Translational Efficiency Research Articles

Prediction of the effects of the top 10 synonymous mutations from 26645 SARS-CoV-2 genomes of early pandemic phase.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had led to a global pandemic since December 2019. SARS-CoV-2 is a single-stranded RNA virus, which mutates at a higher rate. Multiple works had been done to study nonsynonymous mutations, which change protein sequences. However, there is little study on the effects of SARS-CoV-2 synonymous mutations, which may affect viral fitness. This study aims to predict the effect of synonymous mutations on the SARS-CoV-2 genome. A total of 26645 SARS-CoV-2 genomic sequences retrieved from Global Initiative on Sharing all Influenza Data (GISAID) database were aligned using MAFFT. Then, the mutations and their respective frequency were identified. Multiple RNA secondary structures prediction tools, namely RNAfold, IPknot++ and MXfold2 were applied to predict the effect of the mutations on RNA secondary structure and their base pair probabilities was estimated using MutaRNA. Relative synonymous codon usage (RSCU) analysis was also performed to measure the codon usage bias (CUB) of SARS-CoV-2. A total of 150 synonymous mutations were identified. The synonymous mutation identified with the highest frequency is C3037U mutation in the nsp3 of ORF1a. Of these top 10 highest frequency synonymous mutations, C913U, C3037U, U16176C and C18877U mutants show pronounced changes between wild type and mutant in all 3 RNA secondary structure prediction tools, suggesting these mutations may have some biological impact on viral fitness. These four mutations show changes in base pair probabilities. All mutations except U16176C change the codon to a more preferred codon, which may result in higher translation efficiency. Synonymous mutations in SARS-CoV-2 genome may affect RNA secondary structure, changing base pair probabilities and possibly resulting in a higher translation rate. However, lab experiments are required to validate the results obtained from prediction analysis.

Downregulation of CPSF6 leads to global mRNA 3' UTR shortening and enhanced antiviral immune responses.

Alternative polyadenylation (APA) is a widespread mechanism of gene regulation that generates mRNA isoforms with alternative 3' untranslated regions (3' UTRs). Our previous study has revealed the global 3' UTR shortening of host mRNAs through APA upon viral infection. However, how the dynamic changes in the APA landscape occur upon viral infection remains largely unknown. Here we further found that, the reduced protein abundance of CPSF6, one of the core 3' processing factors, promotes the usage of proximal poly(A) sites (pPASs) of many immune related genes in macrophages and fibroblasts upon viral infection. Shortening of the 3' UTR of these transcripts may improve their mRNA stability and translation efficiency, leading to the promotion of type I IFN (IFN-I) signalling-based antiviral immune responses. In addition, dysregulated expression of CPSF6 is also observed in many immune related physiological and pathological conditions, especially in various infections and cancers. Thus, the global APA dynamics of immune genes regulated by CPSF6, can fine-tune the antiviral response as well as the responses to other cellular stresses to maintain the tissue homeostasis, which may represent a novel regulatory mechanism for antiviral immunity.

Comprehensive map of ribosomal 2'-O-methylation and C/D box snoRNAs in Drosophila melanogaster.

During their maturation, ribosomal RNAs (rRNAs) are decorated by hundreds of chemical modifications that participate in proper folding of rRNA secondary structures and therefore in ribosomal function. Along with pseudouridine, methylation of the 2'-hydroxyl ribose moiety (Nm) is the most abundant modification of rRNAs. The majority of Nm modifications in eukaryotes are placed by Fibrillarin, a conserved methyltransferase belonging to a ribonucleoprotein complex guided by C/D box small nucleolar RNAs (C/D box snoRNAs). These modifications impact interactions between rRNAs, tRNAs and mRNAs, and some are known to fine tune translation rates and efficiency. In this study, we built the first comprehensive map of Nm sites in Drosophila melanogaster rRNAs using two complementary approaches (RiboMethSeq and Nanopore direct RNA sequencing) and identified their corresponding C/D box snoRNAs by whole-transcriptome sequencing. We de novo identified 61 Nm sites, from which 55 are supported by both sequencing methods, we validated the expression of 106 C/D box snoRNAs and we predicted new or alternative rRNA Nm targets for 31 of them. Comparison of methylation level upon different stresses show only slight but specific variations, indicating that this modification is relatively stable in D. melanogaster. This study paves the way to investigate the impact of snoRNA-mediated 2'-O-methylation on translation and proteostasis in a whole organism.

