When we eat carbohydrates, they are digested into sugars that circulate in the blood to provide energy for the brain and other parts of the body. But too much blood sugar can be poisonous. The body regulates blood sugar balance using the hormone insulin, which triggers the removal of sugar from the blood into muscle and fat cells. This removal process involves a pore in membranes at the surface of muscle and fat tissue, called a glucose transporter, through which the sugar molecules can pass. During fasting, the glucose transporter remains inside muscle and fat. But after a meal, insulin acts to release the transporter from its storage area to the surface of the tissue. How efficiently this process happens reflects how efficiently sugar can be removed from the blood. When this pathway breaks down, it can lead to diabetes.

In humans, a protein called CHC22 is needed to deliver the glucose transporter to its storage area. In mice, CHC22 is absent. The question arises: do different animals' eating habits influence CHC22's role in controlling blood sugar? The evolutionary history of CHC22 in a number of different animals could reveal what is special about glucose transport after a meal in humans, and how it might fail in diabetes.

By analyzing the genomes of several different species, Fumagalli et al. found that the gene encoding CHC22 first evolved around the time animals began developing a backbone and complex nervous systems. Afterwards, it was lost by some animals – including mice, sheep and pigs. Fumagalli et al. also discovered that CHC22 varies between individual people. A new form of CHC22, which first appeared in ancient humans, is less effective at holding the glucose transporter inside muscle and fat – leading to a tendency to reduce blood sugar levels. This new form became more common in humans over a period witnessing the introduction of cooking, and later farming; both of these technologies are associated with increased sugar in the diet. But not everyone has this new variant of the gene – both the old and newer variants are present in people today.

The history of CHC22 suggests that it was useful for early humans to hold the glucose transporter inside muscle and fat, keeping blood sugar levels high, which contributed to the development of a large brain. But as humans became exposed to higher dietary levels of sugar the newer form of CHC22 allowed blood sugar to be lowered more readily. People with different forms of CHC22 are likely to differ in their ability to control blood sugar after a meal. In some cases, this could lead to heightened blood sugar levels, which in turn can lead to diabetes.

Clathrin-coated vesicles (CCVs) are key players in eukaryotic intracellular membrane traffic (Brodsky, 2012). Their characteristic lattice-like coat is self-assembled from cytoplasmic clathrin proteins, captures membrane-embedded protein cargo and deforms the membrane into a vesicle. This process enables CCVs to mediate protein transport to and from the plasma membrane and between organelles. The triskelion-shaped clathrin molecule is formed from three identical clathrin heavy chain (CHC) subunits. Humans have two genes (CLTC and CLTCL1) that respectively encode CHC17 and CHC22 clathrins (Wakeham et al., 2005). CHC17 clathrin, which has three bound clathrin light chain (CLC) subunits, is expressed uniformly in all tissues and forms CCVs that control receptor-mediated endocytosis, as well as lysosome biogenesis and maturation of regulated secretory granules. These pathways are conventionally associated with clathrin function and are mediated by clathrin in all eukaryotic cells (Brodsky, 2012). In humans CHC22 clathrin is most highly expressed in muscle and adipose tissue and forms separate CCVs that are not involved in endocytosis (Dannhauser et al., 2017). In these tissues, CHC22 CCVs regulate targeting of the glucose transporter 4 (GLUT4) to an intracellular compartment where it is sequestered until released to the cell surface in response to insulin (Vassilopoulos et al., 2009). This insulin-responsive GLUT4 pathway is the dominant mechanism in humans for clearing blood glucose into muscle and fat tissues after a meal (Shepherd and Kahn, 1999). In addition to its distinct tissue expression pattern and biological function, CHC22 does not bind the CLC subunits that associate with CHC17 clathrin, even though the CHC protein sequences are 85% identical (Dannhauser et al., 2017; Liu et al., 2001). This remarkable biochemical and functional divergence evolved since the gene duplication event that gave rise to the two different clathrins during the emergence of chordates (Wakeham et al., 2005). Notably, however, the CLTCL1 gene encoding CHC22 evolved into a pseudogene in the Mus genus, although mice maintain an insulin-responsive GLUT4 pathway for clearing blood glucose. This observation suggests that, despite the importance of the CLTCL1 gene product, backup pathways have evolved to compensate for loss of the CHC22 protein. To understand the evolution of the specialized function of CHC22, and the potential selective processes involved, we here explore the phylogenetic history of the CLTCL1 gene in vertebrates and its population genetics in humans, non-human primates and bears.

Ecological shifts create selective forces that filter variation in cellular genes. These include changes in nutritional conditions (Babbitt et al., 2011), as well as encounters with pathogens (Fumagalli et al., 2011); both documented as selective forces that affect membrane traffic genes (Elde and Malik, 2009; Liu et al., 2014). Recent studies of the evolution of genes involved in membrane traffic have focused on an overview of all eukaryotes with the goals of establishing the origins of membrane-traffic regulating proteins in the last common eukaryotic ancestor and defining the species distribution of various families of traffic-regulating proteins (Rout and Field, 2017; Dacks and Robinson, 2017). These studies have identified common features of proteins that regulate membrane traffic (Rout and Field, 2017) and revealed that extensive gene duplication has allowed lineage-specific diversification of coat proteins and other membrane traffic regulators, such as the Rab GTPases (Diekmann et al., 2011; Guerrier et al., 2017). Our earlier study of available annotated genomes in 2005 suggested that the gene duplication giving rise to the two CHC-encoding genes occurred as a result of one of the whole genome duplications contributing to vertebrate evolution (Wakeham et al., 2005). Here we focus on the more recent evolutionary history of these genes, as well as analyze the increased number of fully annotated vertebrate genomes. We establish that the loss of CLTCL1 in the Mus genus is not unique in vertebrates, identifying at least one additional independent gene loss event in the clade of Cetartiodactyla affecting pigs, cows, sheep, porpoise, and possibly additional related species. Nonetheless, there is strong evidence for CHC22 sequence conservation amongst those species that retain CLTCL1 (Wakeham et al., 2005). This evolutionarily recent gene loss in some lineages and retention of the functional form in others suggested that CLTCL1 may still be under purifying selection, so we examined CLTCL1 variation between individuals within vertebrate populations. Comparing populations, we found CLTCL1 to be considerably more polymorphic than CLTC, which encodes the clathrin found in all eukaryotes, with evidence for strong ancient purifying selection for CHC17 clathrin function and relaxed purifying selection on CHC22 function. Additionally, we identified two common allotypes of human CHC22, which have different functional properties. The derived allele arose in ancient humans and is more frequent in farming populations when compared to hunter-gatherers. We previously observed that CHC22 accumulates at sites of GLUT4 retention in the muscle of insulin-resistant patients with type two diabetes (Vassilopoulos et al., 2009) in addition to its active role in membrane traffic of GLUT4. Thus, CHC22 variation has potential to differentially affect membrane traffic pathways involved in insulin resistance, as well as alter normal glucose metabolism within human and other vertebrate populations. The analyses reported here lead us to propose that variation in the CHC22 clathrin coat may be a response to changing nutritional pressures both between and within vertebrate species.

We studied the phylogenetics and population genetics of CHC22 clathrin to understand the functional variation of this protein in relation to its evolutionary history. CHC22 clathrin is a key player in post-prandial blood glucose clearance in humans through its role in intracellular packaging of the GLUT4 glucose transporter in muscle and fat, the tissues in which CHC22 and GLUT4 are expressed (Vassilopoulos et al., 2009). The CHC22 pathway positions GLUT4 for cell surface release in response to insulin and consequent uptake of glucose into these tissues (Bryant et al., 2002). The CLTCL1 gene encoding CHC22 resulted from gene duplication that we have now dated to 494–451 MYA, early in vertebrate evolution when jawed vertebrates emerged. We had previously shown that CLTCL1 is a pseudogene in mice (Wakeham et al., 2005). Expanding analysis to 56 jawed vertebrate genomes (>5X coverage) we could not detect CLTCL1 in nine of them. Six of these absences can be ascribed to two independent gene loss events in branches of the Rodentia and the Cetartidactylae. The three others may represent additional gene losses or incomplete genome annotation. All vertebrate and non-vertebrate eukaryotes considered here have retained the parent CLTC gene encoding CHC17 clathrin, which mediates endocytosis and other housekeeping membrane traffic pathways. The analysis described here establishes that CLTC is under strong purifying selection. Notable is our evidence for purifying selection on CLTCL1 in the species in which it has been retained, supporting its functional importance in those species. Compared to CLTC, extensive allelic diversity was observed for CLTCL1 in all species for which populations were analyzed, including humans, chimpanzees and bears. Variant alleles were species-specific in most cases. In all human populations, two allelic variants of CLTCL1 are present in high frequency, differing only at one nucleotide, resulting in CHC22 protein with either methionine or valine at position 1316. The V1316 allotype appears specific to humans, but some non-human primates have a different variation at the position 1316. Analysis of ancient humans dated the appearance of the V1316 variant to 500–50 KYA and indicated that M1316, which is fixed in CHC17 clathrin, is the ancestral state. Analyses of human population genetic data provided support for the maintenance of high genetic diversity and two allotypes of CHC22. We hypothesize that selective pressure on CHC22 clathrin comes from its role in nutrient metabolism. Consistent with this hypothesis, we observed functional differences between the two CHC22 allotypes in their capacity to control GLUT4 membrane traffic, as predicted by structural modeling and differences in cellular dynamics of the two allotypes.

