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 Metabolic profiling to discriminate the biochemical basis of Powdery Mildew resistance in Mungbean (Vigna radiata)

Soundhiriyan P V a,*, Kamalakannan Aa, and Paranitharan Va
a1Department of Plant Pathology, Centre for Plant Protection Studies, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu � 636903 India.








Corresponding author:
Corresponding author: Mr. Soundhiriyan P V* 
Mailing address: Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India � 636903, India
Mobil phone: +91-944-260-8455
E-mail:  HYPERLINK "mailto:soundharpv14@gmail.com" soundharpv14@gmail.com




Abstract:

The research work was taken up to study the metabolites associated with the powdery mildew resistance and susceptibility in mungbean. Mungbean (Vigna radiata) is an important legume species belong to the family Fabaceae. One of the significantly important diseases of mungbean is powdery mildew caused by Erysiphe polygoni. The genotypes used for this study are SML 1082 (Highly susceptible), and TARM -18 (Highly Resistant). The induction of metabolites during host pathogen interaction was studied and found that the total of 6 metabolites like Hexadeconic acid, alanine, validane, dimethyl amino benzaldehyde, putrescine and methy ethyl trisulfide were up-regulated in resistant mungbean genotypes upon pathogen inoculation and contributed for the powdery mildew resistance. The research work was taken up to study the metabolites associated with the powdery mildew resistance and susceptibility in mungbean. Mungbean (Vigna radiata) are prone to several diseases, fungi being the major pathogens compromising its cultivation and economic profit around the world. One of the significantly important fungal diseases of mungbean is powdery mildew caused by Erysiphe polygoni. Knowledge of the complexity of mechanisms responsible for resistance to fungus infection of cultivars, such as TARM-18, is necessary for strategies to be defined which will improve resistance in highly susceptible crop species. Metabolic profiles of the Vigna radiata cultivars TARM-18 and SML1082 (resistant and susceptible to fungi, respectively) were analysed by Gas Chromatography /EI-TOF-Mass Spectrometry. The GC-MS output on scans and mass abundance were possessed and the metabolites were identified manually using AMDIS software integrated with NIST library 2014. Different metabolites elaborated under the compatible and incompatible interactions of resistant and susceptible genotypes in response to Erysiphe polygoni pathogenesis have been tentatively identified as the top five spectral hits with the NIST-05 National Institute of Standards Technology. Palisade Corp., NY and Golm Metabolome Database (GMD) libraries, using retention times as reference. In total, 20 metabolites were tentatively identified and were classified into different chemical groups. The integration of datasets obtained through metabolome analysis revealed differences in metabolites between both cultivars. These differences are probably associated with the induced resistance of TARM-18 towards the powdery mildews. Several metabolites related to powdery mildew stress and defence, namely Hexadeconic acid, alanine, validane, dimethyl amino benzaldehyde, putrescine and methy ethyl trisulfide were up-regulated in TARM-18 which are known to confer resistance to fungi. The differences in metabolites detected are discussed in terms of their potential interest to discriminate among resistant and susceptible mungbean cultivars as well as their possible role in plant defence against pathogen attack.

