dataesr/scientific-paragraphs-categorization huggingface dataset:
Strong correlations of immune cell markers with chemokine expression may be due to random effects or to unknown underlying confounding variable(s). Alternatively, they may reflect direct cell attraction by the chemokine or chemokine secretion by the cell, an interpretation that we favored when strengthened by existing biological knowledge. By selecting high correlations in two datasets that are biologically supported and for unidirectional interactions (i. e. , cells secrete chemokine but do not respond to them, or chemokines attract cells but do not secrete them), we propose a stepwise model for T-cell recruitment. For modeling and prediction of T-cell recruitment by chemokines, we applied penalized logistic regression (LASSO) with CDE as binary response variable and a set of 34 chemokines as predictors (dataset GSE62564), to select the best set of chemokines for prediction.
CDE expression was classified as high or low (above or below median expression in each dataset). Gene expression data were z-transformed before training and testing the model. The tuning parameter lambda was selected by 10-fold cross-validation, and cross-validation was repeated 100 times. The mean coefficient for each chemokine was chosen for the final model and for testing datasets. The package glmnet in R (  25 ) was used for training the model and package pROC  (26)  for ROC curve and AUC calculations. A logistic regression model predicting CDE and incorporating only the chemokines CCL and CCL was trained on the dataset GSE62564 using the glm function in base R and tested on datasets GSE3960 or GSE85047. The same analyses were repeated with CDD, CDG, and CD (CD3 zeta) as T-cell markers, with similar results.
CCL2-Driven Recruitment of CCL19-and CCL22-Producing CD1c + DCs may Induce T-Cell Recruitment in Neuroblastoma Tumors To further confirm the role of CCL2/CCR2 axis in APC recruitment to neuroblastoma tumors, we used the public RNA sequencing (RNA-seq) dataset GSE62564  (30)  containing data from 498 samples among which 91 had a high expression of MYCN. The GSE3960 and GSE85047 microarray datasets, containing 102 samples among which 20 were MYCN amplified (GSE3960; ref. 24) , or 283 primary neuroblastoma of which 55 were MYCN amplified (GSE85047), were used to test our findings. We first observed a lower level of expression of CCL in MYCN-high neuroblastoma tumors (Fig. 4A , P < 0. 001; Supplementary Fig. S6A ), likely reflecting the repression of CCL expression by the MYCN oncogene, but some MYNC low neuroblastoma had low levels of CCL transcription, and the reverse was also true.
Although correlation analysis, and predictive models (see Materials and Methods for discussion). The correlation between CCL gene expression level and specific immune cell subsets markers was evaluated in GSE85047. Besides CD and CD (monocytes/macrophages), CDE (T cells), KLRC (NK cells) and HLA-DR and CD (Pan-APC), a short list of genes was defined, allowing identification of DC subsets, based on Villani and colleagues published data  (31). The chosen genes were CDC and CLECA for CD1c + MDC subset, ILRA and IRF for PDC, and THBD and CLECA for BDCA3/CD141 + MDC subset. As shown in Fig. 4B , there was a high correlation (r ≥ 0. 54 and P < 0. 001, above 5% threshold, see Materials and Methods) between CCL expression and pan-APC, CD1c + MDC, and CD141 + MDC markers. A lower correlation was observed between CCL level and monocytes/macrophages, and PDC (IRF) infiltration, suggesting the involvement of CCL2/CCR2 in APC recruitment by neuroblastoma tumors.
These results were confirmed in the GSE3960 microarray dataset (Supplementary Fig. S6 ). Intriguingly, a CCL/ CDE correlation was also observed (r = 0. 61, P < 0. 0001). As CCR2 is poorly expressed by circulating T cells, and CCL2 does not attract T cells in our migration experiments, we looked for an indirect link. We analyzed univariate correlations between T-cell infiltration (CDE level) and the level of expression of 34 chemokines, in the two datasets. As shown in the Supplementary Fig. S7A  correlation maps, CCL, CCL, CCL, and CCL levels were highly correlated with CDE levels, more so than any other chemokine. These high univariate correlations are also shown in Supplementary Fig. S7B , with r > 0. 8 in GSE62564, and r > 0. 4 in GSE3960 (P < 0. 001). In another approach, we used penalized logistic regression to build a model predicting CDE levels (high vs.
low) with a minimal set derived from 34 chemokines. By shrinking some coefficient estimates toward zero, the procedure essentially selects a subset of chemokines for regression, leading to a model with potentially good predictive accuracy. In this instance, the LASSO trained on GSE62564, retained CCL, CCL, CCL, CCL, and CXCL as the main variables for predicting CDE levels (Fig. 4C ). This full logistic model performed well when tested on GSE3960, despite different technologies used to acquire data in the training and testing datasets (Fig. 4D ). Among the selected chemokines, CCL5 can be produced by T cells, and CCL21 is mainly produced by stromal cells, while CCL19 and CCL22 can be produced by DCs or monocytes/macrophages  (32) (33) (34) (35) (36). A conventional logistic regression model trained on GSE62564 and including only these two chemokines exhibited a very good discriminating power when tested on GSE3960 and GSE85047 (Fig.
