dataesr/scientific-paragraphs-categorization huggingface dataset:
a Each locus was determined through LD-based clumping, using 5 1 Mb around index variants, followed by LD threshold of r 2 > 0. 1; ancestry-specific LDs from 1000 Genomes Project were used when clumping within each ancestry and the entire cosmopolitan data were used for trans-ancestry clumping. b Gene names were obtained using variant effect predictor (VEP) from Ensembl. Genes with intragenic index variants are indicated with an asterisk (*). c Positions are based on build 37. d Effect is in mmHg unit. e The most significant p value (between 1 df interaction test and 2 df joint test) is indicated with an asterisk (*). f These loci indicate ''completely novel'' loci, at least 1 Mb away from any of known BP loci. both known and newly identified variants (with their interactions) explain some of the BP variance across ancestry groups.
a Each locus was determined through LD-based clumping, using 5 1 Mb around index variants, followed by LD threshold of r 2 > 0. 1; ancestry-specific LDs from 1000 Genomes Project were used when clumping within each ancestry and the entire cosmopolitan data were used for trans-ancestry clumping. b Gene names were obtained using variant effect predictor (VEP) from Ensembl. Genes with intragenic index variants are indicated with an asterisk (*). c Positions are based on build 37. d Effect is in mmHg unit. e The most significant p value (between 1 df interaction test and 2 df joint test) was set in bold. f These loci indicate ''completely novel'' loci, at least 1 Mb away from any of known BP loci. Each locus is genome-wide significant (p < 5 3 10 À8 ) in the combined analyses of stages 1 and 2 and had FDR q value < 0.
1. Forest plots and LocusZoom plots are shown in Figures  S3  and S4 , respectively. Abbreviations: BP, blood pressure; SBP, systolic BP; DBP, diastolic BP; EA, effect allele; EAF, effect allele frequency; 2 df joint p, p value of the joint test with 2 degrees of freedom of genetic main and interaction effects; 1 df interaction p, p value of the interaction test with 1 degree of freedom. a Each locus was determined through LD-based clumping, using 5 1 Mb around index variants, followed by LD threshold of r 2 > 0. 1; ancestry-specific LDs from 1000 Genomes Project were used when clumping within each ancestry and the entire cosmopolitan data were used for trans-ancestry clumping. b Gene names were obtained using variant effect predictor (VEP) from Ensembl. Genes with intragenic index variants are indicated with an asterisk (*).
The most significant p value (between 1 df interaction test and 2 df joint test) is indicated with an asterisk (*). f These loci indicate ''completely novel'' loci, at least 1 Mb away from any of known BP loci. The majority of the available data on tissue expression are derived from studies with a breadth of tissue types but with small sample sizes that limit the statistical power to detect association. A more in-depth but single-tissue functional annotation, reporting both cis-and trans-acting elements, was recently performed using microarray-based gene and exon expression levels in whole blood from more than 5,000 individuals of the Framingham Heart Study. 35 In this database, a total of 170 variant-transcript pairs (representing 36 variants) were significant at false discovery rate (FDR) < 0.
ancestry) and BP trait (DBP versus SBP; Material and Methods, Tables  S15-S20 ). DEPICT significantly prioritized genes (FDR < 5%) at 12 European DBP loci, 26 European SBP loci, 34 trans-ancestry DBP loci, and 27 trans-ancestry SBP loci (Tables  S15-S19 ). In 43 cases, the prioritized gene for a specific locus differed from the nearest gene of the lead variant. Our DEPICT gene-set enrichment analyses highlighted a role for the identified variants in the cardiovascular system-predominantly affecting blood vessel biology (FDR < 0. 05 for a total of 134 gene-sets across the four analyses, Table  S20 ). To identify connected gene sets and pathways implicated by our findings, we performed GeneGO analysis and text data mining using Literature Lab. 36 The genes near our findings were enriched by GeneGO disease class ''chronic kidney failure'' (p ¼ 9.
