Synthetic Genome Research Articles

The design and engineering of synthetic genomes.

Synthetic genomics seeks to design and construct entire genomes to mechanistically dissect fundamental questions of genome function and to engineer organisms for diverse applications, including bioproduction of high-value chemicals and biologics, advanced cell therapies, and stress-tolerant crops. Recent progress has been fuelled by advancements in DNA synthesis, assembly, delivery and editing. Computational innovations, such as the use of artificial intelligence to provide prediction of function, also provide increasing capabilities to guide synthetic genome design and construction. However, translating synthetic genome-scale projects from idea to implementation remains highly complex. Here, we aim to streamline this implementation process by comprehensively reviewing the strategies for design, construction, delivery, debugging and tailoring of synthetic genomes as well as their potential applications.

Synthetic Genomics in Crop Breeding: Evidence, Opportunities and Challenges

Synthetic Genomics in Crop Breeding: Evidence, Opportunities and Challenges

Optimization of PCA Error Correction Conditions to Improve Efficiency of Virus Genome De Novo Synthesis.

In recent years, there have been frequent global outbreaks of viral epidemics such as Zika, COVID-19, and monkeypox, which have had a huge impact on human health and society and have also spurred innovation in virus engineering technology. The rise of synthetic virus genome technology has provided researchers with a new platform to accelerate vaccine and drug development. Although DNA synthesis technology has made significant progress, the current virus genome synthesis technology still requires the assembly of short oligonucleotides of around 60 bp into kb-level lengths when constructing long segments, a process in which the commonly used polymerase chain reaction assembly (PCA) technology has high error rates and is cumbersome to operate. This study optimized the error correction conditions after PCA assembly, increasing the accuracy of synthesizing 1 kb DNA fragments from 4.2 ± 2.1% before error correction to 31.3 ± 3.1% after two rounds of correction, an improvement of over 6 times. This study provides a more efficient operational process for synthesizing virus genomes from scratch, indicating greater potential for virus engineering in epidemic prevention and control and the field of biomedicine.

A "Goldilocks Zone" for Recruiting BET Proteins with Bromodomain-1-Selective Ligands.

Synthetic genome readers/regulators (SynGRs) are bifunctional molecules that are rationally designed to bind specific genomic sequences and engage cellular machinery that regulates the expression of targeted genes. The prototypical SynGR1 targets GAA trinucleotide repeats and recruits the BET family of transcriptional regulatory proteins via a flexibly tethered ligand, JQ1. This pan-BET ligand binds both tandem bromodomains of BET proteins (BD1 and BD2). Second-generation SynGRs, which substituted JQ1 with bromodomain-selective ligands, unexpectedly revealed that BD1-selective ligands failed to functionally engage BET proteins in living cells despite displaying the ability to bind BD1 in vitro. Mechanistically, recruiting a BET protein via BD1- or BD2-selective SynGRs should have resulted in indistinguishable functional outcomes. Here we report the conversion of inactive BD1-targeting SynGRs into functional gene regulators by a structure-guided redesign of the chemical linker that bridges the DNA-binding molecule to the highly selective BD1 ligand GSK778. The results point to an optimal zone for positioning the BD1-selective ligand for functional engagement of BET proteins on chromatin, consistent with the preferred binding of BD1 domains to distal acetyllysine residues on histone tails. The results not only resolve the mechanistic conundrum but also provide insight into domain-selective targeting and nuanced design of chemo probes and therapeutics.

Synthetic genome start-up gets funding

Synthetic genome start-up gets funding

Biosafety risks, control, and biocontainment of engineered microalgae

Microalgae, with the ability to harness solar energy to fix CO2 and convert it into organic compounds, have emerged as promising green cell factories. With the rapid development of cutting-edge biotechnologies, the research and application of photosynthetic microalgae have been expanding, leading to comprehensive and in-depth engineering of microalgae. The synthetic biology and genome editing technologies have enabled the applications of microalgae in medicine, agriculture, food, energy, and the environment. However, the survival and spreading of engineered microalgae in the natural environment pose potential safety risks to ecosystems and human health. To curb the risks caused by the spreading of engineered microalgae in the environment, biosafety policies should be formulated for engineered microalgae and the prevention and control technologies should be developed. Toward this goal, researchers have developed biocontainment systems, including positive strategies such as the design of toxic protein-based kill switches and passive strategies such as knocking out essential genes to construct the strains with nutritional deficiencies, thereby spatially containing engineered microalgae. This article summarizes the application of cutting-edge biotechnologies in the engineering of microalgae, the biosafety risks and management regulations associated with the escape of engineered microalgae, and the progress in novel biocontainment technologies established for engineered microalgae. Finally, this article gives insights into the future development direction of microalgae biocontainment.

Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures

Enzymes are crucial in numerous biological processes, with the Enzyme Commission (EC) number being a commonly used method for defining enzyme function. However, current EC number prediction technologies have not fully recognized the importance of enzyme active sites and structural characteristics. Here, we propose GraphEC, a geometric graph learning-based EC number predictor using the ESMFold-predicted structures and a pre-trained protein language model. Specifically, we first construct a model to predict the enzyme active sites, which is utilized to predict the EC number. The prediction is further improved through a label diffusion algorithm by incorporating homology information. In parallel, the optimum pH of enzymes is predicted to reflect the enzyme-catalyzed reactions. Experiments demonstrate the superior performance of our model in predicting active sites, EC numbers, and optimum pH compared to other state-of-the-art methods. Additional analysis reveals that GraphEC is capable of extracting functional information from protein structures, emphasizing the effectiveness of geometric graph learning. This technology can be used to identify unannotated enzyme functions, as well as to predict their active sites and optimum pH, with the potential to advance research in synthetic biology, genomics, and other fields.

Reconstruction of a robust bacterial replication module.

Chromosomal DNA replication is a fundamental process of life, involving the assembly of complex machinery and dynamic regulation. In this study, we reconstructed a bacterial replication module (pRC) by artificially clustering 23 genes involved in DNA replication and sequentially deleting these genes from their naturally scattered loci on the chromosome of Escherichia coli. The integration of pRC into the chromosome, moving from positions farther away to close to the replication origin, leads to an enhanced efficiency in DNA synthesis, varying from lower to higher. Strains containing replication modules exhibited increased DNA replication by accelerating the replication fork movement and initiating chromosomal replication earlier in the replication cycle. The minimized module pRC16, containing only replisome and elongation encoding genes, exhibited chromosomal DNA replication efficiency comparable to that of pRC. The replication module demonstrated robust and rapid DNA replication, regardless of growth conditions. Moreover, the replication module is plug-and-play, and integrating it into Mb-sized extrachromosomal plasmids improves their genetic stability. Our findings indicate that DNA replication, being a fundamental life process, can be artificially reconstructed into replication functional modules. This suggests potential applications in DNA replication and the construction of synthetic modular genomes.

Multilevel Systematic Optimization To Achieve Efficient Integrated Expression of Escherichia coli.

Genomic integration of heterologous genes is the preferred approach in industrial fermentation-related strains due to the drawbacks associated with plasmid-mediated microbial fermentation, including additional growth burden, genetic instability, and antibiotic contamination. Synthetic biology and genome editing advancements have made gene integration convenient. Integrated expression is extensively used in the field of biomanufacturing and is anticipated to become the prevailing method for expressing recombinant proteins. Therefore, it is pivotal to strengthen the expression of exogenous genes at the genome level. Here, we systematically optimized the integrated expression system of Escherichia coli from 3 aspects. First, the integration site slmA with the highest expression activity was screened out of 18 sites in the ORI region of the E. coli BL21 (DE3) genome. Second, we characterized 16 endogenous promoters in E. coli and combined them with the T7 promoter. A constitutive promoter, Plpp-T7, exhibited significantly higher expression strength than the T7 promoter, achieving a 3.3-fold increase in expression levels. Finally, to further enhance the T7 expression system, we proceeded with overexpression of T7 RNA polymerase at the chassis cell level. The resulting constitutive efficient integrated expression system (CEIES_Ecoli) showed a 2-fold increase in GFP expression compared to the pET3b recombinant plasmid. Therefore, CEIES_Ecoli was applied to the integrated expression of nitrilase and hyaluronidase, achieving stable and efficient enzyme expression, with enzyme activities of 22.87 and 12,195 U·mL-1, respectively, comparable to plasmid levels. Overall, CEIES_Ecoli provides a stable and efficient method of gene expression without the need for antibiotics or inducers, making it a robust tool for synthetic biology, enzyme engineering, and related applications.

