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Investigating microbial biofilm community mediated processes on surfaces: from single cell genomics to community meta-omics

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Investigating microbial biofilm community mediated processes on surfaces: from single cell genomics to community meta-omics

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INVESTIGATING MICROBIAL BIOFILM COMMUNITY
MEDIATED PROCESSES ON SURFACES: FROM SINGLE
CELL GENOMICS TO COMMUNITY META-OMICS

by

Jeffrey Scott McLean

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(GEOLOGICAL SCIENCES)

August 2013

Copyright 2013 Jeffrey Scott McLean
ii

Dedication
This dissertation is dedicated to my family. To my parents for always encouraging me
and allowing me the freedom to become a scientist. To my loving wife thank you for
your patience and understanding. And to my children, Kellan and Carson, for putting up
with my hectic work schedule and being always being there for their father with smiles.
iii

Acknowledgements
I would like to acknowledge Ken Nealson my advisor for allowing me to pursue my
dreams and gain the knowledge and experience to help me in the future. I will be forever
in his debt. I thank committee members, Jan Amend, Will Berelson, Katrina Edwards
and Steve Finkel for their support and time. The Earth Sciences administrative staff were
extremely helpful and kind to me during the entire process. Their actions are sincerely
appreciated.
iv

Table of Contents
Dedication ......................................................................................................................... ii
Acknowledgements ......................................................................................................... iii
List of Tables ................................................................................................................... vi
List of Figures ................................................................................................................. vii
Abstract .......................................................................................................................... viii
Introduction ...................................................................................................................... 1
Chapter 1: Background, And Previous Research On Microbial Biofilm
Communities ..................................................................................................................... 7
Microbes And Minerals-Geobiology ............................................................................ 43
Bacterial Mediated Dissolution Of Apatites ................................................................. 50
Geobiology Of The Oral Cavity ................................................................................... 54
Summary ....................................................................................................................... 63
Chapter 1 References .................................................................................................... 65
Chapter 2: Candidate Phylum TM6 Genome Recovered from a Biofilm Provides the
First Genomic Insights into this Uncultivated Phylum .............................................. 79
Abstract ......................................................................................................................... 80
Introduction ................................................................................................................... 81
Results ........................................................................................................................... 85
Discussion ..................................................................................................................... 99
Methods....................................................................................................................... 103
Chapter 2 References .................................................................................................. 118
Chapter 3: Genome of the Pathogen Porphyromonas Gingivalis Recovered from a
Biofilm in a Hospital Sink Using a High Throughput Single Cell Genomics Platform
........................................................................................................................................ 123
Abstract ....................................................................................................................... 124
Introduction ................................................................................................................. 125
Results ......................................................................................................................... 130
Discussion ................................................................................................................... 141
Methods....................................................................................................................... 143
v

Acknowledgements ..................................................................................................... 150
Chapter 3 References .................................................................................................. 160
Conclusions and Future Directions ............................................................................ 166
Comprehensive References ......................................................................................... 167

Appendix A ............................................................................................................. 189

Appendix B ................................................................................................................ 190

Appendix C ................................................................................................................ 250

Appendix D ............................................................................................................... 251

Appendix E ................................................................................................................ 252
vi

List of Tables
Table 2-1. Assembly statistics. ................................................................................ 110

Table 2-2. Genome statistics ..................................................................................... 110

Table 3- 1. Read mapping ........................................................................................ 151

Table 3- 2. Comparison of assemblies ...................................................................... 151

Table 3- 3. General features of the PG JCVI SC001 genome ................................... 152

Table 3- 4: JCVI SCOO1 specific CDS .................................................................... 152
vii

List of Figures
Figure 1-1. Illustration of the hydroxyapaptite (HA) demineralization ...................... 2

Figure 1-2. Schematic representation of the structure and complexity of biofilms ...... 4

Figure 1-3. Generalized stages of biofilm development. ............................................ 13

Figure 1-4. Custom integrated Agilent Technologies BioCel 1200 ............................ 25

Figure 1-5. Illustration of the NMR-SIP experimental procedure. ............................. 35

Figure 1-6. In vitro oral biofilm model system ........................................................... 41

Figure 2-1. Summary of genera found in the biofilm sample .................................. 111

Figure 2-2. Circular representation of the TM6SC1 genome ................................... 112

Figure 2-3. Evolutionary relationships of Candidate Division TM6........................ 113

Figure 2-4. Phylogenetic tree illustrating the major lineages ................................... 115

Figure 2-5. Predicted metabolic pathways of phylotype TM6SC1 ........................... 116

Figure 3-1. Distribution of 78 candidate 16S rRNA sequences ............................... 153

Figure 3-2. Circular representation of the draft JCVI SC001 genome ..................... 154

Figure 3-3. Single nucleotide polymorphisms and read coverage ........................... 155

Figure 3-4. Comparison of the polysaccharide capsule locus .................................. 156

Figure 3-5. Comparison of a CRISPR region ........................................................... 157

viii

Abstract
The study of microbial biofilms on reactive surfaces integrates many disciplines
including biogeochemistry, engineering, physics and microbiology and has benefited
greatly from recent approaches based in genomics and bioinformatics. Experimental
biofilm studies have been predominately conducted on model systems containing single
species. Most processes in nature and many in human disease however are driven by
reactions occurring within complex microbial biofilm communities in contrast to a few
processes that are the result of a single species. Many hurdles still remain and
overcoming these will rely on technological and experimental advances that disentangle
the immense complexity of a multispecies biofilm. This dissertation deals with complex
microbial biofilm communities involved in the degradation of minerals: hydroxyapatite-
based biominerals in the oral cavity. This work is inherently and extensively
interdisciplinary, involving microbiology, molecular biology, genomics and
metagenomics, as well as microbial physiology and geology. This work has involved the
development and use of methods for the study of the structure and function of biofilms on
one hand, and for the genomic characterization of interacting community members with
single cell genomics on the other. Both top-down and bottom up approaches are clearly
needed to more fully understand the abiotic and biotic processes that contribute to the
fundamental process of mineral dissolution. The goals of the work are focused on using
this interdisciplinary approach to gain insight into how microbial biofilm communities
interact with surfaces such as human biominerals (teeth enamel made of hydroxyapatite
ix

mineral). However, the approaches and techniques developed here involving sampling a
complex biofilm for single cell genomic reconstruction are widely applicable to the study
of biofilms involved with geobiological activities everywhere.

1

Introduction
Microbial biofilms (hereafter referred to simply as biofilms) are broadly defined as three
dimensional structures composed of microorganisms and their self-produced extracellular
polymeric substances (EPS) which includes proteins, DNA and polysaccharides. They
may be attached to surfaces (substratum or air-water interfaces) or as cells attached to
each other and the matrix material (aggregates, flocs). The physiology of bacteria in
biofilms is profoundly different from that of their planktonic counterparts (Costerton et
al., 1994). Biofilms in nature are almost exclusively made up of multiple bacterial and
sometimes a mixture of prokaryotic and eukaryotic taxa, although unispecies biofilms can
be formed under under appropriate conditions, and are often the subjects of study in the
laboratory. When bacterial cells form such multi-cellular biofilms, they can have
detrimental and/or beneficial impacts. In nature, biofilms are found nearly everywhere
and are studied extensively for their impact in mineral weathering, cycling of nutrients,
applications in bioremediation and sequestration of contaminating organics, metals and
radionuclides as well as exploited in alternative energy applications such as in microbial
fuel cells. These persistent structures can also cost billions of dollars in lost industrial
productivity and capital equipment damage resulting from pipe clogging, corrosion and
water contamination. Biofilms are both “good” (i.e. protective) common components of
healthy ecosystems, including animals as commensal microbiomes, and also “bad”, as a
source of unwanted byproducts (e.g. acid mine drainage) as well as in disease when there
is dysbiosis and the communities shift metabolism in harmful ways. Under such
2

conditions, the antagonistic biofilms often display enhanced resistance to antibiotics, and
as such become the etiological agents of many serious human diseases, including cystic
fibrosis, periodontitis, otitis media (inner ear infections), and bacterial endocarditis, to
name a few. Tooth decay (dental caries disease), which is the loss of enamel that is
composed of the mineral hydroxyapatite (HAP), is one such polymicrobial process that is
thought to be caused by a shift in biofilm populations from “good” to “bad”. The shift to
more acidogenic (acid generating) aciduric (acid-loving) species is thought to drive
demineralization of the HAP crystals. The physical and ecological model of this process
is described briefly in Figure 1-1 and will be a subject of a later section within the chapter
(Section 1.4)

Figure 1-1. Illustration of the hydroxyapaptite (HA) demineralization and
remineralization process in relation to the Ecological Plaque Hypothesis (adapted from
Marsh 1994).

Biofilm communities constitute evolving, multiple-species metabolic networks with a
myriad of interrelated and interconnected functions (Figure 1-2). The types, numbers, and
3

activities of microbes present in biofilms are a function of the environmental (physical,
chemical, and biological) parameters, including the kinds and amounts of nutrient
resources available. Microbial communities may exhibit large and rapid changes in
composition and activity (both in time and space). These complex, non-equilibrium
dynamics are consequences of several factors, including the temporal frequency of
changes in important environmental factors, the rapid pace of physiological adaptation by
microbes to changing environmental conditions, positive and negative interactions
between different microbes, and the capacity for rapid genetic change (through horizontal
gene transfer or genomic mutations). Neither environmental nor biological factors by
themselves really determine the observed spatial and temporal changes. Instead, changes
likely depend on intricate interactions among biotic and environmental factors. As a
consequence of these biological realities, understanding the functionality of biofilm
communities represents one of the grand challenges in microbial ecology.

4

Figure 1-2. Schematic representation of the structure and complexity of biofilms on
surfaces. From biofilm structure and function at the macro-scale to the genes and
molecules at the micro-level.

Unfortunately, most of what is known about biofilm function has been extrapolated from
mono-species culture studies. This is largely because the methods available for biofilm
analysis have lacked the sensitivity and/or resolution required to unravel the complexity
of these mixed species biofilms. In fact, until recently, the approaches being used lacked
sufficient ability to capture the behavior of even known species within a background of a
mixed community.

5

With this grand challenge always in mind, the work presented in this thesis is aimed at
providing a background overview of microbial biofilms, including some recent work
prior to my thesis contributions (Chapter 1), followed by the definition at the genomic
level of some of the community members of complex biofilms (Chapters 2 and 3). In
chapter 1 biofilm structure, function, and microbial species composition are addressed,
with the goal of beginning to understand the species interactions that ultimately govern
biofilm communities….information that is critical to enhancing beneficial biofilms as
well as combatting harmful ones. These methods, in part developed during my pre-
dissertation and current dissertation work (McLean et al., 2001; McLean et al., 2013a;
McLean et al., 2013b; McLean et al., 2008a; McLean et al., 2008b; McLean et al., 2008c;
McLean et al., 2010), have allowed us to begin, to advance the study of multiple species
biofilms, and begin to ask “who is there”, “who is active” and “what are they doing?”
(McLean et al., 2012). In Chapter 2, a modified single cell genomics approach (using
pools of flow sorted cells) was used to “capture” and reconstruct the first genome of any
member of the cosmopolitan but uncultivated phlyum TM6, providing novel insights into
its lifestyle (McLean et al., 2013a). This is the type of approach that can be used to
characterize the potential metabolism of low abundance and novel members of natural
biofilm communities of many kinds. Chapter 3 describes the capture of genomes and
novel genes from environmental biofilms using DNA from only a single cell (McLean et
al., 2013b)– again, such high throughput single cell approaches that can be applied to
biofilms in many natural environments.

6

While this work focuses on the development of biofilm targeted techniques and
ultimately the study of a human-connected ecosystem (the oral cavity and its
microbiome), the process under inquiry (mineral dissolution) is similar in mechanism to
many environmental processes relevant to geobiology and materials science. The oral
microbial system was chosen since it represents the most well characterized microbial
biofilm community to date in many regards and provides a fundamental basis for the
development of methods as well as testing of hypotheses. Furthermore, the methods
developed and described here can be readily adapted to the study of biofilm communities
in nearly any environment imagined, and the results obtained form a basis for the future
study of biofilm communities everywhere.
7

Chapter 1: Background, And Previous Research On Microbial
Biofilm Communities
The problem with microbiology : the uncultivated and unknown majority
Depending on the environment being studied, only a small percentage of the microbes
visible under the microscope will be able to be domesticated in the lab. We call this the
“dark matter of life”, and as will be discussed below, this great uncultivated majority
(Whitman et al., 1998), which includes microbes and even entire divisions of bacterial
phyla that have evaded cultivation and have yet to be sequenced, is a major reason that
biofilms have remained mysterious and enigmatic. It is estimated that bacteria comprise
~10
18
g of global living biomass with 10
6
-10
9
bacteria per gram of soil. Typical soil
genera (e.g., Arthrobacter, Bacillus and Actinomycetes) may comprise only 5% of the
culturable types in soils. Since the realization of this missing diversity in culture attempts
(Staley and Konopka, 1985), estimates now indicate only 1-10% of known bacterial
species (Rappe and Giovannoni, 2003) are thought to be currently cultivated. Fortunately,
great progress is being made for some bacterial communities; for example, about half of
bacterial species within the human oral cavity have been cultivated (Dewhirst et al.,
2010). For soils, it is estimated that 99.9% of soil microbes are uncultivated and
unsequenced (Amann et al., 1996; Amann et al., 1995; Staley and Konopka, 1985). Since
this vast majority of bacteria in the environment as well as those associated with the
human microbiome have eluded standard culturing approaches, their physiology and their
gene content are unknown. In the absence of culture-based physiological analyses, the
8

functional roles of these uncultivated species remain enigmatic despite their apparent
correlations with important processes. These problems have become the limiting step in
studying ecology-based community activities.

With the development and increasing advances in DNA sequencing technologies,
combined with the reduction of sequencing costs, access to the microbial world has
greatly expanded and revealed an even more unprecedented microbial diversity across
nearly every environment. Pioneering large scale environmental shotgun sequencing with
the Sargasso Sea pilot study (Venter et al., 2004) and the larger Global Ocean Sampling
(GOS) expedition (Rusch et al., 2007), focused on marine surface waters. Recently the
Human Microbiome Project (HMP) efforts have revealed remarkable microbial diversity
within and on the human body. HMP also made it painfully obvious that there were
major gaps in terms of the number of available reference genomes. Reference genomes
are critical for capturing species diversity, gene content (metagenomics), gene expression
profiles (metatranscriptomics), expressed proteins (metaproteomics) and small molecules
(meta-metabolomics). Without annotated genes from reference genomes to assign reads
and proteins to, there is no taxonomic or sometimes functional information obtained.
These are referred to as “orphan reads”, and currently the vast majority of sequences
from microbial community studies fall within that category. In the “best of all worlds”,
reference genomes would be obtained, these used to link function to phylotype for
uncultivated microbes, and eventually the information used to guide successful
cultivation of these abundant and enigmatic unknown microbes.
9

Current Needs to Address the Polymicrobial Problem
Most processes in nature, including many in human disease are driven by reactions
occurring in mixed species biofilms: these are termed “polymicrobial processes”. The
HMP results have made it obvious that many human diseases are impacted by these
polymicrobial interactions and therefore require thinking beyond the traditional one
organism-one disease concept. In this way, a single or multiple species can presumably
disrupt the ecosystem balance and cause a shift in the proportions or types of bacteria
present and the overall function of the community. This shift, commonly referred to as
“dybiosis”, forms the ecological basis for many detrimental processes that polymicrobial
biofilms are blamed for (including enamel demineralization as described in Figure 1-1).

In order to gain knowledge to support or refute these hypothesized mechanisms, one
needs to know all the players and be able to track their behavior. Since most microbes
remain uncultivated, little is known about these species except for their 16S rDNA
sequence. Furthermore, although many model bacteria that are known to be one of the
species associated with a particular condition in vivo, they have only typically been
characterized in the laboratory as pure cultures. It is not validated whether these
observed laboratory characteristics are actually maintained in vivo in the presence of a
mixed microbial community. This is where the current techniques are lacking. The
major outstanding questions in the study of mixed microbial communities include:
10

1. What is the behavior (metabolism) and gene expression of known model species
when they are within a mixed species microbial community?
2. What is the role of uncultivated organisms and their contribution to the overall
function of the community?
3. How does a stable microbial community shift to an undesirable state (which
species, metabolic pathways, and genes are involved?) and can this shift be
predicted.

But why has it been so difficult to move from the study of single-species, well defined
biofilms to the study of natural, mixed-species communities? In addition to the issue of
unknown (uncultivated) taxa, there are the simple physical problems related to the size of
microbes and microbial communities. Biofilm communities are often only a few to 100
μm thick, and are composed of individual members at the micrometer size range that are
in close contact with one another. Thus, the challenges in sensitivity and resolution are
great, and the development of appropriate tools for the study of biofilms has moved
slowly. Thus, while the questions that need to be answered are easy to phrase, the
pathway to answering them is not easy, with a few major scientific challenges that must
be met before it will be possible to move from the study of Single to Multi-Species
Biofilm Studies:
1. The need for Microbial Genomes as References for Community Based
Studies. In order to more fully grasp microbial taxonomic and gene diversity as
well as to provide a means to assign metagenomic (DNA) and metatranscriptomic
11

(mRNA) reads to a given species, reference genomes are critical. The rapid
growth of metagenomic sequencing has led to a need for more reference genomes
in order to be able to assign reads to genes and/or bacterial species or gene and
therefore possible taxon-related function within a given biofilm. As reference
genomes appear, the identification of those community members responsible for
“good” and “bad” activities may be possible to determine.
2. The Need for Tools to Address the Large Fraction of Uncultivated Species.
While many new microbes can be identified through 16S rDNA gene based
diversity analyses, further research on many of these microorganisms is hampered
by an inability to uncover their culture requirements. In the absence of a culture,
physiological inferences can be made by obtaining the genome of an uncultivated
species. Further advances on culture methods as well as methods such as single
cell genomics are needed to make substantial headway in this area given the vast
amount of known uncultivated diversity present on earth.
3. The Need for Mixed-Culture Laboratory Model Biofilm Communites:
Understanding individual species function, metabolism and gene expression
profiles in a biofilm is a necessary step in the study of polymicrobial processes.
Importantly, multi-species models that are reproducible and stable will allow for
hypothesis testing, and functional validation of observations made in nature.
Models that contain uncultivated phlyotypes (those species only known by their
16S rRNA gene sequence) are indeed more comprehensive and valuable. Once
again, it should be noted that the need for cultivated strains and reference
12

genomes from these model systems is key in order to more fully understand the
dynamics within laboratory models and the role uncultivated species play.
4. The Need for Methods that enable species level resolution of function within
biofilm communities. Overall, it is likely that model species modify their
behavior in the presence of other community members. How (and how much) the
behavior of a given microbe changes in the presence of the other members of a
complex biofilm community is not presently known. This knowledge gap is a
result of the inability to track the behavior of many individual species. There are
few approaches available to understand the behavior of cultured isolated
organisms when they are put back in a complex community. A number of
exciting new approaches predominately based on deep sequencing technologies
have allowed us to ask questions about “who” is there and “what” are they doing
within a diverse community. The ability to monitor biological functions and link
this observed activity to the identity of the species responsible is an area that is
just starting to become available through techniques such as nucleic acid base
Stable Isotope Probing (SIP) and more recently metatranscriptomics. (see below)

The study of microbial biofilm communities

13

Figure 1-3. Generalized stages of biofilm development. Biofilm development from
planktonic stage, attachment, irreversible attachment, monolayer formation, three
dimensional structure development and maturation to the detachment stages.

Biofilms in nature that are attached to surfaces exhibit a high degree of complexity in
terms of structure, species composition and spatial distribution of cellular functions. This
is further complicated by the interactions that occur in each of the various stages of
biofilm formation which include: 1) attachment; 2) monolayer formation; 3) mature
biofilm formation; and, 4) detachment. Each of these stages represents a key facet of
bacterial adaptation and evolutionary development that has a large body of research
dedicated to it. One of the fundamental differences between planktonic cells and cells
within biofilms is that as the cells grow into complex layered structures; the environment
becomes diffusion limited, resulting in a variety of steep gradients within the biofilm (e.g.
oxygen or other electron acceptors on one hand, and electron donors and/or metabolic by-
products on the other). Thus, in most biofilms (single or multi-species), steep metabolite
concentration gradients develop as a consequence of diffusion limitation and cellular
metabolism. For single species, it is known that accumulation of metabolites such as
organic acids can cause local changes in gene expression leading to spatially varying
14

phenotypes within the structure. In mixed species biofilms, this generates a variety of
growth environments that may ultimately drive species diversity, determine overall
structure and perhaps dictate the spatial arrangement of the community members.

Studies of biofilms composed of pure cultures (model species) have revealed much about
the structure and development of these complex communities, as summarized in a
number of excellent reviews and books (Characklis and Marshall, 1990; Costerton, 2007;
Ghannoum and O'Toole, 2004; Percival et al., 2011). In addition to microbial cells,
biofilms contain many extracellular polymeric substances (EPS) such as: 1)
polysaccharide material; 2) DNA (Whitchurch et al., 2002) - a possible structural and/or
storage molecule for survival and nutrients (Palchevskiy and Finkel, 2009; Pinchuk et al.,
2008); 3) extracellular electron transfer components such as bacterial nanowires (Gorby
et al., 2006); and, 4) cytochromes in the matrix material (Marshall et al., 2006). These
single species studies have led to an understanding of adaptive evolution (Kraigsley and
Finkel, 2009), quorum sensing (Nealson, 1977), gene expression changes and even the
multicellularity or specialization of cell types within a single species biofilm (Klausen et
al., 2003).
Specialized techniques for biofilm studies
Unlike motile planktonic bacteria, EPS-imbedded biofilm cells (Flemming and
Wingender, 2010; Sutherland, 2001) are relatively inaccessible for most techniques
developed for the study of planktonic microbes; the methods that measure bulk
parameters of the environment around cells in planktonic cultures are not adequate for
15

biofilms. This is due to the high cell density and matrix which hinders fluid transport
(diffusion and convection) into the biofilm (Stewart, 2003), resulting in concentration
gradients for oxygen (from oxic to anaerobic), pH, nutrients and metabolic byproducts.
Thus, biofilm bacteria are physiologically and functionally distinct from their free-
floating “siblings”, e.g., being more resistant to antibiotics (Costerton et al., 1999)
(Stewart, 2001). Metabolite concentration fluxes may also vary widely with
hydrodynamic flow since this will impact the boundary layer thickness and serve to vary
the availability of nutrients and the removal of byproducts. Under such conditions, small
metabolites such as organic acids or quorum sensing molecules may cause local changes
in gene expression and cell metabolism that lead to changes in biofilm architecture and
the underlying substratum (e.g., erosion/corrosion).

Metabolic byproduct accumulation itself for example is known to have regulatory effects
in planktonic cell populations and therefore likely to play a major role in gene regulation
inside a diffusion-limited biofilm. Quorum sensing and other global regulatory mediators
are also likely involved with the initiation of coordinated behavior such as complex tower
formation (Klausen et al., 2003) and swarming dispersal (Abee et al., 2011; Dow et al.,
2003; McDougald et al., 2012; Schreiber et al., 2011) and may therefore be a result of
localized nutrient limitation or metabolite buildup in diffusion-limited regions of the
biofilm. There may also be a direct relationship between metabolite concentrations and
the architecture of the biofilm of respiratory bacteria due to limitations imposed by the
electron donor and/or acceptor availability (McLean et al., 2008a). Overall, these spatial
16

variations in metabolite concentrations and hence their fluxes through biofilms are of
fundamental importance to biofilm structure and function. It is these micro- and macro-
scale gradients that likely control the development, spatial organization and sustainability
of mixed species microbial communities. Currently however, limitations exist for
studying processes occurring inside biofilms mainly due to sample size constraints and
spatial dependency of the measurements. Such dynamic and spatially varying metabolic
gradients offer particular challenges in the study of biofilms.
Tools for visualization of biofilm communities
Minimally invasive, non-contacting techniques include optical methods such as confocal
laser scanning microscopy (CLSM) (Kuehn et al., 1998; Lawrence and Neu, 1999;
Lawrence et al., 1994). CLSM is regarded as the gold standard to interrogate biofilms
and has provided considerable insight into biofilms, such as detailed structure, cell
arrangement, size, species viability, phenotype, and polysaccharide composition.
However, optical studies of biofilms are typically confined to thin biofilms due to depth
penetration issues (opacity and scattering). Further, CLSM typically involves genetic
insertion of fluorescent reporters or fluorescent tracers that can affect metabolic activity.
Thus, few techniques exist that can measure biofilm metabolite profiles in a truly non-
invasive and non-destructive (and continuous) fashion with adequate time and spatial
resolution.
17

Previous contributions: Development of novel techniques for biofilm research
Flow Cell Systems: Using fluorescent reporter constructs in a continuous flow cell
system allowed monitoring the temporal development of Shewanella oneidensis MR-1
biofilms on iron oxide (McLean et al., 2008a) and electrode surfaces within a novel
optical compatible microbial fuel cell in real-time (McLean et al., 2010). Previous
collaborative research also involved developing and validating methods to visualize the
internal and external structure in biofilms on surfaces through the use of acoustic
microultrasound techniques (Good et al., 2006), application of surface enhanced Raman
spectroscopy to characterize redox-reactive components of the cell surface (Biju et al.,
2007), and matrix assisted laser desorption/ionization (MALDI) for fingerprinting
characterisitcs of small molecules on cells and biofilms (Wunschel et al., 2005). These
are just some of the advances of non-invasive and non-destructive methods that are
critical for advancing biofilm research.

Tools to measure nutrients and fluxes
In recent years, a number of new techniques have been developed to interrogate intact
biofilm communities (see reviews (McLean et al., 2008a; Nivens et al., 1995; Wagner et
al., 2006; Wuertz et al., 2004). The techniques, described below, vary in terms of degree
of invasiveness, and spatial/temporal resolution. Invasive biofilm-metabolism methods
typically result in sample damage and include direct extraction methods for the analysis
of biomass (dry weight and ash content determinations) and chemical composition of
18

extracts by high pressure liquid chromatography, mass spectrometry and NMR. For
example a major development that led to new insights into spatial and temporal variations
in microbial metabolism within biofilms was microelectrodes with tips less than 10μm in
diameter (Revsbech, 2005). These probes are of moderate-invasiveness however and
require insertion into the biofilm leading to potential disruption of metabolism.
Microelectrodes offer a way to map a single chosen parameter, e.g. pH or pO2
(Revsbech, 2005), however, the probes physically perforate the sample thereby changing
its permeability and therefore potentially its metabolism. Techniques now linking spatial
organization of different microbial species to profiles of metabolites measured using
microelectrodes are an excellent example of the types of measurements leading to an
improved understanding of microbial interactions within biofilms. Nuclear magnetic
resonance (NMR) is a non-invasive method that has been largely used to measure flow
dynamics and diffusion in biofilm systems (Seymour et al., 2004; Van As and Lens,
2001) as well as planktonic cell metabolism (Shanks, 2001). NMR measurements are
often employed with stable isotopes to map metabolic pathways and their flux rates,
making it a powerful tool for metabolic diversity and engineering studies (de Graaf et al.,
2009; de Graaf and Venema, 2008). NMR spectroscopy and spectroscopic imaging are
non-invasive techniques capable of revealing the metabolic and mass transport processes
in live prokaryotic cell suspensions, gel-immobilized cells and extracts (Grivet et al.,
2003).

19

Previous contributions: Devleopment of tools for non-destuctive correlated
diffusion, metabolism and structure measurements within biofilms
In recent studies, NMR as been used to study functionally active and intact biofilms
(Majors et al., 2005b; Majors et al., 2005a; McLean et al., 2008b). The technique for
metabolite measurements, magnetic resonance spectroscopy (MRS) within live oral
biofilms was developed in a carefully designed and regulated single pass flow-through
cell housed inside the NMR that has recently shown to enable both time- and depth-
resolved metabolite concentrations to be measured (McLean, Ona et al. 2008). Using
combined optical laser scanning confocal and nuclear magnetic resonance (NMR)
microscopy, this systems provided for the first time biomass imaging as well as absolute
concentrations of metabolite in active biofilms (McLean et al., 2008b). Both temporal
fluctuations of metabolites and the spatially resolved concentrations can be captured.
These measurements are performed under static as well as hydrodynamic conditions.
The design of the NMR sample chamber facilitated the investigation of biofilm depth-
resolved measurements during laminar flow on effective volumes of only 180 picoliters
(total volume of chamber is 25uL). Data were first obtained for a model biofilm system,
Shewanella oneidensis strain MR-1, a facultative metal reducing bacterium studied for its
bioremediation potential. For S. oneidensis, this technique has resulted in novel metabolic
pathway discoveries and as well as new insights into its metabolic plasticity. In MR-1
the ability of the biofilm to rapidly shift between alternative anaerobic electron acceptors
(fumarate, dimethylsulfoxide and nitrate) without preadaptation was discovered and these
rates of reduction quantified (McLean et al., 2008a). The utility of the in vivo NMR
20

system for studying the metabolism in oral biofilms (McLean et al., 2012; McLean et al.,
2008b) was also demonstrated. In summary, in vivo MRI and MRS combined with
CLSM offers many possibilities for investigating communities of cells on surfaces. These
combined techniques offer unprecedented structural and metabolic information in near-
real time. Importantly, measurements of multiple metabolites over time within a biofilm
structure are not achievable by any other current technologies. Specialized chamber
design such as the one described however were needed to facilitate a controlled
environment for live biofilm measurements as well as providing a logical geometry by
which to spatial encode depth measurements. For example, proton NMR or
1
H NMR
imaging and spectroscopy when correlated, defines the structure and boundaries of the
biomass and enables the interpretation of the metabolite spectra by location. Unlike
optical methods, 1H NMR imaging experiences effectively no depth limitation and is
used to non-invasively monitor biomass distribution and volume over time. This novel
non-invasive and non-destructive capability to monitor real time responses in biofilms
has now allowed further insights into the diffusion and metabolic dynamics of microbial
systems (Cao et al., 2012; Renslow et al., 2012; Renslow et al., 2010).

