dataesr/scientific-paragraphs-categorization huggingface dataset:
A list of 56 up regulated genes in Treg filtered on the FDR and the fold change also establish a dense network of putative PPI, although many members were not connected (Figure  supplemental 1 ). The topology of the network was very similar to the one of the TMS (Figure  1 ), with Foxp3, Il2ra and Ctla4 being both highly differentially expressed and most connected to other members of the network. Of note is that Cpe, coding for Carboxypeptidase E, responsible with other convertases for processing proenkephalin into biologically active MENK peptides  (33)  , was present in this signature. To go further, we next investigated the overlap between the Immuno-Navigator signature and the TMS. There was a 50% overlap in genes differentially expressed in CD4 and Treg from the Immuno-Navigator database relative to our more stringent TMS (Figure  2C ).
Penk expression by Treg in various tissues and inflammatory conditions Following on the surprising observation that Penk was a highly specific Treg genetic marker, we wanted to confirm this observation at the protein level. Deceptively, PENK was not listed in 2 different databases reporting the proteomes of murine Treg and Tconv, preventing any differential expression analysis to be performed  (34, 35). Furthermore, a monoclonal antibody to murine PENK is currently not available, preventing direct detection by cytometry. Then, we asked whether Penk mRNA over expression could be limited to peripheral lymphoid organs, since most of the data analyzed so far were generated in spleens or lymph nodes. A rapid survey of studies examining gene expression in Tconv and Treg in various tissues showed that Penk was found to be over expressed by Treg in the thymus  (21)  and in several tissues, including fat  (17, 18) , and muscle  (22)  (Figure  4A ).
Analysis of the genes most correlated to Penk mRNA In order to gain mechanistic insights into the regulation of Penk expression in Treg, we first established a network linking the genes most correlated to Penk mRNA from the Immuno-Navigator database in all cell types combined (Figure  5A ). This network included several members of the TMS, including Foxp3, Ikzf4, Ncmap, Ctla-4, or Gpr83, showing that Penk was highly correlated with some genes of the TMS defined above. The high correlation between Foxp3 and Penk is illustrated in figure  5B , where the co expression of Foxp3 and Penk in Treg is also clearly visible. We next interrogated the Immuno-Navigator database to establish a network of the genes most correlated to Penk specifically in Treg (Figure  5C ). Penk was highly correlated to 4 TNFR members (Tnfrsf1b (TNFR2), Tnfrsf4 (OX40), Tnfrsf9 (4-1BB) and Tnfrsf18 (GITR)), and to the TF Batf (illustrated in figure  5D ), suggesting that Penk expression in Treg might be regulated by TNFR signaling and BATF.
Finally, BATF was reported to control several immune regulatory networks through AP-1 members JUN or FOS  (37). Interestingly, members of the AP-1 TF were listed as potential regulators of Penk mRNA according to the Ingenuity database (Figure  6D ). Altogether, our results point to a model in which TNFR signaling regulates Penk expression in Treg through modulation of the BATF-AP-1 complex.
Discussion To our knowledge, the present report is the first attempt to define a "universal" Treg signature in mice by a meta analysis of published and available datasets. Our TMS provide a more solid appreciation of Treg-specific genes relative to the Immuno-navigator database which includes many types of heterogeneous samples. Our unbiased approach confirms that the genes belonging to the interleukin-2 signaling pathway (Il2ra, Il2rb) and of the TNF receptor super-family (Tnfrsf4, Tnfrsf9) are central to their identity. Our gene ontology enrichment analysis of the TMS also confirms that the "core" set of genes belongs to biological processes important for Treg function; regulation of T cell proliferation and cytokine signaling pathway. Finally, the preferred cellular localization of the TMS gene products at the cell surface highlights the dependency of Treg to external stimulus for their function and to preserve their identity.
Our analysis significantly extends these observations by showing that Penk expression by Treg is consubstantial of their generation in the thymus, independent of their localization, and can be modulated in the tissues during inflammation. Based on our own and previously published datasets, we propose a model in which TNFR signaling regulate Penk mRNA transcription through modulation of the Batf/AP-1 complex. Interestingly, Penk was down modulated in Treg in the EAE model, where Treg are defective, whereas it was up regulated in Treg of the tumor, where Treg are suppressors, suggesting an immunosuppressive role for Penk-derived opioid peptides. This has been clearly established in vitro on PHA-activated murine T cells (46).
