scholarly journals Turbulence induces clustering and segregation of non-motile, buoyancy-regulating phytoplankton

2019 ◽  
Vol 16 (159) ◽  
pp. 20190324 ◽  
Author(s):  
Matteo Borgnino ◽  
Jorge Arrieta ◽  
Guido Boffetta ◽  
Filippo De Lillo ◽  
Idan Tuval
Keyword(s):  
Microbial Interactions ◽  
Ecological Interactions ◽  
Cell Level ◽  
Chemical Gradients ◽  
Mesoscale Dynamics ◽  
Vertical Fluxes ◽  
Biological Coupling ◽  
Abundant Phytoplankton ◽  
Motile Species ◽  
Community Segregation

Turbulence plays a major role in shaping marine community structure as it affects organism dispersal and guides fundamental ecological interactions. Below oceanographic mesoscale dynamics, turbulence also impinges on subtle physical–biological coupling at the single cell level, setting a sea of chemical gradients and determining microbial interactions with profound effects on scales much larger than the organisms themselves. It has been only recently that we have started to disentangle details of this coupling for swimming microorganisms. However, for non-motile species, which comprise some of the most abundant phytoplankton groups on Earth, a similar level of mechanistic understanding is still missing. Here, we explore by means of extensive numerical simulations the interplay between buoyancy regulation in non-motile phytoplankton and cellular responses to turbulent mechanical cues. Using a minimal mechano-response model, we show how such a mechanism would contribute to spatial heterogeneity and affect vertical fluxes and trigger community segregation.

scholarly journals Disentangling environmental effects in microbial association networks

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Ina Maria Deutschmann ◽  
Gipsi Lima-Mendez ◽  
Anders K. Krabberød ◽  
Jeroen Raes ◽  
Sergio M. Vallina ◽  
...  
Keyword(s):  
Data Processing ◽  
Simulated Data ◽  
Potential Interaction ◽  
Microbial Interactions ◽  
Ecological Interactions ◽  
Sign Pattern ◽  
Interaction Information ◽  
Microbial Association ◽  
Environmental Selection ◽  
Data Processing Inequality

Abstract Background Ecological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association networks. Associations 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 1087 edges, of which 60 were true interactions but 1026 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). 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 8.3% of the associations were environmentally driven, while individual methods predicted 24.8% (data processing inequality), 25.7% (interaction information), and up to 84.6% (sign pattern as well as overlap). The fraction of environmentally driven edges among negative microbial associations in the real network increased rapidly with the number of environmental factors. 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 using EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses.

scholarly journals BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data

2021 ◽  
Vol 17 (9) ◽  
pp. e1009343
Author(s):  
Chenhao Li ◽  
Tamar V. Av-Shalom ◽  
Jun Wei Gerald Tan ◽  
Junmei Samantha Kwah ◽  
Kern Rei Chng ◽  
...  
Keyword(s):  
Ecological Model ◽  
Expectation Maximization Algorithm ◽  
Microbial Interactions ◽  
Ecological Interactions ◽  
Cross Sectional ◽  
Key Species ◽  
Alternate Model ◽  
High Fraction ◽  
And Function ◽  
Microbiome Data

The structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of next-generation sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional microbiome datasets for unravelling ecological structure in a scalable manner thus remains an open problem. We present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. Benchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n = 4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species. Conclusion BEEM-Static provides new opportunities for mining ecologically interpretable interactions and systems insights from the growing corpus of microbiome data.

scholarly journals BEEM-Static: Accurate inference of ecological interactions from cross-sectional metagenomic data

2020 ◽  
Author(s):  
Chenhao Li ◽  
Tamar V. Av-Shalom ◽  
Jun Wei Gerald Tan ◽  
Junmei Samantha Kwah ◽  
Kern Rei Chng ◽  
...  
Keyword(s):  
Ecological Model ◽  
Expectation Maximization Algorithm ◽  
Microbial Interactions ◽  
Metagenomic Data ◽  
Metagenomic Sequencing ◽  
Ecological Interactions ◽  
Cross Sectional ◽  
Key Species ◽  
Alternate Model ◽  
And Function

AbstractMotivationThe structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of metagenomic sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional metagenomic datasets for unravelling ecological structure in a scalable manner thus remains an open problem.MethodsWe present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples.ResultsBenchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n=4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species.ConclusionBEEM-Static provides new opportunities for mining ecologically interpretable interactions and systems insights from the growing corpus of metagenomic data.

scholarly journals A Holling Functional Response Model for Mapping QTLs Governing Interspecific Interactions

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiao-Yu Zhang ◽  
Huiying Gong ◽  
Qing Fang ◽  
Xuli Zhu ◽  
Libo Jiang ◽  
...  
Keyword(s):  
Functional Response ◽  
Species Interactions ◽  
Community Dynamics ◽  
Interspecific Interactions ◽  
Microbial Interactions ◽  
Ecological Communities ◽  
Response Model ◽  
Ecological Interactions ◽  
Dynamic Complexity ◽  
Genome Level

