Genomic Copy Number Variation Study of Nine Macaca Species Provides New Insights into Their Genetic Divergence, Adaptation, and Biomedical Application

Abstract

Copy number variation (CNV) can promote phenotypic diversification and adaptive evolution. However, the genomic architecture of CNVs among Macaca species remains scarcely reported, and the roles of CNVs in adaptation and evolution of macaques have not been well addressed. Here, we identified and characterized 1,479 genome-wide hetero-specific CNVs across nine Macaca species with bioinformatic methods, along with 26 CNV-dense regions and dozens of lineage-specific CNVs. The genes intersecting CNVs were overrepresented in nutritional metabolism, xenobiotics/drug metabolism, and immune-related pathways. Population-level transcriptome data showed that nearly 46% of CNV genes were differentially expressed across populations and also mainly consisted of metabolic and immune-related genes, which implied the role of CNVs in environmental adaptation of Macaca. Several CNVs overlapping drug metabolism genes were verified with genomic quantitative polymerase chain reaction, suggesting that these macaques may have different drug metabolism features. The CNV-dense regions, including 15 first reported here, represent unstable genomic segments in macaques where biological innovation may evolve. Twelve gains and 40 losses specific to the Barbary macaque contain genes with essential roles in energy homeostasis and immunity defense, inferring the genetic basis of its unique distribution in North Africa. Our study not only elucidated the genetic diversity across Macaca species from the perspective of structural variation but also provided suggestive evidence for the role of CNVs in adaptation and genome evolution. Additionally, our findings provide new insights into the application of diverse macaques to drug study.

Significance

Copy number variation (CNV) plays an important role in the adaptation and evolution of mammals. However, CNV in Macaca species has not been thoroughly studied so far, which hinders the understanding of genome features, adaptive evolution, and biomedical application of macaques. Here, we identified and characterized genome-wide interspecific CNVs among nine Macaca species, demonstrating that these macaques mainly diverge from one another in nutritional metabolism, drug metabolism, and immune-related pathways from the perspective of structural variation. Our findings provide not only suggestive evidence for the role of CNVs in the adaptation and evolution of Macaca species but also new insights into the biomedical application of these nonhuman primates.

Introduction

Copy number variations (CNVs) represent a major form of structural genetic variation. CNVs are segments of DNA that have variable copy numbers (CNs) within a species or a lineage (Feuk et al. 2006; Freeman et al. 2006). Widely spread over the genome, CNVs typically range from 1 kb to 5 Mb in the human genome (Redon et al. 2006). A growing amount of evidence suggests that CNVs are associated with local adaptation via phenotypic trait variations (Kondrashov 2012). CNVs may impact variance in expression levels of specific genes via gene dosage effects, positional effects, gene splits, gene fusions, and unmasking of recessive alleles (Lupski and Stankiewicz 2005; Henrichsen et al. 2009; Hou et al. 2012), leading to different phenotypes and susceptibility to diseases (Gökçümen and Lee 2009; Iskow et al. 2012). Additionally, they can modify genome architecture and lead to additional structural variants that promote genome evolution and speciation (Perry et al. 2008; Conrad et al. 2010).

CNV features have been studied in many animal taxa, including birds (chicken [Wang et al. 2010; Jia et al. 2013] and zebra finch [Völker et al. 2010]) and mammals (mice and rats [Cahan et al. 2009; Charchar et al. 2010], dogs and wolves [Nicholas et al. 2011; Alvarez and Akey 2012], pigs [Chen et al. 2012; Paudel et al. 2015], sheep [Yang et al. 2018], cattle [Keel et al. 2016], yaks [Goshu et al. 2019], horse [Jun et al. 2014], and great apes [Marques-Bonet et al. 2009; Gazave et al. 2011; Oetjens et al. 2016]). In particular, CNV studies in humans and other great apes have uncovered the role of CNVs in various phenotypic traits, diseases, and primate evolution (Stranger et al. 2007; Itsara et al. 2009; Zhang et al. 2009; Ventura et al. 2011; Almal and Padh 2012; Sudmant et al. 2013). Compared with the great ape lineage, the study of CNV in Macaca is rather limited. Prior work has mainly focused either on conspecific variations of the rhesus (Lee et al. 2008; Gokcumen et al. 2011) and cynomolgus macaques (Gschwind et al. 2017) or on particular gene families in macaques (Degenhardt et al. 2009; Uno et al. 2010; Ottolini et al. 2014), leaving genomic CNVs across Macaca species scarcely reported and the biological significance of CNVs in ecological and evolutionary processes of macaques unsolved.

The genus Macaca (Primates: Cercopithecidae) is a group of diverse Catarrhini that contains 23 species (Solari and Baker 2006; Li et al. 2015) which have diverged over a short evolutionary timespan. Macaques are the most widely distributed nonhuman primates (NHPs), occupying various habitat types in Asia along with Northern Africa and Southern Europe (Solari and Baker 2006). They have diverged genetically, morphologically, and behaviorally to adapt to a wide range of environmental conditions (Thierry 2007; Roos and Zinner 2015). Therefore, this genus has experienced both rapid speciation and adaptive radiation (Jiang et al. 2016). To date, the general phylogenetic relationships among macaques have been well addressed, and the seven-species-group phylogeny (Zinner et al. 2013; Roos et al. 2014) based on molecular evidence is widely accepted.

Macaques are also important animal models in a wide range of biomedical research including drug development studies. They display very high similarity to humans in development, immunology, pathology, and behavior (Haus et al. 2014). Besides rhesus (Macaca mulatta) and cynomolgus (Macaca fascicularis) macaques, southern pig-tailed (Macaca nemestrina), Barbary (Macaca sylvanus), Tibetan (Macaca thibetana), and Japanese (Macaca fuscata) macaques are increasingly used as NHP models in biomedical research (Hatziioannou et al. 2009; Pouladi et al. 2013; Zhang et al. 2017). The genetic backgrounds of different macaques may strongly affect the results of biomedical studies. For example, because of a species-specific insertion of a retrotransposon in the TRIM5 gene, the southern pig-tailed macaque can be infected by HIV-1, whereas rhesus and cynomolgus macaques cannot (Brennan et al. 2008; Newman et al. 2008). However, detailed exploration of the impact of using different Macaca species on research outcomes is limited. Despite many genome-wide single-nucleotide variant (SNV) studies elucidating the evolutionary history, population genetics, or intraspecific genetic diversity of macaques (Fang et al. 2011; Yan et al. 2011; Higashino et al. 2012; Fan, Zhao, et al. 2014; Zhong et al. 2016; Fan et al. 2018; Liu et al. 2018), the genetic divergence among Macaca species has not been thoroughly surveyed, especially from the perspective of structural variation and CNV.

Recently, easier accessibility of next-generation sequencing (NGS) data of Macaca species combined with well-developed CNV detection approaches allow for comprehensive characterization of genome-wide CNVs in macaques. NGS-based methods have become a popular strategy for CNV detection. Compared with array-based approaches like single-nucleotide polymorphism (SNP) and comparative genomic hybridization arrays (Carter 2007; Li and Olivier 2013), the NGS-based approach has higher resolution, more accurate estimation of CNs and breakpoints, and better capability to identify novel CNVs (Meyerson et al. 2010; Alkan et al. 2011). Various software tools, such as Breakdancer (Fan, Abbott, et al. 2014), and pipelines such as fastCN, which calculates CNs with a read depth (RD)-based approach (Pendleton et al. 2018), have been developed to detect CNVs using NGS data.

