Dataset Information

Assessing soil bacterial community and dynamics by integrated high-throughput absolute abundance quantification.

ABSTRACT: Microbial ecological studies have been remarkably promoted by the high-throughput sequencing approach with explosive information of taxonomy and relative abundance. However, relative abundance does not reflect the quantity of the microbial community and the inter-sample differences among taxa. In this study, we refined and applied an integrated high-throughput absolute abundance quantification (iHAAQ) method to better characterize soil quantitative bacterial community through combining the relative abundance (by high-throughput sequencing) and total bacterial quantities (by quantitative PCR). The proposed iHAAQ method was validated by an internal reference strain EDL933 and a laboratory strain WG5. Application of the iHAAQ method to a soil phenanthrene biodegradation study showed that for some bacterial taxa, the changes of relative and absolute abundances were coincident, while for others the changes were opposite. With the addition of a microbial activity inhibitor (NaN3), the absolute abundances of soil bacterial taxa, including several dominant genera of Bacillus, Flavobacterium, and Paenibacillus, decreased significantly, but their relative abundances increased after 28 days of incubation. We conclude that the iHAAQ method can offer more comprehensive information to reflect the dynamics of soil bacterial community with both relative and absolute abundances than the relative abundance from high-throughput sequencing alone.

SUBMITTER: Lou J

PROVIDER: S-EPMC5857175 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Assessing soil bacterial community and dynamics by integrated high-throughput absolute abundance quantification.

Lou Jun J Yang Li L Wang Haizhen H Wu Laosheng L Xu Jianming J

PeerJ 20180314

Microbial ecological studies have been remarkably promoted by the high-throughput sequencing approach with explosive information of taxonomy and relative abundance. However, relative abundance does not reflect the quantity of the microbial community and the inter-sample differences among taxa. In this study, we refined and applied an integrated high-throughput absolute abundance quantification (iHAAQ) method to better characterize soil quantitative bacterial community through combining the relat ...[more]

PMID: 29576979

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Project description:Progress in molecular methods has enabled the monitoring of bacterial populations in time. Nevertheless, understanding community dynamics and its links with ecosystem functioning remains challenging due to the tremendous diversity of microorganisms. Conceptual frameworks that make sense of time series of taxonomically rich bacterial communities, regarding their potential ecological function, are needed. A key concept for organizing ecological functions is the niche, the set of strategies that enable a population to persist and define its impacts on the surroundings. Here we present a framework based on manifold learning to organize genomic information into potentially occupied bacterial metabolic niches over time. Manifold learning tries to uncover low-dimensional data structures in high-dimensional data sets that can be used to describe the data in reduced dimensions. We apply the method to re-construct the dynamics of putatively occupied metabolic niches using a long-term bacterial time series from the Baltic Sea, the Linnaeus Microbial Observatory (LMO). The results reveal a relatively low-dimensional space of occupied metabolic niches comprising groups of taxa with similar functional capabilities. Time patterns of occupied niches were strongly driven by seasonality. Some metabolic niches were dominated by one bacterial taxon, whereas others were occupied by multiple taxa, depending on the season. These results illustrate the power of manifold learning approaches to advance our understanding of the links between community composition and functioning in microbial systems. IMPORTANCE The increase in data availability of bacterial communities highlights the need for conceptual frameworks to advance our understanding of these complex and diverse communities alongside the production of such data. To understand the dynamics of these tremendously diverse communities, we need tools to identify overarching strategies and describe their role and function in the ecosystem in a comprehensive way. Here, we show that a manifold learning approach can coarse grain bacterial communities in terms of their metabolic strategies and that we can thereby quantitatively organize genomic information in terms of potentially occupied niches over time. This approach, therefore, advances our understanding of how fluctuations in bacterial abundances and species composition can relate to ecosystem functions and it can facilitate the analysis, monitoring, and future predictions of the development of microbial communities.

Dataset Information

Assessing soil bacterial community and dynamics by integrated high-throughput absolute abundance quantification.

Publications

Assessing soil bacterial community and dynamics by integrated high-throughput absolute abundance quantification.

Similar Datasets

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