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Tumor budding is a histopathological biomarker associated with metastases and adverse survival outcomes in colorectal carcinoma (CRC) patients. It is characterized by the presence of single tumor cells or small clusters of cells within the tumor or at the tumor-invasion front. In order to obtain a tumor budding score for a patient, the region with the highest tumor bud density must first be visually identified by a pathologist, after which buds will be counted in the chosen hotspot field. The automation of this process will expectedly increase efficiency and reproducibility. Here, we present a deep learning convolutional neural network model that automates the above procedure. For model training, we used a semi-supervised learning method, to maximize the detection performance despite the limited amount of labeled training data. The model was tested on an independent dataset in which human- and machine-selected hotspots were mapped in relation to each other and manual and machine detected tumor bud numbers in the manually selected fields were compared. We report the results of the proposed method in comparison with visual assessment by pathologists. We show that the automated tumor bud count achieves a prognostic value comparable with visual estimation, while based on an objective and reproducible quantification. We also explore novel metrics to quantify buds such as density and dispersion and report their prognostic value. We have made the model available for research use on the grand-challenge platform., Funding Agencies|European Union [825292]; Dutch Cancer Society [10602/2016-2]; Swiss Cancer Research Foundation [KFS-4427-02-2018]; Alpe dHuZes/Dutch Cancer Society Fund [KUN 2014-7032]

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Tumor budding is a histopathological biomarker associated with metastases and adverse survival outcomes in colorectal carcinoma (CRC) patients. It is characterized by the presence of single tumor cells or small clusters of cells within the tumor or at the tumor-invasion front. In order to obtain a tumor budding score for a patient, the region with the highest tumor bud density must first be visually identified by a pathologist, after which buds will be counted in the chosen hotspot field. The automation of this process will expectedly increase efficiency and reproducibility. Here, we present a deep learning convolutional neural network model that automates the above procedure. For model training, we used a semi-supervised learning method, to maximize the detection performance despite the limited amount of labeled training data. The model was tested on an independent dataset in which human- and machine-selected hotspots were mapped in relation to each other and manual and machine detected tumor bud numbers in the manually selected fields were compared. We report the results of the proposed method in comparison with visual assessment by pathologists. We show that the automated tumor bud count achieves a prognostic value comparable with visual estimation, while based on an objective and reproducible quantification. We also explore novel metrics to quantify buds such as density and dispersion and report their prognostic value. We have made the model available for research use on the grand-challenge platform., Funding Agencies|European Union [825292]; Dutch Cancer Society [10602/2016-2]; Swiss Cancer Research Foundation [KFS-4427-02-2018]; Alpe dHuZes/Dutch Cancer Society Fund [KUN 2014-7032]

Tumor budding is a histopathological biomarker associated with metastases and adverse survival outcomes in colorectal carcinoma (CRC) patients. It is characterized by the presence of single tumor cells or small clusters of cells within the tumor or at the tumor-invasion front. In order to obtain a tumor budding score for a patient, the region with the highest tumor bud density must first be visually identified by a pathologist, after which buds will be counted in the chosen hotspot field. The automation of this process will expectedly increase efficiency and reproducibility. Here, we present a deep learning convolutional neural network model that automates the above procedure. For model training, we used a semi-supervised learning method, to maximize the detection performance despite the limited amount of labeled training data. The model was tested on an independent dataset in which human- and machine-selected hotspots were mapped in relation to each other and manual and machine detected tumor bud numbers in the manually selected fields were compared. We report the results of the proposed method in comparison with visual assessment by pathologists. We show that the automated tumor bud count achieves a prognostic value comparable with visual estimation, while based on an objective and reproducible quantification. We also explore novel metrics to quantify buds such as density and dispersion and report their prognostic value. We have made the model available for research use on the grand-challenge platform., Funding Agencies|European Union [825292]; Dutch Cancer Society [10602/2016-2]; Swiss Cancer Research Foundation [KFS-4427-02-2018]; Alpe dHuZes/Dutch Cancer Society Fund [KUN 2014-7032]

