James N. McGuire

Discovery of the Once-Weekly Glucagon-Like Peptide-1 (GLP-1) Analogue Semaglutide.

Liraglutide is an acylated glucagon-like peptide-1 (GLP-1) analogue that binds to serum albumin in vivo and is approved for once-daily treatment of diabetes as well as obesity. The aim of the present studies was to design a once weekly GLP-1 analogue by increasing albumin affinity and secure full stability against metabolic degradation. The fatty acid moiety and the linking chemistry to GLP-1 were the key features to secure high albumin affinity and GLP-1 receptor (GLP-1R) potency and in obtaining a prolonged exposure and action of the GLP-1 analogue. Semaglutide was selected as the optimal once weekly candidate. Semaglutide has two amino acid substitutions compared to human GLP-1 (Aib(8), Arg(34)) and is derivatized at lysine 26. The GLP-1R affinity of semaglutide (0.38 ± 0.06 nM) was three-fold decreased compared to liraglutide, whereas the albumin affinity was increased. The plasma half-life was 46.1 h in mini-pigs following i.v. administration, and semaglutide has an MRT of 63.6 h after s.c. dosing to mini-pigs. Semaglutide is currently in phase 3 clinical testing.

Characterization of Membrane-shed Microvesicles from Cytokine-stimulated β-Cells Using Proteomics Strategies

Microparticles and exosomes are two of the most well characterized membrane-derived microvesicles released either directly from the plasma membrane or released through the fusion of intracellular multivesicular bodies with the plasma membrane, respectively. They are thought to be involved in many significant biological processes such as cell to cell communication, rescue from apoptosis, and immunological responses. Here we report for the first time a quantitative study of proteins from β-cell-derived microvesicles generated after cytokine induced apoptosis using stable isotope labeled amino acids in cell culture combined with mass spectrometry. We identified and quantified a large number of β-cell-specific proteins and proteins previously described in microvesicles from other cell types in addition to new proteins located to these vesicles. In addition, we quantified specific sites of protein phosphorylation and N-linked sialylation in proteins associated with microvesicles from β-cells. Using pathway analysis software, we were able to map the most distinctive changes between microvesicles generated during growth and after cytokine stimulation to several cell death and cell signaling molecules including tumor necrosis factor receptor superfamily member 1A, tumor necrosis factor, α-induced protein 3, tumor necrosis factor-interacting kinase receptor-interacting serine-threonine kinase 1, and intercellular adhesion molecule 1.

Plasma proteome analysis of patients with type 1 diabetes with diabetic nephropathy

BackgroundAs part of a clinical proteomics program focused on diabetes and its complications we are looking for new and better protein biomarkers for diabetic nephropathy. The search for new and better biomarkers for diabetic nephropathy has, with a few exceptions, previously focused on either hypothesis-driven studies or urinary based investigations. To date only two studies have investigated the proteome of blood in search for new biomarkers, and these studies were conducted in sera from patients with type 2 diabetes. This is the first reported in depth proteomic study where plasma from type 1 diabetic patients was investigated with the goal of finding improved candidate biomarkers to predict diabetic nephropathy. In order to reach lower concentration proteins in plasma a pre-fractionation step, either hexapeptide bead-based libraries or anion exchange chromatography, was performed prior to surface enhanced laser desorption/ionization time-of-flight mass spectrometry analysis.ResultsProteomic analysis of plasma from a cross-sectional cohort of 123 type 1 diabetic patients previously diagnosed as normoalbuminuric, microalbuminuric or macroalbuminuric, gave rise to 290 peaks clusters of which 16 were selected as the most promising biomarker candidates based on statistical performance, including independent component analysis. Four of the peaks that were discovered have been identified as transthyretin, apolipoprotein A1, apolipoprotein C1 and cystatin C. Several yet unidentified proteins discovered by this novel approach appear to have more potential as biomarkers for diabetic nephropathy.ConclusionThese results demonstrate the capacity of proteomic analysis of plasma, by confirming the presence of known biomarkers as well as revealing new biomarkers for diabetic nephropathy in plasma in type 1 diabetic patients.

Mass spectrometry is only one piece of the puzzle in clinical proteomics

Biomarker discovery in clinical proteomics is being performed on relatively large patient cohorts by utilizing the high throughput of laser desorption/ionization mass spectrometry (MALDI- and SELDI-TOF-MS). Dealing directly with patient samples as opposed to working in cell or animal systems requires a host of considerations both before and after mass spectrometric analysis to obtain robust biomarker candidates. The challenges associated with the heterogeneity of typical samples are amplified by the ability to detect hundreds to thousands of proteins simultaneously. Adherence to protocols and consistency, however, can ensure optimal results. A study starts necessarily with a relevant clinical question and proceeds to a planning phase where sample availability, statistical test selection, logistics and bias reduction are key points. The physical analysis requires consistency and standardized protocols that are helped significantly through automation. Data analysis is broken into two stages, screening and final testing, which can detect either single candidates or a pattern of proteins. Biomarker identification can be performed at this point and will help significantly in the last stage, interpretation. Replication should be performed in an independent sample set in a separate study. The candidate biomarkers from an initial study give a wealth of information that can help to pinpoint patient subpopulations for a more exhaustive proteomic study using complementary platforms with limited capacity but extremely high information content. A clinical proteomics pilot project can also lead to better selection of model systems by providing a direct link with patient samples.

