Evidence map›Paper›PMID 33850115›Full record

ArticleNature communications2021

Metabolic pathways inferred from a bacterial marker gene illuminate ecological changes across South Pacific frontal boundaries.

Eric J Raes, Kristen Karsh, Swan L S Sow, Martin Ostrowski, Mark V Brown, Jodie van de Kamp, Rita M Franco-Santos, Levente Bodrossy, Anya M Waite

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
6.2field-weighted citation impact, top 3% of its field
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

11 citing papers in PubMed, 47 citations in OpenAlex.

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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

9 authors at 5 institutions in 3 countries.

Eric J RaesCSIRO Oceans and Atmosphere, Hobart, TAS, Australia. eric.raes@dal.ca.ORCID http://orcid.org/0000-0002-4131-9312
Kristen KarshCSIRO Oceans and Atmosphere, Hobart, TAS, Australia.
Swan L S SowCSIRO Oceans and Atmosphere, Hobart, TAS, Australia.ORCID http://orcid.org/0000-0001-5887-7049
Martin OstrowskiClimate Change Cluster, University of Technology Sydney, Sydney, NSW, Australia.ORCID http://orcid.org/0000-0002-4357-3023
Mark V BrownSchool of Environmental and Life Sciences, The University of Newcastle, Callaghan, NSW, Australia.
Jodie van de KampCSIRO Oceans and Atmosphere, Hobart, TAS, Australia.ORCID http://orcid.org/0000-0003-2167-0938
Rita M Franco-SantosInstitute for Marine and Antarctic Studies, University of Tasmania, Hobart, TAS, Australia.ORCID http://orcid.org/0000-0001-7846-3425
Levente BodrossyCSIRO Oceans and Atmosphere, Hobart, TAS, Australia.ORCID http://orcid.org/0000-0001-6940-452X
Anya M WaiteOcean Frontier Institute and Department of Oceanography, Dalhousie University, Halifax, NS, Canada.
CSIRO Oceans and Atmosphere · AUDalhousie University · CAUniversity of Newcastle Australia · AUUniversity of Tasmania · AUUniversity of Technology Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Global oceanographic monitoring initiatives originally measured abiotic essential ocean variables but are currently incorporating biological and metagenomic sampling programs. There is, however, a large knowledge gap on how to infer bacterial functions, the information sought by biogeochemists, ecologists, and modelers, from the bacterial taxonomic information (produced by bacterial marker gene surveys). Here, we provide a correlative understanding of how a bacterial marker gene (16S rRNA) can be used to infer latitudinal trends for metabolic pathways in global monitoring campaigns. From a transect spanning 7000 km in the South Pacific Ocean we infer ten metabolic pathways from 16S rRNA gene sequences and 11 corresponding metagenome samples, which relate to metabolic processes of primary productivity, temperature-regulated thermodynamic effects, coping strategies for nutrient limitation, energy metabolism, and organic matter degradation. This study demonstrates that low-cost, high-throughput bacterial marker gene data, can be used to infer shifts in the metabolic strategies at the community scale.

Indexed as

BacteriaBacterial Physiological PhenomenaBiodiversityEcologyGenes, BacterialMetabolic Networks and PathwaysMetagenomeMetagenomicsPacific OceanRNA, Ribosomal, 16SSequence Analysis, DNAThermodynamicsRNA, Ribosomal, 16S

Identifiers

PMID33850115
PMCPMC8044245
OpenAlexW3091542727

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.