Quantification of microbial species in solid state fermentation samples using signature genomic sequences

Solid state fermentation processes are mediated by the collective metabolism of specialized microbial communities. Monitoring the relative abundance of dominating species is a critical task in quality control, which is traditionally done by wet lab techniques, such as quantitative PCR (qPCR). In this study, we developed a computational method to quantify microbial species in metagenomes based on their signature genomic sequences, i.e., unique k-mers. Bacterial species found in fermentation starters of a Chinese liquor producer were used as examples to demonstrate the development and application of the method. A database was constructed, comprising 562 complete genome sequences of 93 bacterial species that had been found in relevant fermentation samples. K-mers in length of 12 were extracted from each species and compared against each other to identify the ones that were unique to each species. The quantity of a species was determined by the average frequencies of unique k-mers encountered in the metagenome. Six dominating bacterial species were chosen as reporter species to test the quantification method. Four metagenome datasets were simulated, which contained various portions of sequence reads generated from the genomes of the reporter species. The amount of reads sampled from each reporter species followed a pre-determined ratio, i.e., a known relationship in relative abundance. For each simulated dataset, the cell number of each reporter species was computed based on the unique k-mers found in the metagenome. In all datasets, the computed quantities of the reporter species reflected the expected relative abundance by displaying a linear relationship with the pre-determined ratio. This demonstrates that quantification based on a set of unique k-mers is a reliable way to detect relative abundance among species. Besides industrial fermentation, this method may also be applied to areas such as wastewater treatment, microbiota analysis, etc.