As the world is brought to its knees by the novel Coronavirus, new and exciting research must rise alongside to prevent the fast spread of the virus. While healthcare professionals are battling the virus on the frontlines of hospitals and clinics, back-end support in the form of medicine and a possible cure have become the need of the hour. Many talented individuals are working on various problems surrounding the development of a vaccine for the pandemic and hopefully the time isn’t far before we have a fully operating vaccine.
In the context of new and exciting research, as well as India and my university, the University of Hyderabad, Dr. Seema Mishra has conducted research and published a preprint article titled ‘T Cell Epitope-Based Vaccine Design for Pandemic Novel Coronavirus 2019-nCoV’ (Link) (in preparation for research paper).
In this article that I am writing, I will be summarising the preprint article as well as trying my best to explain the preprint article in a general manner, targeted at a non-science audience specifically. I’d highly recommend you read another article I have written ‘The war against viruses’ to better understand how viruses proliferate and harm our body as well as why it is important to kill off virus-infected cells.
Index
The immune system is a part of our body designed to regulate processes and happenings chiefly responsible for fighting off diseases and foreign attacks on our body by viruses, bacteria and other microorganisms. Think of it as the ‘defence sector’ of our body. The immune system has many ‘soldiers’ enlisted in its ‘army’ one of which being the robust T Cell. T Cells are like ‘scanners’. They ‘scan’ the surface of a cell searching for foreign particles (called antigens). T Cells are of two types:
Killer T Cells (also known as cytotoxic T Cells, or CD8+ T Cells) which destroy the host cell preventing further proliferation of the virus; and,
Helper T Cells (also known as CD4+ T Cells), which will ‘help’ other immune cells fight off viral infection.
The way T cells work is through the following steps:
Usually vaccines are made with the mindset of letting our body experience the effects of the virus without actually being infected by the virus. Vaccines are usually made with other living viruses of similar nature which are not as fatal, weakened viruses, dead viruses or viral particles. Vaccine development by traditional methods, though successful, is time consuming. It involves complicated steps of culturing living organisms, isolating antigens (viral particles) which are specific to that organism and then proceeding to test whether the inactivated antigens produce required immune responses.
The need for epitope-based vaccines have become the matter of the hour because such vaccines are designed to target epitopes that are ‘static’ and not ‘changing (or mutating)’ like other proteins of the virus. This improves their success rates of producing an immunological response (meaning a response by the immune system). Instead of isolating the viral particles and testing each one like it traditionally happens, we theoretically predict how successful each particle is in producing a response and then proceed to test it. This saves us a ton of time.
There are some problems with designing epitope-based vaccines. Figuring out which proteins bind best with respect to the various MHC proteins, is something that cannot be determined experimentally and hence must be predicted using computational tools. This form of predication has come to be known in silico (meaning ‘in silicon’. A reference to the fact that computer chips are made out of silicon). This has led to the development of a new field of biology known as immunoinformatics which centres around solving immunological problems using computational tools.
HLA (meaning Human Leukocyte Antigen) is a gene complex which is known to give ‘instructions’ for the preparation of the MHC protein. It is highly polymorphic, meaning it has many forms hence being able to ‘fine-tune’ the immune system and deal with a larger set of immunological problems. The polymorphism is shown in the MHC proteins binding site. Promiscuous T cell epitopes are therefore important in this sense as they bind at a larger variety of MHC sites and hence can show immune response to larger numbers of antigens. Given the fact that novel Coronavirus is a pandemic, promiscuous T Cell Epitopes are needed to bind with a variety of HLA variants.
The genome is the sum total of the instruction set both useful and not. Genes are part of this information code which are useful and provide instructions for the production of all the proteins in an organism’s body. Knowing more about the gene that is responsible for a particular protein we can then find out more about the composition of the protein. Brilliant researchers have already elucidated the genome of the novel Coronavirus as well as some proteins associated with the virus have been isolated from it. With the availability of the composition of the protein, we can do computational analysis on it to yield interesting results.
What Dr. Seema Mishra has done is take 10 proteins identified from the novel Coronavirus and perform two types of predictions: one for cytotoxic T Cell epitopes and the other for helper T Cell epitopes. She initially predicted for cytotoxic T Cells using computational software (namely NetCTL and Pickpocket). The prediction gave outputs in the term of top high binders and promiscuous epitopes for the various proteins. For the helper T Cells predictions were also done using computational tools (namely NetMHCII) and the epitopes were predicted and arranged in order of the epitope’s binding ability. Prediction in CTL epitope cases was done using the 12 HLA supertypes producing a consensus list of epitopes.
Immunogenicity is essentially a measure of how good the epitope is able to elicit a response from the immune system. High Binding of the epitopes to the MHC is not something that ensures that the epitope will produce an immune response. The epitopes that were generated above, that is, the consensus list of the epitopes were checked on the Immune Epitope Database (IEDB) immunogenicity tool. The tool scanned the epitopes for immunogenic regions.
Cross-reactivity between antigens occurs when an antibody/TCR directed against one specific antigen is successful in binding with another, different/similar antigen. All the epitopes obtained were used to search against the human proteome (entire set of proteins) database from UniProt. This was done to avoid matches between the epitopes and any human proteins, and hence prevent cross-reactivity.
Helper T Cells based epitopes:
The data of these epitopes are currently being manually analysed.
Cytotoxic T Cell based epitopes:
Based on high binding affinity, epitopes were predicted for 10 proteins identified for novel Coronavirus. A unique result obtained here was that the surface and membrane proteins had few promiscuous epitopes and hence would not be viable immunogens. Immunogenicity measurements for the epitopes showed that many of the epitopes would be successful in eliciting an immune response.
Clustering of epitopes provided many useful results. Clustering found two conserved regions across two proteins, and the epitopes containing these conserved regions were eliciting a strong immune response. Further many unique epitopes were found that didn’t fall in the consensus sequence predicted by the clustering algorithm. Further none of the predicted epitopes showed any cross-reactivity.
This was an in-silico approach to the problem. The next step is to test it under lab conditions, that is, in vitro (meaning in glass, with reference to Petri dishes and such) or in living systems (also called in vivo). The tests would centre around the binding of the epitope to MHC and whether it stimulates the T Cell in an in vitro condition. Depending on the success or failure of the procedure, eventually, tests could be run on living systems. If all luck runs well, we might hopefully see drug trials on higher mammals, and then eventually a vaccine for novel Coronavirus will be made.
References:
These are some papers that I went through to help me write this article.
1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3603454/
2. https://link.springer.com/article/10.1007/s12010-018-2804-5
3. https://science.sciencemag.org/content/315/5818/1583
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