Genetic diversity underlying behavioral plasticity in human adaptation

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Genetic diversity underlying behavioral plasticity in human adaptation

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Amy L. Bauernfeinda,b,*, Courtney C. Babbittc a

Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, United States b Department of Anthropology, Washington University in St. Louis, St. Louis, MO, United States c Department of Biology, University of Massachusetts Amherst, Amherst, MA, United States *Corresponding author: e-mail address: [email protected]

Abstract The human brain is notably different from that of other primate species by its size and structure, in addition to its behavioral output. As we seek to understand how the human brain has evolved, many researchers have turned to genomics to help elucidate the biological basis for uniqueness of the human brain. When considering the molecular evolution of the human brain, a common misconception is that molecular evolution should be “unidirectional”—progressing along a single trajectory with the human brain as the ultimate goal. This outlook fails to acknowledge the importance of variability in the evolutionary process. In this review, we review what we know about inter- and intraspecific molecular diversity in the human brain arising from heritable and non-heritable sources. We note that genetic substitutions may not be optimal in brain evolution due to pleiotropic effects. Instead, we focus on other sources of molecular diversity including gene duplications, copy number variations, and transcriptional regulation. With recent advancements in the field of single-cell genomics, we explore what is currently known about gene expression at the cellular level and highlight opportunities to advance our understanding of human uniqueness at the neuronal level.

Keywords Chimpanzee, Gene expression, Pleiotropy, Duplication, Copy number variations, Transcription

1 Introduction How the human brain evolved is one of the most awe-inspiring riddles of the natural world. When compared to any other species, humans are unmatched in their abilities to communicate using complex speech, perform abstract thought, and use complex Progress in Brain Research, Volume 250, ISSN 0079-6123, https://doi.org/10.1016/bs.pbr.2019.06.002 © 2019 Elsevier B.V. All rights reserved.

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tools to manipulate the world around them (Sherwood et al., 2008). Humans are also unique in their behavioral flexibility, including abilities to endure environmental change, thrive in new environments, spread to new habitats, and respond in novel ways to their surroundings (Potts and Faith, 2015). Yet, the ability to define phenotypic specializations in the human brain that account for the behavioral uniqueness of humans—in particular their behavioral flexibility—has been challenging. In his seminal work, The Origin of Species, Charles Darwin wrote “the more diversified the descendants from any one species become in structure, constitution, and habits, by so much will they be better enabled to seize on many and widely diversified places in the polity of nature, and so enabled to increase in numbers” (Darwin, 1859, p. 112). Although Darwin had no knowledge of genetics or genomics—the structure of DNA was to be discovered nearly a century later—he appreciated that variation in phenotype (in this case, both anatomical and behavioral) among individuals of a species was critical to its success through his theory of natural selection. More recently, the term “variability selection” has been coined to describe how gene combinations and behaviors that facilitate resilience and enable novel responses to environmental conditions have accrued in the human lineage as a response to large-scale environmental oscillations occurring as our species evolved (Potts, 1996). When it comes to the brain, diversity in genetics of neurons determines the diversity of interconnectivity among neurons—and by extension, the breadth of behaviors the brain can produce. If humans have evolved to be particularly adaptable, the genetics underlying the human brain is the substrate through which this has occurred. The goal of this chapter is to review different sources of variation affecting the phenotype of the human brain. We begin by examining what we currently know about sequence evolution that occurred in the lineage leading to humans. However, because of the effects of pleiotropy (a change in one gene having two or more apparently unrelated downstream effects), sequence evolution may be somewhat limited in its role in the evolution of the human brain. Next, we explore other ways in which the human genetic code has changed to avoid pleiotropic effects, including gene duplication, larger-scale copy number variation, and expression regulation by transcription factors. As the field of single-cell genomics is relatively new, we highlight the rising potential of this field to inform how human neurons differ from those of other primate species. When discussing gene expression within individual cells, we note that biological “noise” is a non-genetic source of phenotypic variation that may also be adaptive. Our appraisal of genetic variation will focus on the interspecific variation of the human brain compared to that of nonhuman primates, with a particular focus on chimpanzees as our closest living relatives.

