Biology subjects

Rai, S. N.

Publications and source records attributed to Rai, S. N..

6 recordsLinked to original sources

bioRxiv2026

Impact of high-fat Western diet on chronic lymphocytic leukemia disease progression and gut microbiome profile in Eμ-TCL1 mice

BackgroundThe composition and function of the gut microbiome have been shown to contribute to both health and disease. One of the most powerful modulators of microbial composition and function is diet. Materials & MethodsUsing the E{micro}-TCL1 murine model of B-cell chronic lymphocytic leukemia (CLL), we assigned male and female mice to a high-fat, high-carbohydrate Western diet (HF) or standard chow (CH) diet. ResultsMice consuming a HF diet had significantly shorter survival than those consuming a CH diet, irrespective of sex, with female mice exhibiting particularly poor outcomes. We also observed a significant increase in splenic involvement by CLL in the HF diet-fed mice at time of sacrifice. Mice receiving the HF diet demonstrated immediate and profound effects on the gut microbiome, marked by reduced alpha diversity and significantly different community composition as measured by beta diversity. Notably, there was a sustained increase in Akkermansia muciniphila and Bacteroidetes thetaiotaomicron in HF diet-fed mice, coupled with a corresponding increase in microbiome functional pathways related to arginine and histidine biosynthesis, chitin degradation, and nucleotide biosynthesis. DiscussionCollectively our data provides evidence of the profound and sustained impact of a high-fat Western diet upon the gut microbiome community and CLL pathogenesis in the E{micro}-TCL1 murine model of CLL.

cancer biology↗

bioRxiv2025

Potassium channels mediate the inhibitory effect of exosomes on anti-tumor immunity in head and neck cancer

Head and neck squamous cell carcinomas (HNSCC) are aggressive cancers with a relatively low response rate to immunotherapy. Anti-tumor immune responses rely on cytotoxic T and NK cells infiltrating the tumor microenvironment to eliminate cancer cells. However, tumors activate multiple mechanisms to evade these responses. Tumor-derived small extracellular vesicles, also known as exosomes, reshape the tumor microenvironment by impairing cytotoxic T and NK cell function, promoting immune escape and metastasis. Transcriptomic analysis of healthy donor peripheral blood mononuclear cells (PBMCs) exposed to exosomes from HPV-negative HNSCC patient primary cancer cells revealed reduced cytotoxic cells and suppressed immune responses including cytotoxicity, chemokine production, and NK and T cell functions. Bead-based multiplex immunoassay showed that HNSCC-derived exosomes inhibited the release of effector cytokines (IL-2, TNF-, IFN-{gamma}) and cytotoxic molecules from activated CD8 T cells. While ion channels regulate Ca{superscript 2}-dependent cytotoxicity and cytokine production and release, their role in exosome-mediated immune suppression is unexplored. We found that tumor-derived exosomes selectively inhibit KCa3.1 channel activity in CD8 T cells by downregulating calmodulin, ultimately impairing Ca{superscript 2} signaling and IFN-{gamma} release. This study identifies a novel mechanism of exosome-mediated immunosuppression, positioning KCa3.1 as a promising therapeutic target to enhance immune surveillance and immunotherapy response in HNSCC.

immunology↗

bioRxiv2024

PTPN11 Mutation Clonal Hierarchy in Acute Myeloid Leukemia

Mutations in protein tyrosine phosphatase non-receptor type 11 (PTPN11) have been considered late acquired mutations in acute myeloid leukemia (AML) development. To interrogate the ontogeny of PTPN11 mutations, we utilized single-cell DNA sequencing and identified that PTPN11 mutations can occur as initiating events in some AML patients when accompanied by strong oncogenic drivers, commonly NPM1 mutations. The co-driver role of PTPN11 mutations was confirmed in a novel murine model that exhibits an AML phenotype with early expansion of a diverse set of variably differentiated myeloid cells that engrafted into immunodeficient and immunocompetent mice. This immune diversity was reconstituted from early precursor cells when engrafted into immunodeficient mice. Moreover, immune diversity was also observed in the blast component of patient samples with NPM1 and PTPN11 mutations, providing novel antigen targets for immune based approaches in this subset of AML that is resistant to multiple targeted therapies.

cancer biology↗

bioRxiv2023

Inhibition of type III secretion system induced leukotriene B4 production by Yersinia pestis: A mechanism for early immune evasion

