Genome Calligrapher: A Web Tool for Refactoring Bacterial Genome Sequences for de Novo DNA Synthesis

Automated high-throughput DNA synthesis and assembly

DNA synthesis and assembly primarily revolve around the innovation and refinement of tools that facilitate the creation of specific genes and the manipulation of entire genomes. This multifaceted process encompasses two fundamental steps: the synthesis of lengthy oligonucleotides and the seamless assembly of numerous DNA fragments. With the advent of automated pipetting workstations and integrated experimental equipment, a substantial portion of repetitive tasks in the field of synthetic biology can now be efficiently accomplished through integrated liquid handling workstations. This not only reduces the need for manual labor but also enhances overall efficiency. This review explores the ongoing advancements in the oligonucleotide synthesis platform, automated DNA assembly techniques, and biofoundries. The development of accurate and high-throughput DNA synthesis and assembly technologies presents both challenges and opportunities.

Machine learning-aided scoring of synthesis difficulties for designer chromosomes

Designer chromosomes are artificially synthesized chromosomes. Nowadays, these chromosomes have numerous applications ranging from medical research to the development of biofuels. However, some chromosome fragments can interfere with the chemical synthesis of designer chromosomes and eventually limit the widespread use of this technology. To address this issue, this study aimed to develop an interpretable machine learning framework to predict and quantify the synthesis difficulties of designer chromosomes in advance. Through the use of this framework, six key sequence features leading to synthesis difficulties were identified, and an eXtreme Gradient Boosting model was established to integrate these features. The predictive model achieved high-quality performance with an AUC of 0.895 in cross-validation and an AUC of 0.885 on an independent test set. Based on these results, the synthesis difficulty index (S-index) was proposed as a means of scoring and interpreting synthesis difficulties of chromosomes from prokaryotes to eukaryotes. The findings of this study emphasize the significant variability in synthesis difficulties between chromosomes and demonstrate the potential of the proposed model to predict and mitigate these difficulties through the optimization of the synthesis process and genome rewriting.

Investigation of Genome Biology by Synthetic Genome Engineering

Synthetic genomes were designed based on an understanding of natural genomic information, offering an opportunity to engineer and investigate biological systems on a genome-wide scale. Currently, the designer version of the M. mycoides genome and the E. coli genome, as well as most of the S. cerevisiae genome, have been synthesized, and through the cycles of design-build-test and the following engineering of synthetic genomes, many fundamental questions of genome biology have been investigated. In this review, we summarize the use of synthetic genome engineering to explore the structure and function of genomes, and highlight the unique values of synthetic genomics.

Escherichia coli Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data

Microbes are being engineered for an increasingly large and diverse set of applications. However, the designing of microbial genomes remains challenging due to the general complexity of biological systems. Adaptive Laboratory Evolution (ALE) leverages nature’s problem-solving processes to generate optimized genotypes currently inaccessible to rational methods. The large amount of public ALE data now represents a new opportunity for data-driven strain design. This study describes how novel strain designs, or genome sequences not yet observed in ALE experiments or published designs, can be extracted from aggregated ALE data and demonstrates this by designing, building, and testing three novel Escherichia coli strains with fitnesses comparable to ALE mutants. These designs were achieved through a meta-analysis of aggregated ALE mutations data (63 Escherichia coli K-12 MG1655 based ALE experiments, described by 93 unique environmental conditions, 357 independent evolutions, and 13 957 observed mutations), which additionally revealed global ALE mutation trends that inform on ALE-derived strain design principles. Such informative trends anticipate ALE-derived strain designs as largely gene-centric, as opposed to noncoding, and composed of a relatively small number of beneficial variants (approximately 6). These results demonstrate how strain design efforts can be enhanced by the meta-analysis of aggregated ALE data.

The transcriptional landscape of a rewritten bacterial genome reveals control elements and genome design principles

Sequence rewriting enables low-cost genome synthesis and the design of biological systems with orthogonal genetic codes. The error-free, robust rewriting of nucleotide sequences can be achieved with a complete annotation of gene regulatory elements. Here, we compare transcription in Caulobacter crescentus to transcription from plasmid-borne segments of the synthesized genome of C. ethensis 2.0. This rewritten derivative contains an extensive amount of supposedly neutral mutations, including 123'562 synonymous codon changes. The transcriptional landscape refines 60 promoter annotations, exposes 18 termination elements and links extensive transcription throughout the synthesized genome to the unintentional introduction of sigma factor binding motifs. We reveal translational regulation for 20 CDS and uncover an essential translational regulatory element for the expression of ribosomal protein RplS. The annotation of gene regulatory elements allowed us to formulate design principles that improve design schemes for synthesized DNA, en route to a bright future of iteration-free programming of biological systems.

