dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Topoisomerase VI is required for heterochromatin organization  Kirik et al. reported that interphase nuclei of the severe mid mutant in the BIN4/MID subunit presents smaller and less defined chromocenters (CCs) compared to the wild-type (wt), suggesting that heterochromatin organization is affected by the mid mutation  (39). However, this phenotype was not reported in the allelic bin4-1 mutant, which also has a severe phenotype  (38). Therefore, to unequivocally confirm the role of the Arabidopsis Topo VI complex in nuclear organization, we analyzed the nuclear phenotypes of hypomorphic and amorphic mutants of the AtTOP6A subunit, We then used the online positional gene enrichment tool  (48)  to investigate the genomic distribution of misregulated genes identified in our RNA-seq analysis of caa39 (Dataset S2).
This analysis revealed that the 500 most downregulated genes are strikingly over-represented in pericentromeric regions (PRs) and in the heterochromatic knob of chromosome 4 (hk4S, Fig  3A ). In these regions, 94% (181/193) of the non-TE genes that are differentially expressed in caa39 are downregulated (Dataset S2). In contrast, the 500 most highly upregulated genes displayed no preferential localization (SI Appendix, Fig  S4A ). To determine whether this effect is robust in other Topo VI mutant lines, we first examined the expression profiles of bin4-1 and a second allelic mutant, bin4-2, from microarray data that were generated during the initial characterization of these two allelic lines  (38). Despite the fact that bin4-1 and bin4-2 are knock-out mutants that have much more severe developmental defects than caa39, and although two different technical platforms have been used (RNA-seq for caa39 vs Affymetrix ATH1 microarrays for bin4-1 and bin4-2), we found a good correlation between the different transcriptomes (SI Appendix, Fig  S4B ).
In particular, 91% (72/79) of the PR genes that are repressed in caa39 and are detected in both RNA-seq and microarray experiments are also downregulated in bin4-1 or bin4-2 (Fig 3B, Dataset S2). We examined further the expression of seven pericentromeric genes distributed over the five chromosomes and strongly repressed in caa39, by RT-qPCR in caa39, bin4-1 as well as in rhl2-1 plants. These genes were found to be downregulated in all mutants, except for AT4G06634 and AT4G07390 that were not significantly repressed in rhl2-1 and bin4-1  (Fig 3C). This could possibly be due to secondary effects of the bin4-1 and rhl2-1 amorphic mutations as compared to the less severe caa39 mutation. In order to test this hypothesis, we took advantage of the availability of an Arabidopsis BIN4 co-suppressed transgenic line identified during the process of generating lines overexpressing BIN4-CFP.
4C ). Inspection of meta-profiles for each chromosome confirmed highly significant To further strengthen our analysis, we applied diffReps  (50)  on each replicate individually to confirm differential enrichment of H3K9me2 in EIs of caa39 and wt (Dataset S4). This very stringent and not very sensitive analysis (essentially due to the fact that H3K9me2 peaks were barely detectable in wt and that H3K9me2 spreading does not appear to be sequence-specific) could still reveal increased levels of H3K9me2 in some EIs of caa39, but not all (92. 0% in replicate 2, 73. 9% in replicate 3, 27. 4% in replicate 1; Dataset S3). The limited number of EIs identified this way in replicate 1 can be attributed to the fact that it was less deeply sequenced than replicates 2 and 3 (GSE103924). Despite that, there was a large overlap between replicates: among EIs that showed increased levels of H3K9me2 in caa39, only one was identified in replicate 1 but not in replicates 2 or 3, and 96.
