Biology subjects

Daakour, S.

Publications and source records attributed to Daakour, S..

4 recordsLinked to original sources

bioRxiv2026

Coupling of oceanographic state to the dark proteome: a foundation for genome-informed marine productivity modeling

Phytoplankton drive ~46% of global primary production, yet whether their protein repertoires encode ocean conditions - and whether reference databases capture the environmentally responsive fraction - is unknown. We used a protein language model to classify 447.7 million proteins from 2,357 samples and analyzed domain profiles for 231.7 million algal proteins across 2,044 samples. Under spatial block cross-validation, environment predicted individual domain abundances at R2 up to 0.59, while domain profiles predicted sea surface temperature at R2 = 0.38. The strongest coupling lay beyond annotated sequence space: 33,950 families clustered from 201 million Pfam-dark proteins coupled to environment 2.29-fold more strongly than Pfam domains. Cross-taxonomic selection, protein language modeling, and AlphaFold 3 predictions of 138 well-folded, InterPro-unannotated representatives support the Pfam-dark proteome as a biologically structured evolutionary compartment. Thus, the proteins most tightly coupled to ocean state are those least represented in reference databases.

bioinformatics↗

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

Multi-omics decipher the molecular mechanisms driving high-lipid production in an artificially-evolved Chlamydomonas mutant

Enhancing lipid accumulation in microalgae is critical for commercial viability but often compromises growth. We previously identified an artificially evolved Chlamydomonas reinhardtii mutant (H5) that retains wild-type growth (CC-503) while producing significantly more lipids. Here, we present multi-omic analyses that elucidate the molecular basis of this phenotype. Whole-genome sequencing revealed over 3,000 mutations in H5, including 45 in protein-coding genes (e.g., phosphofructokinase, acyl-carrier protein, glycerol kinase). Six corresponding CLiP insertion mutants also showed elevated lipid content. Transcriptomics revealed upregulation of key genes for glycolysis, nutrient uptake, and proliferation (e.g., pyruvate carboxylase, carbonic anhydrase) under nutrient-replete conditions. Metabolomics identified a striking increase in malonate, a metabolite that supports fatty acid synthesis and cell proliferation. Epigenomic profiling showed hypomethylation in triacylglycerol (TAG) biosynthesis genes and hypermethylation in energy balance regulators. Together, these data suggest that accelerated glycolysis and streamlined metabolism drive lipid accumulation in H5 without compromising growth. Our findings provide a blueprint for engineering high-lipid microalgal strains for industrial applications. HIGHLIGHTSO_LIHigh-lipid Chlamydomonas mutant (H5) exhibits cancer-like metabolism: pseudo-hypoxia and nutrient deprivation response C_LIO_LIMulti-omics reveals 45 high-impact mutations synergistically enhance lipid production in H5 C_LIO_LISix CLiP mutants of H5-disrupted genes showed significantly increased lipid content C_LIO_LIMalonate levels increased 10-fold in H5, indicating altered mitochondrial function C_LIO_LIH5 upregulates glycolytic genes while maintaining wild-type growth rates C_LIO_LITranscriptomes from H5 and CC-503 converge after nitrogen deprivation despite replete-state differences C_LIO_LIH5 shows altered lipid composition with increased TAG diversity, decreased DAGs C_LIO_LIEpigenomic profiling reveals 14,720 differentially methylated transcribed regions in H5 C_LI

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↗