dataesr/scientific-paragraphs-categorization huggingface dataset:
33  or Sun et al. 28  respectively. Specifically, 22 QTL signals were shared between all three studies (Supplementary Data 4). pQTL signals, we replicated included IL-1R AcP described above and CA6, discussed later in this paper. pQTL associations under a low-calorie diet challenge. Changes in 104 proteins were associated with BMI variation during the LCD intervention. Our pQTL analysis identified three proteins significantly regulated by four SNPs. All were trans-acting pQTLs (Table  4 ) and intergenic except rs1544241, located in the 5′-upstream region of the pseudogene RP11-672L10. 5. Inclusion of BMI as a confounder in the statistical model did not impact association results (Table  4 ). One SNP located in a large intergenic region on chromosome 6 displayed the strongest pQTL association signal.
Comparison of pQTL signals with two large independent studies  28, 33  , conducted in the general population, further supported our pQTL findings and highlighted 22 pQTL signals that were common between all three studies. Both our functional assays and comparison of association signals with studies of slightly different design, lend support to our results and stress the importance of our identified pQTL signals and their potential role in regulation of proteins like FAM46A and gustin. Our study has some limitations. Proteomics information was available from plasma while eQTL analyses were carried out in samples from adipose tissue biopsies in a smaller subset of participants of the DIOGenes study. This limitation of tissue availability between omics data did not allow us to perform tissue specific pQTL-eQTL comparative analyses like in Chick et al  13.
8 kg)  14. Protein measures. Protein expression was measured using a multiplexed aptamerbased proteomic technology developed by SomaLogic Inc (Boulder, CO). The SOMAscan proteomic assay has been described in previous publications  54, 55. Briefly, this approach uses fluorescently labeled poly-nucleotide aptamers that recognize specific protein epitopes, similar to protein antibodies quantified using relative fluorescence on microarrays. A set of 1129 proteins was measured and quantified directly from plasma samples under fasting conditions before and after intervention. Therefore, resulting protein measurements were reported in relative fluorescence units. Outliers were identified based on interquartile range (IQR; IQR = Q3-Q1 , where Q3 is the third quartile and Q1 is the first quartile of data distribution).
Protein data were available for 530 DIOGenes participants before and after the intervention. Extreme outliers were set as missing if out of protein values ± 6 IQR. Participants were excluded (n = 18) if more than 5% of proteins were outside ± 4 standard deviations at baseline or after LCD. Protein data were log2 transformed for normality for 512 informative DIOGenes participants. Principal component analysis did not highlight effects of age, gender or center on protein variation during the intervention. The three first components explained up to 60% of the variance, respectively 49%, 8%, and 3%. At the time of the analysis, 1,129 proteins were available with the Somalogic Assay. A revision was published recently by the company removing five reagents from future assay results reporting: Alkaline phosphatas, C1s subcomponent, Desmoglein-2, Reticulon-4, and Tumor necrosis factor receptor superfamily member 25.
Finally sequencing was performed on a HiSeq 2000 for 2 × 100 cycles following Illumina's recommendations. A first PCA was performed on all data for outlier detection based on exclusion of samples with principal component 1 and 2 out of twice the interquartile range at each time point. RNAseq data included count information for 54,035 genes. Only subjects with transcriptomics information at CID1 (baseline) and 2 (after LCD intervention) and no outliers based on PCA were kept for further analysis. Count data were filtered and transformed to normality for analysis. Data were normalized by calculating the reads per kilobase per million mapped reads  57  (RPKM) for each gene. RPKM normalized read counts by library size. Expression matrices were derived from RPKM values at the gene level. Data were log2 transformed with a starting value (prior count = 1, i.
The integrated mapping information is based on NCBI's build 37. 498,233 SNPs were genotyped with call rate > 98% and Hardy-Weinberg equilibrium p > 1. 0 × 10 -6. Imputation was performed on the Michigan Imputation Server 58 using a European 1000 Genomes set reference panel. Imputation data was converted to best-guess genotypes (genotype probability threshold 0. 8). Imputed genotypes with an imputation quality score <0. 8 and a minor allele frequency < 5% were discarded. A total of 4,020,654 SNPs were available for analysis in 494 participants with proteomics data and a subset of 151 subjects with gene expression data before and after intervention. A description of the two cohorts is provided in Supplementary Table  2. Protein change between CID1 and CID2 was computed like BMI change as the log 2 fold change between CID2 and CID1.
