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

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Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: an Overview H OH metabolites OH Review Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview Morena M. Tinte 1, Kekeletso H. Chele 1, Justin J. J. van der Hooft 2,* and Fidele Tugizimana 1,3,* 1 Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; [email protected] (M.M.T.); [email protected] (K.H.C.) 2 Bioinformatics Group, Wageningen University, 6708 PB Wageningen, The Netherlands 3 International Research and Development Division, Omnia Group, Ltd., Johannesburg 2021, South Africa * Correspondence: [email protected] (J.J.J.v.d.H.); [email protected] (F.T.) 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 descrip- tions 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 Citation: Tinte, M.M.; Chele, K.H.; the opportunities and challenges offered by omics science, analytical intelligence, computational tools van der Hooft, J.J.J.; Tugizimana, F. and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics Metabolomics-Guided Elucidation of workflows and the use of machine learning and computational tools to decipher the dynamics in the Plant Abiotic Stress Responses in the chemical space that define plant responses to abiotic stress conditions. 4IR Era: An Overview. Metabolites 2021, 11, 445. https://doi.org/ Keywords: abiotic stress; metabolomics; 4IR technologies; automation; machine learning 10.3390/metabo11070445 Academic Editor: Peter Meikle 1. Introduction—A Dawn of a New Era and a Prime to Plant Defenses Received: 25 May 2021 Accepted: 3 July 2021 1.1. The Fourth Industrial Revolution (4IR) Era Published: 8 July 2021 The Fourth Industrial Revolution (4IR) era entails the integration of advanced tech- nologies in the physical, digital and biological domains. This includes the confluence and Publisher’s Note: MDPI stays neutral convergence of emerging technologies such as artificial intelligence (AI), the Internet of with regard to jurisdictional claims in Things (IoT), big data analytics, cloud computing, robotics and wireless telecommunica- published maps and institutional affil- tions [1,2]. These innovative technologies have brought about paradigm shifts and are iations. disruptively boosting many industries globally by encouraging new models that enable the acquisition, sharing, and use of data and resources to produce improved products/services in a faster, cheaper, more effective and sustainable manner [3]. In life sciences, particularly in the field of metabolomics—a multidisciplinary omics science that studies metabolism Copyright: © 2021 by the authors. (Section 2.1)—some of these 4IR technologies have been and are integral components of Licensee MDPI, Basel, Switzerland. metabolomics workflows. It suffices to highlight the use of analytical platforms that are This article is an open access article equipped with analytical and artificial intelligence (A/AI), generation of big data, applica- distributed under the terms and tion and development of big data analytics involving the use of machine and deep learning conditions of the Creative Commons (ML and DL) algorithms (Figure1). Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Metabolites 2021, 11, 445. https://doi.org/10.3390/metabo11070445 https://www.mdpi.com/journal/metabolites Metabolites 20212021,, 1111,, 445x FOR PEER REVIEW 32 of of 4143 Figure 1. 4IR technologies in the plant metabolomics workflow. The 4IR technologies and their implementation within the Figure 1. 4IR technologies in the plant metabolomics workflow. The 4IR technologies and their implementation within the plantplant metabolomicsmetabolomics workflowsworkflows are are indicated indicated based based on on colour, colour, as as highlighted highlighted by by the the key. key. An An illustration illustration of of the the preparation preparation of samplesof samples with with the the assistance assistance of of robotics, robotics, advancements advancements in in analytical analytical platforms, platforms, equipped equipped with with A/AI A/AI forfor sample analysis. TheThe generatedgenerated (big)(big) data can be uploaded on cloud-based servers,servers, e-infrastructures for data analysis, storage and sharing. SomeSome ofof thesethese web-based web-based suits suits include include MetaboAnalyst, MetaboAnalyst, XCMS XCMS Online, Online, MetExplore, MetExplore, PhenoMeNal PhenoMeNal and and GNPS. GNPS. Computational Computa- toolstional in tools these in thesee-infrastructures e-infrastructures involve involve the use the ofuse chemometrics of chemometrics methods, methods, ML ML and and DL DL algorithms. algorithms. Metabolic Metabolic pathwaypathway reconstructionreconstruction and and network network analysis analysis are are of oftenten used used for for biological biological interpretation interpretation of metabolomics of metabolomics data. data. The IoT The isIoT an indis- is an indispensablepensable component component supporting supporting most most of these of these cloud cloud metabolomics metabolomics frameworks. frameworks. AsThis the review field matures,focuses on with the advancementsuse of metabolomics in technologies, in interrogating development plant responses and appli- to cationsadverse ofenvironmental state-of-the-art conditions, bioinformatics with a andparticular computational attention tools,to the equipped4IR technologies with ML in algorithms,this multidisciplinary are gaining omics momentum science. Metabolomics for data mining is increasingly and interpretation enabling [ 4the,5]. decoding Typical widelyof the language adopted examplesof cells at include molecular the Globallevel, advancing Natural Product the understanding Social Molecular of regulatory Network- ingnetwork (GNPS), rules an and ecosystem mechanistic of tandem events mass at cellular spectrometry and chemical (MS/MS) space data of storage, the plant analysis under andconsideration. sharing [6], Plants MetaboLights, are naturally a cloud sessile computing organisms, based and repository are thus susceptible that enables to the changing sharing andenvironmental re-use of data conditions and meta-data such as[ 7abiotic], MS2LDA, stress afactors software that tool include that extractsdrought, co-occurring salinity, ex- masstremefragments high and low and temperatures, neutral losses heavy from MS/MS metals, spectralight and using radiation an unsupervised [13,14]. These ML abiotic algo- rithmstress [factors8], MetaboAnalyst, can negatively a web-based affect plant service growth, consisting development of modules and productivity, for data pretreatment, and sub- miningsequently and the pathway agricultural analysis, yield. XCMS, It is, ther a cloud-basedefore, imperative data analysis to comprehensively suite for preprocessing and pre- untargeteddictively understand liquid chromatography-mass the plant metabolism spectrometry under abiotic (LC-MS) stresses, data, as statisticalsuch fundamental analysis, pathwayand actionable analysis insights and multi-omic(adding to the data current integration, knowledgebase, MetExplore, Section an environment 1.2) will contribute for the curationto the development of metabolic of networks, plants with and enhanced PhenoMeNal, resilience a e-infrastructure and productivity, with and a collection support ofstrategies workflows that andpromote tools plant for metabolomics growth under analysis abiotic stress pipelines conditions [5] (Figure [15,16].1). To The logically digiti- zationarticulate of massthese spectraaspects, to the aid review in biological is struct interpretationured to comprise of plant four metabolomicsmain components. data In is illustratedSections 1.1. in and a study 1.2, ofthe more 4IR era than is 70 brieflyRhamnaceae definedplant and extractsintroduced [9] where as well the as authors the current illu- minatemodels clade-specificof plant defense chemical mechanisms. signatures The annotated second throughmain section an integrative then elaborates computational on 4IR metabolomicstechnologies in workflow. the context of (plant) metabolomics workflows, from sample preparation step toThe the increasing annotation momentum of metabolites. and useAutomation of 4IR technologies and technological in lifesciences, advancements particularly in an- inalytical plant techniques, metabolomics, the whichuse of is machine the focus learning of this review,and computational is redefining tools the ideological to aid in Metabolites
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