dataesr/scientific-paragraphs-categorization huggingface dataset:
Selection and Genotyping of SNPs Ten haplotype-tagging SNPs were selected based on genotype data from the CEU + TSI population (HapMap release #27) using an r 2 cut-off = 0. 9 and a MAF = 0. 1 (with the Multimarker Tagger Algorithm available on the HapMap website, www. hapmap. org, now defunct). In addition, SNP rs13217897 was selected based on its association with MS as reported by an independent group  [2] , and rs28385692 was included because of its potential functional effect, given its location in the coding region. Haplotype-tagging SNPs in the Bilbao cohort and rs28385692 in the Donostia cohort were genotyped using the iPLEX Sequenom MassARRAY platform in the Spanish National Genotyping Center (CEGEN, Santiago de Compostela, Spain, www. cegen. es). Genotyping of rs17066096, rs202573 and rs28385692 in the validation cohorts was performed using Taqman ® Genotyping Assays, following the manufacturer's instructions.
Statistical Analysis The statistical analysis was performed using PLINK v1. 07 (http://zzz. bwh. harvard. edu/plink/) [24]. For the association analysis, a logistic regression with an additive model, adjusted for sex, was applied. The independence of association signals was assessed using a conditional logistic regression analysis. A chi-squared test was used to check for the Hardy-Weinberg equilibrium. A haplotype analysis was performed using Haploview v. 4. 2 (https://www. broadinstitute. org/haploview/haploview) [25]. Only the samples that had genotyping data available for all the SNPs were used for the haplotype calculation (for the Bilbao cohort, 375 cases and 441 controls; for the joint datasets, 6545 cases and 5713 controls). Haplotype blocks were defined by the confidence interval method  [26].
The p. Leu16Pro coding SNP rs28385692 is Associated with Risk for Multiple Sclerosis A total of 15 SNPs in a ~100-kb interval around IL22RA2 were analyzed in the Bilbao cohort. Of these, five showed nominally significant (p < 0. 05) association with MS (Figure  1 , Table  2 ). The most strongly associated SNP was rs202573 (OR = 1. 27, p = 0. 007), which had already been the index SNP in the initial screening  [3]. This intronic SNP is in weak LD in the Bilbao dataset (r 2 = 0. 06, D' = 0. 31) with the most significant GWAS-derived top SNP found at the IL22RA2 locus, i. e. , rs17066096  [1, [4] [5] [6]. Rs17066096 showed no association in the Bilbao dataset. Logistic regression analysis of all SNPs conditioning on rs202573 did not reveal any other independent SNP signal reaching statistical significance including rs17066096, suggesting that the main effect observed in this population is driven by rs202573 (Appendix A Table  A1 ).
Haplotype analysis did not show any haplotype displaying a higher significance than single SNPs in the IL22RA2 region (Table  A2 ). Table  2. Association values of SNPs included in the mapping analysis in the Bilbao dataset. 1 Position is according to the hg19 genome build. 2 RAF: risk allele frequency. 3 OR: odds ratio. Interestingly, an infrequent non-synonymous SNP (rs28385692; changes Leu to Pro at position 16 of the SP shared by the three IL-22BP isoforms) was at the limit of statistical significance in the Bilbao dataset (Figure  1  and Table  2 ; p = 0. 05, OR = 1. 972, 95% CI = 0. 983-3. 954). The risk allele of this SNP (C) is comparatively rare (MAF = 0. 015). Notably, haplotype analyses of all genotyped SNPs revealed that among all the haplotypes that were present with a frequency higher than 0.
5% in the Bilbao cohort, the C allele was present only in one haplotype, which contained the risk alleles of four additional SNPs that displayed significant associations in the Bilbao dataset (Figure  A1 ). The association of rs28385692 with MS strengthened when adding 250 controls and 572 cases from nearby Donostia to our analysis, also located in the Basque Country (p = 0. 03, OR = 1. 88, 95% CI = Table  2. Association values of SNPs included in the mapping analysis in the Bilbao dataset. 1 Position is according to the hg19 genome build. 2 RAF: risk allele frequency. 3 OR: odds ratio. 4  Interestingly, an infrequent non-synonymous SNP (rs28385692; changes Leu to Pro at position 16 of the SP shared by the three IL-22BP isoforms) was at the limit of statistical significance in the Bilbao dataset (Figure  1  and Table  2 ; p = 0.