Insights into a functional synthetic plant genome.

Synthetic genomics involves the design, assembly, and transfer of artificially synthesized DNA fragments into target hosts to replace the native genome and construct viable forms of life. With advances in DNA synthesis and assembly techniques, the application of synthetic genomics in viruses, bacteria, and yeast has improved our knowledge of genome organization and function. Multicellular eukaryotic organisms are characterized by larger genomes, more complex epigenetic regulation, and widespread transposable elements, making genome synthesis challenging. Recently, the first synthetic multicellular eukaryotic organism was generated in the model plant Physcomitrium patens with a partially synthetic chromosome arm. Here, we introduce the design and assembly principles of moss genome synthesis. We also discuss the remaining technical barriers in the application of synthetic genomics in seed plants.

TRANSGENE MANUFACTURING: FROM TOOLS TO APPLICATIONS

Genetic modification generation is an important intersection of genetic engineering and biotechnology and involves the integration of foreign genes into the genome of organisms to produce specific proteins or repair genetic abnormalities. The machine has many applications in medicine, agriculture and biotechnology. The evolution of genetically modified production technologies from traditional methods to advanced genetic engineering tools demonstrates their evolution. This review discusses various methods incorporating modern and advanced technologies and explores their applications and future prospects. Early models of genetic modification generally involved organisms modified to produce human insulin, followed by the evolution of animals and plants. Traditional methods such as microinjection, retrovirus-mediated gene transfer and embryonic stem cell -mediated gene transfer are important for the production of transgenic animals. Similarly, Agrobacterium-mediated transformation and biolistic transformation methods are used to produce genetically modified plants. Although useful, these methods often result in synergistic and differential gene expression. Made with genetic modification. This technology allows modification of the target with minimal impact on the target, thus increasing the predictability and efficiency of genetic modification. Additionally, CRISPR/Cas9's ability to alter multiple genes simultaneously and adaptability to various organisms expands its applications in medicine and agriculture. Synthetic biology continues to advance genetic engineering by designing and building new biological materials and systems, enabling innovations such as genetic engineering, metabolic processes, and minimal genomes. Viral vectors such as adeno-associated virus (AAV), lentivirus, and adenovirus have unique advantages and clinical challenges. Nonviral vectors, including lipid nanoparticles, electroporated, and polymeric vectors, provide alternative delivery methods with varying efficacy and specificity. Painkiller. It can improve crop growth, livestock and disease resistance in agriculture. Applications of biotechnology include biofuels, biopharmaceuticals, and bioremediation. Future directions aim to improve delivery, expand applications, explore synthetic genomics, advance personalized medicine, and develop regenerative agriculture.In summary, with the influence of advances in genetic engineering and technology, genetically modified production has become a complex field with many applications. Its future promises transformative solutions to global challenges in health, food security and environmental sustainability. To reach its full potential, continuous innovation and ethical thinking are essential.

A CRISPR-Cas9-Mediated Large-Fragment Assembly Method for Cloning Genomes and Biosynthetic Gene Cluster.

The ability to clone large DNA fragments from genomes is valuable for both basic and applied research, such as the construction of synthetic genomes, and the expression of biosynthetic gene clusters (BGCs) for natural product discovery. Here, we report a fast and efficient platform for the direct capture of genome DNAs, by combining CRISPR and Gibson assembly. We demonstrate this method with the ability of cloning large DNA fragments ranging from 30 to 77 kb from various host genomes, achieving a near 100% cloning fidelity for DNA fragments below 50 kb. We next demonstrate this method by the cloning of a 40 kb fragment from Streptomyces ceruleus A3(2), which is rich in BGCs for natural products; and used this method cloning the 40 kb fengycin synthetic gene cluster from B. subtilis 168, encoding for a class of peptides with bioactivity. This method provides efficient and simple opportunities for assembling large DNA constructs from distant sources.

