Nucleic acid chemistry is a huge research area that has received new impetus due to the recent explosive success of oligonucleotide therapy. In order for an oligonucleotide to become clinically effective, its monomeric parts are subjected to modifications. Although a large number of redesigned natural nucleic acids have been proposed in recent years, the vast majority of them are combinations of simple modifications proposed over the past 50 years. This review is devoted to the main modifications of the sugar phosphate backbone of natural nucleic acids known to date. Here, we propose a systematization of existing knowledge about modifications of nucleic acid monomers and an acceptable classification from the point of view of chemical logic. The visual representation is intended to inspire researchers to create a new type of modification or an original combination of known modifications that will produce unique oligonucleotides with valuable characteristics.

DNA shuffling is a powerful technique for generating synthetic DNA via recombination of homologous parental sequences. Resulting chimeras are often incorporated into complex libraries for functionality screenings that identify novel variants with improved characteristics. To survey shuffling efficiency, subsequences of chimeras can be computationally assigned to their corresponding parental counterpart, yielding insight into frequency of recombination events, diversity of shuffling libraries and actual composition of final variants. Whereas tools for parental assignment exist, they do not provide direct visualization of the results, making the analysis time-consuming and cumbersome. Here we present ShuffleAnalyzer, a comprehensive, user-friendly, Python-based analysis tool that directly generates graphical outputs of parental assignments and is freely available under a BSD-3 license (https://github.com/joerg-swg/ShuffleAnalyzer/releases). Besides DNA shuffling, peptide insertions can be simultaneously analyzed and visualized, which makes ShuffleAnalyzer a highly valuable tool for integrated approaches often used in synthetic biology, such as AAV capsid engineering in gene therapy applications.

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Algae biotechnology holds immense promise for revolutionizing the bioeconomy through the sustainable and scalable production of various bioproducts. However, their development has been hindered by the lack of advanced genetic tools. This study introduces a synthetic biology approach to develop such tools, focusing on the construction and testing of synthetic promoters. By analyzing conserved DNA motifs within the promoter regions of highly expressed genes across six different algal species, we identified cis-regulatory elements (CREs) associated with high transcriptional activity. Combining the algorithms POWRS, STREME, and PhyloGibbs, we predicted 1511 CREs and inserted them into a minimal synthetic promoter sequence in 1, 2, or 3 copies, resulting in 4533 distinct synthetic promoters. These promoters were evaluated in vivo for their capacity to drive the expression of a transgene in a high-throughput manner through next-generation sequencing post antibiotic selection and fluorescence-activated cell sorting. To validate our approach, we sequenced hundreds of transgenic lines showing high levels of GFP expression. Further, we individually tested 14 identified promoters, revealing substantial increases in GFP expression─up to nine times higher than the baseline synthetic promoter, with five matching or even surpassing the performance of the native AR1 promoter. As a result of this study, we identified a catalog of CREs that can now be used to build superior synthetic algal promoters. More importantly, here we present a validated pipeline to generate building blocks for innovative synthetic genetic tools applicable to any algal species with a sequenced genome and transcriptome data set.

Hydrogen production through water splitting is a vital strategy for renewable and sustainable clean energy. In this study, we developed an approach integrating nanomaterial engineering and synthetic biology to establish a bionanoreactor system for efficient hydrogen production. The periplasmic space (20 to 30 nm) of an electroactive bacterium, Shewanella oneidensis MR-1, was engineered to serve as a bionanoreactor to enhance the interaction between electrons and protons, catalyzed by hydrogenases for hydrogen generation. To optimize electron transfer, we used the microbially reduced graphene oxide (rGO) to coat the electrode, which improved the electron transfer from the electrode to the cells. Native MtrCAB protein complex on S. oneidensis and self-assembled iron sulfide (FeS) nanoparticles acted in tandem to facilitate electron transfer from an electrode to the periplasm. To enhance proton transport, S. oneidensis MR-1 was engineered to express Gloeobacter rhodopsin (GR) and the light-harvesting antenna canthaxanthin. This led to efficient proton pumping when exposed to light, resulting in a 35.6% increase in the rate of hydrogen production. The overexpression of native [FeFe]-hydrogenase further improved the hydrogen production rate by 56.8%. The bionanoreactor engineered in S. oneidensis MR-1 achieved a hydrogen yield of 80.4 μmol/mg protein/day with a Faraday efficiency of 80% at a potential of -0.75 V. This periplasmic bionanoreactor combines the strengths of both nanomaterial and biological components, providing an efficient approach for microbial electrosynthesis.

