High-throughput 5' UTR engineering for enhanced protein production in non-viral gene therapies

Nucleic acids in modern molecular therapies: A realm of opportunities for strategic drug design

RNA vaccines have made evident to society what was already known by the scientific community: nucleic acids will be the "drugs of the future." By modifying the genome, interfering in transcription or translation, and by introducing new catalysts into the cell or by mimicking antibody effects, nucleic acids can generate therapeutic activities that are not accessible by any other therapeutic agents. There are, however, challenges that need to be solved in the next few years to make nucleic acids usable in a wide range of therapeutic scenarios. This review illustrates how simulation methods can help achieve this goal.

Comprehensive translational profiling and STE AI uncover rapid control of protein biosynthesis during cell stress

Translational control is important in all life, but it remains a challenge to accurately quantify. When ribosomes translate messenger (m)RNA into proteins, they attach to the mRNA in series, forming poly(ribo)somes, and can co-localize. Here, we computationally model new types of co-localized ribosomal complexes on mRNA and identify them using enhanced translation complex profile sequencing (eTCP-seq) based on rapid in vivo crosslinking. We detect long disome footprints outside regions of non-random elongation stalls and show these are linked to translation initiation and protein biosynthesis rates. We subject footprints of disomes and other translation complexes to artificial intelligence (AI) analysis and construct a new, accurate and self-normalized measure of translation, termed stochastic translation efficiency (STE). We then apply STE to investigate rapid changes to mRNA translation in yeast undergoing glucose depletion. Importantly, we show that, well beyond tagging elongation stalls, footprints of co-localized ribosomes provide rich insight into translational mechanisms, polysome dynamics and topology. STE AI ranks cellular mRNAs by absolute translation rates under given conditions, can assist in identifying its control elements and will facilitate the development of next-generation synthetic biology designs and mRNA-based therapeutics.

RNA vaccines for cancer: Principles to practice

Vaccines are the most impactful medicines to improve health. Though potent against pathogens, vaccines for cancer remain an unfulfilled promise. However, recent advances in RNA technology coupled with scientific and clinical breakthroughs have spurred rapid discovery and potent delivery of tumor antigens at speed and scale, transforming cancer vaccines into a tantalizing prospect. Yet, despite being at a pivotal juncture, with several randomized clinical trials maturing in upcoming years, several critical questions remain: which antigens, tumors, platforms, and hosts can trigger potent immunity with clinical impact? Here, we address these questions with a principled framework of cancer vaccination from antigen detection to delivery. With this framework, we outline features of emergent RNA technology that enable rapid, robust, real-time vaccination with somatic mutation-derived neoantigens-an emerging "ideal" antigen class-and highlight latent features that have sparked the belief that RNA could realize the enduring vision for vaccines against cancer.

Enhancing recombinant antibody yield in Chinese hamster ovary cells

A range of recombinant monoclonal antibodies (rMAbs) have found application in treating diverse diseases, spanning various cancers and immune system disorders. Chinese hamster ovary (CHO) cells have emerged as the predominant choice for producing these rMAbs due to their robustness, ease of transfection, and capacity for posttranslational modifications akin to those in human cells. Transient transfection and/or stable expression could be conducted to express rMAbs in CHO cells. To bolster the yield of rMAbs in CHO cells, a multitude of approaches have been developed, encompassing vector optimization, medium formulation, cultivation parameters, and cell engineering. This review succinctly outlines these methodologies when also addressing challenges encountered in the production process, such as issues with aggregation and fucosylation.

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.

Interleukins and interferons in mesenchymal stromal stem cell-based gene therapy of cancer

The tumor microenvironment is importantly shaped by various cytokines, where interleukins (ILs) and interferons (IFNs) shape the balance of immune activity within tumor niche and associated lymphoid organs. Their importance in activation and tuning of both innate and adaptive immune responses prompted their use in several clinical trials, albeit with limited therapeutic efficacy and risk of toxicity due to systemic administration. Increasing preclinical evidence suggests that local delivery of ILs and IFNs could significantly increase their effectiveness, while simultaneously attenuate the known side effects and issues related to their biological activity. A prominent way to achieve this is to use cell-based delivery vehicles. For this purpose, mesenchymal stromal stem cells (MSCs) are considered an almost ideal candidate. Namely, MSCs can be obtained in large quantities and from obtainable sources (e.g. umbilical cord or adipose tissue), their ex vivo expansion is relatively straightforward compared to other cell types and they possess very low immunogenicity making them suitable for allogeneic use. Importantly, MSCs have shown an intrinsic capacity to respond to tumor-directed chemotaxis. This review provides a focused and detailed discussion on MSC-based gene therapy using ILs and IFNs, engineering techniques and insights on potential future advancements.

