Methods for multiple outcome meta-analysis of gene-expression data

MiRNA-miRNA interaction network in peripheral blood of patients with myocardial infarction: a gene expression meta-analysis

In recent years, investigations have revealed that microRNAs (miRNAs) can bind together and form a miRNA-miRNA-mRNA regulatory network that alters the consequence of miRNA-mRNA interaction. If we consider the miRNA that binds to mRNA as the primary miRNA and the miRNA that binds to the primary miRNA as the secondary one, secondry miRNAs can act as master regulators upstream of primary miRNAs and their target mRNAs. One of the distinguishing characteristics of secondary miRNAs as master regulators within a diverse set of differentially expressed genes is the absence of direct target mRNA for them. Instead, these master regulators exclusively govern the regulation of miRNAs that target specific mRNAs. Through in silico analysis, we identified 18 miRNAs among 385 differentially expressed miRNAs (DEmiRNAs) with no direct target mRNAs among 58 differentially expressed mRNAs (DEmRNAs) in peripheral blood of patients with myocardial infarction (MI). Instead, these secondary miRNAs targeted 9 primary miRNAs that had 36 direct targets among 58 DEmRNAs. We found that one primary miRNA might be regulated by more than one secondary miRNAs and each secondary miRNA can target more than one primary miRNAs. Among identified miRNA-miRNA-mRNA networks miR-188-5p/miR-299-3p/natural killer cell granule protein (NKG7), miR-200a-3p/miR-199b-5p/granzyme B (GZMB), and miR-377-3p/miR-581/oviductal glycoprotein 1 (OVGP1) exhibited higher scors in terms of expression levels (>2-fold increase or decrease) and strengh of interactions (ΔG < -5). Given the extensive network of miRNA interactions, focusing on master regulators opens up avenues for identifying key regulatory nodes for more effective therapeutic strategies.

Network Analysis of Inflammatory Bowel Disease Research: Towards the Interactome

BACKGROUND AND AIMS

Modern views accept that inflammatory bowel diseases [IBD] emerge from complex interactions among the multiple components of a biological network known as the 'IBD interactome'. These diverse components belong to different functional levels including cells, molecules, genes and biological processes. This diversity can make it difficult to integrate available empirical information from human patients into a collective view of aetiopathogenesis, a necessary step to understand the interactome. Herein, we quantitatively analyse how the representativeness of components involved in human IBD and their relationships ha ve changed over time.

METHODS

A bibliographic search in PubMed retrieved 25 971 abstracts of experimental studies on IBD in humans, published between 1990 and 2020. Abstracts were scanned automatically for 1218 IBD interactome components proposed in recent reviews. The resulting databases are freely available and were visualized as networks indicating the frequency at which different components are referenced together within each abstract.

RESULTS

As expected, over time there was an increase in components added to the IBD network and heightened connectivity within and across functional levels. However, certain components were consistently studied together, forming preserved motifs in the networks. These overrepresented and highly linked components reflect main 'hypotheses' in IBD research in humans. Interestingly, 82% of the components cited in reviews were absent or showed low frequency, suggesting that many aspects of the proposed IBD interactome still have weak experimental support in humans.

CONCLUSIONS

A reductionist and fragmented approach to the study of IBD has prevailed in previous decades, highlighting the importance of transitioning towards a more integrated interactome framework.

MAGE: An Open-Source Tool for Meta-Analysis of Gene Expression Studies

MAGE (Meta-Analysis of Gene Expression) is a Python open-source software package designed to perform meta-analysis and functional enrichment analysis of gene expression data. We incorporate standard methods for the meta-analysis of gene expression studies, bootstrap standard errors, corrections for multiple testing, and meta-analysis of multiple outcomes. Importantly, the MAGE toolkit includes additional features for the conversion of probes to gene identifiers, and for conducting functional enrichment analysis, with annotated results, of statistically significant enriched terms in several formats. Along with the tool itself, a web-based infrastructure was also developed to support the features of this package.

Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach

The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology’s potential, a set of transcriptomic datasets are meta-analyzed as an example.