Retention of CLTC in all vertebrate species is consistent with the encoded CHC17 mediating cellular housekeeping clathrin functions shared by all eukaryotes. On the other hand, CHC22, encoded by the paralogous gene CLTCL1, operates in the specialized insulin-responsive GLUT4 pathway to make the pathway more efficient in those species that retained CLTCL1. Data presented here (Figure 8) and our recent mapping of a novel intracellular location for CHC22 function (Camus et al., 2018) indicate that, in human cells, CHC22 clathrin promotes transport from the secretory pathway to the insulin-responsive GSC. This CHC22 pathway complements the endocytic pathway for GLUT4 targeting to the GSC, so species without CHC22 can rely primarily on endocytosis for GLUT4 trafficking to the GSC, while species with CHC22 use both pathways. Thus, we hypothesize that species with functional CHC22 clathrin are more efficient at intracellular GLUT4 sequestration, resulting in lower surface GLUT4 in the absence of insulin, and tighter regulation of GLUT4 release in response to insulin. The trade-off is that these species have an inherent increased tendency to insulin resistance as their GLUT4 is sequestered more effectively. The two main vertebrate branches that have lost CHC22 comprise the Muridae (mice and rats) who are incessant herbivores and the Cetartiodactyla (sheep, cattle, porpoise and pigs) which include the ruminants (sheep and cattle) whose muscle uptake of glucose is critical for muscle function, but is not a main pathway for glucose clearance (Hocquette et al., 1995). These two groups of species require greater availability of GLUT4 on their cell surfaces, so that more efficient GLUT4 sequestration by CHC22 would not be favorable to their nutritional needs. The fact that CHC22 alters the balance of membrane traffic to the GSC means that species losing CLTCL1 could evolve compensatory pathways more compatible with their diets. Thus, transgenic mice expressing CHC22 over-sequester GLUT4 in their muscle and develop hyperglycemia with aging (Vassilopoulos et al., 2009). The cave fish, which appears to lack CLTCL1, has independently evolved mutations in the insulin receptor, creating natural insulin resistance, such that the presence of CHC22 on top of this mechanism might be detrimental (Riddle et al., 2018). The loss of CLTCL1 from cave fish is consistent with the insulin responsive GLUT4 pathway being a target for natural selection driven by diet, which might also explain CLTCL1 variation or loss for additional vertebrate species during vertebrate evolution.

The allelic variation reported here for CLTCL1 in human and bear populations further supports the hypothesis that CLTCL1 has undergone continued selection during vertebrate evolution in relation to diet. While purifying selection appears to be operating on CLTCL1 in those species that retain it, CLTCL1 is far more variable than CLTC in these species. In humans, we find two major and functionally distinct alleles at remarkably similar frequencies in all populations studied. Statistical analysis comparing early farmer and hunter-gatherer populations shows an apparent increase of the V1316 variant, suggesting a correlation with regular consumption of digestible carbohydrate. Notably, the SNP distinguishing these alleles is human-specific and likely arose 550–50 KYA (i.e. post-Neanderthal split, pre-Neolithic). Other dramatic increases in digestible carbohydrate utilization have been inferred for humans in this timeframe; in particular the advent of cooking (which gelatinizes crystalized starch, making it much easier to digest), salivary amylase gene copy number increase (allowing increased starch digestion capacity) and accelerated brain size increase (which would increase demands for blood glucose) (Hardy et al., 2015). While the co-evolution of these cultural and genetic traits was originally proposed to have occurred some 800 KYA, recent studies indicate a time frame of 450–300 KYA years for cooking (Shahack-Gross et al., 2014), increased oral amylase activity (Inchley et al., 2016) and accelerated brain size increase (Dunbar, 2019). The fact that the two major human CLTCL1 alleles are functionally distinct is consistent with diversifying selection operating on CLTCL1, with a balancing selection possibly caused by heterozygote advantage. While population genetic signatures for balancing or overdominant selection were not entirely robust, some summary statistics were suggestive of an increased diversity that was unlikely to have occurred under neutrality. Other statistics, such as the ones based on allele frequencies, would not be expected to gain significance within the timeframe of the human-specific diversifying selection we detect. The allelic diversity of CLTCL1 in other primate species could have the potential effect of diluting its function. Whilst chimpanzees are omnivores and gorillas herbivores, both rely for nutrition on extensive foraging for carbohydrate. Also notable is that polar bears, who have a very low carbohydrate diet compared to their brown bear relatives, have distinct CHC22 variants with unknown functionality, again consistent with CLTCL1 undergoing selection driven by nutritional ecology.

Clathrins are self-assembling proteins and function as a latticed network in the protein coat that they form on transport vesicles. Our structural modeling predicts that the single amino acid difference between the two main human CHC22 allotypes could influence the strength of molecular interactions in the CHC22 clathrin lattice, as position 1316 occurs at a lattice assembly interface (Figure 6). When expressed in cells, both CHC22 variants gave the same overall intracellular distribution, but CHC22-V1316 shows faster turnover from membranes than CHC22-M1316 (Figure 7) and is less effective at GLUT4 sequestration (Figure 8B). These properties are consistent with the methionine to valine change attenuating GLUT4 retention. This interpretation is further supported by a GLUT4 translocation assay, which indicates that the V1316 variant is less effective in forming the insulin-responsive GSC than the ancestral M1316 form of CHC22 (Figure 8A). Thus, mixed lattices occurring in heterozygous individuals, potentially reflect balancing selection and overdominance, might reduce GLUT4 sequestration compared to M1316 homozygotes. This would have the effect of improving glucose clearance. It can be argued that human consumption of digestible carbohydrate on a regular basis (Hardy et al., 2015), requiring increased glucose clearance, might be a selective force driving this genetic adaptation. This view is consistent with the increased frequency of the V1316 variant in early farmers. It is also possible that some forms of polar bear CHC22 are super-active at GLUT4 sequestration, providing a route to maintain high blood glucose, as occurs through other mutations in the cave fish (Riddle et al., 2018).

Regulators of fundamental membrane traffic pathways have diversified through gene duplication in many species over the timespan of eukaryotic evolution. Retention and loss can, in some cases, be correlated with special requirements resulting from species differentiation, such as the extensive elaboration of genes in the secretory pathway of Tetrahymena (Dacks and Robinson, 2017; Bright et al., 2010). The evolutionary history of CLTCL1, following vertebrate-specific gene duplication, suggests that differentiation of nutritional habits has shaped selection for the presence and absence of CLTCL1 in some vertebrate species, and its diversification in humans and potentially other species. Though its highest expression is in muscle and adipose tissue, transient expression of CHC22 during human brain development has also been documented (Nahorski et al., 2015). This was noted in a study of a very rare null mutant of CLTCL1 that caused loss of pain sensing in homozygotes and no symptoms for heterozygotes (Nahorski et al., 2015). Attenuated CHC22 function of the V1316 variant might lead to a spectrum of pain sensing in humans but this is unlikely to be a strong selective force affecting reproductive success, whereas glucose homeostasis, as suggested by our analysis, is more likely. By exerting efficient control of blood glucose levels, the presence of CHC22 clathrin was likely beneficial in providing the nutrition required to develop the large human brain, as well as affecting reproduction by influencing glucose availability during pregnancy (Hardy et al., 2015). However, over the last 12,500 years in association with farming, or perhaps over the last 450,000 years in association with cooking, salivary amylase activity and starch digestion (Hardy et al., 2015; Shahack-Gross et al., 2014; Inchley et al., 2016), readily available carbohydrate has increased our need to clear glucose from the blood, such that selection continues to act on CLTCL1 in humans. Our cell biology studies have also demonstrated that CHC22 increases GLUT4 retention. While we would not expect the major CLTCL1 polymorphism to directly influence the development of T2D, CHC22 accumulates on the expanded GSC that forms in cases of insulin-resistant T2D (Vassilopoulos et al., 2009), so its variation could potentially exacerbate insulin resistance to different degrees. The genetic diversity that we report here may reflect evolution towards reversing a human tendency to insulin resistance and have relevance to coping with increased carbohydrate in modern diets.

All data generated or analysed during this study are included in the manuscript and supporting files.