TIR predictor and optimizer: Web-tools for accurate prediction of translation initiation rate and precision gene design in Saccharomyces cerevisiae

Translation initiation is the primary determinant of the rate of protein production. The variation in the rate with which this step occurs can cause up to three orders of magnitude differences in cellular protein levels. Several mRNA features, including mRNA stability in proximity to the start codon, coding sequence length, and presence of specific motifs in the mRNA molecule, have been shown to influence the translation initiation rate. These molecular factors acting at different strengths allow precise control of in vivo translation initiation rate and thus the rate of protein synthesis. However, despite the paramount importance of translation initiation rate in protein synthesis, accurate prediction of the absolute values of initiation rate remains a challenge. In fact, as of now, there is no available model for predicting the initiation rate in Saccharomyces cerevisiae. To address this, we train a machine learning model for predicting the in vivo initiation rate in S. cerevisiae transcripts. The model is trained using a diverse set of mRNA transcripts, enabling the comparison of initiation rates across different transcripts. Our model exhibited excellent accuracy in predicting the translation initiation rate and demonstrated its effectiveness with both endogenous and exogenous transcripts. Then, by combining the machine learning model with the Monte-Carlo search algorithm, we have also devised a method to optimize the nucleotide sequence of any gene to achieve a specific target initiation rate. The machine learning model we've developed for predicting translation initiation rates, along with the gene optimization method, are deployed as a web server. Both web servers are accessible for free at the following link: ajeetsharmalab.com/TIRPredictor. Thus, this research advances our fundamental understanding of translation initiation processes, with direct applications in biotechnology.

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.

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.

Advances in recombinant protease production: current state and perspectives

Proteases, enzymes that catalyze the hydrolysis of peptide bonds in proteins, are important in the food industry, biotechnology, and medical fields. With increasing demand for proteases, there is a growing emphasis on enhancing their expression and production through microbial systems. However, proteases' native hosts often fall short in high-level expression and compatibility with downstream applications. As a result, the recombinant production of proteases has become a significant focus, offering a solution to these challenges. This review presents an overview of the current state of protease production in prokaryotic and eukaryotic expression systems, highlighting key findings and trends. In prokaryotic systems, the Bacillus spp. is the predominant host for proteinase expression. Yeasts are commonly used in eukaryotic systems. Recent advancements in protease engineering over the past five years, including rational design and directed evolution, are also highlighted. By exploring the progress in both expression systems and engineering techniques, this review provides a detailed understanding of the current landscape of recombinant protease research and its prospects for future advancements.

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.

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.

scSNV-seq: high-throughput phenotyping of single nucleotide variants by coupled single-cell genotyping and transcriptomics

CRISPR screens with single-cell transcriptomic readouts are a valuable tool to understand the effect of genetic perturbations including single nucleotide variants (SNVs) associated with diseases. Interpretation of these data is currently limited as genotypes cannot be accurately inferred from guide RNA identity alone. scSNV-seq overcomes this limitation by coupling single-cell genotyping and transcriptomics of the same cells enabling accurate and high-throughput screening of SNVs. Analysis of variants across the JAK1 gene with scSNV-seq demonstrates the importance of determining the precise genetic perturbation and accurately classifies clinically observed missense variants into three functional categories: benign, loss of function, and separation of function.

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.

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.

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.

High-fidelity large-diversity monoclonal mammalian cell libraries by cell cycle arrested recombinase-mediated cassette exchange

Mammalian cells carrying defined genetic variations have shown great potentials in both fundamental research and therapeutic development. However, their full use was limited by lack of a robust method to construct large monoclonal high-quality combinatorial libraries. This study developed cell cycle arrested recombinase-mediated cassette exchange (aRMCE), able to provide monoclonality, precise genomic integration and uniform transgene expression. Via optimized nocodazole-mediated mitotic arrest, 20% target gene replacement efficiency was achieved without antibiotic selection, and the improved aRMCE efficiency was applicable to a variety of tested cell clones, transgene targets and transfection methods. As a demonstration of this versatile method, we performed directed evolution of fragment crystallizable (Fc), for which error-prone libraries of over 107 variants were constructed and displayed as IgG on surface of CHO cells. Diversities of constructed libraries were validated by deep sequencing, and panels of novel Fc mutants were identified showing improved binding towards specific Fc gamma receptors and enhanced effector functions. Due to its large cargo capacity and compatibility with different mutagenesis approaches, we expect this mammalian cell platform technology has broad applications for directed evolution, multiplex genetic assays, cell line development and stem cell engineering.

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.

Engineering a Novel Modular Adenoviral mRNA Delivery Platform Based on Tag/Catcher Bioconjugation

mRNA vaccines have attracted widespread research attention with clear advantages in terms of molecular flexibility, rapid development, and potential for personalization. However, current mRNA vaccine platforms have not been optimized for induction of CD4/CD8 T cell responses. In addition, the mucosal administration of mRNA based on lipid nanoparticle technology faces challenges in clinical translation. In contrast, adenovirus-based vaccines induce strong T cell responses and have been approved for intranasal delivery. To leverage the inherent strengths of both the mRNA and adenovirus platforms, we developed a novel modular adenoviral mRNA delivery platform based on Tag/Catcher bioconjugation. Specifically, we engineered adenoviral vectors integrating Tag/Catcher proteins at specific locales on the Ad capsid proteins, allowing us to anchor mRNA to the surface of engineered Ad viruses. In proof-of-concept studies, the Ad-mRNA platform successfully mediated mRNA delivery and could be optimized via the highly flexible modular design of both the Ad-mRNA and protein bioconjugation systems.

