dataesr/scientific-paragraphs-categorization huggingface dataset:
Functional annotation of SNVs was performed using the Variant Classification and the Annotate and Filter tools in the Golden Helix software. Nonexonic SNVs (intronic, intergenic) were removed, and predicted functional SNVs were used to generate an linkage disequilibrium (LD) matrix using Haploview. LD measurements of r 2 >0. 8 were used to define LD haplotype blocks.
Mouse Embryo Analysis and Whole-Mount RNA In Situ Hybridization Gene expression profiling was previously performed to identify differentially expressed genes in the second heart field mesoderm of wild-type mouse embryos. 12 Data from wild-type embryos were extracted for evaluation of specific expression levels in this tissue. For in situ hybridization, RNA probes were generated from mouse embryo cDNA using digoxigenin-uridine triphosphate (Roche Diagnostic Corp, Indianapolis, IN; Table  III  in the Data Supplement). Swiss Webster strain wild-type mouse embryos were isolated at day (E)9. 5 or E10. 5 and used for experiments.
Description of the 22q11. 2DS Population A total of 1472 unrelated subjects with 22q11. 2DS were ascertained through multiple sources (Tables I and II in the Data Supplement) and were genotyped on Affymetrix 6. 0 arrays. Genotypes (n≈6. 6 million SNPs with minor allele frequency >0. 01 and INFO>0. 8) were also imputed. The characteristics of the study subjects are shown in Table  1. These subjects predominantly self-reported as white and non-Hispanic (Table  1 ). This was confirmed by principal component analyses using 4 PCs (we did not observe a difference between usage of 4 or 10 PCs) to estimate genetic ancestry (Figure I in the Data Supplement). The majority (191/217) of Hispanic subjects were recruited at the collection site in Santiago, Chile. Approximately 62% of the subjects (n=906) had CHD at birth.
GWAS to Identify Genetic Loci for TOF The TOF phenotype comprised the largest individual group of subjects with severe intracardiac anomalies in our 22q11. 2DS population (36%; Table  1 ; Figure  1 ). We conducted a GWAS to identify genetic variants associated with TOF. This analysis was based on data from 326 subjects with 22q11. 2DS and TOF and 566 subjects with 22q11. 2DS and normal cardiac anatomy. This study had a power of 80% to detect an odds ratio of >1. 9 for a common SNP with an allele frequency >0. 3 under a log-additive model at P <5×10  -8. Subjects of all races and ethnicities were included, and associations were assessed using logistic regression adjusted with the first 4 PCs of race/ethnicity. As neither deletion size nor sex was significantly associated with TOF in this cohort, these variables were not included in the logistic models.
Definition of the 5q14. 3 Locus To identify nonsynonymous variants that may be in LD with the rs12519770 and to narrow the region containing the association signal on 5q14. 3 based on LD, we performed an LD analysis using existing whole-genome sequence data from 397 individuals with 22q11. 2DS (http://22q11-ibbc. org; unpublished data, International 22q11. 2 Brain and Behavior Consortium authors in Supplementary Table 1, 2017). These individuals comprise a subset of the samples genotyped on Affymetrix 6. 0 arrays and were selected based on psychiatric but not cardiovascular phenotype. There were 9680 SNVs identified in the 2. 7 Mb region around GPR98 (chr5: 8 799 640-90 704 983). There were 161 coding SNVs (102 nonsynonymous, 58 synonymous, and 1 splicing) and 115 SNVs in the 3ʹ or 5ʹ-untranslated regions for a total of 276 SNVs (Table VII in the Data Supplement).