In this study, we used a RD-based method to identify interspecific CNVs and shared duplications genome-wide across nine species of macaques and further analyzed the distribution patterns and potential functions of these CNVs with bioinformatic approaches. In addition to the two flagship species in Macaca, rhesus and cynomolgus macaques, our sample set also includes the only non-Asian species, Barbary macaque (M. sylvanus) and several less studied macaque species. Altogether, this systematic study presents the first comprehensive map of genome-wide interspecific CNV in Macaca. We aimed at not only obtaining a better understanding of the genetic diversity and biomedical application of these Macaca species but also providing new insights into the roles of CNVs in genetic diversity, environmental adaptation, and the evolution of these animals.

Materials and Methods

Genome Data

Whole resequenced genomes of four macaques were produced in-house, including the Japanese (Macaca fuscata, JM), Taiwanese (Macaca cyclopis, TwM), Barbary (M. sylvanus, BM), and lion-tailed (Macaca silenus, LM) macaques, as shown in table 1. These sequence data were combined with public genome data of the Chinese rhesus (Macaca mulatta lasiota, CR) (Yan et al. 2011), cynomolgus (M. fascicularis, CE) (Yan et al. 2011), Tibetan (M. thibetana, TM) (Fan, Zhao, et al. 2014), stump-tailed (Macaca arctoides, SM) (Fan et al. 2018), and southern pig-tailed (Macaca nemestrina, PM) macaques. A genome data set of nine individuals was analyzed, involving nine species and representing six out of seven-species groups in Macaca (table 1). The sequence data for each sample varied from 25× (TwM) to nearly 84× (JM) coverage, allowing sufficient power to detect CNVs.

Table 1.

Information on Genome Data in This Study

Scientific Names	Sample Identifier(s)	GenBank Accession(s)	Sequencing Platform(s)	# Reads	Genome Depth	Total Usable Base Pairs	Sex	Sample Origin(s)	Source(s)
M. mulatta mulatta	Mmul_8	—	Illumina	20,100,000	5.1×	—	Female	Washington National Primate Research Center	Zimin et al. (2014)
M. mulatta lasiota	CR	SRA023856	Illumina	3,299,851,568	45.65×	2,264,143,011	Female	Yunnan, China	Yan et al. (2011)
M. fascicularis	CE	SRA023855	Illumina	3,299,851,568	43.96×	2,245,482,535	Female	Vietnam	Yan et al. (2011)
M. arctoides	SM	SRX1470574	Illumina	1,001,034,260	34.55×	2,280,352,231	Female	Southwestern China	Fan et al. (2018)
M. thibetana	TM	SRP032525	Illumina	1,275,012,390	36.92×	2,281,638,762	Female	Sichuan, China	Fan, Zhao, et al. (2014)
M. nemestrina	PM	SRX1022644	Illumina	770,413,198	25.59×	2,246,079,419	Female	Washington National Primate Research Center	Baylor College of Medicine
M. fuscata	JM	SRR11921216	Illumina	2,258,829,541	83.85×	2,271,704,290	Female	Kyoto Primate Research Center	Fan ZX, Zhou AB, Xing JC, Hey J, Osada N, Melnick DJ, Yue BS, Li J. (unpublished data)
M. cyclopis	TwM	SRR11921217	Illumina	2,279,695,913	24.66×	2,279,695,913	Female	Kyoto Primate Research Center	Fan ZX, Zhou AB, Xing JC, Hey J, Osada N, Melnick DJ, Yue BS, Li J. (unpublished data)
M. sylvanus	BM	SRR11921218, SRR11927939–SRR11927943	Illumina	2,226,490,341	45.91×	2,226,490,341	Female	Columbia University	In this study
M. silenus	LM	SRR11921219, SRR11927944–SRR11927948	Illumina	2,241,953,780	46.49×	2,241,953,780	Female	Columbia University	In this study

CN Estimation Using Short-Read Data

As Macaca species are phylogenetically close, it is reasonable to estimate interspecific CNVs by mapping the short-read sequences from various macaques to the reference genome of the Indian rhesus macaque (Macaca mulatta mulatta; Mmul_8) (Zimin et al. 2014). Prior to mapping, we employed FastQC (v0.11.8) (http://www.bioinformatics.babraham.ac.uk/projects/fastqc) to perform quality control checks on raw sequences, then used Trimmomatic (v0.36) (Bolger et al. 2014) to filter and trim the reads. The cleaned reads were aligned to the reference genome using BWA mem (Li and Durbin 2009).

The program fastCN was designed to efficiently estimate genome CN from short-read data utilizing RD information (https://github.com/KiddLab/fastCN) (Pendleton et al. 2018). Two steps were implemented to estimate CNs with the fastCN pipeline. First, GC correction was performed using custom-defined control regions to remove the GC bias introduced by polymerase chain reaction (PCR) during library preparation and sequencing. Due to the lack of known control regions across macaques, we implemented an iterative process to retrieve effective control regions, which correspond to a CN of two in these diploid genomes. The initial control regions were defined as autosomal genomic regions excluding segments masked by RepeatMasker and Tandem Repeat Finder (Benson 1999), overrepresented 50mers, assembly gaps, and an additional 36 bp flanking each masked segment. RD was converted to estimated CN based on a set of control regions. This calculation was performed in windows that contain an equal number (1,000 bp, 1 kb) of unmasked, nongap positions. As a result, although each window contains the same number of interrogated positions, the actual size of the windows along the genome is variable and individual windows may span assembly gaps. Using these initial data, we then defined a revised set of control regions which appeared to have a fixed CN. Specifically, we optimized the controls for each sample as segments where RD fell into the full width at half maximum of the RD distribution of all 1-kb windows (supplementary fig. S1, Supplementary Material online), which were likely to be regions with a CN of two. We then repeated the GC normalization and CN estimation procedure using the revised control regions for all the samples.

Second, GC-corrected per-bp depths were converted to mean depths in windows containing 1 kb of unmasked sequence. As described above, the windows differ in genomic length, but each window contains 1,000 unmasked, nongap positions. We estimated genome-wide CNs based on window depths by using a correction factor calculated from the average RD of the control regions. The calculation function is as follows:

$$\begin{array}{c}\text{CF}={\text{RD}}_{\text{ctl}}\text{/2}\\ \text{CN}=\text{RD/CF}\end{array},$$

where CF stands for the correction factor, RD represents the read depth of specific genomic window, and RD_ctl is the mean read depth of the control regions. Unplaced contigs were merged as a single “chrUn” in data processing to decrease the CPU time.

