Dataset Information

Revealing myopathy spectrum: integrating transcriptional and clinical features of human skeletal muscles with varying health conditions.

ABSTRACT: Myopathy refers to a large group of heterogeneous, rare muscle diseases. Bulk RNA-sequencing has been utilized for the diagnosis and research of these diseases for many years. However, the existing valuable sequencing data often lack integration and clinical interpretation. In this study, we integrated bulk RNA-sequencing data from 1221 human skeletal muscles (292 with myopathies, 929 controls) from both databases and our local samples. By applying a method similar to single-cell analysis, we revealed a general spectrum of muscle diseases, ranging from healthy to mild disease, moderate muscle wasting, and severe muscle disease. This spectrum was further partly validated in three specific myopathies (97 muscles) through clinical features including trinucleotide repeat expansion, magnetic resonance imaging fat fraction, pathology, and clinical severity scores. This spectrum helped us identify 234 genuinely healthy muscles as unprecedented controls, providing a new perspective for deciphering the hallmark genes and pathways among different myopathies. The newly identified featured genes of general myopathy, inclusion body myositis, and titinopathy were highly expressed in our local muscles, as validated by quantitative polymerase chain reaction.

SUBMITTER: Zhong H

PROVIDER: S-EPMC11006663 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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Revealing myopathy spectrum: integrating transcriptional and clinical features of human skeletal muscles with varying health conditions.

Zhong Huahua H Sian Veronica V Johari Mridul M Katayama Shintaro S Oghabian Ali A Jonson Per Harald PH Hackman Peter P Savarese Marco M Udd Bjarne B

Communications biology 20240410 1

Myopathy refers to a large group of heterogeneous, rare muscle diseases. Bulk RNA-sequencing has been utilized for the diagnosis and research of these diseases for many years. However, the existing valuable sequencing data often lack integration and clinical interpretation. In this study, we integrated bulk RNA-sequencing data from 1221 human skeletal muscles (292 with myopathies, 929 controls) from both databases and our local samples. By applying a method similar to single-cell analysis, we re ...[more]

PMID: 38600180

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Project description:MotivationMicroRNAs (miRNAs) play important roles in general biological processes and diseases pathogenesis. Identifying miRNA target genes is an essential step to fully understand the regulatory effects of miRNAs. Many computational methods based on the sequence complementary rules and the miRNA and mRNA expression profiles have been developed for this purpose. It is noted that there have been many sequence features of miRNA targets available, including the context features of the target sites, the thermodynamic stability and the accessibility energy for miRNA-mRNA interaction. However, most of current computational methods that combine sequence and expression information do not effectively integrate full spectrum of these features; instead, they perceive putative miRNA-mRNA interactions from sequence-based prediction as equally meaningful. Therefore, these sequence features have not been fully utilized for improving miRNA target prediction.ResultsWe propose a novel regularized regression approach that is based on the adaptive Lasso procedure for detecting functional miRNA-mRNA interactions. Our method fully takes into account the gene sequence features and the miRNA and mRNA expression profiles. Given a set of sequence features for each putative miRNA-mRNA interaction and their expression values, our model quantifies the down-regulation effect of each miRNA on its targets while simultaneously estimating the contribution of each sequence feature to predicting functional miRNA-mRNA interactions. By applying our model to the expression datasets from two cancer studies, we have demonstrated our prediction results have achieved better sensitivity and specificity and are more biologically meaningful compared with those based on other methods.Availability and implementationThe source code is available at: http://nba.uth.tmc.edu/homepage/liu/miRNALasso.Supplementary informationSupplementary data are available at Bioinformatics online.ContactYin.Liu@uth.tmc.edu.

Dataset Information

Revealing myopathy spectrum: integrating transcriptional and clinical features of human skeletal muscles with varying health conditions.

Publications

Revealing myopathy spectrum: integrating transcriptional and clinical features of human skeletal muscles with varying health conditions.

Similar Datasets

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