The lack of high-throughput methods to analyze the adipose tissue protein composition limits our understanding of the protein networks responsible for age and diet related metabolic response. We have developed an approach using multiple-dimension liquid chromatography tandem mass spectrometry and extended multiplexing (24 biological samples) with tandem mass tags (TMT) labeling to analyze proteomes of epididymal adipose tissues isolated from mice fed either low or high fat diet for a short or a long-term, and from mice that aged on low high fat diets. The peripheral metabolic health (as measured by body weight, adiposity, plasma fasting glucose, insulin, triglycerides, total cholesterol levels, and glucose and insulin tolerance tests) deteriorated with diet and advancing age, with long-term high fat diet exposure being the worst. In response to short-term high fat diet, 43 proteins representing lipid metabolism ( AACS, ACOX1, ACLY) and red-ox pathways ( CPD2, CYP2E, SOD3) were significantly altered (FDR < 10%). Long-term high fat diet significantly altered 55 proteins associated with immune response ( IGTB2, IFIT3, LGALS1) and rennin angiotensin system ( ENPEP, CMA1, CPA3, ANPEP). Age-related changes on low fat diet significantly altered only 18 proteins representing mainly urea cycle ( OTC, ARG1, CPS1), and amino acid biosynthesis ( GMT, AKR1C6). Surprisingly, high fat diet driven age-related changes culminated with alterations in 155 proteins involving primarily the urea cycle ( ARG1, CPS1), immune response/complement activation ( C3, C4b, C8, C9, CFB, CFH, FGA), extracellular remodeling ( EFEMP1, FBN1, FBN2, LTBP4, FERMT2, ECM1, EMILIN2, ITIH3) and apoptosis ( YAP1, HIP1, NDRG1, PRKCD, MUL1) pathways. Using our adipose tissue tailored approach we have identified both age-related and high fat diet specific proteomic signatures highlighting a pronounced involvement of arginine metabolism in response to advancing age, and branched chain amino acid metabolism in early response to high fat feeding. Data are available via ProteomeXchange with identifier PXD005953.

The identification of biomarkers to noninvasively detect prediabetes/diabetes will facilitate interventions designed to prevent or delay progression to frank diabetes and its attendant complications. The purpose of this study was to characterize the human salivary proteome in type-2 diabetes to identify potential biomarkers of diabetes. Whole saliva from control and type-2 diabetic individuals was characterized by multidimensional liquid chromatography/tandem mass spectrometry (2D-LC-MS/MS). Label-free quantification was used to identify differentially abundant protein biomarkers. Selected potential biomarkers were then independently validated in saliva from control, diabetic, and prediabetic subjects by Western immunoblotting and ELISA. Characterization of the salivary proteome identified a total of 487 unique proteins. Approximately 33% of these have not been previously reported in human saliva. Of these, 65 demonstrated a greater than 2-fold difference in abundance between control and type-2 diabetes samples. A majority of the differentially abundant proteins belong to pathways regulating metabolism and immune response. Independent validation of a subset of potential biomarkers utilizing immunodetection confirmed their differential expression in type-2 diabetes, and analysis of prediabetic samples demonstrated a trend of relative increase in their abundance with progression from the prediabetic to the diabetic state. This comprehensive proteomic analysis of the human salivary proteome in type-2 diabetes provides the first global view of potential mechanisms perturbed in diabetic saliva and their utility in detection and monitoring of diabetes. Further characterization of these markers in a larger cohort of subjects may provide the basis for new, noninvasive tests for diabetes screening, detection, and monitoring.

With the increasing availability of de novo sequencing algorithms for interpreting high-mass accuracy tandem mass spectrometry (MS/MS) data, there is a growing need for programs that accurately identify proteins from de novo sequencing results. De novo sequences derived from tandem mass spectra of peptides often contain ambiguous regions where the exact amino acid order cannot be determined. One problem this poses for sequence alignment algorithms is the difficulty in distinguishing discrepancies due to de novo sequencing errors from actual genomic sequence variation and posttranslational modifications. We present a novel, mass-based approach to sequence alignment, implemented as a program called OpenSea, to resolve these problems. In this approach, de novo and database sequences are interpreted as masses of residues, and the masses, rather than the amino acid codes, are compared. To provide further flexibility, the masses can be aligned in groups, which can resolve many de novo sequencing errors. The performance of OpenSea was tested with three types of data: a mixture of known proteins, a mixture of unknown proteins that commonly contain sequence variations, and a mixture of posttranslationally modified known proteins. In all three cases, we demonstrate that OpenSea can identify more peptides and proteins than commonly used database-searching programs (SEQUEST and ProteinLynx) while accurately locating sequence variation sites and unanticipated posttranslational modifications in a high-throughput environment.

Spontaneous preterm birth (SPTB) is a major contributor to perinatal morbidity and mortality. However, the diagnosis of preterm labor (PTL) that leads to preterm birth is difficult, and there is a pressing need for improved diagnosis. We utilized multidimensional liquid chromatography-tandem mass spectrometry (LC/LC-MS/MS; MudPIT) and Fluorescence two-dimensional differential in-gel electrophoresis (2D-DIGE) to identify potential biomarkers of PTL and SPTB. MudPIT analysis identified 205 proteins in cervical-vaginal fluid (CVF), 28 of which exhibited significant differences in pairwise and progressive comparisons. Calgranulins, annexins, S100 calcium-binding protein A7, and epidermal fatty acid binding protein were abundant in CVF and differentially present in PTL and SPTB samples, as were the serum proteins alpha-1-antitrypsin, alpha1-acid glycoprotein, haptoglobin, serotransferrin, and vitamin D binding protein. 2D-DIGE identified 17 proteins that were significantly differentially present in PTL and SPTB. Immunoblotting with specific antibodies confirmed the differences and trends of selected markers. Further characterization and quantification of these markers in a larger cohort of subjects may provide the basis for new tests for the early, noninvasive positive prediction of SPTB.