Harnessing artificial intelligence-driven approach for enhanced indole-3-acetic acid from the newly isolated Streptomyces rutgersensis AW08

Environ Res. 2024 Jul 1;252(Pt 3):118933. doi: 10.1016/j.envres.2024.118933. Epub 2024 Apr 18.

Abstract

Indole-3-acetic acid (IAA) derived from Actinobacteria fermentations on agro-wastes constitutes a safer and low-cost alternative to synthetic IAA. This study aims to select a high IAA-producing Streptomyces-like strain isolated from Lake Oubeira sediments (El Kala, Algeria) for further investigations (i.e., 16S rRNA gene barcoding and process optimization). Subsequently, artificial intelligence-based approaches were employed to maximize IAA bioproduction on spent coffee grounds as high-value-added feedstock. The specificity was the novel application of the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Box (L-BFGS-B) optimization algorithm. The new strain AW08 was a significant producer of IAA (26.116 ± 0.61 μg/mL) and was identified as Streptomyces rutgersensis by 16S rRNA gene barcoding and phylogenetic inquiry. The empirical data involved the inoculation of AW08 in various cultural conditions according to a four-factor Box Behnken Design matrix (BBD) of Response surface methodology (RSM). The input parameters and regression equation extracted from the RSM-BBD were the basis for implementing and training the L-BFGS-B algorithm. Upon training the model, the optimal conditions suggested by the BBD and L-BFGS-B algorithm were, respectively, L-Trp (X1) = 0.58 %; 0.57 %; T° (X2) = 26.37 °C; 28.19 °C; pH (X3) = 7.75; 8.59; and carbon source (X4) = 30 %; 33.29 %, with the predicted response IAA (Y) = 152.8; 169.18 μg/mL). Our findings emphasize the potential of the multifunctional S. rutgersensis AW08, isolated and reported for the first time in Algeria, as a robust producer of IAA. Validation investigations using the bioprocess parameters provided by the L-BFGS-B and the BBD-RSM models demonstrate the effectiveness of AI-driven optimization in maximizing IAA output by 5.43-fold and 4.2-fold, respectively. This study constitutes the first paper reporting a novel interdisciplinary approach and providing insights into biotechnological advancements. These results support for the first time a reasonable approach for valorizing spent coffee grounds as feedstock for sustainable and economic IAA production from S. rutgersensis AW08.

Keywords: Actinobacteria; Auxin; Bioprocess optimization; Streptomyces rutgersensis.

MeSH terms

  • Algeria
  • Artificial Intelligence*
  • Indoleacetic Acids* / metabolism
  • Phylogeny
  • RNA, Ribosomal, 16S* / genetics
  • Streptomyces* / genetics
  • Streptomyces* / metabolism

Substances

  • indoleacetic acid
  • Indoleacetic Acids
  • RNA, Ribosomal, 16S