Neuron

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Human Brain Evolution: Harnessing the Genomics(R)evolution to Link Genes, Cognition, and Behavior

Genevieve Konopka1,2,3,4 and Daniel H. Geschwind1,2,3,4,*1Program in Neurogenetics, Department of Neurology2Center for Autism Research, Semel Institute for Neuroscience and Human Behavior3Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior4Department of Human GeneticsUniversity of California, Los Angeles, Los Angeles, CA 90095, USA*Correspondence: dhg@mednet.ucla.eduDOI 10.1016/j.neuron.2010.10.012

The evolution of the human brain has resulted in numerous specialized features including higher cognitiveprocesses such as language. Knowledge of whole-genome sequence and structural variation via high-throughput sequencing technology provides an unprecedented opportunity to view human evolution athigh resolution. However, phenotype discovery is a critical component of these endeavors and the use ofnontraditional model organisms will also be critical for piecing together a complete picture. Ultimately, theunion of developmental studies of the brain with studies of unique phenotypes in a myriad of species willresult in a more thorough model of the groundwork the human brain was built upon. Furthermore, these inte-grative approaches should provide important insights into human diseases.

Introduction: Genome Sequence and BeyondAttempts to understand the uniqueness of what it means to be

human necessarily start with delving into the properties of the

human brain. Genetics and genomics play a critical role in this

regard, because identifying the genetic basis of phenotypic

differences provides a causal foothold. Brain development,

from its circuitry to cell and dendritic morphology, is highly

dependent on the environment. Without genetic reference

points, it is challenging to ascribe observed differences between

CNS features in humans and our closest relatives, chimpanzees,

to the forces of evolution (whether neutral or adaptive), or to

differences in diet, lifestyle, or other environmental features.

Studying the brain from an evolutionary perspective and

combining these results with those from development and

pathology, connecting genetic variation to neural circuit devel-

opment and functioning, will yield the best approximation of

how natural forces shaped this organ. Aside from satisfying

basic curiosity about the origins of our abilities, such endeavors

have enormous implications for understanding human diseases

involving cognition and behavior, ranging from intellectual

disability and autism to neurodegenerative dementias.

On a macroscopic level, the evolution of the brain can be

examined using several approaches: neuroanatomical compari-

sons, behavioral comparisons, and the study of skeletal remains

from extinct species. These approaches are necessary for

building a context in which molecular and cellular comparisons

can be placed. In this era of genomics, we are poised to elabo-

rate the numerous divergent properties of the human brain by

collating gene expression and regulation data with phenotype

and behavioral data. Here, we discuss insights into the genetics

and genomics of human brain evolution generated through the

use of technological advances in expression and sequencing

platforms. These comparisons need to be interpreted carefully,

however, because experimental validation of evolutionary

studies is often challenging, and an openness to perform

cross-species comparative studies is needed. Nonetheless,

the integrated comparative analyses that can now be attained

using appropriate analytical and technical approaches are

providing fascinating insights into the story of human brain

evolution.

SequencingGenomes: ReadingOurEvolutionaryHistory

in the Code

The sequencing of the mouse genome followed quickly by the

human genome allowed us our first glimpse into evolutionary

comparisons at a genome-wide level (Lander et al., 2001; Venter

et al., 2001; Waterston et al., 2002). With at least 70 million years

interceding since the common ancestor of mouse and human,

however, there were obviously many differences found between

these genomes. Thus, the sequencing of the chimpanzee

genome a few years later filled in many of those gaps, demon-

strating approximately 4-fold more divergence between human

and chimpanzee than had previously been appreciated, mostly

due to structural chromosomal variation. Not unexpectedly,

these drafts are a true work in progress. For example, the chim-

panzee draft genome was based on one individual (Chimpanzee

Sequencing and Analysis Consortium, 2005). A recent reannota-

tion of the chimpanzee genome using new technology has

uncovered numerous novel transcripts (Wetterbom et al.,

2010). Moreover, the human genome was based on only a few

individuals, and until recently, only a handful of additional individ-

uals had undergone complete genome sequencing. Not unex-

pectedly, therefore, recent whole-genome sequencing has

uncovered much more diversity in the human genome than orig-

inally appreciated (Kidd et al., 2008; Pickrell et al., 2010). Thus,

until the completion of the 1000 genomes project, which aims

to identify most of the human genetic variants that have frequen-

cies of at least 1% of the population (www.1000genomes.org)

and beyond, any comparative analyses using the reference

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Table 1. Sequenced Species that Will Improve Our Understanding of Brain Evolution

Common Name Species

Original Date

Sequenced Unique Features

Mouse Mus musculus 2002 Common model system for neurobehavioral/

neurogenetic experiments

Human Homo sapiens 2001

Rat Rattus norvegicus 2003 Common model system for neurobehavioral

experiments; now available for genetic

manipulations

Fly Drosophila melanogaster 2003 Common model system for neurobehavioral/

neurogenetic experiments

Worm Caenorhabditis elegans 2004 Aging and longevity

Chimpanzee Pan troglodytes 2005 Great ape; most similar to human on genome level

Zebrafish Danio rerio 2005 Easy to visualize brain development; behavior;

genetically malleable

Rhesus macaque Macaca mulatta 2006 Old world monkey; bred in the USA for behavior

and neurophysiology experiments

Honey bee Apis mellifera 2006 Social behavior; aggression

Orangutan Pongo pygmaeus abelii 2007 Great ape; useful for outgroup comparison

with human/chimp

Elephant shark Callorhinchus milii 2007 Outgroup for zebrafish and mouse comparisons

Dolphin Tursiops truncatus 2008 Evidence for vocalization and self-awareness

Zebra finch Taeniopygia guttata 2008 Patterned vocalization

Sea hare Aplysia californica 2008 Learning and memory

Elephant Loxodonta africana 2009 Evidence for vocalization and self-awareness

Pig Sus scrofa 2009 Gyrencephalic cortex

Bat Myotis lucifugus 2010 Echolocation

Ferret Mustela putorius furo in progress Gyrencephalic cortex

Cichlids Tilapia nilotica in progress ‘‘Natural’’ mutants for the study of evolution

Marmoset Callithrix jacchus in progress New world monkey

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human genome need to take into consideration the caveats

associated with incomplete documentation of the range of

normal variation. This is especially relevant given the observation

that a significant percentage of any individual’s genome is vari-

able and diverges from normal diploid copy number.

