Simple Bayesian Gene Network Learning in Populus Drought Transcriptome Data

Constructing a sensibly functional gene interaction network is highly appealing for better understanding system-level biological processes governing various Populus traits. Bayesian Network (BN) learning provides an elegant and compact statistical approach for modeling causal gene-gene relationships in microarray data. Therefore, it could come with the illumination of functional molecular playing in Biology Systems. In the present study, different forms of gene Bayesian networks were detected on Populus cellular transcriptome data. Markov blankets would likely be emerging at every possible gene regulatory Bayesian network level. Results showed that PtpAffx.1257.4.S1_a_at,1.0 hypothetical protein is the most important in its possible regulatory program. This paper illustrates that the gene network regulatory inference is possible to encapsulate within a single BN model. Therefore, such a BN model can serve as a promising training tool for Populus gene expression data for better future experimental scenarios. Bangladesh J. Bot. 50(4): 1077-1086, 2021 (December)

Identification of Hub Genes And Potential Molecular Mechanisms In Peripheral Blood Mononuclear Cells of Patients With Parkinson’s Disease

Parkinson's disease (PD) is the second most common neurodegenerative disease. The pathogenesis of PD remains elusive, however PD appears to be caused by a complex interaction between environmental and genetic factors affecting various biological processes. The purpose of the present study is to identify hub genes and potential molecular mechanisms in peripheral blood mononuclear cells (PBMCs) of PD patients to aid early diagnosis and start treatment promptly. Two gene expression profiles (GSE22491 and GSE100054) were obtained from the Gene Expression Omnibus (GEO) database, in which 20 PBMC samples from PD patients and 17 controls were included, and the genes were analyzed with GEO2R. 1382 and 512 differentially expressed genes (DEGs) were identified in GSE22491 and GSE100054, respectively. Additionally, a total of 80 significant DEGs were found to co-exist in the two microarray datasets via Venn diagram. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, which showed that the DEGs were mainly enriched in platelet degranulation, blood coagulation, nitric oxide mediated signal transduction, positive regulation of GTPase activity and cellular response to lipopolysaccharide. PPI network, microRNA (miRNA) - hub gene network, and transcription factor (TF)- hub gene network were constructed. In summary, the present study provides data of potential diagnostic biomarkers and therapeutic targets for PD. SRC may be a potential target for the treatment of PD. Additionally, three TFs (HNF4A, CDX2 and FUS), three miRNAs (hsa-miR-16-5p, hsa-miR-103a-3p and hsa-miR-107), may be involved in PD.

Key Gene Network Related to Primary Ciliary Dyskinesia in Hippocampus of Patients with Alzheimer’S Disease Revealed by Weighted Gene Co-Expression Network Analysis

Background. Alzheimer's disease (AD) is closely related to aging, showing an increasing incidence rate for years. As one of the main organs involved in AD, hippocampus has been extensively studied due to its association with many human diseases. However, little knowledge is known on its association with primary ciliary dyskinesia (PCD). Material and Methods. The microarray data of hippocampus on AD were retrieved from the Gene Expression Omnibus (GEO) database to construct the co-expression network by weighted gene co-expression network analysis (WGCNA). The gene network modules associated with AD screened with the common genes were further annotated based on Gene Ontology (GO) database and enriched based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The protein-protein interaction (PPI) network was constructed based on STRING database to identify the hub genes in the network. Results. Genes involved in PCD were identified in the hippocampus of AD patients. Functional analysis revealed that these genes were mainly enriched in ciliary tissue, ciliary assembly, axoneme assembly, ciliary movement, microtubule based process, microtubule based movement, organelle assembly, axoneme dynamin complex, cell projection tissue, and microtubule cytoskeleton tissue. A total of 20 central genes, e.g., DYNLRB2, ZMYND10, DRC1, DNAH5, WDR16, TTC25, and ARMC4 were identified as hub genes related to PCD in hippocampus of AD patients. Conclusion. Our study demonstrated that AD and PCD have shared metabolic pathways. These common pathways provide novel evidence for further investigation of the pathophysiological mechanism and the hub genes suggest new therapeutic targets for the diagnosis and treatment of AD and PCD. Subjects Bioinformatics, Cell Biology, Molecular Biology, Neurology

Gap-com: General model selection criterion for sparse undirected gene networks with non-trivial community structure

