Recent technological developments in proteomics shed new light on translational research on diabetic microangiopathy

Comparative proteomics of proliferative diabetic retinopathy in people with Type 2 diabetes highlights the role of inflammation, visual transduction, and extracellular matrix pathways

Purpose

To explore the vitreous humor proteome from type 2 diabetes subjects with proliferative diabetic retinopathy (PDR) in the Indian population.

Methods

We performed mass spectrometry-based label-free quantitative analysis of vitreous proteome of PDR (n = 13) and idiopathic macular hole (IMH; control) subjects (n = 14). Nine samples of PDR and 10 samples of IMH were pooled as case and control, respectively, and compared. Four samples each of PDR and IMH were analyzed individually without pooling to validate the results of the pooled analysis. Comparative quantification was performed using Scaffold software which calculated the fold changes of differential expression. Bioinformatics analysis was performed using DAVID and STRING software.

Results

We identified 469 proteins in PDR and 517 proteins in IMH vitreous, with an overlap of 172 proteins. Also, 297 unique proteins were identified in PDR and 345 in IMH. In PDR vitreous, 37 proteins were upregulated (P < 0.05) and 19 proteins were downregulated compared to IMH. Protein distribution analysis clearly demonstrated a separation of protein expression in PDR and IMH. Significantly upregulated proteins included fibrinogen gamma chain, fibrinogen beta chain, and carbonic anhydrase 1 and downregulated proteins included alpha-1-antitrypsin, retinol-binding protein 3, neuroserpin, cystatin C, carboxypeptidase E and cathepsin-D.

Conclusion

Diabetic retinopathy pathogenesis involves proteins which belong to inflammation, visual transduction, and extracellular matrix pathways. Validation-based experiments using enzyme-linked immunosorbent assay (ELISA) or western blotting are needed to establish cause and effect relationships of these proteins to the disease state, to develop them as biomarkers or drug molecules.

Interpretable machine learning-derived nomogram model for early detection of diabetic retinopathy in type 2 diabetes mellitus: a widely targeted metabolomics study

Objective

Early identification of diabetic retinopathy (DR) is key to prioritizing therapy and preventing permanent blindness. This study aims to propose a machine learning model for DR early diagnosis using metabolomics and clinical indicators.

Methods

From 2017 to 2018, 950 participants were enrolled from two affiliated hospitals of Wenzhou Medical University and Anhui Medical University. A total of 69 matched blocks including healthy volunteers, type 2 diabetes, and DR patients were obtained from a propensity score matching-based metabolomics study. UPLC-ESI-MS/MS system was utilized for serum metabolic fingerprint data. CART decision trees (DT) were used to identify the potential biomarkers. Finally, the nomogram model was developed using the multivariable conditional logistic regression models. The calibration curve, Hosmer–Lemeshow test, receiver operating characteristic curve, and decision curve analysis were applied to evaluate the performance of this predictive model.

Results

The mean age of enrolled subjects was 56.7 years with a standard deviation of 9.2, and 61.4% were males. Based on the DT model, 2-pyrrolidone completely separated healthy controls from diabetic patients, and thiamine triphosphate (ThTP) might be a principal metabolite for DR detection. The developed nomogram model (including diabetes duration, systolic blood pressure and ThTP) shows an excellent quality of classification, with AUCs (95% CI) of 0.99 (0.97–1.00) and 0.99 (0.95–1.00) in training and testing sets, respectively. Furthermore, the predictive model also has a reasonable degree of calibration.

Conclusions

The nomogram presents an accurate and favorable prediction for DR detection. Further research with larger study populations is needed to confirm our findings.

