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Deep sequencing of DNA and mRNA

Genome sequencing, or in the case of microbial community profiling, meta-genome sequencing, provides the entire genome or identity of each organism in the sample under analysis (Table 9.1). The amount of genetic information obtained by this technique is exponentially greater than provided by targeted-sequence analysis (i.e. of housekeeping genes or 16S rDNA). Given that a small handful of genes have not yet proven adequate to distinguish between BSR and non-BSR LAB, the wealth of data from deep DNA and mRNA sequencing is critical for better understanding of the total genetic character and higher-level metabolic regulation that differentiates these two groups of organisms and those LAB able to provide helpful fermentation for craft beers. Further, emerging patterns of species- or genus-level genetic content may be identified and then incorporated into routine brewery-level diagnostic approaches.

Applications of transcriptomics or metatran- scriptomics, or the profiling of the genetic expression (mRNA) within an organism or community, is by far the most accurate means of studying genetic pathways required for growth in a given condition, the interactions between members of a community such as quorum signalling, and overall process and stress regulation mechanisms (Bokulich et al., 2012a,b; Simon and Daniel, 2011; Warnecke and Hess, 2009). (Meta)transcriptomics builds upon genomics or metagenomics to reveal what genetic content is specifically active and therefore important for growth and/or activity of a BSR LAB isolate on its own or in a microbial community (Bron et al., 2012; Table 9.1). Only three transcriptomic studies of BSR LAB isolates have been completed to date: on L. brevis BSO 464 in degassed and gassed beer, and on P. claus- senii ATCC BAA-344T during growth in beer with undetermined dissolved CO2 content and on both organisms grown in the presence of growth-limiting hop concentrations (Bergsveinson et al., 2016a,b; Pittet et al., 2013). These studies have revealed insights into not only the complexity of BSR LAB adaptation to the beer environment, but have also confirmed the importance of plasmids for the beer-growth phenotype. Further, these two studies indicate that cell membrane modification and nutrient scavenging (and general membrane transport) are critical responses to the beer environment, and further confirm the importance of biogenic amines production and metabolism as a common hallmark of LAB beer spoilage (Bergsveinson et al., 2016b; Geissler et al., 2016; Izquierdo-Oulido et al., 1996; Kalac et al., 2002). Overall, transcriptomic studies are beginning to reveal genetic adaptations shared by BSR LAB and indicate important next-step investigation efforts.

Deep-sequencing applications represent the current interface of academic research and industrial interests in the brewing field because though they are readily applied in a research setting, they do not presently lend themselves to routine use within the brewery. However, these technologies continually decrease in cost, and are currently being used in clinical settings, making it reasonable to predict that these methods will become part of routine practice in a variety of fields, including the brewing industry. Until such time, support of current academic research by the brewing industry is important, as omics data has the power to delineate specific markers for LAB beer spoilage ability, allowing for development of better detection methodology for brewery use.

Table 9.1 Omicsa

Technology and purpose





Provides the genetic profile of an organism

DNA extracted from single organism

The entire DNA content is sequenced

Genomic DNA sequence of an organism. Basis for comparing gene content between and among organisms


Details organisms present in a bacterial community and the relative abundance of community members

DNA extracted from a community - a sample with multiple organisms present

One genetic marker (e.g. 16S rDNA) that defines a species/ genus. These sequences are often termed operational taxonomic unit (OTU)

Relative abundance and identity of each OTU that comprises that community


Determines which genetic pathways are important for growth under X condition(s). Changes in expression over time can be detailed (many other questions can be answered)

Extracted messenger RNA (mRNA) from single organism growing under condition(s) of interest

All mRNA sequences extracted

mRNA transcripts are mapped to the genes they originate from and quantified. Thus, it is possible to know what genes or pathways are being highly expressed. This methodology integrates with the simultaneous or previous application of genomics


Tracks how a bacterial community respond to changes in X condition(s). Determines if specific genetic pathways are expressed in specific conditions

Extracted messenger RNA (mRNA) from community/ environmental sample

All mRNA extracted (expression) and total rRNA (abundance)

mRNA sequences are mapped to specific pathways and pathway function is classified. Similar rRNA sequences (OTUs) are grouped to determine relative abundance of community members and how these abundances might shift with treatment. This methodology integrates with the simultaneous or previous application of (meta) genomics


Characterizes the structure/ function/identity/interaction of proteins in a sample at X time, under X condition(s)

Purified proteins from sample of interest at a given time point

Depends on the intention of study - e.g. studying protein-protein interaction or need to characterize the type of proteins present

Detailed information on the nature of proteins produced in a sample. This methodology can be integrated with the application of transcriptomics or metabolomics


Discovery of all proteins present in a community/environmental sample

Purified proteins from community at given time point

Structure or specific molecular signatures of protein(s)

Discovery based-approach: catalogues all proteins present. Potential protein ‘biomarkers’ - a protein that is indicative of a specific physiological state or growth ability. This methodology can be integrated with application of metatranscriptomics or metabolomics


Characterization of specific chemical signals/small molecules that characterize a specific metabolic or chemical process

Total isolated metabolites from a sample (intermediate molecules of metabolism)

Structure, mass/size or polarity of each metabolite to determine identity and function

Total metabolite characterization and quantitation gives a ‘snapshot’ of cell physiology at a given time. This methodology can be integrated with (meta)genomics, (meta)transcriptomics, and (meta) proteomics


Characterization of the total lipid content (lipidomics) within an organism or community sample

Extraction of specific lipid classes (i.e. glycerophospholipids, fatty acids, cholesteryl esters, glycerolipids, sterols)

Structure, mass/size, polarity of lipid samples

Determination of lipid profile of a cell in response to a given sample. This methodology can be integrated with (meta)genomics, (meta)transcriptomics, and (meta) proteomics

'Omics is used denote a study of the totality of something (e.g. genomics, the total genetic content of an organism).

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