Biology subjects

Halushka, M. K.

Publications and source records attributed to Halushka, M. K..

At least 19 recordsLinked to original sources

bioRxiv2026

Early tuberculosis treatment remodels blood mRNA and small RNA, and a baseline host signature is associated with early culture conversion

Tuberculosis (TB) treatment monitoring relies on sputum-based microbiological assays, which are slow and provide limited insight into the host response. Blood-based biomarkers could offer rapid and minimally invasive treatment monitoring. We performed an integrative multi-omics analysis of paired whole-blood samples collected at baseline and four weeks after treatment initiation across three South African cohorts. A meta-analysis combined RNA sequencing data from GSE89403 (86 pairs) and a prospective cohort (11 pairs), with directional concordance assessed in an independent microarray cohort, GSE31348 (27 pairs). In the prospective cohort, small RNA sequencing profiled microRNAs (miRNAs) and tRNA-derived fragments (tRFs). In GSE89403 (n = 76), a k-top scoring pairs signature (EARLY5) was developed using leave-one-out cross-validation to predict week-4 sputum culture conversion from baseline samples. The meta-analysis identified 63 concordantly regulated genes. These changes reflected attenuation of neutrophil, inflammatory and complement programs, and deconvolution showed decreased neutrophils with increased T-cell and NK-cell populations. Seven miRNAs and eight nuclear-encoded tRFs changed during treatment, including erythroid-associated miR-144, with computationally identified candidate interactions involving complement genes. EARLY5 identified participants who converted by week 4 with a cross-validated AUC of 0.76 and remained associated with conversion after adjustment for baseline bacterial burden in logistic regression and performed comparably to existing TB signatures. Early TB treatment induces coordinated remodeling of blood mRNA and small RNA profiles. Baseline host transcriptional profiles were associated with early culture conversion after accounting for bacterial burden, supporting further evaluation in independent cohorts.

immunology↗

bioRxiv2026

An assessment of normalization and differential expression methods for miRNA-seq analysis using a realistic benchmark dataset

MicroRNAs are short noncoding RNAs that regulate gene expression and are commonly profiled by small RNA sequencing (miRNA-seq). Despite the widespread use of miRNA-seq, datasets are often analyzed with RNA-seq method such as DESeq2 or edgeR, which do not take into account the specific characteristics of miRNA-seq data. Here, we present a benchmark study of normalization and differential expression approaches using a realistic ground-truth dataset. By mixing mouse RNA of two organs, we generated expression trends while capturing biological and technical variability. Using monotonicity across the dataset and expected fold changes from the mixture design, we assessed normalization and differential expression methods. Normalization benchmarking showed that within-sample scaling, particularly Read Per Million (RPM), best preserved the expected monotonic trends, outperforming cross-sample methods such as TMM, rlog, and VST. These approaches sometimes recovered apparent monotonicity among abundant miRNAs, but inspection of individual profiles suggested likely over-correction. Regarding differential expression, edgeR consistently ranked among the best-performing methods across several metrics, including log2 fold-change estimation, with performance comparable to miRNA-seq-specific tools such as miRglmm and NBSR. DESeq2, edgeR-v4, and limma-based approaches tended to systematically underestimate log2 fold changes. Applying a common RPM-based normalization substantially improved the performance of cross-sample methods, highlighting the strong influence of normalization on differential expression analysis. Overall, our findings support within-sample scaling methods such as RPM for normalization, and edgeR, miRglmm, or NBSR for differential expression. The dataset has been made publicly available, providing a valuable resource for objective method comparison and future miRNA-seq software development.

bioinformatics↗

bioRxiv2026

PARiS: Probabilistic Assignment and Repartitioning of isomiR Sequences: A data-driven method for denoising isomiR read count data

MicroRNAs (miRNAs) are non-coding RNAs, approximately 18-24 nucleotides in length, with important gene regulatory functions. In small RNA sequencing (sRNA-seq), observed isoforms of miRNA, called isomiRs, arise from my biological and technical processes. Alterations in isomiR expression has been linked to a wide variety of human diseases, from cancers to neurological diseases. However, it is difficult to distinguish between technical and biological isomiRs. We present PARiS, an algorithm for the Probabilistic Assignment and Repartitioning of isomiR Sequences, that identifies technical error isomiRs in sRNA-seq data and reassigns them to their most likely biological source. We assess the ability of PARiS to identify and remove error isomiR sequences in a realistic simulation study. Additionally, we compare PARiS to alternative approaches, focusing on downstream miRNA-level differential expression analysis in a variety of settings, including a set of simulated datasets, an experimental benchmark dataset, and three colorectal adenocarcinoma cell lines.

