Wayne State University
Wayne State University Dissertations
1-1-2013
Insight Into Human Brain Evolution Through
Phylogenetic Analysis And Comparative Genomics
Amy Marie Boddy
Wayne State University,
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INSIGHT INTO HUMAN BRAIN EVOLUTION THROUGH PHYLOGENETIC
ANALYSIS AND COMPARATIVE GENOMICS
by
AMY M BODDY
DISSERTATION
Submitted to the Graduate School
of Wayne State University,
Detroit, Michigan
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
2013
MAJOR: MOLECULAR BIOLOGY & GENETICS
Approved By:
_______________________________________
Advisor
Date
© COPYRIGHT BY
AMY M BODDY
2013
All Rights Reserved
DEDICATION
This work is dedicated to my family. To my husband, Mike, without his love and support
this would not be possible. And to my daughter Reagan, your smile can carry me over
any obstacle.
ii ACKNOWLEDGMENTS
First and foremost, I would like to thank my advisor, Derek Wildman, I am very
grateful for all his guidance throughout the years. His training has shown me how to
become an independent researcher and I have no doubt I would not be here today
without his support. I value his enthusiasm for science and his unique ability to think
outside of the box. He has taught me how to ask the right questions and has shown me
how to think creatively.
I would also like to thank my committee members, Dr. Kapatos, Dr. Krawetz, and
Dr. Sherwood. Their valuable suggestions, constructive criticism, and encouraging
discussions have helped me greatly. I would like to acknowledge the late Dr. Morris
Goodman. I am forever honored to have been a student of his. His dedication to
science, humble attitude, and words of inspiration will never be forgotten. I would like to
thank the CMMG office staff, especially Suzanne Shaw for her continuous assistance
throughout the years. A very special thank you to the late Mary Anne Housey, her
constant encouragement and belief in me will be remembered forever. I am greatly
indebted to Wildman lab members past and present. Their contribution, guidance and
friendship have made this experience unforgettable.
Last but not least, I would like to thank my family and friends. Their endless
support and encouragement have been a major motivating factor throughout my
graduate career.
iii TABLE OF CONTENTS
Dedication ……………………………………………………………………………………… iii
Acknowledgments …………………………………………………………………………..... iv
List of Tables ………………………………………………………………………….............. v
List of Figures …………………………………………………………………………………. vi
Chapter 1: Introduction ……………………………………………………………………….. 1
Chapter 2: Comparative analysis of encephalization in mammals reveals relaxed
constraints on anthropoid primate and cetacean brain scaling (Published
JEB 2013) ……………………………………………………………………….. 25
Chapter 3: Evidence for positive selection and parallel brain evolution in the neocortical
transcriptome of a capuchin monkey ………………………………...……….. 58
Chapter 4: Conclusion ……………………………………………………………………… 93
Appendix A: Chapter 2 Supplemental Material …………………………………………..100
Appendix B: Chapter 3 Supplemental Material …………………………………………. 121
References …………………………………………………………………………............. 128
Abstract ………………………………………………………………………….................. 168
Autobiographical Statement ………………………………………………………………. 170
iv LIST OF TABLES
TABLE 1: Summary of species and mean EQ ………………….....…………….….…… 39
TABLE 2: Summary of sequencing reads …………….…………....……………..……… 69
TABLE 3: Sequencing assembly statistics …………………..……..……………..……... 70
TABLE 4: Genes with adaptively evolving on capuchin and human lineages ……...... 77
TABLE 5: Enriched biological processes on the human lineage …………….…..……. 78
TABLE 6: Enriched biological processes on the capuchin monkey lineage …….….… 79
TABLE 7: Enriched biological processes on encephalized lineages ………….…….… 81
TABLE S1: Brain /body mass and EQ Dataset of 630 mammals ………….………… 100
TABLE S2: PGLS phylogenetic signal ……………………………………………….….. 115
TABLE S3: Parsimony REML summary ……………………………………………..….. 115
TABLE S4: Genes with accelerated rate of evolution on the human lineage ……….. 121
TABLE S5: Genes with accelerated rate of evolution on the capuchin lineage …….. 123
TABLE S6: Biological processes enriched in the human and capuchin accelerated
gene set …………………...………………………………………………..…. 125
v LIST OF FIGURES
FIGURE 1: Brain expansion on the human lineage ….………………………………........ 6
FIGURE 2: Body and brain mass linear regression .....………………………………..... 38
FIGURE 3: Ancestral reconstruction of encephalization quotients in mammals ..……. 41
FIGURE 4: Boxplot of EQ in mammals ……………………………………………...……. 42
FIGURE 5: Boxplot of EQ in Primates and Cetartiodactyla …...…………………..……. 45
FIGURE 6: Homologous genes …………….……………..………………………...……. 66
FIGURE 7: Histogram of assembled contigs ……………..………………………...……. 70
FIGURE 8: Sequence and assembly pipeline …………………………………….……… 71
FIGURE 9: Summary of gene alignments ………………………….…………….....……. 72
FIGURE 10: Human and capuchins have a large relative brain size ………….....……. 73
FIGURE 11: Homo and Cebus demonstrate the largest increase in encephalization .. 74
FIGURE 12: Patterns of adaptive evolution among encephalized lineages …………... 74
FIGURE 13: Adaptive evolution summary results ……………………………………….. 76
FIGURE 14: Relationship between dN/dS and length of sequence ………….......……. 76
FIGURE S1: Colored representation of body/brain relationship in mammals ……….. 116
FIGURE S2: Body/brain regression for female mammals …………………….……..…116
FIGURE S3: Brain/body regressions taking phylogeny into consideration ………….. 117
FIGURE S4: Boxplot of PGLS derived EQ in mammals …………………………..…... 118
FIGURE S5: Boxplot of PGLS derived EQ in Primates ……………………….……….. 119
FIGURE S6: Boxplot of PGLS derived EQ in Cetartiodactyla ………………..……….. 120
vi 1
CHAPTER 1
Introduction
Encephalization, defined as an increase in brain size relative to body size, is a
major hallmark in primate evolution (Jerison 1973). As a species, humans are unique
among primates, especially in respect to their large brain size. Humans are the most
encephalized mammal, with the human brain six-fold larger than expected relative to
body mass (Jerison 1973, Martin 1981). Relevantly, there are distinct differences in
cognitive abilities in humans compared to other species. It is not unreasonable to
presume cognition and brain size may have coevolved (Lefebvre 2012). Although
evolving a large brain may seem advantageous in regards to intelligence, it comes at a
high cost, consuming approximately 20% of total energy in the body (Aiello and Wheeler
1995), and introduces susceptibility to human specific neurodegenerative diseases
(Sherwood, Subiaul et al. 2008). Insight into human brain evolution can help uncover
molecular mechanisms involved in such diseases and can shed light on how humans
evolved these enhanced cognitive abilities.
The aim of my research is to incorporate phylogenetic and comparative
techniques to identify some of the key components involved in the evolution of the
2
primate brain. I am interested in what makes the human brain unique, and the tradeoffs
our ancestors may have had to endure to acquire such a trait. In this regard, I have
implemented two approaches to study primate brain evolution, and these two
approaches are broken down into two chapters (Chapter 2 and 3). The first aim
explores brain size patterns in mammals using phylogenetic analyses. I collected
absolute brain and body mass from published literature from over 20 orders of
Mammalia, represented by 630 species, calculated relative brain size and reconstructed
the ancestral states. The goal of this study was to infer at which points during
mammalian evolution significant changes in brain size occurred. We determined there
was a significant change along the anthropoid primate and cetacean lineages, where
we discovered greater variance and relaxed constraints in these groups. These results
provide evidence for convergent/parallel evolution of brain expansion among specific
clades within Mammalia. We also confirmed the second most encephalized primate was
the capuchin monkey (Cebus sp.). These results set the foundation for the second aim.
As discussed in Chapter 3, I am interested in genetic contributions to primate brain
expansion. I implemented a comparative genomics approach and sequenced the
neocortical brain transcriptome of a capuchin monkey using next-generation sequencing
technologies. I tested for patterns of convergent brain evolution by identifying brain
expressed genes that are adaptively evolving on the human and capuchin monkey
lineages. As humans and capuchin monkeys are the two most encephalized primates,
we would expect genes involved in brain expansion would show similar patterns of
evolution on the two lineages. Because the brain is metabolically expensive, we
hypothesized genes involved in energy metabolism will have evidence for adaptive
3
evolution. Identifying molecular mechanisms involved in primate brain expansion can
help us understand the basis of human cognition.
Background
This section of my thesis will serve as background to my specific aims, and for
the first aim, I will provide support from the literature that there is a link between brain
size and cognition and then give a brief history of brain evolution on the human lineage.
I will discuss why relative brain size is important in phylogenetic studies and then
provide background into proposed life history tradeoffs for acquiring a large brain. For
the second aim, I will provide evidence on why the capuchin monkey is a suitable
candidate to study convergent brain evolution. I will also give an introduction into
comparative genomics, and why it is a powerful tool in evolutionary biology. Finally, I will
present an overview of previously discovered genetic determinants of human brain
evolution.
Expansion of the Primate Brain and Cognition
A key trait in primate evolution is the expansion of the neocortex (Finlay and
Darlington 1995, Rakic 2009). Along with large brains, there is evidence of advanced
intelligence in primates and presumably, it is this increased brain size, or
encephalization, that is the basis of these cognitive abilities. (Jerison 1973, Marino
1998, Marino 2002, Reader and Laland 2002, Williams 2002, Roth and Dicke 2005,
Lefebvre 2012). Measures of intelligence such as innovation frequency (i.e. new food
items and unique foraging techniques), has been shown to have a significant positive
4
correlation between with brain size. (Lefebvre, Whittle et al. 1997, Overington,
Cauchard et al. 2011). Large-brained nonhuman primates innovate, learn from others,
and use tools more frequently than small-brain primates (Reader and Laland 2002).
Individuals able to create and discover a new solution to an ecological challenge may
have a selective advantage over those not capable of such complex tasks. Discovery
and innovation are important traits to acquire, and possibly the driving forces in the
evolution of large brains (Reader and Laland 2002).
Humans acquired a distinctively large brain among primates, and compared to
our sister group, the chimpanzees and bonobos, our brains are three-fold larger
(Jerison 1973, Martin 1981, Preuss, Caceres et al. 2004). We share greater than 99%
DNA sequence similarities at nonsynonymous sites with the chimpanzee (Wildman,
Uddin et al. 2003). However, unlike our closest relatives, we have evolved unique
cognitive
abilities
such
as
abstract
thinking,
complex
communication
(i.e.
modality/stimulus independence), personal ornamentation and religious rituals that thus
far, have not been observed in other species (Sherwood, Subiaul et al. 2008).
Sequencing the chimpanzee genome revealed ~35 million single nucleotide changes
and five million insertion/deletion changes in the chimpanzee when compared to the
human genome (Chimpanzee-Consortium 2005, Nickel, Tefft et al. 2008). Despite these
differences, processes involved in the evolution of human cognition are still yet to be
determined (Chimpanzee-Consortium 2005).
Uncovering the tempo and mode of hominin brain evolution can allow us to
understand the evolution of the brain and it’s cognitive performance. It is estimated that
the human and chimpanzee lineages diverged from their most recent common ancestor
5
six million years ago (Goodman, Porter et al. 1998, Steiper and Young 2006). Brain
morphology and size are defining features of Homo sapiens other hominin fossil species
(Neubauer and Hublin 2012). The paleontological record of endocranial casts
demonstrate moderate brain size increase in Australopithecus (~450 cranial capacity,
cc), as compared with the living great apes, with evidence of more substantial brain size
increases beginning with the appearance of Homo habilis (~600cc), and continuing at a
more rapid pace with Homo erectus (~950cc) (Holloway and Post 1982, McHenry 1982,
Holloway 2009). Although there is a general trend for an increase in brain size along the
Homo lineage (Figure 1), brain size is a highly variable trait and the discovery of the
small-brained ancestor Homo floresiensis (Brown, Sutikna et al. 2004), have allowed
some to explore the notion of decreases in absolute brain mass on the Homo lineage as
well (Montgomery, Capellini et al. 2010).
6
Figure 1: Brain expansion on the human lineage
This figure shows the cranial capacity changes through time. Data for this figure was extracted from
Holloway, Brain Fossils: Endocasts, 2009, where the means of the cranial capacity, body mass, and time
were reported. The x-axis is millions of years before present, the y-axis is cranial volume, while the size of
the bubble is relative brain size, based on the estimated body weights.
Brain Composition in Primate Evolution
It is not completely understood what brain components might contribute to the
increased cognitive abilities in some mammalian orders. Studies have shown increases
in the size of the neocortex among mammals in general (Jerison 1973, Hofman 1983).
However, enlargement of the neocortex is most substantial among anthropoid primates,
with dramatic enlargement within humans. The neocortex is the newest part of the
cerebral cortex to evolve and is known to be involved in higher functions, such as motor
skills, spatial reasoning, and language (Finlay and Darlington 1995, Rakic 2009). There
7
has been debate whether the scaling of the human prefrontal cortex is deviates from
primate allometric scaling laws (Semendeferi, Lu et al. 2002, Bush and Allman 2004,
Smaers, Steele et al. 2011), whereas one group demonstrated the human prefrontal
cortex is exceptionally large when compared to other primates (Schoenemann,
Sheehan et al. 2005). However, an additional study demonstrated there was a
significant difference in primate frontal cortex allometric scaling when compared to
carnivores, yet among all primates, the human prefrontal cortex is not larger than
predicted (Bush and Allman 2004).
In the past, there has been debate on whether it is the size of specific primate
brain regions or the total brain that demonstrate the most significant changes in primate
brain evolution. Finlay and Darlington (1995) researched 11 different brain structures
(medulla, hippocampus, cerebellum, striatum, neocortex, olfactory bulb, paleocortex,
mesencephalon, septum, schizocortex, diencephalon) in the brains of 131 mammalian
species. They concluded that the sizes of these brain regions are conserved and
predictable from absolute brain size and total brain size accounted for 96% of the
variance in these regions, suggesting the best factor is a large total brain, not its parts
(Finlay and Darlington 1995).
In contrast, Barton and Harvey (2000) analyzed the same dataset (Stephan 1981
data) and made the claim that the neocortex in primates demonstrates a five-fold
increase in volume compared to insectivores, even after accounting for scaling with the
total brain size and for phylogeny. This study concludes that mosaic evolution is
important in brain size evolution and that brain components evolved independently,
therefore suggesting that mammalian brains are not just scaled versions of the same
8
design (Barton and Harvey 2000). Additionally, de Winter and Oxnard (2001) provide
support for mosaic brain evolution by examining brain proportions in mammals using
principle component analysis and concluding that anthropoid primates, insectivores and
bats show independent radiations in brain organization (de Winter and Oxnard 2001).
Brain growth in primates occurs mainly during their pre and early postnatal period
(Stiles and Jernigan 2010). Compared to chimpanzees, human neonates have larger
brains. However, bipedalism is a limiting factor for human neonates, where the size of
the head at birth is close to the size of the birth canal, this also known as the obstetrical
dilemma hypothesis (Rosenberg and Trevathan 2002, Neubauer and Hublin 2012). The
constraint on neonatal brain size is relaxed in our closest living relatives, the apes.
Humans experience faster growth rates and longer postnatal brain growth when
compared to chimpanzees (Leigh 2004, DeSilva and Lesnik 2006, Sakai, Hirata et al.
2012). Recently an alternative hypothesis has been proposed, the energetics of
gestation and growth (EGG) hypothesis, that proposes neonatal brain expansion is
limited by maternal energy supply and not the pelvic width (Dunsworth, Warrener et al.
2012).
Other contributions to the development of a large brain are the length and rate of
cell division. Larger brains provide more neurons. In humans, the mature brain is
composed of over ~85-100 billion neurons (Pakkenberg and Gundersen 1997, Azevedo,
Carvalho et al. 2009). Increasing the time of neurogenesis or decreasing the rate of
neuronal death can contribute to the number of neurons in the brain (Finlay and
Darlington 1995, Rakic 2009). Prenatal brain growth can be attributed to neurogenesis,
whereas in postnatal growth, myelination and axon growth may be responsible (Rakic
9
1985, LaMantia and Rakic 1990, Sowell, Thompson et al. 2001, Leigh 2004). In
humans, axon myelination and synapse maturation occur later in life, with prolonged
myelination evident in humans compared to chimpanzees (Miller, Duka et al. 2012).
However, the prolonged synaptogenesis in humans compared the macaques, is also
observed in chimpanzees, suggesting this adaptation occurred prior to the human and
chimpanzee divergence from their most recent common ancestor (Bianchi, Stimpson et
al. 2013). The duration of neurogenesis can cause magnitudes of differences in the
amount of cells produced, as precursors can increase by an exponential function and
this results in a larger byproduct (Finlay and Darlington 1995). It has been demonstrated
that brain volume correlates with total neuron number in primates and there is no
significant difference in neuron size or density (Herculano-Houzel, Collins et al. 2007,
Azevedo, Carvalho et al. 2009). Others have shown humans have the highest ratio of
glia: neuron cells in the neocortex of anthropoid primates, however this increase of glia:
neuron cells is predicted by the allometric scaling laws (Sherwood, Stimpson et al.
2006).
Absolute vs. Relative Brain Size
Despite humans having the largest absolute brain size among primates, we are
not the largest primate in terms of body mass (Jerison 1973, Martin 1981, Stephan,
Frahm et al. 1981). Many species, including elephants, have a substantially larger brain
than humans, and are not considered to have higher cognitive or behavioral flexibility,
suggesting brain size alone is not responsible for intelligence (Roth and Dicke 2005,
Shoshani, Kupsky et al. 2006). Body size is considered a confounding variable and with
10
claims that body weight explains 62% of the variation in brain size across primates
(Rilling 2006). Brain size is negatively correlated with body size (Snell 1891, Dubois
1897, Jerison 1973, Martin 1981). Consequently, as a body size gets bigger, the brain
increases in absolute size, but not in relative size. For humans, the brain takes up 2% of
body mass, however, there are smaller mammals, such as shrews, whose brain takes
up 10% of their body mass (Roth and Dicke 2005).
Consequently, when comparing brain sizes among species with varying
morphologies it is important to correct for the body size of each particular species. As
shown in Chapter 2, we analyze our brain data based on relative brain mass, not
absolute mass. This is based on basic brain:body allometric scaling principles.
Allometry is a quantitative approach used in comparative biology between different
parameters (i.e. traits), and uses the equation y=kxα (Snell 1891). This has been a
fundamental tool in the field of comparative biology for examining the relationship of
different traits such as organ weights, body weights and metabolism. Due to allometric
scaling laws, species with large bodies tend to have large brains (Huxley 1936, Kleiber
1947, Jerison 1973, Hofman 1983). Deviations from this brain:body allometric
relationship, such as encephalization, can be used to infer advanced cognitive abilities
in a species (Jerison 1985, Williams 2002, Roth and Dicke 2005).
Selective Advantage and Energetic Expenses of a Large Brain
In this next section I discuss the potential benefits and costs associated with the
evolution of a large brain. Numerous studies have attempted to correlate brain size with
other life history variables as well, including the length of gestation, length of lactation,
11
lifespan, neonatal brain size, and basal metabolic rates (Sacher 1959, Sacher and
Staffeldt 1974, Martin 1981, Hofman 1983, Harvey and Clutton-Brock 1985, Armstrong
1990, Isler and van Schaik 2006, Barrickman, Bastian et al. 2008). Benefits of a large
brain include the previously discussed cognitive advancements, increased fitness and
survival when exploring new niches (Sol, Bacher et al. 2008, Gonzalez-Lagos, Sol et al.
2010) and a longer reproductive lifespan (Gonzalez-Lagos, Sol et al. 2010). Slower
growth and development may allow more time to learn adult skills (Ross and Jones
1999). Humans are able to reproduce much earlier than other mammals, when
compared to the time adult level skills are reached and the interbirth interval in humans
is shorter of than in apes. It has been suggested this is due to humans intense sharing
of resources and cooperation. Cooperation allowed learning to exceed the reproduction
age (Schuppli, Isler et al. 2012).
Accordingly, brain expansion can be considered an adaptive trait. If so, what
selective forces attributed to massive brain expansion during human descent? A few
theories suggest the major turning point in human evolution was the shift to a huntergatherer lifestyle (Leonard, Snodgrass et al. 2007, Carmody and Wrangham 2009,
Raichlen and Gordon 2011, Raichlen and Polk 2013). Some propose it was the shift to
meat eating, allowing for a diet that had an increase in quality, energy and stability
(Leonard, Snodgrass et al. 2007). An alternate hypothesis was the introduction of
cooking food was of substantial significance to human evolution in regards to an
increase in brain size and cognitive performances. Cooking food likely provided an
increase in energy, as it reduces toxins, increases digestibility, and allowed exploitation
of new resources (Carmody and Wrangham 2009). Another study suggested that
12
increased endurance was the selecting force for brain expansion. The advanced
cognitive abilities that began to take shape were an indirect effect, because increases in
aerobic physical activity increase levels growth factors (e.g. VEGF, BDNF, IGF-1),
which can induce neurogenesis (Raichlen and Gordon 2011, Raichlen and Polk 2013).
Lastly, some propose that surviving and reproducing in complex social groups selected
for increased encephalization in humans, whereas these large social groups exhibit
altruism and cooperative behaviors that allow for help with altricial neonates (Dunbar
1998, Dunbar 2009).
Although valuable to the fitness of an individual, the brain is an energetically
costly structure. Energy requirements that are related to the size of the brain are limited
by the energy supply connected with body metabolism (Armstrong 1990). Larger brains
take longer to grow in utero and to reach maturity, and have prolonged maternal
investment (i.e. longer gestation, lactation) (Martin 1996, Barton and Capellini 2011).
Even though the brain only weighs 2% of body weight for modern humans, it is
estimated to consume at least 20% of the adult body’s energy, making it the most
metabolically expensive organ (Aiello and Wheeler 1995, Gilbert, Dobyns et al. 2005).
Interestingly, basal metabolic rate scales to brain weight, similar to how brain weight
scales to body weight. Kleiber examined the relationship between the metabolic rates of
eutherian mammals (i.e. placental mammals) and their body mass and found that
oxygen consumption (i.e. heat production) of an animal per unit time is proportional to ¾
of the body weight, this is known as Kleiber’s law (Kleiber 1947). There have been
many other hypotheses derived to explain the selective pressures for a large brain;
including the expensive tissue hypothesis (Aiello and Wheeler 1995), the maternal
13
energy hypothesis (Martin 1996), and the social brain hypothesis (Dunbar 1998). These
hypotheses are discussed in more detail at the end of Chapter 2; I highlight results
provided by our phylogenetic analysis of brain size, and provide evidence for these
potential tradeoffs proposed above.
Capuchin Monkeys, Intelligence, and Life History Variables
Capuchin monkeys (genus: Cebus) are medium sized New World monkeys
(platyrrhines) endemic in Central and South America (Jack 2007) and are classified in
the family Cebidae, within the subfamily Cebinae, which also includes squirrel monkeys
(genus: Saimiri) (Wildman, Jameson et al. 2009). Currently there are four recognized
species of Cebus; C. apella, C. albifrons, C. capucinus, and C. olivaceus, and over 30
subspecies, however there is debate whether some of these subspecies should be
considered species. Cebus can be divided into two groups, tufted or robust (C. apella)
and untufted or gracile (C. albifrons, C. capucinus, and C. olivaceus) based on the
presence or absence of hair tufts on the top of their head (Jack 2007, Alfaro, Silva et al.
2012). Due to the differences in morphology (i.e. cranial and dental) and biogeography
between the two groups, there has been recent debate on whether these groups should
be divided into two distinct genera, Cebus (C. albifrons, C. capucinus, and C. olivaceus)
and Sapajus (C. apella) (Alfaro, Silva et al. 2012). The cebine monkeys are considered
the most omnivorous platyrrhines, with their diet consisting of fruit, seeds, and small
vertebrates, including birds and their eggs, lizards, and squirrel (Jack 2007). Capuchins
have short fingers and, “pseudo-opposable thumbs,” with the ability to move all digits
independently (Fleagle 1999, Jack 2007). Furthermore, capuchins are the only New
14
World monkey that use a precision grip with their thumb and forefinger (Padberg,
Franca et al. 2007).
General patterns of body size and life history variables emerge in primates,
whereas the larger body sized species tend to have longer and slower life history
variables. The genus Cebus does not fit this pattern, despite their small body size,
capuchins have a slow life history, long lactation periods, long interbirth intervals and a
long life span, i.e. 47 years (Fleagle 1999). Some have argued that these slower life
history characteristics are related to the fact that the capuchin monkey has a large brain
(Montgomery, Capellini et al. 2010, Boddy, McGowen et al. 2012). Interestingly,
humans have the largest brain and the longest lifespan of any primate (Hawkes,
O'Connell et al. 1998), suggesting a correlation between high encephalization and
longer life history variables. It has been suggested that the large brained Neanderthals
likely had slower life history variables than modern humans (Ponce de Leon,
Golovanova et al. 2008).
Resembling human brain growth, a MRI study of 29 capuchin monkeys (C.
apella) demonstrated rapid postnatal brain growth and motor skill development. Relative
to other primates this postnatal brain growth is unusually fast (Phillips and Sherwood
2008), and in agreement with this pattern, humans also have a non-linear increase in
brain volume during postnatal years (Courchesne, Chisum et al. 2000). These findings
demonstrate evidence for similar brain evolution patterns between the two species. It
should be noted that the emergence of large brains has been shown to have evolved
independently in different mammalian lineages (Finarelli and Flynn 2009), and it is
15
therefore feasible to hypothesize a parallel relationship of adaptive brain evolution
among humans and capuchin monkeys.
Capuchin monkeys are well known for their intelligence, and one method of
determining advanced cognitive abilities among animals includes the use of tools.
Among the New World monkeys, capuchins are considered to be the most varied tool
users (Panger, Perry et al. 2002). Tool use has also been observed in numerous
circumstances among the species, including making cups out of leaves to retrieve water
(Phillips 1998), the use of a club to protect from a venomous snake (Boinski 1988), and
hammer and anvil used to crack oysters (Fernandes 1991) and nuts (Fragaszy, Izar et
al. 2004, Ottoni, de Resende et al. 2005, Visalberghi, Fragaszy et al. 2007, Visalberghi,
Spagnoletti et al. 2009). It should be noted, a variety of primates have been observed
and reported using tools, but repeated reports (over 25) of tool use only include Pan,
Pongo, Cebus, Macaca, Gorilla, and Papio (Panger 2007), with Cebus, the only New
World monkey on the list. Typically, not all primates that use tools are highly
encephalized, but some of the primates that have the largest brains tend to be tool
users (Panger 2007). Tool use has implications in both social and cognitive levels, as
one study showed that among wild capuchins (C. apella), the younger monkeys tended
to watch and learn from more proficient nutcrackers, as opposed to the dominant male
or the social proximity of the nutcracker, suggesting a decision-making process in the
establishment of tool use (Ottoni, de Resende et al. 2005).
Other evidence for advance cognitive abilities includes a variety of social and
behavioral tasks. For example, captive capuchin monkeys can recognize pictorial
representation for real objects and establish the difference between the real and
16
pictorial object. Object picture representation is often used as cognitive test among
animals and suggests perceptual skills and cognitive knowledge (Truppa, Spinozzi et al.
2009). Furthermore, it has been demonstrated that capuchin monkeys chose variety for
the sake of variety, when given the choice between a Variety-token (ten varieties of
food, nine non-preferred and one preferred) and a Monotony-token (ten of a single
preferred food item), they chose the Variety-token significantly more often than the
Monotony-token. The authors of this study conclude that this in beneficial to the species
in a number of ways, including eating new foods for nutritional value, but it may also
satisfy intellectual needs, such as stimulation from the environment (Addessi, Mancini et
al. 2010).
Capuchin monkeys also present evidence for complex social behavior, and have
been shown to demonstrate cross-site differences in foraging behavior, similar to what
is found in the great apes. These studies show that the region-specific foraging
differences among groups of capuchins and propose these differences are not due to
genetic or ecological events, but to social learning process, i.e. “traditions” (Panger,
Perry et al. 2002). Additionally, capuchin monkeys participate in fur rubbing, which
involves intentionally anointing themselves with millipedes in the wild. It is thought that
this behavior actually prevents insect stings, and in particular, those of mosquitoes,
because the millipedes secrete benzoquinones, chemicals known to be insect
repellents. It has also been shown that up to four capuchin monkeys will share a single
millipede, suggesting the cultural importance of such tasks (Valderrama, Robinson et al.
2000). Evidence of a large brain, along with the abilities of tool use, cognitive functions
and complex social environments, suggest there may be a relationship between
17
encephalization and cognitive function, making the capuchin monkey a good model for
comparative studies between highly encephalized species.
Comparative Genomics
Comparative genomics can retell an ancient story of how extant species and their
ancestors evolved. In the genomes of close relatives are novel genes, gene loss,
duplicated genes, and genes that are modified. Evolutionary genomics studies can
reconstruct the past and reveal valuable insights into the evolution of a species. The
basis for these studies is that similar phenotypes of two related species will likely have
genomic DNA that is conserved. However, genomic sequences that encode for proteins
responsible for differences between the two species will likely be divergent (Hardison
2003, Futuyma 2009). Tracing these differences in a genome can help determine
functional phenotype/genotype changes on a certain lineage. These types of
comparisons can identify molecular adaptations among genes under selective pressure
(Hardison 2003). Determining genetic factors that are responsible for phenotypic
changes in primate brain evolution may help better understand basic biology of the
brain, and give insight on how to research to human neurological disorders (Vallender
2011).
An underlying assumption in comparative genomics is that changes in a species
phenotype are typically a result of changes in a species genotype. If we want to
understand what phenotypic changes happened during primate brain evolution, we
should start by looking at the genome. We are not capable of understanding the full
context of human brain evolution without looking at phylogenetic relationships. There
18
are approximately 30 orders within Mammalia living today, including egg-laying
mammals (Monotremata), marsupials and placental mammals (Eutherians) (Wilson and
Reeder 2005). Recent advances in the field of genomics and next generation
sequencing set the stage for comparative genomics, with over 60 sequenced
mammalian genomes currently publicly available in ensembl (Flicek, Amode et al.
2012). As discussed in more detail in Chapter 3, I chose to implement RNA sequencing
of the prefrontal cortex in the capuchin brain in order to shed light on human brain
evolution.
Substitution Rates as a Measure of Natural Selection
An established method in the field of comparative genomics is to test for
adaptively evolving sequences of DNA (Yang, Goldman et al. 1994, Yang and Nielsen
2000, Yang, Nielsen et al. 2000, Yang 2007). This is a computational approach to parse
out sequences that are evolving at a faster rate than neutral expectations would predict.
Comparative approaches can determine if the differences are functionally important, or
just neutral changes, with the assumption that genes (i.e. functional DNA sequences)
will accumulate less change, and have more selection pressure than noncoding DNA
(Jukes and Kimura 1984, Li, Wu et al. 1985). This technique estimates the number of
synonymous substitutions, a change in the DNA that causes no change in the amino
acid due to degeneracy of the genetic code (64 possible codons, but only 20 amino
acids), and nonsynonmous substitutions, whereas the change in DNA changes the
amino acid. Natural selection can then be measured by comparing these rates of
neutral mutations (synonymous) to potentially functional mutations (nonsynonymous)
19
(Czelusniak, Goodman et al. 1982, Jukes and Kimura 1984, Li, Wu et al. 1985, Yang,
Goldman et al. 1994).
In order to have direct changes to the phenotype, such as an increase in brain
mass, there is likely an alteration to the DNA, and that alteration needs to become fixed
in the population due to selection (Jukes and Kimura 1984, Duret and Mouchiroud
2000). Mutations periodically arise and become fixed in a population by genetic drift, yet
many mutations are neutral and have no effect on the individual’s fitness. However,
certain amino acid substitutions may have a selective advantage to become fixed in a
population, if these substitutions are beneficial, the allele frequency tends to increase at
a faster rate (Futuyma 2009). For neutral substitutions, it has been proposed (Jukes and
Kimura 1984) the rate of substitution is equal to the rate of mutation, so an increase in
change from the neutral mutation rate is evidence of selective pressure (Jukes and
Kimura 1984, Duret and Mouchiroud 2000).
Nonsynonymous and synonymous substitution rates vary, where sysnonymous
substitutions per site (dS) occur at a higher frequency than nonsynonmous substitutions
per site (dN), according to the neutral theory of evolution. Consequently, most genes
have a ratio of dN/dS of less than 1, and are considered to be under negative (purifying)
selection (Li, Wu et al. 1985, Yang, Goldman et al. 1994, Yang and Nielsen 2000). The
average dN/dS in primates is ~0.2 (Chimpanzee-Consortium 2005). It is estimated that
5% of the human genome is under purifying selection when compared to the mouse
genome, and thus functional (Waterston, Lindblad-Toh et al. 2002, Hardison 2003).
Majority of protein-coding genes are highly conserved because there are many
important biological processes shared among mammals.
20
However, the rates of evolution vary along lineages and within genomes (Wolfe,
Sharp et al. 1989). Li and colleagues discovered the proportion of synonymous sites
can vary ten-fold among different genes compared within the same species (Li, Wu et
al. 1985). These varying rates have been studied to an extent, and it was found that the
varying synonymous substitution rates are correlated with the base composition of the
gene and nearby DNA (Wolfe, Sharp et al. 1989). Other studies have shown that the
synonymous substitution rate for a gene depends on GC content, whereas higher GC
content was conserved at synonymous sites (Bernardi 1995, Hardison, Roskin et al.
2003, Miller, Makova et al. 2004).
One study looked at the relationship between substitution rates and tissue
specific gene expression patterns. The researchers studied 19 tissues and gene
expression information for human/rodent orthologs and measured the number of
substitutions per site at synonomous sites and nonsynonomous sites. This study
demonstrated that tissue specific proteins had three fold higher substitution rates when
compared to ubiquitous ones. The tissues that had the slowest evolutionary rates were
the brain, muscle, retina and neuron-specific proteins. This study also found 5’ and 3’
UTRs to follow similar trends, suggesting they don’t evolve neutrally and do have
selective pressures as well (Duret and Mouchiroud 2000). Others have found similar
results, whereas there are more constraints on tissue-specific genes, especially in the
brain (Strand, Aragaki et al. 2007).
21
Genetic Determinants in the Human Brain
Many studies have attempted to determine the underlying molecular mechanisms
involved in primate brain evolution through comparing gene expression patterns in the
primate brain (Enard, Khaitovich et al. 2002, Caceres, Lachuer et al. 2003, Karaman,
Houck et al. 2003, Marvanova, Menager et al. 2003, Uddin, Wildman et al. 2004). These
studies used microarray analysis to compare brain tissue samples in human and
nonhuman primates, including chimpanzee, bonobo, orangutan, macaques, and
marmoset. The results had some general similarities, including the pattern of increased
gene expression among the human neocortex when compared to a nonhuman primate
(Preuss, Caceres et al. 2004). Gene ontology classification of these genes includes
categories involved in aerobic energy metabolism, neuronal function, cell growth and/or
maintenance (Caceres, Lachuer et al. 2003, Uddin, Wildman et al. 2004).
Another approach to study the molecular mechanisms of primate brain evolution
is to test for patterns of adaptive evolution at the sequence level. Genes that encode for
proteins involved in aerobic energy metabolism have been one the more interesting
ontologies to study in respect to human brain evolution due to the high amount of
metabolic energy a large brain consumes. Previous investigations have shown evidence
of genes involved in the function of the mitochondria have evolved at an accelerated
rate among the human lineage (Grossman, Wildman et al. 2004), including a variety of
genes that encode for proteins involved in the electron transport chain (Goldberg,
Wildman et al. 2003, Doan, Schmidt et al. 2004, Schmidt, Wildman et al. 2005, Uddin,
Opazo et al. 2008). Furthermore, two recent genome-wide phylogenomic studies
revealed evidence of aerobic energy metabolism genes adaptively evolving in both the
22
human and elephant ancestries (Goodman, Sterner et al. 2009) and the dolphin lineage
(McGowen, Grossman et al. 2012), suggesting convergent patterns of gene evolution
that might be associated with brain size.
