This project aims to investigate the soil microbial diversity at different sites in China Petrochemical Development Corporation (CPDC) An-Shun. We especially focus on establishing a pipeline of the NGS experiments and data analysis. First we extracted total DNA from bacteria in soil and amplified their 16S rDNAs using six primer sets, which products were subject to pyrosequencing. We then developed a data analysis platform to assemble various fragments of 16S rDNA into almost full-length sequences (about 1.5kb). This novel strategy is better than those in previous study where only one or two fragments were sequenced and species identification was only accurate at genus level. Our method identifies species down to the specie level, which is more helpful for follow up applications. This report is split into two parts. One is to evaluate the sensitivity and accuracy of our approach. We tested three samples from Environmental Analysis Laboratory (EAL). Sample one contained Pseudomonas, Leginella and Flavobacterium. Our sequencing and analysis pipeline revealed 595 genera including Pseudomonas, Legionella, Chryseobacterium, Bacillus and Azomonas. That is, we accurately identified all three known genera except Flavobacterium in the sample. Instead, we found Chryseobacterium that is rather closer to Flavobacterium. To confirm our finding, we did Sanger sequencing, a traditional technology with high accuracy, and showed that the bacteria was indeed Chryseobacterium. Sample two from sediments in CPDC contained Legionella and Bacillus. Our results showed that there were 1014 genera, including Sulfurovum, Bacillus and Legionella. This sample was polluted by dioxin and we detected some dioxin-related genera, such as Spirochaeta, Dehalogenimonas, and Desulfuromonas. Sample three contained only Pseudomonas, Legionella, Chryseobacterium, and Bacillus. Our pipeline detected these bacterial species accurately without giving other species. That is, the false positive rate of our approach is low in this case. To summarize, we have established an approach with high accuracy, high sensitivity, and low false positives in identifying microbial species in soils. The second part is to investigate the bacterial communities under different dioxin concentrations. We got seven samples from four locations, i.e., soil containing pentachlorophenol (PCP), sediments, soil containing PVC, and soil from outside factory as a control sample from EAL. EAL staff collected two samples at the same location, which were of different dioxin concentrations. In sample PCP1, with a higher dioxin concentration, we found seven species of Hydrogenophaga. One of the species, Hydrogenophaga intermedia, is a known dioxin-degrading bacterium. Because this sample is the highest in dioxin concentration, the detected species may be dioxin-related bacteria. In sample PCP2, with a lower dioxin concentration, we found fifty-one species, including eight genera, i.e., Aeromonas、Bacillus、Hydrogenophaga、Marinobacterium、Pelagibius、Pelobacter、Pseudomonas and Rheinheimera. After paper searching, we found that Hydrogenophaga intermedia、Pseudomonas pseudoalcaligenes, and Pseudomonas stutzeri are dioxin-degrading bacterium. In the two sediments sample,we found many common species: Actibacter sediminis、Desulfosarcina cetonica、Desulfosarcina ovata, and Desulfosarcina variabilis. In sample PVC1, with a higher dioxin concentration, we found 140 species including fifteen genera, e.g., Acinetobacter、Bacillus、Erythrobacter、Hydrogenophaga and Marinobacter. Studies indicated that Bacillus megaterium and Hydrogenophaga intermedia are dioxin-degrading bacteria. In sample PVC2, with a lower dioxin concentration, we found twenty-six species in seven genera. In sample outside factory, we found fifteen species including seven genera. To summarize, we provided many species that they may have function related to dioxin. We investigated the bacterial community structures at different locations with different dioxin concentrations. We found nine genera highly expressed in samples with more dioxin than with less dioxin, such as Pyramidobacter、Fervidicoccus、Balnearium and Desulfobacca. We also found the bacterial community structures strongly correlate with location. The correlation between bacterial community structures at the same locations was higher than those at different locations. To summarize, we found that location determines the main structure of bacterial community. Thus, bacterial community structures may be used for specifying soil types.

pyrosequencing,dioxin