Engineering the Ultrasensitive Visual Whole-Cell Biosensors by Evolved MerR and 5' UTR for Detection of Ultratrace Mercury

The existing mercury whole-cell biosensors (WCBs, parts per billion range) are not able to meet the real-world requirements due to their lack of sensitivity for the detection of ultratrace mercury in the environment. Ultratrace mercury is a potential threat to human health via the food chain. Here, we developed an ultrasensitive mercury WCB by directed evolution of the mercury-responsive transcriptional activator (MerR) sensing module to detect ultratrace mercury. Subsequently, the mutant WCB (m4-1) responding to mercury in the parts per trillion range after 1 h of induction was obtained. Its detection limit (LOD) was 0.313 ng/L, comparable to those of some analytical instruments. Surprisingly, the m4-1 WCB also responded to methylmercury (LOD = 98 ng/L), which is far more toxic than inorganic mercury. For more convenient detection, we have increased another green fluorescent protein reporter module with an optimized 5' untranslated region (5' UTR) sequence. This yields two visual WCBs with an enhanced fluorescence output. At a concentration of 2.5 ng/L, the fluorescence signals can be directly observed by the naked eye. With the combination of mobile phone imaging and image processing software, the 2GC WCB provided simple, rapid, and reliable quantitative and qualitative analysis of real samples (LOD = 0.307 ng/L). Taken together, these results indicate that the ultrasensitive visual whole-cell biosensors for ultratrace mercury detection are successfully designed using a combination of directed evolution and synthetic biotechnology.

Advances in Nonviral mRNA Delivery Materials and Their Application as Vaccines for Melanoma Therapy

Messenger RNA (mRNA) vaccines are promising platforms for cancer immunotherapy because of their potential to encode for a variety of tumor antigens, high tolerability, and capacity to induce strong antitumor immune responses. However, the clinical translation of mRNA cancer vaccines can be hindered by the inefficient delivery of mRNA in vivo. In this review, we provide an overview of mRNA cancer vaccines by discussing their utility in treating melanoma. Specifically, we begin our review by describing the barriers that can impede mRNA delivery to target cells. We then review native mRNA structure and discuss various modification methods shown to enhance mRNA stability and transfection. Next, we outline the advantages and challenges of three nonviral carrier platforms (lipid nanoparticles, polymeric nanoparticles, and lipopolyplexes) frequently used for mRNA delivery. Last, we summarize preclinical and clinical studies that have investigated nonviral mRNA vaccines for the treatment of melanoma. In writing this review, we aim to highlight innovative nonviral strategies designed to address mRNA delivery challenges while emphasizing the exciting potential of mRNA vaccines as next-generation therapies for the treatment of cancers.

Evaluation of self-amplifying mRNA platform for protein expression and genetic stability: Implication for mRNA therapies

The consecutive launch of mRNA vaccines like mRNA-1273, BNT 162b2, and GEMCOVAC®-19 against COVID-19 has triggered the debate of long-term expression, safety, and genomic integration of the mRNA vaccine platforms. In the present study, we examined the longevity of antigenic protein expression of mRNA-614 and mRNA-S1LC based on self-amplifying mRNA (SAM) in Expi-293F™, HEK-293 T, and ARPE-19 cells. The protein expression was checked by sandwich-ELISA, FACS, luciferase activity assay, and Western blot. The transcribed antigenic mRNA was sequenced and found to be un-mutated. Additionally, no genomic integration of the reverse transcribed mRNA was observed even up to 7 days post-transfection as verified by PCR. Furthermore, we have generated high-quality 3D structures of non-structural proteins (nsPs) in silico and the genes encoding for the nsPs were cloned and expressed using the T7 system. Findings from the current study have strengthened the fact that the alphavirus-based SAM platform has the potential to become a modality in the upcoming years.

Deep flanking sequence engineering for efficient promoter design using DeepSEED

Designing promoters with desirable properties is essential in synthetic biology. Human experts are skilled at identifying strong explicit patterns in small samples, while deep learning models excel at detecting implicit weak patterns in large datasets. Biologists have described the sequence patterns of promoters via transcription factor binding sites (TFBSs). However, the flanking sequences of cis-regulatory elements, have long been overlooked and often arbitrarily decided in promoter design. To address this limitation, we introduce DeepSEED, an AI-aided framework that efficiently designs synthetic promoters by combining expert knowledge with deep learning techniques. DeepSEED has demonstrated success in improving the properties of Escherichia coli constitutive, IPTG-inducible, and mammalian cell doxycycline (Dox)-inducible promoters. Furthermore, our results show that DeepSEED captures the implicit features in flanking sequences, such as k-mer frequencies and DNA shape features, which are crucial for determining promoter properties.

Efficacy and muscle safety assessment of fukutin-related protein gene therapy

Limb-girdle muscular dystrophy type R9 (LGMDR9) is a muscle-wasting disease that begins in the hip and shoulder regions of the body. This disease is caused by mutations in fukutin-related protein (FKRP), a glycosyltransferase critical for maintaining muscle cell integrity. Here we investigated potential gene therapies for LGMDR9 containing an FKRP expression construct with untranslated region (UTR) modifications. Initial studies treated an aged dystrophic mouse model (FKRPP448L) with adeno-associated virus vector serotype 6 (AAV6). Grip strength improved in a dose- and time-dependent manner, injected mice exhibited fewer central nuclei and serum creatine kinase levels were 3- and 5-fold lower compared to those in non-injected FKRPP448L mice. Treatment also partially stabilized the respiratory pattern during exercise and improved treadmill running, partially protecting muscle from exercise-induced damage. Western blotting of C2C12 myotubes using a novel rabbit antibody confirmed heightened translation with the UTR modifications. We further explored the question of FKRP toxicity in wild-type mice using high doses of two additional muscle-tropic capsids: AAV9 and AAVMYO1. No toxic effects were detected with either therapeutic agent. These data further support the feasibility of gene therapy to treat LGMDR9.

