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Dynamic processes underlying cerebral cortical development with lifespan impact
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Dynamic processes underlying cerebral cortical development with lifespan impact
By
Alexandra Lauren Lanjewar
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirement for the Degree
DOCTOR OF PHILOSOPHY
(NEUROSCIENCE)
December 2022
Copyright 2022 Alexandra L. Lanjewar
ii
ACKNOWLEDGEMENTS
This culmination of work would not have been possible without the love, support, and
guidance from countless people. As I hope I will be able to convince you in my dissertation
work, there are so many necessary factors that act in very specific ways and at very
specific times in order to ultimately obtain functional outcomes. My accomplishments do
not stand alone; rather, they stand on the backbones of the many who have helped pave
the way for me and the many who have walked the path beside me- you all have been
my necessary factors. Like anything in life, pursuing a doctorate degree in neuroscience
has been filled with highs and lows, but I can genuinely say that this has been such an
enriching, inspiring, and positive period of my life, and I am so fortunate to have had
amazing people surrounding me for every step of the way.
Dr. Pat Levitt is not only one of the best scientists and mentors I know, but he is
also one of the best human beings. His values, passion, and hardworking ethics shine
through everything he does. Pat, I have learned how to do thoughtful and meticulous
research under you while also witnessing how a successful leader can lead with
compassion and virtue. I have so much appreciation for all you have taught me
scientifically and the true moral goodness that you bring to your professional work. I still
remember our first individual meeting together. I was in my first year of graduate school
and in the process of choosing my final lab to rotate in, and you were the director of the
program. When you asked me what my research interests were and listened to what I
had to say, you immediately suggested that I do a rotation in your lab. I was instantly
iii
relieved because I had been too shy, nervous, and intimidated to ask you if you would
take me as a rotation student, so I happily took your offer, and the rest is history. In a
moment when I was too shy to advocate for myself, you saw a potential in me and took
me under your wing. You have continued to teach me how to speak with more confidence.
Now, even when I feel nervous about advocating for myself, I do it anyway because I do
not want to miss a potential opportunity, like the one that has been an absolute privilege
in receiving my PhD training under your mentorship. Even when I feel nervous and
intimidated about standing up and speaking in a room of hundreds of expert scientists, I
do it anyway. Thank you for teaching me that we each have a place in the room and value
to add. There is a common saying in science that you can train someone to do anything,
but you cannot train someone in how to think. Pat, while you may not have trained me in
how to think, per se, I cannot emphasize enough the impact that you have made in the
way I think. You have taught me to think critically, precisely, carefully, sensitively, fluidly,
meticulously, thoughtfully, dynamically, exhaustively, and I can go on. While our papers
together are tangible evidence of our scientific contributions together, thank you even
more for the intangible wisdom that I will carry with me in all my future endeavors.
One major concept in developmental neuroscience is that environment is one of
the key factors that can influence outcome. Throughout my years of PhD training, I have
had an extremely nourishing environment, in which I have been surrounded and
supported by the other members of the Levitt Lab. I could not have wished for more
intelligent, curious, witty, and kind people to have as my lab mates. While too many to
iv
name, every single person that I have overlapped with in the Levitt Lab over the years
has been a true inspiration to me, both scientifically and personally. Dr. Kathie Eagleson,
you have been such a great mentor and collaborator in my dissertation work. Your
knowledge is so versatile, and you have taught me so much. I will forever be impressed
with your writing and editing skills, especially how logically and clearly you are able to
frame the science. Additionally, I would especially like to specifically thank Dr. Anna
Kamitakahara, Dr. Allison Knoll, Dr. Ryan Kast, Annie Nguyen, Patricia Aguiar, Miranda
Villanueva, Valerie Magalong, Zuhayr Khan, Sonum Jagetia, and Amanda Whipple for
your contributions directly to this work, training me on different techniques, and/or joining
me in adventures outside of the lab to keep my brain refreshed and balanced. Lastly, I
want to specifically thank Ramin Ali Marandi Ghoddousi- my cohort member, my only
overlapping fellow PhD lab mate, my colleague, and my friend. It has been an honor and
a delight to go through this journey with you.
My friends have also been so instrumental in creating a nourishing environment
for me during this vigorous journey. While too many to name, I am blessed to have been
supported by many people, near and far. A few of my most important out-of-lab activities
with friends have included basketball, hiking, trivia nights, beach volleyball, zoom happy
hours and workouts to get through COVID, camping trips, and so much more. These
group activities, filled with laughter, and adventure, always gave my brain a reset, so that
I could maximize my mental energy when in the lab. Thank you to all my friends that have
become my family while I have been away from my family and Maria Dillingham. Thank
v
you to all of those who have stayed in touch from afar and make me feel like no time has
passed when I do get to see you in person. To each one of you, I look forward to a lifetime
of friendship, regardless of where our journeys take us next, and thank you for playing a
pivotal part in my PhD journey.
Lastly, I would like to give the biggest thank you to my family. I love you all. I would
not be the person I am today without the encouragement from my parents to follow my
passions. From a very young age, my parents exposed me to everything. We were
constantly running around from one activity to the next, whether it was a scholastic, active,
or community-based one. This taught me how to manage and optimize my time so that I
can live a fulfilling life. As adults, our lives are only as enriched as we make them. I loved
growing up learning that we can truly do it all if we just plan accordingly and keep going.
This sentiment helped me tremendously through the times of stress and anxiety due to
mounting deadlines. Thank you to my brother, Daniel, and my sister, Samantha. I am
extremely impressed and motivated by your career and life journeys. Rosie, you are such
a good girly, and you were the best COVID lockdown buddy. Aji- I told you that you were
going to see your granddaughter become a doctor. I would also like to thank Grandma,
Pop Pop, and Uncle Neal, who I lost during this journey. Each of you have been
instrumental in who I am and how I view the world. I will carry your spirits with me forever.
This research was supported by the National Institute for Mental Health
(R01MH067842) and the USC Neuroscience Predoctoral Training Grant
(T32GM113859).
vi
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ............................................................................................. ii
LIST OF TABLES ........................................................................................................ viii
LIST OF FIGURES ...................................................................................................... ix
CHAPTER 1 : INTRODUCTION - DEVELOPMENT OF MEDIAL PREFRONTAL
CORTEX ........................................................................................................................... 1
1.1 MECHANISM UNDERLYING AREAL PATTERNING OF THE CEREBRAL
CORTEX ....................................................................................................................... 1
1.2 HOMOLOGY OF PREFRONTAL CORTEX CYTOARCHITECTURE .................. 13
1.3 HOMOLOGY OF NEURAL CIRCUITS IN PREFRONTAL CORTEX ................... 22
1.4 FUNCTIONS OF MEDIAL PREFRONTAL CORTEX AND UNDERLYING
MECHANISMS ............................................................................................................ 29
CHAPTER 2 : SUBCLASS-SPECIFIC EXPRESSION PATTERNS OF MET
RECEPTOR TYROSINE KINASE DURING DEVELOPMENT IN MEDIAL
PREFRONTAL AND VISUAL CORTICES .................................................................... 42
2.1 ABSTRACT ........................................................................................................... 42
2.2 INTRODUCTION .................................................................................................. 43
2.3 MATERIALS AND METHODS .............................................................................. 46
2.4 RESULTS ............................................................................................................. 53
2.5 DISCUSSION ........................................................................................................ 76
CHAPTER 3 : DEVELOPMENTAL EXPRESSION PATTERN AND FUNCTIONAL
ROLE OF FOXP2 IN CORTICAL ONTOGENESIS ....................................................... 84
3.1 ABSTRACT ........................................................................................................... 84
3.2 INTRODUCTION .................................................................................................. 84
3.3 MATERIALS AND METHODS .............................................................................. 86
3.4 RESULTS ............................................................................................................. 93
3.5 DISCUSSION ...................................................................................................... 113
CHAPTER 4 : ONTOLOGY OF CONTEXTUAL FEAR MEMORY IN MICE AND
THE ROLE OF MET IN MEMORY CAPABILITIES ..................................................... 122
4.1 ABSTRACT ......................................................................................................... 122
4.2 INTRODUCTION ................................................................................................ 123
4.3 MATERIALS AND METHODS ............................................................................ 126
vii
4.4 RESULTS ........................................................................................................... 133
4.5 DISCUSSION ...................................................................................................... 147
CHAPTER 5 : VIRAL MAPPING OF MET RECEPTOR TYROSINE KINASE-
EXPRESSING NEURONAL PROJECTION PATTERNS FROM MEDIAL
PREFRONTAL CORTEX ............................................................................................. 152
5.1 ABSTRACT ......................................................................................................... 152
5.2 INTRODUCTION ................................................................................................ 153
5.3 MATERIALS AND METHODS ............................................................................ 155
5.4 PRELIMINARY RESULTS .................................................................................. 159
5.5 PRELIMINARY DISCUSSION ............................................................................ 167
CHAPTER 6 : CONCLUDING REMARKS .................................................................. 169
REFERENCES ............................................................................................................. 174
viii
LIST OF TABLES
Chapter 2
Table 2.1 Post hoc analyses of MET-GFP
+
neurons in layer 5 mPFC ..................... 63
Table 2.2 Post hoc analyses of MET-GFP+ neurons in layer 5 V1 ........................... 63
Table 2.3 Post hoc analyses of MET-GFP+ neurons in layer 6 V1 ........................... 63
Table 2.4 Post hoc analyses of CTIP2
+
neurons that co-express MET-GFP in
layer 5 V1 ........................................................................................................... 66
Table 2.5 Post hoc analyses of MET-GFP
+
neurons that co-express CTIP2 in
layer 5 mPFC ..................................................................................................... 66
Table 2.6 Post hoc analyses of MET-GFP
+
neurons that co-express CTIP2 in
layer 5 V1 ........................................................................................................... 66
Table 2.7 Post hoc analyses of MET-GFP
+
neurons that co-express SATB2 in
layer 5 mPFC ..................................................................................................... 66
Table 2.8 Post hoc analyses of MET-GFP
+
neurons that are CTIP2
+
; SATB2
+
in
layer 5 mPFC ..................................................................................................... 69
Table 2.9 Post hoc analyses of MET-GFP
+
neurons that are CTIP2
+
(SATB2
-
) in
layer 5 mPFC ..................................................................................................... 69
Table 2.10 Post hoc analyses of CTIP2
+
neurons that co-express SATB2 in
layer 5 mPFC ..................................................................................................... 69
Table 2.11 Post hoc analyses of MET-GFP
+
neurons that are CTIP2
+
; SATB2
+
in layer 5 V1 ....................................................................................................... 69
Table 2.12 Post hoc analyses of MET-GFP
+
neurons that are SATB2
+
(CTIP2
-
)
in layer 5 V1 ....................................................................................................... 70
Table 2.13 Post hoc analyses of MET-GFP
+
neurons co-express DARPP-32 in
layer 6 mPFC ..................................................................................................... 74
Table 2.14 Post hoc analyses of DARPP-32
+
neurons that co-express
MET-GFP in layer 6 mPFC ................................................................................ 75
Table 2.15 Post hoc analyses of MET-GFP
+
neurons that are SATB2
+
(CTIP2
-
)
in layer 6 V1 ....................................................................................................... 75
Table 2.16 Post hoc analyses of MET-GFP
+
neurons that are CTIP2
+
; SATB2
-
in layer 6 V1 ....................................................................................................... 75
Table 2.17 Post hoc analyses of SATB2
+
neurons that co-express CTIP2 in
layer 6 V1 ........................................................................................................... 76
Chapter 5
Table 5.1 Assessment of axonal labeling in target brain regions. ........................... 165
ix
LIST OF FIGURES
Chapter 1
Figure 1.1 Cytoarchitectonic map of primate PFC (Walker, 1940) ........................... 16
Figure 1.2 Primate cytoarchitectonic map of lateral (top) and medial (bottom)
PFC (Petrides & Pandya, 1999) ......................................................................... 17
Figure 1.3 Subregions of rat PFC (Van Eden & Uylings, 1985) ................................ 20
Chapter 2
Figure 2.1 The spatial and temporal expression of MET-GFP in mPFC and V1
neurons across developmental ages. ................................................................ 56
Figure 2.2 Colocalization analyses of MET-GFP with CTIP2 and SATB2 in
layer 5 mPFC and V1 across developmental ages. ........................................... 65
Figure 2.3 Colocalization analyses of MET-GFP
+
neurons that co-express
CTIP2 and/or SATB2 in layer 5 mPFC and V1 across developmental ages. .... 68
Figure 2.4 Colocalization analysis of MET-GFP with layer 6 projection neuron
markers, in mPFC and V1 across developmental ages. .................................... 74
Figure 2.5 Summary of Met expression in mPFC and V1. ........................................ 83
Chapter 3
Figure 3.1 FOXP2 is enriched in corticothalamic neurons during cortical
development. ..................................................................................................... 99
Figure 3.2 FOXP2 is nonessential for class-specific anatomical and molecular
phenotypes of corticothalamic neurons. .......................................................... 102
Figure 3.3 FOXP2 is nonessential for the genesis of cortical neurons and their
proper lamination. ............................................................................................ 108
Figure 3.4 FOXP2 is not required for proper corticofugal axon pathfinding. ........... 112
Supplemental
Figure 3.1- Figure Supplement 1 FOXP2 is transiently expressed by subpopulation of
PT neurons. ................................................................................................................. 100
Figure 3.2- Figure Supplement 1 Developmental timing of Cre-mediated recombination
in Ntsr1-cre and Emx1-cre. ......................................................................................... 106
Figure 3.3- Figure Supplement 1 Elimination of Foxp2 transcript and protein from the
forebrain of Emx1-cre; Foxp2
Fx/Fx
embryos ................................................................. 109
x
Chapter 4
Figure 4.1 WT mice do not exhibit contextual fear memory persistence at P15
but have contextual fear memory persistence by P35. .................................... 134
Figure 4.2 Contextual fear memory persistence arises rapidly after P20 in WT
mice. ................................................................................................................ 136
Figure 4.3 Traces of remote contextual fear memory are present at P23 but is
still developing, compared to P35. ................................................................... 141
Figure 4.4 Sustaining MET in the cortex past its normal temporal peak does not
affect contextual fear memory persistence developmentally or in adulthood. . 142
Figure 4.5 Reduction or absence of MET expression in neurons affects
contextual fear memory in adulthood but not during development. ................. 143
Figure 4.6 MET-GFP and c-FOS in layer 5 mPFC at P35 compared to P90. ......... 147
Chapter 5
Figure 5.1 Visualization of viral labeling at the injection site. .................................. 161
Figure 5.2 Visualization of virally labeled mPFC axons. ......................................... 166
1
Chapter 1 : INTRODUCTION - DEVELOPMENT OF MEDIAL PREFRONTAL CORTEX
A journey from a single fertilized egg cell
Upon conception, one fertilized cell begins its journey towards the creation of a
living organism. Through many processes of multiplying and differentiating, over time, the
single fertilized cell becomes many different cells. The cells work together in different
tissues that make up organs so that the mammalian body has the ability to function across
many different domains. Even within a specific organ, such as the brain, there can be
much heterogeneity. Understanding heterogeneity in the body is a key step in
understanding how systems function and how systems can be treated if functioning is
suboptimal. The cerebral cortex of the brain is comprised of heterogenous components,
involved in specialized and complex functions. It can be subdivided based on
cytoarchitecture, projection patterns, and functions, which will be further discussed,
specifically in the medial prefrontal cortex, a cortical region involved in higher order
cognitive abilities.
1.1 MECHANISM UNDERLYING AREAL PATTERNING OF THE CEREBRAL
CORTEX
The nervous system arises during embryonic development when a
pseudostratified flat sheet of neuroepithelium is produced during gastrulation and termed
the neural plate (Schoenwolf, 1992). The neural plate consists of nervous system tissue
that can be distinguished from non-neuronal tissue. The neural plate then invaginates
and is termed the neural fold, whereafter cells fuse together at the midline by extracellular
2
macromolecules to form a neural tube (Sadler, 1978). At the most anterior vesicle of the
neural tube, the forebrain arises. Further invagination separates the telencephalon from
the diencephalon. The dorsal telencephalon forms the cerebral cortex (Couly & Le
Douarin, 1987). The cerebral cortex is then further divided into unique areas by
mechanisms of differentiation, allowing for unique neural processing and specialization
of functions.
Areal patterning, which is the partitioning of the cerebral cortex into areas with
unique identities, occurs during development to change an apparently initially relatively
uniform developing cortex into a highly differentiated and specialized one. The
development of areal patterning in cerebral cortex is now known to be dependent on both
extrinsic and intrinsic mechanisms. It contributes to differences between individuals, as
primary areas of the human neocortex between individuals can vary in size two- to three-
fold (Dougherty et al, 2003). This within species variation can also be seen in mice (Airey
et al, 2005). Cortical area size can have a profound lifelong effect on behavioral outputs.
For example, both increasing and decreasing size of somatosensory cortex leads to
diminished sensorimotor behavior; a decrease results in tactile, motor, balance, and
coordination deficits, whereas an increase results in diminished performance of the more
physically demanding sensorimotor tasks (Leingärtner et al, 2007). This suggests that
there may be an optimal size for each cortical area to have maximal functioning, and
variation of size can lead to differences in abilities from one individual to the next.
3
There are two different cell classes of neurons. Excitatory neurons send excitatory
electrical signals in order to produce an action potential, which leads to the transmission
of neuronal activation from one neuron to the next. Inhibitory neurons do the opposite, in
which they transmit signals to other neurons that reduce the likelihood of neuronal
activation. Most excitatory cortical neurons are generated in the ventricular zone, and
then later in the subventricular zone, and migrate radially into the cortical plate in an
“inside-out” laminar fashion, in which the later the neuron is born, the more superficial the
layer it occupies throughout the cortex. However, as these layers develop,
cytoarchitecture borders also begin to become visible, such that cells within a given layer
of one cortical area have unique densities and morphologies compared to those of
adjacent cortical areas. This, combined with differences in projection patterns and
functions, make the adult cerebral cortex easy to partition into areas. Areal patterning
during development leads to these distinguishable cortical areas that perform specialized
and distinct functions.
Heterotopic transplant studies have been indicative of extrinsic mechanisms
contributing to areal patterning, in which a “protocortex” does not have distinctive areal
features at the time of neurogenesis but arise later in development (O’Leary, 1989). For
example, a piece of fetal visual cortex that is transplanted to somatosensory cortex of
newborn rats develop to have barrel-like morphological properties, a feature of
somatosensory cortex, as opposed to visual cortex morphological features (Schlaggar &
O’Leary, 1991). This suggested that where cortical tissue is located during development,
4
as opposed to where it came from, plays a role in how its architecture becomes
specialized. Interestingly, the ability for cells to be receptive to local environmental cues
for morphological development can be time dependent. Embryonic day (E) 12 rat tissue
from sensorimotor cortex transplanted into postnatal day (P) 1 rat perirhinal cortex takes
on perirhinal cortical neuron identity, based on protein expression, however, E17 rat
tissue from sensorimotor cortex transplanted into P1 perirhinal cortex does not (Barbe &
Levitt, 1991). This demonstrates that there are extrinsic mechanisms that contribute to
cell fates in different cortical regions, however, there is a time window in which cells are
sensitive to the cues of their local environment.
Thalamocortical input studies also provide insight of the presence of extrinsic
mechanisms that contribute to areal patterning. Different thalamic nuclei in the dorsal
thalamus project distinctly to the four primary cortices- motor, auditory, somatosensory,
and visual. These thalamocortical inputs define cortical area specificity, such that each
nucleus relays different sensory information to different cortical areas. This results in each
area having unique properties in a sensory information-dependent fashion. For example,
Jeanmonod, Rice & Van der Loos (1981) found that by cutting a row of whiskers early
postnatally in mice, and thereby eliminating sensory input to the brain, the barrel cortex
develops to have abnormal cytoarchitecture. This provides evidence that sensory
information that is relayed from thalamus to cortex contributes to normal areal
cytoarchitecture properties. Another example of sensory input playing a role in cortical
patterning comes from Hunt, Yamoah & Krubitzer (2006). In congenitally deaf mice, there
5
are cortical function and areal size changes compared to control mice. More specifically,
neurons that would typically respond to auditory stimuli responded to somatosensory,
visual, or both somatosensory and visual stimuli. Interestingly in visual cortex of deaf
mice, neurons that typically respond unimodally to visual stimuli responded to
somatosensory or both somatosensory and visual stimuli, paralleled with an increase in
primary visual cortex size and decrease in auditory cortex size. This demonstrates that
functional and structural reorganization of areal patterning takes place not only in auditory
cortex of deaf mice, but in visual cortex as well. In addition, the biggest functional change
was an increase of neurons that respond to somatosensory stimuli; however, the size of
this area did not change. Therefore, functional modality changes of neurons can occur
independent of areal size changes. These studies demonstrate that specialized functions
and cortical area sizes in the cortex are influenced by thalamic input.
In primary visual cortex (V1), Ephrins/Eph proteins play a role in axon guidance of
thalamocortical outputs based on spatial and temporal expression (Dufour et al., 2003),
suggesting that guidance molecules influence specific topographic organization of
thalamocortical axons between and within cortical areas, which subsequently influences
cortical arealization. Furthermore, at least three different types of thalamocortical cells
have been identified based on morphological and axonal differences, including areal and
laminar targeting (Clascá, Rubio-Garrido & Jabaudon, 2012). All cell types can be found
in almost all thalamic nuclei, but there are differences in ratios of axonal innervation into
different cortical areas, further contributing to differences between cortical areas.
6
Intrinsic mechanisms that contribute to areal patterning was initially suggested in
the “protomap” hypothesis, which posed that a molecular map underlying functional areas
of the cerebral cortex exists early in embryonic development and gives rise to mature,
specialized cortical areas (Rakic, 1988). There is also much evidence of intrinsic
mechanisms that contribute to areal patterning of the cerebral cortex. Fiberblast growth
factors (FGFs) and bone morphogenetic proteins (BMPs) act as morphogens to create
different telencephalic regional identities. More specifically, anterior overexpression of
FGF8 in the telencephalon of E11.5 mice results in a posterior shift of the boundary
between primary somatosensory area and motor areas by P6 (Fukuchi-Shimogori &
Grovehen, 2001). The opposite effects occur by reducing FGF8 signaling, where anterior
cortical areas shrink, and posterior areas shift anteriorly. A second source of FGF8 to the
posterior cortex induces a second barrel field, demonstrating a sufficient role of FGF8 in
defining cortical areas, specifically for anterior identities. FGF17 expression somewhat
overlaps with FGF8 but plays a very different role in arealization. In Fgf17 knockout mice,
dorsal frontal cortex is greatly reduced, while ventral frontal cortex appears normal, and
in adulthood, there is an overall rostro-medial shift of caudal parts of cortex (Cholfin &
Rubenstein, 2007). This suggests that FGF17 plays a specific role in establishing the
dorsal parts of frontal cortex. On the other hand, BMPs and Wnts appear to regulate
posterior-medial cortical regions. Bmp4 has been shown to be necessary to repress FGF8
and subsequently prevent anterior cortical parts from expanding posteriorly (Ohkubo,
Chiang & Rubenstein, 2002). This suggests not only do these proteins play a role in areal
7
patterning, but they do so in an interactive way with one another. BMPs, via uniquely
functioning BMP receptors, have also been shown to influence proliferation, apoptosis,
and differentiation of precursor cells and dorsalize the telencephalon along the rostro-
caudal axis (Panchision et al., 2001). Therefore, based on the amount and activation of
these receptors, cytoarchitectural and cortical thickness differences can occur in different
cortical areas. Wnt3a has been shown to be necessary for posterior-medial cortical region
development by creating a cortical progenitor pool, whereby knocking it out results in no
medial hippocampus formation and reduced lateral hippocampus formation (Lee et al.,
2000), suggesting Wnt3a controls arealization of hippocampus during development.
Combined, these proteins and protein gradients begin to create heterogeneity within the
cerebral cortex.
Miyashita-Lin et al. (1999) showed in mutant mice that did not have thalamic input
to cortex, their neocortex was indistinguishable to wildtype in terms of regionalization and
cytoarchitecture at P0, suggesting that while thalamocortical input contributes to extrinsic
mechanisms of areal patterning, the initial areal distinctions may be regulated by factors
intrinsic to the cortex. This makes sense in the context of the way in which neurons of the
dorsal thalamic nuclei project to the developing cortex. Dorsal thalamic nuclei neurons do
not project widely throughout the cortex. Rather, they project to specific areas,
presumably due to molecular patterning differences, and after which, they contribute to
the emergence of specialized cytoarchitecture. When NT-3, a neurotrophic factor, is
conditionally deleted in embryonic mice telencephalon, there is a reduction of
8
thalamocortical input to visual cortex and retrospinal cortex, but not somatosensory
cortex, and subsequently results in visual impairments (Ma et al., 2002). This suggests
that functional deficits of cortical visual areas can be due to the reduction of
thalamocortical input from lateral geniculate nucleus that is molecularly controlled,
intrinsically. An interesting follow-up to this study would be to determine whether the
neurons in the cortical visual areas are now receptive to other sensory modalities, which
would demonstrate whether or not other cortical areas have expanded into this area in
the absence of NT-3. Presumably, other neurotrophic factors expressed in the cortex
plays similar roles in defining unique thalamocortical input to other brain regions, such as
somatosensory cortex. While sensory input to the cortex via thalamus may refine cortical
arealization and have functional implications, it is not necessary for region-specific
expression patterns of genes early on, which may arise by mechanisms intrinsic to the
cerebral cortex. Overall, intrinsic mechanisms may set up initial arealization, whereafter
further refinement and specialization of areas occur based on sensory input via
thalamocortical input.
Further evidence of intrinsic mechanisms leading to areal patterning in cerebral
cortex comes from the graded expression of transcription factors. EMX2 and PAX6 are
primary transcription factors expressed in a gradient. Emx2 is expressed highly in the
dorsal telencephalon proliferative zone in the caudal-medial domain, which includes
primary visual cortex, to low rostral-laterally, which includes primary somatosensory and
motor cortices (Gulisano et al., 1996). Conversely, Pax6 is expressed high to low rostral-
9
laterally to caudal-medially, respectively (Walther & Gruss, 1991). To determine whether
these two transcription factors influence arealization, Bishop, Goudreau & O’Leary (2000)
examined the neocortex of mutant mice. In Emx2 homozygous mutant mice, rostral and
lateral identities of neocortex, measured by cadherin expression, is expanded caudally
and medially, demonstrating that Emx2 plays a role in inducing caudal and medial
arealization. Likewise to its opposite gradient expression pattern, Pax6 influences
opposite cortical identities compared to Emx2. In Pax6 homozyogous mutant mice, rostral
and lateral identities contract, suggesting a normal role for Pax6 in inducing rostral and
lateral arealization. Since both mutant lines of mice die before birth, Hamasaki et al.
(2004) performed a study by conditionally overexpressing Emx2 from progenitor cells or
deleting one copy of Emx2, neither of which are lethal, to be able to determine its effects
on arealization of specific cortical areas. Emx2 conditional overexpression results in a
smaller primary somatosensory cortex that is shifted rostral and a larger visual cortex that
is shifted rostral in its rostral border, while its caudal border remains unchanged.
Additionally, primary auditory cortex shows a rostral and lateral shift when Emx2 is
conditionally overexpressed in mice. Therefore, overexpression of Emx2 has
disproportional changes in size and position of different sensory areas, and this is
independent of thalamocortical inputs. Furthermore, although Emx2 conditional
overexpression maintains the normal high rostral to low caudal expression gradient, it
leads to a flattened Pax6 gradient, where there is diminished expression rostrally. This
suggests Emx2 may directly repress Pax6, and this repression may also be the cause for
10
the observed phenotypes. When a single copy of Emx2 is knocked out, opposite
arealization phenotypes were observed compared to overexpression in mice. Primary
somatosensory cortex and motor areas were modestly increased in size and shifted
caudally, while somatosensory cortex shifted medially, and visual areas decreased in size
with its rostral border shifted caudally. Therefore, Emx2 affects cortical arealization in a
concentration-dependent manner and preferentially influences caudal-medial identities
such as primary visual cortex. On the other hand, overexpression of Pax6 that maintains
normal expression gradient patterns does not affect cortical arealization (Manuel et al,
2007). Contrary to previous reports, Stocker & O’Leary (2016) found Emx1 is also
required for neocortical area patterning. Similar to Emx2, it has a gradient expression
pattern where there is highest expression posterior-medially and lowest anterior-laterally.
Conditionally knocking out Emx1 from cortex results in anterior areas expanding into
posterior areas. More specifically, primary visual cortex is reduced in size and shifted
posteriorly, but cortical lamination remains normal. Primary somatosensory cortex was
shifted posteriorly and medially. Frontal-motor areas were expanded. Due to similar
phenotypes of Emx2 knockouts, this study suggests that there may be functional
redundancies between Emx1 and Emx2. An interesting follow-up experiment would be to
conditionally knockout one of these genes and then overexpress the other in its normal
gradient expression pattern to see if changes in arealization phenotypes are rescued. If
so, this would better support functional redundancy.
11
The orphan nuclear receptor COUP-TFI also shows a gradient in cortical
expression, where expression is high posterior-laterally and low anterior-medially in
ventricular zone and cortical plate (Liu, Dwyer & O’Leary, 2000). Zhou, Tsai & Tsai (2001)
confirmed the role of COUP-TFI for cortical patterning. They found in global knockout
mice, in terms of connectivity, visual cortex adopted somatosensory cortex identity.
Normal visual cortex laminar distribution, as visualized with different lamina-specific
markers, was also disrupted. Unchanged gradients of EMX2 and PAX6 suggest COUP-
TFI functions in creating cortical arealization independently of these two transcription
factors. However, the expression of COUP-TFI in dorsal thalamus makes it difficult to
confirm whether results of this study are due to COUP-TFI acting within the cortex or
extrinsically. Armentano et al. (2007) conditionally deleted Coup-TFI from cortical neurons
and found a massive expansion of frontal areas, including motor, and compression and
caudal shifts of sensory areas that are caudal to occipital cortex, including
somatosensory, visual and auditory cortices, suggesting a within cortex role of COUP-
TFI in areal patterning for caudal identities through repression of rostral identities. Unlike
in the global knockouts in the previously described study, cytoarchitecture was normal in
all cortical regions, demonstrating that cortical COUP-TFI influences position and size of
cortical areas, but unique laminar properties of these areas are not under cortical COUP-
TFI control. Additionally, Pax6 was found to be upregulated and Emx2 was found to be
downregulated in conditional knockout mice, suggesting that COUP-TFI may interact with
PAX6 and EMX2 in regulating cortical patterning.
12
Cajal-Retzius cells, which are one of the first cortical neurons to develop, migrate
to the molecular zone (layer 1) to form the dorsal boundary during laminar development
of cortex. These cells have been proposed to play a role in structural and functional
organization of mammalian cortex (Marín-Padilla, 1998). To probe the roles of previously
identified subpopulations of Cajal-Rezius cells (Bielle et al., 2005) in cortical development,
Griveau et al. (2010) performed conditional ablations of the different subtypes. They
showed effects in arealization when Cajal-Retzius cells that migrate from the septum are
ablated. More specifically, motor/frontal areas expand laterally. Primary somatosensory
cortex was displaced caudally and laterally. Retrosplenial cortex shifted rostrally. They
also showed Fgf17, among other signaling factors, is expressed at high levels in these
cells and may contribute to the mechanism in which Cajal-Retzius cells contribute to areal
patterning of cerebral cortex.
In summary, mechanisms intrinsic to the cerebral cortex may first arise to initiate
arealization, establishing regions along the anterior-posterior and medial-lateral axes
through expression gradients of morphogens and transcription factors. Local
environmental cues can dictate how a precursor cell develops into specific neuron-types,
in which neuron morphology and functioning can vary from one cortical region/layer to
another. Intrinsic and extrinsic signaling molecules then guide thalamocortical axons of
different nuclei to different cortical areas and layers. This creates a layout for unique
sensory input to innervate different cortical regions for further maturation. Based on
sensory input, cortical areas become even more refined and distinct from one another so
13
that by adolescence, there are very clear boundaries from one cortical area to another,
and these areas have unique connectivity, cytoarchitecture, and function. Therefore,
perturbations to any of these mechanisms during development, intrinsic or extrinsic to the
cerebral cortex, along with other biological and environmental factors, can greatly change
final cortical areal patterning. In turn, perturbations to development of normal areal
patterning can result in functional deficits. Notably, as cortical areas are still developing,
major influences on final cortical architecture and lifelong cortical functioning can result
based on environmental factors. This, combined with genetic variation between
individuals may result in heterogeneous sizes of any given cortical area, although the
general rostro-caudal and medio-lateral organization is conserved. Identifying
mechanisms that govern areal patterning is important because defining specific factors
may facilitate the development of strategies for prevention and treatment of suboptimal
cortical patterning that can lead to lifespan functional deficits. From here on, I will focus
on one cortical region that arises from initial prenatal areal patterning, the prefrontal
cortex.
1.2 HOMOLOGY OF PREFRONTAL CORTEX CYTOARCHITECTURE
In 1948, Rose & Woolsey defined the prefrontal cortex (PFC) as the cortical
projection area of the mediodorsal thalamic nucleus. Using this single anatomical
criterion, PFC, a cortical region involved in executive function, can be defined across
mammalian species. However, a single anatomical criterion should not be considered
sufficient to claim homology for basic neuroscience research that is conducted for the
14
purpose of contributing to translational knowledge and human relevance. So instead,
there are several different criteria that can be used to assess how closely related a brain
region from one species is to another species, and the extent of similarity can contribute
to how well generalizations can be made across species about the particular brain region
(Uylings, Groenwegen & Kolb, 2003). One result of areal patterning of the cerebral cortex
is that developmental differences lead to differences in cell distribution, density, shape,
and size between areas by adulthood, defined as cytoarchitecture, which was first
classified by Korbinian Brodmann in 1903 (Loukas et al, 2011). Defining cytoarchitecture
across species is important for understanding what the comparable brain regions of study
are. Here, the cytoarchitectonic maps of primate and rodent PFC will be summarized.
Walker (1940) created a cytoarchitectonic map of primate PFC (Fig. 1.1). Brain
sections cut in multiple planes were examined to define boundaries of regions within PFC
based solely on cytoarchitecture. Nomenclature of areas was given based on homologies
of what was known about human PFC at the time. In general, primate PFC can be
distinguished from the surrounding premotor and motor cortices by the presence of a
granular layer (layer 4). I will summarize the characteristics used that distinguish each
subregion within PFC. Area 8 has a minimal granular layer. It is subdivided into area 8A
and area 8B. Area 8A has large pyramidal cells in layer 5, while area 8B does not.
Although area 46 does not have a clear transition boundary from area 8, it has a well-
developed granular layer. In area 46, outer layer 5 consists of medium-sized pyramidal
cells, while layer 3 has sparse and small pyramidal cells. Layers 5 and 6 blend with no
15
distinct transition line and are similar in cortical depth to layers 2 and 3. This area also
has a wide molecular layer (layer 1). Area 45 is narrow in cortical depth and does not
have marked striations. It has large pyramidal cells in both layers 3 and 5. Area 12 can
be distinguished by a thin layer 3 and thick infrangranular layers, with a faint granular
layer. Area 11 has a well-defined granular layer with medium-sized pyramidal neurons in
outer layer 5 and small, evenly arranged cells in layer 3. Compared to area 11, area 13
has a thin layer 4. Cells are loosely arranged in area 13, and area 13 can be distinguished
from area 12 by thicker layers 5 and 6. The cortex is very narrow in area 14 but the
molecular layer is thick, in which it occupies about one-third of total cortical thickness.
Here, the granular layer is very limited, and the infragranular layers are thin. Area 10 also
has a thin cortex but a well-developed granular layer. There are medium-sized pyramidal
cells in outer layer 5 of area 10, but the rest of layers 5 and 6 are poorly developed. Area
9 has a narrow layer 4 compared to area 46 and cells do not have an orderly arrangement.
Medially, area 24 is agranular with a broad layer 3. Area 25 serves as a transition zone
between areas 24 and 10, where there is a narrow and more prominent granular layer
compared to area 10.
16
Figure 1.1 Cytoarchitectonic map of primate PFC (Walker, 1940)
In 1999, Petrides & Pandya created an updated map in monkeys based on
cytoarchitectonic properties (Fig. 1.2). A lot of the original designations were maintained,
but some additions to the Walker (1940) map were made. Briefly, area 8A is further
divided into a dorsal (8Ad) and ventral (8Av) part. Area 8Av has a thicker layer 4 and a
layer 3 that contains larger, more densely packed pyramidal cells compared to area 8Ad.
Area 46 was further divided into areas 9/46d and 9/46v due to discrepancies and a lack
of homogeneity throughout. This updated map can be applied to human PFC as well, as
PFC cytoarchitecture in human and monkey brains are comparable (Petrides et al., 2012).
17
Figure 1.2 Primate cytoarchitectonic map of lateral (top) and medial (bottom) PFC
(Petrides & Pandya, 1999)
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The rodent cerebral cortex is proportionally smaller compared to primates, and the
rodent PFC occupies a smaller proportion of the cortical mantle compared to primate PFC
(Uylings & Van Eden, 1991). More specifically, while PFC in rodents occupies less than
ten percent of the entire cerebral cortex, human PFC occupies about thirty percent, and
this increase is also paralleled with an increase in cognitive functions across species
(Carlén, 2017). Rodent PFC subregions have historically been divided differently both
spatially and in nomenclature. It is theorized that the more curved corpus callosum in
primates leads to displacement of subregions compared to the more linear layout of
rodent PFC subregions (Laubach et al, 2018). Van Eden & Uylings (1985) defined rat
cytoarchitectonic properties of PFC (Fig. 1.3) based on subregions previously defined by
Kretteck & Price (1977). In general, all of rat PFC lacks a granular layer (layer 4), which
distinguishes it from the rest of the rat neocortex. Infralimbic (IL) region (not shown),
located on the medial wall, has poor lamination, such that layers 2 to 5 cannot be
distinguished from one another cytoarchitecturally. Layers 5 and 6a are separated by a
thin cell-poor layer. Layer 6b has small, darkly stained cells. Prelimbic (PL) region, located
more dorsally on the medial wall, is more laminated compared to IL. It has a wide
molecular layer (layer 1) that is clearly distinguishable from layer 2. Layer 2 is densely
packed, while layer 3 is not. Unlike in IL, a cell-poor layer does not separate layers 5 and
6. Layer 6 does not contain large pyramidal cells that are common in other cortical areas.
Dorsal anterior cingulate area (ACd) can be distinguished from PL by a thickening in layer
5. Layer 6b cells are more loosely arranged compared to PL. In general, this subregion
19
has a more homogenous distribution of cells compared to PL that are smaller and more
differentiated. Layer 5 contains smaller, pyramidal-like cells. The molecular layer is
thinner than in PL. Layers 2 and 3 contain smaller, more densely packed cells. Medial
precentral area (PrCm) can be distinguished from ACd by a thicker layer 2 with cells that
are less evenly arranged. Layer 6a is thicker and dense compared to ACd. Layer 5 can
be divided into 3 sublayers, where the middle sublayer has less densely packed medium
to large pyramidal-like cells. Layer 6b is thinner and less organized in ventral anterior
cingulate area (ACv) compared to ACd and it has sublayers in layer 5 that can be
distinguished, with the highest density of cells in the middle sublayer. The exact transition
between layers 3 and 5 is unclear because both layers contain cells of similar sizes. Like
in ACd, layer 2 of ACv is denser than layer 3. Lastly, dorsal agranular insular area (AId)
has a thick layer 5 that can also be divided into three sublayers. The upper sublayer
contains small cells, while the middle sublayer contains cells that are darkly labeled. Layer
6b is loosely packed.
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Figure 1.3 Subregions of rat PFC (Van Eden & Uylings, 1985)
Top: medial surface. Bottom: lateral surface
The difference in nomenclature for subregions of rodent PFC compared to the
nomenclature used in primates makes it more difficult to understand the brain regions of
comparability in research. Further, terms such as IL and PL are commonly used in rodent
research but do not necessarily refer to the same subregions between studies in the
rodent (Laubach et al, 2018). In order to address this problem and have more comparable
terms between rodent and primate research, the fifth edition of Paxinos and Franklin’s the
21
Mouse Brain in Stereotaxic Coordinates (Paxinos & Franklin, 2019) has eliminated these
classical names. PFC regions are instead identified and given the same nomenclature as
the comparable primate Brodmann area, so that similar subregions can be more easily
compared across species.
In summary, within both primate and rodent PFC, there are subdivisions that can
be made based on unique cytoarchitectonic properties, as previously described.
However, there are some major differences between species. Most obvious is that rodent
PFC is agranular, lacking a layer 4, while almost all of monkey PFC contains a layer 4.
Therefore, from a cytoarchitectonic standpoint, rodent PFC is most similar to the medial
portion of monkey PFC, area 24, which is also agranular. In addition, layer 5 of rodent
PFC generally has a noticeable absence of large pyramidal cells that are characteristic
of layer 5 in other cortical regions and are present in monkey PFC. In general, homology
of regions across species cannot be based on cytoarchitecture alone. For example,
primary motor cortex is considered homologous in rats and monkeys even though rats
have a granular primary motor cortex, while monkey motor cortex is agranular (Northcutt
& Kaas, 1995). In conclusion, subregions based on cytoarchitecture can be defined in
PFC and are useful in general identification for studies in which there could be phenotypic
differences from one subregion to another. Further insight into homology of rodent and
primate PFC is derived from commonalities in neural circuits and function, which will be
explored in the next subsections of the introduction.
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1.3 HOMOLOGY OF NEURAL CIRCUITS IN PREFRONTAL CORTEX
Here, we focus on known PFC circuits and point out similarities and differences
between rodents and primates. PFC has mostly excitatory transmission, consisting of 80-
90% pyramidal neurons and 10-20% inhibitory neurons (den Boon et al., 2015). Excitatory
neurons can be further subclassed as intratelencephalic (IT) or subcerebral based on
their projection targets. PFC IT neurons are found in both infragranular and supragranular
layers and project to other cortical areas, striatum, and amygdala (Dembrow & Johnston).
Subcerebral PFC projection neurons are found in infragranular layers and project
ipsilaterally to thalamus, striatum, and/or brainstem (Gabbott et al., 2005).
In both rodent and primate brains, PFC shares reciprocal connections with
mediodorsal thalamic nucleus, in which the topography of these connections can be
distinguished based on PFC subregion. Specific topography of these connections in rats
has been described (Groenewegen, 1988): PL and ACv connect reciprocally with the
rostral part of the medial segment of the mediodorsal thalamic nucleus; AId shares
connections with the posterior, ventral part of the medial segment of the nucleus; IL sends
projections to the most medial part of the nucleus, but does not receive projections back;
AIv reciprocally connects with central segment of mediodorsal thalamic nucleus; ACd
shares reciprocal connections with the lateral segement of mediodorsal thalamic nucleus;
PrCm is reciprocally connected with the paralamellar segment of the nucleus. Giguere &
Goldman-Rakic (1988) described topography of thalamocortical projections from
mediodorsal thalamic nucleus to PFC in monkey, based on whether projections arose
23
from lateral, medial, or peripheral mediodorsal thalamic nucleus: From anterograde tracer
injections, they determined that lateral mediodorsal thalamic nucleus mainly projects to
dorsolateral PFC (Walker’s areas 8, 9, and 46). Medial mediodorsal thalamic nucleus
projects to areas 10-14. Peripheral mediodorsal thalamic nucleus projects to areas 6, 8A,
and 9. Retrograde injections of these regions within mediodorsal thalamic nucleus reveals
similar patterns as anterograde injections, demonstrating reciprocal connectivity.
