Lysine deficiency in staple crops like maize, rice, and wheat remains a major cause for global protein malnutrition, underscoring the urgent need for effective biofortification strategies. This review critically examines recent advances in enhancing lysine content, spanning conventional breeding and metabolic engineering to cutting-edge precision genome editing. While conventional breeding, exemplified by Quality Protein Maize, has improved lysine levels, it is often constrained by yield and quality trade-offs. Metabolic engineering strategies, including overexpression of lysine biosynthetic genes, suppression of catabolic genes, and modification of storage proteins, have achieved substantial lysine enrichment but face regulatory and consumer acceptance challenges due to their transgenic nature. The advent of CRISPR/Cas technology now enables precise, transgene-free editing of key enzymes such as DHDPS, AK, and LKR/SDH offering a powerful alternative, though concerns regarding off-target effects and pleiotropy remain. While integrating multi-omics with AI-driven predictive modelling can optimise metabolic flux for higher lysine yield, coupling next-generation genome editing with speed breeding offers a transformative route to develop high-lysine, high-yielding crops for sustainable nutritional security.

Rare diseases refer to a group of neglected diseases with low prevalence that face challenges in diagnostics as well as therapeutics due to phenotypic heterogeneity and ineffective clinical trials. In this study, we evaluated two novel analogs of hydroxyethylamine & phthalimide (LTC-181 and LTC-1717) for their potential effect on the epimerase activity of mutant GNE proteins associated with GNE myopathy. GNE gene encodes a key bifunctional sialic acid biosynthetic enzyme, UDP-N-acetyl Glucosamine 2-epimerase/N-acetyl Mannosamine Kinase; GNE). The compounds have significantly increased the epimerase activity of r-F307C-GNE and r-A555V-GNE mutant proteins in vitro. Reduced GNE epimerase activity and sialic acid content in muscle cell-based model for GNE function (SKM-GNEHz) was increased by 2-fold after addition of these compounds. The proteomic study showed that the compounds affected cytoskeletal organization, autophagy and muscle atrophy. Also, treatment with analogs enhanced the cell viability of SKM-GNEHz cells with increased F-actin polymerization and cell migration, thereby, restoring GNE deficient function. Additionally, effect of these compounds was observed with enhanced autophagy and reduced muscle atrophy function in GNE deficient muscle cell. Docking and interaction studies showed that LTC-1717 stabilize GNE better than LTC-181, indicating better therapeutic potential. Overall, this study indicates that HEA-phthalimide analog could be promising leads for treating GNE myopathy.Supplementary InformationThe online version contains supplementary material available at 10.1186/s10020-025-01377-1.

Airborne microbial contamination, especially involving antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs), poses a growing public health concern in urban environments. This study explores the prevalence and diversity of staphylococci, including methicillin-resistant staphylococci (MRS), in bioaerosols from various urban settings in Delhi, India. Indoor and outdoor air samples showed significantly high staphylococcal loads far above the WHO’s recommended limit of 1000 CFU/m³ for microbial exposure. Seasonal variations revealed a peak in airborne MRS during winter, while monsoon rains reduced outdoor bioaerosol contamination. Eight staphylococcal species were identified, with Staphylococcus epidermidis and Staphylococcus arlettae being the most prevalent human- and animal-associated species, respectively. Notably, 73% of MRS isolates exhibited multidrug resistance (MDR), showing resistance to macrolides, β-lactams, and other commonly used antibiotics. Genotypic analysis confirmed the presence of ARGs among airborne MRS encoding resistance for beta-lactam, trimethoprim, gentamicin, macrolides, chloramphenicol and lincosamides. Notably, with 14 out of 36 MDR isolates carrying the mecA gene encoding for methicillin resistance. This study emphasizes the potential health risks posed by airborne reservoirs of antibiotic resistance in urban environments and underscores the urgent need for comprehensive environmental AMR surveillance to develop effective mitigation strategies.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-95462-4.

By the nineteenth century, numerous religious egalitarian movements had emerged in South India envisioning a casteless society. However, their history largely reflects what Louis Dumont’s observed: ‘A sect cannot survive on Indian soil if it denies caste’. 1 Could a section of Protestant missionaries, known as ‘new missionaries’ for their liberal theology and radical ideology, break this pattern? The article presents intriguing stories and observations from their efforts to socially integrate caste-keeping converts through strategies like the caste test and love feast. It captures mid nineteenth century South India’s social attitudes—how society reacted when students from ‘Pariah’ 2 lower castes entered classrooms, when missionaries encouraged converts to dine together without caste distinctions, and when boarding students had to eat food prepared by lower-caste cooks. Furthermore, how did Protestant caste policies evolve, what challenges did missionaries face in implementing them, and what do these experiments reveal about caste in early modern South India?

