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Extensive profiling of a complex microbial community by high-throughput sequencing.

ABSTRACT: Complex microbial communities remain poorly characterized despite their ubiquity and importance to human and animal health, agriculture, and industry. Attempts to describe microbial communities by either traditional microbiological methods or molecular methods have been limited in both scale and precision. The availability of genomics technologies offers an unprecedented opportunity to conduct more comprehensive characterizations of microbial communities. Here we describe the application of an established molecular diagnostic method based on the chaperonin-60 sequence, in combination with high-throughput sequencing, to the profiling of a microbial community: the pig intestinal microbial community. Four libraries of cloned cpn60 sequences were generated by two genomic DNA extraction procedures in combination with two PCR protocols. A total of 1,125 cloned cpn60 sequences from the four libraries were sequenced. Among the 1,125 cloned cpn60 sequences, we identified 398 different nucleotide sequences encoding 280 unique peptide sequences. Pairwise comparisons of the 398 unique nucleotide sequences revealed a high degree of sequence diversity within the library. Identification of the likely taxonomic origins of cloned sequences ranged from imprecise, with clones assigned to a taxonomic subclass, to precise, for cloned sequences with 100% DNA sequence identity with a species in our reference database. The compositions of the four libraries were compared and differences related to library construction parameters were observed. Our results indicate that this method is an alternative to 16S rRNA sequence-based studies which can be scaled up for the purpose of performing a potentially comprehensive assessment of a given microbial community or for comparative studies.

SUBMITTER: Hill JE

PROVIDER: S-EPMC123911 | biostudies-literature | 2002 Jun

REPOSITORIES: biostudies-literature

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Extensive profiling of a complex microbial community by high-throughput sequencing.

Hill Janet E JE Seipp Robyn P RP Betts Martin M Hawkins Lindsay L Van Kessel Andrew G AG Crosby William L WL Hemmingsen Sean M SM

Applied and environmental microbiology 20020601 6

Complex microbial communities remain poorly characterized despite their ubiquity and importance to human and animal health, agriculture, and industry. Attempts to describe microbial communities by either traditional microbiological methods or molecular methods have been limited in both scale and precision. The availability of genomics technologies offers an unprecedented opportunity to conduct more comprehensive characterizations of microbial communities. Here we describe the application of an e ...[more]

PMID: 12039767

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Project description:Surface-groundwater interactions play an important role in microbial community compositions of river bank filtrates. Surface water contaminations deriving from environmental influences are attenuated by biogeochemical processes in the hyporheic zone, which are essential for providing clean and high-quality drinking water in abstraction wells. Characterizing the flow regime of surface water into the groundwater body can provide substantial information on water quality, but complex hydraulic dynamics make predictions difficult. Thus, a bottom up approach using microbial community shifting patterns as an overall outcome of dynamic water characteristics could provide more detailed information on the influences that affect groundwater quality. The combination of high-throughput sequencing data together with flow cytometric measurements of total cell counts reveals absolute abundances among taxa, thus enhancing interpretation of bacterial dynamics. 16S rRNA high-throughput sequencing of 55 samples among six wells in a well field in Austria that is influenced by river bank filtrate within a time period of 3 months has revealed both, clear differences as well as strong similarity in microbiome compositions between wells and dates. A significant community shift from April to May occurred in four of six wells, suggesting that surface water flow regimes do affect these wells stronger than others. Triplicate sampling and subsequent sequencing of wells at different dates proved the method to be reproducible. Flow cytometric measurements of total cells indicate microbial shifts due to increased cell counts and emphasize the rise of allochthonous microorganisms. Typical freshwater bacterial lineages (Verrucomicrobia, Bacteroidetes, Actinobacteria, Cyanobacteria, Armatimonadetes) were identified as most increasing phyla during community shifts. The changes are most likely a result of increased water abstraction in the wells together with constant river water levels rather than rain events. The results provide important knowledge for future implementations of well utilization in dependency of the nearby Danube River water levels and can help drawing conclusions about the influence of surface water in the groundwater such that hygienically save and clean drinking water with a stable microbial community can be provided.

Project description:BackgroundAdvances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent; however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling.ResultsWe present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, community-scale microbial functional networks using a newly defined metric "MW-score" (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~ 3 h with 40 CPU threads to process ~ 100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~ 45 min, while it takes ~ 5 h to complete hmmsearch for ~ 3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut.ConclusionMETABOLIC enables the consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available under GPLv3 at https://github.com/AnantharamanLab/METABOLIC . Video abstract.

Dataset Information

Extensive profiling of a complex microbial community by high-throughput sequencing.

Publications

Extensive profiling of a complex microbial community by high-throughput sequencing.

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