Furthermore, this paper showed that PFC corticothalamic projections to mediodorsal
thalamic nucleus mainly originates from layer 6, with a much smaller proportion of
projections originating from layer 5. Likewise in rats, corticothalamic neurons are also
almost exclusively found in layer 6 PFC, with a smaller population in layer 5 (Gabbott,
2005). On the other hand, thalamocortical input into rodent PFC arrives in supragranular
layers, mostly into layer 3 (Groenewegen, 1988). This creates a circuit in which deep
layers of PFC project to thalamus, thalamus sends projections back to superficial layers
of PFC, and then supragranular layers of PFC connect locally with infragranular layers,
creating an indirect cortico-thalamo-cortical closed loop, which is involved in both
activating excitatory networks and providing feedforward inhibition (Collins et al., 2018).
In monkeys, MD efferents are confined to layer 4 of PFC (Giguere & Goldman-Rakic,
1988). In conclusion, PFC in both rats and monkeys share reciprocal connections with
mediodorsal thalamic nucleus, but there is also specific topographic organization of these
reciprocal connections based on subregions within PFC and mediodorsal thalamic
nucleus, along with cortical layer specificity of these connections.
24
There are many other conserved circuits between PFC and other brain regions in
both rodents and primates. One such conserved circuit is PFC with basal ganglia
structures. In humans, this circuit is involved in salience detection and directing attention,
giving rise to top-down control of cognition (Peters, Dunlop & Downar, 2016). More
specifically, PFC projects mostly to the head of the caudate nucleus, which then projects
to globus pallidus and substantia nigra, which projects to thalamus, and in turn, thalamus
projects back to PFC, closing the loop. These circuits, to varying extents based on PFC
subregion, also involve inputs from amygdala to PFC in both rodents and primates, in
which PFC and amygdala share reciprocal connectivity (Groenewegen, Wright, & Uylings,
1997; Amaral & Price, 1984; Aggleton et al., 2015; Lapate et al., 2016). Within this general
loop, there are distinct circuits that project in parallel and have specific topographic
organization in each of the brain regions involved for both primates and rodents
(Alexander, DeLong & Strick, 1986; Middleton & Strick, 2001; Uylings, Groenwegen &
Kolb, 2003). Heilbronner et al. (2016) have compared specifically the connectivity of PFC
to striatum in rats and monkeys, with a focus on regions in the striatal emotion processing
network, which they refer to as nucleus accumbens, hippocampal-striatal projection zone,
and amygdala-striatal projection zone. These structures have been shown to be essential
for emotional processing (Goto & Grace, 2008; Phelps & LeDoux, 2005; Phelps, 2004).
They found that IL in rat projects most strongly to striatal emotion processing network
compared to other PFC regions, and IL corticostriatal projections are almost exclusively
limited to medial striatal emotion processing network. Comparable results are seen in
25
monkey Brodmann’s area 25. They also found rat PL has similar cortico-striatal
connectivity to Brodmann’s area 32 in monkeys. Rat PL and monkey Brodmann’s area
32 each project to medial wall of the caudate, and to a lesser extent to the striatal emotion
processing network, compared to ratn IL/monkey Brodmann’s area 25. Divergently,
monkey Brodmann area 24 projects to a large proportion of rostral striatum, while rat ACd
(refered to as cingulate area in this paper) has more limited projections to rostral striatum.
More specifically, rostral is unique from caudal area 24 in monkeys, as rostral Brodmann
area 24 projects widespread to striatum, while caudal projections are more limited to
dorsal striatum. Rat ACd does not have this differentiation, as both the rostral and caudal
portions project mainly to dorsal striatum. In summary, rat IL has homologous
corticostriatal projections to monkey Brodmann area 25, and these connections are
involved in the striatal emotion processing network. PL in rodent and Brodmann area 32
in monkeys are less involved in the striatal emotion processing network and have
homologous cortico-striatal projections. However, cortico-striatal projection patterns of
monkey Brodmann area 24 with rodent ACd differ from each other.
Connectivity between hippocampus and PFC has been implicated in working
memory (Jones & Wilson, 2005 Plos Bio; Sigurdsson et al, 2010 Nature). The PFC
receives inputs from parts of hippocampal formation but does not send projections directly
to hippocampus. Specifically, restricted parts of CA1 and subiculum project to all layers
of PL in rodents, with unique fiber morphology in supragranular layers compared to
infraganular layers (Jay & Witter, 1991). Hippocampal inputs to PFC and thalamic inputs
26
to PFC converge and interact with each other to gate each other’s inputs (Bueno-Junior
& Leite, 2018). Monkeys have a similar pattern of hippocampal-PFC connectivity, in which
CA1 and subiculum project to PFC (Barbas & Blatt, 1995).
PFC receives noradrenergic fibers from locus coeruleus and serotonergic fibers
from dorsal and median raphe nuclei and is the only cortical area that projects back to
these nuclei in primate and rodent. More specifically, primate dorsolateral PFC projects
to locus coeruleus and dorsal and median raphe nuclei (Arnsten, 1997). In rodents, IL,
PL, and AI project to locus coeruleus, while PL and IL project to dorsal and median raphe
nuclei (Hajos et al., 1998; Jodo & Aston-Jones, 1997). Additionally, PFC is part of the
dopaminergic system. Dopamine neurons from the substantia nigra and ventral tegmental
area project to PFC. However, there are species differences in both the abundance of
dopaminergic neuron innervation and the timing of dopamine receptor expression.
Primate PFC has more extensive innervation by dopaminergic afferents from the midbrain
compared to rodents (Puig et al., 2014). In addition, postmortem analysis of human brains
reveals a temporal pattern of different dopamine receptors. Dopamine receptor D2
(DRD2) and DRD5 mRNA expression is highest in neonates and infants and then
decreases overtime. On the contrary, DRD1 mRNA and protein expression increase with
age, being highest during adulthood (Rothmond, Weickert & Webster, 2012). However,
in both rodents and non-human primates, both DRD1 and DRD2 peak during
adolescence and decline into adulthood (Rosenberg and Lewis, 1994; Andersen et al.,
2000).
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Both rodent and primate studies have shown extensive PFC connectivity with other
cortical brain regions. Retrograde and anterograde analyses were performed by Van
Eden, Lamme & Uylings (1992) and revealed which cortical areas PFC receives input
from and project to. PFC layers 3 to 6 (layer 5 to a lesser extent than the others) project
to contralateral PFC. Cortical input from other cortical areas are predominantly ipsilateral.
All cortical layers of perirhinal cortex project to various subregions of PFC including ACd,
PL, and IL, with PL and IL cortical input predominantly from perirhinal cortex. Virtually all
motor and mixed motor-somatosensory areas project to PFC, with primary motor cortex
having highest density of cells that project mainly to ACd. Layers 5 and 6b of caudal
somatosensory cortex, as well as all layers of ventral somatosensory cortex, project to
PFC. Other sensory areas such as visual, auditory and gustatory cortices project to PFC
as well. Other paralimbic cortices that project to PFC are posterior cingulate, retrosplenial,
and parahippocampal. Additional rodent research has shown that connections between
PL and IL with perirhinal and entorhinal cortices are reciprocal (Groenewegen, Wright &
Uylings, 1997). Similar findings have been found in monkeys, such that PFC is
reciprocally connected with motor, somatosensory, visual, olfactory, gustatory, and limbic
cortical areas, and these connections have been shown to be preferential between
cortical areas with similar cytoarchitecture differentiation (Pandya & Yeterian, 1990). Also,
the more complex cytoarchitecture an area in monkey PFC has, the more likely the
cortical inputs originate from one or two cortical areas, with limited cortical limbic input,
while areas with less complex cytoarchitecture receive cortical projections from two or
28
more cortical areas and have strong cortical limbic input (Barbas et al., 2002). Another
finding in monkey is that PFC has strong cortical connections with parietal association
area (Cavada & Goldman-Rakic, 1989). Rodent PFC has also been shown to connect
with parietal cortex (Wilber & Clark et al., 2014). Furthermore, in mice, three major cortical
networks, all with further subnetwork classifications, have been identified and shown to
project onto different areas of PFC, demonstrating PFC is an associative brain region
where information from segregated networks converge (Zingg et al., 2014). Similar
conclusions in monkey PFC had been previously drawn, with monkey PFC part of at least
two different networks that have distinct topographic organization, but also interconnect
(Carmichael & Price, 1996).
In conclusion, PFC connects with an extensive number of brain regions, all of
which have not been covered here, with general conservation across rodents and
primates at the macro-level. However, differences in subregions within PFC and
cytoarchitecture between rodents and primates create differences in connectivity patterns
at a finer level. With the ability to genetically manipulate mice to study diseases and
disorders, careful consideration of PFC homology must be made before making general,
cross-species conclusions about findings. PFC circuits that are conserved across species
may be useful to study in a rodent model, as direct manipulations can be made that cannot
be made readily in monkeys or in humans. Overall, understanding differences in PFC
across species will better guide specific PFC research questions that have the potential
29
for translational relevance, as well as which human research questions may be too
complicated or irrelevant to study in a rodent model.
1.4 FUNCTIONS OF MEDIAL PREFRONTAL CORTEX AND UNDERLYING
MECHANISMS
The classic example that provides insight into human PFC function comes from
the case study of Phineas Gage. Phineas Gage survived an accident in which an iron bar
penetrated his brain. Postmortem analysis of his brain revealed that the majority of
damage occurred bilaterally in PFC (Damasio et al, 1994). Extreme behavioral changes
occurred after the accident, such as deficits in rational decision-making and emotional
processing, including impulsivity, cognitive inflexibility, and inability to maintain social
relationships, giving some insight into the normal functions of PFC.
Animal models can be used to study behavior as well, but every species exhibits
species-specific behaviors that they have adapted to survive. Humans have evolved the
most in the cognitive domain. However, there is substantial evidence that rodents also
are able to carry out executive functions. In rodents, I will focus in on medial PFC (mPFC),
which is comprised of IL, PL, and the anterior cingulate area. Specific cognitive and social
behavioral tasks in rodents have been found to be mPFC-dependent (Ko, 2017 Frontal
Neural Circuits; Wass et al, 2018 Sci Rep; Finlay et al, 2015 Brain Res). The use of rodent
models allows for genetic, environmental, and physiological manipulations that provide
causal insight into biological underpinnings that drive these behaviors and abilities. Lesion
studies and, more recently, optogenetic studies have been performed to better
30
understand mPFC function. Notably, lesion studies often affect entire mPFC and
potentially some other nearby regions. One such study (Kolb et al, 1994) found many
deficits in rats that had mPFC lesions. Morris water test was performed to test escape
latency, which involves being placed in a pool of water and having to find a hidden
platform to escape the water. Not only did mPFC lesioned rats take significantly longer to
escape, but their swim heading was at chance levels. Thus, mPFC lesioned rats swam
around randomly and would only find the hidden escape platform if they happened to
swim into it. Therefore, normally, rats can learn and retain spatial memory, a function that
is impaired with mPFC lesion. A modification to this task was then performed by adding
a visual landmark that rats could use to find the escape platform. Rats with mPFC lesions
once again had significantly longer escape latencies and never learned to swim directly
to the platform. This suggests that even with visual cues, rats with mPFC lesions are
unable to remember their environment. An egocentric radial arm task was also performed
to test knowledge of environmental space in relation to self. Briefly, rats are put randomly
on any one of eight arms of a radial maze and food reward is always placed in the arm
adjacent to placement. Sham rats learn to directly run to the arm with the food, instead of
having to first search all arms. Rats with mPFC lesions, however, had significantly more
errors in the arm they chose to explore. The main source of error in sham animals was
that the rats would sometimes run so fast that they would not be able to make the sharp
turn into the adjacent arm and ended up in the arm one more over. However, in the
lesioned group, rats often just ran straight ahead to the arm on the opposite side. This
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suggests mPFC is involved in rodent memory of self-location in comparison to their
environment. A more recent study in mice used cell-type specific optogenetic inactivation
in mPFC to investigate different cell-type contributions to behavioral flexibility and impulse
control (Nakayama, Ibañez-Tallon & Heintz, 2018). Briefly, mice performed a binary
probabilistic choice task, which requires frequent adjustment of behaviors, to test for the
role of mPFC in cognitive flexibility and impulsivity. Inactivation of pyramidal neurons in
mPFC during the initiation cue led to an increase in premature responses, suggesting
that mPFC pyramidal neurons play a role in impulse control. Inactivation of pyramidal
neurons in mPFC during the initiation period and the start period of the task led to deficits
in reversal learning, suggesting that there is behavioral inflexibility when these neurons
are inactivated. More specifically, when layer 5 corticostriatal projection neurons are
inactivated in mPFC, mice have reversal learning deficits but no change in premature
response rate. Similar results were found when layer 6 corticothalamic neurons were
inactivated. Lastly, photoinhibition of supragranular mPFC corticocortical neurons had no
effect on the task. In conclusion, both behavioral flexibility and impulse control involve
mPFC, and behavioral flexibility involves supragranular layer pyramidal neurons, but not
supragranular pyramidal neurons. These two papers, amongst many others, illustrate that
although rodents may not “cognitively think” in the way humans do, the completion of
behavioral tasks like these in control rodents and deficits in those that have mPFC
manipulations give insight to mPFC-mediated executive function in rodents.
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There are numerous psychiatric disorders, including autism and schizophrenia,
which involve dysfunction of PFC (Gamo & Arnsten, 2011). Therefore, understanding
normal development and functions of PFC is needed to be able to directly address any
potential dysfunction. Behavioral deficits in mPFC-dependent tasks have been reported
in numerous rodent models of neurodevelopmental disorders (Coley & Gao, 2019 Sci
Rep; Anacker et al, 2019 Autism Res; Alexander et al, 2020 Front Synaptic Neurosci;
Grissom et al, 2015 Neuropsychopharmacology), which have piqued interest in
understanding the underlying developmental mechanisms for attaining cognitive and
social functions. One rationale for defining the typical developmental trajectories of
cognitive and social behaviors is the opportunity to identify periods in which vulnerability
exists and dysfunction may originate. There is an emerging literature, which will be further
explored, investigating the developmental trajectories of different mPFC-dependent
behaviors in rodents, as well as evidence that experiences during early postnatal life
mediate mPFC-dependent behavioral maturation, underscoring the influence of genes by
environment interactions for development of lifelong social and cognitive capacities.
Secondly, understanding PFC will give insight into understanding cognition and how it
has advanced across evolution.
There has been limited, but emerging, data defining critical periods (CPs), a time
of peak plasticity, where there is a great opportunity for experience-based circuit
formation for cognitive functioning. The majority of CP studies focus on mechanisms
underlying CPs in primary sensory neocortical areas (e.g., Wang et al, 2019; Macharadze
33
et al, 2019) because of the ability to use sensory deprivation to study behavioral and
anatomical outcomes. As in sensory cortical areas (Hubel & Wiesel, 1970 J Physiol; Van
der Loos & Woolsey, 1973 Science; Harrison et al, 1993 Acta Otolaryngol), there appears
to be competition for target innervation in mPFC, specifically between hippocampus and
amygdala projections (Guirado et al, 2016 Cereb Cortex), suggesting mechanisms that
occur during CPs in sensory systems may be conserved across cortical areas. Defining
the onset and developmental trajectory of different cognitive and social behaviors would
help guide further studies that test for mPFC-based CPs. Some work has been done in
this area, which will be further described.
Fear behavioral paradigms can be used in rodent models to study molecular
underpinnings of learning and memory. Pairing a conditioned stimulus, which is a neutral
stimulus, with an unconditioned stimulus, which has intrinsic motivation, generates an
association. Conditioned stimulus can be a cue or a context (the environment) in which
the unconditioned stimulus is administered. Contextual fear conditioning behavioral
paradigms provide assays to study contingent learning and memory in animal models. In
contextual fear conditional behavioral paradigms, the environment (context), which is
neutral, is associated with foot shocks, which is aversive, causing a conditioned fear
response when returned to the context of the shock, even in the absence of the innate
fear stimulus. The development of learnt fear caused from fear conditioning can be
assayed by recording freeze responses when animals are placed back into a context
where a foot shock had been previously administered. Using different modifications to
34
this paradigm, fear generalization (when a fear response to a specific stimulus is
transferred to other stimuli), fear extinction (when a conditioned fear response declines
following nonreinforced exposure to a feared conditioned stimulus), and passive
avoidance (when a feared conditioned stimulus is avoided) can also be measured.
Studies have shown that aspects of contextual fear learning are mPFC-dependent
(Heroux et al, 2017 Learn Mem; Santos et al, 2017 Neurobiol Learn Mem; Zelikowsky et
al, 2014 J Neurosci; Rizzo et al, 2017 Biol Psychiatry Cogn Neurosci Neuroimaging),
which also is evident in humans (Åhs et al, 2015 Neuroimage; Liberzon & Abelson, 2016
Neuron; Lonsdorf et al, 2014 Soc Cogn Affect Neurosci).
A study performed by Akers et al (2012 Learn Mem) has led to key insights about
the development of contextual fear learning in mice. The earliest age of contextual fear
conditioning that leads to contextual fear memory formation, where freezing responses
can be observed 24 hours after conditioning when placed back in the context, is P15.
However, the earliest age of contextual fear conditioning that leads to contextual fear
memory persistence, defined as a fear response that can be observed at least 7 days
after conditioning, does not arise until some age within the age range of P16-P30.
Therefore, whereas a fear memory can be encoded as early as P15, continued brain
development and maturation prepubertally must be necessary for the memory to persist-
a function that is not yet developed in P15 mice. This study compliments a developmental
study in rats that found the earliest age in which rats can remember their context is P17,
but contextual fear memory acquisition is not expressed until P19-P23, suggesting
35
context representation comes online before the ability to associate the context with fear
an aversive experience (Foster & Burman, 2010 Learn Mem; Rudy 1993 Behav
Neurosci). Contextual fear learning also can lead to remote memory, in which the memory
lasts for at least several weeks in rodents. Remote memory of contextual fear conditioning
arises at some age between P22-P25 in mice (Samifanni & Zhao et al, 2021 Learn Mem).
Numerous studies implicate hippocampal-mPFC circuitry in contextual fear
learning (Marek et al, 2018 Nat Neurosci; Pereira et al, 2019 Mol Neurobiol; Vasquez et
al, 2019 Neurobiol Learn Mem). In adult mice, there is an increase in synchronization
between mPFC and CA1 when remote contextual fear memory is called upon (30 days
after conditioning), which does not occur during fear memory testing 24 hours after
training (Makino et al, 2019 Cell Rep), suggesting that functional hippocampal-mPFC
connections are involved in long-lasting contextual fear memories. Coupled oscillations
between PFC and hippocampus arise in the first postnatal week in rats (Brockmann et al,
2011 Neuron). Although these brain regions already show synchronization, it is debatable
whether there is true functionality in the hippocampal-mPFC circuits at young ages. For
example, in P17 mice, hippocampus is already functional in encoding contextual cues,
but there is not yet functional connectivity between hippocampus and mPFC, measured
by a lack of change in mPFC c-FOS activity at P17 in mice that is observed in adult mice
when hippocampus is injected with a GABAaR antagonist, picrotoxin (Li et al, 2018
Neurobiol Learn Mem). However, functional connectivity between hippocampus and
mPFC may still exist early postnatally through hippocampal glutamatergic neuron
36
projections to mPFC, as optogenetic manipulation of glutamatergic projection neurons
from intermediate/ventral hippocampus disrupts synchrony (Ahlbeck et al, 2018 Elife). In
contrast, immunotoxic lesion of GABAergic neurons at birth does not affect
developmental synchrony between PFC and hippocampus (Bitzenhofer & Hanganu-
Opatz, 2014 Neuropharmacology), Nevertheless, the timing in which the connections
between hippocampus and mPFC mature may be a factor that governs the initial
appearance of contextual fear memory capabilities. Furthermore, in adults, hippocampal
neurons that send collateral excitatory projections to both mPFC and amygdala are
preferentially activated during contextual fear memory responses, facilitating the
synchronization of these three brain regions (Ishikawa & Nakamura, 2006 J Neurophysiol;
Jin & Maren, 2015 Sci Rep; Kim & Cho, 2017 J Neurosci). The exact timing of and the
factors regulating the maturation of hippocampal-mPFC connections has not been
thoroughly studied, including the development of hippocampal neurons that form these
collateral projections. There are indirect pathways connecting hippocampus and mPFC,
as well. Nucleus reuniens of the ventral midline thalamus indirectly connects mPFC and
hippocampus by projecting to hippocampus from neurons that receive input from mPFC,
coordinating activity between these three brain regions (Vertes et al, 2007 Brain Res Bull).
Nucleus reuniens also directly controls coordination between mPFC and hippocampus
from neurons that send collaterals to both brain regions (Hoover & Vertes, 2012 Brain
Struct Funct; Varela et al, 2014 Brain Struct Funct). The coordination of mPFC,
hippocampus, and nucleus reuniens has been implicated in contextual fear learning in
37
rodents (Xu & Südhof; 2013 Science; Ramanathan et al, 2018 Nat Commun; Troyner et
al, 2018 Hippocampus; Ramanathan et al, 2018 J Neurosci). Functionally in humans,
medioventral thalamus, which corresponds to rodent nucleus reuniens, has been shown
to increase coupling activity with PFC to promote durable memory encoding (Wagner et
al, 2019 Neuroimage). Developmentally, functional links between the mPFC and
hippocampus via the ventral midline thalamus is already evident during the first postnatal
week in rats (Hartung et al, 2016 J Neurosci). However, there is limited knowledge
regarding the maturation of this circuit. In neonate rats (P1/P2), nucleus reuniens (along
with other thalamic structures) projects to ipsilateral and contralateral prefrontal/frontal
cortices, while in adults, contralateral projections are greatly reduced (Minciacchi &
Granato, 1989 J Comp Neurol), indicating that thalamic axons innervate brain regions
early in life that eventually get pruned away. Further determination of the time range that
this pruning occurs would help with the understanding of the maturation of thalamo-
prefrontal circuitry.
mPFC reciprocally connects with the amygdala (Krettek & Price, 1977 J Comp
Neurol; Sarter & Markowitsch, 1983 Brain Res Bull; Gabbott et al, 2005 J Comp Neurol).
This circuit has also been implicated in fear learning (Klavir et al, 2017 Nat Neurosci;
Bloodgood et al, 2018 Transl Psychiatry). From a developmental standpoint, tracer
studies have examined the timing of structural connectivity between mPFC and amygdala
early postnatally. In rats at P3, there are already fibers that innervate the mPFC from
amygdala (Verwer et al, 1996 J Comp Neurol). Interestingly, while projection patterns
38
from amygdala to nucleus accumbens and mediodorsal thalamus are stable from P7-P26
in rats, there is protracted development of projections sent from amygdala to mPFC, in
which reorganization occurs between P7 to P11, including a shift from solely innervating
mPFC layer 5 to also include layers 1/2 (Bouwmeester et al, 2002b J Comp Neurol).
Strikingly, there is an increase in fiber density in mPFC of BLA projecting neurons from
P6 to P120 (adulthood) in rats (Cunningham et al, 2002 J Comp Neurol). At P7 in rats,
there is limited retrograde labeling of supragranular mPFC following amygdala injections
(representing mPFC to amygdala projections). By P13, there is a dramatic increase in
labeled neurons in both superficial and deep layers of mPFC (Bouwmeester et al, 2002a
J Comp Neurol). Very similar results are found in mice, in which projections from mPFC
to BLA are very limited at P10, but are abundant by P15, whereas by P10, there already
are dense mPFC projections to other subcortical structures, including nucleus reuniens
and ventromedial thalamus (Arruda-Carvalho et al, 2017 J Neurosci). There is a further
increase in fibers in the BLA from mPFC between P15 to P30, suggesting a continued
increase in connectivity. During fear behavior in adult mice, synchronized 2-6 Hz
oscillations are found in mPFC and amygdala, and optogenetic induction of these
oscillations induces fear behavior (Karalis et al, 2016 Nat Neurosci). In humans,
amygdala-prefrontal functional connectivity is age-dependent. Children younger than 10
years old exhibit a positive relationship between amygdala and prefrontal cortex activity,
while children older than 10 years old show a negative correlation, suggesting
reorganization of connectivity during the transition into adolescence (Gee et al, 2013 J
39
Neurosci). This shift may be due to the maturation of mPFC leading to the ability for it to
exhibit top-down control.
Several cellular and molecular mechanisms contributing to contextual fear learning
have been identified. The differentiation and maturation of oligodendrocytes, which
myelinate axons, in mPFC is necessary for remote memory from contextual fear
conditioning in adult mice, but not for memory acquisition (Pan et al, 2020 Nat Neurosci).
Interestingly, it has been reported that the myelination of axons that project to layer 5
mPFC neurons increases dramatically between P15 and P43 in rats (McDougall et al,
2018 eNeuro). Similarly, in mice, P21-P35 is a period with significant oligodendrocyte
maturation in mPFC (Makinodan et al, 2012 Science). This raises the possibility that the
earliest age at which mice are capable of exhibiting contextual fear memory persistence
and/or remote memory is dependent on the earliest time at which the relevant brain
circuitry has the capability to promote experience-based myelination. Further
developmental studies into the relationship between age of onset for contextual fear
memory persistence and mPFC myelination timing will shed light.
The following chapters will mainly focus on the Met receptor tyrosine kinase (MET).
MET was initially identified as a protooncogene transcript that encodes a cell-surface
receptor that is part of the tyrosine kinase family of growth factor receptors (Park et al,
1987 Proc Acad Natl Sci U S A). There is emerging evidence of pleiotropic roles of MET
in the developing nervous system, including in excitatory synapse development and
maturation (Matsuzaki et al, 2001 Nat Neurosci; Peng et al, 2016 Mol Psychiatry; Chen
40
& Ma et al, 2021 Mol Psychiatry; Ma et al, 2022 Cereb Cortex). Hepatocyte growth factor
(HGF), the only known ligand of MET (Bottaro et al, 1991 Science), binds to and activates
MET, which dimerizes and causes autophosphorylation of tyrosine in the catalytic domain
and phosphorylation of tyrosine in the C-terminal domain. This in turn results in activation
of downstream signaling pathways, including phosphatidylinositol-3 kinase/Akt and
ERK/mitogen-activated protein kinase (Longati et al, 1994 Oncogene; Ponzetto et al,
1994 Cell). A promoter variant in MET reduces transcript levels and increases autism
spectrum disorder (ASD) relative risk (Campbell et al, 2006 Proc Natl Acad Sci U S A).
Since this initial report, further studies have reported similar findings in the same promoter
variant (Jackson et al, 2009 Autism Res) and identified additional single nucleotide
polymorphisms in MET that is enriched in people with ASD (Sousa et al, 2009 Eur J Hum
Genet; Thanseem et al, 2010 Neurosci Res). Additionally, in humans with the MET ASD-
risk promoter variant, functional connectivity and activity in brain regions involved in social
cognition are reduced (Rudie et al, 2012 Neuron). In subjects with ASD, as well as in
subjects specifically diagnosed with Rett Syndrome, a syndromic form of ASD, there is a
depletion of MET protein levels in the post-mortem brain compared to control subjects
(Campbell et al, 2007 Ann Neurol; Plummer et al, 2013 Transl Psychiatry). This emerging
literature suggests a role of MET in normal circuit development, which, when disrupted,
can lead to vulnerabilities in brain circuits that increase risk for ASD. Therefore, further
studies on the normal role of MET in brain development and its relationship with ASD risk
when disrupted are critical. Chapter 2 will focus on the expression patterns of MET in
41
medial prefrontal cortex (mPFC) and primary visual cortex of mice, comparing between
regions and across postnatal development in a layer-specific manner. In Chapter 3, we
aimed to determine whether FOXP2 is a negative regulator of MET in vivo, which was
previously reported in human neuronal progenitor cells in vitro (Mukamel et al, 2011 J
Neurosci). Here, Foxp2 is conditionally deleted in the cortex of mice, and MET
expression, as well as other molecular and anatomical phenotypes in the cortex, is
analyzed. Chapter 4 will explore whether MET has a functional role in the emergence of
contextual fear memory capabilities, a cognitive ability, and Chapter 5 will begin to map
out the specific projection targets of mPFC MET
+
neurons, comparing them to overall
mPFC circuitry.
42
CHAPTER 2 : SUBCLASS-SPECIFIC EXPRESSION PATTERNS OF MET
RECEPTOR TYROSINE KINASE DURING DEVELOPMENT IN MEDIAL
PREFRONTAL AND VISUAL CORTICES
Alexandra L. Lanjewar, Sonum Jagetia, Zuhayr M. Khan, Kathie L. Eagleson, and Pat
Levitt
2.1 ABSTRACT
Met encodes a receptor tyrosine kinase (MET) that is expressed during development and
regulates cortical synapse maturation. Conditional deletion of Met in the nervous system
during embryonic development leads to deficits in adult contextual fear learning, a medial
prefrontal cortex (mPFC)-dependent cognitive task. MET also regulates the timing of critical
period plasticity for ocular dominance in primary visual cortex (V1). However, the underlying
circuitry responsible remains unknown. Therefore, this study determines the broad expression
patterns of MET throughout postnatal development in mPFC and V1 projection neurons (PNs),
providing insight into similarities and differences in the neuronal subtypes and temporal patterns
of MET expression between cortical areas. Using a transgenic mouse line that expresses green
fluorescent protein (GFP) in Met
+
neurons, immunofluorescence and confocal microscopy were
performed to visualize MET-GFP
+
cell bodies and PN subclass-specific protein markers. Analyses
reveal that MET expression is highly enriched in infragranular layers of mPFC, but in
supragranular layers of V1. Interestingly, temporal regulation of the percentage of MET
+
neurons
across development not only differs between cortical regions, but also is distinct between lamina
within a cortical region. Further, MET is expressed predominantly in the subcerebral PN subclass
in mPFC, but the intratelencephalic PN subclass in V1. The data suggest that MET signaling
influences the development of distinct circuits in mPFC and V1 that underlie subcerebral and
intracortical functional deficits following Met deletion, respectively.
43
2.2 INTRODUCTION
The cerebral cortex comprises diverse cell types and circuits required to subserve higher
order brain processes. This diversity arises during development, when molecular
expression of cortical neurons becomes heterogenous and changes over time to meet
developmental demands, including those related to experience-dependent maturation of
circuits (Trevino et al, 2021 Cell; Zhong et al, 2018 Nature; Bruno et al, 2009 J Neurosci;
Dantzker & Callaway, 1998 J Neurosci). This occurs before entering a more stable state
in adulthood. Despite its complexity, great strides have been made in understanding the
development of cortical heterogeneity. For cortical projection neurons (PNs),
heterogeneity has been studied broadly, in the context of areal patterning (Bhaduri &
Sandoval-Espinosa et al, 2021 Nature; Chen et al, 2011 Neuron; Levitt et al, 1997 Annu
Rev Neurosci) and lamina identity within a cortical area (Qian et al, 2020 Cell Stem Cell;
Molinard-Chenu et al, 2020 Ann Clin Transl Neurol; Luo et al, 2011 Proc Natl Acad Sci U
S A), as well as more specifically, at the level of a cell subclass – defined by broad
neuronal projection differences within a cortical layer (Tsyporin et al, 2021 Cell Rep; Kast
& Levitt, 2019 Prog Neurobiol; Gerfen et al, 2018 J Neurosci Res; Hatanaka et al, 2016
Cereb Cortex; Molyneaux et al, 2007 Nat Rev Neurosci; Kassai et al, 2008 Eur J
Neurosci) – and, most recently, at the level of a cell type, which further refines subclass
identity based on RNA composition (Zhang & Zhou et al, 2021 Nature; Kim et al, 2020
Neuron; Zeisel et al, 2015 Science; Luo et al, 2017 Science). These efforts have led to a
greater understanding of the molecular heterogeneity of mature PNs. Currently, however,
there is a more limited understanding of the generalizability of neuronal cell types
44
identified in one cortical region at one stage of development to other cortical regions and
developmental time points. Identification of molecules exhibiting discrete temporal and
spatial patterns in the developing cortex will narrow this knowledge gap.
The c-MET receptor tyrosine kinase (MET) is expressed transiently in cortical PNs
during the peak period of synaptogenesis, with greatly reduced expression during
adolescence (Judson et al, 2009 J Comp Neurol; Eagleson et al, 2016 Dev Neurobiol;
Chen & Ma et al, 2021 Mol Psychiatry). Selective deletion of Met in cells that arise from
the dorsal pallium results in precocious electrophysiological and molecular maturation of
excitatory synapses. In contrast, extending cortical Met expression past its normal
temporal decline results in synapses remaining in a more immature state (Chen & Ma et
al, 2021 Mol Psychiatry; Ma et al, 2022 Cereb Cortex). Thus, the timing of the
downregulation of MET expression modulates the timing of synapse maturation and
stabilization, with functional consequences at the circuit and behavioral level. For
example, the critical period for ocular dominance in the primary visual cortex (V1) is
closed prematurely or opened later by deleting or extending Met expression, respectively
(Chen & Ma et al, 2021 Mol Psychiatry). Disruption of the temporal regulation of MET also
impacts medial prefrontal cortex (mPFC)-mediated functions, including social cognition
(Ma et al, 2022 Cereb Cortex) and contextual fear memory (Thompson & Levitt; 2015 J
Neurodev Disord; Heun-Johnson & Levitt, 2017 Neurobiol Stress; Xia & Wei et al, 2021
Neurobiol Learn Mem). Notably, while deficits in contextual fear memory are apparent in
adults when Met expression is reduced or eliminated developmentally from all neural cells
45
(Thompson & Levitt; 2015 J Neurodev Disord; Heun-Johnson & Levitt, 2017 Neurobiol
Stress) or from cells arising from the dorsal pallium (Xia & Wei et al, 2021 Neurobiol Learn
Mem), there is no effect on the onset of expression of contextual fear memory in weanling
mice (unpublished data). Therefore, while MET expression is involved in appropriate
emergence of visual circuit patterning, the receptor appears to be necessary for long-term
cognitive capabilities in adults, perhaps reflecting differences in the specific circuits
expressing MET in V1 and mPFC.
The identity of PNs contributing to MET-expressing circuits in V1 and mPFC is not
known, although recent studies in mice provide insight. For example, in granular V1,
visual experience drives cell-type differentiation of PNs in layers 2/3, but not infragranular
layer 5 or 6 (Cheng et al, 2022 Cell), suggesting experience-driven critical periods may
involve supragranular plasticity, while neurons in infragranular layers are stable at the
molecular level before eye opening. In contrast, social deficits in autism mouse models
are driven by abnormalities in layer 5 subcortical (SC) PNs in granular mPFC (Brumback,
Elwood & Kjaerby et al, 2018 Mol Psychiatry). Similarly, cognitive flexibility is driven by
activity of infragranular PNs but not supragranular intratelencephalic (IT) PNs (Nakayama
et al, 2018 J Neurosci; Spellman et al, 2021 Cell). These data indicate that, although MET
is expressed in all cerebral cortical areas (Judson et al, 2009 J Comp Neurol), there may
be regional differences in the specific populations that express the receptor. Thus far,
detailed analyses have focused on primary somatosensory cortex (S1), in which MET is
enriched in ITPNs. In this region, MET is also expressed in a smaller subset of SCPNs
46
but is excluded from nearly all granule neurons and corticothalamic (CT) PNs (Kast et al,
2019 Cereb Cortex). Whether the expression patterns of MET in S1 are recapitulated in
V1 and mPFC remain unknown. The developmental contributions of MET signaling, as
well as the different roles of ITPNs and SCPNs in V1 and mPFC, however, raise questions
regarding the specificity of MET-expressing PN phenotypes across cortical regions. Here,
experiments were designed to determine the developmental expression patterns of MET
in mPFC and V1 PNs, providing greater insight into 1) the timing and changes over time
of MET expression by cortical PNs during development; and 2) the similarities and
differences in temporal and spatial MET expression in mPFC and V1.
2.3 MATERIALS AND METHODS
Animals
A Met
EGFP
BAC transgenic mouse line was rederived from the Met-EGFP Bacterial
Artificial Chromosome (BX139), obtained from the GENSAT repository at Rockefeller
University (RRID:SCR_002721), on an FVB background, as previously described in
Kamitakahara, Wu & Levitt (2017 J Comp Neurol) and Kast, Wu & Levitt (2019 Cereb
Cortex). Multiplex in situ hybridization in the brainstem raphe and neocortex has validated
that the expression of green fluorescent protein (GFP) recapitulates endogenous Met
transcript expression in founder lines (Kast et al, 2017 ACS Chem Neurosci;
Kamitakahara et al, 2017 J Comp Neurol; Kast, Wu & Levitt, 2019 Cereb Cortex). Founder
mice were then backcrossed with C57BL/6J mice (The Jackson Laboratory,
RRID:IMSR_JAX:000664) for at least 10 generations. Backcrossed female and male
47
mice homozygous for the Met-EGFP transgene were bred in our facility to produce the
homozygous Met
GFP
pups used in this study. Met
GFP
mice express EGFP under the
control of the Met promoter, which permits visualization of cell bodies that express MET.
Mice were housed on ventilated racks with a 13:11 hour light:dark cycle (lights on at 06:00
hours, lights off at 19:00 hours) at 22°C with ad libitum access to water and a standard
chow diet (PicoLab Rodent Diet 20, #5053, St. Louis, MO). Animal care and procedures
were in accordance with the guidelines set forth by the Children’s Hospital Los Angeles
Institutional Animal Care and Use Committee.
Immunofluorescence Staining
Brain tissue for immunofluorescence staining was collected at several ages between
postnatal day (P) 2 and P35 from male and female Met
GFP
mice. Mice used at P2 were
anesthetized by cold exposure followed by acute decapitation. Brains were then dissected
at room temperature in 0.1M phosphate-buffered saline (PBS) and immersed in fixative
(4% paraformaldehyde (Sigma, St. Louis, MO) in PBS, pH 7.4) at 4°C for 12-18 h. Mice
used on or after P4 were deeply anesthetized by intraperitoneal injection of
ketamine:xylazine (100 mg/kg:10 mg/kg, Henry Schein, Melville, NY). Animals were then
transcardially perfused with fixative, followed by immediate dissection and immersion of
the brain in fixative at 4°C for 2 h. Following post-fixation, brains of all ages were
cryoprotected by sequential incubation in 10%, 20%, and 30% sucrose in PBS,
embedded in Tissue-Tek
®
Optimal Cutting Temperature Compound (VWR, Radnor, PA),
frozen over liquid nitrogen vapors, and stored at -80°C until cryosectioning. Twenty μm-
48
thick coronal sections were collected at -20°C and mounted onto superfrost plus
microscope slides (VWR, Radnor, PA) in a series of five. Slides were stored at -80°C until
immunofluorescence staining. For immunostaining, slides were defrosted at room
temperature for 10 min, dried at 60°C for 15 min in a hybridization oven, and washed in
PBS at room temperature for 10 min. Sections were blocked and permeabilized at room
temperature for 1 h in PBS containing 5% Normal Donkey Serum (Jackson
ImmunoResearch, West Grove, PA) and 0.3% Triton X-100 (Sigma, St. Louis, MO), then
incubated overnight at room temperature in one or more primary antibody diluted in 0.1%
Triton X-100 in PBS. Primary antibodies (characterizations below) used were chicken
anti-green fluorescent protein (GFP; Abcam Cat# ab13970, RRID:AB_300798), rat anti-
CTIP2 (Abcam Cat# ab18465, RRID:AB_2064130), rabbit anti-NeuN (Millipore Cat#
ABN78, RRID:AB_10807945), mouse anti-SATB2 (Abcam Cat # ab51502,
RRID:AB_882455), rabbit anti-DARPP-32 (Cell Signaling Technology Cat# 2306;
RRID:AB_823479), and rabbit anti-PCP4 (J Morgan Laboratory, St. Jude’s). Sections
were washed 5 times for 5 min each at room temperature with 0.2% Tween-20 (Sigma,
St. Louis, MO) in PBS, then incubated at room temperature for 1 h in diluted Alexa Fluor
®
F(ab’)2 conjugated secondary antibodies (1:500; Abcam) in 0.1% Triton X-100 in PBS
and protected from light, hereon. Sections were washed 3 times for 5 min each with 0.2%
Tween-20 in PBS. Sections were then incubated in DAPI (1:15,000; Thermo Fisher
Scientific Cat# D1306) diluted in PBS for 8 min, followed by two washes in PBS for 5 min
each. Sections were embedded with a coverslip using ProLong Gold antifade mountant
49
(Thermo Fisher Scientific Cat# P36930), and the mounting media cured for at least 24 h
before acquiring images using confocal microscopy.
Antibody Characterization
The chicken anti-GFP polyclonal antibody (Abcam, Cat# ab13970, used at 1:500)
immunogen is a recombinant full-length protein, corresponding to GFP. Samples from
transgenic mice expressing GFP analyzed by western blot using this antibody exhibit a
single 25kDa band (manufacturer’s datasheet). Immunofluorescence staining with the
antibody recapitulates endogenous Met transcript in the Met
GFP
line (Kast et al, 2017 ACS
Chem Neurosci; Kamitakahara et al, 2017 J Comp Neurol; Kast, Wu & Levitt, 2019 Cereb
Cortex).
The rat anti-CTIP2 [25B6] monoclonal antibody (Abcam, Cat# ab18465, used at
1:500) immunogen is a recombinant fragment corresponding to human CTIP2 with a GTS
fusion. The antibody detects 2 bands representing CTIP2 at about 120kD and is highly
expressed in brain and in malignant T-cell lines derived from patients with adult T-cell
leukemia/lymphoma (manufacturer’s datasheet). The antibody-stained cell nuclei in
infragranular layers of mouse cortex in a pattern that is identical with previous reports of
CTIP2 expression (Arlotta et al, 2005 Neuron).
The rabbit anti-NeuN polyclonal antibody (Millipore, Cat# ABN78, used at 1:500)
immunogen is a GST-tagged recombinant fragment corresponding to first 97 amino acids
from the N-terminal region of murine NeuN. The antibody detects bands ~48/42 kDa.
50
Uncharacterized bands may be observed in some lysates (manufacturer’s datasheet).
NeuN is a neuronal marker (Dent et al, 2010 FEBS Lett).
The mouse anti-SATB2 [SATBA4B10] - C-terminal monoclonal antibody (Abcam,
Cat # ab51502, used at 1:100) immunogen is a recombinant fragment corresponding to
human SATB2 (C terminal). The antibody detects a single band at 82 kDA
(manufacturer’s datasheet).
The rabbit anti-DARPP-32 monoclonal antibody (Cell Signaling Technology, Cat#
2306, used at 1:250) is produced by immunizing animals with a synthetic peptide
corresponding to residues surrounding Glu160 of human DARPP-32 (manufacturer’s
datasheet).
The antiserum to PCP4 (PEP-19) (J Morgan Laboratory, St. Jude’s, used at
1:3,000) immunogen is a 13 a.a. peptide of PCP4 VAIQSQFRKFQKK (Ziai et al, 1988 J
Neurochem). The specificity of detection for these antibodies has also been confirmed by
colocalization of ISH signals (Watakabe et al, 2012 J Comp Neurol).
Imaging and Analyses
Two cortical regions, mPFC (corresponding to areas 24a, 25, and 32 in Paxinos &
Franklin, 2019) and V1 (corresponding to V1 in Paxinos & Franklin, 2019), were analyzed.
