The Ongoing Quest to Crack the Genetic Code for Protein Production

From Sequence to System: Enhancing IVT mRNA Vaccine Effectiveness through Cutting-Edge Technologies

The COVID-19 pandemic has spotlighted the potential of in vitro transcribed (IVT) mRNA vaccines with their demonstrated efficacy, safety, cost-effectiveness, and rapid manufacturing. Numerous IVT mRNA vaccines are now under clinical trials for a range of targets, including infectious diseases, cancers, and genetic disorders. Despite their promise, IVT mRNA vaccines face hurdles such as limited expression levels, nonspecific targeting beyond the liver, rapid degradation, and unintended immune activation. Overcoming these challenges is crucial to harnessing the full therapeutic potential of IVT mRNA vaccines for global health advancement. This review provides a comprehensive overview of the latest research progress and optimization strategies for IVT mRNA molecules and delivery systems, including the application of artificial intelligence (AI) models and deep learning techniques for IVT mRNA structure optimization and mRNA delivery formulation design. We also discuss recent development of the delivery platforms, such as lipid nanoparticles (LNPs), polymers, and exosomes, which aim to address challenges related to IVT mRNA protection, cellular uptake, and targeted delivery. Lastly, we offer insights into future directions for improving IVT mRNA vaccines, with the hope to spur further progress in IVT mRNA vaccine research and development.

Antisense oligonucleotides as a targeted therapeutic approach in model of acute myeloid leukemia

BACKGROUND

The genetic and epigenetic alterations observed in acute myeloid leukemia (AML) contribute to its heterogeneity, influencing disease progression response to therapy, and patient outcomes. The use of antisense oligonucleotides (ASOs) technology allows for the design of oligonucleotide inhibitors based on gene sequence information alone, enabling precise targeting of key molecular pathways or specific genes implicated in AML.

METHODS AND RESULTS

Midostaurin, a FLT3 specific inhibitor and ASOs targeting particular genes, exons, or mutations was conducted using AML models. This ASOs treatment was designed to bind to exon 7 of the MBNL1 (muscleblind-like) gene. Another target was the FLT3 gene, focusing on two aspects: (a) FLT3-ITD (internal tandem duplication), to inhibit the expression of this aberrant gene form, and (b) the FLT3 in general. Treated and untreated cells were analyzed using quantitative PCR (qPCR), dot blot, and Raman spectroscopy. This study contrasts midostaurin with ASOs that inhibit FLT3 protein production or its isoforms via mRNA degradation. A trend of increased FLT3 expression was observed in midostaurin-treated cells, while ASO-treated cells showed decreased expression, though these changes were not statistically significant.

CONCLUSIONS

In AML, exon 7 of MBNL1 is involved in several cellular processes and in this study, exon 7 of MBNL1 was targeted for method optimization, with the highest block of the exon 7 gene variant observed 48 h post-transfection. Midostaurin, a multitargeted kinase inhibitor, acts against the receptor tyrosine kinase FLT3, a critical molecule in AML pathogenesis. While midostaurin blocks FLT3 signaling pathways, it paradoxically increases FLT3 expression.

Darwinian Evolution of Self-Replicating DNA in a Synthetic Protocell

Replication, heredity, and evolution are characteristic of Life. We and others have postulated that the reconstruction of a synthetic living system in the laboratory will be contingent on the development of a genetic self-replicator capable of undergoing Darwinian evolution. Although DNA-based life dominates, the in vitro reconstitution of an evolving DNA self-replicator has remained challenging. We hereby emulate in liposome compartments the principles according to which life propagates information and evolves. Using two different experimental configurations supporting intermittent or semi-continuous evolution (i.e., with or without DNA extraction, PCR, and re-encapsulation), we demonstrate sustainable replication of a linear DNA template - encoding the DNA polymerase and terminal protein from the Phi29 bacteriophage - expressed in the 'protein synthesis using recombinant elements' (PURE) system. The self-replicator can survive across multiple rounds of replication-coupled transcription-translation reactions in liposomes and, within only ten evolution rounds, accumulates mutations conferring a selection advantage. Combined data from next-generation sequencing with reverse engineering of some of the enriched mutations reveal nontrivial and context-dependent effects of the introduced mutations. The present results are foundational to build up genetic complexity in an evolving synthetic cell, as well as to study evolutionary processes in a minimal cell-free system.

