dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, when the global model was significant, we applied the ordiR2step forward selection procedure of the 'vegan' package based on permutation tests considering the global p-value and adjusted R 2 before applying the selection procedure  (Blanchet et al. 2008 ). This approach is recommended for the selection of MEMs for pattern description because it is robust to type I errors  (Bauman et al. 2018b ). The significance and importance of different variables in the models were defined using the anova function of the 'vegan' package with 10 000 permutations  (Legendre et al. 2011). After selecting the models that were significant, we computed a partial dbRDA to disentangle the exclusive influence of each explanatory variable on the genomic regions potentially reflecting drift and migration (namely neutral SNPs) and selection (namely outliers), separately.
06127 by IFREMER Centre Bretagne BLP, Wiley Online Library on [02/12/2022]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License based on a specific outlier detection method (pcadapt) that is appropriate for our individual-based approach and does not require the a priori grouping of individuals with respect to sampling location or environmental variables. Our continuous sampling scheme may have limited our ability to detect loci under selection, as few genome scan methods are available for individual-based datasets, and different outlier detection approaches can lead to substantially different results  (Lotterhos and Whitlock 2014 , de Villemereuil and Gaggiotti 2015 , Liggins et al.
Distance-based redundancy analysis results for each genomic dataset (X: genomic variation), species (species), seascape environment (Y: seascape data) and reserve (Y: closest reserve to which the sample belongs). Seascape environment includes spatial variation captured by the Moran's Eigenvector Maps (MEMs) and environmental parameters such as temperature, salinity and chlorophyll. Significance is indicated with *p-value < 0. 05, **< 0. 01, *** < 0. 001. Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/ecog. 06127 by IFREMER Centre Bretagne BLP, Wiley Online Library on [02/12/2022]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License.
Funding -This research (RESERVEBENEFIT) was funded through the 2015-2016 BiodivERsA COFUND call for research proposals, with the national funders  ANR (France) ,  Formas (Sweden) ,  DLR (Germany)  and  AEI (Spain). RESERVEBENEFIT benefitted from the  Montpellier Bioinformatics Biodiversity  platform supported by the  LabEx CeMEB , an  ANR 'Investissements d'avenir' program  ( ANR-10-LABX-04-01 ).
Data availability statement All code used for the analysis is available from the Montpellier Bioinformatics Biodiversity platform gitlab at <https:// gitlab. mbb. univ-montp2. fr/reservebenefit/snakemake_ stacks2>. Scripts for the download of all environmental data are available on gitlab at <https://gitlab. mbb. univ-montp2. fr/reservebenefit/environmental_assign_data>. Scripts for analyses are available on <https://github. com/laurabenestan/ seascape_reservebenefit> on the Laura Benestan github page. Bioinformatics scripts are available on the Reservebenefit Gitlab page, and vcf data are available from the Dryad Digital Repository <https://doi. org/10. 5061/dryad. 0cfxpnw1s> (Benestan et al. 2022 ).
Patients and data collection Seventy-two allografted adult or pediatric B-ALL patients who received blina with or without DLI as a salvage therapy after post-transplant relapse were included. Only patients who received at least one complete cycle of blina were included. Patient received blina alone or blina and DLI according to center policy. Blina and DLI exact modalities were at the discretion of each center. Of note, a cycle of blina consisted of 28 days of continuous intravenous infusion of blina followed by a period of 14 days of treatment-free interval  (17). The study was conducted between January 2012 and December 2018 in 25 centers belonging to the Francophone Society of Bone Marrow Transplantation and Cellular Therapy (SFGM-TC). This study was approved by the SFGM-TC scienti c board and was conducted in agreement with the declaration of Helsinki.
Implications of all the available evidence Our findings, together with a recent article in individuals treated with a latency reversing agent (NCT01680094), which identified DNA methylation imprints associated with HIV-1 reservoir size and time to viral rebound after ATI, suggest that the inclusion of DNA methylation studies in future clinical studies might be relevant to identify predictors of viral control prior to the interruption of antiretroviral treatment. Additionally, a few studies have related HIV-1 infection, ART treatment, and HIV-1 control with DNA methylation marks, therefore a comprehensive study of the host epigenome in clinical intervention might be relevant to fine-tune such strategies to induce effective HIV cure.
