Urinary collagen fragments are significantly altered in diabetes: a link to pathophysiology

Urinary collagen peptides: Source of markers for bone metabolic processes in kidney transplant recipients

INTRODUCTION

Kidney transplant recipients (KTRs) are at an increased risk of fractures. Total urinary hydroxyproline excretion served as marker for bone resorption (BR) but was replaced by β-CrossLaps (CTX), a C-terminal collagen α-1(I) chain (COL1A1) telopeptide. We investigated the low-molecular-weight urinary proteome for peptides associated with changes in bone metabolism after kidney transplantation.

METHODS

Clinical and laboratory data including serum levels of CTX in 96 KTR from two nephrology centers were correlated with signal intensities of urinary peptides identified by capillary electrophoresis mass spectrometry.

RESULTS

Eighty-two urinary peptides were significantly correlated with serum CTX levels. COL1A1 was the predominant peptide source. Oral bisphosphonates were administered for decreased bone density in an independent group of 11 KTR and their effect was evaluated on the aforementioned peptides. Study of the peptides cleavage sites revealed a signature of Cathepsin K and MMP9. Seventeen of these peptides were significantly associated with bisphosphonate treatment, all showing a marked reduction in their excretion levels compared to baseline.

DISCUSSION

This study provides strong evidence for the presence of collagen peptides in the urine of KTR that are associated with BR and that are sensitive to bisphosphonate treatment. Their assessment might become a valuable tool to monitor bone status in KTR.

Systematic Review: Urine Biomarker Discovery for Inflammatory Bowel Disease Diagnosis

Inflammatory bowel diseases (IBDs) are chronic, heterogeneous, and inflammatory conditions mainly affecting the gastrointestinal tract. Currently, endoscopy is the gold standard test for assessing mucosal activity and healing in clinical practice; however, it is a costly, time-consuming, invasive, and uncomfortable procedure for the patients. Therefore, there is an urgent need for sensitive, specific, fast and non-invasive biomarkers for the diagnosis of IBD in medical research. Urine is an excellent biofluid for discovering biomarkers because it is non-invasive to sample. In this review, we aimed to summarize proteomics and metabolomics studies performed in both animal models of IBD and humans that identify urinary biomarkers for IBD diagnosis. Future large-scale multi-omics studies should be conducted in collaboration with clinicians, researchers, and industry to make progress toward the development of sensitive and specific diagnostic biomarkers, thereby making personalized medicine possible.

Urinary peptidomic profiles to address age-related disabilities: a prospective population study

BACKGROUND

The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 called for innovation in addressing age-related disabilities. Our study aimed to identify and validate a urinary peptidomic profile (UPP) differentiating healthy from unhealthy ageing in the general population, to test the UPP predictor in independent patient cohorts, and to search for targetable molecular pathways underlying age-related chronic diseases.

METHODS

In this prospective population study, we used data from participants in the Flemish Study on Environment, Genes and Health Outcomes (FLEMENGHO), done in northern Belgium from 1985 to 2019, and invited participants to a follow-up examination in 2005-10. Participants were eligible if their address was within 15 km of the examination centre and if they had not withdrawn consent in any of the previous examination cycles (1985-2004). All participants (2005-10) were also invited to an additional follow-up examination in 2009-13. Participants who took part in both the 2005-10 follow-up examination and in the additional 2009-13 follow-up visit constituted the derivation dataset, which included their 2005-10 data, and the time-shifted internal validation dataset, which included their 2009-13 data. The remaining participants who only had 2005-10 data constituted the synchronous internal validation dataset. Participants were excluded from analyses if they were incapacitated, had not undergone UPP, or had either missing or outlying (three SDs greater than the mean of all consenting participants) values of body-mass index, plasma glucose, or serum creatinine. The UPP was assessed by capillary electrophoresis coupled with mass spectrometry. The multidimensional UPP signature reflecting ageing was generated from the derivation dataset and validated in the time-shifted internal validation dataset and the synchronous validation dataset. It was further validated in patients with diabetes, COVID-19, or chronic kidney disease (CKD). In FLEMENGHO, the mortality endpoints were all-cause, cardiovascular, and non-cardiovascular mortality; other endpoints were fatal or non-fatal cancer and musculoskeletal disorders. Molecular pathway exploration was done using the Reactome and Kyoto Encyclopedia of Genes and Genomes databases.

FINDINGS

778 individuals (395 [51%] women and 383 [49%] men; aged 16·2-82·1 years; mean age 50·9 years [SD 15·8]) from the FLEMENGHO cohort had a follow-up examination between 2005 and 2010, of whom 559 participants had a further follow-up from Oct 28, 2009, to March 19, 2013, and made up the derivation (2005-10) and time-shifted internal validation (2009-13) datasets. 219 were examined once and constituted the synchronous internal validation dataset (2005-10). With correction for multiple testing and multivariable adjustment, chronological age was associated with 210 sequenced peptides mainly showing downregulation of collagen fragments. The trained model relating chronological age to UPP, derived by elastic net regression, included 54 peptides from 17 proteins. The UPP-age prediction model explained 76·3% (r=0·87) of chronological age in the derivation dataset, 54·4% (r=0·74) in the time-shifted validation dataset, and 65·3% (r=0·81) in the synchronous internal validation dataset. Compared with chronological age, the predicted UPP-age was greater in patients with diabetes (chronological age 50·8 years [SE 0·37] vs UPP-age 56·9 years [0·30]), COVID‑19 (53·2 years [1·80] vs 58·5 years [1·67]), or CKD (54·6 years [0·97] vs 62·3 years [0·85]; all p<0·0001). In the FLEMENGHO cohort, independent of chronological age, UPP-age was significantly associated with various risk markers related to cardiovascular, metabolic, and renal disease, inflammation, and medication use. Over a median of 12·4 years (IQR 10·8-13·2), total mortality, cardiovascular mortality, and osteoporosis in the population was associated with UPP-age independent of chronological age, with hazard ratios per 10 year increase in UPP-age of 1·54 (95% CI 1·22-1·95) for total mortality, 1·72 (1·20-2·47) for cardiovascular mortality, and 1·40 (1·06-1·85) for osteoporosis and fractures. The most relevant molecular pathways informed by the proteins involved deregulation of collagen biology and extracellular matrix maintenance.

