Integrated genome-scale discovery of the SFPQ-DNA and -RNA regulatory interactome and its output in cancer biology

Splicing factor proline- and glutamine- rich (SFPQ) is a multifunctional DNA- and RNA-binding protein, part of the drosophila behaviour human splicing (DBHS) family of proteins. First discovered as a splicing factor, it has since been revealed that SFPQ is involved in a large range of cellular mechanisms, including transcriptional regulation, DNA damage repair, and paraspeckle formation. Misregulation of SFPQ is associated with aetiology of neurodegenerative diseases and a range of cancers, including prostate, renal, and colorectal cancers, and is portrayed with significant potential as a diagnostic or prognostic biomarker in cancer. Studies regarding the regulatory role of SFPQ, through interaction with other transcripts have propelled in recent years, however the complete scale of the SFPQ regulatory network remains unknown.

In this study, we mapped the SFPQ regulome exhaustively, constructing an interaction database of novel interactors for further study. We utilised publicly available datasets from several experimental techniques at NCBI GEO and ENCODE, including SFPQ-knockdown RNA-seq, RIP-seq, ChIP-seq, PAR-CLIP and eCLIP datasets. We interrogated transcripts with altered expression levels in multiple cell lines depleted with SFPQ in order to explore the scope of genes regulated by SFPQ function. We next identified RNA physical interactors of SFPQ through integration RIP-seq data targeting SFPQ across multiple cell lines, as well as revealing the binding locations of target of SFPQ upon target transcripts through analysis of eCLIP and PAR-CLIP datasets. To complete the baseline of our interaction database, we integrated ChIP-seq datasets in order to tease out transcripts regulated by SFPQ at the DNA level, and the extent at which SFPQ is involved in transcriptional regulation.

We observed a wide range of biological pathways associated with SFPQ interactors, and compiled results into the mineable database and R package, idbSFPQ. Additionally, our analyses lay template for application of methodology to investigate regulatory networks of other proteins.