Genetic Code Research Articles

Tuning tRNAs for improved translation.

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.

Fluorinated Tags to Study Protein Conformation and Interactions Using 19F NMR.

The incorporation of fluorine atoms into a biomacromolecule provides a background-free and environmentally sensitive reporter of structure, conformation and interactions using 19F NMR. There are several methods to introduce the 19F reporter - either by synthetic incorporation via solid phase peptide synthesis; by suppressing the incorporation or biosynthesis of a natural amino acid and supplementing the growth media with a fluorinated counterpart during protein expression; and by genetic code expansion to add new amino acids to the amino acid alphabet. This review aims to discuss progress in the field of introducing fluorinated handles into biomolecules for NMR studies by post-translational bioconjugation or 'fluorine-tagging'. We will discuss the range of chemical tagging 'warheads' that have been used, explore the applications of fluorine tags, discuss ways to enhance reporter sensitivity and how the signal to noise ratios can be boosted. Finally, we consider some key challenges of the field and offer some ideas for future directions.

3D Vase Design Based on Interactive Genetic Algorithm and Enhanced XGBoost Model

The human–computer interaction attribute of the interactive genetic algorithm (IGA) allows users to participate in the product design process for which the product needs to be evaluated, and requiring a large number of evaluations would lead to user fatigue. To address this issue, this paper utilizes an XGBoost proxy model modified by particle swarm optimization and the graphical interaction mechanism (GIM) to construct an improved interactive genetic algorithm (PXG-IGA), and then the PXG-IGA is applied to 3D vase design. Firstly, the 3D vase shape has been designed by using a bicubic Bézier surface, and the individual genetic code is binary and includes three parts: the vase control points, the vase height, and the texture picture. Secondly, the XGBoost evaluation of the proxy model has been constructed by collecting user online evaluation data, and the particle swarm optimization algorithm has been used to optimize the hyperparameters of XGBoost. Finally, the GIM has been introduced after several generations, allowing users to change product styles independently to better meet users’ expectations. Based on the PXG-IGA, an online 3D vase design platform has been developed and compared to the traditional IGA, KD tree, random forest, and standard XGBoost proxy models. Compared with the traditional IGA, the number of evaluations has been reduced by 58.3% and the evaluation time has been reduced by 46.4%. Compared with other proxy models, the accuracy of predictions has been improved up from 1.3% to 20.2%. To a certain extent, the PXG-IGA reduces users’ operation fatigue and provides new ideas for improving user experience and product design efficiency.

Orb2 enables rare-codon-enriched mRNA expression during Drosophila neuron differentiation

Regulation of codon optimality is an increasingly appreciated layer of cell- and tissue-specific protein expression control. Here, we use codon-modified reporters to show that differentiation of Drosophila neural stem cells into neurons enables protein expression from rare-codon-enriched genes. From a candidate screen, we identify the cytoplasmic polyadenylation element binding (CPEB) protein Orb2 as a positive regulator of rare-codon-dependent mRNA stability in neurons. Using RNA sequencing, we reveal that Orb2-upregulated mRNAs in the brain with abundant Orb2 binding sites have a rare-codon bias. From these Orb2-regulated mRNAs, we demonstrate that rare-codon enrichment is important for mRNA stability and social behavior function of the metabotropic glutamate receptor (mGluR). Our findings reveal a molecular mechanism by which neural stem cell differentiation shifts genetic code regulation to enable critical mRNA stability and protein expression.

Corrigendum: Algebraic and toroidal representation of the genetic code

Corrigendum: Algebraic and toroidal representation of the genetic code

Structure-guided conversion from an anaplastic lymphoma kinase inhibitor into Plasmodium lysyl-tRNA synthetase selective inhibitors

Aminoacyl-tRNA synthetases (aaRSs) play a central role in the translation of genetic code, serving as attractive drug targets. Within this family, the lysyl-tRNA synthetase (LysRS) constitutes a promising antimalarial target. ASP3026, an anaplastic lymphoma kinase (ALK) inhibitor was recently identified as a novel Plasmodium falciparum LysRS (PfLysRS) inhibitor. Here, based on cocrystal structures and biochemical experiments, we developed a series of ASP3026 analogues to improve the selectivity and potency of LysRS inhibition. The leading compound 36 showed a dissociation constant of 15.9 nM with PfLysRS. The inhibitory efficacy on PfLysRS and parasites has been enhanced. Covalent attachment of l-lysine to compound 36 resulted in compound 36K3, which exhibited further increased inhibitory activity against PfLysRS but significantly decreased activity against ALK. However, its inhibitory activity against parasites did not improve, suggesting potential future optimization directions. This study presents a new example of derivatization of kinase inhibitors repurposed to inhibit aaRS.

