dataesr/scientific-paragraphs-categorization huggingface dataset:
C. Hierarchical clustering (Euclidean/Ward. D2) performed on normalized T-cell response values defined as "Weak" (cluster 1) and "Strong" (clusters 2 and 3) vectors. Vectors in red were used to build the initial prediction model (see Model stability and confidence in the Results section). doi:10. 1371/journal. pcbi. 1004801. g001 (PPV) is stable for the two classes ("Weak": 0. 96, "Strong": 0. 93). This 9-vector model is already efficient to classify the vector platform with 0. 94 accuracy. These results led us to construct the final predictive model (called RFM model) including all the 19 datasets, based on the analysis of the 210 signatures across the 1900 bootstrapped datasets. This complete training set contained enough information to discriminate clearly between the 2 vector classes, as demonstrated by the misclassification rate parameter reaching zero after 100 simulated trees.
The RandomForest algorithm provides a ranked list of the signatures based on their importance to the efficacy of the classification in the model. This score is based on the decrease of the Gini impurity criterion for each child node of a split. The result of this calculus is the mean of this decrease for each signature present in the trees of the forest. 27 most important signatures, having a mean decrease score higher than ten, were selected. Clustering methods were then   We then asked whether RFM was biased toward particular vector datasets. We first used the leave-one-out methodology, where 19 models were iteratively built using only 18 out of 19 datasets, and then assessing how accurately such models predict the 100 bootstraps from the left-out dataset. All vectors were classified as expected for at least 96 of the 100 bootstrapped datasets, except MPY_3 for which 16 bootstrapped datasets were misclassified (S3 Table ).
Model validation with novel vectors RFM was then used to predict the vector class of 4 new vectors belonging to 3 vector platforms: 2 batches of lentivirus (LV) vectors -a category of vaccine not represented during the model establishment, one new batch of AP205 (AP205_3) and one of MLV (MLV_2). We had independently determined that LV vectors induced strong antigen-specific T-cell responses after immunization and were classified in the "Strong" vector class (Fig  1 ). As shown in Tables  1  and 2A , these 4 bootstrapped datasets were classified as expected with high precision (>95%) while sensitivity and PPV of the model increased compared to the 9-vector model, especially the sensitivity for the "Weak" vector class now reaching 0. 97 (from 0. 89) with RFM. These results highlight that RFM (i) is not vaccine platform-dependent, (ii) correctly predicts a vector platform unknown to the model, and (iii) efficiently predicts both "Weak" and "Strong" vectors.
Model prediction of whole spleen and PBMC data RFM was built on transcriptome data obtained from sorted spleen DCs. In our next experiment, we assessed whether RFM would be sensitive enough to classify transcriptome datasets derived from whole spleen samples obtained 6 hours after immunization, where DCs represent 1-2% of total splenocytes. As summarized in Table  2B , all bootstrapped datasets from whole spleens were well classified, with at least 91% of the expected classification, thus demonstrating our model's sensitivity in classifying vectors in whole spleen transcriptome datasets. We then tested microarray datasets for whole spleen samples obtained 6, 48 and 72 hours after vaccination with one vector, the rAd vector that we used as a standard. Strikingly, only datasets sampled 6 hours after injection were classified as expected (as "Strong") (Table  2D ).
Similarly, we tested the performance of our model in classifying vectors using PBMCderived microarray datasets. The rationale for this experiment is that PBMCs, less than 1% of which are DCs, offer a more accessible sample source than spleen, especially in humans. As Vector prediction confidence. One hundred different models were created using one representative of each vector platform. The probabilities for a vector being classified as expected were calculated for its 100 bootstrapped datasets and averaged as a vector mean probability. Vector mean probabilities are displayed as boxplots. A value of 1 means that the bootstrapped dataset was successfully predicted 100 times over the 100 models. "Strong" and "Weak" vectors are colored red and yellow, respectively. doi:10. 1371/journal. pcbi.
Model prediction of human PBMC data Finally, we tested whether our model could classify datasets obtained from the literature. We found datasets from the Merck Ad5/HIV trial reported by Zak et al. [25]  PBMC transcriptome data were generated from samples obtained at 6, 24 and 72 hours after vaccination. We bootstrapped the samples of Zak et al. , taking patient-paired samples before and after vaccination. 100% and 91% of the bootstrapped paired samples were predicted as "Strong" at 24 and 72 hours, respectively (Table  3 ), in line with the authors' original observations. The same analysis.
