Infectious disease mRNA vaccines and a review on epitope prediction for vaccine design

Advances in the design and delivery of RNA vaccines for infectious diseases

RNA medicines represent a paradigm shift in treatment and prevention of critical diseases of global significance, e.g., infectious diseases. The highly successful messenger RNA (mRNA) vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were developed at record speed during the coronavirus disease 2019 pandemic. A consequence of this is exceptionally shortened vaccine development times, which in combination with adaptability makes the RNA vaccine technology highly attractive against infectious diseases and for pandemic preparedness. Here, we review state of the art in the design and delivery of RNA vaccines for infectious diseases based on different RNA modalities, including linear mRNA, self-amplifying RNA, trans-amplifying RNA, and circular RNA. We provide an overview of the clinical pipeline of RNA vaccines for infectious diseases, and present analytical procedures, which are paramount for characterizing quality attributes and guaranteeing their quality, and we discuss future perspectives for using RNA vaccines to combat pathogens beyond SARS-CoV-2.

mRNA vaccines contribute to innate and adaptive immunity to enhance immune response in vivo

Messenger RNA (mRNA) therapeutics have been widely employed as strategies for the treatment and prevention of diseases. Amid the global outbreak of COVID-19, mRNA vaccines have witnessed rapid development. Generally, in the case of mRNA vaccines, the initiation of the innate immune system serves as a prerequisite for triggering subsequent adaptive immune responses. Critical cells, cytokines, and chemokines within the innate immune system play crucial and beneficial roles in coordinating tailored immune reactions towards mRNA vaccines. Furthermore, immunostimulators and delivery systems play a significant role in augmenting the immune potency of mRNA vaccines. In this comprehensive review, we systematically delineate the latest advancements in mRNA vaccine research, present an in-depth exploration of strategies aimed at amplifying the immune effectiveness of mRNA vaccines, and offer some perspectives and recommendations regarding the future advancements in mRNA vaccine development.

In Silico Design of a Trans-Amplifying RNA-Based Vaccine against SARS-CoV-2 Structural Proteins

Nucleic acid-based vaccines allow scalable, rapid, and cell-free vaccine production in response to an emerging disease such as the current COVID-19 pandemic. Here, we objected to the design of a multiepitope mRNA vaccine against the structural proteins of SARS-CoV-2. Through an immunoinformatic approach, promising epitopes were predicted for the spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins. Fragments rich in overlapping epitopes were selected based on binding affinities with HLA classes I and II for the specific presentation to B and T lymphocytes. Two constructs were designed by fusing the fragments in different arrangements via GG linkers. Construct 1 showed better structural properties and interactions with toll-like receptor 2 (TLR-2), TLR-3, and TLR-4 during molecular docking and dynamic simulation. A 50S ribosomal L7/L12 adjuvant was added to its N-terminus to improve stability and immunogenicity. The final RNA sequence was used to design a trans-amplifying RNA (taRNA) vaccine in a split-vector system. It consists of two molecules: a nonreplicating RNA encoding a trans-acting replicase to amplify the second one, a trans-replicon (TR) RNA encoding the vaccine protein. Overall, the immune response simulation detected that activated B and T lymphocytes and increased memory cell formation. Macrophages and dendritic cells proliferated continuously, and IFN-γ and cytokines like IL-2 were released highly.

Hybrid Predictive Machine Learning Model for the Prediction of Immunodominant Peptides of Respiratory Syncytial Virus