None of these SNVs were in LD with rs12519770. The SNP, rs6893710, was in weak LD with the synonymous variant, rs41304884 (GPR98, NM_032119. 3, c. 16164G>A;Dʹ=0. 859, r 2 =0. 389; Figure  2B ). There were no nonsynonymous variants related to our association signal. Most GWAS signals that have been previously discovered are in intergenic regions and may mark transcriptional regulatory regions in the genome rather than genes themselves. To test this for TOF in 22q11. 2DS, we examined the LD pattern from available whole-genome sequencing data on the same 397 22q11. 2DS subjects to narrow the interval with SNPs showing the strongest association. We narrowed down the association signal to a 104. 7 kb region on chromosome 5 (chr5: 90 057 563-90 162 285; Figure  2C  and 2D, red block). Most of the common SNPs with P values <10 -5 were located in this region (Figure  2C ).
Genes Mapping to 5q14. 3 ] [28]  To investigate higher-order chromatin-mediated looping, we extracted data for the 5q14. 3 interval from the Hi-C browser. 29 here was chromatin interaction data for 28 different cell lines ranging from H1 embryonic stem cells to cancer cell lines. 29- 31 We focused on TAD contact domains in a 3. 3 Mb region including GPR98 (Figure  3 ; Figure IV in the Data Supplement). Because the 104. 7 kb region with genetic association is within the GPR98 locus, it is possible that variants might affect its expression or that of nearby genes. In addition to GPR98, this region includes 6 additional genes (Figure  3    ). The blue line represents the threshold for suggestive association (P=1×10 -5 ). A single locus marked by the GPR98 (G-protein-coupled receptor V1) gene reached genome-wide significance.
We then examined expression levels of genes in the 5q14. 3 region in existing Affymetrix microarray data from the microdissected distal pharyngeal apparatus. 12 he purpose was to determine whether any of the genes are expressed in this critical tissue. We compared expression levels of genes on 5q14. 3 to the highest expressed genes (Actb, Gapdh), Tbx1 expression, and the lowest expressed genes (Il6, Olfr299) in this tissue as shown in Figure  3C. All of the genes are expressed in the pharyngeal apparatus, albeit Gpr98 is expressed at the lowest level, as can be also seen in Figure  3B. The Encyclopedia of DNA Elements functional genomics data were examined in the 104. 7 kb region of LD with SNP, rs12519770, to identify possible regulatory regions. We found 3 possible regulatory regions defined by binding of multiple transcription factors, with one close to rs12519770 (Figure V in the Data Supplement).
Another limitation of our study is possible population stratification. The majority of our cohort was selfreported as white. Nevertheless, a subset had different ethnicities requiring statistical correction in the analysis. Further, we examined each ethnicity separately and combined each group by performing a meta-analysis. We found that the top SNP in GPR98 was still statistically significant. In addition, the odds ratio was in the same direction in all the subcohorts, supporting our findings despite the limitations. In this report, we used available bioinformatic data to interpret our data. However, this study did not provide proof of causation. This will need to be done by performing functional studies including development of animal models. Despite the limitations of the study, this is the first GWAS to identify common variants that may modify the cardiac phenotype in a large cohort of individuals with 22q11.
Acknowledgments We thank the families with 22q11. 2DSwho provided DNA and clinical information. We acknowledge the  Genomics and Molecular Cytogenetics Cores at Einstein. We also acknowledge  Mark Zeffren ,  Nousin Haque ,  Antoneta Preldakaj , and  Francisco Ujueta  for project management and  John Bruppacher ,  Dan Arroyo ,  Michael Gleeson ,  Dominique Calandrillo , and  Frédérique Bena  for technical support at Einstein. We also greatly appreciate the effort of  Dr Frédérique Bena  who works with S. E. and  S. E. A. (Institute of Genetics and Genomics of Geneva, Switzerland). URLs:  Golden Helix  software: http://goldenhelix. com/;EIGENSOFT: http://genetics. med. harvard. edu/reich/Reich_Lab/Software. html;QUANTO: http://biostats. usc. edu/Quanto. html; IMPUTE: https://mathgen. stats. ox.
