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Dwivedi, B.

Publications and source records attributed to Dwivedi, B..

8 recordsLinked to original sources

Deciphering the Streptococcus mutans essentialome: multi-omic resolution of hypothetical genes and identification of a functional RocS equivalent

Genome-wide viability catalogs produced by transposon sequencing (Tn-seq) and CRISPR interference (CRISPRi) have successfully mapped the essential genome of Streptococcus mutans. In this study, we combined predictive bioinformatics, conditional CRISPRi transcriptional silencing, transmission electron microscopy, transcriptomics, and genetic suppressor screens to investigate nine poorly characterized essential genes in S. mutans. From this screen, phenotypic and genetic analyses identified SMU_393 as a functional homolog of the pneumococcal chromosome segregation factor, RocS. Depletion of SMU_393 resulted in abnormal cell widening, hypersensitivity to DNA damage, and a significant subpopulation of anucleate cells. These phenotypes were bypassed by a spontaneous surface-exposed missense mutation (dnaAQ197E) within the AAA+ ATPase domain of the replication initiator. Together, this study refines annotations within the S. mutans essential genome and provides genetic insights into streptococcal chromosome segregation and cell cycle control.

microbiology↗

Genomic instability and biofilm determinants in Streptococcus mutans: insights from a sequence-defined arrayed transposon library

Streptococcus mutans is a primary architect of dental caries, utilizing complex genetic networks to build resilient, acid-producing biofilms. While pooled screens (Tn-seq) have identified important fitness factors, they often fail to capture extracellular or moderate-effect determinants due to community-level masking. Therefore, to study biofilm phenotypes, we constructed a comprehensive arrayed library of 9,216 mutants and used Cartesian Pooling-Coordinate Sequencing (CP-CSeq) to establish a sequence-defined resource covering 51% of non-essential genes. By screening the entire collection in isolation, we identified several novel biofilm determinants, including the putative metal transporter SMU_635 and the glycosylation-associated protein SMU_2160. However, systematic whole-genome sequencing (WGS) of our hits revealed an interesting level of genomic instability: 25% of biofilm-defective mutants had undergone spontaneous recombination at the gtfBC locus, while 7% had lost the TnSmu1 element, an excision rate 1,000-fold higher than previously reported. While targeted mutagenesis confirmed that TnSmu1 loss does not impact biofilm integrity, the gtfBC deletions directly accounted for the most severe phenotypes, highlighting a systemic risk of misattributing gene functions to primary transposon insertions. Our findings provide a powerful new genetic resource for the S. mutans community while establishing a critical new standard: an arrayed library is only as defined as its underlying genome, making systematic genomic verification an essential prerequisite for accurate functional genomics. ImportanceStreptococcus mutans is a major human pathogen responsible for dental caries, a global public health challenge driven in part by the organisms ability to form resilient, acidogenic biofilms. While traditional pooled genetic screens have identified many fitness factors, they often fail to capture extracellular or moderate-effect determinants because neighboring healthy bacteria can mask these defects. This work provides the scientific community with a sequence-defined arrayed mutant library, an essential resource for dissecting the individual contributions of genes to biofilm integrity in isolation. Beyond identifying well-known machinery, this study uncovers novel determinants, including the putative metal transporter SMU_635 and the putative glycosylation-associated protein SMU_2160. Crucially, the discovery of pervasive genomic instability within the library, specifically at the gtfBC and TnSmu1 loci, reveals a systemic risk in functional genomics: the potential to misattribute phenotypes to primary mutations when the underlying background has undergone large-scale rearrangements. By establishing systematic whole-genome verification as a necessary standard, this research ensures that the identification of future therapeutic targets is built upon a verified genetic foundation.

microbiology↗

Live-cell invasive phenotyping uncovers the ALK2/BMP6 iron homeostasis pathway as a therapeutic vulnerability in LKB1-mutant lung cancer