Respiratory syncytial virus (RSV) is a common respiratory pathogen that infects the human lungs and respiratory tract, often causing symptoms similar to the common cold. Vaccination is the most effective strategy for managing viral outbreaks. Currently, extensive efforts are focused on developing a vaccine for RSV. Traditional vaccine design typically involves using an attenuated form of the pathogen to elicit an immune response. In contrast, peptide-based vaccines (PBVs) aim to identify and chemically synthesize specific immunodominant peptides (IPs), known as T-cell epitopes (TCEs), to induce a targeted immune response. Despite their potential for enhancing vaccine safety and immunogenicity, PBVs have received comparatively less attention. Identifying IPs for PBV design through conventional wet-lab experiments is challenging, costly, and time-consuming. Machine learning (ML) techniques offer a promising alternative, accurately predicting TCEs and significantly reducing the time and cost of vaccine development. This study proposes the development and evaluation of eight hybrid ML predictive models created through the permutations and combinations of two classification methods, two feature weighting techniques, and two feature selection algorithms, all aimed at predicting the TCEs of RSV. The models were trained using the experimentally determined TCEs and non-TCE sequences acquired from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) repository. The hybrid model composed of the XGBoost (XGB) classifier, chi-squared (ChST) weighting technique, and backward search (BST) as the optimal feature selection algorithm (ChST-BST-XGB) was identified as the best model, achieving an accuracy, sensitivity, specificity, F1 score, AUC, precision, and MCC of 97.10%, 0.98, 0.97, 0.98, 0.99, 0.99, and 0.96, respectively. Additionally, K-fold cross-validation (KFCV) was performed to ensure the model's reliability and an average accuracy of 97.21% was recorded for the ChST-BST-XGB model. The results indicate that the hybrid XGBoost model consistently outperforms other hybrid approaches. The epitopes predicted by the proposed model may serve as promising vaccine candidates for RSV, subject to in vitro and in vivo scientific assessments. This model can assist the scientific community in expediting the screening of active TCE candidates for RSV, ultimately saving time and resources in vaccine development.

Physicochemical properties-based hybrid machine learning technique for the prediction of SARS-CoV-2 T-cell epitopes as vaccine targets

Majority of the existing SARS-CoV-2 vaccines work by presenting the whole pathogen in the attenuated form to immune system to invoke an immune response. On the other hand, the concept of a peptide based vaccine (PBV) is based on the identification and chemical synthesis of only immunodominant peptides known as T-cell epitopes (TCEs) to induce a specific immune response against a particular pathogen. However PBVs have received less attention despite holding huge untapped potential for boosting vaccine safety and immunogenicity. To identify these TCEs for designing PBV, wet-lab experiments are difficult, expensive, and time-consuming. Machine learning (ML) techniques can accurately predict TCEs, saving time and cost for speedy vaccine development. This work proposes novel hybrid ML techniques based on the physicochemical properties of peptides to predict SARS-CoV-2 TCEs. The proposed hybrid ML technique was evaluated using various ML model evaluation metrics and demonstrated promising results. The hybrid technique of decision tree classifier with chi-squared feature weighting technique and forward search optimal feature searching algorithm has been identified as the best model with an accuracy of 98.19%. Furthermore, K-fold cross-validation (KFCV) was performed to ensure that the model is reliable and the results indicate that the hybrid random forest model performs consistently well in terms of accuracy with respect to other hybrid approaches. The predicted TCEs are highly likely to serve as promising vaccine targets, subject to evaluations both in-vivo and in-vitro. This development could potentially save countless lives globally, prevent future epidemic-scale outbreaks, and reduce the risk of mutation escape.

Whole proteome analysis of MDR Klebsiella pneumoniae to identify mRNA and multiple epitope based vaccine targets against emerging nosocomial and lungs associated infections

Klebsiella pneumonia is a Gram negative facultative anaerobic bacterium involved in various community-acquired pneumonia, nosocomial and lungs associated infections. Frequent usage of several antibiotics and acquired resistance mechanisms has made this bacterium multi-drug resistance (MDR), complicating the treatment of patients. To avoid the spread of this bacterium, there is an urgent need to develop a vaccine based on immuno-informatics approaches that is more efficient than conventional method of vaccine prediction or development. Initially, the complete proteomic sequence of K. pneumonia was picked over for specific and prospective vaccine targets. From the annotation of the whole proteome, eight immunogenic proteins were selected, and these shortlisted proteins were interpreted for CTL, B-cells, and HTL epitopes prediction, to construct mRNA and multi-epitope vaccines. The Antigenicity, allergenicity and toxicity analysis validate the vaccine's design, and its molecular docking was done with immuno-receptor the TLR-3. The docking interaction showed a stronger binding affinity with a minimum energy of -1153.2 kcal/mol and established 23 hydrogen bonds, 3 salt bridges, 1 disulfide bond, and 340 non-binding contacts. Further validation was done using In-silico cloning which shows the highest CAI score of 0.98 with higher GC contents of 72.25% which represents a vaccine construct with a high value of expression in E. coli. Immune Simulation shows that the antibodies (IgM, IgG1, and IgG2) production exceeded 650,000 in 2 to 3 days but the response was completely neutralized in the 5th day. In conclusion, the study provides the effective, safe and stable vaccine construct against Klebsiella pneumonia, which further needs in vitro and in vivo validations.Communicated by Ramaswamy H. Sarma.

