The promise of omics for BSR LAB research
Recent review articles cover the evolution and current state ofunderstanding of BSR LAB prevalence, genetics (Bokulich and Bamforth, 2013; Sakamoto and Konings, 2003; Suzuki, 2011b) and research techniques used during investigation (Ben-Amor et al., 2007; Bokulich and Mills, 2012b; Bokulich et al., 2012a,b). Though such background knowledge is of critical importance to understanding the current issues facing the brewing field, this chapter is not meant to be exhaustive of all relevant literature history to BSR LAB. Rather, this information is used to highlight the apparent gaps in knowledge and need for the expansion of research methods into omics applications.
The ability for transcriptomics or proteomics to profile, in a rapid and high-throughput manner, how a specific microbe grows under defined conditions and/or provide information on a microbial community's genetics, activities and ecology means that these omic approaches can effectively balance the interests of academia and industry, and overcome the problem of understanding BSR LAB variability. To date, research into BSR LAB has often failed to provide data of equal value to research investigators and brewers tasked with carrying out detection of contaminating BSR LAB. For example, detailed study of genetic or physiological stress response mechanisms of BSR LAB is of great value to LAB and brewing research writ large; however, these data alone present little utility to individual brewers. Further, the targeted analysis ofjust a few genes, or one physiological stressor in few specific isolates, has provided only minimal and incremental expansion to our current knowledge regarding LAB. Most importantly, findings from targeted-analysis experiments are frequently inconsistent for all BSR LAB, thus curtailing the value of these data from both academic and industry perspectives.
Omics approaches have proven to be a powerful way to investigate LAB genetic and metabolic diversity (Claesson et al., 2007; Horvath et al., 2009; Marakova and Koonin, 2007), and when applied broadly, produce large amounts of data that can be mined to give statistically relevant genetic or metabolic markers for beer spoilage that could be effectively screened within breweries. Secondly, these approaches help distinguish potentially helpful LAB from BSR LAB for use in specialty brews, by correlating limited beer-growth ability with desirable genetic or metabolic traits, without having to develop optimal strains through the use of laborious genetic modification techniques. The meta data that is produced from omics approaches thus allows for the conversion of information obtained by broad-scale or community-analysis of BSR LAB to specific application required for application in the brewery.