Keywords: Metabolic Profiling; Mungbean; Powdery mildew; Erysiphe polygoni

1. Introduction

Legumes are important protein (22-24%) source for food diet of humans. The Mungbean [Vigna radiata (L.) Wilcjeck] is being widely cultivated in the region of Tropical Asia ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Shanmugasundaram","given":"S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kim","given":"D. H.","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["1996"]]},"number-of-pages":"137-173.","publisher":"Science Publishers, En�eld, New Hampshire","title":"Mungbean. In: P. N. Bahl, and P. M. Salimath (eds), Genetics, Cytogenetic and Breeding of Crop Plant","type":"book"},"uris":["http://www.mendeley.com/documents/?uuid=e48f49a7-5084-414f-b0d7-a2052a724c37"]}],"mendeley":{"formattedCitation":"[1]","plainTextFormattedCitation":"[1]","previouslyFormattedCitation":"[1]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[1]. India is the largest producer of mungbean about 45% of total world production (Poehlman, 1991) and cultivated as Kharif crop. The Indian national average yield of mungbean is 468 kg ha-1 and it contributed about 8% of total pulse export during 2016-2017 (Indiastat, 2017). Significant foliar disease affecting the mungbean is Powdery mildew (Erysiphe polygoni D.C.), Macrophomina blight (Macrophomina phaseolina) and Cercospora leaf spot (Cercospora canescens) yellow mosaic (Mungbean Yellow Mosaic Virus). Among all the disease, Powdery mildew can cause yield loss from 9-50 per cent and maximum damage occurs when infects just before flowering ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Reddy","given":"K.S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dhanasekar","given":"P.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dhole","given":"V.J.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"J. Food Legumes","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2008"]]},"page":"151-155","title":"A review on powdery mildew disease resistance in mungbean","type":"article-journal","volume":"21"},"uris":["http://www.mendeley.com/documents/?uuid=21d8ad6d-e051-4678-85d0-e3cf2e59118e"]},{"id":"ITEM-2","itemData":{"author":[{"dropping-particle":"","family":"Pande","given":"S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sharma","given":"M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kumari","given":"S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gaur","given":"Pm","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chen","given":"W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"L","given":"Kaur","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"W","given":"Macleod","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"A","given":"Basandrai","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"D","given":"Basandrai","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"A","given":"Bakr","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"J.S","given":"Sandhu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"H.S","given":"Tripathi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gowda","given":"C L L","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"International Conference on Grain Legumes: Quality Improvement, Value Addition and Trade","editor":[{"dropping-particle":"","family":"Ali","given":"Masood","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-2","issued":{"date-parts":[["2009"]]},"page":"143-161","publisher":"Indian Society of Pulses Research and Development, Indian Institute of Pulses Research, Kanpur, India.","title":"Integrated foliar diseases management of legumes","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=472ada37-a918-48a3-bdcb-31f0675b9c55"]}],"mendeley":{"formattedCitation":"[2,3]","plainTextFormattedCitation":"[2,3]","previouslyFormattedCitation":"[2,3]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[2,3]. PM has the extensive geographical range and also have varied host range ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Moseman","given":"J. G.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Annu. Rev. Phytopathol.","id":"ITEM-1","issued":{"date-parts":[["1966"]]},"page":"269-290","title":"Genetics of powdery mildews.","type":"article-journal","volume":"4"},"uris":["http://www.mendeley.com/documents/?uuid=7379ea73-2fc8-4abd-a7ca-990de2e961fd"]},{"id":"ITEM-2","itemData":{"author":[{"dropping-particle":"","family":"Poehlman","given":"J. M.","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-2","issued":{"date-parts":[["1991"]]},"publisher":"Oxford and IBH Publ., New Delhi.","title":"The Mungbean","type":"book"},"uris":["http://www.mendeley.com/documents/?uuid=2539bc38-b458-4baf-84a5-1ebd856f9741"]}],"mendeley":{"formattedCitation":"[4,5]","plainTextFormattedCitation":"[4,5]","previouslyFormattedCitation":"[4,5]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[4,5] and severity is more during cool-dry months. Powdery mildew disease was managed mainly by chemical spray and genetic resistance but this method has not been satisfactory considering the loss of effectiveness of genes as new race evolved and use of chemicals is known to cause undesirable effects such as residual toxicity, development of resistance and not always economically feasible ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3389/fphys.2016.00413","ISSN":"1664042X","PMID":"27708588","abstract":"Previous work has demonstrated that glycerol-3-phosphate (G3P) and oleic acid (18:1) are two important signal molecules associated with plant resistance to fungi. In this article, we provide evidence that a 3% glycerol spray application 1-2 days before powdery mildew infection and subsequent applications once every 4 days was sufficient to stimulate the plant defense responses without causing any significant damage to wheat leaves. We found that G3P and oleic acid levels were markedly induced by powdery mildew infection. In addition, TaGLI1 (encoding a glycerol kinase) and TaSSI2 (encoding a stearoylacyl carrier protein fatty acid desaturase), two genes associated with the glycerol and fatty acid (FA) pathways, respectively, were induced by powdery mildew infection, and their promoter regions contain some fungal response elements. Moreover, exogenous application of glycerol increased the G3P level and decreased the level of oleic acid (18:1). Glycerol application induced the expression of pathogenesis-related (PR) genes (TaPR-1, TaPR-2, TaPR-3, TaPR-4, and TaPR-5), induced the generation of reactive oxygen species (ROS) before powdery mildew infection, and induced salicylic acid (SA) accumulation in wheat leaves. Further, we sprayed glycerol in a wheat field and found that it significantly (p < 0.05) reduced the severity of powdery mildew disease and lessened disease-associated kernel weight loss, all without causing any noticeable degradation in wheat seed quality.","author":[{"dropping-particle":"","family":"Li","given":"Yinghui","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Song","given":"Na","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhao","given":"Chuanzhi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Feng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Geng","given":"Miaomiao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Yuhui","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Wanhui","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xie","given":"Chaojie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sun","given":"Qixin","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Frontiers in Physiology","id":"ITEM-1","issue":"SEP","issued":{"date-parts":[["2016"]]},"page":"1-13","title":"Application of glycerol for induced powdery mildew resistance in Triticum aestivum L.","type":"article-journal","volume":"7"},"uris":["http://www.mendeley.com/documents/?uuid=3b613034-ceb2-41cd-b2a2-f2c5a11f96cf"]}],"mendeley":{"formattedCitation":"[6]","plainTextFormattedCitation":"[6]","previouslyFormattedCitation":"[6]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[6]. Cultivation of resistant genotypes is an effective and cheaper method to combat the disease and the resistant genotypes for PM are TARM-18, TAU-95-1, TM-96-2, VG-1, HPPC 16, HPCC 77, HPCC 95, Rachana, Pant P-9, PH-1, JP 514-A, VG-9 ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Malhotra","given":"S. K.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Singh","given":"Akhilesh","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Leg. Res.","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2000"]]},"page":"69-70","title":"Diversity for resistance to powdery mildew in peas","type":"article-journal","volume":"23"},"uris":["http://www.mendeley.com/documents/?uuid=3863ed98-449d-43ea-b7cb-ca61723f655a"]},{"id":"ITEM-2","itemData":{"author":[{"dropping-particle":"","family":"Jayalakshmi","given":"S. K.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mahalinga","given":"D. M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gangadhar","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Sarhad J. Agric.","id":"ITEM-2","issued":{"date-parts":[["2003"]]},"page":"31-32","title":"Source of resistance powdery mildew disease in mung bean, recent development in the diagnosis and management of plant disease","type":"article-journal","volume":"5"},"uris":["http://www.mendeley.com/documents/?uuid=178b50f1-12b5-4ee3-a77e-47ac2d1a2d6c"]},{"id":"ITEM-3","itemData":{"DOI":"10.5958/0976-1926.2015.00039.X","ISSN":"0971-8184","author":[{"dropping-particle":"","family":"Pathak","given":"Nidhi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Singh","given":"MN","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mishra","given":"MK","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Singh","given":"DK","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pal","given":"Yash","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dhurai","given":"SY","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pathak","given":"Rishi","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Indian Journal of Plant Genetic Resources","id":"ITEM-3","issue":"3","issued":{"date-parts":[["2015"]]},"page":"303","title":"Field Screening of Mungbean Genotypes and the Role of Total Soluble Sugars and Phenols against Powdery Mildew Resistance","type":"article-journal","volume":"28"},"uris":["http://www.mendeley.com/documents/?uuid=2d052c98-302f-32bb-84f6-7848383dd1e7"]}],"mendeley":{"formattedCitation":"[7�9]","plainTextFormattedCitation":"[7�9]","previouslyFormattedCitation":"[7�9]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[7�9]. But several genotypes need to be screened to identify resistant genotypes or to locate the source of resistance and it is time consuming process.  
In order to pyramid genes and to enhance the level of resistance in elite cultivars, the resistance genes and alleles of all types are needed to be identified and included in breeding programmes ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Yang","given":"W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Miller","given":"S.A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Scott","given":"J.W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jone","given":"J.B.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ranscis","given":"D.M.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Acta Hort (ISHS)","id":"ITEM-1","issued":{"date-parts":[["2005"]]},"page":"241-250","title":"Ming tomato genome sequence database for molecular markers: application to bacterial resistance and marker assisted selection","type":"article-journal","volume":"695"},"uris":["http://www.mendeley.com/documents/?uuid=f4fc6bcb-769e-42a4-b84a-60699d4edca0"]}],"mendeley":{"formattedCitation":"[10]","plainTextFormattedCitation":"[10]","previouslyFormattedCitation":"[10]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[10]. Hence, apart from the studies on pathogenesis and host-pathogen interactions using conventional methods, the outcome of �omics' studies will aid in a better understanding of complex response to pathogen infection ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1186/1471-2229-5-8","ISBN":"10.1186/1471-2229-5-8","ISSN":"14712229","PMID":"15927065","abstract":"BACKGROUND: This study analyzes metabolomic data from a rice tillering (branching) developmental profile to define a set of biomarker metabolites that reliably captures the metabolite variance of this plant developmental event, and which has potential as a basis for rapid comparative screening of metabolite profiles in relation to change in development, environment, or genotype. Changes in metabolism, and in metabolite profile, occur as a part of, and in response to, developmental events. These changes are influenced by the developmental program, as well as external factors impinging on it. Many samples are needed, however, to characterize quantitative aspects of developmental variation. A biomarker metabolite set could benefit screening of quantitative plant developmental variation by providing some of the advantages of both comprehensive metabolomic studies and focused studies of particular metabolites or pathways.\\n\\nRESULTS: An appropriate set of biomarker metabolites to represent the plant developmental period including the initiation and early growth of rice tillering (branching) was obtained by: (1) determining principal components of the comprehensive metabolomic profile, then (2) identifying clusters of metabolites representing variation in loading on the first three principal components, and finally (3) selecting individual metabolites from these clusters that were known to be common among diverse organisms. The resultant set of 21 biomarker metabolites was reliable (P = 0.001) in capturing 83% of the metabolite variation in development. Furthermore, a subset of the biomarker metabolites was successful (P = 0.05) in correctly predicting metabolite change in response to environment as determined in another rice metabolomics study.\\n\\nCONCLUSION: The ability to define a set of biomarker metabolites that reliably captures the metabolite variance of a plant developmental event was established. The biomarker metabolites are all commonly present in diverse organisms, so studies of their quantitative relationships can provide comparative information concerning metabolite profiles in relation to change in plant development, environment, or genotype.","author":[{"dropping-particle":"","family":"Tarpley","given":"Lee","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Duran","given":"Anthony L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kebrom","given":"Tesfamichael H.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sumner","given":"Lloyd W.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"BMC Plant Biology","id":"ITEM-1","issued":{"date-parts":[["2005"]]},"title":"Biomarker metabolites capturing the metabolite variance present in a rice plant developmental period","type":"article-journal","volume":"5"},"uris":["http://www.mendeley.com/documents/?uuid=b671516e-7f46-49a1-9b8e-844f9813190d"]}],"mendeley":{"formattedCitation":"[11]","plainTextFormattedCitation":"[11]","previouslyFormattedCitation":"[11]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[11]. It has been well documented that during early phases of infection by plant pathogens, a series of physiological and biochemical changes, together with the molecular response occur in plants. Among them, metabolic alteration is a common response in both compatible and incompatible plant-pathogen interactions ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1021/ac991142i","ISBN":"0003-2700 (Print)\\r0003-2700 (Linking)","ISSN":"00032700","PMID":"10952545","abstract":"Unknown compounds in polar fractions of Arabidopsis thaliana crude leaf extracts were identified on the basis of calculations of elemental compositions obtained from gas chromatography/low-resolution quadrupole mass spectrometric data. Plant metabolites were methoximated and silylated prior to analysis. All known peaks were used as internal references to construct polynomial recalibration curves of from raw mass spectrometric data. Mass accuracies of 0.005 +/- 0.003 amu and isotope ratio errors of 0.5 +/- 0.3% (A + 1/A), respectively, 0.3 +/- 0.2% (A + 2/A), could be achieved. Both masses and isotope ratios were combined when the elemental compositions of unknown peaks were calculated. After calculation, compound identities were elucidated by searching metabolic databases, interpreting spectra, and, finally, by comparison with reference compounds. Sum formulas of more than 70 peaks were determined throughout single GC/MS chromatograms. Exact masses were confirmed by high-resolution mass spectrometric data. More than 15 uncommon plant metabolites were identified, some of which are novel in Arabidopsis, such as tartronate semialdehyde, citramalic acid, allothreonine, or glycolic amide.","author":[{"dropping-particle":"","family":"Fiehn","given":"O","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kopka","given":"J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Trethewey","given":"Richard N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Willmitzer","given":"L.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Analytical Chemistry","id":"ITEM-1","issue":"15","issued":{"date-parts":[["2000"]]},"page":"3573-3580","title":"Identification of uncommon plant metabolites based on calculation of elemental compositions using gas chromatography and quadrupole mass spectrometry","type":"article-journal","volume":"72"},"uris":["http://www.mendeley.com/documents/?uuid=1e13cf1d-e698-4864-9e41-7667f0223bf9"]}],"mendeley":{"formattedCitation":"[12]","plainTextFormattedCitation":"[12]","previouslyFormattedCitation":"[12]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[12]. The disease resistance or susceptibility depends on physiology of plants differing in their metabolic activities ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.pmpp.2005.05.005","ISBN":"0885-5765","ISSN":"08855765","abstract":"Metabolic profiles of spikelets of wheat cultivars, Roblin and Sumai3, respectively, susceptible and resistant to fusarium head blight (FHB) were analyzed using GC/MS to develop a technology to discriminate resistance. More than 700 peaks were detected and a total of 55 compounds were tentatively identified, of which 49 were induced/up-regulated following pathogen inoculation, including 23 in Roblin and 26 in Sumai3. However, only five metabolites were significantly different both between cultivars and inoculations. Metahydroxycinnamic acid, though was detected in all four treatments, exhibited a six-fold increase in abundance in Sumai3 following pathogen inoculation, with no corresponding change in Roblin. The abundance of myo-inositol in Sumai3 was higher than that in Roblin, and in both the abundance increased following pathogen inoculation. The compounds common to all treatments were subjected to factor analysis to identify groups of compounds, based on significant factor-loadings, associated with susceptibility or resistance against FHB. The treatment involving pathogen-inoculation of the resistant cv. Sumai3 was associated with the highest scores for the first and second factors that can be used for the discrimination of resistance against FHB. The first factor was associated with higher abundances of several fatty acids and aromatic compounds, while the second factor was associated with metabolites such as p- and m-coumaric acids, myo-inositol and other sugars, and malonic acid. The treatments involving pathogen-inoculation had higher factor scores for the third factor than the water inoculated, the highest being for the susceptible cultivar Roblin, and may be useful in explaining susceptibility/pathogenesis. The third factor had positive correlation with metabolites from different groups, mostly amino acids, fatty acids, and aromatics. The various compounds detected in this study are discussed, in terms of their possible roles in plant defense against pathogen-stress, their metabolic pathways of synthesis, and their potential application for screening cultivars of wheat for resistance to FHB. � 2005 Elsevier Ltd. All rights reserved.","author":[{"dropping-particle":"","family":"Hamzehzarghani","given":"H.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kushalappa","given":"A. C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dion","given":"Y.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rioux","given":"S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Comeau","given":"A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yaylayan","given":"V.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Marshall","given":"W. D.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mather","given":"D. E.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Physiological and Molecular Plant Pathology","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2005"]]},"page":"119-133","title":"Metabolic profiling and factor analysis to discriminate quantitative resistance in wheat cultivars against fusarium head blight","type":"article-journal","volume":"66"},"uris":["http://www.mendeley.com/documents/?uuid=7239d50d-6078-4b07-b856-c192d721dbbd"]},{"id":"ITEM-2","itemData":{"DOI":"10.1139/B08-052","ISBN":"1916-2790","ISSN":"1916-2790","abstract":"Inoculation with the virulence factor deoxynivalenol (DON) can induce disease symptoms in wheat (Triticum aestivum L.) spikelets, even though it is not needed for the initial invasion by Fusarium graminearum Schwabe, thus the mechanism of plant defense against both the pathogen and DON, was investigated. Wheat cultivars that are resistant ('Sumai3') or susceptible ('Roblin') to fusarium head blight (FHB) were inoculated with F. graminearum, DON, or water. Inoculated spikelets were harvested 48 h after inoculation, the metabolites were extracted in methanol-water and chloroform, then derivatized and analyzed by gas chromatography - mass spectrometry. The metabolite peaks were deconvoluted and identified by manually matching the mass spectra with those in the NIST and GMD libraries. The peaks were aligned, and abundances were measured. A total of 117 metabolites were tentatively identified, including several antimicrobial metabolites and signal molecules or their precursors. Out of these 117 metabolites, 15 and 18 were identified as possible resistance-related (RR) metabolites, following F. graminearum (RRIF) and DON (RRID) inoculations. respectively, with 4 metabolites common to both. Canonical discriminant analysis of marginally significant metabolites (105) identified those with constitutive and induced resistance functions. The metabolites with high canonical loading to the canonical vectors were used to explain these functions. The putative roles of these RR metabolites in plant defense, their metabolic pathways, and their potential application for screening of wheat breeding lines for resistance to FHB are discussed.","author":[{"dropping-particle":"","family":"Paranidharan","given":"V.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Abu-Nada","given":"Y.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hamzehzarghani","given":"H.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kushalappa","given":"A. C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mamer","given":"O.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dion","given":"Y.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rioux","given":"S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Comeau","given":"A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Choiniere","given":"L.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Botany","id":"ITEM-2","issue":"10","issued":{"date-parts":[["2008"]]},"page":"1168-1179","title":"Resistance-related metabolites in wheat against <i>Fusarium graminearum</i> and the virulence factor deoxynivalenol (DON)","type":"article-journal","volume":"86"},"uris":["http://www.mendeley.com/documents/?uuid=dbe8993a-338e-4e97-8edd-8a00d377961f"]}],"mendeley":{"formattedCitation":"[13,14]","plainTextFormattedCitation":"[13,14]","previouslyFormattedCitation":"[13,14]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[13,14]. Hence, comparing metabolic response of resistant and susceptible genotypes upon infection by pathogens possibly represents a powerful tool to unravel the biochemical pathways involved in plant defence. 
The metabolomics is an evolving field in systems biology and it is not only complementary to transcriptomics and proteomics but also have special advantage that they are often the downstream results of gene expression ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1023/A:1013713905833","ISBN":"978-94-010-3903-1","ISSN":"01674412","PMID":"11860207","abstract":"Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.","author":[{"dropping-particle":"","family":"Fiehn","given":"Oliver","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Plant Molecular Biology","id":"ITEM-1","issue":"1-2","issued":{"date-parts":[["2002"]]},"page":"155-171","publisher":"Kluwer Academic Publishers","title":"Metabolomics - The link between genotypes and phenotypes","type":"article-journal","volume":"48"},"uris":["http://www.mendeley.com/documents/?uuid=87dea65e-be71-3119-90d0-cb68d9a4ed86"]}],"mendeley":{"formattedCitation":"[15]","manualFormatting":"(Fiehn, 2002)","plainTextFormattedCitation":"[15]","previouslyFormattedCitation":"[15]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}(Fiehn, 2002). This technology has found momentum to identify and quantify all the metabolites in an organism or a biological system ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/nbt1192","ISBN":"1087-0156","ISSN":"10870156","PMID":"16531992","abstract":"Tomato represents an important source of fiber and nutrients in the human diet and is a central model for the study of fruit biology. To identify components of fruit metabolic composition, here we have phenotyped tomato introgression lines (ILs) containing chromosome segments of a wild species in the genetic background of a cultivated variety. Using this high-diversity population, we identify 889 quantitative fruit metabolic loci and 326 loci that modify yield-associated traits. The mapping analysis indicates that at least 50% of the metabolic loci are associated with quantitative trait loci (QTLs) that modify whole-plant yield-associated traits. We generate a cartographic network based on correlation analysis that reveals whole-plant phenotype associated and independent metabolic associations, including links with metabolites of nutritional and organoleptic importance. The results of our genomic survey illustrate the power of genome-wide metabolic profiling and detailed morphological analysis for uncovering traits with potential for crop breeding.","author":[{"dropping-particle":"","family":"Schauer","given":"Nicolas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Semel","given":"Yaniv","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Roessner","given":"Ute","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gur","given":"Amit","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Balbo","given":"Ilse","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Carrari","given":"Fernando","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pleban","given":"Tzili","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Perez-Melis","given":"Alicia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bruedigam","given":"Claudia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kopka","given":"Joachim","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Willmitzer","given":"Lothar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zamir","given":"Dani","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fernie","given":"Alisdair R.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature Biotechnology","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2006"]]},"page":"447-454","title":"Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement","type":"article-journal","volume":"24"},"uris":["http://www.mendeley.com/documents/?uuid=2bb2cddd-3193-4e06-8cfa-151010a51c46"]}],"mendeley":{"formattedCitation":"[16]","plainTextFormattedCitation":"[16]","previouslyFormattedCitation":"[16]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[16]. Metabolites have been linked to specific genomic positions, and a set of co-localized genes/QTL have been proven to regulate certain metabolic pathways leading to the production of a series of metabolites that are in turn linked to phenotypes ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.tplants.2006.08.007","ISBN":"1360-1385 (Print)\\r1360-1385 (Linking)","ISSN":"13601385","PMID":"16949327","abstract":"Metabolite profiling is a fast growing technology and is useful for phenotyping and diagnostic analyses of plants. It is also rapidly becoming a key tool in functional annotation of genes and in the comprehensive understanding of the cellular response to biological conditions. Metabolomics approaches have recently been used to assess the natural variance in metabolite content between individual plants, an approach with great potential for the improvement of the compositional quality of crops. Here, we assess the contribution of metabolite profiling to these areas. � 2006 Elsevier Ltd. All rights reserved.","author":[{"dropping-particle":"","family":"Schauer","given":"Nicolas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fernie","given":"Alisdair R.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Trends in Plant Science","id":"ITEM-1","issue":"10","issued":{"date-parts":[["2006","10"]]},"page":"508-516","title":"Plant metabolomics: towards biological function and mechanism","type":"article","volume":"11"},"uris":["http://www.mendeley.com/documents/?uuid=1046e36f-6d4e-39c7-aa7e-d6c012f5d8ad"]},{"id":"ITEM-2","itemData":{"DOI":"10.1038/ng1815","ISBN":"1061-4036 (Print)\\n1061-4036 (Linking)","ISSN":"1061-4036","PMID":"16751770","abstract":"Variation for metabolite composition and content is often observed in plants. However, it is poorly understood to what extent this variation has a genetic basis. Here, we describe the genetic analysis of natural variation in the metabolite composition in Arabidopsis thaliana. Instead of focusing on specific metabolites, we have applied empirical untargeted metabolomics using liquid chromatography-time of flight mass spectrometry (LC-QTOF MS). This uncovered many qualitative and quantitative differences in metabolite accumulation between A. thaliana accessions. Only 13.4% of the mass peaks were detected in all 14 accessions analyzed. Quantitative trait locus (QTL) analysis of more than 2,000 mass peaks, detected in a recombinant inbred line (RIL) population derived from the two most divergent accessions, enabled the identification of QTLs for about 75% of the mass signals. More than one-third of the signals were not detected in either parent, indicating the large potential for modification of metabolic composition through classical breeding.","author":[{"dropping-particle":"","family":"Keurentjes","given":"Joost J B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fu","given":"Jingyuan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vos","given":"C H Ric","non-dropping-particle":"de","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lommen","given":"Arjen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hall","given":"Robert D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bino","given":"Raoul J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Plas","given":"Linus H W","non-dropping-particle":"van der","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jansen","given":"Ritsert C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vreugdenhil","given":"Dick","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Koornneef","given":"Maarten","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature Genetics","id":"ITEM-2","issue":"7","issued":{"date-parts":[["2006","7","4"]]},"page":"842-849","publisher":"Nature Publishing Group","title":"The genetics of plant metabolism","type":"article-journal","volume":"38"},"uris":["http://www.mendeley.com/documents/?uuid=e21e475d-c6ee-3e23-96ac-466c9502db21"]}],"mendeley":{"formattedCitation":"[17,18]","plainTextFormattedCitation":"[17,18]","previouslyFormattedCitation":"[17,18]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[17,18]. It is possible that the cultivars of mungbean varying in resistance to Erysiphe polygoni also would produce certain metabolites to resist pathogen invasion. Such metabolites can be used as biomarkers or linked to genomic locations on chromosomes and in the breeding programmes.
Host plant resistance is the most promising method to minimize yield loss due to powdery mildew disease. Several major genes with complete resistance to a specific subset of isolate have been deployed in the development of varieties. The resistance in several varieties having major genes for powdery mildew often breaks down due to the evolution of virulent races of pathogen after few years of the release of resistant varieties. Host plant and pathogen interaction were exciting in terms of richness of metabolism and regulation of metabolite and may act as an ideal model for developing and standardizing of high-throughput metabolomics. It may be possible that the genotype of mungbean varying in resistant also would produce certain metabolites to resist invasion of pathogen.
Hence, it is important to know the metabolite produced before and after infection of resistant and susceptible variety, which helps to identify the resistant source at initial stages.





