Systems biology: opening new avenues in clinical research

The Glomerulus Multiomics Analysis Provides Deeper Insights into Diabetic Nephropathy

Although diabetic nephropathy (DN) is the leading cause of the end-stage renal disease, the exact regulation mechanisms remain unknown. In this study, we integrated the transcriptomics and proteomics profiles of glomeruli isolated from 50 biopsy-proven DN patients and 25 controls to investigate the latest findings about DN pathogenesis. First, 1152 genes exhibited differential expression at the mRNA or protein level, and 364 showed significant association. These strong correlated genes were divided into four different functional modules. Moreover, a regulatory network of the transcription factors (TFs)-target genes (TGs) was constructed, with 30 TFs upregulated at the protein levels and 265 downstream TGs differentially expressed at the mRNA levels. These TFs are the integration centers of several signal transduction pathways and have tremendous therapeutic potential for regulating the aberrant production of TGs and the pathological process of DN. Furthermore, 29 new DN-specific splice-junction peptides were discovered with high confidence; these peptides may play novel functions in the pathological course of DN. So, our in-depth integrative transcriptomics-proteomics analysis provided deeper insights into the pathogenesis of DN and opened the potential avenue for finding new therapeutic interventions. MS raw files were deposited into the proteomeXchange with the dataset identifier PXD040617.

Clinical Proteomics in Kidney Disease: From Discovery to Clinical Application

Proteome analysis has been applied in multiple studies in the context of chronic kidney disease, aiming at improving our knowledge on the molecular pathophysiology of the disease. The approach is generally based on the hypothesis that proteins are key in maintaining kidney function, and disease is a clinical consequence of a significant change of the protein level. Knowledge on critical proteins and their alteration in disease should in turn enable identification of ideal biomarkers that could guide patient management. In addition, all drugs currently employed target proteins. Hence, proteome analysis also promises to enable identifying the best suited therapeutic target, and, in combination with biomarkers, could be used as the rationale basis for personalized intervention. To assess the current status of proteome analysis in the context of CKD, we present the results of a systematic review, of up-to-date scientific research, and give an outlook on the developments that can be expected in near future. Based on the current literature, proteome analysis has already seen implementation in the management of CKD patients, and it is expected that this approach, also supported by the positive results generated to date, will see advanced high-throughput application.

Clinical Proteomics: Closing the Gap from Discovery to Implementation

Clinical proteomics, the application of proteome analysis to serve a clinical purpose, represents a major field in the area of proteome research. Over 1000 manuscripts on this topic are published each year, with numbers continuously increasing. However, the anticipated outcome, the transformation of the reported findings into improvements in patient management, is not immediately evident. In this article, the value and validity of selected clinical proteomics findings are investigated, and it is assessed how far implementation has progressed. A main conclusion from this assessment is that to achieve implementation, well-powered clinical studies are required in the appropriate population, addressing a specific clinical need and with a clear context-of-use. Efforts toward implementation, to be feasible, must be supported by the key players in science: publishers and funders. The authors propose a change on objectives, from additional discovery studies toward studies aiming at validation of the plethora of potential biomarkers that have been described, to demonstrate practical value of clinical proteomics. All elements required, potential biomarkers, technologies, and bio-banked samples are available (based on today's literature), hence a change in focus from discovery toward validation and application is not only urgently necessary, but also possible based on resources available today.

Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis

Background

In spite of its invasive nature and risks, kidney biopsy is currently required for precise diagnosis of many chronic kidney diseases (CKDs). Here, we explored the hypothesis that analysis of the urinary proteome can discriminate different types of CKD irrespective of the underlying mechanism of disease.

Methods

We used data from the proteome analyses of 1180 urine samples from patients with different types of CKD, generated by capillary electrophoresis coupled to mass spectrometry. A set of 706 samples served as the discovery cohort, and 474 samples were used for independent validation. For each CKD type, peptide biomarkers were defined using statistical analysis adjusted for multiple testing. Potential biomarkers of statistical significance were combined in support vector machine (SVM)-based classifiers.

Results

For seven different types of CKD, several potential urinary biomarker peptides (ranging from 116 to 619 peptides) were defined and combined into SVM-based classifiers specific for each CKD. These classifiers were validated in an independent cohort and showed good to excellent accuracy for discrimination of one CKD type from the others (area under the receiver operating characteristic curve ranged from 0.77 to 0.95). Sequence analysis of the biomarkers provided further information that may clarify the underlying pathophysiology.

