UNIVERSITY OF CALIFORNIA

Los Angeles

Characterizing microRNA-128 as a Therapeutic Target for Vocal Communication Deficits

A dissertation submitted in partial satisfaction

of the requirements for the degree Doctor of Philosophy

in Neuroscience

by

Caitlin Marie Aamodt

2021

© Copyright by

Caitlin Marie Aamodt

2021

ABSTRACT OF DISSERTATION

Characterizing microRNA-128 as a Therapeutic Target for Vocal Communication Deficits

by

Caitlin Marie Aamodt

Doctor of Philosophy in Neuroscience

University of California, Los Angeles, 20126

Professor Stephanie A. White, Chair

Autism spectrum disorder (ASD) prevalence is on the rise, with recent estimates of 1 in

68 children affected (cdc.gov). Although social communication deficits are among the two core criteria for diagnosis there are currently no medications available on the market to enhance the efficacy of speech therapy. Despite the urgent and growing need, progress on therapeutic development has been hindered by a lack of understanding of the molecular mechanisms underlying learned vocal communication. One model for how communication deficits arise is that the molecular mechanisms that constrain critical period plasticity come on too early, closing the plasticity window before the patient has the opportunity to learn speech and language

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(LeBlanc & Fagiolini 2011). The brain-enriched microRNA miR-128 increases over the course of development in rodent and human brains, peaking in adulthood (Lek-Tan et al. 2012, Bruno et al. 2011). miR-128 is also aberrantly upregulated in postmortem tissue (Wu et al. 2016) and its targets are downregulated in peripheral blood from autism patients (Gazestani et al. 2019). If miR-128 is a negative regulator of vocal learning, then reducing its levels could restore plasticity, or the capacity to learn to speak, in patients with ASD.

Previously our lab generated activity-dependent gene expression networks in the striatopallidal song nucleus known as Area X in adult and juvenile songbirds to identify master regulators of learned vocal behavior (Hilliard et al. 2012, Burkett et al. 2018). Using this dataset,

I discovered that the two host genes for miR-128, ARPP21 and R3HDM1, are among the top genes whose expression was correlated to how much the adult bird sang. In humans, multiple single nucleotide polymorphisms that reduce the expression of ARPP21 correlate with higher intelligence scores (Savage et al. 2018). I hypothesized that reducing miR-128 during the critical period for vocal plasticity would enhance vocal learning. To test this, I adapted a miR-128 siRNA sponge for use in songbird Area X from a previously published rodent version developed by Bredy and colleagues (Lin et al. 2011). I injected a viral construct bearing either the siRNA sponge or a control sponge with a scrambled sequence bilaterally into Area X at post-hatch day

30 (30d) using sibling-matched controls, then returned birds to their home cage. After collecting recordings at 45d, 60d, and 75d post-hatch I collected tissue and performed song analysis.

Strikingly, miR-128 inhibition in Area X was sufficient to recapitulate the therapeutic rescue of abnormal sequence stereotypy. In human ASD patients, differential gene expression in microglia is correlated to clinical symptom severity (Velmeshev et al. 2019). My results suggest that

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inhibiting miR-128 could normalize microglia reactivity in autism patients to improve clinical symptom severity. To my knowledge, this study is the first to directly link miR-128 to learned vocal communication and among the first to identify miR-128 as a potential therapeutic target for

ASD.

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The dissertation of Caitlin Marie Aamodt is approved.

Daniel H. Geschwind

Pamela J. Kennedy

Marcelo A. Wood

Stephanie A. White, Committee Chair

University of California, Los Angeles

2021

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Dedication

This dissertation is dedicated to the memory of Daniel Gayer, my high school English teacher who, no matter the circumstances, always encouraged me believe that I had something important to offer the world.

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Table of Contents

Chapter I. Introduction...... 1

Chapter II. Birdsong as a window into language origins and evolutionary

neuroscience...... 11

Summary...... 12

Introduction...... 13

The slow but rewarding journey of becoming a vocal learner: intrinsic reward

systems are central to the two-way evolutionary process between vocal learning

and timing of sexual maturation...... 15

From slowing down to speeding up: the role of human-specific and intelligence-

related genes in learned vocal behavior...... 25

On the horizon: key questions for future research...... 34

Conclusion...... 36

Chapter III. Ginsenoside Rh2-mediated inhibition of miR-128 rescues deficits in

learned vocal communication...... 38

Abstract...... 39

Introduction...... 40

Methods...... 42

Results...... 46

Discussion...... 48

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Chapter IV. siRNA-mediated inhibition of miR-128 rescues deficits in learned vocal

communication...... 57

Abstract...... 58

Introduction...... 59

Methods...... 60

Results...... 63

Discussion...... 64

Chapter V. siRNA-mediated inhibition of miR-128 enhances vocal sequence

organization in juveniles...... 69

Abstract...... 70

Introduction...... 71

Methods...... 72

Results...... 76

Discussion...... 77

Chapter VI. Conclusions...... 88

Appendix. FoxP2 isoforms delineate spatiotemporal transcriptional networks for

vocal learning in the zebra finch...... 97

References...... 160

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List of Figures

Figure 2-1: Pie charts illustrate relationships between human intelligence eQTLs and songbird gene expression

Figure 3-1: miR-128 regulates gene networks essential for neural development and functioning

Figure 3-2: miR-128 targets are enriched in ASD gene-related datasets

Figure 3-3: Activity-dependent regulation of miR-128 in adult zebra finch Area X

Figure 3-4: Molecular structure of Ginsenoside Rh2

Figure 3-5: Systemic GRh2 decreases miR-128 in Area X

Figure 3-6: Social isolation and GRh2 rescue timeline schematic

Figure 3-7: Exemplar motifs showing changes in sequence stereotypy in a single bird before and after GRh2 rescue

Figure 3-8: GRh2 rescues deficits in sequence stereotypy in social isolates

Figure 4-1: Region of interest (Area X) and miR-128 antisense viral construct

Figure 4-2: Focal miR-128 inhibition in Area X rescues deficits in learned vocal sequencing

Figure 4-3: Exemplar motifs and transition probability diagrams showing changes in sequence stereotypy in a single isolate before and after focal miR-128 inhibition

Figure 4-4: Focal miR-128 inhibition in Area X rescues deficits in sequence stereotypy in social isolates

Figure 5-1: Timeline schematic

Figure 5-2: Viral construct

Figure 5-3: Exemplar motifs

Figure 5-4: Sequence stereotypy data

Figure 5-5: NS-UD paradigm schematic ix

Figure 5-6: CV for spectral features of NS-UD data

Figure 5-S1: NS-UD Sequence Stereotypy data

Figure 5-S2: Tempo data

Figure 5-S3: Tutor Similarity data

Figure A-1: Overexpression of FoxP2 isoforms

Figure A-2: Overexpression of FoxP2 isoforms affect learning and/or variability in song

Figure A-3: WGCNA yields behaviorally relevant modules

Figure A-4: Area X singing related gene coexpression patterns are not preserved in VSP

Figure A-5: Area X song but not learning modules are preserved into adulthood

Figure A-6: Gene significance and network position implicate MAPK11 as a molecular entry point to vocal learning mechanisms

Figure A-7: Protein-level interactions between song and learning module genes in juvenile Area

X

Figure A-8: Changes in vocal plasticity state between juvenile and adult birds

Figure A-S1: Raw acoustic feature variability in the NS and UD conditions by virus group

Figure A-S2: Juvenile Area X gene coexpression network

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Chapter I is an introduction to the work presented in this dissertation.

Chapter II is a version of Aamodt, CM, Farias-Virgens, M, and White SA. (2019). Birdsong as a window into language origins and evolutionary neuroscience. Phil. Trans. R. Soc. B 375:

20190060. This work was supported by grants NIH F31-MH110209, NIH T32-MH073256

(CMA), the Coordination for Improvement of Higher Education Personnel (CAPES), Brazil,

NSF (grant no. Bio Anthro DDRIG 1613709), the UCLA Eureka and Will Rogers Scholarships, and the Philanthropic Educational Organization’s International Peace Scholarship (MFV), and

NIH RO1MH070712 (SAW). We acknowledge Professors Michael S. Brainard, Nancy F. Day,

Terrence W. Deacon, Daniel H. Geschwind, Emilia Huerta-Sanchez and Kazuo Okanoya for helpful discussions, and we thank Dr. Zachary D. Burkett for insights and feedback on a prior version of the manuscript.

Chapter III is a component of a manuscript in preparation for submission.

Chapter IV is the most current version of a manuscript in preparation for submission.

Chapter V is a component of a manuscript in preparation for submission.

Chapter VI is the conclusion and discussion of the work presented in this dissertation.

Appendix is a version of Burkett ZD, Day NF, Kimball TH, Aamodt CM, Heston JB, Hilliard

AT, Xiao X, White SA. (2018). FoxP2 isoforms delineate spatiotemporal transcriptional networks for vocal learning in the zebra finch. eLife 7: e30649. This work was supported by NIH

5T32HD007228 (ZDB) and NIH RO1MH07012 (SAW). Thanks to Jennifer Morales and Maria

Truong for assistance in analyzing song and non-vocal behavior.

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VITA

2013 Bachelor of Science, Neuroscience

Bachelor of Arts, Anthropology

Minor in Chemistry

University of Alabama at Birmingham

2013 Graduate Dean’s Scholar Award

University of California, Los Angeles

2015-2016 Neurobehavioral Genetics Training Grant

University of California, Los Angeles

2017-2019 National Research Service Award

National Institute of Mental Health

2020 Dissertation Year Fellowship

University of California, Los Angeles

2020-2021 Neuroscience Scholar’s Program Fellowship

Society for Neuroscience

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Publications and Presentations

Aamodt CM, Hemminger ZH, Wollman R, White SA. Inhibition of microRNA-128 rescues deficits in learned vocal communication. (manuscript in preparation)

Aamodt CM. “Pharmacological and genetic reduction in miR-128 rescues learned vocal communication deficits.” Molecular and Cellular Cognition Society Annual Meeting in Chicago,

IL, 18 October 2019. (Talk)

Aamodt CM. "Pharmacological and genetic reduction in miR-128 enhances learned vocal communication." Birdsong and Animal Communication in Millbrook, NY, 28 June 2019. (Talk)

Aamodt CM*, Farias-Virgens MY*, White SA. 2019. Birdsong as a window into language origins and evolutionary neuroscience. Philosophical Transactions of the Royal Society B:

Biological Sciences 375(1789):20190060. (Invited review, *Co-first author)

Burkett ZD, Day NF, Kimball TH, Aamodt CM, Heston JB, Hilliard AT, Xiao X, White SA.

2018. FoxP2 isoforms delineate spatiotemporal transcriptional networks for vocal learning in the zebra finch. eLife 7: e30649.

Cormier LA, Jones S, Carlisle C, Andrews C, Aamodt CM, McCown A, Messersmith M, Wilson

A, Whiteaker L. 2012. A case study of metabolic syndrome without hypertension in a Fijian coastal fishing village. J Hum Ecol 39(2):115-123.

Jones SR, Cormier LA, Aamodt CM, Delisle J, McCown A, Messersmith M, Noojin M. 2012.

Enculturating student anthropologists through fieldwork in Fiji. Building Bridges in

Anthropology: Understanding, Acting, Teaching, Theorizing. Knoxville: Newfound Press. Pp

103-145. xiii

Chapter I: Introduction

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Songbirds as a model system for human communication deficits

Speech and language are central to the human experience, and the acquisition of language is a driving factor in human cognitive development (Vasileva & Balyasnikova 2019). As children develop they first learn to speak with older and more proficient speakers, then speak to themselves, then develop the “inner speech” that typically characterizes human cognition

(Friedrich 2014). Work towards characterizing the cellular and molecular basis of speech and language is essential for understanding the human mind and disorders of cognition.

The zebra finch (Taeniopygia guttata) is the most widely used animal model for speech and language. Famed ethologist Desmond Morris summarized why they rose to popularity as a lab model in 1954: ‘The Zebra Finch is a small Australian Ploceid which is ideally suitable for laboratory observations. It will nest and rear young in small indoor aviaries. New birds, transported to the laboratory in small boxes, will begin to nest-build and court within minutes of their release into an aviary. There are no seasonal difficulties, as it breeds all through the year.

The species is exclusively a seed-eater and the nestlings require no special diet in captivity. The birds are not disturbed by the presence of an unconcealed human observer.’

Analogous brain regions in songbirds and humans comprise circuits for the acquisition and production of learned vocal communication. In vocal learning birds, song production is made possible by the descending vocal motor pathway, which connects a pre-motor-like cortical region called HVC (proper name) to a motor cortex-like region called the robust nucleus of the arcopallium (RA), which then projects to motor neurons innervating the syrinx and trachea

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(Vicario, Naqvi, & Raksin 2001). Song acquisition is made possible by the anterior forebrain pathway. This circuit connects HVC to the striatopallidal song nucleus Area X, which then projects to the dorsolateralis anterior thalami pars medialis (DLM) in the thalamus, then to a cortical region called the lateral magnocellular nucleus of the anterior nidopallium (LMAN), which then projects to RA as well as back to Area X (Brainard 2004). The anterior forebrain pathway is essential during song learning during development but is not required for song production in adulthood (Bottjer, Miesner, & Arnold 1984).

In songbirds and humans, convergent gene expression specializations within analogous brain regions make learned vocal communication possible. In 2014 Pfenning et al. discovered that RA shares gene expression patterns unique to the human laryngeal motor cortex relative to adjacent tissue. Similarly, Area X shares gene expression specializations with a region of the human striatum activated during speech production. Interestingly, songbirds and humans share ongoing basal ganglia neurogenesis in adulthood (Ernst et al. 2014, Vellema et al. 2010). All of the original data generated during my graduate work was carried out in Area X. The genes expressed in these regions include over 1,400 autism and intellectual disability genes. This makes the zebra finch uniquely suited to serve as a model for disorders affecting speech and language.

Convergent evolutionary changes in the timing of development (delayed and prolonged) are present in both humans and vocal learning birds (Aamodt, Farias-Virgens, & White 2019).

The molecular mechanisms underlying learning and memory evolved from those that first evolved in development (Day & Sweatt 2011), suggesting that prolonged development could

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underlie the evolution of language. Like human children, zebra finches learn to communicate during a developmental critical period, a window of time when a higher level of neuroplasticity makes it easy for the brain to acquire a new function, such as vocal communication, through environmental exposure. Many of these mechanisms play a role in both neurodevelopment and vocal learning and understanding their role both prenatally and postnatally will be key to developing effective therapies for neurodevelopmental disorders such as ASD. In Chapter II I elaborate on the similarities between songbirds and humans, with a particular focus on how songbird research can inform our understanding of the evolution of language (Aamodt, Farias-

Virgens, & White 2019).

Modeling early life social isolation-induced deficits

Early life psychosocial deprivation prevents the normal acquisition of learned vocal communication. Evidence for this phenomenon arose in Romania after the regime of Nicolae

Ceaușescu passed laws coercing women to have as many children as possible. This led to an epidemic of childbirths that could not be supported by their families that was then worsened by an economic downturn in the 1980s. The orphaned children were transferred to understaffed orphanages where they were raised in relative silence (Nelson, Fox, & Zeanah 2014).

To better understand the consequences of such extreme psychosocial neglect, Charles

Nelson organized the Bucharest Early Intervention Project, the first randomized controlled trial to study the effects of high-quality foster care versus institutionalization. The researchers found measures of intelligence in institutionalized children were almost two standard deviations below the mean for children reared in a community, and that intellectual delay correlated to how long the child had been institutionalized (Smyke et al. 2007). With regard to language, they found that 4

developmental social deprivation leads to communication deficits (Windsor et al. 2007, Windsor et al. 2013, Almas et al. 2012) and autistic-like behaviors (Federici 1998, Rutter et al. 1999,

Hoksbergen et al. 2005, Levin et al. 2015) in children who were not transferred to foster care before the age of two.

The effects of developmental isolation on social communication can be easily modeled in the zebra finch (Eales 1985, Livingston, White, & Mooney 2000, White et al. 2006, Fehér et al.

2009). An adult zebra finch typically sings a highly stereotyped sequence of individual syllables that together comprise a single motif. In the course of two hours a bird may sing hundreds and sometimes thousands of renditions of his motif. Birds reared without exposure to a song tutor that are placed in social isolation once they can feed themselves (“isolates”) produce song that is impoverished and more variable, both in terms of syllable stability and sequence organization at the level of the motif (Wilbrecht et al. 2006).

In the absence of social input, an isolate’s critical period is extended beyond the normal window. Although some neurogenesis persists in HVC during adulthood, during this time there is a ramp down of new neuron addition. Isolation leads to neurogenesis in HVC to proceed at juvenile levels beyond the normal point when it decreases. Interestingly, greater neuron addition is correlated to reduced syllable stereotypy in isolate song (Wilbrecht et al. 2006).

In Chapters III and IV I capitalize on the social isolate model to generate songbirds with communication deficits, then inhibit miR-128 as a therapeutic approach to normalizing sequence stereotypy deficits. Chapter III focuses on pharmacologic inhibition of miR-128 using a bioactive glycoside found in ginseng, GRh2. In Chapter IV I focally inhibit miR-128 in Area X using a miR-128 antisense construct. Chapter V focuses on the effects of this same miR-128

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antisense construct in typically-reared juvenile zebra finches.

Songbirds as a model system for microRNA-128 in vocal learning

A key mechanism uncovered in my doctoral research is song-induced regulation of miR-

128 (Chapter III). microRNAs are regulatory RNA sequences of ~21-23 nucleotides that inhibit transcription by binding to matching seed sequences on messenger RNA (mRNA) transcripts

(Bartel 2004). The highly conserved miR-128 is also one of the most highly expressed microRNAs in zebra finch (Luo et al. 2012), mouse (He et al. 2012), and human (Shao et al.

2010) brain, making it a broadly relevant candidate therapeutic target.

Previous research has established that microRNAs regulate transcription in vocal learning. Shi and colleagues found that miR-9 and miR-140-5p are upregulated when a bird practices his song by himself and regulate key targets important for learned vocalization, including FoxP2 (2013, 2018). Similarly, Larson and colleagues characterized changes in microRNAs associated with seasonal song plasticity in Gambel's white-crowned sparrows

(2015). Within the auditory forebrain, microRNAs are regulated by passive listening to conspecific song (Gunaratne et al. 2011). Song in zebra finches is sexually dimorphic, and males sing their courtship song to attract a mate. Several studies have identified sex differences in songbird microRNA regulation (Luo et al. 2012, Lin et al. 2014).

My interest in miR-128 stemmed from a broader interest in using a clustering approach called weighted gene co-expression network analysis (WGCNA) to decode essential mechanisms of transcriptional regulation contributing to activity-dependent gene expression. In 2012

Stephanie White’s lab published one of the first papers successfully applying WGCNA to complex behavior (Hilliard et al. 2012). Gene expression data was collected from birds that had

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been singing as well as birds that had sung very little or not at all, then analyzed which co- regulated genes correlated to singing. As an undergraduate student I immediately recognized the importance of this approach and applied to UCLA with the goal of joining Stephanie’s lab, eager to build on this prior work.

My previous research focused on the role of histone variant exchange as a novel epigenetic mechanism in learning and memory, and initially I thought I would continue this type of research in my doctoral studies. The dataset generated by Hilliard and colleagues found that the gene for the histone variant H3.3 is dynamically regulated, a key correlate for detecting H3.3 exchange. I was investigating candidate mechanisms that could promote or constrain histone exchange, and since many polycomb group genes were also activity-regulated I wondered if polycomb repressive complexes may function to constrain histone cycling. In this literature search I came across a paper showing that miR-128 targets integral components of polycomb repressive complexes 1 and 2 (Peruzzi et al. 2013). This paper happened to include the names of the host genes for miR-128, Activity-Regulated Phosphoprotein 21 (ARPP21) and R3H Domain

Containing 1 (R3HDM1). I instantly recognized that these were two of the top genes most strongly correlated to singing in the Hilliard et al. data. This led me to hypothesize that miR-128 could be a key regulator of activity-dependent gene expression in learned vocalization. In

Chapters III, IV and V I present this set of experiments, and in Chapter VI I discuss their implications for ASD.

Prior studies linking miR-128 to cognition and behavior further supported a potential role for miR-128 in vocal learning. In 2011 Tim Bredy’s group found that miR-128 inhibition in the mouse infralimbic prefrontal cortex (ILPFC) decreased fear memory extinction, whereas miR-

128 overexpression promoted the extinction of the memory (Lin et al. 2011). In 2012 Anne 7

Schaefer’s team showed that miR-128 levels in the striatum bidirectionally regulate motor behavior (Lek-Tan et al. 2012). Lower levels of miR-128 were associated with greater neuronal excitability and increased motor activity, whereas higher levels suppressed neuronal activity and associated motor behavior. The authors attributed these findings to miR-128-driven changes in ion channels as well as in the Extracellular Regulated Kinase 2 (ERK2) pathway.

The role of miR-128 in ASD

Communication deficits arise from a wide array of neurodevelopmental disorders, but currently no medications exist to alleviate the suffering of patients and their families (Singh et al.

2014). The total cost of ASD and intellectual disability in the United States is ~$236 billion per year, with the bulk arising from special education in childhood as well as residential accommodations and productivity loss in adulthood (Buescher et al. 2014).

Better pharmacotherapeutics are urgently needed to enhance the efficacy of environmental interventions aimed at enabling the full acquisition of skills such as speech and language, which are critical for function in society. Behavioral drug discovery is best accomplished by investing in relevant models for ASD and other developmental disabilities

(Ruhela et al. 2015). Over the past decade songbirds have emerged as a major neurogenetic animal model (Warren et al. 2010, Jarvis 2019), and are now uniquely suited to contribute to this area of research.

