S. T. Agnandji, A. Huttner, M. E. Zinser, P. Njuguna, C. Dahlke et al., Phase 1 Trials of rVSV Ebola Vaccine in Africa and Europe, N. Engl. J. Med, vol.374, pp.1647-1660, 2016.

D. H. Barouch, J. Liu, L. Peter, P. Abbink, M. J. Iampietro et al., Characterization of humoral and cellular immune responses elicited by a recombinant adenovirus serotype 26 HIV-1 Env vaccine in healthy adults (IPCAVD 001), J. Infect. Dis, vol.207, pp.248-256, 2013.

Y. Benjamini and Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. R. Stat. Soc. B, vol.57, pp.289-300, 1995.

Y. Benjamini and Y. Hochberg, On the adaptive control of the false discovery fate in multiple testing with independent statistics, J. Educ. Behav. Stat, vol.25, pp.60-83, 2000.

D. Betancourt, N. M. De-queiroz, T. Xia, J. Ahn, and G. N. Barber, , 2017.

, Cutting Edge: Innate Immune Augmenting Vesicular Stomatitis Virus Expressing Zika Virus Proteins Confers Protective Immunity, J. Immunol, vol.198, pp.3023-3028

A. L. Boulesteix and K. Strimmer, Partial least squares: a versatile tool for the analysis of high-dimensional genomic data, Brief. Bioinform, vol.8, pp.32-44, 2007.

A. Bukreyev, P. E. Rollin, M. K. Tate, L. Yang, S. R. Zaki et al., Successful topical respiratory tract immunization of primates against Ebola virus, J. Virol, vol.81, pp.6379-6388, 2007.
DOI : 10.1128/jvi.00105-07

URL : https://jvi.asm.org/content/81/12/6379.full.pdf

J. E. Christensen, C. De-lemos, T. Moos, J. P. Christensen, and A. R. Thomsen, CXCL10 is the key ligand for CXCR3 on CD8+ effector T cells involved in immune surveillance of the lymphocytic choriomeningitis virus-infected central nervous system, J. Immunol, vol.176, pp.4235-4243, 2006.

J. H. Dufour, M. Dziejman, M. T. Liu, J. H. Leung, T. E. Lane et al., , 2002.

, CXCL10)-deficient mice reveal a role for IP-10 in effector T cell generation and trafficking, J. Immunol, vol.168, pp.3195-3204

W. C. Fanslow, K. N. Clifford, M. Seaman, M. R. Alderson, M. K. Spriggs et al., Recombinant CD40 ligand exerts potent biologic effects on T cells, J. Immunol, vol.152, pp.4262-4269, 1994.

H. Feldmann, S. M. Jones, K. M. Daddario-dicaprio, J. B. Geisbert, U. Strö-her et al., Effective post-exposure treatment of Ebola infection, PLoS Pathog, vol.3, p.2, 2007.

D. Furman, V. Jojic, B. Kidd, S. Shen-orr, J. Price et al., Apoptosis and other immune biomarkers predict influenza vaccine responsiveness, Mol. Syst. Biol, vol.9, p.659, 2013.
DOI : 10.15252/msb.20145632

URL : http://msb.embopress.org/content/msb/10/9/750.full.pdf

D. Gaucher, R. Therrien, N. Kettaf, B. R. Angermann, G. Boucher et al., Yellow fever vaccine induces integrated multilineage and polyfunctional immune responses, J. Exp. Med, vol.205, pp.3119-3131, 2008.
DOI : 10.1084/jem.20082292

URL : http://jem.rupress.org/content/205/13/3119.full.pdf

T. W. Geisbert, K. M. Daddario-dicaprio, M. G. Lewis, J. B. Geisbert, A. Grolla et al., Vesicular stomatitis virus-based ebola vaccine is well-tolerated and protects immunocompromised nonhuman primates, PLoS Pathog, vol.4, p.1000225, 2008.
DOI : 10.1371/journal.ppat.1000225

URL : https://journals.plos.org/plospathogens/article/file?id=10.1371/journal.ppat.1000225&type=printable

T. W. Geisbert, J. B. Geisbert, A. Leung, K. M. Daddario-dicaprio, L. E. Hensley et al., Single-injection vaccine protects nonhuman primates against infection with marburg virus and three species of ebola virus, J. Virol, vol.83, pp.7296-7304, 2009.

A. M. Henao-restrepo, A. Camacho, I. M. Longini, C. H. Watson, W. J. Edmunds et al., Efficacy and effectiveness of an rVSV-vectored vaccine in preventing Ebola virus disease: final results from the Guinea ring vaccination, open-label, cluster-randomised trial, Ebola C ¸ a Suffit!). Lancet, vol.389, pp.505-518, 2017.

