Synthetic biology (Oxford, England)最新文献

The presence and impact of variability in cells as the source material for genome engineering are important to consider for the design, execution and interpretation of outcomes of a genome-engineering process. Variability may be present at the genotype and phenotype level, yet the impact of these sources of variability on a genome-engineering experiment may not be regularly considered by researchers. In this perspective, we use clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein (Cas) genome editing of mammalian cells to provide examples of how variation within or across cell samples may mislead a researcher in their expectations about the cells they are engineering. Furthermore, we highlight the need for understanding the baseline cell genotype and phenotype to appropriately understand the starting cell material and interpret and attribute the impact of engineering on cells. We emphasize that heterogeneity within a cell pool and the inherent variability in the cellular materials used for genome engineering are complex, but of high value to characterize and account for where possible, to move toward the potential of generating desired and predictable engineered products. Provided is a framework cause-and-effect diagram for CRISPR/Cas9 genome editing toward identifying and mitigating potential sources of variability. We encourage researchers to consider the variability of source materials and undertake strategies, which may include those described here, for detecting, attributing and minimizing additional sources of variability where possible toward the aim of fostering greater reliability, confidence and reproducibility in genome-engineering studies. Graphical Abstract.

Complex therapeutic antibody formats, such as bispecifics (bsAbs) or cytokine fusions, may provide new treatment options in diverse disease areas. However, the manufacturing yield of these complex antibody formats in Chinese Hamster Ovary (CHO) cells is lower than monoclonal antibodies due to challenges in expression levels and potential formation of side products. To overcome these limitations, we performed a clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated protein 9 (Cas9)-based knockout (KO) arrayed screening of 187 target genes in two CHO clones expressing two different complex antibody formats in a production-mimicking set-up. Our findings revealed that Myc depletion drastically increased product expression (>40%) by enhancing cell-specific productivity. The Myc-depleted cells displayed decreased cell densities together with substantially higher product titers in industrially-relevant bioprocesses using ambr15 and ambr250 bioreactors. Similar effects were observed across multiple different clones, each expressing a distinct complex antibody format. Our findings reinforce the mutually exclusive relationship between growth and production phenotypes and provide a targeted cell engineering approach to impact productivity without impairing product quality. We anticipate that CRISPR/Cas9-based CHO host cell engineering will transform our ability to increase manufacturing yield of high-value complex biotherapeutics.

Retrons are a class of retroelements that produce multicopy single-stranded DNA (ssDNA) and participate in anti-phage defenses in bacteria. Retrons have been harnessed for the overproduction of ssDNA, genome engineering and directed evolution in bacteria, yeast and mammalian cells. Retron-mediated ssDNA production in plants could unlock their potential applications in plant biotechnology. For example, ssDNA can be used as a template for homology-directed repair (HDR) in several organisms. However, current gene editing technologies rely on the physical delivery of synthetic ssDNA, which limits their applications. Here, we demonstrated retron-mediated overproduction of ssDNA in Nicotiana benthamiana. Additionally, we tested different retron architectures for improved ssDNA production and identified a new retron architecture that resulted in greater ssDNA abundance. Furthermore, co-expression of the gene encoding the ssDNA-protecting protein VirE2 from Agrobacterium tumefaciens with the retron systems resulted in a 10.7-fold increase in ssDNA production in vivo. We also demonstrated clustered regularly interspaced short palindromic repeats-retron-coupled ssDNA overproduction and targeted HDR in N. benthamiana. Overall, we present an efficient approach for in vivo ssDNA production in plants, which can be harnessed for biotechnological applications. Graphical Abstract.

Standardized deoxyribonucleic acid (DNA) assembly methods utilizing modular components provide a powerful framework to explore designs and iterate through Design-Build-Test-Learn cycles. Biopart Assembly Standard for Idempotent Cloning (BASIC) DNA assembly uses modular parts and linkers, is highly accurate, easy to automate, free for academic and commercial use and enables hierarchical assemblies through an idempotent format. These features enable applications including pathway engineering, ribosome binding site (RBS) tuning, fusion protein engineering and multiplexed guide ribonucleic acid (RNA) expression. In this work, we present basicsynbio, open-source software encompassing a Web App (https://basicsynbio.web.app/) and Python Package (https://github.com/LondonBiofoundry/basicsynbio), enabling BASIC construct design via simple drag-and-drop operations or programmatically. With basicsynbio, users can access commonly used BASIC parts and linkers while designing new parts and assemblies with exception handling for common errors. Users can export sequence data and create instructions for manual or acoustic liquid-handling platforms. Instruction generation relies on the BasicBuild Open Standard, which is parsed for bespoke workflows and is serializable in JavaScript Object Notation for transfer and storage. We demonstrate basicsynbio, assembling 30 vectors using sequences including modules from the Standard European Vector Architecture (SEVA). The BASIC SEVA vector collection is compatible with BASIC and Golden Gate using BsaI. Vectors contain one of six antibiotic resistance markers and five origins of replication from different compatibility groups. The collection is available via Addgene under an OpenMTA agreement. Furthermore, vector sequences are available from within the basicsynbio application programming interface with other collections of parts and linkers, providing a powerful environment for designing assemblies for bioengineering applications. Graphical Abstract.

Sequencing technologies, in particular RNASeq, have become critical tools in the design, build, test and learn cycle of synthetic biology. They provide a better understanding of synthetic designs, and they help identify ways to improve and select designs. While these data are beneficial to design, their collection and analysis is a complex, multistep process that has implications on both discovery and reproducibility of experiments. Additionally, tool parameters, experimental metadata, normalization of data and standardization of file formats present challenges that are computationally intensive. This calls for high-throughput pipelines expressly designed to handle the combinatorial and longitudinal nature of synthetic biology. In this paper, we present a pipeline to maximize the analytical reproducibility of RNASeq for synthetic biologists. We also explore the impact of reproducibility on the validation of machine learning models. We present the design of a pipeline that combines traditional RNASeq data processing tools with structured metadata tracking to allow for the exploration of the combinatorial design in a high-throughput and reproducible manner. We then demonstrate utility via two different experiments: a control comparison experiment and a machine learning model experiment. The first experiment compares datasets collected from identical biological controls across multiple days for two different organisms. It shows that a reproducible experimental protocol for one organism does not guarantee reproducibility in another. The second experiment quantifies the differences in experimental runs from multiple perspectives. It shows that the lack of reproducibility from these different perspectives can place an upper bound on the validation of machine learning models trained on RNASeq data. Graphical Abstract.