Microbial reductive dechlorination of polychlorinated biphenyls (PCBs) plays a major role in detoxifying anoxic contaminated freshwater and marine sediments from PCBs. Known members of the phylum Chloroflexi are typically responsible for this activity in freshwater sediments, whereas less is known about the microorganisms responsible for this activity in marine sediments. PCB-respiring activities were detected in PCB-impacted marine sediments of the Venice Lagoon. The aim of this work was to identify the indigenous organohalide-respiring microorganisms in such environments and assess their dechlorination specificity against spiked Aroclor™ 1254 PCBs under laboratory conditions resembling the in situ biogeochemistry. High PCB dechlorination activities (from 150 ± 7 to 380 ± 44 μmol of chlorine removed kg−1 week−1) were detected in three out of six sediments sampled from different locations of the lagoon. An uncultured non-Dehalococcoides phylotype of the class Dehalococcoidia closely related to Dehalobium chlorocoercia DF-1, namely phylotype VLD-1, was detected and enriched up to 109 16S rRNA gene copies per gram of sediment where dechlorination activities were higher and 25-4/24-4 and 25-2/24-2/4-4 chlorobiphenyls (CB) accumulated as the main tri-/dichlorinated products. Conversely, a different phylotype closely related to the SF1/m-1 clade, namely VLD-2, also enriched highly where lower dechlorination activity and the accumulation of 25-3 CB as main tri-chlorinated product occurred, albeit in the simultaneous presence of VLD-1. Both phylotypes showed growth yields higher or comparable to known organohalide respirers and neither phylotypes enriched in sediment cultures not exhibiting dechlorination. These findings confirm the presence of different PCB-respiring microorganisms in the indigenous microbial communities of Venice Lagoon sediments and relate two non-Dehalococcoides phylotypes of the class Dehalococcoidia to different PCB dechlorination rates and specificities.

Metabolites produced in the human gut are known modulators of host immunity. However, large-scale identification of metabolite–host receptor interactions remains a daunting challenge. Here, we employed computational approaches to identify 983 potential metabolite–target interactions using the Inflammatory Bowel Disease (IBD) cohort dataset of the Human Microbiome Project 2 (HMP2). Using a consensus of multiple machine learning methods, we ranked metabolites based on importance to IBD, followed by virtual ligand-based screening to identify possible human targets and adding evidence from compound assay, differential gene expression, pathway enrichment, and genome-wide association studies. We confirmed known metabolite–target pairs such as nicotinic acid–GPR109a or linoleoyl ethanolamide–GPR119 and inferred interactions of interest including oleanolic acid–GABRG2 and alpha-CEHC–THRB. Eleven metabolites were tested for bioactivity in vitro using human primary cell-types. By expanding the universe of possible microbial metabolite–host protein interactions, we provide multiple drug targets for potential immune-therapies.