1. 1. Adey A
   2. Burton JN
   3. Kitzman JO
   4. Hiatt JB
   5. Lewis AP
   6. Martin BK
   7. Qiu R
   8. Lee C
   9. Shendure J

   (2013) The haplotype-resolved genome and epigenome of the aneuploid HeLa cancer cell line

   Nature 500:207–211.

   https://doi.org/10.1038/nature12064

   • PubMed
   • Google Scholar
2. 1. Adzhubei IA
   2. Schmidt S
   3. Peshkin L
   4. Ramensky VE
   5. Gerasimova A
   6. Bork P
   7. Kondrashov AS
   8. Sunyaev SR

   (2010) A method and server for predicting damaging missense mutations

   Nature Methods 7:248–249.

   https://doi.org/10.1038/nmeth0410-248

   • PubMed
   • Google Scholar
3. 1. Alexander DH
   2. Novembre J
   3. Lange K

   (2009) Fast model-based estimation of ancestry in unrelated individuals

   Genome Research 19:1655–1664.

   https://doi.org/10.1101/gr.094052.109

   • PubMed
   • Google Scholar
4. 1. Amores A
   2. Catchen J
   3. Ferrara A
   4. Fontenot Q
   5. Postlethwait JH

   (2011) Genome evolution and meiotic maps by massively parallel DNA sequencing: spotted gar, an outgroup for the teleost genome duplication

   Genetics 188:799–808.

   https://doi.org/10.1534/genetics.111.127324

   • PubMed
   • Google Scholar
5. 1. Auton A
   2. Brooks LD
   3. Durbin RM
   4. Garrison EP
   5. Kang HM
   6. Korbel JO
   7. Marchini JL
   8. McCarthy S
   9. McVean GA
   10. Abecasis GR
   11. 1000 Genomes Project Consortium

   (2015) A global reference for human genetic variation

   Nature 526:68–74.

   https://doi.org/10.1038/nature15393

   • PubMed
   • Google Scholar
6. 1. Babbitt CC
   2. Warner LR
   3. Fedrigo O
   4. Wall CE
   5. Wray GA

   (2011) Genomic signatures of diet-related shifts during human origins

   Proceedings of the Royal Society B: Biological Sciences 278:961–969.

   https://doi.org/10.1098/rspb.2010.2433

   • Google Scholar
7. 1. Benazzo A
   2. Trucchi E
   3. Cahill JA
   4. Maisano Delser P
   5. Mona S
   6. Fumagalli M
   7. Bunnefeld L
   8. Cornetti L
   9. Ghirotto S
   10. Girardi M
   11. Ometto L
   12. Panziera A
   13. Rota-Stabelli O
   14. Zanetti E
   15. Karamanlidis A
   16. Groff C
   17. Paule L
   18. Gentile L
   19. Vilà C
   20. Vicario S
   21. Boitani L
   22. Orlando L
   23. Fuselli S
   24. Vernesi C
   25. Shapiro B
   26. Ciucci P
   27. Bertorelle G

   (2017) Survival and divergence in a small group: the extraordinary genomic history of the endangered apennine Brown bear stragglers

   PNAS 114:E9589–E9597.

   https://doi.org/10.1073/pnas.1707279114

   • PubMed
   • Google Scholar
8. 1. Benjamin W
   2. Sali A

   (2014) Comparative protein structure modeling using MODELLER

   Current Protocols In Bioinformatics 47:5–6.

   https://doi.org/10.1002/cpbi.3

   • Google Scholar
9. 1. Bojarska K
   2. Selva N

   (2012) Spatial patterns in Brown bear ursus arctos diet: the role of geographical and environmental factors

   Mammal Review 42:120–143.

   https://doi.org/10.1111/j.1365-2907.2011.00192.x

   • Google Scholar
10. 1. Boratyn GM
    2. Camacho C
    3. Cooper PS
    4. Coulouris G
    5. Fong A
    6. Ma N
    7. Madden TL
    8. Matten WT
    9. McGinnis SD
    10. Merezhuk Y
    11. Raytselis Y
    12. Sayers EW
    13. Tao T
    14. Ye J
    15. Zaretskaya I

    (2013) BLAST: a more efficient report with usability improvements

    Nucleic Acids Research 41:W29–W33.

    https://doi.org/10.1093/nar/gkt282

    • PubMed
    • Google Scholar
11. 1. Bright LJ
    2. Kambesis N
    3. Nelson SB
    4. Jeong B
    5. Turkewitz AP

    (2010) Comprehensive analysis reveals dynamic and evolutionary plasticity of rab GTPases and membrane traffic in tetrahymena thermophila

    PLOS Genetics 6:e1001155.

    https://doi.org/10.1371/journal.pgen.1001155

    • PubMed
    • Google Scholar
12. 1. Brodsky FM

    (1985) Clathrin structure characterized with monoclonal antibodies. I. analysis of multiple antigenic sites

    The Journal of Cell Biology 101:2047–2054.

    https://doi.org/10.1083/jcb.101.6.2047

    • PubMed
    • Google Scholar
13. 1. Brodsky FM

    (2012) Diversity of clathrin function: new tricks for an old protein

    Annual Review of Cell and Developmental Biology 28:309–336.

    https://doi.org/10.1146/annurev-cellbio-101011-155716

    • PubMed
    • Google Scholar
14. 1. Broushaki F
    2. Thomas MG
    3. Link V
    4. López S
    5. van Dorp L
    6. Kirsanow K
    7. Hofmanová Z
    8. Diekmann Y
    9. Cassidy LM
    10. Díez-Del-Molino D
    11. Kousathanas A
    12. Sell C
    13. Robson HK
    14. Martiniano R
    15. Blöcher J
    16. Scheu A
    17. Kreutzer S
    18. Bollongino R
    19. Bobo D
    20. Davudi H
    21. Munoz O
    22. Currat M
    23. Abdi K
    24. Biglari F
    25. Craig OE
    26. Bradley DG
    27. Shennan S
    28. Veeramah K
    29. Mashkour M
    30. Wegmann D
    31. Hellenthal G
    32. Burger J

    (2016) Early neolithic genomes from the eastern fertile crescent

    Science 353:499–503.

    https://doi.org/10.1126/science.aaf7943

    • PubMed
    • Google Scholar
15. 1. Bryant NJ
    2. Govers R
    3. James DE

    (2002) Regulated transport of the glucose transporter GLUT4

    Nature Reviews Molecular Cell Biology 3:267–277.

    https://doi.org/10.1038/nrm782

    • PubMed
    • Google Scholar
16. Preprint

    1. Camus SM
    2. Camus MD
    3. Sadacca LA
    4. Esk C
    5. Gould GW
    6. Kioumourtzoglou D
    7. Bryant NJ
    8. Mukherjee S
    9. Brodsky FM

    (2018) CHC22 clathrin diverts GLUT4 from the ER-to-Golgi intermediate compartment for intracellular sequestration

    bioRxiv.

    https://doi.org/10.1101/242941

    • Google Scholar
17. 1. Cann HM
    2. de Toma C
    3. Cazes L
    4. Legrand MF
    5. Morel V
    6. Piouffre L
    7. Bodmer J
    8. Bodmer WF
    9. Bonne-Tamir B
    10. Cambon-Thomsen A
    11. Chen Z
    12. Chu J
    13. Carcassi C
    14. Contu L
    15. Du R
    16. Excoffier L
    17. Ferrara GB
    18. Friedlaender JS
    19. Groot H
    20. Gurwitz D
    21. Jenkins T
    22. Herrera RJ
    23. Huang X
    24. Kidd J
    25. Kidd KK
    26. Langaney A
    27. Lin AA
    28. Mehdi SQ
    29. Parham P
    30. Piazza A
    31. Pistillo MP
    32. Qian Y
    33. Shu Q
    34. Xu J
    35. Zhu S
    36. Weber JL
    37. Greely HT
    38. Feldman MW
    39. Thomas G
    40. Dausset J
    41. Cavalli-Sforza LL

    (2002) A human genome diversity cell line panel

    Science 296:261–262.

    https://doi.org/10.1126/science.296.5566.261b

    • PubMed
    • Google Scholar
18. 1. Charlesworth D

    (2006) Balancing selection and its effects on sequences in nearby genome regions

    PLOS Genetics 2:e64.

    https://doi.org/10.1371/journal.pgen.0020064

    • PubMed
    • Google Scholar
19. 1. Dacks JB
    2. Robinson MS

    (2017) Outerwear through the ages: evolutionary cell biology of vesicle coats

    Current Opinion in Cell Biology 47:108–116.

    https://doi.org/10.1016/j.ceb.2017.04.001

    • PubMed
    • Google Scholar
20. 1. Danecek P
    2. Auton A
    3. Abecasis G
    4. Albers CA
    5. Banks E
    6. DePristo MA
    7. Handsaker RE
    8. Lunter G
    9. Marth GT
    10. Sherry ST
    11. McVean G
    12. Durbin R
    13. 1000 Genomes Project Analysis Group

    (2011) The variant call format and VCFtools

    Bioinformatics 27:2156–2158.

    https://doi.org/10.1093/bioinformatics/btr330

    • PubMed
    • Google Scholar
21. 1. Dannhauser PN
    2. Camus SM
    3. Sakamoto K
    4. Sadacca LA
    5. Torres JA
    6. Camus MD
    7. Briant K
    8. Vassilopoulos S
    9. Rothnie A
    10. Smith CJ
    11. Brodsky FM

    (2017) CHC22 and CHC17 clathrins have distinct biochemical properties and display differential regulation and function

    Journal of Biological Chemistry 292:20834–20844.