bioinformatics↗

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↗

bioRxiv2023

Patterns of unwanted biological and technical expression variation across 49 human tissues

All tissue-based gene expression studies are impacted by biological and technical sources of variation. Numerous methods are used to normalize and batch correct these datasets. A more accurate understanding of all causes of variation could further optimize these approaches. We used 17,282 samples from 49 tissues in the Genotype Tissue Expression (GTEx) dataset (v8) to investigate patterns and causes of expression variation. Transcript expression was normalized to Z-scores and only the most variable 2% of transcripts were evaluated and clustered based on co-expression patterns. Clustered gene sets were solved to different biological or technical causes related to metadata elements and histologic images. We identified 522 variable transcript clusters (median 11 per tissue) across the samples. Of these, 64% were confidently explained, 15% were likely explained, 7% were low confidence explanations and 14% had no clear cause. Common causes included sex, sequencing contamination, immunoglobulin diversity, and compositional tissue differences. Less common biological causes included death interval (Hardy score), muscle atrophy, diabetes status, and menopause. Technical causes included brain pH and harvesting differences. Many of the causes of variation in bulk tissue expression were identifiable in the Tabula Sapiens dataset of single cell expression. This is the largest exploration of the underlying sources of tissue expression variation. It uncovered expected and unexpected causes of variable gene expression. These identified sources of variation will inform which metadata to acquire with tissue harvesting and can be used to improve normalization, batch correction, and analysis of both bulk and single cell RNA-seq data.

bioinformatics↗

bioRxiv2023

Right Ventricular Sarcomere Contractile Depression and the Role of Thick Filament Activation in Human Heart Failure with Pulmonary Hypertension

RationaleRight ventricular (RV) contractile dysfunction commonly occurs and worsens outcomes in heart failure patients with reduced ejection fraction and pulmonary hypertension (HFrEF-PH). However, such dysfunction often goes undetected by standard clinical RV indices, raising concerns that they may not reflect aspects of underlying myocyte dysfunction. ObjectiveTo determine components of myocyte contractile depression in HFrEF-PH, identify those reflected by clinical RV indices, and elucidate their underlying biophysical mechanisms. Methods and ResultsResting, calcium- and load-dependent mechanics were measured in permeabilized RV cardiomyocytes isolated from explanted hearts from 23 HFrEF-PH patients undergoing cardiac transplantation and 9 organ-donor controls. Unsupervised machine learning using myocyte mechanical data with the highest variance yielded two HFrEF-PH subgroups that in turn mapped to patients with depressed (RVd) or compensated (RVc) clinical RV function. This correspondence was driven by reduced calcium-activated isometric tension in RVd, while surprisingly, many other major myocyte contractile measures including peak power, maximum unloaded shortening velocity, and myocyte active stiffness were similarly depressed in both groups. Similar results were obtained when subgroups were first defined by clinical indices, and then myocyte mechanical properties in each group compared. To test the role of thick-filament defects, myofibrillar structure was assessed by X-ray diffraction of muscle fibers. This revealed more myosin heads associated with the thick filament backbone in RVd but not RVc, as compared to controls. This corresponded to reduced myosin ATP turnover in RVd myocytes, indicating less myosin in a cross-bridge ready disordered-relaxed (DRX) state. Altering DRX proportion (%DRX) affected peak calcium-activated tension in the patient groups differently, depending on their basal %DRX, highlighting potential roles for precision-guided therapeutics. Lastly, increasing myocyte preload (sarcomere length) increased %DRX 1.5-fold in controls but only 1.2-fold in both HFrEF-PH groups, revealing a novel mechanism for reduced myocyte active stiffness and by extension Frank-Starling reserve in human HF. ConclusionsWhile there are multiple RV myocyte contractile deficits In HFrEF-PH, clinical indices primarily detect reduced isometric calcium-stimulated force related to deficits in basal and recruitable %DRX myosin. Our results support use of therapies to increase %DRX and enhance length-dependent recruitment of DRX myosin heads in such patients.

