The application of proteomics to diabetes

Rapid Multivariate Analysis Approach to Explore Differential Spatial Protein Profiles in Tissue

Spatially targeted proteomics analyzes the proteome of specific cell types and functional regions within tissue. While spatial context is often essential to understanding biological processes, interpreting sub-region-specific protein profiles can pose a challenge due to the high-dimensional nature of the data. Here, we develop a multivariate approach for rapid exploration of differential protein profiles acquired from distinct tissue regions and apply it to analyze a published spatially targeted proteomics data set collected from Staphylococcus aureus-infected murine kidney, 4 and 10 days postinfection. The data analysis process rapidly filters high-dimensional proteomic data to reveal relevant differentiating species among hundreds to thousands of measured molecules. We employ principal component analysis (PCA) for dimensionality reduction of protein profiles measured by microliquid extraction surface analysis mass spectrometry. Subsequently, k-means clustering of the PCA-processed data groups samples by chemical similarity. Cluster center interpretation revealed a subset of proteins that differentiate between spatial regions of infection over two time points. These proteins appear involved in tricarboxylic acid metabolomic pathways, calcium-dependent processes, and cytoskeletal organization. Gene ontology analysis further uncovered relationships to tissue damage/repair and calcium-related defense mechanisms. Applying our analysis in infectious disease highlighted differential proteomic changes across abscess regions over time, reflecting the dynamic nature of host-pathogen interactions.

Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis

Type 2 diabetes mellitus (T2DM) is a multifactorial and multigenetic disease, and its pathogenesis is complex and largely unknown. In the present study, microarray data (GSE201966) of β-cell enriched tissue obtained by laser capture microdissection were downloaded, including 10 control and 10 type 2 diabetic subjects. A comprehensive bioinformatics analysis of microarray data in the context of protein-protein interaction (PPI) networks was employed, combined with subcellular location information to mine the potential candidate genes for T2DM and provide further insight on the possible mechanisms involved. First, differential analysis screened 108 differentially expressed genes. Then, 83 candidate genes were identified in the layered network in the context of PPI via network analysis, which were either directly or indirectly linked to T2DM. Of those genes obtained through literature retrieval analysis, 27 of 83 were involved with the development of T2DM; however, the rest of the 56 genes need to be verified by experiments. The functional analysis of candidate genes involved in a number of biological activities, demonstrated that 46 upregulated candidate genes were involved in ‘inflammatory response’ and ‘lipid metabolic process’, and 37 downregulated candidate genes were involved in ‘positive regulation of cell death’ and ‘positive regulation of cell proliferation’. These candidate genes were also involved in different signaling pathways associated with ‘PI3K/Akt signaling pathway’, ‘Rap1 signaling pathway’, ‘Ras signaling pathway’ and ‘MAPK signaling pathway’, which are highly associated with the development of T2DM. Furthermore, a microRNA (miR)-target gene regulatory network and a transcription factor-target gene regulatory network were constructed based on miRNet and NetworkAnalyst databases, respectively. Notably, hsa-miR-192-5p, hsa-miR-124-5p and hsa-miR-335-5p appeared to be involved in T2DM by potentially regulating the expression of various candidate genes, including procollagen C-endopeptidase enhancer 2, connective tissue growth factor and family with sequence similarity 105, member A, protein phosphatase 1 regulatory inhibitor subunit 1 A and C-C motif chemokine receptor 4. Smad5 and Bcl6, as transcription factors, are regulated by ankyrin repeat domain 23 and transmembrane protein 37, respectively, which might also be used in the molecular diagnosis and targeted therapy of T2DM. Taken together, the results of the present study may offer insight for future genomic-based individualized treatment of T2DM and help determine the underlying molecular mechanisms that lead to T2DM.

Differential proteome analysis of diabetes mellitus type 2 and its pathophysiological complications

The prevalence of Diabetes Mellitus Type 2 (DM 2) is increasing every passing year due to some global changes in lifestyles of people. The exact underlying mechanisms of the progression of this disease are not yet known. However recent advances in the combined omics more particularly in proteomics and genomics have opened a gateway towards the understanding of predetermined genetic factors, progression, complications and treatment of this disease. Here we shall review the recent advances in proteomics that have led to an early and better diagnostic approaches in controlling DM 2 more importantly the comparison of structural and functional protein biomarkers that are modified in the diseased state. By applying these advanced and promising proteomic strategies with bioinformatics applications and bio-statistical tools the prevalence of DM 2 and its associated disorders i-e nephropathy and retinopathy are expected to be controlled.