INTERPRETATION

The UPP signature indicative of ageing reflects fibrosis and extracellular matrix remodelling and was associated with risk factors and adverse health outcomes in the population and with accelerated ageing in patients. Innovation in addressing disability should shift focus from the ontology of diseases to shared disease mechanisms, in particular ageing-related fibrotic degeneration.

FUNDING

European Research Council, Ministry of the Flemish Community, OMRON Healthcare.

Collagen analysis with mass spectrometry

Mass spectrometry-based techniques can be applied to investigate collagen with respect to identification, quantification, supramolecular organization, and various post-translational modifications. The continuous interest in collagen research has led to a shift from techniques to analyze the physical characteristics of collagen to methods to study collagen abundance and modifications. In this review, we illustrate the potential of mass spectrometry for in-depth analyses of collagen.

Detecting Proteomic Indicators to Distinguish Diabetic Nephropathy from Hypertensive Nephrosclerosis by Integrating Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging with High-Mass Accuracy Mass Spectrometry

INTRODUCTION

Diabetic nephropathy (DN) and hypertensive nephrosclerosis (HN) represent the most common causes of chronic kidney disease (CKD) and many patients progress to -end-stage renal disease. Patients are treated primarily through the management of cardiovas-cular risk factors and hypertension; however patients with HN have a more favorable outcome. A noninvasive clinical approach to separate these two entities, especially in hypertensive patients who also have diabetes, would allow for targeted treatment and more appropriate resource allocation to those patients at the highest risk of CKD progression. Meth-ods: In this preliminary study, high-spatial-resolution matrix-assisted laser desorption/ion-ization (MALDI) mass spectrometry imaging (MSI) was integrated with high-mass accuracy MALDI-FTICR-MS and nLC-ESI-MS/MS analysis in order to detect tissue proteins within kidney biopsies to discriminate cases of DN (n = 9) from cases of HN (n = 9).

RESULTS

Differences in the tryptic peptide profiles of the 2 groups could clearly be detected, with these becoming even more evident in the more severe histological classes, even if this was not evident with routine histology. In particular, 4 putative proteins were detected and had a higher signal intensity within regions of DN tissue with extensive sclerosis or fibrosis. Among these, 2 proteins (PGRMC1 and CO3) had a signal intensity that increased at the latter stages of the disease and may be associated with progression.

DISCUSSION/CONCLUSION

This preliminary study represents a valuable starting point for a future study employing a larger cohort of patients to develop sensitive and specific protein biomarkers that could reliably differentiate between diabetic and hypertensive causes of CKD to allow for improved diagnosis, fewer biopsy procedures, and refined treatment approaches for clinicians.

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.

Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria

Background

The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown.

Methods

Urine samples were obtained from 155 patients with type 2 diabetes and confirmed microalbuminuria. Proteomic analysis was undertaken using capillary electrophoresis coupled to mass spectrometry to determine the CKD273 classifier score. A previously defined CKD273 threshold of 0.343 for identification of DN was used to categorise the cohort in Kaplan–Meier and Cox regression models with all-cause mortality as the primary endpoint. Outcomes were traced through national health registers after 6 years.

Results

CKD273 correlated with urine albumin excretion rate (UAER) (r = 0.481, p = <0.001), age (r = 0.238, p = 0.003), coronary artery calcium (CAC) score (r = 0.236, p = 0.003), N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.190, p = 0.018) and estimated glomerular filtration rate (eGFR) (r = 0.265, p = 0.001). On multivariate analysis only UAER (β = 0.402, p < 0.001) and eGFR (β = − 0.184, p = 0.039) were statistically significant determinants of CKD273. Twenty participants died during follow-up. CKD273 was a determinant of mortality (log rank [Mantel-Cox] p = 0.004), and retained significance (p = 0.048) after adjustment for age, sex, blood pressure, NT-proBNP and CAC score in a Cox regression model.

Conclusion

A multidimensional biomarker can provide information on outcomes associated with its primary diagnostic purpose. Here we demonstrate that the urinary proteomic classifier CKD273 is associated with mortality in individuals with type 2 diabetes and MA even when adjusted for other established cardiovascular and renal biomarkers.

Electronic supplementary material

The online version of this article (10.1186/s12933-018-0697-9) contains supplementary material, which is available to authorized users.

A Urinary Fragment of Mucin-1 Subunit α Is a Novel Biomarker Associated With Renal Dysfunction in the General Population

INTRODUCTION

Sequencing peptides included in the urinary proteome identifies the parent proteins and may reveal mechanisms underlying the pathophysiology of chronic kidney disease.

METHODS

In 805 randomly recruited Flemish individuals (50.8% women; mean age, 51.1 years), we determined the estimated glomerular filtration rate (eGFR) from serum creatinine using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. We categorized eGFR according to the National Kidney Foundation Kidney Disease Outcomes Quality Initiative guideline. We analyzed 74 sequenced urinary peptides with a detectable signal in more than 95% of participants. Follow-up measurements of eGFR were available in 597 participants.

