Development of an in silico multi-epitope vaccine against SARS-COV-2 by précised immune-informatics approaches

Identification of Differentially Expressed Genes and Protein-Protein Interaction in Patients With COVID-19 and Diabetes Peripheral Neuropathy: A Bioinformatics and System Biology Approach

The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact globally, resulting in a higher death toll and persistent health issues for survivors, particularly those with pre-existing medical conditions. Numerous studies have demonstrated a strong correlation between catastrophic COVID-19 results and diabetes. To gain deeper insights, we analysed the transcriptome dataset from COVID-19 and diabetic peripheral neuropathic patients. Using the R programming language, differentially expressed genes (DEGs) were identified and classified based on up and down regulations. The overlaps of DEGs were then explored between these groups. Functional annotation of those common DEGs was performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Bio-Planet, Reactome, and Wiki pathways. A protein-protein interaction (PPI) network was created with bioinformatics tools to understand molecular interactions. Through topological analysis of the PPI network, we determined hub gene modules and explored gene regulatory networks (GRN). Furthermore, the study extended to suggesting potential drug molecules for the identified mutual DEG based on the comprehensive analysis. These approaches may contribute to understanding the molecular intricacies of COVID-19 in diabetic peripheral neuropathy patients through insights into potential therapeutic interventions.

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.

Systematical analyses of large-scale transcriptome reveal viral infection-related genes and disease comorbidities

Perturbation of transcriptome in viral infection patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent transcriptome and identification of robust biomarkers is not complete. In this study, we manually collected 23 datasets related to 6,197 blood transcriptomes across 16 types of respiratory virus infections. We applied a comprehensive systems biology approach starting with whole-blood transcriptomes combined with multilevel bioinformatics analyses to characterize the expression, functional pathways, and protein-protein interaction (PPI) networks to identify robust biomarkers and disease comorbidities. Robust gene markers of infection with different viruses were identified, which can accurately classify the normal and infected patients in train and validation cohorts. The biological processes (BP) of different viruses showed great similarity and enriched in infection and immune response pathways. Network-based analyses revealed that a variety of viral infections were associated with nervous system diseases, neoplasms and metabolic diseases, and significantly correlated with brain tissues. In summary, our manually collected transcriptomes and comprehensive analyses reveal key molecular markers and disease comorbidities in the process of viral infection, which could provide a valuable theoretical basis for the prevention of subsequent public health events for respiratory virus infections.

Prevalence of covid-19 among patients with chronic obstructive pulmonary disease and tuberculosis

BACKGROUND

The exhaustive information about non-communicable diseases associated with COVID-19 and severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) are getting easier to find in the literature. However, there is a lack of knowledge regarding tuberculosis (TB) and chronic obstructed pulmonary disease (COPD), with numerous infections in COVID-19 patients.

OBJECTIVES

Priority is placed on determining the patient's prognosis based on the presence or absence of TB and COPD. Additionally, a comparison is made between the risk of death and the likelihood of recovery in terms of time in COVID-19 patients who have either COPD or TB.

METHODOLOGY

At the DHQ Hospital in Muzaffargarh, Punjab, Pakistan, 498 COVID-19 patients with TB and COPD were studied retrospectively. The duration of study started in February 2022 and concluded in August 2022. The Kaplan-Meier curves described time-to-death and time-to-recovery stratified by TB and COPD status. The Wilcoxon test compared the survival rates of people with TB and COPD in two matched paired groups and their status differences with their standard of living.

RESULTS

The risk of death in COVID-19 patients with TB was 1.476 times higher than in those without (95% CI: 0.949-2.295). The recovery risk in COVID-19 patients with TB was 0.677 times lower than in those without (95% CI: 0.436-1.054). Similarly, patients with TB had a significantly shorter time to death (p=.001) and longer time to recovery (p=.001).

CONCLUSIONS

According to the findings, the most significant contributor to an increased risk of morbidity and mortality in TB and COPD patients was the COVID-19.KEY MESSAGESSARS-Cov-19 is a new challenge for the universe in terms of prevention and treatment for people with tuberculosis and chronic obstructive pulmonary disease, among other diseases.Propensity score matching to control for potential biases.Compared to hospitalized patients with and without (TB and COPD) had an equivalently higher mortality rate.