Additionally, groups have attempted to examine diseases that affect brain
development, such as microcephaly, to provide insight into molecular mechanism
involved in brain size. Investigations on microcephaly, a neuro-development disorder
that causes a reduction in brain growth, have been studied in depth in an attempt to
characterize
genes
involved
in
brain
expansion.
Individuals
diagnosed
with
microcephaly typically have a brain that is at least three standard deviations below the
mean and is accompanied with mental retardation (mild to severe), however brain scans
show normal architecture of the brain (Cox, Jackson et al. 2006). It is thought that the
mechanism that leads to a decrease in brain size will be similar to those responsible for
brain growth, thus an understanding of microcephaly will also shed light on
encephalization. Microcephaly has been linked to many genes, including, MCPH1,
ASPM, CDK5RAP2, CENPJ, STIL, WDR62, DUF1220, and ZNF335 suggesting a n
important role in brain size regulation during development (Jackson, McHale et al. 1998,
Bond, Roberts et al. 2002, Cox, Jackson et al. 2006, Kumar, Girimaji et al. 2009,
Nicholas, Khurshid et al. 2010, Dumas, O'Bleness et al. 2012, Yang, Baltus et al. 2012).
ASPM and MCPH1 are the most studied of the microcephaly genes and both
have been independently shown to be associated with brain size (Wang, Li et al. 2008,
Rimol, Agartz et al. 2010) and have evidence of positive selection in human evolution
(Evans, Gilbert et al. 2005, Mekel-Bobrov, Gilbert et al. 2005), however there was no
evidence for positive selection on the capuchin monkey lineage (Villanea, Perry et al.
23
2012). Interestingly, three of the microcephaly genes, ASPM, CDK5RAP2, and CENPJ,
transcribe proteins involved in spindle pole formation and orientation, while MCPH1 has
been implicated in centrosome formation as well as cell cycle and survival. It is for this
reason that microcephaly is hypothesized to be a disorder of neuro-genic mitosis,
whereas these genes have a role in controlling the number of symmetric divisions,
producing two identical progenitor cells, a cell undergoes during neurogenesis (Cox,
Jackson et al. 2006, Montgomery and Mundy 2010).
Other genes thought to be involved in cognitive development that have showed
positive selection on the human lineage include, HAR1, a novel RNA gene expressed in
neocortical development (Pollard, Salama et al. 2006), GLUD2, a brain specific isoform
of glutamate dehydrogenase (Burki and Kaessmann 2004), AHI1, deleterious mutations
in this gene responsible for Joubert sydrome (Ferland, Eyaid et al. 2004), and the most
well known, FOXP2, a gene suggested to play a role in language and speech in
humans. The human variant of FOXP2, mutations have been found that allowed for two
amino acid substitutions in an otherwise conserved gene, and point mutations in this
gene cause severe articulation including linguistic and grammatical impairment (Enard,
Przeworski et al. 2002).
In Summary
There are potentially many factors that influence primate cognition, however,
evidence suggests brain size is at least one aspect. The following chapters will
demonstrate through phylogenetic analysis that selection pressure on primate brain
evolution relaxed on the lineage leading to anthropoid primates. In an otherwise
24
conserved trait among mammals, primates exhibit the largest variance in relative brain
size, with evidence of brain expansion events on individual lineages, including the
capuchin monkey. Using a comparative genomics approach, I will test for patterns of
convergent evolution adaptively evolving on both the capuchin monkey and human
lineages and provide a list of potential genes contributing to primate encephalization.
25
CHAPTER 2
Comparative Analysis of Encephalization in Mammals
Reveals Relaxed Constraints on Anthropoid Primate and
Cetacean Brain Scaling
This chapter published as:
Boddy AM, McGowen MR, Sherwood CC, Grossman LI, Goodman M, and Wildman DE.
“Comparative analysis of encephalization in mammals reveals relaxed contraints on
anthropoid primate and cetacean brain scaling” J. Evol Biol. 2012 May;25(5):981-994.
Abstract
There is a well-established allometric relationship between brain and body mass
in mammals. Deviation of relatively increased brain size from this pattern appears to
coincide with enhanced cognitive abilities. To examine whether there is a phylogenetic
structure to such episodes of changes in encephaliazation across mammals we used
phylogenetic techniques to analyze brain mass, body mass, and encephalization
26
quotient (EQ) among 630 extant mammalian species. Among all mammals, anthropoid
primates and odontocete cetaceans have significantly greater variance in EQ,
suggesting that evolutionary constraints that result in a strict correlation between brain
and body mass have independently become relaxed. Moreover, ancestral state
reconstructions of absolute brain mass, body mass, and EQ revealed patterns of
increase and decrease in EQ within anthropoid primates and cetaceans. We propose
both neutral drift and selective factors may have played a role in the evolution of
brain:body allometry.
Key Words: allometry, encephalization, phylogenetics, ancestral state reconstruction,
brain size, mammalian evolution
27
Introduction
The scaling relationship between brain mass and body mass has generated
great interest since the early years of evolutionary biology (Darwin 1871, Snell 1891).
This interest is grounded in the quest to understand the biological basis of intelligence in
relatively large brained species such as humans. Snell proposed a model to explain
phylogenetic variation in brain mass encompassing two factors, one dependent on body
mass (slope of the line) and one independent of body mass (y-intercept). Snell (1891)
suggested that brain mass scales with body surface area and therefore expected the
brain:body mass relationship to scale at a 2/3 ratio. After the discovery of the large
brained hominin fossil, Homo erectus, Dubois extended the work of Snell by proposing
the “index of cephalization,” a measure that specified the relative mass of the brain after
body mass was taken into consideration (Dubois 1897). Dubois reduced Snell’s scaling
ratio from 2/3 to 0.56; however, this relationship was calculated from brain and body
measurements of species that he considered equally “intelligent” and, in retrospect, the
small number of samples and subjective grouping were responsible for the reduced
slope (Jerison 1973).
Similar to the “index of cephalization” of Dubois, Jerison introduced the
encephalization quotient (EQ) (Jerison 1973), which provided a quantitative value to
describe relative brain mass that could be compared across a wide range of species of
varying body mass. Encephalization is defined as a higher than expected brain mass
relative to total body mass, and it is often hypothesized that deviations from this
brain:body allometric relationship may correlate with cognitive abilities (Jerison 1985,
Williams 2002, Roth and Dicke 2005). The EQ of a particular species is determined by
28
calculating the ratio of its observed brain mass over its “expected” brain mass. The
expected brain mass is calculated from a prediction equation based on either a
theoretical scaling relationship (i.e., Jerison, 1973) or an empirically determined one
(i.e., (Holloway and Post 1982), Martin, 1981). Thus, the EQ represents how many
times larger (or smaller) a species’ brain is in comparison to what would be expected for
its body mass. Accordingly, a species with an EQ that is greater than 1 has a brain that
is larger than expected for its body mass and an EQ that is less than 1 indicates that the
species has a brain that is smaller than expected. Although the exact EQ depends on
the composition of species in the reference sample used in the analysis, without
exception modern humans have always been found to have the highest EQ in
comparative studies of mammals and primates (Jerison 1973, Marino 1998).
The two most well-established slopes of the regression line are the 2/3 exponent
(Snell 1891, Jerison 1973), which produces the equation EQ=Brain mass / 0.12 x Body
Mass2/3 (Jerison 1973), and the 3/4 exponent (Pilbeam and Gould 1974, Martin 1981),
which produces the equation EQ=Brain mass / 0.059 x Body Mass0.76 (Martin 1984). It
should be noted that the slope of the brain:body mass relationship and the exponent in
scaling relationships can be considered equivalent when the equation is converted into
log form. The 2/3 exponent is derived from the theoretical expectation that brain mass
scales with body surface area (Snell 1891, Jerison 1973). The 3/4 exponent is based on
an empirical fit to large cross-species datasets and seems to accord with the hypothesis
that brain mass scales with basal metabolic rate (BMR), i.e. a species brain mass is
dependent on the maternal energy available during gestation (Martin 1981, Marino
1998). In either case, brain mass has a negative allometric relationship with body mass,
29
indicating that brain mass does not usually increase in mass in equal proportion to
increases in body mass as a whole.
Although allometric studies have provided valuable insights into the brain:body
relationship, most past studies have not explicitly taken phylogeny into consideration
when calculating the brain:body regression lines. Felsenstein (1985) established that
individual data points in comparative studies should not be considered independent due
to the structured pattern of trait similarity among species due to common ancestry.
Moreover, many succeeding reports have studied individual mammalian orders, such as
Primates (Bronson 1981, Armstrong 1985, Isler, Christopher Kirk et al. 2008,
Montgomery, Capellini et al. 2010), Carnivora (Radinsky 1978, Finarelli and Flynn 2006,
Finarelli and Flynn 2007, Finarelli and Flynn 2009), Cetacea (Marino 1998), or
Chiroptera (Hutcheon, Kirsch et al. 2002, Jones and MacLarnon 2004). There are few
studies of encephalization among all mammals (Jerison 1973, Martin 1996, Isler and
van Schaik 2009). Therefore, studying brain mass in the context of a phylogenetic tree
of mammals can assist in refining our estimates of the brain:body relationship as well as
determining the timing and tempo of major changes in mammalian brain:body allometry.
Humans are the most encephalized species, with a brain mass at least six times
larger than expected for a mammalian species of its body mass (i.e. average EQ ≈ 6)
(Jerison, 1973). Presumably, it is this high degree of encephalization that makes
humans unique in cognitive performance, including the skills needed for complex
language and culture (Sherwood, Subiaul et al. 2008). However, recent studies point to
additional factors beyond EQ that might also be involved in the evolution of cognition
including the possible importance of neuron density (Herculano-Houzel, 2011). In
30
addition, encephalization is not exclusive to humans, with evidence of varying degrees
of relative brain mass enlargement among many other mammalian species. In addition,
encephalization is not exclusive to humans, with evidence of varying degrees of relative
brain mass enlargement among many other mammalian species. Primates in particular
demonstrate numerous independent shifts to larger relative brain mass among different
lineages (Harvey, Clutton-Brock et al. 1980, Armstrong 1985, Williams 2002, Isler,
Christopher Kirk et al. 2008, Montgomery, Capellini et al. 2010) and other clades such
as Carnivora (Radinsky 1978, Cutler 1979, Dunbar and Bever 1998, Finarelli and Flynn
2009), Cetacea (Marino 1998, Tartarelli and Bisconti 2007) and Proboscidea (Shoshani,
Kupsky et al. 2006) show independent increases in relative brain mass as well.
Although most past research on brain mass evolution has focused solely on
evolutionary trends of increasing brain mass, there is also evidence that relative brain
mass has been reduced within multiple lineages (Niven 2005, Safi, Seid et al. 2005,
Montgomery, Capellini et al. 2010) including particular clades of bats (Safi, Seid et al.
2005) and primates (Montgomery, Capellini et al. 2010). Safi et al. (2005) argued that a
reduction in brain mass might have benefits as well, such as adaptation to habitat
complexity and flying efficiency in bats.
Because previous publications on brain mass evolution have been limited by the
size of datasets, number of replicate species, and redundancy (i.e., extracting
information from multiple papers that lead back to the same publication), in this study
we curated data on brain and body mass from the largest number of extant mammalian
species yet assembled, including 630 species from 21 mammalian orders. We
calculated the allometric relationship between brain and body mass, and tested whether
31
specific clades deviated from brain:body allometric scaling regularities. In addition, we
traced the phylogenetic history of encephalization and reconstructed the ancestral state
of EQ, brain mass, and body mass for all mammals in our dataset. Comparisons among
mammals using our large dataset can lead to important insights concerning human
evolution, including a more accurate inference of the ancestral brain mass at the stem
of the hominin lineage. Tracing the evolution of brain and body mass in mammals
allows us to estimate the ancestral EQ of multiple species and determine the timing of
major changes in encephalization during mammalian descent. With these data, it is now
possible to design testable hypotheses regarding the evolutionary forces that drove the
numerous increases and decreases in mammalian evolution. We propose that among
anthropoid primates several changes in brain mass may have been advantageous and
thus selected while others may have been due to neutral drift, and we discuss these
changes in the light of population size.
Materials and Methods
Data Collection
All data on body mass and brain mass of mammals were collected from
published literature sources, except for brain masses measured directly from
postmortem specimens in our own collections (by CCS; n=94 individuals), and has been
entered
into
a
MySQL
database
that
is
publicly
available
at
(http://homopan.wayne.edu/brainbodydb/brainbody_list.php). This publicly available
database contains brain and body mass as well as other information from over 2000
individuals in 930 species. We then parsed this dataset into a smaller one (Table S1),
32
where the data points used for this study had to satisfy two main criteria to be included:
1) all measurements were from adult individuals, and 2) published data were obtained
from its original source. If authors noted emaciation, the data were not included in our
analysis. Domesticated species were also removed from the analysis due to the
process of artificial selection and its effects on reduced brain size in domesticated
animals (Kruska 1988). Both male and female data were collected if available and, if
there were multiple measures (male and female) for a single species, the brain mass
and body mass were averaged. For the dataset from Mace et al. (1981), we corrected
for measurement error by subtracting 0.59 g from all rodent species from this dataset as
proposed by (Isler and van Schaik 2006). In some instances, brain size was measured
and reported as endocranial volume, which was converted to brain mass in grams by
multiplying the volume by 1.036 (Stephan, Frahm et al. 1981). For cases that had only
brain mass of an individual and no recorded body measurement, the body weight was
averaged from CRC Handbook of Mammalian Body Masses (Silva and Downing 1995).
These procedures resulted in 630 adult mammalian species from 21 orders (Table S1)
being included in our analysis. The female dataset is a subset of this larger dataset,
including only measurements from adult female individuals (n=130).
Phylogenetic Comparative Methods
Character states in related species should not be considered statistically
independent, because they are inherited from a common ancestor (Felsenstein 1985,
Garland Jr, Harvey et al. 1992, Felsenstein 2008). We corrected for non-independence
of character states using two methods; 1) independent contrasts (Felsenstein 1985) and
33
phylogenetic generalized least squares (PGLS). Both methods were calculated from log
base 10 transformed body and brain mass data and a time-calibrated supertree of
mammals (Bininda-Emonds, Cardillo et al. 2007). Taxa for which we did not have data
were
pruned
from
this
tree.
Independent
contrasts
were
calculated
using
PDAP:PDTREE (Midford, Garland Jr et al. 2005) in MESQUITE version 2.74 (Maddison
and Maddison 2009). We took an approach similar to the generalized least squares
(GLS) method by transforming the branch lengths and accounting for polytomies
(Paradis, 2006). As suggested in Garland et al. (1992), the absolute values of the
contrast were plotted against their standard deviations and were found to have no
correlation (r2=0.001, p=0.35), suggesting an appropriate branch length transformation.
The branch lengths were transformed using the method of (Grafen 1989) with a Rho of
0.5. Polytomies were counted using a Perl script that searched the tree from the tips to
the root and counted the number of branches, indicated by commas in the tree file.
Independent contrasts were standardized by dividing raw contrasts by their standard
deviations.
PGLS was performed in BayesTraits (Pagel,1997) using the random-walk model,
as the directional model can not be performed with ultrametric trees. We followed a
method similar to Capellini et al. (2010, 2011) and tested for phylogenetic signal (λ
parameter) in our data. The λ parameter predicts the pattern of covariance among
species on a given trait, whereas a λ set to 0 allows for no phylogenetic signal (the data
are independent) and can be considered equivalent to ordinary least squares regression
(OLS). A λ set to 1 suggests species are not independent and can be considered similar
to independent contrasts. The λ parameter was estimated by maximum likelihood (ML)
34
and likelihood ratios (LR) were computed, where LR equals the absolute value of
2*[(Likelihood score of the best fitting model)-(Likelihood score of the worst fitting
model)], to determine the best fitting model. The λ estimated with ML was found to be a
significantly better predictor of phylogeny, and this model was used for PGLS analysis.
Encephalization Quotient
Encephalization was quantified by the calculation of an encephalization quotient
(EQ), derived by the allometric formula E=kPα, where E = brain mass, P = body mass, k
= y-intercept (proportionality constant), and α = allometric exponent (Snell 1891, Huxley
1950, Gould 1971, Jerison 1973). After this equation is log-transformed, the slope of the
line corresponds to the allometric exponent, whereas the y-intercept indicates the
encephalization level, independent of body size (Kruska 2005). When this allometric
formula is applied, EQ is a ratio of observed brain mass over “expected” brain mass,
where the expected brain mass is derived from the body mass of that species, using the
following equation, EQ= E / kPα. If the EQ is greater than 1, the brain mass is larger
than expected for a species of that body size. We used our new dataset to derive the
EQ equation from the log body mass vs. log brain mass linear least squares regression
line plotted in this study (Fig. 2A). For all results, we have chosen to use this regression
line to derive the equation rather than the independent contrast or PGLS regression line
because by taking phylogeny into consideration, the tip data has been transformed into
values that are statistically independent and can no longer be used in the same
biological context as the log-log regression line. The 0.746 exponent is the slope of the
log body mass vs. log brain mass regression line (Fig. 2A), and this regression line has
35
a y-intercept of -1.253. Based on the allometric formula E=kPα, and after log
transformation of the y-intercept (see Jerison 1973), the formula is EQ=brain mass /
0.056 x body mass0.746. The independent contrast regression line has a slope of 0.631
and a y-intercept of -0.0008, and the formula is EQ = brain mass/ 0.998 x body
mass0.631 and the PGLS regression line has a slope 0.60 and a y-intercept of -0.89, with
a formula of EQ= brain mass/ 0.128 x body mass0.60. As one can see, the y-intercepts of
the regression lines are very different because the phylogenetically controlled
regression lines are based off residuals of body and brain measurements and not actual
measurements. Because of this, the calculated EQs for the equations are distinctly
different. For example, humans have an EQ of 5.72 when using the log-log regression
line (Fig. 2A) derived equation, whereas the EQ decreases to 1.16 in the independent
contrasts analysis and 12.6 for the PGLS derived equation. There is a significant
correlation between the log-log and independent contrast EQ values (F= 2423, r2=0.79,
p<0.0001), as well as the log-log and PGLS derived EQ values (F= 1557, r2=0.71,
p<0.0001) (Fig. S3). However, to take phylogenetic relationships into consideration, all
statistical tests were performed using the independent contrasts and PGLS derived EQ
as well as the log-log regression line derived EQ. Results remained significant after
accounting for phylogeny (Fig. S3, S5, S6). In order to retain the same biological
context as other encephalization studies, we chose to mainly report EQ values using the
standard log brain mass vs. log body mass regression line in the main body of the text
(Fig. 2A).
36
Statistical Analyses
Linear least squares regression analysis and box plot statistics were performed
using GraphPad PRISM version 5.0 for Mac OS X (GraphPad Software, San Diego
California USA, www.graphpad.com). Least squares regression (LSR) was used to
derive the encephalization quotient because EQ is calculated from a prediction
equation, a ratio of observed over expected brain mass, and LSR allows for uncertainty
regarding the y-variable. To estimate the scaling relationships in clades, we chose to
use reduced major axis regression (RMA), which is appropriate for studies of scaling in
comparative biological data because variation in both the x and y variables is taken into
account. We tested whether Primates and Cetartiodactyla RMA slopes deviated
significantly from other mammals using the software program, Standardised Major Axis
Tests & Routines (SMATR) version 2.0 (Falster, Warton et al. 2006). All regression
analyses were performed with log body mass on the x-axis and log brain mass on the yaxis. To determine if EQs were statistically different in sister clades, we performed a
Mann Whitney two-tailed test on the mean EQ of the clades using GraphPad PRISM.
Levene’s test of Equality of Variance was performed in SPSS.
Ancestral State Reconstruction
Ancestral reconstructions of EQ, body mass, and brain mass were all completed
using the time-calibrated revised supertree of Bininda-Emonds et al. (2007) with absent
species pruned. Weighted squared parsimony model of Maddison (1991) was
performed in MESQUITE version 2.74 (Maddison and Maddison 2009). Maximum
likelihood reconstructions were performed using the Analysis of Phylogenetics and
37
Evolution (APE) package (Paradis, Claude et al. 2004) in R version 2.7.1 (R
Development Core Team 2008), using the method of restricted maximum likelihood
(REML). We used the log-log regression line (Fig. 2A) to calculate the EQ for these
reconstructions. All character state reconstructions were performed using the full
dataset, n=630, and the female-only dataset, n=130.
Results
Allometric relationship between body mass and brain mass in mammals
To calculate the relationship between body mass and brain mass among adult
mammalian species in our dataset (Table 1), we performed linear least squares
regression analysis of log body mass vs. log brain mass (Fig. 2A). Similar to previous
studies, our results demonstrate a significant negative allometric relationship
(F1,628=13230, slope=0.75, r2=0.955, p<0.0001) between these character states (Fig.
2A). Notably, two clades deviated from the general mammalian regression line,
Primates and Cetartiodactyla (Fig. S1). Using the software, SMATR, we tested whether
these clade specific RMA regression lines differed significantly from the brain:body
allometric relationship of other mammals. Cetartiodactyla (slope=0.64, r2=0.762) had a
significantly shallower slope when compared to all other mammals in the analysis
(F=4.62, p=0.032, ANCOVA). Primates (slope=0.82, r2=0.908) had a significantly
steeper slope as compared to other mammals (F=7.81, p=0.006, ANCOVA).
38
Figure 2: Body and brain mass linear regression
A) Relationship of log brain mass (g) versus log body mass (g) in adult mammals (n=630). B)
Relationship of standardized contrasts of log body mass versus log brain mass.
Our full dataset includes measurements from both males and females; however,
sexual dimorphism within species may affect the brain:body allometric scaling
relationship, as comparative studies are highly dependent on these measurements and
even small changes could affect the results (see Discussion). Taking this
intoconsideration, we analyzed a reduced dataset of only adult females representing
130 species from 13 orders. Least squares regression analysis of the log brain mass vs.
log body mass data demonstrated a significant relationship (F1,628=1283, slope=0.68,
r2=0.91, p<0.0001) between body size and brain size (Fig. S2). However, the femaleonly dataset yielded a significantly lower slope than the combined male/female dataset
(p=0.0001, ANCOVA). The female-only dataset was comprised of mostly Primates
(36%) and Carnivora (31%), whereas a large proportion of the male/female dataset
39
consisted of mostly Rodentia (41%) followed by Primates (12%). To take phylogeny into
consideration, we performed both independent contrast and PGLS. Least squares
regression analysis of independent contrasts confirmed a significant negative allometric
relationship (F1,628=4707, slope=0.63, r2=0.88, p<0.0001) to body mass and brain mass.
Prior to running PGLS, we first tested for phylogenetic signal (λ) and found that the ML λ
estimation (λ=0.96) was a significantly better predictor of phylogeny than either λ=1 or
λ=0, (LR=734.6, p<0.0001 and LR=72.8, p<0.0001, respectively) (Table S2). Similar to
independent contrasts, PGLS confirmed a significant negative relationship between
body mass and brain mass (slope=0.60, r2= 0.85, p=<0.0001). We found the
independent contrast and PGLS derived EQ’s (Table S1) to be highly correlated
(F1,628=30676, r2=0.98, p<0.0001) (Fig. S3).
Order
Number of Species
Mean EQ
Monotremata
3
0.80
Didelphimorphia
13
0.82
Paucituberculata
1
1.29
Dasyuromorphia
18
0.87
Peramelemorphia
9
0.52
Diprotodontia
33
0.78
Rodentia
258
0.98
Lagomorpha
15
0.77
Scandentia
3
1.44
Primates
76
2.38
Carnivora
60
1.07
Perissodactyla
3
1.00
Cetartiodactyla
35
1.42
Chiroptera
42
0.99
Eulipotyphla
33
0.89
Xenarthra
9
0.79
Afrosoricida
12
0.64
Macroscelidea
3
1.11
Hyracoidea
1
1.07
Sirenia
1
0.27
Proboscidea
2
1.27
Table 1: Summary of species and mean EQ
Above is a summary of the mammalian orders included in this study, number of species in each order,
and mean EQ for the order.
40
Ancestral reconstruction of relative brain size
In determining the encephalization quotient, we used the following equation, EQ
= Brain Mass / (0.056 x Body Mass
0.746
) derived from the log brain mass vs. log body
mass least squares regression line of our complete adult mammalian data set described
above (Fig. 2A). To trace the evolution of encephalization among mammals, we
reconstructed the ancestral state of the EQ for all mammalian species using the
phylogenetic relationships of Bininda-Emonds et al. (2007). Reconstructions were
performed using both parsimony and ML methods. The inferred ancestral values were
nearly identical, and therefore, we present the parsimony results for the rest of the
paper. Ancestral values for both methods can be found in the supplement. The inferred
EQ for the last common ancestor (LCA) of all extant mammalian species was 0.94.
Eutheria had an inferred EQ value of 1.03, Boreoeutheria was 1.06 (1.15 and 1.02 for
Euarchontoglires and Laurasiatheria, respectively) and Metatheria had an inferred EQ
of 0.88. EQ values extended from a minimum of 0.14 in the fin whale (Balaenoptera
physalus) to a maximum of 5.72 in Homo sapiens.
Figure 3: Ancestral reconstruction of encephalization quotients in mammals (on next page)
Character history reconstruction of the EQ of 630 mammalian species using the weighted squared
parsimony model in MESQUITE and the time-calibrated supertree of Bininda-Emonds et al. (2007). EQ
was divided into 11 bins (see key on top right), with each color representing a specific range of EQ, the
darkest blue indicating the lowest EQ range and red indicated the highest EQ range. If the EQ range is
present in that order, the color range is represented as a gradient on the tree. A) Phylogeny of mammals
condensed so that each box indicates a specific mammalian order and the size of the box represents the
amount of species included form that order. B-C) Detailed view of the evolutionary history of EQ in B)
Cetacea and (C) Anthropoidea. Numbers at each nodes indicate the inferred ancestral EQ.
41
42
As mammalian orders are monophyletic and originated at approximately the
same time (Meredith et al., 2011), we examined the range of EQs found in each order.
This allowed us to broadly examine phylogenetic patterns while also exploring variations
in EQ (Fig. 3). Primates (F593=176.7, p<0.001, Levene’s Test) and Cetartiodactyla
(F552=265.0, p<0.001, Levene’s Test) had a significantly larger variance in EQ when
compared to other mammals. When taking phylogeny into consideration, Primates and
Cetartiodactyla continued to have a significantly larger variance in EQ (Fig. S3). EQ
values within these highly encephalized taxa ranged from 0.90-5.72 in Primates and
0.14-4.43 in Cetartiodactyla. Other clades had considerably less variation in EQ, and
the next two next most variable clades were Rodentia (0.25-2.26) and Eulipotyphla
(0.39-2.92).
Figure 4: Boxplot of EQ in mammals
Box plots representing comparison of mean and variance distributions of EQ in primate sister clade pairs,
including A) Haplorrhini and Strepsirrhini, B) Anthropoidea and Tarsiiformes, C) Catarrhini and Platyrrhini.
Tarsiiformes p-value was not obtainable in this analysis due to the limited number of species (n=1).
43
EQ within Primates and Cetartiodactyla
We calculated the mean and range of EQ for each mammalian order in this
analysis (Fig. 4). As previously observed, the two orders that encompassed the largest
variance in EQ were Primates and Cetartiodactyla; therefore, we investigated these
orders in more detail by comparing the mean EQs of nested sister clades. Within
Primates (Fig. 5), the Haplorrhini (mean EQ=2.64) were significantly different (U=149.0,
p<0.0001) in mean EQ when compared to the Strepsirrhini (mean EQ=1.63). Within
Haplorrhini, the anthropoid primates (mean EQ=2.65) demonstrated the largest mean
EQ; however, there was no significant difference between the anthropoid sister taxa of
Catarrhini (mean EQ=2.57) and Platyrrhini (mean EQ=2.77) (U=337.0, p=0.482),
suggesting neither group has a greater average relative brain size. Anthropoid primate
EQ values ranged from 1.31 in Gorilla gorilla to 5.72 in Homo sapiens (Table S1).
Upon further investigation of the Cetartiodactyla, comparisons among the clades
Ruminantia (mean EQ=0.86) and Cetancodonta (mean EQ=2.28) demonstrated that the
mean EQ of Cetancodonta significantly differed from Ruminantia (U=72, p=0.042).
Within Cetancodonta, Cetacea (mean EQ=2.43) was responsible for the large mean EQ
in comparison to Hippopotamidae (mean EQ=0.34). Comparisons of the sister taxa
Mysticeti (mean EQ=0.21) and Odontoceti (mean EQ=3.10) revealed that the
Odontoceti was solely responsible for the large peak in EQ within Cetartiodactyla (U=0,
p=0.007) (Fig. 5).
In our study, Proboscidea had an average EQ of 1.27 (Table 1; Loxodonta
africana [EQ=1.09] and Elephas maximus [EQ=1.46]). Interestingly, when taking
44
phylogeny into consideration, Proboscidea was the only group to have a notable shift in
relative brain size (Fig. S4). While Carnivora had an average EQ of 1.07, certain groups
within this order had higher than average EQ values for the clade, including Canidae
(mean EQ=1.41) where EQ values ranged from 1.10 in Otocyon megalotis to 1.92 in
Vulpes vulpes, Ursidae (mean EQ=1.38) where EQ values that ranged from 0.71 in
Melursus ursinus to 2.33 in Heloarctos malayanus, and Musteloidae (mean EQ=1.41)
where EQ values ranged from 0.63 in Mephitis mephitis to 2.05 in Bassariscus
sumichrasti. Rodentia, the largest order in the analysis (n=258), had a mean EQ of 0.98
and while most species in this order had an EQ of close to 1, there were a few outliers
including Gerbillus dasyurus (EQ=2.48), Tamiops macclellandi (EQ=2.26), and
Tscherskia triton (EQ=2.09). Unexpectedly, within Eulipothyphla, the Talpidae (mean
EQ=1.69) also had a higher than average EQ, with values ranging from 0.97 in Talpa
europaea to 2.92 in Neurotrichus gibbsii. Neurotrichus is the smallest of the American
moles, with a body mass ranging from 9-11g (Nowak 1991).
45
Figure 5: Boxplot of EQ in Primates and Cetartiodactyla
(LEFT) Box plots representing comparison of mean and variance distributions of EQ in primate sister
clade pairs, including A) Haplorrhini and Strepsirrhini, B) Anthropoidea and Tarsiiformes, C) Catarrhini
and Platyrrhini. Tarsiiformes p-value was not obtainable in this analysis due to the limited number of
species (n=1). (RIGHT) Box plots representing comparison of mean and variance distributions of EQ in
cetartiodactyl clades, including A) Ruminantia and Cetancodonta, B) Cetacea and Hippopotamidae, C)
Mysticeti and Odontoceti. Hippopotamidae p-value was not obtainable in this analysis due to the limited
number of species (n=1).
46
Increases and decreases of relative brain size in anthropoid primates and
cetaceans
Within Primates (crown node EQ=1.45), the LCA of extant Platyrrhini was
estimated to have an EQ of 2.55, and reconstructions within the suborder ranged from
1.82 for crown Alouatta to 3.72 for crown Cebus (see Table S3 for inferred ML ancestor
values). A similar pattern in the change in EQ can be found among Catarrhini as well
(crown EQ=2.33), in which the reconstructed EQ ranged from 1.57 for crown Colobus,
to 3.02 for the most recent common ancestor of humans and chimpanzees. These
results demonstrate that relative brain mass both increased and decreased within major
primate clades during evolution. For cetaceans (crown EQ=1.23), similar patterns of
concomitant expansion and reduction emerged. The ancestral EQ for crown Mysticeti
(EQ=0.45) was much lower than the reconstructed LCA for crown Cetacea, whereas the
ancestral EQ for crown Odontoceti (EQ=2.43) was much higher. The ancestral state
reconstruction on the exclusively female dataset when compared to the full dataset that
included both sexes revealed very similar patterns.
One limitation to reconstructing the ancestral EQ is that this method cannot
determine if an encephalization event was due to a change in brain mass or body mass
with respect to an ancestral species. To address this issue, we separately reconstructed
the ancestral states in both brain and body mass. When using this approach we traced
the body and brain mass reconstructions and determined whether changes in EQ were
due to alterations in brain mass, body mass, or both measures. For example, both
Homo and Cebus demonstrated an increase in EQ when compared to the ancestral
state (Fig. 3). After estimating brain and body mass separately, both genera show a
47
larger increase in brain size than body size when compared to the ancestral state. Body
mass increased from 28.8kg to 65.1kg (~2 fold) in the last ~18 million years on the
Homo lineage, while the brain mass increased nearly 5-fold, from 254.6 to 1250.4g.
Cebus body mass increased slightly over the last ~18 million years from 1.3kg to 2.2kg,
whereas the brain mass more than doubled in size, from 32.4g to 69.1g. Both Alouatta
and Colobus, are inferred to have undergone a decrease in EQ compared to ancestral
state (Fig. 3). According to the body and brain mass reconstructions, Alouatta’s body
mass nearly doubled from 2.5kg to 4.3kg in the last ~19 million years, while the brain
mass stayed nearly constant (47.2g to 48.7g). The brain size decreased in Colobus
from 91.1g to 79.0g, while the body mass increased from 6.6kg to 8.9kg in the last ~19
million years, leading to a decrease in EQ.
Among cetaceans, Mysticeti demonstrated a decrease in relative brain size while
Odontoceti increased in relative brain size as compared to their ancestral EQ (Fig. 3).
Ancestral reconstructions of body and brain mass data reveal that brain mass increased
in crown Odontoceti from the crown Cetacea (471.76g to 1101.36g) while the body
mass decreased slightly from 267.9kg to 242.9kg. Although crown Mysticeti brain mass
also increased by less than one order of magnitude compared to crown Cetacea
(471.76g to 2997.6g), body size increased approximately 1.5 orders of magnitude,
267.9kg to 12380.2kg.
Discussion
We curated data on brain and body mass for new and previously published
measurements in 630 mammalian species. Using this data, we confirmed a scaling
48
exponent value of 0.75 for the complete dataset and calculated the EQ based on the
log-log regression (Fig 2A). There was a reduction in the overall slope when accounting
for phylogeny. It has been suggested that this disparity in slope could be due to a bias
in contrasts within a specific group (Isler et al., 2008). The integration of phylogenetic
history did not eliminate the significant relationship between brain and body size, nor did
it have any effect on statistical significance of this relationship. Additionally, the much
smaller female dataset that consisted of 130 species also displayed a reduction in slope
compared to the overall dataset. This reduction in slope is likely due to lack of small (i.e.
Suncus etruscus) and large (i.e. Balaenoptera physalus) species, which leads to a
smaller range in brain and body mass among the female dataset compared to the
complete dataset.
Evaluations of relative brain mass in mammals revealed highly encephalized
species among primates and cetaceans; however, we were not only interested in the
most encephalized species, but the variance in encephalization within mammalian
orders. By taking a phylogenetic approach, we found primates and cetaceans
encompassed the widest range in EQ among mammals (Fig. 4). Anthropoid primates
and odontocete cetaceans were found to have a significantly larger EQ than their
respective sister clades (Fig 5). Additionally, we used our dataset to infer the ancestral
states of EQ, brain mass and body mass and found evidence of both increasing and
decreasing relative brain mass within primate and cetacean lineages. These results
generally extend findings by Shultz and Dunbar (Shultz and Dunbar 2010), who showed
encephalization patterns vary across different mammalian groups during evolution.
49
Together, these results suggest relaxed phylogenetic constraints on brain and body
mass coevolution in anthropoid primates and cetaceans.
