Phylogenesys and homology modeling in Zika virus epidemic: food for thought

<p>Zika virus (ZIKV) is an emerging Flavivirus that have recently caused an outbreak in Brazil and rapid spread in several countries. In this study, the consequences of ZIKV evolution on protein recognition by the host immune system have been analyzed. Evolutionary analysis was combined with homology modeling and T-B cells epitope predictions. Two separate clades, the African one with the Uganda sequence, as the most probable ancestor, and the second one containing all the most recent sequences from the equatorial belt were identified. Brazilian strains clustered all together and closely related to the French Polynesia isolates. A strong presence of a negatively selected site in the envelope gene (<i>Env</i>) protein was evidenced, suggesting a probable purging of deleterious polymorphisms in functionally important genes. Our results show relative conservancy of ZIKV sequences when envelope and other non-structural proteins (NS3 and NS5) are analyzed by homology modeling. However, some regions within the consensus sequence of NS5 protein and to a lesser extent in the envelope protein, show localized high mutation frequency corresponding to a considerable alteration in protein stability. In terms of viral immune escape, envelope protein is under a higher selective pressure than NS5 and NS3 proteins for HLA class I and II molecules. Moreover, envelope mutations that are not strictly related to T-cell immune responses are mostly located on the surface of the protein in putative B-cell epitopes, suggesting an important contribution of B cells in the immune response as well.</p

table_4_Predicting HLA CD4 Immunogenicity in Human Populations.xlsx

Background<p>Prediction of T cell immunogenicity is a topic of considerable interest, both in terms of basic understanding of the mechanisms of T cells responses and in terms of practical applications. HLA binding affinity is often used to predict T cell epitopes, since HLA binding affinity is a key requisite for human T cell immunogenicity. However, immunogenicity at the population it is complicated by the high level of variability of HLA molecules, potential other factors beyond HLA as well as the frequent lack of HLA typing data. To overcome those issues, we explored an alternative approach to identify the common characteristics able to distinguish immunogenic peptides from non-recognized peptides.</p>Methods<p>Sets of dominant epitopes derived from peer-reviewed published papers were used in conjunction with negative peptides from the same experiments/donors to train neural networks and generate an “immunogenicity score.” We also compared the performance of the immunogenicity score with previously described method for immunogenicity prediction based on HLA class II binding at the population level.</p>Results<p>The immunogenicity score was validated on a series of independent datasets derived from the published literature, representing 57 independent studies where immunogenicity in human populations was assessed by testing overlapping peptides spanning different antigens. Overall, these testing datasets corresponded to over 2,000 peptides and tested in over 1,600 different human donors. The 7-allele method prediction and the immunogenicity score were associated with similar performance [average area under the ROC curve (AUC) values of 0.703 and 0.702, respectively] while the combined methods reached an average AUC of 0.725. This increase in average AUC value is significant compared with the immunogenicity score (p = 0.0135) and a strong trend toward significance is observed when compared to the 7-allele method (p = 0.0938). The new immunogenicity score method is now freely available using CD4 T cell immunogenicity prediction tool on the Immune Epitope Database website (http://tools.iedb.org/CD4episcore).</p>Conclusion<p>The new immunogenicity score predicts CD4 T cell immunogenicity at the population level starting from protein sequences and with no need for HLA typing. Its efficacy has been validated in the context of different antigen sources, ethnicities, and disparate techniques for epitope identification.</p