Although this project was initially motivated by the hypothesis that miR-128 would be a central regulator of vocal learning in birds, several groundbreaking studies have emerged since its inception that strongly suggest that miR-128 plays a convergent role in vocal learning in 8

humans. Wu and colleagues profiled microRNA from postmortem neural tissue from ASD patients to determine if there were differences correlated to diagnosis (2016). They used

WGCNA to group the microRNAs into co-regulated modules, then determined which modules were correlated to diagnosis. Within the module with the strongest positive correlation to diagnosis, miR-128 was among the top ten microRNAs with the strongest correlation to its first principal component. Two other microRNAs within this module, miR-212 and miR-132, were previously linked to seasonal vocal learning plasticity by the Brenowitz lab (Larson et al. 2005), further suggesting that the module identified by Wu et al. could directly related to social communication deficits in ASD.

In a recent study, Gazestani et al. analyzed the leukocyte transcriptome of a deeply phenotyped cohort of 1 to 4-year-olds with and without ASD (2019). They found a network of perturbed genes that correlated to symptom severity in the ASD toddlers. Strikingly, Gene Set

Enrichment Analysis showed that miR-128 was included in the processes enriched in this gene set. This same cohort was analyzed by Lombardo and colleagues in a prior study (2018). The researchers correlated leukocyte gene expression to fMRI data to home in on which genes co- varied with neural responses to speech. Notably, they found a module of genes present only in children with poor language outcomes that was also enriched in songbird genes regulated by singing. They found that this same module was also enriched in genes related to inflammation and immune signaling.

Several important studies have linked microglia dysregulation to ASD. Most recently,

Velmeshev et al. performed single cell RNA sequencing on postmortem tissue from ASD

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patients with associated medical records (2019). They found that microglia were the non- neuronal cell type with the greatest differential gene expression compared to controls. These changes were associated with activation. Importantly, microglia were among the top two cell types that were most predictive of clinical severity.

This work aligns with prior literature supporting a key role for inflammation in ASD.

Several studies identified key features of microglia dysregulated in ASD postmortem tissue, including total number of microglia (Morgan et al. 2010), microglial activation (Vargas et al.

2005, Morgan et al. 2010), and somal volume (Morgan et al. 2010). Supporting evidence also comes from bulk transcriptomic analysis of postmortem ASD brains identifying inflammation and immune signaling as dysregulated processes (Voineagu et al. 2011). Additionally, large scale genome wide association studies (GWAS) have also identified immune pathways as key contributors to ASD (The Network and Pathway Analysis Subgroup of the Psychiatric Genomics

Consortium 2015).

In Chapters III-IV I show that rescuing isolation-induced communication deficits via miR-128 inhibition leads to the normalization of dysregulated song. In Chapter V, I show that reducing miR-128 in juveniles leads to a precocious enhancement in sequence stereotypy. In the closing chapter I present a proposed model for miR-128-driven normalization of chronically reactive microglia and how this is relevant for therapeutically targeting microglia in ASD.

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Chapter II. Birdsong as a window into language origins and evolutionary neuroscience

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Abstract

Humans and songbirds share the key trait of vocal learning, manifested in speech and song, respectively. Striking analogies between these behaviors include that both are acquired during developmental critical periods when the brain’s ability for vocal learning peaks. Both behaviors show similarities in the overall architecture of their underlying brain areas, characterized by cortico-striato-thalamic loops and direct projections from cortical neurons onto brainstem motor neurons that control the vocal organs. These neural analogies extend to the molecular level, with certain song control regions sharing convergent transcriptional profiles with speech-related regions in the human brain. This evolutionary convergence offers an unprecedented opportunity to decipher the shared neurogenetic underpinnings of vocal learning.

A key strength of the songbird model is that it allows for the delineation of activity- dependent transcriptional changes in the brain that are driven by learned vocal behavior. To capitalize on this advantage, we used previously published datasets from our laboratory that correlate gene co-expression net- works to features of learned vocalization within and after critical period closure to probe the functional relevance of genes implicated in language. We interrogate specific genes and cellular processes through converging lines of evidence: human- specific evolutionary changes, intelligence-related phenotypes and relevance to vocal learning gene co-expression in songbirds.

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1. Introduction

In the words of the Argentinean writer Borges (1949), ‘All language is a set of symbols whose use among its speakers assumes a shared past’. Spoken language primarily uses sounds as symbolic vehicles, a repertoire of learned and voluntarily controlled vocal elements that can be connected in a rule-based way to form more complex sequences. Despite its human uniqueness, spoken language shares some of its necessary components with vocal behavior practiced by at least one evolutionarily distant animal group. A robust body of evidence accrued over approximately 100 years demonstrates striking analogies between birdsong and speech, both learned forms of vocalization.

Birdsong and speech are acquired during developmental critical periods when the brain’s ability for vocal learning peaks (Tyack 2020). Both behaviors show similarities in the overall architecture of related brain areas, characterized by cortico-striato-thalamic loops and direct projections from cortical neurons onto brainstem motor neurons that control the vocal organs.

These neural parallels extend to the molecular level, with certain song control regions sharing transcriptional profiles with speech-related regions in the human brain (Pfenning et al. 2014).

This offers an opportunity to decipher the shared neurogenetic underpinnings of vocal learning.

This line of inquiry will additionally yield insight into human disorders of communication.

Indeed, a major barrier in autism and schizophrenia research has been a lack of animal models with measurable behaviors relevant to core symptoms such as deficits in learned vocal communication (Ruhela et al. 2015, Swerdlow et al. 2016, Hunter et al. 2019). With the advent of a well-annotated genome (Warren et al. 2010, Korlach et al. 2017), the songbird model is

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poised to close the gap in the available approaches for addressing language-associated neurodevelopmental disorders.

A key strength of the songbird model is that it allows for the delineation of activity- dependent transcriptional regulation driven by learned vocal behavior. To capitalize on this advantage, we used published datasets that correlate gene expression networks to features of learned vocalization within and after critical period closure to probe the functional relevance of genes implicated in language (Hilliard et al. 2012, Burkett et al. 2018). Evolutionary changes relevant to human brain development include alterations in the timing of neuronal gene expression (e.g. heterochrony) as well as human-specific changes to the genetic sequence itself.

We interrogate specific genes and cellular processes through converging lines of evidence: human-specific evolutionary changes, communication and intelligence-related phenotypes, and relevance to coordinated gene expression patterns related to learning in songbirds.

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2. The slow but rewarding journey of becoming a vocal learner: intrinsic reward systems are central to the two-way evolutionary process between vocal learning and timing of sexual maturation

(a) Dopamine and song variability

Within a given songbird species, there is individual variability in both the acoustic structure of the vocal units themselves and in the order which they are sequenced. In zebra finches, a species in which song is sexually dimorphic and only males learn to sing, social context modulates song variability: it is typically reduced when a male sings to females (i.e. female- directed song—FD) relative to when he sings alone (i.e. undirected song—UD) (Kao et al. 2005). While females prefer FD to UD song (Woolley et al. 2008, Dunning et al. 2014), the more variable UD song is thought to reflect vocal-motor exploration important for motor learning and reinforcement (Troyer & Doupe 2000a, Troyer & Doupe 2000b). The same benefit of variability during training leads to more efficient motor learning and performance in humans

(Kerr & Booth 1978, Sutton & Barto 1998, Wu et al. 2014).

Along these lines, a certain degree of variation in juvenile song is thought to enable vocal learning, as young birds explore a range of vocal gestures in search for motor patterns capable of producing vocal output that matches the template built from the tutor’s song (Doya & Senjowski

1995, Olveczky et al. 2005). Similarly, in adult songbirds, variability appears fundamental for song maintenance, because it allows for the reinforcement of optimal motor patterns capable of producing auditory inputs that match the auditory template built from the bird’s own song

(Hessler & Doupe 1999, Sober et al. 2008). Hence, in a way, variation begets fidelity. Fidelity is fundamental for performance of species-specific song patterns and levels of stereotypy that are adequate to a species’ sensory ecology (e.g. in species recognition), without which successful

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social interactions (e.g. enchanting females) are less likely (Clayton & Prove 1999). On the other hand, the ability to introduce variation creates an openness to innovation and more complex patterns of cultural transmission, leading to increased song complexity, such as simultaneous learning from multiple tutors (Takahashi & Okanoya 2006).

Context-dependent changes in song are correlated with key differences in gene expression in the songbird brain. The earliest indication of this came from the discovery of different patterns of immediate early gene expression in the brain between UD and FD, leading to the original proposal of catecholaminergic modulation in the male basal ganglia song control nucleus, Area X, in context-dependent changes in song (Jarvis et al. 1998). Subsequent studies show that in contexts associated with increased song stereotypy, as in FD singing, extracellular dopamine (DA) levels in Area X are higher than in contexts associated with increased song variability, as in UD singing (Sasaki et al. 2006). These changes are paralleled by another key molecule in vocal learning, the transcription factor FOXP2, for which Area X gene expression decreases during UD but not FD singing (Teramitsu & White 2006, Chen et al. 2013). Rather than coincidental, these concordant changes between FOXP2 gene expression and dopaminergic regulation appear causative: experimental ‘knockdown’ of FOXP2 in Area X both disrupts the possibility of context- dependent modulation of vocal variability and decreases levels of dopamine receptor 1 (D1R) and DA- and cAMP- regulated neuronal phosphoprotein (DARPP-

32), components in the D1R signaling cascade (Murugan et al. 2013).

(b) A role for endocannabinoids in vocal learning

Darwin (1871) noted that although the main function of bird- song during the breeding season is mate attraction, ‘birds continue singing for their own amusement after the season for

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courtship is over’. Several studies now provide evidence supporting the hypothesis that song practice is indeed stimulated and maintained by intrinsic reward mechanisms (Mooney 2009).

These systems operate at two integrated levels: one regarding memetic aspects of song learning

(i.e. of the reward-related memories) and associated dopaminergic neuromodulation, and the other regarding pleasure (i.e. elicited positive affective states), associated with opioid and endocannabinoid neuromodulation (Riters et al. 2019). The role of cannabinoids in song variability and learning is substantiated by the reduction in stereotypy and number of learned notes in the songs of young birds exposed to an endocannabinoid agonist relative to songs of non-exposed juveniles (Soderstrom & Johnson 2003). Notably, the treatment had no measurable effect on the structure of already-learned song patterns in adults, indicating that the mechanisms underlying critical period learning are more sensitive to cannabinoid disruption. Here, we present further evidence supporting the involvement of the endocannabinoid system for vocal learning in humans and songbirds.

In prior work, our group applied an unsupervised weighted gene co-expression network analysis (Zhang & Horvath 2005) to songbird brain transcriptomes and discovered groups of genes whose transcripts exhibit co-expression patterns, which in gene network terminology are referred to as ‘modules’. As part of this unbiased approach, modules are assigned arbitrary color names (e.g. darkgrey) (Hilliard et al. 2012, Burkett et al. 2018). Within the resultant network, some of the modules were unique to Area X and others were unique to the juvenile critical period for learning. Specifically, we tested whether the co-expression relationships observed in juvenile Area X were preserved in a non-song control brain region—neighboring basal ganglia termed the ventral striato-pallidum (VSP)—and in adulthood (i.e. region-specific and age- dependent modules). Statistically significant correlations (q-value < 0.05) to song features were

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also reported including to song production (the number of motifs sung by the bird at the time of the experiment) or learning (the spectral similarity of a pupil’s song to that of its tutor).

In another behavioral measure, we leveraged an experimental paradigm validated in several publications that detect singing-induced changes in zebra finch (Taeniopygia guttata) song variation, as seen during natural social modulation (Heston et al. 2018). Here, we refer to this as the ‘variability induction’ (VI) paradigm (Heston et al. 2018, Miller et al. 2010, Miller et al. 2008, Heston & White 2015). In these experiments, the variability of UD song after a period of non-singing is com- pared to the variability of UD song after an equivalent period of continuous UD singing. This comparison reveals that, on average, continuous UD ‘practice’ in the morning leads to increased song variability, while singing after a morning period of quiescence is more stereotyped, similar to FD song (Heston et al. 2018).

In this prior study, our group identified gene modules that were correlated to song-related behavioral features. Briefly, we found modules of genes whose co-expression was up- or downregulated by the amount of song that birds sang on the morning of the experiment.

Strikingly, these ‘song modules’ were specific to Area X and observed in both adults and juveniles, speaking to the profound influence of singing behavior on the transcriptome (Hilliard et al. 2012, Burkett et al. 2018). Excitingly, the analysis shows modules correlated to the amount of tutor song learning by a pupil, and these were restricted to juveniles. The gene co- expression pattern for these learning-related modules disappeared in adults that were beyond the critical period for learning, suggesting that they are important for the neural plasticity underlying critical period learning. Additional modules were correlated to the morning modulation of song variability as measured by the VI paradigm.

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Here, we newly integrate those prior findings of behaviorally relevant gene co-expression modules in Area X of juvenile birds (Burkett et al. 2018) with findings published by Pfenning et al. (2014), who identified gene expression patterns that are conserved in the brains of humans and song-learning birds. Specifically, the authors analyzed transcriptomes from multiple brain regions from humans and song-learning birds (zebra finch, parrot and hummingbird) as well as vocal non-learning birds (dove and quail) and a non-learning primate species (macaque). They found evidence for transcriptional convergence in vocal-learners’ brains, including between Area

X and the human putamen, a dorsal portion of the human striatum.

Our new analysis points to a total of 45 genes (electronic supplementary material, table

S1) that form behaviorally relevant modules in the juvenile network and exhibit transcriptional convergence between Area X and the human putamen. Among these, monoacylglycerol lipase

(MGLL) shows high intramodular connectivity that is specific to the Area X of juvenile birds, and for which expression correlates with a bird’s ability to introduce variability in its song

(electronic supplementary material, table S1). MGLL is responsible for the metabolism of endocannabinoid 2-arachidonoylglycerol (2-AG) (Blankman et al. 2007). These findings suggest that the regulation of MGLL metabolism of 2-AG in Area X allows for song variability in juvenile birds. Endocannabinoids such as 2-AG mediate activity-dependent changes in synaptic strength via activation of type 1 cannabinoid (CB1) receptors in several regions of the mammalian brain, including the striatum (Kreitzer & Malenka 2005). They retrogradely suppress both inhibition and excitation of dopaminergic neurons by inhibiting neurotransmitter release by

GABAergic medium spiny neurons and glutamatergic terminals, respectively (Cachope 2012).

Endocannabinoid–DA striatal interactions seem to be conserved in songbirds, as components of both systems are regulated in Area X (Sasaki et al. 2006, Hahn et al. 2017). Thus, the regulation

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of MGLL metabolism of 2-AG in Area X during the juvenile critical period likely has consequences for FOXP2- mediated synaptic plasticity (e.g. Grozer et al. 2008, Schreiweis et al.

2014) and dopaminergic neuromodulation.

Sex hormones and related metabolites and enzymes are regulated in a context-dependent fashion and as a function of the developmental trajectory (see §2c below) in several organisms, including songbirds (London et al. 2010) and mammals (Gleeson et al. 2009). In zebra finches, directed FD singing is associated with higher levels of circulating plasma testosterone than during more variable UD singing (Harding et al. 1983, Walters et al. 1991). Moreover, the plastic period of juvenile song learning closes when zebra finches mature and testosterone levels rise

(Prove 1983, Williams et al. 2003). These endogenous hormones have long been recognized to modulate DA in mammalian and songbird brains (Barclay & Harding 1988, Abreu et al. 1988), and have more recently been investigated in endocannabinoid-associated reward systems in both animal groups (Riters et al. 2019, Purves-Tyson TD et al. 2014, DeVries et al. 2016, Conde et al.

2017). Therefore, context-dependent surges in testosterone levels and changes in the timing of sexual maturation impact the function of intrinsic reward systems in both songbird and mammalian brains.

(c) Slow developmental trajectories and the evolution of vocal learning

Human development is delayed and prolonged relative to other great apes, and this distinction is key to understanding human uniqueness (Bogin et al. 2018). Songbird development is also delayed relative to non-vocal learning birds (Charvet & Streidter 2011), suggesting that similar changes in the timing of development (heterochrony) contributed to the emergence of vocal learning in songbirds and humans. Charvet & Striedter argue that changes in the timing of

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development are a necessary prerequisite for the evolution of learned vocal communication

(2009). Developmental timing, though not sufficient, may have been key to the evolution of complex vocal production learning, at least in songbirds and humans. Slowed development supports the vast metabolic demands of the developing songbird (Nowicki et al. 2002) and human brain (Luzawa et al. 2014). This allows the generation of an expanded telencephalon, which is then capable of being adapted for learned vocalization circuitry. With prolonged development, the window of juvenile plasticity also expands, creating an opening for the evolution of complex learned behaviors (Sousa et al. 2017).

In the wild, slow developmental trajectories reflect some of the most varied evolutionary processes, including the effects of limiting environmental conditions (Foster 1987, Greene et al.

2000, Cucco & Malacarne 2000). However, some cases of delayed developmental trajectories may evolve through a change in selective pressures whereby commonly found environmental sources of selection that work as constraints to behavioral plasticity and flexibility, such as pre- dation, foraging and species recognition, are either superseded or overcome by sources of selection imposed by socialization (West-Eberhard 1978, Wobber et al. 2010). This sort of change in evolutionary regime is also observed in processes of domestication (Price 1984).

Incidences of domestication thus serve as fruitful models for studying such evolutionary phenomena (Thomas & Kirby 2018).

Domestication has reduced the time to sexual maturation in a variety of animals, including the domestic chicken. However, another parcel of domestic animals, likely following domestication practices other than selection for earlier reproduction, show slow developmental trajectories and retain in adulthood a suite of behavioral, physiological and morphological traits

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typically found at younger ages in the ancestral species (i.e. the domesticated phenotype/syndrome), a form of heterochrony called ‘neoteny’ (Thomas & Kirby 2018, Wilkins et al. 2014). This is reflected in a reduction in sexually stereotyped traits (e.g. morphological and behavioral dimorphisms) and extended neuroplasticity and learning, which leads to behavioral flexibility and an openness to cultural transmission (Theofanopoulou et al. 2017).

Humans may have undergone a similar process, but one in which we were the solo protagonists of our own domestication (i.e. self-domestication) (Hare et al. 2012). Sources of relaxation of selection during human evolution include the development of collective ambushing strategies to hunt for stronger prey; taking part in ‘traditional learning’ (i.e. the cultural transference of information from generation to generation), and division of labor that allows for the optimization of tool-making, hunting-and-gathering as well as long-range trading, among other fundamental human activities. In addition, increases in affiliative behaviors towards the elderly, children and disabled individuals, minimize the fitness consequences of their vulnerability. In these contexts, human sociality would have entered a feedback loop, reducing common environmental selective pressures, but making itself the main source of selection in our species. Individuals showing more tolerance to social stress, and more cooperative—instead of aggressive— behaviors would have gained selective advantages. Several unique traits that define our species, including language, could have evolved associated with these selective pressures for more peaceful and cooperative living.

Like language, the evolution of birdsong is challenging to study because song is not directly reflected in non-perishable or fossilizable forms. As Don Kroodsma, a pioneer in the field, has pointed out, ‘perhaps the best hope for understanding why some birds learn is to

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examine far more recent evolutionary events [...] in which we can better identify the social circumstances that led to the origins [and] loss of vocal learning’ (2004, p.110). To that goal, recent studies have added the evolution of more complex patterns of vocal learning, such as learning from multiple tutors and more variable song. A preeminent example of this type of evolutionary change is the domesticated Bengalese finch (BF; Lonchura striata domestica)

(Okanoya 2017).

Beginning approximately 250 years ago, BFs were caught and domesticated from their wild Chinese ancestor, the white-backed munia (WBM; Lonchura striata). According to extensive historical reports, the BF was never bred for its singing ability, yet it evolved a much more complex vocal behavior than that found in WBMs, marked by increased sequence variability and simultaneous learning from multiple tutors. Together with less aggressive and less neophobic (i.e. fear of novelty) behaviors, these features complete a suite of neotenic traits in the

BF that enhance exploration. Female choice has been proposed as a major selective force leading to the increase in BF’s vocal complexity (Okanoya 2002). Additionally, relaxation of sources of selection present in the wild, such as species recognition and environmental stress (Kagawa et al.

2012, Suzuki et al. 2012), may have been crucial in allowing for new or continuing sources of positive selection to operate towards the evolution of more complex vocal behavior in the BF

(i.e. female choice for more complex songs) (Okanoya 2015, Deacon 2010).

To sum up thus far, the delayed developmental trajectories in both humans and songbirds hold parallel implications for reward systems leading to the evolution of vocal learning.

Evidence suggests that, rather than the work of a single evolutionary force, this was the product

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of a similar interplay of adaptive pressures relating to the enrichment of social over environmental demands in both groups.

This advance holds timely relevance to the current path of our species in meeting modern-day socio-political and cli- mate-related challenges on the global scale.

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3. From slowing down to speeding up: the role of human-specific and intelligence-related genes in learned vocal behavior

(a) Human accelerated regions and vocal learning

Human accelerated regions (HARs) are segments of DNA that are conserved among our closest relatives but have undergone recent selection in humans (Levchenko et al. 2018,

O’Bleness et al. 2012). HARs are identified by higher rates of single-nucleotide substitutions in the human lineage (Hubisz & Pollard 2014) or by segmental duplication and copy number variation (Levchenko et al. 2018). HARs are typically found in non-coding regions of the genome. These areas are often poorly annotated yet likely regulate gene expression (Capra et al.

2013).

Among the approximately 3000 currently identified HARs with potential biological effects, a subset have been directly linked to brain development and function (Franchini &

Pollard 2017). A popular method for validating the functional relevance of HARs to human- specific traits is to create transgenic reporter animals or cell lines and monitor how the human versus the chimpanzee region differentially regulates gene expression during development

(Capra et al. 2013). We tested for the presence of 10 highly validated HAR-related genes within juvenile and adult activity-dependent gene expression networks (Hilliard et al. 2012, Burkett et al. 2018). Eight were detected within juvenile Area X modules that were correlated to vocal learning (AUTS2, PTBP2, SRGAP2, NPAS3, CUX1, FZD8, GPC4, ARHGAP11A) and five were correlated to the modulation of song variability in juveniles as measured in the VI paradigm

(NPAS3, CUX1) or to the amount of singing (i.e. the number of motifs sung on the day of the experiment) in adults (AUTS2, PTBP2, SRGAP2). We discuss the implications of these

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relationships for evolutionary neuroscience and develop- mental disorders of vocal communication for two of these, below.