Anti-Idiotypic mRNA Vaccine to Treat Autoimmune Disorders

The 80+ existing autoimmune disorders (ADs) affect billions with little prevention or treatment options, except for temporary symptomatic management, leading to enormous human suffering and a monumental financial burden. The autoantibodies formed in most ADs have been identified, allowing the development of novel anti-idiotypic antibodies to mute the autoantibodies using vaccines. Nucleoside vaccines have been successfully tested as antigen-specific immunotherapies (ASI), with mRNA technology offering multi-epitope targeting to mute multiple autoantibodies. This paper proposes using mRNA technology to produce anti-idiotypic antibodies with broad effectiveness in preventing and treating them. This paper delves into the state-of-the-art mRNA design strategies used to develop novel ASIs by selecting appropriate T cell and B cell epitopes to generate anti-idiotypic antibodies. The low cost and fast development of mRNA vaccines make this technology the most affordable for the global control of ADs.

From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches

A significant surge in research endeavors leverages the vast potential of high-throughput omic technology platforms for broad profiling of biological responses to vaccines and cutting-edge immunotherapies and stem-cell therapies under development. These profiles capture different aspects of core regulatory and functional processes at different scales of resolution from molecular and cellular to organismal. Systems approaches capture the complex and intricate interplay between these layers and scales. Here, we summarize experimental data modalities, for characterizing the genome, epigenome, transcriptome, proteome, metabolome, and antibody-ome, that enable us to generate large-scale immune profiles. We also discuss machine learning and network approaches that are commonly used to analyze and integrate these modalities, to gain insights into correlates and mechanisms of natural and vaccine-mediated immunity as well as therapy-induced immunomodulation.

Advances of Reverse Vaccinology for mRNA Vaccine Design against SARS-CoV-2: A Review of Methods and Tools

mRNA vaccines are a new class of vaccine that can induce potent and specific immune responses against various pathogens. However, the design of mRNA vaccines requires the identification and optimization of suitable antigens, which can be challenging and time consuming. Reverse vaccinology is a computational approach that can accelerate the discovery and development of mRNA vaccines by using genomic and proteomic data of the target pathogen. In this article, we review the advances of reverse vaccinology for mRNA vaccine design against SARS-CoV-2, the causative agent of COVID-19. We describe the steps of reverse vaccinology and compare the in silico tools used by different studies to design mRNA vaccines against SARS-CoV-2. We also discuss the challenges and limitations of reverse vaccinology and suggest future directions for its improvement. We conclude that reverse vaccinology is a promising and powerful approach to designing mRNA vaccines against SARS-CoV-2 and other emerging pathogens.

Optimization, characterization, comparison of self-assembly VLP of capsid protein L1 in yeast and reverse vaccinology design against human papillomavirus type 52

BACKGROUND

Vaccination is the one of the agendas of many countries to reduce cervical cancer caused by the Human papillomavirus. Currently, VLP-based vaccine is the most potent vaccine against HPV, which could be produced by a variety of expression systems. Our study focuses on a comparison of recombinant protein expression L1 HPV52 using two common yeasts, Pichia pastoris and Hansenula polymorpha that have been used for vaccine production on an industrial scale. We also applied bioinformatics approach using reverse vaccinology to design alternative multi-epitope vaccines in recombinant protein and mRNA types.

RESULTS

Our study found that P. pastoris relatively provided higher level of L1 protein expression and production efficiency compared to H. polymorpha in a batch system. However, both hosts showed self-assembly VLP formation and stable integration during protein induction. The vaccine we have designed exhibited high immune activation and safe in computational prediction. It is also potentially suitable for production in a variety of expression systems.

CONCLUSION

By monitoring the overall optimization parameter assessment, this study can be used as the basis reference for large-scale production of the HPV52 vaccine.