Acknowledgments The author wish to thank  Dr J Divoux  for performing the MCA tumor experiments for RNA-Seq analysis,  Dr Maryam Khosravi  for performing the EAE experiments for RNA-Seq experiments,  Dr Martina Lubrano di Ricco  for performing the TNFR agonists for RNA-Seq experiments,  Dr Armanda Casrouge  and  Mr Claude Baillou  for cell sorting,  Dr Béhazine Combadière  for access to Ingenuity Pathway Analysis software,  Dr Aurélien Corneau  for running the CyTOF, all members of the ITAC team and all the members of the  CIMI-PARIS research center  for their encouragements and criticisms, and the curators of the different databases used in this study.
Funding: This work was supported by the  Institut Universitaire de Cancérologie (Sorbonne University) , the  Cancéropôle Ile-de-France  to G. M, the  ENLIGHT-TEN program , the  Fondation pour la Recherche Médicale  (Equipes FRM 2015), and the  Agence Nationale de la Recherche  ( ANR-15-CE15-0015-01  and  ANR-17-CE15-0030-01 ) to B. L. S. N. A is a recipient of a doctoral fellowship from the  Association pour la Recherche sur le Cancer (ARC). , Regulatory T cells; Tconv, Conventional T cells; TNFRSF,.
Sequencing was performed by the IGBMC Microarray and Sequencing platform, a member of the 'France Génomique' consortium (ANR-10-INBS-0009). After quality trimming and adapter removal using Trimmomatic v0. 36  (Bolger et al. 2014) , reads were mapped sequentially using bowtie v1. 1. 2 (Langmead et al. 2009)  to the following references: rRNA's, microRNA's, FHVΔB2eGFP, TEs and Drosophila genome (dm6), using piPipes v1. 5. 0  (Han et al. 2015) , allowing 1 mismatch for all references except for the Drosophila genome (2 mismatches allowed). Library and mapping statististics are shown in Table  S1. Analysis of mapped reads was done using custom R scripts. Counts of reads mapping to FHVΔB2eGFP and gypsy were normalized against the total number of reads excluding ribosomal RNA and micro RNA mapping reads.
Library quality was assessed in Agilent Bioanalyzer 2100 (Agilent), using the High Sensitivity DNA assay. Libraries were sequenced in Illumina HiSeq 4000 (Illumina, San Diego, CA, USA), with a single-end 50 base-pairs (bp) read-length strategy. Sequencing was performed by the IGBMC Microarray and Sequencing platform. After quality trimming and adapter removal using Trimmomatic, reads were mapped using STAR v2. 5. 2b  (Dobin et al. 2013 ) to a modified Drosophila genome and annotation (r6. 04 with the sequences/annotations of the FHVΔB2eGFP, UAS-CrPV-1A and UAS-mCherry∷NLS transgenes the transgenes added as extra chromosomes). Reads mapping to the sense strand of the transcripts or transgenes were counted with featureCounts v1. 5. 2  (Liao et al. 2014)  using the modified Drosophila annotation files.
Differential gene expression of transcripts present in $25% of the libraries with at least 5 reads across all libraries was done using DESeq2 v1. 20  (Love et al. 2014). Variance was estimated using the local fitting method. Read counts and normalized read counts are shown in Tables  S4-S5. Transcripts with log2 difference in expression $ 1. 5 and Benjamini & Hochberg corrected p-value , 0. 05 were considered differentially expressed.
Reagents and materials LPS purified from Salmonella enterica subsp. enterica serovar Minnesota S strain (smooth LPS) and Re595 mutant strain (rough LPS) were purchased from Sigma-Aldrich (Saint Louis, MO, USA). The molecular weight of our LPS molecules was checked by polyacrylamide gel electrophoresis. Briefly, 0. 8 μg of LPS was heated at 70°C for 10 min in a Tricine buffer containing SDS (Thermo Fisher, Illkirch, France) and submitted to electrophoresis on a 10-20% polyacrylamide gradient gel in Tricine SDS buffer (Novex, Thermo Fisher) prior to silver staining (Pierce Silver Staining Kit, Thermo Fisher). Apparent molecular weights were determined by comparison with protein standards (Mark 12, Thermo Fisher; Protein Ladder, Euromedex, Souffelweyersheim, France) that were submitted to electrophoresis together with the samples, and calculations were performed by using GraphPad Prism 6 software with interpolation from nonlinear regression curve.
Cytokine Measurements Interleukin (IL) 6, IL-10, tumor necrosis factor alpha (TNF-α), macrophage chemoattractant protein 1 (MCP-1), and interferon gamma (IFN-γ) in mouse plasma were assayed by cytometric bead array using commercially available kits (CBA mouse inflammation kit, BD Biosciences, San Diego, CA, USA) according to the manufacturer's instructions. Signal quantification was performed on a Guava EasyCyte Plus flow cytometer (Millipore, Billerica, MA, USA) and analyzed with FCAP Array software (SoftFlow, Pecs, Hungary).
