University of Southern California Dissertations and Theses

Iron-dependent response mechanisms of the nitrogen-fixing cyanobacterium Crocosphaera to climate change

(USC Thesis Other)

Iron-dependent response mechanisms of the nitrogen-fixing cyanobacterium Crocosphaera to climate change

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content

IRON-DEPENDENT RESPONSE MECHANISMS OF THE NITROGEN-FIXING
CYANOBACTERIUM CROCOSPHAERA TO CLIMATE CHANGE

by

Nina Yang

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfilment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(MARINE BIOLOGY AND BIOLOGICAL OCEANOGRAPHY)

December 2022

Copyright 2022 Nina Yang

ii
Dedication
To my parents and grandparents who have been on this bumpy ride from the very beginning.

iii
Acknowledgments

I’m grateful for the years of funding to the Hutchins Lab that have financially supported
my thesis research. This work was supported by U.S. National Science Foundation (NSF)
Biological Oceanography program grants OCE 1260490, OCE 1538525, OCE 1657757,
OCE 1851222, and OCE 2149837. My time as a graduate student was also funded in part
by a USC Provost Fellowship that provided travel and research support related to
fieldwork in my first year. Moreover, the molecular work for Chapter 4 was made possible
in part by a grant from the PADI Foundation.

They say, “it takes a village…” and that has certainly been true in my case. There is a long
list of people who contributed to the completion of my dissertation work, and I am deeply
grateful for their support, mentorship, leadership, camaraderie, and friendship over the
years. I am thankful to Dave for giving me a spot in his lab and for his unwavering faith in
me throughout, especially during the times when I did not believe in myself. Dave has an
incredible ability for piecing together and writing compelling narratives from complex
datasets and I am lucky to have learned from the best.

To my defense committee members, Eric Webb, Naomi Levine, and Seth John, I am
indebted to you for your mentorship, helpful suggestions, and kindness, especially in the
final months of this saga. I would not have made it without you all. Thank you for believing
in me.

To Seth and the Marine Trace Element Laboratory (Nick, Rachel, and Xiaopeng), thank you
for sharing your clean lab space, knowledge, and expertise with trace metal chemistry and
analysis. The iron measurements central to all three chapters’ work would not have been
as insightful or relevant. To Naomi, math has always been a daunting subject for me, but
your enthusiasm and modeling class made me want to learn and to improve. The modeling
framework in Chapter 2 and the doors that have opened since would not have been
possible without you. To Eric, none of this work would be possible without your lab’s
resources, your expertise, and your willingness to talk life and science (often, crisis
management) for hours on end. Chapters 3 and 4 would not have been written with such
alacrity without you.

Thank you also to Sergio Sañudo-Wilhelmy and Jim Moffett who always asked thought-
provoking questions (especially during my Qualifying Exams) that I didn’t think I would
ever need to know. Of course, that knowledge has ended up shaping significant parts of
this thesis. I am grateful for your interest in my work and growth as a scientist.

This work would not have been possible without these coauthors and other collaborators
involved, including the several high school and undergraduate students I have had the
privilege of mentoring and sharing my research with. Much of this work involves manual
and physical labor, making hundreds of liters of seawater and countless hours of cell
iv
counts that they endured. Vicky, Marlen, Carlin, Michelle, and Bryan –I am a better mentor,
scholar, and person after knowing you. Your futures are so bright, and I will always be
rooting for you.

To the faculty who strive every day to make this department a better place for students,
my gratitude for your time and commitment knows no bounds. Eric, Jim, Cameron, and
especially to Carly, Naomi, Suzanne, thank you for your time, dedication, and service to
the department and to the MEB community and for all your efforts in advancing diversity,
equity, inclusion, and justice at USC and beyond. I have only had a small taste of what it
feels like to have an inordinate amount of work and responsibilities at work and at home.
My gratitude for your positivity and willingness to engage is endless. No matter where I
end up next, I hope to channel that same energy in all that I do.

To Don Bingham, there are no words to describe what you do for MEB. Thank you for
being around and taking care of us, from the early mornings to late evenings. I always felt
safer during overnight experiments knowing you were likely around. Please know that you
are truly appreciated by so many.

I am fortunate to have shared my graduate experience with some fantastic students and
alumni of the USC MEB community. I am lucky to have had support, training, and
friendship of fellow Hutchins Lab students (now alumni) including Nate, Mike, Pingping,
and Josh. To Kyla and Cara, what a rollercoaster it has been. The pandemic really threw
us for a loop, and I am so grateful to have had our little lab trio through it all. I am also
grateful to the TriCoLim crew (Babak, Elaina, Yubin, and Andrea and Massimo from
Stockholm) for our TMP bubble memories and a truly global friendship through the years.
To Alexis and Anjali, thank you for continuing the important work of serving the student
body through MEB and Women in Science and Engineering. It was a huge relief to know
that I could pass the torch to enormously capable and caring people. The world needs
more of you.

To the (best) 2017 cohort, it has been a whirlwind ride with you all and I will miss our
annual retreat shenanigans. Colette, Heidi, and Gerid, the last 2 years would not have been
as meaningful without your friendship. It is always a joy to hear what you are up to, and I
am excited for where our journeys will lead us next. I hope our paths continue to cross
throughout our lives.

It is a privilege to be surrounded by those who care deeply about their work and their
people, and I will cherish our memories and conversations for years to come.

To my family and friends who have been with me from the start, thank you for tolerating
me over the years. Mom and dad, none of this would have been possible if you didn’t dare
to dream.

Finally, to Ryan, the best dog dad, and my best friend. Sandy and I are so incredibly blessed
to have you in our lives. I love you.
v
Table of Contents

Dedication .............................................................................................................................................. ii
Acknowledgments ............................................................................................................................... iii
List of Figures ..................................................................................................................................... vii
Abstract ............................................................................................................................................... viii
Chapter 1: Introduction ...................................................................................................................... 1
1. Primary Production in the Ocean ............................................................................................ 1
2. Nitrogen fixation in low-latitude oceans ............................................................................... 2
2.1. Marine nitrogen fixers ....................................................................................................... 2
2.2. Variables controlling marine N2 fixation ........................................................................ 4
3. Climate Change in the oligotrophic gyres ............................................................................. 8
4. Accounting for Multi-stressor effects on N2 fixation .......................................................... 9
References ........................................................................................................................................... 11
Chapter 2. Warming iron-limited oceans enhance nitrogen fixation and drive
biogeographic specialization of the globally important cyanobacterium Crocosphaera .... 19
Abstract ................................................................................................................................................ 20
1. Introduction ................................................................................................................................ 22
2. Methods ...................................................................................................................................... 26
2.1. Culturing Methods ............................................................................................................ 26
2.2. Elemental Stoichiometry ................................................................................................. 28
2.3. Nitrogen Fixation Rates ................................................................................................... 28
2.4. Carbon Fixation Rates ..................................................................................................... 28
2.5. Intracellular Iron Content ................................................................................................ 29
2.6. Fe Quotas and Resource Use Efficiencies .................................................................. 30
2.7. Statistical Analyses ........................................................................................................... 30
2.8. Modeling Methods ........................................................................................................... 30
3. Results .......................................................................................................................................... 36
4. Discussion ................................................................................................................................... 39
References ........................................................................................................................................... 48
Figures .................................................................................................................................................. 53
Supplementary Materials ................................................................................................................. 57
Chapter 3: Molecular mechanisms underlying iron and phosphorus co-limitation
responses in the nitrogen fixing cyanobacterium Crocosphaera ............................................. 76
Abstract ................................................................................................................................................ 77
1. Introduction ................................................................................................................................ 78
2. Methods ...................................................................................................................................... 81
2.1. Culturing Methods ............................................................................................................ 81
2.2. Physiological Measurements .......................................................................................... 82
vi
2.3. Statistical Analyses ........................................................................................................... 83
2.4. RNA Extractions and Sequencing ................................................................................. 84
2.5. Transcriptomic analysis and visualizations ................................................................. 85
3. Results & Discussion ................................................................................................................ 86
3.1. Crocosphaera physiology and function under different nutrient conditions ....... 86
3.2. Crocosphaera diel transcriptome under Fe, P, and Fe/P (co)-limitation ............... 89
3.3. Transcriptomic response of nutrient-limited versus nutrient replete
Crocosphaera ................................................................................................................................... 93
3.4. Linking nutrient-limited diel gene expression to Fe and P-limited physiology ... 94
3.5. Fe limitation moderates P limitation in a unique Fe/P co-limited phenotype .... 96
4. Conclusions ................................................................................................................................. 98
References ........................................................................................................................................ 100
Figures ............................................................................................................................................... 110
Supplementary Materials .............................................................................................................. 116
Chapter 4: Contrasting transcriptomic responses underlie temperature and iron-limited
physiology in the marine diazotroph Crocosphaera watsonii ................................................. 154
Abstract ............................................................................................................................................. 154
1. Introduction ............................................................................................................................. 156
2. Methods ................................................................................................................................... 161
2.1. Culturing Methods ......................................................................................................... 161
2.2. Elemental Stoichiometry .............................................................................................. 163
2.3. Rate Measurements & Resource Use Efficiencies ................................................. 164
2.4. Statistical Analyses ........................................................................................................ 166
2.5. RNA Extractions and Sequencing .............................................................................. 166
2.6. Transcriptomic analysis and visualizations .............................................................. 168
3. Results & Discussion ............................................................................................................. 170
3.1. Day and night physiological responses to temperature and iron availability .. 170
3.2. Molecular underpinnings of temperature-driven physiology .............................. 171
3.3. Temperature-specific responses to iron limitation ................................................ 176
4. Conclusions .............................................................................................................................. 181
References ........................................................................................................................................ 183
Figures ............................................................................................................................................... 193
Supplementary Materials .............................................................................................................. 197
Chapter 5: Conclusions ................................................................................................................. 212
References ........................................................................................................................................ 219

vii
List of Figures
Chapter 2
Figure 1: Cell-specific growth rates of Crocosphaera grown under Fe-replete and Fe-
limited conditions across 5 temperatures (20°C, 22°C, 27°C, 32°C, and 36°C) that
span their thermal range…...………………………...……………………………………...……………………….58

Figure 2: Resource use efficiencies of Crocosphaera grown in triplicate under Replete
and Limited iron conditions at 20°C, 22°C, 27°C, 32°C, and 36°C……..………………………......59

Figure 3: Percent change in the modeled response of Crocosphaera N-IUE (mol N
fixed hr
-1
mol Fe
-1
) and N2-fixation (fmol N hr
-1
cell
-1
) for the years 2010 and 2100
under the IPCC RCP 8.5 warming scenario.……………………………….......……..………………………60

Figure 4: Comparative change in the modelled responses of Crocosphaera and
Trichodesmium N-IUE and N2-fixation for the years 2010 and 2100 under the IPCC
RCP 8.5 warming…..……………………………………………………………………………………..………………61

Chapter 3
Figure 1: Nutrient replete and nutrient-limited Crocosphaera physiology……………….…..120

Figure 2: Calculated elemental use efficiencies (RUEs, mol C or N fixed / hour / mol
intracellular P or Fe) of nutrient replete and nutrient-limited Crocosphaera……………….…121

Figure 3: Gene expression trends of commonly used Fe- and P limitation biomarkers…..122

Figure 4: Diel transcriptomes and core functional enrichment analysis of
differentially expressed genes..………………………………………….……………………………….………124

Chapter 4
Figure 1: Fe replete and Fe-limited Crocosphaera physiology at sub-optimal, optimal,
and supra-optimal growth temperatures (23°C, 27°C, and 32°C)….………………..………...…209

Figure 2: Redundancy analysis (RDA) of Crocosphaera gene expression across
different temperatures and iron (Fe) concentration..…………………………………………....….….210

Figure 3. Dot plot visualization of day and night enriched GO Biological Process (BP)
terms and KEGG Pathways of differentially expressed genes unique to each
temperature for Fe-limited relative to Fe-replete cells.……………..…………………………………211

Figure 4. Heatmap analysis showing the DESeq2-normalized gene expression scaled
as the number of standard deviations from the row mean (Z-score) for enriched
Gene Ontology terms and KEGG Pathways calculated from pairwise comparisons
for Fe-limited cells at 23°C and 32°C…………………………..………………………………………………212
viii
Abstract
The unicellular cyanobacterium Crocosphaera efficiently leverages limited sources
of the micronutrient iron (Fe) to carry out nitrogen fixation (N2 fixation). N2 fixation is an
essential ecosystem function that transforms inert nitrogen into bioavailable ammonia, a
key source of nitrogen that fuels primary production. Across large regions of the ocean,
Fe is primary limiting nutrient for N2 fixation, and so constrains the abundance and
distribution of Crocosphaera and other N2-fixing cyanobacteria. However, other key
environmental controls also play an important role, including phosphorus (P) and
temperature. As the ocean responds to anthropogenic climate change, nutrient availability
and temperature will undoubtedly change. This thesis focuses on the interplay between
temperature and nutrient availability and its impact on N2 fixation. Through a combined
approach incorporating culture-based studies, gene expression analyses, and
biogeochemical modeling, I identify key response mechanisms to climate change to better
understand the associated consequences for marine biogeochemistry.
P availability is also important for N2 fixation, especially in regions of the ocean
where episodic aeolian inputs temporarily alleviate Fe-limitation. In addition to Fe-limited
and P-limited ocean basins, oceanographic surveys have revealed naturally occurring Fe
and P gradients where both nutrients can be simultaneously limiting. In a warming ocean,
the availability of both nutrients is expected to shift due to indirect impacts of warming-
enhanced stratification and nutrient depletion of the surface ocean. To understand the
mechanisms underlying Crocosphaera’s response to varying Fe and P availability, I cultured
Crocosphaera under individual Fe and P limitation as well as Fe/P co-limitation conditions.
ix
I then conducted a differential gene expression analysis to identify mechanisms driving
Crocosphaera’s physiological responses. My experimental data showed that relative to
nutrient replete and Fe-limited cultures, Crocosphaera experience reduced growth and
nitrogen fixation under P-limitation. Counterintuitively, when Crocosphaera are co-limited
by both P and Fe, growth and nitrogen fixation rates recover to exceed P-limited rates.
This suggests that Fe-limitation may trigger a shift in Crocosphaera’s metabolic profile
affecting growth, photosynthesis, nitrogen fixation, and resource allocation that
ameliorates the deleterious effects of P-limitation.
I then directly assessed the impacts of ocean warming through a differential gene
expression analysis of Crocosphaera grown under a 3x2 matrix of temperature and iron
availability. The results suggest that Crocosphaera modulates the cellular balance of carbon
and nitrogen to cope with stressors that manifest under increasing temperature and low
iron availability, producing a metabolically efficient phenotype within a prescribed thermal
limit. I incorporated these results into a simple biogeochemical model simulating the IPCC
RCP 8.5 warming scenario which suggested that as oceans
warm, Crocosphaera distribution and N2 fixation may shrink in lower latitudes and expand
to higher latitudes, contrasting with the thermal response of another nitrogen
fixer, Trichodesmium.
Collectively, this work characterizes the indirect and direct impacts of ocean
warming on Crocosphaera and N2 fixation, information that is necessary to develop
diagnostic biomarkers for climate change and advance our understanding of a future,
warmer ocean.

1
Chapter 1: Introduction
1. Primary Production in the Ocean
The ocean plays a central role in regulating Earth’s climate, serving as a sink for
carbon dioxide (CO2) and heat (Yan et al., 2016; Romanou et al., 2017; Gruber et al., 2019).
Carbon (C) biogeochemical cycling in the ocean is determined in part by biological
processes mediated by marine microorganisms through the biological carbon pump (BCP).
The BCP plays a critical role in climate regulation such that atmospheric CO2 levels would
be ~400 ppm higher if the BCP did not exist (Ito and Follows, 2005; Boyd, 2015).
Phytoplankton serve as the major conduit of the biological carbon pump (BCP). The
BCP includes primary production, or the photosynthetic fixation of inorganic C (e.g. CO2)
into new organic matter, as well as the dynamics of organic matter transport into the deep
ocean where C can be sequestered over long time scales (e.g. months to millennia)
(Falkowski et al., 2003; De La Rocha and Passow, 2014; Siegel et al., 2022). Phytoplankton
contribute nearly half of the Earth’s net primary production (Field et al., 1998; Behrenfeld
et al., 2006) and support food webs in both the sunlit surface ocean (euphotic zone) and
deeper, mesopelagic waters (Siegel et al., 2022).
Their importance to the C cycle as well as other vital biogeochemical processes in
the ocean has spurred intensive research efforts to determine the abiotic and biotic
controls of phytoplankton abundance, activity, and biogeography. Years of theoretical and
empirical studies including laboratory and modeling efforts and field work have identified
nutrient availability as a key variable (Dugdale and Goering, 1967; Dufour et al., 1999;
2
Moore et al., 2001, 2013; Boyd et al., 2007; Davey et al., 2008; Garcia et al., 2015; Bristow
et al., 2017; Buchanan et al., 2021). These many studies show that different nutrients drive
phytoplankton dynamics across the different oceanic basins. For example, macronutrients,
especially nitrogen (N), are considered limiting across large portions of the ocean,
especially the subtropical gyres. In contrast, in the Equatorial Pacific Ocean, North Pacific
Ocean, and the Southern Ocean, low iron (Fe) supply relative to macronutrient availability
is considered the limiting factor (Dugdale and Goering, 1967; Martin, 1991; Martin et al.,
1994; Davey et al., 2008; Moore et al., 2013; Ustick et al., 2021).
The low-latitude, nutrient-poor or oligotrophic ocean gyres represent the world’s
largest ecosystem, spanning ~40% of the Earth’s surface (Karl, 1999). Despite nutrient
scarcity, these subtropical and tropical regions are biogeochemically important
contributors to global primary productivity, C export out of the euphotic zone, and C
sequestration in the deep ocean (Richardson and Jackson, 2007; Yang et al., 2019;
Nowicki et al., 2022).

2. Nitrogen fixation in low-latitude oceans
2.1. Marine nitrogen fixers
Across the oligotrophic gyres, a group of microorganisms have evolved the ability
to transform or “fix” inert dinitrogen (N2) gas into ammonia, a more bioavailable N form
that fuels phytoplankton growth and primary production in these largely N-limited regimes
(Sohm et al., 2011). This “N2 fixation” is catalyzed by prokaryotic microorganisms
collectively termed N2-fixers or diazotrophs, which include two globally important
3
cyanobacteria, the filamentous, colony-forming Trichodesmium and the unicellular
Crocosphaera (Sohm et al., 2011). These cyanobacterial diazotrophs provide a significant
amount of fixed N that can fuel up to half of the new production in oligotrophic low-
latitude waters (Karl et al., 1997). Trichodesmium has long been recognized as a key
contributor to global marine N2 fixation (Capone and Carpenter, 1982; Carpenter and
Romans, 1991; Capone et al., 1994, 1997, 2005; Sohm et al., 2011; Bergman et al., 2013)
while the contributions of unicellular cyanobacteria, including Crocosphaera, have only
recently been recognized in the last two decades (Montoya et al., 2004; Moisander et al.,
2010; Zehr, 2011; Zehr and Capone, 2020).
Both of these diazotrophic cyanobacteria carry out photosynthesis and N2 fixation
in their cells, despite these being considered incompatible processes. This is due to the
sensitivity of the N2-fixing nitrogenase enzymes, which can be easily inactivated by O2
evolved during photosynthesis (Gallon, 1981; Berman-Frank et al., 2001; Zehr and
Capone, 2020). Thus, N2 fixers must employ various strategies to accommodate both
metabolisms. Whereas Crocosphaera temporally segregates photosynthesis from N2
fixation into daytime and nighttime activities, Trichodesmium is unique in its ability to
simultaneously photosynthesize and fix N2 during the day. N2 fixation is an energetically
intensive reaction that requires 16 ATP and 8 electrons to fix one N2 molecule into 2
molecules of ammonia (NH3) (Sohm et al., 2011; Yang et al., 2011; Zehr and Capone,
2020). A potential mechanistic driver of these different strategies may be the contrasting
use of the C storage compound glycogen to fuel N2 fixation (Held et al., 2022).
During the day, Crocosphaera accumulates glycogen while photosynthesizing, thus
building up C stores for nighttime N2 fixation. At night, respiration burns glycogen stores
4
to produce ATP, which draws down cellular O2 levels, simultaneously providing energy
and creating a nearly anoxic environment for N2 fixation to take place (Inomura et al.,
2019). A recent study demonstrated that Trichodesmium balances photosynthesis and N2
fixation through a rapid and dynamic cycling of its daytime proteome. The energy
produced during periods of photosynthesis may be directly funneled to N2 fixation,
bypassing the need for glycogen stores (Held et al., 2022). While it remains unclear how
Trichodesmium protects nitrogenase from O2 inhibition, simultaneously balancing
photosynthesis and N2 fixation enables Trichodesmium colonies to inhabit their high-light
niche.

2.2. Variables controlling marine N2 fixation
2.2.1. Iron (Fe)
These distinct strategies to accommodate photosynthesis and N2 fixation also
determine the nutrient requirements for Crocosphaera and Trichodesmium, especially for
the scarce micronutrient iron (Fe). Fe is an important cofactor in photosynthesis and
related pathways including chlorophyll biosynthesis and electron transport, with ~24 Fe
atoms spread across the photosynthetic apparatus (Shi et al., 2007). N2 fixers also have a
much higher cellular Fe demand than other phytoplankton because nitrogenase,
composed of the NifH and NifDK proteins, is an Fe-rich metalloenzyme complex requiring
34 Fe atoms (Yang et al., 2011; Schoffman et al., 2016).
Due to the enhanced Fe demand of N2 fixation coupled with Fe scarcity in the
ocean, the biogeography and activity of N2 fixers are primarily constrained by Fe (Capone
5
et al., 2005; Moore et al., 2013; Hutchins and Boyd, 2016; Schoffman et al., 2016).
Phytoplankton have evolved several ways to cope with Fe-limitation including the
replacement of the Fe-containing ferredoxin with Fe-free flavodoxin for electron
transport (Leonhardt and Straus, 1992; LaRoche et al., 1996; Chappell et al., 2012). In
addition, Fe-limitation downregulates photosynthesis, especially Fe-rich photosystem I
(PSI) (Shi et al., 2007). To compensate for decreased light harvesting and photosynthetic
capacity, cells produce chlorophyll-binding IsiA proteins which form a light-harvesting ring
structure around PSI to optimize photosynthetic efficiency under low-Fe stress, as well as
other stressors including high light and oxidative stress (Bibby et al., 2001; Chen et al.,
2018; Toporik et al., 2019; Cheng et al., 2020).
Of the two diazotrophic cyanobacteria, Trichodesmium has a higher cellular Fe
requirement relative to Crocosphaera, because it has to juggle photosynthesis and N2
fixation simultaneously. As such, Trichodesmium colonies have evolved the ability to use
Fe bound in dust particles by harboring epibiotic bacteria that dissolve and extract Fe to
access an Fe source that is largely unavailable to most other microorganisms (Basu et al.,
2019; Kessler et al., 2020; Held et al., 2021). Crocosphaera’s temporal separation of
photosynthesis and N2 fixation enables the sharing or “hotbunking” of Fe between the two
pathways (Saito et al., 2011). At the end of the night, nitrogenase is degraded and Fe is
funneled towards the synthesis of various Fe-containing photosynthetic proteins. This
process is reciprocated at night with the degradation of photosynthesis proteins that then
supplies Fe for the de novo synthesis of nitrogenase. This diel Fe hotbunking reduces
Crocosphaera’s cellular requirements by ~40% and contributes to the niche differentiation
between Trichodesmium and Crocosphaera, whereby Crocosphaera may outcompete
6
Trichodesmium in regions characterized by severe Fe-limitation (e.g. the Pacific Ocean)
(Saito et al., 2011; Sohm et al., 2011).

2.2.2. Phosphorus (P)
Phosphorus (P) can also limit N2 fixation and affect diazotrophic community
composition across different oceanic basins, especially in areas where the relative
availability of P is scarcer than Fe (Sohm et al., 2011; Zehr and Capone, 2020). P is an
essential macronutrient that makes up core cellular components including membrane
lipids, ribosomes, nucleic acids, and energy-related functions (ATP). It also plays an
important role in the cell’s ability to sense and respond to environmental changes through
two-component regulatory systems (Capra and Laub, 2012; Lin et al., 2016; Held et al.,
2019).
A biogeochemical transition from P limitation to Fe limitation is evident in the
Atlantic Ocean (Capone, 2014) where close proximity to episodic Saharan dust inputs
alleviates Fe-limitation seasonally in the North Atlantic, yielding a P-limited system
(Behrenfeld et al. 1996; Falkowski et al. 1998). In contrast, chronic Fe-limitation prevails
in the South Atlantic and Pacific Oceans due to a lack of external Fe inputs (Behrenfeld
and Kolber, 1999).
When dissolved inorganic P (DIP) is growth-limiting, N2 fixers upregulate the PstS
transporter for high-affinity phosphate transport (Orchard et al., 2009; Pereira et al.,
2019). When DIP is scarce, both Trichodesmium and Crocosphaera can use different forms
of dissolved organic P (DOP) as a coping mechanism for P limitation (Dyhrman and Haley,
7
2006; Dyhrman et al., 2006). DOP represents a significant pool of dissolved P in the
oligotrophic gyres (~80%) and the ability to access this resource could help relieve local P-
limitation of N2 fixation (Rabouille et al., 2022). Trichodesmium and Crocosphaera
upregulate the enzyme alkaline phosphatase (AP) that facilitates the hydrolysis of
phosphomonoesters for uptake. However, Trichodesmium can also access phosphonates
through the C-P lyase pathway, while this ability has yet to be demonstrated in
Crocosphaera, suggesting that Trichodesmium are comparatively well-suited for P-scarce
environments (Dyhrman and Haley, 2006; Dyhrman et al., 2006; Rabouille et al., 2022).

2.2.3. Temperature
Both Crocosphaera and Trichodesmium are found throughout the warm, low-latitude
ocean, suggesting that temperature may also be an important control of N2 fixation.
Studies on the thermal response of diazotroph growth and N2 fixation revealed differing
thermal ranges for Crocosphaera and Trichodesmium. Crocosphaera exhibit a relatively
narrow thermal range spanning ~20°C to 35°-36°C, with a broad thermal growth optimum
(~26°C-30°C) that also applies to N2 fixation rates (Fu et al., 2014; Yang et al., 2021). In
contrast, Trichodesmium has a broader thermal range from ~16°C to 35°C, as well as a
wider growth optimum (~24°C-30°C) (Fu et al., 2014; Jiang et al., 2018). However, optimal
growth and N2 fixation rates have been observed to be higher for Crocosphaera than
Trichodesmium. These thermal range differences suggest resource competition dynamics
and potential niche differentiation of N2 fixers that can affect diazotroph community
8
structure and activity throughout various ocean basins, with implications for global
biogeochemical cycling.

3. Climate Change in the oligotrophic gyres
Given its role as a significant heat sink for anthropogenically-induced climate
change, the ocean is warming at a rate 40% faster than previously forecasted (Cheng et
al., 2019). Temperature is a principle determinant of cellular biochemistry and function
that influences important metabolic processes including photosynthesis, respiration, and
N2 fixation (Huertas et al., 2011; Thomas et al., 2012; Toseland et al., 2013; Hutchins and
Fu, 2017; Boscolo-Galazzo et al., 2018; Hutchins and Sañudo-Wilhelmy, 2021).
Warming can also alter the fundamental chemical and physical properties of the
ocean that determine nutrient availability, which in turn controls N2 fixation and primary
productivity (Behrenfeld et al., 2006; Hutchins and Boyd, 2016; Letelier et al., 2019).
Warming is already driving the expansion of the warm subtropical and tropical oligotrophic
gyres (Karl, 1999; Polovina et al., 2008) and increasing temperatures will likely intensify
the existing vertical stratification that suppresses the supply of advected deep-water
nutrients to the euphotic zone (Hutchins and Fu, 2017). This projected reduction in
advected nutrients including P coupled with increased aeolian Fe input suggests that
future N2 fixation may be more P-limited, which has implications for diazotroph
community structure and activity and consequences for primary productivity (Hutchins
and Boyd, 2016; Hutchins and Capone, 2022). Thus accelerated ocean warming and its
impacts on biogeochemical cycling have raised important questions regarding the
9
resiliency and future state of marine ecosystems (Hutchins and Fu, 2017; Cavicchioli et
al., 2019; Burger et al., 2022; Lomas et al., 2022).

4. Accounting for Multi-stressor effects on N2 fixation
The body of work summarized here has been critical in establishing a foundation of
knowledge on diazotroph nutrient requirements, thermal growth curves, community
dynamics, coping mechanisms for stress, and the associated biogeochemical implications.
However, these studies primarily focus on one variable as a sole driver of diazotroph
responses to changing environmental conditions. We now know that a singular focus on
one environmental driver is not an accurate reflection of the natural conditions that
diazotrophs are exposed to, as they have adapted to a suite of co-occurring environmental
variables including simultaneously varying temperature, Fe, and P regimes (Saito et al.,
2014; Browning et al., 2017). Throughout my PhD, I drew upon this foundational
knowledge to build my research around elucidating the responses of N2 fixers and N2
fixation to multiple stressors in the context of climate change, with an emphasis on gaining
a better understanding of the interactions between Fe and P, and between Fe and
temperature in these biogeochemically-critical cyanobacteria. Large regions of the ocean
are Fe-limited, yet few studies evaluate the responses of N2 fixers and N2 fixation to other
environmental variables under Fe-limiting conditions.
Chapter 2 characterizes the molecular mechanisms underpinning Crocosphaera’s
physiological response to Fe-limitation, P-limitation, and Fe/P co-limitation. As global
10
climate change shifts nutrient availability and distribution across different oceanic basins,
it is critical that we are able to identify corresponding changes to N2 fixation.
Chapters 3 and 4 explore the responses of N2 fixation to simultaneous impacts of
increasing temperature and Fe-limitation (Fe/warming) and the biogeochemical
implications for future N2 fixation in a warmer ocean. Chapter 3 characterizes the
molecular mechanisms underpinning Crocosphaera’s physiological response to
Fe/warming. Chapter 4 incorporates physiological responses from Crocosphaera to
extrapolate laboratory observations globally, using temperature and Fe projections for a
current ocean and a future ocean under the Intergovernmental Panel for Climate Change
(IPCC) RCP8.5 emissions scenario that predicts a ~4°C rise in temperature. Chapter 4 also
compares Crocosphaera’s response to previously published findings on Trichodesmium to
understand how intergeneric response mechanisms to Fe/warming will affect diazotroph
distribution and N2 fixation in a future, warming ocean.

11
References
Basu, S., Gledhill, M., de Beer, D., Prabhu Matondkar, S. G., and Shaked, Y. (2019). Colonies
of marine cyanobacteria Trichodesmium interact with associated bacteria to acquire
iron from dust. Commun Biol 2, 284.

Behrenfeld, M. J., and Kolber, Z. S. (1999). Widespread iron limitation of phytoplankton in
the South Pacific Ocean. Science 283, 840–843.

Behrenfeld, M. J., O’Malley, R. T., Siegel, D. A., McClain, C. R., Sarmiento, J. L., Feldman, G.
C., et al. (2006). Climate-driven trends in contemporary ocean productivity. Nature
444, 752.

Bergman, B., Sandh, G., Lin, S., Larsson, J., and Carpenter, E. J. (2013). Trichodesmium - a
widespread marine cyanobacterium with unusual nitrogen fixation properties.
FEMS Microbiol. Rev. 37, 286–302.

Berman-Frank, I., Lundgren, P., Chen, Y.-B., Küpper, H., Kolber, Z., Bergman, B., et al.
(2001). Segregation of nitrogen fixation and oxygenic photosynthesis in the marine
cyanobacterium Trichodesmium. Science 294, 1534.

Bibby, T. S., Nield, J., and Barber, J. (2001). Iron deficiency induces the formation of an
antenna ring around trimeric photosystem I in cyanobacteria. Nature 412, 743–745.

Boscolo-Galazzo, F., Crichton, K. A., Barker, S., and Pearson, P. N. (2018). Temperature
dependency of metabolic rates in the upper ocean: A positive feedback to global
climate change? Glob. Planet. Change 170, 201–212.

Boyd, P. W. (2015). Toward quantifying the response of the oceans’ biological pump to
climate change. Frontiers in Marine Science 2. doi: 10.3389/fmars.2015.00077.

Boyd, P. W., Jickells, T., Law, C. S., Blain, S., Boyle, E. A., Buesseler, K. O., et al. (2007).
Mesoscale Iron Enrichment Experiments 1993-2005: Synthesis and Future
Directions. Science 315, 612.

Bristow, L. A., Mohr, W., Ahmerkamp, S., and Kuypers, M. M. M. (2017). Nutrients that
limit growth in the ocean. Curr. Biol. 27, R474–R478.

Browning, T. J., Achterberg, E. P., Rapp, I., Engel, A., Bertrand, E. M., Tagliabue, A., et al.
(2017). Nutrient co-limitation at the boundary of an oceanic gyre. Nature 551, 242–
246.

12
Buchanan, P. J., Aumont, O., Bopp, L., Mahaffey, C., and Tagliabue, A. (2021). Impact of
intensifying nitrogen limitation on ocean net primary production is fingerprinted by
nitrogen isotopes. Nat. Commun. 12, 6214.

Burger, F. A., Terhaar, J., and Frölicher, T. L. (2022). Compound marine heatwaves and
ocean acidity extremes. Nat. Commun. 13, 4722.

Capone, D. G. (2014). An iron curtain in the Atlantic Ocean forms a biogeochemical divide.
Proc. Natl. Acad. Sci. U. S. A. 111, 1231–1232.

Capone, D. G., Burns, J. A., Montoya, J. P., Subramaniam, A., Mahaffey, C., Gunderson, T.,
et al. (2005). Nitrogen fixation by Trichodesmium spp.: An important source of new
nitrogen to the tropical and subtropical North Atlantic Ocean. Global Biogeochem.
Cycles 19, 17.

Capone, D. G., and Carpenter, E. J. (1982). Nitrogen fixation in the marine environment.
Science 217, 1140–1142.

Capone, D. G., Ferrier, M. D., and Carpenter, E. J. (1994). Amino acid cycling in colonies of
the planktonic marine cyanobacterium Trichodesmium thiebautii. Appl. Environ.
Microbiol. 60, 3989–3995.

Capone, D. G., Zehr, J. P., Paerl, H. W., Bergman, B., and Carpenter, E. J. (1997).
Trichodesmium, a globally significant marine cyanobacterium. Science 276, 1221–
1229.

Capra, E. J., and Laub, M. T. (2012). Evolution of two-component signal transduction
systems. Annu. Rev. Microbiol. 66, 325–347.

Carpenter, E. J., and Romans, K. (1991). Major role of the cyanobacterium Trichodesmium
in nutrient cycling in the North Atlantic Ocean. Science 254, 1356–1358.

Cavicchioli, R., Ripple, W. J., Timmis, K. N., Azam, F., Bakken, L. R., Baylis, M., et al. (2019).
Scientists’ warning to humanity: microorganisms and climate change. Nat. Rev.
Microbiol. 17, 569–586.

Chappell, P. D., Moffett, J. W., Hynes, A. M., and Webb, E. A. (2012). Molecular evidence
of iron limitation and availability in the global diazotroph Trichodesmium. ISME J. 6,
1728–1739.

Chen, H. Y. S., Bandyopadhyay, A., and Pakrasi, H. B. (2018). Function, regulation and
distribution of IsiA, a membrane-bound chlorophyll a-antenna protein in
cyanobacteria. Photosynthetica 56, 322–333.

13
Cheng, L., Abraham, J., Hausfather, Z., and Trenberth, K. E. (2019). How fast are the oceans
warming?. Science 363, 128.

Cheng, Y., Zhang, T., Wang, L., and Chen, W. (2020). Transcriptome analysis reveals IsiA-
regulatory mechanisms underlying iron depletion and oxidative-stress acclimation
in Synechocystis sp. Strain PCC 6803. Appl. Environ. Microbiol. 86. doi:
10.1128/AEM.00517-20.

Davey, M., Tarran, G. A., Mills, M. M., Ridame, C., Geider, R. J., and LaRoche, J. (2008).
Nutrient limitation of picophytoplankton photosynthesis and growth in the tropical
North Atlantic. Limnol. Oceanogr. 53, 1722–1733.

De La Rocha, C. L., and Passow, U. (2014). The Biological Pump. Treatise on Geochemistry,
93–122. https://doi.org/10.1016/B978-0-08-095975-7.00604-5.

Dufour, P., Charpy, L., Bonnet, S., and Garcia, N. (1999). Phytoplankton nutrient control in
the oligotrophic South Pacific Subtropical Gyre (Tuamotu Archipelago). Mar. Ecol.
Prog. Ser. 179, 285–290.

Dugdale, R. C., and Goering, J. J. (1967). Uptake of new and regenerated forms of nitrogen
in primary productivity. Limnol. Oceanogr. 12, 196–206.

Dyhrman, S. T., Chappell, P. D., Haley, S. T., Moffett, J. W., Orchard, E. D., Waterbury, J.
B., et al. (2006). Phosphonate utilization by the globally important marine
diazotroph Trichodesmium. Nature 439, 68–71.

Dyhrman, S. T., and Haley, S. T. (2006). Phosphorus scavenging in the unicellular marine
diazotroph Crocosphaera watsonii. Appl. Environ. Microbiol. 72, 1452–1458.

Falkowski, P. G., Laws, E. A., Barber, R. T., and Murray, J. W. (2003). Phytoplankton and
their role in primary, new, and export production. Ocean Biogeochemistry: The Role
of the Ocean Carbon Cycle in Global Change, 99–121. https://doi.org/10.1007/978-
3-642-55844-3_5.

Field, C. B., Behrenfeld, M. J., Randerson, J. T., and Falkowski, P. (1998). Primary
production of the biosphere: integrating terrestrial and oceanic components.
Science 281, 237–240.

Fu, F. X., Yu, E., Garcia, N. S., Gale, J., Luo, Y. S., Webb, E. A., et al. (2014). Differing
responses of marine N2 fixers to warming and consequences for future diazotroph
community structure. Aquat. Microb. Ecol. 72, 33–46.

Gallon, J. R. (1981). The oxygen sensitivity of nitrogenase: a problem for biochemists and
microorganisms. Trends Biochem. Sci. 6, 19–23.

14
Garcia, N. S., Fu, F., Sedwick, P. N., and Hutchins, D. A. (2015). Iron deficiency increases
growth and nitrogen-fixation rates of phosphorus-deficient marine cyanobacteria.
ISME J. 9, 238–245.

Gruber, N., Clement, D., Carter, B. R., Feely, R. A., van Heuven, S., Hoppema, M., et al.
(2019). The oceanic sink for anthropogenic CO2 from 1994 to 2007. Science 363,
1193–1199.

Held, N. A., McIlvin, M. R., Moran, D. M., Laub, M. T., and Saito, M. A. (2019). Unique
patterns and biogeochemical relevance of two-component sensing in marine
bacteria. mSystems 4. doi: 10.1128/msystems.00317-18.

Held, N. A., Sutherland, K. M., Webb, E. A., McIlvin, M. R., Cohen, N. R., Devaux, A. J., et
al. (2021). Mechanisms and heterogeneity of in situ mineral processing by the
marine nitrogen fixer Trichodesmium revealed by single-colony metaproteomics.
ISME Communications 1, 1–9.

Held, N. A., Waterbury, J. B., Webb, E. A., Kellogg, R. M., McIlvin, M. R., Jakuba, M., et al.
(2022). Dynamic diel proteome and daytime nitrogenase activity supports
buoyancy in the cyanobacterium Trichodesmium. Nat Microbiol 7, 300–311.

Huertas, I. E., Rouco, M., López-Rodas, V., and Costas, E. (2011). Warming will affect
phytoplankton differently: evidence through a mechanistic approach. Proceedings
of the Royal Society B: Biological Sciences 278, 3534–3543.

Hutchins, D. A., and Boyd, P. W. (2016). Marine phytoplankton and the changing ocean
iron cycle. Nat. Clim. Chang. 6, 1072.

Hutchins, D. A., and Capone, D. G. (2022). The marine nitrogen cycle: new developments
and global change. Nat. Rev. Microbiol. doi: 10.1038/s41579-022-00687-z.

Hutchins, D. A., and Fu, F. (2017). Microorganisms and ocean global change. Nat Microbiol
2, 17058.

Hutchins, D. A., and Sañudo-Wilhelmy, S. A. (2021). The enzymology of ocean global
change. Ann. Rev. Mar. Sci. doi: 10.1146/annurev-marine-032221-084230.

Inomura, K., Deutsch, C., Wilson, S. T., Masuda, T., Lawrenz, E., Bučinská, L., et al. (2019).
Quantifying oxygen management and temperature and light dependencies of
nitrogen fixation by Crocosphaera watsonii. mSphere 4, e00531-19.

Ito, T., and Follows, M. J. (2005). Preformed phosphate, soft tissue pump and atmospheric
CO2. J. Mar. Res. 63, 813–839.

15
Jiang, H.-B., Fu, F.-X., Rivero-Calle, S., Levine, N. M., Sañudo-Wilhelmy, S. A., Qu, P.-P., et
al. (2018). Ocean warming alleviates iron limitation of marine nitrogen fixation. Nat.
Clim. Chang. 8, 709–712.

Karl, D., Letelier, R., Tupas, L., Dore, J., Christian, J., and Hebel, D. (1997). The role of
nitrogen fixation in biogeochemical cycling in the subtropical North Pacific Ocean.
Nature 388, 533–538.

Karl, D. M. (1999). A sea of change: biogeochemical variability in the North Pacific
Subtropical Gyre. Ecosystems 2, 181–214.

Kessler, N., Kraemer, S. M., Shaked, Y., and Schenkeveld, W. D. C. (2020). Investigation of
siderophore-promoted and reductive dissolution of dust in marine
microenvironments such as Trichodesmium colonies. Frontiers in Marine Science 7.
doi: 10.3389/fmars.2020.00045.

LaRoche, J., Boyd, P. W., McKay, R. M. L., and Geider, R. J. (1996). Flavodoxin as an in situ
marker for iron stress in phytoplankton. Nature 382, 802.

Leonhardt, K., and Straus, N. A. (1992). An iron stress operon involved in photosynthetic
electron transport in the marine cyanobacterium Synechococcus sp. PCC 7002. J.
Gen. Microbiol. 138 Pt 8, 1613–1621.

Letelier, R. M., Björkman, K. M., Church, M. J., Hamilton, D. S., Mahowald, N. M., Scanza,
R. A., et al. (2019). Climate-driven oscillation of phosphorus and iron limitation in
the North Pacific Subtropical Gyre. Proc. Natl. Acad. Sci. U. S. A. 116, 12720–12728.

Lin, S., Litaker, R. W., and Sunda, W. G. (2016). Phosphorus physiological ecology and
molecular mechanisms in marine phytoplankton. J. Phycol. 52, 10–36.

Lomas, M. W., Bates, N. R., Johnson, R. J., Steinberg, D. K., and Tanioka, T. (2022). Adaptive
carbon export response to warming in the Sargasso Sea. Nat. Commun. 13, 1211.

Martin, J. (1991). Iron, liebig’s law, and the greenhouse. Oceanography 4, 52–55.

Martin, J. H., Coale, K. H., Johnson, K. S., Fitzwater, S. E., Gordon, R. M., Tanner, S. J., et
al. (1994). Testing the iron hypothesis in ecosystems of the equatorial Pacific
Ocean. Nature 371, 123.

Moisander, P. H., Beinart, R. A., Hewson, I., White, A. E., Johnson, K. S., Carlson, C. A., et
al. (2010). Unicellular cyanobacterial distributions broaden the iceanic N2 fixation
domain. Science 327, 1512–1514.

16
Montoya, J. P., Holl, C. M., Zehr, J. P., Hansen, A., Villareal, T. A., and Capone, D. G. (2004).
High rates of N2 fixation by unicellular diazotrophs in the oligotrophic Pacific
Ocean. Nature 430, 1027–1032.

Moore, C. M., Mills, M. M., Arrigo, K. R., Berman-Frank, I., Bopp, L., Boyd, P. W., et al.
(2013). Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701–
710.