The acquisition of invasive properties is a prerequisite for tumor progression and metastasis. Molecular subtypes of KRAS-driven lung cancer exhibit distinct modes of invasion that likely contribute to unique growth properties and therapeutic susceptibilities. Despite this, pre-clinical discovery strategies designed to exploit invasive phenotypes are lacking. To address this, we designed an experimental system to screen for targetable signaling pathways linked to active early invasion phenotypes in the two most prominent molecular subtypes, TP53 and LKB1, of KRAS-driven lung adenocarcinoma (LUAD). By combining live-cell imaging of human bronchial epithelial cells in a 3D invasion matrix with RNA transcriptome profiling, we identified the LKB1-specific upregulation of bone morphogenetic protein 6 (BMP6). Examination of early-stage lung cancer patients confirmed upregulation of BMP6 in LKB1-mutant lung tumors. At the molecular level, we find that the canonical iron regulatory hormone Hepcidin is induced via BMP6 signaling upon LKB1 loss, where intact LKB1 kinase activity is necessary to maintain signaling homeostasis. Furthermore, pre-clinical studies in a novel Kras/Lkb1-mutant syngeneic mouse model show that potent growth suppression was achieved by inhibiting the ALK2/BMP6 signaling axis with single agents that are currently in clinical trials. We show that alterations in the iron homeostasis pathway are accompanied by simultaneous upregulation of ferroptosis protection proteins. Thus, LKB1 is sufficient to regulate both the gas and breaks to finely tune iron-regulated tumor progression.

cancer biology↗

Targeting TRIP13 in Wilms Tumor with Nuclear Export Inhibitors

Wilms tumor (WT) is the most common renal malignancy of childhood. Despite improvements in the overall survival, relapse occurs in ~15% of patients with favorable histology WT (FHWT). Half of these patients will succumb to their disease. Identifying novel targeted therapies in a systematic manner remains challenging in part due to the lack of faithful preclinical in vitro models. We established ten short-term patient-derived WT cell lines and characterized these models using low-coverage whole genome sequencing, whole exome sequencing and RNA-sequencing, which demonstrated that these ex-vivo models faithfully recapitulate WT biology. We then performed targeted RNAi and CRISPR-Cas9 loss-of-function screens and identified the nuclear export genes (XPO1 and KPNB1) as strong vulnerabilities. We observed that these models are sensitive to nuclear export inhibition using the FDA approved therapeutic agent, selinexor (KPT-330). Selinexor treatment of FHWT suppressed TRIP13 expression, which was required for survival. We further identified in vitro and in vivo synergy between selinexor and doxorubicin, a chemotherapy used in high risk FHWT. Taken together, we identified XPO1 inhibition with selinexor as a potential therapeutic option to treat FHWTs and in combination with doxorubicin, leads to durable remissions in vivo.

cancer biology↗

Survival Genie: A web platform for single cell data, gene ratio, and cell composition based survival analyses across adult and pediatric cancers

The genomics data-driven identification of gene signatures and pathways has been routinely explored for predicting cancer survival and making decisions related to targeted treatments. A large number of packages and tools have been developed to correlate gene expression/mutations to the clinical outcome but lack the ability to perform such analysis based on pathways, gene sets, and gene ratios. Furthermore, in this single-cell omics era, the cluster markers from cancer single-cell transcriptomics studies remain an underutilized prognostic option. Additionally, no bioinformatics online tool evaluates the associations between the enrichment of canonical cell types and survival across cancers. Here we have developed Survival Genie, a web tool to perform survival analysis on single-cell RNA-seq (scRNA-Seq) data and a variety of other molecular inputs such as gene sets, genes ratio, tumor infiltrating immune cells proportion, gene expression profile scores, and tumor mutation burden. For a comprehensive analysis, Survival Genie contains 53 datasets of 27 distinct malignancies from 11 different cancer programs related to adult and pediatric cancers. Users can upload scRNA-Seq data or gene sets and select a gene expression partitioning method (i.e., mean, median, quartile, cutp) to determine the effect of expression levels on survival outcomes. The tool provides comprehensive results including box plots of low and high-risk groups, Kaplan-Meier plots with univariate Cox proportional hazards model, and correlation of immune cell enrichment and molecular profile. The analytical options and comprehensive collection of cancer datasets make Survival Genie a unique resource to correlate gene sets, pathways, cellular enrichment, and single-cell signatures to clinical outcomes to assist in developing next-generation prognostic and therapeutic biomarkers. Survival Genie is open-source and available online at https://bbisr.shinyapps.winship.emory.edu/SurvivalGenie/.