Over-representation analysis of angiogenic factors in immunosuppressive mechanisms in neoplasms and neurological conditions during COVID-19

BACKGROUND

Recent studies emphasized the necessity to identify key (human) biological processes and pathways targeted by the Coronaviridae family of viruses, especially Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Coronavirus Disease (COVID-19) caused up to 33-55 % death rates in COVID-19 patients with malignant neoplasms and Alzheimer's disease. Given this scenario, we identified biological processes and pathways involved in various diseases which are most likely affected by COVID-19.

METHODS

The COVID-19 DisGeNET data set (v4.0) contains the associations between various diseases and human genes known to interact with viruses from Coronaviridae family and were obtained from the IntAct Coronavirus data set annotated with DisGeNET data. We constructed the disease-gene network to identify genes that are involved in various comorbid diseased states. Communities from the disease-gene network were identified using Louvain method and functional enrichment through over-representation analysis methodology was used to discover significant biological processes and pathways shared between COVID-19 and other diseases.

RESULT

The COVID-19 DisGeNET data set (v4.0) comprised of 828 human genes and 10,473 diseases (including various phenotypes) that together constituted nodes in the disease-gene network. Each of the 70,210 edges connects a human gene with an associated disease. The top 10 genes linked to most number of diseases were VEGFA, BCL2, CTNNB1, ALB, COX2, AGT, HLA-A, HMOX1, FGF2 and COMT. The most vulnerable group of patients thus discovered had comorbid conditions such as carcinomas, malignant neoplasms and Alzheimer's disease. Finally, we identified 15 potentially useful biological processes and pathways for improved therapies. Vascular endothelial growth factor (VEGF) is the key mediator of angiogenesis in cancer. It is widely distributed in the brain and plays a crucial role in brain inflammation regulating the level of angiopoietins. With a degree of 1899, VEGFA was associated with maximum number of diseases in the disease-gene network. Previous studies have indicated that increased levels of VEGFA in the blood results in dyspnea, Pulmonary Edema (PE), Acute Lung Injury (ALI) and Acute Respiratory Distress Syndrome (ARDS). In case of COVID-19 patients with neoplasms and other neurological symptoms, our results indicate VEGFA as a therapeutic target for inflammation suppression. As VEGFs are known to disproportionately affect cancer patients, improving endothelial permeability and vasodilation with anti-VEGF therapy could lead to suppression of inflammation and also improve oxygenation. As an outcome of our study, we make case for clinical investigations towards anti-VEGF therapies for such comorbid conditions affected by COVID-19 for better therapeutic outcomes.

In silico and immunoinformatics based multiepitope subunit vaccine design for protection against visceral leishmaniasis

Visceral leishmaniasis (VL) is a vector-borne neglected tropical protozoan disease with high fatality and no certified vaccine. Conventional vaccine preparation is challenging and tedious. Here in this work, we created a global multiepitope subunit vaccination against VL utilizing innovative immunoinformatics technique based on the extensively conserved epitopic regions of the PrimPol protein of Leishmania donovani consisting of four subunits which were analyzed and studied, out of which DNA primase large subunit and DNA polymerase α subunit B were evaluated as antigens by Vaxijen 2.0. The multiepitope vaccine design includes a single adjuvant β-defensins, eight CTL epitopes, eight HTL epitopes, seven linear BCL epitopes and one discontinuous BCL epitope to induce innate, cellular and humoral immune responses against VL. The Expasy ProtParam tool characterized the physiochemical parameters of the vaccine. At the same time, SOLpro evaluated our vaccine constructs to be soluble upon expression. We also modeled the stable tertiary structure of our vaccine construct through Robetta modeling for molecular docking studies with toll-like receptor proteins through HADDOCK 2.4. Simulations based on molecular dynamics revealed an intact vaccine and TLR8 complex, supporting our vaccine design's immunogenicity. Also, the immune simulation of our vaccine by the C-ImmSim server demonstrated the potency of the multiepitope vaccine construct to induce proper immune response for host defense. Codon optimization and in silico cloning of our vaccine further assured high expression. The outcomes of our study on multiepitope vaccine design significantly produced a potential candidate against VL and can potentially eradicate the disease in the future after clinical investigations.Communicated by Ramaswamy H. Sarma.