RESULTS

In multivariable analyses, baseline eGFR decreased (P ≤ 0.022) with urinary fragments of mucin-1 (standardized association size expressed in ml/min/1.73 m², -4.48), collagen III (-2.84), and fibrinogen (-1.70) and was bi-directionally associated (P ≤ 0.0006) with 2 urinary collagen I fragments (+2.28 and -3.20). The eGFR changes over 5 years (follow-up minus baseline) resulted in consistent estimates (P ≤ 0.025) for mucin-1 (-1.85), collagen (-1.37 to 1.43) and fibrinogen (-1.45) fragments. Relative risk of having or progressing to eGFR <60 ml/min/1.73 m² was associated with mucin-1. Partial least-squares analysis confirmed mucin-1 as the strongest urinary marker associated with decreased eGFR, with a score of 2.47 compared with 1.80 for a collagen I fragment as the next contender. Mucin-1 predicted eGFR decline to <60 ml/min/1.73 m² over and above microalbuminuria (P = 0.011) and retained borderline significance (P = 0.05) when baseline eGFR was accounted for.

DISCUSSION

In the general population, mucin-1 subunit α, an extracellular protein that is shed from renal tubular epithelium, is a novel biomarker associated with renal dysfunction.

Identification of novel biomarkers to monitor β-cell function and enable early detection of type 2 diabetes risk

A decline in β-cell function is a prerequisite for the development of type 2 diabetes, yet the level of β-cell function in individuals at risk of the condition is rarely measured. This is due, in part, to the fact that current methods for assessing β-cell function are inaccurate, prone to error, labor-intensive, or affected by glucose-lowering therapy. The aim of the current study was to identify novel circulating biomarkers to monitor β-cell function and to identify individuals at high risk of developing β-cell dysfunction. In a nested case-control study from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) cohort (n = 1157), proteomics and miRNA profiling were performed on fasting plasma samples from 43 individuals who progressed to impaired glucose tolerance (IGT) and 43 controls who maintained normal glucose tolerance (NGT) over three years. Groups were matched at baseline for age, gender, body mass index (BMI), insulin sensitivity (euglycemic clamp) and β-cell glucose sensitivity (mathematical modeling). Proteomic profiling was performed using the SomaLogic platform (Colorado, USA); miRNA expression was performed using a modified RT-PCR protocol (Regulus Therapeutics, California, USA). Results showed differentially expressed proteins and miRNAs including some with known links to type 2 diabetes, such as adiponectin, but also novel biomarkers and pathways. In cross sectional analysis at year 3, the top differentially expressed biomarkers in people with IGT/ reduced β-cell glucose sensitivity were adiponectin, alpha1-antitrypsin (known to regulate adiponectin levels), endocan, miR-181a, miR-342, and miR-323. At baseline, adiponectin, cathepsin D and NCAM.L1 (proteins expressed by pancreatic β-cells) were significantly lower in those that progressed to IGT. Many of the novel prognostic biomarker candidates were within the epithelial-mesenchymal transition (EMT) pathway: for example, Noggin, DLL4 and miR-181a. Further validation studies are required in additional clinical cohorts and in patients with type 2 diabetes, but these results identify novel pathways and biomarkers that may have utility in monitoring β-cell function and/ or predicting future decline, allowing more targeted efforts to prevent and intercept type 2 diabetes.

A review of the emerging potential therapy for neurological disorders: human embryonic stem cell therapy

The first human embryonic stem cell (hESC) line was developed in the late nineties. hESCs are capable of proliferating indefinitely and differentiate into all the three embryonic germ layers. Further, the differentiation of hESC lines into neural precursor cells and neurons, astrocytes and oligodendrocytes showed their potential in treating several incurable neurological disorders such as spinal cord injury (SCI), cerebral palsy (CP), Parkinson's disease (PD). In this review, we will discuss the global scenario of research and therapeutic use of hESCs in the treatment of neurological disorders. Following this, we will discuss the development of a unique hESC line, how it differs from the other available hESC lines and its use in the treatment of neurological disorders. hESCs were isolated from mixture of neuronal and non-neuronal progenitor cells in their pre progenitor state in a Good Laboratory Practices, Good Tissue Practices and Good Manufacturing Practices compliant laboratory. Blastomere cells have served as a source to derive the hESCs and the xeno-free culture was demonstrated to be more safe and effective in clinical therapeutic application of hESCs. All the patients showed a remarkable improvement in their conditions and no serious adverse events were reported. This study concluded that hESC lines could be scalable and used in the treatment of various neurological disorders such as SCI, CP, and PD.

Insights into Diabetic Kidney Disease Using Urinary Proteomics and Bioinformatics

A number of proteomic and peptidomic analyses of urine from diabetic subjects have been published in the quest for a biomarker that predicts progression of nephropathy. Less attention has been paid to the relationships between urinary proteins and the underlying biological processes revealed by the analyses. In this review, we focus on the biological processes identified by studying urinary proteins and protein-protein interactions at each stage of diabetic nephropathy to provide an overview of the events underlying progression of kidney disease reflected in the urine. In uncomplicated diabetes, proteomic/peptidomic analyses indicate that early activation of fibrotic pathways in the kidney occurs before the onset of microalbuminuria. In incipient nephropathy, when albumin excretion rates are abnormal, proteomic/peptidomic analyses suggest that changes in glomerular permselectivity and tubular reabsorption account, at least in part, for the proteins and peptides that appear in the urine. Finally, overt nephropathy is characterized by proteins involved in wound healing, ongoing fibrosis, and inflammation. These findings suggest that there is a spectrum of biological processes in the diabetic kidney and that assessing protein networks may be more informative than individual markers with respect to the stage of disease and the risk of progression.

