Influenza A HA's conserved epitopes and broadly neutralizing antibodies: a prediction method

Shotgun Immunoproteomic Approach for the Discovery of Linear B-Cell Epitopes in Biothreat Agents Francisella tularensis and Burkholderia pseudomallei

Peptide-based subunit vaccines are coming to the forefront of current vaccine approaches, with safety and cost-effective production among their top advantages. Peptide vaccine formulations consist of multiple synthetic linear epitopes that together trigger desired immune responses that can result in robust immune memory. The advantages of linear compared to conformational epitopes are their simple structure, ease of synthesis, and ability to stimulate immune responses by means that do not require complex 3D conformation. Prediction of linear epitopes through use of computational tools is fast and cost-effective, but typically of low accuracy, necessitating extensive experimentation to verify results. On the other hand, identification of linear epitopes through experimental screening has been an inefficient process that requires thorough characterization of previously identified full-length protein antigens, or laborious techniques involving genetic manipulation of organisms. In this study, we apply a newly developed generalizable screening method that enables efficient identification of B-cell epitopes in the proteomes of pathogenic bacteria. As a test case, we used this method to identify epitopes in the proteome of Francisella tularensis (Ft), a Select Agent with a well-characterized immunoproteome. Our screen identified many peptides that map to known antigens, including verified and predicted outer membrane proteins and extracellular proteins, validating the utility of this approach. We then used the method to identify seroreactive peptides in the less characterized immunoproteome of Select Agent Burkholderia pseudomallei (Bp). This screen revealed known Bp antigens as well as proteins that have not been previously identified as antigens. Although B-cell epitope prediction tools Bepipred 2.0 and iBCE-EL classified many of our seroreactive peptides as epitopes, they did not score them significantly higher than the non-reactive tryptic peptides in our study, nor did they assign higher scores to seroreactive peptides from known Ft or Bp antigens, highlighting the need for experimental data instead of relying on computational epitope predictions alone. The present workflow is easily adaptable to detecting peptide targets relevant to the immune systems of other mammalian species, including humans (depending upon the availability of convalescent sera from patients), and could aid in accelerating the discovery of B-cell epitopes and development of vaccines to counter emerging biological threats.

Bioinformatic screening and detection of allergen cross-reactive IgE-binding epitopes

Protein allergens can be related by cross‐reactivity. Allergens that share relevant sequence can cross‐react, those lacking sufficient similarity in their IgE antibody‐binding epitopes do not cross‐react. Cross‐reactivity is based on shared epitopes that is based on shared sequence and higher level structure (charge and shape). Epitopes are important in predicting cross‐reactivity potential and may provide the potential to establish criteria that identify homology among allergens. Selected allergen's IgE‐binding epitope sequences were used to determine how the FASTA algorithm could be used to identify a threshold of significance. A statistical measure (expectation value, E‐value) was used to identify a threshold specific to identifying cross‐reactivity potential. Peanut Ara h 1 and Ara h 2, shrimp tropomyosin Pen a 1, and birch tree pollen allergen, Bet v 1 were sources of known epitopes. Each epitope or set of epitopes was inserted into random amino acid sequence to create hypothetical proteins used as queries to an allergen database. Alignments with allergens were noted for the ability to match the epitope's source allergen as well as any cross‐reactive or other homologous allergens. A FASTA expectation value range (1 × 10⁻⁵–1 × 10⁻⁶) was identified that could act as a threshold to help identify cross‐reactivity potential.

Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences

Background

The broad heterogeneity of antigen-antibody interactions brings tremendous challenges to the design of a widely applicable learning algorithm to identify conformational B-cell epitopes. Besides the intrinsic heterogeneity introduced by diverse species, extra heterogeneity can also be introduced by various data sources, adding another layer of complexity and further confounding the research.

Results

This work proposed a staged heterogeneity learning method, which learns both characteristics and heterogeneity of data in a phased manner. The method was applied to identify antigenic residues of heterogenous conformational B-cell epitopes based on antigen sequences. In the first stage, the model learns the general epitope patterns of each kind of propensity from a large data set containing computationally defined epitopes. In the second stage, the model learns the heterogenous complementarity of these propensities from a relatively small guided data set containing experimentally determined epitopes. Moreover, we designed an algorithm to cluster the predicted individual antigenic residues into conformational B-cell epitopes so as to provide strong potential for real-world applications, such as vaccine development. With heterogeneity well learnt, the transferability of the prediction model was remarkably improved to handle new data with a high level of heterogeneity. The model has been tested on two data sets with experimentally determined epitopes, and on a data set with computationally defined epitopes. This proposed sequence-based method achieved outstanding performance - about twice that of existing methods, including the sequence-based predictor CBTOPE and three other structure-based predictors.

Conclusions

The proposed method uses only antigen sequence information, and thus has much broader applications.

Heterosubtypic protection conferred by the human monoclonal antibody PN-SIA28 against influenza A virus lethal infections in mice

PN-SIA28 is a human monoclonal antibody (Hu-MAb) targeting highly conserved epitopes within the stem portion of the influenza virus hemagglutinin (HA) (N. Clementi, et al, PLoS One 6:e28001, 2011, http://dx.doi.org/10.1371/journal.pone.0028001). Previous in vitro studies demonstrated PN-SIA28 neutralizing activities against phylogenetically divergent influenza A subtypes. In this study, the protective activity of PN-SIA28 was evaluated in mice inoculated with lethal influenza A/WSN/33 (H1N1), A/Quebec/144147/09 (H1N1)pdm09, and A/Victoria/3/75 (H3N2) viruses. At 24 h postinoculation (p.i.), animals received PN-SIA28 intraperitoneally (1 or 10 mg/kg of body weight) or 10 mg/kg of unrelated Hu-MAb (mock). Body weight loss and mortality rate (MR) were recorded for 14 days postinfection (p.i.). Lung viral titers (LVT) were determined at day 5 p.i. In A/WSN/33 (H1N1)-infected groups, all untreated and mock-receiving mice died, whereas MRs of 87.5% and 25% were observed in mice that received PN-SIA28 1 and 10 mg/kg, respectively. In influenza A(H1N1) pdm09-infected groups, an MR of 75% was recorded for untreated and mock-treated groups, whereas the PN-SIA28 1-mg/kg and 10-mg/kg groups had rates of 62.5% and 0%, respectively. In A/Victoria/3/75 (H3N2)-infected animals, untreated and mock-treated animals had MRs of 37.5% and 25%, respectively, and no mortalities were recorded after PN-SIA28 treatments. Accordingly, PN-SIA28 treatments significantly reduced weight losses and resulted in a ≥ 1-log reduction in LVT compared to the control in all infection groups. This study confirms that antibodies targeting highly conserved epitopes in the influenza HA stem region, like PN-SIA28, not only neutralize influenza A viruses of clinically relevant subtypes in vitro but also, more importantly, protect from a lethal influenza virus challenge in vivo.