Biology subjects

Solano, L. E.

Publications and source records attributed to Solano, L. E..

3 recordsLinked to original sources

bioRxiv2026

Flow Orchestrated Regulatory Genomics Engine (FORGE): A Configurable Nextflow Pipeline for End-to-End snMultiome Analysis

MOTIVATION Single-nucleus resolution multiome (snMultiome) assays concurrently profile gene expression and chromatin accessibility in the same nucleus. Yet regulatory inference from such analyses are difficult to scale, audit, and reproduce; moreover, as a field, snMultiomics and its toolset remains far from standardized. To address these challenges we developed FORGE, a configureable Nextflow workflow that carries paired data from raw counts and fragments through regulatory network inference with a comprehensive differential testing suite. Execution is containerized, tracks provenance, robust to interruption, optimized for cluster-based compute environments, and allows for nuanced customization of specific processes. SUMMARY Single-nucleus multiome assays jointly profile gene expression and chromatin accessibility, yet their analysis typically requires bespoke chaining of modality-specific tools, creating barriers to reproducibility, scalability, and regulatory interpretation. We present FORGE (Flow Orchestrated Regulatory Genomics Engine), a configurable workflow that automates standalone snRNA-seq and snATAC-seq analysis, integrates the pair through complementary linear and nonlinear latent-variable models, and carries them through regulatory-network inference and differential testing. We evaluated FORGE on four human and mouse datasets spanning blood, brain, and kidney and two multiome chemistries, including a twelve-sample CRND8 Alzheimers disease cohort. We report cross-modal agreement alongside missing-modality reconstruction and an accounting of computational cost. In the Alzheimer's cohort, FORGE nominated a glial Mef2c-associated program defensible across expression, co-accessibility, footprinting, and eRegulon evidence. In human PBMC, FORGE layered evidence models also provide nuanced interpretations that largely corroborate previously published regulatory links while also proposing an additional CD83 myeloid module.

bioinformatics↗

bioRxiv2025

Dynamic Lipidome Reorganization in Response to Heat Shock Stress

The heat shock response (HSR) is a conserved cellular mechanism critical for adaptation to environmental and physiological stressors, with broad implications for cell survival, immune responses, and cancer biology. While the HSR has been extensively studied at the proteomic and transcriptomic levels, the role of lipid metabolism and membrane reorganization remains underexplored. Here, we integrate mass spectrometry-based lipidomics with RNA sequencing to characterize global lipidomic and transcriptomic changes in HeLa cells exposed to three conditions: control, heat shock (HS), and HS with eight hours of recovery. Heat shock-induced extensive lipid remodeling, including significant increases in fatty acids, glycerophospholipids, and sphingolipids, with partial normalization during recovery. Transcriptomic analysis identified over 2,700 upregulated and 2,300 downregulated genes under heat shock, with GO enrichment suggesting potential transcriptional contributions to lipid metabolism. However, transcriptional changes alone did not fully explain the observed lipidomic shifts, suggesting additional layers of regulation. Joint pathway analysis revealed enrichment in glycerophospholipid and sphingolipid metabolism, while network analysis identified lipid transport regulators (STAB2, APOB), stress-linked metabolic nodes (KNG1), and persistent sphingolipid enrichment during recovery. These findings provide a comprehensive framework for understanding lipid-mediated mechanisms of the HSR and highlight the importance of multi-omics integration in stress adaptation and disease biology.

bioinformatics↗

bioRxiv2024

PRRGO: A Tool for Visualizing and Mapping Globally Expressed Genes in Public Gene Expression Omnibus RNA-Sequencing Studies to PageRank-scored Gene Ontology Terms

We herein report PageRankeR Gene Ontology (PRRGO), a downloadable web application that can integrate differentially expressed gene (DEG) data from the gene expression omnibus (GEO) GEO2R web tool with the gene ontology (GO) database [1]. Unlike existing tools, PRRGO computes the PageRank for the entire GO network and can generate both interactive GO networks on the web interface and comma-separated values (CSV) files containing the DEG statistics categorized by GO term. These hierarchical and tabular GO-DEG data are especially conducive to hypothesis generation and overlap studies with the use of PageRank data, which can provide a metric of GO term centrality. We verified the tool for accuracy and reliability across nine independent heat shock (HS) studies for which the RNA-seq data was publicly available on GEO and found that the tool produced increasing concordance between study DEGs, GO terms, and select HS-specific GO terms.

bioinformatics↗