Genome-wide impact of codon usage bias on translation optimization in Drosophila melanogaster

Accuracy and efficiency are fundamental to mRNA translation. Codon usage bias is widespread across species. Despite the long-standing association between optimized codon usage and improved translation, our understanding of its evolutionary basis and functional effects remains limited. Drosophila is widely used to study codon usage bias, but genome-scale experimental data are scarce. Using high-resolution mass spectrometry data from Drosophila melanogaster, we show that optimal codons have lower translation errors than nonoptimal codons after accounting for these biases. Genomic-scale analysis of ribosome profiling data shows that optimal codons are translated more rapidly than nonoptimal codons. Although we find no long-term selection favoring synonymous mutations in D. melanogaster after diverging from D. simulans, we identify signatures of positive selection driving codon optimization in the D. melanogaster population. These findings expand our understanding of the functional consequences of codon optimization and serve as a foundation for future investigations.

Using yeasts for the studies of nonfunctional factors in protein evolution

The evolution of protein sequence is driven not only by factors directly related to protein function and shape but also by nonfunctional factors. Such factors in protein evolution might be categorized as those connected to energetic costs, synthesis efficiency, and avoidance of misfolding and toxicity. A common approach to studying them is correlational analysis contrasting them with some characteristics of the protein, like amino acid composition, but these features are interdependent. To avoid possible bias, empirical studies are needed, and not enough work has been done to date. In this review, we describe the role of nonfunctional factors in protein evolution and present an experimental approach using yeast as a suitable model organism. The focus of the proposed approach is on the potential negative impact on the fitness of mutations that change protein properties not related to function and the frequency of mutations that change these properties. Experimental results of testing the misfolding avoidance hypothesis as an explanation for why highly expressed proteins evolve slowly are inconsistent with correlational research results. Therefore, more efforts should be made to empirically test the effects of nonfunctional factors in protein evolution and to contrast these results with the results of the correlational analysis approach.

Re-defining how mRNA degradation is coordinated with transcription and translation in bacteria

In eukaryotic cells, transcription, translation, and mRNA degradation occur in distinct subcellular regions. How these mRNA processes are organized in bacteria, without employing membrane-bound compartments, remains unclear. Here, we present generalizable principles underlying coordination between these processes in bacteria. In Escherichia coli, we found that co-transcriptional degradation is rare for mRNAs except for those encoding inner membrane proteins, due to membrane localization of the main ribonuclease, RNase E. We further found, by varying ribosome binding sequences, that translation affects mRNA stability not because ribosomes protect mRNA from degradation, but because low translation leads to premature transcription termination in the absence of transcription-translation coupling. Extending our analyses to Bacillus subtilis and Caulobacter crescentus, we established subcellular localization of RNase E (or its homolog) and premature transcription termination in the absence of transcription-translation coupling as key determinants that explain differences in transcriptional and translational coupling to mRNA degradation across genes and species.

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.

Translational efficiency in gas-fermenting bacteria: Adding a new layer of regulation to gene expression in acetogens

Major advances in mastering metabolism of single carbon (C1) gaseous feedstocks in acetogenic microorganisms are primed to fuel the transition toward environmentally sustainable and cost-efficient production schemes of biofuels and value-added biochemicals. Since acetogens grow under autotrophic energy-limited conditions, protein synthesis is expected to be controlled. This survey integrated publicly available RNA sequencing and ribosome profiling studies of several acetogens, providing data on genome-scale transcriptional and translational responses of A. woodii, E. limosum, C. drakei, and C. ljungdahlii to autotrophic and heterotrophic growth conditions. The extent of translational efficiency turned out to vary across key functional modules in acetogens' metabolism. Translational control was confirmed to support stoichiometric protein production in multimeric complexes. Comparing the autotrophic to the heterotrophic growth condition revealed growth-dependent regulation of translational efficiency, pointing at translational buffering as a widespread phenomenon shared by acetogens.

The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian

Gene expression inherently gives rise to stochastic variation ("noise") in the production of gene products. Minimizing noise is crucial for ensuring reliable cellular functions. However, noise cannot be suppressed below a certain intrinsic limit. For constitutively expressed genes, this limit is typically assumed to be Poissonian noise, wherein the variance in mRNA numbers is equal to their mean. Here, we demonstrate that several cell division genes in fission yeast exhibit mRNA variances significantly below this limit. The reduced variance can be explained by a gene expression model incorporating multiple transcription and mRNA degradation steps. Notably, in this sub-Poissonian regime, distinct from Poissonian or super-Poissonian regimes, cytoplasmic noise is effectively suppressed through a higher mRNA export rate. Our findings redefine the lower limit of eukaryotic gene expression noise and uncover molecular requirements for achieving ultralow noise, which is expected to be important for vital cellular functions.

