Developing a microbial community structure index (MCSI) as an approach to evaluate and optimize bioremediation performance.

Much attention is placed on organohalide-respiring bacteria (OHRB), such as Dehalococcoides, during the design and performance monitoring of chlorinated solvent bioremediation systems. However, many OHRB cannot function effectively without the support of a diverse group of other microbial community members (MCMs), who play key roles fermenting organic matter into more readily useable electron donors, producing corrinoids such as vitamin B12, or facilitating other important metabolic processes or biochemical reactions. While it is known that certain MCMs support dechlorination, a metric considering their contribution to bioremediation performance has yet to be proposed. Advances in molecular biology tools offer an opportunity to better understand the presence and activity of specific microbes, and their relation to bioremediation performance. In this paper, we test the hypothesis that a specific microbial consortium identified within 16S ribosomal ribonucleic acid (rRNA) gene next generation sequencing (NGS) data can be predictive of contaminant degradation rates. Field-based data from multiple contaminated sites indicate that increasing relative abundance of specific MCMs correlates with increasing first-order degradation rates. Based on these results, we present a framework for computing a simplified metric using NGS data, the Microbial Community Structure Index, to evaluate the adequacy of the microbial ecosystem during assessment of bioremediation performance.