Biology subjects

Khatri, P.

Publications and source records attributed to Khatri, P..

33 records · Page 2Linked to original sources

bioRxiv2022

Crosstalk of Histone Modifications in the Healthy Human Immune System

Chromatin remodeling through post-translational modifications of histone tails (HPTM) is fundamental for regulation and maintenance of DNA-centered processes. Systems level understanding of coordination and interactions between HPTMs and their impact on the functional state of the immune cells remain unexplored due to the technical reasons. We leveraged large biologically heterogeneous data (>27 million cells), comprising of primary human immune cells profiled for 33 HPTMs and 4 histone variants at the single-cell level using high-dimensional mass cytometry (EpiTOF), to discover and map relations between HPTMs at the systems level. Briefly, we elucidated a comprehensive epigenetic network of HPTM interactions, discovered a novel subset of hematopoietic progenitors with distinct epigenetic profile, and revealed hitherto undescribed associations between a decrease in global methylations, modulation of one-carbon metabolism, and immune cell life span. Ultimately our work lays a foundation for future studies aimed at understanding complexity of HPTM interactions in immune response in infectious or autoimmune diseases, cancers, and vaccination.

immunology↗

bioRxiv2021

Identification and targeting of a pan-genotypic influenza A virus RNA structure that mediates packaging and disease.

Currently approved anti-influenza drugs target viral proteins, are subtype limited, and are challenged by rising antiviral resistance. To overcome these limitations, we sought to identify a conserved essential RNA secondary structure within the genomic RNA predicted to have greater constraints on mutation in response to therapeutics targeting this structure. Here, we identified and genetically validated an RNA stemloop structure we termed PSL2, which serves as a packaging signal for genome segment PB2 and is highly conserved across influenza A virus (IAV) isolates. RNA structural modeling rationalized known packaging-defective mutations and allowed for predictive mutagenesis tests. Disrupting and compensating mutations of PSL2s structure give striking attenuation and restoration, respectively, of in vitro virus packaging and mortality in mice. Antisense Locked Nucleic Acid oligonucleotides (LNAs) designed against PSL2 dramatically inhibit IAV in vitro against viruses of different strains and subtypes, possess a high barrier to the development of antiviral resistance, and are equally effective against oseltamivir carboxylate-resistant virus. A single dose of LNA administered 3 days after, or 14 days before, a lethal IAV inoculum provides 100% survival. Moreover, such treatment led to the development of strong immunity to rechallenge with a ten-fold lethal inoculum. Together, these results have exciting implications for the development of a versatile novel class of antiviral therapeutics capable of prophylaxis, post-exposure treatment, and "just-in-time" universal vaccination against all IAV strains, including drug-resistant pandemics. One Sentence SummaryTargeting a newly identified conserved RNA structure in the packaging signal region of influenza segment PB2 abrogates virus production in vitro and dramatically attenuates disease in vivo.

microbiology↗

bioRxiv2021

Proximal and Distal Nephron-specific Adaptation to Furosemide

Furosemide, a widely prescribed diuretic for edema-forming states, inhibits sodium reabsorption in the thick ascending limb of the nephron. Tubular adaptation to diuretics has been observed, but the range of mechanisms along the nephron has not been fully explored. Using morphometry, we show that furosemide induces renal tubular epithelial hyperplasia selectively in distal nephron segments. By comparison, we find progressive cellular hypertrophy in proximal and distal nephron segments. We next utilize single cell RNA sequencing of vehicle- and furosemide-treated mice to define potential mechanisms of diuretic resistance. Consistent with distal tubular cell hyperplasia, we detect a net increase in DCT cell number and Birc5, an anti-apoptotic and pro-growth gene, in a subset of DCT cells, as the most prominently up-regulated gene across the nephron. We also map a gradient of cell-specific transcriptional changes congruent with enhanced distal sodium transport. Furosemide stimulates expression of the mitogen IGF-1. Thus, we developed a mouse model of inducible deletion of renal tubular IGF-1 receptor and show reduced kidney growth and proximal, but not distal, tubular hypertrophy by furosemide. Moreover, genes that promote enhanced bioavailability of IGF-1 including Igfbp1 and Igfbp5 are significantly and differentially expressed in proximal tubular segments and correspond to IGF-1R-dependent hypertrophy. In contrast, downstream PI3-kinase signaling genes including Pdk1, Akt1, Foxo3, FKBP4, Eif2BP4, and Spp1 are significantly and differentially expressed in distal nephron segments and correspond to IGF-1R-independent hypertrophy. These findings highlight novel mechanisms of tubular remodeling and diuretic resistance, provide a repository of transcriptional responses to a common drug, and expand the implications of long-term loop diuretic use for human disease.