Discussion Understanding and predicting innate immune response to vector platforms is primordial for fast and effective production of new vaccination or gene therapy protocols. Systems biology tools efficiently extract information from large datasets in computing predictive models and have already played a major role in recent discoveries in this field  [5, 27]. In this paper, we initially focused on early transcriptomic changes of DCs since these are first-line players in the innate immune response and directly contribute to the triggering of the adaptive response. Our aim was to identify transcriptomic signatures predictive of the late CD8+ CTL responses to the LCMV gp33-41 model antigen conveyed by a variety of vaccine vectors. Based on molecular signatures extracted using the non-supervised ICA method  [20, 22] , we produced and validated a prediction model taking into account 19 available datasets generated with different vector platforms.
No specific molecular pathway was clearly identified by IPA annotations for the other 26 important signatures in our model. This is somewhat surprising since these signatures have been selected by the model to best distinguish "Strong" and "Weak" vectors and are therefore expected to represent differentially regulated biological pathways. In this line, none of the 27 signatures corresponds to a peculiar behavior of a vector but they rather reveal similar behavior within "Strong" or "Weak" groups (Fig  3 ). Moreover, the identified signatures were extracted from 13 out of 19 different vector datasets (9 "Strong" and 4 "Weak" vectors). We believe that these signatures are unlikely artifactual but related to yet undefined biological processes. Indeed, the constant improvement of annotation databases can reveal secondary or additional functions of genes.
For example, CH25H, a gene found in one of the 26 signatures and clearly upregulated in "Strong" vectors, is primarily involved in cholesterol metabolism, but has recently been shown to play a role in the early stage of DC maturation  [26]. Fig  5  shows how the expression of this gene is related to dendritic cell through direct or indirect interactions with STAT-1 or IFNγ, both members of Sig1, and with several genes known to be important in early dendritic cell activation: for example, MYD88 is a gene involved in toll-like receptor signaling  [35] , DUSP5 is known to be upregulated during dendritic cell maturation  [36] , and ABCG1 is a gene playing a role in adaptive immune responses  [37]. The comparative analysis of gene expression modulation of this interaction network shown in Fig  5 reveals  a similar pattern of differential expression for "Strong" vectors (rAd_1, AP205_1) different than that observed for "Weak" vector datasets (BCG_2, MPY_3bis).
Microarray data Experimental groups comprised of 3 to 6 mice immunized with vaccine candidates by the intravenous route. Mice were sacrificed 6 hours after immunization. Spleen DCs were purified with CD11c+-conjugated MACS magnetic beads (Miltenyi Biotec) according to the manufacturer's instructions. After incubation for 20 minutes at 4°C, cells were washed and passed over a MACS column. Purity was checked routinely by FACS and found to be greater than 96±2%. 2x10 6 CD11c+ cells were used for total RNA extraction using Nucleospin RNAII (Macherey Nagel). For test dataset generation, whole PBMCs and/or whole splenocytes and/or sorted spleen DCs were collected at 6 hours, and at 48-and 72-hour time points for the kinetic followup. RNA was checked for quality using gel electrophoresis and for quantity using a Nanodrop spectrophotometer (Thermo Scientific).
Microarrays were performed using either Applied Microarrays (CodeLink Mouse Whole Genome Bioarray) or Illumina (WG6 Mouse BeadArray) technologies (S1 Table ). The MessageAmp II aRNA Amplification Kit (Ambion) was used for cDNA and cRNA production from 1 μg of total RNA. 10 μg of amplified cRNA was subsequently fragmented and hybridized for 20 hours using the Applied Microarrays hybridization and washing buffer kit. Slides were scanned using the GenePix Personal 4100A scanner for CodeLink array or the Illumina BeadArray 500GX Reader for Illumina array. Hybridization and raw data extraction were performed using either GenePix Pro 6. 0 (for CodeLink array) or BeadStudio (for Illumina array) software, respectively (GEO accession GSE66991). Each tested vector dataset comprised "vector-immunized" and corresponding PBS control samples.
