dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Zebrafish nup133 ortholog is broadly expressed at early stages and becomes restricted to specific tissues at later stages. Query of the latest version of the genome databases identified a unique nup133 orthologue gene in zebrafish, Dr nup133 (ZFIN:ZDB-GENE-040426-2941; Ensembl:ENSDARG00000010078) containing 26 exons located on the forward strand of chromosome 1. The open reading frame is predicted to encode a protein of 1136 amino acids (aa) that shares 62. 3% overall amino acid identity with human Nup133 (Supplementary Fig. S1 ). To determine the spatio-temporal localization of nup133 transcripts in zebrafish embryonic tissues, we performed whole-mount in situ hybridization (ISH) at different developmental stages (Fig. 1a-f ). The nup133 sense RNA probe was used as negative control (Supplementary Fig.
1g , and Supplemental Fig. S2 ). Consistent with these ISH data and with a previous genome wide RNA-seq dataset  24  quantitative RT-PCR performed at 1, 2 and 5 dpf revealed a progressive decrease of nup133 mRNA level during development as compared to beta2 actin mRNA (actb2, previously reported to be extremely stable at these stages of zebrafish development (Supplementary Fig. S2f )  24, 25.
Whole-mount in situ hybridization. Sequences were identified and annotated using combined information from expressed sequence tags and genome databases (GeneBank, http://www. ncbi. nlm. nih. gov; Ensembl, http://www. ensembl. org/index. html). The primers used for probe preparation are listed in supplementary Table  S1. nup133, pax2a, wt1a and cdh17 cDNAs were all isolated by RT-PCR from total RNA from 24-48 hpf embryos and cloned into TOPO pCRII vector (TA Cloning Kit Dual Promoter, Invitrogen) as previously described  64. The resulting plasmids were linearized for SP6 and T7 in vitro transcription and purified with phenol-chloroform. Digoxigenin (DIG)-labeled antisense (or sense, as negative control for nup133 expression) RNA probes were generated using DIG-RNA-labeling kit (Roche Diagnostic).
Following rapid washes in PBS-T, slides were then incubated for 2 hours at room temperature with CY3-conjugated anti-mouse antibody (Jackson ImmuoResearch, # 715-165-151) diluted 1/500 in PBS-T-BSA containing 0. 1 µg/ml DAPI. The slides were then washed in PBS-T and mounted in Moviol medium. Spinning disk images were acquired on an inverted microscope (DMI8; Leica) with a CSU-W1 spinning disk head (Yokogawa) and a sCMOS ORCA-Flash 4 V2+ camera (Hamamatsu) using 100×/1. 4 oil objective or a 20×/0. 75 oil objective. Images were acquired without camera binning using MetaMorph (Universal Imaging Corp. ), processed using ImageJ/Fiji software and assembled using Adobe Photoshop CS. A unique plane is presented. Glomerular filtration assay. Zebrafish larvae at 4 dpf were anesthetized with 200 mg/ml 3-aminobenzoic acid methyl ester (MESAB, Sigma-Aldrich).
Images were acquired using the Gatan Microscopy Software. For quantification of FP density, images acquired from 3 control and 3 nup133-MO treated embryos were analyzed. For each embryo, the number of foot processes and the length of the basement membrane were measured from 3-5 distinct fields each. Images used for quantification, the selected ROI, and original Excel file used to generate the graph are available at Mendeley under https://doi. org/10. 17632/j78ddshctz. 1. Statistical analyses were performed using Wilcoxon-Mann-Whitney Rank Sum Test provided by the KaleidaGraph software.
Acknowledgements We are grateful to  Dr. Christoph Englert  ( Leibniz Age Research, Jena, Germany ) for sharing the Tg(wt1b:EGFP) line. We also thank  Zhiyong Chen ,  Yuya Sugano ,  Ruxandra Bachmann-Gagescu  and  Matthias Gesemann  and members of our labs for support and helpful advices and  Benoit Palancade  for critical reading of the manuscript. We acknowledge the assistance and support of the  Center for Microscopy and Image Analysis, University of Zurich  for support with electron microscopy experiments. We also acknowledge the  ImagoSeine core facility  of the  Institut Jacques Monod , member of IBISA and of the  France-Bioimaging  ( ANR-10-INBS-04 ) infrastructures. Work in the laboratory of VD is supported by the  Centre National de la Recherche Scientifique (CNRS) , the  "Fondation pour la Recherche Médicale" (Foundation for Medical Research)  under grant No  DEQ.
