Aberrant isoform expression of chromatin-associated proteins can induce epigenetic programs related to disease. The MDS1 and EVI1 complex locus (MECOM) encodes PRDM3, a protein with an N-terminal PR-SET domain, as well as a shorter isoform, EVI1, lacking the N-terminus containing the PR-SET domain (ΔPR). Imbalanced expression of MECOM isoforms is observed in multiple malignancies, implicating EVI1 as an oncogene, while PRDM3 has been suggested to function as a tumor suppressor through an unknown mechanism. To elucidate functional characteristics of these N-terminal residues, we compared the protein interactomes of the full-length and ΔPR isoforms of PRDM3 and its closely related paralog, PRDM16. Unlike the ΔPR isoforms, both full-length isoforms exhibited a significantly enriched association with components of the NuRD chromatin remodeling complex, especially RBBP4. Typically, RBBP4 facilitates chromatin association of the NuRD complex by binding to histone H3 tails. We show that RBBP4 binds to the N-terminal amino acid residues of PRDM3 and PRDM16, with a dissociation constant of 3.0 μM, as measured by isothermal titration calorimetry. Furthermore, high-resolution X-ray crystal structures of PRDM3 and PRDM16 N-terminal peptides in complex with RBBP4 revealed binding to RBBP4 within the conserved histone H3-binding groove. These data support a mechanism of isoform-specific interaction of PRDM3 and PRDM16 with the NuRD chromatin remodeling complex.

Background Immunohistochemistry (IHC) is often used in personalisation of cancer treatments. Analysis of large data sets to uncover predictive biomarkers by specialists can be enormously time-consuming. Here we investigated crowdsourcing as a means of reliably analysing immunostained cancer samples to discover biomarkers predictive of cancer survival. Methods We crowdsourced the analysis of bladder cancer TMA core samples through the smartphone app ‘Reverse the Odds’. Scores from members of the public were pooled and compared to a gold standard set scored by appropriate specialists. We also used crowdsourced scores to assess associations with disease-specific survival. Results Data were collected over 721 days, with 4,744,339 classifications performed. The average time per classification was approximately 15 s, with approximately 20,000 h total non-gaming time contributed. The correlation between crowdsourced and expert H-scores (staining intensity × proportion) varied from 0.65 to 0.92 across the markers tested, with six of 10 correlation coefficients at least 0.80. At least two markers (MRE11 and CK20) were significantly associated with survival in patients with bladder cancer, and a further three markers showed results warranting expert follow-up. Conclusions Crowdsourcing through a smartphone app has the potential to accurately screen IHC data and greatly increase the speed of biomarker discovery.