TissueCoCoPUTs: Novel Human Tissue-Specific Codon and Codon-Pair Usage Tables Based on Differential Tissue Gene Expression

Codon-optimization in gene therapy: promises, prospects and challenges

Codon optimization has evolved to enhance protein expression efficiency by exploiting the genetic code's redundancy, allowing for multiple codon options for a single amino acid. Initially observed in E. coli, optimal codon usage correlates with high gene expression, which has propelled applications expanding from basic research to biopharmaceuticals and vaccine development. The method is especially valuable for adjusting immune responses in gene therapies and has the potenial to create tissue-specific therapies. However, challenges persist, such as the risk of unintended effects on protein function and the complexity of evaluating optimization effectiveness. Despite these issues, codon optimization is crucial in advancing gene therapeutics. This study provides a comprehensive review of the current metrics for codon-optimization, and its practical usage in research and clinical applications, in the context of gene therapy.

Synthetic biology approach revealed enhancement in haeme oxygenase-1 gene expression by codon pair optimization while reduction by codon deoptimization

Haem oxygenase-1 (HO-1) is a ubiquitously expressed gene involved in cellular homoeostasis, and its imbalance in expression results in various disorders. To alleviate such disorders, HO-1 gene expression needs to be modulated. Codon usage bias results from evolutionary forces acting on any nucleotide sequence and determines the gene expression. Like codon usage bias, codon pair bias also exists, playing a role in gene expression. In the present study, HO-1 gene was recoded by manipulating codon and codon pair bias, and four such constructs were made through codon/codon pair deoptimization and codon/codon pair optimization to reduce and enhance the HO-1 gene expression. Codon usage analysis was done for these constructs for four tissues brain, heart, pancreas and liver. Based on codon usage in different tissues, gene expression of these tissues was determined in terms of the codon adaptation index. Based on the codon adaptation index, minimum free energy, and translation efficiency, constructs were evaluated for enhanced or decreased HO-1 expression. The analysis revealed that for enhancing gene expression, codon pair optimization, while for reducing gene expression, codon deoptimization is efficacious. The recoded constructs developed in the study could be used in gene therapy regimens to cure HO-1 over or underexpression-associated disorders.

SARS-CoV-2 continuously optimizes its codon usage to adapt to human lung environment

Viruses need to utilize the resources from host cells to reproduce themselves. RNA translation rate, which is largely determined by codon usage, is the rate-limiting step across the life cycle of viruses. Adapting to the codon usage of hosts would help virus better proliferate. We retrieved the time-course mutation profile of millions of world-wide SARS-CoV-2 sequences. For synonymous mutations, we defined whether a mutation elevate or reduce the relative synonymous codon usage (RSCU). We found that if a synonymous mutation in SARS-CoV-2 increases the RSCU (calculated from human lungs), denoted as delta RSCU > 0, then this mutation is positively selected because the allele frequency (AF) of this mutation increases with time, and vice versa. The results suggest that in SARS-CoV-2, the synonymous mutations that increase codon optimality are beneficial to the virus and are favored by natural selection. For the first time, we used the dynamics of allele frequency to demonstrate that SARS-CoV-2 is continuously optimizing its codon usage to adapt to human lungs. Nevertheless, adaptation to other human tissues cannot be excluded. These results warn us that under this global pandemic, synonymous mutations in SARS-CoV-2 should not be automatically ignored since they indeed change the fitness of the virus.

Growth-dependent Gene Expression Variation Influences the Strength of Codon Usage Biases

The most highly expressed genes in microbial genomes tend to use a limited set of synonymous codons, often referred to as "preferred codons." The existence of preferred codons is commonly attributed to selection pressures on various aspects of protein translation including accuracy and/or speed. However, gene expression is condition-dependent and even within single-celled organisms transcript and protein abundances can vary depending on a variety of environmental and other factors. Here, we show that growth rate-dependent expression variation is an important constraint that significantly influences the evolution of gene sequences. Using large-scale transcriptomic and proteomic data sets in Escherichia coli and Saccharomyces cerevisiae, we confirm that codon usage biases are strongly associated with gene expression but highlight that this relationship is most pronounced when gene expression measurements are taken during rapid growth conditions. Specifically, genes whose relative expression increases during periods of rapid growth have stronger codon usage biases than comparably expressed genes whose expression decreases during rapid growth conditions. These findings highlight that gene expression measured in any particular condition tells only part of the story regarding the forces shaping the evolution of microbial gene sequences. More generally, our results imply that microbial physiology during rapid growth is critical for explaining long-term translational constraints.