Human Embryonic Stem Cell Therapy in Chronic Spinal Cord Injury: A Retrospective Study

Human embryonic stem cells (hESCs) have a role in treating neurological disorders. The efficacy and safety of hESC in treating spinal cord injury (SCI) was reported in our previous study. In the present study, we have evaluated the efficacy and safety of hESC therapy in 226 patients with SCI. In the first treatment phase (T1), 0.25 mL hESCs were administered intramuscularly twice daily, 1 mL every 10 days i.v., and 1-5 mL every 7 days. Of 153 patients in the American Spinal Injury Association (ASIA) scale A at the beginning of T1, a significant number of patients (n = 80; 52.3%) moved to lower scales at the end of T1 (p = 0.01). At the end of T2, of 32 patients in ASIA scale A, 12 patients (37.5%) moved to scale B (p = 0.01). Of 19 patients, 3 patients (37.5%) moved to scale B at the end of T3 (p = 0.02). No serious adverse events (AEs) were observed. hESC transplantation is safe and effective.

TWEAK-Fn14 Signaling Activates Myofibroblasts to Drive Progression of Fibrotic Kidney Disease

The identification of the cellular origins of myofibroblasts has led to the discovery of novel pathways that potentially drive myofibroblast perpetuation in disease. Here, we further investigated the role of innate immune signaling pathways in this process. In mice, renal injury-induced activation of pericytes, which are myofibroblast precursors attached to endothelial cells, led to upregulated expression of TNF receptor superfamily member 12a, also known as fibroblast growth factor-inducible 14 (Fn14), by these cells. In live rat kidney slices, administration of the Fn14 ligand, TNF-related weak inducer of apoptosis (TWEAK), promoted pericyte-dependent vasoconstriction followed by pericyte detachment from capillaries. In vitro, administration of TWEAK activated and differentiated pericytes into cytokine-producing myofibroblasts, and further activated established myofibroblasts in a manner requiring canonical and noncanonical NF-κB signaling pathways. Deficiency of Fn14 protected mouse kidneys from fibrogenesis, inflammation, and associated vascular instability after in vivo injury, and was associated with loss of NF-κB signaling. In a genetic model of spontaneous CKD, therapeutic delivery of anti-TWEAK blocking antibodies attenuated disease progression, preserved organ function, and increased survival. These results identify the TWEAK-Fn14 signaling pathway as an important factor in myofibroblast perpetuation, fibrogenesis, and chronic disease progression.

Diabetic Kidney Disease in Adolescents With Type 2 Diabetes: New Insights and Potential Therapies

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease (ESRD) and dialysis in the Western world. Early DKD, including microalbuminuria and renal hyperfiltration, is common in adolescents with type 2 diabetes (T2D). Furthermore, youth-onset T2D carries a higher risk of progressive DKD than adult-onset T2D of similar diabetes duration. DKD is characterized by a long clinically silent period without signs of disease. Therefore, a major challenge in preventing DKD is the difficulty in identifying high-risk T2D patients at an early stage. The Type 2 Diabetes in Adolescents and Youth (TODAY) study demonstrated a high initial prevalence that increased over time, irrespective of treatment arm. This key observation underscores the importance of discovering new therapeutic targets to supplement conventional management, in order to reduce DKD risk. In this review, we focus on early DKD in T2D and summarize potential novel biomarkers and therapeutic targets.

Modulation of the Association between the PEPD Variant and the Risk of Type 2 Diabetes by n-3 Fatty Acids in Chinese Hans

BACKGROUND/AIMS

Type 2 diabetes (T2D) is modulated by the interactions between genetic and dietary factors. This study sought to examine whether the associations of genome-wide association study (GWAS)-identified genetic variants with T2D risk were modulated by n-3 fatty acids in Chinese Hans.

METHODS

Six hundred and twenty-two T2D patients and 293 healthy controls were recruited. Erythrocyte phospholipid fatty acids were measured by standard methods. Nine GWAS-identified T2D-related single-nucleotide polymorphisms (SNPs) were genotyped. These SNPs were all identified in GWAS of Asian populations with a high minor allele frequency (>0.2).

RESULTS

Among the 9 SNPs, only rs3786897 at PEPD (peptidase D) showed a significant interaction with n-3 fatty acids (p(interaction) after Bonferroni correction = 0.027). The rs3786897 A allele was associated with a higher risk of T2D [GA+AA vs. GG: odds ratio (OR) = 2.16, 95% confidence interval (CI) 1.32-3.55] when n-3 fatty acids were lower than the population median, but no significant association (GA+AA vs. GG: OR = 0.63, 95% CI 0.35-1.12) was observed when n-3 fatty acids were higher than the median.

CONCLUSIONS

The association between the PEPD genetic variant and the risk of T2D was modulated by n-3 fatty acids. Higher n-3 fatty acids may abolish the adverse effect of the risk allele at PEPD for T2D.

Detection of urinary biomarkers in reservoir hosts of leptospirosis by capillary electrophoresis-mass spectrometry

PURPOSE

Pathogenic leptospires colonize the renal tubules of reservoir hosts of infection and are excreted via urine into the environment. Asymptomatic reservoir hosts include a wide range of domestic and wild animal species and include cattle, dogs, and rats that can persistently excrete large numbers of pathogenic leptospires over many months. A similar presentation has been observed in humans categorized as "long-term asymptomatic individuals" as they excreted leptospires in the absence of any clinical symptoms or positive serology.

EXPERIMENTAL DESIGN

In the current study, the urine of experimentally infected rats, which showed no clinical signs or positive serology, was analyzed by CE-MS to identify urinary biomarkers of chronic infection.