For qualitative analyses, images of mPFC or V1 were acquired on a Zeiss LSM 700
inverted confocal microscope using a 10×/0.45 Plan-APOCHROMAT or a Leica
51
STELLARIS 5: 10×/0.40 HC PL APO CS2, respectively. For quantitative analyses,
images of mPFC were acquired on a Zeiss LSM 700 inverted confocal microscope using
a 20×/0.8NA Plan-APOCHROMAT objective lens, using refractive index correction, and
images of V1 were acquired on a Leica STELLARIS 5 inverted confocal microscope using
a 20×/.75 air lens. Images were collected at 2μm z-stacks through the entire thickness of
the section at 1AU (Zeiss: 0.313 x 0.313 x 2 μm; Leica: 0.757 x 0.757 x 2 μm). Three
brain sections, at least 100μm apart and corresponding to rostral, middle, and caudal
mPFC or V1, were imaged and counted per animal. Some brains were used for mPFC
and V1 analyses, while other brains were used for only one brain region. For each
analysis, no more than two mice of the same sex from a single litter were used. Further,
at least three litters and approximately equal numbers of males and females are
represented in each analysis to minimize potential litter and sex effects. Criteria for an
immuno-labeled cell, for which a positive profile included the fluorescent signal
representing the marker of interest and a DAPI
+
nucleus, were established by two
researchers and validated through independent counts to confirm inter-rater reliability.
Manual counts of immuno-labeled cell bodies in discrete cortical layers were then
performed using the ‘cell counter’ plugin in FIJI software version 2.3.0 (https://fiji.sc/,
RRID:SCR_002285). First, images were cropped to the layer of interest, based on DAPI
and CTIP2 immunostaining. Thickness of each cortical layer crop varied based on the
depth of the layer to capture the full thickness, while width of the cortical crop was held
consistent in each brain region (mPFC: 321µm; V1: 861µm). The number of cells
52
immunolabeled with MET-GFP or with a specific marker, as well as the number of cells
immunolabeled with MET-GFP and the marker, were counted for each section. The
percentage of co-labeled cells was determined for each section, and percentages for the
three sections were averaged to obtain one value per animal (per brain region).
Statistical Analysis
Data were statistically analyzed and graphed using GraphPad PRISM software version
9.1.2 (http://www.graphpad.com/, RRID:SCR_002798) and then figures of the graphs
were created using Microsoft PowerPoint version 16.63, with the exception of the
graphical abstract, which was created with BioRender (http://biorender.com,
RRID:SCR_018361). Mean + standard error of the mean is reported in the text, with
individual values and sample sizes reported in the figure legends. For each analysis,
sample size was determined to obtain at least 80% power with α = 0.05 (SPH Analytics,
statistical power calculator using average values). D’Agostino & Pearson normality test,
when n>8, or Shapiro Wilk normality test, when n<8, was used to determine normal
distribution for each data set. For data with normal distributions, two-tailed unpaired t-test
(test statistic: t), ordinary one-way ANOVA (equal standard deviation; test statistic: F) or
Welch’s ANOVA (not equal standard deviation; test statistic: W) was used to determine
statistically significant differences in percentage of colocalization between ages. For the
ANOVAs, if the omnibus test detected a significant difference, a post hoc Tukey’s multiple
comparisons test (ordinary one-way ANOVA) or Dunnett’s T3 multiple comparisons test
(Welch’s ANOVA) was performed to determine the ages at which differences occurred.
53
For data without normal distributions, the Kruskal-Wallis test (test statistic: H) was
performed to determine statistically significant differences in percentage of colocalization
between ages. If the omnibus test detected a significant difference, a post hoc Dunn’s
multiple comparisons test was performed to determine at which ages the differences
occurred.
2.4 RESULTS
The Met
GFP
transgenic mouse line was used to profile GFP-expressing (MET-GFP
+
)
neurons in the medial prefrontal cortex (mPFC) and primary visual cortex (V1) across
early postnatal development. There are two patterns of MET-GFP
+
expression that are
described: spatial, related to enrichment differences in laminar location, and temporal,
related to changes in the percentage of neurons that express MET-GFP over
developmental time. In mPFC, MET-GFP
+
neurons are largely restricted to layers 5 and
6 (Fig. 2.1a). Quantitative analysis reveals no significant effect of age on the percentage
of layer 5 MET-GFP
+
neurons, defined by co-expression with NeuN, with approximately
one-fifth double-labeled across the first five postnatal weeks (F = 1.8263; p = 0.1457; P2:
13.84 + 0.82%; P7: 20.87 + 1.25%; P12: 19.23 + 1.62%; P15: 18.29 + 2.90%; P21: 18.50
+ 4.15%; P35: 17.20 + 1.32%; Fig. 2.1b). This contrasts with layer 6, in which there is a
significant effect of age on the percentage of neurons expressing MET-GFP over the
same time period (F = 52.7200; p < 0.0001; Fig. 2.1c). Post hoc analyses demonstrate a
significant reduction in the percentage of neurons expressing MET-GFP after the first
postnatal week. Specifically, the mean percentage at P2 (57.40 + 2.59%) and P7 (49.83
54
+ 2.07%) are significantly different from those at P12 (26.57 + 1.91%), P15 (28.49 +
3.68%), P21 (21.88 + 1.10%) and P35 (17.12 + 2.14%), as are those between P15 and
P35; all other comparisons are not significantly different (Table 2.1). In comparison to
mPFC, the laminar distribution of MET-GFP
+
neurons is substantially different in V1. Most
notably, in addition to the infragranular layers, MET-GFP
+
neurons are abundant in layers
2/3 (Fig. 2.1d), similar to previous findings in primary somatosensory cortex (Kast, Wu &
Levitt, 2019 Cereb Cortex). The high packing density of neurons in relatively immature
layers 2/3 at P4 precluded quantitative analysis at that age, but analyses at later ages
revealed no significant difference in the percentage of MET-GFP
+
neurons at P7 and P21
(t = 1.8817; p = 0.0843; P7: 18.10 + 2.51%; P21: 25.50 + 3.02%; Fig. 2.1e). Further, the
temporal pattern of changes in the percentage of MET-GFP
+
neurons in layers 5 and 6
are distinct from that in mPFC. Specifically, in V1 layer 5, there is a significant effect of
age on the percentage of neurons that express MET-GFP (H = 8.1109; p = 0.0112; Fig.
2.1f), driven by a decrease over the first postnatal week (p = 0.0209; P4: 15.95 + 8.61%;
P7: 7.25 + 1.02%); there is no significant difference between P4 or P7 and P21 (P21:
7.77 + 1.24%; Table 2.2). There also is a significant effect of age in layer 6 (W = 7.7554;
p = 0.0148; Fig. 2.1g), driven by a decrease between P4 (13.33 + 0.85%) and P21 (9.96
+ 0.29%); P7 (12.91 + 1.59%) does not differ significantly from either P4 or P21 (Table
2.3). Collectively, the data suggest there are divergent laminar patterns of MET
expression in mPFC and V1 neurons, as well as distinct differences in the stability of
expression over time.
55
(a)P2 P35
6 5 2/3 6 5 2/3
CTIP2
MET-GFP
P2 P35
P2 P7 P12 P15 P21 P35
0
20
40
60
80
100
Age
% MET-GFP Neurons
MET-GFP Neurons in
Layer 5 mPFC
P2 P7 P12 P15 P21 P35
0
20
40
60
80
100
Age
% MET-GFP Neurons
MET-GFP Neurons in
Layer 6 mPFC
****
*
P2
NeuN
MET-GFP
(b)
NeuN
MET-GFP
P35
P21 P7
2/3
5
6
4
CTIP2
MET-GFP
2/3
5
6
4
P4
P7
P21
P4 P7 P21
0
20
40
60
80
100
Age
% MET-GFP Neurons
MET-GFP Neurons in
Layer 5 V1
✱
P21
P4
P7 P21
0
20
40
60
80
100
Age
% MET-GFP Neurons
MET-GFP Neurons in
Layers 2/3 V1
MET-GFP NeuN
P4 P7 P21
0
20
40
60
80
100
Age
% MET-GFP Neurons
MET-GFP Neurons in
Layer 6 V1
✱
P4
P21
MET-GFP NeuN
MET-GFP NeuN
(c)
(d) (e)
(f) (g)
56
Figure 2.1 The spatial and temporal expression of MET-GFP in mPFC and V1
neurons across developmental ages.
(a) Representative images at P2 (left panel) and P35 (right panel) of MET-GFP (green)
expression in mPFC, with CTIP2 (magenta) as marker for infragranular layers 5 & 6.
Boundaries of layers 2/3, 5, and 6 are denoted. (b) Representative images at P2 (left
panel) and P35 (middle panel) of MET-GFP (green) and NeuN (red) overlayed
expressions in layer 5 mPFC. Arrows denote examples of colocalization between and
MET-GFP and NeuN. Quantification of the percentage of layer 5 mPFC neurons that
express MET-GFP at developmental ages between P2 and P35 (right panel). n = 5 for
each age. There is no significant effect of age, analyzed by ordinary one-way ANOVA. (c)
Representative images at P2 (left panel) and P35 (middle panel) of MET-GFP (green)
and NeuN (red) overlayed expression in layer 6 mPFC. Arrows denote examples of
colocalization between NeuN and MET-GFP. Quantification of the percentage of layer 6
mPFC neurons that express MET-GFP at developmental ages between P2 and P35 (right
panel). n = 5 for P2, P7, P21, and P35, n = 4 for P12 and P15. ‘*’ indicates p < .05, ‘****’
indicates p < .0001, analyzed by ordinary one-way ANOVA followed by Tukey’s multiple
comparisons test. (d) Representative images at P7 (left panel) and P21 (right panel) of
MET-GFP (green) expression in V1, with CTIP2 (magenta) as marker for infragranular
layers 5 & 6. Boundaries of layers 2/3, 5, and 6 are denoted. (e) Representative images
at P7 (top left panel) and P21 (bottom left panel) of MET-GFP (green) and NeuN (red)
overlayed expressions in layers 2/3 V1. Arrows denote examples of colocalization
between and MET-GFP and NeuN. Quantification of the percentage of layer 2/3 V1
neurons that express MET-GFP at developmental ages between P7 and P21 (right
panel). n = 7 for each age. There is no significant difference between ages, analyzed by
unpaired t test. (f) Representative images at P7 (top left panel) and P21 (bottom left
panel) of MET-GFP (green) and NeuN (red) overlayed expressions in layer 5 V1. Arrows
denote examples of colocalization between and MET-GFP and NeuN. Quantification of
the percentage of layer 5 V1 neurons that express MET-GFP at developmental ages
between P4 and P21 (right panel). n = 6 for P4, n = 7 for P7 and P21. ‘*’ indicates p < .05,
analyzed by Kruskal-Wallis test followed by Dunn’s multiple comparisons test. (g)
Representative images at P7 (top left panel) and P21 (bottom left panel) of MET-GFP
(green) and NeuN (red) overlayed expressions in layer 6 V1. Arrows denote examples of
colocalization between and MET-GFP and NeuN. Quantification of the percentage of
layer 6 V1 neurons that express MET-GFP at developmental ages between P4 and P21
(right panel). n = 6 for each age. ‘*’ indicates p < .05, analyzed by Welch’s ANOVA test
followed by Dunnett’s T3 multiple comparisons test. All scale bars = 50µm. The
brightness and contrast of each channel were increased globally in images for
visualization purposes.
57
In the cerebral cortex, MET expression is enriched in excitatory projection neurons
(PNs; Judson et al, 2009 J Comp Neurol; Eagleson et al, 2011 Autism Res). In layer 5,
two broad subclasses of PNs, long-range subcerebral PNs (SCPNs) and
intratelencephalic PNs (ITPNs), can be identified using molecular markers (Woodworth &
Greig et al, 2012 Cell). The SCPN subclass broadly includes PNs that project
subcortically to target regions including midbrain, brain stem, and spinal cord, while the
ITPN subclass broadly includes PNs that project to cortical targets (Harris & Shepherd,
2015 Nat Neurosci). We next determined if the divergent patterns of MET expression in
infragranular layers of mPFC and V1 are reflected by differential co-expression of MET
with CTIP2, which is involved in the development of SCPNs (Arlotta et al, 2005 Neuron),
or with SATB2, which enables neurons to project cortically (ITPNs) through repression of
Ctip2 (Alcamo et al, 2008 Neuron). First, we determined the proportion of CTIP2
+
(Fig.
2.2a-b) or SATB2
+
(Fig. 2.2c-d) layer 5 neurons that express MET-GFP in each region.
In mPFC, there is no significant effect of age on the percentage of CTIP2
+
neurons that
express MET-GFP (F = 0.5279; p = 0.5982; Fig. 2.2e). Specifically, approximately 20%
of CTIP2
+
neurons express MET-GFP across the three ages examined (P7: 19.97 +
1.31%; P15: 22.43 + 2.48%; P21: 19.91 + 1.63%). In V1, however, MET-GFP is almost
completely absent from the CTIP2
+
population at P7 (1.28 + 0.32%). Further, there is a
significant effect of age on the percentage of CTIP2
+
neurons that express MET-GFP (F
= 4.4144; p = 0.0192; Fig. 2.2f), with post hoc analyses demonstrating a significant
increase at P15 (5.82 + 2.60%) and P21 (6.79 + 1.09%) compared to P7; the percentage
58
of V1 CTIP2 neurons that express MET-GFP at P12 (4.68 + 0.70%) is not statistically
different from any other age analyzed (Table 2.4). In mPFC, similar to the CTIP2
+
population, there is no significant effect of age on the percentage of SATB2
+
neurons that
express MET-GFP (F = 0.5994; p = 0.5592; Fig. 2.2g), with approximately 10% of SATB2
+
neurons expressing MET-GFP at the three ages examined (P7: 12.60 + 0.86%; P15:
11.51 + 2.06%; P21: 9.87 + 0.87%). A similar percentage of SATB2
+
neurons express
MET-GFP in V1, again with no significant effect of age (F = 0.4538; p = 0.7182; P7: 10.57+
1.54%; P12: 9.96 + 0.96%; P15: 9.56 + 1.73%; P21: 8.40 + 1.17%; Fig. 2.2h). Together,
these data suggest that less than one quarter of the total layer 5 CTIP2
+
and SATB2
+
population of neurons express MET in mPFC and V1, consistent with MET expression in
less than one quarter of the total neurons in layer 5 of these regions (Fig. 2.1b, 2.1f).
Next, we determined the percentage of the total population of layer 5 MET-GFP
+
neurons that express CTIP2 or SATB2. In mPFC, there is a significant effect of age on
the percentage of MET-GFP
+
neurons expressing CTIP2 (F = 3.5450; p = 0.0492; Fig.
2.2i). Post hoc analyses reveal no significant difference between individual ages, with
approximately 90% of MET-GFP
+
neurons expressing CTIP2 over time (P7: 94.72 +
0.57%; P15: 91.06 + 1.28%; P21: 89.81 + 2.32%; Table 2.5). In V1, there is also a
significant effect of age on the percentage of MET-GFP
+
neurons that express CTIP2 in
layer 5 (F = 7.3029; p = 0.0027; Fig. 2.2j). In contrast to mPFC, however, few MET-GFP
+
neurons express CTIP2 at P7 (9.13 + 2.58%). Post hoc analyses demonstrate a
significant increase in MET-GFP
+
neurons that express CTIP2 at P15 (47.51 + 9.76%)
59
and P21 (49.61 + 7.04%) compared to P7, such that approximately half of MET-GFP
+
neurons also express CTIP2 by the end of the second postnatal week; there is no
significant difference between P12 (31.33 + 14.22%) and any other age analyzed (Table
2.6). In mPFC, there is a significant age effect on the percentage of MET
+
neurons that
express SATB2 (F = 7.4194; p = 0.0042; Fig. 2.2k), with post hoc analyses demonstrating
a significant decrease between P7 (61.01 + 6.18%) and P21 (19.38 + 3.83%); P15 (40.40
+ 7.94%) is not significantly different than P7 or P21 (Table 2.7). In contrast, in V1 MET-
GFP
+
neurons expressing SATB2 are abundant in layer 5, with approximately 90% of the
MET-GFP
+
neurons expressing SATB2, and there is no significant effect of age (F
=1.8300; p = 0.1823; P7: 92.92 + 2.46%; P12: 96.82 + 1.86%; P15: 87.58 + 3.31%; P21:
91.07 + 3.45%; Fig. 2.2l). Together, these data suggest that the majority of MET-
expressing layer 5 neurons are molecularly distinct between cortical regions, expressing
CTIP2 in mPFC but SATB2 in V1.
In the previous analyses, over 100% of the MET-GFP
+
population were accounted
for at each age when considering MET-GFP co-expression with either CTIP2 or SATB2,
alone, indicating there is a population of MET-GFP neurons that co-expresses both CTIP2
and SATB2. In this context, while initial reports indicated that CTIP2 and SATB2 are
expressed in largely non-overlapping populations of SCPNs and ITPNs, respectively, via
repression mechanisms (Alcamo et al, 2008 Neuron; Chen et al, 2008 Proc Natl Acad Sci
U S A; Britanova et al, 2008 Neuron; Baranek et al, 2012 Proc Natl Acad Sci U S A), more
recent studies have demonstrated co-expression of SATB2 and CTIP2 in subpopulations
60
of excitatory PNs (Lickiss et al, 2012 J Anat; Leone et al, 2015 Cereb Cortex; Harb et al,
2016 eLife; Paolino et al, 2020 Proc Natl Acad Sci U S A). These studies further showed
that a double-labeled cell represents either a SCPN or an ITPN, rather than a PN that
sends collaterals to both targets. We therefore performed a more nuanced analysis of the
molecular phenotype of layer 5 MET-GFP
+
neurons, in which a CTIP2
+
; SATB2
+
cell
represents either an SCPN or ITPN, CTIP2
+
(SATB2
-
) cells represent SCPNs, and
SATB2
+
(CTIP2
-
) cells represent ITPNs, in mPFC (Fig. 2.3a) and V1 (Fig. 2.3b). In mPFC
at P7, approximately 55% of MET-GFP
+
neurons are CTIP2
+
; SATB2
+
, 40% are
CTIP2
+
(SATB2
-
), and 5% are SATB2
+
(CTIP2
-
). There are significant effects of age for the
percentage of MET-GFP
+
neurons that are CTIP2
+
; SATB2
+
(F = 8.5190; p = 0.0023; Fig.
2.3c) or CTIP2
+
(SATB2
-
) (F = 7.0515; p = 0.0051; Fig. 2.3d). Post hoc analyses
demonstrate a significant decrease in CTIP2
+
; SATB2
+
MET-GFP
+
neurons between P7
(56.62 + 6.00%) and P21 (13.06 + 2.75%; Table 2.8) that is paralleled by a significant
increase in the percentage of CTIP2
+
(SATB2
-
) MET-GFP
+
neurons between P7 (38.10 +
6.07%) and P21 (76.76 + 3.76%; Table 2.9). For both populations, the percentage of
MET-GFP
+
neurons at P15 (CTIP2
+
; SATB2
+
: 33.58 + 7.98%; CTIP2
+
(SATB2
-
): 57.48 +
7.45%;) is not significantly different from P7 or P21 (Table 2.8 & 2.9). There is no
significant effect of age on the percentage of MET-GFP
+
neurons expressing
SATB2
+
(CTIP2
-
) in mPFC (F = 1.4951; p = 0.2494; P7: 4.39 + 0.71%; P15: 6.82 + 1.05%;
P21: 6.32 + 1.77%; Fig. 2.3e). The percentage of MET-GFP
+
neurons in mPFC is stable
across the second and third postnatal week (Fig. 2.1b). These findings indicate that over
61
this time period, at least 75% of the layer 5 MET population in mPFC represent the SCPN
subclass, with the majority initially co-expressing CTIP2 and SATB2, but subsequently
downregulating SATB2. In contrast, less than 10% of the MET-GFP
+
population is the
ITPN subclass, with the remaining approximately 15% that continue to co-express both
CTIP2 and SATB2 representing either class. Notably, age-dependent co-expression of
SATB2 and CTIP2 in the MET-GFP
+
subset of neurons follows a near identical pattern to
all layer 5 mPFC neurons. Specifically, there is a significant effect of age on the
percentage of CTIP2
+
neurons that express SATB2, independent of MET-GFP, (F =
9.2545; p = 0.0016; Fig. 2.3f), with post hoc analyses demonstrating a significant
difference between P7 (59.77 + 4.62%) and P21 (23.84 + 3.64%), and between P15
(49.02 + 6.09%) and P21; all other comparisons are not significantly different (Table 2.10).
Together, these findings indicate that the downregulation of SATB2 in the MET
+
SCPN
subpopulation is similar to that in the whole SCPN population. The pattern of CTIP2
+
;
SATB2
+
in V1 layer 5 is strikingly different from that in mPFC. At P7, less than 10% of
MET-GFP
+
neurons are CTIP2
+
; SATB2
+
, few are CTIP2
+
(SATB2
-
), and almost 90% are
SATB2
+
(CTIP2
-
). There is a significant effect of age on the percentage of MET-GFP
+
neurons expressing CTIP2 and SATB2 (F = 5.7723; p = 0.0071; Fig. 2.3g). Post hoc
analyses demonstrate a significant increase between P7 (6.18 + 2.22%) and P15 (38.40
+ 10.05%), and P7 and P21 (43.86 + 6.97%), with all other comparisons not significantly
different (Table 2.11). There is no significant effect of age on the percentage of MET-
GFP
+
neurons that are CTIP2
+
(SATB2
-
) (H = 3.5917; p = 0.3091; P7: 2.95 + 1.21%; P12:
62
1.35 + 0.97%; P15: 9.11 + 4.54%; P21: 5.76 + 2.48%; Fig. 2.3h). There also is an effect
of age on the percentage of SATB2
+
(CTIP2
-
) MET-GFP
+
neurons (F = 6.5618; p = 0.0042;
Fig. 2.3i). Post hoc analyses reveal that, paralleling the increase in the percentage of
CTIP2
+
; SATB2
+
MET-GFP
+
layer 5 V1 neurons (Fig. 2.3g), there is a significant decrease
in the percentage of SATB2
+
(CTIP2
-
) MET-GFP
+
neurons between P7 (86.74 + 2.78%)
and P15 (38.40 + 10.05%), and P7 and P21 (47.21 + 7.51%); all other comparisons are
not significantly different (P12: 66.84 + 7.09%; Table 2.12). As in mPFC, the percentage
of MET-GFP
+
neurons is stable across the second and third postnatal week in V1 (Fig.
2.1f). The data indicate, however, that, in contrast to mPFC, over 85% of the layer 5 MET-
GFP
+
population in V1 is the ITPN subclass. This ITPN population initially expresses
SATB2, but not CTIP2, and then upregulates CTIP2 across the second and third postnatal
weeks. In contrast, less than 10% of the MET-GFP
+
population in this layer is the SCPN
subclass, with the remaining approximately 5% that expresses both subclass markers at
P7 representing either ITPNs or SCPNs. We note that, independent of MET-GFP, there
is no significant effect of age on the percentage of the V1 SATB2 layer 5 population that
also express CTIP2 at these ages (F = 0.4250; p = 0.7377; P7: 31.79 + 2.03%; P12: 34.76
+ 3.54%; P15: 37.18% + 11.95%; P21: 32.46% + 3.34%; Fig. 2.3j), suggesting that there
is a bias towards the suppression of CTIP2 in the MET-GFP
+
subpopulation of ITPNs
early postnatally.
63
Table 2.1 Post hoc analyses of MET-GFP
+
neurons in layer 5 mPFC
Table 2.2 Post hoc analyses of MET-GFP+ neurons in layer 5 V1
Table 2.3 Post hoc analyses of MET-GFP+ neurons in layer 6 V1
Tukey’s multiple
comparisons test
Adjusted p
value
P2 vs. P7 0.1953
P2 vs. P12 <0.0001
P2 vs. P15 <0.0001
P2 vs. P21 <0.0001
P2 vs. P35 <0.0001
P7 vs. P12 <0.0001
P7 vs. P15 <0.0001
P7 vs. P21 <0.0001
P7 vs. P35 <0.0001
P12 vs. P15 0.9934
P12 vs. P21 0.7223
P12 vs. P35 0.0881
P15 vs. P21 0.3821
P15 vs. P35 0.0262
P21 vs. P35 0.6572
Dunn’s multiple
comparisons test
Adjusted p
value
P4 vs. P7 0.0209
P4 vs. P21 0.0791
P7 vs. P21 >0.9999
Dunnett’s T3
multiple
comparisons test
Adjusted p
value
P4 vs. P7 0.9928
P4 vs. P21 0.0248
P7 vs. P21 0.2999
64
P7 P12 P15 P21
0
20
40
60
80
100
Age
%MET-GFP
+
; SATB2
+
/Total MET-GFP
+
Layer 5 MET+ neurons
that co-express SATB2
in V1
P7 P12 P15 P21
0
20
40
60
80
100
Age
%MET-GFP
+
; CTIP2
+
/Total MET-GFP
+ Layer 5 MET+ neurons that
co-express CTIP2
in V1
✱✱
✱✱
P7 P12 P15 P21
0
20
40
60
80
100
Age
%MET-GFP
+
; CTIP2
+
/Total CTIP2
+
Layer 5 CTIP2+ neurons
that co-express MET
in V1
✱
(a) (b)
(c) (d)
(g) (e)
P7 P21
MET-GFP
CTIP2
P7
P21
MET-GFP CTIP2
(f)
MET-GFP
SATB2
(i) (j) (k) (l)
MET-GFP SATB2
P7 P21 P7
P21
P7 P15 P21
0
20
40
60
80
100
Age
%MET-GFP
+
; CTIP2
+
/Total CTIP2
+
Layer 5 CTIP2+ neurons
that co-express MET
in mPFC
P7 P15 P21
0
20
40
60
80
100
Age
%MET-GFP
+
; SATB2
+
/Total SATB2
+
Layer 5 SATB2+ neurons
that co-express MET
in mPFC
P7 P12 P15 P21
0
20
40
60
80
100
Age
%MET-GFP
+
; SATB2
+
/Total SATB2
+
Layer 5 SATB2+ neurons
that co-express MET
in V1
(h)
P7 P15 P21
0
20
40
60
80
100
Age
%MET-GFP
+
; CTIP2
+
/Total MET-GFP
+
Layer 5 MET+ neurons that
co-express CTIP2
in mPFC
P7 P15 P21
0
20
40
60
80
100
Age
%MET-GFP
+
; SATB2
+
/Total MET-GFP
+
Layer 5 MET+ neurons that
co-express SATB2
in mPFC
✱✱
65
Figure 2.2 Colocalization analyses of MET-GFP with CTIP2 and SATB2 in layer 5
mPFC and V1 across developmental ages.
(a) Representative images at P7 (left) and P21 (right) of MET-GFP (green) and CTIP2
(magenta) overlayed expressions in layer 5 mPFC. Arrows denote examples of
colocalization between MET-GFP and CTIP2. (b) Representative images at P7 (top) and
P21 (bottom) of MET-GFP (green) and CTIP2 (magenta) overlayed expressions in layer
5 V1. Arrows denote examples of colocalization between MET-GFP and CTIP2. (c)
Representative images at P7 (left) and P21 (right) of MET-GFP (green) and SATB2
(yellow) overlayed expressions in layer 5 mPFC. Arrows denote examples of
colocalization between MET-GFP and SATB2. (d) Representative images at P7 (top) and
P21 (bottom) of MET-GFP (green) and SATB2 (yellow) overlayed expressions in layer 5
V1. Arrows denote examples of colocalization between MET-GFP and SATB2. (e)
Quantification of the percentage of layer 5 mPFC CTIP2
+
neurons that co-express MET-
GFP at P7, P15, and P21. n = 8 for P7, n = 9 for P15, n = 5 for P21. There is no significant
effect of age, analyzed by an ordinary one-way ANOVA. (f) Quantification of the
percentage of layer 5 V1 CTIP2
+
neurons that co-express MET-GFP at P7, P12, P15,
and P21. n = 5 for each age. ‘*’ indicates p < .05, analyzed by ordinary one-way ANOVA
followed by Tukey’s multiple comparisons test. (g) Quantification of the percentage of
layer 5 mPFC SATB2
+
neurons that co-express MET-GFP at P7, P15, and P21. n = 8 for
P7, n = 9 for P15, n = 5 for P21. There is no significant effect of age, analyzed by an
ordinary one-way ANOVA. (h) Quantification of the percentage of layer 5 V1 SATB2
+
neurons that co-express MET-GFP at P7, P12, P15, and P21. n = 5 for each age. There
is no significant effect of age, analyzed by an ordinary one-way ANOVA. (i) Quantification
of the percentage of layer 5 mPFC MET-GFP
+
neurons that co-express CTIP2 at P7, P15,
and P21. n = 8 for P7, n = 9 for P15, n = 5 for P21. There is no significant difference
between ages, analyzed by an ordinary one-way ANOVA followed by Tukey’s multiple
comparisons test. (j) Quantification of the percentage of layer 5 V1 MET-GFP
+
neurons
that co-express CTIP2 at P7, P12, P15, and P21. n = 5 for each age. ‘**’ indicates p <
.01, analyzed by ordinary one-way ANOVA followed by Tukey’s multiple comparisons
test. (k) Quantification of the percentage of layer 5 mPFC MET-GFP
+
neurons that co-
express SATB2 at P7, P15, and P21. n = 8 for P7, n = 9 for P15, n = 5 for P21. ‘**’
indicates p < .01, analyzed by ordinary one-way ANOVA followed by Tukey’s multiple
comparisons test. (l) Quantification of the percentage of layer 5 mPFC MET-GFP
+
neurons that co-express SATB2 at P7, P12, P15, and P21. n = 5 for each age. There is
no significant effect of age, analyzed by an ordinary one-way ANOVA. All scale bars =
50µm. The brightness and contrast of each channel were increased globally in images
for visualization purposes.
66
Table 2.4 Post hoc analyses of CTIP2
+
neurons that co-express MET-GFP in layer 5 V1
Table 2.5 Post hoc analyses of MET-GFP
+
neurons that co-express CTIP2 in layer 5
mPFC
Table 2.6 Post hoc analyses of MET-GFP
+
neurons that co-express CTIP2 in layer 5 V1
Table 2.7 Post hoc analyses of MET-GFP
+
neurons that co-express SATB2 in layer 5
mPFC
Tukey’s multiple
comparisons test
Adjusted p
value
P7 vs. P12 0.3818
P7 vs. P15 0.0161
P7 vs. P21 0.0719
P12 vs. P15 0.3138
P12 vs. P21 0.7396
P15 vs. P21 0.8675
Tukey’s multiple
comparisons test
Adjusted p
value
P7 vs. P15 0.1155
P7 vs. P21 0.0654
P15 vs. P21 0.8086
Tukey’s multiple
comparisons test
Adjusted p
value
P7 vs. P12 0.1480
P7 vs. P15 0.0061
P7 vs. P21 0.0039
P12 vs. P15 0.3798
P12 vs. P21 0.2804
P15 vs. P21 0.9963
Tukey’s multiple
comparisons test
Adjusted p
value
P7 vs. P15 0.0944
P7 vs. P21 0.0032
P15 vs. P21 0.1476
67
(a) (b)
(c) (d) (e)
(g)
P7
MET-GFP
CTIP2
MET-GFP CTIP2 SATB2
(i) (h)
SATB2
P21
P7
P21
P7 P12 P15 P21
0
20
40
60
80
100
Age
%SATB2
+
(CTIP2
-
);GFP
+
/Total GFP
+
Layer 5 MET+ neurons that
are SATB2+(CTIP2-)
in V1
✱✱
✱✱
P7 P12 P15 P21
0
20
40
60
80
100
Age
%CTIP2+(SATB2-);GFP
+
/Total GFP
+
Layer 5 MET+ neurons that
are CTIP2+(SATB2-)
in V1
P7 P12 P15 P21
0
20
40
60
80
100
Age
%CTIP2
+
;SATB2
+
;GFP
+
/Total GFP
+
Layer 5 MET+ neurons that
are CTIP2+; SATB2+
in V1
✱
✱✱
P7 P15 P21
0
20
40
60
80
100
Age
%CTIP2
+
;SATB2
+
;GFP
+
/Total GFP
+ Layer 5 MET+ neurons that
are CTIP2+; SATB2+
in mPFC
✱✱
P7 P15 P21
0
20
40
60
80
100
Age
%SATB2
+
(CTIP2
-
);GFP
+
/Total GFP
+ Layer 5 MET+ neurons that
are SATB2+(CTIP2-)
in mPFC
P7 P15 P21
0
20
40
60
80
100
Age
%CTIP2+(SATB2-);GFP
+
/Total GFP
+ Layer 5 MET+ neurons that
are CTIP2+(SATB2-)
in mPFC
✱✱
P7 P15 P21
0
20
40
60
80
100
Age
%CTIP2
+
;SATB2
+
/Total CTIP2
+
Layer 5 CTIP2+ neurons that
express SATB2
in mPFC
✱✱
✱
(f)
P7 P12 P15 P21
0
20
40
60
80
100
Age
%CTIP2+;SATB2+/Total SATB2
+
Layer 5 SATB2+ neurons that
express CTIP2
in V1
(j)
68
Figure 2.3 Colocalization analyses of MET-GFP
+
neurons that co-express CTIP2
and/or SATB2 in layer 5 mPFC and V1 across developmental ages.
(a) Representative images at P7 (left panel) and P21 (right panel) of MET-GFP (green),
CTIP2 (magenta), and SATB2 (yellow) overlayed expressions in layer 5 mPFC. White
arrows denote examples of colocalization between MET-GFP, CTIP2, and SATB2.
Magenta arrows denote examples of MET-GFP
+
; CTIP2
+
( SATB2
-
) neurons. Yellow
arrows denote examples of MET-GFP
+
; SATB2
+
(CTIP2
-
) neurons. (b) Representative
images at P7 (top panel) and P21 (bottom panel) of MET-GFP (green), CTIP2 (magenta),
and SATB2 (yellow) overlayed expressions in layer 5 V1. White arrows denote examples
of colocalization between MET-GFP, CTIP2, and SATB2. Magenta arrows denote
examples of MET-GFP
+
; CTIP2
+
( SATB2
-
) neurons. Yellow arrows denote examples of
MET-GFP
+
; SATB2
+
(CTIP2
-
) neurons. (c) Quantification of the percentage of layer 5
mPFC MET-GFP
+
neurons that co-express both CTIP2 and SATB2 at P7, P15, and P21.
n = 8 for P7, n = 9 for P15, n = 5 for P21. ‘**’ indicates p < .01, analyzed by ordinary one-
way ANOVA followed by Tukey’s multiple comparisons test. (d) Quantification of the
percentage of layer 5 mPFC MET-GFP
+
neurons that are CTIP2
+
(SATB2
-
) at P7, P15,
and P21. n = 8 for P7, n = 9 for P15, n = 5 for P21. ‘**’ indicates p < .01, analyzed by
ordinary one-way ANOVA followed by Tukey’s multiple comparisons test. (e)
Quantification of the percentage of layer 5 mPFC MET-GFP
+
neurons that are
SATB2
+
(CTIP2
-
) at P7, P15, and P21. n = 8 for P7, n = 9 for P15, n = 5 for P21. There is
no significant effect of age, analyzed by ordinary one-way ANOVA. (f) Quantification of
the percentage of layer 5 mPFC CTIP2
+
neurons that express SATB2 at P7, P15, and
P21. n = 8 for P7, n = 9 for P15, n = 5 for P21. ‘*’ indicates p < .05, ‘**’ indicates p < .01,
analyzed by ordinary one-way ANOVA followed by Tukey’s multiple comparisons test. (g)
Quantification of the percentage of layer 5 V1 MET-GFP
+
neurons that co-express both
CTIP2 and SATB2 at P7, P12, P15, and P21. n = 5 for each age. ‘*’ indicates p < .05, ‘**’
indicates p < .01, analyzed by ordinary one-way ANOVA followed by Tukey’s multiple
comparisons test. (h) Quantification of the percentage of layer 5 V1 MET-GFP
+
neurons
that are CTIP2
+
(SATB2
-
) at P7, P12, P15, and P21. n = 5 for each age. There is no
significant effect of age, analyzed by Kruskal-Wallis test. (i) Quantification of the
percentage of layer 5 V1 MET-GFP
+
neurons that are SATB2
+
(CTIP2
-
) at P7, P12, P15,
and P21. n = 5 for each age. ‘**’ indicates p < .01, analyzed by ordinary one-way ANOVA
followed by Tukey’s multiple comparisons test. (j) Quantification of the percentage of layer
5 V1 SATB2
+
neurons that express CTIP2 at P7, P12, P15, and P21. n = 5 for each age.
There is no significant effect of age, analyzed by ordinary one-way ANOVA.All scale bars
= 50µm. The brightness and contrast of each channel were increased globally in images
for visualization purposes.
69
Table 2.8 Post hoc analyses of MET-GFP
+
neurons that are CTIP2
+
; SATB2
+
in
layer 5 mPFC
Table 2.9 Post hoc analyses of MET-GFP
+
neurons that are CTIP2
+
(SATB2
-
) in
layer 5 mPFC
Table 2.10 Post hoc analyses of CTIP2
+
neurons that co-express SATB2 in layer 5
mPFC
Table 2.11 Post hoc analyses of MET-GFP
+
neurons that are CTIP2
+
; SATB2
+
in
layer 5 V1
Tukey’s multiple
comparisons test
Adjusted p
value
P7 vs. P15 0.0525
P7 vs. P21 0.0019
P15 vs. P21 0.1515
Tukey’s multiple
comparisons test
Adjusted p
value
P7 vs. P15 0.0999
P7 vs. P21 0.0040
P15 vs. P21 0.1683
Tukey’s multiple
comparisons test
Adjusted p
value
P7 vs. P15 0.3127
P7 vs. P21 0.0011
P15 vs. P21 0.0168
Tukey’s multiple
comparisons test
Adjusted p
value
P7 vs. P12 0.1108
P7 vs. P15 0.0215
P7 vs. P21 0.0070
P12 vs. P15 0.8250
P12 vs. P21 0.5068
P15 vs. P21 0.9431
70
Table 2.12 Post hoc analyses of MET-GFP
+
neurons that are SATB2
+
(CTIP2
-
) in
layer 5 V1
We next focused on determining the phenotype of MET-GFP
+
neurons in layer 6,
which contains two major PN subclasses – corticothalamic (CT) and IT (Woodworth and
Greig et al, 2012 Cell). In mPFC, DARPP-32 serves as a marker of CTPNs (Ouimet, 1991
Brain Res). There is a significant age effect on the percentage of MET-GFP
+
neurons that
express DARPP-32 (F = 25.0550; p < 0.0001; Fig. 2.4a-b). Post hoc analyses
demonstrate a significant increase in this percentage between P7 (29.30 + 1.85%) and
P9 (P9: 57.33 + 2.45%), and P7 and P15 (57.62 + 4.25%); there is no significant
difference between P9 and P15 (Table 2.13). This increase is paralleled by a decrease in
the percentage of MET-GFP
+
neurons in layer 6 mPFC over the same period (Fig. 2.1c),
suggesting that as the percentage of MET-GFP
+
neurons decrease, those that continue
expressing MET are most likely in the CTPN subclass. There also is an age effect on the
percentage of DARPP-32 neurons that co-express MET-GFP in layer 6 mPFC (F =
26.2137; p < 0.0001; Fig. 2.4c). Post hoc comparisons reveal at P2, over 60% of DARPP-
32 neurons co-express MET (64.76 + 2.99), which decreases significantly to
Tukey’s multiple
comparisons test
Adjusted p
value
P7 vs. P12 0.2443
P7 vs. P15 0.0095
P7 vs. P21 0.0064
P12 vs. P15 0.3383
P12 vs. P21 0.2550
P15 vs. P21 0.9973
71
approximately 40% by P9 (43.23 + 2.76); all other comparisons are not significant (P15:
35.90 + 2.97; Table 2.14). These results, together with the finding that the percentage of
MET-GFP neurons decreases after P7 (Fig. 2.1c), suggest that MET is expressed in a
large population of CTPNs in the first postnatal week, and as the percentage of MET-
GFP neurons decreases, the total population of CTPNs that co-express MET also
decreases.
DARPP-32 is not a validated marker of CTPNs in V1 and its developmental
expression patterns have not been mapped in this region. We observed low expression
of DARPP-32 at P7 and widespread expression across all cortical layers at P21 (data not
shown), indicating that in V1, DARPP-32 is not a selective marker of CTPNs. Therefore,
PCP4, a previously validated marker of CTPNs in primary sensory areas (Watakabe et
al, 2012 J Comp Neurol), was used to identify this population, as reported previously in
S1 cortex (Kast, Wu & Levitt, 2019 Cereb Cortex). In V1, there is little overlap of MET-
GFP and PCP4 at any age examined, demonstrating that MET is largely excluded from
CTPNs (Fig. 2.4d). Rather, at P7 the majority of MET-GFP
+
layer 6 neurons in V1 are
ITPNs, expressing SATB2
+
(CTIP2
-
) (77.46 + 3.02%; Fig. 2.4e-f). There is, however, a
significant age effect on the percentage of MET-GFP
+
neurons expressing SATB2 alone
(F = 11.7631; p = 0.0003; Fig. 2.4f), with post hoc analyses demonstrating a significant
decrease between P7 and P15 (P15: 42.85 + 3.42%), P7 and P21 (P21: 40.67 + 5.93%),
and P12 (62.23 + 6.85%) and P21; all other comparisons are not significantly different
(Table 2.15). Given the stable expression of MET-GFP
+
neurons in layer 6 V1 across
72
these ages (Fig. 2.1g), we next determined whether the decline in MET-GFP
+
neurons
expressing SATB2 alone reflected an increase in those co-expressing CTIP2 and SATB2.
Indeed, there is a significant effect of age on percentage of MET-GFP
+
neurons that are
CTIP2
+
; SATB2
+
(F = 12.9124; p = 0.0002; Fig. 2.4g), with post hoc analyses
demonstrating a significant increase in the mean percentage between P7 (19.18 + 2.58%)
and P15 (51.60 + 3.39%), P7 and P21 (55.42 + 4.98%), and P12 (34.34 + 6.60%) and
P21; all other comparisons were not significantly different (Table 2.16). A similar pattern
is observed when considering the entire layer 6 V1 SATB2 population, for which there is
a significant effect of age on the percentage of SATB2 neurons that co-express CTIP2 (F
= 5.5736; p = 0.0082; Fig. 2.4h). Post hoc analyses demonstrate a significant increase in
this measure between P7 (35.88 + 2.60%) and P15 (59.67 + 3.48%), and P7 and P21
(62.75 + 4.56%), with all other comparisons not statistically significant (P12: 43.27 +
8.94%; Table 2.17). Together, these data indicate that the MET neurons in layer 6 of V1
are predominantly ITPNs and that the increasing co-expression of CTIP2 in the MET
population is consistent for the entire ITPN population.
