Early urinary biomarkers of diabetic nephropathy in type 1 diabetes mellitus show involvement of kallikrein-kinin system

Recent progress in mass spectrometry-based urinary proteomics

Serum or plasma is frequently utilized in biomedical research; however, its application is impeded by the requirement for invasive sample collection. The non-invasive nature of urine collection makes it an attractive alternative for disease characterization and biomarker discovery. Mass spectrometry-based protein profiling of urine has led to the discovery of several disease-associated biomarkers. Proteomic analysis of urine has not only been applied to disorders of the kidney and urinary bladder but also to conditions affecting distant organs because proteins excreted in the urine originate from multiple organs. This review provides a progress update on urinary proteomics carried out over the past decade. Studies summarized in this review have expanded the catalog of proteins detected in the urine in a variety of clinical conditions. The wide range of applications of urine analysis-from characterizing diseases to discovering predictive, diagnostic and prognostic markers-continues to drive investigations of the urinary proteome.

Maternal serum proteomic profiles of pregnant women with type 1 diabetes

Despite improvement in the care of diabetes over the years, pregnancy complicated by type 1 diabetes (T1DM) is still associated with adverse maternal and neonatal outcomes. To date, proteomics studies have been conducted to identify T1DM biomarkers in non-pregnant women, however, no studies included T1DM pregnant women. In this study serum proteomic profiling was conducted in pregnant women with T1DM in the late third trimester. Serum samples were collected from 40 women with T1DM and 38 healthy controls within 3 days before delivery at term pregnancy. Significant differences between serum proteomic patterns were revealed, showing discriminative peaks for complement C3 and C4-A, kininogen-1, and fibrinogen alpha chain. Quantification of selected discriminative proteins by ELISA kits was also performed. The serum concentration of kininogen-1 was significantly lower in women with T1DM than in controls. There were no significant differences in serum concentrations of complement C3 and complement C4-A between study groups. These data indicate that pregnant women with T1DM have a distinct proteomic profile involving proteins in the coagulation and inflammatory pathways. However, their utility as biomarkers of pregnancy complications in women with T1DM warrants further investigation.

Proteomic Analysis of Serum Lysine Acetylation in Uyghur Patients With T2DM

Lysine acetylation is a reversible modification process after protein translation, which plays a key regulatory role in various metabolic diseases such as diabetes. The prevalence of type 2 diabetes mellitus (T2DM) in the Uyghur population is high, but the acetylation status of proteomics in Uyghur with T2DM is still unclear. Herein, we performed a quantitative proteomic study of lysine acetylation in T2DM patients using Tandem Mass Tags (TMTs) labeling, acetylation enrichment techniques, and high-resolution liquid chromatography-tandem mass spectrometry. We quantified 422 acetylation sites on 120 proteins, of which 347 sites of 103 proteins contained quantitative information. Compared with the control, we found that a total of eight acetylated sites within proteins were significantly differentially expressed with three upregulated and five downregulated, including histones H4 and H3.3C. Meanwhile, we completed bioinformatics analysis, including protein annotation, functional classification, functional enrichment, and cluster analysis, based on functional enrichment. In addition, the mRNA (ApoB-100, histones H4 and H3.3C) and protein (histones H4 and H3.3C) levels were verified through 60 samples. Besides, we also performed histone H4 chromatin immunoprecipitation analysis at the level of INS-1 cells. These could be potentially useful markers for the prediction of prediabetes and also provided a basis for the pathogenesis of T2DM.

Urinary metabolite profiling and risk of progression of diabetic nephropathy in 2670 individuals with type 1 diabetes

AIMS/HYPOTHESIS

This prospective, observational study examines associations between 51 urinary metabolites and risk of progression of diabetic nephropathy in individuals with type 1 diabetes by employing an automated NMR metabolomics technique suitable for large-scale urine sample collections.

METHODS

We collected 24-h urine samples for 2670 individuals with type 1 diabetes from the Finnish Diabetic Nephropathy study and measured metabolite concentrations by NMR. Individuals were followed up for 9.0 ± 5.0 years until their first sign of progression of diabetic nephropathy, end-stage kidney disease or study end. Cox regressions were performed on the entire study population (overall progression), on 1999 individuals with normoalbuminuria and 347 individuals with macroalbuminuria at baseline.