Signature database and enrichment analysis Following our two-step ICA!GSEA signature discovery strategy  [20] , signatures were extracted using the fastICA algorithm R package  [40]  following modifications in  [22]. Parameters were set as default, except for the unmixing matrix A -1 convergence threshold set to 10 -6. Ranked gene lists were calculated using the limma modified t-test. ES were calculated using GSEA  [41]  with the pre-ranked gene list protocol. Normalized ES are then calculated based on the permutation performed on gene sets collection, allowing comparison between experiments. The ICA-extracted signature database was complemented with the MsigDB C2 (curated gene sets of biological pathways) and C5 (Gene Ontology gene sets) databases (www. broad. mit. edu/ gsea) in order to increase universe of genes available for permutation of gene sets.
Random forest classification and validation For each model produced in the Results section, classification was performed on a matrix of fastICA extracted signature NES values (see above section) calculated for bootstrapped vector datasets (100 bootstraps per vector dataset), using the random forest algorithm implemented in the randomForest R package to produce a forest of 2000 trees  [42]. The number of randomly selected signatures used at each of the 2000 runs was set according to the mtry function implemented in the randomForest package. The class prediction of the new dataset was deduced by the probability to be "Weak" or "Strong" > 0. 5. The overall vector class was then obtained as the majority of "Weak" or "Strong" class assignments over the 100 bootstraps. For classification model validation, we implemented the leave-one-out methodology consisting in creating models with n-1 datasets, where n is the total number of datasets, and classifying the dataset left out.
In addition, we implemented a "multi-model" methodology based on the classification of bootstrapped datasets over 100 models created as above. Each model was computed on an NES matrix of a random selection of one representative vector dataset of each of the 7 represented vector platforms (see Vector platforms section and S1 Table ). Vector mean probabilities were calculated as the average probability of being "Weak" or "Strong" over the 100 bootstrapped vector datasets, and their distribution over the 100 models was analyzed.
Acknowledgments We thank  Bruno Gouritin  and  Fabien Pitoiset  for sample preparation,  Wassila Carpentier  for microarray datasets generated on the P3S platform ( Groupe Hospitalier Pitié-Salpétrière, Paris ), and  Wahiba Chaara  and  Ioannis Drakos  for proofreading. The authors wish to thank all the  CompuVac consortium members  (www. compuvac. eu).
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. 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.
This study was supported by the  French National Research Agency  (http://www. agencenationale-recherche. fr/)( DIMYVIR ANR-13-BSV3-0007-01 ) to JLH and LK, the  Fondation pour la Recherche Medicale (FRM)  ( DEQ20150331719 ) (https://www. frm. org/) to LK, by the  European Community 's  Seventh Framework Program  ( FP7-PEOPLE-2011-ITN ) (http://cordis. europa. eu/fp7/)under the  Marie-Curie Initial Training Network LK GL JLH.
Immunization of NHP with the humanized anti-human DCIR recombinant IgG4 fused to HIV-1 gag p24 without adjuvant elicits a robust anti-Gag p24 IgG response  [7]. Vaccination of NHPs with a humanized anti-human LOX-1 recombinant IgG4 antibody fused to the HIV-1 Env protein (gp140 from clade C 96ZM651) generates multifunctional and broad specific CD4 + T cells, a specific CD8+ T cell immunity, as well as specific IgG and IgA responses, in particular when given with poly ICLC as the adjuvant  [13]. Human in vitro and preclinical studies in NHPs are promising in the fields of HIV, cancer, and autoimmunity  [28]. However, only a few clinical studies testing the immune efficacy of DC targeting vaccines in HIV and cancer are registered on clinicaltrials. gov. The clinical studies reported in the database indicate that DC therapies in HIV infection, cancer, and autoimmune diseases are mostly based on administration of in situ generated DCs.
These results are reminiscent of our recent findings that antigen-specific Tregs and not "bulk" Tregs correlate with HIV-vaccine induced T-cell responses (manuscript in preparation and  [5] ). These types of more sophisticated assays are of interest especially if used in combination with novel methods such as high-throughput proteomics, high-resolution genomics, and transcriptomics. Understanding the molecular biology underlying vaccines is one of the first steps needed to clarify the complexity of immune responses to vaccines. This approach can be of great use especially when combined with multi-parameter cellular immunology. In recent years, systems biology approaches have provided a novel tool to define immunological correlates. Systems biology uses mathematical modelling to integrate multifaceted datasets and make profiles of immune responses.