Proteomics identifies new potential therapeutic targets of diabetic retinopathy

Retinal pigment epithelium (RPE) is an important component of the outer blood-retinal barrier and plays a critical role in maintaining retinal homeostasis. Alterations in RPE can be detected during the early stages of diabetic retinopathy (DR). However, the molecular mechanisms underlying these early changes remain unclear. We investigated the molecular changes induced in the RPE by high glucose concentrations by constructing a high glucose-induced ARPE-19 cell injury model simulating the DR environment in vitro. Proteomic analysis was conducted to measure differences in protein expression between cells treated with normal (5 mM) and high (25 mM) glucose concentrations, and bioinformatics techniques were used to analyze the mechanism of action. The results of the proteomic analyses were validated using western blotting. High glucose levels inhibited the proliferation of ARPE-19 cells. We identified 88 upregulated proteins and 114 downregulated proteins. Six of these proteins were selected for further validation. Changes in the proteome mainly affected the lysosome and cell cycle pathways. Proteomic differences between ARPE-19 cells treated with normal and high glucose concentrations indicate that damage to the RPE in DR may be caused by specific mechanisms. Our study verified protein changes in ARPE-19 cells in a high-glucose environment and may provide new strategies for understanding the molecular mechanisms underlying DR.

Diabetes and Its Cardiovascular Complications: Comprehensive Network and Systematic Analyses

Diabetes mellitus is a worldwide health problem that usually comes with severe complications. There is no cure for diabetes yet and the threat of these complications is what keeps researchers investigating mechanisms and treatments for diabetes mellitus. Due to advancements in genomics, epigenomics, proteomics, and single-cell multiomics research, considerable progress has been made toward understanding the mechanisms of diabetes mellitus. In addition, investigation of the association between diabetes and other physiological systems revealed potentially novel pathways and targets involved in the initiation and progress of diabetes. This review focuses on current advancements in studying the mechanisms of diabetes by using genomic, epigenomic, proteomic, and single-cell multiomic analysis methods. It will also focus on recent findings pertaining to the relationship between diabetes and other biological processes, and new findings on the contribution of diabetes to several pathological conditions.

Possible Differential Diagnosis of the Degrees of Rheological Disturbances in Patients with Type 2 Diabetes Mellitus by Dielectrophoresis of Erythrocytes

Hemorheological disorders in structural and functional parameters of erythrocytes are involved in the pathological process in type 2 diabetes mellitus (DM). Aim: to investigate the feasibility of differential diagnosis of the degrees of rheological disturbances in patients with type 2 DM by dielectrophoresis of erythrocytes. Methods: 62 subjects (58.7 ± 1.6 years) with type 2 DM diagnosed according to the criteria of the ADA were subdivided into two groups: medium (n = 47) and high (n = 15) risk of microcirculatory disturbances (EASD, 2013). Electric and viscoelastic parameters of erythrocytes were determined by dielectrophoresis using an electric optical system of cell detection. Results: the progression of rheological disturbances in the patients with type 2 DM was accompanied by significant decreases in deformation amplitude; dipole moment; polarizability; and membrane capacity; and increases in conductivity, viscosity, rigidity, hemolysis, and formation of aggregates (p < 0.05). Combined use of the parameters increased sensitivity (97.8%) and specificity (86.7%) for diagnosis of rheological disturbances in type 2 DM. Conclusion: the proposed experimental approach possesses low invasiveness, high productivity, shorter duration, vividness of the results. The method allows to evaluate not only local (renal and ocular) but also systemic status of microcirculation using more than 20 parameters of erythrocytes.

Proteomic Biomarkers of Retinal Inflammation in Diabetic Retinopathy

Diabetic retinopathy (DR), a sight-threatening neurovasculopathy, is the leading cause of irreversible blindness in the developed world. DR arises as the result of prolonged hyperglycemia and is characterized by leaky retinal vasculature, retinal ischemia, retinal inflammation, angiogenesis, and neovascularization. The number of DR patients is growing with an increase in the elderly population, and therapeutic approaches are limited, therefore, new therapies to prevent retinal injury and enhance repair are a critical unmet need. Besides vascular endothelial growth factor (VEGF)-induced vascular proliferation, several other mechanisms are important in the pathogenesis of diabetic retinopathy, including vascular inflammation. Thus, combining anti-VEGF therapy with other new therapies targeting these pathophysiological pathways of DR may further optimize treatment outcomes. Technological advancements have allowed for high-throughput proteomic studies examining biofluids such as aqueous humor, vitreous humor, tear, and serum. Many DR biomarkers have been identified, especially proteins involved in retinal inflammatory processes. This review attempts to summarize the proteomic biomarkers of DR-associated retinal inflammation identified over the last several years.