SHIELD: a platform for high-throughput screening of barrier-type DNA elements in human cells

Chromatin boundary elements contribute to the partitioning of mammalian genomes into topological domains to regulate gene expression. Certain boundary elements are adopted as DNA insulators for safe and stable transgene expression in mammalian cells. These elements, however, are ill-defined and less characterized in the non-coding genome, partially due to the lack of a platform to readily evaluate boundary-associated activities of putative DNA sequences. Here we report SHIELD (Site-specific Heterochromatin Insertion of Elements at Lamina-associated Domains), a platform tailored for the high-throughput screening of barrier-type DNA elements in human cells. SHIELD takes advantage of the high specificity of serine integrase at heterochromatin, and exploits the natural heterochromatin spreading inside lamina-associated domains (LADs) for the discovery of potent barrier elements. We adopt SHIELD to evaluate the barrier activity of 1000 DNA elements in a high-throughput manner and identify 8 candidates with barrier activities comparable to the core region of cHS4 element in human HCT116 cells. We anticipate SHIELD could facilitate the discovery of novel barrier DNA elements from the non-coding genome in human cells.

Using Synthetic DNA Libraries to Investigate Chromatin and Gene Regulation

Despite the recent explosion in genome-wide studies in chromatin and gene regulation, we are still far from extracting a set of genetic rules that can predict the function of the regulatory genome. One major reason for this deficiency is that gene regulation is a multi-layered process that involves an enormous variable space, which cannot be fully explored using native genomes. This problem can be partially solved by introducing synthetic DNA libraries into cells, a method that can test the regulatory roles of thousands to millions of sequences with limited variables. Here, we review recent applications of this method to study transcription factor (TF) binding, nucleosome positioning, and transcriptional activity. We discuss the design principles, experimental procedures, and major findings from these studies and compare the pros and cons of different approaches.

The mRNA Vaccine Revolution: COVID-19 Has Launched the Future of Vaccinology

During the COVID-19 pandemic, mRNA (mRNA) vaccines emerged as leading vaccine candidates in a record time. Nonreplicating mRNA (NRM) and self-amplifying mRNA (SAM) technologies have been developed into high-performing and clinically viable vaccines against a range of infectious agents, notably SARS-CoV-2. mRNA vaccines demonstrate efficient in vivo delivery, long-lasting stability, and nonexistent risk of infection. The stability and translational efficiency of in vitro transcription (IVT)-mRNA can be further increased by modulating its structural elements. In this review, we present a comprehensive overview of the recent advances, key applications, and future challenges in the field of mRNA-based vaccinology.

Generative and predictive neural networks for the design of functional RNA molecules

RNA is a remarkably versatile molecule that has been engineered for applications in therapeutics, diagnostics, and in vivo information-processing systems. However, the complex relationship between the sequence and structural properties of an RNA molecule and its ability to perform specific functions often necessitates extensive experimental screening of candidate sequences. Here we present a generalized neural network architecture that utilizes the sequence and structure of RNA molecules (SANDSTORM) to inform functional predictions. We demonstrate that this approach achieves state-of-the-art performance across several distinct RNA prediction tasks, while learning interpretable abstractions of RNA secondary structure. We paired these predictive models with generative adversarial RNA design networks (GARDN), allowing the generative modelling of novel mRNA 5' untranslated regions and toehold switch riboregulators exhibiting a predetermined fitness. This approach enabled the design of novel toehold switches with a 43-fold increase in experimentally characterized dynamic range compared to those designed using classic thermodynamic algorithms. SANDSTORM and GARDN thus represent powerful new predictive and generative tools for the development of diagnostic and therapeutic RNA molecules with improved function.

Deep learning for optimization of protein expression

Recent progress in high-throughput DNA synthesis and sequencing has enabled the development of massively parallel reporter assays for strain characterization. These datasets map a large number of DNA sequences to protein expression levels, sparking increased interest in data-driven methods for sequence-to-expression modeling. Here, we highlight advances in deep learning models of protein expression and their potential for optimizing strains engineered to produce recombinant proteins. We review recent works that built highly accurate models and discuss challenges that hinder adoption by end users. There is a need to better align this technology with the constraints encountered in strain engineering, particularly the cost of acquiring large amounts of data and the requirement for interpretable models that generalize beyond the training data. Overcoming these barriers will help to incentivize academic and industrial laboratories to tap into a new era of data-centric strain engineering.

