http://223.31.159.10:8080/jspui/handle/123456789/11 Collections of Institutional Publications of NIPGR Scientists Thu, 30 Apr 2026 12:06:11 GMT 2026-04-30T12:06:11Z http://223.31.159.10:8080/jspui/handle/123456789/1808 Title: Developing together: The elementome and biogeochemical niche of the mutualistic occupants of a fig microcosm Authors: Kulkarni, Manasa; Naik, Nehal Vijay; Vadassery, Jyothilakshmi; Borges, Renee M. Abstract: In brood-site pollination mutualisms, where flowers provide nutrition and shelter to pollinator offspring in exchange for pollination, resource allocation to inflorescences is directly related to plant and pollinator fitness. We determine resource allocation to components of an enclosed monoecious Ficus inflorescence or syconium that, besides seeds, also houses and provides nutrition to pollinator wasp offspring, each developing within individual uniovulate galled flowers. Besides biomass, we determine elemental concentrations as parameters of resource allocation. For the first time, we apply the biogeochemical niche (BN) concept to a mutualism and construct the BN of syconial occupants using the elementomes and stoichiometric ratios of plant, seed and pollinator tissue. We predicted that BNs of seeds and galls containing wasps should differ due to differences in tissue type, facilitating their co-development. We also measure trophic stoichiometric ratios (TSRs) for various elements to determine resource mismatch between consumers and resources. We found that the syconium wall, which insulates and protects developing seeds and wasps, constituted 58% of syconial biomass. Individual pollinators and their galls were significantly heavier than seeds indicating that their development is resource-intensive. As predicted, seeds and adult female pollinators had significantly different BNs, highlighting differences in nutritional needs of these mutualistic occupants within a shared nutrient-providing resource. Pollinators had significantly lower C:N and C:P ratios than the syconial wall indicating limitation of N and P within host resources. The BN of pollinator wasps was distinguished by significantly higher concentrations of nitrogen, phosphorus, zinc and sulphur compared to the syconium wall or seeds. TSRs of >4 for nitrogen and sulphur highlight the heightened resource mismatch that pollinators likely face for these elements during their development. We found no overlap in the BNs of male and female pollinator wasps, likely due to their starkly different anatomical and functional traits. Overall, our study demonstrates how BNs and TSRs can reveal trading of resources within mutualisms highlighting non-overlapping requirements for elements and the potential limitations they can pose for resource providers and consumers. These parameters can serve as common currencies for comparisons across mutualistic interactions. Description: Accepted date: 13 February 2026 Thu, 01 Jan 2026 00:00:00 GMT http://223.31.159.10:8080/jspui/handle/123456789/1808 2026-01-01T00:00:00Z http://223.31.159.10:8080/jspui/handle/123456789/1807 Title: An artificial neural network–based deep learning model to predict combined stress impact and interaction in plants Authors: Priya, Piyush; Pandey, Prachi; Jain, Rubi; Kandpal, Manu; Jain, Shradha; Chaudhury, Rim; Ramegowda, Venkategowda; Senthil-Kumar, Muthappa Abstract: Premise: Plants are frequently exposed to combinations of abiotic and biotic stresses that pose a greater threat to yield and productivity than individual stresses. However, knowledge of the impact of many stress combinations in numerous plants is limited due to the lack of experimental data, which could take decades to generate. To overcome this limitation, we utilized existing literature data from various plant species and stress combinations to derive biological inferences, thereby gaining a comprehensive understanding of plant responses through a computational tool. Methods: Public databases were used to gather literature on the impact of various abiotic and biotic stress combinations. Then, a composite artificial neural network (ANN)–based multi-target classification and regression deep learning model was developed using machine learning algorithms. Results: The model predicted the impact of stress interactions in plants, including the morphological parameters affected and percentage changes in those parameters, with an overall accuracy of 76.33%. Predicted reductions in yield were validated in rice under combined drought and heat stress. Discussion: The ANN-based model developed in this study is a valuable resource for plant researchers seeking to understand the impact of stress combinations. The tool can make use of multivariate and complex combined stress datasets. Description: Accepted date: 8 November 2025 Thu, 01 Jan 2026 00:00:00 GMT http://223.31.159.10:8080/jspui/handle/123456789/1807 2026-01-01T00:00:00Z http://223.31.159.10:8080/jspui/handle/123456789/1806 Title: OsNUOR enhances disease susceptibility by interfering with reactive oxygen species homeostasis and ferroptosis-like cell death Authors: Sahoo, Debashis; Chandan, Ravindra K; Goel, Naveen; Jha, Gopaljee Abstract: Necrotrophic fungal pathogens such as Rhizoctonia solani, the causal agent of rice (Oryza sativa) sheath blight disease, enhance reactive oxygen species (ROS) production to induce necrosis in infected tissues. Here, we present evidence that the host alternative NADH:ubiquinone oxidoreductase (OsNUOR) facilitates R. solani infection by promoting an oxidative-stress-enriched environment and inducing iron-dependent ferroptosis-like cell death. OsNUOR overexpression (OE) lines exhibit enhanced disease susceptibility, whereas knock-out (KO) lines developed through genome editing demonstrate increased resistance. Infected OE lines have enhanced accumulation of ROS, lipid peroxides, and ferric ions (Fe3+); a significant reduction in antioxidative enzyme (including glutathione peroxidase) activity; and depletion of glutathione levels. In KO lines, the redox status of infected tissues is maintained, and the antioxidative defense is activated. Our data suggest that upregulation of OsNUOR induces mitochondrial ROS accumulation and modulates redox signalling, leading to Fe3+ accumulation and lipid peroxidation that promote necrosis in rice. KO lines are compromised in these processes and therefore exhibit disease resistance. We demonstrate that treatment with ferroptosis inhibitors prevents necrotic lesions, whereas ferroptosis inducers enhance disease severity. Overall, our study reveals the importance of ferroptosis-like cell death in promoting necrosis during R. solani infection in rice. Description: Accepted date: 16 April 2026 Thu, 01 Jan 2026 00:00:00 GMT http://223.31.159.10:8080/jspui/handle/123456789/1806 2026-01-01T00:00:00Z http://223.31.159.10:8080/jspui/handle/123456789/1805 Title: The intersection of AI and genomics in health and disease: Advancements and applications Authors: Kaushik, Love; Vivek, A T; Arora, Simran; Hamid, Fiza; Mukherjee, Kanka; Bisht, Niyati; Chaudhary, Sakshi; Shukla, Jagriti; Nawani, Sakshi; Kumar, Shailesh Abstract: AI and genomics are revolutionizing precision medicine by using machine learning (ML) to analyze large-scale next-generation sequencing (NGS) data, identifying genetic mutations and biomarkers for personalized therapies. In practice, this accelerates drug discovery and enhances variant detection, while in cancer genomics, AI enables early detection via liquid biopsies and refines treatment by integrating multi-omics data to improve therapeutic precision. However, challenges such as data biases in underrepresented populations, limited model interpretability, and ethical concerns regarding privacy and algorithmic inequity hinder clinical adoption and demand robust governance. Efforts to diversify datasets also face standardization hurdles, although explainable AI and federated learning provide promising solutions for improving transparency and privacy. In this chapter, we discuss the role of AI in advancing genomics from diagnostics to novel therapies and emphasize the need for equitable frameworks to ensure responsible implementation, thereby paving the way for breakthroughs in personalized medicine. Description: Accepted date: 13 April 2026 Thu, 01 Jan 2026 00:00:00 GMT http://223.31.159.10:8080/jspui/handle/123456789/1805 2026-01-01T00:00:00Z