An Approach for a Synthetic CTL Vaccine Design against Zika Flavivirus Using Class I and Class II Epitopes Identified by Computer Modeling

Peptide Hamming Graphs: A network representation of peptides presented through specific HLAs to identify potential epitope clusters

BACKGROUND

Class I Major Histocompatibility Complex plays a critical role in the adaptive immune response by binding to peptides processed by Proteasome and Transporter associated with antigen processing complex and presenting them on the cell surface to cytotoxic T-cells. Understanding the process of peptide presentation and studying how presented peptides are distributed in the huge space of all potential epitopes could have a dramatic impact in the context of vaccine design, transplantation, autoimmunity, and cancer development.

METHODS

In the present work we propose a graph-driven approach to investigate the landscape of both self (human) and viral (254 organisms) peptides presented on cell surface through class I Major Histocompatibility Complex considering specific HLAs. For each considered HLA (N = 89) we designed a network, namely Peptide Hamming Graph, where nodes are peptides predicted to be presented by a given HLA and an edge is set when the Hamming distance between two peptides is equal or smaller than 2 (i.e. the same amino acid occurs in at least 7 positions of the two sequences).

RESULTS

Through the analysis of Peptide Hamming Graphs we studied how predicted presented peptides are distributed in the whole configurational space for different HLAs, identifying sets of viral peptides that can constitute a potential target for the immune system. In particular we selected connected components of the graph made exclusively of viral peptides and sets of viral peptides with high node degree interacting exclusively with viral neighbours.

CONCLUSIONS

This work constitutes an innovative approach to study potential cytotoxic T-cell epitopes relying on a network approach, overcoming the classical paradigm based on the identification of potential epitopes only considering their features as single peptides. T-cell cross-reactivity plays a focal role for the efficacy of this strategy increasing the probability of recognition, and consequently a stronger immune response, of presented peptides far from self, sharing a common pattern in terms of sequence similarity.

Survivin (BIRC5) Peptide Vaccine in the 4T1 Murine Mammary Tumor Model: A Potential Neoadjuvant T Cell Immunotherapy for Triple Negative Breast Cancer: A Preliminary Study

A triple negative breast cancer model using the murine 4T1 tumor cell line was used to explore the efficacy of an adjuvanted survivin peptide microparticle vaccine using tumor growth as the outcome metric. We first performed tumor cell dose titration studies to determine a tumor cell dose that resulted in sufficient tumor takes but allowed multiple serial measurements of tumor volumes, yet with minimal morbidity/mortality within the study period. Later, in a second cohort of mice, the survivin peptide microparticle vaccine was administered via intraperitoneal injection at the study start with a second dose given 14 days later. An orthotopic injection of 4T1 cells into the mammary tissue was performed on the same day as the administration of the second vaccine dose. The mice were followed for up to 41 days with subcutaneous measurements of tumor volume made every 3–4 days. Vaccination with survivin peptides was associated with a peptide antigen-specific gamma interferon enzyme-linked immunosorbent spot response in the murine splenocyte population but was absent from the control microparticle group. At the end of the study, we found that vaccination with adjuvanted survivin peptide microparticles resulted in statistically significant slower primary tumor growth rates in BALB/c mice challenged with 4T1 cells relative to the control peptideless vaccination group. These studies suggest that T cell immunotherapy specifically targeting survivin might be an applicable neoadjuvant immunotherapy therapy for triple negative breast cancer. More preclinical studies and clinical trials are needed to explore this concept further.

Proteome-Wide and Protein-Specific Multi-Epitope Vaccine Constructs Against the Rift Valley Fever Virus Outbreak Using Integrated Omics Approaches