biophysics↗

bioRxiv2022

Direct observation of the evolution of cell-type specific microRNA expression signatures supports the hematopoietic origin model of endothelial cells

The evolution of specialized cell-types is a long-standing interest of biologists, but given the deep time-scales very difficult to reconstruct or observe. microRNAs have been linked to the evolution of cellular complexity and may inform on specialization. The endothelium is a vertebrate specific specialization of the circulatory system that enabled a critical new level of vasoregulation. The evolutionary origin of these endothelial cells is unclear. We hypothesized that Mir-126, an endothelial cell-specific microRNA may be informative. We here reconstruct the evolutionary history of Mir-126. Mir-126 likely appeared in the last common ancestor of vertebrates and tunicates, a species without an endothelium, within an intron of the evolutionary much older EGF Like Domain Multiple (Egfl) locus. Mir-126 has a complex evolutionary history due to duplications and losses of both the host gene and the microRNA. Taking advantage of the strong evolutionary conservation of the microRNA among Olfactores, and using RNA in situ hybridization (RISH), we localized Mir-126 in the tunicate Ciona robusta. We found exclusive expression of the mature Mir-126 in granular amebocytes, supporting a long-proposed scenario that endothelial cells arose from hemoblasts, a type of proto-endothelial amoebocyte found throughout invertebrates. This observed change of expression of Mir-126 from proto-endothelial amoebocytes in the tunicate to endothelial cells in vertebrates is the first direct observation of the evolution of a cell-type in relation to microRNA expression indicating that microRNAs can be a prerequisite of cell-type evolution. Research HighlightsO_LIdirect observation of cell-type evolution C_LIO_LIhigh conservation of sequence enables for simple RISH experiment of expression C_LIO_LIMir-126 follows the evolution of hematopoetic cells to endothelial cells C_LI

cell biology↗

bioRxiv2022

Expression Microdissection for use in qPCR based analysis of miRNA in a single cell type

Cell-specific microRNA (miRNA) expression estimates are important in characterizing the localization of miRNA signaling within tissues. Much of this data is obtained from cultured cells, a process known to significantly alter miRNA expression levels. Thus, our knowledge of in vivo cell miRNA expression estimates is poor. We previously demonstrated expression microdissection-miRNA-sequencing (xMD-miRNA-seq) as a means to acquire in vivo estimates, directly from formalin fixed tissues, albeit with limited yield. Here we optimized each step of the xMD process including tissue retrieval, tissue transfer, film preparation, and RNA isolation to increase RNA yields and ultimately show strong enrichment for in vivo miRNA expression by qPCR array. These method improvements, including the development of a non-crosslinked ethylene vinyl acetate (EVA) membrane, resulted in a 23-45 fold increase in miRNA yield, depending on cell type. By qPCR, miR-200a was increased 14-fold in xMD-derived small intestine epithelial cells, with a concurrent 336-fold reduction in miR-143, relative to the matched non-dissected duodenal tissue. xMD is now an optimized method to obtain robust in vivo miRNA expression estimates from cells.

genomics↗

bioRxiv2022

Considerations for Deconvolution: A Case Study with GTEx Coronary Artery Tissues

Differential expression analyses are ubiquitous in the realm of statistical genomics, used to estimate functional differences between genomes of groups of subjects. However, differences in tissue composition between groups may contribute to changes in gene expression, potentially obscuring the detection of functionally significant genes of interest. Deconvolution techniques allow researchers to estimate the abundance of each cell type assumed to be in a tissue. While deconvolution is a useful tool to estimate composition, several crucial considerations must be made when setting up and employing such a workflow in an analysis. We perform a deconvolution on GTEx coronary artery data using CIBERSORT and discuss the challenges and limitations in order to highlight future areas of improvement in the deconvolution framework.