Quantitative Proteomic Analysis of Hepatic Tissue of T2DM Rhesus Macaque

Type 2 diabetes mellitus (T2DM) is a metabolic disorder that severely affects human health, but the pathogenesis of the disease remains unknown. The high-fat/high-sucrose diets combined with streptozotocin- (STZ-) induced nonhuman primate animal model of diabetes are a valuable research source of T2DM. Here, we present a study of a STZ rhesus macaque model of T2DM that utilizes quantitative iTRAQ-based proteomic method. We compared the protein profiles in the liver of STZ-treated macaques as well as age-matched healthy controls. We identified 171 proteins differentially expressed in the STZ-treated groups, about 70 of which were documented as diabetes-related gene in previous studies. Pathway analyses indicated that the biological functions of differentially expressed proteins were related to glycolysis/gluconeogenesis, fatty acid metabolism, complements, and coagulation cascades. Expression change in tryptophan metabolism pathway was also found in this study which may be associations with diabetes. This study is the first to explore genome-wide protein expression in hepatic tissue of diabetes macaque model using HPLC-Q-TOF/MS technology. In addition to providing potential T2DM biomarkers, this quantitative proteomic study may also shed insights regarding the molecular pathogenesis of T2DM.

Differentiation of type 2 diabetes mellitus with different complications by proteomic analysis of plasma low abundance proteins

BACKGROUND

Few biomarkers of type 2 diabetes mellitus (T2DM) are replicable in the differentiation of T2DM with different complications. We aimed to identify proteomic biomarkers among T2DM patients with nephropathy or retinopathy.

METHODS

Plasma low abundance proteins were enriched by depletion of 14 high abundance proteins using an affinity removal system, and subjected to nanoflow liquid chromatography electrospray ionization (nano LC-ESI) mass spectrometry after a gel electrophoresis with in-gel digestion. The plasma differential proteomes between normal adults and diabetic patients were validated by another cohort of 149 T2DM patients.

RESULTS

A total of 826 proteins in plasma were consistently identified from 8 plasma samples of normal adults, and 817 proteins were consistently identified in 8 plasma samples of T2DM patients. Using the MetaCore analysis, low abundance proteins in plasma between normal adults and T2DM patients were significantly different in 5 functional pathways. Moreover, plasma prolactin-induced protein (PIP), thrombospondin-2 (THBS2), L1 cell adhesion molecule (L1CAM) and neutrophil gelatinase-associated lipocalin (NGAL) levels were higher in T2DM patients. Further, PIP, THBS2 and NGAL were significantly higher in T2DM patients with nephropathy (albuminuria) but not in those with retinopathy, while L1CAM levels were higher in T2DM patients with retinopathy.

CONCLUSIONS

This study identified that higher PIP, THBS2 and/or NGAL levels were significantly associated with nephropathy of T2DM, and higher L1CAM but normal PIP, THBS2 or NGAL was significantly associated with retinopathy of T2DM.

