MyoData: An expression knowledgebase at single cell/nucleus level for the discovery of coding-noncoding RNA functional interactions in skeletal muscle

The role of microRNAs in dexamethasone-induced skeletal muscle atrophy

Muscle atrophy is characterized by a decrease in muscle mass, strength, and activity. Recently, it was determined that microRNAs (miRNAs) can regulate muscle atrophy and that dexamethasone (Dex), an allergy and autoimmune disorder treatment that can induce muscle atrophy. Therefore, this study was designed to identify miRNAs expressed in Dex-induced muscle atrophy in mice using small RNA sequencing. A total of 820 miRNAs were identified, with 58 miRNAs expressed explicitly in atrophic muscles. Dex-induced muscle atrophy miRNAs clustered separately from the differential miRNAs in aging, disuse, and cancer-induced muscle atrophy models. The target genes of Dex-induced muscle atrophy miRNAs were independently enriched in inositol phosphate metabolism, hypoxia-inducible factor-1 signaling pathway, etc. Of note, there was a significant increase in the volume of fat cells and adipose weight in the Dex group, suggesting that fat deposition during Dex-induced skeletal muscle atrophy is a unique and typical feature. SIMPLE SUMMARY: Dexamethasone (Dex) is a glucocorticoid used to treat allergic and autoimmune diseases, but excessive use can lead to skeletal muscle atrophy. We used dexamethasone (Dex) to build a muscle atrophy model in mice, and obvious changes had taken place in mouse body weight, muscle tissue morphology and related genes. A large number of microRNAs were found to be differentially expressed, and their functions were enriched in pathways related to muscle development. At the same time, we compared the similarities and differences of microRNAs and their functions between Dex induced muscle atrophy model and other muscle atrophy models. Finally, we were surprised to find that Dex induced muscle atrophy is specifically accompanied by the accumulation of body fat.

Detailed role of Let-7e in human diseases

As part of the epigenetic machinery, microRNAs (miRNAs) are extensively utilized by eukaryotes. By modulating gene expression in a variety of ways, these short RNAs mediate crucial physiological processes. This suggests that abnormalities in miRNA biogenesis and expression can be traced back to a variety of diseases. In addition, miRNAs are promising clinical candidates, especially for preclinical diagnosis. The Let family of miRNAs was one of the first to be discovered. As a prominent member of this category, extensive research has been conducted on Let-7e. The vast majority of evidence indicates an association between let-7e dysregulation and the onset and progression of disease, including malignancies. Because their effect depends on the genetic profile of disease and the affected tissue, different miRNAs play diverse roles in various diseases. However, what counts in miRNA studies is that just one miRNA may target numerous mRNAs in a cell at the exact time, therefore summarizing the effect of a single miRNA in human diseases can provide better insights into disease detection and treatment. The goal of this study is to gain a deeper understanding of how let-7e functions in human cells so that it can be utilized more effectively in clinical settings for diagnosis, prognosis, and treatment. We have reviewed the research on let-7e, focusing on the molecular underpinnings of biological processes controlled by this miRNA that contribute to the development and etiology of numerous disorders.

Integrated procedures for accelerating, deepening, and leading genetic inquiry: A first application on human muscle secretome

PURPOSE

Beyond classical procedures, bioinformatic-assisted approaches and computational biology offer unprecedented opportunities for scholars. However, these amazing possibilities still need epistemological criticism, as well as standardized procedures. Especially those topics with a huge body of data may benefit from data science (DS)-assisted methods. Therefore, the current study dealt with the combined expert-assisted and DS-assisted approaches to address the broad field of muscle secretome. We aimed to apply DS tools to fix the literature research, suggest investigation targets with a data-driven approach, predict possible scenarios, and define a workflow.

METHODS

Recognized scholars with expertise on myokines were invited to provide a list of the most important myokines. GeneRecommender, GeneMANIA, HumanNet, and STRING were selected as DS tools. Networks were built on STRING and GeneMANIA. The outcomes of DS tools included the top 5 recommendations. Each expert-led discussion has been then integrated with an DS-led approach to provide further perspectives.

RESULTS

Among the results, 11 molecules had already been described as bona-fide myokines in literature, and 11 molecules were putative myokines. Most of the myokines and the putative myokines recommended by the DS tools were described as present in the cargo of extracellular vesicles.

CONCLUSIONS

Including both supervised and unsupervised learning methods, as well as encompassing algorithms focused on both protein interaction and gene represent a comprehensive approach to tackle complex biomedical topics. DS-assisted methods for reviewing existent evidence, recommending targets of interest, and predicting original scenarios are worth exploring as in silico recommendations to be integrated with experts' ideas for optimizing molecular studies.