A proteomic signature that reflects pancreatic beta-cell function

AIM

Proteomics has the potential to enhance early identification of beta-cell dysfunction, in conjunction with monitoring the various stages of type 2 diabetes onset. The most routine method of assessing pancreatic beta-cell function is an oral glucose tolerance test, however this method is time consuming and carries a participant burden. The objectives of this research were to identify protein signatures and pathways related to pancreatic beta-cell function in fasting blood samples.

METHODS

Beta-cell function measures were calculated for MECHE study participants who completed an oral glucose tolerance test and had proteomic data (n = 100). Information on 1,129 protein levels was obtained using the SOMAscan assay. Receiver operating characteristic curves were used to assess discriminatory ability of proteins of interest. Subsequent in vitro experiments were performed using the BRIN-BD11 pancreatic beta-cell line. Replication of findings were achieved in a second human cohort where possible.

RESULTS

Twenty-two proteins measured by aptamer technology were significantly associated with beta-cell function/HOMA-IR while 17 proteins were significantly associated with the disposition index (p ≤ 0.01). Receiver operator characteristic curves determined the protein panels to have excellent discrimination between low and high beta-cell function. Linear regression analysis determined that beta-endorphin and IL-17F have strong associations with beta-cell function/HOMA-IR, β = 0.039 (p = 0.005) and β = -0.027 (p = 0.013) respectively. Calcineurin and CRTAM were strongly associated with the disposition index (β = 0.005 and β = 0.005 respectively, p = 0.012). In vitro experiments confirmed that IL-17F modulated insulin secretion in the BRIN-BD11 cell line, with the lower concentration of 10 ng/mL significantly increasing glucose stimulated insulin secretion (p = 0.043).

CONCLUSIONS

Early detection of compromised beta-cell function could allow for implementation of nutritional and lifestyle interventions before progression to type 2 diabetes.

Retinol-binding protein 4 and its potential roles in hypercholesterolemia revealed by proteomics

Effects of hypercholesterolemia on alterations of serum proteins have not been fully elucidated. Herein, using two-dimensional gel electrophoresis (2-DE) in conjunction with LC-MS searching has successfully been carried out to investigate the change of protein expression profiles as consequences of raised blood cholesterol at different levels (normal group: total cholesterol 200 mg/dL; borderline high group: total cholesterol 200-239 mg/dL; and high group: total cholesterol ≥ 240 mg/dL) (n = 45). Results revealed that down-regulation of retinol-binding protein 4 (RBP4) (-2.26 fold), transthyretin (-1.25 fold) and gelsolin (-1.47 fold) was observed in the high group. Meanwhile, the other proteins such as haptoglobin, complement factor B and CD5 antigen-like protein were up-regulated upto +3.24, +1.96 and +2.04 fold, respectively. Confirmation by Western blotting revealed a significant reduction of RBP4 (approximately 50 %) in individual samples derived from the high group. Presumptive conclusion can be drawn that down-regulation of RBP4 might be attributable to the inflammation of adipocytes caused by the release of proinflammatory cytokines (e.g. tumor necrosis factor α and interleukin-1β) from adipose tissues. Moreover, the decrease of transthyretin might also be taken into accounts since it is known that the transthyretin usually forms complex with RBP4 to prevent glomerular filtration and excretion through the kidney. The suppressing effect on RBP4 should be potentiated by the increase of complement factor B and CD5 antigen-like protein, which rendered the adipose tissues to overwhelm the liberation of RBP4 to blood circulation by metabolic and inflammatory processes. Such inflammation could further modulate the induction of cytokine release (e.g. IL-6 and IL-1β), resulting in the synthesis of acute phase protein, in particular, haptoglobin and C-reactive proteins from hepatocytes. However, the mechanism of gelsolin reduction remains unclear. Among these differentially expressed proteins, the RBP4 has been proposed as a major linkage between hypercholesterolemia, adipose tissues, liver and kidney, which is believed to be a potential biomarker for metabolic and cardiovascular disorders associated with dyslipidemia in the future.

