Use of nCounter mRNA Profiling to Identify at-arrival Gene Expression Patterns for Predicting Bovine Respiratory Disease in Beef Cattle

Abstract

BackgroundTranscriptomics has identified at-arrival differentially expressed genes associated with bovine respiratory disease (BRD) development; however, their use as prediction molecules necessitates further evaluation. Therefore, we aimed to selectively analyze and corroborate at-arrival mRNA expression from multiple independent populations of beef cattle. In a nested case-control study, we evaluated the expression of 56 mRNA molecules from at-arrival blood samples of 234 cattle across seven populations via NanoString nCounter gene expression profiling. Analysis of mRNA was performed with nSolver Advanced Analysis software (p<0.05), comparing cattle groups based on the diagnosis of clinical BRD within 28 days of facility arrival (n=115 Healthy; n=119 BRD); BRD was further stratified for severity based on frequency of treatment and/or mortality (Treated_1, n=89; Treated_2+, n=30). Gene expression homogeneity of variance, receiver operator characteristic (ROC) curve, and decision tree analyses were performed between severity cohorts.ResultsIncreased expression of mRNAs involved in specialized pro-resolving mediator synthesis (ALOX15, HPGD), leukocyte differentiation (LOC100297044, GCSAML, KLF17), and antimicrobial peptide production (CATHL3, GZMB, LTF) were identified in Healthy cattle. BRD cattle possessed increased expression of CFB, and mRNA related to granulocytic processes (DSG1, LRG1, MCF2L) and type-I interferon activity (HERC6, IFI6, ISG15, MX1). Healthy and Treated_1 cattle were similar in terms of gene expression, while Treated_2+ cattle were the most distinct. ROC cutoffs were used to generate an at-arrival treatment decision tree, which classified 90% of Treated_2+ individuals. ConclusionsIncreased expression of complement factor B, pro-inflammatory, and type I interferon-associated mRNA hallmark the at-arrival expression patterns of cattle that develop severe clinical BRD. Here, we corroborate at-arrival mRNA markers identified in previous transcriptome studies and generate a prediction model to be evaluated in future studies. Further research is necessary to evaluate these expression patterns in a prospective manner.

CompCorona: A Web Portal for Comparative Analysis of the Host Transcriptome of PBMC and Lung SARS-CoV-2, SARS-CoV, and MERS-CoV

“…This gene is well associated with hypertension (Benjafield et al, 1998; Yamamoto et al, 2004; Danoviz et al, 2006; Grove et al, 2007) and is congruent with the rise in blood pressure during the SARS-CoV-2 infection (e.g., Ran et al, 2020; Angeli et al, 2022; Laffin et al, 2022). The deficiency of SENP2 (sentrin/sumo-specific protease 2), which is another down-regulated gene in our analysis, has been related to early mortality in mice (Qi et al, 2014), mitochondrial dysfunction (Nan et al, 2022), embryonic heart and brain development in placenta (Maruyama et al, 2016), and SMA-like phenotype (Zhang et al, 2021). Despite the little relation between newborn health and SARS-CoV-2 infection (Kyle et al, 2020), we here would like to propound this anomaly that appeared in our analysis.…”

“…HERC6 (Probable E3 ubiquitin-protein ligase 6) is a member of the HERC ubiquitin ligase family and exhibits antiviral activity induced by interferon (Paparisto et al, 2018). When HERC6 is overexpressed, it promotes the inflammatory response (Cao et al, 2022; Scott et al, 2022). It has been mentioned that this gene is overexpressed in SARS-CoV-2 infection patients (Li et al, 2021).…”

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

“…Based on our previous work, it can be inferred that host gene expression captured at facility arrival is variable across BRD severity cohorts [20,40,41]. Therefore, we assessed whether the at-arrival co-expression patterns and modules found in this study were well preserved across an RNA-Seq data set from an independent population of cattle.…”

“…BRD-associated hub gene identification and in silico protein-protein interaction and clustering analyses Hub gene identification analysis included co-expressed genes from the following modules: violet (54), orange (68), royalblue (100), mediumpurple3 (41), and steelblue (59). The k ME and GS value cutoffs within each module resulted in 24, 46, 30, 22, and 32 BRD-associated hub genes from the violet, orange, royalblue, mediumpurple3, and steelblue modules, respectively (Supplemental Table S5).…”