FUNDING This work was supported by the  INSERM (Institut National de la Santé Et de la Recherche Médicale) , the  Regional Council of Burgundy ; the  University of Burgundy ; the  Fondation de France  (grant  RAF13002MMA ); and by a  French Government  grant managed by the  French National Research Agency (ANR)  under the program " Investissements d' Avenir " (reference  ANR-11-LABX-0021-01-LipSTIC LabEx ).
Epigenetic players in viral infections: filling the gap between basic research and clinical application Powered by the supportive results in cell cultures and mouse models, epigenetic drug candidates are recently being clinically evaluated as potential antiviral drugs. In this section, some completed and ongoing clinical trials are cited, as an attempt to present preliminary data about the use of epigenetic drugs to manipulate viral infections or viral infection-related malignancies (Table  2 ). As most trials are being published recently, analyzing toxicity, schedules, doses, and measuring clinical response stand up as the main aim. In the setting of HIV infection, several HDAC inhibitors were tested as a combination with antiretroviral therapy: panobinostat (NCT01680094), vorinostat (NCT01319383), and romidepsin (NCT02092116, NCT01933594), in addition to VPA (NCT00289952).
Although with varying degrees, all HDAC inhibitors showed an increase in viral transcription with no significant effect on the size of the HIV-1 functional reservoir, as no inhibitor has demonstrated complete clearance of latent infection  [195] [196] [197] [198]. This is possibly attributable to incomplete latency reversal or   [199]. Importantly, caution should be engaged as the increased efficacy implicated by those combinations could be complemented with adverse effects not noted with the used of HDAC inhibitors alone. Not limited to HIV infection, some HDAC inhibitors have been tested in the context of viral-induced malignancies. Treatment with belinostat demonstrated tumor stabilization in unresectable hepatocellular carcinoma (NCT00321594)  [200]. In addition, mocetinostat (MGCD0103) showed promising disease control in patients with relapsed classical Hodgkin lymphoma (NCT00358982)  [201].
Tractinostat (VRx-3996) in combination with valganciclovir is currently under investigation in EBV-associated lymphoid malignancies (NCT03397706). Interestingly, HDAC inhibitors could induce EBV lytic-phase gene expression and act as sensitizers to antivirals for the treatment of EBV-associated lymphomas  [202]  as in the case of arginine butyrate and ganciclovir  [203]. Another epigenetic player, the DNMT inhibitor azacitidine, was shown to reverse the dense CpG methylation and potentially triggering gene re-expression in patients with EBV-positive tumors  [204]. Although those inhibitors might be future promising candidates for inclusion in the current therapeutic management, caution should be taken as they could reactivate some latent DNA viruses like HBV or EBV in the setting of other conditions treatment  [205].
HIVconsv HIV infection No published results yet Phase I NCT02616874 vaccine Broadly neutralizing HIV infection Ongoing Phase II NCT03041012 antibody (3BNC117) Valproic acid Antiretroviral HIV infection No significant reductions in Phase II NCT00289952 [198] therapy the frequency of CD4+ T cells harboring replication-competent HIV Belinostat None Unresectable -Tumor stabilization Phases I-II NCT00321594 [200] hepatocellular -Well tolerated carcinoma Mocetinostat None Relapsed and refractory -Decrease in tumor measurements Phase II NCT00358982 [201] classical Hodgkin lymphoma -Grade 3 and 4 adverse events (neutropenia and pneumonia) Tractinostat Valganciclovir EBV-associated lymphoid Ongoing Phase Ib/II NCT03397706 malignancies *Retrieved from Clinicaltrials. gov.
However, alongside these rounded microglia, a few branched cells were also observed in the disease context (Figures  1B, B' ). Given that microglial cells are highly dynamic, we used in vivo real-time confocal imaging combined with Imaris software (Bitplane Inc. ) image analysis to determine whether the presence of hTau P301L -expressing neurons modified microglia dynamics. In Tg(ApoE-eGFP) embryos, microglia displayed dynamic processes that were constantly extending and retracting, while their cell bodies remained almost immobile (Figures  1H, J , Supplementary Videos 1, 5, Supplementary Figures  1A, C ). By contrast, in Tg(ApoE-eGFP; HuC-hTau P301L :DsRed) embryos, microglia were highly mobile with their cell bodies traveling over longer distances (Figures  1I, K , Supplementary Videos 1, 6, Supplementary Figures  1B, D ).