We introduce a new model selection criterion for sparse complex gene network modeling where gene co-expression relationships are estimated from data. This is a novel formulation of the gap statistic and it can be used for the optimal choice of a regularization parameter in graphical models. Our criterion favors gene network structure which differs from a trivial gene interaction structure obtained totally at random. We call the criterion the gap-com statistic (gap community statistic). The idea of the gap-com statistic is to examine the difference between the observed and the expected counts of communities (clusters) where the expected counts are evaluated using either data permutations or reference graph (the Erdős-Rényi graph) resampling. The latter represents a trivial gene network structure determined by chance. We put emphasis on complex network inference because the structure of gene networks is usually non-trivial. For example, some of the genes can be clustered together or some genes can be hub genes. We evaluate the performance of the gap-com statistic in graphical model selection and compare its performance to some existing methods using simulated and real biological data example.

Exploration of the Potential Mechanisms of Wumei Pill for the Treatment of Ulcerative Colitis by Network Pharmacology

Background. Wumei pill (WMP) has a long history of colitis treatment in China, but the protective mechanisms have not been elucidated. To uncover the potential mechanisms of WMP against ulcerative colitis (UC), the network pharmacology approach was utilized in this study. Methods. Public databases were utilized to identify the potential targets of WMP and genes related to UC. Based on the identified overlapping common targets, drug-ingredient-target gene network, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and protein-protein interaction (PPI) analysis were conducted. Molecular docking was carried out to verify the selected key active ingredients and core targets. Results. 129 active ingredients and 622 target genes were obtained. The drug-ingredient-target gene network revealed 52 active ingredients of WMP acting on 73 targets related to UC. GO analysis revealed that biological processes were mainly associated with oxidative stress, such as, reactive oxygen species metabolic processes, response to oxidative stress, cellular response to oxidative stress, response to reactive oxygen species, and regulation of reactive oxygen species metabolic processes. KEGG analysis revealed that the immune- and inflammation-related pathways, tumor-related signaling pathways, and microbial infection-related signaling pathways were the most significant. PPI network identified 13 core target genes. The molecular docking results indicated the formation of stable bonds between the active ingredients and core target genes. Conclusions. The approach of network pharmacology reveals the key ingredients, potential core targets, and biological process of WMP in the treatment of UC. The mechanisms of action of WMP involve anti-inflammation, antioxidation, and modulation of immunity, which provides evidence for the therapeutic role of WMP in UC.

Gene Co-expression Network Characterizing Microenvironmental Heterogeneity and Intercellular Communication in Pancreatic Ductal Adenocarcinoma: Implications of Prognostic Significance and Therapeutic Target

Background Pancreatic ductal adenocarcinoma (PDAC) is characterized by intensive stroma involvement and heterogeneity. Pancreatic cancer cells interplay with surrounding tumor micro-environment (TME), leading to exacerbated tumorigenesis, dismal prognosis and tenacious therapy resistance. Herein, we aim to ascertain a gene-network indicative of vicious features of TME, then find a vulnerability for pancreatic cancer. Methods Single cell RNA sequencing data was processed by Seurat package, retrieving the cell component marker genes (CCMGs). Correlation networks/modules of CCMGs were determined by WGCNA algorithm in a combined PDAC mRNA expression dataset. The gene modules that statistically associate with prognosis were chosen for classifying TME subgroups, constructing neural network and designing the risk score system. Cell-cell communication analysis was achieved by NATMI software. The tumor suppressive effect of ITGA2 inhibitor was evaluated in vivo by using a Kras G12D -driven murine pancreatic cancer model.Results WGCNA analysis categorized cell component marker genes into eight co-expression networks. From gene modules with the maximum and minimum hazard ratio, we stratify PDAC samples based on TME gene patterns, resulting in two main TME subclasses with contrasting survival periods. Furthermore, we generated a neural network model and a risk score model which robustly predict prognosis and therapeutic outcomes. The hub genes in both gene modules were also gathered for functional enrichment analysis, elucidating a crucial role of cell communication-mediating integrins in TME associated PDAC malignancy. To perform a confirmatory experiment underpinning the significance of hub gene targeting, the mice with spontaneously developed pancreatic cancer were orally treated with an integrin inhibitor. The in vivo assays unraveled that pharmacologically inhibiting ITGA2 counteracts cancer-promoting micro-environment, and ameliorates pancreatic lesions. Conclusions By recapitulating gene-network across various cell types, we exploited novel PDAC prognosis-predicting strategies. Medically interfering ITGA2, a key factor guiding cellular reciprocal interaction, attenuated tumor development. These findings may open new avenue about PDAC targeting therapy.