4G ). Basal migration of CD4 + T cells (20%) was higher than basal migration of CD8 + T cells (2%), but both subsets were attracted by activated DC culture supernatants (Supplementary Fig. S7C ). Altogether, these data suggest that CCL19 and CCL22 produced by activated DC play an important role in recruiting T cells to the neuroblastoma microenvironment in vitro and in vivo. To decipher which APC may be responsible for CCL19 and CCL22 secretions in neuroblastoma tumors, we analyzed correlations between these chemokine levels and APC-specific markers. CDC was highly correlated with CDE in both datasets (r > 0. 5 in GSE3960, and r > 0. 8 in GSE62564, P < 0. 001; Supplementary Fig. S7D ). Both CCL and CCL expression levels were highly correlated with the level of CDC in both dataset (r > 0.
7 in GSE62564 and, r > 0. 4, in GSE3960 P < 0. 001; Fig. 4H ; Supplementary Fig. S7E ), and CCL was correlated with CD. Altogether, our results suggest that CCL2 produced by neuroblastoma cells initiate the recruitment of monocytes, myeloid and plasmacytoid DCs, and that among these cells, the CD1c + subset when activated may recruit T cells by means of CCL19/CCL22 secretion. Besides recruitment of immune cells, the efficacy of antitumor immune responses also relies on the correct processing and presentation of antigens by the tumor cells. We measured the expression of HLA class I molecules at the surface of the neuroblastoma cell lines. As described already (37-39), we observed that MYCN-amplified neuroblastoma cells lines (DZ and N91) lacked expression of HLA class I molecules, whereas AS and SH expressed it at high level (Supplementary Fig.
S8A ). On a heatmap, we represented the expression of CCL, CCL, CCL, and a set of genes coding for proteins expressed by APC subsets, by T cells, and for molecules associated with HLA class I presentation pathway for GSE62564 (Supplementary Fig. S8B ). Samples were separated according to MYCN amplification status and sorted on the basis of CCL level. CCL and HLA class I genes were found more often downregulated in neuroblastoma tumors with MYCN amplification. In agreement with our in vitro data, we observed an increased representation of DC-associated genes in neuroblastoma with higher CCL expression, further illustrating the role of CCL2 in the active recruitment of DCs.
Our data suggest that monocytes and DC recruitment is directly associated with CCL2 secretion by neuroblastoma cells, as already shown for iNKT  (20, 21). Further investigation of the bidirectional cross-talks that may occur between APC and iNKT in neuroblastoma TME, could pave the way for development of new therapeutic strategies harnessing the biology of these cells  (43). It has been shown that CCL2 expression is repressed in MYCN-amplified neuroblastoma cells  (21) , and our analysis using two different public transcriptomic datasets confirm that CCL transcription was lower in MYCN-amplified neuroblastoma (which may arise from tumor or immune cells). We also observed a continuum of CCL expression with low CCL expression in some MYCN-nonamplified neuroblastoma, suggesting that other mechanisms could repress CCL transcription.
Our results suggest that CCL2 is crucial for the initial recruitment of monocytes, MDC and PDC by neuroblastoma, and paves the way for the further recruitment of effector cells in these tumors. Using transcriptomic public datasets, we found that 4 chemokines (CCL5, CCL19, CCL21, and CCL22) were strongly correlated with T lymphocyte infiltration. Both CCL19 and CCL22 can be produced by MDC after activation  (36) , there are however no data available for chemokines production by CD141 + MDC, certainly in relation with their very low numbers that make their study difficult. In the simplified model we propose, CCL19 and CCL22 are sufficient to predict T lymphocyte recruitment. Interestingly, CCL22 was found in supernatants of cocultured DC and neuroblastoma cells, but activation of DC was needed to induce CCL19 and T-cell migration in the same settings.
