An equation-free method reveals the ecological interaction networks within complex microbial ecosystems

AbstractMapping the network of ecological interactions is key to understanding the composition, stability, function and dynamics of microbial communities. In recent years various approaches have been used to reveal microbial interaction networks from metagenomic sequencing data, such as time-series analysis, machine learning and statistical techniques. Despite these efforts it is still not possible to capture details of the ecological interactions behind complex microbial dynamics.We developed the sparse S-map method (SSM), which generates a sparse interaction network from a multivariate ecological time-series without presuming any mathematical formulation for the underlying microbial processes. The advantage of the SSM over alternative methodologies is that it fully utilizes the observed data using a framework of empirical dynamic modelling. This makes the SSM robust to non-equilibrium dynamics and underlying complexity (nonlinearity) in microbial processes.We showed that an increase in dataset size or a decrease in observational error improved the accuracy of SSM whereas, the accuracy of a comparative equation-based method was almost unchanged for both cases and equivalent to the SSM at best. Hence, the SSM outperformed a comparative equation-based method when datasets were large and the magnitude of observational errors were small. The results were robust to the magnitude of process noise and the functional forms of inter-specific interactions that we tested. We applied the method to a microbiome data of six mice and found that there were different microbial interaction regimes between young to middle age (4-40 week-old) and middle to old age (36-72 week-old) mice.The complexity of microbial relationships impedes detailed equation-based modeling. Our method provides a powerful alternative framework to infer ecological interaction networks of microbial communities in various environments and will be improved by further developments in metagenomics sequencing technologies leading to increased dataset size and improved accuracy and precision.

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Interactions between strains govern the eco-evolutionary dynamics of microbial communities

10.1101/2021.01.04.425224 ◽

2021 ◽

Author(s):

Akshit Goyal ◽

Leonora S. Bittleston ◽

Gabriel E. Leventhal ◽

Lu Lu ◽

Otto X. Cordero

Keyword(s):

Microbial Communities ◽

Evolutionary Dynamics ◽

Metagenomic Sequencing ◽

Ecological Interactions ◽

Base Pairs ◽

Pitcher Plant ◽

Naturally Occurring ◽

Relevant Level ◽

Over Time

AbstractGenomic data has revealed that genotypic variants of the same species, i.e., strains, coexist and are abundant in natural microbial communities. However, it is not clear if strains are ecologically equivalent, or if they exhibit distinct interactions and dynamics. Here, we address this problem by tracking 10 microbial communities from the pitcher plant Sarracenia purpurea in the laboratory for more than 300 generations. Using metagenomic sequencing, we reconstruct their dynamics over time and across scales, from distant phyla to closely related genotypes. We find that interactions between naturally occurring strains govern eco-evolutionary dynamics. Surprisingly, even fine-scale variants differing only by 100 base pairs can exhibit vastly different dynamics. We show that these differences may stem from ecological interactions in the communities, which are specific to strains, not species. Finally, by analyzing genomic differences between strains, we identify major functional hubs such as transporters, regulators, and carbohydrate-catabolizing enzymes, which might be the basis for strain-specific interactions. Our work shows that strains are the relevant level of diversity at which to study the long-term dynamics of microbiomes.

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Population-Resources Dynamics

10.32545/encyclopedia201901.0003.v8 ◽

2019 ◽

Author(s):

Charles Telles

Keyword(s):

Nonlinear Dynamics ◽

Time Series ◽

Phase Space ◽

Time Series Analysis ◽

Ecological Interactions ◽

Ecological Interaction ◽

Time Frequency ◽

Series Analysis ◽

Interaction Variables ◽

Dynamics Of Population

Dynamics of population and resources symmetries are investigated and a model of ecological interaction that fits with empirical behavior was obtained. It was observed how variables in these dynamics are in recurrence considering parameters such as time, frequency, iteration and interaction. Variables investigated are the consume, time and supply mechanics. Time series analysis of these variables indicated a possible phase space formation of the phenomena. The main conclusion leads to a nonlinear dynamics of the  ecological interactions of the organisms and resources symmetries. (Item cover credit: Amber, Mabel. Aug. 12, 2018)

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Altered structure and stability of bat-prey interaction networks in logged tropical forests revealed by metabarcoding

10.1101/2020.03.20.000331 ◽

2020 ◽

Cited By ~ 1

Author(s):

David R. Hemprich-Bennett ◽

Victoria A. Kemp ◽

Joshua Blackman ◽

Matthew J. Struebig ◽

Owen T. Lewis ◽

...