Genes play an important role in community ecology and evolution, but how to identify the genes that affect community dynamics at the whole genome level is very challenging. Here, we develop a Holling type II functional response model for mapping quantitative trait loci (QTLs) that govern interspecific interactions. The model, integrated with generalized Lotka-Volterra differential dynamic equations, shows a better capacity to reveal the dynamic complexity of inter-species interactions than classic competition models. By applying the new model to a published mapping data from a competition experiment of two microbial species, we identify a set of previously uncharacterized QTLs that are specifically responsible for microbial cooperation and competition. The model can not only characterize how these QTLs affect microbial interactions, but also address how change in ecological interactions activates the genetic effects of the QTLs. This model provides a quantitative means of predicting the genetic architecture that shapes the dynamic behavior of ecological communities.

scholarly journals The planktonic protist interactome: where do we stand after a century of research?

2019 ◽  
Vol 14 (2) ◽  
pp. 544-559 ◽  
Author(s):  
Marit F. Markussen Bjorbækmo ◽  
Andreas Evenstad ◽  
Line Lieblein Røsæg ◽  
Anders K. Krabberød ◽  
Ramiro Logares
Keyword(s):  
Large Scale ◽  
Microbial Interactions ◽  
Global Ocean ◽  
Ecological Interactions ◽  
Evolutionary Trends ◽  
Bipartite Networks ◽  
Host Interactions ◽  
Interaction Database ◽  
Marine Realm ◽  
Earth Ecosystem

Abstract Microbial interactions are crucial for Earth ecosystem function, but our knowledge about them is limited and has so far mainly existed as scattered records. Here, we have surveyed the literature involving planktonic protist interactions and gathered the information in a manually curated Protist Interaction DAtabase (PIDA). In total, we have registered ~2500 ecological interactions from ~500 publications, spanning the last 150 years. All major protistan lineages were involved in interactions as hosts, symbionts (mutualists and commensalists), parasites, predators, and/or prey. Predation was the most common interaction (39% of all records), followed by symbiosis (29%), parasitism (18%), and ‘unresolved interactions’ (14%, where it is uncertain whether the interaction is beneficial or antagonistic). Using bipartite networks, we found that protist predators seem to be ‘multivorous’ while parasite–host and symbiont–host interactions appear to have moderate degrees of specialization. The SAR supergroup (i.e., Stramenopiles, Alveolata, and Rhizaria) heavily dominated PIDA, and comparisons against a global-ocean molecular survey (TARA Oceans) indicated that several SAR lineages, which are abundant and diverse in the marine realm, were underrepresented among the recorded interactions. Despite historical biases, our work not only unveils large-scale eco-evolutionary trends in the protist interactome, but it also constitutes an expandable resource to investigate protist interactions and to test hypotheses deriving from omics tools.

scholarly journals Disentangling environmental effects in microbial association networks

2020 ◽  
Author(s):  
Ina Maria Deutschmann ◽  
Gipsi Lima-Mendez ◽  
Anders K. Krabberød ◽  
Jeroen Raes ◽  
Sergio M. Vallina ◽  
...  
Keyword(s):  
Data Processing ◽  
Simulated Data ◽  
Potential Interaction ◽  
Microbial Interactions ◽  
Ecological Interactions ◽  
Sign Pattern ◽  
Interaction Information ◽  
Microbial Association ◽  
Environmental Selection ◽  
Data Processing Inequality

Abstract 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.

scholarly journals The Deep Rocky Biosphere: New Geomicrobiological Insights and Prospects

2021 ◽  
Vol 12 ◽  
Author(s):  
Hinako Takamiya ◽  
Mariko Kouduka ◽  
Yohey Suzuki
Keyword(s):  
Microbial Interactions ◽  
Radioactive Elements ◽  
Cell Level ◽  
High Iron Content ◽  
Microbial Life ◽  
Technological Advances ◽  
Energy Inputs ◽  
Microbial Proliferation ◽  
Rocky Habitats ◽  
Life On Earth

Rocks that react with liquid water are widespread but spatiotemporally limited throughout the solar system, except for Earth. Rock-forming minerals with high iron content and accessory minerals with high amounts of radioactive elements are essential to support rock-hosted microbial life by supplying organics, molecular hydrogen, and/or oxidants. Recent technological advances have broadened our understanding of the rocky biosphere, where microbial inhabitation appears to be difficult without nutrient and energy inputs from minerals. In particular, microbial proliferation in igneous rock basements has been revealed using innovative geomicrobiological techniques. These recent findings have dramatically changed our perspective on the nature and the extent of microbial life in the rocky biosphere, microbial interactions with minerals, and the influence of external factors on habitability. This study aimed to gather information from scientific and/or technological innovations, such as omics-based and single-cell level characterizations, targeting deep rocky habitats of organisms with minimal dependence on photosynthesis. By synthesizing pieces of rock-hosted life, we can explore the evo-phylogeny and ecophysiology of microbial life on Earth and the life’s potential on other planetary bodies.