The Topo VI subunit BIN4 physically associates with MAT3 To gain knowledge on the molecular mechanism by which Topo VI contributes to the delimitation of chromatin boundaries, we used the BIN4 subunit as a bait to screen a yeast two hybrid (Y2H) cDNA library (Hybrigenics). A strong interaction with the Topo VI subunit RHL1 was detected, thereby demonstrating the reliability of the screening procedure (Dataset S6). Among the eleven additional interacting partners, three proteins belong to the S-AdenosylMethionine (SAM) biosynthesis pathway, the universal methyl group donor  (52). The first one, 5-methylthioribose-1phosphate isomerase (MTI1, AT2G05830), is involved in the methionine salvage pathway whereas the two others, Methionine Synthase 1 (MS1, AT5G17920) and Methionine Adenosyltransferase 3 (MAT3, AT2G36880) are the ultimate enzymes of the SAM cycle.
In order to identify BIN4-interacting proteins in planta, we also performed a CoIP-MS experiment using the Arabidopsis mid-1 35S:BIN4/MID-YFP line, which consists of the mid-1 allelic mutant of BIN4 complemented with YFPtagged BIN4/MID  (39) , and a wt line as control. To exclude nonspecific proteins, we discarded proteins that were not detected in both BIN4/MID-YFP CoIP-MS replicates, as well as plastidial, mitochondrial and peroxisomal proteins. The presence of the RHL1 and TOP6B Topo VI subunits in BIN4/MID-YFP CoIPs validated our procedure (Dataset S7). Remarkably, MAT3 coimmunoprecipitated strongly with BIN4. MAT4 also co-immunoprecipitated with BIN4, but apparently to a much lesser extent (Dataset S7). We further investigated the genetic and biochemical interactions between BIN4 and enzymes of the SAM cycle, particularly the very last enzyme MAT3, using Bimolecular Fluorescence Complementation (BiFC).
5 kb flanking regions (Fig 3D). We identified 232 EIs containing 540 EI genes this way, among which 6 correspond to unsequenced gaps (https://jbrowse. arabidopsis. org/?data=Araport11) and were discarded in subsequent analyses (Dataset S3). Looking for EIs directly flanked by state 9 chromatin did not increase the number of EIs identified, showing that chromatin state 8 is always present in the proximal border (Fig 3D, SI Appendix, Fig S6C). In contrast, the number of detected EIs started to decrease to 215 when considering two-nucleosome-long flanking regions in state 8, suggesting that 11 EIs have only one proximal nucleosome in state 8 (SI Appendix, Fig S6C). With respect to state 9, the number of extracted islands began to drop from 4-nucleosome-long flanking regions, suggesting that the state 8 proximal border is always flanked by at least 3-nucleosome-long state 9 regions (SI Appendix, Fig S6C).
Firstly, because H3K9me2 and non-CG (CHG and CHH) DNA methylation are strongly inter-dependent (51), we tested whether H3K9me2 spreading into EIs might in turn, or reciprocally, affect DNA methylation. Whole-genome bisulfite sequencing revealed that DNA methylation levels in all sequence contexts were overall unaltered in caa39 relative to wt in EIs (Fig 4F and I, SI Appendix, Fig S8). Differentially methylated region (DMR) analyses confirmed that there was no global significant increase of DNA methylation in EIs (Dataset S5). Surprisingly, despite CHGs being known to be methylated through a feedback loop with H3K9me2, DMR analyses rather revealed a very slight decrease of CHG, with 51 hypo-CHG DMRs and 16 hyper-CHG DMRs observed in EIs of caa39 (with differences in the methylation percentage higher than 10%, P < 0.
High-throughput ChIP-seq was performed at the TGML platform, supported by grants from  Inserm ,  GIS IBiSA, Aix-Marseille Université , and  ANR-10-INBS-0009-10. LVM, FV, CV, LD, MR, and CB performed the experiments. CL and MT performed the bioinformatic analyses. LST was in charge of the sequencing. LVM, CL, FB and CL analyzed the data. LVM, KS, FB and CL designed the research. LVM, FB and CL wrote the manuscript. All authors read and approved the final manuscript.