Keyword(s):

Interaction Networks ◽

Ecosystem Functions ◽

List Type ◽

Ecological Interactions ◽

Old Growth ◽

Insectivorous Bats ◽

Old Growth Forest ◽

Dna Metabarcoding ◽

Prey Interaction ◽

The Impact

AbstractHabitat degradation is pervasive across the tropics and is particularly acute in Southeast Asia, with major implications for biodiversity. Much research has addressed the impact of degradation on species diversity; however, little is known about how ecological interactions are altered, including those that constitute important ecosystem functions such as pest consumption.We examined how rainforest degradation alters trophic interaction networks linking insectivorous bats and their prey. We used DNA metabarcoding to study the diets of forest-dwelling insectivorous bat species, and compared bat-prey interaction networks between old growth forest and forest degraded by logging in Sabah, Borneo.We predicted that rainforest degradation would cause measurable reductions in the numbers of prey consumed by individual bats, and that this degradation would yield networks in logged forest with lower functional complementarity, modularity and nestedness than those in old growth forest.Compared to bats in old growth rainforest, bats in logged sites consumed a lower diversity of prey. Their interaction networks were less nested and had a more modular structure in which bat species had lower closeness centrality scores than in old growth forest. These network structures were associated with reduced network redundancy and thus increased vulnerability to perturbations in logged forests.Our results show how ecological interactions change between old growth and logged forests, with potentially negative implications for ecosystem function and network stability. We also highlight the potential importance of insectivorous bats in consuming invertebrate pests.Malay abstractDegradasi habitat merupakan suatu fenomena yang berleluasa dikawasan tropika, terutamanya di Asia Tenggara dengan implikasi yang besar ke atas biodiversiti. Banyak kajian telahpun meneliti impak degradasi habitat atas kepelbagaian spesis. Walau bagaimanapun, dari segi mana interaksi ekologi diubah suai kurang diselidik, termasuk interaksi yang membentuk fungsi ekosistem yang penting seperti pemakanan binatang perosak.Kami telah memeriksa bagaimana degradasi hutan hujan tropika dapat mengubah suai interaksi antara tahap trofik yang menghubungkan kelawar yang memakan serangga dan mangsa mereka. Kami telah menggunakan “DNA metabarcoding” untuk mengenal pasti kandungan artropod dalam sampel najis kelawar and membandingkan jaringan interaksi kelawar dan mangsa mereka diantara hutan dara dan hutan yang telah dibalak di Sabah, Borneo.Kami meramalkan bahawa degradasi hutan hujan akan menyebabkan kekurangan dalam bilangan nod mangsa yang dimakan oleh setiap individu kelawar yang dapat diukur. Degradasi ini pula boleh menghasilkan jaringan yang mempunyai fungsi saling melengkapi dan modulariti yang rendah, dan lebih berkelompok atau “mempunyai “nestedness” yang lebih tinggi di hutan yang dibalak berbanding hutan dara.Kelawar di kawasan hutan yang dibalak memakan diversiti mangsa yang lebih rendah dengan kelawar di habitat hutan hujan dara. Jaringan-jaringan interaksi mereka kurang berkelompok dan mempunyai stuktur yang lebih modular dimana spesis kelawar mempunyai pemarkahan kerapatan berpusat yang lebih rendah daripada sepesis kelawar di hutan dara. Struktur-struktur jaringan ini berkait dengan lebihan jaringan atau “network redundancy” yang lebih rendah and ini membawa kepada kerentantan yang meningkat terhadap gangguan luar di hutan yang telah dibalak.Keputusan kami menunjukkan bagaimana interaksi ekologi berubah diantara hutan dara dan hutan yang dibalak, dengan potensi implikasi negatif untuk fungsi ekosistem dan kestabilan jaringan. Kami juga telah menunjukkan potensi kepentingan kelawar yang memakan serangga dalam fungsi mereka untuk makan perosak invertebrat.Data Accessibility StatementData are currently archived at the Centre for Ecology and Hydrology Environmental Information Data Centre (https://doi.org/10.5285/8b106445-d8e0-482c-b517-5a372a09dc91) and will be released from embargo following publication. Specific analysis scripts are available on GitHub with links given in the manuscript and will be archived on Zenodo prior to publication.Statement of authorshipSR, EC, DHB, MS and OTL conceived the project, DHB, VK and JB undertook field collections and laboratory work, DHB analysed the data with input from EC, and DHB wrote the manuscript with input from all authors.