20150734355 , "Equipe FRM 2015" and the  Labex Who Am I?  ( ANR-11-LABX-0071 ; Idex  ANR-11-IDEX-0005-02 ). AB received PhD fellowships from the  "Ministère de l'Enseignement Supérieur et de la Recherche" and the "Ligue Nationale contre le Cancer " and a "transition post-doc" grant from the  Labex Who Am I? SCNF  and JL are supported by research funds from the  Swiss National Science Foundation  ( 31003A_173083  and  310030_143929/1 , respectively). CCC received a fellowship and research funds from the  Italian Ri. MED foundation.
Data Availability pBluescript KSM vector containing 3xHA-mCherry-Dr nup133 and its sequences are available upon request. Images used for EM quantification (Fig. 7g ) and for the quantifications of kidney cysts appearance, LR asymmetry (based on pancreas positioning) and head sizes (Supplemental Fig. S5 ) are available at Mendeley under https:// doi. org/10. 17632/j78ddshctz. 1.
Therefore, direct assessment of allelic differences in transcriptional regulation could help prioritize likely functional variants among multiple variants tied by LD. For example, a recent study adopted MPRA to test 2,756 variants from 75 GWAS loci for red blood cell traits and identified 32 functional variants from 23 loci 20. In addition, expression quantitative trait loci (eQTL) analysis can be a powerful approach for identifying susceptibility genes from GWAS loci, as it informs on genes for which expression levels are correlated with trait-associated variants. While there are a number of publicly available eQTL datasets using tissues representing different human organs including those through the GTEx project 23 , most of them are based on bulk tissue samples (e. g. , skin tissues).
4 as opposed to individual cell types. Importantly, melanomas arise from melanocytes, but they account for less than 5% of a typical skin biopsy. To dissect cell-type specific gene expression regulation implicated in melanoma predisposition, a melanocyte eQTL dataset using primary cultures of melanocytes from 106 individuals was established and mapped six melanoma GWAS loci (30% of all the loci) to melanocyte eQTLs  24. This dataset outperformed eQTLs from bulk skin tissues, other tissue types from GTEx, and melanoma tumors  24  , highlighting the utility of cell-type specific eQTL dataset for functional follow-up of GWAS regions. In this study, we combine MPRA and cell-type specific melanocyte eQTL to scale up the functional annotation process for melanoma GWAS loci and nominate the best candidates for testing in a zebrafish model.
01), and then further selected those with either allele displaying a significant departure from the null (scrambled core sequence; FDR < 0. 01) (Supplementary Fig  3 ). After applying these cutoffs, 39 of the 832 tested variants (~4. 7%) qualified as displaying allelic transcriptional activity in the UACC903 melanoma cell dataset alone as well as in the combined total dataset (Methods;    7 ; Supplementary Fig 9). Transcriptional activities of these 39 variants were significantly higher than those of negative controls (8 variants of high LD with the lead SNP but located in non-DHS/nonpromoter/enhancer histone mark in melanocytes/melanoma cells; P < 2. 2e-16, effect size = 0. 137; Mann-Whitney U test; Supplementary Fig 8B ) as well as the rest of the variants (nonsignificant variants; P < 2.
To incorporate melanoma-relevant annotations to this fine-mapping, we included select functional annotations of primary melanocytes (melanocyte-specific expressed genes from our melanocyte dataset, melanocyte enhancers, TF-binding sites, and histone marks from ENCODE and Roadmap database). When overlaid with these probability scores, the 39 significant MPRA variants (FDR < 0. 01) displayed the highest median probability score compared to other variant groups with varying FDR cutoffs, which was a 2. 12-fold enrichment over all the tested variants with probability scores (Supplementary Fig  10B ). These data demonstrated that MPRA can quickly narrow down to a small number of plausible functional candidate variants from melanoma GWAS loci using allelic transcriptional activity.
Integration of MPRA and melanocyte eQTLs identified functional variants and genes from multiple melanoma loci To prioritize functional variants that contribute to melanoma risk through regulation of nearby gene expression, we turned to cell-type specific melanocyte eQTL data from 106 individuals  24. 597,335 significant cis-eQTL SNPs (+/-1 Mb of TSS, FDR < 0. 05, not LD-pruned) were identified in this dataset, with 6 of 20 melanoma GWAS loci displaying significant colocalization/TWAS  24. As five of these six loci (1q21. 3, 1q42. 12, 2p22. 2, 21q22. 3, and 22q13. 1)    were tested in our MPRA, we overlaid MPRA-significant variants from these loci with genomewide significant melanocyte eQTLs. Four loci had variants that were significant in both assays, and nine of these variants displayed a consistent direction, in which the direction of allelic expression of local genes matches those of MPRA allelic transcriptional activity (Supplementary Table  7 ; Supplementary Fig  10C).