Subverting the host immune response to inhibit inflammation is a key virulence factor of Yersinia pestis. The inflammatory cascade is tightly controlled via the sequential action of lipid and protein mediators of inflammation. Because delayed inflammation is essential for Y. pestis to cause lethal infection, defining the mechanisms used by Y. pestis to manipulate the inflammatory cascade is necessary to understand this pathogens virulence. While previous studies have established that Y. pestis actively inhibits the expression of host proteins that mediate inflammation, there is currently a gap in our understanding of inflammatory lipid mediator response during plague. Here we use in vivo lipidomics to define the synthesis of lipid mediators of inflammation within the lungs during pneumonic plague. Interestingly, while we observed an early cyclooxygenase response during pneumonic plague, there was a significant delay in the synthesis of leukotriene B4 (LTB4), a pro-inflammatory lipid chemoattractant and activator of immune cells. Furthermore, in vitro studies with primary leukocytes from mice and humans further revealed that Y. pestis actively inhibited the synthesis of LTB4. Finally, using Y. pestis mutants in the Ysc type 3 secretion system (T3SS) and Yersinia outer protein (Yop) effectors, we demonstrate that leukocytes recognize the T3SS to initiate the synthesis of LTB4 rapidly. However, the Yop effectors secreted through the same system effectively inhibit this host response. Together, these data demonstrate that Y. pestis actively inhibits the synthesis of LTB4, an inflammatory lipid, required for rapid recruitment of leukocytes to the site of infection. Author SummaryYersinia pestis, the bacteria that causes plague, targets the hosts innate immune response to inhibit inflammation. Because the generation of this non-inflammatory environment is required for infection, we are interested in mechanisms used by Y. pestis to block inflammation. Lipid mediators are potent signaling molecules that regulate multiple host immune responses, including inflammation. While there have been studies on how Y. pestis blocks the proteins that mediate inflammation, there is a gap in our understanding of the inflammatory lipid mediator response during plague. Here we show that Y. pestis inhibits the production of one of these critical lipid mediators, leukotriene B4, by host immune cells. Furthermore, we identify both the signals that induce LTB4 production by leukocytes and the mechanisms used by Y. pestis to inhibit this process. Together, these data represent the first comprehensive analysis of inflammatory lipids produced during plague and improve our current understanding of how Y. pestis manipulates the host immune response to generate a permissive non-inflammatory environment required for bacterial colonization.

microbiology↗

bioRxiv2018

Inferring metabolite interactomes via molecular structure informed Bayesian graphical model selection with an application to coronary artery disease

IntroductionWhile the generation of reference genomes facilitates the elucidation of gene-phenome associations, reference models of the metabolome that are specific to organism, sample type (e.g. plasma, serum, urine, cell-culture), and state (including disease), remain uncommon. In studying heart disease in humans, a reference model describing the relationships between metabolites in plasma has not been determined but would have great utility as a reference for comparing acute disease states such as myocardial infarction.\n\nMaterials and MethodsWe present a methodology for deriving probabilistic models that describe the partial correlation structure of metabolite distributions (\"interactomes\") from metabolomics data. As determining partial correlation structures requires estimating p*(p-1)/2 parameters for p metabolites, the dimension of the search space for parameter values is immense. Consequently, we have developed a Bayesian methodology for the penalized estimation of model parameters in which the magnitude of penalization is drawn from probability distributions with hyperparameters linked to molecular structure similarity. In our work, structural similarity was determined as the Tanimoto coefficient of algorithmically-generated \"atom colors\" that capture the local structure around each atom within each structure. A Gibbs sampler (a Markov chain Monte Carlo technique) was implemented for simulating the posterior distribution of model parameters. We have made software for implementing this methodology publicly available via the R package BayesianGLasso.\n\nResults / ConclusionsFirst, we demonstrate robust performance of our methodology (sensitivity, specificity, and measures of accuracy) for recovering the true underlying partial correlation structure over simulated datasets (with simulated metabolite abundances and simulated known structural similarity). We then present an interactome model for stable heart disease inferred from non-targeted mass spectrometry data via this methodology. Inspection of the local graph topology about cholate reveals probabilistic interactions with other primary bile acids, secondary bile acids, and many steroid hormones sharing the same precursors.

systems biology↗

bioRxiv2017

Evaluation Of Classifier Performance For Multiclass Phenotype Discrimination In Untargeted Metabolomics

Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes and for furnishing diagnostic and prognostic phenotype predictions in medicine. Despite this, a comprehensive and rigorous evaluation of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics data, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines, and Neural Network classification techniques for discriminating phenotype. We evaluated the techniques on simulated data generated to mimic global untargeted metabolomics data by incorporating realistic block-wise correlation and partial correlation structures for mimicking the correlations and metabolite clustering generated by biological processes. Over the simulation studies, covariance structures, means, and effect sizes were randomly simulated to provide consistent estimates of classifier performance over a wide range of possible scenarios. The presence of non-normal error distributions and the effect of prior-significance filtering (dimension reduction) were evaluated. In each simulation, classifier parameters (such as the number of hidden nodes in a neural network) were tuned by cross-validation to minimize the probability of detecting spurious results due to poorly tuned classifiers. Classifier performance was then evaluated using real clinical metabolomics datasets of varying sample medium, sample size, and experimental design. We report that in the scenarios without a significant presence of non-normal error distributions over metabolite clusters, Neural Network and PLS-DA classifiers performed poorly relative to Sparse PLS-DA (sPLS-DA), Support Vector Machine (SVM), and Random Forest classifiers. When non-normal error distributions were introduced, the performance of PLS-DA classifiers deteriorated further relative to the remaining techniques. Simultaneously, while the relative performance of Neural Network classifiers improved relative to PLS-DA classifiers, Neural Network classifier performance remained poor compared sPLS-DA, SVM, and Random Forest classifiers. Over the real datasets, a trend of better performance of SVM and Random Forest classifier performance was observed.

bioinformatics↗