dataesr/scientific-paragraphs-categorization huggingface dataset:
( which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted April 17, 2022. ; https://doi. org/10. 1101/2022. 04. 16. 488569 doi: bioRxiv preprintBiological materialsWolffia australiana wa7733 was from Prof. Hongwei Hou Lab at the National Aquatic Biological Resource Center, Institute of Hydrobiology, Chinese Academy of Sciences. W. australiana plants used for genome sequencing and transcriptome sequencing were cultured in half MS (Solarbio) solid medium (1% sucrose, pH 5. 5) under long-day (LD: 16-h light/8-h dark) conditions at 26 °C. The fresh plants cultured for 10-15 days were used for the extraction of genomic DNA for genome sequencing and total RNA for transcriptome sequencing from plants in a population under cultured conditions (Dataset, Table.
( which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted April 17, 2022. ; https://doi. org/10. 1101/2022. 04. 16. 488569 doi: bioRxiv preprint 35 (B) UMAP visualization of 15,983 cells into 16 clusters (for detailed information, see Dataset, Table.
Gb of ultra-long reads, 22. 85 Gb of Illumina data, 253. 08 Gb of Gb of clean reads after quality-control steps and the removal of organellar and bacterial (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Bionano data, and 36. 55 Gb of Hi-C data (Dataset, Table S1). To help with later genome annotation, we produced 202. 46 Gb of data by transcriptome deep sequencing (RNA-seq). To survey the genome, we used 11. 3 Gb of Illumina paired-end reads (out of 22. 85 Gb), resulting in 8. 3.
Letter Exome sequencing identifies breast cancer susceptibility genes and defines the contribution of coding variants to breast cancer risk Linkage and candidate gene studies have identified several breast cancer susceptibility genes, but the overall contribution of coding variation to breast cancer is unclear. To evaluate the role of rare coding variants more comprehensively, we performed a meta-analysis across three large whole-exome sequencing datasets, containing 26,368 female cases and 217,673 female controls. Burden tests were performed for protein-truncating and rare missense variants in 15,616 and 18,601 genes, respectively. Associations between protein-truncating variants and breast cancer were identified for the following six genes at exome-wide significance (P < 2. 5 × 10 -6 ): the five known susceptibility genes ATM, BRCA1, BRCA2, CHEK2 and PALB2, together with MAP3K1.
Breast cancer is the leading cause of cancer-related mortality for women worldwide. Genetic susceptibility to breast cancer is known to be conferred by common variants, identified through genome-wide association studies (GWAS), together with rarer coding variants conferring higher disease risks. The latter, identified through genetic linkage or targeted sequencing studies, includes protein-truncating variants (PTVs) and some rare missense variants in ATM, BARD1, BRCA1, BRCA2, CHEK2, RAD51C, RAD51D, PALB2 and TP53 (ref. 1). However, these variants together explain less than half the familial relative risk (FRR) of breast cancer  2. The contribution of rare coding variants in other genes remains largely unknown. Here we used data from the following three large whole-exome sequencing (WES) studies, primarily of European ancestry, to assess the role of rare variants in all coding genes: the Breast Cancer Risk after Diagnostic Gene Sequencing (BRIDGES) dataset that included samples from eight studies in the Breast Cancer Association Consortium (BCAC), the PERSPECTIVE (Personalized Risk assessment for prevention and early detection of breast cancer: integration and implementation) dataset that included three BCAC studies (Supplementary Table  1 ) and UK Biobank (UKB).
After quality control (QC; Methods), these datasets comprised 26,368 female cases and 217,673 female controls (Supplementary Table  2 ). We considered the following two main categories of variants: PTVs and rare missense variants (minor allele frequency <0. 001). Single-variant association tests are generally underpowered for rare variants; however, burden tests, in which variants are collapsed together, can be more powerful if the associated variants have similar effect sizes  3. To further improve power, we incorporated data on family history of breast cancer (Methods)  4. Association tests were conducted for all genes in which there was at least one carrier of a variant (15,616 genes for PTVs and 18,601 genes for rare missense variants). Letter https://doi. org/10. 1038/s41588-023-01466-z We next considered missense variants predicted deleterious combined with PTVs.
