Methods for transcriptomic analyses of the porcine host immune response: application to Salmonella infection using microarrays

Abstract

Technological developments in both the collection and analysis of molecular genetic data over the past few years have provided new opportunities for an improved understanding of the global response to pathogen exposure. Such developments are particularly dramatic for scientists studying the pig, where tools to measure the expression of tens of thousands of transcripts, as well as unprecedented data on the porcine genome sequence, have combined to expand our abilities to elucidate the porcine immune system. In this review, we describe these recent developments in the context of our work using primarily microarrays to explore gene expression changes during infection of pigs by Salmonella. Thus while the focus is not a comprehensive review of all possible approaches, we provide links and information on both the tools we use as well as alternatives commonly available for transcriptomic data collection and analysis of porcine immune responses. Through this review, we expect readers will gain an appreciation for the necessary steps to plan, conduct, analyze and interpret the data from transcriptomic analyses directly applicable to their research interests.

Fig. 1.

Overview of transcriptomic/bioinformatics analyses. A schematic view of approaches our collaborative group has developed to analyze porcine transcriptomic data. See text in Sections 2.3.1–2.3.7 for details.

Fig. 2.

Schematic diagram of the major parts of the ANEXdb.org website and database for porcine transcriptomic data storage and analysis. See text in Section 2.3.1 for details on the main functions of this bioinformatic resource.

Fig. 3.

An example of using hierarchical clustering of gene expression patterns to find co-expressed clusters of genes and the general functions represented by such gene clusters. Adapted from Wang et al. (2008b).

Fig. 4.

Genes with significant shed x infection interaction may be clues as to an effective immune response pathway. For example, nearly 50% of genes (215 of 448) showing high up-regulation response to infection in low shedders (LS +1.5) are in common with high down-regulated genes in Persistent shedding animals (PS −1.5). The response of such genes to infection is dependent on the class of animal (LS or PS) in which they are present. The global function of such genes may be useful in understanding variation in immune response to Salmonella.