Biology subjects

Metwally, S.

Publications and source records attributed to Metwally, S..

3 recordsLinked to original sources

bioRxiv2026

Deciphering the Genetic Architecture of Sorghum Grain Oil Content via Lipidome-Integrated Genome-Wide Association Analysis

Grain oil content and composition are complex quantitative traits that shape cereal grain quality and nutritional value. Sorghum (Sorghum bicolor), a heat- and drought-adapted C crop essential for global food and feed security, remains insufficiently characterized with respect to grain lipidome diversity and its genetic architecture. Here, we integrated population-scale whole-grain lipidomics with genome-wide association studies (GWAS) in 266 sorghum accessions. Lipidome profiling revealed extensive natural variation in triacylglycerols (TAGs), accompanied by coordinated shifts in phosphatidylcholines (PCs) and phosphatidylethanolamines (PEs), explaining 87% of population-level differences in total grain oil. Lipidome-wide GWAS identified approximately 1.6 million significant variant-trait associations and resolved 55 loci linked to plastidial fatty acid synthesis, TAG assembly, lipid transport, and membrane remodeling. These loci, many undetected in previous GWAS of bulk oil content, demonstrated the increased mapping resolution achieved through lipidomics. Integration with metabolic gene clusters revealed significant enrichment of lipid-associated variants within terpene and saccharide-terpene biosynthetic clusters, indicating coordinated genetic regulation between central lipid metabolism and specialized metabolic pathways. Variants within these clusters explained more than 50% of the variance in measured grain oil content and exhibited additive effects of favorable alleles. Haplotype analyses further identified 27 elite sorghum accessions and 12 linked markers for marker-assisted improvement of sorghum grain oil. These findings elucidate the multilayered genetic architecture of sorghum grain lipid diversity and showcase the value of large-scale lipidomics integrated with GWAS for accelerating C crop grain quality improvement.

bioinformatics↗

bioRxiv2025

Matrix mechanics governs mechano-metabolic adaptation across cancer grades in bladder spheroids

Extracellular matrix (ECM) mechanics critically influence cancer progression, yet the interplay between ECM viscoelasticity, architecture, and tumor cell adaptation remains incompletely understood. Here, we engineered collagen-hyaluronan hydrogels with tunable stiffness to mimic soft and stiff tumor microenvironments and studied bladder cancer spheroids representing benign, low-invasive, and highly invasive stages. Using hydraulic force spectroscopy, rheometry, and molecular analyses, we found that matrix stiffness differentially modulates spheroid morphology, migration, and expression of adhesion and metabolic markers. Active ECM remodeling via metalloproteinase MMP-2 facilitated migration in compliant but not rigid matrices, while mechano-metabolic coupling varied with cancer progression stage. These findings reveal how bladder cancer cells adapt to mechanical cues through coordinated biomechanical and metabolic responses, underscoring the importance of integrating cellular and matrix mechanics in modeling tumor invasion and developing targeted therapies.

biophysics↗

bioRxiv2025

Integrative metabolome-genome analysis reveals the genetic architecture of metabolic diversity in sorghum grain

Natural variation in the grain metabolome plays a central role in shaping nutritional quality and end-use traits in grass crops. Understanding the genetic basis of this metabolic diversity is therefore essential, yet population-scale integration of metabolomics and genomics remains limited in sorghum, a climate-resilient C4 crop renowned for its exceptional heat and drought tolerance. Here, we integrated large-scale untargeted metabolomic profiling, population genomics, and artificial intelligence (AI)-based machine learning to systematically dissect grain metabolic diversity and its genetic architecture in sorghum. Untargeted metabolomic profiling of mature grains of the Sorghum Association Panel (SAP) identified 4,877 compounds, revealing extensive quantitative variation relevant to grain nutritional improvement. Metabolite-based genome-wide association studies (mGWAS) identified [~]4.15 million significant SNP-metabolite associations, revealing the heterogeneous genetic architecture of metabolic traits. Associated variants were enriched in genic and regulatory regions but depleted in intergenic regions, consistent with functional constraint. A total of 38 metabolite gene clusters revealed coordinated genetic control of core metabolic pathways. We further applied machine learning to identify key metabolites that underlie grain color variation and to prioritize associated candidate genes, demonstrating the utility of predictive models integrating genotype, metabolome, and end trait. Collectively, this work establishes a population-scale atlas of sorghum grain metabolomic and genetic diversity, available through the Sorghum Grain Metabolite Diversity Atlas (SorGMDA). This resource enables integrated metabolomics and genomic analyses and supports systems-level breeding strategies for improving grain nutritional quality.

genomics↗