CNV and Shared Duplication Identification

Due to the absence of multiple individuals of the same species, we used the maximum copy number difference (CND_m) to define interspecific CNV, which is the difference between the maximum CN (CN_max) and the minimum CN (CN_min) of the samples:

$${\text{CND}}_{\text{m}}={\text{CN}}_{\text{max}}-{\text{CN}}_{\text{min}}.$$

Theoretically, duplications are regions with CN of at least three copies, deletions are segments with CN of one (heterozygous deletion) or zero (homozygous deletion), and CNVs are bins where the CN difference is equal to or greater than one copy for any two samples, which means CND_m ≥ 1 among the nine samples. To correct for noise in the CNs, we checked the modal value of CND (∼0.6) which should approximate zero, and thus set the CND_m threshold for CNV as 1.6 (1 + 0.6) copies (supplementary fig. S2, Supplementary Material online). Duplications were defined as windows with 2.7 (3 − 0.6/2) or more copies and deletions were defined as bins with 1.3 (1 + 0.6/2) or less copies among the nine samples. Duplications shared by all macaques are considered to be fixed in this genus, which are of research importance. To reduce false positives, we only kept CNVs or shared duplications no shorter than 3 kb. After merging consecutive 1-kb windows and calculating the mean CNs for merged windows, we filtered out these failing the thresholds or that were shorter than three windows (∼3 kb). CNVs on chrUn were excluded from subsequent analyses. We employed the UCSC genome browser to examine the CN patterns using custom track files.

CNV-Dense Region Detection

We surveyed CNV density across the genome using a sliding window of 10 Mb with a custom python script. CNV density was defined as the CNV count in each window. According to the count distribution, bins with ten or more CNVs were empirically considered to be a CNV-dense region. LiftOver (https://genome.ucsc.edu/cgi-bin/hgLiftOver) was used to convert the coordinates to match the human reference genome hg19. We investigated if the CNV-dense regions overlapped with CNV hotspots shared by human, chimpanzee, and rhesus macaque identified in Gokcumen et al. (2011).

Gene-Based Annotation and Functional Enrichment Analyses

To delineate the functional impact of CNVs, we performed gene annotation and enrichment analyses. Gene-based annotation was implemented with “bedtools window” (Quinlan and Hall 2010). Because CNVs can regulate the expression and function of adjacent genes, we set the intersecting window size between CNVs and genes as 5 kb. Annotations of gene models in rhesus macaque genome were obtained from Ensembl (http://ftp.ensembl.org/pub/release-92). The gene ontology (GO) and KEGG enrichment analyses were performed with standalone KOBAS 3.0 (Xie et al. 2011) on genes that intersected CNVs or shared duplications with 5-kb window allowance. The background gene set contained all Ensembl genes in the rhesus macaque. We chose “Fisher’s exact test” and “Benjamini and Hochberg (1995)” as the statistical and FDR correction methods, respectively. Small terms with five or less genes were dropped from our analyses. Functional enrichment with g:Profiler (Reimand et al. 2007) was also conducted on genes intersecting with CNV-dense regions. Hierarchical filtering was set as “best per parent group”. The size of the functional categories ranged from 5 to 2,000 genes. Benjamini–Hochberg FDR was employed to calculate significant threshold.

Permutation Test

To inspect if there was any positional bias of the CNVs or shared duplications, we calculated empirical significance by performing 1,000 genome-wide permutations. We shuffled both the locations of the CNVs or shared duplications and locations of genes with “bedtools shuffle” (Quinlan and Hall 2010) to examine the following three factors: 1) the number of CNVs or shared duplications intersecting with genes, 2) the number of genes intersecting with CNVs or shared duplications, and 3) the lengths of intersecting genes. The shuffling tested the following hypotheses, 1) if the genes were overlapped with CNVs or shared duplications incidentally, 2) if large genes were more likely to emerge in the enriched pathways, and 3) if the enriched CNV intersecting genes tended to emerge together. P values were defined as the possibility of the observation in the distribution of the permutation data.

Lineage-Specific CNV Screening

Lineage-specific CNVs were screened to investigate the evolutionary features or adaptive characteristics of macaques. We utilized Picard (v1.98; http://broadinstitute.github.io/picard/) and GATK (v3.2) (Depristo et al. 2011) to identify the genome-wide interspecific SNVs of the nine species. After hard filtration suggested by the GATK website (QualByDepth [QD] < 2.0; QUAL < 30.0; FisherStrand [FS] > 60.0; RMSMappingQuality [MQ] < 40.0; StrandOddsRatio [SOR] > 4.0; MQRankSum < −12.5; ReadPosRankSum < −8.0), the SNVs were thinned to 500,000 sites with PLINK (v1.07) (Purcell et al. 2007) to estimate a phylogenetic tree using the Neighbor-Joining (NJ) method by SNPhylo (Lee et al. 2014). Bootstrap replicates (n = 1,000) were employed to assess branch support. Clade-specific duplication was defined as CN ≥ 2.7 for a clade and around two copies (1.7 ≤ CN ≤ 2.3) for others. Correspondingly, a lineage-specific CNV deletion was called when CN ≤ 1.3 for a lineage but 1.7 ≤ CN ≤ 2.3 for others.

Genomic Quantitative Polymerase Chain Reaction Validation

To validate the CNVs in drug metabolism genes including CYP2C76, UGT2B33, UGT1A1, GSTM5, and GSTM1, real-time quantitative polymerase chain reaction (qPCR) was conducted on genomic DNA. Primers were designed with Primer3Plus (https://primer3plus.com/cgi-bin/dev/primer3plus.cgi) for the CNVs and a diploid internal control, part of RPP30 with no CN alteration among macaques (supplementary fig. S3, Supplementary Material online). Primer information is available in supplementary table S1, Supplementary Material online. The fidelity of the primers was checked in silico.

Blood samples of Chinese rhesus (CR-AB, CR-OB), cynomolgus (CE-3), Tibetan (TM-4), stump-tailed (SM-2), and Japanese (JM-5) macaques were collected from Chengdu Zoo and Hengshu Bio-Technology Company. Due to lack of Indian rhesus macaque, Chinese rhesus macaque (CR-AB) was used as calibrator for all qPCR experiments. As southern pig-tailed macaque is not distributed in China, we used northern pig-tailed macaque (Macaca leonine, PM-6, feces, Chengdu Zoo) instead, which is the phylogenetically closest species to the southern pig-tailed macaque in China, and both belong to silenus group (Zinner et al. 2013; Roos et al. 2014). Genomic DNA was extracted using TIANamp Genomic DNA Kit (TIANGEN, Peking, China). All samples were obtained in accordance with Chinese regulations for the implementation of protection of terrestrial wild animals (State Council Decree [1992] No.13), and all laboratory work was approved by the Guidelines for Care and Use of Laboratory Animals and the Ethics Committee of Sichuan University (Chengdu, China). Throughout the procedure, care was taken to ensure animal welfare for all monkeys.

By referring to previous studies (Jung et al. 2013; Wang et al. 2018), relative quantification with ΔΔCT method was employed, and CN of each target was calculated as 2 × 2^−ΔΔCT. Genomic qPCR was performed using the real-time qPCR system as recommended by the manufacturer’s instruction. In brief, a 10 μl of reaction mixture contained 10 ng of genomic DNA, 1× Taq SYBRGreen qPCR Mix (Innovagene, Changsha, China), and 5 pmol of each primer. Thermal cycling conditions consisted of one cycle of 3 min at 94 °C followed by 40 cycles of 20 s at 94 °C, 40 s at 60 °C, and 20 s at 72 °C. All qPCR experiments were triplicate.