2. Materials and methods 

2.1. Collection and isolation of Erysiphe polygoni D.C.

Powdery mildew infected mungbean leaf samples were collected from Department of Pulses, Tamil Nadu Agricultural University, Coimbatore-India during July 2017. Although powdery mildew was an obligate pathogen, we cannot able to maintain culture on artificial media. So sick plot for powdery mildew was maintained throughout the entire research period to get fresh inoculum. 
The Powdery mildew spores from the infected samples were collected from severely infected leaf using a camel brush in 1.5mL micro-centrifuge tube containing one ml of double sterile water under aseptic condition and it is further used for spraying in sick plot.
Powdery mildew fungus was maintained under natural conditions during Kharif 2017�2018. Powdery mildew susceptible mungbean variety SML 1082 was raised in micro plot. All the recommended package of practices were followed in raising the crop with protective irrigations. Conidia were collected from the infected mungbean leaves using a camel brush in sterilized 1.5mL micro-centrifuge tube containing one ml of double sterile water under aseptic condition, desirable concentration of conidial suspension (106conidia/ml) was prepared. Glycerol at the rate of ten microliter was added in micro-centrifuge tube and inoculated on the healthy leaves at 30 days after sowing by spray method. The pathogen was maintained by continuous sowing of susceptible mungbean plants (SML-1028) for further studies.

