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      SETD2: from chromatin modifier to multipronged regulator of the genome and beyond

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          Abstract

          Histone modifying enzymes play critical roles in many key cellular processes and are appealing proteins for targeting by small molecules in disease. However, while the functions of histone modifying enzymes are often linked to epigenetic regulation of the genome, an emerging theme is that these enzymes often also act by non-catalytic and/or non-epigenetic mechanisms. SETD2 (Set2 in yeast) is best known for associating with the transcription machinery and methylating histone H3 on lysine 36 (H3K36) during transcription. This well-characterized molecular function of SETD2 plays a role in fine-tuning transcription, maintaining chromatin integrity, and mRNA processing. Here we give an overview of the various molecular functions and mechanisms of regulation of H3K36 methylation by Set2/SETD2. These fundamental insights are important to understand SETD2’s role in disease, most notably in cancer in which SETD2 is frequently inactivated. SETD2 also methylates non-histone substrates such as α-tubulin which may promote genome stability and contribute to the tumor-suppressor function of SETD2. Thus, to understand its role in disease, it is important to understand and dissect the multiple roles of SETD2 within the cell. In this review we discuss how histone methylation by Set2/SETD2 has led the way in connecting histone modifications in active regions of the genome to chromatin functions and how SETD2 is leading the way to showing that we also have to look beyond histones to truly understand the physiological role of an ‘epigenetic’ writer enzyme in normal cells and in disease.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

            Intratumor heterogeneity may foster tumor evolution and adaptation and hinder personalized-medicine strategies that depend on results from single tumor-biopsy samples. To examine intratumor heterogeneity, we performed exome sequencing, chromosome aberration analysis, and ploidy profiling on multiple spatially separated samples obtained from primary renal carcinomas and associated metastatic sites. We characterized the consequences of intratumor heterogeneity using immunohistochemical analysis, mutation functional analysis, and profiling of messenger RNA expression. Phylogenetic reconstruction revealed branched evolutionary tumor growth, with 63 to 69% of all somatic mutations not detectable across every tumor region. Intratumor heterogeneity was observed for a mutation within an autoinhibitory domain of the mammalian target of rapamycin (mTOR) kinase, correlating with S6 and 4EBP phosphorylation in vivo and constitutive activation of mTOR kinase activity in vitro. Mutational intratumor heterogeneity was seen for multiple tumor-suppressor genes converging on loss of function; SETD2, PTEN, and KDM5C underwent multiple distinct and spatially separated inactivating mutations within a single tumor, suggesting convergent phenotypic evolution. Gene-expression signatures of good and poor prognosis were detected in different regions of the same tumor. Allelic composition and ploidy profiling analysis revealed extensive intratumor heterogeneity, with 26 of 30 tumor samples from four tumors harboring divergent allelic-imbalance profiles and with ploidy heterogeneity in two of four tumors. Intratumor heterogeneity can lead to underestimation of the tumor genomics landscape portrayed from single tumor-biopsy samples and may present major challenges to personalized-medicine and biomarker development. Intratumor heterogeneity, associated with heterogeneous protein function, may foster tumor adaptation and therapeutic failure through Darwinian selection. (Funded by the Medical Research Council and others.).
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              DNA methylation and its basic function.

              In the mammalian genome, DNA methylation is an epigenetic mechanism involving the transfer of a methyl group onto the C5 position of the cytosine to form 5-methylcytosine. DNA methylation regulates gene expression by recruiting proteins involved in gene repression or by inhibiting the binding of transcription factor(s) to DNA. During development, the pattern of DNA methylation in the genome changes as a result of a dynamic process involving both de novo DNA methylation and demethylation. As a consequence, differentiated cells develop a stable and unique DNA methylation pattern that regulates tissue-specific gene transcription. In this chapter, we will review the process of DNA methylation and demethylation in the nervous system. We will describe the DNA (de)methylation machinery and its association with other epigenetic mechanisms such as histone modifications and noncoding RNAs. Intriguingly, postmitotic neurons still express DNA methyltransferases and components involved in DNA demethylation. Moreover, neuronal activity can modulate their pattern of DNA methylation in response to physiological and environmental stimuli. The precise regulation of DNA methylation is essential for normal cognitive function. Indeed, when DNA methylation is altered as a result of developmental mutations or environmental risk factors, such as drug exposure and neural injury, mental impairment is a common side effect. The investigation into DNA methylation continues to show a rich and complex picture about epigenetic gene regulation in the central nervous system and provides possible therapeutic targets for the treatment of neuropsychiatric disorders.
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                Author and article information

                Contributors
                fred.v.leeuwen@nki.nl
                Journal
                Cell Mol Life Sci
                Cell Mol Life Sci
                Cellular and Molecular Life Sciences
                Springer International Publishing (Cham )
                1420-682X
                1420-9071
                6 June 2022
                6 June 2022
                2022
                : 79
                : 6
                : 346
                Affiliations
                [1 ]GRID grid.430814.a, ISNI 0000 0001 0674 1393, Division of Gene Regulation, , Netherlands Cancer Institute, ; 1066CX Amsterdam, The Netherlands
                [2 ]GRID grid.7177.6, ISNI 0000000084992262, Department of Medical Biology, , Amsterdam UMC Location University of Amsterdam, ; Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
                Author information
                http://orcid.org/0000-0002-0061-3566
                http://orcid.org/0000-0002-7267-7251
                Article
                4352
                10.1007/s00018-022-04352-9
                9167812
                35661267
                2f07f025-a731-49e9-a2cf-2a89c7e126ff
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 February 2022
                : 9 April 2022
                : 5 May 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004622, KWF Kankerbestrijding;
                Award ID: KWF-NKI2018-1/11490
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003246, Nederlandse Organisatie voor Wetenschappelijk Onderzoek;
                Award ID: NWO-VICI-016.130.627
                Award Recipient :
                Categories
                Review
                Custom metadata
                © Springer Nature Switzerland AG 2022

                Molecular biology
                setd2,set2,h3k36 methylation,histone methyltransferase,chromatin,transcription,cancer
                Molecular biology
                setd2, set2, h3k36 methylation, histone methyltransferase, chromatin, transcription, cancer

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