Biology subjects

Colucci, M.

Publications and source records attributed to Colucci, M..

5 recordsLinked to original sources

bioRxiv2026

Gut Microbiota Production of Phenylacetate Programs Vascular Niche Senescence and Drives Atherosclerosis

Vascular senescence is a key contributor to ageing-related diseases, including atherosclerosis. Initial intervention is based on aggressive management of traditional risk factors, yet microbial metabolites remain underestimated as modifiable factors. We recently identified phenylacetate (PAA), a gut microbiota-linked metabolite, as a potent accelerator of endothelial senescence, raising the question of its causal role in atherosclerosis. Here, we show that PAA promotes vascular niche senescence and perivascular adipose tissue (PVAT) dysfunction, associated with atherosclerosis in humans and mice. Furthermore, PAA administration to atherosclerosis-prone mice was sufficient to drive atherosclerosis without altering lipid profile. Mechanistically, we found that PAA induces senescence-messaging secretome, containing IL-6, from endothelial cells, which stimulates Notch1 and disrupts insulin signaling in adipocytes. Blocking the PAA-IL-6-Notch1 axis as well as senolytics rescued adipocyte senescence and dysfunction. Identification of the strong link between PAA and atherosclerosis opens new avenues for microbiome-targeted preventive and therapeutic strategies in ageing. HighlightsO_LIAge-dependent increase in gut microbial metabolite PAA causally promotes vascular niche senescence C_LIO_LIPAA indirectly accelerates PVAT senescence through endothelial senescence-messaging secretome C_LIO_LISASP upregulates Notch1 signaling, leading to PVAT dysfunction C_LIO_LISenolytic therapy restores PVAT function C_LIO_LIPAA-induced vascular niche senescence contributes to atherosclerosis progression C_LI

cell biology↗

bioRxiv2025

tidygenclust: Clustering for Population Genetics in R

BackgroundPopulation structure analysis is crucial for evolutionary research and medical genomics. Clustering methods, broadly categorized as model-based (e.g. ADMIXTURE) or non-model-based (e.g. SCOPE), differ in their methodology and computational efficiency. Recently, fastmixture, a model-based approach, has improved scalability and performance, while replicate alignment tools, such as Clumppling, extend previous methods by also aligning the modes across K values. However, all the existing tools are standalone and generate numerous untracked text files, as well as offering limited plot customisability. ResultsWe introduce an R package, tidygenclust, which brings the functionalities of the original ADMIXTURE, fastmixture and Clumppling software into R, enabling a streamlined and integrated workflow. By integrating with tidypopgen, a package designed to handle large SNP datasets, these new tools maintain metadata, simplify data handling, and produce results as customisable ggplot2 objects for flexible visualisation. ConclusionsThe R package tidygenclust advances population genetic analysis by combining computational efficiency with reproducible workflows and user-friendly plotting. The source code and instructions can be accessed on https://github.com/EvolEcolGroup/tidygenclust.

bioinformatics↗

bioRxiv2025

Malaria shaped human spatial organisation for the last 74 thousand years

The mechanisms driving the spatial organisation of early human societies in Africa are typically addressed through climate variables 1-3. However, genetic and archaeological studies have also suggested diseases as a major source of selection in the Pleistocene. Here, we explore whether P. falciparum-induced malaria, a major world disease, drove habitat choice in human societies between 74 and 5 thousand years ago (kya). Using species distribution models of three main mosquito complexes with palaeoclimatic reconstructions and combining the results with epidemiological information, we estimated an index of malaria transmission risk in sub-Saharan Africa through time. We then correlated it with an independent reconstruction of the human niche over the same time period and region. Our results show that humans strongly avoided or were unsuccessful in potential malaria hotspots. The effects of these choices shaped human demography for the last 74 kya, and likely much earlier, by fragmenting human societies over time and contributing to the formation of modern population structure. Our results highlight the importance of considering disease distributions when modelling estimates of past human demography, demonstrating that factors beyond climate underlay patterns of human habitat choice, exchange, and dispersal. Sentence summaryMalaria shaped human habitat choice, exchange, and dispersal since the late Pleistocene in sub-Saharan Africa.

evolutionary biology↗

bioRxiv2025

Pan-African metapopulation model explains Homo sapiens genetic and morphological evolution

A growing body of evidence has challenged the traditional assumption of a single-region origin for Homo sapiens, suggesting instead that our species originated from multiple geographically distinct populations in Africa, which intermittently exchanged genes and culture. However, our understanding of how this Pan-African metapopulation would have evolved through time is still limited. Furthermore, the drivers of such changes are uncertain, and quantitative models of the respective contributions of different African regions are lacking. Here we provide a complete reconstruction of the meta-population dynamics over the last 200,000 years by quantitatively integrating an ecological niche model, informed by archaeological sites, within a spatially explicit population genetic framework. The inferred metapopulation dynamics account for the divergence among all available contemporary and ancient genomes of African hunter-gatherers used to calibrate the model. In addition, it also accurately predicts the patterns of craniometric diversification across the continent from the Late Middle Pleistocene to the present. Finally, we show how the climate-driven changes in population sizes and connectivity are congruent with major patterns of archaeological and phenotypic diversification over the last 200,000 years across the African continent.

evolutionary biology↗

bioRxiv2023

tidysdm: leveraging the flexibility of tidymodels for Species Distribution Modelling in R

In species distribution modelling (SDM), it is common practice to explore multiple machine-learning algorithms and combine their results into ensembles. This is no easy task in R: different algorithms were developed independently, with inconsistent syntax and data structures. Specialised SDM packages integrate multiple algorithms by creating a complex interface between the user (providing a unified input and receiving a unified output), and the back-end code (that tackles the specific needs depending on the algorithm). This requires a lot of work to create and maintain the right interface, and it prevents an easy integration of other methods that may become available. Here we present tidysdm, an R package that solves this problem by taking advantage of the tidymodels universe. Being part of the tidyverse, (i) it has standardised grammar and data structures providing a coherent interface for modelling, (ii) includes packages designed for fitting, tuning, and validating various models, and (iii) allows easy integration of new algorithms and methods. tidysdm allows easy, flexible and quick species distribution modelling by supporting standard algorithms, including additional SDM-oriented functions, and giving the opportunity of using any algorithm or procedure to fit, tune and validate a large number of different models. Additionally, it provides further functions to easily fit models based on paleo/time-scattered data. The package includes two vignettes detailing standard procedures for present-day and time-scattered data. These vignettes also showcase the integration with pastclim (Leonardi et al. 2023) to allow easier access to palaeoclimatic data series, if needed, but users can bring in their own climatic data in standard formats.

ecology↗