Biology subjects

Rajasekaran, S.

Publications and source records attributed to Rajasekaran, S..

8 recordsLinked to original sources

bioRxiv2025

Comparative immuno-biology at clinical recognition of early multiple organ dysfunction syndrome in pediatric and adult patients using single-cell transcriptomics

Globally, sepsis remains a major health issue, with Multiple Organ Dysfunction Syndrome (MODS) being a leading cause of mortality. MODS, a severe condition often seen in intensive care units, typically results from infections or trauma and involves complex pathophysiological processes requiring various clinical interventions. Although infections are the main triggers, the mechanisms driving MODS remain unclear. To investigate the transition of sepsis to MODS, we generated a single cell RNA sequencing dataset comprising 86,839 immune cells from pediatric sepsis patients at the clinical onset of MODS patients and age-matched controls, identifying 22 distinct cell types. A cluster of S100 genes, located in the same genomic region, was highly expressed in neutrophils in MODS patients, demonstrating strong diagnostic potential across cohorts (AUC=0.94- 0.99) and potential as therapeutic targets. We found that many B and T cells showed heightened inflammation and increased apoptotic activity during early MODS. Additionally, specific transcription regulators and surface proteins associated with inflammation and S100 regulations were uniquely expressed in MODS. Pseudotime analysis revealed distinct S100 gene expression patterns between controls and MODS. Cell-cell interaction analysis highlighted dendritic cells as key mediators, enhancing communication between plasma cells and V{delta} T cells while activating inflammatory and immunosuppressive pathways. We also analyzed 116,803 immune cells from adult MODS patients, revealing stronger immune dysregulation compared to pediatric MODS, including altered S100 gene expression, and enhanced cell-cell interactions. These findings suggest that S100 genes may serve as a marker for MODS. Furthermore, insights gained from adult MODS could improve our understanding of rare pediatric MODS and contribute to the development of better therapeutics for all MODS patients.

bioinformatics↗

bioRxiv2023

Transcriptomic analysis of melanoma cells reveals an association of α-synuclein with regulation of the inflammatory response.

The Parkinsons disease protein, alpha-synuclein (-syn/SNCA), is highly expressed in neurons and melanomas. The goal of this study was to reveal the mechanism(s) of -syns involvement in melanoma pathogenesis. To decipher the genes and pathways affected by -syn, we conducted an RNA sequencing analysis of human SK-MEL-28 cells and several SK-MEL-28 SNCA-KO clones. We identified 1098 significantly up-regulated genes and 660 significantly down-regulated genes. Several of the upregulated genes are related to the immune system, i.e., the inflammatory response and the matrisome. We validated five upregulated genes (IL-1y, SAA1, IGFBP5, CXCL8, and CXCL10) by RT-qPCR and detected IGFBP5 and IL-1y in spent media of control and SNCA-KO cells. The levels of each of these secreted proteins were significantly higher in the spent media of the SNCA-KO clones than control cells. We suggest that the loss of -syn expression unleashes chemokine/cytokine secretion, which could help melanoma cells evade the immune system.

cancer biology↗

bioRxiv2023

Knocking out alpha-synuclein in melanoma cells downregulates L1CAM and decreases motility

The Parkinsons disease (PD) associated protein, alpha-synuclein (-syn/SNCA), is highly expressed in aggressive melanomas, which raises the possibility that -syn has a pro-survival function in melanoma. Herein, we asked whether -syn modulates the expression of the pro-oncogenic adhesion molecules L1CAM and N-cadherin. We used two human melanoma cell lines (SK-MEL-28, SK-MEL-29), SNCA-knockout (KO) clones, and two human SH-SY5Y neuroblastoma cell lines. In the melanoma lines, loss of -syn expression resulted in significant decreases in the expression of L1CAM and N-cadherin and concomitant significant decreases in motility. On average, there was a 75% reduction in motility in the four SNCA-KOs tested compared to control cells. Strikingly, comparing neuroblastoma SH-SY5Y cells that have no detectable -syn to SH-SY5Y cells that stably express -syn (SH/+S), we found that expressing -syn increased L1CAM and single-cell motility by 54% and 597%, respectively. The reduction in L1CAM level in SNCA-KO clones was not due to a transcriptional effect, rather we found that L1CAM is more efficiently degraded in the lysosome in SNCA-KO clones than in control cells. We propose that -syn is pro-survival to melanoma (and possibly neuroblastoma) because it promotes the intracellular trafficking of L1CAM.

