Whole-Blood Transcriptome Analysis of Feedlot Cattle With and Without Bovine Respiratory Disease

Abstract

Bovine respiratory disease (BRD) is one of the main factors leading to morbidity and mortality in feedlot operations in North America. A complex of viral and bacterial pathogens can individually or collectively establish BRD in cattle, and to date, most disease characterization studies using transcriptomic techniques examine bronchoalveolar and transtracheal fluids, lymph node, and lung tissue as well as nasopharyngeal swabs, with limited studies investigating the whole-blood transcriptome. Here, we aimed to identify differentially expressed (DE) genes involved in the host immune response to BRD using whole blood and RNA sequencing. Samples were collected from heifers (average arrival weight = 215.0 ± 5.3 kg) with (n = 25) and without (n = 18) BRD at a commercial feedlot in Western Canada. RNAseq analysis showed a distinct whole-blood transcriptome profile between BRD and non-BRD heifers. Further examination of the DE genes revealed that those involved in the host inflammatory response and infectious disease pathways were enriched in the BRD animals, while gene networks associated with metabolism and cell growth and maintenance were downregulated. Overall, the transcriptome profile derived from whole blood provided evidence that a distinct antimicrobial peptide-driven host immune response was occurring in the animals with BRD. The blood transcriptome of the BRD animals shows similarities to the transcriptome profiles obtained from lung and bronchial lymph nodes in other studies. This suggests that the blood transcriptome is a potential diagnostic tool for the identification of biomarkers of BRD infection and can be measured in live animals and used to further understand infection and disease in cattle. It may also provide a useful tool to increase the understanding of the genes involved in establishing BRD in beef cattle and be used to investigate potential therapeutic applications.

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

“…RNA sequencing data from feedlot cattle with and without BRD were obtained from the Gene Expression Omnibus (GEO) database at the National Center for Biotechnology Information (NCBI) under the accession number of GSE162156. Moreover, clinical traits of BRD were obtained from the supplementary material section of the original paper ( Jiminez et al, 2021 ) and then filtered for useful measurements. The data included samples from the whole blood of 25 and 18 mixed-breed beef heifers with and without BRD, respectively.…”

“…An Illumina HiSeq 4000 platform was used to generate 43 paired-end (2 × 100 bp) libraries that included 18 healthy and 25 BRD samples. More information about the data can be found in the original paper ( Jiminez et al, 2021 ).…”

“…Functional genomic methods such as RNA-sequencing-based transcriptomics can provide a global gene expression profile, and their use in BRD studies can accelerate the understanding of disease mechanisms and the development of diagnosis ( Rai et al, 2015 ). Several transcriptome studies have been performed on BRD in various tissues, such as the lung, bronchial lymph nodes ( Behura et al, 2017 ; Johnston et al, 2019 ), and whole blood ( Jiminez et al, 2021 ; Scott et al, 2021 ). For example, Sun et al (2020) reported differentially expressed genes (DEGs) in the blood tissue as biomarkers to recognize BRD cattle at entry, such as MX1 , IFIT3 , ISG15 , OAS2 , and IFI6 involved in the interferon signaling pathway.…”

“…In the present study, we used previously published RNA-seq data ( Jiminez et al, 2021 ) and the WGCNA method for constructing weighted gene coexpression networks to better understand the molecular regulatory mechanisms responsible for the immune response to BRD. It should be noted that in the current study we used two distinct WGCNA methods to identify key modules, their hub genes, hub–hub genes, and regulatory factors involved in BRD: 1) module–trait relationships analysis across all samples to identify significant highly correlated modules with clinical traits of BRD, and 2) module preservation analysis between healthy samples (as reference set) and BRD samples (as test set) to identify nonpreserved modules between these conditions.…”

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

“…RNA sequencing (RNA-Seq) has become an invaluable tool for evaluated genomic and gene-byenvironment associations related to clinical disease [21,22,23]. Previous studies have detected differentially expressed genes and enriched mechanisms associated with peak clinical disease after experimental challenge with known BRD-associated pathogens and related to naturally occurring disease [24,25,26,27,28,29]. Additionally, our previous research has de ned genes and pathways related to proin ammatory mediation/resolution and antiviral defense to be increased at arrival in high-risk beef cattle that resist or succumb to naturally occurring BRD, respectively [30,31,32].…”