(i) AUTS2

AUTS2 was among the shared set of genes upregulated throughout the songbird and human striatum detected by Pfenning et al. (2014). In the juvenile network that we identified

(Burkett et al. 2018), AUTS2 exhibited high module membership in the cyan song-related module (0.85; p = 0.000005). In humans, three unique intronic regions of Autism Susceptibility

Candidate 2 (AUTS2) have undergone accelerated evolution (Pollard et al. 2006, Prabhakar et al.

2006). Notably, in a scan for selective sweeps in humans versus Neanderthals, AUTS2 was within the region with the greatest significance, containing 293 consecutive single-nucleotide polymorphisms within non-coding regions throughout the first half of the gene (Green et al.

2010). Functionally, AUTS2 has been implicated in transcriptional repression (Franchini &

Pollard 2017) as well as regulation of neuritogenesis and neuronal migration (Pollard et al.

2006). Knockdown of AUTS2 in zebrafish leads to a reduced head size, reduced movement and a smaller number of midbrain neurons, suggesting that the human-specific evolutionary changes could be associated with expanded brain size (Oksenberg et al. 2013).

AUTS2 was first identified in a pair of monozygotic twins concordant for autism (Sultana et al. 2002). Given that deficits in social communication are one of the three key diagnostic criteria that define autism spectrum disorder, this indicates that AUTS2 may be important for social communication. In addition to autism, AUTS2 variants have been linked to information

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processing speed (Luciano et al. 2011) and dyslexia (Girirajan et al. 2011), further suggesting a role in language-related processes. AUTS2 has also been linked to human intelligence (Savage et al. 2018). Expression quantitative trait loci (eQTLs) are single nucleotide variants that increase or decrease expression of a gene and are correlated to a trait (see §3b). Intelligence-related eQTLs in AUTS2 increase its expression as measured in whole blood, which is often used as a proxy for brain tissue (Savage et al. 2018, Kanduri et al. 2015). In juveniles, AUTS2 was significantly correlated to the darkgrey Area X gene co-expression module that is enriched for human intelligence- related genes.

As noted above, birdsong is procedurally learned and associated with basal ganglia reward systems, so specializations of the underlying processes become of interest. AUTS2 is highly expressed in reward-related brain regions, including tyrosine hydroxylase-positive dopaminergic neurons of the substantia nigra and the ventral tegmental area (Bedogni et al.

2010). Variants in AUTS2 are linked to alcohol consumption (Schumann et al. 2011), and

AUTS2 mRNA and protein levels decrease in the nucleus accumbens after chronic heroin administration (Zhu et al. 2016), further supporting a link between the underlying genetics of reward and vocal learning.

(ii) SRGAP2

Another HAR gene with relevance to vocal learning is Slit-Robo Rho GTPase activating protein 2 (SRGAP2) (Levchenko et al. 2018). The ancestral SRGAP2 gene has undergone three consecutive partial duplications in humans, resulting in paralogues SRGAP2 B, C and D.

SRGAP2 B and D are RNA genes, whereas C encodes a human-specific truncated form of the ancestral protein that arose 2–3 Ma. The truncated form acts as a dominant negative to suppress

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the function of ancestral SRGAP2 (Fortna et al. 2004, Sudmant et al. 2010, Dennis et al. 2012,

Fossati et al. 2016). The SLIT-ROBO pathway regulates axon guidance and neuronal migration

(Brose et al. 1999). Human mutations in SRGAP2 lead to intellectual disability and psychomotor delay (Saitsu et al. 2012). SRGAP2 negatively regulates neuronal migration (Guerrier et al.

2009) and promotes dendritic and synaptic maturation (Fossati et al. 2016, Charrier et al. 2012).

The human specific SRGAP2C inhibits the ancestral SRGAP2A to prolong the phase of spine development and retain a more plastic, neotenous neuronal phenotype (Charrier et al. 2012).

The role of SLIT-ROBO signaling in vocal learning has been well characterized by Jarvis and co-workers (Wang et al. 2015). SLIT1 is selectively downregulated in the primary motor cortex song control nucleus, known as RA, of vocal learning birds. RA neurons make direct projections onto tracheosyringeal motor neurons. Conversely, the SLIT1 receptor, ROBO1, is upregulated in RA during developmental critical periods for vocal learning (Wang et al. 2015).

In humans, SLIT1 is also downregulated in the primary motor cortex, specifically in a region that directly projects to laryngeal motor neurons, reinforcing the molecular and functional parallels of neural circuitry for vocal learning in both species.

In the Area X gene co-expression networks, SRGAP2 is downregulated during singing in adult birds (Hilliard et al. 2012, Burkett et al. 2018). Likewise, in juveniles, SRGAP2 levels are inversely correlated with the song- and learning-related modules. Although a truncated form has not been identified in songbirds, these observations suggest that singing-driven inhibition of

SRGAP2 expression promotes learning-related plasticity mechanisms.

(b) Human intelligence-related genes and vocal learning

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A meta-analysis of 2,748 twin studies showed that human intelligence is a highly heritable trait (Polderman et al. 2015), although a consensus about the theory, definition or model of human intelligence is currently lacking (Conway & Kovacs 2015). A subsequent meta- analysis of 269,867 individuals identified eQTLs in 1016 genes associated with intelligence, as determined by various measures operationalized to index a common latent g factor capturing multiple dimensions of cognitive functioning (Savage et al. 2018). Expression quantitative trait loci (eQTLs) are single- nucleotide variants that alter the expression of a gene and are correlated to a phenotype. Notably, intelligence-related eQTL genes were most strongly expressed in medium spiny neurons, the major striatal cell type. Historically, studies of human cognition have focused primarily on cortical regions, but there is mounting interest in how the basal ganglia contribute to systems-level circuits for complex behavior, like speech and language.

We examined the overlap between these 1,016 intelligence- related genes and the 10,348 genes in the Area X juvenile vocal learning network. Among these 1016 intelligence-related genes, orthologues of 412 were found in our juvenile dataset (Burkett et al. 2018). The human genes include 360 variants in non-coding regions and 52 genes with non-synonymous variants in protein coding exons (electronic supplementary material, tables S2 and S3) (Burkett et al. 2018,

Savage et al. 2018). We next looked for enrichment of human intelligence-related genes within specific modules (Burkett et al. 2018). Rather than a ‘winner take all’ approach where each gene is assigned to only one module, we included all genes significant for a module as determined by the false discovery rate-corrected p-value, q, on the grounds that a single gene can play an important role in multiple modules. One module, darkgrey, was significantly enriched for human intelligence-related genes as measured by a hypergeometric gene-set enrichment test (p = 7.8 ×

10−3, R Gene Overlap package) (electronic supplementary material, figure S1) (Shen & Sinai

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2018). This was the only module correlated to overall song stability and one of several modules correlated with the morning modulation of song variability measured in the VI paradigm. As a control, we compared an equivalent number of brain-expressed chicken genes to this dataset and did not find any gene enrichment relationships, demonstrating that the association was not simply owing to using gene lists for brain-expressed genes (Xu et al. 2018).

To better understand the relationship between variability and human intelligence variants, we compared directional changes in the expression of the 860 intelligence-related gene eQTLs to directional expression changes in the 2064 genes correlated to juvenile song modulation. Based on the total distribution, we would expect to see 66% negatively correlated to variability, 34% positively correlated to variability and an even split for intelligence eQTL gene expression

(figure 1a). Instead, we found a bias for downregulation of genes associated with greater variability after 2 h of undirected singing (χ2 11.81, p = 0.008; figure 1b). This relationship was observed in the full dataset of 19 birds and preserved within a subset of three control birds

(Burkett et al. 2018). As a control, we compared the same intelligence genes to an equivalent number of genes not correlated to variability. Broadly construed, these parallels suggest links between cognition and the modulation of vocal variability in both humans and songbirds.

We interpret this to suggest that the ability to control variability is advantageous in the evolution of vocal learning. An athlete able to make very subtle modifications to their free throw is going to more precisely hone in and perfect their shot. This also suggests that functional validation of at least a subset of human intelligence genes could be carried out in songbirds using a vocal variability paradigm, particularly shared mechanisms in the basal ganglia.

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Five intelligence-related genes that were related to prolonged stereotypy were also among the convergent genes in the songbird and human striatum: ARPP21, CACNA2D3, CACNA1E,

FOXP1 and SMPD3. Below, we characterize three intelligence-related genes relevant for vocal learning.

(i) ARPP21

One intelligence-related gene selectively expressed in DA- innervated brain regions is cAMP-regulated phosphoprotein 21kD (ARPP21) (Girault et al. 1990). Computational modelling suggests that ARPP21 and DARPP-32, both downstream effectors of cyclic AMP, work together to coordinate the timing of calcium and DA signal integration in medium spiny neurons of the basal ganglia (Nair et al. 2016). Rehfeld et al. (2018) recently demonstrated that ARPP21 is important for regulating dendrite formation: knockdown leads to reduced dendritic complexity, whereas complexity is enhanced by ARPP21 overexpression. ARPP21 works as an mRNA binding protein to interact with eukaryotic translational initiation factor eIF4F to positively regulate transcription.

Interestingly, one intron of ARPP21 includes miR-128, a brain-enriched microRNA previously implicated in procedural learning and memory (Lin et al. 2011, Lek-Tan et al. 2013) and neurodevelopment (Bruno et al. 2011, Franzoni et al. 2015, Zhang et al. 2016). ARPP21 prevents miR-128-mediated repression of a subset of genes related to neural development, suggesting that the interplay between host gene and miRNA may be important for striatal function and cognition (Rehfeld et al. 2018). Molecular mechanisms that change over development are prime candidates for mechanisms that could be altered by heterochrony.

Expression of miR-128 increases over pre- and post-natal development, peaking in adulthood

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(Lin et al. 2011, Bruno et al. 2011, Franzoni et al. 2015, Zhang et al. 2016). This makes miR-128 and ARPP21 promising candidate mechanisms underlying the link between heterochrony and vocal learning.

Multiple lines of evidence suggest that ARPP21 and miR-128 are relevant for cognition in humans. Mutations affecting ARPP21 lead to intellectual disability (Liu et al. 2001, Marangi et al. 2013), and miR-128 is dysregulated in autism (Abu-Elneel et al. 2008, Wu et al. 2016).

ARPP21 is convergently regulated in the songbird and human striatum, suggesting a role in vocal learning. In songbirds, higher expression of ARPP21 correlates to higher variability after 2 h of undirected singing in juveniles, and ARPP21 levels decrease with singing in adults. Human eQTLs related to intelligence are associated with downregulation of ARPP21, following the broader trend described in figure 1c (Savage et al. 2018).

(ii) BCL11A

Among the gene specializations exclusive to vocal-learners identified by Pfenning et al.

(2014), two were independently identified as human intelligence-related genes: BCL11A and

PCDH17 (Savage et al. 2018). BCL11A encodes B-Cell CLL/Lymphoma 11A, a zinc-finger transcription factor and a putative member of the BAF SWI/SNF chromatin-remodeling complex

(Dias et al. 2016). BCL11A is an intellectual disability gene associated with language delays, dysarthria and childhood apraxia of speech (Dias et al. 2016, Soblet et al. 2018).

While loss of BCL11A is associated with severe speech and language deficits, BCL11A is upregulated in William’s Syndrome (WS), a developmental disorder associated with low non- verbal intelligence yet strikingly enhanced verbal fluency and speech production (Kimura et al.

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2018). WS results from haploinsufficiency of approximately 25 genes on chromosome 7 including Williams Syndrome Transcription Factor, also known as BAZ1B (Barnett et al. 2012).

The knockdown of BAZ1B during zebrafish development results in defects in neural crest migration and maintenance, a severe version of a milder phenotype posited to reflect a

‘domestication syndrome’ (Thomas & Kirby 2018, Wilkins et al. 2014). In this sense, BAZ1B may be connected to neural crest changes that occur during neotenization and influence a subset of specialized traits including verbal fluency and sociality that are enhanced in WS patients.

Notably, BCL11A was among the most highly interconnected genes in a transcriptional network generated from peripheral blood from WS patients (Kimura et al. 2018). In the juvenile

Area X, expression levels of BCL11A and BAZ1B positively correlate with levels of morning song modulation measured by the VI paradigm (Burkett et al. 2018). These parallels suggest links between the modulation of vocal variability and fluency in verbal production in both humans and songbirds.

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4. On the horizon: key questions for future research

Over the past decades, exciting progress has been made in relating the previously separate fields of language evolution and birdsong. Below, we propose new horizons of investigation based on these insights of convergent evolution, with a focus on the implications of songbird neurogenomics to evolutionary medicine and developmental disorders of speech and language.

(a) Can songbird genes be used to drive discovery of novel syndromic intellectual disability genes?

With more than 1200 human intellectual disability genes identified in our juvenile gene expression dataset alone (Burkett et al. 2018), it is increasingly apparent that convergent mechanisms govern vocal learning in songbirds and humans. Genes causing deficits in humans have been manipulated in songbirds to determine how they affect song, but could this approach also work both ways? We predict that in the future consortiums such as the undiagnosed diseases network (Ramoni et al. 2017) will capitalize on gene lists from the songbird model of vocal learning to pinpoint mutations in humans that could be validated for their causal effect.

(b) Can song-related gene expression modules be used to identify subtypes of autism?

Autism is a highly heterogeneous disorder that varies between affected individuals at every level of analysis—from underlying genetics to developmental trajectories (Geschwind &

Levitt 2007, Happe et al. 2006, Lai et al. 2013). New strategies are needed to identify subtypes of autism and accurately predict how a patient will respond to treatment. Lombardo et al. (2018) have pioneered this approach by linking gene expression in songbirds and humans to early 34

language outcome subgroups. They found that 902 of the 1267 differentially expressed genes that we identified in adult Area X (Burkett et al. 2018) were present in their autism gene-related modules. Future work could use songbird gene expression data to identify shared modules of genes that can best be targeted for therapeutic intervention. For example, modules that persist in adulthood could be targeted directly, whereas critical period modules may require pharmacological restoration of critical period plasticity (Gervain et al. 2013) to have an effect.

(c) Can songbirds model the negative symptoms of schizophrenia?

Neuropsychiatric disorders are highly polygenic, with autism and schizophrenia being particularly well correlated (Gandal et al. 2018). Given that the negative symptoms of schizophrenia center on deficits in social expression and verbal communication, songbirds may offer novel ways to model schizophrenia and identify interventions to normalize symptoms.

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5. Conclusion

Birdsong and human speech are highly convergent at multiple levels of analysis. A better understanding of the convergently evolved patterns of gene regulation that govern learned vocalization will accelerate progress towards deciphering the mechanistic basis for this complex behavior. As we enter the era of songbird transgenics, we predict that the similarities between human and songbird will also yield the development of better animal models of disorders affecting communication, which can then generate breakthrough progress in translational research.

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Figure 2-1: Pie charts illustrate relationships between human intelligence eQTLs and songbird gene expression

(a) The expected distribution of overlap if there is no correlation between intelligence gene expression and genes underlying vocal variability in songbirds. (b) The observed correlation showing that, among directional gene expression changes correlated to intelligence, genes correlated to higher variability in songbirds are more likely to be downregulated in humans. (c)

A control dataset showing a random distribution when using songbird genes with no correlation to vocal variability.

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Chapter III: Ginsenoside Rh2-mediated inhibition of miR-128 rescues deficits in learned vocal communication

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Abstract

The molecular mechanisms underlying learned vocal communication are poorly understood, and this limitation has been a major barrier in translational drug discovery for disorders like autism spectrum disorder (ASD). Social experience is required for vocal learning, suggesting that the early establishment of gene regulation patterns is necessary for learning to produce complex sequences of sounds. Using a songbird model, I have found that social isolation-induced deficits in learned vocal sequencing can be effectively recovered after critical period closure by the oral administration of ginsenoside Rh2 (GRh2; 10mg/kg), a steroid-like glycoside isolated from ginseng. In human glioma cell cultures, GRh2 treatment alters levels of miR-128, a brain-enriched microRNA that increases over development in songbirds and humans.

The same dose of GRh2 that remediates isolation-induced deficits decreases miR-128 in the basal ganglia song control region when given acutely to adult birds. Overall, this chapter introduces the zebra finch as a model system for translational drug discovery and shows that common ASD-related mechanisms governing vocal learning in songbirds and humans can be pharmacologically targeted to rescue disordered communication.

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Introduction

The total cost of ASD and intellectual disability in the United States is ~$236 billion per year, with the bulk arising from special education in childhood as well as residential accommodations and productivity loss in adulthood (Buescher et al. 2014).

Pharmacotherapeutics are urgently needed to enhance the efficacy of environmental interventions aimed at enabling the full acquisition of skills such as speech and language, which are critical for function in society. Currently, no pharmacological interventions exist for vocal communication deficits arising from ASD or intellectual disability. This is largely due to the lack of pre-clinical models with construct validity (Ruhela et al. 2015), since learned vocal communication cannot be modeled in rodents or cell culture.

Fortuitously, recent advances in songbird genetics show that gene expression in the striatum of songbirds is highly similar to that in humans, indicating evolutionary convergence of the molecular mechanisms that govern vocal learning (Pfenning et al. 2014). Within a recently published data set from our lab (Burkett et al. 2018) I identified ~1000 human ASD and intellectual disability-related genes that are expressed in the striatal song control nucleus, Area

X, during the developmental critical period for song learning. These genes are part of larger transcriptional modules that are correlated to key features of learned vocal behavior, including several that are significantly enriched for high confidence ASD-related genes. This suggests that the songbird is not only uniquely suited as a model for human speech, but for specific forms of communication deficits as well.

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Based on the finding that Ginsenside Rh2 (GRh2, Figure 3-3) alters levels of miR-128 in vitro (Wu et al. 2011) I predicted that GRh2 would have a therapeutic effect on vocal learning. In this chapter, I hypothesized that social isolates with deficits in sequence organization could be rescued by administering an oral dose of GRh2 for four weeks. In this study I exploit the advantages of the songbird model to characterize the mechanism-of-action of a novel candidate therapeutic as a first step to identifying new treatments for disorders of communication.

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Methods

Subjects

Subjects were juvenile male zebra finches (Taeniopygia guttata), beginning at 10 days post-hatch

(30d) and raised to ~150d. A total of 12 birds were raised in conditions of social isolation (see below), while an additional five birds were reared normally with both parents. After 120d birds were primarily housed in home cages with parents and siblings, unless being recorded individually in a sound attenuation chamber. The vivarium and recording chambers are humidity- and temperature-controlled (22°C) and on a 12 hr light: dark cycle with half hour

‘dusks’ and ‘dawns’. Birdseed, water, millet, cuttlebone, and grit were provided ad libitum.

Baths, hardboiled egg, and vegetables were provided weekly. Animal use was in accordance with the Institutional Animal Care and Use Committee at the University of California, Los Angeles and complied with the American Veterinary Medical Association Guidelines.

Experimental Timeline

Experiments were performed as schematized in Figure 3-4. Birds were isolated from the tutor at

10d and housed in a recording chamber with the mother and another female for care support.

Birds were isolated in a recording chamber at 35d. At 120d isolated birds were returned to their home cage with both parents. Six birds were treated by oral administration 10mg/kg GRh2

(Abcam ab142385) dissolved in DMSO and diluted in unsweetened almond milk and six siblings were treated with DMSO only in almond milk as a control. Birds were housed in their home cage for four weeks, with a break at two weeks to record changes in song. At ~150d birds were

42

returned to recording chambers for behavioral experiments. On the final day birds were allowed to sing for two hours and then sacrificed.

Audio Recording.

Recordings were collected in sound attenuation chambers. Songs were digitally recorded (Sound

Analysis Pro; SAP; Tchernichovski et al., 2000) using a PreSonus FirePod or Audiobox (44.1 kHz sampling rate; 24-bit depth) and stored uncompressed.

Song Analysis and Statistics

Enrichment Map

1,215 predicted miR-128 targets from TargetScan (Agarwal et al. 2015) were analyzed for pathway enrichment using g:Profiler (Raudvere et al. 2019), including the biological processes of

Gene Ontology (Ashburner et al. 2000, The Gene Ontology Consortium 2021) and molecular pathways of Reactome (Jassal et al. 2020). The resulting Generic Enrichment Map file was visualized using the Enrichment Map application (Merico et al. 2010) in Cytoscape (Shannon et al. 2003).

Hypergeometric Overlap Gene Enrichment Test

Gene enrichment analysis (Figure 1A) was performed using the GeneOverlap package in R

(https://github.com/shenlab-sinai/geneoverlap). The Simons Foundation Autism Research

Initiative (SFARI Gene) database provided a comprehensive list of autism candidate genes

(Abrahams et al., 2013; gene.sfari.org). My analysis includes all available genes from the SFARI database SFARI genes found in songbird Area X (Burkett et al., 2018, Hilliard et al., 2012),

43

CHD8 targets at enhancers [CHD8- enhancers] and promoters [CHD8- promoters] in human fetal cortex (Cotney et al. 2015), Fragile-X mental retardation protein target genes [FMRP] from

P11–P25 mouse whole brain extracts (Darnell et al. 2011), and genes differentially expressed in

Down Syndrome [Down Syndrome] postmortem brain using samples from multiple regions spanning mid-fetal development to adulthood (Olmos-Serrano et al. 2016) as a negative control.

In these datasets I tested for enrichment in miR-128 target genes, mRNA binding protein (and miR-128 host gene), ARPP21 target genes, and genes targeted by both miR-128 and ARPP21

(Rehfeld et al., 2018). The background was limited to 14,313 brain-expressed genes. Statistically significant gene overlap is calculated using Fisher's Exact Test followed by FDR correction.

Sequence Stereotypy

Sequence stereotypy was measured by calculating syllable transition probability using Vocal

Inventory Clustering Engine (VoICE; [Burkett et al., 2015]; https://github.com/zburkett/VoICE).