001 after allowing for the six exome-wide significant genes (Fig. 2 ). However, 28 of the 30 associations at P < 0. 001 correspond to an increased risk, compared with ~15 that would be expected by chance. This imbalance suggests some of the other associations may be genuine. We performed additional analyses by age and (within the BCAC dataset) the following disease subtypes: estrogen receptor (ER) + or ER -, progesterone receptor (PR) + or PR -and triple-negative disease. When restricting the age of cases to <50 years, 40 genes were associated (all with increased risk) at P < 0. 001, suggesting an enrichment of associations in this age group (Supplementary Table  5 ), MGAT5 met exome-wide significance, in addition to BRCA2, BRCA1, CHEK2, PALB2, ATM and MAP3K1. In the subtype-specific analyses (Supplementary Table  6 ), the expected associations for known genes were observed, importantly, the higher OR for ER -and triple-negative disease for BRCA1 and higher OR for ER + disease for CHEK2, but no other genes were associated with subtype-specific disease at exome-wide significance.
MDM4 encodes a p53 repressor overexpressed in a variety of tumors  28  and is also a GWAS locus for triple-negative breast cancer  13, 29. Pathology information was available for cases in the BCAC dataset. We tabulated pathology characteristics for carriers of variants in genes with P < 1 × 10 -4 in the meta-analysis of PTVs or the meta-analysis of predicted deleterious (CADD) rare missense variants combined with PTVs (Supplementary Tables  11  and 12 ). These data suggest a slightly higher proportion of mixed lobular and ductal tumors for LZTR1 PTV carriers and MRPL27 deleterious rare missense variant or PTV carriers. There was a slightly higher proportion diagnosed >50 years for ATRIP PTV carriers and a higher proportion of HER2+ tumors for EXOC4 deleterious rare missense variant or PTV carriers.
10). To evaluate the overall contribution of PTVs to the FRR, we fitted models to the effect size using an empirical Bayes approach. We used whole-genome sequencing data in UKB to estimate the missing contribution due to large rearrangements. Under the assumption of an exponentially distributed effect size, the estimated proportion of risk genes (a) was 0. 0047 with a median OR of 1. 38. Under this model, an estimated 10. 61% of the FRR would be explained by all PTVs, of which 9. 64% would be due to the five genes BRCA1, BRCA2, ATM, CHEK2 and PALB2 and 0. 97% due to all other genes combined with MAP3K1 contributing 0. 14% (Supplementary Table  15 ). Only the six genes reaching exome-wide significance for PTVs had a posterior probability of association >0. 90. We repeated these analyses using the subsets of genes including breast cancer driver genes and target genes of GWAS signals identified in ref.
13, the list of cancer predisposition genes identified in ref. 30, Catalogue Of Somatic Mutations In Cancer (COSMIC) TSGs  31  and the top pathways identified by GSEA (Supplementary Table  16 ). The largest contributions to the FRR, after excluding the five known genes, were for the BioCarta CD40 pathway (0. 657%, n = 16, a = 0. 628) and COSMIC TSGs (0. 639%, n = 320, a = 0. 196). Thus, these results suggest that the majority of the remaining risk genes are TSGs. These results demonstrate that large exome sequencing studies, combined with efficient burden analyses, can identify additional breast cancer susceptibility genes. The excess of positive associations at P < 0. 001 indicates that further genes should be identifiable through large datasets-the heritability analyses suggest the number of associated genes might be of the order of 90, with the majority of these being TSGs.
Although the estimated risks associated with the new genes, in particular MAP3K1 PTVs, would be large enough to be of clinical relevance, the effect sizes might be over-estimated due to the 'winner's curse'  32. Thus, further replication in larger datasets will also be necessary to provide more precise estimates for variants in the new genes, to define the set of variants in these genes associated with breast cancer, the clinic-pathological characteristics of tumors in variant carriers and the combined effects of pathogenic variants and other risk factors. The heritability analyses suggest that most of the contribution of PTVs is mediated through the five genes BRCA1, BRCA2, ATM, CHEK2 and PALB2, commonly tested for in clinical cancer genetics  33. These analyses assume dominant inheritance and recessive genes may also contribute to the familial risk, while subsets of missense variants may also make important contributions (exemplified by CDKN2A and SAMHD1).
UKB The UKB is a population-based prospective cohort study of more than 500,000 subjects. More detailed information on the UKB is given elsewhere  34, 35. The study received ethics approval from the North West Multi-center Research Ethics Committee. All participants signed written informed consent before participating. WES data for 450,000 subjects were released in October 2021 and accessed via the UKB DNA Nexus platform  36. QC metrics were applied to Variant Call Format (VCF) files as discussed in ref. 37, including genotype level filters for depth and genotype quality. Samples with disagreement between genetically determined and self-reported sex, sex aneuploidy or excess relatives in the dataset were excluded. Excess relatives were identified by considering pairs of individuals with kinship >0.
