Codon adaptation index as a measure of dominating codon bias

Mutational features of chromids and chromosomes in Pseudoalteromonas provide new insights into the evolution of secondary replicons

The genomes of multi-replicon bacteria are composed of a primary replicon (the chromosome) and secondary replicons (chromids). Currently, there is a lack of understanding of the mutation features and evolutionary patterns of these different replicons. Specifically, in the genus Pseudoalteromonas, the chromids of multi-replicon species exhibit both unidirectional and bidirectional replication. Here, we investigated the similarities and differences between chromosomes and chromids in sequence composition and gene synteny of Pseudoalteromonas species by comparative genomic analysis, as well as the spontaneous mutation features of different replicons by mutation accumulation (MA) experiments combined with whole-genome sequencing strategy (MA-WGS). MA-WGS analysis revealed that there was no significant difference between chromids and chromosomes in the mutation rate or mutation spectrum of P. sp. LC0214 (where the chromid is unidirectional in replication) and P. sp. JCM12884T (where the chromid is bidirectional in replication). In addition, the context-dependence and variation pattern of the base-pair substitutions (BPSs) rates of the entire replicons exhibited differences that may be caused by the different replication directions of the chromids. The results of this study provide a new theoretical foundation for an in-depth understanding of the origin and evolution of chromids in multi-replicon bacterial species and facilitate further exploration of the complex mechanisms of bacterial diversity.IMPORTANCEDe novo mutations are a critical driving force in species evolution. Currently, there is a lack of sufficient research on the influence of replicon types on the occurrence of genomic mutations in bacteria. Moreover, the scarcity in systematic analysis and comparison of spontaneous mutation features between different replicons results in the limited information on the evolutionary dynamics of multi-replicon species. The diversity of replication direction in the multi-replicon species of the genus Pseudoalteromonas provides a unique opportunity for studying the impact of replication direction on the patterns of mutation. In addition to the composition characteristics between chromosomes and chromids, the spontaneous mutation rates in the context-dependence and variation pattern of the base-pair substitutions (BPSs) across different replicons within Pseudoalteromonas species revealed in this study provide valuable insights into the evolutionary dynamics of bacterial secondary replicons.

Does metabolic rate influence genome-wide amino acid composition in the course of animal evolution?

Natural selection is believed to shape amino acid usage of the proteome by minimizing the energy cost of protein biosynthesis. Although this hypothesis explains well the amino acid frequency (AAfrequency) difference among the 20 common amino acids within a given genome (species), whether it is applicable to cross-species difference remains to be inspected. Here, we proposed and tested a "metabolic rate hypothesis," which suggests that metabolic rate impacts genome-wide AAfrequency, considering that the energy allocated to protein biosynthesis is under selection pressure due to metabolic rate constraint. We performed integrated phylogenetic comparative analyses on proteomic sequence and metabolic rate data of 166 species covering 130 eumetazoan orders. We showed that resting metabolic rate (RMR) was significantly linked to AAfrequency variation across animal lineages, with a contribution comparable to or greater than genomic traits such as GC content and codon usage bias. Consistent with the metabolic rate hypothesis, low-energy-cost amino acids are observed to be more likely at higher frequency in animal species with high (residual) metabolic rate. Correlated evolution of RMR and AAfrequency was further inferred being driven by adaptation. The relationship between RMR and AAfrequency varied greatly among amino acids, most likely reflecting a trade-off among various interacting factors. Overall, there exists no "one-size-fits-all" predictor for AAfrequency, and integrated investigation of multilevel traits is indispensable for a fuller understanding of AAfrequency variation and evolution in animal.

Designing a broad-spectrum multi-epitope subunit vaccine against leptospirosis using immunoinformatics and structural approaches

Introduction

Leptospirosis, caused by Leptospira interrogans, is a neglected zoonotic disease that poses a significant global health risk to both humans and animals. The rise of antimicrobial resistance and the inefficacy of existing vaccines highlight the urgent need for new preventive strategies.

Methods

An immunoinformatics approach was employed to design a multi-epitope subunit vaccine (MESV) against leptospirosis. B-cell, cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) epitopes were selected from five key Leptospira proteins. These epitopes were fused with a heparin-binding hemagglutinin (HBHA) adjuvant and appropriate linkers to construct the broad-spectrum vaccine. The physicochemical properties of the vaccine were assessed, including antigenicity, immunogenicity, allergenicity, and conservation. The vaccine's 3D structure was modeled, optimized, and validated. Molecular docking, molecular dynamics simulations, and MM-GBSA analysis were performed to assess the vaccine's binding interactions with Toll-like receptors (TLR2 and TLR4). Immune simulations and in silico cloning were also conducted to evaluate the vaccine's immune response and expression potential.

Results

The MESV demonstrated high antigenicity, immunogenicity, non-allergenicity, and conservation across different Leptospira strains. Population coverage analysis revealed that T-cell epitopes significantly interacted with HLA molecules, covering 95.7% of the global population. Molecular docking showed strong and stable binding with TLR2 and TLR4, with binding energies of -1,357.1 kJ/mol and -1,163.7 kJ/mol, respectively. Molecular dynamics simulations and MM-GBSA analysis confirmed the stability of these interactions and accurately calculated the intermolecular binding free energies. Immune simulations indicated robust B and T cell responses, and in silico cloning demonstrated that the vaccine could be successfully expressed in E. coli.

Discussion

These findings suggest that MESV is a promising candidate for leptospirosis prevention, providing robust immune responses and broad population coverage. However, further in vivo studies are necessary to validate its efficacy and safety.

Codon usage analysis in selected virulence genes of Staphylococcal species

The Staphylococcus genus, composed of Gram-positive bacteria, includes several pathogenic species such as Staphylococcus aureus, S. epidermidis, S. haemolyticus, and S. saprophyticus, each implicated in a range of infections. This study investigates the codon usage patterns in key virulence genes, including Autolysin (alt), Elastin Binding protein (EbpS), Lipase, Thermonuclease, Intercellular Adhesion Protein (IcaR), and V8 Protease, across four Staphylococcus species. Using metrics such as the Effective Number of Codons (ENc), Relative Synonymous Codon Usage (RSCU), Codon Adaptation Index (CAI), alongside neutrality and parity plots, we explored the codon preferences and nucleotide composition biases. Our findings revealed a pronounced AT-rich codon preference, with AT-rich genomes likely aiding in energy-efficient translation and bacterial survival in host environments. These insights provide a deeper understanding of the evolutionary adaptations and translational efficiency mechanisms that contribute to the pathogenicity of Staphylococcus species. This knowledge could pave the way for novel therapeutic interventions targeting codon usage to disrupt virulence gene expression.

In silico design of an epitope-based vaccine ensemble for fasliolopsiasis

Introduction

Fasciolopsiasis, a food-borne intestinal disease is most common in Asia and the Indian subcontinent. Pigs are the reservoir host, and fasciolopsiasis is most widespread in locations where pigs are reared and aquatic plants are widely consumed. Human infection has been most commonly documented in China, Bangladesh, Southeast Asia, and parts of India. It predominates in school-age children, and significant worm burdens are not uncommon. The causal organism is Fasciolopsis buski, a giant intestinal fluke that infects humans and causes diarrhoea, fever, ascites, and intestinal blockage. The increasing prevalence of medication resistance and the necessity for an effective vaccination make controlling these diseases challenging.