Rift Valley fever (RVF) is a viral disease caused by a member of the Bunyavirales family causing severe infections in humans. The RVF virus is an enveloped, negative-sense, single-stranded RNA virus that can infect both animals and humans. The symptoms associated with these infections span from minor (fever and headaches) to severe (meningoencephalitis and hemorrhagic fever syndrome) symptoms. Despite the outbreaks of the RVF virus being reported in different parts of the world, no effective therapy is available. Herein, the development of an efficient vaccine is critical for the control of infections associated with the RVF virus. Moreover, computational vaccine approaches are helpful in the design of specific, safe, and stable peptide-based designs when compared to the conventional methods of vaccine development. In this study, the whole proteome of the virus, comprising four proteins (NP, L, GP, and NSP), was screened to find putative vaccine epitope sequences (T cell, B cell, and HTL) specific for each protein. These shortlisted epitopes were then combined with flexible linkers to design protein-specific and proteome-wide immunogenic multi-epitope-based vaccine constructs. The results revealed that these multi-epitope vaccine constructs (MEVCs) are strongly antigenic and non-allergenic in nature. The efficacy of these constructs was further validated by docking with immune receptors, which revealed strong binding interactions with human TLR8. Using the MD simulation approach, the binding stability and residual flexibility of the best vaccine construct (proteome-wide) were confirmed, which revealed stable dynamic and favorable features. Furthermore, in-silico cloning and immune simulation analysis confirmed the expression and production of immune factors, that is, IgM, IgG, and IL-6, against the proposed vaccine designs. Additionally, 3D models of all the MEVC constructs have been developed and evaluated for potential immunization against the RVF virus. Finally, the proteome-wide vaccine candidate (MEVC-PW-RVFV) with the highest immune reinforcement potential provides new insights into the development of future vaccines against the emerging RVF virus.

Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review

The only part of an antigen (a protein molecule found on the surface of a pathogen) that is composed of epitopes specific to T and B cells is recognized by the human immune system (HIS). Identification of epitopes is considered critical for designing an epitope-based peptide vaccine (EBPV). Although there are a number of vaccine types, EBPVs have received less attention thus far. It is important to mention that EBPVs have a great deal of untapped potential for boosting vaccination safety-they are less expensive and take a short time to produce. Thus, in order to quickly contain global pandemics such as the ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), as well as epidemics and endemics, EBPVs are considered promising vaccine types. The high mutation rate of SARS-CoV-2 has posed a great challenge to public health worldwide because either the composition of existing vaccines has to be changed or a new vaccine has to be developed to protect against its different variants. In such scenarios, time being the critical factor, EBPVs can be a promising alternative. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. This paper presents a systematic review of various state-of-the-art and relevant ML-based methods and tools for predicting T- and B-cell epitopes. Special emphasis is placed on highlighting and analyzing various models for predicting epitopes of SARS-CoV-2, the causative agent of COVID-19. Based on the various methods and tools discussed, future research directions for epitope prediction are presented.

In silico construction of a multiepitope Zika virus vaccine using immunoinformatics tools

Zika virus (ZIKV) is an arbovirus from the Flaviviridae family and Flavivirus genus. Neurological events have been associated with ZIKV-infected individuals, such as Guillain-Barré syndrome, an autoimmune acute neuropathy that causes nerve demyelination and can induce paralysis. With the increase of ZIKV infection incidence in 2015, malformation and microcephaly cases in newborns have grown considerably, which suggested congenital transmission. Therefore, the development of an effective vaccine against ZIKV became an urgent need. Live attenuated vaccines present some theoretical risks for administration in pregnant women. Thus, we developed an in silico multiepitope vaccine against ZIKV. All structural and non-structural proteins were investigated using immunoinformatics tools designed for the prediction of CD4 + and CD8 + T cell epitopes. We selected 13 CD8 + and 12 CD4 + T cell epitopes considering parameters such as binding affinity to HLA class I and II molecules, promiscuity based on the number of different HLA alleles that bind to the epitopes, and immunogenicity. ZIKV Envelope protein domain III (EDIII) was added to the vaccine construct, creating a hybrid protein domain-multiepitope vaccine. Three high scoring continuous and two discontinuous B cell epitopes were found in EDIII. Aiming to increase the candidate vaccine antigenicity even further, we tested secondary and tertiary structures and physicochemical parameters of the vaccine conjugated to four different protein adjuvants: flagellin, 50S ribosomal protein L7/L12, heparin-binding hemagglutinin, or RS09 synthetic peptide. The addition of the flagellin adjuvant increased the vaccine's predicted antigenicity. In silico predictions revealed that the protein is a probable antigen, non-allergenic and predicted to be stable. The vaccine’s average population coverage is estimated to be 87.86%, which indicates it can be administered worldwide. Peripheral Blood Mononuclear Cells (PBMC) of individuals with previous ZIKV infection were tested for cytokine production in response to the pool of CD4 and CD8 ZIKV peptide selected. CD4 + and CD8 + T cells showed significant production of IFN-γ upon stimulation and IL-2 production was also detected by CD8 + T cells, which indicated the potential of our peptides to be recognized by specific T cells and induce immune response. In conclusion, we developed an in silico universal vaccine predicted to induce broad and high-coverage cellular and humoral immune responses against ZIKV, which can be a good candidate for posterior in vivo validation.