Association between each protein and BMI was performed using a linear regression adjusted for age, gender, and center. To identify proteins associated with BMI level in obese participants, a first analysis was performed using data at baseline. Then, we conducted a second analysis using proteins and BMI level change between CID1 and CID2 in order to identify proteins associated with weight loss. p values were corrected for multiple testing using Benjamini-Hochberg standard false discovery rate correction  48. A 5% p value cutoff was used to define the association as significant. Despite a log2 transformation, protein distribution may still have a non-normal distribution, which may imply non-normal residuals and affect association results. The same analysis was performed using a rank-based inverse normal transformation which forces normality of protein distribution based on rank of expression values.
p values from both analyses were compared and did not highlighted differences. Correlation and network and canonical pathway analysis. Correlation between proteins was presented in a heatmap. Protein residuals were obtained regressing corresponding expression on age, center, and gender. Protein residuals pairwise correlation was quantified by mean of the Spearman ρ correlation coefficient. Results were plotted in a symmetric heatmap matrix using R function heatmap. 2. IPA (Ingenuity Systems, Redwood City, CA) was used to create molecular networks based on protein-coding genes name, expression and p values for each protein available in our data and integrative information from a large structured collection of observations with nearly 5 million findings manually curated from the biomedical literature or integrated from third-party databases  59.
Proteins were clustered into canonical pathways and p values determining an enrichment of selected proteins in these pathways computed using a Fisher's exact test as proposed by IPA. A Benjamini-Hochberg correction for multiple testing was applied and only pathways that met the p value cutoff p < 0. 05 were considered. Default parameters were used limiting source of data from mammalian studies in the Ingenuity Knowledge Base. The reference set of genes to consider for test p value computation was limited to genes coding for proteins available on the proteomics assay. QTL mapping. pQTLs were mapped using a linear mixed model (LMM) with GCTA software  60. Sex, age, and center were added to the model as confounding variables with a random polygenic term modeling genome-wide sample structure (the genetic background of all patients in the study).
To avoid confounding effects of BMI, pQTL GWAS were performed with and without addition of BMI as confounding factor. GCTA software was used for LMM computation with the 'loco' option that excludes all SNPs belonging to the same chromosome as the SNP under study avoiding multi-collinearity. For each pQTL analysis, the genomic inflation factor (GIF) was estimated using the estlambda function available in the GenABEL R package  61. At baseline, all but one pQTL analysis (pQTL analysis for tPA protein which did not provide any significant results) displayed GIFs ranged from 0. 99 to 1. 07 (Supplementary Fig. 20a ) suggesting that population stratification (if any after correction for population genetic structure using GCTA) was limited. During LCD, GIFs ranged from 0. 98 to 1. 08 (Supplementary Fig.
20b ) leading to the same conclusion. Correction for multiple testing was performed using SLIDE  62  (Sliding-window method for Locally Inter-correlated markers with asymptotic Distribution Errors corrected). SLIDE is based on a multivariate normal distribution to provide an efficient multiple testing correction which is much faster than the gold standard permutation test with similar accuracy  62  and more tractable when applied to association between 1,129 proteins and millions of common variants. For each gene, cis-acting SNPs were located within ±500 Kb of the gene and trans-acting outside this region. Functional annotation of SNPs was performed using SNPnexus tool  63  based on NCBI RefSeq database. We first limited our analysis to SNPs available on the Illumina chip used for genotyping with a minor allele frequency (MAF) > 0.
We used the Genotype-Tissue Expression (GTEx) database to investigate whether identified pQTL SNPs were also associated with the expression levels of contiguous genes in different tissues  65. Basically, a SNP was compared with reported cis-eQTL SNPs from GTEx database. Investigation was limited to SNPs with cis-eQTL p values below or equal to gene-specific nominal p value thresholds as defined by Ardlie et al. 65  for adipose tissues (subcutaneous with data available from 298 samples with donor genotype and visceral omentum tissues with data available from 185 samples with donor genotype). pQTL SNPs were evaluated for association with BMI in the Locke et al. 2 published large GWAS. To assess the significance of the n observed pQTL SNPs, we used a resampling approach where n SNPs were drawn at random, and we counted how many of those were associated with BMI (with unadjusted alpha 5%).
Acknowledgments We are grateful to the  Centre National de Génotypage, Evry, France  for RNAseq data generation, to  M. Wabitsch ,  University of Ulm, Germany  for providing the SGBS cells and to  Margharita Springer  for reviewing the manuscript. The DIOGenes trial was funded by the  European Commission , contract no. FP6-2005-513946. The funding source had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. RNAseq analysis was supported through ESGI funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 262055.