    https://doi.org/10.1074/jbc.M117.816256

    • PubMed
    • Google Scholar
22. 1. de Manuel M
    2. Kuhlwilm M
    3. Frandsen P
    4. Sousa VC
    5. Desai T
    6. Prado-Martinez J
    7. Hernandez-Rodriguez J
    8. Dupanloup I
    9. Lao O
    10. Hallast P
    11. Schmidt JM
    12. Heredia-Genestar JM
    13. Benazzo A
    14. Barbujani G
    15. Peter BM
    16. Kuderna LF
    17. Casals F
    18. Angedakin S
    19. Arandjelovic M
    20. Boesch C
    21. Kühl H
    22. Vigilant L
    23. Langergraber K
    24. Novembre J
    25. Gut M
    26. Gut I
    27. Navarro A
    28. Carlsen F
    29. Andrés AM
    30. Siegismund HR
    31. Scally A
    32. Excoffier L
    33. Tyler-Smith C
    34. Castellano S
    35. Xue Y
    36. Hvilsom C
    37. Marques-Bonet T

    (2016) Chimpanzee genomic diversity reveals ancient admixture with bonobos

    Science 354:477–481.

    https://doi.org/10.1126/science.aag2602

    • PubMed
    • Google Scholar
23. 1. Dereeper A
    2. Guignon V
    3. Blanc G
    4. Audic S
    5. Buffet S
    6. Chevenet F
    7. Dufayard JF
    8. Guindon S
    9. Lefort V
    10. Lescot M
    11. Claverie JM
    12. Gascuel O

    (2008) Phylogeny.fr: robust phylogenetic analysis for the non-specialist

    Nucleic Acids Research 36:W465–W469.

    https://doi.org/10.1093/nar/gkn180

    • PubMed
    • Google Scholar
24. 1. Diekmann Y
    2. Seixas E
    3. Gouw M
    4. Tavares-Cadete F
    5. Seabra MC
    6. Pereira-Leal JB

    (2011) Thousands of rab GTPases for the cell biologist

    PLOS Computational Biology 7:e1002217.

    https://doi.org/10.1371/journal.pcbi.1002217

    • PubMed
    • Google Scholar
25. 1. dos Reis M
    2. Thawornwattana Y
    3. Angelis K
    4. Telford MJ
    5. Donoghue PC
    6. Yang Z

    (2015) Uncertainty in the timing of origin of animals and the limits of precision in molecular timescales

    Current Biology 25:2939–2950.

    https://doi.org/10.1016/j.cub.2015.09.066

    • PubMed
    • Google Scholar
26. 1. Dunbar RIM

    (2019)

    Landscapes of Human Evolution: Contributions in Honour of John Gowlett

    Fire and the biogeography of Palaeo hominins, Landscapes of Human Evolution: Contributions in Honour of John Gowlett, Oxford, ArcheoPress.

    • Google Scholar
27. 1. Elde NC
    2. Malik HS

    (2009) The evolutionary conundrum of pathogen mimicry

    Nature Reviews Microbiology 7:787–797.

    https://doi.org/10.1038/nrmicro2222

    • PubMed
    • Google Scholar
28. 1. Esk C
    2. Chen CY
    3. Johannes L
    4. Brodsky FM

    (2010) The clathrin heavy chain isoform CHC22 functions in a novel endosomal sorting step

    Journal of Cell Biology 188:131–144.

    https://doi.org/10.1083/jcb.200908057

    • PubMed
    • Google Scholar
29. 1. Ewing G
    2. Hermisson J

    (2010) MSMS: a coalescent simulation program including recombination, demographic structure and selection at a single locus

    Bioinformatics 26:2064–2065.

    https://doi.org/10.1093/bioinformatics/btq322

    • PubMed
    • Google Scholar
30. 1. Fotin A
    2. Cheng Y
    3. Sliz P
    4. Grigorieff N
    5. Harrison SC
    6. Kirchhausen T
    7. Walz T

    (2004) Molecular model for a complete clathrin lattice from electron cryomicroscopy

    Nature 432:573–579.

    https://doi.org/10.1038/nature03079

    • PubMed
    • Google Scholar
31. 1. Fu Q
    2. Li H
    3. Moorjani P
    4. Jay F
    5. Slepchenko SM
    6. Bondarev AA
    7. Johnson PL
    8. Aximu-Petri A
    9. Prüfer K
    10. de Filippo C
    11. Meyer M
    12. Zwyns N
    13. Salazar-García DC
    14. Kuzmin YV
    15. Keates SG
    16. Kosintsev PA
    17. Razhev DI
    18. Richards MP
    19. Peristov NV
    20. Lachmann M
    21. Douka K
    22. Higham TF
    23. Slatkin M
    24. Hublin JJ
    25. Reich D
    26. Kelso J
    27. Viola TB
    28. Pääbo S

    (2014) Genome sequence of a 45,000-year-old modern human from western Siberia

    Nature 514:445–449.

    https://doi.org/10.1038/nature13810

    • PubMed
    • Google Scholar
32. 1. Fu YX
    2. Li WH

    (1993)

    Statistical tests of neutrality of mutations

    Genetics 133:693–709.

    • PubMed
    • Google Scholar
33. 1. Fumagalli M
    2. Sironi M
    3. Pozzoli U
    4. Ferrer-Admetlla A
    5. Ferrer-Admettla A
    6. Pattini L
    7. Nielsen R

    (2011) Signatures of environmental genetic adaptation pinpoint pathogens as the main selective pressure through human evolution

    PLOS Genetics 7:e1002355.

    https://doi.org/10.1371/journal.pgen.1002355

    • PubMed
    • Google Scholar
34. 1. Garud NR
    2. Messer PW
    3. Buzbas EO
    4. Petrov DA

    (2015) Recent selective sweeps in North American Drosophila melanogaster show signatures of soft sweeps

    PLOS Genetics 11:e1005004.

    https://doi.org/10.1371/journal.pgen.1005004

    • PubMed
    • Google Scholar
35. 1. Garud NR
    2. Rosenberg NA

    (2015) Enhancing the mathematical properties of new haplotype homozygosity statistics for the detection of selective sweeps

    Theoretical Population Biology 102:94–101.

    https://doi.org/10.1016/j.tpb.2015.04.001

    • PubMed
    • Google Scholar
36. 1. Guerrier S
    2. Plattner H
    3. Richardson E
    4. Dacks JB
    5. Turkewitz AP

    (2017) An evolutionary balance: conservation vs innovation in ciliate membrane trafficking

    Traffic 18:18–28.

    https://doi.org/10.1111/tra.12450

    • PubMed
    • Google Scholar
37. 1. Guindon S
    2. Dufayard JF
    3. Lefort V
    4. Anisimova M
    5. Hordijk W
    6. Gascuel O

    (2010) New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0

    Systematic Biology 59:307–321.

    https://doi.org/10.1093/sysbio/syq010

    • PubMed
    • Google Scholar
38. 1. Gutenkunst RN
    2. Hernandez RD
    3. Williamson SH
    4. Bustamante CD

    (2009) Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data

    PLOS Genetics 5:e1000695.

    https://doi.org/10.1371/journal.pgen.1000695

    • PubMed
    • Google Scholar
39. 1. Haak W
    2. Lazaridis I
    3. Patterson N
    4. Rohland N
    5. Mallick S
    6. Llamas B
    7. Brandt G
    8. Nordenfelt S
    9. Harney E
    10. Stewardson K
    11. Fu Q
    12. Mittnik A
    13. Bánffy E
    14. Economou C
    15. Francken M
    16. Friederich S
    17. Pena RG
    18. Hallgren F
    19. Khartanovich V
    20. Khokhlov A
    21. Kunst M
    22. Kuznetsov P
    23. Meller H
    24. Mochalov O
    25. Moiseyev V
    26. Nicklisch N
    27. Pichler SL
    28. Risch R
    29. Rojo Guerra MA
    30. Roth C
    31. Szécsényi-Nagy A
    32. Wahl J
    33. Meyer M
    34. Krause J
    35. Brown D
    36. Anthony D
    37. Cooper A
    38. Alt KW
    39. Reich D

    (2015) Massive migration from the steppe was a source for Indo-European languages in Europe

    Nature 522:207–211.

    https://doi.org/10.1038/nature14317

    • PubMed
    • Google Scholar
40. 1. Haga Y
    2. Ishii K
    3. Suzuki T

    (2011) N-glycosylation is critical for the stability and intracellular trafficking of glucose transporter GLUT4

    Journal of Biological Chemistry 286:31320–31327.

    https://doi.org/10.1074/jbc.M111.253955

    • PubMed
    • Google Scholar
41. 1. Hardy K
    2. Brand-Miller J
    3. Brown KD
    4. Thomas MG
    5. Copeland L

    (2015) The importance of dietary carbohydrate in human evolution

    The Quarterly Review of Biology 90:251–268.

    https://doi.org/10.1086/682587

    • PubMed
    • Google Scholar
42. 1. Hedges SB
    2. Marin J
    3. Suleski M
    4. Paymer M
    5. Kumar S

    (2015) Tree of life reveals clock-like speciation and diversification

    Molecular Biology and Evolution 32:835–845.

    https://doi.org/10.1093/molbev/msv037

    • PubMed
    • Google Scholar
43. 1. Hocquette JF
    2. Bornes F
    3. Balage M
    4. Ferre P
    5. Grizard J
    6. Vermorel M

    (1995) Glucose-transporter (GLUT4) protein content in oxidative and glycolytic skeletal muscles from calf and goat

    Biochemical Journal 305 ( Pt 2:465–470.

