A Combined Mass Spectrometry and Data Integration Approach to Predict the Mitochondrial Poly(A) RNA Interacting Proteome

In order to synthesize the 13 oxidative phosphorylation proteins encoded by mammalian mtDNA, a large assortment of nuclear encoded proteins is required. These include mitoribosomal proteins and various RNA processing, modification and degradation enzymes. RNA crosslinking has been successfully applied to identify whole-cell poly(A) RNA-binding proteomes, but this method has not been adapted to identify mitochondrial poly(A) RNA-binding proteomes. Here we developed and compared two related methods that specifically enrich for mitochondrial poly(A) RNA-binding proteins and analyzed bound proteins using mass spectrometry. To obtain a catalog of the mitochondrial poly(A) RNA interacting proteome, we used Bayesian data integration to combine these two mitochondrial-enriched datasets as well as published whole-cell datasets of RNA-binding proteins with various online resources, such as mitochondrial localization from MitoCarta 2.0 and co-expression analyses. Our integrated analyses ranked the complete human proteome for the likelihood of mtRNA interaction. We show that at a specific, inclusive cut-off of the corrected false discovery rate (cFDR) of 69%, we improve the number of predicted proteins from 185 to 211 with our mass spectrometry data as input for the prediction instead of the published whole-cell datasets. The chosen cut-off determines the cFDR: the less proteins included, the lower the cFDR will be. For the top 100 proteins, inclusion of our data instead of the published whole-cell datasets improve the cFDR from 54% to 31%. We show that the mass spectrometry method most specific for mitochondrial RNA-binding proteins involves ex vivo 4-thiouridine labeling followed by mitochondrial isolation with subsequent in organello UV-crosslinking.