Peptide-encoding gene transfer to modulate intracellular protein-protein interactions

Peptide drug discovery has great potential, but the cell membrane is a major obstacle when the target is an intracellular protein‒protein interaction (PPI). It is difficult to target PPIs with small molecules; indeed, there are no intervention tools that can target any intracellular PPI. In this study, we developed a platform that enables the introduction of peptides into cells via mRNA-based gene delivery. Peptide-length nucleic acids do not enable stable ribosome binding and exhibit little to no translation into protein. In this study, a construct was created in which the sequence encoding dihydrofolate reductase (DHFR) was placed in front of the sequence encoding the target peptide, together with a translation skipping sequence, as a sequence that meets the requirements of promoting ribosome binding and rapid decay of the translated protein. This enabled efficient translation from the mRNA encoding the target protein while preventing unnecessary protein residues. Using this construct, we showed that it can inhibit Drp1/Fis1 binding, one of the intracellular PPIs, which governs mitochondrial fission, an important aspect of mitochondrial dynamics. In addition, it was shown to inhibit pathological hyperfission, normalize mitochondrial dynamics and metabolism, and inhibit apoptosis of the mitochondrial pathway.

Treemendous: an R package for integrating taxonomic information across backbones.

Standardizing and translating species names from different databases is key to the successful integration of data sources in biodiversity research. There are numerous taxonomic name-resolution applications that implement increasingly powerful name-cleaning and matching approaches, allowing the user to resolve species relative to multiple backbones simultaneously. Yet there remains no principled approach for combining information across these underlying taxonomic backbones, complicating efforts to combine and merge species lists with inconsistent and conflicting taxonomic information. Here, we present Treemendous, an open-source software package for the R programming environment that integrates taxonomic relationships across four publicly available backbones to improve the name resolution of tree species. By mapping relationships across the backbones, this package can be used to resolve datasets with conflicting and inconsistent taxonomic origins, while ensuring the resulting species are accepted and consistent with a single reference backbone. The user can chain together different functionalities ranging from simple matching to a single backbone, to graph-based iterative matching using synonym-accepted relations across all backbones in the database. In addition, the package allows users to 'translate' one tree species list into another, streamlining the assimilation of new data into preexisting datasets or models. The package provides a flexible workflow depending on the use case, and can either be used as a stand-alone name-resolution package or in conjunction with existing packages as a final step in the name-resolution pipeline. The Treemendous package is fast and easy to use, allowing users to quickly merge different data sources by standardizing their species names according to the regularly updated database. By combining taxonomic information across multiple backbones, the package increases matching rates and minimizes data loss, allowing for more efficient translation of tree species datasets to aid research into forest biodiversity and tree ecology.

DERNA Enables Pareto Optimal RNA Design.

The design of an RNA sequence that encodes an input target protein sequence is a crucial aspect of messenger RNA (mRNA) vaccine development. There are an exponential number of possible RNA sequences for a single target protein due to codon degeneracy. These potential RNA sequences can assume various secondary structure conformations, each with distinct minimum free energy (MFE), impacting thermodynamic stability and mRNA half-life. Furthermore, the presence of species-specific codon usage bias, quantified by the codon adaptation index (CAI), plays a vital role in translation efficiency. While earlier studies focused on optimizing either MFE or CAI, recent research has underscored the advantages of simultaneously optimizing both objectives. However, optimizing one objective comes at the expense of the other. In this work, we present the Pareto Optimal RNA Design problem, aiming to identify the set of Pareto optimal solutions for which no alternative solutions exist that exhibit better MFE and CAI values. Our algorithm DEsign RNA (DERNA) uses the weighted sum method to enumerate the Pareto front by optimizing convex combinations of both objectives. We use dynamic programming to solve each convex combination in time and space. Compared with a CDSfold, previous approach that only optimizes MFE, we show on a benchmark data set that DERNA obtains solutions with identical MFE but superior CAI. Moreover, we show that DERNA matches the performance in terms of solution quality of LinearDesign, a recent approach that similarly seeks to balance MFE and CAI. We conclude by demonstrating our method's potential for mRNA vaccine design for the SARS-CoV-2 spike protein.