RESULTS

A discriminating peptide pattern of 43 polypeptides provided a sensitivity of 93%, a specificity of 83%, and an accuracy of 90% for the identification of urine from chronically infected rats (p < 0.05, AUC > 90%). The majority of discriminating peptides were decreased in abundance in urine of chronically infected rats, including a peptide derived from neprilysin, a membrane metalloendopeptidase, the expression of which has previously been shown to be diminished in infected urine.

CONCLUSION AND CLINICAL RELEVANCE

Results highlight the diagnostic capabilities of urinary biomarkers to identify reservoir hosts of leptospirosis using CE coupled to MS.

Renal allograft fibrosis: biology and therapeutic targets

Renal tubulointerstitial fibrosis is the final common pathway of progressive renal diseases. In allografts, it is assessed with tubular atrophy as interstitial fibrosis/tubular atrophy (IF/TA). IF/TA occurs in about 40% of kidney allografts at 3-6 months after transplantation, increasing to 65% at 2 years. The origin of renal fibrosis in the allograft is complex and includes donor-related factors, in particular in case of expanded criteria donors, ischemia-reperfusion injury, immune-mediated damage, recurrence of underlying diseases, hypertensive damage, nephrotoxicity of immunosuppressants, recurrent graft infections, postrenal obstruction, etc. Based largely on studies in the non-transplant setting, there is a large body of literature on the role of different cell types, be it intrinsic to the kidney or bone marrow derived, in mediating renal fibrosis, and the number of mediator systems contributing to fibrotic changes is growing steadily. Here we review the most important cellular processes and mediators involved in the progress of renal fibrosis, with a focus on the allograft situation, and discuss some of the challenges in translating experimental insights into clinical trials, in particular fibrosis biomarkers or imaging modalities.

Impact of a 6-wk olive oil supplementation in healthy adults on urinary proteomic biomarkers of coronary artery disease, chronic kidney disease, and diabetes (types 1 and 2): a randomized, parallel, controlled, double-blind study

BACKGROUND

Olive oil (OO) consumption is associated with cardiovascular disease prevention because of both its oleic acid and phenolic contents. The capacity of OO phenolics to protect against low-density lipoprotein (LDL) oxidation is the basis for a health claim by the European Food Safety Authority. Proteomic biomarkers enable an early, presymptomatic diagnosis of disease, which makes them important and effective, but understudied, tools for primary prevention.

OBJECTIVE

We evaluated the impact of supplementation with OO, either low or high in phenolics, on urinary proteomic biomarkers of coronary artery disease (CAD), chronic kidney disease (CKD), and diabetes.

DESIGN

Self-reported healthy participants (n = 69) were randomly allocated (stratified block random assignment) according to age and body mass index to supplementation with a daily 20-mL dose of OO either low or high in phenolics (18 compared with 286 mg caffeic acid equivalents per kg, respectively) for 6 wk. Urinary proteomic biomarkers were measured at baseline and 3 and 6 wk alongside blood lipids, the antioxidant capacity, and glycation markers.

RESULTS

The consumption of both OOs improved the proteomic CAD score at endpoint compared with baseline (mean improvement: -0.3 for low-phenolic OO and -0.2 for high-phenolic OO; P < 0.01) but not CKD or diabetes proteomic biomarkers. However, there was no difference between groups for changes in proteomic biomarkers or any secondary outcomes including plasma triacylglycerols, oxidized LDL, and LDL cholesterol.

CONCLUSION

In comparison with low-phenolic OO, supplementation for 6 wk with high-phenolic OO does not lead to an improvement in cardiovascular health markers in a healthy cohort.

Advances in urinary protein biomarkers for urogenital and non-urogenital pathologies

The discovery of protein biomarkers that reflect the biological state of the body is of vital importance to disease management. Urine is an ideal source of biomarkers that provides a non-invasive approach to diagnosis, prognosis and prediction of diseases. Consequently, the study of the human urinary proteome has increased dramatically over the last 10 years, with many studies being published. This review focuses on urinary protein biomarkers that have shown potential, in initial studies, for diseases affecting the urogenital tract, specifically chronic kidney disease and prostate cancer, as well as other non-urogenital pathologies such as breast cancer, diabetes, atherosclerosis and osteoarthritis. PubMed was searched for peer-reviewed literature on the subject, published in the last 10 years. The keywords used were "urine, biomarker, protein, and/or prostate cancer/breast cancer/chronic kidney disease/diabetes/atherosclerosis/osteoarthritis". Original studies on the subject, as well as a small number of reviews, were analysed including the strengths and weaknesses, and we summarized the performance of biomarkers that demonstrated potential. One of the biggest challenges found is that biomarkers are often shared by several pathologies so are not specific to one disease. Therefore, the trend is shifting towards implementing a panel of biomarkers, which may increase specificity. Although there have been many advances in urinary proteomics, these have not resulted in similar advancements in clinical practice due to high costs and the lack of large data sets. In order to translate these potential biomarkers to clinical practice, vigorous validation is needed, with input from industry or large collaborative studies.

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.

Deciphering the peptidome of urine from ovarian cancer patients and healthy controls

Background

Ovarian cancer (OvCa) is the most lethal gynecological malignancy. The emergence of high-throughput technologies, such as mass spectrometry, has allowed for a paradigm shift in the way we search for novel biomarkers. Urine-based peptidomic profiling is a novel approach that may result in the discovery of noninvasive biomarkers for diagnosing patients with OvCa. In this study, the peptidome of urine from 6 ovarian cancer patients and 6 healthy controls was deciphered.

