H. Akaike, Statistical predictor identification, Annals of the institute of Statistical Mathematics, vol.22, issue.1, pp.203-217, 1970.

P. Alquier and P. Doukhan, Sparsity considerations for dependent variables, Electron. J. Statist, vol.5, pp.750-774, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00564291

C. Audoin, V. Cocandeau, O. Thomas, A. Bruschini, S. Holderith et al., Metabolome consistency : Additional parazoanthines from the mediterranean zoanthid parazoanthus axinellae. Metabolites, vol.4, pp.421-432, 2014.

F. R. Bach, Consistency of the group lasso and multiple kernel learning, Journal of Machine Learning Research, vol.9, pp.1179-1225, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00164735

B. Bai, O. Novák, K. Ljung, J. Hanson, and L. Bentsink, Combined transcriptome and translatome analyses reveal a role for tryptophan-dependent auxin biosynthesis in the control of DOG1-dependent seed dormancy, New Phytologist, vol.217, issue.3, pp.1077-1085, 2018.

B. Bai, A. Peviani, S. Horst, M. Gamm, L. Bentsink et al., Extensive translational regulation during seed germination revealed by polysomal profiling, New Phytologist, vol.214, issue.1, pp.233-244, 2017.

O. Banerjee, L. E. Ghaoui, and A. , Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data, Journal of Machine Learning Research, vol.9, pp.485-516, 2008.

O. Banerjee, L. E. Ghaoui, and A. Aspremont, Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data, Journal of Machine learning research, vol.9, pp.485-516, 2008.

D. Bates and M. Maechler, Matrix : Sparse and Dense Matrix Classes and Methods, 2017.

A. Belloni, V. Chernozhukovand, W. , and L. , Square-root lasso : pivotal recovery of sparse signals via conic programming, Biometrika, vol.98, issue.4, pp.791-806, 2011.

P. J. Bickel and E. Levina, Covariance regularization by thresholding, Ann. Statist, vol.36, issue.6, pp.2577-2604, 2008.

J. Bien and R. J. Tibshirani, Sparse estimation of a covariance matrix, Biometrika, vol.98, issue.4, pp.807-820, 2011.

Y. Blum, M. Houee-bigot, and D. Causeur, FANet : Sparse Factor Analysis model for high dimensional gene co-expression Networks, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01211038

Y. Blum, M. Houée-bigot, and D. Causeur, Sparse factor model for co-expression networks with an application using prior biological knowledge. Statistical applications in genetics and molecular biology, vol.15, pp.253-272, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01340542

J. Boccard and S. Rudaz, Exploring omics data from designed experiments using analysis of variance multiblock orthogonal partial least squares, Analytica Chimica Acta, vol.920, pp.18-28, 2016.

P. Brockwell and R. Davis, Time Series : Theory and Methods. Springer Series in Statistics, 1991.

P. J. Brockwell and R. A. Davis, Time Series : Theory and Methods, 1990.

T. Cai, W. Liu, and X. Luo, A constrained l1 minimization approach to sparse precision matrix estimation, Journal of the American Statistical Association, vol.106, issue.494, pp.594-607, 2011.

T. T. Cai and M. Yuan, Adaptive covariance matrix estimation through block thresholding, Ann. Statist, vol.40, issue.4, pp.2014-2042, 2012.

R. B. Cattell, The scree test for the number of factors, Multivariate behavioral research, vol.1, issue.2, pp.245-276, 1966.

Y. Chen, P. Du, W. , and Y. , Variable selection in linear models, Wiley Interdisciplinary Reviews : Computational Statistics, vol.6, issue.1, pp.1-9, 2014.

J. Chiquet, T. Mary-huard, R. , and S. , Structured regularization for conditional Gaussian graphical models, Statistics and Computing, vol.27, issue.3, pp.789-804, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01531660

L. D. Desboulets, A Review on Variable Selection in Regression Analysis, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01954386

E. Devijver and M. Gallopin, Block-diagonal covariance selection for high-dimensional gaussian graphical models, Journal of the American Statistical Association, vol.113, issue.521, pp.306-314, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01227608

F. Dieterle, A. Ross, G. Schlotterbeck, and H. Senn, Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. application in 1h nmr metabonomics, Analytical Chemistry, vol.78, issue.13, pp.4281-4290, 2006.

