03 JUN

Microbial genetic "recombination" technology

  • Life Style
  • Cindy
  • Aug 21,2021
  • 115

Microbial genetic "recombination" technology

Researchers from Lawrence Berkeley National Laboratory (Berkeley Lab) have achieved unprecedented success in modifying microbes to efficiently produce a compound of interest using computational models and CRISPR-based gene editing. Their approach could significantly accelerate the research and development phase of new biomanufacturing processes and get cutting-edge biobased products, such as sustainable fuels and plastic alternatives, on the shelves faster.


The process uses computer algorithms - based on real-world experimental data - to determine which genes in the "host" microbe can be turned off to redirect the organism's energy to produce large amounts of the target compound instead of its normal metabolites.

Currently, many scientists in this field still rely on ad hoc, trial-and-error experiments to determine which gene modifications will lead to improvements. In addition, most microbes used in biomanufacturing processes that produce non-native compounds - meaning that the genes to make such compounds have been inserted into the host's genome - can only produce large amounts of the target compound after the microbe reaches a certain growth stage, resulting in a slow process that wastes energy while incubating the microbe.


The team's streamlined metabolic remodeling process, called "product/substrate pairing," makes the microbe's entire metabolism relevant to making the compound at any given time. To test "product/substrate pairing," the team conducted experiments with a promising emerging host - a soil microbe called Pseudomonas putida, which is designed to carry a gene for making indigoidine, a blue pigment. The scientists evaluated 63 potential rewiring strategies and used a workflow that systematically assessed the possible outcomes of ideal host characteristics, determining that only one of them was experimentally realistic. They then performed CRISPR interference to block the expression of 14 genes, as guided by computational predictions.