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. Author manuscript; available in PMC: 2015 Apr 1.
Published in final edited form as: Virology. 2014 Mar 21;0:291–298. doi: 10.1016/j.virol.2014.02.029

Positive Selection of Primate Genes that Promote HIV-1 Replication

Nicholas R Meyerson a, Paul A Rowley a, Christina H Swan a,b, Dona T Le a, Greg K Wilkerson c, Sara L Sawyer a,*
PMCID: PMC4028154  NIHMSID: NIHMS573720  PMID: 24725956

Abstract

Evolutionary analyses have revealed that most host-encoded restriction factors against HIV-1 have experienced virus-driven selection during primate evolution. However, HIV also depends on the function of many human proteins, called host factors, for its replication. It is not clear whether virus-driven selection shapes the evolution of host factor genes to the extent that it is known to shape restriction factor genes. We show that 5 out of 40 HIV host factor genes (13%) analyzed do bear strong signatures of positive selection. Some of these genes (CD4, NUP153, RANBP2/NUP358) have been characterized with respect to the HIV lifecycle, while others (ANKRD30A/NY-BR-1 and MAP4) remain relatively uncharacterized. One of these, ANKRD30A, shows the most rapid evolution within this set of genes and is induced by interferon stimulation. We discuss how evolutionary analysis can aid the study of host factors for viral replication, just as it has the study of host immunity systems.

Keywords: host factors, co-factors, arms race, genetic conflict, paleovirology

Introduction

HIV exploits a vast network of human proteins in order to replicate inside of human cells. These human proteins, referred to here as HIV host factors, are involved in processes such as transcription, translation, and transport of the virus (Friedrich et al., 2011; Goff, 2007). Experimentally, the depletion or disabling of HIV host factors decreases viral replication. For this reason, these host proteins constitute novel and potentially highly effective antiviral drug targets. For example, the FDA has approved the first anti-HIV drug targeting such a protein, Maraviroc, which is a small molecule antagonist of the HIV co-receptor CCR5. For this reason, there is intense interest in identifying the human proteins that promote the replication of viruses that cause human disease, including HIV, influenza, West Nile virus, Dengue virus, and others (reviewed in (Fernandez-Garcia et al., 2009; Friedel & Haas, 2011; Hirsch, 2010; Houzet & Jeang, 2011; Hsu & Spindler, 2012; Shaw, 2011; Telenti & Johnson, 2012; Watanabe et al., 2010)).

In several genetic screens aimed at identifying HIV host factors, RNA interference was used to systematically deplete human gene transcripts, and then the effects of depletion on HIV replication were evaluated (reviewed in (Bushman et al., 2009; Goff, 2008)). Collectively, these screens uncovered approximately a thousand genes that lead to reduced HIV replication when their expression is decreased (Brass et al., 2008; König et al., 2008; Yeung et al., 2009; Zhou et al., 2008). Another proteomics-based study recently employed affinity purification and mass spectrometry to uncover 435 human proteins that interact with HIV proteins (Jäger et al., 2012). While these high-throughput technologies have undoubtedly led to new insights, one surprise has been the low overlap of candidates generated between the different screens, and between the screens and the interactions previously reported in the literature (Bushman et al., 2009; Jäger et al., 2012). Similar scenarios have played out in other fields of virology where high-throughput studies are being employed (de Chassey et al., 2012; Friedel & Haas, 2011). While there are certainly many important new host factor genes being identified, the task now at hand is to triage these gene lists for further, more in depth mechanistic studies.

Advantageous mutations are preferentially inherited in populations due to the forces of positive natural selection. Repeated rounds of positive selection have been faithfully detected in all well-characterized HIV restriction factor genes (TRIM5, APOBECs, Tetherin, SAMHD1) (Compton et al., 2012; Gupta et al., 2009; Laguette et al., 2012; Lim et al., 2010; 2012; McNatt et al., 2009; OhAinle et al., 2006; Sawyer et al., 2004; 2005). This evolutionary signature results from the millions-of-years long struggle for survival between retroviruses and the primates that they infect (Compton & Emerman, 2013; Gifford, 2012; Stoye, 2012). Restriction factors block the replication of HIV upon recognition of and interaction with specific viral targets (Malim & Bieniasz, 2012). Retroviruses, in turn, often encode antagonist proteins that specifically recognize and inhibit restriction factor proteins (Malim & Emerman, 2008). The evolutionary battles between restriction factors and viruses play out at physical interaction interfaces between host and virus proteins. Both parties (host and virus) are continuously selected for mutations that modulate this interaction. For instance, the gene encoding the TRIM5α restriction factor has experienced continuous positive selection for mutations that allow TRIM5α to better recognize its target, the retroviral capsid (Sawyer et al., 2005), while capsid continuously evolves to escape interaction with TRIM5α (Kirmaier et al., 2010; McCarthy et al., 2013). This continual evolutionary struggle is referred to as an evolutionary “arms race” and, because it plays out at the level of protein-protein interactions, results in the rapid evolution of each of the interacting host and viral proteins (Meyerson & Sawyer, 2011). This signature of rapid evolution is so typical of restriction factors that it has even be used to predict novel virus-binding domains of restriction factors (Daugherty & Malik, 2012).