cancer biology↗

Single Cell Transcriptomic Landscape of Diabetic Foot Ulcers

To understand the diabetic wound healing microenvironment, we profiled 174,962 single cells from foot, forearm, and PBMCs using single-cell RNA sequencing (scRNASeq) approach. Our analysis shows enrichment of a unique population of fibroblasts overexpressing MMP1, MMP3, MMP11, HIF1A, CHI3L1, and TNFAIP6 genes and M1 macrophage polarization in the DFU patients with healing wounds. Further, scRNASeq of spatially separated samples from same patient and spatial transcriptomics (ST) revealed preferential localization of these healing associated fibroblasts toward deep wound/ulcer bed as compared to wound edge or non-wounded skin. ST also validated our findings of higher enrichment of M1 macrophages in healers and M2 macrophages in non-healers. Our analysis provides deep insights into the wound healing microenvironment, identifying cell types that could be critical in promoting DFU healing, and may inform novel therapeutic approaches for DFU treatment.

genomics↗

The level of oncogenic Ras controls the malignant transformation of Lkb1 mutant tissue in vivo.

The genetic and metabolic heterogeneity of RAS-driven cancers has confounded therapeutic strategies in the clinic. To address this, rapid and genetically tractable animal models are needed that recapitulate the heterogeneity of RAS-driven cancers in vivo. Here, we generate a Drosophila melanogaster model of Ras/Lkb1mutant carcinoma. We show that low-level expression of oncogenic Ras (RasLo) promotes the survival of Lkb1 mutant tissue, but results in autonomous cell cycle arrest and non-autonomous overgrowth of wild-type tissue. In contrast, high-level expression of oncogenic Ras (RasHi) transforms Lkb1 mutant tissue resulting in lethal malignant tumors. Using simultaneous multiview light-sheet microcopy, we have characterized invasion phenotypes of Ras/Lkb1 tumors in living larvae. Our molecular analysis reveals sustained activation of the AMPK pathway in malignant Ras/Lkb1 tumors, and demonstrate the genetic and pharmacologic dependence of these tumors on CaMK-activated Ampk. We further show that LKB1 mutant human lung adenocarcinoma patients with high levels of oncogenic KRAS exhibit worse overall survival and increased AMPK activation. Our results suggest that high levels of oncogenic KRAS is a driving event in the malignant transformation of LKB1 mutant tissue, and uncover a novel vulnerability that may be used to target this aggressive genetic subset of RAS-driven tumors. One Sentence SummaryA multivariable Ras-driven Drosophila model reveals a novel LKB1 mutant lung adenocarcinoma patient subpopulation and targetable effector pathway.

cancer biology↗

NOJAH: Not Just Another Heatmap for Genome-Wide Cluster Analysis

Since their inception, several tools have been developed for cluster analysis and heatmap construction. The application of such tools to the number and types of genome-wide data available from next generation sequencing (NGS) technologies requires the adaptation of statistical concepts, such as in defining a most variable gene set, and more intricate cluster analyses method to address multiple omic data types. Additionally, the growing number of publicly available datasets has created the desire to estimate the statistical significance of a gene signature derived from one dataset to similarly group samples based on another dataset. The currently available number of tools and their combined use for generating heatmaps, along with the several adaptations of statistical concepts for addressing the higher dimensionality of genome-wide NGS-derived data, has created a further challenge in the ability to replicate heatmap results. We introduce NOJAH (NOt Just Another Heatmap), an interactive tool that defines and implements a workflow for genome-wide cluster analysis and heatmap construction by creating and combining several tools into a single user interface. NOJAH includes several newly developed scripts for techniques that though frequently applied are not sufficiently documented to allow for replicability of results. These techniques include: defining a most variable gene set (a.k.a., core genes), estimating the statistical significance of a gene signature to separate samples into clusters, and performing a result merging integrated cluster analysis. With only a user uploaded dataset, NOJAH provides as output, among other things, the minimum documentation required for replicating heatmap results. Additionally, NOJAH contains five different existing R packages that are connected in the interface by their functionality as part of a defined workflow for genome-wide cluster analysis. The NOJAH application tool is available at http://bbisr.shinyapps.winship.emory.edu/NOJAH/ with corresponding source code available at https://github.com/bbisr-shinyapps/NOJAH/.

bioinformatics↗