Lineages with evidence of encephalization
Along with primates and cetaceans (discussed in detail below), various studies
have indicated a possible increase in EQ in other mammalian orders, including
Proboscidea and Carnivora (Hart, Hart et al. 2008, Mazur and Seher 2008, Finarelli and
Flynn 2009). In the current study, the family Ursidae ranged widely in EQ, from EQ=0.71
in Melursus ursinus to EQ=2.33 in Helarctos malayanus. Consistent with our results,
Finarelli and Flynn (2009) reported that Canidae, Ursidae, and Musteloidea have
independent and significant increases in brain size. Social learning abilities are
correlated with brain size in primates (Reader and Laland 2002), and black bears have
demonstrated social learning through food conditioning, teaching cubs to forage for
human food or trash as opposed to wild foraging (Mazur and Seher 2008).
Elephants, who possess the largest absolute brain size among terrestrial
mammals, exhibit complex social and cognitive abilities, and have demonstrated
examples of tool use (Hart, Hart et al. 2008). Prior to taking phylogeny into
consideration, these large mammals were not identified as having high EQs relative to
other mammals (Loxodonta africana [EQ=1.09] and Elephas maximus [EQ=1.46]). Our
results are consistent with Shoshani et al (2006), which calculated the EQs of elephants
to range from 1.13-2.36 (Shoshani, Kupsky et al. 2006). When phylogeny was taken
into account, elephants display an increase in relative brain size (Fig. S4), suggesting
that their closest living relatives have an affect on their brain:body scaling.
50
Adaptive explanations for variation in brain size
Previous studies have proposed adaptationist explanations in which physiological
and ecological factors have been hypothesized to support increases in brain size
among mammals. The maternal energy hypothesis (Martin 1996) proposes that the
mother’s investment in the offspring influences brain size and demonstrates a
correlation between the maternal BMR (basal metabolic rate) and the brain size of
offspring. An alternative explanation for the evolution of encephalization is the
expensive tissue hypothesis (Aiello and Wheeler 1995), which proposed that instead of
increasing BMR to supply more energy for increased brain size, reductions in gut size or
other energetically expensive organs allow for the reallocation of energy to the brain. In
contrast to these two hypotheses concerning the evolution of encephalization, the social
brain hypothesis, does not take into consideration brain energetics (Dunbar, 1998).
Instead, this adaptationist perspective proposes that the evolution of a relatively large
neocortex is a direct result of complex social demands within a species, as social
relationships are hypothesized to be cognitively demanding.
In support of the expensive tissue hypothesis (Aiello & Wheeler, 1995), we found
increases and decreases in EQ values of certain primate lineages over time. For
example, the EQs of ancestral species that were phylogenetically reconstructed in the
current study demonstrate that howler and colobus monkeys (Alouatta and Colobus),
both folivores with large guts (Milton, 1998), independently underwent decreases in EQ
during their evolution. These folivores have an enlarged large intestine to help digest
the carbohydrates that are predominant in leaves, grasses and stems (Chivers & Hladik,
51
1980). As proposed by the expensive tissue hypothesis, freed energy from a reduced
gut may have allowed for the increase in brain size among primates. The same
hypothesis can be applied here in reverse; reducing the brain size might have freed up
energy, allowing for a reallocation of energy to maintain a large gut size. This possibly
allowed these folivorous primates (e.g. leaf eating colobus monkeys) to exploit a new
diet rich in leaves and grasses. Interestingly, similar patterns are demonstrated in
gorillas. 85% of their diet consists of leaves, shoots and stems (Nowak, 1991) and
gorillas have the lowest EQ (1.31) among anthropoid primates. However, there are
conflicting views regarding correlation between diet quality or gut size and relative brain
size (Allen and Kay, 2011, Hartwig et al., 2011, Navarrete et al., 2011).
While it is possible to relate an individual’s metabolic allocation among organs to
the evolution of encephalization, other perspectives exist. Martin (1996) suggested that
the primary link in the evolution of encephalization is that between the developing brain
of offspring and the mother’s metabolic capacity. Thus, leaf eating monkey mother’s
whose diet is relatively poor will have less metabolic capacity from which they can
provide nutrients to their developing offspring resulting in relatively small brains.
Moreover, the low metabolic capacity in mothers of these species may constrain the
development of large social groups (Martin, 1996).
In addition to metabolic allocation (Aiello and Wheeler 1995) and maternal
metabolic capacity (Martin, 1996) as driving forces of adaptations associated with the
evolution of encephalization, social complexity has been implicated (Dunbar, 1998,
Shultz and Dunbar, 2010). In this view increased sociality drives brain evolution (i.e. the
social brain hypothesis). It has been proposed that the emergence of complex sociality
52
requires enhanced cognitive abilities (Dunbar 1998, Whiten and van Schaik 2007). This
viewpoint first assumes that a brain which is larger than expected by body mass
enhances cognitive abilities, and it also assumes that social behavior is the driving
cause of cognitive enhancements. In other words, there is a selective advantage gained
by social acumen and a relatively large brain facilitates social advantages. It is thus not
surprising that we observed deviations in brain:body scaling in cetaceans, specifically
odontocetes, as this has been previously observed in other studies (Worthy and Hickie
1986, Marino 1998, Marino 2002, Marino, McShea et al. 2004) and, in support of the
social brain hypothesis, several odontocete cetacean species are characterized by
complex social groups (i.e., cooperative actions and fission-fusion societies) (Marino
2002). Similar to primates, cetaceans demonstrate encephalization and deencephalization among lineages; for example, as compared to the reconstructed EQ of
the stem Cetacea, there is a pronounced decrease in EQ in the Mysticeti clade and a
distinct increase in the EQ of the Odontoceti. Ancestral reconstructions of body and
brain mass revealed there was a rapid increase in mysticete body mass compared to
the reconstructed EQ of the cetacean last common ancestor; the brain mass also
increased, though at a much slower rate. The size of baleen whales may be related to
the massive biomechanical forces needed to open their mouths when feeding
(Goldbogen et al., 2007). With respect to odontocetes, high encephalization was
acquired at least 10 million years after the adoption of a fully aquatic lifestyle by
Archaeoceti ancestors in the Eocene, indicating that the relatively large brains of
odontoctes, and particularly delphinids, could have been related to the emergence of
53
social complexity and/or the acquisition of the novel sense of echolocation (Marino,
Connor et al. 2007); however, this remains to be explicitly tested.
Neutral evolution and brain:body allometry
It has long been appreciated that primates and toothed whales have larger brains
than would be predicted by body mass in other mammals (Jerison 1973, Martin 1981,
Worthy and Hickie 1986, Marino 1998, Marino 2002, Barton 2006) and in the present
study we have demonstrated that increased variance, in addition to an increase in
relative brain mass, characterize the most encephalized mammalian lineages. Primates
(0.90-5.72) and cetaceans (0.14-4.43) have a significantly greater range in EQ than
other mammalian orders. That is, these two groups encompass the most encephalized
species (i.e. humans and dolphins), as well as species with lower than expected relative
brain size (i.e. lemurs), to the smallest relative brain size (i.e. baleen whales). While
adaptive explanations for the evolution of encephalization, such as those discussed
above, are attractive, other possible explanations should also be considered.
As in neutral mutation at the molecular level, we suggest that small changes in
phenotype may have little if any consequences on the fitness of an organism. Ohta
(Ohta 1973, Ohta 1974) proposed that mutations that are nearly neutral (slightly
deleterious) are more likely to become fixed in populations with small sizes. This is
because the effects of random drift are stronger in small populations (Hartl & Clark,
2007, Hedrick, 2011). At the phenotypic level, the expectation of the nearly neutral
theory would be that shifts in phenotypes would become fixed more rapidly in small
populations, resulting in increased phenotypic diversity among related lineages with
54
small population sizes and more homogenous phenotypes among related lineages with
large populations. For example, primates are notable among mammals in that they
generally have small population sizes in comparison to other orders such as rodents
(Ohta 1998). It is thus reasonable to consider that some of the phenotypic diversity in
primate brain:body allometry might not be strictly associated with selective pressures
(i.e. social complexity), but rather a result of neutral, or at least nearly neutral evolution
in small populations. Indeed some other studies have emphasized the importance of
other features in the evolution of the brain irrespective of brain size such as subtle
modifications of neocortical circuits (Hakeem, Sherwood et al. 2009, Jacobs, Lubs et al.
2010), increased gyrification of the neocortex (Zilles, Armstrong et al. 1989, Marino,
Connor et al. 2007, Rogers, Kochunov et al. 2010), and neuronal density (HerculanoHouzel, 2011).
Study limitations
It should be noted that there are limitations in accurately measuring whole brain
mass and body mass, with the resulting calculation of EQ depending on these
measurements. Most studies in the primary literature containing empirical data from
brain and body measurements across diverse species are over 50-100 years old.
Additionally, many studies do not describe details such as when (i.e., how long after
time of death) the individuals were measured or if the animal was ill prior to death. Due
to the difficulties of obtaining well-controlled brain and body mass measurements
without sacrificing the animal, it is probable that some of the animals in this study were
55
weighed after signs of emaciation, as they may have been deceased for hours or days
prior to being weighed.
We acknowledge another limitation to this study is the lack of inclusion of data
derived from extinct species; however, the uncertainties of body mass reconstruction as
well as taxonomic uncertainty among fossil taxa poses a serious challenge to direct
evaluation of the evolution of encephalization. Nonetheless, the careful addition of data
from the fossil record could provide a secondary assessment of the accuracy of
ancestral state reconstructions (Finarelli and Flynn 2006). In this study, the
reconstructed EQs for stem taxa may be overestimates if there is a trend toward
increasing brain size within a lineage over time (Shultz and Dunbar 2010). For example,
fossil taxa in early anthropoid primates appear to be less encephalized than their
modern descendants (Simons, Seiffert et al. 2007). In our dataset the most recent
common ancestor of humans and chimpanzees was estimated to weigh 52.8kg and
have a brain mass of 477.5g. According to the fossil record, Ardipithecus ramidus from
4.4 Ma is estimated to have had a body mass of 51kg and brain mass of 300-350g
(Lovejoy, Suwa et al. 2009, Suwa, Asfaw et al. 2009). Australopithecus afarensis from
approximately 3.7 – 2.9 Ma is estimated to have had a body mass of 35kg and brain
mass of 430g (McHenry 1982). Although it is not entirely clear how closely these
species represent the character states of the most recent common ancestor of humans
and chimpanzees, they are usually considered to fall early on the lineage leading to
humans, prior to a general trend of increasing brain size to Homo sapiens. From these
estimates we can conclude that our ancestral reconstructions are reasonable with
56
respect to both brain and body mass for the hominin stem, but the addition of fossil data
would aide in generating more accurate estimates.
Conclusion
In this study we examined brain mass and body mass data from adult
mammalian species, calculated the EQ for each, and traced encephalization, body
mass, and brain mass through time. We confirmed there was a significant relationship
between body mass and brain mass among species in our dataset, with an exponent of
0.75 that agrees with several previous studies (Pilbeam and Gould 1974, Martin 1981).
The relationship remained significant after correcting for the nonindependence of the
character traits, although the value of the exponent was decreased. This strong
relationship between brain and body mass allows one to calculate an EQ for a given
species in the dataset based on the scaling law that generally characterizes mammals.
Results demonstrated that anthropoid primates and cetaceans exhibit the greatest
variance in EQ values among mammals, and we suggest that changes in relative brain
mass may not always be due to natural selection. Ancestral reconstructions revealed
evidence for both increases and decreases in brain size throughout evolutionary history,
most distinctively in primates and cetaceans.
Acknowledgments
This research was supported by the National Science Foundation, grant award numbers
BCS-0550209 and BCS-0827546. Our dear colleague and coauthor, Dr. Morris
57
Goodman passed away during the preparation of this manuscript. We are ever grateful
for his intellectual honesty and dedication to the study of evolution
58
CHAPTER 3
Evidence for Positive Selection and Parallel Brain
Evolution in the Neocortical Transcriptome of a Capuchin
Monkey
Abstract
The capuchin monkey (Cebus) is the most encephalized nonhuman
primate. Interestingly, capuchin monkeys, like humans, exhibit many complex cognitive
skills including tool use and complex social behavior. Evidence for a relatively large
neocortex, along with the presence of those cognitive abilities, indicate that capuchin
monkeys represent an excellent taxon to investigate parallelism in primate brain
evolution; however, there have yet to be any large genomic studies published in Cebus.
To address the idea of parallelism in primate brain evolution at the molecular level we
sequenced and assembled the prefrontal cortex transcriptome of a male tufted capuchin
monkey, Cebus apella. Using these data and publically available data from seven other
primate species, we conducted large-scale analyses of protein-coding evolution to
identify genes that have undergone adaptive evolution on the capuchin lineage. In this
59
study, we identify genes that have accelerated rate of evolution on the human lineage
and the capuchin monkey lineage. We provide support for genes involved in shared
biological processes, such as microtubule organization and metabolism evolving at
accelerated rates on these two highly encephalized primate lineages.
60
Introduction
Neocortical expansion is a key trait of primate brain evolution (Rakic 2009).
Evidence suggests both expansion and reduction in brain size occurred on multiple
lineages throughout primate history and has resulted in varying degrees of both
absolute and relative brain sizes among anthropoids (Montgomery, Capellini et al. 2010,
Boddy, McGowen et al. 2012), with the terminal Homo lineage demonstrating an
exceptional enlargement (Jerison 1973). The implication of this event, along with the
cognitive performances in primates, and more specifically the cognition in humans, has
lead to many investigations of the evolution of the primate brain (Sherwood, Subiaul et
al. 2008). Evidence of multiple brain expansion events among anthropoid primates
supports the hypothesis for parallelism in brain evolution.
The capuchin monkey (Cebus) is a genus of New World monkey (Jack 2007) that
is the second most encephalized primate, with a brain size four times larger than
expected for a primate of its body mass (Montgomery, Capellini et al. 2010, Boddy,
McGowen et al. 2012). Capuchins are considered to be highly intelligent, with their
ability to use tools (Phillips 1998) and social learning processes, such as region-specific
foraging (Valderrama, Robinson et al. 2000, Panger, Perry et al. 2002, Truppa, Spinozzi
et al. 2009, Addessi, Mancini et al. 2010). Unlike other small bodied primates, capuchin
monkeys are similar to humans in having slow life history variables (Fleagle 1999) and
unusually fast early postnatal brain growth (Courchesne, Chisum et al. 2000, Phillips
and Sherwood 2008). As such, we believe the capuchin monkey is an ideal primate for
studying the genetic mechanisms of encephalization.
61
There are three distinct types of comparative evolution: divergent, parallel and
convergent. Divergent evolution occurs when two species have the same trait and over
time evolve to have two different traits. Convergent evolution is the change from two
different traits to the same trait on two separate lineages. Parallel evolution occurs when
the same change occurs on two independent lineages to produce the same final trait
(Futuyma 2009). We can define a trait as a general characteristic of an organism, where
it can be phenotypic, (i.e brain mass), or genotypic (i.e amino acid state). The eye
(Kozmik, Ruzickova et al. 2008), stomach lysozymes in leaf eating primates and cows
(Stewart, Schilling et al. 1987, Zhang and Kumar 1997), echolocation in bats and
dolphins (Liu, Cotton et al. 2010) as well as brain size among humans, elephants and
dolphins (Goodman, Sterner et al. 2009, McGowen, Grossman et al. 2012) are
examples of traits that have undergone parallel evolution.
In general, the brain is one of the most conserved functional organs in mammals
(Duret and Mouchiroud 2000, Khaitovich, Hellmann et al. 2005, Strand, Aragaki et al.
2007) making it a complex tissue to study. Between humans and chimpanzees, the
brain has fewer differences in the expression level of protein-coding genes than in
heart, kidney, liver, or testis (Khaitovich, Hellmann et al. 2005). Gene expression in the
brain is not only conserved among primates, but among mammals as well. Indeed,
while distinct gene expression profiles of cortex, striatum, and cerebellum exist within
both human and mice, a strong conservation exists among the region specific gene
expression (Strand et al 2007). Furthermore, a recent study demonstrated that out of six
organs studied (brain, cerebellum, heart, kidney, liver and testis), the brain gene
expression evolves significantly slower than all other tissues in 10 mammalian species
62
(placentas, marsupials, and monotremes) (Brawand, Soumillon et al. 2011). Thus it is
not surprising that the brain has more evolutionary constraints, it is a critical organ and
small changes could have major effects.
One way to elucidate distinct characteristics of large-brained primates is to
appreciate not only the gene expression patterns, but also the genomic differences
between related species. One of the goals of comparative genomics is to identify genes
that are under positive selection in a particular lineage and are likely candidates for a
specific trait being studied (Hardison 2003). Comparative genomics can provide a
functional explanation for differing phenotypes, such as brain expansion. Previous
studies have taken a candidate gene approach, such as studying candidate genes
linked to microcephaly (MCPH1, ASPM, CDK5RAP2, CENPJ, STIL, WDR62, ZNF335),
a disease involved in the reduction of brain size (Jackson, McHale et al. 1998, Bond,
Roberts et al. 2002, Cox, Jackson et al. 2006, Kumar, Girimaji et al. 2009, Nicholas,
Khurshid et al. 2010, Yang, Baltus et al. 2012). Previously, ASPM was shown to have
evidence of positive selection across anthropoid primates (Montgomery, Capellini et al.
2011), however there is no evidence for adaptive evolution on the capuchin monkey
lineage (Villanea, Perry et al. 2012). Other comparative analyses have studied genes
that encode for proteins involved in the electron transport chain, which is essential for
supplying the brain with greater energy (Goldberg, Wildman et al. 2003, Doan, Schmidt
et al. 2004, Schmidt, Wildman et al. 2005, Uddin, Opazo et al. 2008). Despite these
studies, there is still little known about the molecular changes involved in brain
expansion in primates, possibly due to the limited genomic data available for largebrained primates.
63
There have been significant advances in the field of molecular evolution with the
development and progression of next-generation sequencing, including, transcriptomics.
Massively parallel sequencing of RNA (RNA-seq) has the ability to detect novel
transcripts, splice variants, allelic-specific expression and sequence variations
(Cloonan, Forrest et al. 2008, Morin, Bainbridge et al. 2008). These techniques are
sensitive enough to detect low transcript levels (Sultan, Schulz et al. 2008). RNA-seq
has the ability to produce massive amounts of sequence information without the need
for a reference genome, is more cost effective, and requires less computational power
than the sequencing of complete genome, and in this regard, it is the ideal technique for
exploring the evolution of protein-coding genes (Grabherr, Haas et al. 2011, Martin and
Wang 2011, Miller, Biggs et al. 2012). Currently, no genome or transcriptome has been
published for any species of capuchin monkey.
In this study, we explored the genetic basis of the parallel evolution of large
brains within the primate lineage. Previous work has demonstrated greater variance in
brain size within anthropoid primates relative to other mammals (Boddy, McGowen et al.
2012). Furthermore, encephalization arose numerous times within Primates, most
spectacularly along the lineage leading to humans and that leading to capuchin
monkeys. This provides a foundation for testing the molecular patterns of parallel
evolution among independent brain expansion events within anthropoid primates, as
well as revealing clues to the general molecular underpinnings of brain expansion and
potential relationships to complex cognition. Accordingly, we have sequenced the
transcriptome from the prefrontal cortex of a male tufted capuchin monkey, Cebus
apella, and performed a comparative genomics analysis with other primate species. We
64
identify key genes that show evidence of adaptive evolution on human and capuchin
monkey lineages.
Material and Methods
Tissue Collection, Library Preparation and Sequencing
Cebus apella
The fresh frozen whole brain of a male infant capuchin monkey was obtained
from collaborator, Dr. Kim Phillips (Trinity University in San Antonio, Texas, USA). Brain
tissue samples were extracted from the frontal pole of the left hemisphere by Dr. Mary
Ann Raghanti (Kent State University in Kent, Ohio, USA) Total RNA was isolated and
the sample was determined to have a RNA integrity number (RIN) of 9 using a
Bioanalyzer
2100
(Agilent
Technologies,
Wilmington,
DE,
USA)
and
an
A260nm/A280nm ratio of 2.06 using a Nanodrop ND-1000 (Thermo Scientific, Hanover,
IL, USA). RNA (2.5ug/per lane) was sent to Wayne State University’s Applied Genomics
Technology Center, where library construction with an insert size of ~200bp was
performed using Illumina’s paired-end RNA-seq protocol. Two lanes of the flow cell
were run on Ilumina’s Genome Analyzer II with a read length of 76bp. (Table 2). The
raw reads from the images and the quality values were obtained by the Illumina 1.3
pipeline.
Assembly
After obtaining raw sequencing reads and before assembling the reads, the data
was
checked
using
FASTQC
v0.10.0
65
(http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to determine if there were
any adaptors present or overrepresented sequences included in the reads. A de novo
assembly of the brain transcriptome was performed using the RNA-seq assembler,
Trinity (r2012-10-05), with a kmer length of k=25 (Grabherr, Haas et al. 2011). To
scaffold the contigs, CAP3 (Huang and Madan 1999) was run on the assembled Trinity
results using the default parameters. Contigs and singletons were combined into one
file. To produce a consensus file, we first mapped the raw sequencing reads back to
assembled contigs generated from Trinity/CAP3 assemblers using Bowtie v0.12.7
(Langmead, Trapnell et al. 2009) and used the alignReads.pl script provided by the
Trinity package. Because the data is paired-end and Bowtie, by default, can sometimes
miss valid paired-end alignments, each read is aligned separately and then matched
back with its pair. Mapping was run using the quality-aware parameters (-n 2 –l 28 –e
70). We then created a consensus file by using the mpileup tool in SAMtools v0.1.18 (Li,
Handsaker et al. 2009). Likely coding sequences of the newly assembled contigs were
extracted by identifying the longest open reading frame (ORF) within the transcript,
using the transcripts_to_best_scoring_orfs.pl perl script provided by the Trinity package.
Annotation
We performed BLAST searches against human cDNA to identify putative
homologous transcripts. We used NCBI’s BLAST 2.25+ and implemented, BLASTn to
search all Cebus contigs against human transcripts (ensembl v37.69). Homologous
genes have fundamentally two types, orthologs, which arise by speciation events and
paralogs, which arise within species due to duplication events (Futuyma 2009). After a
66
duplication event, different functional constraints can be placed on these genes, and
thus it is important to study orthologs when comparing rates of evolution (Koonin 2005).
To identify 1:1 orthologs, we then performed a reciprocal BLAST search on the C.
apella protein-coding transcripts. We used a cutoff e-value of 10-6 and reported the top
five best hits. In addition, we performed the same procedure on transcripts for the
Angolan black-and-white colobus monkey (Colobus angolensis) exome (George,
McVicker et al. 2011). We added the colobine monkey to the study to improve our
sampling of anthropoid primates (see below). The end goal of this study is to align the
transcriptome/exome to six additional primate genomes available in ensembl, including
Homo.
Figure 6: Homologous Genes
Depicted above is a gene duplication event, where a gene can duplicate on a chromosome and result in
two homologous genes. We demonstrate the difference between the two types of homologous genes,
orthologs and paralogs, where orthologs arise by speciation and paralogs are produces by duplication.
67
Alignments
Transcript identifiers used in reciprocal best BLAST against C. apella and C.
angolensis were matched to gene names using the BioMart web interface (Smedley,
Haider et al. 2009). All sequence matches from C. apella and C. angolensis were added
to the 1:1 orthology alignments produced by the recent Gorilla genome paper (Scally,
Dutheil et al. 2012). Primates in the Gorilla alignments included: Homo sapiens, Pan
troglodytes, Gorilla gorilla, Pongo abelii; Macaca mulatta, and Callithrix jacchus.
Alignments were performed using a codon aware alignment package, MACSE v0.9b1
(Ranwez, Harispe et al. 2011). Alignments that did not contain sequence data for all 8 of
the taxa in the study were discarded. For alignments that contained sequence for
multiple contigs from C. apella or C. angolensis, the translated sequence was compared
to the human reference sequence. The contig that had the highest ratio of identical
amino acids to mismatches over the whole length of the human sequence was selected
as the best transcript and all other transcripts were discarded. The translated sequence
of each species was compared to that of the human using a 10 amino acid sliding
window.
Any window with < 70% amino acid identity to the corresponding human
sequence was completely removed from the DNA alignment and replaced with a 30 bp
gap.
If the entire length of a sequence was removed in this step, the gene was
removed from the analysis. These alignments were further processed with Gblocks
v0.91b (Castresana 2000, Talavera and Castresana 2007) with sequence type set to
codon, minimum number of sequences for a conserved position 5, minimum number of
sequences for a flank position 6, maximum number of contiguous nonconserved
positions 3, minimum length of a block 10, and no allowed gap positions.
68
Adaptive Evolution
To test for adaptive evolution, we measured the ratio of the rate of
nonsynonymous substitutions to the rate of synonymous substitions, dN/dS (ω) using a
branch model test in the codon-based maximum likelihood (codeml) program in PAML
v4.0 (Yang 2007). The branch model allows dN/dS to vary across branches. In this
study, we were testing whether or not dN/dS varies among the human and capuchin
monkey branches. Each gene was tested twice, once to detect genes adaptively
evolving on the human lineage and then to detect genes adaptively evolving on the
capuchin monkey lineage. We compared two models for each test using likelihood ratio
tests (LRTs) and then using a χ2 distribution (degrees of freedom=1) to assess
significance. The two models were: (i) a null model, where the branches for all species
have the same dN/dS and (ii) an alternative model, where the dN/dS is set to vary for
either the human or capuchin monkey branch. Genes were discarded if they had any of
the following i) total length of alignment sequences less than 50 codons, ii) S*dS=0 for
the lineage being tested, iii) on the capuchin monkey lineage if dN>0.02 or dS>0.1.
Gene Ontology
For functional analyses, we used the functional annotation tool DAVID v6.7
(Huang da, Sherman et al. 2009) to determine enrichments in biological processes.
Gene list for both the human lineage and capuchin monkey lineage were input
separately and then combined to test for enrichment in shared biological processes.
69
The enrichment score All datasets were compared to the background gene set of
alignments (~5,500 genes).
Results
de novo assembly of a New World monkey brain transcriptome
We sequenced the brain transcriptome of a male infant capuchin monkey, Cebus
apella, and generated 4.7Gb of data (Table 2). Currently, there are no reference
genomes available for capuchin monkey, and the closest New World monkey relative
with a fully annotated genome is the common marmoset, Callithrix jacchus. These
species are estimated to have diverged approximately 17-20 million years ago (Opazo
et al. 2006; Steiper and Young 2006). Due to the rapid advancement of next-generation
sequencing analyses tools (Grabherr, Haas et al. 2011), we assembled this
transcriptome de novo. Reads were assembled into contigs, and as a quality control
measure, we then mapped the reads back to the assembled contigs to remove poor
quality sequences (Miller, Biggs et al. 2012) (Table 3). This resulted in 145,708 contigs
with a N50 of 1,809bp and transcripts sizes ranged with the longest contigs of
17,354bp, with a total size of ~114Mb (Figure 6).
Species
C. apella
C. apella
Reads (per PE)
17,928,044
13,771,675
Length (bp)
76
76
Total Bases
2,725,062,688
2,093,294,600
Table 2: Summary of Sequencing
Summary statistics for the sequencing reads. Transcriptome was sequenced using a paired-end (PE)
library by an Illumina GAII. Two lanes of a flow cell were sequenced. The amount of reads is reported in
the second column, length (bp) is the size of the reads, and total number of bases sequenced.
70
Figure 7: Histogram of Assembled Contigs
Depicted above is frequency of assembled contigs (A) and protein coding contigs (B), with the contig
length plotted on a log scale (x-axis). Contigs were separated into 100bp bins. This demonstrates contigs
range from 200bp to over 17,000bp, with the majority of contigs falling under 5,000bp.
No. contigs
Total Length
Max Length
N50
N90
Assembly
148,414
118,820,479
17,374
1,762
274
Mapping
145,708
114,613,284
17,354
1,809
267
Protein Coding
50,716
50,706,519
16,299
1,365
435
Table 3: Assembly Summary Statistics
This is a table of the summary statistics for the de novo assembly, mapping and extraction of protein
coding regions for sequenced transcriptome of neocortical brain regions of the capuchin monkey. The
summary statistics can be defined as followed: No. contigs are the number of contigs, Total Length is the
total length of all the contigs, Max Length represents the longest contig in the dataset, N50 and N90 are
calculated by sorting all contigs from largest to smallest and determining the minimum set of contigs
whose size total 50% and 90% of the entire assembly.
71
Figure 8: Sequence and Assembly Pipeline
A flow chart of the sequence and assembly pipeline performed for this study. First, poly-A RNA was
isolated, it was then fragmented and reverse transcribed into complementary DNA (cDNA). Paired-end
Illumina RNA-seq was performed and the raw reads were de novo assembled. We then extracted the
protein coding regions to perform evolutionary analyses.
Gene orthology and alignments
We extracted the likely protein coding regions from the prefrontal cortex
transcriptome. This resulted in 50,716 protein-coding regions with an N50 of 1,365bp
and a maximum length of 16,299bp (Table 3). These transcripts were annotated using
BLASTn to human transcriptome. We report 45,192 hits. Currently, there is only one Old
World monkey (Macaca mulatta) and one New World monkey (Callithrix jacchus)
genome available in ensemble (v69). To strengthen the study, we added another Old
72
World monkey to our analysis, the previously published colobine monkey exome (C.
angolensis) (George, McVicker et al. 2011). Along with our capuchin monkey
transcriptome, we now have genomic data for at least two species for all three major
groups in anthropoid primates, catarrhines (Apes, Old World monkeys) and platyrrhines
(New World monkeys). To establish orthology, we performed reciprocal BLAST and
found 14,936 orthologues for the capuchin monkey and 12,733 for the colobine monkey.
Figure 9: Summary of Gene Alignments
Flow chart with the summary of the 1:1 orthologs used in this study.
73
The capuchin monkey transcriptome, and the colobine exome were aligned to six
primate (H. sapiens, P. troglodytes, G. gorilla, P. abelii, M. mulatta and C. jacchus) gene
alignments from the previously published Gorilla genome paper (Scally, Dutheil et al.
2012). This resulted in a total of 5,935 gene alignments shared for all eight taxa in the
study. As the dN/dS metric is highly sensitive to misaligned and miscalled bases
(Mallick, Gnerre et al. 2009), especially over short evolutionary distances where dS is
expected to be very small, we performed quality control measures to remove poor and
divergent sequences (see Methods for details). This resulted in 5,672 reliable multiple
sequence alignments.
Figure 10: Humans and capuchin monkeys have a large relative brain size
This figure illustrates the brain mass (y-axis), body mass (x-axis) and encephalization quotient, which is
represented by the relative size of the bubble (A) for all eight primate species in this study (B). The brain
mass, body mass, and encephalization quotient was extracted from Boddy et al 2012 and phylogeny from
Goodman et al 1998. The color of the bubble in (A) indicates the representative primate in (B).
74
Figure 11: Homo and Cebus demonstrate the largest increase in encephalization
Illustrated above is the primate phylogeny for all eight primates included in our analyses. The color on the
branches indicates the range of EQ, as seen in Chapter 2. The number on the branches indicates the
inferred ancestral EQ (data from Chapter 2).
Figure 12: Patterns of adaptive evolution among encephalized lineages
We tested each gene for positive selection twice, once on the Cebus lineage (left) and once on the Homo
lineage (right).
Testing for patterns of parallel brain expansion in coding regions
We searched for accelerated rates of amino acid substitution among the highly
encephalized human and capuchin monkey lineages. First, we determined whether
dN/dS was significantly higher (p<0.05) on the human lineage compared to all other
primate lineages. In a second test, we identified genes with a significantly higher
75
(p<0.05) dN/dS on the capuchin monkey lineage. These tests yielded 68 and 105 genes
with an accelerated rate of evolution on the human and capuchin monkey lineages,
respectively (Tables S4 and S5). Among these genes, 17 on the capuchin lineage and
13 on the human lineage showed evidence for adaptive evolution, with a dN/dS >1
(Table 4). Included in the human specific positively selected gene set, three genes,
ASXL1, FAM53C and ADAM15, were previously shown to have adaptive evolution on
the human lineage (Uddin, Goodman et al. 2008). We also found one gene involved in
myeloproliferative neoplasms (ASXL1), two in transcriptional regulation (ASXL1,
ZNF263) (Tefferi 2010), one in cell growth (DHX35) and two linked to Alzheimer’s
(APLP1, APBB3) (Bayer, Paliga et al. 1997, McLoughlin and Miller 2008). Additionally,
of the 17 genes showing evidence for adaptive evolution on the capuchin monkey
lineage (Table 4), two were involved in transcriptional regulation (ZNF174, KLHDC2,
TAF12) (Williams, Khachigian et al. 1995), one in cell proliferation (FBXW8) (Okabe,
Lee et al. 2006, Tsutsumi, Kuwabara et al. 2008), one in neurogenesis (ADAM10)
(Pruessmeyer and Ludwig 2009, Jorissen, Prox et al. 2010), and three were linked to
intellectual disabilities (TACO1, FUCA1, SLC989) (Willems, Seo et al. 1999, Geschwind
2008, Weraarpachai, Antonicka et al. 2009, Seeger, Schrank et al. 2010). The
alignments for these tests were of varying lengths (150bp-9,975bp) due to incomplete
sequencing information (see Methods). We found the length of the aligned sequences
had no effect dN/dS on the human lineage, and little effect on the capuchin monkey
lineage (Figure 12).
76
Figure 13: Adaptive Evolution Summary Results
Of the 5,672 primate gene alignments, we found 105 genes to have an accelerated rate of evolution on
the capuchin lineage, where 17 of these genes have a dN/dS >1. On the human lineage, we found
evidence of 68 genes with an accelerated rate of evolution, where 13 of these genes had a dN/dS >1.
Figure 14: Relationship between dN/dS and length of sequence
The relationship between sequence length (y-axis) and dN/dS (x-axis) in genes with evidence of an
2
2
accelerated rate of evolution in A) capuchin monkeys (n=105, r =0.06, p=0.01) and B) humans (n=68, r =
0.02, p=0.25).
77
Gene Name
dN/dS
additional sex combs like 1
2.20
C2 calcium-dependent domain containing 3
2.13
DEAH (Asp-Glu-Ala-His) boxy polypeptide 35
2.05
family with sequence similarity 53, member C
1.73
amyloid beta (A4) precursor protein-binding, family B,
APBB3
1.73
member 3
ZNF263
zinc finger protein 263
1.70
SUGP2
SURP and G patch domain containing 2
1.69
ADAM15
ADAM metallopeptidase domain 15
1.63
PDCL
phosducin-like
1.38
TNKS1BP1
tankyrase 1binding protein
1.23
APLP1
amyloid beta (A4) precursor-like protein 1
1.13
ARHGEF11
Rho guanine nucleotide exchange factor (GEF) 11
1.04
TAF12 RNA polymerase II, TATA box binding protein (TBP)TAF12
1.02
associated factor
Gene Symbol
Gene Name
dN/dS
PGM3
phosphoglucomutase 3
5.71
FUCA1
fucosidase, alpha-L- 1, tissue
4.88
translational activator of mitochondrially encoded cytochrome
TACO1
2.75
c oxidase I
ANKRD42
ankyrin repeat domain 42
2.25
SLC44A3
solute carrier family 44, member 3
2.23
ZNF174
zinc finger protein 174
2.13
SLC9A9
solute carrier family 9, member 9
2.00
STK19
serine/threonine kinase 19
1.86
FBXW8
F-box and WD repeat domain containing 8
1.65
POP5
processing of precursor 5, ribonuclease P/MRP subunit
1.53
ENTPD6
ectonucleoside triphosphate diphosphohydrolase 6
1.48
INTS12
integrator complex subunit 12
1.45
MUS81
MUS81 endonuclease homolog
1.27
KLHDC2
kelch domain containing 2
1.17
ADAM10
ADAM metallopeptidase domain 10
1.12
SCCPDH
saccharopine dehydrogenase
1.11
ARFGAP3
ADP-ribosylation factor GTPase activating protein 3
1.02
Table 4: Genes with evidence of adaptive evolution
List of significantly adaptively evolving genes (dN/dS >1) on the human and capuchin monkey lineages.