8À10  To overcome these limitations, clinical trials combining LRAs with therapeutic T cell vaccination aim to control rebounding HIV by intensifying the anti-HIV specific CTL responses. 11À13  However, the effects of LRAs administration on non-viral, host genes involved in antiviral immunity are poorly understood, although they may play an important role in shaping effective vaccine-induced immunity. BCN02 (NCT02616874) was a pilot study that combined the MVA. HIVconsv  14  vaccination before and after the administration of three cycles of RMD in early treated individuals rolled-over from the BCN01 (NCT01712425) clinical trial. 13, 15 In BCN02, RMD led to a transient increase of HIV transcription and histone 3 acetylation levels (H3Ac) that were short-lived and returned to basal levels one week after the last RMD cycle.
Study design BCN02 (NCT02616874) was a pilot kick-and-kill study that included 15 early-treated individuals who were rolled-over from the prior clinical trial BCN01 (NCT01712425) (Table  S1 ). 15 In BCN01, individuals were immunized with a heterologous prime/boost regimen delivering the HIVconsv immunogen. In BCN02, the participants were vaccinated again with MVA. HIVconsv before and after the administration of RMD (1 week after 3 cycles of RMD infusion) (Figure  S1a ). The BCN02 included a monitored antiretroviral pause (MAP) to evaluate the viral rebound kinetics whereby participants were monitored for viral load rebounds on a weekly basis for up to 32 weeks. Two participants were excluded from MAP evaluations, one who did not meet the criteria for treatment interruption, and one who showed a protocol deviation during this phase.
Specifically, the present omics sub-study was based on the 14 male individuals enrolled in the BCN02, of which 12 were eligible for MAP. These 12 individuals were classed in Early rebound (n = 8) or Late rebound group (n = 4) according if they reached the threshold of 2000 HIV RNA copies/mL and restart of ART before or after the 4 weeks of treatment interruption. In order to evaluate the contribution of RMD-only treatment on gene expression profiles in isolated CD4 T cells and whole PBMCs, samples from the part A of the clinical trial REDUC (NCT02092116)  6  were used. REDUC was designed to evaluate the in vivo effect of RMD administration alone (1 week after 3 cycles of RMD infusions) in ART-treated individuals and showed that RMD could disrupt latency and led to a 2. 4 to 5 fold increased HIV-1 transcription (cell-associated un-spliced HIV-1 RNA).
18. 66. 3) and followed by generation of FASTQ sequence files by CASAVA. RNA-seq pairedend reads were mapped against the human (GRCh38) genome using STAR version 2. 5. 3a 18 with ENCODE parameters. Annotated genes (genecode version 28) were quantified using RSEM version 1. 3. 0 with default parameters. 19 The quality control of the mapping and quantification steps was performed with GEMtools (https://gemtools. github. io/) and custom python scripts. Following RSEM expected counts were normalized with TMM method from edgeR R/Bioconductor and genes with less than 5 counts were filtered out. 20 inally, counts were transformed with the voom function in limma R/Bioconductor and used in downstream analyses. 21.
DNA methylation array Genomic DNA was bisulfite converted using EZ DNA methylation Kit (Zymo) following the manufacturer's protocol. Next, 4uL of bisulfite-converted DNA were hybridized to Infinium HumanMethylation450 Bead-Chip following Illumina Infinium HD Methylation protocol. For samples obtained during MAP, Infinium MethylationEPIC BeadChip kit was used. Chip analysis was performed using Illumina HiScan SQ fluorescent scanner and the intensities of the images were extracted using GenomeStudio (2010. 3) Methylation module (1. 8. 5) software. Quality control, background correction and quantile normalization across arrays was performed using ChAMP R/Bioconductor package with functions extracted from Minfi R/Bioconductor package. 22À24.
Differential gene expression and DNA methylation analysis In exploratory data analysis of both -omics data sets, MDS (Multidimensional scaling) plots were used to identify batch effects and the major source of variation. In the DNA methylation dataset, the only female participant appeared as an outlier in the MDS plot. Additionally, the impossibility to take sex variation into account due to the presence of a single female in the study, this participant was removed from the omics substudy (Figure  S1b ). BCN02 was conducted across two hospitals in the Barcelona metropolitan area and biological samples were processed at the respective laboratories. The sample processing laboratory parameter was also identified as a source of variation in gene expression data (Figure  S1c ). Therefore, ComBat function from SVA R/Biconductor was used to correct for this effect in unsupervised analysis.