Single Cell Methods for Microbial Metabolism
Combining species level identification with substrate uptake by fluorescence in situ
hybridization and microautoradiography (FISH–MAR) has been an important
development that has enabled quantification of substrate utilization (Lee et al., 1999a) at
the single cell level. Another approach enables one to use destructive methods to capture
21

snapshots of the community metabolism by using CLSM to simultaneously image a
single labeled metabolite using microautoradiography (MAR) and specific bacterial
species using 16S rRNA targeted FISH probes (FISH-MAR) (Lee et al., 1999b) from a
fixed and sectioned biofilm. These techniques are however limited with regards to the
number of measurable metabolites and species that can be analyzed in a single sample.
While useful in evaluating species and metabolite distribution, these methods do not
directly provide information on which species is responsible for formation of observed
metabolites since the targeted species need to be known a priori. New and novel assays
to decipher metabolic and genetic functions among different species within the microbial
communities with a particular emphasis on revealing the biological functions of
uncultured species are needed.

22

Single Cell Genomic Sequencing: Capturing Reference Genomes of Rare
and Uncultivated Microbes

Sequencing from single bacterial cells, first achieved in 2005, was demonstrated by the
Lasken lab using Escherichia coli, Myxococcus xanthus, and Bacillus subtilis cells
isolated by flow cytometry (Raghumathan et al., 2005). This breakthrough was enabled
by the development of the MDA reaction (Dean et al., 2002; Dean et al., 2001), which
can amplify a single genome copy more than a billion fold: this enabled the amplification
and sequencing of DNA from very low (femtogram) levels (about the amount of DNA in
a single bacterial cell).

Bacteria that have not been cultivated by conventional culturing techniques are the
central target of single-cell genomics (Hutchison and Venter, 2006; Ishoey et al., 2008;
Lasken, 2005; Lasken, 2012a; Raghunathan et al., 2005) The recent advancements in
DNA sequencing of single bacterial cells (Raghunathan et al., 2005) has accelerated the
study of uncultivated microbes (Lasken, 2012a), providing genomic assemblies for
species previously known only from 16S rRNA clone libraries and metagenomic data
(Binga et al., 2008; Dupont et al., 2011; Eloe et al., 2011; Marcy et al., 2007; Podar et al.,
2007; Youssef et al., 2011a). Using these approaches, the so-called “dark matter of life”
(microbes and even entire divisions of bacterial phyla (candidate division Phlya)) is
slowly becoming known by reference genomes and/or gene content.
23

The single cell genomics approach has had a number of notable successes, including
uncultured soil and marine microbes, and a commensal bacterium from termite gut
involved in degradation of cellulose (Hongoh et al., 2008a) , a species of the ancient and
poorly understood Chrenarchaeal group (Kvist, Ahring et al. 2007) and the confirmation
of a chemolithoautotrophic metabolism (Mussmann, Hu et al. 2007) proposed for
Beggiatoa in 1888. Single cell whole genome amplification techniques have now
allowed partial recovery of genomes from the elusive Candidate Divisions (CDs); TM7
(Marcy et al., 2007; Podar et al., 2007), OP11 (Youssef et al., 2011b), and Poribacteria, a
candidate division belonging to the Planctomycetes–Verrucomicrobia–Chlamydiae
superphylum symbiotically associated with marine sponges (Siegl et al., 2011). In a
similar approach, whole genomes of the Termite Group 1 (TG1) division were recovered
from amplification of pooled clonal single cells (Hongoh et al., 2008a) from within a
single protist host cell.

Given the number and diversity of taxa found within biofilms, it becomes important to
know the members of these communities (and their activities) at as higher level of
resolution than allowed by the most commonly used detection and identification
methodologies. Culture-independent surveys using the 16S rRNA gene as a marker are
currently the most widely used approach; however genetic strain differences reflecting
potential different metabolisms and phenotypes are often difficult to resolve due to this
gene being highly conserved amongst many bacterial strains. Quantitative PCR and direct
24

culturing are focused on either a handful of predetermined species or what can be readily
cultivated which we already know to be only a minor portion of the species in any given
environment. Metagenomic surveys are becoming common but so far, our ability is
limited with regard to predict taxonomic affiliation at the species or strain level from
highly diverse and complex datasets. Additionally, a whole genome comparative
genomic study on the evolution and transmission of a low abundance organism of interest
that resides in a microbial community requires substantial amounts of DNA or a cultured
strain from the community which often cannot be obtained.

A new strategy was recently reported for sequencing and assembly of single cell genomes
of bacteria (Chitsaz et al., 2011) and viruses (Allen et al., 2011) including novel
uncultivated bacteria from environmental samples (Chitsaz et al., 2011; Dupont et al.,
2011; Eloe et al., 2011). The workflow consists of: 1) delivery of single bacterial cells
into 384 well microtiter wells by Fluorescence Activated Cell Sorting (FACS); 2) use of a
robotic platform to perform 384 well automated cell lysis and amplification of DNA by
the (MDA) method (Dean et al., 2002; Dean et al., 2001; Hosono et al., 2003) to create
libraries of genomic DNA derived from single cells; 3) PCR and cycle sequencing of 16S
rRNA genes to profile the taxonomy and diversity of the libraries; 4) selection of
candidate amplified genomes for whole genome sequencing; and 5) sequencing and
assembly of selected genomes using assembly tools designed specifically for MDA
amplified single cells (Bankevich et al., 2012a; Chitsaz et al., 2011). In Chapters 2 and 3,
and Appendix E of this thesis, a highly automated single cell genomics system (Figure 1-
25

4) was developed and validated for the application to diverse and difficult-to-sample
environmental biofilms (McLean et al., 2013a; McLean et al., 2013b) (Nurk et al.,
2013b).

Figure 1-4. Custom integrated Agilent Technologies BioCel 1200 liquid handling
automated platform for high throughput single cell genomics. The BioCel platform
allows processing of more than 5,000 single cells per week through a multi-stage protocol
that includes multiple displacement amplification (MDA) of DNA, MDA dilution and
16S rDNA PCR. After classification of 16S rRNA gene sequences, candidate genomes
can be deeply sequenced followed by assembly and annotation of generated contigs.

26

Capturing Gene Expression of Entire Communities: Metatranscriptomics

Next Generation Sequencing (NGS) technologies have provided a new way to assess the
gene expression (transcription activity) of bacteria, predominately referred to as RNA-seq
(Mader et al., 2011; Pinto et al., 2011). The strength of this methodology relies on the
very large number of sequence reads generated with NGS platforms. This enormous
quantity of reads generated; now allow the number of expressed transcripts (mRNA) to
be determined by mapping reads to reference genomes or assembled de novo. Uniquely,
RNA-seq also permits the quantification of novel transcripts such as small RNAs within
intergenic regions that might not have been previously predicted and targeted by
microarrays or qRT-PCR primers. Furthermore, the decreasing costs associated with
RNASeq in comparison to conventional DNA-microarray hybridization techniques
justifyies the use of this approach. Until recently however, techniques used to
characterize gene expression in more complex natural microbial communities have been
challenged by the overwhelming genetic diversity and metabolic complexity of these
consortia. The working hypothesis for metatranscriptomics applied to microbial
communities is that transcripts associated with the active genes responding to each stage
of the described interactions will be more highly abundant. Specifically,
metatranscriptomic analyses can be applied to communities in a number of defined
interactions to delineate the expressed genes thereby moving closer to the real functions
of the bacteria under specific conditions. Species-specific genes that are up-regulated in
27

response to a conditions such as low pH would ideally give insight into the mechanisms
that bacteria use for acid tolerance and maintenance of cytoplasmic pH.

Critical to the successful employment of metatranscriptomic analyses for microbial
communities were techniques for the effective removal of rRNA sequences that normally
comprise over 95% of the total RNA recovered from cells. With the advent of mRNA
enrichment strategies to deplete the ribosomal RNA along with advances in deep
sequencing, metatranscriptome analysis now enables comprehensive gene expression
profiles to be created from complex microbial assemblages. This technique has been
successfully applied to soil, oligotrophic oceans, eutrophic coastal marine waters and
human microbiome samples (Booijink et al., 2010; Turnbaugh et al., 2010).

Nucleic acid and cDNA preparation and sequencing
The generalized scheme for preparing cDNA for sequencing involves first extraction and
purification of total RNA which would be performed by using a RNA extraction kit. The
critical step of removal of rRNA typically now follows. The early procedures used
subtractive hybridization to reduce all rRNAs present in the samples (Stewart et al.
2010). In the other studies, protocols adapted to remove community-specific rRNAs by
using biotinylated rDNA amplicons obtained from the community of interest, in
combination with a streptavidine- bead based capture approach (Stewart et al. 2010). By
applying this protocol it was possible to remove the majority of all rRNA species (16S,
23S, 18S and 28S) present in the samples. Now commercially available kits such as
28

Ribozero (Epicenter) effectively remove most bacterial rRNA from total RNA samples to
obtain mRNA enriched samples for deep metatranscriptomic analyses. The mRNA
purification and rRNA removal process can be evaluated by using a Bioanalyzer assay
following the RNA quantification protocol. The most dominant pools of rRNAs and
small RNAs (sRNAs) are then removed when using the further clean up and size
selection kits. This approach allows removing 85-95% of the rRNA from the total RNA
extracts and thus greatly enhances the proportion of mRNA in RNA-seq experiments
(McLean, J. Unpublished), (Giannoukos et al., 2012). Uni- or bi-directional cDNA
libraries are then generated using such library preparation kits as ScriptseqV2
(Epicentre). The resulting cDNA library of each culture can be sequenced using such
platforms as the Illumina Hi Seq 2000 (~40GB per lane or ~600 million reads for 2x100
paired end reads).

Analysis of genomic and metatranscriptomic datasets
Analysis of the metatranscriptomic data will ideally allow identification of both structural
and regulatory genes coding for the molecular mechanisms involved in bacterial
physiology. The types of functional analyses looking at gene expression include:
1) Comparative metatranscriptomics: Differential expression (DE) of genes or
clusters of genes in regards to a reference condition or temporal change in
expression patterns.
2) De novo assembly and in silico metabolomics. Annotation information such as
enzyme commission (EC) number assignments, COGs and hidden Markov models
29

(HMMs) can be assigned to reads that assembled into partial or full genes and
used to construct metabolic pathways to the extent possible to gain new insights
into metabolic pathways present in a community. EC number assignments, for
example, can be used to populate KEGG metabolic maps and can be enhanced
with expression level information. The assignment of EC numbers, COGs, and
HMMs can then be used to perform statistical analyses to determine over-
representation of pathways/processes in the transcriptomes from each sample
relative to one another.

3) Phylogenetic profiling. Patterns of present and absent of gene families or
proteins in different genomes can be compared to the distribution of phenotypic
characters of interest. Genes (or combinations of genes) found preferentially in the
genomes of organisms sharing a particular phenotype can be considered functional
candidates of interest.

There are many technical and bioinformatics based challenges associated with
interpretation of microbial gene expression patterns in mixed species biofilm
communities, including precision and sampling times effecting mRNA degradation,
accurate mapping parameters, length of sequence reads limiting accuracy to a single
genome. One of the major concerns that needs to be addressed is the existence of
phylogenetically closely related strains in the community: the identification of a unique
read to a given strain can often be done only when a reference genome of that strain is
30

available. For the present, in most biofilm communities, a great majority of reads do not
map to any known reference genome.

A generalized workflow for metatranscriptomics involves mapping reads onto reference
and assembled genomes/metagenomes using such short read mapping tools such as the
Burrows-Wheeler Aligner (BWA) tool (Li and Durbin, 2009). Counts across multiple
genomes can be divided to attempt to account for highly conserved genes present in
multiple genomes. The counts for each genomic region can then be extracted and
tabulated. Comparative gene expression analyses between sequencing libraries can be
performed using tools and approaches developed to handle the dynamic range of RNA-
seq datasets as opposed to methods developed for microarrays. In addition, the
approaches for normalization of the data built into these processing tools and the
statistical tests are key to determine the significance of the genes expressed. Gene
transcription boundaries, regulatory regions and expression of small RNA can be
analyzed in detail by using more sophisticated approaches once they are identified in the
genome of interest. In addition, de novo assembly of the transcripts can then be
performed which allows coding regions to be determined from the community that could
assemble into new and novel genes. These open reading frames (ORFs) can be annotated
and compared to existing genomes representing closely related bacterial genomes.
31

Linking Function to Phlyogeny: Nucleic Acid Stable Isotope Probing
In the field of microbial ecology it is well understood that most prokaryotes have not
been cultivated in the laboratory. As a result, there is at present little understanding of the
metabolic potential and ecological roles of most microbial species. Determining which
microbes are responsible for metabolizing substrates in a mixed microbial community is
one of the biggest challenges in microbial ecology. Stable Isotope Probing (SIP)
(Boschker et al., 1998; Radajewski et al., 2000) methods offer great potential to identify
the uncultivated microorganisms that metabolize and assimilate specific substrates in lab
and field samples, and to identify metabolic networks that define functional microbial
communities. When a substrate enriched in
13
C or
15
N (stable isotopes with higher-than-
average mass number) is added to a sample, the native microorganisms that metabolize
the substrate incorporate the isotope into their biomass. These targeted organisms can be
identified by the analysis of labeled biomarkers i.e. macromolecules that incorporate
labeled carbon during metabolic processes and can be identified with the organism via
their chemical structure. SIP methods are proving very useful tools for investigations in
microbial ecology (Dumont and Murrell, 2005; Dumont et al., 2006; Neufeld et al.,
2007a; Radajewski et al., 2003; Uhlik et al., 2008) and have been almost exclusively
applied to environmental samples. Given what little is known about the metabolism of
uncultivated bacteria in the lab or field, the application of SIP in studies of multi-species
biofilms holds considerable promise for meeting this challenge.

32

There are three stable isotope probing (SIP) approaches traditionally defined by the
biomarker that is analyzed: DNA-SIP, RNA-SIP and PLFA-SIP. DNA-SIP allows for the
recovery of the genomes of the active microbial population and has the advantage of
providing great opportunities for downstream applications, most notably metagenomics
(Friedrich, 2006). RNA-SIP employs a similar technique for physical separation of
labeled molecules (isopycnic density centrifugation). To date, RNA-SIP has been applied
less frequently (Madsen, 2006) and has focused on analysis of stable, ribosomal RNA to
determine the phylogenetic identity of metabolizing organisms. Under nutritional
conditions where rRNA synthesis occurs faster than overall growth labeled rRNA may
accumulate in a shorter incubation time than labeled DNA. Phospholipid-derived fatty
acid (PLFA) or
13
C-labeling of lipids is an extremely sensitive technique compared with
the methods above and this is its main strength. An enrichment bias, which is a major
concern, particularly for DNA-SIP studies is therefore virtually eliminated. However,
PLFAs have poor phylogenetic resolution in comparison to ribosomal RNA gene
sequences.

In brief, SIP of nucleic acids entails adding a labeled substrate to a metabolically active
culture of cells which can range from a
13
C label on a sugar (Egert et al., 2007) or other
substrate (Osaka et al., 2008), labeled CO
2
(Griffiths et al., 2004) and also
15
N labeled
amino acids (Buckley et al., 2007; Cadisch et al., 2005; Cupples et al., 2007). After
waiting a predetermined time for incorporation of the labeled material into biomass,
extraction of nucleic acids is performed. The key to this technology development was the
33

separation of the heavy isotopically labeled nucleic acids from the light unlabeled using
isopycnic density gradient centrifugation into fractions. Both DNA (DNA-SIP) (Neufeld
et al., 2007b) or RNA (RNA-SIP) (Whiteley et al., 2007) can be isolated in this fashion.
The species represented in the collected fractions can be identified by PCR amplification
and, depending on the level of identification needed, applying either fingerprinting
methods such as denaturing gradient gel electrophoresis (DGGE) or clone library
generation by sequencing of 16S rDNA genes.

Initially DNA-SIP studies were performed in microcosms, in some cases with substrate
concentrations far exceeding those found in natural environments (Dumont et al., 2006).
SIP field studies have been shown to be effective at identifying key players in
biotransformation processes in both field and bioreactor studies on bioremediation studies
(Madsen, 2006). Novel species and insights into phenol degradation (Derito and Madsen,
2008; DeRito et al., 2005; Mahmood et al., 2005; Manefield et al., 2007; Sueoka et al.,
2009), cellulolytic bacteria (Haichar et al., 2007), long-chain fatty acid degradation
(Hatamoto et al., 2007), anaerobic benzene-degrading bacteria (Kasai et al., 2006)
perchloroethene-respiring microorganisms (Kittelmann and Friedrich, 2008), bacteria
involved in degradation of pentachlorophenol (Mahmood et al., 2005), salicylate,
naphthalene, or phenanthrene (Singleton et al., 2005; Yu and Chu, 2005), and
degradation of PCBs (Tillmann et al., 2005) have been successfully achieved using the
SIP approach. Functional comparisons of activated sludge communities (Manefield et al.,
2005) has also revealed active members of the community.
34

Several SIP-related, high-resolution analytical techniques have now been reported, all of
which enable direct detection of heavy isotopic signatures in single cells. Orphan et al.
(Orphan et al., 2001) report a cultivation-independent study of marine microbial
assemblages in anoxic methane-rich sediments that combined microbial cell
identification using ribosomal RNA-targeted fluorescent in situ hybridization (FISH)
with secondary ion mass spectrometry (SIMS). Recent work (Mayali et al., 2012; Pett-
Ridge and Weber, 2012) using NanoSIMS to scan hybridization patterns of heavy
isotopic labeled phylogenetic microarrays have the potential to circumvent time
consuming steps involved with density gradient centrifugation steps. To examine the
potential of protein stable isotope probing (protein-SIP), Jehmlich et al. (Jehmlich et al.,
2008) conducted an experiment identifying the species responsible for anoxic toluene
degradation in an artificial mixed culture fed with gluconate and [
13
C7]-toluene under
denitrifying conditions. The mass spectrometric analysis of the proteomes following 2-
DE enabled them to identify the active degrading species in a bacterial consortium. Stable
isotope labeling offers a more focused approach in many cases to reveal the identity and
genomic content of active members in microbial communities in a bulk fashion (Chen
and Murrell, 2010). Several groups have successfully coupled the use of SIP with
sequencing of the heavy labeled DNA with shotgun metagenomics (Dumont et al., 2006;
Kalyuzhnaya et al., 2008; Neufeld et al., 2008a; Neufeld et al., 2008b; Sul et al., 2009)
allowing more detailed understanding of the genes active in the organisms of interest.
35

SIP application in Oral biofilms
Recent efforts in our laboratory have combined nucleic acid based SIP with in situ non-
invasive Magnetic Resonance Spectroscopy (MRS) to link organic acid production and
the specific bacterial species active in oral biofilms (McLean et al., 2012) (See Appendix
A). The experimental procedure involved incubating plaque samples from healthy
juvenile human subjects with isotopically labeled carbon sources (
13
C- glucose or
13
C-
lactate) in a defined minimal medium under various pH and buffering conditions. The
temporal metabolite profiles of these live samples were monitored by inserting the
biofilms into the NMR and performing spectroscopy with
1
H MRS. In addition, DNA and
RNA SIP were performed on these samples and clone libraries were constructed of the
heavy fractions representing the metabolically active species as well as the light fractions
representing the overall sample diversity (Figure 1-3).

Figure 1-5. Illustration of the NMR-SIP experimental procedure. See text below for
description of steps. (McLean et al. 2012).
36

The study was based on the hypothesis that a low pH environment simulates the time at
which the dissolution rate is highest and only those bacteria that can tolerate and continue
to metabolize 13C-glucose (and byproducts) will be detected in the heavy labeled isotope
fractions. The experimental procedure for such a study (Figure 1-3) involves incubating
microbial biofilm samples with isotopically labeled carbon sources in a minimal medium
under different pH conditions, employing live NMR (McLean et al., 2008b) to quantify
the substrate uptake and acidic metabolite production over time, sample extraction,
followed by density centrifugation for RNA and DNA isotopic separation. After
collecting the heavy and light fractions, a 16S rRNA clone library is made on selected
fractions from the light and heavy samples followed with phylogenetic identification
Using this novel application, McLean et al. (McLean et al., 2012) demonstrated that this
approach allows reconstruction the community interactions by identifying potential novel
acid active species (including uncultivated species) under a set of conditions that were
relevant to the enamel (hydroxyapatite) dissolution (Appendix A)

Collectively, the work described above provides important steps that can be used to study
both microbe-microbe and microbe-mineral interactions. For example, our initial findings
through the use of SIP revealed that species other than the model species of mutans
streptococci are active and that lactobacilli are able to function at pH 5.5 and pH 4.5. In
addition, the study revealed that there were many uncultivated taxa that were active at
low pH (McLean et al., 2012). Does this indicate that shared genetic attributes such as
37

acid tolerance may give these species a competitive advantage and overall increase the
rate and extent of demineralization? It is these uncultivated species that intrigue us the
most. Beginning to address these outstanding questions with the use of advanced
methods to link phylogeny with function is a promising start.

Advanced Biofilm Models: From Single to Mixed Species
Most model systems for biofilms have utilized single-species systems, with the goal of
understanding the various processes that occur during the “life cycle” of a biofilm formed
in the laboratory. Model systems also drive technological advances since they provide a
test bed for new technologies and approaches. They have proven to be valuable for the
elucidation of the fundamental aspects of microbial biofilm formation, but suffer from the
inherent difficulties of manipulating variables. In general, growth models attempt to
mimic in situ conditions as much as possible and to control input and environmental
parameters so that cell-cell interactions can be understood. While some successes have
been achieved in predicting biofilm responses to environmental perturbations, until multi-
species systems are developed and studied, the relevance of this approach for
understanding in situ biofilms will be minimal.

There are considerable difficulties inherent in the development of a multi-species biofilm
model system. A range of approaches and microbial communities of varying complexity
has been utilized, with different uses, strengths, and limitations. Admittedly, each is a
38

compromise between the actual ecosystem and the simplification and controllability
necessary to gain meaningful, useful results in the laboratory. The model systems thus
tend to be limited in the range of uses, with potentially high variability, and difficult in
terms of the interpretation of results (Sissons, 1997). The further development of existing
model systems and the development of new complex multi-species systems that are
particularly well suited to address fundamental questions of biofilm community structure
and function are truly needed. With such systems, we can begin to address some of the
issues specific to the study of biofilms, including: 1) temporal and spatial heterogeneity
in environmental parameters; 2) heterogeneity in growth rates; 3) small sample sizes;
and, 4) fast dynamic temporal changes.

Several physical models suitable for visualizing, documenting, and predicting biofilm
growth have been reviewed (McLean, 2008; McLean et al., 1999; Nivens et al., 1995;
Sissons, 1997) and include: 1) bioreactors (biofilms grow on surfaces within the growth
chambers, and can be run in chemostat or continuous culture mode); 2) optically
compatible parallel plate flow chambers (Berg and Block, 1984; Nivens et al., 1995);
and, 3) Constant Depth Film Fermenters (CDFF)(Pratten, 2007; Wilson, 1999) to name
but a few.
Development and Application of Model Biofilm Growth Systems
In my previous studies, chemostats were used to control the growth of S. oneidensis MR-
1 to allow multi-omic analyses such as proteomics (Elias et al., 2008), transcriptomics
(McLean et al., 2008c). Within large volume, relatively homogenous, well stirred
39

bioreactors cells are controlled by varying single parameters (e.g., electron donor or
electron acceptor levels). Many Insights into MR-1 physiology were gained including the
discovery of microbial nanowires in response to electron acceptor limitation (Gorby et
al., 2006). In one study, we were able to, in a novel manner, control aspects of biofilm
growth that made them inaccessible to controlling parameters such as growth rate. This
was accomplished by keeping cells in a suspended biofilm form (biofilm-like aggregates)
that multiplied and divided at a rate roughly equal to the dilution rate of the chemostat.
(McLean et al., 2008c). In aerobic chemostat cultures maintained at 50%
dissolved O
2
tension (3.5 mg l-1 dissolved O
2
), MR-1 rapidly aggregated upon
addition CaCl
2
and retained this multicellular phenotype at high dilution rates.
Global transcriptome analysis of genes differentially expressed by planktonic versus
aggregated cells at high oxygen showed trends that were similar to published results on
the genes and proteins expressed when comparing planktonic cells to surface attached
biofilms. The major trends seen in biofilm arrays and our chemostat study (McLean et
al., 2008c) are as follows; 1) switch from motile to non-motile phenotype; 2)
upregulation of exopolysaccharides; 3) stationary phase-like character; 4) upregulation of
stress induced pathways; 5) prevalence of genes with unknown function.

As mentioned in the previous sections, developments in magnetic resonance spectroscopy
(MRS) enabled the study of biofilms pre-grown in a CDFF that allowed controlled
biofilm morphology. This in turn enabled depth resolved quantification of metabolites
and diffusion rates to be performed for the first time (McLean et al., 2008b). Parallel
40

plate flow cell chambers that were optically accessible allowed detailed studies of the
biofilm development within the metal reducing bacterium S. oneidenis MR-1 leading to
discovery of a late stage dispersion mechanism seen in other organisms named
“swarming dispersal” that was thought to be triggered by a limitation in electron acceptor
(McLean et al., 2008a). Later, a flow chamber was retrofitted with components of a
microbial fuel cell (MFC) to generate a novel optical compatible MFC that allowed the
first report on the quantification of extracellular electron transfer rates for single cells (~
200 fA per cell) and the biofilm structure development under these conditions (McLean
et al., 2010).

Recently, our efforts to develop a mixed microbial in vitro biofilm model of the oral
cavity was achieved through iterative manipulation of media components and monitoring
the species diversity compared to a pool of saliva from healthy subjects (Tian et al.,
2010). Appendix B, contains an unpublished manuscript describing how this multi-
species model system was developed and investigated. Using this model system we have
monitored bulk pH dynamics in response to kinds and levels of sugar (Appendix B
(manuscript submitted for publication)). Our aim was to develop a mixed-community
biofilm model system comprising the highest possible cultivable bacterial diversity
representative of the resident saliva-derived microbiome responsible for plaque formation
in the human oral cavity. The general outline of the model is shown in Figure 1-4 and it
is fully described in Appendix B. The use of a laboratory model system with a highly
complex bacterial diversity that also supports the growth of otherwise uncultivated
41

species is highly desirable as it can be manipulated and studied over a longer period of
time in a controlled environment. To our knowledge, the model we developed here is the
first validated system that fulfills these criteria and that can be readily used for example
to target changes in taxa, regulation of metabolic pathways and signaling molecules by
using next generation sequencing and ‘omics’ methodologies (stable isotope probing,
metatranscriptomics) described earlier. Specifically such a model system will greatly help
facilitate experimental approaches that seek answers to questions related to the roles each
bacteria plays in enamel dissolution or remineralization. This type of model system
should be the goal of biofilm studies in other geobiologically-relevant environments.

Figure 1-6. In vitro oral biofilm model system. Schematic of in vitro model system
growth and 16S rRNA gene sequencing results.

These are just a few demonstrations in my past and current research that proves the
power of biofilm models to advance understanding as well the development of new
unique capabilities. However, despite our considerable excitement over this model, one
42

must keep in mind that without the availability of reference genomes, the resolution of
the data we gather will be lacking. Thus, Chapters 2 and 3 describe the capture and
reconstruction of genomes from single cells for two microbes, one an oral bacterium and
the second, a Candidate Phyla (e.g., had no cultivated members, and no reference
genomes). Both of these organisms came from a sink drain biofilm in a hospital; now
that we have begun to identify the active members of the natural oral biofilm in our
model system, we can move to elucidating their genomes using similar techniques.