RESULTS

Seven urinary metabolites were associated with overall progression after adjustment for baseline albuminuria and chronic kidney disease stage (p < 8 × 10^-4): leucine (HR 1.47 [95% CI 1.30, 1.66] per 1-SD creatinine-scaled metabolite concentration), valine (1.38 [1.22, 1.56]), isoleucine (1.33 [1.18, 1.50]), pseudouridine (1.25 [1.11, 1.42]), threonine (1.27 [1.11, 1.46]) and citrate (0.84 [0.75, 0.93]). 2-Hydroxyisobutyrate was associated with overall progression (1.30 [1.16, 1.45]) and also progression from normoalbuminuria (1.56 [1.25, 1.95]). Six amino acids and pyroglutamate were associated with progression from macroalbuminuria.

CONCLUSIONS/INTERPRETATION

Branched-chain amino acids and other urinary metabolites were associated with the progression of diabetic nephropathy on top of baseline albuminuria and chronic kidney disease. We found differences in associations for overall progression and progression from normo- and macroalbuminuria. These novel discoveries illustrate the utility of analysing urinary metabolites in entire population cohorts.

Development of Biomarkers and Molecular Therapy Based on Inflammatory Genes in Diabetic Nephropathy

Diabetic Nephropathy (DN) is a debilitating consequence of both Type 1 and Type 2 diabetes affecting the kidney and renal tubules leading to End Stage Renal Disease (ESRD). As diabetes is a world epidemic and almost half of diabetic patients develop DN in their lifetime, a large group of people is affected. Due to the complex nature of the disease, current diagnosis and treatment are not adequate to halt disease progression or provide an effective cure. DN is now considered a manifestation of inflammation where inflammatory molecules regulate most of the renal physiology. Recent advances in genetics and genomic technology have identified numerous susceptibility genes that are associated with DN, many of which have inflammatory functions. Based on their role in DN, we will discuss the current aspects of developing biomarkers and molecular therapy for advancing precision medicine.

Does diabetes modify the effect of heparin on plasma proteins? - A proteomic search for plasma protein biomarkers for diabetes-related endothelial dysfunction

AIM

Heparin administration affects the concentrations of many plasma proteins through their displacement from the endothelial glycocalyx. A differentiated protein response in diabetes will therefore, at least partly, reflect glycocalyx changes. This study aims at identifying biomarkers of endothelial dysfunction in diabetes by statistical exploration of plasma proteome data for interactions between diabetes status and heparin treatment.

METHODS

Diabetes-by-heparin interactions in relation to protein levels were inspected by regression modelling in plasma proteome data from 497 patients admitted for acute angiography. Analyses were conducted separately for all 273 proteins and as set-based analyses of 44 heparin-relevant proteins identified by gene ontology analysis and 42 heparin-influenced proteins previously reported.

RESULTS

Seventy-five patients had diabetes and 361 received heparin before hospitalization. The proteome-wide analysis displayed no proteins with diabetes-heparin interaction to pass correction for multiple testing. The overall set-based analyses revealed significant association for both protein sets (p-values<2*10^-4), while constraining on opposite directions of effect in diabetics and none-diabetics was insignificant (p-values = 0.11 and 0.17).

CONCLUSIONS

Our plasma proteome-wide interaction approach supports that diabetes influences heparin effects on protein levels, however the direction of effects and individual proteins could not be definitively pinpointed, likely reflecting a complex protein-basis for glycocalyx dysfunction in diabetes.

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

BACKGROUND

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

OBJECTIVE

The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

METHODS

Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

RESULTS

In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

CONCLUSION

In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

Decreased plasma kallikrein activity is associated with reduced kidney function in individuals with type 1 diabetes

AIMS/HYPOTHESIS

Plasma kallikrein is the central mediator of the plasma kallikrein-kinin system, which is involved both in vascular control and thrombin formation cascades. The plasma kallikrein-kinin system has also been considered protective in pathological conditions, but the impact of plasma kallikreins on diabetic nephropathy remains unknown. The objective of this cross-sectional study was to explore the association of plasma kallikrein with diabetic nephropathy.

METHODS

We measured plasma kallikrein activity in 295 individuals with type 1 diabetes at various stages of diabetic nephropathy, and we tested the genetic association between the plasma kallikrein-kinin system and kidney function in 4400 individuals with type 1 diabetes.

