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Managing Contamination and Diverse Bacterial Loads in 16S rRNA Deep Sequencing of Clinical Samples: Implications of the Law of Small Numbers.

ABSTRACT: In this article, we investigate patterns of microbial DNA contamination in targeted 16S rRNA amplicon sequencing (16S deep sequencing) and demonstrate how this can be used to filter background bacterial DNA in diagnostic microbiology. We also investigate the importance of sequencing depth. We first determined the patterns of contamination by performing repeat 16S deep sequencing of negative and positive extraction controls. This process identified a few bacterial species dominating across all replicates but also a high intersample variability among low abundance contaminant species in replicates split before PCR amplification. Replicates split after PCR amplification yielded almost identical sequencing results. On the basis of these observations, we suggest using the abundance of the most dominant contaminant species to define a threshold in each clinical sample from where identifications with lower abundances possibly represent contamination. We evaluated this approach by sequencing of a diluted, staggered mock community and of bile samples from 41 patients with acute cholangitis and noninfectious bile duct stenosis. All clinical samples were sequenced twice using different sequencing depths. We were able to demonstrate the following: (i) The high intersample variability between sequencing replicates is caused by events occurring before or during the PCR amplification step. (ii) Knowledge about the most dominant contaminant species can be used to establish sample-specific cutoffs for reliable identifications. (iii) Below the level of the most abundant contaminant, it rapidly becomes very demanding to reliably discriminate between background and true findings. (iv) Adequate sequencing depth can be claimed only when the analysis also picks up background contamination. IMPORTANCE There has been a gradual increase in 16S deep sequencing studies on infectious disease materials. Management of bacterial DNA contamination is a major challenge in such diagnostics, particularly in low biomass samples. Reporting a contaminant species as a relevant pathogen may cause unnecessary antibiotic treatment or even falsely classify a noninfectious condition as a bacterial infection. Yet, there are few studies on how to filter contamination in clinical microbiology. Here, we demonstrate that sequencing of extraction controls will not reveal the full spectrum of contaminants that could occur in the associated clinical samples. Only the most abundant contaminant species were consistently detected, and we present how this can be used to set sample specific thresholds for reliable identifications. We believe this work can facilitate the implementation of 16S deep sequencing in diagnostic laboratories. The new data we provide on the patterns of microbial DNA contamination is also important for microbiome research.

SUBMITTER: Dyrhovden R

PROVIDER: S-EPMC8262989 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundBacterial community composition in the marine environment differs from one geographical location to another. Reports that delineate the bacterial diversity of different marine samples from geographically similar location are limited. The present study aims to understand whether the bacterial community compositions from different marine samples harbour similar bacterial diversity since these are geographically related to each other.Methods and principal findingsIn the present study, 16S rRNA deep sequencing analysis targeting V3 region was performed using Illumina bar coded sequencing. A total of 22.44 million paired end reads were obtained from the metagenomic DNA of Marine sediment, Rhizosphere sediment, Seawater and the epibacterial DNA of Seaweed and Seagrass. Diversity index analysis revealed that Marine sediment has the highest bacterial diversity and the least bacterial diversity was observed in Rhizosphere sediment. Proteobacteria, Actinobacteria and Bacteroidetes were the dominant taxa present in all the marine samples. Nearly 62-71% of rare species were identified in all the samples and most of these rare species were unique to a particular sample. Further taxonomic assignment at the phylum and genus level revealed that the bacterial community compositions differ among the samples.ConclusionThis is the first report that supports the fact that, bacterial community composition is specific for specific samples irrespective of its similar geographical location. Existence of specific bacterial community for each sample may drive overall difference in bacterial structural composition of each sample. Further studies like whole metagenomic sequencing will throw more insights to the key stone players and its interconnecting metabolic pathways. In addition, this is one of the very few reports that depicts the unexplored bacterial diversity of marine samples (Marine sediment, Rhizosphere sediment, Seawater) and the host associated marine samples (Seaweed and Seagrass) at higher depths from uncharacterised coastal region of Palk Bay, India using next generation sequencing technology.