    https://doi.org/10.1042/bj3050465

    • PubMed
    • Google Scholar
44. 1. Hofmanová Z
    2. Kreutzer S
    3. Hellenthal G
    4. Sell C
    5. Diekmann Y
    6. Díez-Del-Molino D
    7. van Dorp L
    8. López S
    9. Kousathanas A
    10. Link V
    11. Kirsanow K
    12. Cassidy LM
    13. Martiniano R
    14. Strobel M
    15. Scheu A
    16. Kotsakis K
    17. Halstead P
    18. Triantaphyllou S
    19. Kyparissi-Apostolika N
    20. Urem-Kotsou D
    21. Ziota C
    22. Adaktylou F
    23. Gopalan S
    24. Bobo DM
    25. Winkelbach L
    26. Blöcher J
    27. Unterländer M
    28. Leuenberger C
    29. Çilingiroğlu Ç
    30. Horejs B
    31. Gerritsen F
    32. Shennan SJ
    33. Bradley DG
    34. Currat M
    35. Veeramah KR
    36. Wegmann D
    37. Thomas MG
    38. Papageorgopoulou C
    39. Burger J

    (2016) Early farmers from across Europe directly descended from neolithic Aegeans

    PNAS 113:6886–6891.

    https://doi.org/10.1073/pnas.1523951113

    • PubMed
    • Google Scholar
45. 1. Inchley CE
    2. Larbey CD
    3. Shwan NA
    4. Pagani L
    5. Saag L
    6. Antão T
    7. Jacobs G
    8. Hudjashov G
    9. Metspalu E
    10. Mitt M
    11. Eichstaedt CA
    12. Malyarchuk B
    13. Derenko M
    14. Wee J
    15. Abdullah S
    16. Ricaut FX
    17. Mormina M
    18. Mägi R
    19. Villems R
    20. Metspalu M
    21. Jones MK
    22. Armour JA
    23. Kivisild T

    (2016) Selective sweep on human amylase genes postdates the split with Neanderthals

    Scientific Reports 6:37198.

    https://doi.org/10.1038/srep37198

    • PubMed
    • Google Scholar
46. 1. Katoh K
    2. Standley DM

    (2013) MAFFT multiple sequence alignment software version 7: improvements in performance and usability

    Molecular Biology and Evolution 30:772–780.

    https://doi.org/10.1093/molbev/mst010

    • PubMed
    • Google Scholar
47. 1. Kong A
    2. Gudbjartsson DF
    3. Sainz J
    4. Jonsdottir GM
    5. Gudjonsson SA
    6. Richardsson B
    7. Sigurdardottir S
    8. Barnard J
    9. Hallbeck B
    10. Masson G
    11. Shlien A
    12. Palsson ST
    13. Frigge ML
    14. Thorgeirsson TE
    15. Gulcher JR
    16. Stefansson K

    (2002) A high-resolution recombination map of the human genome

    Nature Genetics 31:241–247.

    https://doi.org/10.1038/ng917

    • PubMed
    • Google Scholar
48. 1. Kumar P
    2. Henikoff S
    3. Ng PC

    (2009) Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm

    Nature Protocols 4:1073–1081.

    https://doi.org/10.1038/nprot.2009.86

    • PubMed
    • Google Scholar
49. 1. Lampson MA
    2. Racz A
    3. Cushman SW
    4. McGraw TE

    (2000)

    Demonstration of insulin-responsive trafficking of GLUT4 and vpTR in fibroblasts

    Journal of Cell Science 113:4065–4076.

    • PubMed
    • Google Scholar
50. 1. Landry JJ
    2. Pyl PT
    3. Rausch T
    4. Zichner T
    5. Tekkedil MM
    6. Stütz AM
    7. Jauch A
    8. Aiyar RS
    9. Pau G
    10. Delhomme N
    11. Gagneur J
    12. Korbel JO
    13. Huber W
    14. Steinmetz LM

    (2013) The genomic and transcriptomic landscape of a HeLa cell line

    G3: Genes|Genomes|Genetics 3:1213–1224.

    https://doi.org/10.1534/g3.113.005777

    • Google Scholar
51. 1. Lazaridis I
    2. Patterson N
    3. Mittnik A
    4. Renaud G
    5. Mallick S
    6. Kirsanow K
    7. Sudmant PH
    8. Schraiber JG
    9. Castellano S
    10. Lipson M
    11. Berger B
    12. Economou C
    13. Bollongino R
    14. Fu Q
    15. Bos KI
    16. Nordenfelt S
    17. Li H
    18. de Filippo C
    19. Prüfer K
    20. Sawyer S
    21. Posth C
    22. Haak W
    23. Hallgren F
    24. Fornander E
    25. Rohland N
    26. Delsate D
    27. Francken M
    28. Guinet JM
    29. Wahl J
    30. Ayodo G
    31. Babiker HA
    32. Bailliet G
    33. Balanovska E
    34. Balanovsky O
    35. Barrantes R
    36. Bedoya G
    37. Ben-Ami H
    38. Bene J
    39. Berrada F
    40. Bravi CM
    41. Brisighelli F
    42. Busby GB
    43. Cali F
    44. Churnosov M
    45. Cole DE
    46. Corach D
    47. Damba L
    48. van Driem G
    49. Dryomov S
    50. Dugoujon JM
    51. Fedorova SA
    52. Gallego Romero I
    53. Gubina M
    54. Hammer M
    55. Henn BM
    56. Hervig T
    57. Hodoglugil U
    58. Jha AR
    59. Karachanak-Yankova S
    60. Khusainova R
    61. Khusnutdinova E
    62. Kittles R
    63. Kivisild T
    64. Klitz W
    65. Kučinskas V
    66. Kushniarevich A
    67. Laredj L
    68. Litvinov S
    69. Loukidis T
    70. Mahley RW
    71. Melegh B
    72. Metspalu E
    73. Molina J
    74. Mountain J
    75. Näkkäläjärvi K
    76. Nesheva D
    77. Nyambo T
    78. Osipova L
    79. Parik J
    80. Platonov F
    81. Posukh O
    82. Romano V
    83. Rothhammer F
    84. Rudan I
    85. Ruizbakiev R
    86. Sahakyan H
    87. Sajantila A
    88. Salas A
    89. Starikovskaya EB
    90. Tarekegn A
    91. Toncheva D
    92. Turdikulova S
    93. Uktveryte I
    94. Utevska O
    95. Vasquez R
    96. Villena M
    97. Voevoda M
    98. Winkler CA
    99. Yepiskoposyan L
    100. Zalloua P
    101. Zemunik T
    102. Cooper A
    103. Capelli C
    104. Thomas MG
    105. Ruiz-Linares A
    106. Tishkoff SA
    107. Singh L
    108. Thangaraj K
    109. Villems R
    110. Comas D
    111. Sukernik R
    112. Metspalu M
    113. Meyer M
    114. Eichler EE
    115. Burger J
    116. Slatkin M
    117. Pääbo S
    118. Kelso J
    119. Reich D
    120. Krause J

    (2014) Ancient human genomes suggest three ancestral populations for present-day europeans

    Nature 513:409–413.

    https://doi.org/10.1038/nature13673

    • PubMed
    • Google Scholar
52. 1. Lippincott-Schwartz J
    2. Snapp E
    3. Kenworthy A

    (2001) Studying protein dynamics in living cells

    Nature Reviews Molecular Cell Biology 2:444–456.

    https://doi.org/10.1038/35073068

    • PubMed
    • Google Scholar
53. 1. Liu SH
    2. Towler MC
    3. Chen E
    4. Chen CY
    5. Song W
    6. Apodaca G
    7. Brodsky FM

    (2001) A novel clathrin homolog that co-distributes with cytoskeletal components functions in the trans-Golgi network

    The EMBO Journal 20:272–284.

    https://doi.org/10.1093/emboj/20.1.272

    • PubMed
    • Google Scholar
54. 1. Liu S
    2. Lorenzen ED
    3. Fumagalli M
    4. Li B
    5. Harris K
    6. Xiong Z
    7. Zhou L
    8. Korneliussen TS
    9. Somel M
    10. Babbitt C
    11. Wray G
    12. Li J
    13. He W
    14. Wang Z
    15. Fu W
    16. Xiang X
    17. Morgan CC
    18. Doherty A
    19. O'Connell MJ
    20. McInerney JO
    21. Born EW
    22. Dalén L
    23. Dietz R
    24. Orlando L
    25. Sonne C
    26. Zhang G
    27. Nielsen R
    28. Willerslev E
    29. Wang J

    (2014) Population genomics reveal recent speciation and rapid evolutionary adaptation in polar bears

    Cell 157:785–794.

    https://doi.org/10.1016/j.cell.2014.03.054

    • PubMed
    • Google Scholar
55. 1. Löytynoja A
    2. Goldman N

    (2005) An algorithm for progressive multiple alignment of sequences with insertions

    PNAS 102:10557–10562.