Transfer RNAs have been extensively explored as the molecules that translate the genetic code into proteins. At this interface of genetics and biochemistry, tRNAs direct the efficiency of every major step of translation by interacting with a multitude of binding partners. However, due to the variability of tRNA sequences and the abundance of diverse post-transcriptional modifications, a guidebook linking tRNA sequences to specific translational outcomes has yet to be elucidated. Here, we review substantial efforts that have collectively uncovered tRNA engineering principles that can be used as a guide for the tuning of translation fidelity. These principles have allowed for the development of basic research, expansion of the genetic code with non-canonical amino acids, and tRNA therapeutics.

Cell-free gene expression systems are used in numerous applications, including medicine making, diagnostics, and educational kits. Accurate quantification of nonfluorescent proteins in these systems remains a challenge. To address this challenge, we report the adaptation and use of an optimized tetra-cysteine minihelix both as a fusion protein and as a standalone reporter with the FlAsH dye. The fluorescent reporter helix is short enough to be encoded on a primer pair to tag any protein of interest via PCR. Both the tagged protein and the standalone reporter can be detected quantitatively in real time or at the end of cell-free expression reactions with standard 96/384-well plate readers, an RT-qPCR system, or gel electrophoresis without the need for staining. The fluorescent signal is stable and correlates linearly with the protein concentration, enabling product quantification. We modified the reporter to study cell-free expression dynamics and engineered ribosome activity. We anticipate that the fluorescent minihelix reporter will facilitate efforts in engineering in vitro transcription and translation systems.

Although members of the genus Pseudomonas share specific morphological, metabolic, and genomic traits, the diversity of niches and lifestyles adopted by the family members is vast. One species of the group, Pseudomonas putida, thrives as a colonizer of plant roots and frequently inhabits soils polluted with various types of chemical waste. Owing to a combination of historical contingencies and inherent qualities, a particular strain, P. putida KT2440, emerged time ago as an archetype of an environmental microorganism amenable to recombinant DNA technologies, which was also capable of catabolizing chemical pollutants. Later, the same bacterium progressed as a reliable platform for programming traits and activities in various biotechnological applications. This article summarizes the stepwise upgrading of P. putida KT2440 from being a system for fundamental studies on the biodegradation of aromatic compounds (especially when harboring the TOL plasmid pWW0) to its adoption as a chassis of choice in metabolic engineering and synthetic biology. Although there are remaining uncertainties about the taxonomic classification of KT2440, advanced genome editing capabilities allow us to tailor its genetic makeup to meet specific needs. This makes its traditional categorization somewhat less important, while also increasing the strain\'s overall value for contemporary industrial and environmental uses.

Data science is playing an increasingly important role in the design and analysis of engineered biology. This has been fueled by the development of high-throughput methods like massively parallel reporter assays, data-rich microscopy techniques, computational protein structure prediction and design, and the development of whole-cell models able to generate huge volumes of data. Although the ability to apply data-centric analyses in these contexts is appealing and increasingly simple to do, it comes with potential risks. For example, how might biases in the underlying data affect the validity of a result and what might the environmental impact of large-scale data analyses be? Here, we present a community-developed framework for assessing data hazards to help address these concerns and demonstrate its application to two synthetic biology case studies. We show the diversity of considerations that arise in common types of bioengineering projects and provide some guidelines and mitigating steps. Understanding potential issues and dangers when working with data and proactively addressing them will be essential for ensuring the appropriate use of emerging data-intensive AI methods and help increase the trustworthiness of their applications in synthetic biology.