Host factor genes may also have the potential to undergo positive selection for new allelic forms. In fact, we recently showed that the TFR1 gene, which encodes a receptor used by several viruses for cellular entry, has undergone multiple rounds of positive selection (Demogines et al., 2013; Kaelber et al., 2012). In the case of host factors, host genomes will experience selection for alleles that reduce interactions with viruses. However, there are two critical differences between restriction factor and host factor genes. First, gain-of-function alleles of restriction factor genes are expected to have a dominant effect on viral replication. In contrast, loss-of-function alleles of host factor genes are expected to have a recessive or, at best, semi-dominant effect on viral replication because there is another allele to supply the host factor required by HIV. Second, it is not known whether HIV host factor genes have the functional flexibility to evolve under positive selection like restriction factors. Unlike restriction factors which are typically dedicated proteins of the innate immune system (Blanco-Melo et al., 2012), host factors have important roles in cellular physiology and are expected to have significantly more evolutionary constraint acting upon them.

Results

To address the question of whether or not HIV host factors also experience positive selection, we looked at the evolution of genes recently identified in several genome-wide RNA interference screens for HIV-1 host factors (Brass et al., 2008; König et al., 2008; Yeung et al., 2009; Zhou et al., 2008). Specifically, we focused on the 40 human genes that were identified in two or more of these screens (Fig. 1A) (Bushman et al., 2009; Yeung et al., 2009). For each, we calculated the dN/dS ratio, which summarizes the rate at which non-synonymous (amino-acid altering; dN) and synonymous (silent; dS) mutations have accumulated in a gene over evolutionary time. Repeated rounds of positive natural selection for non-synonymous mutations results in dN/dS > 1, whereas conservation of protein-coding sequence results in dN/dS < 1 (Goldman & Yang, 1994). We gathered sequences for each of these 40 genes from the human, chimpanzee, and rhesus macaque genome projects, and generated a 3-species multiple alignment for each gene. One of the 40 genes, RGPD8, was excluded because it is a recent duplication of another gene on this list, RANBP2, and could not be clearly identified in other primate genomes (Ciccarelli, 2005). We find that the remaining 39 host factor genes generally have lower whole-gene dN/dS values than genes encoding restriction factors (Fig. 1B), consistent with previous reports (Ortiz et al., 2009) and with their important roles as housekeeping genes.

Fig 1. Evolutionary analysis of genes encoding HIV host factors.

Fig 1

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(A) Each circle represents a whole-genome RNA interference screen previously conducted to identify human genes important for HIV replication (Brass et al., 2008; König et al., 2008; Yeung et al., 2009; Zhou et al., 2008). Indicated within overlap regions are the numbers of human genes identified in multiple screens. (B) A histogram shows whole-gene dN/dS values previously calculated for 10,376 orthologous gene trios from the human, chimpanzee, and rhesus genomes (Rhesus Macaque Genome Sequencing and Analysis Consortium et al., 2007). On the x-axis is the average dN/dS calculated over the length of each gene for this three-species tree. On the y-axis is the number of gene trios with this average dN/dS value. On this distribution are overlayed the dN/dS values for similar trios hand-curated for known HIV restriction factors (red asterisks), and for the 39 putative host factors analyzed in this study (gray asterisks). dN/dS values were calculated using the M0 model in PAML (Yang, 1997). The restriction factors are (from left to right on the distribution): SAMHD1, Tetherin, ZAP, TRIM22, TRIM5, APOBEC3H, APOBEC3DE, APOBEC3G. (C) Example sliding window analyses for CXCR4 and CD4. The x-axis is in base pairs and represents the length of each gene. dN/dS was calculated in sliding windows moving along the length of the gene. The three pairwise species comparisons made are color-coded (blue, human vs orangutan; red, human vs rhesus; green, rhesus vs marmoset). For each comparison, the window with the highest dN/dS value was tested for statistical significance using a Monte Carlo simulation (Comeron, 1999), and two windows where dN/dS is significantly greater than 1 (p < 0.001 and p < 0.004) are indicated. (D) The maximum dN/dS value observed in each pairwise sliding window analysis is summarized. An asterisk (*) indicates instances where dN/dS is significantly greater than 1 (p < 0.05).

