Mutant NPM1 Hijacks Transcriptional Hubs to Maintain Pathogenic Gene Programs in Acute Myeloid Leukemia

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Nucleophosmin (NPM1) is a ubiquitously expressed nucleolar protein with a wide range of biological functions. In 30% of acute myeloid leukemia (AML), the terminal exon of NPM1 is often found mutated, resulting in the addition of a nuclear export signal and a shift of the protein to the cytoplasm (NPM1c). AMLs carrying this mutation have aberrant expression of the HOXA/B genes, whose overexpression leads to leukemogenic transformation. Here, for the first time, we comprehensively prove that NPM1c binds to a subset of active gene promoters in NPM1c AMLs, including well-known leukemia-driving genes—HOXA/B cluster genes and MEIS1. NPM1c sustains the active transcription of key target genes by orchestrating a transcription hub and maintains the active chromatin landscape by inhibiting the activity of histone deacetylases. Together, these findings reveal the neomorphic function of NPM1c as a transcriptional amplifier for leukemic gene expression and open up new paradigms for therapeutic intervention.

Significance:

NPM1 mutation is the most common mutation in AML, yet the mechanism of how the mutant protein results in AML remains unclear. Here, for the first time, we prove mutant NPM1 directly binds to active chromatin regions and hijacks the transcription of AML-driving genes.

See related article by Uckelmann et al., p. 746.

This article is highlighted in the In This Issue feature, p. 517

Related collections

Most cited references 62

Record: found
Abstract: found
Article: found

Is Open Access

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Michael Love, Wolfgang Huber, Simon Anders (2014)

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.

0 comments Cited 26643 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

The Sequence Alignment/Map format and SAMtools

Heng Li, Bob Handsaker, Alec Wysoker … (2009)

Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk

0 comments Cited 15312 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

STAR: ultrafast universal RNA-seq aligner.

Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)

Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.