Exploring miRNA-target gene pair detection in disease with coRmiT.

A wide range of approaches can be used to detect micro RNA (miRNA)-target gene pairs (mTPs) from expression data, differing in the ways the gene and miRNA expression profiles are calculated, combined and correlated. However, there is no clear consensus on which is the best approach across all datasets. Here, we have implemented multiple strategies and applied them to three distinct rare disease datasets that comprise smallRNA-Seq and RNA-Seq data obtained from the same samples, obtaining mTPs related to the disease pathology. All datasets were preprocessed using a standardized, freely available computational workflow, DEG_workflow. This workflow includes coRmiT, a method to compare multiple strategies for mTP detection. We used it to investigate the overlap of the detected mTPs with predicted and validated mTPs from 11 different databases. Results show that there is no clear best strategy for mTP detection applicable to all situations. We therefore propose the integration of the results of the different strategies by selecting the one with the highest odds ratio for each miRNA, as the optimal way to integrate the results. We applied this selection-integration method to the datasets and showed it to be robust to changes in the predicted and validated mTP databases. Our findings have important implications for miRNA analysis. coRmiT is implemented as part of the ExpHunterSuite Bioconductor package available from https://bioconductor.org/packages/ExpHunterSuite.

CoMentG: comprehensive retrieval of generic relationships between biomedical concepts from the scientific literature.

The CoMentG resource contains millions of relationships between terms of biomedical interest obtained from the scientific literature. At the core of the system is a methodology for detecting significant co-mentions of concepts in the entire PubMed corpus. That method was applied to nine sets of terms covering the most important classes of biomedical concepts: diseases, symptoms/clinical signs, molecular functions, biological processes, cellular compartments, anatomic parts, cell types, bacteria and chemical compounds. We obtained more than 7 million relationships between more than 74 000 terms, and many types of relationships were not available in any other resource. As the terms were obtained from widely used resources and ontologies, the relationships are given using the standard identifiers provided by them and hence can be linked to other data. A web interface allows users to browse these associations, searching for relationships for a set of terms of interests provided as input, such as between a disease and their associated symptoms, underlying molecular processes or affected tissues. The results are presented in an interactive interface where the user can explore the reported relationships in different ways and follow links to other resources. Database URL: https://csbg.cnb.csic.es/CoMentG/.

Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease.

Hirschsprung's disease (HSCR) is a rare developmental disorder in which enteric ganglia are missing along a portion of the intestine. HSCR has a complex inheritance, with RET as the major disease-causing gene. However, the pathogenesis of HSCR is still not completely understood. Therefore, we applied a computational approach based on multi-omics network characterization and clustering analysis for HSCR-related gene/miRNA identification and biomarker discovery. Protein-protein interaction (PPI) and miRNA-target interaction (MTI) networks were analyzed by DPClusO and BiClusO, respectively, and finally, the biomarker potential of miRNAs was computationally screened by miRNA-BD. In this study, a total of 55 significant gene-disease modules were identified, allowing us to propose 178 new HSCR candidate genes and two biological pathways. Moreover, we identified 12 key miRNAs with biomarker potential among 137 predicted HSCR-associated miRNAs. Functional analysis of new candidates showed that enrichment terms related to gene ontology (GO) and pathways were associated with HSCR. In conclusion, this approach has allowed us to decipher new clues of the etiopathogenesis of HSCR, although molecular experiments are further needed for clinical validations.

Transcriptomic analysis identifies dysregulated pathways and therapeutic targets in PMM2-CDG.

PMM2-CDG (MIM # 212065), the most common congenital disorder of glycosylation, is caused by the deficiency of phosphomannomutase 2 (PMM2). It is a multisystemic disease of variable severity that particularly affects the nervous system; however, its molecular pathophysiology remains poorly understood. Currently, there is no effective treatment. We performed an RNA-seq based transcriptomic study using patient-derived fibroblasts to gain insight into the mechanisms underlying the clinical symptomatology and to identify druggable targets. Systems biology methods were used to identify cellular pathways potentially affected by PMM2 deficiency, including Senescence, Bone regulation, Cell adhesion and Extracellular Matrix (ECM) and Response to cytokines. Functional validation assays using patients' fibroblasts revealed defects related to cell proliferation, cell cycle, the composition of the ECM and cell migration, and showed a potential role of the inflammatory response in the pathophysiology of the disease. Furthermore, treatment with a previously described pharmacological chaperone reverted the differential expression of some of the dysregulated genes. The results presented from transcriptomic data might serve as a platform for identifying therapeutic targets for PMM2-CDG, as well as for monitoring the effectiveness of therapeutic strategies, including pharmacological candidates and mannose-1-P, drug repurposing.

Bone metabolism and inflammatory biomarkers in radiographic and non-radiographic axial spondyloarthritis patients: a comprehensive evaluation.

Introduction: Axial spondyloarthritis (axSpA) is a heterogeneous disease that can be represented by radiographic axSpA (r-axSpA) and non-radiographic axSpA (nr-axSpA). This study aimed to evaluate the relationship between the markers of inflammation and bone turnover in r-axSpA patients and nr-axSpA patients. Methods: A cross-sectional study included 29 r-axSpA patients, 10 nr-axSpA patients, and 20 controls matched for age and sex. Plasma markers related to bone remodeling such as human procollagen type 1 N-terminal propeptide (P1NP), sclerostin, tartrate-resistant acid phosphatase 5b (TRACP5b), receptor activator of nuclear factor kappa B ligand (RANKL), and osteoprotegerin (OPG) were measured by an ELISA kit. A panel of 92 inflammatory molecules was analyzed by proximity extension assay. Results: R-axSpA patients had decreased plasma levels of P1NP, a marker of bone formation, compared to controls. In addition, r-axSpA patients exhibited decreased plasma levels of sclerostin, an anti-anabolic bone hormone, which would not explain the co-existence of decreased plasma P1NP concentration; however, sclerostin levels could also be influenced by inflammatory processes. Plasma markers of osteoclast activity were similar in all groups. Regarding inflammation-related molecules, nr-axSpA patients showed increased levels of serum interleukin 13 (IL13) as compared with both r-axSpA patients and controls, which may participate in the prevention of inflammation. On the other hand, r-axSpA patients had higher levels of pro-inflammatory molecules compared to controls (i.e., IL6, Oncostatin M, and TNF receptor superfamily member 9). Correlation analysis showed that sclerostin was inversely associated with IL6 and Oncostatin M among others. Conclusion: Altogether, different inflammatory profiles may play a role in the development of the skeletal features in axSpA patients particularly related to decreased bone formation. The relationship between sclerostin and inflammation and the protective actions of IL13 could be of relevance in the axSpA pathology, which is a topic for further investigation.