It was also partly facilitated by the web-based tools GeneMatcher  20  and DECIPHER. 21 his study was approved by both the CHU de Nantes ethics committee (comiteconsultatif sur le traitement de l'information en matiere de recherche no. 14. 556) and the Baylor College of Medicine institutional review board. All participants were clinically assessed by at least one expert clinical geneticist from one of the participating centers. Written informed consent was obtained from all study participants. The main clinical features of our cohort are summarized in Table  1. More detailed clinical information for all subjects is provided in the Supplemental Note and Table  S1 , and corresponding Human Phenotype Ontology terms are reported in Tables  S2  and S3. Three de novo nonsense SNVs in PSMD12 (GenBank: NM_002816.
3)-c. 367C>T (p. Arg123*) in subject 1, c. 1274T>G (p. Leu425*) in subject 2, and c. 601C>T (p. Arg201*) in subject 3-were found by subject-parent trio-based whole-exome sequencing. The protocols used Genital þ þ þ þ À þ À À À þ Skeletal þ þ À À À þ þ À þ À Other À þ À À À À À À À À Craniofacial Abnormalities Ophthalmological þ þ ND þ þ þ þ À þ þ Ears þ þ ND þ À þ À þ þ þ (Micro)retrognathia þ À ND À þ À þ À þ À Other À À ND þ þ þ þ þ þ þ 3] [24]  These three variants were confirmed by Sanger sequencing. They were unique events observed in our inhouse database of about 350 exomes (including 75 trios from families with simplex ID) for subject 1 (HUGODIMS and CHU de Nantes); in over 40,000 exomes, including 2,300 trios with various developmental disorders, for subject 2 (Boston Children's Hospital and GeneDX); and in 2,500 trios with autism spectrum disorders for subject 3 (Simons Simplex Collection) (Figure  S2 ).
These three variants are also absent in public variant databases (dbSNP138, 1000 Genomes, NHLBI GO Exome Sequencing Project, and the Exome Aggregation Consortium [ExAC] Browser). In addition, a query of over 7,000 clinical exomes in the BG database, according to the previously defined clinical diagnostics protocol,  25, 26   1  and Table  S4 ). Minimal and maximal coordinates of the CNV deletions are indicated in Tables  1  and S1. In the BG in-house database, apparently similarly sized ~270 kb 17q24. 2 duplication CNVs (chr17: 65,081,882-65,388,883 and 65,120,043-65,458,702; UCSC Genome Browser hg19), involving the entire PSMD12, were observed in three unrelated families. In two of these families, the duplication was inherited from the reportedly asymptomatic parents. Moreover, in one of these families, a pathogenic de novo CNV deletion in chromosomal region 2p14p15, explaining the subject's phenotype, was detected.
Thus, this duplication most likely represents a rare nonpathogenic CNV. No constitutive de novo small duplication was recorded in DECIPHER  21  or in the Database of Genomic Variants. 29 ll subjects from the case series exhibited developmental delay (DD) or ID (n ¼ 10) and had variable dysmorphic features, among which low-set ears (n ¼ 6), hypertelorism (n ¼ 5), and retrognathia or microretrognathia (n ¼ 4) were the most frequent. All but one individual had additional neurological features, including abnormal behavior (n ¼ 7, mostly autistic features or hyperactivity), hypotonia (n ¼ 6), or epilepsy (n ¼ 3, including seizure disorder, reflex seizures, and tonic convulsion). Additionally, two of six subjects had abnormalities detected on brain imaging (pineal cyst [n ¼ 2], cerebral atrophy, and periventric-ular hypomyelination [n ¼ 1]).
Considering the presence of disrupting mutations, we decided to use CRISPR/Cas9 technology to generate a mutant for the zebrafish ortholog of PSMD12. Using reciprocal BLAST, we identified a single zebrafish PSMD12 ortholog (GenBank: NM_201578 and NP_963872) with 86% similarity and designed short guide RNA (sgRNA) targeting exon 3 of psmd12 (Figure  S5 ). (A) Depiction of the PSMD12 constructs used in this study. The truncated PSMD12 1-424 and PSMD12 1-122 variants were constructed to reflect the Leu425* and Arg123* nonsense mutants, respectively. All PSMD12 constructs were cloned into the pcDNA3. 1/Zeo(þ) expression vector (Invitrogen) and C-terminally tagged with a double human influenza hemagglutinin (HA) epitope (GYPYDVPDYAMGGY PYDVPYAGT), as indicated. (B) HeLa cells were cultivated in RPMI medium supplemented with 10% fetal calf serum and 1% penicillin and streptomycin (all purchased from Biochrom AG) and transfected with each of the three PSMD12 variants for 24 hr with Lipofectamin 2000 according to the manufacturer's instructions.
