<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kumar, Yashwant</style></author><author><style face="normal" font="default" size="100%">Dholakia, Bhushan B.</style></author><author><style face="normal" font="default" size="100%">Panigrahi, Priyabrata</style></author><author><style face="normal" font="default" size="100%">Kadoo, Narendra Y.</style></author><author><style face="normal" font="default" size="100%">Giri, Ashok P.</style></author><author><style face="normal" font="default" size="100%">Gupta, Vidya S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Metabolic profiling of chickpea-Fusarium interaction identifies differential modulation of disease resistance pathways</style></title><secondary-title><style face="normal" font="default" size="100%">Phytochemistry</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Chickpea</style></keyword><keyword><style  face="normal" font="default" size="100%">Fusarium wilt</style></keyword><keyword><style  face="normal" font="default" size="100%">LC-MS</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolomics</style></keyword><keyword><style  face="normal" font="default" size="100%">OPLS-DA</style></keyword><keyword><style  face="normal" font="default" size="100%">Phytoalexin</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">AUG</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">PERGAMON-ELSEVIER SCIENCE LTD</style></publisher><pub-location><style face="normal" font="default" size="100%">THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND</style></pub-location><volume><style face="normal" font="default" size="100%">116</style></volume><pages><style face="normal" font="default" size="100%">120-129</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Chickpea is the third most widely grown legume in the world and mainly used as a vegetarian source of human dietary protein. Fusarium wilt, caused by Fusarium oxysporum f. sp. ciceri (Foc), is one of the major threats to global chickpea production. Host resistance is the best way to protect crops from diseases; however, in spite of using various approaches, the mechanism of Foc resistance in chickpea remains largely obscure. In the present study, non-targeted metabolic profiling at several time points of resistant and susceptible chickpea cultivars using high-resolution liquid chromatography-mass spectrometry was applied to better understand the mechanistic basis of wilt resistance or susceptibility. Multivariate analysis of the data (OPLS-DA) revealed discriminating metabolites in chickpea root tissue after Foc inoculation such as flavonoids, isoflavonoids, alkaloids, amino acids and sugars. Foc inoculated resistant plants had more flavonoids and isoflavonoids along with their malonyl conjugates. Many antifungal metabolites that were induced after Foc infection viz, aurantion-obstine beta-glucosides and querecitin were elevated in resistant cultivar. Overall, diverse genetic and biochemical mechanisms were operational in the resistant cultivar for Foc defense as compared to the susceptible plant. The resistant chickpea plants employed the above-mentioned metabolic pathways as potential defense strategy against Foc. (C) 2015 Elsevier Ltd. All rights reserved.&lt;/p&gt;</style></abstract><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">2.779</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kumar, Yashwant</style></author><author><style face="normal" font="default" size="100%">Zhang, Limin</style></author><author><style face="normal" font="default" size="100%">Panigrahi, Priyabrata</style></author><author><style face="normal" font="default" size="100%">Dholakia, Bhushan B.</style></author><author><style face="normal" font="default" size="100%">Dewangan, Veena</style></author><author><style face="normal" font="default" size="100%">Chavan, Sachin G.</style></author><author><style face="normal" font="default" size="100%">Kunjir, Shrikant M.</style></author><author><style face="normal" font="default" size="100%">Wu, Xiangyu</style></author><author><style face="normal" font="default" size="100%">Li, Ning</style></author><author><style face="normal" font="default" size="100%">Rajmohanan, Pattuparambil R.</style></author><author><style face="normal" font="default" size="100%">Kadoo, Narendra Y.</style></author><author><style face="normal" font="default" size="100%">Giri, Ashok P.</style></author><author><style face="normal" font="default" size="100%">Tang, Huiru</style></author><author><style face="normal" font="default" size="100%">Gupta, Vidya S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fusarium oxysporum mediates systems metabolic reprogramming of chickpea roots as revealed by a combination of proteomics and metabolomics</style></title><secondary-title><style face="normal" font="default" size="100%">Plant Biotechnology Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Chickpea</style></keyword><keyword><style  face="normal" font="default" size="100%">fusarium oxysporum</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolomics</style></keyword><keyword><style  face="normal" font="default" size="100%">NMR</style></keyword><keyword><style  face="normal" font="default" size="100%">plant-pathogen interaction</style></keyword><keyword><style  face="normal" font="default" size="100%">proteomics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JUL</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">7</style></number><publisher><style