Large-scale investigation of zoonotic viruses in the era of high-throughput sequencing

Zoonotic diseases considerably impact public health and socioeconomics. RNA viruses reportedly caused approximately 94% of zoonotic diseases documented from 1990 to 2010, emphasizing the importance of investigating RNA viruses in animals. Furthermore, it has been estimated that hundreds of thousands of animal viruses capable of infecting humans are yet to be discovered, warning against the inadequacy of our understanding of viral diversity. High-throughput sequencing (HTS) has enabled the identification of viral infections with relatively little bias. Viral searches using both symptomatic and asymptomatic animal samples by HTS have revealed hidden viral infections. This review introduces the history of viral searches using HTS, current analytical limitations, and future potentials. We primarily summarize recent research on large-scale investigations on viral infections reusing HTS data from public databases. Furthermore, considering the accumulation of uncultivated viruses, we discuss current studies and challenges for connecting viral sequences to their phenotypes using various approaches: performing data analysis, developing predictive modeling, or implementing high-throughput platforms of virological experiments. We believe that this article provides a future direction in large-scale investigations of potential zoonotic viruses using the HTS technology.

Nanotechnology-Driven Delivery Systems in Inoculation Therapies

Nanotechnology and genomics are the newest allies of inoculation design. In recent years, nucleic acids have been targeted as sources of therapeutics to stimulate immune responses, to both fight disease and create memory to trigger further responses to threat. A myriad of promising findings in cancer research and virology has been reported in the current literature. Nanosystems are demonstrating their capabilities as efficient carriers, improving the efficacy of drug delivery, including nucleic acids as therapeutics, at focal sites, in living systems. This chapter approaches major elements involved in the successful use of nanotechnology as delivery platforms to optimise the efficacy of nucleic acids-driven therapeutics, particularly mRNA vectors as coding engines for targeted viral proteins. Latest findings in nanotechnological design are highlighted, key discoveries associated with the success of nanodelivery platforms are presented, and key characteristics of nanodelivery systems in nucleic acids-based vaccine technology are discussed, to illustrate their distinct advantages and disadvantages.

Latest Trends in Nucleic Acids' Engineering Techniques Applied to Precision Medicine

Nucleic acids are paving the way for advanced therapeutics. Unveiling the genome enabled a better understanding of unique genotype-phenotype profiling. Methods for engineering and analysis of nucleic acids, from polymerase chain reaction to Cre-Lox recombination, are contributing greatly to biomarkers' discovery, mapping of cellular signaling cascades, and smart design of therapeutics in precision medicine. Investigating the different subtypes of DNA and RNA via sequencing and profiling is empowering the scientific community with valuable information, to be used in advanced therapeutics, tracking epigenetics linked to disease. Recent results from the application of nucleic acids in novel therapeutics and precision medicine are very encouraging, demonstrating great potential to treat cancer, viral infections via inoculation (e.g., SAR-COV-2 mRNA vaccines), along with metabolic and genetic disorders. Limitations posed by challenges in delivery mode are being addressed to enable efficient guided-gene-programmed precision therapies. With the focus on genetic engineering and novel therapeutics, more precisely, in precision medicine, this chapter discusses the advance enabled by knowledge derived from these innovative branches of biotechnology.

A systematic and thematic analysis of the top 100 cited articles on mRNA vaccine indexed in Scopus database

The success of mRNA vaccines against SARS-CoV-2 implies that this technology can be applied to target any pathogen. However, the scientific production and research trends using the bibliometric method are still unknown. The top 100 most cited articles on mRNA vaccine research were obtained from the Scopus database from 1995 to 2021. Bibliometrix, an R-Package, and VOSviewer 1.6.11 were used for data analysis. There is a rapid growth in scientific outputs with a gradual increase in 2021. The United States produced 45 (45%) of the articles, followed by Germany with 15 (15%) and Israel with 10 (10%). The New England Journal of Medicine published the most papers in this field 13 (13%), followed by Nature 6(6%). Barney S. Graham was the most productive author among the top 100 most cited mRNA vaccine articles. University of Pennsylvania Perelman School of Medicine, US, was the top ranking institution, having 37 (37%). The visualization map clearly and spontaneously displayed the current state and research hot spots of mRNA research from a specific perspective. The most frequent keywords were COVID-19, vaccine, mRNA vaccine, mRNA, SARS-CoV-2, and immunogenicity, among others. A systematic review of the articles provided evidence that out of 100 articles, approximately 25 (25%) were focused on vaccine production and evaluation, followed by 26 (26%) in mRNA vaccine safety and efficacy, 23 (23%) were into mRNA vaccination, 23 (23%) considered risk factors associated with mRNA vaccination, while 8 (8%) of the articles covered the issue of mRNA vaccine delivery. In addition, 42% of the articles focused on COVID-19, 17% on cancer, 8% on influenza virus, 4% on COVID-19 and kidney disease, 3% COVID-19 and myocarditis, and 3% on rabies virus, among others. The findings of this systematic and thematic analysis provided the knowledge basis for further research on mRNA vaccines globally.