Redundant Residue Number System (RRNS) Di-Base Table for SOLiD Sequencing

The next-generation sequencing (NGS) methodology, sequencing by oligonucleotide ligation and detection (SOLiD) uses a di-base table, a somewhat unusual method, to decode sequences. Its coding scheme is based on the binary number system. The di-base table is not connected to the genetic code, nor is the coding scheme structured in the space of an entire number system. Gamow also revealed the hidden attribute of a 4 × 4 code for the di-base table, supporting his proposal of a 4 × 4 × 4 codons for the genetic code. Consideration for digital applications has focused more on the Residue Number System (RNS) and Redundant Residue Number System (RRNS) lately. Consequently, an RRNS di-base table based on the number tree concept is designed. The designed RRNS di-base table deviates from the canonical di-base table but retains every attribute necessary for effective SOLiD decoding. It shares a close relationship with the RNS-Genetic code and this presents a compelling argument for creating a single instrument that possesses the capabilities of both the genetic code and the di-base table.

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.

A tRNA modification pattern that facilitates interpretation of the genetic code.

Interpretation of the genetic code from triplets of nucleotides to amino acids is fundamental to life. This interpretation is achieved by cellular tRNAs, each reading a triplet codon through its complementary anticodon (positions 34-36) while delivering the amino acid charged to its 3'-end. This amino acid is then incorporated into the growing polypeptide chain during protein synthesis on the ribosome. The quality and versatility of the interpretation is ensured not only by the codon-anticodon pairing, but also by the post-transcriptional modifications at positions 34 and 37 of each tRNA, corresponding to the wobble nucleotide at the first position of the anticodon and the nucleotide on the 3'-side of the anticodon, respectively. How each codon is read by the matching anticodon, and which modifications are required, cannot be readily predicted from the codon-anticodon pairing alone. Here we provide an easily accessible modification pattern that is integrated into the genetic code table. We focus on the Gram-negative bacterium Escherichia coli as a model, which is one of the few organisms whose entire set of tRNA modifications and modification genes is identified and mapped. This work provides an important reference tool that will facilitate research in protein synthesis, which is at the core of the cellular life.

The Great Codon Escape: Vacating Codons for Genetic Code Expansion and Ribosome Stalling

AbstractIn ribosomal synthesis of peptides and proteins, genetic information is translated into an amino acid polymer according to the genetic code, which describes the translational command encoded by each codon. However, parts of the genetic code can be adjusted to customize translations. One option is to remove decoding for a specific codon, resulting in a vacant codon. Such vacant codons can be used to stall the ribosome for mechanistic studies and display techniques. Alternatively, the liberated codon can be assigned to encode for incorporation of a noncanonical building block for expansion of the genetic code. In this review we provide an overview of the methods currently available for vacating codons in prokaryotic translation (agnostic of how these are later applied), targeting factors such as amino‐acyl tRNA synthetases, tRNA, release factors, and the initiation machinery. Moreover, we assess applicability and compatibility of the currently available techniques and discuss which have the potential to develop into even more powerful approaches in the future.

Genetic code expansion, click chemistry, and light-activated PI3K reveal details of membrane protein trafficking downstream of receptor tyrosine kinases.

Ligands such as insulin, epidermal growth factor, platelet derived growth factor, and nerve growth factor (NGF) initiate signals at the cell membrane by binding to receptor tyrosine kinases (RTKs). Along with G-protein coupled receptors, RTKs are the main platforms for transducing extracellular signals into intracellular signals. Studying RTK signaling has been a challenge, however, due to the multiple signaling pathways to which RTKs typically are coupled, including MAP/ERK, PLCγ, and Class 1A phosphoinositide 3-kinases (PI3K). The multi-pronged RTK signaling has been a barrier to isolating the effects of any one downstream pathway. Here, we used optogenetic activation of PI3K to decouple its activation from other RTK signaling pathways. In this context, we used genetic code expansion to introduce a click chemistry noncanonical amino acid into the extracellular side of membrane proteins. Applying a cell- impermeant click chemistry fluorophore allowed us to visualize delivery of membrane proteins to the plasma membrane in real time. Using these approaches, we demonstrate that activation of PI3K, without activating other pathways downstream of RTK signaling, is sufficient to traffic the TRPV1 ion channels and insulin receptors to the plasma membrane.

Deciphering the genetic code of neuronal type connectivity through bilinear modeling.