Natural variation in codon bias and mRNA folding strength interact synergistically to modify protein expression in Saccharomyces cerevisiae

Codon bias and mRNA folding strength (mF) are hypothesized molecular mechanisms by which polymorphisms in genes modify protein expression. Natural patterns of codon bias and mF across genes as well as effects of altering codon bias and mF suggest that the influence of these 2 mechanisms may vary depending on the specific location of polymorphisms within a transcript. Despite the central role codon bias and mF may play in natural trait variation within populations, systematic studies of how polymorphic codon bias and mF relate to protein expression variation are lacking. To address this need, we analyzed genomic, transcriptomic, and proteomic data for 22 Saccharomyces cerevisiae isolates, estimated protein accumulation for each allele of 1,620 genes as the log of protein molecules per RNA molecule (logPPR), and built linear mixed-effects models associating allelic variation in codon bias and mF with allelic variation in logPPR. We found that codon bias and mF interact synergistically in a positive association with logPPR, and this interaction explains almost all the effects of codon bias and mF. We examined how the locations of polymorphisms within transcripts influence their effects and found that codon bias primarily acts through polymorphisms in domain-encoding and 3' coding sequences, while mF acts most significantly through coding sequences with weaker effects from untranslated regions. Our results present the most comprehensive characterization to date of how polymorphisms in transcripts influence protein expression.

Optimization and Deoptimization of Codons in SARS-CoV-2 and Related Implications for Vaccine Development

The spread of coronavirus disease 2019 (COVID-19), caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2), has progressed into a global pandemic. To date, thousands of genetic variants have been identified among SARS-CoV-2 isolates collected from patients. Sequence analysis reveals that the codon adaptation index (CAI) values of viral sequences have decreased over time but with occasional fluctuations. Through evolution modeling, it is found that this phenomenon may result from the virus's mutation preference during transmission. Using dual-luciferase assays, it is further discovered that the deoptimization of codons in the viral sequence may weaken protein expression during virus evolution, indicating that codon usage may play an important role in virus fitness. Finally, given the importance of codon usage in protein expression and particularly for mRNA vaccines, it is designed several codon-optimized Omicron BA.2.12.1, BA.4/5, and XBB.1.5 spike mRNA vaccine candidates and experimentally validated their high levels of expression. This study highlights the importance of codon usage in virus evolution and provides guidelines for codon optimization in mRNA and DNA vaccine development.

Enhanced expression of xylanase in Aspergillus niger enabling a two-step enzymatic pathway for extracting β-glucan from oat bran

The high cost and process complexity limit the enzymatic extraction of β-glucan. In this study, β-glucan was extracted from oat bran in a two-step enzymatic pathway using a recombinant strain of Aspergillus niger AG11 overexpressing the endogenous xylanase (xynA) and amylolytic enzyme. First, co-optimization of promoter and signal peptide and a fusion of glucoamylase (glaA) fragment were integrated into the β-glucosidase (bgl) locus to improve xynA expression. Then, the optimized expression cassette was simultaneously integrated into bgl, α-amylase amyA, and acid α-amylase ammA loci, yielding the Rbya with 3,650-fold and 31.2% increase in xynA and amylolytic enzyme activity than the wild-type strain, respectively. Finally, Rbya's supernatants at 72 h (rich in xynA and amylolytic enzyme) and 10 d (rich in proteases) were used to decompose xylan/starch and proteins in oat bran, respectively, to obtain 85.1% pure β-glucan. Rbya could be a robust candidate for the cost-effective extraction of β-glucan.

