Random Mutagenesis as a Promising Tool for Microalgal Strain Improvement towards Industrial Production

Mafalda Trovão, Lisa M. Schüler, Adriana Machado, Gabriel Bombo, Sofia Navalho, Ana Barros, Hugo Pereira, Joana Silva, Filomena Freitas, João Varela

Research output: Contribution to journalReview articlepeer-review

35 Citations (Scopus)
32 Downloads (Pure)


Microalgae have become a promising novel and sustainable feedstock for meeting the rising demand for food and feed. However, microalgae‐based products are currently hindered by high production costs. One major reason for this is that commonly cultivated wildtype strains do not possess the robustness and productivity required for successful industrial production. Several strain improvement technologies have been developed towards creating more stress tolerant and productive strains. While classical methods of forward genetics have been extensively used to determine gene function of randomly generated mutants, reverse genetics has been explored to generate specific mutations and target phenotypes. Site‐directed mutagenesis can be accomplished by employing different gene editing tools, which enable the generation of tailor‐made genotypes. Nevertheless, strategies promoting the selection of randomly generated mutants avoid the introduction of foreign genetic material. In this paper, we review different microalgal strain improvement approaches and their applications, with a primary focus on random mutagenesis. Current challenges hampering strain improvement, selection, and commercialization will be discussed. The combination of these approaches with high‐throughput technologies, such as fluorescence‐activated cell sorting, as tools to select the most promising mutants, will also be discussed.

Original languageEnglish
Article number440
Number of pages25
JournalMarine Drugs
Issue number7
Publication statusPublished - Jul 2022


  • adaptive laboratory evolution
  • fluorescence‐activated cell sorting
  • genetic engineering
  • reverse and forward genetics
  • selection methods


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