Metabolite-Based Biomarkers for Plant Genetics and Breeding

Zabotina, Olga A.

doi:10.1007/978-94-007-5687-8_14

Cited by 7 publications

(4 citation statements)

References 109 publications

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“…Recent advances in ‘omics’ technologies have made the discovery of new candidate genes or metabolites for MAS possible in crops with limited or even unavailable genomic information (Gebhardt, ; Zabotina, ). However, marker candidates and the respective prediction models need to be tested with an independent set of samples to ensure robustness and generalizability (Schudoma et al ., ; Zabotina, ). The first implementation and validation of MAS for polygenic tuber quality traits in potato was conducted by Li et al .…”

Section: Introductionmentioning

confidence: 99%

Metabolite and transcript markers for the prediction of potato drought tolerance

Sprenger

Erban

Seddig

et al. 2017

Plant Biotechnology Journal

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SummaryPotato (Solanum tuberosum L.) is one of the most important food crops worldwide. Current potato varieties are highly susceptible to drought stress. In view of global climate change, selection of cultivars with improved drought tolerance and high yield potential is of paramount importance. Drought tolerance breeding of potato is currently based on direct selection according to yield and phenotypic traits and requires multiple trials under drought conditions. Marker‐assisted selection (MAS) is cheaper, faster and reduces classification errors caused by noncontrolled environmental effects. We analysed 31 potato cultivars grown under optimal and reduced water supply in six independent field trials. Drought tolerance was determined as tuber starch yield. Leaf samples from young plants were screened for preselected transcript and nontargeted metabolite abundance using qRT‐PCR and GC‐MS profiling, respectively. Transcript marker candidates were selected from a published RNA‐Seq data set. A Random Forest machine learning approach extracted metabolite and transcript markers for drought tolerance prediction with low error rates of 6% and 9%, respectively. Moreover, by combining transcript and metabolite markers, the prediction error was reduced to 4.3%. Feature selection from Random Forest models allowed model minimization, yielding a minimal combination of only 20 metabolite and transcript markers that were successfully tested for their reproducibility in 16 independent agronomic field trials. We demonstrate that a minimum combination of transcript and metabolite markers sampled at early cultivation stages predicts potato yield stability under drought largely independent of seasonal and regional agronomic conditions.

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Section: Introductionmentioning

confidence: 99%

Metabolite and transcript markers for the prediction of potato drought tolerance

Sprenger

Erban

Seddig

et al. 2017

Plant Biotechnology Journal

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“…They have both been used to proxy the nitrogen status in plants for the sustainable fertilization of wheat, barley and maize (Justes et al 1997 ; Uddling et al 2007 ) through measurements of nitrate in stem fluids or chlorophyll in leaves respectively. Because plant scientists and breeders are eager to improve crop performances in challenging conditions for human food security and to find varieties selected for more complex traits, metabolic markers are also becoming popular in plant science and breeding (Herrmann and Schauer 2013 ; Zabotina 2013 ). However, the use of metabolic markers is not straightforward.…”

Section: Introductionmentioning

confidence: 99%

Fortune telling: metabolic markers of plant performance

et al. 2016

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Background In the last decade, metabolomics has emerged as a powerful diagnostic and predictive tool in many branches of science. Researchers in microbes, animal, food, medical and plant science have generated a large number of targeted or non-targeted metabolic profiles by using a vast array of analytical methods (GC–MS, LC–MS, 1H-NMR….). Comprehensive analysis of such profiles using adapted statistical methods and modeling has opened up the possibility of using single or combinations of metabolites as markers. Metabolic markers have been proposed as proxy, diagnostic or predictors of key traits in a range of model species and accurate predictions of disease outbreak frequency, developmental stages, food sensory evaluation and crop yield have been obtained.Aim of review(i) To provide a definition of plant performance and metabolic markers, (ii) to highlight recent key applications involving metabolic markers as tools for monitoring or predicting plant performance, and (iii) to propose a workable and cost-efficient pipeline to generate and use metabolic markers with a special focus on plant breeding.Key messageUsing examples in other models and domains, the review proposes that metabolic markers are tending to complement and possibly replace traditional molecular markers in plant science as efficient estimators of performance.

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“…The robustness of phenotyping criteria for assessing disease resistance in cassava has been already addressed, and has highlighted the effects of the environment on the visible symptoms of disease [1] and on the development rate of its vector, the whiteflies [2]. In the absence of reliable or clearly distinguishable phenotypes, metabolite analysis has been introduced as a tool for aiding selection and assessing effects of genetic differences, both among candidate donors (parents), siblings or even families derived from genetic crosses [3][4][5][6]. Food security is one of the major challenges facing society in the 21 st century and over 800 million of people rely on cassava production to subsist.…”

Section: Introductionmentioning

confidence: 99%

The metabotyping of an East African cassava diversity panel: A core collection for developing biotic stress tolerance in cassava

et al. 2020

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Cassava will have a vital role to play, if food security is to be achieved in Sub-Saharan Africa, especially Central and East Africa. The whitefly Bemisia tabaci poses a major threat to cassava production by small holder farmers in part due to their role as a vector of cassava mosaic begomoviruses (CMBs) and cassava brown streak ipomoviruses (CBSIs). In the present study untargeted metabolomics has been used as a tool to assess natural variation, similarities and attempts to identify trait differentiators among an East African cassava diversity panel that displayed tolerance/resistance to the effects of Bemisia tabaci infestation. The metabolome captured, was represented by 1529 unique chemical features per accession. Principal component analysis (PCA) identified a 23% variation across the panel, with geographical origin/adaption the most influential classification factors. Separation based on resistance and susceptible traits to Bemisia tabaci could also be observed within the data and was corroborated by genotyping data. Thus the metabolomics pipeline represented an effective metabotyping approach. Agglomerative Hierarchical Clustering Analysis (HCA) of both the metabolomics and genotyping data was performed and revealed a high level of similarity between accessions. Specific differentiating features/metabolites were identified, including those potentially conferring vigour to whitefly tolerance on a constitutive manner. The implications of using these cassava varieties as parental breeding material and the future potential of incorporating more exotic donor material is discussed.

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Metabolite-Based Biomarkers for Plant Genetics and Breeding

Cited by 7 publications

References 109 publications

Metabolite and transcript markers for the prediction of potato drought tolerance

Metabolite and transcript markers for the prediction of potato drought tolerance

Fortune telling: metabolic markers of plant performance

The metabotyping of an East African cassava diversity panel: A core collection for developing biotic stress tolerance in cassava

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