Seventh, subjects were grouped into each ancestry based on self-reported information instead of genetically computed ancestry. Finally, the use of multiple hypothesis tests, multiple phenotypes and exposures, and multiple ancestries may contribute to inflation at some level. Striking a balance between false positives and false negatives, especially in the context of interactions, remains a challenge. In summary, our study identified a total of 137 genomewide significant loci; 56 known loci, 15 new loci identified in stage 1 and formally replicated in stage 2, and 66 additional BP loci identified through the combined analysis of stages 1 and 2 and validated through low FDR. Our ability to identify this many loci is likely due to four factors: focus on gene-smoking interactions, consideration of multiple ancestries, the large aggregate sample sizes available, and the densely imputed data using the recent 1000 Genomes Project reference panel in stage 1 analysis.
Acknowledgments The  various Gene-Lifestyle Interaction  projects, including this one, are largely supported by a grant from the  U. S. National Heart, Lung, and Blood Institute (NHLBI) , the  National Institutes of Health ,  R01HL118305. A Career Development Award  ( K25HL121091 ), also from the  NHLBI , enabled  Y. J. S. to lead this project. Full set of study-specific funding sources and acknowledgments appear in the Supplemental Note.
Indeed, MG132 treatment resulted in p62 degradation in both control and Shield1treated cells (Fig. 1e ). Together, these data established that ATG5 DD was degraded by the proteasome and that Shield1 treatment restored the capacity to undergo autophagy in ATG5 DD -expressing Atg5 -/-cells. We validated that our cell-based system rescued autophagy by testing whether chikungunya virus (CHIKV) propagation was inhibited upon Shield1 treatment (Fig. 1f )  37. We next assessed autophagy in the context of IAV infection, exposing ATG5 DD cells to IAV A/PR/8/1934 (H1N1, PR8 strain) for 20 h in the presence or absence of Shield1. Using imaging flow cytometry, we delineated infected and uninfected cells (Fig. 1g ) and measured autophagic cells within each population (Fig. 1h ). IAVinduced LC3 puncta accumulated in IAV-infected cells (green) but not in uninfected cells (purple) (Fig.
After 4 h of culture, protein extracts were subjected to immunoblot analysis using anti-p62, anti-LC3 and anti-GAPDH antibodies. f ATG5 DD cells, pretreated or not with Shield1 (Sh1), were infected with GFP-expressing chikungunya virus at an MOI of 0. 1. The number of green cells were monitored through live imaging. Graph shows mean and standard deviation of three biological replicates, and data are representative of three experiments. ns, not significant; *q < 0. 01, **q < 0. 01 (two-tailed unpaired t-test followed by Holm's multiple testing correction). g-k ATG5 DD expression was stabilized by Sh1 treatment and cells were infected with influenza A virus (IAV). After 20 h, cells were fixed, permeabilized and stained using anti-NP antibody to quantify IAV infection and anti-LC3 antibody to visualize autophagy puncta.
From the results of three independent experiments, we found that the inability to undergo autophagy during infection led to higher expression of many pro-inflammatory genes as compared to autophagy-competent cells, including established ISGs, such as Psmb10, Cd274, Cxcl10, Irf1 and Tap1 (Fig. 3a  and Supplementary Table  S1 ). Of note, two members of the class I major histocompatibility complex (MHC I) pathway, Psmb10 and Tap1, were decreased in autophagy-competent cells (Fig. 3a ). To identify biological processes and signalling pathways, rather than single genes, impacted by autophagy capacity in a robust fashion, we performed a gene set enrichment analysis (GSEA)  40. Among the 129 gene sets tested (corresponding to sets from databases that included at least five genes measured in this experiment and with expression levels consistently above the lower limit of quantification), we identified four pathways that were significantly enriched in genes differentially expressed by autophagy-competent (Shield1-treated) vs.
