A framework for non-preserved consensus gene module detection in Johne's disease.

Johne's disease caused by Mycobacterium avium subsp. paratuberculosis (MAP) is a major concern in dairy industry. Since, the pathogenesis of the disease is not clearly known, it is necessary to develop an approach to discover molecular mechanisms behind this disease with high confidence. Biological studies often suffer from issues with reproducibility. Lack of a method to find stable modules in co-expression networks from different datasets related to Johne's disease motivated us to present a computational pipeline to identify non-preserved consensus modules. Two RNA-Seq datasets related to MAP infection were analyzed, and consensus modules were detected and were subjected to the preservation analysis. The non-preserved consensus modules in both datasets were determined as they are modules whose connectivity and density are affected by the disease. Long non-coding RNAs (lncRNAs) and TF genes in the non-preserved consensus modules were identified to construct integrated networks of lncRNA-mRNA-TF. These networks were confirmed by protein-protein interactions (PPIs) networks. Also, the overlapped hub genes between two datasets were considered hub genes of the consensus modules. Out of 66 consensus modules, 21 modules were non-preserved consensus modules, which were common in both datasets and 619 hub genes were members of these modules. Moreover, 34 lncRNA and 152 TF genes were identified in 12 and 19 non-preserved consensus modules, respectively. The predicted PPIs in 17 non-preserved consensus modules were significant, and 283 hub genes were commonly identified in both co-expression and PPIs networks. Functional enrichment analysis revealed that eight out of 21 modules were significantly enriched for biological processes associated with Johne's disease including "inflammatory response," "interleukin-1-mediated signaling pathway", "type I interferon signaling pathway," "cytokine-mediated signaling pathway," "regulation of interferon-beta production," and "response to interferon-gamma." Moreover, some genes (hub mRNA, TF, and lncRNA) were introduced as potential candidates for Johne's disease pathogenesis such as TLR2, NFKB1, IRF1, ATF3, TREM1, CDH26, HMGB1, STAT1, ISG15, CASP3. This study expanded our knowledge of molecular mechanisms involved in Johne's disease, and the presented pipeline enabled us to achieve more valid results.

Consensus gene modules related to levels of bone mineral density (BMD) among smokers and nonsmokers.

Osteoporosis, as a common metabolic disorder characterized by the decrease of bone mass, can cause fractures, thereby threatening the life quality of females, especially postmenopausal women. Thus, it is necessary to reveal the genes involved in osteoporosis and explore biomarkers for osteoporosis. In this study, two groups, smokers and nonsmokers with different bone mineral density (BMD) levels, were collected from the Gene Expression Omnibus (GEO) database GSE13850. Consensus modules of the two groups were identified; the variety of gene modules between smokers and nonsmokers with different BMD levels was observed; and a consensus module, including 390 genes significantly correlated with different BMD levels, was identified. Function analysis revealed the significantly enriched osteoporosis-related pathways, such as the PI3K-Akt signaling pathway. Hub genes analysis revealed the critical role of CXCL12 and CHRM2 in modules related to BMD levels. Based on the support vector machine recursive feature elimination (SVM-RFE) analysis, the model containing 10 genes (TNS4, IRF2, BSG, GZMM, ARRB2, COX15, RALY, TP53, RPS6KA3, and SYNPO) with good performance in identifying people with different BMD levels was constructed. Among them, the roles of RALY and SYNPO in the osteogenic differentiation of hBMSCs were verified experimentally. Overall, this study provides a strategy to explore the biomarkers for osteoporosis through analysis of consensus modules.

Multivariate genome wide association and network analysis of subcortical imaging phenotypes in Alzheimer's disease.