Moore, J. K., Doney, S. C., Glover, D. M., and Fung, I. Y. (2001). Iron cycling and nutrient-
limitation patterns in surface waters of the World Ocean. Deep Sea Res. Part 2 Top.
Stud. Oceanogr. 49, 463–507.

Nowicki, M., DeVries, T., and Siegel, D. A. (2022). Quantifying the carbon export and
sequestration pathways of the ocean’s biological carbon pump. Global Biogeochem.
Cycles 36. doi: 10.1029/2021gb007083.

Orchard, E. D., Webb, E. A., and Dyhrman, S. T. (2009). Molecular analysis of the
phosphorus starvation response in Trichodesmium spp. Environ. Microbiol. 11,
2400–2411.

Pereira, N., Shilova, I. N., and Zehr, J. P. (2019). Use of the high-affinity phosphate
transporter gene, pstS, as an indicator for phosphorus stress in the marine
diazotroph Crocosphaera watsonii (Chroococcales, Cyanobacteria). J. Phycol. 55, 752–
761.

Polovina, J. J., Howell, E. A., and Abecassis, M. (2008). Ocean’s least productive waters are
expanding. Geophys. Res. Lett. 35. doi: 10.1029/2007GL031745.

Rabouille, S., Tournier, L., Duhamel, S., Claquin, P., Crispi, O., Talec, A., et al. (2022).
Organic phosphorus scavenging supports efficient growth of diazotrophic
cyanobacteria under phosphate depletion. Front. Microbiol. 13, 848647.

Richardson, T. L., and Jackson, G. A. (2007). Small phytoplankton and carbon export from
the surface ocean. Science 315, 838.

Romanou, A., Marshall, J., Kelley, M., and Scott, J. (2017). Role of the ocean’s AMOC in
setting the uptake efficiency of transient tracers. Geophys. Res. Lett. 44, 5590–
5598.

Saito, M. A., Bertrand, E. M., Dutkiewicz, S., Bulygin, V. V., Moran, D. M., Monteiro, F. M.,
et al. (2011). Iron conservation by reduction of metalloenzyme inventories in the
marine diazotroph Crocosphaera watsonii. Proc. Natl. Acad. Sci. U. S. A. 108, 2184–
2189.

17
Saito, M. A., McIlvin, M. R., Moran, D. M., Goepfert, T. J., DiTullio, G. R., Post, A. F., et al.
(2014). Multiple nutrient stresses at intersecting Pacific Ocean biomes detected by
protein biomarkers. Science 345, 1173.

Schoffman, H., Lis, H., Shaked, Y., and Keren, N. (2016). Iron-nutrient interactions within
phytoplankton. Front. Plant Sci. 7, 1223.

Shi, T., Sun, Y., and Falkowski, P. G. (2007). Effects of iron limitation on the expression of
metabolic genes in the marine cyanobacterium Trichodesmium erythraeum IMS101.
Environ. Microbiol. 9, 2945–2956.

Siegel, D. A., DeVries, T., Cetinić, I., and Bisson, K. M. (2022). Quantifying the ocean’s
biological pump and its carbon cycle impacts on global scales. Ann. Rev. Mar. Sci.
doi: 10.1146/annurev-marine-040722-115226.

Sohm, J. A., Webb, E. A., and Capone, D. G. (2011). Emerging patterns of marine nitrogen
fixation. Nat. Rev. Microbiol. 9, 499–508.

Thomas, M. K., Kremer, C. T., Klausmeier, C. A., and Litchman, E. (2012). A global pattern
of thermal adaptation in marine phytoplankton. Science 338, 1085.

Toporik, H., Li, J., Williams, D., Chiu, P.-L., and Mazor, Y. (2019). The structure of the stress-
induced photosystem I-IsiA antenna supercomplex. Nat. Struct. Mol. Biol. 26, 443–
449.

Toseland, A., Daines, S. J., Clark, J. R., Kirkham, A., Strauss, J., Uhlig, C., et al. (2013). The
impact of temperature on marine phytoplankton resource allocation and
metabolism. Nat. Clim. Chang. 3, 979–984.

Ustick, L. J., Larkin, A. A., Garcia, C. A., Garcia, N. S., Brock, M. L., Lee, J. A., et al. (2021).
Metagenomic analysis reveals global-scale patterns of ocean nutrient limitation.
Science 372, 287–291.

Yan, X.-H., Boyer, T., Trenberth, K., Karl, T. R., Xie, S.-P., Nieves, V., et al. (2016). The global
warming hiatus: Slowdown or redistribution? Earths Future 4, 472–482.

Yang, B., Emerson, S. R., and Quay, P. D. (2019). The subtropical ocean’s biological carbon
pump determined from O2 and DIC/DI
13
C tracers. Geophys. Res. Lett. 46, 5361–
5368.

Yang, Z.-Y., Danyal, K., and Seefeldt, L. C. (2011). Mechanism of Mo-dependent
nitrogenase. Nitrogen Fixation: Methods and Protocols, 9–29.
https://doi.org/10.1146/annurev.biochem.78.070907.103812

18
Zehr, J. P. (2011). Nitrogen fixation by marine cyanobacteria. Trends Microbiol. 19, 162–
173.

Zehr, J. P., and Capone, D. G. (2020). Changing perspectives in marine nitrogen fixation.
Science 368, eaay9514.

19
Chapter 2. Warming iron-limited oceans enhance nitrogen fixation
and drive biogeographic specialization of the globally important
cyanobacterium Crocosphaera

Nina Yang
1
, Carlin A. Merkel
1
, Yu-An Lin
1
, Naomi M. Levine
1,2
, Nicholas J. Hawco
2*
, Hai-
Bo Jiang
3,4†
, Ping-Ping Qu
1††
, Michelle A. DeMers
1
, Eric A. Webb
1
, Fei-Xue Fu
1
, David A.
Hutchins
1

1
Department of Biological Sciences, University of Southern California, Los Angeles, CA,
USA

2
Department of Earth Sciences, University of Southern California, Los Angeles, CA, USA

3
School of Life Sciences, Central China Normal University, Wuhan, Hubei, China
4
Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, Guangdong,
China

Correspondence:
David A. Hutchins
dahutch@usc.edu

*
Present address: Department of Oceanography, School of Ocean and Earth Sciences and
Technology, University of Hawaiʻi at Mānoa, Honolulu, HI, USA
†
Present address: School of Marine Sciences, Ningbo University, Ningbo, Zhejiang, China

††
Present address: Department of Psychiatry and Behavioral Sciences, Stanford University
School of Medicine, Stanford, CA, USA

This manuscript was published in 2021 as:
Yang N, Merkel CA, Lin Y-A, Levine NM, Hawco NJ, Jiang H-B, et al. Warming iron-limited
oceans enhance nitrogen fixation and drive biogeographic specialization of the globally
important cyanobacterium Crocosphaera. Frontiers in Marine Science. 2021;
https://doi.org/10.3389/fmars.2021.628363.

20
Abstract
Primary productivity in the nutrient-poor subtropical ocean gyres depends on new
nitrogen inputs from nitrogen fixers that convert inert dinitrogen gas into bioavailable
forms. Temperature and iron (Fe) availability constrain marine nitrogen fixation, and both
are changing due to anthropogenic ocean warming. We examined the physiological
responses of the globally important marine nitrogen fixer, Crocosphaera watsonii across its
full thermal range as a function of iron availability. At the lower end of its thermal range,
from 22°C to 27°C, Crocosphaera growth, nitrogen fixation, and Nitrogen-specific Iron Use
Efficiencies (N-IUEs, mol N fixed hour
-1
mol Fe
-1
) increased with temperature. At an
optimal growth temperature of 27°C, N-IUEs were 66% higher under iron-limited
conditions than iron-replete conditions, indicating that low-iron availability increases
metabolic efficiency. However, Crocosphaera growth and function decrease from 27°C to
32°C, temperatures that are predicted for an increasing fraction of tropical oceans in the
future. Altogether, this suggests that Crocosphaera are well adapted to iron-limited, warm
waters, within prescribed limits. A model incorporating these results under the IPCC RCP
8.5 warming scenario predicts that Crocosphaera N-IUEs could increase by a net 47% by
2100, particularly in higher-latitude waters. These results contrast with published
responses of another dominant nitrogen fixer (Trichodesmium), with predicted N-IUEs that
increase most in low-latitude, tropical waters. These models project that differing
responses of Crocosphaera and Trichodesmium NIUEs to future warming of iron-limited
oceans could enhance their current contributions to global marine nitrogen fixation with
rates increasing by ~91% and ~22%, respectively, thereby shifting their relative
21
importance to marine new production and also intensifying their regional divergence.
Thus, interactive temperature and iron effects may profoundly transform existing
paradigms of nitrogen biogeochemistry and primary productivity in open ocean regimes.

22
1. Introduction
Marine phytoplankton are important facilitators of biogeochemical cycling in the
ocean and contribute nearly half of the of the Earth’s net primary production (Behrenfeld
et al., 2006). Ocean warming threatens the stability and resiliency of marine ecosystems
and will undoubtedly also drive shifts in the distribution and activity of these keystone
phytoplankton communities (Barton et al., 2016; Hutchins and Fu, 2017). In particular,
temperature exerts significant influence over phytoplankton biogeography as a principal
determinant of cellular biochemistry and metabolic processes, including growth and
photosynthesis (Breitbarth et al., 2007; Huertas et al., 2011; Thomas et al., 2012; Boyd et
al., 2013).
In the subtropical and tropical ocean gyres where large expanses of nutrient-poor
surface waters persist, low concentrations of bioavailable nitrogen generally limit
phytoplankton growth (Moore et al., 2013). In these regions, nitrogen-fixing cyanobacteria
(N2-fixers), or diazotrophs, provide an essential ecosystem service by converting or “fixing”
inert dinitrogen gas (N2) into bioavailable ammonia (NH3) that fuels primary productivity
(Zehr et al., 2001; Sohm et al., 2011). While many microbial groups contribute to biological
nitrogen fixation (N2-fixation), cyanobacteria of the filamentous Trichodesmium and
unicellular Crocosphaera genera are estimated to account for nearly half of the global total
(Zehr and Capone, 2020). These two diazotrophic groups are key components of the
marine nitrogen cycle and so, will help to determine how the availability of this limiting
nutrient responds to ongoing changes in the ocean environment (Hutchins and Fu, 2017).
23
Culturing several strains of Trichodesmium and Crocosphaera across a range of
temperatures has demonstrated that the two diazotrophs have differing thermal ranges
for growth. Trichodesmium has a broader thermal range that can span from ~18ºC to 32ºC
while Crocosphaera’s range is narrower from ~24ºC to 32ºC (Fu et al., 2014). That study
found that warmer temperatures within these thermal limits may enhance diazotrophic
growth and N2-fixation rates and also revealed that Crocosphaera may be better adapted
to grow faster at warmer temperatures than Trichodesmium (Fu et al., 2014). However,
because Trichodesmium and Crocosphaera are found in already-warm waters of ~30ºC,
future ocean warming of ~3.7ºC under IPCC’s “business as usual” RCP 8.5 scenario (IPCC,
2014) may surpass the thermal maxima of these N2-fixers (~35-36ºC, Fu et al., 2014 and
this study) and lead to their disappearance from some regions, with potentially negative
implications for biogeochemistry across the low-latitude oceans (Breitbarth et al., 2007;
Thomas et al., 2012).
The distribution, abundance, and function of these diazotrophs in open ocean
regimes are likewise often constrained by the availability of the essential micronutrient
iron (Fe), which is required as a cofactor for photosynthesis, respiration, and nitrogen-
fixation enzymes (Moore et al., 2001; Schoffman et al., 2016). However, supplies of this
essential micronutrient are insufficient to meet phytoplankton requirements in ~40% of
today’s oceans (Moore et al., 2001, Moore et al., 2013), and are likely to be further altered
in the future ocean by multiple global change processes (Hutchins and Boyd, 2016). To
persist in low-Fe waters, N2-fixers have evolved adaptive responses to acquire and
efficiently use Fe under Fe-limiting conditions. For example, when Fe-limited, both
Trichodesmium and Crocosphaera are able to substitute the iron-sulfur electron transport
24
protein ferredoxin with iron-free flavodoxin (Roche et al., 1996; Chappell and Webb,
2010).
Previous studies have demonstrated that the two N2-fixers differ in Fe
requirements for their photosynthetic apparatus and for iron-rich nitrogenase, the
metalloenzyme catalyst for N2-fixation (Raven et al., 1999; Saito et al., 2011). This is in
part due to differing strategies to protect their nitrogenase from oxygen, which inhibits its
activity and consequently suppresses nitrogen fixation (Inomura et al., 2019). Thus,
photosynthetic N2-fixers that evolve oxygen as a byproduct must employ various
strategies to partition photosynthesis and nitrogen fixation, which partly dictates their
cellular iron requirements (Schoffman et al., 2016). Trichodesmium is capable of
photosynthesizing while simultaneously fixing nitrogen, but at the cost of having higher
cellular Fe requirements to maintain both metabolic pathways (Küpper et al., 2008; Saito
et al., 2011). In contrast, Crocosphaera employs a repertoire of Fe-conservation strategies
to reduce its cellular requirements relative to Trichodesmium. These include temporal
separation of photosynthesis (daytime) and N2-fixation (nighttime), daily synthesis and
breakdown of metalloproteins to recycle Fe between the photosynthetic and N2-fixation
metalloenzymes, and increased expression of flavodoxin at night (during N2-fixation) even
under Fe-replete conditions (Saito et al., 2011). In addition, Crocosphaera is better able to
access dissolved Fe due to its smaller cell size and corresponding increase in the cell
surface area-to-volume ratio compared to Trichodesmium (Finkel et al., 2010). Altogether,
these strategies enable Crocosphaera to reduce its cellular Fe quota relative to
Trichodesmium and allow Crocosphaera to outcompete Trichodesmium in Fe-scarce
25
oligotrophic regimes (Saito et al., 2011). Thus, Crocosphaera may be the dominant N2-fixer
in regions of severe and chronic Fe-limitation, such as much of the Pacific Ocean.
Trichodesmium occurs in greater abundance in areas like the North Atlantic, where episodic
Saharan dust inputs alleviate Fe-limitation seasonally (Behrenfeld et al., 1996; Falkowski
et al., 1998; Campbell et al., 2005; Sohm et al., 2011). How these distributions will respond
to a warmer, Fe-limited future ocean is currently poorly understood.
Jiang et al. (2018) observed the interactive responses of Trichodesmium growth and
N2-fixation rates to warming relative to Fe availability (Jiang et al., 2018). They found that
when Trichodesmium was Fe-limited, growth at warmer temperatures synergistically
enhanced Nitrogen-specific Iron Use Efficiency (N-IUE, nitrogen fixation rate per unit Fe)
by reducing cellular Fe requirements and increasing N2-fixation rates. By applying their
physiological measurements to IPCC’s RCP 8.5 warming scenario for years 2010 and
2100, they projected a ~76% increase in IUEs corresponding to a 21.5% increase in global
marine N2-fixation. Thus, warming may alleviate Fe-limitation for Trichodesmium across
large expanses of the oligotrophic gyres in a warmer, future ocean (Jiang et al., 2018). It is
unknown whether Crocosphaera will exhibit similar responses to warming under low-Fe
conditions.
In this study, we experimentally assess the interactive responses of Crocosphaera
to warming under low-Fe conditions and use a modeling framework to incorporate our
results and those of Jiang et al. (2018). We then use this approach to explore the impacts
of a projected warmer ocean on the biogeographic distributions and biogeochemical
functions of both Trichodesmium and Crocosphaera on a global scale.

26
2. Methods
2.1. Culturing Methods
Triplicate cultures of Crocosphaera watsonii strain WH0005 were grown at five
ecologically relevant temperatures spanning the thermal range of Crocosphaera: 20°C,
22°C, 27°C, 32°C, and 36°C. Cultures were maintained under two Fe conditions, Fe-
replete and Fe-limited, in microwave-sterilized medium made with 0.2 micron-filtered
surface seawater collected from the Sargasso Sea using a trace metal clean towfish
system. The medium was amended with Aquil concentrations of phosphate (10 μM)
passed through an activated Chelex 100 resin column (BioRad Laboratories, Hercules, CA,
USA) to remove contaminating Fe, and with vitamins and a modified Aquil trace metals
stock (1.21 x 10
-7
M Mn, 7.97 x 10
-8
M Zn, 1.00 x 10
-7
M Mo, and 5.03 x 10
-8
M Co) (Sunda
et al., 2005). Fe-replete medium was amended with 250 nM Fe, while 5 nM was directly
added to Fe-limited cultures during periodic dilutions. The media was buffered with 25
μM EDTA, and the resulting average concentration of dissolved free inorganic iron, which
is the form most bioavailable to phytoplankton, was calculated for the different
experimental conditions following Jabre and Bertrand (2020) (see Supplementary
Methods, Table S1, Figure S1). The Fe concentrations for the replete and limited media
include the added Fe-EDTA, and a measured background Fe concentration in the Sargasso
seawater of 0.54 nM (Held et al., 2020).
As in almost all laboratory studies, both the dissolved Fe and cell concentrations
used in our culture experiments were necessarily higher than those found in the open
ocean (Jiang et al., 2018). This Fe concentration increase in our culture experiments allows
27
us to maintain a sufficient density of cells to carry out the required physiological
measurements as well as molecular sampling for future analyses. Thus, the growth-limiting
Fe concentration was determined as the concentration that would induce lower growth
rates relative to the higher, replete growth Fe concentration, rather than concentrations
expected from oligotrophic regimes.
Cultures were maintained semi-continuously in 2.5 L polycarbonate bottles on a
12:12 light:dark cycle in temperature-controlled incubators at 150 μmol photons m
-2
s
-1
,
and diluted every three days with media adjusted to the experimental temperature to
maintain steady-state exponential growth for at least 2 months. All bottles used in the
study were soaked in a 1% Citranox detergent for 24 hours, rinsed in Milli-Q (18.2 Ω)
water, and then soaked in 10% HCl for a week, rinsed in Milli-Q and microwave-sterilized
before use. 0.2 micron filter-sterilized nutrients, trace metals, and vitamins were amended
to the natural seawater base using sterile pipette tips rinsed three times with 1% HCl and
three times with microwave sterilized Milli-Q water immediately prior to use.
Dilutions were conducted based on in-vivo fluorescence measured in real-time on
a 10AU Fluorometer (Turner Designs, San Jose, CA). Cell samples were preserved in 0.5%
0.2-μm filtered glutaraldehyde to validate in-vivo growth rates using on an Olympus BX51
epifluorescence microscope. The specific growth rate (μ) was then calculated using the
equation μ = (ln N1 – ln N0) / t, where N refers to cell densities and t is time in days. The
cell size was determined by measuring the cell diameters of at least 20 cells per sample
using the CaptaVision Imaging Software (Commack, NY, USA).

28
2.2. Elemental Stoichiometry
To measure particulate organic carbon and nitrogen (POC and PON), 30-40 mL of
culture from each experimental treatment was filtered onto pre-combusted glass
microfiber filters (Whatman, Grade GF/F), dried in an oven at ~60°C, and then pelleted
and analyzed on a 4010 Costech Elemental Analyzer calibrated with methionine and
acetanilide (Jiang et al., 2018).

2.3. Nitrogen Fixation Rates
N2-fixation rates were measured using the acetylene reduction assay following
previously described methods (Garcia et al., 2013). Briefly, duplicate 40 mL culture
samples were collected from the triplicate experimental cultures and 6 mL of acetylene
was injected into 35 mL of headspace at the start of the dark period in 75 mL sealed-top
bottles. All-night (~12 hours) accumulation of acetylene was measured at the end of the
incubation period on a gas chromatograph GC-8a (Shimadzu Scientific Instruments,
Columbia, Maryland), and the measured ethylene was converted to fixed N2 using a ratio
of 3:1 and a Bunsen coefficient of 0.086. Converted N2-fixation rates were then
normalized to particulate organic nitrogen (N-specific N2-fixation rates).

2.4. Carbon Fixation Rates
To approximate net primary productivity (C-fixation), 10 mL sub-cultures from
each experimental replicate were incubated for 5 hours with H
14
CO3 beginning 2 hours
29
after the start of the light period under the same experimental growth conditions (e.g.
light, temperature, etc.). Samples were then filtered onto glass microfiber filters (GF/F)
and stored in the dark overnight before analysis using a Wallac System 1400 liquid
scintillation counter (Jiang et al., 2018). Calculated rates were then normalized to
particulate organic carbon (C-specific C-fixation rates).

2.5. Intracellular Iron Content
Intracellular Fe samples were obtained by filtering cultures onto acid-washed
0.2μm Supor polyethersulfone filters (Pall Laboratory) and rinsed with oxalate reagent to
remove extracellular trace metals (Tovar-Sanchez et al., 2003). All filtration and sample
processing steps were conducted in a class 100 trace metal clean environment. Filters
were then digested with 5 mL of 50% nitric acid (HNO3) amended with 10 ppb Indium as
an internal standard at 95°C for 5 days in individual, 30 mL perfluoroalkoxy vials (Savillex).
Following acid-digestion, the filters were removed with plastic tweezers, and samples
were dried overnight at 100ºC. Samples were resolubilized in 200 µL of 1:1 concentrated
HNO3 and hydrochloric acid (HCl), sealed, and heated for ~2-3 hours and then allowed to
cool. The sample was dried and resuspended in 5 mL of 0.1M distilled HNO3 and then
analyzed by inductively coupled plasma mass spectrometry (ICP-MS, Element 2, Thermo).
Intensities of
56
Fe were calibrated with a 0.1-300 ppb metal reference standard curve.
115
In was monitored as an internal standard to correct for matrix suppression and any
sample loss during digestion (Hawco et al., 2020). Two procedural blank filters for each
treatment were also analyzed and subtracted from the measured sample values.
30

2.6. Fe Quotas and Resource Use Efficiencies
Fe quotas (µmol Fe / mol POC) were calculated using Fe concentrations measured
via ICP-MS and POC (see above). Nitrogen-specific Iron Use Efficiencies (N-IUEs) were
calculated by normalizing measured N2-fixation rates to intracellular Fe content (mol N
fixed hr
-1
mol cellular Fe
-1
) (Kustka et al., 2003). Similarly, Carbon-specific Iron Use
Efficiencies (C-IUEs, mol C fixed hr
-1
mol cellular Fe
-1
) were calculated by normalizing
measured C-fixation rates to intracellular iron.

2.7. Statistical Analyses
The significance of Crocosphaera’s response to warming under Fe-replete and Fe-
limited conditions for all reported physiological parameters was assessed via two-way
ANOVA and a Tukey post-hoc analysis at p-value < 0.05.

2.8. Modeling Methods
We modeled Crocosphaera under Fe limitation and warming using two different
models, an additive model and an interactive model, following Jiang et al. (2018). The
additive model assumes that growth is independently affected by temperature and iron
limitation whereby changes to one variable does not affect the other variable’s impact on
growth. While this is the traditional relationship used in biogeochemical models, recent
studies have demonstrated interactive relationships between temperature and iron such
31
that warming may influence phytoplankton iron use efficiency (Jiang et al., 2018; Jabre
and Bertrand 2020). Thus, our interactive model attempts to capture the observed multi-
stressor impacts of Fe limitation and warming. For our additive model, we assumed a fixed
half-saturation constant for Fe indicating that temperature did not affect Fe use. Our
interactive model incorporates a flexible half-saturation constant that varies with
temperature.

2.8.1. Additive model
We assumed that Fe-limited growth followed Monod growth kinetics where
growth rate (μ) is defined as (Figure S2):

μadditive = μTmax
[Fe]
[Fe]+KspCroco
Eq. 1

μTmax is the calculated maximum growth rate (see Equations 3 & 5), and [Fe] is the iron
concentration. KspCroco is the fixed half-saturation constant for iron under the optimal
growth temperature (27ºC) calculated by deriving Equation 1 to set μTmax of replete and
limited growth as equal, and then solving for KspCroco:

KspCroco =
(FelimitFereplete)(μreplete - μlimit)
(Ferepleteμlimit - Felimitμreplete)
Eq. 2

32
where replete and limit subscripts denote the Fe-limited and replete experimental
conditions for Fe concentrations and growth rates (μ) at 27ºC.

The temperature dependence for growth rate was estimated by calculating μTmax
for each experimental temperature as:

μTmax =
μreplete(Fereplete + KspT)
Fereplete
Eq. 3

where KspT is the calculated Ksp at each experimental temperature using a similar
equation to Eq 2:

KspT =
(FeTlimitFeTreplete)(μTreplete - μTlimit)
(FeTrepleteμTlimit - FeTlimitμTreplete)
Eq. 4

Treplete and Tlimit subscripts denote the Fe-limited and replete experimental conditions
for Fe concentrations and growth rates (μ).

Calculated μTmax rates were then fit to a polynomial curve to model temperature limited
growth, (Breitbarth et al. 2007) (Figure S3A):

μTmax = -0.005816T
2
+ 0.32290T – 4.13443 Eq. 5

33
μTmax was bounded by the experimental thermal response norm with growth rates below
20ºC and above 36ºC set to 0.
Since the Fe concentrations used in the culture study were higher than that found
naturally in oligotrophic regimes, the Ksp values calculated from these cultures are
correspondingly higher than Ksps in the ocean. To reconcile this discrepancy, both KspCroco
and the calculated temperature-dependent Ksps (KspT) were scaled down to convert Fe
concentrations used in our biomass-dense cultures to those reflective of open-ocean
conditions as in Jiang et al. (2018). In that study with Trichodesmium, a similar Fe scaling
factor (αFe_Tricho) was calculated as the ratio of the Ksp of in situ Trichodesmium (Kspfield)
based on a pre-established threshold of ~0.33 nM Fe for isiB (Fe stress biomarker) gene
expression, and the Ksp at the optimal growth temperature in culture. As the Kspfield is
unknown for Crocosphaera, the effective Fe concentration used in our modeling input was
based on a modified scaling factor for Crocosphaera (αFe_Croco) calculated using the
Trichodesmium Fe scaling factor (αFe_Tricho = 0.02564, Jiang et al., 2018) and the ratios of
the Ksp values under optimal growth conditions for Crocosphaera (2.4 nM, Eq. 2) and
Trichodesmium (12.9 nM, Jiang et al. 2018):

αFe_Croco = αFe_Tricho
KspCroco
KspTricho
= 0.00478 Eq. 6

αFe_Croco was used to convert all experimentally derived Ksp values (KspCroco and KspT for
each experimental temperature) so that they could be used with model outputs. To verify
34
this scaling exercise, we can use the POC:dissolved Fe ratio as a simple proxy for relative
Fe availability to the algal cells in order to compare the values in our cultures with field
samples. The ratio of Crocosphaera biomass to total Fe added (POC:dissolved Fe) in our
limited cultures under the optimum growth temperature of 27°C was calculated to be
4.26x10
4
mol:mol, which roughly corresponds to the calculated POC:dFe ratio of 3.64x10
4

based on oligotrophic POC field data (Martiny et al., 2013) and a dissolved Fe
concentration of 0.11 nM, which is reasonable for low-Fe regimes (Jiang et al., 2018). See
Supplementary Materials for further discussion on scaling Fe quota values.

2.8.2. Interactive model
The interactive model replaced KspCroco in Equation 1 with KspTi, the temperature
dependent half saturation constant, in order to capture the interactive effect of Fe
limitation and warming.

μinteractive = μTmax
[Fe]
[Fe]+KspTi
Eq. 7

The interactive relationship between the calculated KspT (Equation 4) and temperature (T)
was then estimated as the best polynomial fit to the data, KspTi (Figure S3B):

KspTi = 0.00018T
2
– 0.01049T + 0.161015 Eq. 8

35
2.8.3. Modeling N2-fixation rates
A linear relationship was used to relate N2-fixation and growth when modelling
Trichodesmium N2-fixation rates in Jiang et al., 2018 (Hutchins et al. 2013, Jiang et al.
2018). For Crocosphaera, N2-fixation rates (fmol N hr
-1
cell
-1
) and cell-specific growth rates
across all growth temperature and Fe experimental conditions were measured at four time
points during steady-state exponential growth to establish the linear relationship using a
two-way least squares fit between the two rates under varying temperature and Fe
availability (Figure S4).

Nitrogen fixation = 18.022μ - 0.1923 Eq. 9

Interactive or additive effects of temperature and Fe on N2-fixation are thus determined
by either additive or interactive growth (μ).

2.8.4. Modeling Iron Use Efficiencies (IUEs)
N-IUEs were determined as in Jiang et al. (2018). Briefly, calculated N-IUEs from
each temperature treatment were linearly related to the scaled Fe concentration (Figure
S5A). Then, the N-IUE slope (m) and intercepts (b) were individually related to temperature
via a best polynomial fit to establish the impact of temperature (T) on the relationships
between the slope (m) and intercepts (b) of calculated IUEs and the scaled Fe
concentration (Figure S5B-C). The maximum modeled response for m outside of the
36
experimental growth temperatures (22°C, 27°C, 32°C) was set to 13.4616 while the
minimum modeled response for b was set to 5.1718.

m = 2.3195T
2
- 126.6903T + 1672.5701 Eq. 10

b = -5.2694T
2
+ 289.2894T - 3808.7982 Eq. 11

Finally, N-IUE was estimated as a linear function of Fe concentration and temperature
where m is slope represented by Eq. 10, b is the intercept represented by Eq. 11, and [𝐹𝑒]
is the scaled Fe concentration.

N-IUE = m[Fe] + b Eq. 12

3. Results
The thermal optimum for Crocosphaera growth under both Fe-replete and Fe-
limited treatments was at 27°C, with the thermal minimum and maximum at 20°C and
36°C, respectively. As expected, under Fe-limitation Crocosphaera growth rates were
significantly lower relative to Fe-replete cultures across experimental temperatures of
22°C, 27°C, and 32°C where cultures exhibited active growth (Figure 1). In addition,
growth at 32°C (supra-optimum) was significantly higher than growth at 22°C (sub-
37
optimum). At the optimum growth temperature of 27ºC, both Fe-replete and Fe-limited
cells had significantly smaller diameters than cells growing at 22ºC (Figure S6). In addition,
under Fe-limited conditions, cell diameter was larger at 32ºC than 27ºC.
Calculated Nitrogen-specific Iron Use Efficiencies (N-IUEs, mol N fixed hr
-1
mol
intracellular Fe
-1
), showed that at the optimal growth temperature of 27°C, Fe-limited N-
IUE increased by 66% relative to Fe-replete cultures (Figure 2A). N-IUEs were not
significantly different between Fe treatments at 22°C (sub-optimum) and 32°C (supra-
optimum) (p-value > 0.05), although 22°C N-IUEs exhibited a 64% decrease between
replete and limited cultures. Crocosphaera N-IUE increased significantly from the cooler
temperature to the optimum N-IUE temperature, especially under Fe-limiting conditions
where N-IUE increased by nearly 141% from 22°C to 27°C (Figure 2A).
Carbon-specific-IUEs (C-IUEs, mol C fixed hr
-1
mol intracellular Fe
-1
) exhibited
similar patterns to N-IUEs, such that C-IUEs were also highest at the growth thermal
optimum (27°C), with higher IUEs under Fe-limiting conditions (Figure 2B). The minimum
C-IUE for both Fe treatments occurred at 22°C. The increase from 22°C to 27°C for Fe-
replete cultures was 280%, and the IUE differential was even higher under Fe-limiting
conditions, with an increase of >1,600%. Again, like N-IUEs, C-IUEs dropped from 27°C
to 32°C by 65% to 68% for Fe-limited and Fe-replete cultures, respectively (Figure 2B).
Similar to growth rates, Crocosphaera N-specific N2-fixation rates (hr
-1
) were
significantly lower under Fe-limiting relative to Fe-replete conditions. As with IUEs, there
was also a trend of increases in N2-fixation rates from 22°C to 27°C. Regardless of Fe
availability, N2-fixation rates were lowest at 22°C while cultures grown at the warmer
38
temperatures (27°C, 32°C) had the highest rates, ranging from 338-388% higher under
Fe-limiting conditions (Figure 2C).
C-fixation rates (hr
-1
) significantly decreased between Fe-replete and Fe-limited
cultures at 22°C, whereas at the warmer temperatures of 27°C and 32° Fe-limited rates
were comparable to Fe-replete rates (Figure 2D). Consistent with the other three
metabolic parameters, Fe-limited cultures also exhibited a significant temperature-driven
increase in C-fixation rates of 332% between 22°C and 27°C. However, at the higher end
of their thermal range (27°C to 32°C), Fe-limited C-fixation rates decreased significantly
from 27°C to 32°C by nearly 14.5% (Figure 2D).
To place these culture-based results in a global context, we modeled N2-fixation
and N-IUE using the experimentally derived equations as a function of temperature and
Fe (see Methods). National Center for Atmospheric Research (NCAR) Community Earth
System Model (CESM) projected Fe concentrations and temperatures under the IPCC RCP
8.5 “business-as-usual” emissions scenario were then applied to these equations to model
global N2-fixation and N-IUE outputs. Model results projected an average global increase
of 47% in Crocosphaera N-IUEs from 2010 to 2100, with some regions experiencing
increases that exceeded 1,300%, particularly the higher latitudes bordering the subtropical
oceans (Figure 3A, sensitivity analysis in Figure S7). The average global N-IUEs increases
also included decreases of over 183% in some of the warmest regions of the tropics, such
as the western Pacific and the central Indian Ocean. The model projected that
Crocosphaera N2-fixation rates will increase globally in 2100 by 91% on average, but with
similarly large regional differences (Figure 3B, sensitivity analysis in Figure S8). The largest
increases were concentrated in the higher latitudes bordering the central gyres, areas
39
where current temperatures are well below Crocosphaera’s thermal growth optimum and
likewise constrain IUEs (Figure 3A). Conversely, the low-latitude tropical oceans where
future warming will likely exceed Crocosphaera’s thermal range and reduce IUEs will also
experience decreased rates of N2-fixation by this cyanobacterium (Figure 3B).

4. Discussion
Our study demonstrates that temperature may be an important determinant of Iron
Use Efficiency and Fe-dependent metabolism in Crocosphaera, with important implications
for global biogeochemistry in a warming ocean. Low temperatures may reduce enzyme
activity and impede metabolic functions, constraining some diazotrophs to the warm
subtropical and tropical oceans (Webb et al., 2009; Sohm et al., 2011; Fu et al., 2014). For
example, decreased respiration rates reduce the ability to remove intracellular O2, which
can directly inhibit N2-fixation (Inomura et al., 2019). Similarly, one study on the unicellular
N2-fixing cyanobacterium Cyanothece suggested that low temperature may impede the
onset of N2-fixation, due to possible delays in de-novo synthesis of functional nitrogenases
for nighttime N2-fixation (Brauer et al., 2013). Thus, thermal rate enhancement of key
metabolic processes may be an important response mechanism to compensate for Fe
limitation in low-Fe oceanic regimes.
Warmer temperatures may enhance photosynthesis and respiration in
Crocosphaera, which could in turn indirectly promote N2-fixation (Raven and Geider, 1988;
Großkopf and LaRoche, 2012; Inomura et al., 2019), perhaps by helping to offset the
energy costs of daily degradation and de novo synthesis of nitrogenase. In fact, maintaining
40
photosynthetic function may be a key aspect of retaining N2-fixing capabilities by
providing carbon for respiration, which supplies energy necessary for nitrogenase
function. N2-fixation is a notoriously slow, energy-intensive process requiring 16 ATP to
reduce one molecule of N2 into two molecules of ammonia (Sohm et al., 2011).
Previous studies have suggested that both temperature (Finkel et al., 2010) and
nutrient stress (Peter and Sommer, 2013) may cause a reduction in phytoplankton cell size.
Furthermore, it has been shown that Crocosphaera will reduce its cell size under Fe-limiting
growth conditions (Jacq et al., 2014). A smaller cell size and thus, a larger surface area-to-
volume ratio, is beneficial for Crocosphaera under Fe stress because it enables better
access to Fe relative to larger cells. Our results indicated that warming may significantly
reduce cell size, with Crocosphaera growing at 27ºC measuring significantly smaller than
cells growing at 22ºC. In addition, we also saw a small nutrient stress effect whereby Fe-
limited cells at 27ºC were slightly smaller than replete cells at the same growth
temperature. These patterns of temperature and Fe-induced cell size reduction at 27ºC
may contribute to the enhanced IUEs observed for Fe-limited Crocosphaera. Surprisingly,
cell size actually increased from 27ºC to 32ºC. Crocosphaera cell size varies through its cell
cycle which can be affected by cell division, photosynthesis, and carbon catabolism to
power N2-fixation (Dron et al., 2012). While this response may have important
implications for Crocosphaera function at the upper bound of its thermal range, the
mechanisms underlying this response remain poorly investigated.
We compared the physiological parameters measured for Crocosphaera against the
responses of Trichodesmium reported by Jiang et al. (2018). Both Trichodesmium and
Crocosphaera exhibited the slowest growth and lowest N2-fixation and C-fixation rates at
41
22°C, with Fe-limitation driving further significant decreases in growth and function.
However, under warmer temperatures, a thermally driven alleviation of Fe-limitation was
observed in both diazotrophs, albeit in contrasting ways. For example, Trichodesmium
exhibited higher N-IUEs at 32°C that corresponded with a shift in the thermal optimum
for N2-fixation from 27°C under Fe-replete conditions to 32°C under Fe-limited
conditions, indicating an interactive effect between warming and low-Fe availability that
may offset Fe limitation to enhance function (Figure S9, Jiang et al. 2018). In contrast,
Crocosphaera N2-fixation and IUE thermal optima did not shift, and instead remained
constant at 27°C. However, at this thermal optimum, Crocosphaera IUEs were much higher
under Fe-limited conditions relative to Fe-replete conditions. By comparison, Fe-limitation
led to significant decreases in Trichodesmium IUE across all experimental temperatures,
likewise suggesting an interactive effect of temperature and Fe-limitation in enhancing
IUE.
It is possible that the higher IUEs for Fe-limited Crocosphaera relative to the Fe-
replete cultures are influenced by luxury uptake of Fe under replete conditions. Thus, for
replete cultures, the calculated IUEs can be considered a minimum IUE estimate. However,
the same trend is not seen for Trichodesmium, where Fe-limited cultures exhibited lower
IUEs across the three experimental temperatures compared to Fe-replete cultures (Jiang
et al. 2018), suggesting that additional mechanisms besides luxury uptake need to be
considered.
These differences in diazotrophic IUEs appear to drive the varying responses of Fe-
limited N2-fixation and C-fixation rates to warming. Trichodesmium N2-fixation and C-
fixation rates follow a similar pattern to growth in response to temperature and Fe-
42
limitation, such that the rates increase in a step-wise manner as a function of temperature
with a thermal optimum at 32°C, where N-IUE and C-IUE were also highest (Jiang et al.,
2018). For Crocosphaera, while IUEs were significantly higher at 27°C than at 32°C, N2-
fixation and C-fixation rates were comparable at those temperatures. It is unclear why
these rates do not reflect the responses of growth or IUE to Fe-limitation and warming.
Higher concentrations of intracellular iron (Fe quotas) at 32°C relative to 27°C under both
Fe-replete and Fe-limiting conditions suggest that metabolic rates may be maintained at
the expense of increasing intracellular Fe requirements (Table S2). One possibility is lower
biodilution rates of cellular Fe, whereby Fe uptake rates might be unchanged while growth
rates are slowed at 32°C relative to 27°C, resulting in higher cellular Fe quotas.
Another potential explanation is that warming beyond Crocosphaera’s optimal
growth temperature disrupts the Fe-conserving diel patterns of photosynthetic and N
2
-
fixing enzyme synthesis and degradation, thereby increasing the cellular Fe demand
required to maintain functionality. This temperature-induced disruption could also prompt
changes to protein expression that enable Crocosphaera to maintain photosynthetic
activity and drive N2-fixation. For example, Saito et al. (2011) noted that when Fe-limited,
Crocosphaera seemingly increases the abundance of proteins involved in C-fixation, which
indicates the existence of mechanisms that increase C-fixation efficiency under stress
(Saito et al., 2011). While the mechanisms of Crocosphaera warming responses are
unknown, warmer temperatures in conjunction with limited Fe availability may
interactively influence protein abundance and activity patterns that underlie our observed
physiological trends. Trichodesmium is unique in its ability to simultaneously
43
photosynthesize while fixing N
2
and has thus developed strategies that may benefit from
increased temperatures under Fe-limitation. These include both spatial and temporal
sequestration of nitrogenase and N2-fixation throughout the photoperiod (Bergman et al.,
2013). More work is needed to better understand the mechanistic underpinnings of
thermally enhanced metabolic rates at the expense of higher Fe requirements.
The incorporation of experimental data into quantitative models allows us to better
understand the impacts of warming on diazotroph function on a global scale, and to
evaluate the potential biogeochemical implications in a future, warmer ocean scenario.
Here we compare the predictions of an additive model and an interactive model using
experimentally observed relationships between Fe availability and temperature on IUE
and metabolic rates. Our results generated from an interactive model show that ocean
warming projected under IPCC’s RCP 8.5 scenario may alleviate prevailing Fe-limitation of
N2-fixation by the globally important diazotrophs Trichodesmium and Crocosphaera
throughout much of the world’s oligotrophic oceans, with N2-fixation rates increasing by
~22% (Jiang et al. 2018) and 91%, respectively. Under projected future ocean conditions,
the additive and interactive models largely predict similar Crocosphaera N2-fixation rates
across the global subtropical and tropical oceans with a <1% average difference between
the two models (Figure S10A). The small difference between the two projections was
driven by slightly higher N2-fixation rates projected by the additive model compared to
the interactive model in regions where water temperatures were either above (equatorial
Pacific) or below (high-latitudes) Crocosphaera’s thermal optimum. This is in contrast to the
published Trichodesmium response showing that the additive effects of temperature and
44
iron were lower than the synergistic interactive effects, especially in the equatorial Pacific
where warm temperatures enhanced N2-fixation rates (Jiang et al. 2018, Figure S10B).
Regardless of the modeling approach used, it is clear that warming enhances N2-fixation
despite Fe scarcity, resulting in significant increases of new nitrogen input across the Fe-
limited oceans. Moreover, due to a projected near doubling of Crocosphaera nitrogen
fixation rates under future conditions, Crocosphaera’s contribution to global nitrogen
fixation may increase relative to Trichodesmium in the future. Collectively, these results
suggest that although the current low latitude oceans are predominantly nitrogen-limited
(Moore et al., 2013), thermal rate enhancement of diazotrophy may help to alleviate this
stress, even when Fe is also limiting or co-limiting with nitrogen.
To assess the biogeographical implications of the distinct Trichodesmium and
Crocosphaera responses to Fe-limitation and warming, we compared the interactive model
outputs for the percent changes in N-IUE and N2-fixation from 2010 to 2100. These are
based on the assumption that physiological responses depend on Fe-availability and vary
as a function of temperature. Since the magnitude of change is different between the two
diazotrophs, we used quartiles to identify regions with the largest change for each group.
Here we present results using the 75% quartile (top 25%), but the conclusions are
independent of this threshold (Figure S11 and S12). Figure 4A shows the regions where
the N-IUE percent change from 2010 to 2100 for Crocosphaera and Trichodesmium were
within this top quartile. Minimal overlap occurs for N-IUEs, indicating a niche
differentiation in thermal responses and Fe requirements that could drive spatial
divergence between these two diazotrophs in the future. In a warmer ocean scenario,
Trichodesmium is most dominant in the Fe-limited tropical oceans, as the greatest increases
45
in its IUEs occur at the upper end of its thermal range, from 27ºC to 32ºC (Jiang et al.
2018). In contrast, IUEs in Crocosphaera increase most in the lower portion of its thermal
curve, from 22ºC to 27ºC, facilitating its range expansion into the newly warmed higher
latitudes. Thus, taxon-specific changes to IUEs directly influence the prevalence and
patterns of N2-fixation. Coincidentally, the contrasting thermal responses of both groups
lead to net diazotrophic IUE increases that encompass virtually the entire basins of the
oligotrophic oceans where conditions are otherwise suitable for N2-fixation (i.e., where
temperatures and nitrate levels remain below critical thresholds, Figure 4A).
A similar assessment using projected increases in N2-fixation shows a parallel trend
of future latitudinal partitioning between the two nitrogen fixers (Figure 4B). In this
analysis, the regions of overlap are more expansive between Trichodesmium and
Crocosphaera, suggesting a potential for competition. However, based on our N-IUE
analysis it is likely that Crocosphaera may emerge as the winner in the higher latitude
oceans, since projected temperatures at those latitudes increase to near Crocosphaera’s
thermal optimum.
It is important to note that while our analysis highlighted regions where net
thermally enhanced N2-fixation rates are predicted to occur, our models also identified
regions where ocean warming may exceed the thermal optimum of Crocosphera and result
in negative changes to N2-fixation rates in the future. These patterns can be found in the
individually modeled response for both diazotrophs as well as in the overlap analysis.
Whereas Trichodesmium function may decrease across parts of the western Equatorial
Pacific as well as the Indian Ocean (Jiang et al., 2018), our study found that Crocosphaera
may experience decreases in N2-fixation throughout the tropical low-latitude oceans as
46
water temperature greater than 27°C become an increasingly prevalent feature across this
region.
Of course, changes in cell-specific rates are only one part of the equation in
determining overall changes to global new nitrogen inputs; we also need to consider
overall and group-specific diazotroph biomass in the context of regional temperature-
driven changes. However, in both Crocosphaera and Trichodesmium, increases in N2
fixation rates such as those observed and modeled here are closely linearly correlated with
higher growth rates (Fig. S4, Hutchins et al., 2013; Jiang et al., 2018), and thus potentially
with biomass increases as well. Our current model does not resolve the net change to
diazotroph biomass between 2010 and 2100 and references the trends in NIUEs and N2-
fixation rates as a proxy for net new nitrogen input. Including a biomass parameter through
the use of nifH genes to quantify group-specific marine diazotroph biomass could be an
illuminating parameter in future iterations of this modeling exercise.
While our culture experiments and model analysis focused on temperature and Fe
availability, other important constraints on diazotrophic cyanobacteria including
phosphate (P) limitation and Fe/P co-limitation should also be considered (Moore et al.,
2013; Zehr and Capone, 2020). Studies have shown that Fe availability influences the
nitrogen and phosphorus cycles (Browning et al., 2017). If warming changes iron use
efficiency, then it will also change the interactions with these other cycles in currently
unknown ways. Ocean warming necessitates a deeper understanding of the interacting
effects of temperature and all other major drivers of primary productivity. Only when this
is achieved can we quantify the net impacts of these environmental drivers on microbially-
mediated biogeochemistry. In this study, we incorporated our experimental observations
47
into global projections for N-IUE and N2-fixation rates that revealed potential implications
for the marine nitrogen cycle in a warmer, Fe-limited ocean. By comparing the model
results of Crocosphaera and Trichodesmium, we found that genus-specific responses of the
two cyanobacterial N2-fixers may alter existing spatiotemporal patterns of marine nitrogen
fixation and thus, the regional availability of limiting nutrients in the future oligotrophic
ocean. These thermally-driven shifts in diazotroph biogeography, and the accompanying
overall increases in future global marine N2-fixation, may profoundly transform existing
patterns of biological productivity and Fe and nitrogen biogeochemistry in open ocean
regimes. Additional research efforts are needed to uncover the molecular underpinnings
of these response mechanisms and provide more accurate predictions of diazotroph
distributions and activity changes due to future ocean warming.