Methods

Over the last decade, we have achieved major advances in our understanding of intestinal fluke biology by in-depth interrogation and analysis of evolving F. buski omics datasets. The creation of large omics datasets for F. buski by our group has accelerated the discovery of key molecules involved in intestinal fluke biology, toxicity, and virulence that can be targeted for vaccine development. Finding successful vaccination antigen combinations from these huge number of genes/proteins in the available omics datasets is the key in combating these neglected tropical diseases. In the present study, we developed an in silico workflow to select antigens for composing a chimeric vaccine, which could be a significant technique for developing a fasciolopsiasis vaccine that prevents the parasite from causing serious harm.

Results and discussion

This chimeric vaccine can now be tested experimentally and compared to other vaccine candidates to determine its potential influence on human health. Although the results are encouraging, additional validation is needed both in vivo and in vitro. Considering the extensive genetic data available for intestinal flukes that has expanded with technological advancements, we may need to reassess our methods and suggest a more sophisticated technique in the future for identifying vaccine molecules.

Codon optimization of voraxin α sequence enhances the immunogenicity of a recombinant vaccine against Hyalomma anatolicum infestation in rabbits

Research has shown that voraxin α derived from male ticks stimulates blood feeding to engorge in female ticks. Whereas, the oviposition rate, egg weight, and body weight of female ticks were reduced in animals vaccinated with recombinant (r-) voraxin α. These data suggest a potential role of r-voraxin α as a functional anti-tick antigen in Rhipicephalus appendiculatus and Amblyomma hebraeum tick infestation. This study investigated the immunogenicity of r-voraxin α protein from Hyalomma anatolicum (H. anatolicum) tick as an anti-tick vaccine in rabbits. The H. anatolicum voraxin α sequence was optimized according to the codon usage in E. coli before being sub-cloned into pQE30. The gene sequence of the voraxin α was synthesized, verified by DNA sequencing, cloned in a pQE30 vector, and transformed into E. coli. Then, the expression of the r-voraxin α protein was confirmed by SDS-PAGE and Western blot analysis. Subsequently, three rabbits were immunized with the r-voraxin α as the vaccinated group, whereas three rabbits without injection were considered the control group. The result indicated the success of cloning of codon-optimized H. anatolicum voraxin α gene. Moreover, the expression of the r-voraxin α protein (approximately 18 kDa) in the bacterial expression system was confirmed by SDS-PAGE and Western blot analysis. The results of this study showed that the mortality rate in vaccine recipients increased compared to the control group (P < 0.01). Also, the egg weight, oviposition rate, and engorgement weight of female ticks fed from vaccinated animals were significantly reduced compared to the control group (P < 0.01). The results confirmed that the codon-optimized H. anatolicum voraxin α gene expressed in the bacterial expression system could be a suitable anti-tick vaccine against H. anatolicum tick infestation.

Comparative genome wise analysis of codon usage of Staphylococcus Genus

The genus Staphylococcus encompasses a diverse array of bacteria with significant implications for human health, including disreputable pathogens such as Staphylococcus aureus and Staphylococcus epidermidis. Understanding the genetic composition and codon usage patterns of Staphylococcus species is crucial for unraveling their evolutionary dynamics, adaptive strategies, and pathogenic potential. In this study, we conducted a comprehensive analysis of codon usage patterns across 48 species within the Staphylococcus genus. Our findings uncovered variations in genomic G-C content across Staphylococcus species, impacting codon usage preferences, with a notable preference for A/T-rich codons observed in pathogenic strains. This preference for A/T-rich codons suggests an energy-saving strategy in pathogenic organisms. Analysis of dinucleotide pair expression patterns unveiled insights into genomic dynamics, with overrepresented codon pairs reflecting trends in dinucleotide expression across genomes. Additionally, a significant correlation between CAI and genomic G-C content underscored the intricate relationship between codon usage patterns and gene expression strategies. Amino acid usage analysis highlighted preferences for energetically cheaper amino acids, suggesting adaptive strategies promoting energy efficiency. This comprehensive analysis sheds light on the evolutionary dynamics and adaptive mechanisms employed by Staphylococcus species, providing valuable insights into their pathogenic potential and clinical implications. Understanding these genomic features is crucial for devising strategies to combat staphylococcal infections and improve public health outcomes.

Revolutionizing Nipah virus vaccinology: insights into subunit vaccine development strategies and immunological advances

The Nipah virus (NiV), a zoonotic virus in the Henipavirus genus of the Paramyxoviridae family, emerged in Malaysia in 1998 and later spread globally. Diseased patients may have a 40- 70% chance of fatality depending on the severity and early medication. The recent outbreak of NiV was reported in Kerala (India) by a new strain of MCL-19-H-1134 isolate. Currently, no vaccines are available, highlighting the critical need for a conclusive remedy. Our study aims to develop a subunit vaccine against the NiV by analyzing its proteome. NiV genome and proteome sequences were obtained from the NCBI database. A phylogenetic tree was constructed based on genome alignment. T-cell, helper T-cell, and B-cell epitopes were predicted from the protein sequences using NetCTL-1.2, NetMHCIIPan-4.1, and IEDB servers, respectively. High-affinity epitopes for human receptors were selected to construct a multi-epitope vaccine (MEV). These epitopes' antigenicity, toxicity, and allergenicity were evaluated using VaxiJen, AllergenFP-v.1.0, and AllergenFP algorithms. Molecular interactions with specific receptors were analyzed using PyRx and ClusPro. Amino acid interactions were visualized and analyzed using PyMOL and LigPlot. Immuno-simulation was conducted using C-ImmSim to assess the immune response elicited by the MEV. Finally, the vaccine cDNA was inserted into the pET28a(+) expression vector using SnapGene tool for in silico cloning in an E. coli host. The potential for an imminent outbreak cannot be overlooked. A subunit vaccine is more cost-effective and time-efficient. With additional in vitro and in vivo validation, this vaccine could become a superior preventive measure against NiV disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00246-9.

Large-scale investigation of species-specific orphan genes in the human gut microbiome elucidates their evolutionary origins

Species-specific genes, also known as orphans, are ubiquitous across life's domains. In prokaryotes, species-specific orphan genes (SSOGs) are mostly thought to originate in external elements such as viruses followed by horizontal gene transfer, whereas the scenario of native origination, through rapid divergence or de novo, is mostly dismissed. However, quantitative evidence supporting either scenario is lacking. Here, we systematically analyzed genomes from 4644 human gut microbiome species and identified more than 600,000 unique SSOGs, representing an average of 2.6% of a given species' pangenome. These sequences are mostly rare within each species yet show signs of purifying selection. Overall, SSOGs use optimal codons less frequently, and their proteins are more disordered than those of conserved genes (i.e., non-SSOGs). Importantly, across species, the GC content of SSOGs closely matches that of conserved ones. In contrast, the ∼5% of SSOGs that share similarity to known viral sequences have distinct characteristics, including lower GC content. Thus, SSOGs with similarity to viruses differ from the remaining SSOGs, contrasting an external origination scenario for most of them. By examining the orthologous genomic region in closely related species, we show that a small subset of SSOGs likely evolved natively de novo and find that these genes also differ in their properties from the remaining SSOGs. Our results challenge the notion that external elements are the dominant source of prokaryotic genetic novelty and will enable future studies into the biological role and relevance of species-specific genes in the human gut.