g ATG5 DD cells, pretreated or not with Shield1 (Sh1), were infected for 16 h at the indicated MOI. NP RNA expression in supernatants was analysed by RT-qPCR. h ATG5 DD cells, pretreated or not with Shield1 (Sh1), were infected at MOI 3 for the indicated times; haemagglutination assays were used to quantify of the number of haemagglutinin units (HAUs) per millilitre of supernatant. i Atg5 +/+ or Atg5 -/-were infected with IAV at the indicated MOI for 16 h. Percentages of NP-expressing cells was determined by flow cytometry. Graphs show mean and standard deviation of biological triplicates, and data are representative of two experiments. ns, not significant (one-tailed unpaired t-test followed by Holm's multiple testing correction) ROS content. Measurement of both total and mitochondrial ROS revealed that Shield1 treatment did not impact ROS content, arguing that differences in ROS are not responsible for the hyperinflammatory phenotype of autophagy-incompetent cells (Supplementary Figure  S4 ).
4h ). Together, we conclude that Fig. 3  IAV-induced expression of type I IFN-stimulated genes is reduced when ATG5 DD is stabilized. a ATG5 DD cells, pretreated for 16 h with Shield1 (Sh1), were infected with IAV PR8 at MOI 3 for 4 or 12 h followed by RNA extraction. mRNA levels of 561 genes (see Materials and methods) were quantified using Nanostring nCounter technology. Volcano plots show the p-value determined by two-tailed paired t-tests and fold change of gene expression in control vs. Sh1-treated cells. Iso z-value curves are depicted. Data were generated from three independent experiments. b Gene set enrichment analysis was performed after ranking genes according to their differential expression in control vs. Sh1-treated samples (see Materials and methods for computation of t-statistic).
Three hours post-infection, Ifnb1 expression was assayed by RT-qPCR. d ATG5 DD cells were pretreated or not with Sh1 and infected with IAV. Sixteen hours post-infection, surface CD274 expression was monitored by flow cytometry. c, d Graphs show mean and standard deviation of three biological replicates, and data are representative of three experiments. ns, not significant; *q < 0. 05, **q < 0. 01, ***q < 0. 001 (one-tailed unpaired ∅ followed by Holm's multiple testing correction) blocking solution with gentle shaking at 4 °C: anti-ATG5 (Abcam, ab108327), anti-GAPDH (CST, 2118), anti-LC3B (CST, 2775 S), anti-p62 (CST, 5114), anti-M2 (Abcam, ab5416), anti-IκBα (CST, 9242), and anti-PSMB10 (Abcam, ab77735) antibodies. Membranes were then incubated in 1:1000 horseradish peroxidase-conjugated anti-mouse antibody (CST, 7076 S) or anti-rabbit antibody (CST, 7074 S) in blocking solution for 1 h at room temperature with gentle shaking.
Intra-incubator microscopy To monitor cell growth, cells were plated at 10,000 cells per well in 24-well plates. To monitor infection of cells by CHIKV harbouring a green fluorescent protein (GFP) coding sequence after the subgenomic promoter (a kind gift of Marco Vignuzzi, Institut Pasteur), 200,000 cells were plated in 12-well plates 24 h before infection. To monitor NF-κB transcription factor activity, cells were transfected with pNF-κB-GFP plasmid (a kind gift from Eliane Meurs, Institut Pasteur) using lipofectamine 2000 (ThermoFisher Scientific, 11668027) according to the manufacturer's guidelines 24 h before plating (200,000 cells were plated in 12-well plates) and 40 h before infection. Images were taken every hour using Incucyte ZOOM System and analysis was performed using Incucyte ZOOM software (EssenBioScience).
