Biology subjects

Shanmugam, V.

Publications and source records attributed to Shanmugam, V..

6 recordsLinked to original sources

bioRxiv2025

Rice NH2 Functions as a Positive Regulator of Salicylic Acid Mediated Defense Responses Against Sheath Blight and Bacterial Blight

The rice genome encodes five non-expressors of pathogenesis-related (NPR) homologs, with OsNPR1/NH1 and OsNPR3/NH3 emerging as pivotal players in salicylic acid (SA)-mediated defense responses. Investigating the functional implications of the remaining NPR/NH genes is critical for the development of disease-resistant rice cultivars. This study explores the role of OsNH2 in rice defense against sheath blight (ShB) using CRISPR/Cas9-edited mutants of the susceptible cultivar ASD16 and the moderately resistant CO51. OsNH2 knockout mutants showed increased susceptibility to ShB, as evidenced by dense mycelial growth, wider hyphae, and elevated superoxide radical content. Two in-frame deletion mutants lacking 15-17 amino acids in the BTB/POZ domain also showed higher susceptibility, highlighting the importance of an intact OsNH2 protein for resistance. qRT-PCR analysis revealed significant downregulation of OsNH1, OsNH3, key transcription factors (WRKY4, WRKY45, WRKY80, TGA2 and TGA3), pathogenesis-related (PR) genes (PR1, PR3 and PR5), and SA biosynthesis genes (PAL and ICS1) in the mutants. Additionally, OsNH2 mutants in both cultivars exhibited reduced endogenous SA levels upon Rhizoctonia solani infection. Exogenous SA treatment partially restored resistance and upregulated OsNH1/3 expression in mutants, though not to wild-type levels. These results suggest that OsNH2 is essential for maintaining SA-mediated defense signaling and optimal expression of NPR1 homologs. Moreover, OsNH2 mutants also showed increased susceptibility to bacterial leaf blight (BLB). Collectively, this research highlights the critical role of OsNH2 in coordinating with OsNH1 and OsNH3 in SA-mediated defense against ShB and BLB in rice. HighlightsCRISPR/Cas9-edited OsNH2 knockout mutants, along with in-frame deletion mutants lacking 15-17 amino acids in the BTB/POZ domain, exhibited increased susceptibility to sheath blight disease in rice. OsNH2 disruption led to reduced endogenous salicylic acid (SA) levels and significant downregulation of OsNH1, OsNH3, key WRKY and TGA transcription factors, and pathogenesis-related (PR) genes. Exogenous SA treatment partially restored resistance and upregulated OsNH1/3 expression in mutants, though not to wild-type levels--highlighting OsNH2s essential role in sustaining SA-mediated defense signaling. OsNH2 mutants also showed increased susceptibility to bacterial leaf blight (BLB), emphasizing its coordination with OsNH1 and OsNH3 in defense against multiple rice pathogens. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/688593v1_ufig1.gif" ALT="Figure 1000"> View larger version (51K): org.highwire.dtl.DTLVardef@f0c18forg.highwire.dtl.DTLVardef@33b59eorg.highwire.dtl.DTLVardef@d8bf27org.highwire.dtl.DTLVardef@1edc5fc_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2025

Genome-scale spatial mapping of the Hodgkin lymphoma microenvironment identifies tumor cell survival factors