Immunoinformatics and structural aided approach to develop multi-epitope based subunit vaccine against Mycobacterium tuberculosis

Tuberculosis is a highly contagious disease caused by Mycobacterium tuberculosis (Mtb), which is one of the prominent reasons for the death of millions worldwide. The bacterium has a substantially higher mortality rate than other bacterial diseases, and the rapid rise of drug-resistant strains only makes the situation more concerning. Currently, the only licensed vaccine BCG (Bacillus Calmette-Guérin) is ineffective in preventing adult pulmonary tuberculosis prophylaxis and latent tuberculosis re-activation. Therefore, there is a pressing need to find novel and safe vaccines that provide robust immune defense and have various applications. Vaccines that combine epitopes from multiple candidate proteins have been shown to boost immunity against Mtb infection. This study applies an immunoinformatic strategy to generate an adequate multi-epitope immunization against Mtb employing five antigenic proteins. Potential B-cell, cytotoxic T lymphocyte, and helper T lymphocyte epitopes were speculated from the intended proteins and coupled with 50 s ribosomal L7/L12 adjuvant, and the vaccine was constructed. The vaccine's physicochemical profile demonstrates antigenic, soluble, and non-allergic. In the meantime, docking, molecular dynamics simulations, and essential dynamics analysis revealed that the multi-epitope vaccine structure interacted strongly with Toll-like receptors (TLR2 and TLR3). MM-PBSA analysis was performed to ascertain the system's intermolecular binding free energies accurately. The immune simulation was applied to the vaccine to forecast its immunogenic profile. Finally, in silico cloning was used to validate the vaccine's efficacy. The immunoinformatics analysis suggests the multi-epitope vaccine could induce specific immune responses, making it a potential candidate against Mtb. However, validation through the in-vivo study of the developed vaccine is essential to assess its efficacy and immunogenicity profile, which will assure active protection against Mtb.

Analysis of codon usage bias of exonuclease genes in invertebrate iridescent viruses

Invertebrate iridescent viruses (IIVs) are double-stranded DNA viruses that belong to the Iridoviridae family. IIVs result diseases that vary in severity from subclinical to lethal in invertebrate hosts. Codon usage bias (CUB) analysis is a versatile method for comprehending the genetic and evolutionary aspects of species. In this study, we analyzed the CUB in 10 invertebrate iridescent viruses exonuclease genes by calculating and comparing the nucleotide contents, effective number of codons (ENC), codon adaptation index (CAI), relative synonymous codon usage (RSCU), and others. The results revealed that IIVs exonuclease genes are rich in A/T. The ENC analysis displayed a low codon usage bias in IIVs exonuclease genes. ENC-plot, neutrality plot, and parity rule 2 plot demonstrated that besides mutational pressure, other factors like natural selection, dinucleotide content, and aromaticity also contributed to CUB. The findings could enhance our understanding of the evolution of IIVs exonuclease genes.

Investigation of CaMV-host co-evolution through synonymous codon pattern

Cauliflower mosaic virus (CaMV) has a double-stranded DNA genome and is globally distributed. The phylogeny tree of 121 CaMV isolates was categorized into two primary groups, with Iranian isolates showing the greatest genetic variations. Nucleotide A demonstrated the highest percentage (36.95%) in the CaMV genome and the dinucleotide odds ratio analysis revealed that TC dinucleotide (1.34 ≥ 1.23) and CG dinucleotide (0.63 ≤ 0.78) are overrepresented and underrepresented, respectively. Relative synonymous codon usage (RSCU) analysis confirmed codon usage bias in CaMV and its hosts. Brassica oleracea and Brassica rapa, among the susceptible hosts of CaMV, showed a codon adaptation index (CAI) value above 0.8. Additionally, relative codon deoptimization index (RCDI) results exhibited the highest degree of deoptimization in Raphanus sativus. These findings suggest that the genes of CaMV underwent codon adaptation with its hosts. Among the CaMV open reading frames (ORFs), genes that produce reverse transcriptase and virus coat proteins showed the highest CAI value of 0.83. These genes are crucial for the creation of new virion particles. The results confirm that CaMV co-evolved with its host to ensure the optimal expression of its genes in the hosts, allowing for easy infection and effective spread. To detect the force behind codon usage bias, an effective number of codons (ENC)-plot and neutrality plot were conducted. The results indicated that natural selection is the primary factor influencing CaMV codon usage bias.

Comprehensive Genomics Investigation of Neboviruses Reveals Distinct Codon Usage Patterns and Host Specificity

Neboviruses (NeVs) from the Caliciviridae family have been linked to enteric diseases in bovines and have been detected worldwide. As viruses rely entirely on the cellular machinery of the host for replication, their ability to thrive in a specific host is greatly impacted by the specific codon usage preferences. Here, we systematically analyzed the codon usage bias in NeVs to explore the genetic and evolutionary patterns. Relative Synonymous Codon Usage and Effective Number of Codon analyses indicated a marginally lower codon usage bias in NeVs, predominantly influenced by the nucleotide compositional constraints. Nonetheless, NeVs showed a higher codon usage bias for codons containing G/C at the third codon position. The neutrality plot analysis revealed natural selection as the primary factor that shaped the codon usage bias in both the VP1 (82%) and VP2 (57%) genes of NeVs. Furthermore, the NeVs showed a highly comparable codon usage pattern to bovines, as reflected through Codon Adaptation Index and Relative Codon Deoptimization Index analyses. Notably, yak NeVs showed considerably different nucleotide compositional constraints and mutational pressure compared to bovine NeVs, which appear to be predominantly host-driven. This study sheds light on the genetic mechanism driving NeVs' adaptability, evolution, and fitness to their host species.

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.

A new perspective on codon usage, selective pressure, and phylogenetic implications of the plastomes in the Telephium clade (Crassulaceae)

The Telephium clade of the Crassulaceae family contains many medicinal, ornamental, and ecologically restorative plants. However, the phylogenetic relationships within the clade remain debated, and comprehensive analyses of codon usage and selection pressure in Telephium plastomes are limited. In this study, we assembled and annotated four plastomes and performed extensive analyses. The plastomes exhibited a typical quadripartite structure and high conservation. The lengths ranged from 151,357 bp to 151,641 bp with 134 genes identified. The GC content was the highest within IR, followed by LSC, and lowest in the SSC region. Meanwhile, a unique inversion was observed within the LSC region of Meterostachys sikokianus. Polymorphisms analysis revealed minimum nucleotide diversity in the IR regions, with over ten highly polymorphic regions identified. Phylogenetically, two subclades formed within the monophyletic Telephium clade, with Umbilicus as the sister group to the remaining Hylotelephium subclade members. Notably, no significant positive selection was found among the 79 plastid genes, which showed varying evolutionary patterns. However, 19 genes contained codons under positive selection. The specific functions of these sites require further investigation. Synonymous codon usage was biased and conserved across the tested plastomes, shaped by natural selection, mutations and other factors of varying influence. We also identified 34 taxon-specific codon aversion motifs from 49 plastid genes. Our plastomic analyses elucidate phylogenetic relationships and evolutionary patterns in this medicinal clade, providing a foundation for further research on these ecologically and pharmaceutically important plants.