E. Conclusion-dobriban, Permutation methods for factor analysis and PCA, 2018.

N. R. Draper and H. Smith, Applied Regression Analysis, 1998.

T. C. Durand, G. Cueff, B. Godin, B. Valot, G. Clément et al., Combined proteomic and metabolomic profiling of the arabidopsis thaliana vps29 mutant reveals pleiotropic functions of the retromer in seed development, International journal of molecular sciences, vol.20, issue.2, p.362, 2019.

C. Eckart and G. Young, The approximation of one matrix by another of lower rank, Psychometrika, vol.1, issue.3, pp.211-218, 1936.

J. Fan and R. Li, Variable selection via nonconcave penalized likelihood and its oracle properties, Journal of the American Statistical Association, vol.96, issue.456, pp.1348-1360, 2001.

J. Fan, L. Yuan, and L. Han, An overview of the estimation of large covariance and precision matrices, The Econometrics Journal, vol.19, issue.1, pp.1-32, 2016.

J. J. Faraway, Linear Models with R, 2004.

B. Field, G. Cardon, M. Traka, J. Botterman, G. Vancanneyt et al., Glucosinolate and amino acid biosynthesis in arabidopsis, Plant Physiology, vol.135, issue.2, pp.828-839, 2004.

J. Friedman, T. Hastie, and R. Tibshirani, Sparse inverse covariance estimation with the graphical lasso, Biostatistics, vol.9, issue.3, p.432, 2008.

J. Friedman, T. Hastie, and R. Tibshirani, Regularization paths for generalized linear models via coordinate descent, Journal of Statistical Software, vol.33, issue.1, pp.1-22, 2010.

J. Friedman, T. Hastie, and R. Tibshirani, Regularization paths for generalized linear models via coordinate descent, Journal of Statistical Software, vol.33, issue.1, pp.1-22, 2010.

M. Galland, R. Huguet, E. Arc, G. Cueff, D. Job et al., Dynamic proteomics emphasizes the importance of selective mrna translation and protein turnover during arabidopsis seed germination, Molecular & Cellular Proteomics, vol.13, issue.1, pp.252-268, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01204034

D. Gianola, M. Perez-enciso, and M. A. Toro, On marker-assisted prediction of genetic value : beyond the ridge, Genetics, vol.163, issue.1, pp.347-365, 2003.

C. Giraud, Introduction to High-Dimensional Statistics, Chapman & Hall/CRC Monographs on Statistics & Applied Probability, 2014.

J. N. Haddad, On the closed form of the covariance matrix and its inverse of the causal arma process, Journal of Time Series Analysis, vol.25, issue.4, pp.443-448, 2004.

D. Harville, Matrix Algebra : Exercises and Solutions : Exercises and Solutions, 2001.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. Springer Series in Statistics, 2001.

G. Heinze, C. Wallisch, and D. Dunkler, Variable selection -A review and recommendations for the practicing statistician, Biom J, vol.60, issue.3, pp.431-449, 2018.

N. J. Higham, Computing the nearest correlation matrix -a problem from finance, IMA Journal of Numerical Analysis, vol.22, issue.3, pp.329-343, 2002.

H. Hoefling, A path algorithm for the fused lasso signal approximator, Journal of Computational and Graphical Statistics, vol.19, issue.4, pp.984-1006, 2010.

H. Hoefling, FusedLasso : Solves the generalized Fused Lasso, 2014.

J. L. Horn, A rationale and test for the number of factors in factor analysis, Psychometrika, vol.30, issue.2, pp.179-185, 1965.

R. A. Horn and C. R. Johnson, Matrix Analysis, 1986.

Z. Hossain, L. Amyot, B. Mcgarvey, M. Gruber, J. Jung et al., The translation elongation factor eef-1b?1 is involved in cell wall biosynthesis and plant development in arabidopsis thaliana, PLoS One. e30425, 2012.

M. J. Hosseini, S. Lee, D. D. Lee, M. Sugiyama, U. V. Luxburg et al., Learning sparse gaussian graphical models with overlapping blocks, Advances in Neural Information Processing Systems, vol.29, pp.3808-3816, 2016.

O. Hrydziuszko and M. R. Viant, Missing values in mass spectrometry based metabolomics : an undervalued step in the data processing pipeline, Metabolomics, vol.8, issue.1, pp.161-174, 2012.

