Biology subjects

Gadkari, M.

Publications and source records attributed to Gadkari, M..

5 recordsLinked to original sources

bioRxiv2025

Glucocorticoids regulate the human non-coding genome

Glucocorticoids (GCs) are widely used anti-inflammatory and immunosuppressive agents known to induce dramatic changes in gene expression, yet their effects on non-coding RNAs such as long non-coding RNAs (lncRNAs) or microRNAs (miRNAs) remain poorly characterized. We present the first comprehensive and systematic analysis of GC regulation of the non-coding genome across nine human primary cell types from both hematopoietic and non-hematopoietic lineages. In vitro GC treatment was applied to each cell type, with transcriptomic profiling (total RNA-seq and small RNA-seq) at 2 and 6 hours post-treatment. We identified over 2,000 GC-responsive non-coding transcripts, including 654 annotated lncRNAs, 1,376 novel lncRNAs, and 39 miRNAs. The non-coding RNA response to GCs was highly cell type-dependent: 80% of GC-responsive lncRNAs and 97% of miRNAs were unique to a single cell type. Hematopoietic cells exhibited a greater magnitude of lncRNA induction than non-hematopoietic cells. Notably, dozens of facultative lncRNAs (undetectable at baseline) were induced de novo by GC. GC-responsive transcripts spanned diverse lncRNA classes, with enrichment of host lncRNAs. By contrast, only a limited number of miRNAs were GC-responsive. GC regulation of transcript abundance for miRNAs and their host lncRNAs appears to be independent. Our results, which are accessible through an interactive web application, establish a framework for studying how non-coding transcription contributes to physiological and clinical heterogeneity in GC responses. The newly identified GC-responsive non-coding transcripts could represent biomarkers of GC exposure, determinants of GC sensitivity or resistance, or candidate regulators of tissue-specific GC effects.

genomics↗

bioRxiv2023

LOXL2 inhibition ameliorates pulmonary artery remodeling in pulmonary hypertension

BackgroundConduit pulmonary arterial stiffening and the resultant increase in pulmonary vascular impedance has emerged as an important underlying driver of pulmonary arterial hypertension (PAH). Given that matrix deposition is central to vascular remodeling, we evaluated the role of the collagen crosslinking enzyme lysyl oxidase like 2 (LOXL2) in this study. Methods and ResultsHuman pulmonary artery smooth muscle cells (PASMCs) subjected to hypoxia showed increased LOXL2 secretion. LOXL2 activity and expression were markedly higher in primary PASMCs isolated from pulmonary arteries of the rat Sugen 5416 + hypoxia (SuHx) model of severe PH. Similarly, LOXL2 protein and mRNA levels were increased in pulmonary arteries (PA) and lungs of rats with PH (SuHx and monocrotaline (MCT) models). Pulmonary arteries (PAs) isolated from rats with PH exhibited hypercontractility to phenylephrine and attenuated vasorelaxation elicited by acetylcholine, indicating severe endothelial dysfunction. Tensile testing revealed a a significant increase in PA stiffness in PH. Treatment with PAT-1251, a novel small-molecule LOXL2 inhibitor, improved active and passive properties of the PA ex vivo. There was an improvement in right heart function as measured by right ventricular pressure volume loops in-vivo with PAT-1251. Importantly PAT-1251 treatment ameliorated PH, resulting in improved pulmonary artery pressures, right ventricular remodeling, and survival. ConclusionHypoxia induced LOXL2 activation is a causal mechanism in pulmonary artery stiffening in PH, as well as pulmonary artery mechanical and functional decline. LOXL2 inhibition with PAT-1251 is a promising approach to improve pulmonary artery pressures, right ventricular elastance, cardiac relaxation, and survival in PAH. New & NoteworthyPulmonary arterial stiffening contributes to the progression of PAH and the deterioration of right heart function. This study shows that LOXL2 is upregulated in rat models of PH. LOXL2 inhibition halts pulmonary vascular remodeling and improves PA contractility, endothelial function and improves PA pressure, resulting in prolonged survival. Thus, LOXL2 is an important mediator of PA remodeling and stiffening in PH and a promising target to improve PA pressures and survival in PH.