Development of multi-epitope based subunit vaccine against Mycobacterium Tuberculosis using immunoinformatics approach

The etiological agent of tuberculosis (TB), Mycobacterium tuberculosis, is a deadly pathogen that adapts to thrive within the host. Since 2020, the COVID-19 pandemic has had colossal health, societal, and economic consequences, which have affected the reporting of new incidences and mortality cases of TB. As per the WHO 2022 report, 10.6 million people were diagnosed with TB, and 1.6 million died worldwide. The increase in resistant strains of tuberculosis is making it more burdensome to reach the End TB strategy. A reliable and efficient TB vaccine that may avert both primary infection and recurrence of latent TB in adults and adolescents is of the utmost importance. In this study, we used computational techniques to predict the ability of HLA molecules to display epitopes for six TB proteins (PPE68, PE_PGRS17, EspC, LDT4, RpfD, and RpfC) to design the multi-epitope subunit vaccine. From the aimed proteins, the potential B-cell, helper T lymphocyte (HTL), and cytotoxic T lymphocyte (CTL) epitopes were predicted and linked together with LPA adjuvant, and the vaccine was designed. The vaccine's physicochemical analysis demonstrates that it is non-allergic, non-toxic, and antigenic. Then, the vaccine structure was predicted, improved, and verified to yield the optimal structure. The developed vaccine's binding mechanism with distinct immunogenic receptors (Tlr2 and MHC-II) was assessed utilizing molecular docking. The molecular dynamic simulation and MMPBSA analysis were performed to comprehend the complexes' dynamics and stability. The immune simulation was utilized to anticipate the vaccine's immunogenic attributes. In silico cloning was employed to demonstrate the efficient expression of the designed vaccine in E. coli as a host. Moreover, in vitro and in vivo animal testing is required to determine the efficacy of the in silico developed vaccine.Communicated by Ramaswamy H. Sarma.

Codon usage pattern of the ancestor of green plants revealed through Rhodophyta

Rhodophyta are among the closest known relatives of green plants. Studying the codons of their genomes can help us understand the codon usage pattern and characteristics of the ancestor of green plants. By studying the codon usage pattern of all available red algae, it was found that although there are some differences among species, high-bias genes in most red algae prefer codons ending with GC. Correlation analysis, Nc-GC3s plots, parity rule 2 plots, neutrality plot analysis, differential protein region analysis and comparison of the nucleotide content of introns and flanking sequences showed that the bias phenomenon is likely to be influenced by local mutation pressure and natural selection, the latter of which is the dominant factor in terms of translation accuracy and efficiency. It is worth noting that selection on translation accuracy could even be detected in the low-bias genes of individual species. In addition, we identified 15 common optimal codons in seven red algae except for G. sulphuraria for the first time, most of which were found to be complementary and bound to the tRNA genes with the highest copy number. Interestingly, tRNA modification was found for the highly degenerate amino acids of all multicellular red algae and individual unicellular red algae, which indicates that highly biased genes tend to use modified tRNA in translation. Our research not only lays a foundation for exploring the characteristics of codon usage of the red algae as green plant ancestors, but will also facilitate the design and performance of transgenic work in some economic red algae in the future.

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.

gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm

The tRNA adaptation index (tAI) is a translation efficiency metric that considers weighted values (S _ij values) for codon-tRNA wobble interaction efficiencies. The initial implementation of the tAI had significant flaws. For instance, generated S _ij weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. Consequently, a species-specific approach (stAI) was developed to overcome those limitations. However, the stAI method employed a hill climbing algorithm to optimize the S _ij weights, which is not ideal for obtaining the best set of S _ij weights because it could struggle to find the global maximum given a complex search space, even after using different starting positions. In addition, it did not perform well in computing the tAI of fungal genomes in comparison with the original implementation. We developed a novel approach named genetic tAI (gtAI) implemented as a Python package (https://github.com/AliYoussef96/gtAI), which employs a genetic algorithm to obtain the best set of S _ij weights and follows a new codon usage-based workflow that better computes the tAI of genomes from the three domains of life. The gtAI has significantly improved the correlation with the codon adaptation index (CAI) and the prediction of protein abundance (empirical data) compared to the stAI.

A computational approach to design a polyvalent vaccine against human respiratory syncytial virus

Human Respiratory Syncytial Virus (RSV) is one of the leading causes of lower respiratory tract infections (LRTI), responsible for infecting people from all age groups-a majority of which comprises infants and children. Primarily, severe RSV infections are accountable for multitudes of deaths worldwide, predominantly of children, every year. Despite several efforts to develop a vaccine against RSV as a potential countermeasure, there has been no approved or licensed vaccine available yet, to control the RSV infection effectively. Therefore, through the utilization of immunoinformatics tools, a computational approach was taken in this study, to design a multi-epitope polyvalent vaccine against two major antigenic subtypes of RSV, RSV-A and RSV-B. Potential predictions of the T-cell and B-cell epitopes were followed by extensive tests of antigenicity, allergenicity, toxicity, conservancy, homology to human proteome, transmembrane topology, and cytokine-inducing ability. The peptide vaccine was modeled, refined, and validated. Molecular docking analysis with specific Toll-like receptors (TLRs) revealed excellent interactions with suitable global binding energies. Additionally, molecular dynamics (MD) simulation ensured the stability of the docking interactions between the vaccine and TLRs. Mechanistic approaches to imitate and predict the potential immune response generated by the administration of vaccines were determined through immune simulations. Subsequent mass production of the vaccine peptide was evaluated; however, there remains a necessity for further in vitro and in vivo experiments to validate its efficacy against RSV infections.

Fasciola gigantica vaccine construct: an in silico approach towards identification and design of a multi-epitope subunit vaccine using calcium binding EF-hand proteins

Continuous attempts have been made to pinpoint candidate vaccine molecules and evaluate their effectiveness in order to commercialise such vaccines for the treatment of tropical fascioliasis in livestock. The pathophysiology of fascioliasis can be related to liver damage brought on by immature flukes that migrate and feed, as well as immunological reactions to chemicals produced by the parasites and alarm signals brought on by tissue damage. Future research should, in our opinion, concentrate on the biology of invasive parasites and the resulting immune responses, particularly in the early stages of infection. The goal of the current study was to use the calcium-binding proteins from F. gigantica to create a multi-epitope subunit vaccine. The adjuvant, B-cell epitopes, CTL epitopes, and HTL epitopes that make up the vaccine construct are all connected by certain linkers. The antigenicity, allergenicity, and physiochemical properties of the vaccine construct were examined. The vaccine construct was docked with toll-like receptor 2, and simulations of the molecular dynamics of the complex's stability, interaction, and dynamics were run. After performing in silico cloning and immunosimulation, it was discovered that the construct was suitable for further investigation. New vaccination technologies and adjuvant development are advancing our food safety procedures since vaccines are seen as safe and are accepted by the user community. This research is also applicable to the F. hepatica system.

Identification and design of a multi-epitope subunit vaccine against the opportunistic pathogen Staphylococcus epidermidis: An immunoinformatics approach

Staphylococcus epidermidis is one of the major causes of nosocomial infections around the globe that leads to a high rate of mortality and morbidity in both immunocompromised patients and preterm infants. Despite the alarming increase in multi-drug resistance, no promising vaccines are readily available against this pathogen. Thus, the present study is focused on designing a multi-epitope subunit vaccine using five antigenic proteins of S. epidermidis through an immunoinformatics approach. The final vaccine comprised B-cell, HTL, and CTL binding epitopes followed by Lipoprotein LprA adjuvant added at N-terminal to augment the immunogenicity. Physicochemical assessment of the vaccine reveals the antigenic and non-allergic nature. The vaccine structure was designed, refined, validated, and disulfide engineered to obtain the best model. Molecular docking and dynamics simulation of the proposed vaccine with toll-like receptors (TLR-2 and TLR-4) showed strong and stable interactions. MM-PBSA analysis was implemented as an efficient tool to determine the intermolecular binding free energies of the system. The vaccine was subjected to immune simulation to predict its immunogenic profile. In silico cloning suggested that the proposed vaccine can be expressed efficiently in E.coli. Furthermore, in vivo animal experiment is needed to determine the effectiveness of the in silico designed vaccine.Communicated by Ramaswamy H. Sarma.

