Enhancement of isoprene production in engineered Synechococcus elongatus UTEX 2973 by metabolic pathway inhibition and machine learning-based optimization strategy

• Published in: Bioresource technology 2023-11, Vol.387, p.129677-129677, Article 129677
• Main Authors: Yadav, Indrajeet, Rautela, Akhil, Gangwar, Agendra, Wagadre, Lokesh, Rawat, Shweta, Kumar, Sanjay
• Format: Article
• Language: English
• Subjects: Alendronate; Artificial neural network-genetic algorithm; Cyanobacteria; Isoprene; Statistical optimization; Synechococcus elongatus UTEX 2973
• ISSN: 0960-8524; 1873-2976
• DOI: 10.1016/j.biortech.2023.129677

[Display omitted] •Engineered S. elongatus UTEX 2973 has potential to produce isoprene in a sustainable way.•Use of alendronate, an inhibitor of CrtE, significantly improved isoprene production.•ANN-GA model outperforms statistical model for process factors optimization.•A 29.52-fold increase in isoprene yield was seen at ANN-GA optimized conditions with alendronate.•Engineered cyanobacteria could be a suitable platform for renewable production of chemicals. An engineered Synechococcus elongatus UTEX 2973-IspS.IDI is used to enhance isoprene production through geranyl diphosphate synthase (CrtE) inhibition and process parameters (light intensity, NaHCO3 and growth temperature) optimization approach. A cumulative isoprene production of 1.21 mg/gDCW was achieved with productivity of 12.6 μg/gDCW/h in culture supplemented with 20 μg/mL alendronate. This inhibition strategy improvises the cumulative isoprene production 5.76-fold in presence of alendronate. The maximum cumulative production of isoprene is observed to be 5.22 and 6.20 mg/gDCW (54.4 and 64.6 μg/gDCW/h) at statistical and artificial neural network genetic algorithm (ANN-GA) optimized conditions, respectively. The overall increase of isoprene production is found to be 29.52-fold using an integrated approach of inhibition and ANN-GA optimization in comparison to unoptimized cultures without alendronate. This study reveals that alendronate use as a potential inhibitor and machine learning based optimization is a better approach in comparison to statistical optimization to enhance the isoprene production.