Growth-dependent gene expression variation influences the strength of codon usage biases

The most highly expressed genes in microbial genomes tend to use a limited set of synonymous codons, often referred to as "preferred codons." The existence of preferred codons is commonly attributed to selection pressures on various aspects of protein translation including accuracy and/or speed. However, gene expression is condition-dependent and even within single-celled organisms transcript and protein abundances can vary depending on a variety of environmental and other factors. Here, we show that growth rate-dependent expression variation is an important constraint that significantly influences the evolution of gene sequences. Using large-scale transcriptomic and proteomic data sets in Escherichia coli and Saccharomyces cerevisiae, we confirm that codon usage biases are strongly associated with gene expression but highlight that this relationship is most pronounced when gene expression measurements are taken during rapid growth conditions. Specifically, genes whose relative expression increases during periods of rapid growth have stronger codon usage biases than comparably expressed genes whose expression decreases during rapid growth conditions. These findings highlight that gene expression measured in any particular condition tells only part of the story regarding the forces shaping the evolution of microbial gene sequences. More generally, our results imply that microbial physiology during rapid growth is critical for explaining long-term translational constraints.

In silico methods for predicting functional synonymous variants

Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous SNVs are previously considered to be "silent," but mounting evidence has revealed that these variants can cause RNA and protein changes and are implicated in over 85 human diseases and cancers. Recent improvements in computational platforms have led to the development of numerous machine-learning tools, which can be used to advance synonymous SNV research. In this review, we discuss tools that should be used to investigate synonymous variants. We provide supportive examples from seminal studies that demonstrate how these tools have driven new discoveries of functional synonymous SNVs.

The structural plasticity of polyglutamine repeats

From yeast to humans, polyglutamine (polyQ) repeat tracts are found frequently in the proteome and are particularly prominent in the activation domains of transcription factors. PolyQ is a polymorphic motif that modulates functional protein-protein interactions and aberrant self-assembly. Expansion of the polyQ repeated sequences beyond critical physiological repeat length thresholds triggers self-assembly and is linked to severe pathological implications. This review provides an overview of the current knowledge on the structures of polyQ tracts in the soluble and aggregated states and discusses the influence of neighboring regions on polyQ secondary structure, aggregation, and fibril morphologies. The influence of the genetic context of the polyQ-encoding trinucleotides is briefly discussed as a challenge for future endeavors in this field.

Human Tissues Exhibit Diverse Composition of Translation Machinery

While protein synthesis is vital for the majority of cell types of the human body, diversely differentiated cells require specific translation regulation. This suggests the specialization of translation machinery across tissues and organs. Using transcriptomic data from GTEx, FANTOM, and Gene Atlas, we systematically explored the abundance of transcripts encoding translation factors and aminoacyl-tRNA synthetases (ARSases) in human tissues. We revised a few known and identified several novel translation-related genes exhibiting strict tissue-specific expression. The proteins they encode include eEF1A1, eEF1A2, PABPC1L, PABPC3, eIF1B, eIF4E1B, eIF4ENIF1, and eIF5AL1. Furthermore, our analysis revealed a pervasive tissue-specific relative abundance of translation machinery components (e.g., PABP and eRF3 paralogs, eIF2B and eIF3 subunits, eIF5MPs, and some ARSases), suggesting presumptive variance in the composition of translation initiation, elongation, and termination complexes. These conclusions were largely confirmed by the analysis of proteomic data. Finally, we paid attention to sexual dimorphism in the repertoire of translation factors encoded in sex chromosomes (eIF1A, eIF2γ, and DDX3), and identified the testis and brain as organs with the most diverged expression of translation-associated genes.