Results

Urine samples underwent ultrafiltration and the filtrate was subjected to solid phase extraction, followed by fractionation using strong cation exchange chromatography. These fractions were analyzed using an Orbitrap mass spectrometer. Over 4600 unique endogenous urine peptides arising from 713 proteins were catalogued, representing the largest urine peptidome reported to date. Each specimen was processed in triplicate and reproducibility at the protein (69-76%) and peptide (58-63%) levels were noted. More importantly, over 3100 unique peptides were detected solely in OvCa specimens. One such promising biomarker was leucine-rich alpha-2-glycoprotein (LRG1), where multiple peptides were found in all urines from OvCa patients, but only one peptide was found in one healthy control urine sample.

Conclusions

Mining the urine peptidome may yield highly promising novel OvCa biomarkers.

Markers of and risk factors for the development and progression of diabetic kidney disease

Diabetic kidney disease (DKD) occurs in 25%-40% of patients with diabetes. Given the dual problems of a significant risk of progression from DKD to end-stage renal disease (ESRD) and increased cardiovascular morbidity and mortality, it is important to identify patients at risk of DKD and ESRD and initiate protective renal and cardiovascular therapies. The importance of preventive therapy is emphasized further by worldwide increases in the incidence of diabetes. This review summarizes the evidence regarding the prognostic value and benefits of targeting established and novel risk markers for DKD development and progression. Family history of DKD, smoking history, and glycemic, blood pressure, and plasma lipid level control are established factors for identifying people at greatest risk of DKD development and progression. Absolute albumin excretion rate (AER) and glomerular filtration rate (GFR) measurements also are important, although AER categorization generally lacks the necessary specificity and sensitivity, and estimates of declining GFR are compromised by methodological limitations for GFRs in the normal-to-high range. Emerging risk markers for progressive loss of kidney function include markers of oxidation and inflammation, profibrotic cytokines, uric acid, advanced glycation end products, functional and structural markers of vascular dysfunction, kidney structural changes, and tubular biomarkers. Among these, the most promising are serum uric acid and soluble tumor necrosis factor receptor (type 1 and type 2) levels, especially in relation to GFR changes. At present, these can only be considered as risk markers because they only identify an individual at increased risk of progressive DKD and not necessarily related to the causal pathway promoting kidney damage. Further work is needed to establish whether modulating these factors improves the prognosis in DKD. Although change in urinary peptidome levels also is a promising marker, there currently is neither a clinical assay nor adequate studies defining its prognostic value. Until these or other novel markers become available for clinical use, predictive accuracy often may be increased with greater attention to established markers.

The highly abundant urinary metabolite urobilin interferes with the bicinchoninic acid assay

Estimation of total protein concentration is an essential step in any protein- or peptide-centric analysis pipeline. This study demonstrates that urobilin, a breakdown product of heme and a major constituent of urine, interferes considerably with the bicinchoninic acid (BCA) assay. This interference is probably due to the propensity of urobilin to reduce cupric ions (Cu(2+)) to cuprous ions (Cu(1+)), thus mimicking the reduction of copper by proteins, which the assay was designed to do. In addition, it is demonstrated that the Bradford assay is more resistant to the influence of urobilin and other small molecules. As such, urobilin has a strong confounding effect on the estimate of total protein concentrations obtained by BCA assay and thus this assay should not be used for urinary protein quantification. It is recommended that the Bradford assay be used instead.

A peptidomic approach to biomarker discovery for bovine mastitis

Bovine mastitis is usually caused by either Gram positive or Gram negative bacteria, reducing the quantity and quality of milk produced. This investigation using capillary electrophoresis and mass spectroscopy, studied peptides in milk from cows with clinical mastitis in comparison to milk from healthy cows to identify biomarkers for mastitis. In addition, the milk peptidome from udders infected with Gram positive Staphylococcus aureus (S. aureus) or with Gram negative Escherichia coli (E. coli), was examined to assess differential diagnosis between the causative agent. Comparison of the peptidome between healthy (n=10) and mastitic milk (n=27) identified 154 peptides for a biomarker panel which in a model for diagnosis of mastitis showed 100% sensitivity and specificity. β-casein and α(s1) casein provided the majority of peptides identified in this model. The peptidome comparison of milk from mastitis cases caused by S. aureus (n=8) or E. coli (n=11) revealed a biomarker panel of 47 peptides which discriminated between cause of infection with a sensitivity of 75% and a specificity of 100%. β-casein fragments were the most common of the peptides in this model. Peptide biomarkers of milk could be used in the diagnosis of mastitis and can discriminate between these two bacterial causes.

BIOLOGICAL SIGNIFICANCE

The paper describes an innovative approach to the use of gel free proteomics to identify the peptides that are present in milk during clinical mastitis, which is a major cause of loss of production to dairy farmers worldwide. The use of capillary electrophoresis, liquid chromatography and mass spectrometry has been able to identify panels of peptides which can be used for disease diagnosis and for differential diagnosis of the causative bacteria of the infections of the mammary gland. As well as contributing to our knowledge of the pathophysiology of bovine mastitis the results could be the basis of improved detection and differential diagnosis of the disease.

Proteome-base biomarkers in diabetes mellitus: progress on biofluids' protein profiling using mass spectrometry

The worldwide number of individuals suffering from diabetes mellitus (DM) has been projected to rise from 171 million in 2000 to 366 million in 2030. Identification of specific biomarkers for prediction and monitoring of DM is needed not only for the adequate screening diagnosis but also to assist the design of interventions to prevent or delay progression of this pathology and its attendant complications. Proteomic methods based on MS hold special promise for the identification of novel biomarkers that might form the foundation for new clinical tests, but to date, their contribution has been somehow unfruitful. Indeed, from more than 300 proteins found differently modulated in body fluids from diabetic patients, approximately 50 were validated with other approaches like ELISA or Western blotting and the clinical trials are being initiated to employ biofluids' proteomics (specifically urinary proteomics) in clinical decision. This review provides an overview of MS-based applications in the identification of potential biomarkers for DM, emphasizing the methodological challenges involved.

