Nooruddin Khan

Bio: Nooruddin Khan is an academic researcher from University of Hyderabad. The author has contributed to research in topics: Immune system & Dengue virus. The author has an hindex of 18, co-authored 36 publications receiving 1947 citations. Previous affiliations of Nooruddin Khan include Jawaharlal Nehru Centre for Advanced Scientific Research & Centre for DNA Fingerprinting and Diagnostics.

Systems biology of vaccination for seasonal influenza in humans

Abstract: Here we have used a systems biology approach to study innate and adaptive responses to vaccination against influenza in humans during three consecutive influenza seasons. We studied healthy adults vaccinated with trivalent inactivated influenza vaccine (TIV) or live attenuated influenza vaccine (LAIV). TIV induced higher antibody titers and more plasmablasts than LAIV did. In subjects vaccinated with TIV, early molecular signatures correlated with and could be used to accurately predict later antibody titers in two independent trials. Notably, expression of the kinase CaMKIV at day 3 was inversely correlated with later antibody titers. Vaccination of CaMKIV-deficient mice with TIV induced enhanced antigen-specific antibody titers, which demonstrated an unappreciated role for CaMKIV in the regulation of antibody responses. Thus, systems approaches can be used to predict immunogenicity and provide new mechanistic insights about vaccines.

Predicting Network Activity from High Throughput Metabolomics

Abstract: The functional interpretation of high throughput metabolomics by mass spectrometry is hindered by the identification of metabolites, a tedious and challenging task. We present a set of computational algorithms which, by leveraging the collective power of metabolic pathways and networks, predict functional activity directly from spectral feature tables without a priori identification of metabolites. The algorithms were experimentally validated on the activation of innate immune cells.

The amino acid sensor GCN2 controls gut inflammation by inhibiting inflammasome activation

Abstract: The integrated stress response (ISR) is a homeostatic mechanism by which eukaryotic cells sense and respond to stress-inducing signals, such as amino acid starvation. General controlled non-repressed (GCN2) kinase is a key orchestrator of the ISR, and modulates protein synthesis in response to amino acid starvation. Here we demonstrate in mice that GCN2 controls intestinal inflammation by suppressing inflammasome activation. Enhanced activation of ISR was observed in intestinal antigen presenting cells (APCs) and epithelial cells during amino acid starvation, or intestinal inflammation. Genetic deletion of Gcn2 (also known as Eif2ka4) in CD11c(+) APCs or intestinal epithelial cells resulted in enhanced intestinal inflammation and T helper 17 cell (TH17) responses, owing to enhanced inflammasome activation and interleukin (IL)-1β production. This was caused by reduced autophagy in Gcn2(-/-) intestinal APCs and epithelial cells, leading to increased reactive oxygen species (ROS), a potent activator of inflammasomes. Thus, conditional ablation of Atg5 or Atg7 in intestinal APCs resulted in enhanced ROS and TH17 responses. Furthermore, in vivo blockade of ROS and IL-1β resulted in inhibition of TH17 responses and reduced inflammation in Gcn2(-/-) mice. Importantly, acute amino acid starvation suppressed intestinal inflammation via a mechanism dependent on GCN2. These results reveal a mechanism that couples amino acid sensing with control of intestinal inflammation via GCN2.

Vaccine Activation of the Nutrient Sensor GCN2 in Dendritic Cells Enhances Antigen Presentation

Abstract: The yellow fever vaccine YF-17D is one of the most successful vaccines ever developed in humans. Despite its efficacy and widespread use in more than 600 million people, the mechanisms by which it stimulates protective immunity remain poorly understood. Recent studies using systems biology approaches in humans have revealed that YF-17D–induced early expression of general control nonderepressible 2 kinase (GCN2) in the blood strongly correlates with the magnitude of the later CD8+ T cell response. We demonstrate a key role for virus-induced GCN2 activation in programming dendritic cells to initiate autophagy and enhanced antigen presentation to both CD4+ and CD8+ T cells. These results reveal an unappreciated link between virus-induced integrated stress response in dendritic cells and the adaptive immune response.

Interleukin-10 (IL-10) mediated suppression of IL-12 production in RAW 264.7 cells also involves c-rel transcription factor

Abstract: Interleukin-10 (IL-10) is known to inhibit IL-12 production in macrophages primarily at the transcriptional level with the involvement of p50 and p65 nuclear factor-kappaB (NF-kappaB). We demonstrate that the c-rel transcription factor also plays a major role in IL-10-mediated IL-12 suppression. Treatment of macrophages with recombinant IL-10 inhibited nuclear c-rel levels, whereas addition of neutralizing anti-IL-10 antibody up-regulated both nuclear c-rel levels and IL-12 production by macrophages. Decreased nuclear c-rel was associated with a reduction in phosphorylation of inhibitory kappa B alpha (IkappaBalpha) in the cytoplasm, indicating that IL-10 prevents degradation of IkappaBalpha and the subsequent translocation of c-rel into the nucleus. Treatment with leptomycin B, a known inhibitor of c-rel at a concentration of 10 nm, when used with anti-IL-10 antibody, resulted in reduced expression of IL-12. In a complementary experiment, in vitro transient expression of p65 NF-kappaB could not rescue the inhibitory effect of IL-10 on IL-12 production, suggesting that NF-kappaB alone was not sufficient to restore IL-12 production during IL-10 treatment. However, over-expression of c-rel resulted in IL-12 restoration upon stimulation with lipopolysaccharide plus interferon-gamma during IL-10 treatment. Our studies highlight the involvement of c-rel in IL-10-mediated IL-12 regulation.

“Bioinformatics” 특집을 내면서

Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis.

Abstract: We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.

Determining cell type abundance and expression from bulk tissues with digital cytometry.

Abstract: Single-cell RNA-sequencing has emerged as a powerful technique for characterizing cellular heterogeneity, but it is currently impractical on large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. We previously developed an approach for digital cytometry, called CIBERSORT, that enables estimation of cell type abundances from bulk tissue transcriptomes. We now introduce CIBERSORTx, a machine learning method that extends this framework to infer cell-type-specific gene expression profiles without physical cell isolation. By minimizing platform-specific variation, CIBERSORTx also allows the use of single-cell RNA-sequencing data for large-scale tissue dissection. We evaluated the utility of CIBERSORTx in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade. We anticipate that digital cytometry will augment single-cell profiling efforts, enabling cost-effective, high-throughput tissue characterization without the need for antibodies, disaggregation or viable cells. CIBERSORTx, a suite of computational tools, enables inference of cell type abundance and cell-type-specific gene expression profiles from bulk RNA profiles.

The Microbiota-Gut-Brain Axis

Abstract: The importance of the gut-brain axis in maintaining homeostasis has long been appreciated. However, the past 15 yr have seen the emergence of the microbiota (the trillions of microorganisms within ...

MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights.

Abstract: Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca.