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Defining the functional roles of neurotransmitters and neuropeptides in neural circuits

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Defining the functional roles of neurotransmitters and neuropeptides in neural circuits

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DEFINING THE FUNCTIONAL ROLES OF NEUROTRANSMITTERS AND
NEUROPEPTIDES IN NEURAL CIRCUITS

by

Courtney Hudson-Paz

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOLOGY OF AGING)
August 2022

Copyright 2022 Courtney Hudson-Paz

ii
DEDICATION
First and foremost, I dedicate my dissertation work to Ori, my light, my someone to learn
with. I wouldn’t be here without your continuous support and heroic levels of
encouragement. Thank you for filling our life with beautiful words and play, for speaking
all my languages of love, and pushing me to always dream bigger.

To Mom and Dad: I view my life as a continuation of yours. You gave me everything:
endless shameless selfless love, support to explore my passions, and no pressure to fit
into a something I’m not. Your wish for me was always happiness. I will continually aim
to live up to the potential you see in me and try to make you proud.

To JD: The great luck of my life is to call you brother. Thank you for your friendship,
being my champion and cheerleader.

This dissertation is dedicated in loving memory of my grandfather, Jim Hudson. Your
and Oma’s love for me knows no bounds. You taught me the value of presence, instilled
in me a love for stories and a deep curiosity about the world and how it worked.

To all my family: given, chosen, and gained. I love you. I love you. I love you.

iii
ACKNOWLEDGEMENTS
I cannot begin to thank adequately those who have helped me through my
doctoral training. Pursuing this degree would not have been possible without the help
from so many.
As I write this, I have before me my manuscript displaying thoughtful and tactful
notes from my advisor, Dr. Jennifer Garrison. It is thanks to her that my doctoral work,
beautifully encapsulated within this document, was possible. Jennifer, thank you for
your support, guidance, and mentorship. Thank you for believing in me and taking me
on as your first PhD student. I’m thankful for all of the opportunities you have given me.
I will take the lessons you taught me, through your words and actions, into my next
chapter. I am deeply grateful for the time you have invested in me as a person and a
scientist.
I would like to thank Dr. Zachary Knight. Zack, your passion and excitement for
science is contagious. Thank you for sharing your time, advise, and immense expertise.
I am also grateful for the efforts you made to make me feel like part of your lab.
Thank you to my dissertation committee, Drs. Gordon Lithgow, Julie Andersen,
Tara Tracy, and Sean Curran. I have drawn on your wisdom and support. Your
guidance has been instrumental to my development as a scientist.
Thank you to all my fellow Garrison Lab members, who were unfailingly
generous and kindly showed an endless amount of patience and encouragement. I
would like to especially thank Jacqueline Lo, who even helped me run my mass spec
samples while in labor. Tony Liang and Kristin Obrochta Moss who I cherish as mentors
and showed me that science is just as much about the people you meet along the way.

iv
Heeun Jang, thank you for your help analyzing my drinking data. To “the minds that
grew with mine”
1
: especially Katelyn Adam, Lizzie Poznyakov, and Alexandra Ongpin
who helped me with experiments, puzzles, and for infusing my days with laughter.
Thank you to the undergraduate research assistants, Shamieh Banihani and Keith Kim,
whose excitement for learning reminded me why I love science.
Thank you to all my lab mates in the Knight Lab. I’m beyond grateful for your
heaps of patience, continuous support, and happy hours. I feel lucky to have two lab
families. I would like to especially thank Jamie Ahn, Usan Dan, Nilla Sivakumar for
helping with experiments and surgeries. This wouldn’t have been possible without your
help.
A huge thank you to my collaborators in the Baggerman Lab: Jusal Quanico,
Eline Berghmans, and Geert Baggerman. Thank you for hard work illuminating the
neuropeptides I’m trying to knockdown.
Eamonn, thank you for giving me tools when I needed them most.
Thank you to the Buck Institute and everyone there who made it feel like home
throughout my time here. Especially the turkeys and deer, I’ll never tire of you pecking
at my window.
Finally, I would like to acknowledge and thank all of the animals sacrificed in the
name of scientific discovery. We are forever indebted to you.

v

TABLE OF CONTENTS
Dedication ......................................................................................................................... ii
Acknowledgements .......................................................................................................... iii
List of Tables .................................................................................................................. vii
List of Figures ................................................................................................................ viii
Abbreviations ................................................................................................................... ix
Abstract ............................................................................................................................. x
Chapter 1: Introduction .................................................................................................... 1
Neuropeptide Signaling ......................................................................................... 1
What is a Neuropeptide? .............................................................................. 1
Evolutionary considerations ......................................................................... 3
Biosynthesis ................................................................................................. 4
Release ........................................................................................................ 9
Targets and Receptors ............................................................................... 12
Colocalization ............................................................................................. 17
Why are neuropeptides difficult to study? .................................................. 20
Tools for Manipulating Neurotransmission .......................................................... 21
Whole neuron manipulations ...................................................................... 22
Genetic dissection of gene functions ......................................................... 25
Lessons from neuropeptide knockouts ...................................................... 28
Lessons from neuropeptide processing enzyme knockouts ...................... 29
Neuropeptide Signaling in Energy Homeostasis ................................................. 32
The central melanocortin system - first order neurons ............................... 33
Second order neurons ................................................................................ 37
Neuropeptide Signaling in Aging ......................................................................... 42
Melanocortin system .................................................................................. 43
Gonadotropin-releasing hormone and hypothalamic inflammation ............ 44
Neuropeptide Y .......................................................................................... 45
Chapter 2: Cell-type specific Neurotransmitter Silencing (CNS) development .............. 47
Abstract ............................................................................................................... 47
Introduction ......................................................................................................... 47
Results ................................................................................................................ 49
Testing CRISPR and CRISPRi variations for CNS .................................... 49
Staphylococcus aureus CRISPRi development and characterization ........ 51
Staphylococcus aureus CRISPR reduced expression of oxytocin ............. 54
Streptococcus pyogenes CRISPRi effectively reduced gene expression .. 55
CNS efficiently silences gene expression in vitro ....................................... 56

vi
Transgenic mouse lines expressing dCas9 ............................................... 61
CNS efficiently silences gene expression in the mouse brain .................... 64
Transgenic expression of sgRNA array facilitates simultaneous
knockdown of multiple genes ...................................................................... 68
Viral delivery of an array of sgRNAs enables simultaneous knockdown
of multiple genes ........................................................................................ 72
Chapter 3: Defining the functional role of neuropeptides and neurotransmitters
regulating energy homeostasis ...................................................................................... 77
Abstract ............................................................................................................... 77
Introduction ......................................................................................................... 77
Results ................................................................................................................ 80
Glutamate in PVN plays a key role in body weight regulation in males,
but not females ........................................................................................... 80
Targeted knockdown of neuropeptide or glutamatergic output in Sim1+
PVN neurons impacts body weight on different timescales ....................... 82
Disrupting neuropeptide output globally leads to energy homeostasis
and behavioral deficits ............................................................................... 84
Viral toxicity in PVH leads to massive obesity ............................................ 89
Conditional inactivation of neuropeptide output does not impact body
weight ......................................................................................................... 91
Viral disruption of neuropeptide maturation in key brain areas
controlling energy homeostasis led to changes in body weight ................. 93
Chapter 4: Discussion .................................................................................................... 96
Summary of findings ........................................................................................... 96
Cell-type Specific Neurotransmitter Silencing - the method to our madness ...... 97
Global neuropeptide knockdown ....................................................................... 101
Paraventricular hypothalamus: a microcosm of homeostatic control ................ 103
Sex differences in body weight regulation ........................................................ 107
Neuropeptide signaling in ARC and NTS .......................................................... 108
Neuropeptide signaling - Flexible, resilient, adaptable, responsive, and
elastic ................................................................................................................ 110
Conclusion ........................................................................................................ 111
Materials and Methods ................................................................................................ 112
References .................................................................................................................. 122

vii
LIST OF TABLES
Table 1: Considerations for CRISPR based gene knockdown methods ....................... 50

viii
LIST OF FIGURES
Figure 1. Neuropeptide Biosynthesis ............................................................................... 5
Figure 2. Overview of CRISPR interference (CRISPRi) ................................................ 27
Figure 3: The central melanocortin system .................................................................... 34
Figure 4. CRISPRi and CRISPR-mediated gene silencing ............................................ 51
Figure 5. Virally delivered Staphylococcus aureus CRISPRi dCas9-induced cell
death ....................................................................................................................... 53
Figure 6. CRISPR based gene disruption led to a reduction in oxytocin expression
in mouse brain ........................................................................................................ 55
Figure 7. sp CRISPRi efficiently silences oxytocin expression in mouse brain ............. 56
Figure 8. CNS efficiently silences gene expression in vitro ........................................... 58
Figure 9. Multiple sgRNAs targeting the same gene did not enhance repression
efficiency of CPE .................................................................................................... 60
Figure 10. dCas9-KRAB delivery via transgenic mice ................................................... 64
Figure 11. CNS efficiently silences gene expression in vivo ......................................... 66
Figure 12. Image Analysis Pipeline ............................................................................... 68
Figure 13. Transgenic expression of sgRNA array facilitates multiplex gene
knockdown .............................................................................................................. 71
Figure 14. Viral delivery of an array of sgRNAs enables flexible multiplex gene
knockdown .............................................................................................................. 75
Figure 15. Silencing glutamate in PVN leads to a sex-specific increase in body
weight ..................................................................................................................... 81
Figure 16. Silencing neuropeptide or glutamatergic output in Sim1+ PVN neurons
impacts body weight on different timescales in male mice ..................................... 83
Figure 17. Disrupting neuropeptide output globally leads to energy homeostasis and
behavioral deficits ................................................................................................... 89
Figure 18. Viral toxicity in PVN leads to massive obesity .............................................. 91
Figure 19. The LSL dCas9 mouse was not functional ................................................... 93
Figure 20. Viral disruption of neuropeptide maturation in NTS and PVN led to
changes in body weight .......................................................................................... 95

ix
ABBREVIATIONS
Agouti related protein (AgRP)
Alpha melanocyte stimulating hormone (αMSH)
Arcuate nucleus (ARC)
beta melanocyte stimulating hormone (bMSH)
Carboxypeptidase D (CPD)
Cell-type specific Neurotransmitter Silencing (CNS)
Cerebral spinal fluid (CSF)
Clozapine-N-oxide (CNO)
Clustered regularly interspaced palindromic repeats (CRISPR)
Corticotropin-releasing hormone (CRH)
CRISPR activation (CRISPRa)
CRISPR interference (CRISPRi)
Deactivated Cas9 (dCas9)
Dense-core vesicles (DCV)
Designer receptors exclusively activated by designer drugs (DREADDs)
Double-floxed inverse open reading frame (DIO)
Fluorescent in situ hybridization (FISH)
G-protein coupled receptors (GPCRs)
gamma melanocyte stimulating hormone (gMSH)
Growth hormone releasing hormone (GHRH)
Growth hormone-releasing hormone (GHRH)
Human synapsin (hSyn)
Intracerebroventricular (ICV)
Krüppel-associated box (KRAB)
Lateral parabrachial nucleus (LPBN)
Lox-stop-lox (LSL)
Melanin-concentrating hormone (MCH)
Melanocortin-4 receptor (MC4R)
Neuropeptide Y (NPY)
Non-homologous end joining (NHEJ)
Pro-opiomelanocortin (POMC)
Quantitative reverse-transcription PCR (qRT-PCR)
Region of interest (ROI)
RNA interference (RNAi)
single guide RNA (sgRNA)
Translating ribosome affinity purification (TRAP)
Vesicle-associated membrane proteins (VAMP)
Woodchuck hepatitis virus post-transcriptional regulatory element (WPRE)
β-endorphin (β-END)

x
ABSTRACT
Most neurons in the brain use multiple neuropeptides, monoamine
neurotransmitters, and fast acting amino acid neurotransmitters to communicate.
Although fast neurotransmitter signaling and slow neuropeptide signaling function
together, they have distinct functional roles, while acting on different timescales, over an
expansive range of distances to activate a wide variety of targets. A key question in
neuroscience remains largely unanswered: how does the brain use the cocktail of
chemical messengers to control physiology and coordinate behavior? The vast number
of chemical signals co-expressed within intermixed heterogenous cell-types in the brain
makes it challenging to evaluate the individual contributions of each type of signal.
Moreover, neuropeptides have a wide range of functional roles; however, it has been
difficult to do a simple acute loss of function experiment that would link individual
transmitter to a specific functional role with the current tools available. While there is still
much to learn, we gain more insight with each step in the evolution of tool development
in neurobiology.
Here, I introduce a flexible system, Cell-type specific Neurotransmitter Silencing
(CNS) which uses CRISPR interference (CRISPRi) to silence neurotransmitters in a
cell-type specific manner individually, or as an entire class in the mammalian brain. I
show acute simultaneous silencing of multiple genes with spatial and temporal
specificity. By targeting common neuropeptide processing enzymes, I successfully
reduced neuropeptide signaling from specific cell types and brain areas. I used CNS to
dissect some of the chemical signals controlling energy homeostasis. Through a century
of research, our understanding of how the brain monitors and controls food intake and

xi
energy homeostasis has dramatically increased but has not translated to effective
therapies for obesity. Many of the brain areas and cell types controlling feeding have
been identified however, the contributions of the individual chemical messengers within
these regions and circuits remain unknown. Using CNS, I found sex specific differences
in the role of glutamate within the paraventricular hypothalamus, while I demonstrated
that both neuropeptides and glutamate play a significant role in body weight regulation.
Disrupting neuropeptide maturation globally led to deficits in the ability of mice to cope
with social isolation stress and maintain energy and fluid homoeostasis. This system
provides an important tool for identifying roles of individual transmitters in specific
phenotypes and linking neuropeptides to their function.

1
CHAPTER 1: INTRODUCTION
While it was once thought that only a subset of neurons and brain regions
contain neuropeptides, it is becoming more apparent that most neurons in the brain
contain a mix of multiple neuropeptides, monoamine neurotransmitters, and fast acting
amino acid neurotransmitters. Despite being the largest and most diverse class of
signaling molecules in the brain, there is still a considerable amount we do not
understand about neuropeptides and how they function. With each step in the evolution
of tool development in neurobiology, we gain more insight on how the brain uses this
chemical signaling cocktail to control behavior and physiology. This introduction covers
the past several decades of research to unveil what is already known about
neuropeptide signaling, the questions that remain, and strategies for uncovering their
mysteries.

Neuropeptide Signaling
What is a Neuropeptide?
Neuropeptides are polymers of amino acids that are processed, stored, and
released from dense core vesicles (DCVs) and function as cell-to-cell chemical
messengers
2
. They are the largest and most diverse class of signaling molecules in the
brain, and they exert pronounced effects on behavior along with energy homeostasis,
fluid homeostasis, reproduction, growth, memory, stress, circadian rhythms, mood, and
reward among many other processes
2–20
. As there is often confusion about the

2
differences between neuropeptides and other types of bioactive peptides, is imperative
to review the common features of neuropeptides.
Neuropeptides share several key distinguishing features. They are expressed
and synthesized by a wide variety of cells, including but not limited to neurons
21,22
. They
undergo controlled secretion, and they act via G-protein coupled receptors (GPCRs) to
modulate biological activity
23
. Neuropeptides are only active once released from a cell
and participate in intercellular and intertissue communication
2,23–25
. Neuropeptides are
synthesized as part of larger proprotein precursors
26–28
, which do not have any function
on their own. During biogenesis these precursors are cleaved to produce smaller
bioactive peptides by a set of processing and modifying enzymes in the secretory
pathway and primarily inside the DCVs
29–33
. Many have post-translational modifications
that are required for function and render them resistant to non-specific degradation
34
.
The final bioactive peptides vary in length ranging from 3 to more than 40 amino acids
35
.
DCVs, loaded with approximately 84,000-85,000 mature bioactive peptides
36–38
, are
stored within the cell until they are stimulated for release. In response to electrical
activity and elevated Ca
2+
levels, the vesicles release the peptides into the extracellular
space. Once released, neuropeptides can signal locally but are not confined spatially by
synaptic wiring; there is debate about just how far these signals can travel but they do
have long half-lives in the CNS
39
. Neuropeptides act on multiple scales: they can have
autocrine effects on the cell they were released from, paracrine effects on their
neighboring cells, or signal long distances to diverse targets
40
. Most neuropeptides
exert their effects through G-protein coupled receptors (GPCRs) on target cells both
within and outside of the central nervous system. Neuropeptides bind to GPCRs with

3
extremely high affinities, as a result they are potent at low nanomolar concentrations –
facilitating long range interactions
41
. GPCR signaling generates an intracellular cascade
of molecular actions with diverse outcomes and is slower, acting on the timescale of
seconds or longer, than signaling through ion channels, which act on the millisecond
scale
42,43
. Neuropeptides have powerful effects on their targets reaching far beyond
changes in membrane excitability to effects on gene transcription, modulation of local
blood flow
44–47
, morphological organization and plasticity
48
, among other effects on
neuronal and non-neuronal tissue.

Evolutionary considerations
Neuropeptides are ancient chemical messengers. In fact, nervous systems in the
first animals they evolved in, cnidarians, are primarily peptidergic
49
. In these animals,
neuropeptides functioned in a similar way to the fast type of neurotransmission seen in
classical neurotransmission, signaling through ionotropic receptors
50
. Neuropeptide
signaling was vital for shaping the development of the nervous systems through
hundreds of millions of years
51
. Additionally, since many neuropeptides functional roles
are non-essential, they are excellent mediators of flexibility in the nervous system.
Neuropeptides are remarkably conserved through strong selection pressure, especially
the bioactive region of the peptides
52
. The peptide families known today developed into
their current system through a process involving gene duplication and point mutations,
which allowed neuropeptide signaling to expand and diverge distinct roles and refine
their precise functions
2
.

4
Biosynthesis
Neuropeptide synthesis is a complex process notably different from that of
classic neurotransmitters. In addition to describing what is known about neuropeptide
biosynthesis, release, and targets, the following three sections will outline how
neuropeptide signaling compares to classical neurotransmitter signaling to illustrate the
significance of the differences between the two.
Neuropeptides are derived from larger inactive precursors that undergo a series
of endoproteolytic cleavages and post-translational modification to transform them into
active peptides that are ready for release
26,27
(Figure 1). Neuropeptide proprotein
precursors may encode several copies of one peptide, as is the case with proTRH
which contains five copies of the peptide thyrotropin-releasing hormone
53
, or many
different peptides like pro-opiomelanocortin (POMC), which produces
adrenocorticotropic hormone, corticotropin-like intermediate peptide, α-MSH, β-
endorphin, β-MSH, and γ-MSH
54
. This process begins with a synthesized gene product
that contains an N-terminal signal peptide around 20-25 amino acids long, which targets
the translating ribosome to the endoplasmic reticulum (ER)
55
. Like every other protein
entering the regulated secretory pathway, the signal peptide is cleaved off by signal
peptidase at the ER membrane and the proprotein precursor is translocated into the ER
lumen
56,57
. The neuropeptide precursors continue through the regulated secretory
pathway from the endoplasmic reticulum to the Golgi in small vesicles, then to the trans-
Golgi where they are sorted into immature secretory granules
58
. Beginning in the trans-
Golgi, enzymes begin cleaving and modifying the neuropeptide precursors, but the
majority of the modifications occur via specific neuropeptide processing enzymes in the

5
maturing secretory granule or DCV
59
. There are multiple possible processing enzymes
for each step of neuropeptide maturation, and their specific expression patterns
determine which collection of enzymes are available in a particular cell, adding an
important layer of regulation to neuropeptide expression at the level of biogenesis. For
example, POMC encodes 8 peptides that have different biological effects, which
peptides are generated is dependent upon which enzymes are expressed due to their
cut site preference and specificity
60
.

Figure 1. Neuropeptide Biosynthesis
Neuropeptides (coral, teal, mustard) are cleaved out of larger inactive proprotein
precursors. Endopeptidases, Furin (trans Golgi), prohormone convertase 1 and 2
(PCSK1 and PCSK2) (DCVs) cut after pairs of basic residues (RR; KR) creating
processing intermediates. Carboxypeptidase D (CPD) (trans Golgi) and
Carboxypeptidase E (CPE) (DCVs) trim off the basic residues. Following that the
peptides undergo post translational modifications, such as amidation by
peptidyl-alpha-amidating monooxygenase (PAM) (DCVs). Adapted from Fricker 2012
59
.
Each neuropeptide processing enzyme recognizes specific clusters of basic
amino acids, arginine and lysine, within the neuropeptide precursor
61
. Furin is

6
sometimes the first endoprotease to cleave the neuropeptide precursor within the trans-
Golgi. It is membrane bound and cleaves peptides after the final arginine in a sequence
that contains another basic residue 1 or 3 amino acids before it
59,62,63
. While furin has
been shown to be involved in the maturation of the neuropeptides from the gene
proSAAS
64
, due to its location in the trans-Golgi and expression in non-neuronal
tissues, neuropeptide biosynthesis is likely not furin’s primary function
63,65
.
Most cleavages at basic residues are mediated by the endopeptidases,
prohormone convertase 1 and 2 (PCSK1 and PCSK2). PCSK1 and PCSK2 are
packaged into immature secretory granules with the neuropeptide precursors
62
. There,
the Ca
2+
dependent processing enzymes, which are optimally active in pH range of 5-6
matching the internal conditions of DCVs, are activated when the vesicles fully mature
66
.
Both PCSK1 and 2 are expressed primarily in neuroendocrine cells
67
and are expressed
either individually or together to create different final assortments of bioactive
peptides
59
. PCSK1 and PCSK2 usually cut after pairs of basic amino acids but also
recognize sites where the basic amino acid pair is separated by an even number of
amino acids and, rarely, they have been shown to cut at a one single amino acid
68–70
.
PCSK1 has an affinity for larger precursors, while PCSK2 is able to cleave more types
of sites more easily, including sequences that have proline, positively or negatively
charged residues, or aromatic residues
69
. This broad and overlapping substrate
specificity has made it difficult to predict potential cleavage sites. Though they tend to
cleave different sites, PCSK1 and PCSK2 both have regulatory binding proteins.
ProSAAS is a potent inhibitor of PCSK1 via its C-terminal region
71,72
. ProSAAS is also
cleaved into smaller fully processed peptides with action independent of PCSK1

7
inhibition, including big and little PEN, LEN, GAV and SAAS
73
that play roles in many
processes, including body weight regulation
74,75
, and may play a role as a biomarker of
Alzheimer’s disease
76,77
. PCSK2’s binding protein, 7B2, is required for the activation of
PCSK2
78
. After proPCSK2 folds in the endoplasmic reticulum, 7B2 binds to it allowing it
to exit the ER
79
. Its main role is to bind and prevent aggregation of proPCSK2
80
. With
help from 7B2, PCSK2 cleaves neuropeptide precursors leaving C-terminally extended
peptide processing intermediates ready for processing by carboxypeptidases.
After the endopeptidases cleave the precursors at basic residues,
Carboxypeptidases D and E are zinc-binding carboxypeptidases, primarily responsible
for trimming the basic residues off the C-terminus neuropeptide processing
intermediates. The first to encounter the intermediates is Carboxypeptidase D (CPD)
because of its localization in the trans-Golgi, like Furin
81
. Similarly to Furin, CPD is
membrane bound, expressed more broadly than neuronal tissues
82
, and plays a
minimal role in neuropeptide processing
83
. CPD’s role in neuropeptide processing was
demonstrated via neuropeptidomics studies of Cpe knockout mice
84
. Carboxypeptidase
E (CPE) is the major enzyme for cleaving basic residues in mammalian neuropeptide
biosynthesis, but by no means the only one. CPE is expressed in neuroendocrine tissue
and its soluble form is localized to the secretory granules or DCV
85,86
. Like PCSK1 and
PCSK2, that are also localized to the DCVs, CPE is activated with the maturation of the
DCV, as the pH lowers
87
. Recent studies have revealed additional non-enzymatic roles
of CPE as a sorting receptor to direct proneuropeptides to the secretory pathway when
it is membrane bound
88
and a trophic factor with neuroprotective effects when the N-
terminal region is truncated
89,90
.