cancer biology↗

bioRxiv2021

Novel graph theoretic biological pathway network analytics methods for analyzing and discovering Alzheimer's disease related genes

BackgroundAlzheimers disease (AD) is the most common form of dementia among older people. It is a complex disease and the genetics and environmental factors behind it are not conclusive yet. Traditional statistical analyses are inadequate to identify variants, genes, or pathways capable of explaining AD as a unit. In this context, pathway network analysis based on a set of curated AD-specific genes identified in the literature can elucidate biological mechanisms underneath AD. Through the network, we can infer influential pathways that can together explain AD. Consequently, we can target those pathways and corresponding genes for further analysis to develop new drugs, discover novel AD-related genes, combine multiple hypotheses, and so forth. MethodsWe have developed a novel graph theoretic algorithm that can elucidate complex biology from a given set of disease-related genes. It constructs a weighted network of enriched pathways where similarity score between a pair of pathways is defined in a context-specific manner. To make the network robust, we employ topological overlap techniques on top of the raw similarity measure. We then provide the importance of each pathway with respect to the entire network, functional modules and importance of each pathway in a specific module, gene clusters, and so forth. We also provide a method to identify a set of novel genes that can further explain the disease-related genes and the disease itself. ResultsWe have employed our algorithms onto a set of AD-specific genes. It identified three distinct functional modules that are related to metabolism, cancer, and infectious disease related pathways. These findings are matched with three recognized hypotheses in Alzheimers disease, e.g. "metabolism hypothesis," "cell cycle hypothesis," and "infectious disease hypothesis." By analyzing the curated genes common among those functional modules, we can attain more understanding about this fateful disease. We have also identified 24 novel AD-related genes of which at least 14 genes are known to be involved in AD. ConclusionsWe developed a computational framework for analyzing biological pathways in a context-specific manner. It can be used in any sets of disease-related genes. We manifest its efficacy, reliability, and accuracy by employing a set of AD-specific genes.

bioinformatics↗

bioRxiv2020

A novel algorithm to accurately classify metagenomic sequences

Widespread availability of next-generation sequencing (NGS) technologies has prompted a recent surge in interest in the microbiome. As a consequence, metagenomics is a fast growing field in bioinformatics and computational biology. An important problem in analyzing metagenomic sequenced data is to identify the microbes present in the sample and figure out their relative abundances. In this article we propose a highly efficient algorithm dubbed as "Hybrid Metagenomic Sequence Classifier" (HMSC) to accurately detect microbes and their relative abundances in a metagenomic sample. The algorithmic approach is fundamentally different from other state-of-the-art algorithms currently existing in this domain. HMSC judiciously exploits both alignment-free and alignment-based approaches to accurately characterize metagenomic sequenced data. To demonstrate the effectiveness of HMSC we used 8 metagenomic sequencing datasets (2 mock and 6 in silico bacterial communities) produced by 3 different sequencing technologies (e.g., HiSeq, MiSeq, and NovaSeq) with realistic error models and abundance distribution. Rigorous experimental evaluations show that HMSC is indeed an effective, scalable, and efficient algorithm compared to the other state-of-the-art methods in terms of accuracy, memory, and runtime. Availability of data and materialsThe implementations and the datasets we used are freely available for non-commercial purposes. They can be downloaded from: https://drive.google.com/drive/folders/132k5E5xqpkw7olFjzYwjWNjyHFrqJITe?usp=sharing