Lipid Nanoparticle (LNP) Chemistry Can Endow Unique In Vivo RNA Delivery Fates within the Liver That Alter Therapeutic Outcomes in a Cancer Model

Within the field of lipid nanoparticles (LNPs) for RNA delivery, the focus has been mainly placed on organ level delivery, which can mask cellular level effects consequential to therapeutic applications. Here, we studied a pair of LNPs with similar physical properties and discovered how the chemistry of the ionizable amino lipid can control the endogenous LNP identity, affecting cellular uptake in the liver and altering therapeutic outcomes in a model of liver cancer. Although most LNPs accumulate in the liver after intravenous administration (suggesting that liver delivery is straightforward), we observed an unexpected behavior when comparing two similar LNP formulations (5A2-SC8 and 3A5-SC14 LNPs) that resulted in distinct RNA delivery within the organ. Despite both LNPs possessing similar physical properties, ability to silence gene expression in vitro, strong accumulation within the liver, and a shared pKa of 6.5, only 5A2-SC8 LNPs were able to functionally deliver RNA to hepatocytes. Factor VII (FVII) activity was reduced by 87%, with 5A2-SC8 LNPs carrying FVII siRNA (siFVII), while 3A5-SC14 LNPs carrying siFVII produced baseline FVII activity levels comparable to the nontreatment control at a dosage of 0.5 mg/kg. Protein corona analysis indicated that 5A2-SC8 LNPs bind apolipoprotein E (ApoE), which can drive LDL-R receptor-mediated endocytosis in hepatocytes. In contrast, the surface of 3A5-SC14 LNPs was enriched in albumin but depleted in ApoE, which likely led to Kupffer cell delivery and detargeting of hepatocytes. In an aggressive MYC-driven liver cancer model relevant to hepatocytes, 5A2-SC8 LNPs carrying let-7g miRNA were able to significantly extend survival up to 121 days. Since disease targets exist in an organ- and cell-specific manner, the clinical development of RNA LNP therapeutics will require an improved understanding of LNP cellular tropism within organs. The results from our work illustrate the importance of understanding the cellular localization of RNA delivery and incorporating further checkpoints when choosing nanoparticles beyond biochemical and physical characterization, as small changes in the chemical composition of LNPs can have an impact on both the biofate of LNPs and therapeutic outcomes.

mRNA vaccines: A novel weapon to control infectious diseases

Infectious diseases have always threatened human life, but with the development of vaccines, effective strategies for preventing and controlling these diseases have become available. The global outbreak of COVID-19 ushered in the advent of mRNA vaccine technologies, which quickly led to the introduction of mRNA vaccines effective against SARS-CoV-2. The success of this approach has stimulated research into the use of mRNA vaccines in the fight against other emerging as well as remerging infectious diseases. This review examines the constructive strategies and delivery systems used in mRNA vaccines and provides an overview of current clinical trials of those vaccines in the prevention of infectious diseases. The underlying mechanisms of mRNA vaccines are also discussed, including the double-edged sword of the innate immune response. Finally, the challenges but also the potential of mRNA vaccines are considered.

