Human 5' UTR design and variant effect prediction from a massively parallel translation assay

Recent progress in mRNA cancer vaccines

The research and development of messenger RNA (mRNA) cancer vaccines have gradually overcome numerous challenges through the application of personalized cancer antigens, structural optimization of mRNA, and the development of alternative RNA-based vectors and efficient targeted delivery vectors. Clinical trials are currently underway for various cancer vaccines that encode tumor-associated antigens (TAAs), tumor-specific antigens (TSAs), or immunomodulators. In this paper, we summarize the optimization of mRNA and the emergence of RNA-based expression vectors in cancer vaccines. We begin by reviewing the advancement and utilization of state-of-the-art targeted lipid nanoparticles (LNPs), followed by presenting the primary classifications and clinical applications of mRNA cancer vaccines. Collectively, mRNA vaccines are emerging as a central focus in cancer immunotherapy, offering the potential to address multiple challenges in cancer treatment, either as standalone therapies or in combination with current cancer treatments.

Design and evaluation of mRNA encoding recombinant neutralizing antibodies for botulinum neurotoxin type B intoxication prophylaxis

Among all natural and synthetic toxins, botulinum neurotoxins (BoNTs), produced by Clostridium botulinum in an anaerobic environment, are the most toxic polymer proteins. Currently, the most effective modalities for botulism prevention and treatment are vaccination and antitoxin use, respectively. However, these modalities are associated with long response time for active immunization, side effects, and donor limitations. As such, the development of more promising botulism prevention and treatment modalities is warranted. Here, we designed an mRNA encoding B9-hFc - a heavy-chain antibody fused to VHH and human Fc that can neutralize BoNT serotype B (BoNT/B) effectively - and assessed its expression in vitro and in vivo. The results confirmed that our mRNA demonstrates good expression in vitro and in vivo. Moreover, a single mRNA lipid nanoparticle injection effectively prevents BoNT/B intoxication in vivo, with effects comparable to those of protein antibodies. In conclusion, we explored and clarified whether mRNA drugs encoding neutralizing antibodies prevent BoNT/B intoxication. Our results provide an efficient strategy for further research on the prevention and treatment of intoxication by botulinum toxin.

Pan-cancer transcriptome analysis reveals widespread regulation through alternative tandem transcription initiation

Abnormal transcription initiation from alternative first exon has been reported to promote tumorigenesis. However, the prevalence and impact of gene expression regulation mediated by alternative tandem transcription initiation were mostly unknown in cancer. Here, we developed a robust computational method to analyze alternative tandem transcription start site (TSS) usage from standard RNA sequencing data. Applying this method to pan-cancer RNA sequencing datasets, we observed widespread dysregulation of tandem TSS usage in tumors, many of which were independent of changes in overall expression level or alternative first exon usage. We showed that the dynamics of tandem TSS usage was associated with epigenomic modulation. We found that significant 5' untranslated region shortening of gene TIMM13 contributed to increased protein production, and up-regulation of TIMM13 by CRISPR-mediated transcriptional activation promoted proliferation and migration of lung cancer cells. Our findings suggest that dysregulated tandem TSS usage represents an addtional layer of cancer-associated transcriptome alterations.

Emerging mRNA Technology for Liver Disease Therapy

Liver diseases have consistently posed substantial challenges to global health. It is crucial to find innovative methods to effectively prevent and treat these diseases. In recent times, there has been an increasing interest in the use of mRNA formulations that accumulate in liver tissue for the treatment of hepatic diseases. In this review, we start by providing a detailed introduction to the mRNA technology. Afterward, we highlight types of liver diseases, discussing their causes, risks, and common therapeutic strategies. Additionally, we summarize the latest advancements in mRNA technology for the treatment of liver diseases. This includes systems based on hepatocyte growth factor, hepatitis B virus antibody, left-right determination factor 1, human hepatocyte nuclear factor α, interleukin-12, methylmalonyl-coenzyme A mutase, etc. Lastly, we provide an outlook on the potential of mRNA technology for the treatment of liver diseases, while also highlighting the various technical challenges that need to be addressed. Despite these difficulties, mRNA-based therapeutic strategies may change traditional treatment methods, bringing hope to patients with liver diseases.

In silico design of DNA sequences for in vivo nucleosome positioning

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

Next-generation sequencing strategies in venous thromboembolism: in whom and for what purpose?

This invited review follows the oral presentation "To Sequence or Not to Sequence, That Is Not the Question; But 'When, Who, Which and What For?' Is" given during the State of the Art session "Translational Genomics in Thrombosis: From OMICs to Clinics" of the International Society on Thrombosis and Haemostasis 2023 Congress. Emphasizing the power of next-generation sequencing technologies and the diverse strategies associated with DNA variant analysis, this review highlights the unresolved questions and challenges in their implementation both for the clinical diagnosis of venous thromboembolism and in translational research.

Unveil cis-acting combinatorial mRNA motifs by interpreting deep neural network

SUMMARY

Cis-acting mRNA elements play a key role in the regulation of mRNA stability and translation efficiency. Revealing the interactions of these elements and their impact plays a crucial role in understanding the regulation of the mRNA translation process, which supports the development of mRNA-based medicine or vaccines. Deep neural networks (DNN) can learn complex cis-regulatory codes from RNA sequences. However, extracting these cis-regulatory codes efficiently from DNN remains a significant challenge. Here, we propose a method based on our toolkit NeuronMotif and motif mutagenesis, which not only enables the discovery of diverse and high-quality motifs but also efficiently reveals motif interactions. By interpreting deep-learning models, we have discovered several crucial motifs that impact mRNA translation efficiency and stability, as well as some unknown motifs or motif syntax, offering novel insights for biologists. Furthermore, we note that it is challenging to enrich motif syntax in datasets composed of randomly generated sequences, and they may not contain sufficient biological signals.

AVAILABILITY AND IMPLEMENTATION

The source code and data used to produce the results and analyses presented in this manuscript are available from GitHub (https://github.com/WangLabTHU/combmotif).

Optimizing 5'UTRs for mRNA-delivered gene editing using deep learning

mRNA therapeutics are revolutionizing the pharmaceutical industry, but methods to optimize the primary sequence for increased expression are still lacking. Here, we design 5'UTRs for efficient mRNA translation using deep learning. We perform polysome profiling of fully or partially randomized 5'UTR libraries in three cell types and find that UTR performance is highly correlated across cell types. We train models on our datasets and use them to guide the design of high-performing 5'UTRs using gradient descent and generative neural networks. We experimentally test designed 5'UTRs with mRNA encoding megaTALTM gene editing enzymes for two different gene targets and in two different cell lines. We find that the designed 5'UTRs support strong gene editing activity. Editing efficiency is correlated between cell types and gene targets, although the best performing UTR was specific to one cargo and cell type. Our results highlight the potential of model-based sequence design for mRNA therapeutics.