Novel biomarkers in type 1 diabetes

Biomarkers are useful tools for research into type 1 diabetes (T1D) for a number of purposes, including elucidation of disease pathogenesis, risk prediction, and therapeutic monitoring. Susceptibility genes and islet autoantibodies are currently the most useful biomarkers for T1D risk prediction. However, these markers do not fully meet the needs of scientists and physicians for several reasons. First, improvement of the specificity and sensitivity is still desirable to achieve better positive predictive values. Second, autoantibodies appear relatively late in the disease process, thus limiting their value in early disease prediction. Third, the currently available biomarkers are not useful for assessing therapeutic outcomes because some are not involved in the disease process (autoantibodies) and others do not change during disease progression (susceptibility genes). Therefore, considerable effort has been devoted to the discovery of novel T1D biomarkers in the last three decades. The advent of high-throughput technologies for genetic, transcriptomic, and proteomic studies has allowed genome-wide examinations of genetic polymorphisms, global gene changes, and protein expression changes in T1D patients and prediabetic subjects. These large-scale studies resulted in the discovery of a large number of susceptibility genes and changes in gene and protein expression. While these studies have provided a number of novel biomarker candidates, their clinical benefits remain to be evaluated in prospective studies, and no new "star biomarker" has been identified until now. Previous studies suggest that significant improvements in study design and analytical methodologies have to be made to identify clinically relevant biomarkers. In this review, we discuss progress, opportunities, challenges, and future directions in the development of T1D biomarkers, mainly by focusing on the genetic, transcriptomic, and proteomic aspects.

Evaluation of the Zucker diabetic fatty (ZDF) rat as a model for human disease based on urinary peptidomic profiles

Representative animal models for diabetes-associated vascular complications are extremely relevant in assessing potential therapeutic drugs. While several rodent models for type 2 diabetes (T2D) are available, their relevance in recapitulating renal and cardiovascular features of diabetes in man is not entirely clear. Here we evaluate at the molecular level the similarity between Zucker diabetic fatty (ZDF) rats, as a model of T2D-associated vascular complications, and human disease by urinary proteome analysis. Urine analysis of ZDF rats at early and late stages of disease compared to age- matched LEAN rats identified 180 peptides as potentially associated with diabetes complications. Overlaps with human chronic kidney disease (CKD) and cardiovascular disease (CVD) biomarkers were observed, corresponding to proteins marking kidney damage (eg albumin, alpha-1 antitrypsin) or related to disease development (collagen). Concordance in regulation of these peptides in rats versus humans was more pronounced in the CVD compared to the CKD panels. In addition, disease-associated predicted protease activities in ZDF rats showed higher similarities to the predicted activities in human CVD. Based on urinary peptidomic analysis, the ZDF rat model displays similarity to human CVD but might not be the most appropriate model to display human CKD on a molecular level.

How to get proteomics to the clinic? Issues in clinical proteomics, exemplified by CE-MS

Clinical proteomics is defined as application of proteome analysis aiming at improving the current clinical situation. As such, the success of clinical proteomics should be assessed based on the clinical impact following implementation of the findings. While we have experienced significant technological advancements in mass spectrometry in the last years, based on the above measure, this has not at all resulted in similar advancements in clinical proteomics. Although a large number of proteomic biomarkers have been described, most of them were not subsequently validated, and certainly have had no impact in clinical decision making as yet. Under the current conditions, it appears likely that the situation will not change significantly: we will be flooded by reports on biomarkers, but not see any implementation. In this article, some key issues in proteomic biomarker research are pinpointed, based on the experience with CE-MS, likely also holding true for biomarkers resulting from other analysis domains.

Perspectives on systems biology applications in diabetic kidney disease

Diabetic kidney disease (DKD) is a microvascular complication of type 1 and 2 diabetes with a devastating impact on individuals with the disease, their families, and society as a whole. DKD is the single most frequent cause of incident chronic kidney disease cases and accounts for over 40% of the population with end-stage renal disease. Contributing factors for the high prevalence are the increase in obesity and subsequent diabetes combined with an improved long-term survival with diabetes. Environment and genetic variations contribute to DKD susceptibility and progressive loss of kidney function. How the molecular mechanisms of genetic and environmental exposures interact during DKD initiation and progression is the focus of ongoing research efforts. The development of standardized, unbiased high-throughput profiling technologies of human DKD samples opens new avenues in capturing the multiple layers of DKD pathobiology. These techniques routinely interrogate analytes on a genome-wide scale generating comprehensive DKD-associated fingerprints. Linking the molecular fingerprints to deep clinical phenotypes may ultimately elucidate the intricate molecular interplay in a disease stage and subtype-specific manner. This insight will form the basis for accurate prognosis and facilitate targeted therapeutic interventions. In this review, we present ongoing efforts from large-scale data integration translating "-omics" research efforts into improved and individualized health care in DKD.

Mass spectrometry for diabetic nephropathy monitoring: new effective tools for physicians

The main aim of diabetic nephropathy monitoring is to identify molecular markers, that is, to find changes occurring at metabolome and proteome levels indicative of the disease's development. The mass spectrometry methods available today have been successfully applied to this field. This paper provides a short description of the basic aspects of the mass spectrometric methods used for diabetic nephropathy monitoring, reporting and discussing the results obtained using different approaches.

