Multivariate Analysis of Complex DNA Sequence Electropherograms for High-Throughput Quantitative Analysis of Mixed Microbial Populations

High-throughput quantification of genetically coherent units (GCUs) is essential for deciphering population dynamics and species interactions within a community of microbes. Current techniques for microbial community analyses are, however, not suitable for this kind of high-throughput application. Here, we demonstrate the use of multivariate statistical analysis of complex DNA sequence electropherograms for the effective and accurate estimation of relative genotype abundance in cell samples from mixed microbial populations. The procedure is no more labor-intensive than standard automated DNA sequencing and provides a very effective means of quantitative data acquisition from experimental microbial communities. We present results with the Campylobacter jejuni strain-specific marker gene gltA, as well as the 16S rRNA gene, which is a universal marker across bacterial assemblages. The statistical models computed for these genes are applied to genetic data from two different experimental settings, namely, a chicken infection model and a multispecies anaerobic fermentation model, demonstrating collection of time series data from model bacterial communities. The method presented here is, however, applicable to any experimental scenario where the interest is quantification of GCUs in genetically heterogeneous DNA samples.