Evidence map›Paper›PMID 35288949›Full record

ArticleThe New phytologist2022

Predictive metabolomics of multiple Atacama plant species unveils a core set of generic metabolites for extreme climate resilience.

Thomas Dussarrat, Sylvain Prigent, Claudio Latorre, Stéphane Bernillon, Amélie Flandin, Francisca P Díaz, Cédric Cassan, Pierre Van Delft, Daniel Jacob, Kranthi Varala and 5 more

Abstract read
In one paragraph

Article in The New phytologist, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

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  15. Cardioprotective effects ofFrontiers in pharmacology · 2025
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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

15 authors.

Thomas DussarratDepartamento de Genética Molecular y Microbiología, Pontificia Universidad Católica de Chile, FONDAP Center for Genome Regulation and Millenium Institute for Integrative Biology (iBio), Av Libertador Bernardo O'Higgins 340, Santiago, Chile.ORCID 0000-0001-6245-3652
Sylvain PrigentUniv. Bordeaux, INRAE, UMR1332 BFP, 33882, Villenave d'Ornon, France.ORCID 0000-0001-5146-0347
Claudio LatorreDepartamento de Ecología, Pontificia Universidad Católica de Chile, Av Libertador Bernardo O'Higgins 340, Santiago, Chile.ORCID 0000-0003-4708-7599
Stéphane BernillonUniv. Bordeaux, INRAE, UMR1332 BFP, 33882, Villenave d'Ornon, France.ORCID 0000-0003-4955-0161
Amélie FlandinUniv. Bordeaux, INRAE, UMR1332 BFP, 33882, Villenave d'Ornon, France.
Francisca P DíazDepartamento de Genética Molecular y Microbiología, Pontificia Universidad Católica de Chile, FONDAP Center for Genome Regulation and Millenium Institute for Integrative Biology (iBio), Av Libertador Bernardo O'Higgins 340, Santiago, Chile.ORCID 0000-0002-1100-7801
Cédric CassanUniv. Bordeaux, INRAE, UMR1332 BFP, 33882, Villenave d'Ornon, France.ORCID 0000-0003-3022-4278
Pierre Van DelftBordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 33140, Villenave d'Ornon, France.ORCID 0000-0001-5839-8427
Daniel JacobUniv. Bordeaux, INRAE, UMR1332 BFP, 33882, Villenave d'Ornon, France.ORCID 0000-0002-6687-7169
Kranthi VaralaDepartment of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN, 47907, USA.ORCID 0000-0003-1051-6636
Jérôme JoubesLaboratoire de Biogenèse Membranaire, CNRS, Univ. Bordeaux, UMR 5200, Villenave d'Ornon, France.ORCID 0000-0001-5385-450X
Yves GibonUniv. Bordeaux, INRAE, UMR1332 BFP, 33882, Villenave d'Ornon, France.ORCID 0000-0001-8161-1089
Dominique RolinUniv. Bordeaux, INRAE, UMR1332 BFP, 33882, Villenave d'Ornon, France.ORCID 0000-0001-5952-0949
Rodrigo A GutiérrezDepartamento de Genética Molecular y Microbiología, Pontificia Universidad Católica de Chile, FONDAP Center for Genome Regulation and Millenium Institute for Integrative Biology (iBio), Av Libertador Bernardo O'Higgins 340, Santiago, Chile.ORCID 0000-0002-5961-5005
Pierre PétriacqUniv. Bordeaux, INRAE, UMR1332 BFP, 33882, Villenave d'Ornon, France.ORCID 0000-0001-8151-7420

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current crop yield of the best ideotypes is stagnating and threatened by climate change. In this scenario, understanding wild plant adaptations in extreme ecosystems offers an opportunity to learn about new mechanisms for resilience. Previous studies have shown species specificity for metabolites involved in plant adaptation to harsh environments. Here, we combined multispecies ecological metabolomics and machine learning-based generalized linear model predictions to link the metabolome to the plant environment in a set of 24 species belonging to 14 families growing along an altitudinal gradient in the Atacama Desert. Thirty-nine common compounds predicted the plant environment with 79% accuracy, thus establishing the plant metabolome as an excellent integrative predictor of environmental fluctuations. These metabolites were independent of the species and validated both statistically and biologically using an independent dataset from a different sampling year. Thereafter, using multiblock predictive regressions, metabolites were linked to climatic and edaphic stressors such as freezing temperature, water deficit and high solar irradiance. These findings indicate that plants from different evolutionary trajectories use a generic metabolic toolkit to face extreme environments. These core metabolites, also present in agronomic species, provide a unique metabolic goldmine for improving crop performances under abiotic pressure.

Indexed as

BrassicaceaeEcosystemClimate ChangeHumansMetabolomicsPlantsSpecies Specificityadaptationextreme environmentsmultiple speciesplant metabolismpredictive metabolomics

Identifiers

PMID35288949
PMCPMC9324839

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Registered trials

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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.