Biology subjects

Martin, P. C. N.

Publications and source records attributed to Martin, P. C. N..

5 recordsLinked to original sources

bioRxiv2026

PersonaAI: An Interactive Agentic-AI Framework for Autonomous Hypothesis Generation and Validation in Aging

Elucidating the mechanisms of aging is impeded by its stochastic, multi-scale nature and cellular heterogeneity, challenges that are compounded by the overwhelming volume of biomedical literature and the complexity of genome-wide datasets. To overcome these barriers, we present PersonaAI, an interactive agentic-AI framework that acts as a digital co-scientist. By integrating literature-based reasoning with autonomous in silico validation, PersonaAI synthesizes over 560,000 aging-related publications via retrieval-augmented generation (RAG) to propose mechanistic hypotheses. These hypotheses are subsequently validated by autonomous agents utilizing single-cell RNA-seq data. Using a temporal cutoff strategy restricted to pre-2020 literature, we demonstrate that PersonaAI can generate hypotheses effectively validated by post-2021 discoveries, proving its capacity for inference beyond simple information retrieval. In application, the system identified senescent Cirbp+ hepatocytes as a liver-intrinsic aging program and uncovered a middle-aged, male-specific decline in adipose stem and progenitor cells, driven by vascular niche deterioration and disrupted VEGF-VEGFR signaling. These results establish PersonaAI as a scalable platform that augments human intuition with autonomous data-driven validation, providing a generalizable platform for accelerating discovery in aging biology.

bioinformatics↗

bioRxiv2023

Identification of mammalian transcription factors that bind to inaccessible chromatin

Transcription factors (TFs) are proteins that affect gene expression by binding to regulatory regions of DNA in a sequence specific manner. The binding of TFs to DNA is controlled by many factors, including the DNA sequence, concentration of TF, chromatin accessibility and co-factors. Here, we systematically investigated the binding mechanism of hundreds of TFs by analysing ChIP-seq data with our explainable statistical model, ChIPanalyser. This tool uses as inputs the DNA sequence binding motif; the capacity to distinguish between strong and weak binding sites; the concentration of TF; and chromatin accessibility. We asked whether TFs preferred to bind to DNA in open or dense chromatin conformation and found that approximately one third of TFs are predicted to bind the genome in a DNA accessibility independent fashion. Our model predicted this to be the case when the TF binds to its strongest binding regions in the genome, and only a small number of TFs have the capacity to bind dense chromatin at their weakest binding regions, such as CTCF USF2 and CEBPB. Our study demonstrated that the binding of hundreds of human and mouse TFs is predicted by ChIPanalyser with high accuracy and showed that many TFs can bind dense chromatin.

genomics↗

bioRxiv2022

Detecting Cell Contact-dependent Gene Expression from Spatial Transcriptomics Data

Cells have evolved communication methods to sense their microenvironments and send biological signals. In addition to the communication using ligands and receptors, cells use diverse channels including gap junctions to communicate with their immediate neighbors. Current approaches, however, cannot effectively capture the influence of various microenvironments. Here, we propose a novel approach that identifies cell neighbor-dependent gene expression (CellNeighborEX). After categorizing cells based on their microenvironment from spatial transcriptomics (ST) data, CellNeighborEX identifies diverse gene sets associated with partnering cell types, providing further insight. To categorize cells along with their environment, CellNeighborEX uses direct cell location or the mixture of transcriptome from multiple cells depending on the ST technology. We show that cells express different gene sets depending on the neighboring cell types in various tissues including mouse embryos, brain, and liver cancer. These genes were associated with development (in embryos) or metastases (liver cancer). We further validate that gene expression can be induced by neighboring partners. The neighbor-dependent gene expression suggests new potential genes involved in cell-cell interactions beyond what ligand-receptor co-expression can discover.

bioinformatics↗

bioRxiv2021

Vesalius: Tissue anatomy from spatial transcriptomic data.

Characterization of tissue architecture promises to deliver insights into development, cell communication and disease. In silico spatial domain retrieval methods have been developed for spatial transcriptomics (ST) data assuming transcriptional similarity of neighboring barcodes. However, domain retrieval approaches with this assumption cannot work in complex tissues composed of multiple cell types. This task becomes especially challenging in cellular resolution ST methods. We developed Vesalius to decipher tissue anatomy from ST data by applying image processing technology. Vesalius uniquely detected territories composed of multiple cell types and successfully recovered tissue structures in high-resolution ST data including in mouse brain, embryo, liver, and colon. Utilizing this tissue architecture, Vesalius identified tissue morphology specific gene expression and regional specific gene expression changes for astrocytes, interneuron, oligodendrocytes, and entorhinal cells in the mouse brain.

genomics↗

bioRxiv2019

Dissecting the binding mechanisms of transcription factors to DNA using a statistical thermodynamics framework.

Transcription Factors (TFs) bind to DNA and control activity of target genes. Here, we present ChIPanalyser, a user-friendly, versatile and powerful R/Bioconductor package predicting and modelling the binding of TFs to DNA. ChIPanalyser performs similarly to state-of-the-art tools, but is an explainable model and provides biological insights into binding mechanisms of TFs. We focused on investigating the binding mechanisms of three TFs that are known architectural proteins CTCF, BEAF-32 and su(Hw) in three Drosophila cell lines (BG3, Kc167 and S2). While CTCF preferentially binds only to a subset of high affinity sites located mainly in open chromatin, BEAF-32 binds to most of its high affinity binding sites available in open chromatin. In contrast, su(Hw) binds to both open chromatin and also partially closed chromatin. Most importantly, differences in TF binding profiles between cell lines for these TFs are mainly driven by differences in DNA accessibility and not by differences in TF concentrations between cell lines. Finally, we investigated binding of Hox TFs in Drosophila and found that Ubx binds only in open chromatin, while Abd-B and Dfd are capable to bind in both open and partially closed chromatin. Overall, our results show that TFs display different binding mechanisms and that our model is able to recapitulate this diverse repertoire of mechanisms.

genomics↗