Z. Huang, S. Footitt, and W. E. Finch-savage, The effect of temperature on reproduction in the summer and winter annual arabidopsis thaliana ecotypes bur and cvi, Annals of botany, vol.113, issue.6, pp.921-929, 2014.

A. Jacquard, Eloge de la différence. La génétique et les hommes, 1978.

R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical Analysis, 1988.

B. Karahalil, Overview of systems biology and omics technologies, Current medicinal chemistry, vol.23, issue.37, pp.4221-4230, 2016.

S. L. Kendall, A. Hellwege, P. Marriot, C. Whalley, I. A. Graham et al., Induction of dormancy in arabidopsis summer annuals requires parallel regulation of dog1 and hormone metabolism by low temperature and cbf transcription factors, The Plant Cell, vol.23, issue.7, pp.2568-2580, 2011.

E. Kerdaffrec and M. Nordborg, The maternal environment interacts with genetic variation in regulating seed dormancy in swedish arabidopsis thaliana, PloS one, issue.12, p.190242, 2017.

J. Kirwan, D. Broadhurst, R. Davidson, and M. Viant, Characterising and correcting batch variation in an automated direct infusion mass spectrometry (dims) metabolomics workflow, Analytical and Bioanalytical Chemistry, vol.405, issue.15, pp.5147-5157, 2013.

C. Kuhl, R. Tautenhahn, C. Boettcher, T. R. Larson, and S. Neumann, Camera : an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets, Analytical Chemistry, vol.84, pp.283-289, 2012.

,. Lê-cao, S. Boitard, and P. Besse, Sparse pls discriminant analysis : biologically relevant feature selection and graphical displays for multiclass problems, BMC Bioinformatics, vol.12, issue.1, p.253, 2011.

O. Ledoit and M. Wolf, A well-conditioned estimator for large-dimensional covariance matrices, Journal of Multivariate Analysis, vol.88, issue.2, pp.365-411, 2004.

W. Lee and Y. Liu, Simultaneous Multiple Response Regression and Inverse Covariance Matrix Estimation via Penalized Gaussian Maximum Likelihood, J. Multivar. Anal, vol.111, pp.241-255, 2012.

S. Leung, X. Liu, L. Fang, X. Chen, T. Guo et al., The cytokine milieu in the interplay of pathogenic Th1/Th17 cells and regulatory T cells in autoimmune disease, Cell. Mol. Immunol, vol.7, issue.3, pp.182-189, 2010.

H. Lütkepohl, New introduction to multiple time series analysis, 2005.

D. R. Macgregor, S. L. Kendall, H. Florance, F. Fedi, K. Moore et al., Seed production temperature regulation of primary dormancy occurs through control of seed coat phenylpropanoid metabolism, New Phytologist, vol.205, issue.2, pp.642-652, 2015.

C. L. Mallows, Technometrics, vol.15, issue.4, pp.661-675, 1973.

K. Mardia, J. Kent, and J. Bibby, Multivariate analysis. Probability and mathematical statistics, 1979.

K. V. Mardia, J. T. Kent, and J. M. Bibby, Multivariate Analysis (Probability and Mathematical Statistics), 1980.

T. Mehmood, K. H. Liland, L. Snipen, and S. Saebo, A review of variable selection methods in partial least squares regression, vol.118, pp.62-69, 2012.

N. Meinshausen and P. Bühlmann, Stability selection, Journal of the Royal Statistical Society : Series B (Statistical Methodology), vol.72, issue.4, pp.417-473, 2010.

N. Meinshausen and P. Buhlmann, Stability selection, Journal of the Royal Statistical Society, vol.72, issue.4, pp.417-473, 2010.

C. Meng, B. Kuster, A. C. Culhane, and A. M. Gholami, A multivariate approach to the integration of multi-omics datasets, BMC Bioinformatics, vol.15, issue.1, p.162, 2014.

A. J. Molstad, G. Weng, C. R. Doss, and A. J. Rothman, An explicit mean-covariance parameterization for multivariate response linear regression, 2018.

T. R. Mosmann, H. Cherwinski, M. W. Bond, M. A. Giedlin, and R. L. Coffman, Two types of murine helper t cell clone. i. definition according to profiles of lymphokine activities and secreted proteins, The Journal of immunology, vol.136, issue.7, pp.2348-2357, 1986.

T. R. Mosmann and R. Coffman, Th1 and th2 cells : different patterns of lymphokine secretion lead to different functional properties, Annual review of immunology, vol.7, issue.1, pp.145-173, 1989.

