Biology subjects

Salimi, S.

Publications and source records attributed to Salimi, S..

3 recordsLinked to original sources

bioRxiv2024

Characterization and Organization of Telomeric-Linked Helicase (TLH) Gene Families in Fusarium oxysporum

Telomere-linked RecQ helicase (TLH) genes have been reported in several fungi and a choanoflagellate in the regions adjacent to the terminal telomere repeats. In this study, we explore the Telomere-linked RecQ helicase (TLH) genes in four strains of Fusarium oxysporum, offering new insights into their genomic structure, functional motifs, and impact on chromosomal ends. We conducted a comprehensive analysis, comparing the TLH genes of F. oxysporum with those previously identified in other organisms and uncovering significant similarities. Through comparative genomics, we identified conserved protein motifs across these genes, including a TLH domain, C2H2, and RecQ helicase motifs. Our phylogenetic analysis positions the F. oxysporum TLH genes in a cluster with other known TLHs, suggesting a shared evolutionary origin. Mutation analysis revealed a relatively low level of deleterious mutations in TLH gene paralogs, with a notable proportion of full-size structures maintained across strains. Analysis of subtelomeric sequences indicates that a region with almost identical sequences flanks the majority of chromosome ends, termed TLH-containing region (TLHcr), across these strains. The presence of TLHcrs at chromosome ends, either as single entities or in arrays, underscores their potential role in telomere function and genome stability. Our findings provide a detailed examination of TLH genes in four strains of F. oxysporum, laying the groundwork for future studies on their biological significance and evolutionary history in fungal genomes.

bioinformatics↗

bioRxiv2023

Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

genetics↗

bioRxiv2021

PathoClock and PhysioClock in mice recapitulate human multimorbidity and heterogeneous aging

Multimorbidity is a public health concern and an essential component of aging and healthspan but understudied because investigative tools are lacking that can be translatable to capture similarities and differences of the aging process across species and variability between individuals and individual organs. To help address this need, body organ disease number (BODN) borrowed from human studies was applied to C57BL/6 (B6) and CB6F1 mouse strains at 8, 16, 24 and 32 months of age, as a measure of systems morbidity based on pathology lesions to develop a mouse PathoClock resembling clinically based Body Clock in humans, using Bayesian inference. A mouse PhysioClock was also developed based on measures of physiological domains including cardiovascular, neuromuscular, and cognitive function in the same two mouse strains so that alignment with BODN was predictable. The results revealed between- and within-age variabilities in PathoClock and PhysioClock, as well as between-strain variabilities. Both PathoClock and PhysioClock correlated with chronological age more strongly in CB6F1 than C57BL/6. Prediction models were then developed, designated as PathoAge and PhysioAge, using regression models of pathology and physiology measures on chronological age. PathoAge better predicted chronological age than PhysioAge as the predicted chronological and observed chronological age for PhysioAge were complex rather than linear. In conclusion, PathoClock and PhathoAge can be used to capture biological changes that predict BODN, a metric developed in human, and compare multimorbidity across species. These mouse clocks are potential translational tools that could be used in aging intervention studies.

pathology↗