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Population-Resources Dynamics

10.32545/encyclopedia201901.0003.v3 ◽

2019 ◽

Author(s):

Charles Telles

Keyword(s):

Nonlinear Dynamics ◽

Time Series ◽

Phase Space ◽

Time Series Analysis ◽

Ecological Interactions ◽

Ecological Interaction ◽

Time Frequency ◽

Series Analysis ◽

Dynamics Of Population

Dynamics of population and resources symmetries are investigated and a model of ecological interaction was obtained. It was observed how variables in these dynamics are in recurrence considering parameters such as time, frequency, iteration, interaction and frequency of iteration. Variables investigated are the consume, idleness and supply mechanics and time series analysis of these variables indicated a possible phase space formation of the phenomena. The main conclusion leads to a nonlinear dynamics of the  ecological interactions of the organisms and resources symmetries.

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The LDE-Type Flare Occurrence (1969-1993)

International Astronomical Union Colloquium ◽

10.1017/s0252921100025458 ◽

1994 ◽

Vol 144 ◽

pp. 279-282

Author(s):

A. Antalová

Keyword(s):

Time Series ◽

Fourier Analysis ◽

Sunspot Cycle ◽

List Type ◽

Type Number ◽

Solar Cycles ◽

Type Iv ◽

Long Duration ◽

Cycle Maximum ◽

Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

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Development of a time-series shotgun metagenomics database for monitoring microbial communities at the Pacific coast of Japan

Scientific Reports ◽

10.1038/s41598-021-91615-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Kazutoshi Yoshitake ◽

Gaku Kimura ◽

Tomoko Sakami ◽

Tsuyoshi Watanabe ◽

Yukiko Taniuchi ◽

...

Keyword(s):

Time Series ◽

Microbial Communities ◽

Pacific Coast ◽

Three Dimensional ◽

Amplicon Sequencing ◽

Metagenomic Data ◽

Shotgun Metagenomics ◽

Functional Features ◽

Pacific Coast Of Japan ◽

Marine Microbial Communities

AbstractAlthough numerous metagenome, amplicon sequencing-based studies have been conducted to date to characterize marine microbial communities, relatively few have employed full metagenome shotgun sequencing to obtain a broader picture of the functional features of these marine microbial communities. Moreover, most of these studies only performed sporadic sampling, which is insufficient to understand an ecosystem comprehensively. In this study, we regularly conducted seawater sampling along the northeastern Pacific coast of Japan between March 2012 and May 2016. We collected 213 seawater samples and prepared size-based fractions to generate 454 subsets of samples for shotgun metagenome sequencing and analysis. We also determined the sequences of 16S rRNA (n = 111) and 18S rRNA (n = 47) gene amplicons from smaller sample subsets. We thereafter developed the Ocean Monitoring Database for time-series metagenomic data (http://marine-meta.healthscience.sci.waseda.ac.jp/omd/), which provides a three-dimensional bird’s-eye view of the data. This database includes results of digital DNA chip analysis, a novel method for estimating ocean characteristics such as water temperature from metagenomic data. Furthermore, we developed a novel classification method that includes more information about viruses than that acquired using BLAST. We further report the discovery of a large number of previously overlooked (TAG)n repeat sequences in the genomes of marine microbes. We predict that the availability of this time-series database will lead to major discoveries in marine microbiome research.

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673 Precision microbiome mapping identifies a microbiome signature predictive of Immune checkpoint inhibitor response across multiple research study cohorts

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-sitc2020.0673 ◽

2020 ◽

Vol 8 (Suppl 3) ◽

pp. A711-A711

Author(s):

Matthew Robinson ◽

Kevin Vervier ◽

Simon Harris ◽

David Adams ◽

Doreen Milne ◽

...