image_1_Predicting HLA CD4 Immunogenicity in Human Populations.PDF

Background<p>Prediction of T cell immunogenicity is a topic of considerable interest, both in terms of basic understanding of the mechanisms of T cells responses and in terms of practical applications. HLA binding affinity is often used to predict T cell epitopes, since HLA binding affinity is a key requisite for human T cell immunogenicity. However, immunogenicity at the population it is complicated by the high level of variability of HLA molecules, potential other factors beyond HLA as well as the frequent lack of HLA typing data. To overcome those issues, we explored an alternative approach to identify the common characteristics able to distinguish immunogenic peptides from non-recognized peptides.</p>Methods<p>Sets of dominant epitopes derived from peer-reviewed published papers were used in conjunction with negative peptides from the same experiments/donors to train neural networks and generate an “immunogenicity score.” We also compared the performance of the immunogenicity score with previously described method for immunogenicity prediction based on HLA class II binding at the population level.</p>Results<p>The immunogenicity score was validated on a series of independent datasets derived from the published literature, representing 57 independent studies where immunogenicity in human populations was assessed by testing overlapping peptides spanning different antigens. Overall, these testing datasets corresponded to over 2,000 peptides and tested in over 1,600 different human donors. The 7-allele method prediction and the immunogenicity score were associated with similar performance [average area under the ROC curve (AUC) values of 0.703 and 0.702, respectively] while the combined methods reached an average AUC of 0.725. This increase in average AUC value is significant compared with the immunogenicity score (p = 0.0135) and a strong trend toward significance is observed when compared to the 7-allele method (p = 0.0938). The new immunogenicity score method is now freely available using CD4 T cell immunogenicity prediction tool on the Immune Epitope Database website (http://tools.iedb.org/CD4episcore).</p>Conclusion<p>The new immunogenicity score predicts CD4 T cell immunogenicity at the population level starting from protein sequences and with no need for HLA typing. Its efficacy has been validated in the context of different antigen sources, ethnicities, and disparate techniques for epitope identification.</p

table_3_Predicting HLA CD4 Immunogenicity in Human Populations.xlsx

Background<p>Prediction of T cell immunogenicity is a topic of considerable interest, both in terms of basic understanding of the mechanisms of T cells responses and in terms of practical applications. HLA binding affinity is often used to predict T cell epitopes, since HLA binding affinity is a key requisite for human T cell immunogenicity. However, immunogenicity at the population it is complicated by the high level of variability of HLA molecules, potential other factors beyond HLA as well as the frequent lack of HLA typing data. To overcome those issues, we explored an alternative approach to identify the common characteristics able to distinguish immunogenic peptides from non-recognized peptides.</p>Methods<p>Sets of dominant epitopes derived from peer-reviewed published papers were used in conjunction with negative peptides from the same experiments/donors to train neural networks and generate an “immunogenicity score.” We also compared the performance of the immunogenicity score with previously described method for immunogenicity prediction based on HLA class II binding at the population level.</p>Results<p>The immunogenicity score was validated on a series of independent datasets derived from the published literature, representing 57 independent studies where immunogenicity in human populations was assessed by testing overlapping peptides spanning different antigens. Overall, these testing datasets corresponded to over 2,000 peptides and tested in over 1,600 different human donors. The 7-allele method prediction and the immunogenicity score were associated with similar performance [average area under the ROC curve (AUC) values of 0.703 and 0.702, respectively] while the combined methods reached an average AUC of 0.725. This increase in average AUC value is significant compared with the immunogenicity score (p = 0.0135) and a strong trend toward significance is observed when compared to the 7-allele method (p = 0.0938). The new immunogenicity score method is now freely available using CD4 T cell immunogenicity prediction tool on the Immune Epitope Database website (http://tools.iedb.org/CD4episcore).</p>Conclusion<p>The new immunogenicity score predicts CD4 T cell immunogenicity at the population level starting from protein sequences and with no need for HLA typing. Its efficacy has been validated in the context of different antigen sources, ethnicities, and disparate techniques for epitope identification.</p

table_2_Predicting HLA CD4 Immunogenicity in Human Populations.xlsx

Background<p>Prediction of T cell immunogenicity is a topic of considerable interest, both in terms of basic understanding of the mechanisms of T cells responses and in terms of practical applications. HLA binding affinity is often used to predict T cell epitopes, since HLA binding affinity is a key requisite for human T cell immunogenicity. However, immunogenicity at the population it is complicated by the high level of variability of HLA molecules, potential other factors beyond HLA as well as the frequent lack of HLA typing data. To overcome those issues, we explored an alternative approach to identify the common characteristics able to distinguish immunogenic peptides from non-recognized peptides.</p>Methods<p>Sets of dominant epitopes derived from peer-reviewed published papers were used in conjunction with negative peptides from the same experiments/donors to train neural networks and generate an “immunogenicity score.” We also compared the performance of the immunogenicity score with previously described method for immunogenicity prediction based on HLA class II binding at the population level.</p>Results<p>The immunogenicity score was validated on a series of independent datasets derived from the published literature, representing 57 independent studies where immunogenicity in human populations was assessed by testing overlapping peptides spanning different antigens. Overall, these testing datasets corresponded to over 2,000 peptides and tested in over 1,600 different human donors. The 7-allele method prediction and the immunogenicity score were associated with similar performance [average area under the ROC curve (AUC) values of 0.703 and 0.702, respectively] while the combined methods reached an average AUC of 0.725. This increase in average AUC value is significant compared with the immunogenicity score (p = 0.0135) and a strong trend toward significance is observed when compared to the 7-allele method (p = 0.0938). The new immunogenicity score method is now freely available using CD4 T cell immunogenicity prediction tool on the Immune Epitope Database website (http://tools.iedb.org/CD4episcore).</p>Conclusion<p>The new immunogenicity score predicts CD4 T cell immunogenicity at the population level starting from protein sequences and with no need for HLA typing. Its efficacy has been validated in the context of different antigen sources, ethnicities, and disparate techniques for epitope identification.</p