Using protein-per-mRNA differences among human tissues in codon optimization

BACKGROUND

Codon usage and nucleotide composition of coding sequences have profound effects on protein expression. However, while it is recognized that different tissues have distinct tRNA profiles and codon usages in their transcriptomes, the effect of tissue-specific codon optimality on protein synthesis remains elusive.

RESULTS

We leverage existing state-of-the-art transcriptomics and proteomics datasets from the GTEx project and the Human Protein Atlas to compute the protein-to-mRNA ratios of 36 human tissues. Using this as a proxy of translational efficiency, we build a machine learning model that identifies codons enriched or depleted in specific tissues. We detect two clusters of tissues with an opposite pattern of codon preferences. We then use these identified patterns for the development of CUSTOM, a codon optimizer algorithm which suggests a synonymous codon design in order to optimize protein production in a tissue-specific manner. In human cell-line models, we provide evidence that codon optimization should take into account particularities of the translational machinery of the tissues in which the target proteins are expressed and that our approach can design genes with tissue-optimized expression profiles.

CONCLUSIONS

We provide proof-of-concept evidence that codon preferences exist in tissue-specific protein synthesis and demonstrate its application to synthetic gene design. We show that CUSTOM can be of benefit in biological and biotechnological applications, such as in the design of tissue-targeted therapies and vaccines.

Analysis of 3.5 million SARS-CoV-2 sequences reveals unique mutational trends with consistent nucleotide and codon frequencies

BACKGROUND

Since the onset of the SARS-CoV-2 pandemic, bioinformatic analyses have been performed to understand the nucleotide and synonymous codon usage features and mutational patterns of the virus. However, comparatively few have attempted to perform such analyses on a considerably large cohort of viral genomes while organizing the plethora of available sequence data for a month-by-month analysis to observe changes over time. Here, we aimed to perform sequence composition and mutation analysis of SARS-CoV-2, separating sequences by gene, clade, and timepoints, and contrast the mutational profile of SARS-CoV-2 to other comparable RNA viruses.

METHODS

Using a cleaned, filtered, and pre-aligned dataset of over 3.5 million sequences downloaded from the GISAID database, we computed nucleotide and codon usage statistics, including calculation of relative synonymous codon usage values. We then calculated codon adaptation index (CAI) changes and a nonsynonymous/synonymous mutation ratio (dN/dS) over time for our dataset. Finally, we compiled information on the types of mutations occurring for SARS-CoV-2 and other comparable RNA viruses, and generated heatmaps showing codon and nucleotide composition at high entropy positions along the Spike sequence.

RESULTS

We show that nucleotide and codon usage metrics remain relatively consistent over the 32-month span, though there are significant differences between clades within each gene at various timepoints. CAI and dN/dS values vary substantially between different timepoints and different genes, with Spike gene on average showing both the highest CAI and dN/dS values. Mutational analysis showed that SARS-CoV-2 Spike has a higher proportion of nonsynonymous mutations than analogous genes in other RNA viruses, with nonsynonymous mutations outnumbering synonymous ones by up to 20:1. However, at several specific positions, synonymous mutations were overwhelmingly predominant.

CONCLUSIONS

Our multifaceted analysis covering both the composition and mutation signature of SARS-CoV-2 gives valuable insight into the nucleotide frequency and codon usage heterogeneity of SARS-CoV-2 over time, and its unique mutational profile compared to other RNA viruses.