Systematic discovery of recombinases for efficient integration of large DNA sequences into the human genome

Large serine recombinases (LSRs) are DNA integrases that facilitate the site-specific integration of mobile genetic elements into bacterial genomes. Only a few LSRs, such as Bxb1 and PhiC31, have been characterized to date, with limited efficiency as tools for DNA integration in human cells. In this study, we developed a computational approach to identify thousands of LSRs and their DNA attachment sites, expanding known LSR diversity by >100-fold and enabling the prediction of their insertion site specificities. We tested their recombination activity in human cells, classifying them as landing pad, genome-targeting or multi-targeting LSRs. Overall, we achieved up to seven-fold higher recombination than Bxb1 and genome integration efficiencies of 40-75% with cargo sizes over 7 kb. We also demonstrate virus-free, direct integration of plasmid or amplicon libraries for improved functional genomics applications. This systematic discovery of recombinases directly from microbial sequencing data provides a resource of over 60 LSRs experimentally characterized in human cells for large-payload genome insertion without exposed DNA double-stranded breaks.

Optimization of 5'UTR to evade SARS-CoV-2 Nonstructural protein 1-directed inhibition of protein synthesis in cells

Maximizing the expression level of therapeutic proteins in cells is the general goal for DNA/mRNA therapies. It is particularly challenging to achieve efficient protein expression in the cellular contexts with inhibited translation machineries, such as in the presence of cellular Nonstructural protein 1 (Nsp1) of coronaviruses (CoVs) that has been reported to inhibit overall protein synthesis of host genes and exogenously delivered mRNAs/DNAs. In this study, we thoroughly examined the sequence and structure contexts of viral and non-viral 5'UTRs that determine the protein expression levels of exogenously delivered DNAs and mRNAs in cells expressing SARS-CoV-2 Nsp1. It was found that high 5'-proximal A/U content promotes an escape from Nsp1-directed inhibition of protein synthesis and results in selective protein expression. Furthermore, 5'-proximal Cs were found to significantly enhance the protein expression in an Nsp1-dependent manner, while Gs located at a specific window close to the 5'-end counteract such enhancement. The distinct protein expression levels resulted from different 5'UTRs were found correlated to Nsp1-induced mRNA degradations. These findings ultimately enabled rational designs for optimized 5'UTRs that lead to strong expression of exogenous proteins regardless of the translationally repressive Nsp1. On the other hand, we have also identified several 5'-proximal sequences derived from host genes that are capable of mediating the escapes. These results provided novel perspectives to the optimizations of 5'UTRs for DNA/mRNA therapies and/or vaccinations, as well as shedding light on the potential host escapees from Nsp1-directed translational shutoffs. KEY POINTS: • The 5'-proximal SL1 and 5a/b derived from SARS-CoV-2 genomic RNA promote exogenous protein synthesis in cells expressing Nsp1 comparing with non-specific 5'UTRs. • Specific 5'-proximal sequence contexts are the key determinants of the escapes from Nsp1-directed translational repression and thereby enhance protein expressions. • Systematic mutagenesis identified optimized 5'UTRs that strongly enhance protein expression and promote resistance to Nsp1-induced translational repression and RNA degradation.

mRNA melanoma vaccine revolution spurred by the COVID-19 pandemic

The advent of mRNA vaccines represents a significant advance in the field of vaccinology. While several vaccine approaches (mRNA, DNA, recombinant protein, and viral-vectored vaccines) had been investigated at the start of the COVID-19 pandemic, mRNA vaccines quickly gained popularity due to superior immunogenicity at a low dose, strong safety/tolerability profiles, and the possibility of rapid vaccine mass manufacturing and deployment to rural regions. In addition to inducing protective neutralizing antibody responses, mRNA vaccines can also elicit high-magnitude cytotoxic T-cell responses comparable to natural viral infections; thereby, drawing significant interest from cancer immunotherapy experts. This mini-review will highlight key developmental milestones and lessons we have learned from mRNA vaccines during the COVID-19 pandemic, with a specific emphasis on clinical trial data gathered so far for mRNA vaccines against melanoma and other forms of cancer.

Engineered Living Materials For Sustainability

Recent advances in synthetic biology and materials science have given rise to a new form of materials, namely engineered living materials (ELMs), which are composed of living matter or cell communities embedded in self-regenerating matrices of their own or artificial scaffolds. Like natural materials such as bone, wood, and skin, ELMs, which possess the functional capabilities of living organisms, can grow, self-organize, and self-repair when needed. They also spontaneously perform programmed biological functions upon sensing external cues. Currently, ELMs show promise for green energy production, bioremediation, disease treatment, and fabricating advanced smart materials. This review first introduces the dynamic features of natural living systems and their potential for developing novel materials. We then summarize the recent research progress on living materials and emerging design strategies from both synthetic biology and materials science perspectives. Finally, we discuss the positive impacts of living materials on promoting sustainability and key future research directions.

mRNA-Based Therapeutics in Cancer Treatment

Over the past two decades, significant technological innovations have led to messenger RNA (mRNA) becoming a promising option for developing prophylactic and therapeutic vaccines, protein replacement therapies, and genome engineering. The success of the two COVID-19 mRNA vaccines has sparked new enthusiasm for other medical applications, particularly in cancer treatment. In vitro-transcribed (IVT) mRNAs are structurally designed to resemble naturally occurring mature mRNA. Delivery of IVT mRNA via delivery platforms such as lipid nanoparticles allows host cells to produce many copies of encoded proteins, which can serve as antigens to stimulate immune responses or as additional beneficial proteins for supplements. mRNA-based cancer therapeutics include mRNA cancer vaccines, mRNA encoding cytokines, chimeric antigen receptors, tumor suppressors, and other combination therapies. To better understand the current development and research status of mRNA therapies for cancer treatment, this review focused on the molecular design, delivery systems, and clinical indications of mRNA therapies in cancer.