Modeling neurodevelopmental disorder-associated human AGO1 mutations in Caenorhabditis elegans Argonaute alg-1

MicroRNAs (miRNA) associate with Argonaute (AGO) proteins and repress gene expression by base pairing to sequences in the 3' untranslated regions of target genes. De novo coding variants in the human AGO genes AGO1 and AGO2 cause neurodevelopmental disorders (NDD) with intellectual disability, referred to as Argonaute syndromes. Most of the altered amino acids are conserved between the miRNA-associated AGO in Homo sapiens and Caenorhabditis elegans, suggesting that the human mutations could disrupt conserved functions in miRNA biogenesis or activity. We genetically modeled four human AGO1 mutations in C. elegans by introducing identical mutations into the C. elegans AGO1 homologous gene, alg-1. These alg-1 NDD mutations cause phenotypes in C. elegans indicative of disrupted miRNA processing, miRISC (miRNA silencing complex) formation, and/or target repression. We show that the alg-1 NDD mutations are antimorphic, causing developmental and molecular phenotypes stronger than those of alg-1 null mutants, likely by sequestrating functional miRISC components into non-functional complexes. The alg-1 NDD mutations cause allele-specific disruptions in mature miRNA profiles, accompanied by perturbation of downstream gene expression, including altered translational efficiency and/or messenger RNA abundance. The perturbed genes include those with human orthologs whose dysfunction is associated with NDD. These cross-clade genetic studies illuminate fundamental AGO functions and provide insights into the conservation of miRNA-mediated post-transcriptional regulatory mechanisms.

Kinetic characterization of human mRNA guanine-N7 methyltransferase

The 5′-mRNA-cap formation is a conserved process in protection of mRNA in eukaryotic cells, resulting in mRNA stability and efficient translation. In humans, two methyltransferases, RNA cap guanine-N7 methyltransferase (hRNMT) and cap-specific nucleoside-2′-O-methyltransferase 1 (hCMTr1) methylate the mRNA resulting in cap0 (N7mGpppN-RNA) and cap1 (N7mGpppN2′-Om-RNA) formation, respectively. Coronaviruses mimic this process by capping their RNA to evade human immune systems. The coronaviral nonstructural proteins, nsp14 and nsp10-nsp16, catalyze the same reactions as hRNMT and hCMTr1, respectively. These two viral enzymes are important targets for development of inhibitor-based antiviral therapeutics. However, assessing the selectivity of such inhibitors against human corresponding proteins is crucial. Human RNMTs have been implicated in proliferation of cancer cells and are also potential targets for development of anticancer therapeutics. Here, we report the development and optimization of a radiometric assay for hRNMT, full kinetic characterization of its activity, and optimization of the assay for high-throughput screening with a Z-factor of 0.79. This enables selectivity determination for a large number of hits from various screening of coronaviral methyltransferases, and also screening hRNMT for discovery of inhibitors and chemical probes that potentially could be used to further investigate the roles RNMTs play in cancers.

Fitness benefits of a synonymous substitution in an ancient EF-Tu gene depend on the genetic background.

