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Unbiased RNA-protein interaction screen by quantitative proteomics.

ABSTRACT: Mass spectrometry (MS)-based quantitative interaction proteomics has successfully elucidated specific protein-protein, DNA-protein, and small molecule-protein interactions. Here, we developed a gel-free, sensitive, and scalable technology that addresses the important area of RNA-protein interactions. Using aptamer-tagged RNA as bait, we captured RNA-interacting proteins from stable isotope labeling by amino acids in cell culture (SILAC)-labeled mammalian cell extracts and analyzed them by high-resolution, quantitative MS. Binders specific to the RNA sequence were distinguished from background by their isotope ratios between bait and control. We demonstrated the approach by retrieving known and novel interaction partners for the HuR interaction motif, H4 stem loop, "zipcode" sequence, tRNA, and a bioinformatically-predicted RNA fold in DGCR-8/Pasha mRNA. In all experiments we unambiguously identified known interaction partners by a single affinity purification step. The 5' region of the mRNA of DGCR-8/Pasha, a component of the microprocessor complex, specifically interacts with components of the translational machinery, suggesting that it contains an internal ribosome entry site.

SUBMITTER: Butter F

PROVIDER: S-EPMC2697111 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundQuantitative proteomics studies are often used to detect proteins that are differentially expressed across different experimental conditions. Functional enrichment analyses are then typically used to detect annotations, such as biological processes that are significantly enriched among such differentially expressed proteins to provide insights into the molecular impacts of the studied conditions. While common, this analytical pipeline often heavily relies on arbitrary thresholds of significance. However, a functional annotation may be dysregulated in a given experimental condition, while none, or very few of its proteins may be individually considered to be significantly differentially expressed. Such an annotation would therefore be missed by standard approaches.ResultsHerein, we propose a novel graph theory-based method, PIGNON, for the detection of differentially expressed functional annotations in different conditions. PIGNON does not assess the statistical significance of the differential expression of individual proteins, but rather maps protein differential expression levels onto a protein-protein interaction network and measures the clustering of proteins from a given functional annotation within the network. This process allows the detection of functional annotations for which the proteins are differentially expressed and grouped in the network. A Monte-Carlo sampling approach is used to assess the clustering significance of proteins in an expression-weighted network. When applied to a quantitative proteomics analysis of different molecular subtypes of breast cancer, PIGNON detects Gene Ontology terms that are both significantly clustered in a protein-protein interaction network and differentially expressed across different breast cancer subtypes. PIGNON identified functional annotations that are dysregulated and clustered within the network between the HER2+, triple negative and hormone receptor positive subtypes. We show that PIGNON's results are complementary to those of state-of-the-art functional enrichment analyses and that it highlights functional annotations missed by standard approaches. Furthermore, PIGNON detects functional annotations that have been previously associated with specific breast cancer subtypes.ConclusionPIGNON provides an alternative to functional enrichment analyses and a more comprehensive characterization of quantitative datasets. Hence, it contributes to yielding a better understanding of dysregulated functions and processes in biological samples under different experimental conditions.

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Unbiased RNA-protein interaction screen by quantitative proteomics.

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