FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2991615522> ?p ?o ?g. }

• W2991615522 abstract "Article21 November 2019free access Human chromosome-specific aneuploidy is influenced by DNA-dependent centromeric features Marie Dumont Institut Curie, PSL Research University, CNRS, UMR144, Paris, France Search for more papers by this author Riccardo Gamba Institut Curie, PSL Research University, CNRS, UMR144, Paris, France Search for more papers by this author Pierre Gestraud Institut Curie, PSL Research University, CNRS, UMR144, Paris, France PSL Research University, Institut Curie Research Center, INSERM U900, Paris, France MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France Search for more papers by this author Sjoerd Klaasen Oncode Institute, Hubrecht Institute—KNAW (Royal Netherlands Academy of Arts and Sciences), Utrecht, The Netherlands Search for more papers by this author Joseph T Worrall Barts Cancer Institute, Queen Mary University of London, London, UK Search for more papers by this author Sippe G De Vries Oncode Institute, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands Search for more papers by this author Vincent Boudreau Department of Biology, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Catalina Salinas-Luypaert Institut Curie, PSL Research University, CNRS, UMR144, Paris, France Search for more papers by this author Paul S Maddox Department of Biology, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Susanne MA Lens Oncode Institute, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands Search for more papers by this author Geert JPL Kops orcid.org/0000-0003-3555-5295 Oncode Institute, Hubrecht Institute—KNAW (Royal Netherlands Academy of Arts and Sciences), Utrecht, The Netherlands Search for more papers by this author Sarah E McClelland Barts Cancer Institute, Queen Mary University of London, London, UK Search for more papers by this author Karen H Miga Center for Biomolecular Science & Engineering, University of California Santa Cruz, Santa Cruz, CA, USA Search for more papers by this author Daniele Fachinetti Corresponding Author [email protected] orcid.org/0000-0002-8795-6771 Institut Curie, PSL Research University, CNRS, UMR144, Paris, France Search for more papers by this author Marie Dumont Institut Curie, PSL Research University, CNRS, UMR144, Paris, France Search for more papers by this author Riccardo Gamba Institut Curie, PSL Research University, CNRS, UMR144, Paris, France Search for more papers by this author Pierre Gestraud Institut Curie, PSL Research University, CNRS, UMR144, Paris, France PSL Research University, Institut Curie Research Center, INSERM U900, Paris, France MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France Search for more papers by this author Sjoerd Klaasen Oncode Institute, Hubrecht Institute—KNAW (Royal Netherlands Academy of Arts and Sciences), Utrecht, The Netherlands Search for more papers by this author Joseph T Worrall Barts Cancer Institute, Queen Mary University of London, London, UK Search for more papers by this author Sippe G De Vries Oncode Institute, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands Search for more papers by this author Vincent Boudreau Department of Biology, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Catalina Salinas-Luypaert Institut Curie, PSL Research University, CNRS, UMR144, Paris, France Search for more papers by this author Paul S Maddox Department of Biology, University of North Carolina, Chapel Hill, NC, USA Search for more papers by this author Susanne MA Lens Oncode Institute, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands Search for more papers by this author Geert JPL Kops orcid.org/0000-0003-3555-5295 Oncode Institute, Hubrecht Institute—KNAW (Royal Netherlands Academy of Arts and Sciences), Utrecht, The Netherlands Search for more papers by this author Sarah E McClelland Barts Cancer Institute, Queen Mary University of London, London, UK Search for more papers by this author Karen H Miga Center for Biomolecular Science & Engineering, University of California Santa Cruz, Santa Cruz, CA, USA Search for more papers by this author Daniele Fachinetti Corresponding Author [email protected] orcid.org/0000-0002-8795-6771 Institut Curie, PSL