Ultradeep characterisation of translational sequence determinants refutes rare-codon hypothesis and unveils quadruplet base pairing of initiator tRNA and transcript

Translation is a key determinant of gene expression and an important biotechnological engineering target. In bacteria, 5'-untranslated region (5'-UTR) and coding sequence (CDS) are well-known mRNA parts controlling translation and thus cellular protein levels. However, the complex interaction of 5'-UTR and CDS has so far only been studied for few sequences leading to non-generalisable and partly contradictory conclusions. Herein, we systematically assess the dynamic translation from over 1.2 million 5'-UTR-CDS pairs in Escherichia coli to investigate their collective effect using a new method for ultradeep sequence-function mapping. This allows us to disentangle and precisely quantify effects of various sequence determinants of translation. We find that 5'-UTR and CDS individually account for 53% and 20% of variance in translation, respectively, and show conclusively that, contrary to a common hypothesis, tRNA abundance does not explain expression changes between CDSs with different synonymous codons. Moreover, the obtained large-scale data provide clear experimental evidence for a base-pairing interaction between initiator tRNA and mRNA beyond the anticodon-codon interaction, an effect that is often masked for individual sequences and therefore inaccessible to low-throughput approaches. Our study highlights the indispensability of ultradeep sequence-function mapping to accurately determine the contribution of parts and phenomena involved in gene regulation.

The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian

Stochastic variation in gene products ("noise") is an inescapable by-product of gene expression. Noise must be minimized to allow for the reliable execution of cellular functions. However, noise cannot be suppressed beyond an intrinsic lower limit. For constitutively expressed genes, this limit is believed to be Poissonian, meaning that the variance in mRNA numbers cannot be lower than their mean. Here, we show that several cell division genes in fission yeast have mRNA variances significantly below this limit, which cannot be explained by the classical gene expression model for low-noise genes. Our analysis reveals that multiple steps in both transcription and mRNA degradation are essential to explain this sub-Poissonian variance. The sub-Poissonian regime differs qualitatively from previously characterized noise regimes, a hallmark being that cytoplasmic noise is reduced when the mRNA export rate increases. Our study re-defines the lower limit of eukaryotic gene expression noise and identifies molecular requirements for ultra-low noise which are expected to support essential cell functions.

CoDe: a web-based tool for codon deoptimization

Summary

We have developed a web-based tool, CoDe (Codon Deoptimization) that deoptimizes genetic sequences based on different codon usage bias, ultimately reducing expression of the corresponding protein. The tool could also deoptimize the sequence for a specific region and/or selected amino acid(s). Moreover, CoDe can highlight sites targeted by restriction enzymes in the wild-type and codon-deoptimized sequences. Importantly, our web-based tool has a user-friendly interface with flexible options to download results.

Availability and implementation

The web-based tool CoDe is freely available at https://web.iitm.ac.in/bioinfo2/codeop/landing_page.html.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Engineering microbial cell viability for enhancing chemical production by second codon engineering

Microbial cell factories offer a promising strategy for the sustainable production of industrial chemicals from renewable biomass feedstock. However, their performance is often limited by poor microbial cell viability (MCV). Here, MCV was engineered to enhance chemical production by optimizing the regulation of lifespan-specific genes to reduce the accumulation of reactive oxygen species (ROS). In Escherichia coli, MCV was improved by reducing ROS accumulation using second codon engineering to regulate hypoxia-inducible transcription factor (arcA), resulting in lysine production up to 213 g L^-1 with its productivity 5.90 g L^-1·h^-1. In Saccharomyces cerevisiae, MCV was increased by decreasing ROS accumulation using second codon engineering to fine-tune ceramide synthase (lag1), leading to glucaric acid production up to 9.50 g L^-1 with its productivity 0.057 g L^-1·h^-1. These results demonstrate that engineering MCV is a potential strategy to boost the performance of microbial cell factories in industrial processes.

iCodon customizes gene expression based on the codon composition

Messenger RNA (mRNA) stability substantially impacts steady-state gene expression levels in a cell. mRNA stability is strongly affected by codon composition in a translation-dependent manner across species, through a mechanism termed codon optimality. We have developed iCodon (www.iCodon.org), an algorithm for customizing mRNA expression through the introduction of synonymous codon substitutions into the coding sequence. iCodon is optimized for four vertebrate transcriptomes: mouse, human, frog, and fish. Users can predict the mRNA stability of any coding sequence based on its codon composition and subsequently generate more stable (optimized) or unstable (deoptimized) variants encoding for the same protein. Further, we show that codon optimality predictions correlate with both mRNA stability using a massive reporter library and expression levels using fluorescent reporters and analysis of endogenous gene expression in zebrafish embryos and/or human cells. Therefore, iCodon will benefit basic biological research, as well as a wide range of applications for biotechnology and biomedicine.