2 Searching for a genetic basis for the phenotype of the human brain The heritable phenotype of any organism must be genetic to be passed to subsequent generations. In the simplest terms, random mutations accrue within the genome over a series of generations that changes the phenotype of individuals. When natural

2 Searching for a genetic basis for the phenotype of the human brain

selection “selects” for the trait within a population, the frequency of that trait increases within a population over subsequent generations. Essential to the evolutionary process is having phenotypic diversity (and therefore, genetic diversity) across a population through which natural selection can act (Kirschner and Gerhart, 1998). In other words, variation arises within a species before a divergence of diversity between two species is apparent. Therefore under fluctuating environmental conditions, such as those predicted during the evolution of the human species (Potts, 1998), organisms with high levels of genotypic variation may be more adaptable than species with limited diversity (Hoffman and Meril€a, 1999; Holloway et al., 1990) (Fig. 1). King and Wilson (1975) are well known for their observation that protein sequences of humans and chimpanzees contained far fewer amino acid substitutions than would be expected given the differences in behavioral phenotype of these two species. Their insights predicted a relatively recent common ancestry between humans and chimpanzees before this fact was confirmed from DNA sequencing. However, the authors based their theory on observations of hemoglobin proteins that may not have been expected to accrue many nonsynonymous substitutions because of its conserved function in transporting oxygen in the blood. Given the remarkable

FIG. 1 How greater phenotypic diversity could respond to directional selection. (A) The frequency of a phenotype is represented in two different populations. Although the mean is the same in both distributions, one population (red line) has a greater diversity of phenotype than the other population (blue line). When directional selection is applied at a low threshold (black line with arrow), the population with a lower diversity in expressed phenotype has a greater frequency of individuals above the threshold. This scenario occurs during weak selection of a relatively stable environment and demonstrates that the population with less diversity is favored. (B) In contrast, when directional selection is applied with a higher threshold, the population with more variation in the phenotype is favored. The scenario occurs during the tight selection of a highly variable environment and demonstrates that the population with more diversity is favored. (C) Over generations, the diversity of phenotype increases under tight selection and decreases under weak selection. Image is modified from Eldar, A., Elowitz, M.B., 2010. Functional roles for noise in genetic circuits. Nature 467, 167–173.

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phenotypic differences between the brains of humans and chimpanzees, might changes in genetic sequence be more appreciable in proteins that have brain-specific functions? The publication of the entire chimpanzee genome has enabled researchers to ask questions about the patterns of adaptive evolution that have occurred across the entire genetic code. Measuring the rate of evolutionary change in protein sequence is frequently determined by the Ka/Ks ratio, where Ks is the synonymous mutation rate and Ka is the nonsynonymous mutation rate (Yang et al., 2000). A Ka/Ks ratio of 1 would indicate a neutral mutation rate, Ka/Ks < 1 purifying selection, and Ka/Ks > 1 positive selection. When examining the molecular differences between humans and chimpanzees, one might predict that proteins with specific neural functions would have nonsynonmous substitutions accruing at a faster rate within protein coding regions, indicative of positive selection (Ka/Ks > 1). Across the entire genome, about 30% of homologous proteins are identical in humans and chimpanzees, while the rest of proteins differ by an average of two amino acids (Chimpanzee Sequencing and Analysis Consortium, 2005). Contrary to expectations, genes that have specific functional roles in the central nervous system have far fewer changes between primate species than do genes that are expressed in other tissues, indicative of purifying selection (Duret and Mouchiroud, 2000; Khaitovich et al., 2005). This may occur because genes expressed in the brain, or any other complex organ, tend to have strong functional constraints due to pleiotropy, limiting the tolerance of amino acid substitutions (Hayakawa et al., 2005). Pleiotropy occurs when a single variant within a gene affects more than one trait. Because the brain contains components that are interrelated and complementary— including incredibly complex cells with particular connectivity patterns which are subject to specific developmental programs—the brain might be highly constrained due to pleiotropy.