ACKNOWLEDGMENTS We thank  Bettina Schmid  ( DZNE, Munich, Germany ) for providing us with the Tg(HuC-hTau P301L :DsRed) transgenic line,  Francesca Peri  ( University of Zurich, Zurich, Switzerland ) for providing us with the Tg(ApoE-eGFP) and  Michael Redd  ( University College London, London, United Kingdom ) for providing us with the rabbit anti-zebrafish L-plastin polyclonal antibody. We also thank the imaging facility IMAG' IC (Cochin Institute, Paris). We also thank  Christiane Romain  and  Olivier Bar  ( INSERM UMR 1141 ) for their technical assistance.
FUNDING This work was supported by  Institut National de la Santé et la Recherche Médicale (INSERM) , the  National Center for Scientific Research (CNRS) , the  French National Research Agency  ( ANR-16-CE18-0010 ), and  Fondation NRJ (Institut de France ) to NS-Y. Funding sources had no involvement in study design, collection, analysis or interpretation of data, or decision to publish.
3b  and Supplementary Fig. 3e ). Next, we sought to investigate the role of A 198 and A 242 on HIV-1 full-length RNA metabolism. A comparison between wild type and the ΔA 198 /A 242 provirus showed that the double mutant virus accumulates more intracellular Gag but releases significantly less viral particles (Fig. 3c  and Supplementary Fig. 3f ). Moreover, quantification of the full-length RNA from equal amounts of viral particles revealed a defect in packaging in the ΔA 198 /A 242 double mutant provirus thus, confirming the critical relevance of these two adenosine residues for the incorporation of the HIV-1 full-length RNA into viral particles (Fig. 3c ). An analysis of 890 sequences from the HIV database (www. hiv. lanl. gov) indicate that A 198 and A 242 are highly conserved within the 5´-UTR of isolates suggesting that this epitranscriptomic regulation must be a common feature of different HIV-1 subtypes including the highly prevalent subtypes C and B as well as circulating recombinant forms (Fig.
FUNDING This work was funded through the  FONDECYT Program  ( Nº 1160176  and  1190156  to RSR and  1180798  to FVE). CPM, FGdeG, SRB, BRA and PAC are recipients of a National Doctoral fellowship from  CONICYT. Research at BS laboratory was funded by  CNRS ,  Université Paris Descartes  and a grant from  Fondation pour la Recherche Médicale  (FRM # DBI20141231337 ). Exchanges between RSR and BS laboratories were funded by a LIA grant from  CNRS. GdB is a recipient of a fellowship from the  French Ministry for "Enseignement Superieur et Recherche ".
Discussion Immunotherapy is a promising new approach to achieve long-term remission, or even cure, of HIV infection. Currently the only way to access the efficacy of these interventions is to interrupt antiretroviral therapy (ART) and observe whether viral rebound occurs. In this study we analyzed data from a collection of trials conducted in macaques to test three new immunotherapies -a smallmolecule TLR7 agonist, a therapeutic vaccine (Ad26/MVA), and a monoclonal broadly neutralizing antibody (PGT121). We developed and applied a mathematical model of viral dynamics in order to characterize rebound kinetics in detail and better understand the mechanisms of action of each therapy. Using a statistically rigorous group-level fitting framework, we were able to quantify the effect of each therapy on several immunological parameters and on the latent reservoir.
Model parameters and identifiability The data available for fitting this model consists only of longitudinal values of viral load (V ), and therefore all parameters of the model are unlikely to be identifiable. While some longitudinal values of total CD4+ T cells were collected, the relationship between this number and target cell density T is unclear for the reasons discussed in the previous section, and, this measurement is notoriously noisy in non-human primates sampled under anesthesia. Actively infected cells (I) are difficult to 30 quantify separately from forms of latent or defective infection. Characterization of antiviral immune responses (related to P , E) was only done once before and after rebound. We conducted both analytic and numeric investigation of the model to determine principled ways to reduce the number of parameters to be estimated from the data.
We used the Monolix software version 2018R2  [44]  to estimate the values of all the model parameters (µ, ω, and ζ) by maximizing the likelihood of the data given the model and parameters  [44]. The software uses a frequentist version of the stochastic approximation expectation maximization (SAEM) algorithm  [91]. SAEM is an iterative algorithm that essentially consists of constructing Markov chains that converge to the conditional distributions of the parameters given the data. The final parameters estimates are given by the mean parameters values over the iterations during the smoothing phase of the Markov chain, and their standard errors the uncertainty of the estimated population parameters. Standard errors are calculated via estimation of the Fisher Information Matrix  [49] , which is derived from the second derivative of the log-likelihood evaluated by importance sampling  [92].