8
Additional post-translational modifications found on neuropeptides have a critical
impact on their activity such as cyclization, acetylation, phosphorylation, glycosylation,
pyroglutamylation, C-terminal amidation, and sulfation
2,59
. C-terminal amidation is an
important modification to peptides as it provides protection from non-specific digestion
by extracellular peptidases after the peptide is released, enabling more temporal and
spatial flexibility in its signal transmission
34
. In fact, around half of all bioactive peptides
are C-terminally amidated
91,92
. C-terminal amidation is achieved through the enzyme
peptidyl-alpha-amidating monooxygenase (PAM) in mature secretory granules or DCVs.
If there is a glycine at the C-terminus, PAM catalyzes a reaction to remove the carbon
atoms of the glycine leaving an amide
93
. PAM is actually two enzymes, peptidylglycine
alpha-hydroxylating monooxygenase (PHM) and peptidyl-alpha-hydroxyglycine
alpha-amidating lyase (PAL)
94
, and can be membrane bound or soluble
95
. Following the
final post-translational modifications, the mature peptides are bioactive and stored in
DCVs, ready for release.
The biosynthesis of neuropeptides is a complex and intricate process. In
contrast, classical neurotransmitters are small molecules (not peptides), and their
biogenesis occurs closer to the site of release and via a step-wise series of reactions by
biosynthetic enzymes
35
. There is far less biodiversity in the number of small molecule
neurotransmitters, around 10 (acetylcholine, glutamate, GABA, glycine, aspartate,
histamine, dopamine, norepinephrine, epinephrine, and serotonin), compared to the
hundreds of neuropeptides. Classical neurotransmitters are produced locally in
presynaptic locations by enzymes that are transported from the cell body. Here, they
are packaged into small clear synaptic vesicles, which are physically and functionally

9
distinct from DCVs
96
. Additionally, most neurotransmitters are recycled into the
presynaptic neuron via reuptake transporters and not made de novo like
neuropeptides
35
.

Release
Following neuropeptide biosynthesis, the bioactive peptides, packed into DCVs,
await a signal for release. Controlled secretion is a fundamental feature of neuropeptide
signaling. To understand neuropeptide signaling, it is critical to know which cells
express neuropeptides, where in the cell neuropeptides are released from, what
neuropeptides are released in response to, and how neuropeptide release compares to
classical neurotransmitter release.
It was once thought that peptide-releasing neurons were rare and unique, but
years of research have made apparent that most neurons and many non-neuronal cells
use neuropeptides to communicate
97
. The hypothalamus has been recognized as a
brain area rich with neuropeptide expression and diversity but the presence of
neuropeptides here is not unique. The role of neuropeptides is becoming apparent in
other brain regions as well, such as the hippocampus
98
and the cortex
99
. In fact, new
research has unveiled that neuropeptides and their receptors are the best markers for
neuron-types in the brain
99
. Additionally, neuropeptides are not only made or used the
brain. In reality, neuropeptides are expressed in all parts of the nervous system: the
brain, spinal cord, gastrointestinal system, and the sensory and autonomic ganglia
2
.
Neuropeptide expression is also not localized to neurons, deviating from their formal
definition: “small proteinaceous substances produced and released by neurons through

10
the regulated secretory route and acting on neural substrates”
23
. There is accumulating
evidence that neuropeptides are expressed in astrocytes
21
, oligodendrocytes, microglia,
Schwann cells
22
, and non-neuronal tissues
100,101
. Each neuropeptide has a specific
expression pattern and mode of expression that generally fall into three categories: high
levels at any time, normally low or undetectable levels that are upregulated under
specific circumstances, and those expressed in early development with little expression
in adulthood
2
. However, the mode of expression is dependent on the neuron type and
the same peptide may occupy all three categories. In addition, peptide levels rise and
fall with the circadian clock and estrous cycle and may be one of the ways the brain
coordinates these states
102–104
.
Unlike classical neurotransmitters, which are released in response to action
potentials
35
, neuropeptides can be released in response to diverse signals including
increases in calcium levels
105,106
. One reason for cytoplasmic calcium levels change is
electrical stimulation. The amount of neuropeptide release increases with the number of
action potentials up to a limit
39,97
. For magnocellular neurons, a peptidergic cell with
peptide release well characterized, it takes approximately ~400 spikes for one DCV to
release
107
and these spikes typically come in bursts, that are especially efficient at
releasing peptides
108–110
. This is a very large amount of stimulation emphasizing that
DCV release from electrical stimulation is extremely rare. It is probably more dependent
upon release from intracellular stores, which is capable of inducing neuropeptide
release
39,40,106
. There are two mechanisms for DCV release: activity dependent and
stored-regulated
39
. Activity dependent release occurs when DCVs are released from the
readily releasable pool in response to action potentials and a calcium influx. Stored

11
regulated release moves DCVs from the reserve pool to the readily releasable pool. It is
induced by rises in calcium by intracellular sources, such as intracellular organelles,
such as the mitochondria, and endoplasmic reticulum
111
.
DCVs release neuropeptides mostly from non-synaptic parts of the neuron,
including soma, axonal varicosities, and dendrites
39,107,112
. Using electron microscopy,
investigators found that DCVs are occasionally found at synaptic boutons in low
quantities
97,113
. It should not be overlooked that most neurons make and use multiple
chemical messengers so although neuropeptides are mostly released from DCVs
outside of synapses, these peptide-containing neurons still make many synaptic
connections, for fast neurotransmitter signaling. This differential localization also allows
for an additional layer of regulation between the different parts of the neuron for
regulating neuropeptide and neurotransmitter release or coordinating multiple
neuropeptides from the same neuron. In fact, DCVs release in axons and dendrites can
be independently controlled. This is possible through local changes in calcium levels by
discrete localization of specific types of calcium channels in axons or dendrites
114–116
.
Another means of differential regulation is through discrete localization of specific types
of exocytosis proteins
117,118
. Dendric release of peptides serves as a means to recruit,
inhibit, or synchronize activity of neighboring cells and can be self-sustaining
39
. For
example, neurons that express oxytocin and vasopressin also express the cognate
receptor, and when the peptide they release binds to receptors on their cells,
intracellular calcium levels rise and release more peptide via autoregulation
39,119
. This
creates a peptide feedback loop with long lasting effects.

12
Classic neurotransmitters and neuropeptides perform different functions and
likewise their mechanisms of release differ greatly. Neuropeptides in DCVs are primarily
released from non-synaptic areas of the neuron, while neurotransmitters in small clear
vesicles are densely located in synapses
35
. While both neurotransmitter and
neuropeptide release is governed by a rise in calcium, small clear vesicles are released
by a concentrated increase in calcium in response to action potentials and DCVs are
released in response to a gradual diffuse rise in calcium
107
. Moreover, far less
stimulation is needed to release small clear vesicles. Although DCV release is far more
rare than small clear vesicle release, DCVs contain 10 times more messengers
107
.
Small clear vesicles can go through repeated rounds of release and are reloaded with
recycled transmitters from reuptake transporters. Neuropeptides do not have a known
method of recycling and after exocytosis, it may take hours to replenish the supply of
neuropeptides form that site of release because their biogenesis requires translation
and processing
40,120
.

Targets and Receptors
After release, neuropeptides travel through the extracellular space to reach their
target cells; the distance to those targets can vary greatly because neuropeptides are
not restricted temporally due to their long half-life, nor spatially by synaptic signaling.
For example, oxytocin has a half-life of 20 minutes in the CNS
121
. While they can travel
over distances, many neuropeptides only travel within a few microns of their release to
act locally on nearby targets
40
. This idea is supported by the time it would take to
replenish, and the density of the astrocytes surrounding impeding diffusion. Although,

13
more work is needed to map the 3D topography of the extracellular space around
release sites to see how far neuropeptides can travel. However, there is evidence that
some peptides travel long distances through what is known as volume transmission
122
.
This model of signaling stemmed from data showing a mismatch between the location
and projections of peptidergic neurons and their corresponding receptors. Volume
transmission as an idea was introduced in 1986, when investigators examined areas of
β-endorphin synthesis and saw a discrepancy to the location of the corresponding
receptors
123
. This mismatch is a common feature in many other neuropeptide
families
124
. Oxytocin provides another striking example as there is dense expression of
oxytocin receptors in areas without oxytocin projections, including the olfactory bulb,
amygdala, and the ventromedial nucleus
107
. Some peptides are directly released into
blood, where they can travel long distances, acting as hormones. Oxytocin- and
Vasopressin-expressing neurons, for example, project to the anterior pituitary where
they secrete neuropeptides into the blood to control important physiological functions.
Oxytocin released into the bloodstream activates milk letdown during lactation and
uterine contractions during childbirth
125
; and vasopressin regulates the retention of
water by the kidneys
126
.
Another means of distant neuropeptide signaling is through the extracellular fluid,
cerebral spinal fluid (CSF), in the ventricles. The CSF serves a key role in providing the
brain with protection, nutrients, and clearing waste
127
. The CSF is in constant motion in
the brain, turning over three to five times per day

in healthy young individuals
128
but the
rate of turn-over decreases with age and can dramatically disrupt normal functioning
and exasperate neurodegeration
129
. The CSF is made in the lateral ventricles by the

14
choroid plexus and combined with interstitial fluid. It moves through series of ventricles
before being cleared into the circulation in the sub-arachnoid space. Movement of
peptides through the ventricles is metabolically advantageous, requiring minimal energy
input. The ventricles are innervated by axons that release neuropeptides into the
CSF
130,131
. Furthermore, neuronal activity increases following a rise in neuropeptide
levels in the ventricles
130,132
. In a recent study, researchers, for the first time,
demonstrated the functional effects of volume transmission in the CSF by linking
amount of melanin-concentrating hormone (MCH) in CSF to food intake
130
. Use of
retrograde tracers injected into the ventricles revealed that even regions distant from the
peri-ventricular zones send projections to the ventricles
131
. These findings support the
view that neuropeptides travel through the ventricles as a means for long distant
signaling. The complexity of long-distance neuronal communication through
neuropeptides layers on important implications for connectome research, where an
oversized focus is placed on wired, synaptic connections. Importantly, anatomical
connectivity is less important than the location and amount of receptor expressed.
Almost all peptides exert their actions through G protein-coupled receptors
(GPCRs)
23
. Neuropeptides bind to the extracellular region of the receptor; this leads to a
confirmational change in the transmembrane region and subsequent activation of
intracellular signaling
133
. This was previously explained using a lock and key model
which implied that a receptor can be in two confirmational states, 'on' or 'off', but there
are now more known conformational states for the GPCRs
133
. Agonists have the ability
to stabilize the activated form of the GPCR, antagonists compete with the agonist for
binding, and inverse agonist which can stabilize the inactive form. For instance, the

15
melanocortin 4 receptor has two peptides that regulate its activity. AgRP acts on the
melanocortin 4 receptor as an inverse agonist causing an animal to eat, while α-MSH,
the POMC derived peptide, is an agonist that completes with AgRP, signaling
satiation
134
. With the activation of the receptor, the alpha subunit of the G protein
disassociates and acts as a second messenger to effect changes, like opening ion
channels, activating enzymes, initiating gene transcription, and altering neuronal
metabolism and responsiveness
59,135,136
. Through GPCR signaling on non-neuronal
targets neuropeptides can alter blood flow
44
and induce structural changes in glia
48
. The
actions of the second messengers depend on the type of alpha subunit associated: Gs,
Gi, Go, and Gq
42
. This signal can either be terminated by the ligand diffusing away from
the receptor, or the phosphorylation of the intracellular part of the receptor and
association with β-arrestin to mark it for endocytosis
133
.
The amount of information conveyed by neuropeptide signaling is vast and the
signaling network is revealed to be more complex with each detail unveiled. In different
cells the same GPCR can associate with a different G proteins and signal through
different downstream pathways with drastically different intracellular outcomes. Adding
to the complexity, some receptors are able to associate with each other and form homo-
and heteromers, affecting their activity and functional characteristics
137
. Furthermore,
one neuropeptide can bind to multiple receptors and multiple different peptides can
activate the same receptor
138
. Neuropeptides can regulate the affective component of
behavior based on where GPCRs are expressed. For instance, pair bonding in prairie
voles depends on the discrete expression pattern of vasopressin receptors
139
and their
behavioral traits can be manipulated by manipulating the receptor expression

16
pattern
140,141
. Another means of neuropeptide signal regulation comes from the amount
of receptor expressed. By increasing the density of receptors, a similar effect can be
initiated from a reduced concentration of peptide
142
.
Neuropeptides are broken down by extracellular aminopeptidases, also known as
neuropeptidases
143
. Neuropeptidases can serve as a means of signal termination,
enhancement, and specificity. Neuropeptidases can be membrane bound or soluble
143
.
Most cleavages by neuropeptidases leave inactive products and are a way for the signal
to be turned off
59
. However, depending on the particular peptide and enzyme
combination, the resulting product’s biological activity may only be modulated, and the
cleaved peptide can still be active. For example, bradykinin is cleaved extracellularly by
a kallinkrein and the product binds with higher affinity to its target receptor
144,145
. Finally,
neuropeptidases can regulate the temporal and spatial specificity of signal so that a
peptide does not stimulate activity in an unintended area or during specific times
143
.
There is still much to learn about this level of peptide regulation.
Due to the differences in information conveyed by neuropeptides and classical
neurotransmitters, there are great differences in signaling at the receptor level. Small
molecule neurotransmitters are restricted by anatomical connectivity and confined to
synaptic signaling, while where and when the peptide is released from, receptor location
and density, and localization of neuropeptidases are all significant for neuropeptide
communication. Neuropeptides are not confined spatially or temporally. Conversely,
neurotransmitters signal over very small distances, tens of nanometers
40
, and their
signal is very short, around 5 milliseconds
39
. In contrast to long distance neuropeptide
signaling, there are multiple methods employed to minimize synaptic crosstalk and

17
prevent synapse spillover in neurotransmitter signaling, including reuptake and
enzymatic degradation
35
. This allows a greater level temporal specificity for neuronal
communication via neurotransmitter, compared to communication via neuropeptides
107
.
When neurotransmitters are released, their concentration within the synapse is very
high. Consequently, they bind to their receptors at very low affinities
35
. This is also
advantageous to creating a brief signal. Most neurotransmitter signaling occurs through
ionotropic receptors that directly and rapidly lead to a change in membrane excitability
of the postsynaptic neuron while this is only one possibility of neuropeptide receptor
binding. Neurotransmitters also have a few associated GPCR they are able signal
through, like the metabotropic glutamate receptors
146
.

Colocalization
A key question in neuroscience remains largely unanswered: how does the brain
use this cocktail of chemical messengers to control physiology and coordinate
behavior? While there is still much to learn, data gathered using tools currently available
provide valuable clues into how neuropeptides and neurotransmitters cooperate to
orchestrate complex behaviors and adapt to stress while maintaining the homeostatic
balance of many systems simultaneously.
Most neurons express a mix of multiple neuropeptides
40,147–149
and most peptide-
containing neurons also express a small molecule neurotransmitter
113,150–152
. Although
fast neurotransmitter signaling and slow neuropeptide signaling function together, they
have distinct functional roles and act on different timescales, over an expansive range
of distances, to activate a wide variety of targets. Leng and Ludwig describe their

18
functional differences and subsequent signaling purposes clearly, “neurotransmitters
pass whispered secrets from one particular cell to another, they carry a message that
matters only at a particular time and a particular place. By contrast, peptides are public
announcements, the messages endure, at least for a while; they are messages not from
one cell to another, but from one population of neurones to another”
107
. It must be
understood that the activity of single neuron does not alone convey any coherent
message. To accurately decipher relevant information, one must look to aggregate
activity on a population level
107,153
. The same notion applies to chemical signals
thatneurons use to communicate, individually they have discordant messages but
together they coordinate complex behaviors and physiological functions across varying
distances and timescales.
One role of neuropeptides is to modulate classical neurotransmitter synaptic
activity. A recent study highlights how neuropeptides work with a co-expressed fast-
acting neurotransmitter counterpart, to modulate the actions of fast acting classical
neurotransmitters within a circuit and enable spatial memory formation
154
. This study
focused on a population of melanin-concentrating hormone (MCH) neurons. This
population, through the fast-acting neurotransmitters glutmate, simultaneously inhibits
synaptic activity in its projection targets in dorsolateral septum and, through longer
distance MCH signaling, enhances the strength of an input to the dorsolateral septum
from the hippocampus by synchronizing the neuronal population activity. This work
provides an excellent example of how the modes of signaling are differentially
regulated. Importantly, the location of the receptors dictates the actions of the
neuropeptide signaling. For instance, the MCH receptors are not expressed in the area

19
that MCH-expressing neurons project to, but rather to a nearby area that has dense
receptor expression. This enables divergent spatial specificity of the two different modes
of signaling. Fast acting transmitters convey one message in the dorsolateral septum,
while the peptide can travel farther and modulate the activity of the neurons projecting
to the region. Release governs another means of differential regulation.
Neurotransmitters are released in response to different stimuli. Here, glutamate is
released first but it is depleted quickly. MCH release requires higher frequency and
longer stimulation or could be uncoupled from the neuronal firing rate altogether. It is
important to note that these effects observed are on a short timescale and the diversity
of signal from neuropeptide signaling is not fully appreciated, as it can act on a much
longer timescale and even change which genes are transcribed in the target cells. Also,
this population of MCH neurons makes other neuropeptides that could be contributing to
this coordinated action. This work highlights how multiple modes of signaling converge
to process complex information.
Peptidergic neurons typically express multiple peptides simultaneously
40,147–149
.
Multiple peptides can be derived from the same precursor and multiple precursors are
often expressed in the same population of neurons. The co-expressed peptides can
have similar, opposing, or completely independent actions. Similar effects from two co-
expressed neuropeptides perhaps reflects degeneracy. Degeneracy is “the ability of
structurally different elements to perform the same function.”
153
AgRP and neuropeptide
Y (NPY) are co-expressed in a population of neurons in the arcuate nucleus of the
hypothalamus and both have orexigenic effects
155
. Mice that lack either, AgRP, NPY, or
both have normal body weight and were able to maintain homeostatic balance through

20
the co-expressed fast neurotransmitter GABA
156
. Co-expressed neuropeptides can also
have opposing actions. Dynorphin, an inhibitory opioid peptide, is expressed with
vasopressin, a typically excitatory peptide
157–159
. While these are seemingly opposite
acting neuropeptides, dynorphin is packaged into DCVs with vasopressin and released
dendritically from vasopressin neurons coordinating the phasic burst activity needed for
vasopressin release
160
. Co-expressed neuropeptides can also be packaged into
different DCV and located to different parts of the same neuron to regulate divergent
signal release, as is the case with proTRH derived peptides
161
. These examples of
neuropeptide co-expression with other neuropeptides and fast acting neurotransmitters
provide a glimpse into an approach that the nervous system uses to encode flexibly in
circuit communication. Our understanding of how multiple messengers converge to
orchestrate complex tasks is still in its infancy and more focus on the complexity of
neuropeptide signaling is needed.

Why are neuropeptides difficult to study?
There are numerous challenges in studying the complex and diverse signaling of
neuropeptides. First, the concentration of neuropeptides can vary greatly, and even low
concentrations can have potent effects on their targets. This requires sensitive detection
methods for identification and quantification
162
. This is further complicated by the
numerous possible post-translational modifications that can occur on neuropeptides.
Neuropeptide release and DCV visualization has been challenging to study due to the
typical methods for fixation strategies used in electron microscopy (EM) – standard EM
fixation methods do not preserve DCVs
163
. Due to their slower temporal dynamics and

21
longer distance signaling, linking peptide release with the associated response in target
cells is difficult. Unraveling the function of neuropeptides is also a challenge. Biologists
infer function typically by three types of experiments: by observing a system in different
states and measuring the response of a system after activation or inhibition
153
. Since
neuropeptide systems are extremely robust, these latter types of manipulations can be
difficult to interpret. For instance, when injected into the brain, most peptides evoke a
measurable response, but it is nearly impossible to know if it is a physiologically
relevant response seen with endogenous peptide release. Inject too much peptide and it
could bind to other peptide’s receptors or in areas that are never activated together in
normal conditions. Removing or inhibiting a neuropeptide to deduce its function could
seem like a better approach, but many peptide knockouts do not show any behavioral
phenotype
156,164–167
. This could be either an actual biological effect, or reflect
developmental compensation by another analogous signaling system, or reveal
degeneracy or redundancy in the system. Redundancy among neuropeptides is
common
168,169
. There is still much to learn about neuropeptide signaling and the
emergence of new tools will be key in expanding our understanding of how
neuropeptides contribute to coordinate complex functions.

Tools for Manipulating Neurotransmission
Scientific progress depends on technological innovation. With each step in the
evolution of tool development, the field of neurobiology gains more insight into how the
brain controls behavior and physiology. New technology has revolutionized how
neurons are visualized and classified, providing insights into what different neuronal

22
populations express and how they function. Repurposing parts of biological systems
enables more precise manipulation over neural signaling and leads to a greater
understanding of the complex mechanisms of circuits and networks that function to
make us, us. Biological function is typically assessed by three types of experiments: one
can observe the activity of a system in different states or measure the response of a
system after activation or silencing
153
. This section covers the key tools that are
currently used to uncover the function of neuronal signaling, as well as their strengths
and pitfalls.

