Literature DB >> 34357339

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview.

Morena M Tinte1, Kekeletso H Chele1, Justin J J van der Hooft2, Fidele Tugizimana1,3.   

Abstract

Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

Entities:  

Keywords:  4IR technologies; abiotic stress; automation; machine learning; metabolomics

Year:  2021        PMID: 34357339     DOI: 10.3390/metabo11070445

Source DB:  PubMed          Journal:  Metabolites        ISSN: 2218-1989


  200 in total

1.  Machine learning approaches and their current application in plant molecular biology: A systematic review.

Authors:  Jose Cleydson F Silva; Ruan M Teixeira; Fabyano F Silva; Sergio H Brommonschenkel; Elizabeth P B Fontes
Journal:  Plant Sci       Date:  2019-04-04       Impact factor: 4.729

2.  Exploring open cheminformatics approaches for categorizing per- and polyfluoroalkyl substances (PFASs).

Authors:  Bo Sha; Emma L Schymanski; Christoph Ruttkies; Ian T Cousins; Zhanyun Wang
Journal:  Environ Sci Process Impacts       Date:  2019-10-02       Impact factor: 4.238

3.  Mass spectrometry searches using MASST.

Authors:  Mingxun Wang; Alan K Jarmusch; Fernando Vargas; Alexander A Aksenov; Julia M Gauglitz; Kelly Weldon; Daniel Petras; Ricardo da Silva; Robert Quinn; Alexey V Melnik; Justin J J van der Hooft; Andrés Mauricio Caraballo-Rodríguez; Louis Felix Nothias; Christine M Aceves; Morgan Panitchpakdi; Elizabeth Brown; Francesca Di Ottavio; Nicole Sikora; Emmanuel O Elijah; Lara Labarta-Bajo; Emily C Gentry; Shabnam Shalapour; Kathleen E Kyle; Sara P Puckett; Jeramie D Watrous; Carolina S Carpenter; Amina Bouslimani; Madeleine Ernst; Austin D Swafford; Elina I Zúñiga; Marcy J Balunas; Jonathan L Klassen; Rohit Loomba; Rob Knight; Nuno Bandeira; Pieter C Dorrestein
Journal:  Nat Biotechnol       Date:  2020-01       Impact factor: 54.908

Review 4.  Metabolomics by numbers: acquiring and understanding global metabolite data.

Authors:  Royston Goodacre; Seetharaman Vaidyanathan; Warwick B Dunn; George G Harrigan; Douglas B Kell
Journal:  Trends Biotechnol       Date:  2004-05       Impact factor: 19.536

Review 5.  Solid-phase extraction strategies to surmount body fluid sample complexity in high-throughput mass spectrometry-based proteomics.

Authors:  Marco R Bladergroen; Yuri E M van der Burgt
Journal:  J Anal Methods Chem       Date:  2015-01-27       Impact factor: 2.193

6.  Performance prediction of crosses in plant breeding through genotype by environment interactions.

Authors:  Javad Ansarifar; Faezeh Akhavizadegan; Lizhi Wang
Journal:  Sci Rep       Date:  2020-07-13       Impact factor: 4.379

Review 7.  Machine Learning in Agriculture: A Review.

Authors:  Konstantinos G Liakos; Patrizia Busato; Dimitrios Moshou; Simon Pearson; Dionysis Bochtis
Journal:  Sensors (Basel)       Date:  2018-08-14       Impact factor: 3.576

Review 8.  Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine.

Authors:  Dmitry Grapov; Johannes Fahrmann; Kwanjeera Wanichthanarak; Sakda Khoomrung
Journal:  OMICS       Date:  2018-08-20

Review 9.  Bioactive Phenolic Compounds From Agri-Food Wastes: An Update on Green and Sustainable Extraction Methodologies.

Authors:  Lucia Panzella; Federica Moccia; Rita Nasti; Stefania Marzorati; Luisella Verotta; Alessandra Napolitano
Journal:  Front Nutr       Date:  2020-05-07

10.  Accessible and reproducible mass spectrometry imaging data analysis in Galaxy.

Authors:  Melanie Christine Föll; Lennart Moritz; Thomas Wollmann; Maren Nicole Stillger; Niklas Vockert; Martin Werner; Peter Bronsert; Karl Rohr; Björn Andreas Grüning; Oliver Schilling
Journal:  Gigascience       Date:  2019-12-01       Impact factor: 6.524

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  2 in total

1.  Computational Metabolomics Tools Reveal Metabolic Reconfigurations Underlying the Effects of Biostimulant Seaweed Extracts on Maize Plants under Drought Stress Conditions.

Authors:  Morena M Tinte; Keabetswe Masike; Paul A Steenkamp; Johan Huyser; Justin J J van der Hooft; Fidele Tugizimana
Journal:  Metabolites       Date:  2022-05-27

2.  Metabolic and Physiological Changes in the Roots of Two Oat Cultivars in Response to Complex Saline-Alkali Stress.

Authors:  Yugang Gao; Yongling Jin; Wei Guo; Yingwen Xue; Lihe Yu
Journal:  Front Plant Sci       Date:  2022-03-29       Impact factor: 5.753

  2 in total

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