Taxonomic Precision of Different Hypervariable Regions of 16S rRNA Gene and Annotation Methods for Functional Bacterial Groups in Biological Wastewater Treatment

Abstract

High throughput sequencing of 16S rRNA gene leads us into a deeper understanding on bacterial diversity for complex environmental samples, but introduces blurring due to the relatively low taxonomic capability of short read. For wastewater treatment plant, only those functional bacterial genera categorized as nutrient remediators, bulk/foaming species, and potential pathogens are significant to biological wastewater treatment and environmental impacts. Precise taxonomic assignment of these bacteria at least at genus level is important for microbial ecological research and routine wastewater treatment monitoring. Therefore, the focus of this study was to evaluate the taxonomic precisions of different ribosomal RNA (rRNA) gene hypervariable regions generated from a mix activated sludge sample. In addition, three commonly used classification methods including RDP Classifier, BLAST-based best-hit annotation, and the lowest common ancestor annotation by MEGAN were evaluated by comparing their consistency. Under an unsupervised way, analysis of consistency among different classification methods suggests there are no hypervariable regions with good taxonomic coverage for all genera. Taxonomic assignment based on certain regions of the 16S rRNA genes, e.g. the V1&V2 regions -provide fairly consistent taxonomic assignment for a relatively wide range of genera. Hence, it is recommended to use these regions for studying functional groups in activated sludge. Moreover, the inconsistency among methods also demonstrated that a specific method might not be suitable for identification of some bacterial genera using certain 16S rRNA gene regions. As a general rule, drawing conclusions based only on one sequencing region and one classification method should be avoided due to the potential false negative results.

Taxonomic relatedness shapes bacterial assembly in activated sludge of globally distributed wastewater treatment plants

“…, the third bar of each phylum). As expected, ∼37.5% (18 genera) of the AS generalists were found as previously reported functional groups (Guo et al ., ), including three nitrifying bacteria, nine denitrifiers, two nitrogen‐fixing bacteria, three BFB, and one glycogen‐accumulating organism (GAO) (the underlined genera, Fig. ; Supporting Information http://onlinelibrary.wiley.com/doi/10.1111/1462-2920.12355/suppinfo), implicating the prevalence of a core set of functional generalists in all 50 globally collected municipal and industrial AS samples (Kwon et al ., ; Zhang et al ., ; Ibarbalz et al ., ).…”

Applications of sequencing technology in clinical microbial infection

“…At the same time, the reads from mNGS are relatively short, so it is difficult to obtain information such as the full‐length antibiotic resistance gene sequences in pathogenic microorganisms, and it is impossible to associate the drug resistance gene with the corresponding pathogenic microorganism species. Moreover, short‐read sequencing will introduce bias . Third, the detection rate of some low‐content intracellular bacteria, such as M tuberculosis , Legionella , Brucella and fungi with thick cell walls, is low.…”

The bacterial community associated with adult vine weevil (Otiorhynchus sulcatus) in UK populations growing on strawberry is dominated by Candidatus Nardonella

“…These findings are consistent with those reported previously in insect bacterial community studies, which revealed a similarly low diversity of bacterial microbiota dominated by members of the Proteobacteria phylum, compared with analogous studies on vertebrates or soil (Broderick et al., ; Fierer & Jackson, ; Vasanthakumar et al., ; Corby‐Harris et al., ; Chandler et al., ; Douglas, ; Ishak et al., ; Wong et al., ; Colman et al., ; Jones et al., ; Bansal et al., ; Yun et al., ; Gauthier et al., ; Bili et al., ; Robertson‐Albertyn et al., ). This bacterial microbiota pattern seems to be common across insect clades even when targeting different 16S rRNA gene hypervariable regions (Suzuki & Giovannoni, ; Baker et al., ; Guo et al., ; Yang et al., ) or applying different DNA extraction procedures (Martin‐Laurent et al., ). The reasons underlying such an intriguing pattern remain undetermined, although several hypotheses have been proposed to explain low microbial diversity in insects.…”