Tumor budding (TB), the presence of single cells or small clusters of up to 4 tumor cells at the invasive front of colorectal cancer (CRC), is a proven risk factor for adverse outcomes. International definitions are necessary to reduce interobserver variability. According to the current international guidelines, hotspots at the invasive front should be counted in hematoxylin and eosin (H & E)-stained slides. This is time-consuming and prone to interobserver variability; therefore, there is a need for computer-aided diagnosis solutions. In this study, we report an artificial intelligence-based method for detecting TB in H & E-stained whole slide images. We propose a fully automated pipeline to identify the tumor border, detect tumor buds, characterize them based on the number of tumor cells, and produce a TB density map to identify the TB hotspot. The method outputs the TB count in the hotspot as a computational biomarker. We show that the proposed automated TB detection workflow performs on par with a panel of 5 pathologists at detecting tumor buds and that the hotspot-based TB count is an independent prognosticator in both the univariate and the multivariate analysis, validated on a cohort of n 1/4 981 patients with CRC. Computer-aided detection of tumor buds based on deep learning can perform on par with expert pathologists for the detection and quantification of tumor buds in H & E-stained CRC histopathology slides, strongly facilitating the introduction of budding as an independent prognosticator in clinical routine and clinical trials. & COPY; 2023 THE AUTHORS. Published by Elsevier Inc. on behalf of the United States & Canadian Academy of Pathology. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/)., Funding Agencies|European Union [825292]; Dutch Cancer Society [10602/2016-2]; Swiss Cancer Research Foundation [KFS-4427-02-2018]; Alpe dHuZes/Dutch Cancer Society Fund [KUN 2014-7032]

Tumor budding is a histopathological biomarker associated with metastases and adverse survival outcomes in colorectal carcinoma (CRC) patients. It is characterized by the presence of single tumor cells or small clusters of cells within the tumor or at the tumor-invasion front. In order to obtain a tumor budding score for a patient, the region with the highest tumor bud density must first be visually identified by a pathologist, after which buds will be counted in the chosen hotspot field. The automation of this process will expectedly increase efficiency and reproducibility. Here, we present a deep learning convolutional neural network model that automates the above procedure. For model training, we used a semi-supervised learning method, to maximize the detection performance despite the limited amount of labeled training data. The model was tested on an independent dataset in which human- and machine-selected hotspots were mapped in relation to each other and manual and machine detected tumor bud numbers in the manually selected fields were compared. We report the results of the proposed method in comparison with visual assessment by pathologists. We show that the automated tumor bud count achieves a prognostic value comparable with visual estimation, while based on an objective and reproducible quantification. We also explore novel metrics to quantify buds such as density and dispersion and report their prognostic value. We have made the model available for research use on the grand-challenge platform., Funding Agencies|European Union [825292]; Dutch Cancer Society [10602/2016-2]; Swiss Cancer Research Foundation [KFS-4427-02-2018]; Alpe dHuZes/Dutch Cancer Society Fund [KUN 2014-7032]

Tumor budding is a histopathological biomarker associated with metastases and adverse survival outcomes in colorectal carcinoma (CRC) patients. It is characterized by the presence of single tumor cells or small clusters of cells within the tumor or at the tumor-invasion front. In order to obtain a tumor budding score for a patient, the region with the highest tumor bud density must first be visually identified by a pathologist, after which buds will be counted in the chosen hotspot field. The automation of this process will expectedly increase efficiency and reproducibility. Here, we present a deep learning convolutional neural network model that automates the above procedure. For model training, we used a semi-supervised learning method, to maximize the detection performance despite the limited amount of labeled training data. The model was tested on an independent dataset in which human- and machine-selected hotspots were mapped in relation to each other and manual and machine detected tumor bud numbers in the manually selected fields were compared. We report the results of the proposed method in comparison with visual assessment by pathologists. We show that the automated tumor bud count achieves a prognostic value comparable with visual estimation, while based on an objective and reproducible quantification. We also explore novel metrics to quantify buds such as density and dispersion and report their prognostic value. We have made the model available for research use on the grand-challenge platform., Funding Agencies|European Union [825292]; Dutch Cancer Society [10602/2016-2]; Swiss Cancer Research Foundation [KFS-4427-02-2018]; Alpe dHuZes/Dutch Cancer Society Fund [KUN 2014-7032]

Prior colorectal neoplasia is the strongest predictor of pouch neoplasia in inflammatory bowel disease, but the underlying mechanism is unknown. We observed clonality between colorectal and pouch neoplasia in 30% of patients, indicating that most pouch neoplasia develops clonally independent from prior colorectal lesions.

