The role of a newly identified SET domain-containing protein, SETD3, in oncogenesis

Chen, Zhangguo; Yan, Catherine T.; Dou, Yali; Viboolsittiseri, Sawanee S.; Wang, Jing H.

doi:10.3324/haematol.2012.066977

Cited by 37 publications

(51 citation statements)

References 26 publications

(29 reference statements)

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“…Finally, we describe an example of a highly pathogenic somatic duplication (Fig 5), which directly overlapped with the SETD3 gene, a histone methyltransferase that is implicated in many diseases, including cancers 33 . The SETD3 gene also plays a role in cell cycle regulation, dell death, and chromosomal translocation 34,35 . Furthermore, the overexpression of the SETD3 gene leads to cell proliferation and tumor growth in liver cancer cells 36 .…”

Section: Case Studies Highlighting High Impact Somatic Deletion and Dmentioning

confidence: 99%

SVFX: a machine-learning framework to quantify the pathogenicity of structural variants

Kumar

Harmanci

Vytheeswaran

et al. 2019

Preprint

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A rapid decline in sequencing cost has made large-scale genome sequencing studies feasible. One of the fundamental goals of these studies is to catalog all pathogenic variants. Numerous methods and tools have been developed to interpret point mutations and small insertions and deletions. However, there is a lack of approaches for identifying pathogenic genomic structural variations (SVs). That said, SVs are known to play a crucial role in many diseases by altering the sequence and three-dimensional structure of the genome. Previous studies have suggested a complex interplay of genomic and epigenomic features in the emergence and distribution of SVs. However, the exact mechanism of pathogenesis for SVs in different diseases is not straightforward to decipher. Thus, we built an agnostic machine-learning-based workflow, called SVFX, to assign a "pathogenicity score" to somatic and germline SVs in various diseases. In particular, we generated somatic and germline training models, which included genomic, epigenomic, and conservation-based features for SV call sets in diseased and healthy individuals. We then applied SVFX to SVs in six different cancer cohorts and a cardiovascular disease (CVD) cohort. Overall, SVFX achieved high accuracy in identifying pathogenic SVs. Moreover, we found that predicted pathogenic SVs in cancer cohorts were enriched among known cancer genes and many cancer-related pathways (including Wnt signaling, Ras signaling, DNA repair, and ubiquitin-mediated proteolysis). Finally, we note that SVFX is flexible and can be easily extended to identify pathogenic SVs in additional disease cohorts.

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Section: Case Studies Highlighting High Impact Somatic Deletion and Dmentioning

confidence: 99%

SVFX: a machine-learning framework to quantify the pathogenicity of structural variants

Kumar

Harmanci

Vytheeswaran

et al. 2019

Preprint

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“…SETD3 is a conserved histone H3 methyltransferase1. It is abundantly expressed in many tissues, including muscle, where it promotes myocyte differentiation by regulating the transcription of muscle-related genes2.…”

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confidence: 99%

“…SETD3 was identified as novel biomarker for renal cell carcinoma (RCC)3: SETD3 expression was significantly higher in a set of RCC samples compared to normal renal tissues, and high expression of SETD3 was inversely correlated with disease-free survival3. In addition, it has been shown that a truncated version of SETD3 lacking the SET domain is highly expressed in lymphoma and that it displays oncogenic properties1. Overexpression of SETD3 in zebrafish was shown to lead to decreased cell viability and induction of apoptosis4.…”

mentioning

confidence: 99%

Chromatin associated SETD3 negatively regulates VEGF expression

Cohn

Feldman

Weil

et al. 2016

Sci Rep

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SETD3 is a member of the protein lysine methyltransferase (PKMT) family, which catalyzes the addition of methyl group to lysine residues. Accumulating data suggest that PKMTs are involved in the regulation of a broad spectrum of biological processes by targeting histone and non-histone proteins. Using a proteomic approach, we have identified 172 new SETD3 interacting proteins. We show that SETD3 binds and methylates the transcription factor FoxM1, which has been previously shown to be associated with the regulation of VEGF expression. We further demonstrate that under hypoxic conditions SETD3 is down-regulated. Mechanistically, we find that under basal conditions, SETD3 and FoxM1 are enriched on the VEGF promoter. Dissociation of both SETD3 and FoxM1 from the VEGF promoter under hypoxia correlates with elevated expression of VEGF. Taken together, our data reveal a new SETD3-dependent methylation-based signaling pathway at chromatin that regulates VEGF expression under normoxic and hypoxic conditions.