Big data and deep learning for RNA biology

The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.

Transfer learning for cross-context prediction of protein expression from 5'UTR sequence

Model-guided DNA sequence design can accelerate the reprogramming of living cells. It allows us to engineer more complex biological systems by removing the need to physically assemble and test each potential design. While mechanistic models of gene expression have seen some success in supporting this goal, data-centric, deep learning-based approaches often provide more accurate predictions. This accuracy, however, comes at a cost - a lack of generalization across genetic and experimental contexts that has limited their wider use outside the context in which they were trained. Here, we address this issue by demonstrating how a simple transfer learning procedure can effectively tune a pre-trained deep learning model to predict protein translation rate from 5' untranslated region (5'UTR) sequence for diverse contexts in Escherichia coli using a small number of new measurements. This allows for important model features learnt from expensive massively parallel reporter assays to be easily transferred to new settings. By releasing our trained deep learning model and complementary calibration procedure, this study acts as a starting point for continually refined model-based sequence design that builds on previous knowledge and future experimental efforts.

Decoding polygenic diseases: advances in noncoding variant prioritization and validation

Genome-wide association studies (GWASs) provide a key foundation for elucidating the genetic underpinnings of common polygenic diseases. However, these studies have limitations in their ability to assign causality to particular genetic variants, especially those residing in the noncoding genome. Over the past decade, technological and methodological advances in both analytical and empirical prioritization of noncoding variants have enabled the identification of causative variants by leveraging orthogonal functional evidence at increasing scale. In this review, we present an overview of these approaches and describe how this workflow provides the groundwork necessary to move beyond associations toward genetically informed studies on the molecular and cellular mechanisms of polygenic disease.

RNAirport: a deep neural network-based database characterizing representative gene models in plants

A 5'-leader, known initially as the 5'-untranslated region, contains multiple isoforms due to alternative splicing (aS) and alternative transcription start site (aTSS). Therefore, a representative 5'-leader is demanded to examine the embedded RNA regulatory elements in controlling translation efficiency. Here, we develop a ranking algorithm and a deep-learning model to annotate representative 5'-leaders for five plant species. We rank the intra-sample and inter-sample frequency of aS-mediated transcript isoforms using the Kruskal-Wallis test-based algorithm and identify the representative aS-5'-leader. To further assign a representative 5'-end, we train the deep-learning model 5'leaderP to learn aTSS-mediated 5'-end distribution patterns from cap-analysis gene expression data. The model accurately predicts the 5'-end, confirmed experimentally in Arabidopsis and rice. The representative 5'-leader-contained gene models and 5'leaderP can be accessed at RNAirport (http://www.rnairport.com/leader5P/). The Stage 1 annotation of 5'-leader records 5'-leader diversity and will pave the way to Ribo-Seq open-reading frame annotation, identical to the project recently initiated by human GENCODE.

Methods for Functional Characterization of Genetic Polymorphisms of Non-Coding Regulatory Regions of the Human Genome

Currently, numerous associations between genetic polymorphisms and various diseases have been characterized through the Genome-Wide Association Studies. Majority of the clinically significant polymorphisms are localized in non-coding regions of the genome. While modern bioinformatic resources make it possible to predict molecular mechanisms that explain influence of the non-coding polymorphisms on gene expression, such hypotheses require experimental verification. This review discusses the methods for elucidating molecular mechanisms underlying dependence of the disease pathogenesis on specific genetic variants within the non-coding sequences. A particular focus is on the methods for identification of transcription factors with binding efficiency dependent on polymorphic variations. Despite remarkable progress in bioinformatic resources enabling prediction of the impact of polymorphisms on the disease pathogenesis, there is still the need for experimental approaches to investigate this issue.

Predicting functional UTR variants by integrating region-specific features

The untranslated region (UTR) of messenger ribonucleic acid (mRNA), including the 5'UTR and 3'UTR, plays a critical role in regulating gene expression and translation. Variants within the UTR can lead to changes associated with human traits and diseases; however, computational prediction of UTR variant effect is challenging. Current noncoding variant prediction mainly focuses on the promoters and enhancers, neglecting the unique sequence of the UTR and thereby limiting their predictive accuracy. In this study, using consolidated datasets of UTR variants from disease databases and large-scale experimental data, we systematically analyzed more than 50 region-specific features of UTR, including functional elements, secondary structure, sequence composition and site conservation. Our analysis reveals that certain features, such as C/G-related sequence composition in 5'UTR and A/T-related sequence composition in 3'UTR, effectively differentiate between nonfunctional and functional variant sets, unveiling potential sequence determinants of functional UTR variants. Leveraging these insights, we developed two classification models to predict functional UTR variants using machine learning, achieving an area under the curve (AUC) value of 0.94 for 5'UTR and 0.85 for 3'UTR, outperforming all existing methods. Our models will be valuable for enhancing clinical interpretation of genetic variants, facilitating the prediction and management of disease risk.

Novel Efficient Lipid-Based Delivery Systems Enable a Delayed Uptake and Sustained Expression of mRNA in Human Cells and Mouse Tissues

Over the past decade, mRNA-based therapy has displayed significant promise in a wide range of clinical applications. The most striking example of the leap in the development of mRNA technologies was the mass vaccination against COVID-19 during the pandemic. The emergence of large-scale technology and positive experience of mRNA immunization sparked the development of antiviral and anti-cancer mRNA vaccines as well as therapeutic mRNA agents for genetic and other diseases. To facilitate mRNA delivery, lipid nanoparticles (LNPs) have been successfully employed. However, the diverse use of mRNA therapeutic approaches requires the development of adaptable LNP delivery systems that can control the kinetics of mRNA uptake and expression in target cells. Here, we report effective mRNA delivery into cultured mammalian cells (HEK293T, HeLa, DC2.4) and living mouse muscle tissues by liposomes containing either 1,26-bis(cholest-5-en-3β-yloxycarbonylamino)-7,11,16,20-tetraazahexacosane tetrahydrochloride (2X3) or the newly applied 1,30-bis(cholest-5-en-3β-yloxycarbonylamino)-9,13,18,22-tetraaza-3,6,25,28-tetraoxatriacontane tetrahydrochloride (2X7) cationic lipids. Using end-point and real-time monitoring of Fluc mRNA expression, we showed that these LNPs exhibited an unusually delayed (of over 10 h in the case of the 2X7-based system) but had highly efficient and prolonged reporter activity in cells. Accordingly, both LNP formulations decorated with 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[amino(polyethylene glycol)-2000] (DSPE-PEG2000) provided efficient luciferase production in mice, peaking on day 3 after intramuscular injection. Notably, the bioluminescence was observed only at the site of injection in caudal thigh muscles, thereby demonstrating local expression of the model gene of interest. The developed mRNA delivery systems hold promise for prophylactic applications, where sustained synthesis of defensive proteins is required, and open doors to new possibilities in mRNA-based therapies.