Cebus apella
Homo sapiens
Gene Symbol
ASXL1
C2CD3
DHX35
FAM53C
Biological processes involved in rapidly evolving genes
To determine the biological processes overrepresented in genes with an
accelerated rate of evolution, we performed Gene Ontology analyses on the lineage
specific capuchin monkey and human gene lists. For human-specific genes with
evidence of accelerated evolution, enriched processes include multiple categories
involved in cell adhesion, morphogenesis of embryonic epithelium, microtubule-based
process, and establishment of cell polarity (Table 5). Two genes with evidence of
78
positive selection (dN/dS >1) were included in these categories; ADAM15 (dN/dS=1.63)
was categorized into cell adhesion and C2CD3 (dN/dS=2.13) in morphogenesis of
embryonic epithelium.
Term
No.
%
p-value
Fold
cell adhesion
10
14.93
6.19E-03
2.88
biological adhesion
10
14.93
6.35E-03
2.87
cell-cell adhesion
morphogenesis of
embryonic epithelium
microtubule-based
process
morphogenesis of an
epithelium
5
7.46
2.49E-02
4.39
3
4.48
3.48E-02
9.97
4
5.97
8.73E-02
3.74
3
4.48
9.66E-02
5.61
10
14.93
9.97E-02
1.76
phosphate metabolic
process
Genes
RET, NRXN2, TNC, ASTN1,
PTK7, PTPRU, CERCAM,
CDH11, APLP1, ADAM15
RET, NRXN2, TNC, ASTN1,
PTK7, PTPRU, CERCAM,
CDH11, APLP1, ADAM15
RET,
ASTN1,
PTPRU,
CERCAM, CDH11
RET, C2CD3, PTK7
TUBGCP3, RET,
MAP3K11
KIF1B,
RET, C2CD3, PTK7
RET, EPHB6, PAN3, CLK3,
SBF2, PTK7, NEK9, PTPRU,
MAP3K11, DUSP6
RET, EPHB6, PAN3, CLK3,
SBF2, PTK7, NEK9, PTPRU,
MAP3K11, DUSP6
phosphorus
metabolic process
10
14.93 9.97E-02
1.76
Table 5: Enriched biological processes on human lineage
Above list the enriched gene ontology categories for the genes that have evidence of an accelerated rate
of evolution on the human lineage.
Top enriched categories in the capuchin monkey specific gene set were involved
in metabolic and catabolic processes (Table 6). Also included on the list were processes
involved in protein complex localization, regulation of mitotic cell cycle, macromolecular
complex subunit organization, and histone acetylation. The following genes, ADAM10
(dN/dS=1.12), FBXW8 (dN/dS=1.65), FUCA1 (dN/dS=4.88), all have evidence of
positive selection on the capuchin lineage and belong to the macromolecule catabolic
process.
79
Term
nucleoside
monophosphate
metabolic process
macromolecular
complex subunit
organization
proteolysis involved in
cellular protein
catabolic process
No.
%
p-value
Fold
Genes
4
3.85
2.09E-02
6.64
11
10.58
3.49E-02
2.07
10
9.62
3.58E-02
2.18
3.75E-02
2.16
cellular protein
catabolic process
10
protein catabolic
process
10
9.62
4.39E-02
2.10
10
9.62
5.42E-02
2.02
9
8.65
5.64E-02
2.12
9
8.65
5.64E-02
2.12
11
10.58
6.43E-02
1.86
ADCY1, ADSSL1, PDE4A, IMPDH2
KIF2C, RANBP9, NOD1, SLC6A1,
CLP1, FKBP4, PML, SF1, SLC7A9,
ACTN2, ZW10
ADAM10, DCUN1D2, FBXW8,
FBXL20, HACE1, CDC20, CDC16,
FBXL14, FEM1A, RNF41
ADAM10, DCUN1D2, FBXW8,
FBXL20, HACE1, CDC20, CDC16,
FBXL14, FEM1A, RNF41
ADAM10, DCUN1D2, FBXW8,
FBXL20, HACE1, CDC20, CDC16,
FBXL14, FEM1A, RNF41
RANBP9, NOD1, SLC6A1, CLP1,
FKBP4, PML, SF1, SLC7A9,
ACTN2, ZW10
DCUN1D2, FBXW8, FBXL20,
HACE1, CDC20, CDC16, FBXL14,
FEM1A, RNF41
DCUN1D2, FBXW8, FBXL20,
HACE1, CDC20, CDC16, FBXL14,
FEM1A, RNF41
ADAM10, DCUN1D2, FBXW8,
FBXL20, HACE1, CDC20, CDC16,
FBXL14, FEM1A, FUCA1, RNF41
5
4.81
6.43E-02
3.26
PACSIN3, PML, CDC20, CDC16,
DNMT3B
3
2.8
6.57E-02
7.02
ADCY1, ADSSL1, IMPDH2
5
4.81
8.23E-02
2.99
PACSIN3, PML, CDC20, CDC16,
DNMT3B
4
3
3.85
2.88
8.43E-02
8.81E-02
3.81
5.94
TMEM8B, PML, CDC16, ZW10
PHF17, BRPF3, DMAP1
3
2.88
8.81E-02
5.94
PHF17, BRPF3, DMAP1
ADAM10, DCUN1D2, FBXW8,
FBXL20, HACE1, CDC20, CDC16,
FBXL14, FEM1A, RNF41
macromolecular
complex assembly
modification-dependent
macromolecule
catabolic process
modification-dependent
protein catabolic
process
macromolecule
catabolic process
positive regulation of
cellular protein
metabolic process
nucleoside
monophosphate
biosynthetic process
positive regulation of
protein metabolic
process
regulation of mitotic cell
cycle
histone acetylation
protein amino acid
acetylation
9.62
cellular macromolecule
catabolic process
10
9.62
8.81E-02
1.83
positive regulation of
histone modification
2
1.92
9.25E-02 20.58
PML, DNMT3B
protein complex
localization
2
1.92
9.25E-02 20.58
FKBP4, DNMT3B
Table 6: Enriched biological processes on capuchin monkey lineage
Above list the enriched gene ontology categories for the genes that have evidence of an accelerated rate
of evolution on the capuchin lineage
80
To test for patterns of enrichment between both encephalized lineages, we
performed GO analysis on a combined gene list to determine if there was enrichment of
shared processes. This combined analysis shows enrichment for similar processes
described above, however includes additional genes in these categories (Table 7 and
Table S6). For example, cell adhesion was enriched in the human specific gene set with
10 genes, yet the combine analysis demonstrates that six of the genes with evidence of
accelerated evolution in the capuchin specific gene set also fall into this category.
Other categories of interest include establishment of cell polarity (n=3) and microtubulebased process (n=7). Interestingly, new categories in the combined analysis include, M
phase, microtubule cytoskeleton organization, GMP metabolic process, and mitosis.
81
Term
No. Cebus Homo
%
p-value
Fold
nucleoside monophosphate metabolic
process
5
4
1
2.92
1.78E-02
4.86
cell adhesion
16
6
10
9.36
1.83E-02
1.91
biological adhesion
16
6
10
9.36
1.89E-02
1.90
establishment of cell polarity
3
1
2
1.75
2.65E-02
11.31
purine nucleoside monophosphate
biosynthetic process
3
2
1
1.75
3.33E-02
10.05
purine ribonucleoside
monophosphate biosynthetic process
3
2
1
1.75
3.33E-02
10.05
nucleoside monophosphate
biosynthetic process
4
3
1
2.34
3.41E-02
5.48
purine ribonucleoside
monophosphate metabolic process
3
2
1
1.75
4.07E-02
9.05
purine nucleoside monophosphate
metabolic process
3
2
1
1.75
4.07E-02
9.05
microtubule-based process
7
3
4
4.09
4.23E-02
2.71
proteolysis involved in cellular protein
catabolic process
14
10
4
8.18
4.56E-02
1.79
cellular protein catabolic process
14
10
4
8.18
4.82E-02
1.77
histone acetylation
4
3
1
2.34
5.23-02
4.64
protein amino acid acetylation
4
3
1
2.34
5.23-02
4.64
modification-dependent
macromolecule catabolic process
13
9
4
7.60
5.39E-02
1.80
modification-dependent protein
catabolic process
13
9
4
7.60
5.39E-02
1.80
cell-cell adhesion
7
5
2
7.09
5.45E-02
2.54
ribonucleoside monophosphate
biosynthetic process
3
2
1
1.75
5.73E-02
7.54
protein catabolic process
14
10
4
8.18
5.84E-02
1.72
GMP metabolic process
2
1
1
1.17
6.48E-02
30.16
GMP biosynthetic process
2
1
1
1.17
6.48E-02
30.16
ribonucleoside monophosphate
metabolic process
3
2
1
1.75
6.62E-02
6.96
macromolecule catabolic process
16
10
6
9.36
7.31E-02
1.59
protein amino acid acylation
4
2
2
2.34
7.43E-02
4.02
microtubule cytoskeleton organization
5
3
2
2.92
7.63E-02
3.08
cellular macromolecule catabolic
process
15
10
5
8.77
7.69E-02
1.61
histone H3 acetylation
3
2
1
1.75
8.55E-02
6.03
mitosis
6
4
2
3.51
8.72E-02
2.51
nuclear division
6
4
2
3.51
8.72E-02
2.51
M phase of mitotic cell cycle
6
4
2
3.51
9.54E-02
2.45
Table 7: Enriched biological processes on encephalized lineages
Above list the enriched gene ontology categories for the genes that have evidence of an accelerated rate
of evolution on the capuchin and human lineage
82
Discussion
Our study has yielded two novel contributions to the field of primate comparative
genomics. The first contribution is the neocortical transcriptome from an infant male
capuchin monkey. We have implemented an RNA-seq approach, where massive
amounts of sequencing information were used to inventory brain expressed genes. We
then used these sequences to perform a large-scale primate comparative genomics
study. The second contribution is the support for parallel brain evolution in humans and
capuchin monkeys. We found genes with an increased rate of evolution share the same
biological processes on both lineages.
Researchers have been performing large-scale genomic comparative studies in
primates for close to a decade (Clark, Glanowski et al. 2003, Chimpanzee-Consortium
2005, Nielsen, Bustamante et al. 2005, Gibbs, Rogers et al. 2007). These early studies
did not include many species and the positively selected gene lists have little to no
overlap (Mallick, Gnerre et al. 2009). As Eric Vallender eloquently pointed out, many of
these studies provide evidence for several genes involved in primate brain evolution,
however they often have no follow up (Vallender 2012). The addition of species gives us
better power to detect significant changes, especially for closely related species
(Anisimova, Bielawski et al. 2002), such as primates. If the species included in the
analyses are too divergent, the study may not have enough power to detect lineage
specific changes. In this study, we provide the largest comparative genomics primate
study to date, with the inclusion of four Apes, two Old World monkeys, and two New
World monkeys. Furthermore, the addition of the highly encephalized capuchin monkey
83
transcriptome provides a strong foundation for uncovering genes involved in primate
brain evolution.
Protein Coding Regions Adaptively Evolving on the Capuchin and Human
Lineages
One study showed recently evolved genes (primate-specific genes) have higher
expression in the neocortex when compared to young genes (rodent-specific genes) in
a mouse developing brain. These expressed young genes had a significant enrichment
in transcription factors, suggesting that the evolution of regulatory networks play a large
role in shaping human brain evolution (Zhang, Landback et al. 2011). In our study, we
uncovered five genes that encode for proteins involved in transcriptional regulation with
evidence of positive selection on the capuchin (ZNF174, KLHDC2) and human lineage
(ASXL1, ZNF263, TAF12). Interestingly, both lineages include an adaptively evolving
zinc finger protein (ZNF).
Other genes of interest include FBXW8, F-box and WD repeat domain contain 8,
which was found to have evidence of positive selection on the capuchin lineage.
Mutations in FBXW8 cause severe reduction in cell proliferation and is thought to play a
role in growth control (Okabe, Lee et al. 2006). Homozygous knockout mice are viable
however, they demonstrate pre- and postnatal growth retardation (Tsutsumi, Kuwabara
et al. 2008). On the human lineage, C2CD3, C2 calcium-dependent domin containing
3, is a regulator of the hedgehog signaling pathway. C2CD3 was found to be essential
for embryonic development in mice and cilia biogenesis (Hoover, Wynkoop et al. 2008).
84
Many of the genes with evidence for positive selection on the capuchin monkey
(n=17) and human lineages (n=13), have been linked to diseases with mental
disabilities. TACO1, translational activator of mitochondrially encoded cytochrome c
oxidase I, a gene with evidence of adaptive evolution on the capuchin monkey lineage
(dN/dS=2.75)
has
been
linked
to
Leigh
syndrome,
a
neurological
disease
(Weraarpachai, Antonicka et al. 2009, Seeger, Schrank et al. 2010). Also, evolving
adaptively on the capuchin lineage, FUCA1, fucosidase alpha-L-1, (dN/dS=4.88)
mutations are linked to fucosidosis, a lysosomal storage disease that leads to
neurodegeneration and mental retardation. Interestingly, patients with this disease also
have growth retardation as well (Willems, Seo et al. 1999). A polymorphism in SLC9A9,
solute carrier family 9, member 9, (dN/dS=2.00) has been linked to attention
deficit/hyperactivity disorder (ADHD) (Mick, Todorov et al. 2010) and autism
(Geschwind 2008). Two genes linked to Alzheimer’s disease were found to be
adaptively evolving on the human lineage, (APLP1, amyloid beta A4 precursor-like
protein 1, dN/dS=1.13, APBB3, amyloid beta A4 precursor protein-binding family B
member 3, dN/dS=1.73). Accumulation of APLP1 was found in plaques of patients with
Alzheimer’s (Bayer, Paliga et al. 1997). Increased expression of APLP1 in the brain
induces apoptosis and neurodegeneration. In one study, researchers increased brain
magnesium concentrations in macaques in an attempt to mimic neurodegenerative
disorders, and APLP1 was found to be the most highly up-regulated gene in the
magnesium exposed group (Guilarte, Burton et al. 2008).
Of special interest, ADAM10 (dN/dS=1.12, capuchin monkey lineage) and
ADAM15 (dN/dS=1.63, human lineage) are part of the ADAM family of membrane
85
bound metalloproteases. ADAM10 is predominantly expressed in the brain, including
oligodendrocytes and developing neurons, whereas ADAM15 is ubiquitously expressed,
including the brain (Novak 2004, Tousseyn, Thathiah et al. 2009). In Drosophila,
ADAM10 (also known as kuz) controls Notch signaling, and deletion of the
metalloprotease domain leads to an increase in primary neurons in Xenopus embryos
(Pan and Rubin 1997). ADAM10 conditional knockout mice (cKO - inactivation was
restricted in the CNS), demonstrated cortical disorganization, suggesting a possible role
in neuronal migration during development. Additionally, these cKO mice increased the
number of postmitotic neurons, but a reduced number of progenitor cells, suggesting
lack of ADAM10 causes premature differentiation (Jorissen, Prox et al. 2010).
Overexpression of ADAM10 was shown to be protective in Alzheimer’s disease, and is
involved in cleaving amyloid-beta peptide sequences. Interestingly, ADAM10 can by
cleaved by ADAM15, once cleaved, and no longer membrane bound, ADAM10 has
demonstrated involvement in gene regulation through cell signaling (Jorissen, Prox et
al. 2010).
Evidence for Parallel Evolution in Brain Expansion
It is possible that both the human and capuchin monkey shared similar molecular
mechanisms in the evolution of a large brain. It is unclear whether these molecular level
changes occurred in the same genes, however we provide support for changes in
shared biological processes. Large brains have arisen many times and there are likely
multiple genetic factors involved. Previous studies have provided evidence for genes
enriched for energy metabolism adaptively evolving on the large-brained lineages of
86
human (Grossman, Wildman et al. 2004, Uddin, Opazo et al. 2008), elephant
(Goodman, Sterner et al. 2009) and dolphin (McGowen, Grossman et al. 2012). In this
study, genes with accelerated rates of evolution on the capuchin lineage were enriched
in metabolic (ADCY1, ADSSL1, PDE4A, and IMPDH2) and catabolic processes
(ADAM10, DCUN1D2, FBXW8, FBXL20, HACE1, CDC20, CDC16, FBXL14, FEM1A
and RNF4). These genes were found to have a significantly higher rate of change
compared to the rest of the primates in this study. Furthermore, the most significantly
enriched biological process (p=0.002) in the combined human and capuchin monkey
dataset is nucleoside monophosphate metabolic process (n=3 in capuchins; n=1 in
humans).
The brain is one of the most metabolically expensive organs (Aiello and Wheeler
1995). Interestingly, protein amino acid composition can be directly affected by energy
and metabolism. The energy used to make the different amino acids has been linked to
patterns of amino acid composition. It was shown that in a genome-wide analysis of 37
mammalian genomes, encephalization is correlated with amino acid composition (R2 =
0.785, P<0.0001) (Gutierrez 2011) This was determined by multiple regression analysis
including all 20 amino acid frequencies per species and the correlation holds true after
correcting for phylogeny. No significant correlations in brain mass, body mass, genome
size, and lifespan were found. This study concludes that the high energy demands of a
large brain gives selective pressure, possibly involving energy shortages, thus shifting
the individual to make more energy efficient or “cheaper” amino acids.
Previous brain studies propose candidate genes involved in the disease
microcephaly are important in brain size evolution (Evans, Gilbert et al. 2005, Mekel-
87
Bobrov, Gilbert et al. 2005, Cox, Jackson et al. 2006, Evans, Mekel-Bobrov et al. 2006,
McGowen, Montgomery et al. 2011, Montgomery and Mundy 2012, Yang, Baltus et al.
2012). This disease, thus far, has been linked to several genes involved in microtubule
formation, mitosis and centrosome formation. Defects in the function of these genes can
affect neurogenesis. A mechanism has been proposed whereas a change from
symmetrical to asymmetrical cell division during neurogenesis can lead to a decrease in
neuron production and produced the microcephaly phenotype (Rieder, Faruki et al.
2001, Thornton and Woods 2009). Of the genes previously reported to be involved in
microcephaly, three (CDK5RAP2, WDR62 and ZNF335) were expressed in the
capuchin brain, however they did not demonstrate evidence for adaptive evolution on
the capuchin lineage. The lack of transcript data for genes linked to microcephaly may
be due to the age of the capuchin brain, and a fetal brain could potentially express more
of these genes. Intriguingly, this study provides support for cell division playing a large
role in brain size evolution. Our combined dataset for genes with accelerated rates of
evolution in both the human and capuchin lineages demonstrates enrichment in mitosis
(capuchin, n=4; human, n=2), microtubule cytoskeleton organization (capuchin, n=3;
human, n=2), and cell polarity (capuchin, n=1; human, n=2).
Limitations to the Study
One of the greatest challenges to this study was the processing of the massive
amounts of short sequence reads. Difficulties in alignment can arise if sequence reads
match to multiple locations in the genome, especially short reads in long stretches of
repetitive regions (Wang, Gerstein et al. 2009). To address these issues, our
88
methodology was conservative and performed assembly prior to mapping. We
performed a three phase assembly procedure i) de novo assembly of the short contigs,
using two assembly programs (Trinity and CAP3) ii) mapped the raw reads back to
newly assembled transcripts, and iii) created a consensus pileup sequences.
Furthermore, we extracted the contigs that contained open reading frames because our
end goal was comparing protein-coding regions of multiple primates. A recent study of
the placenta transcriptome of an African elephant (Loxodonta Africana), reported 55,910
annotated contigs in the placenta, similar to the number of protein coding contigs
resolved in our brain transcriptome (n=50,716) (Hou, Sterner et al. 2012). The
alignments in our study had to include sequencing information for all eight of the primate
species and we performed quality control measures (i.e. sliding window and Gblocks) to
mask regions of poor sequencing and divergent alignments. Although these procedures
may have lost additional data, we have produced over 5,500 highly reliable gene
alignments in eight primate species. For comparative genomic analyses, it is essential
for the sequence quality to be high to limit the number of false positives (Mallick, Gnerre
et al. 2009).
Consequently, the main limitation to this study is the relatively few adaptively
evolving genes (dN/dS >1) we have uncovered with this conservative approach. Recent
studies performing whole genome scans for positive selection on the human lineage
have estimated as few as 68 to over 500 genes adaptively evolving on the human
terminal branch (Uddin, Goodman et al. 2008, Goodman, Sterner et al. 2009). Below we
would like to compare and contrast three recent genome wide studies that tested for
adaptive evolution on multiple mammalian lineages with our results. First, the Uddin et
89
al (2008) study obtained genomic data from 10 vertebrate species: human (H. sapiens),
chimpanzee (P. troglodytes), macaque (M. mulatta), mouse (M. musculus), rat (R.
norvegicus), dog (C. familiaris), cow (B. taurus), opossum (M. domestica), chicken (G.
gallus), and frog (X. tropicalis) and reported a total of 585 genes out of the aligned
23,945 (2.44%) adaptively evolving on the human terminal lineage. These genes
showed
enrichment
in
mitochondrion,
electron
carrier
activity
and
oxidative
phosphorylation (Uddin, Goodman et al. 2008). McGowen et al. (2012) reported a
genome wide scan of data from 10 vertebrates: dolphin (T. truncatus), cow (B. taurus),
horse (E. caballus), dog (C. familiaris), mouse (M. musculus), human (H.sapiens),
elephant (L. africana), opossum (M. domestica), platypus (O. anatinus) and chicken (G.
gallus). Similar to the Uddin et al. paper, the number of genes exhibiting positive
selection on select lineages was ~2.5-3% of the total number of genes in the study
(n=10,025). For the dolphin lineage, they reported 228 (2.26%) genes with a dN/dS >1,
446 (4.77%) on the horse lineage, 374 (3.76%) on the cow lineage and 238 on the dog
(2.54%). Lastly, the Goodman et al. (2009) study aligned 7,768 1:1 orthologues from six
mammalian species: human (H. sapiens), mouse (M. musculus), elephant (L. africana),
tenrec (E. telfairi), cow (B. taurus) and dog (C. familiaris). This study reported 68 genes
with a dN/dS > 1 on the human lineage (0.88%), nine genes on the mouse lineage
(0.12%), 105 genes on the elephant lineage (1.35%), and 10 genes on the tenrec
lineage (0.13%). These three reports demonstrate genes adaptively evolving on the
human lineage ranged from 0.88%-2.44% of the total amount of genes used in the
study. Based on these estimates we would expect ~45-138 genes adaptively evolving
on the human lineage in this study (n=5,672). However, we uncovered 13 capuchin
90
brain expressed genes with a dN/dS >1 on the human linage (0.23%) and 17 genes with
a dN/dS >1 on the capuchin lineage (0.30%). We reported 68 (1.20%) and 105 (1.85%)
with an accelerated rate of evolution on the human and capuchin monkey lineages,
respectively.
Accordingly, we propose two contributions that can affect the number of genes
adaptively evolving on terminal lineages; i) the source of the genes in the study and ii)
the length of the alignments. They key difference in this study is the dataset is restricted
to genes expressed in the neocortical brain of a capuchin monkey, while the other
studies described above used whole genome alignments. If genes adaptively evolving
on the human lineage were not expressed in the capuchin brain, then they will not be
included in this study. Furthermore, it has been demonstrated that dN/dS levels are
significantly lower for brain-specific expressed protein-coding genes when compared to
other tissue-specific genes, suggesting genes expressed in the brain are under tight
constraints (Khaitovich et al 2005). Additionally, previous studies have shown a
negative correlation with dN/dS and expression levels in tissues, that is, high expressed
genes evolve slower than low expressed genes (Dummond et al 2005, Kosiol et al
2008). It is possible that we did not have power to detect all adaptively evolving genes
as a limitation to RNA-seq analysis, especially genes with low expression in the brain.
The length of the alignments in this study (150bp to 9,975bp) was considerably
shorter than the McGowen et al. study (150bp to 26,255bp), but comparable to the
Goodman et al. study (150bp to 7,698bp). Interestingly, the Goodman et al. study also
reported substantially fewer genes with a dN/dS >1 on the terminal lineages (i.e.
human, n=68 and mouse, n=9). It should be noted that this study did not include
91
alignments of any nonhuman primates, and thus we cannot resolve whether these are
human specific or primate specific genes. Additionally, we have found the sequence
length to have a small effect on the dN/dS (capuchin, r2=0.06, p=0.01; human, r2= 0.02,
p=0.25) (Figure 8). We provide two explanations for the length of our alignments, the
coverage of sequencing information and the quality of the sequence data. Coverage
was limited due to the incorporation of the capuchin transcriptome, as, some transcripts
were not assembled fully. Furthermore, we were concerned about the quality of the
alignments, because estimations of dN/dS rely heavily on the quality of the sequence
data (Mallick, Gnerre et al. 2009). Thus, we chose to sacrifice the length of the
alignments in order to ensure high quality.
This study, although limited, has provided a list of genes on the human lineage
and on the capuchin monkey lineage that have accelerated rates of evolution. We have
laid the framework for additional comparative genomic studies. These gene lists
establish a starting point for more in depth evolutionary studies. Obtaining more
sequencing data from multiple capuchin species, as well as the addition of closer sister
taxa, such as the recently released squirrel monkey (Saimiri) genome would increase
the power of this study.
Conclusion
In this study, we sequenced the neocortical brain transcriptome of the second
most highly encephalized primate, the capuchin monkey. We performed de novo
assembly, extracted the likely protein coding regions and annotated based on the
human transcriptome. Of the 50,716 newly sequenced protein coding regions from the
92
capuchin brain transcriptome, we reported 45,192 hits to the human cDNA database,
and 14,936 1:1 orthologs. This new dataset, along with previously sequenced primate
genomes, allowed us to test for patterns of parallel evolution among the large-brained
human and capuchin monkey lineages. We provide evidence of accelerated rates of
evolution in 105 genes on the capuchin linage and 68 on the human, with approximately
15 genes demonstrating positive selection (dN/dS >1) in each dataset. We found no
overlapping genes in the two datasets. However, combined gene ontology analyses
revealed overlapping enrichment in specific pathways, including mitosis and microtubule
organization. These results suggest different genes with shared biological processes
may have contributed to neocortical brain expansion in humans and capuchin monkeys.
93
CHAPTER 4
Conclusion
An extraordinarily large brain is one of the most remarkable features in modern
humans and presumably this feature is linked to our enhanced cognitive abilities
(Jerison 1973, Martin and Martin 1990). The aim of my research was to contribute to the
age-old question of what makes the human brain unique. In this regard, I first asked
whether brain expansion was a unique feature in not only primates, but all mammals, by
performing
phylogenetic
methods
to
analyze
brain
mass,
body
mass
and
encephalization among 630 extant mammalian species. Secondly, I addressed this
question at the molecular level, by sequencing the brain transcriptome of an
encephalized New World monkey, Cebus apella, in order to test for similar patterns of
adaptively evolving brain expressed genes. This study provides both phenotypic
(Chapter 2) and molecular (Chapter 3) evidence for parallel brain expansion.
94
Phenotypic Evidence for Parallel Brain Expansion
In Chapter 2, I tested whether encephalization was prominent on many lineages,
unique to just primates, or ubiquitous among mammals. By performing a large a
phylogenetic study, I tested numerous assumptions including, i) Humans are the most
encephalized mammals, ii) Primates show a unique signature of encephalization, and
iii) Capuchin monkeys are the most encephalized nonhuman primates. Numerous
studies have been published on the topic of encephalization (Pirlot and Stephan 1970,
O'Shea and Reep 1990, Marino 1998, Williams 2002, Shoshani, Kupsky et al. 2006,
Finarelli and Flynn 2007, Ashwell 2008), but relatively few studies have performed
comparisons in the context of all mammals (Martin 1981). To address the first
assumption, I confirmed that humans are the most encephalized primate. Furthermore,
by reconstructing the ancestral body mass, brain mass, and encephalization quotient, I
reported the Homo lineage had a two-fold increase in body mass and a nearly five-fold
increase in brain mass since the LCA of apes (~18 Myr). Although humans demonstrate
the largest increase in brain mass, the studied revealed patterns of encephalization
among multiple other mammalian lineages, including elephants (EQ=1.27), multiple
species in Carnivora, (red fox; EQ=1.92), (Malayan sun bear; EQ=2.33), and the harbor
porpoise (EQ=4.43). I conclude brain expansion is not unique to humans, however it is
the most extreme example of encephalization among all mammalian orders.
My study also demonstrated that primates do not have distinctive patterns of
encephalization. We found remarkably similar trends for brain size evolution among the
order Cetartiodactyla (i.e. dolphins, whales and hippopotamus), where EQs ranged from
0.14-4.43 in Cetartiodactyla and 0.90-5.72 in Primates. Within these orders, I found both
95
anthropoid primates (mean EQ=2.57) and Cetacea (mean EQ=2.43) to be the driving
force of encephalization in these groups. These results are consistent with other studies
(Marino 1998, Marino, Rilling et al. 2000, Marino 2002). Unexpectedly, I found evidence
for pronounced decreases in encephalization compared to the reconstructed ancestral
state in certain lineage of both anthropoid primates (i.e. howler and colobine monkeys)
and Cetacea (i.e. fin and humpback whales). If brain expansion is an adaptive trait, why
is there evidence of brain reduction occurring multiple times in mammalian evolution?
Although the brain is an adaptive trait in regards to cognitive behaviors (Lefebvre,
Whittle et al. 1997, Reader and Laland 2002, Overington, Cauchard et al. 2011), it
comes at a high cost to maintain (Aiello and Wheeler 1995). This high cost needs to be
addressed by either an increase in energy input (i.e. increase quality of food) or a
reallocation of energy (i.e. reduction of gut size) (Aiello and Wheeler 1995, Isler and van
Schaik 2006, Isler and van Schaik 2009). However, this cost may be too high in certain
environments, and it may be advantageous to reduce the brain and, thus, free energy
for other adaptive traits such as the enlarged gut in colobines (Chivers and Hladik 1980,
Milton 1998) and biomechanical forces to open mouths when feeding in baleen whales
(Goldbogen, Pyenson et al. 2007). Accordingly, I concluded there were relaxed
constraints on anthropoid primate and Cetacea brain:body scaling laws to allow for such
variation in encephalization.
Lastly, I confirmed that capuchin monkeys are the most encephalized nonhuman
primate (EQ=4). Additionally, there is evidence for an increase in brain mass compared
to ancestral reconstructions. The Cebus lineage more than doubled in brain size since
the LCA, ~ 18 Myr. This study demonstrates that among anthropoid primates, there is
96
no significant difference in mean encephalization quotients between New World
monkeys (Platyrrhini) and Old World monkeys (Catarrhini). Combined, these results
further support the hypothesis for parallel brain evolution among primates.
Molecular Evidence for Parallel Brain Expansion
In Chapter 3, I sought to uncover the genetic underpinnings of encephalization
and empirically tested whether shared molecular changes were present on the human
and capuchin monkey lineage. I hypothesized that genes involved in energy metabolism
that have adaptively changed in the human lineage will also have evidence for adaptive
evolution in the capuchin monkey lineage. In order to do this, I first obtained genetic
data to test for these adaptive changes. I implemented RNA-sequencing of the
neocortical brain of a male tufted capuchin monkey. This approach yields significantly
more genomic data than by standard PCR sequencing methods. We assembled and
annotated over 60,000,000 high quality paired-end reads. This assembly resulted in
~150,00 contigs, with an N50 length of 1,809bp, a total length of 114Gb and the longest
contig assembled is 17,354bp. We extracted the protein coding regions of these
sequences and aligned them to the protein coding regions of seven additional
anthropoid primates (chimpanzee, gorilla, orangutan, macaque, colobine monkey and
marmoset).
To address this hypothesis of enrichment of metabolism related genes adaptively
evolving on the capuchin lineage, I found the most significant gene ontology categories
for genes with accelerated rates of evolution on the capuchin lineage to be involved in
metabolic and catabolic processes. However, in contrast to what I originally proposed, I
97
found no overlap in specific genes adaptively evolving on the human and capuchin
lineages. This could be the result of multiple factors. First, there is the possibility that
there is no molecular evidence for parallelism in primate brain expansion. Another
possibility is that there is molecular evidence for parallelism, however, I did not have
enough power to detect these genes due to limited sequencing coverage (see Chapter
3 limitations). Lastly, it is possible that there is no overlap in specific genes involved in
the evolution of brain expansion, instead it is genes involved in shared pathways,
interactions and biological processes.
To illustrate this last point further, I would like to offer two examples from my
results that provide molecular support for parallel brain expansion in human and
capuchin monkey lineages. In the first example, I found a shared gene family of
membrane-bound metallopeptidases (ADAMs) evolving adaptively on the human
lineage (ADAM15, dN/dS=1.63) and the capuchin lineage (ADAM10, dN/dS=1.17).
ADAM10 is predominantly expressed in the brain, while ADAM15 is ubiquitously
expressed, including the brain (Novak 2004, Tousseyn, Thathiah et al. 2009). ADAM10
plays a critical role in the developing brain and is an important controller in Notch
signaling pathways (Jorissen, Prox et al. 2010). Additionally, ADAM10 can be cleaved
by ADAM15, whereas it is thought be involved in gene regulation (Tousseyn, Thathiah
et al. 2009). In the second example, I show when the datasets for human lineage and
the capuchin monkey lineage were combined, there was an enrichment in new
biological processes, such as mitosis (human, n=2; capuchin, n=4), establishment of
cell polarity (human, n=2; capuchin, n=1), and GMP metabolic process (human, n=1;
capuchin, n=1), as well additional support for enrichment in the other previously
98
established categories, such as microtubule-based processes (human, n=4; capuchin,
n=3) and cell adhesion (human, n=10; capuchin, n=6). These results suggest that there
is indeed evidence of parallelism in primate encephalization.
Finally, I conclude that the results from this study are consistent with previous
work that genes linked to cell division, microtubule formation, and centrosome formation
may play a critical role in brain size determination (i.e. microcephaly studies) (Jackson,
McHale et al. 1998, Evans, Gilbert et al. 2005, Mekel-Bobrov, Gilbert et al. 2005,
Kumar, Girimaji et al. 2009, Rakic 2009, Thornton and Woods 2009, Montgomery and
Mundy 2010). I found several genes important in neurogenesis (ADAM10), microtubule
cytoskeleton organization (KIF2C, TUBGCP3, RANBP9, RET, ZW10), mitosis (KIF2C,
NEK9, CDC20, CDC16, PES1, ZW10) and cell polarity (PTK7, ARHGEF11, ZW10)
evolving faster on either the human or capuchin monkey lineage compare to other
primates including in the study. In neurogenesis, the role of cellular division plays an
important role in the size of a developing brain. During symmetrical division in
neurogenesis, each progenitor produces two identical progenitor cells during one round
of mitosis. Symmetrical division acts in an exponential fashion, whereas just one
additional round of symmetric division doubles the number of cells. During asymmetrical
division, the progenitor cell produces one postmitotic neuron and one progenitor cell
(Rakic 1988, Rakic 1995, Rakic 2009). Accordingly, an increase in neuron number can
be the result of: i) the initial number of neurons in the ‘founder population’ (i.e. number
of proliferating cells at the onset of neurogenesis) and/or ii) prolongation of
neurogenesis (Caviness, Takahashi et al. 1995, Rakic 1995). These data provide
support that the molecular underpinnings controlling brain size are involved in the time
99
and mode of cellular division. However, whether the role of symmetrical vs.
asymmetrical division is responsible for large brains or in response to another molecular
mechanism is yet to be determined.
