Browsing by Author "Namrata Bhagat"

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Artificial Neural Network - Multi-Objective Genetic Algorithm based optimization for the enhanced pigment accumulation in Synechocystis sp. PCC 6803
(BioMed Central Ltd, 2025) Namrata Bhagat; Guddu Kumar Gupta; Amritpreet K. Minhas; Deepak Chhabra; Pratyoosh Shukla
Background: Natural colorants produced by the cyanobacterium include carotenoids, chlorophyll a and phycocyanin. The current study used the Synechocystis sp. PCC 6803 to examine how abiotic stress conditions, such as low temperature as well as high light intensity, affect the pigment accumulations in comparison to the control conditions. Additionally, using the response surface methodology (RSM) and artificial neural network - multi-objective genetic algorithm (ANN-MOGA), the impact of several nitrogen sources such as urea, ammonium chloride, and sodium nitrate as nutritional stress on the pigment accumulations in the Synechocystis sp. PCC 6803 was examined. Results: The results showed that the pigment accumulation was more pronounced when urea and ammonium chloride was used in combination with nitrate, respectively, as nitrogen source. With the help of our prediction model that used ANN-MOGA, we were able to enhance the synthesis of chlorophyll a, carotenoids, and phycocyanin by 21.93 µg/mL, 9.78 µg/mL, and 0.05 µg/mL, respectively compared to control with 6.37, 3.88 and 0.008 µg/mL. The significant scavenging activity of pigment was showed with 7.66 ± 0.001 values of IC50. Additionally, a very good correlation of coefficient (R2) value 0.99, 0.99 and 0.92 was obtained for APX, CAT and GPX enzyme activity, respectively. Conclusions: The findings lays the groundwork for future attempts to turn cyanobacteria into a commercially viable source of natural pigments by demonstrating the benefits of using the RSM and machine learning techniques like ANN-MOGA to optimise the production of cyanobacterial pigments. The significant scavenging and antioxidant activities like CAT, GPX and APX were also shown by the pigments of the Synechocystis sp. PCC 6803. Furthermore, these machine learning tools can be used as a model to improve and optimize the yields for other metabolites production. © The Author(s) 2025.
Correction: Artificial Neural Network - Multi-Objective Genetic Algorithm based optimization for the enhanced pigment accumulation in Synechocystis sp. PCC 6803 (BMC Biotechnology, (2025), 25, 1, (23), 10.1186/s12896-025-00955-9)
(BioMed Central Ltd, 2025) Namrata Bhagat; Guddu Kumar Gupta; Amritpreet K. Minhas; Deepak Chhabra; Pratyoosh Shukla
Following publication of the original article [1], the authors updated Fig. 8 and changed the caption as follows: From: (a) APX, (b) CAT and (c) GPX radical scavenging activities of various concentrations of carotenoid extracts by Synechocystis sp. PCC 6803 where the R2 ≥ 0. 998, 0.995 and 0.923. Mean ± SD, n = 3 To: Linear regression plot of absorbance of (a) APX, (b) CAT and (c) GPX activity of carotenoid extract by Synechocystis sp. PCC 6803 where the R2 ≥ 0. 998, 0.995 and 0.923. Mean ± SD, n = 3 The original article has been corrected. © The Author(s) 2025.
Current insights into molecular mechanisms of environmental stress tolerance in Cyanobacteria
(Springer Science and Business Media B.V., 2025) Preeti Rai; Ruchi Pathania; Namrata Bhagat; Riya Bongirwar; Pratyoosh Shukla; Shireesh Srivastava
The photoautotrophic nature of cyanobacteria, coupled with their fast growth and relative ease of genetic manipulation, makes these microorganisms very promising factories for the sustainable production of bio-products from atmospheric carbon dioxide. However, both in nature and in cultivation, cyanobacteria go through different abiotic stresses such as high light (HL) stress, heavy metal stress, nutrient limitation, heat stress, salt stress, oxidative stress, and alcohol stress. In recent years, significant improvement has been made in identifying the stress-responsive genes and the linked pathways in cyanobacteria and developing genome editing tools for their manipulation. Metabolic pathways play an important role in stress tolerance; their modification is also a very promising approach to adapting to stress conditions. Several synthetic as well as systems biology approaches have been developed to identify and manipulate genes regulating cellular responses under different stresses. In this review, we summarize the impact of different stresses on metabolic processes, the small RNAs, genes and heat shock proteins (HSPs) involved, changes in the metabolome and their adaptive mechanisms. The developing knowledge of the adaptive behaviour of cyanobacteria may also be utilised to develop better stress-responsive strains for various applications. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.