dataesr/scientific-paragraphs-categorization huggingface dataset:
PAR-1 gene expression is correlated with lung tumor stroma activation To explore the association of PAR-1 expression with the NSCLC stroma, we correlated PAR-1 gene expression levels with specific markers of different stromal cell types, including macrophages, endothelial cells, epithelial cells and (myo) fibroblasts in resected tumor specimens using publicly available microarray datasets. To this end, 3 markers were selected for each stromal cell type, except for (myo) fibroblasts for which we included markers of differentiated fibroblasts and markers for extracellular matrix (ECM) produced by myofibroblasts. Interestingly, tumors with higher PAR-1 levels also displayed elevated expression levels of markers for macrophages, endothelial cells and (myo) fibroblasts on the microarrays. Using the GSE3141 dataset (Fig.
These observed correlations (and lack of correlation in epithelial cells) were confirmed in four additional independent microarray datasets from NSCLC (Table  2 ). However, no correlation between PAR-1 and stromal markers was observed in the healthy control group included in the Hou et al. set (GSE19188), suggesting the correlation between PAR-1 gene expression and stroma activity specifically exists in tumor microenvironment. To confirm the identity of the stromal cell types expressing PAR-1, we performed immunohistochemistry with different cell type markers on consecutive lung cancer slides. As shown in Additional file 1: Figure  S1 , PAR-1 positive areas are also positive for CD31 (endothelial marker), CD68 (macrophage marker) and aSMA (myofibroblast marker).
PAR-1 is overexpressed in stroma of primary pulmonary tumors on TMAs To confirm the presence of PAR-   1 ). Overall, strong PAR-1 expression was seen in stroma of all different types of NSCLC (ADC, SCC and large-cell carcinoma) as opposed to weak PAR-1 staining on control sections (Fig. 2 ). In line with our observations in the tumor microarray datasets, the stromal cells (fibroblastlike cells, inflammatory cells and endothelial cells) were all intensively stained for PAR-1, while cancer cells were negative for PAR-1 or showed only weak PAR-1 staining. Subsequent quantifications showed that 93 out of the 94 cases had PAR-1 expression in the stroma, with an average score of 2, while 1 SSC patient was PAR-1 negative. Importantly, the average PAR-1 score in control lungs was significantly lower as in NSCLC stroma (average score of 1; Fig.
Availability of data and materials The microarray datasets analyzed during the current study were derived from the Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/gds) using the R2 microarray analysis and visualization platform (http://r2. amc. nl). The five microarray datasets include Bild (n = 114, GSE3141), Peitsch (n = 150, GSE43580), EXPO (n = 121, GSE2109), Mao (n = 124, GSE 31852) and Hou (n = 156, GSE 19188). The data obtained from TMAs and biopsies are available upon reasonable request from the corresponding author.
257 9. 6e-07 0. 388 1. 0e-05 0. 389 1. 0e-12 0. 585 0. 24 0. 124 0. 05 0. 249 FN1 6. 4e-09 0. 511 3. 3e-07 0. 402 7. 8e-07 0. 431 1. 6e-10 0. 535 1. 9e-05 0. 432 0. 23 0. 150 Selection of the datasets is based on the patient group size ≥50. Bild (n = 114, GSE3141), Peitsch (n = 150, GSE43580), EXPO (n = 121, GSE2109), Mao (n = 124, GSE 31852), Hou (n = 91, control group nc = 65, GSE 19188). Linear regression analysis was used to determine p-value of correlation. Significant correlations are indicated in red.
Fastq files were generated by using Miseqreporter (Illumina), and then aligned with SeqNext (JSI Medical Systems, Ettenheim, Germany) and Surecall (Agilent) software. The design included the coding region of following genes with RefSeq nomenclature: alkaline phosphatase, liver, bone, and kidney (ALPL (NM_000478), COL1A1 (NM_000088), COL1A2 (NM_000089), EN1 (NM_001426. 3), LRP5 (NM_002335), LRP6 (NM_002336. 2), PLS3 (NM_005023), TNFSF11 (NM_003701), TNFRSF11A (NM_001270949), TNFRSF11B (NM_002546), VDR (NM_001017536), WNT1 (NM_005430), WNT16 (NM_057168) for exon regions and intron-exon boundaries, and ALPL (12 exons, NG_008940), COL1A1 (51 exons, NG_007400), COL1A2 (52 exons, NG_007405), EN1 (NG_007123), LRP5 (23 exons, NG_015835), LRP6 (NG_016168), PLS3 (18 exons, NG_012518), WNT1 (4 exons, NG_033141), TNFSF11 (8 exons, NG_008990), TNFRSF11A (12 exons, NG_008098), TNFRSF11B (5 exons, NG_012202), WNT16 (4 exons, NG_029242), and VDR (NG_008731) for exon numbering and the intronic region.