The pattern of coding sequences in the chloroplast genome of Atropa belladonna and a comparative analysis with other related genomes in the nightshade family

Atropa belladonna is a valuable medicinal plant and a commercial source of tropane alkaloids, which are frequently utilized in therapeutic practice. In this study, bioinformaticmethodologies were used to examine the pattern of coding sequences and the factors thatmight influence codon usage bias in the chloroplast genome of Atropa belladonna andother nightshade genomes. The chloroplast engineering being a promising field in modernbiotechnology, the characterization of chloroplast genome is very important. The resultsrevealed that the chloroplast genomes of Nicotiana tabacum, Solanum lycopersicum, Capsicum frutescens, Datura stramonium, Lyciumbarbarum, Solanum melongena, and Solanumtuberosum exhibited comparable codon usage patterns. In these chloroplast genomes, weobserved a weak codon usage bias. According to the correspondence analysis, the genesisof the codon use bias in these chloroplast genes might be explained by natural selection,directed mutational pressure, and other factors. GC12 and GC3S were shown to have nomeaningful relationship. Further research revealed that natural selection primarily shapedthe codon usage in A. belladonna and other nightshade genomes for translational efficiency. The sequencing properties of these chloroplast genomes were also investigated by investing the occurrences of palindromes and inverted repeats, which would be useful forfuture research on medicinal plants.

Analysis of Heat Shock Proteins Based on Amino Acids for the Tomato Genome

This research aimed to investigate heat shock proteins in the tomato genome through the analysis of amino acids. The highest length among sequences was found in seq19 with 3534 base pairs. This seq19 was reported and contained a family of proteins known as HsfA that have a domain of transcriptional activation for tolerance to heat and other abiotic stresses. The values of the codon adaptation index (CAI) ranged from 0.80 in Seq19 to 0.65 in Seq10, based on the mRNA of heat shock proteins for tomatoes. Asparagine (AAT, AAC), aspartic acid (GAT, GAC), phenylalanine (TTT, TTC), and tyrosine (TAT, TAC) have relative synonymous codon usage (RSCU) values bigger than 0.5. In modified relative codon bias (MRCBS), the high gene expressions of the amino acids under heat stress were histidine, tryptophan, asparagine, aspartic acid, lysine, phenylalanine, isoleucine, cysteine, and threonine. RSCU values that were less than 0.5 were considered rare codons that affected the rate of translation, and thus selection could be effective by reducing the frequency of expressed genes under heat stress. The normal distribution of RSCU shows about 68% of the values drawn from the standard normal distribution were within 0.22 and -0.22 standard deviations that tend to cluster around the mean. The most critical component based on principal component analysis (PCA) was the RSCU. These findings would help plant breeders in the development of growth habits for tomatoes during breeding programs.

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.

Composition and Codon Usage Pattern Results in Divergence of the Zinc Binuclear Cluster (Zn(II)2Cys6) Sequences among Ascomycetes Plant Pathogenic Fungi

Zinc binuclear cluster proteins (ZBC; Zn(II)2Cys6) are unique to the fungi kingdom and associated with a series of functions, viz., the utilization of macromolecules, stress tolerance, and most importantly, host-pathogen interactions by imparting virulence to the pathogen. Codon usage bias (CUB) is the phenomenon of using synonymous codons in a non-uniform fashion during the translation event, which has arisen because of interactions among evolutionary forces. The Zn(II)2Cys6 coding sequences from nine Ascomycetes plant pathogenic species and model system yeast were analysed for compositional and codon usage bias patterns. The clustering analysis diverged the Ascomycetes fungi into two clusters. The nucleotide compositional and relative synonymous codon usage (RSCU) analysis indicated GC biasness toward Ascomycetes fungi compared with the model system S. cerevisiae, which tends to be AT-rich. Further, plant pathogenic Ascomycetes fungi belonging to cluster-2 showed a higher number of GC-rich high-frequency codons than cluster-1 and was exclusively AT-rich in S. cerevisiae. The current investigation also showed the mutual effect of the two evolutionary forces, viz. natural selection and compositional constraints, on the CUB of Zn(II)2Cys6 genes. The perseverance of GC-rich codons of Zn(II)2Cys6 in Ascomycetes could facilitate the invasion process. The findings of the current investigation show the role of CUB and nucleotide composition in the evolutionary divergence of Ascomycetes plant pathogens and paves the way to target specific codons and sequences to modulate host-pathogen interactions through genome editing and functional genomics tools.

Bioinformatic Assessment of Factors Affecting the Correlation between Protein Abundance and Elongation Efficiency in Prokaryotes

Protein abundance is crucial for the majority of genetically regulated cell functions to act properly in prokaryotic organisms. Therefore, developing bioinformatic methods for assessing the efficiency of different stages of gene expression is of great importance for predicting the actual protein abundance. One of these steps is the evaluation of translation elongation efficiency based on mRNA sequence features, such as codon usage bias and mRNA secondary structure properties. In this study, we have evaluated correlation coefficients between experimentally measured protein abundance and predicted elongation efficiency characteristics for 26 prokaryotes, including non-model organisms, belonging to diverse taxonomic groups The algorithm for assessing elongation efficiency takes into account not only codon bias, but also number and energy of secondary structures in mRNA if those demonstrate an impact on predicted elongation efficiency of the ribosomal protein genes. The results show that, for a number of organisms, secondary structures are a better predictor of protein abundance than codon usage bias. The bioinformatic analysis has revealed several factors associated with the value of the correlation coefficient. The first factor is the elongation efficiency optimization type-the organisms whose genomes are optimized for codon usage only have significantly higher correlation coefficients. The second factor is taxonomical identity-bacteria that belong to the class Bacilli tend to have higher correlation coefficients among the analyzed set. The third is growth rate, which is shown to be higher for the organisms with higher correlation coefficients between protein abundance and predicted translation elongation efficiency. The obtained results can be useful for further improvement of methods for protein abundance prediction.

Complete chloroplast genome sequences of Acanthocalyx alba and Acanthocalyx nepalensis subsp. delavayi (Caprifoliaceae)

In this study, the chloroplast genomes of Acanthocalyx alba (Hand.-Mazz., 1925) and Acanthocalyx nepalensis subsp. delavayi (Franchet, 1885) were sequenced, and their total lengths were 148,720 bp and 149,253 bp, respectively. The A. alba genome contained two inverted repeat regions (IRs) of 21,849 bp, a large single-copy region (LSC) of 89,084 bp, and a small single-copy region (SSC) of 15,938 bp, whereas A. nepalensis subsp. delavayi contained two IRs of 21,736 bp, one LSC of 89,034 bp, and one SSC of 16,747 bp. The chloroplast genomes of both A. alba and A. nepalensis subsp. delavayi contained 109 genes, including 72 mRNA, 33 tRNA, and four rRNA genes. Phylogenetic analysis suggested that A. alba is in a clade with A. nepalensis subsp. delavayi. This study provides useful data for further phylogenetic studies of A. alba and A. nepalensis subsp. delavayi.