Build-A-Genome and the “awesome power of undergraduates”

Abstract The Elizabeth W. Jones Award for Excellence in Education recognizes individuals or groups who have had significant, sustained impact on genetics education at any level, from K-12 through graduate school and beyond. The 2024 Elizabeth W. Jones Award for Excellence in Education recipient Jef Boeke considers himself a geneticist turned engineer. He transformed his landmark synthetic yeast genome project into a research-heavy teaching course, revolutionizing molecular biology and genetics education. The Build-A-Genome course was developed to teach students basic practical molecular genetics while also providing the raw materials for a global genome synthesis project, Yeast 2.0. The course evolved over two decades to reflect the changing needs and opportunities for the project and the development of new technologies. In addition to educating a generation of college and high school students in a new way, it also developed a cadre of educators who developed similar courses and projects at a wide variety of research and educational institutions.

Transformer model generated bacteriophage genomes are compositionally distinct from natural sequences.

Novel applications of language models in genomics promise to have a large impact on the field. The megaDNA model is the first publicly available generative model for creating synthetic viral genomes. To evaluate megaDNA's ability to recapitulate the nonrandom genome composition of viruses and assess whether synthetic genomes can be algorithmically detected, compositional metrics for 4969 natural bacteriophage genomes and 1002 de novo synthetic bacteriophage genomes were compared. Transformer-generated sequences had varied but realistic genome lengths, and 58% were classified as viral by geNomad. However, the sequences demonstrated consistent differences in various compositional metrics when compared to natural bacteriophage genomes by rank-sum tests and principal component analyses. A simple neural network trained to detect transformer-generated sequences on global compositional metrics alone displayed a median sensitivity of 93.0% and specificity of 97.9% (n = 12 independent models). Overall, these results demonstrate that megaDNA does not yet generate bacteriophage genomes with realistic compositional biases and that genome composition is a reliable method for detecting sequences generated by this model. While the results are specific to the megaDNA model, the evaluated framework described here could be applied to any generative model for genomic sequences.

Towards a Yersinia pestis lipid A recreated in an Escherichia coli scaffold genome.

Synthetic biology and genome engineering capabilities have facilitated the utilization of bacteria for a myriad of applications, ranging from medical treatments to biomanufacturing of complex molecules. The bacterial outer membrane, specifically the lipopolysaccharide (LPS), plays an integral role in the physiology, pathogenesis, and serves as a main target of existing detection assays for Gram-negative bacteria. Here we use CRISPR/Cas9 recombineering to insert Yersinia pestis lipid A biosynthesis genes into the genome of an Escherichia coli strain expressing the lipid IVa subunit. We successfully inserted three genes: kdsD, lpxM, and lpxP into the E. coli genome and demonstrated their expression via reverse transcription PCR (RT-PCR). Despite observing expression of these genes, analytical characterization of the engineered strain's lipid A structure via MALDI-TOF mass spectrometry indicated that the Y. pestis lipid A was not recapitulated in the E. coli background. As synthetic biology and genome engineering technologies advance, novel applications and utilities for the detection and treatments of dangerous pathogens like Yersinia pestis will continue to be developed.

Enhanced eMAGE applied to identify genetic factors of nuclear hormone receptor dysfunction via combinatorial gene editing

Technologies that generate precise combinatorial genome modifications are well suited to dissect the polygenic basis of complex phenotypes and engineer synthetic genomes. Genome modifications with engineered nucleases can lead to undesirable repair outcomes through imprecise homology-directed repair, requiring non-cleavable gene editing strategies. Eukaryotic multiplex genome engineering (eMAGE) generates precise combinatorial genome modifications in Saccharomyces cerevisiae without generating DNA breaks or using engineered nucleases. Here, we systematically optimize eMAGE to achieve 90% editing frequency, reduce workflow time, and extend editing distance to 20 kb. We further engineer an inducible dominant negative mismatch repair system, allowing for high-efficiency editing via eMAGE while suppressing the elevated background mutation rate 17-fold resulting from mismatch repair inactivation. We apply these advances to construct a library of cancer-associated mutations in the ligand-binding domains of human estrogen receptor alpha and progesterone receptor to understand their impact on ligand-independent autoactivation. We validate that this yeast model captures autoactivation mutations characterized in human breast cancer models and further leads to the discovery of several previously uncharacterized autoactivating mutations. This work demonstrates the development and optimization of a cleavage-free method of genome editing well suited for applications requiring efficient multiplex editing with minimal background mutations.

Synthetic genomes unveil the effects of synonymous recoding.