Project description:The 16S ribosomal RNA gene is the most widely used marker gene in microbial ecology. Counts of 16S sequence variants, often in PCR amplicons, are used to estimate proportions of bacterial and archaeal taxa in microbial communities. Because different organisms contain different 16S gene copy numbers (GCNs), sequence variant counts are biased towards clades with greater GCNs. Several tools have recently been developed for predicting GCNs using phylogenetic methods and based on sequenced genomes, in order to correct for these biases. However, the accuracy of those predictions has not been independently assessed. Here, we systematically evaluate the predictability of 16S GCNs across bacterial and archaeal clades, based on ??6,800 public sequenced genomes and using several phylogenetic methods. Further, we assess the accuracy of GCNs predicted by three recently published tools (PICRUSt, CopyRighter, and PAPRICA) over a wide range of taxa and for 635 microbial communities from varied environments. We find that regardless of the phylogenetic method tested, 16S GCNs could only be accurately predicted for a limited fraction of taxa, namely taxa with closely to moderately related representatives (?15% divergence in the 16S rRNA gene). Consistent with this observation, we find that all considered tools exhibit low predictive accuracy when evaluated against completely sequenced genomes, in some cases explaining less than 10% of the variance. Substantial disagreement was also observed between tools (R2<0.5) for the majority of tested microbial communities. The nearest sequenced taxon index (NSTI) of microbial communities, i.e., the average distance to a sequenced genome, was a strong predictor for the agreement between GCN prediction tools on non-animal-associated samples, but only a moderate predictor for animal-associated samples. We recommend against correcting for 16S GCNs in microbiome surveys by default, unless OTUs are sufficiently closely related to sequenced genomes or unless a need for true OTU proportions warrants the additional noise introduced, so that community profiles remain interpretable and comparable between studies.

Project description:BACKGROUND:High-throughput sequencing of bacterial 16S rRNA gene (16S-seq) is a useful and common method for studying bacterial community structures. However, contamination of the 16S rRNA genes from the mitochondrion and plastid hinders the sensitive bacterial 16S-seq in plant microbiota profiling, especially for some plant species such as rice. To date, efficiently mitigating such host contamination without a bias is challenging in 16S rRNA gene-based amplicon sequencing. RESULTS:We developed Cas-16S-seq method to reduce abundant host contamination for plant microbiota profiling. This method utilizes the Cas9 nuclease and specific guide RNA (gRNA) to cut 16S rRNA targets during library construction, thereby removing host contamination in 16S-seq. We used rice as an example to validate the feasibility and effectiveness of Cas-16S-seq. We established a bioinformatics pipeline to design gRNAs that specifically target rice 16S rRNA genes without bacterial 16S rRNA off-targets. We compared the effectiveness of Cas-16S-seq with that of the commonly used 16S-seq method for artificially mixed 16S rRNA gene communities, paddy soil, rice root, and phyllosphere samples. The results showed that Cas-16S-seq substantially reduces the fraction of rice 16S rRNA gene sequences from 63.2 to 2.9% in root samples and from 99.4 to 11.6% in phyllosphere samples on average. Consequently, Cas-16S-seq detected more bacterial species than the 16S-seq in plant samples. Importantly, when analyzing soil samples, Cas-16S-seq and 16S-seq showed almost identical bacterial communities, suggesting that Cas-16S-seq with host-specific gRNAs that we designed has no off-target in rice microbiota profiling. CONCLUSION:Our Cas-16S-seq can efficiently remove abundant host contamination without a bias for 16S rRNA gene-based amplicon sequencing, thereby enabling deeper bacterial community profiling with a low cost and high flexibility. Thus, we anticipate that this method would be a useful tool for plant microbiomics. Video Abstract.

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Managing Contamination and Diverse Bacterial Loads in 16S rRNA Deep Sequencing of Clinical Samples: Implications of the Law of Small Numbers.

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