Synonymous mutations are changes to DNA sequence, which occur within translated genes but which do not affect the protein sequence. Although often referred to as silent mutations, evidence suggests that synonymous mutations can affect gene expression, mRNA stability, and even translation efficiency. A collection of both experimental and bioinformatic data has shown that synonymous mutations can impact cell phenotype, yet less is known about the molecular mechanisms and potential of beneficial or adaptive effects of such changes within evolved populations. Here, we report a beneficial synonymous mutation acquired via experimental evolution in an essential gene variant encoding the translation elongation factor protein EF-Tu. We demonstrate that this particular synonymous mutation increases EF-Tu mRNA and protein levels as well as global polysome abundance on RNA transcripts. Although presence of the synonymous mutation is clearly causative of such changes, we also demonstrate that fitness benefits are highly contingent on other potentiating mutations present within the genetic background in which the mutation arose. Our results underscore the importance of beneficial synonymous mutations, especially those that affect levels of proteins that are key for cellular processes.IMPORTANCEThis study explores the degree to which synonymous mutations in essential genes can influence adaptation in bacteria. An experimental system whereby an Escherichia coli strain harboring an engineered translation protein elongation factor-Tu (EF-Tu) was subjected to laboratory evolution. We find that a synonymous mutation acquired on the gene encoding for EF-Tu is conditionally beneficial for bacterial fitness. Our findings provide insight into the importance of the genetic background when a synonymous substitution is favored by natural selection and how such changes have the potential to impact evolution when critical cellular processes are involved.

Evaluation of size-exclusion chromatography, multi-angle light scattering detection and mass photometry for the characterization of mRNA

The recent approval of messenger ribonucleic acid (mRNA) as vaccine to combat the COVID-19 pandemic has been a scientific turning point. Today, the applicability of mRNA is being demonstrated beyond infectious diseases, for example in cancer immunotherapy, protein replacement therapy and gene editing. mRNA is produced by in vitro transcription (IVT) from a linear DNA template and modified at the 3′ and 5′ ends to improve translational efficiency and stability. Co-existing impurities such as RNA fragments and double-stranded RNA (dsRNA), amongst others, can drastically impact mRNA quality and efficacy. In this study, size-exclusion chromatography (SEC) is evaluated for the characterization of IVT-mRNA. The effect of mobile phase composition (ionic strength and organic modifier), pH, column temperature and pore size (300 Å, 1000 Å, and 2000 Å) on the separation performance and structural integrity of IVT-mRNA varying in size is described. Non-replicating, self-amplifying (saRNA), temperature degraded, and ribonuclease (RNase) digested mRNA, the latter to characterize the 3′ poly(A) tail, were included in the study. Beyond ultraviolet (UV) detection, refractive index (RI) and multi-angle light scattering (MALS) detection were implemented to accurately determine molecular weight (MW) of mRNA. Finally, mass photometry is introduced as a complementary methodology to study mRNA under native conditions.

The NP protein of Newcastle disease virus dictates its oncolytic activity by regulating viral mRNA translation efficiency.

Newcastle disease virus (NDV) has been extensively studied as a promising oncolytic virus for killing tumor cells in vitro and in vivo in clinical trials. However, the viral components that regulate the oncolytic activity of NDV remain incompletely understood. In this study, we systematically compared the replication ability of different NDV genotypes in various tumor cells and identified NP protein determines the oncolytic activity of NDV. On the one hand, NDV strains with phenylalanine (F) at the 450th amino acid position of the NP protein (450th-F-NP) exhibit a loss of oncolytic activity. This phenotype is predominantly associated with genotype VII NDVs. In contrast, the NP protein with a leucine amino acid at this site in other genotypes (450th-L-NP) can facilitate the loading of viral mRNA onto ribosomes more effectively than 450th-F-NP. On the other hand, the NP protein from NDV strains that exhibit strong oncogenicity interacts with eIF4A1 within its 366-489 amino acid region, leading to the inhibition of cellular mRNA translation with a complex 5' UTR structure. Our study provide mechanistic insights into how highly oncolytic NDV strains selectively promote the translation of viral mRNA and will also facilitate the screening of oncolytic strains for oncolytic therapy.

