Biology subjects

Vinod, P. K.

Publications and source records attributed to Vinod, P. K..

9 recordsLinked to original sources

bioRxiv2025

Integrated Cross-Organ Transcriptomic Analysis Uncovers Conserved Gene Signatures Predictive of Allograft Rejection

Long-term transplant success is limited by allograft rejection, a complex process traditionally studied on an organ-specific basis. To establish a unified framework beyond organ-specific studies, we performed a network-based systems biology analysis of transcriptomic data from 672 liver, kidney, and heart transplant biopsies to identify a conserved, pan-organ molecular framework of rejection. By constructing and comparing organ-specific gene co-expression networks, we identified a consensus, six-module immune cascade that captures the hierarchical nature of the alloimmune response. In addition, we also uncovered a highly conserved 24-gene cell cycle signature consistently upregulated in rejecting allografts, implicating cellular proliferation as a core feature of rejection pathology. From this framework, we derived a 172-gene immune signature and applied machine learning models to assess its predictive performance, achieving accuracy comparable to established benchmarks. We further refined this to a minimal, high-performance 20-gene immune signature (AUC > 0.96). Both the immune and cell cycle signatures demonstrated robust, pan-organ utility when independently validated in a lung transplant cohort (n=243). Collectively, these findings define a pan-organ molecular framework for rejection and highlight cell cycle dysregulation as a conserved hallmark, offering a foundation for standardized, cross-organ diagnostic platforms to improve allograft surveillance and patient outcomes.

systems biology↗

bioRxiv2024

DeepGraphMut: A graph-based deep learning methodfor cancer prognosis using somatic mutation profile

Cancer remains a leading cause of morbidity and mortality worldwide. Despite advances in genomics, identifying clinically relevant subtypes of cancer remains challenging due to its complex and heterogeneous nature. In this work, we propose DeepGraphMut (DGM), a novel graph-based deep-learning pipeline that integrates somatic mutation data with protein-protein interaction (PPI) networks. By employing a graph autoencoder with a graph attention layer and a node-level attention decoder, DGM generates patient-specific clinically relevant encodings for unsupervised and supervised tasks. We demonstrate the effectiveness of DGM across 16 cancer types comprising of 7352 samples from The Cancer Genome Atlas (TCGA). Unsupervised clustering reveals distinct subtypes with significant survival differences in 11 cancer types. In supervised analysis using a Cox regression model, DGM demonstrates excellent performance in predicting survival outcomes, achieving a high concordance index (c-index) value in the range of 0.7 across most cancers, underscoring its robust predictive performance using only somatic mutation data. Furthermore, DGM outperforms its lightweight variant and the network-based stratification method in both unsupervised and supervised analyses. In summary, this study presents a promising approach for cancer subtype identification and prognosis, especially in resource-limited settings where multi-omics data may not be readily available. By leveraging the strengths of graph learning and network biology, DGM offers a valuable tool for advancing personalized medicine.

systems biology↗

bioRxiv2024

Hypometabolism in Autism Spectrum Disorder: Insights from Brain and Blood Transcriptomics

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by challenges in social communication, repetitive behaviors, and restricted interests. Recent research has emphasized the importance of metabolic dysfunctions in the pathophysiology of ASD. This study investigates metabolic alterations associated with ASD by analyzing transcriptomic data obtained from the prefrontal cortex (bulk tissue and single-nucleus) and data from peripheral blood mononuclear cells (PBMC). We assessed the metabolic activity of each patient based on gene expression profiles, revealing significant downregulation of vital metabolic pathways, including glycolysis, the tricarboxylic acid (TCA) cycle, and oxidative phosphorylation, indicative of hypometabolism. Our analysis also highlighted dysregulation in lipid, vitamin, amino acid, and heme metabolism, which may contribute to the neurodevelopmental delays associated with ASD. Cell-specific metabolic activities in the ASD brain showed altered pathways in astrocytes, oligodendrocytes, excitatory neurons, and interneurons. Furthermore, we identified critical metabolic pathways and genes from PBMC gene expression data that distinguish ASD patients from typically developing individuals. Our findings demonstrate a consistent pattern of metabolic dysfunction across brain and blood samples. This research provides a comprehensive understanding of metabolic alterations in ASD, paving the way for exploring potential therapeutic strategies targeting metabolic dysregulation.

systems biology↗

bioRxiv2024

A Single-Cell Network Approach to Decode Metabolic Regulation in Gynecologic and Breast Cancers