    https://doi.org/10.1073/pnas.0409137102

    • PubMed
    • Google Scholar
56. 1. Mallick S
    2. Li H
    3. Lipson M
    4. Mathieson I
    5. Gymrek M
    6. Racimo F
    7. Zhao M
    8. Chennagiri N
    9. Nordenfelt S
    10. Tandon A
    11. Skoglund P
    12. Lazaridis I
    13. Sankararaman S
    14. Fu Q
    15. Rohland N
    16. Renaud G
    17. Erlich Y
    18. Willems T
    19. Gallo C
    20. Spence JP
    21. Song YS
    22. Poletti G
    23. Balloux F
    24. van Driem G
    25. de Knijff P
    26. Romero IG
    27. Jha AR
    28. Behar DM
    29. Bravi CM
    30. Capelli C
    31. Hervig T
    32. Moreno-Estrada A
    33. Posukh OL
    34. Balanovska E
    35. Balanovsky O
    36. Karachanak-Yankova S
    37. Sahakyan H
    38. Toncheva D
    39. Yepiskoposyan L
    40. Tyler-Smith C
    41. Xue Y
    42. Abdullah MS
    43. Ruiz-Linares A
    44. Beall CM
    45. Di Rienzo A
    46. Jeong C
    47. Starikovskaya EB
    48. Metspalu E
    49. Parik J
    50. Villems R
    51. Henn BM
    52. Hodoglugil U
    53. Mahley R
    54. Sajantila A
    55. Stamatoyannopoulos G
    56. Wee JT
    57. Khusainova R
    58. Khusnutdinova E
    59. Litvinov S
    60. Ayodo G
    61. Comas D
    62. Hammer MF
    63. Kivisild T
    64. Klitz W
    65. Winkler CA
    66. Labuda D
    67. Bamshad M
    68. Jorde LB
    69. Tishkoff SA
    70. Watkins WS
    71. Metspalu M
    72. Dryomov S
    73. Sukernik R
    74. Singh L
    75. Thangaraj K
    76. Pääbo S
    77. Kelso J
    78. Patterson N
    79. Reich D

    (2016) The simons genome diversity project: 300 genomes from 142 diverse populations

    Nature 538:201–206.

    https://doi.org/10.1038/nature18964

    • PubMed
    • Google Scholar
57. 1. Mathieson S
    2. Mathieson I

    (2018) FADS1 and the timing of human adaptation to agriculture

    Molecular Biology and Evolution 35:2957–2970.

    https://doi.org/10.1093/molbev/msy180

    • PubMed
    • Google Scholar
58. 1. McKenna A
    2. Hanna M
    3. Banks E
    4. Sivachenko A
    5. Cibulskis K
    6. Kernytsky A
    7. Garimella K
    8. Altshuler D
    9. Gabriel S
    10. Daly M
    11. DePristo MA

    (2010) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data

    Genome Research 20:1297–1303.

    https://doi.org/10.1101/gr.107524.110

    • PubMed
    • Google Scholar
59. 1. Messer PW
    2. Petrov DA

    (2013) Population genomics of rapid adaptation by soft selective sweeps

    Trends in Ecology & Evolution 28:659–669.

    https://doi.org/10.1016/j.tree.2013.08.003

    • PubMed
    • Google Scholar
60. 1. Meyer M
    2. Kircher M
    3. Gansauge MT
    4. Li H
    5. Racimo F
    6. Mallick S
    7. Schraiber JG
    8. Jay F
    9. Prüfer K
    10. de Filippo C
    11. Sudmant PH
    12. Alkan C
    13. Fu Q
    14. Do R
    15. Rohland N
    16. Tandon A
    17. Siebauer M
    18. Green RE
    19. Bryc K
    20. Briggs AW
    21. Stenzel U
    22. Dabney J
    23. Shendure J
    24. Kitzman J
    25. Hammer MF
    26. Shunkov MV
    27. Derevianko AP
    28. Patterson N
    29. Andrés AM
    30. Eichler EE
    31. Slatkin M
    32. Reich D
    33. Kelso J
    34. Pääbo S

    (2012) A high-coverage genome sequence from an archaic denisovan individual

    Science 338:222–226.

    https://doi.org/10.1126/science.1224344

    • PubMed
    • Google Scholar
61. 1. Nahorski MS
    2. Al-Gazali L
    3. Hertecant J
    4. Owen DJ
    5. Borner GH
    6. Chen YC
    7. Benn CL
    8. Carvalho OP
    9. Shaikh SS
    10. Phelan A
    11. Robinson MS
    12. Royle SJ
    13. Woods CG

    (2015) A novel disorder reveals clathrin heavy chain-22 is essential for human pain and touch development

    Brain 138:2147–2160.

    https://doi.org/10.1093/brain/awv149

    • PubMed
    • Google Scholar
62. 1. Näthke IS
    2. Heuser J
    3. Lupas A
    4. Stock J
    5. Turck CW
    6. Brodsky FM

    (1992) Folding and trimerization of clathrin subunits at the triskelion hub

    Cell 68:899–910.

    https://doi.org/10.1016/0092-8674(92)90033-9

    • PubMed
    • Google Scholar
63. 1. Nei M

    (1973) Analysis of gene diversity in subdivided populations

    PNAS 70:3321–3323.

    https://doi.org/10.1073/pnas.70.12.3321

    • PubMed
    • Google Scholar
64. 1. Olalde I
    2. Allentoft ME
    3. Sánchez-Quinto F
    4. Santpere G
    5. Chiang CW
    6. DeGiorgio M
    7. Prado-Martinez J
    8. Rodríguez JA
    9. Rasmussen S
    10. Quilez J
    11. Ramírez O
    12. Marigorta UM
    13. Fernández-Callejo M
    14. Prada ME
    15. Encinas JM
    16. Nielsen R
    17. Netea MG
    18. Novembre J
    19. Sturm RA
    20. Sabeti P
    21. Marquès-Bonet T
    22. Navarro A
    23. Willerslev E
    24. Lalueza-Fox C

    (2014) Derived immune and ancestral pigmentation alleles in a 7,000-year-old mesolithic European

    Nature 507:225–228.

    https://doi.org/10.1038/nature12960

    • PubMed
    • Google Scholar
65. 1. Ostlund G
    2. Schmitt T
    3. Forslund K
    4. Köstler T
    5. Messina DN
    6. Roopra S
    7. Frings O
    8. Sonnhammer EL

    (2010) InParanoid 7: new algorithms and tools for eukaryotic orthology analysis

    Nucleic Acids Research 38:D196–D203.

    https://doi.org/10.1093/nar/gkp931

    • PubMed
    • Google Scholar
66. 1. Paradis E

    (2010) Pegas: an R package for population genetics with an integrated-modular approach

    Bioinformatics 26:419–420.

    https://doi.org/10.1093/bioinformatics/btp696

    • PubMed
    • Google Scholar
67. 1. Pettersen EF
    2. Goddard TD
    3. Huang CC
    4. Couch GS
    5. Greenblatt DM
    6. Meng EC
    7. Ferrin TE

    (2004) UCSF chimera--a visualization system for exploratory research and analysis

    Journal of Computational Chemistry 25:1605–1612.

    https://doi.org/10.1002/jcc.20084

    • PubMed
    • Google Scholar
68. 1. Prado-Martinez J
    2. Sudmant PH
    3. Kidd JM
    4. Li H
    5. Kelley JL
    6. Lorente-Galdos B
    7. Veeramah KR
    8. Woerner AE
    9. O'Connor TD
    10. Santpere G
    11. Cagan A
    12. Theunert C
    13. Casals F
    14. Laayouni H
    15. Munch K
    16. Hobolth A
    17. Halager AE
    18. Malig M
    19. Hernandez-Rodriguez J
    20. Hernando-Herraez I
    21. Prüfer K
    22. Pybus M
    23. Johnstone L
    24. Lachmann M
    25. Alkan C
    26. Twigg D
    27. Petit N
    28. Baker C
    29. Hormozdiari F
    30. Fernandez-Callejo M
    31. Dabad M
    32. Wilson ML
    33. Stevison L
    34. Camprubí C
    35. Carvalho T
    36. Ruiz-Herrera A
    37. Vives L
    38. Mele M
    39. Abello T
    40. Kondova I
    41. Bontrop RE
    42. Pusey A
    43. Lankester F
    44. Kiyang JA
    45. Bergl RA
    46. Lonsdorf E
    47. Myers S
    48. Ventura M
    49. Gagneux P
    50. Comas D
    51. Siegismund H
    52. Blanc J
    53. Agueda-Calpena L
    54. Gut M
    55. Fulton L
    56. Tishkoff SA
    57. Mullikin JC
    58. Wilson RK
    59. Gut IG
    60. Gonder MK
    61. Ryder OA
    62. Hahn BH
    63. Navarro A
    64. Akey JM
    65. Bertranpetit J
    66. Reich D
    67. Mailund T
    68. Schierup MH
    69. Hvilsom C
    70. Andrés AM
    71. Wall JD
    72. Bustamante CD
    73. Hammer MF
    74. Eichler EE
    75. Marques-Bonet T

    (2013) Great ape genetic diversity and population history

    Nature 499:471–475.