Proteomics and systems biology for understanding diabetic nephropathy

Like many diseases, diabetic nephropathy is defined in a histopathological context and studied using reductionist approaches that attempt to ameliorate structural changes. Novel technologies in mass spectrometry-based proteomics have the ability to provide a deeper understanding of the disease beyond classical histopathology, redefine the characteristics of the disease state, and identify novel approaches to reduce renal failure. The goal is to translate these new definitions into improved patient outcomes through diagnostic, prognostic, and therapeutic tools. Here, we review progress made in studying the proteomics of diabetic nephropathy and provide an introduction to the informatics tools used in the analysis of systems biology data, while pointing out statistical issues for consideration. Novel bioinformatics methods may increase biomarker identification, and other tools, including selective reaction monitoring, may hasten clinical validation.

Assessment of high-throughput high-resolution MALDI-TOF-MS of urinary peptides for the detection of muscle-invasive bladder cancer

PURPOSE

There is a need for better biomarkers to both detect bladder cancer and distinguish muscle-invasive (stage T2+) from non-invasive (stage Ta/T1) disease. We assess whether MALDI-TOF-MS of the urine peptidome can achieve this.

EXPERIMENTAL DESIGN

We analysed urine from 751 patients with bladder cancer and 127 patients without bladder cancer. Endogenous peptide profiling was performed using a Bruker Ultraflextreme MALDI-TOF-MS.

RESULTS

Significant differences were seen between the spectra of urine from patients with and without T2+ disease. Albumin, total protein and haematuria were also elevated in T2+ patients. Haematuria was detected in 39% of patients with Ta/T1 disease and in 77% of patients with T2+ disease. Class prediction models based on MALDI data produced areas under receiver-operator characteristic curves of up to 0.76 but did not significantly outperform a model based on total protein alone. Many peptides significantly associated with invasive disease are fragments of abundant blood proteins and are also associated with haematuria.

CONCLUSIONS AND CLINICAL RELEVANCE

Microscopic haematuria is strongly associated with invasive disease; even traces of blood/plasma strongly influence the urinary peptidome. This needs to be taken into consideration when using 'omic' methods to search for urinary biomarkers as blood proteins may give false-positive results.

Proteomics and systems biology: current and future applications in the nutritional sciences

In the last decade, advances in genomics, proteomics, and metabolomics have yielded large-scale datasets that have driven an interest in global analyses, with the objective of understanding biological systems as a whole. Systems biology integrates computational modeling and experimental biology to predict and characterize the dynamic properties of biological systems, which are viewed as complex signaling networks. Whereas the systems analysis of disease-perturbed networks holds promise for identification of drug targets for therapy, equally the identified critical network nodes may be targeted through nutritional intervention in either a preventative or therapeutic fashion. As such, in the context of the nutritional sciences, it is envisioned that systems analysis of normal and nutrient-perturbed signaling networks in combination with knowledge of underlying genetic polymorphisms will lead to a future in which the health of individuals will be improved through predictive and preventative nutrition. Although high-throughput transcriptomic microarray data were initially most readily available and amenable to systems analysis, recent technological and methodological advances in MS have contributed to a linear increase in proteomic investigations. It is now commonplace for combined proteomic technologies to generate complex, multi-faceted datasets, and these will be the keystone of future systems biology research. This review will define systems biology, outline current proteomic methodologies, highlight successful applications of proteomics in nutrition research, and discuss the challenges for future applications of systems biology approaches in the nutritional sciences.

Human urinary peptide database for multiple disease biomarker discovery

PURPOSE

Human urine is an ideal candidate for use in clinical diagnostics. It is easily available, as untrained personnel can collect it. It correlates well with the pathophysiology of a number of diseases, making it a useful source for clinical proteomics.

EXPERIMENTAL DESIGN

In this article, we give an update of the human urinary peptide database derived from over 13,000 data sets of CE-MS by now.

RESULTS

Urine samples from both patients and healthy subjects were analyzed by CE-MS; these included 47 different pathophysiological conditions. Besides defining biomarkers by their experimental parameters, information on their sequences provides fundamental data into the pathological pathways of diseases. Therefore, we have sequenced 953 urinary peptides by using state-of-the-art top-down MS/MS. Identified biomarkers of all clinical proteomic CE-MS studies including their regulation are also listed in this work.

CONCLUSIONS AND CLINICAL RELEVANCE

Biomarker discovery can be used in the management of a wide range of diseases, by combining these data sets of the database. Taking this approach, we can reveal details, at a molecular level, on the pathogenesis of a number of diseases, in particular those associated with urine production and excretion.

Addressing the challenge of defining valid proteomic biomarkers and classifiers

Background

The purpose of this manuscript is to provide, based on an extensive analysis of a proteomic data set, suggestions for proper statistical analysis for the discovery of sets of clinically relevant biomarkers. As tractable example we define the measurable proteomic differences between apparently healthy adult males and females. We choose urine as body-fluid of interest and CE-MS, a thoroughly validated platform technology, allowing for routine analysis of a large number of samples. The second urine of the morning was collected from apparently healthy male and female volunteers (aged 21-40) in the course of the routine medical check-up before recruitment at the Hannover Medical School.

Results

We found that the Wilcoxon-test is best suited for the definition of potential biomarkers. Adjustment for multiple testing is necessary. Sample size estimation can be performed based on a small number of observations via resampling from pilot data. Machine learning algorithms appear ideally suited to generate classifiers. Assessment of any results in an independent test-set is essential.

Conclusions

Valid proteomic biomarkers for diagnosis and prognosis only can be defined by applying proper statistical data mining procedures. In particular, a justification of the sample size should be part of the study design.