Differential Expression Analysis of CNV Genes Based on Population Transcriptome Data

To explore if CNVs affected gene expression and further biological functions, we investigated the expression levels of CNVs intersecting genes (CNVGs) whose CNs were distinct (CND ≥ 1.6) between the Chinese rhesus macaque (CR) and the Tibetan macaque (HT) using the expression matrices in Yan (2019), which studied the blood transcriptomes of 28 Chinese rhesus macaques and 24 Tibetan macaques. For the CNVGs with detectable expression, differentially expressed genes (DEGs) were identified using threshold of P < 0.05 and q < 0.05. To test if the percentage of differentially expressed CNVGs was significant, we randomly resampled the same number of CNVs between the Chinese rhesus and Tibetan macaques from all interspecific CNVs 1,000 times and compared the observation with the percentages of differentially expressed CNVGs in the resampled data. We also reviewed the main functions of these DEGs to infer the biological impact of these CNVs.

Results

Duplicated Regions across the Nine Species

We estimated genomic CN from NGS data of nine Macaca species based on the rhesus macaque reference genome (Mmul_8) using the fastCN algorithm (Pendleton et al. 2018). To improve the performance of CN estimation, we created an iterative process to identify control regions for GC normalization of the observed sequencing depth as described in Materials and Methods. The improved controls lead to an effective GC correction for all species (supplementary fig. S4, Supplementary Material online).

Using the estimated CNs, gains and losses that spanned three or more 1-kb windows were detected on a per sample basis for the nine species. There were 2,183 (M. fascicularis, CE) to 2,686 (M. thibetana, HT) gains identified per sample. The cumulative lengths of duplications across all chromosomes for each sample are shown in figure 1A. The genomic distribution of CN gains shows a highly uneven pattern. For example, chromosome 19 harbors the largest proportion of duplications, varying from 6.42% for the Tibetan macaque to 10.36% for the Chinese rhesus macaque. The lowest percentage of duplications was found on chromosome 18, fluctuating from 0.04% for the Chinese rhesus, Japanese, and crab-eating macaques to 0.11% for the lion-tailed macaque. Additionally, we identified an excess of duplications on chromosome 14 in the Japanese macaque. This excess was driven by a 4-Mb event that was absent in all other samples (fig. 1B and 1C). This large duplication overlapped with 17 genes, including five genes in the TRIM family (TRIM49, putative TRIM49B, TRIM51, putative TRIM64B, and TRIM77) and three genes associated with the nervous system (GRM5, FOLH1, and NAALAD2), and also harbored a small shared duplication intersecting a homolog to human TRIM64.

Fig. 1.

Genomic patterns of duplications across the nine macaque species. (A) Cumulative lengths of duplications detected in three or more 1-kb windows across all chromosomes for each sample. (B) Proportion of cumulative duplication lengths for each chromosome. (C) Copy number (blue histograms) across the large duplication on chromosome 14 of Japanese macaque is visualized in UCSC genome browser relative to other macaques (species symbols on left) and in the context of Ensembl gene models (red). Copy number was estimated in windows containing 1 kb of nongap, nonmasked sequence. As a result, the genomic span of individual windows is variable and may include positions annotated as assembly gaps.

Shared Duplications

We searched for regions that are duplicated in all analyzed macaques, regardless of the estimated CN. Although each species was represented by one individual, the shared duplicated regions likely represent genomic regions expanded across Macaca, indicating the common genomic features of this group. In total, 1,560 duplications were shared by all assessed macaques (fig 2 and supplementary fig. S5, Supplementary Material online). Shared duplications on chromosome 7 were longer than these on other chromosomes. Chromosome 19 displayed the highest abundance, whereas chromosome 18 held the lowest percentage of shared duplications (fig. 2B). Due to their across-genus distribution, these CN gains likely resulted from duplications that occurred in the last common ancestor of these species.

Fig. 2.

Cumulative lengths of the shared duplications detected in three or more 1-kb windows on each chromosome in the nine Macaca species. (A) Average ratios of the cumulative lengths of shared duplications to the cumulative length of duplications per chromosome across the nine samples. Error bars represent the standard deviations of the ratios among the nine species. (B) The cumulative lengths (green bars) of the shared duplications per chromosome and the percentage (red line) of shared duplications on each chromosome in terms of length.

We observed that 1,166 shared duplications and their 5-kb flanking sequences intersected 1,656 Ensembl genes annotated in the rhesus macaque genome. To assess the genomic location patterns of these duplicates, we conducted permutation tests by randomly shuffling the coordinates (n = 1,000 shuffles) of the duplications or the Ensembl genes and found that shared duplicates are significantly enriched in genic regions (P = 0.001) and that more genes intersected with shared duplications than expected by chance (P = 0.001). We also noted that the lengths of these genes were shorter than expected (P = 0.001) (supplementary figs. S6 and S7, Supplementary Material online). This was opposite to the expectation that CNVs would overlap with long genes due to random chance, suggesting that the enrichment may be driven by gene clustering, given that clustered genes are usually short in length.

Functional enrichment analysis of the 1,656 intersecting genes using KOBAS 3.0 (P ≤ 0.01, supplementary table S2, Supplementary Material online) showed that the majority of the enriched pathways were metabolic pathways, including metabolism of steroid hormone, xenobiotics, retinol, pentose and glucuronate, starch and sucrose, aldarate, and porphyrin. The enriched categories also contained many ribosome-related terms, coincident with the finding of a CNV study of horse (Doan et al. 2012) that 11.9% of ribosomal RNA genes in horse were affected by CNVs, ranking the first among all kinds of genes. To explore if these functional terms were enriched incidentally due to colocalization of genes belonging a shared biological pathway, permutation tests were conducted again by carrying out enrichment analyses using shuffled duplication data sets or shuffled gene sets. Permutations showed that the observed count of enriched pathways was >90% of the pathway counts found in the permutations and that the count of significantly enriched GO categories was larger than that found in >95% of the permutations (supplementary figs. S8 and S9, Supplementary Material online). This suggests the observed results reflect true enrichment signals rather than spurious hits due to random chance.

Genome-Wide Interspecific CNVs

A total of 1,479 regions were identified as CNs variable across the nine Macaca species with lengths of three or more consecutive 1-kb windows (fig. 3 and supplementary data set S1 and fig. S10, Supplementary Material online), including 1,106 gains and 451 losses. Of these, 78 were complex CNVs containing both gains and losses. Gains were ∼2.5-fold more common than losses and displayed comparatively larger average sizes. These CNVs totaled 39.7 Mb, or 1.41% of the genome. The individual lengths of CNVs ranged from 3,001 to 1,086,528 bp with a mean and median of 26,857 and 12,007 bp, respectively. The majority of identified CNVs were relatively small, as ∼70% were between 3 and 20 kb. The count comparison of CNVs with different lengths (≥1-kb, ≥3× 1-kb, and ≥10× 1-kb windows) is shown in supplementary table S3, Supplementary Material online.

Fig. 3.

Genomic distribution of all interspecific CNVs (detected in three or more 1-kb windows) across the nine Macaca species. The blue rectangles represent duplication CNVs and the red rectangles represent deletion CNVs.

Functional Annotation of CNVs

Using 5-kb intersecting windows, 854 out of 1,479 CNVs overlapped with 1,420 Ensembl genes. Specifically, 727 (65.73%) duplications intersected with 1,287 genes, and 164 (36.36%) deletions encompassed 302 genes (supplementary fig. S11, Supplementary Material online). In total, 52.81% of CNVs are directly located in genic regions, which is concordant with the study of Lee et al. (2008) where 55% (68/124) CNVs identified in rhesus macaque were genic. Genes overlapping with CNVs and their 5-kb flanking regions (CNVGs) can be separated into three categories: duplicated genes overlapped by CNV gains, deleted genes intersecting with CNV losses, and mixtures where genes colocalize with loci harboring both gains and losses.