43

Microbes And Minerals-Geobiology
Understanding microbe/mineral interactions requires the disciplines of microbiology,
chemistry and geology. It gets even more challenging, however: depending on what part
of microbe/mineral interactions one is focused, a number of sub-disciplines and processes
are involved, including biochemistry, geomicrobiology, toxicology, biomineralization,
ore deposition, biofouling, biocorrosion, ore recovery, mineral prospecting, metal
recovery, and bioremediation of metal contamination. Interestingly, the interaction of
bacteria with minerals of human origin such as bones or teeth doesn’t have a devoted
subdiscipline and currently geobiomedicine falls within the study of oral microbiology. In
many instances while investigating the potential of isolated bacteria in practical
applications such as the conversion of metals for bioremediation or release of P for
agricultural purposes, insights into the global biogeochemical cycling of metals are made.
Importantly, through the practical application of studying dental caries disease which
is the loss of hydroxyapaptite mineral (tooth enamel), insight is made into the detailed
dissolution mechanisms mediated by bacteria and relevant to various geochemical
processes. Before moving to Chapters 2 and 3, a short overview of microbe/mineral
interactions, and their influences on mineral formation and dissolution is presented.

General Overview of Bacterial Influence on Metals and Minerals
Bacteria have adapted to and subsequently impact almost every conceivable environment
from heavy metal-laden acidic mine wastes, thermal hot springs, marine vents, deep
subsurface and even within eukaryotic organisms such as humans. It is widely accepted
44

that bacteria play a dominant role in the speciation, fate and transport of metal ions in the
environment and the global geochemical cycling of these elements. A diversity of
mechanisms, spanning the spectrum from direct to indirect, can be prescribed to bacteria
and subsequent biofilm formation greatly influencing the transformation and cycling of
elements. Since bacteria play a predominant role in the global geochemical cycling of
metals and because these effects are generally relevant to soil science, mining industry
and bioremediation of metal contamination, fundamental research into metal-microbe
interactions has been the focus of many studies. Consequently, a variety of reviews and
book chapters have been devoted to describing these interactions (Banfield et al., 1997;
Ehrlich, 1981; Gadd, 2010; Jan P. Amend and Lyons, 2004; Konhauser, 2007; McLean et
al., 2001; Nealson, 1997; Nealson et al., 2002; Newman and Banfield, 2002; Staley,
1984; Walker, 1981).

The implications of bacterial de-mineralization/dissolution and solubilization of minerals
are far reaching. Geochemical modeling of metal ion speciation and transport however is
only beginning to describe and predict how bacteria act as geochemically active surfaces.
Bacteria can alter the chemistry of the microenvironment in so many varied ways, it is
very challenging in most cases to understand any microbe/mineral interactions well
enough to develop predictive models: e.g., thermodynamic equilibrium modeling of
aqueous metal species cannot fully account for the diversity of microorganisms, their
active metabolism, and the microenvironments they create. As bacteria change the
chemistry of their environment and actively or passively sequester metals, they may
45

control the speciation and bioavailability of these elements. Processes that are involved in
metal-microbe interactions include; adsorption, complexation, precipitation, oxidation,
reduction, methylation and demethylation. The physical and chemical characteristics of
bacteria, such as their large surface area-to-volume ratio, serve to increase the metal
binding capacity of their charged surfaces leading to precipitation and formation of
mineral phases on their cell walls or other surface polymers. The number of bacteria
present in any given environment may, however, be of less importance than their relative
levels of activity and the role(s) that certain genera play in the cycling of nutrients and
metals. As mediators of chemical phase transitions, certain bacterial species direct major
processes in the environment (e.g., metal reducers, denitrifiers, methanogens, etc.). The
available space on minerals for example, provides a habitat for bacteria and allows them
to interact with the surrounding aqueous phase. Interfaces between biofilms and soil
particles are highly complex regions where the speciation and fate of metals can be
influenced by the active and passive functions of indigenous microbial communities. At
the microbe/mineral interface, dissolution may occur releasing ions into the aqueous
phase where they may be more bioavailable or may reform into secondary mineral phases
(Banfield and Hamers, 1997).

Cell surfaces impact metal ion concentrations. Prokaryotes (Bacteria and Archaea)
come in a variety of shapes that maximize their surface areas, and are typically 1.0 to
5.0 µm in length and 0.5 to 1.0 µm in diameter (Beveridge, 1981). The high surface area-
to-volume ratio of individual cells combined with a high net negative surface charge
46

density allows bacteria to concentrate metals such as cations very effectively. From a
geochemical standpoint, the reactive layers of bacteria provide active sites for adsorption
and complexation of dissolved aqueous metal species. The surfaces of bacteria vary in
that they may have several different layers or a combination of layers, each of which may
possess metal binding sites. Some bacteria may only have a cell wall in contact with the
aqueous environment whereas others can generate additional closely associated layers
such as capsules and S-layers, or more loosely associated materials such as sheaths and
“slimes”.

As with many environmental particles, the net charge on the surfaces of bacteria at
circumneutral pH is negative due to the ionization of carboxyl and phosphoryl groups
that act as sinks for positive charged ions. Likewise, if cells are planktonic in nature or
bound to suspended particulates, they may contribute to the overall mobility and transport
of metals in the subsurface. Alternatively, if the cells are growing as a biofilm on a
surface, they may sequester positively charged ions in more immobile and less
bioavailable forms. In addition to energy and detoxification, however, there may be a
number of benefits bacteria gain by this sequence of reactions. For example, as
biominerals are formed on the surfaces of bacteria, the cell can be protected from toxic
oxygen species and ultraviolet radiation, as well as from physical and/or biochemical
attack from other microorganisms (Ghiorse, 1984).

47

Cell surfaces as nucleation sites. Metals prone to hydrolysis in solution bind strongly to
cells. Under favorable geochemical conditions, the binding of metals may lead to further
metal complexation and the formation of precipitates. The resulting mineral is a function
of the cell surface, the microenvironment around the cell, and the type and abundance of
the negatively charged counter ions (for example, sulfate, sulfide, phosphate, carbonate
or silicate ions). The proposed mechanism of precipitation by bacterial surfaces involves
a two-step process, the first being a stoichiometric binding of metal cations with the
charged surface functional groups, and the second being a nucleation reaction (Beveridge
and Murray, 1976). Nucleation reactions proceed as the microenvironment around the
cell exceeds the activation energy barrier (i.e., the energy to inhibit the spontaneous
formation of insoluble precipitates (Stumm, 1992)). The process of precipitate growth
will continue as long as the microenvironment is saturated with respect to the mineral
phase. Depending on how the surface is modeled, though, the continuum from surface
complexation to precipitation may occur on the surface under saturated or understaurated
conditions (Stumm, 1992). Since nucleation and precipitation of mineral phases may
proceed spontaneously under favorable conditions once the activation energy barrier is
overcome (Warren and Ferris, 1998), enhanced ion immobilization can be achieved.
Surface Layers and Binding Sites of Bacteria.
Eubacteria can be divided into gram-positive and gram-negative forms which are
profoundly different in their cell wall structure. Each different cell wall results in a
variable number of metal binding sites which control complexation constants and total
metal accumulating capacity. Gram-positive cell walls are usually 20 to 50nm thick and
48

are 40 to 90% comprised of peptidoglycan, a molecule containing linear chains of the
disaccharide N-acetylglucosamine- β-1,4-N-acetylmuramic acid. Secondary polymers,
such as teichoic or teichuronic acids, are cross-linked into the peptidoglycan network
through the N-acetylmuramyl residues and are intercalated throughout the inter-
peptidoglycan spaces. Carboxylates (peptidoglycan, teichuronic acid and protein) and
phosphates (teichoic acid) are distributed on the cell surface as the dominant ionizable
chemical groups at pH ~7 (Beveridge, 1981). Therefore, the electronegative character of
the cell wall at circumneutral pH arises mainly from the contribution of free carboxyl and
phosphoryl groups within the wall as well as the negatively charged secondary polymers
(teichoic and teichuronic acids) attached to the peptidoglycan matrix. The structure of
gram-negative cell walls is complex and consists of inner and outer bilayer membranes
separated by a thin layer of peptidoglycan. Although a thick layer of peptidoglycan is
responsible for most metal-binding in gram-positive cells, gram-negative peptidoglycan
is not believed to exert the same influence. It is thin (ca. 2nm) and comprises only 10% or
less of the wall mass. In addition, it is surrounded by a dense periplasm and is shielded
from the external environment by the outer membrane. There is a variety of negatively
charged lipopolysaccharides (LPS), phospholipids and proteins in the outer membrane.
The net electronegative surface charge arises mainly from the ionization of several
phosphate and carboxylate groups in the LPS as well as the phosphate groups of the
major phospholipid (phosphidylethanolamine) at the inner face of the membrane.

49

Capsules and slimes are found on some species of bacteria although they are hard to
examine by conventional electron microscopical methods; the differentiation between
capsules and slimes is that the capsule is physically attached to the bacterial surface
whereas slime is not. The presence of these loose hydrated structures may serve as a
concentrating filter for dissolved metal ions. Depending on the chemical properties of a
particular capsule, the estimates of thermodynamic metal binding constants vary with the
type of organic polymer present as well as with the geochemical conditions which control
the availability of reactive sites (Costerton et al., 1978; Jang et al., 1989).

Previous Contributions: Composition and reactivity of biofilm components

Since biofilms are not homogenous smooth matrices containing bacteria, models need to
begin to also incorporate the active and passive contributions of biofilm structure and
components. Moreover, microenvironments within biofilms are formed by the diversity
of microorganisms growing within them, and the resulting interrelationships between
neighboring species and surfaces. All of these complex, variable and dynamic features
have a bearing on metal binding and mineralization. In addition to the cell surface
characteristics of gram+ and gram- bacteria discussed above, there are additional
considerations in a biofilm. Bacteria within a biofilm are bound together in a matrix of
hydrous exopolysaccharides that is known to contain extracellular DNA (McLean et al.,
2008a; Pinchuk et al., 2008), reactive proteins such as decaheme cytochromes (Marshall
et al., 2006; McLean et al., 2008a; Pinchuk et al., 2008), cytochrome containing outer-
50

membrane vesicles (Gorby et al., 2008) and extracellular reactive appendages (pili,
flagella, nanowires) (Gorby et al., 2006; McLean et al., 2008c). Many of these
components have a variety of binding sites for charged ions and can reduce or oxidize
metals and insoluble metal oxides. Each of these areas is a discipline on its own or is at
the forefront of current research. An example being the new area of characterization and
discovery of the role bacterial nanowires play in transferring electrons to solid phase
electron acceptors.

Bacterial Mediated Dissolution Of Apatites
Weathering and dissolution of minerals is a process central to global biogeochemical
cycling. In terrestrial environments it provides nutrients to sustain forest ecosystems. The
mechanisms by which bacteria initiate the dissolution of minerals in bulk solution vary
widely, and may be a combination of biochemical and surface mediated reactions during
the process. As described earlier, bacterial surface layers may passively adsorb and
indirectly serve to reduce the concentration of divalent cations below the mineral
saturation level. Bacteria can more directly initiate mineral dissolution by producing
reactive compounds which also bind ions such as chelating agents (e.g. oxalates).

Apatite (Ca
5
PO
4
)
3
∙ (F, Cl, OH, CO
3
) is the most commonly occurring phosphate mineral
in nature found in igneous, metamorphic and sedimentary rocks, and can have
substitutions leading to forms with varying solubility such as fluoroapatite (FAP) and
hydroxyapatite (HAP). Carbonate substitution can occur within the phosphate site.
51

Igneous fluorapatite and sedimentary carbonate fluorapatite both precipitated
inorganically, are the two most abundant available mineral forms. Forms of apatite,
namely HAP and FAP can also be found in the human and animal bodies making up our
teeth and bones. Several extensive reviews on natural and biological calcium
orthophosphates are available (Dorozhkin, 2013; Dorozhkin, 2009). Apatite dissolution
can provide a source of phosphorus (P) to the environment and especially in depleted
environments as well as provide calcium in base-poor forest ecosystems (Blum et al.,
2002). P is an essential nutrient for all living cells being a component of RNA, DNA,
ATP, ADP and other key molecules. The net ecosystem production in terrestrial and
marine environments may be controlled by the availability of P from mineral weathering
(Guidry and Mackenzie, 2003). Although the impact(s) on global biogeochemical cycles
and net biological productivity via the liberation of P in the environment through
enhancement of mineral dissolution mediated by microbial processes is widely accepted,
the process is very poorly understood in terms of the specific microbes and the key
metabolic functions involved.

In general, apatite, like other mineral forms, is more likely to dissolve at low pH due to
the hydration effects of the protons. Apatite is relatively insoluble at near neutral pH,
although its solubility and reactivity vary as a function of apatite composition (Welch,
Taunton et al. 2002). The crystal structure, availability of high energy sites, and
impurities can also impact the dissolution kinetics. Modeling of the dissolution of apatite
crystals has provided insights into the potentially important factors, such as: solution
52

chemistry (pH, composition, saturation and hydrodynamics), bulk solid properties
(chemical composition, solubility, particle sizes) and surface interactions (defects,
adsorbed ions, etc) (Dorozhkin 1997). Most research has, however, been directed
towards abiotic reactions on laboratory synthesized HAP and FAP due to the larger effort
on human relevant materials for implant and regenerative/restoration development.

The dissolution of environmental apatites and the HAP formed within human oral cavity
may share many commonalities. Natural mineral forms of apatite (NAP), those found in
igneous and sedimentary environments, and biologically formed apatites (BAP) such as
tooth enamel, are very similar in their primary crystal structure. The unit cell of the
apatite crystal contains 10 Ca
2+
. 6 PO
4
3-
and 2 OH
-
ions. The histological structure of
tooth enamel is formed by hydroxyapatite crystallites grouped into clusters (prisms ~4-5
µm in length). Each prism is packed with these crystallites that have a length on the
order of 0.5-1 µm while the diameter is about 40 nm. In cross-section the shape is a
flattened hexagon. The interprismatic space is also filled with HAP crystallites but have
a less ordered arrangement. Dissolution features found in BAP are generally between the
hexagonal prisms (interprismatic spaces) (Arends et al., 1992; Cury and Tenuta, 2009)
and although little research has been conducted on NAP, the few studies that show the
dissolution features under abiotic or abiotic conditions also indicate a similar process of
acid etching (Welch et al., 2002). The dissolution features were described as a formation
of elongated spires parallel to the c-axis. From NAP and BAP studies in general, the
prism faces are worn but the interprismatic dissolution is higher. This could be explained
53

by the un-ordered nature of the spaces between the prisms and/or the impurities in the
crystal structure such as CO
3
-
ions.
Experiments on abiotic dissolution with various organic acids of varying strength on
NAP showed that acetic acid leads to a small degree of rate enhancement, but the
presence of oxalic and citric acid strongly enhances apatite dissolution rates (Hutchens et
al., 2006). In a study by Welch et al. (Welch et al., 2002), apatite dissolution rates in the
inorganic, acetate, and oxalate solutions increase as pH decreases from approximately 10
-
11
mol/m
-2
.s
-1
at initial pH 5.5 to 10
-7
at initial pH 2. They suggested that, if microbes can
lower the pH of their environment by 2 to 3 pH units, production of organic ligands in
microenvironments could accelerate apatite dissolution by one to two orders of
magnitude compared to inorganic abiotic conditions. Microbes were also suggested to
influence the uptake PO4
-3
or bind/uptake Ca
2+
which could change the solution
saturation state thereby increasing the dissolution rates. A study by Hutchens et al.
(2006), performed dissolution experiments in closed-system reactors in the presence of
Bacillus megaterium, a common heterotrophic aerobe. Apatite dissolution in indirect
contact with B. megaterium was 50 to 900% faster than abiotic controls. Bacterial rate
enhancement was, however, 3 to over 10 times lower when B. megaterium was in direct
contract versus indirect contact with the apatite surfaces (Hutchens et al., 2006). One of
the conclusions of this work was that microbially mediated dissolution may be less
effective when bacteria are in direct contact with mineral surfaces. Although this is
intriguing hypothesis to follow up on with detailed studies, it does suggests the organic
acids and chelating capacity of organic acids such as oxalate could account for the
54

majority of the dissolution. In many environments, the cells are likely to be in close
association with the surface as biofilms. The net mineral loss could be modulated by the
cell surface and secondary mineralization occurring within and on cells. Clearly, more
studies are needed in this important area that has major impacts on global nutrient cycling
as well as human disease.

Geobiology Of The Oral Cavity
Microbial Mediated Apatite Dissolution: From Natural Systems to the
Oral Cavity
Very few studies have attempted thus far to identify the species involved in dissolution of
natural apatites and next to nothing is known of the metabolic potential encoded in the
genomes of these bacteria. Studies by Welch et al. (Welch et al., 2002), and Taunton et
al. both suggested that biological mediation of apatite dissolution was occurring, but
neither sought to identify the organisms present. In a separate study, the dissolution of
apatite was enhanced by cells of B. megaterium, providing some insight into the
metabolites produced and the types of metabolism that might play a role. (Hutchens et al.,
2006). In contrast, the oral microbial community is a well described, rapidly developed,
easily accessible model, and therefore this system provides a framework to study who
and how processes lead to demineralization. A model to develop approaches that should
be applicable to metal dissolution in other environments.

55

Common processes thought to be the major mechanisms enhancing apatite dissolution,
such as acidolysis and chelation, should be applicable to both NAP and HAP. In
particular as carbohydrates are converted to organics acids and protons are excreted, the
pH declines rapidly, reaching below the point of saturation for apatite, and driving the
dissolution process. Analogous reactions occur in the oral cavity driving
demineralization of HAP (tooth enamel). From many years of study, it is clear that
collectively it the community shifts metabolism through an extreme pH change from
neutral to pH 4.0 and then rapidly returns to near neutrality. This overall balanced system
in terms of pH can be upset however; the pH can be driven below the critical
demineralization point (pH 5.5), resulting in net dissolution and mineral loss. Some of
the key aspects of both NAP and HAP bacterial enhanced demineralization is derived
from the ability of cells in biofilms to adapt and tolerate acid stress and maintain low pH
metabolism.

Oral Biofilms as a Model for Understanding Microbial Demineralization
at the Species and Ultimately the Molecular Level
With the advent of advanced molecular techniques for bacterial identification and next
generation sequencing, we can now increase our understanding in the area of
microbe/mineral interactions. The major hurdles to microbial community research that
were highlighted earlier still need to be overcome in order to fully understand this process
in the complexity of a natural environmental system and within the human oral cavity.
56

Current understanding of bacterial processes leading to demineralization
Dental caries disease (tooth decay) is one of the most prevalent and costly bacterial
infections in humans. According to the Surgeon General Report on "Oral Health in
America” dental caries is one of the most common childhood diseases. Currently, the
annual expenditures on dental services in the United States exceed $80 billion with the
majority of these costs attributable to dental caries (http://www.ada.org/). In addition to
having significant health related relevance, dental plaque, is one of the best-described
microbial communities (Kolenbrander, 2000; Kolenbrander et al., 2007; Kuramitsu et al.,
2007; Marsh and Bradshaw, 1995). The oral populations present a very convenient
system for demonstrating the species interactions and functional analysis of multi-species
communities in general (Foster et al., 2003; Kolenbrander et al., 2007). An oral biofilm
on BAP is a much more well defined model system than a natural community on NAP in
terms of : 1) the biogeochemical reactions leading to HAP dissolution; 2) the identity of
species present, the number of genomes available and; 3) the ease of conducting
biological experiments. Despite many years of research however, demineralization of
BAP remains enigmatic in terms of critical interactions that occur in the community at
the species level. It is clear that demineralization of BAP exhibits a strong correlation
with biofilm induced pH reduction, but in reality, a detailed understanding of the
metabolic processes responsible for pH reduction is still lacking.

57

Most of what is known about bacterially mediated deminerlizaation in the oral cavity
comes from the research on one model organism Streptococcus mutans. It is commonly
reported that lactic acid is the major organic acid produced by the S. mutans and that
lactic acid accumulation at the biofilm-enamel interface is responsible for the large pH
shift leading to enamel demineralization. This is a grossly simplified view since very few
studies have addressed which organic acids are produced during the metabolism of the
common sugars (glucose and/or sucrose) under conditions found in a biofilm. None have
been able to non-invasively quantify the absolute concentration of multiple organic acids
in live biofilms, especially in a temporally or spatially-resolved manner. Furthermore we
know that there is a large diversity of species with metabolic information encoded in their
genomes that could produce enzymes that could lead to pH alteration.

In fact, other studies support the importance of other organic acids toward
demineralization even in the model species S. mutans. Yamada et al. (Yamada and
Carlsson, 1975; Yamada et al., 1985), showed early on that S. mutans given excess
glucose exclusively produces lactic acid under aerobic conditions. Under anaerobic
conditions, lactate, formate, acetate and ethanol are formed, with lactate making up less
than 50% of the total acids produced. Since biofilms are typically stratified with respect
to oxygen concentration, the findings suggest that the outermost biofilm layer (where
oxygen concentrations are higher) would present high levels of lactic acid metabolites
while internal regions of the biofilm that are closer to the enamel surface and exposed to
reduced oxygen tensions would give rise to other acids in combination with lactate.
58

Furthermore, overall higher yields of acid occurred when cells were grown anaerobically
rather than aerobically with acetate and formate being the dominant acids present. An
NMR study of perchlorate extracts of oral biofilms demonstrated the utility of
1
H-NMR
for the simultaneous analysis of ~30 chemical constituents, including lactate and other
corrosive organic acids. Their results indicated that lactate, acetate, pyruvate, propionate,
formate and n-butyrate are produced in abundance, with acetate and formate being
produced at higher concentrations than lactate. Considering the larger dissociation
constants of these organic acids, they concluded that formic and pyruvic acids contribute
significantly to the decreased pH values. The authors of this NMR study stated that
previous studies of carious lesions have failed to detect and, therefore, to consider the
contribution of formic and pyruvic acids to demineralization of tooth surfaces. While
supporting the hypothesis that acids other than lactic acid are likely to be important
players in cavity formation, this approach could not differentiate between acids produced
extracellularly where they can interact with surfaces from those retained within the
cytoplasm of the cells. This information is essential for determining which acids are
most important in demineralization processes.

Previous Contribtions: Real-time NMR of metabolism in oral biofilms revealing
organic acid profiles
To our knowledge, no direct measurements of metabolite types, quantities, or distribution
over time have been conducted in in situ, or in vivo model oral biofilms. These types of
measurement are essential to gain an understanding of the metabolic processes that are
59

responsible for cavity formation. The in vivo metabolite profiles of a supra-gingival oral
community under “diseased” (low pH) and “healthy” (neutral pH) environmental
conditions are therefore unknown. Recently, our laboratory has conducted the first
experiments to gain insight into the temporal dynamics of metabolism. NMR based
metabolite measurements were performed in a non-invasive manner on active oral
biofilm communities, a S. mutans biofilm model (McLean et al., 2008b) and human
derived plaque samples (McLean et al., 2012). The metabolite profiles presented in the
McLean et al. (2012) study allow a more detailed look at the temporal dynamics of the
bacteria-derived acidic metabolites from glucose metabolism. Magnetic resonance
spectroscopy (MRS) results confirm that the conversion of glucose was complete when
begun at pH 7 but not at a starting pH of 5.5 under unbuffered conditions. Under buffered
conditions at a starting pH of 7, the profiles clearly demonstrate the conversion of
glucose to lactate and acetate followed by a shift in the metabolism toward lactate
utilization when glucose became limiting. The main end products at 36 hrs for both
conditions were lactate and acetate. Minor amounts of propionate, formate and ethanol
were produced under buffered pH 7 conditions whereas propionate was not detectable
under pH 5.5. The final pH at the end of the experiment was pH 6.02 and 5.03 for the
buffered pH 7 and unbuffered pH 5.5 experiments respectively. Although only endpoint
pH measurements were attainable for these in-magnet experiments, it is assumed that
these plaque samples have a qualitatively similar pH profile first described in the 1940’s
which is now referred to as the “Stephan curve” (Stephan, 1945) (Figure 1-1. This pH
60

drop and subsequent rise is initially induced from glucose addition but the increase of pH
is relatively uncharacterized and certainly not ascribed to any particular species.

Current Knowledge on the Species in Oral Biofilms
Through isolation/culture and culture-independent methods, the species present as
attached cells in biofilms within the oral cavity have been estimated to comprise a diverse
community of more than 700 phylotypes inclusive of bacterial and archaeal species,
although less than 100 phylotypes are found in a typical individual (Dewhirst et al.,
2008). The HMP initiative with recent advances in genomics have now allowed for a far
more comprehensive survey of the microbial species and their associated genes present in
the oral cavity through the Human Oral Microbiome Database (HOMD;
www.HOMD.org) (Dewhirst et al., 2008). Many oral cultured representatives are part of
the planned expansion of the reference dataset and oral metagenomic studies have been
conducted and are planned. Much of what is known about all of these sequenced species
to date however has been derived from pure culture approaches and laboratory
experimentation, which almost certainly does not reflect their actual function in complex
microbial communities. Furthermore roughly half of species identified through culture
independent methods in the oral cavity are classified as uncultivated phylotypes, many of
which, have been found in deep cavities and therefore possibly linked to demineralization
processes. The metabolic function of these species and their contribution to diseased
states has not yet been established.

61

Dynamic metabolic and population shifts leading to sustained
demineralization and the ecological plaque hypothesis
Possibly the best example in the development of overarching hypotheses governing the
shift in microbial populations that results from a healthy (balanced neutral pH) to a
“diseased” state (extended periods of low pH) comes from extensive studies of the oral
cavity, beginning with the specific plaque hypothesis developed by Loesh in 1986
(Loesche, 1986). This hypothesis implicated that only a few species were responsible for
oral caries. S. mutans, identified very early as a major player in the onset of caries
disease, has been the primary subject of most caries research studies. With further
investigations, it became evident that many other bacteria termed “cariogenic or oral
pathogens” such as Streptococcus sobrinus and Lactobacillus spp. exhibit metabolic
behavior similar to that of S. mutans. The non-specific plaque hypothesis developed
years later (Theilade, 1986), implicated the microbial community as a whole as being
responsible for caries. With more extensive studies, Marsh and colleagues later
developed the more widely accepted “ecological plaque hypothesis” (Marsh, 1994)
stating in essence that oral caries disease (tooth decay) and periodontal diseases arise as a
result of environmental perturbations that lead to a shift in the balance of the resident
microflora. Key features of this hypothesis are that (a) the selection of ‘pathogenic’
bacteria is directly coupled to changes in the environment, and, (b) diseases need not
have a specific etiology; any species with relevant traits can contribute to the disease
process. Thus, the significance to disease of newly discovered species can be predicted
on the basis of their physiological characteristics. For caries, the environmental
62

perturbation arises from the intermittent introduction of dietary sugars during feeding
leading to cycling of pH (Stephan, 1945). If the pH remains low for sustained periods, a
shift in the bacterial populations to more aciduric organisms occurs (Kleinberg, 2002;
Marsh and Bradshaw, 1997). This was documented through laboratory culturing studies
in chemostats using defined mixed communities. In vivo it is envisioned that under
disease conditions, the low pH drives the dissolution of calcium and phosphate in the
hydroxyapatite crystalline structure of the tooth and ultimately leads to cavitation. The
cariogenic bacteria are then thought to thrive under these acidic conditions, increasing in
proportion and worsening the diseased state.

From numerous clinical investigations it is clear that certain acidogenic (acid producing)
and aciduric (acid tolerating) species such as S. mutans and Lactobacillus spp. are highly
correlated with active caries. There are, however, many other species are likely to be
relevant as evidenced by the diverse microbial populations present in caries in young
children that contain Actinomyces, Fusobacterium, Porphyromonas, Selenomonas,
Bacteriodetes, and Haemophlilus (Corby et al., 2005). Through Denaturing Gradient Gel
Electrophoresis (DGGE) profiling at least 30 species were found in active caries sites
including Gemella, Kingella, Leptotrichia, Streptococcus, Veillonella (Li et al., 2007).
Clearly, substantial clinical evidence is available to support the hypothesis that cariogenic
activity of an oral biofilm is impacted by multiple members of the community.

63

Overall, the contributions of each species to the healthy and diseased state remain largely
unknown. Although, it is clear that dental caries is closely related to the pathogenic
(acidogenic) state of oral microbial flora, there remain significant fundamental
knowledge gaps about the disease process. For example, while the properties of many of
the identified cariogenic bacteria such as S. mutans are known in pure culture, knowledge
of their physiological and metabolic behaviors in a multi-species dental biofilm is scarce.
It is clear from investigation in model systems that in-vivo characteristics can be greatly
impacted. For example, commensal bacteria in dental plaque biofilms may impact the
processes of acid loving species (cariogenic pathogens) (Takahashi and Nyvad, 2008)
indirectly by modulating the pH decrease by the production of ammonia from arginine
(Takahashi, 2003) in addition, species such Veillonella can reduce the residence time of
lactic acid on the enamel surface thereby reducing demineralization (Wang and
Kuramitsu, 2005). It has also been shown that it is possible to impact virulence in a more
direct manner such as the example of Streptococcus sanguinis inhibiting S. mutans
growth through the production of H
2
0
2
(Kreth et al., 2005).