Translation of in vitro-transcribed RNA therapeutics

In vitro transcribed, modified messenger RNAs (IVTmRNAs) have been used to vaccinate billions of individuals against the SARS-CoV-2 virus, and are currently being developed for many additional therapeutic applications. IVTmRNAs must be translated into proteins with therapeutic activity by the same cellular machinery that also translates native endogenous transcripts. However, different genesis pathways and routes of entry into target cells as well as the presence of modified nucleotides mean that the way in which IVTmRNAs engage with the translational machinery, and the efficiency with which they are being translated, differs from native mRNAs. This review summarises our current knowledge of commonalities and differences in translation between IVTmRNAs and cellular mRNAs, which is key for the development of future design strategies that can generate IVTmRNAs with improved activity in therapeutic applications.

Race with virus evolution: The development and application of mRNA vaccines against SARS-CoV-2

Since the COVID-19 pandemic was declared, vaccines against SARS-CoV-2 have been urgently developed around the world. On the basis of the mRNA vaccine technology developed previously, COVID-19 mRNA vaccines were promptly tested in animals, advanced to clinical trials, and then authorized for emergency use in humans. The administration of COVID-19 mRNA vaccines has successfully reduced the hospitalization and mortality caused by the viral infection, although the virus continuously evolves with its transmission. Therefore, the development of mRNA vaccine technology, including RNA modification and delivery systems, is well recognized for its contribution to moderating the harms caused by the COVID-19 pandemic. The scientists who developed these technologies, Katalin Karikó, Drew Weissman, and Pieter Cullis, were awarded the 2022 Tang Prize in Biopharmaceutical Science. In this review, we summarize the principles, safety and efficacy of as well as the immune response to COVID-19 mRNA vaccines. Since mRNA vaccine approaches could be practical for the prevention of infectious diseases, we also briefly describe mRNA vaccines against other human viral pathogens in clinical trials.

Establishment, optimization, and application of genetic technology in Aspergillus spp

Aspergillus is widely distributed in nature and occupies a crucial ecological niche, which has complex and diverse metabolic pathways and can produce a variety of metabolites. With the deepening of genomics exploration, more Aspergillus genomic informations have been elucidated, which not only help us understand the basic mechanism of various life activities, but also further realize the ideal functional transformation. Available genetic engineering tools include homologous recombinant systems, specific nuclease based systems, and RNA techniques, combined with transformation methods, and screening based on selective labeling. Precise editing of target genes can not only prevent and control the production of mycotoxin pollutants, but also realize the construction of economical and efficient fungal cell factories. This paper reviewed the establishment and optimization process of genome technologies, hoping to provide the theoretical basis of experiments, and summarized the recent progress and application in genetic technology, analyzes the challenges and the possibility of future development with regard to Aspergillus.

Strategies for Bottlenecks of rAAV-Mediated Expression in Skeletal and Cardiac Muscle of Duchenne Muscular Dystrophy

Gene therapy using the adeno-associated virus (rAAV) to deliver mini/micro- dystrophin is the current promising strategy for Duchenne Muscular Dystrophy (DMD). However, the further transformation of this strategy still faces many “bottlenecks”. Most gene therapies are only suitable for infants with strong muscle cell regeneration and immature immune system, and the treatment depends heavily on the high dose of rAAV. However, high-dose rAAV inevitably causes side effects such as immune response and acute liver toxicity. Therefore, how to reduce the degree of fibrosis and excessive immune response in older patients and uncouple the dependence association between therapeutic effect and high dose rAAV are crucial steps for the transformation of rAAV-based gene therapy. The article analyzes the latest research and finds that the application of utrophin, the homologous protein of dystrophin, could avoid the immune response associated with dystrophin, and the exploration of methods to improve the expression level of mini/micro-utrophin in striated muscle, combined with the novel MyoAAV with high efficiency and specific infection of striated muscle, is expected to achieve the same therapeutic efficacy under the condition of reducing the dose of rAAV. Furthermore, the delivery of allogeneic cardio sphere-derived cells (CDCs) with anti-inflammatory and anti-fibrotic characteristics combined with immune suppression can provide a continuous and appropriate “window period” for gene therapy. This strategy can expand the number of patients who could benefit from gene therapy.

Self-amplifying mRNA vaccines: Mode of action, design, development and optimization

The mRNA-based vaccines are quality-by-design (QbD) immunotherapies that provide safe, tunable, scalable, streamlined and potent treatment possibilities against different types of diseases. The self-amplifying mRNA (saRNA) vaccines, as a highly advantageous class of mRNA vaccines, are inspired by the intracellular self-multiplication nature of some positive-sense RNA viruses. Such vaccine platforms provide a relatively increased expression level of vaccine antigen(s) together with self-adjuvanticity properties. Lined with the QbD saRNA vaccines, essential optimizations improve the stability, safety, and immunogenicity of the vaccine constructs. Here, we elaborate on the concepts and mode-of-action of mRNA and saRNA vaccines, articulate the potential limitations or technical bottlenecks, and explain possible solutions or optimization methods in the process of their design and development.