Acknowledgments We thank all the staff in the department of oncology pediatric,  Grenoble-Alpes University Hospital (Pr Dominique Plantaz) , and in  EFS R&D laboratory. We are grateful to  Jean-Paul Molens  ( EFS R&D lab ),  Céline Dard  ( EFS HLA laboratory ), and  Sandrine Blanchet  ( Institute for Advanced Biosciences ) for their help. We are also grateful to voluntary blood donors for their wonderful and generous donation, and to EFS staff in blood collection and support departments. This work was possible thanks to the financial support of  EFS-Auvergne-Rhone-Alpes. Received November 15, 2021; revised March 28, 2022; accepted June 09, 2022; published first July  05 ,  2022.
Data Availability The data generated in this study are available within the article and the Supplementary Data. Data analyzed were obtained from GEO at: https: //www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE62564, https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE3960, and https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE85047.
(E) Transcription factor (TFs) analysis showing the top five enriched TFs associated with target genes that are present in the ENCODE database. For TFs with data from multiple experiments, set intersection was applied to obtain consensus. The number of dysregulated genes in our transcriptome over the total number of consensus target genes associated with each TF is indicated on the graph. (F) TFs analysis showing the top five enriched TFs associated with TF binding sites (TFBS) detected at the promoter of genes that are present in TRANSFAC and JASPAR databases. The number of dysregulated genes in our transcriptome over the total number of TFBS associated with each TF target gene is indicated on the graph. IH, 8h per day during their sleeping period (IH from 8 am to 4 pm and NO the rest of the day) (Figure  1A ).
Microarray Experiment RNA concentration and integrity were assessed with the Agilent 2100 Bioanalyzer (Agilent Technologies) and amplified with the Illumina RNA amplification kit according to the manufacturer's protocol (Ambion) to obtain biotin-labeled complementary RNA from 250 ng total RNA. Hybridization processes were performed with Illumina MouseRef-8 v2. 0 Expression BeadChip (Illumina Inc). Hybridized probes were detected with cyanin-3-streptavidin (1 mg/mL; Amersham Biosciences, GE Health Care) and scanned using an Illumina BeadArray Reader. Raw data were extracted with GenomeStudio 2011. 1 software using the default settings and without any additional normalization. The difference in gene expression between the two conditions (NO or IH) was determined using SAM software (Significant Analysis of Microarray, Stanford University, CA, USA, https:// statweb.
TUNEL Assay The analysis of DNA fragmentation was evaluated by applying terminal deoxynucleotidyl transferase-mediated dUTP nick endlabeling (TUNEL) assay according to the manufacturer's protocol (Abcam). Briefly 5 µm thick paraffin embedded liver tissues were processed for TUNEL assay and the Axioscan fluorescent microscope (Zeiss) was used to visualize TUNEL-positive cells and nuclei counter-staining with 4,6-diamidino-2-phenylindole (DAPI). The TUNEL assay was performed on three biological replicates per group and four areas per biological replicate were analyzed using Image J R software (National Institutes of Health). TUNEL-positive cells were expressed as the percentage of liver area.
Dihydroethidium (DHE) Staining Cells are permeable to Dihydroethidium (DHE), which, upon reaction with a superoxide anion (O 2 -), forms ethidium bromide (a red fluorescent product) that intercalates in DNA. Frozen livers were cryosectioned at 10 µm thick, collected onto Superfrost plus slides (Dutscher France) and allowed to air dry for 15 min at room temperature. Slides were stained with 10 µM DHE (Sigma-Aldrich) for 30 min at 37 • C (PBS was used as control) in a dark moist chamber. After washing, cover slips were added and the fluorescent signal was recorded using confocal microscopy (Zeiss, LSM510 Meta confocal microscope) and analyzed with ImageJ software. DHE-positive cells were expressed as the percentage of liver area.
Then colorimetric substrate was added (E109, HistoGreen, Novus Biological) and slices were counterstained with Mayer hematoxylin (51275, Sigma Aldrich). Vectamount permanent medium (Vectorlab) was used to fix the coverslips on the slides. Images were then acquired using an Axio Scan microscope (Zeiss) and Zen R software (Zeiss).
NRF-Dependent Hepatic Transcription in Response to IH To decipher the transcriptional pathways rewired by IH, transcription factor binding sites (TFBS) and consensus target genes analysis were performed on significantly altered hepatic transcripts using EnrichR to identify associated transcription factors (TFs) (Figures  1E, F ). Nuclear Respiratory Factor 2 (NRF2) consensus target genes (Figure  1E ) and Nuclear Respiratory Factor 1 (NRF1) binding sites were highly enriched in our dataset (Figure  1F ). NRF1 and NRF2 are cap-n-collar basic leucine zipper (CNC-bZIP) transcription factors that regulate mitochondrial respiration and antioxidant defenses  (18) (19) (20) , there was thus a strong rationale to target mitochondria for subsequent experiments.