05, OR = 1. 972, 95% CI = 0. 983-3. 954). The risk allele of this SNP (C) is comparatively rare (MAF = 0. 015). Notably, haplotype analyses of all genotyped SNPs revealed that Cells 2020, 9, 175 8 of 23 among all the haplotypes that were present with a frequency higher than 0. 5% in the Bilbao cohort, the C allele was present only in one haplotype, which contained the risk alleles of four additional SNPs that displayed significant associations in the Bilbao dataset (Figure  A1 ). The association of rs28385692 with MS strengthened when adding 250 controls and 572 cases from nearby Donostia to our analysis, also located in the Basque Country (p = 0. 03, OR = 1. 88, 95% CI = 1. 08-3. 282). Next, we validated this finding in independent Spanish and European cohorts comprising a total of 8960 cases and 7613 controls.
The joint analysis of the discovery and validation cohorts confirmed the association of rs28385692 with MS (p = 3. 6 × 10 -4 , OR = 1. 262, 95% CI = 1. 11-1. 434 (Figure  2a ). To further delineate the findings of this mapping effort, our original index SNP, rs202573, and the GWAS-derived top SNP, rs17066096  [4, 6] , were also genotyped in all validation datasets except France. Upon combining all available datasets, we found a significant association of rs17066096 (p = 9. 9 × 10 -5 , OR = 1. 11, 95% CI = 1. 054-1. 172), but not rs202573 (p = 0. 12, OR = 1. 04, 95% CI = 0. 99-1. 09) (Figure  2b, c ). Logistic regression analysis on the combined datasets revealed that the associations of rs17066096 and rs28385692 are statistically independent, since conditioning on one SNP did not abolish association of the other (Table  3 ).
None of the haplotypes formed by these three SNPs increased the statistical evidence for association compared to the single SNPs in the combined datasets, but, as seen in the dataset from Bilbao, the C allele of the infrequent SNP rs28385692 appeared only in the same haplotype as the risk alleles of the other two SNPs (Figure  A2 ). We next attempted to define which of the associated SNPs were most likely to have functional effects based on in silico predictions. To this purpose, we analyzed the five significant variants from the original Bilbao SNP screen and SNPs that were significantly associated in the validation effort, i. e. , rs17066096 and rs28385692, using VEP and RegulomeDB (Table  4 ). As this study was based on a haplotype-tagging method, the observed association signals were considered to be representative of SNPs in linkage disequilibrium (LD) with the associated markers, and therefore, proxies for the associated SNPs were also included in the analysis.
Neither rs202573, the most associated SNP in the Bilbao dataset, nor the only proxy of this SNP which met the r 2 threshold (r 2 = 0. 8) to be included in the analysis, were predicted to overlap with any regulatory feature. rs17066096 did not show evidence to be functional either, but two of its perfect proxies (r 2 = 1), rs17066063 and rs62420820, displayed a moderate regulatory potential based on RegulomeDB. rs17066063 was the SNP with the highest regulatory potential based on concordance between RegulomeDB (score 3a) and VEP (consequence: TF binding site variant). rs28385692 was predicted to lie within a regulatory region, although the level of evidence supporting this is modest (score 5 according to RegulomeDB). To further delineate the findings of this mapping effort, our original index SNP, rs202573, and the GWAS-derived top SNP, rs17066096  [4, 6] , were also genotyped in all validation datasets except France.
Upon combining all available datasets, we found a significant association of rs17066096 (p = 9. 9 × 10 -5 , OR = 1. 11, 95% CI = 1. 054-1. 172), but not rs202573 (p = 0. 12, OR = 1. 04, 95% CI = 0. 99-1. 09) (Figure  2b, c ). Logistic regression analysis on the combined datasets revealed that the associations of rs17066096 and rs28385692 are statistically independent, since conditioning on one SNP did not abolish association of the other (Table  3 ). None of the haplotypes formed by these three SNPs increased the statistical evidence for association compared to the single SNPs in the combined datasets, but, as seen in the dataset from Bilbao, the C allele of the infrequent SNP rs28385692 appeared only in the same haplotype as the risk alleles of the other two SNPs (Figure  A2 ). We next attempted to define which of the associated SNPs were most likely to have functional effects based on in silico predictions.
To this purpose, we analyzed the five significant variants from the original Bilbao SNP screen and SNPs that were significantly associated in the validation effort, i. e. , rs17066096 and rs28385692, using VEP and RegulomeDB (Table  4 ). As this study was based on a haplotype-tagging method, the observed association signals were considered to be representative of SNPs in linkage disequilibrium (LD) with the associated markers, and therefore, proxies for the associated SNPs were also included in the analysis. Neither rs202573, the most associated SNP in the Bilbao dataset, nor the only proxy of this SNP which met the r 2 threshold (r 2 = 0. 8) to be included in Cells 2020, 9, 175 9 of 23 the analysis, were predicted to overlap with any regulatory feature. rs17066096 did not show evidence to be functional either, but two of its perfect proxies (r 2 = 1), rs17066063 and rs62420820, displayed a moderate regulatory potential based on RegulomeDB.