73
P2 P9 P15
0
20
40
60
80
100
Age
%MET-GFP
+
; DARPP-32
+
/Total DARPP-32
+
Layer 6 DARPP-32+
neurons that co-express MET
in mPFC
✱✱✱
✱✱✱✱
(a) (b) (c)
(d) (e)
(f)
P2
MET-GFP
DARPP-32
(g)
P15
P21
MET-GFP CTIP2 SATB2
MET-GFP PCP4
P7
P2 P9 P15
0
20
40
60
80
100
Age
%MET-GFP
+
; DARPP-32
+
/Total MET-GFP
+ Layer 6 MET+ neurons that
co-express DARPP-32
in mPFC
✱✱✱✱
✱✱✱✱
P15
P7
P7 P12 P15 P21
0
20
40
60
80
100
Age
%SATB2
+
(CTIP2
-
);GFP
+
/Total GFP
+
Layer 6 MET+ neurons that
are SATB2+(CTIP2-)
in V1
✱✱✱
✱✱✱
✱
P7 P12 P15 P21
0
20
40
60
80
100
Age
%CTIP2
+
;SATB2
+
;GFP
+
/Total GFP
+
Layer 6 MET+ neurons that
are CTIP2+; SATB2+
in V1
✱✱✱
✱✱✱
✱
(h)
P7 P12 P15 P21
0
20
40
60
80
100
Age
%CTIP2+;SATB2+/Total SATB2
+
Layer 6 SATB2+ neurons that
express CTIP2
in V1
✱
✱
74
Figure 2.4 Colocalization analysis of MET-GFP with layer 6 projection neuron
markers, in mPFC and V1 across developmental ages.
(a) Representative images at P2 (left panel) and P15 (right panel) of MET-GFP (green)
and DARPP-32 (blue) overlayed expressions in layer 6 mPFC. Arrows denote examples
of colocalization between MET-GFP and DARPP-32. (b) Quantification of the percentage
of layer 6 mPFC MET-GFP
+
neurons that co-express DARPP-32 at P2, P9, and P15. n =
6 for P2 and P9, n = 7 for P15. ‘****’ indicates p < .0001, analyzed by ordinary one-way
ANOVA followed by Tukey’s multiple comparisons test. (c) Quantification of the
percentage of layer 6 mPFC DARPP-32
+
neurons that co-express MET-GFP at P2, P9,
and P15. n = 6 for P2 and P9, n = 7 for P15. ‘***’ indicates p < .001, ‘****’ indicates p <
.0001, analyzed by ordinary one-way ANOVA followed by Tukey’s multiple comparisons
test. (d) Representative images at P7 (top panel) and P15 (bottom panel) of MET-GFP
(green) and PCP4 (blue) overlayed expressions in layer 6 V1. (e) Representative images
at P7 (top panel) and P21 (bottom panel) of MET-GFP (green), CTIP2 (magenta), and
SATB2 (yellow) overlayed expressions in layer 6 V1. White arrows denote examples of
colocalization between MET-GFP, CTIP2, and SATB2. Magenta arrows denote examples
of MET-GFP
+
; CTIP2
+
( SATB2
-
) neurons. (f) Quantification of the percentage of layer 6
V1 MET-GFP
+
neurons that are SATB2
+
(CTIP2
-
) at P7, P12, P15, and P21. n = 5 for each
age. ‘*’ indicates p < .05, ‘***’ indicates p < .001, analyzed by ordinary one-way ANOVA
followed by Tukey’s multiple comparisons test. (g) Quantification of the percentage of
layer 6 V1 MET-GFP
+
neurons that co-express both CTIP2 and SATB2 at P7, P12, P15,
and P21. n = 5 for each age. ‘*’ indicates p < .05, ‘***’ indicates p < .001, analyzed by
ordinary one-way ANOVA followed by Tukey’s multiple comparisons test. (h)
Quantification of the percentage of layer 6 V1 SATB2
+
neurons that express CTIP2 at P7,
P12, P15, and P21. n = 5 for each age. ‘*’ indicates p < .05, analyzed by ordinary one-
way ANOVA followed by Tukey’s multiple comparisons test. All scale bars = 50µm. The
brightness and contrast of each channel were increased globally in images for
visualization purposes.
Table 2.13 Post hoc analyses of MET-GFP
+
neurons co-express DARPP-32 in layer
6 mPFC
Tukey’s multiple
comparisons test
Adjusted p
value
P2 vs. P9 <0.0001
P2 vs. P15 <0.0001
P9 vs. P15 0.9977
75
Table 2.14 Post hoc analyses of DARPP-32
+
neurons that co-express MET-GFP in
layer 6 mPFC
Table 2.15 Post hoc analyses of MET-GFP
+
neurons that are SATB2
+
(CTIP2
-
) in
layer 6 V1
Table 2.16 Post hoc analyses of MET-GFP
+
neurons that are CTIP2
+
; SATB2
-
in
layer 6 V1
Tukey’s multiple
comparisons test
Adjusted p
value
P2 vs. P9 0.0003
P2 vs. P15 <0.0001
P9 vs. P15 0.2045
Tukey’s multiple
comparisons test
Adjusted p
value
P7 vs. P12 0.1876
P7 vs. P15 0.0010
P7 vs. P21 0.0005
P12 vs. P15 0.0674
P12 vs. P21 0.0377
P15 vs. P21 0.9900
Tukey’s multiple
comparisons test
Adjusted p
value
P7 vs. P12 0.1384
P7 vs. P15 0.0008
P7 vs. P21 0.0003
P12 vs. P15 0.0780
P12 vs. P21 0.0255
P15 vs. P21 0.9365
76
Table 2.17 Post hoc analyses of SATB2
+
neurons that co-express CTIP2 in layer 6
V1
2.5 DISCUSSION
The present study determined specific temporal and spatial patterns of MET expression
– a protein that is present transiently during development and modulates the timing of
cerebral cortical synapse development and maturation by cortical PNs. Here, we
examined during postnatal development two architecturally distinct cortical regions,
agranular, association mPFC and granular primary sensory V1. The analyses revealed
discrete temporal, laminar and PN subclass-specific patterns in mPFC and V1 (Fig. 2.5).
First, in mPFC, MET is expressed in layers 5 and 6, with very limited expression in layer
2/3 neurons. In contrast, in V1, similar to findings reported in S1 (Kast, Wu & Levitt, 2019
Cereb Cortex), MET is enriched in layers 2/3, expressed in different PN subclasses in
layers 5 and 6, and nearly absent from layer 4 neurons. Second, in mPFC, the percentage
of MET
+
neurons is stable across the first five postnatal weeks in layer 5, but declines
after the first week in layer 6. In V1, MET expression is stable in layers 2/3 between P7
and P21, declines modestly in the first postnatal week in layer 5, and declines by P21 in
Tukey’s multiple
comparisons test
Adjusted p
value
P7 vs. P12 0.7764
P7 vs. P15 0.0330
P7 vs. P21 0.0149
P12 vs. P15 0.1887
P12 vs. P21 0.0946
P15 vs. P21 0.9777
77
layer 6. The data reveal differences in the temporal regulation of MET between the two
cortical areas, and at a more discrete level, layer-specific temporal regulation within a
given cortical area. Finally, there is an enrichment of MET expression in ITPNs in V1,
similar to that reported previously in S1 (Kast, Wu & Levitt, 2019 Cereb Cortex). Both
primary sensory neocortical areas contrast with mPFC, in which MET is expressed
primarily in SCPNs, including CTPNs. Given the role of MET signaling as a modulator of
synapse development and maturation, the data indicate that the receptor operates within
discrete circuits for each cortical region.
Previous studies using Western blot analyses indicated that MET protein levels in
mouse cortex peak in the second postnatal week (Judson et al, 2009 J Comp Neurol;
Eagleson et al, 2016 Dev Neurobiol). These analyses included whole cortex and as such
do not reflect potential region or layer specific differences in expression across time.
Further, the detection of the MET protein is a combination of the receptor synthesized
locally by PNs in the cerebral cortex as well as the receptor expressed in projections from
different sources (e.g., hippocampus), in which MET protein is trafficked down the axon
to presynaptic terminals. Finally, Western blot analyses do not discriminate between a
general reduction in quantity of protein expressed across all MET-expressing neurons
and a reduction in the number of MET-expressing neurons over time. As such, the current
analyses add key complementary spatial information, demonstrating region- and layer-
specific trajectories of MET
+
neurons in the cortex during postnatal development. Given
that downregulation of MET acts as a modulator for the timing of synapse maturation and
78
stabilization, the present findings pose interesting questions. Future studies will need to
address whether sustained MET expression in layer 5 mPFC and layers 2/3 V1 are
indicative of a more prolonged maturation process for the synapses of these PNs
compared to those in which MET is turned off earlier in postnatal development. It also will
be important to identify comparable proteins that regulate synapse maturation in PNs that
do not express MET during development. The demonstration of substantial differences in
spatial, specifically interlaminar and between cortical regions, and temporal expression
of MET suggests that there are distinct molecular regulatory mechanisms upstream of
Met.
Amongst the most striking findings of the current study is the difference in MET-
expressing cortical PN subclasses between mPFC and V1. This may reflect differences
in function and circuit organization between the two regions. mPFC, an agranular
association cortex involved in social communication and executive functions, can be
considered a hub that communicates with many other cortical and subcortical regions,
issuing top-down control of behaviors and learning (Zhang et al, 2021 Int Rev Neuorbiol;
Reinert et al, 2021 Nature; Zarr & Brown, 2016 Neuroimage; Anastasiades & Carter, 2021
Trends Neurosci). Neurons in infragranular, but not supragranular, layers of mPFC have
been implicated in cognitive flexibility (Nakayama et al, 2018 J Neurosci). SCPNs in
mPFC are involved in active behavioral states (Warden et al, 2012 Nature), decision-
making behavior for goals that involve both reward and punishment (Kim et al, 2017 Cell),
and social-spatial learning (Murugan et al, 2017 Cell). Additionally, there are
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abnormalities specific to SCPNs in animal models of autism, and these neurons affect
social behavior (Brumback, Elwood, Kjaerby et al, 2018 Mol Psychiatry). In contrast, V1
is a granular sensory cortex, with a major input from the lateral geniculate nucleus (LGN)
in the thalamus. The LGN relays visual information directly from the retina to layer 4 of
V1, in which visual information, such as static versus moving objects, orientation, and
pattern recognition, are processed and further relayed to other brain regions (Resulaj,
2021 Front Neural Circuits; Mazade & Alonso, 2017 Vis Neurosci; Glickfeld et al, 2013 J
Neurosci). Projections from V1 to subcortical structures are involved in oculomotor reflex-
driven functions, such as optokinetic nystagmus (Liu et al, 2016 Nature), and visually
evoked innate behaviors (Liang et al, 2015 Neuron). Projections from V1 to cortical
structures are involved in relaying information for further visual processing, such as
motion direction discrimination (Marques et al, 2018 Curr Biol), contour detection and
discrimination (van Kerkoerle et al, 2018 Proc Natl Acad Sci U S A), and spatial attention
(Tiesinga & Buia, 2009 Neural Netw). ITPNs in V1 have also been shown to process
coincidental multisensory events (Knöpfel et al, 2019 Nat Comm) and enhance neural
selectivity to learn visual discrimination tasks (Poort et al, 2015 Neuron). As
aforementioned, MET is predominantly expressed in infragranular layers of mPFC and,
more specifically, in SCPNs, including CTPNs, rather than ITPNs, reflecting a role in
mPFC circuits that connect outside of the cortex. In V1, MET expression in supragranular
ITPNs and in predominantly infragranular ITPNs, rather than in SCPNs or CTPNs,
indicate that in primary sensory cortices, MET functions mainly in neurons that connect
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to other cortical regions. Based on the known roles of SCPNs in mPFC and ITPNs in V1,
this suggests that MET expression is enriched in developing circuits that are involved in
higher-order functions. We note that in both mPFC and V1, there was a minor population
of layer 5 neurons that remained dual-labeled with CTIP2 and SATB2, so their PN
subclass remained ambiguous; however, this did not change the overall
conclusions. Future detailed tracing studies will determine specific connectivity maps of
MET
+
mPFC and V1 neurons.
Maturation and refinement of V1 are affected by and result in numerous
developmental processes that occur postnatally in mice: retinotopy occurs by P8 (Cang
et al, 2005 Neuron), eye-opening around P14, orientation selection around the period of
eye-opening, independent of visual stimulus (Rochefort et al, 2011 Neuron), and the
critical period for ocular dominance between P19 and P32 (Gordon & Stryker, 1996 J
Neurosci). With the abundant expression of MET in supragranular layers 2/3 remaining
unchanged from P7 to P21, it can first be inferred that eye-opening, and subsequent
visual information reaching V1, does not influence the percentage of neurons expressing
MET. Further, MET modulates the timing of the critical period for ocular dominance (Chen
& Ma et al, 2021 Mol Psychiatry). Thus, the sustained MET expression until P21 in layers
2/3 may suggest that MET
+
neurons in these layers modulate the timing of the critical
period, rather than the infragranular MET
+
neurons, in which downregulation occurs
earlier postnatally. This is supported by a recent report demonstrating visual experience-
driven maturation in layers 2/3, but not layer 5 or 6 (Cheng et al, 2022 Cell). Conversely,
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the temporal regulation of infragranular MET expression may play a role in developmental
processes that occur prior to the critical period. Nevertheless, follow-up studies are
needed to test the layer-specific contributions of MET in V1 developmental processes.
It is notable that MET is predominantly expressed in a particular PN subclass in
each region, but not in all neurons of that PN subclass. Expression of MET within only a
subpopulation of the PN subclass may allow for asynchrony of synapse maturation, even
within neurons projecting to the same brain region. While differences in timing of
maturation of circuits subserving different functions has been reported (Huttenlocher &
Dabholkar, 1997 J Comp Neurol; Hensch, 2004 Annu Rev Neurosci; Moyer & Zuo, 2018
Curr Opin Neurobiol), at a finer level, differences in timing of maturation of synapses
within circuits involved in the same function may also be advantageous. For example,
critical period plasticity represents a time when circuits have maximum opportunity to
undergo experience-based modification, but this is at the expense of the brain being in a
more vulnerable state (Hensch, 2003 Neurosci Res; Nelson & Gabard-Durnam, 2020
Trends Neurosci). Closure of critical periods allow the brain to function in a more stable,
resilient state. We speculate that if the timing of closure of a critical period of plasticity
was completely uniform for all circuits that underlie a specific function, this could lead to
greater vulnerability, for which an insult may affect each circuit equally. Mechanisms that
allow for differential timing of maturation of circuits for a given function would provide
opportunities for select circuits to stabilize, while others remain in a more plastic state,
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leading to further optimization. Therefore, the expression of MET within subpopulations
of PN subclasses may contribute to differential timing of maturation.
While this study focused on one synapse-enriched receptor, it underscores the
dynamic process of development, during which many molecular and structural changes
are occurring. The temporal features of our study emphasize the need to model
neurodevelopmental disorders that include developmental phenotypes to capture
biological mechanisms associated with transient processes of maturation that may be
disrupted during development. Even developmental studies using single-cell sequencing
may not capture the spatial and temporal dynamics of genes and proteins of interest due
to limitations in spatial resolution and the practical limitations of sampling a large number
of developmental timepoints. Lastly, while the cortex, as a whole, has properties and
functions that are unique compared to subcortical brain structures, cortical regions are
heterogeneous, such that findings in one region do not necessarily translate to other
regions. Continuing to compare and contrast the development of different cortical regions
will provide greater understanding of the emergence and maturation of specific functions
and their underlying molecular contributions.
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Figure 2.5 Summary of Met expression in mPFC and V1.
Percentage of MET-expressing neurons in medial prefrontal (mPFC) and primary visual
(V1) cortices varies between layer and age in mice. The predominant subclass of MET
+
projection neurons (PN) in mPFC is subcortical (SCPN), including corticothalamic
(CTPN), whereas it is intratelencephalic (ITPN) in V1, demonstrating stark heterogeneity
between cortical regions.
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Chapter 3 : DEVELOPMENTAL EXPRESSION PATTERN AND FUNCTIONAL ROLE OF FOXP2 IN
CORTICAL ONTOGENESIS
Ryan J. Kast*, Alexandra L. Lanjewar*, Colton D. Smith, and Pat Levitt
*these authors contributed equally
3.1 ABSTRACT
The expression patterns of the transcription factor FOXP2 in the developing mammalian
forebrain have been described, and some studies have tested the role of this protein in
the development and function of specific forebrain circuits by diverse methods and in
multiple species. Clinically, mutations in FOXP2 are associated with severe
developmental speech disturbances, and molecular studies indicate that impairment
of Foxp2 may lead to dysregulation of genes involved in forebrain histogenesis. Here,
anatomical and molecular phenotypes of the cortical neuron populations that express
FOXP2 were characterized in mice. Additionally, Foxp2 was removed from the
developing mouse cortex at different prenatal ages using two Cre-recombinase driver
lines. Detailed molecular and circuit analyses were undertaken to identify potential
disruptions of development. Surprisingly, the results demonstrate that Foxp2 function is
not required for many functions that it has been proposed to regulate, and therefore plays
a more limited role in cortical development than previously thought.
3.2 INTRODUCTION
The proper function of the cerebral cortex requires the formation of highly stereotyped
circuits during development. These circuits are built through interdependent processes,
including proliferation of neural progenitors, migration of neurons to their appropriate
85
positions, morphological and physiological differentiation of diverse neuron subtypes, and
the formation of synapses of requisite strength between appropriate pairs of neurons.
Impairments in these fundamental aspects of development can lead to lifelong
dysfunction of the cortex, which is believed to contribute to core symptoms of many
neurodevelopmental disorders (Rubenstein and Rakic, 2013).
The winged helix transcription factor, FOXP2, has been implicated in ontogenetic
processes relevant to the development of the cerebral cortex (Vernes et al., 2011; Chiu
et al., 2014; Chen et al., 2016), and some studies have directly implicated FOXP2 in
cortical ontogeny (Tsui et al., 2013; Garcia-Calero et al., 2016). FOXP2 expression in the
developing cortex is restricted to subpopulations of post-mitotic neurons in the deep
cortical layers, a pattern that is highly conserved across mammalian species (Ferland et
al., 2003; Takahashi et al., 2003; Campbell et al., 2009; Mukamel et al., 2011). Mutations
in FOXP2 cause a severe developmental speech and language disorder, known as
childhood apraxia of speech (Lai et al., 2001; MacDermot et al., 2005). Human
neuroimaging and animal studies have identified alterations in basal ganglia function that
could contribute to the clinical disorder symptoms (Vargha-Khadem et al., 1998; Belton
et al., 2003; Groszer et al., 2008; French et al., 2012; Chen et al., 2016), but whether
changes in cerebral cortical organization and function are critically involved in
impairments associated with FOXP2 mutations is currently unknown.
The present study aimed to establish a more detailed understanding of the cell-
type identity of FOXP2
+
neurons in the developing murine cerebral cortex, using
86
molecular and neuroanatomical phenotyping approaches. Further, this study applied
gold-standard conditional mouse genetics to selectively remove Foxp2 from the
developing cerebral cortex at different prenatal ages to ascertain its putative roles in the
normal histogenic processes that generate the canonical six layers, specific cell types
based on gene expression, and basic axon targeting to subcortical structures. The results
show that FOXP2 expression is limited to specific corticofugal neuron populations and
suggest that the gene plays a more limited role in mouse corticogenesis than previously
concluded based on results obtained by other experimental methodologies.
3.3 MATERIALS AND METHODS
Animals
All animal procedures used in this study were in accordance with the guidelines of the
Institutional Animal Care and Use Committee at Children’s Hospital Los Angeles. Mice
were housed on a 13:11 hr light-dark cycle and were provided with food and water ad
libitum. Mice harboring the conditional Foxp2 allele (Foxp2
Fx
; French et al., 2007), Rosa-
TdTomato allele (Ai14), the Ntsr1-Cre transgene (GN220), or the Emx1-Cre transgene
(B6.129S2-Emx1tm1(cre)Krj/J; obtained from Jackson Laboratories) were maintained on
an isogenic C57Bl/6J background. MetEGFP BAC transgenic (Met
GFP
) mice were re-
derived on the FVB background using the BX139 BAC from the GENSAT collection (Gong
et al., 2003). Founder mice were backcrossed to C57Bl/6J for at least two generations
prior to experimental breeding, such that experiments involving Met
GFP
mice were carried
out on a mixed C57Bl/6J x FVB background. Emx1-Cre first exhibits recombination of
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floxed loci at embryonic day (E) 10 in mice (Gorski et al., 2002), as confirmed in the
present study. Based on data reported here, Ntsr1-Cre exhibits recombination initially at
E17.
Retrograde Tracing
On postnatal day (P) 12, mice were anesthetized with vaporized isoflurane (5% induction,
1.5–2% maintenance) and stabilized in a Narishige SG-4N small animal head holder.
Mice were maintained at 37 ˚C for the duration of the surgical procedure through a TCAT-
2 temperature control device (Physitemp Intruments, Inc) and respiratory rate was
continuously monitored to assess depth of anesthesia. Through stereotaxic guidance, a
picospritzer connected to a pulled borosilicate pipette (28 µm tip diameter) was used to
inject 50–100 nl of Cholera Toxin Subunit B, Alexa Fluor Conjugate (Invitrogen) into the
desired cortical or subcortical target. To minimize contamination of unintended brain
regions along the needle tract, the pipette was left in place for 5 min before being slowly
retracted. Mice received a subcutaneous injection of the non-steroidal anti-inflammatory
drug (NSAID) ketoprofen (5 mg/kg) immediately before the surgery and provided with
ibuprofen (0.2 mg/mL) in the drinking water until the end of the experiment. After 2 days
of recovery, on P14, mice were transcardially perfused with 4% paraformaldehyde
dissolved in 1X phosphate buffered saline (PBS) and tissue was processed for
immunohistochemical analysis as described below. Stereotaxic coordinates used for P12
mice are as follows: ventrobasal thalamus, AP −1.7, ML 1.3 mm, Depth 3.15 mm; primary
88
motor cortex, AP 0.25 mm, 1.5 mm, 0.9 mm; cerebral peduncle, AP −3.5 mm, 1 mm, 4.8
mm.
In situ hybridization
The BaseScope assay (Advanced Cell Diagnostics) was performed on embryonic brain
sections prepared from Foxp2
Fx/Fx
and Emx1-cre; Foxp2
Fx/Fx
embryos that were
harvested at embryonic day (E) 14.5, with noon on the day of vaginal plug (identified
following overnight mating) designated as E0.5. Briefly, the pregnant dam was deeply
anesthetized with saturated isoflurane vapor, and cervical dislocation ensured euthanasia
prior to embryo dissection. Embryos were decapitated and the brains were submerged in
optimal cutting temperature (OCT, Tissue-Tek) compound and frozen in a pre-chilled dry
ice and isopropanol slurry, and subsequently stored at −80 ˚C until cryosectioning. 20 µm
coronal cryosections were collected onto SuperFrost Plus (Fisher Scientific) microscope
slides, and then stored at −80 ˚C until in situ hybrization procedures. Slide-mounted
sections were removed from −80 ˚C and immediately fixed by submerging in prechilled
4% PFA in 1X DEP-C PBS for 30 min on ice with gentle agitation. Sections were
dehydrated in 50%, 70%, and two 100% EtOH washes at room temperature for 5 min
each. Tissue was stored in 100% EtOH overnight at −20°C. Sections were pretreated
with RNAscope Hydrogen Peroxide for 10 min at room temperature and then with
RNAscope Protease IV for 15 min at room temperature. Tissue was washed in 1X DEP-
C PBS at room temperature. A custom Foxp2 BaseScope probe (that hybridizes to the
floxed region, bases 1832–1977 of Foxp2 transcript variant 2 (Refseq ID NM_212435.1),
89
of the Foxp2
Fx
allele) was hybridized for 2 hr at 40°C. Sections were washed twice with
1X wash buffer. Amplification and signal detection steps followed the protocol provided in
the BaseScope users manual. Slides were counterstained in 25% Hematoxylin solution
modified according to Gill III for 2 min at room temperature. Slides were washed in H2O,
and then in 0.02% ammonia for 15 s, and H2O once more. Slides were then incubated at
55°C for 15 min and then mounted in VectaMount (Vector Laboratories). Slides were
stored at −20 ˚C until imaged with a Leica DFC295 color camera using brightfield
microscopy through a 20x objective lens.
Immunohistochemistry
Somatosensory cortex (SSC) was the focus of all data analyses reported in the present
study. Embryos were decapitated and brains were either immediately submerged in OCT
and frozen in a pre-chilled dry ice and isopropanol slurry (‘fresh frozen’) and stored at −80
˚C until cryosectioning, or brains were transferred to 4% paraformaldehyde dissolved in
PBS (pH 7.4) and incubated at 4 ˚C for 12–18 hr. Early postnatal (P0) mouse brains were
dissected in room temperature PBS, transferred to 4% paraformaldehyde dissolved in
PBS (pH 7.4) and incubated at 4 ˚C for 12–18 hr. Mice aged P4 or older were perfused
transcardially with 4% paraformaldehyde dissolved in PBS. Following perfusion, brains
were immediately removed, transferred to 4% paraformaldehyde and incubated at 4 ˚C
for 12–18 hr. Following overnight fixation, brains were incubated sequentially in 10%, 20%
and 30% sucrose dissolved in PBS for 12–24 hr each. Next, brains were embedded in
Clear Frozen Section Compound (VWR International) and placed on a weigh boat floating
90
in liquid nitrogen. Once frozen, embedded brains were stored at −80 ˚C until
cryosectioning. Twenty µm coronal or sagittal cryosections were cut and collected on
SuperFrost Plus slides (Fisher Scientific) at −20 ˚C (PFA fixed tissue) or −15 ˚C (fresh
frozen), and then stored at −80 ˚C until immunohistochemical analysis. Before
immunostaining, fresh frozen sections were thawed to room temperature, fixed in 4%
paraformaldehyde dissolved in PBS at room temperature with agitation for 1 hr and 40
min, and then washed in PBS three times for five minutes each. For immunostaining,
sections were warmed at room temperature for 10 min, dried in a hybridization oven at
55 ˚C for 15 min, and then incubated in PBS for 10 min. Blocking and permeabilization
were done by incubating sections in PBS containing 5% normal donkey serum and 0.3%
Triton X-100 for 1 hr at room temperature. Sections were incubated subsequently in
primary antibodies diluted in 0.1% Triton X-100 in PBS overnight at room temperature.
Sections were washed five times for five minutes each with 0.2% Tween 20 in PBS.
Sections were incubated in Alexa Fluor conjugated secondary antibodies (1:500) diluted
in 0.1% Triton X-100 in PBS for 1 hr at room temperature. Sections were washed three
times for five minutes each with 0.2% Tween 20 in PBS. Sections were then incubated in
950 nM DAPI in PBS for 8 min, and then subjected to two additional five minute PBS
washes. Sections were mounted in Prolong Gold antifade reagent (Life Technologies),
and the mounting medium cured for at least 24 hr before collecting confocal microscopy
images. Primary antibodies used were as follows: Goat anti-Foxp2 (1:100; Santa Cruz
sc-21069), Chicken anti-GFP (1:500; Abcam #ab13970), Rat anti-Ctip2 (1:500; Abcam #
91
ab18465), Guinea Pig anti-ppCCK (1:500; T. Kaneko Lab), Rabbit anti-PCP4 (1:3000; J.
Morgan Lab), Rabbit anti-Fog2 (1:250; Santa Cruz sc-10755), Rabbit anti-DARPP-32
(1:500; Cell Signaling #2306).
Co-localization Analyses
Co-localization analysis in SSC was performed as described previously (Kast et al.,
2019). Briefly, confocal images were collected through a 20x/0.8NA Plan-APOCHROMAT
objective lens mounted on a Zeiss LSM 700 confocal microscope with refractive index
correction. Optical sections were collected at 1 A.U. and 2 µm z-steps through the entire
thickness of each 20 µm section. Colocalization analysis was performed in three-
dimensional renderings of each confocal z-stack using IMARIS software (Bitplane).
Cortical Thickness Measurements
Fluorescent images of DAPI-stained sections containing SSC were collected through a
5x objective lens mounted on an Axionplan II upright fluorescent microscope (Zeiss), an
Axiocam MRm camera (Zeiss) and Axiovision software 4.1 (Zeiss). The images were
opened in ImageJ and three lines separated by ≥50 µm were drawn in the posteromedial
barrel subfield from the pia to the white matter. The length of the nine lines (3 lines x three
images) were averaged to give a value for the radial thickness of SSC for each mouse.
92
Cell Type Quantification
Maximum Z-projections were created from confocal z-stacks and custom written ImageJ
macros were run to quantify the number of FOG2 +and CTIP2+/FOG2- nuclei at P0. The
numbers of FOG2+ and CTIP2+/FOG2- cells for each animal were averaged from three
300 µm wide fields (each separated rostrocaudally by ≥200 µm) of the cortex representing
the anterior, middle and posterior portions of SSC. The numbers of CCK+ cells within
layer 6 at P14 were manually counted by an observer blind to genotype, as the punctate
and discontinuous distribution of the immunofluorescent ppCCK signal prevented
accurate automated quantitation. Similarly, numbers of FOG2
+
and CTIP2+/FOG2
-
nuclei
at P14 were manually quantified by an observer blind to genotype, due to challenges in
automated detection of the lower level expression at this time point.
Image Adjustments and Figure Preparation
Figures were prepared using Adobe Photoshop and Adobe Illustrator (CS6). Only linear
adjustments (i.e. gamma = 1.0) were made to the contrast and signal levels of
fluorescence microscopy images, and were done in an identical manner across
genotypes.
Experimental Design and Statistics
Numbers of biological replicates (number of animals) for each experiment are included in
the figure legends. Numbers of animals in each group were chosen in accordance with
93
group numbers in previous publications reporting differences in murine cortical
phenotypes similar to those measured in the current study (Han et al., 2011; Woodworth
et al., 2016). Summary statistics and specific statistical tests used are described in the
Results section. Parametric tests were used in some cases, although tests for normality
were not possible given the modest sample sizes. Statistical analyses were performed
using Prism 6 (GraphPad).
3.4 RESULTS
Foxp2 expression is enriched in developing corticothalamic projection neurons
Initial Foxp2 expression mapping studies determined that Foxp2 transcript and
protein expression begin prenatally, with the onset of protein expression delayed relative
to the mRNA, and with protein present primarily in postmitotic neurons (Ferland et al.,
2003). However, other more recent studies have reported that FOXP2 protein is also
expressed in mitotic progenitor cells (Tsui et al., 2013), where it regulates cortical
neurogenesis. FOXP2 immunohistochemistry of coronal sections of the embryonic
forebrain suggested that FOXP2 protein expression begins between embryonic day (E)
14.5 and E16.5 within postmitotic neurons of the infragranular layers (Figure 3.1A,B).
Postnatally, it is well established that Foxp2 expression is limited to glutamatergic
neurons of the infragranular cortical layers, with robust expression predominantly in layer
6 (Ferland et al., 2003; Hisaoka et al., 2010; Sundberg et al., 2018). Layer 6 contains
many FOXP2
+
neurons, whereas layer 5 contains some FOXP2
+
neurons that are more
abundant in medial cortical areas at early postnatal stages (Ferland et al., 2003; Campbell
94
et al., 2009). Layer 6, where most of the FOXP2
+
neurons are located, is comprised of
two primary glutamatergic cortical neuron populations, corticothalamic (CT) and
corticocortical (CC) neurons (Thomson, 2010; Petrof et al., 2012; Harris and Shepherd,
2015). To determine whether FOXP2 expression is selective to one of these populations
or is expressed in both layer 6 projection neuron subtypes during development, on
postnatal day (P) 12, the neuroanatomical tracer cholera toxin subunit B (CTB) was
injected into primary somatosensory thalamus or primary motor cortex in separate cohorts
of mice. Cell bodies of CT or CC neurons residing in layer 6 of primary SSC were
retrogradely labeled. Sections through primary SSC were then stained with an anti-
FOXP2 antibody and cellular expression of FOXP2 was assessed among retrogradely
labeled neurons ipsilateral to the tracer injections. FOXP2 expression was evident in most
CT neurons (Mean ± SEM, 78.3 ± 2.9%), but was expressed by very few CC neurons
(Mean ± SEM, 6.4 ± 1.7%; Figure 3.1C–E).
Next, FOXP2 expression was assessed among molecularly-defined CT and CC
neurons at several postnatal developmental stages. The CT-specific cre-driver mouse,
Ntsr1-cre, was crossed with a cre-dependent Rosa-tdTomato reporter line (Ai14) to
selectively and comprehensively label layer 6 CT neurons with tdTomato. FOXP2
immunohistochemistry revealed that nearly all tdTomato-positive neurons in the primary
SSC co-expressed FOXP2 at P0, P7, and P14 (Figure 3.1F–G, Mean ± SEM: P0 = 90 ±
2%, P7 = 87 ± 2%, P14 = 91 ± 1%). For further molecular characterization, we also
examined FOXP2 overlap with the receptor tyrosine kinase MET, which is expressed in
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CC neurons of layer 6, but nearly excluded from layer 6 CT neurons in SSC (Kast et al.,
2019). Using co-labeling in Met
GFP
reporter mice (Kast et al., 2019), analysis of FOXP2
expression among GFP
+
layer 6 CC neurons revealed relatively few double-positive
neurons at P0, P7, and P14 (Figure 3.1H–I, Mean ± SEM: P0 = 19 ± 2%, P7 = 16 ± 2%,
P14 = 5 ± 2%). This finding is consistent with the limited expression of FOXP2 by layer 6
CC neurons observed through retrograde tracing. It is noteworthy, however, that there
are FOXP2 and Met
GFP
double-positive cells positioned in the subplate/layer 6B, similar
to the observation of Ntsr1-cre and Met
GFP
colocalization in deep layer six previously
reported (Kast et al., 2019). Notably, the FOXP2 and GFP double-labeled neurons
became quite sparse by P14, perhaps due to the downregulation of GFP in the subplate
or to the programmed cell death of some subplate neuron populations, as has been
reported previously during early postnatal development (Hoerder-Suabedissen and
Molnár, 2013). We also examined two other layer 6 neuronal subtype marker genes,
PCP4 and ppCCK (Watakabe et al., 2012). There was extensive colocalization between
FOXP2 and PCP4, a marker of corticothalamic neurons, in layer 6, but there was minimal
colocalization between FOXP2 and ppCCK, a marker of CC neurons (Figure 3.1J).
Together, the molecular and connectivity analysis of FOXP2+ neurons in layer six indicate
that FOXP2 expression is highly enriched in CT neurons of primary SSC during postnatal
development. This is consistent with analysis in primary visual cortex of adult mice, which
showed that Foxp2/FOXP2 expression is nearly exclusive to Ntsr1-cre expressing (CT)
neurons in the adult (Tasic et al., 2016; Sundberg et al., 2018). The present study builds
96
on previous findings by demonstrating minimal colocalization with two layer 6 CC markers
(MET and CCK). Notably, lower level expression of Foxp2 transcript has been detected
in molecularly-defined subcerebral projection neurons by RNA-sequencing at perinatal
stages (Molyneaux et al., 2015), consistent with the low-level expression observed in
layer 5 at P0 (Figure 3.1F). Colocalization analysis with the pyramidal-tract (PT) neuron
marker gene CTIP2 (Arlotta et al., 2005), confirmed that these layer 5 neurons are PT-
type neurons (Figure 3.1—figure supplement 1). However, in contrast to the temporally
stable and relatively uniform expression of FOXP2 by CT neurons in layer 6 of the
postnatal SSC (Figure 3.1), FOXP2 was expressed by a minor subset of CTIP2
+
PT
neurons at P0 (34 ± 3%) and P7 (10 ± 2%) (Figure 3.1—figure supplement 1A–C), and
was almost completely absent from retrogradely labeled PT neurons in Layer 5 of SSC
by P14 (4.4 ± 1.7%) (Figure 3.1—figure supplement 1D,E).
FOXP2 is not required for normal histogenesis of the cerebral cortex
Given the enrichment of FOXP2 in corticofugal neuron subclasses in SSC, coupled with
previous reports of fundamental roles for Foxp2 in cortical neuron development (Clovis et
al., 2012; Tsui et al., 2013; Garcia-Calero et al., 2016), we next used a direct genetic
deletion strategy to examine putative involvement of FOXP2 in the development of
anatomical and molecular properties of CT neurons. For these studies, mice harboring
a Foxp2 conditional allele (Foxp2
fx
) were bred with the CT-specific reporter line Ntsr1-cre;
Rosa-tdTomato (Bortone et al., 2014; Kim et al., 2014). Immunohistochemistry verified
the elimination of FOXP2 from tdTomato+ neurons of Ntsr1-cre; Foxp2
fx/fx
mice (Figure
97
3.2A, inset). tdTomato-expressing cell bodies remained limited to layer 6 of the cerebral
cortex across genotypes, consistent with the absence of gross changes in laminar
patterns due to Foxp2 deletion. Confocal microscopy further revealed no overt changes
in the distribution of tdTomato-labeled neurites in more superficial layers of cortex,
suggesting minimal morphological rearrangement of the CT neuron population in
response to Foxp2 deletion. Inspection of tdTomato-labeled efferent axons arising from
the deep layer neurons revealed a nearly identical pattern of CT innervation
across Foxp2 genotypes (Figure 3.2A). There was normal fasciculation within the internal
capsule and extensive axonal elaboration in the dorsal thalamus and thalamic reticular
nuclei of Foxp2 conditional knockout mice, their heterozygous littermates and Ntsr1-cre;
Rosa-tdTomato mice on a wild-type C57Bl/6J background. Consistent with the reported
cell-type specificity of the Ntsr1-cre driver line, no tdTomato-expressing axons in the
corpus callosum or cerebral peduncle were evident for any Foxp2 genotype at the ages
examined. This suggests that FOXP2 is dispensable for the typical positioning of CT
neurons in layer 6 and the guidance of their axons to reach and ramify in their normal
dorsal thalamic targets.
98
99
Figure 3.1 FOXP2 is enriched in corticothalamic neurons during cortical
development.
(A) Low magnification (left) and high magnification (right) images of FOXP2 (yellow)
immunohistochemical labeling of E14.5 forebrain reveals absence of expression in the
dorsal pallium, whereas the developing striatum is robustly labeled at this timepoint
(N = 5). (B) Images of FOXP2 immunolabeling at E16.5 demonstrates the presence of
FOXP2 expression within the deep layers of the developing cortical plate (N = 3). (C)
Retrograde labeling of layer 6 corticothalamic neurons (magenta) by injection of CTB into
the ventrobasal thalamus, combined with FOXP2 (yellow) immunohistochemistry at P14.
(D) Corticocortical neurons (cyan) labeled by injection of CTB into the ipsilateral primary
motor cortex, combined with FOXP2 (yellow) immunohistochemistry at P14. White arrows
denote retrogradely labeled projection neurons. (E) Quantification of the percentages of
retrogradely labeled corticothalamic (N = 4 mice) and corticocortical (N = 5) neurons that
express FOXP2. (F) FOXP2 (yellow) immunohistochemistry in sections of P0, P7, and
P14 somatosensory cortex from Ntsr1-cre; tdTomato mice (tdTomato is magenta); white
asterisks denote relatively low-level expression in layer 5 at P0. (G) Quantification of the
percentages of tdTomato-positive neurons that express FOXP2 at each age (P0, N = 3;
P7, N = 3; P14, N = 3). (H) FOXP2 (magenta) immunohistochemistry in sections of P0,
P7, and P14 somatosensory cortex from Met
GFP
(green) mice. Cyan arrowheads denote
sparse FOXP2
+
and GFP
+
double-labeled cells localized to layer 6B/subplate. (I)
Quantification of the percentages of GFP+ neurons that co-express Foxp2 at each age
(P0, N = 3; P7, N = 4; P14, N = 3). (J) FOXP2 (yellow) colocalizes with PCP4 (magenta),
but not ppCCK (cyan) (N = 3). Scale bars: 500 µm, A, B low magnification; 100 µm A, B
high magnification; 50 µm C, D, F, H and J.
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Figure 3.1- Figure Supplement 1 FOXP2 is transiently expressed by
subpopulation of PT neurons.
(A) FOXP2 (red) and CTIP2 (cyan) immunohistochemistry in sections of P0
somatosensory cortex. (B) FOXP2 (red) and CTIP2 (cyan) immunohistochemistry in
sections of P7 somatosensory cortex. (C) Quantification of the percentage of CTIP2
+
PT
neurons in somatosensory cortex that express FOXP2 at P0 (N = 3) and P7 (N = 5). (D)
Lack of expression of FOXP2 in most retrogradely (CTB) labeled PT neurons at P14
(N = 5). (E) Quantification of the percentage of CTB-labeled PT neurons that express
FOXP2. All scale bars, 100 µm.
101
102
Figure 3.2 FOXP2 is nonessential for class-specific anatomical and molecular
phenotypes of corticothalamic neurons.
(A) At P14 tdTomato (magenta) expression in layer 6 corticothalamic neurons of Ntsr1-
cre; Rosa-tdTomato mice reveals similar organization of corticothalamic innervation
in Foxp2 conditional knockout mice and heterozygous littermates – boxed inset shows
removal of FOXP2 protein (green) from tdTomato
+
corticothalamic neurons of Ntsr1-cre;
Foxp2
Fx/Fx
mouse. (B) Met
GFP
(green) and tdTomato (magenta) label distinct cell
populations in Foxp2 conditional knockout mice, heterozygous littermates, and wild-type
C57Bl/6J mice. (C) Quantification of co-expression of GFP by tdTomato
+
corticothalamic
neurons across Foxp2 genotypes (WT = Ntsr1 cre; Rosa-tdTomato, no Flox alleles, N = 3
mice; cHET = Ntsr1 cre; Foxp2
Fx/+
, N = 2 mice; cKO = Foxp2 Fx/Fx, N = 3 mice). (D)
ppCCK expression is excluded from layer 6 corticothalamic neurons
across Foxp2 genotypes as indicated by the segregation of tdTomato (magenta) and
ppCCK (cyan). (E) Quantification of co-expression of CCK by tdTomato
+
corticothalamic
neurons (N for each group same as panel C). (F) FOG2 expression by corticothalamic
neurons does not require Foxp2, as nearly all tdTomato
+
cells express FOG2 (green)
across Foxp2 genotypes. (G) Quantification of FOG2 coexpression by
tdTomato
+
corticothalamic neurons (N for each group same as panel C). All scale bars,
50 µm. Abbreviations: Ctx, cortex; Hpc, hippocampus; Thal, thalamus; TRN, thalamic
reticular nucleus.
Recent studies have identified neuronal target genes directly regulated by FOXP2,
many of which are directly repressed upon FOXP2 binding (Spiteri et al., 2007; Konopka
et al., 2009; Mukamel et al., 2011; Vernes et al., 2011). Two such genes, Cck and Met,
display largely non-overlapping expression with FOXP2 within layer 6 (Figure 3.1H–J).
Given the repressive effect of FOXP2 on Met and Cck expression (Spiteri et al.,
2007; Mukamel et al., 2011; Vernes et al., 2011), the hypothesis that FOXP2 is required
to prevent ectopic expression of Met and Cck among CT neurons was tested. Mice
carrying Ntsr1-cre; Rosa-tdTomato and Met
GFP
reporter alleles were bred
with Foxp2
fx
mice. The fraction of tdTomato
+
CT neurons that co-expressed GFP was
103
then quantified in Foxp2 conditional knockout mice, their heterozygous littermates
and Ntsr1-cre; Rosa-tdTomato mice on a wild-type C57Bl/6J background at P14.
