Biology subjects

Patil, A. H.

Publications and source records attributed to Patil, A. H..

9 recordsLinked to original sources

bioRxiv2026

Genetic architecture of miRNA expression in human brain and its contribution to brain disorders

MicroRNAs (miRNAs) regulate nearly all protein-coding genes and play critical roles in gene regulation, yet the mechanisms governing miRNA regulation remain poorly understood. Here, we examined the genetic architecture of miRNA expression in 995 human brain tissues spanning four regions and two ancestries (African and European) from neurotypical controls and individuals with three psychiatric disorders (schizophrenia, major depressive disorder, and bipolar disorder). We found that miRNA expression is highly dynamic across brain regions, with region-specific differences exceeding those attributable to ancestry or psychiatric diagnosis. Through miRNA expression quantitative trait loci (miR-eQTL) analyses, we identified genetic variants associated with miRNA expression in a region- and ancestry-dependent manner. Overall, miRNAs exhibited heritability levels comparable to protein-coding genes and were positively co-regulated with their host transcripts. Notably, miRNA-regulating genetic variants were enriched in enhancers active in oligodendrocytes and in binding sites of the transcription factor OLIG2, suggesting miRNA-mediated gene regulation in oligodendrocyte lineage. Finally, we identified 15 miRNAs as likely causative factors for psychiatric and neurodegenerative disorders such as major depressive disorder and Alzheimers disease. Together, our results reveal the genetic underpinnings of miRNA regulation in the human brain and suggest that miRNAs serve as key intermediaries linking genetic variation to complex neuropsychiatric and neurological phenotypes.

genomics↗

bioRxiv2025

Profiling miRNAs involved in Human Oligodendrocyte Precursor Cell Differentiation and Maturation

MicroRNAs (miRNAs) are evolutionarily conserved post-transcriptional regulators that play critical roles in cellular development and differentiation across species. Although the importance of miRNAs in oligodendrocyte lineage cell (OLLC) differentiation has been extensively studied in rodent models, their roles in human OL development remain less understood. To address this gap, we used a human embryonic stem cell (hESC) reporter system designed to study human OLs and OL progenitor cells (OPCs). Using an optimized differentiation protocol, we used the reporter hESCs to generate and isolate well-characterized OLLCs at specific developmental stages and performed next-generation sequencing-based miRNA profiling to identify stage-specific miRNAs enriched during OL lineage specification and maturation. In addition to canonical miRNAs known to be enriched at various stages of OL development, our study identified several lesser-known miRNAs with distinct stage-specific enrichment patterns that may serve as useful molecular markers for classifying human CNS cell types in future studies. Target analysis of OPC-and OL-enriched miRNAs revealed key genes, including transcription factors ZNF488 and DLX1, cytoskeletal regulator CSNK2B, and potassium channel gene KCNJ1, along with key signaling pathways such as AKT, SMAD2/3, estrogen receptor, and insulin signaling, which regulate OPC and OL lineage function. These findings advance our understanding of the OLLC-specific miRNAs, and miRNA-mediated regulatory networks governing human OL differentiation and maturation and provide promising therapeutic targets for future studies aimed at restoring myelin integrity and improving outcomes in demyelinating diseases.

neuroscience↗

bioRxiv2024

miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA level differential expression and uncovers variable differential expression between isomiRs

MicroRNA-seq data is produced by aligning small RNA sequencing reads of different miRNA transcript isoforms, called isomiRs, to known microRNAs. Aggregation to microRNA-level counts discards information and violates core assumptions of differential expression (DE) methods developed for mRNA-seq data. We establish miRglmm, a DE method for microRNA-seq data, that uses a generalized linear mixed model of isomiR-level counts, facilitating detection of miRNA with differential expression or differential isomiR usage. We demonstrate that miRglmm outperforms current DE methods in estimating DE for miRNA, whether or not there is significant isomiR variability, and simultaneously provides estimates of isomiR-level DE.