Whole neuron manipulations
Silencing a neuronal population is an important tool in the neuroscience arsenal.
Neuronal ablation is a blunt loss-of-function approach used to study the function of a
group of neurons. This method can be used on a genetically defined group of neurons
early in the life of the animal, which often leads to problems with developmental
compensation, or initiated in adulthood. A common method for neuron ablation employs
diphtheria toxin. The diphtheria toxin receptor can be expressed in a genetically defined
population, then the experimenter can administer diphtheria toxin, which crosses the
blood brain barrier, to ablate the neurons expressing the receptor
170
. A similar approach
uses a genetically engineered caspase
171
. This can be virally delivered to a specific cell-
type to induce cell death
172
. Neuronal death can have detrimental effects on the
surrounding tissue and is a strong limitation of this method. These effects are avoided
when using a tetanus toxin approach. The tetanus toxin approach suppresses
neurotransmitter release by cleaving vesicle-associated membrane proteins (VAMP) in

23
the SNARE complex
173,174
. The release of both synaptic vesicles and DCVs are blocked
by tetanus toxin meaning that neurotransmitter and neuropeptide secretion is
inhibited
175
. Interestingly, an experiment recently showed a tetanus insensitive VAMP2
could be combined with tetanus toxin to selectively inhibit neuropeptide release while
leaving fast neurotransmission intact
175
. However, this was only performed in cultured
primary cells. Tetanus toxin approaches are more elegant than neural ablation, but it is
challenging to confirm that secretion is blocked in the target cells. Moreover, ablating
the entire cell is likely to lead to more drastic phenotypes than removing a signaling
component of those cells. Neuronal ablation and blocking neurotransmitter release are
effective loss-of-function methods that can aid in understanding the circuits they are
used in.
Optogenetic and chemogenetic techniques enable selective stimulation or
silencing of neural circuits with reversible, temporal control and have been essential to
unraveling the functional output of many circuits and key cell types
176
. Optogenetics
allows researchers to activate or inhibit neurons using light. This is possible through the
light sensitive ion channels, including channelrhodopsin and halorhodopsin. These
receptors can be virally delivered or expressed in transgenic animals to enable cell type
specific control
176
. A caveat of this system is that optogenetic activation often fails to
efficiently release neuropeptides
177
. Special stimulation protocols and control
experiments are necessary to confirm peptide release. Another light inducible form of
stimulation may be better for neuropeptide release. Using Beggiatoa-photoactivated
adenylyl cyclase investigators were able to stimulate cAMP signaling and evoke
neuropeptide release
178
. Another consideration with optogenetic activation is choosing a

24
physiological relevant stimulation pattern, which requires much trial and error. While
optogenetics uses light as a controller for neuronal activity, chemogenetics relies on
drugs. A common chemogenetic platform is designer receptors exclusively activated by
designer drugs (DREADDs)
179
. There are many iterations of types of receptors but the
most used are GPCRs engineered to not have endogenous ligands. A synthetic ligand
for this receptor can be administered to the animal to activate the receptor
180
. These
GPCRs can bind to different G alpha proteins to induce different states in the target
cells, including activation, inactivation and stimulating signaling cascades in the cell.
When using this method, it is important to be aware of possible basal activity if the
receptor expression is too high, receptor desensitization with repeated activations, and
attributing the results to cell activation or inhibition when intracellular signaling could
play a role
179
. Another important consideration is the inert ligand used. For a long time,
Clozapine-N-oxide (CNO) was the ligand of choice. However, it has come to light that
CNO is not completely inert, can be converted to converted to clozapine, and act on
serotonin and dopamine receptors
181
. Due to this limitation, a ligand only control is
necessary. There are also now ligand alternatives with no endogenous activity
182
.
These methods for whole neuron manipulations are valuable tools for
deciphering the role of cell types and circuits. However, these tools fail to elucidate the
role of the transmitter systems mediating the function of a target cell type. It is critical to
understand the roles of the individual transmitters in order to design effective
interventions because drugs do not target cell types or circuits, they target transmitting
systems. These manipulations silence or activate the entire neuron and neurons are
profoundly multimodal. Neurons typically express a several neuropeptides and a fast-

25
acting neurotransmitter. When the whole neuron is activated or inhibited and a behavior
is seen, it is difficult to attribute an individual transmitter in that cell type. Accordingly, a
clearer understanding of the functional role of individual transmitters is imperative.

Genetic dissection of gene functions
To investigate the molecular and cellular mechanisms involved in neuronal
communication, an expanding toolkit enables manipulation of gene expression at the
DNA, RNA, and protein level. A blunt loss of function tool at the DNA level is a genetic
knockout. This is possible through various mechanisms, but a similar result is achieved,
the expression of a particular gene is permanently inhibited. Genetic knockouts can be
a powerful tool, but since the gene disruption is for the life of the animal, developmental
compensation could mask the actual function of that gene or alter the system in such a
way to lead to false correlations. To avoid developmental compensation, recombinase-
based conditional gene knockout can be used to increase temporal specificity and
knockout gene expression once the animal is past development
183
. This same method
could be used to specify knockdown to a particular cell type. It should be noted that
generating these genetic lines is costly and time consuming. Creating a breeding
scheme to enable both cell-type and temporal specificity for a genetic knockout is even
more complex, involving a tamoxifen-inducible Cre recombinase setup
184
. Despite these
challenges and considerations, these methods remain a powerful tool for assessing
gene function.
The gene editing field was revolutionized with the discovery and manipulation of
the bacterial defense system against viruses, clustered regularly interspaced

26
palindromic repeats (CRISPR)-Cas9, into a mammalian gene editor
185–187
. CRISPR-
Cas9 uses a single guide RNA (sgRNA) to direct an endonuclease, Cas9, to a specified
DNA sequence to create double stranded break. The CRISPR system has been
engineered to perform more functions beyond gene editing, including regulating gene
transcription. CRISPR interference (CRISPRi) enables gene transcription knockdown,
while CRISPR activation (CRISPRa) increases transcription. CRISPRi and CRISPRa
rely on a catalytically inactive or nuclease deficient Cas9 (dCas9) (Figure 2). To create
dCas9, point mutations were introduced at the RuvC1 and HNH domains
188
. dCas9
maintains its ability for target specificity and DNA binding. Transcriptional effector
dCas9 fusions can be expressed with an sgRNA for transcriptional control. CRISPRi
uses a dCas9 fused to the transcriptional repression domain, Krüppel-associated box
domain of Kox1 (KRAB)
189
. This fusion protein serves as a potent transcriptional
repressor that can be targeted to the promoter of any endogenous gene using
appropriately designed sgRNAs. This system has been shown to enable high-efficiency
(routinely >90%) knockdown of multiple genes simultaneously in mammalian cells, and,
compared to traditional CRISPR-mediated gene editing
190
, results in a much lower
prevalence of off-target. Multiple sgRNAs can target the same gene to enhance the
repression
189,191–193
. CRISPRi-mediated transcriptional repression is exquisitely specific
because sgRNAs must target a narrow window around the transcriptional start site to be
effective
194
. Alterations of the dCas9 fused effector domains have improved knockdown
for CRISPRi, such as the dCas9-KRAB-MeCP2
195
and the more potent KRAB domain,
ZIM3 KRAB–dCas9
196
. In 2018, it was demonstrated that CRISPRi works in the brain
197
.
dCas9 and sgRNAs were introduced via lentivirus to inhibit gene transcription of

27
synaptic proteins
197
. This study further confirmed low level of off target effects by using
sgRNAs containing mismatches to show essential no change in the expression of the
target gene. As with any technology there are potential pitfalls, including adequate
delivery and sufficient silencing. Partial silencing of the target gene could limit the
measurable behavioral effects and cause the experimental groups to be impossible to
discriminate. Despite the promises of CRISPRi technology, its use in neurons in vivo
has been sparse to date.

Figure 2. Overview of CRISPR interference (CRISPRi)
The CRISPRi system consists of a gene-specific sgRNA and catalytically inactive Cas9
(dCas9) fused to the Krüppel associated box (KRAB) effector domain (dCas9-KRAB
fusion protein). The dCas9-KRAB binds to the sgRNA and forms a protein-RNA
complex that binds to specific DNA targets and represses transcription initiation by RNA
polymerase (RNAP) through steric interactions and by inducing heterochromatinization
via the KRAB domain. Transcriptional repression is inducible (through expression of
sgRNAs), reversible, and can target essential genes.
Manipulation of gene expression at the level of RNA have primarily been done
through RNA interference (RNAi). RNAi relies on double stranded RNAs to silence
complementary messenger RNA sequences
198
. However, this method has many pitfalls.
RNAi relies on endogenous cellular machinery and using RNAi can overload the cells
natural systems leading to unintended side effects
199,200
. Additionally, there is growing
evidence of significant off-target effects
201–203
. Moreover, in a head-to-head comparison

28
knockdown via RNAi in mammalian brain in vivo was shown to be not as effective as
CRISPRi mediated knockdown
197
. In these respects, CRISPRi is analogous to a more
efficient, scalable, and selective form of RNA interference and importantly does not rely
on endogenous proteins to function.
Protein level manipulations rely on structure-specific interactions between the
protein and molecules that interact with the protein, like antibodies. These promote
degradation of mutant proteins and can be valuable when studying and treating mutant
proteins in disease states
204
. All in all, the manipulations outlined here are only a few of
the tools used to understand how the brain functions. Additionally, the development of
new tools will be integral to this process.

Lessons from neuropeptide knockouts
Since neuropeptides play key roles in many processes that are vital to our
existence, when their expression or their receptor expression is disrupted, typically
these processes are as well. Some neuropeptides knockouts are lethal, signaling an
essential function in development. Vasopressin knockout mice are unable to survive
past birth. Some neuropeptide knockouts survive and have distinct phenotypes.
Kisspeptin knockout mice are infertile
205,206
. Oxytocin knockout mice are unable to nurse
their pups
167
, reduced infant vocalization
207
, abnormal social behavior
207
, impaired
social recognition
208
, increased preference for sweet foods and carbohydrates
209
, and
late-life obesity
210
. Naturally occurring vasopressin knockdown rats are unable to
concentrate their urine
153,211
. Orexin deficient mice experience a narcolepsy
phenotype
212
. Many neuropeptide knockouts, however, show no noticeable phenotype.

29
For instance, neuropeptides AgRP and NPY have a known role in feeding, but the
knockout mice have normal body weight
156,166
. Oxytocin has a known role in
reproduction and parturition, but the knockout mice are able to reproduce and deliver
their pups
167
. This mismatch between known functions and lack of phenotypes in
genetic knockouts can either signify redundancy or developmental compensation.
Conditional neuropeptide or neuropeptide receptor knockouts that are initiated after
development are better at revealing the function of neuropeptides due to the lack of
developmental compensation. For example, neuropeptide Y receptor 2 hypothalamic
knockout mice, showed a decrease in body weight and food intake
213,214
. However, this
too was subject to compensation the effect on body weight and food intake lasted for 34
days following knockout and the effect diminished over time with the upregulation of
peptides with a similar function. This is exemplary of how robust neuropeptide signaling
systems are. Genetic knockouts are an important tool to understand neuropeptide
signaling but the results must be approached using caution.

Lessons from neuropeptide processing enzyme knockouts
Genetic knockouts for the neuropeptide processing enzymes that process
peptides from their precursor to their mature bioactive form have been generated to
better understand neuropeptide biosynthesis and the functional roles of the
neuropeptides these enzymes process. PCSK1 and PCSK2 are important enzymes that
cut the peptide precursor at dibasic amino acids. Pcsk1 mutations show a number of
metabolic phenotypes. Certain polymorphism in Pcsk1 in humans are associated with a
higher chance of obesity
215
. In Pcsk1 deficient mouse models, some, but not all, of the

30
Pcsk1 mutant mice exhibit obesity, depending on where the mutation is within the
gene
216
. The most pronounced phenotype in global Pcsk1 knockout mice is their
extreme growth defect. The adult mice are 60% of the size of wildtype mice, possibly
due to PCSK1’s role in processing growth hormone-releasing hormone (GHRH)
217
.
Similarly, Pcsk2 knockout mice have slow growth, but not as dramatic as Pcsk1
knockout. PCSK2 knockout mice are not obese but have difficulty maintaining glucose
homeostasis
218
. Peptidomic analysis on these knockout models have shown
differentially processed peptides providing insight into the enzyme recognition site
preferences
70,219–221
. However, in the single Pcsk1 or Pcsk2 knockouts, there is
speculation that they may compensate for each other, so this possible confounding
factor should considered
219,222,223
.
After the peptides are cut out of the proprotein precursor, the basic amino acids
are trimmed off by a carboxypeptidase, often Carboxypeptidase E (CPE). The first
mutation in the Cpe gene occurred spontaneously in 1973
224
. These mice developed
obesity around 6-8 weeks and were called ‘fat’ mice. A true Cpe knockout mouse was
made in 2004 and confirmed a similar phenotype seen in the ‘fat’ mice
225
. These mice
eat more and are less physiologically active. They have elevated fasting glucose levels,
poor glucose clearance, and are insulin resistant. Cpe knockout mice are infertile, have
diminished muscle strength, and reflex response
225
. They also show memory deficits
and hippocampal neurodegeneration
226
. Knockout males are also longer than their
wildtype counterparts at 30 weeks
225
with lower bone density
227
. Interestingly, a
truncated form of CPE has another role as a neuroprotective trophic factor
228,229
. Stress
evoked on Cpe knockout mice at time of weaning, blocked the development of the CA3

31
region of the hippocampus
90
. Using quantitative peptidomics, researchers have
cataloged and quantified changes in neuropeptide levels in the Cpe knockout mice
230
.
This work provided insights into how CPE processes neuropeptides and highlighted the
roles of these neuropeptides.
A final step in neuropeptide processing is peptide amidation which extends the
life of the peptide and can increase its affinity for binding to the receptor. Peptides are
amidated by the PAM enzyme. Homozygous Pam knockout mice die in utero likely due
to its vital role in embryonic cardiac formation and function. Heterozygous Pam
knockout mice have increased fat stores and a glucose intolerance but similar body
weight to wildtype
231
. Additionally, they also exhibit a strong anxiety phenotype.
Interestingly, PAM is a copper dependent enzyme and copper supplementation
ameliorated this anxiety phenotype in Pam heterozygous knockout mice
231
.
To increase specificity of knockout, conditional knockouts have been developed
for enzymes, PCSK1, CPE, and PAM. The conditional knockout of Pam localized the
knockdown to excitatory forebrain neurons and showed a reduction in anxiety and an
increase in an ability to maintain core body temperature in cold conditions
232
. Both
Pcsk1 and Cpe conditional knockouts were used to investigate the role of
neuropeptides in POMC neurons, a population important for control over satiety. The
conditional Pcsk1 knockout experiment used a tamoxifen-inducible Cre recombinase to
eliminate Pcsk1 expression specifically in POMC neurons only in adulthood
233
. While
POMC knockouts are obese, the conditional Pcsk1 POMC knockout mice were not.
This is likely because the levels of the key POMC peptide mediating the effect on
satiety, alpha melanocyte stimulating hormone (αMSH) were not reduced. PCSK2 could

32
be compensating for PCSK1 in these neurons to maintain levels of αMSH. The
conditional knockout for Cpe in POMC neurons was specific for POMC neurons
however it was for the life of the animal
234
. While this paradigm resulted in a 90%
reduction in αMSH, there was still not an increase in body weight. This does not mean
POMC peptides are not important for body weight regulation, αMSH when administered,
is the strongest known inhibitor of food intake
235
. Instead, the C-terminally extended α-
MSH could still be biologically active. A combination of developmental compensation
and receptor upregulation could also be masking the phenotype of these peptides.
Moreover, since the Cpe knockout was for the life of the animal, it is critical to think
about the effects on cells that share developmental origin because Cre could remove
Cpe expression from those cell types as well. POMC neurons originate from the same
line as AgRP, NPY and kisspeptin neurons
236,237
, critical neurons regulating feeding and
energy homeostasis. It is imperative to consider all of these possibilities when
interpreting results from global and conditional knockdown experiments.

Neuropeptide Signaling in Energy Homeostasis
Investigation into how the brain integrates signals from the body to orchestrate
the delicate balance of energy homeostasis has continued for more than a century. In
addition to being neuropeptide rich, the hypothalamus has emerged as the control
center for many homeostatic processes that are vital for life, including thermoregulation,
thirst, sleep, arousal, and hunger
153,238–246
. Early studies revealed that lesions of certain
hypothalamic nuclei affected feeding behavior
247–254
. Since then, a vast body of
literature has established the hypothalamic melanocortin circuit as a key regulator in

33
energy homeostasis
255–260
. Technological advances have allowed for more specific
identification of cellular and molecular mechanisms regulating feeding behavior and
energy homeostasis
261
. This section will examine the key brain areas, circuits, cell
types, neuropeptides, and transmitters important in energy homeostasis.

The central melanocortin system - first order neurons
The melanocortin system has been key in explaining neuronal control of energy
homeostasis. The melanocortin system consists of two populations of first-order
neurons that receive and integrate metabolic signals
262
(Figure 3). These neurons
converge on downstream targets, or second-order neurons expressing melanocortin
receptors. The first order melanocortin neuronal populations, located in the
hypothalamic arcuate nucleus (ARC), are pro-opiomelanocortin (POMC) and agouti
related protein (AgRP)/neuropeptide Y (NPY) neurons. These non-overlapping neuronal
populations have opposite effects, inhibiting and stimulating the motivational drive for
food intake, respectively
263–266
. In this way, they are often thought of as the ‘gas’ and the
‘brake’ of food intake. The amount of information this neural circuit must interpret is
astounding. They simultaneously assess current energy reserves and predict changes
in energy balance to drive food intake. Recent technological advances have allowed
researchers to begin to unravel the intricacies of how the brain orchestrates the delicate
balance of energy homeostasis. This was a challenging feat due to the location of this

34
neuronal population deep in the brain, intermixed among heterogeneous cell types.

Figure 3: The central melanocortin system
Located in the hypothalamus (hypothalamic nuclei shown in grey; third ventricle shown
in black), the central melanocortin system is composed of first order neurons, POMC
(red) and AgRP/NPY neurons (green) in the arcuate nucleus. They send projections to
MC4R second order neurons in the paraventricular hypothalamus (blue). POMC
neurons activate MC4R neurons and AgRP/NPY neurons inhibit both the MC4R
neurons in the PVN paraventricular hypothalamus and POMC neurons in the arculate
nucleus.
The classic view was that POMC and AgRP/NPY neuronal populations activity
are controlled by signals from the blood, such as leptin, a hormone produced in adipose
tissue
267
, because POMC and AgRP/NPY neurons are situated partially outside the
blood brain barrier
268
, However, recent studies revealed the story is more complex.
Recordings of AgRP and POMC neuron’s calcium dynamics have demonstrated that
the mere presentation of food (without food consumption) is sufficient to decrease

35
activity in AgRP neurons and increase activity in POMC neurons. This indicates that
AgRP neurons are responding to anticipatory signals such as the sight, smell, and
memory of food
269,270
. Another recent study demonstrated these neuronal populations
are not only calculating long-term energy stores through changes in leptin or fast
anticipatory changes through sensory signals and memories, but also through signals
from the gastrointestinal tract
271
. In order to separate these signals from sensory
anticipation, researchers administered mixtures of macronutrients by gastrointestinal
infusion and discovered that AgRP activity inhibition was proportional to calorie content
on the timescale of minutes
271
. Together these studies have identified AgRP/NPY and
POMC neurons as the critical regulators of energy homeostasis. These neurons
integrate information from long-term signals of internal energy stores on the timescale of
hours, intermediate information regarding the size of meals from signals in the
gastrointestinal tract on the timescale of minutes, and quick changes from the
anticipatory sensory input on the timescale of seconds to drive feeding behavior and
regulate energy homeostasis.
Innovations in tools that control neuronal activity and manipulate gene
expression, such as optogenetics
176,261
, chemogenetics
179
, and genetic knockouts
183,184
,
have allowed researchers to develop a more refined model of how first order neurons in
the melanocortin system regulates hunger and satiety. AgRP/NPY activation through
optogenetic and chemogenetic strategies caused animals to seek food voraciously
within minutes and reduced energy consumption
134,272
conversely, POMC neurons
require stimulation on a greater timescale to drive satiety
273–275
. Chemogenetic silencing
of POMC ARC neurons does not result in a short-term effect on satiety, but it does give

36
rise to a long-term increase in food intake
276
. Satiety is regulated on a shorter timescale;
however, it appears this is achieved through more indirect modulations from POMC
neurons that regulate the plasticity of the circuit
277
. POMC and AgRP/NPY neurons
have strong reciprocal projections downstream to second-order neurons in various
hypothalamic and extra-hypthalamic nuclei. This includes one of the most important
hypothalamic nuclei for mediating feeding behavior, the paraventricular nucleus of the
hypothalamus (PVN). In fact, researchers have physically severed the connections
between these hypothalamic nuclei and found that it increased food intake
278
.
The understanding of circuits controlling energy homeostasis is incomplete
without explaining which chemical messengers are mediating the signals. Due to the
nomenclature used to describe genetically defined neuronal populations, it may be
tempting to think of these neurons are solely expressing the chemical messenger the
population is named for, but it is important to remember that neuronal signaling is
complex. Neurons typically express several neuropeptides and a fast-acting
neurotransmitter
40,147–151
. AgRP/NPY neurons express AgRP and NPY, of course, but
also express the fast-acting neurotransmitter GABA and a subpopulation of them
express the neuropeptide somatostatin
279
. This begs the question which of these
chemical messengers is responsible for causing feeding. An injection of AgRP or NPY
into the hypothalamus induces feeding on different timescales. NPY causes a rapid
response with AgRP takes a longer time to stimulate feeding behavior
235,280,281
. Genetic
knockouts of AgRP, NPY, or both have small changes in feeding, highlighting the
redundancy of chemical messengers in this population
156,166
. Knocking out GABA in this
population resulted in a slightly leaner mice who were resistant to diet induced

37
obesity
282
. The rapid behavioral response of AgRP/NPY neurons has been attributed to
the co-expressed fast acting neurotransmitter GABA and NPY through deletions of
these neurotransmitters within the AgRP population
283
. It is important to note that these
described manipulations were for the life of the animals, therefore developmental
compensation could be obscuring the true functional role of these peptides while
simultaneously demonstrating the robustness of this vital circuit. In AgRP neuron
ablations, body weight decreased quickly
156,284,285
, highlighting the necessity of one of
the three chemical signals to this circuit.
POMC neurons express several neuropeptides including: alpha, beta, and
gamma melanocyte stimulating hormone (αMSH, bMSH, gMSH), β-endorphin (β-END)
and adrenal corticotrophic hormone (ACTH)
54
. This population of neurons is
heterogeneous and has subset that partners with glutamate and GABA and sometimes
both
286
. Both genetic disruption and toxin mediated ablation in POMC ARC neurons in
adulthood results in obesity and hyperphagia
284,287
. The POMC derived neuropeptide,
αMSH has emerged as the mediator of the effects on feeding. αMSH and AgRP act
opposingly on the melanocortin-4 receptor (MC4R) in second-order neurons within the
PVN, αMSH as an agonist and AgRP as an inverse agonist
288
. Together, POMC and
AgRP NPY neurons and the chemical messengers they express are the key driving
force in the control of energy homeostasis.