K. E. Muller and P. W. Stewart, Linear Model Theory : Univariate, Multivariate, and Mixed Models, 2006.

J. K. Nicholson, J. C. Lindon, and E. Holmes, 'metabonomics' : understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological nmr spectroscopic data, Xenobiotica, vol.29, issue.11, pp.1181-1189, 1999.

R. Nishii, Asymptotic properties of criteria for selection of variables in multiple regression, The Annals of Statistics, pp.758-765, 1984.

H. Park, Z. Li, X. O. Yang, S. H. Chang, R. Nurieva et al., A distinct lineage of cd4 t cells regulates tissue inflammation by producing interleukin 17, Nature immunology, vol.6, issue.11, p.1133, 2005.

M. Perrot-dockès, C. Lévy-leduc, J. Chiquet, L. Sansonnet, M. Brégère et al., A variable selection approach in the multivariate linear model : an application to lc-ms metabolomics data, Statistical applications in genetics and molecular biology, p.17, 2018.

M. Perrot-dockès, C. Lévy-leduc, L. Sansonnet, and J. Chiquet, Variable selection in multivariate linear models with high-dimensional covariance matrix estimation, Journal of Multivariate Analysis, vol.166, pp.78-97, 2018.

M. Perrot-dockès, C. Lévy-leduc, and J. Chiquet, MultiVarSel : Variable Selection in a Multivariate Linear Model, 2019.

E. Perthame, C. Friguet, and D. Causeur, Stability of feature selection in classification issues for high-dimensional correlated data, Statistics and Computing, vol.26, issue.4, pp.783-796, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01256508

E. Perthame, C. Friguet, and D. Causeur, FADA : Variable Selection for Supervised Classification in High Dimension, 2019.

M. Pourahmadi, High-Dimensional Covariance Estimation, Wiley Series in Probability and Statistics, 2013.

N. J. Provart, J. Alonso, S. M. Assmann, D. Bergmann, S. M. Brady et al., 50 years of arabidopsis research : highlights and future directions, New Phytologist, vol.209, issue.3, pp.921-944, 2016.

. R-core-team, R : A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2017.

L. Rajjou, K. Gallardo, I. Debeaujon, J. Vandekerckhove, C. Job et al., The effect of ?-amanitin on the arabidopsis seed proteome highlights the distinct roles of stored and neosynthesized mrnas during germination, Plant physiology, vol.134, issue.4, pp.1598-1613, 2004.
URL : https://hal.archives-ouvertes.fr/hal-01508727

P. Raven, S. Singer, G. Johnson, K. Mason, J. Losos et al., , 2017.

S. Ren, A. A. Hinzman, E. L. Kang, R. D. Szczesniak, and L. J. Lu, Computational and statistical analysis of metabolomics data, Metabolomics, vol.11, issue.6, pp.1492-1513, 2015.

E. Riekeberg and R. Powers, New frontiers in metabolomics : from measurement to insight, 1000.

A. Rinaldo, Properties and refinements of the fused lasso, The Annals of Statistics, vol.37, issue.5B, pp.2922-2952, 2009.

A. J. Rothman, Positive definite estimators of large covariance matrices, Biometrika, vol.99, issue.3, pp.733-740, 2012.

A. J. Rothman, P. J. Bickel, E. Levina, and J. Zhu, Sparse permutation invariant covariance estimation, Electron. J. Statist, vol.2, pp.494-515, 2008.

A. J. Rothman, E. Levina, and J. Zhu, Sparse multivariate regression with covariance estimation, Journal of Computational and Graphical Statistics, vol.19, issue.4, pp.947-962, 2010.

E. Saccenti, H. C. Hoefsloot, A. K. Smilde, J. A. Westerhuis, and M. M. Hendriks, Reflections on univariate and multivariate analysis of metabolomics data, Metabolomics, vol.10, issue.3, pp.361-374, 2013.

G. Schwarz, Estimating the dimension of a model. The annals of statistics, vol.6, pp.461-464, 1978.

C. Smith, E. Want, G. O'maille, R. Abagyan, and G. Siuzdak, XCMS : Processing mass spectrometry data for metabolite profiling using Nonlinear peak alignment, matching, and identification, Analytical Chemistry, vol.78, issue.3, pp.779-787, 2006.