Permutation tests highlighted the role of gene clustering in this enrichment. Although the observed CNVs did not overlap with a gene more often than expected by chance (P = 0.142, supplementary fig. S12A, Supplementary Material online), the total number of genes that intersected with a CNVs was greater than expected (P = 0.001, supplementary fig. S12B, Supplementary Material online), and the length of the intersecting genes was significantly shorter than expected (P = 0.001, supplementary fig. S12C, Supplementary Material online). The observed intersection counts were substantially different from those found when the positions of genes were randomly shuffled (supplementary fig. S13, Supplementary Material online). Thus, gene clustering, that is, the nonuniform placement of genes along the genome, partially accounts for the increased number of genes that overlap with CNVs. Furthermore, we observed that CNVGs were significantly shorter than expected (supplementary figs. S12 and S13, Supplementary Material online). This may reflect a real genome feature or represent a bias due to the comparatively low quality of the macaque genome assembly or gene model annotation.

We propose two hypotheses for the origin of the “bias” in CNV gene length: 1) the rhesus macaque reference genome is incompletely assembled, and/or 2) the gene models in CNV regions may be inaccurate. To investigate these assumptions, we compared not only the protein coding gene lengths in control regions and CNV regions but also the quality of gene models from rhesus (Mmul_8), chimpanzee (Pan_troglodytes-2), and human (GRCh38) reference genomes. The genomic lengths of all protein coding genes in the macaque genome were similar to that in the chimpanzee genome, but shorter than those in the human genome (supplementary table S4a, Supplementary Material online), suggesting that assembly quality may affect our results. The comparison between control and CNV regions also indicates quality of gene models in CNVs may contribute to the observation (supplementary table S4b, Supplementary Material online).

We again performed enrichment analyses with KOBAS 3.0 and subsequent permutation tests for CNVs. CNVGs were generally overrepresented (P ≤ 0.01) in three main categories: nutritional metabolism, xenobiotics/drug metabolism, and immune-related pathways, but some enrichments lacked strong support from the corrected P value (table 2 and supplementary table S5, Supplementary Material online). Enrichment outputs of duplicated genes were quite similar to that of all CNVGs, mainly because copy gains outnumbered copy losses. Deleted genes were enriched in “olfactory transduction” and disease pathways including “Viral myocarditis” (mcc05416), “Asthma” (mcc05310), and “Graft-versus-host disease” (mcc05332) (supplementary table S6, Supplementary Material online). These pathways were in accordance with the enriched GO terms related to signaling receptor activity. Results from randomized permutations based on locations of both CNVs and genes showed that there were 90% more enriched KEGG pathways and GO terms than expected by chance (supplementary figs. S14 and S15, Supplementary Material online), confirming the functional enrichment in these CNVGs. Additionally, we note that enriched genes (ribosome genes, HLA loci, and olfactory genes) tended to be clustered in the genome (Younger et al. 2001; Ishii et al. 2006).

Table 2.

Enrichment Outputs of Genes Intersecting the CNVs and Their 5-kb Flanking Sequences Using KOBAS 3.0: (A) Enriched KEGG Pathways and (B) Enriched GO Terms (Only Exhibiting the Highest Category in the Tree for GO Terms Containing Exactly the Same Genes)

Term	ID	Input No.	Background No.	P Value	Corrected P Value	Gene Symbols a	CNV ID
(A) Enriched KEGG pathways
Pentose and glucuronate interconversions	mcc00040	6	21	1.9E-05	0.034	ALDH3A2, LOC706528, UGT2B33, UGT1A1, UGT2B15, ENSMMUG00000012355	chr16-19414797–19421121, chr3-160661084–160869232, chr5-65506980–65623157, chr5-65628860–65729639, chr12-116232453–116238344, chr5-65466947–65505089
Ascorbate and aldarate metabolism	mcc00053	5	14	4.0E-05	0.037	UGT2B15, ALDH3A2, UGT1A1, UGT2B33, ENSMMUG00000012355	chr5-65628860–65729639, chr16-19414797–19421121, chr12-116232453–116238344, chr5-65506980–65623157, chr5-65466947–65505089
Viral myocarditis	mcc05416	7	42	7.8E-05	0.047	ABL2, MAMU-DRB1, LOC106992470, SGCD, CASP9, MAMU-DPB1, ACTB	chr1-188324769–188338450, chr4-33338105–33352404, chr4-33355720–33386474, chr4-33406118–33412221, chr4-30203434–30413162, chr6-154795623–154806159, chr1-14308663–14373529, chr4-33928581–33933207, chr3-39508480–39512271
Retinol metabolism	mcc00830	7	45	1.1E-04	0.052	UGT2B33, CYP2C76, LOC713738, UGT1A1, UGT2B15, ALDH1A2, ENSMMUG00000012355	chr5-65506980–65623157, chr5-65628860–65729639, chr9-90297250–90344108, chr11-55695239–55754415, chr12-116232453–116238344, chr7-34700918–34706034, chr5-65466947–65505089
Chemical carcinogenesis	mcc05204	7	55	3.5E-04	0.013	GSTM5, UGT2B33, CYP2C76, UGT1A1, UGT2B15, CYP2A23, ENSMMUG00000012355	chr1-110697591–110747857, chr5-65506980–65623157, chr5-65628860–65729639, chr9-90297250–90344108, chr12-116232453–116238344, chr19-36763842–36769753, chr5-65466947–65505089
Antigen processing and presentation	mcc04612	6	44	6.7E-04	0.15	KIR2DL4, MAMU-DRB1, LOC106992470, KLRC3, KLRC1, MAMU-DPB1	chr19-50037741–50073879, chr4-33338105–33352404, chr4-33355720–33386474, chr4-33406118–33412221, chr4-30203434–30413162, chr11-10736952–10744681, chr11-10746926–10788644, chr4-33928581–33933207
Porphyrin and chlorophyll metabolism	mcc00860	5	29	7.4E-04	0.15	UGT2B15, UGT1A1, UGT2B33, ENSMMUG00000012355, FXN	chr5-65628860–65729639, chr12-116232453–116238344, chr5-65506980–65623157, chr5-65466947–65505089, chr15-85248111–85260241
Drug metabolism: cytochrome P450	mcc00982	6	48	0.001	0.17	GSTM5, UGT2B33, CYP2C76, UGT1A1, UGT2B15, ENSMMUG00000012355	chr1-110697591–110747857, chr5-65506980–65623157, chr5-65628860–65729639, chr9-90297250–90344108, chr12-116232453–116238344, chr5-65466947–65505089
Type I diabetes mellitus	mcc04940	5	32	0.0011	0.17	MAMU-DPB1, HSPD1, MAMU-DRB1, LOC106992470, LOC693438	chr4-33928581–33933207, chr2-148896350–148910750, chr4-33338105–33352404, chr4-33355720–33386474, chr4-33406118–33412221, chr4-30203434–30413162, chr5-105546464–105549989
Metabolism of xenobiotics by cytochrome P450	mcc00980	6	49	0.0011	0.17	GSTM5, UGT2B33, UGT1A1, UGT2B15, CYP2A23, ENSMMUG00000012355	chr1-110697591–110747857, chr5-65506980–65623157, chr5-65628860–65729639, chr12-116232453–116238344, chr19-36763842–36769753, chr5-65466947–65505089
Starch and sucrose metabolism	mcc00500	5	36	0.0018	0.25	UGT2B15, UGT1A1, UGT2B33, ENSMMUG00000012355, AMY2B	chr5-65628860–65729639, chr12-116232453–116238344, chr5-65506980–65623157, chr5-65466947–65505089, chr1-104405951–104421047
RNA degradation	mcc03018	6	64	0.0039	0.46	HSPD1, LOC693438, PARN, PABPC1, EXOSC1, PFKP	chr2-148896350–148910750, chr5-105546464–105549989, chr20-14709561–14721562, chr8-99416856–99422405, chr9-92935279–92941800, chr9-2908518–2937562, chr9-2980923–3000708
Drug metabolism: other enzymes	mcc00983	4	32	0.0072	0.46	UGT2B15, UGT1A1, UGT2B33, ENSMMUG00000012355	chr5-65628860–65729639, chr12-116232453–116238344, chr5-65506980–65623157, chr5-65466947–65505089
(B) Enriched GO terms
Glucuronosyltransferase activity	GO:0015020	3	5	5.1E-04	0.13	UGT1A1, UGT2B33, UGT2B15	chr12-116232453–116238344, chr5-65506980–65623157, chr5-65628860–65729639
UDP-glycosyltransferase activity	GO:0008194	3	14	0.0053	0.46	UGT1A1, UGT2B33, UGT2B15	chr12-116232453–116238344, chr5-65506980–65623157, chr5-65628860–65729639
DNA conformation change	GO:0071103	3	15	0.0063	0.46	HMGB3, NCAPD2, H2BFWT	chrX-144618359–144621948, chr11-6773682–6777531, chrX-97761715–97768667
Flavonoid metabolic process	GO:0009812	2	5	0.009	0.46	UGT2B33, UGT2B15	chr5-65506980–65623157, chr5-65628860–65729639