Understanding how different neuronal types connect and communicate is critical to interpreting brain function and behavior. However, it has remained a formidable challenge to decipher the genetic underpinnings that dictate the specific connections formed between neuronal types. To address this, we propose a novel bilinear modeling approach that leverages the architecture similar to that of recommendation systems. Our model transforms the gene expressions of presynaptic and postsynaptic neuronal types, obtained from single-cell transcriptomics, into a covariance matrix. The objective is to construct this covariance matrix that closely mirrors a connectivity matrix, derived from connectomic data, reflecting the known anatomical connections between these neuronal types. When tested on a dataset of Caenorhabditis elegans, our model achieved a performance comparable to, if slightly better than, the previously proposed spatial connectome model (SCM) in reconstructing electrical synaptic connectivity based on gene expressions. Through a comparative analysis, our model not only captured all genetic interactions identified by the SCM but also inferred additional ones. Applied to a mouse retinal neuronal dataset, the bilinear model successfully recapitulated recognized connectivity motifs between bipolar cells and retinal ganglion cells, and provided interpretable insights into genetic interactions shaping the connectivity. Specifically, it identified unique genetic signatures associated with different connectivity motifs, including genes important to cell-cell adhesion and synapse formation, highlighting their role in orchestrating specific synaptic connections between these neurons. Our work establishes an innovative computational strategy for decoding the genetic programming of neuronal type connectivity. It not only sets a new benchmark for single-cell transcriptomic analysis of synaptic connections but also paves the way for mechanistic studies of neural circuit assembly and genetic manipulation of circuit wiring.

Deciphering the genetic code of neuronal type connectivity through bilinear modeling

Understanding how different neuronal types connect and communicate is critical to interpreting brain function and behavior. However, it has remained a formidable challenge to decipher the genetic underpinnings that dictate the specific connections formed between neuronal types. To address this, we propose a novel bilinear modeling approach that leverages the architecture similar to that of recommendation systems. Our model transforms the gene expressions of presynaptic and postsynaptic neuronal types, obtained from single-cell transcriptomics, into a covariance matrix. The objective is to construct this covariance matrix that closely mirrors a connectivity matrix, derived from connectomic data, reflecting the known anatomical connections between these neuronal types. When tested on a dataset of Caenorhabditis elegans, our model achieved a performance comparable to, if slightly better than, the previously proposed spatial connectome model (SCM) in reconstructing electrical synaptic connectivity based on gene expressions. Through a comparative analysis, our model not only captured all genetic interactions identified by the SCM but also inferred additional ones. Applied to a mouse retinal neuronal dataset, the bilinear model successfully recapitulated recognized connectivity motifs between bipolar cells and retinal ganglion cells, and provided interpretable insights into genetic interactions shaping the connectivity. Specifically, it identified unique genetic signatures associated with different connectivity motifs, including genes important to cell-cell adhesion and synapse formation, highlighting their role in orchestrating specific synaptic connections between these neurons. Our work establishes an innovative computational strategy for decoding the genetic programming of neuronal type connectivity. It not only sets a new benchmark for single-cell transcriptomic analysis of synaptic connections but also paves the way for mechanistic studies of neural circuit assembly and genetic manipulation of circuit wiring.

Several supplementary concepts for applied category-theoretical states over an extended Petri net using an example relating to genetic coding: Toward an abstract algebraic formulation of molecular/genetic biology

Several supplementary concepts for applied category-theoretical states over an extended Petri net using an example relating to genetic coding: Toward an abstract algebraic formulation of molecular/genetic biology

Detection and Characterisation of SARS-CoV-2 in Eastern Province of Zambia: A Retrospective Genomic Surveillance Study.

Mutations have driven the evolution and development of new variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with potential implications for increased transmissibility, disease severity and vaccine escape among others. Genome sequencing is a technique that allows scientists to read the genetic code of an organism and has become a powerful tool for studying emerging infectious diseases. Here, we conducted a cross-sectional study in selected districts of the Eastern Province of Zambia, from November 2021 to February 2022. We analyzed SARS-CoV-2 samples (n = 76) using high-throughput sequencing. A total of 4097 mutations were identified in 69 SARS-CoV-2 genomes with 47% (1925/4097) of the mutations occurring in the spike protein. We identified 83 unique amino acid mutations in the spike protein of the seven Omicron sublineages (BA.1, BA.1.1, BA.1.14, BA.1.18, BA.1.21, BA.2, BA.2.23 and XT). Of these, 43.4% (36/83) were present in the receptor binding domain, while 14.5% (12/83) were in the receptor binding motif. While we identified a potential recombinant XT strain, the highly transmissible BA.2 sublineage was more predominant (40.8%). We observed the substitution of other variants with the Omicron strain in the Eastern Province. This work shows the importance of pandemic preparedness and the need to monitor disease in the general population.