Quantitative iTRAQ-Based Proteomic Identification of Candidate Biomarkers for Diabetic Nephropathy in Plasma of Type 1 Diabetic Patients

IntroductionAs part of a clinical proteomics programme focused on diabetes and its complications, it was our goal to investigate the proteome of plasma in order to find improved candidate biomarkers to predict diabetic nephropathy.MethodsProteins derived from plasma from a cross-sectional cohort of 123 type 1 diabetic patients previously diagnosed as normoalbuminuric, microalbuminuric or macroalbuminuric were enriched with hexapeptide library beads and subsequently pooled within three groups. Proteins from the three groups were compared by online liquid chromatography and tandem mass spectrometry in three identical repetitions using isobaric mass tags (iTRAQ). The results were further analysed with ingenuity pathway analysis. Levels of apolipoprotein A1, A2, B, C3, E and J were analysed and validated by a multiplex immunoassay in 20 type 1 diabetic patients with macroalbuminuria and 10 with normoalbuminuria.ResultsA total of 112 proteins were identified in at least two out of three replicates. The global protein ratios were further evaluated by ingenuity pathway analysis, resulting in the recognition of apolipoprotein A2, B, C3, D and E as key nodes in the top-rated network. The multiplex immunoassay confirmed the overall protein expression patterns observed by the iTRAQ analysis.ConclusionThe candidate biomarkers discovered in this cross-sectional cohort may turn out to be progression biomarkers and might have several clinical applications in the treatment and monitoring of diabetic nephropathy; however, they need to be confirmed in a longitudinal cohort.

Finding diabetic nephropathy biomarkers in the plasma peptidome by high-throughput magnetic bead processing and MALDI-TOF-MS analysis

Purpose and experimental design: Diabetic nephropathy (DN) is the most common cause of end‐stage renal disease and improved biomarkers would help identify high‐risk individuals. The aim of this study was to discover candidate biomarkers for DN in the plasma peptidome in an in‐house cross‐sectional cohort (n=122) of type 1 diabetic patients diagnosed with normo‐, micro‐, and macroalbuminuria.

Screening newborns for candidate biomarkers of type 1 diabetes

Combining samples from a national neonatal screening programme with the information from a national health registry allow for unique opportunities in analysing newborn blood for protein changes that could predict eventual disease development. A nested case-control cohort (n = 54 cases, 108 controls) was analysed by proteomics as a new way of looking for biomarkers that could bolster prediction of T1D risk in newborns. Protein extraction and haemoglobin depletion were automated and samples were processed and analysed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). The data set was reduced to the highest quality peaks and analysed using conditional logistic regression. A total of 25 protein peaks were found to differ between the two groups. The automated haemoglobin depletion provides a platform for further proteomics studies of individual patient material. The method opens a door to a wealth of patient material stored as dried blood spots.

N-glycosylation proteome enrichment analysis in kidney reveals differences between diabetic mouse models

BackgroundDiabetic nephropathy (DN) is a late complication in both type 1 diabetes mellitus (T1DM) and T2DM. Already at an early stage of DN morphological changes occur at the cell surface and in the extracellular matrix where the majority of the proteins carry N-linked glycosylations. These glycosylated proteins are highly important in cell adhesion and cell–matrix processes but not much is known about how they change in DN or whether the distinct etiology of T1DM and T2DM could have an effect on their abundances.MethodWe enriched for the N-glycosylated kidney proteome in db/db mice dosed with insulin or vehicle, in streptozotocin-induced (STZ) diabetic mice and healthy control mice dosed with vehicle. Glycopeptides were analyzed with label-free shotgun mass spectrometry and differential protein abundances identified in both mouse models were compared using multivariate analyses.ResultsThe majority of the N-glycosylated proteins were similarly regulated in both mouse models. However, distinct differences between the two mouse models were for example seen for integrin-β1, a protein expressed mainly in the glomeruli which abundance was increased in the STZ diabetic mice while decreased in the db/db mice and for the sodium/glucose cotransporter-1, mainly expressed in the proximal tubules which abundance was increased in the db/db mice but decreased in the STZ diabetic mice. Insulin had an effect on the level of both glomerular and tubular proteins in the db/db mice. It decreased the abundance of G-protein coupled receptor-116 and of tyrosine-protein phosphatase non-receptor type substrate-1 away from the level in the healthy control mice.ConclusionsOur finding of differences in the N-glycosylation protein profiles in the db/db and STZ mouse models suggest that the etiology of DN could give rise to variations in the cell adhesion and cell–matrix composition in T1DM and T2DM. Thus, N-glycosylated protein differences could be a clue to dissimilarities in T1DM and T2DM at later stages of DN. Furthermore, we observed insulin specific regulation of N-glycosylated proteins both in the direction of and away from the abundances in healthy control mice.

Effects of insulin and the glucagon‐like peptide 1 receptor agonist liraglutide on the kidney proteome in db/db mice

Diabetes mellitus (DM) is a worldwide disease that affects 9% of the adult world population and type 2 DM accounts for 90% of those. A common consequence of DM is kidney complications, which could lead to kidney failure. We studied the potential effects of treatment with insulin and the glucagon‐like peptide 1 receptor (GLP‐1R) agonist liraglutide on the diabetic kidney proteome through the use of the db/db mouse model system and mass spectrometry (MS). Multivariate analyses revealed distinct effects of insulin and liraglutide on the db/db kidney proteome, which was seen on the protein levels of, for example, pterin‐4 α‐carbinolamine dehydratase/dimerization cofactor of hepatocyte nuclear factor‐1α (PCBD1), neural precursor cell expressed developmentally down‐regulated‐8 (NEDD8), transcription elongation factor‐B polypeptide‐1 (ELOC) and hepcidin (HEPC). Furthermore, the separation of the insulin, liraglutide and vehicle db/db mouse groups in multivariate analyses was not mainly related to the albumin excretion rate (AER) or the level of glycated hemoglobin A1c (HbA1c%) in the mice. In summary, we show that insulin and liraglutide give rise to separate protein profiles in the db/db mouse kidney.