17. If one individual in a pair was a case and one was a control then the case was preferentially selected; otherwise, one individual was selected at random. Genetic ancestry was estimated using genetic principal components and the Gilbert-Johnson-Keerthi distance algorithm  38. If genetic principal components were not available, self-reported ancestry was used. Samples of ancestry other than European were excluded. The final dataset for analysis included 419,307 samples with 227,393 females. Cases were defined by having invasive breast cancer (International Classification of Diseases (ICD)-10 code C50) or carcinoma in situ (D05), as determined by linkage to the National Cancer Registration and Analysis Service (NCRAS), or self-reported breast cancer. Both prevalent and incident cases were included.
Only breast cancers that were an individual's first or second diagnosed cancer were included as cases. By this definition, 17,958 female and 94 male cases were included. For structural variants, we accessed the structural variant population VCF files for the initial release of UKB whole-genome sequencing via the DNA Nexus platform. These deletions, duplications and insertions were called using GraphTyper (2. 7. 1) (refs. 39,40). We identified any structural variant that passed the GraphTyper QC filters and overlapped an exon of the MANE transcripts of each gene. The samples were filtered using the above exclusions leaving 64,264 samples (4,847 female breast cancer cases and 59,417 female controls). The frequency of structural variants was then calculated for each gene and used to adjust the PTV frequency (Supplementary Methods).
The BCAC datasets The BRIDGES and PERSPECTIVE samples were from studies in the BCAC (BRIDGES: eight studies, PERSPECTIVE: three studies; Supplementary Table  1 ). All studies were approved by ethical review boards (Supplementary Table  17 ). All subjects provided written informed consent. Most samples were previously included in a targeted panel sequencing project  1. Phenotype data were based on the BCAC database v14. Samples were oversampled for early-onset (age of diagnosis below 50 years) or family history of breast cancer. Cases were preferentially selected to have information on tumor pathology. Samples with previously identified pathogenic mutations in BRCA1, BRCA2 or PALB2 (348 cases, 176 controls) were not included. For BRIDGES, library preparation was conducted in the three laboratories using the Nextera DNA Exome kit (Illumina) for tagmentation, barcoding and amplification steps.
Subsequently, 500 ng of DNA per sample was pooled in 12-plex and concentrated using a vacuum system. Afterward, hybridization capture reagents for DNA libraries were used for overnight hybridization with the xGen Exome Research panel (Integrated DNA Technologies), capture and amplification. Barcoded pooled libraries of 96 samples were sequenced on each lane of a NovaSeq 6000 S4 flowcell (Illumina) using NovaSeq XP 4-Lane Kit (2 × 100 bp). For PERSPECTIVE, library preparation was conducted using Agilent SureSelect Human all exon V7 (48. 2 Mb). Barcoded libraries of 88 samples were sequenced on a NovaSeq 6000 S4 flowcell (Illumina) using NovaSeq XP 4-Lane Kit (2 × 100 bp). The same pipeline for variant calling was applied to both the BRIDGES and PERSPECTIVE data and followed the Genome Analysis Toolkit (GATK) best practices.
Briefly, raw sequence data (FASTQ format) were preprocessed to produce BAM files. This involved alignment to the reference genome (hg38) using BWA (v0. 7. 17) and the sorting and indexing of the reads using samtools (v1. 10). Identification and removal of duplicate read pairs from the same DNA fragments were performed using Picard's MarkDuplicates (v2. 1. 1). Base recalibration included the generation of a base quality score recalibration table with the GATK BaseRecalibrator software (v4. 1. 4. 1), later applied to the read bases to adjust their quality scores and increase the accuracy of the variant calling algorithms with the GATK BQSR (v4. 1. 4. 1). An intermediate and informal QC was performed for a sanity check, including coverage and alignment mapping metrics using samtools flagstat software (v1.
10) and Picard (v2. 22. 2). Variants were then called using GATK Haplotype-Caller (v4. 1. 4. 1). The GATK GenotypeGVCFs (v4. 1. 4. 1) tool was used for the joint genotyping step on each genomic database. The variants with excess heterozygosity were filtered out using GATK VariantFiltration (v4. 1. 4. 1) and GATK MakeSitesOnlyVcf (v4. 1. 4. 1). The GATK VariantRecalibrator (v4. 1. 4. 1) software was used to produce tranches files on SNPs and indels separately. Finally, the tranches files were used to apply the recalibration using the GATK ApplyVQSR (v4. 1. 4. 1). Further details are provided in Supplementary Methods. For the final dataset, similar QC filtering as for the UKB was applied, using VCFtools (v0. 1. 15), BCFtools (v1. 9), Picard (v2. 22. 2) and PLINK (v1. 90b). At the genotype level, SNPs were excluded with sequencing depth <7 or heterozygous allele balance <0.
15 or >0. 85. Indels were excluded with sequencing depth <10 or allele balance <0. 2 or >0. 8. On males' X chromosomes, depth filters were reduced to 5 for SNPs and 7 for indels. Samples with missing calls for >15% and variants with missing calls for >15% of samples were excluded. Variants with Hardy-Weinberg equilibrium P value 10 -15 were also removed. We also excluded samples where the genotypes were inconsistent with previous array genotyping or targeted sequencing data  1, 2. The BRIDGES study sequenced 6,912 samples, of which 3,461 cases and 3,200 controls remained in the final dataset after QC. The PERSPEC-TIVE study sequenced 10,523 samples, of which 4,777 cases and 5,210 remained in the final dataset.
Data preparation For both the UKB and BCAC datasets, Ensembl Variant Effect Predictor (VEP) v101. 0 was used to annotate variants  41. Annotations included the 1000 genomes phase 3 allele frequency, sequence ontology variant consequences and exon/intron number. For each gene, the MANE Select  42  transcript was used if it was available for that gene, or the RefSeq Select transcript  43. Annotation files were used to identify PTVs and rare (allele frequency <0. 001 in both the 1000 genomes dataset and the current dataset) missense variants. PTVs in the last exon of each gene and the last 50 bp of the penultimate exon were excluded as these are generally predicted to escape nonsense-mediated mRNA decay. VEP was also used to annotate missense variants by CADD score (v1. 6) (ref. 6 ). Here CADD ≥ 20 was used to define variants predicted to be deleterious.
The rationale for this weighting is that, for small effect sizes, the log-OR associated with a positive first-degree relative is approximately ½ that associated with the disease. (The approach of using family history as a surrogate was suggested in ref. 44. This method differs in that family history is included with a weight of ½ rather than 1, or using a 2-degree freedom test. ) For the BCAC dataset, we adjusted for country and library preparation method (BRIDGES versus PERSPECTIVE). Ancestry was not adjusted for as within each country ancestry was constant. For the UKB dataset, we adjusted for the first ten principal components and sex. For genes on chromosome X, only females were used in the analysis. When looking at case-control analyses for subtypes of the disease, for example ER status, in the BCAC dataset logistic regression was also used.
NUDT11 was excluded because missing genotypes (which were treated as noncarriers) led to spurious associations, although the variants passed QC filtering. AFF1 was also removed as the PTV frequency was high in PERSPECTIVE but rare in BRIDGES and UKB. This was likely due to a single PTV artifact within the PERSPECTIVE dataset. To combine the results from the BCAC and UKB datasets in a meta-analysis, the association tests for each gene were converted to z scores. The combined z score was defined as z M = ∑ j w j z j √ w 2 j. Here z M is the combined z score, z j is the z score for study j and w j is the weight associated with study j. A standard meta-analysis would define the weights w j using inverse variance or effective sample sizes. However, the effect sizes from the BCAC and UKB may not be comparable, because the BCAC studies oversampled for family history and early age at onset, which may have increased the estimated effect.
Therefore, we defined weights by using the associations in the known risk gene CHEK2 as a standard-we rationalized that the CHEK2 PTVs provided the best standard as the association is well-established and the OR is highly reproducible  1, 5, 45. Moreover, the OR (~2 to 3) was representative of the size of effects we hoped to detect for other genes. Thus, we defined (w 1 , w 2 ) = ( z 1 z 2 , 1), where z 1 z 2 is the ratio of z scores for CHEK2 for the BCAC dataset and UKB. The approach is equivalent to a meta-analysis of risk per unit dose in studies with different levels of exposure or dose (with dose here being the log-OR for CHEK2)  46, 47. As a sensitivity analysis, we also derived weightings based on a combined analysis of the five known genes ATM, BRCA1, BRCA2, CHEK2 and PALB2. This gives slightly more weight to UKB (BCAC: UKB 0.
Pathway analysis We performed GSEA  49  to evaluate the enrichment of genes in KEGG  50  , Reactome  51  and BioCarta 52 pathways in breast cancer risk using the R package clusterProfiler  53. Pathway lists were accessed using MSigDB  54, 55. We used z scores from the PTV meta-analysis to create an ordered gene list. We did this using all genes in each pathway and excluding BRCA1, BRCA2, CHEK2, PALB2 and ATM. Results were presented in terms of false discovery rates (q).