Proteomic analysis of adipose tissue: informing diabetes research

Diabetes, one of the most common endocrine diseases worldwide, results from complex pathophysiological mechanisms that are not fully understood. Adipose tissue is considered a major endocrine organ and plays a central role in the development of diabetes. The identification of the adipose tissue-derived factors that contribute to the onset and progression of diabetes will hopefully lead to the development of preventive and therapeutic interventions. Proteomic techniques may be useful tools for this purpose. In the present review, we have summarized the studies conducting adipose tissue proteomics in subjects with diabetes and insulin resistance, and discussed the proteins identified in these studies as candidates to exert important roles in these disorders.

Gender dimorphism in regulation of plasma proteins in streptozotocin-induced diabetic rats

In the present study, we examined differentially regulated plasma proteins between healthy control and streptozotocin (STZ)-induced male and female diabetic rats by 2DE-based proteomic analysis. Animal experiments revealed that significantly lower plasma insulin levels were observed in female diabetic rats, consequently resulting in higher blood glucose levels in female diabetic rats. Importantly, plasma levels of sex hormones were significantly altered in a gender-dependent manner before and after STZ treatment. Results of the animal experiment indicated the existence of sexual dimorphism in the regulation of plasma proteins between healthy control and diabetic rats. Plasma proteome analysis enabled us to identify a total of 38 proteins showing sexual dimorphic regulation patterns. In addition, for the first time, we identified several differentially regulated plasma proteins between healthy control and diabetic rats, including apolipoprotein E, fetuin B, α-1-acid glycoprotein, β-2-glycoprotein 1, 3-hydroxyanthranilate 3,4-dioxygenase, and serum amyloid P-component. To the best of our knowledge, this is the first proteomic approach to address sexual dimorphism in diabetic animals. These proteomic data on gender-dimorphic regulation of plasma proteins provide valuable information that can be used for evidence-based gender-specific clinical treatment of diabetes.

Application of proteomics to the study of polycystic ovary syndrome

BACKGROUND

Clinical proteomics consists of qualitative and quantitative profiling of proteins present in clinical specimens such as body fluids or tissues, with the aim of discovering novel proteins and cellular pathways associated with the disease of interest.

AIM

To review the proteomic studies conducted to date that addressed different aspects of the pathogenesis of polycystic ovary syndrome (PCOS).

METHODS

Descriptive review of studies that applied proteomic techniques to the study of PCOS. Published articles were identified using the Entrez-PubMed online search facilities.

RESULTS

Most studies conducted to date focused on protein variations in plasma and different target tissues. Plasma proteomics analysis revealed that PCOS associates changes in protein expression in several acute-phase response proteins. Moreover, some of these molecules play major roles in iron metabolism and low-grade chronic inflammation. Studies using omental adipose tissue from morbidly obese women with or without PCOS revealed differences in abundance of proteins that may be involved in lipid and glucose metabolism, oxidative stress processes, and adipocyte differentiation. Moreover, identification of differentially expressed proteins in ovarian tissue, granulosa cells or T lymphocites may help to characterize more clearly some aspects of this disorder.

CONCLUSIONS

Although the application of proteomic techniques to the study of PCOS is in its early infancy, studies conducted to date highlight its heterogeneous nature. Aside from androgen excess, several pathways related to intermediate metabolism, oxidative stress processes, inflammation and iron metabolism appear to be involved in the pathophysiology of PCOS.

Biomarkers for Diabetes Complications: The Results of Several Clinical Studies

Diabetes is a common metabolic disorder. Its microvascular and macrovascular complications contribute to death, disabilities, and reduction in life expectancy in diabetes. It is a costly disease, and affects not only the patient and family, but also the public health, communities and society. It takes an increasing proportion of the national health care expenditure. The prevention of the development of diabetes and its complications is a major concern. Biomarkers have been investigated for understanding the mechanisms of the development and progression of diabetic complications. In this paper, the biomarkers which are recommended in the clinical practice and laboratory medicine guidelines, and which have been investigated for prediction or diagnosis of diabetes complications, have been reviewed. The results of several clinical studies will be summarized.

Differentially displayed proteins as a tool for the development of type 2 diabetes

Background

Type 2 diabetes is a complex disease that still requires a great deal of work to be carried out to understand the pathophysiology. Recently, researchers have focused on studying the organs and tissues known to be involved in the development of the type 2 phenotype using a proteomic approach. Little work has been reported on plasma of type 2 diabetics in whom the clinical status has been well characterized. In this study, changes in plasma proteins of type 2 diabetics were investigated by proteomic analysis in well-characterized individuals with type 2 diabetes (early and late stage) and control groups (with or without a family history of diabetes).