Syllables were hand segmented and a SAP Feature Batch was generated. Then VoICE was used to cluster syllables by similarity. Once syllable types were defined, VoICE was used to calculate syllable transition probability and generate a stereotypy score.

microRNA Assay

Total RNA (including small RNA) was isolated using a TRIzol Reagent kit (Zymo Research).

Primers specific for miR-128 and U6 snRNA as a loading control, were used to reverse- transcribe microRNA using and the TaqMan microRNA reverse transcription kit (Applied

44

Biosystems). Expression was analyzed using the TaqMan microRNA assay kit (Applied

Biosystems) according to the manufacturer’s instructions.

45

Results

Relevance of miR-128 to vocal communication

To investigate the potential role of miR-128 in social communication, I first explored the functional role of miR-128. Using the database TargetScan (Agarwal et al. 2015) I identified

1,215 predicted miR-128 target genes. These targets were then further analyzed for pathway enrichment using g:Profiler (Raudvere et al. 2019), including the biological processes of Gene

Ontology (Ashburner et al. 2000, The Gene Ontology Consortium 2021) and molecular pathways of Reactome (Jassal et al. 2020). This tests for the enrichment of groups of genes that serve a specific biological function, providing evidence that those functions may be implicated in the sample data.

The results from g:Profiler are summarized in an Enrichment Map (Merico et al. 2010,

Shannon et al. 2003) shown in Figure 3-1. Major roles of the miR-128 targets included “RNA metabolic process,” “Regulation of phosphorylation,” “Axon projection,” “Embryonic and neural development,” and “Cell migration.” These results are supported by previous studies showing that miR-128 regulates neural proliferation by targeting the nonsense-mediated mRNA decay pathway (Bruno et al. 2011).

I next sought to determine if miR-128 target genes are broadly implicated in ASD. Given that miR-128 and its host gene product, the mRNA binding protein ARPP21, promote downregulation or upregulation of shared targets (Rehfeld et al. 2018), I also checked for enrichment of ARPP21 target genes as well as genes targeted by both miR-128 and ARPP21.

Gene enrichment analysis revealed that genes disrupted in ASD broadly, as well as syndromic

46

forms of ASD, were all enriched for miR-128 targets (Figure 3-2). In contrast, genes differentially expressed in Down syndrome, a syndromic form of intellectual disability that is unrelated to ASD, were not enriched in miR-128 targets.

I next sought to investigate the role of activity-dependent miR-128 regulation in an animal model of learned vocal communication. In adult zebra finches, miR-128 levels in Area X were negatively correlated to the number of motifs a bird sang within a two-hour period prior to tissue collection (Figure 3-3).

Ginsenoside Rh2 in vocal sequence organization

Based on previous results showing that GRh2 (Figure 3-4) enhances cognition (Yang et al. 2009, Hou et al. 2013), I hypothesized that GRh2 could potentially rescue deficits in communication in songbirds. I measured miR-128 levels in Area X from adult birds acutely treated with oral GRh2 or vehicle alone (Figure 3-5). An acute dose of oral GRh2 decreases miR-128 in Area X of adults, pointing to a mechanism-of-action for the potential enhancement of learned vocalization.

I tested the hypothesis that GRh2 remediates learned vocal communication deficits in songbirds as follows: I used a social isolate paradigm to generate songbirds with communication deficits. Isolated birds were returned to their social environment, including tutors, well after the normal critical period closure then treated with oral GRh2 (solubilized with DMSO then diluted in almond milk) or vehicle for four weeks. Birds that received vehicle alone failed to organize their syllables into stable sequences, whereas GRh2 treatment enhanced syllable sequencing (Fig

3-6:7).

47

Discussion

As a whole, these results suggest that the molecular mechanisms underlying speech and language can be pharmacologically targeted to accelerate the development of novel therapeutics for disorders like ASD and intellectual disability. This study is the first to identify a novel candidate therapeutic that targets a microRNA to ameliorate communication deficits. Given their role as master regulators of processes relevant to ASD and vocal learning, microRNAs may be tractable targets for future therapeutic design.

It is clear that miR-128 plays a key role in development, but many of these developmental genes appear to have been evolutionarily co-opted for mechanisms of vocal communication functioning throughout the lifespan as well. Although several pioneering studies from the Stephanie White lab have begun to characterize activity-dependent gene regulation in learned vocalization, more work is needed to characterize these mechanisms and how dysregulation during development may be partially rescued postnatally by correcting processes that play an ongoing role in cognition.

In this chapter I establish the zebra finch as a preclinical model for drug discovery and drug development. Focusing on communication deficits pathways that can be modeled in behaving animals will expedite the development of much needed therapeutics to enhance the efficacy of speech therapy and improve patient quality of life.

48

Figure 3-1: miR-128 regulates gene networks essential for neural development and function

Embryonic neural development Axon projection

Synaptic signaling

Growth factor signaling Cell migration

Regulation of phosphorylation

RNA metabolic process

Predicted miR-128 targets were analyzed for pathway enrichment and then clustered by similarity to generate an enrichment map. Cluster annotations are summarized with adjacent labels in black.

49

Figure 3-2: miR-128 targets are enriched in ASD-related gene datasets

Heat map shows gene enrichment analysis for miR-128 target genes, ARPP21 target genes, or both [Overlap] in autism-related gene lists: all autism risk genes from the Simons Foundation

Autism Research Initiative (SFARI) database [SFARI (total)], SFARI genes expressed in Area X

[SFARI (Area X)], CHD8 targets at enhancers [CHD8- enhancers] and promoters [CHD8- promoters], Fragile-X mental retardation protein target genes [FMRP targets]. Neural genes differentially expressed in Down syndrome [DS DEGs] and all genes expressed in the adult zebra finch basal ganglia [All ZF BG expressed genes] are included as negative controls. FDR corrected p-value indicated by * p<5.00E-02, ** p<5.00E-12.

50

Figure 3-3: Activity-dependent regulation of miR-128 in adult zebra finch Area X

miR-128 undergoes activity-dependent regulation in the vocal communication region of the basal ganglia in adult songbirds (R2=0.695).

51

Figure 3-4: Molecular structure of GRh2

Diagram showing the steroid-like structure of GRh2.

52

Figure 3-5: Systemic Ginsenoside Rh2 decreases miR-128 in Area X

Oral administration of GRh2 decreases miR-128 in Area X (p = 0.013, resampling t-test, N=4 per group).

53

Figure 3-6: Social isolation and Ginsenoside Rh2 rescue timeline schematic

Timeline of experimental procedures. Developmental critical periods are shown in blue; critical period closure is shown in red. Green indicates the window of oral GRh2 or vehicle administration.

54

Figure 3-7: Exemplar motifs showing changes in sequence stereotypy in a single bird before and after GRh2 rescue

A) Prior to treatment

B) Post-treatment

Sequencing of individual syllables (indicated by black letters underneath) in a single bird was markedly improved after four weeks of treatment with oral GRh2 (10mg/kg).

55

Figure 3-8: GRh2 rescues deficits in sequence stereotypy in social isolates.

GRh2 enhances song sequencing in social isolates as analyzed by Vocal Inventory Clustering

Engine (VoICE; Burkett et al., 2015). VoICE weighted stereotypy scores analysis indicate that only the GRh2 group significantly improved syllable sequencing after treatment (p=0.038, paired resampling t-test, N=6 per group for GRh2 and Vehicle, N=5 for Typically reared and Iso-No tutor). GRh2=Social isolate treated with GRh2 while receiving a tutor in adulthood;

Vehicle=Social isolate treated with almond milk and DMSO while receiving a tutor in adulthood; Typically reared=no social isolation or treatment; Iso-NO tutor=Social isolate not treated or tutored.

56

Chapter IV: Deficits in learned vocalization sequencing are rescued by inhibition of microRNA-128

57

Abstract

Novel approaches for autism drug discovery are urgently needed to treat communication deficits in severely affected patients. The brain-enriched microRNA-128 is aberrantly upregulated in neural postmortem tissue from autism patients (Wu et al. 2016). However, the potential therapeutic effect of targeting miR-128 to treat vocal communication deficits is currently unknown. Social deprivation during development leads to severe deficits in vocal communication. Here I show that inhibition of miR-128 after critical period closure restores the capacity to improve song sequence organization. Focal siRNA-mediated inhibition of miR-128 in the striatal song control nucleus Area X was sufficient to rescue deficits in vocal communication. I conclude that miR-128 is a promising potential therapeutic target for enhancing the efficacy of speech therapy.

58

Introduction

Learned vocalization is the central means of social communication in songbirds and humans. Children who experience social deprivation during development fail to acquire normal speech and language skills and present with autism spectrum disorder (ASD)-like deficits in social communication (Levin et al. 2015). Social deprivation-induced deficits can be easily modeled in the zebra finch songbird by rearing juveniles in the absence of a song tutor. Such birds develop abnormal ‘isolate’ song (White, 2001).

One of the most highly brain expressed microRNAs, miR-128, is elevated in postmortem neural tissue from ASD patients (Wu et al. 2016), suggesting that it may play a role in vocal communication. Loss of Fragile-X mental retardation protein (FMRP) leads to elevation of miR-

128 (Men et al. 2020), indicating that increased miR-128 may be a convergent mechanism shared between idiopathic and syndromic forms of ASD. Previous work has also linked miR-128 to learning and memory (Lin et al. 2011, Lek-Tan et al. 2012), further supporting this potential connection.

In Chapter III I show that administration of a compound that reduces miR-128 in the basal ganglia, GRh2, rescues isolation-induced deficits in sequence stereotypy. While these results were exciting, the question remained whether GRh2 was acting specifically within the basal ganglia to effect these changes. A second remaining question was whether these changes were due to changes in miR-128 or alternatively some other off-target effect. To answer these questions, I adapted a previously published miR-128 siRNA construct (Lin et al. 2011) to inhibit miR-128 directly within Area X. 59

Methods

Subjects

Subjects were juvenile male zebra finches (Taeniopygia guttata), beginning at 30 days post-hatch

(30d) and raised to 75d. A total of 20 birds underwent stereotaxic neurosurgeries targeting Area

X bilaterally (see below). Ten birds from seven breeding pairs were treated by focal injection of an AAV bearing a miR-128 sponge sequence and 10 siblings were treated with a scrambled sequence as a control. Birds were primarily housed in home cages with parents and siblings, unless being recorded individually in a sound attenuation chamber. The vivarium and recording chambers are humidity- and temperature-controlled (22°C) and on a 12 hr light: dark cycle with half hour ‘dusks’ and ‘dawns’. Birdseed, water, millet, cuttlebone, and grit were provided ad libitum. Baths, hardboiled egg, and vegetables were provided weekly. Animal use was in accordance with the Institutional Animal Care and Use Committee at the University of

California, Los Angeles and complied with the American Veterinary Medical Association

Guidelines.

Experimental Timeline

Experiments were performed as schematized in Figure 3-1. Birds were isolated from the tutor at

10d and housed in a recording chamber with the mother and another female for care support.

Birds were isolated in a recording chamber at 35d. At 120d surgeries were performed and the birds were allowed to recover for two weeks, then returned to their home cage with both parents.

Birds were housed in their home cage for four weeks, with a break at two weeks to record changes in song. At ~165d birds were returned to recording chambers for behavioral

60

experiments. On the final day birds were allowed to sing for two hours and then tissue was collected.

Audio Recording

Recordings were collected in sound attenuation chambers. Songs were digitally recorded (Sound

Analysis Pro; SAP; Tchernichovski et al., 2000) using a PreSonus FirePod or Audiobox (44.1 kHz sampling rate; 24-bit depth) and stored uncompressed.

Sequence Stereotypy

Sequence stereotypy was measured by calculating syllable transition probability using Vocal

Inventory Clustering Engine (VoICE; Burkett et al., 2015); https://github.com/zburkett/VoICE).

Syllables were hand segmented and a SAP Feature Batch was generated. Then VoICE was used to cluster syllables by similarity. Once syllable types were defined, VoICE was used to calculate syllable transition probability and generate a stereotypy score.

Stereotaxic Surgery.

At 30d, neurosurgeries were performed as described (Burkett et al., 2018, Heston and White,

2015). Briefly, subjects were administered preoperative meloxicam and then anesthetized using

2–4.5% isoflurane in pure oxygen. A custom-built avian stereotaxic apparatus was used to target

Area X at the following coordinates: 45 ̊ head angle, 5.15 mm rostral of the bifurcation of the midsagittal sinus, 1.60 mm lateral of the midline, at a depth of 3.25 mm. Virus was injected bilaterally using a Drummond Nanoject II via a glass micropipet (~40 um inner diameter). Six

50.6 nL injections were performed in Area X at a depth of 3.25 mm, 3.20 mm, 3.15 mm, 3.10

61

mm, 3.05 mm, and 3.0 mm to maximize the area transfected. Injections were performed with a

15 s pause to allow virus to disperse, with a final 5-minute wait before removing the micropipet.

The scalp incision was closed using Vetbond (3M, St. Paul, MN, USA), then covered with dental cement (get brand and location). Birds remained on oxygen for ~2 min until alert, then returned to their home cages.

siRNA Constructs and Viral Vectors.

An siRNA sponge previously shown to inhibit miR128 function (Lin et al. 2011) and a scrambled control construct were adapted for use in zebra finches and cloned into an AAV1 viral vector (Virovek, Hayward, CA). The sponge consisted of three “bulged” miR-128 binding site sequences separated by 4nt spacers. The scramble construct differed only in that the control sequence contained a standard scramble sponge provided by Virovek. Both sponge and scramble sequences were driven by the U6 promoter. Both viruses contained a GFP reporter sequence driven by a 100bp supercore promoter (SCP) designed by Rachael Neve (Mass, General

Hospital), followed by a 72bp SV40 intron to enhance transcription. Both viral titers were 10^13 vg/ml. 303.6 nl of virus were delivered to each hemisphere in six injections of 50.6 nl spaced 5 um apart.

62

Results

I hypothesized that using an antisense construct to decrease miR-128 in Area X (Figure

4-1) would recapitulate the therapeutic effects of GRh2 (Chapter III) and at least partially restore the capacity for vocal learning. To test this, I isolated zebra finches during the critical period for vocal learning, then injected them as adults with either an AAV construct encoding either a miR-

128 siRNA sponge or scramble sequence control. Two weeks later, well past critical period closure, I re-exposed birds to their tutors for 28 days (Figure 4-2).

As in previous literature, developmental isolation caused deficits in syllable sequence organization. Focal inhibition of miR-128 in adult birds who received the miR-128 sponge normalized their sequencing deficits, whereas birds who received the scramble control failed to substantially alter their song (Figure 4-3:4). Interestingly, although both groups had a stutter relative to typically reared controls prior to treatment, the group that received the miR-128 siRNA construct exhibited a reduction in stutter (Figure 4-3:4).

63

Discussion

Zebra finches are the key model system for studying how activity-dependent gene regulation underlies developmental vocal communication learning. Mechanisms that limit or constrain plasticity for vocal learning are particularly of interest, because premature critical period closure could be a major contributor to communication deficits in humans. In this chapter

I show that focal reduction of miR-128, which is aberrantly elevated in ASD patients, rescues deficits in complex vocal sequences in adult zebra finches, well past the normal closure of the critical period for vocal learning.

One interpretation of the results presented here and in Chapter III is that miR-128 contributes to the closure of the developmental critical period for learned vocal communication.

Therefor pharmacologically or genetically decreasing miR-128 restores the capacity to learn more normalized vocal communication. This raises the possibility that in at least some ASD patients, early elevation of miR-128 may reduce the capacity to learn speech and language. It is exciting to imagine that there may be patients whose communication could be improved by simply removing molecular barriers to enhance the efficacy of speech therapy. Future work in

ASD patients and songbirds will help close this knowledge gap.

64

Figure 4-1: Region of interest (Area X) and miR-128 antisense viral construct

(Left) Diagram showing viral targeting of Area X. (Right) Schematic characterizing the antisense construct used to inhibit miR-128. Adeno-associated virus 1 vector (AAV1), inverted terminal repeats to maintain construct integrity (ITR). The type III RNA polymerase III promoter U6 was used to drive expression of the miR-128 siRNA sponge. A proprietary supercore promotor designed by Rachael Neve was used to drive GFP.

65

Figure 4-2: Focal miR-128 inhibition in Area X rescues deficits in learned vocal sequencing

Timeline schematic for developmental social isolation, surgeries, and tutor exposure.

66

Figure 4-3: Exemplar motifs and transition probability diagrams showing changes in sequence stereotypy in a single isolate before and after focal miR-128 inhibition.

A) Prior to treatment

turquoise

purple

black

white

aliceblue

B) Post-treatment

turquoise

purple

black

white

aliceblue

Left side shows exemplar motifs for a social isolate. Top two motif exemplars are from song prior to miR-128 inhibition and tutor exposure. Top motif shows a stutter at syllable “C”. Bottom two motif exemplars are from after these interventions. Right side shows syllable transition probability diagram for same bird before (Top) and after (Bottom) miR-128 inhibition and tutor exposure.

67

Figure 4-4: Focal miR-128 inhibition in Area X rescues deficits in sequence stereotypy in social isolates.

Analysis of song sequence organization in social isolates and typically reared controls before and after viral intervention and tutor exposure. The y axis shows the syllable transition probability score normalized by average stutter. Top asterisk compares isolates who received the sponge before and after the interventions (p=0.03, bootstrap t-test). Right asterisk compares post- intervention scores between sponge isolates and scramble isolates (p=0.04, bootstrap t-test).

68

Chapter V: siRNA-mediated inhibition of miR-128 enhances vocal sequence organization in juveniles

69

Abstract

Many diverse molecular mechanisms are thought to contribute to the development of learned vocal behavior. The brain-enriched microRNA-128 increases over pre- and post-natal development, peaking in adulthood (Lek-Tan et al. 2012, Bruno et al. 2011, Rehfeld et al. 2018).

Prior chapters showed that pharmacologic (Chapter III) or focal inhibition of miR-128 in the basal ganglia (Chapter IV) rescue deficits in learned vocalization in adult songbirds. However, the effects of inhibiting miR-128 during juvenile development have yet to be explored. Here I show that inhibition of miR-128 prior to critical period closure enhances song sequence organization in typically reared juvenile zebra finches.

70

Introduction

The previous studies described in Chapters III and IV show that miR-128 may be a viable therapeutic target for treating communication deficits in autism spectrum disorder (ASD).

However, an effective therapeutic intervention for communication deficits would likely need to be administered as early as possible to have a maximal effect. Thus, an ideal medication would need to be safe to use in juveniles and have no adverse effects on children who may not go on to develop ASD.

In this study, I characterize the effects of siRNA-mediated inhibition of miR-128 in the basal ganglia of healthy juvenile birds raised under normal conditions (Figure 5-1). Surgeries were performed at 30 days post-hatch, prior to the canonical definition of the onset of the sensorimotor critical period for vocal learning. Sibling-matched controls were injected with an

AAV vector containing a miR-128 antisense sponge (Figure 5-2) or a scrambled sequence as a control. Both siblings were then raised in their home cage with both parents to age 75d, at which point behavioral analyses were conducted.

71

Methods

Subjects

Subjects were juvenile male zebra finches (Taeniopygia guttata), beginning at 30 days post-hatch

(30d) and raised to 75d. A total of 20 birds underwent stereotaxic neurosurgeries targeting Area

X bilaterally (see below). Ten birds from seven breeding pairs were treated by focal injection of an AAV bearing a miR-128 sponge sequence and 10 siblings were treated with a scrambled sequence as a control. Birds were primarily housed in home cages with parents and siblings, unless being recorded individually in a sound attenuation chamber. The vivarium and recording chambers are humidity- and temperature-controlled (22°C) and on a 12 hr light: dark cycle with half hour ‘dusks’ and ‘dawns’. Birdseed, water, millet, cuttlebone, and grit were provided ad libitum. Baths, hardboiled egg, and vegetables were provided weekly. Animal use was in accordance with the Institutional Animal Care and Use Committee at the University of

California, Los Angeles and complied with the American Veterinary Medical Association

Guidelines.

Experimental Timeline.

Experiments were performed as schematized in Figure 5-1. Subjects were housed in their home cage with both parents except during short periods to record song individually. At 30d surgeries were performed and the birds were allowed to recover in their home. At ~70d birds were returned to recording chambers for behavioral experiments. On the final day birds were allowed to sing for two hours and then tissue was collected.

72

siRNA Constructs and Viral Vectors

An siRNA sponge previously shown to inhibit miR128 function (Lin et al. 2011) and a scrambled control construct were adapted for use in zebra finches and cloned into an AAV1 viral vector (Virovek, Hayward, CA). The sponge consisted of three “bulged” miR-128 binding site sequences separated by 4nt spacers. The scramble construct differed only in that the control sequence contained a standard scramble sponge provided by Virovek. Both sponge and scramble sequences were driven by the U6 promoter. Both viruses contained a GFP reporter sequence driven by a 100bp supercore promoter (SCP) designed by Rachael Neve (Mass, General

Hospital), followed by a 72bp SV40 intron to enhance transcription. Both viral titers were 10^13 vg/ml. 303.6 nl of virus were delivered to each hemisphere in six injections of 50.6nl spaced 5 um apart.

Stereotaxic Surgery.

At 30d, neurosurgeries were performed as described (Burkett et al., 2018, Heston and White,

2015). Briefly, subjects were administered preoperative meloxicam and then anesthetized using

2–4.5% isoflurane in pure oxygen. A custom-built avian stereotaxic apparatus was used to target

Area X at the following coordinates: 45 ̊ head angle, 5.15 mm rostral of the bifurcation of the midsagittal sinus, 1.60 mm lateral of the midline, at a depth of 3.25 mm. Virus was injected bilaterally using a Drummond Nanoject II via a glass micropipet (~40 um inner diameter). Six

50.6 nL injections were performed in Area X at a depth of 3.25 mm, 3.20 mm, 3.15 mm, 3.10 mm, 3.05 mm, and 3.0 mm to maximize the area transfected. Injections were performed with a

15 s pause to allow virus to disperse, with a final 5-minute wait before removing the micropipet.