M6A modification regulates tumor suppressor DIRAS1 expression in cervical cancer cells

ABSTRACT DIRAS family GTPase 1 (DIRAS1) has been reported as a potential tumor suppressor in other human cancer. However, its expression pattern and role in cervical cancer remain unknown. Knockdown of DIRAS1 significantly promoted the proliferation, growth, migration, and invasion of C33A and SiHa cells cultured in vitro. Overexpression of DIRAS1 significantly inhibited the viability and motility of C33A and SiHa cells. Compared with normal cervical tissues, DIRAS1 mRNA levels were significantly lower in cervical cancer tissues. DIRAS1 protein expression was also significantly reduced in cervical cancer tissues compared with para-cancerous tissues. In addition, DIRAS1 expression level in tumor tissues was significantly negatively correlated with the pathological grades of cervical cancer patients. DNA methylation inhibitor (5-Azacytidine) and histone deacetylation inhibitor (SAHA) resulted in a significant increase in DIRAS1 mRNA levels in C33A and SiHa cells, but did not affect DIRAS1 protein levels. FTO inhibitor (FB23–2) significantly down-regulated intracellular DIRAS1 mRNA levels, but significantly up-regulated DIRAS1 protein levels. Moreover, the down-regulation of METTL3 and METTL14 expression significantly inhibited DIRAS1 protein expression, whereas the down-regulation of FTO and ALKBH5 expression significantly increased DIRAS1 protein expression. In conclusion, DIRAS1 exerts a significant anti-oncogenic function and its expression is significantly downregulated in cervical cancer cells. The m6A modification may be a key mechanism to regulate DIRAS1 mRNA stability and protein translation efficiency in cervical cancer.

A homogenized two-phase computational framework for meso- and macroscale blood flow simulations

Background and ObjectiveOwing to the complexity of physics linked with blood flow and its associated phenomena, appropriate modeling of the multi-constituent rheology of blood is of primary importance. To this effect, various kinds of computational fluid dynamic models have been developed, each with merits and limitations. However, when additional physics like thrombosis and embolization is included within the framework of these models, computationally efficient scalable translation becomes very difficult. Therefore, this paper presents a homogenized two-phase blood flow framework with similar characteristics to a single fluid model but retains the flow resolution of a classical two-fluid model. The presented framework is validated against four different sets of experiments. MethodsThe two-phase model of blood presented here is based on the classical diffusion-flux framework. Diffusion flux models are known to be less computationally expensive than two-fluid multiphase models since the numerical implementation resembles single-phase flow models. Diffusion flux models typically use empirical slip velocity correlations to resolve the motion between phases. However, such correlations do not exist for blood. Therefore, a modified slip velocity equation is proposed, derived rigorously from the two-fluid governing equations. An additional drag law for red blood cells (RBCs) as a function of volume fraction is evaluated using a previously published cell-resolved solver. A new hematocrit-dependent expression for lift force on RBCs is proposed. The final governing equations are discretized and solved using the open-source software OpenFOAM. ResultsThe framework is validated against four sets of experiments: (i) flow through a rectangular microchannel to validate RBC velocity profiles against experimental measurements and compare computed hematocrit distributions against previously reported simulation results (ii) flow through a sudden expansion microchannel for comparing experimentally obtained contours of hematocrit distributions and normalized cell-free region length obtained at different flowrates and inlet hematocrits, (iii) flow through two hyperbolic channels to evaluate model predictions of cell-free layer thickness, and (iv) flow through a microchannel that mimics crevices of a left ventricular assist device to predict hematocrit distributions observed experimentally. The simulation results exhibit good agreement with the results of all four experiments. ConclusionThe computational framework presented in this paper has the advantage of resolving the multiscale physics of blood flow while still leveraging numerical techniques used for solving single-phase flows. Therefore, it becomes an excellent candidate for addressing more complicated problems related to blood flow, such as modeling mechanical entrapment of RBCs within blood clots, predicting thrombus composition, and visualizing clot embolization.

Automated Generation and Analysis of Molecular Images Using Generative Artificial Intelligence Models.

The development of scanning probe microscopy (SPM) has enabled unprecedented scientific discoveries through high-resolution imaging. Simulations and theoretical analysis of SPM images are equally important as obtaining experimental images since their comparisons provide fruitful understandings of the structures and physical properties of the investigated systems. So far, SPM image simulations are conventionally based on quantum mechanical theories, which can take several days in tasks of large-scale systems. Here, we have developed a scanning tunneling microscopy (STM) molecular image simulation and analysis framework based on a generative adversarial model, CycleGAN. It allows efficient translations between STM data and molecular models. Our CycleGAN-based framework introduces an approach for high-fidelity STM image simulation, outperforming traditional quantum mechanical methods in efficiency and accuracy. We envision that the integration of generative networks and high-resolution molecular imaging opens avenues in materials discovery relying on SPM technologies.

