Mock Bacterial Community Research Articles

Improved DNA Extraction and Amplification Strategy for 16S rRNA Gene Amplicon-Based Microbiome Studies.

Next-generation sequencing technology has driven the rapid advancement of human microbiome studies by enabling community-level sequence profiling of microbiomes. Although all microbiome sequencing methods depend on recovering the DNA from a sample as a first critical step, lysis methods can be a major determinant of microbiome profile bias. Gentle enzyme-based DNA preparation methods preserve DNA quality but can bias the results by failing to open difficult-to-lyse bacteria. Mechanical methods like bead beating can also bias DNA recovery because the mechanical energy required to break tougher cell walls may shear the DNA of the more easily lysed microbes, and shearing can vary depending on the time and intensity of beating, influencing reproducibility. We introduce a non-mechanical, non-enzymatic, novel rapid microbial DNA extraction procedure suitable for 16S rRNA gene-based microbiome profiling applications that eliminates bead beating. The simultaneous application of alkaline, heat, and detergent ('Rapid' protocol) to milligram quantity samples provided consistent representation across the population of difficult and easily lysed bacteria equal to or better than existing protocols, producing sufficient high-quality DNA for full-length 16S rRNA gene PCR. The novel 'Rapid' method was evaluated using mock bacterial communities containing both difficult and easily lysed bacteria. Human fecal sample testing compared the novel Rapid method with a standard Human Microbiome Project (HMP) protocol for samples from lung cancer patients and controls. DNA recovered from both methods was analyzed using 16S rRNA gene sequencing of the V1V3 and V4 regions on the Illumina platform and the V1V9 region on the PacBio platform. Our findings indicate that the 'Rapid' protocol consistently yielded higher levels of Firmicutes species, which reflected the profile of the bacterial community structure more accurately, which was confirmed by mock community evaluation. The novel 'Rapid' DNA lysis protocol reduces population bias common to bead beating and enzymatic lysis methods, presenting opportunities for improved microbial community profiling, combined with the reduction in sample input to 10 milligrams or less, and it enables rapid transfer and simultaneous lysis of 96 samples in a standard plate format. This results in a 20-fold reduction in sample handling time and an overall 2-fold time advantage when compared to widely used commercial methods. We conclude that the novel 'Rapid' DNA extraction protocol offers a reliable alternative for preparing fecal specimens for 16S rRNA gene amplicon sequencing.

Evaluation of Metabarcoding Methods for Plant Disease Surveillance

Metabarcoding holds great potential for general plant pathogen surveillance by providing an untargeted profile of the host microbiome. However, the standard marker utilized for microbiome analysis of prokaryotes, the 16 rRNA gene, offers limited diagnostic utility as it provides phylogenetic resolution primarily at the genus level, and universal primers often co-amplify plant DNA. Here, we evaluated two recently published universal primer sets targeting the DNA gyrase and RNA polymerase ß subunit genes ( gyrB and rpoB, respectively), relative to a plant discriminating 16S primer set (799F/1115R), as disease surveillance tools. Comparative analysis of a mock bacterial community, as well as naturally infected citrus variegated chlorosis samples, indicated that the gyrB method displayed optimal performance in the amplification of DNA across a broad taxonomic spectrum of plant-pathogenic bacteria, providing resolution at the species and, in some cases, subspecies levels. It also generated high-quality datasets with minimal to no co-amplification of plant DNA, outperforming the rpoB and 16S assays in these regards. Further evaluation revealed that the gyrB method displayed an overall linear trend in the detection of several diverse bacterial pathogens; however, it was at least an order of magnitude less sensitive than a standard real-time PCR assay. Finally, we demonstrate the potential of this method to disentangle a mixed population of Pantoea spp. associated with a rice-bacterial blight outbreak and identify putative novel pathogens that would otherwise be overlooked with conventional PCR-based tests. This work represents steps toward establishing a robust, untargeted metabarcoding method for general plant disease surveillance. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Bacterial viability in the built environment of the home.