The scalp incision was closed using Vetbond (3M, St. Paul, MN, USA), then covered with dental

73

cement. Birds remained on oxygen for ~2 min until alert, then returned to their home cages.

Audio Recording

Recordings were collected in sound attenuation chambers. Songs were digitally recorded (Sound

Analysis Pro; SAP; Tchernichovski et al., 2000) using a PreSonus FirePod or Audiobox (44.1 kHz sampling rate; 24-bit depth) and stored uncompressed.

Song Analysis and Statistics

Sequence Stereotypy

Sequence stereotypy was measured by calculating syllable transition probability using Vocal

Inventory Clustering Engine (VoICE; Burkett et al., 2015; https://github.com/zburkett/VoICE).

Syllables were hand segmented and a SAP Feature Batch was generated. Then VoICE was used to cluster syllables by similarity. Once syllable types were defined, VoICE was used to calculate syllable transition probability and generate a stereotypy score.

NS-UD Paradigm

'NS-UD' experiments were performed as follows and as described in Figure 5-5, as well as prior publications (Burkett et al., 2018, Heston and White, 2015; Miller et al., 2010; Chen et al.,

2013). Briefly, on one morning, individual birds were distracted from singing during the first two hours after lights on, then recorded to capture song after the non-singing (NS) state. No birds sang >10 motifs in this condition. Prior work shows that the gene expression profiles of NS birds are similar to those of birds that voluntarily did not sing in enclosed chambers, suggesting that this paradigm does not induce a stress response. NS songs were compared to songs from the

74

same birds that were recorded on a different day following two hours of singing in a single- housed chamber (undirected singing: UD). Syllables from these two conditions were hand segmented and acoustic features were quantified in a SAP Feature Batch. Syllables were clustered using VoICE. To calculate the effect size, I used the formula (NS-UD)/NS+UD).

Tutor Similarity

As a methodological note, each non-isolated control sibling remained in an open space rather than a sound attenuation chamber during development, so tutor similarity scores would be expected to be lower given that the tutor song was not the only template in the environment. The

SAP Similarity Batch was used to compare the acoustic similarity of each pupil's song to his tutor (Tchernichovski et al. 2000). Ten tutor motifs were compared to 20 renditions of pupil song using asymmetric comparisons. The mean of these comparisons is reported as the final tutor similarity score. A two-tailed paired t-test with resampling that compared tutor similarity between miR-128 knockdown birds and scramble controls was not significant, as depicted in

Supplementary Figure 5-S3.

Calculation of Song Tempo

Tempo was defined as the number of syllables per motif divided by the duration of the motif in seconds. Tempo scores were averaged across 20 motifs for each bird.

75

Results

Syllable sequence organization was significantly improved in juvenile birds who received the miR-128 antisense construct relative to sibling-matched controls raised under normal conditions (Figure 5-3:4). To further characterize the effects of the miR-128 inhibitor on learned vocal behavior I employed the “NS-UD paradigm” developed by the White lab (Figure 5-5,

Burkett et al., 2018, Heston and White, 2015; Miller et al., 2010; Chen et al., 2013). When a bird sings by himself (undirected) the spectral features of birdsong become more variable in correlation to the amount of time the bird has been singing (Teramitsu & White 2006). On one day a bird was recorded after two hours of singing, while on a separate day the same bird was recorded after two hours of non-singing. Analysis of the coefficient of variation for the spectral features on both days showed that birds who received the sponge had more variable song in the

UD versus NS condition, in keeping with previous findings. However, the miR-128 antisense birds did not show this same increase in spectral feature variability. Results reached significance for amplitude and entropy and approached significance for pitch goodness (Figure 5-6).

Motif level analysis of syllable sequence stereotypy did not uncover differences between the NS and UD conditions; however, it did replicate the results in Figure 5-4 with independent data sets, showing that the result was consistent across days and robust even after two hours of singing when song is typically less stereotyped (Figure 5-S1). Additionally, I characterized song tempo (Figure 5-S2) and similarity to tutor (Figure 5-S3), but these results were not significant.

76

Discussion

In this chapter I show that siRNA-mediated inhibition of miR-128 enhances syllable sequence organization in juvenile songbirds. Interestingly, in the birds that received the miR-128 siRNA construct I did not see changes in the NS-UD paradigm that are typically associated with prolonged singing. This raises interesting questions about the evolution of learned vocal communication. Perhaps the evolution of stereotyped vocal sequence production starts with a smaller window when an organism can perform this behavior, and then evolution selects for individuals with greater endurance for producing learned vocalizations in a precise manner.

Although some variability is necessary for any form of motor learning (Fee 2014, Chapter II), it may be that the required changes can be more subtle and decrease as the bird learns, similarly to how juvenile birds produce more variable song than adults.

While previous chapters focused on rescuing isolation-induced deficits in adult birds

(Chapter III, Chapter IV), this study provides evidence that therapeutically targeting miR-128 has beneficial effects on vocal learning in juveniles. Inhibition of miR-128 may be a safe approach for future therapeutic design for children with ASD and other developmental disabilities that impair speech and language development.

77

Figure 5-1: Timeline schematic

Sensorimotor Critical Period → Sensory Critical Period miR-128 knockdown or scramble control Hatch 15 30 45 60 75 days Surgery Record Record Record song song song & Sacrifice

Timeline of experimental procedures. Developmental critical periods are shown in blue.

78

Figure 5-2: Viral construct

Schematic characterizing the antisense construct used to inhibit miR-128. Adeno-associated virus 1 vector (AAV1), inverted terminal repeats to maintain construct integrity (ITR). The type

III RNA polymerase III promoter U6 was used to drive expression of the miR-128 siRNA sponge. A proprietary supercore promotor designed by Rachael Neve was used to drive GFP.

79

Figure 5-3: Exemplar motifs

A) Tutor

B) Pupil (miR-128 AS)

C) Pupil (Scramble)

Exemplar motifs from an adult male (Tutor), a juvenile son who received the miR-128 antisense construct (Pupil (miR-128 AS)), and another juvenile son from the same clutch who received the scramble control construct (Pupil (Scramble)).

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Figure 5-4: Sequence stereotypy data Stereotypy Score 75d (Sibling matched)

1.0 *

0.9

0.8

0.7

0.6 VoICE Stereotypy Score Stereotypy VoICE 0.5 miR-128 AS Scramble

Analysis of song sequence organization in 75d sibling-matched miR-128 antisense and scramble control birds (N=10/group), p=0.014, Wilcoxon signed-rank test.

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Figure 5-5: NS-UD paradigm schematic

Schematic illustrating the Non-singing vs Singing paradigm. On separate days a test subject is either allowed to sing for four hours straight (undirected to a female, or UD) or is prevented from singing for two hours and then allowed to sing for the subsequent two hours. Previous studies from this lab have found that variability is positively correlated to the amount of time singing

(Burkett et al. 2018, Heston & White 2015).

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Figure 5-6: CV for spectral features of NS-UD data

A) CV Amplitude * 0.10

0.05 Coefficient of variation 0.00 NS UD NS UD miRmiR-128-128 ASKD Scramble Scramble B) CV Entropy

0.25 *

0.20

0.15

0.10

0.05 Coefficient of variation 0.00 NS UD NS UD miRmiR-128-128 KDAS Scramble Scramble C) CV Pitch Goodness

0.3 p=0.076

0.2

0.1 Coefficient of variation 0.0 NS UD NS UD miRmiR-128-128 ASKD Scramble Scramble 83

Coefficient of variation (CV) of spectral features of song following two hours of singing (UD) or silence (NS). Syllables from five miR-128 antisense birds (N=26) and five sibling-matched scramble controls (N=25) were analyzed, *p<0.05 bootstrap t-test. A) CV Amplitude. B) CV

Entropy. C) CV Pitch goodness.

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Figure 5-S1: NS-UD Sequence Stereotypy Data NS-UD Motif Stereotypy (Sibling matched)

1.0 * * miR-128 AS 0.9 Scramble

0.8

0.7

0.6 VoICE Stereotypy Score Stereotypy VoICE 0.5 NS UD

Sequence stereotypy results in NS and UD conditions for miR-128 antisense (AS) and Scramble sibling-matched controls.

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Figure 5-S2: Tempo data Tempo 75d (Sibling matched)

0.20

0.15 Tempo

0.10

miR-128 AS Scramble

Tempo data for sibling matched miR-128 antisense and scramble control birds, N=10/group (not significant).

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Figure 5-S3: Tutor Similarity data Tutor Similarity 75d (Sibling matched)

8

6

4 Motif Identity

2 miR-128 AS Scramble

Tutor similarity data for sibling-matched miR-128 antisense and scramble control birds

(N=8/group), not significant.

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Chapter VI: Conclusions

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The broad aim of my dissertation research has been to better understand how the convergent evolution of vocal learning in songbirds and humans can be leveraged to identify new therapeutic targets in human disorders of vocal motor learning. These questions are first introduced in Chapter I. In Chapter II I delve into the mechanistic role of key genes that show human-specific evolutionary changes and are also important for vocal learning in songbirds. In

Chapter III I show that the capacity to improve developmental vocal communication deficits can be rescued in adulthood by administering a compound that inhibits miR-128, GRh2. Chapter IV recapitulates the effects of GRh2 by focal inhibition of miR-128 in the basal ganglia. In Chapter

V I show that this same construct enhances sequence stereotypy in typically reared juvenile songbirds. In the Appendix I include collaborative work on the differential contributions of

FoxP2 splice variants to vocal learning. In this chapter I describe a broader model for the role of miR-128 in vocal learning and microglial/immune signaling in ASD. It’s important to note that this mechanism is likely one of many roles miR-128 plays in vocal development.

Previous work shows that miR-128 is upregulated in the postmortem tissue ASD patients

(Wu et al. 2016). In younger ASD patients aged 1-4, miR-128 targets are downregulated in leukocytes (Gazestani et al. 2019), suggesting that the aberrant elevation of miR-128 happens early on, and could even be a driver of ASD pathogenesis. Here I will describe a model for the role of miR-128 in vocal communication over the course of the lifetime of an individual with

ASD.

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Prenatal contributions of miR-128 to cell fate

It’s widely accepted that ASD is a neurodevelopmental disorder that begins prenatally

(Courchesne, Gazestani, & Lewis 2020, Courchesne et al. 2019). Cell proliferation, neurogenesis, and cell fate are all disrupted in prenatal ASD pathogenesis. Evidence suggests that miR-128 plays a surprisingly broad role in contributing to these aspects of pathology.

Studies using induced pluripotent stem cells (IPSCs) generated from ASD patients with brain overgrowth show that neural precursor cell (NPC) proliferation is enhanced and that these

NPCs differentiate earlier than they do in controls (Marchetto et al. 2017). A second study generating IPSCs from ASD patients without macrocephaly found similar rates of proliferation between ASD patients and controls but did find dysregulation that is typically indicative of premature neuronal maturation (Adhya et al. 2020).

One proposed mechanism for how miR-128 disrupts neurogenesis focuses on the nonsense mediated mRNA decay (NMD) pathway. In a paper ahead of its time (2011), Miles

Wilkinson’s team showed that miR-128 directly regulates NMD. NMD was first discovered to be responsible for degrading transcripts with premature stop codons, but subsequent studies show that it is a key regulator of alternative splicing in regular transcripts as well. By targeting core

NMD factors, UPF1 and MLN51, miR-128 can upregulate transcripts by preventing their normal degradation by NMD.

miR-128 increases over prenatal development, with a large increase between embryonic day (E) 9.5 and E14.5 in the mouse brain (Bruno et al. 2011). Elevated miR-128 promotes

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neuronal differentiation, triggering both neuron-specific differential gene expression as well as alternative splicing. Genes upregulated by miR-128 are associated with neuron-specific processes, while those downregulated are associated with cell proliferation, indicating that miR-

128 functions as a molecular switch to drive neuronal differentiation.

A 2016 study by Zhang and colleagues replicated Bruno et al.’s finding that miR-128 drives neurogenesis while inhibiting cell proliferation. They found that miR-128’s effect on neural differentiation was dependent on inhibition of pericentriolar material 1 (PCM1), which encodes a protein that is essential for cell division. These studies provide important insights into how miR-128 may contribute to dysregulated neurogenesis in ASD.

Notably, many of the transcripts regulated by miR-128 in the Bruno et al. study had

NMD-inducing features, in particular a long 3’ untranslated region (UTR) (2011). Alternative polyadenylation can generate a long 3′ UTR, which then triggers NMD via a UPF1-dependent mechanism (Hogg and Goff 2010). Bruno and colleagues found that 36% of upregulated NSC transcripts and 35% of alternatively spliced transcripts induced by miR-128 had a long 3’ UTR.

A quantitative trait locus (QTL) is a polymorphic DNA region that correlates to a quantitative trait. A recent study by Mariella et al. identified alternative polyadenylation QTLs in 2,530 genes from 373 European subjects, then looked to see how they correlated to complex traits (2019).

Among these genes they found that the largest enrichments for genes previously implicated by genome wide association studies (GWAS) were for immune system disorders and neurological disorders. When they searched for enrichment in 95 complex traits they found that the largest odds ratio was for ASD (OR = 42.6, 95% CI = 32.9−55.5, P = 2.36 × 10−174).

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Additionally, several studies have implicated alternative splicing as a contributor to ASD pathology. A study by Gandal and colleagues analyzed isoform differences in postmortem ASD, schizophrenia (SCZ), and bipolar disorder (BD) cortical tissue and found alternative 3’ splice site usage in 5–18% of the 472 transcripts differentially spliced across all three disorders (2018), suggesting potential shared underlying pathology.

Contributions of miR-128 to cell migration and circuit formation

Cell migration and lamination are also disrupted in ASD (Stoner et al. 2014). In a simple yet elegant experiment Franzoni and colleagues used in utero electroporation in mice to demonstrate that miR-128 inhibition leads to overmigration of upper layer neurons, whereas premature miR-128 expression leads to migration defects (2015). They found that migration errors were dependent on reduction of the expression of miR-128 target PHF6.

Neurite outgrowth, synaptogenesis, and neural network organization are among the processes that are dysregulated in the early post-natal ASD brain (Courchesne, Gazestani, and

Lewis 2020). Although there are often a greater number of neurons, the neurons retain an immature morphology and diminished synapse formation. Neurons prematurely expressing miR-

128 also show dysregulated radial morphology. These neurons began forming processes earlier than control neurons, but ultimately dendritic arbor complexity was reduced.

Another key mechanism for ASD pathogenesis is an imbalance of cortical excitation and inhibition, with ASD subjects suffering from cortical hyperexcitability. Work by Lek-Tan and

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colleagues shows that loss of miR-128 leads to aberrant striatal excitability and seizures in rodents (2012). Subsequent research by Franzoni et al. further implicated miR-128 in regulating excitation/ inhibition balance (2015). In addition to migration errors and premature branching, they found that miR-128 overexpression led to an increase in intrinsic excitability that was normalized by restoring PHF6 levels.

Contributions of miR-128 to developmental and adult behavior

miR-128 continues to increase over postnatal development, peaking in adulthood (Bruno et al. 2011, Lek-Tan et al. 2012, Rehfeld et al. 2018). A key finding in Chapter III is that miR-

128 undergoes activity-dependent downregulation in songbird Area X. In some ways these results complement Shi et al.’s finding that miR-9 and miR-140-5p are upregulated during undirected song and play a key role in song learning (2013, 2018). These changes are associated with diminished FoxP2 and increased levels of Zenk. FoxP2 is primarily repressive, while Zenk is an immediate early gene (IEG) indicative of transcriptional activation. Like miR-128, miR-9 also functions to regulate cell fate decisions during neurodevelopment (Shibata et al. 2011,

Coolen et al. 2012). In the case of adult song, both microRNAs are dynamically regulated to permit a transient window of plasticity for song learning, maintenance, and refinement.

Previous work has implicated miR-128 in learning and memory. In 2011, Tim Bredy’s group discovered that miR-128 in the infralimbic prefrontal cortex is elevated after fear extinction learning (Lin et al. 2011). They designed the sponge I adapted in Chapter III to determine how miR-128 levels affect extinction. Decreasing miR-128 impaired fear-extinction learning, whereas it was accelerated by overexpressing miR-128. The relationships between

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memory retention, forgetting, and extinction have been an ongoing source of discussion, and I regret that I do not have the space to get into this topic here. A simple interpretation of these results could be that lower levels of miR-128 promote memory retention over extinction, since much of the groundwork for adult behavior is laid during juvenile development.

A second paper implicating miR-128 in behavior was published by Anne Schaefer’s team. Mice with miR-128-2 knocked out consistently showed hyperactive motor behavior, but ultimately developed fatal seizures (Lek-Tan et al., 2012). Valproate was sufficient to prevent seizures.

miR-128 in microglia and immune signaling

miR-128 inhibition may be associated with the normalization of an aberrant microglia cluster in control animals with impoverished song. A wealth of previous literature supports the role of immune signaling and microglia in ASD pathogenesis (Velmeshev et al. 2019, Gupta et al. 2014, Vargas et al. 2005, Morgan et al. 2010, Voineagu et al. 2011, The Network and

Pathway Analysis Subgroup of the Psychiatric Genomics Consortium 2015).

Several of these studies directly link immune and microglial dysregulation to clinical symptoms, including speech and language. Velmeshev and colleagues collected postmortem tissue from ASD patients with detailed medical records and performed single cell RNA sequencing (2019). They found that microglia were the non-neuronal cell type with the greatest differential gene expression compared to controls, and that the changes were associated with cellular activation. When they correlated their data to clinical severity they discovered that

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microglia were among the top two cell types that were most predictive of more debilitating symptoms.

A second set of studies comes from a deeply phenotyped cohort of 1 to 4-year-olds with and without ASD profiled by the Autism Center of Excellence at the University of California-

San Diego. Lombardo and colleagues correlated leukocyte gene expression to fMRI data to determine which genes co-varied with neural responses to speech (2018). A module of genes present only in children with poor language outcomes was also enriched in songbird genes that undergo activity-dependent regulation driven by singing. Interestingly, this same module was enriched in genes related to inflammation and immune signaling. In a follow up analysis I found that this was also the only module enriched in miR-128 targets.

In a second study of this same cohort, Gazestani et al. analyzed the leukocyte transcriptome to identify a network of perturbed genes correlated to symptom severity in the

ASD toddlers (2019). Strikingly, Gene Set Enrichment Analysis showed that regulation by miR-

128 was among processes enriched in this gene set.

Although data on miR-128 in microglia is limited, evidence from other disorders can inform how miR-128 contributes to macrophage dysregulation. One study of multiple sclerosis patients showed that miR-128 is elevated in CD4+ T cells, leading to a pro-inflammatory phenotype (Guerau-de-Arellano et al. 2011).

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In sum, this body of research supports the potential role of miR-128 as a therapeutic target for treating communication deficits in ASD patients.

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Appendix: FoxP2 Isoforms Delineate Spatiotemporal Transcriptional Networks for Vocal Learning in the Zebra Finch

Zachary D. Burkett, Nancy F. Day, Todd H. Kimball, Caitlin M. Aamodt, Jonathan B. Heston, Austin T. Hilliard, Xinshu Xiao, Stephanie A. White

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Abstract

Human speech is one of the few examples of vocal learning among mammals, yet ~half of avian species exhibit this ability. Its genetic basis is unknown beyond a shared requirement for

FoxP2 in both humans and zebra finches. Here we manipulated FoxP2 isoforms in Area X during a critical period for song development, delineating, for the first time, unique contributions of each to vocal learning. We used weighted gene coexpression network analysis of RNA-seq data to construct transcriptional profiles and found gene modules correlated to singing, learning, or vocal variability. The juvenile song modules were preserved adults, whereas the learning modules were not. The learning modules were preserved in the striatopallidum adjacent to Area

X whereas the song modules were not. The confluence of learning and singing coexpression in juvenile, but not adult, Area X may underscore molecular processes that drive vocal learning in zebra finches and, by analogy, humans.

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Introduction

The ability to learn new vocalizations is a key subcomponent of language. Complex behaviors such as human speech and birdsong typically involve suites of numerous interacting genes, making the attribution of their direct molecular underpinnings a challenge. While language is unique to humans, learned vocal behavior is present in a number of animal taxa.

Among laboratory animals, a champion vocal learner is the zebra finch (Taeniopygia guttata), whose song learning shares numerous parallels with human speech development (Doupe & Kuhl

1999). For example, both species share corticostriatal loops for producing vocalizations and have direct projections from cortical neurons onto brainstem motor neurons that control the vocal organs, a connection that is lacking or reduced in non-vocal learners (Lemon 2008, Jürgens

2002, Arriaga et al. 2012). In the brains of avian vocal learners, neurons controlling vocal production learning are uniquely clustered together within the surrounding corticostriatal circuitry, offering tractable targets for experimental manipulation. Despite their evolutionary distance, humans and zebra finches exhibit shared transcriptional profiles in key brain regions for vocal learning that are unique from surrounding brain areas and from the brains of non-vocal learning species (Pfenning et al. 2014).

The forkhead box P2 (FOXP2) transcription factor was the first molecule shown to be important for vocal learning in both humans and songbirds. Forkhead box proteins are characterized by the presence of DNA-binding FOX domains (Clark et al. 1993) and FOXP subfamily members form homo or heterodimers at zinc finger and leucine zipper domains in order to bind DNA. In humans, a heterozygous mutation in the FOX domain of FOXP2 causes a rare heritable speech and language disorder in a cohort known as the KE family (Lai et al. 2001,

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Vargha-Khadem et al. 1998), potentially by altering the subcellular localization of the molecule

(Vernes et al. 2006). While the mutation clearly disrupts vocal learning, multiple FOXP2 isoforms are endogenous to both songbirds and humans, including one that lacks the DNA binding domain. This truncated variant is referred to as FOXP2.10+ because, although it lacks the FOX domain, it retains the dimerization domains plus an additional 10 amino acids.

