Integrated Network Analysis to Identify Key Modules and Potential Hub Genes Involved in Bovine Respiratory Disease: A Systems Biology Approach

Abstract

Background: Bovine respiratory disease (BRD) is the most common disease in the beef and dairy cattle industry. BRD is a multifactorial disease resulting from the interaction between environmental stressors and infectious agents. However, the molecular mechanisms underlying BRD are not fully understood yet. Therefore, this study aimed to use a systems biology approach to systematically evaluate this disorder to better understand the molecular mechanisms responsible for BRD.Methods: Previously published RNA-seq data from whole blood of 18 healthy and 25 BRD samples were downloaded from the Gene Expression Omnibus (GEO) and then analyzed. Next, two distinct methods of weighted gene coexpression network analysis (WGCNA), i.e., module–trait relationships (MTRs) and module preservation (MP) analysis were used to identify significant highly correlated modules with clinical traits of BRD and non-preserved modules between healthy and BRD samples, respectively. After identifying respective modules by the two mentioned methods of WGCNA, functional enrichment analysis was performed to extract the modules that are biologically related to BRD. Gene coexpression networks based on the hub genes from the candidate modules were then integrated with protein–protein interaction (PPI) networks to identify hub–hub genes and potential transcription factors (TFs).Results: Four significant highly correlated modules with clinical traits of BRD as well as 29 non-preserved modules were identified by MTRs and MP methods, respectively. Among them, two significant highly correlated modules (identified by MTRs) and six nonpreserved modules (identified by MP) were biologically associated with immune response, pulmonary inflammation, and pathogenesis of BRD. After aggregation of gene coexpression networks based on the hub genes with PPI networks, a total of 307 hub–hub genes were identified in the eight candidate modules. Interestingly, most of these hub–hub genes were reported to play an important role in the immune response and BRD pathogenesis. Among the eight candidate modules, the turquoise (identified by MTRs) and purple (identified by MP) modules were highly biologically enriched in BRD. Moreover, STAT1, STAT2, STAT3, IRF7, and IRF9 TFs were suggested to play an important role in the immune system during BRD by regulating the coexpressed genes of these modules. Additionally, a gene set containing several hub–hub genes was identified in the eight candidate modules, such as TLR2, TLR4, IL10, SOCS3, GZMB, ANXA1, ANXA5, PTEN, SGK1, IFI6, ISG15, MX1, MX2, OAS2, IFIH1, DDX58, DHX58, RSAD2, IFI44, IFI44L, EIF2AK2, ISG20, IFIT5, IFITM3, OAS1Y, HERC5, and PRF1, which are potentially critical during infection with agents of bovine respiratory disease complex (BRDC).Conclusion: This study not only helps us to better understand the molecular mechanisms responsible for BRD but also suggested eight candidate modules along with several promising hub–hub genes as diagnosis biomarkers and therapeutic targets for BRD.

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

“…Because the gene co-expression analysis is very sensitive to outliers, the distance-based adjacency metrics of samples was calculated and samples with a standardized connectivity < −2.5 were removed, considered as an outlier. In addition, samples and genes with > 50% missing entries and genes with zero variance were identified and excluded from WGCNA analysis ( 35 ). Briefly, a correlation matrix of expression values was constructed using pairwise bi-weight mid-correlation coefficients between all pairs of genes across the selected samples.…”

“…Therefore, non-preserved modules between healthy and disease samples are important candidates for biological investigation of a disease such as COVID-19. The module preservation approach of WGCNA is valuable for differential network analysis ( 17 , 34 ) and has been successfully used for several human ( 35 – 37 ) and animal ( 16 , 38 ) diseases.…”

Hematological and gene co-expression network analyses of high-risk beef cattle defines immunological mechanisms and biological complexes involved in bovine respiratory disease and weight gain

“…An adjacency matrix was constructed from the calculated signed Pearson coefficients between all genes across all samples. We utilized signed networks as they better capture gene expression trends (up- and down-regulation) and classify co-expressed gene modules which improve the ability to identify functional enrichment, when compared to unsigned networks [24,35,36,37]. Soft thresholding was used to calculate the power parameter (β) required to exponentially raise the adjacency matrix, to reach a scale-free topology fitting index (R 2 ) of >80%; β = 8 was selected for this study.…”

“…As gene expression operates in tandem with biological regulatory networks and complexes, investigation of gene co-expression levels may reveal transcriptional coordination, distinguish protein production relationships, and measure cellular composition and function relevant to specific disease states such as BRD [21,22]. This analysis approach falls into the field of systems biology, where, in contrast to reductionist biology, molecular components are pieced and scaled together to better understand disease and generate novel hypotheses [23,24]. In this respect, we sought to build networks of co-expressed genes, utilizing the full structure of previously published gene expression data [20], and discover relationships between gene expression and cellular hematological components, which may elucidate and/or further confirm genes and mechanisms related to BRD development or resistance.…”

lncRNA–miRNA–mRNA ceRNA Network Involved in Sheep Prolificacy: An Integrated Approach

“…An integrated approach is needed to decipher the large-scale data generated with high-throughput technologies. Integrated analyses can combine multilevel views of physiology data into a holistic interpretation of nonlinear molecular procedures [ 30 , 31 ]. Currently, large databases of big biological/computational data are available including interactions and records of protein functions.…”