3 Gene sequence evolution in human brain evolution Determining how gene sequences contribute to phenotypic differences in behavior or cognition between any two species is incredibly challenging. One way around this problem is to consider candidate genes where appreciable changes in phenotype can be linked to specific mutations. The most well-cited example is the transcription factor, FOXP2. FOXP2, a member of the homeobox family of transcription factors, originally gained attention when it was discovered that a family that had profound speech deficits also had a single point mutation in the gene (Lai et al., 2001). Although relatively conserved across mammals, the human FOXP2 protein is known to differ from the chimpanzee variant by two amino acid substitutions, evidence consistent with a selective sweep in recent human evolution (Enard et al., 2002; but see Atkinson et al., 2018). When a humanized form of FOXP2 was bred into mice, the mice produced different vocalizations than wildtype mice without the insertion and had differing neuronal phenotypes, including greater dendritic lengths and synaptic

4 Adaptation through mechanisms that minimize the effects of pleiotropy

densities of medium spiny neurons within the basal ganglia (Enard et al., 2009). There does not appear to be much doubt that the nonsynonymous substitutions in FOXP2 account for phenotypic changes in humans but determining whether there is functional relevance for other point mutations remains challenging. The genes MCPH1 (microcephalin) and ASPM have inferred relevance to encephalization due to the fact that they are genetic loci associated with primary microcephaly, a disorder with profound phenotypic consequences (Vallender et al., 2008; Woods et al., 2005). Primary microcephaly is a genetically heterogeneous, autosomal recessive neurodevelopmental disorder associated with 12 distinct genetic loci that results in impaired cognitive function, drastically smaller than average brain, but otherwise normal brain architecture (Faheem et al., 2015). ASPM has been found to be under positive selection at time points that would account for an aggregation of nonsynonymous duplications in humans, including after the separation of great apes from Old World primates and since the human lineage split with chimpanzees (Evans et al., 2004; Kouprina et al., 2004; Zhang, 2003). Strong positive selection has been found within MCPH1 and ASPM in recent human evolution, causing genetic sweeps of specific variants within the last 6000 and 40,000 years respectively (Evans et al., 2005; Mekel-Bobrov et al., 2005). Although it is clear that these genetic loci associated with primary microcephaly continue to evolve within modern humans (Faheem et al., 2015), allelic variation of these two genes have not been correlated with any phenotypic differences in brain size or cognition in modern humans (Mekel-Bobrov et al., 2007; Timpson et al., 2007), so the effect of this variation remains unclear. We reviewed several genes that have accumulated mutations within their coding regions that may contribute to human behavior and brain phenotype. Next, we turn our attention to the adaptation through mechanisms that minimize the effects of pleiotropy, including gene duplications, larger-scale copy number variations, and changes in the regulatory regions of genes (Stern, 2000). In fact, the rate of single-base-pair substitutions across the genome that occurs within coding regions has slowed in the lineage leading to humans and chimpanzees relative to other primates, suggesting that point mutations are unlikely the sole driver of evolution in these species (Elango et al., 2006; Li et al., 1987; Moorjani et al., 2016). Examples of other changes in the genetic code that may account for phenotypic differences between humans and chimpanzees are discussed in the paragraphs that follow.

4 Adaptation through mechanisms that minimize the effects of pleiotropy One way that the function of a gene can be modified without sacrificing its original purpose is through gene duplication, most commonly caused by unequal crossing over during meiosis or retroposition. Having two or more copies of a gene allows the duplicated gene to acquire a novel or modified function without sacrificing its original purpose (Ohno et al., 1968) (Fig. 2A). GLUD2 is an X-linked intronless gene

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FIG. 2 A schematic of the genomic changes seen between humans (H) and chimpanzees (C) to avoid the consequences of pleiotropy in changing gene expression. The top shows a schematic gene with amino acid (a.a.) (colored rectangles) and a transcriptional start site (black arrowhead). Here, as in the human and chimpanzee genomes, most a.a. residues are conserved between species (here, the coding regions of the two species differ by a single a.a.). (A, B). Duplication and copy number variations(CNVs). (A) Gene duplication, where duplicated paralogs are able to evolve a.a. changes without changing the function of the original genes. (B) CNV in the genome, where new copies of a region can also accumulate change in the protein sequence. (C, D) cis-Regulatory change. (C) Changes in the gene regulatory region, upstream of the transcription start site, where transcription factor binding modules (triangles) can change between species, without changing the protein-coding region. (D) Coordinated regulation, where one common regulator can bind to the transcription factor binding site of more than one gene.

that likely originated from the exon of the GLUD1 gene on chromosome 10 (Shashidharan et al., 1994). GLUD1 and GLUD2 code for two isotypes of glutamate dehydrogenase (GDH), an enzyme that causes the deamination of the neurotransmitter glutamate to α-ketoglutarate which is necessary for carbohydrate metabolism. GLUD2 differs from GLUD1 in two important respects. It is expressed highly in the brain due to having been inserted in the genome near a brain-specific promoter, and it acquired several amino acid substitutions that may optimize its function within the brain (Burki and Kaessmann, 2004). Interestingly, GLUD2 is present in humans and other great apes but absent in Old World monkeys, indicating that this gene likely came into existence about 20 million years ago (Burki and Kaessmann, 2004). This evidence suggests that species that have GLUD2 may be able to better optimize energy use in the brain, particularly under low energy states (Spanaki et al., 2012). Indeed, the rate of genomic duplications surged in the lineage leading to humans and the other great apes, and the enriched functions of the affected genes include neuronal activities and signal transduction (Hahn et al., 2007; Marques-Bonet et al., 2009).

5 Transcription factors contribute to variability of multiple genes

Copy number variations (CNVs) or structural variants (SVs) are the gain or loss of segments of genomic DNA >1 kb in length that encompass one or more genes and are caused by unequal crossover (Fig. 2B). In the lineage leading to humans and the other great apes, duplication events have clustered around ancestral loci, producing complex mosaics of genomic segments that can form the basis of novel genes or gene families (Bailey et al., 2002; Eichler, 2001; Johnson et al., 2001; Vandepoele et al., 2005). A recent analysis of the human genome in comparison with new high-quality great ape genomes found nearly 18,000 human-specific SVs (Kronenberg et al., 2018). The Olduvai protein domain (previously called DUF1220) is an example of a region that shows a dramatic elevation in copy numbers of constituent genes within the human lineage compared to other great apes (roughly 126 of 300 total copies are human-specific) (Dumas et al., 2012; Popesco et al., 2006). It has been shown that the number of CNVs in this domain is positively correlated to brain size during the evolutionary history of humans, and within human intraspecific diversity (Dumas et al., 2012; Zimmer and Montgomery, 2015). However, neurological conditions including schizophrenia and autism have been associated with the extreme variant numbers of the Olduvai domain: gene deletions in this region are associated with smaller brain size and schizophrenia, while gene duplications are associated with larger brain size and autism (Dumas and Sikela, 2009). The evolutionary advantage of increasing CNVs of the Olduvai protein domain likely contributes to instability of the region in humans, potentially contributing to neurological disorders (Sikela and Searles Quick, 2018).