Bactericidal effect of tetracycline in E. coli strain ED1a may be associated with ribosome dysfunction

Ribosomes translate the genetic code into proteins. Recent technical advances have facilitated in situ structural analyses of ribosome functional states inside eukaryotic cells and the minimal bacterium Mycoplasma. However, such analyses of Gram-negative bacteria are lacking, despite their ribosomes being major antimicrobial drug targets. Here we compare two E. coli strains, a lab E. coli K-12 and human gut isolate E. coli ED1a, for which tetracycline exhibits bacteriostatic and bactericidal action, respectively. Using our approach for close-to-native E. coli sample preparation, we assess the two strains by cryo-ET and visualize their ribosomes at high resolution in situ. Upon tetracycline treatment, these exhibit virtually identical drug binding sites, yet the conformation distribution of ribosomal complexes differs. While K-12 retains ribosomes in a translation-competent state, tRNAs are lost in the vast majority of ED1a ribosomes. These structural findings together with the proteome-wide abundance and thermal stability assessments indicate that antibiotic responses are complex in cells and can differ between different strains of a single species, thus arguing that all relevant bacterial strains should be analyzed in situ when addressing antibiotic mode of action.

A mathematical framework for understanding the spontaneous emergence of complexity applicable to growing multicellular systems.

In embryonic development and organogenesis, cells sharing identical genetic codes acquire diverse gene expression states in a highly reproducible spatial distribution, crucial for multicellular formation and quantifiable through positional information. To understand the spontaneous growth of complexity, we constructed a one-dimensional division-decision model, simulating the growth of cells with identical genetic networks from a single cell. Our findings highlight the pivotal role of cell division in providing positional cues, escorting the system toward states rich in information. Moreover, we pinpointed lateral inhibition as a critical mechanism translating spatial contacts into gene expression. Our model demonstrates that the spatial arrangement resulting from cell division, combined with cell lineages, imparts positional information, specifying multiple cell states with increased complexity-illustrated through examples in C.elegans. This study constitutes a foundational step in comprehending developmental intricacies, paving the way for future quantitative formulations to construct synthetic multicellular patterns.

Engineering Ribosomal Machinery for Noncanonical Amino Acid Incorporation.

The introduction of noncanonical amino acids into proteins has enabled researchers to modify fundamental physicochemical and functional properties of proteins. While the alteration of the genetic code, via the introduction of orthogonal aminoacyl-tRNA synthetase:tRNA pairs, has driven many of these efforts, the various components involved in the process of translation are important for the development of new genetic codes. In this review, we will focus on recent advances in engineering ribosomal machinery for noncanonical amino acid incorporation and genetic code modification. The engineering of the ribosome itself will be considered, as well as the many factors that interact closely with the ribosome, including both tRNAs and accessory factors, such as the all-important EF-Tu. Given the success of genome re-engineering efforts, future paths for radical alterations of the genetic code will require more expansive alterations in the translation machinery.

Journeys Through Genomics: Co-Producing Visual Resources to Communicate Patient Experiences

Journeys through Genomics is a series of illustrations co-produced with patients and families to communicate their experiences of seeking genomic explanations for a health condition and the wider impact on their lives. The resources are embedded within qualitative longitudinal research exploring patient’s experiences of genomic medicine. This research takes place as genomic medicine becomes an integral part of mainstream care within the UK healthcare system. The depiction of genomic medicine often focuses on its technological components and the speed by which genetic code can be analysed, but here, we present a dynamic and situated understanding of the challenges genomic testing presents for patients and families. We describe the process of working with research participants and an artist to co-produce visual resources that illustrate the complexity of participant’s journeys, situating genomic testing within the broader context of their lives. These resources are designed to help future patients, families, and healthcare professionals understand the process, opportunities, and challenges they may face.

Order of amino acid recruitment into the genetic code resolved by Last Universal Common Ancestor's protein domains.

The current "consensus" order in which amino acids were added to the genetic code is based on potentially biased criteria such as absence of sulfur-containing amino acids from the Urey-Miller experiment which lacked sulfur. Even if inferred perfectly, abiotic abundance might not reflect abundance in the organisms in which the genetic code evolved. Here, we instead exploit the fact that proteins that emerged prior to the genetic code's completion are likely enriched in early amino acids and depleted in late amino acids. We identify the most ancient protein-coding sequences born prior to the archaeal-bacterial split. Amino acid usage in protein sequences whose ancestors date back to a single homolog in the Last Universal Common Ancestor (LUCA) largely matches the consensus order. However, our findings indicate that metal-binding (cysteine and histidine) and sulfur-containing (cysteine and methionine) amino acids were added to the genetic code much earlier than previously thought. Surprisingly, even more ancient protein sequences - those that had already diversified into multiple distinct copies in LUCA - show a different pattern to single copy LUCA sequences: significantly less depleted in the late amino acids tryptophan and tyrosine, and enriched rather than depleted in phenylalanine. This is compatible with at least some of these sequences predating the current genetic code. Their distinct enrichment patterns thus provide hints about earlier, alternative genetic codes.