bioinformatics↗

bioRxiv2023

Brooklyn plots to identify co-expression dysregulation in single cell sequencing

Altered open chromatin regions, impacting gene expression, is a feature of some human disorders. We discovered it is possible to detect global changes in genomically-related gene co-expression within single cell RNA sequencing (scRNA-seq) data. We built a software package to generate and test non-randomness using Brooklyn plots to identify the percent of genes significantly co-expressed from the same chromosome in [~]10MB intervals across the genome. These plots establish an expected low baseline of co-expression in scRNA-seq from most cell types, but, as seen in dilated cardiomyopathy cardiomyocytes, altered patterns of open chromatin appear. These may relate to larger regions of transcriptional bursting, observable in single cell, but not bulk datasets.

bioinformatics↗

bioRxiv2022

A curated human cellular microRNAome based on 196 primary cell types

BackgroundAn incomplete picture of the expression distribution of microRNAs (miRNAs) across human cell types has long hindered our understanding of this important regulatory class of RNA. With the continued increase in available public small RNA sequencing datasets, there is an opportunity to more fully understand the general distribution of miRNAs at the cell level. ResultsFrom the NCBI Sequence Read Archive, we obtained 6,054 human primary cell datasets and processed 4,184 of them through the miRge3.0 small RNA-seq alignment software. This dataset was curated down, through shared miRNA expression patterns, to 2,077 samples from 196 unique cell types derived from 175 separate studies. Of 2,731 putative miRNAs listed in miRBase (v22.1), 2,452 (89.8%) were detected. Among reasonably expressed miRNAs, 108 were designated as cell specific/near specific, 59 as infrequent, 52 as frequent, 54 as near ubiquitous and 50 as ubiquitous. The complexity of cellular microRNA expression estimates recapitulates tissue expression patterns and informs on the miRNA composition of plasma. ConclusionsThis study represents the most complete reference, to date, of miRNA expression patterns by primary cell type. The data is available through the human cellular microRNAome track at the UCSC Genome Browser (https://genome.ucsc.edu/cgi-bin/hgHubConnect) and an R/Bioconductor package (https://bioconductor.org/packages/microRNAome/).

genomics↗

bioRxiv2021

No evidence of paralogous loci or new bona fide microRNAs in telomere to telomere (T2T) genomic data

The telomere to telomere (T2T) genome project discovered and mapped [~]240 million additional base pairs of primarily telomeric and centromeric reads. Much of this sequence was comprised of satellite sequences and large segmental duplications. We evaluated the extent to which human bona fide microRNAs (miRNAs) may be found in additional paralogous genomic loci or if previously undescribed microRNAs are present in these newly sequenced regions of the human genome. New genomic regions of the T2T project spanning [~]240 million bp of sequence were obtained and evaluated by blastn for the human miRNAs contained in MirGeneDB2.0 (N=556) and miRBase (N = 1917) along with all species of MirGeneDB2.0 miRNAs (N=10,899). Additionally, bowtie was used to compare unmapped reads from >4,000 primary cell samples to the new T2T sequence. Based on sequence and structure, no bona fide miRNAs were identified. Ninety-seven miRNAs of questionable authenticity (frequently known repeat elements) were identified from the miRBase dataset across the newly described regions of the human genome. These 97 represent only 51 miRNA families due to paralogy of highly similar miRNAs such as 24 members of the hsa-mir-548 family. Altogether, this data strongly supports our having identified widely expressed bona fide miRNAs in the human genome and move us further toward the completion of human miRNA discovery.