R. Smith, A. Mathis, and J. Prince, Proteomics, lipidomics, metabolomics : a mass spectrometry tutorial from a computer scientist's point of view, BMC Bioinformatics, p.15, 2014.

J. M. Stuart, E. Segal, D. Koller, and S. K. Kim, A gene-coexpression network for global discovery of conserved genetic modules, Science, vol.302, issue.5643, pp.249-255, 2003.

M. L. Thompson, Selection of variables in multiple regression : Part i. a review and evaluation, International Statistical Review/Revue Internationale de Statistique, pp.1-19, 1978.

R. Tibshirani, Regression shrinkage and selection via the Lasso, 1996.

, J. Royal. Statist. Soc B, vol.58, issue.1, pp.267-288

J. Varah, A lower bound for the smallest singular value of a matrix, Linear Algebra and its Applications, vol.11, issue.1, pp.3-5, 1975.

D. Verdegem, D. Lambrechts, P. Carmeliet, and B. Ghesquière, Improved metabolite identification with midas and magma through ms/ms spectral datasetdriven parameter optimization, Metabolomics, vol.12, issue.6, pp.1-16, 2016.

J. T. Vogel, D. Cook, S. G. Fowler, and M. F. Thomashow, The cbf cold response pathways of arabidopsis and tomato. Cold Hardiness in Plants : Molecular Genetics, Cell Biology and Physiology, pp.11-29, 2006.

E. Volpe, M. Touzot, N. Servant, M. Marloie-provost, P. Hupé et al., Multiparametric analysis of cytokine-driven human th17 differentiation reveals a differential regulation of il-17 and il-22 production, Blood, vol.114, issue.17, pp.3610-3614, 2009.

U. Von-luxburg, A tutorial on spectral clustering, Statistics and Computing, vol.17, issue.4, pp.395-416, 2007.

A. Walker, Asymptotic properties of least-squares estimates of parameters of the spectrum of a stationary non-deterministic time-series, Journal of the Australian Mathematical Society, vol.4, issue.3, pp.363-384, 1964.

F. Wen, Y. Yang, P. Liu, and R. C. Qiu, Positive definite estimation of large covariance matrix using generalized nonconvex penalties, IEEE Access, vol.4, pp.4168-4182, 2016.

D. Wittenburg, F. Teuscher, J. Klosa, and N. Reinsch, Covariance between genotypic effects and its use for genomic inference in half-sib families, Genes, Genomes, Genetics, vol.3, issue.9, pp.2761-2772, 2016.

U. Wittstock and B. A. Halkier, Glucosinolate research in the arabidopsis era, Trends in plant science, vol.7, issue.6, pp.263-270, 2002.

B. Xin, Y. Kawahara, Y. Wang, and W. Gao, Efficient generalized fused lasso and its application to the diagnosis of alzheimer's disease, Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014.

J. Yang, B. Benyamin, B. P. Mcevoy, S. Gordon, A. K. Henders et al., , 2010.

, Common snps explain a large proportion of the heritability for human height, Nature genetics, vol.42, issue.7, p.565

Y. Yang and H. Zou, gglasso : Group Lasso Penalized Learning Using a Unified BMD Algorithm, 2017.

M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society Series B, vol.68, pp.49-67, 2006.

M. Yuan and Y. Lin, Model selection and estimation in the gaussian graphical model, Biometrika, vol.94, issue.1, pp.19-35, 2007.

A. Zhang, H. Sun, P. Wang, Y. Han, W. et al., Modern analytical techniques in metabolomics analysis, Analyst, vol.137, pp.293-300, 2012.

H. Zhang, Y. Zheng, G. Yoon, Z. Zhang, T. Gao et al., Regularized estimation in sparse high-dimensional multivariate regression, with application to a DNA methylation study, Stat Appl Genet Mol Biol, vol.16, issue.3, pp.159-171, 2017.

P. Zhao and B. Yu, On model selection consistency of lasso, Journal of Machine Learning Research, vol.7, pp.2541-2563, 2006.

X. Zheng and W. Loh, ng/mL), flu (13), and zymosan (10 mg/mL) MoDCs-in which CD80 and CD86 were highly expressed and predicted an effect on 15 distinct Th outputs (Figure 5B), 11 of which were successfully experimentally validated (STAR Methods). The positive role of CD80 and CD86 on IL-3 and IL-31, to our knowledge, have not been described elsewhere. The predictions we failed to validate were for IL-4, IL-5, IL-10, and TNF-a (Figure S4A), Journal of the American Statistical Association, vol.90, issue.429, pp.151-156, 1995.

