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Genome-Based Targeted Sequencing as a Reproducible Microbial Community Profiling Assay.

ABSTRACT: Current sequencing-based methods for profiling microbial communities rely on marker gene (e.g., 16S rRNA) or metagenome shotgun sequencing (mWGS) analysis. We present an approach based on a single-primer extension reaction using a highly multiplexed oligonucleotide probe pool. This approach, termed MA-GenTA (microbial abundances from genome tagged analysis), enables quantitative, straightforward, cost-effective microbiome profiling that combines desirable features of both 16S rRNA and mWGS strategies. The use of multiple probes per target genome and rigorous probe design criteria enabled robust determination of relative abundance. To test the utility of the MA-GenTA assay, probes were designed for 830 genome sequences representing bacteria present in mouse stool specimens. Comparison of the MA-GenTA data with mWGS data demonstrated excellent correlation down to 0.01% relative abundance and a similar number of organisms detected per sample. Despite the incompleteness of the reference database, nonmetric multidimensional scaling (NMDS) clustering based on the Bray-Curtis dissimilarity metric of sample groups was consistent between MA-GenTA, mWGS, and 16S rRNA data sets. MA-GenTA represents a potentially useful new method for microbiome community profiling based on reference genomes.IMPORTANCE New methods for profiling the microbial communities can create new approaches to understanding the composition and function of those communities. In this study, we combined bacterial genome-specific probe design with a highly multiplexed single primer extension reaction as a new method to profile microbial communities, using stool from various mouse strains as a test case. This method, termed MA-GenTA, was benchmarked against 16S rRNA gene sequencing and metagenome sequencing methods and delivered similar relative abundance and clustering data. Since the probes were generated from reference genomes, MA-GenTA was also able to provide functional pathway data for the stool microbiome in the assayed samples. The method is more informative than 16S rRNA analysis while being less costly than metagenome shotgun sequencing.

SUBMITTER: Benjamino J

PROVIDER: S-EPMC8546724 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Introduction:Microbiome research based on high-throughput sequencing has grown exponentially in recent years, but methodological variations can easily undermine the reproducibility across studies. Objectives:To systematically evaluate the comparability of sequencing results of 16S rRNA gene sequencing (16Ss)- and shotgun metagenomic sequencing (SMs)-based microbial community profiling in laboratories under routine conditions. Methods:We designed a multicenter study across 35 participating laboratories in China using designed mock communities and homogenized fecal samples. Results:A wide range of practices and approaches was reported by the participating laboratories. The observed microbial compositions of the mock communities in 46.2% (12/26) of the 16Ss and 82.6% (19/23) of the SMs laboratories had significant correlations with the expected result (Spearman r>0.59, P <0.05). The results from laboratories with near-identical protocols showed slight interlaboratory deviations. However, a high degree of interlaboratory deviation was found in the observed abundances of specific taxa, such as Bacteroides spp. (range: 0.3%-53.5%), Enterococci spp. (range: 0.8%-43.9%) and Fusobacterium spp. (range: 0.1%-39.8%). SMs performed better than 16Ss in detecting low-abundance bacteria (B. bifidum). The differences in DNA extraction methods, amplified regions and bioinformatics analysis tools (taxonomic classifiers and database) were important factors causing interlaboratory deviations. Addressing laboratory contamination is an urgent task because various sources of unexpected microbes were found in negative control samples. Conclusions:Well-defined control samples, such as the mock communities in this study, should be routinely used in microbiome research for monitoring potential biases. The findings in this study will provide guidance in the choice of more reasonable operating procedures to minimize potential methodological biases in revealing human microbiota composition.

Project description:Microbial soil communities form commensal relationships with plants to promote the growth of both parties. The optimization of plant-microbe interactions to advance sustainable agriculture is an important field in agricultural research. However, investigation in this field is hindered by a lack of model microbial community systems and efficient approaches for building these communities. Two key challenges in developing standardized model communities are maintaining community diversity over time and storing/resuscitating these communities after cryopreservation, especially considering the different growth rates of organisms. Here, a model synthetic community (SynCom) of 16 soil microorganisms commonly found in the rhizosphere of diverse plant species, isolated from soil surrounding a single switchgrass plant, has been developed and optimized for in vitro experiments. The model soil community grows reproducibly between replicates and experiments, with a high community α-diversity being achieved through growth in low-nutrient media and through the adjustment of the starting composition ratios for the growth of individual organisms. The community can additionally be cryopreserved with glycerol, allowing for easy replication and dissemination of this in vitro system. Furthermore, the SynCom also grows reproducibly in fabricated ecosystem devices (EcoFABs), demonstrating the application of this community to an existing in vitro plant-microbe system. EcoFABs allow reproducible research in model plant systems, offering the precise control of environmental conditions and the easy measurement of plant microbe metrics. Our results demonstrate the generation of a stable and diverse microbial SynCom for the rhizosphere that can be used with EcoFAB devices and can be shared between research groups for maximum reproducibility. IMPORTANCE Microbes associate with plants in distinct soil communities to the benefit of both the soil microbes and the plants. Interactions between plants and these microbes can improve plant growth and health and are therefore a field of study in sustainable agricultural research. In this study, a model community of 16 soil bacteria has been developed to further the reproducible study of plant-soil microbe interactions. The preservation of the microbial community has been optimized for dissemination to other research settings. Overall, this work will advance soil microbe research through the optimization of a robust, reproducible model community.

Project description:BACKGROUND: Analyses of DNA sequences from cultivated microorganisms have revealed genome-wide, taxa-specific nucleotide compositional characteristics, referred to as genome signatures. These signatures have far-reaching implications for understanding genome evolution and potential application in classification of metagenomic sequence fragments. However, little is known regarding the distribution of genome signatures in natural microbial communities or the extent to which environmental factors shape them. RESULTS: We analyzed metagenomic sequence data from two acidophilic biofilm communities, including composite genomes reconstructed for nine archaea, three bacteria, and numerous associated viruses, as well as thousands of unassigned fragments from strain variants and low-abundance organisms. Genome signatures, in the form of tetranucleotide frequencies analyzed by emergent self-organizing maps, segregated sequences from all known populations sharing < 50 to 60% average amino acid identity and revealed previously unknown genomic clusters corresponding to low-abundance organisms and a putative plasmid. Signatures were pervasive genome-wide. Clusters were resolved because intra-genome differences resulting from translational selection or protein adaptation to the intracellular (pH approximately 5) versus extracellular (pH approximately 1) environment were small relative to inter-genome differences. We found that these genome signatures stem from multiple influences but are primarily manifested through codon composition, which we propose is the result of genome-specific mutational biases. CONCLUSIONS: An important conclusion is that shared environmental pressures and interactions among coevolving organisms do not obscure genome signatures in acid mine drainage communities. Thus, genome signatures can be used to assign sequence fragments to populations, an essential prerequisite if metagenomics is to provide ecological and biochemical insights into the functioning of microbial communities.

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Genome-Based Targeted Sequencing as a Reproducible Microbial Community Profiling Assay.

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