BMC Biology (Oct 2022)

Genetic mechanisms underlying increased microalgal thermotolerance, maximal growth rate, and yield on light following adaptive laboratory evolution

  • Robin Barten,
  • Dirk-Jan M. van Workum,
  • Emma de Bakker,
  • Judith Risse,
  • Michelle Kleisman,
  • Sofia Navalho,
  • Sandra Smit,
  • Rene H. Wijffels,
  • Harm Nijveen,
  • Maria J. Barbosa

DOI
https://doi.org/10.1186/s12915-022-01431-y
Journal volume & issue
Vol. 20, no. 1
pp. 1 – 14

Abstract

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Abstract Background Adaptive laboratory evolution (ALE) is a powerful method for strain optimization towards abiotic stress factors and for identifying adaptation mechanisms. In this study, the green microalga Picochlorum sp. BPE23 was cultured under supra-optimal temperature to force genetic adaptation. The robustness and adaptive capacity of Picochlorum strains turned them into an emerging model for evolutionary studies on abiotic stressors such as temperature, salinity, and light. Results Mutant strains showed an expanded maximal growth temperature of 44.6 °C, whereas the maximal growth temperature of the wild-type strain was 42 °C. Moreover, at the optimal growth temperature of 38 °C, the biomass yield on light was 22.3% higher, and the maximal growth rate was 70.5% higher than the wild type. Genome sequencing and transcriptome analysis were performed to elucidate the mechanisms behind the improved phenotype. A de novo assembled phased reference genome allowed the identification of 21 genic mutations involved in various processes. Moreover, approximately half of the genome contigs were found to be duplicated or even triplicated in all mutants, suggesting a causal role in adaptation. Conclusions The developed tools and mutant strains provide a strong framework from whereupon Picochlorum sp. BPE23 can be further developed. Moreover, the extensive strain characterization provides evidence of how microalgae evolve to supra-optimal temperature and to photobioreactor growth conditions. With this study, microalgal evolutionary mechanisms were identified by combining ALE with genome sequencing.

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