bioinformatics↗

bioRxiv2020

RSGSA: a Robust and Stable Gene Selection Algorithm

Nowadays we are observing an explosion of gene expression data with phenotypes. It enables researchers to efficiently identify genes responsible for certain medical condition as well as classify them for drug target. Like any other phenotype data in medical domain, gene expression data with phenotypes also suffers from being very underdetermined system. In a very large set of features but a very small sample size domains (e.g., DNA microarray, RNA-seq data, GWAS data, etc.), it is often reported that several different spurious feature subsets may yield equally optimal results. This phenomenon is known as instability. Considering these facts, we have developed a very robust and stable supervised gene selection algorithm to select the most discriminating non-spurious set of genes from the gene expression datasets with phenotypes. Stability and robustness is ensured by class and instance levels perturbations, respectively. We have performed rigorous experimental evaluations using 10 real gene expression microarray datasets with phenotypes. It revealed that our algorithm outperforms the state-of-the-art algorithms with respect to stability and classification accuracy. We have also done biological enrichment analysis based on gene ontology-biological processes (GO-BP) terms, disease ontology (DO) terms, and biological pathways.

bioinformatics↗

bioRxiv2020

SARS-CoV2 (COVID-19) Structural/Evolution Dynamicome: Insights into functional evolution and human genomics.

The SARS-CoV-2 pandemic, starting in 2019, has challenged the speed at which labs perform science, ranging from discoveries of the viral composition to handling health outcomes in humans. The small ~30kb single-stranded RNA genome of Coronaviruses makes them adept at cross species spread and drift, increasing their probability to cause pandemics. However, this small genome also allows for a robust understanding of all proteins coded by the virus. We employed protein modeling, molecular dynamic simulations, evolutionary mapping, and 3D printing to gain a full proteome and dynamicome understanding of SARS-CoV-2. The Viral Integrated Structural Evolution Dynamic Database (VIStEDD) has been established (prokoplab.com/vistedd), opening future discoveries and educational usage. In this paper, we highlight VIStEDD usage for nsp6, Nucleocapsid (N), and Spike (S) surface glycoprotein. For both nsp6 and N we reveal highly conserved surface amino acids that likely drive protein-protein interactions. In characterizing viral S protein, we have developed a quantitative dynamics cross correlation matrix insight into interaction with the ACE2/SLC6A19 dimer complex. From this quantitative matrix, we elucidated 47 potential functional missense variants from population genomic databases within ACE2/SLC6A19/TMPRSS2, warranting genomic enrichment analyses in SARS-CoV-2 patients. Moreover, these variants have ultralow frequency, but can exist as hemizygous in males for ACE2, which falls on the X-chromosome. Two noncoding variants (rs4646118 and rs143185769) found in ~9% of African descent individuals for ACE2 may regulate expression and be related to increased susceptibility of African Americans to SARS-CoV-2. This powerful database of SARS-CoV-2 can aid in research progress in the ongoing pandemic.

genetics↗

bioRxiv2020

Reversal of Infected Host Gene Expression Identifies Repurposed Drug Candidates for COVID-19

The repurposing of existing drugs offers the potential to expedite therapeutic discovery against the current COVID-19 pandemic caused by the SARS-CoV-2 virus. We have developed an integrative approach to predict repurposed drug candidates that can reverse SARS-CoV-2-induced gene expression in host cells, and evaluate their efficacy against SARS-CoV-2 infection in vitro. We found that 13 virus-induced gene expression signatures computed from various viral preclinical models could be reversed by compounds previously identified to be effective against SARS- or MERS-CoV, as well as drug candidates recently reported to be efficacious against SARS-CoV-2. Based on the ability of candidate drugs to reverse these 13 infection signatures, as well as other clinical criteria, we identified 10 novel candidates. The four drugs bortezomib, dactolisib, alvocidib, and methotrexate inhibited SARS-CoV-2 infection-induced cytopathic effect in Vero E6 cells at < 1 {micro}M, but only methotrexate did not exhibit unfavorable cytotoxicity. Although further improvement of cytotoxicity prediction and bench testing is required, our computational approach has the potential to rapidly and rationally identify repurposed drug candidates against SARS-CoV-2. The analysis of signature genes induced by SARS-CoV-2 also revealed interesting time-dependent host response dynamics and critical pathways for therapeutic interventions (e.g. Rho GTPase activation and cytokine signaling suppression).

genomics↗