Invented by Vytas SunSpiral, Jennifer Fredlund, Hassan Abdulla, Paolo Boccazzi, Sean Poust, Sara da Luz Areosa Cleto, Brian Chaikind, Dylan Vaughan, Kenneth S. Bruno, Patrick Westfall, Edyta SZEWCZYK, Kyle ROTHSCHILD-MANCINELLI, Arthur Muir FONG, III, Zymergen Inc
The Zymergen Inc invention works as followsThis platform integrates molecular science, automation and advanced machine-learning protocols. It is computationally driven. This platform integrates HTP molecular tools to create HTP design libraries that are derived, among other things, from scientific insight and iterative patterns recognition. “Methods for isolating fungal spore-derived clonal population are also provided.
Background for HTP Genomic Engineering Platform for Improving Fungal Strains
Eukaryotic cell are the preferred organisms to produce polypeptides, secondary metabolites and other secondary metabolites. The filamentous fungi can express native and heterologous protein at high levels. This makes them ideal for large-scale productions of enzymes, other proteins, and animal health products for industrial and pharmaceutical applications. The use of filamentous mushrooms for large-scale production often requires genetic modification of the fungi, as well as automated machinery and equipment. Certain aspects of filamentous fungal growth cycle can make genetic handling and manipulation difficult.
For example DNA introduced into a fungal genome integrates randomly, leading to mostly random DNA fragments. These can quite often be integrated as multiple repeats in tandem (see, for example, Casqueiro and al., J. Bacteriol. 181:1181-1188). This uncontrolled “at random multiple integration” is a potentially harmful process. This uncontrolled?at random multiple integration?
The present transfection system for filamentous fungi is very labor-intensive (see review Fincham 1989, Microbiol. Rev. This is a relatively small-scale process. This may involve protoplasts, handling viscous liquids (e.g. Polyethylene glycol solution, one-by-one spiraling of glass tubes, and selective plating can be involved. Protoplasting conditions can be hard to predict and the yields are often low. The protoplasts may contain multiple nuclei, so that a genetic manipulation could lead to heterokaryotic proplasts which are difficult to distinguish from homokaryotic ones.
Further filamentous cells, such as those that are derived from protoplasts can grow into long fibers known as hyphae, which can form dense mycelium networks. These hyphae may contain different nuclei with a genotype that is distinct. The hyphae are able to differentiate and produce spores which can easily be dispersed into the air. The spores also contain a mix of nuclei if the hyphae have nuclei from different genotypes. This aspect of fungal development means that genetic manipulation will result in a mixture of nuclei. To assess the effect of genetic changes, this population must be homogenized. In an automated environment, spores may also contaminate equipment, which could affect the ability to purify the strains or contaminate other work done on the equipment.
To reduce the dispersal of aerial spores by fungi, submerged cultures can be used. The mycelium produced by the growth of hyphal filamentous mushrooms in submerged cultures may affect the rheological characteristics of the broth. The higher the viscosity, the less uniform oxygen and nutrient distribution, and the greater the energy needed to agitate the culture. The viscosity in some broths due to filamentous hyphal growth can be so high that it interferes with oxygen and nutrients dissolving, which adversely affects the growth of fungi, and ultimately, the yield and productivity.
There is therefore a need for new methods to engineer filamentous fungi that do not suffer the drawbacks of traditional strain-building programs and which greatly speed up the process of discovering beneficial mutations.
The present disclosure presents a high-throughput genomic engineering platform (HTP) for coenocytic species such as filamentous fungi, which does not suffer the many problems associated with conventional microbial strain improvements programs. The methods described herein were tested on filamentous fungi. However, the same methods could be used in other coenocytic species. In one embodiment, the filamentous fungus can be selected from Achlya (e.g. Myceliophthora), Acremonium (e.g. Myceliophthora thermophila), Aspergillus (e.g. Myceliophthora niger), Aureobasidium (e.g. Aureobasidium), Bjerkandera (“Ceriporiopsis”), Cephalosporium (“Chrysosporium”) Aspergillus is the filamentous fungus that can be used for the HTP genome engineering platform and the methods.
The HTP platform, which is taught in this article, can also be used to restore filamentous fungal strains after they have acquired non-beneficial mutants from decades of random mutation-based strain improvement.