Direct in vivo CAR T cell engineering

T cells modified to express intelligently designed chimeric antigen receptors (CARs) are exceptionally powerful therapeutic agents for relapsed and refractory blood cancers and have the potential to revolutionize therapy for many other diseases. To circumvent the complexity and cost associated with broad-scale implementation of ex vivo manufactured adoptive cell therapy products, alternative strategies to generate CAR T cells in vivo by direct infusion of nanoparticle-formulated nucleic acids or engineered viral vectors under development have received a great deal of attention in the past few years. Here, we outline the ex vivo manufacturing process as a motivating framework for direct in vivo strategies and discuss emerging data from preclinical models to highlight the potency of the in vivo approach, the applicability for new disease indications, and the remaining challenges associated with clinical readiness, including delivery specificity, long term efficacy, and safety.

Integrated multiplexed assays of variant effect reveal determinants of catechol-O-methyltransferase gene expression

Multiplexed assays of variant effect are powerful methods to profile the consequences of rare variants on gene expression and organismal fitness. Yet, few studies have integrated several multiplexed assays to map variant effects on gene expression in coding sequences. Here, we pioneered a multiplexed assay based on polysome profiling to measure variant effects on translation at scale, uncovering single-nucleotide variants that increase or decrease ribosome load. By combining high-throughput ribosome load data with multiplexed mRNA and protein abundance readouts, we mapped the cis-regulatory landscape of thousands of catechol-O-methyltransferase (COMT) variants from RNA to protein and found numerous coding variants that alter COMT expression. Finally, we trained machine learning models to map signatures of variant effects on COMT gene expression and uncovered both directional and divergent impacts across expression layers. Our analyses reveal expression phenotypes for thousands of variants in COMT and highlight variant effects on both single and multiple layers of expression. Our findings prompt future studies that integrate several multiplexed assays for the readout of gene expression.

Attenuating ribosome load improves protein output from mRNA by limiting translation-dependent mRNA decay

Developing an effective mRNA therapeutic often requires maximizing protein output per delivered mRNA molecule. We previously found that coding sequence (CDS) design can substantially affect protein output, with mRNA variants containing more optimal codons and higher secondary structure yielding the highest protein outputs due to their slow rates of mRNA decay. Here, we demonstrate that CDS-dependent differences in translation initiation and elongation rates lead to differences in translation- and deadenylation-dependent mRNA decay rates, thus explaining the effect of CDS on mRNA half-life. Surprisingly, the most stable and highest-expressing mRNAs in our test set have modest initiation/elongation rates and ribosome loads, leading to minimal translation-dependent mRNA decay. These findings are of potential interest for optimization of protein output from therapeutic mRNAs, which may be achieved by attenuating rather than maximizing ribosome load.

Optimizing Nucleic Acid Delivery Systems through Barcode Technology

Conventional biological experiments often focus on in vitro assays because of the inherent limitations when handling multiple variables in vivo, including labor-intensive and time-consuming procedures. Often only a subset of samples demonstrating significant efficacy in the in vitro assays can be evaluated in vivo. Nonetheless, because of the low correlation between the in vitro and in vivo tests, evaluation of the variables under examination in vivo and not solely in vitro is critical. An emerging approach to achieve high-throughput in vivo tests involves using a barcode system consisting of various nucleotide combinations. Unique barcodes for each variant enable the simultaneous testing of multiple entities, eliminating the need for separate individual tests. Subsequently, to identify crucial parameters, samples were collected and analyzed using barcode sequencing. This review explores the development of barcode design and its applications, including the evaluation of nucleic acid delivery systems and the optimization of gene expression in vivo.

Enhancement of SARS-CoV-2 mRNA Vaccine Efficacy through the Application of TMSB10 UTR for Superior Antigen Presentation and Immune Activation

The development of effective vaccines against SARS-CoV-2 remains a critical challenge amidst the ongoing global pandemic. This study introduces a novel approach to enhancing mRNA vaccine efficacy by leveraging the untranslated region (UTR) of TMSB10, a gene identified for its significant mRNA abundance in antigen-presenting cells. Utilizing the GEO database, we identified TMSB10 among nine genes, with the highest mRNA abundance in dendritic cell subtypes. Subsequent experiments revealed that TMSB10's UTR significantly enhances the expression of a reporter gene in both antigen-presenting and 293T cells, surpassing other candidates and a previously optimized natural UTR. A comparative analysis demonstrated that TMSB10 UTR not only facilitated a higher reporter gene expression in vitro but also showed marked superiority in vivo, leading to enhanced specific humoral and cellular immune responses against the SARS-CoV-2 Delta variant RBD antigen. Specifically, vaccines incorporating TMSB10 UTR induced significantly higher levels of specific IgG antibodies and promoted a robust T-cell immune response, characterized by the increased secretion of IFN-γ and IL-4 and the proliferation of CD4+ and CD8+ T cells. These findings underscore the potential of TMSB10 UTR as a strategic component in mRNA vaccine design, offering a promising avenue to bolster vaccine-induced immunity against SARS-CoV-2 and, potentially, other pathogens.

A 5' UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions

The 5' UTR, a regulatory region at the beginning of an mRNA molecule, plays a crucial role in regulating the translation process and impacts the protein expression level. Language models have showcased their effectiveness in decoding the functions of protein and genome sequences. Here, we introduced a language model for 5' UTR, which we refer to as the UTR-LM. The UTR-LM is pre-trained on endogenous 5' UTRs from multiple species and is further augmented with supervised information including secondary structure and minimum free energy. We fine-tuned the UTR-LM in a variety of downstream tasks. The model outperformed the best known benchmark by up to 5% for predicting the Mean Ribosome Loading, and by up to 8% for predicting the Translation Efficiency and the mRNA Expression Level. The model also applies to identifying unannotated Internal Ribosome Entry Sites within the untranslated region and improves the AUPR from 0.37 to 0.52 compared to the best baseline. Further, we designed a library of 211 novel 5' UTRs with high predicted values of translation efficiency and evaluated them via a wet-lab assay. Experiment results confirmed that our top designs achieved a 32.5% increase in protein production level relative to well-established 5' UTR optimized for therapeutics.

