Biology subjects

Martin, A. J.

Publications and source records attributed to Martin, A. J..

5 recordsLinked to original sources

bioRxiv2026

Auditable recovery of single-cell RNA-seq zeros with SPARE

In Single-cell RNA-seq, observed zeroes are the mix between biological absence and technical limitations. However, current evaluation metrics fail to distinguish between these two states, focusing on reconstruction accuracy rather than the biological validity of edits. We introduce SPARE, a partition-aware framework that audits the imputation process by cataloging observed zeros as edited, unchanged, or marker-vetoed prior to sequence reconstruction. Across multiple tissue benchmarks, SPARE successfully recovered masked expression while cataloging edit burden, marker leakage and raw-state movement. Protein and disease-context audits showed that recovered expression must be accepted, restricted or rejected endpoint by endpoint. SPARE reframes imputation as auditable zero editing rather than generic matrix completion.

bioinformatics↗

bioRxiv2025

SARS-CoV-2 Variant-Dependent Alterations in Nasopharyngeal Microbiota and Host Inflammatory Response

The SARS-CoV-2 pandemic saw multiple outbreaks occur over short periods. This was linked to the viruss high infectivity and rapid mutation rate, which hindered the development of effective treatments and a comprehensive understanding of COVID-19 pathophysiology. The nasopharyngeal tract, the main entry site of SARS-CoV-2, interacts with angiotensin-converting enzyme 2 (ACE2) receptors via the viral spike glycoprotein, triggering pro-inflammatory responses that affect both tissue integrity and the resident microbiota. Viral infection induces dysbiosis by modulating microbiome diversity. Although distinct variants produce independent symptoms and viral loads, their impact on the nasopharyngeal microbiota and host inflammatory profile remains poorly understood. To address this, we analyzed nasopharyngeal samples from Chilean individuals collected during different phases of the pandemic. The microbiota was characterized by 16S rRNA gene sequencing (V3-V4 region), and cytokine expression was quantified by RT-qPCR. Finally, applied rigorous data processing and machine-learning models Random Forest and K-Nearest Neighbors to identify associations between SARS-CoV-2 variants, inflammatory markers, and opportunistic bacterial genera. Our results reveal that SARS-CoV-2 infection promotes distinct immunomicrobial signatures marked by TNF--driven inflammation and the expansion of opportunistic taxa. These variant-dependent alterations indicate that host inflammatory responses and microbial dysbiosis are closely intertwined in the nasopharyngeal environment. This study provides a comprehensive framework integrating microbial and host factors to better understand the mechanisms underlying COVID-19 pathogenesis and highlights the potential of combining microbiome and cytokine profiling with machine-learning approaches to differentiate infection outcomes across viral variants. ImportanceUnderstanding how SARS-CoV-2 infection alters the upper respiratory microbiota and host immune responses is essential to uncover the mechanisms underlying COVID-19 severity. In this study, we integrate bacterial 16S rRNA sequencing, inflammatory markers, and machine learning to identify key descriptors of infection such as TNF-, Acinetobacter, Prevotella, and Staphylococcus. These markers define a conserved immunomicrobial signature across viral variants, linking inflammatory activation with microbial dysbiosis in the nasopharyngeal environment. Our findings highlight the value of the correct and robust application of machine learning models and nested cross-validation strategies in high-dimensional (wide) datasets to improve variant discrimination, reveal host-microbe interactions, and support the development of therapeutic strategies for respiratory viral infections.