Ontogeny of the B- and T-cell response in a primary Zika virus infection of a dengue-naïve individual during the 2016 outbreak in Miami, FL

<div><p>Zika virus (ZIKV) is a mosquito-borne flavivirus of significant public health concern. In the summer of 2016, ZIKV was first detected in the contiguous United States. Here we present one of the first cases of a locally acquired ZIKV infection in a dengue-naïve individual. We collected blood from a female with a maculopapular rash at day (D) 5 and D7 post onset of symptoms (POS) and we continued weekly blood draws out to D148 POS. To establish the ontogeny of the immune response against ZIKV, lymphocytes and plasma were analyzed in a longitudinal fashion. The plasmablast response peaked at D7 POS (19.6% of CD19⁺ B-cells) and was undetectable by D15 POS. ZIKV-specific IgM was present at D5 POS, peaked between D15 and D21 POS, and subsequently decreased. The ZIKV-specific IgG response, however, was not detected until D15 POS and continued to increase after that. Interestingly, even though the patient had never been infected with dengue virus (DENV), cross-reactive IgM and IgG binding against each of the four DENV serotypes could be detected. The highest plasma neutralization activity against ZIKV peaked between D15 and D21 POS, and even though DENV binding antibodies were present in the plasma of the patient, there was neither neutralization nor antibody dependent enhancement (ADE) of DENV. Interestingly, ADE against ZIKV arose at D48 POS and continued until the end of the study. CD4⁺ and CD8⁺ T-cells recognized ZIKV-NS2A and ZIKV-E, respectively. The tetramer positive CD8⁺ T-cell response peaked at D21 POS with elevated levels persisting for months. In summary, this is the first study to establish the timing of the ontogeny of the immune response against ZIKV.</p></div

ZIKV induced maculopapular rash.

<p>Upon admission into the study photos of the patient’s rash were taken D3 POS. This rash was neither itchy, nor sensitive to the touch. (A) A close-up of the initiation point of the rash on the front of the torso near the midline. (B) The rash then spread from the torso to the neck and head.</p

Neutralization titers against ZIKV and DENV.

<p>(A) Neutralization titers were performed by flow cytometry at several time points against ZIKV-Paraiba/2015 and the NEUT₅₀ was calculated based on a non-linear regression. Peak NEUT₅₀ occurred at D15 POS. Hu0002 was used as a flavivirus-naïve control and Hu0004 was a DENV- and ZIKV-exposed control. (B) Plaque reduction neutralization tests (PRNTs) were also performed against ZIKV and all four DENV serotypes. PRNT₅₀ was calculated as 50% neutralization of plaques based on control virus wells and reported as a dilution of patient plasma. (N/A = samples not run).</p

Ontogeny of the plasmablast response of a primary ZIKV infection in a flavivirus-naïve individual.

<p>The blue box in each flow graph represents the frequency of Pbs from isolated PBMCs as a percentage of total CD19⁺ B-Cells. The Pb frequency was significantly elevated at D5 POS when compared to a naïve uninfected individual as well as to convalescent phase PBMCs from Hu0015 (D148 POS).</p

Phylogenetic tree of ZIKV isolated from Hu0015 compared to previously sequenced ZIKV genomes.

<p>A detailed maximum likelihood phylogenetic analysis of published ZIKV genomes from the Pacific and Americas (Asian genotype, from 2013–2016). The pink branches represent isolates from the 2016 ZIKV outbreak in Florida. Hu0015 is one of the first ZIKV sequences from autochthonous transmission in the contiguous US and clades with one of four ZIKV lineages detected during the outbreak in Florida. The scale of 0.005 represents nucleotide substitutions per site in the viral genome.</p