Next, we gathered additional sequence from two other available primate genome projects (Sumatran orangutan and common marmoset), and used three different sequence pairs for each gene to calculate dN/dS in sliding windows along the length of each gene (Parmley & Hurst, 2007). Rather than just averaging dN/dS across a gene, this analysis can highlight domains of dN/dS > 1. The analyses of two genes, CXCR4 and CD4, illustrate how sliding window analysis can reveal conservation (CXCR4) or positive selection (CD4), despite the small number of sequences going into the analysis (Fig. 1C). Sliding window analysis has an inherent multiple testing problem that is difficult to correct (Schmid & Yang, 2008). As an ad hoc method for reducing false positive signatures, we sought genes with regions of dN/dS significantly > 1 in at least two out of three pairwise primate comparisons made. We find that 8 out of 39 genes meet this criterion (highlighted in Fig. 1D).

To verify these signatures of positive selection in a more statistically robust fashion, we next generated large primate datasets for each of these 8 candidate genes. Each gene was sequenced from 15 simian primate species and these sequences were combined with those gathered from the five genome projects mentioned above (the 20 species included in this dataset are indicated with asterisks in Fig. 2). In all, 153 gene sequences were generated for the 8 genes of interest.

Fig 2. ANKRD30Aand MAP4 exhibit signatures of positive selection.

Fig 2

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A cladogram illustrates the relationship of the primate species analyzed in this study. The 20 species used in the positive selection analysis are indicated with an asterisk next to the species name. Numbers at the top refer to codon coordinates in two genes, ANKRD30A and MAP4. These four codons shown were identified as evolving under positive selection, and as expected the amino acids encoded at these sites are highly variable between species. The arbitrarily colored boxes represent different amino acids encoded. All codons under positive selection in ANKRD30A and MAP4 were sequenced from individuals representing small population sets of different primate species (indicated with a triangle at the end of the branch on the primate cladogram). For humans, SNPs were identified in human SNP databases. Non-synonymous SNPs identified are indicated by 2 squares next to each other. A hyphen indicates lack of information, because ANKDR30A could not be sequenced from these species. These four codons were chosen for illustration because they are under positive selection and also bear SNPs in human or primate populations.

The multiple sequence alignment generated for each gene was analyzed for positive selection with PAML (Yang, 1997). Alignments were fit to a neutral model (Model M8a) where all codon positions are constrained to evolve with dN/dS ≤ 1, and a positive selection model (Model M8) where a dN/dS > 1 category of codons is allowed. A likelihood ratio test was then used to compare the positive selection model to the neutral model. The neutral model is rejected (p<0.005) in favor of the positive selection model in five of the eight datasets analyzed: ANKRD30A, CD4, MAP4, NUP153, and RANBP2 (Table 1). As expected, amino acids encoded by codons in the dN/dS > 1 class are highly variable between species (Fig. 2). We next acquired blood or tissue for small population groups representing 7 different primate species, and we investigated human polymorphism using the 1000 Genomes and dbSNP databases. As has been found in restriction factor genes (Meyerson & Sawyer, 2011), we identified polymorphism at several codon positions under positive selection, as illustrated for ANKRD30A and MAP4 (Fig. 2). This suggests that the functions of these genes could be variable between individuals in a species, as has been shown for other host factors and restriction factors (Duggal et al., 2013; Kirmaier et al., 2010; O'Brien & Nelson, 2004). In ANKRD30A, we find SNPs at one site of positive selection (codon/residue 1037) in both human and rhesus macaque populations. We were unable to sequence ANKRD30A from owl monkeys. Unlike all other genes analyzed herein, the inability to sequence ANKRD30A from some species was a consistent problem in our study (Table 1). This may suggest that this gene is being lost, duplicated, or rearranged in different species. This gene has also been identified as copy number variable in the human population (Park et al., 2010), and gene dosage of a host factor could theoretically contribute to variable HIV susceptibility and/or disease progression.