One of the most exciting advances in the realm of sequence

comparison was the unveiling of a first-pass Neandertal genome

(Briggs et al., 2009; Burbano et al., 2010; Green et al., 2010).

Though controversies such as experimental contamination and

unknown admixture effects had stymied confidence in the initial

analysis (Green et al., 2009; Wall and Kim, 2007), these data,

together with the sequencing of other hominid species (Krause

et al., 2010), should provide evidence for human-specific

features that have promoted our continued evolution and

survival. Although this represents a technical tour de force, inter-

pretation at the whole-genome level is limited by the relatively

sparse average fold-coverage published. Detailed analysis of

three genes in the Neandertal genome has been conducted:

that of FOXP2 (Krause et al., 2007), the melanocortin 1 receptor

(Lalueza-Fox et al., 2007), and microcephalin (Lari et al., 2010).

None of these studies provided any insight into potential unique

features of humans compared with those of Neandertals,

because all three genes had variation in line with known human

variation, suggesting that these changes occurred in an earlier

232 Neuron 68, October 21, 2010 ª2010 Elsevier Inc.

ancestor. However, targeted resequencing of the Neandertal

genome identified 83 genes with nonsynonymous substitutions

that became fixed on the human lineage (Burbano et al., 2010).

Functional analyses of the effect of these changes in the human

proteins need to be undertaken.

The study of nonhuman primates is critical for understanding

human brain evolution, and the use of multiple primate species

is necessary for identifying changes that occur specifically in

the human lineage (Khaitovich et al., 2006a; Preuss et al.,

2004; Varki et al., 2008). However, we expect that the inclusion

of nontraditional model organisms in the study of brain evolution

will further inform our understanding of specific brain processes

or phenotypes, as well as uncover lineage-specific changes.

To this end, a number of little-studied species on the molecular

level, such as honeybees, songbirds, and elephants, have

recently had their genomes sequenced (Table 1).

Once the complete sequences of genomes are in place, an

often-utilized approach for determining the evolution of any

given gene or region of the genome, which relies on direct

sequence comparisons between two species, can be under-

taken. Measures of positive selection are calculated by dividing

the number of nonsynonymous amino acid changes by what is

considered neutral background, typically the number of synony-

mous changes or intronic variants; values greater than one are

Figure 1. Combining ComparativeGenomics with Phenotype and ExpressionData Leads to Disease InsightsMeasures of positive selection are typically calcu-lated by dividing the number of nonsynonymouschanges (depicted in red) by the number of synon-ymous changes (depicted in blue); a value greaterthan one is used as evidence for positive selection.It should be emphasized that this is arbitrary andonly takes into account known protein codingregions. Screening genomes for genes under posi-tive selection is one important step; however, othermeasures such as links to behavior and expressionneed to be incorporated. Furthermore, a genedoes not have to have undergone positive selec-tion to be a disease-susceptibility gene. Otherchanges in evolution such as timing or location ofexpression could make a gene or signalingpathway vulnerable in disease.

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considered indicative of adaptive evolution (Figure 1). Of course,

much of what has been considered neutral is rapidly changing as

noncoding regions of the genome are identified as functional

(Varki et al., 2008). Additionally, for any specific gene, these

metrics of adaptive evolution based on straight sequence

comparisons do not take into account tissue specificity of

expression. For example, strong evidence of adaptive evolution

for brain-expressed genes at the gene level is often taken as

evidence for that gene’s involvement in brain evolution.

However, unless a gene is essentially brain specific, or some

other form of experimental evidence is provided (Konopka

et al., 2009), one cannot be certain that the sequence changes

have been selected due to their brain function, rather than their

function in other organ systems. Therefore, it is important to

directly study genomics in the tissue of interest and on multiple

levels, and not just examine the genome sequence, to be able

to make convincing evolutionary arguments.

In addition to straightforward sequence comparisons, there

are other comparisons that can be made at the genome level.

Characterization of evolutionary breakpoints has provided

a list of regions where chromosomal rearrangements may

have led to both loss-of-function and gain-of-function mutations

in genes important for mammalian evolution (Larkin et al., 2009;

Murphy et al., 2005). The initial study uncovered 40 breakpoints

that were likely primate-specific (Murphy et al., 2005), and

further analysis derived 44 primate-specific breakpoints with

two being human specific (Larkin et al., 2009). These data

suggest that the human genome has achieved a good measure

of stability, and therefore when new mutations arise within these

breakpoints, disease can often occur. Other data suggests that

some genomic rearrangements such as insertion of repetitive

elements or mitochondrial DNA sequences into the nuclear

genome were not the result of positive selection, but rather

neutral drift (Gherman et al., 2007). But ultimately, as the

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prescient hypothesis of King and Wilson

(1975) has suggested, changes at the

level of gene regulation are a greater

driver of diversity between humans and

chimpanzees than changes in coding

sequences alone.

To uncover the complexity of gene expression and regulation

during brain evolution, a multipronged approach needs to be

taken (Figure 2). First, the power of next-generation sequencing

(NGS) needs to be leveraged to ascertain the changing landscape

onmany levels: noncodingsequences,epigenetics, splicing, tran-

scriptional regulation, microRNA regulation, expression, and

ultimately translation. These data need to be obtained using

large-scale studies, appropriately analyzed, and completely inte-

grated. Second, a greater understandingof developmentalmech-

anisms needs to be unveiled, because these are likely integral to

how the brain adapted. Third, a multitude of organisms should

be investigated, not just traditional model organisms, and behav-

ioral phenotypes with expression data in these organisms need

to be combined. Many of these organisms are far more expert

than humans at certain functions that may be related to the

CNS, or have adapted to unique environments, so they provide

excellent substrates for the comparative investigation of brain

evolution. Finally, the incorporation of human brain disease attri-

butes will highlight the most recent steps in brain evolution, and

how they have yielded significant cognition and a more disease-

vulnerable organ (Crespi, 2010; Preuss et al., 2004; Vernier

et al., 2004).

Evo-GenoIt is difficult to fathom how the relatively small number of genes in

the human genome (a little over 20,000) has led to the highly

complex social organism that is human.Changes inmanyparam-

eters have built upon the archetypal mammalian brain to yield an

organof unprecedented complexity and versatility. Differences in

gene regulationandexpressionhave led to theemergenceof new

cell types and brain regions, and more remain to be discovered.