Second order neurons
POMC and AgRP/NPY send dense projections from the ARC to the
paraventricular nucleus of the hypothalamus (PVN)
289,290
(Figure 3). The PVN acts as a

38
key convergence point where the signals from diverse neural systems are integrated
and then redistributed through projections to extrahypothalamic brain regions. Lesions
of this region or disruption of its development lead to overeating and obesity
254,291–294
.
Activation of neurons in the PVN suppresses appetite
276
. In order to achieve this
complex information processing, the PVN contains an extraordinary diversity of
neurochemically distinct and functionally specialized cell types
295,296
. These cell types
are primarily glutamatergic but also co-express a vast array of neuropeptides
297
. How
the PVN uses this array of chemical messengers to coordinate this complex task is still
unknown, but research has provided several clues. Signifying the importance of
glutamate signaling to energy homeostasis, adult-onset conditional knockout of
glutamate within the PVN resulted in obesity and increased food intake
298
, however the
degree of contribution from neuropeptides remains largely unknown.
Within the PVN, the vast majority of neurons are labeled by Sim-1, a critical gene
in development of the PVN. Within the Sim-1 population, there are two non-overlapping
populations of neurons that independently control satiety, prodynorphin neurons and
melanocortin 4 receptor (MC4R) neurons
299
(Figure 3). Both populations of neurons
project to different parts of the parabrachial complex. The MC4R neurons, the more
characterized of the two populations, are the targets of POMC and AgRP/NPY neurons
from the ARC. Early pharmacological studies led to the first insights into the function of
this receptor. Intracerebroventricular (ICV) injections of the MC4R primary agonist
αMSH, a product of the POMC gene, and its analogs decreased food intake, diminished
the hyperphagia phenotype in obesity mouse models, and decreased body
weight
155,235,300,301
. Specifically, agonizing this receptor decreased food intake by

39
altering the meal microstructure, decreasing the size and duration of the meals
302
.
Conversely, application of antagonists increased food intake and body weight. The
MC4R is unique in that is it one of the only systems with an endogenous antagonist, the
neuropeptide AgRP. In fact, AgRP acts as both a competitive antagonist and inverse
agonist
288,303,304
. On the MC4R, AgRP not only blocks the action of the agonist, αMSH,
but it also leads to internalization of the MC4R thereby preventing the agonist from
contacting the receptor
305
. Through these pharmacological manipulations, researchers
showed how MC4R+ neurons in the PVN receive bi-directional signals from satiety-
promoting POMC neurons and hunger-driving AgRP/NPY neurons to mediate energy
balance. Genetic manipulations further confirmed the critical role of the MC4R in
maintaining energy homeostasis. In 1997, a report of a MC4R knockout mouse showed
that the targeted deletion of the MC4R resulted in obesity, hyperphagia and increased
linear growth
301
. More specific genetic manipulations have helped to clarify the complex
role of MC4R in energy homeostasis. In an experiment that enabled selective
reactivation of the MC4R in genetically defined cell types in the global MC4R knockout
mouse, researchers restored MC4R expression in glutamatergic neurons. The
phenotypes observed in the MC4R knockout were reversed and the animals had normal
food intake, energy homeostasis, and body weight
306
. Real time circuit manipulations
shed light on how PVN MC4R neurons mediate their effect on satiety. The median
eminence, central lateral parabrachial nucleus (LPBN), nucleus of the solitary tract, and
dorsal motor nucleus of the vagus were identified as the targets of PVN MC4R neurons
by synaptic tracing
307
. By injecting a virus expressing Cre-dependent channel rhodopsin
into the PVN of MC4R-Cre mice and implanting optic fibers in the four target areas,

40
PVN MC4R projections to the LPBN were identified as the site of functional outflow for
the satiety effects of PVN MC4R neurons. Optogenetic activation of this circuit reduced
food intake by altering the meal microstructure of the animals. It reduced the number of
times the animals ate as well as the duration of the meals and increased the time
between meals
307
. Altogether, this evidence strongly indicates that MC4R PVN neurons
are critical in controlling food intake.
Even though PVN MC4R neurons have a well-established role in satiety, a
fundamental question remains. What chemical messengers are mediating the function
of this population of neurons in each phenotype? Some studies have attempted to
determine the identity of these neurons through staining, pharmacological, and genetic
manipulations. Several neuropeptides and neuromodulators have been indirectly
implicated in MC4R function. One of which, brain derived neurotrophic factor (BDNF),
was proposed as a mediator of MCR4 signaling because an anti-BDNF antibody
blocked the decrease in food intake from an MC4R agonist
308
. Additionally, an ICV
infusion of a MC4R selective agonist stimulated BDNF expression in the ventromedial
hypothalamus
309
. The neuropeptide corticotropin-releasing hormone (CRH) is another
candidate mediating the function of MC4R neurons. Within the PVN, MC4R neurons
display overlapping expression with a sub-population of CRH-expressing neurons using
in situ hybridization
310
. An ICV injection of MC4R agonist increased the expression of
CRH
311
. Similarly, thyrotropin-releasing hormone (TRH) is increased following an ICV
injection of a MC4R agonist and prevents a fasting-induced suppressed expression
311–
313
. Notably, TRH neurons receive inputs from AgRP and POMC neurons. Oxytocin and
MC4R+ neurons are expressed in non-overlapping cells within the PVN
307
. Moreover,

41
researchers restored expression of MC4R in specific neuronal subtypes including CRH,
OXT, AVP, pDYN; however, there was no difference in body weight
314
. A rescue of
MC4R in dopamine neurons partially rescued the obesity and hyperphagic
phenotype
315
. It is important to realize that these manipulations were global, for the life
of the animal, and therefore subject to developmental compensation often seen when
manipulating neuropeptide signaling. When MC4R expression was selectively restored
function in glutamatergic neurons, this rescued the phenotype of obesity and
hyperphagia
306
. While this established that glutamatergic neurons in Sim-1 Cre neurons
are mediating this effect, it doesn’t offer much specificity. Most of the neurons in this
population are glutamatergic and express many other neuropeptides. Uncovering the
role of the peptides in this population remains an open question.
Understanding the functional role of these individual chemical messengers is
essential to unraveling how the brain regulates energy homeostasis. Although our
understanding of how the brain monitors and controls food intake and energy
homeostasis has dramatically increased, these advances have not translated into
effective treatments for obesity. Obesity and its associated conditions are among the
most expensive, prevalent, and chronic disorders affecting more than one-third of adults
in the United States, yet we still lack the fundamental understanding of the molecules
that regulate feeding behavior and energy balance
316
. Much of the research over the
past few decades has been focused determining the important circuits and cell types.
While understanding the circuits and cell types involved is important, it is not the full
story. A greater knowledge of the role of the individual chemical messengers mediating
the messages in these circuits is essential to designing effective interventions, because

42
drugs do not target cell types or circuits, they target transmitting systems. Therefore, to
intervene in these transmitter systems, a clearer understanding is crucial.

Neuropeptide Signaling in Aging
Aging is characterized by a gradual and progressive decline of many
physiological functions including a breakdown of energy homeostasis, circadian
rhythms, hormonal regulation, and reproduction
317
. This breakdown increases the risk of
acquiring many diseases that drive morbidity and mortality, including stroke
318
,
cancer
319
, diabetes, cardiovascular disorders, and neurodegenerative diseases
320,321
.
Interestingly, these physiological functions are controlled and regulated by the
hypothalamus
317,321,322
, a highly peptidergic region of the brain. Due to the
hypothalamus’s key role in mediating communication between the brain and the
periphery, we hypothesize that it may be significant in the control of systemic aging. If
the hypothalamus is a regulator for systemic aging, then the cellular and circuit
mechanisms operating in hypothalamus are targets for aging interventions. Moreover,
the hypothalamus uses neuropeptidergic signaling as long-range chemical messengers
to signal to the body. Accordingly, the gaps in our understanding of neuropeptide
signaling and the role it plays in aging is imperative to resolve. For example, research
on oxytocin signaling in aging has been scarce and of varying quality but results from a
few studies are promising. Single cell RNA-sequencing of the aging hypothalamus
revealed oxytocin is highly downregulated in aged mice
323
. This age-related decrease in
oxytocin could be contributing to sarcopenia in the periphery. In fact, administering
oxytocin to aged mice resulted in increased repair and maintenance of the skeletal

43
muscle, though this study was missing some key controls
324
. More research is needed
to identify the role that neuropeptides play in the aging process to discover effective
strategies for mitigating the deleterious process of aging. This section will highlight a
few neuropeptidergic signaling systems, their role and therapeutic potential in aging.

Melanocortin system
A hallmark of aging is the loss of the ability to maintain energy homeostasis.
There are similar trends that occur in rodents and humans as they age, there is a
gradual increase in weight, mid-life obesity, followed by a decrease in weight, aging
anorexia
325–327
. Both trends are dangerous, with obesity predisposing individuals to
many diseases
316
and aging anorexia leading to chronic undernutrition, sarcopenia, and
increased disability in the aging populations
325
. The development of aging anorexia is
multifactorial
326–329
although the loss of appetite is a huge component, suggesting the
melanocortin may have an important role in this dysregulation. Aging humans and mice
have trouble coping with homeostatic perturbations. After the challenge of a 72 hour
fast, aging animals exhibit a delay in regaining lost body weight
330
. Demonstrating the
intertwined nature of energy homeostasis and lifespan, AgRP knockout mice have a
longer lifespan on a high fat diet and MC4R knockout mice has a shortened
lifespan
331,332
.
POMC neurons are especially affected by aging. Single cell RNA-sequencing of
the aging hypothalamus revealed POMC expressing neurons exhibited altered
pathways controlling DNA repair, oxidative phosphorylation, the unfolded protein
response
323
. Aging also effects the activity of POMC neurons. In mice, after six months

44
of age, the activity of this population of neurons plummeted
333
. By overexpressing
POMC in aged mice via AAV, mice had an initial decrease in food intake and body
weight. However, the effect on food intake waned with continued reductions in body
weight
334
. Underlying cellular mechanisms within these neurons are also contributing to
the metabolic changes with aging. In fact, mTOR signaling is raised in POMC neurons
of aged mice
333
. Known aging treatment that inhibits mTOR signaling, rapamycin,
administered systemically or by intracerebral injection via an osmotic pump, improved
the health of POMC neurons. The POMC neurons in these animals, increased the
density of their projections to the PVN and were more excitable. A greater
understanding of the mechanisms of underlying age-related dysfunction in energy
intake will facilitate the development of preventative and therapeutic treatments that will
improve quality of life during aging.

Gonadotropin-releasing hormone and hypothalamic inflammation
With age, there is a decrease gonadotropin-releasing hormone (GnRH)
expression in the hypothalamus
335
. Researchers linked this decrease in GnRH with a
hypothalamic increase in inflammation involving nuclear factor κB and IκB kinase-β
335
.
The increase in inflammation was specific to mediobasal hypothalamus. This research
demonstrated hypothalamic control over systemic aging. Impressively, when inhibiting
these inflammatory signals in mid-life within the mediobasal hypothalamus, there was a
corresponding increase in longevity and health span markers including muscle size,
skin thickness, and bone mass. Inhibiting the inflammatory signals in microglia in this
region, led to improvements in cognitive decline and signs of muscle aging, highlighting

45
the significance of the microglia–neuron communication. Inhibiting the inflammatory
signals increased the expression of GnRH. Intracerebral injections of GnRH increased
neurogenesis in the hypothalamus and hippocampus. Underscoring the potential of
GnRH as an aging treatment, peripheral administration of GnRH reduced age-related
cognitive decline and signs of muscle aging
335
. Interestingly, a recent study found NF-
κB nuclear oscillations negatively regulated GnRH
336
. Future investigations are needed
for elucidating the cause of the inflammation and the true potential of GnRH as an aging
intervention.

Neuropeptide Y
Neuropeptide Y (NPY) is one of the most abundant neuropeptides in the brain. It
has been implicated in many processes including energy homeostasis, learning and
memory, stress, and circadian rhythms
337
, processes impacted during aging. NPY’s role
in aging is largely unknown but research provides some interesting clues. Transgenic
mice overexpressing NPY have an extended lifespan
338
. While knocking out an NPY
receptor, NPY Y2, has cognitive and memory deficits
339
. This implicates NPY could be
beneficial to an aging brain although the exact pathway is unknown. It has been
suggested that this effect on lifespan extension is through a similar pathway to calorie
restriction
340
. This is likely due to NPY’s integral role in energy homeostasis, since NPY
expression increases in the arcuate hypothalamus during fasting. In fact, the benefits
seen with calorie restriction, increases in life and healthspan, are ameliorated in NPY
knockout mice
340
. Additionally, NPY has been shown to have anti-inflammatory effects
partly through decreasing levels of inflammatory factors in microglia
341–344
and actions

46
on GnRH neurons
345
. The direct mechanism in the relationship is unknown. More
research is needed to uncover the role that NPY plays in aging.

47
CHAPTER 2: CELL-TYPE SPECIFIC NEUROTRANSMITTER SILENCING (CNS)
DEVELOPMENT
Abstract
Most neurons in the brain contain a mix of neuropeptides, neuromodulators, and
fast-acting amino acid neurotransmitters; however, understanding the functional role of
these individual transmitters in vivo remains challenging. Here, we introduce a flexible
system, Cell-type specific Neurotransmitter Silencing (CNS) which uses CRISPR
interference (CRISPRi) to silence neurotransmitters individually, or as an entire class in
the mammalian brain. We show acute simultaneous silencing of multiple genes with
spatial and temporal specificity. By targeting common neuropeptide processing
enzymes, we were able to knockout down neuropeptide signaling from a specific cell
type in the brain. This system provides a critical tool in neural circuit analysis for
identifying roles of individual transmitters.

Introduction
Most neurons in the brain make and release multiple neuropeptides, monoamine
neurotransmitters, and fast acting amino acid neurotransmitters to signal
40,147–149
.
Despite the widespread nature of neuropeptide signaling, the relative functional role of
these individual transmitters remains a mystery. Neuropeptides have a wide range of
functional roles; however, due to the heterogeneous nature of the brain, it has been
difficult to do a simple acute loss of function experiment that would link individual
transmitter to a specific functional role, with the current tools available. While
neuroscience has made great progress identifying functions of specific cell types, it is

48
critical to identify and characterize the function of individual transmitters because drugs
in the brain target these transmitter systems, not cell types. Current methods either
manipulate the whole neuron and do not allow the manipulation of individual transmitter
systems or are initiated from birth for the life of the organism and confound the data with
developmental compensation. Conditional recombination systems can be used but
require complex breeding strategies that are time consuming. In order to link individual
transmitters to specific phenotypes, it is critical to have a method that would allow for an
acute loss of function experiment in the adult brain.
Programmable spatial and temporal control over gene expression is essential to
understanding biological mechanisms. The CRISPR/Cas9 system has emerged as a
powerful gene editing tool
185–187
. The system employs a short guide RNA (sgRNA) to
direct a Cas9 endonuclease to a specific DNA sequence to create a double stranded
break in the DNA. More recent developments have identified the essential regions for
DNA cutting and mutated them to create a catalytically inactive Cas9 (dCas9)
188
. When
dCas9 is linked to a transcriptional activator or repressor, it facilitates sequence-specific
gene regulation. The CRISPRi system uses dCas9 linked to Krüppel-associated box
(KRAB), a transcriptional repressor, to efficiently and specifically silence genes by
targeting only a small region around the transcriptional start site (TSS)
189,191–193
. The
CRISPRi system has been demonstrated to efficiently knockdown gene expression
both both in vitro
189,346
and in vivo
197,347–349
. An important advantage of CRISPRi is that
it allows precise and efficient knockdown of multiple genes simultaneously
350–352
, and
that it does not utilize endogenous cellular machinery. Moreover, because CRISPRi
only works when dCas9 is targeted to a small (~100 bp) region around the TSS, it is

49
more specific than RNAi with fewer off-target effects than both RNAi and CRISPR
189,353
. CRISPRi provides and elegant highly specific method for acute loss of function
experiments. Our work builds upon this development of CRISPRi and establishes a
toolkit for manipulating transmitter systems.
We developed a flexible system, Cell-type specific Neurotransmitters Silencing
(CNS), to silence individual transmitters in specific cell types in the adult mouse brain.
We tested several subtypes of CRISPRi based tools to optimize specificity of target
knockdown, designed multiple methods of sgRNA and dCas9 delivery, and confirmed
CNS’s ability to silence genes in vitro and in in vivo. Using CNS, we developed a
scheme to investigate the collective role of neuropeptides by silencing neuropeptide
output from a cell type while leaving the fast-acting amino acid neurotransmission intact.

Results

Testing CRISPR and CRISPRi variations for CNS
Following the initial manipulation of CRISPR Cas9 from Streptococcus pyogenes,
more variants were discovered in different microbial species, diverging in endonuclease
size and target DNA binding requirements
354–357
. We tested three different approaches
for our CNS system including Staphylococcus aureus (sa) CRISPRi, sa CRISPR, and
Streptococcus pyogenes (sp) CRISPRi (outlined in Table 1). The main difference
between the approaches is the size of the endonuclease. Cas9 from Staphylococcus
aureus is small enough (3.2 kb) to fit in the limited ∼4.5 kb of an Adeno-associated virus
(AAV). This enables all components of saCRISPRi and saCRISPR approaches to be

50
delivered virally, while the sp dCas9 from the sp CRISPRi is over 1kb larger and must
be delivered using a knock-in transgenic mouse. Another difference is the protospacer
adjacent motif (PAM) for is longer for Staphylococcus aureus. The PAM sequence is a
short sequence located directly downstream of the target DNA. The target region must
match the sgRNA and the PAM sequence for proper binding
185
. This required matching
region provides another means for specificity. In fact, a longer PAM sequences
increases specificity by reducing possibilities of off-target effects, which are already low
with CRISPRi but could be an advantage for CRISPR. Both CRISPR-based targeted
gene disruption and CRISPRi gene expression knockdown approaches were tested for
the CNS method. CRISPRi blocks the initiation or elongation of transcription (Error! R
eference source not found.A), while CRISPR initiates a double stranded break in the
DNA and error prone non-homologous end joining (NHEJ) repairs the DNA and can
cause an indel leading to gene silencing (Error! Reference source not found.B).

Table 1. Considerations for CRISPR based knockdown methods

sa CRISPRi sa CRISPR sp CRISPRi
sgRNA delivery
AAV AAV AAV
Cas9 delivery
AAV AAV transgenic mouse
Mouse
any mouse; any CRE mouse any mouse sp dCas9 mouse
Pam sequence
NGGRRT NGGRRT NGG

51

Figure 4. CRISPRi and CRISPR-mediated gene silencing
(A) CRISPRi silences genes uses dCas9-KRAB, which binds to a sgRNA to form a
protein-RNA complex. This complex binds to a specific DNA target sequence to repress
transcription initiation or elongation by RNA polymerase. (B) CRISPR-based gene
silencing uses Cas9 to induce double-stranded breaks. When the breaks are repaired
via NHEJ small insertions or deletions are introduced which create either mutant or
truncated proteins.

Staphylococcus aureus CRISPRi development and characterization
To create an entirely virally delivered gene silencing approach for the CNS
method, we modified sa Cas9 for CRISPRi. We first designed and assembled dCas9
and sgRNA expression AAV vectors. For the dCas9 construct (Figure 5A), we
introduced mutations in sa Cas9 at the RuvC1 and HNH domains to inactivate the
endonuclease, fused a KRAB transcriptional repressor, and drove the expression using
a human synapsin (hSyn) promoter to limit the expression to neurons. I also generated
a Cre-recombinase dependent double-floxed inverse open reading frame (DIO) vector
to allow for Cre-dependent expression. For the sgRNA construct (Figure 5B), we
arranged the type III RNA polymerase III promoter, U6, to drive the guide RNA and a
hSyn promoter to drive the markers. The hSyn promoter is specific to neurons and is

52
advantageous to use in AAVs due to its compact size
358
. I developed several versions
of each construct with different markers. I used enhanced green fluorescent protein
(EGFP) or polypeptide tags, FLAG and Myc, were fused to a ribosomal protein L10.
This tag enables transcriptional profiling of the neurons that received the virus via the
translating ribosome affinity purification (TRAP) technique to screen for both on-target
and off-target effects. In our hands, the introduction of AAV-sa dCas9-KRAB induced
cell death (Figure 5C). To determine if toxicity was due to the virus titer, we tried and
range of virus titers and consistently saw cell death. At the time we were conducting
these experiments, sa Cas9 had only been used in vivo in mouse liver
359
so we were
concerned the toxicity of sa dCas9 was specific to our use in the brain or that the
mutations we had created to generate dCas9 were stimulating an immune response, as
has been seen with CRISPR components before
360
. In the time following these
experiments, sa dCas9 has been used to silence Pcsk9 in mouse liver
361
signaling a
single vector delivery is possible. We designed and cloned sgRNAs into the sgRNA
constructs targeting the neuropeptides, Oxytocin and AgRP along with the neuropeptide
processing enzyme, PCSK2 (Figure 5D). The neurotoxicity results highlight the need
for an alternate gene silencing approach.

53

Figure 5. Virally delivered Staphylococcus aureus CRISPRi dCas9-induced cell
death
(A) Schematic of dCas9 constructs. Flanked by inverted terminal repeat sequences
(ITRs), the construct begins with the hSyn promoter is driving sa dCas9 and KRAB with
KRAB attached to the N-terminal side of dCas9. KRAB-dCas9 has a nuclear localization
signal (NLS) and 3 hemagglutinin (HA) peptide tags fused. The construct concludes
with a polyA tail for added stability. The second version introduces a LoxP site based
genetic switch, or Double-Floxed Inverted Open reading frame (DIO). When Cre is
present, the sequence is inverted and transcribed. (B) Schematic of sgRNA constructs.
These constructs are flanked by ITRs for insertion in the the AAV genome. Within the
construct, the U6 promoter drives the sgRNA and s. aureus Cas9 scaffold. Next, the
hSyn promoter drives the markers, either EGFP or Polypeptide tags, Flag-Myc. The
markers are fused to ribosomal subunit L10 (L10). Woodchuck hepatitis virus post-
transcriptional regulatory element (WPRE) is inserted following the markers to increase
stability and expression of the construct. (C) Immunohistochemistry staining of AAV-sa
dCas9-KRAB stereotactic injection into paraventricular hypothalamus (D) Outline of
sgRNAs designed and cloned.

54
Staphylococcus aureus CRISPR reduced expression of oxytocin
The second approach we tested was CRISPR-based gene silencing. We used a
previously developed plasmid
362
to express both sgRNA and sa Cas9 in a single vector
in an AAV (Figure 6A). To test the efficiency of this approach in vivo, we designed
guides targeting the neuropeptide oxytocin, which is expressed in the bisymmetrical
region, the paraventricular nucleus of the hypothalamus (PVN). The sides of the PVN
are close to one another but separated by the third ventricle, creating an ideal system
for testing efficiency of knockdown because each animal has its own internal control.
We then stereotactically injected the sa Cas9/oxt sgRNA expressing AAV unilaterally
into the PVN in wild type mice, then waited two weeks for viral expression and
knockdown to occur. Next, we stained for oxytocin, which has reliable antibodies that
work well in the brain. We saw adequate infectivity in the PVN via the viral GFP marker
and a reduction in the expression of the target gene, oxytocin, on the injected side of
the PVN (Figure 6B). Despite these promising results, going forward with this approach
would not be feasible due to the massive undertaking that verifying off target effects for
a large number of experiments would require. Previously described validation
190

required deep sequencing of extracted nuclei to be analyzed separately. These results
indicate that gene disruption is possible using sa CRISPR in the mouse brain, but this
approach was not optimal for CNS due to the complexity of validation.

55

Figure 6. CRISPR based gene disruption led to a reduction in oxytocin expression
in mouse brain
(A) Schematic of sa CRISPR vector. ITRs flank the construct. A Cytomegalovirus
(CMV) promoter drives expression of Cas9 with nuclear localization signals and 3 HA
tags. Next, a U6 promoter drives expression of the sgRNA and sa sgRNA scaffold. (B)
Immunohistochemistry staining of AAV targeting Oxytocin unilaterally in paraventricular
hypothalamus.

Streptococcus pyogenes CRISPRi effectively reduced gene expression
The third and final approach tested was sp CRISPRi, and we developed multiple
approaches to deliver each component of the system. We used a transgenic mouse to
express sp dCas9 ubiquitously. Using a previously developed plasmid, we cloned
sgRNAs and had them packaged into AAVs
190
. The sgRNA is driven by the U6
promoter and a EGFP marker is driven by a hSyn promoter (Figure 7A). We utilized a
similar strategy for initial validation (described in previous section). Following a
unilateral injection into the paraventricular nucleus of the hypothalamus (PVN), we
observed a knockdown of oxytocin on the side of injection (Figure 7B). Since this
approach functions at the level of transcription, validation is simpler than traditional

56
CRISPR for on and off-target effects
359
. Separate delivery of the sgRNA and dCas9
allowed for greater flexibility for refining temporal, spatial and cell-type specificity.