However, its replacement, Pro16, is a helix-breaker neutral residue with restricted conformational flexibility that has no free hydrogen to contribute to helix stability. Results obtained with various signal peptide cleavage site and secondary structure prediction tools are represented in Figure  3a. While SignalP-3. 0 [34]  and PSIPRED  [36]  did not predict any change in the cleavage site, Phobius  [35]  predicted the p. Leu16Pro polymorphism to shift the signal peptide cleavage site from the 21st to 20th residue. The three software applications coincided in predicting a shortening of the hydrophobic core of the signal peptide. Other prediction tools were applied as well and these predicted similar structural effects (Figure  A4 ).
obtained with various signal peptide cleavage site and secondary structure prediction tools are represented in Figure  3a. While SignalP-3. 0 [34]  and PSIPRED  [36]  did not predict any change in the cleavage site, Phobius  [35]  predicted the p. Leu16Pro polymorphism to shift the signal peptide cleavage site from the 21st to 20th residue. The three software applications coincided in predicting a shortening of the hydrophobic core of the signal peptide. Other prediction tools were applied as well and these predicted similar structural effects (Figure  A4 ).
Thus, residual confounding due to undetected population stratification may have impacted some of our results and may account for some of the heterogeneity of the observed effect estimates. However, the effect size for the GWAS-derived SNP rs17066096 estimated in our datasets (OR = 1. 11) was very similar to the one described in the original GWAS (OR = 1. 14)  [4] [5] [6] , suggesting that the impact of population substructure on our results is minor. Lastly, observing both genetic risk association and an effect of the genotypes on gene expression or quantitative protein production does not imply causality. Therefore, additional studies are necessary to characterize the exact molecular mechanisms of the IL22RA2 association signal in MS. In summary, we successfully identified a comparatively rare genetic variant in IL22RA2 that is strongly-and likely independently of the primary GWAS signal-associated with MS.
Cells 2019, 8 , 23 Figure 1. 8231 Figure 1. A single nucleotide polymorphism (SNP) screen of the IL22RA2 locus in the Bilbao dataset. SNPs, depicted with dots in different colors depending on r 2 values with respect to the index SNP rs202573, are plotted as a function of their log-converted p-value (left Y axis) and their position on chromosome 6 according to hg19 assembly of the human genome (X axis). The recombination rate across the locus is provided on the right Y axis. The red line represents the significance threshold (p = 0. 05). SNPs genotyped in the primary screening [3]  are shown in italics. rs202573, which was genotyped both in the previous and in the present study, is underlined.
Finally, by merging APL-specific and malignant-related lncRNAs, we totally obtained 137 lncRNAs (78 upregulated and 59 downregulated) that had the oncogenic potential in APL development, defined as pathogenesisrelated lncRNAs in APL (Fig. 1c , Supplementary Table  S3 ). The importance of these lncRNAs was also validated using transcriptome data of APL versus normal promyelocytes (GSE12662 9 ) (Fig. 1d ). These pathogenesis-related lncRNAs included some already known to be involved in APL. For example, HOTAIRM1 has been reported to contribute to myeloid differentiation, especially ATRAinduced differentiation of APL cells  10, 11. High expression of MEG3 has been observed in APL patients and regulated miRNA expression in APL  12. Furthermore, we also found some lncRNAs known to be involved in many types of solid tumors but without clear roles defined in APL, such as CRNDE  [13] [14] [15] [16]  and ITGB2-AS1  17.
Oncogenic lncRNA CRNDE coordinates with PML/RARα in promoting leukemogenesis in APL Next, we were interested in investigating the biological function of pathogenesis-related lncRNAs in APL. CRNDE drew our interest because of the following characteristics. First, CRNDE expression was remarkedly higher in APL samples, as compared with non-APL AML (GSE13159  18, 19  ), as well as normal counterparts, including normal promyelocytes, CD34 + hematopoietic stem cells and polymorphonuclear neutrophils (GSE12662 9 ) (Fig. 2a ). Second, CRNDE exhibited a high degree of sequence conservation in vertebrates (Fig. 2b ), suggesting its functional importance. Third, the epigenetic status of the CRNDE locus in APL cells showed a high level of active histone marks (GSE18886 20 ) (Fig. 2c ), such as H3K4 trimethylation and H3K9K14 acetylation, and an enriched binding of histone acetyltransferase P300, along with the loss of repressive marks, such as H3K9me3.