48
References
Barton, A. D., Irwin, A. J., Finkel, Z. V., and Stock, C. A. (2016). Anthropogenic climate
change drives shift and shuffle in North Atlantic phytoplankton communities. Proc.
Natl. Acad. Sci. U. S. A. 113, 2964. doi:10.1073/pnas.1519080113.

Behrenfeld, M. J., Bale, A. J., Kolber, Z. S., Aiken, J., and Falkowski, P. G. (1996).
Confirmation of iron limitation of phytoplankton photosynthesis in the equatorial
Pacific Ocean. Nature 383, 508. doi:10.1038/383508a0.

Behrenfeld, M. J., O’Malley, R. T., Siegel, D. A., McClain, C. R., Sarmiento, J. L., Feldman, G.
C., et al. (2006). Climate-driven trends in contemporary ocean productivity. Nature
444, 752. doi:10.1038/nature05317.

Bergman, B., Sandh, G., Lin, S., Larsson, J., and Carpenter, E. J. (2013). Trichodesmium - a
widespread marine cyanobacterium with unusual nitrogen fixation properties.
FEMS Microbiol. Rev. 37, 286–302. doi:10.1111/j.1574-6976.2012.00352.x.

Boyd, P. W., Rynearson, T. A., Armstrong, E. A., Fu, F., Hayashi, K., Hu, Z., et al. (2013).
Marine phytoplankton temperature versus growth responses from polar to tropical
waters - outcome of a scientific community-wide study. PLoS One 8, e63091.
doi:10.1371/journal.pone.0063091.

Brauer, V. S., Stomp, M., Rosso, C., van Beusekom, S. A. M., Emmerich, B., Stal, L. J., et al.
(2013). Low temperature delays timing and enhances the cost of nitrogen fixation
in the unicellular cyanobacterium Cyanothece. ISME J. 7, 2105.
doi:10.1038/ismej.2013.103.

Breitbarth, E., Oschlies, A., and LaRoche, J. (2007). Physiological constraints on the global
distribution of Trichodesmium: effect of temperature on diazotrophy. Biogeosciences
4, 53–61. doi:10.5194/bg-4-53-2007.

Browning, T. J., Achterberg, E. P., Yong, J. C., Rapp, I., Utermann, C., Engel, A., et al. (2017).
Iron limitation of microbial phosphorus acquisition in the tropical North Atlantic.
Nat. Commun. 8, 15465. doi:10.1038/ncomms15465.

Campbell, L., Carpenter, E., Montoya, J., Kustka, A., and Capones, D. (2005). Picoplankton
community structure within and outside a Trichodesmium bloom in the
southwestern Pacific Ocean. Vie Milieu Paris 55, 185–195.

Chappell, P. D., and Webb, E. A. (2010). A molecular assessment of the iron stress response
in the two phylogenetic clades of Trichodesmium. Environ. Microbiol. 12, 13–27.
doi:10.1111/j.1462-2920.2009.02026.x.
49
Dron, A., Rabouille, S., Claquin, P., Le Roy, B., Talec, A., and Sciandra, A. (2012). Light-dark
(12:12) cycle of carbon and nitrogen metabolism in Crocosphaera watsonii WH8501:
relation to the cell cycle. Environ. Microbiol. 14, 967–981. doi:10.1111/j.1462-
2920.2011.02675.x.

Falkowski, P. G., Barber, R. T., and Smetacek, V. (1998). Biogeochemical controls and
feedbacks on ocean primary production. Science 281, 200. Available at:
http://science.sciencemag.org/content/281/5374/200.abstract.

Finkel, Z. V., Beardall, J., Flynn, K. J., Quigg, A., Rees, T. A. V., and Raven, J. A. (2010).
Phytoplankton in a changing world: cell size and elemental stoichiometry. J.
Plankton Res. 32, 119–137. doi:10.1093/plankt/fbp098.

Fu, F. X., Yu, E., Garcia, N. S., Gale, J., Luo, Y. S., Webb, E. A., et al. (2014). Differing
responses of marine N2 fixers to warming and consequences for future diazotroph
community structure. Aquat. Microb. Ecol. 72, 33–46. doi:10.3354/ame01683.

Garcia, N. S., Fu, F.-X., Breene, C. L., Yu, E. K., Bernhardt, P. W., Mulholland, M. R., et al.
(2013). Combined effects of CO2 and light on large and small isolates of the
unicellular N2-fixing cyanobacterium Crocosphaera watsonii from the western
tropical Atlantic Ocean. Eur. J. Phycol. 48, 128–139.
doi:10.1080/09670262.2013.773383.

Großkopf, T., and LaRoche, J. (2012). Direct and indirect costs of dinitrogen fixation in
Crocosphaera watsonii WH8501 and possible implications for the nitrogen cycle.
Front. Microbiol. 3, 236. doi:10.3389/fmicb.2012.00236.

Hawco, N. J., Fu, F., Yang, N., Hutchins, D. A., and John, S. G. (2020). Independent iron and
light limitation in a low-light-adapted Prochlorococcus from the deep chlorophyll
maximum. ISME J. doi:10.1038/s41396-020-00776-y.

Held, N. A., Sutherland, K. M., Webb, E.A., McIlvin, M. R., Cohen, N. R., Devaux, A. J., et al.
(2020) Mechanisms and heterogeneity of mineral use by natural colonies of theh
cyanobacterium Trichodesmium. In Review. doi:
https://doi.org/10.1101/2020.09.24.295147

Huertas, I. E., Rouco, M., López-Rodas, V., and Costas, E. (2011). Warming will affect
phytoplankton differently: evidence through a mechanistic approach. Proceedings
of the Royal Society B: Biological Sciences 278, 3534–3543.
doi:10.1098/rspb.2011.0160.

Hutchins, D. A., and Boyd, P. W. (2016). Marine phytoplankton and the changing ocean
iron cycle. Nat. Clim. Chang. 6, 1072. doi:10.1038/nclimate3147.

50
Hutchins, D. A., and Fu, F. (2017). Microorganisms and ocean global change. Nat Microbiol
2, 17058. doi:10.1038/nmicrobiol.2017.58.

Hutchins, D. A., Fu, F.-X., Webb, E. A., Walworth, N., and Tagliabue, A. (2013). Taxon-
specific response of marine nitrogen fixers to elevated carbon dioxide
concentrations. Nat. Geosci. 6, 790–795. doi:10.1038/ngeo1858.

Inomura, K., Deutsch, C., Wilson, S. T., Masuda, T., Lawrenz, E., Bučinská, L., et al. (2019).
Quantifying oxygen management and temperature and light dependencies of
nitrogen fixation by Crocosphaera watsonii. mSphere 4, e00531-19.
doi:10.1128/mSphere.00531-19.

IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and
III to the Fifth Assessment Report of the Intergovernmental Panel on Climate
Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva,
Switzerland, 151 pp.

Jabre, L., and Bertrand, E. M. (2020). Interactive effects of iron and temperature on the
growth of Fragilariopsis cylindrus. Limnology and Oceanography Letters.
doi:10.1002/lol2.10158.

Jacq, V., Ridame, C., L’Helguen, S., Kaczmar, F., and Saliot, A. (2014). Response of the
Unicellular diazotrophic cyanobacterium Crocosphaera watsonii to iron limitation.
PLoS One 9, 9. doi:10.1371/journal.pone.0086749.

Jiang, H.-B., Fu, F.-X., Rivero-Calle, S., Levine, N. M., Sañudo-Wilhelmy, S. A., Qu, P.-P., et
al. (2018). Ocean warming alleviates iron limitation of marine nitrogen fixation. Nat.
Clim. Chang. 8, 709–712. doi:10.1038/s41558-018-0216-8.

Küpper, H., Šetlík, I., Seibert, S., Prášil, O., Šetlikova, E., Strittmatter, M., et al. (2008). Iron
limitation in the marine cyanobacterium Trichodesmium reveals new insights into
regulation of photosynthesis and nitrogen fixation. New Phytol. 179, 784–798.
doi:10.1111/j.1469-8137.2008.02497.x.

Kustka, A. B., Sañudo-Wilhelmy, S. A., Carpenter, E. J., Capone, D., Burns, J., and Sunda,
W. G. (2003). Iron requirements for dinitrogen- and ammonium-supported growth
in cultures of Trichodesmium (IMS 101): Comparison with nitrogen fixation rates
and iron: carbon ratios of field populations. Limnol. Oceanogr. 48, 1869–1884.
doi:10.4319/lo.2003.48.5.1869.

Martiny, A. C., Vrugt, J. A., Primeau, F. W., and Lomas, M. W. (2013). Regional variation in
the particulate organic carbon to nitrogen ratio in the surface ocean. Global
Biogeochem. Cycles 27, 723–731. doi:10.1002/gbc.20061.
51
Moore, C. M., Mills, M. M., Arrigo, K. R., Berman-Frank, I., Bopp, L., Boyd, P. W., et al.
(2013). Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701–
710. doi:10.1038/ngeo1765.

Moore, J. K., Doney, S. C., Glover, D. M., and Fung, I. Y. (2001). Iron cycling and nutrient-
limitation patterns in surface waters of the World Ocean. Deep Sea Res. Part 2 Top.
Stud. Oceanogr. 49, 463–507. doi:10.1016/S0967-0645(01)00109-6.

Peter, K. H., and Sommer, U. (2013). Phytoplankton cell size reduction in response to
warming mediated by nutrient limitation. PLoS One 8, e71528–e71528.
doi:10.1371/journal.pone.0071528.

Raven, J. A., Evans, M. C. W., and Korb, R. E. (1999). The role of trace metals in
photosynthetic electron transport in O2-evolving organisms. Photosynth. Res. 60,
111–150. doi:10.1023/A:1006282714942.

Raven, J. A., and Geider, R. J. (1988). Temperature and algal growth. New Phytol. 110, 441–
461. doi:10.1111/j.1469-8137.1988.tb00282.x.

Roche, J. L., Boyd, P. W., McKay, R. M. L., and Geider, R. J. (1996). Flavodoxin as an in situ
marker for iron stress in phytoplankton. Nature 382, 802. doi:10.1038/382802a0.

Saito, M. A., Bertrand, E. M., Dutkiewicz, S., Bulygin, V. V., Moran, D. M., Monteiro, F. M.,
et al. (2011). Iron conservation by reduction of metalloenzyme inventories in the
marine diazotroph Crocosphaera watsonii. Proc. Natl. Acad. Sci. U. S. A. 108, 2184–
2189. doi:10.1073/pnas.1006943108.

Schoffman, H., Lis, H., Shaked, Y., and Keren, N. (2016). Iron–nutrient interactions within
phytoplankton. Front. Plant Sci. 7, 1223. Available at:
https://www.frontiersin.org/article/10.3389/fpls.2016.01223.

Sohm, J. A., Webb, E. A., and Capone, D. G. (2011). Emerging patterns of marine nitrogen
fixation. Nat. Rev. Microbiol. 9, 499–508. doi:10.1038/nrmicro2594.

Sunda, W. G., Price, N. M., and Morel, F. M. M. (2005). Trace metal ion buffers and their
use in culture studies. Algal Culturing Techniques, 35–63. doi:10.1016/b978-
012088426-1/50005-6.

Thomas, M. K., Kremer, C. T., Klausmeier, C. A., and Litchman, E. (2012). A global pattern
of thermal adaptation in marine phytoplankton. Science 338, 1085.
doi:10.1126/science.1224836.

52
Tovar-Sanchez, A., Sañudo-Wilhelmy, S. A., Garcia-Vargas, M., Weaver, R. S., Popels, L. C.,
and Hutchins, D. A. (2003). A trace metal clean reagent to remove surface-bound
iron from marine phytoplankton. Mar. Chem. 82, 91–99. doi:10.1016/S0304-
4203(03)00054-9.

Webb, E. A., Ehrenreich, I. M., Brown, S. L., Valois, F. W., and Waterbury, J. B. (2009).
Phenotypic and genotypic characterization of multiple strains of the diazotrophic
cyanobacterium, Crocosphaera watsonii, isolated from the open ocean. Environ.
Microbiol. 11, 338–348. doi:10.1111/j.1462-2920.2008.01771.x.

Zehr, J. P., and Capone, D. G. (2020). Changing perspectives in marine nitrogen fixation.
Science 368, doi:10.1126/science.aay9514.

Zehr, J. P., Waterbury, J. B., Turner, P. J., Montoya, J. P., Omoregie, E., Steward, G. F., et
al. (2001). Unicellular cyanobacteria fix N2 in the subtropical North Pacific Ocean.
Nature 412, 635–638. doi:10.1038/35088063.

53
Figures

Figure 1. Cell-specific growth rates of Crocosphaera grown under Fe-replete and Fe-
limited conditions across 5 temperatures (20°C, 22°C, 27°C, 32°C, and 36°C) that span
their thermal range. Error bars are standard deviations of the means of triplicates, and
different letters represent statistical significance between growth rate means at p-value <
0.05.

54

Figure 2. Nitrogen-specific Iron Use Efficiencies (N-IUEs, mols of N fixed hr
-1
mol
intracellular Fe
-1
) (A), Carbon-specific Iron Use Efficiencies (C-IUEs, mols of C fixed hr
-1

mol intracellular Fe
-1
) (B), Nitrogen-specific nitrogen fixation rates (hr
-1
) (C), and Carbon-
specific carbon fixation rates (hr
-1
) (D) of Crocosphaera grown in triplicate under Replete
and Limited iron conditions at 20°C, 22°C, 27°C, 32°C, and 36°C. Error bars are standard
deviations from the mean and different letters represent statistical significance between
mean values at p-value < 0.05.

55

Figure 3. Percent change in the modeled response of Crocosphaera N-IUE (mol N fixed hr
-
1
mol Fe
-1
) (A) and N2-fixation (fmol N hr
-1
cell
-1
) (B) for the years 2010 and 2100 under
the IPCC RCP 8.5 warming scenario. Grey masked areas exclude areas where nitrate
concentrations are > 5 µM or where temperatures are outside the thermal range, and
where modeled growth rates are consequently assumed to be negligible (<0.01 d
-1
).

56

Figure 4. Comparative change in the modelled responses of Crocosphaera and
Trichodesmium N-IUE (A) and N2-fixation (B) for the years 2010 and 2100 under the IPCC
RCP 8.5 warming. Colored areas mark where the percent change in physiology is predicted
to increase the most (above the 75% quantile) for either diazotroph, green (Crocosphaera)
and purple (Trichodesmium) with the overlap between the two diazotrophs colored in
orange. Light grey masked areas mark areas where activity is below the 75% quantile for
either diazotroph. Grey masks regions of assumed no activity where nitrate concentrations
are > 5 µM or where temperatures are outside the thermal range, and where modeled
growth rates are consequently assumed to be negligible (<0.01 d
-1
).

57
Supplementary Materials
Supplementary Methods

Dissolved iron calculations: In the Fe-EDTA buffered medium used in this experiment, light
and temperature can affect the availability of free, dissolved iron (Feʹ) in the media (Sunda
and Huntsman, 2003). We calculated the average concentration of Feʹ ([Feʹ]) for our
experimental conditions following Jabre and Bertrand (2020) and using data from Sunda
and Huntsman (1995) to calculate dissociation constants for light and dark conditions
relevant to our experimental temperatures that enabled active Crocosphaera growth: 22ºC,
27ºC, and 32ºC. We then applied these temperature-specific dissociation constants to
calculate average [Feʹ] (Table S1) using the following equation (Sunda and Huntsman,
2003):

,Fe
'
-=
. FeEDTA
*
/ × 0K
'
d
+ I
hv
K
hv
h/241
. EDTA
*
/

Such that:
[FeEDTA*]: Total Fe concentrations added to the media: 2.5 x 10
-7
M (250 nM) for replete
and 5.00 x 10
-9
M (5 nM) for Fe-limited media.

Kʹd: The Fe-EDTA dissociation constant in the dark determined for each experimental
temperature via a linear regression where T = temperature (Figure S1):

58
K
'
d = -0.005583*T - 6.9308

Ihv: Ratio of the experimental light conditions relative to conditions used to measure Khv in
Sunda and Huntsman, 2003 (150 µmol m
-2
s
-1
/ 500 µmol m
-2
s
-1
= 0.3)

Khv: The Fe-EDTA dissociation constant in the light determined for each experimental
temperature via a linear regression where T = temperature (Figure S1):

Khv = -0.0405*T - 5.66

h: The number of light hours used in the experiment = 12.

[EDTA*]: The free EDTA concentration was calculated by subtracting the total
concentration of trace metals in the trace metals stock (Mn + Zn + Co + Mo = 3.51 x 10
-7
)
and the experimental Fe concentration ([FeEDTA*]) from the total EDTA concentration
(2.5 x 10
-5
M).

Sensitivity analyses: The maximum and minimum observed values in our experimental data
for N-IUE and N2-fixation rates were used to parameterize a “high sensitivity” and “low
sensitivity” analyses, respectively, to assess the uncertainty in the modeled outputs. These
results were then compared to our “best” estimate outputs based on the mean
experimental values (Figure S7-S8). While these upper and lower bound estimates
59
changed the magnitude of the N-IUE and N2-fixation response to iron and temperature,
the general trends and conclusions remain unchanged (Figure S7, Figure S8).

Spatial overlap analysis: Different thresholds were used to establish a baseline of
comparison for the modeled responses of Crocosphaera and Trichodesmium N-IUE and N2-
fixation in 2100 relative to 2010. Colored areas indicate where the percent change in
physiology is predicted to increase above the 25%, 50%, 75%, and 90% quantiles to assess
areas where diazotroph activity was most affected by warming to infer biogeographical
implications (Figure S11-S12). Increasing quantiles reflected an increase in the percent
change of both diazotrophs, and the areas exhibiting percent change above the 75%
quantile suggesting potential biogeographic specialization in warming oceans with minimal
functional overlap between the two diazotrophs (Figure S11C, Figure S12C). Areas of the
greatest change (90%) further reinforce this trend (Figure S11D, Figure S12D).

60
Supplementary Figures

Figure S1. Log of Fe-EDTA dissociation constants (Kd) as a function of light levels (PAR)
and temperature reproduced from Sunda and Huntsman, 2003. The solid circles represent
the K'd (dark Kd, N=9) and the open circles represent the Khv (light Kd, N=10) shown in log
scale. The solid lines are the linear least squares fit of the Kd recorded under each light
condition at 10ºC and 20ºC.

61

Figure S2. Crocosphaera growth as a function of total iron concentration (scaled) and
temperature. The experimental observations are shown as black open circles and the
means of the experimental data are shown in black asterisks. The solid-colored lines
represent the modeled fit following Monod growth kinetics, and the dashed colored lines
indicate the upper and lower uncertainties in the data.

62

Figure S3. Temperature response of maximum growth rates (A) and Ksp (B). Experimental
observations are shown as dark blue open circles and the means of the experimental data
are shown in dark blue asterisks. The solid blue line represents the modeled fit and the
dashed blue lines indicate the upper and lower uncertainty in the data.
20 22 24 26 28 30 32 34 36
Temperature (
o
C)
0.01
0.015
0.02
0.025
0.03
0.035
Ksp
observations
mean
best fit
20 22 24 26 28 30 32 34 36
Temperature (
o
C)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Max Growth Rate (d
-1
)
observations
mean
best fit
A
B
63

Figure S4. Two-way least squares fit of aggregated Crocosphaera growth versus N2-
fixation responses, at all tested iron concentrations and temperatures across 4 time points
during steady state growth. Experimental treatments were growth in triplicate for a total
N=72.

0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
Growth (d
-1
)
0
1
2
3
4
5
6
7
8
9
10
N
2
-fixation (fmol N hr
-1
cell
-1
)
y=18.021673x-0.192299, r=0.905257
64

Figure S5. Linear response of N-IUEs to temperature and iron concentration (A). The slope
(m) and intercept (b) were then related to temperature (B and C, respectively).
20 22 24 26 28 30 32 34 36
Temperature (
o
C)
-80
-60
-40
-20
0
20
40
Slope (m)
observations
mean
best fit
20 22 24 26 28 30 32 34 36
Temperature (
o
C)
0
20
40
60
80
100
120
140
160
180
Intercept (b)
observations
mean
best fit
A
B
C
65
Experimental observations are shown as open circles and the means of the experimental
data are shown in asterisks. The solid lines represent the modeled fit and the dashed lines
indicate the upper and lower uncertainty in the data. For (A), different colors represent
the three temperature treatments (22°C, 27°C, and 32°C). The maximum m value as a
function of temperature was set to 13.4616 (B) while the minimum b value was set to
5.1718 (C).

66

Figure S6. Cell diameter measurements of Crocosphaera for Fe-replete and Fe-limited
conditions across three experimental growth temperatures, 22°C, 27°C, 32°C. Different
letters of the boxplot represent statistical significance between mean values at p-value <
0.05. The number of measurements (n), mean, median, and interquartile (iqr) range of the
cell diameter measurements are shown in the table.

67

Figure S7. Sensitivity analysis of the modeled response of Crocosphaera N-IUE. Percent
change of N-IUE for the years 2010 and 2100 under the IPCC RCP8.5 warming scenario
of the best fit model (A), low sensitivity model (B), and high sensitivity model (C). Dark grey
masked areas exclude areas where nitrate concentrations are > 5 µM or where
temperatures are outside the thermal range, and where modeled growth rates are
consequently assumed to be negligible (<0.01 d
-1
).

%
A
B
%
C
%
68

Figure S8. Sensitivity analysis of the modeled response of Crocosphaera N2-fixation.
Percent change of N2-fixation for the years 2010 and 2100 under the IPCC RCP8.5
warming scenario relative to 2010 of the best fit model (A), low sensitivity model (B), and
high sensitivity model (C). Dark grey masked areas exclude areas where nitrate
concentrations are > 5 µM or where temperatures are outside the thermal range, and
where modeled growth rates are consequently assumed to be negligible (<0.01 d
-1
).

C
%
A
B
% %
69

Figure S9. N-IUEs (A), C-IUEs (B), N2-fixation rates (C), and C-fixation rates (D) of the N2-
fixing cyanobacterium Trichodesmium grown in triplicate under Replete and Limited iron
conditions at 22°C, 27°C, 32°C, and 35°C. Error bars are standard deviations from the
mean and different letters represent statistical significance between mean values at p-
value < 0.05. Modified from Jiang et al., 2018, with permission.

B
C D
A
70

Figure S10. Percent difference in the modeled interactive vs. additive responses of
Crocosphaera (A) and Trichodesmium (modified with permission from Jiang et al., 2018) (B).
Shown are percent differences in the two types of models for N2-fixation rate responses
to Fe and warming in 2100. Grey masked areas exclude areas where nitrate concentrations
are > 5 µM or where temperatures are outside the thermal range, and where modeled
growth rates are consequently assumed to be negligible (<0.01 d
-1
).

%
A
B
%
71

Figure S11. Comparative change in modeled responses of Crocosphaera and
Trichodesmium N-IUE in 2100 under the IPCC RCP8.5 warming scenario relative to 2010.
Colored areas mark where the percent change in physiology is predicted to increase above
assigned thresholds: top 25% (75% quantile) (A), bottom 25% (25% quantile) (B), top 50%
(50% quantile) (C), and top 90% (90% quantile) (D) for either diazotroph. Crocosphaera is
highlighted in green and Trichodesmium in purple with the overlap between the two
diazotrophs colored in orange. Light grey masked areas mark areas where activity is below
the assigned quantiles for either diazotroph. Dark grey masks regions of assumed no
activity where nitrate concentrations are > 5 µM or where temperatures are outside the
thermal range, and where modeled growth rates are consequently assumed to be
negligible (<0.01 d
-1
).
Both (> 75%)
Crocosphaera (> 75%)
Trichodesmium (> 75%)
Both (> 25%)
Crocosphaera (> 25%)
Trichodesmium (> 25%)
Both (> 50%)
Crocosphaera (> 50%)
Trichodesmium (> 50%)
Both (> 90%)
Crocosphaera (> 90%)
Trichodesmium (> 90%)
A
B
C
D
72

Figure S12. Comparative change in modelled responses of Crocosphaera and
Trichodesmium N2-fixation in 2100 under the IPCC RCP8.5 warming scenario relative to
2010. Colored areas mark where the percent change in physiology is predicted to increase
above assigned thresholds: top 25% (75% quantile) (A), bottom 25% (25% quantile) (B),
top 50% (50% quantile) (C), and top 90% (90% quantile) (D) for either diazotroph.
Crocosphaera is highlighted in green and Trichodesmium in purple with the overlap between
the two diazotrophs colored in orange. Light grey masked areas mark areas where activity
is below the assigned quantiles for either diazotroph. Dark grey masks regions of assumed
no activity where nitrate concentrations are > 5 µM or where temperatures are outside
the thermal range, and where modeled growth rates are consequently assumed to be
negligible (<0.01 d
-1
).
Both (> 75%)
Crocosphaera (> 75%)
Trichodesmium (> 75%)
Both (> 25%)
Crocosphaera (> 25%)
Trichodesmium (> 25%)
Both (> 50%)
Crocosphaera (> 50%)
Trichodesmium (> 50%)
Both (> 90%)
Crocosphaera (> 90%)
Trichodesmium (> 90%)
A
B
C
D
73
Supplementary Tables:
Table S1: Calculated average [Fe'] in Replete and Limited media for total Fe concentrations
used: 250 nM for Fe-Replete and 5 nM for Fe-Limited cultures.

Temperature
(°C)
[Fe']-Replete
(pM)
[Fe']-Limited
(pM)
22
1340 26.5
27
1120 22.2
32
966 19.1

Table S2: Mean intracellular Fe quotas (µmol Fe:mol C) ± the standard deviation for
Crocosphaera across three temperature and two iron experimental conditions. The
different letters represent the statistical significance between mean values at p-value <
0.05.

Temperature Fe-Replete Fe-Limited
22°C 78.8±15.4
ab
57.4±1.9
abc

27°C 28±6.7
bc
14.5±1.9
c

32°C 94.4±71.5
a
33.1±5.7
bc

74
Extended Discussion on scaling Fe quotas:
While it would be ideal to base our modeling parameters on laboratory data that
reflect in situ field measurements, it is currently logistically impossible to measure the
intracellular Fe content of Crocosphaera cells while they are growing in situ as part of a
mixed natural plankton community, so these values do not exist in the literature. In fact,
even studies in lab cultures are lacking, and it is difficult to make comparisons between
the two lab studies that do exist, due to methodological differences including varying
light:dark cycles and the use of different Fe and EDTA concentrations which can impact
Fe availability and thus, intracellular Fe content. Furthermore, the two prior studies that
have reported Fe quota (Fe:C) values only reported them for a small, non-extracellular
polysaccharide (EPS) producing strain of Crocosphaera (WH8501). No values have been
produced for the larger, EPS-producing strains including our strain (WH0005) and there
are no values available that incorporate the impact of temperature on Fe quotas. While
we still need to scale the culture-based Fe parameters to reflect in situ conditions in our
models, we can use the published values as a benchmark to compare against our own
data. All our values fall within the ranges reported by Tuit et al., 2004 and our Fe-replete
and Fe-limited trends follow those of Fu et al., 2008 where Fe quotas at optimum
growth temperatures are higher for Fe-replete cultures than Fe-limited cultures.
Altogether, these results show that given the existing constraints, our data remain the
best available and are thus appropriate for parameterizing our models.

75
Supplementary References:

Fu, F.-X., Mulholland, M. R., Garcia, N. S., Beck, A., Bernhardt, P. W., Warner, M. E., et al.
(2008). Interactions between changing pCO2, N2 fixation, and Fe limitation in the
marine unicellular cyanobacterium Crocosphaera. Limnol. Oceanogr. 53, 2472–2484.
doi:10.4319/lo.2008.53.6.2472.

Jabre, L., and Bertrand, E. M. (2020). Interactive effects of iron and temperature on the
growth of Fragilariopsis cylindrus. Limnology and Oceanography Letters.
doi:10.1002/lol2.10158.

Jiang, H.-B., Fu, F.-X., Rivero-Calle, S., Levine, N. M., Sañudo-Wilhelmy, S. A., Qu, P.-P., et
al. (2018). Ocean warming alleviates iron limitation of marine nitrogen fixation.
Nat. Clim. Chang. 8, 709–712. doi:10.1038/s41558-018-0216-8.

Sunda, W. G., and Huntsman, S. A. (1995). Iron uptake and growth limitation in oceanic
and coastal phytoplankton. Mar. Chem. 50, 189–206. doi:10.1016/0304-
4203(95)00035-P.

Sunda, W., and Huntsman, S. (2003). Effect of pH, light, and temperature on Fe–EDTA
chelation and Fe hydrolysis in seawater. Mar. Chem. 84, 35–47.
doi:10.1016/S0304-4203(03)00101-4.

Tuit, C., Waterbury, J., and Ravizza, G. (2004). Diel variation of molybdenum and iron in
marine diazotrophic cyanobacteria. Limnol. Oceanogr. 49, 978–990.
doi:10.4319/lo.2004.49.4.0978.

76
Chapter 3: Molecular mechanisms underlying iron and phosphorus
co-limitation responses in the nitrogen fixing cyanobacterium
Crocosphaera

Nina Yang
1
, Yu-An Lin
1
, Carlin A. Merkel
1
, Michelle A. DeMers
1
, Ping-Ping Qu
1†
, Eric A.
Webb
1
, Fei-Xue Fu
1
, David A. Hutchins
1

1
Department of Biological Sciences, University of Southern California, Los Angeles, CA,
USA

Corresponding Author:
David A. Hutchins
dahutch@usc.edu

†
Present address: Department of Psychiatry and Behavioral Sciences, Stanford University
School of Medicine, Stanford, CA, USA

This manuscript was published in 2022 as:
Yang N, Lin Y-A, Merkel CA, DeMers MA, Qu P-P, Webb, EA et al. Molecular mechanisms
undelrying iron and phosphorus co-limitation responses in the nitrogen fixing
cyanobacterium Crocosphaera. ISME Journal. 2022; https://doi.org/10.1038/s41396-
022-01307-7.

77
Abstract
In the nitrogen-limited subtropical gyres, diazotrophic cyanobacteria, including
Crocosphaera, provide an essential ecosystem service by converting dinitrogen (N2) gas
into ammonia to support primary production in these oligotrophic regimes. Natural
gradients of phosphorus (P) and iron (Fe) availability in the low-latitude oceans constrain
the biogeography and activity of diazotrophs with important implications for marine
biogeochemical cycling. Much remains unknown regarding Crocosphaera’s physiological
and molecular responses to multiple nutrient limitations. We cultured C. watsonii under Fe,
P, and Fe/P (co)-limiting scenarios to link cellular physiology with diel gene expression and
observed unique physiological and transcriptional profiles for each treatment.
Counterintuitively, reduced growth and N2 fixation resource use efficiencies (RUEs) for Fe
or P under P limitation were alleviated under Fe/P co-limitation. Differential gene
expression analyses show that Fe/P co-limited cells employ the same responses as single-
nutrient limited cells that reduce cellular nutrient requirements and increase
responsiveness to environmental change including smaller cell size, protein turnover (Fe-
limited), and upregulation of environmental sense-and-respond systems (P-limited).
Combined, these mechanisms enhance growth and RUEs in Fe/P co-limited cells. These
findings are important to our understanding of nutrient controls on N2 fixation and the
implications for primary productivity and microbial dynamics in a changing ocean.

78
1. Introduction
The availability of the biologically essential macronutrients nitrogen (N) and
phosphorus (P), and micronutrients like iron (Fe), plays a critical role in the productivity
and distribution of phytoplankton communities in the ocean (Redfield, 1958; Moore et al.,
2013). In the nutrient-poor or oligotrophic open ocean ecosystems where N scarcity
generally limits phytoplankton growth (Moore et al., 2013), a group of specialized
cyanobacteria carry out nitrogen fixation (N2 fixation), converting dinitrogen (N2) gas into
a more biologically accessible form of N, ammonia (Sohm et al., 2011; Zehr and Capone,
2020). This input of “new” N by cyanobacterial N2-fixers or diazotrophs, including the
filamentous, colony-forming Trichodesmium and unicellular diazotrophs (e.g. the obligate
symbiont UCYN-A, and free-living Crocosphaera), supports a significant portion of primary
production in the subtropical and tropical oligotrophic waters (Karl et al., 1997; Sohm et
al., 2011; Tang et al., 2019, 2020). While Trichodesmium has been intensely studied for
decades as a primary contributor to marine N2 fixation (Capone and Carpenter, 1982;
Capone et al., 1997, 2005; Orchard et al., 2009; Qu et al., 2019), the ecological and
biogeochemical relevance of unicellular diazotrophs, including Crocosphaera, has only
been recognized more recently (Zehr et al., 2001; Moisander et al., 2010; Shi et al., 2010;
Bench et al., 2013; Dugenne et al., 2020; Detoni et al., 2022).
Despite our growing recognition of Crocosphaera’s importance in oligotrophic
systems, we still have limited understanding of how the availability of nutrients, especially
P and Fe, constrain their biogeography and activity (Webb et al., 2001; Jacq et al., 2014;
Garcia et al., 2015). P is an essential component of various cellular molecules including
79
membranes, ribosomes, nucleic acids, and the energy source, adenosine triphosphate
(ATP) (Lin et al., 2016). It is also crucial in two-component regulatory systems that enable
the cell to sense and respond to environmental changes (Capra and Laub, 2012; Held et
al., 2019). Fe is an essential micronutrient for phytoplankton, serving as a cofactor of
numerous metabolic processes including photosynthesis, chlorophyll biosynthesis and
respiratory electron transport (Shi et al., 2007). In addition, N2 fixers including
Crocosphaera have a much higher cellular iron demand than non-diazotrophic
phytoplankton because the nitrogenase metalloenzyme that facilitates N2 fixation is an
Fe-rich enzyme complex (Schoffman et al., 2016; Hutchins and Sañudo-Wilhelmy, 2021).
Thus, in oligotrophic ecosystems, the availability of P and Fe may impact core diazotrophic
cellular processes and metabolisms that play a key role in marine biogeochemical cycling.
Previous studies have suggested that the distributions of N2-fixers and N2 fixation
rates in the subtropical and tropical open oceans are shaped and constrained by the
relative availability of P and Fe across different oceanic basins (Sohm et al., 2011; Capone,
2014; Zehr and Capone, 2020). For example, the North Atlantic Subtropical Gyre, which
receives Fe via episodic inputs of aeolian dust from the Sahara Desert, is relatively more
P-limited than the North Pacific Subtropical Gyre, where a lack of aeolian Fe input yields
a more Fe-limited system (Moore et al., 2013; Rouco et al., 2018; Zehr and Capone, 2020).
Under low P or Fe conditions, N2 fixers have evolved various strategies to acquire
and conserve these elements efficiently. These nutrient acquisition systems have been
applied as Fe and/or P limitation indicators. Under P limitation, these strategies include a
mechanism for high-affinity phosphate transport via the upregulation of the phosphate-
binding gene, pstS (Dyhrman and Haley, 2006; Orchard et al., 2009; Pereira et al., 2019).
80
Another mitigating response is the use of dissolved organic phosphorus (e.g.
phosphomonoesters), indicated by upregulation of the alkaline phosphatase (phoA/B,
phoX) genes (Dyhrman and Haley, 2006; Orchard et al., 2009; Pereira et al., 2016;
Frischkorn et al., 2019).
When Fe-limited, cells downregulate gene expression of Fe-rich Photosystem I
(PSI) protein complexes while upregulating the expression of an iron stress-induced
chlorophyll-binding gene, isiA (Bibby et al., 2001; Chen et al., 2018). IsiA proteins form Fe-
free light-harvesting antennae that shield the photosynthetic apparatus from oxidative
damage (Hewson et al., 2009; Bench et al., 2013). In addition, cyanobacterial diazotrophs
can substitute the Fe-containing electron transfer protein ferredoxin with the Fe-free
flavodoxin, IsiB (LaRoche et al., 1996; Chappell and Webb, 2010). Unlike Trichodesmium,
which simultaneously photosynthesize and fix N2 during the day, Crocosphaera temporally
separate these two processes and fix N2 at night (Held et al., 2022). This unicellular
diazotroph has evolved the ability to shuttle cellular Fe between Fe-containing proteins in
the photosynthetic apparatus and those in the nitrogenase complex through diel synthesis
and degradation of these core metalloenzymes (Saito et al., 2011). This Fe-conservation
strategy substantially reduces Crocosphaera’s cellular Fe requirements that may better
enable Crocosphaera to inhabit Fe-depleted waters compared to Trichodesmium (Saito et
al., 2011).
Recent lab and field studies have found that N2 fixers may be well-adapted to
environments that are low in both P and Fe, or Fe/P co-limited (Mills et al., 2004; Held et
al., 2020; Cérdan-García et al., 2021). Fe/P co-limitation can produce unexpected
physiological responses, including enhanced growth and N2 fixation rates, and cell size
81
reductions compared to either P or Fe single-nutrient limitation responses (Garcia et al.,
2015; Walworth et al., 2016; Held et al., 2020; Cérdan-García et al., 2021). Most of these
studies focus on the molecular mechanisms underlying the Fe/P co-limited response in
Trichodesmium, which include a shift in protein abundance patterns unique to the Fe/P co-
limited response in various metabolic pathways linked to cell size reduction and increased
growth rates (Walworth et al., 2016).
To our knowledge, only one study has been conducted on the Fe/P co-limited
physiological response in Crocosphaera, while the molecular response mechanisms
involved remain uncharacterized (Garcia et al., 2015). To study the molecular response,
we grew one Crocosphaera watsonii isolate under Fe, P, and Fe/P (co)-limiting conditions
and conducted differential gene expression analyses to link physiology with the underlying
mechanisms of nutrient-limited responses and their implications for marine nitrogen
biogeochemistry.

2. Methods
2.1. Culturing Methods
Triplicate cultures of C. watsonii strain WH0005 were grown at 28ºC in microwave-
sterilized Aquil medium made with 0.2 µm-filtered artificial seawater (ASW) (Price et al.,
1989). The seawater base was amended with Fe buffered with 25 μM EDTA, P, vitamins,
and trace metals (Yang et al., 2021). In an approach modified from Walworth et al. (2016),
cultures were maintained semi-continuously under Fe/P co-limitation for ~3 months and
single-nutrient and replete treatments were generated by adding back Fe (P-limited), P
82
(Fe-limited), and Fe + P (Replete) followed by an additional 1.5 months of growth before
sampling (Walworth et al., 2016). Culture maintenance followed previously described
trace metal-clean methods (Yang et al., 2021). Prior to sampling, cultures were grown for
two weeks (~3-7 generations) in ASW passed through an activated Chelex 100 resin
column (BioRad Laboratories, Hercules, CA, USA) to remove contaminating Fe followed
by nutrient amendments described above. The entire media recipe with nutrient
amendments is detailed in the Supplementary Methods.

2.2. Physiological Measurements
Previously published methods were used for physiological measurements (Garcia
et al., 2013; Jiang et al., 2018; Yang et al., 2021) and are detailed further in the
Supplementary Methods. Briefly, cell counts were used to calculate specific growth rates
(μ) using the equation μ = (ln N1 – ln N0) / t, where N refers to cell densities and t is time
in days. Cell size was determined by measuring cell diameters of 65 cells per sample at
400x magnification using the CaptaVision Imaging Software (Commack, NY, USA).
Net primary productivity or carbon fixation (C fixation) was assessed using the
radiocarbon labeled bicarbonate (H
14
CO3) method. Sub-cultures (10 mL) were incubated
for 6 hours with H
14
CO3, filtered onto glass microfiber filters (Whatman, Grade GF/F),
stored in the dark overnight, and subsequently analyzed on a Beckman LS 6000 liquid
scintillation counter (Beckman Coulter Inc., Fullerton, CA, USA). N2 fixation was measured
using the acetylene reduction assay. Sub-cultures (40 mL) from each replicate were
injected with 6 mL of acetylene (~17% of headspace) at the start of the dark period. All-
83
night (~12 hours) accumulation of ethylene was measured on a GC-8A gas chromatograph
(Shimadzu Scientific Instruments, Columbia, Maryland) and converted to fixed N2 using a
ratio of 3:1 and a Bunsen coefficient of 0.086. Measured C fixation and N2 fixation rates
were then normalized to daytime and nighttime cell counts, respectively.
Resource Use Efficiencies (RUEs) were calculated by normalizing measured C
fixation and N2 fixation rates to cellular P (C-PUEs, N-PUEs, mol C or N fixed hr
-1
mol
cellular P
-1
) or to cellular Fe content (C-IUEs, and N-IUEs, mol C or N fixed hr
-1
mol cellular
Fe
-1
) (Kustka et al., 2003; Yang et al., 2021). Cellular C, N, P, and Fe samples to calculate
RUEs and elemental ratios were obtained following previously published methods (Jiang
et al., 2018; Hawco et al., 2020; Yang et al., 2021) (see Supplementary Methods).

