In the present study plasma samples from 15 systemic lupus erythematosus (SLE) patients and 16 healthy controls of initially unknown haptoglobin (Hp) phenotype were separated by 2-DE, and tryptic digests of the excised Hpalpha polypeptide chain spots were analyzed by MALDI-TOF-MS. Selected tryptic peptides were sequenced by nano-(n)ESI-IT MS/MS. The six major Hp phenotypes were present, although with distinct frequencies in controls and SLE patients. Thus, there were an increased proportion of SLE patients with Hp 2-2, or Hp 2-1S phenotypes. The Hp phenotype distribution resulted in allele frequencies of 0 625 (Hp(2)), 0.281 (Hp(1S)), and 0.093 (Hp(1F)) in healthy controls, correlating fairly well with the allele frequencies of European populations. In contrast, the Hp allele frequencies of the SLE patients were 0.733 (Hp(2)), 0.233 (Hp(1S)), and 0.033 (Hp1(1F)), which clearly indicated an increased frequency of Hp(2), a similar proportion of Hp(1S) and a diminished proportion of Hp(1F) in SLE patients compared with that in healthy controls. Preferential Hpalpha2 expression in SLE patients may contribute to some of the clinical manifestations of the disease such as hypergammaglobulinemia, systemic vasculitis, and cardiovascular disorders.

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Collagen-type-II-induced arthritis (CIA) is an autoimmune disease, which involves a complex host systemic response including inflammatory and autoimmune reactions. CIA is milder in CD38(-/-) than in wild-type (WT) mice. ProteoMiner-equalized serum samples were subjected to 2D-DiGE and MS-MALDI-TOF/TOF analyses to identify proteins that changed in their relative abundances in CD38(-/-) versus WT mice either with arthritis (CIA(+) ), with no arthritis (CIA(-) ), or with inflammation (complete Freund's adjuvant (CFA)-treated mice). Multivariate analyses revealed that a multiprotein signature (n = 28) was able to discriminate CIA(+) from CIA(-) mice, and WT from CD38(-/-) mice within each condition. Likewise, a distinct multiprotein signature (n = 16) was identified which differentiated CIA(+) CD38(-/-) mice from CIA(+) WT mice, and lastly, a third multiprotein signature (n = 18) indicated that CD38(-/-) and WT mice could be segregated in response to CFA treatment. Further analyses showed that the discriminative power to distinguish these groups was reached at protein species level and not at the protein level. Hence, the need to identify and quantify proteins at protein species level to better correlate proteome changes with disease processes. It is crucial for plasma proteomics at the low-abundance protein species level to apply the ProteoMiner enrichment. All MS data have been deposited in the ProteomeXchange with identifiers PXD001788, PXD001799 and PXD002071 (http://proteomecentral.proteomexchange.org/dataset/PXD001788, http://proteomecentral.proteomexchange.org/dataset/PXD001799 and http://proteomecentral.proteomexchange.org/dataset/PXD002071).

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This data article presents the results of all the statistical analyses applied to the relative intensities of the detected 2D-DiGE protein spots for each of the 3 performed DiGE experiments. The data reveals specific subsets of protein spots with significant differences between WT and CD38-deficient mice with either Collagen-induced arthritis (CIA), or with chronic inflammation induced by CFA, or under steady-state conditions. This article also shows the MS data analyses that allowed the identification of the protein species which serve to discriminate the different experimental groups used in this study. Moreover, the article presents MS data on the citrullinated peptides linked to specific protein species that were generated in CIA+ or CFA-treated mice. Lastly, this data article provides MS data on the efficiency of the analyses of the transferrin (Tf) glycopeptide glycosylation pattern in spleen and serum from CIA+ mice and normal controls. The data supplied in this work is related to the research article entitled “identification of multiple transferrin species in spleen and serum from mice with collagen-induced arthritis which may reflect changes in transferrin glycosylation associated with disease activity: the role of CD38” [1]. All mass spectrometry data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with identifiers PRIDE: PXD002644, PRIDE: PXD002643, PRIDE: PXD003183 and PRIDE: PXD003163.

We evaluated the use of the peptide mass fingerprint (PMF) obtained by matrix assisted laser desorption and ionization (MALDI) time-of-flight mass spectrometry (TOF-MS) to track changes in the structure of a protein. The first problem we had to overcome was the inherent complexity of the PMF, which makes it difficult to compare. We dealt with this problem by developing a cluster-based comparison algorithm which takes into account the proportional error made by the mass spectrometer. This procedure involves grouping together similar masses in an intelligent manner, so that we can determine which data correspond to the same peptide (any slight differences can be explained as experimental errors), and which of them are too different and thus more likely to represent different peptides. The proposed algorithm was applied to track changes in a commercially available monoclonal antibody (mAb), namely rituximab (RTX), prepared under the usual hospital conditions and stored refrigerated (4 °C) and frozen (-20 °C) for a long term study. PMFs were obtained periodically over three months. For each checked time, five replicates of the PMFs were obtained in order to evaluate the similarities between them by means of the occurrences of the particular peptides (m/z). After applying the algorithm to the PMF, different approaches were used to analyse the results. Surprisingly, all of them suggested that there were no differences between the two storage conditions tested, i.e. the RTX samples were almost equally well preserved when stored refrigerated at 4 °C or frozen at -20 °C. The cluster-based methodology is new in protein mass spectrometry and could be useful as an easy test for major changes in proteins and biopharmaceutics for diverse applications in industry and other fields, and could provide additional stability data in relation to the practical use of anticancer drugs.