Machine Learning-Based Ensemble Model for Zika Virus T-Cell Epitope Prediction

Zika virus (ZIKV), the causative agent of Zika fever in humans, is an RNA virus that belongs to the genus Flavivirus. Currently, there is no approved vaccine for clinical use to combat the ZIKV infection and contain the epidemic. Epitope-based peptide vaccines have a large untapped potential for boosting vaccination safety, cross-reactivity, and immunogenicity. Though many attempts have been made to develop vaccines for ZIKV, none of these have proved to be successful. Epitope-based peptide vaccines can act as powerful alternatives to conventional vaccines due to their low production cost, less reactogenic, and allergenic responses. For designing an effective and viable epitope-based peptide vaccine against this deadly virus, it is essential to select the antigenic T-cell epitopes since epitope-based vaccines are considered safe. The in silico machine-learning-based approach for ZIKV T-cell epitope prediction would save a lot of physical experimental time and efforts for speedy vaccine development compared to in vivo approaches. We hereby have trained a machine-learning-based computational model to predict novel ZIKV T-cell epitopes by employing physicochemical properties of amino acids. The proposed ensemble model based on a voting mechanism works by blending the predictions for each class (epitope or nonepitope) from each base classifier. Predictions obtained for each class by the individual classifier are summed up, and the class with the majority vote is predicted upon. An odd number of classifiers have been used to avoid the occurrence of ties in the voting. Experimentally determined ZIKV peptide sequences data set was collected from Immune Epitope Database and Analysis Resource (IEDB) repository. The data set consists of 3,519 sequences, of which 1,762 are epitopes and 1,757 are nonepitopes. The length of sequences ranges from 6 to 30 meter. For each sequence, we extracted 13 physicochemical features. The proposed ensemble model achieved sensitivity, specificity, Gini coefficient, AUC, precision, F-score, and accuracy of 0.976, 0.959, 0.993, 0.994, 0.989, 0.985, and 97.13%, respectively. To check the consistency of the model, we carried out five-fold cross-validation and an average accuracy of 96.072% is reported. Finally, a comparative analysis of the proposed model with existing methods has been carried out using a separate validation data set, suggesting the proposed ensemble model as a better model. The proposed ensemble model will help predict novel ZIKV vaccine candidates to save lives globally and prevent future epidemic-scale outbreaks.

Paired SARS-CoV-2 spike protein mutations observed during ongoing SARS-CoV-2 viral transfer from humans to minks and back to humans

A mutation analysis of SARS-CoV-2 genomes collected around the world sorted by sequence, date, geographic location, and species has revealed a large number of variants from the initial reference sequence in Wuhan. This analysis also reveals that humans infected with SARS-CoV-2 have infected mink populations in the Netherlands, Denmark, United States, and Canada. In these animals, a small set of mutations in the spike protein receptor binding domain (RBD), often occurring in specific combinations, has transferred back into humans. The viral genomic mutations in minks observed in the Netherlands and Denmark show the potential for new mutations on the SARS-CoV-2 spike protein RBD to be introduced into humans by zoonotic transfer. Our data suggests that close attention to viral transfer from humans to farm animals and pets will be required to prevent build-up of a viral reservoir for potential future zoonotic transfer.

Immunoinformatics prediction of potential B-cell and T-cell epitopes as effective vaccine candidates for eliciting immunogenic responses against Epstein-Barr virus

BACKGROUND

The ongoing search for viable treatment options to curtail Epstein Barr Virus (EBV) pathogenicity has necessitated a paradigmatic shift towards the design of peptide-based vaccines. Potential B-cell and T-cell epitopes were predicted for nine antigenic EBV proteins that mediate epithelial cell-attachment and spread, capsid self-assembly, DNA replication and processivity.