Synthesis Success Calculator: Predicting the Rapid Synthesis of DNA Fragments with Machine Learning

The synthesis and assembly of long DNA fragments has greatly accelerated synthetic biology and biotechnology research. However, long turnaround times or synthesis failures create unpredictable bottlenecks in the design-build-test-learn cycle. We developed a machine learning model, called the Synthesis Success Calculator, to predict whether a long DNA fragment can be readily synthesized with a short turnaround time. The model also identifies the sequence determinants associated with the synthesis outcome. We trained a random forest classifier using biophysical features and a compiled data set of 1076 DNA fragment sequences to achieve high predictive performance (F₁ score of 0.928 on 251 unseen sequences). Feature importance analysis revealed that repetitive DNA sequences were the most important contributor to synthesis failures. We then applied the Synthesis Success Calculator across large sequence data sets and found that 84.9% of the Escherichia coli MG1655 genome, but only 34.4% of sampled plasmids in NCBI, could be readily synthesized. Overall, the Synthesis Success Calculator can be applied on its own to prevent synthesis failures or embedded within optimization algorithms to design large genetic systems that can be rapidly synthesized and assembled.

Repository-based plasmid design

There was an explosion in the amount of commercially available DNA in sequence repositories over the last decade. The number of such plasmids increased from 12,000 to over 300,000 among three of the largest repositories: iGEM, Addgene, and DNASU. A challenge in biodesign remains how to use these and other repository-based sequences effectively, correctly, and seamlessly. This work describes an approach to plasmid design where a plasmid is specified as simply a DNA sequence or list of features. The proposed software then finds the most cost-effective combination of synthetic and PCR-prepared repository fragments to build the plasmid via Gibson assembly^®. It finds existing DNA sequences in both user-specified and public DNA databases: iGEM, Addgene, and DNASU. Such a software application is introduced and characterized against all post-2005 iGEM composite parts and all Addgene vectors submitted in 2018 and found to reduce costs by 34% versus a purely synthetic plasmid design approach. The described software will improve current plasmid assembly workflows by shortening design times, improving build quality, and reducing costs.

Absolute Measurements of mRNA Translation in Caulobacter crescentus Reveal Important Fitness Costs of Vitamin B12 Scavenging

Caulobacter crescentus is a model system of the bacterial cell cycle culminating in asymmetric cell division, with each daughter cell inheriting a distinct set of proteins. While a genetic network of master transcription factors coordinates the cell cycle timing of transcription for nearly 20% of Caulobacter genes, we lack knowledge of how many of each protein “part” encoded in the genome are synthesized. Therefore, to determine the absolute production rates across the genome, we performed ribosome profiling, providing, for the first time, a quantitative resource with measurements of each protein “part” needed to generate daughter cells. This resource furthers the goal of a systems-level understanding of the genetic network controlling asymmetric cell division. To highlight the utility of this data set, we probe the protein synthesis cost of a B₁₂ utilization pathway and provide new insights into Caulobacter’s adaptation to its natural environments.

Caulobacter crescentus is a model for the bacterial cell cycle which culminates in asymmetric cell division, yet little is known about the absolute levels of protein synthesis of the cellular parts needed to complete the cell cycle. Here we utilize ribosome profiling to provide absolute measurements of mRNA translation in C. crescentus, providing an important resource with quantitative genome-wide measurements of protein output across individual genes. Analysis of protein synthesis rates revealed ∼4.5% of cellular protein synthesis is for genes related to vitamin B₁₂ import (btuB) and B₁₂-independent methionine biosynthesis (metE) when grown in common growth media lacking B₁₂. While its facultative B₁₂ lifestyle provides a fitness advantage in the absence of B₁₂, we find that it provides a fitness disadvantage of the cells in the presence of B₁₂, potentially explaining why many Caulobacter species have lost the metE gene and become obligates for B₁₂.

IMPORTANCECaulobacter crescentus is a model system of the bacterial cell cycle culminating in asymmetric cell division, with each daughter cell inheriting a distinct set of proteins. While a genetic network of master transcription factors coordinates the cell cycle timing of transcription for nearly 20% of Caulobacter genes, we lack knowledge of how many of each protein “part” encoded in the genome are synthesized. Therefore, to determine the absolute production rates across the genome, we performed ribosome profiling, providing, for the first time, a quantitative resource with measurements of each protein “part” needed to generate daughter cells. This resource furthers the goal of a systems-level understanding of the genetic network controlling asymmetric cell division. To highlight the utility of this data set, we probe the protein synthesis cost of a B₁₂ utilization pathway and provide new insights into Caulobacter’s adaptation to its natural environments.

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Chemical synthesis rewriting of a bacterial genome to achieve design flexibility and biological functionality

The fundamental biological functions of a living cell are stored within the DNA sequence of its genome. Classical genetic approaches dissect the functioning of biological systems by analyzing individual genes, yet uncovering the essential gene set of an organism has remained very challenging. It is argued that the rewriting of entire genomes through the process of chemical synthesis provides a powerful and complementary research concept to understand how essential functions are programed into genomes.