Tumor budding (TB), the presence of single cells or small clusters of up to 4 tumor cells at the invasive front of colorectal cancer (CRC), is a proven risk factor for adverse outcomes. International definitions are necessary to reduce interobserver variability. According to the current international guidelines, hotspots at the invasive front should be counted in hematoxylin and eosin (H & E)-stained slides. This is time-consuming and prone to interobserver variability; therefore, there is a need for computer-aided diagnosis solutions. In this study, we report an artificial intelligence-based method for detecting TB in H & E-stained whole slide images. We propose a fully automated pipeline to identify the tumor border, detect tumor buds, characterize them based on the number of tumor cells, and produce a TB density map to identify the TB hotspot. The method outputs the TB count in the hotspot as a computational biomarker. We show that the proposed automated TB detection workflow performs on par with a panel of 5 pathologists at detecting tumor buds and that the hotspot-based TB count is an independent prognosticator in both the univariate and the multivariate analysis, validated on a cohort of n 1/4 981 patients with CRC. Computer-aided detection of tumor buds based on deep learning can perform on par with expert pathologists for the detection and quantification of tumor buds in H & E-stained CRC histopathology slides, strongly facilitating the introduction of budding as an independent prognosticator in clinical routine and clinical trials. & COPY; 2023 THE AUTHORS. Published by Elsevier Inc. on behalf of the United States & Canadian Academy of Pathology. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/)., Funding Agencies|European Union [825292]; Dutch Cancer Society [10602/2016-2]; Swiss Cancer Research Foundation [KFS-4427-02-2018]; Alpe dHuZes/Dutch Cancer Society Fund [KUN 2014-7032]

Both lymph node metastases (LNMs) and tumour deposits (TDs) are included in colorectal cancer (CRC) staging, although knowledge regarding their biological background is lacking. This study aimed to compare the biology of these prognostic features, which is essential for a better understanding of their role in CRC spread. Spatially resolved transcriptomic analysis using digital spatial profiling was performed on TDs and LNMs from 10 CRC patients using 1,388 RNA targets, for the tumour cells and tumour microenvironment. Shotgun proteomics identified 5,578 proteins in 12 different patients. Differences in RNA and protein expression were analysed, and spatial deconvolution was performed. Image-based consensus molecular subtype (imCMS) analysis was performed on all TDs and LNMs included in the study. Transcriptome and proteome profiles identified distinct clusters for TDs and LNMs in both the tumour and tumour microenvironment segment, with upregulation of matrix remodelling, cell adhesion/motility, and epithelial-mesenchymal transition (EMT) in TDs (all p < 0.05). Spatial deconvolution showed a significantly increased abundance of fibroblasts, macrophages, and regulatory T-cells (p < 0.05) in TDs. Consistent with a higher fibroblast and EMT component, imCMS classified 62% of TDs as poor prognosis subtype CMS4 compared to 36% of LNMs (p < 0.05). Compared to LNMs, TDs have a more invasive state involving a distinct tumour microenvironment and upregulation of EMT, which are reflected in a more frequent histological classification of TDs as CMS4. These results emphasise the heterogeneity of locoregional spread and the fact that TDs should merit more attention both in future research and during staging. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Tumor budding is a long-established independent adverse prognostic marker in colorectal cancer, yet methods for its assessment have varied widely. In an effort to standardize its reporting, a group of experts met in Bern, Switzerland, in 2016 to reach consensus on a single, international, evidence-based method for tumor budding assessment and reporting (International Tumor Budding Consensus Conference [ITBCC]). Tumor budding assessment using the ITBCC criteria has been validated in large cohorts of cancer patients and incorporated into several international colorectal cancer pathology and clinical guidelines. With the wider reporting of tumor budding, new issues have emerged that require further clarification. To better inform researchers and health-care professionals on these issues, an international group of experts in gastrointestinal pathology participated in a modified Delphi process to generate consensus and highlight areas requiring further research. This effort serves to re-affirm the importance of tumor budding in colorectal cancer and support its continued use in routine clinical practice., Funding Agencies|Dutch Cancer SocietyKWF Kankerbestrijding [10602/2016-2]