Insights into Synonymous Codon Usage Bias in Hepatitis C Virus and Its Adaptation to Hosts

Hepatitis C virus (HCV) is enveloped RNA virus, encoding for a polyprotein that is processed by cellular proteases. The virus is responsible for liver cirrhosis, allograft rejection, and human hepatocellular carcinoma. Based on studies including compositional analysis, odds ratio analysis, parity analysis, skew analysis, relative synonymous codon usage, codon bias, and protein properties, it was evident that codon usage bias in HCV is dependent upon the nucleotide composition. Codon context analysis revealed CTC-CTG as a preferred codon pair. While CGA and CGT codons were rare, none of the codons were rare in HCV-like viruses envisaged in the present study. Many of the preferred codon pairs were valine amino acid-initiated, which possibly infers viral infectivity; hence the role of selection forces appears to act on the HCV genome, which was further validated by neutrality analysis where selection accounted for 87.28%, while mutation accounted for 12.72% force shaping codon usage. Furthermore, codon usage was correlated with the length of the genome. HCV viruses prefer valine-initiated codon pairs, while HCV-like viruses prefer alanine-initiated codon pairs. The HCV host range is very narrow and is confined to only humans and chimpanzees. Based on indices including codon usage correlation analysis, similarity index, and relative codon deoptimization index, it is evident in the study that the chimpanzee is the primary host of the virus. The present study helped elucidate the preferred host for HCV. The information presented in the study paved the way for generating an attenuated vaccine candidate through viral recoding, with finely tuned nucleotide composition and a perfect balance of preferred and rare codons.

Implementing computational methods in tandem with synonymous gene recoding for therapeutic development

Synonymous gene recoding, the substitution of synonymous variants into the genetic sequence, has been used to overcome many production limitations in therapeutic development. However, the safety and efficacy of recoded therapeutics can be difficult to evaluate because synonymous codon substitutions can result in subtle, yet impactful changes in protein features and require sensitive methods for detection. Given that computational approaches have made significant leaps in recent years, we propose that machine-learning (ML) tools may be leveraged to assess gene-recoded therapeutics and foresee an opportunity to adapt codon contexts to enhance some powerful existing tools. Here, we examine how synonymous gene recoding has been used to address challenges in therapeutic development, explain the biological mechanisms underlying its effects, and explore the application of computational platforms to improve the surveillance of functional variants in therapeutic design.

Pan-cancer analyses of synonymous mutations based on tissue-specific codon optimality

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Developed tissue-specific codon optimality in 29 human tissues.

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Applied these to analyze synonymous mutations in ∼10,000 tumor and normal samples.

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Synonymous mutations frequently increase optimal codons in most cancer types.

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Synonymous mutations frequently increase optimal codons cell cycle-related genes.

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Frequency of optimal codon gain relates to proliferation, DDR deficiency, and survival.

Codon optimality has been demonstrated to be an important determinant of mRNA stability and expression levels in multiple model organisms and human cell lines. However, tissue-specific codon optimality has not been developed to investigate how codon optimality is usually perturbed by somatic synonymous mutations in human cancers. Here, we determined tissue-specific codon optimality in 29 human tissues based on mRNA expression data from the Genotype-Tissue Expression project. We found that optimal codons were associated with differentiation, whereas non-optimal codons were correlated with proliferation. Furthermore, codons biased toward differentiation displayed greater tissue specificity in codon optimality, and the tissue specificity of codon optimality was primarily present in amino acids with high degeneracy of the genetic code. By applying tissue-specific codon optimality to somatic synonymous mutations in 8532 tumor samples across 24 cancer types and to those in 416 normal cells across six human tissues, we found that synonymous mutations frequently increased optimal codons in tumor cells and cancer-related genes (e.g., genes involved in cell cycle). Furthermore, an elevated frequency of optimal codon gain was found to promote tumor cell proliferation in three cancer types characterized by DNA damage repair deficiency and could act as a prognostic biomarker for patients with triple-negative breast cancer. In summary, this study profiled tissue-specific codon optimality in human tissues, revealed alterations in codon optimality caused by synonymous mutations in human cancers, and highlighted the non-negligible role of optimal codon gain in tumorigenesis and therapeutics.

