Metabolic engineering of vitamin C production in Arabidopsis

Genomes of diverse Actinidia species provide insights into cis-regulatory motifs and genes associated with critical traits

BACKGROUND

Kiwifruit, belonging to the genus Actinidia, represents a unique fruit crop characterized by its modern cultivars being genetically diverse and exhibiting remarkable variations in morphological traits and adaptability to harsh environments. However, the genetic mechanisms underlying such morphological diversity remain largely elusive.

RESULTS

We report the high-quality genomes of five Actinidia species, including Actinidia longicarpa, A. macrosperma, A. polygama, A. reticulata, and A. rufa. Through comparative genomics analyses, we identified three whole genome duplication events shared by the Actinidia genus and uncovered rapidly evolving gene families implicated in the development of characteristic kiwifruit traits, including vitamin C (VC) content and fruit hairiness. A range of structural variations were identified, potentially contributing to the phenotypic diversity in kiwifruit. Notably, phylogenomic analyses revealed 76 cis-regulatory elements within the Actinidia genus, predominantly associated with stress responses, metabolic processes, and development. Among these, five motifs did not exhibit similarity to known plant motifs, suggesting the presence of possible novel cis-regulatory elements in kiwifruit. Construction of a pan-genome encompassing the nine Actinidia species facilitated the identification of gene DTZ79_23g14810 specific to species exhibiting extraordinarily high VC content. Expression of DTZ79_23g14810 is significantly correlated with the dynamics of VC concentration, and its overexpression in the transgenic roots of kiwifruit plants resulted in increased VC content.

CONCLUSIONS

Collectively, the genomes and pan-genome of diverse Actinidia species not only enhance our understanding of fruit development but also provide a valuable genomic resource for facilitating the genome-based breeding of kiwifruit.

Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering

Metabolic engineering aims to maximize the production of bio-economically important substances (compounds, enzymes, or other proteins) through the optimization of the genetics, cellular processes and growth conditions of microorganisms. This requires detailed understanding of underlying metabolic pathways involved in the production of the targeted substances, and how the cellular processes or growth conditions are regulated by the engineering. To achieve this goal, a large system of experimental techniques, compound libraries, computational methods and data resources, including multi-omics data, are used. The recent advent of multi-omics systems biology approaches significantly impacted the field by opening new avenues to perform dynamic and large-scale analyses that deepen our knowledge on the manipulations. However, with the enormous transcriptomics, proteomics and metabolomics available, it is a daunting task to integrate the data for a more holistic understanding. Novel data mining and analytics approaches, including Artificial Intelligence (AI), can provide breakthroughs where traditional low-throughput experiment-alone methods cannot easily achieve. Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms.