Grouping of large populations into few CTL immune ‘response‐types’ from influenza H1N1 genome analysis

Abstract

Despite extensive work on influenza, a number of questions still remain open about why individuals are differently susceptible to the disease and why only some strains lead to epidemics. Here we study the effect of human leukocyte antigen (HLA) genotype heterogeneity on possible cytotoxic T-lymphocyte (CTL) response to 186 influenza H1N1 genomes. To enable such analysis, we reconstruct HLA genotypes in different populations using a probabilistic method. We find that epidemic strains in general correlate with poor CTL response in populations. Our analysis shows that large populations can be classified into a small number of groups called response-types, specific to a given viral strain. Individuals of a response-type are expected to exhibit similar CTL responses. Extent of CTL responses varies significantly across different populations and increases with increase in genetic heterogeneity. Overall, our analysis presents a conceptual advance towards understanding how genetic heterogeneity influences disease susceptibility in individuals and in populations. We also obtain lists of top-ranking epitopes and proteins, ranked on the basis of conservation, antigenic cross-reactivity and population coverage, which provide ready short-lists for rational vaccine design. Our method is fairly generic and has the potential to be applied for studying other viruses.

Long-term adaptation of the influenza A virus by escaping cytotoxic T-cell recognition

“…To account for the fact that two different alleles at the same locus is a unit difference in epitopes, we deviate from the classical Jaccard distance by dividing by | T i ∪ T j | instead of | S i ∪ S j |. Notice that | S i | = | T i |, because a virus can at most have one epitope at a given locus, and that the difference between the classical Jaccard distance, e.g., as used elsewhere 37 , and our distance is small, since only a few epitopes in our compendium contain multiple alleles.…”

In-silico study of influence of HLA heterogeneity on CTL responses across ethnicities to SARS-CoV-2

“…From the set of hypothetically generated HLA genotypes, the ones with a frequency crossing a threshold value of 5 × 10 -7 were considered a part of the population for the particular ethnic group. The algorithm used to construct synthetic populations is similar to that used by Mukherjee & Chandra [17] .…”

Role of genetic heterogeneity in determining the epidemiological severity of H1N1 influenza

“…Each sub-population is defined according to their specific susceptibility to the viral strain. To define these sub-populations in practice, starting from biological data, we employ the probabilistic method developed in [ 51 ]. This method uses well-tested and benchmarked algorithms for epitope prediction [ 52 ] to predict the viral epitopes presented by individuals represented by different HLA class-I genotypes.…”

“…In a previous paper [ 51 ], we developed a method to group together individuals who can be expected to have a similar CTL response, using the frequency of occurrence of HLA class-I alleles and the full proteome of the pathogen. We formulated an algorithm to generate all possible HLA genotypes given the frequency of occurrence of each allele in a particular population.…”

“…Clustering was then carried out on these HLA genotypes based on the number of epitopes presented from each viral protein. In this work, we use the algorithm presented in [ 51 ] to generate HLA genotypes and thereby predict high affinity epitopes presented by each such genotype. We thus identify sub-populations of individuals with comparable susceptibility to the virus.…”