Summary

With the high level of effort to characterize the diversity and functions of bacteria
associated with surfaces, a logical (and very important) next step is to increase our
limited understanding of the process through community level physiological and
molecular based studies. The huge challenges remaining, such as the vast uncultivated
64

species and the lack of reference genomes limit our understanding. Capturing genomes of
yet-to-be cultivated species which serve not only to gain insight into their potential
physiology but will enable verification of this predicted metabolism using sequencing
based approaches such as metatranscriptomics to measure the expression of genes while
they are within the community. In order to assign the mRNA reads and therefore
function to a particular organism, these genomes need to be generated. Development and
application of such methods to capture genomes from biofilms are the topic of Chapters 2
and 3. Ultimately, once more genomes become available, we can apply concomitantly,
the arsenal of approaches as described earlier including non-invasive biofilm specific
imaging and metabolic analysis methods followed downstream by such tools as stable
isotope probing and the expression profiling of all species within a mixed species biofilm.
Logically these are likely best applied first in more controlled model systems and as the
technology and capabilities evolve, expand to natural environments.

65

Chapter 1 References

Abee, T., Kovacs, A. T., Kuipers, O. P., and van der Veen, S., 2011, Biofilm formation
and dispersal in Gram-positive bacteria: Curr Opin Biotechnol, v. 22, no. 2, p.
172-179.
Allen, L. Z., Ishoey, T., Novotny, M. A., McLean, J. S., Lasken, R. S., and Williamson,
S. J., 2011, Single virus genomics: a new tool for virus discovery: PLoS ONE, v.
6, no. 3, p. e17722.
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79

Chapter 2: Candidate Phylum TM6 Genome Recovered from a Biofilm
Provides the First Genomic Insights into this Uncultivated Phylum
(McLean et al. 2013)

Jeffrey S. McLean
1,7*
, Mary-Jane Lombardo
1
, Jonathan H. Badger
1
, Anna Edlund
1
, Mark
Novotny
1
, Joyclyn Yee-Greenbaum
1
, Nikolay Vyahhi
2
, Adam P. Hall
1
, Youngik Yang
1
,
Chris L. Dupont
1
, Michael G. Ziegler
3
, Hamid Chitsaz
4
, Andrew E. Allen
1
, Shibu
Yooseph
1
, Glenn Tesler
5
, Pavel A. Pevzner
2,6
, Robert Friedman
1
, Kenneth H. Nealson
1,7
,
J. Craig Venter
1
and Roger S. Lasken
1

1
Microbial and Environmental Genomics, J. Craig Venter Institute, San Diego, CA, USA
2
Algorithmic Biology Laboratory, St. Petersburg Academic University, Russian
Academy of Sciences, St. Petersburg, 194021, Russia
3
Department of Medicine, University of California, San Diego, California, USA.
4
Department of Computer Science, Wayne State University, Detroit, Michigan, USA.
5
Department of Mathematics, University of California, San Diego, La Jolla, California,
USA.
6Department of Computer Science, University of California, San Diego, La Jolla,
California, USA.
7Department of Earth Sciences, University of Southern California, Los Angeles, CA,
USA

*Corresponding Author: Jeffrey S. McLean jmclean@jcvi.org
80

Abstract
The “dark matter of life” describes microbes and even entire divisions of bacterial phyla
that have evaded cultivation and have yet to be sequenced. We present the first genome
from the globally distributed but elusive Candidate Phylum TM6 and uncover its
metabolic potential. TM6 was detected in a biofilm from a sink drain within a hospital
restroom by analyzing cells using a highly automated single cell genomics platform. We
developed an approach for increasing throughput and effectively improving the
likelihood of sampling rare events based on forming small random pools of single flow
sorted cells, amplifying their DNA by multiple displacement amplification (MDA) and
sequencing all cells in the pool, creating a “mini-metagenome”. A recently developed
single-cell assembler, SPAdes, in combination with contig binning methods, allowed the
reconstruction of genomes from these mini-metagenomes. A total of 1.07 Mb was
recovered in seven contigs for this member of TM6 (JCVI TM6SC1), estimated to
represent 90% of its genome. High nucleotide identity between a total of three TM6
genome drafts generated from pools that were independently captured, amplified and
assembled provided strong confirmation of a correct genomic sequence. TM6 is likely a
gram-negative organism and possibly a symbiont of an unknown host (non-free living) in
part based on its small genome, low GC-content and lack of biosynthesis pathways for
most amino acids and vitamins. Phylogenomic analysis of conserved single copy genes
confirms that TM6SC1 is a deeply branching Phylum.

81

Introduction
Bacteria that have not been obtained by conventional culturing techniques are the central
target of single-cell sequencing (Lasken, 2012b), which is accomplished using Multiple
Displacement Amplification (MDA) (Dean et al., 2002; Dean et al., 2001; Hosono et al.,
2003; Raghunathan et al., 2005) of genomic DNA to obtain sufficient template. In this
study, we applied a high throughput strategy to capture and sequence genomes of bacteria
from a biofilm in a hospital sink including novel and uncultivated members. Despite the
fact that a typical person spends approximately 90 percent of their time indoors (Klepeis
et al., 2001), our knowledge of the microbial diversity of the indoor environment has only
recently begun to be explored using culture-independent methods (Hospodsky et al.,
2012; Tringe et al., 2008). Biofilms within water distribution systems in particular are
thought to be diverse microbial communities and potential reservoirs of disease-causing
organisms in the indoor environment. Several pathogens including Escherichia coli,
Legionella pneumophila (Declerck, 2010; Giao et al., 2011; Murga et al., 2001; Walker et
al., 1993), Vibrio cholerae (Shikuma and Hadfield, 2010) and Helicobacter pylori (Linke
et al., 2010; Percival and Thomas, 2009) have been detected in biofilms within water
distribution systems. A recent 16S rRNA gene molecular survey also revealed significant
loads of Mycobacterium avium in showerhead biofilms (Feazel et al., 2009a). Based on
these findings indoor environments can clearly serve as significant reservoirs of
pathogenic bacteria and therefore there is great interest to investigate the rare and
abundant bacterial type species within biofilms in these environments.
82

One approach to capture uncultivated bacteria is to isolate single bacterial cells by
fluorescent activated cell sorting (FACS). The DNA of the sorted cells can then be
amplified by MDA and screened for the presence of amplified bacterial genomes,
typically by PCR and sequencing of the 16S rRNA gene (abbreviated henceforth as 16S
unless otherwise stated) (Raghunathan et al., 2005). However, environmentally derived
biofilms pose particularly difficult challenges since they can contain low overall cell
numbers and there are abundant inorganic and organic particulates present, which can
contribute fluorescent signals that can be mistaken for bacteria. Less than 1% of the
single cell MDA reactions initially attempted in pilot studies were positive for 16S
sequences and therefore discovery of rare species was not statistically favored. Indeed, if
a rare bacterial species ‘X’ represents just 0. 1% of cells in a sample then sequencing one
thousand randomly selected cells would result in only a 37% chance of capturing a single
cell from that particular species. However, if one generates pools consisting of one
hundred randomly selected single cells (mini-metagenome), then ten pools would be
sufficient to capture a cell of interest at the same probability within one of the pools but
more economically. However, this strategy, while attractive, faces two computational
challenges: (i) assembling a mini-metagenome consisting of up to one hundred genomes
with highly non-uniform coverage, and (ii) identifying contigs from the species of
interest within a metagenome with high confidence. In this paper we mainly focus on the
latter challenge as well as on the experimental techniques for generating mini-
metagenomes from biofilms. The former computational challenge is addressed with
simulated and real single cell datasets in a separate publication (Nurk et al., 2013a). By
83

flow-sorting pools of one hundred fluorescent detection events into 384 well plates,
approximately 19-60% were positive for bacterial DNA based on 16S compared to the
1% success rate previously obtained. Since there is no accurate way to determine the
total number of cells that were in the original pool, the number of cells that lysed with the
single lysis method employed, and the number of resulting genomes successfully
amplified, several species are assumed to be pooled together. Shotgun metagenomic
sequencing of very highly diverse communities combined with methods for assembly and
binning contigs have previously revealed genomes of uncultivated organisms including
acidophilic members from a low complexity biofilm community (Tyson et al., 2004),
symbionts (Woyke et al., 2006), rumen host-associated organisms (Hess et al., 2011),
Marine group II Euryarchaeota (Iverson et al., 2012; Rusch et al., 2010) as well as the
Candidate Division (CD) designated WWE1 (Pelletier et al., 2008). In addition,
approaches leveraging metagenomic and single cell data sets have enabled reconstruction
of genomes from uncultivated marine organisms (Dupont et al., 2012; Woyke et al.,
2009). While recently developed assemblers (Chitsaz et al., 2011) enhance sequencing of
single cells, assembly of our MDA-obtained metagenomes (of up to one hundred
different bacterial species in this case) is a more difficult computational problem,
particularly since mixed cells feature even more non-uniform read coverage as compared
to single cell sequencing, for example, due to differing GC content between species
(Abulencia et al., 2006; Yilmaz et al., 2010).

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A strategy we refer to as a mini-metagenomic approach was therefore employed to
increase the likelihood of capturing low abundance bacteria. Partial 16S sequences
representing a member of the Candidate Phylum TM6 were recovered from three
different wells from a MDA amplified pool of one hundred events. The Candidate Phyla
TM6 and TM7 were first identified by Rheims et al. (Rheims et al., 1996) based on
culture independent molecular surveys, and appear to include common, low abundance
members of microbial communities in diverse environments including domestic water
sources. A new assembly tool, designed for coping with the wide variations in coverage
from MDA samples, SPAdes (Bankevich et al., 2012b), was used to assemble the mini-
metagenomes. Computational strategies were used to reconstruct and bin contigs
representing genomes of individual species from the mixed genomes similar to published
metagenomic methods, revealing a near complete genome for TM6. Single cell whole
genome amplification techniques have previously allowed partial recovery of genomes
from several elusive CD organisms: TM7 (Marcy et al., 2007; Podar et al., 2007), OP11
(Youssef et al., 2011b), and Poribacteria symbiotically associated with marine sponges
(Siegl et al., 2011). In contrast, complete genomes of the Elusimicrobia (previously
named Termite Group 1 division) were recovered from amplification of pooled clonal
single cells (Hongoh et al., 2008b). The mini-metagenomic approach in combination
with single cell assembly tools and contig binning methods that we employed here,
resulted in the recovery of 1.07 Mb of a TM6 genome (TM6SC1) within only seven
contigs. Analysis of core single copy marker genes from these contigs resulted in a
conservative estimate of 91% recovery for this TM6 genome. High nucleotide identity
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between a total of three TM6 genome drafts generated from pools that were
independently captured, amplified and assembled provided strong confirmation of a
correct genomic sequence. From the genomic information available, this TM6 is likely a
gram-negative and facultatively anaerobic representative. Based on its small genome,
AT-bias and apparent lack of biosynthetic capability for most amino acids and vitamins,
it may represent an obligate community member or symbiont of an unknown host.

Results
Sampling and Sorting Cells from Biofilm Samples
We modified our single-cell genomic methods developed for marine samples (Chitsaz et
al., 2011; Dupont et al., 2012) and healthy human microbiome samples (gastrointestinal,
oral, and skin) (Fodor et al., 2012; Lasken, 2012b) to acquire microbial genomes from
biofilms in the indoor environment. The marine derived samples contained relatively
high bacterial content and were a rich source of single cells for FACS isolation and
genomic sequencing, with about 20-30% of single cell amplifications yielding a positive
16S (Chitsaz et al., 2011; Dupont et al., 2012). In contrast, FACS analysis of the
untreated biofilm samples from this environment (indoor surface) was more challenging
to analyze due to: 1) the presence of autofluorescent non-bacterial particles that produced
elevated background signals and 2) difficulties in disrupting the intact biofilm to access
individual cells. The typical success rate for capturing single cells from these difficult
indoor environmental samples was roughly 1% (wells yielding a positive 16S). To
address these issues, the biofilm sample was vortexed, filtered through a 5 µm filter, and
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concentrated to purify the bacterial fraction within a Nycodenz gradient (Materials and
Methods) prior to FACS and DNA amplification by MDA. This processing raised the
number of FACS positive DNA stained events within the bacterial size-range to roughly
20% and the overall success rate of bacterial cell sorting to 18% based on sequencing of
16S PCR products derived from the MDA reactions. From the high-fluorescence gate,
we sorted single events into a total of 416 wells and in order to more fully capture the
bacterial diversity as well as increase the odds of capturing low abundant species in the
sample, we also sorted multiple events into wells. A total of 128 wells received 20 events
and another 128 wells received 100 events. In addition, 32 wells from a low-fluorescence
gate in a defined forward scatter size range received 100 events (SI Appendix C, Figure
S2-1). The overall success rate for a positive 16S sequence increased to 60% in these
high fluorescent multi-event wells and 19% for the wells that received low fluorescent
events.

Plates containing the sorted events were processed on an automated high throughput
single cell platform (Materials and Methods, SI Appendix C, Figure S2-2) to amplify
genomes by MDA and screen the amplified DNAs for 16S sequences. The relative
abundance of various genera found in the 100 cell low-fluorescent sort as well as a
parallel high throughput single cell and multiple cell sort from the high fluorescent
population from this same sink biofilm sample are shown in Figure 1. Across all gated
events, 232 total 16S sequences were identified from 23 different genera within the
domain Bacteria. Some of the most highly detected genera such as Acinetobacter and
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Sphingomonas are consistent with those found in microbial communities associated with
drinking water distribution systems (Feazel et al., 2009a; Pavissich et al., 2010; Revetta
et al., 2010). From the 32 wells with 100 low fluorescent events in each, 6 wells
produced a 16S sequence. Two wells contained an unclassified member of the genus
Spirosoma at 91% sequence identity and 1 well a member of genus Afipia (97% identity).
The final three wells contained nearly identical sequences (>99.5%) which had a
maximum level of sequence identity to a previously deposited clone (GenBank accession
belonging to the CD TM6 at ~94% identity (GenBank accession GU368367). The 3
amplified DNAs containing the partial TM6 16S sequences were sequenced on the Roche
454 and Illumina GAIIx platforms.

Genome Assembly and Contig Identification
Three different assembly approaches were used to obtain the assemblies for these
genomes. Due to the fact that there was no close reference genome for distantly related
CD TM6, both unsupervised and supervised contig classification and binning approaches
were then needed to confidently identify those contigs belonging to this organism. For
assembly, we employed one assembler designed for sequences of cultured cells (CLC,
http://www.clcbio.com) and two assembly tools specifically designed for data generated
from single cell MDA reactions: Velvet-SC (Chitsaz et al., 2011) and SPAdes
(Bankevich et al., 2012b) (Table 1). Previous studies (Bankevich et al., 2012b; Chitsaz
et al., 2011) demonstrated that the Velvet-SC and SPAdes assemblers are significantly
better than Velvet (Zerbino and Birney, 2008) and SoapDenovo (Li et al., 2010) in
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assembly of single cell datasets because they are able to cope with the wide variations in
coverage characteristic of MDA samples. SPAdes was further designed to cope with the
elevated number of chimeric reads and read pairs characteristic of single cell assemblies.
For this TM6 study, more complete assemblies were obtained with SPAdes than with
Velvet-SC or CLC by most assembly metrics (Table 2-1). The SPAdes TM6 assembly
showed remarkably superior results with respect to N50 and longest contig size (Table 2-
1) and since this software has a low rate of assembly errors (Bankevich et al., 2012b), it
was chosen for this study.

A 273 kb contig in TM6 MDA2 with an average GC content of 36% contained a 16S
rRNA gene with a flanking 23S. Taxonomic affiliations of the predicted protein
sequences derived from this contig were assigned using APIS (Badger et al., 2006) which
generates a phylogenetic tree for each ORF in a genomic or metagenomic sample using
homologous proteins from complete genomes. APIS classifies each ORF taxonomically
and functionally based on phylogenetic position. APIS trees showed that the majority of
ORFs of this 273 kb contig were very distantly related to any sequenced genome,
consistent with the contig belonging to an uncharacterized organism such as CD TM6.

An independent metagenomic binning approach using an autonomous method, Principal
Coordinate Analyses (PCA) of the penta-nucleotide frequency, followed by k-means
clustering, revealed a small grouping of contigs clustered near the putative TM6 contig.
In a second independent approach for taxonomic classification of the contigs, MGTAXA,
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software that performs taxonomic classification of metagenomic sequences with machine
learning techniques (http://andreyto.github.com/mgtaxa) http://mgtaxa.jcvi.org, was used
to classify the contigs. MGTAXA, which is also fundamentally based on the frequency of
kmers, can allow users to input sequences as training sets to then further classify their
own metagenomic sequences. Using the putative TM6 16S and 23S rRNA gene-
containing contig as a training sequence, MGTAXA identified contigs (contigs of length
>300 bp) with similar taxonomic classification (SI Appendix C, Figure S2-3).
Taxonomic affiliations of the non-TM6 contigs were dominated by Bacteriodetes
(Sphingobacteriales) and Flavobacteriaceae (Chryseobacterium) (SI Appendix C,
Figure 3). Contigs identified as TM6 from the intersection of these independent
approaches sharing a GC content of 36 +/- 2% were chosen as the final set. Each
approach was in general agreement for the final contig set that was originally identified
using MGTAXA, providing confidence in the final contigs chosen. The nucleotide
frequency approach identified 8 additional contigs compared to MGTAXA but these
either deviated slightly from the expected GC (within +/-5%) or were classified as
belonging to Bacteriodetes by MGTAXA and/or APIS and were therefore excluded from
the set. In the case of uncultivated genomes where there is no closely related reference
genome, the identification and use of contigs containing a marker gene for the genome of
interest (such as the 16S rRNA gene) is helpful to guide nucleotide frequency binning
and critical for the effective grouping of contigs.

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All three amplified TM6 SPAdes assemblies were processed as described above to yield
draft TM6 genomes. MDA2 contained the largest TM6 assembly with 1,074,690 bp
contained in 7 contigs (Figure 2). Comparative genomic analyses on the three sets of
contigs using ProgressiveMauve (Darling et al., 2010) and LAST alignments (Frith et al.,
2010) confirmed that the assembled contigs of MDA1 and MDA3 were contained within
MDA2 with highly conserved synteny (SI Appendix C, Figure S2-4). At the nucleotide
level, BLASTN comparisons on the concatenated contigs representing genome MDA2
(Figure 2) indicate nearly identical assemblies. To further confirm the agreement
between the MDA1, 2 and 3 TM6 contigs, all reads for each MDA were mapped to the
MDA2 genome and SNP analyses performed. The MDA2 TM6 genome recruited 33%
(5.8 of 15M), 64% (16.8 of 26M) and 70% (16.9 of 24M) of the reads for MDA1,
MDA2, and MDA3 respectively (SI Appendix C, Figure S2-5). At a minimum 10×
coverage and a cutoff at 50% frequency, there were fewer than 20 SNPs identified (SI
Appendix C, Figure S2-5). Unless stated, further analyses focus on only the assembled,
binned and annotated TM6 contigs from MDA2 (designated as TM6SC1).

Genome General Features
The coding density of the TM6SC1 assembly is relatively high at 89% (CDS and RNA)
and there is excellent fit to the expected number of CDS per genome size (based on plots
of predicted CDS per genome size) (SI Appendix C, Figure S2-6). Analysis of
conserved single copy marker genes using a set of 111 genes (Dupont et al., 2012)
revealed that the assembly includes 101 of 111 genes (SI Appendix C, Table S2), and
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thus a conservative estimate of genome completeness is 91% for this TM6 genome. In
the recent single cell genomic study describing a partial genome of 270 kb for CD OP11
(designated ZG1) (Youssef et al., 2011b), roughly 45% of the 423 protein-coding genes
had no function prediction (27% of those with no prediction were conserved hypothetical
proteins with similarities in the databases, and 72% were hypothetical proteins unique to
the ZG1 genome). The authors noted that several candidate division genomes shared a
similar percentage of protein-coding annotated genes, e.g., 54% for CD TM7 (Marcy et
al., 2007) and 48% for CD WWE1 (Pelletier et al., 2008) compared to Escherichia coli
K-12 (14%) and Bacillus licheniformis ATCC 14580 (27%) (Youssef et al., 2011b). ZG1
also has the lowest percentage of protein-coding genes assigned to clusters of orthologous
groups (COGs; 41%) relative to genomes of other CDs (e.g., 53% for TM7, 63% for
WWE1, and 80% for Elusimicrobia). The TM6 genome (TABLE 2-2) also has a low
percentage of functionally annotated genes (43%) with 34% of these assigned to COGs.
Based on these studies, it is clear that single cell sequencing techniques can tap into
diverse novel genomes with few similar ORF matches in existing databases, greatly
expanding the known diversity.

Phylogenetic and Phylogenomic Analyses of Candidate Phylum TM6.
A large number of TM6 related 16S rRNA gene sequences have been identified from
geographically varied sampling sites (Figure 3a and high resolution SI Appendix C,
Figure S2-7), which suggests that this Phylum has a cosmopolitan distribution, although
typically found at low relative abundance. Its ecological distribution (derived from
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published and unpublished studies which deposited related sequences in GenBank)
includes domestic water sources (Feazel et al., 2009a; Henne et al., 2012; Pavissich et al.,
2010), acidic cave biofilms, acid mine drainage biofilms (Lear et al., 2009), wastewater
biofilms (Kwon et al., 2010), soil, contaminated groundwater and subsurface sites (Fields
et al., 2005; Lin et al., 2012), aquatic moss, hypersaline mats, peat bogs and peat swamps
(Dedysh et al., 2006; Rheims et al., 1996). These and additional environments where
TM6 was detected in 16S rRNA gene clone libraries including a number of biofilm
related samples are highlighted in SI Appendix C, Table S1. Notably, only a few TM6
sequence signatures have so far been identified as associated with a human host
(Maldonado-Contreras et al., 2011). We designate the clade that our TM6 16S fell within
as TM6 Clade I (Figure 3a and 3b) since it also includes the 16S from a peat bog clone
library that led to the designation of TM6 (Rheims et al., 1996). The name is derived
from “Torf, Mittlere Schicht " (“peat, middle layer") (Rheims et al., 1996) (Figure 3b).
Candidate division TM7 was also first designated based on a sequence from that clone
library. Interestingly, at the time of this study, the closest sequence in the NCBI nr
database to our assembled TM6 genome is from a biofilm in a corroded copper water
pipe (GU368367) (Pavissich et al., 2010) (Figure 3b). Several studies indicate that TM6
16S sequences are commonly detected in biofilms from domestic water systems (SI
Appendix C, Table S1). Five such sequences were discovered along with potential
opportunistic pathogens in showerhead biofilms (Feazel et al. 2009). A recent study of a
more than 20 year old drinking water network that compared bacterial core communities
in bulk water and associated biofilms revealed that the biofilm samples contained a
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unique community with no overlapping phylotypes with the bulk water samples (Henne
et al., 2012). TM6 represented 11% of the clones observed in these biofilms. Given the
occurrence of TM6 organisms in biofilm communities and their apparent enrichment in
biofilm samples, it is interesting to speculate that they may play a role in biofilm
development or be dependent upon communal living.

The number of identified candidate phyla within the domain Bacteria has grown from the
11 that were recognized in 1987 (Woese, 1987), to 26 in 1998 (Hugenholtz et al., 1998),
to the most recent list of around 30 (http://www.arb-silva.de) (Pruesse et al., 2007). Early
phylogenetic identification of the bacterial candidate phyla, including TM6, was
accomplished using 16S rRNA gene phylogeny (Hugenholtz et al., 1998; Hugenholtz et
al., 2001; Rappe and Giovannoni, 2003). In these studies, the TM6 sequences available
did not find phylogenetic congruence with existing divisions, and was designated as a
Phylum level candidate division. Our goal was to resolve its phylogenetic position with
the genome information now available. For this purpose we employed the automated
pipeline for phylogenomic analyses (AMPHORA2) that uses multiple marker gene
analysis (Wu and Eisen, 2008; Wu and Scott, 2012). We began with a set of 29 genes
(out of 31 supported by AMPHORA2) that could be identified in the TM6 genome and
that were previously chosen based on their universality, low copy number, phylogenetic
signal, and low rates of horizontal gene transfer (Wu and Eisen, 2008). These sequences
were aligned and compare by using the AMPHORA2 seed alignment through HMM. We
masked the resulting alignments to remove poorly conserved regions using the
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AMPHORA2 supplied masks, and concatenated them together to serve as input to Phyml.
The resulting phylogeny showed that the TM6 sequences that were obtained in this study
were representative of a deep branching Phylum that claded closest to the Acidobacteria
and Aquificae Phyla (Figure 4 and SI Appendix C, Fig.S9). A similar topology, where
16S rRNA genes representative of TM6 were clading closest to the Acidobacteria
Phylum was also observed when using the SSU- Align program (Nawrocki, 2009) (SI
Appendix C, Fig.S8). Its other closest 16S rRNA gene-neighbor represent the
Elusimicriobim Phylum (earlier named Candidate Phylum Termite group I) (SI Appendix
C, Figure S2-8) which is in line with previous 16S rRNA gene analyses (Hugenholtz et
al. 2001; Rappe and Giovannoni, 2003). However, due to the more robust AMPHORA2
analyses where multiple marker genes with phylogentic signals were used, we propose
that the TM6 Phylum is most closely realted to the Acidobacteria and Aquificeae Phyla.
Additional genomes will be needed to further refine this phylogenetic position.
Pathways and Processes
Due to the distant homology of TM6 proteins with existing genomes, only 43% of the
protein coding regions were functionally annotated (428 genes). As with many genomes
of uncultivated species using single cell genomic techniques, it is still possible to gain
insight into the predicted metabolic abilities of TM6SC1 using the captured genomic
information. In our case having an estimated 91% of a genome, we are still cautious in
our interpretation. The fact that we generated three separate sets of contigs that are nearly
identical at the nucleotide level provides additional confidence in the functional
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interpretations based on presence or absence of key genes and operons but does not rule
out the possibility that we are missing these from the three assemblies.

Cell Wall biogenesis and Pili
TM6SC1 contains evidence for a gram-negative envelope including outer membrane
proteins Omp18, YaeT, RomA, OmpH and outer membrane related genes homologous to
the Type II general secretion pathway (gspD, gspE, and gspG) as well as murJ
(peptidoglycan lipid II flippase). There were very few genes that gave an indication of a
possible phenotype but there is some evidence that this organism may form a type of
spore as it contains genes with homology to sporulation, SpoIID/lytB domain and SpoVG
(SI Appendix C, Table S3). An ability to form a spore-like feature (such as an
endospore) is consistent with our enrichment of these organisms in the small, weakly
fluorescent population (spores are typically of low fluorescent profile). Only one gene
with homology to flagellar genes was found (fliC), indicating this organism may not be
motile via flagella, although it is possible that it could be motile via a gliding motility
using the type IV pili related genes (pilA and pilB). The genome also encodes a sigma
factor 54 which is a central transcriptional regulator in many bacteria and has been linked
to a multitude of processes like nitrogen assimilation, motility, virulence (host
colonization) and biofilm formation (Francke et al., 2011).
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Energy Production and Conservation
The predicted metabolic pathways of TM6SC1 are shown in Fig 5. The genome
retains a non-catalytic glycoside binding protein, a lectin B chain and a β-glucosidase,
suggesting that TM6 can bind complex carbohydrates and perform extracellular
hydrolysis of the cellulose derived disaccharide cellobiose. Cellobiose can enter the
bacterial cell either via a phosphoenolpyruvate-dependent phosphotransferase system or
via protein-dependent ATP-binding cassette (ABC) transporters; the latter were identified
at several locations in the genome. β-glucosidase acts on the glucose- β(1,4)-linkage in
cellobiose which results in the production of β–D-glucose, the unphosphorylated
substrate needed for the pentose phosphate pathway (PPP) for which the genome harbors
all enzymes. A putative sodium dependent bicarbonate transporter and a carbonic
anhydrase (E.C. 4.2.1.1), responsible for inorganic carbon (CO
2
) uptake (Dagnall and
Saier, 1997) and rapid interconversion of carbon dioxide and water to bicarbonate and
protons, respectively, were also identified. Potentially the latter two are used to remove
CO
2
produced by the oxidative arm of the pentose phosphate pathway and prevent
acidification of the cytoplasm. Alternatively, a pathway for autotrophic carbon fixation
unique to TM6 is possibly present. These features reflect an unexpected mixotrophic
lifestyle that is unusual for bacteria with small genomes sizes (Yooseph et al., 2010). The
genome contains only one enzyme from the glycolysis pathway, a phosphoglycerate
mutase (PGM) that catalyzes the conversion of 3-phosphoglycerate to 2-
phosphoglycerate. It lacks any identifiable tricaboxylic acid cycle-enzymes but has a
modified electron transport chain consisting of a F-type ATPase synthase, a DsbD
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cytochrome (a protein-disulfide reductase) and five thioredoxin reductases that
previously were identified as essential for aerobic growth of facultative anerobic bacteria
(Serata et al., 2012). Thioredoxin reductases are known for acting on sulfur groups of
electron donors with NAD
+
or NADP
+
as acceptors and for shuffling electrons from
cytoplasm to periplasm without involvement of additional co-factors such as quinone. A
demethylmenaquinone was identified which functions as a reversible redox component of
the electron transfer chain, mediating electron transfer between hydrogenases and
cytochromes in anaerobic conditions however both of these proteins are absent. Another
gene that indicates TM6’s potential to grow anaerobically is the nifU gene that is
responsible for Fe-S cluster assembly and functions as an electron transfer component.
Although no other nif genes were identified and hence nitrogenase activity, which
requires additional genes (e.g. NifH, NifS, NifV) is unlikely. Instead, it seems likely that
Fe-S clusters are synthesized for HemN, the only protein that appears to use this cofactor.
The remainder of the heme synthesis pathway appears to be missing, thus hemN may be
used to scavenge porphyrins. Also, a V-type pyrophosphate-energized proton pump that
can generate a proton motive force (PMF) or utilize existing PMF to drive pyrophosphate
synthesis such as in acidic environments is present. The genome harbors manganese and
iron superoxide dismutases but no catalase for H
2
O
2
degradation. However, it has two
peroxidase enzymes, which derive electrons from NADH
2
to reduce peroxide to H
2
O.
The genome also encodes a putative copper ion transporter ATPase for copper efflux,
which also can prevent oxidative stress in aerobic conditions. Together, these features
suggest that TM6SC1 is a facultative anaerobic bacterium able to generate energy from
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organic carbon sources with a modified electron transport chain adapted both for aerobic
and anaerobic conditions.