Consistent with its lack of a FOX domain, in vitro assays of FOXP2.10+ indicated that it does not localize to the nucleus (Vernes et al. 2008). Since it retains the dimerization domain, it has been hypothesized to act as a cytoplasmic sink, binding to other FOXP proteins and preventing their entry to the nucleus and interaction with DNA. Investigation of FoxP2 function in zebra finches has revealed remarkable parallels with humans. Both the full-length (FoxP2.FL) and FoxP2.10+ isoforms are present in each species (Teramitsu & White 2006), and similar

FoxP2 expression patterns occur in developing human and zebra finch brains (Teramitsu et al.

2004). In zebra finches, knockdown of FoxP2 in the song dedicated striatopallidal nucleus, Area

X, during vocal development impaired vocal mimicry of tutor songs (Haesler et al. 2007), much as the KE family mutation impairs speech. These observations indicate that functional FoxP2 is necessary for proper vocal learning in both species.

The unique organization of song control circuit neurons enabled the discovery that FoxP2 is dynamically downregulated within Area X when zebra finches practice their songs, termed

‘undirected’ (UD) singing (Teramitsu & White 2006, Miller et al. 2008, Hall 1962, Immelmann

1962, Dunn & Zann 1996). This FoxP2 decrease is accompanied by increased vocal variability

(Hilliard et al. 2012, Miller et al. 2010), and blockade of FoxP2 downregulation impairs birds’ ability to induce variability in their songs, thought to be a form of vocal exploration. A poor

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learning phenotype emerged following FoxP2 overexpression that was remarkably similar to that observed following knockdown (Heston & White 2015). Taken together, these results indicate that the dynamic regulation of at least FoxP2.FL and thereby the behavior-linked up- and down- regulation of its transcriptional targets is necessary for the proper learning of vocalizations. No specific role in vocal behavior has yet been attributed to the FoxP2.10+ isoform.

These observations pinpoint FoxP2 as a molecular entry point to the pathways underlying vocal learning. In adult birds, we previously used Weighted Gene Coexpression Network

Analysis (WGCNA) to identify thousands of genes regulated by singing specifically in Area X

(Hilliard et al. 2012, Langfelder & Horvath 2008). Since adult zebra finches sing stable, or crystallized, songs, the transcription patterns underlying vocal learning were not identified. Here we conduct a new study with two goals: 1) Determine whether FoxP2.10+ plays a role in vocalization and, 2) Manipulate FoxP2 isoforms in juveniles to generate a broad range of behavioral and transcriptional states upon which to apply WGCNA and thereby reveal learning- related gene modules. Toward the first goal, overexpression of FoxP2.10+ revealed a unique role for this truncated isoform in the acute modulation of vocal variability. Toward the second goal, overexpression of either GFP or one of the two FoxP2 isoforms created three distinct groups of juvenile birds: one that was good at learning and acutely modulating variability (GFP), one that was poor at learning and acutely modulating variability (FoxP2.FL), and one that was good at learning but injected stability into song (FoxP2.10+). We applied WGCNA to the Area X transcriptome of birds across this behavioral continuum and discovered striatopallidal coexpression patterns that were positively correlated to learning. These learning- related patterns were present in juvenile but not adult Area X. However, singing-driven coexpression patterns in

Area X were largely preserved between juveniles and adults, suggesting that: 1) singing-related

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modules are independent of learning state and 2) the spatiotemporal co-occurrence of both singing and learning-related gene modules in juvenile Area X is fundamental to vocal learning.

Methods

Subjects

All animal use was in accordance with NIH guidelines for experiments involving vertebrate animals and approved by the University of California, Los Angeles Chancellor’s Institutional

Animal Care and Use Committee. Birds were selected from breeding pairs in our colony.

Experimental Timeline

Our procedures closely followed those of Heston and White (2015), which are detailed further in the Supplemental Experimental Procedures and schematized in Figure 2-2A. In brief, 30d male birds were injected with virus to overexpress GFP, FoxP2.FL, or FoxP2.10+ bilaterally in Area

X, then recorded constantly until reaching 65d, when they were sacrificed by decapitation and brains rapidly frozen by liquid nitrogen. A total of 19 birds were injected (7 GFP, 6 FoxP2.FL, 6

FoxP2.10+). Sample sizes were selected so as to replicate the n used by Heston and White (2015) where an n of 5 to 8 animals were required to observe a virus effect on tutor percentage similarity. In addition, the authors of the WGCNA R package recommend a minimum of 15 samples for building a network

(https://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/faq.html), so we ensured at least 5 animals per group. At ~60d, an NS-UD experiment was performed where an experimenter kept the animal from singing for the first two hours following lights-on to compare to the previous or following day’s songs recorded after 2 hours of singing. 102

Surgery and Viruses

AAV1 identical to those used by Heston and White (2015) and produced by Virovek (Hayward,

CA) were used in this study. AAV1s expressing GFP, FoxP2.FL, or FoxP2.10+ behind the CMV early enhancer/chicken β actin (CAG) promoter were injected to Area X of 30d birds. Virus titers were all ~2.24E+13 vg/ml, thus equal volumes were delivered to each bird irrespective of the virus being injected. Immunostains (Figure 2-1D) were performed on tissue injected with a custom engineered HSV-1 expressing FoxP2.10+ behind the IE 4/5 promoter and GFP behind the CMV promoter. HSV was prepared by the virus core at The McGovern Institute for Brain

Research at the Massachusetts Institute of Technology (Cambridge, MA).

Further information about the surgical procedure and viruses are in the Supplemental

Experimental Procedures.

Song Analysis and Statistics

Motif Similarity: To quantify the acoustic similarity between pupil and tutor, a metric for quantity of learning, we utilized the Sound Analysis Pro (SAP) (Tchernichovski et al. 2010)

Similarity Batch module. Asymmetric comparisons were performed between 10 tutor motifs from the final day before the pupil was isolated from the home cage and 20 pupil motifs every three days following the onset of singing in isolation following injection of virus. We used the average percentage similarity from these comparisons as a representative of how well the pupil had learned its tutor’s song as of a given analysis day. Statistical significance of motif similarity data was calculated by performing one- way ANOVA on the average percentage similarity score of each animal across virus groups within each time bin, as depicted in Figure 2-2D. If the

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ANOVA yielded a significant result, Tukey’s Honest Significant Difference (HSD) was used as a post-hoc test.

Overall Vocal Variability: To broadly assess the amount of variability in the animal’s song preceding sacrifice, asymmetric comparisons between 20 pupil motifs and themselves were conducted. We calculated the motif identity for all motif-motif comparisons as the product of their percentage similarity and accuracy divided by 100. Higher identity scores indicate lower variability within the batch.

Acute Vocal Variability Modulation: For finer-grained analyses of acoustic variability as presented in Figures 2-2C and 2-S1, we utilized SAP and VoICE (Burkett et al. 2015)

(https://github.com/zburkett/VoICE). Syllables from the first 20 minutes following two hours of non-singing or undirected singing on the NS-UD experiment days were hand segmented, had their acoustic features quantified in the SAP Feature Batch, then clustered by VoICE. Data for analyses of acoustic features were taken from the VoICE output. Effect sizes were calculated using the formula (NS-UD)/(NS+UD), where values were the CV of a given acoustic feature following two hours of NS or UD. Thus, negative values were indicative of increased song variability after UD singing (For more detail regarding this transformation, see Supplemental

Information: Song Analysis). Statistical significance for each song feature was assessed by one- way ANOVA on the CV effect size for all syllables from all animals within each group. Tukey’s

HSD was used as a post-hoc test in the instance of a significant ANOVA result. For the raw acoustic data, as presented in Figure 2-S1, the syllables

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were considered paired within virus construct and across singing context. Paired T-tests were used to assess whether two hours’ silence vs. two hours’ singing caused a significant change in the coefficient of variation for each acoustic feature.

Immunostaining

Tissue was prepared for immunostaining by sacrificing the animal 3-5 days following HSV injection then perfusing warm saline followed by ice cold 4% paraformaldehyde in 0.1 M phosphate buffer. Tissue was sectioned on a cryostat at 20 μM, thaw mounted on glass microscope slides, and stored at -80oC. Thawed tissue was incubated with goat-anti-FoxP2 at

1:500 dilution (Abcam, Cambridge, UK, Thompson et al. 2013) and mouse-anti-Xpress at 1:500 dilution (ThermoFisher Scientific, Waltham, MA) overnight. AlexaFluor 546 donkey-anti-goat at

1:500 dilution and AlexaFluor 405 donkey-anti-mouse at 1:250 dilution secondary antibodies were used to bind the anti-FoxP2 and anti-Xpress signals, respectively. The tissue was visualized using a Zeiss (Oberkochen, Germany) LSM 800 confocal microscope and processed using NIH

ImageJ (Schneider et al. 2012).

Tissue Collection and Processing, RNA Extraction, cDNA Library Preparation, and Sequencing

Two hours following lights-on at ~65d, birds were sacrificed by decapitation. Brains were rapidly extracted and frozen on liquid nitrogen, then stored at -80oC until all brains were collected. As in Hilliard et al. (2012), tissue micropunches of Area X and VSP were performed.

Brains were sectioned on a Cryostat until Area X became visible. Area X and outlying VSP were punched using a 20 gauge Luer adapter and stored in RNAlater (Qiagen, Germantown, MD) at -

80oC until

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RNA extraction was performed. Sections were then collected for validation of punch accuracy.

Total RNA extraction was performed in the same manner as in Hilliard et al. (2012). Samples in the present study were processed randomly and in parallel with another sequencing project.

Tissue punches from both studies were processed in batches of 8. We used Qiagen RNeasy

Micro Kits following the manufacturer’s protocol and used QIAzol as the lysis reagent. We also performed an additional wash each in RW1 and RPE buffers beyond the manufacturer’s protocol. Final elution volume was 20 μL. Extracted total RNA were stored at -80oC until all

RNA extractions were completed. All extractions were completed over the course of two weeks.

Total RNA was provided to the UCLA Neuroscience Genomics Core (UNGC; https://www.semel.ucla.edu/ungc) where RNA quality was assessed on an Agilent TapeStation

(Agilent Technologies, Santa Clara, California). RNA of sufficient quality was then used to generate cDNA libraries using the Illumina TruSeq Stranded Poly-A Prep Kit (Illumina, San

Diego, California). Libraries for each sample were divided across two lanes and sequenced in a total of 8 lanes in an Illumina HiSeq 2500 in high output mode, generating between 15 and 35 million 50bp paired-end reads per library.

RNA-Seq Preprocessing & WGCNA

FASTQ files for all 19 samples from UNGC were quality controlled and then aligned to the zebra finch genome assembly 3.2.4 (http://www.ncbi.nlm.nih.gov/assembly/524908) using

Spliced Transcripts Alignment to a Reference (STAR) (Dobin et al. 2013). Reads mapping uniquely to exons were counted using the featureCounts() function in the Rsubread R package

(Liao et al. 2013, Liat et al. 2014), then exon 28 counts were summed to the gene level and

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transformed to transcripts per million (TPM). Expression data were then log2 transformed and preprocessed for WGCNA as described in the Supplemental Experimental Procedures. Finally, we checked for batch effect on average expression resultant of RNA extraction group, RNA extraction experimenter, and across sequencing lanes. No batch effects were observed.

Signed weighted gene coexpression networks were constructed using the WGCNA R package

(Langfelder & Horvath 2008) using custom written code for iteratively building networks, as described in the Supplemental Experimental Procedures. Soft thresholding power was set at 18 and 14 for the Area X and VSP networks, respectively. Minimum module sizes for both networks were set to 100 and the deepSplit parameters were set to 4 and 2 for Area X and VSP networks, respectively. All other input parameters were left at their default values.

Correlation of Behavior to Gene Expression

Calculation of gene significance to a trait requires the definition of a single value to which the amount of gene expression in each sample is correlated. Gene significances were calculated for the following traits: motifs, defined as the number of motifs each animal sang in the two hours following lights-on on the day of sacrifice; tutor similarity, defined as the percentage similarity between the pupil and its tutor on the day of sacrifice; variability induction, defined by inserting

Wiener entropy CV scores into the equation (NS-UD)/(NS+UD) from the first twenty syllable renditions sung during the NS-UD experiment performed at ~60d; motif identity, defined as the product of the similarity and accuracy scores divided by 100 of the last 20 motifs sung by each bird before sacrifice. Song variability was assessed on the motif level for the purpose of gene significance calculations so as to obtain a single value for each animal.

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Following network construction, modules were summarized by calculating a module eigengene, defined as the first principal component of the module’s expression data using the moduleEigengenes function in the WGCNA R package. The relationship between a module and a behavior was assessed by determining the Pearson correlation between the module eigengene and continuous behavioral traits as defined in ‘Song Analysis and Statistics’, above. Significance was then determined by calculating the Fisher transformation of each correlation using the corPvalueFisher() function in the WGCNA R package.

Gene Ontology, Module Significance, and Term Significance

At the time of our study, annotation of the zebra finch genome is relatively sparse, thus we used the HGNC gene symbols for the human homologs of the zebra finch genes in our study for gene ontology analyses. Genes with no known human homolog were excluded. Symbols were submitted to the GeneCards GeneAnalytics suite at http://geneanalytics.genecards.org (Ben-Ari

Fuchs et al. 2016). GeneCards enrichment scores were converted into p-values, which were used as the input to module significance calculations. Module significance of a term was defined as the product of the average module membership for each gene annotated with a term and one minus the p-value for that term such that the genes with the highest module membership and lowest p-value prioritize the terms. Term significance was defined by weighting the module significance score by the gene significance for a given behavioral metric.

Transcription Factor Binding Site Analysis

The FoxP2 consensus binding sequence from the JASPAR database (Nelson et al. 2013,

Mathelier et al. 2016) was converted into a position-weight matrix (PWM) and used to scan the

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promoter (defined as the first 1000 base pairs upstream of the transcription start site) for each gene in the zebra finch genome. Putative FoxP2 binding sites were identified using the matchPWM function in the Biostrings R package

(https://bioconductor.org/packages/release/bioc/html/Biostrings.html) with a minimum hit score of 80%.

Protein Interaction Networks and Scaling of Interaction Confidence Scores

STRING is a comprehensive database of known and predicted protein-protein interactions derived from experimental data, coexpression data, automated text mining, and also pulls information from other interaction databases. STRING accepts gene symbols as input, then mines for interactions between those genes and assigns a confidence score between 0 and 1 based on the evidence in the database for the genes’ interaction. We submitted gene symbols for the human homologs of module members to STRING then operated on the highest confidence interactions (≥ 0.9) in downstream analyses.

Interaction scores were scaled by different metrics to emphasize or deemphasize network position and/or relationship to behavior. Those metrics are:

1. The product of each gene’s connectivity in juvenile Area X network: emphasizes interactions between the most connected genes in the juvenile network 31

2. The product of each gene’s differential connectivity between juvenile and adult Area X networks: emphasizes interactions between genes that are of high network importance in juveniles but not adults

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3. The product of each gene’s gene significance for learning or singing: emphasizes interactions between genes that are strongly correlated to behavior independent of their connectivity

4. The product of each gene’s connectivity and gene significance: emphasizes interactions between genes that are strongly correlated to behavior and of highly connected in the juvenile network

Network Visualization and Interactive Figures

Network plots presented in this manuscript were constructed using the freely available plotting software, Gephi (https://gephi.org), using edge lists prepared in R and exported in the .GEXF format. Interactive figures were exported from Gephi using the Sigma.js Exporter plugin

(https://github.com/oxfordinternetinstitute/gephi-plugins). A more detailed description of their construction is presented in the Supplemental Experimental Procedures.

Accession Information

Raw and processed RNA-seq and behavioral data for each bird are available at the Gene

Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) at accession number

GSE96843.

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Results

Virus-mediated overexpression of FoxP2 isoforms affected song learning and/or vocal variability

Adeno-associated viral constructs were used to drive expression of FoxP2.FL or

FoxP2.10+ in Area X of developing males. To verify isoform-specific overexpression, we used two riboprobes in in situ hybridization: one antisense to a region common to both transcripts

(mid probe) and one antisense to a region near the 3’ end of FoxP2.FL (3’ probe; Figure 2-1A).

Robust signals in the striatopallidum were observed in both hemispheres using the mid probe but only in the hemisphere injected with the FoxP2.FL construct using the 3’ probe (Figure 2-1B).

These results indicate that each viral construct over-expressed its FoxP2 isoform and was thus suitable for bilateral injection into juvenile males at 35d. To quantify this, we performed qRT-

PCR with a set of primers that amplifies a region common to both transcripts (Haesler et al.

2007, Olias et al. 2014) and another set specific to the FoxP2.10+ (see Supplemental

Experimental Procedures). The first primer set indicated that FoxP2 levels were higher in birds injected with either construct relative to control levels. When quantified by the second primer set 111

we found elevated PCR product only in the animals injected with the FoxP2.10+ construct

(Figure 2-1C). Taken together, these results indicate that both constructs were effective in elevating levels of their encoded FoxP2 isoform throughout the 30d experimental period.

Overexpression of a tagged form of FoxP210+ in SH-SY5Y suggested that FoxP2.10+ acts as a posttranslational regulator of FoxP2.FL through heterodimerization and the formation of cytoplasmic aggresomes (Vernes et al. 2006). We thus examined the protein-level distribution of FoxP2.10+ and FoxP2.FL in the finch striatopallidum following overexpression of an N- terminus Xpress tagged FoxP2.10+ linked to a GFP reporter (see Methods). Transfected cells shared the distinctive FoxP2.10+ staining pattern of aggresomes seen previously. In FoxP2+ cells that co-expressed the Xpress tag and GFP reporter, endogenous FoxP2.FL signal was interspersed among Xpress- positive puncta. (Vernes et al. 2006) (Figure 2-1D).

We previously found that in unmanipulated birds, two hours of UD singing is sufficient to decrease Area X FoxP2 mRNA (as measured by both the mid and 3’ probes) and protein

(Teramitsu & White 2006,33]. This decrease is accompanied by increased vocal variability

(Hilliard et al. 2012, Miller et al. 2010) which is abolished by overexpression of FoxP2.FL

(Heston & White 2015). These observations indicate that downregulation of full length FoxP2 is necessary for acute vocal variability but we did not directly manipulate FoxP2.10+. Here, we repeated our behavioral protocols to test for the induction of vocal variability and included the

FoxP2.10+ injected animals (Figure 2-2A, 2-2B). To assess whether UD singing drove an increase in vocal variability, we quantified the effect of two hours’ UD singing on the coefficient of variation (CV) of acoustic features in ~60d birds overexpressing GFP, FoxP2.FL, or

FoxP2.10+. As predicted, GFP-expressing animals exhibited a negative effect size for most

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acoustic features and FoxP2.FL overexpression diminished these practice-induced changes in vocal variability, replicating our previous findings (Heston & White 2015) (Figure 2-2C).

Unexpectedly, in animals overexpressing FoxP2.10+, song variability after two hours of

UD singing was significantly less than that after two hours of NS for syllable duration, amplitude modulation, and Wiener entropy (Figure 2-2C). Thus, rather than increasing vocal variability, the act of UD singing promoted invariability when behavior-driven down-regulation of FoxP2.10+ was blocked. We also examined variability in the raw acoustic features of NS and UD song and found that expression of either FoxP2 isoform did not dramatically alter variability, indicating that the virus specifically affected the modulation of variability and not its overall level (Figure

2-S1 and Supplemental Experimental Procedures). Surprisingly, despite its acute effect on practice- induced changes in song variability, overexpression of FoxP2.10+ did not impair overall vocal learning (Figure 2-2D, 2-2E). These results suggest that the ability to modulate between relatively low and high variability states is important for proper vocal learning.

In sum, our AAV constructs allowed us to generate groups of animals in distinct states of vocal variability and learning. GFP-injected birds learned well and displayed singing-induced variability in the acoustic features of song. FoxP2.FL birds learned poorly and had no difference in their songs’ acoustic variability following practice. FoxP2.10+ birds learned well but seemed to exist in a state where practice drives invariability in vocal acoustics. As such, a broad degree of both learning and variability induction existed across groups (Figure 2-2F). Next, we used these behavioral metrics as correlates to gene coexpression patterns to interrogate the transcriptional profiles underlying these traits.

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Gene modules in juvenile Area X that correlate to vocal behavior were enriched for communication and intellectual disability risk genes

We used RNA-seq to quantify gene transcription in Area X of 65d juveniles overexpressing GFP, FoxP2.FL or FoxP2.10+, then used WGCNA to identify gene coexpression modules and link them to song learning. In our final network (see Supplemental Experimental

Procedures), 7461 genes formed 21 modules (Figures 2-3A, 2-3B). A strength of WGCNA is that coexpression networks are built in an unsupervised fashion blind to phenotypic trait data, thus making any correlations between coexpression modules and traits highly compelling. After network construction, we found significant correlations between module eigengenes and the following behaviors: tutor percentage similarity (i.e. vocal learning; modules: black, blue, darkgreen, orange, royalblue), number of motifs (i.e. amount of singing; modules: darkred, green, greenyellow), singing-induced acoustic variability (i.e. variability induction; modules: black, brown, darkgreen, darkgrey, magenta, orange, pink, purple, turquoise), and motif identity

(i.e. overall vocal variability; module: darkgrey) (0.00008 < p < 0.05; Figure 2-3B). We examined all modules whose p-value was ≤ 0.05 and calculated the relationship between module membership and gene significance (For definitions of WGCNA and network terms, please see

Supplemental Information: WGCNA and Network Terminology). For most modules, strong significant correlations were observed for each trait, indicating that the genes most representative of the module’s overall expression profile were those most strongly related to the behavior

(Figure 2- 3C).

Connectivity is the core network concept and genes with high connectivity have the strongest coexpression relationships across the entire network, indicating greater importance to

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overall network structure and biological significance. The purple, green, and pink modules contained the most densely interconnected genes (Figure 2-S2), and were all significantly correlated to percentage similarity to tutor (green) or singing-induced variability (purple, pink)

(Figure 2-3B, 2-3D). Altogether, these findings indicate that information about the relationships between molecular activity and behavior was embedded in the actual structure of the network, and that a gene’s relationship to a module or a module’s relationship to the network was predictive of strong behavioral relevance. Therefore, we examined the most well-connected/hub genes within the context of their module (genes with the greatest intramodular connectivity,

Figure 2-3G) or the entire network (genes with the greatest whole-network connectivity). We discovered that many of these hub genes were known risk genes for human disease (Figure 2-

3G). For example, of the 7462 genes in our network, Fragile X Mental Retardation 1 (FMR1) had the third highest connectivity and was the most well connected member of the green module

(Figure 2-3G). Deficiency in FMR1 gives rise to Fragile X Syndrome, a genetic disease with a multitude of symptoms including intellectual deficiency and speech and language impairment.

