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.

This mapping gives us an initial representation of the relative proportions of each strain, but results may be biased by the size of the genomes and their common sequences. Further tools can be applied that use statistical normalization methods to return a more accurate count of the relative amounts of each organism in the sample.

An example is provided in the figure below which is taken from a yeast-based fermentation.  The curves show the distribution of RNA from the top families (except for S. cerevisiae) during a fermentation of roughly 24 hours. This plot reflects a common pattern found in both propagation and fermentation processes. Initially, there is some level of microbial presence, but as the yeast reproduces and thrives the other microbial families decrease almost to zero. Later, after the yeast has reached a steady state and stopped reproducing, contaminants begin to increase and reach or even surpass their earlier levels.

In the specific case of ethanol fermentations from organic feedstocks, the ethanol itself kills off many contaminating organisms; however, in fermentations for specialty chemicals, the product itself may not be sufficient to slow contaminant growth. In such fermentations, understanding prevalent contaminant organisms at each fermentation phase is important for planning and executing mitigation strategies.

A thorough metagenomic analysis yields fascinating and often unexpected insights into the contaminants present during fermentation. Knowledge of toxic or inhibitory contaminants is a necessary step in improving fermentation speed and purity and maximizing yield.

Does your organization have a fermentation process that is subject to contamination?  Are you looking for rapid and efficient ways to identify the contaminants and understand their growth during the production process? Contact Mimetics and let’s discuss how we can help. 

Jacob Pritt Ph.D – Computational Biologist