Application of Functional Genomics for Bovine Respiratory Disease Diagnostics

Abstract

Bovine respiratory disease (BRD) is the most common economically important disease affecting cattle. For developing accurate diagnostics that can predict disease susceptibility/resistance and stratification, it is necessary to identify the molecular mechanisms that underlie BRD. To study the complex interactions among the bovine host and the multitude of viral and bacterial pathogens, as well as the environmental factors associated with BRD etiology, genome-scale high-throughput functional genomics methods such as microarrays, RNA-seq, and proteomics are helpful. In this review, we summarize the progress made in our understanding of BRD using functional genomics approaches. We also discuss some of the available bioinformatics resources for analyzing high-throughput data, in the context of biological pathways and molecular interactions. Although resources for studying host response to infection are avail-able, the corresponding information is lacking for majority of BRD pathogens, impeding progress in identifying diagnostic signatures for BRD using functional genomics approaches.

Keywords: AgBase; bovine genome atlas; bovine respiratory disease (BRD); functional genomics; host–pathogen interaction database (HPIDB).

Figure 1

A flowchart describing functional genomics-based approach for BRD diagnostics. The availability of sequenced genomes for bovine and BRD pathogens enables the study of genome expression using various high-throughput approaches such as microarrays, RNA-seq, and proteomics, which often generate a list of genes/proteins. Continuous updates to existing structural and functional annotations enhance the quality of functional genomics findings. At the core of these annotation approaches as well as analysis of functional genomics data in the context of biological pathways and networks is the necessary bioinformatics analysis and resources. An integrated systems biology framework for analyzing functional genomics data is expected to identify accurate biomarkers for prediction of BRD resistance and susceptibility, as well as disease stratification.