These, in turn, have resulted in new behaviors and phenotypes,

and have ultimately improved our cognitive abilities. Under-

standing themechanismsunderlying thesechanges in thehuman

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Figure 2. Multiple Layers of RegulationUnderlie Human Brain EvolutionMore than genomic comparisons need to beconsidered when building our understanding ofhuman brain evolution. Regulation at the levelof the epigenome (e.g., methylome), regulationof expression by changes in transcription or smallRNA regulation, novel isoforms through differentialsplicing, changes in regional and subcellularexpression, and posttranslational modifications(e.g., kinome) all need to be taken into account,and many of these changes can be queried usingNGS techniques. Also, the incorporation of animalmodels and cell-specific lines of inquiry need toundertaken. Finally, the integration of all of thesedata will lead to phenotype discovery and hypoth-esis-testing that ultimately will inform polymor-phism discovery in human brain diseases. In thismanner, modern neurogenetic investigations arenot only an exercise in advanced data analysis,but comprehensive data integration.

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brain from both a genomic and developmental perspective

should shed some light on the evolutionary process.

Gene Expression and Gene Splicing

Perhaps the most direct, truly genome-wide method for inves-

tigating evolutionary divergence in the brain is to measure

differences in gene expression levels. Several studies have con-

ducted comparative transcriptomic analyses in the brains of

primates (Oldham and Geschwind, 2009; Preuss et al., 2004).

Initial reports used microarrays for assessment and identified

a few hundred genes that differed between human and

chimpanzee brain (Caceres et al., 2003; Enard et al., 2002a;

Khaitovich et al., 2005; Khaitovich et al., 2004a). Further studies

have also utilized this approach of comparing the two species

using analysis of microarray data (Khaitovich et al., 2006b;

Nowick et al., 2009; Oldham et al., 2006; Somel et al., 2010).

One of the more salient findings was an enrichment of KRAB-

zinc finger transcription factors in human brain (Nowick et al.,

2009). These genes have already been linked to both brain

development and disease, and are therefore important for

further consideration in functional analyses. Another compelling

finding was the difference in gene expression with aging in the

human brain specifically as a potential mechanism for the

increased longevity of the human species (Somel et al., 2009).

In addition, the genes most correlated with aging are also those

demonstrating less evolutionary conservation (Somel et al.,

2010). These data suggest that positive selection is having

a significant effect on genes involved in aging, and likely

neurodegenerative susceptibility as well. Subcellular localiza-

tion of expression may also play a pivotal role in evolution. At

least one example, GLUD2, is a hominid-specific gene that

localizes specifically to mitochondria in comparison with the

ancestral form GLUD1 (Rosso et al., 2008). Because GLUD2

has acquired some brain-specific functions, this change in

localization may have been essential for these new properties

in the primate brain.

234 Neuron 68, October 21, 2010 ª2010 Elsevier Inc.

One of the difficulties inherent in analysis of gene expression

data is determining positive selection from the neutral back-

ground (Khaitovich et al., 2004b; Oldham and Geschwind,

2009), because standard methods for analysis of differential

expression cannot determine which changes are functional.

Here, gene network analysis may serve as a framework for

assessment of the functionality in changes in expression

(Oldham and Geschwind, 2009; Oldham et al., 2006) and has

been utilized to examine changes in gene coexpression

networks between humans and both chimpanzees (Oldham

et al., 2006) and mice (Miller et al., 2010). These analyses

produced several important insights into the human brain tran-

scriptome. The first insight was that gene connectivity in the

newly evolved cortex was less preserved in other species

compared with networks in the older caudate nucleus (Oldham

et al., 2006). In addition, this measure of differential network

connectivity between the species is more sensitive than differen-

tial expression in ascertaining evolutionary divergence (Miller

et al., 2010; Oldham et al., 2006). Finally, this approach is able

to pinpoint highly connected genes in human-specific modules

with known associations to human-specific neurodegenerative

disorders (Miller et al., 2010). Together, these studies highlight

the importance of applying unbiased analytical methods to

large-scale expression data sets to yield novel underlying struc-

ture (Geschwind and Konopka, 2009).

One of the interesting areas of convergence in gene expres-

sion studies is the identification of energy metabolism-related

genes (Caceres et al., 2003; Goodman et al., 2009; Grossman

et al., 2001), mitochondria, and other related pathways suggest-

ing differences in cellular energetics between humans and

nonhuman primates (Goodman and Sterner, 2010; Preuss

et al., 2004). Some of these pathways, especially mitochondria,

are also related to human aging and neurodegenerative disease

(Miller et al., 2008). This suggests the interesting hypothesis that

a potential consequence of having a human cerebral cortex and

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increased energy demands may be a predisposition to neurode-

generative conditions.

While some amount of diversity in the brain is certainly driven

by changes in the amount, timing, and location of gene expres-

sion, it has become increasingly apparent that changes in the

splicing of the mRNAs themselves are dramatically different

among species, even among primates. Comparisons of splicing

in primate transcriptomes have been conducted using both exon

arrays for brain tissue (Calarco et al., 2007; Irimia et al., 2009;

Lin et al., 2010) and NGS for liver tissue (Blekhman et al.,

2010). One study found that human-specific splicing is likely

regulated by cis-regulatory elements (Lin et al., 2010), and

another found that although similar percentages of genes

between humans and chimpanzees are alternatively spliced,

these do not significantly overlap the differentially expressed

genes (Calarco et al., 2007). Similar analyses are needed using

NGS in the brains of primates, because NGS will allow for the

discovery of previously unannotated isoforms.

Targets of known specific splicing factors such as Nova1/2

have also been examined in vivo at a genome-wide level in the

brain, whereas a bioinformatic approach has been taken to

examine the targets of the neuronal splicing factors Fox-1

(A2BP1) and Fox-2 (RBM9) in brain (Licatalosi et al., 2008; Ule

et al., 2005; Zhang et al., 2008; 2010). A discovery-based

approach has also been taken to identify novel CNS-specific

splicing factors. Using this methodology, a neural-specific

splicing factor, nSR100, was uncovered and shown to be integral

for neuronal differentiation both by the identification of its target

genes, which are important for CNS development, and by in vivo

loss of function studies (Calarco et al., 2009). Loss of Magoh,

a gene involved in RNA splicing, leads to a disorganized, micro-

cephalic brain with fewer neurons (Silver et al., 2010). Moreover,

splicing ofMAGOH itself has a human-specific pattern (Lin et al.,

2010). Another example of how differential splicing can affect

brain function can be found upon studying the Liprin-alpha

protein family. These proteins have essential roles in both

dendrites and synapses, and human-specific patterns of splicing

occur, and are likely to regulate human-specific functions of this

family of genes (Zurner and Schoch, 2009). Together, these data

point to a critical role both for splicing in general and of genes

such as MAGOH and Liprin in the evolution of the human brain.