From the genomic interval defined as the TAD encompassing rs398206 (chr21:42,480,000-44,320,000; hg19; retrieved from Hi-C data of SKMEL5 melanoma cell line generated for ENCODE dataset via http://promoter. bx. psu. edu/hi-c/), a total of 21 genes were significantly expressed in melanocytes, for which eQTL analyses were performed. The results demonstrated that MX2 displayed the most significant eQTL with rs398206 (P = 6. 6e-15), while none of the other genes in the TAD displayed even a marginally significant eQTL after adjusting for multiple testing (Bonferroni-corrected cutoff at P < 0. 0024 for 21 genes; Supplementary Table  10 ). These data determined that MX2 is the most likely susceptibility gene at the 21q22. 3 melanoma susceptibility locus. To complement the eQTL data, we also assessed allele-specific expression (ASE) of MX2 in melanocytes.
Melanocyte-specific MX2 expression accelerates melanoma formation in zebrafish MX2 is best known for its function in innate immunity as an HIV-1 restriction gene  41, 42. In GTEx tissue types, the highest MX2 expression levels are observed in EBV-transformed lymphocytes, whole blood, and spleen, reflecting its main role in innate immune response as an interferon-stimulated gene (GTEx portal; https://gtexportal. org). On the other hand, a previous made available for use under a CC0 license. study also demonstrated that MX2 has cell-autonomous function in the proliferation of HeLa cells without IFNα-mediated induction  43. In our primary melanocyte dataset, MX2 is expressed at a relatively high level (median expression ranked at top 26. 5% of all expressed genes) without IFNα stimulation. To assess co-expressed genes and enriched pathways in melanocytes expressing MX2 at a higher level, we profiled differentially expressed genes between MX2-high (top 25%; n = 28) and MX2-low (bottom 25%; n = 28) melanocytes.
Discussion In this study, we adopted an integrative approach combining MPRA with cell-type specific epigenomic and eQTL data to efficiently nominate functional variants and susceptibility genes from 20 known melanoma GWAS loci. Molecular characterization of functional variants and susceptibility genes from a GWAS locus can represent a significant commitment of time made available for use under a CC0 license. and effort as the functions of these genes and variants could be obscure unless the relevant cell types and molecular contexts are considered. By using cell-type specific eQTL to prioritize candidate variants from MPRA, we were able to maximize the probability of finding the most plausible candidates for intense characterization in a time-efficient way. In the future, incorporation of MPRA and cell-type specific eQTL with additional genome-scale datasets, including cell-type specific chromatin interaction data as well as chromatin features of different cellular contexts will further identify strong leads for additional loci with candidate melanoma susceptibility genes and variants.
3. SNPs of r 2 > 0. 4 with the primary or secondary lead SNPs using 1000 Genomes phase3 EUR or CEU populations were selected as "high-LD variants" (n = 2,748) To prioritize high-LD variants overlapping melanocyte/melanoma open chromatin regions and/or active promoter/enhancer histone marks, we used one or more of the following criteria: 1. Variant is located within a human melanocyte DHS peak from one or more individuals of three available through ENCODE and Epigenome Roadmap database. Based on the above criteria, 832 melanoma GWAS variants were selected to be tested by MPRA. We also included 8 additional variants from Chr1q21. 3 that were of r 2 > 0. 8 with the lead SNP but did not overlap with any functional signature listed above and assigned them as negative controls. Of 832 variants, 306 as well as 8 negative controls were repeated in two libraries to ensure cross-library consistency (see MPRA oligo library design).
sequencing read. Linux command "fgrep" was used and only 100% sequence match was kept. We then counted the total read depth for each variant represented by the matched sequences, and then calculated the proportion of variant sequences that were verified. For both library 1 and library 2, more than 97% of unique sequences representing the variants in the library were detected with at least 10 sequencing reads. In addition, we found similar proportion and read depth for sequences representing both forward and reverse directions in both libraries. If we use the actual tag sequences as a bait, 82% of tags could be verified, with a caveat that some tags were amplified but not detected because of relatively poor sequencing quality in this position of the amplicon. Sequence-verified oligo libraries were first cloned into pMPRA1 vector (Addgene) using SfiI site by electroporation into 10 times higher number of bacterial cells than the number of unique sequences in the oligo library.
