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Results for “Synthetic Biology”

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bioRxiv2025

Drug treatment alters performance in a neural microphysiological system of information processing

Assessment of pharmacological intervention on in vitro neural systems often emphasizes molecular and structural changes. However, neural systems fundamentally process and act on information. For preclinical assays to predict drug efficacy, they must model these physiological functions. DishBrain, an in vitro synthetic biological intelligence (SBI) assay embodying a neural system in a simulated game-world, enables the quantification of this information-processing capacity, however the question remains whether such a system permits classical pharmacological interrogation and dose-response profiling. Hyperactive glutamatergic dysregulation is linked to neurological disorders including epilepsy, and inducible overexpression of neurogenin 2 (NGN2) in human induced pluripotent stem cells (hiPSCs) generates glutamatergic cultures with dysregulated hyperactivity. We therefore tested three anti-seizure medications (ASMs), phenytoin, perampanel, and carbamazepine, on NGN2 neurons from day 21 of differentiation in this system. The key finding was that, while all compounds altered spontaneous firing, carbamazepine 200 {micro}M significantly improved gameplay metrics. This marks the first demonstration of altered SBI following exogenous drug treatment. Notably, only inhibitory compounds enhanced goal-directed activity, linking glutamatergic attenuation to performance. Neurocomputational analysis revealed nuanced pharmacological responses during closed-loop stimulation, highlighting insights beyond spontaneous activity metrics.

neuroscience↗

bioRxiv2025

Beyond RNA Structure Alone: Complex-Aware Feature Fusion for Tertiary Structure-based RNA Design

Tertiary structure-based RNA design plays a crucial role in synthetic biology and therapeutics. While existing methods have explored structure-to-sequence mappings, they focus solely on RNA structures and overlook the role of complex-level information, which is crucial for effective RNA design. To address this limitation, we propose tthe Complex-Aware tertiary structure-based RNA Design model, CARD, that integrates complex-level information to enhance tertiary structure-based RNA sequence design. To be specific, our method incorporates protein features extracted by protein language model (e.g., ESM-2), enabling the design model to generate more accurate and complex relevant sequences. Considering the biological complexity of protein-RNA interactions, we introduce a distance-aware filtering for local features from protein representation. Furthermore, we design a high-affinity design framework that combines our CARD with an affinity evaluation model. In this framework, candidate RNA sequences are generated and rigorously screened based on affinity and structural alignment to produce high-affinity RNA sequences. Extensive experiments demonstrate the effectiveness of our method with an improvement of 5.6% compared with base model without our complex-aware feature integration. A concrete case study for 2LBS further validates the superiority of our CARD.

bioinformatics↗

bioRxiv2025

TIPs-VF: An augmented vector-based representation for variable-length DNA fragments with sequence, length, and positional awareness

The ability to accurately encode and represent genetic sequences in machine learning process is critical for advancements in biotechnology, specifically in genetic engineering and synthetic biology. Traditional sequence encoding method face significant limitations in handling sequence variability, maintaining reading frame integrity, and preserving biologically relevant features. This preliminary study presents TIPs-VF (Translator-Interpreter Pre-seeding for Variable-length Fragments), a simple and efficient encoding framework designed to address some of the key challenges in representing genetic sequences for machine learning. The results showed that TIPs- VF enables a variable-length sequence representation that retains biological context while ensuring the alignment of encodings with codon boundary, making it particularly suited for modular genetic construction. TIPs-VF demonstrated superior performance in truncation and fragmentation analysis, sequence homology detection, domain assessment, and splice junction identification. Unlike conventional methods that require fixed-length inputs, TIPs-VF dynamically adapts to sequence length variations, preserving essential features such as domain similarities and sequence motifs. Additionally, TIPs-VF improves open reading frame recognition and enhances the identification of vector parts and plasmid elements by unifying sequence embeddings with the three possible open reading frame. Overall, TIPs-VF offers a robust, biologically meaningful encoding framework that overcomes the constraints of traditional sequence representations by incorporating sequence, length, and positional awareness. The TIPs-VF encoding infrastructure is available at https://tips.logiacommunications.com.

bioinformatics↗

bioRxiv2025

The growth benefits and toxicity of quinone synthesis are balanced by a dual regulatory mechanism and substrate limitations