100
APPENDIX A
CHAPTER 2 SUPPLEMENTAL MATERIAL
Supplemental Tables
Table S1: Brain / body mass and EQ Dataset of 630 mammals
Published in DRYAD: http://datadryad.org/handle/10255/dryad.37957
Species Name
Order
Brain Mass (g)
Body Mass (g)
EQ
Chrysochloris stuhlmanni
Afrosoricida
1.06
50.05
1.02
Potamogale velox
Afrosoricida
4.05
650.00
0.58
Microgale cowani
Afrosoricida
0.42
15.20
0.98
Limnogale mergulus
Afrosoricida
1.15
92.00
0.70
Microgale talazaci
Afrosoricida
0.79
50.40
0.76
Microgale dobsoni
Afrosoricida
0.56
32.60
0.74
Chrysochloris asiatica
Afrosoricida
0.70
49.00
0.69
Oryzorictes hova
Afrosoricida
0.58
44.20
0.61
Setifer setosus
Afrosoricida
1.51
248.00
0.44
Hemicentetes semispinosus
Afrosoricida
0.83
110.00
0.44
Tenrec ecaudatus
Afrosoricida
2.84
907.00
0.31
Echinops telfairi
Afrosoricida
0.62
87.50
0.39
Helarctos malayanus
Carnivora
385.50
45020.00
2.32
Ursus americanus
Carnivora
248.00
25990.00
2.25
Arctocephalus galapagoensis
Carnivora
291.25
45950.00
1.73
Zalophus californianus
Carnivora
405.00
91000.00
1.44
Phoca vitulina
Carnivora
315.00
73635.00
1.32
Bassariscus sumichrasti
Carnivora
41.90
2722.00
2.05
Lobodon carcinophagus
Carnivora
586.25
222250.00
1.07
Vulpes vulpes
Carnivora
49.64
3722.71
1.92
Odobenus rosmarus
Carnivora
1410.25
1022225.00
0.83
Ommatophoca rossii
Carnivora
495.00
179400.00
1.06
Arctocephalus gazella
Carnivora
340.00
96600.00
1.16
Lynx rufus
Carnivora
65.00
6350.00
1.69
Arctocephalus philippii
Carnivora
415.00
140000.00
1.07
Hydrurga leptonyx
Carnivora
712.50
345500.00
0.94
Arctocephalus tropicalis
Carnivora
326.25
101250.00
1.07
Potos flavus
Carnivora
33.08
2241.50
1.87
Otaria byronia
Carnivora
506.25
222000.00
0.93
Arctocephalus forsteri
Carnivora
320.00
109690.00
0.99
Arctocephalus australis
Carnivora
307.50
103750.00
0.99
Nasua nasua
Carnivora
37.50
3187.50
1.63
Panthera leo
Carnivora
259.50
84000.00
0.98
Ursus arctos
Carnivora
335.97
131022.67
0.91
101
Phoca fasciata
Carnivora
248.75
87580.00
0.91
Phoca largha
Carnivora
253.75
91500.00
0.90
Urocyon cinereoargenteus
Carnivora
37.28
3749.00
1.44
Monachus schauinslandi
Carnivora
370.00
173000.00
0.82
Neophoca cinerea
Carnivora
388.75
189275.00
0.80
Ailurus fulgens
Carnivora
46.80
5590.00
1.34
Callorhinus ursinus
Carnivora
334.83
150916.67
0.82
Arctocephalus pusillus
Carnivora
369.38
178750.00
0.80
Leopardus pardalis
Carnivora
63.10
9525.50
1.21
Monachus monachus
Carnivora
480.00
280500.00
0.74
Leptonychotes weddellii
Carnivora
564.17
378666.67
0.69
Ursus maritimus
Carnivora
507.00
317000.00
0.71
Canis mesomelas
Carnivora
54.80
8000.00
1.20
Procyon lotor
Carnivora
41.07
4975.67
1.28
Erignathus barbatus
Carnivora
460.00
281000.00
0.71
Phoca hispida
Carnivora
196.25
69085.00
0.86
Cystophora cristata
Carnivora
455.00
282840.00
0.70
Nasua narica
Carnivora
44.17
6250.00
1.16
Crocuta crocuta
Carnivora
175.00
62370.00
0.83
Paradoxurus hermaphroditus
Carnivora
25.95
2773.50
1.25
Melursus ursinus
Carnivora
267.00
136080.00
0.71
Puma concolor
Carnivora
154.00
54432.00
0.81
Mustela erminea
Carnivora
4.57
156.43
1.88
Lynx canadensis
Carnivora
69.50
14969.00
0.95
Eumetopias jubatus
Carnivora
661.25
643775.00
0.55
Phocarctos hookeri
Carnivora
393.75
273500.00
0.62
Phoca caspica
Carnivora
162.50
62750.00
0.76
Panthera pardus
Carnivora
135.00
48000.00
0.78
Halichoerus grypus
Carnivora
307.50
194000.00
0.62
Otocyon megalotis
Carnivora
26.09
3335.00
1.10
Phoca sibirica
Carnivora
187.50
89500.00
0.68
Leptailurus serval
Carnivora
54.10
11340.00
0.91
Mirounga leonina
Carnivora
1205.00
2006500.00
0.43
Genetta tigrina
Carnivora
15.35
1525.00
1.16
Ichneumia albicauda
Carnivora
28.30
4400.00
0.97
Panthera tigris
Carnivora
269.73
195000.00
0.54
Mustela putorius
Carnivora
7.87
915.00
0.87
Mephitis mephitis
Carnivora
10.15
1980.00
0.63
Phocoena phocoena
Cetartiodactyla
1735.00
142430.00
4.43
Lagenorhynchus obliquidens
Cetartiodactyla
1136.75
89750.00
4.10
Stenella coeruleoalba
Cetartiodactyla
883.75
63500.00
4.12
Tursiops truncatus
Cetartiodactyla
1573.00
170480.00
3.51
Delphinus delphis
Cetartiodactyla
797.26
65086.96
3.65
102
Stenella longirostris
Cetartiodactyla
450.00
33600.00
3.38
Phocoenoides dalli
Cetartiodactyla
833.67
98333.33
2.81
Orcinus orca
Cetartiodactyla
6052.00
3273000.00
1.49
Delphinapterus leucas
Cetartiodactyla
1921.00
498250.00
1.93
Pseudorca crassidens
Cetartiodactyla
4307.00
2000000.00
1.53
Rangifer tarandus
Cetartiodactyla
278.00
71700.00
1.18
Damaliscus pygargus
Cetartiodactyla
254.60
62500.00
1.20
Alces alces
Cetartiodactyla
436.00
200000.00
0.86
Tayassu pecari
Cetartiodactyla
145.80
32233.33
1.13
Connochaetes taurinus
Cetartiodactyla
443.00
212230.00
0.84
Odocoileus virginianus
Cetartiodactyla
210.00
65090.00
0.96
Cervus elaphus
Cetartiodactyla
409.30
200000.00
0.81
Tragelaphus scriptus
Cetartiodactyla
165.00
44225.00
1.01
Aepyceros melampus
Cetartiodactyla
175.00
57610.00
0.88
Boselaphus tragocamelus
Cetartiodactyla
271.75
125000.00
0.77
Axis axis
Cetartiodactyla
219.00
88450.00
0.80
Madoqua kirkii
Cetartiodactyla
37.00
4570.00
1.23
Tragelaphus eurycerus
Cetartiodactyla
389.00
253000.00
0.65
Gazella thomsonii
Cetartiodactyla
91.80
24370.00
0.88
Syncerus caffer
Cetartiodactyla
647.50
665440.00
0.52
Megaptera novaeangliae
Cetartiodactyla
6100.00
30050000.00
0.29
Kobus ellipsiprymnus
Cetartiodactyla
290.00
188200.00
0.60
Sus scrofa
Cetartiodactyla
169.80
86772.73
0.63
Moschiola meminna
Cetartiodactyla
16.93
1997.00
1.04
Tragulus napu
Cetartiodactyla
18.50
2510.00
0.96
Phacochoerus aethiopicus
Cetartiodactyla
125.00
65320.00
0.57
Giraffa camelopardalis
Cetartiodactyla
700.00
1209000.00
0.36
Hippopotamus amphibius
Cetartiodactyla
720.00
1351000.00
0.34
Balaenoptera borealis
Cetartiodactyla
4900.00
36666666.67
0.20
Balaenoptera physalus
Cetartiodactyla
5100.00
62500000.00
0.14
Pteropus rufus
Chiroptera
7.30
374.86
1.57
Pteropus lylei
Chiroptera
6.13
314.29
1.50
Rousettus egyptiacus
Chiroptera
2.60
130.00
1.23
Desmodus rotundus
Chiroptera
0.90
29.00
1.30
Cynopterus brachyotis
Chiroptera
0.88
29.00
1.27
Diaemus youngi
Chiroptera
0.97
34.60
1.23
Cynopterus horsfieldi
Chiroptera
1.23
53.00
1.14
Eonycteris spelaea
Chiroptera
1.18
50.00
1.14
Noctilio leporinus
Chiroptera
1.26
58.50
1.08
Phyllostomus discolor
Chiroptera
0.89
33.00
1.17
Artibeus jamaicensis
Chiroptera
0.93
37.50
1.11
Hypsignathus monstrosus
Chiroptera
3.40
340.00
0.78
Casinycteris argynnis
Chiroptera
0.92
40.50
1.04
103
Uroderma bilobatum
Chiroptera
0.53
16.40
1.17
Platyrrhinus helleri
Chiroptera
0.42
11.60
1.20
Sturnira lilium
Chiroptera
0.52
17.10
1.12
Glossophaga soricina
Chiroptera
0.39
11.20
1.15
Artibeus lituratus
Chiroptera
1.05
58.70
0.90
Carollia perspicillata
Chiroptera
0.47
16.00
1.06
Nycteris arge
Chiroptera
0.36
10.60
1.10
Mormoops megalophylla
Chiroptera
0.42
15.60
0.97
Leptonycteris curasoae
Chiroptera
0.55
24.50
0.90
Myotis bechsteini
Chiroptera
0.27
7.50
1.05
Hipposideros caffer
Chiroptera
0.32
10.70
0.98
Pteronotus davyi
Chiroptera
0.31
10.60
0.95
Rhinolophus hipposideros
Chiroptera
0.24
7.15
0.97
Myotis daubentoni
Chiroptera
0.23
7.00
0.96
Myotis mystacinus
Chiroptera
0.16
4.00
1.02
Myotis myotis
Chiroptera
0.48
25.00
0.78
Myotis nattereri
Chiroptera
0.22
7.00
0.92
Mimon crenulatum
Chiroptera
0.34
14.80
0.81
Hipposideros bicolor
Chiroptera
0.24
8.40
0.88
Saccopteryx bilineata
Chiroptera
0.22
8.00
0.83
Miniopterus schreibersi
Chiroptera
0.29
12.70
0.78
Myotis bocagei
Chiroptera
0.21
7.60
0.83
Noctilio albiventris
Chiroptera
0.55
38.50
0.64
Chaerephon pumila
Chiroptera
0.29
13.30
0.75
Myotis dasycneme
Chiroptera
0.31
15.00
0.73
Molossus molossus
Chiroptera
0.26
13.00
0.69
Mormopterus jugularis
Chiroptera
0.22
11.50
0.64
Myotis nigricans
Chiroptera
0.12
4.50
0.70
Rhynchonycteris naso
Chiroptera
0.11
4.30
0.66
Myrmecobius fasciatus
Dasyuromorphia
4.40
405.40
0.89
Parantechinus apicalis
Dasyuromorphia
1.24
60.50
1.04
Antechinus stuartii
Dasyuromorphia
0.79
29.80
1.12
Dasycercus byrnei
Dasyuromorphia
1.55
98.40
0.90
Phascogale calura
Dasyuromorphia
0.94
43.80
1.00
Antechinus swainsonii
Dasyuromorphia
1.13
63.50
0.91
Murexia rothschildi
Dasyuromorphia
1.09
62.00
0.89
Antechinus minimus
Dasyuromorphia
0.77
36.30
0.94
Sminthopsis laniger
Dasyuromorphia
0.49
17.80
1.01
Antechinus flavipes
Dasyuromorphia
0.66
30.70
0.92
Sminthopsis griseoventer
Dasyuromorphia
0.51
19.80
0.98
Dasyurus hallucatus
Dasyuromorphia
3.50
513.00
0.59
Sminthopsis crassicaudata
Dasyuromorphia
0.37
13.30
0.97
Sminthopsis macroura
Dasyuromorphia
0.41
17.00
0.89
104
Antechinus leo
Dasyuromorphia
0.78
60.20
0.65
Antechinus bellus
Dasyuromorphia
0.65
45.80
0.67
Planigale gilesi
Dasyuromorphia
0.18
6.10
0.82
Murexia longicaudata
Dasyuromorphia
1.74
296.50
0.44
Caluromys philander
Didelphimorphia
3.63
277.00
0.98
Thylamys elegans
Didelphimorphia
0.74
20.10
1.40
Caluromys derbianus
Didelphimorphia
3.52
289.00
0.92
Marmosa rubra
Didelphimorphia
1.24
60.00
1.05
Marmosa mexicana
Didelphimorphia
1.04
46.70
1.05
Metachirus nudicaudatus
Didelphimorphia
3.63
390.00
0.76
Marmosa murina
Didelphimorphia
1.14
60.00
0.96
Philander opossum
Didelphimorphia
3.97
570.00
0.62
Caluromys lanatus
Didelphimorphia
3.10
384.00
0.65
Monodelphis brevicaudata
Didelphimorphia
0.93
64.60
0.74
Didelphis marsupialis
Didelphimorphia
6.05
1535.00
0.45
Marmosa robinsoni
Didelphimorphia
0.73
50.50
0.69
Didelphis virginiana
Didelphimorphia
6.96
2797.00
0.33
Gymnobelideus leadbeateri
Diprotodontia
2.95
90.50
1.83
Onychogalea fraenata
Diprotodontia
13.50
1433.00
1.07
Thylogale billardierii
Diprotodontia
22.90
3618.00
0.91
Bettongia gaimardi
Diprotodontia
12.12
1273.00
1.05
Macropus eugenii
Diprotodontia
24.55
4425.00
0.84
Bettongia penicillata
Diprotodontia
9.90
981.00
1.04
Dendrolagus dorianus
Diprotodontia
33.46
7897.00
0.74
Lagorchestes conspicillatus
Diprotodontia
14.50
2015.00
0.89
Petrogale concinna
Diprotodontia
11.40
1407.00
0.91
Dactylopsila trivirgata
Diprotodontia
6.04
506.00
1.04
Thylogale thetis
Diprotodontia
19.89
3694.00
0.77
Macropus rufogriseus
Diprotodontia
38.15
12257.50
0.61
Petrogale penicillata
Diprotodontia
24.76
5990.00
0.67
Vombatus ursinus
Diprotodontia
61.12
27192.00
0.54
Macropus parryi
Diprotodontia
42.79
15194.00
0.58
Trichosurus vulpecula
Diprotodontia
11.40
1761.00
0.77
Macropus parma
Diprotodontia
17.40
3760.00
0.67
Petrogale persephone
Diprotodontia
22.27
5732.00
0.63
Dendrolagus inustus
Diprotodontia
31.18
10119.00
0.57
Macropus antilopinus
Diprotodontia
58.84
31286.00
0.47
Phalanger vestitus
Diprotodontia
9.88
1672.00
0.70
Macropus robustus
Diprotodontia
61.12
35540.00
0.44
Spilocuscus maculatus
Diprotodontia
13.88
3127.00
0.61
Cercartetus lepidus
Diprotodontia
0.37
7.80
1.44
Pseudocheirus peregrinus
Diprotodontia
5.78
759.00
0.73
Dorcopsis luctuosa
Diprotodontia
21.34
7392.00
0.50
105
Phalanger orientalis
Diprotodontia
8.61
1674.00
0.61
Phalanger carmelitae
Diprotodontia
8.80
1754.00
0.60
Distoechurus pennatus
Diprotodontia
0.97
45.40
1.01
Wallabia bicolor
Diprotodontia
32.70
16400.00
0.42
Phalanger ornatus
Diprotodontia
7.27
1448.00
0.57
Petauroides volans
Diprotodontia
4.86
900.00
0.54
Cercartetus concinnus
Diprotodontia
0.31
9.50
1.03
Neurotrichus gibbsii
Eulipotyphla
0.91
10.00
2.92
Gerbillus dasyurus
Eulipotyphla
1.32
20.50
2.48
Scalopus aquaticus
Eulipotyphla
1.48
39.60
1.70
Scapanus townsendii
Eulipotyphla
2.02
70.00
1.52
Condylura cristata
Eulipotyphla
1.37
50.00
1.32
Galemys pyrenaicus
Eulipotyphla
1.33
57.50
1.16
Desmana moschata
Eulipotyphla
4.00
440.00
0.76
Talpa europaea
Eulipotyphla
1.40
77.83
0.97
Solenodon paradoxus
Eulipotyphla
4.67
900.00
0.52
Sorex trowbridgii
Eulipotyphla
0.21
5.30
1.08
Sorex arcticus
Eulipotyphla
0.27
8.20
1.00
Sorex monticolus
Eulipotyphla
0.23
6.30
1.04
Atelerix albiventris
Eulipotyphla
2.24
280.00
0.60
Sorex cinereus
Eulipotyphla
0.17
3.90
1.10
Erinaceus europaeus
Eulipotyphla
3.67
720.44
0.48
Sorex palustris
Eulipotyphla
0.31
11.90
0.87
Surdisorex polulus
Eulipotyphla
0.40
18.60
0.81
Hemiechinus auritus
Eulipotyphla
1.90
250.00
0.55
Sorex vagrans
Eulipotyphla
0.17
5.30
0.87
Sorex araneus
Eulipotyphla
0.23
8.90
0.79
Atelerix algirus
Eulipotyphla
3.20
790.00
0.39
Neomys fodiens
Eulipotyphla
0.31
16.35
0.68
Sorex fumeus
Eulipotyphla
0.21
8.90
0.73
Sorex minutus
Eulipotyphla
0.13
4.60
0.74
Blarina brevicauda
Eulipotyphla
0.29
17.70
0.60
Notiosorex crawfordi
Eulipotyphla
0.15
6.00
0.70
Sorex hoyi
Eulipotyphla
0.09
3.05
0.73
Crocidura russula
Eulipotyphla
0.19
10.45
0.59
Crocidura fuscomurina
Eulipotyphla
0.13
5.60
0.64
Suncus murinus
Eulipotyphla
0.39
35.00
0.49
Sorex ornatus
Eulipotyphla
0.12
5.00
0.65
Cryptotis parva
Eulipotyphla
0.12
5.30
0.62
Suncus etruscus
Eulipotyphla
0.07
2.24
0.69
Procavia capensis
Hyracoidea
19.47
2321.67
1.07
Lepus europaeus
Lagomorpha
14.35
1954.00
0.90
Sylvilagus bachmani
Lagomorpha
6.58
712.40
0.87
106
Lepus californicus
Lagomorpha
13.23
2314.90
0.73
Ochotona hyperborea
Lagomorpha
2.22
120.00
1.11
Lepus nigricollis
Lagomorpha
11.49
1961.00
0.72
Lepus americanus
Lagomorpha
9.72
1500.00
0.74
Lepus timidus
Lagomorpha
15.10
3274.50
0.64
Sylvilagus floridanus
Lagomorpha
7.56
1036.00
0.76
Sylvilagus brasiliensis
Lagomorpha
4.50
439.00
0.86
Oryctolagus cuniculus
Lagomorpha
11.09
2157.60
0.65
Sylvilagus audubonii
Lagomorpha
6.07
792.50
0.75
Ochotona princeps
Lagomorpha
2.39
169.00
0.93
Lepus arcticus
Lagomorpha
15.80
4231.00
0.56
Lepus capensis
Lagomorpha
10.78
2412.50
0.58
Ochotona rufescens
Lagomorpha
2.75
250.00
0.80
Rhynchocyon petersi
Macroscelidea
5.40
370.00
1.17
Rhynchocyon cirnei
Macroscelidea
5.90
507.50
1.01
Elephantulus fuscipes
Macroscelidea
1.33
57.00
1.16
Zaglossus bruijni
Monotremata
37.35
7500.00
0.86
Tachyglossus aculeatus
Monotremata
20.73
4250.00
0.73
Ornithorhynchus anatinus
Monotremata
10.08
1389.00
0.81
Rhyncholestes raphanurus
Paucituberculata
0.67
20.00
1.29
Peroryctes raffrayana
Peramelemorphia
6.56
977.00
0.69
Perameles bougainville
Peramelemorphia
3.33
457.00
0.62
Isoodon macrourus
Peramelemorphia
4.58
822.00
0.55
Isoodon obesulus
Peramelemorphia
4.03
691.00
0.55
Macrotis lagotis
Peramelemorphia
6.81
1859.00
0.44
Perameles gunnii
Peramelemorphia
4.66
1002.00
0.48
Perameles nasuta
Peramelemorphia
5.14
1273.00
0.44
Echymipera rufescens
Peramelemorphia
3.86
798.00
0.47
Echymipera clara
Peramelemorphia
4.64
1207.00
0.42
Equus burchellii
Perissodactyla
612.00
250000.00
1.03
Tapirus bairdii
Perissodactyla
85.00
14260.00
1.21
Equus asinus
Perissodactyla
434.00
234000.00
0.77
Varecia variegata
Primates
31.20
2705.50
1.53
Trachypithecus francoisi
Primates
94.40
9100.00
1.88
Theropithecus gelada
Primates
130.00
7710.00
2.92
Tarsius bancanus
Primates
2.70
77.60
1.88
Semnopithecus entellus
Primates
111.50
7010.00
2.69
Saimiri sciureus
Primates
23.35
578.70
3.62
Saimiri oerstedii
Primates
22.45
605.00
3.37
Saimiri boliviensis
Primates
24.06
750.00
3.08
Saguinus oedipus
Primates
9.64
327.14
2.29
Saguinus geoffroyi
Primates
14.27
634.67
2.07
Pygathrix nemaeus
Primates
77.00
7500.00
1.77
107
Propithecus verreauxi
Primates
26.70
3480.00
1.09
Procolobus badius
Primates
78.00
7000.00
1.89
Pongo pygmaeus
Primates
341.99
54229.04
1.80
Pithecia monachus
Primates
35.00
1500.00
2.67
Perodicticus potto
Primates
12.07
929.33
1.32
Papio hamadryas
Primates
142.00
12020.00
2.29
Pan troglodytes
Primates
354.81
60433.16
1.72
Pan paniscus
Primates
329.70
39700.00
2.18
Nycticebus pygmaeus
Primates
7.80
480.00
1.39
Nycticebus coucang
Primates
12.74
655.50
1.80
Microcebus murinus
Primates
1.84
58.00
1.59
Mandrillus sphinx
Primates
159.20
11500.00
2.66
Macaca sylvanus
Primates
87.70
11200.00
1.49
Macaca sinica
Primates
58.15
1970.00
3.62
Macaca nigra
Primates
97.50
3452.00
3.99
Macaca nemestrina
Primates
110.00
4456.00
3.72
Macaca mulatta
Primates
87.99
4612.78
2.90
Macaca maura
Primates
94.48
6846.00
2.32
Macaca fascicularis
Primates
66.93
3109.45
2.96
Macaca assamensis
Primates
90.50
3655.00
3.55
Macaca arctoides
Primates
100.70
7630.00
2.28
Loris tardigradus
Primates
6.00
322.00
1.44
Lophocebus albigena
Primates
96.80
5125.00
2.95
Leontopithecus rosalia
Primates
13.05
512.38
2.22
Lemur catta
Primates
21.63
2090.00
1.29
Lagothrix lagotricha
Primates
89.35
3905.00
3.34
Indri indri
Primates
38.30
6250.00
1.01
Hylobates syndactylus
Primates
134.80
12172.00
2.16
Hylobates muelleri
Primates
95.31
5954.88
2.60
Hylobates lar
Primates
93.99
5550.00
2.70
Hylobates agilis
Primates
88.10
5528.75
2.54
Homo sapiens
Primates
1250.43
65142.86
5.72
Gorilla gorilla
Primates
454.55
120975.00
1.31
Galagoides demidoff
Primates
3.38
81.00
2.28
Galago senegalensis
Primates
5.90
300.33
1.49
Eulemur rubriventer
Primates
24.90
1015.00
2.54
Eulemur mongoz
Primates
24.03
1559.33
1.78
Eulemur macaco
Primates
22.60
2086.17
1.35
Erythrocebus patas
Primates
100.20
7376.00
2.33
Daubentonia madagascariensis
Primates
45.15
2800.00
2.16
Colobus guereza
Primates
83.90
10281.25
1.52
Colobus angolensis
Primates
74.40
9670.00
1.41
Chlorocebus aethiops
Primates
64.13
3452.67
2.63
108
Cheirogaleus medius
Primates
3.34
179.67
1.24
Cheirogaleus major
Primates
6.80
450.00
1.27
Cercopithecus mona
Primates
67.00
3001.00
3.05
Cercopithecus mitis
Primates
75.00
6300.00
1.96
Cercopithecus cephus
Primates
76.00
3508.33
3.08
Cercocebus agilis
Primates
95.30
4700.00
3.10
Cebus olivaceus
Primates
72.50
2684.50
3.58
Cebus capucinus
Primates
70.14
2104.88
4.16
Cebus apella
Primates
71.30
2589.00
3.62
Cebus albifrons
Primates
62.95
1620.00
4.53
Callithrix pygmaea
Primates
4.64
134.75
2.14
Callithrix jacchus
Primates
7.73
347.45
1.76
Callicebus moloch
Primates
19.00
900.00
2.12
Avahi laniger
Primates
10.49
1285.00
0.90
Ateles paniscus
Primates
108.96
3430.00
4.48
Ateles geoffroyi
Primates
104.96
5774.00
2.93
Ateles fusciceps
Primates
113.60
9026.50
2.27
Aotus trivirgatus
Primates
16.04
701.68
2.16
Aotus lemurinus
Primates
113.50
9026.50
2.27
Alouatta seniculus
Primates
45.50
2827.50
2.16
Alouatta palliata
Primates
50.04
5952.00
1.37
Alouatta caraya
Primates
50.70
5012.50
1.57
Elephas maximus
Proboscidea
5211.25
2765457.50
1.46
Loxodonta africana
Proboscidea
5436.40
4301340.00
1.09
Rattus norvegicus
Rodentia
8.03
359.50
1.78
Callosciurus caniceps
Rodentia
6.02
240.00
1.80
Agouti paca
Rodentia
35.22
4607.00
1.16
Tamiops macclellandi
Rodentia
1.95
39.00
2.26
Xerus inauris
Rodentia
7.65
400.00
1.56
Dremomys rufigenis
Rodentia
5.41
240.00
1.62
Callosciurus notatus
Rodentia
4.91
209.50
1.63
Marmota sibirica
Rodentia
18.10
1890.00
1.16
Tscherskia triton
Rodentia
1.29
25.00
2.09
Sciurus niger
Rodentia
7.91
516.32
1.34
Callosciurus nigrovittatus
Rodentia
4.44
202.00
1.51
Sciurus vulgaris
Rodentia
5.93
327.40
1.41
Funisciurus pyrropus
Rodentia
4.38
200.00
1.50
Ammospermophilus nelsoni
Rodentia
2.31
70.00
1.73
Heliosciurus ruwenzorii
Rodentia
5.40
291.00
1.40
Dasyprocta leporina
Rodentia
21.26
2873.00
1.00
Heliosciurus rufobrachium
Rodentia
5.75
326.00
1.37
Sciurus carolinensis
Rodentia
7.41
503.17
1.28
Atherurus africanus
Rodentia
21.89
3152.50
0.96
109
Tamias panamintinus
Rodentia
1.83
51.20
1.73
Callosciurus prevostii
Rodentia
6.52
439.00
1.24
Tamias palmeri
Rodentia
1.99
60.80
1.66
Sciurus aureogaster
Rodentia
7.81
595.00
1.19
Tamias dorsalis
Rodentia
1.98
62.20
1.62
Tamiasciurus hudsonicus
Rodentia
3.83
188.90
1.37
Tamias townsendii
Rodentia
2.44
89.40
1.53
Funisciurus anerythrus
Rodentia
4.14
218.00
1.33
Iomys horsfieldii
Rodentia
3.38
155.50
1.40
Tamias minimus
Rodentia
1.60
45.30
1.66
Sciurus granatensis
Rodentia
5.91
400.00
1.21
Xerus rutilus
Rodentia
5.09
317.50
1.24
Rhinosciurus laticaudatus
Rodentia
4.28
240.00
1.28
Jaculus orientalis
Rodentia
2.50
98.00
1.46
Tamias speciosus
Rodentia
1.86
59.90
1.57
Hydrochaeris hydrochaeris
Rodentia
75.00
28500.00
0.64
Ratufa affinis
Rodentia
9.85
982.50
1.03
Thryonomys swinderianus
Rodentia
13.28
1625.00
0.95
Proechimys semispinosus
Rodentia
6.87
544.75
1.12
Funisciurus carruthersi
Rodentia
4.48
268.00
1.24
Tamias quadrimaculatus
Rodentia
2.16
82.30
1.44
Spermophilopsis leptodactylus
Rodentia
6.18
495.00
1.08
Allactaga sibirica
Rodentia
3.50
193.00
1.23
Ratufa bicolor
Rodentia
11.01
1320.00
0.92
Aeromys tephromelas
Rodentia
10.34
1189.00
0.94
Dasyprocta punctata
Rodentia
18.34
3172.00
0.80
Funambulus pennantii
Rodentia
2.10
85.90
1.35
Tamias quadrivittatus
Rodentia
1.70
62.30
1.39
Ammospermophilus leucurus
Rodentia
2.33
105.90
1.28
Tamias striatus
Rodentia
2.17
94.30
1.30
Erethizon dorsatum
Rodentia
24.60
5397.00
0.72
Lagidium viscacia
Rodentia
15.08
2460.50
0.79
Dolichotis patagonum
Rodentia
30.50
8000.00
0.67
Glaucomys sabrinus
Rodentia
3.01
174.00
1.15
Tamias amoenus
Rodentia
1.39
50.80
1.33
Heliosciurus gambianus
Rodentia
3.99
295.00
1.02
Melomys levipes
Rodentia
1.86
83.40
1.22
Petaurista petaurista
Rodentia
11.77
1811.50
0.78
Glaucomys volans
Rodentia
1.71
73.10
1.24
Cavia aperea
Rodentia
4.04
306.45
1.01
Spermophilus lateralis
Rodentia
3.21
209.70
1.06
Petaurista elegans
Rodentia
7.76
924.00
0.85
Xerus erythropus
Rodentia
6.68
722.50
0.88
110
Hylopetes spadiceus
Rodentia
1.87
87.50
1.19
Dipodomys ordii
Rodentia
1.45
57.10
1.26
Stenomys verecundus
Rodentia
1.48
59.40
1.26
Tatera afra
Rodentia
1.56
65.00
1.24
Hoplomys gymnurus
Rodentia
4.13
330.00
0.97
Aplodontia rufa
Rodentia
7.04
806.00
0.85
Pogonomelomys sevia
Rodentia
1.49
61.40
1.23
Jaculus jaculus
Rodentia
1.53
64.20
1.22
Gerbillurus paeba
Rodentia
0.84
24.00
1.40
Gerbillus campestris
Rodentia
0.90
27.50
1.36
Gerbillus nanus
Rodentia
0.60
14.00
1.50
Melomys rubex
Rodentia
1.27
49.70
1.23
Ratufa indica
Rodentia
11.40
1935.00
0.72
Pogonomys sylvestris
Rodentia
1.13
41.20
1.26
Dipodomys merriami
Rodentia
1.10
39.40
1.27
Dipodomys panamintinus
Rodentia
1.60
74.00
1.15
Cavia porcellus
Rodentia
4.83
476.00
0.87
Microdipodops pallidus
Rodentia
0.55
12.90
1.46
Melomys rufescens
Rodentia
1.29
54.70
1.16
Dipodomys heermanni
Rodentia
1.44
65.65
1.13
Stenomys niobe
Rodentia
1.10
42.10
1.21
Ammospermophilus harrisii
Rodentia
2.24
138.80
1.01
Spermophilus beldingi
Rodentia
3.28
262.90
0.92
Microdipodops megacephalus
Rodentia
0.56
13.85
1.41
Spermophilus tridecemlineatus
Rodentia
2.38
156.40
0.98
Podomys floridanus
Rodentia
0.94
33.47
1.22
Dipodomys agilis
Rodentia
1.34
61.40
1.11
Paraxerus cepapi
Rodentia
2.85
216.00
0.92
Neotoma fuscipes
Rodentia
2.66
193.30
0.94
Castor canadensis
Rodentia
52.21
27670.00
0.45
Peromyscus grandis
Rodentia
1.53
77.32
1.07
Spermophilus beecheyi
Rodentia
5.12
587.50
0.79
Pseudomys australis
Rodentia
1.16
50.00
1.12
Peromyscus crinitus
Rodentia
0.53
13.56
1.35
Ototylomys phyllotis
Rodentia
1.33
63.00
1.08
Notomys alexis
Rodentia
0.96
37.00
1.16
Onychomys leucogaster
Rodentia
0.80
27.39
1.21
Cynomys ludovicianus
Rodentia
6.01
793.50
0.74
Neotoma floridana
Rodentia
3.04
257.70
0.86
Rattus lutreolus
Rodentia
1.64
92.70
1.00
Pogonomys macrourus
Rodentia
1.43
73.90
1.03
Peromyscus guatemalensis
Rodentia
1.16
52.83
1.07
Rattus nitidus
Rodentia
1.70
101.00
0.97
111
Rattus tunneyi
Rodentia
1.41
76.20
0.99
Spalacopus cyanus
Rodentia
1.58
93.00
0.96
Leopoldamys siporanus
Rodentia
3.39
332.70
0.80
Grammomys cometes
Rodentia
0.96
40.70
1.08
Tatera brantsii
Rodentia
1.56
91.70
0.96
Peromyscus truei
Rodentia
0.75
27.35
1.13
Clethrionomys gapperi
Rodentia
0.56
16.90
1.21
Melomys cervinipes
Rodentia
1.31
70.00
0.98
Massoutiera mzabi
Rodentia
2.32
182.60
0.85
Rattus morotaiensis
Rodentia
1.92
134.10
0.89
Spermophilus richardsonii
Rodentia
3.37
343.67
0.77
Dipodomys microps
Rodentia
1.22
62.80
0.99
Peromyscus mexicanus
Rodentia
1.02
46.55
1.03
Malacomys edwardsi
Rodentia
1.18
60.00
0.99
Peromyscus aztecus
Rodentia
0.92
39.73
1.05
Chiropodomys gliroides
Rodentia
0.70
25.30
1.12
Acomys wilsoni
Rodentia
0.58
18.50
1.17
Mystromys albicaudatus
Rodentia
1.39
80.00
0.94
Clethrionomys rutilus
Rodentia
0.56
17.60
1.18
Meriones unguiculatus
Rodentia
1.13
57.40
0.98
Zapus hudsonius
Rodentia
0.55
17.47
1.16
Onychomys torridus
Rodentia
0.59
20.02
1.14
Peromyscus yucatanicus
Rodentia
0.70
26.30
1.09
Praomys tullbergi
Rodentia
0.86
37.20
1.03
Spermophilus parryii
Rodentia
5.25
763.72
0.66
Pteromyscus pulverulentus
Rodentia
3.55
400.00
0.73
Dipodomys spectabilis
Rodentia
1.93
144.55
0.84
Gerbillus gleadowi
Rodentia
0.69
26.10
1.08
Peromyscus boylii
Rodentia
0.71
27.47
1.07
Dipodomys deserti
Rodentia
1.74
122.45
0.86
Spermophilus franklinii
Rodentia
3.82
455.50
0.71
Hybomys trivirgatus
Rodentia
1.10
57.50
0.96
Apodemus sylvaticus
Rodentia
0.59
20.10
1.11
Reithrodontomys megalotis
Rodentia
0.40
10.70
1.22
Lophuromys sikapusi
Rodentia
1.13
60.70
0.94
Microtus pennsylvanicus
Rodentia
0.74
30.13
1.04
Peromyscus leucopus
Rodentia
0.60
21.21
1.10
Rattus sordidus
Rodentia
1.70
120.67
0.85
Peromyscus melanophrys
Rodentia
0.94
45.00
0.98
Cricetomys gambianus
Rodentia
6.57
1150.00
0.61
Micromys minutus
Rodentia
0.29
6.40
1.30
Tatera indica
Rodentia
1.84
139.70
0.82
Gerbillus pyramidum
Rodentia
0.88
40.90
0.99
112
Myoxus glis
Rodentia
1.90
148.00
0.82
Rattus fuscipes
Rodentia
1.69
122.00
0.84
Orthogeomys cherriei
Rodentia
3.47
405.00
0.70
Peromyscus californicus
Rodentia
0.91
43.53
0.97
Neofiber alleni
Rodentia
2.72
270.50
0.74
Perognathus longimembris
Rodentia
0.33
8.15
1.23
Cynomys gunnisoni
Rodentia
5.16
796.30
0.63
Oligoryzomys longicaudatus
Rodentia
0.67
26.60
1.03
Uranomys ruddi
Rodentia
0.77
33.60
1.00
Spermophilus undulatus
Rodentia
4.84
725.30
0.63
Arvicanthis niloticus
Rodentia
1.33
84.50
0.87
Liomys irroratus
Rodentia
1.06
57.70
0.92
Nyctomys sumichrasti
Rodentia
1.08
60.00
0.91
Clethrionomys glareolus
Rodentia
0.52
17.90
1.08
Chaetodipus baileyi
Rodentia
0.70
29.75
1.00
Peromyscus gossypinus
Rodentia
0.68
28.25
1.00
Microtus agrestis
Rodentia
0.74
32.40
0.98
Spermophilus columbianus
Rodentia
3.74
488.20
0.66
Orthogeomys hispidus
Rodentia
3.98
542.10
0.65
Peromyscus merriami
Rodentia
0.56
20.72
1.04
Cynomys leucurus
Rodentia
5.69
992.10
0.59
Peromyscus megalops
Rodentia
1.12
66.20
0.88
Orthogeomys heterodus
Rodentia
4.31
630.00
0.63
Peromyscus maniculatus
Rodentia
0.57
21.65
1.03
Zygogeomys trichopus
Rodentia
3.95
545.00
0.64
Peromyscus eremicus
Rodentia
0.54
19.93
1.03
Peromyscus melanocarpus
Rodentia
1.03
58.80
0.88
Isthmomys pirrensis
Rodentia
1.71
138.00
0.77
Peromyscus polionotus
Rodentia
0.42
13.32
1.09
Microtus townsendii
Rodentia
0.88
45.80
0.91
Chaetodipus fallax
Rodentia
0.54
20.30
1.02
Synaptomys cooperi
Rodentia
0.59
23.80
0.99
Perognathus parvus
Rodentia
0.49
17.30
1.04
Chaetodipus californicus
Rodentia
0.62
26.00
0.98
Spermophilus tereticaudus
Rodentia
1.82
156.60
0.75
Dinomys branickii
Rodentia
26.95
14000.00
0.39
Oryzomys palustris
Rodentia
0.88
47.10
0.89
Apodemus flavicollis
Rodentia
0.70
32.30
0.94
Pappogeomys gymnurus
Rodentia
4.19
637.00
0.60
Chaetodipus penicillatus
Rodentia
0.46
16.25
1.03
Chaetodipus formosus
Rodentia
0.49
18.10
1.01
Lophuromys flavopunctatus
Rodentia
1.01
60.00
0.85
Geocapromys ingrahami
Rodentia
4.46
717.30
0.59
113
Spermophilus townsendii
Rodentia
2.06
199.00
0.71
Pectinator spekei
Rodentia
1.93
180.00
0.72
Lemniscomys striatus
Rodentia
0.85
46.20
0.87
Perognathus flavus
Rodentia
0.31
8.40
1.11
Hydromys chrysogaster
Rodentia
4.33
698.00
0.58
Heteromys desmarestianus
Rodentia
1.13
74.65
0.81
Neotoma albigula
Rodentia
2.17
223.10
0.69
Lagostomus maximus
Rodentia
12.66
4270.75
0.44
Praomys morio
Rodentia
0.84
46.50
0.86
Myocastor coypus
Rodentia
17.09
7052.00
0.41
Holochilus sciureus
Rodentia
1.41
112.30
0.74
Neotoma cinerea
Rodentia
2.65
331.30
0.62
Microtus oregoni
Rodentia
0.52
22.00
0.93
Microtus pinetorum
Rodentia
0.56
24.90
0.91
Microtus californicus
Rodentia
0.78
43.30
0.84
Chaetodipus spinatus
Rodentia
0.48
19.10
0.94
Liomys pictus
Rodentia
0.77
43.35
0.83
Myomys daltoni
Rodentia
0.68
35.00
0.86
Geomys pinetis
Rodentia
2.51
313.50
0.61
Marmota monax
Rodentia
10.83
3624.75
0.43
Saccostomus campestris
Rodentia
0.82
49.35
0.80
Nannospalax ehrenbergi
Rodentia
1.88
197.00
0.65
Liomys salvini
Rodentia
0.77
44.90
0.80
Chaetodipus hispidus
Rodentia
0.68
37.10
0.82
Galea musteloides
Rodentia
2.72
375.00
0.58
Geomys bursarius
Rodentia
1.83
197.25
0.63
Rattus exulans
Rodentia
0.89
60.30
0.75
Thomomys talpoides
Rodentia
1.24
105.65
0.69
Microtus montanus
Rodentia
0.69
39.70
0.79
Rhabdomys pumilio
Rodentia
0.69
39.71
0.79
Arvicola terrestris
Rodentia
1.64
168.30
0.64
Neotoma micropus
Rodentia
2.66
378.30
0.57
Meriones hurrianae
Rodentia
0.97
71.30
0.72
Beamys hindei
Rodentia
1.11
89.75
0.69
Megadontomys thomasi
Rodentia
1.26
111.00
0.67
Cryptomys hottentotus
Rodentia
1.17
98.40
0.68
Ondatra zibethicus
Rodentia
5.03
1136.45
0.47
Microtus ochrogaster
Rodentia
0.71
43.80
0.76
Zapus princeps
Rodentia
0.50
24.50
0.82
Napaeozapus insignis
Rodentia
0.48
23.20
0.82
Cricetus cricetus
Rodentia
2.20
297.00
0.56
Aethomys chrysophilus
Rodentia
1.25
117.00
0.64
Aethomys hindei
Rodentia
1.42
146.30
0.61
114
Tachyoryctes splendens
Rodentia
1.88
234.00
0.57
Microtus arvalis
Rodentia
0.55
30.40
0.77
Otomys irroratus
Rodentia
1.38
141.00
0.61
Lagurus lagurus
Rodentia
0.42
20.10
0.80
Peromyscus melanotis
Rodentia
0.63
39.60
0.72
Thomomys bottae
Rodentia
1.47
162.95
0.59
Microtus longicaudus
Rodentia
0.70
47.40
0.70
Clethrionomys rufocanus
Rodentia
0.61
38.30
0.72
Mesembriomys gouldii
Rodentia
4.59
1110.00
0.44
Myopus schisticolor
Rodentia
0.56
33.50
0.73
Heliophobius argenteocinereus
Rodentia
1.43
160.00
0.58
Lemmus lemmus
Rodentia
0.86
68.90
0.65
Mus platythrix
Rodentia
0.50
29.20
0.72
Microtus guentheri
Rodentia
0.69
51.30
0.65
Aethomys namaquensis
Rodentia
0.89
79.40
0.61
Dicrostonyx torquatus
Rodentia
0.84
73.00
0.61
Marmota flaviventris
Rodentia
10.29
5000.00
0.32
Mus booduga
Rodentia
0.31
14.60
0.75
Sigmodon hispidus
Rodentia
1.14
132.50
0.53
Dicrostonyx groenlandicus
Rodentia
0.72
68.40
0.55
Bathyergus suillus
Rodentia
3.75
1175.00
0.34
Heterocephalus glaber
Rodentia
0.52
60.80
0.43
Otomys angoniensis
Rodentia
0.42
95.00
0.25
Apodemus agrarius
Rodentia
0.20
33.30
0.26
Tupaia minor
Scandentia
2.58
70.00
1.94
Urogale everetti
Scandentia
4.28
275.00
1.16
Tupaia glis
Scandentia
3.20
170.00
1.24
Trichechus manatus
Sirenia
364.00
756000.00
0.27
Myrmecophaga tridactyla
Xenarthra
84.00
18940.00
0.97
Tamandua tetradactyla
Xenarthra
27.50
4361.00
0.95
Bradypus tridactylus
Xenarthra
24.90
4097.00
0.90
Choloepus didactylus
Xenarthra
28.20
5070.00
0.87
Cabassous unicinctus
Xenarthra
23.68
3930.00
0.88
Euphractus sexcinctus
Xenarthra
33.50
7990.00
0.73
Choloepus hoffmanni
Xenarthra
24.54
5048.00
0.76
Bradypus variegatus
Xenarthra
16.03
3263.00
0.68
Dasypus novemcinctus
Xenarthra
8.50
2743.50
0.41
115
Table S2: PGLS phylogenetic signal
ML= Maximum Likelihood, PIC= Phylogenetic Independent Contrast, OLS= Ordinary Least Squares,
LR=Likelihood Ratio
lambda
Likelihood
Y-intercept
Slope
r2
ML
0.955
431.44
-0.892
0.599
0.853
PIC
1
395.02
-0.808
0.57
0.805
OLS
0
64.16
-1.25
0.746
0.955
ML vs PIC
LR
734.56
ML vs OLS
P
<0.0001
LR
72.83
P
<0.0001
Table S3: Parsimony REML Summary
WSP= Weighted Square Parsimony, REML= Restricted Maximum Likelihood
Cetacea
Primates
Body Mass
WSP
Node
Brain Mass
REML
Esti
mate
28.8k
g
95%
CI
NA
Node
Estimate
312
28.8kg
Homo
Sapiens
518
Cebus
563
1.3kg
NA
331
1.2kg
Alouatta
536
2.5kg
NA
321
2.5kg
Colobus
469
6.6kg
NA
287
6.6kg
Odontoceti
655
242.9
kg
NA
385
262.0kg
Mysticeti
670
1238
0.2kg
NA
394
12562.4kg
WSP
95%C
I
11.274.6k
g
0.62.5kg
1.15.7kg
2.915.1k
g
82.1836.7
kg
3849.