METHODS

Predictive algorithms incorporated in the Immune Epitope Database (IEDB) resources were used to determine potential B-cell epitopes based on their physicochemical attributes. These were combined with a string-kernel method and an antigenicity predictive AlgPred tool to enhance accuracy in the end-point selection of highly potential antigenic EBV B-cell epitopes. NetCTL 1.2 algorithms enabled the prediction of probable T-cell epitopes which were structurally modeled and subjected to blind peptide-protein docking with HLA-A*02:01. All-atom molecular dynamics (MD) simulation and Molecular Mechanics Generalized-Born Surface Area methods were used to investigate interaction dynamics and affinities of predicted T-cell peptide-protein complexes.

RESULTS

Computational predictions and sequence overlapping analysis yielded 18 linear (continuous) and discontinuous (conformational) subunit epitopes from the antigenic proteins with characteristic surface accessibility, flexibility and antigenicity, and predictive scores above the threshold value (1) set. A novel site was identified on HLA-A*02:01 with preferential affinity binding for modeled BMRF2, BXLF1 and BGLF4 T-cell epitopes. Interaction dynamics and energies were also computed in addition to crucial residues that mediated complex formation and stability.

CONCLUSION

This study implemented an integrative meta-analytical approach to model highly probable B-cell and T-cell epitopes as potential peptide-vaccine candidates for the treatment of EBV-related diseases.

A Synthetic Peptide CTL Vaccine Targeting Nucleocapsid Confers Protection from SARS-CoV-2 Challenge in Rhesus Macaques

BACKGROUND

Persistent transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has given rise to a COVID-19 pandemic. Several vaccines, conceived in 2020, that evoke protective spike antibody responses are being deployed in mass public health vaccination programs. Recent data suggests, however, that as sequence variation in the spike genome accumulates, some vaccines may lose efficacy.

METHODS

Using a macaque model of SARS-CoV-2 infection, we tested the efficacy of a peptide-based vaccine targeting MHC class I epitopes on the SARS-CoV-2 nucleocapsid protein. We administered biodegradable microspheres with synthetic peptides and adjuvants to rhesus macaques. Unvaccinated control and vaccinated macaques were challenged with 1 × 108 TCID50 units of SARS-CoV-2, followed by assessment of clinical symptoms and viral load, chest radiographs, and sampling of peripheral blood and bronchoalveolar lavage (BAL) fluid for downstream analysis.

RESULTS

Vaccinated animals were free of pneumonia-like infiltrates characteristic of SARS-CoV-2 infection and presented with lower viral loads relative to controls. Gene expression in cells collected from BAL samples of vaccinated macaques revealed a unique signature associated with enhanced development of adaptive immune responses relative to control macaques.

CONCLUSIONS

We demonstrate that a room temperature stable peptide vaccine based on known immunogenic HLA class I bound CTL epitopes from the nucleocapsid protein can provide protection against SARS-CoV-2 infection in nonhuman primates.

The farther the better: Investigating how distance from human self affects the propensity of a peptide to be presented on cell surface by MHC class I molecules, the case of Trypanosoma cruzi

More than twenty years ago the reverse vaccinology paradigm came to light trying to design new vaccines based on the analysis of genomic information in order to select those pathogen peptides able to trigger an immune response. In this context, focusing on the proteome of Trypanosoma cruzi, we investigated the link between the probabilities for pathogen peptides to be presented on a cell surface and their distance from human self. We found a reasonable but, as far as we know, undiscovered property: the farther the distance between a peptide and the human-self the higher the probability for that peptide to be presented on a cell surface. We also found that the most distant peptides from human self bind, on average, a broader collection of HLAs than expected, implying a potential immunological role in a large portion of individuals. Finally, introducing a novel quantitative indicator for a peptide to measure its potential immunological role, we proposed a pool of peptides that could be potential epitopes and that can be suitable for experimental testing. The software to compute peptide classes according to the distance from human self is free available at http://www.iasi.cnr.it/~dsantoni/nullomers.