Decoding molecular factors shaping human angiotensin converting enzyme 2 receptor usage by spike glycoprotein in lineage B beta-coronaviruses

Acquiring the human ACE2 receptor usage trait enables the coronaviruses to spill over to humans. However, the origin of the ACE2 usage trait in coronaviruses is poorly understood. Using a multi-disciplinary approach combining evolutionary bioinformatics and molecular dynamics simulation, we decode the principal driving force behind human ACE2 receptor recognition in coronaviruses. Genomic content, evolutionary divergence, and codon usage bias analysis reveal that SARS-CoV2 is evolutionarily divergent from other human ACE2-user CoVs, indicating that SARS-CoV2 originates from a different lineage. Sequence analysis shows that all the human ACE2-user CoVs contain two insertions in the receptor-binding motif (RBM) that directly interact with ACE2. However, the insertion sequences in SARS-CoV2 are divergent from other ACE2-user CoVs, implicating their different recombination origins. The potential of mean force calculations reveals that the high binding affinity of SARS-CoV2 RBD to human ACE2 is primarily attributed to its ability to form a higher number of hydrogen bonds than the other ACE2-user CoVs. The adaptive branch-site random effects likelihood method identifies positive selection bias across the ACE2 user CoVs lineages. Recombination and selection forces shape the spike evolution in human ACE2-using beta-CoVs to optimize the interfacial hydrogen bonds between RBD and ACE2. However, these evolutionary forces work within the constraints of nucleotide composition, ensuring optimum codon adaptation of the spike (S) gene within the host cell.

A large-scale analysis of codon usage bias in 4868 bacterial genomes shows association of codon adaptation index with GC content, protein functional domains and bacterial phenotypes

Multiple synonymous codons code for the same amino acid, resulting in the degeneracy of the genetic code and in the preferred used of some codons called codon bias usage (CBU). We performed a large-scale analysis of codon usage bias analysing the distribution of the codon adaptation index (CAI) and the codon relative adaptiveness index (RA) in 4868 bacterial genomes. We found that CAI values differ significantly between protein functional domains and part of the protein outside domains and show how CAI, GC content and preferred usage of polymerase III alpha subunits are related. Additionally, we give evidence of the association between CAI and bacterial phenotypes.

Dispersion of synonymous codon usage patterns in hepatitis E virus genomes derived from various hosts

Hepatitis E virus (HEV) is an important zoonotic pathogen infecting a wide range of host species. It has a positive-sense, single-stranded RNA genome encoding three open reading frames (ORFs). Synonymous codon usages of viruses essentially determine their survival and adaptation to susceptible hosts. To better understand the interplay between the ever-expanding host range and synonymous codon usages of HEV, we quantified the dispersion of synonymous codon usages of HEV genomes isolated from different hosts via V_s calculation and information entropy. HEV ORFs show species-specific synonymous codon usage patterns. Ruminant-derived HEV ORFs own the most synonymous codons with stable usage patterns (V_s value <0.1) which leads to the stable overall codon usage patterns (R value being close to zero). Swine-derived HEV ORFs own more concentrated synonymous codons than those from wild boar. Compared with HEV strains isolated from other hosts, the human-derived HEV exhibits a distinct pattern at the overall codon usage (R < 0). Generally, ORF1 contains more synonymous codons with stable usage patterns (V_s  < 0.1) than those of ORFs 2 and 3. Moreover, ORF3 contains more synonymous codons with varied patterns (V_s  > 1.0) than ORFs 1 and 2. The host factor serving as one of the evolutionary dynamics probably influences synonymous codon usage patterns of the HEV genome. Taken together, synonymous codons with stable usage patterns in ORF1 might help to sustain the infection, while that with varied usage patterns in ORF3 may facilitate cross-species infection and expand the host range.

Immunoinformatic Design of a Multivalent Peptide Vaccine Against Mucormycosis: Targeting FTR1 Protein of Major Causative Fungi

Mucormycosis is a potentially fatal illness that arises in immunocompromised people due to diabetic ketoacidosis, neutropenia, organ transplantation, and elevated serum levels of accessible iron. The sudden spread of mucormycosis in COVID-19 patients engendered massive concern worldwide. Comorbidities including diabetes, cancer, steroid-based medications, long-term ventilation, and increased ferritin serum concentration in COVID-19 patients trigger favorable fungi growth that in turn effectuate mucormycosis. The necessity of FTR1 gene-encoded ferrous permease for host iron acquisition by fungi has been found in different studies recently. Thus, targeting the transit component could be a potential solution. Unfortunately, no appropriate antifungal vaccine has been constructed as of yet. To date, mucormycosis has been treated with antiviral therapy and surgical treatment only. Thus, in this study, the FTR1 protein has been targeted to design a convenient and novel epitope-based vaccine with the help of immunoinformatics against four different virulent fungal species. Furthermore, the vaccine was constructed using 8 CTL, 2 HTL, and 1 LBL epitopes that were found to be highly antigenic, non-allergenic, non-toxic, and fully conserved among the fungi under consideration. The vaccine has very reassuring stability due to its high pI value of 9.97, conclusive of a basic range. The vaccine was then subjected to molecular docking, molecular dynamics, and immune simulation studies to confirm the biological environment’s safety, efficacy, and stability. The vaccine constructs were found to be safe in addition to being effective. Finally, we used in-silico cloning to develop an effective strategy for vaccine mass production. The designed vaccine will be a potential therapeutic not only to control mucormycosis in COVID-19 patients but also be effective in general mucormycosis events. However, further in vitro, and in vivo testing is needed to confirm the vaccine’s safety and efficacy in controlling fungal infections. If successful, this vaccine could provide a low-cost and effective method of preventing the spread of mucormycosis worldwide.

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.

Identification of a Potential mRNA-based Vaccine Candidate against the SARS-CoV-2 Spike Glycoprotein: A Reverse Vaccinology Approach

The emergence of the novel coronavirus (SARS-CoV-2) in December 2019 has generated a devastating global consequence which makes the development of a rapidly deployable, effective and safe vaccine candidate an imminent global health priority. The design of most vaccine candidates has been directed at the induction of antibody responses against the trimeric spike glycoprotein of SARS-CoV-2, a class I fusion protein that aids ACE2 (angiotensin-converting enzyme 2) receptor binding. A variety of formulations and vaccinology approaches are being pursued for targeting the spike glycoprotein, including simian and human replication-defective adenoviral vaccines, subunit protein vaccines, nucleic acid vaccines and whole-inactivated SARS-CoV-2. Here, we directed a reverse vaccinology approach towards the design of a nucleic acid (mRNA-based) vaccine candidate. The "YLQPRTFLL" peptide sequence (position 269-277) which was predicted to be a B cell epitope and likewise a strong binder of the HLA*A-0201 was selected for the design of the vaccine candidate, having satisfied series of antigenicity assessments. Through the codon optimization protocol, the nucleotide sequence for the vaccine candidate design was generated and targeted at the human toll-like receptor 7 (TLR7). Bioinformatics analyses showed that the sequence "UACCUGCAGCCGCGUACCUUCCUGCUG" exhibited a strong affinity and likewise was bound to a stable cavity in the TLR7 pocket. This study is therefore expected to contribute to the research efforts directed at securing definitive preventive measures against the SARS-CoV-2 infection.

The Dynamic Codon Biaser: calculating prokaryotic codon usage biases

Bacterial genomes often reflect a bias in the usage of codons. These biases are often most notable within highly expressed genes. While deviations in codon usage can be attributed to selection or mutational biases, they can also be functional, for example controlling gene expression or guiding protein structure. Several different metrics have been developed to identify biases in codon usage. Previously we released a database, CBDB: The Codon Bias Database, in which users could retrieve precalculated codon bias data for bacterial RefSeq genomes. With the increase of bacterial genome sequence data since its release a new tool was needed. Here we present the Dynamic Codon Biaser (DCB) tool, a web application that dynamically calculates the codon usage bias statistics of prokaryotic genomes. DCB bases these calculations on 40 different highly expressed genes (HEGs) that are highly conserved across different prokaryotic species. A user can either specify an NCBI accession number or upload their own sequence. DCB returns both the bias statistics and the genome’s HEG sequences. These calculations have several downstream applications, such as evolutionary studies and phage–host predictions. The source code is freely available, and the website is hosted at www.cbdb.info.

