Biology subjects

Gelly, J.-C.

Publications and source records attributed to Gelly, J.-C..

4 recordsLinked to original sources

bioRxiv2025

Macrophages restrict tumor permissiveness to immune infiltration by controlling local collagen topography through a Tcf4-Collagen3 fibrotic axis

During tumorigenesis, the extracellular matrix (ECM), which constitutes the structural scaffold of tissues, is profoundly remodeled. While the impact of such remodeling on tumor growth and invasion has been extensively investigated, much less is known on the consequences of ECM remodeling on tumor infiltration by immune cells. By combining tissue imaging and machine-learning, we here show that the localization of T lymphocytes and neutrophils, which orchestrate antitumor immune responses, can be predicted by defined topographical features of fibrillar collagen networks. We further show that these collagen topographies result from the activation of a fibrotic pathway controlled by the transcription factor Tcf4 upon depletion of tumor-associated macrophages at late tumor stages. This pathway promotes the deposition of collagen 3 by both tumor and stromal cells, resulting in intermingled collagen networks that favor intra-tumoral T cell and neutrophil localization. Importantly, analysis of human colorectal cancer public bulk RNAseq databases showed a strong correlation between Tcf4 and collagen 3, as well as between the expression of these genes and tumor infiltration by T lymphocytes and neutrophils, attesting the clinical relevance of our findings. This study highlights the key structural role of macrophages on the tumor extracellular matrix and identifies collagen network topographies as a major regulator of tumor infiltration by immune cells.

immunology↗

bioRxiv2024

CONSTRUCT: an algorithmic tool for identifying functional or structurally important regions in protein tertiary structure

In the realm of protein-coding genes, evolutionary rates show considerable variability. Essential or highly expressed proteins evolve more slowly, and within a protein, different amino acid sites evolve at different rates. Accurately modeling this variation is critical for identifying structurally or functionally important amino acid sites. Standard methods such as Rate4Site assume independent substitution rates across sites, and the most conserved ones are widely distributed in protein tertiary structure. This is biologically unrealistic because functional sites often cluster together. Here, we developed CONSTRUCT, an improved strategy for identifying functional and structurally important regions in protein tertiary structure. Given a set of orthologous sequences, CONSTRUCT first calculates site-specific substitution rates using the Rate4site model, which are then weighted by the rates of neighboring amino acid sites within a range of window sizes. The optimal window size is determined by the strongest spatial correlation, if present. This method can be analyzed using either C atoms or the center of mass of amino acid sites to account for side chain orientation. Validation on 14 functionally characterized proteins of different size, conservation level and kingdom/domain of species has demonstrated the relevance of CONSTRUCT.

bioinformatics↗

bioRxiv2024

Assessment of variant effect predictors unveils variants difficulty as a critical performance indicator

Amino acid substitutions in protein sequences are generally harmless, but a certain number of these changes can lead to disease. Accurate prediction of the impact of genetic variants is crucial for clinicians as it accelerates the diagnosis of patients with missense variants associated with health issues. Numerous computational tools have been developed for prediction of the pathogenicity of genetic variants based on different methodologies. Nowadays, many approaches are based on Machine Learning. Assessment of the performance of these diverse computational tools is crucial to provide guidance to both future users and especially clinicians. In this study, a large-scale study of 65 tools was conducted. Variants from both clinical and functional context have been used, incorporating data from the ClinVar database and bibliographic sources. The analysis showed that AlphaMissense is often performing very well and is actually the best option among existing tools. Additionally, meta-predictors, as expected, are of high quality and perform well on average. Tools using evolution information demonstrated highest performances on functional variants. These results also highlighted some variations in the difficulty to predict some specific variants while others are always well categorized. Strikingly, the majority of variants from the ClinVar database appear to be easy to predict, while variants from other sources of data are more challenging. These results demonstrate that this variant predictability can be classified into three distinct classes: easy, moderate and hard to predict. We analyzed the parameters leading to these differences and show that classes are linked to structural and functional information.

bioinformatics↗

bioRxiv2021

Intergenic ORFs as elementary structural modules of de novo gene birth and protein evolution

The noncoding genome plays an important role in de novo gene birth and in the emergence of genetic novelty. Nevertheless, how noncoding sequences properties could promote the birth of novel genes and shape the evolution and the structural diversity of proteins remains unclear. Therefore, by combining different bioinformatic approaches, we characterized the fold potential diversity of the amino acid sequences encoded by all intergenic ORFs (Open Reading Frames) of S. cerevisiae with the aim of (i) exploring whether the large structural diversity observed in proteomes is already present in noncoding sequences, and (ii) estimating the potential of the noncoding genome to produce novel protein bricks that can either give rise to novel genes or be integrated into pre-existing proteins, thus participating in protein structure diversity and evolution. We showed that amino acid sequences encoded by most yeast intergenic ORFs contain the elementary building blocks of protein structures. Moreover, they encompass the large structural diversity of canonical proteins with strikingly the majority predicted as foldable. Then, we investigated the early stages of de novo gene birth by identifying intergenic ORFs with a strong translation signal in ribosome profiling experiments and by reconstructing the ancestral sequences of 70 yeast de novo genes. This enabled us to highlight sequence and structural factors determining de novo gene emergence. Finally, we showed a strong correlation between the fold potential of de novo proteins and the one of their ancestral amino acid sequences, reflecting the relationship between the noncoding genome and the protein structure universe.

evolutionary biology↗