To attribute biological meaning to the modules, zebra finch gene symbols were converted to their Human Genome Organization (HUGO) Gene Nomenclature Committee (HGNC) paralogs, then submitted to GeneAnalytics, a comprehensive tool for the contextualization of gene set data that integrates across multiple databases (Ben-Ari Fuchs et al. 2016). We calculated a module significance score for the resulting disease, gene ontology, and pathway annotations returned from GeneAnalytics. This was done by scaling the module membership of each gene that was annotated with a term. Specifically, the p-value for that term was subtracted from 1 such that the terms related to the genes most representative of the module received the highest scores

(Hilliard et al. 2012). The top five terms for the black song module (negatively correlated to

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singing), the brown module (positively correlated to variability induction and henceforth referred to as a ‘variability induction module’), and green learning module (positively correlated to learning) are shown in Figure 2-3F. Since most modules contained hundreds of genes, prioritizing the ontology terms by the connectivity their annotated genes allowed genes with the greatest network importance (Figure 2-3F) to emphasize the terms with the greatest biological importance (Figure 2-3E).

Juvenile Area X modules for learning, but not singing, were preserved in ventral striatopallidum

To validate the specificity of the Area X modules to vocal behavior, we compared the

Area X network to a network constructed from the adjacent unspecialized ventral striatopallidum

(VSP) from the same animals. Area X and VSP networks were constructed using the genes that were common to both standalone networks. We hypothesized that all of the modules related to singing in Area X would have no correlation to behavior in VSP since, despite its close proximity and similar cell type composition, it is not linked into the song circuit. Moreover, a significant body of evidence suggests that the song circuit evolved as a specialization of existing motor circuitry (Pfenning et al. 2014,79-81]. As predicted, no module in the VSP network displayed any correlation to any of the singing or learning behaviors (Figure 2-4A). We calculated module preservation statistics between the two brain regions and observed that the song modules were among the most poorly preserved across the two networks (Figures 2-4B, 2-

4C), further underscoring that Area X is specialized for song. This lack of preservation was not the product of differential gene expression between the two regions (Figure 2-4D, top) but instead reflected altered connectivity among similar genes (Figure 2-4D, bottom). In striking

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contrast to the song modules, the green learning module was very strongly preserved in VSP

(Figure 2-4B, Figure 2-3B), indicating a generalized learning- related coexpression state exists in the juvenile striatopallidum that is specialized for singing in Area X.

Juvenile Area X modules for singing, but not learning, were preserved in adult Area X

To provide further context for the modules observed in our network and how they relate to learned vocalization, we compared them with prior data from adult zebra finch Area X

(Hilliard et al. 2012). Our present network captures a point in zebra finch development when birds were actively learning how to improve their songs whereas in adulthood, the learning process has ended and adult songs are “crystallized”. Contrasts between juvenile and adult networks highlight gene coexpression patterns that change between the two learning states, and inform their molecular underpinnings.

Our previous study in adults found multiple modules in Area X that were significantly correlated to singing crystallized songs. We reasoned that if very similar co-expression patterns were present in juveniles, then they would likely be unrelated to learning. In this case, the capacity to learn a song might be attributable to other genes and/or the relationships between them. To compare across studies, we first built new, separate networks for both age groups composed only of genes common to the two original networks and then computed gene significance scores for all genes in both networks. We found a remarkable correlation between gene significances to singing in juveniles and adults (Figure 2-5A), showing that genes in Area X shared similar relationships to singing, whether it be positive, negative, or nonexistent, independent of the animal’s age and learning state. The replicated discovery of specific sets of

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song-related genes across studies and ages speaks to the profound effect that singing behavior has on gene transcription profiles within the song-dedicated basal ganglia.

We next calculated module preservation across the two studies, which assesses how well the coexpression relationships between genes persist across ages (Langfelder et al. 2011). We observed strong relationships between module preservation and correlation to singing, and genes related to singing clustered together independent of age (Figures 2-5B, 2-5C). These results indicate that not only are the relationships between genes and singing consistent across ages but those genes’ coexpression patterns are preserved as well.

Since singing-driven gene coexpression patterns were similar between juvenile and adult

Area X the capacity to learn vocalizations is not a product of large-scale differences in coexpression of the song module genes. We therefore looked for any modules that differed between juvenile and adult Area X. We found that the green, greenyellow and darkred learning modules that were significantly correlated to tutor similarity in juveniles were poorly preserved in adult Area X (Figures 2-5B, 2-5C). Like the genes in the song modules, learning module genes were activated with singing (Figure 2-5D, top row). For both well- and poorly-preserved modules, the ranked gene expression within each module displayed a positive correlation (Figure

2-5D, middle row). However, only the song modules showed positive correlations between connectivity in juvenile and adult Area X (Figure 2-5D, bottom row). These results attribute the difference between juvenile and adult Area X not to differential expression or altered correlation to behavior, but to differential connectivity in adults of modules that are correlated to tutor similarity in juveniles. Therefore, the capacity to alter vocalizations does not reside in the absolute expression level of a given gene but instead the transcriptional context in which that

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gene expresses. For example, FMR1 was poorly connected in the adult network but was positioned as a hub gene in the juvenile network, indicating the gene’s importance during a developmental period when vocalizations are being actively modified but not during their maintenance.

A bioinformatics approach indicates MAPK11 as an entry point to neuromolecular networks for vocal learning

Above we described two classes of coexpression modules: 1) learning modules that were preserved throughout the striatopallidum but only present in juveniles, 2) song modules that were preserved across age but specific to Area X. Therefore, song modules and learning modules exist simultaneously only in juveniles, and their co-occurrence within Area X may reflect the capacity to dramatically alter vocalizations during sensorimotor learning. Therefore, we hypothesized that interactions between these two modules may drive the vocal learning process.

To test this idea using bioinformatics, we examined any genes linked to FoxP2, whose overexpression drove the broad range of tutor song copying in our animals. The gene with the greatest gene significance to learning was MAPK11 (Figure 2-6A). Interestingly, in Foxp2 heterozygous knockout mice, MAPK11 levels increase, supporting the interaction we observed here (Enard et al. 2009). To test whether MAPK11 could be a target of FoxP2 in the zebra finch, we scanned the MAPK11 promoter for sequences corresponding to the FoxP2 binding motif from the JASPAR database (see Methods) (Nelson et al. 2013, Mathelier et al. 2016). We found a match with a single base difference beginning 288 base pairs upstream of the zebra finch

MAPK11 transcription start site (Figure 2-6B). Taken together, these data suggest that birds overexpressing FoxP2.FL may be limited in their learning capacity to learn due to FoxP2

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repression of this gene. In line with this, both the FoxP2.10+ and GFP animals had higher

MAPK11 gene significances for tutor similarity than did FoxP2.FL animals (Figure 2-6A).

A strength of WGCNA is the “guilt by association” approach whereby genes in close network proximity to a gene of interest become candidates for a role in the same biological processes. With this in mind, we used MAPK11 as an entry point to pathways related to vocal learning. We first scanned for genes with high topological overlap with MAPK11 (e.g. the closest network neighbors to MAPK11). Many of these genes were well-connected members of the green learning module (Figure 2-6C). One such gene, ATF2, had the fifth highest green intramodular connectivity and third highest whole network connectivity. ATF2 protein is necessary for proper development of the nervous system (Reimold et al. 1996) and serves a dual purpose in affecting transcription by binding to cAMP response elements and also by acetylating histones H2B and H4 (Bruhat et al. 2007, Kawasaki et al. 2000). Like FMR1, ATF2 is poorly connected in the adult network (Hilliard et al. 2012).

While its role in development of the nervous system has been defined, no specific relationship between ATF2 and learned vocalization has been described. In our network, the

ATF2 acetylation target histone H2B sorted into the blue module, which is strongly and positively correlated to the act of singing (Figure 2-3B) and has been linked to learning and memory in rat hippocampus (Bousiges et al. 2013). These observations illustrate a pathway wherein overexpression of FoxP2 represses the expression of its putative target, MAPK11. As a consequence, less MAPK11 phosphorylation of learning module hub gene ATF2 would occur, decreasing acetylation of song module member histone H2B. This pathway represents an interaction between a network hub in a learning module (ATF2) and a song module gene

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(histone H2B) at a developmental time point at which the bird is actively learning its vocalizations.

To generalize this strategy, we used the Search Tool for the Retrieval of Interacting

Genes/Proteins (STRING) database (Szklarczyk et al. 2015) examine additional interactions between learning-related network hubs and singing genes in Area X. We submitted genes from the green, greenyellow, and darkred learning modules and the black, blue, darkgreen, orange, and royalblue song modules, then filtered for cross-module interactions and scaled the confidence scores by the average intramodular connectivity of each gene in the interaction. This yielded a ranked list of interactions between genes positively regulated by learning and those regulated by singing, which was prioritized by weighted confidence score to yield the highest confidence interactions between genes with the greatest network importance. These interactions were visualized as a network (Figure 2-7). This approach allowed us to not only visualize the confidence in gene interactions but also the local neighborhoods formed by the protein interaction network, providing emphasis on genes of potentially greater importance in the vocal learning process based on the number of interactions they have.

We ranked interactions by four different metrics designed to emphasize or deemphasize gene significance, intramodular connectivity, and differential connectivity in juveniles vs. adults

(see Methods). These metrics provide a basis for selecting protein-protein interactions based on the relationship to the genes and their most strongly correlated behavior, the coexpression network importance of the genes, or the change in connectivity between juvenile and adult birds.

In using the latter metric, the decreased connectivity of learning-related genes ATF2 and FMR1 in adulthood is accounted for and interactions involving those genes are prioritized. Interactions

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between ATF2 and IRF2, DUSP5, and FOS are among the highest scoring interactions using this metric.

Discussion

In this study, we overexpressed FoxP2 isoforms to create groups of birds across a continuum of learning and ability to induce variability in their songs (Figure 2-2F), ideal for transcriptome profiling and WGCNA. We constructed an Area X gene network and discovered modules related to singing, learning, and vocal variability. The network properties of these modules revealed strong relationships between gene module membership and the behavior(s) to which the modules correlated.

To understand how gene coexpression patterns change across the boundary of the critical period for vocal learning, we performed comparisons between the network constructed here in juvenile Area X to one constructed from adult Area X (Hilliard et al. 2012). We had competing 122

hypotheses about whether the inability to learn new songs as an adult is resultant of changes to the song modules observed in juveniles or whether it is from some other transcriptional change.

Module preservation statistics revealed very strong preservation of the juvenile Area X song modules in the adult network, supporting the latter hypothesis. In striking contrast, the densely interconnected juvenile striatopallidal green learning module was poorly preserved in adults, indicating that at least part of the learning related transcriptome is altered by aging.

Because we also created networks from VSP of the same animals, we were able to compare how well the Area X modules were preserved in a similar brain region lacking the specialization for singing. As observed in Hilliard et al. (2012), Area X song modules were poorly preserved in VSP. In contrast, the densely interconnected green module was strongly preserved in VSP, suggesting that a learning related gene coexpression pattern generalizes across the juvenile striatopallidum. These experiments define juvenile Area X as a nexus wherein the striatopallidal learning-related modules exist in tandem with singing-specific modules. As the brain ages, singing continues to drive transcriptional patterns but the learning related patterns are lost (Figure 2-8A). This hypothesis is supported by the preservation of the juvenile Area X song modules in adult Area X and preservation of the juvenile Area X green learning module in the outlying VSP (Figure 2-8B). Our findings suggest a model for the molecular basis for complex learned vocal behavior as not specific genes or coexpression modules, but the spatiotemporal combination of “singing” and “learning” building blocks that we observed in Area X. Like Area

X, the other song nuclei of the finch brain likely evolved as specializations of existing motor circuitry (Pfenning et al. 2014,79]. We expect a similar principle to exist across the songbird brain where nonspecialized/learning related and specialized/singing related coexpression patterns converge to permit song learning.

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Our findings here validate prior results where overexpression of FoxP2.FL made birds unable to induce variability into their songs and poorly learned their tutors’ songs. This result supports the hypothesis that the behavior linked cycling of FoxP2 instead of the absolute level per se is critical for driving the vocal learning process. In addition, we described a behavioral role for the FoxP2.10+ isoform as we observed singing induced vocal invariability following its overexpression. A similar phenomenon was observed in a different species of passerine songbird, the Bengalese finch (Lonchura striata domestica), where two hours’ UD singing resulted in less variable songs than those after two hours’ NS (Chen et al. 2013). In both species, the inability to induce variability into song did not affect vocal learning, suggesting that the ability to have relatively low or high variability states in singing are necessary to properly learn a song regardless of whether those differential variability states precede or follow singing.

WGCNA identified Fmr1 as a gene of great importance in a learning module. FMR protein is expressed throughout the zebra finch song circuit primarily in neurons, and the song system itself has been suggested as an interesting model system within which to study the gene’s function (Winograd et al. 2008, Winograd & Ceman 2012). FMR1 codes for an RNA-binding protein and therefore its level of expression could have a profound effect on a number of targets in the network (Ascano et al. 2012). Here, we observed a correlative link between FMR1 expression and how well the animal copied its tutor’s song, a novel association that could be reasonably hypothesized given the speech and language phenotype concurrent with deficiency of the gene in humans. A key strength of WGCNA is the ability to query the network around genes known to be associated with a trait. FMR1’s close network neighbors included ATF2, which is associated with learning but has no prior link to vocal behavior. We believe that further

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investigation into learning modules is likely to reveal molecules that are fundamental to learning behavior.

To identify those molecules that may interact at this particular developmental time point and brain region, we selected MAPK11 – a likely FoxP2 target (Enard et al. 2009) and the gene with the greatest significance to learning – to further investigate as an entry point to the pathways underlying learning behavior. Local neighborhood analysis of MAPK11 in the coexpression network revealed high topological overlap with many strongly connected members of green module, including the hub gene ATF2. ATF2 is a phosphorylation target of MAPK11 and this phosphorylation enhances its histone-acetyltransferase activity (Enslen et al. 1998, Stein et al.

1997). A known enzymatic substrate of ATF2 is histone H2B (Kawasaki et al. 2000), a member of the blue module that is positively correlated to singing. To probe for additional protein-protein interactions such as these, we mined the STRING database using song and learning module members, then prioritized the interactions based on the network properties and/or behavioral significance of the input genes. A prioritized list of interactions and a complex network emerged, highlighting genes based on their coexpression network importance and/or the number of protein level interactions in the database (Figure 2-7).

While there are differences in overall gene expression between the juvenile and adult brain, the context within which genes express, i.e. their connectivity, is drastically altered, especially in the learning modules. Changes in connectivity are not necessarily indicative of changes in the absolute level of a gene’s expression, as evidenced by the comparisons between

Area X and VSP (Figure 2-4D) or juvenile and adult Area X (Figure 2-5D), where expression levels correlate positively but connectivity does not. These data support the idea that the

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coexpression patterns, and thereby the genes’ connectivity and network importance, contribute to the transition from a state of learning to a state of non-learning.

In using connectivity as a measure of network importance and protein interaction as a measure of functional biological output, the protein interaction landscape underlying learned vocal behavior shifts across the two developmental time points analyzed here. The local interaction network around green module hub ATF2 (defined as all those neighbors within two steps and with high confidence of protein interaction) is composed of well-connected genes in the learning and song modules (Figure 2-8C, top). Moreover, the connections to learning related genes are inputs to well-connected network hubs. As the juvenile crosses over into adulthood, the connectivity of many of the learning-related genes, including ATF2, dramatically decreases. As part of the same process, the adjacencies between genes in the interaction network shift such that a connection to a learning-related gene is no longer one with a hub (Figure 2-8C, bottom). This shift in network importance may present a pattern underlying song maintenance rather than song learning, and potentially the closure of the critical period in which the bird can change its song.

To understand the mechanisms underlying the transition between the two learning states, our data highlight the importance of the network position of a gene, beyond simply its absolute expression level at a given time. To enable vocal plasticity after the closing of a critical period, a goal critically relevant to social and communication disorders, manipulations that coordinate gene expression such that poorly connected genes are reestablished as network hubs are likely required. Using exogenous sources to attain these goals is methodologically complex, but the pathways prioritized and presented here provide a foundation for breaking down the components of vocal learning behavior.

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In sum, we have described the transcriptome at a developmentally significant point in the vocal learning process and provided context for it in terms of aging and brain region specificity.

We have also suggested numerous coexpression and protein level interactions that our data indicate are significant to vocal learning. Due to the large amount of data generated by this study, we have generated as a supplement to the figures in the manuscript, interactive graphics describing the coexpression and protein interaction networks and compiled descriptive statistics and have hosted them on our laboratory website

(https://www.ibp.ucla.edu/research/white/genenetwork.html). We strongly encourage the reader to explore these datasets to mine for coexpression and protein interactions among their genes of interest. Further investigation of these pathways in the zebra finch is necessary for confirming their validity and providing the molecule-to-behavior links suggested herein. By using a network based approach, we are able to prioritize the interactions between genes and identify pathways so as to begin the process of teasing out the complex interactions underlying a complicated behavior.

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Supplemental Information

WGCNA and Network Terminology

WGCNA is a well-established technique for gleaning biologically relevant clusters of coexpressed and functionally related genes from microarray and sequencing data. WGCNA methods and terminology are summarized and defined in numerous manuscripts (Zhang &

Horvath 2005, Hilliard et al. 2012, Dong & Horvath 2007, Zhao et al. 2010, Yip & Horvath

2007, Horvath 2011). For the sake of completeness, we provide working definitions of network terms that we use in the main text of this manuscript. Definitions of greater detail are available in the manuscripts cited above.

• Adjacency (a): The first step of network construction is to generate an adjacency matrix β 128

where Aij = Sij , where i and j are genes, S is the expression correlation across samples, and β is an empirically derived power to which the correlation is raised such that the resulting network approximates a scale free topology.

• Connectivity (k): Connectivity is a measure of connectedness of a given gene, either in the context of its module (kIN) or the entire network (kTotal). Connectivity is defined as follows:

where i and j are genes, N is all of the genes in the module or network, and a is the adjacency between genes i and j.

• Topological overlap: Adjacency is transformed to topological overlap as a method of calculating the interconnectedness (or similarity) between two nodes. Topological overlap is defined as follows:

and , where u represents all genes besides i and j. A and k are defined above.

• Gene significance: The Pearson correlation between a gene’s expression profile and, in

our work, a given behavioral metric.

• Module eigengene: The first principal component of a module’s gene expression profile,

a method of summarizing an entire module in one vector.

• Module membership: The correlation between an individual gene expression profile and

a module eigengene. Genes with high module membership tend to have high

intramodular connectivity and are referred to as intramodular hubs. Of note, genes can

have high module membership in more than one module.

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• Zsummary: Along with median rank, a term for quantifying preservation of gene

coexpression patterns between two independent datasets (Langfelder et al. 2011), such as

between juvenile and adult Area X or juvenile Area X and juvenile VSP. Zsummary is a

composite preservation score defined as the average of Zdensity and Zconnectivity,

which assess the preservation of connection strength among network nodes (e.g. Are

strongly connected nodes in one network also strongly connected in the other?) and the

connectivity patterns between nodes (e.g. Do the patterns of connection between specific

nodes exist in both networks?), respectively, following permutation tests under the null

hypothesis. Higher Zsummary scores indicate better preservation.

Supplemental Experimental Procedures

Song Recording

Birds were recorded constantly from the initial placement of their home cage into a sound attenuation chamber at ~20 d to sacrifice at 65d. Countryman EMW or Shure SM93 omnidirectional lavalier microphones were used. Sounds were digitized using PreSonus FirePod or PreSonus Audioboxes at a 44.1 kHz sampling rate and 24-bit depth. Recordings were managed by Sound Analysis Pro 2011 software (SAP, Tchernichovski et al. 2010).

Detailed Experiment Timeline

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Following methods established by Heston and White (2015), we isolated breeding cages that contained candidate experimental birds along with their parents and siblings when the juveniles reached ~20d. The breeding cages were recorded constantly upon isolation so as to capture tutor vocalizations and ensure juveniles did not sing prior to surgery. At 30d, juvenile males were bilaterally injected with AAV1 to overexpress either FoxP2.FL, FoxP2.10+, or GFP and returned to their breeding cages following surgery. At 40d, juvenile males were isolated from all other birds and recorded constantly. At ~60d, an ‘NS-UD’ experiment was performed following the methods of Miller et al., Chen et al., and Heston et al. (2010, 2015, 89] to assess the bird’s ability to induce variability into its song resulting from practice. On the ‘NS’ day, for the first two hours following lights-on, birds were distracted if they attempted to sing. (Those that sang >10 motifs were excluded from that day’s experiment). On the ‘UD’ day, birds were allowed to sing unrestricted for the first two hours following lights-on. The level of variability in the animal’s songs immediately following those two hours was then quantified. Birds were later sacrificed at

65d following two hours of unrestricted singing. In order to assure a broad range of song amounts immediately preceding sacrifice (and to thereby capture a range of singing-induced gene expression), we prevented one bird in the GFP group from singing during the two hours preceding sacrifice.

Stereotaxic Surgery and Viruses

As described in Heston et al. (2015), 30d juvenile males were anesthetized using 2-4% isoflurane in pure oxygen and secured in a custom-built avian stereotaxic apparatus, then injected with virus to overexpress FoxP2.FL, FoxP2.10+, or GFP bilaterally into Area X at the following coordinates: 45o head angle, 5.15 mm rostral of the bifurcation of the midsaggital sinus, 1.60

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mm lateral of the midline, and to a depth of 3.3 mm. Virus was injected via a Drummond

Nanoject II through a glass microelectrode with inner diameter between 30 and 50 μM backfilled with mineral oil. Three 27.6 nL injections were performed with a 15 second wait between injections and a 10- minute wait before retraction of the electrode so as to minimize vacuum action pulling the virus away from the injection site. Incisions in the scalp were closed with

Vetbond (3M, St. Paul, MN, USA) and the animals given oxygen for 1-2 minutes until alert, upon which they were returned to their home cages.