540995
.9kg
REML
No
de
518
Estim
ate
254.6
g
95%
CI
NA
Node
312
Estimat
e
254.8g
563
32.4g
NA
331
29.4g
536
47.2g
NA
321
47.5g
469
91.2g
NA
287
90.7g
655
1101.
4g
NA
385
1145.6g
670
2997.
6g
NA
394
3016.6g
*Weighted Sq Parsimony reconstructions were performed in MESQUITE
** Maximum Likelihood reconstructions were performed using the APE package in R
95%CI
139.3466.1g
18.247.4g
28.380.0g
53.6153.7g
547.72396.5
g
1422.6
6397.0
g
116
Supplemental Figures
Figure S1: Colored representation of body/brain relationship in mammals
A colored graphical representation of the log brain mass vs log body mass plot (Figure 1A in manuscript).
Each color/shape (depicted in the key on the right) represents a different mammalian order (n=21).
Figure S2: Body/brain regression for female mammals
A graphical representation of the relationship between log brain mass vs log body mass in our adult
female dataset (n=130), linear regression analysis demonstrated a significant relationship (F1,128=1283,
2
slope=0.68, r =0.909, p<0.0001) between body mass and brain mass.
117
Figure S3: Brain/body regressions taking phylogeny into consideration
To take phylogeny into consideration, we have calculated the EQ based on three regression lines, the log
brain vs. log body regression line of Figure 1A, the independent contrast regression line of Figure 1B and
the PGLS regression line. The equation for the log-log regression line reads EQ=brain mass / 0.056 x
0.746
0.631
body mass
, while the independent contrast formula reads EQ = brain mass/ 0.998 x body mass
0.60
and the PGLS formula reads EQ=brain mass/ 0.128 x body mass . A) The EQs calculated from the loglog regression and the independent contrast regression were plotted and we found a significant
2
correlation between these EQ values (F1,628=2423, r =0.79, p<0.0001). B) The EQs calculated from the
log-log regression and the PGLS regression were plotted and we found a significant correlation between
2
these EQ values (F1,628=1557, r =0.71, p<0.0001). C) The EQs calculated from the independent contrast
regression and the PGLS regression were plotted and we found a very significant correlation between
2
these EQ values (F1,628=30676, r =0.98, p<0.0001), with one outlier, Cebus olivaceus.
118
Figure S4: Boxplot of PGLS derived EQ in mammals
A comparison of the mean and range of EQs for the 21 different mammalian orders for the log brain vs.
log body regression line derived EQ (A), the independent contrast derived EQ (B), and the PGLS derived
EQ (C). Proboscidea was noted to have a shift in EQ when taking phylogeny into consideration.
119
Figure S5: Boxplot of PGLS derived EQ in Primates
A comparison of the mean and variance distributions of EQ in primate sister clade pairs, for the log brain
vs. log body regression line derived EQ (A, B, C), the independent contrast derived EQ (D, E, F), and the
PGLS derived EQ (G, H, I). Tarsiiformes p-value was not obtainable in this analysis due to the limited
number of species (n=1).
120
Figure S6: Boxplot of PGLS derived EQ in Cetartiodactyla
A comparison of the mean and variance distributions of EQ in cetartiodactyl clades, for the log brain vs.
log body regression line derived EQ (A, B, C), the independent contrast derived EQ (D, E, F), and the
PGLS derived EQ (G, H, I). Hippopotamidae p-value was not obtainable in this analysis due to the limited
number of species (n=1).
121
APPENDIX B
CHAPTER 3 SUPPLEMENTAL MATERIAL
Table S4: Genes with accelerated rate on human lineage
List of genes that have significantly higher dN/dS on the human lineage compared to all other primate
lineages in this study.
gene
t
N
S
dN/dS
dN
dS
N*dN
S*dS
p-value
ASXL1
0.017
1453.1
661.9
2.200
0.007
0.003
9.9
2
0.037
C2CD3
0.010
1315.6
559.4
2.123
0.004
0.002
5.1
1
0.028
DHX35
0.036
361.9
154.1
2.053
0.014
0.007
5.1
1
0.000
FAM53C
0.014
752.1
327.9
1.734
0.005
0.003
4
1
0.024
APBB3
0.011
978
420
1.730
0.004
0.002
4.1
1
0.045
ZNF263
0.012
911.2
390.8
1.698
0.004
0.003
4
1
0.036
SUGP2
0.006
1767.6
743.4
1.687
0.002
0.001
4
1
0.025
ADAM15
0.011
996.5
401.5
1.633
0.004
0.003
4
1
0.046
PDCL
0.022
500
184
1.380
0.008
0.006
4
1.1
0.007
TNKS1BP1
0.009
1981.8
808.2
1.226
0.003
0.003
6
2
0.022
APLP1
0.009
1024.7
376.3
1.133
0.003
0.003
3
1
0.015
ARHGEF11
0.006
2544.2
1058.8
1.043
0.002
0.002
5.1
2
0.006
TAF12
0.019
321.7
158.3
1.018
0.006
0.006
2.1
1
0.044
GTF3C3
0.007
838.8
406.2
0.970
0.002
0.003
2
1
0.009
EPHB6
0.008
1163.4
450.6
0.966
0.003
0.003
3.1
1.2
0.010
ZNF76
0.014
1076.2
402.8
0.933
0.005
0.005
5.1
2
0.007
DPP10
0.013
720.6
290.4
0.927
0.004
0.005
3
1.3
0.016
PHF1
0.008
802.4
352.6
0.890
0.003
0.003
2
1
0.025
KIAA0195
0.003
2310.6
626.4
0.881
0.001
0.001
2
0.6
0.018
CERCAM
0.020
461.7
141.3
0.856
0.007
0.008
3
1.1
0.037
SCUBE2
0.010
1581.7
671.3
0.838
0.003
0.004
5.1
2.6
0.048
FKBP10
0.008
1206.5
335.5
0.797
0.003
0.003
3
1
0.005
GNL1
0.006
1074.7
425.3
0.795
0.002
0.002
2
1
0.029
PDE1B
0.011
593.7
216.3
0.795
0.004
0.004
2.1
0.9
0.043
THOC5
0.005
1211.4
468.6
0.775
0.002
0.002
2
1
0.028
FBXO38
0.004
1837.5
709.5
0.755
0.001
0.001
2
1
0.017
KLHL18
0.006
1149.2
425.8
0.741
0.002
0.002
2
1
0.008
MYO16
0.010
1608.3
656.7
0.729
0.003
0.004
5.1
2.8
0.040
FAM5B
0.004
1538.4
522.6
0.711
0.001
0.002
2.1
1
0.015
ST6GALNAC4
0.016
604.6
109.4
0.651
0.005
0.008
3
0.8
0.001
RET
0.024
1426.9
451.1
0.650
0.007
0.011
10.3
5
0.000
MYEF2
0.009
762.1
269.9
0.644
0.003
0.004
2
1.1
0.029
RASAL2
0.003
2245.3
895.7
0.564
0.001
0.002
2
1.4
0.014
OSGIN2
0.013
892.6
313.4
0.520
0.003
0.007
3
2.1
0.023
SBF2
0.008
1949.2
864.8
0.507
0.002
0.004
4.1
3.6
0.041
122
NEK9
0.006
656.6
300.4
0.473
0.002
0.003
1
1
0.040
TNC
0.010
3288.4
1217.6
0.467
0.003
0.005
8.2
6.5
0.019
ABCF3
0.006
1388.1
549.9
0.459
0.002
0.003
2
1.8
0.045
PANK4
0.017
707.2
207.8
0.453
0.004
0.010
3.1
2
0.016
KBTBD4
0.005
920.8
402.2
0.445
0.001
0.003
1
1
0.006
SLC9A6
0.006
778.3
337.7
0.428
0.001
0.003
1
1
0.034
SH3RF1
0.014
947.4
426.6
0.416
0.003
0.008
3
3.3
0.047
TUBGCP3
0.011
408.8
170.2
0.415
0.003
0.006
1
1
0.008
KLHL15
0.005
951.1
392.9
0.415
0.001
0.003
1
1
0.043
CDH11
0.005
1620
675
0.415
0.001
0.003
2
2
0.035
KPNA1
0.004
1144.2
451.8
0.395
0.001
0.002
1
1
0.007
PAN3
0.012
364.2
142.8
0.388
0.003
0.007
1
1
0.016
PES1
0.013
926.4
279.6
0.371
0.003
0.009
2.9
2.4
0.021
ZFP64
0.012
1438.9
382.1
0.342
0.003
0.008
4.1
3.2
0.008
MAP3K11
0.008
612.7
203.3
0.328
0.002
0.005
1
1
0.033
PTK7
0.011
2045.6
663.4
0.318
0.003
0.008
5.1
5.2
0.015
CLK3
0.006
795.5
257.5
0.315
0.001
0.004
1
1
0.010
CACHD1
0.006
1744.5
733.5
0.314
0.001
0.004
2
2.7
0.018
CHPF
0.018
842
358
0.311
0.004
0.011
3
4
0.038
KIF1B
0.009
1213.5
532.5
0.301
0.002
0.006
2.1
3
0.029
ZBTB16
0.009
1005.7
311.3
0.293
0.002
0.007
2
2.1
0.042
IMPDH1
0.006
817.3
247.7
0.286
0.001
0.004
1
1.1
0.038
EFEMP2
0.007
688.8
181.2
0.257
0.002
0.006
1
1
0.025
PRDM10
0.012
956.7
360.3
0.248
0.002
0.009
2
3.1
0.019
ASTN1
0.006
2518.7
850.3
0.247
0.001
0.005
3
4.1
0.007
WDR7
0.014
2533.2
1021.8
0.228
0.002
0.011
6.1
10.7
0.006
PTPRU
0.006
1773
459
0.202
0.001
0.006
2
2.6
0.024
SORL1
0.012
4193.1
1515.9
0.194
0.002
0.010
8.1
15.1
0.030
PATZ1
0.006
1044.2
371.8
0.176
0.001
0.006
1
2
0.048
NRXN2
0.005
2244.1
590.9
0.174
0.001
0.005
2
3.1
0.044
SLIT3
0.016
1916.5
450.5
0.166
0.003
0.016
5.1
7.2
0.038
DUSP6
0.008
889.7
250.3
0.144
0.001
0.008
1
2
0.049
ENC1
0.014
1205.8
483.2
0.058
0.001
0.015
1
7.1
0.021
123
Table S5: Genes with accelerated rate on capuchin monkey lineage
List of genes that have significantly higher dN/dS on the capuchin lineage compared to all other primate
lineages in this study.
gene
PGM3
t
0.009
N
518.4
S
231.6
dN/dS
5.741
dN
0.004
dS
0.001
N*dN
2
S*dS
0.2
p-value
0.041
FUCA1
0.027
354.2
152.8
4.884
0.012
0.002
4.1
0.4
0.029
TACO1
0.043
352.9
160.1
2.751
0.018
0.006
6.2
1
0.020
ANKRD42
0.030
527.9
198.1
2.246
0.012
0.005
6.3
1
0.018
SLC44A3
0.035
425.5
186.5
2.226
0.014
0.006
6
1.2
0.044
ZNF174
0.033
526.4
214.6
2.129
0.013
0.006
6.9
1.3
0.001
SLC9A9
0.038
277.1
124.9
1.998
0.015
0.008
4.1
0.9
0.013
STK19
0.039
320.9
126.1
1.862
0.015
0.008
4.8
1
0.024
FBXW8
0.031
754.1
373.9
1.647
0.012
0.007
9.1
2.7
0.001
POP5
0.025
331.5
130.5
1.531
0.009
0.006
3.1
0.8
0.016
ENTPD6
0.021
479
181
1.478
0.008
0.005
3.7
0.9
0.035
INTS12
0.025
462.9
179.1
1.453
0.009
0.006
4.2
1.1
0.032
MUS81
0.029
598.6
217.4
1.274
0.010
0.008
6.2
1.8
0.001
KLHDC2
0.026
335.5
132.5
1.173
0.009
0.008
3.1
1
0.002
ADAM10
0.026
343.5
130.5
1.124
0.009
0.008
3
1
0.003
SCCPDH
0.026
354.2
140.8
1.107
0.009
0.008
3.1
1.1
0.037
ARFGAP3
0.041
608.4
258.6
1.019
0.014
0.014
8.4
3.5
0.029
MBD1
0.023
817.6
361.4
0.912
0.008
0.008
6.1
3
0.019
SIAE
0.041
665.8
270.2
0.890
0.013
0.015
8.9
4
0.016
MCFD2
0.035
183.3
80.7
0.866
0.011
0.013
2
1
0.046
NHLRC2
0.033
644.1
240.9
0.848
0.010
0.012
6.7
2.9
0.049
SLC4A3
0.012
679
230
0.842
0.004
0.005
2.7
1.1
0.033
SLC7A9
0.061
617.4
207.6
0.836
0.019
0.023
11.9
4.8
0.004
LIMD1
0.029
232.4
85.6
0.732
0.009
0.012
2
1
0.039
SLC25A42
0.031
428.6
129.4
0.728
0.010
0.013
4.1
1.7
0.003
HEXA
0.030
1040.7
429.3
0.715
0.009
0.013
9.3
5.4
0.031
VRK3
0.049
294.6
131.4
0.703
0.015
0.021
4.3
2.7
0.048
SUPV3L1
0.031
1059.3
443.7
0.699
0.009
0.013
9.8
5.9
0.036
GLI3
0.015
470.2
180.8
0.695
0.004
0.006
2
1.1
0.011
FBXL20
0.007
523.1
265.9
0.614
0.002
0.003
1
0.9
0.018
TRPM4
0.047
421
92
0.586
0.014
0.024
5.9
2.2
0.008
LMO7
0.047
1655.6
672.4
0.570
0.013
0.023
21.4
15.2
0.023
BLVRA
0.035
321
123
0.567
0.010
0.017
3.1
2.1
0.042
DNMT3B
0.063
409.2
130.8
0.565
0.018
0.031
7.2
4.1
0.001
LRRTM3
0.009
1290.9
446.1
0.526
0.002
0.005
3.1
2
0.011
PML
0.043
904.4
259.6
0.518
0.012
0.023
10.7
5.9
0.030
NOD1
0.023
802.7
232.3
0.512
0.006
0.013
5.1
2.9
0.043
124
RC3H1
0.021
914
385
0.507
0.006
0.011
5.1
4.2
0.039
ACAD9
0.063
789.9
242.1
0.506
0.017
0.034
13.5
8.2
0.010
COL6A3
0.045
823.4
415.6
0.488
0.011
0.023
9.2
9.5
0.015
FKBP4
0.035
817.2
331.8
0.487
0.009
0.018
7.3
6
0.029
IPPK
0.017
934.2
340.8
0.470
0.004
0.009
4
3.1
0.003
HACE1
0.008
933.6
335.4
0.461
0.002
0.005
2
1.6
0.038
ARPP21
0.039
1057.3
478.7
0.451
0.009
0.021
9.9
9.9
0.003
BRPF3
0.026
1271.2
492.8
0.438
0.006
0.015
8.2
7.2
0.025
CLP1
0.016
536
232
0.428
0.004
0.009
2
2
0.002
NT5C2
0.006
659.2
261.8
0.422
0.002
0.004
1
0.9
0.009
ZW10
0.031
842.4
348.6
0.415
0.007
0.018
6.2
6.1
0.043
KLHDC5
0.027
458
118
0.373
0.007
0.018
3
2.1
0.012
KIAA0355
0.020
1387.7
616.3
0.362
0.004
0.012
5.9
7.3
0.048
TIAL1
0.008
557.9
207.1
0.345
0.002
0.005
1
1.1
0.023
TRMT1
0.073
900.7
323.3
0.338
0.016
0.048
14.5
15.4
0.021
DHDDS
0.038
647.8
255.2
0.337
0.008
0.024
5.2
6.1
0.016
MYOM2
0.061
1115.9
381.1
0.334
0.014
0.041
15.2
15.5
0.002
CDC20
0.083
128.6
63.4
0.330
0.017
0.050
2.1
3.2
0.025
DCUN1D2
0.077
190.3
73.7
0.329
0.016
0.050
3.1
3.7
0.040
KIF2C
0.053
444.7
146.3
0.328
0.012
0.036
5.2
5.2
0.014
TMEM106B
0.019
243.5
77.5
0.318
0.004
0.013
1
1
0.034
SLC22A6
0.046
609.3
188.7
0.313
0.010
0.033
6.2
6.1
0.033
IMPDH2
0.018
584.8
249.2
0.298
0.004
0.012
2.1
2.9
0.014
SMAP2
0.018
860.7
363.3
0.297
0.004
0.012
3
4.3
0.021
LRTM2
0.027
684.1
173.9
0.287
0.006
0.021
4.2
3.7
0.018
KIAA0182
0.036
662.4
210.6
0.286
0.008
0.026
5
5.5
0.014
FRS3
0.023
931.6
322.4
0.244
0.004
0.018
4
5.7
0.042
SGK1
0.023
483.1
179.9
0.240
0.004
0.017
2
3.1
0.019
CCDC6
0.025
250.1
127.9
0.239
0.004
0.017
1
2.1
0.040
HGS
0.030
883.7
238.3
0.223
0.006
0.026
5.1
6.2
0.011
RGS17
0.038
311.7
114.3
0.211
0.006
0.030
2
3.4
0.044
GEM
0.026
260
106
0.198
0.004
0.020
1
2.1
0.039
IP6K1
0.013
911.7
282.3
0.188
0.002
0.012
2
3.3
0.048
FOXJ3
0.026
1046.8
378.2
0.174
0.004
0.022
4
8.3
0.018
TMEM8B
0.030
459.7
155.3
0.171
0.004
0.026
2
4
0.028
PHF17
0.024
886.8
313.2
0.162
0.003
0.021
3
6.6
0.014
CCNY
0.018
495.2
206.8
0.139
0.002
0.015
1
3.1
0.040
SLITRK1
0.015
1579
491
0.137
0.002
0.014
3.1
7
0.011
ARFGEF2
0.020
1721.7
690.3
0.132
0.002
0.018
4.1
12.4
0.032
TPCN1
0.036
874.2
253.8
0.129
0.005
0.037
4.1
9.3
0.037
ABCG4
0.030
1022.7
279.3
0.125
0.004
0.032
4.1
9
0.017
PDE4A
0.016
427.7
88.3
0.125
0.002
0.019
1
1.7
0.020
DOC2A
0.064
264.9
80.1
0.119
0.008
0.066
2.1
5.3
0.044
125
ARHGAP39
0.019
1591
254
0.118
0.003
0.027
5.1
6.9
0.004
PACSIN3
0.043
749.6
204.4
0.112
0.005
0.048
4
9.8
0.026
ADCY1
0.027
1648.3
511.7
0.106
0.003
0.028
4.9
14.4
0.020
FHL3
0.034
572.6
192.4
0.102
0.004
0.034
2
6.6
0.036
RNF41
0.017
666.4
284.6
0.101
0.002
0.015
1
4.3
0.030
ADSSL1
0.064
762.5
242.5
0.100
0.007
0.067
5.1
16.3
0.030
AAMP
0.038
851
328
0.099
0.004
0.037
3.1
12
0.025
ZBTB43
0.047
701
247
0.093
0.004
0.047
3.1
11.6
0.047
RANBP9
0.014
818
322
0.091
0.001
0.013
1
4.3
0.037
YWHAH
0.059
563.2
171.8
0.090
0.006
0.065
3.3
11.2
0.013
HAS3
0.039
292.2
106.8
0.090
0.004
0.039
1
4.2
0.040
ZNF436
0.099
511.1
196.9
0.087
0.008
0.097
4.3
19.2
0.027
FBLN5
0.021
564.1
194.9
0.081
0.002
0.022
1
4.3
0.036
FEM1A
0.043
1248.5
254.5
0.080
0.005
0.061
6.1
15.5
0.048
CORO2B
0.040
626.8
198.2
0.073
0.003
0.045
2.1
9
0.008
CDK5R1
0.023
379.4
61.6
0.070
0.003
0.038
1
2.3
0.039
ACTN2
0.027
1865.2
564.8
0.069
0.002
0.032
4.1
17.9
0.014
DMAP1
0.033
854.1
324.9
0.068
0.002
0.034
2
11.1
0.043
NPTX1
0.025
431.6
72.4
0.054
0.002
0.044
1
3.2
0.035
DENND1A
0.030
619.2
223.8
0.049
0.002
0.034
1
7.5
0.046
SF1
0.028
818.3
390.7
0.046
0.001
0.026
1
10.3
0.047
CDC16
0.032
706.8
307.2
0.044
0.001
0.032
1
9.9
0.047
FBXL14
0.025
638.3
153.7
0.043
0.002
0.037
1
5.6
0.039
SLC6A1
0.028
1163.4
258.6
0.018
0.001
0.048
1
12.3
0.036
LPHN1
0.029
1244.8
258.2
0.016
0.001
0.053
1
13.7
0.037
Table S6: Biological processes enriched in the human and capuchin accelerated gene set
Gene ontology results for biological processes enriched in the combined human (n=68) and capuchin
monkey (n=106) accelerated rates of evolution gene sets.
Term
nucleoside
monophosphate
metabolic process
No.
C
e
b
u
s
5
4
1
2.92
1.78E02
4.86
cell adhesion
16
6
1
0
9.36
1.83E02
1.91
biological adhesion
16
6
1
0
9.36
1.89E02
1.90
H
o
m
o
%
pvalue
Fold
Genes
ADCY1, ADSSL1, PDE4A,
IMPDH1, IMPDH2
CDK5R1, RET, NRXN2, TNC,
ASTN1, PTK7, LMO7, ACTN2,
PTPRU, CERCAM, APLP1,
TMEM8B, FBLN5, COL6A3,
ADAM15, CDH11
CDK5R1, RET, NRXN2, TNC,
ASTN1, PTK7, LMO7, ACTN2,
PTPRU, CERCAM, APLP1,
126
TMEM8B, FBLN5, COL6A3,
ADAM15, CDH11
establishment of cell
polarity
purine nucleoside
monophosphate
biosynthetic process
3
1
2
1.75
2.65E02
11.31
PTK7, ARHGEF11, ZW10
3
2
1
1.75
3.33E02
10.05
ADSSL1, IMPDH1, IMPDH2
3
2
1
1.75
3.33E02
10.05
ADSSL1, IMPDH1, IMPDH2
4
3
1
2.34
3.41E02
5.48
ADCY1, ADSSL1, IMPDH1,
IMPDH2
3
2
1
1.75
4.07E02
9.05
ADSSL1, IMPDH1, IMPDH2
3
2
1
1.75
9.05
7
3
4
4.09
4.07E02
4.23E02
proteolysis involved
in cellular protein
catabolic process
14
1
0
4
8.19
4.56E02
cellular protein
catabolic process
14
1
0
4
8.19
1.77
ADSSL1, IMPDH1, IMPDH2
KIF2C, TUBGCP3, RANBP9,
RET, KIF1B, MAP3K11, ZW10
ADAM10, FBXL20, ENC1,
HACE1, CDC20, ZBTB16,
CDC16, FEM1A, FBXO38,
DCUN1D2, FBXW8, KLHL15,
FBXL14, RNF41
ADAM10, FBXL20, ENC1,
HACE1, CDC20, ZBTB16,
CDC16, FEM1A, FBXO38,
DCUN1D2, FBXW8, KLHL15,
FBXL14, RNF41
4
3
1
2.34
4.64
PHF17, TAF12, BRPF3, DMAP1
4.64
PHF17, TAF12, BRPF3, DMAP1
FBXL20, ENC1, HACE1, CDC20,
ZBTB16, CDC16, FEM1A,
FBXO38, DCUN1D2, FBXW8,
KLHL15, FBXL14, RNF41
FBXL20, ENC1, HACE1, CDC20,
ZBTB16, CDC16, FEM1A,
FBXO38, DCUN1D2, FBXW8,
KLHL15, FBXL14, RNF41
CDK5R1, RET, ASTN1, LMO7,
PTPRU, CERCAM, CDH11
purine ribonucleoside
monophosphate
biosynthetic process
nucleoside
monophosphate
biosynthetic process
purine ribonucleoside
monophosphate
metabolic process
purine nucleoside
monophosphate
metabolic process
microtubule-based
process
2.71
1.79
histone acetylation
protein amino acid
acetylation
modificationdependent
macromolecule
catabolic process
4
3
1
2.34
4.82E02
5.23E02
5.23E02
13
9
4
7.60
5.39E02
modificationdependent protein
catabolic process
13
9
4
7.60
7
5
2
4.09
5.39E02
5.45E02
3
2
1
1.75
5.73E02
7.54
protein catabolic
process
14
1
0
4
8.19
5.84E02
1.72
ADSSL1, IMPDH1, IMPDH2
ADAM10, FBXL20, ENC1,
HACE1, CDC20, ZBTB16,
CDC16, FEM1A, FBXO38,
DCUN1D2, FBXW8, KLHL15,
FBXL14, RNF41
GMP metabolic
2
1
1
1.17
6.48E-
30.16
IMPDH1, IMPDH2
cell-cell adhesion
ribonucleoside
monophosphate
biosynthetic process
1.80
1.80
2.54
127
process
GMP biosynthetic
process
ribonucleoside
monophosphate
metabolic process
02
2
1
1
1.17
6.48E02
30.16
IMPDH1, IMPDH2
3
2
1
1.75
6.62E02
6.96
16
1
0
6
9.36
1.59
4
2
2
2.34
7.31E02
7.43E02
ADSSL1, IMPDH1, IMPDH2
PAN3, ADAM10, FBXL20, ENC1,
HACE1, CDC20, ZBTB16,
CDC16, FEM1A, FUCA1,
FBXO38, DCUN1D2, FBXW8,
KLHL15, FBXL14, RNF41
4.02
PHF17, TAF12, BRPF3, DMAP1
5
3
2
2.92
7.63E02
cellular
macromolecule
catabolic process
histone H3
acetylation
15
1
0
5
8.77
3
2
1
1.75
mitosis
6
4
2
3.51
nuclear division
M phase of mitotic
cell cycle
6
4
2
3.51
6
4
2
3.51
macromolecule
catabolic process
protein amino acid
acylation
microtubule
cytoskeleton
organization
7.69E02
8.55E02
8.72E02
8.72E02
9.54E02
3.08
1.61
6.03
2.51
2.51
2.45
KIF2C, TUBGCP3, RANBP9,
RET, ZW10
PAN3, ADAM10, FBXL20, ENC1,
HACE1, CDC20, ZBTB16,
CDC16, FEM1A, FBXO38,
DCUN1D2, FBXW8, KLHL15,
FBXL14, RNF41
PHF17, TAF12, BRPF3
KIF2C, NEK9, CDC20, CDC16,
PES1, ZW10
KIF2C, NEK9, CDC20, CDC16,
PES1, ZW10
KIF2C, NEK9, CDC20, CDC16,
PES1, ZW10
128
REFERENCES
Addessi, E., A. Mancini, L. Crescimbene, D. Ariely and E. Visalberghi (2010). "How to
spend a token? Trade-offs between food variety and food preference in tufted
capuchin monkeys (Cebus apella)." Behav Processes 83(3): 267-275.
