ABSTRACT: Comparison of mucus proteins from 6 different segments (from stomach to distal colon). 6 biological replicates, 2 MS replicates. The mucus that protects the surface of the gastrointestinal tract is rich in specialized O-glycoproteins called mucins, but little is known about other mucus proteins or their variability along the tract. We combined collection of mucus from explant tissues with FASP processing and single-shot mass spectrometry in an LTQ-Orbitrap system, to characterize the proteome of the murine mucus from stomach to distal colon. We identified ~1,300 proteins in the mucus, and found no differences in the protein composition or abundance between genders, but clear differences in the different gastrointestinal locations. Qualitatively, there was a relatively stable core proteome (~80% of the total proteins identified). Quantitatively, we found significant differences (~40% of the proteins) that could reflect mucus specialization throughout the gastrointestinal tract. Hierarchical clustering pinpointed a number of proteins that correlated with the main intestinal mucin, Muc2 (e.g. Clca1, Zg16, Klk1). This study provides a deeper knowledge of the gastrointestinal mucus proteome that will be important in further understanding this poorly studied mucosal protection system. Bioinformatics pipeline: Data was acquired in a hybrid LTQ-Orbitrap XL instrument (Thermo Scientific) in dependent mode, measuring full MS in the Orbitrap and selecting the 8 most abundant multiply charged ions for collision (CID, 30% normalized collision energy) and acquisition in the LTQ. Full MS scans were performed in the m/z 350-2,000 range, with internal calibration by lock mass (m/z 371.1012, siloxane), and resolution of 60,000. MS/MS scans were set at a target value of 100,000, with isolation width of 3 amu. The raw files obtained were analyzed in the MaxQuant 1.2.2.5 environment. Data were searched with the Andromeda search engine integrated in MaxQuant against an in-house database containing all the mucin sequences available (http://www.medkem.gu.se/mucinbiology/databases/), the UniProt-SwissProt mouse database (version 1203, reviewed sequences), and the standard MaxQuant contaminant database. Oxidation of methionines and acetylation of the protein N-terminus were set as variable modifications, and carbamidomethylation of cysteines as fixed; enzyme cleavage rules were defined for trypsin/P, with a maximum of 2 missed cleavages. Tolerances were limited as maximum 5 modifications per peptide, 20 ppm error for the first search and 6 ppm for the main search. Isoleucine and leucine were considered indistinguishable. The false discovery rate was calculated from searches against a reversed database, and set to 0.01 for proteins, peptides and modified sites. The identification rate was improved by matching between runs through remapped retention time (window of 2 min). The resulting data were loaded as protein groups into Perseus 1.3.0.4. Protein quantities were calculated as intensity values (from the extracted ion current: XIC) and normalized in ppm to the total XIC for the standard labeled peptides (AQUA) in each run.
Ana M. Rodriguez-Pineiro 