For M2 staining, cells were then incubated in 1:500 dilution of Alexa Fluor 647 goat anti-mouse IgG (H+L) (Jackson ImmunoResearch, 115-606-062) for 45 min at 4 °C. Cells were then washed in PWB buffer twice and in PBS once before resuspension in PBS and acquisition using a BD LSR Fortessa flow cytometer. Data analysis was performed using FlowJo 9 (Flowjo, LLC). For imaging flow cytometry, the antibodies used for ICS were: mouse anti-LC3 antibody (MBL International) at 1:500 dilution and rabbit FITC-conjugated anti-IAV NP antibody (Abcam, ab20921) at 1:100 dilution. Staining of LC3 was performed before washing and staining with Alexa Fluor 647 goat anti-mouse IgG (H+L) (Jackson ImmunoResearch, 115-606-062). After washes, the anti-NP antibody was introduced. Cells were then washed in PWB buffer twice and in PBS once before resuspension in PBS and acquisition using Amnis Imagestream X imaging flow cytometer.
In all, 2 µL of cDNA diluted 4 times in water was used at concentration per qPCR reaction. qPCR was performed using Taqman Fast Advanced Master Mix (ThermoFisher Scientific) according to the provider's protocol. StepOnePlus Real-Time PCR System (ThermoFisher Scientific) was used for thermocycling and data acquisition using the Fast Advanced Master Mix recommended conditions. The StepOnePlus software (ThermoFisher Scientific) was used for analysis.
Nanostring nCounter assays, normalization and analysis Infected cells were harvested and RNA was extracted as detailed above. nCounter Mouse Immunology Kit was used, following Nanostring guidelines. Raw RNA counts were exported from the nSolver Analysis Software (Nanostring). Raw RNA counts were normalized by housekeeping genes to account for the inter-sample variation of RNA quantity. The selected housekeeping gene pool was built from the 14 candidate control genes provided by Nanostring, following the geNorm method  39. Briefly, for each two genes j ≠ k, pairwise variation coefficient V jk is defined as V jk ¼ sd i log 2 a ij a ik ; where a ij is the number of counts for the gene j in the sample i. The gene stability measure M j for control gene j is the arithmetic mean of all pairwise variations V jk for k ≠ j.
Genes were ranked by decreasing score, and gene set enrichment analysis was performed using GSEA v. 2. 2. 1 (Broad Institute), with the following settings: method, pre ranked gene list; gene setsdatabase, reactome (c2. cp. reactome. v5. 1. symbols. gmt); number of permutations, 10,000; enrichment statistic, classic; min set size, 5; max set size, 100; and all other parameters as default.
Acknowledgements We thank the  Centre for Human Immunology and the Centre for Translational Science of Institut Pasteur  for access and support for the Incucyte ZOOM system and ISX technologies. This work was supported by  Canceropôle Île-de-France  and  Fondation pour la Recherche médicale  (to BP); Ecole normale superieure (to JB);  Inca and Plan Cancer, INSERM  (to NY);  Medical Research Council  [ MR/L00870X/1  and  MR/L018578/1 ] and the  European Union Seventh Framework Programme  [ FP7-PEOPLE-2012-CIG: 333955 ] (to JDR);  European Union Seventh Framework Programme Marie Curie Action  ( PCIG11-GA-2012-3221170 ) (to MAI); and  ANR ,  ImmunoOnco LabEx  ( ANR-10-LABX-15 ),  La Ligue contre le Cancer  (to MLA). The authors thank the  Milieu Intérieur consortium  for access to data prior to publication.
Acknowledgement The partial funding support from the  Singapore National Research Foundation  (under its  NMRC-CBRG grant  ( NMRC/CBRG/0096/2015 ) and administered by the  Singapore Ministry of Health's National Medical Research Council ); and  MOE Tier 1 grant  ( RG148/14 ) are acknowledged. William Ong would like to thank  Nanyang Technological University (NTU) Interdisciplinary Graduate School  for its Graduate Scholarship.