physiology↗

bioRxiv2020

Multicohort Analysis of Publicly-available Monocyte Expression Data Identifies Gene Signatures to Accurately Monitor Subset-specific Changes in Human Diseases

Monocytes and monocyte-derived cells play important roles in the regulation of inflammation, both as precursors as well as effector cells. Monocytes are heterogeneous and characterized by three distinct subsets in humans. Classical and non-classical monocytes represent the most abundant subsets, each carrying out distinct biological functions. Consequently, altered frequencies of different subsets have been associated with inflammatory conditions, such as infections and autoimmune disorders including lupus, rheumatoid arthritis, inflammatory bowel disease, and, more recently, COVID-19. Dissecting the contribution of different monocyte subsets to disease is currently limited by samples and cohorts, often resulting in underpowered studies and, consequently, poor reproducibility. Public transcriptomes provide an alternative source of data characterized by high statistical power and real world heterogeneity. However, most transcriptome datasets profile bulk blood or tissue samples, requiring the use of in silico approaches to quantify changes in the levels of specific cell types. Here, we integrated 853 publicly available microarray expression profiles of sorted human monocyte subsets from 45 independent studies to identify robust and parsimonious gene expression signatures, consisting of 10 genes specific to each subset. These signatures, although derived using only datasets profiling healthy individuals, maintain their accuracy independent of the disease state in an independent cohort profiled by RNA-sequencing (AUC = 1.0). Furthermore, we demonstrate that our signatures are specific to monocyte subsets compared to other immune cells such as B, T, dendritic cells (DCs) and natural killer (NK) cells (AUC = 0.87~0.88, p<2.2e-16). This increased specificity results in estimated monocyte subset levels that are strongly correlated with cytometry-based quantification of cellular subsets (r = 0.69, p = 6.7e-4). Consequently, we show that these monocyte subset-specific signatures can be used to quantify changes in monocyte subsets levels in expression profiles from patients in clinical trials. Finally, we show that proteins encoded by our signature genes can be used in cytometry-based assays to specifically sort monocyte subsets. Our results demonstrate the robustness, versatility, and utility of our computational approach and provide a framework for the discovery of new cellular markers.

systems biology↗

bioRxiv2020

Basal Neural Stem Cells in the Subventricular Zone Drive Postnatal Neurogenesis with Apical Stem Cells Acting as Proliferation Gatekeepers

According to the current consensus, neural stem cells (NSCs) apically contacting the lateral ventricle generate differentiated progenitors by rare asymmetric divisions or by relocating to the basal side of the ventricular-subventricular zone V-SVZ. Both processes will then ultimately lead to the generation of adult-born olfactory bulb (OB) interneurons. In contrast to this view, we here found that adult-born OB interneurons largely derive from an additional NSC type resident in the basal V-SVZ. Despite being both capable of self-renewal and long-term quiescence, apical and basal NSCs differ in Nestin expression, primary cilia extension and frequency of cell division. The expression of Notch-related genes also differed between the two NSC groups and Notch-activation was greatest in apical NSCs. Apical downregulation of Notch-effector Hes1 decreased Notch activation while increasing proliferation across the niche and neurogenesis from apical NSCs. Underscoring their different roles in neurogenesis, lactation-dependent increase in neurogenesis was paralleled by extra activation of basal but not apical NSCs. Thus, basal NSCs support OB neurogenesis whereas apical NSCs impart Notch-mediated lateral inhibition across the V-SVZ.

cell biology↗

bioRxiv2020

SIMON: open-source knowledge discovery platform

Data analysis and knowledge discovery has become more and more important in biology and medicine with the increasing complexity of the biological datasets, but necessarily sophisticated programming skills and in-depth understanding of algorithms needed pose barriers to most biologists and clinicians to perform such research. We have developed a modular open-source software SIMON to facilitate the application of 180+ state-of-the-art machine learning algorithms to high-dimensional biomedical data. With an easy to use graphical user interface, standardized pipelines, automated approach for machine learning and other statistical analysis methods, SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.

systems biology↗

bioRxiv2020

Multiplexed imaging of human tuberculosis granulomas uncovers immunoregulatory features conserved across tissue and blood