Integrated functional pathways analysis of genes and CpG positions modulated during the kick-and-kill therapy For integration of the differentially expressed genes (DEGs, p-value < 0. 05) and differentially methylated positions (DMP p-value < 0. 05) data sets corresponding to one week after RMD administration (Vacc+RMD) and the baseline (BSL) (Figure  S1 ), a published protocol was followed. 26 For functional analyses, a pre-ranked GSEA was run in GSEA JAVA desktop program (MacOSX Version 4. 03). Genes were ranked according to the Àlog10 p-value multiplied by the sign of log2 Fold-Change. 26 As database, a combination of the REACTOME subset of Canonical pathways from Molecular Signature database (MSigDB, v7. 4), and the blood transcriptional modules (BTMs) previously described elsewhere  27  were used.
Chromatin state and histone marks enrichment To identify the chromatin regions and histone marks in which DEGs or DMPs were enriched, either based on differences between time points or between early or late rebound, a genomic regions enrichment was run with LOLA R/Bioconductor 30 based on the publicly available Roadmap datasets of 18-chromatin states (ChromHMM) and histone marks of PBMCs (E062, https://egg2. wustl. edu/roadmap/web_portal/chr_state_ learning. html). As background, non-differentially expressed genes or the non-differentially methylated CpGs positions were used.
Evaluation of DNA methylation and gene expression capacity to discriminate between early and late rebound To test the capacity of DNA methylation and gene expression to differentiate Early or Late rebound at the different time points analysed, a Principal Component Analysis (PCA) in each of the datasets for each time point was used, and a logistic regression was fitted with PC1 and PC2 as predictors. Area Under de Curve (AUC) values showed how well the PC1 and PC2 classified the Early or Late rebounder groups. Based on the DMPs between Early and Late Rebound at Vacc+RMD time point, a GSEA analysis was applied using canonical pathways and BTMs as described in methods above. Based on GSEA results, 6 modules of DMPs were identified, and the Z-score methylation levels (M-values) of DMPs on genes in these pathways were represented in a heatmap (ComplexHeatmap R/Bioconductor).
Statistics The moderated t-test in limma was used to identify the differentially methylated positions or the differentially expressed genes between different time points or between Early and Late rebound. Although false discovery rate (FDR) adjusted p-value was calculated for each gene transcript or CpG position, due to the small sample size and the exploratory character of the present study, an uncorrected p-value < 0. 05 was used to define the subset of differentially expressed genes (DEGs) and differentially methylated positions (DMPs). Chisquare tests were applied to evaluate the different abundance of DMPs or DEGs in certain chromosomes in contrast to the evaluated CpG positions or transcripts. The same test was applied for DMPs in relation to island (island, open sea, shore or self) or in relation to gene (5'UTR, TSS200, TSS1500, 1stExon, Body, 3'UTR) and for DEGs in relation to biotypes.
Data deposition and material sharing Transcriptomics and DNA Methylation is uploaded at Gene Expression Omnibus (GEO): GSE184653, contains the transcriptomics data for BCN02 study; GSE185391, the DNA Methylation data for BCN02 study; and GSE185027, the transcriptomics data for the individuals from the REDUC study. The R code used in the analysis can be found in the GitHub https://github. com/hostimmuneOMICS under the repository BCN02_OmicsAnalysis.
Results Gene expression and DNA methylation changes one week after MVA. HIVconsv vaccination in the BCN02 study To evaluate the impact of the first MVA. HIVconsv vaccination (Vacc) on the host transcriptional and epigenetic signatures, whole genome gene expression and DNA methylation screenings were conducted. Multidimensional scaling analysis (MDS) and volcano plot of data from samples drawn one week after the HIV. consv vaccination, revealed a strong impact on the host gene transcriptional program (Figures  S2a  and 1a ). Specifically, compared to basal time point, 673 upregulated (Figure  1a , red) and 524 downregulated (Figure  1a , blue) genes were identified (p-value < 0. 05, limma model, Table  S3 ). After adjusting for multiple comparisons with false discovery rate (FDR), there were 95 DEGs (adjusted p-value < 0.