Identification of a Novel Function of Adipocyte Plasma Membrane-Associated Protein (APMAP) in Gestational Diabetes Mellitus by Proteomic Analysis of Omental Adipose Tissue

Gestational diabetes mellitus (GDM) is considered as an early stage of type 2 diabetes mellitus. In this study, we compared demographic and clinical data between six GDM subjects and six normal glucose tolerance (NGT; healthy controls) subjects and found that homeostasis model of assessment for insulin resistance index (HOMA-IR) increased in GDM. Many previous studies demonstrated that omental adipose tissue dysfunction could induce insulin resistance. Thus, to investigate the cause of insulin resistance in GDM, we used label-free proteomics to identify differentially expressed proteins in omental adipose tissues from GDM and NGT subjects (data are available via ProteomeXchange with identifier PXD003095). A total of 3528 proteins were identified, including 66 significantly changed proteins. Adipocyte plasma membrane-associated protein (APMAP, a.k.a. C20orf3), one of the differentially expressed proteins, was down-regulated in GDM omental adipose tissues. Furthermore, mature 3T3-L1 adipocytes were used to simulate omental adipocytes. The inhibition of APMAP expression by RNAi impaired insulin signaling and activated NFκB signaling in these adipocytes. Our study revealed that the down-regulation of APMAP in omental adipose tissue may play an important role in insulin resistance in the pathophysiology of GDM.

Vitreous humor in the pathologic scope: insights from proteomic approaches

The vitreous humor (VH) is the largest component of the eye. It is a colorless, gelatinous, highly hydrated matrix that fills the posterior segment of the eye between the lens and retina in vertebrates. In VH, a diversity of proteins that can influence retinal physiology is present, including growth factors, hormones, proteins with transporter activity, and enzymes. More importantly, the protein composition of VH has been described as being altered in a number of disease states. Therefore, attempts aiming at establishing a map of VH proteins and detecting putative biomarkers for ocular illness or protein fluctuations with putative physiologic significance were conducted over the last two decades, using proteomic approaches. Proteomic strategies often involve gel-based or LC techniques as sample fractioning approaches, subsequently coupled with MS procedures. This set of studies resulted in the proteomic characterization of a range of ocular disease samples, with particular incidence on diabetic retinopathy. However, practical therapeutic applications arising from these studies are scarce at the moment. A pertinent example of therapeutic targets arising from VH proteomics has emerged concerning vasoproliferative factors present in the vitreous, which should be involved in neovascularization and subsequent fibrovascular proliferation of the retina, in ocular disease context. Therefore, this review attempts to sum up the information acquired from the proteomic approaches to ocular disease conducted in VH samples, highlighting its clinical potential for disclosing ocular disease mechanisms and engendering pharmacological therapeutic treatments.

Sample treatment methods involving combinatorial peptide ligand libraries for improved proteomes analyses

If used in an optimized manner, the technology of combinatorial peptide solid-phase libraries can easily improve the analytical determinations of proteomes by several factors. The discovery of novel species and of early stage biomarkers becomes thus reachable with a simple sample treatment. This report describes the most important point to consider (overloading and full recovery) along with a minimum scientific background and gives then detailed recipes to laboratory technicians. Orientations for optional routes are also given according to the objective of the experimental investigations. This covers different approaches to capture proteins of very low abundance. Total protein harvestings to prevent partial losses are also described such as single exhaustive desorption and fractionated elutions for more detailed analyses. Documented results are also reported demonstrating the capability of the technology well beyond what is the common assumption.

Mass spectrometry-based proteomic quest for diabetes biomarkers

Diabetes mellitus (DM) is a metabolic disorder characterized by chronic hyperglycemia, which affects hundreds of millions of individuals worldwide. Early diagnosis and complication prevention of DM are helpful for disease treatment. However, currently available DM diagnostic markers fail to achieve the goals. Identification of new diabetic biomarkers assisted by mass spectrometry (MS)-based proteomics may offer solution for the clinical challenges. Here, we review the current status of biomarker discovery in DM, and describe the pressure cycling technology (PCT)-Sequential Window Acquisition of all Theoretical fragment-ion (SWATH) workflow for sample-processing, biomarker discovery and validation, which may accelerate the current quest for DM biomarkers. This article is part of a Special Issue entitled: Medical Proteomics.