Model Selection In order to focus on the strongest effects and control the number of hypotheses, we determined the location of treatment effects in each dataset by employing an iterative model selection approach. Starting from a model without treatment effects but with random effects on each parameter, we applied two independent variable selection methods. First, we applied the method Stepwise Covariate Modeling (SCM)  [94]. SCM has two phases: each covariate's potential effect on each parameter is examined, and the effect leading to the best improvement in the target criteria is incorporated in the following iteration. Next, when no further improvement is possible, each previously introduced effect is removed if it fails to contribute to an improvement in the target criteria. Second, we applied the method COnditional Sampling for Stepwise Approach based on Correlation tests (COSSAC)  [95].
HIV data To create a calibrated model of HIV rebound we used data from a collection of individual ACTG studies (5024,5068,5197) in which participants were sampled at least weekly after ART interruption. These studies were previously assembled by Li et. al. [55]  and included 69 individuals. All subjects in these cohorts originally initiated ART during chronic infection and received no additional immunological interventions along with ART. Median time on ART was ∼5 years. At the time of stopping ART, all participants had viral loads less than 50 copies/mL, and ∼40% were taking drug combinations involving an NNRTI (EFV or NVP), with the remaining ∼60% on non-NNRTI based regimes (NRTI only or PI-based). The original studies were conducted between years 2001 and 2007.
Accounting for numerical errors For all differential equation solvers we have tested (e. g. R, Matlab, Monolix), numerical errors often arise when evaluating Eqs. (S22) or (S25) when t a is large. This occurs due to the extremely small values of a ef f and w (and therefore I 0 ) and the extremely small values of I(t) inferred for t < t a with this continuous approximation. We can get around this by choosing not to start integration for I(t) right at t = 0 if both a ef f and I 0 are very small, but instead, choosing a time t go such that at least one of them is larger. We also want to make sure this time is not too large, because then, infection levels could have grown out of the simple regime we are considering now (no target cell limitation, no immune control). An easy solution is to choose a t go such that I(t go ) = I 1.
Acknowledgments This work was supported by  NIH  grants  DP5OD019851  (ALH), P01AI131365 (ALH, JBW), and  P01AI131385  (ALH, MP), and  P30AI060354  (Harvard CFAR; ALH, JG), as well as funding from the  French National Institute for Research in Computer Science and Automation (Inria)  through an  International Associate Team award  (MP, ALH). We thank  Jesse Fajnzylber ,  Ronald Bosch ,  Evgenia Aga  and the  Cure Transformative Science Group  for assistance with accessing HIV rebound data from the  AIDS Clinical Trials Group (ACTG)  studies. The ACTG is supported by  NIH   UM1 AI068636 , and their Statistical and Data Management Center by  NIH   UM1 AI068634. We are grateful for feedback on the analysis provided by  Yves Levy ,  Rodolphe Thiebaut , and  Martin Nowak.
However, major variations of p14 were observed between patients in this series, with unknown clinical significance  [2]. Furthermore, it is currently unknown whether p14 could undergo renal elimination. We performed a post hoc analysis based on the data from a previously published prospective cohort of severe septic patients  [3]. Ninety-nine patients underwent measurement of p14 on EDTA plasma. Plasmatic endocan cleavage ratio (ECR) was calculated as plasma p14/(endocan + p14) ratio (endocan and p14 being expressed in pmol/mL) on baseline and 24 h, 48 h, and 72 h following ICU admission. Baseline characteristics of patients are exposed in Additional file 1. In this cohort, ECR on enrolment was correlated with baseline SAPS 2 (ρ = 0. 36, 95% CI (0. 17-0. 53); p < 10 -3 ) and SOFA (ρ = 0. 21 (0-0.
at 4°C: hCD45-BUV805 (HI30, BD), hCD3-PECyn7 (SK7, BD), hCD4-PerCP (RPA-T4, Biolegend), hCD8-APC-H7 (SK1, BD), hKi67-AF700 (B56, BD), hCD270-BV421 (cw10, BD), and mCD45-BUV395 (30-F11, BD) hGranzymeB-APC (GB11, eBioscience), hPerforin-PE (B-D48, Biolegend) and mCD45-BUV395 (30-F11, BD). For intracellular staining, Foxp3/Transcription Factor Staining (eBioscience) or Cytofix/Cytoperm (BD) buffer sets were used. Cells were washed with 1X PBS before acquisition on an X20 cytometer (Becton Dickinson (BD), San Jose, CA). The absolute count of different populations was determined by adding 50 µL of Cell Counting Beads (Bangs Laboratories) before acquisition. Data were analyzed using FlowJo software (TreeStar, Ashland, OR, USA).