Engineering the genetic code of an organism provides the basis for (i) making any organism safely resistant to natural viruses and (ii) preventing genetic information flow into and out of genetically modified organisms while (iii) allowing the biosynthesis of genetically encoded unnatural polymers1-4. Achieving these three goals requires the reassignment of multiple of the 64 codons nature uses to encode proteins. However, synonymous codon replacement-recoding-is frequently lethal, and how recoding impacts fitness remains poorly explored. Here, we explore these effects using whole-genome synthesis, multiplexed directed evolution, and genome-transcriptome-translatome-proteome co-profiling on multiple recoded genomes. Using this information, we assemble a synthetic Escherichia coli genome in seven sections using only 57 codons to encode proteins. By discovering the rules responsible for the lethality of synonymous recoding and developing a data-driven multi-omics-based genome construction workflow that troubleshoots synthetic genomes, we overcome the lethal effects of 62,007 synonymous codon swaps and 11,108 additional genomic edits. We show that synonymous recoding induces transcriptional noise including new antisense RNAs, leading to drastic transcriptome and proteome perturbation. As the elimination of select codons from an organism's genetic code results in the widespread appearance of cryptic promoters, we show that synonymous codon choice may naturally evolve to minimize transcriptional noise. Our work provides the first genome-scale description of how synonymous codon changes influence organismal fitness and paves the way for the construction of functional genomes that provide genetic firewalls from natural ecosystems and safely produce biopolymers, drugs, and enzymes with an expanded chemistry.

Reversible Nucleic Acid Storage in Deconstructable Glassy Polymer Networks.

The rapid decline in DNA sequencing costs has fueled the demand for nucleic acid collection to unravel genomic information, develop treatments for genetic diseases, and track emerging biological threats. Current approaches to maintaining these nucleic acid collections hinge on continuous electricity for maintaining low-temperature and intricate cold-chain logistics. Inspired by the millennia-long preservation of fossilized biological specimens in calcified minerals or glassy amber, we present Thermoset-REinforced Xeropreservation (T-REX): a method for storing DNA in deconstructable glassy polymer networks. Key to T-REX is the development of polyplexes for nucleic acid encapsulation, streamlining the transfer of DNA from aqueous to organic phases, replete with initiators, monomers, cross-linkers, and thionolactone-based cleavable comonomers required to form the polymer networks. This process successfully encapsulates DNA that spans different length scales, from tens of bases to gigabases, in a matter of hours compared to days with traditional silica-based encapsulation. Further, T-REX permits the extraction of DNA using comparatively benign reagents, unlike the hazardous hydrofluoric acid required for recovery from silica. T-REX provides a path toward low-cost, time-efficient, and long-term nucleic acid preservation for synthetic biology, genomics, and digital information storage, potentially overcoming traditional low-temperature storage challenges.

In silico design of DNA sequences for in vivo nucleosome positioning.

The computational design of synthetic DNA sequences with designer in vivo properties is gaining traction in the field of synthetic genomics. We propose here a computational method which combines a kinetic Monte Carlo framework with a deep mutational screening based on deep learning predictions. We apply our method to build regular nucleosome arrays with tailored nucleosomal repeat lengths (NRL) in yeast. Our design was validated in vivo by successfully engineering and integrating thousands of kilobases long tandem arrays of computationally optimized sequences which could accommodate NRLs much larger than the yeast natural NRL (namely 197 and 237bp, compared to the natural NRL of ∼165bp). RNA-seq results show that transcription of the arrays can occur but is not driven by the NRL. The computational method proposed here delineates the key sequence rules for nucleosome positioning in yeast and should be easily applicable to other sequence properties and other genomes.

Synthetic Genomics: Repurposing Biological Systems for Applications in Engineering Biology.

Substantial improvements in DNA sequencing and synthesis technologies and increased understanding of genome biology have empowered the development of synthetic genomics. The ability to design and construct engineered living cells boosted up by synthetic chromosomes provides opportunities to tackle enormous current and future challenges faced by humanity and the planet. Here we review the progresses, considerations, challenges, and future direction of the "design-build-test-learn" cycle used in synthetic genomics. We also discuss future applications enabled by synthetic genomics as this emerging field shapes and revolutionizes biomanufacturing and biomedicine.