Log2 transformed TPM ratios of scrambled sequences were regressed against those of either reference or alternative allele while using the same covariates ("Strand" and "Transfection"). Log2 TPM ratio for each tag in each transfection was considered as an experimental replicate for regression. The same set of analyses was done only using data from UACC903 melanoma cells and further dropping data for repeated variants from one of two libraries (library 1) to allow subsequent enrichment analyses. Variants showing FDR < 0. 01 for both allelic difference and departure from null (for either allele) in both UACC903 only and combined set were called as significant MPRA variants. Complete processed MPRA data can be found in Supplementary File 1 as an R object.
Motif analysis made available for use under a CC0 license. Prediction of variant effects on transcription factor binding sites was performed using the motifbreakR package  28  and a comprehensive collection of human transcription factor binding sites models (HOCOMOCO)  56. We selected the information content algorithm and used a threshold of 0. 001 as the maximum P-value for a transcription binding site match in motifbreakR. Log2 fold change between alternative allele score and reference allele score were used to predict the transcription factor motif effect for each variant. Statistical fine-mapping using PAINTOR PAINTOR 3. 0 (http://bogdan. bioinformatics. ucla. edu/software/paintor) was used to estimate the posterior probability of any SNP within a melanoma locus to be causal. We used default parameters in PAINTOR (window size of 100Kb, max causals 2) and filtered out all the SNPs with P-value > 0.
5 for computational efficiency. The pairwise LD between all SNPs in each window was computed using the 1000 Genomes EUR data. Functional annotations were provided as part of PAINTOR software which was complimented with a melanocyte specific gene set annotation  24. In order to determine which annotations are relevant to the phenotype being considered, we ran PAINTOR on each annotation independently and then selected 4 annotations specific to primary melanocytes with high sum of log Bayes factors for the final model to compute trait-specific posterior probabilities for causality. These 4 annotations include melanocyte-specific expressed genes from our melanocyte dataset  24  , melanocyte enhancers, TF-binding sites, and histone marks from ENCODE and Roadmap. Aside from the variants not meeting our analysis parameters, 462 out of 832 MPRA-tested variants were assigned a posterior probability by PAINTOR and were used for enrichment analyses.
0024 for 21 genes was then applied to select the genes showing marginal eQTL with rs398206. For sQTL, meQTL, and RNA stability QTL, we performed similar QTL analyses as our previous eQTL study using the same genotype data, population structure covariates, and statistical approaches. We replaced normalized gene expression levels with normalized splice junction events (sQTL), normalized methylation values (meQTL), and normalized mRNA stability measures (RNA stability QTL). We also re-calculated the top 15 PEER factors according to these phenotype values. For sQTL analysis, STAR  58  was used to map the RNA-Seq reads onto the genome (hg19) and then LeafCutter  31  was applied to quantify the splice junctions following the procedures described by the authors (http://davidaknowles. github. io/leafcutter/articles/sQTL.
MX2 isoform analysis Taqman assays targeting unique junctions of MX2 transcript isoforms were obtained from Thermo Fisher (full-length transcript: Hs01550809_m1 and AP323EZ; ENST00000543692: APYMKKU; ENST00000418103: AP2W9U3). Custom assay design was based on Ensembl75 GRCh37 annotation. RNA was isolated from primary cultures of melanocyte from 106 individuals mainly of European decent  24  , and cDNA was synthesized using iScript Advanced cDNA Synthesis Kit (Bio-Rad). Taqman assays were performed in triplicates (technical replicates) to be averaged to single data points and normalized to TBP levels. TBP was selected among 16 conventional human control genes as being one of the least variable genes in melanocyte dataset based on RNAseq data.
qPCR triplicates (technical replicates) were averaged to be considered as one data point. Cells were also harvested for protein isolation from each biological replicate to assess knockdown efficiency by Western blot analysis. Total cell lysates were generated with RIPA buffer (Thermo Scientific, Pittsburgh, PA) and subjected to water bath sonication. Samples were resolved by 4-12% Bis-Tris ready gel (Invitrogen, Carlsbad, CA) electrophoresis. The primary antibodies used were rabbit anti-YY1 (sc-1703X, Santa Cruz), and mouse anti-GAPDH (sc-51907, Santa Cruz).