The Long Road Toward COVID-19 Herd Immunity: Vaccine Platform Technologies and Mass Immunization Strategies

There is an urgent need for effective countermeasures against the current emergence and accelerating expansion of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Induction of herd immunity by mass vaccination has been a very successful strategy for preventing the spread of many infectious diseases, hence protecting the most vulnerable population groups unable to develop immunity, for example individuals with immunodeficiencies or a weakened immune system due to underlying medical or debilitating conditions. Therefore, vaccination represents one of the most promising counter-pandemic measures to COVID-19. However, to date, no licensed vaccine exists, neither for SARS-CoV-2 nor for the closely related SARS-CoV or Middle East respiratory syndrome-CoV. In addition, a few vaccine candidates have only recently entered human clinical trials, which hampers the progress in tackling COVID-19 infection. Here, we discuss potential prophylactic interventions for SARS-CoV-2 with a focus on the challenges existing for vaccine development, and we review pre-clinical progress and ongoing human clinical trials of COVID-19 vaccine candidates. Although COVID-19 vaccine development is currently accelerated via so-called fast-track programs, vaccines may not be timely available to have an impact on the first wave of the ongoing COVID-19 pandemic. Nevertheless, COVID-19 vaccines will be essential in the future for reducing morbidity and mortality and inducing herd immunity, if SARS-CoV-2 becomes established in the population like for example influenza virus.

An effective CTL peptide vaccine for Ebola Zaire Based on Survivors' CD8+ targeting of a particular nucleocapsid protein epitope with potential implications for COVID-19 vaccine design

The 2013-2016 West Africa EBOV epidemic was the biggest EBOV outbreak to date. An analysis of virus-specific CD8+ T-cell immunity in 30 survivors showed that 26 of those individuals had a CD8+ response to at least one EBOV protein. The dominant response (25/26 subjects) was specific to the EBOV nucleocapsid protein (NP). It has been suggested that epitopes on the EBOV NP could form an important part of an effective T-cell vaccine for Ebola Zaire. We show that a 9-amino-acid peptide NP44-52 (YQVNNLEEI) located in a conserved region of EBOV NP provides protection against morbidity and mortality after mouse adapted EBOV challenge. A single vaccination in a C57BL/6 mouse using an adjuvanted microsphere peptide vaccine formulation containing NP44-52 is enough to confer immunity in mice. Our work suggests that a peptide vaccine based on CD8+ T-cell immunity in EBOV survivors is conceptually sound and feasible. Nucleocapsid proteins within SARS-CoV-2 contain multiple Class I epitopes with predicted HLA restrictions consistent with broad population coverage. A similar approach to a CTL vaccine design may be possible for that virus.

Current Status of Zika Virus Vaccines: Successes and Challenges

The recently emerged Zika virus (ZIKV) spread to the Americas, causing a spectrum of congenital diseases including microcephaly in newborn and Guillain-Barré syndrome (GBS) in adults. The unprecedented nature of the epidemic and serious diseases associated with the viral infections prompted the global research community to understand the immunopathogenic mechanisms of the virus and rapidly develop safe and efficacious vaccines. This has led to a number of ZIKV vaccine candidates that have shown significant promise in human clinical trials. These candidates include nucleic acid vaccines, inactivated vaccines, viral-vectored vaccines, and attenuated vaccines. Additionally, a number of vaccine candidates have been shown to protect animals in preclinical studies. However, as the epidemic has waned in the last three years, further development of the most promising vaccine candidates faces challenges in clinical efficacy trials, which is needed before a vaccine is brought to licensure. It is important that a coalition of government funding agencies and private sector companies is established to move forward with a safe and effective vaccine ready for deployment when the next ZIKV epidemic occurs.

A review of Zika virus: hurdles toward vaccine development and the way forward

The Zika virus (ZIKV) epidemic has recently emerged as a public health threat due to its teratogenic nature and association with the serious neurological condition Guillain-Barré syndrome (GBS). To date, no approved antiviral therapeutics to treat, nor vaccines to prevent, ZIKV infection are available. In order to develop effective anti-ZIKV vaccines, improved animal models and a better understanding of immunological correlates of protection against ZIKV are required. In this paper, we discuss the recent progress in developing vaccines against ZIKV and the hurdles to overcome in making efficacious anti-ZIKV vaccines. Here, we propose strategies to make efficacious and safe vaccines against ZIKV by using novel approaches including molecular attenuation of viruses and TLR-based nanoparticle vaccines. The question of exacerbating dengue virus infection or causing GBS through the production of cross-reactive immunity targeting viral or host proteins have been addressed in this paper. Challenges in implementing immunogenic and protective ZIKV vaccine trials in immunodepressed target populations (for example, pregnant women) have also been discussed.