All variants were confirmed by Sanger technology with Life Technologies products and software on an ABI3130 sequencer (ThermoFischer, Les Ulis, France). Large deletions of LRP5, COL1A1, and COL1A2 involved use of multiplex ligationdependent probe amplification (MLPA) reagent on the ABI3130 sequencer with coffalyser software (MRC, Amsterdam, Netherland). Variant pathogenicity was evaluated by using the prediction software Polyphen (http://genetics. bwh. harvard. edu/pph2), Mutationtaster (http://www. mutationtaster. org), and SIFT (http://sift. bii. a-star. edu. sg). The known pathogenic mutations were searched in the Human Gene Mutation database (HGMD) or Leiden Open Variation Database (LOVD). All novel variants will be reported in ClinVar database.
All experiments were realized in triplicate. Cells were plated at 1. 25 Â 105 cells/well in 24-well plates for transfection. Cells were cultured in Wnt3a-conditioned medium or not for 48 hours before transfection. (18) The luciferase assay involved the Dual-Glo Assay system (Promega) according to the manufacturer's instructions. Data were normalized by Renilla firefly activity and are presented as a fold change compared to vehicle-treated cells.
Statistical analysis Statistical analysis was conducted in the 112 patients carrying LRP5 variants or no variant (74 patients in Negative or p. Ala1330Val group, 22 in p. Val667Met group and 16 in Novel or very rare group). Diagnostic tests were performed to detect nonnormality of data and outliers. Bartlett's test was used to test homoscedasticity. Quantitative variables are expressed by mean AE SD. Qualitative variables are expressed by percentages. ANOVA was used to compare multiple groups, followed by a post hoc test in case of a significant effect. The post hoc test consisted of a pairwise comparison by using t tests with pooled SD, and p value adjustment to control for false discovery rate. Chi-square test was used to assess the association between genetic groups and gender, and because the theoretical effectives were <5 for the fracture approach, we used Fisher exact tests to study the association with genetic group variables.
Ala1330Val variant alone (14. 5%); 22 with the p. Val667Met variant (17. 8%), including three at the homozygous level (1. 6%); and 16 carrying a novel or very rare variant (13%). Finally, 56 patients (45. 5%) harbored no variants of genes included in the panel (COL1A1, ALPL, EN1, LRP5, LRP6, WNT16, TNFSF11, TNFRSF11A, and TNFRSF11B genes). Clinical and DXA characteristics of the cohort are reported in Table  1. Table  2  summarizes the clinical data for 11 patients and variants in genes previously reported in osteogenesis imperfecta and idiopathic osteoporosis. Four patients showed variants in COL1A2. We found two known pathogenic heterozygous mutations p. (Gly193Ser) (HGMD ID CM062556) and p. (Gly247Cys), known at this location in HGMD and in LOVD, in patients 1 and 2 presenting one vertebral fracture and one peripheral fracture associated with markedly low spinal BMD.
The known p. (Arg708Gln) variant in COL1A2 was identified in one woman (patient 4) with fractures from childhood. (19) The pathogenicity was verified by the SIFT, Mutationtaster, and Polyphen software. The coding COL1A2 p. (Pro471Leu) variant of unknown significance (VUS) in patient 3 was not found in the dbSNP database, but was reported with the following allele frequency 8. 827e-06 in the Exome Aggregation Consortium database (ExAc) with a potential damaging effect according to the SIFT, MutationTaster, and Polyphen software. This VUS was, however, associated with an unexpected fracture of the femur although femoral BMD was within the normal range in our case. Four novel heterozygous variants of WNT1 were identified: p. (Gly36Asp), p. (Gly134Arg), p. (Asp168Asn), and p. (Arg182Trp). These variants were not found in any database (ExAc, dbSNP, and Exome Sequencing Project [ESP]), but were considered probably pathogenic according to the prediction software Polyphen-2, MutationTaster, and SIFT.
Z-score BMD at the spine was significantly lower with the p. Val667Met variant or novel/rare variants than without a variant. The three groups did not differ in BMD at the hip. Moreover, the proportion of patients with vertebral fractures was significantly higher with the p. Val667Met variant or novel/very rare variants than a p. (Ala1330Val) or no variant. Biochemical markers were measured in a subgroup of patients before the initiation of treatment. The three groups did not differ in circulating levels of bone alkaline phosphatase, P1NP, osteocalcin, or tartrate-resistant acid phosphatase. software Polyphen-2, MutationTaster, and SIFT (Table  4 ). The clinical phenotype varied in patients with LRP5 mutations but was associated with vertebral fractures in 13 of 19 patients. In four patients, the novel/very rare variant was in combination with the p.