Research University, CNRS, UMR144, Paris, France Search for more papers by this author Author Information Marie Dumont1,‡, Riccardo Gamba1,‡, Pierre Gestraud1,2,3, Sjoerd Klaasen4, Joseph T Worrall5, Sippe G De Vries6, Vincent Boudreau7, Catalina Salinas-Luypaert1, Paul S Maddox7, Susanne MA Lens6, Geert JPL Kops4, Sarah E McClelland5, Karen H Miga8 and Daniele Fachinetti *,1 1Institut Curie, PSL Research University, CNRS, UMR144, Paris, France 2PSL Research University, Institut Curie Research Center, INSERM U900, Paris, France 3MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France 4Oncode Institute, Hubrecht Institute—KNAW (Royal Netherlands Academy of Arts and Sciences), Utrecht, The Netherlands 5Barts Cancer Institute, Queen Mary University of London, London, UK 6Oncode Institute, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands 7Department of Biology, University of North Carolina, Chapel Hill, NC, USA 8Center for Biomolecular Science & Engineering, University of California Santa Cruz, Santa Cruz, CA, USA ‡These authors are contributed equally to this work *Corresponding author. Tel: +33 1 56246335: E-mail: [email protected] EMBO J (2020)39:e102924https://doi.org/10.15252/embj.2019102924 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Intrinsic genomic features of individual chromosomes can contribute to chromosome-specific aneuploidy. Centromeres are key elements for the maintenance of chromosome segregation fidelity via a specialized chromatin marked by CENP-A wrapped by repetitive DNA. These long stretches of repetitive DNA vary in length among human chromosomes. Using CENP-A genetic inactivation in human cells, we directly interrogate if differences in the centromere length reflect the heterogeneity of centromeric DNA-dependent features and whether this, in turn, affects the genesis of chromosome-specific aneuploidy. Using three distinct approaches, we show that mis-segregation rates vary among different chromosomes under conditions that compromise centromere function. Whole-genome sequencing and centromere mapping combined with cytogenetic analysis, small molecule inhibitors, and genetic manipulation revealed that inter-chromosomal heterogeneity of centromeric features, but not centromere length, influences chromosome segregation fidelity. We conclude that faithful chromosome segregation for most of human chromosomes is biased in favor of centromeres with high abundance of DNA-dependent centromeric components. These inter-chromosomal differences in centromere features can translate into non-random aneuploidy, a hallmark of cancer and genetic diseases. Synopsis Intrinsic genomic features of individual chromosomes can contribute to chromosome-specific aneuploidy. Here, DNA-dependent centromeric features rather than centromere length are found to correlate with varying missegregation rates within human cells. Chromosome-specific aneuploidy occurs in mitosis following centromere perturbations. Human centromeres are intrinsically heterogeneous at the level of centromeric DNA-dependent features. Heterogeneity of human centromeric DNA determines the chromosome segregation fidelity of individual human chromosomes during mitosis. Introduction Defects during cell division can lead to loss or gain of chromosomes in the daughter cells, a phenomenon called aneuploidy. This alters gene copy number and cell homeostasis, leading to genomic instability and pathological conditions including genetic diseases and various types of cancers (Gordon et al, 2012; Santaguida & Amon, 2015). While it is known that selection is a key process in maintaining aneuploidy in cancer, a preceding mis-segregation event is required. It was shown that chromosome-specific aneuploidy occurs under conditions that compromise genome stability, such as treatments with microtubule poisons (Caria et al, 1996; Worrall et al, 2018), heterochromatin hypomethylation (Fauth & Scherthan, 1998), or following ionizing radiation (Balajee et al, 2014). This suggests that certain human chromosomes are more prone to mis-segregate than others, indicating the existence of a heterogeneity between chromosomes that could be at the origin of chromosome-specific aneuploidy. Centromeres are key components in mediating equal distribution of