The built environment (BE) consists of human-made structures and, much like living organisms, is colonized by bacteria that make up the BE microbiome. The BE microbiome can potentially affect human health because of the constant proximity of these bacteria to humans. This has led to increasing public concern of whether the bacteria in the BE are harmful. Previous studies have used approaches based on DNA sequencing to assess the composition of the BE microbiome. However, the extent to which the bacterial DNA in the BE represents viable bacterial cells that could infect human hosts remains unknown. To address this open question we used both culture-based and culture-independent molecular methods to profile bacterial viability of the microbiomes from several BE sites. As part of an undergraduate-led project, we found that the vast majority of the bacterial DNA from the BE is not associated with viable bacteria, suggesting that most bacteria in the BE are dead. To begin to understand the determinants of bacterial viability in the BE we used mock bacterial communities to investigate the effects of temperature, relative humidity, and human interaction on bacterial viability. We found that relative humidity, temperature, and surface material did not have statistically significant effects on BE microbiome viability, but environmental exposure decreased bacterial viability. These results update our conception of the BE microbiome and begin to define the factors that affect BE microbiome viability.

Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data

Most experiments studying bacterial microbiomes rely on the PCR amplification of all or part of the gene for the 16S rRNA subunit, which serves as a biomarker for identifying and quantifying the various taxa present in a microbiome sample. Several computational methods exist for analyzing 16S amplicon sequencing. However, the most-used bioinformatics tools cannot produce high quality genus-level or species-level taxonomic calls and may underestimate the potential accuracy of these calls. We used 16S sequencing data from mock bacterial communities to evaluate the sensitivity and specificity of several bioinformatics pipelines and genomic reference libraries used for microbiome analyses, concentrating on measuring the accuracy of species-level taxonomic assignments of 16S amplicon reads. We evaluated the tools DADA2, QIIME 2, Mothur, PathoScope 2, and Kraken 2 in conjunction with reference libraries from Greengenes, SILVA, Kraken 2, and RefSeq. Profiling tools were compared using publicly available mock community data from several sources, comprising 136 samples with varied species richness and evenness, several different amplified regions within the 16S rRNA gene, and both DNA spike-ins and cDNA from collections of plated cells. PathoScope 2 and Kraken 2, both tools designed for whole-genome metagenomics, outperformed DADA2, QIIME 2 using the DADA2 plugin, and Mothur, which are theoretically specialized for 16S analyses. Evaluations of reference libraries identified the SILVA and RefSeq/Kraken 2 Standard libraries as superior in accuracy compared to Greengenes. These findings support PathoScope and Kraken 2 as fully capable, competitive options for genus- and species-level 16S amplicon sequencing data analysis, whole genome sequencing, and metagenomics data tools.

Evaluation of 96-well high-throughput DNA extraction methods for 16S rRNA gene metabarcoding.

Gaining meaningful insights into bacterial communities associated with animal hosts requires the provision of high-quality nucleic acids. Although many studies have compared DNA extraction methods for samples with low bacterial biomass (e.g. water) or specific PCR inhibitors (e.g. plants), DNA extraction bias in samples without inherent technical constraint (e.g. animal samples) has received little attention. Furthermore, there is an urgent need to identify a DNA extraction methods in a high-throughput format that decreases the cost and time for processing large numbers of samples. We here evaluated five DNA extraction protocols, using silica membrane-based spin columns and a 96-well microplate format and based on either mechanical or enzymatic lysis or a combination of both, using three bacterial mock communities and Illumina sequencing of the V4 region of the 16SrRNA gene. Our results showed that none of the DNA extraction methods fully eliminated bias associated with unequal lysis efficiencies. However, we identified a DNA extraction method with a lower bias for each mock community standard. Of these methods, those including an enzymatic lysis showed biases specific to some bacteria. Altogether, these results again demonstrate the importance of DNA extraction standardization to be able to compare the microbiome results of different samples. In this attempt, we advise for the use of the 96-well DNeasy Blood and Tissue kit (Qiagen) with a zirconia bead-beating procedure, which optimizes altogether the cost, handling time and bacteria-specific effects associated with enzymatic lysis.

Efficient taxa identification using a pangenome index.