Understanding how to program biological functions into artificial DNA sequences remains a key challenge in synthetic genomics. Here, we report the chemical synthesis and testing of Caulobacter ethensis-2.0 (C. eth-2.0), a rewritten bacterial genome composed of the most fundamental functions of a bacterial cell. We rebuilt the essential genome of Caulobacter crescentus through the process of chemical synthesis rewriting and studied the genetic information content at the level of its essential genes. Within the 785,701-bp genome, we used sequence rewriting to reduce the number of encoded genetic features from 6,290 to 799. Overall, we introduced 133,313 base substitutions, resulting in the rewriting of 123,562 codons. We tested the biological functionality of the genome design in C. crescentus by transposon mutagenesis. Our analysis revealed that 432 essential genes of C. eth-2.0, corresponding to 81.5% of the design, are equal in functionality to natural genes. These findings suggest that neither changing mRNA structure nor changing the codon context have significant influence on biological functionality of synthetic genomes. Discovery of 98 genes that lost their function identified essential genes with incorrect annotation, including a limited set of 27 genes where we uncovered noncoding control features embedded within protein-coding sequences. In sum, our results highlight the promise of chemical synthesis rewriting to decode fundamental genome functions and its utility toward the design of improved organisms for industrial purposes and health benefits.

High-fidelity de novo synthesis of pathways using microchip-synthesized oligonucleotides and general molecular biology equipment

Engineering and evaluation of synthetic routes for generating valuable compounds require accurate and cost-effective de novo synthesis of genetic pathways. Here, we present an economical and streamlined de novo DNA synthesis approach for engineering a synthetic pathway with microchip-synthesized oligonucleotides (oligo). The process integrates entire oligo pool amplification, error-removal, and assembly of long DNA molecules. We utilized this method to construct a functional lycopene biosynthetic pathway (11.9 kb encoding 10 genes) in Escherichia coli using a highly error-prone microchip-synthesized oligo pool (479 oligos) without pre-purification, and the error-frequency was reduced from 14.25/kb to 0.53/kb. This low-equipment-dependent and cost-effective method can be widely applied for rapid synthesis of biosynthetic pathways in general molecular biology laboratories.

Genome Partitioner: A web tool for multi-level partitioning of large-scale DNA constructs for synthetic biology applications

Recent advances in lower-cost DNA synthesis techniques have enabled new innovations in the field of synthetic biology. Still, efficient design and higher-order assembly of genome-scale DNA constructs remains a labor-intensive process. Given the complexity, computer assisted design tools that fragment large DNA sequences into fabricable DNA blocks are needed to pave the way towards streamlined assembly of biological systems. Here, we present the Genome Partitioner software implemented as a web-based interface that permits multi-level partitioning of genome-scale DNA designs. Without the need for specialized computing skills, biologists can submit their DNA designs to a fully automated pipeline that generates the optimal retrosynthetic route for higher-order DNA assembly. To test the algorithm, we partitioned a 783 kb Caulobacter crescentus genome design. We validated the partitioning strategy by assembling a 20 kb test segment encompassing a difficult to synthesize DNA sequence. Successful assembly from 1 kb subblocks into the 20 kb segment highlights the effectiveness of the Genome Partitioner for reducing synthesis costs and timelines for higher-order DNA assembly. The Genome Partitioner is broadly applicable to translate DNA designs into ready to order sequences that can be assembled with standardized protocols, thus offering new opportunities to harness the diversity of microbial genomes for synthetic biology applications. The Genome Partitioner web tool can be accessed at https://christenlab.ethz.ch/GenomePartitioner.

Streamlining the Design-to-Build Transition with Build-Optimization Software Tools

Scaling-up capabilities for the design, build, and test of synthetic biology constructs holds great promise for the development of new applications in fuels, chemical production, or cellular-behavior engineering. Construct design is an essential component in this process; however, not every designed DNA sequence can be readily manufactured, even using state-of-the-art DNA synthesis methods. Current biological computer-aided design and manufacture tools (bioCAD/CAM) do not adequately consider the limitations of DNA synthesis technologies when generating their outputs. Designed sequences that violate DNA synthesis constraints may require substantial sequence redesign or lead to price-premiums and temporal delays, which adversely impact the efficiency of the DNA manufacturing process. We have developed a suite of build-optimization software tools (BOOST) to streamline the design-build transition in synthetic biology engineering workflows. BOOST incorporates knowledge of DNA synthesis success determinants into the design process to output ready-to-build sequences, preempting the need for sequence redesign. The BOOST web application is available at https://boost.jgi.doe.gov and its Application Program Interfaces (API) enable integration into automated, customized DNA design processes. The herein presented results highlight the effectiveness of BOOST in reducing DNA synthesis costs and timelines.