Conclusions

Our data indicate that urinary proteome analysis has the potential to identify various types of CKD defined by pathological assessment of renal biopsies and current clinical practice in general. Moreover, these approaches may provide information to model molecular changes per CKD.

Proteomics for Biomarker Identification and Clinical Application in Kidney Disease

Treatment effectiveness for kidney disease is limited by lack of accuracy, sensitivity, specificity of diagnostic, prognostic, and therapeutic biomarkers. The gold standard test renal biopsy along with serum creatinine and proteinuria is often necessary to establish a diagnosis, particularly in glomerular disease. Proteomics has become a powerful tool for novel biomarker discovery in kidney disease. Novel proteomics offer earlier and more accurate diagnosis of renal pathology than possible with traditional biomarkers such as serum creatinine and urine protein. In addition, proteomic biomarkers could also be useful to choose the most suitable therapeutic targets. This review focuses on the current status of proteomic biomarkers from animal models (5/6 nephrectomy, unilateral ureteral obstruction, and diabetic nephropathy) and human studies (chronic kidney disease, glomerular diseases, transplantation, dialysis, acute and drug-induced kidney injury) to assess relevant findings and clinical usefulness. Current issues and problems related to the discovery, validation, and clinical application of proteomic biomarkers are discussed. We also describe several proteomic strategies highlighting technologic advancements, specimen selection, data processing and analysis. This review might provide help in future proteomic studies to improve the diagnosis and management of kidney disease.

A system of recurrent neural networks for modularising, parameterising and dynamic analysis of cell signalling networks

In this paper, we show how to extend our previously proposed novel continuous time Recurrent Neural Networks (RNN) approach that retains the advantage of continuous dynamics offered by Ordinary Differential Equations (ODE) while enabling parameter estimation through adaptation, to larger signalling networks using a modular approach. Specifically, the signalling network is decomposed into several sub-models based on important temporal events in the network. Each sub-model is represented by the proposed RNN and trained using data generated from the corresponding ODE model. Trained sub-models are assembled into a whole system RNN which is then subjected to systems dynamics and sensitivity analyses. The concept is illustrated by application to G1/S transition in cell cycle using Iwamoto et al. (2008) ODE model. We decomposed the G1/S network into 3 sub-models: (i) E2F transcription factor release; (ii) E2F and CycE positive feedback loop for elevating cyclin levels; and (iii) E2F and CycA negative feedback to degrade E2F. The trained sub-models accurately represented system dynamics and parameters were in good agreement with the ODE model. The whole system RNN however revealed couple of parameters contributing to compounding errors due to feedback and required refinement to sub-model 2. These related to the reversible reaction between CycE/CDK2 and p27, its inhibitor. The revised whole system RNN model very accurately matched dynamics of the ODE system. Local sensitivity analysis of the whole system model further revealed the most dominant influence of the above two parameters in perturbing G1/S transition, giving support to a recent hypothesis that the release of inhibitor p27 from Cyc/CDK complex triggers cell cycle stage transition. To make the model useful in a practical setting, we modified each RNN sub-model with a time relay switch to facilitate larger interval input data (≈20min) (original model used data for 30s or less) and retrained them that produced parameters and protein concentrations similar to the original RNN system. Results thus demonstrated the reliability of the proposed RNN method for modelling relatively large networks by modularisation for practical settings. Advantages of the method are its ability to represent accurate continuous system dynamics and ease of: parameter estimation through training with data from a practical setting, model analysis (40% faster than ODE), fine tuning parameters when more data are available, sub-model extension when new elements and/or interactions come to light and model expansion with addition of sub-models.

Pro: urine proteomics as a liquid kidney biopsy: no more kidney punctures!