A novel deep generative model for mRNA vaccine development: Designing 5' UTRs with N1-methyl-pseudouridine modification

Efficient translation mediated by the 5' untranslated region (5' UTR) is essential for the robust efficacy of mRNA vaccines. However, the N1-methyl-pseudouridine (m1Ψ) modification of mRNA can impact the translation efficiency of the 5' UTR. We discovered that the optimal 5' UTR for m1Ψ-modified mRNA (m1Ψ-5' UTR) differs significantly from its unmodified counterpart, highlighting the need for a specialized tool for designing m1Ψ-5' UTRs rather than directly utilizing high-expression endogenous gene 5' UTRs. In response, we developed a novel machine learning-based tool, Smart5UTR, which employs a deep generative model to identify superior m1Ψ-5' UTRs in silico. The tailored loss function and network architecture enable Smart5UTR to overcome limitations inherent in existing models. As a result, Smart5UTR can successfully design superior 5' UTRs, greatly benefiting mRNA vaccine development. Notably, Smart5UTR-designed superior 5' UTRs significantly enhanced antibody titers induced by COVID-19 mRNA vaccines against the Delta and Omicron variants of SARS-CoV-2, surpassing the performance of vaccines using high-expression endogenous gene 5' UTRs.

Engineered mRNA Delivery Systems for Biomedical Applications

Messenger RNA (mRNA)-based therapeutic strategies have shown remarkable promise in preventing and treating a staggering range of diseases. Optimizing the structure and delivery system of engineered mRNA has greatly improved its stability, immunogenicity, and protein expression levels, which has led to a wider range of uses for mRNA therapeutics. Herein, a thorough analysis of the optimization strategies used in the structure of mRNA is first provided and delivery systems are described in great detail. Furthermore, the latest advancements in biomedical engineering for mRNA technology, including its applications in combatting infectious diseases, treating cancer, providing protein replacement therapy, conducting gene editing, and more, are summarized. Lastly, a perspective on forthcoming challenges and prospects concerning the advancement of mRNA therapeutics is offered. Despite these challenges, mRNA-based therapeutics remain promising, with the potential to revolutionize disease treatment and contribute to significant advancements in the biomedical field.

RNA-encapsulating lipid nanoparticles in cancer immunotherapy: From pre-clinical studies to clinical trials

Nanoparticle-based delivery systems such as RNA-encapsulating lipid nanoparticles (RNA LNPs) have dramatically advanced in function and capacity over the last few decades. RNA LNPs boast of a diverse array of external and core configurations that enhance targeted delivery and prolong circulatory retention, advancing therapeutic outcomes. Particularly within the realm of cancer immunotherapies, RNA LNPs are increasingly gaining prominence. Pre-clinical in vitro and in vivo studies have laid a robust foundation for new and ongoing clinical trials that are actively enrolling patients for RNA LNP cancer immunotherapy. This review explores RNA LNPs, starting from their core composition to their external membrane formulation, set against a backdrop of recent clinical breakthroughs. We further elucidate the LNP delivery avenues, broach the prevailing challenges, and contemplate the future perspectives of RNA LNP-mediated immunotherapy.

Messenger RNA-encoded antibody approach for targeting extracellular and intracellular tau

Monoclonal antibodies have emerged as a leading therapeutic agent for the treatment of disease, including Alzheimer's disease. In the last year, two anti-amyloid monoclonal antibodies, lecanemab and aducanumab, have been approved in the USA for the treatment of Alzheimer's disease, whilst several tau-targeting monoclonal antibodies are currently in clinical trials. Such antibodies, however, are expensive and timely to produce and require frequent dosing regimens to ensure disease-modifying effects. Synthetic in vitro-transcribed messenger RNA encoding antibodies for endogenous protein expression holds the potential to overcome many of the limitations associated with protein antibody production. Here, we have generated synthetic in vitro-transcribed messenger RNA encoding a tau-specific antibody as a full-sized immunoglobulin and as a single-chain variable fragment. In vitro transfection of human neuroblastoma SH-SY5Y cells demonstrated the ability of the synthetic messenger RNA to be translated into a functional tau-specific antibody. Furthermore, we show that the translation of the tau-specific single-chain variable fragment as an intrabody results in the specific engagement of intracellular tau. This work highlights the utility of messenger RNA for the delivery of antibody therapeutics, including intrabodies, for the targeting of tau in Alzheimer's disease and other tauopathies.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

Recent advances in nanoparticulate RNA delivery systems

Nanoparticle-based RNA delivery has shown great progress in recent years with the approval of two mRNA vaccines for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and a liver-targeted siRNA therapy. Here, we discuss the preclinical and clinical advancement of new generations of RNA delivery therapies along multiple axes. Improvements in cargo design such as RNA circularization and data-driven untranslated region optimization can drive better mRNA expression. New materials discovery research has driven improved delivery to extrahepatic targets such as the lung and splenic immune cells, which could lead to pulmonary gene therapy and better cancer vaccines, respectively. Other organs and even specific cell types can be targeted for delivery via conjugation of small molecule ligands, antibodies, or peptides to RNA delivery nanoparticles. Moreover, the immune response to any RNA delivery nanoparticle plays a crucial role in determining efficacy. Targeting increased immunogenicity without induction of reactogenic side effects is crucial for vaccines, while minimization of immune response is important for gene therapies. New developments have addressed each of these priorities. Last, we discuss the range of RNA delivery clinical trials targeting diverse organs, cell types, and diseases and suggest some key advances that may play a role in the next wave of therapies.

Holistic One Health Surveillance Framework: Synergizing Environmental, Animal, and Human Determinants for Enhanced Infectious Disease Management

Recent pandemics, including the COVID-19 outbreak, have brought up growing concerns about transmission of zoonotic diseases from animals to humans. This highlights the requirement for a novel approach to discern and address the escalating health threats. The One Health paradigm has been developed as a responsive strategy to confront forthcoming outbreaks through early warning, highlighting the interconnectedness of humans, animals, and their environment. The system employs several innovative methods such as the use of advanced technology, global collaboration, and data-driven decision-making to come up with an extraordinary solution for improving worldwide disease responses. This Review deliberates environmental, animal, and human factors that influence disease risk, analyzes the challenges and advantages inherent in using the One Health surveillance system, and demonstrates how these can be empowered by Big Data and Artificial Intelligence. The Holistic One Health Surveillance Framework presented herein holds the potential to revolutionize our capacity to monitor, understand, and mitigate the impact of infectious diseases on global populations.