Table 1. PAML analysis of genes encoding HIV host factors.

Gene a Number of species a 2Δl b p-value b Positive selection? dN/dS c % sites c Codons with dN/dS>1 d * P>0.95 ** P>0.99
ANKRD30A aa 973-1335 13 36 p<0.001 yes 3.5 34% A985, D986*, Q1015**, I1022*, V1027, N1032, T1037, C1049**, A1063*, S1100, Q1107*, I1169**, C1174**, E1191, L1211*, R1233**, M1239, F1268*, H1285, H1299*, E1302, L1309
CD4 aa 28-424 25 15 p<0.001 yes 2.2 18% T15, T17*, Q20*,S23*, N32, I34, N39*, N52*, A55*, D88*, K206*, W214, R240*
IBTK aa 1-1353 20 0.9 p=0.340 no na na na
MAP4 aa 10-1152 20 8.2 p<0.005 yes 5.5 1.0% L566, T631, E640**, A814
NUP98/96 aa 80-1636 20 0.5 p=0.464 no na na na
NUP153 aa 1-1475 20 8.9 p<0.003 yes 6.4 0.5% I794, V1189**
RANBP2 aa 2002-3224 20 15 p<0.001 yes 3.7 2.2% H2418**, A2724*, M2786*, M2813*, A2890*, T3153, Q3163, K3177*
WNK1 aa 742-2073 20 0.07 p=0.795 no na na na
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a

Each dataset consists of gene orthologs from 20 primate species, with two exceptions. ANKRD30A could not be amplified from 7 primate species so the final dataset consisted of only 13 species. In the case of CD4, sequences from 5 additional primate species beyond the core set of 20 species were available on Genbank. Two of the genes, ANKRD30A and RANBP2, were difficult to amplify from the available primate tissues, so we focused on sequencing the portion of these genes corresponding to the location of dN/dS > 1 peaks in the sliding window analysis. The gene regions analyzed are indicated (by encoded amino acids, aa).

b

Twice the difference in the natural logs of the likelihoods (2Δl) of the two models (M8a-M8) being compared. The p-value indicates the confidence with which the neutral model (M8a) can be rejected in favor of the model of positive selection (M8).

c

dN/dS value of the dN/dS>1 class of codons in M8, and the percent of codons falling in that class.

d

Codons assigned to the dN/dS>1 class in M8 with a posterior probability of P>0.90 by bayes empirical bayes analysis. Codon coordinates and the encoded amino acid correspond to the human protein

Of the five genes under positive selection, all except ANKRD30A have established roles in the HIV lifecycle. CD4 is the primary receptor for primate immunodeficiency viruses, including HIV-1. In concordance with the findings of a previous study of CD4, signatures of positive selection are concentrated in the D1 domain of CD4 (Zhang et al., 2008), coinciding with the known binding region with HIV gp120 (Kwong et al., 1998). Ten of the thirteen sites identified in our evolutionary analysis of CD4 fall in this region. NUP153 and RANBP2, both associated with the nuclear pore, are known to be important for the trafficking of pre-integration complexes into the nucleus (Di Nunzio et al., 2012; Matreyek & Engelman, 2011; Woodward et al., 2009; Schaller et al., 2011). The C-terminal cyclophilin domain in RANBP2 directly interacts with HIV capsid (Schaller et al., 2011), and three residues in this cyclophilin domain are under positive selection (T3153, Q3163, K3177). NUP153 has been shown to interact with both integrase and capsid via its C-terminal FXFG hydrophobic repeat domain (Matreyek et al., 2013; Woodward et al., 2009) in which we have identified residue V1189 to be evolving under positive selection. Recently, a role for the microtubule-associated protein MAP4 was reported (Gallo & Hope, 2012). Depletion of MAP4 was shown to impact HIV-1 reverse transcription but not nuclear translocation. It is currently unknown whether or not MAP4 directly interacts with viral proteins, and if so, whether or not this interaction surface is correlated to the sites in MAP4 that we have identified to be evolving under positive selection.

