Meta-analysis of Liver and Heart Transcriptomic Data for Functional Annotation Transfer in Mammalian Orthologs

Comparative Analyses of Gene Co-expression Networks: Implementations and Applications in the Study of Evolution

Similarities and differences in the associations of biological entities among species can provide us with a better understanding of evolutionary relationships. Often the evolution of new phenotypes results from changes to interactions in pre-existing biological networks and comparing networks across species can identify evidence of conservation or adaptation. Gene co-expression networks (GCNs), constructed from high-throughput gene expression data, can be used to understand evolution and the rise of new phenotypes. The increasing abundance of gene expression data makes GCNs a valuable tool for the study of evolution in non-model organisms. In this paper, we cover motivations for why comparing these networks across species can be valuable for the study of evolution. We also review techniques for comparing GCNs in the context of evolution, including local and global methods of graph alignment. While some protein-protein interaction (PPI) bioinformatic methods can be used to compare co-expression networks, they often disregard highly relevant properties, including the existence of continuous and negative values for edge weights. Also, the lack of comparative datasets in non-model organisms has hindered the study of evolution using PPI networks. We also discuss limitations and challenges associated with cross-species comparison using GCNs, and provide suggestions for utilizing co-expression network alignments as an indispensable tool for evolutionary studies going forward.

Meta-Analysis of Peripheral Blood Transcriptome Datasets Reveals a Biomarker Panel for Tuberculosis in Patients Infected With HIV

Biomarkers are critical for rapid diagnosis of tuberculosis (TB) and could benefit patients with AIDS where diagnosis of TB co-infection is challenging. Meta-analysis is an approach to combine the results of the studies with standard statistical method by weighting each study with different sample size. This study aimed to use meta-analysis to integrate transcriptome datasets from different studies and screen for TB biomarkers in patients who were HIV-positive. Five datasets were subjected to meta-analysis on whole-blood transcriptomes from 640 patients infected with HIV. A total of 293 differentially expressed genes (DEGs) were identified as significant (P<0.0001) using the random effective model to integrate the statistical results from each study. DEGs were enriched in biological processes related to TB, such as “Type I interferon signaling” and “stimulatory C-type lectin receptor signaling”. Eighteen DEGs had at least a two-fold change in expression between patients infected with HIV who were TB-positive and those who were TB-negative. GBP4, SERPING1, ATF3 and CDKBN3 were selected as a biomarker panel to perform multivariable logistic regression analysis on TB status and relative gene expression levels. The biomarker panel showed excellent accuracy (AUC>0.90 for HIV+TB) in clinical trial and suggests that meta-analysis is an efficient method to integrate transcriptome datasets from different studies.

Meta-analysis of transcriptome datasets: An alternative method to study IL-6 regulation in coronavirus disease 2019

In coronavirus disease 2019 (COVID-19) patients, interleukin (IL)-6 is one of the leading factors causing death through cytokine release syndrome. Hence, identification of IL-6 downstream from clinical patients’ transcriptome is very valid for analyses of its mechanism. However, clinical study is conditional and time consuming to collect optional size of samples, as patients have the clinical heterogeneity. A possible solution is to deeply mine the relative existing data. Several transcriptome-based studies on other diseases or treatments have revealed different genes to be regulated by IL-6. Through our meta-analysis of these transcriptome datasets, 352 genes were suggested to be regulated by IL-6 in different biological conditions, some of which were related to virus infection and cardiovascular disease. Among them, 232 genes were not identified by current transcriptome studies from clinical research. ICAM1 and PFKFB3 were the most significantly upregulated genes in our meta-analysis and could be employed as biomarkers in patients with severe COVID-19. In general, a meta-analysis of transcriptome datasets could be an alternative way to analyze the immune response and complications of patients suffering from severe COVID-19 and other emergency diseases.