Biology subjects

Ramaswami, S.

Publications and source records attributed to Ramaswami, S..

4 recordsLinked to original sources

bioRxiv2025

Large-scale skin metagenomics reveals extensive prevalence, coordination, and functional adaptation of skin microbiome dermotypes across body sites

While skin microbiome studies have increasingly highlighted its importance in health and disease, our understanding of inter-individual heterogeneity in structure and function remains limited, impacting the ability to develop microbiome-based stratification and therapeutics. Powered by comprehensive skin microbiome characterization in a multi-ethnic population-based cohort (>3,550 shotgun metagenomes across 18 sampling sites), we established significant undescribed inter-individual heterogeneity and the extensive prevalence of distinct microbial configurations (17 species-resolution dermotypes) in seven out of nine body sites. Combining functional in silico and in vitro studies revealed insights into how these dermotypes assemble as a function of niche-dependent microbial interactions (e.g. hypoxia-dependent inhibition of S. hominis by S. epidermidis/M. luteus) and metabolic resource utilization (e.g. differential galactose and histidine metabolism). Integration of demographic, skin physiological, and behavioral data further identified >30 significant associations with host attributes. Cross-site analysis revealed remarkable coordination across disparate skin regions (predictive AUC-ROC>0.8) and bilateral consistency (Pearson {pi}>0.95), emphasizing the role of specific microbial and host factors in shaping dermotypes. Finally, we provide multiple lines of evidence that dermotype states impact the risk for skin discomfort (e.g. irritation, itch) and diseases (e.g. eczema), that when combined with our highly accurate dermotype classifiers (AUC-ROC>0.98), provide a new paradigm for understanding skin microbiome function and stratifying patients in the context of skin and other diseases.

genomics↗

bioRxiv2024

Characterization of tumor heterogeneity through segmentation-free representation learning

The interaction between tumors and their microenvironment is complex and heterogeneous. Recent developments in high-dimensional multiplexed imaging have revealed the spatial organization of tumor tissues at the molecular level. However, the discovery and thorough characterization of the tumor microenvironment (TME) remains challenging due to the scale and complexity of the images. Here, we propose a self-supervised representation learning framework, CANVAS, that enables discovery of novel types of TMEs. CANVAS is a vision transformer that directly takes high-dimensional multiplexed images and is trained using self-supervised masked image modeling. In contrast to traditional spatial analysis approaches which rely on cell segmentations, CANVAS is segmentation-free, utilizes pixel-level information, and retains local morphology and biomarker distribution information. This approach allows the model to distinguish subtle morphological differences, leading to precise separation and characterization of distinct TME signatures. We applied CANVAS to a lung tumor dataset and identified and validated a monocytic signature that is associated with poor prognosis.

cancer biology↗

bioRxiv2023

MethNet: a robust approach to identify regulatory hubs and their distal targets in cancer

Aberrations in the capacity of DNA/chromatin modifiers and transcription factors to bind non-coding regions can lead to changes in gene regulation and impact disease phenotypes. However, identifying distal regulatory elements and connecting them with their target genes remains challenging. Here, we present MethNet, a pipeline that integrates large-scale DNA methylation and gene expression data across multiple cancers, to uncover novel cis regulatory elements (CREs) in a 1Mb region around every promoter in the genome. MethNet identifies clusters of highly ranked CREs, referred to as hubs, which contribute to the regulation of multiple genes and significantly affect patient survival. Promoter-capture Hi-C confirmed that highly ranked associations involve physical interactions between CREs and their gene targets, and CRISPRi based scRNA Perturb-seq validated the functional impact of CREs. Thus, MethNet-identified CREs represent a valuable resource for unraveling complex mechanisms underlying gene expression, and for prioritizing the verification of predicted non-coding disease hotspots.

cancer biology↗

bioRxiv2022

Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma

Early-stage lung adenocarcinoma is typically treated by surgical resection of the tumor. While in the majority of cases surgery can lead to cure, approximately 30% of patients progress. Despite intense efforts to map the genetic landscape of early-stage lung tumors, there has been limited success in discovering accurate biomarkers that can predict clinical outcomes. Meanwhile, the role of the tumor-adjacent tissue in cancer progression has been largely ignored. To test whether tumor-adjacent tissue can be informative of progression-free survival and to probe the underlying molecular pathways involved, we designed a multi-omic study in both tumor and matched tumor-adjacent histologically normal lung tissue from the same patient. Our study includes 143 treatment naive stage I cases with long-term patient follow-up and is, to our knowledge, the largest such study with the longest follow-up. We performed a comprehensive histologic characterization of all tumors, mapped the mutational landscape and probed the transcriptome of both tumor and adjacent normal tissue. We evaluated the predictive power of each data modality and showed that the transcriptome of tumor-adjacent histologically normal lung tissue is the only reliable predictor of clinical outcome. Unbiased discovery of co-expressed gene modules revealed that inflammatory pathways are upregulated in the tumor-adjacent tissue of patients at high risk for disease progression. Furthermore, single-cell transcriptome analysis in the tumor-adjacent lung demonstrated that progression-associated inflammatory signatures were broadly expressed by both immune and non-immune cells including mesothelial cells, alveolar type 2 cells and fibroblasts, CD1 dendritic cells and MAST cells. Collectively, our studies suggest that molecular profiling of tumor-adjacent tissue can identify patients that are at high risk for disease progression.

bioinformatics↗