Biology subjects

Duprey, A.

Publications and source records attributed to Duprey, A..

3 recordsLinked to original sources

bioRxiv2026

Dynamic Consistency Reveals Predictable Genes in Cross-Cell Type Temporal scRNA-Seq Data

Understanding how gene expression evolves over time after trauma is central to modeling immune responses, yet single-cell temporal data remain sparse and heterogeneous across cell types. Using a temporal trauma scRNA-seq dataset, we formulate the task of predicting next-time gene expression from earlier observations under a cross-cell-type generalization setting. We introduce the Dynamic Consistency Index (DCI), which quantifies how consistently a genes temporal trajectory aligns across cell types, serving as a measure of biological regularity and predictability. High-DCI genes exhibit reproducible temporal dynamics and are markedly easier to model. By integrating DCI-based gene selection with a recurrent neural architecture trained under a Gaussian negative log-likelihood objective, we achieve superior accuracy and well-calibrated uncertainty compared to deterministic baselines. Overall, DCI reliably identifies dynamically consistent genes, and uncertainty-aware recurrent modeling provides a robust framework for capturing cross-cell-type gene-expression evolution.

bioinformatics↗

bioRxiv2025

CSLAN: Cross-Species Latent Alignment Network for Trauma-Related Cell-Type Classification

Understanding cellular identity and function across species is fundamental to deciphering conserved biological principles and translating insights from model organisms to human health. However, integrating single-cell transcriptomic data, particularly from resource-limited human studies, with comprehensive datasets from model organisms like mice remains a significant challenge due to evolutionary divergence and technical variability. Here, we introduce the Cross-Species Latent Alignment Network (CSLAN), a transfer learning framework that effectively bridges this gap for robust cell type classification. CSLAN employs a novel strategy involving focused feature selection on source (mouse) data followed by pretraining an encoder-decoder architecture. Crucially, for transfer to human data, only the encoder is fine-tuned while the latent space processor and the decoder, which encapsulate learned, conserved cell type signatures, remains fixed. This targeted adaptation mechanism allows CSLAN to achieve exceptional accuracy (e.g., 96.67% for human immune cells in a trauma context) by efficiently aligning human gene expression profiles to a biologically informed latent space derived from mouse data. Our findings establish a powerful and broadly applicable paradigm for cross-species knowledge transfer, enabling deeper biological insights from limited human scRNA-seq data by leveraging the wealth of information from model systems, with significant implications for translational research and the construction of comprehensive, cross-species cell atlases.

bioinformatics↗

bioRxiv2019

The phytopathogenic nature of Dickeya aquatica 174/2 and the dynamic early evolution of Dickeya pathogenicity

Originality-Significance statementAlthough the reach of large-scale comparative studies has spread exponentially over the years, the phytopathogenic Dickeya group remains overlooked. In this work, we sequence the complete genome of Dickeya aquatica type strain, a species isolated from water that was first assumed to be non-phytopathogenic. We show that the proteome of D. aquatica contains a wide number of proteins involved in Dickeya virulence, including plant cell wall degrading enzymes, suggesting that this species could be in fact pathogenic. Using experimental approaches, we confirm this prediction and uncover the particular affinity of D. aquatica for acidic fruits. In-depth phylogenomic analyses reveal that Dickeya species display a great degree of genetic plasticity in the pathogenicity determinants, explaining how this bacterial group was able to colonize a wide variety of plants growing in different climates. These observations greatly advance our understanding of how bacteria adapt to new ecological niches.\n\nSummaryDickeya is a genus of phytopathogenic enterobacterales causing soft rot in a variety of plants (e.g. potato, chicory, maize). Among the species affiliated to this genus, Dickeya aquatica, described in 2014, remained particularly mysterious because it had no known host. Furthermore, while D. aquatica was proposed to represent a deep-branching species among Dickeya genus, its precise phylogenetic position remained elusive.\n\nHere, we report the complete genome sequence of the D. aquatica type strain 174/2. We demonstrate the affinity of D. aquaticaT for acidic fruits such as tomato and cucumber, and show that exposure of this bacterium to acidic pH induces twitching motility. An in-depth phylogenomic analysis of all available Dickeya proteomes pinpoints D. aquatica as the second deepest branching lineage within this genus and reclassifies two lineages that likely correspond to new genomospecies (gs.): Dickeya gs. poaceaephila (Dickeya sp NCPPB 569) and Dickeya gs. undicola (Dickeya sp 2B12), together with a new putative genus, tentatively named Prodigiosinella. Finally, from comparative analyses of Dickeya proteomes we infer the complex evolutionary history of this genus, paving the way to study the adaptive patterns and processes of Dickeya to different environmental niches and hosts. In particular, we hypothetize that the lack of xylanases and xylose degradation pathways in D. aquatica could reflects adaptation to aquatic charophyte hosts which, in contrast to land plants, do not contain xyloglucans.

genomics↗