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

TAZ/TEAD complex regulates TGF-β1-mediated fibrosis in iPSC-derived renal organoids

Chronic kidney disease (CKD) progresses by replacement of functional tissue compartments with fibrosis, representing a maladaptive repair process. Shifting kidney repair towards a physiologically-intact architecture, rather than fibrosis, is key to blocking CKD progression. In this study, we developed a fibrosis model that uses human induced pluripotent stem cell (iPSC)-based three-dimensional renal organoids, in which exogenous TGF-{beta}1 induces production of extracellular matrix. In these organoids, TGF- {beta}1 increased transcription factor tafazzin (TAZ) expression. Further, in human kidney biopsies, nuclear TAZ expression was markedly increased in mild and moderate fibrosis. In cultured renal tubular cells expressing a fibrogenic program, TAZ formed a trimeric complex with phosphorylated mothers against decapentaplegic homolog 3 (p-SMAD3) and TEA domain protein (TEAD)-4. Overexpression of TEAD4 protein suppressed collagen-11 (COL1A1) promoter activity, and expression of TAZ attenuated this inhibition. INT-767, a dual bile acid receptor agonist binding farnesoid X receptor (FXR) and the Takeda G protein-coupled receptor 5 (TGR5), decreased the TGF-{beta}1-induced increase in p-SMAD3 and TAZ, and preserved renal organoid architecture. These data demonstrate, in an iPSC-derived renal organoid fibrosis model, that INT767 prevents fibrosis programs early in the course of tubular injury through modulation of the TEAD4/TAZ pathway.

cell biology↗

bioRxiv2021

Sex, age, tissue, and disease patterns of matrisome expression in GTEx transcriptome data

The extracellular matrix (ECM) has historically been explored through proteomic methods. Whether or not global transcriptomics can yield meaningful information on the human matrisome is unknown. Gene expression data from 17,382 samples across 52 tissues, were obtained from the Genotype-Tissue Expression (GTEx) project. Additional datasets were obtained from The Cancer Genome Atlas (TCGA) program and the Gene Expression Omnibus for comparisons. Gene expression levels generally recapitulated proteome-derived matrisome expression patterns. Further, matrisome gene expression properly clustered tissue types, with some matrisome genes including SERPIN family members having tissue-restricted expression patterns. Deeper analyses revealed 388 genes varied by age and 222 varied by sex in at least one tissue, with expression correlating with digitally imaged histologic tissue features. A comparison of TCGA tumor, TCGA adjacent normal and GTEx normal tissues demonstrated robustness of the GTEx samples as a generalized control, while also determining a common primary tumor matrisome. Additionally, GTEx tissues served as a useful non-diseased control in a separate study of idiopathic pulmonary fibrosis matrix changes. Altogether, these findings indicate that the transcriptome, in general, and GTEx in particular, has value in understanding the state of organ ECM.

molecular biology↗

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↗

bioRxiv2019

Basal Contamination of Bulk Sequencing: Lessons from the GTEx dataset

One of the challenges of next generation sequencing (NGS) is read contamination. We used the Genotype-Tissue Expression (GTEx) project, a large, diverse, and robustly generated dataset, to understand the factors that contribute to contamination. We obtained GTEx datasets and technical metadata and validating RNA-Seq from other studies. Of 48 analyzed tissues in GTEx, 26 had variant co-expression clusters of four known highly expressed and pancreas-enriched genes (PRSS1, PNLIP, CLPS, and/or CELA3A). Fourteen additional highly expressed genes from other tissues also indicated contamination. Sample contamination by non-native genes was associated with a sample being sequenced on the same day as a tissue that natively expressed those genes. This was highly significant for pancreas and esophagus genes (linear model, p=9.5e-237 and p=5e-260 respectively). Nine SNPs in four genes shown to contaminate non-native tissues demonstrated allelic differences between DNA-based genotypes and contaminated sample RNA-based genotypes, validating the contamination. Low-level contamination affected 4,497 (39.6%) samples (defined as 10 PRSS1 TPM). It also led [≥] to eQTL assignments in inappropriate tissues among these 18 genes. We note this type of contamination occurs widely, impacting bulk and single cell data set analysis. In conclusion, highly expressed, tissue-enriched genes basally contaminate GTEx and other datasets impacting analyses. Awareness of this process is necessary to avoid assigning inaccurate importance to low-level gene expression in inappropriate tissues and cells.

genomics↗