中央研究院 生物化學研究所

Functional genomics and synthetic biology at the Joint Genome Institute: capabilities and examples of internal and collaborative enabled science 

Ian Blaby

US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

The rapid design and assembly of synthetic DNA assemblies and engineered microbial chassis strains have become a crucial component of biological engineering projects via iterative design–build–test–learn cycles. In view of this, the Joint Genome Institute (JGI) provides capabilities that integrate semi-automated design and laboratory automation for the design, assembly, and expression of functional DNA assemblies for expression in custom microbial chassis strains. These capabilities are available to both academic and industrial researchers through a competitive peer-reviewed proposal system. For awarded proposals, the DNA synthesis platform provides a spectrum of custom DNA-based assemblies ranging from designed, population verified gRNA libraries through >150kb biosynthetic gene clusters, synthetic pathways and artificial chromosomes. A recent addition is the provision of DNA assemblies chromosomally integrated into an array of modularized microbial host strains, spanning 7 phyla and three kingdoms. Each of these capabilities is supported by in-house developed tools for BioCAD, construct verification and management/workflow tracking. More recently, and in line with JGI’s transition to an AI-centric user facility, we are in the process of evolving the group into a BioDesign Platform, by integrating AI-driven design approaches, novel molecular technologies and optimized workflows, enabling closed-loop iterative cycles of engineering.

I will provide an overview of our capabilities and technologies, highlighting recent developments and future plans across in silico, molecular, and microbial efforts. I will also present examples of internal and collaborative projects enabled by these platforms. Examples include a genome-scale CRISPR functional analysis in the model cyanobacterium Synechocystis sp. PCC 6803. Here, we screening pooled libraries across 30 environmental conditions—spanning trophic states, light intensity, pH, and micronutrient limitation and excess, identifying 1,210 of 3,670 genes with significant fitness effects in at least one condition. I’ll also discuss projects where we have used CRISPR approaches to define microbial engineering of elevated levels of isoprenol, and have mutagenized all amino acids across the length of proteins encoding RuBisCO and members of the haloacid dehalogenase-like superfamily to identify key residues contributing to function.