5 Transcription factors contribute to variability of multiple genes Due to pleiotropy and other factors, transcription factor binding sites and other cis-regulatory sequences may be particularly important in evolution and adaptation (Carroll, 2005; Hoekstra and Coyne, 2007; Wray, 2007). Modifications of transcription factor binding sites can specify the timing, location, and efficiency of transcription, and therefore control the dynamic nature of gene expression. Transcription factor binding sites are often located in small functional clusters or modules upstream of a gene. This modular architecture means that a mutation in one module might affect only one aspect of regulation, like where, when, or by how much a gene is expressed (Stern, 2000) (Fig. 2C). Given the degree of behavioral divergence between humans and non-human primates, it is surprising that the brain shows more constraint in gene expression levels compared to other organs (Brawand et al., 2011; Khaitovich et al., 2005). Several studies have reported regions of the human genome under positive selection (Blekhman et al., 2008; Enard et al., 2010, 2014; Haygood et al., 2007, 2010; Reinius et al., 2008; Sabeti et al., 2006), but a greater proportion of these regions under positive selection are located in regulatory regions compared to protein coding regions (Enard et al., 2014). It has been shown that transcription factors are among the genes

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that have the most elevated expression in humans compared to other primates (Gilad et al., 2006). However, transcription factors are not among the genes with elevated expression levels in chimpanzees, suggesting that transcription factors have undergone directional selection within the human lineage. Understanding just how these changes to the genome are correlated with changes in phenotype is an exciting challenge. A systematic examination of interspecific differences in transcription factor binding on a genome-wide scale may compare how generally accessible those binding sites are between species. One way that this can be done is to examine histone modifications associated with regulatory functions and how they change over the course of development, providing differential access to transcription factor binding sites. Comparative epigenetic profiling of human, rhesus macaque, and mouse during cortical development have identified thousands of promoters and enhancers that have gained activity in humans (Reilly et al., 2015). Importantly, these gains were significantly enriched in genes that function in neuronal proliferation, migration, and cortical patterning. A similar study of regional patterning of adult human, chimpanzee, gorilla, gibbon, and macaques found that epigenetic changes affecting gene expression were linked to spatial expression differences across different brain regions (Xu et al., 2018). Modules of genes with correlated expression patterns also tend to have similar transcription factor binding sites, suggesting that common regulatory mechanisms affect genes within a module (Reilly et al., 2015; Xu et al., 2018) (Fig. 2D). Moreover, when affecting noncoding promoter regions, single nucleotide polymorphisms and SVs have the ability to change the binding of transcription factors, which is correlated with expression changes (Kasowski et al., 2010). Genetic variations in transcription factor binding are apparent between humans and chimpanzees, but also differ considerably across individuals of a species, suggesting a role for binding variation in inter- and intraspecific variation in phenotype (Kasowski et al., 2010).

6 Gene expression in individual neurons Beyond understanding which changes to the human genetic code has affected evolution of the human brain, we have yet to uncover how these genetic differences are manifested in individual neurons. We have reason to suspect that this question is overwhelming in its complexity. Consider that the human brain contains roughly 85 billion neurons (Azevedo et al., 2009) that can be categorized into disparate types based on their dendritic arbor, neurotransmitter, connectivity, firing pattern, and location within a specific brain region or cortical layer (Migliore and Shepherd, 2005). Yet, neurons comprise only 50% of the cells within the brain and assessing genomic diversity of single cells must also consider or exclude the other 85 billion non-neuronal cells, including glia. The recent development of technologies to perform single-cell RNA-seq (scRNA-seq) provides abundant opportunities to uncovering the genomics that