Another level of speciesdivergence lies in the regulationof gene

expression. Understanding epigenetic regulation in the nervous

system is an area of active study that should provide a first level

of input into potential differences in regulation (Borrelli et al.,

2008; Dulac, 2010). Changes in the epigenome, which include

methylation andhistonemodifications, canderive fromnumerous

sources including the environment, stress, and neuronal activity.

In addition, epigenetics can lead to transgenerational evolution

through effects on gene expression (Sharma and Singh, 2009).

Other work has focused on identifying either cis-regulatory

elements that have undergone positive selection, or the acquisi-

tion of novel transcriptional activity with evolution. Elegant work

has detected evolutionary conserved noncoding sequences

(CNSs) (Pennacchio et al., 2006), followed by a further refined

analysis to identify specific human-accelerated conserved non-

coding sequences (haCNSs) (Prabhakar et al., 2006, 2008) and

human accelerated regions (HARs) (Pollard et al., 2006a,

2006b).Ofnote,haCNSsareenrichednearneuronal cell adhesion

genes (Prabhakar et al., 2006), genes spatially differentially ex-

pressed in human fetal brain (Johnson et al., 2009), and genes

differentially regulated by human and chimpanzee FOXP2

(Konopka et al., 2009). One haCNS, HACNS1, has been shown

to be important for human-specific digit and limb patterning,

and therefore may have been instrumental in the development

of features such as opposable thumbs in humans (Prabhakar

et al., 2008). The most significant HAR, HAR1, forms an RNA

gene that is expressed in Cajal-Retzius neurons (see below) in

human fetal brain (Pollard et al., 2006b). Thus, the importance of

newly evolved noncoding elements in the human genome cannot

be underestimated in terms of their potential for regulating critical

processes during brain development.

A well-characterized example of differential transcriptional

activity in evolutionary terms is the study of the transcription

factor FOXP2. This gene has undergone accelerated evolution

along the human lineage due to changes at two amino acids

(Enard et al., 2002b), and pathogenic variants of FOXP2 have

been found in people with a specific form of verbal dyspraxia

(Fisher and Scharff, 2009; Lai et al., 2001). However, FOXP2 is

also highly expressed in the lung, so changes at the genome level

cannot definitively be assigned to changes in brain function. Here

is where experimental tests of changes at the genome level are

important. In this regard, support for the relationship of the

sequence changeswith brain evolutionwasprovidedbycompar-

isons of expression of the human form of FOXP2 in human cells

versus the chimpanzee form. This difference led to a different

transcriptional program, and thesedifferentially expressedgenes

significantly overlapwith differentially expressed genes in human

and chimpanzee brains (Konopka et al., 2009). Remarkably,

differentially expressed genes included many involved in cranio-

facial development, suggesting coevolution of the CNS capacity

for language with the vocal apparatus in humans (Konopka et al.,

2009). Furthermore, mice expressing the human FOXP2 in place

of the mouse gene display novel behavioral phenotypes,

including changes in ultrasonic vocalization, and differences in

neuronal morphology and activity (Enard et al., 2009). Together,

these studies suggest that positive selection of even one tran-

scription factor can potentially have a dramatic difference in the

evolution of neuronal signaling and circuitry.

Studies of Primates and Other Vertebrate Species

and the Importance of Outgroups

Although all of the above studies have produced useful informa-

tion as to which genes are different in the human brain, they are

not able to determine which genes changed specifically along

the human lineage unless an outgroup (e.g., rhesus macaque)

is included. The use of an outgroup allows one to identify which

lineage the observed changes occurred upon. In comparisons of

humans and chimpanzees, another primate with a common

ancestor, such as rhesus macaque, is often included. Thus,

any difference that holds between humans and chimpanzees

as well as humans and macaques (but not chimpanzees and

macaques) can be considered to have occurred more recently

in time on the human lineage. The closer in evolutionary time

two species are to one another, the more confident one can be

about the relevance of the comparison. A few brain gene expres-

sion studies comparing humans and chimpanzees have indeed

Neuron 68, October 21, 2010 ª2010 Elsevier Inc. 235

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included outgroup data (Caceres et al., 2003; Enard et al., 2002a;

Khaitovich et al., 2006b; Somel et al., 2009); however, the

majority of these gene expression reports were still limited by

either the number of brain regions or samples used in the anal-

ysis. Moreover, cross-hybridization issues reduced the number

of transcripts examined because the chimpanzee samples

were queried on human microarrays. To alleviate this potential

bias, NGS, which is agnostic to species, is now being used to

conduct comparative transcriptome analysis by directly

sequencing all expressed RNAs (RNA-seq). At least two

RNA-seq studies have been completed comparing gene expres-

sion in the brains of humans, chimpanzees, and macaques and

have uncovered noncoding RNAs in the human lineage with

potential functions in directing gene expression (Babbitt et al.,

2010; Xu et al., 2010). All of these reports are important for under-

standing human-specific patterns of expression; however, as

will become evident below, different cells and areas of the brain

conduct differentially evolved functions. Therefore, large-scale

comparative expression analyses of many more brain regions

are critical. Another vertebrate outgroup that may be critical for

understanding brain development is the elephant shark. This

vertebrate can be used for comparison with other bony

vertebrates such as the mouse and zebrafish. In fact, the

elephant shark genome is more similar to the human genome

than the zebrafish genome is to the human genome (Venkatesh

et al., 2007). A recent study identified the homologous Dlx

enhancer in the elephant shark genome, and showed that this

enhancer had similar activity to the mouse enhancer in the fore-

brain but also displayed some unique characteristics (MacDon-

ald et al., 2010). Further comparative expression studies using

species such as the elephant shark should provide additional

insights into unique and conserved brain evolution pathways in

jawed vertebrates.