Acknowledgements Sequencing and analysis for this project were funded by the  European Union 's  Horizon 2020 Research and Innovation Program  (BRIDGES: grants  634935  to A. G. -N. , A. M. D. , A. K. , P. D. and D. F. E. ), the  PERSPECTIVE   I&I  project (funded by the  Government of Canada through Genome Canada  and the  Canadian Institutes of Health Research, the Ministère de l'Économie et de l'Innovation du Québec through Genome Québec , the  Quebec Breast Cancer Foundation ,  Agilent Technologies Canada and Illumina Canada Ulc  to J. S. ), the  Wellcome Trust  (grant  v203477/Z/16/Z  to S. H. T. and D. F. E. ) and the  Medical Research Council  (unit programs  MC_UU_12015/2  and  MC_UU_00006/2  to S. H. T. ). BCAC is funded by the  European Union   Horizon 2020 Research and Innovation Program  (grants  634935  for BRIDGES and 633784 for B-CAST to M.
Data availability Meta-analysis summary statistics are available from the GWAS Catalog (https://www. ebi. ac. uk/gwas/; https://ftp. ebi. ac. uk/pub/databases/ gwas/summary_statistics/), accession numbers GCST90267995, GCST90267996, GCST90267997 and GCST90267998. Summary statistics are provided for all ancestries combined as the sample size for non-European ancestry subjects is too small to provide meaningful statistics. Individual level data for the BCAC data are not publicly available due to ethical review board constraints but are available on request through the BCAC Data Access Co-ordinating Committee (BCAC@medschl. cam. ac. uk). Requests for access to UK Biobank data should be made to the UK Biobank Access Management Team (access@ ukbiobank. ac. uk).
Code availability Quality control filtering of VCF files was performed using VCFtools (v0. 1. 15), BCFtools (v1. 9), Picard (v2. 22. 2) and PLINK (v1. 90b), as outlined in the Methods. Variants were annotated using Ensembl Variant Effect Predictor v101 with assembly GRCh38. The code for each software is available on the website of each package. Data manipulation and analysis were performed using R-4. 13 with packages clusterProfiler (4. 2. 2), data. table (1. 14. 2), dplyr (1. 0. 9), gtools (3. 9. 2. 1), HGNChelper (0. 8. 9), msigdbr (7. 5. 1), tibble (3. 1. 7) and tidyr (1. 2. 0). Plots were created using R-4. 13 using additional packages ggplot2 (3. 3. 6) and ggrepel (0. 9. 1). The code for each of the R packages can be found in their associated vignettes.
The most famous and well-studied CRO is the galactose oxidase (GalOx) from the phytopathogen Fusarium graminearum (FgrGalOx), discovered in 1959  [5]  and structurally characterized in 1991 (PDB: 1GOF)  [6]. CROs are classified within the Auxiliary Activity family 5 (AA5) of the CAZy database (www. cazy. org/) [7] , itself divided into two subfamilies. The AA5_1 subfamily contains (methyl)-glyoxal oxidases (GlOx; EC1. 2. 3. 15) that oxidize several aldehydes and a-hydroxy carbonyl compounds generated during lignin and carbohydrates degradation  [8] [9] [10]. The AA5_2 subfamily historically contains GalOx enzymes (EC1. 1. 3. 9) [5, 11, 12]  and has more recently included raffinose oxidases (RafOx; EC1. 1. 3. -) [13] , as well as other enzymes with varying substrate specificities, i. e. alcohol oxidases (AlcOx, EC1.
First generation of CgrAlcOx loop variants To identify the main structural differences between AA5_2 enzymes, we aligned the sequences and overlayed the 3D structures of CgrAlcOx (PDB: 5C92) and FgrGalOx (PDB: 1GOF), two well-characterized enzymes displaying a high degree of conservation of their tertiary structure (r. m. s. d. 0. 98 A; Figs  2  and 3 ). As previously noticed  [14] , the first striking difference stands on the substitution of the tryptophan (W290) of FgrGalOx, constituting the second sphere of metal coordination, by phenylalanine (F138) in CgrAlcOx (Figs  1  and 2 ). Another striking difference between CgrAlcOx and FgrGalOx relies upon the amino acid composition of the four loops surrounding the catalytic pocket (i. e. Loop1, Loop2, Loop3 and Loop4; see Fig. 2 ). We therefore generated a first set of multiple mutants consisting in substituting the four loops of CgrAlcOx with the corresponding ones from FgrGa-lOx.