Methods

Samples were analysed by two-dimensional gel electrophoresis and significantly differentiated proteins were identified by nano-LC-ESI-MS.

Results

A total of 12 protein signatures that were differentially displayed with high significance compared with controls were selected. Four of the differentially displayed proteins were identified as haptoglobin alpha2, haptoglobin Hp2(fragment) and transthyretin and Chain A (formerly prealbumin), and all were up-regulated. Thiol-specific antioxidant protein, Chain A, tertiary structures of three amyloidogenic transthretin variants and haptoglobin-related protein precursor were all down-regulated in controls with a family history of diabetes, early and late diabetic patients in comparison with the control.

Conclusion

A proteomic-based approach was used to discover and identify the differentially expressed proteins in various states of type 2 diabetes.

Localized-statistical quantification of human serum proteome associated with type 2 diabetes

BACKGROUND

Recent advances in proteomics have shed light to discover serum proteins or peptides as biomarkers for tracking the progression of diabetes as well as understanding molecular mechanisms of the disease.

RESULTS

In this work, human serum of non-diabetic and diabetic cohorts was analyzed by proteomic approach. To analyze total 1377 high-confident serum-proteins, we developed a computing strategy called localized statistics of protein abundance distribution (LSPAD) to calculate a significant bias of a particular protein-abundance between these two cohorts. As a result, 68 proteins were found significantly over-represented in the diabetic serum (p<0.01). In addition, a pathway-associated analysis was developed to obtain the overall pathway bias associated with type 2 diabetes, from which the significant over-representation of complement system associated with type 2 diabetes was uncovered. Moreover, an up-stream activator of complement pathway, ficolin-3, was observed over-represented in the serum of type 2 diabetic patients, which was further validated with statistic significance (p = 0.012) with more clinical samples.

CONCLUSIONS

The developed LSPAD approach is well fit for analyzing proteomic data derived from biological complex systems such as plasma proteome. With LSPAD, we disclosed the comprehensive distribution of the proteins associated with diabetes in different abundance levels and the involvement of ficolin-related complement activation in diabetes.

The application of proteomics technology to thrombosis research: the identification of potential therapeutic targets in cardiovascular disease

Thrombus formation underpins the development of cardiovascular diseases, including acute coronary syndromes and ischaemic stroke. A number of well-characterised cardiovascular risk factors which contribute to the development of the majority of cardiovascular events have been identified, including dyslipidaemia, hypertension and diabetes. Individuals with type 2 diabetes mellitus (T2DM) have a 3- to 5-fold increased risk for development of cardiovascular disease (CVD). They may have a cluster of haemostatic abnormalities, including elevated levels of plasminogen activator inhibitor-1 (PAI-1) and fibrinogen, which contribute to acute thrombotic events. It is clear that additional unidentified risk factors contribute to the pathogenesis of cardiovascular events, and so the search for novel biomarkers and effectors, particularly in individuals with T2DM, remains a major challenge of cardiovascular medicine. Plasma and cellular proteins which contribute to thrombus formation have the potential to confer a pro-thrombotic state and represent a link between genotype, environment and disease phenotype. The comprehensive analysis of these proteins is now increasingly facilitated through the continued development of proteomic technologies which provide multifaceted approaches to the identification of novel biomarkers and/or effectors of thrombus formation and on which future anticoagulant and thrombolytic therapies may be based. This review provides an overview of current proteomic technologies. It focuses on the recent studies in which these technologies have been applied in the search for novel proteins that may confer increased risk of acute cardiovascular diseases and therefore that may influence disease progression and therapy.

Proteomics for diabetes research: an update and future perspectives

Diabetes is a common disease worldwide and can cause several complications, leading to systemic derangements and end-organ damage. Despite blood sugar control and adequate therapy with currently available drugs, diabetic complications remain a serious issue in clinical practice, indicating that our knowledge of diabetes and its complications is only at the tip of the iceberg. Better understanding of its pathogenesis and pathophysiology is crucial to achieve better therapeutic outcomes and to prevent its complications. This review provides an overview of proteomics and introduces proteomic technologies commonly used for diabetes research. Recent proteomic studies for the investigation of diabetes and its complications are summarized. Finally, the future perspectives for the field of proteomics in diabetes research are discussed.