Construction and validation of a multi-epitope in silico vaccine model for lymphatic filariasis by targeting Brugia malayi: a reverse vaccinology approach

BACKGROUND

Lymphatic filariasis (LF), often referred to as elephantiasis, has been identified as one of the 17 neglected tropical diseases by the World Health Organization. Currently, there are no vaccines available to treat this infection in humans. Therefore, with the objective of devising a novel preventive measure, we exploited an immunoinformatics approach to design a multi-epitope-based subunit vaccine for LF, that can elicit a variety of immune responses within the host. In this study, different B cell, T_C cell, and T_H cell-binding epitopes were screened from the antigenic proteins of Brugia malayi and they were passed through several immunological filters to determine the optimal epitopes.

RESULTS

As a result, 15 CD8+, 3 CD4+, and 3 B cell epitopes were found to be prominent, antigenic, non-toxic, immunogenic and non-allergenic. The presence of conformational B cell epitopes and cytokine-inducing epitopes confirmed the humoral and cell-mediated immune response that would be triggered by the constructed vaccine model. Following that, the selected epitopes and TLR-4-specific adjuvant were ligated by appropriate peptide linkers to finalize the vaccine construct. Protein-protein docking of the vaccine structure with the TLR4 receptor predicted strong binding affinity and hence putatively confirms its ability to elicit an immune response. Further, the efficiency of the vaccine candidate to provide a long-lasting protective immunity was assessed by in silico immune simulation. The reverse translated vaccine sequence was also virtually cloned in the pET28a (+) plasmid after the optimization of the gene sequence.

CONCLUSION

So taken together, by monitoring the overall in silico assessment, we hypothesize that our engineered peptide vaccine could be a viable prophylactic approach in the development of vaccines against the threat of human lymphatic filariasis.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s42269-023-01013-0.

Immunoinformatics Study: Multi-Epitope Based Vaccine Design from SARS-CoV-2 Spike Glycoprotein

The coronavirus disease 2019 outbreak has become a huge challenge in the human sector for the past two years. The coronavirus is capable of mutating at a higher rate than other viruses. Thus, an approach for creating an effective vaccine is still needed to induce antibodies against multiple variants with lower side effects. Currently, there is a lack of research on designing a multiepitope of the COVID-19 spike protein for the Indonesian population with comprehensive immunoinformatic analysis. Therefore, this study aimed to design a multiepitope-based vaccine for the Indonesian population using an immunoinformatic approach. This study was conducted using the SARS-CoV-2 spike glycoprotein sequences from Indonesia that were retrieved from the GISAID database. Three SARS-CoV-2 sequences, with IDs of EIJK-61453, UGM0002, and B.1.1.7 were selected. The CD8+ cytotoxic T-cell lymphocyte (CTL) epitope, CD4+ helper T lymphocyte (HTL) epitope, B-cell epitope, and IFN-γ production were predicted. After modeling the vaccines, molecular docking, molecular dynamics, in silico immune simulations, and plasmid vector design were performed. The designed vaccine is antigenic, non-allergenic, non-toxic, capable of inducing IFN-γ with a population reach of 86.29% in Indonesia, and has good stability during molecular dynamics and immune simulation. Hence, this vaccine model is recommended to be investigated for further study.

In silico design of a multi-epitope vaccine against the spike and the nucleocapsid proteins of the Omicron variant of SARS-CoV-2

Computational studies can comprise an effective approach to treating and preventing viral infections. Since 2019, the world has been dealing with the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The most important achievement in this short period of time in the effort to reduce morbidity and mortality was the production of vaccines and effective antiviral drugs. Although the virus has been significantly suppressed, it continues to evolve, spread, and evade the host's immune system. Recently, researchers have turned to immunoinformatics tools to reduce side effects and save the time and cost of traditional vaccine production methods. In the present study, an attempt has been made to design a multi-epitope vaccine with humoral and cellular immune response stimulation against the Omicron variant of SARS-CoV-2 by investigating new mutations in spike (S) and nucleocapsid (N) proteins. The population coverage of the vaccine was evaluated as appropriate compared to other studies. The results of molecular dynamics simulation and molecular mechanics/generalized Born surface area (MM/GBSA) calculations predict the stability and proper interaction of the vaccine with Toll-like receptor 4 (TLR-4) as an innate immune receptor. The results of the immune simulation show a significant increase in the coordinated response of IgM and IgG after the third injection of the vaccine. Also, in the continuation of the research, spike proteins from BA.4 and BA.5 lineages were screened by immunoinformatics filters and effective epitopes were suggested for vaccine design. Despite the high precision of computational studies, in-vivo and in-vitro research is needed for final confirmation.Communicated by Ramaswamy H. Sarma.

Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics

Objective

To analyze and identify the core genes related to the expression and prognosis of lung cancer including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by bioinformatics technology, with the aim of providing a reference for clinical treatment.

Methods

Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database. After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein-protein interaction (PPI) network was constructed by STRING online software for further study, and the core genes were determined by Cytoscape software and KEGG pathway enrichment analysis. The clustering heat map was drawn by Excel software to verify its accuracy. In addition, we used the University of Alabama at Birmingham Cancer (UALCAN) website to analyze the expression of core genes in P53 mutation status, confirmed the expression of crucial core genes in lung cancer tissues with Gene Expression Profiling Interactive Analysis (GEPIA) and GEPIA2 online software, and evaluated their prognostic value in lung cancer patients with the Kaplan-Meier online plotter tool.

Results

CHEK1, CCNB1, CCNB2, and CDK1 were selected. The expression levels of these four genes in lung cancer tissues were significantly higher than those in normal tissues. Their increased expression was negatively correlated with lung cancer patients (including LUAD and LUSC) prognosis and survival rate.

Conclusion

CHEK1, CCNB1, CCNB2, and CDK1 are the critical core genes of lung cancer and are highly expressed in lung cancer. They are negatively correlated with the prognosis of lung cancer patients (including LUAD and LUSC) and closely related to the formation and prediction of lung cancer. They are valuable predictors and may be predictive biomarkers of lung cancer.

Identifying the Effect of COVID-19 Infection in Multiple Myeloma and Diffuse Large B-Cell Lymphoma Patients Using Bioinformatics and System Biology

The severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), also referred to as COVID-19, has spread to several countries and caused a serious threat to human health worldwide. Patients with confirmed COVID-19 infection spread the disease rapidly throughout the region. Multiple myeloma (MM) and diffuse large B-cell lymphoma (DLBCL) are risk factors for COVID-19, although the molecular mechanisms underlying the relationship among MM, DLBCL, and COVID-19 have not been elucidated so far. In this context, transcriptome analysis was performed in the present study to identify the shared pathways and molecular indicators of MM, DLBCL, and COVID-19, which benefited the overall understanding of the effect of COVID-19 in patients with MM and DLBCL. Three datasets (GSE16558, GSE56315, and GSE152418) were downloaded from the Gene Expression Omnibus (GEO) and searched for the shared differentially expressed genes (DEGs) in patients with MM and DLBCL who were infected with SARS-CoV-2. The objective was to detect similar pathways and prospective medicines. A total of 29 DEGs that were common across these three datasets were selected. A protein-protein interaction (PPI) network was constructed using data from the STRING database followed by the identification of hub genes. In addition, the association of MM and DLBCL with COVID-19 infection was analyzed through functional analysis using ontologies terms and pathway analysis. Three relationships were observed in the evaluated datasets: transcription factor-gene interactions, protein-drug interactions, and an integrated regulatory network of DEGs and miRNAs with mutual DEGs. The findings of the present study revealed potential pharmaceuticals that could be beneficial in the treatment of COVID-19.

Mapping Potential Vaccine Candidates Predicted by VaxiJen for Different Viral Pathogens between 2017-2021-A Scoping Review

