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      The SAGA acetyltransferase module is required for the maintenance of MAF and MYC oncogenic gene expression programs in multiple myeloma

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          Abstract

          Despite recent advances in therapeutic treatments, multiple myeloma (MM) remains an incurable malignancy. Epigenetic factors contribute to the initiation, progression, relapse, and clonal heterogeneity in MM, but our knowledge on epigenetic mechanisms underlying MM development is far from complete. The SAGA complex serves as a coactivator in transcription and catalyzes acetylation and deubiquitylation. Analyses of datasets in the Cancer Dependency Map Project revealed many SAGA components are selective dependencies in MM. To define SAGA-specific functions, we focused on ADA2B, the only subunit in the lysine acetyltransferase (KAT) module that specifically functions in SAGA. Integration of RNA-seq, ATAC-seq, and CUT&RUN results identified pathways directly regulated by ADA2B include MTORC1 signaling, MYC, E2F, and MM-specific MAF oncogenic programs. We discovered that ADA2B is recruited to MAF and MYC gene targets, and that MAF shares a majority of its targets with MYC in MM cells. Furthermore, we found the SANT domain of ADA2B is required for interaction with both GCN5 and PCAF acetyltransferases, incorporation into SAGA, and ADA2B protein stability. Our findings uncover previously unknown SAGA KAT module-dependent mechanisms controlling MM cell growth, revealing a vulnerability that might be exploited for future development of MM therapy.

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          Most cited references75

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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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            AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

            The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk ) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.
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              The molecular classification of multiple myeloma.

              To better define the molecular basis of multiple myeloma (MM), we performed unsupervised hierarchic clustering of mRNA expression profiles in CD138-enriched plasma cells from 414 newly diagnosed patients who went on to receive high-dose therapy and tandem stem cell transplants. Seven disease subtypes were validated that were strongly influenced by known genetic lesions, such as c-MAF- and MAFB-, CCND1- and CCND3-, and MMSET-activating translocations and hyperdiploidy. Indicative of the deregulation of common pathways by gene orthologs, common gene signatures were observed in cases with c-MAF and MAFB activation and CCND1 and CCND3 activation, the latter consisting of 2 subgroups, one characterized by expression of the early B-cell markers CD20 and PAX5. A low incidence of focal bone disease distinguished one and increased expression of proliferation-associated genes of another novel subgroup. Comprising varying fractions of each of the other 6 subgroups, the proliferation subgroup dominated at relapse, suggesting that this signature is linked to disease progression. Proliferation and MMSET-spike groups were characterized by significant overexpression of genes mapping to chromosome 1q, and both exhibited a poor prognosis relative to the other groups. A subset of cases with a predominating myeloid gene expression signature, excluded from the profiling analyses, had more favorable baseline characteristics and superior prognosis to those lacking this signature.
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                Author and article information

                Contributors
                Role: conceived and directed the projectRole: designed and performed the experimentsRole: analyzed data and interpreted results
                Role: performed gene expression and protein studies including qRT-PCR and co-IP, and helped with data analysis and interpretation
                Role: conceived and directed the projectRole: acquired the funding
                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                08 April 2024
                : 2024.03.26.586811
                Affiliations
                [1 ]Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
                [2 ]The Center for Cancer Epigenetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
                Author notes

                Author Contributions

                YL performed bioinformatics analyses of the sequencing data with assistance from KL. Y-JCC wrote the original draft of the manuscript with SYRD. All authors reviewed and approved the final version of the manuscript.

                Author information
                http://orcid.org/0000-0002-6290-0771
                Article
                10.1101/2024.03.26.586811
                10996596
                38585845
                d28f8c4c-7ebf-4e30-b67d-2ba834d9c715

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

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                saga complex,ada2b,maf,myc,oncogenic gene expression programs,sant domain,multiple myeloma

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