Unexpectedly, the percentage of GFP and tdTomato double-positive neurons was
minimal and indistinguishable across genotypes (Figure 3.2B,C; one-way ANOVA,
p=0.441; Ntsr1-cre; Foxp2
+/+
(WT) mean ± SEM = 1.9 ± 0.4; Ntsr1-cre;
Foxp2
Fx/+
(cHET), mean ± SEM = 2.7 ± 0.1; Ntsr1-cre; Foxp2
Fx/Fx
(cKO),
mean ± SEM = 2 ± 0.5), indicating that the exclusion of Met expression from CT neurons
occurs independent of transcriptional regulation by FOXP2. Next, to determine whether
FOXP2 is required to repress CCK expression in CT neurons, colocalization of CCK and
tdTomato was quantified. Despite abundant CCK expression among layer 6 neurons,
there was minimal co-expression of CCK by tdTomato
+
neurons across Foxp2 genotypes
(Figure 3.2C,D; one-way ANOVA, p=0.5016; Ntsr1-cre; Foxp2
+/+
(WT)
mean ± SEM = 0.6 ± 0.4; Ntsr1-cre; Foxp2
Fx/+
(cHET), mean ± SEM = 1.2 ± 0.4; Ntsr1-
cre; Foxp2
Fx/Fx
(cKO), mean ± SEM = 0.6 ± 0.4), consistent with selective CCK
expression by layer 6 CC neurons and exclusion from CT neurons, as previously reported
(Watakabe et al., 2012; Kast et al., 2019). These data indicate that FOXP2 is not required
for the exclusion of CCK and Met expression from CT neurons. To determine whether
molecular markers unique to CT neurons continue to be expressed in their normal pattern
in the absence of FOXP2, labeling of FOG2 among tdTomato-expressing CT neurons
was assessed in Foxp2 conditional knockouts and their heterozygous littermates. FOG2
immunolabeling was detected in nearly 100% of CT neurons, independent
104
of Foxp2 genotype (Figure 3.2E,F; one-way ANOVA, p=0.3163; Ntsr1-cre; Foxp2
+/+
(WT)
mean ± SEM = 97.1 ± 0.1; Ntsr1-cre; Foxp2
Fx/+
(cHET),
mean ± SEM = 97.5 ± 0.7; Ntsr1-cre; Foxp2
Fx/Fx
(cKO), mean ± SEM = 96.1 ± 0.7).
Because Ntsr1-cre is selectively expressed in CT neurons, it is likely Cre
expression begins postmitotically, but the timing of the developmental onset of Cre-
mediated recombination in the Ntsr1-cre mouse line has not been reported. Given the
normal development of CT neurons in Ntsr1-cre; Foxp2
Fx
conditional knockout mice, we
reasoned that the potentially late timing of the developmental deletion of Foxp2 could
have influenced the lack of abnormal CT phenotypes. This possibility was investigated
first by determining the onset of Cre-dependent tdTomato expression in Ntsr1-cre; Rosa-
tdTomato embryos. Coronal sections of the embryonic forebrain were analyzed on
embryonic days (E)14.5, E16.5, and E17.5. Expression of tdTomato was not detected
until E17.5, when it was localized in a sparse population of subplate and layer 6 neurons
(Figure 3.2—figure supplement 1). Thus, cre-mediated recombination in the Ntsr1-
cre line does not begin until approximately E17, well after Foxp2 expression is initiated in
the cerebral cortex (Figure 3.2—figure supplement 1; Ferland et al., 2003), and at a
developmental time point only shortly preceding the initial innervation of the thalamus by
descending CT axons (Grant et al., 2012; Torii et al., 2013). Thus, it is possible
that Foxp2 operates in an earlier developmental window, prior to Ntsr1-cre mediated
recombination and the subsequent depletion of previously transcribed and
translated Foxp2/FOXP2. To determine whether Foxp2 might play a role earlier in the
105
development of cortical neurons, the Emx1-cre driver line was employed, which exhibits
cre-mediated recombination in dorsal pallial progenitors beginning at E10.5, when they
have just started to proliferate (Gorski et al., 2002). Evaluation of Emx1-cre embryos
confirmed cre-dependent tdTomato expression by E10.5 (Figure 3.2—figure supplement
1B). In situ hybridization revealed the selective removal of the floxed portion of
the Foxp2
Fx
allele, which encodes the DNA-binding domain, at E14.5 (Figure 3.3A), prior
to FOXP2 protein production in the dorsal pallium (Figure 3.1A,B). FOXP2
immunohistochemistry performed on sections of the E16.5 forebrain demonstrated the
absence of FOXP2 protein in Emx1-cre; Foxp2
Fx
embryos at the earliest timepoint that
FOXP2 could be detected in Foxp2
Fx
embryos (Figure 3.3B, Figure 3.3—figure
supplement 1B). Thus, the removal of Foxp2 prior to the expression of FOXP2 by any
dorsal pallial cells, using Emx1-cre, provided the opportunity to assess the developmental
role of Foxp2 function from the earliest stages of cortical development and in the subset
of layer 5 PT neurons that normally express FOXP2 (Figure 3.1—figure supplement 1),
but that do not express Ntsr1-cre.
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Figure 3.2- Figure Supplement 1 Developmental timing of Cre-mediated
recombination in Ntsr1-cre and Emx1-cre.
(A) Cre-dependent tdTomato (magenta) expression begins at E17.5 (N = 3) in the cortex
of Ntsr1-cre mice, when there are abundant FOXP2+ (green) neurons in the subplate and
layer 6 that do not yet express tdTomato. (B) Cre-dependent tdTomato (red) expression
begins at E10.5 (N = 3) in the cortex of Emx1-cre mice, where the majority of dorsal
pallium (DP) progenitors are tdTomato+. (CP, cortical plate; IZ, intermediate zone; Str,
striatum; SVZ, subventricular zone; VZ, ventricular zone; WM, white matter). Scale Bars:
100 µm.
107
108
Figure 3.3 FOXP2 is nonessential for the genesis of cortical neurons and their
proper lamination.
A) Foxp2 in situ hybridization based on the BaseScope method reveals expression
of Foxp2 transcript (Red) in Foxp2
Fx/Fx
embryos (N = 4) and selective removal of exons
12–14 (DNA-binding domain) from the dorsal pallium including the cortical plate (white
arrowhead) of Emx1-cre; Foxp2
Fx/Fx
mice (N = 6) by E14.5. (B) Immunohistochemical
analysis of FOXP2 protein in E16.5 embryos (N = 3 each genotype) demonstrates
selective elimination of FOXP2 protein (green) from the infragranular layers of the dorsal
pallium of Emx1-cre;Foxp2
Fx/Fx
mouse embryos. (C) FOXP2 immunohistochemistry on
coronal sections of P0 Foxp2
Fx/Fx
and Emx1-cre; Foxp2
Fx/Fx
mice reveals absence of
FOXP2 (black) in the infragranular cortical layers (red bracket) of Emx1-cre;
Foxp2
Fx/Fx
mice.Inset (red outline) shows selective loss of FOXP2 in the cortex at higher
magnification. (D) FOG2 (green) and CTIP2 (magenta) immunohistochemistry in coronal
sections of the primary somatosensory cortex of Foxp2Fx/Fx and Emx1-cre;
Foxp2Fx/Fx mice reveals similar distributions of laminar specific markers at P0. (E)
Quantification of FOG2
+
cells in layer 6 across genotypes at P0 (Fx/Fx, Foxp2
fx/fx
, N = 7;
cHET, Emx1-cre; Foxp2
fx/+
, N = 6; cKO, Emx1-cre;Foxp2
fx/fx
, N = 7; Emx1-cre, N = 6). (F)
Quantification of CTIP2+/Fog2- cells in layer 5 across genotypes at P0 (N for each group,
same as panel E). (G) ppCCK (cyan) and PCP4 (magenta) immunohistochemistry in
coronal sections of conditional knockout and control littermates at P14. (H) Quantification
of ppCCK
+
cells in layer 6 of SSC across genotypes at P14 (Foxp2
Fx/Fx
, N = 3 mice; Emx1-
cre; Foxp2
Fx/Fx
, N = 4 mice). (I) Quantification of FOG2
+
cells in layer 6 of SSC across
genotypes at P14 (Foxp2
Fx/Fx
, N = 3 mice; Emx1-cre; Foxp2
Fx/Fx
, N = 4 mice). (J) DAPI-
staining of coronal sections of Foxp2
Fx/Fx
and Emx1-cre; Foxp2
Fx/Fx
mice reveals similar
size of cortex, including the thickness of primary somatosensory cortex (indicated by cyan
bracket). (K) Quantification of somatosensory cortex thickness across genotypes at P14
(Foxp2
Fx/Fx
, N = 3 mice; Emx1-cre; Foxp2
Fx/Fx
, N = 4 mice). Scale Bars: A(inset), 100 µm;
B, E, 50 µm; H, 500 µm.
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Figure 3.3- Figure Supplement 1 Elimination of Foxp2 transcript and protein from
the forebrain of Emx1-cre; Foxp2
Fx/Fx
embryos
(A) BaseScope RNAscope demonstrating selective removal of Foxp2 exons 12–14
(DNA-binding domain) from the dorsal pallium at E14.5 (note that high magnification
insets are the same images as Figure 3.3A). (B) FOXP2 immunohistochemistry
demonstrates selective removal of FOXP2 protein from the infragranular layers (red
bracket) of the dorsal pallium at the earliest timepoint that it can be detected, E16.5. Scale
bar = 500 µm.
Foxp2 is reportedly important for cortical neurogenesis and cell migration (Tsui et
al., 2013; Garcia-Calero et al., 2016); these ontogenetic events thus were evaluated
in Emx1-cre; Foxp2
Fx/Fx
conditional knockout mice. FOXP2 immunohistochemistry
validated the removal of Foxp2 from the cortex of Emx1-cre; Foxp2
Fx/Fx
mice at E16.5
(Figure 3.2—figure supplement 1C,D), at P0 (Figure 3.3A), as well as P4 and P14 (data
not shown). Immunohistochemistry of the layer-specific markers FOG2, CTIP2 (Figure
3.3D) and DARPP-32 (data not shown) at P0 revealed normal patterns of lamination and
cell-density in conditional knockout mice. There also were no overt differences in the
thickness of layers as revealed by DAPI staining at P0 or P14 (Figure 3.3C,J). In addition,
high-level expression of CTIP2 remained restricted to layer 5, and neurons expressing
110
FOG2, a marker of CT neurons that are enriched for Foxp2, were found in their normal
position, in layer 6 (Figure 3.3D). The numbers of CTIP2
+
/FOG2
-
cells in layer five were
not different between genotypes at P0 (Figure 3.3E Kruskal-Wallis test, p=0.). The
numbers of FOG2
+
cells in layer 6 were comparable across genotypes (Figure 3.3F;
Kruskal-Wallis test, p=0.0437, Foxp2Fx, mean ± SEM = 416 ± 28; cHET,
mean ± SEM = 440 ± 68; cKO, mean ± SEM = 357 ± 37; Emx1-cre,
mean ± SEM = 430 ± 52), as no statistically significant pairwise differences were
observed between genotypes (Dunn’s Multiple Comparisons test: cKO vs. Foxp2Fx,
p=0.3688; cKO vs. Emx1-cre, p=0.1353; cKO vs. cHET, p=0.0674; cHET vs. Foxp2Fx,
p=0.9999; cHET vs. Emx1-cre, p=0.9999; Foxp2Fx vs. Emx1-cre, p=0.9999). Importantly,
at P14, the numbers of FOG2
+
layer 6 CT cells were similar in Emx1-cre;
Foxp2
Fx/Fx
and Foxp2
Fx/Fx
littermates (Figure 3.3I, unpaired two-tailed t-test,
p=0.6912 Foxp2
Fx/Fx
, mean ± SEM = 209 ± 21; Emx1-cre; Foxp2
Fx/Fx
(cKO),
mean ± SEM = 199 ± 13), as were the numbers of CTIP2
+
/FOG2
-
cells in layer 5
(unpaired two-tailed t-test, p=0.99; Foxp2
Fx/Fx
, mean ± SEM = 77 ± 14; Emx1-cre;
Foxp2
Fx/Fx
(cKO), mean ± SEM = 79 ± 7). These data demonstrate that the corticofugal
populations that express FOXP2 during development do not require FOXP2 for their
proper specification. Additionally, the number of ppCCK+ layer 6 CC cells was
indistinguishable between conditional knockout and control groups (Figure 3.3G,H.
unpaired two-tailed t-test, p=0.5178; Foxp2
Fx/Fx
, mean ± SEM = 139 ± 7; Emx1-cre;
Foxp2
Fx/Fx
(cKO), mean ± SEM = 147 ± 9). Thus, the infragranular layers, which contain
111
the neurons that express FOXP2, develop their normal complement of diverse projection
neuron subtypes in normal numbers in the absence of Foxp2. Finally, the radial thickness
of the SSC was indistinguishable across genotypes at P14 (Figure 3.3J,K, unpaired two-
tailed t-test, p=0.583), suggesting that FOXP2 function also is dispensable to produce a
histogenically and architecturally normal SSC.
Next, to evaluate the putative role of FOXP2 in axon guidance (Vernes et al.,
2011), Emx1-cre; Rosa-tdTomato mice were crossed with Foxp2
Fx
mice. In agreement
with the results from the Ntsr1-cre; Rosa-tdTomato experiments, tdTomato-labeled
subcortical innervation revealed nearly identical patterns in the internal capsule,
thalamus, cerebral peduncle, and pyramidal decussation across Foxp2 genotypes at
P0 (data not shown) and P4 (Figure 3.4). This result, using a genetic deletion strategy
prior to cortical neurogenesis, indicates that Foxp2 is dispensable for proper specification
of the cortical neuron subtypes that normally express FOXP2 in SSC, and the appropriate
guidance and targeting of their efferent axons.
112
Figure 3.4 FOXP2 is not required for proper corticofugal axon pathfinding.
A) tdTomato reporter (black) reveals similar patterns of corticofugal axon growth in
sagittal sections of Emx1-cre;Foxp2
+/+
(WT, top panel, N = 4) Emx1-cre;
Foxp2
Fx/+
(middle, N = 3) and Emx1-cre; Foxp2
Fx/Fx
(bottom, N = 3). Note the
fasciculation of axons in the internal capsule (blue asterisks) and the comparable growth
of axons into the thalamus (blue arrows) and pyramidal decussation (red arrowheads).
(B) Higher magnification images of the corticothalamic innervation patterns in each
genotype. Scale Bars = 500 µm.
113
3.5 DISCUSSION
The current study focused on putative roles for FOXP2 in murine cortical histogenesis,
using conditional mouse genetics. The results demonstrate that Foxp2 is not required for
establishing basic developmental organization, molecular phenotypes or efferent
connectivity of Foxp2-expressing neurons in SSC of mice. The role of FOXP2 in neural
development has been of significant interest following the identification
of FOXP2 mutations that cause developmental apraxia of speech in humans (Lai et al.,
2001; MacDermot et al., 2005). Diverse methods and genetic models have been used to
interrogate FOXP2 function in a variety of brain areas and species (French and Fisher,
2014). Notably, recent studies primarily in mice have implicated Foxp2 in many
developmental processes including cortical neurogenesis (Tsui et al., 2013), neuronal
migration (Garcia-Calero et al., 2016), neuron subtype specification (Chiu et al., 2014),
neural tissue patterning (Ebisu et al., 2017), neurite outgrowth and axon guidance
(Vernes et al., 2011), synapse formation (Chen et al., 2016), and synaptic plasticity
(Groszer et al., 2008). However, assessment of Foxp2 function during cerebral cortical
development by means of conditional mouse genetics had not been thoroughly pursued.
Here, Foxp2 was deleted at different prenatal ages using dorsal pallial- and cell-type
specific Cre-recombinase mouse lines.
114
FOXP2 and cortical projection neuron phenotypes
FOXP2 expression is enriched in the deepest layers of the developing and mature
neocortex of mammalian species ranging from mice to humans (Ferland et al.,
2003; Campbell et al., 2009; Mukamel et al., 2011), suggesting conservation of the
cortical cell types that utilize the gene. The connectivity and molecular phenotyping data
generated here demonstrate that, in mice, FOXP2 expression within the infragranular
layers of the developing SSC is present in nearly all CT neurons, excluded from most
layer 6 CC neurons, and transiently expressed by a minor subset of PT neurons. This is
consistent with observations of enrichment of Foxp2 in CT neurons in the primary visual
cortex of adult mice by single cell RNA-sequencing and immunohistochemistry (Tasic et
al., 2016; Sundberg et al., 2018), and previous findings that FOXP2 is expressed by
subsets of layer five neurons (Ferland et al., 2003; Hisaoka et al., 2010; Molyneaux et al.,
2015), which we show here are PT-type neurons. Analysis of SSC at three postnatal ages
demonstrated that expression of FOXP2 by CT-type neurons is stable, whereas PT-type
neurons express FOXP2 transiently, with no detectable FOXP2 expression in most PT-
type neurons at P14. The developmental cell-type selectivity of FOXP2 expression raises
important questions regarding the function of FOXP2.
With the enrichment of FOXP2 in CT neurons, and very limited expression in CC
neurons, the current study addressed whether Foxp2 expression is required to repress
expression of two putative target genes, Cck and Met, which are generally excluded from
CT neurons. Such a role would be consistent with the non-overlapping expression
115
patterns of these genes with Foxp2 in layer 6, as well as previous reports of direct
repressive control by FOXP2 in vitro (Spiteri et al., 2007; Mukamel et al., 2011; Vernes et
al., 2011). However, deletion of Foxp2 failed to alter expression patterns of Met or Cck,
suggesting other transcriptional mechanisms may mediate their cell type-specific
expression in infragranular layers in vivo. Additionally, two important molecular features
unique to FOXP2
+
neurons, FOG2 and DARPP-32 expression, are unchanged following
either very early or late prenatal deletion of Foxp2. Much broader molecular profiling is
warranted, but the data indicate that Foxp2 is neither required for the specification of
some of the unique molecular features of FOXP2
+
layer 6 CT neurons, nor for the
regulation of alternate cell-type molecular signatures that were predicted from previous
analysis of FOXP2 transcriptional regulatory targets.
FOXP2 and early cortical neuron development
Using the early expressing Emx1-cre driver line (E10.5), the data show
that Foxp2 deletion does not disrupt the normal generation and migration of neurons in
SSC. Thus, unlike other transcriptional regulators of cell-type identity (e.g. CTIP2, FEZF2,
TBR1), for which dramatic changes in cell type numbers and projection phenotypes
develop upon mutation (Arlotta et al., 2005; Chen et al., 2005; Han et al., 2011; McKenna
et al., 2011; Molyneaux et al., 2005), FOXP2 appears dispensable for the general
production of cortical neurons and the specification of the specific projection populations
that normally express FOXP2. This conclusion is consistent with the absence of
developmental defects recently reported in Nex1-cre; Foxp2
Fx
mice, in which Foxp2 is
116
deleted from postmitotic excitatory neurons of the dorsal pallium (Medvedeva et al.,
2019). These results were unexpected given the data in previous studies using in
utero electroporation (IUEP)-mediated Foxp2 shRNA knockdown, which demonstrated
atypical cortical neurogenesis and migration (Tsui et al., 2013; Garcia-Calero et al.,
2016). In fact, using IUEP and identical shRNA reagents, we observed similar phenotypes
to those previously reported (data not shown) (Tsui et al., 2013). Several explanations,
based on distinct methodologies, could account for the discrepant findings using IUEP
knockdown compared to hetero- and homozygous genetic deletion. Recent studies have
revealed compensatory transcriptional responses by orthologous transcripts in some
mutant mouse lines, and thus it is possible that other Foxp family members could
compensate for Foxp2 removal in our studies (discussed more below). Additionally, when
crossed with the Foxp2
Fx
conditional allele, Emx1-cre leads to uniform removal
of Foxp2 from dorsal pallial progenitors, whereas in utero electroporation reduces gene
expression in a much smaller subpopulation of cells. This generates a mosaic of Foxp2-
positive and negative cells. The altered neurogenesis phenotype observed following IUEP
mediated-knockdown could result from the removal of Foxp2 in a mosaic fashion in the
dorsal pallium resulting in atypical interactions between neighboring FOXP2
+
and
FOXP2
-
cells. A similar mosaic effect could explain the altered migration observed
following IUEP of Foxp2 shRNA (Tsui et al., 2013). Mosaic expression of mutant and wild-
type alleles can influence cortical development, shown recently in mouse models of X-
linked Pcdh19 epilepsy (Pederick et al., 2018). Resolving whether
117
mosaic Foxp2 expression can disrupt neurogenesis and migration will require many
additional studies using methods distinct from shRNA knockdown, such as in
utero electroporation of Cre-expressing plasmid constructs into Foxp2
Fx/Fx
embryos. We
note, however, the importance of determining the relevance of such mosaic effects, may
depend on the identification of a natural context, in humans or developing mouse models,
in which mosaic Foxp2 function occurs in the cortex.
An alternative explanation is that the Foxp2-targeting shRNA used in ours and
previous studies may lead to incomplete reduction of Foxp2 expression. This would in
turn result in different adaptive responses compared to complete deletion
of Foxp2 genetically. Similar mechanistic explanations have been posed for the
discrepant observations of germline versus IUEP-mediated manipulation of doublecortin
(Bai et al., 2003). Noteworthy is the finding that ectopic overexpression of Foxp2 results
in a paradoxically similar arrest in the radial migration of cortical neurons caused
by Foxp2 shRNA knockdown (Clovis et al., 2012). While no direct evidence currently
exists, non-physiological mosaic reduction or overexpression of FOXP2 could create an
imbalance that itself disrupts cortical development, while genetic disruptions fail to
produce the same phenotypes. Finally, shRNA knockdown does have the technical
caveat of potential ‘off-target’ effects, which cannot be ruled out unequivocally. For
example, it is possible that the Foxp2 shRNAs also impact the expression of other genes
including other Foxp family members, such as Foxp1, which are expressed in the cortex
and could hypothetically compensate for Foxp2 function in our knockout studies.
118
However, in vitro analysis of the specificity of the shRNA constructs suggested that they
did not alter the levels of Foxp1 or Foxp4 expression (Tsui et al., 2013). Additionally, it is
noteworthy that, in a previous study, quantification of Foxp1 and Foxp4 transcript levels
demonstrated that these genes were expressed at similar levels in WT
and Foxp2 conditional knockout embryos (French et al., 2007). Thus, recombination of
the Foxp2
Fx
allele does not necesarrily trigger genetic compensation by other Foxp family
members. Although unidentified compensatory mechanisms may mask a dispensable
role that Foxp2 plays in the core aspects of cortical development studied here, based on
results obtained following complete genetic removal of Foxp2 function, we conclude
that Foxp2 does not play an essential role in murine SSC neurogenesis, neuron
migration, subtype specification or axonal pathfinding, contrary to conclusions of other
studies.
Functional implications of FOXP2 in developmental disorders
The lack of overt changes in the generation, migration, differentiation, or axon pathfinding
of SSC neurons following conditional Foxp2 deletion, using two different Cre driver lines,
is important to consider in several contexts. The results may have implications for
understanding the involvement of the cerebral cortex in speech and language
impairments associated with FOXP2 mutations in humans (Vargha-Khadem et al., 2005).
The results suggest that loss of FOXP2 function likely does not contribute to these deficits
through altered cortical histogenesis. However, it is important to note that the present
studies were carried out in mice, and thus it remains possible that novel species-specific
119
roles for Foxp2 may have been acquired in the human lineage. Nonetheless, the results
provide foundational knowledge that will be essential when designing studies to further
address the role of FOXP2 in the development and function of specific neuronal cell types
in the cortex.
The normal development of cortical phenotypes in Foxp2 conditional knockout
mice is consistent with conventional magnetic resonance imaging of brain structure in
patients with FOXP2 mutations, which did not identify substantive alterations in gray and
white matter structure of the cerebral cortex (Vargha-Khadem et al., 2005). However,
more refined analysis using voxel-based morphometric analysis identified spatially
restricted, minor alterations in the gray matter in perisylvian cortical areas of patients
with FOXP2 mutations (Belton et al., 2003). Area-restricted deficits in the development of
cerebral cortical anatomy may occur in Foxp2 conditional knockout mice, but were not
detected in the present study due to the focus of the analysis on primary SSC. More
expansive studies will need to be pursued. In addition, given the lack of speech and
language homologous regions in the cerebral cortex of mice, discovery of regional
disruptions relevant to humans with FOXP2 mutations may not be possible.
Here, the genetic deletion of Foxp2 in mice is distinct from the most common
mutations observed in human patients with inherited speech and language abnormalities
(Lai et al., 2001; MacDermot et al., 2005). Cre-mediated recombination of the
conditional Foxp2 allele produces a nonsense mutation that eliminates DNA-binding
ability and causes near complete loss of FOXP2 protein (French et al., 2007). Functionally
120
analogous, truncating FOXP2 mutations have been identified in some patients with
speech and language disorders (MacDermot et al., 2005). However, missense mutations
like the one in the KE pedigree are more common and are the best characterized in terms
of their associated brain abnormalities (Vargha-Khadem et al., 1998; Lai et al., 2001).
Thus, the conditional knockout mice used here may not fully recapitulate aberrant FOXP2
functions caused by single amino acid changes, which are proposed to elicit dominant-
negative functions that could be distinct from the simple loss-of-function caused by
conditional Foxp2 deletion (Tsui et al., 2013). For example, missense FOXP2 mutations
could lead to gain-of-function impairments by influencing the activity of other transcription
factors. Importantly, transgenic mouse models that carry the same missense mutations
as those observed in human populations have been generated and functionally, but not
developmentally, characterized (French and Fisher, 2014). Irrespective of the differences
in the genetic strategies used to disrupt Foxp2, the present results strongly suggest that
the histogenesis of murine SSC does not depend on transcriptional regulation by FOXP2.
Finally, in Foxp2 constitutive knockout mice, medium spiny neurons of the striatum
display decreased mEPSC frequency, decreased dendritic spine density, and increased
mEPSC amplitudes, whereas the macro-level organization and cell-type composition of
the striatum remains intact (Chen et al., 2016). It would be interesting to investigate
whether the cortical neurons that express Foxp2 display similar synaptic abnormalities
in Foxp2 knockout mice. Altered excitability of CT neurons could contribute to atypical
activity within cortico-striato-thalamocortical loops that are important for motor control and
121
information processing, which could significantly alter speech related functions. The
demonstration that the histological organization of the somatosensory cortex is unaffected
by the removal of Foxp2 warrants more detailed characterization of cortical circuit
function in Foxp2 mutant mice.
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Chapter 4 : ONTOLOGY OF CONTEXTUAL FEAR MEMORY IN MICE AND THE ROLE OF MET IN
MEMORY CAPABILITIES
Alexandra L. Lanjewar, Kathie L. Eagleson, and Pat Levitt
4.1 ABSTRACT
Cognitive impairment is a common phenotype of many neurodevelopmental disorders,
but how these deficits arise remains elusive. Determining the precise onset of discrete
cognitive capabilities facilitates studies in probing the biological mechanisms underlying
their emergences. Here, we focused on the expression of contextual fear memory, a
learned memory that results in freezing responses on testing day (24 hours later or
beyond) by mice that were shocked on training day. First, we determined the age of onset
of contextual fear memory persistence capabilities, defined as the ability to retain a fear
memory for at least 7 days, in C57Bl/6J male and female mice. Contextual fear memory
persistence emerged rapidly during the early fourth postnatal week, with no sex
differences in age of onset. In contrast, robust remote memory – a memory retained for
at least 30 days following training – was not observed until later in development. Previous
studies demonstrated deficits in contextual fear learning by adult mice when the gene
encoding the MET receptor tyrosine kinase (MET) is conditionally deleted during
development, but how and when these deficits come about remains unknown. To
determine if MET impacts the emergence of contextual fear memory persistence, we
generated mice with either conditional expression of a Met transgene past the normal
window of peak expression or conditional deletion during development. The timing of
emergence of these memory capabilities is not impacted by either manipulation of Met
123
expression. However, we determined that adult mice have increased activity in MET
+
medial prefrontal cortex neurons during memory tests compared to younger ages.
Overall, the studies reveal the precise onset of contextual fear memory capabilities and
determines a role of MET in adult contextual fear learning independent to onset of the
cognitive capability.
4.2 INTRODUCTION
Cognitive development is a protracted process, during which brain regions involved in
higher order cognitive processes, such as prefrontal cortex, continue to mature into early
adulthood (Calabro et al, 2020 Cereb Cortex; Gogtay et al, 2004 Proc Natl Acad U S A;
Shaw et al, 2008 J Neurosci; Sowell et al, 1999 Nat Neurosci; Mills et al, 2014 Soc Cogn
Affect Neurosci). This maturation process coincides with increased and optimized
cognitive processing capabilities (Best, Miller & Naglieri, 2011 Learn Individ Differ; Luna
et al, 2004 Child Dev; Luciana et al, 2005 Child Dev). Cognitive deficits are common in
neurodevelopmental disorders (NDDs), including autism spectrum disorder, attention-
deficit/hyperactivity disorder, and schizophrenia (Reichenberg et al, 2010 Am J
Psychiatry; Kahn & Keefe, 2013 JAMA Psychiatry; Keefe, Eesley & Poe, 2005 Biol
Psychiatry; Cantio et al, 2018 Autism Res; Damaj et al, 2015 Eur J Hum Genet; Kallweit
et al, 2020 J Clin Exp Neuropsycol; Loyer Carbonneau et al, 2021 J Atten Disord);
however, how and when such deficits arise during brain development remain elusive.
In rodents, contextual fear learning paradigms are used to assess cognitive
abilities, specifically learning and memory functions. In mice, perturbations in the
124
expression of NDD risk genes from the onset of normal expression during development
lead to contextual fear learning deficits, with most studies focusing analyses only in adults
(Nakamoto et al, 2020 PLoS One; Haji et al, 2020 Mol Autism; Huang et al, 2021
Theranostics). Elucidating the precise onset of cognitive deficits, however, is important
for determining the origins of dysfunction. Studies have begun to focus on the ontology
of contextual fear learning abilities in wild type mice. As early as postnatal day (P) 15,
mice are able to form a fear memory that lasts at least 1 day; the memory, however, does
not persist for 7 days (Akers et al, 2012 Learn Mem). By P25, longer-term memory
capabilities are present, such that mice can now retain fear memories for at least 30 days
(Samifanni & Zhao et al, 2021 Learn Mem). Together, these studies identify a broad
window of cognitive development, spanning P15 to P25, during which longer-term
memory retention capabilities arise. Identification of the developmental processes
underlying the biological mechanisms that contribute to the onset of longer intervals of
memory retention requires a more precise definition of the age at which this cognitive
capability arises. Once this precise trajectory is determined, biological mechanisms can
be tested to pinpoint the underlying developmental processes that give rise to memory
retention capabilities and determine whether deficits seen in adults are due to deficits
from the very onset of cognitive development.
Accumulating evidence underscores a key role for the MET receptor tyrosine
kinase (MET) in the development of discrete circuits within the forebrain, including those
subserving cognitive function. MET expression in cortex peaks during the period of peak
125
synaptogenesis and modulates dendritic and synapse development and maturation
(Judson et al, 2009 J Comp Neurol; Judson et al, 2010 J Comp Neurol; Eagleson et al,
2016 Dev Neurobiol; Chen & Ma et al, 2021 Mol Psychiatry; Ma et al, 2022 Cereb Cortex;
Xie et al, 2016 eNeuro). Prolonging or eliminating MET expression alters the timing of
critical period plasticity for ocular dominance, shifting the critical period later or earlier,
respectively (Chen & Ma et al, 2021 Mol Psychiatry). Additionally, prolonging MET
expression during a social cognition critical period alters social behavior in adult mice (Ma
et al, 2021 Cereb Cortex). Finally, adult mice in which Met had been conditionally deleted
embryonically in all neural cells (Thompson & Levitt, 2015 J Neurodev Disord; Heun-
Johnson & Levitt, 2017 Neurobiol Stress) or in cells arising from the dorsal pallium only
(Xia et al, 2021 Neurobiol Learn Mem) exhibit contextual fear learning deficits. Thus far,
however, no study has examined when these learning deficits arise. Combined, the
previous studies position MET as a candidate for regulating the timing of onset for
contextual fear memory capabilities.
Here, we determine the precise developmental trajectory for onset of 7-day
memory persistence capabilities and whether this coincides with the onset of the ability
to exhibit remote memory capabilities, defined as a memory that is retained for at least
30 days (Frankland et al, 2006 Learn Mem; Silva, Bruns & Gräff, 2019
Psychopharmacology). Lastly, we determine whether MET signaling affects contextual
fear memory abilities prior to adulthood and compare the activity of MET
+
neurons in
medial prefrontal cortex (mPFC) during memory testing of adults versus adolescence.
126
4.3 MATERIALS AND METHODS
Animals
All mice were bred in the Children’s Hospital Los Angeles vivarium and housed on
ventilated racks with a 13:11 hour light:dark cycle (lights on at 06:00 hours, lights off at
19:00 hours) at 22°C with ad libitum access to water and a standard chow diet (PicoLab
Rodent Diet 20, #5053, St. Louis, MO). To determine the developmental trajectory for
retaining a 7-day persistent memory or a 30-day remote memory, C57Bl/6J were used as
the wild type (WT) mouse line. Additionally, two transgenic mouse lines were used to
determine the potential involvement of the gene encoding the Met receptor tyrosine
kinase in regulating the developmental onset of the expression of persistence and/or
remote memory. To sustain MET expression, a controllable transgenic overexpression
for MET (cto-Met; Ma et al, 2022 Cereb Cortex) mouse line a transgenic mouse line, in
which a Met transgene is expressed under the control of the CAMKII promoter in all dorsal
pallial excitatory neurons on a C57Bl/6J background, as described in Chen et al. (2021,
Mol Psychiatry), was used. In experimental mice (Met
ctg
/tTA, cto-Met from hereon), the
Met transgene is expressed abundantly by postnatal day (P) 16 (Chen et al, 2021 Mol
Psychiatry) and sustained throughout the course of experiments. Littermates in which the
Met transgene is not expressed (WT/WT, Met
ctg
/WT, or WT/tTA), were considered
control. Additionally, to delete the Met gene, Met
fx/fx
females and Nestin
cre
; Met
fx/+
males,
both on a C57Bl/6J background, were bred to produce control (Cre-negative), conditional
heterozygous (cHet; Nestin
cre
/Met
fx/+
), and conditional homozygous (cKO;
127
Nestin
cre
/Met
fx/fx
) pups (Thompson & Levitt, 2015 J Neurodev Disord). The cHet mice
produce 50% of normal MET levels in neurons, while the cKO mice do not produce any
MET in neurons. Lastly, Met
EGFP
BAC transgenic mouse line was used to be able to
visualize green fluorescent protein (GFP) in MET-expressing neurons, as previously
described in Kamitakahara, Wu & Levitt (2017 J Comp Neurol) and Kast, Wu & Levitt
(2019 Cereb Cortex). Backcrossed for at least 10 generations with C57BL/6J mice (The
Jackson Laboratory, RRID:IMSR_JAX:000664), female and male mice homozygous for
the Met-EGFP transgene were bred in our facility to generate the homozygous Met
GFP
offspring used in this study. The day of birth for all mice was designated P0. At P21 (+/-1
day), mice were weaned and housed with same-sex littermates (2-5/cage). Male and
female mice at various ages between P15 and P106 from each of these lines were used
for analyses. To minimize potential litter and sex effects, each experimental group
included a maximum of 2 males and 2 females from a single litter, with a minimum of
three litters and approximately equal numbers of males and females represented. Animal
care and experiments conformed to the guidelines set forth by the Children’s Hospital Los
Angeles Institutional Animal Care and Use Committee.
Contextual Fear Conditioning and Testing
Contextual fear conditioning and fear memory testing were performed as described in
Akers et al. (2012 Learn Mem) using the NIR Video Fear Conditioning Package for Mouse
(MED-VFC2-USB-M; Med Associates Inc, Georgia, VT). Fear conditioning chambers
(Med Associates VFC-008-LP) were 29.53 cm length x 23.5 cm width x 20.96 cm height
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with shock-grid floors comprising stainless-steel grid rods arranged in a straight horizontal
plane, spaced 0.813 cm apart (Med Associates VFC-005A). Shocks were generated by
a standalone aversive stimulator/scrambler (Med Associates ENV-414S). Separate
cohorts of mice from each mouse line were trained on P15, P20, P21, P22, P23, P35,
P50 (post pubertal adolescences, between P46 and P53) or P90 (adults, between P89
and P99). Briefly, mice designated “Shock” mice were acclimated in the chamber for two
minutes and then presented with three unsignaled 2-sec foot shocks of 0.5 mA intensity
spaced 1 min apart. One minute after the last shock, mice were removed from the
chamber and returned to their home cage. Shock delivery was confirmed by mice jumping
and/or vocalizing during the shock. In the rare instances that shock delivery could not be
confirmed for all three shocks, mice were excluded from further testing. These
occurrences were rare, due either to mice briefly hanging on the side of the chamber and
therefore were not shocked, or there was a hardware malfunction. There were no age,
sex, or genotype differences in testing day exclusions. Age-matched littermates that were
designated “No Shock” mice, were placed in the chamber for the same length of time
without receiving foot shocks and served as controls for spontaneous (non-memory
induced) freezing. No Shock mice were trained separately from Shock mice to eliminate
the confound of shock-induced vocalizations. Memory trials were conducted 1 (formation
of fear memory), 7 (persistence of fear memory), 14 (longer persistence of fear memory),
or 30 (remote fear memory) days later. On the testing day, mice were returned to the
chamber and allowed to explore for 5 minutes (or 2 minutes in cFOS experiments) without
129
any foot shocks presented. Throughout the behavioral paradigm, investigators were blind
to the genotype of the mice from the Met-manipulated transgenic mouse lines.
Behavioral Analysis
During the memory trials on testing day, freezing responses were recorded by a
monochrome video camera (Med Associates VID-CAM-MONO-5). A freeze response
was considered no movement above an 18au threshold for at least 1 second (30 frames),
analyzed by automated software (Video Freeze, SOF-843, Med Associates). The
percentage of time freezing over the 5-minute (or 2-minute) trial on testing day was
calculated by the automated software.
Immunofluorescence Staining
Brain tissue for immunofluorescence staining was collected at P36 or P91, 90 minutes
after contextual fear memory formation testing, from male and female Met
GFP
mice. Mice
were deeply anesthetized by intraperitoneal injection of ketamine:xylazine (100 mg/kg:10
mg/kg, Henry Schein, Melville, NY), transcardially perfused with fixative (4%
paraformaldehyde (Sigma, St. Louis, MO) in 0.1M phosphate-buffered saline (PBS), pH
7.4), and brains were immediately dissected and immersed in fixative at 4°C for 2 h. Next,
brains were sequentially incubated in 10%, 20%, and 30% sucrose in PBS for
cryoprotection, embedded in Tissue-Tek
®
Optimal Cutting Temperature Compound
(VWR, Radnor, PA), frozen over liquid nitrogen vapors, and stored in -80°C until
cryosectioning. Sections were collected coronally in a series of 5 at 20 μm-thick, mounted
130
onto superfrost plus microscope slides (VWR, Radnor, PA), and stored at -80°C until
immunofluorescence staining. For immunostaining, slides were defrosted at room
temperature for 10 min, dried at 60°C for 15 min in a hybridization oven, washed in PBS
at room temperature for 10 min, blocked and permeabilized at room temperature for 1 h
in PBS containing 5% Normal Donkey Serum (Jackson ImmunoResearch, West Grove,
PA) and 0.3% Triton X-100 (Sigma, St. Louis, MO), and then incubated overnight at room
temperature in primary antibodies (1:500) diluted in 0.1% Triton X-100 in PBS. The
primary antibodies used were chicken anti-green fluorescent protein (GFP; Abcam Cat#
ab13970, RRID:AB_300798), rat anti-CTIP2 (Abcam Cat# ab18465, RRID:AB_2064130),
and rabbit anti-CFOS (Cell Signaling Technology Cat# 2250, RRID:AB_2247211). After
overnight incubation, sections were washed 5 times for 5 min each at room temperature
with 0.2% Tween-20 (Sigma, St. Louis, MO) in PBS, incubated at room temperature for
1 h in diluted Alexa Fluor
®
F(ab’)2 conjugated secondary antibodies (1:500; Abcam) in
0.1% Triton X-100 in PBS, and protected from light, hereon. Next, sections were washed
3 times for 5 min each with 0.2% Tween-20 in PBS, incubated in DAPI (1:15,000; Thermo
Fisher Scientific Cat# D1306) diluted in PBS for 8 min, and wash two times in PBS for 5
min each. Finally, sections were embedded with a coverslip using ProLong Gold antifade
mountant (Thermo Fisher Scientific Cat# P36930). The mounting media was allowed to
cure for at least 24 h before acquiring images using confocal microscopy.
131
Imaging and Cell Counting Analysis
Sections were imaged in mPFC (corresponding to areas 24a, 25, and 32 in Paxinos &
Franklin, 2019) on a Zeiss LSM 700 inverted confocal microscope using a 10×/0.45 Plan-
APOCHROMAT with a 20×/0.8NA Plan-APOCHROMAT objective lens, using refractive
index correction. 2μm z-stacks were acquired through the entire thickness of the section
at 1AU (Zeiss: 0.313 x 0.313 x 2 μm). Three brain sections, at least 100μm apart, were
imaged, counted, and averaged per animal. Manual counts were performed using the ‘cell
counter’ plugin in FIJI software version 2.3.0 (https://fiji.sc/, RRID:SCR_002285). First,
images were cropped by layer based on CTIP2 immunostaining and DAPI, so that they
could be analyzed in a layer-specific manner. The width of the cortical crop was held
consistent in each brain region (mPFC: 321µm; V1: 861µm), while the thickness varied
to capture the full depth of each layer. The number of total DAPI nuclei, toal CFOS
+
cells,
total GFP
+
cells, and CFOS
+
;GFP
+
colocalized cells were counted. The marker of interest
was considered a positive count only if there was both immunofluorescence signal and a
DAPI
+
nucleus.
Statistical Analyses
Data are reported as mean ± standard error of the mean to the second decimal place.
Individual measures and sample sizes are reported in the figures. For each measure, an
individual animal represents a single sample. Sample sizes were determined using a
power analysis at a = 0.05 and 1-b = 0.8 (SPH Analytics, statistical power calculator using
132
average values). For all tests, test statistics and p values are reported to the fourth
decimal place. Statistical analyses were performed using GraphPad Prism software
version 9.1.2 (GraphPad Software, Inc, La Jolla, CA). For each statistical analysis, a
D’Agostino & Pearson normality test, when n>8, or a Shapiro-Wilk normality test, when
n<8, was performed. To determine statistically significant differences in freezing response
on testing day between age-matched No Shock and Shock mice or between Shock mice
across two ages, two-tailed unpaired Kolmogorov-Smirnov tests (not normal distribution;
test statistic: D) were performed. To determine statistically significant differences in
freezing responses in three or more groups of Shock mice across ages or for in age-
matched Shock mice across different training-testing intervals, Kruskal-Wallis tests were
performed (not normal distribution; test statistic: H). If the omnibus test detected a
significant difference, a post hoc Dunn’s multiple comparisons test was performed to
determine between which training-testing intervals that the differences in freezing
responses occurred. Two-tailed unpaired t-tests (normal distribution; test statistic: t) were
used to determine statistically significant differences in freezing responses on testing day
between the genotype groups of age-matched Shock mice from the cto-Met line and to
cell count differences between two ages in mPFC of mice from the Met
GFP
line. An
ordinary one-way ANOVA (normal distribution; test statistic: F) or a Kruskal-Wallis test,
followed by a post hoc Dunn’s multiple comparisons test if statistically significant, was
performed to determine statistically significant differences in freezing responses on
testing day of age-matched Shock mice of different genotypes from the Nestin-cre; Met
fx
133
line. If the omnibus test detected a significant difference, a post hoc Tukey multiple
comparisons test was performed to determine between which genotypes that the
differences in freezing responses occurred. Two-way ANOVA was used to determine
memory testing, age, and interaction effect in the percentage of c-FOS cells in layer 6
mPFC, followed by Šídák’s multiple comparisons test to determine which age(s) have a
memory testing difference.