Tumor budding is a long-established independent adverse prognostic marker in colorectal cancer, yet methods for its assessment have varied widely. In an effort to standardize its reporting, a group of experts met in Bern, Switzerland, in 2016 to reach consensus on a single, international, evidence-based method for tumor budding assessment and reporting (International Tumor Budding Consensus Conference [ITBCC]). Tumor budding assessment using the ITBCC criteria has been validated in large cohorts of cancer patients and incorporated into several international colorectal cancer pathology and clinical guidelines. With the wider reporting of tumor budding, new issues have emerged that require further clarification. To better inform researchers and health-care professionals on these issues, an international group of experts in gastrointestinal pathology participated in a modified Delphi process to generate consensus and highlight areas requiring further research. This effort serves to re-affirm the importance of tumor budding in colorectal cancer and support its continued use in routine clinical practice., Funding Agencies|Dutch Cancer SocietyKWF Kankerbestrijding [10602/2016-2]

Tumor budding is a long-established independent adverse prognostic marker in colorectal cancer, yet methods for its assessment have varied widely. In an effort to standardize its reporting, a group of experts met in Bern, Switzerland, in 2016 to reach consensus on a single, international, evidence-based method for tumor budding assessment and reporting (International Tumor Budding Consensus Conference [ITBCC]). Tumor budding assessment using the ITBCC criteria has been validated in large cohorts of cancer patients and incorporated into several international colorectal cancer pathology and clinical guidelines. With the wider reporting of tumor budding, new issues have emerged that require further clarification. To better inform researchers and health-care professionals on these issues, an international group of experts in gastrointestinal pathology participated in a modified Delphi process to generate consensus and highlight areas requiring further research. This effort serves to re-affirm the importance of tumor budding in colorectal cancer and support its continued use in routine clinical practice., Funding Agencies|Dutch Cancer SocietyKWF Kankerbestrijding [10602/2016-2]

Tumor budding is a long-established independent adverse prognostic marker in colorectal cancer, yet methods for its assessment have varied widely. In an effort to standardize its reporting, a group of experts met in Bern, Switzerland, in 2016 to reach consensus on a single, international, evidence-based method for tumor budding assessment and reporting (International Tumor Budding Consensus Conference [ITBCC]). Tumor budding assessment using the ITBCC criteria has been validated in large cohorts of cancer patients and incorporated into several international colorectal cancer pathology and clinical guidelines. With the wider reporting of tumor budding, new issues have emerged that require further clarification. To better inform researchers and health-care professionals on these issues, an international group of experts in gastrointestinal pathology participated in a modified Delphi process to generate consensus and highlight areas requiring further research. This effort serves to re-affirm the importance of tumor budding in colorectal cancer and support its continued use in routine clinical practice.

Tumor budding is a long-established independent adverse prognostic marker in colorectal cancer, yet methods for its assessment have varied widely. In an effort to standardize its reporting, a group of experts met in Bern, Switzerland, in 2016 to reach consensus on a single, international, evidence-based method for tumor budding assessment and reporting (International Tumor Budding Consensus Conference [ITBCC]). Tumor budding assessment using the ITBCC criteria has been validated in large cohorts of cancer patients and incorporated into several international colorectal cancer pathology and clinical guidelines. With the wider reporting of tumor budding, new issues have emerged that require further clarification. To better inform researchers and health-care professionals on these issues, an international group of experts in gastrointestinal pathology participated in a modified Delphi process to generate consensus and highlight areas requiring further research. This effort serves to re-affirm the importance of tumor budding in colorectal cancer and support its continued use in routine clinical practice.