Topo VI is required for the silencing of heterochromatic transposable elements A role for Arabidopsis Topo VI in heterochromatin-dependent transcriptional gene silencing may contribute to TE activation, although the precise causal heterochromatin spatial reorganization was highlighted by the reactivation of TRANSCRIPTIONALLY SILENT INFORMATION (TSI) in mid mutant mechanism is unknown. plants (39). However, reactivation was not observed in the bin4-1 allelic mutant (38). Therefore, to unambiguously assess the involvement of Topo VI in transcriptional silencing and get a more global understanding of Topo VI influence on TE repression, we performed a RNA-seq analysis of caa39 and wt transcripts. Multiple heterochromatic TEs (176 TEs with log2(FC) > 2), particularly from the LTR/Gypsy, LTR/Copia and En-Spm/CACTA superfamilies (47), are reactivated in caa39 plants (Fig 2A, Dataset S1).
Conversely, 91 TEs are repressed in caa39 (log2(FC) < -2); unlike reactivated TEs, these repressed TEs are rarely in the most inaccessible and repressive heterochromatin state 9 (SI Appendix, Fig S2A). To test for TE silencing defects in other Topo VI mutants, we selected several de- repressed heterochromatic TEs loci (Dataset S1) for which robust primer design was feasible, and measured their relative transcript abundance by RT-qPCR in rhl2-1 and bin4-1 mutants along with the caa39 and wt lines. A clear increase in TE transcript abundance was observed for all three tested Topo VI mutant lines (Fig 2B). Although we found no global decrease of H3K9me2 and 5-meC in caa39 (Fig 1B and 1D), more subtle local changes could account for TE reactivation. We first assessed DNA methylation levels at individual TEs in Topo VI mutants as compared to wt and ddm1-8 plants by digestion with the methylation-dependent restriction enzyme McrBC.
To find common genomic lesions on a focal and arm level, Infinium OmniExpress arrays were initially processed with Illumina Genome Studio v2. 0. 4. Following this, Log R Ratio (LRR) data was extracted for all probes and array annotation obtained from Illumina (InfiniumOmniExpress-24v1-3_A1). LRR data was then smoothed and segmentation called using the CBS algorithm from the DNACopy 54,55 v1. 60. 0 package in R. A minimum number of 5 probes was required to call a segment, and segments where analysed using GenomicRanges 56 v1. 38. 0. Definitions of amplification, gain, loss and deletion events where as outlined in Bashton, et al. 57. Segmentation data was then analysed in GISTIC 58 v2. 023. For PDX models, genomic DNA from sorted populations (LMPP: hCD45 + Lin -CD34 + CD38 - CD45RA + CD90 -and GMP: hCD45 + Lin -CD34 + CD38 + CD45RA + CD123 + ) was extracted using QIAamp DNA Mini Kit (Qiagen).
SNP-CGH array hybridization was performed using the Affymetrix Cytoscan® HD (Thermo Fisher Scientific) according to the manufacturer's recommendations. DNA amplification was checked using BioSpec-nano TM spectrophotometer (Shimadzu) with expected concentrations between 2,500 and 3,400ng/μL. DNA length distribution post-fragmentation was checked using D1000 ScreenTapes on Tapestation 4200 instrument (Agilent Technologies). Cytoscan HD array includes 2. 6 million markers combining SNP and non-polymorphic probes for copy number evaluation. Raw data CEL files were analysed using the Chromosome Analysis Suite software package (v4. 1, Affymetrix) with genome version GRCh37 (hg19) only if achieving the manufacturer's quality cut-offs. Only CNAs > 10kb were reported in the analysis presented in Extended Data Fig.