4.4 RESULTS
To determine the precise developmental onset of contextual fear memory persistence
capabilities, the same training paradigm as Akers et al. (2012) was used (Fig 4.1A).
Separate cohorts of mice that were P15 or P35 were exposed to the context paired with
foot shocks (Shock group) and were compared to age-matched mice that were exposed
to the context but did not receive any foot shocks (No Shock group). The No Shock group
controls for age-specific, non-specific freezing that may occur during development. An
age at which Shock mice exhibit significantly different freezing responses compared to
No Shock mice was deemed as an age that contextual fear memory persistence
capabilities are present.
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Figure 4.1 WT mice do not exhibit contextual fear memory persistence at P15 but
have contextual fear memory persistence by P35.
(A) Model of contextual fear memory persistence paradigm. (B) Quantification of the
percentage time freezing on testing day of No Shock and Shock mice trained on P15 and
tested 7 d later. n = 7 for No Shock, n = 8 for Shock. No significant difference was
determined as analyzed by two-tailed unpaired Kolmogorov-Smirnov test. (C)
Quantification of the percentage time freezing on testing day of No Shock and Shock mice
trained on P35 and tested 7 d later. n = 14 for No Shock, n = 15 for Shock. ‘****’ indicates
p < .0001 as analyzed by two-tailed unpaired Kolmogorov-Smirnov test.
There was no significant difference in percentage time freezing between the No
Shock groups seven days after training on P15 (D = 0.2500; p = 0.6867; No Shock: 0.56
+ 0.34; Shock: 1.65 + 1.05; Fig 4.1B). This contrasted with mice trained on P35 that
showed significant differences in percentage time freezing (D = 0.9333; p < 0.0001;
Shock: 16.92 + 4.80; No Shock: 0.44 + 0.16; Fig 4.1C). Together, these results
demonstrate that the ability to retain a contextual fear memory for at least seven days
B. A.
Training Day Testing Day
or
7 days
C.
No Shock
Shock
0
20
40
60
80
100
Training Day Condition
% Time Freezing P35 WT Mice- Contextual
Fear Persistence Test
(7 days later)
✱✱✱✱
Freezing
responses
recorded
No Shock
Shock
0
20
40
60
80
100
Training Day Condition
% Time Freezing P15 WT Mice- Contextual
Fear Persistence Test
(7 days later)
135
emerges between P15 and P35 in C57Bl/6J (WT) mice, consistent with the previous
report in a mixed background strain (Akers et al, 2012 Learn Mem).
We next trained separate cohorts of mice at various ages between P15 and P35
and tested them seven days later. For mice trained on P20, the Shock group exhibited no
significant difference in percentage time freezing compared to the age-matched No Shock
group (D = 0.5000; p = 0.2491; Shock: 2.80 + 1.56; No Shock: 0.68 + 0.14; Fig 4.2A).
Remarkably, when mice were trained just one day later (P21), a significant difference in
percentage time freezing was observed between the Shock and No Shock groups (D =
1.0000; p < 0.0001; Shock: 23.08 + 6.87; No Shock: 0.09 + 0.05; Fig 4.2B). There is no
difference in freezing response between No Shock groups at P21, P22, P23, and P35 (H
= 3.2029; p = 0.3614; P21: 0.09 + 0.05; P22: 1.43 + 0.98; P23: 0.46 + 0.32; P35: 0.44 +
0.16; Fig 4.2C), demonstrating no baseline differences in freezing responses due to age.
Therefore, we next compared freezing responses between the Shock groups of these
ages. We find no significant age effect in percentage time freezing (H = 6.6094; p =
0.0854; P21: 23.08 + 6.87; P22: 29.08 + 7.43; P23: 31.97 + 6.94; P35: 16.92 + 4.80; Fig
4.2C), indicating no further development of persistent memory expression within two
weeks after P21. Together, these data show rapid onset of seven-day contextual fear
memory persistence capabilities at P21, with comparable memory expression to mice of
older ages.
136
Figure 4.2 Contextual fear memory persistence arises rapidly after P20 in WT
mice.
(A) Quantification of the percentage time freezing on testing day of No Shock and Shock
mice trained on P20 and tested 7 d later. n = 6 for No Shock, n = 8 for Shock. No
significant difference was determined as analyzed by two-tailed unpaired Kolmogorov-
Smirnov test. (B) Quantification of percentage time freezing on testing day of No Shock
and Shock mice trained on P15 and tested 7 d later. n = 7 for No Shock, n = 8 for Shock.
‘****’ indicates p < .0001 as analyzed by two-tailed unpaired Kolmogorov-Smirnov test.
(C) Quantification of the percentage time freezing on testing day of No Shock
mice of different ages and tested 7 d later. n = 12 for P21, n = 10 for P22, n = 6 for P23,
n = 14 for P35. No significant difference was determined as analyzed by Kruskal-Wallis
test. (D) Quantification of the percentage time freezing on testing day of Shock mice of
different ages and tested 7 d later. n = 13 for P21, n = 11 for P22, n = 10 for P23, n = 15
for P35. No significant difference was determined as analyzed by Kruskal-Wallis test.
A. B.
C.
No Shock
Shock
0
20
40
60
80
100
Training Day Condition
% Time Freezing P21 WT Mice- Contextual
Fear Persistence Test
(7 days later)
✱✱✱✱
No Shock
Shock
0
20
40
60
80
100
Training Day Condition
% Time Freezing P20 WT Mice- Contextual
Fear Persistence Test
(7 days later)
P21 P22 P23 P35
0
20
40
60
80
100
Age
% Time Freezing
WT No Shock Mice- Contextual
Fear Persistence Test
(7 days later)
D.
P21 P22 P23 P35
0
20
40
60
80
100
Age
% Time Freezing
WT Shock Mice- Contextual
Fear Persistence Test
(7 days later)
137
A recent study reported remote contextual fear memory capabilities were present
at P25 but not at P21 in C57Bl/6J (WT) mice using a 5-shock paradigm (Samifanni et al,
2021 Learn Mem). We next determined whether remote fear memory capabilities also
emerge rapidly within this timeframe. More specifically, we investigated whether mice
exhibit remote contextual fear memories following training at P23, one of the earliest ages
that contextual fear memory persistent capabilities are present. Thirty days following
training at P23, there was a significant difference between the Shock and No Shock
groups in percentage of time freezing (D = 0.7778; p < 0.0001; Shock: 7.05 + 2.24; No
Shock: 0.02 + 0.02; Fig 4.3A). The freezing response, however, appeared blunted
compared to that observed after the 7-day training-testing interval (Fig 4.2C). We
therefore measured the freezing response of Shock mice at different training-testing
intervals (1, 7, 14 and 30 days). For mice trained at P23, there was a significant effect of
training-testing interval on percentage of time freezing (H = 20.9168; p = 0.0001; Fig 4.3B)
that was driven by significant differences after a 1- or 7-day interval (1 d: 29.12 + 4.80; 7
d: 31.97 + 6.94) compared to a 30-day interval (7.05 + 2.24; 1 vs. 30: p = 0.0013; 7 vs
30: p = 0.0009). All other post-hoc comparisons were not significant (14 d: 15.34 + 4.26).
Thus, there is a blunted remote fear memory freezing response after training at P23
compared to age-matched mice tested for memory formation or persistence. In contrast,
there was no effect of the training-testing interval on the percentage of time freezing when
mice were trained at P35 (H = 6.8754; p = 0.0760; 1d: 5.73 + 1.15; 7 d: 16.92 + 4.80; 14
d: 28.92 + 6.78; 30 d: 18.98 + 5.08; Fig 4.3C), demonstrating that at this age, there is a
138
sustained freezing response robustness 30 days after training compared to those tested
after shorter time intervals.
With the developmental trajectories for 7-day persistent and remote memories
defined, we next tested the hypothesis that regulation of MET expression influences the
timing of contextual fear memory onset by utilizing two transgenic lines that either extends
the period of Met expression (cto-Met) or eliminates Met expression conditionally (Nestin-
cre; Met
fx
).
First, we used the cto-Met transgenic line to investigate whether eliminating the
developmental downregulation of Met in cortex disrupts the onset of contextual fear
memory. In this line, MET expression is sustained beyond the normal period of
expression. Separate cohorts of Met-cto and littermate control mice were trained between
P23 and P90 and tested 7 or 30 days later. Since there were no genotype differences
found at any age for the No Shock mice (data not shown), only genotype effects between
the age-matched Shock mice were tested. There was no difference between control and
Met-cto Shock mice in percentage time freezing for those trained on P23, one of the
earliest ages that memory persistence is present in WT mice, and tested 7 days later (t =
0.0455; p = 0.9640; control: 32.75 + 4.19; cto-Met: 32.39 + 7.10; Fig 4.4A). We next
probed whether sustaining MET expression disrupts remote memory during
development. Notably, P35 cto-Met mice do not have 7-day persistent memory deficits (t
= 0.0592; p = 0.9533; control: 27.64 + 3.48; Met-cto: 27.32 + 3.81; Fig 4.4B). P35 mice
were trained and tested 30 days later for remote memory. There was no difference
139
between control and Met-cto Shock mice in percentage time freezing on testing day (t =
0.3277; p = 0.7480; control: 23.69 + 5.97; cto-Met: 21.01 + 5.59; Fig 4.4C). Together,
these data indicate that downregulation of MET expression is not required for normal
onset of contextual fear memory persistence or remote contextual fear memory. Lastly,
we determined whether continued expression of cortical MET disrupts contextual fear
memory persistence in adulthood. There was no difference between P90 control and Met-
cto Shock mice in percentage time freezing on testing day 7 days later (t = 0.7795; p =
0.4448; control: 18.46 + 4.06; cto-Met: 14.20 + 3.65; Fig 4.4D), indicating the
downregulation of MET is not necessary for memory persistence capabilities in adulthood.
Next, we determine whether MET expression is necessary for onset of contextual
fear memory using the Nestin-cre; Met
fx
line. First, we investigated whether the 3-shock
paradigm used in the current study was sufficient to induce similar memory formation
deficits in Nestin-cre; Met
fx
adult mice, as previously reported (Thompson & Levitt, 2015
J Neurodev Disord; Heun-Johnson & Levitt, 2017 Neurobiol Stress). There was a
significant genotype effect at P90 on percentage time freezing in Shock mice tested one
day later (F = 6.8204; p = 0.0034), with cHet (2.53 + 1.16) and cKO (4.14 + 1.17) mice
exhibiting reduced freezing compared to the control group (11.99 + 2.18; control vs cHet:
p = 0.0127; control vs. cKO: p = 0.0127; Fig 4.5A). In contrast to adults, there were no
genotype differences in percentage of time freezing one day after training on P23 (F =
0.2290; p = 0.7440; control: 35.10 + 5.9414; cHet: 37.82 + 6.05; cKO: 30.78 + 7.39; Fig
4.5B). Similarly, there was no significant difference between genotypes in percentage of
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time freezing 7 days following training on P23 (H = 0.9181; p = 0.6477; control: 35.38 +
4.32; cHet: 32.24 + 5.43; cKO: 44.83 + 12.80; Fig. 4.5C). Together, the results
demonstrate that elimination or reduction of MET expression does not disrupt the
developmental onset of contextual fear memory formation or persistence. We next probed
whether MET expression is necessary for normal onset of remote contextual fear memory
during development. Control, cHet, and cKO Shock mice were trained on P35, an age
when robust remote memory expression is present, and tested 30 days later for remote
memory. There was no significant genotype effect on percentage time freezing (H =
4.6269; p = 0.0989; control: 17.05 + 3.77; cHet: 12.51 + 4.40; cKO: 9.07 + 3.14; Fig 4.5D),
suggesting MET expression is not necessary for onset of remote fear memory during
development. Lastly, we probed whether the reduction or elimination of Met leads to
memory deficits prior to adulthood. P50 mice, an age considered post-pubertal
adolescence (Plochocki, 2009 J Orthop Surg Res) were tested for remote fear memory.
There is once again no genotype effect on percentage of time freezing (H = 2.9520; p =
0.2286; control: 11.23 + 4.18; cHet: 8.31 + 3.64; 6.09 + 3.05; Fig 4.5E). Overall, these
data suggest that the reduction or elimination of Met in neural cells does not cause
developmental issues in onset of contextual fear memory capabilities but causes an adult-
specific deficit.
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Figure 4.3 Traces of remote contextual fear memory are present at P23 but is still
developing, compared to P35.
(A) Quantification of the percentage time freezing on testing day of No Shock and Shock
mice trained on P23 and tested 30 d later. n = 17 for No Shock, n = 18 for Shock. ‘****’
indicates p < .0001 as analyzed by two-tailed unpaired Kolmogorov-Smirnov test. (B)
Quantification of the percentage of time freezing on testing day of Shock mice.
conditioned on P23 and tested 1, 7, 14, 30 d later. n = 9 for 1 day, n = 10 for 7 d, n = 8
for 14 d, n = 18 for 30 d. ‘**’ indicates p < .01, ‘****’ indicates p < 0.0001 as analyzed by
a Kruskal-Wallis test followed by Dunn’s multiple comparisons test. (C) Quantification of
the percentage time freezing on testing day of Shock mice conditioned at P35 and tested
1, 7, 14, or 30 d later. n = 10 for 1 d, n = 15 for 7 d, n = 11 for 14 d, n = 9 for 30 d. No
significant difference was determined as analyzed by Kruskal-Wallis test.
A. C. B.
No Shock
Shock
0
20
40
60
80
100
Training Day Condition
% Time Freezing P23 WT Mice- Contextual
Fear Persistence Test
(30 days later)
✱✱✱✱
1 7 14 30
0
20
40
60
80
100
Delay (Days)
% Time Freezing
P23 WT Shocked Mice-
Contextual Fear
Memory Tests
✱✱
✱✱✱
No Shock
Shock
0
20
40
60
80
100
Training Day Condition
% Time Freezing P23 WT Mice- Contextual
Fear Persistence Test
(30 days later)
✱✱✱✱
1 7 14 30
0
20
40
60
80
100
Delay (Days)
% Time Freezing
P23 WT Shocked Mice-
Contextual Fear
Memory Tests
✱✱
✱✱✱
7 14 30
0
20
40
60
80
100
Delay (Days)
% Time Freezing
P35 WT Shocked Mice-
Contextual Fear
Memory Tests
7 14 30
0
20
40
60
80
100
Delay (Days)
% Time Freezing
P35 WT Shocked Mice-
Contextual Fear
Memory Tests
1 7 14 30
0
20
40
60
80
100
Delay (Days)
% Time Freezing
P35 WT Shocked Mice-
Contextual Fear
Memory Tests
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Figure 4.4 Sustaining MET in the cortex past its normal temporal peak does not
affect contextual fear memory persistence developmentally or in adulthood.
(A) Quantification of the percentage time freezing on testing day of control and ctg;tTA
Shock mice genotypes conditioned on P23 and tested 7 d later. n = 23 for control, n = 10
for ctg; tTA. No significant difference between genotypes was determined by two-tailed
unpaired t test. (B) Quantification of the percentage time freezing on testing day of control
and ctg;tTA Shock mice genotypes conditioned on P35 and tested 7 d later. n = 16 for
control, n = 9 for ctg; tTA. No significant difference between genotypes was determined
by two-tailed unpaired t test. (C) Quantification of the percentage time freezing on testing
day of control and ctg;tTA Shock mice genotypes conditioned on P35 and tested 30 d
later. n = 8 for control, n = 8 for ctg; tTA. No significant difference between genotypes was
determined by two-tailed unpaired t test. (D) Quantification of the percentage time
freezing on testing day of control and ctg;tTA Shock mice genotypes conditioned on P90
and tested 7 d later. n = 11 for control, n = 11 for ctg; tTA. No significant difference
between genotypes was determined by two-tailed unpaired t test.
A. B.
control
ctg+; tTA+
0
20
40
60
80
100
Genotype
% Time Freezing
P23 cto-Met Shocked Mice-
Contextual Fear Persistence
Memory Test (7d)
control
ctg+; tTA+
0
20
40
60
80
100
Genotype
% Time Freezing
P35 cto-Met Shocked Mice-
Contextual Remote Fear
Memory Test (30d)
C.
control
ctg+; tTA+
0
20
40
60
80
100
Genotype
% Time Freezing
P90 cto-Met Shocked Mice-
Contextual Fear Persistence
Memory Test (7d)
control
ctg+; tTA+
0
20
40
60
80
100
Genotype
% Time Freezing
P35 cto-Met Shocked Mice-
Contextual Fear Memory
Persistence Test (7d)
D.
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Figure 4.5 Reduction or absence of MET expression in neurons affects contextual
fear memory in adulthood but not during development.
(A) Quantification of the percentage time freezing on testing day of control, cHet, and cKO
Nestin
cre
/Met
fx
Shock mice conditioned on P90 and tested 1 d later. n = 17 for control, n
= 7 for cHet, n = 11 for cKO. ‘*’ indicates p < 0.05 as analyzed by an ordinary one-way
ANOVA test followed by Tukey multiple comparisons test. (B) Quantification of the
percentage time freezing on testing day of control, cHet, and cKO Nestin
cre
/Met
fx
Shock
mice conditioned on P23 and tested 1 d later. n = 10 for control, n = 11 for cHet, n = 9 for
cKO. No significant difference between genotypes was determined by ordinary one-way
ANOVA. (C) Quantification of the percentage time freezing on testing day of control, cHet,
and cKO Nestin
cre
/Met
fx
Shock mice conditioned on P23 and tested 7 d later. n = 9 for
control, n = 8 for cHet, n = 5 for cKO. No significant difference between genotypes was
determined by Kruskal-Wallis test. (D) Quantification of the percentage time freezing on
testing day of control, cHet, and cKO Nestin
cre
/Met
fx
Shock mice conditioned on P35 and
tested 30 d later. n = 17 for control, n = 12 for cHet, n = 15 for cKO. No significant
A. B.
C.
D.
control
cHet
cKO
0
20
40
60
80
100
Genotype
% Time Freezing
P90 Nestin
cre
; Met
fx
Shocked
Mice- Contextual Fear Memory
Formation Test (1d)
✱
✱
control
cHet
cKO
0
20
40
60
80
100
Genotype
% Time Freezing
P23 Nestin
cre
; Met
fx
Shocked Mice- Contextual
Fear Memory Formation
Test (1d)
control
cHet
cKO
0
20
40
60
80
100
Genotype
% Time Freezing
P23 Nestin
cre
; Met
fx
Shocked Mice- Contextual
Fear Memory Persistence
Test (7d)
control
cHet
cKO
0
20
40
60
80
100
Genotype
% Time Freezing
P35 Nestin
cre
; Met
fx
Shocked
Mice- Contextual Remote
Fear Memory Test (30d)
E.
control
cHet
cKO
0
20
40
60
80
100
Genotype
% Time Freezing
P50 Nestin
cre
; Met
fx
Shocked
Mice- Contextual Remote
Fear Memory Test (30d)
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difference between genotypes was determined by Kruskal-Wallis test. (E) Quantification
of the percentage time freezing on testing day of control, cHet, and cKO Nestin
cre
/Met
fx
Shock mice conditioned on P50 and tested 30 d later. n = 10 for control, n = 9 for cHet, n
= 12 for cKO. No significant difference between genotypes was determined by Kruskal-
Wallis test.
mPFC is involved in contextual fear memory circuitry (Stevenson, 2011 Neurobiol
Learn Mem; Maren et al, 2013 Nat Rev Neurosci), and MET is enriched in subcerebral
projection neurons in infragranular layers of mPFC throughout postnatal development in
mice (Lanjewar et al, 2022 J Comp Neurol). Whether there is a change in the percentage
of MET-expressing neurons in adulthood has not been determined. Therefore, using
Met
GFP
mice in order to visualize GFP in Met-expressing cells, we next compared MET-
GFP cell density (GFP
+
/total DAPI) between P35 and P90 in layer 5 of mPFC (Fig 4.6A).
There is no significant difference between ages (t = 0.3203; p = 0.0.7648; P35: 16.01 +
0.71; P90: 16.57 + 1.58; Fig 4.6B), demonstrating no change in mPFC MET-GFP cell
density is correlated to adult behavioral deficits when Met is reduced or absent (Fig 4.5A).
We next compared whether there were differences in the percentage of layer 5 mPFC c-
FOS between P35 and P90 after contextual fear memory formation testing and compared
to age-matched baseline activity (Fig 4.6C-D). Two-way ANOVA reveals both a memory
testing effect (p = 0.0023) and an age effect (p = 0.0482) but no interaction effect (0.1421).
Post-hoc multiple comparisons reveal no significant difference in percentage of c-FOS
cells at P35 between age-matched baseline (15.40 + 3.73) versus memory tested c-FOS
145
(24.01 + 0.44; p = 0.2770) but a significantly higher percentage of c-FOS cells at P90
between age-matched baseline (17.83 + 2.17) versus memory tested c-FOS (38.88 +
5.73; p = 0.0045). This demonstrates that mPFC is preferentially engaged during memory
expression at P90 compared to age-matched baseline expression but not at P35. Notably,
this is independent of freezing levels, whereas there is no difference in freezing responses
between the two ages during the 2-minute formation test (D = 0.5000; p = 0.5635; P35:
23.61 + 10.16; P90: 13.41 + 3.46; Fig 4.6E). Lastly, we determine whether there is
differential percentage of c-FOS cells that express MET-GFP between these two ages in
mice that were fear conditioned and memory test (Fig 4.6F). There are significantly more
c-FOS
+
; GFP
+
cells, normalized to total c-FOS, at P90 compared to P35 (t = 3.9820; p =
0.0053; P35: 28.65 + 2.31; P90: 50.83 + 4.58; Fig 4.6G). This demonstrates that in
adulthood, MET-GFP cells in layer 5 mPFC are more engaged in fear memory compared
to at P35, which correlates with adult deficits when Met is reduced or deleted (Fig 4.5A)
but no deficits at younger ages (Fig 4.5B-D).
146
A. B.
C.
D.
P35
DAPI
MET-GFP
P90
P35- baseline P35- memory tested P90- baseline P90- memory tested
P35 P90
0
20
40
60
80
100
Age
% GFP+/total DAPI
MET-GFP+ Cells
in Layer 5 mPFC
age-matched baseline
memory tested
age-matched baseline
memory tested
0
20
40
60
80
100
Condition
% cFOS+/total DAPI
cFOS+ Cells in
Layer 5 mPFC
P35
P90
✱✱
E.
P35 P90
0
20
40
60
80
100
Age
% Time Freezing
Met
GFP
Shock Mice- Contextual
Fear Formation Test
(1 day later)
F. G.
P35 P90
0
20
40
60
80
100
Age
% cFOS+GFP+/total cFOS
cFOS+ GFP+ Normalized to
cFOS in Layer 5 mPFC
✱✱
DAPI
c-FOS
P35 P90
c-FOS
MET-GFP
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Figure 4.6 MET-GFP and c-FOS in layer 5 mPFC at P35 compared to P90.
(A) DAPI (blue) and MET-GFP (green) in layer 5 mPFC at P35 (1
st
panel) and P90 (2
nd
panel). (B) Quantification of the percentage of MET-GFP
+
cells in layer 5 mPFC between
P35 and P90. n = 3 for each age. No significant difference between age was determined
by two-tailed unpaired t test. (C) DAPI (blue) and c-FOS (red) in layer 5 mPFC at P35
baseline (1
st
panel), P35 after 1 d memory test (2
nd
panel), P90 baseline (3
rd
panel), and
P90 after 1 d memory test (4
th
panel). (D) Quantification of the percentage of c-FOS
+
cells
in layer 5 mPFC between P35 and P90 baseline and contextual fear memory formation
trained mice. n = 5 for P35 age-matched baseline, n = 4 for P35 memory tested, n = 4 for
P90 age-matched baseline, n = 5 for P90 memory tested. ‘**’ indicates p < 0.01 as
analyzed by a two-way ANOVA test followed by Šídák’s multiple comparisons test for
conditioning effect. (E) Quantification of the percentage time freezing on testing day of
P35 and P90 Met
GFP
Shock mice conditioned and tested 1 d later. n = 4 for P35, n = 5 for
P90. No significant difference was determined by two-tailed unpaired Kolmogorov-
Smirnov test. (F) c-FOS (red) and MET-GFP (green) in layer 5 mPFC at P35 (1
st
panel)
and P90 (2
nd
panel). White arrows denote colocalization. (G) Quantification of the
percentage of double labeled MET-GFP
+
, c-FOS
+
cells out of all c-FOS cells in layer 5
mPFC after formation memory testing between mice conditioned on P35 or P90. n = 4 for
P35, n = 5 for P90. ‘**’ indicates p < 0.01 as analyzed by two-tailed unpaired t test. All
images were taken at 20x magnification. Scale bars = 50 µm. Brightness and contrast of
images were globally adjusted for visualization purposes.
4.5 DISCUSSION
Here, we determined a precise developmental trajectory for onset of contextual fear
memory persistence in mice, defined as the earliest age in which mice have the ability to
retain a fear memory for at least 7 d. Using this trajectory, as well as previously defined
ages in which memory formation (1 d memory) and remote memory (30 d memory) is
present (Akers et al, 2012 Learn Mem; Samifanni & Zhao et al, 2021 Learn Mem) we
determined that previously reported deficits in contextual fear memory formation when
Met is conditionally reduced or deleted embryonically in neural cells (Thompson & Levitt,
148
2015 J Neurodev Disord; Heun-Johnson & Levitt, 2017 Neurobiol Stress) only results in
deficits in adulthood but not during development when the cognitive function first comes
online. We next determined that there is a higher percentage of cells that are c-FOS
activated in layer 5 mPFC after P90 memory formation test compared to at P35, even
though there is no difference in fear expression between these two ages. Lastly, we
determined that there is a higher proportion of MET-GFP expression in c-FOS activated
mPFC layer 5 cells at P90 after contextual fear formation memory testing compared to at
P35.
The developmental trajectory for contextual fear memory persistence in mice had
not been previously defined. The results in this study highlight some important points
about acquisition of learning and memory capabilities. First, there is an abrupt onset of
contextual fear memory persistence between P20 and P21. We note that this is
independent of weaning, as litters weaned a day early or a day later did not affect the
timing of contextual fear memory persistence onset (data not shown). Follow-up studies
focusing on changes in brain circuitry, electrophysiology, and molecular expression at this
time will shed further insight into why at P20 this ability is not present in mice, but 1 d
later, it is robust. These results highlight a potential sensitive period in memory
development, in which brain changes allow for this cognitive capability to now be
functional. Secondly, the onset of contextual fear memory persistence comes at an age
after which mice are able to retain a memory for 1 d but before the ability of 30 d remote
memory is robust. Therefore, the ability for longer-term memory capabilities develops in
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a stepwise fashion, first with shorter term abilities that develop into longer-term
capabilities, over time.
Extending cortical Met expression shifts the critical period for ocular dominance in
visual cortex to a later age and affects social behavior in adult mice (Chen & Ma et al,
2021 Mol Psychiatry; Ma et al, 2022 Cereb Cortex). Therefore, we reasoned that
extending cortical Met may shift the onset of contextual fear memory abilities. However,
extending cortical Met had no implications for the timing of onset of contextual fear
learning and did not affect fear memory expression into adulthood. Therefore, an “off”
signal of Met is not necessary for normal development of contextual fear memory.
Interestingly, absence of neural Met also does not affect normal onset of contextual
fear memory abilities but results in deficits by adulthood. Therefore, MET may have an
adult-specific role in circuitry involving contextual fear memory. This is supported with the
data shown in mPFC. First of all, independent of MET-GFP, there is a difference in c-FOS
expression in mPFC after contextual fear memory formation testing between P35 and
P90. This could mean that brain development that occurs between P35 and P90 results
in more reliance of mPFC activation for memory expression at P90, while a different part
of the brain may be more engaged at younger ages. While contextual fear memory
persistence is present at P23, the underlying circuits may still be developing and
optimizing before becoming in a stable state for adult functioning. This suggests another
potential sensitive period, in which capabilities are present but are not functioning in the
same way as in the adult brain. Additionally, while the percentage of MET-GFP
150
expressing cells does not change from P35 to P90, there is a higher percentage of c-FOS
cells that express MET. This demonstrates that the additional mPFC cells that are now
engaged in the memory expression at P90, that are not at P35, are biasedly expressing
MET. While further testing is needed, as this is only correlative, this suggests an adult-
specific engagement of MET expressing cells in mPFC for adult contextual fear memory.
Overall, the results of this study pose that deficits found when Met is conditionally
deleted may be due to the absence of MET that is normally expressed in adult memory-
engaged cells in mPFC. Therefore, while the role of cortical MET has been largely
focused in developmental mechanisms, there is still a gap in understanding its role in
adulthood. Since cortical neurons with sustained MET expression have more
biochemically immature synapses (Chen & Ma et al, 2021 Mol Psychiatry), adult
expression of MET in cortical neurons may be important in allowing for some synapses
to remain in a more plastic state for learning and memory capabilities.
Importantly, we chose to focus on c-FOS and MET-GFP expression in mPFC, but
there could be other brain regions involved in the adult deficits when Met is conditionally
deleted, as well as other molecular mechanisms involved in onset of contextual fear
memory abilities. This study was an important first step in defining the timing of contextual
fear memory onset, so that other biological mechanisms can also be tested for
involvement in the development and maintenance of this cognitive ability. While MET is
considered an autism risk gene (Campbell et al, 2006 Proc Natl Acad Sci U S A) and
largely studied in the cortex in a developmental manner, this study shows that adult
151
deficits cannot be assumed to arise from a neurodevelopmental disorder-like phenotype.
During development, the brain is in a very dynamic state, and therefore studying
molecular mechanisms at these critical time points is necessary for understanding the
underpinnings of deficits, as well as how they could be relevant in human brain
development and disorders.
This study used a contextual fear memory paradigm in mice in order to study
development of higher-order cognitive capabilities. In general, studying cognitive
development has its limitations in rodent models, but there are species-specific
paradigms that can be used to assess cognitive development, like the one used here.
Studies that aim to address mechanisms underlying neurodevelopmental disorders and
the cognitive deficits that are often associated with them would benefit if other cognitive
assays performed in rodents had a precise developmental trajectory defined. Since this
study only focused on contextual fear memory, we can conclude that MET is not involved
in onset of this ability, but there is the possibility that MET affects onset of other cognitive
abilities. Therefore, this study does not exclude MET in having a role in cognitive
development but highlights an adult role in fear memory.
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Chapter 5 : VIRAL MAPPING OF MET RECEPTOR TYROSINE KINASE-EXPRESSING
NEURONAL PROJECTION PATTERNS FROM MEDIAL PREFRONTAL CORTEX
Alexandra L. Lanjewar, Zuhayr M, Khan, Kathie L. Eagleson, and Pat Levitt
5.1 ABSTRACT
MET receptor tyrosine kinase (MET) is a synaptic regulator that peaks temporally in the
cerebral cortex during the active period of synaptogenesis. In medial prefrontal cortex
(mPFC), a cortical region involved in higher order cognitive functions, MET is expressed
during development in a subset of subcerebral projection neurons (SCPNs), while largely
absent from intralencephalic PNs. The role of the neuron subtype-specific expression of
MET in mPFC remains unknown. To determine 1) the specific projection targets of MET
+
mPFC neurons and 2) whether there are projection target specificities of MET
+
mPFC
SCPNs compared to overall mPFC SCPNs, connectomics analyses were performed. At
postnatal day (P) 12, in a transgenic mouse line that expresses high levels of green
fluorescent protein (GFP) in MET
+
neurons (Met
GFP
mice), the mPFC was stereotaxically
injected with Cre Dependent on GFP viruses (CRE-DOG) and flex-tdTomato to
permanently label the mPFC MET-GFP expressing neurons and their axons with
tdTomato. A separate cohort of Met
GFP
mice were injected in mPFC at P12 with AAV2/1-
hSyn-mTurqoise2 virus to permanently label all transfected neurons at the injection site
and their axons with mTurqoise2, independent of MET-GFP. Brains were then collected
at various ages between P19 and P60 and processed for immunofluorescence and
confocal microscopy. We find that MET
+
mPFC projections are abundant in many of the
expected mPFC subcerebral targets, including modest axonal labeling in midline dorsal
153
thalamic nuclei and dense labeling in preoptic areas of the hypothalamus and zona
incerta. Surprisingly, very few labeled axons were present in the basolateral amygdala, a
target that receives dense innervation from mPFC neurons, independent of MET. These
results suggest that MET
+
mPFC SCPNs biasedly target specific subcortical areas,
providing an opportunity to perform future studies on the involvement of MET-specific
circuits in specific functions.
5.2 INTRODUCTION
The capacity of an organism to perform a wide variety of functions as an adult can be
attributed to a vast array of specialized circuits that comprise the brain. During the
development of the cerebral cortex, which is the outermost layer of cells in the brain
involved in higher-level processing, spatial and temporal molecular heterogeneity
contribute to the diversification of circuits (Klingler et al, 2021 Nature; Lake et al, 2016
Science; Lee et al, 2009 Neurochem Res). Dysregulation of the medial prefrontal cortex
(mPFC), a cortical region involved in higher-order cognitive abilities, and its circuitry can
result in cognitive deficits that are common in neurodevelopmental disorders (Miller, 2000
Nat Rev Neurosci; Watanabe, 1998 Rev Neurosci; Haehl et al, 2020 Biol Psychol;
Collado-Torres et al, 2019 Neuron; Paylor et al, 2018 eNeuro; Lupien-Meilleur et al, 2021
Mol Psychiatry). Substantial progress has been made in understanding mPFC
development and functioning; however, knowledge of the heterogeneous molecular
mechanisms underpinning the development and optimization of diverse mPFC circuits
remain elusive. Therefore, determining the processes underlying typical mPFC circuit
154
development is needed to ultimately understand and treat what may go awry when
cognitive dysfunction occurs.
The expression pattern of MET receptor tyrosine kinase (MET), a protein involved
in synapse development in the cerebral cortex (Eagleson et al, 2016 Dev Neurobiol; Chen
& Ma et al, 2021 Mol Psychiatry; Ma et al, 2022 Cereb Cortex; Xie et al, 2016 eNeuro),
has recently been characterized spatially and temporally in mPFC across development
and contributes to molecular heterogeneity of specific neuronal populations (Lanjewar et
al, 2022 J Comp Neurol). Differing from both primary visual cortex and primary
somatosensory cortex, in which MET is enriched in intratelencephalic projection neurons
(PNs), in mPFC, MET is enriched in subcerebral (SC) PNs (Lanjewar et al, 2022 J Comp
Neurol; Kast et al, 2019 Cereb Cortex). SCPNs define a broad range of connections from
the cortex to subcortical regions of the brain (O’Leary & Koester, 1993 Neuron;
Woodworth et al, 2012 Cell). However, MET is only expressed in a subpopulation of these
PN subclasses, making it a candidate protein in contributing to timing of differential circuit
maturation due to its heterogeneous expression patterns. Questions remain regarding
whether MET expression in mPFC neurons is confined to SCPNs that project to specific
mPFC SC target regions, or is expressed in a proportion of SCPNs that project to all of
the well-known SC target brain regions. Determination of the mPFC MET
+
SCPN
connectivity patterns during development will provide insight into the mPFC circuits that
MET is involved in. Here, we use a strategy to selectively label and characterize the
155
projection targets of MET
+
mPFC neurons, comparing innervation patterns to overall
mPFC targets.
5.3 MATERIALS AND METHODS
Animals
Mice housed on a 13:11 hour light:dark cycle at 22°C were provided with ad libitum access
to standard chow (PicoLab Rodent Diet 20, #5053, St. Louis, MO). Animal care and
experimental procedures were performed in accordance with the Institutional Animal Care
and Use Committee of the Saban Research Institute, Children’s Hospital Los Angeles
and the standard ethical guidelines (European Community Guidelines and French
Agriculture and Forestry Ministry Guidelines for Handling Animals decree 87849) for AAV
virus tracing experiments. Met
GFP
mice (described in Kamitakahara et al, 2017 J Comp
Neurol; Kast et al, 2019 Cereb Cortex; Lanjewar et al, 2022 J Comp Neurol) maintained
on a C57Bl/6J background and bred in our facility were used. More specifically, mice
homozygous for the Met-EGFP transgene (Met
GFP
mice) were used experimentally.
Met
GFP
mice express, with high fidelity, green fluorescent protein (GFP) under the control
of the Met promoter in cell bodies of Met
+
neurons.
Stereotaxic Viral Injections into mPFC
Viral injections were performed on postnatal day (P) 12, an age when MET-GFP
expression is high in mPFC (Lanjewar et al, 2022 J Comp Neurol). Met
GFP
mice were
anesthetized with vaporized isoflurane (4% induction, 1.5-2% maintenance), placed in a
156
mouse gas anesthesia head holder (Kopf Model 934-B) on an ultra-precise small animal
stereotaxic instrument (Kopf Model 963), and maintained at 37 °C for the duration of the
surgery. To reduce postoperative pain, ibuprofen (0.2 mg/mL) was provided in drinking
water the day before surgery and for 3 days post-surgery and, immediately before
surgery, mice received a subcutaneous injection of ketoprofen (5 mg/kg). Respiratory rate
was continuously monitored visually to ensure deep anesthesia. A pulled pipette (15 μm
tip diameter) attached to a picospritzer was used to inject 10 pulses (at 10 psi) of Cre-
DOG (cre-dependent on GFP; Tang et al, 2015 Nat Neurosci) with AAV2/1-Flex-tdTomato
or control virus (AAV2/1-hSyn-mTurqoise2) into left mPFC using stereotaxic guidance
(AP: 1.2 mm, ML: 0.045 mm, Depth: 2.4 mm). Cre-DOG; flex-tdTomato viral cocktail
allows for the specific and permanent labeling of any infected cell that also expresses
GFP, which is required for the split-Cre viruses to come together and produce the reporter
protein tdTomato, as well as specific and permanent labeling of the axon of infected cells.
Here, P12 MET-GFP
+
neurons and their axons were labeled with tdTomato. Importantly,
even with MET-GFP expression declining over time (Judson et al, 2009 J Comp Neurol;
Eagleson et al, 2016 Dev Neurobiol; Lanjewar et al, 2022 J Comp Neurol), tdTomato
represents permanent labeling of MET-GFP
+
neurons at P12. The control virus was used
in a separate cohort of Met
GFP
mice and labels all transfected neurons at the injection site
and their axons with mTurqoise2. The pipette was left in place for 10 minutes after
injections before being slowly retracted in order to minimize leakage into unintended brain
regions. At either P22 (10 days post injection) or P33 (21 days post injection), mice were
157
transcardially perfused with fixative (4% paraformaldehyde dissolved in 1X phosphate
buffered saline (PBS)). Brains were dissected and incubated in fixative for 2 hours
following perfusion and then were cryoprotected in sequential sucrose solutions (10%,
20%, and 30% sucrose in PBS) prior to being embedded in OCT, frozen over liquid
nitrogen vapors, and stored at –80 °C until further processing.
Immunohistochemistry Staining
Every third 20 μm coronal or sagittal cryosection was collected at –20 °C through the
entire length or width of the brain, respectively, and mounted on SuperFrost Plus slides
(Fisher Scientific) in a series of 5. Slides were stored at –80 °C until
immunohistochemistry was performed. On the day of staining, slides were thawed at
room temperature for 10 minutes, dried at 55 °C in a hybridization oven, and then washed
for 10 minutes in PBS. Sections were blocked and permeabilized through incubation at
room temperature in PBS solution with 5% normal donkey serum and 0.3% Triton X-100
for 1 hour. Next, sections were incubated at room temperature in primary antibodies
diluted in 0.1 Triton X-100 at room temperature. Sections were washed 5 times for 5
minutes each with 0.2% Tween-20 in PBS. Then, sections were incubated at room
temperature for 1 hour in a solution consisting of Alexa Fluor conjugated secondary
antibodies (1:500) in 0.1% Triton-X 100, followed by 3 washes for 5 minutes each in 0.2%
Tween 20 in PBS. Sections were then counterstained for 8 minutes in 1:15,000 DAPI in
PBS and washed 2 additional times for 5 minutes each. Sections were coverslipped with
Prolong Gold Antifade Reagent (Life Technologies), and mounting media was left to cure
158
for at least 24 hours before imaging. Primary antibodies used were rabbit anti-red
fluorescent protein (RFP) (1:500, Rockland Immunochemicals), chicken anti-green
fluorescent protein (GFP) (1:500, Abcam #ab13970), and rat anti-CTIP2 (1:500, Abcam
#ab18465).
Confocal Imaging/Qualitative Analysis
Confocal images of full sections from the rostral tip of the forebrain through the
mesencephalon were acquired on a Leica STELLARIS 5 HC PL APO CS2 inverted
confocal microscope using a 20×/.75 air lens. Two µm z-stacks through the entire
thickness of the section were collected at 1AU (0.757 × 0.757 × 2 µm) and tiles were
stitched together for full section images. First, the injection site of either Cre-DOG or
control virus in mPFC was accessed. Descriptions of the extent of labeling at the injection
site was noted for each brain. If injection site met criteria of viral expression in mPFC and
did not spread to motor cortex, septum or contralateral mPFC, a series of sections from
the brain were imaged subsequently for qualitative mapping of axonal projections. Brains
for which injections did not meet criteria for restriction to mPFC were not further analyzed.
Through this process, atlases of viral injections were created, in which rostral-caudal
position between Cre-DOG and control virus brains were matched to compare and
contrast axonal labeling in subcerebral target brain regions. Finally, tables of target brain
regions were made to describe positive or negative labeling in Cre-DOG brains versus
control brains. A qualitative score of minus, no innervation (-), or plus, innervation (+),
was assigned.
159
5.4 PRELIMINARY RESULTS
Results in this section are preliminary and ongoing due to small sample size. Further
analyses are also needed, as the results presented here are from crude, qualitative
assessments.
First, we demonstrate that injection sites between brains that pass the injection
site criteria are relatively consistent between control and Cre-DOG viruses (Fig 5.1), but
we have also noted whether subregions have injection site labeling, or not, to help
understand projection pattern variabilities (Table 5.1). The biggest variability is in the 2
P22 control virus brains, in which control #1 has broader labeling (Fig 5.1C), and control
#2 has more limited labeling (Fig 5.1D).
Next, we created brain atlases of the labeled projections from mPFC to target brain
regions in control and Cre-DOG brains for qualitative comparison of overall mPFC
projections compared to MET
+
mPFC projections, as well comparison of projection
patterns of brains collected at P33 versus P22 (Fig 5.2). For visualization purposes, we
do not show images of P22 control #2 in the figure since P22 control #1 injection site is
more comparable to P22 Cre-DOG (Table 5.1), and we only show two different rostral-
caudal levels. Some notable findings are abundant labeling in mediodorsal thalamus and
reuniens thalamic nucleus in all brains (Fig 5.2A-D), demonstrating MET expression
mPFC neurons that project to thalamus. Interestingly, while there is abundant labeling of
projections in reticular nucleus at both ages of control brains and at P33 in the Cre-DOG
brain, there is an absence of projections in P22 Cre-DOG brain (Fig 5.2A-D; Table 5.1).
