Biology subjects

Rudra, P.

Publications and source records attributed to Rudra, P..

4 recordsLinked to original sources

bioRxiv2026

Excessive sample reagent incubation time increases the risk of false rifampin resistance results in Xpert(R) MTB/RIF Ultra tests performed on paucibacillary Mycobacterium tuberculosis sputum.

A high rate of false rifampicin resistance (RIF-R) was recently reported in paucibacillary sputum samples tested with the Xpert(R) MTB/RIF Ultra (Ultra) assay. However, 96% of the studied facilities started the test 8 - 12 hours after addition of sample reagent (SR), exceeding the manufacturers recommended limit of 4 hours. Assessed whether excessive long SR incubation could lead to similar false RIF-R results in paucibacillary samples in a reference laboratory setting. We evaluated the performance of the Ultra assay following incubation of TB-negative sputum spiked with 300 CFU/mL and 100,000 CFU/mL levels of Mycobacterium tuberculosis (Mtb) H37Rv in SR for different durations (15 min to 24hrs.) at different temperatures (-20 to 370C) to determine the effect on RIF result calls. No false RIF-R results were observed in these samples with 105 CFU/mL Mtb at any of the conditions tested, even when they deviated largely from recommended limits. Similarly, freezing the 300 CFU/mL SR treated samples at minus 200C for 18 h also prevented the development of false RIF-R results. However, sputum spiked with 300 CFU/ml Mtb, produced false RIF-R calls at longer incubation times and higher incubation temperatures than are recommended by the manufacturer. Approximately half of the results displayed melt patterns consistent with hetero-resistance, indicating SR-induced mutagenesis in a wild-type background. This study demonstrates that extended off-label SR incubation of paucibacillary specimens beyond the recommended 4-hour limit can generate false RIF-R results. Freezing paucibacillary specimens in SR may mitigate this risk although it is not yet an approved procedure.

microbiology↗

bioRxiv2024

cytoKernel: Robust kernel embeddings for assessing differential expression of single cell data

High-throughput sequencing of single-cell data can be used to rigorously evlauate cell specification and enable intricate variations between groups or conditions. Many popular existing methods for differential expression target differences in aggregate measurements (mean, median, sum) and limit their approaches to detect only global differential changes. We present a robust method for differential expression of single-cell data using a kernel-based score test, cytoKernel. cytoKernel is specifically designed to assess the differential expression of single cell RNA sequencing and high-dimensional flow or mass cytometry data using the full probability distribution pattern. cytoKernel is based on kernel embeddings which employs the probability distributions of the single cell data, by calculating the pairwise divergence/distance between distributions of subjects. It can detect both patterns involving aggregate changes, as well as more elusive variations that are often overlooked due to the multimodal characteristics of single cell data. We performed extensive benchmarks across both simulated and real data sets from mass cytometry data and single-cell RNA sequencing. The cytoKernel procedure effectively controls the False Discovery Rate (FDR) and shows favourable performance compared to existing methods. The method is able to identify more differential patterns than existing approaches. We apply cytoKernel to assess gene expression and protein marker expression differences from cell subpopulations in various publicly available single-cell RNAseq and mass cytometry data sets. The methods described in this paper are implemented in the open-source R package cytoKernel, which is freely available from Bioconductor at http://bioconductor.org/packages/cytoKernel.

bioinformatics↗

bioRxiv2021

Tools, strains, and strategies to effectively conduct anaerobic and aerobic transcriptional reporter screens and assays in Staphylococcus aureus

Transcriptional reporters are reliable and time-tested tools to study gene regulation. In Staphylococcus aureus, {beta}-galactosidase (lacZ)-based genetic screens are not widely used because of the necessity of selectable markers for strain construction and the production of staphyloxanthin pigment which obfuscates results. We describe a series of vectors that allow for markerless insertion of codon-optimized lacZ-based transcriptional reporters. The vectors encode for different ribosomal binding sites allowing for tailored lacZ expression. A {Delta}crtM::kanR deletion insertion mutant was constructed that prevents the synthesis of staphyloxanthin, thereby permitting blue-white screening without the interference of carotenoid production. We demonstrate the utility of these vectors to monitor aerobic and anaerobic transcriptional activity. For the latter, we describe the use of a ferrocyanide-ferricyanide redox system (Fe(CN)63-/4-) permitting blue-white screening in the absence of oxygen. We also describe additional reporter systems and methods for monitoring transcriptional activity during anaerobic culture including a FAD-binding fluorescent protein (EcFbFP), alpha-hemolysin (hla), or lipase (geh). The systems and methods described are compatible with vectors utilized to create and screen high-density transposon mutant libraries. ImportanceStaphylococcus aureus is a human pathogen and a leading cause of infectious disease-related illness and death worldwide. For S. aureus to successfully colonize and invade host tissues, it must tightly control the expression of genes encoding for virulence factors. Oxygen tension varies greatly at infection sites and many abscesses are devoid of oxygen. In this study, we have developed novel tools and methods to study how and when S. aureus alters transcription of genes. A key advantage to these methods and tools is that they can be utilized in the presence and absence of oxygen. A better understanding of anaerobic gene expression in S. aureus will provide important insight into the regulation of genes in low oxygen environments.

microbiology↗

bioRxiv2021

Compositional Data Analysis using Kernels in Mass Cytometry Data

MotivationCell type abundance data arising from mass cytometry experiments are compositional in nature. Classical association tests do not apply to the compositional data due to their non-Euclidean nature. Existing methods for analysis of cell type abundance data suffer from several limitations for high-dimensional mass cytometry data, especially when the sample size is small. ResultsWe proposed a new multivariate statistical learning methodology, Compositional Data Analysis using Kernels (CODAK), based on the kernel distance covariance (KDC) framework to test the association of the cell type compositions with important predictors (categorical or continuous) such as disease status. CODAK scales well for high-dimensional data and provides satisfactory performance for small sample sizes (n < 25). We conducted simulation studies to compare the performance of the method with existing methods of analyzing cell type abundance data from mass cytometry studies. The method is also applied to a high-dimensional dataset containing different subgroups of populations including Systemic Lupus Erythematosus (SLE) patients and healthy control subjects. Availability and ImplementationCODAK is implemented using R. The codes and the data used in this manuscript are available on the web at http://github.com/GhoshLab/CODAK/. Supplementary informationSupplementary Materials.pdf.

bioinformatics↗