Rapid Inference of Direct Interactions in Large-Scale Ecological Networks from Heterogeneous Microbial Sequencing Data

Tackmann, Janko; Rodrigues, João F. Matias; Mering, Christian von

doi:10.1016/j.cels.2019.08.002

Cited by 109 publications

(122 citation statements)

References 68 publications

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“…Co-occurrence networks were constructed using filtered ASV occurrence data sets in which the ASV were filtered to only those that were present in at least five samples resulting in a 102 ASV data set. The 102 x 63 matrix was provided as input to FlashWeave v1.0 (74) using default parameters, and visualized in Gephi v. 0.9.2 (75). Then to consider whether the ASVs in the Core80, Dynamic50, or Variable fractions of the SaM were affiliated with particular levels of PalA in the ascidian lobes, PalA niche robust optimum and range were computed using the occurrence and dry weight normalized contextual data (76).…”

Section: Methodsmentioning

confidence: 99%

Uncovering the core microbiome and distributions of palmerolide inSynoicum adareanumacross the Anvers Island archipelago, Antarctica

Murray

Avalon

Bishop

et al. 2020

Preprint

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Section: Methodsmentioning

confidence: 99%

Uncovering the core microbiome and distributions of palmerolide inSynoicum adareanumacross the Anvers Island archipelago, Antarctica

Murray

Avalon

Bishop

et al. 2020

Preprint

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“…The lack of null model and/or statistical test on these observations, however, did not allow to determine whether biologic or random processes were responsible of the patterns. In a more recent study, global gut microbiome co-occurrence networks were found to have significant higher phylogenetic assortativity than in randomize networks overall (Tackmann, Matias Rodrigues, & von Mering, 2019), while size effect was not quantified at distinct distance classes and no interpretation was provided on these observations. Our new approach has the potential to uncover inter-dependencies between phylogenetic relatedness and co-occurrence and co-exclusion of any micro-organisms in any environment.…”

Section: Discussionmentioning

confidence: 94%

Relating network analyses to phylogenetic relatedness to infer protistan co-occurrences and co-exclusions in marine and terrestrial environments

Lentendu

Dunthorn

2020

Preprint

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We used two large-scale metabarcoding datasets to evaluate phylogenetic signals at global marine and regional terrestrial scales using co-occurrence and co-exclusion networks. Phylogenetic relatedness was estimated using either global pairwise sequence distance or phylogenetic distance and the significance of observed patterns relating networks and phylogenies were evaluated against two null models. In all datasets, we found that phylogenetically close OTUs significantly cooccurred more often, and OTUs with intermediate phylogenetic relatedness co-occurred less often, than expected by chance. Phylogenetically close OTUs co-excluded less often than expected by chance in the marine datasets only. Simultaneous excess of co-occurrences and co-exclusions were observed in the inversion zone between close and intermediate phylogenetic distance classes in marine surface. Similar patterns were observed by using either pairwise sequence or phylogenetic distances, and by using both null models. These results suggest that environmental filtering and dispersal limitation are the preponderant forces driving co-occurrence of protists in both environments, while signal of competitive exclusion was only detected in the marine surface environment. The discrepancy in the co-exclusion pattern is potentially linked to the individual 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 environments: water bodies are more homogeneous while tropical forest soils contain a myriad of nutrient rich micro-environment reducing the strength of mutual exclusion.

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“…There are multiple methods to determine associations (normally based on correlations) between microorganisms using their abundances (e.g. eLSA Xia et al (2011Xia et al ( , 2012, CoNet Faust and Raes (2016), SPIEC-EASI Kurtz et al (2015), or FlashWeave Tackmann et al (2019)). These abundance-based associations compose a network, where nodes represent microorganisms and edges represent either co-presence (positive association) or mutual exclusion (negative association) relationships, which constitute microbial interaction hypotheses.…”

Section: Association Network To Generate Microbial Interaction Hypotmentioning

confidence: 99%

Disentangling environmental effects in microbial association networks

Deutschmann

Lima‐Mendez

Krabberød

et al. 2020

Preprint

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Background: Ecolocial interctions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions by associations across time and space, which can be represented as association networks. Links in these networks could result from either ecological interactions between microorganisms, or from environmental selection, where the association is environmentally-driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly if it is due to environmental selection or not.Results: We present EnDED (Environmentally-Driven Edge Detection), an implementation of four approaches as well as their combination to predict which links between microorganisms in an association network are environmentally-driven. The four approaches are Sign Pattern, Overlap, Interaction Information, and Data Processing Inequality. We tested EnDED on networks from simulated data of 50 microorganisms. The networks contained on average 50 nodes and 1,087 edges, of which 60 were true interactions but 1,026 false associations (i.e. environmentally-driven or due to chance). Applying each method individually, we detected a moderate to high number of environmentally-driven edges—87% Sign Pattern and Overlap, 67% Interaction Information, and 44% Data Processing Inequality. Combining these methods in an intersection approach resulted in retaining more interactions, both true and false (32% of environmentally-driven associations). The addition of noise to the simulated datasets did not alter qualitatively these results. After validation with the simulated datasets, we applied EnDED on a marine microbial network inferred from 10 years of monthly observations of microbial-plankton abundance. The intersection combination predicted that 14.2% of the associations were environmentally-driven, while individual methods predicted 31.4% (Data Processing Inequality), 38.3% (Interaction Information), and up to 83.4% (Sign Pattern as well as Overlap).Conclusions: To reach accurate hypotheses about ecological interactions, it is important to determine, quantify, and remove environmentally-driven associations in marine microbial association networks. For that, EnDED offers up to four individual methods as well as their combination. However, especially for the intersection combination, we suggest to use EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses.

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Rapid Inference of Direct Interactions in Large-Scale Ecological Networks from Heterogeneous Microbial Sequencing Data

Cited by 109 publications

References 68 publications

Uncovering the core microbiome and distributions of palmerolide inSynoicum adareanumacross the Anvers Island archipelago, Antarctica

Uncovering the core microbiome and distributions of palmerolide inSynoicum adareanumacross the Anvers Island archipelago, Antarctica

Relating network analyses to phylogenetic relatedness to infer protistan co-occurrences and co-exclusions in marine and terrestrial environments

Disentangling environmental effects in microbial association networks

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