Data pre-processing For each cell, the FASTQ file containing both targeted gDNA and cDNA-derived sequencing reads was aligned to the human reference genome (GRCh37/hg19) using Burrow-Wheeler Aligner (BWA v0. 7. 17)  31  and STAR (v2. 6. 1d)  60. Custom perl scripts were used to demultiplex the gDNA and mRNA reads in the BAM file into separate SAM files based on targeted-sequencing primer coordinates (https://github. com/albarmeira/TARGET-seq). Next, Samtools (v1. 9) 61 was used to concatenate the BAM header to the resulting SAM files before re-converting the SAM file to BAM format, which was subsequently sorted by genomic coordinates and indexed. Both gDNA and mRNA reads were tagged with the cell's unique identifier using Picard (v2. 3. 0) "AddOrReplaceReadGroups" and duplicate reads were subsequently marked using Picard "MarkDuplicates".
Then, SCITE was used (git revision 2016b31, downloaded from https://github. com/cbgethz/SCITE. git 66 ) to sample 1000 mutation trees from the posterior for every single-cell genotype matrix corresponding to a particular patient, where all possible mutation trees are equally likely a priori. For patients in which several disease timepoints were available, all timepoints were merged for SCITE analysis. As parameters for every SCITE run "-fd 0. 01" (corresponding to the allelic dropout rate of reference allele in our adapted SCITE model), "-ad 0. 01" (corresponding to the allelic dropout of the alternate allele), a chain length (-l) of 1e6, and a thinning interval of 1 while marginalizing out cell attachments (-p 1 -s) were used. To summarize the posterior tree sample distribution, the number of times a particular sample matched each tree was computed.
Computational validation of TP53 biallelic status from single-cell targeted genotyping datasets To further validate the biallelic status of TP53 mutations in our dataset, the patterns of allelic dropout in TARGET-seq single-cell genotyping data from patient carrying at least 2 different TP53 mutations were investigated (n=6; Extended Data Fig. 1j ). To test the hypothesis that the observed TP53-WT/TP53-homozygous (TP53-WT/HOM; or (0,2)) cells are the result of a chromosomal loss (and therefore, in different alleles), the following null hypothesis (H0) was formulated: observed TP53-WT/HOM cells are double allelic dropout events. Under H0, every TP53-WT/HOM cell (0,2), TP53-HOM/WT cell (2,0), TP53-HOM/HOM (2,2) as well as an unknown number of TP53-WT/WT (0,0) are the result of a TP53-HET/HET (1,1) cell undergoing allelic dropout (ADO) at both sites.
Single cell 3'-biased RNA-sequencing data pre-processing FASTQ files for each single cell were generated using bcl2fastq (version 2. 20) with default parameters and the following read configuration: Y8N*, I8, Y63N*. Read 1 corresponds to a cell-specific barcode, index read correspond to an i7 index sequence from each cDNA pool, and read 2 corresponds to the cDNA molecule. PolyA tails were trimmed from demultiplexed FASTQ files with TrimGalore (version 0. 4. 1). Reads were then aligned to the human genome (hg19) using STAR (version 2. 4. 2a) and counts for each gene were obtained with FeatureCounts (version 1. 4. 5-p1; options --primary). Counts were then normalized by dividing each gene count by the total library size of each cell and multiplying this value by the median library size of all cells processed, as implemented in the "normalize_UMIs" function from the SingCellaR package 67 (https://github.
com/supattlab/SingCellaR). A summary of the pre-processing pipeline can be found in https://github. com/albarmeira/TARGET-seq-WTA. Quality control was performed using the following parameters: number of genes detected>500, percentage of ERCC derived reads<35%, percentage of mitochondrial reads<0. 25%, percentage of unmapped reads<75%. Cells with less than 2000 reads in batch1, 5000 reads in batch2 and 10000 reads in batch3 were further excluded. This QC step was performed independently for each sequencing batch owing to differences in sequencing depth (mean library size: 42949 batch 1, 93580 batch 2 and 173145 batch3). After these QC steps, 7200 cells passed QC for batch1, 5838 for batch2 and 6490 for batch 3 (78. 5%, 75. 0% and 82. 4% of cells processed, respectively). Then, 2733 cells from a previously published study  16  corresponding to 8 myelofibrosis patients and 2 normal donor controls were further integrated, encompassing a final dataset of 22261 cells in total.