6 Gene expression in individual neurons

underlie diversity of cellular phenotypes. Algorithms have been developed that can sort transcriptomic profiles of neurons and glia according to reference panels (Darmanis et al., 2015; Li et al., 2018). Even so, by characterizing the expression of thousands of genes by scRNA-seq, neurons can be classified by a seemingly endless array of trait combinations (Andrews and Hemberg, 2018; Johnson and Walsh, 2017; Luo and Zhang, 2018). New cell types will certainly be “discovered” based upon nuanced differences in molecular expression (Harbom et al., 2016). In recent years, scRNA-seq has been used to analyze neuronal subtypes from mouse tissue samples (Tasic et al., 2016; Zeisel et al., 2015). However, isolating individual neurons from human brain tissue is more challenging due to the extent of myelination of the tissue and factors pertaining to the storage of samples (Darmanis et al., 2015). Due to these limitations, research with direct application to humans has employed mRNA isolated from single nuclei of particular neurons (single-nucleus RNA-seq [snRNA-seq]) because the neuronal nuclei are easier to isolate than whole neurons and contain the majority of the cell’s mRNA. An alternative approach to studying the transcriptomes of individual neurons is to study those derived from induced pluripotent stem cells (iPSCs) that offer the advantage of being sampled at different developmental time points, offering insight on developmental processes. After performing snRNA-seq on neurons from several different human brain regions, researchers discovered that the neuronal transcriptome profiles sorted into eight excitatory and eight inhibitory subtypes (Lake et al., 2016). Interestingly, the transcriptomes of these subtypes differed substantially by region of the brain (i.e., layer IV excitatory neurons differ in frontal cortex compared to visual cortex), indicating a regional specificity of neurons. Sousa et al. (2017) also confirmed that transcriptomic differences among humans, chimpanzees, and macaques that are apparent in specific regions extend to the level of individual cells. Furthermore, expression patterns that are unique to humans include upregulation of genes associated with dopamine biosynthesis within the striatum suggestive evolutionary alterations in this neuromodulatory system within the human lineage. Further investigations of single neurons will refine our knowledge of neuronal biology, allowing subtle differences between humans and nonhuman species to be revealed. Using human neurons developed from induced pluripotent stem cells (iPSCs), scRNA-seq has revealed that neuronal progenitor cells share initial transcriptional trajectories but increase in diversity as both excitatory glutamatergic and inhibitory GABAergic neurons mature (Mayer et al., 2018; Nowakowski et al., 2017). Because the progenitor cells of neurons are similar in gene expression, transcriptional programs affecting neuronal development appear to trigger diversification of neurons into their endless array of subtypes. It would seem unlikely that major differences in gene expression of neuronal progenitor cells would affect neuronal progenitor cells due to functional constraints that would be amplified with maturation. Instead, interspecific differences in mature neurons are much more likely to be caused by the postmitotic developmental programs that underlie the diversity of neuronal phenotypes.

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Cerebral organoids derived from human iPSCs has been established as a promising tool for studying differences between the human and chimpanzee brains across developmental time periods (Mora-Bermu´dez et al., 2016; Pollen et al., 2019). These methods are particularly effective in assessing the genetic changes underlying brain growth and cortical expansion. Over 70% of coexpression modules were highly correlated (>0.7) between primary tissue and organoid tissue, confirming that laboratory-derived tissue can be effective to studying human brain development (Pollen et al., 2019). Over the next several years, studies of brain organoids derived from human and nonhuman primate iPSCs will illuminate differences in the developmental process between primate species and the genetic basis for those differences.

7 Unexpected complexities: Variability resulting from non-heritable sources To advance our knowledge of how nervous systems work and evolve, we need to understand their constituent parts. Therefore, it might seem that the recent shift of focus from the brain (or its different regions) to single neurons would deepen our understanding of the brain’s biology. Far from simplifying the biology underlying the brain, the advancement of scRNA-seq has revealed additional layers of complexity that we had not previously known existed (Harbom et al., 2018). Even in cell cultures, where one would predict that expression of genes and proteins would be rather homogenous among genetically identical cells raised under the same environmental conditions, individual cells show a remarkable degree of heterogeneity in their molecular expression levels (Cai et al., 2006; Raj and van Oudenaarden, 2008). There are at least a couple reasons why the expression of genes and proteins can vary at the level of individual cells. First, the relatively random nature of biochemical events and stochastic balance of transcription and translation means that the resulting molecular expression in individual cells can be a bit erratic (Raj and van Oudenaarden, 2009). Additionally, because transcription and translation are energetically demanding processes, variability in expression levels at the cellular level may be linked to cellular metabolism. Guantes et al. (2015) reported that the number of mitochondria present in specific cells in culture accounts for about 50% of the variability observed in protein levels and that this effect occurs genome-wide. Given the myriad of functions that the energetic output of mitochondria must support within the brain, irregular metabolic support of transcription and translation may not be surprising (Muir et al., 2016). It is unknown—but provocative to speculate—whether species with the highest energy budgets for their brains, like humans, have more diversity in molecular expression simply by nature of what cellular metabolism can support. Sampling from cells within the human frontal cortex, McConnell et al. (2013) discovered large sections of the genome, ranging from 3 Mb to an entire chromosome, to be duplicated or deleted within individual neurons. Interestingly, although CNVs were much more common in neurons than anticipated, about 15% of neurons accounted for the majority of duplications and deletions. This is a fascinating finding as a relatively small proportion of neurons would be “eccentric” in their profiles