microbiology↗

bioRxiv2025

Exploring Single-Cell Gene Regulatory Dynamics in Rett Syndrome

Rett syndrome is a monogenic disorder with an incidence of 95% in women, characterized by the complexity of studying the associated phenotype due to the heterogeneity in patient tissues from the stochastic silencing of the affected X chromosome. Furthermore, we are largely unaware of the cascade of alterations that occur in neurons due to transcriptional changes induced by the affected MECP2 gene. To address these challenges, an in-depth network analysis was implemented using organoid single-cell transcriptomic data derived from human patients. We performed a Weighted correlation network analysis and trajectory analysis to understand the differences in the developmental processes between samples, we followed by the generation of gene regulatory networks for each relevant cell developmental pathways to assess the master regulator that are involved in this process, with the differential expressed genes with potential therapeutic implications being identified by integration with SFARI and Genes4Epi. The results were adapted into dynamic Boolean models fitted with the transcriptomic data for validation in which we evaluated the attractor field from each reachable state. These approaches allowed us to explore differences in regulatory behavior in the developmental pathways. Our study provides an insight that pinpoints the cellular stages on which the regulation and compensatory mechanism activate and regulate Rett syndrome. We identified 19 Master regulators for the Dopaminergic developmental trajectory, as well as 34 Master regulator genes for the Gabaergic developmental trajectory. Dynamic Boolean modeling of these systems showcased a comprehensive understanding of the disrupted developmental pathways of Rett syndrome, highlighting the transitional states of potential within maturation trajectories as the key point of divergence in regulation for Rett syndrome. After complementing with enrichment and clinical relevant variant analysis, we identify the key actors in this system as NR2F1 and TCF4, with TCF4 suggesting a symmetrical compensatory relationship with MeCP2, and NR2F1 as possible link with wider developmental conditions, this concluded with highlighting the possibility of regulation in this condition being affected by the MAPK-ERK pathway of transcriptional regulation, offering a novel angle for targeted research.

neuroscience↗

bioRxiv2024

scExplorer: A Comprehensive Web Server for Single-Cell RNA Sequencing Data Analysis

Single-cell RNA sequencing (scRNA-seq) has redefined our ability to investigate cellular heterogeneity at unprecedented resolution. However, the computational analysis required for scRNA-seq data interpretation remains a significant barrier for many researchers. We present scExplorer, an interactive web application that streamlines the analysis workflow from preprocessing through dimensional reduction to differential expression analysis. The platform provides an intuitive interface for real-time data exploration and visualization while maintaining compatibility with established analysis frameworks in both R and Python environments. scExplorer significantly reduces the technical knowledge and computational requirements needed to perform scRNA-seq analysis, enabling researchers to focus on biological insights rather than computational complexity. The application modular design ensures scalability and robust performance, making it a valuable tool for both small-scale and large-scale single-cell studies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=148 SRC="FIGDIR/small/622710v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@171afe8org.highwire.dtl.DTLVardef@d3b878org.highwire.dtl.DTLVardef@1f3fff1org.highwire.dtl.DTLVardef@285e95_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

Automated generation of context-specific Gene Regulatory Networks with a weighted approach in D. melanogaster

MotivationThe regulation of gene expression is a key factor in the development and maintenance of life in all organisms. This process is carried out mainly through the action of transcription factors (TFs), although other actors such as ncRNAs are involved. In this work, we propose a new method to construct Gene Regulatory Networks (GRNs) depicting regulatory events in a certain context for Drosophila melanogaster. Our approach is based on known relationships between epigenetics and the activity of transcription factors. ResultsWe developed method, Tool for Weighted Epigenomic Networks in D. melanogaster (Fly T-WEoN), which generates GRNs starting from a reference network that contains all known gene regulations in the fly. Regulations that are unlikely taking place are removed by applying a series of knowledge-based filters. Each of these filters is implemented as an independent module that considers a type of experimental evidence, including DNA methylation, chromatin accessibility, histone modifications, and gene expression. Fly T-WEoN is based on heuristic rules that reflect current knowledge on gene regulation in D. melanogaster obtained from literature. Experimental data files can be generated with several standard procedures and used solely when and if available.Fly T-WEoN is available as a Cytoscape application that permits integration with other tools, and facilitates downstream network analysis. In this work, we first demonstrate the reliability of our method to then provide a relevant application case of our tool: early development of D. melanogaster. AvailabilityFly T-WEoN, together with its step-by-step guide is available at https://weon.readthedocs.io Contactalberto.martin@umayor.cl

bioinformatics↗