Using Metagenomics To Track Biological Contamination Of Fermentations
Industrial fermentations are highly susceptible to contamination from unwanted microorganisms. These microbes may be present in incompletely cleaned fermentation tanks, and many more are present in fermentation feedstocks such as corn mash and sugarcane. Although the primary fermentation microbe is often able to quickly outcompete rival organisms, some of these contaminants can produce toxins or deplete nutrients to negatively impact fermentation efficiency and outcomes. Here we describe a method using tools from metagenomics to detect, identify, and even quantitate contaminant microorganisms present in fermentations.
To provide an unbiased exploratory analysis, we use a wide database of representative organisms from all bacterial, fungal, and viral families that have been sequenced. This database does not contain every single species and subspecies but is sufficiently detailed to accurately capture any microbial family present in a sample. If we find any concerning or intriguing families in this initial exploration, a more detailed taxon-specific database can be constructed to provide a deeper analysis.
Given a database of organisms, each read from a sample can be assigned to its lowest common ancestor in the taxonomic tree. Generic reads from common sequences will map to common nodes in the tree and give little information about specific organisms, while strain-specific sequences map to unique leaf nodes and give more useful information on the specific organisms present. The figure below shows a sample result for a taxonomic tree containing 5 organisms. This sample contains strains A, D, and E, with D being the most common and A the least common.