Note.—The term description and ID are provided along with the number of genes identified near our CNVs (input) compared with the total Ensembl gene set of the rhesus macaque (background). The raw and corrected P values are indicated. Input gene names with enrichment signals (P ≤ 0.01) can be found in the last second column.

^aThe novel genes without gene symbols are indicated with Ensembl gene IDs.

Drug metabolism genes were highly enriched among the CNV intersecting genes, including CYP2C76, UGT2B33, UGT2B15, UGT1A1, and GSTM5. Notably, the CN patterns of CYP2C76 fit the expectations for Macaca species under the evolutionary phylogeny reconstructed from genomic SNVs with high bootstrap support values (supplementary fig. S16, Supplementary Material online). In detail, the species that diverged early in the phylogenetic tree (BM, LM, and PM) maintained two copies of CYP2C76, whereas others had around four copies. We observed distinct CN difference across macaques in this region on the UCSC genome browser, with a short shared duplication (∼1 kb, 8–9 copies) embedded in the CNV (fig. 4A). We also uncovered 26 apparent SNVs in the CNV intersecting CYP2C76 which were heterozygous in all macaques except for BM, LM, and PM, whose genotypes were homozygous at each locus, validating this CNV with SNV genotyping. The CN pattern at GSTM5 was generally consistent with the phylogenetic topology of this genus as well. The clade of CE, CR, and TwM, representing recently diverged macaques, had 5–6 copies of GSTM5, but only 2–3 copies were found in other species (fig. 4B).

Fig. 4.

Copy number patterns of CYP2C76 and GSTM5 across the nine Macaca species. (A) CYP2C76 (chr9: 90,280,000–90,360,000) and (B) GSTM5 (chr1: 110,670,000–110,770,000). The black baselines in the tracks indicate copy number of two, and CNV regions are indicated with black dashed box. As in figure 1, copy number was estimated in windows containing 1 kb of nongap, nonmasked sequence.

CNV-Dense Regions

We detected 26 CNV-dense regions (containing 479 CNVs), which displayed at least ten CNVs per 10-Mb segment. These CNV-dense regions were distributed across the genome, and chromosome 7 harbored four such regions, ranking the first among all chromosomes. Fifteen of the 26 CNV-dense regions are first reported here, whereas the remaining 11 regions overlap with human, chimpanzee, and rhesus macaque-shared CNV regions identified by Gokcumen et al. (2011), which are very likely to be CNV hotspots in both great apes and Catarrhini. Several important immunity-related genes, such as HLA, HCG9, DEFA, and DEFB, were located in the overlapping segments, along with members of the most polymorphic CNV-enriched gene families: olfactory receptor (OR), TRIM, and ZNF. Studies involving more species are needed to investigate this pattern further.

Functional enrichment analyses performed with g:Profiler showed that the 722 genes intersecting CNV-dense regions were enriched in immune function, as suggested by the most significantly enriched pathway “antigen processing and presentation” (mcc04612, P = 1.81E-05) and enriched GO terms including immunoglobulin production (GO:0002377, P = 4.4E-08), antigen processing and presentation of peptide antigen via MHC class I (GO:0002474, P = 1.08E-06), MHC protein complex (GO:0042611, P = 1.9E-08), and peptide antigen binding (GO:0042605, P = 0.00229).

Lineage-Specific CNVs

To address evolutionary issues of these sibling species, we reconstructed the Macaca phylogeny with SNPhylo using thinned genomic SNVs with the NJ method and surveyed the lineage-specific CNVs according to the resulting topology (fig. 5). Six of the seven-species groups defined by Zinner et al. (2013) and Roos et al. (2014) in Macaca are included in this study. We did not identify any CNV specific to group mulatta (CR, TwM, and JM), fascicularis (CE), sinica (TM), or arctoides (SM). This absence may reflect the very short species divergence time in the four species groups.

Fig. 5.

Lineage-specific interspecific CNVs are displayed on the branches of the NJ tree of nine Macaca species, which was generated by SNPhylo based on thinned genomic SNVs (500k sites). Bootstrap values are at each node, as determined by 1,000 bootstraps. Species groups defined by Zinner et al. (2013) and Roos et al. (2014) are labeled in green ovals on the tree.

We observed that BM, the only member in sylvanus, formed the longest branch of the phylogenetic tree and there were 12 gains and 40 losses specific to this clade (fig. 5). It is intriguing that the number of specific loss events was more than twice the number of specific gain events. BM-specific (sylvanus-specific) CNVs overlapped with genes related to metabolism and immunity, including duplications in PRKD1/PKD1 and CD55, and deletions in ADIPOR2, TBX20, and SERINC5 (supplementary fig. S17, Supplementary Material online). These CNVs also intersect LRP1B, a gene whose deletion or downregulation significantly correlates with acquired chemotherapy resistance in high-grade serious cancers (Cowin et al. 2012).

Twenty-two duplications and one deletion were specific to the large group comprising all other species. These CNVs intersect with genes that are mainly involved in metabolism and immunity, too, such as ACBD7, APOBEC3F, and IGHV7-4-1. The silenus species group (composed of LM and PM) displayed 16 shared CNVs, including three duplications and 13 deletions. One of the silenus-specific duplications is just ∼500 bp away from the gene NPTX2, a member of neuronal pentraxin family associated with neurological disorders and cancers.