Biosynthesis of Amino Acids, Nucleotides and Coenzymes
Evidence for the capacity for de novo synthesis of amino acids is currently absent in the
assembled genome since it only contains a few enzymes (e.g. glycine hydroxymethyl
transferase) that can be employed to synthesize serine, glycine and cysteine only from
intermediate metabolites (Fig 5). Also, a glutamin-fructose-6-phosphate transaminase is
present and has the potential to catalyze the formation of glutamate. Several amino
peptidases that catalyze the cleavage of amino acids of proteins or peptide substrates
were identified (e.g. PepA, pepP, methionyl aminopeptidase,). In addition, cotransporting
proteins such as sodium/proline and sodium/alanine symporters were present suggesting
that proline and alanine can be imported from the environment. Taken together, these
results suggest that TM6 relies on energy saving salvatory pathways including peptidase
activity and amino acid import. The same pattern is observed for pyrimidine and purine
biosynthesis as the required enzymes for synthesis of the building block inosine 5’-
monophosphate (IMP) from inosine are absent, which excludes the potential for de novo
synthesis. However, several enzymes with the capacity to both catabolize and synthesize
ADP, GDP, ATP, GTP, dATP and dGTP are present. The coenzymes vitamin K epoxide
reductase which is involved in vitamin K recycling, the NADP+ reducer Glycerol-3-
phosphate-NADP dehydrogenase and the pyridoxamine 5'-phosphate oxidase, the latter
catalyzes the biologically active form of vitamin B
6
, were identified as electron carriers.
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The genome also harbors a symporter for sodium/panthothenate (Vitamin B
5
) showing
that this essential vitamin unlikely to be synthesized de novo as in many other bacterial
species (Genschel, 2004).

We also applied less stringent comparison rules than were applied by the automated
annotation to address the unclassified ORFs. We manually re-annotated representative
“unclassified” ORFs from MG-RAST that were located around interesting gene
signatures and might possibly correspond to horizontal gene transfer events (e.g., phage
signatures and short sequence repeats) (SI Appendix C, Table S3). The amino acid
sequences of these ORFs were manually compared to existing protein sequences by using
BLASTP with a low stringency. This resulted in identification of several genes coding
for proteins broadly classified as involved in virulence. In addition, a number of potential
Archaeal genes were also identified (SI Appendix C, Table 4).

Discussion
We have demonstrated a mini-metagenomic approach based on single cell
genomic sequencing methodology, to be employed with deep sequencing and
downstream assembly methods optimized for MDA samples, with the aim of
reconstructing genomes from small pools of cells. Using this approach, a near complete
genome of a member of the low abundant yet globally distributed Candidate Phylum
TM6 was recovered in a biofilm from a hospital sink. Previous studies have identified
TM6 using a phylogenetic marker (16S rRNA gene) in a number of diverse environments
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with a global distribution. They appear to be low abundance members of many microbial
communities including being found in domestic water sources such as drinking water
distribution systems (Henne et al., 2012) and showerhead biofilms (Feazel et al., 2009a).
At the time of this study, the closest sequence in the NCBI nr database to our assembled
TM6 genome is from a biofilm in a corroded copper water pipe (GU368367) (Pavissich
et al., 2010) supporting our discovery of this organism in a sink drain biofilm.

From one of our pooled samples, we reconstructed a draft genome (1.07 Mb in 7 contigs)
for this species of TM6 that we designated TM6SC1. As with many genomes of
currently uncultivated organisms that have been recovered with single cell or
metagenomic approaches, the genome of TM6 presented here is only a portion of the
whole and this makes interpretations difficult to confirm or refute without more genomes
or an actual isolate. The near perfect nucleotide identity between a total of three
independently amplified and assembled samples however, provided strong confirmation
of a correct genomic sequence for the portion of the genome recovered. An analysis of
the conserved single copy genes using a set of 111 genes (Dupont et al., 2012) revealed
that the assembly included 101 of the 111 genes, and thus an estimated 91% recovery of
the TM6 genome.

From the available genes that were assembled and functionally annotated, TM6 is
cautiously a gram-negative and facultative. The predicted genome size of TM6 falls
within the range of some of the smallest sequenced bacteria which are predominantly
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symbionts. This raises the question as to whether TM6 might be a free living organism
or it has formed a symbiotic relationship with unknown host. The TM6 genome is in
general agreement with some of the characteristics of symbionts reported to date. These
features include reduced genome size, adenine–thymine (AT) bias, and loss of
biosynthetic pathways (Andersson and Kurland, 1998; Moran and Wernegreen, 2000). In
particular, amino acid biosynthetic genes are often lost in obligate symbiotic bacterial
genomes (obligate host pathogens and obligate endosymbionts) (Andersson and Kurland,
1998; Schmitz-Esser et al., 2010; Yu et al., 2009), where amino acids are obtained from
the host environment.

Several additional lines of evidence point towards TM6 as possibly having a
symbiotic lifestyle. The phylogenetic affiliations of roughly 10% of the CDSs had best
hits to known facultative symbionts or obligate symbionts including Parachlamydia
acanthamoebae, Candidatus Protochlamydia Amoebophilia (Chlamydiae), Candidatus
Ameobophilus asiaticus, Legionella, Francisella (Gammaproteobacteria) Rickettsia
(Alphaproteobacteria), Borrelia (Spirochaetales), and Planctomyces
(Planctomycetaceae). (SI Appendix C, Table S5). A recent study reported that an
obligate endosymbiont, Candidatus Ameobophilus asiaticus, of a free living amoeba, also
lacking almost all amino acid biosynthesis pathways, contains a large fraction of proteins
with eukaryotic domains (Collingro et al., 2011; Schmitz-Esser et al., 2010). It was
demonstrated that these domains are also significantly enriched in the genomes of other
amoeba associated bacteria (including Legionella pneumophilia, Ricksettsia bellii,
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Francisella tularensis and Mycobacterium avium). TM6 also contains several of these
eukaryotic domains within predicted coding regions such as ankyrin repeats (8
identified), an F-box domain protein, a tetratricopeptide repeat TPR_1 and WD-40
repeats (5 identified). Based on the fact that TM6 remains uncultivated to date despite
being observed globally and across diverse environments also suggests a host such as a
free-living amoeba. Such bacteria would likely only yield to cultivation if it was co-
isolated with the host species for which it is symbiotically associated. Amoebas are also
well known to be globally distributed across diverse environments (Horn and Wagner,
2004; Schmitz-Esser et al., 2010) and could explain the distribution of TM6. In relation
to where we recovered this TM6 genome, often studies of hospital water networks (taps
and showerheads) yield many amoeba and their associated bacteria (Guindon and
Gascuel, 2003; Nurk et al., 2013a). These studies are mainly conducted to evaluate the
role of pathogenic amoeba-associated bacteria such as Legionella and Parachlamydia in
hospital acquired infections. A direct report of finding TM6 related 16S rRNA gene
signatures associated amoeba hosts was not found in our investigations. So far, in terms
of host related systems, they have been reported as part of the consortia of bacteria
intimately associated with marine sponges (Siegl et al., 2011). In absence of direct
evidence, further detailed work is needed to determine the association, if any exists,
between members of TM6 and eukaryotic hosts.

Overall, the genomic information presented here may help guide cultivation
efforts and efforts to further elucidate the function and ecology for this organism. Further
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application of this approach in other environments may greatly increase the likelihood of
capturing and assembling genomes of elusive, low abundance microorganisms that
continue to remain unyielding to culturing approaches.

Methods
Isolation of Bacterial Cells from Sink Material. Sink drain samples were collected
with sterile cotton tipped swabs from a publicly accessible restroom adjacent to an
emergency waiting room. The initial sample was fixed with ethanol and vortexed briefly
for 20 sec (SI Appendix C , Figure S2-1). The sample was filtered through a 35 µm
filter. A two ml cushion of prechilled Nycodenz gradient solution (Nycoprep Universal,
Axis Shield) was placed in a 17 ml ultracentrifuge tube, and 6 ml of supernatant was
placed gently over the Nycodenz cushion. The pair of balanced tubes was centrifuged at
9000 rpm for 20 min at 4 °C in an ultracentrifuge SW32.1 rotor. The visible cloudy
interface containing the bacterial cells was collected gently, and mixed by inversion to
create a suspension.

Sorting of Single Cells by Flow Cytometry. Single cell sorting was performed on a
custom FACS Aria II equipped with a 100 mW 488 nm laser, forward scatter
photomultiplier tube (FSC-PMT) 488 nm filtered, side scatter (SSC-PMT) 488 nm
filtered, and green fluorescence (SYBR-PMT) 512 nm filtered detectors (BD
Biosciences, San Jose, CA). An Olympus IX70 inverted epifluorescent microscope was
used to visualize bacterial cells, and also in detecting fluorescent beads used for the
104

sorting setup in targeting a 384-well PCR plate. After confirmation of accurate targeting
of sorted beads, single cell sorting proceeded. FACS detection was performed on the
Nycodenz fractionated bacterial enriched sample. Filter sterilized (0.2 µm) phosphate
buffered saline (PBS, 1×) was used as sheath fluid, and for sample dilution. Unstained
and SYBR Green I (0.5×) stained material was 35 µm filtered and a 1:1000 dilution was
assessed for event rate at low flow rate (<2,000 total events/sec) and adjusted if
necessary. A low flow rate is critical to reduce the likelihood of sorting coincident
events. Fluorescent events were collected using 549V FSC-PMT, 220V SSC, and 700V
SYBR Green Channel, with thresholds of 200, 200, and 500 respectively, with no
compensation. Bi-exponential scatter plots were generated using FSC-PMT vs. SSC and
FSC-PMT vs. SYBR Green (SI Appendix C, Figure S2-1). Unstained material had few
or no green fluorescent events (SI Appendix C, Figure S2-1b). 1000 events from each
gate were sorted at a lower purity setting onto glass slides for fluorescent microscopy at
60× magnification to confirm the presence of cellular morphologies. A bead targeting
strategy (see above) was employed to ensure only single cells were sorted from the
biofilm sample into each well of a 384-well PCR plate (FrameStar). Events were sorted
into 4 µL of a low EDTA TE (10 mM Tris 0.1 mM EDTA, pH 8.0) and immediately
frozen on dry ice and held there until transfer to -80C for storage prior to processing.

Multiple Displacement Amplification. MDA of single cell genomes was performed in
a 384-well format using GenomiPhi HY kit (GE Healthcare) using a custom Agilent
BioCel robotic system (outlined in SI Appendix C , Figure S2-2). Briefly, cells were
105

lysed by addition of 2 μl of alkaline lysis solution (645 mM KOH, 265 mM DTT, 2.65
mM EDTA pH 8.0) and then incubated for 10 min at 4
o
C. After lysis, 7 μl of a
neutralization solution (2.8 μl of 1290 mM TrisCl pH 4.5, and 4.2 μl of GE Sample
Buffer) was added, followed by 12 μl of GenomiPhi master mix (10.8 μl of GE Reaction
Buffer and 1.2 μl GE Enzyme Mix) for a reaction volume of 25 μl. Reactions were
incubated at 30 °C for 16 h followed by a 10 min inactivation step at 65 °C. MDA yield
was determined by Picogreen assay. MDAs with yields ≥ 50 ng/μl were set aside for the
purpose of this study as the relationship between yield and MDA quality is unclear. No
template control (NTC) MDA reactions were included to reveal any contaminating
sequences, and processed in parallel through 16S rRNA gene PCR analysis. These
negative controls lacking a sorted cell were run in parallel to determine the relative
amount and identity of contaminating bacterial DNA in the MDA reagents, a necessary
standard practice in single cell genomics due to the highly processive strand displacement
activity of the phi29 DNA polymerase (Allen et al., 2011; Blainey and Quake, 2011;
Woyke et al., 2011). Further amplification of selected MDAs to generate 100-200 μg for
sequencing and archival storage was performed as described above with 150–1500 ng of
the original MDA as template.

PCR and Analysis of 16S rRNAs. Using the Biocel robotics platform processing plates
in 384 well format, 16S rRNA was amplified from diluted MDA product (1:20 into TE)
using universal bacterial primers 27f and 1492r (Lane, 1991) as follows: 94°C for 3 min,
35 cycles of 94°C for 30 sec, 55°C for 30 sec, 72 °C for 90 sec, and 72°C for 10 min. PCR
106

products were treated with Exonuclease I and shrimp alkaline phosphatase (both from
Fermentas) prior to direct cycle sequencing with 27f and 1492r primers at the sequencing
center Joint Technology Center (JTC, J. Craig Venter Institute, Rockville, MD). 16S
rRNA gene tracefiles were analyzed and trimmed with the CLC Workbench software
program (CLC bio, Muehltal, Germany). Sanger read lengths of less than 200 bp were
discarded. Only a minority of 16S rRNA read pairs could form a contig, and in some
cases only the forward or reverse read was used to establish taxonomy. Chromatogram
quality was assessed manually, and MDAs with both forward and reverse reads of poor
quality were excluded from further analysis. MDAs with 16S taxonomy similar to those
in MDA and 16S PCR reactions with no template DNA added were excluded from
further analysis.

All full length 16S rRNA sequences from the three assemblies were 100% identical. A
BLASTN analysis against the SILVA SSU ref NR 102 database (Pruesse et al., 2007)
was performed to classify the 16S sequences taxonomically, and to determine their
relationship to TM6. An additional analysis was performed against public databases to
retrieve neighboring TM6 and related bacterial sequences for generation of 16S rRNA
phylogenetic trees. The 16S rRNA gene phylogenetic tree was created by aligning the
related sequences against the SILVA alignment with mothur 1.19.4 (Schloss et al.,
2009b), triming to eliminate gap only positions, and creating a maximum likelihood tree
with PhyML version 20110919 (Guindon and Gascuel, 2003). A phylogenetic-marker
gene tree was created by using the AMPHORA2 pipeline (Wu and Eisen, 2008; Wu and
107

Scott, 2012). AMPHORA2 uses a hidden Markov model trained on a reference database
of 571 fully sequenced bacterial genomes to identify and align gene sequences belonging
to 31 marker genes. 29 of these genes could be identified in TM6 and was used in the
downstream phylogenic analyses. The 29 genes were rplS, rplB, rpsB, nusA, dnaG,
rpoB, rplM, rpsJ, rplD, rplP, rpsK, rpsI, rplF, rpsS, rpsM, rpmA, rplN, rplT, rpsE, rplL,
rplA, smpB, rplC, rplE, rpsC, frr, infC, tsf and rplK. A single large alignment was
generated by concatenating the masked HMM-generated AMPHORA2 alignments from
29 genes. As the AMPHORA2 alignments contained information from hundreds of
genomes, a phylogenetically representative subset of the alignment was created for
computational feasibility. This alignment was used to create a maximum likelihood tree
with PhyML version 20110919 (Guindon and Gascuel, 2003) using the WAG amino acid
evolutionary model (Whelan and Goldman, 2001).

Library Construction and Sequencing. Illumina sequencing on the GAII platform was
performed on the amplified genomic material using the Genome Analyzer II System
according to the manufacturer’s specifications. Three TM6 MDAs were barcoded and
pooled for a single lane generating reads totaling 23 GB of data and 85 million reads that
passed quality score >20.

Single Cell Assemblies. Assemblies were produced using Velvet-SC (Chitsaz et al.,
2011) and SPAdes (Bankevich et al., 2012b) and CLC Bio Version 5.1 (CLC Bio,
Muehltal, Germany). All three are based on the de Bruijn graph. The first two
108

assemblers have been adapted for uneven coverage found in single-cell MDA datasets.
For Velvet-SC, we assembled the data with vertex size k=55. For SPAdes, we iterated
over vertex sizes k=21, 33, and 55.

Contig Binning Methods and Annotation. A 273 kb contig in MDA2 with an average
GC content of 36% contained a 16S rRNA gene with a flanking 23S. The 16S rRNA
gene had a top BLAST hit to a member of TM6. Taxonomic affiliations of the predicted
protein sequences derived from this contig were also assigned using APIS (Badger et al.,
2006). APIS generates a phylogenetic tree for each ORF in a genomic or metagenomic
sample using homologous proteins from PhyloDB 1.05, a JCVI database containing
proteins from all publically available complete genomes as of August 15, 2012. APIS
classifies each ORF taxonomically and functionally based on their phylogenetic position.
An independent metagenomic binning approach using an autonomous method, Principal
Coordinate Analyses (PCA) of the penta-nucleotide frequency, followed by k-means
clustering was employed. A second independent approach used MGTAXA software
(http://mgtaxa.jcvi.org), which performs taxonomic classification of metagenomic
sequences with machine learning techniques. The 273 kb contig containing the TM6 16S
rRNA was used as the input sequence in training sets to classify all remaining contigs
from the three assemblies using MGTAXA. Identified contigs from the intersection of
the separate approaches sharing a GC content of 36 +/- 2% were chosen as the final set of
contigs. MDA2 had the largest number of base pairs and was concatenated to allow
comparisons to the MDA1 and MDA3 contig sets. Whole contig set comparisons were
109

carried out using ProgressiveMauve (Darling et al., 2010) and the LAST alignment tool
(Frith et al., 2010). The assembly from MDA2 that represented the largest assembled
genome was annotated using the JCVI metagenomic annotation pipeline
(http://www.jcvi.org/cms/research/projects/annotation-service/overview/) which uses
Metagene for gene calling (Noguchi et al., 2006). A combination of databases and tools
including BLAST, RAST (Aziz et al., 2008), as well as MG-RAST (Meyer et al., 2008)
uploaded with nucleotide sequences for the CDS, were used to assess a consensus on the
pathways and processes predicted for the TM6 genome.

Acknowledgements
This work was supported by grants to J.S.M. from the National Institute of General
Medical Sciences (NIH 1R01GM095373), to R.F., J.C.V. and R.S.L. by the Alfred P.
Sloan Foundation (Sloan Foundation-2007-10-19), to P.P. and G.T. from the National
Institutes of Health (NIH 3P41RR024851-02S1), to R.S.L. from the National Institutes of
Health (NIH 2R01 HG003647), to P.P. from the Government of the Russian Federation
(grant 11.G34.31.0018), to M.G.Z. from the National Institutes of Health
(UL1TR000100). We thank Pamela Mishra and Mathangi Thiagarajan (J. Craig Venter
Institute) for bioinformatics support.

110

Table 2-1. Assembly Statistics.

Table 2-2. Statistics characterizing the assembled and annotated TM6SC1 genome.
Genome Features
Assembly size (bp) 1074690
% GC content 36
No. of ORFs 1056
No. of tRNA genes 29
No. of rRNA genes 2
Protein coding genes (CDS) 993
No. conserved single copy genes

101/111 (91%)
No. ORFs with functional annotation 428
No. ORFs without function prediction 565
Average CDS length 952
No. ORFs connected to KEGG pathways
322

111

Figure 2-1. Summary of genera found in the biofilm sample from single and multi-
event sorts. The total number of 16S rRNA gene sequences for each observed bacterial
genera recovered in individual MDA amplified wells. Data is presented for wells in
which 1, 20 or 100 events were sorted from either high or low fluorescent event
population. . Data from the 20 and 100 event wells that were sorted from the high
fluorescent population are grouped together.
112

Figure 2-2. Circular representation of the TM6SC1 genome as a pseudomolecule
derived from the concatenated contigs for MDA2. From the inner to the outer ring:
GCskew-, GCskew+, G+C content, BLASTN alignment against MDA1 contigs,
BLASTN alignment against MDA3 contigs, predicted CDS, rRNA, and MDA2 contigs
(contigs were ordered by length and then concatenated).

113

114

Figure 2-3. Evolutionary relationships of Candidate Division TM6. a) Phylogenetic
relationship of 16S rRNA gene sequences designated as members of TM6 in public
databases reveal the global distribution and sequence diversity within this group.
*indicates TM6 sequence from this study. b) Unrooted 16S rRNA gene tree based on
maximum likelihood analysis of representative Candidate Division TM6 and
Proteobacteria sequences. One thousand bootstrapped replicate resampled datasets were
analyzed. Bootstrap values are indicated as percentages and not shown if below 50%. *
and red indicates TM6 sequence from this study.
115

Figure 2-4. Phylogenetic tree illustrating the major lineages (phyla) of the domain
Bacteria analyzed with AMPHORA2 and 29 protein phylogenetic markers. The
TM6 gene sequences were aligned against the AMPHORA2 seed alignment consisting of
sequences from over 1,000 genomes through HMM. Tree branch lengths ≤ 0.4 were
collapsed. For original tree see SI Appendix C, Figure S2-9.

116

Figure 2-5. Predicted metabolic pathways of phylotype TM6SC1. Predicted ABC
transporters (e.g. amino acid importers, nucleotide/nucleoside importers, divalent ion importers)
(red) as well as a cellobiose importer (orange). ATP-driven transporters are indicated by the ATP
hydrolysis reaction. The copper ion transporting P-type ATPase is proposed to serve as both
uptake and efflux systems, which is shown by a bidirectional arrow. Several protein-secretion
components belonging to the type-II secretion pathway; GSP and Sec-proteins were identified as
well as five prepilin related domains and a type IV pilB ATPase (green). A modified electron
transport chain was also identified consisting of 7 thioredoxins and 5 thioredoxin reductases, a V-
type pyrophosphatase, an F-type ATPase synthase and a DsbD Cytochrome
(blue). Thiolperoxidases (Bcp and bacterioferretine-like), superoxide dismutases (manganese
and iron), were identified as cytosolic and periplasmic enzymes protecting against oxidative
stress. A β-glucosidase that catalyzes the formation of β-D-glucose from disaccharides (e.g.
cellobiose) was identified as well as all enzymes involved in the pentose phosphate pathway.
However, other enzymes (e.g. enzymes from the Calvin cycle or the Citric acid cycle) involved in
the conversion of 3-Glyceraldehyde-3-P could not be identified. Enzymes for de novo synthesis
of amino acids were absent while several amino acid importing membrane proteins, i.e., ABC
117

transporters and 14 cytosolic and proteolytic peptidases were identified as well indicating that
TM6 has a high capacity for amino acid scavenging.
118

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123

Chapter 3: Genome of the Pathogen Porphyromonas Gingivalis
Recovered from a Biofilm in a Hospital Sink Using a High Throughput
Single Cell Genomics Platform

Jeffrey S. McLean
1,6
*, Mary-Jane Lombardo
1
, Mike Ziegler
2
, Mark Novotny
1
, Joyclyn
Yee-Greenbaum
1
, Jonathan H. Badger
1
, Glenn Tesler
3
, Sergey Nurk
4
, Valery Lesin
4
,
Daniel Brami
1
, Adam P. Hall
1
, Anna Edlund
1
, Lisa Z. Allen
1
, Scott Durkin
1
, Sharon
Reed
2
, Francesca Torriani
2
, Kenneth H. Nealson
1,6
, Pavel A. Pevzner
4,5
, Robert
Friedman
1
, J. Craig Venter
1
, Roger S. Lasken
1
*

1
Microbial and Environmental Genomics, The J. Craig Venter Institute, San Diego, CA,
USA
2
Department of Medicine, University of California, San Diego, California, USA.
3
Department of Mathematics, University of California, San Diego, La Jolla, California,
USA.
4
Algorithmic Biology Laboratory, St. Petersburg Academic University, Russian
Academy of Sciences, St. Petersburg, Russia
5
Department of Computer Science, University of California, San Diego, La Jolla,
California, USA.
6
Department of Earth Sciences, University of Southern California, Los Angeles, CA,
USA

*Corresponding Authors

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Abstract

Although biofilms have been shown to be reservoirs of pathogens, our knowledge of the
microbial diversity in biofilms within critical areas, such as health care facilities, is
limited. Available methods for pathogen identification and strain typing have some
inherent restrictions. In particular, culturing will yield only a fraction of the species
present, PCR of virulence or marker genes is mainly focused on a handful of known
species, and shotgun metagenomics is limited in the ability to detect strain variations. In
this study we present a single cell genome sequencing approach to address these
limitations and demonstrate it by specifically targeting bacterial cells within a complex
biofilm from a hospital bathroom sink drain. A newly developed, automated platform
was used to generate genomic DNA by the multiple displacement amplification (MDA)
technique from hundreds of single cells in parallel. MDA reactions were screened and
classified by 16S rRNA gene PCR sequence which revealed a broad range of bacteria
covering 25 different genera representing environmental species, human commensals and
opportunistic human pathogens. Here we focus on the recovery of a nearly complete
genome representing a novel strain of the periodontal pathogen Porphyromonas
gingivalis (P. gingivalis JCVI SC001) using the single cell assembly tool, SPAdes. Single
cell genomics is becoming an accepted method to capture novel genomes, primarily in
the marine and soil environments. Here we show for the first time that it also enables
comparative genomic analysis of strain variation in a pathogen captured from complex
biofilm samples in a healthcare facility.
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Introduction
Ongoing efforts to understand the genomic diversity of microbes in nature and human
health are hampered by the limited availability of cultivated organisms and their genomes
(Consortium, 2010). Only 1-10% of known bacterial species (Rappe and Giovannoni,
2003) are thought to be currently cultivated, although great progress is being made for
some bacterial communities; for example, about half of bacterial species within the
human oral cavity have been cultivated (Dewhirst et al., 2010). The recent advancements
in DNA sequencing of single bacterial cells (Raghunathan et al., 2005) has accelerated
the discovery of uncultivated microbes (Lasken, 2012a), providing genomic assemblies
for species previously known only from 16S rRNA clone libraries and metagenomic data
(Binga et al., 2008; Dupont et al., 2011; Eloe et al., 2011; Marcy et al., 2007; Podar et al.,
2007; Youssef et al., 2011a). This newly developed methodology provides a culture-
independent approach to capture the genomes of uncultivated organisms, which can then
be integrated into many intensive genomics-based studies. A high throughput strategy
was recently established to sequence and assemble single cell genomes of bacteria
(Chitsaz et al., 2011) and viruses (Allen et al., 2011) including novel uncultivated
bacteria from environmental samples (Chitsaz et al., 2011; Dupont et al., 2011; Eloe et
al., 2011). The workflow consists of: 1) delivery of single bacterial cells into 384 well
microtiter wells by Fluorescence Activated Cell Sorting (FACS); 2) use of a robotic
platform to perform 384 well automated cell lysis and amplification of DNA by the
Multiple Displacement Amplification (MDA) method (Dean et al., 2002; Dean et al.,
2001; Hosono et al., 2003) to create libraries of genomic DNA derived from single cells;
126

3) PCR and cycle sequencing of 16S rRNA genes to profile the taxonomy and diversity
of the libraries; 4) selection of candidate amplified genomes for whole genome
sequencing; and 5) sequencing and assembly of selected genomes using assembly tools
designed specifically for MDA amplified single cells (Bankevich et al., 2012a; Chitsaz et
al., 2011). A highly integrated robotic platform, described in this paper for the first time,
was employed to increase the throughput, ease and overall cost of processing single cells.

