Biology subjects

Murga-Moreno, J.

Publications and source records attributed to Murga-Moreno, J..

4 recordsLinked to original sources

bioRxiv2023

Quantifying Adaptive Evolution of the Human Immune Cell Landscape

The human immune system is under constant evolutionary pressure, primarily through its role as first line of defence against pathogens. Accordingly, population genomics studies have shown that immune-related genes have a high rate of adaptive evolution. These studies, however, are mainly based on protein-coding genes without cellular context, leaving the adaptive role of cell types and states uncharted. Inferring the rate of protein-coding genes adaptation in developing and adult immune cells at cellular resolution, we found cell types from both the lymphoid and myeloid compartments to harbour significantly increased adaptation rates. Specific cell states, such as foetal Pre-Pro B cells and adult T resident memory CD8+ cells show highly elevated rates of adaptation. We further analysed activated cell states, specifically, iPSC-derived macrophages responding to various challenges, including pro- and anti-inflammatory cytokines or bacterial and viral infections, the latter simulating the evolutionary arms race between humans and pathogens. Here, we found positive selection to be concentrated in early immune responses, suggesting benefits for the host to adapt to early stages of infection to control pathogen numbers and spread. Together, our study reveals spatio-temporal and functional biases in human immune populations with evidence of rapid adaptive evolution and provides a retrospect of forces that shaped the complexity, architecture, and function of the human body.

immunology↗

bioRxiv2023

An efficient and robust ABC approach to infer the rate and strength of adaptation

Inferring the effects of positive selection on genomes remains a critical step in characterizing the ultimate and proximate causes of adaptation across species, and quantifying positive selection remains a challenge due to the confounding effects of many other evolutionary processes. Robust and efficient approaches for adaptation inference could help characterize the rate and strength of adaptation in non-model species for which demographic history, mutational processes, and recombination patterns are not currently well-described. Here, we introduce an efficient and user-friendly extension of the McDonald-Kreitman test (ABC-MK) for quantifying long-term protein adaptation in specific lineages of interest. We characterize the performance of our approach with forward simulations and find that it is robust to many demographic perturbations and positive selection configurations, demonstrating its suitability for applications to non-model genomes. We apply ABC-MK to the human proteome and a set of known Virus Interacting Proteins (VIPs) to test the long-term adaptation in genes interacting with viruses. We find substantially stronger signatures of positive selection on RNA-VIPs than DNA-VIPs, suggesting that RNA viruses may be an important driver of human adaptation over deep evolutionary time scales.

evolutionary biology↗

bioRxiv2022

Stability evolution as a major mechanism of human protein adaptation in response to viruses

Pathogens were a major driver of genetic adaptation during human evolution. Viruses in particular were a dominant driver of adaptation in the thousands of proteins that physically interact with viruses (VIPs for Virus-Interacting Proteins). This however poses a conundrum. The best understood cases of virus-driven adaptation in specialized immune antiviral factors or in host viral receptors are numerically vastly insufficient to explain abundant adaptations in VIPs. What adaptive mechanisms can then at least partly close this gap? VIPs tend to be broadly conserved proteins with conserved host native molecular functions. Because many amino acid changes in a protein can alter its stability -the balance between the folded and unfolded forms of a protein- without destroying conserved native activities, here we ask if stability evolution was an important mechanism of virus-driven human protein adaptation. Using predictions of protein stability changes based on Alphafold 2 structures and validated by multiple lines of evidence, we find that amino acid changes that altered stability experienced highly elevated adaptative evolution in VIPs, compared to changes with a weaker impact on stability. We further find that RNA viruses, rather DNA viruses, predominantly drove strong adaptation through stability changes in VIPs. We also observe that stability in immune antiviral VIPs preferentially evolved under directional selection. Conversely, stability in proviral VIPs needed by viruses evolved under compensatory evolution following viral epidemics. Together, these results suggest that stability evolution, and thus functional host protein abundance evolution, was a prominent mechanism of host protein adaptation during viral epidemics.

evolutionary biology↗

bioRxiv2021

Drosophila Evolution over Space and Time (DEST) - A New Population Genomics Resource

Drosophila melanogaster is a leading model in population genetics and genomics, and a growing number of whole-genome datasets from natural populations of this species have been published over the last 20 years. A major challenge is the integration of these disparate datasets, often generated using different sequencing technologies and bioinformatic pipelines, which hampers our ability to address questions about the evolution and population structure of this species. Here we address these issues by developing a bioinformatics pipeline that maps pooled sequencing (Pool-Seq) reads from D. melanogaster to a hologenome consisting of fly and symbiont genomes and estimates allele frequencies using either a heuristic (PoolSNP) or a probabilistic variant caller (SNAPE-pooled). We use this pipeline to generate the largest data repository of genomic data available for D. melanogaster to date, encompassing 271 population samples from over 100 locations in >20 countries on four continents based on a combination of 121 unpublished and 150 previously published genomic datasets. Several of these locations have been sampled at different seasons across multiple years. This dataset, which we call Drosophila Evolution over Space and Time (DEST), is coupled with sampling and environmental meta-data. A web-based genome browser and web portal provide easy access to the SNP dataset. Our aim is to provide this scalable platform as a community resource which can be easily extended via future efforts for an even more extensive cosmopolitan dataset. Our resource will enable population geneticists to analyze spatio-temporal genetic patterns and evolutionary dynamics of D. melanogaster populations in unprecedented detail.

genomics↗