The critically endangered western gorillas (Gorilla gorilla) are divided into two subspecies: the western lowland (G. g. gorilla) and the Cross River (G. g. diehli) gorilla. Given the difficulty in sampling wild great ape populations and the small estimated size of the Cross River gorilla population, only one whole genome of a Cross River gorilla has been sequenced to date, hindering the study of this subspecies at the population level. In this study, we expand the number of whole genomes available for wild western gorillas, generating 41 new genomes (25 belonging to Cross River gorillas) using single shed hairs collected from gorilla nests. By combining these genomes with publicly available wild gorilla genomes, we confirm that Cross River gorillas form three population clusters. We also found little variation in genome-wide heterozygosity among them. Our analyses reveal long runs of homozygosity (>10 Mb), indicating recent inbreeding in Cross River gorillas. This is similar to that seen in mountain gorillas but with a much more recent bottleneck. We also detect past gene flow between two Cross River sites, Afi Mountain Wildlife Sanctuary and the Mbe Mountains. Furthermore, we observe past allele sharing between Cross River gorillas and the northern western lowland gorilla sites, as well as with the eastern gorilla species. This is the first study using single shed hairs from a wild species for whole genome sequencing to date. Taken together, our results highlight the importance of implementing conservation measures to increase connectivity among Cross River gorilla sites.

How populations adapt to their environment is a fundamental question in biology. Yet, we know surprisingly little about this process, especially for endangered species, such as nonhuman great apes. Chimpanzees, our closest living relatives, are particularly notable because they inhabit diverse habitats, from rainforest to woodland-savannah. Whether genetic adaptation facilitates such habitat diversity remains unknown, despite it having wide implications for evolutionary biology and conservation. By using newly sequenced exomes from 828 wild chimpanzees (388 postfiltering), we found evidence of fine-scale genetic adaptation to habitat, with signatures of positive selection in forest chimpanzees in the same genes underlying adaptation to malaria in humans. This work demonstrates the power of noninvasive samples to reveal genetic adaptations in endangered populations and highlights the importance of adaptive genetic diversity for chimpanzees.

Modern human contamination is a common problem in ancient DNA studies. We provide evidence that this issue is also present in studies in great apes, which are our closest living relatives, for example in noninvasive samples. Here, we present a simple method to detect human contamination in short-read sequencing data from different species: HuConTest. We demonstrate its feasibility using blood and tissue samples from these species. This test is particularly useful for more complex samples (such as museum and noninvasive samples) which have smaller amounts of endogenous DNA, as we show here.

Captive breeding programmes represent the most intensive type of ex situ population management for threatened species. One example is the Cuvier’s gazelle programme that started in 1975 with only four founding individuals and after more than four decades of management in captivity, a reintroduction effort was undertaken in Tunisia in 2016, to establish a population in an area historically included within its range. Here, we aim to determine the genetic consequences of this reintroduction event by assessing the genetic diversity of the founder stock as well as of their descendants. We present the first whole-genome sequencing dataset of 30 Cuvier’s gazelles including captive-bred animals, animals born in Tunisia after a reintroduction, and individuals from a genetically unrelated Moroccan population. Our analyses revealed no difference between the founder and the offspring cohorts in genome-wide heterozygosity and inbreeding levels, and in the amount and length of runs of homozygosity. The captive but unmanaged Moroccan gazelles have the lowest genetic diversity of all genomes analyzed. Our findings demonstrate that the Cuvier’s gazelle captive breeding programme can serve as source populations for future reintroductions of this species. We believe that this study can serve as a starting point for global applications of genomics to the conservation plan of this species.

Importance: The time to accurately diagnose rare pediatric diseases often spans years. Assessing the diagnostic accuracy of an LLM-based tool on real pediatric cases can help reduce this time, providing quicker diagnoses for patients and their families. Objective: To evaluate the clinical utility of DxGPT as a support tool for differential diagnosis of both common and rare diseases. Design: Unicentric descriptive cross-sectional exploratory study. Anonymized data from 50 pediatric patients’ medical histories, covering common and rare pathologies, were used to generate clinical case notes. Each clinical case included essential data, with some expanded by complementary tests. Setting: This study was conducted at a reference pediatric hospital, Sant Joan de Déu Barcelona Children’s Hospital. Participants: A total of 50 clinical cases were diagnosed by 78 volunteer doctors (medical diagnostic team) with varying experience, each reviewing 3 clinical cases. Interventions: Each clinician listed up to five diagnoses per clinical case note. The same was done on the DxGPT web platform, obtaining the Top-5 diagnostic proposals. To evaluate DxGPT’s variability, each note was queried three times. Main Outcome(s) and Measure(s): The study mainly focused on comparing diagnostic accuracy, defined as the percentage of cases with the correct diagnosis, between the medical diagnostic team and DxGPT. Other evaluation criteria included qualitative assessments. The medical diagnostic team also completed a survey on their user experience with DxGPT. Results: Top-5 diagnostic accuracy was 65% for clinicians and 60% for DxGPT, with no significant differences. Accuracies for common diseases were higher (Clinicians: 79%, DxGPT: 71%) than for rare diseases (Clinicians: 50%, DxGPT: 49%). Accuracy increased similarly in both groups with expanded information, but this increase was only stastitically significant in clinicians (simple 52% vs. expanded 69%; p =0.03). DxGPT’s response variability affected less than 5% of clinical case notes. A survey of 48 clinicians rated the DxGPT platform 3.9/5 overall, 4.1/5 for usefulness, and 4.5/5 for usability. Conclusions and Relevance: DxGPT showed diagnostic accuracies similar to medical staff from a pediatric hospital, indicating its potential for supporting differential diagnosis in other settings. Clinicians praised its usability and simplicity. These tools could provide new insights for challenging diagnostic cases. Key Points: Question: Is DxGPT, a large language model-based (LLM-based) tool, effective for differential diagnosis support, specifically in the context of a clinical pediatric setting? Findings: In this unicentric cross-sectional study, diagnostic accuracy, measured as the proportion of clinical cases where any of the five diagnostic options included the correct diagnosis, showed comparable results between clinicians and DxGPT. Top-5 accuracy was 65% for clinicians and 60% for DxGPT. Meaning: These findings highlight the potential of LLM-based tools like DxGPT to support clinicians in making accurate and timely diagnoses, ultimately improving patient care.