36% in Europe in databases such as dbSNP, ExAc, and ESP. The unexpected high frequency at the homozygous level (frequency according to ExAc, 0% to 0. 15%) might be explained by the selected population being BMD-based and fracture-based rather than population-based. Indeed, three patients harbored the homozygous p. Val667Met variant, although osteoporosis and fractures occurred in adulthood without eye involvement, as described in osteoporosis pseudoglioma syndrome. (28) The absence of a full phenotype despite the homozygous variant suggests that this variant is not a full loss of function in osteoporosis pseudoglioma syndrome phenotype. Here, heterozygous/homozygous p. Val667Met and novel variants in LRP5 were found associated with a significantly lower Z-score BMD, which increases the risk of fractures.
Only serum b-crosslaps levels were higher in with p. (Val667Met) (483. 6 AE 176 pg/mL) or novel/very rare variants (570. 6 AE 205. 3 pg/mL) than a p. (Ala1330Val) or no variant (385. 4AE210pg/mL, p < 0. 05). In this cohort, 16 patients harbored heterozygous novel or very rare LRP5 mutations and three patients harbored homozygous rare LRP5. The clinical and genetic characteristics are in Table4. The following variants were not found in the databases: p. (Lys265Gln), p. (Cys336Gly), p. (Met473Thr), c. 1413-2A>G, p. (Lys772Argfs Ã 26), p. (Ala1418Profs), and p. (Glu1597 Ã ). Some variants were reported as rare in a population according to ExAc: p. (Asp587Asn), p. (Trp560Cys), p. (Glu829Gln) p. (Arg1188Gln), p. (Ala1196Thr), and p. (Asp1288Gly). Except for p. (Ala1196Thr), the missense variants were classified as probably pathogenic, with high scores, according to the prediction Clinical and DXA Characteristics of Idiopathic Osteoporosis Cohort.
Acknowledgments This work was supported by the European program SFP7 Sybil. Authors' roles: C. Collet: Conception of the study, perform the experiments, analysis of the results, writing the manuscrpt. A. Ostertag: statistical analysis. M. Ricquebourg: performed the genetix experiments. M. Delecourt, G. Tueur: collection of the data. B. Isidor,  P. Guillot ,  RM Javier ,  E. Schaefer ,  T. Funck-Brentano , P. Orcel: selected the patients, signed inform consent and analyzed the data. JL Laplanche: perform experiments and analyzed the data. MCS: Conception of the study, perform the experiments, analysis of the results, writing the manuscrpt.
The initially published S1 Table is missing some accession numbers necessary to retrieve raw sequence reads used to identity single nucleotide polymorphisms listed in S2 table. The revised version includes the full list of accession numbers together with links to facilitate the downloading of the associated data. More information for downloading from the European Nucleotide Archive (ENA) can be found at http://www. ebi. ac. uk/ena/. CIP strains indexed 1 to 7 were initially obtained from the "Collection de Ressources Biologiques de l'Institut Pasteur" (CRBIP). We especially thank the CRBIP for the provision of these highly valuable historical strains. List of strains used in this study. The geographic origin and year of isolation are indicated when known, together with the SNP lineage assignment (canSNP), accession numbers, sequencing level, and links to the corresponding data.
Studies Stage 2 Data from 14 independent studies, totaling 87 360 individuals, and the first release of UK Biobank, totaling 140 886 individuals, were combined to replicate the findings from stage 1 (ie, totaling 228 245 individuals). Stage 2 study details, including full study names, are given in Table  S6  and included 3C-Dijon (n=4061), Airwave (n=14 023), ASCOT-SC (n=2462), ASCOT-UK (n=3803), BRIGHT (n=1791), GAPP (n=1685), GoDARTs (n=7413), GS:SFHS (n=9749), HCS (n=2112), JUPITER (n=8718), LifeLines (n=13 376), NEO (n=5731), TwinsUK (n=4973), UK Biobank-CMC (n=140 886), and UKHLS (n=7462). Analysis was undertaken using the same methods as described for stage 1 studies. UK Biobank-CMC used a newer imputation reference panel than the other studies, and where a requested variant was not available, a proxy was used (next most significant P value with LD r 2 >0.