These suggest that CRNDE is activated in APL cells, whose high degree of conservation indicates a vital function in vertebrates. More importantly, we found that CRNDE was only highly expressed in the disease state of APL but not in the preleukemic stage of APL (Fig. 2d ). Previous studies have shown that PML/RARα is necessary but not sufficient to cause leukemia, and cooperating events are required for the development of APL  7, 8. PML/RARα transgenic murine APL models mimic this notion well, in which PML/ RARα only does not induce leukemia (PML/RARα transgenic preleukemic mice) and additional secondary events cooperating with PML/RARα promote APL development (PML/RARα transgenic APL mice). We retrieved mouse microarray data (GSE40022  21  ) on promyelocytes from PML/RARα transgenic preleukemic mice (no APL occurred) and blasts from PML/RARα transgenic APL mice (APL occurred), as well as normal promyelocytes from wild type mice as control.
We found that CRNDE expression was only significantly increased after APL occurred but was not induced in the promyelocytes from PML/RARα transgenic preleukemic mice, as compared with wild type mice (Fig. 2d ). These indicated that the oncogenic potential of CRNDE was independent of PML/RARα and CRNDE might coordinate with PML/ RARα to exacerbate leukemogenesis in APL. To verify this finding, we detected the expression of CRNDE in the PML/RARα-inducible model U937-PR9 cells and found that CRNDE expression remained unchanged upon PML/ RARα induction after ZnSO 4 treatment (Fig. 2e ). Moreover, detailed inspection of chromatin immunoprecipitation sequencing (ChIP-seq) data on the CRNDE locus indeed supported that there was no enrichment of PML or RARα binding from 50 Kb upstream or downstream of CRNDE (GSE18886 20 ) (Fig.
CRNDE knockdown induces differentiation and inhibits proliferation of APL cells and represses APL progression in vivo To elucidate the biological function of CRNDE in APL, we used shRNA-mediated knockdown approach to stably downregulate CRNDE expression in the APL cell line NB4. We first updated the possible variants using the latest human NCBI AceView database and showed that the CRNDE locus produced 12 different transcript variants (Supplementary Fig. S1a ). We performed primerspecific qRT-PCR assays to validate the expression of transcript variants of CRNDE and found that CRNDE-g was the most abundant transcript in NB4 (Supplementary Fig. S1b ). The following experiments thus focused on CRNDE-g. Two shRNA sequences (sh-CRNDE1 and sh-CRNDE2) efficiently attenuated the expression level of CRNDE (Fig.
The expression of representative differentially expressed genes was further verified by qRT-PCR in CRNDE knockdown cells (Fig. 3d ). These data suggested a critical role for CRNDE in blocking differentiation and accelerating proliferation in APL. We further addressed the in vivo effect of CRNDE repression using a transplantable murine APL model. The leukemic blasts from PML/RARα transgenic APL mice were electrotransfected with siRNA targeting Crnde, and then injected into tail vein of the recipient FVB/NJ mice (Fig. 3e ). Leukemia engraftment was determined by flow cytometry 19 days after transplantation. As shown in Fig. 3f , compared with negative control mice, si-Crnde-treated mice exhibited a lower percentage of GFP-positive leukemic cells in the peripheral blood. Notably, the white blood cell counts were fewer in si-Crnde-treated mice than those from control mice (Fig.
4f ). Next, we analyzed the potential role of the miR-181 family members in APL cells. First, overexpression of these miRNA mimics promoted differentiation of NB4 cells, as detected by flow cytometry analysis (Fig. 4g ). Second, the cell growth of NB4 cells was suppressed after overexpressing the miR-181c-5p or miR-181d-5p mimic, but was not observed with the miR-181a-5p and miR-181b-5p mimics (Fig. 4g ). Third, to search for the downstream targets of CRNDE/miR-181 in APL cells, we used the encyclopedia of RNA interactome (ENCORI) database based on the large-scale CLIP-seq data. We found that NOTCH2, a well-known member of NOTCH signaling activated in APL development  28  , had strong binding signals with miR-181 (ClipExpNum = 28) (Fig. 4h ). Indeed, we also observed that NOTCH2 was downregulated upon CRNDE knockdown (Fig.