Vaccinia Virus Attenuation by Codon Deoptimization of the A24R Gene for Vaccine Development

Poxviruses have large DNA genomes, and they are able to infect multiple vertebrate and invertebrate animals, including humans. Despite the eradication of smallpox, poxvirus infections still remain a significant public health concern. Vaccinia virus (VV) is the prototypic member in the poxviridae family and it has been used extensively for different prophylactic applications, including the generation of vaccines against multiple infectious diseases and/or for oncolytic treatment. Many attempts have been pursued to develop novel attenuated forms of VV with improved safety profiles for their implementation as vaccines and/or vaccines vectors. We and others have previously demonstrated how RNA viruses encoding codon-deoptimized viral genes are attenuated, immunogenic and able to protect, upon a single administration, against challenge with parental viruses. In this study, we employed the same experimental approach based on the use of misrepresented codons for the generation of a recombinant (r)VV encoding a codon-deoptimized A24R gene, which is a key component of the viral RNA polymerase. Similar to our previous studies with RNA viruses, the A24R codon-deoptimized rVV (v-A24cd) was highly attenuated in vivo but able to protect, after a single intranasal dose administration, against an otherwise lethal challenge with parental VV. These results indicate that poxviruses can be effectively attenuated by synonymous codon deoptimization and open the possibility of using this methodology alone or in combination with other experimental approaches for the development of attenuated vaccines for the treatment of poxvirus infection, or to generate improved VV-based vectors. Moreover, this approach could be applied to other DNA viruses. IMPORTANCE The family poxviridae includes multiple viruses of medical and veterinary relevance, being vaccinia virus (VV) the prototypic member in the family. VV was used during the smallpox vaccination campaign to eradicate variola virus (VARV), which is considered a credible bioterrorism threat. Because of novel innovations in genetic engineering and vaccine technology, VV has gained popularity as a viral vector for the development of vaccines against several infectious diseases. Several approaches have been used to generate attenuated VV for its implementation as vaccine and/or vaccine vector. Here, we generated a rVV containing a codon-deoptimized A24R gene (v-A24cd), which encodes a key component of the viral RNA polymerase. v-A24cd was stable in culture cells and highly attenuated in vivo but able to protect against a subsequent lethal challenge with parental VV. Our findings support the use of this approach for the development of safe, stable, and protective live-attenuated VV and/or vaccine vectors.

Design of typical genes for heterologous gene expression

Heterologous protein expression is an important method for analysing cellular functions of proteins, in genetic circuit engineering and in overexpressing proteins for biopharmaceutical applications and structural biology research. The degeneracy of the genetic code, which enables a single protein to be encoded by a multitude of synonymous gene sequences, plays an important role in regulating protein expression, but substantial uncertainty exists concerning the details of this phenomenon. Here we analyse the influence of a profiled codon usage adaptation approach on protein expression levels in the eukaryotic model organism Saccharomyces cerevisiae. We selected green fluorescent protein (GFP) and human α-synuclein (αSyn) as representatives for stable and intrinsically disordered proteins and representing a benchmark and a challenging test case. A new approach was implemented to design typical genes resembling the codon usage of any subset of endogenous genes. Using this approach, synthetic genes for GFP and αSyn were generated, heterologously expressed and evaluated in yeast. We demonstrate that GFP is expressed at high levels, and that the toxic αSyn can be adapted to endogenous, low-level expression. The new software is publicly available as a web-application for performing host-specific protein adaptations to a set of the most commonly used model organisms ( https://odysseus.motorprotein.de ).

Intragenomic variation in non-adaptive nucleotide biases causes underestimation of selection on synonymous codon usage