    https://doi.org/10.1038/nature12228

    • PubMed
    • Google Scholar
69. 1. Prüfer K
    2. Racimo F
    3. Patterson N
    4. Jay F
    5. Sankararaman S
    6. Sawyer S
    7. Heinze A
    8. Renaud G
    9. Sudmant PH
    10. de Filippo C
    11. Li H
    12. Mallick S
    13. Dannemann M
    14. Fu Q
    15. Kircher M
    16. Kuhlwilm M
    17. Lachmann M
    18. Meyer M
    19. Ongyerth M
    20. Siebauer M
    21. Theunert C
    22. Tandon A
    23. Moorjani P
    24. Pickrell J
    25. Mullikin JC
    26. Vohr SH
    27. Green RE
    28. Hellmann I
    29. Johnson PL
    30. Blanche H
    31. Cann H
    32. Kitzman JO
    33. Shendure J
    34. Eichler EE
    35. Lein ES
    36. Bakken TE
    37. Golovanova LV
    38. Doronichev VB
    39. Shunkov MV
    40. Derevianko AP
    41. Viola B
    42. Slatkin M
    43. Reich D
    44. Kelso J
    45. Pääbo S

    (2014) The complete genome sequence of a Neanderthal from the Altai mountains

    Nature 505:43–49.

    https://doi.org/10.1038/nature12886

    • PubMed
    • Google Scholar
70. 1. Raghavan M
    2. Skoglund P
    3. Graf KE
    4. Metspalu M
    5. Albrechtsen A
    6. Moltke I
    7. Rasmussen S
    8. Stafford TW
    9. Orlando L
    10. Metspalu E
    11. Karmin M
    12. Tambets K
    13. Rootsi S
    14. Mägi R
    15. Campos PF
    16. Balanovska E
    17. Balanovsky O
    18. Khusnutdinova E
    19. Litvinov S
    20. Osipova LP
    21. Fedorova SA
    22. Voevoda MI
    23. DeGiorgio M
    24. Sicheritz-Ponten T
    25. Brunak S
    26. Demeshchenko S
    27. Kivisild T
    28. Villems R
    29. Nielsen R
    30. Jakobsson M
    31. Willerslev E

    (2014) Upper palaeolithic Siberian genome reveals dual ancestry of native Americans

    Nature 505:87–91.

    https://doi.org/10.1038/nature12736

    • PubMed
    • Google Scholar
71. 1. Raineri E
    2. Dabad M
    3. Heath S

    (2014) A note on exact differences between beta distributions in genomic (Methylation) studies

    PLOS ONE 9:e97349.

    https://doi.org/10.1371/journal.pone.0097349

    • PubMed
    • Google Scholar
72. 1. Read TD
    2. Petit RA
    3. Joseph SJ
    4. Alam MT
    5. Weil MR
    6. Ahmad M
    7. Bhimani R
    8. Vuong JS
    9. Haase CP
    10. Webb DH
    11. Tan M
    12. Dove ADM

    (2017) Draft sequencing and assembly of the genome of the world's largest fish, the whale shark: Rhincodon typus Smith 1828

    BMC Genomics 18:532.

    https://doi.org/10.1186/s12864-017-3926-9

    • PubMed
    • Google Scholar
73. 1. Reynolds J
    2. Weir BS
    3. Cockerham CC

    (1983)

    Estimation of the coancestry coefficient: basis for a short-term genetic distance

    Genetics 105:767–779.

    • PubMed
    • Google Scholar
74. 1. Riddle MR
    2. Aspiras AC
    3. Gaudenz K
    4. Peuß R
    5. Sung JY
    6. Martineau B
    7. Peavey M
    8. Box AC
    9. Tabin JA
    10. McGaugh S
    11. Borowsky R
    12. Tabin CJ
    13. Rohner N

    (2018) Insulin resistance in cavefish as an adaptation to a nutrient-limited environment

    Nature 555:647–651.

    https://doi.org/10.1038/nature26136

    • PubMed
    • Google Scholar
75. 1. Rout MP
    2. Field MC

    (2017) The evolution of organellar coat complexes and organization of the eukaryotic cell

    Annual Review of Biochemistry 86:637–657.

    https://doi.org/10.1146/annurev-biochem-061516-044643

    • PubMed
    • Google Scholar
76. 1. Seguin-Orlando A
    2. Korneliussen TS
    3. Sikora M
    4. Malaspinas AS
    5. Manica A
    6. Moltke I
    7. Albrechtsen A
    8. Ko A
    9. Margaryan A
    10. Moiseyev V
    11. Goebel T
    12. Westaway M
    13. Lambert D
    14. Khartanovich V
    15. Wall JD
    16. Nigst PR
    17. Foley RA
    18. Lahr MM
    19. Nielsen R
    20. Orlando L
    21. Willerslev E

    (2014) Paleogenomics. genomic structure in Europeans dating back at least 36,200 years

    Science 346:1113–1118.

    https://doi.org/10.1126/science.aaa0114

    • PubMed
    • Google Scholar
77. 1. Shahack-Gross R
    2. Berna F
    3. Karkanas P
    4. Lemorini C
    5. Gopher A
    6. Barkai R

    (2014) Evidence for the repeated use of a central hearth at middle pleistocene (300 ky ago) Qesem Cave, Israel

    Journal of Archaeological Science 44:12–21.

    https://doi.org/10.1016/j.jas.2013.11.015

    • Google Scholar
78. 1. Shepherd PR
    2. Kahn BB

    (1999) Glucose transporters and insulin action--implications for insulin resistance and diabetes mellitus

    New England Journal of Medicine 341:248–257.

    https://doi.org/10.1056/NEJM199907223410406

    • PubMed
    • Google Scholar
79. 1. Skoglund P
    2. Malmström H
    3. Raghavan M
    4. Storå J
    5. Hall P
    6. Willerslev E
    7. Gilbert MT
    8. Götherström A
    9. Jakobsson M

    (2012) Origins and genetic legacy of neolithic farmers and hunter-gatherers in Europe

    Science 336:466–469.

    https://doi.org/10.1126/science.1216304

    • PubMed
    • Google Scholar
80. 1. Skoglund P
    2. Malmström H
    3. Omrak A
    4. Raghavan M
    5. Valdiosera C
    6. Günther T
    7. Hall P
    8. Tambets K
    9. Parik J
    10. Sjögren KG
    11. Apel J
    12. Willerslev E
    13. Storå J
    14. Götherström A
    15. Jakobsson M

    (2014) Genomic diversity and admixture differs for stone-age Scandinavian foragers and farmers

    Science 344:747–750.

    https://doi.org/10.1126/science.1253448

    • PubMed
    • Google Scholar
81. 1. Speir ML
    2. Zweig AS
    3. Rosenbloom KR
    4. Raney BJ
    5. Paten B
    6. Nejad P
    7. Lee BT
    8. Learned K
    9. Karolchik D
    10. Hinrichs AS
    11. Heitner S
    12. Harte RA
    13. Haeussler M
    14. Guruvadoo L
    15. Fujita PA
    16. Eisenhart C
    17. Diekhans M
    18. Clawson H
    19. Casper J
    20. Barber GP
    21. Haussler D
    22. Kuhn RM
    23. Kent WJ

    (2016) The UCSC genome browser database: 2016 update

    Nucleic Acids Research 44:D717–D725.

    https://doi.org/10.1093/nar/gkv1275

    • PubMed
    • Google Scholar
82. 1. Stöver BC
    2. Müller KF

    (2010) TreeGraph 2: combining and visualizing evidence from different phylogenetic analyses

    BMC Bioinformatics 11:7.

    https://doi.org/10.1186/1471-2105-11-7

    • PubMed
    • Google Scholar
83. 1. Suyama M
    2. Torrents D
    3. Bork P

    (2006) PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments

    Nucleic Acids Research 34:W609–W612.

    https://doi.org/10.1093/nar/gkl315

    • PubMed
    • Google Scholar
84. 1. Tajima F

    (1989)

    Statistical method for testing the neutral mutation hypothesis by DNA polymorphism

    Genetics 123:585–595.

    • PubMed
    • Google Scholar
85. 1. Teixeira JC
    2. de Filippo C
    3. Weihmann A
    4. Meneu JR
    5. Racimo F
    6. Dannemann M
    7. Nickel B
    8. Fischer A
    9. Halbwax M
    10. Andre C
    11. Atencia R
    12. Meyer M
    13. Parra G
    14. Pääbo S
    15. Andrés AM

    (2015) Long-Term balancing selection in LAD1 maintains a missense Trans-Species polymorphism in humans, chimpanzees, and bonobos

    Molecular Biology and Evolution 32:1186–1196.

    https://doi.org/10.1093/molbev/msv007

    • PubMed
    • Google Scholar
86. 1. Towler MC
    2. Gleeson PA
    3. Hoshino S
    4. Rahkila P
    5. Manalo V
    6. Ohkoshi N
    7. Ordahl C
    8. Parton RG
    9. Brodsky FM

    (2004) Clathrin isoform CHC22, a component of neuromuscular and myotendinous junctions, binds sorting nexin 5 and has increased expression during myogenesis and muscle regeneration

    Molecular Biology of the Cell 15:3181–3195.

    https://doi.org/10.1091/mbc.e04-03-0249

    • PubMed
    • Google Scholar
87. 1. Trefely S
    2. Khoo PS
    3. Krycer JR
    4. Chaudhuri R
    5. Fazakerley DJ
    6. Parker BL
    7. Sultani G
    8. Lee J
    9. Stephan JP
    10. Torres E
    11. Jung K
    12. Kuijl C
    13. James DE
    14. Junutula JR
    15. Stöckli J