Subtractive Proteomics and Immuno-informatics Approaches for Multi-peptide Vaccine Prediction Against Klebsiella oxytoca and Validation Through In Silico Expression

Klebsiella oxytoca is a gram-negative bacterium. It is opportunistic in nature and causes hospital acquired infections. Subtractive proteomics and reverse vaccinology approaches were employed to screen out the best proteins for vaccine designing. Whole proteome of K. oxytoca strain ATCC 8724, consisting of 5483 proteins, was used for designing the vaccine. Total 1670 cytotoxic T lymphocyte (CTL) epitope were predicted through NetCTL while 1270 helper T lymphocyte (HTL) epitopes were predicted through IEDB server. The epitopes were screened for non-toxicity, allergenicity, antigenicity and water solubility. After epitope screening 300 CTL and 250 HTL epitopes were submitted to IFN-γ epitope server to predict their Interferon-γ induction response. The selected IFN-γ positive epitopes were tested for their binding affinity with MHCI-DRB1 by MHCPred. The 15 CTL and 13 HTL epitopes were joined by linkers AAY and GPGPG respectively in vaccine construct. Chain C of Pam3CSK4 (PDB ID; 2Z7X) was linked to the vaccine construct as an adjuvant. A 450aa long vaccine construct was submitted to I-TASSER server for 3D structure prediction. Thirteen Linear B cells were predicted by ABCPred server and 10 sets of discontinues epitopes for 3D vaccine structure were predicted by DiscoTope server. The modeled 3D vaccine construct was docked with human Toll-like receptor 2 (PDB ID: 6NIG) by PatchDock. The docked complexes were refined by FireDock. The selected docked complex showed five hydrogen bonds and one salt bridge. The vaccine sequence was reverse transcribed to get nucleotide sequence for In silico cloning. The reverse transcribed sequence strand was cloned in pET28a(+) expression vector. A clone containing 6586 bp was constructed including the 450 bp of query gene sequence.

Analysis of Codon Usage Patterns in Giardia duodenalis Based on Transcriptome Data from GiardiaDB

Giardia duodenalis, a flagellated parasitic protozoan, the most common cause of parasite-induced diarrheal diseases worldwide. Codon usage bias (CUB) is an important evolutionary character in most species. However, G. duodenalis CUB remains unclear. Thus, this study analyzes codon usage patterns to assess the restriction factors and obtain useful information in shaping G. duodenalis CUB. The neutrality analysis result indicates that G. duodenalis has a wide GC3 distribution, which significantly correlates with GC12. ENC-plot result—suggesting that most genes were close to the expected curve with only a few strayed away points. This indicates that mutational pressure and natural selection played an important role in the development of CUB. The Parity Rule 2 plot (PR2) result demonstrates that the usage of GC and AT was out of proportion. Interestingly, we identified 26 optimal codons in the G. duodenalis genome, ending with G or C. In addition, GC content, gene expression, and protein size also influence G. duodenalis CUB formation. This study systematically analyzes G. duodenalis codon usage pattern and clarifies the mechanisms of G. duodenalis CUB. These results will be very useful to identify new genes, molecular genetic manipulation, and study of G. duodenalis evolution.

Subtractive proteomic analysis of antigenic extracellular proteins and design a multi-epitope vaccine against Staphylococcus aureus

Staphylococcus aureus is a versatile Gram's positive bacterium that can reside as an asymptomatic colonizer, which can cause a wide range of skin, soft-tissue, and nosocomial infections. A vaccine against multi-drug resistant S. aureus, therefore, is urgently needed. Subtractive proteomics and reverse vaccinology are newly emerging techniques to design multiepitope-based vaccines. The analysis of 7290 proteomes (sensitive and resistant strains), five potent nonhuman homologous vaccine targets [(UNIPORT ID Q2FZL3 (Staphopain B), Q2G2R8 (Staphopain A), Q2FWP0 (uncharacterized leukocidin-like protein 1), Q2G1S6 (uncharacterized protein), and Q2FWV3 (Staphylokinase, putative)] were selected. These proteins were absent in the gut microbiome, which further enhances the significance of these proteins in vaccine design. These five virulence-associated proteins mainly have a role in the invasion mechanism in the host phagocyte cells. MHC I, MHC II, and B cell epitopes were identified in these five proteins. Finalized epitopes were examined by different online servers to screen suitable epitopes for multi-epitope based vaccine design. Shortlisted antigenic and nonallergenic associated epitopes were joined with linkers to design 30 variants (VSA1-VSA30) of multi-epitope vaccine conjugates. The antigenicity and allergenicity of all the 30 vaccine constructs were identified, and VSA30 was found to have the highest antigenicity and lowest allergenicity, and hence was selected for further study. Accordingly, VSA30 was docked with different HLA allelic variants, and the best-docked complex (VSA30-1SYS) was further analyzed by molecular dynamics simulation (MDS). The MDS result confirms the interaction of VSA30 with MHC (HLA-allelic variant). Thus, the final vaccine construct was in silico cloned in the pET28a vector for suitable expression in a heterologous system. Therefore, the designed vaccine construct VSA-30 can be developed as an appropriate vaccine to target S. aureus infection. VSA-30 still needs experimental validation to assure the antigenic and immunogenic properties.

Edging on Mutational Bias, Induced Natural Selection From Host and Natural Reservoirs Predominates Codon Usage Evolution in Hantaan Virus

The molecular evolutionary dynamics that shape hantaviruses’ evolution are poorly understood even now, besides the contribution of virus-host interaction to their evolution remains an open question. Our study aimed to investigate these two aspects in Hantaan virus (HTNV)—the prototype of hantaviruses and an emerging zoonotic pathogen that infects humans, causing hemorrhagic fever with renal syndrome (HFRS): endemic in Far East Russia, China, and South Korea—via a comprehensive, phylogenetic-dependent codon usage analysis. We found that host- and natural reservoir-induced natural selection is the primary determinant of its biased codon choices, exceeding the mutational bias effect. The phylogenetic analysis of HTNV strains resulted in three distinct clades: South Korean, Russian, and Chinese. An effective number of codon (ENC) analysis showed a slightly biased codon usage in HTNV genomes. Nucleotide composition and RSCU analyses revealed a significant bias toward A/U nucleotides and A/U-ended codons, indicating the potential influence of mutational bias on the codon usage patterns of HTNV. Via ENC-plot, Parity Rule 2 (PR2), and neutrality plot analyses, we would conclude the presence of both mutation pressure and natural selection effect in shaping the codon usage patterns of HTNV; however, natural selection is the dominant factor influencing its codon usage bias. Codon adaptation index (CAI), Relative codon deoptimization index (RCDI), and Similarity Index (SiD) analyses uncovered the intense selection pressure from the host (Human) and natural reservoirs (Striped field mouse and Chinese white-bellied rat) in shaping HTNV biased codon choices. Our study clearly revealed the evolutionary processes in HTNV and the role of virus-host interaction in its evolution. Moreover, it opens the door for a more comprehensive codon usage analysis for all hantaviruses species to determine their molecular evolutionary dynamics and adaptability to several hosts and environments. We believe that our research will help in a better and deep understanding of HTNV evolution that will serve its future basic research and aid live attenuated vaccines design.

Learning the Regulatory Code of Gene Expression

Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology.