Here we have focused this approach on the indoor environment. Despite the fact that a
typical person spends approximately 90 percent of their time indoors (Klepeis et al.,
2001), there is little known about the microbial diversity of this environment. Of
particular interest is the prevalence of species affecting human health, including both
opportunistic and primary pathogens. Recent studies of indoor environments using
culture-independent molecular methods indicate an unexpectedly high bacterial diversity
on surfaces within daycare facilities and public bathroom facilities (Flores et al., 2011;
Lee et al., 2007) where the majority of organisms in the latter environment were human-
associated (Flores et al., 2011). Another study shows that bacterial diversity is lower in
indoor air at a health-care facility compared to outdoor air; however, the indoor air
contained a higher number of potential human pathogens as shown by 16S rRNA gene
sequence analyses (Kembel et al., 2012). Biofilms in particular are thought to be
reservoirs of disease-causing organisms in both outdoor and indoor environments.
Several pathogens, including Escherichia coli, Vibrio cholerae (Shikuma and Hadfield,
2010) and Helicobacter pylori (Linke et al., 2010; Percival and Thomas, 2009), have
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been detected in biofilms within water distribution systems. Also, the long-term
persistence of Legionella pneumophila, the causative agent of Legionnaire’s disease, in
biofilms within natural and human-impacted freshwater environments is well known
(Declerck, 2010; Giao et al., 2011; Murga et al., 2001; Walker et al., 1993). Recent 16S
rRNA gene molecular surveys have revealed a significant load of Mycobacterium avium
in showerhead biofilms (Feazel et al., 2009b), and studies on biofilms growing on shower
curtains suggest that these communities also harbor potential opportunistic pathogens that
can threaten immune-compromised patients (Kelley et al., 2004). In another study, the
source of a deadly outbreak of a multidrug resistant strain of Pseudomonas aeruginosa
was traced to biofilms in hand hygiene sink drains where its viable cells could be
identified (Hota et al., 2009).

There is great interest, therefore, to investigate biofilms as reservoirs of pathogens at
higher resolution than allowed by the most commonly used detection and identification
methodologies. Culture-independent surveys using the 16S rRNA gene as a marker is
currently the most widely used approach; however, genetic strain differences reflecting
pathogenicity are often difficult to resolve due to this gene being highly conserved
amongst many bacterial strains. Quantitative PCR and direct culturing are focused on
either a handful of predetermined pathogens or what can be readily cultivated.
Metagenomic surveys are becoming common but so far, our ability is limited to
accurately predict taxonomic affiliation at species or strain levels from highly diverse and
complex datasets. Additionally, a whole genome comparative genomic study on the
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evolution and transmission of a pathogen requires substantial amounts of DNA or a
cultured strain, which often cannot be obtained. It has been demonstrated in a controlled
experiment with 10 pg of extracted DNA provided as a template, that MDA amplified
genotyping call and accuracy rates were only slightly lower than those for genomic DNA
isolated directly from cultured cells (Giardina et al., 2009). Using single cell genomic
approaches, partial to near complete genomes should be obtainable without cultivation,
from difficult samples within critical indoor environments such as healthcare facilities.
In-depth analyses of this genomic data can then provide accurate and detailed information
of strain specific pathogen-gene signatures and other virulence factors.

The aim of this study was to investigate for the first time, the bacteria present in a
healthcare facility with a high-throughput single cell genomics approach. Based on the
known prevalence of pathogens in biofilms, we focused on a sink drain biofilm from a
public restroom adjoining an emergency waiting room. Sequencing of 16S rRNA genes
PCR amplified from 416 single cell MDA reactions, we found 18 candidate commensal
and potentially pathogenic species which were selected for 454 shallow sequencing.
Initial read mapping and de novo assembly of the low coverage 454 sequence data
confirmed that we had obtained genomic sequences for the pathogen Streptococcus
pneumoniae as well as bacterial species highly similar to and those reported to be
potentially pathogenic, including Sphingobacterium spiritivorum (Kampfer et al., 2005;
Tronel et al., 2003), Leptotrichia buccalis (Hammann et al., 1993; Hot et al., 2008), and
the host-associated oral bacteria, Streptococcus mitis and Veillonella parvula. Of
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particular note, we found three MDA products with sequences for the oral pathogen P.
gingivalis, which is a periodontal pathogen involved in periodontal bone loss that has also
been linked to progression of atherosclerotic disease (Pussinen et al., 2007; Yilmaz,
2008). P. gingivalis possesses many virulence factors, including functions that allow it to
survive intracellularly and to be transmitted between different types of host cells (Li et
al., 2008). Despite being detected at a very low abundance in the oral cavity, P.
gingivalis can strongly disrupt the host-microbial homeostasis (Hajishengallis et al.,
2011). As with many pathogens, the environmental reservoirs and mode(s) of
transmission of P. gingivalis are not fully understood, yet it is a globally important
pathogen with only three sequenced genomes available at the time of this report. It was
recently stated by a CDC report that nearly 50% of American adults have mild, moderate
or severe periodontitis and this percentage rises to 70% in adults greater than 65(Eke et
al., 2012). To our knowledge, there are no previous reports detecting P. gingivalis
outside of a host.

Three MDA amplified genomes with 16S rRNA gene sequences identified as P.
gingivalis were chosen for additional deep sequencing on the Illumina GA IIx platform,
and the resulting reads were mapped to P. gingivalis genomes. One MDA-read dataset
had ~90% sequence coverage to P. gingivalis strain TDC60, which was isolated from a
patient in Japan with severe periodontitis (Watanabe et al., 2011). A new single cell de
novo assembly algorithm, SPAdes (Bankevich et al., 2012a), was used to generate contigs
of the highest coverage MDA product, which produced a 2.35 Mb draft genome (PG
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JCVI SC001). Comparative genomics and pangenome analyses were performed with the
three other available P. gingivalis genomes; virulent strains W83 (Nelson et al., 2003)
and TDC60 (Watanabe et al., 2011), and the less virulent strain ATCC 33277 (Naito et
al., 2008). We demonstrate that single cell genomics is a powerful approach that can
produce highly accurate sequence data, enabling comparative genomic studies of
pathogens obtained from a complex heterogeneous environmental sample.

Results

Sampling and sorting cells from biofilms
Seawater derived samples contain relatively high bacterial counts and were a rich source
for sorting single cells by FACS (Chitsaz et al. 2011; Dupont et al. 2011) with about 20-
30% of single cell amplifications yielding amplified genomic DNA based upon
sequencing of PCR amplified 16S rRNA genes (Methods). In contrast, attempts to
randomly sort single bacterial cells from the indoor environment, such as from surface
swabs (unpublished) and sink drains, yielded <1% amplified bacterial genomes due to
failure to lyse cells or due to noise from fluorescence background signals of non-bacterial
particles during sorting. To reduce the background noise of non-bacterial events, which
obscure bacterial cells stained with SYBR Green, the biofilm sample was vortexed,
filtered through a 5 µm filter and processed through a Nycodenz cushion (Methods).
After processing, the percentage of positive stained events that were in the bacterial size
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range approached 20% of the total particle count (Supplemental Figure 3-S1). This
positive gate was used to sort single events into 384 well microtiter plates.

Microtiter plates containing single sorted events were then processed on the highly
integrated high throughput single cell platform (Methods and Supplemental Figure 3-
S2). In total, 78 MDAs of 416 sorted wells were identified as candidates based on the
taxonomy of their 16S rRNA gene sequence. This 19% overall success rate, does not
include 16S rRNA gene sequences that can be attributed to common bacterial MDA
reagent contaminants detected in control MDA reactions without a sorted cell (NTC-no
template control). NTCs were always run in parallel with each sort of single cells to
determine the relative amount and identity of contaminating bacterial DNA in the MDA
reagents; this a necessary standard practice in single cell genomics due to the highly
processive strand displacement activity of the phi29 DNA polymerase and the near
ubiquitous presence of bacterial DNAs in reagents (Allen et al., 2011; Blainey and
Quake, 2011; Woyke et al., 2011). Based on 16S rRNA gene analysis, a wide diversity
of genera was found in this random sort from the sink biofilm community (Figure 3-1).

Screening MDA products using multiplexed 454 sequencing

A total of 18 candidate MDA products from the sink biofilm sample were of interest in
terms of their relationship to human health being reported as potentially pathogenic or a
commensal species. These were each sequenced as barcoded libraries on 1/4
th
of a 454
plate. A range of 5,500-13,500 reads were obtained for the 18 libraries, with overall
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average read length of 321 bp. Complex environmental samples, such as a sink biofilm,
may be difficult to lyse, had poorer amplification, and are more likely to contaminate
single cell MDA reactions with free DNA from other organisms. The 454 datasets for the
18 candidate MDAs were therefore screened for quality, contamination and overall
suitability for analysis by several criteria: 1) the presence of reads having BLAST
matches (NCBI nr database) to the same genera indicated by the 16S rRNA gene; 2)
confirmation of identity for contigs generated from de novo assembly; and 3) successful
mapping of reads to a representative sequenced genome. A total of 9 of the original 18
MDAs met these criteria. These comprise species previously reported to be pathogenic
or potentially pathogenic, including Streptococcus pneumoniae (1 MDA product),
Sphingobacterium spiritivorum (1), Leptotrichia buccalis (1), P. gingivalis (3),
Streptococcus mitis (2), and the commensal oral bacteria Veillonella parvula (1). These
454 reads were assembled and the open reading frames were annotated to determine the
genes captured from these species, as well as to assess potential for full genome
sequencing (Supplemental Table 3- 3- S1). Although many of these products were very
intriguing, P. gingivalis was chosen for detailed analysis because there was more than
one MDA representing a genome, they passed all the quality criteria, and all three of the
MDAs contained a high proportion of genomic sequences from P. gingivalis (labeled
MDA 1, 2 and 3). These were chosen for deep sequencing using the Illumina GAIIx
platform on 1/3
rd
of a lane each.
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Read based analyses of the P. gingivalis MDA products

Although Porphyromonas is a widespread and well recognized pathogen, only three P.
gingivalis genomes have been sequenced: strain W83 (virulent) (Nelson et al., 2003),
ATCC 33277 (less virulent than W83) (Naito et al., 2008), and the most recent, TDC60
(virulent) (Watanabe et al., 2011). A majority of the raw 100 bp paired-end reads for
each of the three MDA products yielded BLAST hits to the newly completed genome of
a virulent P. gingivalis strain (TDC60) from a severe periodontal lesion in a Japanese
patient (Watanabe and Frommel, 1993), confirming the 16S rRNA gene PCR sequence
matches. The total reads passing quality filtering, along with the results of mapping the
reads of the single cells to strain TDC60, are summarized in Table 3- 3- 1. Coverage of
the reference genome varied with 41%, 87% and 91% for MDA1, 2, and 3, respectively.
As with many MDA-derived single cell genomes, the coverage depth varied widely
across the genome: the average coverage for MDA3 was 237×, with approximately 90%
of the genome covered at 10× or greater (Supplemental Figure 3-S3). Single nucleotide
polymorphism (SNP) and Deletion Insertion and Polymorphism (DIP) analyses revealed
847 shared SNPs between the three amplified genomes with respect to TDC60, with 791
in coding regions (202 missense) (Supplemental Figure 3-S4). There were 75 shared
DIPs in total. Although MDA1 had the highest number of reads, it had the lowest
reference genome coverage. Mapping of reads from all three MDAs did not increase the
percentage of the reference genome mapped compared to coverage by MDA3 alone.

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De novo assembly of P. gingivalis genomes

All three MDA sequence datasets were assembled de novo. Our assembly analysis
described here is restricted to MDA3, which corresponds to the MDA having the highest
genome coverage to TDC60. Two newly developed de novo assembly tools specifically
designed to assemble reads generated from single cell MDA reactions, Euler correction
with Velvet-Single Cell E+V-SC (Chitsaz et al., 2011) and SPAdes (Bankevich et al.,
2012a), were compared (Table 3- 3- 2) to the Velvet assembly tool (Zerbino and Birney,
2008). For single-cell projects, these significantly improve on traditional assemblers
(designed for mono-species cultured samples), since they are able to cope with the wide
variations in coverage and elevated numbers of chimeric reads and read-pairs
characteristic of MDA reactions. Completeness of the assemblies was assessed by using
Plantagora (Barthelson et al., 2011) to compare to the reference. Plantagora determines
“mis-assembly breakpoints” with the assumption that the dataset being assembled is a
sample of the reference genome. It is emphasized here that since contigs are compared
against a reference from a similar but non-identical strain, the term “mis-assembly
breakpoints” does not apply and the term “breakpoints vs TDC60” is used instead.
Assemblers were compared based on the fraction of the genome covered by the
assembled contigs, and an adjusted N50, in which assembly contigs are broken into
multiple contigs at the breakpoints determined by Plantagora. Sequence in the contigs
that does not align to the TDC60 reference is not counted. Note that the adjusted N50 is
likely to eliminate mis-assemblies from the N50 computation (which result in incorrectly
135

overestimating N50), but this is at the expense of underestimating the true N50. While
assembly of sample MDA3 with Velvet resulted in only 52% coverage of the TDC60
reference genome with a very small adjusted N50 (Table 3- 2), assemblies with Velvet-
SC and SPAdes resulted in 88% and 90% coverage, respectively, which are close to the
91% coverage by reads (Table 3- 1). The adjusted N50 was 10,732 for Velvet-SC and
13,589 for SPAdes. For MDA2, SPAdes yielded a much better assembly than Velvet-
SC: 58% coverage of TDC60 for Velvet-SC vs. 78% for SPAdes (while the reads have
87% coverage; see Table 3- 1), and adjusted N50 of 1,495 for Velvet-SC vs. 5,887 for
SPAdes. Based on this benchmarking, SPAdes assemblies only were used for further
analyses.

Following assembly, contigs resulting from non-target environmental sequences, as well
as MDA contaminants, were identified and removed from the three MDA assemblies
using a combination of BLAST and the Automated Phylogenetic Inference System
(APIS) (Badger et al., 2006), as described previously (Chitsaz et al., 2011; Dupont et al.,
2011). APIS performs a BLAST analysis of each ORF against reference genomes and,
when possible, generates a phylogenetic tree for each ORF. APIS analysis confirmed
that the majority of ORFs on the contigs were placed within the genera Porphyromonas,
APIS also helped to identify contaminant contigs as those containing ORFs that were
phylogenetically similar to each other and distinct from the rest of the assembly (Chitsaz
et al., 2011; Dupont et al., 2011). We also assessed the relative proportion and
phylogenetic association of target and non-target contigs by MGTAXA
136

(http://mgtaxa.jcvi.org) (Supplemental Figure 3-S5), which performs taxonomic
classification of metagenomic sequences with machine learning techniques. This process
also confirmed that the majority of contigs for MDA3 belonged to P. gingivalis.

We investigated the three assembled genomes by remapping the three sets of MDA reads
to the final contigs generated by MDA3. We reported SNPs using the same criteria as
described earlier, revealing an insignificant number of SNPs in a few genes between the
datasets (Supplemental Table 3- S2). We also confirmed that the three shared identical
16S rRNA gene sequences based on 938 bp of alignment from initial PCR amplified 16S
rRNA gene results as well as identical full length 16S rRNA gene sequences in the three
assemblies. Although the data may provide support that these three cells are likely the
same strain and the assemblies represent the same genome, it is not conclusive, so we
chose to treat them as separate genomes rather than combine reads to attempt to generate
a better assembly. Given the fact that MDA3 had the highest mapped coverage of the
reference genome and the largest number of contigs classified as P. gingivalis, we further
restricted our analyses to MDA3. In total, 288 contigs from MDA3 were chosen for
further annotation and genomic comparisons.

General genomic features
The single cell genome assembled from MDA3, designated PG JCVI SC001, was 2.35
Mbp in size and is similar to the other three P. gingivalis genomes for a number of
genome parameters (Table 3- 3). A total of 1,869 genes (86%) were found to have some
137

level of homology in ATCC33277, W83, or TDC60 genomes (Supplemental Table 3-
S3), with a total of 1500 genes making up the pangenome encompassing all three
genomes (Supplemental Figure 3-S6). The 524 genes unique to PG JCVI SC001, i.e.,
no ortholog to reference genomes, were primarily annotated as hypothetical proteins
(Table 3- 4, and full Table 3- in Supplemental Table 3- S4). Contigs were then
ordered based on the TDC60 reference genome and concatenated to provide a scaffold
for further comparative genomics. The circular representation of the genome (Figure 3-
2) displays the ordered contigs, predicted CDSs as well as BLASTN analyses to the three
P. gingivalis genomes and a distant relative, Prevotella buccae ATCC33574.

Multiple locus sequence typing

We used the seven gene Multiple Locus Sequence Typing (MLST) scheme for P.
gingivalis (Enersen et al., 2008a; Jolley et al., 2004) and sequence types (STs) from
human periodontitis isolates of P. gingivalis in the MLST database
(www.pubmlst.org/pgingivalis) (Jolley et al., 2004) to type the PG JCVI SC001 genome.
MLST detects allelic variation at multiple housekeeping loci accumulating slowly in
bacterial populations. This database has been used to investigate the clonality of P.
gingivalis; which was previously reported to have a weakly clonal population structure
comparable with Neisseria menigitidis (Enersen, 2011). Using the PG JCVI SC001
contig sequences as input, the MLST database searches revealed that the sequenced
single cell has five exact matches to previously sequenced genes of the seven MLST loci
(ftsQ, gpdxJ, hagB, mcmA, pepO, pga, recA). Strain TDC60 also had only 5 exact
138

matches to the database, indicating they have a unique allele pattern for hagB (SC001),
gpdxJ (TDC60), pga (both) absent in the database. Using existing MLST database tools
that generate trees based on the allelic profiles of the 138 sequence types
(www.pubmlst.org), we confirmed that the nearest sequence type to PG JCVI SC001 is
ST-68 having three identical matches (pepO, pga and recA) (Supplemental Figure 3-
S7).

Analysis of virulence factors

Diversity in P. gingivalis is reported to arise by genetic recombination rather than
mutation (Frandsen et al., 2001; Koehler et al., 2003; Nadkarni et al., 2004). In addition
to the MLST data, the structure and function of major virulence factors, such as fimbriae
(fimA) and the capsular polysaccharide biosynthesis locus (CPS), provide insight into
strain variation and degrees of pathogenicity (Lamont and Jenkinson, 1998). The single
cell read data and de novo assembled contigs provide the opportunity to examine strain
variation and pathogenicity of this uncultivated strain. In some cases, mapping reads to
the reference TD60 genome provided deep coverage sufficient to detect SNPs, whereas in
other highly variable regions, such as the CPS region, reads could not be accurately
mapped. The CPS region could, however, be recovered in the assembled contigs and
used for comparative genome analyses.
139

Coverage and analysis of fimbriae gene
It is becoming evident that the fimbriae A gene (fimA), encoding the major fimbrial
subunit of P. gingivalis, is one of the main virulent factors of this organism. Based on
fimA, P gingivalis is classified into six genotypes (genotype I, Ib, II, III, IV, and V).
Epidemiological studies have shown that advanced periodontitis patients harbor fimA type
II (Enersen, 2011; Enersen et al., 2008a; Enersen et al., 2008b). It should be noted that
fimA type II P. gingivalis is most frequently detected in cardiovascular disease patients
(Nakagawa et al., 2006) and have been shown to invade epithelial cells (Nakagawa et al.,
2006). TDC60 has the type II fimbriae and our analysis of read mapping and de novo
assemblies confirm that our single cell fimA sequences are related to the fimA of TDC60.
A total of 6 SNPS in fimA are shared by all three MDAs with greater than 10X coverage
(Figure 3-3), giving further confidence that these 6 SNPs are valid and not assembly
error or MDA artifact.

Variation in the capsular polysaccharide biosynthesis locus
The CPS locus has been described as a virulence factor of various pathogenic bacteria
involved in evasion of the host immune system. Encapsulated P. gingivalis strains such
as W83 have been shown to be more virulent than non-encapsulated strains (e.g. ATCC
33277 in the mouse infection model) (Brunner et al., 2010a; Brunner et al., 2010b; Laine
and van Winkelhoff, 1998). The CPS loci were poorly covered by reads of all three
MDA datasets suggesting that these may be highly variable regions within the genome.
The arrangement of the genes between the three sequenced genomes and JCVI SC001
140

single cell genome from MDA3 (Figure 3-4) reveals how the region is bounded by
shared homology in the gene encoding glycosyl transferase, group 4 family protein and
epsC (UDP-N-acetyl-D-mannosaminuronic acid dehydrogenase) upstream and a pair of
downstream genes encoding UDP-N-acetylglucosamine 2-epimerase (epsD) and DNA-
binding protein HU. The synteny of genes in this region were more similar between the
virulent TDC60 (Watanabe et al., 2011) and the less virulent ATCC 33277 than between
the two more virulent strains W83 and TDC60. Our MDA3 assembly has only 5 ORFs
between epsD and epsC and appears very distinct compared to the reference sequences.
A recent report has shown that a loss in the ability to produce a capsule, by deletion of
the glycosyl transferase, group 4 family protein, increases biofilm formation by W83 and
ATCC 33277. Although we do not have an isolate to test if these single cells would
produce a capsule, one may speculate based on the genomic data that PG JCVI SC001
lacks a capsule.

Evidence of unique CRISPR region
Differences between species can be observed in Clustered Regularly Interspaced Short
Palindromic Repeats (CRISPR) sequences, which are non-continuous direct repeats
separated by variable (spacer) sequences that have been shown to confer immunity to
phage (Barrangou et al., 2007; Jansen et al., 2002). Amplification and de novo assembly
was successful for the previously identified CRISPR (designated 36-30) within W83; this
CRISPR contains 8 repeats of 36 bp. Comparisons of this region with other genomes
(Figure 3-5) reveals identical repeat sequences between all genomes. This region,
141

however, varies in the number of repeats, number of spacer sequence and spacer identity.
Additional confirmed CRISPR repeat sequences of 46 bp (5 repeats and 4 spacers) as
well as putative CRISPR direct repeat sequences of 30, 38, 45, 52 bp and associated
spacers were found in PG JCVI SC001 using the CRIPSR finder database (http://crispr.u-
psud.fr/crispr/). The 36-30 region is not flanked by CRISPR-associated (cas) genes in
W83 or TDC60, all three MDA assemblies also failed to capture any cas genes or any
homologous genes related to the cas system. Zegans et al. (Zegans et al., 2009) noted
that loss or disruption of five of the six cas genes, results in a restoration of biofilm
formation in Pseudomonas aeruginosa strains that were infected with a lysogenic phage.
It is interesting to speculate that a lack of cas genes in PG JCVI SC001 may also provide
an advantage for this strain to integrate into a biofilm community, although no prophage
regions were detected in the MDA products.

Discussion
A vast majority of bacteria in the environment as well as those associated with the human
microbiome have eluded standard culturing approaches and therefore their physiology
and their gene content are unknown. This leaves a large gap in our knowledge of the
potential roles for these organisms in the environment and also in human health and
disease. This is the first report describing the recovery of genomes of bacterial pathogens
from single cells out of an environmental sample. We demonstrate that a single cell
approach enables analysis of the genetic diversity between the captured environmental
142

cells and sequenced pathogen genomes, permits identification of variations in virulence
factors and supports discovery of variant genes in the genome. Single cell genomics
comparing multiple single cells enables analysis of genetic diversity within a population
and, although it has inherent biases of its own, may potentially be free from biasing
effects that can occur during subculturing, such as gene loss (Karch et al., 1992; Nair et
al., 2004). Based on the work reported here, capturing genomes from environmental
samples using single cell approaches could support studies on the prevalence and
genotype of pathogens from environmental sources and may ultimately help reveal their
possible modes of transmission between the host and environment.

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Methods

Sample collection and isolation of bacterial cells from sink material

Sink drain samples were collected with sterile cotton tipped swabs from a publicly
accessible restroom adjacent to an emergency waiting room within a Medical School
Hospital (University of California, San Diego, Medical Center – Hillcrest, in San Diego,
CA, USA). The initial sample was fixed with ethanol and vortexed briefly for 20 seconds
(Supplemental Figure 3-S1). The sample was filtered through a 35 µm filter. A two ml
cushion of prechilled Nycodenz gradient solution (Nycoprep Universal, Axis Shield) was
placed in a 17 ml ultracentrifuge tube, and 6 ml of supernatant was placed gently over the
Nycodenz cushion. The pair of balanced tubes was centrifuged at 9000 rpm for 20 min at
4 °C in an ultracentrifuge SW32.1 rotor. The visible cloudy interface containing the
bacterial cells was collected gently, mixed by inversion to create a suspension and diluted
1000 fold for flow cytometry. For further details, see the Supplemental Methods.
FACS detection and single cell sorting

FACS detection was performed on the Nycodenz fractionated bacteria. Filter sterilized
(0.2 µm) phosphate buffered saline (PBS, 1×) was used as sheath fluid, and for sample
dilution. Unstained and SYBR Green I (0.5×) stained material was 35 µm filtered and a
1:1000 dilution was assessed for event rate at low flow rate (<2,000 total events/sec) and
adjusted if necessary. The low flow rate is critical to reduce the likelihood of sorting
144

coincident events. Fluorescent events were collected using using gating parameters FSC-
PMT vs. SSC and FSC-PMT vs. SYBR Green (Supplemental Figure 3-S1). 1000 events
from each gate were sorted at a lower purity setting onto glass slides for viewing with
fluorescent microscopy on an Olympus I×70 inverted fluorescence microscope at 60×
magnification to confirm the presence of cellular morphologies. A bead targeting
strategy (see supplemental methods) was employed to ensure only single cells were
sorted from the sample into each well of a 384-well PCR plate (FrameStar). Single cell
events were sorted into 4 µL of a low EDTA TE (10 mM Tris 0.1 mM EDTA, pH 8.0)
and immediately frozen on dry ice and held there until transfer to -80
o
C for storage prior
to processing. For further details, see the Supplemental Methods.
Multiple displacement amplification

MDA of single cell genomes was performed in a 384-well format using GenomiPhi HY
kit (GE Healthcare) using a custom Agilent BioCel robotic system (detailed schematic in
Supplementary Figure 3-S2). Briefly, cells were lysed by addition of 2 μl of alkaline
lysis solution (645 mM KOH, 265 mM DTT, 2.65 mM EDTA pH 8.0) then incubated for
10 min at 4
o
C. After lysis, 7μl of a neutralization solution (2.8 μl of 1290 mM TrisCl pH
4.5, and 4.2 μl of GE Sample Buffer) was added followed by 12 μl of GenomiPhi master
mix (10.8 μl of GE Reaction Buffer and 1.2 μl GE Enzyme Mix) for a reaction volume of
25 μl. Reactions were incubated at 30 °C for 16 h followed by a 10 min inactivation step
at 65°C. MDA yield was determined by Picogreen assay. 384 no template control (NTC)
MDA reactions were included to reveal any contaminating sequences, and processed in
145

parallel through 16S PCR analysis. These negative controls lacking a sorted cell were
run in parallel to determine the relative amount and identity of contaminating bacterial
DNA in the MDA reagents, a necessary standard practice in single cell genomics due to
the highly processive strand displacement activity of the phi29 DNA polymerase (Allen
et al., 2011; Blainey and Quake, 2011; Woyke et al., 2011). For further details, see the
Supplemental Methods.

PCR and analysis of 16S rRNA genes

16S rRNA genes were amplified from diluted MDA product using universal bacterial
primers 27f and 1492r (Lane, 1991) according to previously established protocols
(Chitsaz et al., 2011; Dupont et al., 2011; Eloe et al., 2011) (Supplemental Methods).
BLASTN analysis against the SILVA SSU ref NR 102 database (Pruesse et al., 2007)
was performed to classify the 16S rRNA gene sequences taxonomically, and to determine
their relationship to sequenced bacterial genomes and 16S rRNA gene sequences. An
additional BLASTN analysis was performed against a curated database of near full
length to full length 16S sequences (at least 900 bp) from human fecal samples 16S
rRNA sequences (at least 900 bp) from several survey studies (Dethlefsen et al., 2008;
Eckburg et al., 2005; Gill et al., 2006; Tap et al., 2009; Turnbaugh et al., 2009) as well as
16S rRNA sequences from the Human Oral Microbiome Database (HOMD). In both
cases sequences were clustered by cd-hit at the 99% level to remove redundancy. This
reduced the combined dataset of 57,894 sequences to 28,335 OTU representatives at 99%
146

identity. As the survey sequences had no taxonomy assigned to them, they were
classified using the SILVA taxonomy via the classification feature of MOTHUR (Schloss
et al., 2009a). MDA products with 16S rRNA gene taxonomy similar to those NTC
reactions were excluded from further analysis. For further details, see the Supplemental
Methods.

Sequencing

Nextera transposition based 454 compatible fragment libraries were constructed for each
MDA as per the manufacturer’s instructions (Epicentre Technologies, Madison, WI)
using MDA genomic DNA as template, including incorporation of the 48 Nextera 454
barcodes and use of Zymo DNA Clean & Concentrator-5 columns (Zymo Research,
Irvine, CA) following the transposition and PCR amplification steps. Nextera
transposition based Illumina fragment libraries were constructed for each MDA as per the
manufacturer’s instructions (Epicentre Technologies, Madison, WI). The library
concentrations were determined by absorbance on a Nanodrop spectrophotometer. The
column purified barcoded fragment libraries were pooled at approximately 1:1, without
size selection, and emulsion PCR and sequencing were performed at JTC. Illumina
sequencing (Bentley, 2006) was performed on the MDAs using the Genome Analyzer II
System according to the manufacturer’s specifications. The three MDAs assigned to P.
gingivalis were barcoded and pooled on a half lane; this generated 11.5 GB of data and
47 M reads that passed quality score >20.
147

Reference mapping

Reference mapping was conducted using CLC Genomics Workbench, using the reference
Porphyromonas gingivalis (NC-015571). Mapping parameters were as follows: local
alignment with mismatch cost 2, insertion cost 3, deletion cost 3, length fraction 0.9, and
similarity 0.9 (90% of the read length needed to be aligned at 90% similarity).