Reverse vaccinology (RV) is a promising alternative to traditional vaccinology. RV focuses on in silico methods to identify antigens or potential vaccine candidates (PVCs) from a pathogen's proteome. Researchers use VaxiJen, the most well-known RV tool, to predict PVCs for various pathogens. The purpose of this scoping review is to provide an overview of PVCs predicted by VaxiJen for different viruses between 2017 and 2021 using Arksey and O'Malley's framework and the Preferred Reporting Items for Systematic Reviews extension for Scoping Reviews (PRISMA-ScR) guidelines. We used the term 'vaxijen' to search PubMed, Scopus, Web of Science, EBSCOhost, and ProQuest One Academic. The protocol was registered at the Open Science Framework (OSF). We identified articles on this topic, charted them, and discussed the key findings. The database searches yielded 1033 articles, of which 275 were eligible. Most studies focused on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), published between 2020 and 2021. Only a few articles (8/275; 2.9%) conducted experimental validations to confirm the predictions as vaccine candidates, with 2.2% (6/275) articles mentioning recombinant protein expression. Researchers commonly targeted parts of the SARS-CoV-2 spike (S) protein, with the frequently predicted epitopes as PVCs being major histocompatibility complex (MHC) class I T cell epitopes WTAGAAAYY, RQIAPGQTG, IAIVMVTIM, and B cell epitope IAPGQTGKIADY, among others. The findings of this review are promising for the development of novel vaccines. We recommend that vaccinologists use these findings as a guide to performing experimental validation for various viruses, with SARS-CoV-2 as a priority, because better vaccines are needed, especially to stay ahead of the emergence of new variants. If successful, these vaccines could provide broader protection than traditional vaccines.

A novel vaccine based on SARS-CoV-2 CD4+ and CD8+ T cell conserved epitopes from variants Alpha to Omicron

COVID-19 caused, as of September, 1rst, 2022, 599,825,400 confirmed cases, including 6,469,458 deaths. Currently used vaccines reduced severity and mortality but not virus transmission or reinfection by different strains. They are based on the Spike protein of the Wuhan reference virus, which although highly antigenic suffered many mutations in SARS-CoV-2 variants, escaping vaccine-generated immune responses. Multiepitope vaccines based on 100% conserved epitopes of multiple proteins of all SARS-CoV-2 variants, rather than a single highly mutating antigen, could offer more long-lasting protection. In this study, a multiepitope multivariant vaccine was designed using immunoinformatics and in silico approaches. It is composed of highly promiscuous and strong HLA binding CD4⁺ and CD8⁺ T cell epitopes of the S, M, N, E, ORF1ab, ORF 6 and ORF8 proteins. Based on the analysis of one genome per WHO clade, the epitopes were 100% conserved among the Wuhan-Hu1, Alpha, Beta, Gamma, Delta, Omicron, Mµ, Zeta, Lambda and R1 variants. An extended epitope-conservancy analysis performed using GISAID metadata of 3,630,666 SARS-CoV-2 genomes of these variants and the additional genomes of the Epsilon, Lota, Theta, Eta, Kappa and GH490 R clades, confirmed the high conservancy of the epitopes. All but one of the CD4 peptides showed a level of conservation greater than 97% among all genomes. All but one of the CD8 epitopes showed a level of conservation greater than 96% among all genomes, with the vast majority greater than 99%. A multiepitope and multivariant recombinant vaccine was designed and it was stable, mildly hydrophobic and non-toxic. The vaccine has good molecular docking with TLR4 and promoted, without adjuvant, strong B and Th1 memory immune responses and secretion of high levels of IL-2, IFN-γ, lower levels of IL-12, TGF-β and IL-10, and no IL-6. Experimental in vivo studies should validate the vaccine’s further use as preventive tool with cross-protective properties.

COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning

Rational vaccine design, especially vaccine antigen identification and optimization, is critical to successful and efficient vaccine development against various infectious diseases including coronavirus disease 2019 (COVID-19). In general, computational vaccine design includes three major stages: (i) identification and annotation of experimentally verified gold standard protective antigens through literature mining, (ii) rational vaccine design using reverse vaccinology (RV) and structural vaccinology (SV) and (iii) post-licensure vaccine success and adverse event surveillance and its usage for vaccine design. Protegen is a database of experimentally verified protective antigens, which can be used as gold standard data for rational vaccine design. RV predicts protective antigen targets primarily from genome sequence analysis. SV refines antigens through structural engineering. Recently, RV and SV approaches, with the support of various machine learning methods, have been applied to COVID-19 vaccine design. The analysis of post-licensure vaccine adverse event report data also provides valuable results in terms of vaccine safety and how vaccines should be used or paused. Ontology standardizes and incorporates heterogeneous data and knowledge in a human- and computer-interpretable manner, further supporting machine learning and vaccine design. Future directions on rational vaccine design are discussed.