, IL-23, and TGF-b) condition to detect additional synregarding IL-21, IL-31, and IL-22 (Figa similar strategy to validate predictions regarding ICOSL using an anti-ICOS agonistic antibody. Overall, we validated 10 of 16 predictions (Figure 5E and S4C

, IL-6, and IL-22 production. Using the Th2 condition, we further validated the inhibitory role of IL-12 on IL-10 and IL-31. The effects of IL-12 on TNF-b, IL-31, and IL-6 have not been described previously (Table S3), Exp Fold, and TFN-b. We also validated the inhibitory role of IL-12 on Th2 cytokine (IL-4, IL-5, and IL-13)

, Experimental scheme of the ''adding'' validation procedure used in

, DC-free validation experiment studying the effect of adding IL-1b in Th0, Th2, and Th17. Naive T cells were stimulated by anti-CD3/CD28 beads

, DC-free validation experiment studying the effect of adding ICOS in Th0 and Th17. Naive T cells were stimulated by coated anti-CD3 and ICOS antibodies and soluble anti-CD28

, IL-12 validation experiments in the DC-free system. Naive T cells were stimulated by anti-CD3/CD28 beads under Th0 and Th2 conditions

, IL-12 at 10 ng/mL was added for 6 days; n = 6 donors. For (B) and (D)-(G), each panel shows the mean and SD of cytokine concentration, measured on restimulated Th supernatants (Wilcoxon test), under Th0, Th2, IL-1b, IL-12, IL-12+IL-1b, and Th17 conditions (Table S6). The 63 genes included master regulators of the Th1 and Th2 subsets, such as T-bet and GATA3, respectively, and Th17 regulators, 2012.

, Cells from the IL-12+IL-1b condition had an intermediate expression pattern between the IL-12 (Th1) and Th17 conditions. By decomposing the PCA space into vectors for each variable, we found that IL-17F, IL-23R, ICOS, and T-bet projected predominantly along the IL-12+IL-1b condition (Figure S7B), again pointing to mixed Th1/Th17 features. We then addressed the link between IL-12 and IL-17A in various contexts. IL-12 with IL-23 was predicted to induce IL-17A but not IL-17F (Figure 6C). In a DC-free Th polarization assay, we used IL-12, IL-23, or IL-12+IL-23 and found that none of these conditions induced IL-17A (Figure 7C). We hypothesized that a third input could explain the positive link between ''IL-12_with_IL-23'' and IL-17A. Using an unsupervised analysis, we found IL-1 as a top variable with the highest correlation (Figure S7C), IL-17A and IL-17F regulation at the mRNA level mirrored the protein level (Figure S6H). IL-12+IL-1b induced significantly more RORc, BATF, and Bcl6 than IL-12 or IL-1b alone (Figure S6H), which could explain the induction of IL-17F and IL-21. Still, the levels of RORc and Bcl6 were lower in IL-12+IL-1b than under the Th17 condition (Figure S6H)

, We validated a significant induction of IL-17A with no effect on IL-17F when IL-12 and IL-23 were given in the presence of IL-1b compared with IL-12 or IL-23 (Figure 7C), Therefore, we tested whether IL-12+IL-23 would induce IL-17A in the presence of IL-1b

, To establish the cytokine co-expression profiles of IL-12+IL-1b-treated Th cells at the single-cell level, we per-(IL-1b, IL-6, TGF-b, and IL-23more insight into the functional properties of these ''Th17F'' cells, we studied their co-production with IL-21, IFN-g, and IL-22, all relevant to the Th17 and/or IL-12 pathways, in vitro (Figure S7J) and ex vivo (Figure S7K). Among IL-17F + IL-17A À cells generated with IL-12 and IL-1b, Finally, we observed that our modeling strategy always identi-, IL-17A and IL-17F were not induced by anti-CD2 alone under the Th0 condition

, Day 5 intracellular FACS analysis of Th cells differentiated as in (B). Dot plots show a representative donor

, Quantification of live total CD4 T cells producing either IL-17A or IL-17F; n = 6 donors

, Representative donor of CD4 memory T cells with intracellular FACS staining for IL-17A versus IL-17F