Tools that classify sequencing reads against a database of reference sequences require efficient index data-structures. The r-index is a compressed full-text index that answers substring presence/absence, count, and locate queries in space proportional to the amount of distinct sequence in the database: [Formula: see text] space, where r is the number of Burrows-Wheeler runs. To date, the r-index has lacked the ability to quickly classify matches according to which reference sequences (or sequence groupings, i.e., taxa) a match overlaps. We present new algorithms and methods for solving this problem. Specifically, given a collection D of d documents, [Formula: see text] over an alphabet of size σ, we extend the r-index with [Formula: see text] additional words to support document listing queries for a pattern [Formula: see text] that occurs in [Formula: see text] documents in D in [Formula: see text] time and [Formula: see text] space, where w is the machine word size. Applied in a bacterial mock community experiment, our method is up to three times faster than a comparable method that uses the standard r-index locate queries. We show that our method classifies both simulated and real nanopore reads at the strain level with higher accuracy compared with other approaches. Finally, we present strategies for compacting this structure in applications in which read lengths or match lengths can be bounded.

An In Vitro Small Intestine Model Incorporating a Food Matrix and Bacterial Mock Community for Intestinal Function Testing.

Consumed food travels through the gastrointestinal tract to reach the small intestine, where it interacts with the microbiota, forming a complex relationship with the dietary components. Here we present a complex in vitro cell culture model of the small intestine that includes human cells, digestion, a simulated meal, and a microbiota represented by a bacterial community consisting of E. coli, L. rhamnosus, S. salivarius, B. bifidum, and E. faecalis. This model was used to determine the effects of food-grade titanium dioxide nanoparticles (TiO2 NPs), a common food additive, on epithelial permeability, intestinal alkaline phosphatase activity, and nutrient transport across the epithelium. Physiologically relevant concentrations of TiO2 had no effect on intestinal permeability but caused an increase in triglyceride transport as part of the food model, which was reversed in the presence of bacteria. Individual bacterial species had no effect on glucose transport, but the bacterial community increased glucose transport, suggesting a change in bacterial behavior when in a community. Bacterial entrapment within the mucus layer was reduced with TiO2 exposure, which may be due to decreased mucus layer thickness. The combination of human cells, a synthetic meal, and a bacterial mock community provides an opportunity to understand the implications of nutritional changes on small intestinal function, including the microbiota.

A Refined, Controlled 16S rRNA Gene Sequencing Approach Reveals Limited Detection of Cerebrospinal Fluid Microbiota in Children with Bacterial Meningitis.

Advances in both laboratory and computational components of high-throughput 16S amplicon sequencing (16S HTS) have markedly increased its sensitivity and specificity. Additionally, these refinements have better delineated the limits of sensitivity, and contributions of contamination to these limits, for 16S HTS that are particularly relevant for samples with low bacterial loads, such as human cerebrospinal fluid (CSF). The objectives of this work were to (i) optimize the performance of 16S HTS in CSF samples with low bacterial loads by defining and addressing potential sources of error, and (ii) perform refined 16S HTS on CSF samples from children diagnosed with bacterial meningitis and compare results with those from microbiological cultures. Several bench and computational approaches were taken to address potential sources of error for low bacterial load samples. We compared DNA yields and sequencing results after applying three different DNA extraction approaches to an artificially constructed mock-bacterial community. We also compared two postsequencing computational contaminant removal strategies, decontam R and full contaminant sequence removal. All three extraction techniques followed by decontam R yielded similar results for the mock community. We then applied these methods to 22 CSF samples from children diagnosed with meningitis, which has low bacterial loads relative to other clinical infection samples. The refined 16S HTS pipelines identified the cultured bacterial genus as the dominant organism for only 3 of these samples. We found that all three DNA extraction techniques followed by decontam R generated similar DNA yields for mock communities at the low bacterial loads representative of CSF samples. However, the limits of detection imposed by reagent contaminants and methodologic bias precluded the accurate detection of bacteria in CSF from children with culture-confirmed meningitis using these approaches, despite rigorous controls and sophisticated computational approaches. Although we did not find current DNA-based diagnostics to be useful for pediatric meningitis samples, the utility of these methods for CSF shunt infection remains undefined. Future advances in sample processing methods to minimize or eliminate contamination will be required to improve the sensitivity and specificity of these methods for pediatric meningitis. IMPORTANCE Advances in both laboratory and computational components of high-throughput 16S amplicon sequencing (16S HTS) have markedly increased its sensitivity and specificity. These refinements have better delineated the limits of sensitivity, and contributions of contamination to these limits, for 16S HTS that are particularly relevant for samples with low bacterial loads such as human cerebrospinal fluid (CSF). The objectives of this work were to (i) optimize the performance of 16S HTS in CSF samples by defining and addressing potential sources of error, and (ii) perform refined 16S HTS on CSF samples from children diagnosed with bacterial meningitis and compare results with those from microbiological cultures. We found that the limits of detection imposed by reagent contaminants and methodologic bias precluded the accurate detection of bacteria in CSF from children with culture-confirmed meningitis using these approaches, despite rigorous controls and sophisticated computational approaches.