Cancer metabolism is characterized by significant heterogeneity, presenting challenges for treatment efficacy and patient outcomes. Understanding this heterogeneity and its regulatory mechanisms at single-cell resolution is crucial for developing personalized therapeutic strategies. In this study, we employed a single-cell network approach to characterize malignant heterogeneity in gynecologic and breast cancers, focusing on the transcriptional regulatory mechanisms driving metabolic alterations. By leveraging single-cell RNA sequencing (scRNA-seq) data, we assessed the metabolic pathway activities and inferred cancer-specific protein-protein interactomes (PPI) and gene regulatory networks (GRNs). We explored the crosstalk between these networks to identify key alterations in metabolic regulation. Clustering cells by metabolic pathways revealed tumor heterogeneity across cancers, highlighting variations in oxidative phosphorylation, glycolysis, cholesterol, fatty acid, hormone, amino acid, and redox metabolism. Our analysis identified metabolic modules associated with these pathways, along with their key transcriptional regulators. Notably, transcription factors related to ER stress, immune response, and cell proliferation, along with hypoxia-inducible factor and sterol regulatory element-binding proteins were found to drive metabolic reprogramming. These findings provide new insights into the complex interplay between metabolic rewiring and transcriptional regulation in gynecologic and breast cancers, offering potential avenues for targeted therapeutic strategies in precision oncology. Furthermore, this pipeline for dissecting coregulatory metabolic networks can be broadly applied to decipher metabolic regulation in any disease at single-cell resolution.

systems biology↗

bioRxiv2023

Identification and characterization of metabolic subtypes of endometrial cancer using systems-level approach

BackgroundEndometrial cancer (EC) is the most common gynaecological cancer worldwide. Understanding the metabolic adaptation and its heterogeneity in tumor tissues may provide new insights and help in cancer diagnosis, prognosis, and treatment. In this study, we investigated metabolic alterations of EC to understand the variations in the metabolism within tumor samples. MethodsWe integrated the TCGA transcriptomics data of EC (RNA-Seq) with the human genome-scale metabolic model (HMR2.0) and performed unsupervised learning to identify the metabolic subtypes of EC and uncover the underlying dysregulated metabolic pathways and reporter metabolites in each subtype. The relationship between metabolic subtypes and clinical variables was explored. Further, we characterized each subtype at the molecular level and correlated the subtype-specific metabolic changes occurring at the transcriptome level with the genomic alterations. ResultsEC patients are stratified into two robust metabolic subtypes (cluster-1 and cluster-2) that significantly correlate to patient survival, tumor stages, mutation, and copy number variations. We observed coactivation of pentose phosphate pathway and one-carbon metabolism along with genes involved in controlling estrogen levels in cluster-2, which is linked to poor survival. PNMT and ERBB2 are also upregulated in cluster-2 samples and present in the same chromosome locus 17q12, which is amplified. PTEN and TP53 mutations show mutually exclusive behavior between subtypes and display a difference in survival. ConclusionThis work identifies metabolic subtypes with distinct characteristics at the transcriptome and genome levels, highlighting the metabolic heterogeneity within EC.

systems biology↗

bioRxiv2021

Systems-level analysis of transcriptome reorganization during liver regeneration

Tissue homeostasis and regeneration depend on the reversible transitions between quiescence (G0) and proliferation. The liver has a remarkable capacity to regenerate after injury or resection by cell growth and division. During regeneration, the liver needs to maintain the essential metabolic tasks and meet the metabolic requirements for hepatocyte growth and division. Understanding the regulatory mechanisms involved in balancing the liver function and proliferation demand after injury or resection is crucial. In this study, we analyzed high-resolution temporal RNA sequencing data of liver regeneration after two-thirds partial hepatectomy (PHx) using network inference and mathematical modeling approaches. The reconstruction of the dynamic regulatory network of liver regeneration reveals the trajectories of different metabolic pathways, protein processing in the endoplasmic reticulum (ER), ribosome biogenesis, RNA transport, spliceosome, immune response, and cell cycle. We further developed a mathematical model of the integrated circuit of liver regeneration that accounts for underlying features of compensatory metabolism, proliferation, and epithelial-to-mesenchymal transition during liver regeneration. We show that a mutually exclusive behavior emerges due to the bistable inactivation of HNF4A, which controls the initiation and termination of liver regeneration and different population-level expressions observed in single-cell RNA sequencing data of liver regeneration.