physiology↗

bioRxiv2022

Single-cell analysis reveals the range of transcriptional states of circulating human neutrophils

Neutrophils are the most abundant leukocytes in human blood and are essential components of innate immunity. Until recently, neutrophils were considered homogeneous and transcriptionally inactive cells, but both concepts are being challenged. To date, neutrophils have been characterized based on discrete parameters including cell-surface markers, buoyancy, maturation status, or tissue localization. Single-cell RNA sequencing (scRNA-seq) offers an unbiased view of cells along a continuum of transcriptional states. However, the use of scRNA-seq to characterize neutrophils has proven technically difficult, explaining in part the paucity of published single-cell data on neutrophils. We have found that modifications to the data analysis pipeline, rather than to the existing scRNA-seq chemistries, can significantly increase the detection of human neutrophils in scRNA-seq. We have then applied a modified pipeline to the study of human peripheral blood neutrophils. Our findings indicate that circulating human neutrophils are transcriptionally heterogeneous cells, which can be classified into one of four transcriptional clusters that are reproducible among healthy human subjects. We demonstrate that peripheral blood neutrophils shift from relatively immature (Nh0) cells, through a transitional phenotype (Nh1), into one of two endpoints defined by either relative transcriptional inactivity (Nh2) or high expression of type I interferon-inducible genes (Nh3). Transitions among states are characterized by the expression of specific transcription factors. By simultaneously measuring surface proteins and intracellular transcripts at the single-cell level, we show that these transcriptional subsets are independent of the canonical surface proteins that are commonly used to define and characterize human neutrophils. These findings provide a new view of human neutrophil heterogeneity, with potential implications for the characterization of neutrophils in health and disease.

immunology↗

bioRxiv2020

High-throughput imaging of mRNA at the single-cell level in human primary immune cells

Measurement of gene expression at the single-cell level has led to important advances in the study of transcriptional regulation programs in healthy and disease states. In particular, single-cell gene expression approaches have shed light on the high level of transcriptional heterogeneity of individual cells, both at baseline and in response to experimental or environmental perturbations. We have developed a method for High-Content Imaging (HCI)-based quantification of transcript abundance at the single-cell level in primary human immune cells and have validated its performance under multiple experimental conditions to demonstrate its general applicability. This method, which we abbreviate as hcHCR, combines the high sensitivity of the hybridization chain reaction (HCR) for the visualization of mRNA molecules in single cells, with the speed, scalability, and technical reproducibility of HCI. We first tested eight microscopy-compatible attachment substrates for short-term culture of primary human B cells, T cells, monocytes, or neutrophils. We then miniaturized HCR in a 384-well format and documented the ability of the method to detect increased or decreased transcript abundance at the single-cell level in thousands of cells for each experimental condition by HCI. Furthermore, we demonstrated the feasibility of multiplexing gene expression measurements by simultaneously assaying the abundance of two transcripts per cell, both at baseline and in response to an experimental stimulus. Finally, we tested the robustness of the assay to technical and biological variation. We anticipate that hcHCR will be a suitable and cost-effective assay for low- to medium-throughput chemical, genetic or functional genomic screens in primary human cells, with the possibility of performing personalized screens or screens on cells obtained from patients with a specific disease.

genomics↗

bioRxiv2020

A Deep Learning Pipeline for Nucleus Segmentation

Deep learning is rapidly becoming the technique of choice for automated segmentation of nuclei in biological image analysis workflows. In order to evaluate the feasibility of training nuclear segmentation models on small, custom annotated image datasets that have been augmented, we have designed a computational pipeline to systematically compare different nuclear segmentation model architectures and model training strategies. Using this approach, we demonstrate that transfer learning and tuning of training parameters, such as the composition, size and pre-processing of the training image dataset, can lead to robust nuclear segmentation models, which match, and often exceed, the performance of existing, off-the-shelf deep learning models pre-trained on large image datasets. We envision a practical scenario where deep learning nuclear segmentation models trained in this way can be shared across a laboratory, facility, or institution, and continuously improved by training them on progressively larger and varied image datasets. Our work provides computational tools and a practical framework for deep learning-based biological image segmentation using small annotated image datasets.

cell biology↗