Multi-factorial examination of amplicon sequencing workflows from sample preparation to bioinformatic analysis

BackgroundThe development of sequencing technologies to evaluate bacterial microbiota composition has allowed new insights into the importance of microbial ecology. However, the variety of methodologies used among amplicon sequencing workflows leads to uncertainty about best practices as well as reproducibility and replicability among microbiome studies. Using a bacterial mock community composed of 37 soil isolates, we performed a comprehensive methodological evaluation of workflows, each with a different combination of methodological factors spanning sample preparation to bioinformatic analysis to define sources of artifacts that affect coverage, accuracy, and biases in the resulting compositional profiles.ResultsOf the workflows examined, those using the V4-V4 primer set enabled the highest level of concordance between the original mock community and resulting microbiome sequence composition. Use of a high-fidelity polymerase, or a lower-fidelity polymerase with an increased PCR elongation time, limited chimera formation. Bioinformatic pipelines presented a trade-off between the fraction of distinct community members identified (coverage) and fraction of correct sequences (accuracy). DADA2 and QIIME2 assembled V4-V4 reads amplified by Taq polymerase resulted in the highest accuracy (100%) but had a coverage of only 52%. Using mothur to assemble and denoise V4-V4 reads resulted in a coverage of 75%, albeit with marginally lower accuracy (99.5%).ConclusionsOptimization of microbiome workflows is critical for accuracy and to support reproducibility and replicability among microbiome studies. These considerations will help reveal the guiding principles of microbial ecology and impact the translation of microbiome research to human and environmental health.

High-resolution single-molecule long-fragment rRNA gene amplicon sequencing of bacterial and eukaryotic microbial communities

Sequencing of hypervariable regions as well as internal transcribed spacer regions of ribosomal RNA genes (rDNA) is broadly used to identify bacteria and fungi, but taxonomic and phylogenetic resolution is hampered by insufficient sequencing length using high throughput, cost-efficient second-generation sequencing. We developed a method to obtain nearly full-length rDNA by assembling single DNA molecules combining DNA co-barcoding with single-tube long fragment read technology and second-generation sequencing. Benchmarking was performed using mock bacterial and fungal communities as well as two forest soil samples. All mock species rDNA were successfully recovered with identities above 99.5% compared to the reference sequences. From the soil samples we obtained good coverage with identification of more than 20,000 unknown species, as well as high abundance correlation between replicates. This approach provides a cost-effective method for obtaining extensive and accurate information on complex environmental microbial communities.

Multi-amplicon microbiome data analysis pipelines for mixed orientation sequences using QIIME2: Assessing reference database, variable region and pre-processing bias in classification of mock bacterial community samples.