Identification of highly variable genes Highly variable genes above technical noise were identified by fitting a gamma generalized linear model (GLM) model of the log2(mean expression level) and coefficient of variation for each gene, using the "get_variable_genes_by_fitting_GLM_model" from SingCellaR package and the following options: mean_expr_cutoff = 1, disp_zscore_cutoff = 0. 1, quantile_genes_expr_for_fitting = 0. 6, quantile_genes_cv2_for_fitting = 0. 2. Those genes with a coefficient of variation above the fitted model and expression cut-off were selected for further analysis, excluding those annotated as ribosomal or mitochondrial genes.
CNA inference from single cell transcriptomes InferCNV was used to identify CNAs in single-cell transcriptomes  68  (https://github. com/broadinstitute/inferCNV/wiki). Briefly, inferCNV creates genomic bins from gene expression matrices and computes the average level of expression for each of these bins. The expression across each bin is then compared to a set of normal control cells, and CNAs are predicted using a hidden markov model. For each patient, inferCNV was performed with the following parameters: "cutoff=0. 1, denoise=T, HMM=T", compared to the same set of normal donor control cells (n=992). To identify CNA subclones, inferCNV in analysis_mode='subclusters' was used. CNAs identified by inferCNV were manually curated by removing those with size<10kb, merging adjacent CNA calls with identical CNA status into larger CNA intervals and comparing them to SNP-Array bulk CNA calls.
Dimensionality reduction, data integration and clustering PCA was performed using "runPCA" function from the SingCellaR R package, and Forcedirected graph analysis was subsequently performed using the "runFA2_ForceDirectedGraph" with the top 30 PCA dimensions and the following options: n. neighbors=5, fa2_n_iter=1000 to generate the plots in Extended Data Fig. 4a. For the analysis of patient IF0131 presented in Extended Data Fig. 3m , PCA was performed using "runPCA" function from the SingCellaR R package and then UMAP was performed using the "runUMAP" function with the top 10 PCA dimensions and the following options: n. neighbors=20, uwot. metric = "correlation", uwot. min. dist=0. 30, n. seed = 1. Integration of TARGET-seq single-cell transcriptomes from 10538 cells corresponding to 14 TP53-sAML samples was performed using "runHarmony" function implemented in the SingCellaR package, using the patient ID as covariate and the following options: n.
Pseudotime trajectory analysis Monocle3 69 (https://cole-trapnell-lab. github. io/monocle3/) was used to infer differentiation trajectories from single cell transcriptomes. Raw UMI count matrix and clustering annotations were extracted from the SingCellaR object to build a Monocle3 'cds' object. 'learn_graph' function was then used calculate the trajectory, using TP53-WT preleukemic cell cluster as the root node. Pseudotime was calculated using 'order_cells' function and overlayed on the diffusion map embeddings to generate the plot in Fig. 2b.
Differential expression analysis Differentially expressed genes from TARGET-seq datasets were identified using a combination of non-parametric Wilcoxon test, to compare the expression values for each group, and Fisher's exact test, to compare the frequency of expression for each group, as previously described  17. Logged normalized counts were used as input for this comparison, including genes expressed in at least 2 cells. Combined p-values were calculated using were considered. Violin plots (Extended Data Fig. 9l ,m) from selected differentially expressed genes were generated using "ggplot2" package in R.
Gene-Set Enrichment analysis For analysis involving <500 cells per group (Fig. 4c , TableS5) GSEA was performed using GSEA software (https:/www. gsea-msigdb. org/gsea/index. jsp) with default parameters and 1000 permutations on the phenotype, using log2(normalized counts). For analysis involving >500 cells per group (Fig. 3k  and Extended Data Fig. 4c ), GSEA was performed with "identifyGSEAPrerankedGene" function from SingCellaR R package with default options. Briefly, differential expression analysis was performed between two cell populations using Wilcoxon rank sum test and the resulting p-values were adjusted for multiple testing using the Benjamini-Hochberg approach. Prior to the differential expression analysis, down-sampling was performed so that both cell populations had the same number of cells.