systems biology↗

bioRxiv2020

Host metabolic reprogramming in response to SARS-Cov-2 infection

Understanding the pathogenesis of SARS-CoV-2 is important for developing effective treatment strategies. Viruses hijack the host metabolism to redirect the resources for their replication and survival. How SARS-CoV-2 influences the host metabolism is still unclear. In this study, we analyzed transcriptomic data obtained from different human respiratory cell lines and patient samples (Swab, PBMC, lung biopsy, BALF) to understand the metabolic alterations in response to SARS-CoV-2 infection. For this purpose, the expression pattern of metabolic genes in the human genome-scale metabolic network model Recon3D was explored. We identified metabolic genes and pathways and reporter metabolites under each SARS-CoV-2-infected condition and compared them to identify common and unique changes in the metabolism. Our analysis revealed host-dependent dysregulation of glycolysis, mitochondrial metabolism, amino acid metabolism, glutathione metabolism, polyamine synthesis, and lipid metabolism. We observed different metabolic changes that are pro- and antiviral in nature. We generated hypotheses on how antiviral metabolism can be targeted/enhanced for reducing viral titers. These warrant further exploration with more samples and in vitro studies to test predictions.

bioinformatics↗

bioRxiv2019

A deep learning approach for Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images

Histopathological images contain morphological markers of disease progression that have diagnostic and predictive values. However, complex morphological information remains unutilized in unaided approach to histopathology. In this study, we demonstrate how deep learning framework can be used for an automatic classification of Renal Cell Carcinoma (RCC) subtypes, and for identification of features that predict survival outcome from digital histopathological images. Convolutional neural networks (CNNs) trained on whole-slide images distinguish clear cell and chromophobe RCC from normal tissue with a classification accuracy of 93.39 % and 87.34 %, respectively. Further, a CNN trained to distinguish clear cell, chromophobe and papillary RCC achieves a classification accuracy of 92.61 %. Here, we introduced a novel support vector machine based method to deal with data imbalance in multi-class classification to improve the accuracy. Finally, we extracted the morphological features from high probability tumor regions identified by the CNN to predict patient survival outcome of most common clear cell RCC. The generated risk index based on both tumor shape and nuclei features are significantly associated with patient survival outcome. These results highlight that deep learning can play a role in both cancer diagnosis and prognosis.

pathology↗

bioRxiv2018

Integration and segregation in Autism Spectrum Disorders modulated by age, disease, and interaction: A graph-theoretic study of intrinsic functional connectivity

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting 1 in 50 children between the ages of 6 and 17 years. Brain connectivity and graph theoretic methods have been particularly very useful in shedding light on the differences between high functioning autistic children compared to typically developing (TD) ones. However, very recent developments in network measures raise a cautionary note by highlighting gross under- and over-connectivity in ASD may be an oversimplified hypothesis. Thus the primary aim of our study is to investigate these notions in functional connectomics of ASD versus TD by subjecting the data to reproducibility experiments using two independent datasets.\n\nFurther, we tested the hypothesis of alteration in network segregation and integration in the ASD subjects. We have analyzed the resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) data from the University of California Los Angeles (UCLA) multimodal connectivity database (n=42 ASD, n=37 TD) and rs-fMRI data from the Autism Brain Imaging Data Exchange (ABIDE) (n=187 ASD, n=176 TD) dataset. We assessed the differences in connection strength between TD and ASD subjects. We also performed graph theoretical analysis to analyze the effect of disease on various network measures. Further, using the larger ABIDE dataset, we performed two-factor ANOVA test, to study the effect of age, disease and their interaction by classifying the TD and ASD participants into two cohorts: children (9-12 years, n=73 TD and n=87 ASD) and adolescents (13-16 years, n=103 TD and n=100 ASD). In ASD, we show the existence of atypical connectivity within and between functional networks as compared to TD. We also found in ASD both hypo-and hyper-connectivity within functional networks such as the default mode network (DMN). Further, graph theoretic analysis showed that there is significant effect of age and disease on modularity, clustering coefficient, and local efficiency. We also identified specific areas within the DMN, sensorimotor, visual and attention networks that are affected by age, disease and their interaction. Overall, our findings suggest that maturation, disease and their interaction are critical for unraveling the biological basis and developmental trajectory in ASD and other neuropsychiatric disorders.

neuroscience↗