Microbiome research relies on next-generation sequencing and on downstream data analysis workflows. Several manufacturers have introduced multi-amplicon kits for microbiome characterization, improving speciation, but present unique challenges for analysis. The goal of this methodology study was to develop two analysis pipelines specific to mixed-orientation reads from multi-hypervariable (V) region amplicons. A secondary aim was to assess agreement with expected abundance, considering database and variable region. Mock community sequence data (n = 41) generated using the Ion16S™ Metagenomics Kit and Ion Torrent Sequencing Platform were analyzed using two workflows. Amplicons from V2, V3, V4, V6-7, V8 and V9 were deconvoluted using a specialized plugin based on CutPrimers. A separate workflow using Cutadapt is also presented. Three reference databases (Ribosomal Database Project, Greengenes and Silva) were used for taxonomic assignment. Bray-Curtis, Euclidean and Jensen-Shannon distance measures were used to evaluate overall annotation consistency, and specific taxon agreement was determined by calculating the ratio of observed to expected relative abundance. Reads that mapped to regions V2-V9 varied for both CutPrimers and Cutadapt-based methods. Within the CutPrimers-based pipeline, V3 amplicons had the best agreement with the expected distribution, tested using global distance measures, while V9 amplicons had the worst agreement. Accurate taxonomic annotation varied by genus-level taxon and V region analyzed. For the first time, we present a microbiome analysis pipeline that employs a specialized plugin to allow microbiome researchers to separate multi-amplicon data from the Ion16S Metagenomics Kit into V-specific reads. We also present an additional analysis workflow, modified for Ion Torrent mixed orientation reads. Overall, the global agreement of amplicons with the expected mock community abundances differed across V regions and reference databases. Benchmarking data should be referenced when planning a microbiome study to consider these biases related to sequencing and data analysis for multi-amplicon sequencing kits.

Effect of bacterial DNA enrichment on detection and quantification of bacteria in an infected tissue model by metagenomic next-generation sequencing

Before implementing metagenomic next-generation sequencing (mNGS) in the routine diagnostic laboratory, several challenges need to be resolved. To address strengths and limitations of mNGS in bacterial detection and quantification in samples with overwhelming host DNA abundance, we used the pig muscle tissue spiked with a home-made bacterial mock community, consisting of four species from different phyla. From the spiked tissue, we extracted DNA using: (i) a procedure based on mechanical/chemical lysis (no bacterial DNA enrichment); (ii) the Ultra-Deep Microbiome Prep (Molzym) kit for bacterial DNA enrichment; and (iii) the same enrichment kit but replacing the original proteinase K treatment for tissue solubilization by a collagenases/thermolysin digestion and cell filtration. Following mNGS, we determined bacterial: ‘host’ read ratios and taxonomic abundance profiles. We calculated the load of each mock-community member by combining its read counts with read counts and microscopically-determined cell counts of other co-spiked bacteria. In unenriched samples, bacterial quantification and taxonomic profiling were fairly accurate but at the expense of the sensitivity of detection. The removal of ‘host’ DNA by the modified enrichment protocol substantially improved bacterial detection in comparison to the other two extraction procedures and generated less distorted taxonomic profiles as compared to the original enrichment protocol.

Deep Learning Encoding for Rapid Sequence Identification on Microbiome Data.

We present a novel approach for rapidly identifying sequences that leverages the representational power of Deep Learning techniques and is applied to the analysis of microbiome data. The method involves the creation of a latent sequence space, training a convolutional neural network to rapidly identify sequences by mapping them into that space, and we leverage the novel encoded latent space for denoising to correct sequencing errors. Using mock bacterial communities of known composition, we show that this approach achieves single nucleotide resolution, generating results for sequence identification and abundance estimation that match the best available microbiome algorithms in terms of accuracy while vastly increasing the speed of accurate processing. We further show the ability of this approach to support phenotypic prediction at the sample level on an experimental data set for which the ground truth for sequence identities and abundances is unknown, but the expected phenotypes of the samples are definitive. Moreover, this approach offers a potential solution for the analysis of data from other types of experiments that currently rely on computationally intensive sequence identification.

Measuring and mitigating PCR bias in microbiota datasets.

PCR amplification plays an integral role in the measurement of mixed microbial communities via high-throughput DNA sequencing of the 16S ribosomal RNA (rRNA) gene. Yet PCR is also known to introduce multiple forms of bias in 16S rRNA studies. Here we present a paired modeling and experimental approach to characterize and mitigate PCR NPM-bias (PCR bias from non-primer-mismatch sources) in microbiota surveys. We use experimental data from mock bacterial communities to validate our approach and human gut microbiota samples to characterize PCR NPM-bias under real-world conditions. Our results suggest that PCR NPM-bias can skew estimates of microbial relative abundances by a factor of 4 or more, but that this bias can be mitigated using log-ratio linear models.