Single nucleotide polymorphism (SNP) and deletion insertion and
polymorphism (DIP) analysis

Parameters for SNP analysis using CLC Genomics Workbench: max # of gaps and
mismatches 2, minimum average of quality of surrounding bases 30, minimum quality of
central base 30, minimum coverage 10, Minimum paired coverage =10, minimum variant
frequency 80%. DIP analysis parameters using CLC Genomics Workbench: minimum
coverage 4, minimum variant frequency 35%. Coverage above 30× with a minimum
count of 10 reads with a frequency of 80% cutoff was considered for the detection of
shared SNPs.

148

Single-cell assemblies

Assemblies were produced using Velvet-SC (Chitsaz et al., 2011) and SPAdes
(Bankevich et al., 2012a). Both of these assemblers are based on the de Bruijn graph, and
both have been adapted for uneven coverage found in single-cell MDA datasets. SPAdes
was also adapted for the elevated numbers of chimeric reads and read-pairs in MDA
amplified datasets. For Velvet-SC, we assembled the data with kmer size k=55. For
SPAdes, we iterated over kmer sizes k=21, 33, and 55. As described in Results, we
selected the SPAdes assembly of MDA3 for detailed analysis.

Contig analyses

Assemblies were manually curated using a conservative approach for single cell MDA
data as described in (Chitsaz et al., 2011; Dupont et al., 2011; Eloe et al., 2011). Briefly,
all contigs less than 150 bp in length were removed. Taxonomic affiliations of the
predicted protein sequences on the contigs were assigned using APIS (Badger et al.,
2006) and contigs that contained a majority of proteins with taxonomic affiliations other
than the genus-level classification Porphyromonas were removed. Further verification of
correct taxonomic classification of the contigs was performed using MGTAXA software,
which performs taxonomic classification of metagenomic sequences and is fundamentally
based on the frequency of kmers, (http://mgtaxa.jcvi.org). Although, the use of combined
approaches for enrichment of target contigs of interest can remove a fraction of
149

potentially new genomic information, it also provided confidence in the final datasets
since taxonomic affiliation of the sequences are in accordance with the 16S rRNA
phylogeny.
Gene annotation
The assembly from MDA3 that represented most of the genome was annotated using the
JCVI prokaryotic annotation pipeline
(http://www.jcvi.org/cms/research/projects/annotation-service/overview/). In addition,
the XBASE rapid annotation service (http://www.xbase.ac.uk/annotation/) was used to
annotate homologs in this genome to P. gingivalis Strain W83, which XBASE provides
as the nearest reference genome for the purpose of rapid annotation.

Orthology
Reciprocal best BLASTp analysis of genes for reference genomes: P. gingivalis TDC60,
ATCC33277, W83 and predicted genes of PG JCVI SCOO1 at a cutoff of 1e-9.
Annotations for genes that were found only in PG JCVI SC001 (524, 22.8%) were from
the JCVI prokaryotic annotation pipeline.
Multi locus sequence typing

This publication made use of the P. gingivalis Multi Locus Sequence Typing website
(http://pubmlst.org/pgingivalis/) developed by Keith Jolley and sited at the University of
Oxford.
150

Data access
The genome data have been submitted to the NCBI GenBank (http://
www.ncbi.nlm.nih.gov/genbank/) Accession: PRJNA167667 ID:167667

Acknowledgements
This work was supported by grants to J.S.M. from the National Institutes of Health
(1R01GM095373 and 1R01DE020102)to R.F., J.C.V and R.S.L by the Alfred P. Sloan
Foundation (Sloan Foundation-2007-10-19);; to P.P. and G.T. from the National
Institutes of Health (NIH 3P41RR024851-02S1); to P.P. from the Government of the
Russian Federation (grant 11.G34.31.0018); and to M.G.Z. from the National Institutes of
Health (UL1TR000100). We thank Mark Adams for helpful discussions and Mathangi
Thiagaran (J. Craig Venter Institute) for bioinformatics support.

151

TABLE 3-S
Table 3- 1. Individual amplified single cells and combined single cell read mapping
against P. gingivalis TDC60 reference.
MDA1 MDA2 MDA3 MDA123
Total read count 8,873,267 990,119 5,756,473 15,619,859
% of reference mapped 40 87 91 91
Maximum coverage
a
20,717 1,074 10,059 21,726
Average coverage
b
366 41 238 644
a
The highest coverage in the region.
b
Average coverage is calculated by summing up the bases of the aligned part of all the reads divided by the
length of the reference sequence including zero coverage regions.

a
Quantities in the Table 3- are based on the contigs of size at least 201 bases.
b
The best value in each category is boldfaced when possible; some categories cannot be interpreted as
having a best value.
c
N50 is the largest contig length, L, such that contigs of size ≥ L comprise at least half of the bases in the
reference genome (TDC60).
d
Adjusted N50 is computed by first aligning the contigs to the TDC60 reference genome, removing the
non-aligning parts, and breaking the contigs up into blocks at alignment boundaries. With an exact
reference genome, this would prevent miss-assemblies (incorrect contig joins) and contaminants from
inflating the value of N50; however, there may be true differences between this strain and TDC60, which
this adjustment penalizes. The remaining statistics are also based on the same alignments and alignment
breakpoints.
e
A contig is partially aligned (“part”) when it has alignment(s) to the reference genome, but they comprise
less than 99% of the contig’s length.
f
The rate of matches in the portions of the contigs that align to the TDC60 reference genome.
g
The fraction of the TDC60 reference genome to which contigs are aligned.
Table 3- 2. Comparison of assemblies of single-cell P. gingivitis MDA3.
2
Assembly SPAdes E+V-SC Velvet
Number of contigs
a
614 452 3,162
Total length 2,537,623
b
2,352,771 1,442,312
Largest contig 101,845 82,017 3,788
N50
c
23,369 13,391 283
Adjusted N50
d
13,589 10,732 220

Reference length (strain TDC60) 2,339,898 2,339,898 2,339,898
# Breakpoints vs. TDC60 115 96 7
# Contigs with breakpoints 65 55 5
# Bases in contigs with breakpoints 1,235,715 866,637 6,678
# Unaligned contigs
e
351 + 23 part 91 + 40 part 493 + 74 part
# Unaligned bases 194,790 135,565 205,376
Average identity (%)
f
96.720 96.830 98.860
Mapped genome (%)
g
90.139 87.682 52.175
152

Table 3- 3. General features of the PG JCVI SC001 genome and comparisons with
sequenced Porphyromonas gingivalis genomes.
a
Based on Glimmer gene prediction and JCVI prokaryotic annotation pipeline.
b
Based on XBase (http://www.xbase.ac.uk/annotation/ ) gene annotation (using P. gingivalis W83
as a reference).

Table 3- 4: JCVI SCOO1 specific CDS (top 12 of 524)
identified via reciprocal best blast analysis.

Raw
Count %
a
Annotation
246 46.68 hypothetical protein
91 17.27 conserved hypothetical protein
31 5.88 conserved domain protein
4 0.76 cleaved adhesin domain protein
4 0.76 site-specific recombinase, phage integrase family
3 0.57 PcfJ-like protein
3 0.57 peptidase, S9A/B/C family
3 0.57 TraM recognition site of TraD and TraG
3 0.57 transposase, IS4-like family protein
3 0.57 lipoprotein, putative
3 0.57 DNA-binding helix-turn-helix protein
3 0.57 glycosyltransferase, group 1 family protein
a
Percentage of CDS specific to JCVI SC001.
Strain CRISPR
Count
GC
%
Coding Base
Count
Genom
e Size
Gene
Count
CDS
Count
CDS % RNA
Count
ATCC 3 48 2,046,172 2,354,886 2,155 2,090 96.98 65
W83 4 48 1,954,527 2,343,476 1,984 1,909 96.22 75
TDC60 5 48 2,040,041 2,339,898 2,283 2,217 97.11 66
JCVI SC001
a
1 48 2,0327,27 2,350,571 2,344 2,293 97.88 45
JCVI SC001
b
1 48 1,948,482 2,350,571 2,165 2,120 97.98 48
153

152 found in single cell sorted wells from the sink biofilm sample. 16S rRNA
sequences from single cell amplifications observed for this FACS sorted sample.
154

Figure 3-2. Circular representation of the draft JCVI SC001 genome. The
assembled draft genome is the SPAdes assembly of MDA3 with the contigs ordered to
the TDC60 reference genome. From the inner to the outer ring: coordinates in the
assenmbled and concatenated JCVI SC001genome, G+C content, GCskew, ordered
contigs, predicted CDS, tblastn alignment showing percent identity against P. gingivalis
TDC60, W83, ATCC 33277 and Prevotella buccae ATCC33574 (near neighbor)
reference genomes.
155

Figure 3-3. Single nucleotide polymorphisms and read coverage across fimA of the
reference strain TDC60. Row 1) reference gene fimA; Row 2) shared SNPs across the
three single cell genomes with 100% frequency at a coverage of 10× ; Row 3) shared
SNPs at a coverage of 30×; Row 4-8) SNPs at 10× and 30× for each single cell
amplification; Rows 9-10) mapped deletions; Rows 11-13) mapped reads; Row 14)
mapped 454 reads.
156

Figure 3-4. Comparison of the polysaccharide capsule locus found in MDA3
(bottom) with W83, ATCC 33277 and TDC60. Genes of the same color are from the
same orthologous group.
157

Figure 3-5. Comparison of a clustered regularly interspaced short palindromic
repeat region (CRISPR). Successful multiple displacement amplification and de novo
assembly of the repeats in CRISPR region 36-30. This region was first identified in
strain W83 and all three genomes have 100% identical repeat sequences. The regions
vary in the number of repeats, number of spacer sequences and spacer identity.
158

Supplemental Tables and Figure Legends.
Appendix C (Information for Tables and Figures available online )
(http://genome.cshlp.org/content/23/5/867/suppl/DC1)
Supplemental Figure 3- S1. Biofilm sample biomass and Fluorescence Activated
Cell Sorting (FACS) biofilm sample. (A) Sample biomass from sink drain biofilm
obtained within a restroom adjacent to an emergency waiting room. Image show the
collected biomass prior to any processing. (B) Sorting gates used to sort events after
staining with sybergreen DNA stain. P1 represented high fluorescent particle events and
background represents the region unstained sample events were located. After DNA
staining, the high fluorescent P1 region was chosen as a sort gate.

Supplementary Figure S2. Custom integrated Agilent Technologies BioCel 1200
liquid handling automated platform for high throughput single cell genomics. The
BioCel platform allows processing of more than 5,000 single cells per week through a
multi-stage protocol that includes multiple displacement amplification (MDA) of DNA,
MDA dilution and 16S rDNA PCR, MDA and PCR hit picking, Picogreen (Life
Technologies) DNA quantitation, 16S Syto 9 (Life Technologies) melt curve assay, 16S
rDNA Taqman qPCR, and SAP/Exonuclease I (Affymetrix) PCR treatment. All liquid
handling is performed on the BioCel with the BioRAPTR (Beckman Coulter) and Bravo
(Agilent) performing non-contact dispensing and liquid transfer steps, respectively. The
MDA isothermal reaction and PCR are performed offline on GeneAmp PCR system 9700
thermocyclers (Applied Biosystems), while TaqMan or melt curve analysis are performed
in-line on the ABI 7900HT (Applied Biosystems). The platform includes barcode
tracking of 384-well plates, and is integrated with a JCVI Laboratory Information
Management System (LIMS).
Supplemental Figure S3. Variation in coverage levels for the three single cell P.
gingivalis MDAs. The percent of bases with at least the indicated coverage is plotted.

Supplemental Figure S4. Single nucleotide polymorphisms and read coverage
across the reference strain TDC60. Row 1) genes; Row 2) mobile elements; Rows 3-5)
MDA SNPs at a coverage of 30×; Rows 6-8) Illumina read coverage; Rows 9-11) 454
read coverage. Read coverage is given for each on the x-axis and TDC60 genome
position on the y-axis.

Supplemental Figure S5. Taxonomic classification of the SPAdes de novo assembled
contigs. The largest proportion of the contigs of each assembled genome were classified
as belonging to Porphyromonas gingivalis species using the MGTAXA tool.

Supplemental Figure S6: Venn diagram depicting pangenome of P. gingivalis. Data
resulted from reciprocal BLASTp at 1e-9 cutoff analysis. Total gene counts are written
above genome ID. Reference genomes are listed by their strain designation.
159

Supplemental Figure S7. Comparisons of the allelic profiles for the 138 sequence
types (isolates) available in the MLST database with the allelic profile for JCVI
SC001. A) Trees were generated using PHYLIP suite of programs (web-based tools
available for the MLST database) to create trees from allelic profile data. A graphical
representation of the tree was generated with Phylodendron. Scale bar is 0.1. B)
Minimum-spanning tree. A minimum-spanning tree was generated from MLST profile
data. (www.pubmlst.org). BURST clustering is used to give meaningful results. The two
STs with the greatest number of single locus and then double locus variants are linked
first, preferably using intermediate STs. ST-68 is the nearest sequence type and this
sequence type is shared with P. gingivalis strain TDC60.

Supplemental Table 3- S1. Taxonomic diversity of bacterial open reading frames
identified from assembled contigs derived from shallow 454 pyrosequencing
belonging to the additional wells of interest. The number of 454 reads ranged between
9,000 -10,000 for each sample. ORFs were identified and annotated by FragGeneScan
(Rho et al. 2010) and METAREP (Goll et al. 2010), respectively.

Supplemental Table 3- S2. Read coverage and single nucleotide polymorphisms
determined by mapping reads from individual MDA sets to the de novo assembled
contigs generated from MDA3. There were 3 SNPs shared in all MDAs limited to the
41% of the MDA3 contigs covered by MDA1 reads.

Supplemental Table 3- S3. Table 3- of gene orthologs in JCVI SC001 across the
available P. gingivalis genomes.

Supplemental Table 3- S4. JCVI SCOO1 specific genes (524) identified via
reciprocal best blast analysis.

160

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166

Conclusions and Future Directions

It is clear that in order to determine what is happening, we need to know all the
functional contributors and the generation of genomic information is a start on
understanding the potential metabolisms encoded in the DNA of these organisms. While
many new associated microbes have been identified through 16S rDNA based diversity
analyses, further research on most of these microorganisms has been hampered by an
inability to uncover their genomes. As many human microbial infectious diseases
including dental caries are polymicrobial in nature, and closely associated with ecological
conditions of the microbial communities. Understanding first the species present, the
genomic potential s, gene expression profiles and their metabolism and as well as inter-
species interactions within these complex microbial communities are necessary steps for
deep understanding of the process and also finding new solutions to modulate the activity
of communities.
In future work developing from these advancements, a particular emphasis will be
focused on the discovery of low pH metabolism, low pH adaptations and organic acid
production most relevant to mineral (hydroxyapatite) dissolution process. Overall the
combination of these approaches on any microbial systems of interest will reveal species
(cultured and uncultured) involved in dissolution processes and provide new insights into
specific species, genes/domains, gene products, and metabolic pathways that define the
synergistic and competitive contributions to both neutral pH and low pH in a complex
microbial community.
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189

Appendix A: Manuscript McLean et al. 2012

Identifying Low pH Active and Lactate-Utilizing Taxa within Oral
Microbiome Communities from Healthy Children Using Stable Isotope
Probing Techniques

Citation: McLean JS, Fansler SJ, Majors PD, McAteer K, Allen LZ, et al. (2012) Identifying
Low pH Active and Lactate-Utilizing Taxa within Oral Microbiome Communities from
Healthy Children Using Stable Isotope Probing Techniques. PLoS ONE 7(3): e32219.
doi:10.1371/journal.pone.0032219

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0032219

190

Appendix B:

A novel in vitro biofilm model maintaining a high species and metabolic
diversity similar to the human oral microbiome

Anna Edlund
1‡
, Youngik Yang
1‡
, Adam P. Hall
1
, Lihong Gou
2
, Renate Lux
2
, Xuesong
He
2
, Karen E. Nelson
3
, Kenneth H. Nealson
4
, Shibu Yooseph
1
, Wenyuan Shi
2
, Jeffrey S.
McLean
1*

1 Microbial and Environmental Genomics, J. Craig Venter
Institute, San Diego, CA, USA, 2 School of Dentistry,
University of California Los Angeles, California, USA, 3 J.
Craig Venter Institute, Rockville, MD, USA, 4 Department of
Earth Sciences, University of Southern California, Los
Angeles, CA USA

‡
These authors contributed equally to this work.
*Correspondence: Jeffrey S. McLean, Microbial and Environmental Genomics, the J.
Craig Venter Institute, San Diego, CA, USA. Tel: +001 858 750 1843; email:
jmclean@jcvi.org

191

Abstract

Our knowledge of microbial diversity and its role in the human oral cavity has vastly
expanded during the last two decades of research. However, much of what is known
about the behavior of these species to date derives from pure culture approaches and
limited mixed species laboratory experimentation, which does not fully reflect their
function in complex microbial communities. In the light of this, our primary goal was
to develop a robust in vitro biofilm-model system that can be applied to generate
fundamental knowledge of the complexity of the oral microbiome and its dynamic
response to environmental changes from the community to the molecular level. We
employed deep sequencing to further investigate this biofilm model with regards to
bacterial taxonomic and metabolic diversity and show high reproducibility of the
taxonomic carriage and proportions between individual biofilm samples, between
batches and research laboratories. Comparative metagenomic analyses confirmed the
high similarity of metabolic potential in biofilms to recently published oral
metagenomes from healthy subjects as part of the Human Microbiome Project. Our
study demonstrates that we now have the capability to grow complex oral microbial
biofilms containing more than one hundred operational taxonomic units (OTU) in vitro
where 60-80% of the original inoculum diversity is maintained. To our knowledge, this
represents the highest oral bacterial diversity that reported for an in vitro model system
thus far. This robust model will help investigate the known virulence properties for
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many oral pathogens not solely restricted to pure culture systems, but within multi-
species plaques.
Introduction
The human oral cavity harbors a highly diverse and unique microbiome, which
exist in a continuously changing environment where pH, organic carbon and oxygen
levels fluctuate on a hundred-fold or even a thousand-fold scale within minutes (Lemos et
al., 2005; Takahashi and Yamada, 1999). To gain a deeper understanding of the
complexity of this ecosystem, research has slowly begun to move beyond single-species
approaches to defined mixed oral communities limited to isolated and characterized
species. Currently available mixed-species growth model systems include chemostats
(Bradshaw et al., 1989), the constant depth film fermentor (CDFF) (Kinniment et al.,
1996), saliva-conditioned flow cells (Cook et al., 1998; Foster and Kolenbrander, 2004)
and artificial mouths (Sissons et al., 1991). Using mixed-species models consisting of up
to 10 defined species in a chemostat led to the synthesis of the “ecological plaque
hypothesis” which proposes that selection of “cariogenic bacteria” is directly coupled to
alterations in the environment that shift the balance of the community (Marsh, 1991).
According to this hypothesis, if the pH remains below the “critical pH” for
demineralization of 5.5 for extended time periods after a carbohydrate pulse, a shift in the
bacterial populations to more cariogenic organisms that are acid-producing (acidogenic)
and acid-tolerant (aciduric) occurs (Hodgson et al., 2001; Marsh, 1997). Another
important aspect of this hypothesis is that any species with relevant traits can contribute
to the disease process (Bradshaw and Marsh, 1998; Marsh, 1991). This was also
193
supported by multiple findings that bacterial species, other than well-known pathogens
(e.g. Streptococcus mutans) are present in caries active sites (Aas et al., 2008; Gross et
al., 2012). Also, a recent study shows that one can detect many low-pH active species
present in a healthy plaque which may be responsible for the onset of caries disease
(Ahmed et al., 2012).
In order to fill in the knowledge gaps in species diversity for these complex
communities, the Human Microbiome Project (HMP) (Consortium, 2012; Turnbaugh et
al., 2007) and the Human Oral Microbiome Database (HOMD) (Chen et al., 2010) have
been established. The major goal of the HMP is to characterize bacterial communities
that are associated with several different body sites as well as generate a catalog of
reference genomes from species derived from human hosts (Human Microbiome
Jumpstart Reference Strains et al., 2010). The HOMD specifically contains information
on the prokaryotic species present in the human oral cavity and has the capacity to link
sequence data with phenotypic, phylogenetic, clinical and bibliographic information. The
curated version of HOMD contains approximately 619 validated taxa with 1,178 total
taxa identified, of which 24% are named, 8% are cultivated but unnamed and 68%
represented uncultivated phylotypes (Dewhirst et al., 2010). A human oral taxa is defined
as sharing 98% similarity in 16S rRNA (16S) gene sequences. A major hurdle to
understanding the oral microbiome is the unknown contribution of the very large
uncultivated fraction of the existing bacterial diversity. In fact, the greatest number of the
“most wanted taxa” (i.e. those that have been seen by 16S sequencing but remain
uncultivated) targeted for whole genome sequencing in the human body reside in the oral
194
cavity (Fodor et al., 2012). The identity of these oral phylotypes can only be linked to
possible functions by using techniques such as nucleic acid based stable isotope probing
(SIP) (McLean et al., 2012) or single-cell genomics based sequencing approaches
(Lasken, 2012a; Marcy et al., 2007). Moreover, due to the complexity and the high
taxonomic variability of the oral microbiome between study subjects it is extremely
difficult to track species and strains temporally and spatially (Consortium, 2012). Also,
small sample sizes that are dictated by availability of volunteers and costs, limit the
statistical power needed to detect small, but important differences among communities.
Hence, to gain a deeper ecological understanding of the processes that are involved in the
gradual succession of healthy oral microbiomes to disease-associated microbiomes, it is
important to continue the development of oral microbial model systems where
experiments can be conducted in a controlled environment. The advantages with such
systems are many as they provide novel opportunities to study microbial community
ecology with systems biology perspectives by using global ‘omics’ experimental tools
(metagenomics, metatrascriptomics, metabolomics). A model system also allows for
collecting replicate samples and contributes to the analyses of large samples that are
needed to obtain reliable spatial and temporal dynamics data of bacterial populations
within a community.
In this study, our aim was to develop a mixed-community biofilm model system
comprising the highest possible cultivable bacterial diversity representative of the
resident saliva-derived microbiome responsible for plaque formation in the human oral
cavity. We used a recently developed growth medium (SHI medium) that previously was
195
shown to support growth of a highly diverse oral microbiome and that had high coverage
of those in original saliva samples obtained from healthy adults (Tian et al., 2010). To
evaluate the reproducibility of this in vitro model system and to address its overall
metabolic potential we applied suites of molecular techniques consisting of the
conventional community fingerprinting tool DGGE and two next generation sequencing
techniques. Firstly, the reproducibility of the mixed biofilm community model was tested
between research laboratories by addressing 16S community fingerprints from DGGE
profiles. Secondly, to obtain a broader understanding of the whole biofilm community
diversity, deep sequencing of 16S genes via 454 pyrosequencing and whole genome
shotgun (WGS) on the Illumina HiSeq platform was performed. To our knowledge, the
model we have developed represents the highest oral bacterial diversity that has been
reported for an in vitro system thus far. This model will aid in the understanding of oral
microbial communities by facilitating discovery and functional characterization of known
as well as uncultivated bacteria within a mixed-species system that is approaching the
diversity of in vivo conditions. Importantly, it will also allow systematic investigation of
species, specific genes/domains, gene products and metabolic pathways that define the
synergistic and competitive contributions to health and disease in the complex oral
microbiome.
Materials and methods
Saliva collection
Saliva samples were collected from six healthy subjects, age 25-35 years as described in
Tian et al. (2010). Subjects were asked to refrain from any food or drink 2 hours before
196
donating saliva and to spit directly into the saliva collection tube, 5 ml saliva was
collected from each person. Saliva samples were pooled together and centrifuged at 2600
g for 10 minutes to spin down large debris and eukaryotic cells. The supernatant was
referred to as pooled saliva and used throughout this study. Cell-free saliva was also used
for coating wells prior to growing the biofilms.

Culturing and growth of saliva-derived biofilms using SHI medium
Prior to saliva inoculation in SHI medium (Tian et al., 2010) in a sterile 24-well plate,
200 μl of saliva supernatant was added to each well to pre-coat the wells, and plates were
incubated at 37°C with the lid open for 1 hour to dry the saliva coating. Plates were then
sterilized under UV light for 1 h before 10 μl of pooled saliva was inoculated into pre-
coated well containing 10 μl of sucrose (0.5%), 980 μl SHI medium. Plates were
incubated at 37°C under anaerobic conditions for 16 hours to allow biofilm formation.
The biofilms were carefully washed twice with an buffered chemically defined medium
(CDM) (Ahmed et al., 2012). After the washing steps, biofilms were starved (i.e.
incubated without carbon source) in fresh CDM medium (pH 7) for 2 hours in 37°C and
incubated under anaerobic conditions. After starvation the biofilms were harvested.

Glucose and pH monitoring of biofilm growth medium
After the 2 hour period of starvation, 1 mL of 0.5% glucose in fresh CDM (pH 7) was
added to each biofilm well. Glucose levels were then measured in the biofilm growth
medium by using the glucose-specific TRUE2go Blood Monitoring System (CVS
197
Pharmacy, Inc., Woonsocket, RI). A 3 µl sample was withdrawn from a glucose-
designated sample well throughout the biofilm incubation. This sample was added to a
sterile plastic surface inside the anaerobic chamber prior to applying the test strip, which
was then mounted onto the electronic glucose meter reader. Three replicate samples were
analyzed at incubation time points: zero, 2, 4, 5, 6 hours. The lower limit for glucose
detection of the TRUE2GO Blood Monitoring devise is 20mg/dl, which was reached
after 5 hours of incubation. Biofilm-growth medium pH was monitored in near real-time
within replicate pH-designated incubation wells and measured by combining pH
Laboratory Electrodes (EW-05990-65, Cole-Parmer, Court Vernon Hills, IL) with a
wireless sensor network platform consisting of a pH transmitter (UWPH-2-NEMA,
OMEGA, Stamford, CT) and a pH receiver (UWTC-REC1, OMEGA). pH measurements
were monitored and downloaded onto a PC computer by using a TC central software for
UWTC (OMEGA). Real-time pH was recorded for 24 hours every 30 seconds within
each growth well.

DNA extraction and Processing of Pyrosequencing data
DNA was isolated as described in (McLean et al., 2012) by using the DNeasy Blood and
Tissue Kit (Qiagen Inc, USA) and eluted in a final volume of 200 μL water. Biofilms
representing the Batch 2 samples were a part of a Stable Isotope Probing (SIP) time-
series study (unpublished) in which a series of samples are subjected to
13
C-labelled
glucose amendments in the CDM buffer as described in McLean et al. 2012. However,
the Batch 2 samples described in this study were not fed labeled
13
C-glucose since they
198
served as controls and were collected immediately (time point zero hours of incubation).
The DNA from the SIP-treated biofilm replicate samples (Batch 2 samples) was
separated by centrifugation against a CsCl gradient (McLean et al 2012). The sample
processing of these particular samples was conducted as follows: (1) entire DNA was
extracted from each sample and loaded into the gradient solution; (2) gradient formation
was achieved by centrifugation at 265,000×g for 66 hours in a Beckman VTi65 rotor
(Beckman Coulter, Inc., Fullerton, CA); (3) fractions were collected (400 µL) and the
DNA was isolated using a Ym-100 Microcon column (Millipore); (4) columns were
washed four times with TE buffer and purified DNA was eluted into 50 μL volumes.
Biofilm samples representing Batch 1 and Batch 3 were not subjected to
ultracentrifugation. After genomic DNA extraction and quantification, samples were
prepared for 16S amplification and titanium based 454 sequencing at the (J. Craig Venter
Institute) JCVI Joint Technology Center (JTC). Genomic DNA sample concentrations
were normalized to 2-6 ng/μl. The V3-V5 region of 16S genes was amplified according
to the previously developed protocol available at
http://www.nature.com/nature/journal/v486/n7402/extref/nature11209-s1.pdf
(Consortium, 2012). Using each sample's individual barcodes, the 454 sequence data was
deconvolved into the respective samples. After trimming the bar codes, low-quality and
short sequences (<100 bp) were removed by using the JCVI 16S pipeline. Subsequently,
the remaining filtered reads were aligned against the SILVA database of 16S to verify
that the reads were indeed 16S. The Chimera Slayer tool was used to filter out
potentially chimeric reads (Haas et al., 2011).
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PCR-DGGE analysis
Reproducibility of 16S gene diversity was tested by two different research laboratories,
i.e. JCVI, San Diego, CA and at the School of Dentistry, University California Los
Angeles, CA. Both research laboratories carried out saliva inoculation from the same
saliva pool into SHI medium prepared independently at each lab. Samples were incubated
and washed at the different locations as described above. Genomic DNA extractions, PCR
amplification and DGGE analyses were carried out at the UCLA laboratory. Amplification of
bacterial 16S genes and DGGE analysis was carried out as described in previous
published protocol (Tian et al., 2010). The universal primer set Bac1 and Bac2 (Sheffield
et al., 1989) was used to amplify an approximately 300-base-pair internal 16S fragment
of the 16S gene.