Aiello, L. C. and P. Wheeler (1995). "The Expensive-Tissue Hypothesis: The brain and
the digestive system in human and primate evolution." Current Anthropology
36(2): 199-221.
Alfaro, J. W., J. D. Silva, Jr. and A. B. Rylands (2012). "How different are robust and
gracile capuchin monkeys? An argument for the use of sapajus and cebus." Am J
Primatol 74(4): 273-286.
Anisimova, M., J. P. Bielawski and Z. Yang (2002). "Accuracy and power of bayes
prediction of amino acid sites under positive selection." Mol Biol Evol 19(6): 950958.
Armstrong, E. (1985). "Relative brain size in monkeys and prosimians." Am J Phys
Anthropol 66(3): 263-273.
Armstrong, E. (1990). "Brains, bodies and metabolism." Brain Behav Evol 36(2-3): 166176.
Ashwell, K. W. (2008). "Encephalization of Australian and New Guinean marsupials."
Brain Behav Evol 71(3): 181-199.
129
Azevedo, F. A., L. R. Carvalho, L. T. Grinberg, J. M. Farfel, R. E. Ferretti, R. E. Leite,
W. Jacob Filho, R. Lent and S. Herculano-Houzel (2009). "Equal numbers of
neuronal and nonneuronal cells make the human brain an isometrically scaled-up
primate brain." J Comp Neurol 513(5): 532-541.
Barrickman, N. L., M. L. Bastian, K. Isler and C. P. van Schaik (2008). "Life history costs
and benefits of encephalization: a comparative test using data from long-term
studies of primates in the wild." J Hum Evol 54(5): 568-590.
Barton, R. A. (2006). "Primate brain evolution: Integrating comparative,
neurophysiological, and ethological data." Evol Anthropol 15: 224-236.
Barton, R. A. and I. Capellini (2011). "Maternal investment, life histories, and the costs
of brain growth in mammals." Proc Natl Acad Sci U S A.
Barton, R. A. and P. H. Harvey (2000). "Mosaic evolution of brain structure in
mammals." Nature 405(6790): 1055-1058.
Bayer, T. A., K. Paliga, S. Weggen, O. D. Wiestler, K. Beyreuther and G. Multhaup
(1997). "Amyloid precursor-like protein 1 accumulates in neuritic plaques in
Alzheimer's disease." Acta Neuropathol 94(6): 519-524.
Bernardi, G. (1995). "The human genome: organization and evolutionary history." Annu
Rev Genet 29: 445-476.
Bianchi, S., C. D. Stimpson, T. Duka, M. D. Larsen, W. G. Janssen, Z. Collins, A. L.
Bauernfeind, S. J. Schapiro, W. B. Baze, M. J. McArthur, W. D. Hopkins, D. E.
130
Wildman, L. Lipovich, C. W. Kuzawa, B. Jacobs, P. R. Hof and C. C. Sherwood
(2013). "Synaptogenesis and development of pyramidal neuron dendritic
morphology in the chimpanzee neocortex resembles humans." PNAS
(Submitted).
Bininda-Emonds, O. R., M. Cardillo, K. E. Jones, R. D. MacPhee, R. M. Beck, R.
Grenyer, S. A. Price, R. A. Vos, J. L. Gittleman and A. Purvis (2007). "The
delayed rise of present-day mammals." Nature 446(7135): 507-512.
Boddy, A. M., M. R. McGowen, C. C. Sherwood, L. I. Grossman, M. Goodman and D. E.
Wildman (2012). "Comparative analysis of encephalization in mammals reveals
relaxed constraints on anthropoid primate and cetacean brain scaling." J Evol
Biol 21(10): 1420-9101.
Boinski, S. (1988). "Use of a club by a wild white-faced capuchin (Cebus capucinus) to
attack a venemous snake (Bothrops asper)." Am J Primatol 14: 177-179.
Bond, J., E. Roberts, G. H. Mochida, D. J. Hampshire, S. Scott, J. M. Askham, K.
Springell, M. Mahadevan, Y. J. Crow, A. F. Markham, C. A. Walsh and C. G.
Woods (2002). "ASPM is a major determinant of cerebral cortical size." Nat
Genet 32(2): 316-320.
Brawand, D., M. Soumillon, A. Necsulea, P. Julien, G. Csardi, P. Harrigan, M. Weier, A.
Liechti, A. Aximu-Petri, M. Kircher, F. W. Albert, U. Zeller, P. Khaitovich, F.
Grutzner, S. Bergmann, R. Nielsen, S. Paabo and H. Kaessmann (2011). "The
131
evolution of gene expression levels in mammalian organs." Nature 478(7369):
343-348.
Bronson, R. (1981). "Brain weight-body weight relationships in 12 species of nonhuman
primates." American Journal of Physical Anthropology 56(1).
Brown, P., T. Sutikna, M. J. Morwood, R. P. Soejono, Jatmiko, E. W. Saptomo and R. A.
Due (2004). "A new small-bodied hominin from the Late Pleistocene of Flores,
Indonesia." Nature 431(7012): 1055-1061.
Burki, F. and H. Kaessmann (2004). "Birth and adaptive evolution of a hominoid gene
that supports high neurotransmitter flux." Nat Genet 36(10): 1061-1063.
Bush, E. C. and J. M. Allman (2004). "The scaling of frontal cortex in primates and
carnivores." Proc Natl Acad Sci U S A 101(11): 3962-3966.
Caceres, M., J. Lachuer, M. A. Zapala, J. C. Redmond, L. Kudo, D. H. Geschwind, D. J.
Lockhart, T. M. Preuss and C. Barlow (2003). "Elevated gene expression levels
distinguish human from non-human primate brains." Proc Natl Acad Sci U S A
100(22): 13030-13035.
Carmody, R. N. and R. W. Wrangham (2009). "The energetic significance of cooking." J
Hum Evol 57(4): 379-391.
Castresana, J. (2000). "Selection of conserved blocks from multiple alignments for their
use in phylogenetic analysis." Mol Biol Evol 17(4): 540-552.
132
Caviness, V. S., Jr., T. Takahashi and R. S. Nowakowski (1995). "Numbers, time and
neocortical neuronogenesis: a general developmental and evolutionary model."
Trends Neurosci 18(9): 379-383.
Chimpanzee-Consortium (2005). "Initial sequence of the chimpanzee genome and
comparison with the human genome." Nature 437(7055): 69-87.
Chivers, D. J. and C. M. Hladik (1980). "Morphology of the gastrointestinal tract in
primates: comparisons with other mammals in relation to diet." J Morphol 166(3):
337-386.
Clark, A. G., S. Glanowski, R. Nielsen, P. D. Thomas, A. Kejariwal, M. A. Todd, D. M.
Tanenbaum, D. Civello, F. Lu, B. Murphy, S. Ferriera, G. Wang, X. Zheng, T. J.
White, J. J. Sninsky, M. D. Adams and M. Cargill (2003). "Inferring nonneutral
evolution from human-chimp-mouse orthologous gene trios." Science 302(5652):
1960-1963.
Cloonan, N., A. R. Forrest, G. Kolle, B. B. Gardiner, G. J. Faulkner, M. K. Brown, D. F.
Taylor, A. L. Steptoe, S. Wani, G. Bethel, A. J. Robertson, A. C. Perkins, S. J.
Bruce, C. C. Lee, S. S. Ranade, H. E. Peckham, J. M. Manning, K. J. McKernan
and S. M. Grimmond (2008). "Stem cell transcriptome profiling via massive-scale
mRNA sequencing." Nat Methods 5(7): 613-619.
Courchesne, E., H. J. Chisum, J. Townsend, A. Cowles, J. Covington, B. Egaas, M.
Harwood, S. Hinds and G. A. Press (2000). "Normal brain development and
133
aging: quantitative analysis at in vivo MR imaging in healthy volunteers."
Radiology 216(3): 672-682.
Cox, J., A. P. Jackson, J. Bond and C. G. Woods (2006). "What primary microcephaly
can tell us about brain growth." Trends Mol Med 12(8): 358-366.
Cutler, R. G. (1979). "Evolution of longevity in ungulates and carnivores." Gerontology
25(2): 69-86.
Czelusniak, J., M. Goodman, D. Hewett-Emmett, M. L. Weiss, P. J. Venta and R. E.
Tashian (1982). "Phylogenetic origins and adaptive evolution of avian and
mammalian haemoglobin genes." Nature 298(5871): 297-300.
Darwin, C. (1871). The Descent of Man, and Selection in Relation to Sex. London, John
Murray.
de Winter, W. and C. E. Oxnard (2001). "Evolutionary radiations and convergences in
the structural organization of mammalian brains." Nature 409(6821): 710-714.
DeSilva, J. and J. Lesnik (2006). "Chimpanzee neonatal brain size: Implications for
brain growth in Homo erectus." J Hum Evol 51(2): 207-212.
Doan, J. W., T. R. Schmidt, D. E. Wildman, M. Uddin, A. Goldberg, M. Huttemann, M.
Goodman, M. L. Weiss and L. I. Grossman (2004). "Coadaptive evolution in
cytochrome c oxidase: 9 of 13 subunits show accelerated rates of
nonsynonymous substitution in anthropoid primates." Mol Phylogenet Evol 33(3):
944-950.
134
Dubois, E. (1897). "Ueber die Abhangigkeit des Hirngewichtes von der Korpergrosse
bei den Saugetieren." Arch Anthropol 25: 1-28.
Dumas, L. J., M. S. O'Bleness, J. M. Davis, C. M. Dickens, N. Anderson, J. G. Keeney,
J. Jackson, M. Sikela, A. Raznahan, J. Giedd, J. Rapoport, S. S. Nagamani, A.
Erez, N. Brunetti-Pierri, R. Sugalski, J. R. Lupski, T. Fingerlin, S. W. Cheung and
J. M. Sikela (2012). "DUF1220-domain copy number implicated in human brainsize pathology and evolution." Am J Hum Genet 91(3): 444-454.
Dunbar, R. and J. Bever (1998). "Neocortex size predicts group size in carnivores and
some insectivores." Ethology 104(8): 695-708.
Dunbar, R. I. (1998). "The social brain hypothesis." Evol Anthropol 6: 178-190.
Dunbar, R. I. (2009). "The social brain hypothesis and its implications for social
evolution." Ann Hum Biol 36(5): 562-572.
Dunsworth, H. M., A. G. Warrener, T. Deacon, P. T. Ellison and H. Pontzer (2012).
"Metabolic hypothesis for human altriciality." Proc Natl Acad Sci U S A 109(38):
15212-15216.
Duret, L. and D. Mouchiroud (2000). "Determinants of substitution rates in mammalian
genes: expression pattern affects selection intensity but not mutation rate." Mol
Biol Evol 17(1): 68-74.
Enard, W., P. Khaitovich, J. Klose, S. Zollner, F. Heissig, P. Giavalisco, K. NieseltStruwe, E. Muchmore, A. Varki, R. Ravid, G. M. Doxiadis, R. E. Bontrop and S.
135
Paabo (2002). "Intra- and interspecific variation in primate gene expression
patterns." Science 296(5566): 340-343.
Enard, W., M. Przeworski, S. E. Fisher, C. S. Lai, V. Wiebe, T. Kitano, A. P. Monaco
and S. Paabo (2002). "Molecular evolution of FOXP2, a gene involved in speech
and language." Nature 418(6900): 869-872.
Evans, P. D., S. L. Gilbert, N. Mekel-Bobrov, E. J. Vallender, J. R. Anderson, L. M.
Vaez-Azizi, S. A. Tishkoff, R. R. Hudson and B. T. Lahn (2005). "Microcephalin, a
gene regulating brain size, continues to evolve adaptively in humans." Science
309(5741): 1717-1720.
Evans, P. D., N. Mekel-Bobrov, E. J. Vallender, R. R. Hudson and B. T. Lahn (2006).
"Evidence that the adaptive allele of the brain size gene microcephalin
introgressed into Homo sapiens from an archaic Homo lineage." Proc Natl Acad
Sci U S A 103(48): 18178-18183.
Falster, D., D. Warton and I. Wright (2006). SMATR: Standardised major axis tests and
routines.
Felsenstein, J. (1985). "Phylogenies and the comparative method." Am Nat 125(1): 115.
Felsenstein, J. (2008). "Comparative methods with sampling error and within-species
variation: contrasts revisited and revised." Am Nat 171(6): 713-725.
136
Ferland, R. J., W. Eyaid, R. V. Collura, L. D. Tully, R. S. Hill, D. Al-Nouri, A. AlRumayyan, M. Topcu, G. Gascon, A. Bodell, Y. Y. Shugart, M. Ruvolo and C. A.
Walsh (2004). "Abnormal cerebellar development and axonal decussation due to
mutations in AHI1 in Joubert syndrome." Nat Genet 36(9): 1008-1013.
Fernandes, M. (1991). "Tool use and predation of oysters (Crassostrea rhizophorea) by
the tufted capuchin, Cebus apella apella, in brackish water mangrove swamp."
Primates 32(529-531).
Finarelli, J. A. and J. J. Flynn (2006). "Ancestral state reconstruction of body size in the
Caniformia (Carnivora, Mammalia): the effects of incorporating data from the
fossil record." Syst Biol 55(2): 301-313.
Finarelli, J. A. and J. J. Flynn (2007). "The evolution of encephalization in caniform
carnivorans." Evolution 61(7): 1758-1772.
Finarelli, J. A. and J. J. Flynn (2009). "Brain-size evolution and sociality in Carnivora."
Proc Natl Acad Sci U S A 106(23): 9345-9349.
Finlay, B. L. and R. B. Darlington (1995). "Linked regularities in the development and
evolution of mammalian brains." Science 268(5217): 1578-1584.
Fleagle, J. G. (1999). Primate Adaptation and Evolution. San Diego, Academic Press.
Flicek, P., M. R. Amode, D. Barrell, K. Beal, S. Brent, D. Carvalho-Silva, P. Clapham, G.
Coates, S. Fairley, S. Fitzgerald, L. Gil, L. Gordon, M. Hendrix, T. Hourlier, N.
Johnson, A. K. Kahari, D. Keefe, S. Keenan, R. Kinsella, M. Komorowska, G.
137
Koscielny, E. Kulesha, P. Larsson, I. Longden, W. McLaren, M. Muffato, B.
Overduin, M. Pignatelli, B. Pritchard, H. S. Riat, G. R. Ritchie, M. Ruffier, M.
Schuster, D. Sobral, Y. A. Tang, K. Taylor, S. Trevanion, J. Vandrovcova, S.
White, M. Wilson, S. P. Wilder, B. L. Aken, E. Birney, F. Cunningham, I. Dunham,
R. Durbin, X. M. Fernandez-Suarez, J. Harrow, J. Herrero, T. J. Hubbard, A.
Parker, G. Proctor, G. Spudich, J. Vogel, A. Yates, A. Zadissa and S. M. Searle
(2012). "Ensembl 2012." Nucleic Acids Res 40(Database issue): D84-90.
Fragaszy, D., P. Izar, E. Visalberghi, E. B. Ottoni and M. G. de Oliveira (2004). "Wild
capuchin monkeys (Cebus libidinosus) use anvils and stone pounding tools." Am
J Primatol 64(4): 359-366.
Futuyma, D. J. (2009). Evolution. Sunderland, MA, Sinauer Associates, Inc.
Garland Jr, T., P. H. Harvey and A. R. Ives (1992). "Procedures for the analysis of
comparative data using phylogenetically independent contrasts." Syst Biol 41(1):
18-32.
George, R. D., G. McVicker, R. Diederich, S. B. Ng, A. P. MacKenzie, W. J. Swanson,
J. Shendure and J. H. Thomas (2011). "Trans genomic capture and sequencing
of primate exomes reveals new targets of positive selection." Genome Res
21(10): 1686-1694.
Geschwind, D. H. (2008). "Autism: many genes, common pathways?" Cell 135(3): 391395.
138
Gibbs, R. A., J. Rogers, M. G. Katze, R. Bumgarner, G. M. Weinstock, E. R. Mardis, K.
A. Remington, R. L. Strausberg, J. C. Venter, R. K. Wilson, M. A. Batzer, C. D.
Bustamante, E. E. Eichler, M. W. Hahn, R. C. Hardison, K. D. Makova, W. Miller,
A. Milosavljevic, R. E. Palermo, A. Siepel, J. M. Sikela, T. Attaway, S. Bell, K. E.
Bernard, C. J. Buhay, M. N. Chandrabose, M. Dao, C. Davis, K. D. Delehaunty,
Y. Ding, H. H. Dinh, S. Dugan-Rocha, L. A. Fulton, R. A. Gabisi, T. T. Garner, J.
Godfrey, A. C. Hawes, J. Hernandez, S. Hines, M. Holder, J. Hume, S. N.
Jhangiani, V. Joshi, Z. M. Khan, E. F. Kirkness, A. Cree, R. G. Fowler, S. Lee, L.
R. Lewis, Z. Li, Y. S. Liu, S. M. Moore, D. Muzny, L. V. Nazareth, D. N. Ngo, G.
O. Okwuonu, G. Pai, D. Parker, H. A. Paul, C. Pfannkoch, C. S. Pohl, Y. H.
Rogers, S. J. Ruiz, A. Sabo, J. Santibanez, B. W. Schneider, S. M. Smith, E.
Sodergren, A. F. Svatek, T. R. Utterback, S. Vattathil, W. Warren, C. S. White, A.
T. Chinwalla, Y. Feng, A. L. Halpern, L. W. Hillier, X. Huang, P. Minx, J. O.
Nelson, K. H. Pepin, X. Qin, G. G. Sutton, E. Venter, B. P. Walenz, J. W. Wallis,
K. C. Worley, S. P. Yang, S. M. Jones, M. A. Marra, M. Rocchi, J. E. Schein, R.
Baertsch, L. Clarke, M. Csuros, J. Glasscock, R. A. Harris, P. Havlak, A. R.
Jackson, H. Jiang, Y. Liu, D. N. Messina, Y. Shen, H. X. Song, T. Wylie, L.
Zhang, E. Birney, K. Han, M. K. Konkel, J. Lee, A. F. Smit, B. Ullmer, H. Wang, J.
Xing, R. Burhans, Z. Cheng, J. E. Karro, J. Ma, B. Raney, X. She, M. J. Cox, J.
P. Demuth, L. J. Dumas, S. G. Han, J. Hopkins, A. Karimpour-Fard, Y. H. Kim, J.
R. Pollack, T. Vinar, C. Addo-Quaye, J. Degenhardt, A. Denby, M. J. Hubisz, A.
Indap, C. Kosiol, B. T. Lahn, H. A. Lawson, A. Marklein, R. Nielsen, E. J.
Vallender, A. G. Clark, B. Ferguson, R. D. Hernandez, K. Hirani, H. Kehrer-
139
Sawatzki, J. Kolb, S. Patil, L. L. Pu, Y. Ren, D. G. Smith, D. A. Wheeler, I.
Schenck, E. V. Ball, R. Chen, D. N. Cooper, B. Giardine, F. Hsu, W. J. Kent, A.
Lesk, D. L. Nelson, E. O'Brien W, K. Prufer, P. D. Stenson, J. C. Wallace, H. Ke,
X. M. Liu, P. Wang, A. P. Xiang, F. Yang, G. P. Barber, D. Haussler, D.
Karolchik, A. D. Kern, R. M. Kuhn, K. E. Smith and A. S. Zwieg (2007).
"Evolutionary and biomedical insights from the rhesus macaque genome."
Science 316(5822): 222-234.
Gilbert, S. L., W. B. Dobyns and B. T. Lahn (2005). "Genetic links between brain
development and brain evolution." Nat Rev Genet 6(7): 581-590.
Goldberg, A., D. E. Wildman, T. R. Schmidt, M. Huttemann, M. Goodman, M. L. Weiss
and L. I. Grossman (2003). "Adaptive evolution of cytochrome c oxidase subunit
VIII in anthropoid primates." Proc Natl Acad Sci U S A 100(10): 5873-5878.
Goldbogen, J. A., N. D. Pyenson and R. E. Shadwick (2007). "Big gulps require high
drag for fin whale lunge feeding." Mar. Ecol. Prog. Ser.349: 289-301.
Gonzalez-Lagos, C., D. Sol and S. M. Reader (2010). "Large-brained mammals live
longer." J Evol Biol 23(5): 1064-1074.
Goodman, M., C. A. Porter, J. Czelusniak, S. L. Page, H. Schneider, J. Shoshani, G.
Gunnell and C. P. Groves (1998). "Toward a phylogenetic classification of
Primates based on DNA evidence complemented by fossil evidence." Mol
Phylogenet Evol 9(3): 585-598.
140
Goodman, M., K. N. Sterner, M. Islam, M. Uddin, C. C. Sherwood, P. R. Hof, Z. C. Hou,
L. Lipovich, H. Jia, L. I. Grossman and D. E. Wildman (2009). "Phylogenomic
analyses reveal convergent patterns of adaptive evolution in elephant and human
ancestries." Proc Natl Acad Sci U S A.
Gould, S. J. (1971). "Geometric similarity in allometric growth: A contribution to the
problem of sclaing in the evolution of size." American Naturalist 105(941): 113136.
Grabherr, M. G., B. J. Haas, M. Yassour, J. Z. Levin, D. A. Thompson, I. Amit, X.
Adiconis, L. Fan, R. Raychowdhury, Q. Zeng, Z. Chen, E. Mauceli, N. Hacohen,
A. Gnirke, N. Rhind, F. di Palma, B. W. Birren, C. Nusbaum, K. Lindblad-Toh, N.
Friedman and A. Regev (2011). "Full-length transcriptome assembly from RNASeq data without a reference genome." Nat Biotechnol 29(7): 644-652.
Grafen, A. (1989). "The phylogenetic regression." Philos Trans R Soc Lond B Biol Sci
326(1233): 119-157.
Grossman, L. I., D. E. Wildman, T. R. Schmidt and M. Goodman (2004). "Accelerated
evolution of the electron transport chain in anthropoid primates." Trends Genet
20(11): 578-585.
Guilarte, T. R., N. C. Burton, T. Verina, V. V. Prabhu, K. G. Becker, T. Syversen and J.
S. Schneider (2008). "Increased APLP1 expression and neurodegeneration in
the frontal cortex of manganese-exposed non-human primates." J Neurochem
105(5): 1948-1959.
141
Hakeem, A. Y., C. C. Sherwood, C. J. Bonar, C. Butti, P. R. Hof and J. M. Allman
(2009). "Von Economo neurons in the elephant brain." Anat Rec (Hoboken)
292(2): 242-248.
Hardison, R. C. (2003). "Comparative genomics." PLoS Biol 1(2): E58.
Hardison, R. C., K. M. Roskin, S. Yang, M. Diekhans, W. J. Kent, R. Weber, L. Elnitski,
J. Li, M. O'Connor, D. Kolbe, S. Schwartz, T. S. Furey, S. Whelan, N. Goldman,
A. Smit, W. Miller, F. Chiaromonte and D. Haussler (2003). "Covariation in
frequencies of substitution, deletion, transposition, and recombination during
eutherian evolution." Genome Res 13(1): 13-26.
Hart, B. L., L. A. Hart and N. Pinter-Wollman (2008). "Large brains and cognition: where
do elephants fit in?" Neurosci Biobehav Rev 32(1): 86-98.
Harvey, P. and T. Clutton-Brock (1985). "Life history variation in primates." Evolution:
559-581.
Harvey, P. H., T. H. Clutton-Brock and G. M. Mace (1980). "Brain size and ecology in
small mammals and primates." Proc Natl Acad Sci U S A 77(7): 4387-4389.
Hawkes, K., J. F. O'Connell, N. G. Jones, H. Alvarez and E. L. Charnov (1998).
"Grandmothering, menopause, and the evolution of human life histories." Proc
Natl Acad Sci U S A 95(3): 1336-1339.
Herculano-Houzel, S., C. E. Collins, P. Wong and J. H. Kaas (2007). "Cellular scaling
rules for primate brains." Proc Natl Acad Sci U S A 104(9): 3562-3567.
142
Hofman, M. A. (1983). "Energy metabolism, brain size and longevity in mammals." Q
Rev Biol 58(4): 495-512.
Holloway, R. L. (2009). Brain Fossils: Endocasts. Encyclopedia of Neuroscience.
Oxford, Academic Press. 2: 353-361.
Holloway, R. L. and D. Post (1982). The relativity of relative brain measures and
Hominid evolution. Primate brain evolution. E. Armstrong and D. Falk. New York,
Pelnum: 57-76.
Hoover, A. N., A. Wynkoop, H. Zeng, J. Jia, L. A. Niswander and A. Liu (2008). "C2cd3
is required for cilia formation and Hedgehog signaling in mouse." Development
135(24): 4049-4058.
Hou, Z. C., K. N. Sterner, R. Romero, N. G. Than, J. M. Gonzalez, A. Weckle, J. Xing,
K. Benirschke, M. Goodman and D. E. Wildman (2012). "Elephant transcriptome
provides insights into the evolution of eutherian placentation." Genome Biol Evol
4(5): 713-725.
Huang da, W., B. T. Sherman and R. A. Lempicki (2009). "Systematic and integrative
analysis of large gene lists using DAVID bioinformatics resources." Nat Protoc
4(1): 44-57.
Huang, X. and A. Madan (1999). "CAP3: A DNA sequence assembly program."
Genome Res 9(9): 868-877.
143
Hutcheon, J. M., J. A. Kirsch and T. Garland Jr (2002). "A comparative analysis of brain
size in relation to foraging ecology and phylogeny in the Chiroptera." Brain Behav
Evol 60(3): 165-180.
Huxley, J. S. (1936). "Terminology of Relative Growth." Nature 137(3471): 780-781.
Huxley, J. S. (1950). "A discussion on the measurement of growth and form; relative
growth and form transformation." Proc R Soc Lond B Biol Sci 137(889): 465-469.
Isler, K., E. Christopher Kirk, J. M. Miller, G. A. Albrecht, B. R. Gelvin and R. D. Martin
(2008). "Endocranial volumes of primate species: scaling analyses using a
comprehensive and reliable data set." J Hum Evol 55(6): 967-978.
Isler, K. and C. P. van Schaik (2006). "Metabolic costs of brain size evolution." Biol Lett
2(4): 557-560.
Isler, K. and C. P. van Schaik (2009). "The Expensive Brain: a framework for explaining
evolutionary changes in brain size." J Hum Evol 57(4): 392-400.
Jack, K. M. (2007). The Cebines: Toward an explanation of Variable Social Structure.
New York, Oxford Press.
Jackson, A. P., D. P. McHale, D. A. Campbell, H. Jafri, Y. Rashid, J. Mannan, G.
Karbani, P. Corry, M. I. Levene, R. F. Mueller, A. F. Markham, N. J. Lench and C.
G. Woods (1998). "Primary autosomal recessive microcephaly (MCPH1) maps to
chromosome 8p22-pter." Am J Hum Genet 63(2): 541-546.
144
Jacobs, B., J. Lubs, M. Hannan, K. Anderson, C. Butti, C. C. Sherwood, P. R. Hof and
P. R. Manger (2010). "Neuronal morphology in the African elephant (Loxodonta
africana) neocortex." Brain Struct Funct.
Jerison, H. J. (1973). Evolution of the brain and intelligence. New York, Academic
Press.
Jerison, H. J. (1985). "Animal intelligence as encephalization." Philos Trans R Soc Lond
B Biol Sci 308(1135): 21-35.
Jones, K. E. and A. M. MacLarnon (2004). "Affording larger brains: testing hypotheses
of mammalian brain evolution on bats." Am Nat 164(1): E20-31.
Jorissen, E., J. Prox, C. Bernreuther, S. Weber, R. Schwanbeck, L. Serneels, A.
Snellinx, K. Craessaerts, A. Thathiah, I. Tesseur, U. Bartsch, G. Weskamp, C. P.
Blobel, M. Glatzel, B. De Strooper and P. Saftig (2010). "The
disintegrin/metalloproteinase ADAM10 is essential for the establishment of the
brain cortex." J Neurosci 30(14): 4833-4844.
Jukes, T. H. and M. Kimura (1984). "Evolutionary constraints and the neutral theory." J
Mol Evol 21(1): 90-92.
Karaman, M. W., M. L. Houck, L. G. Chemnick, S. Nagpal, D. Chawannakul, D. Sudano,
B. L. Pike, V. V. Ho, O. A. Ryder and J. G. Hacia (2003). "Comparative analysis
of gene-expression patterns in human and African great ape cultured fibroblasts."
Genome Res 13(7): 1619-1630.
145
Khaitovich, P., I. Hellmann, W. Enard, K. Nowick, M. Leinweber, H. Franz, G. Weiss, M.
Lachmann and S. Paabo (2005). "Parallel patterns of evolution in the genomes
and transcriptomes of humans and chimpanzees." Science 309(5742): 18501854.
Kleiber, M. (1947). "Body Size and Metabolic Rate." Physiol Rev 27(4): 511-541.
Kleiber, M. (1947). "Body size and metabolic rate." Physiol Rev 27(4): 511-541.
Koonin, E. V. (2005). "Orthologs, paralogs, and evolutionary genomics." Annu Rev
Genet 39: 309-338.
Kozmik, Z., J. Ruzickova, K. Jonasova, Y. Matsumoto, P. Vopalensky, I. Kozmikova, H.
Strnad, S. Kawamura, J. Piatigorsky, V. Paces and C. Vlcek (2008). "Assembly
of the cnidarian camera-type eye from vertebrate-like components." Proc Natl
Acad Sci U S A 105(26): 8989-8993.
Kruska, D. (1988). "Mammalian domestication and its effect on brain structure and
behavior." NATO ASI series. Series G: ecological sciences 17: 211-250.
Kruska, D. C. (2005). "On the evolutionary significance of encephalization in some
eutherian mammals: effects of adaptive radiation, domestication, and
feralization." Brain Behav Evol 65(2): 73-108.
Kumar, A., S. C. Girimaji, M. R. Duvvari and S. H. Blanton (2009). "Mutations in STIL,
encoding a pericentriolar and centrosomal protein, cause primary microcephaly."
Am J Hum Genet 84(2): 286-290.
146
LaMantia, A. S. and P. Rakic (1990). "Axon overproduction and elimination in the
corpus callosum of the developing rhesus monkey." J Neurosci 10(7): 21562175.
Langmead, B., C. Trapnell, M. Pop and S. L. Salzberg (2009). "Ultrafast and memoryefficient alignment of short DNA sequences to the human genome." Genome Biol
10(3): R25.
Lefebvre, L. (2012). "Primate encephalization." Prog Brain Res 195: 393-412.
Lefebvre, L., P. Whittle, E. Lascaris and A. Finkelstein (1997). "Feeding innovations and
forebrain size in birds." Animal Behavoir 53(3): 549-560.
Leigh (2004). "Brain growth, life history, and cognition in primate and human evolution."
62(3): 139-164.
Leonard, W. R., J. J. Snodgrass and M. L. Robertson (2007). "Effects of brain evolution
on human nutrition and metabolism." Annu Rev Nutr 27: 311-327.
Li, H., B. Handsaker, A. Wysoker, T. Fennell, J. Ruan, N. Homer, G. Marth, G. Abecasis
and R. Durbin (2009). "The Sequence Alignment/Map format and SAMtools."
Bioinformatics 25(16): 2078-2079.
Li, W. H., C. I. Wu and C. C. Luo (1985). "A new method for estimating synonymous
and nonsynonymous rates of nucleotide substitution considering the relative
likelihood of nucleotide and codon changes." Mol Biol Evol 2(2): 150-174.
147
Liu, Y., J. A. Cotton, B. Shen, X. Han, S. J. Rossiter and S. Zhang (2010). "Convergent
sequence evolution between echolocating bats and dolphins." Curr Biol 20(2):
R53-54.
Lovejoy, C. O., G. Suwa, S. W. Simpson, J. H. Matternes and T. D. White (2009). "The
great divides: Ardipithecus ramidus reveals the postcrania of our last common
ancestors with African apes." Science 326(5949): 100-106.
Maddison, W. P. and D. R. Maddison (2009). Mesquite: A modular system for
evolutionary analysis.
Mallick, S., S. Gnerre, P. Muller and D. Reich (2009). "The difficulty of avoiding false
positives in genome scans for natural selection." Genome Res 19(5): 922-933.
Marino, L. (1998). "A comparison of encephalization between odontocete cetaceans
and anthropoid primates." Brain Behav Evol 51(4): 230-238.
Marino, L. (2002). "Convergence of complex cognitive abilities in cetaceans and
primates." Brain Behav Evol 59(1-2): 21-32.
Marino, L., R. C. Connor, R. E. Fordyce, L. M. Herman, P. R. Hof, L. Lefebvre, D.
Lusseau, B. McCowan, E. A. Nimchinsky, A. A. Pack, L. Rendell, J. S.
Reidenberg, D. Reiss, M. D. Uhen, E. Van der Gucht and H. Whitehead (2007).
"Cetaceans have complex brains for complex cognition." PLoS Biol 5(5): e139.
Marino, L., D. W. McShea and M. D. Uhen (2004). "Origin and evolution of large brains
in toothed whales." Anat Rec A Discov Mol Cell Evol Biol 281(2): 1247-1255.
148
Marino, L., J. K. Rilling, S. K. Lin and S. H. Ridgway (2000). "Relative volume of the
cerebellum in dolphins and comparison with anthropoid primates." Brain Behav
Evol 56(4): 204-211.
Martin, J. A. and Z. Wang (2011). "Next-generation transcriptome assembly." Nat Rev
Genet 12(10): 671-682.
Martin, R. and A. Martin (1990). Primate origins and evolution: a phylogenetic
reconstruction, Chapman and Hall London.
Martin, R. D. (1981). "Relative brain size and basal metabolic rate in terrestrial
vertebrates." Nature 293(5827): 57-60.
Martin, R. D. (1984). Body size, brain size and feeding strategies. Food, acquisition and
processing in primates. D. J. Chivers, B. Wood and A. Bilsborough. New York,
Plenum Press.
Martin, R. D. (1996). "Scaling of the Mammalian Brain: the Maternal Energy
Hypothesis." News Physiol Sci 11: 149-156.
Marvanova, M., J. Menager, E. Bezard, R. E. Bontrop, L. Pradier and G. Wong (2003).
