Dataset Information

TRAP150 interacts with the RNA-binding domain of PSF and antagonizes splicing of numerous PSF-target genes in T cells.

ABSTRACT: PSF (a.k.a. SFPQ) is a ubiquitously expressed, essential nuclear protein with important roles in DNA damage repair and RNA biogenesis. In stimulated T cells, PSF binds to and suppresses the inclusion of CD45 exon 4 in the final mRNA; however, in resting cells, TRAP150 binds PSF and prevents access to the CD45 RNA, though the mechanism for this inhibition has remained unclear. Here, we show that TRAP150 binds a region encompassing the RNA recognition motifs (RRMs) of PSF using a previously uncharacterized, 70 residue region we have termed the PSF-interacting domain (PID). TRAP150's PID directly inhibits the interaction of PSF RRMs with RNA, which is mediated through RRM2. However, interaction of PSF with TRAP150 does not appear to inhibit the dimerization of PSF with other Drosophila Behavior, Human Splicing (DBHS) proteins, which is also dependent on RRM2. Finally, we use RASL-Seq to identify ?40 T cell splicing events sensitive to PSF knockdown, and show that for the majority of these, PSF's effect is antagonized by TRAP150. Together these data suggest a model in which TRAP150 interacts with dimeric PSF to block access of RNA to RRM2, thereby regulating the activity of PSF toward a broad set of splicing events in T cells.

SUBMITTER: Yarosh CA

PROVIDER: S-EPMC4605305 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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TRAP150 interacts with the RNA-binding domain of PSF and antagonizes splicing of numerous PSF-target genes in T cells.

Yarosh Christopher A CA Tapescu Iulia I Thompson Matthew G MG Qiu Jinsong J Mallory Michael J MJ Fu Xiang-Dong XD Lynch Kristen W KW

Nucleic acids research 20150810 18

PSF (a.k.a. SFPQ) is a ubiquitously expressed, essential nuclear protein with important roles in DNA damage repair and RNA biogenesis. In stimulated T cells, PSF binds to and suppresses the inclusion of CD45 exon 4 in the final mRNA; however, in resting cells, TRAP150 binds PSF and prevents access to the CD45 RNA, though the mechanism for this inhibition has remained unclear. Here, we show that TRAP150 binds a region encompassing the RNA recognition motifs (RRMs) of PSF using a previously unchar ...[more]

PMID: 26261210

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Project description:Precise identification of target sites of RNA-binding proteins (RBP) is important to understand their biochemical and cellular functions. A large amount of experimental data is generated by in vivo and in vitro approaches. The binding preferences determined from these platforms share similar patterns but there are discernable differences between these datasets. Computational methods trained on one dataset do not always work well on another dataset. To address this problem which resembles the classic "domain shift" in deep learning, we adopted the adversarial domain adaptation (ADDA) technique and developed a framework (RBP-ADDA) that can extract RBP binding preferences from an integration of in vivo and vitro datasets. Compared with conventional methods, ADDA has the advantage of working with two input datasets, as it trains the initial neural network for each dataset individually, projects the two datasets onto a feature space, and uses an adversarial framework to derive an optimal network that achieves an optimal discriminative predictive power. In the first step, for each RBP, we include only the in vitro data to pre-train a source network and a task predictor. Next, for the same RBP, we initiate the target network by using the source network and use adversarial domain adaptation to update the target network using both in vitro and in vivo data. These two steps help leverage the in vitro data to improve the prediction on in vivo data, which is typically challenging with a lower signal-to-noise ratio. Finally, to further take the advantage of the fused source and target data, we fine-tune the task predictor using both data. We showed that RBP-ADDA achieved better performance in modeling in vivo RBP binding data than other existing methods as judged by Pearson correlations. It also improved predictive performance on in vitro datasets. We further applied augmentation operations on RBPs with less in vivo data to expand the input data and showed that it can improve prediction performances. Lastly, we explored the predictive interpretability of RBP-ADDA, where we quantified the contribution of the input features by Integrated Gradients and identified nucleotide positions that are important for RBP recognition.

Dataset Information

TRAP150 interacts with the RNA-binding domain of PSF and antagonizes splicing of numerous PSF-target genes in T cells.

Publications

TRAP150 interacts with the RNA-binding domain of PSF and antagonizes splicing of numerous PSF-target genes in T cells.

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