BACKGROUND: Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer's disease (AD). However single marker level association discovery may not be able to address the underlying biological interactions with disease mechanism. RESULTS: In this paper, we used the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool to perform multivariate GWAS on eight AD-relevant subcortical imaging measures. We conducted multiple iPINBPA (integrative Protein-Interaction-Network-Based Pathway Analysis) network analyses on MGAS findings using protein-protein interaction (PPI) data, and identified five Consensus Modules (CMs) from the PPI network. Functional annotation and network analysis were performed on the identified CMs. The MGAS yielded significant hits within APOE, TOMM40 and APOC1 genes, which were known AD risk factors, as well as a few new genes such as LAMA1, XYLB, HSD17B7P2, and NPEPL1. The identified five CMs were enriched by biological processes related to disorders such as Alzheimer's disease, Legionellosis, Pertussis, and Serotonergic synapse. CONCLUSIONS: The statistical power of coupling MGAS with iPINBPA was higher than traditional GWAS method, and yielded new findings that were missed by GWAS. This study provides novel insights into the molecular mechanism of Alzheimer's Disease and will be of value to novel gene discovery and functional genomic studies.

Genome-wide Network-assisted Association and Enrichment Study of Amyloid Imaging Phenotype in Alzheimer's Disease.

BACKGROUND: The etiology of Alzheimer's disease remains poorly understood at the mechanistic level, and genome-wide network-based genetics have the potential to provide new insights into the disease mechanisms. OBJECTIVE: The study aimed to explore the collective effects of multiple genetic association signals on an AV-45 PET measure, which is a well-known Alzheimer's disease biomarker, by employing a network assisted strategy. METHODS: First, we took advantage of a dense module search algorithm to identify modules enriched by genetic association signals in a protein-protein interaction network. Next, we performed statistical evaluation to the modules identified by dense module search, including a normalization process to adjust the topological bias in the network, a replication test to ensure the modules were not found randomly , and a permutation test to evaluate unbiased associations between the modules and amyloid imaging phenotype. Finally, topological analysis, module similarity tests and functional enrichment analysis were performed for the identified modules. RESULTS: We identified 24 consensus modules enriched by robust genetic signals in a genome-wide association analysis. The results not only validated several previously reported AD genes (APOE, APP, TOMM40, DDAH1, PARK2, ATP5C1, PVRL2, ELAVL1, ACTN1 and NRF1), but also nominated a few novel genes (ABL1, ABLIM2) that have not been studied in Alzheimer's disease but have shown associations with other neurodegenerative diseases. CONCLUSION: The identified genes, consensus modules and enriched pathways may provide important clues to future research on the neurobiology of Alzheimer's disease and suggest potential therapeutic targets.

Preservation affinity in consensus modules among stages of HIV-1 progression.

BACKGROUND: Analysis of gene expression data provides valuable insights into disease mechanism. Investigating relationship among co-expression modules of different stages is a meaningful tool to understand the way in which a disease progresses. Identifying topological preservation of modular structure also contributes to that understanding. METHODS: HIV-1 disease provides a well-documented progression pattern through three stages of infection: acute, chronic and non-progressor. In this article, we have developed a novel framework to describe the relationship among the consensus (or shared) co-expression modules for each pair of HIV-1 infection stages. The consensus modules are identified to assess the preservation of network properties. We have investigated the preservation patterns of co-expression networks during HIV-1 disease progression through an eigengene-based approach. RESULTS: We discovered that the expression patterns of consensus modules have a strong preservation during the transitions of three infection stages. In particular, it is noticed that between acute and non-progressor stages the preservation is slightly more than the other pair of stages. Moreover, we have constructed eigengene networks for the identified consensus modules and observed the preservation structure among them. Some consensus modules are marked as preserved in two pairs of stages and are analyzed further to form a higher order meta-network consisting of a group of preserved modules. Additionally, we observed that module membership (MM) values of genes within a module are consistent with the preservation characteristics. The MM values of genes within a pair of preserved modules show strong correlation patterns across two infection stages. CONCLUSIONS: We have performed an extensive analysis to discover preservation pattern of co-expression network constructed from microarray gene expression data of three different HIV-1 progression stages. The preservation pattern is investigated through identification of consensus modules in each pair of infection stages. It is observed that the preservation of the expression pattern of consensus modules remains more prominent during the transition of infection from acute stage to non-progressor stage. Additionally, we observed that the module membership values of genes are coherent with preserved modules across the HIV-1 progression stages.