Proteogenomics of Adenosine-to-Inosine RNA Editing in the Fruit Fly.

Adenosine-to-inosine RNA editing is one of the most common types of RNA editing, a posttranscriptional modification made by special enzymes. We present a proteomic study on this phenomenon for Drosophila melanogaster. Three proteome data sets were used in the study: two taken from public repository and the third one obtained here. A customized protein sequence database was generated using results of genome-wide adenosine-to-inosine RNA studies and applied for identifying the edited proteins. The total number of 68 edited peptides belonging to 59 proteins was identified in all data sets. Eight of them being shared between the whole insect, head, and brain proteomes. Seven edited sites belonging to synaptic vesicle and membrane trafficking proteins were selected for validation by orthogonal analysis by Multiple Reaction Monitoring. Five editing events in cpx, Syx1A, Cadps, CG4587, and EndoA were validated in fruit fly brain tissue at the proteome level using isotopically labeled standards. Ratios of unedited-to-edited proteoforms varied from 35:1 ( Syx1A) to 1:2 ( EndoA). Lys-137 to Glu editing of endophilin A may have functional consequences for its interaction to membrane. The work demonstrates the feasibility to identify the RNA editing event at the proteome level using shotgun proteomics and customized edited protein database.

Proteogenomics of Malignant Melanoma Cell Lines: The Effect of Stringency of Exome Data Filtering on Variant Peptide Identification in Shotgun Proteomics.

The identification of genetically encoded variants at the proteome level is an important problem in cancer proteogenomics. The generation of customized protein databases from DNA or RNA sequencing data is a crucial stage of the identification workflow. Genomic data filtering applied at this stage may significantly modify variant search results, yet its effect is generally left out of the scope of proteogenomic studies. In this work, we focused on this impact using data of exome sequencing and LC-MS/MS analyses of six replicates for eight melanoma cell lines processed by a proteogenomics workflow. The main objectives were identifying variant peptides and revealing the role of the genomic data filtering in the variant identification. A series of six confidence thresholds for single nucleotide polymorphisms and indels from the exome data were applied to generate customized sequence databases of different stringency. In the searches against unfiltered databases, between 100 and 160 variant peptides were identified for each of the cell lines using X!Tandem and MS-GF+ search engines. The recovery rate for variant peptides was ∼1%, which is approximately three times lower than that of the wild-type peptides. Using unfiltered genomic databases for variant searches resulted in higher sensitivity and selectivity of the proteogenomic workflow and positively affected the ability to distinguish the cell lines based on variant peptide signatures.