The landscape of mRNA nanomedicine

Messenger RNA (mRNA) is an emerging class of therapeutic agent for the prevention and treatment of a wide range of diseases. The recent success of the two highly efficacious mRNA vaccines produced by Moderna and Pfizer-BioNTech to protect against COVID-19 highlights the huge potential of mRNA technology for revolutionizing life science and medical research. Challenges related to mRNA stability and immunogenicity, as well as in vivo delivery and the ability to cross multiple biological barriers, have been largely addressed by recent progress in mRNA engineering and delivery. In this Review, we present the latest advances and innovations in the growing field of mRNA nanomedicine, in the context of ongoing clinical translation and future directions to improve clinical efficacy.

A synthetic transcription platform for programmable gene expression in mammalian cells

Precise, scalable, and sustainable control of genetic and cellular activities in mammalian cells is key to developing precision therapeutics and smart biomanufacturing. Here we create a highly tunable, modular, versatile CRISPR-based synthetic transcription system for the programmable control of gene expression and cellular phenotypes in mammalian cells. Genetic circuits consisting of well-characterized libraries of guide RNAs, binding motifs of synthetic operators, transcriptional activators, and additional genetic regulatory elements express mammalian genes in a highly predictable and tunable manner. We demonstrate the programmable control of reporter genes episomally and chromosomally, with up to 25-fold more activity than seen with the EF1α promoter, in multiple cell types. We use these circuits to program the secretion of human monoclonal antibodies and to control T-cell effector function marked by interferon-γ production. Antibody titers and interferon-γ concentrations significantly correlate with synthetic promoter strengths, providing a platform for programming gene expression and cellular function in diverse applications.

High-throughput identification of RNA localization elements in neuronal cells

Hundreds of RNAs are enriched in the projections of neuronal cells. For the vast majority of them, though, the sequence elements that regulate their localization are unknown. To identify RNA elements capable of directing transcripts to neurites, we deployed a massively parallel reporter assay that tested the localization regulatory ability of thousands of sequence fragments drawn from endogenous mouse 3' UTRs. We identified peaks of regulatory activity within several 3' UTRs and found that sequences derived from these peaks were both necessary and sufficient for RNA localization to neurites in mouse and human neuronal cells. The localization elements were enriched in adenosine and guanosine residues. They were at least tens to hundreds of nucleotides long as shortening of two identified elements led to significantly reduced activity. Using RNA affinity purification and mass spectrometry, we found that the RNA-binding protein Unk was associated with the localization elements. Depletion of Unk in cells reduced the ability of the elements to drive RNAs to neurites, indicating a functional requirement for Unk in their trafficking. These results provide a framework for the unbiased, high-throughput identification of RNA elements and mechanisms that govern transcript localization in neurons.

An OX40L mRNA vaccine inhibits the growth of hepatocellular carcinoma

mRNA cancer vaccines show therapeutic potential for malignant tumors, including hepatocellular carcinoma (HCC). We optimized and synthesized stable mRNA encoding costimulator Oxford 40 ligand (OX40L). For systemic delivery, OX40L mRNAs were loaded into lipid nanoparticles (LNPs). The expression and costimulatory effects of OX40L were investigated in vitro. OX40L was expressed on the cell surface and costimulated T cells. In vivo, intratumoral injection of LNPs encapsulating OX40L mRNAs significantly reduced tumor growth and increased the survival of mice bearing H22 tumors. Importantly, CD4+ and CD8+ T cells were significantly increased in the OX40L mRNA group in vivo. Taken together, our findings provide a promising clinical strategy for immunotherapy for HCC using mRNA vaccines.

Selective Xi reactivation and alternative methods to restore MECP2 function in Rett syndrome

The human X-chromosome harbors only 4% of our genome but carries over 20% of genes associated with intellectual disability. Given that they inherit only one X-chromosome, males are more frequently affected by X-linked neurodevelopmental genetic disorders than females. However, despite inheriting two X-chromosomes, females can also be affected because X-chromosome inactivation enables only one of two X-chromosomes to be expressed per cell. For Rett syndrome and similar X-linked disorders affecting females, disease-specific treatments have remained elusive. However, a cure may be found within their own cells because every sick cell carries a healthy copy of the affected gene on the inactive X (Xi). Therefore, selective Xi reactivation may be a viable approach that would address the root cause of various X-linked disorders. Here, we discuss Rett syndrome and compare current approaches in the pharmaceutical pipeline to restore MECP2 function. We then focus on Xi reactivation and review available methods, lessons learned, and future directions.