Figure 7. sp CRISPRi efficiently silences oxytocin expression in mouse brain
(A) Schematic of sa CRISPR vector. The construct was flanked by inverted terminal
repeats for incorporation of the sequence into the AAV genome. Here, a U6 promoter
drives expression of the sgRNA and sp scaffold. Next, the hSyn promoter was used to
drive the marker EGFP-KASH. The KASH signal localizes the EGFP to the nuclear
membrane and is advantageous when sorting cells in vitro. (B) Immunohistochemistry
staining of AAV targeting Oxytocin unilaterally in paraventricular hypothalamus.

CNS efficiently silences gene expression in vitro
We designed guide RNAs to maximize efficiency and minimize off-target effects
using previously developed ranking
363–365
. This ranking algorithm assesses off-target
effects by looking for potential sequence similarly of the sgRNA sequences with a focus
on perfect nucleotide matches, one nucleotide deletions, one nucleotide insertions and
one nucleotide substitutions
366
. Additionally, the ranking also considers mismatches
within the PAM sequence. The perfect matches provided the most off-target activity
followed by the one nucleotide substitutions; however, potion and identity of the
mismatch were important and taken into account. Almost no off-target activity was
detected with the one nucleotide deletion or insertions
364
. On-target activity ranking

57
depends upon a sgRNA spacer length of 19 nucleotides, an increase in presence of
purines in the sgRNA spacer, and a guanine at the 5′ end of sgRNA sequence
365
. Top
ranking sgRNAs were cloned into expression vectors (Figure 7A). Before investing time
and budget to make AAVs, we investigated the efficiency of each sgRNA by first
creating a stable cell line expressing dCas9 and an mCherry marker in Neuro2A (N2A)
cells
367
. We chose this cell line because it showed endogenous expression of the target
genes of interest
368,369
. I generated the dCas9 N2A stable cell line using a lentivirus
expressing dCas9 (gift from Michael McManus, University of California San Francisco)
and puromycin selection. Next, I performed a single cell selection using a serial dilution
to grow clonal populations. I then selected positive clones by puromycin selection,
expanded, and froze down the dCas9 N2A cells. To evaluate the efficiency of the
sgRNAs, I transfected the plasmids containing the sgRNA and a EGFP marker into
dCas9 N2A cells, then sorted for EGFP and mCherry positive cells and measured gene
of interest expression levels using quantitative reverse-transcription PCR (qRT-PCR)
(Figure 8A-B). We performed quantitative analysis using the ΔΔCT method and
using ActB and Gapdh as housekeeping genes on CFX Maestro Software (BioRad). We
saw efficient knockdown of two neuropeptide processing enzymes: Carboxypeptidase E
(CPE) (CPE sgRNA #1: 79% knockdown), and peptidyl-alpha-amidating
monooxygenase (PAM) (Pam sgRNA #1: 77% knockdown) (Figure 8C). Our results
indicate the CNS system facilitates efficient gene silencing in vitro.

58

Figure 8. CNS efficiently silences gene expression in vitro
(A) Flowchart for in vitro sgRNA validation strategy. Plasmids are transfected into
dCas9-KRAB stable cell line [1], FACS sorted [2], and gene expression levels are
evaluated with qPCR [3]. (B) Representative images N2A dCas9 cells, top left panel
bright field view (BF). Cells are immunostained for the marker for dCas9 expression,
mCherry (red), and EGFP (green) labeling cells transfected with sgRNA plasmid. (C)
qRT-PCR analysis of sgRNAs (x axis). Relative mRNA expression of Cpe (pink) and
Pam (green) compared to negative control, scramble sgRNA. Statistical significance
was assessed by ordinary one-way ANOVA and Dunnett's test for multiple comparisons
to negative control. Data obtained from 1 - 3 independent samples per sgRNA, on a
minimum of 3 separate days. Data shown as mean ± SEM. *p < 0.05, **p < 0.01, ***p <
0.001 ****p < 0.0001 ns = not significant

It has been reported that CRISPRi mediated repression can be enhanced by
targeting multiple sgRNAs to the same gene
370–373
. There are conflicting reports of the
minimum distance between sgRNAs for designing multiplex combinations. Some
studies only specify that the protospacer and PAM sequence of the guides cannot
overlap
188,374
, while others are more cautious and recommend a minimum distance of
30-bp between guides to allow for efficient binding. We designed and tested several
combinations of guides targeting either Cpe or Pam to test this empirically, distanced
between guides spanning from 30bp to 180bp (Figure 8C) (Figure 9). In our hands,

59
single sgRNAs were equally to slightly more efficient as multiple sgRNAs for
combinations targeting either Cpe or Pam. In fact for Cpe sgRNA combinations (Figure
9A), knockdown efficiency between guides targeting Cpe did not vary greatly, and none
were significantly different when assessed by two-way ANOVA with Tukey for pairwise
comparison. The greatest difference was between Combo 3 vs. #1, at p = 0.1783, with
single sgRNA #1 performing the best (Figure 9B-C). For Pam, we also saw minimal
differences, with the most efficient knockdown from single sgRNA #1. It is possible
sgRNA #1 are the most efficient and we are seeing a ceiling effect of possible gene
silencing with the current dCas9-repressor pair (dCas9-KRAB), and we would only see
greater repression with and enhanced transcriptional repressor
195,375
. It is also possible
that there is different transcriptional regulation of these genes in vitro than in vivo, such
as different transcription start sites. It is also possible that this effect was specific to our
target genes.

60

Figure 9. Multiple sgRNAs targeting the same gene did not enhance repression
efficiency of CPE
(A) Schematic of sgRNA position on gene, Cpe, for multiplexing combinations. sgRNA
#1 (light pink), sgRNA #2 (orange), sgRNA #3 (teal), sgRNA #4 (yellow), promoter
(grey), TSS (black arrow), 5’ UTR (blue). Combo 1: sgRNA #1, sgRNA #2, sgRNA #3.
Combo 2: sgRNA #1, sgRNA #3, sgRNA #4. Combo 3: sgRNA #3, sgRNA #4. Distance
between guides is listed below schematic. (B) in vitro validation of sgRNAs targeting
Cpe. qRT-PCR analysis of sgRNAs (x axis). Relative mRNA expression of Cpe,
compared to negative control scramble sgRNA. (C) Mean score of relative mRNA
expression compared to scramble negative control. Statistical significance for efficiency
of sgRNAs vs. negative control was assessed by ordinary one-way ANOVA and
Dunnett's test for multiple comparisons. Data obtained from 1 - 3 independent samples
per sgRNA, on a minimum of 3 separate days. Two-way ANOVA with Tukey for
pairwise comparison was used to compare combinations vs. single guides. Data shown
as mean ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001 ****p < 0.0001 ns = not significant

61
Transgenic mouse lines expressing dCas9
To deliver the dCas9 protein, we generated three transgenic mice (Figure 10A).
The first mouse enables broad ubiquitous expression of dCas9-KRAB under the CAG
promoter. The CAG promoter is used to drive high levels of gene expression
376
. To
enable cell-type specificity for CNS, we developed two additional knock-in mouse
strains where dCas9-KRAB expression is Cre-dependent. A lox-stop-lox (LSL) cassette
limits the expression of dCas9-KRAB to cells expressing Cre recombinase. We
engineered the mice to express dCas9-KRAB under the control of a hSyn promoter
enabling neuronal specificity (Figure 10C). To provide a marker for recombination, we
placed a P2A self-cleaving peptide after the dCas9-KRAB and before a mCherry
fluorescent transcriptional reporter. When Cre is expressed in the cell, the LSL cassette
is removed and dCas9-KRAB and mCherry are expressed. To evaluate the specificity of
conditional expression, we crossed the LSL dCas9 mouse to Vglut2
Cre
(Slc17a6),
Vgat
Cre
(Slc32a1), and Sim1
Cre
(Sim1) (Figure 10D). VGLUT2 is a vesicular glutamate
transporter which transports glutamate from the cytosol into small clear vesicles prior to
release from presynaptic terminals. By crossing the LSL mouse to the Vglut2
Cre

(Slc17a6) mouse, we expected to see expression in glutamatergic cells. Similarly,
VGAT serves that same function in GABAergic cells
377
. Sim1 is important for the
development of and a marker for the PVN, supraoptic nucleus, and some cells in the
medial amygdala
378
. We observed highly specific expression matching the expression
patterns of the genes to the corresponding Cre strains. To determine if there was leaky
expression the LSL dCas9 mouse was crossed to wildtype strain (Figure 10D). No
mCherry expression was observed in this cross. These results indicate Cre-dependent

62
expression of dCas9-KRAB is specific. We developed the final mouse using an
enhanced transcriptional repressor to increase gene expression knockdown
195
. Here,
we employed KRAB-MeCP2 in place of KRAB alone (Figure 10E). A recent study
indicated that an additional transcriptional repressor could be added to the dCas9-
KRAB to increase the gene expression knockdown
195
. We developed the enhanced
CRISPRi mouse using a similar design as the LSL dCas9 mouse (Figure 10F-G). When
we crossed the enhanced CRISPRi mouse to the Vglut2
Cre
(Slc17a6) mouse we saw
specific expression matching the Vglut2 expression pattern (Figure 10H). These results
indicated dCas9-KRAB can be expressed via transgenic mouse to tailor specific and
efficient knockdown of gene expression.

63

64
Figure 10. dCas9-KRAB delivery via transgenic mice
(A) Diagram highlighting transgenic mice for expressing dCas9 (B) Schematic showing
the conditional mouse can be crossed to any Cre mouse to limit the expression to a
genetically defined cell type. (C) The LSL mouse uses a human synapsin promoter,
limiting expression to neurons, to drive LSL cassette followed by dCas9-KRAB-2A-
mCherry. When Cre is expressed, the LSL cassette is removed and dCas9-KRAB-2A-
mCherry can be expressed. (D) Representative immunofluorescence images of
mCherry expression pattern in coronal sections in Cre mouse crosses. Last row shows
in situ hybridization data from Allen Institute Brain Atlas for comparison. (E) Schematic
showing enhanced CRISPRi mouse. dCas9 fused to KRAB and MeCP2. (F) Schematic
indicating the mouse breeding paradigm for validating recombinase: enhanced CRISPRi
mouse crossed to Vglut2
Cre
(Slc17a6) (G) The enhanced CRISPRi mouse uses a
human synapsin promoter to drive LSL followed by dCas9-KRAB-MeCP2-2A-mCherry.
When Cre is expressed, the LSL is removed and dCas9-KRAB-MeCP2-2A-mCherry
can be expressed. (H) Allen Institute Brain in situ hybridization Atlas image of Vglut2
(Slc17a6) expression in thalamus. Representative in situ hybridization image of Vglut2
(Slc17a6) (green) and dCas9 (red).

CNS efficiently silences gene expression in the mouse brain
To measure efficiency of silencing genes in the mouse brain, we developed a
scheme using the ubiquitous dCas9 mouse. To start, we packaged the most efficient
sgRNAs from our in vitro results into AAVs, then stereotactically injected into one side of
the bilateral PVN. Then we compared the mRNA expression on the injected side vs.
non-injected side to determine the sgRNA efficiency of knockdown using in situ
hybridization because the genes we are targeting do not have commercially available
antibodies that work on brain tissue (Figure 11A-C). The sides of the PVN are close to
one another but separated by the third ventricle, creating an ideal system for testing
efficiency of knockdown because each animal has its own internal control. To target all
peptidergic output from an individual cell type, we investigated the efficiency of sgRNAs
targeting neuropeptide processing genes: Pcsk2, Cpe, Pam and to target glutamate
signaling, the vesicular glutamate transporter Vglut2. Using fluorescent in situ

65
hybridization (FISH), we found robust gene knockdown with guides targeting Pcsk2 and
Cpe (80-85% knockdown) and moderate knockdown of targets Pam and Vglut2 (50-
70% knockdown). Pam has a complex gene structure. PAM actually encodes two
enzymes, peptidylglycine alpha-hydroxylating monooxygenase (PHM) and
peptidyl-alpha-hydroxyglycine alpha-amidating lyase (PAL)
91,93,94,379
, and has two major
transcription start sites that are >100,000 base pairs apart, surrounded by pronounced
secondary structure
380
. We ultimately designed sgRNAs targeting both and saw
increased efficiency at the later TSS both in vitro and in vivo, although there appears to
be some tissue specific effects, as guide #4 targeting the early TSS performed better in
vivo than in vitro (Figure 11C) (Figure 8C). Using guides targeting the neuropeptide
oxytocin, we were able to measure efficiency and specificity of knockdown. To do this,
we injected ubiquitous dCas9 animals unilaterally with sgRNAs targeting oxytocin. By
comparing oxytocin expression on the injected and noninjected side, we were able to
evaluate the efficiency of silencing. To measure specificity, we could compare the
amount of the peptide vasopressin, which is expressed in intermingled cell population in
the PVN. Vasopressin and oxytocin arose from a gene duplication event and share a
high level of sequence identity (96.65%)
381
so if there were any off-target effects, we
would expect to see them is vasopressin. Histology confirmed specific knockdown of
oxytocin while leaving vasopressin expression unaffected. (Figure 11D-E). Timing of
injection was an important factor to consider in these experiments as AAVs take 7-10
days to have their full effect and we wanted to give the DCV reserve pool enough time
to become depleted and show knockdown. We tested the most efficient guides at 2, 3, 4
and 6 weeks and saw similar knockdown so all the data shown here are at two weeks.

66
Depending on the question one is asking, the type and onset of viral expression (i.e.
HSV expression turns on more rapidly than AAVs but expression is not stable over
time
382,383
, whereas AAVs turn on after 2 weeks but are stable over 6-12 months
384,385
)
or an inducible system could be a better choice. These results demonstrate the efficacy
of CNS to efficiently and specifically to silence gene expression in adult mouse brains.

Figure 11. CNS efficiently silences gene expression in vivo
(A) Scheme for evaluating the efficiency of sgRNAs mouse brain. AAVs are injected
unilaterally into the PVN and expression in the injected vs. non-injected side is
compared (B) Fluorescent in situ hybridization representative image of in vivo validation
for sgRNA targeting Pcsk2 (C) Quantification of knockdown using fluorescent in situ
hybridization (for description of methodology see Figure 12) (D) Representative
immunofluorescence image of in vivo validation measuring specificity and efficiency of
sgRNAs targeting oxytocin. (E) Quantification of knockdown comparing number of
oxytocin cell bodies on the injected and non-injected side of the PVH to vasopressin
ratio of cell bodies on injected and noninjected sides. Statistical significance was
assessed by two-way ANOVA and Šídák test for multiple comparisons. 1-3 brain slices

67
processed per animal, n = 3 animals per sgRNA. Data shown as mean ± SEM. *p <
0.05, **p < 0.01

To quantify the level of knockdown (Figure 11C), we created an imaging analysis
pipeline (Figure 12). We unilaterally injected dCas9 ubiquitous mice with an AAV
expressing a sgRNA (Figure 12A). We waited two weeks for the virus to express and
gene knockdown to occur. Then we performed FISH to label mRNA from our target
gene and GFP to quantify level of viral expression. We then imaged the slices and
created an imaging analysis pipeline, similar to previously described analysis
methods
386–388
(Figure 12B). The pipeline performs the following steps: identify the
region of interest (ROI), apply a lower threshold to remove background signal (Figure
12C), quantify fluorescent intensities within ROI for all channels (DAPI, target gene,
GFP) (Figure 12D-E). To quantify the level of knockdown, fluorescent intensity was
normalized to the area of ROI resulting in mRNA fluorescent intensity/mm
2
. For AAV-
mediated knockdown, the ROIs compared were the injected side of the PVH and the
non-injected side of the PVH, with each animal serving at its own control, to provide a
ratio for relative RNA expression.

68

Figure 12. Image Analysis Pipeline
(A) To measure efficiency of each sgRNA, sgRNAs are packaged into AAVs and
injected unilaterally into PVN, then the noninjected side of the PVN will serve as a
control. (B) After two weeks, the brains are harvested, and FISH is performed to
visualized mRNA distribution of target gene (Pcsk2, red) and virus (GFP, green) and we
take z-stack images of the injected region (C) The image analysis pipeline begins with
identifying the injected and noninjected regions of interest and measures the intensity of
target gene (Pcsk2), GFP, and DAPI (a nuclei marker). Additonally, the pipeline
quantifies the GFP and target gene (Pcsk2) particles. Finally, the distribution of
intensities of GFP (D) and the target gene in the injected and noninjected regions can
be compared to quantify knockdown. (E) In this example, Pcsk2 = 90% knockdown

Transgenic expression of sgRNA array facilitates simultaneous knockdown of
multiple genes
Most neurons in the brain make and release multiple neuropeptides, monoamine
neurotransmitters, and fast acting amino acid neurotransmitters to signal
40,147–149
. In
order to achieve our aim of isolating and identifying the functional effects of the different
modes of signaling, we needed a means to silence all neuropeptide output from a

69
neuron. To that end, we employed a novel approach to target common neuropeptide
processing enzymes which are localized inside the DCVs, and thereby disrupt all
neuropeptide biogenesis in a specific cell type. To disrupt neuropeptide signaling as a
class we needed the capability to target multiple neuropeptide processing genes. We
achieved this by targeting multiple genes simultaneously using an array of guides. We
designed and generated a transgenic mouse expressing this array of guides targeting
three neuropeptide processing enzyme genes: Pcsk2, Cpe, Pam. To validate this
delivery approach, we crossed the dCas9 ubiquitous expression mouse to the sgRNA
array mouse (NPPE;dCas9) (Figure 13A). Gene expression knockdown of the three
target genes at the RNA level was confirmed by in situ hybridization within the PVN
(Psck2 48%, Cpe >99%, Pam 89% knockdown) (Figure 13B-C), and whole brain qPCR
(Pcsk2 45%, Cpe 91%, Pam 72% knockdown) (Figure 13G). By targeting the common
neuropeptide processing enzymes, we saw disruption of neuropeptide expression. We
saw a downregulation of oxytocin (4.5-fold decrease) and upregulation of vasopressin
(2.9-fold increase) by immunohistochemistry compared to the control mice (Figure 13D-
F). The observed increase in vasopressin by immunohistochemistry does not
necessarily mean there is an increase of biologically active vasopressin because the
antibody could be recognizing an accumulation of the inactive precursor (Figure 1). It is
probable that the increase in vasopressin immunofluorescence is a real biological
consequence of developmental compensation. Since we do not know if the antibody is
specific for the mature bioactive peptide, we needed a way to characterize and quantify
the peptide content in the brains of the NPPE;dCas9 animals. We used quantitative
neuropeptidomics approach to determine the effect on mature forms of neuropeptides.

70
Peptidomics is an approach to identify endogenous peptides
389,390
. It is distinct
from proteomics, in that its goal is identify to native forms of peptides, including post-
translational modifications. Peptidomics requires a unique sample preparation without
enzymes, like trypsin, a complicated instrument setup and challenging data analysis
389–
392
. Using a neuropeptidomic strategy we developed, we assessed the neuropeptide
expression in the thalamus (Figure 13H). To do this, we perfused the animals with
isotonic saline containing protease inhibitors, micro-dissected our brain regions of
interest, heat inactivated the tissue in water with protease inhibitors, and flash froze the
tissue. To extract the neuropeptides, we homogenized the tissue, sonicated the
samples in extraction buffer twice, and desalted with a C18 spin column. We found a
downregulation of neuropeptides, Little SAAS (4.1-fold decrease) (Figure 13I) and
somatostatin-28 (10.3-fold decrease) (Figure 13J); and we found an upregulation of
Met-enkephalin (1.8-fold increase) (Figure 13K) compared to the control mice.
Somatostatin-28 is processed by furin, PCSK1, PCSK2, CPE
393,394
. In this experiment,
we silenced PCSK2 and CPE, this dramatic decrease is likely due to the reduced
processing of those enzymes. Interestingly, neuropeptidomic analysis of Pcsk2 genetic
knockouts showed decreased levels of Little SAAS and met-enkephalin
395
. We found a
similar trend with little SAAS so this decrease could be attributed to a reduction in
PCSK2. Though interesting, the neuropeptidomics data is preliminary and we need to
increase our sample size to confirm the effect of silencing neuropeptide processing
enzymes on neuropeptide levels. Taken together, our data indicate that CNS can be
used to target multiple genes simultaneously and targeting neuropeptide processing
enzymes leads to disruption of multiple neuropeptides simultaneously.

71

Figure 13. Transgenic expression of sgRNA array facilitates multiplex gene
knockdown

72
(A) Transgenic mice expressing array of sgRNAs were crossed to the ubiquitous dCas9
mice. (B) in situ hybridization quantification of PVH expression of target genes n = 3-4
mice per genotype. Immunofluorescence Intensity of PVH normalized to area. Unpaired
two-tailed Student’s t-tests were used, Pcsk2 p = 0.0291 Cpe p = 0.0001, Pam p =
0.0415 (C) Representative coronal sections of in situ hybridization through PVN
illustrating expression of target genes in neuropeptide processing enzyme/dCas9
(NPPE;dCas9) mice and littermate controls (D-E) Representative immunofluorescent
images of oxytocin (OXT) and vasopressin (AVP) (F) Quantification of oxytocin and
vasopressin expression. Paired two-tailed Student’s t-tests were used, Oxytocin p =
0.0200 Vasopressin p = 0.0286 (G) whole brain qPCR quantification for target genes.
Statistical significance was assessed by two-way ANOVA and Šídák test for multiple
comparisons. Pcsk2 p = 0.019 Cpe p < 0.0001, Pam p = 0.0009 (H) Neuropeptidomics
was performed on thalamic tissue (purple) from NPPE;dcas9 and control brains. Allen
reference atlas displayed here for visualization. Several peptides were downregulated
(I) Little SAAS (4.1-fold decrease) (J) somatostatin (10.3-fold decrease) and we found
an upregulation of (K) Met-enkephalin (1.8-fold increase). Data shown as mean ± SEM.
*p < 0.05, **p < 0.01, ***p < 0.001 ****p < 0.0001 ns = not significant

Viral delivery of an array of sgRNAs enables simultaneous knockdown of multiple
genes
Since we saw that the array targeting neuropeptide processing enzymes
disrupted neuropeptide output, the next step towards developing a flexible tool to
measure the functional role of neuropeptides was to develop a virally delivered sgRNA
targeting all four neuropeptide processing enzymes simultaneously. Increased temporal
and spatial flexibility of knockdown was enabled by packaging the guides into an array
and delivering it virally. It also permitted acute knockdown experiments that are
essential for measuring the functional role of neuropeptides. Additionally, it allowed
more rapid testing of guides to determine the best combination for efficient knockdown
compared to expressing the array through a transgenic mouse. This method is superior
to mixing multiple viruses expressing a single sgRNA together because that runs the
risk of mosaic silencing when not every cell receives a copy of every virus
352
. By

73
expressing the sgRNAs in an array, each cell that receives the virus is expressing all
the sgRNAs. Moreover, the gene encoding the enzyme PAM has more complex gene
regulation than previously thought, including multiple transcription start sites
380,396,397
.
Viral delivery of an array of sgRNAs permits targeting both TSS simultaneously. Finally,
this experimental design allowed me to target both PCSK1 and PCSK2 enzymes,
compared to the transgenic mouse sgRNA array that only targeted the PCSK2 enzyme.
Ideally, this change will help to avoid possible compensation from the complementary
convertase. I first generated an AAV that expresses five to six sgRNAs. We chose this
number of sgRNAs to test in order to maximize the number for the amount of space that
was remaining in the AAV with the EGFP marker. We wanted to target the most
common four neuropeptide processing enzyme genes simultaneously: Pcsk1, Pcsk2,
Cpe, Pam. Each sgRNA is expressed under its own U6 promoter (Figure 14A). The
expression vector contains a EGFP fluorescent reporter driven by a hSyn promoter.
While there are numerous studies that have used arrays to express multiple
sgRNAs
197,398–402
, we wanted to maximize our targeting potential, while avoiding
possible pitfalls. For example, there is a possibility for “Promoter Cross talk Effects” with
the pol III promoters arranged so close together in tandem
403–406
. Another concern is
that the with the increased expression of sgRNAs, the sgRNAs are forced to compete
for a declining amount of dCas9 endonucleases
407
. To validate the efficiency of
knockdown of the guide array, the virus was injected into one side of the PVN while the
other side was used as the control for comparison (Figure 14B). We evaluated four
sgRNA array combinations (Figure 14A). Following a prescreen, we saw the
combinations with five guides show the greatest knockdown across the tested genes

74
and those arrays were subsequently fully characterized. We saw knockdown of each
gene targeted. Combination #8 resulted in the greatest levels of gene silencing across
the targets resulting in 86% Pcsk1 knockdown, 72% Pcsk2 knockdown, 81% Cpe
knockdown, 56% Pam knockdown (Figure 14C). We identified a possible placement
effect of the order of the guides, with the first guide in each array consistently producing
the greatest levels of knockdown. Although more experiments are necessary to clarify
this order effect. Additionally, we identified a possible efficiency limit on the number of
guides expressed in the array with five guides showing more efficient knockdown than
six guides. This is possibly due to the diminishing pool of dCas9 that the sgRNAs must
compete for or possibly the promoter interference as discussed above. Unfortunately,
despite targeting both transcriptional start sites for Pam, I did not see an increase in
repression and future work will be needed to enhance the repression of this enzyme.
These observations indicate that CNS can facilitate the flexible silencing of multiple
genes simultaneously.