Observations, data reduction and analysis 2. 1. K2 Photometry EPIC212521166 was observed during Campaign 6 of the K2 mission. We downloaded the pixel data from the Mikulski Archive for Space Telescopes (MAST) 1 and used a modified version of the CoRoT imagette pipeline to extract the light curve. Based on signal-to-noise of each pixel we computed an optimal aperture of 25 pixels. The background was estimated using the 3σ clipped median of all the pixels in the image and subtracted. We also calculated the centroid using the Modified Moment Method by  Stone (1989). In order to correct for flux variations due to the star's apparent motion on the charge-coupled device (CCD) we used a self-flat-fielding procedure similar to  Vanderburg & Johnson (2014). This assumes the movement of the satellite was mainly in one direction, as described in  Barros et al.
PASTIS analysis We jointly analysed the HARPS/HARPS-N RVs, K2 photometric light curve within 3h of the transit midtime and the spectral energy distribution (SED) as observed by the APASS, 2-MASS   (Díaz et al. 2014; Santerne et al. 2015). It models the light curve using the jktebop package  (Southworth 2008 ) assuming an oversampling factor (Kipping 2010) of 30 to account for the long integration time of the K2 data. The SED was modelled using the BT-SETTL library of stellar atmosphere  (Allard et al. 2012 ). The RVs were modelled with a Keplerian orbit. A Markov Chain Monte Carlo (MCMC) method was used to analyse the data. The results from the spectral analysis described in Section 2. 4 were used as priors for the host star. The spectroscopic parameters were converted into fundamental stellar parameters in the MCMC using the Darthmouth evolution tracks  (Dotter et al.
Planet parameters Planet mass M p [M ⊕ ] 16. 7 ± 3. 2 Planet radius R p [R ⊕ ] 2. 592 ± 0. 098 Planet density ρ p [g. cm -3 ] 5. 2 ± 1. 2 Equilibrium temperature T eq [K] 640 ± 20 Notes. All the uncertainties provided here are only the statistical ones. Errors on the models are not considered, as they are unknown. Stellar parameters are derived from the combined analysis of the data and not from the spectral analysis. We assumed R =695 508km, M =1. 98842×10 30 kg, R ⊕ =6 378 137m, M ⊕ =5. 9736×10 24 kg, and 1AU=149 597 870. 7km. strong evidence for a planet. However, we also performed additional tests to ensure that the signal was not due to, for example, a blended eclipsing binary. Using the cross-correlation function with which we computed RVs, we can exclude to 3-sigma all secondaries with ∆mag< 6.
Acknowledgements. We are grateful to the pool of HARPS observers who conducted part of the visitor-mode observations at  La Silla Observatory :  Fatemeh Motalebi ,  Aurélien Wyttenbach ,  Baptiste LaVie ,  Pedro Figueira ,  Alessandro Sozzetti , and  Anaël Wunsche. The Portuguese team acknowledges the support from the  Fundação para a Ciência e Tecnologia (FCT)  through national funds and by  FEDER  through  COMPETE2020  by grants  UID/FIS/04434/2013 & POCI-01-0145-FEDER-007672 ,  PTDC/FIS-AST/1526/2014 & POCI-01-0145-FEDER-016886  and  PTDC/FIS-AST/7073/2014& POCI-01-0145-FEDER-016880. S. C. C. B. , N. C. S. e S. G. S. also acknowledge support from  FCT  through Investigador FCT contracts numbers  IF/01312/2014/CP1215/CT0004 ,  IF/00169/2012/CP0150/CT0002  and  IF/00028/2014/CP1215/CT0002.