Moving from Tissue to Cells and Beyond: Cellular,

Synapse, and Circuit Phenotype Discovery

Cellular phenotype discovery is a critical aspect of the human

evolutionary-genomics enterprise in this age of molecular and

cellular neurobiology. Little is understood about the differences

between cell types, such as neurons and glia, and key aspects

of neuronal morphology, such as dendritic structures and the

synapse, between humans and other vertebrates. Genomic

investigations provide essentially unlimited opportunities for

foundational investigations in this regard.

Though it is debatable as to whether the pronounced increase

in glia along the human lineage was more important for brain

evolution than neuronal modifications, due to the unique physi-

ology of neurons, investigations into the specific evolution of

neurons are an essential line of inquiry. This approach has

been thoughtfully undertaken at the proteomic level. Compar-

ison of the synaptic proteome in mouse and fly revealed core

proteins that were further extended and elaborated upon in

vertebrates to yield synapses with increasing functional abilities

(Emes et al., 2008). Moreover, a significant number of proteins in

the core synaptic proteome are enriched for genes already

known to be implicated in several neuropsychiatric disorders,

most prominently schizophrenia (Fernandez et al., 2009). This

explains the debilitating effects that are seen when disruption

to these genes occurs. Combining these proteomic data with

236 Neuron 68, October 21, 2010 ª2010 Elsevier Inc.

that from genomic and transcriptomic studies will provide

a greater understanding of how translational regulation may

also be playing a role in divergent evolution. Furthermore, exam-

ination of the synaptic proteome needs to be carried out in

human and nonhuman primate brain, preferably during brain

development, to identify any human-specific adaptations.

An even more challenging question in terms of human speciali-

zation iswhether there are different amounts or functions of partic-

ular neurons in the human brain. Even in the well-studied mouse,

wehaveonly justbegun toappreciateandunderstand thediversity

of neuronal subtypes in the brain. Thus, knowledge of neuronal

subclasses in humans and other species is even sparser. Here,

we illustrate two human neuronal subtypes with potential implica-

tions in human brain evolution. The first examples are von Econ-

omo neurons (VENs) (von Economo, 1926) that are only present

in the anterior cingulate cortex and frontoinsular cortex, regions

important for social behavior. These neurons are present in great-

est numbers in the human brain, and have only been identified in

great apes, cetaceans, and elephants (Allman et al., 2010; Butti

et al., 2009; Hakeem et al., 2009; Nimchinsky et al., 1999), all

species that have passed tests for self-awareness (Byrne and

Bates, 2010; Craig, 2009; Povinelli et al., 1993). There is a signifi-

cant reduction in the number of VENs in the brains of patients

with frontotemporal dementia (FTD) (Seeley et al., 2006), a disease

that presentswith deterioration of social function,whereas there is

an increase in the number of VENs in the brains of autistic patients

(Santos et al., 2010), a disease with dysfunctional social interac-

tions. It is therefore possible that VENs represent a specialized

class of neurons with a role in social interactions. Though expres-

sion of a few genes has been described in VENs (Allman et al.,

2010; Allman et al., 2005), targeted whole-genome expression

profiling of VENs could provide some insight into their evolution

and function in the human brain.

Another specialized neuronal cell type that is not unique to

primate, but displays differential characteristics along the

primate lineage, is the Cajal-Retzius neuron. These cells, as

well as Reelin, which is expressed in these cells, are critical for

cortical lamination (Marın-Padilla, 1998). There is an increase in

Reelin along the primate lineage (Abellan et al., 2010a; Zecevic

and Rakic, 2001), and LIM-homeodomain transcription factors

also display differential expression in Cajal-Retzius neurons in

the cortex of multiple species (Abellan et al., 2010a, 2010b).

Finally, though a few studies have examined gene expression

profiles specifically in these neurons (Soriano and Del Rıo,

2005), agnostic, developmental, time course, whole-genome

expression studies in multiple species are warranted to identify

the entire coterie of primate and human-specific expression

patterns. In addition, cross-species comparisons of neuronal

plasticity, activity-dependent gene expression, and dendritic

structures are also needed.

Brain Size Isn’t Everything, but It Is Relevant

While clearly a relatively gross phenotype, brain size is correlated

with overall intellectual function (Deaner et al., 2007; Roth and

Dicke, 2005; Rushton and Ankney, 2009). So, the identification

of genes involved in brain size has also provided insight into the

molecular mechanisms underlying human brain evolution.

However, one logical fallacy is that the relativelyweakbut positive

correlation between brain size and intelligence means that every

Neuron

Perspective

gene contributing to brain size is related to intelligence, or to

its evolution. These are related but independent phenotypes.

Many genes contributing to brain size are not expected to be

related to intelligence. Study of common genetic variation in

each of these genes to assess its contributions to normal varia-

tion in brain size, and to intelligence in turn, will be necessary.

Several genetic loci associatedwithmicrocephalywere first iden-

tified followed by the further identification of mutations in several

genes in these regions including ASPM, CDK5RAP2, CENPJ,

MCPH1, SLC25A19, and STIL. Interestingly, there is some

evidence for positive selection of ASPM and MCPH1 in humans

(Evans et al., 2005; Mekel-Bobrov et al., 2005); however, other

reports have suggested different factors as the driving force for

variation in these genes (Currat et al., 2006; Timpson et al.,

2007; Woods et al., 2006; Yu et al., 2007). This controversy high-

lights the difficulty in conducting comparative genomic studies

and drawing conclusions about the evolutionary implications. A

recent report used the new technology of whole-exome

sequencing to define the causative gene in one microcephaly

locus, WDR62 (Bilguvar et al., 2010). Of note, WDR62 is ex-

pressed in the nucleus of neural progenitors and therefore may

be important for regulating the transition from precursor to post-

mitotic neuron. Loss-of-function studies in model systems

should address this potential mechanism.

Another parameter with a potential effect on brain size is the

control of the cell cycle in neural progenitor pools (Kriegstein

et al., 2006). Changes in cell cycle duration in these cells in

primates are thought to be one adaptive mechanism for the

increase in neocortical size in primates (Dehay and Kennedy,

2007; Rakic, 1995). Area-specific modulation of cell cycle length

can also result in differing sensitivities bewteen proliferation and

differentiation signals (Lukaszewicz et al., 2005). In vivo manipu-

lation of genes important in cell cycle progression results in

mouse cortical tissue subsuming a thickened, more primate-like

phenotype (Chenn andWalsh, 2002; Pilaz et al., 2009). Recently,

a primate-specific PAX6 target, RFPL1, was shown to influence

cell cycle progression with a potential implication for this function

in brain development (Bonnefont et al., 2010). Themicrocephaly-

related genes ASPM and Mcph1 are also involved in regulating

brain size through mitotic spindle activity (Bond et al., 2002) and

cell cycle progression (Xu et al., 2004), respectively. The identifi-

cation of additional primate or human-specific mRNA patterns

for genes involved in cell cycle progression should permit further

study of the role of the cell cycle in the evolution of the brain, and

regional differences in expression are probably also key.