Inhibition of OCT4 binding at the MYCN locus induces neuroblastoma cell death accompanied by downregulation of transcripts with high-open reading frame dominance.

Amplification of MYCN is observed in high-risk neuroblastomas (NBs) and is associated with a poor prognosis. MYCN expression is directly regulated by multiple transcription factors, including OCT4, MYCN, CTCF, and p53 in NB. Our previous study showed that inhibition of p53 binding at the MYCN locus induces NB cell death. However, it remains unclear whether inhibition of alternative transcription factor induces NB cell death. In this study, we revealed that the inhibition of OCT4 binding at the MYCN locus, a critical site for the human-specific OCT4-MYCN positive feedback loop, induces caspase-2-mediated cell death in MYCN-amplified NB. We used the CRISPR/deactivated Cas9 (dCas9) technology to specifically inhibit transcription factors from binding to the MYCN locus in the MYCN-amplified NB cell lines CHP134 and IMR32. In both cell lines, the inhibition of OCT4 binding at the MYCN locus reduced MYCN expression, thereby suppressing MYCN-target genes. After inhibition of OCT4 binding, differentially downregulated transcripts were associated with high-open reading frame (ORF) dominance score, which is associated with the translation efficiency of transcripts. These transcripts were enriched in splicing factors, including MYCN-target genes such as HNRNPA1 and PTBP1. Furthermore, transcripts with a high-ORF dominance score were significantly associated with genes whose high expression is associated with a poor prognosis in NB. Because the ORF dominance score correlates with the translation efficiency of transcripts, our findings suggest that MYCN maintains the expression of transcripts with high translation efficiency, contributing to a poor prognosis in NB. In conclusion, the inhibition of OCT4 binding at the MYCN locus resulted in reduced MYCN activity, which in turn led to the downregulation of high-ORF dominance transcripts and subsequently induced caspase-2-mediated cell death in MYCN-amplified NB cells. Therefore, disruption of the OCT4 binding at the MYCN locus may serve as an effective therapeutic strategy for MYCN-amplified NB.

LncRNA as a regulator in the development of diabetic complications.

Diabetes is a metabolic disease characterized by hyperglycemia, which induces the production of AGEs, ROS, inflammatory cytokines, and growth factors, leading to the formation of vascular dysfunction and target organ damage, promoting the development of diabetic complications. Diabetic nephropathy, retinopathy, and cardiomyopathy are common complications of diabetes, which are major contributors to disability and death in people with diabetes. Long non-coding RNAs affect gene transcription, mRNA stability, and translation efficiency to influence gene expression for a variety of biological functions. Over the past decade, it has been demonstrated that dysregulated long non-coding RNAs are extensively engaged in the pathogenesis of many diseases, including diabetic complications. Thus, this review discusses the regulations of long non-coding RNAs on the primary pathogenesis of diabetic complications (oxidative stress, inflammation, fibrosis, and microvascular dysfunction), and some of these long non-coding RNAs may function as potential biomarkers or therapeutic targets for diabetic complications.

Efficiency of transcription and translation of cell-free protein synthesis systems in cell-sized lipid vesicles with changing lipid composition determined by fluorescence measurements.