S. M. Jones, H. Feldmann, U. Strö-her, J. B. Geisbert, L. Fernando et al., Live attenuated recombinant vaccine protects nonhuman primates against Ebola and Marburg viruses, Nat. Med, vol.11, pp.786-790, 2005.
DOI : 10.1038/nm1258

T. H. Kang, H. C. Bae, S. H. Kim, S. H. Seo, S. W. Son et al., Modification of dendritic cells with interferon-gammainducible protein-10 gene to enhance vaccine potency, J. Gene Med, vol.11, pp.889-898, 2009.

T. H. Kang, K. W. Kim, H. C. Bae, S. Y. Seong, K. et al., Enhancement of DNA vaccine potency by antigen linkage to IFN-g, 2011.

, Int. J. Cancer, vol.128, pp.702-714

C. W. Law, Y. Chen, W. Shi, and G. K. Smyth, voom: Precision weights unlock linear model analysis tools for RNA-seq read counts, Genome Biol, vol.15, p.29, 2014.

K. A. L^-e-cao, D. Rossouw, C. Robert-granié, and P. Besse, A sparse PLS for variable selection when integrating omics data, Stat. Appl. Genet. Mol. Biol, vol.7, p.35, 2008.

S. Li, N. Rouphael, S. Duraisingham, S. Romero-steiner, S. Presnell et al., Molecular signatures of antibody responses derived from a systems biology study of five human vaccines, Nat. Immunol, vol.15, pp.195-204, 2014.

B. Liquet, K. A. L^-e-cao, H. Hocini, R. Thié-baut, B. Liquet et al., A novel approach for biomarker selection and the integration of repeated measures experiments from two assays, BMC Bioinformatics, vol.13, pp.35-42, 2012.
URL : https://hal.archives-ouvertes.fr/inserm-00814235

P. Lu, J. F. Urban, X. D. Zhou, S. J. Chen, K. Madden et al., CD40-mediated stimulation contributes to lymphocyte proliferation, antibody production, eosinophilia, and mastocytosis during an in vivo type 2 response, but is not required for T cell IL-4 production, J. Immunol, vol.156, pp.3327-3333, 1996.

A. Marzi, S. J. Robertson, E. Haddock, F. Feldmann, P. W. Hanley et al., EBOLA VACCINE. VSV-EBOV rapidly protects macaques against infection with the 2014/15 Ebola virus outbreak strain, Science, vol.349, pp.739-742, 2015.

R. Mazumder, T. Hastie, and R. Tibshirani, Spectral Regularization Algorithms for Learning Large Incomplete Matrices, J. Mach. Learn. Res, vol.11, pp.2287-2322, 2010.

L. A. Mitchell, A. J. Henderson, and S. W. Dow, Suppression of vaccine immunity by inflammatory monocytes, J. Immunol, vol.189, pp.5612-5621, 2012.

H. I. Nakaya, J. Wrammert, E. K. Lee, L. Racioppi, S. Marie-kunze et al., Systems biology of vaccination for seasonal influenza in humans, Nat. Immunol, vol.12, pp.786-795, 2011.

S. Paust and U. H. Von-andrian, Natural killer cell memory, Nat. Immunol, vol.12, pp.500-508, 2011.

S. Paust, H. S. Gill, B. Z. Wang, M. P. Flynn, E. A. Moseman et al., Critical role for the chemokine receptor CXCR6 in NK cell-mediated antigen-specific memory of haptens and viruses, Nat. Immunol, vol.11, pp.1127-1135, 2010.

B. Pulendran, S. Li, and H. I. Nakaya, Systems vaccinology. Immunity, vol.33, pp.516-529, 2010.

X. Qiu, L. Fernando, J. B. Alimonti, P. L. Melito, F. Feldmann et al., Mucosal immunization of cynomolgus macaques with the VSVDeltaG/ZEBOVGP vaccine stimulates strong ebola GP-specific immune responses, PLoS ONE, vol.4, p.5547, 2009.

T. D. Querec, R. S. Akondy, E. K. Lee, W. Cao, H. I. Nakaya et al., Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans, Nat. Immunol, vol.10, pp.116-125, 2009.

D. M. Sansom, C. N. Manzotti, and Y. Zheng, What's the difference between CD80 and CD86?, Trends Immunol, vol.24, pp.314-319, 2003.

T. Shimaoka, N. Kume, M. Minami, K. Hayashida, H. Kataoka et al., Molecular cloning of a novel scavenger receptor for oxidized low density lipoprotein, SR-PSOX, on macrophages, J. Biol. Chem, vol.275, pp.40663-40666, 2000.

J. E. Teigler, M. J. Iampietro, and D. H. Barouch, Vaccination with adenovirus serotypes 35, 26, and 48 elicits higher levels of innate cytokine responses than adenovirus serotype 5 in rhesus monkeys, J. Virol, vol.86, pp.9590-9598, 2012.