    (2015) Kinome screen identifies PFKFB3 and glucose metabolism as important regulators of the Insulin/Insulin-like growth factor (IGF)-1 signaling pathway

    Journal of Biological Chemistry 290:25834–25846.

    https://doi.org/10.1074/jbc.M115.658815

    • PubMed
    • Google Scholar
88. 1. Vassilopoulos S
    2. Esk C
    3. Hoshino S
    4. Funke BH
    5. Chen CY
    6. Plocik AM
    7. Wright WE
    8. Kucherlapati R
    9. Brodsky FM

    (2009) A role for the CHC22 clathrin heavy-chain isoform in human glucose metabolism

    Science 324:1192–1196.

    https://doi.org/10.1126/science.1171529

    • PubMed
    • Google Scholar
89. 1. Venkatesh B
    2. Lee AP
    3. Ravi V
    4. Maurya AK
    5. Lian MM
    6. Swann JB
    7. Ohta Y
    8. Flajnik MF
    9. Sutoh Y
    10. Kasahara M
    11. Hoon S
    12. Gangu V
    13. Roy SW
    14. Irimia M
    15. Korzh V
    16. Kondrychyn I
    17. Lim ZW
    18. Tay BH
    19. Tohari S
    20. Kong KW
    21. Ho S
    22. Lorente-Galdos B
    23. Quilez J
    24. Marques-Bonet T
    25. Raney BJ
    26. Ingham PW
    27. Tay A
    28. Hillier LW
    29. Minx P
    30. Boehm T
    31. Wilson RK
    32. Brenner S
    33. Warren WC

    (2014) Elephant shark genome provides unique insights into gnathostome evolution

    Nature 505:174–179.

    https://doi.org/10.1038/nature12826

    • PubMed
    • Google Scholar
90. 1. Wakeham DE
    2. Abi-Rached L
    3. Towler MC
    4. Wilbur JD
    5. Parham P
    6. Brodsky FM

    (2005) Clathrin heavy and light chain isoforms originated by independent mechanisms of gene duplication during chordate evolution

    PNAS 102:7209–7214.

    https://doi.org/10.1073/pnas.0502058102

    • PubMed
    • Google Scholar
91. 1. Watterson GA

    (1975) On the number of segregating sites in genetical models without recombination

    Theoretical Population Biology 7:256–276.

    https://doi.org/10.1016/0040-5809(75)90020-9

    • PubMed
    • Google Scholar
92. 1. Wilbur JD
    2. Hwang PK
    3. Brodsky FM

    (2005) New faces of the familiar clathrin lattice

    Traffic 6:346–350.

    https://doi.org/10.1111/j.1600-0854.2005.00277.x

    • PubMed
    • Google Scholar
93. 1. Yang Z

    (2007) PAML 4: phylogenetic analysis by maximum likelihood

    Molecular Biology and Evolution 24:1586–1591.

    https://doi.org/10.1093/molbev/msm088

    • PubMed
    • Google Scholar
94. 1. Yates A
    2. Akanni W
    3. Amode MR
    4. Barrell D
    5. Billis K
    6. Carvalho-Silva D
    7. Cummins C
    8. Clapham P
    9. Fitzgerald S
    10. Gil L
    11. Girón CG
    12. Gordon L
    13. Hourlier T
    14. Hunt SE
    15. Janacek SH
    16. Johnson N
    17. Juettemann T
    18. Keenan S
    19. Lavidas I
    20. Martin FJ
    21. Maurel T
    22. McLaren W
    23. Murphy DN
    24. Nag R
    25. Nuhn M
    26. Parker A
    27. Patricio M
    28. Pignatelli M
    29. Rahtz M
    30. Riat HS
    31. Sheppard D
    32. Taylor K
    33. Thormann A
    34. Vullo A
    35. Wilder SP
    36. Zadissa A
    37. Birney E
    38. Harrow J
    39. Muffato M
    40. Perry E
    41. Ruffier M
    42. Spudich G
    43. Trevanion SJ
    44. Cunningham F
    45. Aken BL
    46. Zerbino DR
    47. Flicek P

    (2016) Ensembl 2016

    Nucleic Acids Research 44:D710–D716.

    https://doi.org/10.1093/nar/gkv1157

    • PubMed
    • Google Scholar
95. 1. Ybe JA
    2. Brodsky FM
    3. Hofmann K
    4. Lin K
    5. Liu SH
    6. Chen L
    7. Earnest TN
    8. Fletterick RJ
    9. Hwang PK

    (1999) Clathrin self-assembly is mediated by a tandemly repeated superhelix

    Nature 399:371–375.

    https://doi.org/10.1038/20708

    • PubMed
    • Google Scholar
96. 1. Yuan Y
    2. Zhang P
    3. Wang K
    4. Liu M
    5. Li J
    6. Zheng J
    7. Wang D
    8. Xu W
    9. Lin M
    10. Dong L
    11. Zhu C
    12. Qiu Q
    13. Li S

    (2018) Genome sequence of the freshwater Yangtze finless porpoise

    Genes 9:213.

    https://doi.org/10.3390/genes9040213

    • Google Scholar

Author details

1. Matteo Fumagalli

   1. Department of Life Sciences, Imperial College London, Ascot, United Kingdom
   2. Research Department of Structural and Molecular Biology, Division of Biosciences, University College London, London, United Kingdom
   3. Research Department of Genetics, Evolution and Environment, Division of Biosciences, University College London, London, United Kingdom
   4. UCL Genetics Institute, University College London, London, United Kingdom

   Contribution

   Formal analysis, Investigation, Visualization, Methodology, Writing—review and editing

   Contributed equally with

   Stephane M Camus and Yoan Diekmann

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4084-2953

2. Stephane M Camus

   Research Department of Structural and Molecular Biology, Division of Biosciences, University College London, London, United Kingdom

   Contribution

   Formal analysis, Investigation, Visualization, Methodology, Writing—review and editing

   Contributed equally with

   Matteo Fumagalli and Yoan Diekmann

   Competing interests

   No competing interests declared

3. Yoan Diekmann

   1. Research Department of Genetics, Evolution and Environment, Division of Biosciences, University College London, London, United Kingdom
   2. UCL Genetics Institute, University College London, London, United Kingdom

   Contribution

   Formal analysis, Investigation, Visualization, Methodology, Writing—review and editing

   Contributed equally with

   Matteo Fumagalli and Stephane M Camus

   Competing interests

   No competing interests declared

4. Alice Burke

   Research Department of Structural and Molecular Biology, Division of Biosciences, University College London, London, United Kingdom

   Contribution

   Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

5. Marine D Camus

   Research Department of Structural and Molecular Biology, Division of Biosciences, University College London, London, United Kingdom

   Contribution

   Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

6. Paul J Norman

   1. Division of Bioinformatics and Personalized Medicine, University of Colorado, Aurora, United States
   2. Department of Microbiology and Immunology, University of Colorado, Aurora, United States

   Contribution

   Resources, Formal analysis, Writing—review and editing

   Competing interests

   No competing interests declared

7. Agnel Joseph

   Institute of Structural and Molecular Biology, Birkbeck College and University College London, London, United Kingdom

   Contribution

   Investigation, Visualization, Writing—review and editing

   Competing interests

   No competing interests declared

8. Laurent Abi-Rached

   Aix-Marseille Univ, IRD, MEPHI, IHU Méditerranée Infection, CNRS, Marseille, France

   Contribution

   Resources, Writing—review and editing

   Competing interests

   No competing interests declared

9. Andrea Benazzo

   Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy

   Contribution

   Resources, Data curation, Writing—review and editing

   Competing interests

   No competing interests declared

10. Rita Rasteiro

    School of Biological Sciences, University of Bristol, Bristol, United Kingdom

    Contribution

    Resources, Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4217-3060

11. Iain Mathieson

    Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

    Contribution

    Formal analysis, Investigation, Writing—review and editing

    Competing interests

    No competing interests declared

12. Maya Topf

    Institute of Structural and Molecular Biology, Birkbeck College and University College London, London, United Kingdom

    Contribution

    Supervision, Writing—review and editing

    Competing interests

    No competing interests declared

13. Peter Parham

    1. Department of Structural Biology, Stanford University, Stanford, CA, United States
    2. Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States

    Contribution

    Supervision, Writing—review and editing

    Competing interests

    No competing interests declared

14. Mark G Thomas

    1. Research Department of Genetics, Evolution and Environment, Division of Biosciences, University College London, London, United Kingdom
    2. UCL Genetics Institute, University College London, London, United Kingdom

    Contribution

    Conceptualization, Supervision, Writing—review and editing

    Competing interests

    No competing interests declared

15. Frances M Brodsky

    1. Research Department of Structural and Molecular Biology, Division of Biosciences, University College London, London, United Kingdom
    2. Institute of Structural and Molecular Biology, Birkbeck College and University College London, London, United Kingdom

    Contribution

    Conceptualization, Supervision, Funding acquisition, Writing—original draft, Project administration

    For correspondence

    f.brodsky@ucl.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1334-9258

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