Geochemical and microbial community determinants of reductive dechlorination at a site biostimulated with glycerol

Abstract

Biostimulation is widely used to enhance reductive dechlorination of chlorinated ethenes in contaminated aquifers. However, the knowledge on corresponding biogeochemical responses is limited. In this study, glycerol was injected in an aquifer contaminated with cis-dichloroethene (cDCE), and geochemical and microbial shifts were followed for 265 days. Consistent with anoxic conditions and sulfate reduction after biostimulation, MiSeq 16S rRNA gene sequencing revealed temporarily increased relative abundance of Firmicutes, Bacteriodetes and sulfate reducing Deltaproteobacteria. In line with C cDCE enrichment and increased Dehalococcoides mccartyi (Dcm) numbers, dechlorination was observed toward the end of the field experiment, albeit being incomplete with accumulation of vinyl chloride. This was concurrent with (i) decreased concentrations of dissolved organic carbon (DOC), reduced relative abundances of fermenting and sulfate reducing bacteria that have been suggested to promote Dcm growth by providing electron donor (H ) and essential corrinoid cofactors, (ii) increased sulfate concentration and increased relative abundance of Epsilonproteobacteria and Deferribacteres as putative oxidizers of reduced sulfur compounds. Strong correlations of DOC, relative abundance of fermenters and sulfate reducers, and dechlorination imply the importance of syntrophic interactions to sustain robust dechlorination. Tracking microbial and environmental parameters that promote/preclude enhanced reductive dechlorination should aid development of sustainable bioremediation strategies.

NG-Tax, a highly accurate and validated pipeline for analysis of 16S rRNA amplicons from complex biomes

“…However, these efforts have arguably enhanced replicability rather than reproducibility, by providing widely adopted defaults 5 . To this end, Drummond 6 suggested that exact replication of an experiment (i.e., replicability) is less informative (although a necessary pre-requisite for any scientific endeavour) than the corroboration of findings by reproduction in different independent setups (i.e., reproducibility) 7 , because biological findings that are robust to independent methodologies are arguably more dependable than any single-track analysis 5 . This distinction is highly relevant for the field of microbial ecology, where replicability is often confused with reproducibility, which is apparent from many often non-interchangeable methodologies.…”

“…Although new clustering algorithms that reduce the influence of clustering parameters, such as a hard cutoff for cluster similarity, have been specifically developed for amplicons 17 , cluster generation is context-dependent, i.e. different datasets generate different clusters, and different algorithms may produce different endresults 5,18 . Therefore, even though the same analysis framework is used, independent studies remain incomparable at OTU level.…”

“…The consequence of this approach is that the 'quality' of the clustering of the reference set propagates to reference-picked OTUs. Clustering has limited robustness 5,18,22 , and unbalances in databases due to over-or under-representation of certain species as well as error hotspots that are not necessarily matched to the variable regions 23 , can potentially lead to a biased cluster formation, driven by non-biological factors. These effects have been previously ignored or underestimated in reference OTU picking protocols 5 .…”

“…There are several factors that can lead to the commonly observed highly regionspecific behaviour across datasets: 1) PCR bias of varying degrees 23,27,30 , 2) different regions are associated with different error profiles and different rates of chimera formation 23,31 , and 3) the actual variation contained in the sequence is dissimilar (e.g. some regions are not variable enough to differentiate between genera, while others are), which in turn can affect clustering 5 .…”

“…Methodology rather than biology has often been shown to be the largest driver of variation in microbiome studies 5,18,23,27,29,[32][33][34] , and this aspect of amplicon sequencing is increasingly addressed in literature. Nevertheless, a satisfactory solution has not been found.…”