Genetic changes and evolutionary analysis of canine circovirus

Canine circovirus (canineCV) has been found to be associated with vasculitis, hemorrhage, hemorrhagic enteritis, and diarrhea of canines. CanineCV, like other circoviruses, may also be associated with lymphoid depletion and immunosuppression. This circovirus has been detected worldwide in different countries and species. Recombination and mutation events in the canineCV genome have been described, indicating that the virus is continuing to evolve. However, the origin, codon usage patterns, and host adaptation of canineCV remain to be studied. Here, the coding sequences of 93 canineCV sequences available in the GenBank database were used for analysis. The results showed that canineCV sequences could be classified into five genotypes, as confirmed by phylogenetic and principal component analysis (PCA). Maximum clade credibility (MCC) and maximum-likelihood (ML) trees suggested that canineCV originated from bat circovirus. G/T and A/C nucleotide biases were observed in ORF1 and ORF2, respectively, and a low codon usage bias (CUB) was found in canineCV using an effective number of codon (ENC) analysis. Correlation analysis, ENC plot analysis and neutrality plot analysis indicated that the codon usage pattern was mainly shaped by natural selection. Codon adaptation index (CAI) analysis, relative codon deoptimization index (RCDI) analysis, and similarity index (SiD) analysis revealed a better adaption to Vulpes vulpes than to Canis familiaris. Furthermore, a cross-species transmission hypothesis that canineCV may have evolved from bats (origin analysis) and subsequently adapted to wolves, arctic foxes, dogs, and red foxes, was proposed. This study contributes to our understanding of the factors related to canineCV evolution and host adaption.

Codon usage patterns and evolution of HSP60 in birds

Heat shock protein 60 (HSP60) is highly conserved from prokaryotic to eukaryotic organisms, acting as molecular chaperone and other vital biological functions. In spite of increasing knowledge of HSP60, its evolutionary mechanism on functional adaption is still far from completely understood. Moreover, analysis of codon usage bias (CUB) is a powerful tool to understand evolutionary association studies. However, so far, as we know, no scientific work on CUB of HSP60 in birds has been reported. In this study, we provide a comprehensive analysis on the codon usage and molecular evolution of HSP60 across birds. The results indicated that HSP60 had a weak codon usage bias with high ENC values (range from 52.66 to 61), low RSCU, and A/T-ending codons were mostly preferred. Meanwhile, it was considered that mutation, natural selection, and genetic drift combined to shape codon usage patterns with different strength proportions among various birds for HSP60. Then, the LRT tests suggested that different lineages of birds might be under similar selective pressures. Besides, the two positive selection sites (151 and 131) were detected and might undergo radical changes. These findings would contribute to understand function diversity and molecular evolution of HSP60 in birds.

Analysis of chromosomes and nucleotides in rice to predict gene expression through codon usage pattern

Amino acids are essential measurements for the potential growth stage because of connecting to protein structures and functions. The objective of this paper was to analyze chromosomes feature at plastid region of rice represented by nucleotide, synonymous codon, and amino acid usage to predict gene expression through codon usage pattern. The results showed that the values of the codon adaption index ranged from 0.733 in chromosome 9 to 0.631 in chromosome 8 with full length of these two chromosomes were 3738 and 1635 respectively. The higher value of guanine and cytosine content was 60% in chromosomes 9 while the lower values was 37% in chromosomes 11. Eight chromosomes (ch1, ch2, ch3, ch5, ch7, ch8, ch10, and ch12) were greater value of modified relative codon bias than threshold (threshold: 0.66) especially in cysteine for ch1, ch2, ch5, ch10, and ch12. While other remaining chromosomes were less than the threshold. Relative synonymous codon usage found that the over-represented of amino acids were asparagine, aspartate, cysteine, glutamate, and phenylalanine across all 12 chromosomes. These results would establish a platform for more and further projects concerning rice breeding and genetics and codon optimization in the amino acids for developing varieties. These results also will help breeders to select desirable genes through the genome for improve target traits.

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.

Prediction of gene expression under drought stress in spring wheat using codon usage pattern

Spring wheat (Triticum aestivum) is a staple food providing sources of essential proteins for human. In fact, gene expressions of wheat play an important role in growth and productivity that are affected by drought stress. The objective of this work focused on analysis gene feature on spring wheat represented by nucleotide and gene expressions under drought stress. It was found that the higher codon adaptation index was in both wheat root and L-galactono-1, 4-lactone dehydrogenase. It was also found that guanine and cytosine content were high (55.56%) in wheat root. Whereas, guanine and cytosine content were low (41.28%) in L-galactono-1, 4-lactone dehydrogenase. Moreover, the higher relative synonymous codon usage value was observed in codon CAA (1.20), GAA (1.33), GAT (1.00), and ATG (1.00) in wheat root and thus about 62.95% of the total variation in relative synonymous codon was explained by principal component analysis. Additionally, high averages frequency number of codon were (above 15.76) in Met, Lys, Ala, Gly, Phe, Asp, Glu, His, and Tyr; whereas, low averages were in remaining amino acids and majority (90%) of modified relative codon bias values was between 0.40 and 0.90. Shortly, calculations and analysis of codon usage pattern under drought stress would help for genetic engineering, molecular evolution, and gene prediction in wheat studies for developing varieties that associate with drought tolerance.

Phenotypic and Phylogenetic Characterization of Cu Homeostasis among Xylella fastidiosa Strains

Xylella fastidiosa is a bacterial pathogen causing severe diseases and asymptomatic colonization in more than 600 plants worldwide. Copper (Cu) is a widely used antimicrobial treatment for various plant diseases, including those affecting X. fastidiosa hosts. Cu homeostasis among X. fastidiosa strains from different geographical locations and host species has not been characterized. Here, we assessed minimum inhibitory concentration (MIC) of Cu for 54 X. fastidiosa strains. We observed strain-level variation in MIC values within each subspecies. We hypothesized that these differences could be explained by sequence variation in Cu homeostasis genes. Phylogenies based on copA, copB, copL, and cutC were created using 74 genomes (including 43 strains used in vitro) of X. fastidiosa, showing that the phylogenetic clustering of Cu homeostasis associated with clustering was based on core genome phylogenies, rather than on pattern of MIC. No association was found among Cu MIC, subspecies classification, and host and location of isolation, probably due to uneven and limited group of strains whose genomes are available. Further analysis focused on a subgroup of isolates from Georgia’s vineyards that shared similar Cu-related phenotypes. Further research is needed to better understand the distribution of Cu homeostasis for this pathogen.

Evolutionary and codon usage preference insights into spike glycoprotein of SARS-CoV-2

Interaction of SARS-CoV-2 spike glycoprotein with the ACE2 cell receptor is very crucial for virus attachment to human cells. Selected mutations in SARS-CoV-2 S-protein are reported to strengthen its binding affinity to mammalian ACE2. The N501T mutation in SARS-CoV-2-CTD furnishes better support to hotspot 353 in comparison with SARS-CoV and shows higher affinity for receptor binding. Recombination analysis exhibited higher recombination events in SARS-CoV-2 strains, irrespective of their geographical origin or hosts. Investigation further supports a common origin among SARS-CoV-2 and its predecessors, SARS-CoV and bat-SARS-like-CoV. The recombination events suggest a constant exchange of genetic material among the co-infecting viruses in possible reservoirs and human hosts before SARS-CoV-2 emerged. Furthermore, a comprehensive analysis of codon usage bias (CUB) in SARS-CoV-2 revealed significant CUB among the S-genes of different beta-coronaviruses governed majorly by natural selection and mutation pressure. Various indices of codon usage of S-genes helped in quantifying its adaptability in other animal hosts. These findings might help in identifying potential experimental animal models for investigating pathogenicity for drugs and vaccine development experiments.