Genotype and Gene Expression We searched for signals of association of genotype with gene expression for the 22 signals (including 8 novel) described in this study (Table  S7 ) and all signals reported prior to our study (Table  S10 )  3- 16,18,24  in 3 whole-blood data sets, 1 kidney data set, and the genotype-tissue expression multiple tissue data resource, which included whole blood. 25 We selected cis signals of association, which were significant after controlling for 5% false discovery rate. The 3 wholeblood expression quantitative trait loci (eQTL) data sets were the NHLBI Systems Approach to Biomarker Research in Cardiovascular Disease initiative whole-blood eQTL resource (SABRe) (microarray, n=5257), NESDA-NTR (microarray, n=4896), BIOS (RNAseq, n=2116). The whole-blood data from genotype-tissue expression was based on data from 338 samples.
LD Lookup The 1000 Genomes Project phase 3 release of variant calls was used (February 20, 2015) using 503 subjects of European ancestry. 22 r 2 between the sentinel SNPs and all other biallelic SNPs within the corresponding 2 Mb area were calculated using the Tabix and PLINK software package (v1. 07). 27, 28 Annotation was performed using the ANNOVAR software package. 29.
Network Analysis To construct a functional association network, we combined 2 prioritized candidate gene sets into a single query gene set as (1) genes mapping to the nonsynonymous SNPs in high LD (r 2 >0. 5) with the corresponding sentinel BP-associated SNP and (2) genes with eQTL evidence from 3 or 4 of the blood eQTL resources. Three sentinel SNPs (rs185819, rs926552, and rs805303) mapping to the HLA region on chromosome 6 were excluded from downstream analyses. The single query gene set was then used as input for the functional network analysis. 31 We used the Cytoscape 32 software platform extended by the GeneMANIA 33 plugin (Data Version: August 12, 2014). 34 All the genes in the composite network, either from the query or the resulting gene sets, were then used for functional enrichment analysis against GO terms  35  to identify the most relevant GO terms using the same plugin.
Drug-Gene Interactions Genes used for pathway and GO enrichment analyses were further investigated for potential druggable or drugged targets using the drug gene interaction database. Known drug-gene interactions were interrogated across 15 source databases in drug gene interaction database and include all types of interactions. The analysis performed for druggability prediction included all 9 databases exclusively inspecting expert curated data only. We also evaluate genes for known tool compounds using Chembl (www. ebi. ac. uk/ chembl/; version 22. 1).
38  Kidney was chosen because of the many renal pathways that regulate BP and outstanding questions about the relevance of kidney pathways to the genetic component of BP regulation in the general population. 3, 15  eQTL signals were filtered by false discovery rate (<5%), and we examined cis (within 1 Mb) associations only (Methods and supporting information in the online-only Data Supplement). The 4 blood eQTL data sets were NESDA-NTR, 39,40 SABRe,  15  the BIOS resource,  41  and genotype-tissue expression  25  (Methods and supporting information in the online-only Data Supplement). The BIOS resource (n=2116) has not previously been used in the analysis of BP associations, and findings from NESDA-NTR and SABRe have been reported for a subset of the previously published signals. 16, 18  For a total of 369 genes, gene expression was associated with the BP SNP in ≥1 of the 4 blood data sets at experiment-wide significance (Table  S11 ).
This included 14 genes for 6 of the 8 novel signals. For 110 genes, we found eQTL evidence in 2 out of 4 data sets (Figure ), including 4 genes for 2 of the novel signals: EIF4B and TNS2 for rs73099903 and MAP3K10 and PLD3 for rs9710247. SNP rs73099903 was in strong LD (r 2 > 0. 9), with the SNP most strongly associated with TNS2 expression in the BIOS resource. TNS2 encodes a tensin focal adhesion molecule and may have a role in renal function. 42  For 48 genes, we found evidence in 3 out of the 4 resources (Table  2 ), suggesting robustness of the SNP-gene expression F1 Results from stage 1 and stage 2, and the meta-analysis of stage 1 and stage 2, for all novel genome-wide significant signals of association. P values of association for all 3 traits from a meta-analysis of stages 1 and 2 are also presented.
AQ16 Remya Art:HYP09438 September 2017 correlation signal and highlighting those genes as potential candidates in genetic BP regulation. Of the 48 genes, 28 have not previously been described in eQTL analyses using BP-associated SNPs, and all were correlated with previously reported BP association signals. In the kidney data set (TransplantLines),  26  there was association of gene expression and genotype for 9 SNPs and 13 genes (Table  2  and Figure; Table  S12 ). Nine of the SNP-gene expression associations were also observed in the whole-blood eQTL data sets, suggesting that those signals may not be unique to the kidney. We report 3 signals that were unique to the kidney and not previously reported (C4orf34, HIP2, and ASIC1) and confirm a previously reported kidney eQTL signal for an antisense RNA for PSMD5.