Once again, IH caused a significant augmentation in DNA breaks in the liver (Figure  5B ). It is known that oxidative stress leads to NRF2 stabilization and translocation into the nucleus where it induces the expression of cytoprotective target genes. NRF2-dependant transcription had been highlighted in our transcriptomic dataset (Figure  1E ) and accumulation of NRF2 in the nucleus was also observed by immunohistochemistry upon IH (Figure  5C ). Accordingly, the expression of representative target genes implicated in antioxidant defenses was markedly increased upon IH (Figures  5D, E  and Supplementary Figure  2 ). In particular, Sod1 and Sod2 genes encoding for the enzymes that catalyze the dismutation of superoxide radicals to molecular oxygen. Taken together, these results indicate that IH causes liver oxidative stress and induces a transcriptional adaptation involving the transcription factor NRF2.
Our findings contribute to the understanding of the mechanisms underlying NAFLD development and progression during SAS and provide a rationale for novel therapeutic targets and biomarker discovery. University of Grenoble-Alpes (UGA), the Fondation Agir Pour les Maladies Chroniques (APMC, Project Temporize Liver), the Agence Nationale pour la Recherche (ANR, Project Temporize, ANR-19-CE14-0037-01), and the ANR Initiatives d'Excellence de l'UGA (ANR-IDEX-UGA, Project LiFE, 15-IDEX-0002). This work has been partially supported by UGA e-health chair and MIAI @ university Grenoble Alpes (ANR-19-P3IA-0003). JG was the recipient of the postdoctoral fellowship from the ANR-IDEX-UGA Initiative de Recherche Stratégique (ANR-IDEX-UGA-IRS, Project HypoClock). GV received a grant from Société Francophone du Diabète.
Next, we analyzed the mutation frequencies over the course of the three independent runs of experimental evolution (Fig. 3b, c  and Supplementary Fig. 3c ). We validated that the consensus sequence obtained by deep-sequencing of the parental inoculum used for our experimental evolutions perfectly matches the clinical isolate reference sequence (GenBank ID KX197192). To identify mutations whose frequency significantly varied through time, for each run, we computed position-wise standard deviations of the frequency of the most frequent variant. As shown in Supplementary Fig. 4a , the standard deviations are distributed in two categories: (i) a high density of very lowfrequency variants, likely corresponding to polymorphisms due to the error-prone feature of the NS5 polymerase and/or generated during library preparation, despite the CirSeq protocol and (ii) low density of polymorphisms showing higher standard deviations, corresponding to mutations that have reached a high frequency during at least one time-point of one experiment.
e. , between the 1 and 2% rarest variants). Variants C1786T, T5663C, and C10007T were thus present in the initial inoculum at the highest frequency. The non-synonymous C1786T mutation was detected within the course of viral adaptation with an increasing frequency over time. This leads to a substitution at position 270 of E envelope protein from V to A. Since A270 is present in the clinical isolate database sequence (ZIKV PE243, KX197192)  39  V270 is likely resulting from initial viral amplification of the clinical isolate in Vero cells, and A270 is actually a reversion when passaged in Huh7. 5. 1 cells. Variant C2194T was particularly rare, and variants C2341T and T7173C had unremarkable frequencies at the start of the experiment, in the middle of the distribution of variant frequencies. Among the variants, C2341T, T7173C, C1786T, and C2194T are non-synonymous mutations within the coding part of the ZIKV polyprotein and are thus most likely to have phenotypic effects on the virus.
Foci size analysis. Foci sizes were determined by infection using Huh7. 5. 1 cells and Vero cells with a carboxymethylcellulose (CMC) overlay in 12-well plate. Forty-eight hours later, cells were washed with PBS and fixed by 4% PFA. Foci were detected by anti-E protein (4G2 clone), as described above. Imaging by Olympus CKX53 microscope of randomly picked foci were processed using Image J software. Size index was determined as the ratio of E staining intensity relative to Hoechst staining with normalization of each intensity on the mean intensity of three randomly selected cell units. More than 80 foci were acquired for parental and passaged virus comparison, and more than 50 foci for the molecular clone analysis. Immunostaining and FACS analysis of monocyte-derived macrophages. Surface immunostainings were performed at day 6 post-isolation of the CD14 + cells from PBMCs.