2.2. Pathogenicity of Erysiphe polygoni in Mungbean

Conidial suspension of Erysiphe polygoni was prepared by scrapping the conidia from the infected leaves. The conidial concentration was adjusted to 106conidia/ml using a haemocytometer. The suspension was sprayed on the healthy leaves of 30 days old mungbean genotype SML-1082 grown in pots under glass house condition. The plants were covered by polythene bags for 24 hours after inoculation to maintain humidity of 80 per cent for disease development. The development of disease was observed at periodical interval after inoculation ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Vimala","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Suriachandraselvan","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"J. of Biopesticides","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2008"]]},"page":"130-133","title":"Influence of antagonistic agent, plant products and chemical agents on the powdery mildew disease of bhendi and its production","type":"article-journal","volume":"1"},"uris":["http://www.mendeley.com/documents/?uuid=9f6254c6-03bf-4d24-8634-ae0e4e7f58e0"]}],"mendeley":{"formattedCitation":"[19]","plainTextFormattedCitation":"[19]","previouslyFormattedCitation":"[19]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[19].
2.3. Metabolic profiling of susceptible and resistant mungbean genotypes inoculated with Erysiphe polygoni 
2.3.1. Greenhouse studies

The experiment was conducted in earthen pots (30 cm dia.) filled with 10 kg of autoclaved pot mixture (Sterilized). Seeds of mungbean varieties viz., SML 1082 (Highly susceptible) and TARM 18 (Highly resistant) were sown at three seeds per pot. Plants were grown in controlled condition. The recommended package of practices was followed to raise the mungbean crop. Artificial inoculation of powdery mildew pathogen was done by spraying of conidial suspension (106 conidia/ml). Pathogen treated plants (sprayed with powdery mildew conidia) and non-treated plants (control) were kept separately in different chamber under controlled conditions.

2.3.2. Sampling for metabolite extraction 

Sampling was carried out as described by ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s11306-010-0218-7","ISBN":"1573-3882","ISSN":"15733882","PMID":"20676379","abstract":"Bacterial leaf blight (BLB), caused by Xanthomonas oryzae pv. oryzae (Xoo), gives rise to devastating crop losses in rice. Disease resistant rice cultivars are the most economical way to combat the disease. The TP309 cultivar is susceptible to infection by Xoo strain PXO99. A transgenic variety, TP309_Xa21, expresses the pattern recognition receptor Xa21, and is resistant. PXO99�%raxST, a strain lacking the raxST gene, is able to overcome Xa21-mediated immunity. We used a single extraction solvent to demonstrate comprehensive metabolomics and transcriptomics profiling under sample limited conditions, and analyze the molecular responses of two rice lines challenged with either PXO99 or PXO99�%raxST. LC TOF raw data file filtering resulted in better within group reproducibility of replicate samples for statistical analyses. Accurate mass match compound identification with molecular formula generation (MFG) ranking of 355 masses was achieved with the METLIN database. GC TOF analysis yielded an additional 441 compounds after BinBase database processing, of which 154 were structurally identified by retention index/MS library matching. Multivariate statistics revealed that the susceptible and resistant genotypes possess distinct profiles. Although few mRNA and metabolite differences were detected in PXO99 challenged TP309 compared to mock, many differential changes occurred in the Xa21-mediated response to PXO99 and PXO99�%raxST. Acetophenone, xanthophylls, fatty acids, alkaloids, glutathione, carbohydrate and lipid biosynthetic pathways were affected. Significant transcriptional induction of several pathogenesis related genes in Xa21 challenged strains, as well as differential changes to GAD, PAL, ICL1 and Glutathione-S-transferase transcripts indicated limited correlation with metabolite changes under single time point global profiling conditions.","author":[{"dropping-particle":"","family":"Sana","given":"Theodore R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fischer","given":"Steve","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wohlgemuth","given":"Gert","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Katrekar","given":"Anjali","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jung","given":"Ki hong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ronald","given":"Pam C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fiehn","given":"Oliver","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Metabolomics","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2010","9","27"]]},"page":"451-465","publisher":"Springer US","title":"Metabolomic and transcriptomic analysis of the rice response to the bacterial blight pathogen Xanthomonas oryzae pv. oryzae","type":"article-journal","volume":"6"},"uris":["http://www.mendeley.com/documents/?uuid=335890d7-b10b-3016-9804-3236624a796c"]}],"mendeley":{"formattedCitation":"[20]","manualFormatting":"Sana et al. (2010)","plainTextFormattedCitation":"[20]","previouslyFormattedCitation":"[20]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}Sana et al. (2010). Leaf samples from both inoculated and control plants of each mungbean genotype was collected at 0 h (immediately after inoculation) and 24 h, 48 h & 72 h of post inoculation days. Non-treated plants (control plans) and pathogen-inoculated plants were used for comparison. Three replicates were maintained for each time points. Samples were frozen immediately in liquid nitrogen and stored at -70 �C. These stored powdered sample were further used for the metabolite extraction.

2.3.3. Metabolite extraction and derivatization 

Polar primary metabolites were extracted from the leaf tissues and then subjected to derivatization by following the procedure described by ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/81137","ISBN":"1087-0156 (Print)\\r1087-0156 (Linking)","ISSN":"1087-0156","PMID":"11062433","abstract":"Multiparallel analyses of mRNA and proteins are central to today's functional genomics initiatives. We describe here the use of metabolite profiling as a new tool for a comparative display of gene function. It has the potential not only to provide deeper insight into complex regulatory processes but also to determine phenotype directly. Using gas chromatography/mass spectrometry (GC/MS), we automatically quantified 326 distinct compounds from Arabidopsis thaliana leaf extracts. It was possible to assign a chemical structure to approximately half of these compounds. Comparison of four Arabidopsis genotypes (two homozygous ecotypes and a mutant of each ecotype) showed that each genotype possesses a distinct metabolic profile. Data mining tools such as principal component analysis enabled the assignment of \"metabolic phenotypes\" using these large data sets. The metabolic phenotypes of the two ecotypes were more divergent than were the metabolic phenotypes of the single-loci mutant and their parental ecotypes. These results demonstrate the use of metabolite profiling as a tool to significantly extend and enhance the power of existing functional genomics approaches","author":[{"dropping-particle":"","family":"Fiehn","given":"O.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kopka","given":"J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dormann","given":"P.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Altmann","given":"T.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Trethewey","given":"R. 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(2000), ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/nprot.2006.59","ISBN":"1750-2799","ISSN":"17542189","PMID":"17406261","abstract":"The concept of metabolite profiling has been around for decades, but technical innovations are now enabling it to be carried out on a large scale with respect to the number of both metabolites measured and experiments carried out. Here we provide a detailed protocol for gas chromatography mass spectrometry (GC-MS)-based metabolite profiling that offers a good balance of sensitivity and reliability, being considerably more sensitive than NMR and more robust than liquid chromatography�linked mass spectrometry. We summarize all steps from collecting plant material and sample handling to derivatization procedures, instrumentation settings and evaluating the resultant chromatograms. We also define the contribution of GC-MS�based metabolite profiling to the fields of diagnostics, gene annotation and systems biology. Using the protocol described here facilitates routine determination of the relative levels of 300�500 analytes of polar and nonpolar extracts in B400 experimental samples per week per machine. INTRODUCTION Although metabolite measurements have been carried out for decades owing to the fundamental regulatory importance of these molecules as components of metabolic pathways, the impor-tance of some metabolites in the human diet and their use as diagnostic markers for a wide range of biological conditions, including disease and response to chemical treatment, is only now being recognized 1 . Historically, the measurement of metabo-lites was achieved either by spectrophotometric assays capable of detecting single metabolites or by simple chromatographic separa-tion of mixtures of low complexity. Over the past decade, however, several methods offering both high accuracy and sensitivity for the analysis of highly complex mixtures of compounds have been established 2�8 . These methods include GC-MS, liquid chromato-graphy mass spectrometry (LC-MS), capillary electrophoresis mass spectrometry (CE-MS) and Fourier transform ion cyclotron reso-nance mass spectrometry (FT-ICR-MS). In addition, chromato-graphically coupled NMR technologies have found great utility in addressing specific issues, particularly in the medical field 9,10 and perhaps most importantly with respect to the unequivocal deter-mination of metabolite structures 11 . Nevertheless, NMR shows relatively low sensitivity and thus can be used for highly abundant metabolites when profiling complex mixtures. GC-MS facilitates the identification and robust quantification of a few hundred metabolite�","author":[{"dropping-particle":"","family":"Lisec","given":"Jan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schauer","given":"Nicolas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kopka","given":"Joachim","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Willmitzer","given":"Lothar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fernie","given":"Alisdair R.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature Protocols","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2006"]]},"page":"387-396","title":"Gas chromatography mass spectrometry-based metabolite profiling in plants","type":"article-journal","volume":"1"},"uris":["http://www.mendeley.com/documents/?uuid=c9dd58cf-7a2f-4cee-9467-5e95b34c46a9"]}],"mendeley":{"formattedCitation":"[22]","manualFormatting":"Lisec et al. (2006)","plainTextFormattedCitation":"[22]","previouslyFormattedCitation":"[22]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}Lisec et al. (2006) & ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/978-1-59745-244-1_2","ISBN":"978-1-58829-561-3","ISSN":"1064-3745","PMID":"17035678","abstract":"Gas chromatography-mass spectrometry (GC-MS) is routinely applied to the metabolite profiling of biological samples. Time-of-flight (TOF)-GC-MS metabolite profiling is based on highly reproducible electron impact ionization. Single chromatograms may comprise 200-1000 mass spectral components. The nature and composition of these mass spectral components depend on the choice of metabolite extraction, type of biological sample, and experimental condition. The components represent mass spectral tags (MSTs) of volatile metabolites or metabolite derivatives. Identification of MSTs is the major challenge in GC-MS metabolite profiling. We describe methods suitable for the automated construction of mass spectral and retention time index databases from large sets of TOF-GC-MS profiles. Application of these libraries for automated identification by pure reference compounds and classification of hitherto unidentified MSTs from biological sources is demonstrated.","author":[{"dropping-particle":"","family":"Erban","given":"Alexander","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schauer","given":"Nicolas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fernie","given":"Alisdair R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kopka","given":"Joachim","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Methods in molecular biology (Clifton, N.J.)","id":"ITEM-1","issued":{"date-parts":[["2007"]]},"page":"19-38","title":"Nonsupervised Construction and Application of Mass Spectral and Retention Time Index Libraries From Time-of-Flight Gas Chromatography-Mass Spectrometry Metabolite Profiles","type":"chapter","volume":"358"},"uris":["http://www.mendeley.com/documents/?uuid=c68ecc6a-5cdf-3d1f-bc81-75c510c44386"]}],"mendeley":{"formattedCitation":"[23]","manualFormatting":"Erban et al. (2007)","plainTextFormattedCitation":"[23]","previouslyFormattedCitation":"[23]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}Erban et al. (2007). Mungbean leaf samples were ground in a sterilized, pre-cooled pestle and mortar. The powdered leaf (300 mg) was transferred to 2 ml round- bottom micro-centrifuge tubes followed by 1.4 ml of 100% methanol and 50�L of internal standard ribitol were added and mixed well by vortex. Then, 50�L of de-ionized water was added. The mixtures was incubated at 70�C for 15 minutes on a shaker at 950 rpm and then centrifuged at 12,500rpm for 20 minutes. The supernatant was transferred into Ultafree-CL 0.22 �L filter tubes and 1.4 ml of water was added in the same tube. Further, 750 �1 of chloroform was added to the pellet then vortexed for uniform mixing and shaked at 37oC for 10 minutes. This mixture was further centrifuged at 12,500 rpm for 20 minutes. The supernatant was transfered and combined into the same Ultafree-CL 0.22 �L filter tubes. This tube was then vortexed and centrifuged at 4000 rpm for 15 minutes. The upper polar phase was separated into new eppendorf tubes and closed the tubes with parafilm and punched a little hole in the parafilm and dried in a SpeedVac concentrator overnight. The dried samples were then stored at -70 0C until derivatization. For derivatization, 300 �1 of methoxyamination reagent (5 mg of 4-dimethylaminopyridine and 40 mg of methoxyamine hydrochloride dissolved in 1 ml of pyridine) was added to the micro-centrifuge tube and shaken at 37�C for 2 hour. Then the sample was vortexed every 30 minutes during two hours of shaking to dissolve the sample completely in the reagent. After methoxyamination, the samples were centrifuged at 10,000rpm for one minutes. The supernatant (40 �l ) from the above was transferred to 1.5 ml of micro-centrifuge tube followed by 70 �l of N, O-Bis (trimethylsilyl) trifluoroacetamide (BSTFA) and 10 �l of retention time standards (n-alkanes) were added to the same tube and shaken at 37�C for 30 minutes. The n-alkanes mixture was prepared by using pyridine at a final concentration of 0.22 mg/ml each. The samples were then centrifuged at 10,000rpm for one minutes and 95 �1 was transferred to GC.MS glass vial for analysis using GC/EI-TOF-MS.

