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

Abstract

Bovine respiratory disease (BRD), the leading disease complex in beef cattle production systems, remains highly elusive regarding diagnostics and disease prediction. Previous research has employed cellular and molecular techniques to describe hematological and gene expression variation that coincides with BRD development. Here, we utilized weighted gene co-expression network analysis (WGCNA) to leverage total gene expression patterns from cattle at arrival and generate hematological and clinical trait associations to describe mechanisms that may predict BRD development. Gene expression counts of previously published RNA-Seq data from 23 cattle (2017; n = 11 Healthy, n = 12 BRD) were used to construct gene co-expression modules and correlation patterns with complete blood count (CBC) and clinical datasets. Modules were further evaluated for cross-populational preservation of expression with RNA-Seq data from 24 cattle in an independent population (2019; n = 12 Healthy, n = 12 BRD). Genes within well-preserved modules were subject to functional enrichment analysis for significant Gene Ontology terms and pathways. Genes which possessed high module membership and association with BRD development, regardless of module preservation (“hub genes”), were utilized for protein-protein physical interaction network and clustering analyses. Five well-preserved modules of co-expressed genes were identified. One module (“steelblue”), involved in alpha-beta T-cell complexes and Th2-type immunity, possessed significant correlation with increased erythrocytes, platelets, and BRD development. One module (“purple”), involved in mitochondrial metabolism and rRNA maturation, possessed significant correlation with increased eosinophils, fecal egg count per gram, and weight gain over time. Fifty-two interacting hub genes, stratified into 11 clusters, may possess transient function involved in BRD development not previously described in literature. This study identifies co-expressed genes and coordinated mechanisms associated with BRD, which necessitates further investigation in BRD-prediction research.

Influence of the At-Arrival Host Transcriptome on Bovine Respiratory Disease Incidence during Backgrounding

“…Our objective in this study was to identify differences in gene expression and associated host-driven biological systems that may be impacted by marketing decisions and how these influence eventual BRD morbidity after backgrounding arrival, in order to explore potential mechanisms of BRD development or resistance that may be leveraged in future studies. While both our research group and others have used the blood RNA-Seq methodology to identify potential predictive markers and mechanisms related to BRD [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ], this study, to our knowledge, is one of the first to utilize said technologies to evaluate how marketing decisions, with relationship to BRD, influence inflammatory- and immune-mediated mechanisms in cattle. Importantly, exposure to an auction market setting and an order-buyer facility for only three days was associated with the differential expression of 2961 genes representing 113 biological processes.…”

Characterizing the influence of various antimicrobials used for metaphylaxis against bovine respiratory disease on host transcriptome responses

“…Trials utilizing tissue samples such as lung, lymph nodes, and tonsils have demonstrated how each viral and bacterial agent acts as a pathogen, causing clinical disease ( 11 , 12 ). More recently, the whole blood transcriptome has been investigated for potential predictive molecules, protective immunological mechanisms, and regulatory patterns of BRD in beef cattle ( 13 , 14 , 36 , 37 ). Whole genome sequencing has also been used to examine bacterial transcriptome profiles and identify ARGs and the drug classifications of which species have developed resistance ( 10 , 38 , 39 ).…”