Peptide-Based Subunit Vaccine Design of T- and B-Cells Multi-Epitopes against Zika Virus Using Immunoinformatics Approaches

The Zika virus disease, also known as Zika fever is an arboviral disease that became epidemic in the Pacific Islands and had spread to 18 territories of the Americas in 2016. Zika virus disease has been linked to several health problems such as microcephaly and the Guillain-Barré syndrome, but to date, there has been no vaccine available for Zika. Problems related to the development of a vaccine include the vaccination target, which covers pregnant women and children, and the antibody dependent enhancement (ADE), which can be caused by non-neutralizing antibodies. The peptide vaccine was chosen as a focus of this study as a safer platform to develop the Zika vaccine. In this study, a collection of Zika proteomes was used to find the best candidates for T- and B-cell epitopes using the immunoinformatics approach. The most promising T-cell epitopes were mapped using the selected human leukocyte antigen (HLA) alleles, and further molecular docking and dynamics studies showed a good peptide-HLA interaction for the best major histocompatibility complex-II (MHC-II) epitope. The most promising B-cell epitopes include four linear peptides predicted to be cross-reactive with T-cells, and conformational epitopes from two proteins accessible by antibodies in their native biological assembly. It is believed that the use of immunoinformatics methods is a promising strategy against the Zika viral infection in designing an efficacious multiepitope vaccine.

Viral peptides-MHC interaction: Binding probability and distance from human peptides

Identification of peptides binding to MHC class I complex can play a crucial role in retrieving potential targets able to trigger an immune response. Affinity binding of viral peptides can be estimated through effective computational methods that in the most of cases are based on machine learning approach. Achieving a better insight into peptide features that impact on the affinity binding rate is a challenging issue. In the present work we focused on 9-mer peptides of Human immunodeficiency virus type 1 and Human herpes simplex virus 1, studying their binding to MHC class I. Viral 9-mers were partitioned into different classes, where each class is characterized by how far (in terms of mutation steps) the peptides belonging to that class are from human 9-mers. Viral 9-mers were partitioned in different classes, based on the number of mutation steps they are far from human 9-mers. We showed that the overall binding probability significantly differs among classes, and it typically increases as the distance, computed in terms of number of mutation steps from the human set of 9-mers, increases. The binding probability is particularly high when considering viral 9-mers that are far from all human 9-mers more than three mutation steps. A further evidence, providing significance to those special viral peptides and suggesting a potential role they can play, comes from the analysis of their distribution along viral genomes, as it revealed they are not randomly located, but they preferentially occur in specific genes.

Prevention and Control Strategies to Counter Zika Virus, a Special Focus on Intervention Approaches against Vector Mosquitoes-Current Updates

Zika virus (ZIKV) is the most recent intruder that acquired the status of global threat creating panic and frightening situation to public owing to its rapid spread, attaining higher virulence and causing complex clinical manifestations including microcephaly in newborns and Guillain Barré Syndrome. Alike other flaviviruses, the principal mode of ZIKV transmission is by mosquitoes. Advances in research have provided reliable diagnostics for detecting ZIKV infection, while several drug/therapeutic targets and vaccine candidates have been identified recently. Despite these progresses, currently there is neither any effective drug nor any vaccine available against ZIKV. Under such circumstances and to tackle the problem at large, control measures of which mosquito population control need to be strengthened following appropriate mechanical, chemical, biological and genetic control measures. Apart from this, several other known modes of ZIKV transmission which have gained importance in recent past such as intrauterine, sexual intercourse, and blood-borne spread need to be checked and kept under control by adopting appropriate precautions and utmost care during sexual intercourse, blood transfusion and organ transplantation. The virus inactivation by pasteurization, detergents, chemicals, and filtration can effectively reduce viral load in plasma-derived medicinal products. Added to this, strengthening of the surveillance and monitoring of ZIKV as well as avoiding travel to Zika infected areas would aid in keeping viral infection under check. Here, we discuss the salient advances in the prevention and control strategies to combat ZIKV with a focus on highlighting various intervention approaches against the vector mosquitoes of this viral pathogen along with presenting an overview regarding human intervention measures to counter other modes of ZIKV transmission and spread. Additionally, owing to the success of vaccines for a number of infections globally, a separate section dealing with advances in ZIKV vaccines and transmission blocking vaccines has also been included.