V. Zh. A. and E. D. M. also acknowledge support from  FCT  through  Investigador FCT  contracts  IF/00650/2015/CP1273/CT0001 ,  IF/00849/2015/CP1273/CT0003 , and by the fellowship  SFRH/BPD/70574/2010 ,  SFRH/BPD/76606/2011  funded by  FCT (Portugal)  and  POPH/FSE (EC). S. H. acknowledges support by the  fellowship PD/BD/128119/2016  funded by  FCT (Portugal)  and  POPH/FSE (EC). A. S. was supported by the  EU  under a  Marie Curie Intra-European Fellowship for Career Developmentwith reference   FP7-PEOPLE-2013-IEF , number  627202. AS is supported by the  EU  under a  Marie Curie Intra-European Fellowship for Career Development  with reference  FP7-PEOPLE-2013-IEF , number  627202. DJAB acknowledges support from the  UKSA  and the  University of Warwick. J. L-B acknowledges support from the  Marie Curie Actions of the European Commission  ( FP7-COFUND) JMA  acknowledges funding from the  European Research Council  under the  ERC Grant  Agreement n.
337591-ExTrA. D. J. A. and D. P acknowledge funding from the  European Union Seventh Framework programme  ( FP7/2007-2013)  under grant agreement No. 313014  (ETAEARTH). OD acknowledges support by  CNES  through contract 567133 P. A. W acknowledges the support of the  French Agence Nationale de la Recherche (ANR) , under programme  ANR-12-BS05-0012   Exo-Atmos ". Some of the data presented in this paper were obtained from the  Mikulski Archive for Space Telescopes (MAST). STScI is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS5-26555. Support for MAST is provided by  NASA  grant  NNX09AF08G  & by other grants and contracts. This paper includes data collected by the Kepler mission. Funding for the Kepler mission is provided by the  NASA Science Mission directorate.
6 ). Amongst the above mentioned conserved 170 DMGs, high methylation compartment contains 84 (49%) (54 hypomethylated after diuron, and 30 hypermethylated). However, their abs(log2) expression differences were <2. In summary, it appeared that DNA methylation changes correlated directly with RNA level only in a very small group of genes, and only for genes located in the high methylation compartment of the (epi)genome. For this analysis, we used pooled BS-Seq data and compared them with pooled RNA-Seq data. A potential caveat of this approach is that potential individual differences in RNA abundance and DNA methylation were leveled out by absence of such differences in the replicates. Therefore, we performed qPCR experiments targeting arbitrarily selected candidate genes in DMRs using cDNA from the same individuals used for the BS-Seq experiment.
3 ). Since it has been suggested that intragenic DNA methylation plays a role in splice variant regulation  [42] , we investigated the occurrence of different splice variants between the D and SWC group. We amplified by conventional PCR long fragments that covered almost the full CDS for three candidate genes (XLOC_005912/CGI_10018082, XLOC_032353/CGI_10010582 and XLOC_032353/CGI_10010585) in 15 individuals per condition including the individuals which had been used for the BS-Seq experiment. The size of amplified products was checked on agarose gels and we observed a unique fragment of the expected size in both conditions for each targeted locus (data not shown), suggesting that differential methylation did not systematically lead to splice variants. Taken together, our data indicated that although the diuron exposure strongly affected the DNA methylation profile of spat from exposed genitors, differential methylation had generally no direct impact on RNA abundance and splicing.
Methodology Genitor Origin, Parental Diuron Exposure, and Production of the Next Generation The biological material used for the present study was obtained at the experimental hatchery of Ifremer La Tremblade in the frame of the GIMEPEC project funded by the French National Agency for Research (ANR-CESA-01601)  [37]. Briefly, wild adult oysters from Marennes-Oleron Bay (France) were transferred from the field to the hatchery. Seawater was pumped directly from the Seudre river Estuary, filtered through a sand filter (40 lm) and passed through UV rays before draining the tanks in a continuous oxygenated flow system. Oysters were fed daily with a mixture of four marine microalgae and the water temperature was maintained at 8 C (61 C) throughout the 1 month acclimatization period. In order to initiate the oyster gonad maturation process, the temperature was then raised by two degrees per day for 1 week, to reach 19.