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“…This view was further strengthened by a finding that SETD3 is predominantly localized in the cytosol of human cells, while only small portion of the enzyme is present in the nucleus [Cheng et al, 2017]. Though, we have not examined the activity of recombinant SETD3 towards histones, as isolated histones are extremely poor substrates [Chen et al, 2013], this activity seems to be well-documented and confirmed by several studies. Our present results are therefore complementary to the above-listed findings in that they disclose actin as the cytosolic substrate of SETD3 enzyme (perhaps the most abundant one), and show that SETD3 exhibits plausibly a dual methyltransferase specificity towards both Lys and His residues of proteins.…”

Section: Molecular Identity Of Rat Actin-specific Histidine N-methyltmentioning

confidence: 53%

“…Several reports describing the role of SETD3 in the regulation of cell cycle and apoptosis [Kim et al, 2011], myocytes differentiation [Eom et al, 2011], cell response to hypoxic conditions [Cohn et al, 2016] and tumorigenesis [Pires-Luis et al, 2015;Cheng et al, 2017] have been published so far. Some of these studies identified methylation of histones as a primary mechanism of SETD3 action [Kim et al, 2011;Eom et al, 2011], while others suggested that the enzyme may work rather by modifing nonhistone substrates present in the cytosol [Chen et al, 2013, Cohn et al, 2016. The existence of two distinct domains in the enzyme, i.e.…”

Section: Biological Importance Of Setd3 Methyltransferasementioning

confidence: 99%

SETD3 protein is the actin-specific histidineN-methyltransferase

Kwiatkowski

Seliga

Veiga‐da‐Cunha

et al. 2018

Preprint

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Protein methylation is one of the most common post-translation modification (PTM) in eukaryotic cells [for review see Clarke, 2013] and histones are probably the best studied substrates of protein Nmethyltransferases. Much is therefore known about methylation of lysine and arginine residues in histones as well as the fundamental role of such modifications in the epigenetic control of mammalian gene expression [Greer and Shi, 2012]. However, an increasing number of studies show methylation of non-histone proteins as a prevalent PTM that regulates diverse biological processes, including protein synthesis and signal transduction [for review see Biggar and Li, 2015]. More interestingly, non-histone proteins may be modified on atypical sites for methylation such as glutamate, glutamine, cysteine, and histidine residues [Clarke, 2013]. Methylation of histidine residues has been long reported just for a few proteins in Nature, including -actin [Johnson et al., 1967], S100A9 protein [Raftery et al., 1996], myosin [Elzinga and Collins, 1977] and its kinase [Meyer i Mayr, 1987], and ribosomal protein RPL3 [Webb et al., 2010], whereas a recent study of Ning and coworkers [2016] suggests that such modification may be a quite common phenomenon, involving dozens if not hundreds of intracellular proteins in mammalian cells.Knowledge of protein histidine N-methyltransferases is rather limited, and yeast YIL110W (Hpm1p) protein, which catalyzes methylation of histidine 243 in ribosomal RPL3 protein, remains the first and only histidine protein methyltransferase molecularly identified up to date [Webb et al, 2010].The enzyme responsible for actin histidine methylation has been partially purified, but no gene encoding this enzyme has been identified yet [Vijayasarathy and Rao, 1987; Raghavan et al., 1992].Actin is one of the most abundant protein in eukaryotic cells and a major component of the cytoskeleton [for review, Pollard and Cooper, 2009]. It is highly evolutionary conserved (90% identity between yeast and human protein), and in vertebrates, three main isoforms of this protein differing by only a few amino acids at their N-terminus have been identified. -Actins are expressed in skeletal, cardiac and smooth muscle, while -and -isoforms are present in nonmuscle and muscle cells. Under physiological conditions, actin exists as a 42-kDa monomeric globular protein (G-actin) that binds ATP and spontaneously polymerize into stable filaments (F-actin). Once filament has been assembled, the terminal phosphate of the bound ATP is hydrolyzed and slowly released from the protein, leading to the sequential depolymerization of the filament. The maintenance of a pool of actin monomers, the initialization of polymerization process as well as the regulation of assembly and turnover of filaments are controlled and tuned by more than 100 interacting partner proteins, making actin to contribute to more protein-protein interactions than any other protein does [Lappalainen, 2016].As actin is essential for the survival of cells, particip...

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The role of a newly identified SET domain-containing protein, SETD3, in oncogenesis

Cited by 37 publications

References 26 publications

SVFX: a machine-learning framework to quantify the pathogenicity of structural variants

SVFX: a machine-learning framework to quantify the pathogenicity of structural variants

Chromatin associated SETD3 negatively regulates VEGF expression

SETD3 protein is the actin-specific histidineN-methyltransferase

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