2.3.4. Gas chromatography and mass spectrometry (GC-MS) analysis 

The GC-MS was performed as per the method described by ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.5740/jaoacint.12-140","ISSN":"10603271","abstract":"A novel, simple, direct, and selective stability- indicating GC/MS procedure was developed for the determination of the anti-ischemic drug trimetazidine dihydrochloride (TMZ) in the presence of two of its related substances (potential impurities), namely, 2,3,4-trimethoxybenzyl alcohol (T1) and 2,3,4-trimethoxybenzaldehyde (T2). The method involved resolution of the underivatized compounds using a 100% dimethylpolysiloxane (Rtx-1) capillary column, and MS detection was carried out in the electron-impact mode. The peaks of the three compounds eluted at retention times 11.69, 11.92, and 15.47 min for T1, T2, and TMZ, respectively. Quantification of the parent drug TMZ was based on measuring its peak area. The reliability and analytical performance of the proposed method, including linearity, range, precision, accuracy, selectivity, detection, and quantification limits, were statistically validated. The calibration curve of TMZ was linear over the range 100-600 �g/mL. The proposed method was successfully applied to the assay of TMZ in several commercially available pharmaceutical formulations with recoveries not less than 96.2%. [ABSTRACT FROM AUTHOR]","author":[{"dropping-particle":"","family":"Belal","given":"Tarek S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Awad","given":"Tamer","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Clark","given":"C. Randall","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of AOAC International","id":"ITEM-1","issue":"6","issued":{"date-parts":[["2014","11","1"]]},"page":"1514-1518","title":"Stability-indicating determination of trimetazidine dihydrochloride in the presence of two of its related substances using a direct GC/MS method","type":"article-journal","volume":"97"},"uris":["http://www.mendeley.com/documents/?uuid=b13ac499-9c0b-388f-81a6-cfdf782d9098"]}],"mendeley":{"formattedCitation":"[24]","plainTextFormattedCitation":"[24]","previouslyFormattedCitation":"[24]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[24]. The sample was injected into  PERKIN ELMER CLARUS SQ8C Gas Chromatograph with DB-5 MS column (30 m x 0.25 mm, 0.25 �m film thickness) . The temperature was programmed at 30 �C (1 minutes), 230 �C (20 minutes) at the rate of 4 �C per minute. The detector was heated at 250�C, injector at 230�C. Helium was used as carrier gas at 5 Psi pressure. Mass spectra were obtained by electron ionization at 70 eV.

2.3.5. Identification of metabolites

Different metabolites elaborated under the compatible and incompatible interactions of resistant and susceptible genotypes in response to Erysiphe polygoni pathogenesis have been tentatively identified as the top five spectral hits with the NIST-05 National Institute of Standards Technology. Palisade Corp., NY and Golm Metabolome Database (GMD) ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/bioinformatics/bti236","ISSN":"1367-4803","PMID":"15613389","abstract":"UNLABELLED Metabolomics, in particular gas chromatography-mass spectrometry (GC-MS) based metabolite profiling of biological extracts, is rapidly becoming one of the cornerstones of functional genomics and systems biology. Metabolite profiling has profound applications in discovering the mode of action of drugs or herbicides, and in unravelling the effect of altered gene expression on metabolism and organism performance in biotechnological applications. As such the technology needs to be available to many laboratories. For this, an open exchange of information is required, like that already achieved for transcript and protein data. One of the key-steps in metabolite profiling is the unambiguous identification of metabolites in highly complex metabolite preparations from biological samples. Collections of mass spectra, which comprise frequently observed metabolites of either known or unknown exact chemical structure, represent the most effective means to pool the identification efforts currently performed in many laboratories around the world. Here we present GMD, The Golm Metabolome Database, an open access metabolome database, which should enable these processes. GMD provides public access to custom mass spectral libraries, metabolite profiling experiments as well as additional information and tools, e.g. with regard to methods, spectral information or compounds. The main goal will be the representation of an exchange platform for experimental research activities and bioinformatics to develop and improve metabolomics by multidisciplinary cooperation. AVAILABILITY http://csbdb.mpimp-golm.mpg.de/gmd.html CONTACT Steinhauser@mpimp-golm.mpg.de SUPPLEMENTARY INFORMATION http://csbdb.mpimp-golm.mpg.de/","author":[{"dropping-particle":"","family":"Kopka","given":"J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schauer","given":"N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Krueger","given":"S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Birkemeyer","given":"C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Usadel","given":"B.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bergmuller","given":"E.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dormann","given":"P.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Weckwerth","given":"W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gibon","given":"Y.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Stitt","given":"M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Willmitzer","given":"L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fernie","given":"A. R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Steinhauser","given":"D.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Bioinformatics","id":"ITEM-1","issue":"8","issued":{"date-parts":[["2005","4","15"]]},"page":"1635-1638","title":"The Golm Metabolome Database","type":"article-journal","volume":"21"},"uris":["http://www.mendeley.com/documents/?uuid=49e22a21-7e2b-3f16-9953-e2cede07da7e"]}],"mendeley":{"formattedCitation":"[25]","plainTextFormattedCitation":"[25]","previouslyFormattedCitation":"[25]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[25] libraries, using retention times as reference. For each peak, the spectra of four replicates of a treatment were compared and matched with that of NIST/GMD spectra. Analytes with good match factor (>60%) were considered and tentatively identified.



