A ribosome-related signature in peripheral blood CLL B cells is linked to reduced survival following treatment.

We have used polysome profiling coupled to microarray analysis to examine the translatome of a panel of peripheral blood (PB) B cells isolated from 34 chronic lymphocytic leukaemia (CLL) patients. We have identified a 'ribosome-related' signature in CLL patients with mRNAs encoding for ribosomal proteins and factors that modify ribosomal RNA, e.g. DKC1 (which encodes dyskerin, a pseudouridine synthase), showing reduced polysomal association and decreased expression of the corresponding proteins. Our data suggest a general impact of dyskerin dysregulation on the translational apparatus in CLL and importantly patients with low dyskerin levels have a significantly shorter period of overall survival following treatment. Thus, translational dysregulation of dyskerin could constitute a mechanism by which the CLL PB B cells acquire an aggressive phenotype and thus have a major role in oncogenesis.

A role for eukaryotic initiation factor 4B overexpression in the pathogenesis of diffuse large B-cell lymphoma.

Dysregulated expression of factors that control protein synthesis is associated with poor prognosis of many cancers, but the underlying mechanisms are not well defined. Analysis of the diffuse large B-cell lymphoma (DLBCL) translatome revealed selective upregulation of mRNAs encoding anti-apoptotic and DNA repair proteins. We show that enhanced synthesis of these proteins in DLBCL is mediated by the relief of repression that is normally imposed by structure in the 5'-untranslated regions of their corresponding mRNAs. This process is driven by signaling through mammalian target of rapamycin, resulting in increased synthesis of eukaryotic initiation factor (eIF) 4B complex (eIF4B), a known activator of the RNA helicase eIF4A. Reducing eIF4B expression alone is sufficient to decrease synthesis of proteins associated with enhanced tumor cell survival, namely DAXX, BCL2 and ERCC5. Importantly, eIF4B-driven expression of these key survival proteins is directly correlated with patient outcome, and eIF4B, DAXX and ERCC5 are identified as novel prognostic markers for poor survival in DLBCL. Our work provides new insights into the mechanisms by which the cancer-promoting translational machinery drives lymphomagenesis.

Translational repression and eIF4A2 activity are critical for microRNA-mediated gene regulation.

MicroRNAs (miRNAs) control gene expression through both translational repression and degradation of target messenger RNAs (mRNAs). However, the interplay between these processes and the precise molecular mechanisms involved remain unclear. Here, we show that translational inhibition is the primary event required for mRNA degradation. Translational inhibition depends on miRNAs impairing the function of the eIF4F initiation complex. We define the RNA helicase eIF4A2 as the key factor of eIF4F through which miRNAs function. We uncover a correlation between the presence of miRNA target sites in the 3' untranslated region (3'UTR) of mRNAs and secondary structure in the 5'UTR and show that mRNAs with unstructured 5'UTRs are refractory to miRNA repression. These data support a linear model for miRNA-mediated gene regulation in which translational repression via eIF4A2 is required first, followed by mRNA destabilization.

Protein function annotation from sequence: prediction of residues interacting with RNA.

MOTIVATION: All eukaryotic proteomes are characterized by a significant percentage of proteins of unknown function. Comp-utational function prediction methods are therefore essential as initial steps in the function annotation process. This article describes an annotation method (PiRaNhA) for the prediction of RNA-binding residues (RBRs) from protein sequence information. A series of sequence properties (position specific scoring matrices, interface propensities, predicted accessibility and hydrophobicity) are used to train a support vector machine. This method is then evaluated for its potential to be applied to RNA-binding function prediction at the level of the complete protein. RESULTS: The 5-fold cross-validation of PiRaNhA on a dataset of 81 RNA-binding proteins achieves a Matthews Correlation Coefficient (MCC) of 0.50 and accuracy of 87.2%. When used to predict RBRs in 42 proteins not used in training, PiRaNhA achieves an MCC of 0.41 and accuracy of 84.5%. Decision values from the PiRaNhA predictions were used in a second SVM to make predictions of RNA-binding function at the protein level, achieving an MCC of 0.53 and accuracy of 76.1%. The PiRaNhA RBR predictions allow experimentalists to perform more targeted experiments for function annotation; and the prediction of RNA-binding function at the protein level shows promise for proteome-wide annotations. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at www.bioinformatics.sussex.ac.uk/PIRANHA or http://piranha.protein.osaka-u.ac.jp. SUPPLEMENTARY INFORMATION: Supplementary data are available at the Bioinformatics online.