Analysis of RNA-seq and microarray gene expression data The RNA-seq data of AML patients were downloaded from The Cancer Genome Atlas (TCGA) Data Portal (https://tcga-data. nci. nih. gov/tcga/) (n = 151) and Beat AML (http://vizome. org/aml/)  50 (n = 249). The GSE12662 microarray expression data set was retrieved from the Gene Expression Omnibus (GEO) Database. The detailed data analysis is available in Supplementary Methods and the code is publicly accessible on GitHub at https://github. com/ rstatistics/AML-CRNDE-Analysis.
Statistical analysis The association between CRNDE expression and the genotype status of AML was determined by the Cochran-Armitage test for trend (Statistical Analysis System, SAS Studio). Values with P < 0. 01 were considered statistically significant. For experiments, results are presented as arithmetic means ± s. d. of at least three independent experiments. A two-tailed Student t-test or Mann-Whitney test was performed to analyze the significance of the differences between two groups using GraphPad Prism 6 where appropriate. A P-value < 0. 05 was considered significant. Kaplan-Meier survival curves for mice and P-value was analyzed using a log-rank (Mantel-Cox) test.
see figure on previous page) Fig. 4 CRNDE exerts its oncogenic effects by sponging miR-181. a Subcellular localization of CRNDE in NB4 cells was assessed by the cellular fractionation assay. GAPDH was used as a cytoplasmic control and SNORA19 as a nuclear control. b The target miRNAs of CRNDE were predicted by miRanda, DIANA-LncBase version 2 and ENCORI algorithms. c Scatter plot representing the log 2 RPM expression values of 12 predicted miRNAs in 17 APL patient samples from the TCGA database. RPM, reads per million. d The prediction of CRNDE binding sites on four miR-181 family members and miR-136-5p. The capital letter nucleotides are the seed sequences. e In vivo binding of CRNDE and its potential target miRNAs was analyzed by RAP assays in NB4 cells. The expression levels of CRNDE (left panel), and 4 members of the miR-181 family, miR-136 (right panel) were detected by qRT-PCR in RNA samples precipitated with CRNDE-specific oligonucleotide probes from lysates of fixed NB4 cells.
An irrelevant control probe was used as negative control (NC). **P < 0. 01. f Luciferase activity of Luc-CRNDE was detected by cotransfection of miRNA mimics with or without CRNDE overexpression. *P < 0. 05, **P < 0. 01. g The functional analysis of miR-181 family members was determined in NB4 cells by overexpression assays. Differentiation of NB4 cells was detected by flow cytometric analysis of the percentage of CD11b positive cells (left panel). The proliferation of NB4 cells was analyzed by CCK8 cell viability assays (right panel). The bar graph shows the average values derived from three independent experiments, and data are represented as the mean ± s. d. *P < 0. 05, **P < 0. 01. h The relationship between miR-181 and NOTCH2 was validated by CLIP-seq data from the ENCORI database. i The relative expression of NOTCH2 was analyzed by qRT-PCR after overexpression of miR-181 mimics in NB4 cells.
( see figure on previous page) Fig. 4 CRNDE exerts its oncogenic effects by sponging miR-181. a Subcellular localization of CRNDE in NB4 cells was assessed by the cellular fractionation assay. GAPDH was used as a cytoplasmic control and SNORA19 as a nuclear control. b The target miRNAs of CRNDE were predicted by miRanda, DIANA-LncBase version 2 and ENCORI algorithms. c Scatter plot representing the log 2 RPM expression values of 12 predicted miRNAs in 17 APL patient samples from the TCGA database. RPM, reads per million. d The prediction of CRNDE binding sites on four miR-181 family members and miR-136-5p. The capital letter nucleotides are the seed sequences. e In vivo binding of CRNDE and its potential target miRNAs was analyzed by RAP assays in NB4 cells. The expression levels of CRNDE (left panel), and 4 members of the miR-181 family, miR-136 (right panel) were detected by qRT-PCR in RNA samples precipitated with CRNDE-specific oligonucleotide probes from lysates of fixed NB4 cells.
This research employs the Bayesian network modeling approach, and the Markov chain Monte Carlo technique, to learn about the role of lies and violence in teachings of major religions, using a unique dataset extracted from long-standing Vietnamese folktales. The results indicate that, although lying and violent acts augur negative consequences for those who commit them, their associations with core religious values diverge in the final outcome for the folktale characters. Lying that serves a religious mission of either Confucianism or Taoism (but not Buddhism) brings a positive outcome to a character (β T_and_Lie_O = 2. 23; β C_and_Lie_O = 1. 47; β T_and_Lie_O = 2. 23). A violent act committed to serving Buddhist missions results in a happy ending for the committer (β B_and_Viol_O = 2. 55).