Screening physiological capacities of BSR LAB: contaminant versus fermentor?

It is obvious that beer spoilage is not a binary phenotype mediated by the presence/absence of one or even a few genes, as antibiotic resistance or other phenotypes like motility most often are (Schwarz et al., 2014). Instead, BSR LAB isolates exist on a scale of beer-spoiling virulence, just as LAB isolates in other industries sit on a continuum of capabilities and efficiency in performing a task (Mozzi et al., 2013). Indeed the extent to which a LAB isolate can grow and metabolize is important for its classification as either a contaminant or fer- mentor. Contaminants are considered thus because their growth is at once unexpected, and uncontrollable by the environmental conditions posed by production of a given beer; however, the damage they impose on the beer product is relative across isolates as a result of their growth ability and metabolic by-products. On the other hand, ideal LAB participants in sour beer fermentations contribute some specific flavour component(s), but do not over-produce these compounds through cellular overgrowth, as this leads to flavour imbalance (i.e. spoilage). Thus, to prevent a helpful LAB isolate from being considered a contaminant, either the genetic makeup or surrounding environment of an isolate must limit its own growth and metabolism.

The relative ability for LAB to establish rapid growth in beer or produce flavour compounds in a moderate fashion is genetically based, which strongly points to subtle differences in the genetics and metabolism of BSR LAB and fermenting LAB. However, these changes are not readily interrogated using targeted analytical methods such as MLST. To distinguish contaminant from helpful isolate, the analytical method must be able to take into account the influence of the total beer environment (i.e. available fermentable sugars and other nutrients, ethanol levels, hop levels, pH, dCO2) when describing the beer spoilage virulence of a given LAB. Thus, it is only through the use of meta-genomics, global transcriptomics and phenotype correlation, that researchers and the brewery industry will be able to effectively profile helpful, fermenting LAB for development of new beer product, as has been done in other non-brewing industries. For example, efforts are under way to preform en masse genome analysis of LAB Oeno- coccus oeni isolates relevant to the wine industry, in order to link genotype with an isolate's winemaking properties and wine characteristics (Bartowsky et al., 2011, 2014; Borneman et al., 2013a,b). Linkage analysis allows for the distinction between content diversity (specifically, gene presence/absence) and genome diversity (organization, regulation, plasmid and phage presence) and their link to overall isolate phenotype such as flavour profile produced. Importantly, this large-scale analysis ameliorates the potential bias that isolate-selection has on between-isolate comparisons (Cai et al., 2009; Pfeiler and Klaenhammer, 2007; Sun et al., 2014).

Within the brewing industry, non-academic institutions are beginning to conduct analyses similar to those ofWhite Labs Ltd (San Diego, CA, USA) who are analysing brewing yeast genomes in relation to the flavour profile of beers that the yeasts impact (Herkewitz, 2014). It is reasonable to expect that comparable analyses could be performed not only for LAB in relation to the styles and composition of beer they are able to spoil, but also for the characterization of helpful, fermenting LAB for sour beer production and for defining LAB exhibiting beneficial interactions with yeast in specialty brews.

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