Evidence map›Paper›PMID 37081491›Full record

ArticleMicrobiome2023

Disentangling temporal associations in marine microbial networks.

Ina Maria Deutschmann, Anders K Krabberød, Francisco Latorre, Erwan Delage, Cèlia Marrasé, Vanessa Balagué, Josep M Gasol, Ramon Massana, Damien Eveillard, Samuel Chaffron and 1 more

Abstract readVideo-Audio Media
In one paragraph

Article in Microbiome, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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  10. Ecological associations distribution modelling of marine plankton at a global scale.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
    Article
  11. 3D intrusions transport active surface microbial assemblages to the dark ocean.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  12. Article
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  15. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Ina Maria DeutschmannInstitute of Marine Sciences (ICM), CSIC, Passeig Marítim de La Barceloneta, 37-49, 08003, Barcelona, Spain. ina.m.deutschmann@gmail.com.
Anders K KrabberødDepartment of Biosciences/Section for Genetics and Evolutionary Biology (EVOGENE), University of Oslo, p.b. 1066 Blindern, N-0316, Oslo, Norway.
Francisco LatorreInstitute of Marine Sciences (ICM), CSIC, Passeig Marítim de La Barceloneta, 37-49, 08003, Barcelona, Spain.
Erwan DelageNantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000, Nantes, France.
Cèlia MarraséInstitute of Marine Sciences (ICM), CSIC, Passeig Marítim de La Barceloneta, 37-49, 08003, Barcelona, Spain.
Vanessa BalaguéInstitute of Marine Sciences (ICM), CSIC, Passeig Marítim de La Barceloneta, 37-49, 08003, Barcelona, Spain.
Josep M GasolInstitute of Marine Sciences (ICM), CSIC, Passeig Marítim de La Barceloneta, 37-49, 08003, Barcelona, Spain.
Ramon MassanaInstitute of Marine Sciences (ICM), CSIC, Passeig Marítim de La Barceloneta, 37-49, 08003, Barcelona, Spain.
Damien EveillardNantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000, Nantes, France.
Samuel ChaffronNantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000, Nantes, France.
Ramiro LogaresInstitute of Marine Sciences (ICM), CSIC, Passeig Marítim de La Barceloneta, 37-49, 08003, Barcelona, Spain. ramiro.logares@icm.csic.es.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMicrobial interactions are fundamental for Earth's ecosystem functioning and biogeochemical cycling. Nevertheless, they are challenging to identify and remain barely known. Omics-based censuses are helpful in predicting microbial interactions through the statistical inference of single (static) association networks. Yet, microbial interactions are dynamic and we have limited knowledge of how they change over time. Here, we investigate the dynamics of microbial associations in a 10-year marine time series in the Mediterranean Sea using an approach inferring a time-resolved (temporal) network from a single static network.

resultsA single static network including microbial eukaryotes and bacteria was built using metabarcoding data derived from 120 monthly samples. For the decade, we aimed to identify persistent, seasonal, and temporary microbial associations by determining a temporal network that captures the interactome of each individual sample. We found that the temporal network appears to follow an annual cycle, collapsing, and reassembling when transiting between colder and warmer waters. We observed higher association repeatability in colder than in warmer months. Only 16 associations could be validated using observations reported in literature, underlining our knowledge gap in marine microbial ecological interactions.

conclusionsOur results indicate that marine microbial associations follow recurrent temporal dynamics in temperate zones, which need to be accounted for to better understand the functioning of the ocean microbiome. The constructed marine temporal network may serve as a resource for testing season-specific microbial interaction hypotheses. The applied approach can be transferred to microbiome studies in other ecosystems. Video Abstract.

Indexed as

EcosystemMicrobiotaBacteriaMicrobial ConsortiaMicrobial InteractionsSeasonsAssociation networkMicrobial interactionsMicroorganismsOceanPlanktonTemporal networkTime series

Identifiers

PMID37081491
PMCPMC10120119

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.