2.3. Statistical Analyses
Statistical significance for physiological measurements and RUEs were calculated
for all four nutrient treatments and for nutrient-limited treatments by one-way ANOVA
with Tukey’s HSD post-hoc analysis (p value < 0.05) using R v4.1.1. Welch’s t test was
conducted for pairwise comparisons of two treatments (p value < 0.05). The cellular Fe
value of one Fe-limited replicate was removed from analyses due to likely contamination
during sample collection, and IUEs and elemental ratios were calculated with the mean
cellular Fe value of the remaining two replicates (see Supplementary Methods).

84
2.4. RNA Extractions and Sequencing
Sub-cultures (400 mL, ~7-8x10
7
cells) were collected 5-6 hours into the light period
and 5-6 hours after dark (24 samples total) via centrifugation (Supplementary Methods).
The pellets were transferred to cryogenic vials, flash frozen, and stored in liquid N2 until
extraction.
RNA was extracted using guanidium thiocyanate-phenol-chloroform (TRI Reagent,
Sigma Aldrich) and Zymo’s Direct-zol RNA Miniprep kit (Zymo Research, Irvine, CA) with
a Dnase treatment following kit instructions. RNA purity was checked using a Nanodrop
spectrophotometer (Thermo Scientific) and sent to UC Davis’ DNA Technologies Core for
library preparation, and 150-bp paired-end sequencing on a NovaSeq S4 (Illumina).
Raw paired-end reads were quality checked using FastQC (Andrews, 2010) and
trimmed using Trimmomatic v0.39 in paired-end mode with the following settings to
remove adapters: TRAILING:10 SLIDINGWINDOW:5:20 MINLEN:36 (Bolger et al., 2014).
Trimmed sequences were mapped onto a new low-contig Crocosphaera WH0005 genome
annotated with KofamScan (Qu et al., 2022) using end-to-end alignment mode in Bowtie2
v2.4.2 (Langmead and Salzberg, 2012). Alignments were converted to BAM files, sorted
by read name, and filtered by mapping quality score (MAPQ) of 10 or higher using
SAMtools v1.11 (Li et al., 2009). Sequences were tabulated using featureCounts from the
Subread package v2.0.1 in stranded mode (Liao et al., 2014).

85
2.5. Transcriptomic analysis and visualizations
Gene counts were assigned Kyoto Encyclopedia of Genes and Genomes database
(KEGG) Orthology identifiers (KO) from the KofamScan-annotated genome (Kanehisa and
Goto, 2000). The isiA gene was identified by a nucleotide BLAST querying known isiA
sequences from a previous Crocosphaera WH0005 genome (Bench et al., 2013) and the
reference genome. The isiA and isiB (flavodoxin) genes are often polycistronic (Leonhardt
and Straus, 1992; Bench et al., 2013) and as expected, both were on the same contig in
our reference genome. KO-annotated and isiA read counts were summed to remove
duplicate gene identifiers. Then, genes with low counts were removed if the mean for all
treatments was less than five, yielding 3,770 “unique genes”.
All transcriptomic analyses and visualizations were performed in R v4.1.1.
Differential gene expression (DGE) was conducted with DESeq2 v1.32.0, which uses a
negative binomial generalized linear model to assess DGE for a design formula (Love et al.,
2014; Cérdan-García et al., 2021). Pairwise comparisons for day and night samples for
each nutrient treatment (diel genes) and for nutrient-limited treatments relative to the
replete treatment were assessed by the default Wald test in DESeq2 with Benjamin-
Hochberg (BH) adjusted p value < 0.05).
Over-representation analysis of differentially expressed genes (DEGs) was
conducted using enrichKEGG (KEGG) and enricher (Gene Ontology, GO) functions from
clusterProfiler v4.0.5, which calculates overrepresented or “enriched” biological pathways
and functions using a hypergeometric test with a BH-adjusted p value < 0.05 (Yu et al.,
2012). For GO enrichment, the genome was annotated following published methods using
86
DIAMOND (blastx mode, more-sensitive) against the NCBI nr database downloaded on
August 31, 2021 (median E-value = 2.66x10
-75
, median bitscore = 235), adding an
additional 110 annotations to the 4,502 KEGG-annotated reads. The DIAMOND output
was then used for GO-annotation by Blast2GO, InterProScan, and UniProt (Conesa et al.,
2005; Jones et al., 2014; Buchfink et al., 2021; Chille et al., 2021).
Gene counts were “regularized log” transformed using the rlog function (DESeq2)
for redundancy analysis (RDA) using the rda function from the vegan v2.5-7 package
(Oksanen et al., 2020). Gene counts normalized using the median of ratios method
(DESeq2) were used for boxplot and heatmap visualizations. Venn diagrams and heatmaps
were generated using VennDiagram v1.6.20 and ComplexHeatmap v2.8.0 packages,
respectively. Heatmap Z-scores were calculated for each gene by subtracting the gene
expression from the row mean and then divided by the row standard deviation. All other
graphs were generated using ggplot2 v3.3.5.

3. Results & Discussion
3.1. Crocosphaera physiology and function under different nutrient conditions
We measured physiological parameters for Crocosphaera grown under different Fe
and P limitation scenarios to compare and contrast their response to single-nutrient (Fe or
P) and dual-nutrient (Fe/P) (co)-limitation (hereafter, co-limited or co-limitation). We
define co-limitation as Crocosphaera’s growth response to simultaneous low Fe and P
concentrations, either of which would be growth-limiting alone. In our study, the limiting
concentrations of Fe and P for the co-limiting treatment were the same as those used in
87
the single Fe- or P-limited conditions. All three nutrient-limited conditions were compared
to the replete condition for a baseline comparison. As expected, all nutrient-limited
Crocosphaera growth rates were less than replete rates (Figure 1A). Co-limited
Crocosphaera had the fastest growth rates of the nutrient-limited treatments, growing
14% faster than Fe-limited cells (p = 0.050) and ~44% faster than P-limited cells (p <
0.001). P-limited cells had the slowest growth.
On average, nutrient-limited cells were also smaller than replete cells, with mean
cell diameters < 5 µm for limited cells and > 5 µm for the replete cells (Figure 1B, p <
0.001). Co-limited and P-limited cells were similar in size with mean cell diameters of 4.05
µm and 4.1 µm, respectively. Fe-limited cells had a mean diameter of 4.7 µm. A smaller
cell size increases the surface area-to-volume ratio and enables better access to nutrients
than larger cells while also reducing the cellular requirements for limiting nutrients like Fe
and P (Finkel et al., 2010; Garcia et al., 2015). Previous studies showed similar size changes
under co-limited and Fe-limited conditions (Jacq et al., 2014; Garcia et al., 2015) but did
not report a decrease in size for P-limited cells (Garcia et al., 2015). Both Garcia et al.
(2015) [20] and our study used a large-cell Crocosphaera phenotype (WH0005 and
WH0003, respectively) with similar genomic capacity for Fe and P-scavenging (Bench et
al., 2013). However, our study used a relatively low P concentration and had higher
biomass than Garcia et al., (2015), which may have increased the severity of P limitation
in our study and produced smaller cells that were not previously observed.
Cell-normalized C fixation and N2 fixation rates showed similar patterns across all
nutrient treatments (Figure 1C-D). Replete cells had the highest C fixation and N2 fixation
rates, and Fe-limited cells had the highest rates of the three nutrient-limited treatments.
88
While not statistically significant, co-limited C fixation rates were slightly lower than P-
limited rates (p = 0.085) and Fe/P co-limited N2 fixation rates were ~2 fold higher than P-
limited rates (p > 0.1).
RUEs can be used as a physiological proxy of enzyme activity and resource
allocation by integrating metabolic productivity and resource requirements (Hutchins and
Sañudo-Wilhelmy, 2021; Yang et al., 2021). Calculated co-limited and P-limited C-PUEs
were the highest of the nutrient-limited cells (Figure 2A-B). Co-limited N-PUEs were
comparable to Fe-limited N-PUEs and were more than 2.5-fold higher than P-limited N-
PUEs (p < 0.05). Fe-limited cells had the highest IUEs while P-limited cells had the lowest
(Figure 2C-D). Co-limited C-IUEs were intermediate of Fe-limited and P-limited cells (p <
0.01 and p < 0.05, respectively), and although the nutrient-limited one-way ANOVA was
not statistically significant (p = 0.166), Co-limited N-IUEs were ~10 times higher than P-
limited N-IUEs (p < 0.05 using Welch’s unequal variances t test).
We observed similar growth and N2 fixation rates as Garcia et al. (2015) where co-
limited cells grew the fastest and exhibited intermediate N2 fixation rates compared to
cells under single-nutrient limitation. Our calculated PUEs and IUEs for P-limited and Fe-
limited Crocosphaera, respectively, reflect recently published RUEs (Deng et al., 2021;
Yang et al., 2021) with the exception of P-limited N-PUEs, which were previously reported
to be higher than replete N-PUEs (Deng et al., 2021) but were observed in our study to be
significantly lower. This contrasting N-PUE response could stem from our study using
lower P concentrations as well as a large-cell Crocosphaera phenotype instead of the small-
cell isolate WH8501; the two phenotypes differ in genome size and the number of P
acquisition genes (Bench et al., 2013), possibly leading to varying N-PUEs. Regardless, our
89
P-limited C-PUEs were similar to co-limited C-PUEs, but N-PUEs were lower. Cellular P
was comparable between the two treatments (Supplementary Figure S1), suggesting that
P-limited cells may be allocating resources differently compared to other treatments. In
particular, high ratios of C fixation to N2 fixation suggest that P-limited cells favor C
fixation compared to cells grown under Fe-limited and co-limited conditions
(Supplementary Table S1). This observed nutrient-dependent balance between C and N2
fixation and elemental ratios (Supplementary Table S2) may have implications for
biogeochemical cycling of both major and trace elements across oligotrophic regimes and
warrants further study.

3.2. Crocosphaera diel transcriptome under Fe, P, and Fe/P (co)-limitation
To uncover the mechanisms underlying Crocosphaera’s physiological response, we
conducted a comparative transcriptomic analysis using DESeq2 on samples collected
during peak C fixation (daytime) and N2 fixation (nighttime). RDA showed samples
clustering by nutrient treatment during the day and night (Supplementary Figure S2A-B)
and that sampling time accounts for a large portion of the Crocosphaera transcriptomic
response (Supplementary Figure S2C).
We evaluated daytime and nighttime gene expression profiles of commonly used
Fe limitation (isiA, isiB) and P limitation (phoA/B, pstS) biomarkers to assess cellular nutrient
status and found expected gene expression patterns for nutrient-limited cells relative to
replete cells (Figure 3). P limitation genes (phoA/B and pstS) were upregulated for P-limited
and Fe/P co-limited cells, while Fe limitation genes (isiA and isiB) were upregulated for Fe-
90
limited and Fe/P co-limited cells. These nutrient biomarkers also exhibited diel trends
whereby isiA was significantly upregulated during the day for Fe/P co-limited, Fe-limited,
and replete treatments while isiB was significantly upregulated at night for P-limited and
replete treatments (p < 0.05). Nighttime isiB expression for Fe-limited and Fe/P co-limited
was higher than daytime gene expression, but the difference was not statistically
significant (p = 0.08 and p > 0.1, respectively).
Given the ecological significance of Crocosphaera in oligotrophic systems, many
studies have sought to identify biomarkers of nutrient limitation that can be used to survey
and assess microbial communities in situ (Hook et al., 2014; Saito et al., 2014; Frischkorn
et al., 2019; Pereira et al., 2019; Walworth et al., 2021). Our analyses show that frequently
used Fe and P biomarkers can be used to indicate co-limitation in Crocosphaera. In
addition, previous studies observed diel cycling of pstS (Pereira et al., 2016) and isiB (Shi
et al., 2010) gene biomarkers in Crocosphaera. While our experiment only sampled two
time points, all four biomarkers used in our study showed diel regulation (Figure 3),
affirming that the time of day for sampling and analysis are important considerations when
using biomarkers to link nutrient availability, cellular physiology, and ocean
biogeochemistry.
Diel regulation of Crocosphaera’s transcriptome, especially the temporal separation
of photosynthesis and N2 fixation has been well documented (Mohr et al., 2010; Saito et
al., 2011; Dron et al., 2012; Wilson et al., 2017), with one field study suggesting that nearly
half of the transcriptome exhibits a diel pattern (Shi et al., 2010). Similarly, our pairwise
analysis of day and night gene expression (p < 0.05) showed that diel genes accounted for
half (51.2%) of Crocosphaera’s total transcriptome under replete conditions and
91
demonstrated that nutrient limitation affects diel gene regulation by increasing or
decreasing the number of diel genes (Figure 4A). Fe-limited Crocosphaera had the largest
diel transcriptome with 58.0 % of genes exhibiting a diel pattern (n = 2,188) while P-limited
Crocosphaera had the smallest with 45.8% (n = 1,725). Fe/P co-limited cells fell in between
at 55.8% (n = 2,102).
Genes can be further categorized as “core” genes that are diel across all treatments,
“shared” genes upregulated by at least 2 treatments, and “unique” genes that are diel for
only one treatment (Supplementary Figure S3). A total of 1,018 upregulated core genes
(day = 522, night = 496) were identified, comprising more than half of the P-limited (59.0%)
and replete (52.7%) diel transcriptomes, but less than half of the co-limited (48.4%) and
Fe-limited (46.5%) diel transcriptomes. Co-limited cells had the highest percentage of diel-
regulated shared genes and lowest percentage of unique genes which contrasted with P-
limited cells.
Overrepresentation analysis identified enriched GO terms and KEGG pathways
across core, shared, and unique diel genes (Supplementary Table S3). Core diel genes were
enriched in photosynthesis and translation genes for daytime and nighttime, respectively
(Figure 4B). Heatmap visualizations of core GO Biological Process (BP) photosynthesis and
translation terms showed that P-limited Crocosphaera downregulate both processes
compared to other nutrient treatments (Figure 4C-D, Supplementary Figure S4, S5A).
Hierarchical clustering indicated a similar photosynthesis response for co-limited and Fe-
limited cells, while the co-limited translation response resembled P-limited Crocosphaera,
although P-limited cells have lower gene expression for most of the core genes. While N2
fixation was not identified through over-representation analysis, the iron protein gene
92
(nifH) and molybdenum-iron alpha and beta chain (nifDK) nitrogenase genes are core night
genes that were significantly upregulated for P-limited cells compared to other nutrient-
limited treatments (Supplementary Figure S6).
Cell division, regulation of cell shape, and peptidoglycan biosynthetic process were
also identified as core day GO BP terms with replete and P-limited treatments clustering
together and co-limited and Fe-limited clustering together (Supplementary Figure S5B-D).
Fe-limited and co-limited upregulation of cell division, cell shape, and peptidoglycan
biosynthesis process correlate with higher growth rates (increased cell division) under Fe
limitation and co-limitation compared to P limitation. Upregulation of peptidoglycan
biosynthesis may increase the cell’s ability to adapt to changing environmental conditions
through rearrangement and restructuring of the cell wall (Scheffers and Pinho, 2005;
Silhavy et al., 2010; Cava and de Pedro, 2014; Mueller and Levin, 2020). Moreover, co-
limited and Fe-limited cells shared diel genes for membrane proteins that were
constitutively expressed in other treatments (Supplementary Table S3). These results
suggest cell wall and membrane flexibility that could benefit Fe-limited and co-limited cells
and warrants further study.
Proteolysis genes exhibited a different clustering pattern where the co-limited
response clustered more with Fe-limited and replete treatments than with the P-limited
treatment (Supplementary Figure S5E). P-limited cells downregulated a suite of proteolysis
genes that were upregulated for Fe-limited and co-limited cells. This includes the ftsH gene
encoding the FtsH protease responsible for membrane and photosystem protein
degradation (Langklotz et al., 2012; Bonisteel et al., 2018), which hints at a possible
93
disruption of Crocosphaera’s diel protein cycling and may explain the differences in diel
transcriptomes across the nutrient-limited conditions.

3.3. Transcriptomic response of nutrient-limited versus nutrient replete Crocosphaera
Pairwise analyses of nutrient-limited treatments (Fe-limited, P-limited, or co-
limited) and the replete treatment for day and night (Supplementary Figure S7A-B) also
identified core, shared, and unique DEGs. During the day, the P-limited transcriptome has
the most unique DEGs (77%) compared to the replete transcriptome. Fe-limited cells had
the least with 41.0% as unique genes (Supplementary Figure S8). At night, P-limited cells
also have the largest portion of unique DEGs (68.8%), but co-limited cells have the lowest
(29.9%). For both time points, co-limited cells have the largest portion of shared genes,
overlapping with both P-limited (36.5%, 30.9%) and Fe-limited (17.0%, 30.8%) treatments
for day and night, respectively. Core DEGs comprised a small fraction (i.e., ~2-12%) of the
nutrient-limited transcriptomes.
Overrepresentation analysis of unique, upregulated genes indicated that co-limited
cells prioritized cell division and regulation of cell shape (GO) and peptidoglycan
biosynthesis (KEGG) processes during the day, while P-limited cells upregulated
transmembrane transport (GO) and ATP-binding cassette (ABC)-type transporters (KEGG,
GO) (Supplementary Figure S7C). Fe-limited cells were enriched in genes for DNA repair
(GO) at night. P-limited cells downregulated genes involved in photosynthesis,
transcription, and translation relative to replete cells that were not downregulated by Fe-
limited or co-limited cells (Supplementary Figure S9).
94
Co- and P-limited cells upregulated shared genes involved in transmembrane
transport and downregulated translation (Supplementary Table S4). At night, these cells
also upregulated mechanisms that enable their detection and response to environmental
stimuli including two-component systems. Simultaneously, co-limited and Fe-limited cells
downregulate shared genes predicted to be involved in metal ion binding and iron-sulfur
cluster binding.

3.4. Linking nutrient-limited diel gene expression to Fe and P-limited physiology
Physiologically, Fe-limited Crocosphaera efficiently use Fe and P for both C and N2
fixation while P-limited cells are only P-efficient for C fixation and have the lowest RUEs
for N2 fixation. In Crocosphaera, low-Fe availability triggers a unique response that
moderates P limitation, producing co-limited cells that are more Fe-efficient and highly P-
efficient for both C and N2 fixation, relative to P-limited cells. Core diel genes represent
critical cellular functions that maintain a diel pattern regardless of Fe and P availability and
constitute a major portion of the diel transcriptome. Thus, differences in gene expression
profiles between treatments may contribute to Crocosphaera’s observed physiological
response to nutrient limitation. Specific nutrient-limited responses can be assessed
looking at the unique and shared DEGs relative to the replete treatment.
Of the nutrient-limited treatments, Fe-limited cells had the highest C and N2
fixation rates and IUEs likely driven by Fe conservation strategies including recycling Fe
between photosynthesis and N2 fixation pathways (Saito et al., 2011), replacing ferredoxin
with flavodoxin (LaRoche et al., 1996), and downregulating Fe-rich Photosystem (PSI)
95
genes (Figure 4C, top cluster) (Shi et al., 2007). Of the core diel processes, Fe-limited and
replete cells have similar expression profiles for translation genes, suggesting that Fe
limitation does not limit ribosomal biogenesis or directly reduce protein synthesis (Figure
4D, Supplementary Figure S4). Furthermore, Fe-limited cells (relative to replete cells)
uniquely upregulate DNA repair (GO) genes, including recF, which is involved in DNA
replication and repair (Supplementary Figure S7C, Supplementary Table S4). Under Fe-
limiting conditions, nighttime DNA repair could help offset DNA damage that occurs
during photosynthesis induced cellular oxidative stress (Latifi et al., 2009; Schoffman et
al., 2016).
In our study, P-limited cultures were more affected by nutrient limitation than Fe-
limited and co-limited cultures, with lower growth and metabolic rates, and RUEs
correlating with substantial downregulation of core diel photosynthesis and ribosomal
genes that could lead to decreases in cellular function. Crocosphaera degrade nitrogenase
enzymes during the day and re-synthesize them de novo at night (Shi et al., 2010; Saito et
al., 2011), such that downregulating translation limits their ability to produce nitrogenase
proteins and reduces N2 fixation rates. Accordingly, P-limited Crocosphaera had the lowest
N2 fixation rates. Surprisingly though, low rates which suggest low nitrogenase protein
abundance corresponded with the highest nighttime nifHDK gene counts (Supplementary
Figure S6). Some previous studies have observed that nitrogenase gene expression
correlated well with measured N2 fixation rates (Zehr et al., 2007; Benavides et al., 2020)
while others did not (Turk et al., 2011; Turk-Kubo et al., 2012). Regulatory control of N2
fixation involves transcriptional and post-translational mechanisms that may have
decoupled in P-limited cultures (Zehr et al., 1993; Dixon and Kahn, 2004). In addition,
96
contrasting P-limited C fixation rates and C-PUEs with N2 fixation rates and N-PUEs
suggest that cells may be allocating more P (e.g. in the form of P-rich ribosomes and ATP)
to C fixation. Moreover, functional enrichment suggests that P-limited cells are shifting
cellular resources towards building ABC transporters to import nutrients and other growth
substrates (Tang et al., 2012).
Collectively, these responses indicate that P-limited cells may be N/P co-limited,
which was evident through additional analyses of N-metabolism biomarkers,
cyanophycinase (cphB) and the global nitrogen regulator (ntcA) (Supplementary Figure
S10). Cyanophycinase (cphB) breaks down cyanophycin granules used for N storage and
P-limited upregulation of cphB during the day and night suggests a need to free up N stores
(Finzi-Hart et al., 2009; Dron et al., 2012; Watzer and Forchhammer, 2018). Additionally,
the global nitrogen regulatory gene ntcA in cyanobacteria is linked to low N availability
and controls the transcription of proteins involved in N assimilation and uptake (Lindell et
al., 1998; Tolonen et al., 2006; Saito et al., 2014). In our study, P-limited cells were the
only nutrient-limited treatment to significantly upregulate ntcA relative to replete cells,
suggesting N-limitation stemming from an inability to synthesize nitrogenase proteins at
night.

3.5. Fe limitation moderates P limitation in a unique Fe/P co-limited phenotype
The combination of Fe limitation and P limitation responses produce a unique Fe/P
co-limited Crocosphaera phenotype that displays a suite of physiological and molecular
characteristics observed in Fe-limited and P-limited cells. A smaller cell size is an important
97
response under nutrient-limitation that can reduce nutrient quotas while increasing
nutrient uptake. Both co-limited and P-limited cells were comparably small, suggesting P-
availability affects cell size.
Previous proteomic analyses of Trichodesmium found that while only co-limited
cells decreased in size, proteins involved in cell division and size were also abundant for
Fe-limited cells, demonstrating that low Fe availability may also exert regulatory control
over division and size (Walworth et al., 2016). In our study, diel transcriptional patterns
showed that co-limited and Fe-limited cells similarly upregulated genes for cell division,
cell shape, and peptidoglycan biosynthesis, which contribute to cell size (Chien et al., 2012;
Si et al., 2019). While it is unclear the exact factors regulating Crocosphaera cell size
flexibility, these results suggest that both Fe and P play an important role.
Altogether, our results suggest that Crocosphaera employ two major strategies to
manage nutrient limitation: reduction of cellular nutrient requirements and increased
responsiveness to environmental change (Figure 5). Fe limitation in Crocosphaera appears
to be a strong transcriptional modulator that offsets deleterious effects of P limitation by
reducing nutrient requirements through substitution and conservation pathways that
were not observed in P-limited cells. P-limited cells primarily increase cellular
responsiveness to environmental change (e.g. upregulate two-component regulatory
systems) to acquire more P, both inorganic and organic. Fe-limited cells however, prioritize
nutrient reduction by substituting out Fe-rich proteins with Fe-free alternatives, and
conserving nutrients, especially N, through diel protein turnover. These single-nutrient
limitation mechanisms along with a small cell size manifest simultaneously under Fe/P co-
limitation and likely underlie their faster growth rates.
98
4. Conclusions
Our study is among the first to link the physiological and molecular responses of
the globally important diazotroph Crocosphaera to Fe, P, and Fe/P (co)-limitation, and
interpret these relationships in the context of diel gene regulation. The low latitude oceans
where N2 fixers are found are often characterized as being either putatively P-limited or
Fe-limited [27, 28]. Our research expands a growing body of work that suggests the reality
is more complex than a single nutrient-limited state. The relative availability of nutrients,
for example a “balanced” limitation where the normal condition is that both Fe and P are
scarce, versus an episodic “imbalanced” state where one nutrient is transitorily available
in excess seems to be an important determinant of cellular physiology [43]. We observed
a broad metabolic restructuring in Crocosphaera in response to P limitation that reduced
N2 fixation and induced cellular N deficiency. In co-limited cells, N deficiency was
potentially minimized by the combination of mechanisms upregulated under both P and
Fe limitation to produce a unique, comparatively fast-growing and resource-efficient
phenotype (Figure 5). Recent transcriptomic analyses showed that in N-scarce waters,
temporal partitioning of N uptake and assimilation enables different microbial groups to
coexist (Muratore et al., 2022), suggesting an important regulatory role for N on diel
transcription and microbial community structure. In Crocosphaera, our analyses showed
that Fe and P also exert a regulatory role on diel gene expression. Varying Fe and P
availability alter diel gene expression patterns that trigger Crocosphaera to dynamically
reallocate resources between core cellular processes (e.g. nutrient acquisition, translation,
99
or metabolic rates). These molecular responses underlie unique nutrient-limited
physiological responses with implications for the input of new N in oligotrophic systems.
This intersection of N, Fe, and P biogeochemistry reinforces the need to consider
differing diazotrophic responses to multiple nutrient limitations. Climate change may alter
the existing Fe and P gradient in the ocean. N2 fixation is projected to shift more towards
being P-limited, as intensified stratification reduces advective supplies of P while at the
same time, Fe availability increases due to rising anthropogenic aeolian inputs (Hutchins
and Boyd, 2016; Hutchins and Capone, 2022). A better understanding of the nutrient-
limited mechanisms and controls of marine N2 fixation using tools that can capture
complex microbial community dynamics (e.g., metatranscriptomics and metaproteomics)
will improve our ability to project changes to biogeochemical cycling and primary
productivity in a changing ocean.

100
References
Andrews, S. (2010). FastQC: A quality control tool for high throughput sequence data.
Available at: https://qubeshub.org/resources/fastqc.

Benavides, M., Duhamel, S., Van Wambeke, F., Shoemaker, K. M., Moisander, P. H.,
Salamon, E., et al. (2020). Dissolved organic matter stimulates N2 fixation and nifH
gene expression in Trichodesmium. FEMS Microbiol. Lett. 367. doi:
10.1093/femsle/fnaa034.

Bench, S. R., Heller, P., Frank, I., Arciniega, M., Shilova, I. N., and Zehr, J. P. (2013). Whole
genome comparison of six Crocosphaera watsonii strains with differing phenotypes.
J. Phycol. 49, 786–801. doi: 10.1111/jpy.12090 PMID - 27007210.

Bibby, T. S., Nield, J., and Barber, J. (2001). Iron deficiency induces the formation of an
antenna ring around trimeric photosystem I in cyanobacteria. Nature 412, 743–745.
doi: 10.1038/35089098.

Bolger, A. M., Lohse, M., and Usadel, B. (2014). Trimmomatic: a flexible trimmer for
Illumina sequence data. Bioinformatics 30, 2114–2120. doi:
10.1093/bioinformatics/btu170.

Bonisteel, E. M., Turner, B. E., Murphy, C. D., Melanson, J.-R., Duff, N. M., Beardsall, B. D.,
et al. (2018). Strain specific differences in rates of Photosystem II repair in
picocyanobacteria correlate to differences in FtsH protein levels and isoform
expression patterns. PLoS One 13, e0209115. doi: 10.1371/journal.pone.0209115.

Buchfink, B., Reuter, K., and Drost, H.-G. (2021). Sensitive protein alignments at tree-of-
life scale using DIAMOND. Nat. Methods 18, 366–368. doi: 10.1038/s41592-021-
01101-x.

Capone, D. G. (2014). An iron curtain in the Atlantic Ocean forms a biogeochemical divide.
Proc. Natl. Acad. Sci. U. S. A. 111, 1231–1232. doi: 10.1073/pnas.1322568111.

Capone, D. G., Burns, J. A., Montoya, J. P., Subramaniam, A., Mahaffey, C., Gunderson, T.,
et al. (2005). Nitrogen fixation by Trichodesmium spp.: An important source of new
nitrogen to the tropical and subtropical North Atlantic Ocean. Global Biogeochem.
Cycles 19, 17. doi: 10.1029/2004gb002331.

Capone, D. G., and Carpenter, E. J. (1982). Nitrogen fixation in the marine environment.
Science 217, 1140–1142. doi: 10.1126/science.217.4565.1140.

Capone, D. G., Zehr, J. P., Paerl, H. W., Bergman, B., and Carpenter, E. J. (1997).
Trichodesmium, a globally significant marine cyanobacterium. Science 276, 1221–
1229. doi: 10.1126/science.276.5316.1221.
101
Capra, E. J., and Laub, M. T. (2012). Evolution of two-component signal transduction
systems. Annu. Rev. Microbiol. 66, 325–347. doi: 10.1146/annurev-micro-092611-
150039.

Cava, F., and de Pedro, M. A. (2014). Peptidoglycan plasticity in bacteria: emerging
variability of the murein sacculus and their associated biological functions. Curr.
Opin. Microbiol. 18, 46–53. doi: 10.1016/j.mib.2014.01.004.

Cérdan-García, E., Baylay, A., Polyviou, D., Woodward, E. M. S., Wrightson, L., Mahaffey,
C., et al. (2021). Transcriptional responses of Trichodesmium to natural inverse
gradients of Fe and P availability. ISME J. doi: 10.1038/s41396-021-01151-1.

Chappell, P. D., and Webb, E. A. (2010). A molecular assessment of the iron stress response
in the two phylogenetic clades of Trichodesmium. Environ. Microbiol. 12, 13–27. doi:
10.1111/j.1462-2920.2009.02026.x.

Chen, H. Y. S., Bandyopadhyay, A., and Pakrasi, H. B. (2018). Function, regulation and
distribution of IsiA, a membrane-bound chlorophyll a-antenna protein in
cyanobacteria. Photosynthetica 56, 322–333. doi: 10.1007/s11099-018-0787-7.

Chien, A.-C., Hill, N. S., and Levin, P. A. (2012). Cell size control in bacteria. Curr. Biol. 22,
R340-9. doi: 10.1016/j.cub.2012.02.032.

Chille, E., Strand, E., Neder, M., Schmidt, V., Sherman, M., Mass, T., et al. (2021).
Developmental series of gene expression clarifies maternal mRNA provisioning and
maternal-to-zygotic transition in a reef-building coral. BMC Genomics 22, 815. doi:
10.1186/s12864-021-08114-y.

Conesa, A., Götz, S., García-Gómez, J. M., Terol, J., Talón, M., and Robles, M. (2005).
Blast2GO: a universal tool for annotation, visualization and analysis in functional
genomics research. Bioinformatics 21, 3674–3676. doi:
10.1093/bioinformatics/bti610.

Deng, L., Cheung, S., Kang, C.-K., Liu, K., Xia, X., and Liu, H. (2021). Elevated temperature
relieves phosphorus limitation of marine unicellular diazotrophic cyanobacteria.
Limnol. Oceanogr. doi: 10.1002/lno.11980.

Detoni, A. M. S., Subramaniam, A., Haley, S. T., Dyhrman, S. T., and Calil, P. H. R. (2022).
Cyanobacterial diazotroph distributions in the Western South Atlantic. Frontiers in
Marine Science 9. doi: 10.3389/fmars.2022.856643.

Dixon, R., and Kahn, D. (2004). Genetic regulation of biological nitrogen fixation. Nat. Rev.
Microbiol. 2, 621–631. doi: 10.1038/nrmicro954.

102
Dron, A., Rabouille, S., Claquin, P., Le Roy, B., Talec, A., and Sciandra, A. (2012). Light-dark
(12:12) cycle of carbon and nitrogen metabolism in Crocosphaera watsonii WH8501:
relation to the cell cycle. Environ. Microbiol. 14, 967–981. doi: 10.1111/j.1462-
2920.2011.02675.x.

Dugenne, M., Henderikx Freitas, F., Wilson, S. T., Karl, D. M., and White, A. E. (2020). Life
and death of Crocosphaera sp. in the Pacific Ocean: Fine scale predator–prey
dynamics. Limnol. Oceanogr. 65, 2603–2617. doi: 10.1002/lno.11473.

Dyhrman, S. T., and Haley, S. T. (2006). Phosphorus scavenging in the unicellular marine
diazotroph Crocosphaera watsonii. Appl. Environ. Microbiol. 72, 1452–1458. doi:
10.1128/AEM.72.2.1452-1458.2006.

Finkel, Z. V., Beardall, J., Flynn, K. J., Quigg, A., Rees, T. A. V., and Raven, J. A. (2010).
Phytoplankton in a changing world: cell size and elemental stoichiometry. J.
Plankton Res. 32, 119–137. doi: 10.1093/plankt/fbp098.

Finzi-Hart, J. A., Pett-Ridge, J., Weber, P. K., Popa, R., Fallon, S. J., Gunderson, T., et al.
(2009). Fixation and fate of C and N in the cyanobacterium Trichodesmium using
nanometer-scale secondary ion mass spectrometry. Proc. Natl. Acad. Sci. U. S. A.
106, 6345–6350. doi: 10.1073/pnas.0810547106.

Frischkorn, K. R., Haley, S. T., and Dyhrman, S. T. (2019). Transcriptional and proteomic
choreography under phosphorus deficiency and re-supply in the N2 fixing
cyanobacterium Trichodesmium erythraeum. Front. Microbiol. 10, 330. doi:
10.3389/fmicb.2019.00330.

Garcia, N. S., Fu, F., Sedwick, P. N., and Hutchins, D. A. (2015). Iron deficiency increases
growth and nitrogen-fixation rates of phosphorus-deficient marine cyanobacteria.
ISME J. 9, 238–245. doi: 10.1038/ismej.2014.104.

Garcia, N. S., Fu, F.-X., Breene, C. L., Yu, E. K., Bernhardt, P. W., Mulholland, M. R., et al.
(2013). Combined effects of CO2 and light on large and small isolates of the
unicellular N2-fixing cyanobacterium Crocosphaera watsonii from the western
tropical Atlantic Ocean. Eur. J. Phycol. 48, 128–139. doi:
10.1080/09670262.2013.773383.

Hawco, N. J., Fu, F., Yang, N., Hutchins, D. A., and John, S. G. (2020). Independent iron and
light limitation in a low-light-adapted Prochlorococcus from the deep chlorophyll
maximum. ISME J. doi: 10.1038/s41396-020-00776-y.

Held, N. A., McIlvin, M. R., Moran, D. M., Laub, M. T., and Saito, M. A. (2019). Unique
patterns and biogeochemical relevance of two-component sensing in marine
bacteria. mSystems 4. doi: 10.1128/msystems.00317-18.
103
Held, N. A., Waterbury, J. B., Webb, E. A., Kellogg, R. M., McIlvin, M. R., Jakuba, M., et al.
(2022). Dynamic diel proteome and daytime nitrogenase activity supports
buoyancy in the cyanobacterium Trichodesmium. Nat Microbiol 7, 300–311. doi:
10.1038/s41564-021-01028-1.

Held, N. A., Webb, E. A., McIlvin, M. M., Hutchins, D. A., Cohen, N. R., Moran, D. M., et al.
(2020). Co-occurrence of Fe and P stress in natural populations of the marine
diazotroph Trichodesmium. Biogeosciences 17, 2537–2551. doi: 10.5194/bg-17-
2537-2020.

Hewson, I., Poretsky, R. S., Beinart, R. A., White, A. E., Shi, T., Bench, S. R., et al. (2009). In
situ transcriptomic analysis of the globally important keystone N2-fixing taxon
Crocosphaera watsonii. ISME J. 3, 618–631. doi: 10.1038/ismej.2009.8.

Hook, S. E., Gallagher, E. P., and Batley, G. E. (2014). The role of biomarkers in the
assessment of aquatic ecosystem health. Integr. Environ. Assess. Manag. 10, 327–
341. doi: 10.1002/ieam.1530.

Hutchins, D. A., and Boyd, P. W. (2016). Marine phytoplankton and the changing ocean
iron cycle. Nat. Clim. Chang. 6, 1072. doi: 10.1038/nclimate3147
https://www.nature.com/articles/nclimate3147.

Hutchins, D. A., and Capone, D. G. (2022). The marine nitrogen cycle: new developments
and global change. Nat. Rev. Microbiol. doi: 10.1038/s41579-022-00687-z.

Hutchins, D. A., and Sañudo-Wilhelmy, S. A. (2021). The enzymology of ocean global
change. Ann. Rev. Mar. Sci. doi: 10.1146/annurev-marine-032221-084230.

Jacq, V., Ridame, C., L’Helguen, S., Kaczmar, F., and Saliot, A. (2014). Response of the
unicellular diazotrophic cyanobacterium Crocosphaera watsonii to iron limitation.
PLoS One 9, e86749. doi: 10.1371/journal.pone.0086749.

Jiang, H.-B., Fu, F.-X., Rivero-Calle, S., Levine, N. M., Sañudo-Wilhelmy, S. A., Qu, P.-P., et
al. (2018). Ocean warming alleviates iron limitation of marine nitrogen fixation. Nat.
Clim. Chang. 8, 709–712. doi: 10.1038/s41558-018-0216-8.

Jones, P., Binns, D., Chang, H.-Y., Fraser, M., Li, W., McAnulla, C., et al. (2014).
InterProScan 5: genome-scale protein function classification. Bioinformatics 30,
1236–1240. doi: 10.1093/bioinformatics/btu031.

Kanehisa, M., and Goto, S. (2000). KEGG: kyoto encyclopedia of genes and genomes.
Nucleic Acids Res. 28, 27–30. doi: 10.1093/nar/28.1.27.

104
Karl, D., Letelier, R., Tupas, L., Dore, J., Christian, J., and Hebel, D. (1997). The role of
nitrogen fixation in biogeochemical cycling in the subtropical North Pacific Ocean.
Nature 388, 533–538. doi: 10.1038/41474.

Kustka, A. B., Sañudo-Wilhelmy, S. A., Carpenter, E. J., Capone, D., Burns, J., and Sunda,
W. G. (2003). Iron requirements for dinitrogen- and ammonium-supported growth
in cultures of Trichodesmium (IMS 101): Comparison with nitrogen fixation rates
and iron: carbon ratios of field populations. Limnol. Oceanogr. 48, 1869–1884. doi:
10.4319/lo.2003.48.5.1869.

Langklotz, S., Baumann, U., and Narberhaus, F. (2012). Structure and function of the
bacterial AAA protease FtsH. Biochim. Biophys. Acta 1823, 40–48. doi:
10.1016/j.bbamcr.2011.08.015.

Langmead, B., and Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nat.
Methods 9, 357–359. doi: 10.1038/nmeth.1923.

LaRoche, J., Boyd, P. W., McKay, R. M. L., and Geider, R. J. (1996). Flavodoxin as an in situ
marker for iron stress in phytoplankton. Nature 382, 802. doi: 10.1038/382802a0.

Latifi, A., Ruiz, M., and Zhang, C.-C. (2009). Oxidative stress in cyanobacteria. FEMS
Microbiol. Rev. 33, 258–278. doi: 10.1111/j.1574-6976.2008.00134.x.

Leonhardt, K., and Straus, N. A. (1992). An iron stress operon involved in photosynthetic
electron transport in the marine cyanobacterium Synechococcus sp. PCC 7002. J.
Gen. Microbiol. 138 Pt 8, 1613–1621. doi: 10.1099/00221287-138-8-1613.

Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., et al. (2009). The
Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079.
doi: 10.1093/bioinformatics/btp352.

Liao, Y., Smyth, G. K., and Shi, W. (2014). featureCounts: an efficient general purpose
program for assigning sequence reads to genomic features. Bioinformatics 30, 923–
930. doi: 10.1093/bioinformatics/btt656.

Lin, S., Litaker, R. W., and Sunda, W. G. (2016). Phosphorus physiological ecology and
molecular mechanisms in marine phytoplankton. J. Phycol. 52, 10–36. doi:
10.1111/jpy.12365.

Lindell, D., Padan, E., and Post, A. F. (1998). Regulation of ntcA expression and nitrite
uptake in the marine Synechococcus sp. strain WH 7803. J. Bacteriol. 180, 1878–
1886. doi: 10.1128/JB.180.7.1878-1886.1998.

105
Love, M. I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and
dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550. doi:
10.1186/s13059-014-0550-8.

Mills, M. M., Ridame, C., Davey, M., LaRoche, J., and Geider, R. J. (2004). Iron and
phosphorus co-limit nitrogen fixation in the eastern tropical North Atlantic. Nature
429, 292–294. doi: 10.1038/nature02550.

Mohr, W., Intermaggio, M. P., and LaRoche, J. (2010). Diel rhythm of nitrogen and carbon
metabolism in the unicellular, diazotrophic cyanobacterium Crocosphaera watsonii
WH8501. Environ. Microbiol. 12, 412–421. doi: 10.1111/j.1462-
2920.2009.02078.x.

Moisander, P. H., Beinart, R. A., Hewson, I., White, A. E., Johnson, K. S., Carlson, C. A., et
al. (2010). Unicellular cyanobacterial distributions broaden the oceanic N2 fixation
domain. Science 327, 1512–1514. doi: 10.1126/science.1185468.

Moore, C. M., Mills, M. M., Arrigo, K. R., Berman-Frank, I., Bopp, L., Boyd, P. W., et al.
(2013). Processes and patterns of oceanic nutrient limitation. 701–710. doi:
10.1038/ngeo1765.

Mueller, E. A., and Levin, P. A. (2020). Bacterial cell wall quality control during
environmental stress. MBio 11. doi: 10.1128/mBio.02456-20.

Muratore, D., Boysen, A. K., Harke, M. J., Becker, K. W., Casey, J. R., Coesel, S. N., et al.
(2022). Complex marine microbial communities partition metabolism of scarce
resources over the diel cycle. Nat Ecol Evol 6, 218–229. doi: 10.1038/s41559-021-
01606-w.

Oksanen, J., Guillaume Blanchet, F., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., et al.
(2020). vegan: Community Ecology Package. R package version 2.5-7.

Orchard, E. D., Webb, E. A., and Dyhrman, S. T. (2009). Molecular analysis of the
phosphorus starvation response in Trichodesmium spp. Environ. Microbiol. 11,
2400–2411. doi: 10.1111/j.1462-2920.2009.01968.x.

Pereira, N., Shilova, I. N., and Zehr, J. P. (2016). Molecular markers define progressing
stages of phosphorus limitation in the nitrogen-fixing cyanobacterium,
Crocosphaera. J. Phycol. 52, 274–282. doi: 10.1111/jpy.12396.

Pereira, N., Shilova, I. N., and Zehr, J. P. (2019). Use of the high-affinity phosphate
transporter gene, pstS, as an indicator for phosphorus stress in the marine
diazotroph Crocosphaera watsonii (Chroococcales, Cyanobacteria). J. Phycol. 55, 752–
761. doi: 10.1111/jpy.12863.
106
Price, N. M., Harrison, G. I., Hering, J. G., Hudson, R. J., Nirel, P. M. V., Palenik, B., et al.
(1989). Preparation and chemistry of the artificial algal culture medium Aquil.
Biological Oceanography 6, 443–461. doi: 10.1080/01965581.1988.10749544.

Qu, P., Fu, F.-X., Kling, J. D., Huh, M., Wang, X., and Hutchins, D. A. (2019). Distinct
responses of the nitrogen-fixing marine cyanobacterium Trichodesmium to a
thermally variable environment as a function of phosphorus availability. Front.
Microbiol. 10, 1282. doi: 10.3389/fmicb.2019.01282.

Qu, P.-P., Fu, F.-X., Wang, X.-W., Kling, J. D., Elghazzawy, M., Huh, M., et al. (2022). Two
co-dominant nitrogen-fixing cyanobacteria demonstrate distinct acclimation and
adaptation responses to cope with ocean warming. Environ. Microbiol. Rep. doi:
10.1111/1758-2229.13041.