Reads with <95% of nucleotides with a quality phred-score equal or higher than 26 were discarded with the Filter by quality tool. Interlace of forward and reverse libraries per sample were performed to discard unpaired reads. De-interlace step was performed per sample to separate forward and reverse libraries previous to mapping. The reference genome, CgigasV9  [34] , was modified for the subsequent mapping step by discarding contigs 5 Kb. Bismark  [94]  was used to map the filtered reads to the modified genome with zero mismatch allowed in the 100 bp reads. Bismark sorted alignment output (BAM file) were converted to SAM file on a local Galaxy instance. Coverage was analyzed using "bedtools makewindows"  [95]  and "samtools bedcov"  [96]. The methylkit software (R packages)  [97]  was used for comparative statistical analysis between the samples using the SAM files as input.
For the bioinformatic pipeline, scripts are available upon request. Visual inspection of the methylation profile of each replicates in IGV software was performed in order to identify DMRs for which all biological replicates were affected in the same way. We determined the genomic features of these target DMRs as being localized either within coding region (exon or intron), promoter or intergenic region using the gene annotation obtained with previous RNA-seq data  [40]. A 500 bp DMRs which was encompassing two genomic features were counted as being located in the two contexts. Promoter regions were considered to be the 1 kb region upstream of the first exon of an annotated gene. For accurate exonic position within the genes, we defined 13 possible positions: position in unique exon, in last exon, in last exon À1 to last exon À10 and others which concerns genes with more than 10 exons.
3 ml of the cDNA matrix were used. PCR product sizes were checked by electrophoresis (TAE 1%). For high resolution analysis on cDNA sequence, a 2. 198 kb fragment from CGI_10018082 was amplified with XLOC _005912. 3for and XLOC_005912. 5rev. 1 ml of this PCR product was cloned into pCR4 (TOPO TA Cloning kit, Invitrogen) and the inserts (17 inserts per condition) derived from amplified cDNA of D (D3-17, D3-12, D3-19) and SWC (TE1-11, TE1-14, TE1-19) individuals were sequenced with primers (M13F, M13R and XLOC_005912. 1rev) using the genoscreen sequencing facilities (http://www. genoscreen. fr/fr/). Chromatograms were edited using Sequencher Software, and a total of 34 sequences (combining several clones of each individual from each condition) were thoroughly examined for final analysis. Phylogeny analysis was performed using the on line phylogeny analysis tools  [105].
Real time PCR (qPCR) were performed in duplicates in a total volume of 10 ml with 1. 5 ml of H 2 O, 0. 5 ml of each primers, 5 ml of MelTaq and 2. 5 ml of cDNA (Roche kit: LightCyclerV R 480 SYBR Green I Master). qPCR were accomplished on the LightCycler480 II (Roche Diagnostics). For each reaction, the crossing point (Cp) was determined using the second derivative maximum method using Light Cycler Software version 3. 3 (Roche Diagnostics). For each reaction, the level of transcription was normalized using the mean geometric transcription rate of three reference sequences: Elongation factor 1: EKC33063. 1 Ubiquitin 60S ribosomal protein L40: EKC19428. 1 40S ribosomal protein S6: AFJ91756. 1 as previously described  [106]  (Supplementary Table  S1 ). Difference between SWC and D individuals was tested statistically using Student's test on R software.
Acknowledgements This work was supported by the  French National Agency for Research  ( GIMEPEC  project,  ANRCESA-01601 ), by the  CNRS program  EC2CO (GENEPIH), by the  Region Languedoc Roussillon  ( TRANSGIGAS, Chercheur  d'avenir 2015), the  "Institut Francais de Recherche pour l'Exploitation de la Mer" ("French Research Institute for Sea Exploration"-Ifremer) , the  "Centre National de la Recherche Scientifique" (National Centre for Scientific Research-CNRS)  and the  University of Perpignan  via Domitia. R. Rondon  was supported by a PhD grant from Ifremer.