K. L. Warfield, J. G. Perkins, D. L. Swenson, E. M. Deal, C. M. Bosio et al., Role of natural killer cells in innate protection against lethal ebola virus infection, J. Exp. Med, vol.200, pp.169-179, 2004.

K. L. Warfield, D. L. Swenson, G. G. Olinger, W. V. Kalina, M. J. Aman et al., Ebola virus-like particle-based vaccine protects nonhuman primates against lethal Ebola virus challenge, J. Infect. Dis, vol.196, issue.2, pp.430-437, 2007.
DOI : 10.1086/520583

URL : https://academic.oup.com/jid/article-pdf/196/Supplement_2/S430/18019057/196-Supplement_2-S430.pdf

L. Cao, sample size and searches 177 the optimal linear combinations of multidimensional, highly correlated, explanatory variables (here innate immune 178 response variables), which best explain the variability in the multivariate outcome (here antibody responses). The 179 linear combinations of variables are summarized in latent components. The sparse version, sPLS, allows for variable 180 down-selection by including a Lasso penalization, Boulesteix and Strimmer, vol.181, issue.2012, 2007.

, The predictive value of the multivariable linear model was assessed by the root square residuals (RSR)

, Missing values of cell surface markers were imputed by non-parametric simple imputation based on Random 189

, Forests, a method that takes into account the multidimensional and correlated nature of the immune markers 190, 2001.

, Kernel estimation of the density function windowed between 0 and the corresponding Low Level Of Quantification 192 (LLOQ) of the standard curve. The cytokine marker selected by sPLS (IP-10 plasma concentration on day 3) was 193 not concerned by this imputation

, For the analyses of differential gene expression after vaccination, we used the voom/limma pipeline, p.195

. Law, We first applied a filter to exclude genes with 197 very low mean count from statistical comparisons (exclusion of genes with mean raw count <5) and then used TMM 198 (Trimmed Mean of M-values) normalization and logCPM, 2014.

O. Robinson, Using these weights, the limma empirical Bayes analysis pipeline, based on the normal 204 linear model, can then be applied to RNA Seq data. We tested the null hypothesis of log2 fold change =0 (compared 205 to Day 0) for each gene, and used the Benjamini-Hochberg procedure for controlling FDR to account for multiple 206 testing, TMM allows for between-sample normalization under the null hypothesis and serves as correction factor for library 200 size in the analyses, 1995.

, The voom method relies on linear modelling of logCPM transformed normalized data, with TMM library size 212 correction, with an estimation of mean-variance trend through a LOWESS curve. This non-parametric approach 213 avoids the negative binomial distribution assumption by instead estimating precision weights. Using these weights, 214 the limma empirical Bayes analysis pipeline, our analysis, we first applied a filter to exclude genes with very low mean count from statistical comparisons 208 (exclusion of genes with mean raw count <5) and then used TMM, 2010.

, Benjamini-Hochberg procedure for controlling FDR to account for multiple testing, 1995.

A. Selidji-todagbe, , p.222

U. Tübingen, ). Germany, and S. Krishna,

U. Tropenmedizin and . Tübingen, , p.224

G. Peter, J. S. Kremsner, and . Brosnahan,

. Tübingen,

, Philip Bejon and Patricia Njuguna, vol.226

M. M. Addo, , p.227

, Germany

S. Becker and V. Krähling,

M. Kieny, V. Moorthy, and P. Fast, , p.229

O. Savarese and . Lapujade, World Health Organization

H. Y. Benjamini-y, Controlling the false discovery rate: a practical and powerful approach to multiple 233 testing, J Roy Stat Soc Ser B, vol.57, pp.289-300, 1995.

B. , A. L. Strimmer, and K. , Partial least squares: a versatile tool for the analysis of high235 dimensional genomic data, Brief Bioinform, vol.8, pp.32-44, 2007.

B. and L. , Random forests, Machine Learning, vol.45, pp.5-32, 2001.

L. , C. W. Chen, Y. Shi, W. Smyth, and G. K. , voom: Precision weights unlock linear model analysis 238 tools for RNA-seq read counts, Genome Biol, vol.15, p.29, 2014.

L. E. Cao, K. A. Rossouw, D. Robert-granie, C. Besse, and P. , A sparse PLS for variable selection 240 when integrating omics data, Stat Appl Genet Mol Biol, vol.7, p.35, 2008.

L. , B. De-micheaux, P. L. Hejblum, B. P. Thiebaut, and R. , Group and sparse group partial 242 least square approaches applied in genomics context, Bioinformatics, vol.32, pp.35-42, 2016.

L. , B. Le-cao, K. A. Hocini, H. Thiebaut, and R. , A novel approach for biomarker selection and 244 the integration of repeated measures experiments from two assays, BMC Bioinformatics, vol.13, p.325, 2012.

R. , M. D. Oshlack, and A. , A scaling normalization method for differential expression analysis of 246 RNA-seq data, Genome Biol, p.25, 2010.