Reexpression of CCR7 in DCs, however, was sufficient to reverse the development of such fibrosis. IFN regulatory factor 4-positive (IRF4 + ) DCs constitutively interacted with collecting lymphatics, and selective ablation of this DC subset in Cd11c-Cre Irf4 fl/fl mice also rendered lymphatic collecting vessels hyperpermeable and fibrotic. Together, our data reveal that CCR7 plays multifaceted roles in regulating collecting vessel permeability and fibrosis, with one of the key players being IRF4-dependent DCs. CCR7 and IRF4-dependent dendritic cells regulate lymphatic collecting vessel permeability Stoyan Ivanov, 1 Joshua P. Scallan, 2 Ki-Wook Kim, 1 Kathrin Werth, 3 Michael W. Johnson, 1,4 Brian T. Saunders, 1  Peter L. Wang, 1 Emma L. Kuan, 5 Adam C. Straub, 6 Melissa Ouhachi, 7 Erica G. Weinstein, 5 Jesse W.
Results Ccr7 -/-mice exhibit fibrotic lymphatic collecting vessels and disrupted interactions with DCs. In contrast with lymphatic capillaries, lymphatic collecting vessels have not been observed as functioning as White inset is placed around the major lymphatic collecting vessel afferent to the brachial LNs and shown at greater magnification on the right. (B) Contact area between DCs and lymphatic collecting vessels in Cd11c-YFP Ccr7 +/+ and Cd11c-YFP Ccr7 -/-mice was quantified from single videos prepared during 2-photon intravital imaging, as described in Methods (see also Supplemental Video 2). Each data point represents analysis of 1 Z-stack, with 5 to 7 Z-stacks examined per mouse. Other measurements assessed from these videos were mean DC velocity (C), meandering index (D), and motility coefficient  (E).
From 4 to 5 mice per genotype were examined, with multiple areas along the lymphatic collecting vessel analyzed per mouse. For C-E, each data point represents tracking from an individual cell, with 7 to 10 cells tracked from 4 to 5 mice per genotype. In C-E, each mouse is plotted using a distinct symbol shape. Mann-Whitney U tests were performed for assessment of statistical significance. **P < 0. 01; ***P < 0. 001. jci. org Volume 126 Number 4 April 2016 not recapitulated in Rag2 -/-mice lacking lymphocytes (Figure  2B ), suggesting that adventitial wall thickening was not driven by a loss of lymphocyte interactions. This was an important issue since, besides DCs, lymphocytes depend upon CCR7 for trafficking. We also tested to determine whether other chemokine receptors on DCs gave rise to similar outcomes.
Fibroblasts were also observed within the excess adventitial collagen observed in Ccr7 -/-mice (Figure  3 , compare A to C, B to D). At higher magnification, individual collagen fibrils were discernible (Figure  3, E  and F ). These fibrils were denser and had a distinct appearance in Ccr7 -/-collecting lymphatics (Figure  3, E  and F ). This analysis confirms that the increased second harmonic generation quantified in Ccr7 -/-mice compared with Ccr7 +/+ mice (Figure  2 ) relates to an accumulation of extracellular matrix around the Ccr7 -/-vessels. Overall, the data indicate lymphatic collecting vessel fibrosis occurs progressively in mice lacking CCR7. ground only briefly interacted or failed to interact with collecting vessels (Supplemental Video 1, Ccr7 -/-analysis on the right). We thus developed a method using MATLAB to quantify the surface of lymphatic vessels that were in contact with DCs (Supplemental Video 4) in the 2 genotypes.
From 3 to 6 mice per genotype were examined at multiple areas along the lymphatic collecting vessel analyzed per mouse. Each data point represents the mean result from 1 mouse after analysis of randomly selected time points from 2-photon images with 3 to 5 Z-stacks examined per mouse. For statistical evaluation, 1-way ANOVA with Tukey's post hoc test was carried out. *P < 0. 05; **P < 0. 01. to the change in permeability and (b) that, given the robust interaction we observed between collecting vessels and DCs (Figure  1  and ref. 14) , the role of CCR7 in controlling permeability involves a requirement for DCs in regulating lymphatic permeability. Support for the first hypothesis was obtained by the demonstration that, whereas fibrosis is a progressive phenotype in Ccr7 -/-mice not observed until 8 weeks of age, we found that the permeability was pervasively elevated, that is, we were able to cannulate lymphatic collecting vessels from mice as young as 5 weeks of age, and their permeability was elevated in these young mice similarly to the way it was in the older mice (Figure  4C ).