Binary Metabolic Phenotypes and Phenotype Diversity Metrics for the Functional Characterization of Microbial Communities.

The profiling of 16S rRNA revolutionized the exploration of microbiomes, allowing to describe community composition by enumerating relevant taxa and their abundances. However, taxonomic profiles alone lack interpretability in terms of bacterial metabolism, and their translation into functional characteristics of microbiomes is a challenging task. This bottom-up approach minimally requires a reference collection of major metabolic traits deduced from the complete genomes of individual organisms, an accurate method of projecting these traits from a reference collection to the analyzed amplicon sequence variants (ASVs), and, ultimately, an approach to a microbiome-wide aggregation of predicted individual traits into physiologically relevant cumulative metrics to characterize and compare multiple microbiome samples. In this study, we extended a previously introduced computational approach for the functional profiling of complex microbial communities, which is based on the concept of binary metabolic phenotypes encoding the presence (“1”) or absence (“0”) of various measurable physiological properties in individual organisms that are termed phenotype carriers or non-carriers, respectively. Derived from complete genomes via metabolic reconstruction, binary phenotypes provide a foundation for the prediction of functional traits for each ASV identified in a microbiome sample. Here, we introduced three distinct mapping schemes for a microbiome-wide phenotype prediction and assessed their accuracy on the 16S datasets of mock bacterial communities representing human gut microbiome (HGM) as well as on two large HGM datasets, the American Gut Project and the UK twins study. The 16S sequence-based scheme yielded a more accurate phenotype predictions, while the taxonomy-based schemes demonstrated a reasonable performance to warrant their application for other types of input data (e.g., from shotgun metagenomics or qPCR). In addition to the abundance-weighted Community Phenotype Indices (CPIs) reflecting the fractional representation of various phenotype carriers in microbiome samples, we employ metrics capturing the diversity of phenotype carriers, Phenotype Alpha Diversity (PAD) and Phenotype Beta Diversity (PBD). In combination with CPI, PAD allows to classify the robustness of metabolic phenotypes by their anticipated stability in the face of potential environmental perturbations. PBD provides a promising approach for detecting the metabolic features potentially contributing to disease-associated metabolic traits as illustrated by a comparative analysis of HGM samples from healthy and Crohn’s disease cohorts.

Evaluation of Established Methods for DNA Extraction and Primer Pairs Targeting 16S rRNA Gene for Bacterial Microbiota Profiling of Olive Xylem Sap.

Next-generation sequencing has revolutionized our ability to investigate the microbiota composition of diverse and complex environments. However, a number of factors can affect the accuracy of microbial community assessment, such as the DNA extraction method, the hypervariable region of 16S rRNA gene targeted, or the PCR primers used for amplification. The aim of this study was to assess the influence of commercially available DNA extraction kits and different primer pairs to provide a non-biased vision of the composition of bacterial communities present in olive xylem sap. For that purpose, branches from “Picual” and “Arbequina” olive cultivars were used for xylem sap extraction using a Scholander chamber device. The DNA extraction protocol significantly affected xylem sap bacterial community assessment. That resulted in significant differences in alpha (Richness) and beta diversity (UniFrac distances) metrics among DNA extraction protocols, with the 12 DNA extraction kits evaluated being clustered in four groups behaving differently. Although the core number of taxa detected by all DNA extraction kits included four phyla, seven classes, 12 orders, 16 or 21 families, and 12 or 14 genera when using the Greengenes or Silva database for taxonomic assignment, respectively, some taxa, particularly those identified at low frequency, were detected by some DNA extraction kits only. The most accurate depiction of a bacterial mock community artificially inoculated on sap samples was generated when using the PowerPlant DNA extraction kit, the combination of 799F/1193R primers amplifying the hypervariable V5–V7 region, and the Silva 132 database for taxonomic assignment. The DESeq2 analysis displayed significant differences among genera abundance between the different PCR primer pairs tested. Thus, Enterobacter, Granulicatella, Prevotella, and Brevibacterium presented a significant higher abundance in all PCR protocols when compared with primer pair 799F/1193R, while the opposite was true for Pseudomonas and Pectobacterium. The methodological approach followed in this study can be useful to optimize plant-associated microbiome analysis, especially when exploring new plant niches. Some of the DNA extraction kits and PCR primers selected in this study will contribute to better characterize bacterial communities inhabiting the xylem sap of olives or other woody crop species.