8 Conclusion

while the rest are “reliable.” Although the neurons with “eccentric” expression profiles may in fact be deleterious to resulting neural circuitry, the brain is well-suited to minimize the influence of these neurons (Macosko and McCarroll, 2013). During development, the brain overproduces synapses and subsequently prunes extraneous connections, allowing the neural circuitry to mature through a sort of trial and error. It would also seem likely that these “eccentric” neurons with more CNVs may contribute to the diversification of the behavioral repertoire by expanding the selection of circuitry that could result from a given genetic code. In an interesting development, Chronister et al. (2019) found that CNVs decrease drastically with age, most likely due to age-related atrophy of some of these “eccentric” neurons. Whether or not these neurons are particularly vulnerable to age-related cell death is unknown. It is also unknown whether or not the loss of these neurons contributes to age-related cognitive decline. Does the variation in molecular expression at the cellular level—whether due to molecular noise or de novo CNVs—serve any adaptive purpose? In fact, cellular diversity may be beneficial across the spectrum of life: from populations of unicellular organisms to cells within mammals (Bala´zsi et al., 2011; Eldar and Elowitz, 2010). Molecular variation alleviates the pressure for single cells to perform all necessary roles within an organism, allowing for specialization of function (Wahl, 2002a,b). In addition to division of labor (i.e., cellular tasks) across a population of cells, genomic variation is advantageous to survival in environmental fluctuations and resource scarcity (Acar et al., 2008; Cag˘atay et al., 2009). Molecular noise expands the array of resulting phenotypes that can be accounted for by genetics alone, which can be particularly advantageous in environmental fluctuations (Eldar and Elowitz, 2010). Although inconvenient for scientists attempting to define causal relationships between genotype and phenotype, it is important to remember that some aspects of brain structure may not be under the influence of genetic factors at all. Go´mezRobles et al. (2015) found that the overall cortical organization of humans is less heritable than that of chimpanzees in associative areas of the brain that are late to develop. Because regions that develop later in human neurodevelopment are also those that are most expanded human evolution (Hill et al., 2010), the authors surmised that the reason for the variability in human associative areas is derived from the prolonged neurodevelopmental sequence of humans. This study proposes that genetic control of cortical organization may be relaxed in humans to permit environmental, social, and cultural factors to influence gross structure of the brain (and by implication underlying microcircuitry of the cortex). Although the exact mechanism for how this is possible is not known, the implication for the study is that genetic flexibility allows a greater degree of learning in the human brain compared to other species.

8 Conclusion One of the defining features of the human brain is its ability to produce astounding behavioral flexibility. In this review, we explored how substitutions in the coding regions of the human genome may be functionally constrained, reducing the

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likelihood that differences in phenotype between humans and chimpanzees are a result of point mutations. We reviewed how gene duplications, CNVs, and regulation of expression from transcription factors more likely to account for the large-scale appreciable differences between these two species. The development of the field of single-cell transcriptomics offers countless possibilities for exploring interspecific differences but also introduces the complex variable of biological “noise.” Regardless of its origin, genetic diversity expands the spectrum of phenotypic variation—from cells, to circuitry, to behavior.

Acknowledgments We would like to thank funding from the Leakey Foundation (A.L.B. and C.C.B.), the National Science Foundation BCS-1750377 (C.C.B.), and the Wenner Gren Foundation (C.C.B.).

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