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Somatic cancer mutations in the MLL3-SET domain alter the catalytic properties of the enzyme.

ABSTRACT: Somatic mutations in epigenetic enzymes are frequently found in cancer tissues. The MLL3 H3K4-specific protein lysine monomethyltransferase is an important epigenetic enzyme, and it is among the most recurrently mutated enzymes in cancers. MLL3 mainly introduces H3K4me1 at enhancers.We investigated the enzymatic properties of MLL3 variants that carry somatic cancer mutations. Asn4848 is located at the cofactor binding sites, and the N4848S exchange renders the enzyme inactive. Tyr4884 is part of an aromatic pocket at the active center of the enzyme, and Y4884C converts MLL3 from a monomethyltransferase with substrate preference for H3K4me0 to a trimethyltransferase with H3K4me1 as preferred substrate. Expression of Y4884C leads to aberrant H3K4me3 formation in cells.Our data show that different somatic cancer mutations of MLL3 affect the enzyme activity in distinct and opposing manner highlighting the importance of experimentally studying the effects of somatic cancer mutations in key regulatory enzymes in order to develop and apply targeted tumor therapy.

SUBMITTER: Weirich S

PROVIDER: S-EPMC4379744 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Somatic cancer mutations in the MLL3-SET domain alter the catalytic properties of the enzyme.

Weirich Sara S Kudithipudi Srikanth S Kycia Ina I Jeltsch Albert A

Clinical epigenetics 20150328

<h4>Background</h4>Somatic mutations in epigenetic enzymes are frequently found in cancer tissues. The MLL3 H3K4-specific protein lysine monomethyltransferase is an important epigenetic enzyme, and it is among the most recurrently mutated enzymes in cancers. MLL3 mainly introduces H3K4me1 at enhancers.<h4>Results</h4>We investigated the enzymatic properties of MLL3 variants that carry somatic cancer mutations. Asn4848 is located at the cofactor binding sites, and the N4848S exchange renders the ...[more]

PMID: 25829971

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Project description:BackgroundLarge-scale tumor sequencing projects are now underway to identify genetic mutations that drive tumor initiation and development. Most studies take a gene-based approach to identifying driver mutations, highlighting genes mutated in a large percentage of tumor samples as those likely to contain driver mutations. However, this gene-based approach usually does not consider the position of the mutation within the gene or the functional context the position of the mutation provides. Here we introduce a novel method for mapping mutations to distinct protein domains, not just individual genes, in which they occur, thus providing the functional context for how the mutation contributes to disease. Furthermore, aggregating mutations from all genes containing a specific protein domain enables the identification of mutations that are rare at the gene level, but that occur frequently within the specified domain. These highly mutated domains potentially reveal disruptions of protein function necessary for cancer development.ResultsWe mapped somatic mutations from the protein coding regions of 100 colon adenocarcinoma tumor samples to the genes and protein domains in which they occurred, and constructed topographical maps to depict the "mutational landscapes" of gene and domain mutation frequencies. We found significant mutation frequency in a number of genes previously known to be somatically mutated in colon cancer patients including APC, TP53 and KRAS. In addition, we found significant mutation frequency within specific domains located in these genes, as well as within other domains contained in genes having low mutation frequencies. These domain "peaks" were enriched with functions important to cancer development including kinase activity, DNA binding and repair, and signal transduction.ConclusionsUsing our method to create the domain landscapes of mutations in colon cancer, we were able to identify somatic mutations with high potential to drive cancer development. Interestingly, the majority of the genes involved have a low mutation frequency. Therefore, the method shows good potential for identifying rare driver mutations in current, large-scale tumor sequencing projects. In addition, mapping mutations to specific domains provides the necessary functional context for understanding how the mutations contribute to the disease, and may reveal novel or more refined gene and domain target regions for drug development.

Dataset Information

Somatic cancer mutations in the MLL3-SET domain alter the catalytic properties of the enzyme.

Publications

Somatic cancer mutations in the MLL3-SET domain alter the catalytic properties of the enzyme.

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