Table 1.
Optional Bioconductor chip reading libraries.
Fig 1.
Systems overview of RPTS Application Capabilities.
A) System overview of the user interface with RPTS. Input may come from either a published tape archive (.tar) GEO dataset, or directly from a high-throughput sequencing system, such as Affymetrix. Input data may come from multiple sources, RPTS has options the user may set in order to combine data from multiple sources. B) The algorithm of RPTS is described in detail in this panel. General flow of decision points and data analysis are described, key points of decision are highlighted in red-boxes. Output from the program is highlighted in green-boxes, with a description of what is contained.
Fig 2.
3’-UTRs become divergent across evolution while conserving regulatory motifs.
A) Representation of the lengths of divergent species 3’-UTR Jak2 lengths. There is a significant degree of variation across evolution, however as seen in panel B regions of these UTRs have been conserved. B) This alignment was generated from RPTS, it utilizes the multiple sequence comparison by log-expectation (Clustal-Ω) alignment method to determine regions of highest conservation in Jak2 [16–18]. Purines and Pyrmidines are differentiated by pink and blue, respectively. C) Our training GEO set GSE40466 yielded a consensus nucleic acid sequence specific to binding hnRNP E1, in addition to determining a consensus sequence, the output includes the identity of genes that contain this motif. When the consensus sequence was analyzed by BLASTn, 92.7% of the genes predicted by BLAST analysis were also predicted by RPTS.
Fig 3.
RPTS predicted motif containing genes interact with hnRNP E1.
A) To assess the TGFβ induced serine-43 phosphorylation of hnRNP E1, we combined an immunoprecipitation and subsequent western blot analysis. Cytosolic hnRNP E1 was immunoprecipitated by α-hnRNP E1 / protein-A-sepharose, an antibody specific to the phosphorylated form of the phosphorylation consensus sequence that contains serine-43 of hnRNP E1 was used in a subsequent western blot to assay p-Ser43-hnRNP E1. NMuMG-/- hnRNP E1 samples were used as a negative control due to its lack of hnRNP E1. A steady rise of p-Ser43-hnRNP E1 is seen as NMuMG cells are treated with TGFβ in a time dependent manner. Levels of total hnRNP E1 remain unchanged as an effect of TGFβ. B) Schematic representation of TGFβ stimulation on hnRNP E1. TGFβ induces kinase activity of Akt2 to cause phosphorylation on serine-43 of hnRNP E1, causing dissociation of this RBP from its target consensus motif. C) After determining the kinetics of hnRNP E1 phosphorylation, we utilized these time points to assess and confirm the binding of RPTS predicted motif-containing genes. The top panel of individual gene assays shows the loss of binding through RNA immunoprecipitation (RIP) and subsequent reverse transcriptase polymerase chain reaction (rtPCR). We utilized IgG as a negative control to show the specificity for RIP samples with α-hnRNP E1. As seen in panel A, the phosphorylation levels of serine-43 peak around 3 hours, RIP analysis shows a significant loss of binding for these motif-containing genes in a direct relationship to the hnRNP E1 phosphorylation kinetics. The lower panel of gene assays shows total RNA levels as a function of TGFβ by rtPCR of total cellular RNA.
Fig 4.
Predicted motif containing genes global set enrichment analysis.
A) The list of motif-containing genes predicted by RPTS was analyzed by GSEA for pathway enrichment of known oncogenic pathways. Each individual block is a functional grouping of pathways, individual pathways are noted horizontally, gene names are noted vertically. B) GSEA individual pathway enrichment analysis. Each pathway had a p-value of less than 0.05, and an enrichment score of greater than 1.7-fold enrichment. When analyzed as a total group, many of these pathways are implicated in the progression of EMT, cancer, and metastasis. C) Previous data suggests the mutation of single bases in RBP consensus motifs causes a disruption of the complex formation. This panel shows the number of SNP records contained in dbSNP (Entrez) for each RPTS motif-containing prediction. There are significant numbers of SNP mutations that occur in the loci of these genes, it is statistically plausible that some of these mutations occur within the boundaries of individual RBP consensus motif.