Neoadjuvant therapy before surgical resection is indicated for patients with locally advanced rectal cancer. However, a significant number of patients show minimal or no response to neoadjuvant therapy. Unfortunately, we are currently unable to predict response and identify non-responding patients before neoadjuvant treatment is given. Genomic mutational status might provide valuable prognostic information. However, it is unclear whether predictions based on genomic mutational status in single preoperative biopsies are reliable due to intra-tumoral heterogeneity. In this study we aim to investigate the reliability of genomic mutations found in single pre-operative biopsies by comparing genomic mutations to four other locations within the same tumor using next generation sequencing. Rectal cancer patients undergoing primary resection without neoadjuvant therapy were included. From each patient, one biopsy, two deep and two superficial samples were obtained and sequenced using a targeted next generation sequencing gene panel. Concordance between these five samples was assessed. In this feasibility study we included 11 patients. In 7 out of 11 (64%) patients, all 5 samples showed concordant mutations. In 4 out of 11 patients (36%) discordant mutations were observed. In conclusion, assessment of mutational status on a single pre-operative biopsy shows discordance with tumor tissue from other locations in 36% of cases. These results warrant careful interpretation of biopsy material analysis, as these might be influenced by tumor heterogeneity.

Tumor budding is a long-established independent adverse prognostic marker in colorectal cancer, yet methods for its assessment have varied widely. In an effort to standardize its reporting, a group of experts met in Bern, Switzerland, in 2016 to reach consensus on a single, international, evidence-based method for tumor budding assessment and reporting (International Tumor Budding Consensus Conference [ITBCC]). Tumor budding assessment using the ITBCC criteria has been validated in large cohorts of cancer patients and incorporated into several international colorectal cancer pathology and clinical guidelines. With the wider reporting of tumor budding, new issues have emerged that require further clarification. To better inform researchers and health-care professionals on these issues, an international group of experts in gastrointestinal pathology participated in a modified Delphi process to generate consensus and highlight areas requiring further research. This effort serves to re-affirm the importance of tumor budding in colorectal cancer and support its continued use in routine clinical practice., Funding Agencies|Dutch Cancer SocietyKWF Kankerbestrijding [10602/2016-2]

Tumor budding is a long-established independent adverse prognostic marker in colorectal cancer, yet methods for its assessment have varied widely. In an effort to standardize its reporting, a group of experts met in Bern, Switzerland, in 2016 to reach consensus on a single, international, evidence-based method for tumor budding assessment and reporting (International Tumor Budding Consensus Conference [ITBCC]). Tumor budding assessment using the ITBCC criteria has been validated in large cohorts of cancer patients and incorporated into several international colorectal cancer pathology and clinical guidelines. With the wider reporting of tumor budding, new issues have emerged that require further clarification. To better inform researchers and health-care professionals on these issues, an international group of experts in gastrointestinal pathology participated in a modified Delphi process to generate consensus and highlight areas requiring further research. This effort serves to re-affirm the importance of tumor budding in colorectal cancer and support its continued use in routine clinical practice., Funding Agencies|Dutch Cancer SocietyKWF Kankerbestrijding [10602/2016-2]

DNA methylation is a central epigenetic modification that is established by de novo DNA methyltransferases. The mechanisms underlying the generation of genomic methylation patterns are still poorly understood. Using mass spectrometry and a phosphospecific Dnmt3a antibody, we demonstrate that CK2 phosphorylates endogenous Dnmt3a at two key residues located near its PWWP domain, thereby downregulating the ability of Dnmt3a to methylate DNA. Genome-wide DNA methylation analysis shows that CK2 primarily modulates CpG methylation of several repeats, most notably of Alu SINEs. This modulation can be directly attributed to CK2-mediated phosphorylation of Dnmt3a. We also find that CK2-mediated phosphorylation is required for localization of Dnmt3a to heterochromatin. By revealing phosphorylation as a mode of regulation of de novo DNA methyltransferase function and by uncovering a mechanism for the regulation of methylation at repetitive elements, our results shed light on the origin of DNA methylation patterns., SCOPUS: ar.j, info:eu-repo/semantics/published

