Deciphering microbial interactions and detecting keystone species with co-occurrence networks

Berry, David; Widder, Stefanie

doi:10.3389/fmicb.2014.00219

Cited by 1,247 publications

(997 citation statements)

References 63 publications

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“…Evidence is emerging for their applicability at the micro scale as well-for example, in describing the microbial dynamics in a cheese model community (Mounier et al, 2008) and within individuals (Gerber 2014), as well as their shifts in response to environmental perturbations (Pepper and Rosenfeld, 2012). Previous investigation in this area mostly tested standard correlation metrics not developed for microbiome data (Berry and Widder, 2014). We created two-and six-species Lotka-Volterra interactions (Supplementary Figure 15) and tested whether tools accurately capture these relationships when they are embedded in random noisy signals.…”

Section: Sampling Significantly Alters Edge Inferencesmentioning

confidence: 99%

“…Without an understanding of these important properties, correlation analysis risks diverting attention from meaningful interactions and leading to wasteful pursuit of expensive in vitro or in vivo validations of mechanisms. One previous effort in this area tested mainly basic correlation measures for one type of model system (Berry and Widder, 2014). …”

Section: Introductionmentioning

confidence: 99%

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Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

et al. 2016

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Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to distinguish signals from noise, and detect a range of ecological and time-series relationships. Finally, we provide specific recommendations for correlation technique usage. Although some methods perform better than others, there is still considerable need for improvement in current techniques.

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Section: Sampling Significantly Alters Edge Inferencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

et al. 2016

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“…Calculation of network indices for individual nodes further corroborated the pivotal role of the MCG lineage in the structuration of the network (Table 1). Indeed, looking at the ranking of nodes for the 10 highest values of each index, MCG nodes were the most represented and particularly for closeness centrality, which has been recently related to the concept of keystone species (Berry and Widder, 2014). In this concept initially developed for macroorganisms, keystone species are commonly understood as the 'backbone' of the community on which the stability of the entire system depends (Paine, 1969).…”

Section: Evolutionary Relationships Between Freshwater and Marine Mcgmentioning

confidence: 99%

Insights in the ecology and evolutionary history of the Miscellaneous Crenarchaeotic Group lineage

et al. 2015

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Members of the archaeal Miscellaneous Crenarchaeotic Group (MCG) are among the most successful microorganisms on the planet. During its evolutionary diversification, this very diverse group has managed to cross the saline-freshwater boundary, one of the most important evolutionary barriers structuring microbial communities. However, the current understanding on the ecological significance of MCG in freshwater habitats is scarce and the evolutionary relationships between freshwater and saline MCG remains poorly known. Here, we carried out molecular phylogenies using publicly available 16S rRNA gene sequences from various geographic locations to investigate the distribution of MCG in freshwater and saline sediments and to evaluate the implications of salinefreshwater transitions during the diversification events. Our approach provided a robust ecological framework in which MCG archaea appeared as a core generalist group in the sediment realm. However, the analysis of the complex intragroup phylogeny of the 21 subgroups currently forming the MCG lineage revealed that distinct evolutionary MCG subgroups have arisen in marine and freshwater sediments suggesting the occurrence of adaptive evolution specific to each habitat. The ancestral state reconstruction analysis indicated that this segregation was mainly due to the occurrence of a few saline-freshwater transition events during the MCG diversification. In addition, a network analysis showed that both saline and freshwater MCG recurrently co-occur with archaea of the class Thermoplasmata in sediment ecosystems, suggesting a potentially relevant trophic connection between the two clades.

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“…Co-occurrence networks examine interactions between members of a community (Faust and Raes, 2012;Berry and Widder, 2014). Co-occurrence networks can reveal ecologically important correlations in data by testing each pair-wise interaction between features (OTUs) in a data set.…”

Section: Co-occurence Networkmentioning

confidence: 99%

Inhibitory bacteria reduce fungi on early life stages of endangered Colorado boreal toads (Anaxyrus boreas)

et al. 2015

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Increasingly, host-associated microbiota are recognized to mediate pathogen establishment, providing new ecological perspectives on health and disease. Amphibian skin-associated microbiota interact with the fungal pathogen, Batrachochytrium dendrobatidis (Bd), but little is known about microbial turnover during host development and associations with host immune function. We surveyed skin microbiota of Colorado's endangered boreal toads (Anaxyrus boreas), sampling 181 toads across four life stages (tadpoles, metamorphs, subadults and adults). Our goals were to (1) understand variation in microbial community structure among individuals and sites, (2) characterize shifts in communities during development and (3) examine the prevalence and abundance of known Bd-inhibitory bacteria. We used high-throughput 16S and 18S rRNA gene sequencing (Illumina MiSeq) to characterize bacteria and microeukaryotes, respectively. Life stage had the largest effect on the toad skin microbial community, and site and Bd presence also contributed. Proteobacteria dominated tadpole microbial communities, but were later replaced by Actinobacteria. Microeukaryotes on tadpoles were dominated by the classes Alveolata and Stramenopiles, while fungal groups replaced these groups after metamorphosis. Using a novel database of Bd-inhibitory bacteria, we found fewer Bd-inhibitory bacteria in post-metamorphic stages correlated with increased skin fungi, suggesting that bacteria have a strong role in early developmental stages and reduce skinassociated fungi.

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Deciphering microbial interactions and detecting keystone species with co-occurrence networks

Cited by 1,247 publications

References 63 publications

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

Insights in the ecology and evolutionary history of the Miscellaneous Crenarchaeotic Group lineage

Inhibitory bacteria reduce fungi on early life stages of endangered Colorado boreal toads (Anaxyrus boreas)

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