75

Figure 14. Viral delivery of an array of sgRNAs enables flexible multiplex gene
knockdown
(A) AAV construct for expressing array of sgRNAs. The construct is flanked by ITRs for
integration into the AAV genome. Each sgRNA is driven by a separate U6 promoter. A
GFP marker is driven by a hSyn promoter. The four combinations tested are illustrated
Combo 1, 3, 7, & 8. (B) Representative coronal sections of in situ hybridization through
PVN illustrating expression of target genes. We employed a similar strategy that was
used to test the efficiency of individual sgRNAs in (Figure 12). AAVs were injected
unilaterally into the PVN and expression on injected vs noninjected sides were
compared. Then FISH was used to visualize the mRNA expression of targets (C)

76
Quantification of in situ hybridization Pcsk1 (orange), Pcsk2 (yellow), Cpe (pink), Pam
(green)

77
CHAPTER 3: DEFINING THE FUNCTIONAL ROLE OF NEUROPEPTIDES AND
NEUROTRANSMITTERS REGULATING ENERGY HOMEOSTASIS
Abstract
Through a century of research, our understanding of how the brain monitors and
controls food intake and energy homeostasis has dramatically increased but this has
not translated to effective therapies for obesity. Many of the brain areas and cell types
controlling feeding are identified however, the contributions of the individual chemical
messengers within these regions and circuits remain unknown. We used a new tool,
CNS, to identify the functional role of and assess the contribution of the chemical
signals in brain regions and cell types. We found sex-specific differences in the role of
glutamate within the paraventricular nucleus of the hypothalamus (PVN), while both
neuropeptides and glutamate play a significant role body weight regulation. Disrupting
neuropeptide maturation globally led to deficits in the ability of mice to cope with social
isolation stress and maintain energy and fluid homoeostasis. Acute disruption of
neuropeptide biosynthesis in the PVN and nucleus of the solitary tract (NTS)
demonstrated an important role for neuropeptides controlling body weight in those
regions. This system provides a critical tool for identifying roles of individual transmitters
in energy homeostasis.

Introduction
The brain coordinates internal and external signals to maintain the delicate
balance of energy homeostasis. Dysregulation of the neural circuits governing energy
homeostasis can lead to obesity. Although our understanding of how the brain monitors

78
and controls food intake and energy homeostasis has dramatically increased, these
advances have not translated into effective treatments for obesity. Obesity and its
associated conditions are among the most expensive, prevalent, and chronic disorders
affecting more than one-third of adults in the United States
408
, yet we still lack the
fundamental understanding of the molecules that regulate feeding behavior and energy
balance.
Decades of research, fueled by advancing technology, reveal many of the brain
areas and cell-types controlling energy homeostasis, however due to the number of
chemical messengers within those regions, the individual contributions of each chemical
messengers remain a challenge to understand. A greater knowledge of the role of the
individual chemical messengers mediating control over energy homeostasis is critical to
designing effective interventions, because drugs do not target cell types or circuits, they
target neurotransmitter systems. The hypothalamus plays a central role in controlling
innate behaviors required for maintenance of energy homeostasis and survival,
including the regulation of hunger and feeding behavior
241–243,409
. Neuronal populations
in the in the hypothalamic arcuate nucleus (ARC), pro-opiomelanocortin (POMC) and
agouti related protein (AgRP)/neuropeptide Y (NPY) neurons, have opposite effects,
inhibiting and stimulating the motivational drive for food intake, respectively
263–266
.
These populations of neurons express the peptides they are named for but also express
multiple other neuropeptides, as well as fast acting amino-acid
neurotransmitters
282,283,286
. They send dense projections to the paraventricular nucleus
of the hypothalamus (PVN)
289,290
, a key brain structure for maintaining energy balance
and regulating feeding
148,254,291,296,299,306,410,411
. Within the hypothalamus, the

79
paraventricular nucleus (PVN) acts as a key convergence point where the signals from
diverse neural systems are integrated and then redistributed through projections to
extrahypothalamic brain regions
299,412
. Lesions of this region or disruption of its
development lead to overeating and obesity
291–294
. To achieve this complex information
processing, the PVN contains an extraordinary diversity of neurochemically distinct and
functionally specialized cell types. These cell types are primarily glutamatergic but also
co-express a vast array of neuropeptides
297,323
. The functional capacity of these
peptides has been demonstrated by classical pharmacology, as well as numerous
genetic studies
40,307,310–313,334,413
. Yet for each of these cell types, it remains unclear as
to what extent their behavioral effects are mediated by fast glutamatergic versus slow
neuropeptidergic signaling. This knowledge gap reflects the difficulty of performing the
simple loss-of-function experiment that is necessary to determine the contribution of a
single neurotransmitter within a circuit to behavior. For these reasons, we recently
developed a tool, called Cell-type-specific Neurotransmitter Silencing (CNS), for
silencing individual transmitters with temporal, spatial, and cell type specificity using
CRISPRi. This tool is a simple yet powerful means to systemically define the
contribution of neurotransmitters and neuropeptides to behavior. Here, we applied this
tool to functionally define the chemical messengers controlling energy homeostasis in
the PVN and interrogated the global effect of neuropeptide signaling. We examined
neuropeptides contribution to body weight regulation in genetically defined cell-types, as
well as key brain areas.

80
Results
Glutamate in PVN plays a key role in body weight regulation in males, but not
females
The PVN is a critical regulator of feeding and energy homeostasis, and physical
and genetic disruption of this area leads to extreme obesity
291–294
. A remarkable feature
of this brain region is its level of neurochemical diversity with intermingled cells that
express over 20 neuropeptides and glutamate
414,415
. The specific functional roles of
these peptides are poorly defined. The simple fundamental question remains: to what
extend is the PVN’s effects on feeding mediated by neuropeptidergic or glutamatergic
signaling? A conditional knockout of glutamate within the PVN has demonstrated its
importance
298
. As an initial step to validate the CNS method, we examined glutamate’s
role within the PVN. To do this, we designed and generated sgRNAs targeting Vglut2, a
glutamate transporter that loads glutamate from the cytosol into small clear vesicles
locally at the synapse, effectively reducing glutamatergic output. The sgRNAs were
packaged into AAVs and delivered by stereotactic injection to the PVN of mice
expressing dCas9 ubiquitously (Figure 15A). An AAV expressing GFP was used in the
control group. CNS produced a robust reduction of target Vglut2, compared to the GFP
control as visualized using fluorescent in situ hybridization (Figure 15B-C). Body weight
was measured for a baseline period of 30 days prior to injection. Following injection, an
increase in body weight was seen in male mice, but not females, compared to the
control group (Figure 15D-E). There are many distinct sex-differences in energy
balance between males and females, including where fat is stored, influence of gonadal
hormones, and the way their brains respond to signals from the body
416
. It is possible

81
that female body weight regulation is more dependent on slower neuropeptidergic
signaling and influenced by the expression of estrogen receptors in the parvocellular
region of the PVN
417–419
. Taken together, these results highlight the importance of
examining both sexes and show that disrupting glutamate output does lead to an
increase in body weight in male mice.

Figure 15. Silencing glutamate in PVN leads to a sex-specific increase in body
weight
(A) Transgenic mice expressing dCas9 ubiquitously were bilaterally injected in the PVN
with an AAV expressing sgRNAs targeting Vglut2 and a GFP marker or GFP, as a
control. Fluorescent in situ hybridization representative image of (B) sgRNA targeting
Vglut2 or (C) GFP bilaterally injected into the PVN. (D) Weekly body weights for
individual mice, male (blue) and female (pink) animals prior to and following sgRNA
AAV injections. (E) Weekly body weights for male (blue) and female (pink) animals prior
to and following GFP AAV injections.

82
Targeted knockdown of neuropeptide or glutamatergic output in Sim1+ PVN
neurons impacts body weight on different timescales
While most of the neurons in the PVN are glutamatergic, it is one of the most
neurochemically diverse areas of the brain, expressing many neuropeptides in
overlapping cell populations
323,414
. To determine the relative functional contribution of
neuropeptides and glutamate in the PVN, we used CNS to compare silencing
glutamatergic or neuropeptidergic output. For cell-type specific knockdown, we used the
LSL dCas9 mouse crossed to Sim1
Cre
mouse, which limited the expression of dCas9 to
Sim1 positive neurons. SIM1 is expressed in most neurons in the PVN and supraoptic
nucleus, with sparse labeling in the medial amygdala
378
. We delivered the sgRNAs via a
bilateral AAV injection into the PVN of adult mice which imparted cell-type, spatial, and
temporal control of knockdown. To disrupt neuropeptide output, we targeted common
neuropeptides processing enzyme genes, Pcsk2, Cpe, and Pam. To silence
glutamatergic signaling, we chose a similar strategy as the previous experiment and
targeted Vglut2. As a control, we used an AAV expressing GFP (Figure 16A). Mice with
glutamate output reduced had an elevation in body weight by one week following AAV
injection (Figure 16C). Mice with neuropeptide processing enzymes silenced showed a
delayed response and did not show a significant increase in body weight until week
four, possibly reflecting a longer turnover rate of neuropeptides and their processing
enzymes (Figure 16B). Due to the PVN’s influence in thermogenesis and energy
expenditure through brown adipose tissue
411,420
, we also monitored basal temperature
levels prior to and following surgery. Interestingly, following surgery, we saw an initial
increase in temperature followed by a consistent reduction below baseline when

83
neuropeptide output was disrupted but not glutamatergic output (Figure 16D). This
indicates that thermoregulation is reliant on neuropeptide signaling from the PVN.
Indeed, the neuropeptides oxytocin, corticotropin-releasing hormone, and thyrotropin-
releasing hormone are implicated in the initiation of the thermogenic response
421–427
,
and these neuropeptides are processed by the target enzymes PCSK2, CPE, and
PAM
379,428–434
. Taken together, these data show that both neuropeptides and glutamate
are important in the regulation of body weight. Additionally, these data suggest that the
PVN’s effect on thermoregulation is primarily mediated through neuropeptidergic
signaling.

Figure 16. Silencing neuropeptide or glutamatergic output in Sim1+ PVN neurons
impacts body weight on different timescales in male mice
(A) Transgenic LSL dCas9 mice were crossed to Sim1
Cre
mice. Double-positive
heterozygous mice were bilaterally injected with AAVs expressing: sgRNAs targeting
neuropeptide processing enzymes (NPPE), sgRNAs targeting Vglut2, or GFP for the
control animals (B) Body weight measurements (normalized to baseline body weight)
following AAV injection of NPPE (blue, n = 2) and control mice (black, n = 2) or (C)
Vglut2 (green, n =2) and control mice (black, n = 2) (D) Temperature measurements
following AAV injection, normalized to baseline measurements prior to AAV injection.
Two-way ANOVA and Dunnett's test for multiple comparisons were used to compare to
control mice, who received a GFP only virus. Data shown as mean ± SEM. *p < 0.05,
**p < 0.01, ***p < 0.001 ****p < 0.0001 ns = not significant

84

Disrupting neuropeptide output globally leads to energy homeostasis and
behavioral deficits
To address the possibility that viral delivery of sgRNAs is insufficient and to avoid
mosaic knockdown when targeting multiple genes simultaneously, we designed and
generated a transgenic mouse expressing an array of guides targeting three
neuropeptide processing enzyme genes: Pcsk2, Cpe, Pam (NPPE) (Figure 13). To
validate this delivery approach, we crossed the dCas9 ubiquitous expression mouse to
the sgRNA array mouse targeting neuropeptide processing genes (NPPE;dCas9)
(Figure 17A). Next, we assessed the functional consequences of disrupting
neuropeptide output via silencing neuropeptide processing enzymes delivered through a
transgenic mouse. We functionally characterized the NPPE;dCas9 mice. For both males
and females, double-heterozygous mice had delayed growth and were significantly
smaller compared to littermate controls at three and four weeks of age (25% body
weight difference at 3 weeks for males and females) (Figure 17A-B). At week 5, the
NPPE;dCas9 animals surpassed their littermates and developed obesity. The dramatic
increase in body weight could not be explained by an increase in body length or
hyperphagia (Figure 17E-H). The development of obesity mirrored the single
neuropeptide processing enzyme knockout of neuropeptide processing gene, Cpe
435
.
Considering this was the most potently silenced gene (Figure 13), these effects are
consistent with the effects of chronic Cpe gene knockout. However, Cpe single gene
knockout animals had an increase in food consumption that their weight gain was linked
to, but we did not observe increased food consumption with the NPPE;dCas9 animals

85
(Figure 17E-F). Interestingly, the smaller size we observed in young animals did not
match the phenotypes of any of the single neuropeptide processing enzyme knockout
animals. Since this is a broad knockdown, it is impossible to identify single cause of this
phenotype, however, there are many possibilities. For example, CPE also has known
roles as a sorting receptor loading secretory vesicles
436
and has been shown to interact
with growth hormone
437
. Additionally, growth hormone releasing hormone, a
neuropeptide integral in the regulation of body growth, is processes by furin and
PCSK1
217
, both enzymes were not targeted with this sgRNA array. However, growth
hormone releasing hormone is amidated by PAM and this modification required for its
full activity
438
, which was targeted in this sgRNA array. Thus, impairing growth hormone
releasing hormone through targeting PAM could result in reduced growth, as was
observed when growth hormone releasing hormone expressing neurons were
ablated
439
. Further supporting for this theory, PAM is copper-dependent enzyme and in
mice with impaired copper metabolism, growth hormone-releasing hormone neurons
had reduced peptide content and disrupted peptide localization
440
.
Strikingly, when the NPPE;dCas9 mice were single caged at weaning, they failed
to cope with the social isolation stress and were unable to maintain body weight (Figure
17C-D). This could be a result the disruption of several neuropeptides implicated the in
the stress response, such as CRH, or social isolation stress, such as TAC2. Previous
reports have revealed an increase in TAC2 following social isolation stress, led to
specific behaviors, such as aggression
386
. It is possible that disruption of TAC2
activation, that normally happens during social isolation to turn on adaptive pathways,
caused the reduction in body weight and development deficits. Another possibility is this

86
major disruption in neuropeptide signaling is buffered by social learning in a group
housed environment and without the feeding cues of their littermates, animals with
neuropeptide signaling disrupted fail to adequately obtain nutrients or water
12
. Cpe
genetic knockouts have known memory deficits
441
.
Fasting and basal levels of glucose were elevated in the NPPE;dCas9 female
mice but not the NPPE;dCas9 male (Figure 17I-N). We measured fasting levels at both
7 and 11 weeks of age to evaluate if the body weight gained during that period would
have an effect, but still, the male mice did not show elevated blood glucose levels at 7
or 11 weeks of age (Figure 17N). In comparison to the single neuropeptide knockout,
the female NPPE;dCas9 animals phenocopied the Cpe knockout mice in their impaired
glucose homeostasis compared to Pcsk2 knockout animals that showed fasting
hypoglycemia
442
, while the male animals did not show elevated fasting glucose levels
compared to controls. In the Cpe genetic knockout, there were no sex-specific
phenotypic differences.
Because we observed an upregulation of vasopressin in the characterization of
the NPPE;dCas9 animals rather than the expected downregulation, (Figure 13E-F) and
considering its important role in fluid homeostasis
126,239,240
, we next investigated
NPPE;dCas9 mice’s ability to maintain fluid balance and cope with a homeostatic
challenge. We administered a peripheral injection of hypertonic saline (2M NaCl) and
measured the drinking response. The hypertonic saline injection resulted in an
increased drinking response compared to controls for female mice (Figure 17O-P).
While there was a similar trend towards increased drinking response in males, it was
not as significant increase as the increase observed in females (Figure 17Q-R).

87
Increased drinking response to a hypertonic saline injection matches the phenotype of
vasopressin knockdown mice
443
and a reduction in the amount of available bioactive
vasopressin is likely responsible for this effect. However, further investigation of their
drinking behavior without a homeostatic challenge showed that compared to control
mice, overnight water consumption was not significantly different (Figure 17S-T) which
did not match the phenotype of vasopressin knockdown mice
443
. It is possible that the
upregulation of pro-vasopressin we observed, in response to a probable decrease in
mature bioactive vasopressin, is sufficient to raise levels of vasopressin enough buffer
the animals from the effects on basal drinking but is insufficient to cope with the
homeostatic challenge. In examining their urine and blood osmolality under basal
conditions, we saw a trend that the NPPE;dCas9 female mice had more dilute urine
than the control mice but this did not reach significance. The male NPPE;dCas9
animal’s urine osmolality matched the control animals (Figure 17U-V). If the
NPPE;dCas9 do have decreased levels of mature bioactive vasopressin, they would
have impaired ability to concentrate their urine which is likely the case in females. Both
male and female NPPE;dCas9 mice had normal blood osmolality (Figure 17W-X).
Taken together, these findings indicate impaired maturation of neuropeptides leads to
deficits in the ability cope with stress and to maintain energy and fluid homeostasis.

88

89
Figure 17. Disrupting neuropeptide output globally leads to energy homeostasis
and behavioral deficits
Transgenic mice expressing array of sgRNAs were crossed to the ubiquitous dCas9
mice. Weekly body weight measurements of (A) group house females (NPPE;dCas9 n
= 10, control n = 10 ), (B) group housed males (NPPE;dCas9 n = 7, control n = 10), (C)
single housed females (NPPE;dCas9 n = 7, control n = 6), (D) and single house males
(NPPE;dCas9 n = 5, control n = 8). For body weight, statistical significance was
assessed by two-way ANOVA and Šídák test for multiple comparisons. (E) 24-h food
intake in group housed female mice (NPPE;dCas9 n = 8, control n = 10) (F) 24-h food
intake in group housed male mice (NPPE;dCas9 n = 6, control n = 5) (G) Body length
measured at 10 weeks (tip of nose to base of tail). Females (NPPE;dCas9 n = 7, control
n = 12) (H) Males (NPPE;dCas9 n = 4, control n = 4) (I) Longitudinal basal blood
glucose measurements. Females (NPPE;dCas9 n = 6 - 10, control n = 9 - 15 (J) Males
(NPPE;dCas9 n = 5 - 7, control n = 6 - 12) (K) fasting blood glucose levels, 7-weeks old.
Females (NPPE;dCas9 n = 9, control n = 15) (L) Males (NPPE;dCas9 n = 6, control n =
9) (M) fasting blood glucose levels, 11-weeks of age. Females (NPPE;dCas9 n = 5,
control n = 11) (N) Males (NPPE;dCas9 n = 5, control n = 5) (O) Averaged traces
showing cumulative licks following IP injection of hypertonic saline (2M NaCl). Females,
6-weeks of age (NPPE;dCas9 n = 5, control n = 7) (P) Total water consumption (area
under curve) during 90-min period following hypertonic saline injection challenge males
and females (NPPE;dCas9 n = 5, control n = 11) (Q) Averaged traces showing
cumulative licks following IP injection of hypertonic saline (2M NaCl). Males, 6-weeks of
age (NPPE;dCas9 n = 6, control n = 7) (R) Total water consumption (area under curve)
during 90-min period following hypertonic saline injection challenge (S) Total water
consumption (area under curve) during 16-hr overnight drinking session. (Average of 4
nights recorded from 6-10 weeks of age, males and females) (NPPE;dCas9 n = 4,
control n = 4) (T) Normalized water intake to body weight (U) Urine osmolality
measurements under basal conditions 9 weeks of age. Females (NPPE;dCas9 n = 6,
control n = 6) (V) Males (NPPE;dCas9 n = 7, control n = 7) (W) Blood osmolality
measurements under basal conditions 10 - 11 weeks of age. Females (NPPE;dCas9 n
= 7, control n = 10) (X) Males (NPPE;dCas9 n = 4, control n = 4) Unpaired two-tailed
Student’s t-tests were used. Data shown as mean ± SEM. *p < 0.05, **p < 0.01, ***p <
0.001 ****p < 0.0001 ns = not significant

Viral toxicity in PVH leads to massive obesity
Since the transgenic delivery of sgRNAs was effective, we next used this mouse
to determine the functional effect of neuropeptidergic signaling in the PVN by a
complementary means. To do this, we crossed the mouse expressing sgRNAs targeting
neuropeptide processing enzymes (NPPE) to the conditional expression dCas9 mouse

90
and injected the animals bilaterally into the PVN with an AAV expressing Cre under the
hSyn promoter, effectively turning on knockdown in neurons that the virus infected
(Figure 18A). Animals positive for only NPPE or LSL dCas9 were used as controls.
Following injection, many animals began rapidly gaining a great deal of body weight
(Figure 18B). One animal gained 60% of its starting weight within 40 days following
injection (Figure 18C). Unexpectedly, many control animals gained weight, as well
(Figure 18B). Histology confirmed viral toxicity at the site of injection in the animals with
elevated body weight. This was unexpected because we tested viral titer concentrations
and amounts. However, for our initial tests, we only waited two weeks following injection
and perhaps we did not observe full viral transduction in our tests. Further confirming
this hypothesis, through immunofluorescent staining for mCherry, a marker of
recombination for the LSL mouse, we saw that the virus had spread outside of the
targeted PVN, which was not observed in our tests with the same viral amount. Despite
the viral toxicity, these data show how vital the PVN is in the regulation of body weight.