"Microarray analysis of nonhuman primates: validation of experimental models in
neurological disorders." FASEB J 17(8): 929-931.
Mazur, R. and V. Seher (2008). "Socially learned foraging behaviour in wild black bears,
Ursus americanus." Animal Behavoir 75(4): 1503-1508.
149
McGowen, M. R., L. I. Grossman and D. E. Wildman (2012). "Dolphin genome provides
evidence for adaptive evolution of nervous system genes and a molecular rate
slowdown." Proc Biol Sci 279(1743): 3643-3651.
McGowen, M. R., S. H. Montgomery, C. Clark and J. Gatesy (2011). "Phylogeny and
adaptive evolution of the brain-development gene microcephalin (MCPH1) in
cetaceans." BMC Evol Biol 11: 98.
McHenry, H. (1982). "The pattern of human evolution: Studies on bipedalism,
mastication, and encephalization." Ann Rev Anthropol 11: 151-173.
McLoughlin, D. M. and C. C. Miller (2008). "The FE65 proteins and Alzheimer's
disease." J Neurosci Res 86(4): 744-754.
Mekel-Bobrov, N., S. L. Gilbert, P. D. Evans, E. J. Vallender, J. R. Anderson, R. R.
Hudson, S. A. Tishkoff and B. T. Lahn (2005). "Ongoing adaptive evolution of
ASPM, a brain size determinant in Homo sapiens." Science 309(5741): 17201722.
Mick, E., A. Todorov, S. Smalley, X. Hu, S. Loo, R. D. Todd, J. Biederman, D. Byrne, B.
Dechairo, A. Guiney, J. McCracken, J. McGough, S. F. Nelson, A. M. Reiersen,
T. E. Wilens, J. Wozniak, B. M. Neale and S. V. Faraone (2010). "Family-based
genome-wide association scan of attention-deficit/hyperactivity disorder." J Am
Acad Child Adolesc Psychiatry 49(9): 898-905 e893.
Midford, P. E., T. Garland Jr and W. P. Maddison (2005). PDAP Package of Mesquite.
150
Miller, D. J., T. Duka, C. D. Stimpson, S. J. Schapiro, W. B. Baze, M. J. McArthur, A. J.
Fobbs, A. M. Sousa, N. Sestan, D. E. Wildman, L. Lipovich, C. W. Kuzawa, P. R.
Hof and C. C. Sherwood (2012). "Prolonged myelination in human neocortical
evolution." Proc Natl Acad Sci U S A 109(41): 16480-16485.
Miller, H. C., P. J. Biggs, C. Voelckel and N. J. Nelson (2012). "De novo sequence
assembly and characterisation of a partial transcriptome for an evolutionarily
distinct reptile, the tuatara (Sphenodon punctatus)." BMC Genomics 13: 439.
Miller, W., K. D. Makova, A. Nekrutenko and R. C. Hardison (2004). "Comparative
genomics." Annu Rev Genomics Hum Genet 5: 15-56.
Milton, K. (1998). "Physiological Ecology of Howlers (Alouatta): Energetic and Digestive
Considerations and Comparison with the Colobinae." Int J Primatology 19(3):
513-548.
Montgomery, S. H., I. Capellini, R. A. Barton and N. I. Mundy (2010). "Reconstructing
the ups and downs of primate brain evolution: implications for adaptive
hypotheses and Homo floresiensis." BMC Biol 8: 9.
Montgomery, S. H., I. Capellini, C. Venditti, R. A. Barton and N. I. Mundy (2011).
"Adaptive evolution of four microcephaly genes and the evolution of brain size in
anthropoid primates." Mol Biol Evol 28(1): 625-638.
Montgomery, S. H. and N. I. Mundy (2010). "Brain Evolution: Microcephaly Genes
Weigh In." Curr Biol 20(5): R244-R246.
151
Montgomery, S. H. and N. I. Mundy (2012). "Evolution of ASPM is associated with both
increases and decreases in brain size in primates." Evolution 66(3): 927-932.
Morin, R., M. Bainbridge, A. Fejes, M. Hirst, M. Krzywinski, T. Pugh, H. McDonald, R.
Varhol, S. Jones and M. Marra (2008). "Profiling the HeLa S3 transcriptome
using randomly primed cDNA and massively parallel short-read sequencing."
Biotechniques 45(1): 81-94.
Neubauer, S. and J. Hublin (2012). "The evolution of human brain development." Evol
Biol 39: 568-586.
Nicholas, A. K., M. Khurshid, J. Desir, O. P. Carvalho, J. J. Cox, G. Thornton, R.
Kausar, M. Ansar, W. Ahmad, A. Verloes, S. Passemard, J. P. Misson, S.
Lindsay, F. Gergely, W. B. Dobyns, E. Roberts, M. Abramowicz and C. G. Woods
(2010). "WDR62 is associated with the spindle pole and is mutated in human
microcephaly." Nat Genet 42(11): 1010-1014.
Nickel, G. C., D. L. Tefft, K. Goglin and M. D. Adams (2008). "An empirical test for
branch-specific positive selection." Genetics 179(4): 2183-2193.
Nielsen, R., C. Bustamante, A. G. Clark, S. Glanowski, T. B. Sackton, M. J. Hubisz, A.
Fledel-Alon, D. M. Tanenbaum, D. Civello, T. J. White, J. S. J, M. D. Adams and
M. Cargill (2005). "A scan for positively selected genes in the genomes of
humans and chimpanzees." PLoS Biol 3(6): e170.
Niven, J. E. (2005). "Brain evolution: getting better all the time?" Curr Biol 15(16): R624626.
152
Novak, U. (2004). "ADAM proteins in the brain." J Clin Neurosci 11(3): 227-235.
Nowak, R. M. (1991). Walker's Mammals of the World Baltimore and London, The John
Hopkins University Press. 2.
O'Shea, T. and R. Reep (1990). "Encephalization quotients and life-history traits in the
Sirenia." Journal of mammalogy: 534-543.
Ohta, T. (1973). "Slightly deleterious mutant substitutions in evolution." Nature
246(5428): 96-98.
Ohta, T. (1974). "Mutational pressure as the main cause of molecular evolution and
polymorphism." Nature 252(5482): 351-354.
Ohta, T. (1998). "Evolution by nearly-neutral mutations." Genetica 102-103(1-6): 83-90.
Okabe, H., S. H. Lee, J. Phuchareon, D. G. Albertson, F. McCormick and O. Tetsu
(2006). "A critical role for FBXW8 and MAPK in cyclin D1 degradation and cancer
cell proliferation." PLoS One 1: e128.
Ottoni, E. B., B. D. de Resende and P. Izar (2005). "Watching the best nutcrackers:
what capuchin monkeys (Cebus apella) know about others' tool-using skills."
Anim Cogn 8(4): 215-219.
Overington, S., L. Cauchard, K. Cote and L. Lefebvre (2011). "Innovative foraging
behaviour in birds: What characterizes an innovator?" Behav Processes 87(3):
274-285.
153
Padberg, J., J. G. Franca, D. F. Cooke, J. G. Soares, M. G. Rosa, M. Fiorani, Jr., R.
Gattass and L. Krubitzer (2007). "Parallel evolution of cortical areas involved in
skilled hand use." J Neurosci 27(38): 10106-10115.
Pakkenberg, B. and H. J. Gundersen (1997). "Neocortical neuron number in humans:
effect of sex and age." J Comp Neurol 384(2): 312-320.
Pan, D. and G. M. Rubin (1997). "Kuzbanian controls proteolytic processing of Notch
and mediates lateral inhibition during Drosophila and vertebrate neurogenesis."
Cell 90(2): 271-280.
Panger, M. A. (2007). Tool use and cognition in Primates. New York, Oxford Press.
Panger, M. A., S. Perry, L. Rose, J. Gros-Louis, E. Vogel, K. C. Mackinnon and M.
Baker (2002). "Cross-site differences in foraging behavior of white-faced
capuchins (Cebus capucinus)." Am J Phys Anthropol 119(1): 52-66.
Paradis, E., J. Claude and K. Strimmer (2004). "APE: Analyses of Phylogenetics and
Evolution in R language." Bioinformatics 20(2): 289-290.
Phillips, K. A. (1998). "Tool use in wild capuchin monkeys (Cebus albifrons trinitatis)."
Am J Primatol 46(3): 259-261.
Phillips, K. A. and C. C. Sherwood (2008). "Cortical development in brown capuchin
monkeys: a structural MRI study." Neuroimage 43(4): 657-664.
Pilbeam, D. and S. J. Gould (1974). "Size and scaling in human evolution." Science
186(4167): 892-901.
154
Pirlot, P. and H. Stephan (1970). "Encephalization in Chiroptera." Can. J. Zool 48: 433444.
Pollard, K. S., S. R. Salama, N. Lambert, M. A. Lambot, S. Coppens, J. S. Pedersen, S.
Katzman, B. King, C. Onodera, A. Siepel, A. D. Kern, C. Dehay, H. Igel, M. Ares,
Jr., P. Vanderhaeghen and D. Haussler (2006). "An RNA gene expressed during
cortical development evolved rapidly in humans." Nature 443(7108): 167-172.
Ponce de Leon, M. S., L. Golovanova, V. Doronichev, G. Romanova, T. Akazawa, O.
Kondo, H. Ishida and C. P. Zollikofer (2008). "Neanderthal brain size at birth
provides insights into the evolution of human life history." Proc Natl Acad Sci U S
A 105(37): 13764-13768.
Preuss, T. M., M. Caceres, M. C. Oldham and D. H. Geschwind (2004). "Human brain
evolution: insights from microarrays." Nat Rev Genet 5(11): 850-860.
Pruessmeyer, J. and A. Ludwig (2009). "The good, the bad and the ugly substrates for
ADAM10 and ADAM17 in brain pathology, inflammation and cancer." Semin Cell
Dev Biol 20(2): 164-174.
Radinsky, L. (1978). "Evolution of brain size in carnivores and ungulates." American
Naturalist: 815-831.
Raichlen, D. A. and A. D. Gordon (2011). "Relationship between exercise capacity and
brain size in mammals." PLoS One 6(6): e20601.
155
Raichlen, D. A. and J. D. Polk (2013). "Linking brains and brawn: exercise and the
evolution of human neurobiology." Proc Biol Sci 280(1750): 20122250.
Rakic, P. (1985). "Limits of neurogenesis in primates." Science 227(4690): 1054-1056.
Rakic, P. (1988). "Specification of cerebral cortical areas." Science 241(4862): 170-176.
Rakic, P. (1995). "A small step for the cell, a giant leap for mankind: a hypothesis of
neocortical expansion during evolution." Trends Neurosci 18(9): 383-388.
Rakic, P. (2009). "Evolution of the neocortex: a perspective from developmental
biology." Nat Rev Neurosci 10(10): 724-735.
Ranwez, V., S. Harispe, F. Delsuc and E. J. Douzery (2011). "MACSE: Multiple
Alignment of Coding SEquences accounting for frameshifts and stop codons."
PLoS One 6(9): e22594.
Reader, S. M. and K. N. Laland (2002). "Social intelligence, innovation, and enhanced
brain size in primates." Proc Natl Acad Sci U S A 99(7): 4436-4441.
Rieder, C. L., S. Faruki and A. Khodjakov (2001). "The centrosome in vertebrates: more
than a microtubule-organizing center." Trends Cell Biol 11(10): 413-419.
Rilling, J. K. (2006). "Human and NonHuman Primate Brains: Are They Allometrically
Scaled Versions of the Same Design?" Evol Anthropol 15: 65-77.
Rimol, L. M., I. Agartz, S. Djurovic, A. A. Brown, J. C. Roddey, A. K. Kahler, M.
Mattingsdal, L. Athanasiu, A. H. Joyner, N. J. Schork, E. Halgren, K. Sundet, I.
Melle, A. M. Dale and O. A. Andreassen (2010). "Sex-dependent association of
156
common variants of microcephaly genes with brain structure." Proc Natl Acad Sci
U S A 107(1): 384-388.
Rogers, J., P. Kochunov, K. Zilles, W. Shelledy, J. Lancaster, P. Thompson, R.
Duggirala, J. Blangero, P. T. Fox and D. C. Glahn (2010). "On the genetic
architecture of cortical folding and brain volume in primates." Neuroimage 53(3):
1103-1108.
Rosenberg, K. R. and W. R. Trevathan (2002). "Birth, obstetrics and human evolution."
BJOG 109: 1199-1206.
Ross, C. and K. Jones (1999). Socioecology and the evolution of primate reproductive
rates. Comparative primate socioecology. P. C. Lee. Cambridge, Cambridge
University Press: 73-110.
Roth, G. and U. Dicke (2005). "Evolution of the brain and intelligence." Trends Cogn Sci
9(5): 250-257.
Sacher, G. (1959). Relation of lifespan to brain weight and body weight in mammals.
Sacher, G. and E. Staffeldt (1974). "Relation of gestation time to brain weight for
placental mammals: implications for the theory of vertebrate growth." American
Naturalist: 593-615.
Safi, K., M. A. Seid and D. K. Dechmann (2005). "Bigger is not always better: when
brains get smaller." Biol Lett 1(3): 283-286.
157
Sakai, T., S. Hirata, K. Fuwa, K. Sugama, K. Kusunoki, H. Makishima, T. Eguchi, S.
Yamada, N. Ogihara and H. Takeshita (2012). "Fetal brain development in
chimpanzees versus humans." Curr Biol 22(18): R791-792.
Scally, A., J. Y. Dutheil, L. W. Hillier, G. E. Jordan, I. Goodhead, J. Herrero, A. Hobolth,
T. Lappalainen, T. Mailund, T. Marques-Bonet, S. McCarthy, S. H. Montgomery,
P. C. Schwalie, Y. A. Tang, M. C. Ward, Y. Xue, B. Yngvadottir, C. Alkan, L. N.
Andersen, Q. Ayub, E. V. Ball, K. Beal, B. J. Bradley, Y. Chen, C. M. Clee, S.
Fitzgerald, T. A. Graves, Y. Gu, P. Heath, A. Heger, E. Karakoc, A. KolbKokocinski, G. K. Laird, G. Lunter, S. Meader, M. Mort, J. C. Mullikin, K. Munch,
T. D. O'Connor, A. D. Phillips, J. Prado-Martinez, A. S. Rogers, S. Sajjadian, D.
Schmidt, K. Shaw, J. T. Simpson, P. D. Stenson, D. J. Turner, L. Vigilant, A. J.
Vilella, W. Whitener, B. Zhu, D. N. Cooper, P. de Jong, E. T. Dermitzakis, E. E.
Eichler, P. Flicek, N. Goldman, N. I. Mundy, Z. Ning, D. T. Odom, C. P. Ponting,
M. A. Quail, O. A. Ryder, S. M. Searle, W. C. Warren, R. K. Wilson, M. H.
Schierup, J. Rogers, C. Tyler-Smith and R. Durbin (2012). "Insights into hominid
evolution from the gorilla genome sequence." Nature 483(7388): 169-175.
Schmidt, T. R., D. E. Wildman, M. Uddin, J. C. Opazo, M. Goodman and L. I. Grossman
(2005). "Rapid electrostatic evolution at the binding site for cytochrome c on
cytochrome c oxidase in anthropoid primates." Proc Natl Acad Sci U S A 102(18):
6379-6384.
158
Schoenemann, P. T., M. J. Sheehan and L. D. Glotzer (2005). "Prefrontal white matter
volume is disproportionately larger in humans than in other primates." Nat
Neurosci 8(2): 242-252.
Schuppli, C., K. Isler and C. P. van Schaik (2012). "How to explain the unusually late
age at skill competence among humans." J Hum Evol.
Seeger, J., B. Schrank, A. Pyle, R. Stucka, U. Lorcher, S. Muller-Ziermann, A. Abicht, B.
Czermin, E. Holinski-Feder, H. Lochmuller and R. Horvath (2010). "Clinical and
neuropathological findings in patients with TACO1 mutations." Neuromuscul
Disord 20(11): 720-724.
Semendeferi, K., A. Lu, N. Schenker and H. Damasio (2002). "Humans and great apes
share a large frontal cortex." Nat Neurosci 5(3): 272-276.
Sherwood, C. C., C. D. Stimpson, M. A. Raghanti, D. E. Wildman, M. Uddin, L. I.
Grossman, M. Goodman, J. C. Redmond, C. J. Bonar, J. M. Erwin and P. R. Hof
(2006). "Evolution of increased glia-neuron ratios in the human frontal cortex."
Proc Natl Acad Sci U S A 103(37): 13606-13611.
Sherwood, C. C., F. Subiaul and T. W. Zawidzki (2008). "A natural history of the human
mind: tracing evolutionary changes in brain and cognition." J Anat 212(4): 426454.
Shoshani, J., W. J. Kupsky and G. H. Marchant (2006). "Elephant brain. Part I: gross
morphology, functions, comparative anatomy, and evolution." Brain Res Bull
70(2): 124-157.
159
Shultz, S. and R. Dunbar (2010). "Encephalization is not a universal macroevolutionary
phenomenon in mammals but is associated with sociality." Proc Natl Acad Sci U
S A.
Silva, M. and J. A. Downing (1995). CRC Handbook of Mammalian Body Masses. Boca
Raton, FL, CRC Press.
Simons, E. L., E. R. Seiffert, T. M. Ryan and Y. Attia (2007). "A remarkable female
cranium of the early Oligocene anthropoid Aegyptopithecus zeuxis (Catarrhini,
Propliopithecidae)." Proc Natl Acad Sci U S A 104(21): 8731-8736.
Smaers, J. B., J. Steele, C. R. Case, A. Cowper, K. Amunts and K. Zilles (2011).
"Primate prefrontal cortex evolution: human brains are the extreme of a
lateralized ape trend." Brain Behav Evol 77(2): 67-78.
Smedley, D., S. Haider, B. Ballester, R. Holland, D. London, G. Thorisson and A.
Kasprzyk (2009). "BioMart--biological queries made easy." BMC Genomics 10:
22.
Snell, O. (1891). "Das Gewicht des Gehirnes und des Hirnmantels der Saugetiere in
Beziehung zu deren geistigen Fahigkeiten. ." Sitzungsberichte Ges. Morphol.
Physiol(7): 90-94.
Sol, D., S. Bacher, S. M. Reader and L. Lefebvre (2008). "Brain size predicts the
success of mammal species introduced into novel environments." Am Nat 172
Suppl 1: S63-71.
160
Sowell, E. R., P. M. Thompson, K. D. Tessner and A. W. Toga (2001). "Mapping
continued brain growth and gray matter density reduction in dorsal frontal cortex:
Inverse relationships during postadolescent brain maturation." J Neurosci 21(22):
8819-8829.
Steiper, M. E. and N. M. Young (2006). "Primate molecular divergence dates." Mol
Phylogenet Evol 41(2): 384-394.
Stephan, H., H. Frahm and G. Baron (1981). "New and revised data on volumes of brain
structures in insectivores and primates." Folia Primatol (Basel) 35(1): 1-29.
Stewart, C. B., J. W. Schilling and A. C. Wilson (1987). "Adaptive evolution in the
stomach lysozymes of foregut fermenters." Nature 330(6146): 401-404.
Stiles, J. and T. L. Jernigan (2010). "The basics of brain development." Neuropsychol
Rev 20(4): 327-348.
Strand, A. D., A. K. Aragaki, Z. C. Baquet, A. Hodges, P. Cunningham, P. Holmans, K.
R. Jones, L. Jones, C. Kooperberg and J. M. Olson (2007). "Conservation of
regional gene expression in mouse and human brain." PLoS Genet 3(4): e59.
Sultan, M., M. H. Schulz, H. Richard, A. Magen, A. Klingenhoff, M. Scherf, M. Seifert, T.
Borodina, A. Soldatov, D. Parkhomchuk, D. Schmidt, S. O'Keeffe, S. Haas, M.
Vingron, H. Lehrach and M. L. Yaspo (2008). "A global view of gene activity and
alternative splicing by deep sequencing of the human transcriptome." Science
321(5891): 956-960.
161
Suwa, G., B. Asfaw, R. T. Kono, D. Kubo, C. O. Lovejoy and T. D. White (2009). "The
Ardipithecus ramidus skull and its implications for hominid origins." Science
326(5949): 68e61-67.
Talavera, G. and J. Castresana (2007). "Improvement of phylogenies after removing
divergent and ambiguously aligned blocks from protein sequence alignments."
Syst Biol 56(4): 564-577.
Tartarelli, G. and M. Bisconti (2007). "Trajectories and Constraints in Brain Evolution in
Primates and Cetaceans." Human Evolution 21(3): 275-287.
Tefferi, A. (2010). "Novel mutations and their functional and clinical relevance in
myeloproliferative neoplasms: JAK2, MPL, TET2, ASXL1, CBL, IDH and IKZF1."
Leukemia 24(6): 1128-1138.
Thornton, G. K. and C. G. Woods (2009). "Primary microcephaly: do all roads lead to
Rome?" Trends Genet 25(11): 501-510.
Tousseyn, T., A. Thathiah, E. Jorissen, T. Raemaekers, U. Konietzko, K. Reiss, E.
Maes, A. Snellinx, L. Serneels, O. Nyabi, W. Annaert, P. Saftig, D. Hartmann and
B. De Strooper (2009). "ADAM10, the rate-limiting protease of regulated
intramembrane proteolysis of Notch and other proteins, is processed by ADAMS9, ADAMS-15, and the gamma-secretase." J Biol Chem 284(17): 11738-11747.
Truppa, V., G. Spinozzi, T. Stegagno and J. Fagot (2009). "Picture processing in tufted
capuchin monkeys (Cebus apella)." Behav Processes 82(2): 140-152.
162
Tsutsumi, T., H. Kuwabara, T. Arai, Y. Xiao and J. A. Decaprio (2008). "Disruption of the
Fbxw8 gene results in pre- and postnatal growth retardation in mice." Mol Cell
Biol 28(2): 743-751.
Uddin, M., M. Goodman, O. Erez, R. Romero, G. Liu, M. Islam, J. C. Opazo, C. C.
Sherwood, L. I. Grossman and D. E. Wildman (2008). "Distinct genomic
signatures of adaptation in pre- and postnatal environments during human
evolution." Proc Natl Acad Sci U S A 105(9): 3215-3220.
Uddin, M., J. C. Opazo, D. E. Wildman, C. C. Sherwood, P. R. Hof, M. Goodman and L.
I. Grossman (2008). "Molecular evolution of the cytochrome c oxidase subunit 5A
gene in primates." BMC Evol Biol 8: 8.
Uddin, M., D. E. Wildman, G. Liu, W. Xu, R. M. Johnson, P. R. Hof, G. Kapatos, L. I.
Grossman and M. Goodman (2004). "Sister grouping of chimpanzees and
humans as revealed by genome-wide phylogenetic analysis of brain gene
expression profiles." Proc Natl Acad Sci U S A 101(9): 2957-2962.
Valderrama, X., J. G. Robinson, A. B. Attygalle and T. Eisner (2000). "Seasonal
Anointment with millipedes in a wild primate: A chemical defense against
insects?" Journal of Chemical Ecology 26(12): 2781-2790.
Vallender, E. J. (2011). "Comparative genetic approaches to the evolution of human
brain and behavior." Am J Hum Biol 23(1): 53-64.
Vallender, E. J. (2012). "Genetic correlates of the evolving primate brain." Prog Brain
Res 195: 27-44.
163
Villanea, F. A., G. H. Perry, G. A. Gutierrez-Espeleta and N. J. Dominy (2012). "ASPM
and the Evolution of Cerebral Cortical Size in a Community of New World
Monkeys." PLoS One 7(9): e44928.
Visalberghi, E., D. Fragaszy, E. Ottoni, P. Izar, M. G. de Oliveira and F. R. Andrade
(2007). "Characteristics of hammer stones and anvils used by wild bearded
capuchin monkeys (Cebus libidinosus) to crack open palm nuts." Am J Phys
Anthropol 132(3): 426-444.
Visalberghi, E., N. Spagnoletti, E. D. Ramos da Silva, F. R. Andrade, E. Ottoni, P. Izar
and D. Fragaszy (2009). "Distribution of potential suitable hammers and transport
of hammer tools and nuts by wild capuchin monkeys." Primates 50(2): 95-104.
Wang, J. K., Y. Li and B. Su (2008). "A common SNP of MCPH1 is associated with
cranial volume variation in Chinese population." Hum Mol Genet 17(9): 13291335.
Wang, Z., M. Gerstein and M. Snyder (2009). "RNA-Seq: a revolutionary tool for
transcriptomics." Nat Rev Genet 10(1): 57-63.
Waterston, R. H., K. Lindblad-Toh, E. Birney, J. Rogers, J. F. Abril, P. Agarwal, R.
Agarwala, R. Ainscough, M. Alexandersson, P. An, S. E. Antonarakis, J.
Attwood, R. Baertsch, J. Bailey, K. Barlow, S. Beck, E. Berry, B. Birren, T.
Bloom, P. Bork, M. Botcherby, N. Bray, M. R. Brent, D. G. Brown, S. D. Brown,
C. Bult, J. Burton, J. Butler, R. D. Campbell, P. Carninci, S. Cawley, F.
Chiaromonte, A. T. Chinwalla, D. M. Church, M. Clamp, C. Clee, F. S. Collins, L.
164
L. Cook, R. R. Copley, A. Coulson, O. Couronne, J. Cuff, V. Curwen, T. Cutts, M.
Daly, R. David, J. Davies, K. D. Delehaunty, J. Deri, E. T. Dermitzakis, C. Dewey,
N. J. Dickens, M. Diekhans, S. Dodge, I. Dubchak, D. M. Dunn, S. R. Eddy, L.
Elnitski, R. D. Emes, P. Eswara, E. Eyras, A. Felsenfeld, G. A. Fewell, P. Flicek,
K. Foley, W. N. Frankel, L. A. Fulton, R. S. Fulton, T. S. Furey, D. Gage, R. A.
Gibbs, G. Glusman, S. Gnerre, N. Goldman, L. Goodstadt, D. Grafham, T. A.
Graves, E. D. Green, S. Gregory, R. Guigo, M. Guyer, R. C. Hardison, D.
Haussler, Y. Hayashizaki, L. W. Hillier, A. Hinrichs, W. Hlavina, T. Holzer, F. Hsu,
A. Hua, T. Hubbard, A. Hunt, I. Jackson, D. B. Jaffe, L. S. Johnson, M. Jones, T.
A. Jones, A. Joy, M. Kamal, E. K. Karlsson, D. Karolchik, A. Kasprzyk, J. Kawai,
E. Keibler, C. Kells, W. J. Kent, A. Kirby, D. L. Kolbe, I. Korf, R. S. Kucherlapati,
E. J. Kulbokas, D. Kulp, T. Landers, J. P. Leger, S. Leonard, I. Letunic, R.
Levine, J. Li, M. Li, C. Lloyd, S. Lucas, B. Ma, D. R. Maglott, E. R. Mardis, L.
Matthews, E. Mauceli, J. H. Mayer, M. McCarthy, W. R. McCombie, S. McLaren,
K. McLay, J. D. McPherson, J. Meldrim, B. Meredith, J. P. Mesirov, W. Miller, T.
L. Miner, E. Mongin, K. T. Montgomery, M. Morgan, R. Mott, J. C. Mullikin, D. M.
Muzny, W. E. Nash, J. O. Nelson, M. N. Nhan, R. Nicol, Z. Ning, C. Nusbaum, M.
J. O'Connor, Y. Okazaki, K. Oliver, E. Overton-Larty, L. Pachter, G. Parra, K. H.
Pepin, J. Peterson, P. Pevzner, R. Plumb, C. S. Pohl, A. Poliakov, T. C. Ponce,
C. P. Ponting, S. Potter, M. Quail, A. Reymond, B. A. Roe, K. M. Roskin, E. M.
Rubin, A. G. Rust, R. Santos, V. Sapojnikov, B. Schultz, J. Schultz, M. S.
Schwartz, S. Schwartz, C. Scott, S. Seaman, S. Searle, T. Sharpe, A. Sheridan,
R. Shownkeen, S. Sims, J. B. Singer, G. Slater, A. Smit, D. R. Smith, B. Spencer,
165
A. Stabenau, N. Stange-Thomann, C. Sugnet, M. Suyama, G. Tesler, J.
Thompson, D. Torrents, E. Trevaskis, J. Tromp, C. Ucla, A. Ureta-Vidal, J. P.
Vinson, A. C. Von Niederhausern, C. M. Wade, M. Wall, R. J. Weber, R. B.
Weiss, M. C. Wendl, A. P. West, K. Wetterstrand, R. Wheeler, S. Whelan, J.
Wierzbowski, D. Willey, S. Williams, R. K. Wilson, E. Winter, K. C. Worley, D.
Wyman, S. Yang, S. P. Yang, E. M. Zdobnov, M. C. Zody and E. S. Lander
(2002). "Initial sequencing and comparative analysis of the mouse genome."
Nature 420(6915): 520-562.
Weraarpachai, W., H. Antonicka, F. Sasarman, J. Seeger, B. Schrank, J. E. Kolesar, H.
Lochmuller, M. Chevrette, B. A. Kaufman, R. Horvath and E. A. Shoubridge
(2009). "Mutation in TACO1, encoding a translational activator of COX I, results
in cytochrome c oxidase deficiency and late-onset Leigh syndrome." Nat Genet
41(7): 833-837.
Whiten, A. and C. P. van Schaik (2007). "The evolution of animal 'cultures' and social
intelligence." Philos Trans R Soc Lond B Biol Sci 362(1480): 603-620.
Wildman, D. E., N. M. Jameson, J. C. Opazo and S. V. Yi (2009). "A fully resolved
genus level phylogeny of neotropical primates (Platyrrhini)." Mol Phylogenet Evol
53(3): 694-702.
Wildman, D. E., M. Uddin, G. Liu, L. I. Grossman and M. Goodman (2003). "Implications
of natural selection in shaping 99.4% nonsynonymous DNA identity between
humans and chimpanzees: enlarging genus Homo." Proc Natl Acad Sci U S A
100(12): 7181-7188.
166
Willems, P. J., H. C. Seo, P. Coucke, R. Tonlorenzi and J. S. O'Brien (1999). "Spectrum
of mutations in fucosidosis." Eur J Hum Genet 7(1): 60-67.
Williams, A. J., L. M. Khachigian, T. Shows and T. Collins (1995). "Isolation and
characterization of a novel zinc-finger protein with transcription repressor
activity." J Biol Chem 270(38): 22143-22152.
Williams, M. F. (2002). "Primate encephalization and intelligence." Med Hypotheses
58(4): 284-290.
Wilson, D. E. and D. M. Reeder (2005). Mammal species of the world. Baltimore, Johns
Hopkins University Press.
Wolfe, K. H., P. M. Sharp and W. H. Li (1989). "Mutation rates differ among regions of
the mammalian genome." Nature 337(6204): 283-285.
Worthy, G. and J. Hickie (1986). "Relative brain size in marine mammals." American
Naturalist 128(4): 445-459.
Yang, Y. J., A. E. Baltus, R. S. Mathew, E. A. Murphy, G. D. Evrony, D. M. Gonzalez, E.
P. Wang, C. A. Marshall-Walker, B. J. Barry, J. Murn, A. Tatarakis, M. A.
Mahajan, H. H. Samuels, Y. Shi, J. A. Golden, M. Mahajnah, R. Shenhav and C.
A. Walsh (2012). "Microcephaly gene links trithorax and REST/NRSF to control
neural stem cell proliferation and differentiation." Cell 151(5): 1097-1112.
Yang, Z. (2007). "PAML 4: phylogenetic analysis by maximum likelihood." Mol Biol Evol
24(8): 1586-1591.
167
Yang, Z., N. Goldman and A. Friday (1994). "Comparison of models for nucleotide
substitution used in maximum-likelihood phylogenetic estimation." Mol Biol Evol
11(2): 316-324.
Yang, Z. and R. Nielsen (2000). "Estimating synonymous and nonsynonymous
substitution rates under realistic evolutionary models." Mol Biol Evol 17(1): 32-43.
Yang, Z., R. Nielsen, N. Goldman and A. M. Pedersen (2000). "Codon-substitution
models for heterogeneous selection pressure at amino acid sites." Genetics
155(1): 431-449.
Zhang, J. and S. Kumar (1997). "Detection of convergent and parallel evolution at the
amino acid sequence level." Mol Biol Evol 14(5): 527-536.
Zhang, Y. E., P. Landback, M. D. Vibranovski and M. Long (2011). "Accelerated
recruitment of new brain development genes into the human genome." PLoS Biol
9(10): e1001179.
Zilles, K., E. Armstrong, K. H. Moser, A. Schleicher and H. Stephan (1989). "Gyrification
in the cerebral cortex of primates." Brain Behav Evol 34(3): 143-150.
168
ABSRACT
INSIGHT INTO HUMAN BRAIN EVOLUTION THROUGH PHYLOGENETIC
ANALYSIS AND COMPARATIVE GENOMICS
by
AMY M BODDY
May 2013
Advisor: Dr. Derek E. Wildman
Major: Molecular Biology and Genetics
Degree: Doctor of Philosophy
As a species, humans are often considered to be unique among mammals, with
respect to their large brain size and enhanced cognitive abilities. Humans are the most
encephalized mammals, with a brain that is six times larger than expected relative to
body mass. Presumably, it is this high degree of encephalization that underlies our
advanced cognitive abilities, including the skills needed for complex language and
culture. Understanding how large brains evolved can shed light on what makes the
human brain unique and introduce possible mechanism for human specific
neurodegenerative diseases. This study takes a both a phenotypic and molecular
approach to study human brain evolution. First, we traced the evolutionary history of
encephalization across mammals through a phylogenetic analysis in order to infer at
which point significant changes in brain size occurred. We demonstrate that variation in
brain size began in anthropoid primates. Furthermore, we show multiple lineages have
evidence of brain expansion, providing support for parallelism in encephalization. To
provide molecular evidence for parallelism in brain expansion among primates, we
169
implement a comparative genomics approach and sequenced the brain transcriptome of
the second most encephalized primate, the capuchin monkey (Cebus). We then test for
similar patterns of adaptive evolution on the capuchin monkey and human lineages and
demonstrate that genes with accelerated rates of change on these large-brained
lineages share similar biological processes, such as microtubule organization, mitosis,
and metabolic processes.
170
AUTOBIOGRAPHICAL STATEMENT
AMY M. BODDY
EDUCATION
PhD. 2006 - 13
Wayne State University – School of Medicine, Molecular Biology &
Genetics
B.S.
2003 - 05
Wayne State University, Biology
A.A.
2000 - 03
Macomb Community College, Liberal Arts
ACADEMIC HONORS & AWARDS
2011-12
Wayne State University Provost Fellowship for Computational Biology
2010
Acceptance into Analyzing Next-Generation Sequencing Data – Michigan
State University
2008
Acceptance into Summer Institute in Statistical Genetics ScholarshipUniversity of Washington
TEACHING
2012
Next-Generation Sequencing, Scientific Communications I
MEMBERSHIPS
2010
Society for the Study of Evolution
2009
American Society of Primatologists
COMMITTEES
2010
Graduate Student Research Day – Chair
2008-09
Summer Undergraduate Research Program - Co-Chair
SELECTED PUBLICATIONS
Sterner KN, Chugani HT, Tarca AL, Sherwood CC, Hof PR, Kuzawa CW, Boddy AM,
Raaum RL, Weckle A, Gregoire L, Lipovich L, Grossman LI, Uddin M, Goodman M,
Wildman DE. Dynamic gene expression in the human cerebral cortex distinguishes
children from adults. PLoS ONE. 2012. 7(5):e37714.
Boddy AM, McGowen MR, Sherwood CC, Grossman LI, Goodman M, Wildman DE.
Comparative analysis of encephalization in mammals reveals relaxed constraints on
anthropoid primate and cetacean brain scaling. J Evol Biol, 2012. 21(10):1420-9101.