Full-length 16S rRNA gene amplicon analysis of human gut microbiota using MinION\u2122 nanopore sequencing confers species-level resolution

BackgroundSpecies-level genetic characterization of complex bacterial communities has important clinical applications in both diagnosis and treatment. Amplicon sequencing of the 16S ribosomal RNA (rRNA) gene has proven to be a powerful strategy for the taxonomic classification of bacteria. This study aims to improve the method for full-length 16S rRNA gene analysis using the nanopore long-read sequencer MinION™. We compared it to the conventional short-read sequencing method in both a mock bacterial community and human fecal samples.ResultsWe modified our existing protocol for full-length 16S rRNA gene amplicon sequencing by MinION™. A new strategy for library construction with an optimized primer set overcame PCR-associated bias and enabled taxonomic classification across a broad range of bacterial species. We compared the performance of full-length and short-read 16S rRNA gene amplicon sequencing for the characterization of human gut microbiota with a complex bacterial composition. The relative abundance of dominant bacterial genera was highly similar between full-length and short-read sequencing. At the species level, MinION™ long-read sequencing had better resolution for discriminating between members of particular taxa such as Bifidobacterium, allowing an accurate representation of the sample bacterial composition.ConclusionsOur present microbiome study, comparing the discriminatory power of full-length and short-read sequencing, clearly illustrated the analytical advantage of sequencing the full-length 16S rRNA gene.

MinION Nanopore-based detection of Clavibacter nebraskensis, the corn Goss's wilt pathogen, and bacteriomic profiling of necrotic lesions of naturally-infected leaf samples.

The Goss’s bacterial wilt pathogen, Clavibacter nebraskensis, of corn is a candidate A1 quarantine organism; and its recent re-emergence and spread in the USA and Canada is a potential biothreat to the crop. We developed and tested an amplicon-based Nanopore detection system for C. nebraskensis (Cn), targeting a purine permease gene. The sensitivity (1 pg) of this system in mock bacterial communities (MBCs) spiked with serially diluted DNA of C. nebraskensis NCPPB 2581T is comparable to that of real-time PCR. Average Nanopore reads increased exponentially from 125 (1pg) to about 6000 reads (1000 pg) after a 3-hr run-time, with 99.0% of the reads accurately assigned to C. nebraskensis. Three run-times were used to process control MBCs, Cn-spiked MBCs, diseased and healthy leaf samples. The mean Nanopore reads doubled as the run-time is increased from 3 to 6 hrs while from 6 to 12 hrs, a 20% increment was recorded in all treatments. Cn-spiked MBCs and diseased corn leaf samples averaged read counts of 5,100, 11,000 and 14,000 for the respective run-times, with 99.8% of the reads taxonomically identified as C. nebraskensis. The control MBCs and healthy leaf samples had 47 and 14 Nanopore reads, respectively. 16S rRNA bacteriomic profiles showed that Sphingomonas (22.7%) and Clavibacter (21.2%) were dominant in diseased samples while Pseudomonas had only 3.5% relative abundance. In non-symptomatic leaf samples, however, Pseudomonas (20.0%) was dominant with Clavibacter at 0.08% relative abundance. This discrepancy in Pseudomonas abundance in the samples was corroborated by qPCR using EvaGreen chemistry. Our work outlines a new useful tool for diagnosis of the Goss’s bacterial wilt disease; and provides the first insight on Pseudomonas community dynamics in necrotic leaf lesions.