Certain human-specific expression patterns may also result in

specialized cell types that underlie cortical size. The recent

demonstration of outer subventricular zone (OSVZ) cells under-

going asymmetric division to generate neurons is of great

interest due to the substantial increase in the number of OSVZ

neurons along the primate lineage (Fietz et al., 2010; Hansen

et al., 2010). One of these reports also demonstrated the exis-

tence of OSVZ-derived neurons in the cortex of ferret. Because

the ferret has a gyrencephalic cortex, this supports the intriguing

hypothesis that OSVZ progenitors are a critical determinant of

brain folding. Thus, the increase in the number of OSVZ progen-

itors in human brain may have been instrumental in generating

a larger brain due to an increase in surface area from folding.

Although the function of a number of genes involved in brain

folding (e.g., LIS1, DCX, and RLN) has been actively studied in

rodent brain, the mouse brain is lissencephalic and therefore

does not lend itself to studies of brain folding. Thus, several

groups have been using either ferret, as mentioned above, or

pig. To this end, a recent report detailed tissue-specific

transcriptomic findings in the ferret brain (Bruder et al., 2010).

Another group examined gene expression in the pig cortex

before and after the onset of convolution using microarrays

and identified differentially expressed genes with a potential

role in cortical folding (Nielsen et al., 2010). Manipulation of these

genes in a genetically tractable organism such as mouse should

provide some mechanistic information on the involved signaling

pathways. Furthermore, deep sequencing of both the pig and

ferret genomes (Table 1) should also assist in interpreting future

gene expression studies using NGS in these species.

Lastly, but perhaps most importantly, while brain size and

brain folding are relevant to the evolution of the brain, it is clear

that the expansion of specific regions in the primate brain has

had a tremendous impact on human brain evolution. In

particular, the increase in the number of association cortex areas

and the development of the prefrontal cortex has been instru-

mental for cognitive abilities (Krubitzer, 2007; Krubitzer and

Kaas, 2005). Again, regional size is not necessarily the important

factor, but it is the evolution of the cortical networks among the

prefrontal cortex and other cortical areas that has likely driven

adaptations in cognition (Semendeferi et al., 2002). This also

includes the development of perisylvian cortical asymmetries

related to language (Geschwind and Miller, 2001; see below).

In addition, the expression and timing of expression of particular

genes within specific regions is also critical for the development

of specific cortical areas (Krubitzer and Kaas, 2005). Examples of

genes with critical roles in arealization include Emx2, Pax6, and

COUP-TFI (Nr2f1) (Krubitzer, 2007). Not surprisingly, variation

in some of these genes leads to cognitive deficits including schi-

zencephaly (EMX2) (Brunelli et al., 1996; Faiella et al., 1997) and

cerebellar ataxia and intellectual disability (Gillespie syndrome;

PAX6) (Graziano et al., 2007; Ticho et al., 2006). A better

understanding of the genes important for arealization inmamma-

lian brain evolution should provide insight into the gene networks

underlying higher cognition.

Evo-DevoBecause ontogeny recapitulates phylogeny to some degree, it is

critical to study the developing human brain. Moreover, proper

arealization or regionalization during brain development is neces-

sary for setting up the brain circuitry needed for higher cognitive

functions. Therefore, the elucidation of regional expression

patterns during human brain development and comparison of

these patterns to those in other species is an important aim.

One of the caveats to this approach is the paucity of fetal human

and great ape brain tissue. Despite this challenge, a recent report

examined gene expression using exon microarrays in 13 regions

in human fetal brain (Johnsonet al., 2009). Almost one-third of ex-

pressed genes are either regionally differentially expressed and/

or differentially spliced. In addition, a significant number of differ-

entially expressed genes have proximal noncoding regions that

have undergone accelerated evolution along the human lineage.

Neuron 68, October 21, 2010 ª2010 Elsevier Inc. 237

Neuron

Perspective

These data suggest that positively selected regulatory elements

may have had a significant impact on driving human brain region-

alization and thus, human cognition. Another study examined

gene expression patterns in perisylvian cortex by comparing

two regions in human fetal brain and identified numerous differ-

entially expressed genes, including several patterning or cell

adhesion molecules (Abrahams et al., 2007). Of note, CNTNAP2

was significantly enriched in human frontal cortex yet did not

show enrichment in rodent cortex. Because variation in

CNTNAP2 is associated with both language endophenotypes

(Alarcon et al., 2008) and autism spectrum disorder (Arking

et al., 2008; Bakkaloglu et al., 2008), this specific pattern of

expression in frontal cortex suggests an instrumental role for

CNTNAP2 in cortical circuitry disrupted in autism, a human-

specific disorder. The spatial overlap of noncoding RNAs with

protein-coding mRNAs may also be playing a role in brain evolu-

tion. A significant number of noncoding RNAs are coexpressed

with adjacent mRNAs specifically in the brain (Ponjavic et al.,

2009). These spatiotemporal pairs denote another level of regula-

tion in the brain that needs to be fully appreciated when attempt-

ing to dissect out the drivers of brain evolution.

The next higher order of brain development that may have

had a significant impact on brain evolution is the lateralization

of the brain. While there is evidence for brain lateralization in

other species (Bisazza et al., 1998), higher cognitive processes,

for example language, which is typically left hemisphere lateral-

ized in right-handers (in the majority; Galaburda et al., 1978),

show remarkable laterality in the human cerebral cortex. Thus,

understanding the process of cerebral lateralization at the

molecular level should provide insight into this feature likely

adapted during evolution for novel cognitive functions in

humans. Several studies have examined whether there are

asymmetrically expressed genes in the hemispheres of the

developing human brain (Johnson et al., 2009; Sun et al.,

2005). Although a number of candidate genes have been uncov-

ered (Sun et al., 2005), it is likely that the process of cerebral

lateralization is actually imparted at an earlier time point in

development (i.e., first trimester), via signals from the underlying

mesoderm (Geschwind and Miller, 2001). In addition to gene

expression, asymmetry has been examined using both neuro-

anatomical methods and neuroimaging techniques, such as

MRI and diffusor tensor imaging (DTI), which can trace the

pattern of white matter tracts in the brain. While both humans

and chimpanzees have asymmetrical brains at the levels of

both gray and white matter (Cantalupo et al., 2009; Hopkins

et al., 2008; Schenker et al., 2010), at least one study has

uncovered a human-specific white matter pattern in the arcuate

fasciculus, a tract important for language (Rilling et al., 2008).