01, Chi-square test) was found when compared to their representation in the methylation array (Figure  S3d , Tables  S9, 10 ). Of interest, RMD administration led to the specific upregulation of DNMT3B and EZH2 enzymes (Figure  S4 ), both key upstream regulators of DNA methylation. 36, 37 Actually, RMD administration, as other HDACi, impact epigenetic cascades at different levels. 38 To further define potential epigenetic landscape changes from baseline to the Vacc+RMD time points, enrichment analyses were applied on DMPs or DEGs using the ChromHMM and Histone marks in PBMCs from the Roadmap database (E062) (https://egg2. wustl. edu/roadmap/web_portal/chr_state_learning. html) (Figure  2dÀg ). These ChromHMM enrichment analyses showed that hypermethylated DMPs at Vacc+RMD time point were enriched in regions of active TSS (Transcription start site) and genic enhancers (EnhG2), while hypomethylated DMPs dominated predominantly in regions of weak transcription (Figure  2d ).
Similarly, downregulated genes were found in regions of active TSS, and upregulated ones, in less active regions or in regions associated with poised expression (Figure  2f ). In addition, histone acetylation marks enrichment followed the same trend, with hypermethylated DMPs and downregulated DEGs being enriched in regions of active transcription (e. g. H3K27ac) (Figure  2e  and g ). These results show that, although RMD is infused to increase histone acetylation, and consequently gene expression, one week after RMD infusion, the majority of upregulated genes were found in regions of transcriptional repression or poised promoters and enhancers rather than regions of active transcription. Since the Vacc+RMD intervention reflected the cumulative changes induced by MVA. HIVconsv vaccination and RMD infusion (Figure  S2g , h), we identified RMD-specific effects, by conducting a transcriptomics assessment on samples from participants in the part A REDUC study (NCT02092116),  6  in which ART-suppressed participants received 3 cycles RMD without prior vaccination.
Restoration of dysregulated gene expression profiles and their underlying epigenetic mechanisms may be critical to induce effective antiviral activity in kick-andkill iinterventions. Therefore, gene set enrichment analyses (GSEA) were conducted to identify the dysregulated pathways upon Vacc+RMD (BCN02) or RMD only treatment (REDUC) (Figure  3 , Table  S13 ). For the combined regimen of therapeutic vaccine plus RMD, the data show a strong impact on the HIV pathways in    3 ). Interestingly, numerous other pathways were shared between the two datasets, including pathways in the metabolism module like Metabolism of RNA or TCA cycle and respiratory electron transport,) the pathway Translation from the protein module, the pathway Signalling by ROBO receptors in the module neuronal-related processes, as well as the pathways DNA double strand break and cell cycle mitotic in modules DNA repair and cell cycle.
In parallel, the pathways in modules transport and cell signalling were mainly impacted at transcriptome level in isolated CD4 cells after RMDonly treatment (REDUC). Finally, only few pathways showed an overlap between the two datasets in the module of immune signature, possibly due to the fact that the two studies differ by the inclusion of a therapeutic vaccine in BCN02. Still, among the immune signature module, pathways including signalling by interleukins, FCERI mediated NF-kB activation and antigen processing cross presentation were dysregulated in isolated CD4 after RMD-only treatment. Interestingly, there was an ChromHMM enrichment and (e) the histone mark enrichment for DMPs. The same was done for DEGs in (f) and (g). (h) and (i) Volcano plots of for DEGs in total PBMCs and isolated CD4 T cell after RMD-only treatment (REDUC), respectively.
Focusing on the BCN02 dataset only (Figure  S5 ), there were multiple pathways suggesting an epigenetic regulation, as they showed opposite NES. Many of these pathways located to the immune signature module, like Enriched in Monocytes, which show a majority of upregulated and hypomethylated genes. Also, T cells and T cell activation, mainly contained hypermethylated DMPs and downregulated expression of genes. The same trend was observed for HIV life cycle pathway and in other pathways of the modules cell cycle, metabolism, gene expression or protein translation. Overall these results suggest that after a Vacc+RMD regimen, a homeostatic mechanism might be activated as the global epigenetic state of the cells attempts to counteract the acetylation effects induced by RMD, whose main effects (4 h post infusion) waned 1 week after its administration.