AAVs used in this study to overexpress FoxP2.FL and GFP were identical to those produced by

Virovek and used by Heston et al (2015). The AAV used to overexpress FoxP2.10+ was otherwise identical to those viruses, except its gene product was the complete coding sequence for

FoxP2.10+, first discovered and cloned in zebra finch by Teramitsu & White (2006) (Genbank

Accession Number DQ285023). All virus titers were 2.24E+13 vg/ml, thus equal volumes of delivery were used for each virus. Using this method, Heston et al. (2015) estimated that

24±5.5% of neurons at the epicenter of the virus injection are transfected by the virus and that

96.7±1.7% of cells that are transfected are neurons. These transfection rates are sufficient to observe a behavioral effect of the virus and were thus used in the present study.

We also used HSV to express a form of FoxP2.10+ labeled tagged with an Xpress epitope and included a GFP transfection reporter. The limited cloning capacity of AAV precluded our ability to express a reporter gene in the viruses that we used for behavior and RNA-seq experiments.

We opted not to include an epitope tag in the AAV to prevent conformational changes to the exogenous FoxP2.FL or FoxP2.10+ proteins that may affect their ability to dimerize and/or

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interact with DNA. FoxP2.10+ is identical to FoxP2.FL except for a 10 amino acid difference at its C-terminus. No antibody specific to this isoform exists, thus we used the Xpress tag. HSV has a considerably larger cloning capacity than AAV, which allowed us to include a GFP reporter that expresses as its own transcript independent of FoxP2.10+. Surgical procedures were identical to those performed with AAV except the virus was diluted to 60% in dPBS immediately preceding injection, per the recommendation of the manufacturer. HSV reaches peak expression much more rapidly than does AAV, thus birds injected with HSV were sacrificed 3-5 days post-injection (Neve et al. 2005).

In Situ Hybridization

In situ hybridizations were performed following the procedures of Jacobs et al. (2000) and using the two probes antisense to different regions of the zebra finch FoxP2 mRNA transcript as described in Teramitsu et al. (2004). 20 μM thick sections were thaw mounted onto Superfrost

Plus microscope slides (ThermoFisher Scientific, Waltham, MA, USA), then postfixed with 4% paraformaldehyde in PBS, pH 7.4. Sections were hybridized with [33P]UTP-labeled RNA probes.

PCR Primers

Due to the shared sequence of the FoxP2.FL and FoxP2.10+ transcripts, we were unable to measure FoxP2.FL independently of FoxP2.10+. The primer pair used for FoxP2.FL has been published previously to quantify FoxP2 knockdown (Haesler et al. 2007,78], thus we also used it here. The forward sequence used was 5’-CCTGGCTGTGAAAGCGTTTG-3’ and reverse

5’ATTTGCACCCGACACTGAGC-3’. We designed a primer pair for FoxP2.10+ using the

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NCBI Primer-BLAST tool (Ye et al. 2012). The input sequence to Primer-BLAST was the

FoxP2.10+ mRNA CDS (GenBank accession DQ285023.1). The forward primer sequence used was 5’- CGCGAACGTCTTCAAGCAAT-3’ and the reverse sequence used was 5’-

AAAGCAATATGCACTTACAGGTT-3’. Primer specificity was determined by obtaining a single peak in melting curve analysis and obtaining a single amplicon of predicted size following qPCR. GAPDH forward and reverse primers were 5’-AACCAGCCAAGTACGATGACAT-3’ and 5’-CCATCAGCAGCAGCCTTCA-3’, respectively. qRT-PCR Experiments

200 ng of RNA from Area X punches was reverse transcribed into cDNA using the Bio- Rad iScript cDNA Synthesis Kit (Hercules, CA, USA). 25 μL qPCR reactions were assembled in

MicroAmp Optical 96-Well Reaction Plates (ThermoFisher Scientific). Reaction components were 0.5 μL cDNA, 200 nM primers, 12.5 μL PowerUp SYBR Green Master Mix

(ThermoFisher Scientific), and 10.75 uL nuclease-free water. Cycling conditions were 50°C for

2 minutes, 95°C for 2 minutes, then 40 cycles of 95°C for 15 seconds and 60°C for 1 minute. A dissociation step of 95°C for 15 seconds, 60°C for 1 minute, 95°C for 15 seconds, and 60°C for

15 seconds was then performed. All reactions were run in triplicate and all samples for an individual animal were run together on the sample plate. FoxP2 expression was quantified

-Δ ΔC relative to GAPDH and normalized to the GFP-injected animals using the 2 T method (Livak

& Schmittgen 2001).

Song Analysis: (NS-UD)/(NS+UD) Effect Size vs. Raw Acoustic Feature CV

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The calculation of effect size was performed because it allows for comparison across virus groups instead of a series of paired comparisons within group (Miller et al. 2015). The transformation normalizes acoustic features so that any observed changes are viewed in the context of the initial values. We present a hypothetical example in the table below where a change of 50 Hz for two syllables is assessed as of higher magnitude following the transformation that we applied for our song data:

RNA-Seq Preprocessing & WGCNA

Raw FASTQ files furnished by UNGC were first quality controlled using FASTQC

(http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). FASTQC returned results indicating high quality across all bases in each read in each sample and no adapter contamination was detected, therefore we did not perform any filtration of the reads before alignment. Reads were aligned to the NCBI zebra finch genome assembly 3.2.4

(http://www.ncbi.nlm.nih.gov/assembly/524908/) using STAR (Dobin et al. 2013). Mismatch tolerance was two base pairs and only uniquely mapping reads were considered in downstream analyses. The featureCounts() function in the Rsubread R package was used to count all reads mapping within exon features, then all exon counts were summed to the gene level so that each gene had a single value of reads mapped to it (Liao et al. 2013, Liao et al. 2014). Gene expression was then quantified by calculation of transcripts per million (TPM). TPM values were

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log2 transformed and genes with zero variance across samples were removed. We used an iterative process of removing gene expression data from single samples whose expression was greater than 2.5 SD of that gene’s expression across all samples, repeating until no samples remained with expression greater than 2.5 SD away from the gene’s average expression across all samples. Finally, we calculated the intrasample correlation (ISC) and used a hard cutoff of 2

SD away from the group ISC for removal of samples from the study. No sample in any group

(Area X or VSP) was greater than 2 SD from the group ISC. Data were quantile normalized as the last step. Final data input to WGCNA was 13665 and 13781 genes for Area X and VSP networks, respectively, across 19 total samples.

We calculated the soft thresholding power for construction of the WGCNA adjacency matrix using the pickSoftThreshold function in the WGCNA R package at 18 for Area X and 14 for

VSP. We then constructed a signed network using the blockwiseModules function in the

WGCNA R package. For the Area X network, we used a minimum module size of 100 genes and deepSplit was set equal to 4 for Area X and 2 for VSP. Genes were required to have at least a connectivity of 0.3 with their module eigengene in order to remain a member of their module and the module ‘core’ ( = minimum module size/3) needed to have a minimum eigengene connectivity of 0.5 for the module to not be disbanded. All other parameters were set to default.

Networks were iteratively constructed with genes in the grey module removed from the expression data after each round of network building and module definition. The networks were considered final after no genes were placed into the grey module.

During network construction, FoxP2 was removed, presumably due to the lack of coexpression with other genes in the network resulting from virus-driven overexpression. Therefore, we added

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FoxP2’s expression data back into the final network and it became the only gene in the grey module. Once coexpression modules were defined, we correlated vocal behavior to the module eigengenes. Since the grey module included only a single gene with no significant behavioral correlations, it was excluded from module-trait analyses.

Network Visualization and Interactive Figures

We have created interactive versions of many of the network plots in this manuscript (Figure 2-

2F), all additional Area X modules (similar to Figure 2-2F but not presented in the manuscript), and the protein interaction network presented in Figure 2-7. They are hosted at our laboratory website (https://www.ibp.ucla.edu/research/white/genenetwork.html) along with high resolution static PDF versions.

In weighted coexpression networks, each node (i.e. gene) is connected to every other node in the network, even if the weight of the edge (i.e. connection) is zero. Therefore, plots depicting nodes and their edges with other genes become exceedingly complicated and unintuitive if all nodes and edges are included. In an effort to sparsify the networks and present the most salient data, we removed edges and genes from the coexpression networks using the following workflow: first, remove ≤ 98% of edges, then remove all disconnected nodes, then remove all nodes that are not part of the network’s main component (e.g. the largest group of connected nodes). The remaining nodes and edges were plotted.

In this manuscript, we present three types of network plots that look similar but convey different data. The three types are as follows:

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1. The entire gene coexpression network, as in Figure 2-S2 and

https://sites.google.com/a/g.ucla.edu/genenet/coexpressionnetwork. In these plots, the

nodes represent genes and their colors represent the module assignment. Edges represent

the adjacency between nodes and the edge color is a combination of the origin and target

node colors. Due to the overwhelming number of edges in this network, the edge weights

are scaled to minimize the range. Node size in this network is equivalent to the node’s

degree (e.g. the number of connections originating or terminating at that node) and the

maximum node size is suppressed so as to provide maximal visual clarity.

2. Individual coexpression modules, as in Figure 2-2F and

https://sites.google.com/a/g.ucla.edu/genenet/modules. These plots are similar to the

preceding except that, potentially, more nodes are present in the module since the

filtration procedures detailed above are applied in a different context (e.g. only the

expression data in the module are considered here vs. the expression data for the entire

network). The same scaling parameters as above are applied to the edges for visual

clarity.

3. Protein interaction network, as in Figure 2-7 and

https://sites.google.com/a/g.ucla.edu/genenet/protein. Nodes represent proteins and their

colors represent the coexpression module assignments. Node size is equivalent to its

degree. Here, the edge width conveys meaning and is helpful in interpreting the

relationship between nodes. An edge is drawn between two nodes when the STRING

database indicates a high confidence interaction (score ≥ 0.9) between them. Edge widths

are the confidence score scaled by the product of the origin and target node’s

intramodular connectivities (kIN). Thus, thick edges indicate a high confidence protein

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level interaction between two genes that are well connected members of learning and

singing related modules. Unlike the previous plots, a node’s size does not necessarily

convey a higher degree of coexpression network importance. Instead, it indicates many

interactions involving this protein described in the database. The thickness of the edges

conveys influence of the gene’s biological importance, as interpreted through their kIN.

Whether a node’s degree or the weight of its connections is the ultimate determinant of

its relationship to vocal learning remains to be determined but the reader should keep the

preceding information in mind when interpreting this network.

Figure A-1: Overexpression of FoxP2 isoforms

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a) Schematics of the full-length (FoxP2.FL) and 10+ (FoxP2.10+) transcripts. The regions targeted by the complementary RNA probes are shown in red. b) Left panel depicts experimental design to test for isoform-specific expression in vivo. Middle and right images depict two sections from the same female brain. For purposes of validation only, the bird’s right hemisphere

(shown on left) was injected with an AAV expressing FoxP2.FL while the left hemisphere was injected with the FoxP2.10+ construct. Two weeks post-injection, robust signals in the

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striatopallidum were observed in both hemispheres using the mid probe but only in the hemisphere injected with the FoxP2.FL construct using the 3’ probe. Signals reflect both the endogenous FoxP2 expression pattern (Teramitsu et al. 2004,32,104] as well as enhanced levels due to viral-driven expression. c) FoxP2 expression quantified by qRT-PCR in juvenile males that were bilaterally injected with one of the constructs at 35d using primers that identify both isoforms (left graph) or the FoxP2.10+ isoform only (right graph). Enhanced expression is observed in the FoxP2.FL (grey; 126.5 ± 13.53%; n=6;) and FoxP2.10+ (red; 162.4 ± 26.77%; n=6) groups relative to levels of birds that received the GFP control construct (green;

100±7.54%; n=7). Values represent percentage relative to GFP ± SEM. * and # denote p =

0.0309 and p = 0.0841, respectively, of an unpaired two-tailed bootstrap vs. GFP. d) A cell in the zebra finch striatopallidum expressing GFP (indicating viral transfection; green), endogenous

FoxP2 as revealed by an antibody directed to the C-terminus (red), and Xpress- FoxP2.10+ revealed by an antibody to the Xpress tag (cyan). The Xpress signal is reminiscent of FoxP2.10+ aggresomes observed by Vernes et al. (2006).

Figure A-2: Overexpression of FoxP2 isoforms affect learning and/or variability in song

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a) Timeline of experimental procedures relative to critical periods in song development. b)

Schematic illustrates NS-UD or UD-UD experiments performed on adjacent days. c) The effect size of two hours’ UD singing on syllable CV was calculated using the formula (NS-

UD)/(NS+UD) after an NS-UD, UD-UD experiment performed at ~60d and 61d as in (b).

Overexpression of FoxP2.FL (grey bars; n = 16 syllables; Duration = -0.059 ± 0.029; AM = -

0.010 ± 0.028; Entropy = -0.038 ± 0.04) diminishes singing induced variability relative to that seen in GFP-expressing controls (green bars; n = 9 syllables; Duration = -0.128 ± 0.071; AM = -

0.065 ± 0.035; Entropy = -0.091 ± 0.034) In contrast, overexpression of FoxP2.10+ (red bars; n

= 13 syllables; Duration = 0.070 ± 0.054; AM = 0.088 ± 0.047; Entropy = 0.048 ± 0.029) causes singing to induce a relative state of invariability. Bar heights represent the average effect size for all syllables within the virus construct group ± SEM. * denotes significant result in one-way

ANOVA (Duration: F(2,35) = 3.95, p = 0.028; AM: F(2,35) = 3.96, p = 0.028; Entropy: F(2,35)

= 3.63, p = 0.037) and Tukey’s HSD post-hoc test (p < 0.05). d) Learning curves plot the relationship between percentage similarity to tutor as a function of time. Animals overexpressing

GFP (green; letter ‘A’; n = 7 birds; ~65d similarity = 67.2 ± 6.64%) or FoxP2.10+ (red, letter

‘B’; n = 5 birds; ~65d similarity = 75.8 ± 2%) learn significantly better than those overexpressing FoxP2.FL (grey, letter ‘C’; n = 5 birds; ~65d similarity = 44.3 ± 10.1%). Data are binned by day (top panel; bold points represent group mean and shifted smaller points are individual birds) or by individuals (bottom panel). Statistically significantly different groups tested by one-way ANOVA (Bin 1: ~40d F(2,11) = 6.06, p = 0.016; Bin 3: ~55d F(2,13) = 6.04, p = 0.014; Bin 4: ~60d F(2,14) = 9.94, p = 0.002; Bin 5: ~65d F(2,14) = 4.76, p = 0.026) and

Tukey HSD post-hoc test (p < 0.05) are denoted by capital and lowercase lettering. e) Exemplar motifs of a tutor and three of his 65d pupils, each of which was injected with a different viral

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construct at 30d. These examples illustrate the percent similarity depicted in panel D. f)

Summary of the learning and variability phenotypes observed after virus injection.

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Figure A-3: WGCNA yields behaviorally relevant modules

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a) Dendrogram (top) illustrates the topological overlap between genes in the juvenile Area X network. Modules delineated by automated tree trimming are shown below and are depicted by arbitrary colors. Beneath the color bar, gene significances to the quantified behaviors (number of motifs sung, tutor similarity, acute variability changes, and overall variability; see Results) are indicated by a heatmap wherein red indicates a positive correlation and blue indicates a negative correlation (see B for scale). b) Correlations between module eigengenes and each behavior are presented as a heatmap. The Pearson’s ⍴ and, in parentheses, Student’s asymptotic p-values for modules where p ≤ 0.05 are displayed. c) For all significant module-trait correlations, the relationship between gene significance and module membership is plotted for each gene in the module. Dashed lines represent the linear regression and the Pearson’s ⍴ (“cor”) and p-value as determined by Fisher’s z-transformation are indicated above each plot. d) The average whole network connectivity (kTotal) within each module reveals that the purple, green, and pink modules are composed of the most strongly connected genes in the network. e) Term significances for the black, darkred, and green modules for are indicated for disease, gene ontology biological process and molecular function, as well as for pathways for categories annotated as ‘neuronal’ in the GeneCards GeneAnalytics software. f) Network plots of the modules presented in panel E where nodes represent genes scaled by the node’s intramodular connectivity and edge width displays the topological overlap between genes.

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Figure A-4: Area X singing related gene coexpression patterns are not preserved in VSP

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a) Dendrogram (top) displays the topological overlap in Area X between genes common to both

Area X and VSP networks. Beneath, the module assignments and the gene significances for each gene as calculated using expression from VSP (“V”) or Area X (“X”) for all behaviors are quantified as in Fig. 3A. Module colors are consistent with those presented in Figure 3. b)

Module preservation (Zsummary) for all modules that were present in both Area X and VSP displayed as a function of ME correlation to motifs. Lower and upper dashed horizontal lines indicate thresholds for low and high preservation, respectively. c) Circle plots display the adjacencies between the 20 most well-connected genes in the Area X black, cyan, green, royalblue, and blue modules. The adjacency between genes is indicated by edge thickness. Genes grouped together in the black, cyan, royalblue, and blue song modules in Area X have numerous and strong connections. Those connections are weakened or nonexistent in VSP such that genes sort into different modules in VSP. In contrast, the green learning module genes maintain their common grouping and connections in VSP. d) Raw gene expression is tightly correlated between

Area X and VSP for the genes in the black, cyan, green, royalblue, and blue modules (top). Only the intramodular connectivity of the genes in the green module is correlated between Area X and

VSP (bottom). Dashed lines represent the linear regression.

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Figure A-5: Area X song but not learning modules are preserved into adulthood

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a) Dendrogram (top) displays the topological overlap in juvenile Area X between genes common to both juvenile and adult Area X networks. The module assignments and the gene significances to motifs in juveniles and adults are presented below. Module colors are consistent with those presented in Figure 3. b) Module preservation (Zsummary) for all modules that were present in both juvenile and adult Area X displayed as a function of ME correlation to motifs. Lower and upper dashed horizontal lines indicate thresholds for low and high preservation, respectively. c)

Circle plots display the adjacencies between the 20 most well-connected genes in the juvenile

Area X black, cyan, green, royalblue, and blue modules. The adjacency between genes are indicated by edge thickness. Genes grouped together in the black, cyan, royalblue, and blue song modules in Area X have numerous and strong connections that are mostly maintained in adulthood. The densely interconnected green learning module genes found in juveniles do not maintain these relationships in adulthood. d) Strong positive correlations between gene significance to motifs exist for all modules (top row). Ranked expression values for the genes in each module also show positive correlation (middle row). Intramodular connectivity is more positively correlated between ages for the black, cyan, royalblue, and blue song modules than for the green learning module.

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Figure A-6: Gene significance and network position implicate MAPK11 as a molecular entry point to vocal learning mechanisms

a) The 20 genes with the highest to lowest gene significances to tutor similarity (sorted from top to bottom) are shown. Each column represents a bird and columns are sorted in order of increasing tutor similarity from left to right. Gene expression is scaled such the highest and lowest expression across samples have the brightest shade of red or blue, respectively. Below, expression of MAPK11 is shown again, here separated by virus group and then sorted by increasing tutor percentage similarity. b) The FoxP2 binding sequence as annotated by the

JASPAR database (top) and a potential binding site found in the promoter upstream of MAPK11.

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c) MAPK11 and its 10 closest network neighbors, including green learning module members and hub gene ATF2, as defined by topological overlap.

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Figure A-7: Protein-level interactions between song and learning module genes in juvenile Area X

A protein interaction network plot using the STRING database between genes in learning

(darkred, green, greenyellow) and song (black, blue, darkgreen, orange, royalblue) modules.

Nodes are scaled by number of connections. Edge width is determined by scaling the STRING

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protein interaction confidence score for the two nodes by the product of each node’s intramodular connectivity. Interactions within learning or song modules are omitted for clarity.

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Figure A-8: Changes in vocal plasticity state between juvenile and adult birds

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a) The juvenile straitopallidum (left) exists in a plastic state in which genes in learning modules

(e.g. green) are densely interconnected and of high importance in the network. Simultaneously, singing driven gene coexpression patterns occur. In the adult striatopallidum (right), song modules exist as they do in juveniles, but the learning modules do not. b) Area X modules in the juvenile brain are plotted to emphasize their preservation in adult Area X (x axis) and juvenile

VSP (y axis). Points representing the module colors are scaled by the module’s absolute correlation to learning (left) or the absolute correlation to singing (right), emphasizing the preservation of singing coexpression patterns into adulthood and learning coexpression patterns in the juvenile striatopallidum. c) Genes in song or learning modules that are within two steps of

ATF2 in the high-confidence protein interaction network are shown. Nodes are scaled by intramodular connectivity in juveniles (top) or adults (bottom) with edge width indicative of adjacency between genes in the coexpression network. The change in coexpression patterns across age groups causes decreased connectivity of many learning-related genes, driving an alteration in the network’s landscape which may underlie the transition from song learning to song maintenance.

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Figure A-S1: Raw acoustic feature variability in the NS and UD conditions by virus group

The raw acoustic feature CVs transformed by the calculation in Figure 1D show the variability relationship between NS and UD contexts for all measured acoustic features. For most song features, UD singing drives increases in CV in the GFP group. This effect is blocked or reversed in the FoxP2.FL and FoxP2.10+ groups, respectively. Notably, the songs of FoxP2.10+ animals following 2 hours of UD song were significantly less variable than those after 2 hours of silence.

Asterisks indicate a significant difference (p < 0.05) in a paired resampling test within virus construct.

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Figure A-S2: Juvenile Area X gene coexpression network

The gene coexpression network is displayed with genes represented as nodes and colors indicating the module assignment of each gene. Nodes are scaled by their degree and edge color is the combination of the module colors of nodes connected by the edge. Poorly connected nodes are excluded. 158

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