The disclosure also includes unique genomic engineering tool sets and procedures that underpin the HTP platform?s functionality for a filamentous fungus system. Aspergillus is one possible species of filamentous fungus. Aspergillus is a species. niger.
The disclosed HTP genome engineering platform is computationally-driven and integrates molecular biological, automation, as well as advanced machine learning protocols. This integrated platform uses a suite HTP molecular tools sets to create HTP gene design libraries. These are derived, inter alia from scientific insight and iterative patterns recognition.
The taught HTP libraries are drivers in the genomic engineering process. They provide libraries of specific genomic alterations to test in filamentous fungi. The microbes that have been engineered using a library or combination of libraries is efficiently screened by HTP for a desired outcome. Production of an interest product. The process of using the HTP libraries to identify specific genomic alterations and testing them in a microbe, and then screening the host genomes that harbor the alterations in an iterative and efficient manner is used. Iterative cycles or rounds are used in some cases. of genomic engineering campaigns can be at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or more iterations/cycles/rounds.
The present disclosure, therefore, includes methods for conducting at least 1, 2, 3 or 4, 5, 6, 7, 8 or 9, 10, 11, 12, 13 and 14, 15, 16, 17 and 18, 19, as well as 20, 21, 22, 23 and 24, 26, 27, 28, 29. It also includes methods for conducting at least 1,000 rounds of HTP genetic engineering (e.g., rounds of SNP swap, PRO swap, Terminator (STOP) swap, or combinations thereof). “The present disclosure teaches methods of conducting at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,
In some embodiments the present disclosure teaches an approach that is linear, where each subsequent HTP round of genetic engineering is based upon the genetic variation found in the previous genetic engineering round. In other embodiments, the present disclosure teaches an non-linear method, where each subsequent HTP Genetic Engineering round is based upon genetic variations identified in previous rounds of genetic engineering. This includes previously conducted analyses and separate HTP Genetic Engineering branches.
The data collected from these iterative cycle enables large-scale data analytics and pattern identification, which are used by the integrative platform for subsequent rounds of HTP Genetic Design Library implementation. The HTP genetic libraries used in the taught platform, are dynamic tools that take advantage of large-scale data pattern recognition algorithms. They also become more insightful with each iteration. This system has not been developed before for filamentous fungi and is urgently needed in the field.
The genetic design libraries described in this disclosure may include at least 1, 2, 3, 5, 6, 7, 8, 9 and 10, as well as at least X number promoter-gene combinations (e.g. the PRO swap library).
The present disclosure, in some embodiments, teaches a method of high-throughput (HTP), genomic engineering, to evolve a filamentous fungus strain to acquire the desired traits. This includes: a. perturbing the genomes in an initial number of filamentous fungus strains, and b. screening and selecting the individual strains from the initial HTP Genetic Design filamentous fungus strain library to achieve the desired traits; c. providing a second plurality filamentous fungus microbes
In some embodiments, this disclosure teaches that an initial HTP genetically designed filamentous fungi library is selected from a group consisting a promoter/stop codon library, SNP swap library, optimized sequence library, terminator swap library or any combination of these.
In some embodiments of the present disclosure, methods are taught for making a plurality of filamentous strains that comprise each a unique combination genetic variations. Each of the combined genetic variants is derived either from the HTP Genetic Design filamentous strain library, or the HTP Genetic Design filamentous strain library in the previous step.
In some embodiments the combination of genetic variation in the subsequent plurality filamentous strains will be a subset all possible combinations of genetic variation in the initial HTP Genetic Design filamentous strain library or in the HTP Genetic Design filamentous strain library in the previous step.
In some embodiments, this disclosure teaches that a subsequent HTP Genetic Design filamentous Fungi strain is derived from genetic variations within the initial HTP Genetic Design filamentous Fungi strain or the HTP Genetic Design filamentous Fungi strain of the previous step.
The order in which mutations are presented is not important. The full combinatorial filamentous fungi library derived from genetic variations in the HTP genetic library of the previous step would consist of six microbes. Each microbe contains either AB or AC unique genetic combinations, BC, BD or CD.
Click here to view the patent on Google Patents.