genomics↗

bioRxiv2021

miRge3.0: a comprehensive microRNA and tRF sequencing analysis pipeline

MicroRNAs and tRFs are classes of small non-coding RNAs, known for their roles in translational regulation of genes. Advances in next-generation sequencing (NGS) have enabled high-throughput small RNA-seq studies, which require robust alignment pipelines. Our laboratory previously developed miRge and miRge2.0, as flexible tools to process sequencing data for annotation of miRNAs and other small-RNA species and further predict novel miRNAs using a support vector machine approach. Although, miRge2.0 is a leading analysis tool in terms of speed with unique quantifying and annotation features, it has a few limitations. We present miRge3.0 which provides additional features along with compatibility to newer versions of Cutadapt and Python. The revisions of the tool include the ability to process Unique Molecular Identifiers (UMIs) to account for PCR duplicates while quantifying miRNAs in the datasets and an accurate GFF3 formatted isomiR tool. miRge3.0 also has speed improvements benchmarked to miRge2.0, Chimira and sRNAbench. Finally, miRge3.0 output integrates into other packages for a streamlined analysis process and provides a cross-platform Graphical User Interface (GUI). In conclusion miRge3.0 is our 3rd generation small RNA-seq aligner with improvements in speed, versatility, and functionality over earlier iterations.

bioinformatics↗

bioRxiv2020

Proteogenomic single cell analysis of skeletal muscle myocytes

Skeletal muscle myocytes have evolved into slow and fast-twitch types. These types are functionally distinct as a result of differential gene and protein expression. However, an understanding of the complexity of gene and protein variation between myofibers is unknown. We performed deep, whole cell, single cell RNA-seq on intact and fragments of skeletal myocytes from the mouse flexor digitorum brevis muscle. We compared the genomic expression data of 171 of these cells with two human proteomic datasets. The first was a spatial proteomics survey of mosaic patterns of protein expression utilizing the Human Protein Atlas (HPA) and the HPASubC tool. The second was a mass-spectrometry (MS) derived proteomic dataset of single human muscle fibers. Immunohistochemistry and RNA-ISH were used to understand variable expression. scRNA-seq identified three distinct clusters of myocytes (a slow/fast 2A cluster and two fast 2X clusters). Utilizing 1,605 mosaic patterned proteins from visual proteomics, and 596 differentially expressed proteins by MS methods, we explore this fast 2X division. Only 36 genes/proteins had variable expression across all three studies, of which nine are newly described as variable between fast/slow twitch myofibers. An additional 414 genes/proteins were identified as variable by two methods. Immunohistochemistry and RNA-ISH generally validated variable expression across methods presumably due to species-related differences. In this first integrated proteogenomic analysis of mature skeletal muscle myocytes we confirm the main fiber types and greatly expand the known repertoire of twitch-type specific genes/proteins. We also demonstrate the importance of integrating genomic and proteomic datasets.

genomics↗

bioRxiv2017

Towards the human cellular microRNAome

microRNAs are short RNAs that serve as master regulators of gene expression and are essential components of normal development as well as modulators of disease. MicroRNAs generally act cell autonomously and thus their localization to specific cell types is needed to guide our understanding of microRNA activity. Current tissue-level data has caused considerable confusion and comprehensive cell-level data does not yet exist. Here we establish the landscape of human cell-specific microRNA expression. This project evaluated 8 billion small RNA-seq reads from 46 primary cell types, 42 cancer or immortalized cell lines, and 26 tissues. It identified both specific and ubiquitous patterns of expression that strongly correlate with adjacent super-enhancer activity. Analysis of unaligned RNA reads uncovered 207 unknown minor strand (passenger) microRNAs of known microRNA loci and 2,632 novel putative microRNA loci. Although cancer cell lines generally recapitulated the expression patterns of matched primary cells, their isomiR sequence families exhibited increased disorder suggesting Drosha and Dicer-dependent microRNA processing variability. Cell-specific patterns of microRNA expression were used to deconvolute variable cellular composition of adipose tissue samples highlighting one use of this cell-specific microRNA expression data. Characterization of cellular microRNA expression across a wide variety of cell types provides a new understanding of this critical regulatory RNA species.

genomics↗