15 The same SNP was also an eQTL for PSMD5 itself in both blood and kidney. ASIC1 encodes the acid sensing ion channel subunit 1, which may interact (and be coexpressed) with ENaC subunits, which mediate transepithelial Na transport in the distal nephron of the kidney. 43 The comparatively small number of signals using kidney tissue (Table  2  and Figure) compared with whole blood could be because of the small sample size. Complete genotype-tissue expression results are given in Table  S13. For genes implicated by eQTL information from whole blood, we tested for enrichment of biological pathways and GOs (Methods in the online-only Data Supplement). We noted enrichment of the 48 genes implicated by 3 or 4 blood eQTL resources (Table  2 ) and a further 53 genes containing a nonsynonymous variant with r 2 >0.
5 with the top SNP (Table  S14 ) in pathways and ontology terms related to actin and striated muscle (Tables  S15  and S16  and Methods in the onlineonly Data Supplement). Network analysis using the same genes highlighted further GO terms relating to muscle function, particularly cardiac muscle (Methods in the online-only Data Supplement and Table  S17 ). We tested the overlap of 161 non-HLA BP-associated variants with DNase hypersensitivity sites identified in the Roadmap and ENCODE cell lines (Methods in the online-only Data Supplement) and identified an overall enrichment in multiple cell and tissue types, including heart, kidney, and smooth muscle (Figure  S5 ). We next investigated these genes for potential suitability as drug targets (druggability), known tool compounds, and clinically approved drugs using the drug-gene interaction database  44  (Table  S18 ).
Twelve genes had known drugs, including 4 genes with known antihypertensive drugs. We noted that drugs modulating all but 1 of the 12 drugged targets had a reported influence on BP, either as a primary antihypertensive indication or as a reported side effect of raised BP. Twenty additional genes were predicted druggable, among these 7 genes have known small molecule tool modulators, based on a query of the Chembl database (www. ebi. ac. uk/chembldb/; version 22. 1).
Art:HYP09438 microarray-based expression levels for the kidney and other blood data sets), or the possibility of false-positive results despite stringent within-experiment significance thresholds. We were unable to distinguish these scenarios using the data available to us, but by selecting genes that were significant in at least 3 resources, and therefore robust to these differences, we identified 48 genes as candidates for further study. These results are limited by the availability of large eQTL resources for whole blood only, which precludes well-powered comparisons across tissue types, particularly, as the origin of BP control is unlikely to be located in the blood. Enrichment and pathway analyses using these genes, and genes containing a correlated functional variant, highlight the potential relevance of muscular tissue and pathways, compatible with a vascular and cardiac origin of BP genetics, extending previous evidence.
Acknowledgments We thank all the study participants of this study for their contributions. Detailed acknowledgment of funding sources is provided in the online-only Data Supplement. Airwave: We thank all participants of the  Airwave Health Monitoring Study. The study is funded by the  UK Home Office  (Grant number  780-TETRA ) with additional support from the  National Institute for Health Research Imperial College Healthcare NHS Trust and Imperial College Biomedical Research Centre. ARIC: The Atherosclerosis Risk in Communities Study is performed as a collaborative study supported by  National Heart, Lung, and Blood Institute contracts  ( HHSN268201100005C ,  HHSN268201100006C ,  HHSN268201100007C ,  HHSN268201100008C ,  HHSN2682011000 09C ,  HHSN268201100010C ,  HHSN268201100011C , and  HHSN2682 01100012C ),  R01HL087641 ,  R01HL59367 , and  R01HL086694 ;  National Human Genome Research Institute  contract  U01HG004402 ; and  National Institutes of Health  contract  HHSN268200625226C.
ASPS: The research reported in this article was funded by the  Austrian Science Fond (FWF)  grant number  P20545-P05  and  P13180. The  Medical University of Graz  supports the databank of the ASPS. We thank the staff and the participants of the ASPS for their valuable contributions. We thank  Birgit Reinhart  for her long-term administrative commitment and  Ing Johann Semmler  for the technical assistance at creating the DNA bank. BRIGHT: This work was supported by the  Medical Research Council of Great Britain  (grant number  G9521010D ) and by the  British Heart Foundation  (grant number  PG/02/128 ). The BRIGHT study is extremely grateful to all the patients who participated in the study and the BRIGHT nursing team. This work forms part of the research programme of the  NIHR Cardiovascular Biomedical Research Unit at Barts.