Computational design of mRNA vaccines

mRNA technology has emerged as a successful vaccine platform that offered a swift response to the COVID-19 pandemic. Accumulating evidence shows that vaccine efficacy, thermostability, and other important properties, are largely impacted by intrinsic properties of the mRNA molecule, such as RNA sequence and structure, both of which can be optimized. Designing mRNA sequence for vaccines presents a combinatorial problem due to an extremely large selection space. For instance, due to the degeneracy of the genetic code, there are over 10632 possible mRNA sequences that could encode the spike protein, the COVID-19 vaccines' target. Moreover, designing different elements of the mRNA sequence simultaneously against multiple objectives such as translational efficiency, reduced reactogenicity, and improved stability requires an efficient and sophisticated optimization strategy. Recently, there has been a growing interest in utilizing computational tools to redesign mRNA sequences to improve vaccine characteristics and expedite discovery timelines. In this review, we explore important biophysical features of mRNA to be considered for vaccine design and discuss how computational approaches can be applied to rapidly design mRNA sequences with desirable characteristics.

Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges

Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.

Regulatory activity is the default DNA state in eukaryotes

Genomes encode for genes and non-coding DNA, both capable of transcriptional activity. However, unlike canonical genes, many transcripts from non-coding DNA have limited evidence of conservation or function. Here, to determine how much biological noise is expected from non-genic sequences, we quantify the regulatory activity of evolutionarily naive DNA using RNA-seq in yeast and computational predictions in humans. In yeast, more than 99% of naive DNA bases were transcribed. Unlike the evolved transcriptome, naive transcripts frequently overlapped with opposite sense transcripts, suggesting selection favored coherent gene structures in the yeast genome. In humans, regulation-associated chromatin activity is predicted to be common in naive dinucleotide-content-matched randomized DNA. Here, naive and evolved DNA have similar co-occurrence and cell-type specificity of chromatin marks, challenging these as indicators of selection. However, in both yeast and humans, extreme high activities were rare in naive DNA, suggesting they result from selection. Overall, basal regulatory activity seems to be the default, which selection can hone to evolve a function or, if detrimental, repress.

Therapeutic synthetic and natural materials for immunoengineering

Immunoengineering is a rapidly evolving field that has been driving innovations in manipulating immune system for new treatment tools and methods. The need for materials for immunoengineering applications has gained significant attention in recent years due to the growing demand for effective therapies that can target and regulate the immune system. Biologics and biomaterials are emerging as promising tools for controlling immune responses, and a wide variety of materials, including proteins, polymers, nanoparticles, and hydrogels, are being developed for this purpose. In this review article, we explore the different types of materials used in immunoengineering applications, their properties and design principles, and highlight the latest therapeutic materials advancements. Recent works in adjuvants, vaccines, immune tolerance, immunotherapy, and tissue models for immunoengineering studies are discussed.

Making sense of mRNA translational "noise"

The importance of translation fidelity has been apparent since the discovery of genetic code. It is commonly believed that translation deviating from the main coding region is to be avoided at all times inside cells. However, ribosome profiling and mass spectrometry have revealed pervasive noncanonical translation. Both the scope and origin of translational "noise" are just beginning to be appreciated. Although largely overlooked, those translational "noises" are associated with a wide range of cellular functions, such as producing unannotated protein products. Furthermore, the dynamic nature of translational "noise" is responsive to stress conditions, highlighting the beneficial effect of translational "noise" in stress adaptation. Mechanistic investigation of translational "noise" will provide better insight into the mechanisms of translational regulation. Ultimately, they are not "noise" at all but represent a signature of cellular activities under pathophysiological conditions. Deciphering translational "noise" holds the therapeutic and diagnostic potential in a wide spectrum of human diseases.

Chemically Modified Platforms for Better RNA Therapeutics

RNA-based therapies have catalyzed a revolutionary transformation in the biomedical landscape, offering unprecedented potential in disease prevention and treatment. However, despite their remarkable achievements, these therapies encounter substantial challenges including low stability, susceptibility to degradation by nucleases, and a prominent negative charge, thereby hindering further development. Chemically modified platforms have emerged as a strategic innovation, focusing on precise alterations either on the RNA moieties or their associated delivery vectors. This comprehensive review delves into these platforms, underscoring their significance in augmenting the performance and translational prospects of RNA-based therapeutics. It encompasses an in-depth analysis of various chemically modified delivery platforms that have been instrumental in propelling RNA therapeutics toward clinical utility. Moreover, the review scrutinizes the rationale behind diverse chemical modification techniques aiming at optimizing the therapeutic efficacy of RNA molecules, thereby facilitating robust disease management. Recent empirical studies corroborating the efficacy enhancement of RNA therapeutics through chemical modifications are highlighted. Conclusively, we offer profound insights into the transformative impact of chemical modifications on RNA drugs and delineates prospective trajectories for their future development and clinical integration.

Application of the iPLUS non-coding sequence in improving biopharmaceuticals production

The biotechnological landscape has witnessed significant growth in biological therapeutics particularly in the field of recombinant protein production. Here we investigate the function of 3'UTR cis-regulatory elements in increasing mRNA and protein levels in different biological therapeutics and model systems, spanning from monoclonal antibodies to mRNA vaccines. We explore the regulatory function of iPLUS - a universal sequence capable of consistently augmenting recombinant protein levels. By incorporating iPLUS in a vector to express a monoclonal antibody used in immunotherapy, in a mammalian cell line used by the industry (ExpiCHO), trastuzumab production increases by 2-fold. As yeast Pichia pastoris is widely used in the manufacture of industrial enzymes and pharmaceuticals, we then used iPLUS in tandem (3x) and iPLUSv2 (a variant of iPLUS) to provide proof-of-concept data that it increases the production of a reporter protein more than 100-fold. As iPLUS functions by also increasing mRNA levels, we hypothesize that these sequences could be used as an asset in the mRNA vaccine industry. In fact, by including iPLUSv2 downstream of Spike we were able to double its production. Moreover, the same effect was observed when we introduced iPLUSv2 downstream of MAGEC2, a tumor-specific antigen tested for cancer mRNA vaccines. Taken together, our study provides data (TLR4) showing that iPLUS may be used as a valuable asset in a variety of systems used by the biotech and biopharmaceutical industry. Our results underscore the critical role of non-coding sequences in controlling gene expression, offering a promising avenue to accelerate, enhance, and cost-effectively optimize biopharmaceutical production processes.