Recent advances in mRNA-LNP therapeutics: immunological and pharmacological aspects

In the last decade, the development of messenger RNA (mRNA) therapeutics by lipid nanoparticles (LNP) leads to facilitate clinical trial recruitment, which improves the efficacy of treatment modality to a large extent. Although mRNA-LNP vaccine platforms for the COVID-19 pandemic demonstrated high efficiency, safety and adverse effects challenges due to the uncontrolled immune responses and inappropriate pharmacological interventions could limit this tremendous efficacy. The current study reveals the interplay of immune responses with LNP compositions and characterization and clarifies the interaction of mRNA-LNP therapeutics with dendritic, macrophages, neutrophile cells, and complement. Then, pharmacological profiles for mRNA-LNP delivery, including pharmacokinetics and cellular trafficking, were discussed in detail in cancer types and infectious diseases. This review study opens a new and vital landscape to improve multidisciplinary therapeutics on mRNA-LNP through modulation of immunopharmacological responses in clinical trials.

Evaluation of the Whole Proteome of Achromobacter xylosoxidans to Identify Vaccine Targets for mRNA and Peptides-Based Vaccine Designing Against the Emerging Respiratory and Lung Cancer-Causing Bacteria

Achromobacter xylosoxidans is a rod-shaped Gram-negative bacterium linked with causing several infections which mostly includes hematological malignancies. It has been recently reported to be associated with the development and progression of lung cancer and is an emerging respiratory disease-causing bacterium. The treatment of individuals infected with A. xylosoxidans bacteremia is difficult due to the fact that this pathogen has both intrinsic and acquired resistance mechanisms, typically resulting in a phenotype of multidrug resistance (MDR). Efforts are needed to design effective therapeutic strategies to curtail the emergence of this bacterium. Computational vaccine designing has proven its effectiveness, specificity, safety, and stability compared to conventional approaches of vaccine development. Therefore, the whole proteome of A. xylosoxidans was screened for the characterization of potential vaccine targets through subtractive proteomics pipeline for therapeutics design. Annotation of the whole proteome confirmed the three immunogenic vaccine targets, such as (E3HHR6), (E3HH04), and (E3HWA2), which were used to map the putative immune epitopes. The shortlisted epitopes, specific against Cytotoxic T Lymphocytes, Helper T-cell Lymphocytes, and linear B-Cell, were used to design the mRNA and multi-epitopes vaccine (MEVC). Initial validations confirmed the antigenic and non-allergenic properties of these constructs, followed by docking with the immune receptor, TLR-5, which resulted in robust interactions. The interaction pattern that followed in the docking complex included formation of 5 hydrogen bonds, 2 salt bridges, and 165 non-bonded contacts. This stronger binding affinity was also assessed through using the mmGBSA approach, showing a total of free binding energy of -34.64 kcal/mol. Further validations based on in silico cloning revealed a CAI score of 0.98 and an optimal percentage of GC contents (54.4%) indicated a putatively higher expression of the vaccine construct in Escherichia coli. Moreover, immune simulation revealed strong antibodies production upon the injection of the designed MEVC that resulted in the highest peaks of IgM+ IgG production (>3,500) between 10 and 15 days. In conclusion the current study provide basis for vaccine designing against the emerging A. xylosoxidans, which demands further experimental studies for in vitro and in vivo validations.

Recent Insights Into the Molecular Mechanism of Toll-Like Receptor Response to Dengue Virus Infection

Dengue is the most prevalent and rapidly spreading mosquito-borne viral disease caused by dengue virus (DENV). Recently, DENV has been affecting humans within an expanding geographic range due to the warming of the earth. Innate immune responses play a significant role in antiviral defense, and Toll-like receptors (TLRs) are key regulators of innate immunity. Therefore, a detailed understanding of TLR and DENV interactions is important for devising therapeutic and preventive strategies. Several studies have indicated the ability of DENV to modulate the TLR signaling pathway and host immune response. Vaccination is considered one of the most successful medical interventions for preventing viral infections. However, only a partially protective dengue vaccine, the first licensed dengue vaccine CYD-TDV, is available in some dengue-endemic countries to protect against DENV infection. Therefore, the development of a fully protective, durable, and safe DENV vaccine is a priority for global health. Here, we demonstrate the progress made in our understanding of the host response to DENV infection, with a particular focus on TLR response and how DENV avoids the response toward establishing infection. We also discuss dengue vaccine candidates in late-stage development and the issues that must be overcome to enable their success.