The commercial viability of bioprocessed products often depends on consistent quality and low cost of manufacture. Delivering high titre while limiting losses during downstream processing can be a formidable technical challenge – one that synthetic biology and the standard techniques used by Contract Development and Manufacture Organisations (CDMOs) struggle to overcome. Fortunately, a new technology that harnesses the power of evolution offers a way to overcome titre limitations and reduce a bioprocess’s cost of goods.
The challenge of biological complexity
Evolution is driven by a highly complex interplay of genetic variation and environmental selection. The genome of an organism is continually subject to random mutations and, with each successive generation, certain alleles are favoured according to environmental conditions. In nature, environments are rarely static, and it is genetic diversity within a population that underlies its ability to adapt.
The scale of the genetic possibility observed in nature is extremely vast. Given a genome of 6,000 genes, each of which consists of hundreds to thousands of alterable bases, the number of possible combinations of alleles is far greater than the 1081 atoms that make up our universe.
Faced with this complexity, CDMOs typically rely on a well-characterised host cell (aka a fixed genetic chassis) to express the product of interest. A limited number of genes may be engineered in an attempt to create a strain that is suitable for a specific process. Artificial intelligence or machine learning can amplify this process by facilitating a greater number of iterations, however genetic diversity will always be restricted by the initial chassis. This means that the resulting phenotype is only one engineered step away from the original chassis.
Furthermore, research has shown that genes interact in complex and unpredictable ways. Limited understanding of these interactions in the context of the whole genome means that engineered strains typically deliver only modest improvements at best. Should more than a few genes be subjected to engineering, then an unstable or “sick” host cell phenotype usually emerges. In light of these constraints, it is clear that a fundamentally different approach is needed to navigate the complexity of biology if strain optimisation is to be achieved.
New technology that optimises breeding
Using new QTL technology, breeding and screening produces the optimum genotype – and therefore the optimum phenotype – for a desired industrial parameter, such as the delivery of high titre at a given temperature. Through controlled breeding, libraries containing vast genomic diversity have been created. These libraries incorporate traits shaped by millions of years of evolution under diverse environmental conditions from around the world, far exceeding the diversity possible from a single genetic chassis.
Putting evolution to work
Two parent strains of Baker’s yeast can produce a more than a billion progeny. Each of these carries a unique genotype that will, in turn, produce a unique phenotype. In other words, the breeding of two parental strains can product billions of distinct genetic chassis, not just one and it is essential to the generation of varied progeny that genetic diversity is present in the parental strains.
Some of the billions of chassis will be better adapted to the conditions of the desired bioprocess than the parent strains. By screening the progeny, the best performers can be identified for QTL analysis, which reveals the genetic traits that underlie their effectiveness. The best progeny can therefore be selected as the next generation of parents, and the cycle of breeding, screening, and QTL analysis is repeated until performance plateaus. This plateau indicates the that the strain has been optimised to the parameter of interest, be this titre or anything else. Optimisation targets can be tailored to the commercial or technical needs of the product, which commonly include maximising titre, achieving highly specific protein secretion, and ensuring consistent product profiles that minimise expensive and yield-sapping downstream processing steps.
By harnessing evolution, bioprocess developers can achieve higher titres, lower costs, and create commercially competitive processes beyond the limits of conventional strain engineering.
