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  • Our assessment also takes into account the identification of

    2018-11-07

    Our assessment also takes into account the identification of alternative proteins (AltProts) (Vanderperre et al., 2013). AltProts are translated from alternative open reading frames (AltORFs). AltORFs can have different localizations: they can overlap annotated protein-coding sequences in a different reading frame, or can be present within untranslated regions (UTRs) of mature mRNAs (Mouilleron et al., 2016; Vanderperre et al., 2013). Thus, alternative proteins are completely different from annotated or reference proteins (Mouilleron et al., 2016; Vanderperre et al., 2013). AltORFs may also be present in transcripts annotated as non-coding RNAs (ncRNAs). Indeed, proteins translated from non-annotated AltORFs were detected in our previous studies by MS. Some of these alternative translation products have also been validated biologically and assessed for their biological activity. For example, we have shown that AltMRVI1 is translated from an AltORF overlapping the MRVI1 coding sequence in a different reading frame and interacts with BRCA1 (Vanderperre et al., 2013). Translation of AltORFs in addition to annotated coding sequences opens the door to proteins that cannot be detected using conventional protein databases. Thus, due to their intriguing role, we aimed at investigating the profiles of the “hidden proteome” and assess their contribution in serous ovarian cancer. Additionally, these AltProts are mainly small proteins and the top-down proteomics strategy seems to be a better alternative rather than the shotgun proteomics for their detection. This is so because, even if the shotgun approach remains the most efficient strategy for high throughput proteomics, the identification of small proteins in this approach can be hampered due to the low amount of generated tryptic mCAP and the generally fewer presence of enzyme cleavage sites. Therefore, top-down proteomics offers a good alternative to identify small proteins or truncated forms as well as some PTMs from the reference or the hidden proteome. Overall, our aim is to identify and characterize reference and altprots as potential markers for serous ovarian cancer pathology.
    Experimental Procedures
    Results
    Discussion This work involves the use of tissue microproteomics to characterize the local proteome in three regions (necrotic/fibrotic tumor, tumor and benign region) of human ovarian cancer. These regions were analyzed by MALDI-MSI and discerned by spatial segmentation analysis (Alexandrov et al., 2011; Bonnel et al., 2011; Bruand et al., 2011), and the proteins were microextracted utilizing LMJ and PAM approaches (Franck et al., 2013; Quanico et al., 2014, 2013; Wisztorski et al., 2016). A total of 237 gene products within the three regions were identified. 61 proteins were specific to the tumor region, 44 to the necrotic/fibrotic tumor region, and 48 to the benign region. The extracted protein profiles from the 3 regions are clearly different and subnetwork analysis revealed a possible progression in the nature of the protein pathways involved in the 3 regions. These results suggest a mechanism in cancer progression from benign to tumor and necrotic/fibrotic tumor regions by a progressive switch in the cell phenotype because we detected proteins common to these regions e.g. SSB, NPM1, YBX1, DDX17, HN1L or PHR1, HMGB1, GYS1, GAGE2B, CFAP44. Utilizing a systems biology approach, pathways implicated in muscle proliferation, cell differentiation, actin, cytoskeleton disorganization, apoptosis, neoplasia, and necrosis with Rho kinase activation are enriched and are likely to be involved in the switch in cell phenotype. In addition, T cell response is observed to be inhibited, leading a tolerant immune response towards the tumor. These results are consistent with spatial segmentation analysis showing that the tumor and necrotic-fibrotic tumor regions had a close histological molecular profile distinct from that of benign regions (see cluster tree, Fig. 1a).