The Ramp Atlas: facilitating tissue and cell-specific ramp sequence analyses through an intuitive web interface

Ramp sequences occur when the average translational efficiency of codons near the 5′ end of highly expressed genes is significantly lower than the rest of the gene sequence, which counterintuitively increases translational efficiency by decreasing downstream ribosomal collisions. Here, we show that the relative codon adaptiveness within different tissues changes the existence of a ramp sequence without altering the underlying genetic code. We present the first comprehensive analysis of tissue and cell type-specific ramp sequences and report 3108 genes with ramp sequences that change between tissues and cell types, which corresponds with increased gene expression within those tissues and cells. The Ramp Atlas (https://ramps.byu.edu/) allows researchers to query precomputed ramp sequences in 18 388 genes across 62 tissues and 66 cell types and calculate tissue-specific ramp sequences from user-uploaded FASTA files through an intuitive web interface. We used The Ramp Atlas to identify seven SARS-CoV-2 genes and seven human SARS-CoV-2 entry factor genes with tissue-specific ramp sequences that may help explain viral proliferation within those tissues. We anticipate that The Ramp Atlas will facilitate personalized and creative tissue-specific ramp sequence analyses for both human and viral genes that will increase our ability to utilize this often-overlooked regulatory region.

Human Codon Usage: The Genetic Basis of Pathogen Latency

Infectious diseases pose two main compelling issues. First, the identification of the molecular factors that allow chronic infections, that is, the often completely asymptomatic coexistence of infectious agents with the human host. Second, the definition of the mechanisms that allow the switch from pathogen dormancy to pathologic (re)activation. Furthering previous studies, the present study (1) analyzed the frequency of occurrence of synonymous codons in coding DNA, that is, codon usage, as a genetic tool that rules protein expression; (2) described how human codon usage can inhibit protein expression of infectious agents during latency, so that pathogen genes the codon usage of which does not conform to the human codon usage cannot be translated; and (3) framed human codon usage among the front-line instruments of the innate immunity against infections. In parallel, it was shown that, while genetics can account for the molecular basis of pathogen latency, the changes of the quantitative relationship between codon frequencies and isoaccepting tRNAs during cell proliferation offer a biochemical mechanism that explains the pathogen switching to (re)activation. Immunologically, this study warns that using codon optimization methodologies can (re)activate, potentiate, and immortalize otherwise quiescent, asymptomatic pathogens, thus leading to uncontrollable pandemics.

Codon Usage Bias: An Endless Tale

Since the genetic code is degenerate, several codons are translated to the same amino acid. Although these triplets were historically considered to be "synonymous" and therefore expected to be used at rather equal frequencies in all genomes, we now know that this is not the case. Indeed, since several coding sequences were obtained in the late '70s and early '80s in the last century, coming from either the same or different species, it was evident that (a) each genome, taken globally, displayed different codon usage patterns, which means that different genomes display a particular global codon usage table when all genes are considered together, and (b) there is a strong intragenomic diversity: in other words, within a given species the codon usage pattern can (and usually do) differ greatly among genes in the same genome. These different patterns were attributed to two main factors: first, the mutational bias characteristic of each genome, which determines that GC- poor species display a general bias towards A/T codons while the reverse is true for GC- rich species. Second, the differences in codon usage among genes from the same species are due to natural selection acting at the level of translation, in such a way that highly expressed genes tend to use codons that match with the most abundant isoacceptor tRNAs. Thus, these genes are translated at a highest rate, which in turn leads to avoid the limiting factor in translation which is the number of available ribosomes per cell. Although these explanations are still valid, new factors are almost constantly postulated to affect codon usage. In this mini review, we shall try to summarize them.

Distinct signatures of codon and codon pair usage in 32 primary tumor types in the novel database CancerCoCoPUTs for cancer-specific codon usage

BACKGROUND

Gene expression is highly variable across tissues of multi-cellular organisms, influencing the codon usage of the tissue-specific transcriptome. Cancer disrupts the gene expression pattern of healthy tissue resulting in altered codon usage preferences. The topic of codon usage changes as they relate to codon demand, and tRNA supply in cancer is of growing interest.