Generating information-dense promoter sequences with optimal string packing

Dense arrangements of binding sites within nucleotide sequences can collectively influence downstream transcription rates or initiate biomolecular interactions. For example, natural promoter regions can harbor many overlapping transcription factor binding sites that influence the rate of transcription initiation. Despite the prevalence of overlapping binding sites in nature, rapid design of nucleotide sequences with many overlapping sites remains a challenge. Here, we show that this is an NP-hard problem, coined here as the nucleotide String Packing Problem (SPP). We then introduce a computational technique that efficiently assembles sets of DNA-protein binding sites into dense, contiguous stretches of double-stranded DNA. For the efficient design of nucleotide sequences spanning hundreds of base pairs, we reduce the SPP to an Orienteering Problem with integer distances, and then leverage modern integer linear programming solvers. Our method optimally packs libraries of 20-100 binding sites into dense nucleotide arrays of 50-300 base pairs in 0.05-10 seconds. Unlike approximation algorithms or meta-heuristics, our approach finds provably optimal solutions. We demonstrate how our method can generate large sets of diverse sequences suitable for library generation, where the frequency of binding site usage across the returned sequences can be controlled by modulating the objective function. As an example, we then show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The nucleotide string packing approach we present can accelerate the design of sequences with complex DNA-protein interactions. When used in combination with synthesis and high-throughput screening, this design strategy could help interrogate how complex binding site arrangements impact either gene expression or biomolecular mechanisms in varied cellular contexts.

Author Summary

The way protein binding sites are arranged on DNA can control the regulation and transcription of downstream genes. Areas with a high concentration of binding sites can enable complex interplay between transcription factors, a feature that is exploited by natural promoters. However, designing synthetic promoters that contain dense arrangements of binding sites is a challenge. The task involves overlapping many binding sites, each typically about 10 nucleotides long, within a constrained sequence area, which becomes increasingly difficult as sequence length decreases, and binding site variety increases. We introduce an approach to design nucleotide sequences with optimally packed protein binding sites, which we call the nucleotide String Packing Problem (SPP). We show that the SPP can be solved efficiently using integer linear programming to identify the densest arrangements of binding sites for a specified sequence length. We show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The presented approach enables the rapid design and study of nucleotide sequences with complex, dense binding site architectures.

Untranslated Region Sequences and the Efficacy of mRNA Vaccines against Tuberculosis

mRNA vaccines have been shown to be effective in combating the COVID-19 pandemic. The amount of research on the use of mRNAs as preventive and therapeutic modalities has undergone explosive growth in the last few years. Nonetheless, the issue of the stability of mRNA molecules and their translation efficiency remains incompletely resolved. These characteristics of mRNA directly affect the expression level of a desired protein. Regulatory elements of RNA-5' and 3' untranslated regions (UTRs)-are responsible for translation efficiency. An optimal combination of the regulatory sequences allows mRNA to significantly increase the target protein's expression. We assessed the translation efficiency of mRNA encoding of firefly luciferase with various 5' and 3'UTRs in vitro on cell lines DC2.4 and THP1. We found that mRNAs containing 5'UTR sequences from eukaryotic genes HBB, HSPA1A, Rabb, or H4C2, or from the adenoviral leader sequence TPL, resulted in higher levels of luciferase bioluminescence 4 h after transfection of DC2.4 cells as compared with 5'UTR sequences used in vaccines mRNA-1273 and BNT162b2 from Moderna and BioNTech. mRNA containing TPL as the 5'UTR also showed higher efficiency (as compared with the 5'UTR from Moderna) at generating a T-cell response in mice immunized with mRNA vaccines encoding a multiepitope antigen. By contrast, no effects of various 5'UTRs and 3'UTRs were detectable in THP1 cells, suggesting that the observed effects are cell type specific. Further analyses enabled us to identify potential cell type-specific RNA-binding proteins that differ in landing sites within mRNAs with various 5'UTRs and 3'UTRs. Taken together, our data indicate high translation efficiency of TPL as a 5'UTR, according to experiments on DC2.4 cells and C57BL/6 mice.

Combining a prioritization strategy and functional studies nominates 5'UTR variants underlying inherited retinal disease

BACKGROUND

5' untranslated regions (5'UTRs) are essential modulators of protein translation. Predicting the impact of 5'UTR variants is challenging and rarely performed in routine diagnostics. Here, we present a combined approach of a comprehensive prioritization strategy and functional assays to evaluate 5'UTR variation in two large cohorts of patients with inherited retinal diseases (IRDs).

METHODS

We performed an isoform-level re-analysis of retinal RNA-seq data to identify the protein-coding transcripts of 378 IRD genes with highest expression in retina. We evaluated the coverage of their 5'UTRs by different whole exome sequencing (WES) kits. The selected 5'UTRs were analyzed in whole genome sequencing (WGS) and WES data from IRD sub-cohorts from the 100,000 Genomes Project (n = 2397 WGS) and an in-house database (n = 1682 WES), respectively. Identified variants were annotated for 5'UTR-relevant features and classified into seven categories based on their predicted functional consequence. We developed a variant prioritization strategy by integrating population frequency, specific criteria for each category, and family and phenotypic data. A selection of candidate variants underwent functional validation using diverse approaches.

RESULTS

Isoform-level re-quantification of retinal gene expression revealed 76 IRD genes with a non-canonical retina-enriched isoform, of which 20 display a fully distinct 5'UTR compared to that of their canonical isoform. Depending on the probe design, 3-20% of IRD genes have 5'UTRs fully captured by WES. After analyzing these regions in both cohorts, we prioritized 11 (likely) pathogenic variants in 10 genes (ARL3, MERTK, NDP, NMNAT1, NPHP4, PAX6, PRPF31, PRPF4, RDH12, RD3), of which 7 were novel. Functional analyses further supported the pathogenicity of three variants. Mis-splicing was demonstrated for the PRPF31:c.-9+1G>T variant. The MERTK:c.-125G>A variant, overlapping a transcriptional start site, was shown to significantly reduce both luciferase mRNA levels and activity. The RDH12:c.-123C>T variant was found in cis with the hypomorphic RDH12:c.701G>A (p.Arg234His) variant in 11 patients. This 5'UTR variant, predicted to introduce an upstream open reading frame, was shown to result in reduced RDH12 protein but unaltered mRNA levels.

CONCLUSIONS

This study demonstrates the importance of 5'UTR variants implicated in IRDs and provides a systematic approach for 5'UTR annotation and validation that is applicable to other inherited diseases.

