Searching across hundreds of databases

Our searching services are busy right now. Your search will reload in five seconds.

X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

Therapeutic Targeting of MLL Degradation Pathways in MLL-Rearranged Leukemia.

Cell | Jan 12, 2017

Chromosomal translocations of the mixed-lineage leukemia (MLL) gene with various partner genes result in aggressive leukemia with dismal outcomes. Despite similar expression at the mRNA level from the wild-type and chimeric MLL alleles, the chimeric protein is more stable. We report that UBE2O functions in regulating the stability of wild-type MLL in response to interleukin-1 signaling. Targeting wild-type MLL degradation impedes MLL leukemia cell proliferation, and it downregulates a specific group of target genes of the MLL chimeras and their oncogenic cofactor, the super elongation complex. Pharmacologically inhibiting this pathway substantially delays progression, and it improves survival of murine leukemia through stabilizing wild-type MLL protein, which displaces the MLL chimera from some of its target genes and, therefore, relieves the cellular oncogenic addiction to MLL chimeras. Stabilization of MLL provides us with a paradigm in the development of therapies for aggressive MLL leukemia and perhaps for other cancers caused by translocations.

Pubmed ID: 28065413 RIS Download

Mesh terms: Animals | Disease Models, Animal | Histone-Lysine N-Methyltransferase | Humans | Interleukin-1 | Interleukin-1 Receptor-Associated Kinases | Leukemia, Biphenotypic, Acute | Mice | Mice, Inbred C57BL | Myeloid-Lymphoid Leukemia Protein | Proteolysis | Ubiquitin-Conjugating Enzymes

Research tools detected in this publication

Data used in this publication

None found

Associated grants

  • Agency: NIGMS NIH HHS, Id: R01 GM120109
  • Agency: NCI NIH HHS, Id: R50 CA211428
  • Agency: NCI NIH HHS, Id: R01 CA117907
  • Agency: NCI NIH HHS, Id: R35 CA197569
  • Agency: NCI NIH HHS, Id: R01 CA101774
  • Agency: NCI NIH HHS, Id: P30 CA046934
  • Agency: NCI NIH HHS, Id: P30 CA060553
  • Agency: NCI NIH HHS, Id: T32 CA080621
  • Agency: NCATS NIH HHS, Id: UL1 TR001082
  • Agency: NCI NIH HHS, Id: R01 CA214035

Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.

This is a list of tools and resources that we have found mentioned in this publication.


MACS

An algorithm for identifying transcript factor binding sites. MACS captures the influence of genome complexity to evaluate the significance of enriched ChIP regions, and MACS improves the spatial resolution of binding sites through combining the information of both sequencing tag position and orientation. MACS can be easily used for ChIP-Seq data alone, or with control sample with the increase of specificity.

tool

View all literature mentions

PRISM

Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.

tool

View all literature mentions

Bowtie

Software tool for gapped-read alignment of sequence reads.

tool

View all literature mentions

edgeR

Software for differential expression analysis of RNA-seq and digital gene expression profiles with biological replication.

tool

View all literature mentions

PANTHER

Classification system that classifies genes by their functions, using published scientific experimental evidence and evolutionary relationships to predict function even in the absence of direct experimental evidence. Proteins are classified by expert biologists according to: * Gene families and subfamilies, including annotated phylogenetic trees * Gene Ontology classes: molecular function, biological process, cellular component * PANTHER Protein Classes * Pathways, including diagrams The PANTHER Classifications are the result of human curation as well as sophisticated bioinformatics algorithms. Details of the methods can be found in (Thomas et al., Genome Research 2003; Mi et al. NAR 2005). Version 8.1 contains 7729 protein families, each with a phylogenetic tree relating modern-day genes in 48 organisms.) PANTHER contains the complete sets of protein coding genes for 48 organisms, obtained from definitive sources. PANTHER uses the Gene Ontology for classifications by molecular function, biological process and cellular component. The PANTHER Protein Class ontology was adapted from the PANTHER/X molecular function ontology, and includes commonly used classes of protein functions, many of which are not covered by GO molecular function. You may download the classes and relationship information. PANTHER uses only a subset of GO terms (GO slim) to facilitate browsing. You may download the PANTHER GO slim. You may Score proteins against the PANTHER HMM library and download PANTHER tools and data. PANTHER Pathway consists of over 176, primarily signaling, pathways, each with subfamilies and protein sequences mapped to individual pathway components. Pathways are drawn using CellDesigner software, capturing molecular level events in both signaling and metabolic pathways, and can be exported in SBML format. The SBGN view of the diagram can also be exported. Pathway diagrams are interactive and include tools for visualizing gene expression data in the context of the diagrams.

tool

View all literature mentions

R Project for Statistical Computing

Software environment and programming language for statistical computing and graphics that provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc) and graphical techniques. R is an integrated suite of software facilities for data manipulation, calculation and graphical display.

tool

View all literature mentions

ngs.plot

A software program that allows you to easily visualize your next-generation sequencing (NGS) samples at functional genomic regions.

tool

View all literature mentions

TopHat

A fast splice junction mapper for RNA-Seq reads.

tool

View all literature mentions