Genomic qPCR Validation of CNVs

We attempted to validate the CNs of drug metabolism genes including CYP2C76, UGT2B33, UGT1A1, GSTM5, and GSTM1 with qPCR conducted on genomic DNA from samples of the same species, but different than the resequenced individuals. Results of qPCR indicated that CNV detection based on RD was credible and also demonstrated that some of the identified CNVs were not fixed in the studied species. In detail, the CN distribution of GSTM5 was consistent with that based on NGS data (fig. 6A). And CN patterns of GSTM1 and UGT1A1 were generally in accord with that obtained from NGS data except for the cynomolgus macaque (fig. 6B and C). Taking UGT1A1 for example, Tibetan and cynomolgus macaques have more copies of this region than others according to the qPCR results, whereas CNV detection with NGS data showed that Tibetan macaque had more copies than other species who displayed similar CNs. These inconsistencies could be due to outstanding genetic variation in the populations of cynomolgus macaques which was demonstrated by Li et al. (2018), Ling et al. (2011), and Satkoski Trask et al. (2013), because the qPCR sample originated from a breeding population in China of unclear geographical source; however, the resequenced individual came from Vietnam (Yan et al. 2011). Therefore, our results suggest that GSTM1 and UGT1A1 are CN polymorphic in cynomolgus macaque. Because of failure in primer design or PCR, we were unable to validate the CNV in CYP2C76 and UGT2B33.

Fig. 6.

Comparison of copy number patterns determined by genomic qPCR and bioinformatic analysis. Copy numbers from qPCR (blue) and bioinformatically estimated (red) approaches are provided for genes (A) GSTM5, (B) GSTM1, and (C) UGT1A1. The whiskers stand for standard errors of copy numbers estimated by independent technical replicates of qPCR experiments.

Differential Expression of the CNV Intersecting Genes

Based on the blood transcriptomes of 28 Chinese rhesus macaques and 24 Tibetan macaques (Yan 2019), we explored if the CNVs had an impact on gene expression. We discovered that a considerable proportion of CNVGs were DEGs. In total, 370 CNVs showed distinct copy numbers (CND ≥ 1.6) between the Chinese rhesus macaque and the Tibetan macaque and intersected with 135 genes with quantifiable expression based on the transcriptome data. Approximately 46% (62/135) of these CNVGs were DEGs (P < 0.05 and q < 0.05, supplementary table S7, Supplementary Material online). However, the ratio was not significantly different from the randomly resampled data (P = 0.065, supplementary fig. S18, Supplementary Material online). Genes playing important roles in metabolism (APOL1, PDK3, and GLUD1), immune function (IL9R, LILRB1, LILRA2, MAMU-A, and MAMU-A3) along with zinc finger genes were included in the DEGs.

Discussion

This study represents the most extensive assessment of CNV across macaques to date, sampling six out of seven-species groups in Macaca and including some less studied species. Our results provide new evidence for the involvement of CNVs in the adaptation and evolution of macaques. In total, 1,479 CNVs, constituting 1.41% of the macaque genome, along with 1,560 duplications shared across species, 26 CNV-dense regions, and dozens of lineage-specific CNVs were identified. High coverage genome data and an improved CNV detection pipeline based on fastCN allowed for a higher-resolution map of CNVs across Macaca species. Although each species was only represented by single individual, our study identified interspecific genetic divergence in Macaca from the perspective of structural variation, in contrast to previous genomic CNV studies in rhesus and cynomolgus macaques that focused on intraspecific genetic polymorphism (Lee et al. 2008; Gokcumen et al. 2011). Function enrichment and expression-level analyses with transcriptome data from Yan (2019) suggest roles for CNV in environmental adaptation and genome evolution of Macaca, with implications for the usage of these NHPs in drug metabolism or diseases research.

Characteristics of the CNV Regions

We uncovered the general CN patterns and chromosomal distributions of CNVs among Macaca species. For example, we found that chromosome 19, the shortest autosome, displayed the most significant enrichment of CNVs. This chromosome is also enriched for genes and microsatellites (Xu et al. 2016) in macaques. In genome-wide CNVs, we observed a higher number of gains (1,106) relative to losses (451). This imbalance was unlikely a bias derived from CNV detection methods, because identification of deletions is very robust in NGS approaches (Pinto et al. 2011; Pang et al. 2014) and previous CNV studies of rhesus macaque (Lee et al. 2008) and human (Sudmant et al. 2015) also detected more genomic gains than losses. Furthermore, genomes are more likely tolerant of duplications than deletions which could result in loss of functions (Brewer et al. 1999) and are typically selected against (Zarrei et al. 2015). However, within a single lineage, gene copy losses generally dominated gains, demonstrating that CNV deletions are also related to the phylogenetic evolution and may be used as phylogenetic markers for Macaca.

Along with shared duplications, CNVs were distributed unevenly in the genomes of macaques. Greater than 52% of CNVs were located directly in genes, whereas only 34–36% of interspecific SNVs were genic for macaques (Li et al. 2018). This is in agreement with previous finding that CNVs were prone to occur in gene-rich regions (Conrad et al. 2010). CNVs and shared duplications overlapped more frequently with relatively short genes than expected, which may be a true trait of these regions attributed to gene clustering or a bias due to the quality of the reference genome. In addition, 26 CNV-dense regions were identified, with 15 regions specific to Macaca and 11 shared by human, chimpanzee, and macaques. According to a human study of such loci (Dumas et al. 2007), CNV-dense regions are prone to gene instability and are possible “gene nurseries” where new gene families may be emerging, facilitating biological innovation and rapid evolution of macaques. Genes overlapping the 11 potential CNV hotspots in primates included several ORs and immunity-related genes, such as HLA, HCG9, DEFA, and DEFB, which suggests that diversity in immunity represents a main evolutionary strategy in primates.

The Possible Role of CNV in Adaptations of Macaca

Although some enriched GO or KEGG terms lacked significant support based on corrected P value, we do find that CNVs are functionally relevant, with a bias toward metabolism and immunity function. CNVGs were mainly enriched for nutritional metabolism, xenobiotics/drug metabolism, and immune-related pathways (table 2). Using expression data of Chinese rhesus and Tibetan macaques (Yan 2019), we found that differentially expressed CNVGs also mainly consisted of metabolic and immune-related genes (e.g., APOL1 and LILRB1). The functional categories were not only partially overlapping with the enrichment outputs of all DEGs between the two species (Yan 2019) but also consistent with results from a comparative transcriptome study (Li et al. 2017) in which these expression differences were found to be mainly in the GO term of nutrient reservoir activity and KEGG subcategories including infectious diseases and immune system. Our results indicate that these monkeys are genetically divergent from one another in metabolism and immunity, agreeing with the conclusions of a SNV study of macaques (Li et al. 2018), and also indicate that CNVs may affect gene functions related to environmental responses such as metabolism and immune response. Given that Macaca species have different foraging habits (Srivastava 1999; Hanya et al. 2011), body sizes (Solari and Baker 2006), and immunity traits (Trichel et al. 2002; De Vries et al. 2012), these findings may reflect adaptation to diverse habitats.