Thus, although the acquisition of an asymmetrical brain

occurred far back in terms of evolutionary time, the further elab-

oration of an asymmetric cerebral cortex with new changes in

gene expression on the human lineage may have been instru-

mental in the development of spoken language.

Evo-PhenoUsing Animal Models to Study Language Evolution

One of the most challenging phenotypes to model and study in

terms of evolution is language (see Newbury and Monaco,

238 Neuron 68, October 21, 2010 ª2010 Elsevier Inc.

2010, this issue). A number of organisms exhibit some form of

vocal communication, but human language is thought to be

unique. One of the more salient human-specific features of

language is the use of recursion, or embedding ideas within

one another. This feature likely co-opted the evolving working

memory system present in lower organisms to expound upon

the new language function. Another human-specific language

feature is the use of language to teach an extrinsic skill or to

use language in the development of theory of mind (Penn

et al., 2008; Premack, 2007).

Two basic genetic approaches can be taken: identification of

common genetic variation and identification of rare, Mendelian

mutations that alter human language. Few common variants

related to language have been identified. Notable exceptions

include variation in LRRTM1 (Francks et al., 2007) that has

been related to brain asymmetry, and CNTNAP2, in which

common variation contributes to language endophenotypes,

such as nonword repetition in humans (Alarcon et al., 2008;

Vernes et al., 2008). Remarkably, CNTNAP2 is regulated by the

transcription factor FOXP2, one of the few genes associated

with Mendelian forms of speech and language dysfunction in hu-

mans (Fisher et al., 2003; Vernes et al., 2008). These data provide

the first glimmer of the emergence of a potential molecular

pathway downstream of FOXP2 that is likely related to develop-

ment of frontal-striatal circuits involved in language learning and

function.

The uniquely human nature of language and evidence for

accelerated evolution of FOXP2 on the human lineage

seriously challenge the use of animal models for the study of

speech and language evolution. However, the human brain

was built on the foundation of its vertebrate ancestors’ nervous

systems (Krubitzer, 2007; Krubitzer and Kaas, 2005), and there

are many processes, such as vocal motor learning, that are

shared in other organisms. In this regard, studies in nonhuman

models have highlighted the conserved features of FOXP2.

Both FoxP1 and FoxP2 show overlapping and highly parallel

expression in cortical basal ganglia circuits involved in vocal

motor learning in the songbird and human fetal brain (Teramitsu

et al., 2004). Remarkably, lentiviral-mediated knockdown of

FoxP2 in the zebra finch has demonstrated that FoxP2 is integral

for learned song production (Haesler et al., 2007). Loss-of-

function studies in mouse have demonstrated that Foxp2 is

necessary for proper ultrasonic vocalizations in newborn mice

(Shu et al., 2005). Further studies using conditional Foxp2

knockout mice should investigate whether Foxp2 is needed for

socially mediated adult vocalizations. Thus, because of the

conservation of phenotypes from songbird to mouse to people,

FoxP2 is not necessarily ‘‘language specific.’’ What is likely is

that FoxP2 has an important role in sensory-motor integration

involving striatal circuits, and in humans this has evolved to serve

an important role in language (Teramitsu et al., 2004; White et al.,

2006). FoxP2 stands as an example of the intricacies of attempt-

ing to study evolution, in particular human-specific phenotypes,

in lower organisms. It can be very difficult to extrapolate these

findings to human features. Nevertheless, these types of

comparative phenotype-genotype studies will inform our under-

standing of the CNS inmany species and ultimately the evolution

of the human brain.

Neuron

Perspective

Evo-PathoIt is interesting to speculate that perhaps one untoward

consequence of the highly evolved human brain has been the

emergence of human-specific neurodegenerative and neuro-

psychiatric diseases (Crespi et al., 2007; Crespi, 2010; Dean,

2009; Preuss et al., 2004; Vernier et al., 2004). Combining an

evolutionary perspective with an understanding of the pathology

of these diseases should uncover the mechanisms that have led

to the development of these diseases in the human lineage.

Expression studies of disease-related genes in human brain

have begun to elucidate human-specific patterns of gene

expression in terms of both levels and localization. An example

of this type of approach is again exemplified in the study of

CNTNAP2 (Alarcon et al., 2008), which is enriched in frontal

pole, an area of the brain critical for executive function

(Abrahams et al., 2007). The results of human brain expression

profiling studies thus far (Johnson et al., 2009) suggest that

comparison of comprehensive gene expression maps in mature

and developing human brain with mouse and nonhuman

primates will provide enormous value.

One of the greatest challenges in studying brain evolution is

the use of the most appropriate model systems. An issue with

incorporating disease-relevant information is that many model

organisms do not succumb to the same diseases that humans

are susceptible to. For example, it has been exceedingly difficult

to model neurodegeneration in mice. A recent study suggests

that differences in gene expression networks and thus gene

function may at least partially explain this observation, consid-

ering that genes such as PSEN1, which causes a dominant

disease in humans, but not in mouse, show significant expres-

sion divergence (Miller et al., 2010). The absence of certain brain

circuits in mice, including certain prefrontal regions, also chal-

lenges the development of mouse models of human neuropsy-

chiatric diseases that involve these areas (Preuss, 2000).

Even more difficult to study than disease in humans is the

evolution of positive attributes such as creativity or intelligence.

Definitional issues certainly challenge this area of research.

Nevertheless, one recent study used the power of comparative

evolutionary genomics to identify genes under positive selection

in the human genome (Bochdanovits et al., 2009). Association of

these genes with intelligence was then conducted, and

a polymorphism in ADRB2 was found to be significantly associ-

ated with cognitive ability. Further functional analyses of this

gene needs to be carried out to place it within the context of brain

development and/or neuronal signaling pathways.

