Precision integrated identification of predictive first-trimester metabolomics signatures for early detection of gestational diabetes mellitus - Sharma, Sapna; Subrahmanyam, Yalamanchili Venkata; Gupta, Payal; Vadivel, Sangeetha; Deepa, Mohan; Tandon, Ansh; Sreedevi, Sreekumar; Ram, Uma; Narad, Priyanka; Parmar, Dharmeshkumar; Anjana, Ranjit Mohan; Raghunathan, Anu; et al. (Cardiovascular Diabetology, 2025)
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 (< 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.
Keywords: First trimester; Gestational diabetes mellitus; Indian women; Mass spectrometry; Metabolomics; Prediction

Novel routes for bioproduction of delta lactone aroma compounds - Srivastava, Sonali; Chandramouli, Aakash; Gupta, Payal; Manzer, Abdur Rahman; Choudhury, Rahul; Reddy, D. Srinivasa; Yazdani, Syed Shams; Kamat, Siddhesh S.; Mohanty, Debasisa; Nandicoori, Vinay K.; Gokhale, Rajesh S. (Metabolic Engineering, 2026)
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.
Keywords: Natural flavour; PKS engineering; S-decalactone; S-dodecalactone; S-lactone