Total RNA was extracted and analyzed by RT-PCR with forward and reverse primers specific to PSMD12 and HA, respectively. Equal loading in each lane was ensured by the amplification of GAPDH. (C) HeLa cells transfected with the empty vector (mock) or each of our PSMD12 variants were subjected to protein extraction at 24 hr after transfection. Whole-cell extracts (20 mg) were resolved on 15% SDS-PAGE and subsequently analyzed by western blotting using anti-HA (clone 16B12, BioLegend) and anti-b-actin (loading control) antibodies, as indicated. CRISPR guide RNA was designed with ChopChop software (see guide sequences in Table  S6 ) and synthesized with the GeneArt Precision gRNA Synthesis Kit (Invitrogen) according to the manufacturer's instructions. In brief, we mixed the forward and reverse target oligonucleotides, the Tracr fragment, and T7 primer mix with 23 Phusion High-Fidelity PCR Master Mix (Invitrogen) and then amplified and ran them on a 1% agarose gel to ensure the quality and appropriate size of the resulting CRISPR guide DNA template.
Accession Numbers PSMD12 sequence variants c. 367C>T (p. Arg123*), c. 1274T>G (p. Leu425*), c. 601C>T (p. Arg201*), and c. 909À2A>G (p. ?) have been deposited in the Leiden Open Variation Database under accession numbers LOVD: 0000132255, 0000132256, 0000132257, and 0000132258, respectively.
Acknowledgments We would like to thank all families for participating in this study. We acknowledge HUGODIMS (a Western France exome-based trio-approach project to identify genes involved in intellectual disability); funding for HUGODIMS (subject 1) is supported by a grant from the  French Ministry of Health  and from the  Health Regional Agency from Poitou-Charentes  (HUGODIMS, 2013,  RC14_0107 ). We thank  Dr. Fre  ´derique Allaire from the  Health Regional Agency of Poitou-Charentes  for supporting this project. We thank  Lea Ferrand  and  Emilie Le Blanc  for grant and data management. We acknowledge  GEM HUGO (Western France Network of Medical Genetics and Genomics). We are most grateful to the  Nantes Genomics core facility (Biogenouest Genomics)  for its technical support and for performing exome sequencing for subject 1 and his parents.
Received : November 3, 2016 Accepted: January 4, 2017 Published: January 26, 2017 Web Resources 1000 Genomes, http://www. 1000genomes. org/ChopChop software, https://chopchop. rc. fas. harvard. edu/Database of Genomic Variants, http://dgv. tcag. ca/dgv/app/home/DECIPHER, https://decipher. sanger. ac. uk/ dbSNP, http://www. ncbi. nlm. nih. gov/projects/SNP/GeneMatcher, https://genematcher. org/ ExAC Browser, http://exac. broadinstitute. org/GenBank, http://www. ncbi. nlm. nih. gov/genbank/Leiden Open Variation Database, http://www. lovd. nl/ NHLBI Exome Sequencing Project (ESP) Exome Variant Server , http://evs. gs. washington. edu/EVS/OMIM, http://www. omim. org/PLINK, http://pngu. mgh. harvard. edu/~purcell/plink/The Human Protein Atlas, http://www. proteinatlas. org/ UCSC Genome Browser , http://genome.
TRPM1 and TYR promoter regions contained not only the welldocumented MITF binding consensus site (the E-box)  12  but also ). Experiment process is shown schematically to the right. f MEFs were transfected with a plasmid for expression of OCT4 or empty vector control (pcDNA). Tyrosinase activity (Cy5, green) was evaluated at day 6 post Dox induction. Nuclei appear blue (DAPI). Green pixel quantification for 10 nuclei from each treatment using ImageJ software is plotted to the right. This was one of n = 2 experiments. g mESCs were transduced with lentiviral vectors for expression of shRNA targeting Oct4 or empty vector as control (sh-control). Mitf, Tyrp2, Trpm1, and Tyrosinase mRNA levels were evaluated at day 6 post Dox induction. Levels were normalized to Gapdh, and fold changes relative to control are shown.