genetic material. They are the chromosomal docking site for assembly of the kinetochore, the protein complex responsible for spindle attachment and chromosome separation during cell division. Centromere position is epigenetically defined by a specific chromatin enriched for the histone H3-variant CENtromere Protein A (CENP-A; Fukagawa & Earnshaw, 2014) via a two-step mechanism (Fachinetti et al, 2013). Centromeres are built on centromeric DNA repeats of 171 base pairs (bp), named alpha-satellites, that span several megabases (Miga, 2017). A fraction of these regions, called CENP-B boxes, are bound by CENP-B, the only DNA sequence-dependent centromeric binding protein identified so far (Muro et al, 1992). Differences in centromere features such as sequence variation (Alexandrov et al, 2001; Aldrup-MacDonald et al, 2016; Contreras-Galindo et al, 2017) and centromere length (Rudd & Willard, 2004; Contreras-Galindo et al, 2017; Dumont & Fachinetti, 2017) could modulate the abundance of centromeric and kinetochore components, as shown for CENP-A and Ndc80 (a subunit of the kinetochore; Irvine et al, 2004; Sullivan et al, 2011; Contreras-Galindo et al, 2017; Drpic et al, 2017), and, thus, have a direct impact on chromosome segregation fidelity. A direct correlation between centromere size and bias in chromosome segregation was demonstrated in mouse asymmetric female meiosis, a phenomenon defined as centromere drive (Henikoff & Malik, 2002). Here it was shown that, between two homologous chromosomes, the chromosome that carries a centromere with a higher amount of centromeric DNA sequences and centromere proteins (a concept globally defined as “centromere strength”) was preferentially retained in the egg during the first meiotic division (Chmátal et al, 2017; Iwata-Otsubo et al, 2017; Lampson & Black, 2017). This could explain part of the molecular mechanisms behind asymmetric division in female gametogenesis. However, if a similar phenomenon occurs also during mitotic division and between non-homologous chromosomes remains untested. We previously showed that CENP-B plays an important role in chromosome segregation by reinforcing centromere function through its interaction with CENP-C (Fachinetti et al, 2015; Hoffmann et al, 2016). CENP-C is a key component of human centromeres recruited by CENP-A (Guse et al, 2011; Hoffmann et al, 2016), and it is necessary to mediate the assembly of the kinetochore prior to mitosis (Fukagawa et al, 1999; Hoffmann et al, 2016; Weir et al, 2016). Interestingly, CENP-B was revealed to be present in varying amounts among different chromosomes (Earnshaw et al, 1989). However, if these different amounts mirror the number of CENP-B boxes within repetitive sequences is unknown. It is also noteworthy that CENP-B binding to DNA might be regulated by DNA methylation (Tanaka et al, 2005), and DNA methylation patterns might be different from centromere to centromere. It is unclear as to whether a correlation exists between centromere length, the number of CENP-B binding sites, and/or the amount of CENP-B molecules at each human centromere. Furthermore, if variation of centromeric DNA translates into differing levels of other centromeric and kinetochore proteins that directly impact on the fidelity of chromosome segregation remains untested. Here, we assessed the direct impact of centromeric DNA on providing strong connections between the chromosomes and the spindle microtubules and, consequently, on chromosome segregation fidelity. We show that in a non-transformed diploid cell line context, chromosome-specific aneuploidy occurs following centromere perturbations. We also show that human centromeres are intrinsically heterogeneous at the level of centromeric DNA and its binding components. Finally, we demonstrate that inter-chromosomal differences in centromeres directly translate into non-random aneuploidy during mitosis. Results Chromosome-specific aneuploidy occurs in centromere perturbation conditions We first measured human chromosome-specific aneuploidies in human female RPE-1 cells with endogenously tagged CENP-AAID/AID alleles (Hoffmann et al, 2016) as a model system. The use of this cell line provides several advantages. It is a non-transformed cell line, thus we can exclude