After a 15 min-incubation step with human Fc blocking reagent (MACS Miltenyi Biotec), cell surface markers were detected by a 30 min-incubation at 4 °C with 4 µg/mL Pacific Blue-conjugated mouse anti-CD14 (clone 63D3, Biolegend), a 1:20 dilution of APC-conjugated mouse anti-CD163 (clone REA812, MACS Miltenyi Biotec), 0. 14 µg/mL PE-conjugated mouse anti-HLA-DR (clone LN3, Invitrogen) and a 1:20 dilution of PE-Cy7-conjugated mouse anti-CD11b (clone ICRF44, BD Biosciences), diluted in staining buffer (PBS -1% FBS). Flow cytometric analysis was performed using a BD FACS Canto II and the data were analyzed with Flow Jo software (Tree Star). Western blot analysis. Cell lysates of the indicated cells were extracted using lysis buffer (150 mM NaCl 50 mM Tris HCl pH 8, 1% NP40, 0. 5% Deoxycholate, 0.
Given the median fragment size of 150 bases, at least two repeats are expected in most fragments, allowing to reduce the theoretical minimum error rate associated with current Illumina sequencing from 10-4 to 10-8 per base. Bioinformatic analysis of the sequencing libraries. Reads were quality-checked and trimmed of sequencing adapters and then mapped using PEAR  59  and in-house software (https://github. com/Kobert/viroMapper). PEAR is an efficient and flexible tool that was used to merge the repeats in the forward and reverse reads, thus generating consensus sequences with improved quality scores and reduced sequencing errors. Viromapper automatically returns a table with statistics per position such as quality quantiles, coverage, raw numbers of A, C, G, T bases, and quality-weighted counts and proportions of A, C, G, T bases.
These tables were then read and analyzed using Python scripts developed in house. To evaluate the efficiency of our protocol to generate tandem repeat, we measured repeat sizes within forward and reverse reads separately using a script developed in house, based on samples of 5000 reads in 22 read files. All libraries contained repeats. The worst library (HV5GLBCXY_ZIKV_17s006139-1-1_DREUX_lane1CirseqD3) had repeats in 59% of its forward reads and 67% of its reverse reads, with sizes ranging between 33 and 97 bases, with a mean at 50. In all other libraries, repeat sizes had similar distributions, but repeats were present in up to 94% of the reads. Datasets for the sequence data file available in SRA with accession numbers SRX9704326-SRX9704344; bioproject PRJNA686429. Bioinformatics analysis for the selection of variants of interest.
4 ). A Jupyter notebook reproducing these analyses is available (https://github. com/Boussau/Notebooks/ blob/master/Notebooks/Viromics/FiguresAndAnalyses_NoTLR3. ipynb). Introduction of selected mutation and analysis of adapted mutants in the ZIKV infectious clone. Mutations were introduced in the genomic length ZIKV infectious clone cDNA, pCCI-SP6-ZIKV BeH819015 plasmid (kindly provided by Dr A. Merits)  40. For adequate comparison with clinical isolate PE243  39  , the R99G and Y122H substitution in NS1 were also introduced in pCCI-SP6-ZIKV BeH819015 plasmid. In a first step, introduction of Y87H in NS4B were performed by overlap-PCR (OL-PCR) using mutagenic primers, as described previously  56. The OL-PCR fragments were purified and transferred using In-Fusion HD Cloning Plus CE technology (638917, Ozyme) into the pCCI-SP6-ZIKV BeH819015 plasmid cleaved using BamHI restriction enzyme.
Acknowledgements We thank  P. Despres  ( PIMIT, Université de La Réunion-INSERM France ) for the anti-E 4G2 antibody;  F. V. Chisari  ( Scripps Research Institute, La Jolla, CA ) for Huh-7. 5. 1 cells;  Dr M Bouloy  ( Institut Pasteur, Paris, France ) for Vero E6 cells and by  Dr M. Koster  ( Helmholtz-Zentrum für Infektionsforschung ) for U6A cells. We are grateful to  Y. Jaillais  for critical reading of the manuscript. We acknowledge the contribution of  SFR Biosciences  ( UMS3444/CNRS ,  US8/Inserm ,  ENS de Lyon, UCBL)  facilities. This work was supported by grants from the  "Agence Nationale pour la Recherche" (ANR-JCJC-EXAMIN) , the  "Agence Nationale pour la Recherche contre le SIDA et les Hépatites Virales"  ( ANRS-AO 2017-01 ), the  European Union 's  Horizon 2020 Research and innovation program  under " ZIKALLIANCE"  (Grant Agreement no.
734548 ) and FINOVI foundation ( AO11 ) to M. D. The grants from " Fondation pour la recherche médicale " (contract Bioinformatic analysis for research in biology,  DBI20141231313  and from the  "Agence Nationale pour la Recherche" LabEx Ecofect  (Grant  ANR-11-LABX-0048 ) to M. D. , B. B. , and A. B. ; A. K. is supported by the  UK Medical Research Council  ( MC_UU_12014/8 ,  MR/N017552/1 ).