Biological processes influencing adaptation have been identified in CNVGs of many animals, including metabolic processes, stress response, and defense response. For example, CNVs in the α-amylase gene facilitated adaptation to dietary starch consumption in both humans (Mandel and Breslin 2012) and dog breeds (Mandel and Breslin 2012; Arendt et al. 2016). CNV-overlapping genes related to drug detoxification and innate or adaptive immunity were overrepresented in human (Freeman et al. 2006; Almal and Padh 2012), pig (Wang et al. 2012; Paudel et al. 2013), dog (Nicholas et al. 2009), and cattle (Fadista et al. 2010; Hou et al. 2011), involving gene families like CYP, ABC, HLA, MHC, BD, IL, and OR, which were also present in the CNVGs identified in this study. This can be explained by a general model that phylogenetically stable genes have core functions in development and physiology, whereas unstable genes have accessory functions associated with unstable environmental interactions such as toxin and pathogen exposure (Thomas 2007).

However, it is worth noting that there are limitations for gene set enrichment analysis even in human where the majority of annotations is generated, and more uncertainty exists in species such as macaques which have less precise gene annotations. For example, some immune-related pathways may include poorly annotated genes containing immunoglobulin-like domains that are evolving fast and hence are subject to duplication, without truly being involved in immunity-related traits. Additionally, we found CNVs tended to overlap with gene clusters, necessitating another layer of cautiousness on the enrichment results. Therefore, more investigation is needed to elucidate the connection between the identified CNVs and adaptive differences between Macaca species.

Implications for the Biomedical Application of Macaca Species

Macaca species have been extensively used as experimental models in drug discovery research and drug safety evaluation, including rhesus, crab-eating, and Barbary macaques (Zuber et al. 2002). Intriguingly, xenobiotics/drug metabolism was one of the most enriched biologic processes for the CNVGs in Macaca, suggesting that various macaques could react differently to drugs. Highly overrepresented CNVGs included CYP2C76, UGT1A1, UGT2B33, and GSTM5, which belong to three well-known drug-metabolizing enzyme families, CYP, UGT, and GST. CYP2C76 and GSTM5 expanded in recently diverged species, such as the crab-eating, Chinese rhesus, and Taiwanese macaques (fig. 4).

Drug metabolism genes can determine drug half-life (Linder et al. 1997; He et al. 2011). These highly polymorphic genes are thus important in pharmaceutical development (Linder et al. 1997; He et al. 2011). A previous study showed that CYP genes in macaques were nearly identical to the orthologs in human (Uno et al. 2011). CNVs in these genes were also observed in human (He et al. 2011; Fuselli 2019), and individuals with more than two copies of CYP2D6 wildtype alleles had elevated CYP2D6 enzyme activity (Ingelman-Sundberg 2005). Additionally, we found that ∼46% of the CNVGs with distinct copies in Chinese rhesus and Tibetan macaques were differentially expressed, suggesting that a large proportion of CNVGs have an altered expression level and may result in different phenotypes. Therefore, we propose that the Barbary, lion-tailed, crab-eating, and Chinese rhesus macaques may differ in drug metabolism of certain substrates due to CNV of drug metabolism-related genes. This CNV study, to some extent, provides theoretical basis for the selection of optimal NHP models for drug research and preclinical toxicology tests. Further functional studies of individuals CNVs are needed to fully address this issue.

Because CNV plays an essential role in phenotypes and diseases (Stranger et al. 2007; Zhang et al. 2009; Almal and Padh 2012), CNVs can affect the outcome and interpretation of biomedical studies in which various Macaca species are employed as NHP models of diseases, especially CNVs overlapping with genes related to immunity or diseases (table 2). Thus, genetic characterization of the macaques is recommended before their usage in biomedical research.

BM-Specific/sylvanus-Specific CNVs

The Barbary macaque is the sole living member of a distinct and ancient species group in Macaca, sylvanus (Fa 2012), and is the only NHP indigenous to North Africa (Taub 1978). It lives for extended periods in snow-covered areas during winter, suffering from not only cold stress but also food shortage. CNVs specific to BM/sylvanus intersected with genes including PRKD1/PKD1, ADIPOR2, and TBX20 (supplementary fig. S17, Supplementary Material online), they may have a role in the adaptation of BM to the harsh environment of its habitats. A recent study of pancreatic β cells found that protein PKD1 controlled the granule degradation in response to nutrient availability and concluded that switching from macroautophagy to insulin granule degradation using a PKD-dependent mechanism was important to keep insulin secretion low upon fasting (GoginashviLi et al. 2015). Therefore, the BM-specific duplication located in the intron of PKD1 may affect its expression and may enable BM to better control insulin secretion during starvation, aiding in winter survival.

A BM-specific deletion overlapped with the first intron of ADIPOR2, a gene that is highly conserved from yeast to human (Tang et al. 2005) and plays important roles in the regulation of glucose and lipid metabolism, inflammation, and oxidative stress. Targeted disruption of ADIPOR2 decreased the activity of PPAR-alpha signaling pathways, affecting lipid metabolism and adaptive thermogenesis (Yamauchi et al. 2007). The partial deletion in the intron may change the metabolic actions of glucose and lipid, and thermogenesis, probably via downregulation of its expression and then increasing the level of adiponectin. Park et al. (2011) found that long-term central infusion of adiponectin improves energy and glucose homeostasis by decreasing fat storage and suppressing hepatic gluconeogenesis without changing food intake, suggesting increased adiponectin leads to high level of glucose homeostasis. It is plausible that this CNV would benefit BM in food shortage and coldness during winter.

Partial deletion of TBX20 was another event specific to BM/sylvanus. Sakabe et al. (2012) found in adult TBX20−/− hearts, additional genes involved in cardiovascular biology and energy metabolism were downregulated, whereas genes related to immune response and cell proliferation were upregulated. This deletion might lower energy metabolism requirements and increase immunity defenses in BM, which could be beneficial to the Barbary macaque in an environment where nutrient shortage is frequent. Further functional studies would help address these hypotheses.

Challenges in CNV Study of Macaques

Several challenges remain for the CNV study of macaques. First, although a single individual is informative of interspecific divergence of Macaca, it is necessary to verify CNVs among Macaca species on a population scale. Some regions may be CN variable among individuals, as demonstrated by the cynomolgus macaque in the genomic qPCR validation. A population-scale CNV study of drug metabolism genes in macaques is highly desirable to assess the impact of such variation on biomedical studies. In addition to the gene set enrichment strategy, more robust evidence is required to clarify the role of CNVs in environmental adaption of Macaca species. The interpretation of gene set enrichment is uncertain even in humans where gene annotations are superior to most species. These uncertainties are exasperated in divergent species like macaques.

In NGS-based methods, the accuracy and sensitivity of CNV identification depend heavily on the quality of the reference genome. Functional analysis of CNVs also calls for accurate gene models. The relatively short lengths of CNVGs suggested that inferior quality of the reference genome (Mmul_8) had an effect on our study. Single molecule, real-time sequencing is a very promising way to improve the continuity of the reference genome with very long reads (McCarthy 2010; Roberts et al. 2013). Recently, the single-molecule assembly of Chinese rhesus macaque (He et al. 2019) has been reported. Along with an increasing number of intensive genome studies, this resource can undoubtedly improve CNV detection in macaques. In turn, CNV surveys can broaden our knowledge of Macaca genome variation.

Supplementary Material

Supplementary data are available at Genome Biology and Evolution online.