Patterns of non-uniform usage of synonymous codons vary across genes in an organism and between species across all domains of life. This codon usage bias (CUB) is due to a combination of non-adaptive (e.g. mutation biases) and adaptive (e.g. natural selection for translation efficiency/accuracy) evolutionary forces. Most models quantify the effects of mutation bias and selection on CUB assuming uniform mutational and other non-adaptive forces across the genome. However, non-adaptive nucleotide biases can vary within a genome due to processes such as biased gene conversion (BGC), potentially obfuscating signals of selection on codon usage. Moreover, genome-wide estimates of non-adaptive nucleotide biases are lacking for non-model organisms. We combine an unsupervised learning method with a population genetics model of synonymous coding sequence evolution to assess the impact of intragenomic variation in non-adaptive nucleotide bias on quantification of natural selection on synonymous codon usage across 49 Saccharomycotina yeasts. We find that in the absence of a priori information, unsupervised learning can be used to identify genes evolving under different non-adaptive nucleotide biases. We find that the impact of intragenomic variation in non-adaptive nucleotide bias varies widely, even among closely-related species. We show that the overall strength and direction of translational selection can be underestimated by failing to account for intragenomic variation in non-adaptive nucleotide biases. Interestingly, genes falling into clusters identified by machine learning are also physically clustered across chromosomes. Our results indicate the need for more nuanced models of sequence evolution that systematically incorporate the effects of variable non-adaptive nucleotide biases on codon frequencies.

Analysis of eukaryotic lincRNA sequences indicates signatures of hindered translation linked to selection pressure

Long intergenic noncoding RNAs (lincRNAs) represent a large fraction of transcribed loci in eukaryotic genomes. Although classified as noncoding, most lincRNAs contain open reading frames (ORFs), and it remains unclear why cytoplasmic lincRNAs are not or very inefficiently translated. Here, we analyzed signatures of hindered translation in lincRNA sequences from five eukaryotes, covering a range of natural selection pressures. In fission yeast and Caenorhabditis elegans, that is, species under strong selection, we detected significantly shorter ORFs, a suboptimal sequence context around start codons for translation initiation, and trinucleotides (“codons”) corresponding to less abundant tRNAs than for neutrally evolving control sequences, likely impeding translation elongation. For human, we detected signatures for cell-type-specific hindrance of lincRNA translation, in particular codons in abundant cytoplasmic lincRNAs corresponding to lower expressed tRNAs than control codons, in three out of five human cell lines. We verified that varying tRNA expression levels between cell lines are reflected in the amount of ribosomes bound to cytoplasmic lincRNAs in each cell line. We further propose that codons at ORF starts are particularly important for reducing ribosome-binding to cytoplasmic lincRNA ORFs. Altogether, our analyses indicate that in species under stronger selection lincRNAs evolved sequence features generally hindering translation and support cell-type-specific hindrance of translation efficiency in human lincRNAs. The sequence signatures we have identified may improve predicting peptide-coding and genuine noncoding lincRNAs in a cell type.

Growth of Biological Complexity from Prokaryotes to Hominids Reflected in the Human Genome

The growth of complexity in evolution is a most intriguing phenomenon. Using gene phylostratigraphy, we showed this growth (as reflected in regulatory mechanisms) in the human genome, tracing the path from prokaryotes to hominids. Generally, the different regulatory gene families expanded at different times, yet only up to the Euteleostomi (bony vertebrates). The only exception was the expansion of transcription factors (TF) in placentals; however, we argue that this was not related to increase in general complexity. Surprisingly, although TF originated in the Prokaryota while chromatin appeared only in the Eukaryota, the expansion of epigenetic factors predated the expansion of TF. Signaling receptors, tumor suppressors, oncogenes, and aging- and disease-associated genes (indicating vulnerabilities in terms of complex organization and strongly enrichment in regulatory genes) also expanded only up to the Euteleostomi. The complexity-related gene properties (protein size, number of alternative splicing mRNA, length of untranslated mRNA, number of biological processes per gene, number of disordered regions in a protein, and density of TF–TF interactions) rose in multicellular organisms and declined after the Euteleostomi, and possibly earlier. At the same time, the speed of protein sequence evolution sharply increased in the genes that originated after the Euteleostomi. Thus, several lines of evidence indicate that molecular mechanisms of complexity growth were changing with time, and in the phyletic lineage leading to humans, the most salient shift occurred after the basic vertebrate body plan was fixed with bony skeleton. The obtained results can be useful for evolutionary medicine.