Characterization and optimization of 5´ untranslated region containing poly-adenine tracts in Kluyveromyces marxianus using machine-learning model

BACKGROUND

The 5´ untranslated region (5´ UTR) plays a key role in regulating translation efficiency and mRNA stability, making it a favored target in genetic engineering and synthetic biology. A common feature found in the 5´ UTR is the poly-adenine (poly(A)) tract. However, the effect of 5´ UTR poly(A) on protein production remains controversial. Machine-learning models are powerful tools for explaining the complex contributions of features, but models incorporating features of 5´ UTR poly(A) are currently lacking. Thus, our goal is to construct such a model, using natural 5´ UTRs from Kluyveromyces marxianus, a promising cell factory for producing heterologous proteins.

RESULTS

We constructed a mini-library consisting of 207 5´ UTRs harboring poly(A) and 34 5´ UTRs without poly(A) from K. marxianus. The effects of each 5´ UTR on the production of a GFP reporter were evaluated individually in vivo, and the resulting protein abundance spanned an approximately 450-fold range throughout. The data were used to train a multi-layer perceptron neural network (MLP-NN) model that incorporated the length and position of poly(A) as features. The model exhibited good performance in predicting protein abundance (average R2 = 0.7290). The model suggests that the length of poly(A) is negatively correlated with protein production, whereas poly(A) located between 10 and 30 nt upstream of the start codon (AUG) exhibits a weak positive effect on protein abundance. Using the model as guidance, the deletion or reduction of poly(A) upstream of 30 nt preceding AUG tended to improve the production of GFP and a feruloyl esterase. Deletions of poly(A) showed inconsistent effects on mRNA levels, suggesting that poly(A) represses protein production either with or without reducing mRNA levels.

CONCLUSION

The effects of poly(A) on protein production depend on its length and position. Integrating poly(A) features into machine-learning models improves simulation accuracy. Deleting or reducing poly(A) upstream of 30 nt preceding AUG tends to enhance protein production. This optimization strategy can be applied to enhance the yield of K. marxianus and other microbial cell factories.

Hold out the genome: a roadmap to solving the cis-regulatory code

Gene expression is regulated by transcription factors that work together to read cis-regulatory DNA sequences. The 'cis-regulatory code' - how cells interpret DNA sequences to determine when, where and how much genes should be expressed - has proven to be exceedingly complex. Recently, advances in the scale and resolution of functional genomics assays and machine learning have enabled substantial progress towards deciphering this code. However, the cis-regulatory code will probably never be solved if models are trained only on genomic sequences; regions of homology can easily lead to overestimation of predictive performance, and our genome is too short and has insufficient sequence diversity to learn all relevant parameters. Fortunately, randomly synthesized DNA sequences enable testing a far larger sequence space than exists in our genomes, and designed DNA sequences enable targeted queries to maximally improve the models. As the same biochemical principles are used to interpret DNA regardless of its source, models trained on these synthetic data can predict genomic activity, often better than genome-trained models. Here we provide an outlook on the field, and propose a roadmap towards solving the cis-regulatory code by a combination of machine learning and massively parallel assays using synthetic DNA.

Tailor made: the art of therapeutic mRNA design

mRNA medicine is a new and rapidly developing field in which the delivery of genetic information in the form of mRNA is used to direct therapeutic protein production in humans. This approach, which allows for the quick and efficient identification and optimization of drug candidates for both large populations and individual patients, has the potential to revolutionize the way we prevent and treat disease. A key feature of mRNA medicines is their high degree of designability, although the design choices involved are complex. Maximizing the production of therapeutic proteins from mRNA medicines requires a thorough understanding of how nucleotide sequence, nucleotide modification and RNA structure interplay to affect translational efficiency and mRNA stability. In this Review, we describe the principles that underlie the physical stability and biological activity of mRNA and emphasize their relevance to the myriad considerations that factor into therapeutic mRNA design.

Artificial Intelligence in battling infectious diseases: A transformative role

It is widely acknowledged that infectious diseases have wrought immense havoc on human society, being regarded as adversaries from which humanity cannot elude. In recent years, the advancement of Artificial Intelligence (AI) technology has ushered in a revolutionary era in the realm of infectious disease prevention and control. This evolution encompasses early warning of outbreaks, contact tracing, infection diagnosis, drug discovery, and the facilitation of drug design, alongside other facets of epidemic management. This article presents an overview of the utilization of AI systems in the field of infectious diseases, with a specific focus on their role during the COVID-19 pandemic. The article also highlights the contemporary challenges that AI confronts within this domain and posits strategies for their mitigation. There exists an imperative to further harness the potential applications of AI across multiple domains to augment its capacity in effectively addressing future disease outbreaks.

RNA-Based Vaccines and Therapeutics Against Intracellular Pathogens

RNA-based vaccines have sparked a paradigm shift in the treatment and prevention of diseases by nucleic acid medicines. There has been a notable surge in the development of nucleic acid therapeutics and vaccines following the global approval of the two messenger RNA-based COVID-19 vaccines. This growth is fueled by the exploration of numerous RNA products in preclinical stages, offering several advantages over conventional methods, i.e., safety, efficacy, scalability, and cost-effectiveness. In this chapter, we provide an overview of various types of RNA and their mechanisms of action for stimulating immune responses and inducing therapeutic effects. Furthermore, this chapter delves into the varying delivery systems, particularly emphasizing the use of nanoparticles to deliver RNA. The choice of delivery system is an intricate process involved in developing nucleic acid medicines that significantly enhances their stability, biocompatibility, and site-specificity. Additionally, this chapter sheds light on the current landscape of clinical trials of RNA therapeutics and vaccines against intracellular pathogens.

Monovalent SARS-COV-2 mRNA vaccine using optimal UTRs and LNPs is highly immunogenic and broadly protective against Omicron variants

The emergence of highly transmissible severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) that are resistant to the current COVID-19 vaccines highlights the need for continued development of broadly protective vaccines for the future. Here, we developed two messenger RNA (mRNA)-lipid nanoparticle (LNP) vaccines, TU88mCSA and ALCmCSA, using the ancestral SARS-CoV-2 spike sequence, optimized 5' and 3' untranslated regions (UTRs), and LNP combinations. Our data showed that these nanocomplexes effectively activate CD4+ and CD8+ T cell responses and humoral immune response and provide complete protection against WA1/2020, Omicron BA.1 and BQ.1 infection in hamsters. Critically, in Omicron BQ.1 challenge hamster models, TU88mCSA and ALCmCSA not only induced robust control of virus load in the lungs but also enhanced protective efficacy in the upper respiratory airways. Antigen-specific immune analysis in mice revealed that the observed cross-protection is associated with superior UTRs [Carboxylesterase 1d (Ces1d)/adaptor protein-3β (AP3B1)] and LNP formulations that elicit robust lung tissue-resident memory T cells. Strong protective effects of TU88mCSA or ALCmCSA against both WA1/2020 and VOCs suggest that this mRNA-LNP combination can be a broadly protective vaccine platform in which mRNA cargo uses the ancestral antigen sequence regardless of the antigenic drift. This approach could be rapidly adapted for clinical use and timely deployment of vaccines against emerging and reemerging VOCs.

