Biology subjects

Salimi, S.

Publications and source records attributed to Salimi, S..

6 recordsLinked to original sources

bioRxiv2026

Phylogenomic Characterization of Two Alternaria Lineages Associated with Cotton Leaf Spot in Tennessee

Alternaria leaf spot is an important foliar disease of cotton (Gossypium hirsutum L.) and has historically been associated primarily with Alternaria macrospora in the United States. In November 2025, symptomatic Upland cotton leaves from a research greenhouse at Tennessee Technological University's Oakley Farm in Livingston, Tennessee, yielded two Alternaria isolates, 2-2 and 9-1. Quantitative conidial characterization showed substantial overlap in dimensions, septation, and shape. Concatenated ITS-RPB2 phylogenetic analysis supported different broad affinities, placing isolate 2-2 within an A. alternata- dominated group and isolate 9-1 within a group associated with the A. arborescens species complex (AASC), although their immediate relationships were weakly resolved. Phylogenomic analysis of 1,501 single-copy BUSCO orthologs from 63 genomes placed isolate 2-2 within the A. alternata lineage, sister to BMP2343, and isolate 9-1 within the AASC, sister to MOD1-FUNGI6. In controlled pathogenicity assays, isolate 9-1 reproduced foliar lesions on cotton, whereas mock-inoculated plants remained symptomless, and molecularly confirmed re-isolation fulfilled Koch's postulates. Isolate 2-2 produced insufficient conidia under the culture conditions used to prepare a standardized inoculum, and its pathogenicity was therefore not determined. These results reveal phylogenetically distinct Alternaria lineages associated with symptomatic cotton and establish an AASC isolate as a causal agent of cotton leaf spot in Tennessee.

pathology↗

bioRxiv2026

Endothelial adaptation to complex flow patterns in a novel in vitro model predicted by computational fluid dynamics

The endothelial cells (ECs) that line blood vessels continuously sense and respond to the physical forces exerted by blood flow. In vivo, pulsatile arterial flow interacts with vessel curvature, branching and other anatomical features to generate complex local hemodynamic environments that dictate the magnitude, direction, pulsatility, and oscillatory nature of wall shear stress experienced by ECs. Currently, accessible and reproducible in vitro models of complex pulsatile flow that recapitulate in vivo vascular anatomy remain limited. Here, we combine a novel rotational-flow endothelial culture platform with detailed computational fluid dynamics (CFD) modeling to characterize four well geometries designed to generate distinct hemodynamic environments. CFD analyses demonstrate that these geometries intrinsically generate pulsatile flow and produce reproducible spatially distinct regions of wall shear stress magnitude, pulsatility, and oscillatory shear within a single culture well. Endothelial alignment mapping and functional assays reveal region-specific cellular responses to the predicted local flow conditions that closely corresponded to the predicted local hemodynamic environment, linking complex flow patterns to endothelial adaptation. The technical advancements of our modeling efforts should support a faster, cheaper, simpler, and--importantly--validated framework for future investigation into EC mechanobiology under complex flow conditions. HIGHLIGHTSO_LISimple engineered well geometries generate distinct hemodynamic microenvironments, mimicking in vivo vascular structures, using a conventional orbital shaker. C_LIO_LIComputational fluid dynamics (CFD) reveals spatially distinct patterns of wall shear stress, pulsatility, and oscillatory shear applied to ECs within individual culture wells. C_LIO_LIHigh average wall shear stress and elevated oscillatory shear index induces a unique perpendicular alignment of ECs to the dominant flow vector. C_LI

cell biology↗

bioRxiv2026

CentroFinder: accurate de novo identification of centromeres in fungal genomes

MotivationCentromeres are essential chromosomal loci, yet their computational identification remains challenging due to rapid sequence evolution, high repeat content, and the absence of conserved defining motifs. This challenge is particularly pronounced in fungi, where centromere architectures vary widely in size, sequence composition, and chromatin organization, limiting the effectiveness of single-feature or motif-based prediction approaches. ResultsWe present CentroFinder, a fungal-specific computational framework for de novo centromere prediction from long-read sequencing-based genome assemblies. CentroFinder integrates multiple genomic and long-read-derived features into a weighted scoring model to identify loci where centromere-associated signals converge. Benchmarking against experimentally mapped centromeres in Cryptococcus deuterogattii, Magnaporthe oryzae, and Neurospora crassa demonstrates that CentroFinder consistently predicts a single centromeric region per chromosome, fully nested within CENP-A-defined domains despite substantial diversity in centromere size, sequence composition, and chromatin context. Availability and ImplementationCentroFinder is freely available as open-source software at https://github.com/RahnamaLab/CentroFinder. The pipeline is designed for high-performance computing environments and leverages features derived from long-read sequencing data.

bioinformatics↗

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↗