Biology subjects

Modrak, M.

Publications and source records attributed to Modrak, M..

8 recordsLinked to original sources

bioRxiv2024

An imbalance of naive and effector T-cell phenotypes in early type 1 diabetes across conventional and regulatory subsets

Type 1 diabetes (T1D) is an autoimmune disease caused by the loss of self-tolerance toward insulin-producing pancreatic {beta}-cells. Although the etiology of T1D is not fully understood, it is linked to dysregulation of the T-cell compartment. To identify T-cell signatures associated with T1D, we performed single-cell transcriptomic analysis of peripheral blood T-cells from newly diagnosed children, the same children after one year, and healthy donors. We observed reduced expression of genes related to effector and cytotoxic T-cell functions across conventional, unconventional, and regulatory T-cell subsets in diabetic children, particularly at the time of diagnosis. These findings were supported by flow cytometry analysis of the same cohort and by reanalysis of publicly available data. Overall, our results suggest that T1D is associated with impaired T-cell effector differentiation, which may contribute to immune dysregulation and loss of self-tolerance.

immunology↗

bioRxiv2024

HelD is a Global Transcription Factor Enhancing Gene Expression in Rapidly Growing Mycobacteria

HelD protein, also named HelR (encoded by MSMEG_2174 in Mycobacterium smegmatis), interacts with mycobacterial RNA polymerase (RNAP) and affects rifampicin resistance in Mycobacterium abscessus. Here, we provide data on rifampicin resistance and helD presence in the genomes of other clinically relevant nontuberculous mycobacteria. We show that helD is primarily found in rapidly growing mycobacteria, such as M. smegmatis, where we detected HelD at a subset of promoters that can also associate with CarD and RbpA. Transcriptome analysis of a helD deletion strain using RNA-seq revealed that HelD enhances gene expression during exponential growth and decreases it in stationary phase, during which we observed reduced levels of CarD, RbpA, and GTP, the initiation nucleotide for the majority of M. smegmatis transcripts. We propose a model in which HelD releases abortive RNAP complexes and confirm that HelD dissociates RNAP from the promoter in vitro. HelD not only helps mycobacteria overcome rifampicin treatment but also supports efficient transcription during rapid growth, which indicates a dual role of this transcription regulator.

microbiology↗

bioRxiv2022

Retinitis pigmentosa associated mutations in mouse Prpf8 cause misexpression of circRNAs and degeneration of cerebellar granule neurons

A subset of patients suffering from a familial retinitis pigmentosa (RP) carry mutations in several spliceosomal components including PRPF8 protein. Here, we established two novel alleles of murine Prpf8 that genocopy or mimic aberrant PRPF8 found in RP patients - the substitution p.Tyr2334Asn and an extended protein variant p.Glu2331ValfsX15. Homozygous mice expressing either of the aberrant Prpf8 variants developed within first 2 months progressive atrophy of the cerebellum due to extensive granule neuron loss. Comparison of transcriptome from pre-degenerative and degenerative tissues revealed a subset of circRNAs that were deregulated in all tissues and both Prpf8-RP mouse strains. To identify potential risk factors that sensitize cerebellum for Prpf8 mutations we monitored expression of several splicing proteins during first eight weeks. We observed downregulation of all selected splicing proteins in wild-type cerebellum, which coincided with neurodegeneration onset. The decrease in splicing protein expression was further pronounced in mouse strains expressing mutated Prpf8. Collectively, we propose a model where physiological reduction of spliceosomal components during postnatal tissue maturation sensitizes cells to expression of aberrant Prpf8 and the subsequent deregulation of circRNAs triggers neuron death.

neuroscience↗

bioRxiv2020

Dissection of prostate tumour, stroma and immune transcription reveals a key contribution by the microenvironment to disease progression

Prostate cancer is caused by genomic aberrations in normal epithelial cells, however clinical translation of findings from analyses of cancer cells alone has been very limited. A deeper understanding of the tumour microenvironment is needed to identify the key drivers of disease progression and reveal novel therapeutic opportunities. In this study, the experimental enrichment of selected cell-types and the development of a Bayesian inference model for continuous differential transcript abundance permitted us to define the transcriptional landscape of the prostate cancer microenvironment along the disease progression axis. An important role of monocytes and macrophages in prostate cancer progression and disease recurrence was uncovered, supported by both transcriptional landscape findings and by differential tissue composition analyses. These findings were corroborated and validated by spatial analyses at the single-cell level using multiplex immunohistochemistry. This study advances our knowledge concerning the role of monocyte-derived recruitment in primary prostate cancer, and supports their key role in disease progression, patient survival and prostate microenvironment immune modulation.