METHODS

We analyzed transcriptome-weighted codon and codon pair usage based on The Cancer Genome Atlas (TCGA) RNA-seq data from 6427 solid tumor samples and 632 normal tissue samples. This dataset represents 32 cancer types affecting 11 distinct tissues. Our analysis focused on tissues that give rise to multiple solid tumor types and cancer types that are present in multiple tissues.

RESULTS

We identified distinct patterns of synonymous codon usage changes for different cancer types affecting the same tissue. For example, a substantial increase in GGT-glycine was observed in invasive ductal carcinoma (IDC), invasive lobular carcinoma (ILC), and mixed invasive ductal and lobular carcinoma (IDLC) of the breast. Change in synonymous codon preference favoring GGT correlated with change in synonymous codon preference against GGC in IDC and IDLC, but not in ILC. Furthermore, we examined the codon usage changes between paired healthy/tumor tissue from the same patient. Using clinical data from TCGA, we conducted a survival analysis of patients based on the degree of change between healthy and tumor-specific codon usage, revealing an association between larger changes and increased mortality. We have also created a database that contains cancer-specific codon and codon pair usage data for cancer types derived from TCGA, which represents a comprehensive tool for codon-usage-oriented cancer research.

CONCLUSIONS

Based on data from TCGA, we have highlighted tumor type-specific signatures of codon and codon pair usage. Paired data revealed variable changes to codon usage patterns, which must be considered when designing personalized cancer treatments. The associated database, CancerCoCoPUTs, represents a comprehensive resource for codon and codon pair usage in cancer and is available at https://dnahive.fda.gov/review/cancercocoputs/ . These findings are important to understand the relationship between tRNA supply and codon demand in cancer states and could help guide the development of new cancer therapeutics.

Nucleotide Composition and Codon Usage Across Viruses and Their Respective Hosts

The genetic material of the three domains of life (Bacteria, Archaea, and Eukaryota) is always double-stranded DNA, and their GC content (molar content of guanine plus cytosine) varies between ≈ 13% and ≈ 75%. Nucleotide composition is the simplest way of characterizing genomes. Despite this simplicity, it has several implications. Indeed, it is the main factor that determines, among other features, dinucleotide frequencies, repeated short DNA sequences, and codon and amino acid usage. Which forces drive this strong variation is still a matter of controversy. For rather obvious reasons, most of the studies concerning this huge variation and its consequences, have been done in free-living organisms. However, no recent comprehensive study of all known viruses has been done (that is, concerning all available sequences). Viruses, by far the most abundant biological entities on Earth, are the causative agents of many diseases. An overview of these entities is important also because their genetic material is not always double-stranded DNA: indeed, certain viruses have as genetic material single-stranded DNA, double-stranded RNA, single-stranded RNA, and/or retro-transcribing. Therefore, one may wonder if what we have learned about the evolution of GC content and its implications in prokaryotes and eukaryotes also applies to viruses. In this contribution, we attempt to describe compositional properties of ∼ 10,000 viral species: base composition (globally and according to Baltimore classification), correlations among non-coding regions and the three codon positions, and the relationship of the nucleotide frequencies and codon usage of viruses with the same feature of their hosts. This allowed us to determine how the base composition of phages strongly correlate with the value of their respective hosts, while eukaryotic viruses do not (with fungi and protists as exceptions). Finally, we discuss some of these results concerning codon usage: reinforcing previous results, we found that phages and hosts exhibit moderate to high correlations, while for eukaryotes and their viruses the correlations are weak or do not exist.

Codon-based indices for modeling gene expression and transcript evolution

Codon usage bias (CUB) refers to the phenomena that synonymous codons are used in different frequencies in most genes and organisms. The general assumption is that codon biases reflect a balance between mutational biases and natural selection. Today we understand that the codon content is related and can affect all gene expression steps. Starting from the 1980s, codon-based indices have been used for answering different questions in all biomedical fields, including systems biology, agriculture, medicine, and biotechnology. In general, codon usage bias indices weigh each codon or a small set of codons to estimate the fitting of a certain coding sequence to a certain phenomenon (e.g., bias in codons, adaptation to the tRNA pool, frequencies of certain codons, transcription elongation speed, etc.) and are usually easy to implement. Today there are dozens of such indices; thus, this paper aims to review and compare the different codon usage bias indices, their applications, and advantages. In addition, we perform analysis that demonstrates that most indices tend to correlate even though they aim to capture different aspects. Due to the centrality of codon usage bias on different gene expression steps, it is important to keep developing new indices that can capture additional aspects that are not modeled with the current indices.