To gain insight into structural features of the inhibitory mechanism of C21, we used SAXS analysis of DOCK5 DHR2 and crystallographic analysis of unbound Rac1-GDP. Together, these data suggest that C21 takes advantage of intramolecular dynamics of DOCK5 and Rac1 to remodel the complex into an unproductive conformation. Based on this allosteric mechanism, we propose that diversion of intramolecular dynamics is a potent mechanism for the inhibition of multidomain regulators of small GTPases. DOCK (dedicator of cytokinesis) proteins activate Rho family GTPases to regulate development, autoimmunity and bone homeostasis and they are involved in cancer and severe developmental and immune-related diseases (reviewed in refs  [1] [2] [3]  ). DOCK proteins are characterized by the presence of a DHR2 domain of about 400 amino acids that carries the GDP/GTP nucleotide exchange (GEF) activity, and an upstream DHR1 domain that binds membrane phosphoinositides.
The structure is from the Dock2 DHR2 -Rac1 complex (PDB entry 2YIN, ref. 4  ). Individual monomers in Dock2 DHR2 are identified by cartoon and cylindrical representations with lobes A, B and C colored in blue, red and green respectively. The Rac GTPase is in cyan. the activation of Rac1 by members of the DOCK A subfamily. C21 was discovered using a yeast-based 3-hybrid screen  14  , in which activation of Rac1 by mouse Dock5 and binding of Rac1-GTP to an effector are simultaneously reconstituted in yeast  15. C21 inhibited the Dock5-dependent formation of the sealing zone, where the bone matrix is resorbed in osteoclasts, and it protected mice against bone degradation in models of bone metastasis, menopause and rheumatoid arthritis  16. An unrelated chemical compound, CPYPP, was identified in a screen for disruptors of the interaction between recombinant Dock2 and Rac1, and effectively inhibited Rac activation in lymphocytes resulting in the reduction of chemotactic response and T cell activation  17.
Small-Angle X-ray Scattering (SAXS) is a powerful method to analyze structural determinants such as flexibility or disorder in proteins, which are pivotal for function and inhibition but are difficult to observe by other methods (reviewed in refs  [18] [19] [20]  ). We found that unbound DOCK5 DHR2 resembles the dimeric structure of Dock2 DHR2 and that it displays conformational dynamics that diminishes upon binding of Rac1. Together, our data suggest a model in which C21 takes advantage of the intramolecular dynamics of DOCK5 and Rac1-GDP to remodel the complex in a conformation that cannot proceed to GDP dissociation. Based on this mechanism, we propose that diversion of intramolecular dynamics can be exploited by chemical compounds to remodel protein complexes into unproductive conformations, and has the potential for a variety of applications to the inhibition of GEFs and multidomain proteins in diseases.
We note that this observation is in contrast to previous reports  17  , possibly arising from different experimental setups. Because CPYPP is not GEF-specific, we focused on C21 for the rest of the analysis. To get further insight into the inhibitory mechanism of C21, we analyzed its effect on the formation of the Rac1-DOCK5 DHR2 complex using size exclusion chromatography. As shown in Fig. 2D , C21 does not disrupt the formation of the complex. Together, these data indicate that C21 discriminates between the DHR2-and DH-containing RacGEF families, and that it functions by a non-competitive mechanism in which the GEF activity of DOCK5 is impaired but not the ability of DOCK5 to bind Rac1.
Structural dynamics of DOCK5 and Rac1 support the allosteric mechanism of C21. To investigate structural features in DOCK5 and Rac1 that could be susceptible to allosteric modulation by C21, we used synchrotron SAXS coupled to size exclusion chromatography (SEC-SAXS) to analyze the structure of DOCK5 DHR2 and of the DOCK5 DHR2 -Rac1 complex in solution, and X-ray crystallography to determine the structure of Rac1-GDP. All SAXS data acquisition and analyses are summarized in Table  1. The scattering profile of DOCK5 DHR2 is shown in Fig. 3A. Interestingly, we noted that although the protein eluted as a single, well-resolved I (0) peak, the radius of gyration (R g , an indicator of the overall spread of the protein) slightly decreased across the frames (Figure  S2A ). The frames before, at the middle of, and after the peak were therefore grouped and analyzed separately.