DNA methylation is a central epigenetic modification that is established by de novo DNA methyltransferases. The mechanisms underlying the generation of genomic methylation patterns are still poorly understood. Using mass spectrometry and a phosphospecific Dnmt3a antibody, we demonstrate that CK2 phosphorylates endogenous Dnmt3a at two key residues located near its PWWP domain, thereby downregulating the ability of Dnmt3a to methylate DNA. Genome-wide DNA methylation analysis shows that CK2 primarily modulates CpG methylation of several repeats, most notably of Alu SINEs. This modulation can be directly attributed to CK2-mediated phosphorylation of Dnmt3a. We also find that CK2-mediated phosphorylation is required for localization of Dnmt3a to heterochromatin. By revealing phosphorylation as a mode of regulation of de novo DNA methyltransferase function and by uncovering a mechanism for the regulation of methylation at repetitive elements, our results shed light on the origin of DNA methylation patterns., SCOPUS: ar.j, info:eu-repo/semantics/published

RNA interference (RNAi) is widespread in eukaryotes and regulates gene expression transcriptionally or post-transcriptionally. In fission yeast, RNAi is tightly coupled to template transcription and chromatin modifications that establish heterochromatin in cis. Exogenous double-stranded RNA (dsRNA) triggers seem to induce heterochromatin formation in trans only when certain silencing proteins are overexpressed. Here, we show that green fluorescent protein (GFP) hairpin dsRNA allows production of high levels of Argonaute-associated small interfering RNAs (siRNAs), which can induce heterochromatin formation at a remote locus. This silencing does not require any manipulation apart from hairpin expression. In cells expressing a ura4-GFP fusion gene, production of GFP siRNAs causes the appearance of ura4 siRNAs from the target gene. Production of these secondary siRNAs depends on RNA-dependent RNA polymerase Rdp1 (RDRP Rdp1) function and other RNAi pathway components. This demonstrates that transitivity occurs in fission yeast and implies that RDRP Rdp1 can synthesize RNA from targeted RNA templates in vivo, generating siRNAs not homologous to the hairpin. © 2010 EUROPEAN MOLECULAR BIOLOGY ORGANIZATION., SCOPUS: ar.j, FLWIN, info:eu-repo/semantics/published

RNA interference (RNAi) is widespread in eukaryotes and regulates gene expression transcriptionally or post-transcriptionally. In fission yeast, RNAi is tightly coupled to template transcription and chromatin modifications that establish heterochromatin in cis. Exogenous double-stranded RNA (dsRNA) triggers seem to induce heterochromatin formation in trans only when certain silencing proteins are overexpressed. Here, we show that green fluorescent protein (GFP) hairpin dsRNA allows production of high levels of Argonaute-associated small interfering RNAs (siRNAs), which can induce heterochromatin formation at a remote locus. This silencing does not require any manipulation apart from hairpin expression. In cells expressing a ura4-GFP fusion gene, production of GFP siRNAs causes the appearance of ura4 siRNAs from the target gene. Production of these secondary siRNAs depends on RNA-dependent RNA polymerase Rdp1 (RDRP Rdp1) function and other RNAi pathway components. This demonstrates that transitivity occurs in fission yeast and implies that RDRP Rdp1 can synthesize RNA from targeted RNA templates in vivo, generating siRNAs not homologous to the hairpin. © 2010 EUROPEAN MOLECULAR BIOLOGY ORGANIZATION., SCOPUS: ar.j, FLWIN, info:eu-repo/semantics/published