3. Results and discussion

In the present study, powdery mildew resistant (TARM-18) and susceptible (SML-1082) mungbean were used to understand the mechanism of resistance against Erysiphe polygoni pathogen at metabolomic level by using GC-MS analysis. About 50 metabolites for each treatment was generated but a total of 20 metabolites were taken due to time and cost factor. The GC-MS output on scans and mass abundance were possessed and the metabolites were identified manually using AMDIS software integrated with NIST library 2014. In total, 20 metabolites were tentatively identified and were classified into different chemical groups (Table 1). 
Among these, eight metabolites were commonly detected in both the genotypes during 0, 24, 48, 72 hours of post-inoculation. Whereas hexadeonic acid, alanine, butyl acetate, diamino benzaldehyde and putrescine, methy ethy trisulfide, pyridine, hydroxyheptanoic acid were detected only the resistant genotype TARM-18. From the 20 metabolites identified, six metabolites were found to be upregulated in resistant mungbean genotype TARM-18 as a result of pathogen infection. Three metabolites viz., hexadeconic acid, alanine and quinic acid were upregulated at 72 hours of inoculation of E. polygoni in resistant genotype. Whereas, Dimethyl amino benzaldehyde, putrescine and methyl ethyl trisulfide were upregulated from 24 hours of inoculation. The compound 2-hexanone was induced in both susceptible and resistant genotype at 72 hours of post inoculation. The metabolite 2-hexanone were produced in both the susceptible and resistant genotype at 72 hours of inoculation. 
The other metabolites include propylene glycol, propyl acetate, methionine, glucose, sucrose, malic acid, oxalic acid, raffanose were detected in both resistant and susceptible genotypes. Whereas, propionic acid and lauric acid were down-regulated from 24 hours of post-infection both in resistant and susceptible genotype. Ethyl propionate were detected in both susceptible and resistant genotype and were down regulated after 72 hours of inoculation. 
In the present study, E. polygoni inoculated plants of TARM-18 showed induction of quinic acid and dimethyl amino benzaldehyde. It has been reported that early induction of phenylpropanoid pathway leads to production of both anti-microbial secondary metabolites and lignin/suberin precursors for cell-wall strengthening in resistance responses ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1073/pnas.0509875103","ISBN":"0027-8424 (Print)\\r0027-8424 (Linking)","ISSN":"0027-8424","PMID":"16380417","abstract":"OsRac1, one of the Rac/Rop family of small GTPases, plays important roles in defense responses, including a role in the production of reactive oxygen species mediated by NADPH oxidase. We have identified an effector of OsRac1, namely rice (Oryza sativa) cinnamoyl-CoA reductase 1 (OsCCR1), an enzyme involved in lignin biosynthesis. Lignin, which is polymerized through peroxidase activity by using H(2)O(2) in the cell wall, is an important factor in plant defense responses, because it presents an undegradable mechanical barrier to most pathogens. Expression of OsCCR1 was induced by a sphingolipid elicitor, suggesting that OsCCR1 participates in defense signaling. In in vitro interaction and two-hybrid experiments, OsRac1 was shown to bind OsCCR1 in a GTP-dependent manner. Moreover, the interaction of OsCCR1 with OsRac1 led to the enzymatic activation of OsCCR1 in vitro. Transgenic cell cultures expressing the constitutively active OsRac1 accumulated lignin through enhanced CCR activity and increased reactive oxygen species production. Thus, it is likely that OsRac1 controls lignin synthesis through regulation of both NADPH oxidase and OsCCR1 activities during defense responses in rice.","author":[{"dropping-particle":"","family":"Kawasaki","given":"T.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Koita","given":"H.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nakatsubo","given":"T.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hasegawa","given":"K.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wakabayashi","given":"K.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Takahashi","given":"H.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Umemura","given":"K.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Umezawa","given":"T.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shimamoto","given":"K.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Proceedings of the National Academy of Sciences","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2006"]]},"page":"230-235","title":"Cinnamoyl-CoA reductase, a key enzyme in lignin biosynthesis, is an effector of small GTPase Rac in defense signaling in rice","type":"article-journal","volume":"103"},"uris":["http://www.mendeley.com/documents/?uuid=db179e8a-babd-4d71-92bf-fe1d80ddd39d"]},{"id":"ITEM-2","itemData":{"DOI":"10.1146/annurev.phyto.45.062806.094325","ISBN":"0066-4286","ISSN":"0066-4286","PMID":"17352660","abstract":"The plant cuticle and cell wall separate microbial pathogens from the products of plant metabolism. While microbial pathogens try to breach these barriers for colonization, plants respond to attempted penetration by a battery of wall-associated defense reactions. Successful pathogens circumvent or suppress plant nonself recognition and basal defense during penetration and during microbial reproduction. Additionally, accommodation of fungal infection structures within intact cells requires host reprogramming. Recent data highlight that both early plant defense to fungal penetration and host reprogramming for susceptibility can function at the host cell periphery. Genetic evidence has also widened our understanding of how fungal pathogens are restricted during penetration at the plant cell wall. This review summarizes the current view of how plants monitor and model their cell periphery during interaction with microbial invaders.","author":[{"dropping-particle":"","family":"H�ckelhoven","given":"Ralph","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Annual Review of Phytopathology","id":"ITEM-2","issue":"1","issued":{"date-parts":[["2007"]]},"page":"101-127","title":"Cell Wall�Associated Mechanisms of Disease Resistance and Susceptibility","type":"article-journal","volume":"45"},"uris":["http://www.mendeley.com/documents/?uuid=678303af-7139-48cd-98d2-83146b33f82e"]},{"id":"ITEM-3","itemData":{"ISSN":"1543-8120","author":[{"dropping-particle":"","family":"Fraser","given":"Christopher M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chapple","given":"Clint","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The Arabidopsis Book","id":"ITEM-3","issued":{"date-parts":[["2011"]]},"page":"e0152","publisher":"BioOne","title":"The phenylpropanoid pathway in Arabidopsis","type":"article-journal"},"uris":["http://www.mendeley.com/documents/?uuid=530846c8-63d2-41fb-a0bd-8301587a8dfa"]}],"mendeley":{"formattedCitation":"[26�28]","plainTextFormattedCitation":"[26�28]","previouslyFormattedCitation":"[26�28]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[26�28]. Methyl ethyl trisulfide, a methyl ester compound responsible for inter-and intra-organism communication between plants, antagonists and mutualistic symbionts both below and above ground ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3389/fpls.2015.00151","ISBN":"1664-462X","ISSN":"1664-462X","PMID":"25821453","abstract":"O-anisaldehyde found to exert the highest inhibition on myceilial growth of fungal pathogen BOtryts cinerea (Zhang et al., 2013)","author":[{"dropping-particle":"","family":"Kanchiswamy","given":"Chidananda Nagamangala","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Malnoy","given":"Mickael","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Maffei","given":"Massimo E.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Frontiers in Plant Science","id":"ITEM-1","issue":"151","issued":{"date-parts":[["2015","3","13"]]},"page":"1-23","title":"Chemical diversity of microbial volatiles and their potential for plant growth and productivity","type":"article-journal","volume":"6"},"uris":["http://www.mendeley.com/documents/?uuid=76f1d241-6fb7-4bbf-9ee2-b5bf20f22c7a"]}],"mendeley":{"formattedCitation":"[29]","plainTextFormattedCitation":"[29]","previouslyFormattedCitation":"[29]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[29]. This showed that the infection by E. polygoni substantially altered the metabolic profile of mungbean.
Plant-pathogen interactions are interesting in terms of metabolite richness and metabolism regulation and can serve as an ideal model for the development and standardization of high-throughput metabolomics. It is possible that the genotype of mungbean varying in resistance also would produce certain metabolites to resist pathogen invasion. Melabolomics is a technology geared to identify and quantify all the metabolites in a biological system ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.tplants.2006.08.007","ISBN":"1360-1385 (Print)\\r1360-1385 (Linking)","ISSN":"13601385","PMID":"16949327","abstract":"Metabolite profiling is a fast growing technology and is useful for phenotyping and diagnostic analyses of plants. It is also rapidly becoming a key tool in functional annotation of genes and in the comprehensive understanding of the cellular response to biological conditions. Metabolomics approaches have recently been used to assess the natural variance in metabolite content between individual plants, an approach with great potential for the improvement of the compositional quality of crops. Here, we assess the contribution of metabolite profiling to these areas. � 2006 Elsevier Ltd. All rights reserved.","author":[{"dropping-particle":"","family":"Schauer","given":"Nicolas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fernie","given":"Alisdair R.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Trends in Plant Science","id":"ITEM-1","issue":"10","issued":{"date-parts":[["2006","10"]]},"page":"508-516","title":"Plant metabolomics: towards biological function and mechanism","type":"article","volume":"11"},"uris":["http://www.mendeley.com/documents/?uuid=1046e36f-6d4e-39c7-aa7e-d6c012f5d8ad"]}],"mendeley":{"formattedCitation":"[17]","plainTextFormattedCitation":"[17]","previouslyFormattedCitation":"[17]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[17]. Metabolites have been linked to specific genomic positions, and a set of co-localized genes/QTLs have been proven to regulate certain metabolic pathways leading to the production of a series of metabolites that are in turn linked to phenotypes ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.tplants.2006.08.007","ISBN":"1360-1385 (Print)\\r1360-1385 (Linking)","ISSN":"13601385","PMID":"16949327","abstract":"Metabolite profiling is a fast growing technology and is useful for phenotyping and diagnostic analyses of plants. It is also rapidly becoming a key tool in functional annotation of genes and in the comprehensive understanding of the cellular response to biological conditions. Metabolomics approaches have recently been used to assess the natural variance in metabolite content between individual plants, an approach with great potential for the improvement of the compositional quality of crops. Here, we assess the contribution of metabolite profiling to these areas. � 2006 Elsevier Ltd. All rights reserved.","author":[{"dropping-particle":"","family":"Schauer","given":"Nicolas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fernie","given":"Alisdair R.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Trends in Plant Science","id":"ITEM-1","issue":"10","issued":{"date-parts":[["2006","10"]]},"page":"508-516","title":"Plant metabolomics: towards biological function and mechanism","type":"article","volume":"11"},"uris":["http://www.mendeley.com/documents/?uuid=1046e36f-6d4e-39c7-aa7e-d6c012f5d8ad"]},{"id":"ITEM-2","itemData":{"DOI":"10.1038/ng1815","ISBN":"1061-4036 (Print)\\n1061-4036 (Linking)","ISSN":"1061-4036","PMID":"16751770","abstract":"Variation for metabolite composition and content is often observed in plants. However, it is poorly understood to what extent this variation has a genetic basis. Here, we describe the genetic analysis of natural variation in the metabolite composition in Arabidopsis thaliana. Instead of focusing on specific metabolites, we have applied empirical untargeted metabolomics using liquid chromatography-time of flight mass spectrometry (LC-QTOF MS). This uncovered many qualitative and quantitative differences in metabolite accumulation between A. thaliana accessions. Only 13.4% of the mass peaks were detected in all 14 accessions analyzed. Quantitative trait locus (QTL) analysis of more than 2,000 mass peaks, detected in a recombinant inbred line (RIL) population derived from the two most divergent accessions, enabled the identification of QTLs for about 75% of the mass signals. More than one-third of the signals were not detected in either parent, indicating the large potential for modification of metabolic composition through classical breeding.","author":[{"dropping-particle":"","family":"Keurentjes","given":"Joost J B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fu","given":"Jingyuan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vos","given":"C H Ric","non-dropping-particle":"de","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lommen","given":"Arjen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hall","given":"Robert D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bino","given":"Raoul J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Plas","given":"Linus H W","non-dropping-particle":"van der","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jansen","given":"Ritsert C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vreugdenhil","given":"Dick","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Koornneef","given":"Maarten","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature Genetics","id":"ITEM-2","issue":"7","issued":{"date-parts":[["2006","7","4"]]},"page":"842-849","publisher":"Nature Publishing Group","title":"The genetics of plant metabolism","type":"article-journal","volume":"38"},"uris":["http://www.mendeley.com/documents/?uuid=e21e475d-c6ee-3e23-96ac-466c9502db21"]}],"mendeley":{"formattedCitation":"[17,18]","plainTextFormattedCitation":"[17,18]","previouslyFormattedCitation":"[17,18]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[17,18]. 
Metabolomics is an evolving field in systems biology and it is not only complementary to transcriptomics and proteomics but also have a special advantage that they are often the downstream results of gene expression ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1023/A:1013713905833","ISBN":"978-94-010-3903-1","ISSN":"01674412","PMID":"11860207","abstract":"Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.","author":[{"dropping-particle":"","family":"Fiehn","given":"Oliver","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Plant Molecular Biology","id":"ITEM-1","issue":"1-2","issued":{"date-parts":[["2002"]]},"page":"155-171","publisher":"Kluwer Academic Publishers","title":"Metabolomics - The link between genotypes and phenotypes","type":"article-journal","volume":"48"},"uris":["http://www.mendeley.com/documents/?uuid=87dea65e-be71-3119-90d0-cb68d9a4ed86"]}],"mendeley":{"formattedCitation":"[15]","plainTextFormattedCitation":"[15]","previouslyFormattedCitation":"[15]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[15]. In metabolite profiles of potato following pathogen inoculation, a 106 peaks were detected, of which 95 metabolites were tentatively identified. Abundance of 42 metabolites were knowingly increased or decreased, and these metabolites were designated as Pathogenesis-Related (PR) metabolites. Plausible satellite-networks of metabolic pathways leading to up-regulation of these families of amino acids and other secondary metabolites, and their potential application for evaluation of horizontal resistance in potato against late blight pathogen was studied by ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s10658-007-9150-8","ISBN":"0929-1873","ISSN":"09291873","abstract":"Metabolite profiles based on GC/MS were used to study the temporal dynamics of metabolites in potato leaves following pathogen inoculation. In the polar and non-polar plant extracts a total of 106 consistent peaks were detected, of which 95 metabolites were tentatively identified. Following pathogen inoculation, the abundances of 42 metabolites were significantly increased or decreased, and these metabolites were designated as Pathogenes is-Related (PR) Metabolites. Factor analysis of the abundance of 106 metabolites identified four plant-pathogen interaction functions: (i) homeostasis; (ii) primary defence; (iii) secondary defence; (iv) collapse of primary and secondary defence responses. During the primary and secondary defence phases, dramatic changes in the amino acids, known precursors of several plant defence-related metabolites, were observed. Plausible satellite-networks of metabolic pathways leading to the up-regulation of these families of amino acids and other secondary metabolites, and their potential application for the evaluation of horizontal resistance in potato against the late blight pathogen is discussed.","author":[{"dropping-particle":"","family":"Abu-Nada","given":"Y.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kushalappa","given":"A. C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Marshall","given":"W. D.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Al-Mughrabi","given":"K.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Murphy","given":"A.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"European Journal of Plant Pathology","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2007","7","3"]]},"page":"375-391","publisher":"Kluwer Academic Publishers","title":"Temporal dynamics of pathogenesis-related metabolites and their plausible pathways of induction in potato leaves following inoculation with Phytophthora infestans","type":"article-journal","volume":"118"},"uris":["http://www.mendeley.com/documents/?uuid=18c0aea2-6a61-3455-a89a-786e8fdb1406"]}],"mendeley":{"formattedCitation":"[30]","manualFormatting":"Abu-Nada, Kushalappa, Marshall, Al-Mughrabi, & Murphy (2007)","plainTextFormattedCitation":"[30]","previouslyFormattedCitation":"[30]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}Abu-Nada, Kushalappa, Marshall, Al-Mughrabi, & Murphy (2007).