cancer biology↗

bioRxiv2020

A Bayesian inference tool for identifying artifactual calls from differential transcript abundance analyses

Relative transcript abundance has proven to be a valuable tool for inferring the phenotype of biological systems from genetic material. Several methods for the analysis of differential transcript abundance have been developed, and some of the most popular are based on negative binomial models. Although most genes are fitted reasonably well by the negative binomial distribution, the presence of outlier observations that do not fit such models can lead to artifactual identification of significant changes in transcription. Identifying those transcripts for the correct interpretation of results is extremely important. A robust and automated tool for detecting sample/transcript pairs that do not fit a negative binomial regression model is currently lacking. Here we propose ppcseq, a robust statistical framework that models hierarchically sample- and gene-wise features such as sequencing depth bias, the association between mean transcript abundance and its over-dispersion, and provides a theoretical transcript abundance distribution, on which the observed transcript abundance can be tested for outliers. We show using a publicly available data set where nearly 10% of differentially abundant transcripts had fold change inflated by the presence of outliers. This method has broad utility in filtering artifactual results of differential transcript abundance analyses based on a negative binomial framework.

bioinformatics↗

bioRxiv2019

Low miRNA abundance disables microRNA pathway in mammalian oocytes

MicroRNAs (miRNAs) are ubiquitous small RNAs, which guide post-transcriptional gene repression in countless biological processes. However, the miRNA pathway in mouse oocytes appears inactive and is dispensable for development. We report that miRNAs do not accumulate like maternal mRNAs during oocyte growth. The most abundant miRNAs total tens of thousands of molecules in fully-grown oocytes, a number similar to that observed in much smaller fibroblasts. The lack of miRNA accumulation acts like miRNA knock-down, where miRNAs can engage their targets but are not abundant enough to produce significant silencing effect. Injection of 100,000 miRNAs was sufficient to restore reporter repression in oocytes, confirming that miRNA inactivity primarily stems from low miRNA abundance and not from an active repression of the miRNA pathway per se. Similar situation was observed in rat, hamster, porcine, and bovine oocytes arguing that miRNA inactivity is not a mouse-specific adaptation but a common mammalian oocyte feature.

molecular biology↗

bioRxiv2019

Comparing quality of reporting between preprints and peer-reviewed articles in the biomedical literature

BackgroundPreprint usage is growing rapidly in the life sciences; however, questions remain on the relative quality of preprints when compared to published articles. An objective dimension of quality that is readily measurable is completeness of reporting, as transparency can improve the readers ability to independently interpret data and reproduce findings. MethodsIn this observational study, we initially compared independent samples of articles published in bioRxiv and in PubMed-indexed journals in 2016 using a quality of reporting questionnaire. After that, we performed paired comparisons between preprints from bioRxiv to their own peer-reviewed versions in journals. ResultsPeer-reviewed articles had, on average, higher quality of reporting than preprints, although the difference was small, with absolute differences of 5.0% [95% CI 1.4, 8.6] and 4.7% [95% CI 2.4, 7.0] of reported items in the independent samples and paired sample comparison, respectively. There were larger differences favoring peer-reviewed articles in subjective ratings of how clearly titles and abstracts presented the main findings and how easy it was to locate relevant reporting information. Changes in reporting from preprints to peer-reviewed versions did not correlate with the impact factor of the publication venue or with the time lag from bioRxiv to journal publication. ConclusionsOur results suggest that, on average, publication in a peer-reviewed journal is associated with improvement in quality of reporting. They also show that quality of reporting in preprints in the life sciences is within a similar range as that of peer-reviewed articles, albeit slightly lower on average, supporting the idea that preprints should be considered valid scientific contributions.

scientific communication and education↗

bioRxiv2018

Reparametrizing the Sigmoid Model of Gene Regulation for Bayesian Inference

This poster describes a novel reparametrization of a fre-quently used non-linear ordinary differential equation (ODE) model of gene regulation. We show that in its commonly used form, the model cannot reliably distinguish between both quantitatively and qualitatively different parameter combinations. The proposed reparametrization makes inference over the model stable and amenable to fully Bayesian treatment with state of the art Hamiltonian Monte Carlo methods.\n\nComplete source code and a more detailed explanation of the model is available at https://github.com/cas-bioinf/genexpi-stan.

bioinformatics↗