miRNA engineering of CHO cells facilitates production of difficult-to-express proteins and increases success in cell line development.

In recent years, coherent with growing biologics portfolios also the number of complex and thus difficult-to-express (DTE) therapeutic proteins has increased considerably. DTE proteins challenge bioprocess development and can include various therapeutic protein formats such as monoclonal antibodies (mAbs), multi-specific affinity scaffolds (e.g., bispecific antibodies), cytokines, or fusion proteins. Hence, the availability of robust and versatile Chinese hamster ovary (CHO) host cell factories is fundamental for high-yielding bioprocesses. MicroRNAs (miRNAs) have emerged as potent cell engineering tools to improve process performance of CHO manufacturing cell lines. However, there has not been any report demonstrating the impact of beneficial miRNAs on industrial cell line development (CLD) yet. To address this question, we established novel CHO host cells constitutively expressing a pro-productive miRNA: miR-557. Novel host cells were tested in two independent CLD campaigns using two different mAb candidates including a normal as well as a DTE antibody. Presence of miR-557 significantly enhanced each process step during CLD in a product independent manner. Stable expression of miR-557 increased the probability to identify high-producing cell clones. Furthermore, production cell lines derived from miR-557 expressing host cells exhibited significantly increased final product yields in fed-batch cultivation processes without compromising product quality. Strikingly, cells co-expressing miR-557 and a DTE antibody achieved a twofold increase in product titer compared to clones co-expressing a negative control miRNA. Thus, host cell engineering using miRNAs represents a promising tool to overcome limitations in industrial CLD especially with regard to DTE proteins. Biotechnol. Bioeng. 2017;114: 1495-1510. © 2017 Wiley Periodicals, Inc.

Enhancing prime editor activity by directed protein evolution in yeast.

Prime editing is a highly versatile genome editing technology that enables the introduction of base substitutions, insertions, and deletions. However, compared to traditional Cas9 nucleases prime editors (PEs) are less active. In this study we use OrthoRep, a yeast-based platform for directed protein evolution, to enhance the editing efficiency of PEs. After several rounds of evolution with increased selection pressure, we identify multiple mutations that have a positive effect on PE activity in yeast cells and in biochemical assays. Combining the two most effective mutations - the A259D amino acid substitution in nCas9 and the K445T substitution in M-MLV RT - results in the variant PE_Y18. Delivery of PE_Y18, encoded on DNA, mRNA or as a ribonucleoprotein complex into mammalian cell lines increases editing rates up to 3.5-fold compared to PEmax. In addition, PE_Y18 supports higher prime editing rates when delivered in vivo into the liver or brain. Our study demonstrates proof-of-concept for the application of OrthoRep to optimize genome editing tools in eukaryotic cells.

Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens.

Base editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable transition of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have great potential as genome editing tools for basic research and gene therapy, their application has been hampered by a broad variation in editing efficiencies on different genomic loci. Here we perform an extensive analysis of adenine- and cytosine base editors on a library of 28,294 lentivirally integrated genetic sequences and establish BE-DICT, an attention-based deep learning algorithm capable of predicting base editing outcomes with high accuracy. BE-DICT is a versatile tool that in principle can be trained on any novel base editor variant, facilitating the application of base editing for research and therapy.