Redfield, A. C. (1958). The biological control of chemical factors in the environment. Am.
Sci. 46, 230A–221.

Rouco, M., Frischkorn, K. R., Haley, S. T., Alexander, H., and Dyhrman, S. T. (2018).
Transcriptional patterns identify resource controls on the diazotroph
Trichodesmium in the Atlantic and Pacific oceans. ISME J. 12, 1486–1495. doi:
10.1038/s41396-018-0087-z.

Saito, M. A., Bertrand, E. M., Dutkiewicz, S., Bulygin, V. V., Moran, D. M., Monteiro, F. M.,
et al. (2011). Iron conservation by reduction of metalloenzyme inventories in the
marine diazotroph Crocosphaera watsonii. Proc. Natl. Acad. Sci. U. S. A. 108, 2184–
2189. doi: 10.1073/pnas.1006943108.

Saito, M. A., McIlvin, M. R., Moran, D. M., Goepfert, T. J., DiTullio, G. R., Post, A. F., et al.
(2014). Multiple nutrient stresses at intersecting Pacific Ocean biomes detected by
protein biomarkers. Science 345, 1173. doi: 10.1126/science.1256450.

Scheffers, D.-J., and Pinho, M. G. (2005). Bacterial cell wall synthesis: new insights from
localization studies. Microbiol. Mol. Biol. Rev. 69, 585–607. doi:
10.1128/MMBR.69.4.585-607.2005.

Schoffman, H., Lis, H., Shaked, Y., and Keren, N. (2016). Iron–nutrient interactions within
phytoplankton. Front. Plant Sci. 7, 1223. doi:
https://doi.org/10.3389/fpls.2016.01223.

Shi, T., Ilikchyan, I., Rabouille, S., and Zehr, J. P. (2010). Genome-wide analysis of diel gene
expression in the unicellular N2-fixing cyanobacterium Crocosphaera watsonii WH
8501. ISME J. 4, 621–632. doi: 10.1038/ismej.2009.148.

107
Shi, T., Sun, Y., and Falkowski, P. G. (2007). Effects of iron limitation on the expression of
metabolic genes in the marine cyanobacterium Trichodesmium erythraeum IMS101.
Environ. Microbiol. 9, 2945–2956. doi: 10.1111/j.1462-2920.2007.01406.x.

Si, F., Le Treut, G., Sauls, J. T., Vadia, S., Levin, P. A., and Jun, S. (2019). Mechanistic origin
of cell-size Control and homeostasis in bacteria. Curr. Biol. 29, 1760-1770.e7. doi:
10.1016/j.cub.2019.04.062.

Silhavy, T. J., Kahne, D., and Walker, S. (2010). The bacterial cell envelope. Cold Spring
Harb. Perspect. Biol. 2, a000414. doi: 10.1101/cshperspect.a000414.

Sohm, J. A., Webb, E. A., and Capone, D. G. (2011). Emerging patterns of marine nitrogen
fixation. Nat. Rev. Microbiol. 9, 499–508. doi: 10.1038/nrmicro2594.

Tang, K., Jiao, N., Liu, K., Zhang, Y., and Li, S. (2012). Distribution and functions of TonB-
dependent transporters in marine bacteria and environments: implications for
dissolved organic matter utilization. PLoS One 7, e41204. doi:
10.1371/journal.pone.0041204.

Tang, W., Cerdán-García, E., Berthelot, H., Polyviou, D., Wang, S., Baylay, A., et al. (2020).
New insights into the distributions of nitrogen fixation and diazotrophs revealed by
high-resolution sensing and sampling methods. ISME J. 14, 2514–2526. doi:
10.1038/s41396-020-0703-6.

Tang, W., Wang, S., Fonseca-Batista, D., Dehairs, F., Gifford, S., Gonzalez, A. G., et al.
(2019). Revisiting the distribution of oceanic N2 fixation and estimating
diazotrophic contribution to marine production. Nat. Commun. 10, 831. doi:
10.1038/s41467-019-08640-0.

Tolonen, A. C., Aach, J., Lindell, D., Johnson, Z. I., Rector, T., Steen, R., et al. (2006). Global
gene expression of Prochlorococcus ecotypes in response to changes in nitrogen
availability. Mol. Syst. Biol. 2, 53. doi: 10.1038/msb4100087.

Turk, K. A., Rees, A. P., Zehr, J. P., Pereira, N., Swift, P., Shelley, R., et al. (2011). Nitrogen
fixation and nitrogenase (nifH) expression in tropical waters of the eastern North
Atlantic. ISME J. 5, 1201–1212. doi: 10.1038/ismej.2010.205.

Turk-Kubo, K. A., Achilles, K. M., Serros, T. R. C., Ochiai, M., Montoya, J. P., and Zehr, J. P.
(2012). Nitrogenase (nifH) gene expression in diazotrophic cyanobacteria in the
Tropical North Atlantic in response to nutrient amendments. Front. Microbiol. 3, 17.
doi: 10.3389/fmicb.2012.00386.

108
Walworth, N. G., Fu, F.-X., Webb, E. A., Saito, M. A., Moran, D., Mcllvin, M. R., et al. (2016).
Mechanisms of increased Trichodesmium fitness under iron and phosphorus co-
limitation in the present and future ocean. Nat. Commun. 7, 12081. doi:
10.1038/ncomms12081.

Walworth, N. G., Saito, M. A., Lee, M. D., McIlvin, M. R., Moran, D. M., Kellogg, R. M., et
al. (2021). Why environmental biomarkers work: Transcriptome-proteome
correlations and modeling of multistressor experiments in the marine bacterium
Trichodesmium. J. Proteome Res. doi: 10.1021/acs.jproteome.1c00517.

Watzer, B., and Forchhammer, K. (2018). Cyanophycin synthesis optimizes nitrogen
utilization in the unicellular cyanobacterium Synechocystis sp. Strain PCC 6803.
Appl. Environ. Microbiol. 84. doi: 10.1128/AEM.01298-18.

Webb, E. A., Moffett, J. W., and Waterbury, J. B. (2001). Iron stress in open-ocean
Cyanobacteria (Synechococcus, Trichodesmium, and Crocosphaera spp.):
Identification of the IdiA Protein. Appl. Environ. Microbiol. 67, 5444. doi:
10.1128/AEM.67.12.5444-5452.2001.

Wilson, S. T., Aylward, F. O., Ribalet, F., Barone, B., Casey, J. R., Connell, P. E., et al. (2017).
Coordinated regulation of growth, activity and transcription in natural populations
of the unicellular nitrogen-fixing cyanobacterium Crocosphaera. Nature Microbiology
2, 9. doi: 10.1038/nmicrobiol.2017.118.

Yang, N., Merkel, C. A., Lin, Y.-A., Levine, N. M., Hawco, N. J., Jiang, H.-B., et al. (2021).
Warming iron-limited oceans enhance nitrogen fixation and drive biogeographic
specialization of the globally important Cyanobacterium Crocosphaera. Front. Mar.
Sci. 8. doi: 10.3389/fmars.2021.628363.

Yu, G., Wang, L.-G., Han, Y., and He, Q.-Y. (2012). clusterProfiler: an R package for
comparing biological themes among gene clusters. OMICS 16, 284–287. doi:
10.1089/omi.2011.0118.

Zehr, J. P., and Capone, D. G. (2020). Changing perspectives in marine nitrogen fixation.
Science 368, eaay9514. doi: 10.1126/science.aay9514.

Zehr, J. P., Montoya, J. P., Jenkins, B. D., Hewson, I., Mondragon, E., Short, C. M., et al.
(2007). Experiments linking nitrogenase gene expression to nitrogen fixation in the
North Pacific Subtropical Gyre. Limnol. Oceanogr. 52, 169–183. doi:
10.4319/lo.2007.52.1.0169.

Zehr, J. P., Waterbury, J. B., Turner, P. J., Montoya, J. P., Omoregie, E., Steward, G. F., et
al. (2001). Unicellular cyanobacteria fix N2 in the subtropical North Pacific Ocean.
Nature 412, 635–638. doi: 10.1038/35088063.
109
Zehr, J. P., Wyman, M., Miller, V., Duguay, L., and Capone, D. G. (1993). Modification of
the Fe protein of nitrogenase in natural populations of Trichodesmium thiebautii.
Appl. Environ. Microbiol. 59, 669–676. doi: 10.1128/aem.59.3.669-676.1993.

110
Figures

Figure 1: Nutrient replete and nutrient-limited Crocosphaera physiology: A) growth rates,
B) cell diameter, C) carbon fixation (C fixation), and D) nitrogen fixation (N2 fixation) rates.
For B) a violin plot shows the distribution of the cell diameter datapoints, while the
embedded diamond marker in the boxplot marks the mean cell diameter. Error bars denote
standard deviation of triplicate values. Different letters on each plot represent statistical
significance (p < 0.05) calculated by one-way ANOVA across all four treatments. Line
segments and corresponding significance markers represent statistical significance
calculated by one-way ANOVA across only the nutrient-limited treatments. Plots without
line segments indicate that one-way ANOVA results with nutrient-limited only treatments
were the same as results calculated with all treatments. Co-lim (Fe/P co-limited, green),
Fe-lim (Fe-limited, dark blue), P-lim (P-limited, light blue), and Replete (Fe/P replete,
peach).. Significance: * (p < 0.05), ** (p < 0.01), *** (p < 0.001).

111

Figure 2: Calculated elemental use efficiencies (RUEs, mol C or N fixed / hour / mol
intracellular P or Fe) of nutrient replete and nutrient-limited Crocosphaera. A) Carbon-
specific Phosphorus Use Efficiencies (C-PUEs), B) Nitrogen-specific Phosphorus Use
Efficiencies (N-PUEs), C) Carbon-specific Iron Use Efficiencies (C-IUEs), and D) Nitrogen-
specific Iron Use Efficiencies (N-IUEs). Error bars denote standard deviation of triplicate
values. Different letters on each plot represent statistical significance (p < 0.05) calculated
by one-way ANOVA across all four treatments. Line segments and corresponding
significance markers represent statistical significance calculated by one-way ANOVA
across only the nutrient-limited treatments. Plots without line segments indicate that one-
way ANOVA results with nutrient-limited only treatments were the same as results
calculated with all treatments. Co-lim (Fe/P co-limited, green), Fe-lim (Fe-limited, dark
blue), P-lim (P-limited, light blue), and Replete (Fe/P replete, peach). Significance: ns* (no
significance, p > 0.1 using one-way ANOVA, p < 0.05 using Welch’s t test), * (p < 0.05), **
(p < 0.01), *** (p < 0.001).

112

Figure 3: Gene expression trends of commonly used Fe- and P limitation biomarkers.
Statistical significance of biomarkers was calculated from DESeq2 pairwise comparisons
of triplicate samples of Co-lim (Fe/P co-limited, green), Fe-lim (Fe-limited, dark blue), P-
lim (P-limited, light blue) treatments compared to the Replete (Fe/P replete, peach)
treatment. Day and night gene expression significance was calculated from DESeq2
analysis of day and night samples for each nutrient treatment. Shaded-in plots indicate
significant upregulation of biomarker genes for both day and night compared to the
replete treatment (p < 0.05). For the co-limited treatment, only daytime isiB gene
expression was significantly upregulated compared to the replete treatment at p < 0.001
(***). Arrows indicate the directionality of statistically significant trends between day and
night gene expression within a treatment (p < 0.05).

113

Figure 4: Diel transcriptomes and core functional enrichment analysis of differentially
expressed genes. A) Venn Diagram of diel genes for each treatment. The percentages
represent the proportion of the overall transcriptome that exhibits a significant diel
pattern. The numbers represent the count of genes upregulated during the day (white)
and at night (black). B) Dot plot analysis of enriched GO Biological Process (BP) and
Molecular Function (MF) terms and KEGG Pathways (KO) containing diel-regulated genes
with p value < 0.05. The size of the dot reflects the number of genes for each term or
pathway and the grayscale colorbar reflects the p value. C) Heatmap analysis showing the
DESeq2-normalized gene expression scaled as the number of standard deviations from
the row mean (Z-score: red = upregulated, blue = downregulated) for all genes under the

114
Photosynthesis GO term and D) Heatmap analysis showing the top 25 genes (based on
row mean) for the Translation GO term. Due to space constraints, the full Translation
heatmap is displayed in the Supplementary Materials (Supplementary Figure S4). Column
dendrograms show similarity based on Euclidean distance and hierarchical clustering and
gene clusters were determined by k-means clustering using Euclidean distance. Nutrient
treatments are Co-lim (Fe/P co-limited, green), Fe-lim (Fe-limited, dark blue), P-lim (P-
limited, light blue), and Replete (Fe/P replete, peach). Time points are Day (goldenrod
yellow) and Night (deep purple). The heatmap color gradient shows low gene expression
(blue) and high gene expression (red). KEGG-annotations were assigned from the genome
annotation while non-KEGG annotations were assigned using DIAMOND blastx described
in Methods.

115

Figure 5: A schematic of the different strategies and mechanisms to manage single-
nutrient limitation (Fe-limited or P-limited) and Fe/P co-limited conditions. For each
mechanism, cells that are shaded in represent whether a mechanism is present for a given
nutrient-limited treatment and the broad strategy group each mechanism falls under. The
partial shading for Fe limitation under the smaller cell size mechanism indicates that while
Fe-limited cells were bigger than P-limited and Fe/P co-limited cells, Fe limitation may also
play a role in cell size reduction (see the main text for a discussion on (Fe limitation
moderates P limitation in a unique Fe/P co-limited phenotype). Nutrient treatments are Co-
lim (Fe/P co-limited, green), Fe-lim (Fe-limited, dark blue), P-lim (P-limited, light blue).
Nutrient status is either unbalanced (black) or balanced (grey). Strategy is either reduces
nutrient requirements (orange) or increases responsiveness to environmental change
(purple).

116
Supplementary Materials
Supplementary Methods

Culturing Media
Aquil artificial seawater (ASW) was amended with Aquil mixed vitamins, and a
modified Aquil trace metals stock (1.21 x 10
-7
M Mn, 7.97 x 10
-8
M Zn, 1.00 x 10
-7
M Mo,
and 5.03 x 10
-8
M Co) (Sunda et al., 2005). For Replete media, 10 μM phosphate
(HNa2PO4, passed through an activated Chelex 100 resin column to remove contaminating
Fe) and 250 nM Fe buffered with 25 μM EDTA (free inorganic Fe = 1,085 pM) was added
directly to the ASW base media. For Fe-limited media, replete phosphate (P)
concentrations (10 μM) were added to the media and 3 nM Fe buffered with 25 μM EDTA
(free inorganic Fe = 19.0 pM) was directly added to Fe-limited cultures during periodic
dilutions. For P-limited media, replete Fe was added directly to the media and 0.15 μM P
was amended directly to the cultures during dilutions. For Fe/P co-limited media, 3 nM Fe
buffered with 25 μM EDTA and 0.15 μM P were directly added to cultures during
dilutions. Dissolved free inorganic Fe, which is the form most bioavailable to
phytoplankton, was calculated for the different experimental conditions following
previously described Methods (Yang et al., 2021).

Carbon and Nitrogen fixation
Carbon fixation. Sub-cultures (10 mL) from each replicate were incubated for 6
hours with H
14
CO3 starting 2 hours into the light period under the same experimental
117
growth conditions, filtered onto glass microfiber filters (Whatman, Grade GF/F), rinsed 6x
with artificial seawater, and stored in the dark overnight in 4.5 ml of scintillation cocktail
(Ultima Gold, PerkinElmer). Samples were analyzed on a Beckman LS 6000 liquid
scintillation counter (Beckman Coulter Inc., Fullerton, CA, USA). One procedural blank for
each nutrient treatment was created using 3.3 ml sub-cultures from each treatment
triplicate and subtracted from the measured sample values of the corresponding
treatment.
Nitrogen fixation. N2 fixation was measured using the acetylene reduction assay.
Sub-cultures (40 mL) from each replicate were incubated with 6 mL of acetylene injected
into the 35 mL headspace of 75 mL sealed-top bottles at the start of the dark period and
gently inverted 3x to mix. All-night (~12 hours) accumulation of ethylene was measured
at the end of the incubation period on a Shimadzu GC-8A gas chromatograph (Shimadzu
Scientific Instruments, Columbia, Maryland) and converted to fixed N2 using a ratio of 3:1
and a Bunsen coefficient of 0.086. Two procedural blanks with ASW-only and spiked with
acetylene were also analyzed and subtracted from the measured sample values.

Cellular P and Fe Content
For cellular P, 30 mL of each replicate was filtered onto pre-combusted (4 hours,
450°C) GF/F filters, rinsed 2x with 0.17M sodium sulfate, and placed in individual pre-
combusted (12 hours, 450°C) borosilicate scintillation vials with 2 ml of 0.017M
magnesium sulfate and left to dry at ~60°C. Prior to analyzing, the dried samples were
combusted for 3 hours at 450°C to convert organic P into inorganic orthophosphate and
118
cooled to room temperature. Samples were then rehydrated in 5 mL of 0.2M hydrochloric
acid (HCl). Orthophosphate levels were determined spectrophotometrically on a Shimadzu
UV-1800 using the molybdate colorimetric assay (Murphy and Riley, 1962) and calibrated
with a 0-20 µM P standard curve.
For cellular Fe, ~100 mL of each replicate was filtered onto acid-washed 0.2μm
Supor polyethersulfone filters (Pall Laboratory) and rinsed with oxalate reagent to remove
extracellular trace metals (Tovar-Sanchez et al., 2003). Filters were then digested in 30 ML
perfluoroalkoxy vials (Savillex) with 5 mL of 50% nitric acid (HNO3) amended with 10 ppb
Indium as an internal standard at 95°C for three days. Filters were then removed, and
samples were dried overnight at 100ºC and subsequently resolubilized in 200 µL of 1:1
concentrated HNO3 and HCl, sealed and heated for ~2-3 hours, and allowed to cool.
Samples were then dried and resuspended in 5 mL of 0.1M distilled HNO3 and analyzed
by inductively coupled plasma mass spectrometry (ICP-MS, Element 2, Thermo) calibrated
with a 0.1-300 ppb metal reference standard curve. Three procedural blank filters for each
treatment were also analyzed and subtracted from the measured sample values. All steps
occurred in a class 100 trace metal-clean environment.
One Fe sample was an order of magnitude higher than the other two replicates
(0.072 µM compared to 0.005 and 0.006 µM) and was removed from data analysis. We
believe the sample was contaminated in an accident during sample processing for the ICP-
MS.

Particulate Organic Carbon and Nitrogen
119
To measure and process particulate organic carbon and nitrogen (POC and PON)
samples, 30-40 mL sub-cultures were obtained from each experimental replicate and
filtered onto pre-combusted glass microfiber filters (Whatman, Grade GF/F), dried in an
oven at ~60°C, and then pelleted and analyzed on a 4010 Costech Elemental Analyzer
calibrated with an acetanilide standard curve.

RNA Extractions and Sequencing
Sub-cultures (200 mL) of each replicate were collected in 250 mL flat-bottom
polycarbonate centrifuge bottles 5-6 hours into the light period and 5-6 hours after dark
(24 samples total) and centrifuged at 12,000 rpm for 12 minutes at 28ºC on a Beckman
Avanti J-E Refrigerated Centrifuge using a J-Lite JLA-16.250 Fixed Angle Rotor. The
supernatant was carefully discarded, and the centrifuge step was repeated for a total of
400 mL centrifuged (total ~7-8x10
7
cells) per replicate at each time point, leaving ~1 mL
of supernatant to transfer the combined pellet into 1.8 mL cryogenic vials using sterile
filter-pipette tips. The cryogenic vials were centrifuged at 4,500 rpm for 8 minutes
(Eppendorf 5810) A final centrifuge step of the cryogenic vials was conducted at 4,500
rpm to pellet the sample and remove the excess liquid. Pellets in cryogenic vials were flash
frozen and stored in liquid N2 until extraction.
RNA was extracted using guanidium thiocyanate-phenol-chloroform (TRI Reagent,
Sigma Aldrich) and Zymo’s Direct-zol RNA Miniprep kit (Zymo Research, Irvine, CA) with
a DNase treatment following kit instructions. RNA purity was checked using a Nanodrop
spectrophotometer (Thermo Scientific). Samples that did not meet purity guidelines
120
(260/280 = 1.8-2.1, 260/230 > 1.5) were cleaned using Zymo’s RNA Clean and
Concentrator kit. Extracted RNA samples were sent to UC Davis’ DNA Technologies Core
for quality check (LabChip GX), library preparation, ribo-depletion (QIAseq FastSelect,
QIAGEN), and 150-bp paired-end sequencing on a NovaSeq S4 (Illumina).

Accounting for Crocosphaera’s diel cycle
Crocosphaera’s gene expression and activity shows a clear diel pattern (Saito et al.,
2011; Wilson et al., 2017). To account for the diel cycle in our study, we measured C
fixation and N2 fixation during the daytime and nighttime, respectively (See
Supplementary Methods, Carbon and Nitrogen Fixation). We also collected transcriptomic
samples at peak activity time points, 5-6 hours into the light period for C fixation and 5-6
hours into the dark period for N2 fixation to serve as representative snapshots. We
normalized measured C and N2 fixation rates to cell count samples collected during the
day and night, respectively to account for potential diel effects on cell size that could affect
rate calculations.
Our overarching goal was to compare the cell size, rates, and RUEs across nutrient
treatments with a minimal focus on how these measurements would fluctuate across the
diel cycle within a nutrient treatment. Thus, due to this scope, we did not measure
nighttime cell size or collect nighttime cellular P and cellular Fe. Previous studies have
shown that cell size and cellular Fe quotas can fluctuate throughout the diel cycle (Tuit et
al., 2004; Dron et al., 2012). Therefore, we sampled elemental quotas and cell size at the
same time of day (during the light period) to avoid diel influences affecting our inter-
treatment comparisons for cell size and RUEs.
121
We note that cellular Fe has been shown to increase at night in the small cell
Crocosphaera strain WH8501 (Tuit et al., 2004). Since we used daytime Fe quotas for our
nitrogen-specific iron use efficiencies (N-IUEs), the N-IUEs we report are likely a maximum
estimate for Crocosphaera. Thus, additional time points would be helpful to develop a
higher resolution understanding of how activity and gene expression patterns oscillate
throughout the diel period within a given treatment and it would be desirable to
incorporate additional time points into future studies.

122
Supplementary References

Dron, A., Rabouille, S., Claquin, P., Le Roy, B., Talec, A., and Sciandra, A. (2012). Light-dark
(12:12) cycle of carbon and nitrogen metabolism in Crocosphaera watsonii WH8501:
relation to the cell cycle. Environ. Microbiol. 14, 967–981. doi: 10.1111/j.1462-
2920.2011.02675.x.

Murphy, J., and Riley, J. P. (1962). A modified single solution method for the determination
of phosphate in natural waters. Anal. Chim. Acta 27, 31–36. doi: 10.1016/S0003-
2670(00)88444-5.

Saito, M. A., Bertrand, E. M., Dutkiewicz, S., Bulygin, V. V., Moran, D. M., Monteiro, F. M.,
et al. (2011). Iron conservation by reduction of metalloenzyme inventories in the
marine diazotroph Crocosphaera watsonii. Proc. Natl. Acad. Sci. U. S. A. 108, 2184–
2189. doi: 10.1073/pnas.1006943108.

Sunda, W. G., Price, N. M., and Morel, F. M. M. (2005). Trace metal ion buffers and their
use in culture studies. Algal Culturing Techniques, 35–63. doi: 10.1016/b978-
012088426-1/50005-6.

Tovar-Sanchez, A., Sañudo-Wilhelmy, S. A., Garcia-Vargas, M., Weaver, R. S., Popels, L. C.,
and Hutchins, D. A. (2003). A trace metal clean reagent to remove surface-bound
iron from marine phytoplankton. Mar. Chem. 82, 91–99. doi: 10.1016/S0304-
4203(03)00054-9.

Tuit, C., Waterbury, J., and Ravizza, G. (2004). Diel variation of molybdenum and iron in
marine diazotrophic cyanobacteria. Limnol. Oceanogr. 49, 978–990. doi:
10.4319/lo.2004.49.4.0978.

Wilson, S. T., Aylward, F. O., Ribalet, F., Barone, B., Casey, J. R., Connell, P. E., et al. (2017).
Coordinated regulation of growth, activity and transcription in natural populations
of the unicellular nitrogen-fixing cyanobacterium Crocosphaera. Nature Microbiology
2, 9. doi: 10.1038/nmicrobiol.2017.118.

Yang, N., Merkel, C. A., Lin, Y.-A., Levine, N. M., Hawco, N. J., Jiang, H.-B., et al. (2021).
Warming iron-limited oceans enhance nitrogen fixation and drive biogeographic
specialization of the globally important Cyanobacterium Crocosphaera. Front. Mar.
Sci. 8. doi: 10.3389/fmars.2021.628363.

123
Supplementary Figures

Supplementary Figure S1: Cellular P quota measurements in nutrient replete and nutrient-
limited Crocosphaera. Error bars denote standard deviation of triplicate values. Different
letters on each plot represent statistical significance (p value < 0.01) calculated by one-
way ANOVA across all four treatments. Treatments: Co-lim (Fe/P co-limited, green), Fe-
lim (Fe-limited, dark blue), P-lim (P-limited, light blue), and Replete (Fe/P replete, peach).

124
Supplementary Figure S2: Redundancy analysis (RDA) of Crocosphaera gene expression
across different nutrient treatments for A) day, B) night, and C) day and night time points.
RDA1 and RDA2 axes quantify the proportion of the variance in the gene expression
dataset explained by each axis. Arrows indicate the explanatory variables constraining the
dataset. Time point: Day (circles) and Night (triangles). Treatments: Fe/P co-limited (Co-
lim, green), Fe-limited (Fe-lim, dark blue), P-limited (P-lim, light blue), and Fe/P replete
(Replete, peach).

125

Supplementary Figure S3: Breakdown of diel genes across different nutrient conditions.
Core genes are genes that are diel across all four treatments, shared genes are diel genes
that occur between at least two treatments, and unique genes are diel genes that occur
for only one treatment. The number of diel genes for each treatment are listed in the main
text. Treatments: Co-lim (Fe/P co-limited), Fe-lim (Fe-limited), P-lim (P-limited), and
Replete (Fe/P replete).

126
Supplementary Figure S4: Heatmap analysis showing the DESeq2-normalized gene
expression scaled as the number of standard deviations from the row mean (Z-score) for
all core genes in the Translation GO term. Figure 4D in the main text is truncated, showing
the top 25 genes based on row mean. Column dendrograms show similarity based on
Euclidean distance and hierarchical clustering and gene clusters were determined by k-
means clustering using Euclidean distance. Nutrient treatments are Co-lim (Fe/P co-
limited, green), Fe-lim (Fe-limited, dark blue), P-lim (P-limited, light blue), and Replete (Fe/P
replete, peach). Time points are Day (goldenrod yellow) and Night (deep purple). The
heatmap color gradient shows low gene expression (blue) and high gene expression (red).

127
Supplementary Figure S5: Heatmap analysis of enriched GO Biological Process (BP) terms
across core genes in daytime samples. A) Photosynthesis, light reaction, B) Cell division, C)
Regulation of cell shape (Cell Shape), D) Peptidoglycan biosynthetic process (PG
biosynthesis), and E) Proteolysis. Column dendrograms show similarity based on Euclidean
distance and hierarchical clustering and gene clusters were determined by k-means
clustering using Euclidean distance. Nutrient treatments Co-lim (Fe/P co-limited, green),
Fe-lim (Fe-limited, dark blue), P-lim (P-limited, light blue), and Replete (Fe/P replete,
peach). Time points are Day (goldenrod yellow) and Night (deep purple). Heatmap color
gradient shows the DESeq2-normalized gene expression scaled as the number of standard
deviations from the row mean (Z-score: red = upregulated, blue = downregulated) for all

128
genes. KEGG-annotations were assigned from the genome annotation while non-KEGG
annotations were assigned using DIAMOND blastx described in Methods.

129

Supplementary Figure S6: DESeq2-normalized gene expression trends of nitrogenase
genes (nifH, nifD, and nifK): nifD, nitrogenase molybdenum-iron protein alpha chain; nifH,
nitrogenase iron protein; nifK, nitrogenase molybdenum-iron protein alpha chain. Gene
expression has been scaled down 1000 times (1000x) for visualization purposes. Gene
expression significance was calculated from DESeq2 pairwise comparisons of day and
night samples for each treatment compared to the P-limited treatment. Significance: ** (p
value < 0.01), *** (p value < 0.001). Treatments: Co-lim (Fe/P co-limited, green), Fe-lim
(Fe-limited, dark blue), P-lim (P-limited, light blue), and Replete (Fe/P replete, peach).

130

Supplementary Figure S7: Venn Diagram of differentially expressed genes based on
DESeq2 pairwise analysis for each nutrient-limited (Fe-limited, P-limited, and Fe/P co-
limited) treatment relative to the replete treatment for A) Day and B) Night. The numbers
represent the count of genes upregulated during the day or night (white) and
downregulated during the day or night (black). C) Dot plot visualization of enriched GO
Biological Process (BP) and Molecular Function (MF) terms and KEGG Pathways (KO) with
p value < 0.05 for unique, upregulated genes under either Fe-limited, P-limited, or Fe/P
co-limited treatments. The size of the dot reflects the number of genes for each term or

131
pathway and the grayscale colorbar indicates the p value. Nutrient treatments are Co-lim
(Fe/P co-limited), Fe-lim (Fe-limited), P-lim (P-limited).

132
Supplementary Figure S8: Breakdown of differentially expressed nutrient-limited genes
relative to the replete treatment. Core genes are up- or down-regulated by all nutrient-
limited treatments, shared genes are nutrient-limited genes that occur between at least
nutrient-limited treatments, and unique genes are genes that occur for only one nutrient-
limited treatment. Treatments: Co-lim (Fe/P co-limited), Fe-lim (Fe-limited), P-lim (P-
limited), and Replete (Fe/P replete).

133

Supplementary Figure S9: Dot plot visualization of the enriched GO Biological Process
(BP) and Molecular Function (MF) terms and KEGG Pathways (KO) with p value < 0.05 for
unique, downregulated genes under nutrient-limited conditions. The size of the dot
reflects the number of genes for each term or pathway and the grayscale colorbar
indicates the p value. Co-lim (Fe/P co-limited), Fe-lim (Fe-limited), P-lim (P-limited), and
Replete (Fe/P replete).

134
Supplementary Figure S10: Gene expression of cyanophycinase (cphB) and the global
nitrogen regulator (ntcA) genes. Gene expression has not been scaled. Boxplot shows
daytime and nighttime gene expression in triplicate across each nutrient treatment: Co-
lim (Fe/P co-limited, green), Fe-lim (Fe-limited, dark blue), P-lim (P-limited, light blue), and
Replete (Fe/P replete, peach). Gene expression significance calculated from DESeq2
pairwise comparisons for either day or night gene expression of the nutrient-limited
treatments relative to the Replete treatment is indicated by asterisks (*). Arrows indicate
the directionality of statistically significant trends between day and night gene expression
within a treatment. Significance: * (p < 0.05), ** (p < 0.01).

135
Supplementary Tables
Supplementary Table S1: Calculated ratios of C fixation (pmol C cell
-1
h
-1
) to N2 fixation
(fmol N cell
-1
h
-1
) for each nutrient treatment in triplicate. Treatment abbreviations: Co-lim
(Fe/P co-limited), Fe-lim (Fe-limited), P-lim (P-limited), and Replete (Fe/P replete).

Treatment Ratio
Co-lim 61.55
Co-lim 58.64
Co-lim 48.25
Fe-lim 52.34
Fe-lim 40.43
Fe-lim 30.69
P-lim 127.91
P-lim 158.92
P-lim 117.89
Replete 41.98
Replete 21.98
Replete 34.30

136
Supplementary Table S2: Calculated elemental stoichiometry ratios. All units are mol:mol
except for Fe:C ratios with units of µmol:mol. Treatment abbreviations: Co-lim (Fe/P co-
limited), Fe-lim (Fe-limited), P-lim (P-limited), and Replete (Fe/P replete).

Treatment C:N C:P N:P Fe:C
Co-lim 9.8 539.4 55.0 36.0
Co-lim 8.7 503.6 58.0 37.6
Co-lim 8.7 466.0 53.3 30.2
Fe-lim 11.4 261.8 23.0 20.8
Fe-lim 11.6 227.7 19.7 21.6
Fe-lim 9.6 246.6 25.7 23.9
P-lim 11.2 462.2 41.4 227.4
P-lim 11.7 559.4 47.7 84.9
P-lim 11.7 527.7 45.0 126.1
Replete 13.5 220.0 16.3 123.3
Replete 11.5 265.7 23.2 47.2
Replete 14.2 234.5 16.5 100.0

137
Supplementary Table S3: Enriched Gene Ontology (GO) terms and KEGG pathways across core, shared, and unique diel
genes identified using clusterProfiler’s v4.0.5 enricher (GO terms) and enrichKEGG (KEGG pathways) functions. The
following columns, diel, go.fxn (GO aspects: BP-biological process, MF-molecular function, CC-cellular component, KO-
KEGG Pathway), treatment, and type and were added separately in R v4.1.1. Under “treatment”, core GO or KEGG
enrichment are differentially expressed across all treatments, shared terms or pathways are indicated by additive treatments
(e.g., shared between Co-lim and P-lim is CoP). A single treatment indicates unique terms or pathways for a given treatment.
“Type” specifies whether the result is core, shared, or unique. Treatment abbreviations: Co (Fe/P co-limited), P (P-limited),
Fe (Fe-limited), and Rep (Replete).

ID Description GeneRatio BgRatio pvalue p.adjust qvalue geneID Count diel go.fxn treatment type
GO:0015979 photosynthesi
s
33/160 55/1206 6E-17 1E-15 1E-15 27_25/K02093/K02094/K02284/K0229
0/K02634/K02635/K02689/K02690/K0
2691/K02692/K02693/K02694/K02697
/K02698/K02699/K02700/K02705/K02
706/K02707/K02708/K02709/K02710/
K02711/K02712/K02713/K02714/K027
15/K02718/K02720/K02723/K05378/K
05380
33 day BP core core
GO:0019684 photosynthesi
s, light
reaction
9/160 13/1206 5E-06 6E-05 5E-05 K02703/K02705/K02706/K02707/K027
08/K02714/K05572/K05579/K05580
9 day BP core core
GO:0051301 cell division 7/160 11/1206 1E-04 1E-03 9E-04 67_60/K00790/K01924/K01928/K0256
3/K03588/K03798
7 day BP core core
GO:0008360 regulation of
cell shape
6/160 10/1206 7E-04 4E-03 3E-03 K00790/K01776/K01924/K01928/K025
63/K11212
6 day BP core core
GO:0009252 peptidoglycan
biosynthetic
process
5/160 10/1206 6E-03 3E-02 2E-02 K00790/K01776/K01924/K02563/K233
93
5 day BP core core
GO:0006508 proteolysis 12/160 43/1206 7E-03 3E-02 2E-02 K00681/K01406/K01423/K03100/K035
92/K03797/K03798/K07259/K08156/K
08641/K15352/K17734
12 day BP core core
138
GO:0006412 translation 45/262 58/1206 1E-20 4E-19 4E-19 K02433/K02434/K02435/K02863/K028
67/K02871/K02874/K02876/K02878/K
02881/K02884/K02886/K02887/K0288
8/K02890/K02892/K02895/K02897/K0
2904/K02906/K02911/K02913/K02916
/K02926/K02931/K02933/K02935/K02
939/K02946/K02948/K02950/K02952/
K02954/K02956/K02959/K02961/K029
63/K02965/K02967/K02982/K02986/K
02988/K02990/K02992/K19032
45 night BP core core
GO:0009055 electron
transfer
activity
10/202 22/1574 2E-04 7E-03 7E-03 K00240/K02634/K02635/K02690/K026
91/K02703/K02708/K02720/K04755/K
08685
10 day MF core core
GO:0048038 quinone
binding
7/202 13/1574 5E-04 1E-02 1E-02 K05572/K05575/K05579/K05580/K055
82/K05583/K05584
7 day MF core core
GO:0003735 structural
constituent of
ribosome
42/311 51/1574 1E-22 7E-21 7E-21 K02863/K02867/K02871/K02874/K028
76/K02878/K02881/K02884/K02886/K
02887/K02888/K02890/K02892/K0289
5/K02897/K02904/K02906/K02911/K0
2913/K02916/K02926/K02931/K02933
/K02935/K02939/K02946/K02948/K02
950/K02952/K02954/K02956/K02959/
K02961/K02963/K02965/K02967/K029
82/K02986/K02988/K02990/K02992/K
19032
42 night MF core core
GO:0019843 rRNA binding 14/311 16/1574 9E-09 2E-07 2E-07 K02863/K02874/K02876/K02878/K028
87/K02890/K02931/K02933/K02948/K
02952/K02954/K02982/K02986/K0299
0
14 night MF core core
GO:0000049 tRNA binding 9/311 12/1574 5E-05 9E-04 8E-04 K00989/K01889/K01890/K02863/K029
31/K02946/K02952/K03536/K05539
9 night MF core core
139
GO:0003723 RNA binding 19/311 43/1574 2E-04 2E-03 2E-03 K01147/K01890/K02600/K02863/K028
86/K02888/K02895/K02952/K02982/K
02986/K02988/K03216/K03218/K0350
0/K03595/K03664/K06180/K06183/K1
2573
19 night MF core core
GO:0009523 photosystem II 15/122 20/685 2E-08 2E-07 1E-07 27_25/K02705/K02706/K02707/K0270
8/K02709/K02710/K02711/K02712/K0
2713/K02714/K02718/K02720/K02723
/K08902
15 day CC core core
GO:0031676 plasma
membrane-
derived
thylakoid
membrane
22/122 40/685 4E-08 2E-07 1E-07 27_25/K02284/K02634/K02635/K0269
0/K02693/K02697/K02700/K02705/K0
2708/K02709/K02710/K02714/K02718
/K03798/K05378/K05572/K05575/K05
579/K05580/K05583/K05584
22 day CC core core
GO:0009522 photosystem I 9/122 11/685 6E-06 2E-05 1E-05 K02690/K02691/K02692/K02693/K026
94/K02697/K02698/K02699/K02700
9 day CC core core
GO:0005840 ribosome 37/126 48/685 2E-20 2E-19 2E-19 K02867/K02871/K02874/K02878/K028
81/K02884/K02886/K02887/K02888/K
02890/K02892/K02895/K02897/K0290
4/K02906/K02913/K02916/K02926/K0
2931/K02933/K02935/K02939/K02946
/K02948/K02950/K02952/K02954/K02
956/K02959/K02961/K02963/K02965/
K02967/K02982/K02988/K02990/K190
32
37 night CC core core
ko00195 Photosynthesi
s
33/223 53/1512 6E-16 7E-14 7E-14 K02634/K02635/K02636/K02640/K026
42/K02643/K02689/K02690/K02691/K
02692/K02693/K02694/K02696/K0269
7/K02698/K02699/K02700/K02703/K0
2705/K02706/K02707/K02708/K02709
/K02710/K02711/K02712/K02713/K02
714/K02718/K02720/K02723/K03689/
K08902
33 day KO core core
ko00550 Peptidoglycan
biosynthesis
10/223 20/1512 2E-04 1E-02 1E-02 K00790/K00887/K01921/K01924/K019
28/K02563/K05366/K07259/K19302/K
23393
10 day KO core core
140
ko03010 Ribosome 43/282 53/1512 9E-24 1E-21 1E-21 K02863/K02864/K02867/K02871/K028
74/K02876/K02878/K02881/K02884/K
02886/K02887/K02888/K02890/K0289
2/K02895/K02897/K02904/K02906/K0
2911/K02913/K02916/K02926/K02931
/K02933/K02935/K02939/K02945/K02
946/K02948/K02950/K02952/K02954/
K02956/K02959/K02961/K02963/K029
65/K02967/K02982/K02986/K02988/K
02990/K02992
43 night KO core core
GO:0015995 chlorophyll
biosynthetic
process
1/2 10/1206 2E-02 2E-02 NA K04037 1 day BP CoRep shared
GO:0006468 protein
phosphorylatio
n
2/11 12/1206 5E-03 4E-02 4E-02 K10297/K21440 2 day BP FeRep shared
GO:0016491 oxidoreductas
e activity
2/2 104/157
4
4E-03 9E-03 5E-03 K04037/K14189 2 day MF CoRep shared
GO:0004672 protein kinase
activity
2/14 10/1574 3E-03 4E-02 4E-02 K10297/K21440 2 day MF FeRep shared
GO:0016021 integral
component of
membrane
36/50 333/685 4E-04 4E-03 3E-03 K00341/K00461/K02037/K02257/K022
59/K02497/K02654/K03296/K03585/K
05384/K06402/K06890/K06946/K0711
4/K07399/K08153/K08218/K08744/K0
8884/K09940/K10257/K10661/K11105
/K11527/K11720/K14825/K16368/K16
705/K17105/K17686/K17784/K18169/
K18233/K21163/K21919/K22804
36 day CC CoFe shared
GO:0016020 membrane 6/7 241/685 9E-03 4E-02 3E-02 115_4/69_20/K04084/K05099/K09971/
K19422
6 day CC CoPRep shared
ko03420 Nucleotide
excision repair
4/71 8/1512 3E-04 2E-02 2E-02 K01972/K03701/K03703/K03723 4 day KO CoFe shared
ko01501 beta-Lactam
resistance
1/2 11/1512 1E-02 4E-02 1E-02 K03587 1 day KO PFe shared
ko00550 Peptidoglycan
biosynthesis
1/2 20/1512 3E-02 4E-02 1E-02 K03587 1 day KO PFe shared
141
ko04626 Plant-
pathogen
interaction
2/6 12/1512 9E-04 7E-03 5E-03 K13408/K16225 2 day KO PFeRep shared
GO:0006935 chemotaxis 3/21 15/1206 2E-03 1E-02 1E-02 K02659/K11524/K11526 3 night BP CoP shared
GO:0007165 signal
transduction
4/21 33/1206 2E-03 1E-02 1E-02 K02659/K07636/K11524/K11526 4 night BP CoP shared
GO:0000160 phosphorelay
signal
transduction
system
4/21 42/1206 5E-03 2E-02 2E-02 K07636/K11523/K11526/K21023 4 night BP CoP shared
GO:0005515 protein
binding
6/22 99/1574 2E-03 3E-02 3E-02 K07636/K10260/K12132/K12795/K210
23/K21832
6 night MF CoP shared
GO:0005737 cytoplasm 11/26 118/685 2E-03 1E-02 1E-02 K00789/K00930/K01507/K01735/K018
87/K01892/K01925/K02356/K02502/K
03531/K16908
11 night CC CoFeRep shared
ko02020 Two-
component
system
6/20 109/151
2
2E-03 4E-02 3E-02 K02650/K02659/K07636/K11523/K115
24/K11526
6 night KO CoP shared
ko04110 Cell cycle 2/16 7/1512 2E-03 4E-02 3E-02 K06636/K06679 2 night KO PCoFe shared
ko05203 Viral
carcinogenesis
2/16 9/1512 4E-03 4E-02 3E-02 K06679/K21662 2 night KO PCoFe shared
ko04113 Meiosis - yeast 2/16 10/1512 4E-03 4E-02 3E-02 K06636/K06679 2 night KO PCoFe shared
ko04114 Oocyte
meiosis
2/16 11/1512 5E-03 4E-02 3E-02 K06636/K06679 2 night KO PCoFe shared
ko04111 Cell cycle -
yeast
2/16 13/1512 8E-03 4E-02 3E-02 K06636/K06679 2 night KO PCoFe shared
ko00140 Steroid
hormone
biosynthesis
1/3 5/1512 1E-02 3E-02 1E-02 K22970 1 night KO PFe shared
ko04626 Plant-
pathogen
interaction
1/3 12/1512 2E-02 4E-02 1E-02 K13440 1 night KO PFe shared
ko00290 Valine, leucine
and isoleucine
biosynthesis
2/23 9/1512 2E-04 7E-03 6E-03 K01649/K01652/K01702 3 night KO PRep shared
ko00300 Lysine
biosynthesis
3/26 10/1512 5E-04 8E-03 6E-03 K00003/K01586/K01778 3 night KO CoPRep shared
ko01230 Biosynthesis
of amino acids
7/26 91/1512 6E-04 8E-03 6E-03 K00003/K01586/K01778/K02501/K038
56/K15633/K22305
7 night KO CoPRep shared
142
ko01110 Biosynthesis
of secondary
metabolites
12/26 332/151
2
5E-03 4E-02 3E-02 K00003/K00214/K00218/K01586/K017
78/K02293/K02496/K02501/K03856/K
04040/K15633/K22305
12 night KO CoPRep shared
ko00260 Glycine, serine
and threonine
metabolism
3/26 24/1512 7E-03 5E-02 4E-02 K00003/K15633/K22305 3 night KO CoPRep shared
GO:0008270 zinc ion
binding
4/31 20/1574 5E-04 1E-02 1E-02 27_115/40_5/K02316/K12574 4 day MF Co unique
GO:0004519 endonuclease
activity
5/36 28/1574 3E-04 7E-03 6E-03 5_54/K07448/K18411/K19158/K20478 5 day MF Rep unique
GO:0004518 nuclease
activity
3/36 14/1574 3E-03 4E-02 3E-02 7_104/72_47/K07448 3 day MF Rep unique
GO:0016021 integral
component of
membrane
12/14 333/685 4E-03 1E-02 9E-03 K01000/K02227/K03101/K03321/K042
10/K06921/K07243/K07300/K09793/K
11749/K18678/K19511
12 day CC Co unique
ko00230 Purine
metabolism
8/55 48/1512 2E-04 2E-02 2E-02 K00525/K00873/K00939/K00951/K015
24/K01933/K22879/K23269
8 night KO Rep unique

143
Supplementary Table S4: Enriched gene ontology (GO) terms and KEGG pathways across core, shared, and unique nutrient-
limited genes that are differentially expressed compared to the Replete treatment identified using clusterProfiler’s v4.0.5
enricher (GO terms) and enrichKEGG (KEGG pathways) functions. The following columns, treatment, diel, reg (up or down
regulation), go.fxn (GO aspects: BP-biological process, MF-molecular function, CC-cellular component, KO-KEGG Pathway),
and type and were added separately in R v4.1.1. Under “treatment”, core GO or KEGG enrichment are differentially
expressed across all treatments, shared terms or pathways are indicated by additive treatments (e.g. shared between Co-lim
and P-lim is CoP). A single treatment indicates unique terms or pathways for a given treatment. “Type” specifies whether the
result is core, shared, or unique. Treatment abbreviations: Co (Fe/P co-limited), P (P-limited), and Fe (Fe-limited).