, Venn Diagrams of IL-17F + /IL17A À Th cells co-producing IL-22, IFN-g, and IL-21 of naive CD4 T cells under the IL-12+IL-1b. IL-12+IL; mean percentage and confidence interval

, Venn Diagrams of IL-17F + /IL17A À Th cells co-producing IL-22, IFN-g, and IL-21 of memory CD4 T cells stimulated for 5 h with PMA and ionomycin

L. Pattarini, B. Fould, W. Cohen, O. Geneste, B. Lockhart et al., ACKNOWLEDGMENTS We thank the Institut Curie Cytometry platform for cell sorting, P. Gestraud and F. Coffin for advice regarding statistical analyses, and O. Lantz and N. Manel for important discussions. We wish to thank, from Center of Clinical Inverstigation IGR-Curie: CIC IGR-Curie 1428 and from Ligue contre le Cancer: EL2016.LNCC/VaS. M.G. was funded by ANRS and ARC. AUTHOR CONTRIBUTIONS

M. G. , C. T. , L. K. , C. C. , and P. , designed the experiments. M.P.-D. performed the statistical analysis

W. Abou-jaoudé, P. T. Monteiro, A. Naldi, M. Grandclaudon, V. Soumelis et al., Model checking to assess T-helper cell plasticity, Front. Bioeng. Biotechnol, vol.2, p.86, 2015.

E. V. Acosta-rodriguez, G. Napolitani, A. Lanzavecchia, and F. Sallusto, Interleukins 1beta and 6 but not transforming growth factor-beta are essential for the differentiation of interleukin 17-producing human T helper cells, Nat. Immunol, vol.8, pp.942-949, 2007.

S. Alculumbre and L. Pattarini, Purification of Human Dendritic Cell Subsets from Peripheral Blood, Methods Mol. Biol, vol.1423, pp.153-167, 2016.

Y. E. Antebi, S. Reich-zeliger, Y. Hart, A. Mayo, I. Eizenberg et al., Mapping differentiation under mixed culture conditions reveals a tunable continuum of T cell fates, PLoS Biol, vol.11, 2013.

S. Balan, C. Arnold-schrauf, A. Abbas, N. Couespel, J. Savoret et al., , 2018.

. Large-scale, Human Dendritic Cell Differentiation Revealing Notch-Dependent Lineage Bifurcation and Heterogeneity, Cell Rep, vol.24, 1902.

J. Banchereau and R. M. Steinman, Dendritic cells and the control of immunity, Nature, vol.392, pp.245-252, 1998.

F. Cariani and L. J. Rips, Conditionals, Context, and the Suppression Effect, Cogn. Sci. (Hauppauge), vol.41, pp.540-589, 2017.

L. Chen and D. B. Flies, Molecular mechanisms of T cell co-stimulation and co-inhibition, Nat. Rev. Immunol, vol.13, pp.227-242, 2013.

M. Ciofani, A. Madar, C. Galan, M. Sellars, K. Mace et al., A validated regulatory network for Th17 cell specification, Cell, vol.151, pp.289-303, 2012.

, Cell, vol.179, pp.432-447, 2019.

V. Da-silva-diz, L. Lorenzo-sanz, A. Bernat-peguera, M. Lopez-cerda, and P. Muñ-oz, Cancer cell plasticity: Impact on tumor progression and therapy response, Semin. Cancer Biol, vol.53, pp.48-58, 2018.

F. Edfors, F. Danielsson, B. M. Hallströ-m, L. Kä-ll, E. Lundberg et al., Gene-specific correlation of RNA and protein levels in human cells and tissues, Mol. Syst. Biol, vol.12, p.883, 2016.

P. Guermonprez, J. Valladeau, L. Zitvogel, C. Thé-ry, A. et al.,

, Antigen presentation and T cell stimulation by dendritic cells, Annu. Rev. Immunol, vol.20, pp.621-667

T. Ito, Y. H. Wang, O. Duramad, T. Hori, G. J. Delespesse et al., , 2005.

, J. Exp. Med, vol.202, pp.1213-1223

I. I. Ivanov, B. S. Mckenzie, L. Zhou, C. E. Tadokoro, A. Lepelley et al., , vol.126, pp.1121-1133, 2006.

R. Keller, Dendritic cells: their significance in health and disease, Immunol. Lett, vol.78, pp.113-122, 2001.

W. Kintsch and P. Mangalath, The construction of meaning, Top. Cogn. Sci, vol.3, pp.346-370, 2011.

T. Korn, E. Bettelli, M. Oukka, and V. K. Kuchroo, IL-17 and Th17 Cells, Annu. Rev. Immunol, vol.27, pp.485-517, 2009.

Y. J. Liu, H. Kanzler, V. Soumelis, and M. Gilliet, Dendritic cell lineage, plasticity and cross-regulation, Nat. Immunol, vol.2, pp.585-589, 2001.