scholarly journals Combining SIMS and mechanistic modelling to reveal nutrient kinetics in an algal-bacterial mutualism

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251643
Author(s):  
Hannah Laeverenz Schlogelhofer ◽  
François J. Peaudecerf ◽  
Freddy Bunbury ◽  
Martin J. Whitehouse ◽  
Rachel A. Foster ◽  
...  
Keyword(s):  
Secondary Ion Mass Spectrometry ◽  
Heterotrophic Bacterium ◽  
Microbial Interactions ◽  
Vitamin B ◽  
Cell Level ◽  
Mechanistic Modelling ◽  
Novel Approach ◽  
Algal Photosynthesis ◽  
Microbial Systems ◽  
Secondary Ion

Microbial communities are of considerable significance for biogeochemical processes, for the health of both animals and plants, and for biotechnological purposes. A key feature of microbial interactions is the exchange of nutrients between cells. Isotope labelling followed by analysis with secondary ion mass spectrometry (SIMS) can identify nutrient fluxes and heterogeneity of substrate utilisation on a single cell level. Here we present a novel approach that combines SIMS experiments with mechanistic modelling to reveal otherwise inaccessible nutrient kinetics. The method is applied to study the onset of a synthetic mutualistic partnership between a vitamin B12-dependent mutant of the alga Chlamydomonas reinhardtii and the B12-producing, heterotrophic bacterium Mesorhizobium japonicum, which is supported by algal photosynthesis. Results suggest that an initial pool of fixed carbon delays the onset of mutualistic cross-feeding; significantly, our approach allows the first quantification of this expected delay. Our method is widely applicable to other microbial systems, and will contribute to furthering a mechanistic understanding of microbial interactions.

scholarly journals The planktonic protist interactome: where do we stand after a century of research?

2019 ◽  
Author(s):  
Marit F. Markussen Bjorbækmo ◽  
Andreas Evenstad ◽  
Line Lieblein Røsæg ◽  
Anders K. Krabberød ◽  
Ramiro Logares
Keyword(s):  
Large Scale ◽  
Microbial Interactions ◽  
Global Ocean ◽  
Ecological Interactions ◽  
Evolutionary Trends ◽  
Bipartite Networks ◽  
Host Interactions ◽  
Interaction Database ◽  
Marine Realm ◽  
Earth Ecosystem

AbstractMicrobial interactions are crucial for Earth ecosystem function, yet our knowledge about them is limited and has so far mainly existed as scattered records. Here, we have surveyed the literature involving planktonic protist interactions and gathered the information in a manually curated Protist Interaction DAtabase (PIDA). In total, we have registered ~2,500 ecological interactions from ~500 publications, spanning the last 150 years. All major protistan lineages were involved in interactions as hosts, symbionts, parasites, predators and/or prey. Symbiosis was the most common interaction (43% of all records), followed by predation (39%) and parasitism (18%). Using bipartite networks, we found that protistan predators seem to be “multivorous”, while parasite-host and symbiont-host interactions appear to have moderate degrees of specialization. The SAR supergroup (i.e. Stramenopiles, Alveolata and Rhizaria) heavily dominated PIDA, and comparisons against a global-ocean molecular survey (TARA Oceans) indicated that several SAR lineages, which are abundant and diverse in the marine realm, were underrepresented among the compiled interactions. All in all, despite historical biases, our work not only unveils large-scale eco-evolutionary trends in the protist interactome, but it also constitutes an expandable resource to investigate protist interactions and to test hypotheses deriving from omics tools.

scholarly journals Disentangling environmental effects in microbial association networks

2021 ◽  
Author(s):  
Ina Maria Deutschmann ◽  
Gipsi Lima-Mendez ◽  
Anders K. Krabberod ◽  
Jeroen Raes ◽  
Sergio M. Vallina ◽  
...  
Keyword(s):  
Data Processing ◽  
Simulated Data ◽  
Potential Interaction ◽  
Microbial Interactions ◽  
Ecological Interactions ◽  
Sign Pattern ◽  
Interaction Information ◽  
Microbial Association ◽  
Environmental Selection ◽  
Data Processing Inequality

Background Ecological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association networks. Associations could result from either ecological interactions between microorganisms, or from environmental selection, where the associations are 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 1087 edges, of which 60 were true interactions but 1026 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). 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 8.3% of the associations were environmentally-driven, while individual methods predicted 24.8% (Data Processing Inequality), 25.7% (Interaction Information), and up to 84.6% (Sign Pattern as well as Overlap). The fraction of environmentally-driven edges among negative microbial associations in the real network increased rapidly with the number of environmental factors. 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 using EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses.

Sign in / Sign up

Export Citation Format

Share Document