On the other hand, hypermethylated DMPs in Early individuals were negatively correlated with the time off antiretroviral treatment (time MAP) and the time with undetectable viral load during MAP (UD MAP). In addition, these DMPs showed an association with the time from HIV infection until cART initiation (HIV to cART) as well as the time on cART at BCN02 entry (Time on cART at BCN02). Although DNA methylation levels were not assessed at time points in early and untreated HIV infection, these data suggest that some epigenetic marks could be defined even before ART initiation and that the intrinsic individual epigenome or the impact of the virus during acute infection might define the ability to control HIV-1. This is in line with a recent study that demonstrated certain epigenetic changes induced upon acute HIV-1 infection not to revert upon ART.
Thus, the present data hint at the possibility that epigenetic signatures, including those assessed before kick-and-kill interventions, could predict eventual virus control in treatment interruption. Furthermore, a recent study has associated specific DNA methylation marks with the size of the viral reservoir and the time to viral rebound during an ATI in a clinical trial administrating a latent reactivation agent (NCT01680094). 61 The potential of epigenome studies in the identification of correlates of control of HIV-1 infection is also in line with a previous report from our group showing that differential capacity to control HIV infection in the absence of ART was associated with a differential methylation profile of genes involved in antiviral response and T-cell immunity. 16, 62 Finally, different DNA methylation in CD4 T cells has been associated with distinct viral progression and can be impacted by cART treatment initiation,  17  again suggesting that epigenetic profiling, even in early stages of HIV infection, may have predictive power for post-treatment control.
Contributors MRR and CB designed the experimental plan. BM, JM and CB contributed with clinical trial, patient data and sample management. BCl, BM, JM, OS, MM, were in charge of patient recruitment and sample provision. BOT, CDC, SC and ALL did the sample processing. BOT, AEC and EG performed the transcriptome analysis and BOT, MRR and FCM analyzed methylome data and their integration with clinical and virological parameters and immune reactivity data. ME and MB performed DNA Methylation assays and helped in data analysis. BM, ALL, MRU and SC assessed HIV specific T cell responses. MCP and JMP determined CA-HIV-1 DNA, CA-HIV-1 RNA and residual viral load. BOT conducted R language and programing for the execution of statistically, bioinformatic and integrative analyses with support of AEC, AS and MLC.
This publication has received funding from the European Union Horizon 2020 Framework Programme for Research and Innovation under Grant Agreement N°6 81137-EAVI2020 and N°847943-MISTRAL, the Ministerio de Ciencia e Innovaci on (SAF2017_89726_R), and the National Institutes of Health À National Institute of Allergy and Infectious Diseases Program Grant P01-AI131568. The funding sources had no role in writing the manuscript nor in data collection, analysis and interpretation or any other aspect of the study. Funding sources only require to submit the publication in open access.
Acknowledgments The authors appreciate the participation of all the volunteers in this study. This publication has received funding from the  European Union   Horizon 2020 Framework Programme for Research and Innovation  under Grant Agreement  N°681137-EAVI2020  and  N°8 47943-MISTRAL , the  Ministerio de Ciencia e Innovaci on  ( SAF2017_89726_R ), and the  National Institutes of Health À National Institute of Allergy and Infectious Diseases Program  Grant  P01-AI131568. This work was also supported in part by  Grifols , a research agreement with  Aelix Therapeutics S,L and the Foundation Dormeur (Vadur, Liechtenstein. The BCN02 study was funded in part by the  ISCIII PI15/01188  grant, the  HIVACAT Catalan research program  for an HIV vaccine and the  Fundaci o Gloria Soler. J. M-P. research program is funded by grant  PID2019-109870RB-I00  from the  Spanish Ministry of Science and Innovation.
MLC was partially supported by the  Spanish Ministry of Economy, Industry and Competitiveness , Reference PID2019-104830RB-I00. AE-C was funded by  ISCIII /MINECO  ( PT17/0009/0019 ) and co-funded by  FEDER. This work was carried out within the framework of the PhD in Advanced Immunology of the  Universitat Aut onoma de Barcelona  for BO-T and CD-C. BO-T received an EMBO short term fellowship.
Funding European Union  Horizon 2020 Framework Programme for Research and Innovation  under Grant Agreement  N°681137-EAVI2020  and  N°847943-MISTRAL , the  Ministerio de Ciencia e Innovaci on  ( SAF2017_89726_R ), and the  National Institutes of HealthÀNational Institute of Allergy and Infectious Diseases Program  Grant  P01-AI131568.