In this article, the benefits of urinary proteomics in comparison with kidney biopsy are discussed. The majority of urinary proteins are generated by the kidney, hence the urinary proteome holds substantial information on the kidney, and assessment of the urinary proteome could be considered a 'liquid biopsy'. The main question is how well the information contained in the urinary proteome can be assessed today, if it is ready to be routinely used, and what are the advantages and possible disadvantages in comparison with current standards. Since chronic kidney disease (CKD) is by far the largest area in nephrology based on the number of patients affected, the focus of this article is on CKD. Substantial progress was made in the last decade in urinary proteomics, and today we have comparable urinary proteome datasets of tens of thousands of subjects available. Clinical proteomics studies in CKD including close to, or even exceeding, 1000 subjects have recently been published, demonstrating a benefit over the current state-of-the-art in diagnosis and especially prognosis. The first large multicentric randomized controlled intervention trial aiming at preventing CKD by employing urinary proteomics-guided intervention has been initiated recently. These data provide ample evidence for the utility and value of urinary proteomics in nephrology. A further consideration is that the purpose of the biopsy, be it 'liquid' or 'solid', is to guide intervention. However, essentially all drug targets are proteins, not microscopic structures. Therefore, obtaining information on the proteome to guide intervention appears to be the most appropriate approach. Presenting more detailed evidence, I argue that urinary proteome analysis can, in most cases, be employed to guide therapeutic intervention, can be repeated multiple times as it is without any direct risk or discomfort and can be considered as a liquid biopsy.

Integrative analysis of extracellular and intracellular bladder cancer cell line proteome with transcriptome: improving coverage and validity of -omics findings

Characterization of disease-associated proteins improves our understanding of disease pathophysiology. Obtaining a comprehensive coverage of the proteome is challenging, mainly due to limited statistical power and an inability to verify hundreds of putative biomarkers. In an effort to address these issues, we investigated the value of parallel analysis of compartment-specific proteomes with an assessment of findings by cross-strategy and cross-omics (proteomics-transcriptomics) agreement. The validity of the individual datasets and of a “verified” dataset based on cross-strategy/omics agreement was defined following their comparison with published literature. The proteomic analysis of the cell extract, Endoplasmic Reticulum/Golgi apparatus and conditioned medium of T24 vs. its metastatic subclone T24M bladder cancer cells allowed the identification of 253, 217 and 256 significant changes, respectively. Integration of these findings with transcriptomics resulted in 253 “verified” proteins based on the agreement of at least 2 strategies. This approach revealed findings of higher validity, as supported by a higher level of agreement in the literature data than those of individual datasets. As an example, the coverage and shortlisting of targets in the IL-8 signalling pathway are discussed. Collectively, an integrative analysis appears a safer way to evaluate -omics datasets and ultimately generate models from valid observations.

Body fluid peptide and protein signatures in diabetic kidney diseases

Body fluid protein-based biomarkers carry the hope of improving patient management in diabetes by enabling more accurate and earlier detection of diabetic kidney disease (DKD), but also of defining the most suitable therapeutic targets. We present the data on some of the best studied individual protein markers in body fluids and conclude that their potential in clinical application for assessing DKD is moderate. Proteome-based approaches aiming at the identification of panels of body fluid biomarkers might be a valid alternative. We discuss the past (first) clinical proteomics studies in DKD, stressing their drawbacks but also the lessons that could be learned from them, as well as the more recent studies that have a potential for actual clinical implementation. We also highlight relevant issues and current problems associated with clinical proteomics from discovery towards application, and give suggestions for solutions that may help guiding proteomic studies, thereby removing some of the current hurdles for implementation of potentially beneficial results.

Diagnostic assessment of osteosarcoma chemoresistance based on Virtual Clinical Trials

Osteosarcoma is the most common primary bone tumor in pediatric and young adult patients. Successful treatment of osteosarcomas requires a combination of surgical resection and systemic chemotherapy, both neoadjuvant (prior to surgery) and adjuvant (after surgery). The degree of necrosis following neoadjuvant chemotherapy correlates with the subsequent probability of disease-free survival. Tumors with less than 10% of viable cells after treatment represent patients with a more favorable prognosis. However, being able to predict early, such as at the time of the pre-treatment tumor biopsy, how the patient will respond to the standard chemotherapy would provide an opportunity for more personalized patient care. Patients with unfavorable predictions could be studied in a protocol, rather than a standard setting, towards improving therapeutic success. The onset of necrotic cells in osteosarcomas treated with chemotherapeutic agents is a measure of tumor sensitivity to the drugs. We hypothesize that the remaining viable cells, i.e., cells that have not responded to the treatment, are chemoresistant, and that the pathological characteristics of these chemoresistant tumor cells within the osteosarcoma pre-treatment biopsy can predict tumor response to the standard-of-care chemotherapeutic treatment. This hypothesis can be tested by comparing patient histopathology samples before, as well as after treatment to identify both morphological and immunochemical cellular features that are characteristic of chemoresistant cells, i.e., cells that survived treatment. Consequently, using computational simulations of dynamic changes in tumor pathology under the simulated standard of care chemotherapeutic treatment, one can couple the pre- and post-treatment morphological and spatial patterns of chemoresistant cells, and correlate them with patient clinical diagnoses. This procedure, that we named 'Virtual Clinical Trials', can serve as a potential predictive biomarker providing a novel value-added decision support tool for oncologists.

