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The incorporation loci of H3.3K36M determine its preferential prevalence in chondroblastomas.

ABSTRACT: The histone H3.3K36M mutation, identified in over 90% of chondroblastoma cases, reprograms the H3K36 methylation landscape and gene expression to promote tumorigenesis. However, it's still unclear how the H3K36M mutation preferentially occurs in the histone H3 variant H3.3 in chondroblastomas. Here, we report that H3.3K36M-, but not H3.1K36M-, mutant cells showed increased colony formation ability and differentiation defects. H3K36 methylations and enhancers were reprogrammed to different status in H3.3K36M- and H3.1K36M-mutant cells. The reprogramming of H3K36 methylation and enhancers was depended on the specific loci at which H3.3K36M and H3.1K36M were incorporated. Moreover, targeting H3K36M-mutant proteins to the chromatin inhibited the H3K36 methylation locally. Taken together, these results highlight the roles of the chromatic localization of H3.3K36M-mutant protein in the reprogramming of the epigenome and the subsequent induction of tumorigenesis, and shed light on the molecular mechanisms by which the H3K36M mutation mainly occurs in histone H3.3 in chondroblastomas.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC7991640 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background & aimsRisk for colorectal cancer (CRC) can be greatly reduced through screening. To aid in the development of screening strategies, we refined models designed to determine risk of CRC by incorporating information from common genetic susceptibility loci.MethodsBy using data collected from more than 12,000 participants in 6 studies performed from 1990 through 2011 in the United States and Germany, we developed risk determination models based on sex, age, family history, genetic risk score (number of risk alleles carried at 27 validated common CRC susceptibility loci), and history of endoscopic examinations. The model was validated using data collected from approximately 1800 participants in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, conducted from 1993 through 2001 in the United States.ResultsWe identified a CRC genetic risk score that independently predicted which patients in the training set would develop CRC. Compared with determination of risk based only on family history, adding the genetic risk score increased the discriminatory accuracy from 0.51 to 0.59 (P = .0028) for men and from 0.52 to 0.56 (P = .14) for women. We calculated age- and sex-specific 10-year CRC absolute risk estimates based on the number of risk alleles, family history, and history of endoscopic examinations. A model that included a genetic risk score better determined the recommended starting age for screening in subjects with and without family histories of CRC. The starting age for high-risk men (family history of CRC and genetic risk score, 90%) was 42 years, and for low-risk men (no family history of CRC and genetic risk score, 10%) was 52 years. For men with no family history and a high genetic risk score (90%), the starting age would be 47 years; this is an intermediate value that is 5 years earlier than it would be for men with a genetic risk score of 10%. Similar trends were observed in women.ConclusionsBy incorporating information on CRC risk alleles, we created a model to determine the risk for CRC more accurately. This model might be used to develop screening and prevention strategies.

Project description:MotivationThe discovery of CRISPR-Cas systems almost 20 years ago rapidly changed our perception of the bacterial and archaeal immune systems. CRISPR loci consist of several repetitive DNA sequences called repeats, inter-spaced by stretches of variable length sequences called spacers. This CRISPR array is transcribed and processed into multiple mature RNA species (crRNAs). A single crRNA is integrated into an interference complex, together with CRISPR-associated (Cas) proteins, to bind and degrade invading nucleic acids. Although existing bioinformatics tools can recognize CRISPR loci by their characteristic repeat-spacer architecture, they generally output CRISPR arrays of ambiguous orientation and thus do not determine the strand from which crRNAs are processed. Knowledge of the correct orientation is crucial for many tasks, including the classification of CRISPR conservation, the detection of leader regions, the identification of target sites (protospacers) on invading genetic elements and the characterization of protospacer-adjacent motifs.ResultsWe present a fast and accurate tool to determine the crRNA-encoding strand at CRISPR loci by predicting the correct orientation of repeats based on an advanced machine learning approach. Both the repeat sequence and mutation information were encoded and processed by an efficient graph kernel to learn higher-order correlations. The model was trained and tested on curated data comprising >4500 CRISPRs and yielded a remarkable performance of 0.95 AUC ROC (area under the curve of the receiver operator characteristic). In addition, we show that accurate orientation information greatly improved detection of conserved repeat sequence families and structure motifs. We integrated CRISPRstrand predictions into our CRISPRmap web server of CRISPR conservation and updated the latter to version 2.0.AvailabilityCRISPRmap and CRISPRstrand are available at http://rna.informatik.uni-freiburg.de/CRISPRmap.Supplementary informationSupplementary data are available at Bioinformatics online.

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The incorporation loci of H3.3K36M determine its preferential prevalence in chondroblastomas.

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