Harnessing genetic engineering to drive economic bioproduct production in algae

Abstract

Our reliance on agriculture for sustenance, healthcare, and resources has been essential since the dawn of civilization. However, traditional agricultural practices are no longer adequate to meet the demands of a burgeoning population amidst climate-driven agricultural challenges. Microalgae emerge as a beacon of hope, offering a sustainable and renewable source of food, animal feed, and energy. Their rapid growth rates, adaptability to non-arable land and non-potable water, and diverse bioproduct range, encompassing biofuels and nutraceuticals, position them as a cornerstone of future resource management. Furthermore, microalgae's ability to capture carbon aligns with environmental conservation goals. While microalgae offers significant benefits, obstacles in cost-effective biomass production persist, which curtails broader application. This review examines microalgae compared to other host platforms, highlighting current innovative approaches aimed at overcoming existing barriers. These approaches include a range of techniques, from gene editing, synthetic promoters, and mutagenesis to selective breeding and metabolic engineering through transcription factors.

Keywords: algae; biofuels; bioproducts; biotechnology; genetic engineering; sustainability; transcription factors.

FIGURE 1

Overview of strategies for establishing microalgae as an industrial platform (created with BioRender.com). The schematic provides a streamlined depiction of the key phases in developing microalgae for industrial applications, starting from bioprospecting and culminating in large-scale industrial production. It highlights the progression through strain development, characterization, and genetic engineering.

FIGURE 2

Schematic of current transcription factor-based engineering strategies in microalgae alongside prospective approaches (created with BioRender.com). The illustration presents current metabolic engineering strategies in microalgae using transcription factors, alongside prospective approaches. It highlights the genome-wide TF-TFBS pair identification via experimental and computational techniques, integrated with multiomics data, to decipher TFs’ regulatory functions in microalgae. DAP-seq denotes DNA Affinity Purification and sequencing; ChIP-Seq denotes Chromatin Immunoprecipitation sequencing; ML denotes machine learning, and DL deep learning models.