ID Description GeneRatio BgRatio pvalue p.adjust qvalue geneID Count treatment diel reg go.fxn type
GO:0008360
regulation of
cell shape
3/40 10/1206 0.003 0.037 0.027 K00790/K01924/K01928 3 Co day up BP unique
GO:0051301 cell division 3/40 11/1206 0.005 0.037 0.027 K00790/K01924/K01928 3 Co day up BP unique
GO:0055085
transmembra
ne transport
30/202 80/1206 0.000 0.000 0.000
K02005/K02006/K02011/K02033
/K02035/K03072/K03327/K0349
8/K05802/K05845/K06147/K070
88/K07300/K07301/K09691/K10
233/K10234/K11004/K11105/K1
1955/K12340/K14445/K16053/K
16786/K16787/K17245/K18138/
K21163/K21642/K22044
30 P day up BP unique
GO:0006281 DNA repair 3/14 24/1206 0.002 0.028 0.028 K03550/K03629/K19171 3 Fe night up BP unique
GO:0055085
transmembra
ne transport
29/113 80/1206 0.000 0.000 0.000
K02006/K02011/K02038/K02575
/K03281/K03296/K03316/K0332
5/K03442/K03498/K05099/K058
02/K05845/K06147/K07088/K07
300/K08153/K08217/K09690/K0
9691/K09971/K11085/K14445/K
16786/K17245/K19341/K20344/
K21163/K21642
29 P night up BP unique
GO:0006418
tRNA
aminoacylati
on for
protein
translation
3/24 14/1206 0.002 0.037 0.037 K01875/K01881/K01893 3 Fe day down BP unique
144
GO:0015979
photosynthes
is
27/140 55/1206 0.000 0.000 0.000
27_25/K02092/K02093/K02095/
K02097/K02290/K02692/K02693
/K02694/K02700/K02706/K0270
8/K02709/K02710/K02712/K027
13/K02714/K02715/K02718/K02
719/K02723/K04035/K04039/K0
5378/K05380/K08901/K08903
27 P day down BP unique
GO:0007156
homophilic
cell adhesion
via plasma
membrane
adhesion
molecules
6/140 11/1206 0.001 0.008 0.007
K06336/K12548/K14825/K16503
/K16507/K16669
6 P day down BP unique
GO:0006355
regulation of
transcription,
DNA-
templated
15/140 61/1206 0.003 0.024 0.020
67_7/K00996/K03711/K03723/K
06378/K07450/K09825/K10697/
K10900/K10914/K11332/K11527
/K18843/K18918/K21744
15 P day down BP unique
GO:0015979
photosynthes
is
34/142 55/1206 0.000 0.000 0.000
112_19/27_25/K02092/K02093/K
02094/K02095/K02097/K02284/
K02285/K02290/K02692/K02693
/K02698/K02699/K02700/K0270
7/K02708/K02709/K02710/K027
11/K02712/K02713/K02714/K02
715/K02718/K02720/K02723/K0
4035/K04039/K05378/K05380/K
08901/K08903/K16071
34 P night down BP unique
GO:0006412 translation 24/142 58/1206 0.000 0.000 0.000
K01056/K02874/K02878/K02881
/K02895/K02899/K02902/K0290
4/K02911/K02913/K02919/K029
31/K02933/K02939/K02954/K02
959/K02961/K02963/K02968/K0
2986/K02988/K02990/K02994/K
02996
24 P night down BP unique
GO:0016787
hydrolase
activity
19/245 58/1574 0.001 0.038 0.038
41_66/K01153/K01561/K02433/
K03651/K04485/K06147/K06999
/K07107/K07116/K08156/K0969
1/K12388/K13282/K16055/K189
59/K19837/K21064/K22390
19 P day up MF unique
145
GO:0140359
ABC-type
transporter
activity
5/132 10/1574 0.001 0.030 0.029
K06147/K09691/K11085/K19341
/K20344
5 P night up MF unique
GO:0005509
calcium ion
binding
9/161 27/1574 0.001 0.041 0.039
K06336/K08901/K12548/K13448
/K14825/K15825/K16503/K1650
7/K16669
9 P day down MF unique
GO:0003735
structural
constituent
of ribosome
23/148 51/1574 0.000 0.000 0.000
K02874/K02878/K02881/K02895
/K02899/K02902/K02904/K0291
1/K02913/K02919/K02931/K029
33/K02939/K02954/K02959/K02
961/K02963/K02968/K02986/K0
2988/K02990/K02994/K02996
23 P night down MF unique
GO:0019843 rRNA binding 8/148 16/1574 0.000 0.001 0.001
K02874/K02878/K02931/K02933
/K02954/K02986/K02990/K0299
4
8 P night down MF unique
GO:0016021
integral
component
of membrane
84/142 333/685 0.003 0.019 0.014
106_63/12_28/15_121/15_30/16_
75/30_70/38_92/53_15/58_30/69
_20/7_38/K00278/K02007/K0201
1/K02030/K02033/K02496/K026
60/K03072/K03118/K03306/K03
309/K03310/K03327/K03498/K0
3587/K03820/K04210/K04773/K
05384/K05568/K05571/K05577/
K05595/K05802/K05874/K06147
/K06199/K06373/K06460/K0694
6/K07052/K07088/K07090/K072
64/K07300/K07301/K07642/K07
679/K08156/K08715/K09133/K1
0233/K11004/K11105/K11212/K
11520/K11690/K11749/K11955/
K12284/K12352/K13487/K14205
/K14340/K14445/K14683/K1527
0/K15445/K15744/K16053/K162
11/K16368/K17979/K18138/K18
814/K19294/K19422/K21084/K2
1163/K21642/K22044/K22615/K
23094
84 P day up CC unique
146
GO:0005886
plasma
membrane
20/142 55/685 0.004 0.019 0.014
15_30/K02007/K02011/K02033/
K03072/K03310/K03498/K04773
/K05595/K05802/K07090/K0726
4/K07678/K08156/K11690/K142
05/K19294/K21642/K22044/K23
094
20 P day up CC unique
GO:0016020 membrane 62/142 241/685 0.012 0.040 0.029
15_121/15_30/16_75/38_92/58_3
0/69_20/K00887/K02000/K02004
/K02005/K02006/K02011/K0203
0/K02033/K02496/K02660/K030
72/K03118/K03306/K03310/K03
327/K03406/K04210/K05384/K0
5577/K05585/K05802/K05845/K
06213/K06373/K06460/K07052/
K07264/K08156/K08715/K09133
/K09691/K09879/K10039/K1023
3/K10234/K12284/K12352/K134
87/K14205/K14340/K14683/K15
270/K15445/K16053/K16786/K1
6787/K17080/K17245/K18138/K
18814/K19422/K21084/K21163/
K21642/K22044/K22615
62 P day up CC unique
GO:0005737 cytoplasm 4/6 118/685 0.010 0.010 NA K01925/K02520/K03629/K21936 4 Fe night up CC unique
147
GO:0016021
integral
component
of membrane
68/90 333/685 0.000 0.000 0.000
108_3/53_15/69_20/7_38/9_72/K
00278/K01537/K02011/K02030/
K02496/K02497/K02575/K02654
/K03118/K03296/K03309/K0331
0/K03316/K03325/K03442/K034
98/K03699/K03820/K05099/K05
384/K05571/K05595/K05802/K0
6125/K06147/K06515/K06946/K
07088/K07240/K07264/K07267/
K07300/K08153/K08156/K08217
/K09690/K09793/K09971/K1066
1/K11085/K11520/K11525/K115
36/K12211/K13487/K14340/K14
445/K14683/K15270/K15272/K1
5537/K15744/K16211/K16368/K
19225/K19294/K19341/K19416/
K19422/K20344/K20469/K21163
/K21642
68 P night up CC unique
GO:0005886
plasma
membrane
19/90 55/685 0.000 0.000 0.000
K02011/K02575/K02654/K03310
/K03325/K03442/K03498/K0509
9/K05595/K05802/K06125/K072
40/K07264/K07678/K08156/K11
525/K15272/K19294/K21642
19 P night up CC unique
GO:0016020 membrane 46/90 241/685 0.001 0.002 0.001
108_3/69_20/9_72/K00887/K020
00/K02006/K02011/K02030/K02
038/K02496/K02654/K03118/K0
3281/K03296/K03310/K03325/K
03442/K05099/K05384/K05802/
K05845/K06515/K07240/K07264
/K08153/K08156/K09690/K0969
1/K09793/K09971/K10661/K115
25/K11536/K13487/K14340/K14
683/K15270/K15272/K16786/K1
7245/K19341/K19416/K19422/K
20469/K21163/K21642
46 P night up CC unique
GO:0005887
integral
component
of plasma
membrane
6/90 16/685 0.012 0.029 0.018
K02008/K02038/K03118/K08992
/K11719/K13292
6 P night up CC unique
148
GO:0009523
photosystem
II
14/80 20/685 0.000 0.000 0.000
27_25/K02706/K02708/K02709/
K02710/K02712/K02713/K02714
/K02718/K02719/K02723/K0890
1/K08902/K08903
14 P day down CC unique
GO:0031676
plasma
membrane-
derived
thylakoid
membrane
17/80 40/685 0.000 0.000 0.000
27_25/K02095/K02110/K02641/
K02693/K02700/K02708/K02709
/K02710/K02714/K02718/K0379
8/K05378/K05579/K05583/K055
84/K08903
17 P day down CC unique
GO:0030089
phycobilisom
e
9/80 24/685 0.001 0.002 0.001
K02092/K02093/K02095/K02097
/K02287/K02290/K02641/K0537
8/K05380
9 P day down CC unique
GO:0009523
photosystem
II
15/94 20/685 0.000 0.000 0.000
27_25/K02707/K02708/K02709/
K02710/K02711/K02712/K02713
/K02714/K02718/K02720/K0272
3/K08901/K08902/K08903
15 P night down CC unique
GO:0005840 ribosome 21/94 48/685 0.000 0.000 0.000
K02874/K02878/K02881/K02895
/K02899/K02902/K02904/K0291
3/K02919/K02931/K02933/K029
39/K02954/K02959/K02961/K02
963/K02968/K02988/K02990/K0
2994/K02996
21 P night down CC unique
GO:0030089
phycobilisom
e
13/94 24/685 0.000 0.000 0.000
K02092/K02093/K02094/K02095
/K02097/K02284/K02285/K0229
0/K05378/K05380/K05385/K053
86/K20713
13 P night down CC unique
GO:0031676
plasma
membrane-
derived
thylakoid
membrane
15/94 40/685 0.000 0.000 0.000
27_25/K02095/K02110/K02284/
K02285/K02693/K02700/K02708
/K02709/K02710/K02714/K0271
8/K05378/K05584/K08903
15 P night down CC unique
GO:0009522
photosystem
I
5/94 11/685 0.010 0.021 0.011
K02692/K02693/K02698/K02699
/K02700
5 P night down CC unique
ko00550
Peptidoglyca
n
biosynthesis
5/52 20/1512 0.000 0.029 0.028
K00075/K00790/K01924/K01928
/K07259
5 Co day up KO unique
149
ko02010
ABC
transporters
17/117 84/1512 0.000 0.012 0.012
K02000/K02006/K02008/K02011
/K02020/K02038/K05845/K0969
0/K09691/K09971/K11085/K157
70/K16786/K17244/K17245/K19
341/K20344
17 P night up KO unique
ko00195
Photosynthes
is
25/171 53/1512 0.000 0.000 0.000
K02110/K02637/K02639/K02640
/K02641/K02692/K02693/K0269
4/K02696/K02700/K02703/K027
06/K02708/K02709/K02710/K02
712/K02713/K02714/K02718/K0
2719/K02723/K03689/K08901/K
08902/K08903
25 P day down KO unique
ko03420
Nucleotide
excision
repair
4/71 8/1512 0.000 0.022 0.021 K03657/K03701/K03723/K10843 4 Co night down KO unique
ko00195
Photosynthes
is
26/177 53/1512 0.000 0.000 0.000
K02110/K02639/K02640/K02642
/K02643/K02692/K02693/K0269
6/K02698/K02699/K02700/K027
07/K02708/K02709/K02710/K02
711/K02712/K02713/K02714/K0
2718/K02720/K02723/K03689/K
08901/K08902/K08903
26 P night down KO unique
ko03010 Ribosome 24/177 53/1512 0.000 0.000 0.000
K02874/K02878/K02881/K02895
/K02899/K02902/K02904/K0291
1/K02913/K02919/K02931/K029
33/K02939/K02954/K02959/K02
961/K02963/K02968/K02985/K0
2986/K02988/K02990/K02994/K
02996
24 P night down KO unique
ko00196
Photosynthes
is – antenna
proteins
14/177 22/1512 0.000 0.000 0.000
K02092/K02093/K02094/K02095
/K02097/K02284/K02285/K0229
0/K05378/K05380/K05382/K053
85/K05386/K20713
14 P night down KO unique
GO:0015979
photosynthes
is
4/6 55/1206 0.000 0.000 0.000 K02094/K02691/K02707/K02720 4 core day down BP core
GO:0006412 translation 2/6 58/1206 0.030 0.045 0.016 K02884/K19032 2 core day down BP core
GO:0006412 translation 12/41 58/1206 0.000 0.000 0.000
K02887/K02888/K02911/K02916
/K02919/K02935/K02954/K0295
6/K02968/K02970/K02986/K029
90
12 CoP day down BP shared
150
GO:0015979
photosynthes
is
2/2 55/1206 0.002 0.002 NA K02635/K02697 2 Pfe day down BP shared
GO:0055085
transmembra
ne transport
2/4 80/1206 0.024 0.024 NA K03287/K03832 2 core day up BP core
GO:0055085
transmembra
ne transport
9/41 80/1206 0.001 0.015 0.014
K02037/K02038/K03316/K03325
/K06188/K08153/K10112/K1108
5/K20344
9 CoP day up BP shared
GO:0006260
DNA
replication
3/21 16/1206 0.002 0.020 0.019 K00525/K01972/K02343 3 core night down BP core
GO:0006412 translation 17/52 58/1206 0.000 0.000 0.000
K02876/K02884/K02886/K02887
/K02888/K02890/K02892/K0289
7/K02906/K02916/K02926/K029
35/K02956/K02965/K02967/K02
970/K19032
17 CoP night down BP shared
GO:0018298
protein-
chromophore
linkage
2/6 15/1206 0.002 0.017 0.013 IsiA/K07718 2 CoFe night up BP shared
GO:0007165
signal
transduction
2/6 33/1206 0.010 0.041 0.032 K07651/K07718 2 CoFe night up BP shared
GO:0006935 chemotaxis 7/41 15/1206 0.000 0.000 0.000
K02659/K02660/K03406/K03408
/K03412/K11524/K11526
7 CoP night up BP shared
GO:0007165
signal
transduction
8/41 33/1206 0.000 0.000 0.000
K02659/K02660/K03406/K03408
/K05874/K07679/K11524/K1152
6
8 CoP night up BP shared
GO:0000160
phosphorelay
signal
transduction
system
8/41 42/1206 0.000 0.000 0.000
K02658/K03412/K07679/K11443
/K11522/K11523/K11526/K1964
1
8 CoP night up BP shared
GO:0009055
electron
transfer
activity
4/10 22/1574 0.000 0.000 0.000 K02691/K02720/K05524/K08906 4 core day down MF core
GO:0020037 heme binding 3/10 17/1574 0.000 0.000 0.000 K02707/K02720/K08906 3 core day down MF core
GO:0005506
iron ion
binding
2/10 20/1574 0.006 0.014 0.004 K02720/K08906 2 core day down MF core
GO:0051539
4 iron, 4
sulfur cluster
binding
2/10 22/1574 0.008 0.014 0.004 K02691/K17892 2 core day down MF core
GO:0003735
structural
constituent
of ribosome
12/60 51/1574 0.000 0.000 0.000
K02887/K02888/K02911/K02916
/K02919/K02935/K02954/K0295
12 CoP day down MF shared
151
6/K02968/K02970/K02986/K029
90
GO:0019843 rRNA binding 4/60 16/1574 0.002 0.032 0.030 K02887/K02954/K02986/K02990 4 CoP day down MF shared
GO:0016491
oxidoreducta
se activity
10/60
104/157
4
0.005 0.041 0.038
K00260/K00301/K00303/K00514
/K00526/K02586/K02592/K0712
4/K10679/K22970
10 CoP day down MF shared
GO:0009055
electron
transfer
activity
1/1 22/1574 0.014 0.042 0.029 K02635 1 Pfe day down MF shared
GO:0042626
ATPase-
coupled
transmembra
ne
transporter
activity
1/1 17/1574 0.011 0.022 0.011 K06158 1 Pfe day up MF shared
GO:0046872
metal ion
binding
20/146 90/1574 0.000 0.005 0.005
K00013/K00240/K00366/K01265
/K01702/K01772/K01920/K0194
5/K01961/K02635/K02689/K035
17/K03795/K03802/K03862/K04
487/K05810/K06402/K08997/K1
7686
20 CoFe night down MF shared
GO:0051536
iron-sulfur
cluster
binding
11/146 37/1574 0.000 0.008 0.007
K00240/K00366/K00392/K02585
/K04487/K05582/K06168/K1002
6/K11473/K11781/K23548
11 CoFe night down MF shared
GO:0051539
4 iron, 4
sulfur cluster
binding
8/146 22/1574 0.000 0.008 0.007
K00240/K00366/K00392/K01702
/K02690/K03517/K05582/K0616
8
8 CoFe night down MF shared
GO:0003735
structural
constituent
of ribosome
17/66 51/1574 0.000 0.000 0.000
K02876/K02884/K02886/K02887
/K02888/K02890/K02892/K0289
7/K02906/K02916/K02926/K029
35/K02956/K02965/K02967/K02
970/K19032
17 CoP night down MF shared
GO:0005840 ribosome 10/25 48/685 0.000 0.000 0.000
K02887/K02888/K02916/K02919
/K02935/K02954/K02956/K0296
8/K02970/K02990
10 CoP day down CC shared
152
GO:0031676
plasma
membrane-
derived
thylakoid
membrane
2/2 40/685 0.003 0.013 0.007 K02635/K02697 2 Pfe day down CC shared
GO:0016021
integral
component
of membrane
19/24 333/685 0.002 0.009 0.006
K01992/K02037/K02040/K02456
/K02650/K03316/K03325/K0612
5/K06188/K08153/K09793/K098
33/K10661/K11085/K12211/K15
272/K17784/K20344/K23379
19 CoP day up CC shared
GO:0005840 ribosome 16/24 48/685 0.000 0.000 0.000
K02884/K02886/K02887/K02888
/K02890/K02892/K02897/K0290
6/K02916/K02926/K02935/K029
56/K02965/K02967/K02970/K19
032
16 CoP night down CC shared
ko00195
Photosynthes
is
4/7 53/1512 0.000 0.000 0.000 K02691/K02707/K02720/K08906 4 core day down KO core
ko03010 Ribosome 2/7 53/1512 0.023 0.034 0.012 K02864/K02884 2 core day down KO core
ko01120
Microbial
metabolism
in diverse
environments
8/20
168/151
2
0.001 0.023 0.019
K00240/K00450/K01698/K01783
/K03389/K05942/K11473/K2172
6
8 CoFe day down KO shared
ko03010 Ribosome 13/58 53/1512 0.000 0.000 0.000
K02887/K02888/K02911/K02916
/K02919/K02935/K02954/K0295
6/K02968/K02970/K02985/K029
86/K02990
13 CoP day down KO shared
ko00195
Photosynthes
is
2/2 53/1512 0.001 0.001 NA K02635/K02697 2 Pfe day down KO shared
ko05130
Pathogenic
Escherichia
coli infection
2/5 5/1512 0.000 0.001 0.000 K19475/K23612 2 core day up KO shared
ko05135
Yersinia
infection
2/5 6/1512 0.000 0.001 0.000 K19475/K23612 2 core day up KO shared
ko04144 Endocytosis 2/5 8/1512 0.000 0.001 0.000 K19475/K23612 2 core day up KO shared
ko05100
Bacterial
invasion of
epithelial
cells
1/5 5/1512 0.016 0.031 0.004 K23612 1 core day up KO shared
153
ko00600
Sphingolipid
metabolism
1/5 7/1512 0.023 0.031 0.004 K00720 1 core day up KO shared
ko05131 Shigellosis 1/5 7/1512 0.023 0.031 0.004 K23612 1 core day up KO shared
ko03010 Ribosome 17/55 53/1512 0.000 0.000 0.000
K02864/K02876/K02884/K02886
/K02887/K02888/K02890/K0289
2/K02897/K02906/K02916/K029
26/K02935/K02956/K02965/K02
967/K02970
17 CoP night down KO shared
ko00190
Oxidative
phosphorylati
on
1/1 41/1512 0.027 0.027 NA K05583 1 Pfe night down KO shared
ko02020
Two-
component
system
21/56
109/151
2
0.000 0.000 0.000
K00575/K01077/K02040/K02650
/K02658/K02659/K02660/K0340
6/K03408/K03412/K05874/K076
79/K07711/K07720/K11443/K11
522/K11523/K11524/K11526/K1
9641/K20976
21 CoP night up KO Shared
ko02030
Bacterial
chemotaxis
5/56 8/1512 0.000 0.000 0.000
K00575/K03406/K03408/K03412
/K05874
5 CoP night up KO Shared

154
Chapter 4: Contrasting transcriptomic responses underlie
temperature and iron-limited physiology in the marine diazotroph
Crocosphaera watsonii

Abstract
Across the vast nutrient-poor oligotrophic ocean gyres, the unicellular
cyanobacterium Crocosphaera provides a crucial ecosystem service by transforming or
“fixing” inert nitrogen gas (N2) into bioavailable ammonia to fuel primary production.
Daytime photosynthesis generates glucose that is stored as glycogen, a critical fuel source
to support nighttime cellular respiration and N2 fixation. These metabolic dynamics reflect
a tight coordination between cellular carbon I and nitrogen (N) status that complements
Crocosphaera’s unique intracellular iron (Fe) cycling. Crocosphaera carries out a diel cycle
of metalloenzyme degradation and biosynthesis, shuttling Fe between photosynthesis
proteins and nitrogenase. Ocean warming across low-Fe regimes may disrupt this unique
choreography. Differential gene expression analysis of Crocosphaera grown under a 3x2
matrix of temperature and iron revealed comparable physiology but contrasting
underlying molecular responses at 23°C and 32°C, representing Crocosphaera’s sub- and
supra-optimal growth temperatures. At sub-optimal growth, enzyme turnover rate is
reduced, limiting glycogen synthesis and C accumulation with downstream consequences
for N2 fixation. At supra-optimal growth, thermally enhanced enzyme activity offsets heat-
induced protein damage, sustaining optimal N2 fixation rates. Fe-limitation exacerbates
these temperature effects and intensifies protein damage especially at 32°C, which
155
induces N limitation and substantial accumulation of cellular C as a possible coping
mechanism. By linking physiological rate measurements, elemental stoichiometry, and
differential gene expression analysis, we present the first response assessment of a
biogeochemically important cyanobacteria to changing temperature and Fe availability
and discuss the implications for C and N biogeochemistry in a warming ocean.

156
1. Introduction
Subtropical and tropical ocean gyres cover ~40% of the Earth’s surface, collectively
representing the largest ecosystem that is expanding with ocean warming (Karl, 1999;
Polovina et al., 2008). These nutrient-scarce or oligotrophic regions are biogeochemically
important, contributing significantly to global primary productivity and carbon export and
sequestration in the deep ocean (Richardson and Jackson, 2007; Yang et al., 2019;
Nowicki et al., 2022). Decades of research focused on elucidating the controls of primary
productivity in these regions have revealed that nutrient availability, especially
bioavailable nitrogen (N), is a key constraint on phytoplankton abundance and primary
production (Dugdale and Goering, 1967; Dufour et al., 1999; Davey et al., 2008; Moore et
al., 2013; Buchanan et al., 2021).
A select group of marine microorganisms provide a critical ecosystem service by
transforming or “fixing” inert dinitrogen gas (N2) into bioavailable ammonia (NH3),
providing an input of new N that drives primary productivity (Sohm et al., 2011a). These
“N2 fixers” or diazotrophs include the unicellular cyanobacterium Crocosphaera, which has
only been recognized in the last two decades as an important contributor to global marine
N2 fixation (Zehr et al., 2001; Montoya et al., 2004; Zehr, 2011).
In the oligotrophic gyres, the distribution and activity of N2 fixers are primarily
constrained by the availability iron (Fe), a scarce micronutrient in the warm surface waters
where Crocosphaera and other cyanobacterial diazotrophs are found (Moore and Doney,
2007; Sohm et al., 2011a; Hutchins and Boyd, 2016; Zehr and Capone, 2020).
Photosynthesis and N2 fixation, both key metabolic processes, are Fe-dependent with ~24
157
Fe atoms distributed throughout the photosynthetic apparatus (Shi et al., 2007) and 34
atoms in a single nitrogenase complex composed of the NifH (4 Fe), and NifDK (30 Fe)
proteins (Yang et al., 2011). Accordingly, many cyanobacterial N2 fixers use various
strategies to cope with the effects of low-Fe or Fe-limitation on their growth and
metabolism. Crocosphaera has evolved the ability to reduce its Fe requirements by ~40%
by alternating its cellular Fe pool between photosynthesis and nitrogenase proteins (Saito
et al., 2011). Fe-containing photosynthesis proteins are degraded as night approaches and
the Fe is used to synthesize nitrogenase enzymes that are subsequently degraded for Fe
resupply. This Fe-conservation “hotbunking” strategy complements Crocosphaera’s
temporal separation of photosynthesis and N2 fixation, which are incompatible processes
as oxygen (O2) produced from photosynthesis inhibits nitrogenase activity (Zehr and
Capone, 2020).
These dynamics suggest a fine-tuned diel choreography of C and N metabolism that
must withstand fluctuations in iron availability and the presence of O2. In Crocosphaera,
nighttime cellular respiration draws down O2 to provide a suitable environment for N2
fixation (Rabouille et al., 2017; Inomura et al., 2019a) and to generate energy to meet ATP
requirements necessary for N2 fixation (Sohm et al., 2011a; Yang et al., 2011). This
nighttime respiration is fueled by daytime accumulation of carbon stored as glycogen in
the cell, which reflects the intimate relationship between C and N in Crocosphaera (Mohr
et al., 2010; Dron et al., 2012; Wilson et al., 2017; Held et al., 2022).
Due to the O2-sensitivity of nitrogenase and N2 fixation, temperature may also play
a determinant role in the biogeography and activity of non-heterocystous N2-fixing
cyanobacteria, including Crocosphaera, across the low-latitude oceans (Stal, 2009). Warm
158
temperatures both decrease O2 solubility, and drive high rates of cellular respiratory
drawdown of O2 so N2 fixation can occur (Brauer et al., 2013; Inomura et al., 2019a).
However, rising temperatures as a result of anthropogenic climate change in already warm
waters may increase ambient temperatures above the thermal optimum for Crocosphaera
and even beyond the thermal maximum, which has implications for their future
biogeography and activity (Hutchins and Fu, 2017; Yang et al., 2021; Wrightson et al.,
2022). For example, modeling studies based on lab-generated data that project
Crocosphaera’s biogeographic responses to temperature and Fe availability suggest a
potential poleward shift as low-latitude waters warm beyond Crocosphaera’s thermal
optimum (~27°C) (Yang et al., 2021). This results in a net decrease in global N2 fixation by
the year 2100 under IPCC’s RCP8.5 “high emissions” scenario, which projects a ~4°C
warming of surface waters (Wrightson et al., 2022).
Given the keystone role of Crocosphaera and other N2 fixers in the ocean, it is
critical to develop an in-depth understanding of the environmental controls that shape
their distribution and impact their function. Fe availability and temperature are two such
controls, and previous studies have laid the groundwork for our understanding of how N2
fixers such as Crocosphaera respond to Fe availability and temperature as individual
parameters (Brauer et al., 2013; Flombaum et al., 2013; Fu et al., 2014; Jacq et al., 2014;
Garcia et al., 2015; Schoffman et al., 2016; Varkey et al., 2016; Hutchins and Fu, 2017;
Inomura et al., 2019a; Yang et al., 2022).
Generally, low-Fe limits photosynthetic activity which is supported by the
downregulation of Fe-containing photosynthesis genes such as ferredoxin and Fe-rich
159
Photosystem I (PSI) genes (Shi et al., 2007; Yang et al., 2022). Simultaneously, cells
upregulate isiA, which encodes the IsiA chlorophyll-binding protein to increase light
harvesting capacity without the need for additional Fe, while also protecting cells from
oxidative stress (Bibby et al., 2001; Cheng et al., 2020). The Fe limitation response also
includes upregulation of flavodoxin (isiB), an Fe-free electron carrier that replaces
ferredoxin (LaRoche et al., 1996; Chappell and Webb, 2010). In N2 fixers, Fe-limitation also
constrains N2 fixation rates and downregulates nitrogenase gene expression (Shi et al.,
2007; Küpper et al., 2008; Yang et al., 2022).
Thermal growth curve studies using Crocosphaera demonstrate a broad thermal
growth optimum (~26-30°C) with a minimum of ~20°C and maximum of ~35°C; these
temperature limits also apply to carbon fixation (C fixation) and N2 fixation rates (Fu et al.,
2014; Yang et al., 2021). Increasing temperatures can enhance enzyme turnover rates,
although supra-optimal temperatures may cause enzymes to denature and lose function.
Thus, the molecular underpinnings of the photosynthetic thermal response are often
characterized by lower photosynthetic efficiency at sub-optimal growth temperatures that
increases as temperatures approach the growth optima (Mackey et al., 2013). Lower
temperatures may also delay the onset of N2 fixation due to decreased respiration and
drawdown of O2, as well as the high energetic cost of de novo nitrogenase synthesis
(Brauer et al., 2013). At supra-optimal growth temperature, heat stress can damage the
Photosystem II (PSII) reaction center and associated light harvesting phycobilisomes as
well as disrupt electron transfer, resulting in oxidative stress that can further impede
activity (Mathur et al., 2014; Pittera et al., 2017; Ferguson et al., 2020). Cells have
160
therefore developed mechanisms to address protein damage, including molecular
chaperones that disaggregate damaged proteins and assist in proper conformational
folding (Hartl et al., 2011).
Despite this foundational thermal response work and the fact that Fe-limitation is
the chronic natural state across many oligotrophic regions, few studies have investigated
the dual influences of temperature and Fe availability on Crocosphaera physiology and
metabolism, and no studies have assessed the underlying molecular mechanisms driving
the physiological response. Recently, a study of Southern Ocean phytoplankton response
to warming and Fe found differing responses between two bloom-forming diatoms,
whereby Pseudo-nitzschia seemed to thrive under warmer, Fe-limited conditions
compared to Fragilariopsis due to increased light harvesting and iron use efficiency (Jabre
and Bertrand, 2020). However, these eukaryotic phytoplankton from the high-nutrient
low-chlorophyll Southern Ocean inhabit a vastly different biogeochemical regime
compared to diazotrophic cyanobacteria from oligotrophic, low-latitude gyres.
To address this significant knowledge gap, we grew Crocosphaera under three
ecologically relevant temperatures and two Fe conditions (Fe-replete and Fe-limited). We
then conducted a differential gene expression analysis for a first look at the molecular
mechanisms underlying temperature and Fe-responsive physiology in this
biogeochemically-important diazotroph.

161
2. Methods
2.1. Culturing Methods
Triplicate cultures of Crocosphaera watsonii strain WH0005 were grown under a
3x2 experimental matrix of temperature and Fe conditions. Three ecologically relevant
temperatures spanning Crocosphaera’s thermal range at 23°C, 27°C, and 32°C were
selected, representing the sub-optimal, optimal, and supra-optimal growth temperatures,
respectively. Two Fe concentrations were selected following concentrations used in
previous studies to represent Fe-replete and Fe-limited conditions (Yang et al., 2021).
Cultures were maintained in microwave-sterilized Aquil medium made with 0.2
micron-filtered artificial seawater (ASW) (Price et al., 1989). The seawater base was
amended with Aquil mixed vitamins, 10 μM phosphate (passed through an activated
Chelex 100 resin column to remove contaminating Fe), and a modified Aquil trace metals
stock (1.21 x 10
-7
M Mn, 7.97 x 10
-8
M Zn, 1.00 x 10
-7
M Mo, and 5.03 x 10
-8
M Co) (Sunda
et al., 2005). For Fe-replete media, 250 nM Fe buffered with 25 μM EDTA (free inorganic
Fe = 1,085 pM) was added directly to the ASW base media. For Fe-limited media, 5 nM
Fe buffered with 25 μM EDTA (free inorganic Fe = 19.0 pM) was directly added to Fe-
limited cultures during periodic dilutions. Dissolved free inorganic Fe, which is the form
most bioavailable to phytoplankton, was calculated for the different experimental
conditions following previously described Methods (Yang et al., 2021).
Experimental cultures were initiated from stock cultures maintained at 27°C and
subcultures were transferred to 23°C and 32°C to generate cell lines at sub-optimal and
supra-optimal temperatures. To avoid shocking cells, cell lines were maintained semi-
162
continuously for ~2 weeks at 25°C and 30°C to gradually acclimate them to low and high
temperature. Once cultures acclimated to experimental temperatures, culture volume was
then scaled up periodically until 2.5 L and cell were subsequently maintained semi-
continuously in 2.5 L polycarbonate bottles and diluted every three days to maintain
steady-state exponential growth for 2 months. All cultures were grown on a 12:12
light:dark cycle in temperature-controlled incubators at 150 μmol photons m
-2
s
-1
. All
bottles used in the study were soaked in a 1% Citranox detergent for 24 hours, rinsed in
Milli-Q (18.2 Ω) water, and then soaked in 10% HCl for a week, rinsed in Milli-Q and
microwave-sterilized before use. Filter-sterilized (0.2 μm) nutrients, trace metals, and
vitamins were amended to the microwave-sterilized ASW base using sterile pipette tips
rinsed three times with 1% HCl and three times with microwave sterilized Milli-Q water
immediately prior to use.
Dilutions were conducted at the same time during the late-light period (~7-8 hours
into the light period) based on in-vivo fluorescence measured in real-time on a 10AU
Fluorometer (Turner Designs, San Jose, CA). Cell count samples were collected during
dilutions and preserved in 1% 0.2-μm filtered glutaraldehyde to validate in-vivo growth
rates on a Zeiss Axiovision Epifluorescent Microscope.
163
2.2. Elemental Stoichiometry
Cellular C and N Content. To measure particulate organic carbon and nitrogen (POC and
PON), sub-cultures (30 mL) from each replicate were filtered onto pre-combusted (4
hours, 450°C) GF/F filters, dried in an oven at ~60°C, and then pelleted and analyzed on
a 4010 Costech Elemental Analyzer calibrated with acetanilide. POC and PON were used
to calculate carbon-to-nitrogen ratios (C:N) and cell-normalized C (pmol cell-1) and N
(pmol cell-1). POC and PON samples were collected during the late-light period (~8 hours
into the light period).

Cellular P Content. For cellular phosphorus (P), sub-cultures (30 mL) from each replicate
were filtered onto pre-combusted (4 hours, 450°C) GF/F filters, rinsed 2x with 0.17M
sodium sulfate, and placed in individual pre-combusted (12 hours, 450°C) borosilicate
scintillation vials with 2 ml of 0.017M magnesium sulfate and left to dry at ~60°C. Prior to
analysis, the dried samples were combusted for 3 hours at 450°C to convert particulate
organic P (POP) into inorganic orthophosphate and cooled to room temperature. Samples
were then rehydrated in 5 mL of 0.2M hydrochloric acid (HCl). Orthophosphate levels
were determined spectrophotometrically on a Shimadzu UV-1800 using the molybdate
colorimetric assay (Murphy and Riley, 1962) and calibrated with a 0-20 µM P standard
curve. POP samples were collected the same time as POC and PON samples.

164
Cellular Fe Content. For cellular Fe, sub-cultures (~100 mL) from each were filtered onto
acid-washed 0.2 μm Supor polyethersulfone filters (Pall Laboratory) and rinsed with
oxalate reagent to remove extracellular trace metals (Tovar-Sanchez et al., 2003). Filters
were then digested in 30 mL perfluoroalkoxy vials (Savillex) with 5 mL of 50% nitric acid
(HNO3) amended with 10 ppb Indium as an internal standard at 95°C for three days. Filters
were then removed, and samples were dried overnight at 100ºC and subsequently
resolubilized in 200 µL of 1:1 concentrated HNO3 and HCl, sealed and heated for ~2-3
hours, and allowed to cool. Samples were then dried and resuspended in 5 mL of 0.1M
distilled HNO3 and analyzed by inductively coupled plasma mass spectrometry (ICP-MS,
Element 2, Thermo) calibrated with a 0.1-300 ppb metal reference standard curve. Three
procedural blank filters for each treatment were also analyzed and subtracted from the
measured sample values. All steps occurred in a class 100 trace metal-clean environment.
Fe samples were collected in the early light period (~1 hour into the light period).

2.3. Rate Measurements & Resource Use Efficiencies
Growth rates. The specific growth rate (μ) was then calculated using the equation μ = (ln
N1 – ln N0) / t, where N refers to cell densities and t is time in days. The cell size was
determined by measuring the cell diameters of 12 cells per sample using the Zen 2012
software (ZEISS, Jena, Germany).

Carbon fixation. Net primary productivity or carbon fixation (C fixation) was assessed using
the radiocarbon labeled bicarbonate (H
14
CO3) method. Sub-cultures (10 mL) from each
165
replicate were incubated for 5 hours with H
14
CO3 starting 2 hours into the light period
under the same experimental growth conditions, filtered onto glass microfiber filters
(Whatman, Grade GF/F), rinsed 6x with artificial seawater, and stored in the dark
overnight in 4.5 ml of scintillation cocktail (Ultima Gold, PerkinElmer). Samples were
analyzed on a Beckman LS 6000 liquid scintillation counter (Beckman Coulter Inc.,
Fullerton, CA, USA). One procedural blank for each nutrient treatment was created using
3.3 ml sub-cultures from each treatment triplicate and subtracted from the measured
sample values of the corresponding treatment.

Nitrogen fixation. N2 fixation was measured using the acetylene reduction assay. Sub-
cultures (40 mL) from each replicate (2 technical replicates per sample) were incubated
with 6 mL of acetylene injected into the 35 mL headspace of 75 mL sealed-top bottles at
the start of the dark period and gently inverted 3x to mix. All-night (~12 hours)
accumulation of ethylene was measured at the end of the incubation period on a Shimadzu
GC-8A gas chromatograph (Shimadzu Scientific Instruments, Columbia, Maryland) and
converted to fixed N2 using a ratio of 3:1 and a Bunsen coefficient of 0.086. Two
procedural blanks with ASW-only and spiked with acetylene were also analyzed and
subtracted from the measured sample values.

Specific Rates. POC-normalized C fixation rates (hr
-1
) and PON-normalized N2 fixation
rates (hr
-1
) were calculated by normalizing measured C fixation and N2 fixation rates to
POC and PON, respectively.
166

Resource Use Efficiencies. Resource use efficiencies (RUEs) were calculated for Fe (iron use
efficiencies, IUEs) and P (phosphorus use efficiencies, PUEs) by normalizing measured C
fixation rates (CIUEs) and N2 fixation rates (NIUEs) to cellular Fe (mol C or N fixed hr
-1
mol
cellular Fe
-1
) and to cellular P (CPUEs and NPUES, mol C or N fixed hr
-1
mol cellular P
-1
)
(Kustka et al., 2003; Yang et al., 2021)).

2.4. Statistical Analyses
Statistical significance for all physiological measurements (Elemental Stoichiometry
and Rate Measurements & Resource Use Efficiencies) were first calculated across the 3x2
matrix of experimental temperature and Fe conditions by two-way ANOVA with Tukey’s
HSD post-hoc analysis (p value < 0.05) using R v4.1.1. Statistical significance was also
calculated across temperature within a given Fe treatment as well as between Fe
treatments at a given temperature by one-way ANOVA (p value < 0.05).

2.5. RNA Extractions and Sequencing
Sub-cultures (400 mL, average ~7x10
7
cells) were collected 5-6 hours into the light
period and 5-6 hours after dark (36 samples total) via centrifugation (Supplementary
Methods). Sub-cultures (200 mL) from each replicate were collected in 250 mL flat-bottom
polycarbonate centrifuge bottles 5-6 hours into the light period and 5-6 hours after dark
(36 samples total) and centrifuged at 12,000 rpm for 12 minutes at 28ºC on a Beckman
Avanti J-E Refrigerated Centrifuge using a J-Lite JLA-16.250 Fixed Angle Rotor. The
167
supernatant was carefully discarded, and the centrifuge step was repeated for a total of
400 mL centrifuged (total ~7-8x10
7
cells) per replicate at each time point, leaving ~1 mL
of supernatant to transfer the combined pellet into 1.8 mL cryogenic vials using sterile
filter-pipette tips. The cryogenic vials were centrifuged at 4,500 rpm for 8 minutes
(Eppendorf 5810) A final centrifuge step of the cryogenic vials was conducted at 4,500
rpm to pellet the sample and remove the excess liquid. Pellets in cryogenic vials were flash
frozen and stored in liquid N2 until extraction. RNA was extracted using guanidium
thiocyanate and phenol solution (TRI Reagent, Sigma Aldrich) and Zymo’s Direct-zol RNA
Miniprep kit (Zymo Research, Irvine, CA) with a Dnase treatment following kit instructions.
RNA purity was checked using a Nanodrop spectrophotometer (Thermo Scientific).
Samples that did not meet purity guidelines (260/280 = 1.8-2.1, 260/230 > 1.5) were
cleaned using Zymo’s RNA Clean and Concentrator kit. Extracted RNA samples were sent
to UC Davis’ DNA Technologies Core for quality check (LabChip GX), library preparation,
ribo-depletion (QIAseq FastSelect, QIAGEN), and 150-bp paired-end sequencing on a
NovaSeq S4.
Raw paired-end reads were quality checked using FastQC [50] and trimmed using
Trimmomatic v0.39 in paired-end mode with the following settings to remove adapters:
TRAILING:10 SLIDINGWINDOW:5:20 MINLEN:36 [51]. Trimmed sequences were
mapped onto a new low-contig Crocosphaera WH0005 genome annotated with
KofamScan (Qu et al., 2022) using end-to-end alignment mode in Bowtie2 v2.4.2 [53].
Alignments were converted to BAM files, sorted by read name, and filtered by mapping
quality score (MAPQ) of 10 or higher using SAMtools v1.11 [54]. Sequences were
168
tabulated using featureCounts from the Subread package v2.0.1 in stranded mode (Liao
et al., 2014).

