Biology subjects

Absar, N.

Publications and source records attributed to Absar, N..

3 recordsLinked to original sources

bioRxiv2026

Integron-Mediated Convergence of Carbapenemase and Disinfectant Resistance in Acinetobacter spp. from Critical Care Units

Acinetobacter spp. represents critical opportunistic pathogens driving severe bloodstream infections (BSIs) in intensive care unit (ICU) and neonatal intensive care unit (NICU) settings. The convergence of carbapenem resistance and emerging biocide tolerance, often mediated by mobile genetic elements, has intensified concerns regarding co-selection and persistence in clinical environments. A total of 90 molecularly confirmed Acinetobacter isolates (ICU = 44; NICU = 46) from bloodstream infections were analyzed. Antimicrobial susceptibility was determined using the Kirby-Bauer disk diffusion method in accordance with CLSI M100 (2024) guidelines and extended-spectrum {beta}-lactamase production was assessed by combined disc diffusion. Polymerase chain reaction (PCR) was employed to detect carbapenemase genes (blaVIM, blaNDM, blaIMP, blaOXA-23, blaOXA-58), biocide resistance determinants (qacE, qac{Delta}E1), and the class 1 integron-integrase gene (intI1). Multidrug-resistant (MDR) and extensively drug-resistant (XDR) phenotypes were identified in 71.1% (64/90) and 22.2% (20/90) of isolates, respectively. High resistance (>71%) was observed against meropenem and cephalosporins, whereas colistin (58.8%, 53/90) and amikacin (47.8%, 43/90) showed moderate susceptibility. The most prevalent genotypes were qac{Delta}E1 (76.6%, 69/90) and blaVIM (56.6%, 51/90). Statistical and network analyses revealed significant correlations between biocide and carbapenemase genes, identifying IntI1 as a primary driver of co-resistance. The findings indicate a significant co-occurrence of carbapenemase genes, biocide resistance determinants, and class 1 integrons among Acinobacter spp. isolates. These associations suggest that mobile genetic elements may contribute to the dissemination of resistance traits.

microbiology↗

bioRxiv2023

Accelerating Cancer Vaccine Development for Human T-Lymphotropic Virus (HTLV) Using a High-Throughput Molecular Dynamics Approach

Human T-lymphotropic virus (HTLV), a retrovirus belonging to the oncovirus family, has long been linked to be associated with various inflammatory and immunosuppressive disorders. To combat the devastating impact of this virus, our study employed a reverse vaccinology approach to design a multi-epitope-based vaccine targeting the highly virulent subtypes of HTLV. We conducted a comprehensive analysis of the molecular interactions between the vaccine and Toll-like receptors (TLRs), providing valuable insights for future research on preventing and managing HTLV-related diseases and any possible outbreaks. The vaccine was designed by focusing on the envelope glycoprotein gp62, a crucial protein involved in the infectious process and immune mechanisms of HTLV inside the human body. Epitope mapping identified T cell and B cell epitopes with low binding energies, ensuring their immunogenicity and safety. Linkers and adjuvants were incorporated to enhance the vaccines stability, antigenicity, and immunogenicity. Two vaccine constructs were developed, both exhibiting high antigenicity and conferring safety. Vaccine construct 2 demonstrated expected solubility and structural stability after disulfide engineering. Molecular docking analyses revealed strong binding affinity between the vaccine construct 2 and both TLR2 and TLR4. Molecular dynamics simulations indicated that the TLR2-vaccine complex displayed enhanced stability, compactness, and consistent hydrogen bond formation, suggesting a favorable affinity. Contact analysis, Gibbs free energy landscapes, and DCC analysis further supported the stability of the TLR2-vaccine complex, while DSSP analysis confirmed stable secondary structures. MM-PBSA analysis revealed a more favorable binding affinity of the TLR4-vaccine complex, primarily due to lower electrostatic energy. In conclusion, our study successfully designed a multi-epitope-based vaccine targeting HTLV subtypes and provided valuable insights into the molecular interactions between the vaccine and TLRs. These findings should contribute to the development of effective preventive and treatment approaches against HTLV-related diseases.

bioinformatics↗

bioRxiv2018

In Silico Analysis of Non Synonymous Single Nucleotide Polymorphisms (nsSNPs) of SMPX Gene in Hearing Impairment

BackgroundMutations in SMPX gene can disrupt the normal activity of the SMPX protein which is involved in hearing process.\n\nObjectiveIn this study, deleterious non-synonymous single nucleotide polymorphisms were isolated from the neutral variants by using several bioinformatics tools.\n\nMethodFirstly, dbSNP database hosted by NCBI was used to retrieve the SNPs of SMPX gene, secondly, SIFT was used primarily to screen the damaging SNPs. Further, for validation PROVEAN, PredictSNP and PolyPhen 2 were used. I-Mutant 3 was utilized to analyze the protein stability change and MutPred predicted the molecular mechanism of protein stability change. Finally evolutionary conservation was done to study their conservancy by using ConSurf server.\n\nResultsA total of 26 missense (0.6517%) and 3 nonsense variants (0.075%) were retrieved and among them 4 mutations were found deleterious by all the tools of this experiment and are also highly conserved according to ConSurf server. rs772775896, rs759552778, rs200892029 and rs1016314772 are the reference IDs of deleterious mutations where the substitutions are S71L, N19D, A29T and K54N. Loss of Ubiquitination, loss of methylation, loss of glycosylation, and loss of MoRF binding motifs are the root causes of protein stability change.\n\nConclusionThis is the first study regarding nsSNPs of SMPX gene where the most damaging SNPs were screened that are associated with the SMPX gene and can be used for further research to study their effect on protein structure and function, their dynamic behavior and how they actually affect proteins flexibility.

bioinformatics↗