Alanine has an important role in plant metabolism and is involved in enhanced defence response to fungi, oomycetes and bacteria ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3389/fpls.2014.00017","ISSN":"1664-462X","author":[{"dropping-particle":"","family":"Rojas","given":"Clemencia M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Senthil-Kumar","given":"Muthappa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tzin","given":"Vered","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mysore","given":"Kirankumar S","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Frontiers in Plant Science","id":"ITEM-1","issue":"February","issued":{"date-parts":[["2014"]]},"page":"1-12","title":"Regulation of primary plant metabolism during plant-pathogen interactions and its contribution to plant defense","type":"article-journal","volume":"5"},"uris":["http://www.mendeley.com/documents/?uuid=8291ed38-11b7-4b84-a24a-2bbaa8484f5a"]}],"mendeley":{"formattedCitation":"[31]","plainTextFormattedCitation":"[31]","previouslyFormattedCitation":"[31]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[31]. The putrescine is one of the aliphatic amines found in all plant cells. It occurs in plants in free form as cations and are often conjugated to small molecules like phenolic aids and also to various macromolecules, such as DNA and RNA. They are necessary for the successful replication of most cells. This have been implicated in the regulation of senescence and morphogenesis in plants, as well as in plant responses to environmental stress such as biotic and abiotic stress. Putrescine is synthesized in plants through decarboxylation of either ornithine or arginine and the respective reactions are catalysed by the enzymes ornithinc decarboxylase and arginine decarboxylase ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1034/j.1399-3054.1997.1000332.x","ISSN":"00319317","abstract":"The diamine putrescine and the polyamines spermidine and spermine are ubiquitous in nature and are essential for cell proliferation. Since polyamine biosynthesis in plants can start from either ornithine or arginine, while fungal polyamine biosynthesis appears to utilise only the ornithine route, it was suggested that specific inhibition of fungal polyamine biosynthesis should be lethal. Indeed, inhibitors of polyamine biosynthesis, e.g. the ornithine decarboxylase inhibitor alpha- difluoromethylomithine, have been shown to inhibit fungal growth in vitro and to control fungal infections on a variety of plants under glasshouse and field conditions. It is now known that polyamine analogues can perturb polyamine metabolism leading to powerful antiproliferative effects in cancer cells. This paper reviews the results of a research programme focused on the synthesis and evaluation of putrescine analogues as novel fungicides. A number of aliphatic, alicyclic and cyclic diamines have been shown to possess considerable fungicidal activity, but although many of these compounds perturb polyamine metabolism in fungal cells, such changes are not considered sufficient to account for the observed antifungal effects. More recent work on spermidine analogues is also described.","author":[{"dropping-particle":"","family":"Walters","given":"D. R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mackintosh","given":"C. A.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Physiologia Plantarum","id":"ITEM-1","issue":"3","issued":{"date-parts":[["1997"]]},"page":"689-695","title":"Control of plant disease by perturbation of fungal polyamine metabolism","type":"article-journal","volume":"100"},"uris":["http://www.mendeley.com/documents/?uuid=d2bfca84-f605-4f13-8085-a4a9d047aa8a"]}],"mendeley":{"formattedCitation":"[32]","plainTextFormattedCitation":"[32]","previouslyFormattedCitation":"[32]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[32]. Hexadeconic acid, a 16 carbon molecule with no double bond which constitutes complex lipids are essential components of cellular membrane. This fatty acid derivative may act as a signalling molecule, modulating normal and disease related physiologies in plants ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1146/annurev-phyto-080508-081820","ISSN":"0066-4286","author":[{"dropping-particle":"","family":"Kachroo","given":"Aardra","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kachroo","given":"Pradeep","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Annual Review of Phytopathology","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2009","9"]]},"page":"153-176","title":"Fatty Acid�Derived Signals in Plant Defense","type":"article-journal","volume":"47"},"uris":["http://www.mendeley.com/documents/?uuid=2970f5cc-4efc-43c5-aad0-704db7a4cd97"]}],"mendeley":{"formattedCitation":"[33]","plainTextFormattedCitation":"[33]","previouslyFormattedCitation":"[33]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[33]. 
Quinic acid is thought to be a reserve compound of the shikimate pathway, which it used for biosynthesis of phenolics. Quinate dehydrogenase (QDH) catalyzes the inter-conversion of quinic acid and 3-dehydroquinic acid and thus connects the pool of quinate with the shikimate pathway. Quinatehydrolyase convert quinic acid directly into shikimic acid in Zea mays cells ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Boudet","given":"A.M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Graziana","given":"A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ranjeva","given":"R.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"InThe Biochemistry of Plant Phenolics","id":"ITEM-1","issued":{"date-parts":[["1985"]]},"page":"135-160","publisher":"C. F. Van Sumere & P. J. Lea. Clarendon Press, London","title":"Recent advances in the regulation of the prearomatic pathway","type":"chapter"},"uris":["http://www.mendeley.com/documents/?uuid=e95ef5d0-98fb-4a25-ad1d-5e11b1912d83"]}],"mendeley":{"formattedCitation":"[34]","plainTextFormattedCitation":"[34]","previouslyFormattedCitation":"[34]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[34]. Shikimic acid and its pathway intermediates act as a precursors for synthesis of phenylpropanoid compounds such as phenyalanine, cinnamic acid and coumaric acid, thus in turn involved in the production of defensive lignans and lignin, phenylpropenes and coumarins ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"0471496413","author":[{"dropping-particle":"","family":"Dewick","given":"Paul M","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2002"]]},"publisher":"John Wiley & Sons","title":"Medicinal natural products: a biosynthetic approach","type":"book"},"uris":["http://www.mendeley.com/documents/?uuid=31f6d103-cd5d-495f-b60d-319266ded60b"]}],"mendeley":{"formattedCitation":"[35]","plainTextFormattedCitation":"[35]","previouslyFormattedCitation":"[35]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[35]. Nuclear Magnetic Resonance Spectroscopy and GC/LC-MS/MS analysis of rice leaves aided to idenify wide range of metabolites ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s10658-010-9718-6","ISBN":"0929-1873","ISSN":"09291873","abstract":"A metabolomics based approach has been used to study the infection of the Hwacheong rice cultivar (Oryza sativa L. cv. Hwacheong) with compatible (KJ201) and incompatible (KJ401) strains of the rice blast fungal pathogen Magnaporthe grisea. The metabolic response of the rice plants to each strain was assessed 0, 6, 12, 24, 36, and 48 h post inoculation. Nuclear Magnetic Resonance (NMR) spectroscopy and Gas and Liquid Chromatography Tandem Mass spectrometry (GC/LC-MS/MS) were used to study both aqueous and organic phase metabolites, collectively resulting in the identification of 93 compounds. Clear metabolic profiles were observed at each time point but there were no significant differences in the metabolic response elicited by each pathogen strain until 24 h post inoculation. The largest change was found to be in alanine, which was ~30% (�9%) higher in the leaves from the compatible, compared to the resistant, plants. Together with several other metabolites (malate, glutamine, proline, cinnamate and an unknown sugar) alanine exhibited a good correlation between time of fungal penetration into the leaf and the divergence of metabolite profiles in each interaction. The results indicate both that a wide range of metabolites can be identified in rice leaves and that metabolomics has potential for the study of biochemical changes in plant-pathogen interactions. � 2010 KNPV.","author":[{"dropping-particle":"","family":"Jones","given":"Oliver A.H.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Maguire","given":"Mahon L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Griffin","given":"Julian L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jung","given":"Young Ho","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shibato","given":"Junko","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rakwal","given":"Randeep","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Agrawal","given":"Ganesh K.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jwa","given":"Nam Soo","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"European Journal of Plant Pathology","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2011"]]},"page":"539-554","title":"Using metabolic profiling to assess plant-pathogen interactions: An example using rice (Oryza sativa) and the blast pathogen Magnaporthe grisea","type":"article-journal","volume":"129"},"uris":["http://www.mendeley.com/documents/?uuid=7c4c481e-ca8b-4583-9cc8-5237893306a7"]}],"mendeley":{"formattedCitation":"[36]","plainTextFormattedCitation":"[36]","previouslyFormattedCitation":"[36]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[36].
ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.phytochem.2006.03.020","ISBN":"0031-9422 (Print)","ISSN":"00319422","PMID":"16714038","abstract":"A study has been undertaken to characterize 15 field grown somaclonal variants derived from cotyledonary tissues of UPASI-10 using morphological, physiological and biochemical characters. Although variants were derived from UPASI-10, a very few variants possessed unique \"Chinery\" characters while others exhibited \"Assam\" characters. However, no variant showed identical morphological characters aligning with the parent. Somaclonal variants showed distinct variation in terms of photosynthetic carbon assimilation, stomatal conductance and diffusion resistance. Proline accumulation and water use efficiency showed marginal variations among the variants. SE 8 and SE 10 recorded higher values of membrane stability index denoting their tolerant nature against stress. Class interval analysis based on physiological parameters grouped these plants into three clusters. Three variants grouped under good category representing higher values of productivity index followed by five variants under moderate category. Green leaf constituents and quality profile of made tea produced with crop shoots of variants exhibited wide variation. Center point radar graph analysis of quality constituents grouped these plants into three clusters. Variants SE 2 and SE 13 were segregated distinctly representing their black tea characters. When considering both the quality and productivity indices, SE 3 and SE 7 fall under moderate category and in future these two variants may be subjected to further quality tests for commercial exploitation. � 2006 Elsevier Ltd. All rights reserved.","author":[{"dropping-particle":"","family":"Thomas","given":"Jibu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Raj Kumar","given":"R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mandal","given":"A. K.A.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Phytochemistry","id":"ITEM-1","issue":"11","issued":{"date-parts":[["2006","6","1"]]},"page":"1136-1142","publisher":"Pergamon","title":"Metabolite profiling and characterization of somaclonal variants in tea (Camellia spp.) for identifying productive and quality accession","type":"article-journal","volume":"67"},"uris":["http://www.mendeley.com/documents/?uuid=04a82575-f592-3917-b222-5b221b73f8f8"]}],"mendeley":{"formattedCitation":"[37]","manualFormatting":"Thomas et al. (2006)","plainTextFormattedCitation":"[37]","previouslyFormattedCitation":"[37]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}Thomas et al. (2006) carried out metabolite profiling and characterization of somaclonal variants in tea (Camellia spp.) for identifying productive and quality accession. Nuclear Magnetic Resonance (NMR) spectroscopy and Gas and Liquid Chromatography Tandem Mass spectrometry (GC/LC-MS/MS) analysis of rice leaves from compatible and incompatible interaction with M. grisea yielded 93 compounds from both organic and aqueous phase extracts ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s10658-010-9718-6","ISBN":"0929-1873","ISSN":"09291873","abstract":"A metabolomics based approach has been used to study the infection of the Hwacheong rice cultivar (Oryza sativa L. cv. Hwacheong) with compatible (KJ201) and incompatible (KJ401) strains of the rice blast fungal pathogen Magnaporthe grisea. The metabolic response of the rice plants to each strain was assessed 0, 6, 12, 24, 36, and 48 h post inoculation. Nuclear Magnetic Resonance (NMR) spectroscopy and Gas and Liquid Chromatography Tandem Mass spectrometry (GC/LC-MS/MS) were used to study both aqueous and organic phase metabolites, collectively resulting in the identification of 93 compounds. Clear metabolic profiles were observed at each time point but there were no significant differences in the metabolic response elicited by each pathogen strain until 24 h post inoculation. The largest change was found to be in alanine, which was ~30% (�9%) higher in the leaves from the compatible, compared to the resistant, plants. Together with several other metabolites (malate, glutamine, proline, cinnamate and an unknown sugar) alanine exhibited a good correlation between time of fungal penetration into the leaf and the divergence of metabolite profiles in each interaction. The results indicate both that a wide range of metabolites can be identified in rice leaves and that metabolomics has potential for the study of biochemical changes in plant-pathogen interactions. � 2010 KNPV.","author":[{"dropping-particle":"","family":"Jones","given":"Oliver A.H.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Maguire","given":"Mahon L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Griffin","given":"Julian L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jung","given":"Young Ho","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shibato","given":"Junko","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rakwal","given":"Randeep","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Agrawal","given":"Ganesh K.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jwa","given":"Nam Soo","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"European Journal of Plant Pathology","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2011"]]},"page":"539-554","title":"Using metabolic profiling to assess plant-pathogen interactions: An example using rice (Oryza sativa) and the blast pathogen Magnaporthe grisea","type":"article-journal","volume":"129"},"uris":["http://www.mendeley.com/documents/?uuid=7c4c481e-ca8b-4583-9cc8-5237893306a7"]}],"mendeley":{"formattedCitation":"[36]","plainTextFormattedCitation":"[36]","previouslyFormattedCitation":"[36]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[36]. The authors observed the induction of metabolites 24 h post inoculation and identified the largest change of metabolite namely, alanine which was ~30% higher in the leaves from the compatible, compared to the resistant plants. They also hypothesized and suggested that successful entry of pathogen will trigger an increase in alanine levels to promote cell death of the infected tissue which M. grisea then exploits to facilitate invasion. 
The involvement of these compounds in various defence mechanisms in plants against wide range of pathogens has been well documented. Mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy provides a powerful complementary technique for the identification and quantitative analysis of plant metabolites either in vivo or in tissue extracts. Metabolite fingerprinting by NMR is a fast, convenient, and effective tool for discriminating between groups of related samples and it identifies the most important regions of the spectrum for further analysis ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/jxb/eri010","ISBN":"0022-0957","ISSN":"00220957","PMID":"15520026","abstract":"Although less sensitive than mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy provides a powerful complementary technique for the identification and quantitative analysis of plant metabolites either in vivo or in tissue extracts. In one approach, metabolite fingerprinting, multivariate analysis of unassigned 1H NMR spectra is used to compare the overall metabolic composition of wild-type, mutant, and transgenic plant material, and to assess the impact of stress conditions on the plant metabolome. Metabolite fingerprinting by NMR is a fast, convenient, and effective tool for discriminating between groups of related samples and it identifies the most important regions of the spectrum for further analysis. In a second approach, metabolite profiling, the 1H NMR spectra of tissue extracts are assigned, a process that typically identifies 20-40 metabolites in an unfractionated extract. These profiles may also be used to compare groups of samples, and significant differences in metabolite concentrations provide the basis for hypotheses on the underlying causes for the observed segregation of the groups. Both approaches generate a metabolic phenotype for a plant, based on a system-wide but incomplete analysis of the plant metabolome. However, a review of the literature suggests that the emphasis so far has been on the accumulation of analytical data and sample classification, and that the potential of 1H NMR spectroscopy as a tool for probing the operation of metabolic networks, or as a functional genomics tool for identifying gene function, is largely untapped.","author":[{"dropping-particle":"","family":"Krishnan","given":"P.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kruger","given":"N J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ratcliffe","given":"R G","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Experimental Botany","id":"ITEM-1","issue":"410","issued":{"date-parts":[["2005","12","23"]]},"page":"255-265","title":"Metabolite fingerprinting and profiling in plants using NMR","type":"paper-conference","volume":"56"},"uris":["http://www.mendeley.com/documents/?uuid=9bc72a3d-6386-346c-96c9-3c675736d167"]}],"mendeley":{"formattedCitation":"[38]","plainTextFormattedCitation":"[38]","previouslyFormattedCitation":"[38]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[38].