Discovery of regulatory motifs in 5' untranslated regions using interpretable multi-task learning models

The sequence in the 5' untranslated regions (UTRs) is known to affect mRNA translation rates. However, the underlying regulatory grammar remains elusive. Here, we propose MTtrans, a multi-task translation rate predictor capable of learning common sequence patterns from datasets across various experimental techniques. The core premise is that common motifs are more likely to be genuinely involved in translation control. MTtrans outperforms existing methods in both accuracy and the ability to capture transferable motifs across species, highlighting its strength in identifying evolutionarily conserved sequence motifs. Our independent fluorescence-activated cell sorting coupled with deep sequencing (FACS-seq) experiment validates the impact of most motifs identified by MTtrans. Additionally, we introduce "GRU-rewiring," a technique to interpret the hidden states of the recurrent units. Gated recurrent unit (GRU)-rewiring allows us to identify regulatory element-enriched positions and examine the local effects of 5' UTR mutations. MTtrans is a powerful tool for deciphering the translation regulatory motifs.

The anticancer compound JTE-607 reveals hidden sequence specificity of the mRNA 3' processing machinery

JTE-607 is an anticancer and anti-inflammatory compound and its active form, compound 2, directly binds to and inhibits CPSF73, the endonuclease for the cleavage step in pre-messenger RNA (pre-mRNA) 3' processing. Surprisingly, compound 2-mediated inhibition of pre-mRNA cleavage is sequence specific and the drug sensitivity is predominantly determined by sequences flanking the cleavage site (CS). Using massively parallel in vitro assays, we identified key sequence features that determine drug sensitivity. We trained a machine learning model that can predict poly(A) site (PAS) relative sensitivity to compound 2 and provide the molecular basis for understanding the impact of JTE-607 on PAS selection and transcription termination genome wide. We propose that CPSF73 and associated factors bind to the CS region in a sequence-dependent manner and the interaction affinity determines compound 2 sensitivity. These results have not only elucidated the mechanism of action of JTE-607, but also unveiled an evolutionarily conserved sequence specificity of the mRNA 3' processing machinery.

The regulatory landscape of 5' UTRs in translational control during zebrafish embryogenesis

The 5' UTRs of mRNAs are critical for translation regulation, but their in vivo regulatory features are poorly characterized. Here, we report the regulatory landscape of 5' UTRs during early zebrafish embryogenesis using a massively parallel reporter assay of 18,154 sequences coupled to polysome profiling. We found that the 5' UTR is sufficient to confer temporal dynamics to translation initiation, and identified 86 motifs enriched in 5' UTRs with distinct ribosome recruitment capabilities. A quantitative deep learning model, DaniO5P, revealed a combined role for 5' UTR length, translation initiation site context, upstream AUGs and sequence motifs on in vivo ribosome recruitment. DaniO5P predicts the activities of 5' UTR isoforms and indicates that modulating 5' UTR length and motif grammar contributes to translation initiation dynamics. This study provides a first quantitative model of 5' UTR-based translation regulation in early vertebrate development and lays the foundation for identifying the underlying molecular effectors.

Highlights

In vivo MPRA systematically interrogates the regulatory potential of endogenous 5' UTRs The 5' UTR alone is sufficient to regulate the dynamics of ribosome recruitment during early embryogenesis The MPRA identifies 5' UTR cis -regulatory motifs for translation initiation control 5' UTR length, upstream AUGs and motif grammar contribute to the differential regulatory capability of 5' UTR switching isoforms.

Effects of Combinations of Untranslated-Region Sequences on Translation of mRNA

mRNA-based therapeutics have been found to be a promising treatment strategy in immunotherapy, gene therapy, and cancer treatments. Effectiveness of mRNA therapeutics depends on the level and duration of a desired protein's expression, which is determined by various cis- and trans-regulatory elements of the mRNA. Sequences of 5' and 3' untranslated regions (UTRs) are responsible for translational efficiency and stability of mRNA. An optimal combination of the regulatory sequences allows researchers to significantly increase the target protein's expression. Using both literature data and previously obtained experimental data, we chose six sequences of 5'UTRs (adenoviral tripartite leader [TPL], HBB, rabbit β-globin [Rabb], H4C2, Moderna, and Neo2) and five sequences of 3'UTRs (mtRNR-EMCV, mtRNR-AES, mtRNR-mtRNR, BioNTech, and Moderna). By combining them, we constructed 30 in vitro transcribed RNAs encoding firefly luciferase with various combinations of 5'- and 3'UTRs, and the resultant bioluminescence was assessed in the DC2.4 cell line at 4, 8, 24, and 72 h after transfection. The cellular data enabled us to identify the best seven combinations of 5'- and 3'UTRs, whose translational efficiency was then assessed in BALB/c mice. Two combinations of 5'- and 3'UTRs (5'Rabb-3'mtRNR-EMCV and 5'TPL-3'Biontech) led to the most pronounced increase in the luciferase amount in the in vivo experiment in mice. Subsequent analysis of the stability of the mRNA indicated that the increase in luciferase expression is explained primarily by the efficiency of translation, not by the number of RNA molecules. Altogether, these findings suggest that 5'UTR-and-3'UTR combinations 5'Rabb-3'mtRNR- EMCV and 5'TPL-3'Biontech lead to high expression of target proteins and may be considered for use in preventive and therapeutic modalities based on mRNA.

Integrated multiplexed assays of variant effect reveal cis-regulatory determinants of catechol-O-methyltransferase gene expression

Multiplexed assays of variant effect are powerful methods to profile the consequences of rare variants on gene expression and organismal fitness. Yet, few studies have integrated several multiplexed assays to map variant effects on gene expression in coding sequences. Here, we pioneered a multiplexed assay based on polysome profiling to measure variant effects on translation at scale, uncovering single-nucleotide variants that increase and decrease ribosome load. By combining high-throughput ribosome load data with multiplexed mRNA and protein abundance readouts, we mapped the cis-regulatory landscape of thousands of catechol-O-methyltransferase (COMT) variants from RNA to protein and found numerous coding variants that alter COMT expression. Finally, we trained machine learning models to map signatures of variant effects on COMT gene expression and uncovered both directional and divergent impacts across expression layers. Our analyses reveal expression phenotypes for thousands of variants in COMT and highlight variant effects on both single and multiple layers of expression. Our findings prompt future studies that integrate several multiplexed assays for the readout of gene expression.