Summary and Future DirectionsTraditional comparative genomics are core tools for the study of

evolution, because changes in the genome may allow one to

determine causality with relation to specific phenotypic differ-

ences between species. However, one also needs to consider

other forces affecting changes on the genome or phenotype

level, such as environmental exposures and nutrition. Moreover,

in the study of the brain, we need to be cognizant of cell- and

circuit-specific factors, as well as emergent properties of human

higher cognition and behavior, such as social and cultural buffers

that could be acting in concert with changes on other levels

(Varki et al., 2008).

Animal model organisms play a crucial role in neuroscience.

However, one unfortunate trend has been a de-emphasis on

studies of human brain. Advances in genomics and genetics

and availability of human brain tissue, coupled with noninvasive

methods for the study of brain function, provide enormous

opportunities. The critical challenge is to embrace the evolu-

tionary differences between humans and model organisms,

and use them as a platform for discovery, rather than pretending

that they don’t exist. This will permit integration of the large

amount of data produced in model organisms with an under-

standing of human brain function, so as to understand the true

relevance of these data to humans. Comparative functional

genomic investigations will provide a molecular basis for

elucidation of brain circuit evolution, and the evolution of human

cognition and behavior.

The technical advance of microarrays allowed unprecedented

access to differential gene expression in brain evolution.

However, due to the biases and static nature of the information

able to be queried using microarrays, this technology is limiting

for cross-species comparisons. For comparative genomic

purposes, NGS is rapidly making expression array issues of

historic interest, but NGS still requires a draft genome for align-

ment. Therefore, as NGS becomes less expensive and computa-

tional resources improve, even individual labs will be able to

conduct de novo sequencing of their unique model organism.

The focus in evolutionary genomics needs to extend work in

traditional experimental organisms such as mouse, rat,

Drosophila, et cetera into a deeper appreciation of the myriad

organisms with a CNS, especially those that might have unique

brain and behavior functions. For example, recent deep

sequencing of small RNAs inmarineworms identified ancient mi-

croRNAs that may have been essential for the evolution of the

CNS (Christodoulou et al., 2010).

As always, integration of multiple levels of genomic, regula-

tory, and RNA and protein expression, while challenging, is

a must. Genome-wide transcription factor binding studies

(ChIP-seq), sometimes combined with RNA-seq, are just begin-

ning to be conducted (Kim et al., 2010; Visel et al., 2009).

However, these and other studies are really only beginning to

scratch the surface of what NGS can uncover, such as allele-

specific expression (Meaburn et al., 2010; Schalkwyk et al.,

2010). Additionally, it will be useful to define molecular pathways

based on functional genomic studies that identify gene sets

expressed in either a tissue- or cell-specific manner (Doyle

et al., 2008), and use these unbiased pathways as a basis for

studying genetic pathway associations in normal cognition and

disease. Such studies will aid in broadening our acceptance of

the role of particular genes in specific tissues.

Phenotype discovery, in general, is a huge area of need within

the field of evolutionary genomics. Thus, the use of imaging data

analysis to determine phenotypes has been essential for this

progress. Because imaging is one of the few windows into the

brain we can utilize in living patients, it is critical that we harness

this technology in combination with genomic data. Perhaps the

greatest challenge for the evolutionary neuroscientist is to estab-

lish how to combine data from functional genomics studies with

behavioral data, again, in particular, imaging data, and genome-

wide association study information. Such integration relies on

Neuron 68, October 21, 2010 ª2010 Elsevier Inc. 239

Neuron

Perspective

being able to determine quantitative, behaviorally relevant

phenotypes. Recent work in the identification of ‘‘phenologs,’’

or phenotypes that arise from disruption of orthologous genes,

is one approach to ascertain such data (McGary et al., 2010).

Through this methodology, phenologs can be detected for

diseases such as amyotrophic lateral sclerosis (ALS; human-

yeast), autism (human-mouse), and intellectual disability

(human-Arabidopsis). Combining this approach with other data

from model systems will identify gene networks essential for

normal human brain function that can be further explored in

lower organisms.

The genomics revolution now permits us to perform key

experiments that could rapidly advance our understanding of

the evolution of higher cognition. But, one caveat for many of

the evolution studies discussed above is the fundamental

assumptions that are made in drawing the particular conclusion

of each study. In particular, we need to be cautious about over-

interpretation of any findings in the setting of uncertain models.

Interspecies comparisons as far as human and mouse were

important first steps, but some basic assumptions may not

have been met and the positive selection of nervous system

genes in humans, as initially suggested (Dorus et al., 2004), is

not yet well established. For example, this and other studies

need to consider the role of relaxation of constraint, or reducing

the restrictions of amino acid changes, rather than only adaptive

evolution in the primate brain. One hypothesis is that relaxation

of constraint presented the occasion for adaptive evolution to

occur. Furthermore, other fundamental characteristics such as

mutation rates and huge differences in population history, gene

choice, and choice of arbitrary relaxed thresholds for deter-

mining protein evolution (Dorus et al., 2004) may hamper

interpretation of these types of comparative studies. In fact,

a number of studies have used other metrics in their analyses,

and there seems to be little consensus in the field as to whether

human brain genes have indeed undergone accelerated evolu-

tion or not (Bakewell et al., 2007; Clark et al., 2003; Nielsen

et al., 2005; Shi et al., 2006). It is notable that studies that assess

the genome in an unbiased manner, rather than picking only one

arbitrary gene set, show no evidence for adaptive evolution in

brain genes overall (e.g., Nielsen et al., 2007; Shi et al., 2006).

Another example is the pathbreaking study of a humanized

Foxp2 mouse (Enard et al., 2009). In this case, are the assump-

tions about evolution made reasonable, and how can we know?

What are the experiments that could even test this? We cannot

obviously expect to generate a talking mouse, and yet, there is

no doubt that many experiments needed to more fully explore

Foxp2 function can be done in mouse and other model organ-

isms. The same issues are multiplied when we begin to study

gene-gene interactions and molecular circuits. Rather than

avoiding this complexity, it will be crucial to embrace the many

levels of analyses and cross-species comparisons that are

necessary if we truly expect to advance to a new level of under-

standing of human brain evolution.

ACKNOWLEDGMENTS

This work is supported by a grant from the NIMH (R37MH60233-06A1) toD.H.G. G.K. is supported by an A.P. Giannini Foundation Medical Research

240 Neuron 68, October 21, 2010 ª2010 Elsevier Inc.

Fellowship, a NARSAD Young Investigator Award, and the NIMH(K99MH090238).

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