Biology subjects

Yeasmin, F.

Publications and source records attributed to Yeasmin, F..

4 recordsLinked to original sources

bioRxiv2025

Temporal transcriptomics and molecular dynamics identify a serine-type endopeptidase as a key regulator of dengue virus infection in Aedes aegypti

The dengue virus (DENV), a major global pathogen causing over 400 million annual infections, relies on the mosquito Aedes aegypti as its primary vector. Intriguingly, A. aegypti sustains persistent DENV infection without exhibiting apparent pathology, indicating a highly adapted and regulated host-virus relationship. However, the temporal gene expression dynamics that govern this finely balanced interaction remain poorly understood. We performed a comprehensive transcriptomic analysis using 12 paired-end RNA-seq datasets from gene expression omnibus (GEO; GSE222893), comparing naive and DENV-infected A. aegypti samples at Days 1, 2, and 7 post-infection. A robust bioinformatics pipeline (STAR [->] FeatureCounts [->] DESeq2 [->] g:Profiler) was employed to identify differentially expressed genes (DEGs), explore functional annotations, and resolve temporal patterns via principal component analysis. Finally, a molecular dynamics simulation (MDS) was performed to check the molecular stability of the highly expressed gene. Our temporal analysis identified LOC5570687, a gene encoding a serine-type endopeptidase, as the most significantly differentially expressed transcript across all infection time points. Functional annotation confirmed its role in proteolysis, implicating it in the cleavage of flaviviral polyproteins, a critical step in viral replication. Principal component analysis revealed distinct transcriptional divergence at Day 1, immune modulation at Day 2, and convergence by Day 7--marking virion maturation. Downregulation of LOC5570687 in DENV-exposed mosquitoes was temporally associated with enhanced viral replication, indicating its potential role as a molecular switch between antiviral defense and viral exploitation. A 100 ns MDS was proof of the structural stability, compactness, and dynamic properties of the highly expressed protein. This study uncovers the temporally dynamic transcriptional landscape of A. aegypti during DENV infection and the serine-type endopeptidase LOC5570687 as a critical regulator of viral pathogenesis. These findings provide a molecular framework for understanding vector competence and propose the LOC5570687 as a promising target for vector-based intervention strategies to disrupt DENV transmission.

microbiology↗

bioRxiv2024

High-Throughput Screening Reveals Potential Inhibitors Targeting Trimethoprim-Resistant DfrA1 Protein in Klebsiella pneumoniae and Escherichia coli

The DfrA1 protein provides trimethoprim resistance in bacteria, especially Klebsiella pneumoniae and Escherichia coli, by modifying dihydrofolate reductase, which reduces the binding efficacy of the antibiotic. Thus, this study aimed to identify inhibitors of the trimethoprim-resistant DfrA1 protein through high-throughput computational screening of 3,601 newly synthesized chemical compounds sourced from the ChemDiv database. We conducted high-throughput computational optimization and screening of a library containing 3,601 compounds against the DfrA1 protein from K. pneumoniae and E. coli to identify potential drug candidates (DCs). Through this extensive approach, we identified six promising DCs, labeled DC1 to DC6, as potential inhibitors of DfrA1. Each DC demonstrated strong initial binding affinity and favorable chemical interactions with the DfrA1 binding sites when compared to the effective drug Iclaprim (effective antibiotic against DfrA1), used as a control. To validate these findings, we further investigated the molecular mechanisms of inhibition, focusing on the thermodynamic properties of the promising DCs. Furthermore, molecular dynamics simulation (MDS) validated the inhibitory efficacy of these six DCs against the DfrA1 protein. Our results showed that DC4 (an organoflourinated compound) and DC6 (a benzimidazol compound) showed superior efficacy against the DfrA1 protein than the control drug, particularly regarding stability, solvent-accessible surface area, solvent exposure, polarity, and binding site interactions, which influence their residence time and efficacy. Overall, findings of this study suggest that DC4 and DC6 have the potential to act as inhibitors against the DfrA1, offering promising prospects for the treatment and management of infections caused by trimethoprim-resistant K. pneumoniae and E. coli in both humans and animals.

bioinformatics↗

bioRxiv2024

Natural Bacteriocins as Potential Drug Candidates Targeting Core Proteins in Mastitis Pathogens of Dairy Cattle

Mastitis poses a major challenge in the dairy industry, with rising antibiotic-resistant strains underscoring the urgent need for alternative antimicrobial strategies. This study aimed to (i) identify essential core proteins in clinical mastitis (CM)-causing pathogens using genomic approach, and (ii) assess the efficacy of natural antimicrobial peptides as novel therapeutic agents targeting the selected core proteins for the rational management of mastitis in dairy cows. Through a core genomic analysis of 16 CM-causing pathogens, including strains of Staphylococcus aureus, S. warneri, Streptococcus agalactiae, S. uberis, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, P. putida, and P. asiatica, we identified 65 core proteins shared among these pathogens. Among them, ten proteins including PhoH, TrpB, FtsZ, HslV, HupB, RibH, InfA, MurA, GlxK, and Rho were found to be essential for the survival and virulence of these pathogens. Importantly, further novelty, resistance, and virulence assessments identified Rho and HupB as potential therapeutic targets. A comprehensive screening of 70 bacteriocin peptides (BPs) revealed 14 BPs that effectively interacted with both Rho and HupB proteins. Further analysis showed that BP8 and BP32 disrupt Rho protein function by blocking transcription termination process, while BP8, BP39, and BP40 prevent HupB from binding to DNA. These findings confirm the promising stability and efficacy of BP8 against both target proteins in CM-pathogens, highlighting it as a promising broad-spectrum therapeutic agent. Our computational study identified Rho and HupB as key proteins in CM-causing pathogens, which can be targeted by natural bacteriocins like BP8, suggesting its potential for developing effective and sustainable therapeutics against mastitis in dairy cattle. Author SummaryMastitis poses a significant threat to the global dairy industry, with rising antibiotic resistance necessitating alternative therapeutic strategies. This study identified essential core proteins in clinical mastitis-causing pathogens through a genomic approach and evaluated natural antimicrobial peptides (bacteriocins) as novel therapeutic agents. Through a core-genomic analysis, Rho and HupB were identified as key therapeutic targets. Bacteriocin peptides such as BP8 demonstrated promising efficacy by disrupting regular transcription termination process and DNA replication, offering a promising solution for next-generation mastitis therapies. The findings underscore the potential of BP8 as a sustainable, broad-spectrum antimicrobial agent, contributing to the rational management of mastitis in dairy cattle.

bioinformatics↗

bioRxiv2021

PanClassif: Improving pan cancer classification of single cell RNA-seq using machine learning

Cancer is one of the major causes of human death per year. In recent years, cancer identification and classification using machine learning have gained momentum due to the availability of high throughput sequencing data. Using RNA-seq, cancer research is blooming day by day and new insights of cancer and related treatments are coming into light. In this paper, we propose PanClassif, a method that requires a very few and effective genes to detect cancer from RNA-seq data and is able to provide performance gain in several wide range machine learning classifiers. We have taken 22 types of cancer samples from The Cancer Genome Atlas (TCGA) having 8287 cancer samples and 680 normal samples. Firstly, PanClassif uses k-Nearest Neighbor (k-NN) smoothing to smooth the samples to handle noise in the data. Then effective genes are selected by Anova based test. For balancing the train data, PanClassif applies an oversampling method, SMOTE. We have performed comprehensive experiments on the datasets using several classification algorithms. Experimental results shows that PanClassif out-perform existing state-of-the-art methods available and shows consistent performance for two single cell RNA-seq datasets taken from Gene Expression Omnibus (GEO). PanClassif improves performances of a wide variety of classifiers for both binary cancer prediction and multi-class cancer classification. PanClassif is available as a python package (https://pypi.org/project/panclassif/). All the source code and materials of PanClassif are available at https://github.com/Zwei-inc/panclassif.

bioinformatics↗