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Data from: Demography, life history trade-offs, and the gastrointestinal virome of wild chimpanzees

Citation

Negrey, Jacob D. et al. (2020), Data from: Demography, life history trade-offs, and the gastrointestinal virome of wild chimpanzees, Dryad, Dataset, https://doi.org/10.5061/dryad.w6m905qmk

Abstract

In humans, senescence increases susceptibility to viral infection. However, comparative data on viral infection in free-living non-human primates—even in our closest living relatives, chimpanzees and bonobos (Pan troglodytes and P. paniscus)—are relatively scarce, thereby constraining an evolutionary understanding of age-related patterns of viral infection. We investigated a population of wild eastern chimpanzees (P. t. schweinfurthii), using metagenomics to characterize viromes (full viral communities) in the feces of 42 sexually mature chimpanzees (22 males, 20 females) from the Kanyawara and Ngogo communities in Kibale National Park, Uganda. We identified 12 viruses from at least four families with genomes of both single-stranded RNA and single-stranded DNA. Although fecal viromes of both sexes varied with chimpanzee age, viral richness increased with age in males but not in females. This effect was largely due to three viruses, salivirus, porprismacovirus, and chimpanzee stool-associated RNA virus (chisavirus), which occurred more frequently in samples from older males. This finding is consistent with the hypothesis that selection on males for early-life reproduction compromises investment in somatic maintenance, which has delayed consequences for health later in life, in this case reflected in viral infection and/or shedding. Fecal viromes may be useful for studying processes related to the divergent reproductive strategies of males and females, aging, and sex differences in longevity.

Methods

The following data sheets contain data on viral presence and load generated from metagenomic analyses of fecal samples from 42 wild chimpanzees. Fecal samples were collected from the Kanyawara and Ngogo chimpanzee communities in 2016. Pooled reads were sequenced on an Illumina MiSeq.

Usage Notes

Viral loads were calculated as the proportion of reads mapping to a given virus out of the total number of trimmed reads per sample. This value was then normalized to one million reads and to the length of the target sequence (i.e., viral contig length). This final value is the number of viral reads per million per kilobase of target (vRPM/kb).

For more information on the generation and analysis of these data, please consult the following: http://dx.doi.org/10.1098/rstb.2019.0613

Funding

National Institutes of Health, Award: 5R01AG049395

National Science Foundation, Award: 1355014

University of New Mexico