03451nas a2200433   4500008004100000245014700041210006900188260000900257300000800266490000700274520201300281653002002294653003402314653001702348653002202365653001702387653001502404100001802419700004002437700001702477700002302494700001702517700001702534700002402551700001302575700002002588700002602608700002602634700002102660700003202681700002302713700002302736700001902759700002502778700003102803700002402834700002702858856013202885       2025                        eng d00aPrecision integrated identification of predictive first-trimester metabolomics signatures for early detection of gestational diabetes mellitus0 aPrecision integrated identification of predictive firsttrimester  cNOV   a4340 v243 a<p>
	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 (&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.</p>
10aFirst trimester10aGestational diabetes mellitus10aIndian women10aMass spectrometry10aMetabolomics10aPrediction1 aSharma, Sapna1 aSubrahmanyam, Yalamanchili, Venkata1 aGupta, Payal1 aVadivel, Sangeetha1 aDeepa, Mohan1 aTandon, Ansh1 aSreedevi, Sreekumar1 aRam, Uma1 aNarad, Priyanka1 aParmar, Dharmeshkumar1 aAnjana, Ranjit, Mohan1 aRaghunathan, Anu1 aBalasubramanyam, Muthuswamy1 aMohan, Viswanathan1 aSengupta, Abhishek1 aAdamski, Jerzy1 aSaravanan, Ponnusamy1 aPanchagnula, Venkateswarlu1 aUsharani, Dandamudi1 aGokulakrishnan, Kuppan  uhttps://library.ncl.res.in/content/precision-integrated-identification-predictive-first-trimester-metabolomics-signatures-early02727nas a2200325   4500008004100000022001400041245006800055210006800123260000800191300001200199490000700211520174100218653002001959653002001979653001801999653002002017653001402037100002302051700002502074700001702099700002602116700002102142700002102163700002502184700002402209700002202233700002602255700002402281856009602305       2026                        eng d  a1096-717600aNovel routes for bioproduction of delta lactone aroma compounds0 aNovel routes for bioproduction of delta lactone aroma compounds  cMAR  a295-3040 v943 a<p>
	Delta lactones are fatty acid-derived aroma compounds that hold tremendous commercial value. As consumer demand for natural flavours continues to rise, the bioproduction of S-lactones, including S-decalactone and S-dodecalactone, is attracting substantial interest. Our study brings forth a novel approach to the bioproduction of S-lactones from glucose, deviating from existing methods that primarily rely on the biotransformation of fatty acids. The high cost of fatty acid raw material constrains the commercial viability of this traditional approach. We engineered surface-lipid producing type I polyketide synthase (PKS) from Mycobacterium smegmatis by incorporating macrolactone thioesterase (TE) domain. Two out of three fusion constructs produced an appropriately engineered PKS-TE fusion protein that facilitated the synthesis of S-lactones. When grown on glucose as the sole carbon source, recombinant E. coli expressing the engineered PKS-TE fusion protein successfully made S-lactones ranging from C8-C18 acyl chains. Our research further highlights the potential of Mycobacterium smegmatis as a cell factory for producing fatty acid-based S-lactones. By genetically designing and engineering Mycobacterium smegmatis to express PKS-TE fusion protein, we achieved bioproduction of various S-lactones. Batch fermentation of the engineered E. coli strain fed with 2 % glucose produced 786 mg/L of S-dodecalactone and 444 mg/L of S-decalactone at 120 h, underscoring the efficacy of our approach. Thus, this study is the first to demonstrate a methodology for redirecting primary metabolic intermediates towards S-lactone biosynthesis in engineered bacteria, enabling the use of inexpensive and renewable feedstocks.</p>
10aNatural flavour10aPKS engineering10aS-decalactone10aS-dodecalactone10aS-lactone1 aSrivastava, Sonali1 aChandramouli, Aakash1 aGupta, Payal1 aManzer, Abdur, Rahman1 aChoudhury, Rahul1 aReddy, Srinivasa1 aYazdani, Syed, Shams1 aKamat, Siddhesh, S.1 aMohanty, Debasisa1 aNandicoori, Vinay, K.1 aGokhale, Rajesh, S.  uhttps://library.ncl.res.in/content/novel-routes-bioproduction-delta-lactone-aroma-compounds