Codon usage and protein length-dependent feedback from translation elongation regulates translation initiation and elongation speed

Essential cellular functions require efficient production of many large proteins but synthesis of large proteins encounters many obstacles in cells. Translational control is mostly known to be regulated at the initiation step. Whether translation elongation process can feedback to regulate initiation efficiency is unclear. Codon usage bias, a universal feature of all genomes, plays an important role in determining gene expression levels. Here, we discovered that there is a conserved but codon usage-dependent genome-wide negative correlation between protein abundance and CDS length. The codon usage effects on protein expression and ribosome flux on mRNAs are influenced by CDS length; optimal codon usage preferentially promotes production of large proteins. Translation of mRNAs with long CDS and non-optimal codon usage preferentially induces phosphorylation of initiation factor eIF2α, which inhibits translation initiation efficiency. Deletion of the eIF2α kinase CPC-3 (GCN2 homolog) in Neurospora preferentially up-regulates large proteins encoded by non-optimal codons. Surprisingly, CPC-3 also inhibits translation elongation rate in a codon usage and CDS length-dependent manner, resulting in slow elongation rates for long CDS mRNAs. Together, these results revealed a codon usage and CDS length-dependent feedback mechanism from translation elongation to regulate both translation initiation and elongation kinetics.

TISIGNER.com: web services for improving recombinant protein production

Experiments that are planned using accurate prediction algorithms will mitigate failures in recombinant protein production. We have developed TISIGNER (https://tisigner.com) with the aim of addressing technical challenges to recombinant protein production. We offer three web services, TIsigner (Translation Initiation coding region designer), SoDoPE (Soluble Domain for Protein Expression) and Razor, which are specialised in synonymous optimisation of recombinant protein expression, solubility and signal peptide analysis, respectively. Importantly, TIsigner, SoDoPE and Razor are linked, which allows users to switch between the tools when optimising genes of interest.

Production of the biocommodities butanol and acetone from methanol with fluorescent FAST-tagged proteins using metabolically engineered strains of Eubacterium limosum

BACKGROUND

The interest in using methanol as a substrate to cultivate acetogens increased in recent years since it can be sustainably produced from syngas and has the additional benefit of reducing greenhouse gas emissions. Eubacterium limosum is one of the few acetogens that can utilize methanol, is genetically accessible and, therefore, a promising candidate for the recombinant production of biocommodities from this C1 carbon source. Although several genetic tools are already available for certain acetogens including E. limosum, the use of brightly fluorescent reporter proteins is still limited.

RESULTS

In this study, we expanded the genetic toolbox of E. limosum by implementing the fluorescence-activating and absorption shifting tag (FAST) as a fluorescent reporter protein. Recombinant E. limosum strains that expressed the gene encoding FAST in an inducible and constitutive manner were constructed. Cultivation of these recombinant strains resulted in brightly fluorescent cells even under anaerobic conditions. Moreover, we produced the biocommodities butanol and acetone from methanol with recombinant E. limosum strains. Therefore, we used E. limosum cultures that produced FAST-tagged fusion proteins of the bifunctional acetaldehyde/alcohol dehydrogenase or the acetoacetate decarboxylase, respectively, and determined the fluorescence intensity and product concentrations during growth.

CONCLUSIONS

The addition of FAST as an oxygen-independent fluorescent reporter protein expands the genetic toolbox of E. limosum. Moreover, our results show that FAST-tagged fusion proteins can be constructed without negatively impacting the stability, functionality, and productivity of the resulting enzyme. Finally, butanol and acetone can be produced from methanol using recombinant E. limosum strains expressing genes encoding fluorescent FAST-tagged fusion proteins.

Non-Immunotherapy Application of LNP-mRNA: Maximizing Efficacy and Safety

Lipid nanoparticle (LNP) formulated messenger RNA-based (LNP-mRNA) vaccines came into the spotlight as the first vaccines against SARS-CoV-2 virus to be applied worldwide. Long-known benefits of mRNA-based technologies consisting of relatively simple and fast engineering of mRNA encoding for antigens and proteins of interest, no genomic integration, and fast and efficient manufacturing process compared with other biologics have been verified, thus establishing a basis for a broad range of applications. The intrinsic immunogenicity of LNP formulated in vitro transcribed (IVT) mRNA is beneficial to the LNP-mRNA vaccines. However, avoiding immune activation is critical for therapeutic applications of LNP-mRNA for protein replacement where targeted mRNA expression and repetitive administration of high doses for a lifetime are required. This review summarizes our current understanding of immune activation induced by mRNA, IVT byproducts, and LNP. It gives a comprehensive overview of the present status of preclinical and clinical studies in which LNP-mRNA is used for protein replacement and treatment of rare diseases with an emphasis on safety. Moreover, the review outlines innovations and strategies to advance pharmacology and safety of LNP-mRNA for non-immunotherapy applications.