Quinones play a central role in maintaining redox balance and conserving energy but can trigger oxidative stress at high levels. However, the mechanisms by which microbes regulate quinone levels remain poorly0020understood, hindering effective metabolic engineering to modulate microbes for quinone production. Here, we show that the synthesis of the menaquinone precursor DHNA in the lactic acid bacterium Lactococcus lactis is regulated by a combined genetic, enzymatic, and metabolic mechanism. Using synthetic biology approaches, we found that enzymes MenF and MenD both contribute to DHNA regulation, with MenD playing a more prominent role in controlling DHNA concentrations. A mathematical model elucidates a two-phase regulatory pattern resulting from the interplay of reversible flux and allosteric feedback inhibition, where either MenF or MenD can serve as the regulatory enzyme, depending on their relative expression ratio. Additionally, the overproduction of DHNA is constrained by substrate availability, ensuring a sufficient but not excessive DHNA level to benefit cell growth while mitigating cytotoxicity. Collectively, these mechanisms maintain a fine-tuned physiological quinone level and suggest that modulating substrate supplement and MenF-to-MenD ratio could be keys for engineering DHNA production

systems biology↗

bioRxiv2025

Intracellular Mannanases Sustain Matrix Polysaccharide Biosynthesis

O_LIMannans with {beta}-1,4-linked backbones are common cell wall components of algae and land plants. Prior challenges to enhance {beta}-mannan content in plants point to unclear metabolic bottlenecks and the potential for hidden biosynthetic players. C_LIO_LIEndo-{beta}-MANNANASEs (MANs) are the main glycosyl hydrolases that mobilize extracellular {beta}-mannans during seed germination. However, we found that Arabidopsis man2 man5 seeds resemble {beta}-mannan biosynthetic mutants. C_LIO_LICELLULOSE SYNTHASE-LIKE A (CSLA) overexpression restored {beta}-mannan synthesis in the man double mutant and increased the distribution of crystalline polymers, but impaired the release of other mucilaginous polysaccharides. C_LIO_LIUsing yeast synthetic biology, we dissected the functional interplay of MAN enzymes with CSLAs. Intracellular MAN2 and MAN5 reduced the quantity of insoluble {beta}-mannan but elevated the content of water-soluble carbohydrates. C_LIO_LIWe propose that Arabidopsis MAN2/5, and orthologous crop enzymes with a transmembrane domain, sustain hemicellulose production in the Golgi apparatus by cleaving insoluble {beta}-mannan polymers into hydrophilic counterparts. C_LI

biochemistry↗

bioRxiv2025

RNA-triggered fluorescence controlled by RNA switches for real-time RNA expression tracking in living plants

Real-time visualization of RNA dynamics across spatial scales in living plants is desirable yet remains challenging, hindered by the absence of imaging tools that simultaneously offer high sensitivity and deployability in whole living plants. Here, we engineered a modular, RNA-triggered fluorescence (RTF) reporter system de novo for spatiotemporal imaging of RNA from cellular to whole-plant scales in vivo. This system integrates three functional modules: a target-specific allosteric RNA switch, a degradable adapter for background suppression, and a fluorescent reporter. All components are stably expressed from a single vector and assemble in vivo, enabling uniform deployment throughout entire plants. The modified RTF (RTFst) achieves single-molecule sensitivity (signal-to-noise ratio >60) for dynamic tracking of developmentally regulated, tissue-specific, circadian, and stress-responsive mRNAs. Crucially, it enables the first direct, real-time observation of graft-transmissible mRNA trafficking across tissue scales and cross-kingdom transfer of the aphid-secreted long non-coding RNA Ya1 to plant cells. RTF establishes a versatile platform for studying gene regulation, signaling, and transcript mobility in plants, with broad applicability in synthetic biology and crop improvement.

molecular biology↗

bioRxiv2025

RNAtranslator: Modeling protein-conditional RNA design as sequence-to-sequence natural language translation

Protein-RNA interactions are essential in gene regulation, splicing, RNA stability, and translation, making RNA a promising therapeutic agent for targeting proteins, including those considered undruggable. However, designing RNA sequences that selectively bind to proteins remains a significant challenge due to the vast sequence space and limitations of current experimental and computational methods. Traditional approaches rely on in vitro selection techniques or computational models that require post-generation optimization, restricting their applicability to well-characterized proteins. We introduce RNAtranslator, a generative language model that formulates protein-conditional RNA design as a sequence-to-sequence natural language translation problem for the first time. By learning a joint representation of RNA and protein interactions from large-scale datasets, RNAtranslator directly generates binding RNA sequences for any given protein target without the need for additional optimization. Our results demonstrate that RNAtranslator produces RNA sequences with natural-like properties, high novelty, and enhanced binding affinity compared to existing methods. This approach enables efficient RNA design for a wide range of proteins, paving the way for new RNA-based therapeutics and synthetic biology applications. The model and the code is released at github.com/ciceklab/RNAtranslator.