A key challenge in cancer research is to identify the secreted factors that contribute to tumor cell survival. Nowhere is this more evident than in Hodgkin lymphoma, where malignant Hodgkin Reed Sternberg (HRS) cells comprise only 1-5% of the tumor mass, the remainder being infiltrating immune cells that presumably are required for the survival of the HRS cells. Until now, there has been no way to characterize the complex Hodgkin lymphoma tumor microenvironment at genome scale. Here, we performed genome-wide transcriptional profiling with spatial and single-cell resolution. We show that the neighborhood surrounding HRS cells forms a distinct niche involving 31 immune and stromal cell types and is enriched in CD4+ T cells, myeloid and follicular dendritic cells, while being depleted of plasma cells. Moreover, we used machine learning to nominate ligand-receptor pairs enriched in the HRS cell niche. Specifically, we identified IL13 as a candidate survival factor. In support of this hypothesis, recombinant IL13 augmented the proliferation of HRS cells in vitro. In addition, genome-wide CRISPR/Cas9 loss-of-function studies across more than 1,000 human cancer cell lines showed that IL4R and IL13RA1, the heterodimeric partners that constitute the IL13 receptor, were uniquely required for the survival of HRS cells. Moreover, monoclonal antibodies targeting either IL4R or IL13R phenocopied the genetic loss of function studies. IL13-targeting antibodies are already FDA-approved for atopic dermatitis, suggesting that clinical trials testing such agents should be explored in patients with Hodgkin lymphoma.

cancer biology↗

bioRxiv2024

Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment

Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.

cancer biology↗

bioRxiv2023

MAPS: Pathologist-level cell type annotation from tissue images through machine learning

Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.

cancer biology↗

bioRxiv2023

Slide-tags: scalable, single-nucleus barcoding for multi-modal spatial genomics

Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed. Missing from these measurements, however, is the ability to routinely and easily spatially localise these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are tagged with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as input into a wide variety of single-nucleus profiling assays. Application of Slide-tags to the mouse hippocampus positioned nuclei at less than 10 micron spatial resolution, and delivered whole-transcriptome data that was indistinguishable in quality from ordinary snRNA-seq. To demonstrate that Slide-tags can be applied to a wide variety of human tissues, we performed the assay on brain, tonsil, and melanoma. We revealed cell-type-specific spatially varying gene expression across cortical layers and spatially contextualised receptor-ligand interactions driving B-cell maturation in lymphoid tissue. A major benefit of Slide-tags is that it is easily adaptable to virtually any single-cell measurement technology. As proof of principle, we performed multiomic measurements of open chromatin, RNA, and T-cell receptor sequences in the same cells from metastatic melanoma. We identified spatially distinct tumour subpopulations to be differentially infiltrated by an expanded T-cell clone and undergoing cell state transition driven by spatially clustered accessible transcription factor motifs. Slide-tags offers a universal platform for importing the compendium of established single-cell measurements into the spatial genomics repertoire.

genomics↗

bioRxiv2021

Cell type-specific differential expression for spatial transcriptomics

Spatial transcriptomics enables spatially resolved gene expression measurements at near single-cell resolution. There is a pressing need for computational tools to enable the detection of genes that are differentially expressed (DE) within specific cell types across tissue context. We show that current approaches cannot learn cell type-specific DE due to changes in cell type composition across space and the fact that measurement units often detect transcripts from more than one cell type. Here, we introduce a statistical method, Cell type-Specific Inference of Differential Expression (C-SIDE), that identifies cell type-specific patterns of differential gene expression while accounting for localization of other cell types. We model spatial transcriptomics gene expression as an additive mixture across cell types of general log-linear cell type-specific expression functions. This approach provides a unified framework for defining and identifying gene expression changes in a wide-range of relevant contexts: changes due to pathology, anatomical regions, physical proximity to specific cell types, and cellular microenvironment. Furthermore, our approach enables statistical inference across multiple samples and replicates when such data is available. We demonstrate, through simulations and validation experiments on Slide-seq and MER-FISH datasets, that our approach accurately identifies cell type-specific differential gene expression and provides valid uncertainty quantification. Lastly, we apply our method to characterize spatially-localized tissue changes in the context of disease. In an Alzheimers mouse model Slide-seq dataset, we identify plaque-dependent patterns of cellular immune activity. We also find a putative interaction between tumor cells and myeloid immune cells in a Slide-seq tumor dataset. We make our C-SIDE method publicly available as part of the open source R package https://github.com/dmcable/spacexr.

genomics↗