No role for ANKRD30A in HIV biology has yet been reported. Interestingly, ANKRD30A has the highest dN/dS value of any HIV host factor in our study (Fig. 1B and 2Δl value shown in Table 1). ANKRD30A was identified in two genome-wide RNAi screens conducted in HeLa cells (Brass et al., 2008; Zhou et al., 2008), but not in similar screens performed in 293T or Jurkat cells (König et al., 2008; Yeung et al., 2009). It was also not identified in a proteomic screen for HIV-interacting proteins which was conducted in both 293 and Jurkat cells (Jäger et al., 2012). In order to investigate this discrepancy, we tested the expression of ANKRD30A in a human cDNA panel, HeLa, 293T, and Jurkat cells. Using a human cDNA panel, we find that ANKRD30A is expressed in several human tissues (Fig. 3A). A close paralog of this gene, ANKRD30B, is also expressed widely but in different tissues (see Figure S3 in the supplemental material). Interestingly, we find that ANKRD30A is not expressed in any of the cell lines used in the RNAi screens (Fig. 3B), making it hard to understand why this gene would have been identified in any screens. The transfection of short interfering RNAs (siRNAs), which was performed in several of these screens, is known to induce the interferon response (Whitehead et al., 2011). It is thought that siRNAs are detected by pattern recognition receptors in the cytoplasm, which then initiate the interferon production cascade. To see if ANKRD30A is induced by interferon, we treated HeLa, 293T, and Jurkat cells with 1000 U/ml interferon-β and found that expression of ANKRD30A is readily detected in HeLa and Jurkat cells, but not in 293T cells (Fig. 3B). These expression patterns may explain why ANKRD30A was found only in screens that combined HeLa cells with siRNA transfection (Fig. 3C). Under these conditions ANKRD30A expression would have been induced and then knocked-down only with on-target interfering RNAs. Jurkat cells are not easily transfectable, so in screens that employed these cells, transduction was used instead. This is an example of how genome-wide screens may be sensitive to the particular cell type used, and why different screens might identify different genes.

Fig 3. ANKRD30A expression in humans.

Fig 3

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(A) A human tissue cDNA panel was probed for ANKDR30A expression using a primer set that spans an intron. As a positive control, CRFK cells were engineered to stably express part of ANKRD30A. Human DNA was used to show the product size obtained from genomic template (with the intron). Because ANKRD30A was originally identified as a gene associated with breast cancers, we also demonstrate its expression in a breast carcinoma line (HCC1937). (B) Expression of ANKRD30A was assessed in various laboratory cell lines. Cells were grown both with and without stimulation by 1000 U/ml interferon-β, and mRNA was harvested. Expressed and spliced transcripts where detected by RT-PCR. Primers were also designed to amplify spliced transcripts of NUP153 as a control for the amount of input material in each reaction. (C) A summary of genome-wide screens for HIV host factors (Brass et al., 2008; Jäger et al., 2012; König et al., 2008; Yeung et al., 2009; Zhou et al., 2008), along with relevant details of the methods used.

Discussion

In summary, we have identified five human genes that both promote HIV replication and contain signatures of positive selection. Ancient lentiviruses may have provided the selective pressure that drove the positive selection of these genes, based on the following observations. First, four out of five of the genes under positive selection have confirmed roles in the lifecycle of HIV/SIV. Second, in the three genes that have been shown to encode HIV-interacting proteins, codons under positive selection map to known HIV-interaction domains. While these observations support the notion that ancient lentiviruses may have driven the positive selection of these genes, further experimental validation is needed to fortify this conclusion. For example, if lentiviruses drove this selection then mutations made specifically at sites under positive selection should alter interactions with one or more lentiviruses. Several studies have demonstrated this for restriction factors, where substitutions of naturally-occurring amino acids at sites under positive selection drastically alter patterns of restriction (Compton & Emerman, 2013; Lim et al., 2010; Sawyer et al., 2005). Also, we have previously demonstrated this for TFR1, a host factor that promotes the replication of retroviruses, arenaviruses, and parvoviruses (Demogines et al., 2013; Kaelber et al., 2012). Although one site under positive selection in CD4 has been shown to alter the specificity of HIV entry (Humes et al., 2012), the other sites that we have identified are uncharacterized with respect to their role in lentiviral glycoprotein binding. Similarly, with ANKRD30A, MAP4, NUP153, and RANBP2, it remains to be determined what role, if any, sites under positive selection in primate datasets play in determining specific interactions with lentiviruses. Mutation of a positively selected residue in RANBP2 was shown to affect its interaction with HIV capsid, although this site was identified in a more extensive mammalian dataset (Schaller et al., 2011).