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis that is distinctly characterized by granuloma formation within infected tissues. Granulomas are dynamic and organized immune cell aggregates that limit dissemination, but can also hinder bacterial clearance. Consequently, outcome in TB is influenced by how granuloma structure and composition shift the balance between these two functions. To date, our understanding of what factors drive granuloma function in humans is limited. With this in mind, we used Multiplexed Ion Beam Imaging by Time-of-Flight (MIBI-TOF) to profile 37 proteins in tissues from thirteen patients with active TB disease from the U.S. and South Africa. With this dataset, we constructed a comprehensive tissue atlas where the lineage, functional state, and spatial distribution of 19 unique cell subsets were mapped onto eight phenotypically-distinct granuloma microenvironments. This work revealed an immunosuppressed microenvironment specific to TB granulomas with spatially coordinated co-expression of IDO1 and PD-L1 by myeloid cells and proliferating regulatory T cells. Interestingly, this microenvironment lacked markers consistent with T-cell activation, supporting a myeloid-mediated mechanism of immune suppression. We observed similar trends in gene expression of immunoregulatory proteins in a confirmatory transcriptomic analysis of peripheral blood collected from over 1500 individuals with latent or active TB infection and healthy controls across 29 cohorts spanning 14 countries. Notably, PD-L1 gene expression was found to correlate with TB progression and treatment response, supporting its potential use as a blood-based biomarker. Taken together, this study serves as a framework for leveraging independent cohorts and complementary methodologies to understand how local and systemic immune responses are linked in human health and disease.

immunology↗

bioRxiv2019

Combined use of metagenomic sequencing and host response profiling for the diagnosis of suspected sepsis.

BackgroundCurrent diagnostic techniques are inadequate for rapid microbial diagnosis and optimal management of patients with suspected sepsis. We assessed the clinical impact of three powerful molecular diagnostic methods. MethodsWith blood samples from 200 consecutive patients with suspected sepsis, we evaluated 1) metagenomic shotgun sequencing together with a Bayesian inference approach for contaminant sequence removal, for detecting bacterial DNA; 2) viral capture sequencing; and 3) transcript-based host response profiling for classifying patients as infected or not, and if infected, with bacteria or viruses. We then evaluated changes in diagnostic decision-making among three expert physicians by unblinding the results of these methods in a staged fashion. ResultsMetagenomic shotgun sequencing confirmed positive blood culture results in 14 of 26 patients. In 17 of 200 patients, metagenomic sequencing and viral capture sequencing revealed organisms that were 1) not detected by conventional hospital tests within 5 days after presentation, and 2) classified as of probable clinical relevance by physician consensus. Host response profiling led at least two of three physicians to change their diagnostic decisions in 46 of 100 patients. The data suggested possible bacterial DNA translocation in 8 patients who were originally classified by physicians as noninfected and illustrate how host response profiling can guide interpretation of metagenomic shotgun sequencing results. ConclusionsThe integration of host response profiling, metagenomic shotgun sequencing, and viral capture sequencing enhances the utility of each, and may improve the diagnosis and management of patients with suspected sepsis.

microbiology↗

bioRxiv2019

A Unified Molecular Signature of Systemic Lupus Erythematosus Revealed by Integrated, Multi-Cohort Transcriptomic Analysis

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that follows an unpredictable disease course and affects multiple organs and tissues. We performed an integrated, multi-cohort analysis of 7,471 transcriptomic profiles from 40 independent studies to identify robust gene expression changes associated with SLE. We identified a 93-gene signature (SLE MetaSignature) that is differentially expressed in the blood of SLE patients compared to healthy volunteers; distinguishes SLE from other autoimmune, inflammatory, and infectious diseases; and persists across diverse tissues and cell types. The SLE MetaSignature correlated significantly with disease activity and other clinical measures of inflammation. We prospectively validated the SLE MetaSignature in an independent cohort of pediatric SLE patients using a microfluidic RT-qPCR array. We found that 14 of the 93 genes in the SLE MetaSignature were independent of interferon-induced and neutrophil-related transcriptional profiles that have previously been associated with SLE. Pathway analysis revealed dysregulation associated with nucleic acid biosynthesis and immunometabolism in SLE. We further refined a neutropoeisis signature and identified under-appreciated transcripts related to immune cells and oxidative stress. Our multi-cohort, transcriptomic analysis has uncovered under-appreciated genes and pathways associated with SLE pathogenesis, with the potential to advance clinical diagnosis, biomarker development, and targeted therapeutics for SLE.

immunology↗

bioRxiv2018

CD38 is a key regulator of enhanced NK cell immune responses during pregnancy through its role in immune synapse formation