Apigenin, luteolin and naringenin were purchased from Carbosynth (Compton, Berkshire, UK); morin from Honeywell Riedel-de-Haën ™ (Seelze, Germany); quercetin, chrysin, bromocresol purple (BCP), sucrose, lactose, glucose, glycerol, dimethylsulfoxide, 2-(N-morpholino)ethanesulfonic acid (MES), sodium acetate from Sigma-Aldrich (Saint-Louis, MO, USA); Ampicillin (Amp), lysozyme, and DNAse I from Euromedex (Souffelweyersheim, France); Durapore ® Membrane Filter 0. 22 µm GV from Merck Millipore (Darmstadt, Deutschland). Molecular modeling and docking studies. The three-dimensional models of the α-(1→2) branching sucrase in complex with sucrose or mono-glucosylated quercetin were generated using as template the coordinates of the crystallographic structures of the α-(1→2) branching sucrase (ΔN 123 -GBD-CD2; PDB accession code: 3TTQ)  20  and L.
reuteri GTF-180 in complex with sucrose (PDB accession code: 3HZ3)  30. Molecules were initially manually docked in the active site of the enzymes. Four mono-glucosylated quercetin obtained from quercetin glucosylation at positions C3′ or C4′ of ring B and at positions C5 or C7 of ring A were constructed starting from the model of the α-(1→2) branching sucrase in complex with sucrose, in which the glucosyl moiety of sucrose was attached to the quercetin moiety, generated using Corina 3D converter (Molecular Networks, Erlangen, Germany). Glucosylation at position 3 of ring C from quercetin was left out due to major steric hindrance. Molecules were positioned using as template the complex of Streptococcus mutans GTF-SI in complex with acarbose inhibitor (PDB accession code: 3AIC)  31. Mutations were introduced using the Biopolymer module of Insight II software package (Accelrys, Saint Louis, USA).
The conformation of the mutated residue side chain was optimized by manually selecting a low-energy conformation from a side-chain rotamer library. Steric clashes (van der Waals overlap) and non-bonded interaction energies (Coulombic and Lennard-Jones) were evaluated for the different side-chain conformations. All energy minimization calculations were performed using the CFF91 force field and steepest descent algorithm (10,000 iterations)  32. Drawings were performed using PyMOL software  33. 7 ). Only flavonoid glucosides for which a mutant allow a production of at least 0,5 mM are represented. For the "Other MG" and "Other LG" only overall quantity of the different products grouped under that denomination is taken in account. Construction of the α-(1→2) branching sucrase libraries. The degenerated primers designed to achieve mutations at each position were: (i) 5′-GGTGGTGATGCTNDTNDTCAAGGTGGTTATCTGAAG-3′ and 5′-GATAACCACCTTGAHNAHNAGCATCACCACCACCATG-3′ for library 2135-2136; (ii) 5′-CCT GGTAATGCANDTGATTTCNDTCTAGCCAACGACGTGG-3′ and 5′-GTCGTTGGCTAGAHN GAAATCAHNTGCATTACCAGGTTGACG-3′ for library 2163-2166.
6 mL/min. Data acquisition and processing were performed using Chromeleon 6. 80 data systems. Reaction samples were diluted in 4 volumes of ultrapure water and centrifuged (2 min, 10000 g) to remove insoluble compounds. The percentages of sucrose consumption (%Sc) were determined as follows: Flavonoids and their glucosylated forms were analyzed by LC-MS analysis, as previously described in Malbert et al. 16  , using a DIONEX Ultima 3000 series chromatograph equipped with a Dionex UVD 340 UV/vis detector and coupled with a simple-quadrupole mass spectrometer (MSQ Plus, Thermo Scientific). HPLC analyses were performed using a reversed phase analytical column (Prontosil Eurobond, 3 μm, 53 mm × 4. 6 mm; Bischoff Chromatography, Germany) maintained at 40 °C. Data acquisition and processing were performed using MSQ Plus 2.
0 SP1 and Chromeleon 6. 80 data systems. Samples were analyzed using the following gradient starting from 15% B to 25% B over 3 min and from 25% B to 50% B over 3. 5 min (A: ultrapure water with 0. 1% formic acid; B: acetonitrile with 0. 1% formic acid) at a flow rate of 0. 5 mL/min. Detection of flavonoid compounds was monitored at 255 and 340 nm. The samples were diluted with 15 volumes of DMSO to solubilize flavonoids. The amount of converted flavonoids were calculated by summing the moles of glucosylated products identified from HPLC analysis after checking that the UV response factors of the various products were similar. Flavonoids conversion were determined as the ratio of consumed flavonoid versus theoretical concentration at initial time (taking into account both the solubilized and the insoluble fraction).
GJA1 Expression and Localization in the Breast We first investigated the tissue localization and level of expression of Cx43 protein in human samples of both morphologically normal breast tissue and tumors using the Human Protein Atlas. This is a public database containing a large collection of normal and cancer tissue slides which have been probed with various antibodies followed by a hematoxylin counterstain  [28]. Cx43 is a membrane channel and is usually considered to be expressed in the myoepithelial cell. A typical punctate staining of junctional plaques formed by Cx43 channels was observed for normal tissues. The staining could be observed in the myoepithelial layer, as expected, but also in some luminal cells (Figure  1a ). Although an under-expression of Cx43 protein is observed in some of the 21 cancer samples available (Figure  1a ), others show a clear over-expression, mostly in well differentiated luminal-like neoplastic cells, which did not appeared to be associated with a basal layer (Figure  1c ).
In other samples, Cx43 could also be seen in a layer of cells separating neoplastic tissue from stroma, although this layer sometimes adhered poorly to both adjacent compartments (Figure  1d, e ). Cx43 was also observed in samples with poorly differentiated cell and tissue morphology (Figure  1f, g ). Interestingly, Cx43 protein could also be found in spindle-shaped cells in the stroma (Figure  1h ). Overall, some normal punctate patterns could be observed in some tumors (Figure  1d ) while the majority of the samples showed either a downregulation or an aberrant cytoplasmic localization of Cx43 in tumor cells. We next compared transcript levels of GJA1 in breast tumor samples and in the non-cancerous adjacent tissues using microarray-based data of The Cancer Genome Atlas project (TCGA) breast invasive carcinoma cohort (BRCA) of clinical samples.
GJA1 Expression Varies with Breast Cancer Subtype We then speculated that GJA1 variability could be linked to the molecular heterogeneity of breast cancer. When we compared GJA1 mRNA expression after stratifying patient samples by their intrinsic subtype (Pam50 by genefu  [29] ) (Figure  2a ), the increase in variance in gene expression of tumor samples relative to normal tissue was observed in every subtype. The LumA had a mean expression level statistically indistinguishable from morphologically normal samples, but a small significant progressive decrease in expression is observed from LumB to basal and Her2e subtypes (Figure  2a ). A similar pattern was observed in the four other datasets, although normal breast tissues were only used in TCGA and Curtis datasets (Figure  A1 ). A similar pattern was also observed at the protein level in the TCGA dataset (Figure  2b ).
Somatic DNA-Level Events of GJA1 Do Not Drive Expression Changes of GJA1 in Breast Cancer We next asked if underlying DNA-level somatic copy number changes in the genomic loci harboring GJA1 influence gene expression levels. For the TCGA dataset, a few tumors had amplification or deletion of GJA1, compared to genes known to be amplified in breast cancer (Figure  3a ). Moreover, tumors with GJA1 amplification did not show an increase in expression while only deep deletions reduced expression (-2 in called copy number, as shown in Figure  3c ). Most luminal tumors with the highest expression of GJA1 were found to have either a normal copy number or single deletion (Figure  3b-d ). Moreover, in tumors with a GJA1 gain or amplification, a slight but significant decrease in expression, rather than an increase, could be observed compared to normal tissues (Figure  3c ).