2.6. Transcriptomic analysis and visualizations
Gene counts were assigned Kyoto Encyclopedia of Genes and Genomes database
(KEGG) Orthology identifiers (KO) from the KofamScan-annotated genome (Kanehisa and
Goto, 2000). The isiA gene was identified by a nucleotide BLAST querying known isiA
sequences from a previous Crocosphaera WH0005 genome (Bench et al., 2013) and the
reference genome. The isiA and isiB (flavodoxin) genes are often polycistronic (Leonhardt
and Straus, 1992; Bench et al., 2013) and as expected, both were on the same contig in
our reference genome. KO-annotated and isiA read counts were summed to remove
duplicate gene identifiers. Then, genes with low counts were removed if the mean for all
treatments was less than five, yielding 3,831 “unique genes”.
All transcriptomic analyses and visualizations were performed in R v4.1.1.
Differential gene expression (DGE) was conducted with DESeq2 v1.32.0, which uses a
negative binomial generalized linear model to assess DGE for a design formula (Love et al.,
2014). A series of pairwise comparisons were conducted to assess the molecular response
for temperature, Fe-limitation, and temperature-specific Fe-limited samples for day and
night by the default Wald test in DESeq2 with Benjamin-Hochberg (BH) adjusted p value
< 0.05). For the temperature analysis, the transcriptomic response at the optimal growth
temperature (27°C) was used as the baseline comparison to assess the impacts of sub-
optimal (23°C) and supra-optimal (32°C) on gene expression. For Fe-limitation, the Fe-
169
limited transcriptome was evaluated against the Fe-replete transcriptome for a given
temperature to identify Fe-responsive genes at each temperature. Finally, the
temperature-specific Fe-limitation analysis only evaluated the transcriptomic response for
Fe-limited samples by using 27°C gene expression as a comparative benchmark to
evaluate the responses at 23°C and 32°C, as well as directly comparing the 23°C and 32°C
response to assess the Fe-responsive transcriptomic changes at contrasting temperatures.
Over-representation analysis of differentially expressed genes (DEGs) was
conducted using enrichKEGG (KEGG) and enricher (Gene Ontology, GO) functions from
clusterProfiler v4.0.5, which calculates over-represented or “enriched” biological
pathways and functions using a hypergeometric test with a BH-adjusted p-value < 0.05
(Yu et al., 2012). For GO enrichment, the genome was annotated following previously
published methods using DIAMOND and the generated output was then used for GO-
annotation by Blast2GO, InterProScan, and UniProt (Conesa et al., 2005; Jones et al.,
2014; Buchfink et al., 2021; Yang et al., 2022).
Gene counts were “regularized log” transformed using the rlog function (DESeq2)
for redundancy analysis (RDA) using the rda function from the vegan v2.5-7 package [64].
Gene counts normalized using the median of ratios method (DESeq2) were used for
boxplot and heatmap visualizations. Venn diagrams and heatmaps were generated using
VennDiagram v1.6.20 and ComplexHeatmap v2.8.0 packages, respectively. Heatmap Z-
scores were calculated for each gene by subtracting the gene expression from the row
mean and then divided by the row standard deviation. Enrichment map plots were
generated using the enrichplot v1.12.2 package to visualize the similarity of enriched
170
KEGG Pathways or GO terms (e.g. high number of overlapping genes) as determined by
the pairwise_termsim function (Wu et al., 2021). All other graphs were generated using
ggplot2 v3.3.5.

3. Results & Discussion

3.1. Day and night physiological responses to temperature and iron availability
We measured growth, C fixation, and N2 fixation rates for Crocosphaera grown
under Fe-replete and Fe-limited conditions at 23°C, 27°C, and 32°C representing sub-,
optimal, and supra-optimal growth temperatures, respectively (Figure 1A-C). Physiological
measurements were similar to previously reported values, in that Fe-replete cells grew
faster than Fe-limited cells at the same temperature (Yang et al., 2021). Rates normalized
to POC (C fixation) and PON (N2 fixation) generally followed growth patterns except under
Fe-replete conditions, where 32°C N-specific N2 fixation rates were comparable to 27°C
rates. Cell size measurements showed that 27°C cells were the largest, while Fe-replete
cells and 32°C cells were the smallest (Supplementary Figure S1). Under Fe-limitation
however, cell size increased significantly with temperature, with the largest cells occurring
at 32°C. Except for 32°C cells, Fe-limited cells were smaller than Fe-replete cells.
Temperature-driven shifts in cellular C and N underscored the decoupling of
normalized metabolic rates resulting in low Fe-replete C:N ratios at 23°C relative to
warmer temperatures and a stepwise increase in Fe-limited C:N ratios with temperature
(Figure 1D, Supplementary Figure S1A). Under Fe-limitation, 23°C and 32°C cells had
171
contrasting C and N content with 32°C cells having the highest cellular C and N
(Supplementary Figure S1B-C).
Resource use efficiencies (RUEs) integrate elemental stoichiometry and metabolic
activity and serve as a physiological proxy to interpret the efficiency of enzyme activity
and cellular resource allocation. IUEs approximate metalloenzyme activity efficiency, and
PUEs can reflect ribosomal translation efficiency (Hutchins and Sañudo-Wilhelmy, 2021;
Wrightson et al., 2022; Yang et al., 2022). Under both Fe conditions, IUEs for C and N2
fixation (CIUEs, NIUEs) followed similar patterns with Fe-limited cells at 27°C having the
highest IUEs, reflecting previously reported IUE trends (Yang et al., 2021). When Fe-
limited, 23°C cells increase NIUEs while 32°C cells decrease NIUEs (Supplementary Figure
S2A-B). Phosphorus use efficiencies for C fixation (CPUEs) were lowest at 23°C and
increased with temperature. PUEs for N2 fixation (NPUEs) are similar across temperatures
for Fe-replete cells but decrease when they are Fe-limited at 23°C and 32°C relative to
27°C (Supplementary Figure S2C-D).

3.2. Molecular underpinnings of temperature-driven physiology
We used transcriptomics to assess differential gene expression patterns to
characterize the metabolic impacts of increasing temperature. We used Crocosphaera’s
molecular response at the optimum temperature of 27°C as a baseline to compare the
cellular responses at 23°C and 32°C. The diel cycle (day or night) is a major factor
partitioning Crocosphaera gene expression (Figure 2A), with temperature and Fe
availability also playing a role (Figure 2B-C). During the day, ~40% of the transcriptome
172
responded to low and high temperatures relative to 27°C. At night however, 23°C cells
increased the number of differentially expressed genes (DEGs) while 32°C cells decreased
DEGs relative to 27°C (Supplementary Figure S3).
During the day, both 23°C and 32°C downregulated genes associated with KEGG
Pathways for biosynthesis of amino acids and secondary metabolites, carbon metabolism
(e.g. glycolysis, citrate cycle, and pentose phosphate pathway), oxidative phosphorylation,
and photosynthesis, and GO terms for ATP synthesis, photosynthesis, and transcriptional
regulation relative to 27°C cells (Supplementary Figure S4A-B). At 32°C, cells uniquely
upregulated genes involved in signal transduction and protein-chromophore linkage.
At night, both 23°C and 32°C cells downregulated N2 fixation relative to the 27°C
response, with 23°C cells exhibiting the lowest gene expression (Supplementary Figure
S5A). Cells at 23°C also continued to downregulate genes involved in biosynthesis of
amino acids, carbon metabolism, and oxidative phosphorylation while 32°C cells
downregulated photosynthesis genes (Supplementary Figure S5B).
To connect Crocosphaera’s molecular response with its metabolic response, we
assessed patterns of differential gene expression in the context of cellular C:N ratios
(Figure 1D). Under Fe-replete conditions, the 27°C C:N ratio was 9.7, reflecting molecular
responses that promoted optimal growth and activity. Comparatively, the 23°C C:N ratio
(6.6) was significantly lower and driven by higher cellular N relative to C, while 32°C cells
had a similar ratio (9.5) despite comparable cellular C and N2 fixation activity between high
and low temperature cells. High temperatures may increase translation and enzyme
activity, offsetting the metabolic cost and downregulation of ATP synthesis necessary for
C and N2 fixation. Additionally, 32°C cells showed a ~16% reduction in cell size relative to
173
27°C cells, which would simultaneously decrease cellular nutrient requirements and
increase nutrient acquisition efficiency relative to larger cells (Finkel et al., 2010; Van de
Waal and Litchman, 2020; Yang et al., 2022). In contrast, cooler temperatures impair
protein translation and enzyme activity (Toseland et al., 2013; Varkey et al., 2016),
increasing nutrient requirements for growth and metabolism, despite a slight decrease in
cell size relative to 27°C cells. These trends are further supported by RUE patterns where
CPUEs and NIUES were significantly lower at 23°C than 32°C (Supplementary Figure S2,
Supplementary Table S1).
While sub-optimal growth temperatures can directly limit cellular metabolism
including photosynthesis, respiration, and N2 fixation (Inoue et al., 2001; Brauer et al.,
2013; Mackey et al., 2013; Varkey et al., 2016; Inomura et al., 2019a), low temperature-
limited respiratory drawdown of O2 may be a stronger inhibitor of Crocosphaera N2 fixation
than reduced enzyme kinetics (Inomura et al., 2019a). In addition, decreased C fixation
rates, C content, and gene expression of associated pathways suggest a reduction in
glycogen stores relative to 27°C cells, corresponding with daytime downregulation of
glycogen synthase (glgA) and nighttime downregulation of glycogen phosphorylase (glgP)
genes for glycogen synthesis and breakdown, respectively (Supplementary Figure S6). The
gene glgC encoding ADP-glucose pyrophosphorylase catalyzes the rate-limiting first step
in glycogen synthesis (Ball and Morell, 2003; Gründel et al., 2012). Whereas daytime glgC
gene expression generally reflected cellular C content trends, nighttime glgC gene
expression was comparable to 27°C and may reflect glycogen requirements for cellular
activity. Overall, gene expression patterns and low glycogen stores suggest that 23°C cells
174
are C-limited, which may further constrain respiration, with implications for O2 drawdown
and energy generation necessary for N2 fixation.
Past a certain optimal-temperature threshold, high temperature disrupts enzyme
stability and function (Hutchins and Sañudo-Wilhelmy, 2021). While 32°C is not expected
to impair nitrogenase function (Gallon et al., 1993; Brauer et al., 2013; Rajaram et al.,
2014), studies on other cyanobacteria, including Synechococcus and the N2-fixing
Anabaena have demonstrated that high temperatures inhibit photosystems (Rajaram et al.,
2014). This is especially true for photosystem II (PSII) as well as the cell’s light harvesting
phycobilisomes, altering cells’ ability to efficiently harvest light energy and manage
electron transport (Mackey et al., 2013; Varkey et al., 2016; Pedersen and Miller, 2017;
Pittera et al., 2017).
At 32°C, Crocosphaera coordinated a photosynthetic stress response that included
upregulation of two-component regulatory system genes nblS, which regulates the
response of photosynthesis genes to high light and nutrient stress (van Waasbergen et al.,
2002), and barA, which plays a regulatory role in carbon metabolism and oxidative stress
response (Pernestig et al., 2003; Sahu et al., 2003) (Supplementary Figures S7A). Genes
for light harvesting were also upregulated including the phycobilisome pigment-proteins,
phycocyanin (cpcA, cpcB) as well as chlorophyll-binding antenna protein IsiA (isiA), and the
Fe-free electron carrier flavodoxin (isiB) (Supplementary Figure S7B). Upregulation of
phycocyanin genes, the least thermostable of phcyobilisome rod proteins, may reflect a
compensatory response to temperature stress on light harvesting (Pittera et al., 2017).
Upregulation of isiA and isiB may reflect a cellular response to oxidative stress or to an
increased need for efficient light harvesting and electron transfer (Havaux et al., 2005;
175
Kojima et al., 2006; Cao et al., 2020). Additionally, while isiB is often referenced as an Fe-
stress indicator (Leonhardt and Straus, 1992; LaRoche et al., 1996; Yang et al., 2022),
elevated gene expression at night under 32°C Fe-replete conditions suggests an increased
need for electron carriers for N2 fixation that may play a role in high N2 fixation rates
observed at that temperature (Figure 1).
Cells also upregulated genes for molecular chaperones that support proper protein
folding in the cell (Hartl et al., 2011) including clpB, which stabilizes and disaggregates
denatured proteins under various stressors including heat (Eriksson and Clarke, 2000;
Alam et al., 2021) (Supplementary Figure S7C). This protein also plays a role in maintaining
protein stability and function at low temperature, and cells at 23°C upregulated clpB
accordingly (Los and Murata, 1999). Genes for the GroEL-GroES chaperonin system were
also upregulated between 23°C and 32°C cells (groEL p = 0.06; groES p < 0.01) suggesting
an increased need for protein folding assistance as temperature increased (Hartl et al.,
2011; Rajaram et al., 2014).
Overall, our results revealed that comparable growth rates at 23°C and 32°C were
driven by contrasting molecular responses that influence the diel coordination between C
and N metabolism. Low growth temperatures reduce enzyme activity and increase
nutrient requirements that is alleviated by higher temperatures. However, cells at 32°C
are clearly under more stress than 27°C cells and must allocate resources to addressing
temperature-induced damage that affects daytime and nighttime processes.

176
3.3. Temperature-specific responses to iron limitation
In addition to temperature-specific responses, we also assessed molecular
mechanisms underlying Crocosphaera’s response to Fe limitation and the simultaneous
effect of Fe and temperature. During the day and night, 27°C cells had relatively low
percentages (20.9%- 22.7%) of differentially expressed genes under Fe-limitation relative
to Fe-replete conditions (hereafter referred to as Fe-responsive genes). Contrastingly, half
of the genes at 32°C (49.8% - 53.2%) were Fe-responsive. Fe-responsive genes at 23°C
increased from 23.8% during the day to 39.3% at night (Supplementary Figure S8).
As expected, Crocosphaera downregulated photosynthesis during the day and night
under Fe-limitation, regardless of growth temperature (Supplementary Table S2) (Shi et
al., 2007; Moore et al., 2013; Schoffman et al., 2016).
At 27°C, the ~20% of Fe-responsive genes included downregulated genes for ATP
synthase and cytochromes as well as PSI and PSII reaction center proteins. Additionally,
cells downregulated genes involved in C and N pathways including 2-oxocarboxylic acid
metabolism, amino acid biosynthesis, and N2 fixation (Figure 3). Despite decreased growth
and metabolic rates under Fe-limitation at 27°C, low number of differentially expressed
genes and comparable C:N ratios between Fe-replete and Fe-limited cells suggest high
tolerance for Fe-limitation in Crocosphaera. Previous studies have described
Crocosphaera’s unique ability to reduce Fe requirements by ~40% through daily cycling of
intracellular Fe between photosynthesis and N2 fixation pathways (Saito et al., 2011; Yang
et al., 2022). In addition, the reduction in cell size under Fe-limitation that we observed at
27°C represents an additional coping strategy by reducing Fe requirements. In our study,
177
the high RUEs further underscore the ability of cells to efficiently use available nutrients,
especially Fe, to carry out key functions.
At 23°C and 32°C, cells differed significantly in their daytime response compared
to Fe-replete cells at the same temperatures. No uniquely enriched pathways were
detected at 23°C, but at 32°C, cells upregulated genes involved in various pathways,
especially C metabolism (including C fixation), amino acid biosynthesis, and peptidoglycan
biosynthesis (Figure 3). Unexpectedly, both 23°C and 32°C Fe-limited cells upregulated
nitrogenase gene expression relative to Fe-replete cells and 27°C Fe-limited cells.
Contrasting Fe-limited C:N ratios driven by divergent cellular C content indicated
that Fe-limitation exacerbates temperature effects on Crocosphaera metabolism (see
Molecular underpinnings of temperature-driven physiology). At these two temperatures,
Fe-liimited cells upregulated nitrogenase genes although N2 fixation rates and NPUEs
were reduced relative to Fe-replete cells, signaling a possible disruption between the
transcriptional and post-transcriptional controls of N2 fixation (Yang et al., 2022). At both
low and high temperatures, Crocosphaera upregulated clpB and groEL-groES molecular
chaperones, with the highest gene expression at 32°C. This suggested Fe-limitation
increased thermal stress on protein conformation and function, especially at supra-optimal
growth temperatures (Supplementary Figure S7C). Decreased protein stability and
function likely underlies the significant disconnect between nitrogenase gene expression
and N2 fixation rates and suggests an increase in N requirements to maintain function
cellular function at both 23°C and 32°C that cells are unable to meet.
At night, Crocosphaera synthesize nitrogenase proteins de novo and hotbunking Fe
between Fe-containing proteins of the photosynthetic apparatus and nitrogenase
178
represents a significant energetic investment (Saito et al., 2011). This presents an
additional challenge to cells at 23°C that are C and energy limited, especially since
chaperonins require energy (ATP) to function. As a potential coping mechanism, 23°C cells
exhibited a 12% reduction in cell size relative to 23°C Fe-replete cells and a 5.5% reduction
relative to 27°C Fe-limited cells that corresponded with a downregulation in
transmembrane transport, likely reducing both cellular C and N requirements in response
to Fe-limitation (Yang et al., 2022).
In contrast to cell size reduction at 23°C, Fe-limited cells at 32°C increased in size
by nearly 20% relative to Fe-replete cells that parallels observed increases in C content
and glycogen synthase (glgA) gene expression. Coupled with expression patterns, C
accumulation indicates N starvation at 32°C as cells funnel excess C and electrons that
are not used in cellular processes (e.g. amino acid biosynthesis) into glycogen stores
(Gründel et al., 2012; Hickman et al., 2013; Welkie et al., 2019).
Furthermore, the diversion of electrons toward glycogen accumulation can also
serve as a mechanism to combat reactive oxygen species (ROS), suggesting that Fe-
limitation at high temperatures intensifies oxidative stress (Gründel et al., 2012; Wyman
and Thom, 2012; Welkie et al., 2019). Since nitrogenase enzymes are sensitive to ROS,
oxidative stress at 32°C may also contribute to the contradictory relationship between N2
fixation rates and gene expression. Thus, oxidative stress and temperature-induced
protein damage may disrupt hotbunking and de novo nitrogenase synthesis. Like other
cyanobacteria, Crocosphaera produce reactive oxygen species during photosynthesis and
have developed various enzymatic and non-enzymatic strategies to relieve oxidative
stress. Superoxide dismutases (SOD) are a major enzymatic defense against ROS (Latifi et
179
al., 2009; Welkie et al., 2019), but neither of the two SODs present in the transcriptome
were upregulated at 32°C (Supplementary Figure S9). Perhaps, heightened protein
damage at 32°C shifts cellular reliance from SODs to non-enzymatic antioxidants including
glutathione and peroxiredoxin (Canini et al., 2001).
Pairwise comparisons of Fe-responsive genes of 23°C and 32°C relative to 27°C
cells as well as between 23°C and 32°C Fe-limited cells revealed that during the day and
night, cells differentially upregulated key pathways in response to low and high
temperature (Figure 4). At 23°C, upregulated genes were associated with C pathways
including photosynthesis and nucleotide sugar biosynthesis (including glycosylation),
which are critical to polysaccharide, lipopolysaccharide, and peptidoglycan biosynthesis
and play essential roles in cellular structure (e.g., cell membrane and wall) and regulatory
control (Varki, 2017; Mikkola, 2020; Gagneux et al., 2022) that can modulate cells’ ability
to respond to environmental stress. Many of these genes are also involved in O-antigen
biosynthesis, which is a component of extracellular polysaccharides (EPS) that are thought
to serve as a sink for excess C (Otero and Vincenzini, 2004; Sohm et al., 2011b). The
upregulation of nucleotide sugar biosynthesis would require energy and carbon resources
that contradicts observed low cellular C content and C fixation rates at 23°C, suggesting
the molecular response reflects what cells are lacking most, carbon. The negative
correlation between cellular C content and nighttime glgC gene expression further
supports this conclusion that cells are unable to meet C requirements at 23°C.
In contrast, 32°C cells downregulated nucleotide (and O-antigen) sugar
biosynthesis at night. However, high daytime C content and glycogen synthesis coupled
with the upregulation of several O-antigen biosynthesis and polysaccharide transport and
180
export genes suggest increased EPS production at high temperature may serve as an
additional mechanism to offload excess C in response to relatively low cellular N
(Supplementary Figure S10). Morever, cells also upregulated genes involved with N
pathways including translation, N2 fixation, and amino acid and protein biosynthesis. This
response may be related to both the inhibitory effects of ROS on protein stability and
activity and compensatory mechanisms for N limitation and oxidative stress. Protein
conformation and function are sensitive to ROS, especially those with sulfur-containing
cysteine and methionine residues (Grant, 2001; Ezraty et al., 2017). As such, cells
upregulated genes for cysteine and methionine synthesis, cysteine and methionine repair
including glutathione synthase and methionine sulfoxide reductases (msrA & msrB) in
addition to other antioxidants to counteract ROS damage (Supplementary Figure S9,
Supplementary Figure S11) (Busch and Montgomery, 2015; Ezraty et al., 2017).
Simultaneously, Fe-limited cells at 32°C also upregulated molecular chaperonins to
counteract protein damage that may also be a result of oxidative stress (Supplementary
Figure S7C). Collectively, these responses reflect N limitation at high temperature where
Fe-limited cells exhibit both suppressed N2 fixation rates and increased cellular N
requirements to cope with protein damage.
The temperature-specific Fe-limited response suggested that Crocosphaera is well
adapted to Fe-limitation under optimal growth conditions, which we used as a
comparative benchmark throughout this study. Our integrated approach revealed that the
Fe-limited cellular response seemingly disrupts the diel coordination of C and N
metabolism at both sub- and supra-optimal growth temperatures. Low temperature
reduces enzyme activity, rendering cells C and energy limited, and reduces respiratory
181
drawdown of O2, all curtailing factors that contribute to decreased N2 fixation. Fe-
limitation presents additional challenges, with upregulation of molecular chaperone genes
signaling decreased protein stability and consequently, enzyme function. This effect is
exacerbated at high temperature and cells must address increased oxidative stress and
protein damage. We observed a substantial decoupling of N2 fixation rates and
nitrogenase gene expression that coupled with an increase in cellular C, suggests heat
stress-induced N limitation. Unexpectedly, these contrasting C:N and molecular responses
at Crocosphaera’s thermal extremes yielded comparable physiology under both Fe
conditions.

4. Conclusions
This study represents, to our knowledge, the first assessment of day and night
physiological responses to temperature and Fe availability and the underlying molecular
drivers of the biogeochemically important cyanobacterium, Crocosphaera. For
Crocosphaera, daytime C fixation and glycogen accumulation is crucial for subsequent
nighttime N2 fixation. Thus, daytime cellular C content and C:N stoichiometry can offer a
diagnostic lens to contextualize changes to physiological rate measurements and gene
expression in response to temperature and Fe availability to better understand the
controls on diazotrophic biogeography, primary productivity, and C and N
biogeochemistry across the warming ocean.
Recently, a South Pacific field study combined with Tara Oceans metagenomic
analysis identified Crocosphaera as a significant contributor to carbon export into the deep
182
ocean despite their small size (Bonnet et al., 2022). Glycogen accumulation may weigh
down cells or cells can aggregate, bound together in a matrix of self-secreted C-rich
extracellular polysaccharides (EPS). Our study showed heat-stressed, Fe-limited cells
produce and accumulate more cellular C likely as both glycogen stores and excreted EPS.
Consequently, as Fe-limited gyres expand in a warmer ocean, Crocosphaera’s contribution
to carbon export may increase. These results point to a potentially expanded role for
Crocosphaera in oligotrophic regimes that we are still just beginning to understand. Ocean
warming is projected to shift Crocosphaera biogeography and N2 fixation from the tropics
to higher-latitudes, potentially altering future dynamics of global C and N biogeochemical
cycling (Yang et al., 2021; Wrightson et al., 2022).
Ultimately, various biotic and abiotic variables control Crocosphaera distribution
and activity including temperature, light, O2, nutrients (especially P and Fe), and grazing,
among others. Studies that link physiology, molecular, and biogeochemical datasets to
investigate Crocosphaera’s simultaneous response to a broad suite of environmental
variables will be critical to our ability to accurately predict global biogeochemical changes
in the context of global climate change.

183
References
Alam, A., Bröms, J. E., Kumar, R., and Sjöstedt, A. (2021). The role of ClpB in bacterial stress
responses and virulence. Front Mol Biosci 8, 668910. doi:
10.3389/fmolb.2021.668910.

Andrews, S. (2010). FastQC: A quality control tool for high throughput sequence data.
Available at: https://qubeshub.org/resources/fastqc.

Ball, S. G., and Morell, M. K. (2003). From bacterial glycogen to starch: understanding the
biogenesis of the plant starch granule. Annu. Rev. Plant Biol. 54, 207–233. doi:
10.1146/annurev.arplant.54.031902.134927.

Bench, S. R., Heller, P., Frank, I., Arciniega, M., Shilova, I. N., and Zehr, J. P. (2013). Whole
genome comparison of six Crocosphaera watsonii strains with differing phenotypes.
J. Phycol. 49, 786–801. doi: 10.1111/jpy.12090 PMID - 27007210.

Bibby, T. S., Nield, J., and Barber, J. (2001). Iron deficiency induces the formation of an
antenna ring around trimeric photosystem I in cyanobacteria. Nature 412, 743–745.
doi: 10.1038/35089098.

Bolger, A. M., Lohse, M., and Usadel, B. (2014). Trimmomatic: a flexible trimmer for
Illumina sequence data. Bioinformatics 30, 2114–2120. doi:
10.1093/bioinformatics/btu170.

Bonnet, S., Benavides, M., Le Moigne, F. A. C., Camps, M., Torremocha, A., Grosso, O., et
al. (2022). Diazotrophs are overlooked contributors to carbon and nitrogen export
to the deep ocean. ISME J. doi: 10.1038/s41396-022-01319-3.

Brauer, V. S., Stomp, M., Rosso, C., van Beusekom, S. A. M., Emmerich, B., Stal, L. J., et al.
(2013). Low temperature delays timing and enhances the cost of nitrogen fixation
in the unicellular cyanobacterium Cyanothece. ISME J. 7, 2105. doi:
10.1038/ismej.2013.103.

Buchanan, P. J., Aumont, O., Bopp, L., Mahaffey, C., and Tagliabue, A. (2021). Impact of
intensifying nitrogen limitation on ocean net primary production is fingerprinted by
nitrogen isotopes. Nat. Commun. 12, 6214. doi: 10.1038/s41467-021-26552-w.

Buchfink, B., Reuter, K., and Drost, H.-G. (2021). Sensitive protein alignments at tree-of-
life scale using DIAMOND. Nat. Methods 18, 366–368. doi: 10.1038/s41592-021-
01101-x.

184
Busch, A. W. U., and Montgomery, B. L. (2015). Interdependence of tetrapyrrole
metabolism, the generation of oxidative stress and the mitigative oxidative stress
response. Redox Biol 4, 260–271. doi: 10.1016/j.redox.2015.01.010.

Canini, A., Leonardi, D., and Caiola, M. G. (2001). Superoxide dismutase activity in the
cyanobacterium Microcystis aeruginosa after surface bloom formation. New Phytol.
152, 107–116. doi: 10.1046/j.0028-646x.2001.00244.x.

Cao, P., Cao, D., Si, L., Su, X., Tian, L., Chang, W., et al. (2020). Structural basis for energy
and electron transfer of the photosystem I-IsiA-flavodoxin supercomplex. Nat
Plants 6, 167–176. doi: 10.1038/s41477-020-0593-7.

Chappell, P. D., and Webb, E. A. (2010). A molecular assessment of the iron stress response
in the two phylogenetic clades of Trichodesmium. Environ. Microbiol. 12, 13–27. doi:
10.1111/j.1462-2920.2009.02026.x.

Cheng, Y., Zhang, T., Wang, L., and Chen, W. (2020). Transcriptome analysis reveals IsiA-
regulatory mechanisms underlying iron depletion and oxidative-stress acclimation
in Synechocystis sp. Strain PCC 6803. Appl. Environ. Microbiol. 86. doi:
10.1128/AEM.00517-20.

Conesa, A., Götz, S., García-Gómez, J. M., Terol, J., Talón, M., and Robles, M. (2005).
Blast2GO: a universal tool for annotation, visualization and analysis in functional
genomics research. Bioinformatics 21, 3674–3676. doi:
10.1093/bioinformatics/bti610.

Davey, M., Tarran, G. A., Mills, M. M., Ridame, C., Geider, R. J., and LaRoche, J. (2008).
Nutrient limitation of picophytoplankton photosynthesis and growth in the tropical
North Atlantic. Limnol. Oceanogr. 53, 1722–1733. doi: 10.4319/lo.2008.53.5.1722.

Dron, A., Rabouille, S., Claquin, P., Le Roy, B., Talec, A., and Sciandra, A. (2012). Light-dark
(12:12) cycle of carbon and nitrogen metabolism in Crocosphaera watsonii WH8501:
relation to the cell cycle. Environ. Microbiol. 14, 967–981. doi: 10.1111/j.1462-
2920.2011.02675.x.

Dufour, P., Charpy, L., Bonnet, S., and Garcia, N. (1999). Phytoplankton nutrient control in
the oligotrophic South Pacific Subtropical Gyre (Tuamotu Archipelago). Mar. Ecol.
Prog. Ser. 179, 285–290. doi: 10.3354/meps179285.

Dugdale, R. C., and Goering, J. J. (1967). Uptake of new and regenerated forms of nitrogen
in primary productivity. Limnol. Oceanogr. 12, 196–206. doi:
10.4319/lo.1967.12.2.0196.

185
Eriksson, M. J., and Clarke, A. K. (2000). The Escherichia coli heat shock protein ClpB
restores acquired thermotolerance to a cyanobacterial clpB deletion mutant. Cell
Stress Chaperones 5, 255–264. doi: 10.1379/1466-
1268(2000)005<0255:techsp>2.0.co;2.

Ezraty, B., Gennaris, A., Barras, F., and Collet, J.-F. (2017). Oxidative stress, protein damage
and repair in bacteria. Nat. Rev. Microbiol. 15, 385–396. doi:
10.1038/nrmicro.2017.26.

Ferguson, J. N., McAusland, L., Smith, K. E., Price, A. H., Wilson, Z. A., and Murchie, E. H.
(2020). Rapid temperature responses of photosystem II efficiency forecast
genotypic variation in rice vegetative heat tolerance. Plant J. 104, 839–855. doi:
10.1111/tpj.14956.

Finkel, Z. V., Beardall, J., Flynn, K. J., Quigg, A., Rees, T. A. V., and Raven, J. A. (2010).
Phytoplankton in a changing world: cell size and elemental stoichiometry. J.
Plankton Res. 32, 119–137. doi: 10.1093/plankt/fbp098.

Flombaum, P., Gallegos, J. L., Gordillo, R. A., Rincon, J., Zabala, L. L., Jiao, N. A. Z., et al.
(2013). Present and future global distributions of the marine Cyanobacteria
Prochlorococcus and Synechococcus. Proc. Natl. Acad. Sci. U. S. A. 110, 9824–9829.
doi: 10.1073/pnas.1307701110.

Fu, F. X., Yu, E., Garcia, N. S., Gale, J., Luo, Y. S., Webb, E. A., et al. (2014). Differing
responses of marine N2 fixers to warming and consequences for future diazotroph
community structure. Aquat. Microb. Ecol. 72, 33–46. doi: 10.3354/ame01683.

Gagneux, P., Hennet, T., and Varki, A. (2022). Biological functions of glycans. Essentials of
Glycobiology. doi: 10.1101/9781621824213.

Gallon, J. R., Pederson, D. M., and Smith, G. D. (1993). The effect of temperature on the
sensitivity of nitrogenase to oxygen in the cyanobacteria Anabaena cylindrica
(Lemmermann) and Gloeothece (Nägeli). New Phytol. 124, 251–257. doi:
10.1111/j.1469-8137.1993.tb03814.x.

Garcia, N. S., Fu, F., Sedwick, P. N., and Hutchins, D. A. (2015). Iron deficiency increases
growth and nitrogen-fixation rates of phosphorus-deficient marine cyanobacteria.
ISME J. 9, 238–245. doi: 10.1038/ismej.2014.104.

Grant, C. M. (2001). Role of the glutathione/glutaredoxin and thioredoxin systems in yeast
growth and response to stress conditions. Mol. Microbiol. 39, 533–541. doi:
10.1046/j.1365-2958.2001.02283.x.

186
Gründel, M., Scheunemann, R., Lockau, W., and Zilliges, Y. (2012). Impaired glycogen
synthesis causes metabolic overflow reactions and affects stress responses in the
cyanobacterium Synechocystis sp. PCC 6803. Microbiology 158, 3032–3043. doi:
10.1099/mic.0.062950-0.

Hartl, F. U., Bracher, A., and Hayer-Hartl, M. (2011). Molecular chaperones in protein
folding and proteostasis. Nature 475, 324–332. doi: 10.1038/nature10317.

Havaux, M., Guedeney, G., Hagemann, M., Yeremenko, N., Matthijs, H. C. P., and Jeanjean,
R. (2005). The chlorophyll-binding protein IsiA is inducible by high light and protects
the cyanobacterium Synechocystis PCC6803 from photooxidative stress. FEBS Lett.
579, 2289–2293. doi: 10.1016/j.febslet.2005.03.021.

Held, N. A., Waterbury, J. B., Webb, E. A., Kellogg, R. M., McIlvin, M. R., Jakuba, M., et al.
(2022). Dynamic diel proteome and daytime nitrogenase activity supports
buoyancy in the cyanobacterium Trichodesmium. Nat Microbiol 7, 300–311. doi:
10.1038/s41564-021-01028-1.

Hickman, J. W., Kotovic, K. M., Miller, C., Warrener, P., Kaiser, B., Jurista, T., et al. (2013).
Glycogen synthesis is a required component of the nitrogen stress response in
Synechococcus elongatus PCC 7942. Algal Research 2, 98–106. doi:
10.1016/j.algal.2013.01.008.

Hutchins, D. A., and Boyd, P. W. (2016). Marine phytoplankton and the changing ocean
iron cycle. Nat. Clim. Chang. 6, 1072. doi: 10.1038/nclimate3147
https://www.nature.com/articles/nclimate3147.

Hutchins, D. A., and Fu, F. (2017). Microorganisms and ocean global change. Nat Microbiol
2, 17058. doi: 10.1038/nmicrobiol.2017.58.

Hutchins, D. A., and Sañudo-Wilhelmy, S. A. (2021). The enzymology of ocean global
change. Ann. Rev. Mar. Sci. doi: 10.1146/annurev-marine-032221-084230.

Inomura, K., Deutsch, C., Wilson, S. T., Masuda, T., Lawrenz, E., Bučinská, L., et al. (2019).
Quantifying oxygen management and temperature and light dependencies of
nitrogen fixation by Crocosphaera watsonii. mSphere 4, e00531-19. doi:
10.1128/mSphere.00531-19.

Inoue, N., Taira, Y., Emi, T., Yamane, Y., Kashino, Y., Koike, H., et al. (2001). Acclimation to
the growth temperature and the high-temperature effects on photosystem II and
plasma membranes in a mesophilic cyanobacterium, Synechocystis sp. PCC6803.
Plant Cell Physiol. 42, 1140–1148. doi: 10.1093/pcp/pce147.

187
Jabre, L., and Bertrand, E. M. (2020). Interactive effects of iron and temperature on the
growth of Fragilariopsis cylindrus. Limnology and Oceanography Letters n/a. doi:
10.1002/lol2.10158.

Jacq, V., Ridame, C., L’Helguen, S., Kaczmar, F., and Saliot, A. (2014). Response of the
unicellular diazotrophic cyanobacterium Crocosphaera watsonii to iron limitation.
PLoS One 9, e86749. doi: 10.1371/journal.pone.0086749.

Jones, P., Binns, D., Chang, H.-Y., Fraser, M., Li, W., McAnulla, C., et al. (2014).
InterProScan 5: genome-scale protein function classification. Bioinformatics 30,
1236–1240. doi: 10.1093/bioinformatics/btu031.

Kanehisa, M., and Goto, S. (2000). KEGG: kyoto encyclopedia of genes and genomes.
Nucleic Acids Res. 28, 27–30. doi: 10.1093/nar/28.1.27.

Karl, D. M. (1999). A Sea of Change: Biogeochemical variability in the North Pacific
Subtropical Gyre. Ecosystems 2, 181–214. doi: 10.1007/s100219900068.

Kojima, K., Suzuki-Maenaka, T., Kikuchi, T., and Nakamoto, H. (2006). Roles of the
cyanobacterial isiABC operon in protection from oxidative and heat stresses.
Physiol. Plant. 128, 507–519. doi: 10.1111/j.1399-3054.2006.00781.x.

Küpper, H., Šetlík, I., Seibert, S., Prášil, O., Šetlikova, E., Strittmatter, M., et al. (2008). Iron
limitation in the marine cyanobacterium Trichodesmium reveals new insights into
regulation of photosynthesis and nitrogen fixation. New Phytol. 179, 784–798. doi:
10.1111/j.1469-8137.2008.02497.x.

Kustka, A. B., Sañudo-Wilhelmy, S. A., Carpenter, E. J., Capone, D., Burns, J., and Sunda,
W. G. (2003). Iron requirements for dinitrogen- and ammonium-supported growth
in cultures of Trichodesmium (IMS 101): Comparison with nitrogen fixation rates
and iron: carbon ratios of field populations. Limnol. Oceanogr. 48, 1869–1884. doi:
10.4319/lo.2003.48.5.1869.

Langmead, B., and Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nat.
Methods 9, 357–359. doi: 10.1038/nmeth.1923.

LaRoche, J., Boyd, P. W., McKay, R. M. L., and Geider, R. J. (1996). Flavodoxin as an in situ
marker for iron stress in phytoplankton. Nature 382, 802. doi: 10.1038/382802a0.

Latifi, A., Ruiz, M., and Zhang, C.-C. (2009). Oxidative stress in cyanobacteria. FEMS
Microbiol. Rev. 33, 258–278. doi: 10.1111/j.1574-6976.2008.00134.x.

Leonhardt, K., and Straus, N. A. (1992). An iron stress operon involved in photosynthetic
electron transport in the marine cyanobacterium Synechococcus sp. PCC 7002. J.
Gen. Microbiol. 138 Pt 8, 1613–1621. doi: 10.1099/00221287-138-8-1613.
188
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., et al. (2009). The
Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079.
doi: 10.1093/bioinformatics/btp352.

Liao, Y., Smyth, G. K., and Shi, W. (2014). featureCounts: an efficient general purpose
program for assigning sequence reads to genomic features. Bioinformatics 30, 923–
930. doi: 10.1093/bioinformatics/btt656.

Los, D. A., and Murata, N. (1999). Responses to cold shock in cyanobacteria. J. Mol.
Microbiol. Biotechnol. 1, 221–230.

Love, M. I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and
dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550. doi:
10.1186/s13059-014-0550-8.

Mackey, K. R. M., Paytan, A., Caldeira, K., Grossman, A. R., Moran, D., McIlvin, M., et al.
(2013). Effect of temperature on photosynthesis and growth in marine
Synechococcus spp. Plant Physiol. 163, 815–829. doi: 10.1104/pp.113.221937.

Mathur, S., Agrawal, D., and Jajoo, A. (2014). Photosynthesis: response to high
temperature stress. J. Photochem. Photobiol. B 137, 116–126. doi:
10.1016/j.jphotobiol.2014.01.010.

Mikkola, S. (2020). Nucleotide sugars in chemistry and biology. Molecules 25. doi:
10.3390/molecules25235755.

Mohr, W., Intermaggio, M. P., and LaRoche, J. (2010). Diel rhythm of nitrogen and carbon
metabolism in the unicellular, diazotrophic cyanobacterium Crocosphaera watsonii
WH8501. Environ. Microbiol. 12, 412–421. doi: 10.1111/j.1462-
2920.2009.02078.x.

Montoya, J. P., Holl, C. M., Zehr, J. P., Hansen, A., Villareal, T. A., and Capone, D. G. (2004).
High rates of N2 fixation by unicellular diazotrophs in the oligotrophic Pacific
Ocean. Nature 430, 1027–1032. doi: 10.1038/nature02824.

Moore, C. M., Mills, M. M., Arrigo, K. R., Berman-Frank, I., Bopp, L., Boyd, P. W., et al.
(2013). Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701–
710. doi: 10.1038/ngeo1765.

Moore, J. K., and Doney, S. C. (2007). Iron availability limits the ocean nitrogen inventory
stabilizing feedbacks between marine denitrification and nitrogen fixation. Global
Biogeochem. Cycles 21. doi: 10.1029/2006GB002762.

189
Murphy, J., and Riley, J. P. (1962). A modified single solution method for the determination
of phosphate in natural waters. Anal. Chim. Acta 27, 31–36. doi: 10.1016/S0003-
2670(00)88444-5.

Nowicki, M., DeVries, T., and Siegel, D. A. (2022). Quantifying the carbon export and
sequestration pathways of the ocean’s biological carbon pump. Global Biogeochem.
Cycles 36. doi: 10.1029/2021gb007083.

Oksanen, J., Guillaume Blanchet, F., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., et al.
(2020). vegan: Community Ecology Package. R package version 2.5-7.

Otero, A., and Vincenzini, M. (2004). Nostoc (cyanophyceae) goes nude: Extracellular
polysaccharides serve as a sink for reducing power under unbalanced C/N
metabolism. J. Phycol. 40, 74–81. doi: 10.1111/j.0022-3646.2003.03-067.x.

Pedersen, D., and Miller, S. R. (2017). Photosynthetic temperature adaptation during niche
diversification of the thermophilic cyanobacterium Synechococcus A/B clade. ISME
J. 11, 1053–1057. doi: 10.1038/ismej.2016.173.

Pernestig, A.-K., Georgellis, D., Romeo, T., Suzuki, K., Tomenius, H., Normark, S., et al.
(2003). The Escherichia coli BarA-UvrY two-component system is needed for
efficient switching between glycolytic and gluconeogenic carbon sources. J.
Bacteriol. 185, 843–853. doi: 10.1128/JB.185.3.843-853.2003.

Pittera, J., Partensky, F., and Six, C. (2017). Adaptive thermostability of light-harvesting
complexes in marine picocyanobacteria. ISME J. 11, 112–124. doi:
10.1038/ismej.2016.102.

Polovina, J. J., Howell, E. A., and Abecassis, M. (2008). Ocean’s least productive waters are
expanding. Geophys. Res. Lett. 35. doi: 10.1029/2007GL031745.

Price, N. M., Harrison, G. I., Hering, J. G., Hudson, R. J., Nirel, P. M. V., Palenik, B., et al.
(1989). Preparation and chemistry of the artificial algal culture medium Aquil.
Biological Oceanography 6, 443–461. doi: 10.1080/01965581.1988.10749544.

Qu, P.-P., Fu, F.-X., Wang, X.-W., Kling, J. D., Elghazzawy, M., Huh, M., et al. (2022). Two
co-dominant nitrogen-fixing cyanobacteria demonstrate distinct acclimation and
adaptation responses to cope with ocean warming. Environ. Microbiol. Rep. doi:
10.1111/1758-2229.13041.