First, whole-mount immunostaining of perinodal adipose tissue around the brachial LNs revealed a partial reduction in local MHC II + cells in total body Irf4 -/-mice com-conditional expression of CCR7 that was limited to DCs (CD11c + ) was sufficient to prevent fibrosis in the collecting lymphatic vasculature (Figure  4D ). Furthermore, there was a trend in the data suggesting that T cell reexpression of CCR7 was also able to reverse fibrosis, although this fell short of reaching statistical significance in our analysis. In relation to our hypothesis that CCR7-competent DCs prevent fibrosis, these data indicate that the expression of CCR7 by DCs indeed prevents fibrosis, but because T cells may be capable of preventing fibrosis as well if they are allowed to appropriately express CCR7, these data are best interpreted as showing that CCR7 + DCs are sufficient for preventing fibrosis, but it is less clear here if the DCs are necessary.
(C) Measurement of the lymphatic collecting vessel permeability in 5-week-old WT and Ccr7 -/-mice (n = 5 WT and 7 Ccr7 -/-mice, mean ± SEM). (D) 2-Photon intravital analysis of adventitial wall thickness, assessed through measurement of second harmonic signal, around lymphatic collecting vessels in mice in which the human CCR7 gene has been used to disrupt the mouse Ccr7 locus (CCR7 stop/stop ) or mice in which human CCR7 is expressed selectively on CD4 + T cells (T-Ccr7 +/+ ) or CD11c + DCs (DC-Ccr7 +/+ ). In control mice, the Ccr7 gene is not disrupted (Total-Ccr7 -/-). Six mice per genotype were examined at multiple areas along the lymphatic collecting vessels. Each data point represents the mean wall thickness of 1 mouse after blind scoring of 9 Z-stacks per mouse. For statistical evaluation, data in A-C were assessed using the Mann-Whitney U test and data in D employed 1-way ANOVA test, with Tukey's post hoc test.
(B) 1 μl of Cy5-conjugated dextran (70 kDa) was injected in the ear pinnae of anesthetized mice, and the decrease of fluorescence intensity associated with its transport from the initial site of deposition was assessed using a fluorescence stereoscope 36 hours after the administration of DT at 4 ng/g of body weight (n = 6 control and 6 Cd11c-DTR + DT mice combined from 2 independent experiments, mean ± SEM). For statistical evaluation, data were assessed using the Mann-Whitney U test. **P < 0. 01. ing permeability assay. In particular, we measured elevated adventitial second harmonic signal in a second CCR7-deficient strain in which the human CCR7 gene was inserted in the mouse Ccr7 locus downstream of a floxed neo cassette  (7). Cre recombinase-mediated induction of CCR7 in DCs was sufficient to reverse the latestage fibrotic development around lymphatic collecting vessels.
However, reexpression in CD4 + cells, mostly T cells, also strongly trended toward correction of the fibrotic phenotype, even though adventitial collagen was not increased in Rag2 -/-mice entirely lacking T cells. It is thus possible that the role of CCR7 in affecting lymphatic collecting vessel functionality can be supplied by multiple CCR7 + cell types, but the role for DCs seems especially clear. In addition, because intravital imaging data revealed that DCs actively interact with lymphatic collecting vessels, we carried out further experiments to investigate the role of DCs in affecting lymphatic collecting vessel function. This analysis revealed that deletion of IRF4 selectively in CD11c + cells reca-pitulated the increased permeability, and ultimately fibrosis, in lymphatic collecting vessels that was observed in Ccr7 -/-mice.
Finally, while our live imaging experiments, specialized functional assays, and genetic analyses link DCs to regulation of collecting vessel physiology, our studies on CCR7 point to roles in lymphatic function that may involve cells other than DCs. Not only do our data hint that CCR7 expression on T cells may affect lymphatic collecting homeostasis, but the removal of fluorescent tracers from the ear interstitium via lymphatic capillary vessels was impaired in Ccr7 -/-mice, but not in mice with DCspecific ablation of IRF4. When we then visualized the lymphatic capillaries in skin, only those in Ccr7 -/-mice were dilated, consistent with fluid accumulation during impaired transport  (38). Since both loss of IRF4 in DCs and CCR7 deficiency led to similarly altered lymphatic collecting vessel permeability, this transport defect in lymphatic capillaries of Ccr7 -/-mice is likely unrelated to altered lymphatic permeability.