Population dynamics and genetic connectivity in recent chimpanzee history

The European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 864203) (to T.M.-B.). BFU2017-86471-P (MINECO/FEDER, UE) (to T.M.-B.). “Unidad de Excelencia María de Maeztu”, funded by the AEI (CEX2018-000792-M) (to T.M.-B.). Howard Hughes International Early Career (to T.M.-B.). NIH 1R01HG010898-01A1 (to T.M.-B.). Secretaria d’Universitats i Recerca and CERCA Program del Departament d’Economia i Coneixement de la Generalitat de Catalunya (GRC 2017 SGR 880) (to T.M.-B.). UCL’s Wellcome Trust ISSF3 award 204841/Z/16/Z (to A.M.A. and J.M.S.). Generalitat de Catalunya (2017 SGR-1040) (to M. Llorente). Wellcome Trust Investigator Award 202802/Z/16/Z (to D.A.H.). The Pan African Program: The Cultured Chimpanzee (PanAf) is generously funded by the Max Planck Society, the Max Planck Society Innovation Fund, and the Heinz L. Krekeler Foundation.Knowledge on the population history of endangered species is critical for conservation, but whole-genome data on chimpanzees (Pan troglodytes) is geographically sparse. Here, we produced the first non-invasive geolocalized catalog of genomic diversity by capturing chromosome 21 from 828 non-invasive samples collected at 48 sampling sites across Africa. The four recognized subspecies show clear genetic differentiation correlating with known barriers, while previously undescribed genetic exchange suggests that these have been permeable on a local scale. We obtained a detailed reconstruction of population stratification and fine-scale patterns of isolation, migration, and connectivity, including a comprehensive picture of admixture with bonobos (Pan paniscus). Unlike humans, chimpanzees did not experience extended episodes of long-distance migrations, which might have limited cultural transmission. Finally, based on local rare variation, we implement a fine-grained geolocalization approach demonstrating improved precision in determining the origin of confiscated chimpanzees.Publisher PDFPeer reviewe

Recent genetic connectivity and clinal variation in chimpanzees.

Funder: Max-Planck-Gesellschaft (Max Planck Society); doi: https://doi.org/10.13039/501100004189Funder: Max Planck Society Innovation Fund Heinz L. Krekeler FoundationMuch like humans, chimpanzees occupy diverse habitats and exhibit extensive behavioural variability. However, chimpanzees are recognized as a discontinuous species, with four subspecies separated by historical geographic barriers. Nevertheless, their range-wide degree of genetic connectivity remains poorly resolved, mainly due to sampling limitations. By analyzing a geographically comprehensive sample set amplified at microsatellite markers that inform recent population history, we found that isolation by distance explains most of the range-wide genetic structure of chimpanzees. Furthermore, we did not identify spatial discontinuities corresponding with the recognized subspecies, suggesting that some of the subspecies-delineating geographic barriers were recently permeable to gene flow. Substantial range-wide genetic connectivity is consistent with the hypothesis that behavioural flexibility is a salient driver of chimpanzee responses to changing environmental conditions. Finally, our observation of strong local differentiation associated with recent anthropogenic pressures portends future loss of critical genetic diversity if habitat fragmentation and population isolation continue unabated

Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database

<div><p>Strategies for discovering common molecular events among disparate diseases hold promise for improving understanding of disease etiology and expanding treatment options. One technique is to leverage curated datasets found in the public domain. The Comparative Toxicogenomics Database (CTD; <a href="http://ctdbase.org/" target="_blank">http://ctdbase.org/</a>) manually curates chemical-gene, chemical-disease, and gene-disease interactions from the scientific literature. The use of official gene symbols in CTD interactions enables this information to be combined with the Gene Ontology (GO) file from NCBI Gene. By integrating these GO-gene annotations with CTD’s gene-disease dataset, we produce 753,000 inferences between 15,700 GO terms and 4,200 diseases, providing opportunities to explore presumptive molecular underpinnings of diseases and identify biological similarities. Through a variety of applications, we demonstrate the utility of this novel resource. As a proof-of-concept, we first analyze known repositioned drugs (e.g., raloxifene and sildenafil) and see that their target diseases have a greater degree of similarity when comparing GO terms vs. genes. Next, a computational analysis predicts seemingly non-intuitive diseases (e.g., stomach ulcers and atherosclerosis) as being similar to bipolar disorder, and these are validated in the literature as reported co-diseases. Additionally, we leverage other CTD content to develop testable hypotheses about thalidomide-gene networks to treat seemingly disparate diseases. Finally, we illustrate how CTD tools can rank a series of drugs as potential candidates for repositioning against B-cell chronic lymphocytic leukemia and predict cisplatin and the small molecule inhibitor JQ1 as lead compounds. The CTD dataset is freely available for users to navigate pathologies within the context of extensive biological processes, molecular functions, and cellular components conferred by GO. This inference set should aid researchers, bioinformaticists, and pharmaceutical drug makers in finding commonalities in disease mechanisms, which in turn could help identify new therapeutics, new indications for existing pharmaceuticals, potential disease comorbidities, and alerts for side effects.</p></div

CTD data content for GO terms inferred to diseases.

<p>CTD data content for GO terms inferred to diseases.</p

Exploring disease mechanisms from a GO perspective.

<p>(A) Using inferred GO-CC data, the number of diseases (red numbers) can be associated with cellular locations, providing an additional level of information for potential, druggable targets. Interactive cell maps can be annotated with these inferences to allow navigation and exploration. The 1,178 diseases mapping to the mitochondrion (boxed arrow) were clustered to MEDIC disease categories (pie chart), and the top four categories are highlighted: nervous system diseases (N), genetic inborn diseases (G), metabolic diseases (M), and cancers (C). (B) The inferred GO-MF terms (blue numbers) for six cancers (red circles) share a subset of 210 molecular functions (blue box), providing core molecular activities informing common mechanisms of cancer.</p

Leveraging CTD content to prioritize drugs for repositioning.

<p>B-cell chronic lymphocytic leukemia (BCLL) and neuroblastoma are diseases that currently do not share any known genes in CTD, but do share 320 inferred GO-BP terms, suggesting molecular similarity (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155530#pone.0155530.g004" target="_blank">Fig 4</a>). (<b>A</b>) Diseases can be compared using CTD’s <i>VennViewer</i> tool by selecting “Disease” analysis (top arrowhead), inputting the two disease terms, choosing to compare curated chemical associations (middle arrowhead), and adding a filter to retrieve only therapeutic interactions (bottom arrowhead). (<b>B</b>) The resulting Venn diagram identified two chemicals (arsenic trioxide and cyclophosphamide) that each have a curated therapeutic relationship with both diseases, as well as 28 chemicals specific to BCLL (which could potentially be repositioned for neuroblastoma; red box), and 39 chemicals specific to neuroblastoma (which could now be repositioned for BCLL; green box) (<b>C</b>) Arsenic trioxide and cyclophosphamide treat both diseases and both chemicals interact with a set of 277 genes (blue Venn circles), information which can be leveraged to help rank the test drugs. (<b>D</b>) The 39 therapeutic drugs for neuroblastoma with potential repositioning towards BCLL (green names on y-axis) were queried in CTD to see how many of the 277 genes interact with each test drug (x-axis). Four of the 39 test drugs interact with more than 50% of the 277 genes (blue dotted box). (<b>E</b>) Venn diagrams summarize how BCLL and neuroblastoma do not currently share any genes in CTD, but do share 320 inferred GO-BP terms (based upon CTD’s new GO-Disease inference dataset), and that 307 of these 320 GO-BP terms are annotated to the 277-gene set used to rank the test drugs for potential repositioning.</p