1207. Combining standard bacterial vaginosis treatment with cystine uptake inhibitors to block growth of Lactobacillus iners is a potential a target for shifting the cervicovaginal microbiota towards health-associated Lactobacillus crispatus-dominant communities

BackgroundCervicovaginal microbiota domination by Lactobacillus crispatus is associated with beneficial health outcomes, whereas L. iners dominance has more adverse associations. However bacterial vaginosis (BV) treatment with metronidazole (MTZ) typically leads to domination by L. iners rather than L. crispatus. L. iners differs from other lactobacilli by its inability to grow in MRS media. We hypothesized that exploring this growth difference would identify targets for selective L. iners inhibition.MethodsBacteria were grown anaerobically. Nutrient uptake and metabolism were assessed using UPLC-MS/MS and isotopically labeled substrates. Bacterial genome annotation employed Prodigal, Roary, and EggNOG. Competition experiments with mock mixed communities were analyzed by 16S rRNA gene sequencing. We confirmed result generalizability using a diverse collection of South African and North American strains and genomes.ResultsSupplementing MRS broth with L-cysteine (Cys) or L-cystine permitted robust L. iners growth, while L. crispatus grew without Cys supplementation. Despite their different growth requirements, neither species could synthesize Cys via canonical pathways. Adding the cystine uptake inhibitors S-methyl-L-cysteine (SMC, Fig 1) or seleno-DL-cystine (SDLC) blocked growth of L. iners but not other lactobacilli, suggesting L. iners lacks mechanisms other lactobacilli use to exploit complex exogenous Cys sources. Notably, cydABCD, an operon with Cys/glutathione transport and redox homeostasis activities, is absent from L. iners but present in non-iners Lactobacillus species. Consistent with possible roles for cydABCD in explaining the observed phenotypes, (1) L. iners failed to take up exogenous glutathione and (2) supplementing MRS with reducing agents permitted L. iners growth, which could be blocked by SMC or SDLC. In growth competitions testing L. iners and L. crispatus within mock BV-like communities, SMC plus MTZ outperformed MTZ alone in promoting L. crispatus dominance (Figs 2&3).Figure 1: S-methyl-L-cysteine (SMC) selectively blocks growth of L. iners but not other cervicovaginal Lactobacillus species in cysteine-supplemented MRS broth. Growth was measured by optical density and inhibition calculated relative to Cys-supplemented no-inhibitor control during exponential growth. Values displayed are median (+/- maximum/minimum) for 3 replicates from a single experiment. In all panels, representative data are shown from 1 of >=2 independent experiments for each bacterial strain and media condition. Results are representative of multiple strains for L. iners (n = 16), L. crispatus (n = 7), and L. jensenii (n = 2). Figure 2: Relative abundance of L. crispatus, L. iners, or various BV-associated bacteria in mock bacterial communities grown in rich, non-selective media with or without metronidazole (MTZ) and/or SMC. Relative abundance was determined by bacterial 16S rRNA gene sequencing. Data are shown for three representative mock communities with 5 replicates per media condition. Figure 3: Ratio of L. crispatus to other species in the mock bacterial communities depicted in Figure 2. Statistical significance determined via 1-way ANOVA of log10-transformed ratios with post-hoc Tukey test; selected pairwise comparisons are shown (***, p < 0.001). Conclusion L. iners has unique requirements for exogenous cysteine/cystine or a reduced environment for growth. Targeting cystine uptake to inhibit L. iners is a potential strategy for shifting cervicovaginal microbiota towards L. crispatus-dominant communities.Disclosures Douglas S. Kwon, MD, PhD, Day Zero Diagnostics (Consultant, Shareholder, Other Financial or Material Support, co-founder)

MetaFlye: scalable long-read metagenome assembly using repeat graphs.

Long-read sequencing technologies have substantially improved the assemblies of many isolate bacterial genomes as compared to fragmented short-read assemblies. However, assembling complex metagenomic datasets remains difficult even for state-of-the-art long-read assemblers. Here we present metaFlye, which addresses important long-read metagenomic assembly challenges, such as uneven bacterial composition and intra-species heterogeneity. First, we benchmarked metaFlye using simulated and mock bacterial communities and show that it consistently produces assemblies with better completeness and contiguity than state-of-the-art long-read assemblers. Second, we performed long-read sequencing of the sheep microbiome and applied metaFlye to reconstruct 63 complete or nearly complete bacterial genomes within single contigs. Finally, we show that long-read assembly of human microbiomes enables the discovery of full-length biosynthetic gene clusters that encode biomedically important natural products.