The RNA-binding protein AKAP8 suppresses tumor metastasis by antagonizing EMT-associated alternative splicing

Alternative splicing has been shown to causally contribute to the epithelial–mesenchymal transition (EMT) and tumor metastasis. However, the scope of splicing factors that govern alternative splicing in these processes remains largely unexplored. Here we report the identification of A-Kinase Anchor Protein (AKAP8) as a splicing regulatory factor that impedes EMT and breast cancer metastasis. AKAP8 not only is capable of inhibiting splicing activity of the EMT-promoting splicing regulator hnRNPM through protein–protein interaction, it also directly binds to RNA and alters splicing outcomes. Genome-wide analysis shows that AKAP8 promotes an epithelial cell state splicing program. Experimental manipulation of an AKAP8 splicing target CLSTN1 revealed that splice isoform switching of CLSTN1 is crucial for EMT. Moreover, AKAP8 expression and the alternative splicing of CLSTN1 predict breast cancer patient survival. Together, our work demonstrates the essentiality of RNA metabolism that impinges on metastatic breast cancer.


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Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Chonghui Cheng Dec 14, 2019 Data were collected on Illumina HiSeq 4000 at the University of Chicago Genomics Facility RNA-seq reads were aligned to the human genome (GRCh37, primary assembly) and transcriptome (Gencode version 24 backmap 37 comprehensive gene annotation) using STAR version 2.6.1a. Differential alternative splicing was quantified using rMATS version 4.0.2. Differential gene expression analysis was performed by counting reads over genes from the same annotation as alignment using featureCounts version 1.5.0. Differential gene expression analysis was conducted using DESeq2 performed on genes with read abundance larger than 10 counts over the smallest library size of all samples analyzed. eCLIP data processing was conducted using the public eCLIP pipeline version 0.2.1a (https://github.com/YeoLab/eclip/releases/tag/0.2.1a) and public merge-peaks pipeline version 0.0.6 (https://github.com/YeoLab/merge_peaks/ releases/tag/0.0.6), derived from a previously published eCLIP pipeline. Data visualization and statistical analyses were conducted in Python and R.
High throughput sequencing data and differential analysis tables have been uploaded to the GEO database under accession number GSE139074 nature research | reporting summary

October 2018
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