Next, -log10(p-value) transformation was performed and the resulting p-values were multiplied by +1 or -1 if the corresponding log2FC was>0. 1 or <-0. 1, respectively. The genelist was ranked using this statistic in ascending order and used as input for GSEA analysis using "fgsea" function from the fgsea R package with default options. MSigDB HALLMARK v7. 4 50-gene sets or previously published signatures (https://www. gseamsigdb. org/gsea/msigdb/cards/GENTLES_LEUKEMIC_STEM_CELL_UP) were used for all analysis. Normalised enrichment scores (NES) were displayed in a heatmap using pheatmap R package. Gene sets with False Discovery Rate (FDR) q-value lower than 0. 25 were considered significant.
Projection of single cell transcriptomes A previously published human haematopoietic atlas was downloaded from https://github. com/GreenleafLab/MPAL-Single-Cell-2019 and used as a normal haematopoietic reference to project TP53-sAML and de novo AML transcriptions using Latent Semantic Index Projection (LSI)  70. Common genes to all datasets were selected and then, TP53-sAML or previously published de novo AML cells 25 were projected using "projectLSI' function for the analysis presented in Fig. 2c, d. A previously published human myelofibrosis atlas  71  was used as a reference to project TP53-sAML multi-hit cells in the analysis presented in Extended Data Fig. 5a ,b, using previously defined force-directed graph embeddings.
Velocyto analysis Loom files were generated for each single cell using velocyto (v0. 17. 13) with options -c and -U, to indicate that each BAM represents an independent cell and reads are counted instead of molecules (UMIs), respectively  72. The individual loom files were subsequently merged using the combine function from the loompy python module. Healthy donors with at least 300 cells with RNA-sequencing data and patients with at least 300 cells consisting of >50 preleukemic (TP53 wildtype) cells and > 50 TP53 multi-hit cells were included for analysis. For each individual, Seurat object was created from the merged loom file and processed for downstream RNA-velocity analysis  73. Specifically, for each patient, the spliced RNA counts were normalised using regularised negative binomial regression with the SCTransform function  74.
Next, linear dimension reduction was performed using RunPCA function and the first 30 principal components were further used to perform non-linear dimension reduction using the RunUMAP function. Ninety-six multiple rate kinetics (MURK) genes previously shown to possess coordinated stepchange in transcription and hence violate the assumptions behind scVelo were removed  75. The processed and MURK gene-filtered Seurat object was then saved as h5Seurat format using the SaveH5Seurat function and finally converted to h5ad format using the Convert function. AnnData object was created from the h5ad file using the scvelo python module for RNA velocity analysis  76. Highly variable genes were identified and the corresponding spliced and unspliced RNA counts were normalized and log2-transformed using the scvelo.
Data retrieval and pre-processing Two publicly available AML cohorts with genetic mutation and RNA-sequencing data available were used to validate findings from our single-cell analysis, namely BeatAML  26  and The Cancer Genome Atlas (TCGA)  27. Gene expression values in FPKM (fragments per kilobase of transcript per million mapped reads) were retrieved from the National Cancer Institute (NIH) Genomic Data Commons (GDC)  77. Gene expression values were then offset by 1 and log2-transformed. TP53 point mutation status was retrieved from the cBio Cancer Genomics Portal (cBioPortal)  78. Clinical data including survival data for BeatAML and TCGA was retrieved from the BeatAML data viewer (Vizome) and NIH GDC, respectively. We selected samples from the BeatAML cohort with an AML diagnosis (540 de novo AML and 96 secondary AML) collected within 1 month of the patient's enrolment in the study, and with both TP53 mutation status and RNA-sequencing data available.