face="normal" font="default" size="100%">WILEY-BLACKWELL</style></publisher><pub-location><style face="normal" font="default" size="100%">111 RIVER ST, HOBOKEN 07030-5774, NJ USA</style></pub-location><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">1589-1603</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Molecular changes elicited by plants in response to fungal attack and how this affects plant-pathogen interaction, including susceptibility or resistance, remain elusive. We studied the dynamics in root metabolism during compatible and incompatible interactions between chickpea and Fusarium oxysporum f. sp. ciceri (Foc), using quantitative label-free proteomics and NMR-based metabolomics. Results demonstrated differential expression of proteins and metabolites upon Foc inoculations in the resistant plants compared with the susceptible ones. Additionally, expression analysis of candidate genes supported the proteomic and metabolic variations in the chickpea roots upon Foc inoculation. In particular, we found that the resistant plants revealed significant increase in the carbon and nitrogen metabolism; generation of reactive oxygen species (ROS), lignification and phytoalexins. The levels of some of the pathogenesis-related proteins were significantly higher upon Foc inoculation in the resistant plant. Interestingly, results also exhibited the crucial role of altered Yang cycle, which contributed in different methylation reactions and unfolded protein response in the chickpea roots against Foc. Overall, the observed modulations in the metabolic flux as outcome of several orchestrated molecular events are determinant of plant's role in chickpea-Foc interactions.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">6.09</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Singh, Priyanka</style></author><author><style face="normal" font="default" size="100%">Kalunke, Raviraj M.</style></author><author><style face="normal" font="default" size="100%">Shukla, Anurag</style></author><author><style face="normal" font="default" size="100%">Tzfadia, Oren</style></author><author><style face="normal" font="default" size="100%">Thulasiram, V. Hirekodathakallu</style></author><author><style face="normal" font="default" size="100%">Giri, Ashok P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Biosynthesis and tissue-specific partitioning of camphor and eugenol in Ocimum kilimandscharicum</style></title><secondary-title><style face="normal" font="default" size="100%">Phytochemistry</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Borneol dehydrogenase</style></keyword><keyword><style  face="normal" font="default" size="100%">Camphor</style></keyword><keyword><style  face="normal" font="default" size="100%">Eugenol</style></keyword><keyword><style  face="normal" font="default" size="100%">Geranyl diphosphate synthase</style></keyword><keyword><style  face="normal" font="default" size="100%">Lamiaceae</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolite partitioning</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Ocimum kilimandscharicum</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptomics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">SEP</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">177</style></volume><pages><style face="normal" font="default" size="100%">112451</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In Ocimum kilimandscharicum, the relative volatile composition of camphor in leaves was as high as 55%, while that of eugenol in roots was 57%. These metabolites were differentially partitioned between the aerial and root tissues. Global metabolomics revealed tissue-specific biochemical specialization, evident by the differential distribution of 2588 putative metabolites across nine tissues. Next-generation sequencing analysis indicated differential expression of 51 phenylpropanoid and 55 terpenoid pathway genes in aerial and root tissues. By integrating metabolomics with transcriptomics, the camphor biosynthesis pathway in O. kilimandscharicum was elucidated. In planta bioassays revealed the role of geranyl diphosphate synthase (gpps) and borneol dehydrogenase (bdh) in camphor biosynthesis. Further, the partitioning of camphor was attributed to tissue-specific gene expression of both the pathway entry point (gpps) and terminal (bdh) enzyme. Unlike camphor, eugenol accumulated more in roots; however, absence of the eugenol synthase gene in roots indicated long distance transport from aerial tissues. In silico co-expression analysis indicated the potential involvement of ATP-binding cassette, multidrug and toxic compound extrusion, and sugar transporters in eugenol transport. Similar partitioning was evident across five other Ocimum species. Overall, our work indicates that metabolite partitioning maybe a finely regulated process, which may have implications on plant growth, development, and defense.&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;3.044&lt;/p&gt;