We cannot rule out the possibility that these host genes have evolved under selection imposed by other pathogens. Indeed, NUP153 has been shown to also mediate influenza replication in an RNA interference screen (König et al., 2010), and it is well documented that many nuclear-replicating viruses interact with the nuclear pore (Cohen et al., 2011). Also, proteins like MAP4 that are involved in intracellular trafficking have also been shown to be a common target of viral hijacking (Radtke et al., 2006). We conclude that positive selection can shape the evolution of host factor genes, consistent with other recent studies on cell surface receptors (Demogines et al., 2013; Kaelber et al., 2012; Martin et al., 2013), components of the nuclear pore (Schaller et al., 2011), and DNA repair machinery (Demogines et al., 2010; Sawyer & Malik, 2006).

We have previously demonstrated that mutations in one host factor for viral replication, TfR1, reduce interactions with virions act in a semi-dominant fashion with regard to viral entry (Demogines et al., 2013). This is because half of the receptor pool is affected. In this way, selection can operate on these alleles even in heterozygous individuals (Meyerson & Sawyer, 2011). Selection might be expected to be less intense on a semi-dominant trait than on a dominant trait. This is consistent with our finding that host factor genes are generally more conserved than restriction factor genes (Fig. 1B), and that many host factor genes analyzed did not experience positive selection at all. Indeed, CXCR4, LEDGF and CPSF6 are examples of human genes that are well-known to be involved in HIV replication, but that are extremely conserved in protein sequence (Cherepanov, 2004; Lee et al., 2012).

More extensive work will be needed to characterize the role of ANKRD30A as a host factor for HIV-1, and to investigate a possible role of ANKRD30B in HIV-1 biology. This could be quite important because, in addition to having the strongest signature of positive selection in our study, ANKRD30A is one of the few candidate HIV host factors that has been categorized as a “druggable” target, based on properties that it shares with proteins that have been already successfully targeted by drugs (Bushman et al., 2009; Hopkins & Groom, 2002).

In summary, a subset of HIV host factor genes are driven to evolve under positive selection just like restriction factor genes are known to be. Evolutionary studies should be useful in the functional characterization of these host factors just as they have been in the characterization of restriction factors.

Materials and Methods

Primate biomaterials used

Primary and immortalized cell lines from various primate species (see Figure S1 in the supplemental material) were grown in standard media supplemented with 15% fetal bovine serum at 37° C and in 5% CO2. Macaque, owl monkey, and squirrel monkey samples included in the population study are listed in Supplemental Fig. S2, and were acquired from either the Michale E Keeling Center for Comparative Medicine and Research (Bastrop, Texas), or from the New England Primate Research Center (Southborough, MA). For these individuals, 2.5 mL of whole blood was collected in PaxGene Blood RNA Tubes (BD, #762165). Alternately, B cell lines were expanded in suspension culture, in RPMI, 20% FBS, Pen/Strep, L-glutamine, HEPES, and AZT. Genomic DNA and RNA from primate blood and cell lines was isolated using the PaxGene miRNA kit (Qiagen, #763134) and/or the Qiagen All Prep DNA/RNA mini kit (Qiagen, #80204).

Primate gene sequences

The method of acquisition for all primate gene sequences is summarized in Figure S1 found in the supplemental material. Human Refseq sequences were obtained from the NCBI “gene” database. Some chimpanzee, orangutan, rhesus macaque, and marmoset gene sequences were obtained from the UCSC genome database (http://genome.ucsc.edu/) using the BLAT alignment tool. Genes were sequenced from 15 additional primate species, and from chimpanzee, orangutan, rhesus and marmoset in instances where the genome-project sequences were of poor quality. PCR or RT-PCR was performed from total RNA, gDNA, or cDNA with SuperScript III One-Step RT-PCR system with Platinum Taq (Invitrogen, #12574-018), PCR SuperMix High Fidelity (Invitrogen, #10790-020), or Phusion High Fidelity PCR Master Mix (NEB, #F-531S). Details of the PCR and Sanger sequencing strategy, along with primer sequences, are given in Table S1 found in the supplemental material. Primate gene sequences have been deposited in GenBank (accession numbers xxxx-xxxx). Macaque, owl monkey, and squirrel monkey population data (species and sampling scheme summarized in Figure S2 found in the supplemental material) were acquired using primers listed in Table S1 found in the supplemental material.