4. Conclusion

The present investigation provides new insight into the mechanisms of resistance to Erysiphe polygoni in mungbean carrying multiple resistance genes at the metabolite level and indicates a considerable number of potential metabolic targets for further in depth investigations. To our knowledge this study represents the first comprehensive analysis of metabolic changes in mungbean during compatible and incompatible interactions with Erysiphe polygoni.

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

Acknowledgements

The support provided by The Professor and Head (Department of Plant Pathology), Director (CPPS) and The Dean (SPGS), Tamil Nadu Agricultural University are deeply acknowledged. The authors would also like to acknowledge DST � FIST for providing infrastructure facilities and UGC-SAP �DRS operated in Department of Plat Pathology for availing fund facilities and also Dr. Soframanien from Baba Atomic Research Centre, Mumbai & Dr. M. Adinarayana, LAM centre, ANGRU for providing mungbean seed material.

References

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[30]	Y. Abu-Nada, A.C. Kushalappa, W.D. Marshall, K. Al-Mughrabi, A. Murphy, Temporal dynamics of pathogenesis-related metabolites anddfstuv���������B	C	J	K	a	������������zod\QDh�;�h��CJPJaJh�!h��CJPJh�!CJPJh�!h-a�CJPJh�!h�!CJPJh�!h�!CJH*PJh�!h-a�CJH*PJh�!h�!CJH*PJaJh�!h�!CJPJaJ
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�����s�dhH$^��`�s�a$gd�4�L�C�S���K���z���U��* m!n"####)#*#�#�#�#�#������������������������dhgd�2dhgd�4�$dhH$a$gdtP������dh7$8$H$^��`���gd*w� their plausible pathways of induction in potato leaves following inoculation with Phytophthora infestans, Eur. J. Plant Pathol. 118 (2007) 375�391. doi:10.1007/s10658-007-9150-8.
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Table legends

Table 1 Changes in metabolites in susceptible (SML-1082) and resistant (TARM-18) mungbean genotype at 0, 24, 48 and 72 hours of inoculation with Erysiphe polygoni.

Tables:
Table 1 Changes in metabolites in susceptible (SML-1082) and resistant (TARM-18) mungbean genotype at 0, 24, 48 and 72 hours of inoculation with Erysiphe polygoni
S.No.MetabolitesRetention IndexMolecular FormulaMolecular WeightSusceptible 
(SML-1082)Resistant
(TARM-18)024487202448721Malic acid1294C4H6O5134.0874++++++++2Methionine1304C5H11NO2S149.2113++++++++3Oxalic acid933C2H2O494.018++++++++4Propylene glycol724C3H8O276.05243++++++++5Propyl acetate811C5H10O2102.0681++++++++6Glucose1974C6H12O6180.156++++++++7Raffinose1876C18H32O16504.438++++++++8Sucrose1096C12H22O11342.2965++++++++9Hydroxyheptanoic acid960C7H14O3146.094----++++10Pyridine674C5H5N79.0422----++++


S.No.MetabolitesRetention IndexMolecular FormulaMolecular WeightSusceptible 
(SML-1082)Resistant
(TARM-18)0244872024487211Methyl ethyl trisulfide1072C3H8S3139.9788-----+++12Putrescine905C4H12N288.10005-----+++13Dimethylaminobenzaldehyde1406C9H11NO149.0841------++14Quinic acid1842C7H12O6192.167-------+15Alanine874C3H7NO289.04768-------+16Hexadecanoic acid1780C16H32O2256.2402-------+172-Hexanone908C6H12O100.0888---+--++18Lauric acid1298C12H24O2200.1776+---+---19Propynoic acid674C3H2O270.00548+---+---20Ethyl propionate811C5H10O2102.0681+++-+++-








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