</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tellis, Meenakshi</style></author><author><style face="normal" font="default" size="100%">Mohite, Sharada</style></author><author><style face="normal" font="default" size="100%">Joshi, Rakesh</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Trehalase inhibition in Helicoverpa armigera activates machinery for alternate energy acquisition</style></title><secondary-title><style face="normal" font="default" size="100%">JOURNAL OF BIOSCIENCES</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Energy metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">glucose</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptomics</style></keyword><keyword><style  face="normal" font="default" size="100%">trehalase</style></keyword><keyword><style  face="normal" font="default" size="100%">validamycin A</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JUL </style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">49</style></volume><pages><style face="normal" font="default" size="100%">74</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">3</style></issue><work-type><style face="normal" font="default" size="100%">Journal Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Indian&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;2.9&lt;/p&gt;
</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sharma, Sapna</style></author><author><style face="normal" font="default" size="100%">Subrahmanyam, Yalamanchili Venkata</style></author><author><style face="normal" font="default" size="100%">Gupta, Payal</style></author><author><style face="normal" font="default" size="100%">Vadivel, Sangeetha</style></author><author><style face="normal" font="default" size="100%">Deepa, Mohan</style></author><author><style face="normal" font="default" size="100%">Tandon, Ansh</style></author><author><style face="normal" font="default" size="100%">Sreedevi, Sreekumar</style></author><author><style face="normal" font="default" size="100%">Ram, Uma</style></author><author><style face="normal" font="default" size="100%">Narad, Priyanka</style></author><author><style face="normal" font="default" size="100%">Parmar, Dharmeshkumar</style></author><author><style face="normal" font="default" size="100%">Anjana, Ranjit Mohan</style></author><author><style face="normal" font="default" size="100%">Raghunathan, Anu</style></author><author><style face="normal" font="default" size="100%">Balasubramanyam, Muthuswamy</style></author><author><style face="normal" font="default" size="100%">Mohan, Viswanathan</style></author><author><style face="normal" font="default" size="100%">Sengupta, Abhishek</style></author><author><style face="normal" font="default" size="100%">Adamski, Jerzy</style></author><author><style face="normal" font="default" size="100%">Saravanan, Ponnusamy</style></author><author><style face="normal" font="default" size="100%">Panchagnula, Venkateswarlu</style></author><author><style face="normal" font="default" size="100%">Usharani, Dandamudi</style></author><author><style face="normal" font="default" size="100%">Gokulakrishnan, Kuppan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Precision integrated identification of predictive first-trimester metabolomics signatures for early detection of gestational diabetes mellitus</style></title><secondary-title><style face="normal" font="default" size="100%">Cardiovascular Diabetology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">First trimester</style></keyword><keyword><style  face="normal" font="default" size="100%">Gestational diabetes mellitus</style></keyword><keyword><style  face="normal" font="default" size="100%">Indian women</style></keyword><keyword><style  face="normal" font="default" size="100%">Mass spectrometry</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Prediction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">NOV </style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">24</style></volume><pages><style face="normal" font="default" size="100%">434</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	Background and aimGestational diabetes mellitus (GDM), a common pregnancy-related metabolic disorder, often goes undiagnosed until the second trimester, limiting early intervention opportunities. Given the higher prevalence of GDM in India, there is a critical need to investigate metabolomic biomarkers among Asian Indians, who exhibit greater insulin resistance and are predisposed to developing type 2 diabetes at an earlier age. This study aimed to identify early pregnancy metabolomic signatures predictive of GDM. MethodsAmong 2115 pregnant women from the STratification of Risk of Diabetes in Early pregnancy (STRiDE) study, we performed untargeted metabolomic profiling using UPLC-MS/MS at early pregnancy (&amp;lt; 16 weeks) plasma samples from 100 women-comprising 50 with GDM and 50 normal (without GDM) based on oral glucose tolerance test (OGTT) at 24-28 weeks. Statistical and machine learning approaches, including logistic regression and random forest (RF), were applied to identify GDM-associated metabolites and construct predictive models. Pathway enrichment analysis was conducted using KEGG database annotations. ResultsA total of 49 metabolites were significantly associated with GDM, primarily involving lipid classes such as phosphatidylcholines, sphingomyelins, and triacylglycerols. RF analysis identified a panel of eight metabolites that achieved best predictive performance (AUC 0.880; 95% CI: 0.809-0.951) for GDM. When combined with conventional clinical risk factors, the integrated model showed comparable prediction of GDM with AUC 0.88;: 95% CI: 0.810-0.952). Enrichment analysis highlighted dysregulated pathways including glycerophospholipid and sphingolipid metabolism, autophagy, and insulin resistance. ConclusionThis study demonstrates the utility of early-pregnancy metabolomic profiling for predicting GDM in Indian women. The eight-metabolite panel offers a promising tool for early risk stratification of GDM, warranting validation in diverse populations.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;
	Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;
	10&lt;/p&gt;
</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Emwas, Abdul-Hamid</style></author><author><style face="normal" font="default" size="100%">Zacharias, Helena U.</style></author><author><style face="normal" font="default" size="100%">Alborghetti, Marcos Rodrigo</style></author><author><style face="normal" font="default" size="100%">Gowda, G. A. Nagana</style></author><author><style face="normal" font="default" size="100%">Raftery, Daniel</style></author><author><style face="normal" font="default" size="100%">Mckay, Ryan T.</style></author><author><style face="normal" font="default" size="100%">Chang, Chung-ke</style></author><author><style face="normal" font="default" size="100%">Saccenti, Edoardo</style></author><author><style face="normal" font="default" size="100%">Gronwald, Wolfram</style></author><author><style face="normal" font="default" size="100%">Schuchardt, Sven</style></author><author><style face="normal" font="default" size="100%">Leiminger, Roland</style></author><author><style face="normal" font="default" size="100%">Merzaban, Jasmeen</style></author><author><style face="normal" font="default" size="100%">Madhoun, Nour Y.</style></author><author><style face="normal" font="default" size="100%">Iqbal, Mazhar</style></author><author><style face="normal" font="default" size="100%">Alsiary, Rawiah A.</style></author><author><style face="normal" font="default" size="100%">Shivapurkar, Rupali</style></author><author><style face="normal" font="default" size="100%">Pain, Arnab</style></author><author><style face="normal" font="default" size="100%">Shanmugam, Dhanasekaran</style></author><author><style face="normal" font="default" size="100%">Ryan, Danielle</style></author><author><style face="normal" font="default" size="100%">Roy, Raja</style></author><author><style face="normal" font="default" size="100%">Schirra, Horst Joachim</style></author><author><style face="normal" font="default" size="100%">Morris, Vanessa</style></author><author><style face="normal" font="default" size="100%">Zeri, Ana Carolina</style></author><author><style face="normal" font="default" size="100%">Alahmari, Fatimah</style></author><author><style face="normal" font="default" size="100%">Kaddurah-Daouk, Rima</style></author><author><style face="normal" font="default" size="100%">Salek, Reza M.</style></author><author><style face="normal" font="default" size="100%">LeVatte, Marcia</style></author><author><style face="normal" font="default" size="100%">Berjanskii, Mark</style></author><author><style face="normal" font="default" size="100%">Lee, Brian</style></author><author><style face="normal" font="default" size="100%">Wishart, David S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Recommendations for sample selection, collection and preparation for NMR-based metabolomics studies of blood</style></title><secondary-title><style face="normal" font="default" size="100%">Metabolomics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Blood</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolites</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolomics</style></keyword><keyword><style  face="normal" font="default" size="100%">NMR</style></keyword><keyword><style  face="normal" font="default" size="100%">Plasma</style></keyword><keyword><style  face="normal" font="default" size="100%">Serum</style></keyword><keyword><style  face="normal" font="default" size="100%">Standardization</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">MAY</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">66</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	BackgroundMetabolic profiling of blood metabolites, particularly in plasma and serum, is vital for studying human diseases, human conditions, drug interventions and toxicology. The clinical significance of blood arises from its close ties to all human cells and facile accessibility. However, patient-specific variables such as age, sex, diet, lifestyle and health status, along with pre-analytical conditions (sample handling, storage, etc.), can significantly affect metabolomic measurements in whole blood, plasma, or serum studies. These factors, referred to as confounders, must be mitigated to reveal genuine metabolic changes due to illness or intervention onset.Review objectiveThis review aims to aid metabolomics researchers in collecting reliable, standardized datasets for NMR-based blood (whole/serum/plasma) metabolomics. The goal is to reduce the impact of confounding factors and enhance inter-laboratory comparability, enabling more meaningful outcomes in metabolomics studies.Key conceptsThis review outlines the main factors affecting blood metabolite levels and offers practical suggestions for what to measure and expect, how to mitigate confounding factors, how to properly prepare, handle and store blood, plasma and serum biosamples and how to report data in targeted NMR-based metabolomics studies of blood, plasma and serum.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><work-type><style face="normal" font="default" size="100%">Review</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;
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	4.1&lt;/p&gt;
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