Human polymorphism analysis

Human SNPs were identified in datasets deposited by the 1000 Genomes Project (http://browser.1000genomes.org, release 13) and NCBI's dbSNP database (http://www.ncbi.nlm.nih.gov/SNP).

Sliding window analysis

Sliding-window dN/dS calculations for each alignment were performed with the SLIDERKK program (Parmley & Hurst, 2007). Human-orangutan, human-rhesus and rhesus-marmoset alignments were analyzed with standard window sizes (Demogines et al., 2010) of 450bp, 306bp and 153bp, respectively, to reflect the increasing level of divergence in these species pairs (window size must be a multiple of nine in this program). In order to generate confidence values for windows with dN/dS > 1, the K-estimator program (Comeron, 1999) was utilized to generate a null distribution of dN/dS values through Monte Carlo simulation in the gene region of interest.

PAML analysis

Codon models were tested with codeml in the PAML 4.1 software package (Yang, 1997). To detect selection, multiple alignments were fit to the NSsites models M8a (neutral model, codon values of dN/dS fit to a beta distribution plus an extra codon class fixed at dN/dS = 1) and M8 (positive selection model, similar to M8a but with the extra class allowed to be dN/dS >1). A likelihood ratio test was performed to assess whether permitting codons to evolve under positive selection gives a significantly better fit to the data (model comparison M8a vs. M8). In situations where the neutral model could be rejected (p < 0.05), posterior probabilities were assigned to individual codons belonging to the class of codons with dN/dS > 1. For the whole-gene dN/dS values indicated on the genome-wide distribution of human/chimpanzee/rhesus gene trios, dN/dS values were calculated using the M0 model in PAML.

Expression analysis of ANKRD30A in human tissues and laboratory cell lines

Primers were designed to amplify a 750bp fragment that spans the last two exons of ANKRD30A. PCR reactions were carried out with PCR SuperMix High Fidelity (Invitrogen, #10790-020) along with primers NRM602 (forward: 5′-TTAGGGAAGAATTAGGAAGAATC-3′) and NRM604 (reverse: 5′-CATTTGACACTGTGTTTCACGTTG-3′). The Human Total RNA Master Panel II (Clontech, #636643) was converted to cDNA using the SuperScript III First-Strand Synthesis System (Invitrogen, #18080-051) and used as a template in PCR reactions. The Human MTC cDNA Panel II (Clontech, #636743) was also utilized. HeLa, 293T, Jurkat, and breast carcinoma (HCC1937) cells were grown in standard media and RNA was harvested using an All Prep DNA/RNA mini kit (Qiagen, #80204). Each of these cell lines was also treated with IFN- β (Betaseron) at a concentration of 1000 units/ml for 24 hours prior to isolation of RNA. ANKRD30A transcripts detected were verified via sequencing.

Supplementary Material

01

Highlights.

  • 153 primate gene sequences were generated for genes that promote HIV replication.

  • ANKRD30A, CD4, MAP4, NUP153, and RANBP2 evolve under positive selection.

  • HIV host factors can be subject to positive selection like HIV restriction factors.

  • ANKRD30A is an interesting candidate HIV host factor for further study.

Acknowledgments

The authors wish to thank Ann Demogines, Maryska Kaczmarek, Scott Kerr, Dianne Lou, and Alex Stabell for critical reading of the manuscript. We thank Nels Elde for human/chimp/rhesus evolutionary data. We thank the Michale E. Keeling Center for Comparative Medicine and Research for providing primate samples. We thank Dr. Welkin Johnson for macaque cell lines. We thank the UCSC Genome Brower team, NCBI, the 1000 Genomes Project, and various primate sequencing consortiums for the valuable resources that they provide, free of charge, to the research community.

This work was supported by grants from amfAR: The Foundation for AIDS Research (107447-45-RGNT), the National Institutes of Health (R01-GM-093086), and the NIH Office of Research Infrastructure Programs (P40 OD010938). N.R.M. is supported by a National Science Foundation Graduate Research Fellowship. S.L.S. holds a Career Award in the Biomedical Sciences from the Burroughs Wellcome Fund, and is an Alfred P. Sloan Research Fellow in Computational and Evolutionary Molecular Biology.

Footnotes

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