Biology subjects

Tuberosa, J.

Publications and source records attributed to Tuberosa, J..

5 recordsLinked to original sources

bioRxiv2025

Nose-to-ground distance drives the evolution of mammalian odorant receptor repertoire size

The largest protein-coding gene family in mammals encodes the repertoire of chemosensors responsible for detecting odorants, namely odorant receptors (ORs). OR gene numbers vary widely across species, ranging from a few hundred in some primates to over two thousand in elephants. Here, we report that proximity to the ground, a rich source of olfactory cues, has broadly influenced the evolutionary expansion and contraction of OR mammalian repertoires. Analyzing 158 terrestrial mammalian species, we find a strong inverse correlation between nose-to-ground distance and the number of functional OR genes. In contrast, no significant association was found between nose-to-ground distance and the size of other chemoreceptor families, including vomeronasal and bitter-taste receptors. As a case study, we examined OR repertoire divergence between giraffe and okapi lineages, revealing that differential rates of gene duplication and pseudogenization resulted in giraffes possessing 25% fewer ORs than okapis. Together, our findings suggest that the chemical complexity of the olfactory environment has shaped the evolution of OR repertoires, providing new insights into gene family size and diversification in mammals.

evolutionary biology↗

bioRxiv2025

High Throughput Information Extraction of Printed Specimen Labels from Large-Scale Digitization of Entomological Collections using a Semi-Automated Pipeline

O_LINatural history museums curate billions of insect specimens, forming a vast but underutilized resource for biodiversity research. While digitization efforts have increased the availability of high-resolution specimen images, extracting metadata from labels remains a major bottleneck, often requiring manual transcription. C_LIO_LIWe developed a semi-automated pipeline, ELIE (Entomological Label Information Extraction), which combines computer vision, convolutional neural networks (CNNs), optical character recognition (OCR), and clustering algorithms to streamline label data extraction. Our pipeline operates in three stages: (1) label detection and classification (printed vs. handwritten), (2) OCR-based text extraction from printed labels using Tesseract or Google Vision, and (3) clustering of extracted text for human validation of outliers. C_LIO_LIBenchmarking on diverse datasets from multiple museum collections showed that our approach successfully extracted and clustered up to 98% of printed labels, significantly reducing manual effort. The pipeline improves efficiency in digitization workflows while maintaining high accuracy in label data capture. C_LIO_LIOur approach demonstrates the potential of integrating AI-driven methods with human validation to accelerate specimen digitization. By reducing manual transcription workload and enabling scalable extraction of insect label metadata, it unlocks biodiversity data for research in ecology, systematics, and conservation globally. C_LI

bioinformatics↗

bioRxiv2021

Transcriptional adaptation of sensory neurons to GPCR identity and activity

Sensory adaptation is critical to extract information from a changing world. Taking advantage of the extensive parallel coding lines present in the olfactory system, we explored the potential variations of neuronal identities before and after olfactory experience. We found that at rest, the transcriptomic profiles of olfactory sensory neuron populations are already highly divergent, specific to the olfactory receptor they express, and are surprisingly associated with the sequence of these latter. These divergent profiles further evolve in response to the environment, as odorant exposure leads to massive reprogramming via the modulation of transcription. Adenylyl cyclase 3, but not other main elements of the olfactory transduction cascade, plays a critical role in this activity-induced transcriptional adaptation. These findings highlight a broad range of sensory neuron identities that are present at rest and that adapt to the experience of the individual, thus providing a novel layer of complexity to sensory coding.

neuroscience↗

bioRxiv2020

SARS-CoV-2 receptor and entry genes are expressed by sustentacular cells in the human olfactory neuroepithelium

Various reports indicate an association between COVID-19 and anosmia, suggesting an infection of the olfactory sensory epithelium, and thus a possible direct virus access to the brain. To test this hypothesis, we generated RNA-seq libraries from human olfactory neuroepithelia, in which we found substantial expression of the genes coding for the virus receptor angiotensin-converting enzyme-2 (ACE2), and for the virus internalization enhancer TMPRSS2. We analyzed a human olfactory single-cell RNA-seq dataset and determined that sustentacular cells, which maintain the integrity of olfactory sensory neurons, express ACE2 and TMPRSS2. We then observed that the ACE2 protein was highly expressed in a subset of sustentacular cells in human and mouse olfactory tissues. Finally, we found ACE2 transcripts in specific brain cell types, both in mice and humans. Sustentacular cells thus represent a potential entry door for SARS-CoV-2 in a neuronal sensory system that is in direct connection with the brain.

neuroscience↗

bioRxiv2017

PSGfinder: fast identification of genes under divergent positive selection using the dynamic windows method

SummaryOrthologous genes evolving under divergent positive selection are those involved in divergent adaptive trajectories between related species. Current methods to identify such genes are complex and conservative or present some imperfections, limiting genome-wide searches. We present a simple method, Dynamic Windows, to detect regions of protein-coding genes evolving under divergent positive selection. This method is implemented in PSGfinder, a user-friendly and flexible software, allowing rapid genome-wide screenings of regions with a dN/dS >1. PSGfinder additionally includes an alignment cleaning procedure and an adapted multiple comparison correction to identify significant signals of positive selection.\n\nAvailability and ImplementationPSGfinder is a software that implements the DWin method, is written in Python and is freely available with its documentation at: https://genev.unige.ch/research/laboratory/Juan-Montoya or at: https://github.com/joel-tuberosa/psgfinder\n\nContactjuan.montoya@unige.ch; joel.tuberosa@unige.ch

bioinformatics↗