bioinformatics↗

bioRxiv2025

Programmable Nucleic Acid Sensing in Human Cells Using Circularizable ssDNA

Programmable technologies that sense specific nucleic acid signatures in living cells and trigger cellular functions hold significant potential for biotechnology and medicine. Here, we developed SONAR (Sensing Of Nucleic acids using ASOs and Reverse-transcriptases), a platform that enables the detection of target DNA and RNA sequences and triggers controlled gene expression in human cells. SONAR operates through circularizable single-stranded DNA (ssDNA) sensors that, upon hybridization with complementary DNA or reverse-transcribed RNA, undergoes target-dependent ligation via endogenous ligases, subsequently driving expression of genetic payloads. For RNA sensing, we employed chemically modified antisense oligonucleotides (ASOs) to prime targeted reverse transcription, generating complementary DNA that promotes ssDNA circularization. We demonstrate SONARs ability to detect DNA, exogenous and endogenous RNA transcripts, coupled with a programmable expression of diverse protein payloads, including reporters, recombinases, and genome editors. This platform establishes a versatile framework for targeted nucleic acid detection and inducible gene expression, with broad applications in diagnostics, therapeutics, and synthetic biology.

bioengineering↗

bioRxiv2025

Derepression of transposable elements in mouse prefrontal cortex disrupts social behavior

Here, we present a synthetic biology approach to assess the social behavioral consequences of altered function of the Kruppel-associated box zinc finger protein (KZFP) interacting protein TRIM28 within the prefrontal cortex (PFC) of male and female mice. We reprogrammed natural TRIM28WT by replacing the transcriptionally repressive domain with an enhanced transcriptional activation domain VP64-p65-Rta (TRIM28VPR), or by excising the transcriptional regulatory domain (TRIM28NFD). In vitro validation confirmed that TRIM28WT represses, and TRIM28VPR activates, the expression of a KZFP-regulated luciferase reporter gene. Upon intra-PFC viral-mediated delivery of TRIM28 variants, we observed that inversion of TRIM28 transcriptional control via HSV-TRIM28VPR reduced the salience of novel social interaction for male and female mice while not affecting non-social behaviors. RNA-sequencing revealed HSV-TRIM28VPR promoted transcriptional escape of all classes of TEs, particularly those located within intronic and distal enhancer regions of downregulated immune genes. HSV-TRIM28VPR-driven social deficits were reversible by intra-PFC repletion of interferon cytokines. These novel data point to PFC KZFP-TRIM28 interactions as necessary to stabilize TEs to enable cis-regulation of key immune gene expression and enhance organismal capacity for complex, pro-social behaviors.

neuroscience↗

bioRxiv2025

Plasmidome-defensome interactions drive adaptation of 'Fervidacidithiobacillus caldus' in natural and industrial extreme acidic environments

Plasmids are major drivers of microbial evolution, enabling horizontal gene transfer (HGT) and facilitating adaptation through the dissemination of relevant functional genes and traits. However, little is known about plasmid diversity and function in extremophiles. Fervidacidithiobacillus caldus, a meso-thermo-acidophilic sulfur oxidizer, is a key player in sulfur cycling in natural and industrially engineered acidic environments. Here, we present a comprehensive analysis of the plasmidome, and associated anti-mobile genetic element (anti-MGE) defense systems (defensome), across genomes of this species and metagenomes from diverse natural and industrial settings harboring F. caldus. We identified >30 distinct plasmids, representing five consistent replication-mobilization families. Plasmids ranged in size between 2.5-65 kb, with gene content and plasmid modularity scaling with element size and copy numbers inversely correlating with size. Plasmids carried variable numbers of hypothetical proteins and transposases, with annotated cargo genes reflecting functional differentiation by habitat. Defensome profiling revealed over 50 anti-MGE systems in sequenced F. caldus isolates, including diverse restriction-modification systems, CRISPR-Cas types IV-A and V-F, and widespread abortive infection and composite defense systems such as Wadjet, Gabija, and Zorya. In environmental populations, an inverse relationship was observed between defensome complexity and plasmidome abundance and diversity, underscoring a pivotal role of the host defensome in modulating persistence, compatibility, and overall plasmid diversity across F. caldus populations. Yet, other plasmids appeared decoupled from both host abundance and defensome complexity, suggesting potential host shifts, environmental persistence, or differential replication under suboptimal growth conditions for the host. Altogether, these findings reveal a modular, adaptive plasmidome shaped by selective pressures and host-plasmid-defensome interactions and positions plasmids as key contributors to adaptation, gene flow, and functional innovation in this extreme acidophile. ImportancePlasmids are key vehicles of gene exchange and adaptation in bacteria, yet their roles in extremophilic systems remain poorly understood. This study provides the first integrated view of the plasmidome and defense systems in Fervidacidithiobacillus caldus, a sulfur-oxidizing acidophile relevant to both natural biogeochemical cycling and industrial bioleaching. We uncover a rich plasmid diversity structured into modular families with variable cargo and backbone features and reveal their coexistence with complex anti-MGE defense repertoires. By combining genomic and metagenomic approaches, we expose principles of plasmid compatibility, persistence, and habitat-specific adaptation. These insights expand current knowledge of mobile genetic elements in extreme environments and provide a foundation for plasmid-based vector design and synthetic biology in acidophiles, with direct implications for biomining and environmental remediation in extreme environments.