Rabouille, S., Cabral, G. S., and Pedrotti, M. L. (2017). Towards a carbon budget of the
diazotrophic cyanobacterium Crocosphaera: effect of irradiance. Mar. Ecol. Prog. Ser.
570, 29–40. doi: 10.3354/meps12087.
190
Rajaram, H., Chaurasia, A. K., and Apte, S. K. (2014). Cyanobacterial heat-shock response:
role and regulation of molecular chaperones. Microbiology 160, 647–658. doi:
10.1099/mic.0.073478-0.

Richardson, T. L., and Jackson, G. A. (2007). Small phytoplankton and carbon export from
the surface ocean. Science 315, 838. doi: 10.1126/science.1133471.

Sahu, S. N., Acharya, S., Tuminaro, H., Patel, I., Dudley, K., LeClerc, J. E., et al. (2003). The
bacterial adaptive response gene, barA, encodes a novel conserved histidine kinase
regulatory switch for adaptation and modulation of metabolism in Escherichia coli.
Mol. Cell. Biochem. 253, 167–177. doi: 10.1023/a:1026028930203.

Saito, M. A., Bertrand, E. M., Dutkiewicz, S., Bulygin, V. V., Moran, D. M., Monteiro, F. M.,
et al. (2011). Iron conservation by reduction of metalloenzyme inventories in the
marine diazotroph Crocosphaera watsonii. Proc. Natl. Acad. Sci. U. S. A. 108, 2184–
2189. doi: 10.1073/pnas.1006943108.

Schoffman, H., Lis, H., Shaked, Y., and Keren, N. (2016). Iron-nutrient interactions within
phytoplankton. Front. Plant Sci. 7, 1223. doi: 10.3389/fpls.2016.01223.

Shi, T., Sun, Y., and Falkowski, P. G. (2007). Effects of iron limitation on the expression of
metabolic genes in the marine cyanobacterium Trichodesmium erythraeum IMS101.
Environ. Microbiol. 9, 2945–2956. doi: 10.1111/j.1462-2920.2007.01406.x.

Sohm, J. A., Webb, E. A., and Capone, D. G. (2011a). Emerging patterns of marine nitrogen
fixation. Nat. Rev. Microbiol. 9, 499–508. doi: 10.1038/nrmicro2594.

Sohm, J., Edwards, B., Wilson, B., and Webb, E. (2011b). Constitutive extracellular
polysaccharide (EPS) production by specific isolates of Crocosphaera watsonii. Front.
Microbiol. 2, 229.

Stal, L. J. (2009). Is the distribution of nitrogen-fixing cyanobacteria in the oceans related
to temperature? Environ. Microbiol. 11, 1632–1645. doi: 10.1111/j.1758-
2229.2009.00016.x.

Sunda, W. G., Price, N. M., and Morel, F. M. M. (2005). Trace metal ion buffers and their
use in culture studies. Algal Culturing Techniques, 35–63. doi: 10.1016/b978-
012088426-1/50005-6.

Toseland, A., Daines, S. J., Clark, J. R., Kirkham, A., Strauss, J., Uhlig, C., et al. (2013). The
impact of temperature on marine phytoplankton resource allocation and
metabolism. Nat. Clim. Chang. 3, 979–984. doi: 10.1038/nclimate1989.

191
Tovar-Sanchez, A., Sañudo-Wilhelmy, S. A., Garcia-Vargas, M., Weaver, R. S., Popels, L. C.,
and Hutchins, D. A. (2003). A trace metal clean reagent to remove surface-bound
iron from marine phytoplankton. Mar. Chem. 82, 91–99. doi: 10.1016/S0304-
4203(03)00054-9.

Van de Waal, D. B., and Litchman, E. (2020). Multiple global change stressor effects on
phytoplankton nutrient acquisition in a future ocean. Philos. Trans. R. Soc. Lond. B
Biol. Sci. 375, 20190706. doi: 10.1098/rstb.2019.0706.

van Waasbergen, L. G., Dolganov, N., and Grossman, A. R. (2002). nblS, a gene involved in
controlling photosynthesis-related gene expression during high light and nutrient
stress in Synechococcus elongatus PCC 7942. J. Bacteriol. 184, 2481–2490. doi:
10.1128/JB.184.9.2481-2490.2002.

Varkey, D., Mazard, S., Ostrowski, M., Tetu, S. G., Haynes, P., and Paulsen, I. T. (2016).
Effects of low temperature on tropical and temperate isolates of marine
Synechococcus. ISME J. 10, 1252–1263. doi: 10.1038/ismej.2015.179.

Varki, A. (2017). Biological roles of glycans. Glycobiology 27, 3–49. doi:
10.1093/glycob/cww086.

Welkie, D. G., Rubin, B. E., Diamond, S., Hood, R. D., Savage, D. F., and Golden, S. S. (2019).
A hard day’s night: Cyanobacteria in diel cycles. Trends Microbiol. 27, 231–242. doi:
10.1016/j.tim.2018.11.002.

Wilson, S. T., Aylward, F. O., Ribalet, F., Barone, B., Casey, J. R., Connell, P. E., et al. (2017).
Coordinated regulation of growth, activity and transcription in natural populations
of the unicellular nitrogen-fixing cyanobacterium Crocosphaera. Nature Microbiology
2, 9. doi: 10.1038/nmicrobiol.2017.118.

Wrightson, L., Yang, N., Mahaffey, C., Hutchins, D. A., and Tagliabue, A. (2022). Integrating
the impact of global change on the niche and physiology of marine nitrogen-fixing
cyanobacteria. Glob. Chang. Biol. doi: 10.1111/gcb.16399.

Wu, T., Hu, E., Xu, S., Chen, M., Guo, P., Dai, Z., et al. (2021). clusterProfiler 4.0: A universal
enrichment tool for interpreting omics data. Innovation (Camb) 2, 100141. doi:
10.1016/j.xinn.2021.100141.

Wyman, M., and Thom, C. (2012). Temporal orchestration of glycogen synthase (GlgA)
gene expression and glycogen accumulation in the oceanic picoplanktonic
cyanobacterium Synechococcus sp. strain WH8103. Appl. Environ. Microbiol. 78,
4744–4747. doi: 10.1128/AEM.00254-12.

192
Yang, B., Emerson, S. R., and Quay, P. D. (2019). The subtropical ocean’s biological carbon
pump determined from O2 and DIC/DI
13
C tracers. Geophys. Res. Lett. 46, 5361–
5368. doi: 10.1029/2018gl081239.

Yang, N., Lin, Y.-A., Merkel, C. A., DeMers, M. A., Qu, P.-P., Webb, E. A., et al. (2022).
Molecular mechanisms underlying iron and phosphorus co-limitation responses in
the nitrogen-fixing cyanobacterium Crocosphaera. ISME J. doi: 10.1038/s41396-
022-01307-7.

Yang, N., Merkel, C. A., Lin, Y.-A., Levine, N. M., Hawco, N. J., Jiang, H.-B., et al. (2021).
Warming iron-limited oceans enhance nitrogen fixation and drive biogeographic
specialization of the globally important cyanobacterium Crocosphaera. Front. Mar.
Sci. 8. doi: 10.3389/fmars.2021.628363.

Yang, Z.-Y., Danyal, K., and Seefeldt, L. C. (2011). Mechanism of Mo-dependent
nitrogenase. Nitrogen Fixation: Methods and Protocols, 9–29.
https://doi.org/10.1146/annurev.biochem.78.070907.103812.

Yu, G., Wang, L.-G., Han, Y., and He, Q.-Y. (2012). clusterProfiler: an R package for
comparing biological themes among gene clusters. OMICS 16, 284–287. doi:
10.1089/omi.2011.0118.

Zehr, J. P. (2011). Nitrogen fixation by marine cyanobacteria. Trends Microbiol. 19, 162–
173. doi: 10.1016/j.tim.2010.12.004.

Zehr, J. P., and Capone, D. G. (2020). Changing perspectives in marine nitrogen fixation.
Science 368. doi: 10.1126/science.aay9514.

Zehr, J. P., Waterbury, J. B., Turner, P. J., Montoya, J. P., Omoregie, E., Steward, G. F., et
al. (2001). Unicellular cyanobacteria fix N2 in the subtropical North Pacific Ocean.
Nature 412, 635–638. doi: 10.1038/35088063.

193
Figures

Figure 1. Fe replete and Fe-limited Crocosphaera physiology at sub-optimal, optimal, and
supra-optimal growth temperatures (23°C, 27°C, and 32°C): A) cell-specific growth rates
(d
-1
), B) C-specific C fixation rates, C) N-specific N2 fixation rates, and D) C:N ratios. Error
bars denote standard deviation of triplicate values. For N2 fixation rates, error bars denote
standard deviation for 6 replicates (including technical duplicates). Different letters on
each plot represent statistical significance (p < 0.05) calculated by two-way ANOVA across
all temperature and iron treatments. Brackets with corresponding significance markers
represent statistical significance between Fe treatments for a given temperature
calculated by one-way ANOVA. Significance: * (p < 0.05) and *** (p < 0.001).

194

Figure 2. Redundancy analysis (RDA) of Crocosphaera gene expression across different
temperatures and iron (Fe) concentration for A) all samples, B) daytime samples, and C)
nighttime samples. RDA1 and RDA2 axes quantify the proportion of the variance in the
gene expression dataset explained by each axis. Arrows indicate the explanatory variables
constraining the dataset. Iron (Fe) concentration: Limited (-Fe, circles) and Replete (+Fe,
triangles). Treatments: 23°C (purple), 27°C (magenta), 32°C (orange). Time points are
represented in lighter shades for Day samples and darker shades for Night samples.

195

Figure 3. Dot plot visualization of day and night enriched GO Biological Process (BP) terms
and KEGG Pathways of differentially expressed genes unique to each temperature with p
value < 0.05 for up- or downregulated genes for Fe-limited relative to Fe-replete cells at
each temperature. Temperature treatments without significant terms or pathways are not
shown. The size of the dot reflects the number of genes for each term or pathway and the
grayscale colorbar indicates the p value.

196

Figure 4. Heatmap analysis showing the DESeq2-normalized gene expression scaled as
the number of standard deviations from the row mean (Z-score) for enriched Gene
Ontology terms and KEGG Pathways calculated from pairwise comparisons of A) day and
B) night gene expression for Fe-limited cells at 23°C and 32°C. Diel and 27°C Fe-limited
gene expression patterns are included in both heatmaps as a comparative reference.
Hierarchical clustering and pathway clusters were determined by k-means clustering using
Euclidean distance. Temperature treatments are 23°C (purple), 27°C (magenta), and 32°C
(orange). The heatmap color gradient shows low gene expression (blue) and high gene
expression (red). The side annotation indicates which cellular resource, carbon (orange),
nitrogen (blue), or both (green) is most relevant to the GO term or KEGG pathway.

197
Supplementary Materials
Supplementary Figures

Supplementary Figure S1. Cell size and elemental stoichiometry of Fe-replete (Replete)
and Fe-limited (Limited) cells at each temperature (23°C, 27°C, and 32°C). A) Cell size
measurements, B) cellular carbon (C), and C) cellular nitrogen (N). The distribution of cell
diameter measurements (N = 12) are shown as violin plots with an embedded diameter
marking in the boxplot. Error bars denote standard deviation of triplicate values. Different
letters on each plot represent statistical significance (p < 0.05) calculated by two-way
ANOVA across all temperature and iron treatments. Brackets with corresponding
significance markers represent statistical significance between Fe treatments for a given
temperature calculated by one-way ANOVA. Significance: * (p < 0.05) and ** (p < 0.01).

198

Supplementary Figure S2. Calculated Resource Use Efficiencies (RUEs, mol C or N fixed /
hour / mol intracellular Fe or P) of Fe-replete or Fe-limited Crocosphaera at sub-optimal,
optimal, and supra-optimal growth temperatures, respectively (23°C, 27°C, and 32°C): A)
Carbon-specific Iron Use Efficiencies (CIUEs), B) Nitrogen-specific Iron Use Efficiencies
(NIUEs), C) Carbon-specific Phosphorus Use Efficiencies (CPUEs), and D) Nitrogen-specific
Phosphorus Use Efficiencies (NPUEs). Error bars denote standard deviation of triplicate
values. For N2 fixation rates, error bars denote standard deviation for 6 replicates
(including technical duplicates). Different letters on each plot represent statistical
significance (p < 0.05) calculated by two-way ANOVA across all temperature and iron
treatments. Brackets with corresponding significance markers represent statistical
significance between Fe treatments for a given temperature calculated by one-way
ANOVA. Significance: * (p < 0.05) and ** (p < 0.01).

199

Supplementary Figure S3. Percent of temperature-responsive differentially expressed
genes (DEGs) genes that are differentially expressed (up- or downregulated) at sub- and
supra-optimal growth temperatures (23°C and 32°C, respectively) compared to the
optimal growth temperature (27°C) during the day and night relative to the whole
transcriptome analyzed in this study. Internal percentages represent DEGs that are unique
to either 23°C or 32°C while external percentages represent the total percent of DEGs
(including DEGs that are shared between 23°C and 32°C). Temperature treatments are
23°C (purple) and 32°C (orange) for Fe-replete conditions.

200

Supplementary Figure S4. Daytime temperature-specific differential gene expression
relative to the optimal growth temperature response. A) Dot plot visualization shows
enriched GO Biological Process (BP) terms and KEGG Pathways (KO) of the differentially
expressed genes with p value < 0.05 for shared (both) and unique (23°C or 32°C) up- or
downregulated genes under Fe-replete conditions. The size of the dot reflects the number
of genes for each term or pathway and the grayscale colorbar indicates the p value. B)

201
Enrichment map plot of KEGG Pathways (KO) downregulated by both (23°C and 32°C)
temperatures relative to 27°C cells. Map nodes show enriched terms and similar pathways
are connected by edges where nodes with greater similarity are connected by shorter,
thicker edges. Nodes that include overlapping genes cluster together. The size of the node
indicates the number of genes for each pathway and the grayscale colorbar indicates the
p value.

202

Supplementary Figure S5. Nighttime temperature-specific differential gene expression
relative to the optimal growth temperature response A) Heatmap analysis showing the
DESeq2-normalized diel gene expression scaled as the number of standard deviations
from the row mean (Z-score) for genes in the nitrogen fixation GO BP term. Hierarchical
clustering and gene clusters were determined by k-means clustering using Euclidean
distance. Diel timepoints are day (grey) and night (black). Temperature treatments are
23°C (purple), 27°C (magenta), and 32°C (orange) for Fe-replete conditions. The heatmap
color gradient shows low gene expression (blue) and high gene expression (red). B) Dot
plot visualization shows enriched GO Biological Process (BP) terms and KEGG Pathways
of the differentially expressed genes with p value < 0.05 for shared (both) and unique
(23°C or 32°C) up- or downregulated genes under Fe-replete conditions. The size of the
dot reflects the number of genes for each term or pathway and the grayscale colorbar
indicates the p value.

203

Supplementary Figure S6. Diel trends in DESeq2-normalized gene expression of glgA
(glycogen synthase), glgC (ADP-glucose pyrophosphorylase), and glgP (glycogen
phosphorylase) genes. Gene expression has been scaled down 1000 times (1000x) for
visualization purposes. Gene expression significance was calculated from DESeq2 pairwise
comparisons of day and night samples for each temperature compared to the 27°C
response within a given Fe treatment (e.g., 23°C vs. 27°C Fe-replete and 23°C and 27°C
Fe-limited). Significance: * (p < 0.05), ** (p < 0.01), *** (p < 0.001), ns* (p = 0.06, not
significant at p < 0.05).

204

205
Supplementary Figure S7. DESeq2-normalized gene expression trends of stress response
genes upregulated at 32°C during the day and/or night under Fe-replete (Replete) and Fe-
limited (Limited) conditions. Genes are ordered as A) signal transduction genes (barA,
sensor histidine kinase BarA and nblS, sensor histidine kinase NblS), B) phycobilisome and
electron transfer genes (cpcA & cpcB, phycocyanin; isiA, iron stress-induced chlorophyll-
binding protein IsiA; isiB, flavodoxin), and C) molecular chaperone genes (clpB, ATP-
dependent Clp protease ATP-binding subunit ClpB; groEL & groES, GroEL-GroES
chaperonin system). Gene expression has been scaled down 1000 times (1000x) for
visualization purposes. Gene expression significance reflects DESeq2 pairwise
comparisons of day and night samples for each temperature compared to the 27°C
response for a given Fe treatment (e.g., 23°C vs. 27°C Fe-replete and 23°C and 27°C Fe-
limited). Significanc brackets indicate significance between gene expression at 23°C and
32°C. Significance: * (p < 0.05), ** (p < 0.01), *** (p < 0.001).

206

Supplementary Figure S8. Percent of Fe-responsive genes reflecting genes that are
differentially expressed (either up- or downregulated) under Fe-limitation during the day
and night relative to the whole transcriptome (3831 genes) analyzed in this study
calculated for each temperature (23°C, 27°C, and 32°C). Temperature treatments are
23°C (purple), 27°C (magenta), and 32°C (orange) for Fe-limited conditions.

207

Supplementary Figure S9. Gene expression patterns for genes related to the
detoxification of reactive oxygen species (ROS). Heatmap analysis shows the DESeq2-
normalized diel gene expression scaled as the number of standard deviations from the row
mean (Z-score). Hierarchical clustering and gene clusters were determined by k-means
clustering using Euclidean distance. Diel timepoints are day (grey) and night (black).
Temperature treatments are 23°C (purple), 27°C (magenta), and 32°C (orange) for Fe-
replete (dark green) and Fe-limited (light green) conditions. The heatmap color gradient
shows low gene expression (blue) and high gene expression (red).

208

Supplementary Figure S10. DESeq2-normalized gene expression trends for genes related
to extracellular polysaccharide production and transport. Gene expression significance
reflects DESeq2 pairwise comparisons of day and night samples between 23°C and 32°C.
EPS-related transport genes are shaded in blue. Genes: polysaccharide biosynthesis
transport protein (exoP), UDP-N-acetyl-D-glucosamine dehydrogenase (wbpA), UDP-2-
acetamido-3-amino-2,3-dideoxy-glucuronate N-acetyltransferase (wbpD), UDP-2-
acetamido-2-deoxy-ribo-hexuluronate aminotransferase (wbpE), O-antigen biosynthesis
protein (wbqV), polysaccharide biosynthesis/export protein (wza), lipopolysaccharide
transport system permease protein (wzm). Significance: * (p < 0.05), ** (p < 0.01), *** (p <
0.001).

209

Supplementary Figure S11. DESeq2-normalized gene expression trends of methionine
sulfoxide reductase genes (msrA & msrB). Gene expression significance reflects DESeq2
pairwise comparisons of day and night samples for each temperature compared to the
27°C response for a given Fe treatment (e.g., 23°C vs. 27°C Fe-replete and 23°C and 27°C
Fe-limited). Significance: *** (p < 0.001).

210
Supplementary Tables

Supplementary Table S1. Mean N-specific Iron Use Efficiencies (NIUEs) and Carbon-
specific Phosphorus Use Efficiencies (NPUEs) ± the standard deviation and p value (p <
0.01) for the Fe-replete treatment calculated by one-way ANOVA at sub-optimal, optimal,
and supra-optimal growth temperatures, respectively (23°C, 27°C, and 32°C)

Temperature
NIUE
(mol N fixed / hour /
mol intracellular Fe)
CPUE
(mol C fixed / hour /
mol intracellular P)
23°C 66.5±4.5
a
17.3±1.3
a

27°C 84.2±13.8
a
25.6±0.7
b

32°C 125.6±11.0
b
22.8±1.9
b

211
Supplementary Table S2. Enriched gene ontology (GO) terms and KEGG pathways (ko) downregulated across all
experimentally temperatures under Fe-limitation relative to Fe-replete conditions. GO terms and KEGG pathways were
identified using clusterProfiler’s v4.0.5 enricher (GO terms) and enrichKEGG (KEGG pathways) functions. Columns:
Description of the term or pathway, ID corresponding to the Description, p value calculated by clusterProfiler, Gene ID of all
the identified differentially expressed genes included in the enrichment analysis, and Count of genes in each enriched
pathway. The “Gene ID” column lists genes annotated as either a KEGG Orthology identifier (K number) or a unique identifier
assigned based on the reference genome if a K number was not assigned.

Description ID p value Gene ID Count
photosynthesis GO:0015979 < 0.001
27_25/K02092/K02094/K02290/K02634
/K02635/K02691/K02694/K02697/K02698
/K02699/K02700/K02707/K02708/K02711
/K02713/K02720/K05380/K08901/K08903
20
photosynthetic electron transport
chain
GO:0009767 < 0.05 K02637/K02707/K02708 3
Photosynthesis ko00195 < 0.001
K02110/K02634/K02635/K02636/K02637
/K02639/K02640/K02642/K02643/K02691
/K02694/K02697/K02698/K02699/K02700
/K02707/K02708/K02711/K02713/K02720
/K08901/K08903/K08906
23

212
Chapter 5: Conclusions
Under Fe-limiting conditions, P-limitation and increasing temperature resulted in
unexpected and novel physiological and molecular responses in Crocosphaera that
augment our understanding of environmental controls on N2 fixation. In Chapter 2, I
explored the molecular responses underpinning Crocosphaera’s Fe/P co-limitation
response. This work builds on previous studies that identified a novel, but counterintuitive
physiological response to Fe/P co-limitation in N2 fixers whereby low availability of both
Fe and P enhanced growth and N2 fixation rates compared to limitation by only one
nutrient, either Fe or P (Garcia et al., 2015; Walworth et al., 2016). While this phenomenon
has been observed in the lab for both Crocosphaera and Trichodesmium, mechanistic
studies to elucidate the molecular drivers of Fe/P co-limitation responses have been
conducted only for Trichodesmium (Walworth et al., 2016).
Thus, the results presented in Chapter 2 represent the first available dataset that
integrates Crocosphaera Fe/P co-limitation physiology with its molecular response, closing
a significant gap in our understanding of N2 fixation controls. In Trichodesmium,
physiological and molecular studies found cell size reduction to be a unique trait for Fe/P
co-limited cells (Walworth et al., 2016). In Crocosphaera, cell size reduction was not a
unique trait for Fe/P co-limitation and instead, was a shared phenotype between Fe/P co-
limited and P-limited cells. However, growth and N2 fixation rates were enhanced under
Fe/P co-limitation, apparently driven by Fe-limitation induced metabolic restructuring that
increased cell wall flexibility and protein turnover working in concert with P-limitation
mechanisms that upregulated two-component regulatory systems.
213
While the oceanographic community generally describes ocean basins as limited by
a single nutrient (Sohm et al., 2011; Zehr and Capone, 2020), research over the years has
observed regions in the ocean where N2 fixation simultaneously exhibits simultaneous Fe
and P stress. This suggests the presence of natural Fe and P gradients throughout the
ocean that Trichodesmium and Crocosphaera may be well adapted to. However, evidence
of Fe/P co-limitation in natural communities has only been recorded for Trichodesmium,
evidenced by transcriptomic and proteomic methods (Held et al., 2020; Cérdan-García et
al., 2021). It is difficult to assess Crocosphaera Fe/P co-limitation in the ocean, due to their
relatively lower biomass and patchy distribution in a diverse microbial community. This is
made even more difficult by the dynamic nature of the ocean across space and time.
Molecular indicators or biomarkers that can be linked to physiological responses can be
applied to sample large swaths of the ocean to quickly identify regions of Fe/P co-
limitation at high resolution. To that end, our study confirmed the utility of specific Fe-
and P-stress biomarkers as indicators for Fe/P co-limitation that can be applied to large-
scale oceanographic surveys that will generate much-needed insights into N2 fixation
nutrient controls, especially as nutrient availability shifts in a warming ocean.
In Chapter 3 and 4, I discuss the implications of Fe-limitation in a warming ocean
(Fe/warming). Similar to Chapter 2, Chapter 3 explores the molecular mechanisms that
drive Crocosphaera’s response to temperature, representing one of the few datasets
available that integrate physiology and metabolic responses for marine phytoplankton and
to my knowledge, the first dataset for cyanobacteria. Surprisingly, I observed similar
physiological rates at the low and high end of Crocosphaera’s thermal growth range that
were driven by contrasting molecular responses. These temperature-specific responses
214
were linked to the diel coordination of carbon and nitrogen metabolism, especially with
the cell’s ability to synthesize and accumulate carbon stores as glycogen. Differences in
glycogen accumulation and N2 fixation rates reflect shifts in cellular C and N status driven
by temperature-dependent enzyme kinetics and temperature-induced cellular damage at
both ends of the thermal range.
Future studies can leverage this work to identify potential biomarkers for heat
stress in Crocosphaera and other N2 fixers such as Trichodesmium. These biomarkers, in
tandem with other established molecular indicators (e.g. for nutrient stress) can serve as a
diagnostic tool to assess the activity of N2 fixers in the ocean and overall ecosystem health
(Hook et al., 2014; Walworth et al., 2021). The development of a suite of reliable
biomarkers that can link microbial diversity, physiology, and environmental conditions will
be critical tool in advancing our understanding of the controls on cellular physiology and
biogeochemistry. Ultimately, this may aid efforts to project the implications of a changing
ocean environment for ecosystem function.
In Chapter 4, I used a simple biogeochemical modeling framework to evaluate the
implications of Fe/warming in a global context. Through this work, I explored the potential
diazotroph community composition and distribution dynamics in response to ocean
warming. Previous studies established the responses of N2 fixers to temperature and Fe
as individual environmental variables (Shi et al., 2007; Fu et al., 2014; Jacq et al., 2014).
These studies have been critical in establishing a foundation of knowledge on
phytoplankton thermal reaction norms and community dynamics under varying
temperature or Fe-availability. However, it is now widely accepted that studies focusing
solely on one environmental driver do not accurately reflect natural conditions where
215
phytoplankton are exposed to and in turn, have adapted to a suite of interacting variables
including temperature and Fe availability. Relatedly, previous studies have characterized
microbial response to multiple drivers as an additive response that sums the individual
effects of each environmental parameter (Moore et al., 2013). However, new studies have
shown that phytoplankton response to multiple drivers are complicated by the interactive
effects of these co-occurring environmental variables (Boyd et al., 2018; Jiang et al., 2018;
Andrew et al., 2019; Boyd, 2019).
I modified a previously published interactive model that varied growth and N2
fixation rates as a function of both temperature and Fe and used projected average Fe
concentrations and sea surface temperatures from the National Center for Atmospheric
Research (NCAR) Community Earth System Model output (NCAR CESM) for the years
2010 and 2100 to extrapolate physiological responses at a global scale. The model was
based on a 4°C temperature increase projected by the IPCC’s RCP8.5 “business as usual”
emissions scenario. This effort revealed that Crocosphaera may benefit from warming
waters to an extent until ambient water temperatures exceed their thermal optimum of
~27°C. Thus, Crocosphaera distribution and N2 fixation may shrink in lower latitudes and
expand into higher latitudes. contrasting with the thermal response of another nitrogen
fixer, Trichodesmium. This modeling approach generated important insights into the
spatiotemporal impacts of ocean warming on N2 fixation that opened the door for a new
collaboration with scientists from the University of Liverpool. While my simple modeling
approach projected a net increase in global N2 fixation, this new study showed a net
decrease in global N2 fixation across all oceanic basins using a state-of-the-art
216
biogeochemical model (Wrightson et al., 2022). This collaboration demonstrated the
importance of considering multiple environmental parameters (e.g., light, multiple nutrient
availability, temperature, grazing, etc.) in modelling the implications for N2 fixation in a
changing ocean. Future work using multi-stressor studies that assess the physiological
response and determine the molecular mechanisms will undoubtedly bolster our
projections.
This body of work represents a significant step forward in our understanding of N2
fixation and the microbial mediators of this critical ecosystem service. However, there is
so much more work to be done. The data generated in this thesis only represents one
Crocosphaera species and strain (C. watsonii WH0005) out of strains that vary
phenotypically and genotypically, suggesting a potential differentiation in their climate
change response. Additional work that “surveys” the responses of other strains and other
N2 fixers will be necessary to close our knowledge gap and ensure accurate predictions of
ocean biogeochemistry. In future pursuits, studies should consider sampling across the diel
cycle to increase the resolution and understanding of molecular responses. For example, I
collected two time points and observed a close coordination between daytime and
nighttime activity where accumulation of carbon (or lack thereof) has implications for the
cell’s ability to function at night and fix N2. The time points I selected corresponded to
peak carbon fixation and N2 fixation but did not provide enough resolution to understand
how environmental controls affect key cellular processes including DNA replication and
protein translation.
All the work presented in this thesis represents short-term acclimation studies.
However, phytoplankton have short generation times and large population sizes, which
217
make them amenable to both acclimation and evolutionary adaptation to new
environmental regimes (Irwin et al., 2015). For example, a decade-long outdoor mesocosm
study found that the green alga Chlamydomonas reinhardtii exhibited adaptive responses
to warming, including higher optimal growth temperatures linked to increased
photosynthetic capacity and photochemical efficiency (Schaum et al., 2017). Recently, a
long-term experimental evolution study on Crocosphaera and Trichodesmium to
temperature observed an evolutionary response in Crocosphaera after 2 years that was
not evident in Trichodesmium, which seemed to be better at acclimating to higher
temperatures than Crocosphaera (Qu et al., 2022). Future work could build on these studies
to explore the evolutionary capacity of N2 fixers relative to multiple stressors.
Critically, there are ecologically relevant N2 fixers that are not yet in culture,
including the symbiont UCYN-A and heterotrophic N2 fixers. This makes it difficult to
understand their responses to ecosystem perturbations (Delmont et al., 2018; Muñoz-
Marín María del Carmen et al., 2019; Chakraborty et al., 2021). This represents a major
research area that could challenge our understanding of the climate consequences for
marine N2 fixation.
Many questions remain and this work can be daunting, but recently accelerated
ocean warming is a major call-to-action that we cannot ignore. Anthropogenic CO2
emissions have triggered a rise in sea surface temperatures that if left unchecked, could
introduce by 2050 a climate state unseen in the last 50 million years (Foster et al., 2017).
Organismal ability to respond to these changing conditions will reshape marine
ecosystems and productivity (Huertas et al., 2011). Undoubtedly, there will be winners
and losers in a future, warmer ocean that will have profound consequences for
218
biogeochemical cycling and vital marine resources.

219
References
Andrew, S. M., Morell, H. T., Strzepek, R. F., Boyd, P. W., and Ellwood, M. J. (2019). Iron
availability influences the tolerance of Southern Ocean phytoplankton to warming
and elevated irradiance. Front. Mar. Sci. 6, 681.

Boyd, P. W. (2019). Physiology and iron modulate diverse responses of diatoms to a
warming Southern Ocean. Nat. Clim. Chang. 9, 148–152. doi: 10.1038/s41558-
018-0389-1.

Boyd, P. W., Collins, S., Dupont, S., Fabricius, K., Gattuso, J.-P., Havenhand, J., et al. (2018).
Experimental strategies to assess the biological ramifications of multiple drivers of
global ocean change-A review. Glob. Chang. Biol. 24, 2239–2261. doi:
10.1111/gcb.14102.

Cérdan-García, E., Baylay, A., Polyviou, D., Woodward, E. M. S., Wrightson, L., Mahaffey,
C., et al. (2021). Transcriptional responses of Trichodesmium to natural inverse
gradients of Fe and P availability. ISME J. doi: 10.1038/s41396-021-01151-1.

Chakraborty, S., Andersen, K. H., Visser, A. W., Inomura, K., Follows, M. J., and Riemann,
L. (2021). Quantifying nitrogen fixation by heterotrophic bacteria in sinking marine
particles. Nat. Commun. 12, 4085. doi: 10.1038/s41467-021-23875-6.

Delmont, T. O., Quince, C., Shaiber, A., Esen, Ö. C., Lee, S. T., Rappé, M. S., et al. (2018).
Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in
surface ocean metagenomes. Nat Microbiol 3, 804–813. doi: 10.1038/s41564-018-
0176-9.

Foster, G. L., Royer, D. L., and Lunt, D. J. (2017). Future climate forcing potentially without
precedent in the last 420 million years. Nat. Commun. 8, 14845. doi:
10.1038/ncomms14845.

Fu, F. X., Yu, E., Garcia, N. S., Gale, J., Luo, Y. S., Webb, E. A., et al. (2014). Differing
responses of marine N2 fixers to warming and consequences for future diazotroph
community structure. Aquat. Microb. Ecol. 72, 33–46. doi: 10.3354/ame01683.

Garcia, N. S., Fu, F., Sedwick, P. N., and Hutchins, D. A. (2015). Iron deficiency increases
growth and nitrogen-fixation rates of phosphorus-deficient marine cyanobacteria.
ISME J. 9, 238–245. doi: 10.1038/ismej.2014.104.

Held, N. A., Webb, E. A., McIlvin, M. M., Hutchins, D. A., Cohen, N. R., Moran, D. M., et al.
(2020). Co-occurrence of Fe and P stress in natural populations of the marine
diazotroph Trichodesmium. Biogeosciences 17, 2537–2551. doi: 10.5194/bg-17-
2537-2020.
220
Hook, S. E., Gallagher, E. P., and Batley, G. E. (2014). The role of biomarkers in the
assessment of aquatic ecosystem health. Integr. Environ. Assess. Manag. 10, 327–
341. doi: 10.1002/ieam.1530.

Huertas, I. E., Rouco, M., López-Rodas, V., and Costas, E. (2011). Warming will affect
phytoplankton differently: evidence through a mechanistic approach. Proceedings
of the Royal Society B: Biological Sciences 278, 3534–3543. doi:
10.1098/rspb.2011.0160 PMID - 21508031.

Irwin, A. J., Finkel, Z. V., Müller-Karger, F. E., and Troccoli Ghinaglia, L. (2015).
Phytoplankton adapt to changing ocean environments. Natl. Acad. Sci. U. S. A. 112,
5762.

Jacq, V., Ridame, C., L’Helguen, S., Kaczmar, F., and Saliot, A. (2014). Response of the
unicellular diazotrophic cyanobacterium Crocosphaera watsonii to iron limitation.
PLoS One 9, e86749. doi: 10.1371/journal.pone.0086749.

Jiang, H.-B., Fu, F.-X., Rivero-Calle, S., Levine, N. M., Sañudo-Wilhelmy, S. A., Qu, P.-P., et
al. (2018). Ocean warming alleviates iron limitation of marine nitrogen fixation. Nat.
Clim. Chang. 8, 709–712. doi: 10.1038/s41558-018-0216-8.

Moore, J., Lindsay, K., Doney, S., Long, M., and Misumi, K. (2013). Marine Ecosystem
Dynamics and biogeochemical cycling in the Community Earth System Model
[CESM1(BGC)]: Comparison of the 1990s with the 2090s under the RCP4.5 and
RCP8.5 Scenarios. J. Clim. 26, 9291–9312. doi: 10.1175/JCLI-D-12-00566.1.

Muñoz-Marín María del Carmen, Shilova Irina N., Shi Tuo, Farnelid Hanna, Cabello Ana
María, and Zehr Jonathan P. (2019). The transcriptional cycle Is suited to daytime
N2 Fixation in the unicellular cyanobacterium Candidatus Atelocyanobacterium
thalassa (UCYN-A). MBio 10, e02495-18. doi: 10.1128/mBio.02495-18.

Qu, P.-P., Fu, F.-X., Wang, X.-W., Kling, J. D., Elghazzawy, M., Huh, M., et al. (2022). Two
co-dominant nitrogen-fixing cyanobacteria demonstrate distinct acclimation and
adaptation responses to cope with ocean warming. Environ. Microbiol. Rep. doi:
10.1111/1758-2229.13041.

Schaum, C. E., Barton, S., Bestion, E., Buckling, A., Garcia-Carreras, B., Lopez, P., et al.
(2017). Adaptation of phytoplankton to a decade of experimental warming linked
to increased photosynthesis. Nature Ecology & Evolution 1, 0094. doi:
10.1038/s41559-017-0094.

Shi, T., Sun, Y., and Falkowski, P. G. (2007). Effects of iron limitation on the expression of
metabolic genes in the marine cyanobacterium Trichodesmium erythraeum IMS101.
Environ. Microbiol. 9, 2945–2956. doi: 10.1111/j.1462-2920.2007.01406.x.
221
Sohm, J. A., Webb, E. A., and Capone, D. G. (2011). Emerging patterns of marine nitrogen
fixation. Nat. Rev. Microbiol. 9, 499–508. doi: 10.1038/nrmicro2594.

Walworth, N. G., Fu, F.-X., Webb, E. A., Saito, M. A., Moran, D., Mcllvin, M. R., et al. (2016).
Mechanisms of increased Trichodesmium fitness under iron and phosphorus co-
limitation in the present and future ocean. Nat. Commun. 7, 12081. doi:
10.1038/ncomms12081.

Walworth, N. G., Saito, M. A., Lee, M. D., McIlvin, M. R., Moran, D. M., Kellogg, R. M., et
al. (2021). Why environmental biomarkers work: Transcriptome-proteome
correlations and modeling of multistressor experiments in the marine bacterium
Trichodesmium. J. Proteome Res. doi: 10.1021/acs.jproteome.1c00517.

Wrightson, L., Yang, N., Mahaffey, C., Hutchins, D. A., and Tagliabue, A. (2022). Integrating
the impact of global change on the niche and physiology of marine nitrogen-fixing
cyanobacteria. Glob. Chang. Biol. doi: 10.1111/gcb.16399.

Zehr, J. P., and Capone, D. G. (2020). Changing perspectives in marine nitrogen fixation.
Science 368, eaay9514. doi: 10.1126/science.aay9514.

Filename: Yang_Dissertation_final.docx
Directory:
/Users/ninayang/Library/Containers/com.microsoft.Word/D
ata/Documents
Template: /Users/ninayang/Library/Group
Containers/UBF8T346G9.Office/User
Content.localized/Templates.localized/Normal.dotm
Title:
Subject:
Author: Nina Yang
Keywords:
Comments:
Creation Date: 12/5/22 4:21:00 PM
Change Number: 2
Last Saved On: 12/5/22 4:21:00 PM
Last Saved By: Nina Yang
Total Editing Time: 0 Minutes
Last Printed On: 12/5/22 4:21:00 PM
As of Last Complete Printing
Number of Pages: 230
Number of Words: 211,682 (approx.)
Number of Characters: 1,289,148 (approx.)

Abstract (if available)

Abstract The unicellular cyanobacterium Crocosphaera efficiently leverages limited sources of the micronutrient iron (Fe) to carry out nitrogen fixation (N2 fixation). N2 fixation is an essential ecosystem function that transforms inert nitrogen into bioavailable ammonia, a key source of nitrogen that fuels primary production. Across large regions of the ocean, Fe is primary limiting nutrient for N2 fixation, and so constrains the abundance and distribution of Crocosphaera and other N2-fixing cyanobacteria. However, other key environmental controls also play an important role, including phosphorus (P) and temperature. As the ocean responds to anthropogenic climate change, nutrient availability and temperature will undoubtedly change. This thesis focuses on the interplay between temperature and nutrient availability and its impact on N2 fixation. Through a combined approach incorporating culture-based studies, gene expression analyses, and biogeochemical modeling, I identify key response mechanisms to climate change to better understand the associated consequences for marine biogeochemistry.
P availability is also important for N2 fixation, especially in regions of the ocean where episodic aeolian inputs temporarily alleviate Fe-limitation. In addition to Fe-limited and P-limited ocean basins, oceanographic surveys have revealed naturally occurring Fe and P gradients where both nutrients can be simultaneously limiting. In a warming ocean, the availability of both nutrients is expected to shift due to indirect impacts of warming-enhanced stratification and nutrient depletion of the surface ocean. To understand the mechanisms underlying Crocosphaera’s response to varying Fe and P availability, I cultured Crocosphaera under individual Fe and P limitation as well as Fe/P co-limitation conditions. I then conducted a differential gene expression analysis to identify mechanisms driving Crocosphaera’s physiological responses. My experimental data showed that relative to nutrient replete and Fe-limited cultures, Crocosphaera experience reduced growth and nitrogen fixation under P-limitation. Counterintuitively, when Crocosphaera are co-limited by both P and Fe, growth and nitrogen fixation rates recover to exceed P-limited rates. This suggests that Fe-limitation may trigger a shift in Crocosphaera’s metabolic profile affecting growth, photosynthesis, nitrogen fixation, and resource allocation that ameliorates the deleterious effects of P-limitation.
I then directly assessed the impacts of ocean warming through a differential gene expression analysis of Crocosphaera grown under a 3x2 matrix of temperature and iron availability. The results suggest that Crocosphaera modulates the cellular balance of carbon and nitrogen to cope with stressors that manifest under increasing temperature and low iron availability, producing a metabolically efficient phenotype within a prescribed thermal limit. I incorporated these results into a simple biogeochemical model simulating the IPCC RCP 8.5 warming scenario which suggested that as oceans warm, Crocosphaera distribution and N2 fixation may shrink in lower latitudes and expand to higher latitudes, contrasting with the thermal response of another nitrogen fixer, Trichodesmium.
Collectively, this work characterizes the indirect and direct impacts of ocean warming on Crocosphaera and N2 fixation, information that is necessary to develop diagnostic biomarkers for climate change and advance our understanding of a future, warmer ocean.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Effects of global change on the physiology and biogeochemistry of the N₂-fixing cyanobacteria Trichodesmium erythraeum and Crocosphaera watsonii

PDF

The molecular adaptation of Trichodesmium to long-term CO₂-selection under multiple nutrient limitation regimes

PDF

Thermal acclimation and adaptation of key phytoplankton groups and interactions with other global change variables

PDF

Future impacts of warming and other global change variables on phytoplankton communities of coastal Antarctica and California

PDF

The connection of the phosphorus cycle to diazotrophs and nitrogen fixation

PDF

Spatial and temporal dynamics of marine microbial communities and their diazotrophs in the Southern California Bight

PDF

B-vitamins and trace metals in the Pacific Ocean: ambient distribution and biological impacts

PDF

Discerning local and long-range causes of deoxygenation and their impact on the accumulation of trace, reduced compounds

PDF

Thermal diversity within marine phytoplankton communities

PDF

Biological nitrogen fixation associated with living and decomposing macroalgae

PDF

The dynamic regulation of DMSP production in marine phytoplankton

PDF

Identifying functional metabolic guilds: a computational approach to classifying heterotrophic diversity in the marine system

PDF

Characterizing protistan diversity and quantifying protistan grazing in the North Pacific Subtropical Gyre

PDF

Investigations on marine metal cycling through a global expedition, a wildfire survey, and a viral infection

PDF

Plant-microbial interactions in mangrove sediments under different nutrient conditions

PDF

Ecological patterns of free-living and particle-associated prokaryotes, protists, and viruses at the San Pedro Ocean Time-series between 2005 and 2018

PDF

The impact of Lagrangian environmental variability on the growth of phytoplankton

PDF

Patterns of molecular microbial activity across time and biomes

PDF

Molecular ecology of marine cyanobacteria: microbial assemblages as units of natural selection

PDF

Multi-dataset analysis of bacterial heterotrophic variability at the San Pedro Ocean Time-series (SPOT): an investigation into the necessity and feasibility of incorporating a dynamic bacterial c...

Asset Metadata

Creator Yang, Nina (author)

Core Title Iron-dependent response mechanisms of the nitrogen-fixing cyanobacterium Crocosphaera to climate change

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Biology (Marine Biology and Biological Oceanography)

Degree Conferral Date 2022-12

Publication Date 12/06/2023

Defense Date 11/02/2022

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag climate change,Crocosphaera,Iron,nitrogen fixation,OAI-PMH Harvest,ocean warming

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Hutchins, David (committee chair), John, Seth (committee member), Levine, Naomi (committee member), Webb, Eric (committee member)

Creator Email nina.yang0208@gmail.com,ninayang@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC112617318

Unique identifier UC112617318

Identifier etd-YangNina-11340.pdf (filename)

Legacy Identifier etd-YangNina-11340

Document Type Dissertation

Format theses (aat)

Rights Yang, Nina

Internet Media Type application/pdf

Type texts

Source 20221207-usctheses-batch-994 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu