Biology subjects

S, R.

Publications and source records attributed to S, R..

6 recordsLinked to original sources

bioRxiv2025

IN SILICO DRUG DISCOVERY FOR NDM -1 METALLO β LACTAMASE INHIBITORS for KLEBSIELLA PUEUMONIAE

In every region of the world, antibiotic resistance is increasing to dangerously high levels. Our ability to cure widespread infectious diseases is being threatened by the emergence of new resistance mechanisms. Klebsiella pneumoniae is one of the most prevalent nosocomial Gram-negative bacteria in the world. NDM-1 is a brand-new class of metallo-{beta}-lactamase (MBL) that makes the bacteria almost total resistant to all {beta}-lactam antibiotics, including penicillins, cephalosporins and carbapenems. Dangerous infections could develop if NDM-1 switches to a bacterium that is already resistant to antibiotics. It could be untreatable and spread quickly among humans. Using the available insilico tools, in the present research work, an attempt has been made to develop inhibitors for NDM-1 {beta} Lactamase of K. pneumoniae. In the present study, 8 standard ligands were identified and docked against the NDM-1 protein using PyRx. Among these standards, Sulfonamide was selected as the best compound and virtual screening of a large number of sulfonamide moieties was conducted with the identified active site in NDM-1 protein using PyRx. On analyzing the obtained results, about 60 molecules were selected as best hits for Docking studies and ADMET studies. On analyzing the ADMET properties and binding energies of the top hits, (3Z)-N-hydroxypenta-1,3-diene-2-sulfonamide (Pubchem CID 118156306) and N-hydroxyfuran-2-sulfonamide (Pubchem CID 46175386). Molecular dynamics simulations were carried out on the complxes formed by these 2 compounds with target protein and the results showed the complexes were stable which validated the earlier findings. Based on the findings of the study, it was concluded that (3Z)-N-hydroxypenta-1,3-diene-2-sulfonamide (Pubchem CID 118156306) and N-hydroxyfuran-2-sulfonamide (Pubchem CID 46175386) had the potential to be used as lead candidates against infections caused by K. pneumoniae producing NDM-1 strains.

bioinformatics↗

bioRxiv2025

Airway smooth muscle--on-a-chip: a microfluidic approach to study alveolar smooth muscle remodelling

Respiratory illnesses, like chronic obstructive pulmonary disease (COPD) and asthma, pose significant global health challenges due to their chronic nature and limited treatment options. Airway smooth muscle (ASM) plays a vital role in respiratory diseases, particularly in airway remodelling and obstruction. ASM, which encircles the bronchial tree and extends to the trachea, plays a vital yet not fully understood role in lung physiology. However, its dysfunction is strongly associated with asthma and COPD progression, leading to excessive contraction, increased inflammatory mediator release, and ASM hypertrophy. However, identifying its precise function is challenging due to limitations in existing research models for assessing ASM contraction. In vivo models offer a comprehensive physiological perspective but possess ethical concerns and they do not allow for the direct measurement of ASM contraction. Meanwhile, ex vivo and in vitro models provide a more direct assessment; however, they lack crucial physiological factors. Understanding how ASM cells interact with their surroundings is essential for gaining deeper insights into respiratory disorders. To address this gap, we aimed to mimic the human airway smooth muscle-on-a-chip model, incorporating ASM cells in a 3D microenvironment. This microfluidic platform provides a physiologically relevant environment, allowing for studying complex mechanisms that drive airway remodelling and dysfunction in respiratory diseases. The ASM-on-a-chip is designed for long-term 3D cell culture of ASM cells that reorient itself to form a smooth muscle fibre. The design provides side channels for manipulating the constituent of the hydrogel to study the effect of compounds on AMS remodelling.

bioengineering↗

bioRxiv2021

Artificial Intelligence uncovers carcinogenic human metabolites

The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats due to its constant exposure to a myriad of heterogeneous compounds. Despite the availability of innate DNA damage response pathways, some genomic lesions trigger cells for malignant transformation. Accurate prediction of carcinogens is an ever-challenging task due to the limited information about bona fide (non)carcinogens. We developed Metabokiller, an ensemble classifier that accurately recognizes carcinogens by quantitatively assessing their electrophilicity as well as their potential to induce proliferation, oxidative stress, genomic instability, alterations in the epigenome, and anti-apoptotic response. Concomitant with the carcinogenicity prediction, Metabokiller is fully interpretable since it reveals the contribution of the aforementioned biochemical properties in imparting carcinogenicity. Metabokiller outperforms existing best-practice methods for carcinogenicity prediction. We used Metabokiller to unravel cells endogenous metabolic threats by screening a large pool of human metabolites and predicted a subset of these metabolites that could potentially trigger malignancy in normal cells. To cross-validate Metabokiller predictions, we performed a range of functional assays using Saccharomyces cerevisiae and human cells with two Metabokiller-flagged human metabolites namely 4-Nitrocatechol and 3,4-Dihydroxyphenylacetic acid and observed high synergy between Metabokiller predictions and experimental validations.

bioinformatics↗

bioRxiv2021

Invariant Differential Expression Analysis Reveals Mechanism of Cancer Resistance to Cell Cycle Inhibitors

Retinoblastoma (RB) is a good model to study drug resistance to cell-cycle inhibitors because it is driven by mutations in the core components of cell-cycle, i.e, Rb gene. However, there is limited gene expression dataset in RB which has major reproducibility issues. We have developed invariant differential expression analysis (iDEA) that improves the state of the art in differential expression analysis (DEA). iDEA uses strong Boolean implication relationships in a large diverse human dataset GSE119087 (n = 25,955) to filter the noisy differentially expressed genes (DEGs). iDEA was applied to RB datasets and a gene signature was computed that led to prediction and mechanism of drug sensitivity. The prediction was confirmed using drugs-sensitive/resistant RB cell-lines and mouse xenograft models using CDC25 inhibitor NSC663284. iDEA improved reproducibility of differential expression across diverse retina/RB cohorts and RB cell-lines with different drug sensitivity (Y79/Weri vs NCC). Pathway analysis revealed WNT/{beta}-catenin involved in distinguishing drug sensitivity to CDC25 inhibitor NSC663284. NSC663284 inhibited tumour cell proliferation in mouse xenograft model containing Y79 cells indicating novel therapeutic option in RB. Invariant differentially expressed genes (iDEGs) are robustly associated with outcome in diverse cancer datasets and supports for a fundamental mechanism of drug resistance.

systems biology↗

bioRxiv2020

CoalQC - Quality control while inferring demographic histories from genomic data: Application to forest tree genomes

Estimating demographic histories using genomic datasets has proven to be useful in addressing diverse evolutionary questions. Despite improvements in inference methods and availability of large genomic datasets, quality control steps to be performed prior to the use of sequentially Markovian coalescent (SMC) based methods remains understudied. While various filtering and masking steps have been used by previous studies, the rationale for such filtering and its consequences have not been assessed systematically. In this study, we have developed a reusable pipeline called "CoalQC", to investigate potential sources of bias (such as repeat regions, heterogeneous coverage, and callability). First, we demonstrate that genome assembly quality can affect the estimation of demographic history using the genomes of several species. We then use the CoalQC pipeline to evaluate how different repeat classes affect the inference of demographic history in the plant species Populus trichocarpa. Next, we assemble a draft genome by generating whole-genome sequencing data for Mesua ferrea (sampled from Western Ghats, India), a multipurpose forest plant distributed across tropical south-east Asia and use it as an example to evaluate several technical (sequencing technology, PSMC parameter settings) and biological aspects that need to be considered while comparing demographic histories. Finally, we collate the genomic datasets of 14 additional forest tree species to compare the temporal dynamics of Ne and find evidence of a strong bottleneck in all tropical forest plants during Mid-Pleistocene glaciations. Our findings suggest that quality control prior to the use of SMC based methods is important and needs to be standardised.

evolutionary biology↗

bioRxiv2018

Deacetylation of catalytic lysine in CDK1 is essential for Cyclin-B binding and cell cycle

Cyclin-dependent-kinases (CDKs) are essential for cell cycle progression. While dependence of CDK activity on Cyclin levels is established, molecular mechanisms that regulate their binding are less studied. Here, we show that CDKl:Cyclin-B interactions are regulated by acetylation, which was hitherto unknown. We demonstrate that cell cycle dependent acetylation of the evolutionarily conserved catalytic lysine in CDK1 or eliminating its charge state abrogates Cyclin-B binding. Opposing activities of SIRT1 and P300 regulate acetylation, which marks a reserved pool of CDK1. Our high resolution structural analyses into the formation of kinase competent CDK1: Cyclin-B complex have unveiled long-range effects of catalytic lysine in configuring the CDK1 interface for Cyclin-B binding. Cells expressing acetylation mimic mutant of Cdc2 in yeast are arrested in G2 and fail to divide. Thus, by illustrating cell cycle dependent deacetylation as a determinant of CDK1:Cyclin-B interaction, our results redefine the current model of CDK1 activation and cell cycle progression.

cell biology↗