Acknowledgements C. L. thanks the following for grant support: the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  726225 ),  I-CORE Gene Regulation in Complex Human Disease , Center No. 41/11  and  Fritz Thyssen Stiftung. D. S. thanks her supportive family. S. P. thanks  Yehudit Auerbach Mehoudar  for generous support. J. H. H. is supported by a generous gift from Ilana and  Pascal Mantoux , and research grants from the:  European Research Council  ( ERC-2016-COG-726497  and  ERC-2015-PoC-692945 ),  Flight Attendant Medical Research Council (FAMRI) ,  Israel Science Foundation (ISF-ICORE, ISF-NFSC ,  ISF-INCPM & ISF-Morasha  programs),  Kamin-Yeda program ,  Minerva fund ,  Israel Cancer Research Fund (ICRF) ,  Human Frontiers Science Program (HFSP) , the  Benoziyo Endowment fund, New York Stem Cell Foundation (NYSCF) ,  Kimmel Innovator Research Award , the  Helen  and  Martin Kimmel Institute  for Stem Cell Research.
The piggyBac transposable element was originally isolated from the cabbage looper moth, Trichoplusia ni, in the 1980s. Despite its early discovery and specificity compared to the other Class II elements, the diversity and evolution of this superfamily have been only partially analyzed. Two main types of elements can be distinguished: the piggyBac-like elements (PBLE) with terminal inverted repeats, untranslated region, and an open reading frame encoding a transposase, and the piggyBac-derived sequences (PGBD), containing a sequence derived from a piggyBac transposase, and which correspond to domesticated elements. To define the distribution, their structural diversity and phylogenetic relationships, analyses were conducted using known PBLE and PGBD sequences to scan databases. From this data mining, numerous new sequences were characterized (50 for PBLE and 396 for PGBD).
2005; Wang et al. 2006; Xu et al. 2006; Hikosaka et al. 2007; Ray et al. 2008; Sun et al. 2008; Wang et al. 2008; Wu et al. 2008; Carpes et al. 2009; Wang et al. 2009; Daimon et al. 2010; Pagan et al. 2010; Luo et al. 2011; Wu et al. 2011; Luo et al. 2014; Wu and Wang 2014). Most mammalian genomes also contain decayed piggyBac transposons. They ceased their activity due to several mutations or rearrangements  (Pace and Feschotte, 2007; Pagan et al. 2010). The picture emerging from the initial analyses of the human, mouse, rat, and dog genomes shows that there is no evidence for piggyBac activity during the past 40 Ma  (Lander et al. 2001; Gibbs et al. 2004; Lindblad-Toh et al. 2005; Pace and Feschotte 2007). However, piggyBac evolution can be different from one lineage to another. For instance, recent data suggest a continuous colonization of the vesper bat genomes.
Data Mining One hundred and seventeen piggyBac transposases were extracted from databases (NCBI, Repbase, and genomes available). Among those, 107 sequences are related to PBLE including 28 sequences from literature and 79 consensus sequences from Repbase with a transposase longer than 300 aa. Sequences containing long truncations, insertions or deletions were not retained. The other ten sequences are related to piggyBac-derived elements (PGBD) including five sequences from Homo sapiens, two sequences orthologous to PGBD5 of Humans, namely, Pma from the agnathe Petromyzon marinus and Bfl from the cephalochordate Branchiostoma floridae, the KOBUTA element from Xenopus sp and two piggyMac sequences, namely PGM from P. tetraurelia and TPB2 from T. termophila (fig. 1  and supplementary material S1, Supplementary Material online).
Each of these copies was used as query to look for homologous sequences in the NCBI nr database and genomes available by TBlastN. The new sequences identified were in turn used as query in order to identify more PBLE and PGBD. The stringency of the mining steps was between 0 and 1E À100 (TBlastN) and only transposases of at least 250 amino acids were retained. Sequences used as vector were removed and in case of isoform proteins or identical sequences only one sequence was selected.
Structural Analysis of Copies The annotation of piggyBac sequences in databases can sometime be confusing. For instance, the term PGBD-like can be used indifferently for PBLE, PGBD or for degenerated copies (with internal deletion, truncation. ). In the present work, PBLE will refer to sequences (active or not) with two TIRs, two untranslated regions (UTRs) and a transposase. PGBD will correspond to domesticated sequences (i. e. , sequences in single copy in orthologous position in different species and a Ka/Ks indicating the existence of positive or purifying selection). Finally, the PGBD-like term will be restricted to the remaining sequence i. e. with one or no TIRs or UTRs and a transposase that can be partially deleted or truncated. The BLAST searches were done using the transposase sequence as query.