To develop artificial cell models that mimic living cells, cell-sized lipid vesicles encapsulating cell-free protein synthesis (CFPS) systems are useful for protein expressions or artificial gene circuits for vesicle-vesicle communications. Therefore, investigating the transcriptional and translational properties of CFPS systems in lipid vesicles is important for maximizing the synthesis and functions of proteins. Although transcription and translation using CFPS systems inside lipid vesicles are more important than that outside lipid vesicles, the former processes are not investigated by changing the lipid composition of lipid vesicles. Herein, we investigated changes in transcription and translation using CFPS systems inside giant lipid vesicles (approximately 5-20μm in diameter) caused by changing the lipid composition of lipid vesicles containing neutral, positively, and negatively charged lipids. After incubating for 30min, 1h, 2h, and 4h, the transcriptional and translational activities in these lipid vesicles were determined by detecting the fluorescence intensities of the fluorogenic RNA aptamer on the 3'-untranslated region of mRNA (transcription) and the fluorescent protein sfCherry (translation), respectively. The results revealed that transcriptional and translational activities in a lipid vesicle containing positively charged lipids were high when the protein was synthesized using the CFPS system inside the lipid vesicle. Thus, the present study provides an experimental basis for constructing complex artificial cell models using bottom-up approaches.

C2-methyladenosine in tRNA promotes protein translation by facilitating the decoding of tandem m2A-tRNA-dependent codons

RNA modification C2-methyladenosine (m2A) exists in both rRNA and tRNA of Escherichia coli (E. coli), installed by the methyltransferase RlmN using a radical-S-adenosylmethionine (SAM) mechanism. However, the precise function of m2A in tRNA and its ubiquity in plants have remained unclear. Here we discover the presence of m2A in chloroplast rRNA and tRNA, as well as cytosolic tRNA, in multiple plant species. We identify six m2A-modified chloroplast tRNAs and two m2A-modified cytosolic tRNAs across different plants. Furthermore, we characterize three Arabidopsis m2A methyltransferases—RLMNL1, RLMNL2, and RLMNL3—which methylate chloroplast rRNA, chloroplast tRNA, and cytosolic tRNA, respectively. Our findings demonstrate that m2A37 promotes a relaxed conformation of tRNA, enhancing translation efficiency in chloroplast and cytosol by facilitating decoding of tandem m2A-tRNA-dependent codons. This study provides insights into the molecular function and biological significance of m2A, uncovering a layer of translation regulation in plants.

Therapeutic Peptides Are Preferentially Solubilized in Specific Microenvironments within PEG-PLGA Polymer Nanoparticles.

Polymeric nanoparticles are a highly promising drug delivery formulation. However, a lack of understanding of the molecular mechanisms that underlie their drug solubilization and controlled release capabilities has hindered the efficient clinical translation of such technologies. Polyethylene glycol-poly(lactic-co-glycolic) acid (PEG-PLGA) nanoparticles have been widely studied as cancer drug delivery vehicles. In this letter, we use unbiased coarse-grained molecular dynamics simulations to model the self-assembly of a PEG-PLGA nanoparticle and its solubulization of the anticancer peptide, EEK, with good agreement with previously reported experimental structural data. We applied unsupervised machine learning techniques to quantify the conformations that polymers adopt at various locations within the nanoparticle. We find that the local microenvironments formed by the various polymer conformations promote preferential EEK solubilization within specific regions of the NP. This demonstrates that these microenvironments are key in controlling drug storage locations within nanoparticles, supporting the rational design of nanoparticles for therapeutic applications.

G3BP1-dependent condensation of translationally inactive viral RNAs antagonizes infection.

G3BP1 is an RNA binding protein that condenses untranslating messenger RNAs into stress granules (SGs). G3BP1 is inactivated by multiple viruses and is thought to antagonize viral replication by SG-enhanced antiviral signaling. Here, we show that neither G3BP1 nor SGs generally alter the activation of innate immune pathways. Instead, we show that the RNAs encoded by West Nile virus, Zika virus, and severe acute respiratory syndrome coronavirus 2 are prone to G3BP1-dependent RNA condensation, which is enhanced by limiting translation initiation and correlates with the disruption of viral replication organelles and viral RNA replication. We show that these viruses counteract condensation of their RNA genomes by inhibiting the RNA condensing function of G3BP proteins, hijacking the RNA decondensing activity of eIF4A, and/or maintaining efficient translation. These findings argue that RNA condensation can function as an intrinsic antiviral mechanism, which explains why many viruses inactivate G3BP proteins and suggests that SGs may have arisen as a vestige of this antiviral mechanism.