RESULTS

Plasma kallikrein activity was associated with diabetes duration (p < 0.001) and eGFR (p < 0.001), and plasma kallikrein activity was lower with more advanced diabetic nephropathy, being lowest in individuals on dialysis. The minor alleles of the KNG1 rs5030062 and rs710446 variants, which have previously been associated with increased plasma pre-kallikrein and/or factor XI (FXI) protein levels, were associated with higher eGFR (rs5030062 β = 0.03, p = 0.01; rs710446 β = 0.03, p = 0.005) in the FinnDiane cohort of 4400 individuals with type 1 diabetes.

CONCLUSIONS/INTERPRETATION

Plasma kallikrein activity and genetic variants known to increase the plasma kallikrein level are associated with higher eGFR in individuals with type 1 diabetes, suggesting that plasma kallikrein might have a protective effect in diabetic nephropathy.

Roles of kininogen-1, basement membrane specific heparan sulfate proteoglycan core protein, and roundabout homolog 4 as potential urinary protein biomarkers in diabetic nephropathy

Diabetic nephropathy, a major complication of diabetes mellitus (DM), is increasing worldwide and the large majority of patients have type 2 DM. Microalbuminuria has been used as a diagnostic marker of diabetic nephropathy. But owing to its insufficient sensitivity and specificity, other biomarkers are being sought. In addition, the pathophysiology of diabetic nephropathy is not fully understood and declines in renal function occur even without microalbuminuria. In this study, we investigated urinary proteins from three study groups (controls, and type 2 diabetic subjects with or without microalbuminuria). Non-targeted label-free Nano-LC QTOF analysis was conducted to discover underlying mechanisms and protein networks, and targeted label-free Nano-LC QTOF with SWATH was performed to qualify discovered protein candidates. Twenty-eight proteins were identified as candidates and functionally analyzed via String DB, gene ontology and pathway analysis. Four predictive mechanisms were analyzed: i) response to stimulus, ii) platelet activation, signaling and aggregation, iii) ECM-receptor interaction, and iv) angiogenesis. These mechanisms can provoke kidney dysfunction in type 2 diabetic patients via endothelial cell damage and glomerulus structural alteration. Based on these analyses, three proteins (kininogen-1, basement membrane-specific heparan sulfate proteoglycan core protein, and roundabout homolog 4) were proposed for further study as potential biomarkers. Our findings provide insights that may improve methods for both prevention and diagnosis of diabetic nephropathy.

Innate immunity in diabetic kidney disease

Increasing evidence suggests that renal inflammation contributes to the pathogenesis and progression of diabetic kidney disease (DKD) and that anti-inflammatory therapies might have renoprotective effects in DKD. Immune cells and resident renal cells that activate innate immunity have critical roles in triggering and sustaining inflammation in this setting. Evidence from clinical and experimental studies suggests that several innate immune pathways have potential roles in the pathogenesis and progression of DKD. Toll-like receptors detect endogenous danger-associated molecular patterns generated during diabetes and induce a sterile tubulointerstitial inflammatory response via the NF-κB signalling pathway. The NLRP3 inflammasome links sensing of metabolic stress in the diabetic kidney to activation of pro-inflammatory cascades via the induction of IL-1β and IL-18. The kallikrein-kinin system promotes inflammatory processes via the generation of bradykinins and the activation of bradykinin receptors, and activation of protease-activated receptors on kidney cells by coagulation enzymes contributes to renal inflammation and fibrosis in DKD. In addition, hyperglycaemia leads to protein glycation and activation of the complement cascade via recognition of glycated proteins by mannan-binding lectin and/or dysfunction of glycated complement regulatory proteins. Data from preclinical studies suggest that targeting these innate immune pathways could lead to novel therapies for DKD.

Diabetic nephropathy: recent advances in pathophysiology and challenges in dietary management

BACKGROUND

Diabetic nephropathy (DN) or diabetic kidney disease refers to the deterioration of kidney function seen in chronic type 1 and type 2 diabetes mellitus patients. The progression of the disease is known to occur in a series of stages and is linked to glycemic and blood pressure control. However, despite aggressive blood sugar control the prevalence of chronic kidney disease (CKD) in diabetic patients has not witnessed any decrease in the last two decades; which has lead to identification of additional factors in its progression. The nutritional status of patients is an important and modifiable factor that may influence CKD processes and outcome. It directly stems from the traditional dietary choices that patients make due to poor nutritional awareness. Dietary management of DN patients is challenging, as the twin factors of diet overload on kidney function needs to be balanced with malnutrition. Patient education seems to be the key in avoiding overindulgence of carbohydrate and protein-rich foods while favoring inclusion of essential fats in their diet.

