Biology subjects

El Assal, D. C.

Publications and source records attributed to El Assal, D. C..

3 recordsLinked to original sources

bioRxiv2026

Conserved metabolic vulnerabilities across pathogenic coronaviruses nominate host-directed therapeutic targets

Pathogenic coronaviruses profoundly rewire host cell metabolism to support viral replication, yet whether these metabolic alterations expose shared and actionable vulnerabilities remains unclear. By integrating transcriptomic profiles from cells infected with SARS-CoV, SARS-CoV-2, and MERS-CoV with genome-scale metabolic models, we identify conserved and virus-specific metabolic perturbations affecting mitochondrial transport, nucleotide biosynthesis, fatty acid metabolism, and redox balance. Despite distinct transcriptional responses, all three viruses converge on a limited set of metabolic reactions whose flux ranges deviate strongly from healthy states. Using a network-based predictive framework, we systematically identify gene-pair perturbations that restore perturbed reaction fluxes toward non-infected metabolic states. Predicted rescue mechanisms reveal shared metabolic dependencies across coronaviruses, as well as time-dependent virus-specific vulnerabilities, and nominate druggable host targets. Notably, several top predictions align with independent experimental and clinical evidence, including metabolic interventions shown to reduce viral replication or disease severity in COVID-19 patients. Together, our results define conserved metabolic rescue pathways in coronavirus infection and provide a general strategy for identifying host-directed therapeutic opportunities from transcriptomic data. HighlightsO_LICoronaviruses converge on shared metabolic vulnerabilities in host cells C_LIO_LINiTRO predicts gene pairs that rescue viral-induced metabolic states C_LIO_LIMitochondrial transport emerges as a key pan-coronavirus target C_LIO_LITop predictions validated by clinical trials and in vitro evidence C_LI eTOC BlurbDohai et al. develop NiTRO, a network-based algorithm that integrates coronavirus-induced transcriptomic changes with genome-scale metabolic models to identify gene-pair perturbations capable of rescuing infected metabolic states. The approach reveals shared and virus-specific druggable metabolic vulnerabilities, with top predictions corroborated by clinical evidence.

systems biology↗

bioRxiv2025

Earth-Observation and Environmental Vision Transformers Reveal Genome-Environment Associations in Macroalgae

Macroalgae thrive in extreme environments, yet the genomic basis of their tolerance remains poorly resolved. We describe nine Arabian Gulf macroalgae and integrate them with 117 published genomes (126 total; 70 Rhodophyta, 43 Ochrophyta, 13 Chlorophyta) to test genome-environment associations. Google Earth Engine (GEE) for broad-scale oceanography and 10-meter resolution AlphaEarth Foundations (AEF) embeddings for fine-scale habitat heterogeneity. We identified 157 significant (FDR q < 0.05) correlations with global GEE variables--including a strong negative temperature association with DUF3570--while AEF embeddings uncovered over 1,000 lineage-specific signals within Rhodophyta and identified climate-driven Pfam modules. The von Willebrand factor type-A domain emerged as uniquely robust across all frameworks and enriched in Arabian Gulf species. In the Arabian Gulf, enrichment of this domain is consistent with selection for adhesion mechanisms capable of withstanding chronic hydrodynamic stress compounded by high temperature and salinity. These results demonstrate that converging remote sensing with deep learning identifies conserved and lineage-specific genomic signatures of ecological differentiation across diverse macroalgal lineages.

genomics↗

bioRxiv2020

Exostosin-1 Glycosyltransferase Regulates Endoplasmic Reticulum Architecture and Dynamics

The endoplasmic reticulum (ER) is a central eukaryotic organelle with a tubular network made of hairpin proteins linked by hydrolysis of GTP nucleotides. Among post-translational modifications initiated at the ER level, glycosylation is the most common reaction. However, our understanding of the impact of glycosylation on ER structure remains unclear. Here, we show that Exostosin-1 (EXT1) glycosyltransferase, an enzyme involved in N-glycosylation, is a key regulator of ER morphology and dynamics. We have integrated multi-omics data and super-resolution imaging to characterize the broad effect of EXT1 inactivation, including ER shape-dynamics-function relationships in mammalian cells. We have observed that, inactivating EXT1 induces cell enlargement and enhances metabolic switches such as protein secretion. In particular, suppressing EXT1 in mouse thymocytes causes developmental dysfunctions associated to ER network extension. Our findings suggest that EXT1 drives glycosylation reactions involving ER structural proteins and high-energy nucleotide sugars, which might also apply to other organelles.

cell biology↗