Dataset Information

Identification and Removal of Potential Contaminants in 16S rRNA Gene Sequence Data Sets from Low-Microbial-Biomass Samples: an Example from Mosquito Tissues.

ABSTRACT: The bacterial microbiota of the mosquito influences numerous physiological processes of the host. As low-microbial-biomass ecosystems, mosquito tissues are prone to contamination from the laboratory environment and from reagents commonly used to isolate DNA from tissue samples. In this report, we analyzed nine 16S rRNA data sets, including new data obtained by us, to gain insight into the impact of potential contaminating sequences on the composition, diversity, and structure of the mosquito tissue microbial community. Using a clustering-free approach based on the relative abundance of amplicon sequence variants (ASVs) in tissue samples and negative controls, we identified candidate contaminating sequences that sometimes differed from, but were consistent with, results found using established methodologies. Some putative contaminating sequences belong to bacterial taxa previously identified as contaminants that are commonly found in metagenomic studies but that have also been identified as part of the mosquito core microbiota, with putative physiological relevance for the host. Using different relative abundance cutoffs, we show that contaminating sequences have a significant impact on tissue microbiota diversity and structure analysis. IMPORTANCE The study of tissue-associated microbiota from mosquitoes (primarily from the gut) has grown significantly in the last several years. Mosquito tissue samples represent a challenge for researchers given their low microbial biomass and similar taxonomic composition commonly found in the laboratory environment and in molecular reagents. Using new and published data sets that identified mosquito tissue microbiota from gut and reproductive tract tissues (and their respective negative controls), we developed a simple method to identify contamination microbiota. This approach uses an initial taxonomic identification without operational taxonomic unit (OTU) clustering and evaluates the relative abundance of control sample sequences, allowing the identification and removal of purported contaminating sequences in data sets obtained from low-microbial-biomass samples. While it was exemplified with the analysis of tissue microbiota from mosquitos, it can be extended to other data sets dealing with similar technical artifacts.

SUBMITTER: Diaz S

PROVIDER: S-EPMC8265668 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Microbial communities are commonly studied using culture-independent methods, such as 16S rRNA gene sequencing. However, one challenge in accurately characterizing microbial communities is exogenous bacterial DNA contamination, particularly in low-microbial-biomass niches. Computational approaches to identify contaminant sequences have been proposed, but their performance has not been independently evaluated. To identify the impact of decreasing microbial biomass on polymicrobial 16S rRNA gene sequencing experiments, we created a mock microbial community dilution series. We evaluated four computational approaches to identify and remove contaminants, as follows: (i) filtering sequences present in a negative control, (ii) filtering sequences based on relative abundance, (iii) identifying sequences that have an inverse correlation with DNA concentration implemented in Decontam, and (iv) predicting the sequence proportion arising from defined contaminant sources implemented in SourceTracker. As expected, the proportion of contaminant bacterial DNA increased with decreasing starting microbial biomass, with 80.1% of the most diluted sample arising from contaminant sequences. Inclusion of contaminant sequences led to overinflated diversity estimates and distorted microbiome composition. All methods for contaminant identification successfully identified some contaminant sequences, which varied depending on the method parameters used and contaminant prevalence. Notably, removing sequences present in a negative control erroneously removed >20% of expected sequences. SourceTracker successfully removed over 98% of contaminants when the experimental environments were well defined. However, SourceTracker misclassified expected sequences and performed poorly when the experimental environment was unknown, failing to remove >97% of contaminants. In contrast, the Decontam frequency method did not remove expected sequences and successfully removed 70 to 90% of the contaminants.IMPORTANCE The relative scarcity of microbes in low-microbial-biomass environments makes accurate determination of community composition challenging. Identifying and controlling for contaminant bacterial DNA are critical steps in understanding microbial communities from these low-biomass environments. Our study introduces the use of a mock community dilution series as a positive control and evaluates four computational strategies that can identify contaminants in 16S rRNA gene sequencing experiments in order to remove them from downstream analyses. The appropriate computational approach for removing contaminant sequences from an experiment depends on prior knowledge about the microbial environment under investigation and can be evaluated with a dilution series of a mock microbial community.

Project description:High-throughput phylogenetic 16S rRNA gene analysis has permitted to thoroughly delve into microbial community complexity and to understand host-microbiota interactions in health and disease. The analysis comprises sample collection and storage, genomic DNA extraction, 16S rRNA gene amplification, high-throughput amplicon sequencing and bioinformatic analysis. Low biomass microbiota samples (e.g. biopsies, tissue swabs and lavages) are receiving increasing attention, but optimal standardization for analysis of low biomass samples has yet to be developed. Here we tested the lower bacterial concentration required to perform 16S rRNA gene analysis using three different DNA extraction protocols, three different mechanical lysing series and two different PCR protocols. A mock microbiota community standard and low biomass samples (108, 107, 106, 105 and 104 microbes) from two healthy donor stools were employed to assess optimal sample processing for 16S rRNA gene analysis using paired-end Illumina MiSeq technology. Three DNA extraction protocols tested in our study performed similar with regards to representing microbiota composition, but extraction yield was better for silica columns compared to bead absorption and chemical precipitation. Furthermore, increasing mechanical lysing time and repetition did ameliorate the representation of bacterial composition. The most influential factor enabling appropriate representation of microbiota composition remains sample biomass. Indeed, bacterial densities below 106 cells resulted in loss of sample identity based on cluster analysis for all tested protocols. Finally, we excluded DNA extraction bias using a genomic DNA standard, which revealed that a semi-nested PCR protocol represented microbiota composition better than classical PCR. Based on our results, starting material concentration is an important limiting factor, highlighting the need to adapt protocols for dealing with low biomass samples. Our study suggests that the use of prolonged mechanical lysing, silica membrane DNA isolation and a semi-nested PCR protocol improve the analysis of low biomass samples. Using the improved protocol we report a lower limit of 106 bacteria per sample for robust and reproducible microbiota analysis.