CONCLUSION

This review will summarize current advances in staging and molecular pathogenesis of DN. It will highlight recent studies focusing on patient-customized dietary interventions that offer new hope as an effective tool in improving quality of life and delaying disease progression in DN patients.

The role of mass spectrometry in studies of glycation processes and diabetes management

In the last decade, mass spectrometry has been widely employed in the study of diabetes. This was mainly due to the development of new, highly sensitive, and specific methods representing powerful tools to go deep into the biochemical and pathogenetic processes typical of the disease. The aim of this review is to give a panorama of the scientifically valid results obtained in this contest. The recent studies on glycation processes, in particular those devoted to the mechanism of production and to the reactivity of advanced glycation end products (AGEs, AGE peptides, glyoxal, methylglyoxal, dicarbonyl compounds) allowed to obtain a different view on short and long term complications of diabetes. These results have been employed in the research of effective markers and mass spectrometry represented a precious tool allowing the monitoring of diabetic nephropathy, cardiovascular complications, and gestational diabetes. The same approaches have been employed to monitor the non-insulinic diabetes pharmacological treatments, as well as in the discovery and characterization of antidiabetic agents from natural products. © 2018 Wiley Periodicals, Inc. Mass Spec Rev 38:112-146, 2019.

Proteomic and bioinformatic discovery of biomarkers for diabetic nephropathy

Diabetes is associated with numerous metabolic and vascular risk factors that contribute to a high rate of micro-vascular and macro-vascular disorders leading to mortality and morbidity from diabetic complications. In this case, the major cause of death in overall diabetic patients results from diabetic nephropathy (DN) or renal failure. The risk factors and mechanisms that correspond to the development of DN are not fully understood and so far, no specific and sufficient diagnostic biomarkers are currently available other than micro- or macroalbuminuria. Therefore, this review describes current and novel protein biomarkers in the context of DN as well as probable proteins biomarkers associated with pathological processes for the early stage of DN via proteomics data together with bioinformatics. In addition, the mechanisms involved in early development of diabetic vascular disorders and complications resulting from glucose induced oxidative stress will also be explored.

Pediatric endocrine and metabolic diseases and proteomics

The principles of Predictive, Preventive and Personalized Medicine (PPPM) dictate the need to recognize individual susceptibility to disease in a timely fashion and to offer targeted preventive interventions and treatments. Proteomics is a state-of-the art technology- driven science aiming at expanding our understanding of the pathophysiologic mechanisms that underlie disease, but also at identifying accurate predictive, diagnostic and therapeutic biomarkers, that will eventually promote the implementation of PPPM. In this review, we summarize the wide spectrum of the applications of Mass Spectrometry-based proteomics in the various fields of Pediatric Endocrinology, including Inborn Errors of Metabolism, type 1 diabetes, Adrenal Disease, Metabolic Syndrome and Thyroid disease, ranging from neonatal screening to early recognition of specific at-risk populations for disease manifestations or complications in adult life and to monitoring of disease progression and response to treatment.

SIGNIFICANCE

Proteomics is a state-of-the art technology- driven science aiming at expanding our understanding of the pathophysiologic mechanisms that underlie disease, but also at identifying accurate predictive, diagnostic and therapeutic biomarkers that will eventually lead to successful, targeted, patient-centric, individualized approach of each patient, as dictated by the principles of Predictive, Preventive and Personalized Medicine. In this review, we summarize the wide spectrum of the applications of Mass Spectrometry-based proteomics in the various fields of Pediatric Endocrinology, including Inborn Errors of Metabolism, type 1 diabetes, Adrenal Disease, Metabolic Syndrome and Thyroid disease, ranging from neonatal screening, accurate diagnosis, early recognition of specific at-risk populations for the prevention of disease manifestation or future complications.

Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps

This review highlights and discusses critically various knowledge gaps in classification modelling using PLS-DA for high dimensional data.