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Tripathy, S.

Publications and source records attributed to Tripathy, S..

4 recordsLinked to original sources

Macrophage-induced rifampin tolerance across Mycobacterium tuberculosis lineages is Rv1258c-dependent

The Mycobacterium tuberculosis (Mtb) Lineage 4 strains CDC1551 and H37Rv develop tolerance to multiple antibiotics upon macrophage residence. Genetic mutation of the efflux pump Rv1258c in CDC1551 abolishes rifampin tolerance but not isoniazid tolerance. Here we show that clinical isolates from the other predominant Mtb lineages developed macrophage-induced isoniazid tolerance. Furthermore, all lineages developed rifampin tolerance except Lineage 2 Beijing strains, which are natural Rv1258c mutants. Thus macrophage-induced antibiotic tolerance is featured across the majority of Mtb lineages. Our findings further link Rv1258c to rifampin tolerance among clinical isolates.

microbiology

Data-driven approaches for improving the interpretability of patch-seq data

Patch-seq, enabling simultaneous measurement of a transcriptomic, electrophysiological, and morphological features, has recently emerged as a powerful tool for neuronal characterization. However, we show the method is susceptible to technical artifacts, including the presence of mRNA contaminants from multiple cells, that limit the interpretability of the data. We present a straightforward marker gene-based approach for controlling for these artifacts and show that our method improves the correspondence between gene expression and electrophysiological features.

neuroscience

NeuroExpresso: A cross-laboratory database of brain cell-type expression profiles with applications to marker gene identification and bulk brain tissue transcriptome interpretation

The identification of cell type marker genes, genes highly enriched in specific cell types, plays an important role in the study of the nervous system. In particular, marker genes can be used to identify cell types to enable studies of their properties. Marker genes can also aid the interpretation of bulk tissue expression profiles by revealing cell type specific changes.\n\nWe assembled a database, NeuroExpresso, of publicly available mouse brain cell type-specific gene expression datasets. We then used stringent criteria to select marker genes highly expressed in individual cell types. We found a substantial number of novel markers previously unknown in the literature and validated a subset of them using in silico analyses and in situ hybridization. We next demonstrate the use of marker genes in analysis of whole tissue data by summarizing their expression into \"cell type profiles\" that can be thought of as surrogates for the relative abundance of the cell types across the samples studied.\n\nFurther analysis of our cell type-specific expression database confirms some recent findings about brain cell types along with revealing novel properties, such as Ddc expression in oligodendrocytes. To facilitate further use of this expanding database, we provide a user-friendly web interface for the visualization of expression data.\n\nSignificance StatementCell type markers are powerful tools in the study of the nervous system that help reveal properties of cell types and acquire additional information from large scale expression experiments. Despite their usefulness in the field, known marker genes for brain cell types are few in number. We present NeuroExpresso, a database of brain cell type specific gene expression profiles, and demonstrate the use of marker genes for acquiring cell type specific information from whole tissue expression. The database will prove itself as a useful resource for researchers aiming to reveal novel properties of the cell types and aid both laboratory and computational scientists to unravel the cell type specific components of brain disorders.

neuroscience

EuMicrobedbLite: A lightweight genomic resource and analytic platform for draft oomycete genomes

We have developed EuMicrobedbLite - A light weight comprehensive genome resource and sequence analysis platform for oomycete organisms. EuMicrobedbLite is a successor of the VBI Microbial Database (VMD) that was built using the Genome Unified Schema (GUS). In this version, the GUS schema has been greatly simplified with removal of many obsolete modules and redesign of others to incorporate contemporary data. Several dependencies such as perl object layers used for data loading in VMD have been replaced with independent light weight scripts. EumicrobedbLite now runs on a powerful annotation engine developed at our lab called \"Genome Annotator Lite\". Currently this database has 26 publicly available genomes and 10 EST datasets of oomycete organisms. The browser page has dynamic tracks presenting comparative genomics analyses, coding and non-coding data, tRNA genes, repeats and EST alignments. In addition, we have defined 44,777 core conserved proteins from twelve oomycete organisms that form 2974 clusters. Synteny viewing is enabled by incorporation of the Genome Synteny Viewer (GSV) tool. The user interface has undergone major changes for ease of browsing. Queryable comparative genomics information, conserved orthologous genes and pathways are among the new key features updated in this database. The browser has been upgraded to enable user upload of GFF files for quick view of genome annotation comparisons. The toolkit page integrates the EMBOSS package and has a gene prediction tool. Annotations for the organisms are updated once every six months to ensure quality. The database resource is available at www.eumicrobedb.org.

bioinformatics