Early Transcriptome Signatures from Immunized Mouse Dendritic Cells Predict Late Vaccine-Induced T-Cell Responses

RESEARCH ARTICLE Early Transcriptome Signatures from Immunized Mouse Dendritic Cells Predict Late Vaccine-Induced T-Cell Responses Nicolas Dérian1,2,3, Bertrand Bellier1,2,3, Hang Phuong Pham1,3¤a, Eliza Tsitoura4¤b, Dorothea Kazazi4, Christophe Huret1,3¤c, Penelope Mavromara4, David Klatzmann1,2,3*, Adrien Six1,2,3* 1 Sorbonne Universités, UPMC Univ Paris 06, UMRS 959, Immunology, Immunopathology, Immunotherapy, Paris, France, 2 AP-HP, Clinical Investigation Center in Biotherapy, Hôpital Pitié-Salpêtrière, Paris, France, 3 INSERM, UMRS 959, "Immunology, Immunopathology, Immunotherapy", Paris, France, 4 Molecular Virology Laboratory, Hellenic Pasteur Institute, Athens, Greece OPEN ACCESS Citation: Dérian N, Bellier B, Pham HP, Tsitoura E, Kazazi D, Huret C, et al. (2016) Early Transcriptome Signatures from Immunized Mouse Dendritic Cells Predict Late Vaccine-Induced T-Cell Responses. PLoS Comput Biol 12(3): e1004801. doi:10.1371/ journal.pcbi.1004801 Editor: Grégoire Altan-Bonnet, Memorial SloanKettering Cancer Center, UNITED STATES Received: May 22, 2015 Accepted: February 8, 2016 Published: March 21, 2016 Copyright: © 2016 Dérian et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Microarray data are available on GEO: http://www.ncbi.nlm.nih.gov/geo/ query/acc.cgi?token=ijejgwimldunhax&acc= GSE66991. Data are publicly available since July 1st, 2015. Funding: This work was sponsored by European Commission (http://ec.europa.eu) under contract No. LSHB-CT-2004-005246 “CompuVac: rational design and standardized evaluation of novel genetic vaccines” and Safer and Faster Evidence-based Translation (T SAFE 115003), and LabEx Transimmunom (ANR-11-IDEX-0004-02, http://www. ¤a Current address: ILTOO Pharma, iPEPS—ICM Hôpital Pitié Salpêtrière, 47/83 Boulevard de l’Hôpital, Paris France ¤b Current address: Laboratory of Molecular and Cellular Pneumonology, Medical School, University of Crete, Heraklion, Greece ¤c Current address: CNRS UMR7216 Epigenetics and Cell Fate, Université Paris Diderot, Sorbonne Paris Cité, Paris, France * (DK); (AS) Abstract Systems biology offers promising approaches for identifying response-specific signatures to vaccination and assessing their predictive value. Here, we designed a modelling strategy aiming to predict the quality of late T-cell responses after vaccination from early transcriptome analysis of dendritic cells. Using standardized staining with tetramer, we first quantified antigen-specific T-cell expansion 5 to 10 days after vaccination with one of a set of 41 different vaccine vectors all expressing the same antigen. Hierarchical clustering of the responses defined sets of high and low T cell response inducers. We then compared these responses with the transcriptome of splenic dendritic cells obtained 6 hours after vaccination with the same vectors and produced a random forest model capable of predicting the quality of the later antigen-specific T-cell expansion. The model also successfully predicted vector classification as low or strong T-cell response inducers of a novel set of vaccine vectors, based on the early transcriptome results obtained from spleen dendritic cells, whole spleen and even peripheral blood mononuclear cells. Finally, our model developed with mouse datasets also accurately predicted vaccine efficacy from literature-mined human datasets. Author Summary Vaccines are designed to elicit effective immune responses against antigens. The various vector platforms used in vaccine development are diverse and complex, rendering the selection of promising vaccines vector challenging. We have designed a modeling strategy PLOS Computational Biology | DOI:10.1371/journal.pcbi.1004801 March 21, 2016 1 / 17 Early Mouse Dendritic Cell Transcriptome Modelling Predicts Vaccine Efficacy agence-nationale-recherche.fr). CH, ET and DKa were supported by the CompuVac consortium (www. compuvac.eu). PHP was supported by a doctoral fellowship from the Ministère de la Recherche et de la Technologie (http://www.enseignementsup-recherche. gouv.fr). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. that predicts the propensity of vaccine vectors to elicit strong late T-cell responses using transcriptome material obtained 6 hours after vaccination. Our model, designed with mouse datasets, also predicted vector efficacy from mined human data. Thus, molecular signatures obtained 6 hours after vaccination can predict vaccine efficacy at 2 weeks post vaccination, which should help in vaccine development. Competing Interests: The authors have declared that no competing interests exist. Introduction The development of vaccines against complex chronic diseases such as HIV or cancer has been largely unsuccessful so far. Novel vaccine technologies are rationally designed to generate appropriate protective immune responses [1], notably efficient T-cell responses. Such vaccine vectors include plasmid DNA, viral and bacterial vectors, and virus-like particles (VLPs). The intrinsic characteristics of these vectors, including their capacity to stimulate innate immunity and to activate and target the antigen to antigen-presenting cells, determine in large part their immunogenicity and thus their potency as vaccine or gene therapy vectors [2–4]. However the rational design of vectors is limited by various aspects, such as the partial understanding of the factors governing the induction of optimal immunity (i.e. the activation of the innate immune system by various vector components, the effect upon adaptive immunity. . .) or the possible dependence of vector efficacy on the specificity of the target diseases. Systems biology has been introduced in vaccine development to assist in circumventing these limitations and shorten the vaccine development process. Systems biology may not only help to better understand, analyze and reconstruct the complex immune interactions between the pathogen/vaccine and host immune system, but may also improve the in silico testing models for vaccine candidates. Systems biology approaches have proven capable to predict immune responses induced after vaccination [5,6]. For example, expression patterns of genes associated with the efficient processing of peptides for major histocompatibility complex presentation have been identified as useful surrogate markers of vaccine efficacy, obviating the need to perform challenge studies [7]. Signatures derived from antibody repertoire profiling on peptide microarrays during the natural course of influenza infection were shown to be predictive of the efficacy of influenza vaccines [8]. Multivariate analysis performed on human peripheral blood mononuclear cell (PBMC) microarray data, obtain (...truncated)