Project description:The bacterial microbiome of human body sites, previously considered sterile, remains highly controversial because it can be challenging to isolate signal from noise when low-biomass samples are being analyzed. We tested the hypothesis that stochastic sequencing noise, separable from reagent contamination, is generated during sequencing on the Illumina MiSeq platform when DNA input is below a critical threshold. We first purified DNA from serial dilutions of Pseudomonas aeruginosa and from negative controls using three DNA purification kits, quantified input using droplet digital PCR, and then sequenced the 16S rRNA gene in four technical replicates. This process identified reproducible contaminant signal that was separable from an irreproducible stochastic noise, which occurred as bacterial biomass of samples decreased. This approach was then applied to authentic respiratory samples from healthy individuals (n = 22) that ranged from high to ultralow bacterial biomass. Using oral rinse, bronchoalveolar lavage (BAL) fluid, and exhaled breath condensate (EBC) samples and matched controls, we were able to demonstrate (i) that stochastic noise dominates sequencing in real-world low-bacterial-biomass samples that contain fewer than 104 copies of the 16S rRNA gene per sample, (ii) that critical examination of the community composition of technical replicates can be used to separate signal from noise, and (iii) that EBC is an irreproducible sampling modality for sampling the microbiome of the lower airways. We anticipate that these results combined with suggested methods for identifying and dealing with noisy communities will facilitate increased reproducibility while simultaneously permitting characterization of potentially important low-biomass communities.IMPORTANCE DNA contamination from external sources (reagents, environment, operator, etc.) has long been assumed to be the main cause of spurious signals that appear under low-bacterial-biomass conditions. Here, we demonstrate that contamination can be separated from another, random signal generated during low-biomass-sample sequencing. This stochastic noise is not reproduced between technical replicates; however, results for any one replicate taken alone could look like a microbial community different from the controls. Using this information, we investigated respiratory samples from healthy humans and determined the narrow range of bacterial biomass where samples transition from producing reproducible microbial sequences to ones dominated by noise. We present a rigorous approach to studies involving low-bacterial-biomass samples to detect this source of noise and provide a framework for deciding if a sample is likely to be dominated by noise. We anticipate that this work will facilitate increased reproducibility in the characterization of potentially important low-biomass communities.

Project description:BackgroundCareful consideration of experimental artefacts is required in order to successfully apply high-throughput 16S ribosomal ribonucleic acid (rRNA) gene sequencing technology. Here we introduce experimental design, quality control and "denoising" approaches for sequencing low biomass specimens.ResultsWe found that bacterial biomass is a key driver of 16S rRNA gene sequencing profiles generated from bacterial mock communities and that the use of different deoxyribonucleic acid (DNA) extraction methods [DSP Virus/Pathogen Mini Kit® (Kit-QS) and ZymoBIOMICS DNA Miniprep Kit (Kit-ZB)] and storage buffers [PrimeStore® Molecular Transport medium (Primestore) and Skim-milk, Tryptone, Glucose and Glycerol (STGG)] further influence these profiles. Kit-QS better represented hard-to-lyse bacteria from bacterial mock communities compared to Kit-ZB. Primestore storage buffer yielded lower levels of background operational taxonomic units (OTUs) from low biomass bacterial mock community controls compared to STGG. In addition to bacterial mock community controls, we used technical repeats (nasopharyngeal and induced sputum processed in duplicate, triplicate or quadruplicate) to further evaluate the effect of specimen biomass and participant age at specimen collection on resultant sequencing profiles. We observed a positive correlation (r = 0.16) between specimen biomass and participant age at specimen collection: low biomass technical repeats (represented by < 500 16S rRNA gene copies/μl) were primarily collected at < 14 days of age. We found that low biomass technical repeats also produced higher alpha diversities (r = - 0.28); 16S rRNA gene profiles similar to no template controls (Primestore); and reduced sequencing reproducibility. Finally, we show that the use of statistical tools for in silico contaminant identification, as implemented through the decontam package in R, provides better representations of indigenous bacteria following decontamination.ConclusionsWe provide insight into experimental design, quality control steps and "denoising" approaches for 16S rRNA gene high-throughput sequencing of low biomass specimens. We highlight the need for careful assessment of DNA extraction methods and storage buffers; sequence quality and reproducibility; and in silico identification of contaminant profiles in order to avoid spurious results.

Dataset Information

Identification and Removal of Potential Contaminants in 16S rRNA Gene Sequence Data Sets from Low-Microbial-Biomass Samples: an Example from Mosquito Tissues.

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