Half the amount of DNA, lipofectamine, and cells were used when conducting 24well format of culture. Cells were treated with 1mg/ml Geneticin (Gibco) 24 hours after transfection. Cells were harvested 48 hours after drug selection for RNA and protein isolation. The experiments were performed in at least 3 biological replicates in sets of 5-6 replicates. Total RNA was isolated using RNeasy kit (Qiagen) and cDNA was generated using iScript Advanced cDNA Synthesis Kit (Bio-Rad). MX2 levels were measured using Taqman probe set (Assay ID: made available for use under a CC0 license. Hs01550809_m1) specifically detecting the full-length isoform and normalized to GADPH levels. qPCR triplicates (technical replicates) were averaged to be considered as one data point. UACC903 cells tested negative for mycoplasma.
Differentially expressed genes in MX2-high melanocytes From the RNA-seq data of primary melanocytes (n = 106), we profiled differentially expressed genes (DEGs) between MX2-high (top 25%; n = 28) and MX2-low (bottom 25%; n = 28) samples. Total counts of mappable reads for each annotated gene (GENCODE v19) was obtained using featureCounts from Rsubread package  66. The SARTools 67 workflow was used to perform quality control, apply differential analysis and generate reports based on the count data made available for use under a CC0 license. from both MX2-high and MX2-low groups. edgeR  68  was selected as the statistical methodology to determine differential expression based on the negative binomial distributions. The final DEG list with criteria FDR < 0. 01 and |log2 fold difference| > 1 was applied to Ingenuity Pathway Analysis (IPA).
Western blotting was performed for each cell line to estimate the level of MX2 induction. Total RNA was isolated in the same way as previously described  24. Sequencing library was constructed following Illumina TruSeq Standard mRNA Library protocol. 150 bp paired-end sequencing was performed on NovaSeq 6000 to achieve at least 50 million reads per sample (range 53. 0-82. 4 M). FASTQ raw data was received and quality control was performed by the MultiQC RNA-Seq module  69  (https://multiqc. info). Quasi-mapping algorithm Salmon  70  was used to provide fast and bias-aware quantification of transcript expression using GENCODE human transcripts database (release 29). A principal component analysis was performed based on the expression qualification, and based on the results, differentially expressed genes (DEGs) were calculated with DESeq2 71 adjusting for cell line, biological replicate, and library construction batch.
Statistical analyses All cell-based experiments were repeated at least three times with separate cell cultures. When a representative set is shown, replicate experiments displayed similar patterns. For all plots, individual data points are shown with the median or mean, range (maximum and minimum), and 25th and 75th percentiles (where applicable). The statistical method, number of data points, and number and type of replicates are indicated in each figure legend. between -1. 342 and -2. 236 (D), where lightest red is closer to 1 and lightest blue is closer to -1. Figure Legends (E) Melanoma-free survival curves of a zebrafish melanoma model  44  (Tg(mitfa:BRAF V600E ), p53-/-, mitfa-/-). The fish were injected at the one cell stage with either miniCoopR mitfa:MX2 or miniCoopR mitfa:EGFP and monitored weekly for melanoma formation.
Isoform quantification from melanocyte RNA-Seq data was performed using RSEM expression quantification package (iterations of Expectation-Maximization algorithms to assign reads to the isoforms from which they originate)  4, 5. rs398206 genotypes are shown (A: risk, C: protective). (B) Expression correlation between isoform ENST00000330714. 3 (full-length, most abundant) and ENST00000543692. 1 (rs398206 is located at the 5' UTR). Pearson correlation r and P-value are shown.
made available for use under a CC0 license. 20   Variant is located within a human melanocyte H3K27Ac ChIP-Seq peak from one or more individuals AND a H3K4Me3 ChIP-Seq peak from one or more individuals of two and three available through Epigenome Roadmap database, respectively. 4. Variant is located within a human melanoma short-term culture FAIRE-Seq peak from one or more individuals of 11 available from Verfaillie et al 26.
Acknowledgements The results appearing here are in part based on data generated by the  TCGA Research Network  (http://cancergenome. nih. gov/). Data were also obtained from the  GTEx Portal  ( V6p ) on 2 December 2015 or dbGaP accession phs000424. v6. p1 on 17 December 2015. We would like to thank  K. Jones ,  S. Bass ,  K. Teshome ,  S. Brodie  and other members at the  National Cancer Institute Cancer Genomics Research Laboratory (CGR)  for help with sequencing efforts and xCELLigence assays,  B. Hennessey ,  H. Kong , and  L. Mehl  from the  National Cancer Institute, Laboratory of Translational Genomics  for proofreading the manuscript, and  A. Jermusyk ,  O. Onabajo ,  L. Jessop , and  L. Amundadottir  from  National Cancer Institute, Laboratory of Translational Genomics  for helpful discussions, and  S.