91

Figure 18. Viral toxicity in PVN leads to massive obesity
(A) Transgenic mice expressing array of sgRNAs were crossed to the LSL dCas9 mice
and injected with AAV expressing Cre recombinase. (B) Weekly body weight
measurements for male and female animals. (C) Example images of mice showing the
massive accumulation of body fat. (D) Representative immunofluorescent images of
viral toxicity in PVN. Oxytocin, shown here as a marker for PVN (green), is absent.
mCherry (orange) is a fluorescent marker in the LSL dCas9 mouse following Cre-
dependent recombination and excision of the STOP codon.

Conditional inactivation of neuropeptide output does not impact body weight
To determine the functional contribution of neuropeptide signaling to body weight
in genetically defined cell-types, we used a triple-cross breeding strategy to generate a
NPPE/LSL dCas9/Cre mouse with either Sim1
Cre
, Pomc
Cre
or Slc17a6
Cre
mice (Figure

92
19A). Given that mice with ablation of Sim1 neurons
444
, conditional knockouts of
glutamate
298
and MeCP2
445
, and haplosuffciency of Sim1
291
are all obese, we monitored
body weight in these triple cross animals but, unexpectedly, we did not see any
differences in body weight regulation of these animals (Figure 19B). We next examined
Pomc
Cre
triple cross animals. POMC is the neuropeptide precursor for αMSH, a key
peptide promoting satiation in the PVN. POMC knockout animals develop late-onset
obesity
446
, therefore we expected silencing neuropeptide signaling from this population
would result in obesity. However, studies published after these experiments were done
of conditional knockouts of Cpe or Pcsk1, neuropeptide processing enzymes, in Pomc
neurons, did not result in obesity
233,447
. Compared to the control mice, we did not
observe a body weight difference in these animals (Figure 19C). In our broadest
conditional knockout with this paradigm, we used Vglut2
Cre
(Slc17a6), disrupting
neuropeptide signaling in all glutamatergic cells. We did not observe a body weight
difference in female or male mice (Figure 19C). These data suggest that for body
weight regulation either neuropeptide signaling within these cell-types is redundant, not
required, developmental compensation is obscuring the effect, or we potentially failed to
achieve strong enough knockdown with the LSL dCas9 mouse or target sgRNAs. It
should also be considered that this knockdown was for the life of the animal, and
perhaps the triple cross animals were able to cope with neuropeptide processing
enzyme silencing by upregulating receptors or neuropeptide translation. Given that we
did not see an effect, even in the broadest knockdown, we were concerned that the LSL
dCas9 mouse did not provide a great enough level of knockdown to continue using this
mouse model and chose to continue our next experiments with the ubiquitous dCas9

93
mouse.

Figure 19. The LSL dCas9 mouse was not functional
(A) Triple cross animals were generated by crossing the transgenic mice expressing
array of sgRNAs (NPPE) to the LSL dCas9 mice the double positive mice were crossed
to mice expressing Cre recombinase under different promoters. Weekly body weight
measurements for (B) Sim1
Cre
mice (C) Pomc
Cre
mice (D) Slc17a6
Cre
mice (C-D shows
individual animal body weights)

Viral disruption of neuropeptide maturation in key brain areas controlling energy
homeostasis led to changes in body weight
To determine the functional contribution of neuropeptide signaling in brain areas
important in body weight regulation, we developed and employed an AAV expressing an
array of sgRNAs targeting neuropeptide processing enzyme genes, Pcsk1, Pcsk2, Cpe,
and Pam in ubiquitous dCas9 mice (Figure 20A). The development of the virally
delivered array allowed for acute silencing, which is key to investigating the functional
effects of the neuropeptide signaling, which are typically resilient and adaptable to

94
disruption. Here, we used the sgRNA array to inhibit neuropeptide maturation in the
PVN, ARC and NTS (Figure 20B). The ARC is a key brain area for first order neurons
in the melanocortin pathway
263–266
. Following injection into the ARC, males did not gain
a statistically significant weight but there was an upward trend from week two through
four (Figure 20C). However, our sample size was low, and we were only able to test in
male mice. It is possible that we would not observe any changes in body weight here
though because recent reports of a conditional Cpe or Pcsk1 knockout in POMC
neurons failed to produce obesity
233,447
. The NTS is a key convergence point for signals
from the gut and plays a central role in meal termination
448
. Inhibiting neuropeptide
signaling from this region resulted in a modest increase in body weight following sgRNA
array injection in female mice on regular chow (Figure 20E) and breeder chow, which
contains a higher fat concentration (Figure 20D). Following a bilateral injection of the
sgRNA array AAV into the PVH, there was a small but significant increase in body
weight in male mice. The male mice gained a significant amount of weight when
measured in grams, as well. We did not see a significant increase in body weight for
female mice, but there was a similar trend and perhaps a larger sample size would bring
clarity to this effect. It should be noted, these experiments were exploratory and have
small sample sizes. Taken together, these data demonstrate that disrupting
neuropeptide output in the NTS and PVN results in increased body weight, while more
animals are needed to tease out the relationship of neuropeptide signaling within the
ARC.

95

Figure 20. Viral disruption of neuropeptide maturation in NTS and PVN led to
changes in body weight
(A) AAV expressing array of sgRNAs were bilaterally injected into ubiquitous expression
dCas9 mice. (B) Key brain areas targeted PVN (blue), ARC (yellow), and NTS (orange).
Weekly body weight measurements following sgRNA array AAV injection into: (C) ARC
males (D) NTS females on breeder chow (E) NTS females on regular chow. Weekly
body weight measurements (normalized to starting body weight) following sgRNA array
AAV injection into: (F) PVN males (G) PVN females (H) amount of weight gained
following 45 days after injection. Statistical significance was assessed by two-way
ANOVA with Tukey for pairwise comparison. *p < 0.05, **p < 0.01, ***p < 0.001 ****p <
0.0001 ns = not significant

96
CHAPTER 4: DISCUSSION
Neurons release classic fast neurotransmitters and multiple neuropeptides to
signal
40,147–149,414
. Together they coordinate complex behaviors and physiological
functions. Although fast neurotransmitter signaling and slow neuropeptide signaling
function together, they have distinct functional roles, while acting on different
timescales, over an expansive range of distances to activate a wide variety of targets.
The vast number of chemical signals co-expressed within intermixed heterogenous cell-
types in the brain makes it challenging to evaluate the individual contributions of each
signal. Moreover, many open questions remain about the functional role of neuropeptide
signaling due to the difficulty of manipulating it in vivo. For example, while administering
peptides often evokes measurable responses, many peptide knockouts show little to no
behavioral phenotypes
156,164–167
. This could reflect developmental compensation,
highlighting the importance of manipulating this mode of signaling acutely in adult
animals, or reveal redundancy within the system. The development of new tools is
crucial in expanding understanding of how neuropeptides contribute to coordinate
complex functions.

Summary of findings
Here, I’ve introduced Cell-type specific Neurotransmitter Silencing (CNS), an
efficient and flexible tool to enable interrogation of the functional effects of individual
transmitters or neuropeptides as a class with temporal and spatial specificity using
CRISPRi. Our data demonstrate CNS can be used to silence gene expression in vitro
and in vivo. Adeno-associated viral expression of sgRNAs in dCas9 transgenic mice

97
enabled efficient silencing of individual or multiple genes simultaneously. Transgenic
expression of an array of guides targeting neuropeptide processing enzymes disrupted
neuropeptide maturation. The CNS toolset provides an approach to evaluate the
function of neuropeptides as a class from a genetically defined cell population. We
chose to apply this toolset to the brain. Notably, disrupting neuropeptide maturation
globally led to deficits in the ability of mice to cope with social isolation stress and
maintain energy and fluid homoeostasis. Armed with CNS, I investigated the
contribution of glutamate and neuropeptides to the control of energy homeostasis in
various brain areas and cell-types. We found that reducing glutamatergic signaling in
PVN unveiled a sex-specific body weight regulation mechanism, while disrupting
neuropeptide signaling in both the PVN and NTS, led to an increase in body weight.
Disrupting neuropeptide signaling in the ARC did not change body weight. The work,
outlined here, highlights the value of the CNS approach and its potential to evaluate
individual transmitter’s functional contributions, while shedding light on the role of
chemical messengers regulating energy homeostasis.

Cell-type Specific Neurotransmitter Silencing - the method to our madness
Our control over gene expression was enabled by the successful adaptation of
CRISPRi to the mouse brain. A prior study has demonstrated that CRISPRi can
efficiently silence gene expression in the brain
197
. Our work confirms the efficacy of
CRISPRi in the brain and builds upon it, to create a toolkit for knockdown of individual
neurotransmitter systems. The use of CRISPRi to selectively silence genes is both more
specific and efficient compared to gene inactivation through RNAi or through mutations

98
introduced through classical CRISPR–Cas9. Using a transgenic mouse to deliver
dCas9-KRAB and either AAV or transgenic delivery of the sgRNAs allows for flexibility
of knockdown with temporal and spatial specificity, depending on the way the
components are combined. Our approach enabled control over individual transmitters
which could be used in combination with whole cell manipulations like DREADDs
179
or
optogenetic manipulations
176
to reveal the role of individual transmitters within a cell-
type, during activation or silencing. Moreover, since our approach enables acute
knockdown in adulthood, developmental compensation that is seen with traditional
genetic knockouts is avoided. Additionally, incomplete knockdown makes this approach
suitable to interrogate the function of genes where the genetic knockouts are not viable.
In fact, it is possible to design multiple sgRNAs with different knockdown efficiencies to
cover a broad range of knockdown for experimental purposes. However, connecting an
individual transmitter to its role for a particular behavior or phenotype may be difficult
with the current levels of knockdown we achieved. In the future, recently developed
improved dCas9 fusion proteins could be used to increase the efficiency of gene
silencing, such as dCas9-KRAB-MeCP2
195
or ZIM3 KRAB–dCas9
375
. Unfortunately, we
found that our conditional dCas9 mouse (LSL-dCas9-KRAB) did not produce sufficient
knockdown to provide useful investigation for our targeted questions (Figure 19).
However, it may be a very useful transgenic line for knocking down other genes in the
brain. Moreover, we did not find consistent recombination in the dCas9-KRAB-MeCP2
mouse to use this line in experiments, but this path should be explored in future studies
using CRISPRi mediated repression (Figure 10).

99
A key step towards developing a flexible means to measure the functional role of
neuropeptides was our development of virally-delivered sgRNAs targeting each of the
four most common neuropeptide processing enzymes (Figure 14). Packaging the
guides into an array delivered virally allowed for increased temporal and spatial
flexibility of knockdown. It also enabled acute knockdown experiments that are essential
for measuring the functional role of these neuropeptides. Additionally, it allowed more
rapid testing of guides to determine the best combination for efficient knockdown
compared to expressing the array through a transgenic mouse. This method is superior
to mixing multiple viruses expressing an individual sgRNA together because that runs
the risk of mosaic silencing, where not every cell received a copy of every virus. By
expressing the sgRNAs in an array, each cell that received the virus is expressing all of
the sgRNAs. Moreover, the gene, Pam, has more complex gene regulation than
previously thought, including multiple transcriptional start sites. Viral delivery of an array
of sgRNAs permits targeting both simultaneously. Unfortunately, despite targeting both
transcriptional start sites, I did not see an increase in repression and future work will be
needed to enhance the repression of this enzyme. It is possible we do not yet fully
understand the how Pam expression is regulated at the level of transcription. Finally,
this experimental design allowed me to target both PCSK1 and PCSK2 enzymes,
compared to the transgenic mouse sgRNA array that only targeted PCSK2. Ideally, this
change will help to avoid possible compensation from the complementary convertase.
During the development of this array, we identified a possible placement effect of the
order of the guides, with the first guide producing the greatest levels of knockdown.
Although additional experiments are necessary to clarify this order effect. Additionally,

100
we identified a possible efficiency limit on the number of guides expressed in the array
with five guides showing more efficient knockdown than six guides. One possibility for
this observation is “Promoter Cross talk Effects” which can occur when pol III promoters
are arranged so close together, in tandem
403–406
. A potential means of mitigation is to
use a system developed in plants, where multiple gRNAs are expressed in one
transcript and an RNA nuclease cuts the individual gRNAs out of the transcript. While
this could also provide a means of inducible control with expression of the RNA
nuclease, there are also potential positional effects with this system
406
. Another
potential concern with expressing multiple guides is with increased expression of
sgRNAs, the sgRNAs are forced to compete for a declining amount of dCas9
endonucleases
407
. Therefore, there may be need to be a limit on the amount of sgRNAs
that are expressed. While the results from our array are promising, greater efficiency
achieved through an enhanced transcriptional repressor will be vital for the usefulness
of the easily deployable optimized sgRNA arrays in future studies.
Additional developments in CRISPR-based gene knockdown will pave the way
for more precise experimental manipulations and increased specificity, such as novel
Cas9 orthologs. Our use of S. pyogenes dCas9 limited our ability for a completely viral
mediated system, as the construct exceeded the capacity of an AAV. While our initial
exploration of S. aureus dCas9 showed viral toxicity in the brain (Figure 5), a recent
publication has shown its efficacy in knocking down gene expression in the liver
449
,
providing hope that this or another Cas9 orthologue could be used for all-viral mediated
CRISPRi knockdown that could be used in any mouse strain. Another route to an
entirely viral mediated CRISPRi system, is the use of lentiviral vector instead of AAV, as

101
was used in previous investigations into learning and memory
197
. Taken together, CNS
offers a powerful approach to identifying roles of individual transmitters and
improvements to the efficacy of CRISPRi are needed to improve CNS’s ability to study
tightly regulated and resilient systems.

Global neuropeptide knockdown
Global knockdown of neuropeptides, using the transgenic delivery dCas9 and of
an array of sgRNAs targeting neuropeptide processing enzymes, demonstrated that
CNS can be used to reduce bioactive mature neuropeptides. This optimized set of
sgRNAs can be used with cell-type specific expression of dCas9 to probe the function of
neuropeptides within a genetically defined cell-type, in the future. This reduction of
mature neuropeptides resulted in dysfunction in the maintenance of energy
homeostasis and development of obesity, mirroring the single neuropeptide processing
enzyme genetic knockout, Cpe
435
. Additionally, the female NPPE;dCas9 animals
phenocopied the Cpe knockout mice in their impaired glucose homeostasis compared
to Pcsk2 knockout animals that showed fasting hypoglycemia
442
, while the male animals
did not show elevated fasting glucose levels compared to controls. Interestingly, the
smaller size young animals did not match the phenotypes of any of the single
neuropeptide processing enzyme knockout animals. While this is a broad knockdown
and it is impossible to identify single causes of this phenotype, there are many
possibilities. For example, in addition to CPE’s role in NP biosynthesis, it also has a role
as a sorting receptor loading secretory vesicles
436
. This form of CPE has been shown to
interact with growth hormone
437
. Additionally, growth hormone releasing hormone

102
(GHRH), a neuropeptide integral in the regulation of body growth, is processes by furin
and PCSK1
217
, neither of these enzymes were targeted with this sgRNA array.
However, growth hormone releasing hormone has been shown to be amidated by PAM
enhancing its activity. Pam was targeted in this sgRNA array. Therefore, impairing
GHRH through targeting PAM could result in reduced growth, which is seen when
ablating GHRH expressing neurons
439
. Further supporting for this theory, PAM is
copper-dependent enzyme and in mice with impaired copper metabolism, GHRH
neurons had reduced peptide content and disrupted peptide localization
440
.
This study, to the best of my knowledge, is the first to characterize fluid
homeostatic deficits in a neuropeptide processing enzyme-deficient animal. We found
an increased response to a hypertonic saline challenge in female mice and a trend
toward more dilute urine, which is consistent with the effect of reduced vasopressin
443
.
While we observed increased vasopressin expression by immunohistochemistry, the
antibody is likely not specific for the mature active peptide, and it could be recognizing
the accumulation of inactive precursors. The increase in precursor is likely due to our
experimental manipulation. The accumulation of precursor and reduction of mature
peptides was observed in a peptidomics study of Cpe
fat/fat
mice
450
. Our inability to tell
what form of the peptide that the antibody binds to highlights the need for
characterization using neuropeptidomics, an important tool to catalogue the changes of
the different forms of the peptides using mass spectrometry
389,390,392
.
The inability of the NPPE;dCas9 animals to cope with social isolation stress,
could be a result the disruption of several neuropeptides implicated the in stress
response, such as CRH, or social isolation stress, such as TAC2. Previous reports have

103
seen an increase in TAC2 following social isolation stress that led to specific behaviors,
such as aggression
386
. It is conceivable that disruption of this signaling pathway, that
initiates stress protection processes, led to the reduction in body weight and
development deficits. Another possibility is this major disruption in neuropeptide
signaling is buffered by social learning in a group housed environment and without the
feeding cues of their littermates, animals with neuropeptide signaling disrupted fail to
adequately obtain nutrients or water
12,13
. Previous reports show that in single
neuropeptide processing enzyme Cpe genetic knockouts, weaning-associated stress
(ear tagging, maternal separation, and tail snipping) was sufficient to cause
hippocampal degeneration
90
. However, this was linked to CPE’s role as a neurotrophic
factor and separated from its enzymatic activity. In any case, our data show that
targeting neuropeptide processing enzymes is an effective means to reduce bioactive
mature neuropeptides. In the future, these optimized sgRNAs could be used with Cre
dependent dCas9 with an enhanced transcriptional repressor to precisely investigate
the role neuropeptide signaling with more temporal, spatial, and cell-type specificity.

Paraventricular hypothalamus: a microcosm of homeostatic control
The PVN is one of the most heterogeneous structures in the brain, with more
than 20 neuropeptides and glutamate expressed in dozens of intermingled cell-
types
414,415,451
. Disruption of this region lead to overeating and obesity
291,452
.
Unintentionally, this was perfectly demonstrated during the accidental viral ablation of
the cells within the PVN, where following a toxic viral injection, the mice rapidly
developed massive obesity (Figure 18). These results are indicative of how critical this

104
brain region is for body weight regulation. In other experiments, we used CNS to
determine the contribution of chemical messengers within this region. Previous work
has demonstrated the functional role of the predominate fast acting neurotransmitter
within this region, glutamate, to the maintenance of energy homeostasis
298
. Conditional
deletion of glutamate within the PVN led to the development of obesity. It should be
noted that the level of weight gain from inhibiting glutamate within the region is not as
great as the level achieved with inducible ablation of Sim1+ neurons
444
. While this is
not a perfect comparison, due to Sim1 expression in supraoptic nucleus and sparse
labeling of the amygdala
378
, it does hint at an important contribution of peptides in the
PVN. Additionally, with CNS, we disrupted glutamatergic signaling and saw a similar
result in male mice (Figure 15, Figure 16). While a previous study using an acute
conditional deletion strategy reported an increase in body weight in female mice, sex-
specific developmental compensation was observed in female mice with glutamate
output disrupted from birth
298
. It is important to note that CRISPRi based knockdown
may not reduce expression to the extent that genetic deletion would. Additionally, the
level of weight gain in males using CNS was more modest than the weight gain seen
with the genetic deletion. It is possible that the remaining low levels of expression of
Vglut2 are sufficient to reveal sex-specific regulation of body weight while a greater
reduction in levels of Vglut2 is needed to increase the amount of weight that the animals
gain. Taken together, these results underscore the value of studying both sexes and
show that disrupting glutamate output does produce an increase in body weight in male
mice.

105
Next, in a cell-type specific knockdown in Sim1 PVN neurons (Figure 16),
silencing Vglut2 produced a similar result rapid modest increase in body weight. Given
that the vast majority of neurons in the PVN are Sim1+, this was not a surprising result
but promising to see consistent trends. In parallel, we targeted neuropeptide processing
enzymes to reduce neuropeptide signaling in this region, a first for silencing
neuropeptides as a class, in a region or cell type specific manner. Interestingly, the mice
gained a similar amount but on a slower timescale. This may be reflective of the speed
of the two signaling systems. Glutamate mainly signals through ionotropic receptors to
change membrane potential, and most neuropeptides signal through GPCRs to ignite a
variety of downstream signaling cascades, including gene transcription. Using this
unique experimental design to probe the role of neuropeptides as a class, we also saw
a dysregulation of body temperature in the mice with neuropeptide signaling disrupted
but not glutamatergic signaling. The PVN is tightly intertwined with regulating energy
expenditure through brown adipose tissue mediated thermogenesis
411,420
. In fact, the
neuropeptides oxytocin, corticotropin-releasing hormone, and thyrotropin-releasing
hormone have been connected to thermogenesis and the generation of a thermogenic
response
421–427
, and the mature neuropeptides are processed by our target enzymes
PCSK2, CPE, and PAM
379,428–434
. Regardless of the mechanism, this data shows that
thermoregulation is reliant on neuropeptide signaling from the PVN. Given this
experiment was limited by small sample size, future work will be needed to expand the
study, determine the mechanism, and identify the specific neuropeptides mediating this
control.

106
We combined the CNS toolset in a variety of ways to investigate the role of
neuropeptides in the PVN. Unfortunately, the LSL dCas9 mouse did not produce
efficient knockdown of neuropeptide processing enzymes when crossed to the mouse
expressing an array of sgRNAs targeting the enzymes, in Sim1+ neurons to produce
any effect on body weight (Figure 19). This spurred our development a viral delivered
sgRNA targeting all four neuropeptide processing enzymes. Use of this array to probe
the functional contribution of neuropeptides in the PVN showed a small increase in body
weight in male mice (Figure 20). These data have a few possible explanations. This
could be a true effect, reflecting that neuropeptides within this region are mostly
redundant and minimally required for body weight regulation. Alternatively, we could
have failed to achieve strong enough knockdown within target brain region through
insufficient delivery or efficacy of the sgRNAs. Although many peptides in the region
have been implicated in energy homeostasis
310,311,313,378,453
, it is also possible the
glutamate is the key transmitter within this region or peptides expressed here work
through altering glutamatergic expression. Supporting this idea, body weight in the
genetic deletions of major peptides in the PVN is normal
454,455
. Additionally, while our
novel experimental design reduced mature neuropeptide levels, it is possible the level of
knockdown was insufficient. Future studies, possibly utilizing dCas9 with an enhanced
transcriptional repressor
195,375
, are needed to identify the full role of neuropeptide
signaling in this brain region. Alternatively, it is possible that time that it takes for an
AAV to turn on (10-14 days), is too extended, thereby allowing time for the system to
cope with the changes as knockdown begins. In this case, an inducible system would
be better suited for measuring the functional contributions of peptides. One way that

107
peptidergic signaling systems could cope with the reduction in functional peptides, is by
increasing receptor density at their sites of action. Another means by which the systems
could cope is an increase in the synthesis of neuropeptide precursors to compensate for
the reduction in mature peptides such is the case with vasopressin in our global
neuropeptide knockout (Figure 13). Moreover, it is possible other neuropeptide
processing enzymes that are not targeted here, such as furin or carboxypeptidase D,
expressed in the trans-golgi, could compensate for the reduction of PCSK1, PCSK2,
CPE, and PAM to generate a sufficient amount of mature peptides to mask a strong
phenotype. Genetic knockouts of CPE maintain a low level of mature processed
peptides
456,457
. Furthermore, it is possible that some of the of the peptide intermediates
remain biologically active, as was hypothesized in a recent study with a conditional
knockout of CPE in POMC neurons. A POMC derived peptide α-MSH, a strong inhibitor
of food intake
235
, was reduced 90% yet the mice maintained normal body weight
234
.
Despite this level of reduction, the extended version of this peptide is predicted to retain
biological activity at target melanocortin-4 receptors in the PVN. Therefore, it is
conceivable that some peptide intermediates in our experimental paradigm could retain
their activity as well.

