Genomics and Applied Biology 2024, Vol.15, No.3, 162-171 http://bioscipublisher.com/index.php/gab 165 significant opportunities, such as the establishment of national germplasm repositories, the development of hybridization-based breeding pipelines, and the application of bioengineering strategies to enhance traits like thermal and disease resistance (Hu et al., 2023). The integration of ecological kelp-microbiome interactions and the use of synthetic biology approaches can further improve the adaptability and performance of hybrid kelp cultivars (Hlavová et al., 2015). 4 Genetic Basis of Cultivated Microalgae 4.1 Overview of microalgae genetics Microalgae are photosynthetic microorganisms with a high degree of genetic diversity, enabling them to adapt to a wide range of environmental conditions. This genetic diversity is crucial for their role in global photosynthesis and CO2 sequestration, making them significant players in environmental sustainability. The genetic makeup of microalgae allows for the production of various primary and secondary metabolites, which have applications in pharmaceuticals, nutraceuticals, and industrial processes (Sreenikethanam et al., 2022). 4.2 Genetic variability in microalgae strains The genetic variability among microalgae strains is a key factor in their adaptability and productivity. This variability can be harnessed through strain improvement techniques to enhance the yield and robustness of microalgae for industrial applications. Traditional methods such as random mutagenesis have been employed to create stress-tolerant and productive strains without introducing foreign genetic material (Trovão et al., 2022). Additionally, the availability of genome sequences and omics datasets from diverse microalgae species has provided a foundation for targeted genetic improvements (Kumar et al., 2020). 4.3 Genetic engineering approaches in microalgae Genetic engineering has emerged as a powerful tool to enhance the metabolic capabilities of microalgae. Techniques such as CRISPR/Cas9, RNAi, ZNFs, and TALENs have been used to modify genes involved in lipid metabolism, fatty acid synthesis, and other metabolic pathways to increase the production of desired compounds (Fayyaz et al., 2020; Muñoz et al., 2021). These approaches have led to significant improvements in lipid content and altered fatty acid profiles, making microalgae more viable for biofuel production and other industrial applications (Muñoz et al., 2021). However, the application of these advanced genetic tools remains underutilized compared to other microorganisms (Kumar et al., 2020) (Figure 2). 4.4 Omics technologies in microalgae breeding Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the breeding and genetic engineering of microalgae. These technologies provide comprehensive insights into the genome structure and metabolic pathways of microalgae, enabling the identification of key genes and regulatory networks involved in the production of high-value compounds (Salama et al., 2019; Kuo et al., 2022). Integrated omics approaches have been particularly effective in optimizing microalgal growth and biofuel production under various stress conditions (Salama et al., 2019). The use of multiomics datasets, big data analysis, and machine learning has further enhanced the ability to discover and manipulate traits for improved biorefinery capabilities and wastewater treatment (Kuo et al., 2022). 5 Breeding Strategies for Cultivated Microalgae 5.1 Selective breeding in microalgae Selective breeding in microalgae involves the identification and propagation of strains with desirable traits. This traditional approach has been used extensively in agriculture and aquaculture to enhance production traits. However, many microalgae species do not possess the sexual characteristics required for traditional breeding, which limits the application of this method. Instead, selective breeding in microalgae often relies on the use of natural variants and mutants generated through chemical and physical mutagenesis (Hlavová et al., 2015).
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