A Path toward SARS-CoV-2 Attenuation: Metabolic Pressure on CTP Synthesis Rules the Virus Evolution

In the context of the COVID-19 pandemic, we describe here the singular metabolic background that constrains enveloped RNA viruses to evolve toward likely attenuation in the long term, possibly after a step of increased pathogenicity. Cytidine triphosphate (CTP) is at the crossroad of the processes allowing SARS-CoV-2 to multiply, because CTP is in demand for four essential metabolic steps. It is a building block of the virus genome, it is required for synthesis of the cytosine-based liponucleotide precursors of the viral envelope, it is a critical building block of the host transfer RNAs synthesis and it is required for synthesis of dolichol-phosphate, a precursor of viral protein glycosylation. The CCA 3'-end of all the transfer RNAs required to translate the RNA genome and further transcripts into the proteins used to build active virus copies is not coded in the human genome. It must be synthesized de novo from CTP and ATP. Furthermore, intermediary metabolism is built on compulsory steps of synthesis and salvage of cytosine-based metabolites via uridine triphosphate that keep limiting CTP availability. As a consequence, accidental replication errors tend to replace cytosine by uracil in the genome, unless recombination events allow the sequence to return to its ancestral sequences. We document some of the consequences of this situation in the function of viral proteins. This unique metabolic setup allowed us to highlight and provide a raison d'être to viperin, an enzyme of innate antiviral immunity, which synthesizes 3'-deoxy-3',4'-didehydro-CTP as an extremely efficient antiviral nucleotide.

Sequence analysis of SARS-CoV-2 genome reveals features important for vaccine design

As the SARS-CoV-2 pandemic is rapidly progressing, the need for the development of an effective vaccine is critical. A promising approach for vaccine development is to generate, through codon pair deoptimization, an attenuated virus. This approach carries the advantage that it only requires limited knowledge specific to the virus in question, other than its genome sequence. Therefore, it is well suited for emerging viruses, for which we may not have extensive data. We performed comprehensive in silico analyses of several features of SARS-CoV-2 genomic sequence (e.g., codon usage, codon pair usage, dinucleotide/junction dinucleotide usage, RNA structure around the frameshift region) in comparison with other members of the coronaviridae family of viruses, the overall human genome, and the transcriptome of specific human tissues such as lung, which are primarily targeted by the virus. Our analysis identified the spike (S) and nucleocapsid (N) proteins as promising targets for deoptimization and suggests a roadmap for SARS-CoV-2 vaccine development, which can be generalizable to other viruses.

Sequence analysis of SARS-CoV-2 genome reveals features important for vaccine design

As the SARS-CoV-2 pandemic is rapidly progressing, the need for the development of an effective vaccine is critical. A promising approach for vaccine development is to generate, through codon pair deoptimization, an attenuated virus. This approach carries the advantage that it only requires limited knowledge specific to the virus in question, other than its genome sequence. Therefore, it is well suited for emerging viruses for which we may not have extensive data. We performed comprehensive in silico analyses of several features of SARS-CoV-2 genomic sequence (e.g., codon usage, codon pair usage, dinucleotide/junction dinucleotide usage, RNA structure around the frameshift region) in comparison with other members of the coronaviridae family of viruses, the overall human genome, and the transcriptome of specific human tissues such as lung, which are primarily targeted by the virus. Our analysis identified the spike (S) and nucleocapsid (N) proteins as promising targets for deoptimization and suggests a roadmap for SARS-CoV-2 vaccine development, which can be generalizable to other viruses.