Keyword(s):

Gut Microbiome ◽

Immune Checkpoint ◽

Immune Checkpoint Inhibitor ◽

Checkpoint Inhibitor ◽

Advanced Melanoma ◽

List Type ◽

Metagenomic Sequencing ◽

Genome Database ◽

Sequencing Platform ◽

Melanoma Patients

BackgroundThe gut microbiome of cancer patients appears to be associated with response to Immune Checkpoint Inhibitor (ICIs) treatment.1–4 However, the bacteria linked to response differ between published studies.MethodsLongitudinal stool samples were collected from 69 patients with advanced melanoma receiving approved ICIs in the Cambridge (UK) MELRESIST study. Pretreatment samples were analysed by Microbiotica, using shotgun metagenomic sequencing. Microbiotica’s sequencing platform comprises the world’s leading Reference Genome Database and advanced Microbiome Bioinformatics to give the most comprehensive and precise mapping of the gut microbiome. This has enabled us to identify gut bacteria associated with ICI response missed using public reference genomes. Published microbiome studies in advanced melanoma,1–3renal cell carcinoma (RCC) and non-small cell lung cancer (NSCLC)4 were reanalysed with the same platform.ResultsAnalysis of the MELRESIST samples showed an overall change in the microbiome composition between advanced melanoma patients and a panel of healthy donor samples, but not between patients who subsequently responded or did not respond to ICIs. However, we did identify a discrete microbiome signature which correlated with response. This signature predicted response with an accuracy of 93% in the MELRESIST cohort, but was less predictive in the published melanoma cohorts.1–3 Therefore, we developed a bioinformatic analytical model, incorporating an interactive random forest model and the MELRESIST dataset, to identify a microbiome signature which was consistent across all published melanoma studies. This model was validated three times by accurately predicting the outcome of an independent cohort. A final microbiome signature was defined using the validated model on MELRESIST and the three published melanoma cohorts. This was very accurate at predicting response in all four studies combined (91%), or individually (82–100%). This signature was also predictive of response in a NSCLC study and to a lesser extent in RCC. The core of this signature is nine bacteria significantly increased in abundance in responders.ConclusionsAnalysis of the MELRESIST study samples, precision microbiome profiling by the Microbiotica Platform and a validated bioinformatic analysis, have enabled us to identify a unique microbiome signature predictive of response to ICI therapy in four independent melanoma studies. This removes the challenge to the field of different bacteria apparently being associated with response in different studies, and could represent a new microbiome biomarker with clinical application. Nine core bacteria may be driving response and hold potential for co-therapy with ICIs.Ethics ApprovalThe study was approved by Newcastle & North Tyneside 2 Research Ethics Committee, approval number 11/NE/0312.ReferencesMatson V, Fessler J, Bao R, et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 2018;359(6371):104–108.Gopalakrishnan V, Spencer CN, Nezi L, et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 2018;359(6371):97–103.Frankel AE, Coughlin LA, Kim J, et al. Metagenomic shotgun sequencing and unbiased metabolomic profiling identify specific human gut microbiota and metabolites associated with immune checkpoint therapy efficacy in melanoma patients. Neoplasia 2017;19(10):848–855.Routy B, Le Chatelier E, Derosa L, et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 2018;359(6371):91–97.

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Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases

Nature Communications ◽

10.1038/s41467-021-23821-6 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Verónica Lloréns-Rico ◽

Sara Vieira-Silva ◽

Pedro J. Gonçalves ◽

Gwen Falony ◽

Jeroen Raes

Keyword(s):

Microbial Communities ◽

Quantitative Methods ◽

Microbial Load ◽

Metagenomic Sequencing ◽

Sampling Depth ◽

Microbiome Research ◽

Microbiome Data ◽

The Impact ◽

Analytical Approaches ◽

Quantitative Approaches

AbstractWhile metagenomic sequencing has become the tool of preference to study host-associated microbial communities, downstream analyses and clinical interpretation of microbiome data remains challenging due to the sparsity and compositionality of sequence matrices. Here, we evaluate both computational and experimental approaches proposed to mitigate the impact of these outstanding issues. Generating fecal metagenomes drawn from simulated microbial communities, we benchmark the performance of thirteen commonly used analytical approaches in terms of diversity estimation, identification of taxon-taxon associations, and assessment of taxon-metadata correlations under the challenge of varying microbial ecosystem loads. We find quantitative approaches including experimental procedures to incorporate microbial load variation in downstream analyses to perform significantly better than computational strategies designed to mitigate data compositionality and sparsity, not only improving the identification of true positive associations, but also reducing false positive detection. When analyzing simulated scenarios of low microbial load dysbiosis as observed in inflammatory pathologies, quantitative methods correcting for sampling depth show higher precision compared to uncorrected scaling. Overall, our findings advocate for a wider adoption of experimental quantitative approaches in microbiome research, yet also suggest preferred transformations for specific cases where determination of microbial load of samples is not feasible.

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