Y. Liu, A. Beyer, A. , and R. , On the Dependency of Cellular Protein Levels on mRNA Abundance, Cell, vol.165, pp.535-550, 2016.

A. Macagno, G. Napolitani, A. Lanzavecchia, and F. Sallusto, Duration, combination and timing: the signal integration model of dendritic cell activation, Trends Immunol, vol.28, pp.227-233, 2007.

N. Manel, D. Unutmaz, and D. R. Littman, The differentiation of human T(H)-17 cells requires transforming growth factor-beta and induction of the nuclear receptor RORgammat, Nat. Immunol, vol.9, pp.641-649, 2008.

N. Meinshausen and P. Bü-hlmann, Stability selection, J. R. Stat. Soc. Series B. Stat. Methodol, vol.72, pp.417-473, 2010.

T. Nakayama, K. Hirahara, A. Onodera, Y. Endo, H. Hosokawa et al., Th2 Cells in Health and Disease, Annu. Rev. Immunol, vol.35, pp.53-84, 2017.

A. Naldi, J. Carneiro, C. Chaouiya, D. ;. Thieffry, C. Lé-vy-leduc et al., A variable selection approach in the multivariate linear model: an application to LC-MS metabolomics data, e1000912. Perrot-Dockè s, vol.6, 2010.

M. Perrot-dockè-s, C. Lé-vy-leduc, L. Sansonnet, and J. Chiquet, Variable selection in multivariate linear models with high-dimensional covariance matrix estimation, J. Multivariate Anal, vol.166, pp.78-97, 2018.

V. Soumelis, P. A. Reche, H. Kanzler, W. Yuan, G. Edward et al., Human epithelial cells trigger dendritic cell mediated allergic inflammation by producing TSLP, Nat. Immunol, vol.3, pp.673-680, 2002.

R. Tibshirani, Regression Shrinkage and Selection via the Lasso, J. R. Stat. Soc. Series B Stat. Methodol, vol.58, pp.267-288, 1996.

I. Tindemans, M. J. Peeters, and R. W. Hendriks, Notch Signaling in T Helper Cell Subsets: Instructor or Unbiased Amplifier?, Front. Immunol, vol.8, p.419, 2017.

M. Touzot, M. Grandclaudon, A. Cappuccio, T. Satoh, C. Martinez-cingolani et al., Combinatorial flexibility of cytokine function during human T helper cell differentiation, Nat. Commun, vol.5, p.3987, 2014.

R. Vento-tormo, M. Efremova, R. A. Botting, M. Y. Turco, M. Vento-tormo et al., Single-cell reconstruction of the early maternal-fetal interface in humans, Nature, vol.563, pp.347-353, 2018.

E. Volpe, N. Servant, R. Zollinger, S. I. Bogiatzi, P. Hupé et al., A critical function for transforming growth factor-beta, interleukin 23 and proinflammatory cytokines in driving and modulating human T(H)-17 responses, Nat. Immunol, vol.9, pp.650-657, 2008.

Y. Wang, X. Chen, W. Cao, and Y. Shi, Plasticity of mesenchymal stem cells in immunomodulation: pathological and therapeutic implications, Nat. Immunol, vol.15, pp.1009-1016, 2014.

N. Yosef, A. K. Shalek, J. T. Gaublomme, H. Jin, Y. Lee et al., Dynamic regulatory network controlling TH17 cell differentiation, Nature, vol.496, pp.461-468, 2013.

J. Zhu, H. Yamane, and W. E. Paul, Differentiation of effector CD4 T cell populations (*), Annu. Rev. Immunol, vol.28, pp.445-489, 2010.

B. Zygmunt and M. Veldhoen, T helper cell differentiation more than just cytokines, Adv. Immunol, vol.109, pp.159-196, 2011.

, Cell, vol.179, p.447, 2019.