160
This suggest MET
+
mPFC projections to this brain region may still be developing between
P22 and P33. More posteriorly, all brain regions also show abundant labeling in zona
incerta (Fig 5.2E-H). Additionally, there is labeling in basolateral amygdaloid nucleus,
anterior part in all brains (Fig 5.2E-H; Table 5.1), but there is much more limited labeling
in Cre-DOG brains (Fig 5.2F, H) compared to control brains (Fig 5.2E, G), suggesting
limited MET involvement in this mPFC circuit. Interestingly, there is labeling in both ages
of control brains in the basomedial amygdaloid nucleus, anterior part (Fig 5.2E, G) but no
labeling at either age in the Cre-DOG brains (Fig 5.2 F, H; Table 5.1), suggesting a
complete absence of MET
+
mPFC projections to this brain region. The presence or
absence of projections in all brains throughout the brain were documented (Table 5.1).
161
Figure 5.1 Visualization of viral labeling at the injection site.
mPFC subregion is denoted in white. Abbreviations are the same used in Paxinos and
Franklin’s the Mouse Brain in Stereotaxic Coordinates, Fifth Edition
A) P33 control B) P33 Cre-DOG
C) P22 control #1 D) P22 control #2
E) P22 Cre-DOG
162
Brain Region P33 control P33 Cre-DOG P22 control #1 P22 control #2 P22 Cre-DOG
A32 - + + - +
MO - + + - +
A24a + + + - +
A24b + + + - +
A25 - - + + +
DP + + + + +
FrA - - + - -
VO - + + - +
LO - - + - +
Dtr - - - - +
AOM - - + - -
DTT - + + - -
VTT - - + - -
fmi - + + - +
CPu - + + - +
IG - + - - -
LSD + + + + -
LSI + + + + -
MS + + + + -
PDZ + + + + -
VDB + + + + -
mfb + + + + +
AcbSh + + - - +
AcbC + - - - -
SHi - + - - -
VP - + - - +
CP + - - - -
CI + + + - +
DEn + - + - +
IEn - - + - +
LTer + - - - -
Bed nuc. + + - + -
aca - - + - +
ic + + + - +
GP - - + - +
MPA + + - + -
LPO + + + + +
Tu + - - - -
HDB - + - - -
LDB - + + - -
IPACL - + - - -
SIB - + - - -
SFi - + + + -
EA - + + - +
PVA + + + + -
PT - - + - +
mdThalamus + + + - +
163
Rt + + + + -
VA - - + - -
mL Hypothalamus + - - - -
Insular Cortex + - - - -
Piriform cortex + - + + -
Rh - + - - +
Re + + + + +
MEAd + + + - -
CM - + - - +
IAM - + - - -
VM - + - - +
PVN + - + - -
vTH + + + - +
ZID - + + + +
ZIV + + + + +
LH + + - - +
PaXi - + - - -
BLA + + + + +
BMA + - + + -
BLP - - + + -
EP - - - - +
MePD - + + + -
CA1 - - - - -
CA2 + - - - -
CA3 - - - + -
LHb - + - - -
hbc - - - - -
VPPC - - + - -
cp - + + - +
scp - - - - +
PaF - - - - +
PHD - + - - -
pHyp + + - + -
dmHyp + - - + -
Ect + - - - -
PRh + - + - -
PMCo + - + + -
PAG + + + - -
rmx + N/A + - -
GrDG - - + + -
RMM + N/A - - -
p1Rt + N/A + - +
ns + N/A - + -
VTA + N/A + - -
RML + N/A - - -
VTM + N/A - - -
DK + N/A - - -
RLi + N/A - - -
164
IF + N/A - - -
ipf + N/A - - -
JNJN + N/A - - -
SNC + N/A - - -
VTAR + N/A + - +
PBP + N/A + - +
mp + N/A - - -
DA8 - - - - +
CA1 + N/A - + -
LPAG - - + - +
vtgx - - + - -
PIF - - + - -
SNC - - + - -
mlf - - + - -
VS + N/A - - -
DR + N/A - - +
CLi + N/A - - +
xxcp + N/A - - -
PRCnE + N/A - - -
DAB + N/A - - -
PBG + N/A - - -
DRD + N/A - - +
DRV + N/A - - +
Me5 + N/A - - -
isRt + N/A - - +
MiTg + N/A - - -
PMnR + N/A - - -
LPAG + N/A + - -
Aq + N/A - - -
mlf + N/A - - -
LDTg + N/A - - -
MPB + N/A - - -
DMTg + N/A - - -
Trigeminical nuc + N/A - - -
IPI - N/A + - -
DpG - - - - +
PnO - - - - +
lfp - - - - +
tfp - - - - +
vtg - - - - +
PnV - - - - +
ECIC - - - - +
165
Table 5.1 Assessment of axonal labeling in target brain regions.
Abbreviations are the same used in Paxinos and Franklin’s the Mouse Brain in
Stereotaxic Coordinates, Fifth Edition. + denotes viral labeling, - denotes no viral labeling.
166
Figure 5.2 Visualization of virally labeled mPFC axons.
More anterior (A-D) and more posterior (E-H) brain sections. Abbreviations are the same
used in Paxinos and Franklin’s the Mouse Brain in Stereotaxic Coordinates, Fifth Edition
A) P33 control B) P33 Cre-DOG
C) P22 control #1 D) P22 Cre-DOG
E) P33 control F) P33 Cre-DOG
G) P22 control #1 H) P22 Cre-DOG
167
5.5 PRELIMINARY DISCUSSION
Here, we assessed the connectivity patterns of P12 MET
+
PNs in mPFC and compared
it to age-matched MET-independent overall mPFC projection patterns. For almost all of
the targets of mPFC SCPNs, we find mPFC MET
+
projections to these regions. This
suggests that MET expression in mPFC is not involved in axon guidance to direct axons
to one SCPN target over another. Therefore, MET expression may contribute to the timing
of synapse maturation for some projections from mPFC to a specific brain region, while
others may be regulated by other mechanisms, allowing for differential timing of synapse
maturation. This would allow some connections to stabilize, while others remain in a more
plastic state to have the ability to optimize based on the environment. More studies are
needed to test this hypothesis.
The limited labeling of MET
+
projections to basolateral amygdaloid nucleus,
anterior part and the complete lack of MET
+
projections to basomedial amygdaloid
nucleus, anterior part was surprising, given these regions receive abundant projections
from mPFC. mPFC and amygdala are brain regions that are involved in contextual fear
learning and memory (Rozeske et al, 2015 Genes Brain Behav; Kitamura et al, 2017
Science; Maren et al, 2013 Nat Rev Neurosci). Interestingly, we have previously
determined that neural Met expression is dispensable for onset of normal contextual fear
memory capabilities during development (Fig 4.5 B-E). It is not until adulthood that deficits
in contextual fear memory in Met conditional heterozygous and knockout mice are found
(Fig 4.5A). Since P12 MET
+
neurons have limited connections with amygdala, we
168
hypothesize that by adulthood, MET
+
neurons may connect with amygdala, and MET
expression in this circuitry is involved in memory encoding. This would be an explanation
for adult-specific deficits when Met is conditionally reduced or deleted embryonically.
Ongoing connectivity experiments of adult injections in mPFC will reveal whether this
hypothesis can be supported, and if so, follow-up studies using Designer Receptors
Exclusively Activated by Designer Drugs (DREADDs) can be used to determine whether
there is a casual link of MET
+
mPFC neuronal activation and contextual fear memory
abilities. These experiments will resolve whether mPFC MET
+
neurons expressed during
adulthood have similar or different circuitry compared to at P12 and whether MET has an
adult-specific role in mPFC-BLA circuitry that contributes to memory capabilities.
Overall, the detailed connectivity atlas of MET SCPN targets will guide future
functional experiments to determine the selective roles of MET in different mPFC circuits.
More refined analysis will also qualitatively assess axonal density in different target
regions. These conclusions are preliminary, and experiments are being further pursued.
169
Chapter 6 : CONCLUDING REMARKS
The biological processes that occur during the period of time in which the brain is
developing are both complex and dynamic. Lifelong brain functions are shaped by the
way brain circuitry forms and matures during development. Therefore, understanding the
normal biological processes that occur during this time, as well as how they are influenced
by the environment, gives insight into typical brain development, what makes our brains
similar to and unique from one another, and strategies for the development of better
treatments for neurodevelopmental deficits. Great strides have been made in the field of
developmental neuroscience in determining typical developmental processes, but we are
far from a complete understanding of the mechanisms for brain maturation and function.
This dissertation contributes to this growing body of knowledge. It will take countless
temporally defined, specific studies, like performed in my dissertation research, to
continue to expand our understanding of neurodevelopment.
In chapter 1, I reviewed a breadth of literature focused on the development of the
cerebral cortex, and then more specifically, the prefrontal cortex (PFC), a cortical area
involved in higher-order cognitive processing. I first focused on mechanisms underlying
arealization of the cerebral cortex, which results in different regions of the cerebral cortex
developing specialized architecture and functions. Intuitively, vast diversity and
specialization within the brain must occur for animals to perform so many different
functions, and mechanisms underlying cortical arealization is central to how areal
heterogeneity arises. Then, I focused on cytoarchitecture of PFC, which allows for further
170
subdividing of this cortical region. I described known PFC neural circuits, functions, and
underlying mechanisms of these functions. I also compared and contrasted PFC
cytoarchitecture, neural circuits, and function between primates and rodents. Since the
work in this dissertation and many other PFC studies is performed in rodent animal
models, it is important to understand the similarity and differences between primates and
rodents, so that the translatability of rodent results to humans can be assessed.
In chapter 2, we provided novel insight into the expression patterns of MET, a
protein involved in synapse formation and maturation, in the cerebral cortex during
postnatal development. MET expression has a unique temporal pattern in the medial PFC
(mPFC) compared to primary visual cortex (V1). Within each cortical region, the temporal
expression of MET is also unique between the different layers. Further, MET is enriched
in intratelencephalic projection neurons (PNs) in V1, similar to previous findings in primary
somatosensory cortex (S1) (Kast et al, 2019 Cereb Cortex). In contrast, MET is enriched
in subcerebral PNs in mPFC. Therefore, the studies determined that there are major
differences in association cortex (mPFC) compared to sensory cortices (V1 and S1) in
MET expression patterns. The data suggest that MET likely influences synapse
maturation in different circuits between primary sensory and association cortices. More
generally, this study underscores the importance of careful interpretations of research
results. The regional and temporal differences identified in my experiments demonstrate
that one cannot assume results are generalizable across cortical areas or ages. There
171
are transient developmental processes that must be studied in a temporally and spatially
precise manner.
The studies in chapter 3 began as an attempt to determine upstream regulators of
Met in order to provide insight into the mechanisms that regulate MET expression patterns
in select PN subclasses. A previous study in human neural stem cells reported that
FOXP2 represses MET (Mukamel et al, 2011 J Neurosci), and we observed MET-GFP
and FOXP2 expression patterns were virtually non-overlapping in mouse S1 cortex,
positioning FOXP2 as an repressor of Met. Follow-up experiments showed that not only
did deletion of cortical Foxp2 not change MET expression, but deletion also did not alter
many developmental processes that had been previously reported to be affected,
including neurogenesis, PN specification, and cortical axon guidance. We concluded that
while FOXP2 may play a role in cortical development, cortical FOXP2 is dispensable in
neurodevelopmental processes. This paper underscored the need for brain regions
outside of the cortex that express FOXP2 to be studied, as they may provide more insight
into how developmental speech-language deficits arise when FOXP2 is mutated.
The work described in chapter 4 was initiated from a knowledge gap in
developmental trajectories for onset of cognitive capabilities mediated by mPFC in
developing mice. While there are many behavioral tests that can measure cognitive
capabilities and deficits in adult mice, in order to identify mechanisms underlying
dysfunction, such as in neurodevelopmental disorders, which is needed for further
research into prevention and therapeutic options, assays must be performed during
172
development, the time during which functions or deficits begin to arise. Therefore, we set
out to precisely characterize the developmental trajectory of a well-known contingent
learning task, contextual fear memory, and then use the trajectory data to determine when
deficits in mice with embryonic reduction or deletion of Met in neural cells arises. Since
studies of MET in the cerebral cortex have focused on the receptor’s expression and
function in postnatal development, we were surprised to find that contextual fear memory
deficits after Met deletion do not arise developmentally, but only in adulthood. My
experiments in adults replicated two other studies from our laboratory, but these previous
studies did not examine fear learning developmentally. While MET expression levels do
not change from adolescence to adulthood in mPFC, MET-expressing neurons in mPFC
are more engaged, based on c-Fos expression, in fear memory in adulthood compared
to adolescence. These results suggest that the developmental expression of MET is not
required for expression of fear memory. Thus, there appears to be an adult-specific role
of MET in fear memory formation. This study underscores the concept that adult deficits
may not necessarily have their origins developmentally. In order to draw conclusions
about underlying mechanisms, studies must be performed across time, from
developmental periods to adulthood.
The work described in chapter 5 is in a preliminary state. The experiments attempt
to structurally tie together the results of chapters 2 and 3. Chapter 2 describes expression
patterns of MET in mPFC, and chapter 3 describes an adult deficit in mPFC-dependent
contextual fear memory when Met is reduced or deleted in neural cells. Therefore, in
173
chapter 5, two different mPFC viral labeling methods were used to determine the specific
targets of MET
+
mPFC neurons in comparison to the targets of overall mPFC neurons.
Initial analyses reveal that MET
+
neurons comprise a subset of overall mPFC projections
to downstream forebrain targets. Future work will focus on whether there are projection
changes over time to specific targets.
The definition of development is simple - change over time. Yet studying
development is highly complex. As researchers focusing on mechanisms underlying
neural developmental, we continue to explore the ever-changing brain architecture and
functional maturation. While the requirement for incorporating many time points in the
design of research experiments leads to an increased need for resources, including time,
money, and sample sizes, we cannot understand neurodevelopment without studying
specific phenotypic changes over time. It will be in the additive findings of all the changes
that occur over time that we finally reach a full understanding of how biology can turn a
single fertilized cell into a human being that has a brain which is capable of the most
efficient and powerful information processing capacity that, even with the continuous
improvements in technology, greatly surpasses the capabilities of any other processor on
the planet.
174
REFERENCES
Aggleton JP, Wright NF, Rosene DL, Saunders RC (2015) Complementary Patterns of
Direct Amygdala and Hippocampal Projections to the Macaque Prefrontal Cortex.
Cereb Cortex 25:4351-4373.
Ahlbeck, J, Song L, Chini M, Bitzenhofer SH, Hanganu-Opatz IL (2018) Glutamatergic
drive along the septo-temporal axis of hippocampus boosts prelimbic oscillations in
the neonatal mouse. Elife 7:e33158.
Åhs F, Kragel PA, Zielinski DJ, Brady R, LaBar KS (2015) Medial prefrontal pathways for
the contextual regulation of extinguished fear in humans. Neuroimage 122:262-271.
Airey DC, Robbins AI, Enzinger KM, Wu F, Collins CE (2005). Variation in the cortical
area map of C57BL/6J and DBA/2J inbred mice predicts strain identity. BMC
Neurosci 6:18.
Akers KG, Arruda-Carvalho M, Josselyn SA, Frankland PW (2012) Ontogeny of
contextual fear memory formation, specificity, and persistence in mice. Learn Mem
19:598-604.
Alcamo EA, Chirivella L, Dautzenberg M, Dobreva G, Farin I, Grosschedl R, McConnell
SK (2008) Satb2 Regulates Callosal Projection Neuron Identity in the Developing
Cerebral Cortex. Neuron 57:364-377.
Alexander GE, DeLong MR, Strick PL (1986) Parallel organization of functionally
segregated circuits linking basal ganglia and cortex. Annu Rev Neurosci 9:357-81.
Alexander JM, Pirone A, Jacob MH (2020) Excessive β-Catenin in Excitatory Neurons
Results in Reduced Social and Increased Repetitive Behaviors and Altered
Expression of Multiple Genes Linked to Human Autism. Front Synaptic
Neurosci 12:14.
Amaral DG, Price JL (1984) Amygdalo-cortical projections in the monkey (Macaca
fascicularis). J Comp Neurol 230:465-496.
Anacker A, Moran JT, Santarelli S, Forsberg CG, Rogers TD, Stanwood GD, Hall BJ,
Delpire E, Veenstra-VanderWeele J, Saxe MD (2019) Enhanced Social Dominance
and Altered Neuronal Excitability in the Prefrontal Cortex of Male KCC2b Mutant
Mice. Autism Res 12:732-743.
Anastasiades PG, Carter AG (2021) Circuit organization of the rodent medial prefrontal
cortex. Trends Neurosci 44:550-563.
175
Andersen SL, Thompson AT, Rutstein M, Hostetter JC, Teicher MH (2000) Dopamine
receptor pruning in prefrontal cortex during the periadolescent period in rats. Synapse
37:167-169.
Arlotta P, Molyneaux BJ, Chen J, Inoue J, Kominami R, Macklis JD (2005) Neuronal
subtype-specific genes that control corticospinal motor neuron development in
vivo. Neuron 45:207-221.
Armentano M, Chou SJ, Tomassy GS, Leingartner A, O'Leary DD, Studer M (2007)
COUP-TFI regulates the balance of cortical patterning between frontal/motor and
sensory areas. Nat Neurosci 10:1277-1286.
Arnsten AF (1997) Catecholamine regulation of the prefrontal cortex. J Psychopharmacol
11:151-162.
Arruda-Carvalho M, Wu WC, Cummings KA, Clem RL (2017) Optogenetic Examination
of Prefrontal-Amygdala Synaptic Development. J Neurosci 37:2976-2985.
Bai J, Ramos RL, Ackman JB, Thomas AM, Lee RV, LoTurco JJ (2003) RNAi reveals
doublecortin is required for radial migration in rat neocortex. Nat Neurosci 6:1277-
1283.
Baranek C, Dittrich M, Parthasarathy S, Bonnon C G, Britanova O, Lanshakov
D, Boukhtouche F, Sommer JE, Colmenares C, Tarabykin V, Atanasoski
S (2012) Protooncogene Ski cooperates with the chromatin-remodeling factor Satb2
in specifying callosal neurons. Proc Natl Acad Sci U S A 109:3546-3551.
Barbas H, Ghasghaei HT, Rempel-Clower NL, Xiao D (2002) Anatomic basis of functional
specialization in prefrontal cortices in primates. Handb Neuropsy 7:1-27.
Barbe MF, Levitt P (1991) The Early Commitment of Fetal Neurons to the Limbic Cortex.
J Neurosci 11:519-533.
Belton E, Salmond CH, Watkins KE, Vargha-Khadem F, Gadian DG (2003) Bilateral brain
abnormalities associated with dominantly inherited verbal and orofacial dyspraxia.
Hum Brain Mapp 18:194-200.
Best JR, Miller PH, Naglieri JA (2011) Relations between Executive Function and
Academic Achievement from Ages 5 to 17 in a Large, Representative National
Sample. Learn Individ Differ 21:327-336.
Bhaduri A, Sandoval-Espinosa C, Otero-Garcia M, Oh I, Yin R, Eze UC, Nowakowski TJ,
Kriegstein AR (2021) An atlas of cortical arealization identifies dynamic molecular
signatures. Nature 598:200-204.
176
Bielle F, Griveau A, Narboux-Nême N, Vigneau S, Sigrist M, Arber S, Wassef M, Pierani
A (2005) Multiple origins of Cajal-Retzius cells at the borders of the developing
pallium. Nat Neurosci 8:1002-1012.
Bishop KM, Goudreau G, O'Leary DDM (2000) Regulation of Area Identity in the
Mammalian Neocortex by Emx2 and Pax6. Science 288:344-349.
Bitzenhofer SH, Hanganu-Opatz IL (2014) Oscillatory coupling within neonatal prefrontal-
hippocampal networks is independent of selective removal of GABAergic neurons in
the hippocampus. Neuropharmacology 77:57-67.
Bloodgood DW, Sugam JA, Holmes A, Kash TL (2018) Fear extinction requires infralimbic
cortex projections to the basolateral amygdala. Transl Psychiatry 8:60.
Bortone DS, Olsen SR, Scanziani M (2014) Translaminar inhibitory cells recruited by layer
6 corticothalamic neurons suppress visual cortex. Neuron 82:474-485.
Bottaro DP, Rubin JS, Faletto DL, Chan AM, Kmiecik TE, Vande Woude GF, Aaronson
SA (1991) Identification of the hepatocyte growth factor receptor as the c-met proto-
oncogene product. Science 251:802-804.
Bouwmeester H, Smits K, Van Ree JM (2002a) Neonatal development of projections to
the basolateral amygdala from prefrontal and thalamic structures in rat. J Comp
Neurol 450:241-255.
Bouwmeester H, Wolterink G, van Ree JM (2002b) Neonatal development of projections
from the basolateral amygdala to prefrontal, striatal, and thalamic structures in the rat.
J Comp Neurol 442:239-249.
Britanova O, de Juan Romero C, Cheung A, Kwan KY, Schwark M, Gyorgy A, Vogel T,
Akopov S, Mitkovski M, Agoston D, Sestan N, Molnar Z, Tarabykin V (2008) Satb2 is
a postmitotic determinant for upper-layer neuron specification in the neocortex.
Neuron 57:378-392.
Brockmann MD, Pöschel B, Cichon N, Hanganu-Opatz IL (2011) Coupled oscillations
mediate directed interactions between prefrontal cortex and hippocampus of the
neonatal rat. Neuron 71:332-347.
Brumback AC, Ellwood IT, Kjaerby C, Iafrati J, Robinson S, Lee AT, Patel T, Nagaraj
S, Davatolhagh F, Sohal VS (2018) Identifying specific prefrontal neurons that
contribute to autism-associated abnormalities in physiology and social behavior. Mol
Psychiatry 23:2078-2089.
177
Bruno RM, Hahn TT, Wallace DJ, de Kock CP, Sakmann B (2009) Sensory experience
alters specific branches of individual corticocortical axons during development. J
Neurosci 29:3172-3181.
Bueno-Junior LS, Leite JP (2018) Input Convergence, Synaptic Plasticity and Functional
Coupling Across Hippocampal-Prefrontal-Thalamic Circuits. Front Neural Circuits
12:40.
Calabro FJ, Murty VP, Jalbrzikowski M, Tervo-Clemmens B, Luna B (2020) Development
of Hippocampal-Prefrontal Cortex Interactions through Adolescence. Cereb
Cortex 30:1548-1558.
Campbell DB, D'Oronzio R, Garbett K, Ebert PJ, Mirnics K, Levitt P, Persico AM (2007)
Disruption of cerebral cortex MET signaling in autism spectrum disorder. Ann Neurol
62:243-250.
Campbell DB, Sutcliffe JS, Ebert PJ, Militerni R, Bravaccio C, Trillo S, Elia M, Schneider
C, Melmed R, Sacco R, Persico AM, Levitt P (2006) A genetic variant that disrupts
MET transcription is associated with autism. Proc Natl Acad Sci U S A 103:16834-
16839.
Campbell P, Reep RL, Stoll ML, Ophir AG, Phelps SM (2009) Conservation and diversity
of Foxp2 expression in muroid rodents: functional implications. J Comp Neurol 512:84-
100.
Cang J, Rentería RC, Kaneko M, Liu X, Copenhagen DR, Stryker
MP (2005) Development of precise maps in visual cortex requires patterned
spontaneous activity in the retina. Neuron 48:797-809.
Cantio C, White S, Madsen GF, Bilenberg N, Jepsen J (2018) Do cognitive deficits persist
into adolescence in autism?. Autism Res 11:1229-1238.
Carlén M (2017) What constitutes the prefrontal cortex?. Science 358:478-482.
Carmichael ST, Price JL (1996) Connectional networks within the orbital and medial
prefrontal cortex of macaque monkeys. J Comp Neurol 371:17-207.
Cavada C, Goldman-Rakic PS (1989) Posterior parietal cortex in rhesus monkey: II.
Evidence for segregated corticocortical networks linking sensory and limbic areas with
the frontal lobe. J Comp Neurol 287:422-45.
Chen B, Schaevitz LR, McConnell SK (2005) Fezl regulates the differentiation and axon
targeting of layer 5 subcortical projection neurons in cerebral cortex. Proc Natl Acad
Sci U S A 102:17184-17189.
178
Chen B, Wang SS, Hattox AM, Rayburn H, Nelson SB, McConnell SK (2008) The Fezf2-
Ctip2 genetic pathway regulates the fate choice of subcortical projection neurons in
the developing cerebral cortex. Proc Natl Acad Sci U S A 105:11382-11387.
Chen CH, Panizzon MS, Eyler LT, Jernigan TL, Thompson W, Fennema-Notestine
C, Jak AJ, Neale MC, Franz CE, Hamza S, Lyons MJ, Grant MD, Fischl B, Seidman
LJ, Tsuang MT, Kremen WS, Dale AM (2011) Genetic influences on cortical
regionalization in the human brain. Neuron 72:537-544.
Chen K, Ma X, Nehme A, Wei J, Cui Y, Cui Y, Yao D, Wu J, Anderson T, Ferguson
D, Levitt P, Qiu S (2021) Time-delimited signaling of MET receptor tyrosine kinase
regulates cortical circuit development and critical period plasticity. Mol
Psychiatry 26:3723-3736.
Chen YC, Kuo HY, Bornschein U, Takahashi H, Chen SY, Lu KM, Yang HY, Chen GM,
Lin JR, Lee YH, Chou YC, Cheng SJ, Chien CT, Enard W, Hevers W, Pääbo S,
Graybiel AM, Liu FC (2016) Foxp2 controls synaptic wiring of corticostriatal circuits
and vocal communication by opposing Mef2c. Nat Neurosci 19:1513-1522.
Cheng S, Butrus S, Tan L, Xu R, Sagireddy S, Trachtenberg JT, Shekhar K, Zipursky
SL (2022) Vision-dependent specification of cell types and function in the developing
cortex. Cell 185:311-327.e24.
Chiu YC, Li MY, Liu YH, Ding JY, Yu JY, Wang TW (2014) Foxp2 regulates neuronal
differentiation and neuronal subtype specification. Dev Neurobiol 74:723-738.
Cholfin JA, Rubenstein JL (2007) Patterning of frontal cortex subdivisions by Fgf17.
PNAS 104:7652-7657.
Clascá F, Rubio-Garrido P, Jabaudon D (2012) Unveiling the diversity of thalamocortical
neuron subtypes. Eur J Neurosci 35:1524-1532.
Clovis YM, Enard W, Marinaro F, Huttner WB, De Pietri Tonelli D (2012) Convergent
repression of Foxp2 3'UTR by miR-9 and miR-132 in embryonic mouse neocortex:
implications for radial migration of neurons. Development 139:3332-3342.
Coley AA, Gao WJ (2019) PSD-95 deficiency disrupts PFC-associated function and
behavior during neurodevelopment. Sci Rep 9:9486.
Collado-Torres L, Burke EE, Peterson A, Shin J, Straub RE, Rajpurohit A, Semick SA,
Ulrich WS; BrainSeq Consortium, Price AJ, Valencia C, Tao R, Deep-Soboslay A,
Hyde TM, Kleinman JE, Weinberger DR, Jaffe AE (2019) Regional heterogeneity in
gene expression, regulation, and coherence in the frontal cortex and hippocampus
across development and schizophrenia. Neuron 103:203-216.e8.
179
Collins DP, Anastasiades PG, Marlin JJ, Carter AG (2018) Reciprocal circuits linking the
prefrontal cortex with dorsal and ventral thalamic nuclei. Neuron 98:366–379.e4.
Couly GF, Le Douarin NM (1987) Mapping of the early neural primordium in quail-chick
chimeras. II. The prosencephalic neural plate and neural folds: implications for the
genesis of cephalic human congenital abnormalities. Dev Biol 120:198-214.
Cunningham MG, Bhattacharyya S, Benes FM (2002) Amygdalo-cortical sprouting
continues into early adulthood: implications for the development of normal and
abnormal function during adolescence. J Comp Neurol 453:116-130.
Damaj L, Lupien-Meilleur A, Lortie A, Riou É, Ospina LH, Gagnon L, Vanasse C,
Rossignol E (2015) CACNA1A haploinsufficiency causes cognitive impairment,
autism and epileptic encephalopathy with mild cerebellar symptoms. Eur J Hum Genet
23:1505-1512.
Damasio H, Grabowski T, Frank R, Galaburda AM, Damasio AR (1994) The return of
Phineas Gage: clues about the brain from the skull of a famous patient. Science
264:1102-1105.
Dantzker JL, Callaway EM (1998) The development of local, layer-specific visual cortical
axons in the absence of extrinsic influences and intrinsic activity. J Neurosci 18:4145-
4154.
Dembrow N, Johnston D (2014) Subcircuit-specific neuromodulation in the prefrontal
cortex. Front Neural Circuits 8:54.
den Boon FS, Werkman TR, Schaafsma-Zhao Q, Houthuijs K, Vitalis T, Kruse CG,
Wadman WJ, Chameau P (2015) Activation of type-1 cannabinoid receptor shifts the
balance between excitation and inhibition towards excitation in layer II/III pyramidal
neurons of the rat prelimbic cortex. Pflugers Arch 467:1551-1564.
Dent MA, Segura-Anaya E, Alva-Medina J, Aranda-Anzaldo A (2010) NeuN/Fox-3 is an
intrinsic component of the neuronal nuclear matrix. FEBS Lett 584:2767-2771.
Dougherty RF, Koch VM, Brewer AA, Fischer B, Modersitzki J, Wandell BA (2003) Visual
field representations and locations of visual areas V1/2/3 in human visual cortex. J
Vis 3:586-598.
Dufour A, Seibt J, Passante L, Depaepe V, Ciossek T, Frisén J, Kullander K, Flanagan
JG, Polleux F, Vanderhaeghen P (2003) Area Specificity and Topography of
Thalamocortical Projections Are Controlled by ephrin/Eph Genes. Neuron 39:453-
465.
180
Eagleson KL, Campbell DB, Thompson BL, Bergman MY, Levitt P (2011) The autism risk
genes MET and PLAUR differentially impact cortical development. Autism Res 4:68-
83.
Eagleson KL, Lane CJ, McFadyen-Ketchum L, Solak S, Wu HH, Levitt P (2016) Distinct
intracellular signaling mediates C-MET regulation of dendritic growth and
synaptogenesis. Dev Neurobiol 76:1160-1181.
Ebisu H, Iwai-Takekoshi L, Fujita-Jimbo E, Momoi T, Kawasaki H (2017) Foxp2 Regulates
Identities and Projection Patterns of Thalamic Nuclei During Development. Cereb
Cortex.
Ferland RJ, Cherry TJ, Preware PO, Morrisey EE, Walsh CA (2003) Characterization of
Foxp2 and Foxp1 mRNA and protein in the developing and mature brain. J Comp
Neurol 460:266-279.
Finlay JM, Dunham GA, Isherwood AM, Newton CJ, Nguyen TV, Reppar PC, Snitkovski
I, Paschall SA, Greene RW (2015) Effects of prefrontal cortex and hippocampal NMDA
NR1-subunit deletion on complex cognitive and social behaviors. Brain Res 1600:70-
83.
Foster JA, Burman MA (2010) Evidence for hippocampus-dependent contextual learning
at postnatal day 17 in the rat. Learn Mem 17:259-66.
Frankland PW, Ding HK, Takahashi E, Suzuki A, Kida S, Silva AJ (2006) Stability of recent
and remote contextual fear memory. Learn Mem 13:451-457.
French CA, Fisher SE (2014) What can mice tell us about Foxp2 function? Curr Opin
Neurobiol 28:72-79.
French CA, Groszer M, Preece C, Coupe AM, Rajewsky K, Fisher SE (2007) Generation
of mice with a conditional Foxp2 null allele. Genesis 45:440-446.
French CA, Jin X, Campbell TG, Gerfen E, Groszer M, Fisher SE, Costa RM (2012) An
aetiological Foxp2 mutation causes aberrant striatal activity and alters plasticity during
skill learning. Mol Psychiatry 17:1077-1085.
Fukuchi-Shimogori T, Grove EA (2001) Neocortex Patterning by the Secreted Signaling
Molecule FGF8. Science 294:1071-1074.
Gabbott PL, Warner TA, Jays PR, Salway P, Busby SJ (2005) Prefrontal cortex in the rat:
projections to subcortical autonomic, motor, and limbic centers. J Comp
Neurol 492:145-177.
181
Gamo NJ, Arnsten AFT (2011) Molecular modulation of prefrontal cortex: Rational
development of treatments for psychiatric disorders. Behav Neurosci 125:282-296.
Garcia-Calero E, Botella-Lopez A, Bahamonde O, Perez-Balaguer A, Martinez S (2016)
FoxP2 protein levels regulate cell morphology changes and migration patterns in the
vertebrate developing telencephalon. Brain Struct Funct 221:2905-2917.
Gee DG, Humphreys KL, Flannery J, Goff B, Telzer EH, Shapiro M, Hare TA, Bookheimer
SY, Tottenham N (2013) A developmental shift from positive to negative connectivity
in human amygdala-prefrontal circuitry. J Neurosci 33:4584-4593.
Gerfen CR, Economo MN, Chandrashekar J (2018) Long distance projections of cortical
pyramidal neurons. J Neurosci Res 96:1467-1475.
Giguere M, Goldman-Rakic PS (1988) Mediodorsal nucleus: areal, laminar, and
tangential distribution of afferents and efferents in the frontal lobe of rhesus
monkeys. J Comp Neurol 277:195-213.
Glickfeld LL, Histed MH, Maunsell JH (2013) Mouse primary visual cortex is used to
detect both orientation and contrast changes. J Neurosci 33:19416-19422.
Gong S, Zheng C, Doughty ML, Losos K, Didkovsky N, Schambra UB, Nowak NJ, Joyner
A, Leblanc G, Hatten ME, Heintz N (2003) A gene expression atlas of the central
nervous system based on bacterial artificial chromosomes. Nature 425:917-925.
Gordon JA, Stryker MP (1996) Experience-dependent plasticity of binocular responses in
the primary visual cortex of the mouse. J Neurosci 16:3274-3286.
Gorski JA, Talley T, Qiu M, Puelles L, Rubenstein JLR, Jones KR (2002) Cortical
Excitatory Neurons and Glia, But Not GABAergic Neurons, Are Produced in the Emx1-
Expressing Lineage. J Neurosci 22:6309–6314.
Gogtay N, Giedd JN, Lusk L, Hayashi KM, Greenstein D, Vaituzis AC, Nugent TF 3rd,
Herman DH, Clasen LS, Toga AW, Rapoport JL, Thompson PM (2004) Dynamic
mapping of human cortical development during childhood through early
adulthood. Proc Natl Acad Sci U S A 101:8174-8179.
Goto Y, Grace AA (2008) Limbic and cortical information processing in the nucleus
accumbens. Trends Neurosci 31:552-8.
Grant E, Hoerder-Suabedissen A, Molnar Z (2012) Development of the corticothalamic
projections. Front Neurosci 6:53.
182
Grissom NM, Herdt CT, Desilets J, Lidsky-Everson J, Reyes TM (2015) Dissociable
deficits of executive function caused by gestational adversity are linked to specific
transcriptional changes in the prefrontal cortex. Neuropsychopharmacology 40:1353-
1363.
Griveau A, Borello U, Causeret F, Tissir F, Boggetto N, Karaz S, Pierani A (2010) A novel
role for Dbx1-derived Cajal-Retzius cells in early regionalization of the cerebral cortical
neuroepithelium. PLoS Biol 8:e1000440.
Groenewegen HJ (1988) Organization of the afferent connections of the mediodorsal
thalamic nucleus in the rat, related to the mediodorsal-prefrontal
topography. Neuroscience 24:379-431.
Groenewegen HJ, Wright CI, Uylings HB (1997) The anatomical relationships of the
prefrontal cortex with limbic structures and the basal ganglia. J Psychopharmacol
11:99-106.
Groszer M, Keays DA, Deacon RM, de Bono JP, Prasad-Mulcare S, Gaub S, Baum MG,
French CA, Nicod J, Coventry JA, Enard W, Fray M, Brown SD, Nolan PM, Pääbo S,
Channon KM, Costa RM, Eilers J, Ehret G, Rawlins JN, Fisher SE (2008) Impaired
synaptic plasticity and motor learning in mice with a point mutation implicated in
human speech deficits. Curr Biol 18:354-362.
Guirado R, Umemori J, Sipilä P, Castrén E (2016) Evidence for Competition for Target
Innervation in the Medial Prefrontal Cortex. Cereb Cortex 26:1287-1294.
Gulisano M, Broccoli V, Pardini C, Boncinelli E (1996) Emx1 and Emx2 show different
patterns of expression during proliferation and differentiation of the developing
cerebral cortex in the mouse. Eur J Neurosci 8:1037-1050.
Haehl W, Mirifar A, Luan M, Beckmann J (2020) Dealing with failure: Prefrontal
asymmetry predicts affective recovery and cognitive performance. Biol Psychol
155:107927.
Haji N, Riebe I, Aguilar-Valles A, Artinian J, Laplante I, Lacaille JC (2020) Tsc1
haploinsufficiency in Nkx2.1 cells upregulates hippocampal interneuron mTORC1
activity, impairs pyramidal cell synaptic inhibition, and alters contextual fear
discrimination and spatial working memory in mice. Mol Autism 11:29.
Hajós M, Richards CD, Székely AD, Sharp T (1998) An electrophysiological and
neuroanatomical study of the medial prefrontal cortical projection to the midbrain
raphe nuclei in the rat. Neuroscience 87:95-108.
183
Hamasaki T, Leingartner A, Ringstedt T, O'Leary DD (2004) EMX2 regulates sizes and
positioning of the primary sensory and motor areas in neocortex by direct specification
of cortical progenitors. Neuron 43:359-372.
Han W, Kwan KY, Shim SS, Lam MMS, Shin Y, Xu X, Zhu Y, Li M, Šestan N (2011) TBR1
directly represses Fezf2 to control the laminar origin and development of the
corticospinal tract. Proc Natl Acad Sci U S A 108:3041-3046.
Harb K, Magrinelli E, Nicolas CS, Lukianets N, Frangeul L, Pietri M, Sun T, Sandoz G,
Grammont F, Jabaudon D, Studer M, Alfano C (2016) Area-specific development of
distinct projection neuron subclasses is regulated by postnatal epigenetic
modifications. Elife 5:e09531.
Harris KD, Shepherd GM (2015) The neocortical circuit: themes and variations. Nat
Neurosci 18:170-181.
Harrison RV, Stanton SG, Ibrahim D, Nagasawa A, Mount RJ (1993) Neonatal cochlear
hearing loss results in developmental abnormalities of the central auditory
pathways. Acta Otolaryngol 113:296-302.
Hartung H, Brockmann MD, Pöschel B, De Feo V, Hanganu-Opatz IL (2016) Thalamic
and Entorhinal Network Activity Differently Modulates the Functional Development of
Prefrontal-Hippocampal Interactions. J Neurosci 36:3676-3690.
Hatanaka Y, Namikawa T, Yamauchi K, Kawaguchi Y (2016) Cortical divergent
projections in mice originate from two sequentially generated, distinct populations of
excitatory cortical neurons with different initial axonal outgrowth characteristics. Cereb
Cortex 26:2257-2270.
Heilbronner SR, Rodriguez-Romaguera J, Quirk GJ, Groenewegen HJ, Haber SN (2016)
Circuit-Based Corticostriatal Homologies Between Rat and Primate. Biol Psychiatry
80:509-521.
Hensch TK (2003) Controlling the critical period. Neurosci Res 47:17-22.
Hensch TK (2004) Critical period regulation. Annu Rev Neurosci 27:549-579.
Heroux NA, Robinson-Drummer PA, Sanders HR, Rosen JB, Stanton ME (2017)
Differential involvement of the medial prefrontal cortex across variants of contextual
fear conditioning. Learn Mem 24:322-330.
Heun-Johnson H, Levitt P (2017) Differential impact of Met receptor gene interaction with
early-life stress on neuronal morphology and behavior in mice. Neurobiol Stress 8:10-
20.
184
Hisaoka T, Nakamura Y, Senba E, Morikawa Y (2010) The forkhead transcription factors,
Foxp1 and Foxp2, identify different subpopulations of projection neurons in the mouse
cerebral cortex. Neuroscience 166:551-563.
Hoerder-Suabedissen A, Molnár Z (2013) Molecular diversity of early-born subplate
neurons. Cereb Cortex 23:1473-1483.
Hoover WB, Vertes RP (2012) Collateral projections from nucleus reuniens of thalamus
to hippocampus and medial prefrontal cortex in the rat: a single and double retrograde
fluorescent labeling study. Brain Struct Funct 217:191-209.
Huang L, Wang J, Liang G, Gao Y, Jin SY, Hu J, Yang X, Lao J, Chen J, Luo ZC, Fan C,
Xiong L, Zhu X, Gao TM, Zhong M, Yang X (2021) Upregulated NMDAR-mediated
GABAergic transmission underlies autistic-like deficits in Htr3a knockout
mice. Theranostics, 11:9296-9310.
Hubel DH, Wiesel TN (1970) The period of susceptibility to the physiological effects of
unilateral eye closure in kittens. J Physiol 206:419-436.
Hunt DL, Yamoah EN, Krubitzer L (2006) Multisensory plasticity in congenitally deaf mice:
how are cortical areas functionally specified?. Neuroscience 139:1507-1524.
Huttenlocher PR, Dabholkar AS (1997) Regional differences in synaptogenesis in human
cerebral cortex. J Comp Neurol 387:167-178.
Ishikawa A, Nakamura S (2006) Ventral hippocampal neurons project axons
simultaneously to the medial prefrontal cortex and amygdala in the rat. J Neurophysiol
96:2134-2138.
Jackson PB, Boccuto L, Skinner C, Collins JS, Neri G, Gurrieri F, Schwartz CE (2009)
Further evidence that the rs1858830 C variant in the promoter region of the MET gene
is associated with autistic disorder. Autism Res 2:232-236.
Jay TM, Witter MP (1991) Distribution of hippocampal CA1 and subicular efferents in the
prefrontal cortex of the rat studied by means of anterograde transport of Phaseolus
vulgaris-leucoagglutinin. J Comp Neurol 313:574-586.
Jeanmonod D, Rice FL, Van der Loos H (1981) Mouse somatosensory cortex: alterations
in the barrelfield following receptor injury at different early postnatal
ages. Neuroscience 6:1503-1535.
Jin J, Maren S (2015) Fear renewal preferentially activates ventral hippocampal neurons
projecting to both amygdala and prefrontal cortex in rats. Sci Rep 5:8388.
185
Jodo E, Aston-Jones G (1997) Activation of locus coeruleus by prefrontal cortex is
mediated by excitatory amino acid inputs. Brain Res 768:327-332.
Jones MW, Wilson MA (2005) Theta rhythms coordinate hippocampal-prefrontal
interactions in a spatial memory task. PLoS Biol 3:e402.
Judson MC, Bergman MY, Campbell DB, Eagleson KL, Levitt P (2009) Dynamic gene
and protein expression patterns of the autism-associated met receptor tyrosine kinase
in the developing mouse forebrain. J Comp Neurol 513:511-531.
Judson MC, Eagleson KL, Wang L, Levitt P (2010) Evidence of cell-nonautonomous
changes in dendrite and dendritic spine morphology in the met-signaling-deficient
mouse forebrain. J Comp Neurol 518:4463-4478.
Kahn RS, Keefe RS (2013) Schizophrenia is a cognitive illness: time for a change in
focus. JAMA Psychiatry 70:1107-1112.