genomics↗

bioRxiv2025

Targeting Regulatory Factors Associated with the Drosophila Myc cis-Elements by Reporter Expression, Gel Shift Assay, and Mass Spectrometric Protein Identification

Transcription factor MYC is highly responsive to mitogenic stimuli to modulate the expression of several targets involved in cell growth, protein biogenesis, proliferation, and differentiation. However, MYCs transcriptional regulation remains incompletely understood. Our previous work demonstrated that key regulatory elements controlling Drosophila Myc reside within its 5 UTR and intergenic regions. In this study, we developed a highly sensitive and selective Solid Surface Magnetic Enrichment Protocol (SSMEP) to purify DNA-protein complexes formed on Myc cis-elements. Using analysis of truncated reporters, Electrophoretic-Mobility Shift Assay (EMSA), and mass spectrometric protein identification, we identified a cis-regulatory module (CRM) within the proximal 5 UTR that appears sufficient to activate lacZ reporter expression in the ovary and embryos. Furthermore, combining CRMs from either the upstream region or the intergenic Downstream Promoter Element (DPE) region with the proximal 5-UTR regulatory unit recapitulates expression patterns previously observed in larval tissues, ovaries, and embryos. The DPE itself exhibits promoter activity when fused in-frame to an adjacent binding site cluster, and drives expression mimicking that of wild-type Myc. Further in vivo validation is necessary to confirm the functional relevance of the associated factors and identified signaling cascades as Myc regulators. Additionally, this highly selective and sensitive protocol has potential applications in other model organisms and various fields of biomedical and synthetic biology research.

molecular biology↗

bioRxiv2025

Global analysis of the cold-shock response in the model antibiotic producing actinomycete, Streptomyces coelicolor A3(2)

Cold-shock adaptation is essential for the survival of soil-dwelling sessile streptomycetes exposed to fluctuating environmental temperatures, yet the precise regulatory mechanisms underlying this response remain poorly understood. Here, we investigated the global transcriptional and translational responses of the model actinomycete, Streptomyces coelicolor A3(2), to cold-shock using integrated RNA-seq and polysome profiling. Cold-shock treatment in minimal liquid medium triggered significant transcriptional changes in 811 genes. Notably, three operons, encoding a CspA homologue, a DEAD-box helicase, and a cystathionine-{beta}-synthase (CBS) domain-containing protein and/or a protein of unknown function (SCO5921-SCO5918, SCO4684-SCO4686, and SCO3731-SCO3733) were identified as central players of the cold-shock response, exhibiting up to 2,000-fold transcriptional induction. Systems-level transcriptomic analysis further revealed the cold-shock induced activation of pathways associated with gluconeogenesis, coenzyme A metabolism, phenylacetate degradation, lipid raft remodelling, and extracellular functions, pointing to extensive metabolic reprogramming coordinated with membrane adaptation during cold acclimation. Polysome profiling unveiled strong translational potentiation of operonic genes downstream from the promoter proximal cspA homologue genes, a process potentially mediated by RNA secondary structures that overlap ribosome binding sites (RBSs). The pronounced induction of DEAD-box RNA helicases and CspA RNA chaperones is presumed to reflect their critical requirement for resolving excessive nucleic acid secondary structures inherent to the high G+C content genome of Streptomyces (>73% G+C), including the RBS-masking stem-loops within their own operons. Together, this study provides a comprehensive, system-level understanding of cold-shock adaptation in Streptomyces, highlighting a multi-layered regulatory architecture that couples metabolic reprogramming with RNA structure-dependent translational control to mitigate thermal stress. IMPORTANCEThis study characterizes the cold-shock response of the model actinomycete, S. coelicolor A3(2), for the first time at both the transcriptome and translatome levels. Combined with a machine-learning based iModulon framework, our findings provide critical insights on both metabolic adaptation and multi-layered regulatory mechanisms, including transcriptional networks and RNA structure-dependent translational control. Beyond advancing our fundamental understanding of cold acclimation in Streptomyces, we identified several putative cis-acting regulatory elements within the intergenic regions between the primary cold-shock genes (SCO4684 and SCO5921) and their downstream DEAD-box helicase-encoding genes. These regulatory elements, coupled with the exceptionally robust transcriptional and translational induction of the core cold-shock operons, significantly expands the synthetic biology toolkit for Streptomyces. Ultimately, these molecular components hold substantial potential for exploitation in optimizing and manipulating cryptic antibiotic biosynthetic gene clusters within this bacterial genus of considerable industrial importance.