confounding effects due to mutations in genes that regulate cell cycle and transcription such as oncogene overexpression and cell checkpoint mutations. Moreover, it does not harbor chromosome rearrangements (with the exception of one known translocation on chromosome X). Additionally, RPE-1 cells have a stable diploid karyotype with very low rates of spontaneous chromosome mis-segregation, allowing us to explicitly test chromosome-specific aneuploidy. To enhance the frequency of aneuploidy (necessary to generate enough data for statistical relevance) without perturbing mitosis with chemical inhibitors, we measured aneuploidy following removal of the epigenetic component of centromere function, CENP-A, as recently described (Hoffmann et al, 2016). This also gives us the advantage of directly assessing the impact of centromeric DNA/CENP-B on mediating chromosome segregation. Indeed, in CENP-A-deficient settings, centromere function and chromosome segregation fidelity depend mainly on CENP-B bound to centromeric DNA as the sole source of centromere/kinetochore interaction (Fachinetti et al, 2015; Hoffmann et al, 2016; Fig 1A). Auxin (IAA) addition leads to rapid, complete, and uniform removal of CENP-A molecules from all centromeres (Fig EV1A). Figure 1. Chromosome-specific aneuploidy arises following CENP-A removal in RPE-1 cells A. Model of centromere strength via CENP-C recruitment supported by DNA sequence and CENP-B. KT = kinetochore, MT = microtubules. IAA = auxin. Cen = centromere. Blue arrows represent CENP-B boxes. Upon IAA addition, AID-tagged CENP-A is degraded. B. Schematic of the experiments shown in (C–E). C–E. Logistic statistical model based on the (C) single-cell sequencing, (D) ImageStream (E) analysis or automated FISH of RPE-1 cells in untreated condition (blue circles) or treated with auxin for 48 h (red squares). Error bars represent the SEM based on the number of cells analyzed (see the statistical method section for details and Datasets EV1 and EV2). Dashed lines indicate the means of aneuploidy rates in untreated (blue line) or auxin-treated (red line) condition. Red asterisks (IAA) and blue (Untreated) indicate significance over the respective mean using a binomial test. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. F. Table summarizing whole-chromosome aneuploidy (fold over the mean) using the indicated methods to measure chromosome mis-segregation rate. Bold numbers represent a statistically significant difference from the mean for each method. Orange to dark red gradient highlights chromosomes that mis-segregate (with at least one method) at a significantly higher rate compared to the mean level (weak chromosomes). Light green to dark green gradient highlights chromosomes that mis-segregate (with at least one method) at a significantly lower rate compared to the mean (stronger chromosomes). Data obtained with only one method were excluded. *centromere 4 probe with image stream was reported to lead to non-specific signal (Worrall et al, 2018). Im. St. = image stream; Seq = single-cell sequencing. Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Chromosome aneuploidy profile of RPE-1 cells after 48 h of CENP-A depletion (related to Fig 1) A. (Left) Representative image of a mitotic chromosome spread stained with CENP-A and CENP-B at the indicated conditions. Scale bar represents 10 μm. (Right) Dot plot showing the mean level of CENP-A at the indicated conditions in different cells treated or not with auxin ± SEM. Each dot represents a centromere (n = 35). B–D. Single-cell sequencing analysis of the frequency of chromosome mis-segregation rates determined (C) in untreated cells (n = 6 cells that show at least one event of chromosome mis-segregation; total cells sequenced = 66), (D) after auxin treatment (n = 485 cells that show at least one event of chromosome mis-segregation; total cells sequenced = 811), (D) number of mis-segregated events per cells. Dashed lines in (B) and (C) show the expected 4.3% frequency of aneuploidy rate for a diploid chromosome. E. Representative images of ImageStream analyzed cells mono-, di-, or trisomic for chromosome 3 (green dots) labeled using a FISH centromeric probe. Scale bar represents 5 μm. F. ImageStream analysis of RPE-1 untreated (gray circles) or auxin-treated cells (48 h, blue circles) cells. Dots represent independent experiments (between ˜ 600 cells to > 10,000 cells for each experiment for every single chromosome). Dashed lines indicate the mean of aneuploidy rate (blue IAA-treated, gray untreated). G, H. Bar graph represents aneuploidy profile with gain and loss frequencies of the chromosomes analyzed by automatic FISH scanning in untreated (G) and auxin-treated (48 h) cells (H). Error bars represent the SEM of three to five independent experiments (between 600 cells and 2,400 cells for each experiment for every chromosome). Dashed lines indicate the mean of aneuploidy rate. Download figure Download PowerPoint Whole-chromosome mis-segregation was measured following IAA treatment for 48 h, corresponding to approximately two full cell cycles. Within this short time frame, CENP-A removal does not lead to cell death (Hoffmann et al, 2016), and therefore does not cause bias in the analysis due to loss of cells with a particular aneuploidy status. To measure aneuploidy, we used three different, complementary, and unbiased approaches (Fig 1B): (i) Single-cell sequencing (Figs 1C and EV1B–D); (ii) ImageStream cytometry to quantify fluorescence in situ hybridization (FISH)-marked individual centromeres in thousands of single cells on most human chromosomes (Figs 1D, and EV1E and F), as recently done (Worrall et al, 2018); and (iii) high-throughput traditional centromeric FISH analysis on selected chromosomes with an automated scanning microscope (Figs 1E, and EV1G and H). Results from the three approaches were largely consistent among each other in detecting chromosome-specific aneuploidy (with some exceptions mainly for the ImageStream data), particularly in identifying the chromosomes that show highest or lowest rates of aneuploidy. Altogether, our analysis of whole-chromosome aneuploidy combined with statistical modeling (see Statistical data in Dataset EV1) revealed that, following CENP-A depletion, specific chromosomes (mainly 3, 6, 16, and X) have a higher probability to mis-segregate, while some others (e.g., 1, 11, 12, 17, and 19) show very low rates of mis-segregation (Fig 1F and Dataset EV2). Although in the untreated condition we do not have sufficient aneuploidy events to draw strong conclusions, nonetheless our data indicate a similar trend (11 out of 18 chromosomes) of chromosome segregation fidelity as that of the CENP-A depleted cells (with chromosomes 3, 16, and X always mis-segregated in at least one method used to detect aneuploidy; Fig 1C–E). Under this condition of centromere inactivation through CENP-A depletion, mis-segregation mainly involved chromosomes that failed to align to the metaphase plate during the second mitosis after IAA addition and were encapsulated into micronuclei (MNs), as observed by following specific chromosomes segregating during mitosis in real time (Fig 2A–E, Movies EV1 and EV2) or on fixed samples (Fig 2F–H). It is important to note that, with all three methods, we analyzed only the main nuclei and not the MNs, therefore explaining why we could detect more chromosome losses rather than gains (Fig EV1B–H). Figure 2. Analysis of chromosome-specific mis-segregation following CENP-A removal A. Schematic of the experimental procedure used in (B–E). Closer view of the labeled chromosome shows the targeted locus for the dCas9. B, C. (B) Micronuclei frequency formation by live cell imaging in untreated (NT) and auxin-treated (IAA) condition. (C) Bar graph represents the type of chromosome mis-segregation (independently of the dCas9 signal) observed in the indicated conditions, with (IAA) or without (NT) auxin, in the cell lines expressing dCas9 mScarlet-I and sgRNA targeting chromosome 1 or 3. Error bars represent the SEM of four independent experiments in which cells labeled for chromosomes 1 and 3 were analyzed together (n = 45–149 cells). D. Representative live cell imaging of RPE-1 cells dCas9 mScarlet-I with sgRNA targeting chromosome 1 (red dots) starting from metaphase and showing example of correct (untreated) or mis-aligned chromosome (auxin) leading to the formation of a micronucleus. Yellow arrows mark mis-aligned- and micronucleus-containing chromosome 1. Cells were imaged every 5 min. The numbers indicate the time point at which the presented images were taken. Cells were fixed at the end of the movie to detect Cas9 with an antibody. Scale bar represents 10 μm. E. Bar graph representing the proportion of chromosome mis-segregation observed in both cell lines after auxin addition. Only movies in which the dCas9 mScarlet-I dots were clearly visible during the whole division were taken into consideration for the analysis (N = 6–10 cells). F. Representative images of mitotic errors leading to aneuploidy in fixed cells after CENP-A depletion (28 h auxin). Chromosomes are stained using whole (X) or centromeric (6) FISH probes. Yellow arrows mark lagging chromosomes. Scale bar represents 5 μm. G, H. Bars represent the (G) proportion of chromosome mis-segregation and (H) the type of the different chromosome mis-segregations observed. Error bars represent the SEM of two replicates. n > 66 mis-segregation events. Paired t-test lagging chromosomes versus mis-alignment chromosomes, *P = 0.0275. Download figure Download PowerPoint High-order repeats vary in abundance among human chromosomes We then investigated if variations in centromere strength—a measure of microtubule binding capacity determined by centromere length and protein composition —could explain the observed variability in chromosome-specific mis-segregation. We first measured the length of centromeric DNA [defined as the sum of the lengths of all alpha-satellite DNA organized into high-order repeat (HOR) arrays present at each centromere] and the abundance of CENP-B boxes in RPE-1 cells using whole-genome sequencing and mapping on centromere reference models (Fig 3A and Table EV1). Of the total reads obtained from whole-genome sequencing, 5% were derived from alpha-satellite DNA, as estimated using a comprehensive library of human alpha-satellite k-mers (Miga et al, 2014; Nechemia-Arbely et al, 2017; see Materials and Methods). Our mapping on centromere reference models allows us to retrieve 97% of these alpha-satellite containing reads, showing that there is no major loss of centromeric sequence information (Fig EV2A–C). Starting from these alignments, we reassigned mis-mapped reads following a pipeline that includes k-mers and FISH analysis to resolve possible ambiguity due to high sequence similarity between some of the centromeric alpha-satellite arrays (Fig EV2D–I and Materials and Methods). It should be noted that the value assigned to each centromere represents the average between the two homologous chromosomes, whose centromeres features cannot be differentially assessed by sequencing. Figure 3. Centromeres of individual chromosomes vary in DNA sequence and CENP-B boxes abundance A. Schematic of the experiments shown in (B–E). B, C. Bar plot showing the mean of (B) centromere length (n = 4) and (C) CENP-B boxes counts (n = 4) as determined by whole-genome sequencing. Error bars represent the SEM of four independent experiments. Acrocentric chromosomes 13, 14, 21, and 22 were marked by a line as we could not assign the respective reads. Dashed lines indicate the mean. Bars were labeled with asterisks according to the significance of their difference from the mean (t-test). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. Bars represent the sum of the length or counts of different HOR arrays (see Table EV2). CENP-B boxes counts are normalized to the average number of mapped reads in each replicate. D. Schematic using representative images of the mFISH labeling followed by CENP-B box FISH method used to identify and quantify centromere specific CENP-B boxes signal in (E). Scale bar represents 10 μm. E. Box and whisker plots of normalized CENP-B boxes intensity over the mean on metaphase spread from three independent experiments (n > 50 cells) using the Tukey plot. One-way ANOVA with post-hoc Tukey's multiple comparison test shows high diversity between chromosomes. t-Test against the mean was used to estimate the statistical significance for each chromosome. **P < 0.01; ***P < 0.001; ****P < 0.0001. F, G. Scatter plot showing a non-significant or significant negative correlation between the mean of (F) centromere length (n = 4) and mis-segregation rate (from Fig 1F) or between the mean of (G) centromere CENP-B boxes FISH signal (n > 50 cells) and mis-segregation rate, respectively (r = Spearman rank coefficient). Lines represent the linear regression with a 95% confidence interval. Data from chr 13, 14, 21, and 22 were excluded from the analysis. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Chromosome-specific centromere length analysis in human cells (related to Fig 3) A. Pie charts representing the fraction of alpha-satellite-containing reads of the total read pool (left) and representing the fraction of alpha-satellite-containing reads that can be mapped on the centromere reference models by using our method (right). Both charts refer to the whole-genome DNA sequencing of RPE-1 cells. B. Barplot showing the average GC percentage of the centromere reference models versus the GC percentage of the single-copy sequences that were used for centromere length determination with standard curve. C. Barplot reporting the GC content across all HOR array consensus sequences used as reference. D. Stepwise procedure of RPE-1 centromere length analysis. E. Example of standard curve used to convert whole-genome sequencing read counts into megabases (Mb), for the determination of centromere length. Each point represents a randomly chosen single-copy region of the genome. F. Centromere length after conversion to Mb using the standard curve shown in (E). The gray bars correspond to the length of centromere-specific HOR arrays; the light and dark blue bars represent the length of the HOR array shared by chr1, 5, 19 (“cen1_1”) and chr13, 14, 21, 22 (“cen13_1”, “cen22_1”), respectively. G. (Left) Centromere length correction after reassignment of the reads from cen16_1 (chr16 specific) to cen1_1 (shared among chr1, 5, 19). The arrow marks the chromosome that changed compared to (F). (Right) Representative plots of read coverage along the HOR sequence of “cen 8”, not showing any misalignment, and “cen16_1”, showing misalignment. H. Centromere length correction after redistribution of read counts between centromere 2 and centromere 18 following k-mer analysis. The arrows mark the chromosomes that changed compared to (G). I. Representative images of the sequential FISH using alpha-satellite cen1/5/19 FISH probe (which recognizes the cen1_1 D1Z1 HOR) followed by chromosome 1, 5, 19 FISH labeling. The scatter plot represents signal quantification of alpha-satellite cen1/5/19. Error bars represent the SEM of two independent experiments (n = 47 cells). Scale bar represents 5 μm. J, K. Scatter plot showing a significant positive correlation between the mean of (J) centromere length (n = 4) and CENP-B boxes count (n = 4) and (K) centromere CENP-B boxes FISH data (n > 50 cells) and CENP-B boxes count (r = Spearman rank coefficient). Data from chr 13, 14, 21 and 22 were excluded from the analysis. The lines represent linear regression with 95% confidence interval. Download figure Download PowerPoint We were thus able to generate a comprehensive analysis of centromere length and abundance of CENP-B boxes in the human model system RPE-1 for all chromosomes, except for the acrocentrics 13, 14, 21, and 22, because they mainly share the same HOR arrays and thus become unassignable (Fig 3B and C, and Tables EV2 and EV3). As somewhat expected, centromere length and CENP-B boxes abundance show a strong correlation profile (Fig EV2J). Sequencing data of CENP-B box a" @default.
• W2991615522 created "2019-12-05" @default.
• W2991615522 creator A5000661571 @default.
• W2991615522 creator A5003883516 @default.
• W2991615522 creator A5011346433 @default.
• W2991615522 creator A5011820963 @default.
• W2991615522 creator A5019420341 @default.
• W2991615522 creator A5020826234 @default.
• W2991615522 creator A5038546519 @default.
• W2991615522 creator A5041589194 @default.
• W2991615522 creator A5043804674 @default.
• W2991615522 creator A5050379587 @default.
• W2991615522 creator A5054315999 @default.
• W2991615522 creator A5056545178 @default.
• W2991615522 creator A5061916596 @default.
• W2991615522 creator A5072775026 @default.
• W2991615522 date "2019-11-21" @default.
• W2991615522 modified "2023-10-15" @default.
• W2991615522 title "Human chromosome‐specific aneuploidy is influenced by<scp>DNA</scp>‐dependent centromeric features" @default.
