Characterizing microbial communities through space and time

Department of Computer Science, University of Colorado at Boulder, Boulder, CO 80309, USA.
Current Opinion in Biotechnology (Impact Factor: 7.12). 06/2012; 23(3):431-6. DOI: 10.1016/j.copbio.2011.11.017
Source: PubMed

ABSTRACT Until recently, the study of microbial diversity has mainly been limited to descriptive approaches, rather than predictive model-based analyses. The development of advanced analytical tools and decreasing cost of high-throughput multi-omics technologies has made the later approach more feasible. However, consensus is lacking as to which spatial and temporal scales best facilitate understanding of the role of microbial diversity in determining both public and environmental health. Here, we review the potential for combining these new technologies with both traditional and nascent spatio-temporal analysis methods. The fusion of proper spatio-temporal sampling, combined with modern multi-omics and computational tools, will provide insight into the tracking, development and manipulation of microbial communities.

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    • "The w value was low across the 6-year experiment, but we still observed a significantly linear decrease in the microbial similarity with time (log transformed) for the in situ samples and the northward and southward transplants. The results strongly support the claim that the community similarity decays over time (time–decay), which underlies key ecological principles; this decay appears to be universal in biology (Chytry et al., 2001; Korhonen et al., 2010; Gonzalez et al., 2012; Shade et al., 2013). We compared the microbial temporal turnover in different habitats and across different temporal scales (Supplementary Figure S6). "
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    ABSTRACT: To understand soil microbial community stability and temporal turnover in response to climate change, a long-term soil transplant experiment was conducted in three agricultural experiment stations over large transects from a warm temperate zone (Fengqiu station in central China) to a subtropical zone (Yingtan station in southern China) and a cold temperate zone (Hailun station in northern China). Annual soil samples were collected from these three stations from 2005 to 2011, and microbial communities were analyzed by sequencing microbial 16S ribosomal RNA gene amplicons using Illumina MiSeq technology. Our results revealed a distinctly differential pattern of microbial communities in both northward and southward transplantations, along with an increase in microbial richness with climate cooling and a corresponding decrease with climate warming. The microbial succession rate was estimated by the slope (w value) of linear regression of a log-transformed microbial community similarity with time (time-decay relationship). Compared with the low turnover rate of microbial communities in situ (w=0.046, P<0.001), the succession rate at the community level was significantly higher in the northward transplant (w=0.058, P<0.001) and highest in the southward transplant (w=0.094, P<0.001). Climate warming lead to a faster succession rate of microbial communities as well as lower species richness and compositional changes compared with in situ and climate cooling, which may be related to the high metabolic rates and intense competition under higher temperature. This study provides new insights into the impacts of climate change on the fundamental temporal scaling of soil microbial communities and microbial phylogenetic biodiversity.The ISME Journal advance online publication, 19 May 2015; doi:10.1038/ismej.2015.78.
    The ISME Journal 05/2015; DOI:10.1038/ismej.2015.78 · 9.30 Impact Factor
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    • "In fact, bacterial diversityis clearly affected by certain environmentalfactors, such as pH, water temperature, water chemistry,nutrient condition, geographical and seasonal variations(Lindstrom et al., 2005; Hahn, 2006; Zeng et al., 2009).However, to predict and describe microbially mediated processes, spatial and temporal patterns of diversity at multiple levels could be investigated.Because of the high diversity of microbial communities, the ability to characterize their fine-scale vertical distribution has only become achievable within the past decade (Scholz et al., 2012). Next-generation 'omics' technologies such as high-throughput amplicon sequencing allow collection of thousands to millions of sequences (Green et al., 2008; DeLong, 2009) and following bystatistical methods detection of numerically dominant as well as uncommon organisms in a system (Bent, Forney, 2008; Gonzalez et al., 2012). The first ones are seem to be responsible for the majority of metabolic activity and energy flux, but uncommon organisms serve as a reservoir of genetic and functional diversity (Yachi,Loreau, 1999; Nandi et al., 2004), often play key roles in ecosystems (Phillips et al., 2000), and can become numerically important if environmental conditions change (Bent, Forney, 2008). "
    Acta Geologica Sinica 12/2014; 88(s1). DOI:10.1111/1755-6724.12266_5 · 1.68 Impact Factor
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    • "However, the purpose of 16S rRNA gene high-throughput surveys extends beyond the taxonomic profiling of microbial communities. It is also important to understand how microbial communities are structured in space [24] and time [25]. "
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    ABSTRACT: The performance of two sets of primers targeting variable regions of the 16S rRNA gene V1-V3 and V4 was compared in their ability to describe changes of bacterial diversity and temporal turnover in full-scale activated sludge. Duplicate sets of high-throughput amplicon sequencing data of the two 16S rRNA regions shared a collection of core taxa that were observed across a series of twelve monthly samples, although the relative abundance of each taxon was substantially different between regions. A case in point was the changes in the relative abundance of filamentous bacteria Thiothrix, which caused a large effect on diversity indices, but only in the V1-V3 data set. Yet the relative abundance of Thiothrix in the amplicon sequencing data from both regions correlated with the estimation of its abundance determined using fluorescence in situ hybridization. In nonmetric multidimensional analysis samples were distributed along the first ordination axis according to the sequenced region rather than according to sample identities. The dynamics of microbial communities indicated that V1-V3 and the V4 regions of the 16S rRNA gene yielded comparable patterns of: 1) the changes occurring within the communities along fixed time intervals, 2) the slow turnover of activated sludge communities and 3) the rate of species replacement calculated from the taxa-time relationships. The temperature was the only operational variable that showed significant correlation with the composition of bacterial communities over time for the sets of data obtained with both pairs of primers. In conclusion, we show that despite the bias introduced by amplicon sequencing, the variable regions V1-V3 and V4 can be confidently used for the quantitative assessment of bacterial community dynamics, and provide a proper qualitative account of general taxa in the community, especially when the data are obtained over a convenient time window rather than at a single time point.
    PLoS ONE 06/2014; 9(6):e99722. DOI:10.1371/journal.pone.0099722 · 3.23 Impact Factor
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