Identifying key genes in glaucoma based on a benchmarked dataset and the gene regulatory network

Candidate SNP Markers Significantly Altering the Affinity of the TATA-Binding Protein for the Promoters of Human Genes Associated with Primary Open-Angle Glaucoma

Primary open-angle glaucoma (POAG) is the most common form of glaucoma. This condition leads to optic nerve degeneration and eventually to blindness. Tobacco smoking, alcohol consumption, fast-food diets, obesity, heavy weight lifting, high-intensity physical exercises, and many other bad habits are lifestyle-related risk factors for POAG. By contrast, moderate-intensity aerobic exercise and the Mediterranean diet can alleviate POAG. In this work, we for the first time estimated the phylostratigraphic age indices (PAIs) of all 153 POAG-related human genes in the NCBI Gene Database. This allowed us to separate them into two groups: POAG-related genes that appeared before and after the phylum Chordata, that is, ophthalmologically speaking, before and after the camera-type eye evolved. Next, in the POAG-related genes' promoters, we in silico predicted all 3835 candidate SNP markers that significantly change the TATA-binding protein (TBP) affinity for these promoters and, through this molecular mechanism, the expression levels of these genes. Finally, we verified our results against five independent web services-PANTHER, DAVID, STRING, MetaScape, and GeneMANIA-as well as the ClinVar database. It was concluded that POAG is likely to be a symptom of the human self-domestication syndrome, a downside of being civilized.

Biomarker Genes Discovery of Alzheimer’s Disease by Multi-Omics-Based Gene Regulatory Network Construction of Microglia

Microglia, the major immune cells in the brain, mediate neuroinflammation, increased oxidative stress, and impaired neurotransmission in Alzheimer’s disease (AD), in which most AD risk genes are highly expressed. In microglia, due to the limitations of current single-omics data analysis, risk genes, the regulatory mechanisms, the mechanisms of action of immune responses and the exploration of drug targets for AD immunotherapy are still unclear. Therefore, we proposed a method to integrate multi-omics data based on the construction of gene regulatory networks (GRN), by combining weighted gene co-expression network analysis (WGCNA) with single-cell regulatory network inference and clustering (SCENIC). This enables snRNA-seq data and bulkRNA-seq data to obtain data on the deeper intermolecular regulatory relationships, related genes, and the molecular mechanisms of immune-cell action. In our approach, not only were central transcription factors (TF) STAT3, CEBPB, SPI1, and regulatory mechanisms identified more accurately than with single-omics but also immunotherapy targeting central TFs to drugs was found to be significantly different between patients. Thus, in addition to providing new insights into the potential regulatory mechanisms and pathogenic genes of AD microglia, this approach can assist clinicians in making the most rational treatment plans for patients with different risks; it also has significant implications for identifying AD immunotherapy targets and targeting microglia-associated immune drugs.

Investigation of Key Signaling Pathways Associating miR-204 and Common Retinopathies

MicroRNAs are a large group of small noncoding RNAs that work in multiple cellular pathways. miR-204, as one of the key axes in the development, maintenance, and pathogenesis of the retina, plays several roles by modulating its target genes. This study was aimed at evaluating the target genes of miR-204 involved in the development and progression of common retinopathies such as glaucoma, retinoblastoma, and age-related macular degeneration. In this study, three datasets related to retinopathies (GSE50195, GSE27276, and GSE97508) were selected from Gene Expression Omnibus. miR-204 target genes were isolated from TargeScan. The shares between retinopathy and miR-204 target genes were then categorized. Using Enrichr and STRING, we highlighted the signaling pathways and the relationships between the proteins. SHC1 events in ERBB2, adherent junction's interactions, NGF signaling via TRKA from the plasma membrane, IRF3-mediated activation of type 1 IFN, pathways in upregulated genes and G0 and early G1, RORA-activated gene expression, PERK-regulated gene expression, adherent junction's interactions, and CREB phosphorylation pathways in downregulated genes were identified in glaucoma, retinoblastoma, and age-related macular degeneration. WEE1, SMC2, HMGB1, RRM2, and POLA1 proteins were also observed to be involved in the progression and invasion of retinoblastoma; SLC24A2 and DTX4 in age-related macular degeneration; and EPHB6, EFNB3, and SHC1 in glaucoma. Continuous bioinformatics analysis has shown that miR-204 has a significant presence and expression in retinal tissue, and approximately 293 genes are controlled and regulated by miR-204 in this tissue; also, target genes of miR-204 have the potential to develop various retinopathies; thus, a study of related target genes can provide appropriate treatment strategies in the future.

The Wheat GENIE3 Network Provides Biologically-Relevant Information in Polyploid Wheat

Gene regulatory networks are powerful tools which facilitate hypothesis generation and candidate gene discovery. However, the extent to which the network predictions are biologically relevant is often unclear. Recently a GENIE3 network which predicted targets of wheat transcription factors was produced. Here we used an independent RNA-Seq dataset to test the predictions of the wheat GENIE3 network for the senescence-regulating transcription factor NAM-A1 (TraesCS6A02G108300). We re-analyzed the RNA-Seq data against the RefSeqv1.0 genome and identified a set of differentially expressed genes (DEGs) between the wild-type and nam-a1 mutant which recapitulated the known role of NAM-A1 in senescence and nutrient remobilisation. We found that the GENIE3-predicted target genes of NAM-A1 overlap significantly with the DEGs, more than would be expected by chance. Based on high levels of overlap between GENIE3-predicted target genes and the DEGs, we identified candidate senescence regulators. We then explored genome-wide trends in the network related to polyploidy and found that only homeologous transcription factors are likely to share predicted targets in common. However, homeologs which vary in expression levels across tissues are less likely to share predicted targets than those that do not, suggesting that they may be more likely to act in distinct pathways. This work demonstrates that the wheat GENIE3 network can provide biologically-relevant predictions of transcription factor targets, which can be used for candidate gene prediction and for global analyses of transcription factor function. The GENIE3 network has now been integrated into the KnetMiner web application, facilitating its use in future studies.

Unified feature association networks through integration of transcriptomic and proteomic data

High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different-omic types (transcripts, proteins, lipids etc). We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm mechanisms of disease.