Increased Production of Riboflavin by Coordinated Expression of Multiple Genes in Operons in Bacillus subtilis

Riboflavin is an essential vitamin widely used in the food, pharmaceutical, and feed industries. However, the insufficient supply of precursors caused by the imbalance of intracellular metabolic flow limits the riboflavin synthesis by industrial strains. Here, we increase riboflavin production by tuning multiple gene expression to balance intracellular metabolic flow. First, we tuned the expression of mCherry and egfp genes within operons by generating libraries of tunable intergenic regions (TIGRs) and confirmed the relative expression of the two reporter genes. The TIGR library can coordinate the expression ratio of reporter genes more than 180 times in Escherichia coli and more than 70 times in Bacillus subtilis. Next, we used this strategy to tune the expression of zwf, ribBA, and ywlf genes within operons through the TIGR library to increase the intracellular precursor pool for riboflavin biosynthesis. Based on the fluorescence characteristics of riboflavin, 96-well plates were used to screen the optimal combination mutants quickly. The best-engineered strain was selected from the library, which produced 2.7 g/L riboflavin, increasing by 64.35% in the shake flask. Finally, the riboflavin titer increased by 59.27% to 11.77 g/L in fed-batch fermentation. The strategy described here will contribute to the industrial production of riboflavin and related products by B. subtilis.

Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics

Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers. We develop an RNA sequencing-based platform called PERSIST-seq to systematically delineate in-cell mRNA stability, ribosome load, as well as in-solution stability of a library of diverse mRNAs. We find that, surprisingly, in-cell stability is a greater driver of protein output than high ribosome load. We further introduce a method called In-line-seq, applied to thousands of diverse RNAs, that reveals sequence and structure-based rules for mitigating hydrolytic degradation. Our findings show that highly structured “superfolder” mRNAs can be designed to improve both stability and expression with further enhancement through pseudouridine nucleoside modification. Together, our study demonstrates simultaneous improvement of mRNA stability and protein expression and provides a computational-experimental platform for the enhancement of mRNA medicines.

The authors develop an RNA sequencing-based platform, PERSIST-seq, to simultaneously delineate in-cell mRNA stability, ribosome load, and in-solution stability of a diverse mRNA library to derive design principles for improved mRNA therapeutics.

Episome-Based Gene Expression Modulation Platform in the Model Diatom Phaeodactylum tricornutum

Chemically inducible gene expression systems have been an integral part of the advanced synthetic genetic circuit design and are employed for precise dynamic control over genetically engineered traits. However, the current systems for controlling transgene expression in most algae are limited to endogenous promoters that respond to different environmental factors. We developed a highly efficient, tunable, and reversible episome-based transcriptional control system in the model diatom alga, Phaeodactylum tricornutum. We assessed the time- and dose-response dynamics of each expression system using a reporter protein (eYFP) as a readout. Using our circuit configuration, we found two inducible expression systems with a high dynamic range and confirmed the suitability of an episome expression platform for synthetic biological applications in diatoms. These systems are controlled by the presence of β-estradiol and digoxin. Addition of either chemical to transgenic strains activates transcription with a dynamic range of up to ∼180-fold and ∼90-fold, respectively. We demonstrated that our episome-based transcriptional control systems are tunable and reversible in a dose- and time-dependent manner. Using droplet digital polymerase chain reaction (PCR), we also confirmed that inducer-dependent transcriptional activation starts within minutes of inducer application without any detectable transcript in the uninduced controls. The system described here expands the molecular and synthetic biology toolkits in algae and will facilitate future gene discovery and metabolic engineering efforts.

Machine Learning for Designing Next-Generation mRNA Therapeutics

Over just the last 2 years, mRNA therapeutics and vaccines have undergone a rapid transition from an intriguing concept to real-world impact. However, whereas some aspects of mRNA therapeutics, such as the use of chemical modifications to increase stability and reduce immunogenicity, have been extensively optimized for over two decades, other aspects, particularly the selection and design of the noncoding leader and trailer sequences which control translation efficiency and stability, have received comparably less attention. In practice, such 5' and 3' untranslated regions (UTRs) are often borrowed from highly expressed human genes with few or no modifications, as in the case for the Pfizer/BioNTech Covid vaccine. Focusing on the 5'UTR, we here argue that model-driven design is a promising alternative that provides unprecedented control over 5'UTR function. We review recent work that combines synthetic biology with machine learning to build quantitative models that relate ribosome loading, and thus translation efficiency, to the 5'UTR sequence. We first introduce an experimental approach that uses polysome profiling and high-throughput sequencing to quantify ribosome loading for hundreds of thousands of 5'UTRs in parallel. We apply this approach to measure ribosome loading in synthetic RNA libraries with a random sequence inserted into the 5'UTR. We then review Optimus 5-Prime, a convolutional neural network model trained on the experimental data. We highlight that very accurate models of biological regulation can be learned from synthetic data sets with degenerate 5'UTRs. We validate model predictions not only on held-out data sets from our random library but also on a large library of over 30 000 human 5'UTR fragments and using translation reporter data collected independently by other groups. Both the experiment and model are compatible with commonly used chemically modified nucleosides, in particular, pseudouridine (Ψ) and 1-methyl-pseudouridine (m¹Ψ). We find that, in general, 5'UTRs have very similar impacts when combined with different protein-coding sequences and even in the context of different chemical modifications. We demonstrate that Optimus 5-Prime can be combined with design algorithms to generate de novo sequences with precisely defined translation efficiencies. We emphasize recent developments in design algorithms that rely on activation maximization and generative modeling to improve both the fitness and diversity of designed sequences. Compared with prior approaches such as genetic algorithms, we show that these approaches are not only faster but also less likely to get stuck in local sequence optima. Finally, we discuss how the approach reviewed here can be generalized to other gene regions and applications.