Natural killer (NK) cells use a diverse array of activating and inhibitory surface receptors to detect threats and provide an early line of defense against viral infections and cancer. Here, we demonstrate that the cell surface protein CD38 is a key human NK cell functional receptor through a role in immune synapse formation. CD38 expression marks a mature subset of human NK cells with a high functional capacity. NK cells expressing high levels of CD38 display enhanced killing and IFN-{gamma} secretion in response to influenza virus-infected and tumor cells. Inhibition of CD38 enzymatic activity does not influence NK cell function, but blockade of CD38 and its ligand CD31 abrogates killing and IFN-{gamma} expression in response to influenza-infected cells. Blockade of CD38 on NK cells similarly inhibits killing of tumor cells. CD38 localizes and accumulates at the immune synapse between NK cells and their targets, and blocking CD38 severely abrogates the ability of NK cells to form conjugates and immune synapses with target cells. Thus, CD38 plays a critical role in NK cell immune synapse formation. These findings open new avenues in immunotherapeutic development for cancer and infection by revealing a critical role for CD38 in NK cell function.

immunology↗

bioRxiv2017

Interpretation of biological experiments changes with evolution of Gene Ontology and its annotations

Gene Ontology (GO) enrichment analysis is ubiquitously used for interpreting high throughput molecular data and generating hypotheses about underlying biological phenomena of experiments. However, the two building blocks of this analysis -- the ontology and the annotations -- evolve rapidly. We used gene signatures derived from 104 disease analyses to systematically evaluate how enrichment analysis results were affected by evolution of the GO over a decade. We found low consistency between enrichment analyses results obtained with early and more recent GO versions. Furthermore, there continues to be strong annotation bias in the GO annotations where 58% of the annotations are for 16% of the human genes. Our analysis suggests that GO evolution may have affected the interpretation and possibly reproducibility of experiments over time. Hence, researchers must exercise caution when interpreting GO enrichment analyses and should reexamine previous analyses with the most recent GO version.

bioinformatics↗

bioRxiv2017

Integrated molecular and clinical analysis for understanding human disease relationships

Existing knowledge of human disease relationships is incomplete. To establish a comprehensive understanding of disease, we integrated transcriptome profiles of 41,000 human samples with clinical profiles of 2 million patients, across 89 diseases. Based on transcriptome data, autoimmune diseases clustered with their specific infectious triggers, and brain disorders clustered by disease class. Clinical profiles clustered diseases according to the similarity of their initial manifestation and later complications, identifying disease relationships absent in prior co-occurrence analyses. Our integrated analysis of transcriptome and clinical profiles identified overlooked, therapeutically actionable disease relationships, such as between myositis and interstitial cystitis. Our improved understanding of disease relationships will identify disease mechanisms, offer novel therapeutic targets, and create synergistic research opportunities.

bioinformatics↗

bioRxiv2017

Leveraging heterogeneity across multiple data sets increases accuracy of cell-mixture deconvolution and reduces biological and technical biases

In silico quantification of cell proportions from mixed-cell transcriptomics data (deconvolution) requires a reference expression matrix, called basis matrix. We hypothesized that matrices created using only healthy samples from a single microarray platform would introduce biological and technical biases in deconvolution. We show presence of such biases in two existing matrices, IRIS and LM22, irrespective of the deconvolution method used. Here, we present immunoStates, a basis matrix built using 6160 samples with different disease states across 42 microarray platforms. We found that immunoStates significantly reduced biological and technical biases. We further show that cellular proportion estimates using immunoStates are consistently more correlated with measured proportions than IRIS and LM22, across all methods. Importantly, we found that different methods have virtually no effect once the basis matrix is chosen. Our results demonstrate the need and importance of incorporating biological and technical heterogeneity in a basis matrix for achieving consistently high accuracy.

bioinformatics↗

bioRxiv2017

Gene annotation bias impedes biomedical research

1We found tremendous inequality across gene and protein annotation resources. We observe that this bias leads biomedical researchers to focus on richly annotated genes instead of those with the strongest molecular data. We advocate for researchers to reduce these biases by pursuing data-driven hypotheses.

bioinformatics↗

bioRxiv2016

Mortality prediction in sepsis via gene expression analysis: a community approach

Improved risk stratification and prognosis in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here three scientific groups were invited to independently generate prognostic models for 30-day mortality using 12 discovery cohorts (N=650) containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance was validated in 5 cohorts of community-onset sepsis patients (N=189) in which the models showed summary AUROCs ranging from 0.765-0.89. Similar performance was observed in 4 cohorts of hospital-acquired sepsis (N=282). Combining the new gene-expression-based prognostic models with prior clinical severity scores led to significant improvement in prediction of 30-day mortality (p<0.01). These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis, improving both resource allocation and prognostic enrichment in clinical trials.

bioinformatics↗