• W2991615522 cites W1558701786 @default.
• W2991615522 cites W1915055765 @default.
• W2991615522 cites W1917235046 @default.
• W2991615522 cites W1965707365 @default.
• W2991615522 cites W1966501744 @default.
• W2991615522 cites W1971165751 @default.
• W2991615522 cites W1975679429 @default.
• W2991615522 cites W1976537757 @default.
• W2991615522 cites W1978279171 @default.
• W2991615522 cites W1989364255 @default.
• W2991615522 cites W1999213586 @default.
• W2991615522 cites W2006981649 @default.
• W2991615522 cites W2010699331 @default.
• W2991615522 cites W2014270114 @default.
• W2991615522 cites W2014541905 @default.
• W2991615522 cites W2040290929 @default.
• W2991615522 cites W2041693122 @default.
• W2991615522 cites W2042967115 @default.
• W2991615522 cites W2043890495 @default.
• W2991615522 cites W2059003504 @default.
• W2991615522 cites W2062575193 @default.
• W2991615522 cites W2065825771 @default.
• W2991615522 cites W2072885333 @default.
• W2991615522 cites W2078545468 @default.
• W2991615522 cites W2093887949 @default.
• W2991615522 cites W2094746916 @default.
• W2991615522 cites W2095259929 @default.
• W2991615522 cites W2095617425 @default.
• W2991615522 cites W2096128575 @default.
• W2991615522 cites W2101066782 @default.
• W2991615522 cites W2103089522 @default.
• W2991615522 cites W2103441770 @default.
• W2991615522 cites W2108234281 @default.
• W2991615522 cites W2114025873 @default.
• W2991615522 cites W2125235548 @default.
• W2991615522 cites W2128711701 @default.
• W2991615522 cites W2132510972 @default.
• W2991615522 cites W2133800605 @default.
• W2991615522 cites W2136801770 @default.
• W2991615522 cites W2138830547 @default.
• W2991615522 cites W2140493627 @default.
• W2991615522 cites W2142642738 @default.
• W2991615522 cites W2146087376 @default.
• W2991615522 cites W2146859140 @default.
• W2991615522 cites W2154528541 @default.
• W2991615522 cites W2159079025 @default.
• W2991615522 cites W2167154024 @default.
• W2991615522 cites W2167279371 @default.
• W2991615522 cites W2321936919 @default.
• W2991615522 cites W2336531194 @default.
• W2991615522 cites W2396522587 @default.
• W2991615522 cites W2414771725 @default.
• W2991615522 cites W2480811611 @default.
• W2991615522 cites W2507133578 @default.
• W2991615522 cites W2511325670 @default.
• W2991615522 cites W2526262591 @default.
• W2991615522 cites W2552027352 @default.
• W2991615522 cites W2559865743 @default.
• W2991615522 cites W2562443513 @default.
• W2991615522 cites W2590824789 @default.
• W2991615522 cites W2605592309 @default.
• W2991615522 cites W2605722421 @default.
• W2991615522 cites W2621496209 @default.
• W2991615522 cites W2685525666 @default.
• W2991615522 cites W2741012700 @default.
• W2991615522 cites W2745812888 @default.
• W2991615522 cites W2749509661 @default.
• W2991615522 cites W2749518727 @default.
• W2991615522 cites W2769904872 @default.
• W2991615522 cites W2788306085 @default.
• W2991615522 cites W2792238040 @default.
• W2991615522 cites W2794258426 @default.
• W2991615522 cites W2803329414 @default.
• W2991615522 cites W2843925039 @default.
• W2991615522 cites W2905753952 @default.
• W2991615522 cites W2912065069 @default.
• W2991615522 cites W2942952616 @default.
• W2991615522 cites W2944727022 @default.
• W2991615522 cites W2948322052 @default.
• W2991615522 cites W2953263404 @default.
• W2991615522 cites W2954087010 @default.

• next