Kallweit C, Paucke M, Strauß M, Exner C (2020) Cognitive deficits and psychosocial
functioning in adult ADHD: Bridging the gap between objective test measures and
subjective reports. J Clin Exp Neuropsychol 42:569-583.
Kamitakahara A, Wu HH, Levitt P (2017) Distinct projection targets define subpopulations
of mouse brainstem vagal neurons that express the autism-associated MET receptor
tyrosine kinase. J Comp Neurol 18:3787-3808.
Karalis N, Dejean C, Chaudun F, Khoder S, Rozeske RR, Wurtz H, Bagur S, Benchenane
K, Sirota A, Courtin J, Herry C (2016) 4-Hz oscillations synchronize prefrontal-
amygdala circuits during fear behavior. Nat Neurosci 19:605-612.
Kassai H, Terashima T, Fukaya M, Nakao K, Sakahara M, Watanabe M, Aiba
A (2008) Rac1 in cortical projection neurons is selectively required for midline crossing
of commissural axonal formation. Eur J Neurosci 28:257-267.
Kast RJ, Levitt P (2019) Precision in the development of neocortical architecture: From
progenitors to cortical networks. Prog Neurobiol 175:77-95.
Kast RJ, Wu HH, Levitt P (2019) Developmental connectivity and molecular phenotypes
of unique cortical projection neurons that express a synapse-associated receptor
tyrosine Kinase. Cereb Cortex 29:189-201.
Kast RJ, Wu HH, Williams P, Gaspar P, Levitt P (2017) Specific Connectivity and Unique
Molecular Identity of MET Receptor Tyrosine Kinase Expressing Serotonergic
Neurons in the Caudal Dorsal Raphe Nuclei. ACS Chem Neurosci 8:1053-1064.
186
Keefe RS, Eesley CE, Poe MP (2005) Defining a cognitive function decrement in
schizophrenia. Biol Psychiatry 57:688-691.
Kim CK, Ye L, Jennings JH, Pichamoorthy N, Tang DD, Yoo AW, Ramakrishnan C,
Deisseroth K (2017) Molecular and circuit-dynamical identification of top-down neural
mechanisms for restraint of reward seeking. Cell 170:1013-1027.e14.
Kim EJ, Zhang Z, Huang L, Ito-Cole T, Jacobs MW, Juavinett AL, Senturk G, Hu M, Ku
M, Ecker JR, Callaway EM (2020) Extraction of distinct neuronal cell types from within
a genetically continuous population. Neuron 107:274-282.e6.
Kim J, Matney CJ, Blankenship A, Hestrin S, Brown SP (2014) Layer 6 corticothalamic
neurons activate a cortical output layer, layer 5a. J Neurosci 34:9656-9664.
Kim WB, Cho JH (2017) Synaptic Targeting of Double-Projecting Ventral CA1
Hippocampal Neurons to the Medial Prefrontal Cortex and Basal Amygdala. J
Neurosci 37:4868-4882.
Kitamura T, Ogawa SK, Roy DS, Okuyama T, Morrissey MD, Smith LM, Redondo RL,
Tonegawa S (2017) Engrams and circuits crucial for systems consolidation of a
memory. Science 356:73-78.
Klavir O, Prigge M, Sarel A, Paz R, Yizhar O (2017) Manipulating fear associations via
optogenetic modulation of amygdala inputs to prefrontal cortex. Nat Neurosci 20:836-
844.
Klingler E, Tomasello U, Prados J, Kebschull JM, Contestabile A, Galiñanes GL, Fièvre
S, Santinha A, Platt R, Huber D, Dayer A, Bellone C, Jabaudon D (2021) Temporal
controls over inter-areal cortical projection neuron fate diversity. Nature 599:453-457.
Knöpfel T, Sweeney Y, Radulescu CI, Zabouri N, Doostdar N, Clopath C, Barnes
SJ (2019) Audio-visual experience strengthens multisensory assemblies in adult
mouse visual cortex. Nat Comm 10:5684.
Ko J (2017) Neuroanatomical Substrates of Rodent Social Behavior: The Medial
Prefrontal Cortex and Its Projection Patterns. Front Neural Circuits 11:41.
Kolb B, Buhrmann K, McDonald R, Sutherland RJ (1994) Dissociation of the medial
prefrontal, posterior parietal, and posterior temporal cortex for spatial navigation and
recognition memory in the rat. Cereb Cortex 4:664-680.
Konopka G, Bomar JM, Winden K, Coppola G, Jonsson ZO, Gao F, Peng S, Preuss TM,
Wohlschlegel JA, Geschwind DH (2009) Human-specific transcriptional regulation of
CNS development genes by FOXP2. Nature 462:213-217.
187
Krettek JE, Price JL (1977) The cortical projections of the mediodorsal nucleus and
adjacent thalamic nuclei in the rat. J Comp Neurol 171:157-191.
Lai CSL, Fisher SE, Hurst JA, Vargha-Khadem FM, Anthony P (2001) A forkhead-domain
gene is mutated in a severe speech and language disorder. Nature 413:519-523.
Lake BB, Ai R, Kaeser GE, Salathia NS, Yung YC, Liu R, Wildberg A, Gao D, Fung HL,
Chen S, Vijayaraghavan R, Wong J, Chen A, Sheng X, Kaper F, Shen R, Ronaghi M,
Fan JB, Wang W, Chun J, Zhang K (2016) Neuronal subtypes and diversity revealed
by single-nucleus RNA sequencing of the human brain. Science 2016 352:1586-90.
Lanjewar AL, Jagetia S, Khan ZM, Eagleson KL, Levitt P (2022) Subclass-specific
expression patterns of MET receptor tyrosine kinase during development in medial
prefrontal and visual cortices. J Comp Neurol 10.1002/cne.25418. Advance online
publication.
Lapate RC, Rokers B, Tromp DPM, Orfali NS, Oler JA, Doran ST, Adluru N, Alexander
AL, Davidson RJ (2016) Awareness of Emotional Stimuli Determines the Behavioral
Consequences of Amygdala Activation and Amygdala-Prefrontal Connectivity. Sci
Rep 6:25826.
Laubach M, Amarante LM, Swanson K, White SR (2018) What, If Anything, Is Rodent
Prefrontal Cortex?. eNeuro 5:ENEURO.0315-18.2018.
Lee KH, Yu DH, Lee YS (2009) Gene expression profiling of rat cerebral cortex
development using cDNA microarrays. Neurochem Res 2009 34:1030-1038.
Lee SM, Tole S, Grove E, McMahon AP (2000) A local Wnt-3a signal is required for
development of the mammalian hippocampus. Development 127:457-467.
Leingärtner A, Thuret S, Kroll TT, Chou SJ, Leasure JL, Gage FH, O'Leary DD (2007)
Cortical area size dictates performance at modality-specific behaviors. Proc Natl Acad
Sci U S A 104:4153-4158.
Leone DP, Heavner WE, Ferenczi EA, Dobreva G, Huguenard JR, Grosschedl R,
McConnell SK (2015) Satb2 regulates the differentiation of both callosal and
subcerebral projection neurons in the developing cerebral cortex. Cereb
Cortex 25:3406-3419.
Levitt P, Barbe MF, Eagleson KL (1997) Patterning and specification of the cerebral
cortex. Annu Rev Neurosci 20:1-24.
Li L, Gao X, Zhou Q (2018) Absence of fear renewal and functional connections between
prefrontal cortex and hippocampus in infant mice. Neurobiol Learn Mem 152:1-9.
188
Liang F, Xiong XR, Zingg B, Ji XY, Zhang LI, Tao HW (2015) Sensory cortical control of
a visually induced arrest behavior via corticotectal projections. Neuron 86:755-767.
Liberzon I, Abelson JL (2016) Context Processing and the Neurobiology of Post-
Traumatic Stress Disorder. Neuron 92:14-30.
Lickiss T, Cheung AF, Hutchinson CE, Taylor JS, Molnár Z (2012) Examining the
relationship between early axon growth and transcription factor expression in the
developing cerebral cortex. J Anat 220:201-211.
Liu BH, Huberman AD, Scanziani M (2016) Cortico-fugal output from visual cortex
promotes plasticity of innate motor behaviour. Nature 538:383-387.
Liu Q, Dwyer ND, O'Leary DD (2000) Differential expression of COUP-TFI, CHL1, and
two novel genes in developing neocortex identified by differential display PCR. J
Neurosci 20:7682-7690.
Longati P, Bardelli A, Ponzetto C, Naldini L, Comoglio PM (1994) Tyrosines1234-1235
are critical for activation of the tyrosine kinase encoded by the MET proto-oncogene
(HGF receptor). Oncogene 9:49-57.
Lonsdorf TB, Haaker J, Kalisch R (2014) Long-term expression of human contextual fear
and extinction memories involves amygdala, hippocampus and ventromedial
prefrontal cortex: a reinstatement study in two independent samples. Soc Cogn Affect
Neurosci 9:1973-1983.
Loukas M, Pennell C, Groat C, Tubbs RS, Cohen-Gadol AA (2011) Korbinian Brodmann
(1868-1918) and his contributions to mapping the cerebral cortex. Neurosurgery 68:6-
11.
Loyer Carbonneau M, Demers M, Bigras M, Guay MC (2021) Meta-Analysis of Sex
Differences in ADHD Symptoms and Associated Cognitive Deficits. J Atten
Disord 25:1640-1656.
Luciana M, Conklin HM, Hooper CJ, Yarger RS (2005) The development of nonverbal
working memory and executive control processes in adolescents. Child Dev 76:697-
712.
Luna B, Garver KE, Urban TA, Lazar NA, Sweeney JA (2004) Maturation of cognitive
processes from late childhood to adulthood. Child Dev 75:1357-1372.
189
Luo C, Keown CL, Kurihara L, Zhou J, He Y, Li J, Castanon R, Lucero J, Nery
JR, Sandoval JP, Bui B, Sejnowski TJ, Harkins TT, Mukamel EA, Behrens MM, Ecker
JR (2017) Single-cell methylomes identify neuronal subtypes and regulatory elements
in mammalian cortex. Science 357:600-604.
Luo R, Jeong SJ, Jin Z, Strokes N, Li S, Piao X (2011) G protein-coupled receptor 56 and
collagen III, a receptor-ligand pair, regulates cortical development and
lamination. Proc Natl Acad Sci U S A 108:12925-12930.
Lupien-Meilleur A, Jiang X, Lachance M, Taschereau-Dumouchel V, Gagnon L, Vanasse
C, Lacaille JC, Rossignol E. Reversing frontal disinhibition rescues behavioural
deficits in models of CACNA1A-associated neurodevelopment disorders (2021) Mol
Psychiatry 26:7225-7246.
Ma L, Harada T, Harada C, Romero M, Hebert JM, McConnell SK, Parada LF (2002)
Neurotrophin-3 is required for appropriate establishment of thalamocortical
connections. Neuron 36:623-634.
Ma X, Wei J, Cui Y, Xia B, Zhang L, Nehme A, Zuo Y, Ferguson D, Levitt P, Qiu
S (2022) Disrupted timing of MET signaling derails the developmental maturation of
cortical circuits and leads to altered behavior in mice. Cereb Cortex 32:1769-1786.
MacDermot KD, Bonora E, Sykes N, Coupe AM, Lai CS, Vernes SC, Vargha-Khadem F,
McKenzie F, Smith RL, Monaco AP, Fisher SE (2005) Identification of FOXP2
truncation as a novel cause of developmental speech and language deficits. Am J
Hum Genet 76:1074-1080.
Macharadze T, Budinger E, Brosch M, Scheich H, Ohl FW, Henschke JU (2019) Early
Sensory Loss Alters the Dendritic Branching and Spine Density of Supragranular
Pyramidal Neurons in Rodent Primary Sensory Cortices. Front Neural Circuits 13:61.
Makino Y, Polygalov D, Bolaños F, Benucci A, McHugh TJ (2019) Physiological Signature
of Memory Age in the Prefrontal-Hippocampal Circuit. Cell Rep 29:3835-3846.e5.
Makinodan M, Rosen KM, Ito S, Corfas G (2012) A critical period for social experience-
dependent oligodendrocyte maturation and myelination. Science 337:1357-1360.
Manuel M, Georgala PA, Carr CB, Chanas S, Kleinjan DA, Martynoga B, Mason JO,
Molinek M, Pinson J, Pratt T, Quinn JC, Simpson TI, Tyas DA, van Heyningen V, West
JD, Price DJ (2007) Controlled overexpression of Pax6 in vivo negatively
autoregulates the Pax6 locus, causing cell-autonomous defects of late cortical
progenitor proliferation with little effect on cortical arealization. Development 134:545-
555.
190
Marek R, Jin J, Goode TD, Giustino TF, Wang Q, Acca GM, Holehonnur R, Ploski JE,
Fitzgerald PJ, Lynagh T, Lynch JW, Maren S, Sah P (2018) Hippocampus-driven feed-
forward inhibition of the prefrontal cortex mediates relapse of extinguished fear. Nat
Neurosci 21:384-392.
Maren S, Phan KL, Liberzon I (2013) The contextual brain: implications for fear
conditioning, extinction and psychopathology. Nat Rev Neurosci 14:417-428.
Marín-Padilla M (1998) Cajal-Retzius cells and the development of the neocortex. Trends
Neurosci 2:64-71.
Marques T, Summers MT, Fioreze G, Fridman M, Dias RF, Feller MB, Petreanu
L (2018) A role for mouse primary visual cortex in motion perception. Curr
Biol 28:1703-1713.e6.
Matsuzaki M, Ellis-Davies GC, Nemoto T, Miyashita Y, Iino M, Kasai H (2001) Dendritic
spine geometry is critical for AMPA receptor expression in hippocampal CA1
pyramidal neurons. Nat Neurosci 4:1086-1092.
Mazade R, Alonso JM (2017) Thalamocortical processing in vision. Vis
Neurosci 34:E007.
McDougall S, Vargas Riad W, Silva-Gotay A, Tavares ER, Harpalani D, Li GL, Richardson
HN (2018) Myelination of Axons Corresponds with Faster Transmission Speed in the
Prefrontal Cortex of Developing Male Rats. eNeuro 5:ENEURO.0203-18.2018.
McKenna WL, Betancourt J, Larkin KA, Abrams B, Guo C, Rubenstein JL, Chen B (2011)
Tbr1 and Fezf2 regulate alternate corticofugal neuronal identities during neocortical
development. J Neurosci 31:549-564.
Medvedeva VP, Rieger MA, Vieth B, Mombereau C, Ziegenhain C, Ghosh T, Cressant A,
Enard W, Granon S, Dougherty JD, Groszer M (2019) Altered social behavior in mice
carrying a cortical Foxp2 deletion. Hum Mol Genet 28:701-717.
Middleton FA, Strick PL (2001) A revised neuroanatomy of frontal–subcortical circuits. In:
Lichter DG, Cummings JL, editors. Frontal–subcortical circuits in psychiatric and
neurological disorders 44-58.
Miller EK (2000) The prefrontal cortex and cognitive control. Nat Rev Neurosci 1:59-65.
Mills KL, Lalonde F, Clasen LS, Giedd JN, Blakemore SJ (2014) Developmental changes
in the structure of the social brain in late childhood and adolescence. Soc Cogn Affect
Neurosci 9:123-131.
191
Minciacchi D, Granato A (1989) Development of the thalamocortical system: transient-
crossed projections to the frontal cortex in neonatal rats. J Comp Neurol 281:1-12.
Miyashita-Lin EM, Hevner R, Wassarman KM, Martinez S, Rubenstein JL (1999) Early
neocortical regionalization in the absence of thalamic innervation. Science 285:906-
909.
Molinard-Chenu A, Fluss J, Laurent S, Laurent M, Guipponi M, Dayer AG (2020) MCF2
is linked to a complex perisylvian syndrome and affects cortical lamination. Ann Clin
Transl Neurol 7:121-125.
Molyneaux BJ, Arlotta P, Hirata T, Hibi M, Macklis JD (2005) Fezl is required for the birth
and specification of corticospinal motor neurons. Neuron 47:817-831.
Molyneaux BJ, Arlotta P, Menezes JR, Macklis JD (2007) Neuronal subtype specification
in the cerebral cortex. Nat Rev Neurosci 8:427-437.
Molyneaux BJ, Goff LA, Brettler AC, Chen HH, Brown JR, Hrvatin S, Rinn JL, Arlotta P
(2015) DeCoN: genome-wide analysis of in vivo transcriptional dynamics during
pyramidal neuron fate selection in neocortex. Neuron 85:275-288.
Moyer CE, Zuo Y (2018) Cortical dendritic spine development and plasticity: Insights from
in vivo imaging. Curr Opin Neurobiol 53:76-82.
Mukamel Z, Konopka G, Wexler E, Osborn GE, Dong H, Bergman MY, Levitt P,
Geschwind DH (2011) Regulation of MET by FOXP2, genes implicated in higher
cognitive dysfunction and autism risk. J Neurosci 31:11437-11442.
Murugan M, Jang HJ, Park M, Miller EM, Cox J, Taliaferro JP, Parker NF, Bhave V, Hur
H, Liang Y, Nectow AR, Pillow JW, Witten IB (2017) Combined social and spatial
coding in a descending projection from the prefrontal cortex. Cell 171:1663-1677.e16.
Nakamoto C, Kawamura M, Nakatsukasa E, Natsume R, Takao K, Watanabe M, Abe M,
Takeuchi T, Sakimura K (2020) GluD1 knockout mice with a pure C57BL/6N
background show impaired fear memory, social interaction, and enhanced
depressive-like behavior. PLoS One 15:e0229288.
Nakayama H, Ibañez-Tallon I & Heintz N (2018) Cell-Type-Specific Contributions of
Medial Prefrontal Neurons to Flexible Behaviors. J Neurosci 38:4490-4504.
Nelson 3rd CA, Gabard-Durnam LJ (2020) Early adversity and critical periods:
Neurodevelopmental consequences of violating the expectable environment. Trends
Neurosci 43:133-143.
192
Northcutt RG, Kaas JH (1995) The emergence and evolution of mammalian
neocortex. Trends Neurosci 18:373-379.
Ohkubo Y, Chiang C, Rubenstein JL (2002) Coordinate regulation and synergistic actions
of BMP4, SHH and FGF8 in the rostral prosencephalon regulate morphogenesis of
the telencephalic and optic vesicles. Neuroscience 111:1-17.
O'Leary DD (1989) Do Cortical Areas Emerge from a Protocortex? TINS 12:400-406.
O'Leary DD, Koester SE (1993) Development of projection neuron types, axon pathways,
and patterned connections of the mammalian cortex. Neuron 10:991-1006.
Ouimet CC (1991) DARPP-32, a dopamine and cyclic AMP-regulated phosphoprotein, is
present in corticothalamic neurons of the rat cingulate cortex. Brain Res 562:85-92.
Pan S, Mayoral SR, Choi HS, Chan JR, Kheirbek MA (2020) Preservation of a remote
fear memory requires new myelin formation. Nat Neurosci 23:487-499.
Panchision DM, Pickel JM, Studer L, Lee SH, Turner PA, Hazel TG, McKay RD (2001)
Sequential actions of BMP receptors control neural precursor cell production and
fate. Genes Dev 15: 2094-2110.
Pandya DN, Yeterian EH (1990) Prefrontal cortex in relation to other cortical areas in
rhesus monkey: architecture and connections. Prog Brain Res 85:63-94.
Paolino A, Fenlon LR, Kozulin P, Haines E, Lim J, Richards LJ, Suárez
R (2020) Differential timing of a conserved transcriptional network underlies divergent
cortical projection routes across mammalian brain evolution. Proc Natl Acad Sci U S
A 117:10554-10564.
Park M, Dean M, Kaul K, Braun MJ, Gonda MA, Vande Woude G (1987) Sequence of
MET protooncogene cDNA has features characteristic of the tyrosine kinase family of
growth-factor receptors. Proc Natl Acad Sci U S A. 84:6379-6383.
Paxinos G, Franklin KB (2019) Paxinos and Franklin's the mouse brain in stereotaxic
coordinates. AP.
Paylor JW, Wendlandt E, Freeman TS, Greba Q, Marks WN, Howland JG, Winship IR
(2018) Impaired Cognitive Function after Perineuronal Net Degradation in the Medial
Prefrontal Cortex. eNeuro 2018 5:ENEURO.0253-18.2018.
193
Pederick DT, Richards KL, Piltz SG, Kumar R, Mincheva-Tasheva S, Mandelstam SA,
Dale RC, Scheffer IE, Gecz J, Petrou S, Hughes JN, Thomas PQ (2018) Abnormal
cell sorting underlies the unique X-Linked inheritance of PCDH19 epilepsy Neuron
97:59-66.
Peng Y, Lu Z, Li G, Piechowicz M, Anderson M, Uddin Y, Wu J, Qiu S (2016) The autism-
associated MET receptor tyrosine kinase engages early neuronal growth mechanism
and controls glutamatergic circuits development in the forebrain. Mol Psychiatry
21:925-935.
Pereira LM, de Castro CM, Guerra L, Queiroz TM, Marques JT, Pereira GS (2019)
Hippocampus and Prefrontal Cortex Modulation of Contextual Fear Memory Is
Dissociated by Inhibiting De Novo Transcription During Late Consolidation. Mol
Neurobiol 56:5507-5519.
Peters SK, Dunlop K, Downar J (2016) Cortico-Striatal-Thalamic Loop Circuits of the
Salience Network: A Central Pathway in Psychiatric Disease and Treatment. Front
Syst Neurosci 10:104.
Petrides M, Pandya DN (1999) Dorsolateral prefrontal cortex: comparative
cytoarchitectonic analysis in the human and the macaque brain and corticocortical
connection patterns. Eur J Neurosci 11:1011-1036.
Petrides M, Tomaiuolo F, Yeterian EH, Pandya DN (2012) The prefrontal cortex:
comparative architectonic organization in the human and the macaque monkey
brains. Cortex 48:46-57.
Petrof I, Viaene AN, Sherman SM (2012) Two populations of corticothalamic and
interareal corticocortical cells in the subgranular layers of the mouse primary sensory
cortices. J Comp Neurol 520:1678-1686.
Phelps EA (2004) Human emotion and memory: interactions of the amygdala and
hippocampal complex. Curr Opin Neurobiol 14:198-202.
Phelps EA, LeDoux JE (2005) Contributions of the amygdala to emotion processing: from
animal models to human behavior. Neuron 48:175-187.
Plochocki JH (2009) Mechanically-induced osteogenesis in the cortical bone of pre- to
peripubertal stage and peri- to postpubertal stage mice. J Orthop Surg Res 4:22.
Plummer JT, Evgrafov OV, Bergman MY, Friez M, Haiman CA, Levitt P, Aldinger KA
(2013) Transcriptional regulation of the MET receptor tyrosine kinase gene by MeCP2
and sex-specific expression in autism and Rett syndrome. Transl Psychiatry 3:e316.
194
Ponzetto C, Bardelli A, Zhen Z, Maina F, dalla Zonca P, Giordano S, Graziani A,
Panayotou G, Comoglio PM (1994) A multifunctional docking site mediates signaling
and transformation by the hepatocyte growth factor/scatter factor receptor family. Cell
77:261-271.
Poort J, Khan AG, Pachitariu M, Nemri A, Orsolic I, Krupic J, Bauza M, Sahani M, Keller
GB, Mrsic-Flogel TD, Hofer SB (2015) Learning enhances sensory and multiple non-
sensory representations in primary visual cortex. Neuron 86:1478-1490.
Puig MV, Rose J, Schmidt R, Freund N (2014) Dopamine modulation of learning and
memory in the prefrontal cortex: insights from studies in primates, rodents, and birds.
Front Neural Circuits 8:93.
Qian X, Su Y, Adam CD, Deutschmann AU, Pather SR, Goldberg EM, Su K, Li S, Lu L,
Jacob F, Nguyen P, Huh S, Hoke A, Swinford-Jackson SE, Wen Z, Gu X, Pierce RC,
Wu H, Briand LA, Chen HI, … Ming GL (2020) Sliced Human Cortical Organoids for
Modeling Distinct Cortical Layer Formation. Cell Stem Cell 26:766-781.e9.
Rakic P (1988) Specification of Cerebral Cortical Areas. Science 241:170-176.
Ramanathan KR, Jin J, Giustino TF, Payne MR, Maren S (2018) Prefrontal projections to
the thalamic nucleus reuniens mediate fear extinction. Nat Commun 9:4527.
Ramanathan KR, Ressler RL, Jin J, Maren S (2018) Nucleus Reuniens Is Required for
Encoding and Retrieving Precise, Hippocampal-Dependent Contextual Fear
Memories in Rats. J Neurosci 38:9925-9933.
Reichenberg A, Caspi A, Harrington H, Houts R, Keefe RS, Murray RM, Poulton R, Moffitt
TE (2010) Static and dynamic cognitive deficits in childhood preceding adult
schizophrenia: a 30-year study. Am J Psychiatry 167:160-169.
Reinert S, Hübener M, Bonhoeffer T, Goltstein PM (2021) Mouse prefrontal cortex
represents learned rules for categorization. Nature 593:411-417.
Resulaj A (2021) Projections of the mouse primary visual cortex. Front Neural
Circuits 15:751331.
Rizzo V, Touzani K, Raveendra BL, Swarnkar S, Lora J, Kadakkuzha BM, Liu XA, Zhang
C, Betel D, Stackman RW, Puthanveettil SV (2017) Encoding of contextual fear
memory requires de novo proteins in the prelimbic cortex. Biol Psychiatry Cogn
Neurosci Neuroimaging 2:158-169.
195
Rochefort NL, Narushima M, Grienberger C, Marandi N, Hill DN, Konnerth
A (2011) Development of direction selectivity in mouse cortical
neurons. Neuron 71:425-432.
Rose JE, Woolsey CN (1948) The orbitofrontal cortex and its connections with the
mediodorsal nucleus in rabbit, sheep and cat. Res Publ Assoc Res Nerv Ment
Dis 2:210-232.
Rosenberg DR, Lewis DA (1994) Changes in the dopaminergic innervation of monkey
prefrontal cortex during late postnatal development: a tyrosine hydroxylase
immunohistochemical study. Biol Psychiatry 36:272-277.
Rothmond DA, Weickert CS, Webster MJ (2012) Developmental changes in human
dopamine neurotransmission: cortical receptors and terminators. BMC Neurosci
13:18.
Rozeske RR, Valerio S, Chaudun F, Herry C (2015) Prefrontal neuronal circuits of
contextual fear conditioning. Genes Brain Behav 14:22-36.
Rubenstein J, Rakic P (2013) Neural Circuit Development and Function in the Brain:
Comprehensive Developmental Neuroscience. AP.
Rudie JD, Hernandez LM, Brown JA, Beck-Pancer D, Colich NL, Gorrindo P, Thompson
PM, Geschwind DH, Bookheimer SY, Levitt P, Dapretto M (2012) Autism-associated
promoter variant in MET impacts functional and structural brain networks. Neuron
75:904-915.
Rudy JW (1993) Contextual conditioning and auditory cue conditioning dissociate during
development. Behav Neurosci 107:887-891.
Sadler TW (1978) Distribution of surface coat material on fusing neural folds of mouse
embryos during neurulation. Anat Rec 19:345-349.
Samifanni R, Zhao M, Cruz-Sanchez A, Satheesh A, Mumtaz U, Arruda-Carvalho M
(2021) Developmental emergence of persistent memory for contextual and auditory
fear in mice. Learn Mem 28:414-421.
Santos TB, Kramer-Soares JC, Favaro VM, Oliveira M (2017) Involvement of the
prelimbic cortex in contextual fear conditioning with temporal and spatial
discontinuity. Neurobiol Learn Mem 144:1-10.
Sarter M, Markowitsch HJ (1983) Convergence of basolateral amygdaloid and
mediodorsal thalamic projections in different areas of the frontal cortex in the rat. Brain
Res Bull 10:607-622.
196
Schlaggar BL, O'Leary DD (1991) Potential of visual cortex to develop an array of
functional units unique to somatosensory cortex. Science 252:1556-1560.
Schoenwolf GC (1992) Morphological and mapping studies of the paranodal and
postnodal levels of the neural plate during chick neurulation. Anat Rec 233:281-290.
Shaw P, Kabani NJ, Lerch JP, Eckstrand K, Lenroot R, Gogtay N, Greenstein D, Clasen
L, Evans A, Rapoport JL, Giedd JN, Wise SP (2008) Neurodevelopmental trajectories
of the human cerebral cortex. J Neurosci 28:3586-3594.
Sigurdsson T, Stark KL, Karayiorgou M, Gogos JA, Gordon JA (2010) Impaired
hippocampal-prefrontal synchrony in a genetic mouse model of
schizophrenia. Nature 464:763-767.
Silva BA, Burns AM, Gräff J (2019) A cFos activation map of remote fear memory
attenuation. Psychopharmacology 236:369-381.
Sousa I, Clark TG, Toma C, Kobayashi K, Choma M, Holt R, Sykes NH, Lamb JA, Bailey
AJ, Battaglia A, Maestrini E, Monaco AP; International Molecular Genetic Study of
Autism Consortium (IMGSAC) (2009) MET and autism susceptibility: family and case-
control studies. Eur J Hum Genet 17:749-758.
Sowell ER, Thompson PM, Holmes CJ, Jernigan TL, Toga AW (1999) In vivo evidence
for post-adolescent brain maturation in frontal and striatal regions. Nat
Neurosci 2:859-861.
Spellman T, Svei M, Kaminsky J, Manzano-Nieves G, Liston C (2021) Prefrontal deep
projection neurons enable cognitive flexibility via persistent feedback
monitoring. Cell 184:2750-2766.e17.
Spiteri E, Konopka G, Coppola G, Bomar J, Oldham M, Ou J, Vernes SC, Fisher SE, Ren
B, Geschwind DH (2007) Identification of the transcriptional targets of FOXP2, a gene
linked to speech and language, in developing human brain. Am J Hum Genet 81:1144-
1157.
Stevenson CW (2011) Role of amygdala-prefrontal cortex circuitry in regulating the
expression of contextual fear memory. Neurobiol Learn Mem 96:315-323.
Stocker AM, O'Leary DD (2016) Emx1 Is Required for Neocortical Area Patterning. PloS
One 11:e0149900.
Sundberg SC, Lindström SH, Sanchez GM, Granseth B (2018) Cre-expressing neurons
in visual cortex of Ntsr1-Cre GN220 mice are corticothalamic and are depolarized by
acetylcholine. J Comp Neurol 526:120-132.
197
Takahashi K, Liu FC, Hirokawa K, Takahashi H (2003) Expression of Foxp2, a gene
involved in speech and language, in the developing and adult striatum. J Neurosci
Res 73:61-72.
Tang JC, Rudolph S, Dhande OS, Abraira VE, Choi S, Lapan SW, Drew IR, Drokhlyansky
E, Huberman AD, Regehr WG, Cepko CL (2015) Cell type-specific manipulation with
GFP-dependent Cre recombinase. Nat Neurosci 18:1334-1341.
Tasic B, Menon V, Nguyen TN, Kim TK, Jarsky T, Yao Z, Levi B, Gray LT, Sorensen SA,
Dolbeare T, Bertagnolli D, Goldy J, Shapovalova N, Parry S, Lee C, Smith K, Bernard
A, Madisen L, Sunkin SM, Hawrylycz M, Koch C, Zeng H (2016) Adult mouse cortical
cell taxonomy revealed by single cell transcriptomics. Nat Neurosci 19:335-346.
Thanseem I, Nakamura K, Miyachi T, Toyota T, Yamada S, Tsujii M, Tsuchiya KJ, Anitha
A, Iwayama Y, Yamada K, Hattori E, Matsuzaki H, Matsumoto K, Iwata Y, Suzuki K,
Suda S, Kawai M, Sugihara G, Takebayashi K, Takei N, Ichikawa H, Sugiyama T,
Yoshikawa T, Mori N (2010) Further evidence for the role of MET in autism
susceptibility. Neurosci Res 68:137-141.
Thompson BL, Levitt P (2015) Complete or partial reduction of the Met receptor tyrosine
kinase in distinct circuits differentially impacts mouse behavior. J Neurodev
Disord 7:35.
Thomson AM (2010) Neocortical layer 6, a review. Front Neuroanat 4:13.
Tiesinga PH, Buia CI (2009) Spatial attention in area V4 is mediated by circuits in primary
visual cortex. Neural Netw 22:1039-1054.
Torii M, Hackett TA, Rakic P, Levitt P, Polley DB (2013) EphA signaling impacts
development of topographic connectivity in auditory corticofugal systems. Cereb
Cortex 23:775-785.
Trevino AE, Müller F, Andersen J, Sundaram L, Kathiria A, Shcherbina A, Farh K, Chang
HY, Pașca AM, Kundaje A, Pașca SP, Greenleaf WJ (2021) Chromatin and gene-
regulatory dynamics of the developing human cerebral cortex at single-cell
resolution. Cell 184:5053-5069.e23.
Troyner F, Bicca MA, Bertoglio LJ (2018) Nucleus reuniens of the thalamus controls fear
memory intensity, specificity and long-term maintenance during
consolidation. Hippocampus 28:602-616.
Tsui D, Vessey JP, Tomita H, Kaplan DR, Miller FD (2013) FoxP2 regulates neurogenesis
during embryonic cortical development. J Neurosci 33:244-258.
198
Tsyporin J, Tastad D, Ma X, Nehme A, Finn T, Huebner L, Liu G, Gallardo
D, Makhamreh A, Roberts JM, Katzman S, Sestan N, McConnell SK, Yang Z, Qiu S,
Chen B (2021) Transcriptional repression by FEZF2 restricts alternative identities of
cortical projection neurons. Cell Rep 35:109269.
Uylings HB, Groenewegen HJ, Kolb B (2003) Do rats have a prefrontal cortex?. Behav
Brain Res 146:3-17.
Uylings HB, van Eden CG (1990) Qualitative and quantitative comparison of the prefrontal
cortex in rat and in primates, including humans. Prog Brain Res 85:31-62.
Van der Loos H, Woolsey TA (1973) Somatosensory cortex: structural alterations
following early injury to sense organs. Science 179:395-398.
Van Eden CG, Lamme VA, Uylings HB (1992) Heterotopic cortical afferents to the medial
prefrontal cortex in the rat. A combined retrograde and anterograde tracer study. Eur
J Neurosci 4:77-97.
Van Eden CG, Uylings HB (1985) Cytoarchitectonic development of the prefrontal cortex
in the rat. J Comp Neurol 241:253-267.
van Kerkoerle T, Marik SA, Meyer Zum Alten Borgloh S, Gilbert CD (2018) Axonal
plasticity associated with perceptual learning in adult macaque primary visual
cortex. Proc Natl Acad Sci U S A 115:10464-10469.
Varela C, Kumar S, Yang JY, Wilson MA (2014) Anatomical substrates for direct
interactions between hippocampus, medial prefrontal cortex, and the thalamic nucleus
reuniens. Brain Struct Funct 219:911-929.
Vargha-Khadem F, Gadian DG, Copp A, Mishkin M (2005) FOXP2 and the neuroanatomy
of speech and language. Nat Rev Neurosci 6:131-138.
Vargha-Khadem F, Watkins KE, Price CJ, Ashburner J, Alcock KJ, Connelly A,
Frackowiak RS, Friston KJ, Pembrey ME, Mishkin M, Gadian DG, Passingham RE
(1998) Neural basis of an inherited speech and language disorder. Proc Natl Acad Sci
U S A 95:12695-12700.
Vasquez JH, Leong KC, Gagliardi CM, Harland B, Apicella AJ, Muzzio IA (2019) Pathway
specific activation of ventral hippocampal cells projecting to the prelimbic cortex
diminishes fear renewal. Neurobiol Learn Mem 161:63-71.
199
Vernes SC, Oliver PL, Spiteri E, Lockstone HE, Puliyadi R, Taylor JM, Ho J, Mombereau
C, Brewer A, Lowy E, Nicod J, Groszer M, Baban D, Sahgal N, Cazier JB, Ragoussis
J, Davies KE, Geschwind DH, Fisher SE (2011) Foxp2 regulates gene networks
implicated in neurite outgrowth in the developing brain. PLoS Genet 7:e1002145.
Vertes RP, Hoover WB, Szigeti-Buck K, Leranth C (2007) Nucleus reuniens of the midline
thalamus: link between the medial prefrontal cortex and the hippocampus. Brain Res
Bull 71:601-609.
Verwer RW, Van Vulpen EH, Van Uum JF (1996) Postnatal development of amygdaloid
projections to the prefrontal cortex in the rat studied with retrograde and anterograde
tracers. J Comp Neurol 376:75-96.
Wagner IC, van Buuren M, Fernández G (2019) Thalamo-cortical coupling during
encoding and consolidation is linked to durable memory
formation. Neuroimage 197:80-92.
Walker AE (1940) A cytoarchitectural study of the prefrontal area of the macaque monkey.
J Comp Neurol 73:59-86.
Walther C, Gruss P (1991) Pax-6, a murine paired box gene, is expressed in the
developing CNS. Development 113:1435-1449.
Wang J, Ni Z, Jin A, Yu T, Yu H (2019) Ocular Dominance Plasticity of Areas 17 and 21a
in the Cat. Front Neurosci 13:1039.
Warden MR, Selimbeyoglu A, Mirzabekov JJ, Lo M, Thompson KR, Kim SY, Adhikari
A, Tye KM, Frank LM, Deisseroth K (2012) A prefrontal cortex-brainstem neuronal
projection that controls response to behavioural challenge. Nature 492:428-432.
Wass C, Sauce B, Pizzo A, Matzel LD (2018) Dopamine D1 receptor density in the mPFC
responds to cognitive demands and receptor turnover contributes to general cognitive
ability in mice. Sci Rep 8:4533.
Watakabe A, Hirokawa J, Ichinohe N, Ohsawa S, Kaneko T, Rockland KS, Yamamori T
(2012) Area-specific substratification of deep layer neurons in the rat cortex. J Comp
Neurol 520:3553-3573.
Watanabe M (1998) Cognitive and motivational operations in primate prefrontal neurons.
Rev Neurosci 9:225-241.
Wilber AA, Clark BJ, Demecha AJ, Mesina L, Vos JM, McNaughton BL (2015) Cortical
connectivity maps reveal anatomically distinct areas in the parietal cortex of the rat.
Front Neural Circuits 8:146.
200
Woodworth MB, Greig LC, Kriegstein AR, Macklis JD (2012) SnapShot: Cortical
development. Cell 151:918-918.e1.
Woodworth MB, Greig LC, Liu KX, Ippolito GC, Tucker HO, Macklis JD (2016) Ctip1
regulates the balance between specification of distinct projection neuron subtypes in
deep cortical layers. Cell Rep 15:999-1012.
Xia B, Wei J, Ma X, Nehme A, Liong K, Cui Y, Chen C, Gallitano A, Ferguson D, Qiu
S (2021) Conditional knockout of MET receptor tyrosine kinase in cortical excitatory
neurons leads to enhanced learning and memory in young adult mice but early
cognitive decline in older adult mice. Neurobiol Learn Mem 179:107397.
Xie Z, Eagleson KL, Wu HH, Levitt P (2016) Hepatocyte Growth Factor Modulates MET
Receptor Tyrosine Kinase and β-Catenin Functional Interactions to Enhance Synapse
Formation. eNeuro 3:ENEURO.0074-16.2016.
Xu W, Südhof TC (2013) A neural circuit for memory specificity and
generalization. Science 339:1290-1295.
Zarr N, Brown JW (2016) Hierarchical error representation in medial prefrontal
cortex. Neuroimage 124:238-247.
Zeisel A, Muñoz-Manchado AB, Codeluppi S, Lönnerberg P, La Manno G, Juréus
A, Marques S, Munguba H, He L, Betsholtz C, Rolny C, Castelo-Branco G, Hjerling-
Leffler J, Linnarsson S (2015) Brain structure. Cell types in the mouse cortex and
hippocampus revealed by single-cell RNA-seq. Science 347:1138-1142.
Zelikowsky M, Hersman S, Chawla MK, Barnes CA, Fanselow MS (2014) Neuronal
ensembles in amygdala, hippocampus, and prefrontal cortex track differential
components of contextual fear. J Neurosci 34:8462-8466.
Zhang Q, Weber MA, Narayanan NS (2021) Medial prefrontal cortex and the temporal
control of action. Int Rev Neurobiol 158:421-441.
Zhang Z, Zhou J, Tan P, Pang Y, Rivkin AC, Kirchgessner MA, Williams E, Lee CT, Liu
H, Franklin AD, Miyazaki PA, Bartlett A, Aldridge AI, Vu M, Boggeman L, Fitzpatrick
C, Nery JR, Castanon RG, Rashid M, Jacobs MW, … Callaway EM (2021) Epigenomic
diversity of cortical projection neurons in the mouse brain. Nature 598:167-173.
Zhong S, Zhang S, Fan X, Wu Q, Yan L, Dong J, Zhang H, Li L, Sun L, Pan N, Xu
X, Tang F, Zhang J, Qiao J, Wang X (2018) A single-cell RNA-seq survey of the
developmental landscape of the human prefrontal cortex. Nature 555:524-528.
201
Zhou C, Tsai SY, Tsai MJ (2001) COUP-TFI: an intrinsic factor for early regionalization
of the neocortex. Genes Dev 15:2054-2059.
Ziai MR, Sangameswaran L, Hempstead JL, Danho W, Morgan JI (1988) An
immunochemical analysis of the distribution of a brain-specific polypeptide, PEP-19.
J Neurochem 51:1771-1776.
Zingg B, Hintiryan H, Gou L, Song MY, Bay M, Bienkowski MS, Foster NN, Yamashita S,
Bowman I, Toga AW, Dong HW (2014) Neural networks of the mouse neocortex. Cell
156:1096-1111.
Abstract (if available)
Abstract
During cerebral cortical development, molecular heterogeneity underlies the development of discrete brain circuits that are capable of carrying out a vast array of brain processes throughout ones lifetime. Disruptions in brain development, even at the molecular level, can result in neurodevelopmental disorders. The biological mechanisms underlying neurodevelopment disorders, however, remain poorly understood. Therefore, studying typical brain development is essential to be able to identify the source of developmental deficits when they occur and find therapeutic treatments. Here, heterogeneity in the cerebral cortex is explored and the expression patterns, regulators, function, and circuit involvement of a receptor tyrosine kinase, MET, is determined. I demonstrate that developmental processes that build cerebral cortex neuronal subtypes and circuits cannot be generalized across ages or across cortical regions. Further, developmental disruptions may not result in a phenotype until after the maturation process is complete. These studies emphasize the importance of a developmental experimental design to reveal the transient and dynamic biological mechanisms that contribute to lifelong brain functions.
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Asset Metadata
Creator
Lanjewar, Alexandra Lauren
(author)
Core Title
Dynamic processes underlying cerebral cortical development with lifespan impact
School
Keck School of Medicine
Degree
Doctor of Philosophy
Degree Program
Neuroscience
Degree Conferral Date
2022-12
Publication Date
11/10/2022
Defense Date
10/31/2022
Publisher
University of Southern California
(original),
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autism,cerebral cortex,Development,Memory,Neuroscience,OAI-PMH Harvest,receptor tyrosine kinase
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theses
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English
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Electronically uploaded by the author
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Eagleson, Kathie (
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), Dias, Brian (
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), Herring, Bruce (
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), Levitt, Pat (
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)
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alexlanje23@gmail.com,lanjewar@usc.edu
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Tags
autism
cerebral cortex
receptor tyrosine kinase