genomics↗

bioRxiv2025

PyFuRNAce: An integrated design engine for RNA origami

To realize the full potential of RNA nanotechnology and RNA origami, user-friendly design tools are needed. Here, we present pyFuRNAce, an open-source, Python-based software package with a graphical user interface that enables the design of complex RNA nanostructures. PyFuRNAce integrates the entire RNA origami workflow--from motif definition and blueprint design to sequence generation and primer selection--into a single, user-friendly platform. Built around a motif-based assembly paradigm, the software enables users to create and modify custom nanostructures through an intuitive web interface with streamlined design steps and real-time 3D visualization. By consolidating multiple design stages into a unified environment, pyFuRNAce reduces the entry barrier for RNA nanotechnology and accelerates the development of functional RNA origami structures for applications in medicine, biotechnology, and synthetic biology.

bioengineering↗

bioRxiv2025

Generative AI-driven artificial DNA design for enhancing inter-species gene activation and enzymatic degradation of PET

Conventional approaches to heterologous gene expression rely on codon optimization, which is limited to swapping synonymous codons and often fails to capture deeper adaptive changes. In contrast, naturally evolved orthologous genes between species often differ by more than just synonymous substitutions - they can include non-synonymous mutations, insertions, and deletions that confer functional adaptation to different host contexts. Here we present OrthologTransformer, a Transformer-based deep learning model that converts orthologous genes between species by learning from large-scale orthologous gene datasets curated for high-quality sequence alignments. The model recapitulates the full spectrum of evolutionary differences - from synonymous codon swaps to amino acid-changing mutations and indels - to predict a coding sequence optimized for a target species while preserving the proteins function. In extensive validation across diverse bacterial species pairs, OrthologTransformer significantly increased the conversion accuracy of generated genes to native target sequences compared to the original source genes, even for pairs with stark disparities in GC content and optimal growth temperature. The Transformers context-aware designs also favored conservative amino acid substitutions, maintaining protein functional integrity. As a proof-of-concept, an OrthologTransformer-designed PETase gene for Bacillus subtilis from the host species Ideonella sakaiensis was synthesized and expressed, yielding robust PET plastic-degradation activity that surpassed synonymous codon-optimized controls. These results establish OrthologTransformer as a powerful tool for de novo cross-species gene adaptation, transcending the limits of traditional codon optimization and enabling more effective heterologous gene performance in synthetic biology applications.

bioinformatics↗

bioRxiv2025

From Likelihood to Fitness: Improving Variant Effect Prediction in Protein and Genome Language Models

Generative models trained on natural sequences are increasingly used to predict the effects of genetic variation, enabling progress in therapeutic design, disease risk prediction, and synthetic biology. In the zero-shot setting, variant impact is estimated by comparing the likelihoods of sequences, under the assumption that likelihood serves as a proxy for fitness. However, this assumption often breaks down in practice: sequence likelihood reflects not only evolutionary fitness constraints, but also phylogenetic structure and sampling biases, especially as model capacity increases. We introduce Likelihood-Fitness Bridging (LFB), a simple and general strategy that improves variant effect prediction by averaging model scores across sequences subject to similar selective pressures. Assuming an Ornstein-Uhlenbeck model of evolution, LFB can be viewed as a way to marginalize the effects of genetic drift, although its benefits appear to extend more broadly. LFB applies to existing protein and genomic language models without requiring retraining, and incurs only modest computational overhead. Evaluated on large-scale deep mutational scans and clinical benchmarks, LFB consistently improves predictive performance across model families and sizes. Notably, it reverses the performance plateau observed in larger protein language models, making the largest models the most accurate when combined with LFB. These results suggest that accounting for phylogenetic and sampling biases is essential to realizing the full potential of large sequence models in variant effect prediction.