Biofilm and saliva OTU diversity in pyrosequencing libraries
To compare operational taxonomic unit (OTU) diversity between saliva and biofilm
samples, the following analysis steps were conducted by using the MOTHUR software
(Schloss et al., 2009b): (1) quality filtered fragment reads were assigned to OTUs in
97% sequence identity level; (2) distances of samples in data sets were calculated in
terms of OTU profile; (3) Yue & Clayton metrics was applied which measures structure
dissimilarities between communities (Yue and Clayton, 2005); (4) the obtained Yue &
Clayton matrix was used to calculate and visualize sample similarities by using
correspondence analysis (CA) (Legendre and Legendre, 1983) in the ADE-4 software
200
(Thioulouse et al., 1997). A bubble chart was generated to show sample similarities for
HOT designations in biofilm samples Batch 2 well 1 to well 2, Batch 3 well 1 to well 3.

Identification and phylogenetic analysis of 16S genes
To identify previously defined oral bacteria taxa in the biofilm and saliva samples, a
reference alignment was initially created by downloading 16S rRNA RefSeq Extended
Version 1.1 from HOMD (Chen et al., 2010), which consists of 1,647 sequences. By
using this reference sequence set we generated 1,642 non-redundant sequences by using
cd-hit-est (Li and Godzik, 2006) with 100% sequence identity and 95% alignment
coverage for the shorter sequence. 1,642 sequences were aligned by cmalign in Infernal
package (Nawrocki et al., 2009). A phylogenetic tree was generated with RAxML
(Stamatakis, 2006) by inputting the alignment. By using blastn (Altschul et al., 1990),
our sequence reads from the saliva and biofilm samples were aligned to the HOMD
reference sequences. Sequence matches were counted at 97% sequence identity cutoff
and 95% sequence coverage. Match counts for each category was scaled in base-10 log.
Reference tree and the counts were visualized by using the online interactive tree of life
(iTol) software (Letunic and Bork, 2011). In the figure, color ranges were shown at
phylum level. Bar charts next to leaf labels shown log scale match counts for saliva and
in vitro biofilm samples, respectively.

201
Metagenomic analyses of biofilms
WGS sequencing was performed on DNA that was extracted from the biofilms at 12 and
16 hours of growth in SHI medium according to the protocol described in McLean et al.
(2012). WGS sequences (fragment and paired end reads) were obtained from the Illumina
HiSeq instrument, quality trimmed and filter by using the CLC workbench software v.
6.0.1 (CLCbio, Aahus, Denmark). The following CLC-parameters were applied during
paired read sequence trimming and quality control: quality score setting: NCBI/Sanger or
Illumina Pipeline 1.8 and later, minimum distance: 180, maximum distance: 250. The
trimmed reads were subjected to sequence assembly by using the CLC workbench
(CLCbio). ORF calling and annotations were performed on the contigs obtained from the
CLC Work Bench according to the JCVI prokaryotic metagenomics pipeline
(Tannenbaum et al. 2010). Metagenome annotations were uploaded for comparative
analyses on METAREP (Goll et al., 2010). 99 previously sequenced metagenomes,
available via the HMP through the JCVI supported METAREP
(http://www.jcvi.org/hmp-metarep/) were selected for metabolic pathway comparison
analysis.

Results and discussion
Reproducibility between research laboratories and across replicate samples
The goal of this study was to develop and validate with deep sequencing, a novel robust
in vitro model system representative of the naturally complex oral microbiome. The
design requirements for the system included relatively simple construction and handling
202
of samples. Other workers have previously developed a variety of in vitro models based
on a handful of already domesticated oral species (Aldsworth and MacFarlane, 2001;
Aspiras et al., 2000; Bradshaw and Marsh, 1998; Palmer et al., 2001). These studies show
intriguing examples of how certain oral bacteria form advantageous partnerships that are
necessary for growth of other species and that these partnerships sometimes are also
observed in vivo (Chestnutt et al., 1994). In line with an earlier study by Tian and
colleagues (2010) we found that it was possible to grow a remarkably high number of
oral taxa in vitro from a small number of human saliva samples that were inoculated and
grown as biofilms in the recently developed SHI medium. Initially, our deep sequencing
experiments sought to confirm the most appropriate concentration of additional sucrose
needed in the SHI medium to best represent the saliva derived oral community that our
previous DGGE results showed more qualitatively (Tian et al., 2010). Two carbon source
concentrations added to the SHI medium (0.1%, 0.5% sucrose) and a control with no
sucrose, were tested by sequencing the resulting in vitro grown biofilms. We found that
the 0.5% sucrose was able to maintain the saliva-derived diversity in biofilms most
similar to the saliva inoculum in terms of the genera observed (see Supplementary Figure
S1). In contrast, the no-sucrose biofilm was less diverse and was dominated by one
species of Streptococcus, S. mitis. The 0.1% sucrose amended sample was similar to the
0.5% sucrose but had a higher proportion of subgingival community members such as
Porphyromonas. This confirmed that 0.5% sucrose was the best choice for our model and
that the taxonomic diversity responds rapidly to environmental changes. After optimizing
the community towards a higher diversity, 16S gene amplification and DGGE analyses
203
were performed from biofilm samples which shared the same pool of saliva but were
grown by two different research laboratories (School of Dentistry, University of
California Los Angeles, CA and J. Craig Venter Institute, San Diego, CA) to test
technical reproducibility (Figure 1). Although the saliva inoculum was shared,
completely independent media reagents and growth procedures were carried out in the
respective labs. The resulting replicate gel images were aligned and 16S band patterns
were compared for samples that had been incubated with or without 0.5% sucrose
showing that despite the variations between labs, the resulting community composition
were highly reproducible. The majority of the DGGE bands showed similar fluorescence
intensity across replicates and only one major band was missing in the replicate samples
deriving from the JCVI laboratory (Figure 1).
To evaluate the reproducibility of our model system further we analyzed 454-
pyrosequencing data obtained from biofilms cultivated on different dates and that were
grown in replicate growth wells from the same freshly thawed saliva pool. The biofilm
samples Batch 1, Batch 2 and Batch 3 were grown and processed at different days to
assess the reproducibly of batches at different time points while replicate growth wells
from Batch 2 and 3 were compared to address the variation between biofilm growth
wells. Similarities in OTU diversity between replicate biofilms from all biofilm batches
and from saliva inoculum samples were estimated by using correspondence analysis (CA)
(Figure 2). In CA, the saliva samples were similar and clustered together but separately
from the biofilm samples (Figure 2). The biofilm libraries that derived from Batch 2 were
also highly similar, however, they clustered separately from biofilm samples representing
204
Batch 1 and Batch 3 that formed a separate cluster. The difference between Batch 2 and
the other biofilm samples was most likely due to that these samples were subjected to the
SIP-treatment that included extra steps of DNA centrifugation. Regardless of this
difference, the community profiles for replicate biofilm samples were highly
reproducible. Of note, this observation was also consistent with the reproducibility
qualitatively determined from DGGE profiles as shown in Tian et al. (2010).

Physiological responses to carbohydrate pulse are similar to in vivo plaque
After initiating biofilm incubation with 0.5% glucose in fresh CDM medium the pH
started to drop instantly (Figure 3). pH was neutral (~7) in the beginning of the
incubation and decreased to 4.5 after 4 hours. At 6 hours it had reached its lowest level
(4.2) and then it started to recover. Glucose concentrations followed the same falling
pattern and after 5 hours it was below detection limit (20mg/dL) showing a rapid turnover
of this substrate by bacteria in the biofilms (Figure 3). Rapid glucose utilization and
conversion to lactate and acetate was also shown in this CDM for S. mutans and fresh
plaque samples derived from children using nuclear magnetic resonance techniques
(Ahmed et al., 2012; McLean et al., 2008b). These results confirm that our in vitro model
system show a similar physiological response to a glucose challenge as previously
described for dental plaque and salivary sediments (Kleinberg et al., 1973; Stephan,
1943). This particular response has been acknowledged for decades and is defined by the
classic Stephan Curve where oral acidogenic bacteria rapidly metabolize fermentable
carbohydrates producing acidic byproducts. Overall, the response of the in vitro model
205
biofilm is consistent with dental plaque containing species capable of lowering and also
raising the pH.

Taxonomic diversity in pooled saliva and biofilm samples
In order to assess the bacterial phylogenetic diversity in saliva and biofilm samples at a
higher resolution, reference sequences from the HOMD were used to taxonomically
classify 16S genes from this study (Figure 4). Matching HOT designations and their log
transformed abundance values were aligned on the outside of an in-house constructed
HOMD-phylogenetic tree (Materials and methods) at their corresponding branch tips
(Figure 4). Within the HOMD classified 16S genes that were obtained from this study
more strain diversity (i.e. sequences showing >98.5% in sequence similarity) was
observed but not included here. In total, six phyla and 43 genera of bacteria could be
identified in the different samples. These corresponded to 264 HOT designations of
which 127 also could be detected in the biofilms. Several of the dominant genera (e.g.
Streptococcus, Prevotella, Klebsiella) were equally abundant in samples grown at
different time points and in samples grown in different incubation wells showing that
the in vitro grown biofilms are highly reproducible (Figure 5). This was also shown by
the low standard deviations of HOT abundance values between replicate libraries which
ranged between 0.1% and 8% across replicate samples (Figure 5). Of particular note, 52
uncutlivated phylotypes belonging to the Streptococcus, Granulicatella, Haemophilus,
Lactobacillus, Parvimonas, Peptostreptococcaceae, Prevotella, Solobacterium,
206
Fusobacterium, Veillonella, Porphyromonas and TM7 G-2 genera could be identified
in the biofilm growth wells.
Streptococci. Members belonging to the Streptococcus genus are predominant
bacterial species of human saliva and supragingival biofilms (Consortium, 2012;
Lazarevic et al., 2009; Nasidze et al., 2009), which also were observed for the biofilm
in vitro model system in this study. The most dominant HOT designations belonged to
S. vestibularis, Streptococcus salivarus, S. mitis, S. parasanguinis and a variety of
understudied Streptococcus sp. strains (Figure 5). S. vestibularis HOT-021 contributed
with approximately 40% to the total 16S abundance in all replicate biofilm samples
while S. salivarus (HOT-755) and the uncultivated phylotype S. sp (HOT-C65) both
contributed with 10% each. S. vestibualris is a normal inhabitant of the oral cavity and
has rarely been associated with human disease except in a few cases of infectious
endocarditis, early neonatal sepsis and bacteremia in cancer patients (Simsek et al.,
2008). Also, S. vestibularis was previously shown to produce only low levels of caries
in rats when compared to other Streptococcus species (e.g. S. salivarus) (Willcox et al.,
1991). The impacts on oral human health of S. salivarus span a broad range from being
strongly cariogenic (Drucker et al., 1984; Horton et al., 1985) to be caries protective by
hydrolyzing urea to ammonia (Chen and Burne, 1996; Tanzer et al., 1985a, b). No
ecological or clinical data is available for the particular strain we identified here,
however its sequenced genome is available through the HMP and hence species and/or
strain specific molecular probes could be constructed to target its metabolic activities
that would indicate its eventual role in our model system. S. parasanguinis strains
207
HOT-721 and HOT-711 were also abundant in the in vitro biofilms but contributed
only half of the sequence read abundance when compared to S. salivarus and S. sp
HOT-C65. The ecological role of S. parasanguinis in oral health is also poorly
understood but due to its capacity to ferment multiple carbohydrates to lactate and other
organic acids it is considered to be a moderately acid tolerant organism and a
cariogenic species (Kikuchi et al., 1995; Whiley et al., 1990). Previous studies show
that it has been significantly associated with both caries in young children as well as
with a healthy oral flora (Becker et al., 2002; Corby et al., 2007). Undoubtedly, our in
vitro biofilm model supports growth of several S. parasangunis stains in concert and
can be used to test these relationships further.
Lactobacilli. Members belonging to the Lactobacillus genus are found in at low
abundance in supragingival samples from healthy subjects and are thought to be late
colonizers in microbial succession (Consortium, 2012). They are commonly observed
in advanced caries and are isolated on low pH agar (Gross et al., 2010). In the in vitro
biofilms two Lactobacillus species (L. fermentum HOT-608 and L. sp. HOT-A89)
could be identified at a low abundance (average 0.8%), which is relatively close to the
abundance of L. fermentum (0.2%) observed in the clone libraries used to generate the
HOMD. This represents an opportunity to challenge the model and track the increase in
these cariogenic species, simulating a transition from a healthy community to a more
disease-like state. As with the previous chemostat studies that included ten species and
from which the ecological plaque hypothesis was developed (Bradshaw et al., 1996;
208
Marsh, 1991), the proportions of cariogenic species S. mutans and L. rhamnosus
increased at low pH under continuous culture conditions.
Veillonella atypical/Veillonella dispar/Veilonella parvula/Veillonella sp. The
presence of these understudied species in our biofilm model finally gives us the
opportunity to learn more about the role of Veillonella in oral community succession
and caries. In previous studies of Veillonella its ecological role has been unclear and
laboratory studies show that effects of pH on Veillonella growth can be mixed (Noorda
et al., 1988; van der Hoeven et al., 1975). It has also been suggested that the presence
of Veillonella could possibly be used as a predictor of future caries in caries free
children and that it has a close and complex relationship to the pathogen S. mutans
(Gross et al., 2012; Liu et al., 2011). Other Firmicutes that were identified in the
samples belonged to the Mogibaterium, Gemella and Parvimonas genera, which all
have been associated with the oral cavity in both health and disease (Figure 5)
(Consortium, 2012). Disease-associated Mogibacterium was previously identified in
infectious lesions in oral periodontal pockets (Ota-Tsuzuki and Mayer, 2010). This
bacterium has not been easy to study since they are difficult to culture and are
unreactive in conventional biochemical test. Disease-associated Parvimonas and
Gemella species have been isolated from patients with serious disease conditions such
as endocarditis, septic arthritis, meningitis and brain abscesses (FitzGerald et al., 2006;
Ota-Tsuzuki and Mayer, 2010). Next to nothing is known about the triggers and sources
of these diseases and if they are directly or indirectly relate to these bacteria. However,
the prevalence of these potential pathogens in biofilms that derive from healthy human
209
saliva raises the question if and to what extent the oral cavity can serve as a reservoir
for disease in different human body sites.
Fusobacterium. Bacterial community members belonging to the Fusobacterium
genus contributed with approximately 10% to the total 16S gene diversity in the biofilm
communities. Like most of the other oral community members they are known to be
associated both with the normal human oral flora and also with certain oral diseases
(Rogers, 1998). Their ability to grow in many different habitats can be explained by
their broad metabolic versatility as they can obtain energy from fermentation of a broad
range of simple sugars and amino acids, free or in the form of peptides (Rogers, 1998).
Three strains representing the invasive host pathogen F. nucleatum were identified here
which suggests that a known host-tissue invasive pathogen also has the capacity to
proliferate in biofilm communities that are not host-associated (Castellarin et al., 2012;
Han et al., 2003).
Only one HOT designation belonging to the Actinobacteria Phylum
(Propionibacterium acnes HOT-530) could be identified in the biofilms while 15 were
found in the saliva-derived samples (Figure 4). Actinobacteria are known as one of the
earliest colonizing representatives of the oral flora and are important in initiating plaque
development (Kolenbrander et al., 1999; Palmer et al., 2001; Yeung, 1999). Taken
together with the results that L. fermentum was a rare species in the in vitro biofilms
and that L. fermentum is considered a late colonizer of oral plaque (Gross et al., 2012) it
is possible that the in vitro biofilm community was at an intermediate state of
succession at the time point of analysis since both early and late stage colonizers were
210
rare. However, to define specific succession stages of the in vitro grown biofilms by
identification of key species, further research of community development is needed.
Several known oral pathogens were identified at notably low 16S gene
abundance in all samples (e.g. S. mutans HOT-686, Rothia mucilaginosa HOT-681,
Abiotrophia defectiva HOT-389, Atopobium rimae HOT-750, Porphyromonas
catoniae, Prevotella melaninogenical HOT-469) (Figures 4 and 5) indicating the
pathogenic potential present in samples derived from healthy oral subjects. A
representative of the elusive candidate Phylum TM7 that was previously identified in
both healthy human subjects and in subjects with periodontal disease and other
inflammatory mucosal infections (Brinig et al., 2003; Fredricks et al., 2005; Rylev et
al., 2011) was also present at a low abundance in the biofilms (Figures 4 and 5).

16S gene diversity in 454 sequencing libraries
Although classification based methods have the benefit of being associated with a
reference-taxonomy, these methods cannot account for the diversity which is missing
from the reference databases. Therefore, we applied an OTU based approach to
complement the above classification approach. These results show that OTU richness was
lower in biofilm samples compared to saliva samples, ranging from 65 OTUs in the
Batch 2 well 1 sample to 156 OTUs in the Batch 3 well 2 sample (Table 1). In line with
these results, the Shannon Entropy (H) index was also slightly higher in the saliva-
derived samples, 4.52- 4.72, compared to the biofilm samples where H ranged from 3.74
to 4.23. Although it is clear that the biofilms at this selected time point do not fully
211
capture every species found in saliva sample, the in vitro biofilms in this study contain
the highest 16S diversity identified so far within an in vitro oral model system.

Metabolic reproducibility as shown by WGS
Using a WGS approach to further investigate the in vitro biofilm community, we
confirmed that the type and proportions of metabolic pathways present in our in vitro
biofilm model were in strong agreement with those recently reported for supragingival
plaque in the HMP studies for 242 “healthy human subjects” (Figure 6) (Consortium,
2012). This was shown in a KEGG pathway comparison-analysis where major
pathways (i.e. secondary metabolite biosynthesis, glycan biosynthesis and metabolism,
metabolism of cofactors, vitamins, terpenoids, polyketides, lipids, amino acids, energy,
nucleotides and translation) were equally abundant and shared between all samples (the
data for 99 of these subjects are shown in Figure 6). The HMP study of these “healthy”
Western subjects revealed that there is variation in carriage of taxa (community
structure) but stable metabolic pathways exist across individuals for many body sites
including supragingival plaque. The comparison of our WGS data with the HMP
dataset was quite unexpected however, our saliva inoculum was also derived from a
pool of healthy subjects and we intentionally developed the SHI medium and sugar
concentrations to produce biofilms that were in general agreement with the major types
and proportions of taxa from the saliva inoculum. This suggests that some variation in
the relative proportions of taxa in a model system such as this may be tolerated since
212
the functional capabilities of the community may remain similarly stable as in the
overall healthy human microbiome.

Conclusions
The principal challenge in most fields of microbiology is still the phenomenon of
microbial cultivability. In the human oral cavity the proportion of uncultured species
are lower (approximately 60%) than in other environments (Marsh et al., 2011;
Vartoukian et al., 2010; Wade, 2002) however, the “missing” species are a significant
impediment to the study of human health. The use of an in vitro model system with a
highly complex bacterial diversity that also supports growth of uncultivated species is
highly desirable as it can be manipulated and studied over a longer period of time in a
controlled environment. To our knowledge, the model we developed here is the first
validated system that fulfills these criteria and that can be readily used for example to
target changes in taxa, regulation of metabolic pathways and signaling molecules by
using next generation sequencing and ‘omics’ methodologies. Its usefulness in human
health research span a wide range as saliva pools with different origin can be collected
and inoculated based on the tested hypotheses (e.g. inoculum can represent saliva from
diseased human subjects or healthy subjects with highly different microbiomes).
Longitudinal studies of these highly different saliva samples by using the in vitro model
system will generate deeper knowledge of the mechanisms regulating bacterial
taxonomic diversity and community functions in both health and disease. Specifically
such a model system will help facilitate experimental approaches that seek answers to
213
questions concerning caries related diseases and how oral pathogens can be eradicated.
We hope that it will be used in future research as a tool to understand and combat the
development of oral disease.

Acknowledgements
This work was supported by grants to W.S. R.L. and J.S.M. from the National Institutes
of Health NIDCR 1R01DE020102 and NIGMS 1R01GM095373 to J.S.M.

Footnotes
Supplementary information accompanies the paper on The ISME Journal website
(http://www.nature.com/ismej)

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241
Figure Legends
Table 1 Diversity and count estimates of 16S genes in pyrosequencing libraries after
sequence trimming by using the SILVA reference alignment and the MOTHUR software.
242

Figure 1 Polymerase chain reaction and denaturing gradient gel electrophoresis (DGGE)
from two different research laboratories. DGGE gel images showing reproducibility of
bacterial 16S gene profiles representing two replicate DNA extractions (left and right
panels) from the saliva derived inoculum cultured in SHI medium with (w/ sucrose) and
without sucrose (w/o sucrose) at two different research laboratories (JCVI and UCLA).
243

Figure 2 Correspondence analysis showing reproducibility and 16S profile similarities
within biofilm and saliva samples. Axis 1 explains 47% of the variation in the data set
while axis 2 explains 19% of the variation. Replicate biofilm samples representing Batch
1 and 3 cluster closely together while Batch 2-biofilms that derive from a SIP experiment
cluster more distantly along the 1
st
ordination axis. Saliva derived replicates (1-3) also
show similar 16S diversity.
244

Figure 3 A & B A) Glucose concentration in replicate biofilm samples after spiking
samples with 0.5% glucose at time point zero hours. After glucose concentrations were
below 20 mg/dl (6 hours) they could no longer be detected (dashed line) and were
considered as 0 mg/dl. B) Replicate pH profiles of biofilm samples after glucose spiking.
pH levels decreased during the first six hours of incubation in parallel with glucose
consumption. pH recovery could be observed after 6 hours of glucose spiking.
245

Figure 4 Phylogenetic tree based on 1,642 HOMD reference sequences. 16S genes that
were obtained from pyrosequencing in this study were matched based on sequence
homology with HOMD reference sequences. Red and blue bars indicate normalized
relative abundance counts of HOT designations that were identified in the saliva and
biofilm samples, respectively. Sequence matches between reference sequences and query
sequences were counted at 97% sequence identity cutoff and 95% coverage.
246

Figure 5 A & B Bubble-charts showing 16S profile reproducibility at HOT designation
(HOT) level (98.5% sequence similarity) in replicate biofilm growth wells representing
batches grown at different time points (Batch 2 and 3). Cultivated species, both unnamed
and unnamed, according to HOMD classification are presented in A. Uncultured
phylotypes are presented in B. Read abundance values were log transformed and all
identified HOTs are presented here. Sizes of bubbles reflect normalized relative
abundance values of individual HOT designations in proportion to total 16S read
abundance for each sample. Standard deviation in HOT abundance between wells from
the same batch ranged between 0.1% and 3% while standard deviation was higher (0.3%-
8%) between batches.

247

Figure 6 Metabolic profile comparisons of HMP-oral metagenomes for supragingival
samples and in vitro biofilm metagenomes. The METAREP tool was used to compare
KEGG-pathways representing the 12 and 16 hours old in vitro biofilms (two bars on the
left in the figure) from this study with 99 human oral metagenomes representing
supragingival samples from healthy subjects. The relative abundance of annotated genes
and their KEGG-pathways are indicated in different colors.

248
249

Supplementary Figure 1. Correspondence analyses of bacterial community structures
based on 16S gene analyses. Levels of sucrose that served as a carbon substrate during
the first 16 hours of biofilm growth in SHI medium. High sugar and low sugar levels
correspond to 0.5% and 0.1% sucrose, respectively. The inoculum sample corresponds to
the pooled saliva sample. 16S rRNA community profiles in the high-sugar biofilms were
more similar to the natural saliva samples.

250
Appendix C: Supporting Information

Supporting Information for McLean et al. “Candidate Phylum TM6 Genome
Recovered from a Hospital Sink Biofilm Provides Genomic Insights into an
Uncultivated Phylum “

http://dx.doi.org/10.1073/pnas.1219809110

http://www.pnas.org/content/suppl/2013/06/06/1219809110.DCSupplemental/sapp.pdf

Direct link (may not be permanent):
http://www.pnas.org/content/early/2013/06/05/1219809110.abstract
251
Appendix D: Supplemental Figures Chapter 3

McLean, J. S.*, Lombardo, M.-J., Ziegler, M. G., Novotny, M., Yee-Greenbaum, J.,
Badger, J. H., Tesler, G., Nurk, S., Lesin, V., Brami, D., Hall, A. P., Edlund, A.,
Allen, L. Z., Durkin, S., Reed, S., Torriani, F., Nealson, K. H., Pevzner, P. A.,
Friedman, R., Venter, J. C., and Lasken, R. S.*, 2013b, Genome of the pathogen
Porphyromonas gingivalis recovered from a biofilm in a hospital sink using a
high-throughput single-cell genomics platform: Genome research, v. 23, no. 5, p.
867-877.

*Corresponding Authors: jmclean@jcvi.org and rlasken@jcvi.org

Online Links to Genome Research
http://genome.cshlp.org/content/23/5/867.abstract
DOI link: http://dx.doi.org/10.1101/gr.150433.112

Main site: http://genome.cshlp.org/
May 2013 issue: http://genome.cshlp.org/content/23/5.toc
May 2013 cover featuring McLean et al: http://genome.cshlp.org/content/23/5.cover-
expansion

Supplemental Material Published Online
http://genome.cshlp.org/content/23/5/867/suppl/DC1
Supplemental Figure S1.pdf
Supplemental Table S2.pdf
Supplemental Table S3.pdf
Supplemental Table S4.pdf
Supplemental Figure S2.pdf
Supplemental Figure S3.pdf
Supplemental Figure S4.pdf
Supplemental Figure S5.pdf
Supplemental Figure S6.pdf
Supplemental Figure S7.pdf
Supplemental Methods.pdf
Supplemental Table S1.pdf
Supplemental Legends.doc
252
Appendix E: Co-Author Manuscript Contributions

Published
Nurk, S., Bankevich, A., Antipov, D., Gurevich, A., Korobeynikov, A., Lapidus, A.,
Prjibelsky, A., Pyshkin, A., Sirotkin, A., Sirotkin, Y., Stepanauskas, R., McLean,
J. S., Lasken, R., Clingenpeel, S. R., Woyke, T., Tesler, G., Alekseyev, M. A.,
and Pevzner, P. A., Assembling Genomes and Mini-metagenomes from Highly
Chimeric Reads, in Proceedings 17th Annual International Conference, RECOMB
2013, Beijing, China, 2013, Volume 7821, Springer, Heidelberg p. in press.

Guo, L., Hu, W., He, X., Lux, R., McLean, J., and Shi, W., 2013, Investigating Acid
Production by Streptococcus mutans with a Surface-Displayed pH-Sensitive
Green Fluorescent Protein: PLoS ONE, v. 8, no. 2, p. e57182.

Ahmed, B., Cao, B., McLean, J. S., Ica, T., Dohnalkova, A., Istanbullu, O., Paksoy, A.,
Fredrickson, J. K., and Beyenal, H., 2012, Fe(III) Reduction and U(VI)
Immobilization by Paenibacillus sp. 300A Isolated from Hanford 300A
Subsurface Sediments: Appl Environ Microbiol.

Abstract (if available)

Abstract The study of microbial biofilms on reactive surfaces integrates many disciplines including biogeochemistry, engineering, physics and microbiology and has benefited greatly from recent approaches based in genomics and bioinformatics. Experimental biofilm studies have been predominately conducted on model systems containing single species. Most processes in nature and many in human disease however are driven by reactions occurring within complex microbial biofilm communities in contrast to a few processes that are the result of a single species. Many hurdles still remain and overcoming these will rely on technological and experimental advances that disentangle the immense complexity of a multispecies biofilm. This dissertation deals with complex microbial biofilm communities involved in the degradation of minerals: hydroxyapatite-based biominerals in the oral cavity. This work is inherently and extensively interdisciplinary, involving microbiology, molecular biology, genomics and metagenomics, as well as microbial physiology and geology. This work has involved the development and use of methods for the study of the structure and function of biofilms on one hand, and for the genomic characterization of interacting community members with single cell genomics on the other. Both top-down and bottom up approaches are clearly needed to more fully understand the abiotic and biotic processes that contribute to the fundamental process of mineral dissolution. The goals of the work are focused on using this interdisciplinary approach to gain insight into how microbial biofilm communities interact with surfaces such as human biominerals (teeth enamel made of hydroxyapatite mineral). However, the approaches and techniques developed here involving sampling a complex biofilm for single cell genomic reconstruction are widely applicable to the study of biofilms involved with geobiological activities everywhere.

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Creator McLean, Jeffrey Scott (author)

Core Title Investigating microbial biofilm community mediated processes on surfaces: from single cell genomics to community meta-omics

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Geological Sciences

Publication Date 08/01/2013

Defense Date 06/06/2013

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

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