Sex differences in body weight regulation
There are many distinct sex differences in energy balance between males and
females, including where fat accumulates in the body, influence of gonadal hormones,
and the manner in which their brains respond to signals from the body
416
. For this
reason, whenever possible, we examined both sexes and analyzed their data

108
independently. The first sex difference we noted was the disruption of glutamate within
the PVN, in which male animals had an increase in body weight while the female
animals did not. It is conceivable that female animal’s body weight regulation is more
dependent on slower neuropeptidergic signaling and influenced by the expression of
estrogen receptors in the parvocellular region of the PVN
417–419
. Another sex difference
of note was in the global disruption of neuropeptides. While, both males and females
had a dramatic increase in body weight the effect was more pronounced in the female
mice. Additionally, the female mice presented deficits in regulating glucose
homeostasis, exhibiting elevated levels of basal and fasting glucose. While there are
known differences in endogenous glucose output and clearance of glucose with
females
458
, since this was a broad knockdown, a more targeted silencing approach
should be used for future investigations into uncovering the sex specific
neuropeptidergic regulation of body weight and glucose homeostasis.

Neuropeptide signaling in ARC and NTS
The ARC is the location of POMC and AgRP/NPY neurons that inhibit and
stimulate the motivational drive for food intake, respectively
263–266
. This brain region
receives signals from the body and sends dense projections to the PVN
289,290
. These
populations of neurons express the peptides they are named for, but also express other
peptides as well as fast acting amino-acid neurotransmitters. Using an sgRNA array
targeting neuropeptide processing enzymes packaged into an AAV, we disrupted
neuropeptide signaling from this region (Figure 20). Unfortunately, we did not observe
any statistically significant changes in body weight. However, our sample size was low,

109
and we were only able to test in male mice. It is possible that we would not observe any
changes in body weight here though because recent reports of a conditional Cpe or
Pcsk1 knockout in POMC neurons failed to produce obesity
233,447
. The Cpe conditional
knockout was able to reduce α-MSH 90%, however the C-terminally extended α-MSH
could still be biologically active and able to activate MC4R at an adequate level to
maintain body weight
447
. For the Pcsk1 knockout, there is a possibility that PCSK2 was
able to compensate for PCSK1
233
. Our experimental design would circumvent those
pitfalls by silencing not a single neuropeptide processing enzyme but silencing four
neuropeptide processing enzymes simultaneously, a clear advantage to the CNS
method. One limitation with our experiments is the efficiency of knockdown via the
sgRNAs we used. It appears that the remaining expression levels of the four
neuropeptide processing enzymes was inadequate to disrupt energy balance. This
experimental design could be improved in the future by using an dCas9 with an
enhanced transcriptional repressor to increase efficiency of knockdown and if this could
be done in a cell-type specific way, the information would be extremely valuable.
Additionally, following an all-neuropeptide disruption, guides could be designed for
specific neuropeptide to evaluate their functional role.
The brain’s control of energy balance is distributed among several brain areas,
including the NTS, in the hindbrain. The NTS is a key regulator of meal size and body
weight
459
. Our experiment using a sgRNA array targeting neuropeptide processing
enzymes to disrupt neuropeptide signaling from this region resulted in an increase in
body weight on both standard lab chow and breeder chow in female mice. Though this
effect was small, it is possible we would have seen a larger effect with increased

110
repression of our targets. It will be interesting for future investigations into the role of
neuropeptide signaling in the NTS to use an enhanced transcriptional repressor.

Neuropeptide signaling - Flexible, resilient, adaptable, responsive, and elastic
There are numerous challenges in studying the complex and diverse functions of
neuropeptides. Biologists assess function by three types of experiments: by observing a
system in diverse states and evaluating the response of a system following activation or
inhibition
153
. Because neuropeptide signaling systems are extremely complex, these
latter types of manipulations can be challenging to interpret. For instance, when injected
into the brain, most peptides evoke a measurable response, but it is nearly impossible
to know if it is a physiologically relevant response seen with endogenous peptide
release. Inject too much peptide and it could bind to other peptide’s receptors or in
areas that are never activated together in normal conditions. Removing or inhibiting a
neuropeptide to deduce its function could seem like a better approach, but many
neuropeptide knockouts often do not show any behavioral phenotype
156,166
. This could
be either an actual biological effect, reflecting developmental compensation by another
analogous signaling system, or reveal degeneracy or redundancy in the system.
Moreover, neuropeptide signaling is robust and flexible. If less or more of a signal is
received, the receiving cell can adjust the levels of receptor to buffer the changes. This
is a probable explanation for why the phenotypes in our neuropeptide silencing
experiments were smaller than expected. The robustness of the signaling systems will
remain an issue in studying neuropeptides in the future, obscuring their true functional
role. An additional caveat is that given enough time, the neurons can upregulate

111
expression of the neuropeptides and without complete silencing of the processing
enzymes, enough mature peptides can be produced to keep the system in balance.
Future experiments will require acute knockdowns to avoid compensation and
improved, almost complete, knockdown efficiency to necessary to surpass the ability of
the system to cope with the manipulations and illuminate their functional roles.

Conclusion
The work described in this thesis highlights the development of a toolset to
silence individual transmitters or neuropeptides as an entire class using CRISPRi in the
adult mouse brain. This system provides an important tool for identifying roles of
individual transmitters and linking neuropeptides to their function. This thesis work hints
at the role of chemical messengers in key regions and cell-types regulating energy
homeostasis. Beyond neurotransmission, using CRISPRi to silence genes in the brain is
a powerful method that can be applied widely to any system. While many questions
remain open and much work remains, my hope is that this work will be another step in
the evolution of tool development in neurobiology on the path to gain more insight on
how the brain uses a chemical signaling cocktail to control behavior and physiology.

112
MATERIALS AND METHODS

Animals
Wild-type mice (C57BL/6J, strain 000664) were obtained from the Jackson Laboratory.
dCas9-KRAB mouse strain was obtained from Michael McManus (UCSF). LSL dCas9,
dCas9-KRAB-MeCP2, and NPPE mouse strain were generated by embryo DNA
microinjection into C57BL/6 mouse strain (Gladstone Transgenic Gene Targeting Core,
San Francisco). Experiments were carried out using mice of both genders. Adult mice
aged 3 - 7 months were used for experiments. All mice were maintained with 12:12-h
light/dark cycle (6pm-6am dark period) with ad libitum access to standard chow
(PicoLab 5053) and water. Mice were group housed unless otherwise specified. All
experimental protocols were approved by the University of California, San Francisco
IACUC following the NIH guidelines for the Care and Use of Laboratory Animals.

Stereotaxic surgery
For stereotaxic injections, mice were anesthetized with 2% isoflurane placed in a
stereotaxic head frame on a heat pad. For PVH injections, 350 – 500 nl of virus was
injected at −0.6 mm AP; −0.3 mm ML; −4.9 mm dorsoventral DV relative to bregma.
Viruses were injected at a rate of 0.05uL per minute. Mice were given subcutaneous
injections of Buprenorphine SR (1.5 mg/kg) and meloxicam (5 mg/kg). Mice used for
viral knock down validation were monitored daily following surgery and allowed to
recover for 14 days in their home cage to allow for viral expression and target
knockdown. All injections were confirmed histologically.

113

Behavior
Overnight drinking and response to salt injection experiments monitored drinking
behavior as previously described
460–462
in sound-isolated behavioral chambers
(Coulbourn Habitest Modular System), using electrical lickometer, and recorded using
Graphic State software (v.4.2, http://www.coulbourn.com/category_s/363.html). To
measure drinking response to salt injection, mice received an intraperitoneal injection of
2M NaCl and water drinking was measured for 90 minutes. Food intake was measured
in the home cages of in individually caged mice.

Metabolic and Phenotypic Measurements
For the global neuropeptide processing enzyme knockdown mice, body weight was
measured weekly. Body length measurements were performed dorsally on 10-week
adult mice, from the tip of the nose to the base of the tail while under anesthesia. Blood
and urine osmolality were measured in triplicate for each sample
Blood plasma was collected at time of sacrifice, isolated by centrifugation (1,000g for 10
min), diluted in deionized water and frozen until measurement. Osmolality was
measured using Wescor Vapro 5600 Vapor Pressure Osmometer. For fasting blood
glucose, mice were food deprived for 18 hours. Blood glucose was measured from tail
blood.

114
Plasmid construction and sgRNA design
The sgRNAs were designed using the Broad Institute CRISPick tool which uses
the scoring principles to maximize activity and minimize off-target effects described in
Doench et al., 2016 and Sanson et al., 2018
363,364
. Additional sgRNAs were designed
using methods described in Larson et al., 2013
370
. The sgRNA vector used for this study
was modified from the SpGuide acceptor plasmid (#60958) obtained from Addgene.
The sgRNA array vectors were assembled using Golden Gate cloning strategy using
the protocol in Vad-Nielsen et al. 2016
463
. Custom AAVs were generated for expressing
sgRNAs including, AAVDJ-U6-sgRNA-hSyn-GFP-KASH (Stanford Vector Core) and
AAV1-U6-sgRNAarray-hSyn-GFP-KASH (Janelia Virus Services).

Cell culture and fluorescent sorting
N2A and N2A dCas9 cells were maintained in Dulbecco’s modified eagle medium
(Dulbecco's Modified Eagle Medium-High Glucose, UCSF Media Production Core)
supplemented with 10% FBS and 1% streptomycin and 100 mg/mL penicillin. The cells
were incubated at 37ºC in 5% CO2. Transfections were performed with lipofectamine
3000 (ThermoFischer) according to the manufacturers recommended protocol, in 12-
well plates using 1.6 μg of sgRNA plasmid. An empty vector was used as the control
plasmid. At 48 hours after transfection, cells were trypsinized to a single cell suspension
for fluorescent activated cell sorting. A FACSAria II cell sorter (BD) was used to gate for
mCherry and GFP double positive cells. Immediately after cells were collected, they
were lysed with RLT buffer (Qiagen) and flash frozen in liquid nitrogen. 1-3 samples
were collected per sgRNA, on a minimum of 3 separate days.

115

Stable cell line generation
N2A dCas9 cell line was generated by infecting the cells with lentivirus CMV-dCas9-
KRAB-2A-mCherry-puromycin resistance (gift from Michael McManus, University of
California, San Francisco) supplementing 8 μg/mL polybrene to increase viral infection
(Sigma). Cells were incubated overnight and changed to fresh media the following day.
Puromycin (Fisher) containing media was added 24 hours later. Clonal populations
were isolated using single cell serial dilution.

RNA isolation
RNA was isolated with the Qiagen RNEasy Plus Micro Kit (Qiagen). Cells were lysed by
adding RLT buffer and vortexing for 30 seconds, then homogenized using QIAshredder
columns (Qiagen). Genomic DNA was removed using gDNA Eliminator columns. RNA
concentration and purity were assessed with a Nanodrop (ND-1000
spectrophotometer). RNA purity was assessed by running four representative samples
through the Bioanalyzer 2100 RNA 6000 Nano Kit (Agilent). RIN numbers averaged 8.4,
with a standard deviation of 0.72.

For whole mouse brain tissue preparation, mice were deeply anesthetized and
decapitated. The brain was quickly dissected, snap-frozen in liquid nitrogen and stored
at -80ºC until processed for RNA extraction. RNA was extracted using TRIzol®
(Invitrogen) according to the manufacturers recommended protocol.

116
cDNA synthesis
cDNA was generated using the iScript cDNA Synthesis Kit using iScript Reverse
Transcriptase (Bio-Rad). ≤1 μg of RNA was used in each well, with wells brought to
exactly 1 μg RNA if possible. Total reaction volume was 20 μL. Reaction was primed
with both Oligo(dT)18 and random hexamer primers. cDNA was generated by
incubations of 5 min at 25°C, 20 min at 46°C, and 1 min @ 95°C, then held at 4°C. No
Reverse Transcriptase Controls were performed and checked later by qPCR, to confirm
the absence of DNA in the RNA samples. cDNA samples were stored at -20°C.

qPCR
qPCR was performed using the iTaq Universal Probes Supermix kit (Bio-Rad),
containing antibody-mediated hot-start iTaq DNA polymerase, and using 10 μL reaction
volume. 0.1 μL to 2 μL of cDNA were used per well, depending on target expression
level. The efficiency of knockdown of each sgRNA was analyzed using one to three sets
of biological replicates on three separate days. Each biological replicate was plated as a
set of three technical replicates. Reactions were set up manually in white well plates,
using Bio-Rad White 96 Well Plates, model HSP 9601. Reference gene calibration was
performed in Thermo Scientific AB-1384 clear 384 well plates. qPCR was run on a
CFX96 Touch or CFX 384 Touch thermocycler (Bio-Rad). Thermocycling parameters
were 1: 95°C for 30s, 2: 95°C for 3s, 3: 60°C for 30s, then repeat steps 2 & 3 40x. No
Template Controls were included as a negative control for contamination. Quantitative
analysis was performed employing the ΔΔCT method and using ActB and Gapdh as the
endogenous controls using CFX Maestro (Bio-Rad).

117

qPCR gene expression assays
All qPCR assays used were commercially produced probe-based PrimeTime qPCR
assays (Integrated DNA Technologies), using FAM fluorophores and both ZEN and
Iowa Black FQ fluorescence quenchers. Forward and reverse primers were at a
concentration of 900 nM each, and the probe was at 250 nM.

Immunofluorescence
Following completion of each experiment, mice were deeply with isoflurane and
transcardially perfused with 1× phosphate buffered saline (PBS) followed by a solution
of 4% paraformaldehyde (PFA) in 1×PBS. Brains were removed and post fixed
overnight in 4% PFA in 1×PBS, then cryoprotected overnight in 30% sucrose. The
brains were sectioned at 35 µm thickness using a cryostat (Thermo Fisher Cryostar
NX70). Sections were washed three times for 5 minutes in 1×PBS and blocked for 1
hour in blocking solution (3% BSA, 2% NGS or NDS, 0.1% Triton X-100 in 1×PBS).
Slices were then incubated in primary antibody mixture at 4°C overnight. Primary
antibodies include rabbit anti-mcherry (1:500, ab167453, Abcam), mouse anti-OXT
(1:1000, MAB5296, Milipore), rabbit anti-AVP (1:1000, AB1565, Millipore), chicken anti-
GFP (1:1000, GFP-1020, Aves Lab). Slices were washed three times for 5 minutes in
1×PBS-T and incubated in the corresponding secondary antibody mixture. Secondary
antibodies include goat anti-mouse A568 (1:1000 Life Technologies A21124), goat anti-
rabbit A633 (1:1000, Life Technologies A21071), goat anti chicken A488 (1:1000, Life
Technologies A11039). Slices were washed three times for 5 minutes in 1×PBS,

118
mounted onto slides, and with covered with DAPI Fluoromount-G (Southern Biotech).
All images were taken using a CSU-W1 Spinning Disk/High Speed Widefield
microscope (Nikon).

Fluorescent in situ hybridization
To spatially detect RNA, RNAScope Fluorescent Multiplex Reagent Kit (Advanced Cell
Diagnostics) was used according to the manufacturers recommended protocol. Briefly,
mice were deeply anesthetized, decapitated, and the brain was quickly dissected. The
brain was embedded in OCT embedding gel (Tissue-Tek®, Sakura Finetek, United
States) and snap frozen in an ethanol dry ice bath. The brain was sliced into 20µm
sections in the cryostat and mounted on Superfrost Plus slides. PVH-containing slides
were fixed in 4% paraformaldehyde for 15 minutes at 4°C, then dehydrated using 50%,
70% and 100% ethanol for five minutes each. A protease solution (pre-treatment IV,
Advanced Cell Diagnostics) was applied to slices for 5-10 minutes at room temperature
and washed with PBS. Next, target probes were applied, and slides were incubated in a
hybridization oven at 40°C for 2 hours, followed by 4 amplification steps at 40°C (Amp1
30 minutes, Amp2 15 minutes, Amp3 30 minutes, Amp4 15 minutes). Slides were then
covered with DAPI Fluoromount-G (Southern Biotech). RNAScope probes Mm-PCSK1
(#466941), Mm-PCSK2 (#503481), Mm-CPE (#454091), Mm-Pam (#457271), and
EGFP (#400281) were used. All images were taken using a CSU-W1 Spinning
Disk/High Speed Widefield microscope (Nikon).

119
Image Analysis
The immunofluorescent and in situ hybridization Z-stack images were captured with 20X
magnification objective lens using CSU-W1 Spinning Disk/High Speed Widefield
microscope (Nikon) and were analyzed using the image analysis software CellProfiler
(Broad Institute). Within each experiment, all imaging parameters were held constant for
all brain sections. In brief, the CellProfiler pipeline performs the following steps: identify
the region of interest (ROI), apply a lower threshold to remove background signal,
quantify fluorescent intensities within ROI for all channels. For the in vivo knockdown
quantification experiment, fluorescent intensity was normalized to the area of ROI
resulting in mRNA fluorescent intensity/mm
2
. For AAV-mediated knockdown, the ROIs
compared were the injected side of the PVH and the non-injected side of the PVH, with
each animal serving at its own control, to provide a ratio for relative RNA expression.
For the oxytocin knockdown, cells were hand counted using ImageJ. For the global
neuropeptide processing enzyme knockdown mice, the ROIs used were both sides of
the PVH and these were compared to littermate controls.

Brain Tissue Preparation and Peptide Extraction
Mice were transcardially perfused with isotonic saline containing protease inhibitors
(0.120 mM EDTA, 14 μM aprotinin, and Roche Complete Protease Inhibitor tablets
(Roche); pH=7.4) then quickly decapitated. Regions of interest were microdissected,
heat inactivated at 95°C in 300 μL water (Honeywell Burdick & Jackson Water, H2O
#AH365-4) with protease inhibitors (0.120 mM EDTA, 14 μM aprotinin, and Roche
Complete Protease Inhibitor tablets (Roche); pH=7.4), placed into low-binding

120
microcentrifuge tube (Thermo Scientific™ Snap Cap Low Retention Microcentrifuge
Tubes Cat # 3451) and flash frozen in liquid nitrogen. To extract peptides, the tissue
was homogenized using dounce homogenizer in ice-cold extraction buffer (water–
methanol–acetic acid solution (89.8:10:0.2, v/v/v)) (10x intervals, 3.75 µL lysis buffer/mg
of tissue; (Southwest Digital Overhead Stirrer - SOS20, Kimble® 886000-0020 Kontes®
3mL Potter-Elvehjem Tissue Grinder with PTFE Pestle and Unground Glass Tube, Size:
20), sonicated (Pulse 20s, 40 off, Amp 1, 80%, 4x; Cole-Parmer Threaded Ultrasonic
Probe Tip, Stepped; 1/8" (3mm) Item # SI-04712-12), placed on dry ice for 15 minutes,
centrifuged (20,000 x g, 20 min, 4 °C) and supernatant was transferred to a clean
centrifuge tube (Thermo Scientific™ Snap Cap Low Retention Microcentrifuge Tubes
Cat # 3451). A second extraction was performed with water–methanol–acetic acid
solution (49.8:50:0.2, v/v/v). Extracts were combined and filtered on a C18 column
(Thermo Scientific™ Pierce™ C18 Spin Columns, Catalog No. PI89873). Protocol
adapted from
464,465
. Eluted peptide mixtures were reconstituted in 2% MeCN, 0.1% FA
and separated using EASY-Spray™ HPLC Column (Thermo Fisher, ES800). The
MS/MS was performed on Q Exactive Orbitrap Mass Spectrometers (Thermo Fisher),
ion source ESI (nano spray), high energy CID fragmentation, FT-ICR/Oritrap MS/MS
scan mode, Linear ion trap MS/MS scan mode. Acquired data were processed with
PEAKS Studio X Plus.

Statistical analysis
The data are shown as means ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001 ****p < 0.0001
ns = not significant. Description of statistical methods and sample sizes are specified in

121
the figure legends. Statistical analyses were conducted using ANOVAs followed by
Dunnett's test, Šídák test, or Tukey for multiple comparisons, and unpaired t-tests when
appropriate. All the data was analyzed using GraphPad Prism version 9. A normal
distribution was assumed. The experiments were not randomized, and the investigators
were not blinded to allocation during experiments and outcome assessment. No
statistical methods were used to predetermine sample sizes, but our sample sizes are
similar to those reported in previous publications.

122
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Abstract (if available)

Abstract Most neurons in the brain use multiple neuropeptides, monoamine neurotransmitters, and fast acting amino acid neurotransmitters to communicate. Although fast neurotransmitter signaling and slow neuropeptide signaling function together, they have distinct functional roles, while acting on different timescales, over an expansive range of distances to activate a wide variety of targets. A key question in neuroscience remains largely unanswered: how does the brain use the cocktail of chemical messengers to control physiology and coordinate behavior? The vast number of chemical signals co-expressed within intermixed heterogenous cell-types in the brain makes it challenging to evaluate the individual contributions of each type of signal. Moreover, neuropeptides have a wide range of functional roles; however, it has been difficult to do a simple acute loss of function experiment that would link individual transmitter to a specific functional role with the current tools available. While there is still much to learn, we gain more insight with each step in the evolution of tool development in neurobiology.
Here, I introduce a flexible system, Cell-type specific Neurotransmitter Silencing (CNS) which uses CRISPR interference (CRISPRi) to silence neurotransmitters in a cell-type specific manner individually, or as an entire class in the mammalian brain. I show acute simultaneous silencing of multiple genes with spatial and temporal specificity. By targeting common neuropeptide processing enzymes, I successfully reduced neuropeptide signaling from specific cell types and brain areas. I used CNS to dissect some of the chemical signals controlling energy homeostasis. Through a century of research, our understanding of how the brain monitors and controls food intake and energy homeostasis has dramatically increased but has not translated to effective therapies for obesity. Many of the brain areas and cell types controlling feeding have been identified however, the contributions of the individual chemical messengers within these regions and circuits remain unknown. Using CNS, I found sex specific differences in the role of glutamate within the paraventricular hypothalamus, while I demonstrated that both neuropeptides and glutamate play a significant role in body weight regulation. Disrupting neuropeptide maturation globally led to deficits in the ability of mice to cope with social isolation stress and maintain energy and fluid homoeostasis. This system provides an important tool for identifying roles of individual transmitters in specific phenotypes and linking neuropeptides to their function.

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Asset Metadata

Creator Hudson-Paz, Courtney Nicole (author)

Core Title Defining the functional roles of neurotransmitters and neuropeptides in neural circuits

School Leonard Davis School of Gerontology

Degree Doctor of Philosophy

Degree Program Biology of Aging

Degree Conferral Date 2022-08

Publication Date 01/20/2024

Defense Date 06/14/2022

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

Tag CRISPR interference,CRISPRi,neuropeptide signaling,neuropeptides,neurotransmitters,OAI-PMH Harvest,obesity

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Garrison, Jennifer (committee chair), Andersen, Julie (committee member), Curran, Sean (committee member), Lithgow, Gordon (committee member), Tracy, Tara (committee member)

Creator Email chudsonpaz@gmail.com,courtneynhudson3@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC111373669

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Rights Hudson-Paz, Courtney Nicole

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