bioinformatics↗

bioRxiv2025

Unravelling the constrained cell growth in engineered living materials

Engineered living materials (ELMs) leverage the integrative advantages of materials science and synthetic biology for advanced functionalities. Predicting and controlling cellular behavior is essential for designing and building ELMs, requiring fundamental understanding of the growth dynamics of encapsulated cells. Here, we interrogate the interference of constrained growth on the engineered functionalities and cellular physiology of cyanobacteria and unveil the dynamic interaction between cell growth and spatial confinements within photosynthetic ELMs. We observed that engineered cyanobacteria within ELMs exhibited compromised performances in growth, uptake of non-natural substrate, and synthesis of customized products, while ELMs could protect encapsulated cells from external stresses. Besides commonly accepted external influences, we identified abnormally high levels of reactive oxygen species and impaired oxygenic photosynthesis inside the cells encapsulated in ELMs. Finally, we illustrated the dynamics of cell growth within the confined spaces enveloped by the material matrices, forming clustered cell aggregates and compressed growth bubbles until the spatial limits. Our study provides a fundamental yet often overlooked connection between cellular behavior and spatial confinements, consolidating the foundation for advanced ELM innovations.

bioengineering↗

bioRxiv2025

Architecture of the bundle-shaped phycobilisome from Gloeobacter violaceus

Cyanobacteria use soluble antenna megacomplexes, phycobilisomes (PBS), to maximize light-harvesting efficiency and small photoswitchable orange carotenoid proteins (OCPs) to down-regulate PBS in high light. Among known PBS morphologies, the one from the basal cyanobacterial genus Gloeobacter still lacks detailed structural characterization. Here, we present the cryo-EM structure of the G. violaceus PBS, a >10-MDa complex with a unique architecture consisting in 516 polypeptide chains totaling nearly 90,000 residues and harboring over 860 bilin chromophores. This unique PBS features diverging, conformationally mobile bundles of rods composed of stacked phycoerythrin and phycocyanin hexamers, stemming from a pentacylindrical allophycocyanin core belted by auxiliary phycocyanin hexamers. We show how two Gloeobacter-specific multidomain linker proteins, Glr1262 and Glr2806, maintain this bundle-shaped architecture and reveal its differential regulation via non-photochemical quenching by two OCP types of G. violaceus that recognize separate binding sites within the allophycocyanin core, including lateral cylinders absent in tricylindrical cores. Our findings provide the high-resolution structural insight into Gloeobacter PBS and its regulation, revealing divergent adaptations in early-branching cyanobacteria. The structure advances understanding of PBS diversity and evolution, offering a framework for bioengineering light-harvesting systems in synthetic biology and biotechnological applications.

biochemistry↗

bioRxiv2025

Purification of post-transcriptionally modified tRNAs for enhanced cell-free translation systems

Transfer RNAs (tRNAs) are utilized by the ribosome to decode the nucleic acid alphabet. tRNA structure, stability, aminoacylation efficiency, and decoding efficacy are governed by their extensive post-transcriptional modifications. In most studies, individual tRNAs are generated using in vitro transcription, which produces tRNAs devoid of these critical site-specific modifications, negatively affecting translation yields and fidelity. To address this, we have developed a purification method which couples tRNA overexpression to DNA hybridization-based purification. Using this approach, we produced native tRNAs from E. coli in high yield and purity while retaining their complement of native post-transcriptional modifications and translational activity. We extend this technique to the purification of [Formula] and [Formula], tRNAs of critical importance for genetic code expansion. We confirmed that both [Formula] and [Formula] contain native E. coli post-transcriptional modifications and provide the first complete modification profiles of each. Moreover, we found that in vivo-generated [Formula] significantly outperforms its in vitro-generated counterpart in amber codon suppression in cell-free translation reactions. Finally, we purified an engineered variant of E. coli [Formula], extending our studies to synthetic tRNAs. We present a flexible method which generates modified tRNAs in high yield and purity, addressing a critical and persistent challenge in RNA biochemistry. This toolkit enables future structural and cell-free studies through scalable access to native and engineered tRNAs, advancing the broader field of translation and synthetic biology.

biochemistry↗