Hydration-free energy (HFE) is a fundamental thermodynamic property with broad relevance in both chemistry and biology, particularly in solvation processes. Traditional methods for computing HFE, such as molecular dynamics simulations, are often computationally intensive and require significant domain-specific calibration. Recent advances in machine learning (ML) have enabled more efficient HFE predictions, especially for small molecules. However, many existing ML models lack interpretability and often rely on large, opaque feature sets. In this study, a graph neural network (GNN) model is used for predicting the HFE of small molecules from the FreeSolv dataset. Our model integrates graph representations of solute and solvent molecules and captures their mutual interactions through a cross-attention mechanism during message passing. To enhance physical interpretability, we incorporate a compact set of six global molecular descriptors: approximate electrostatic energy computed via a closed-form Generalized Born (GB) model, polar surface area (PSA), logarithm of the octanol-water partition coefficient ( ), hydrogen bond donors, hydrogen bond acceptors, and the number of rotatable bonds. We benchmark our model against classical ML methods and recent GNN-based baselines. Our attention-based GNN not only improves prediction accuracy but also maintains transparency in feature importance. Our method outperforms existing baselines, achieving a mean absolute error (MAE) of kcal/mol and a root mean square error (RMSE) of kcal/mol, which is approximately and improvement as compared to the best-performing baseline, respectively. The ablation study reveals that among the global descriptors used for the solute, electrostatic energy and PSA are the most critical in reducing prediction error, followed by features related to hydrogen bonding. This combination of high accuracy and strong interpretability makes our framework well-suited for large-scale, data-driven investigations of solvation-free energies. The validation of the proposed framework on datasets encompassing a broader range of solvents will be undertaken in future investigations.

Elucidating the genetic basis of lipid metabolism in children is essential for early intervention in dyslipidemia and cardiovascular diseases. We performed a two-staged genome-wide association study (GWAS; N = 5412) and an independent exome-wide association study (ExWAS; N = 4750) on lipid parameters-HDL, LDL, Triglycerides (TG), Total Cholesterol (TC) in Indian school-going children - the largest single-cohort paediatric lipid study till date. GWAS identified robust associations at established loci, including CETP for HDL; CELSR2, and PSRC1 for LDL and TC, and GCKR, ZNF259, and TBL2 for TG. We also validated known associations at sub-GWAS significance in FADS2, GATAD2A, PRKCA, and QKI. Exome-based analyses further refined functional variants within these loci and revealed additional known loci in ALDH1A2 for HDL; APOE, APOC1, TM6SF2, CILP2, TOMM40, for LDL and TC; and APOA5, BUD13 for TG and novel loci in ATP8B3, MYH7B, GYS2, and RNF8 for TG. Conditional analysis revealed multiple independent signals at key loci. Gene-based GWAS pinpointed CETP and APOC1 as significant for HDL and LDL, respectively. Rare variant analysis identified significant contribution of loss-of-function missense variants in CETP, TM6SF2, and APOE, in regulating lipid profiles. Associations replicated with consistent directionality in European datasets and Indian adults, reinforcing conserved biology across ancestries and age groups. Functional enrichment analyses emphasized lipid-related pathways and differential expression in liver. These findings lay the foundation for ancestry-informed genetic risk prediction models to identify children at early risk for cardiovascular diseases.

Spinocerebellar ataxia 12 (SCA12) is a progressive degenerative neurological disorder, primarily characterized by impaired coordination and balance. To investigate the correlation between proton (1H) magnetic resonance spectroscopy (MRS) and structural imaging indices in patients with SCA12. T1-weighted MRI, DTI, and single voxel MRS point resolved spectroscopy (PRESS) in the left hemispheric cerebellum were acquired using a 3-T MR scanner in 40 SCA12 patients and 25 healthy controls. Correlations between metabolites, gray and white matter volume of lobules, fractional anisotropy (FA), and clinical, nonclinical, and genetic data were examined. Three machine learning algorithms (KNN, LDA, and SVM) were used to analyze the metabolic feature differences between SCA12 and HC groups. Significant decreases in choline (Cho [GPC (glycerophosphocholine) + PCh (phosphocholine)]) and N-acetyl aspartate (NAA) levels, along with increases in myo-inositol ratios to creatine, FA, and white matter volume values (p < 0.05), were observed in the cerebellum of the SCA12 group compared to healthy controls. Positive correlations were observed between NAA levels and cerebellar lobule volume, the SPM IQ score with the right crus II in the SCA12 group. The International Cooperative Ataxia Rating Scale (ICARS) score showed a negative correlation with white matter and specific cerebellar lobules. Disease duration and cytosine, adenine, and guanine (CAG) repeat length were negatively correlated with right lobule VIIIB, lobule IX, and left lobule X. Machine learning algorithms achieved an accuracy of over 95% in MRS data, and 88.89% in volumetric data. MRS, VBM, and DTI techniques reveal neuronal degeneration in SCA12 compared to healthy individuals.