The formation of heterochromatin at the centromeres in fission yeast depends on transcription of the outer repeats. These transcripts are processed into siRNAs that target homologous loci for heterochromatin formation. Here, high throughput sequencing of small RNA provides a comprehensive analysis of centromere-derived small RNAs. We found that the centromeric small RNAs are Dcr1 dependent, carry 50-monophosphates and are associated with Ago1. The majority of centromeric small RNAs originate from two remarkably well-conserved sequences that are present in all centromeres. The high degree of similarity suggests that this non-coding sequence in itself may be of importance. Consistent with this, secondary structure-probing experiments indicate that this centromeric RNA is partially double-stranded and is processed by Dicer in vitro. We further demonstrate the existence of small centromeric RNA in rdp1D cells. Our data suggest a pathway for siRNA generation that is distinct from the well-documented model involving RITS/RDRC. We propose that primary transcripts fold into hairpin-like structures that may be processed by Dcr1 into siRNAs, and that these siRNAs may initiate heterochromatin formation independent of RDRC activity. The EMBO Journal (2009) 28, 3832-3844. doi: 10.1038/emboj.2009.351; Published online 26 November 2009

In the central domain of fission yeast centromeres, the kinetochore is assembled on CENP-A cnp1 nucleosomes. Normally, small interfering RNAs generated from flanking outer repeat transcripts direct histone H3 lysine 9 methyltransferase Clr4 to homologous loci to form heterochromatin. Outer repeats, RNA interference (RNAi), and centromeric heterochromatin are required to establish CENP-A Cnpl chromatin. We demonstrated that tethering Clr4 via DNA-binding sites at euchromatic loci induces heterochromatin assembly, with or without active RNAi. This synthetic heterochromatin completely substitutes for outer repeats on plasmid-based minichromosomes, promoting de novo CENP-A Cnpl and kinetochore assembly, to allow their mitotic segregation, even with RNAi inactive. Thus, the role of outer repeats in centromere establishment is simply the provision of RNAi substrates to direct heterochromatin formation; H3K9 methylation-dependent heterochromatin is alone sufficient to form functional centromeres., SCOPUS: ar.j, info:eu-repo/semantics/published

In the central domain of fission yeast centromeres, the kinetochore is assembled on CENP-A cnp1 nucleosomes. Normally, small interfering RNAs generated from flanking outer repeat transcripts direct histone H3 lysine 9 methyltransferase Clr4 to homologous loci to form heterochromatin. Outer repeats, RNA interference (RNAi), and centromeric heterochromatin are required to establish CENP-A Cnpl chromatin. We demonstrated that tethering Clr4 via DNA-binding sites at euchromatic loci induces heterochromatin assembly, with or without active RNAi. This synthetic heterochromatin completely substitutes for outer repeats on plasmid-based minichromosomes, promoting de novo CENP-A Cnpl and kinetochore assembly, to allow their mitotic segregation, even with RNAi inactive. Thus, the role of outer repeats in centromere establishment is simply the provision of RNAi substrates to direct heterochromatin formation; H3K9 methylation-dependent heterochromatin is alone sufficient to form functional centromeres., SCOPUS: ar.j, info:eu-repo/semantics/published

The formation of heterochromatin at the centromeres in fission yeast depends on transcription of the outer repeats. These transcripts are processed into siRNAs that target homologous loci for heterochromatin formation. Here, high throughput sequencing of small RNA provides a comprehensive analysis of centromere-derived small RNAs. We found that the centromeric small RNAs are Dcr1 dependent, carry 50-monophosphates and are associated with Ago1. The majority of centromeric small RNAs originate from two remarkably well-conserved sequences that are present in all centromeres. The high degree of similarity suggests that this non-coding sequence in itself may be of importance. Consistent with this, secondary structure-probing experiments indicate that this centromeric RNA is partially double-stranded and is processed by Dicer in vitro. We further demonstrate the existence of small centromeric RNA in rdp1D cells. Our data suggest a pathway for siRNA generation that is distinct from the well-documented model involving RITS/RDRC. We propose that primary transcripts fold into hairpin-like structures that may be processed by Dcr1 into siRNAs, and that these siRNAs may initiate heterochromatin formation independent of RDRC activity. The EMBO Journal (2009) 28, 3832-3844. doi: 10.1038/emboj.2009.351; Published online 26 November 2009, Som manuskript i avhandling. As manuscript in dissertation.