RESUMO
Various species of microalgae have recently emerged as promising host-organisms for use in biotechnology industries due to their unique properties. These include efficient conversion of sunlight into organic compounds, the ability to grow in extreme conditions and the occurrence of numerous post-translational modification pathways. However, the inability to obtain high levels of nuclear heterologous gene expression in microalgae hinders the development of the entire field. To overcome this limitation, we analyzed different sequence optimization algorithms while studying the effect of transcript sequence features on heterologous expression in the model microalga Chlamydomonas reinhardtii, whose genome consists of rare features such as a high GC content. Based on the analysis of genomic data, we created eight unique sequences coding for a synthetic ferredoxin-hydrogenase enzyme, used here as a reporter gene. Following in silico design, these synthetic genes were transformed into the C. reinhardtii nucleus, after which gene expression levels were measured. The empirical data, measured in vivo show a discrepancy of up to 65-fold between the different constructs. In this work we demonstrate how the combination of computational methods and our empirical results enable us to learn about the way gene expression is encoded in the C. reinhardtii transcripts. We describe the deleterious effect on overall expression of codons encoding for splicing signals. Subsequently, our analysis shows that utilization of a frequent subset of preferred codons results in elevated transcript levels, and that mRNA folding energy in the vicinity of translation initiation significantly affects gene expression.