Epidemiologic design and analysis for proteomic studies: a primer on -omic technologies

Proteome analysis is increasingly being used in investigations elucidating the molecular basis of disease, identifying diagnostic and prognostic markers, and ultimately improving patient care. We appraised the current status of proteomic investigations using human samples, including the state of the art in proteomic technologies, from sample preparation to data evaluation approaches, as well as key epidemiologic, statistical, and translational issues. We systematically reviewed the most highly cited clinical proteomic studies published between January 2009 and March 2014 that included a minimum of 100 samples, as well as strategies that have been successfully implemented to enhance the translational relevance of proteomic investigations. Limited comparability between studies and lack of specification of biomarker context of use are frequently observed. Nevertheless, there are initial examples of successful biomarker discovery in cross-sectional studies followed by validation in high-risk longitudinal cohorts. Translational potential is currently hindered, as limitations in proteomic investigations are not accounted for. Interdisciplinary communication between proteomics experts, basic researchers, epidemiologists, and clinicians, an orchestrated assimilation of required resources, and a more systematic translational outlook for accumulation of evidence may augment the public health impact of proteomic investigations.

Proteomic biomarkers in kidney disease: issues in development and implementation

Proteomic biomarkers offer the hope of improving the management of patients with kidney diseases by enabling more accurate and earlier detection of renal pathology than is possible with currently available biomarkers, serum creatinine and urinary albumin. In addition, proteomic biomarkers could also be useful to define the most suitable therapeutic targets in a given patient or disease setting. This Review describes the current status of proteomic and protein biomarkers in the context of kidney diseases. The valuable lessons learned from early clinical studies of potential proteomic biomarkers in kidney disease are presented to give context to the newly identified biomarkers, which have potential for actual clinical implementation. This article also includes an overview of protein-based biomarker candidates that are undergoing development for use in nephrology, focusing on those with the greatest potential for clinical implementation. Relevant issues and problems associated with the discovery, validation and clinical application of proteomic biomarkers are discussed, along with suggestions for solutions that might help to guide the design of future proteomic studies. These improvements might remove some of the current obstacles to the utilization of proteomic biomarkers, with potentially beneficial results.

Clinical proteomics in obstetrics and neonatology

Clinical proteomics has been applied to the identification of biomarkers of obstetric and neonatal disease. We will discuss a number of encouraging studies that have led to potentially valid biomarkers in the context of Down's syndrome, preterm birth, amniotic infections, preeclampsia, intrauterine growth restriction and obstructive uropathies. Obtaining noninvasive biomarkers (e.g., from the maternal circulation, urine or cervicovaginal fluid) may be more feasible for obstetric diseases than for diseases of the fetus, for which invasive methods are required (e.g., amniotic fluid, fetal urine). However, studies providing validated proteomics-identified biomarkers are limited. Efforts should be made to save well-characterized samples of these invasive body fluids so that many valid biomarkers of pregnancy-related diseases will be identified in the coming years using proteomics based analysis upon adoption of 'clinical proteomics guidelines'.

Oral mucosal injury caused by cancer therapies: current management and new frontiers in research

This invited update is designed to provide a summary of the state-of-the-science regarding oral mucosal injury (oral mucositis) caused by conventional and emerging cancer therapies. Current modeling of oral mucositis pathobiology as well as evidence-based clinical practice guidelines for prevention and treatment of oral mucositis are presented. In addition, studies addressing oral mucositis as published in the Journal of Oral Pathology and Medicine 2008-2013 are specifically highlighted in this context. Key research directions in basic and translational science associated with mucosal toxicity caused by cancer therapies are also delineated as a basis for identifying pathobiologic and pharmacogenomic targets for interventions. This collective portfolio of research and its ongoing incorporation into clinical practice is setting the stage for the clinician in the future to predict mucosal toxicity risk and tailor therapeutic interventions to the individual oncology patient accordingly.

Oral mucosal injury in oncology patients: perspectives on maturation of a field

In the past decade, there have been important strategic advances relative to pathobiological modeling as well as clinical management for oral mucositis caused by cancer therapies. Prior to the 1990s, research in this field was conducted by a relatively small number of basic and clinical investigators. Increasing interest among researchers and clinicians over the past twenty years has produced a synergistic outcome characterized by a number of key dynamics, including novel discovery models for pathobiology, increased experience in designing and conducting clinical trials, and creation of international collaborations among cancer care professionals who in turn have modeled clinical care paradigms based on state-of-the-science evidence. This maturation of the science and its clinical translation has positioned investigators and oncology providers to further accelerate both the foundational research and the clinical modeling for patient management in the years ahead. The stage is now set to further capitalize upon optimizing the interactions across this interface, with the goal of strategically enhancing management of patients with cancer at risk for this toxicity while reducing the cost of cancer care.

Molecular models of the cardiorenal syndrome

Molecular profiling techniques have provided extensive sets of molecular features characterizing clinical phenotypes, but further extrapolation to mechanistic molecular models of disease pathophysiology faces major challenges. Here, we describe a computational procedure for delineating molecular disease models utilizing omics profiles, and exemplify the methodology on aspects of the cardiorenal syndrome describing the clinical association of declining kidney function and increased cardiovascular event rates. Individual molecular features as well as selected molecular processes were identified as linking cardiovascular and renal pathology as a combination of cross-organ mediators and common pathophysiology. The molecular characterization of the disease presents as a set of molecular processes together with their interactions, composing a molecular disease model of the cardiorenal syndrome. Integrating omics profiles describing aspects of cardiovascular disease and respective profiles for advanced chronic kidney disease on molecular interaction networks, computation of disease term-specific subgraphs, and complemented by subgraph segmentation allowed delineation of disease term-specific molecular models, at their intersection providing contributors to cardiorenal pathology. Building such molecular disease models allows in a generic way to integrate multi-omics sources for generating comprehensive sets of molecular processes, on such basis providing rationale for biomarker panel selection for further characterizing clinical phenotypes.

From -omics to personalized medicine in nephrology: integration is the key

Large-scale gene, protein and metabolite measurements ('omics') have driven the resolution of biology to an unprecedented high definition. Passing from reductionism to a system-oriented perspective, medical research will take advantage of these high-throughput technologies unveiling their full potential. Integration is the key to decoding the underlying principles that govern the complex functions of living systems. Extensive computational support and statistical modelling is needed to manage and connect the -omic data sets but this, in turn, is speeding up the hypothesis generation in biology enormously and yielding a deep insight into the pathophysiology. This systems biology approach will transform diagnostic and therapeutic strategies with the discovery of novel biomarkers that will enable a predictive and preventive medicine leading to personalized medicine.

Effects of pooling samples on the performance of classification algorithms: a comparative study

A pooling design can be used as a powerful strategy to compensate for limited amounts of samples or high biological variation. In this paper, we perform a comparative study to model and quantify the effects of virtual pooling on the performance of the widely applied classifiers, support vector machines (SVMs), random forest (RF), k-nearest neighbors (k-NN), penalized logistic regression (PLR), and prediction analysis for microarrays (PAMs). We evaluate a variety of experimental designs using mock omics datasets with varying levels of pool sizes and considering effects from feature selection. Our results show that feature selection significantly improves classifier performance for non-pooled and pooled data. All investigated classifiers yield lower misclassification rates with smaller pool sizes. RF mainly outperforms other investigated algorithms, while accuracy levels are comparable among all the remaining ones. Guidelines are derived to identify an optimal pooling scheme for obtaining adequate predictive power and, hence, to motivate a study design that meets best experimental objectives and budgetary conditions, including time constraints.

The impact of CDK inhibition in human malignancies associated with pronounced defects in apoptosis: advantages of multi-targeting small molecules

Malignant cells in chronic lymphocytic leukemia (CLL) and related diseases are heterogeneous and consist primarily of long-lived resting cells in the periphery and a minor subset of dividing cells in proliferating centers. Both cell populations have different molecular signatures that play a major role in determining their sensitivity to therapy. Contemporary approaches to treating CLL are heavily reliant on cytotoxic chemotherapeutics. However, none of the current treatment regimens can be considered curative. Pharmacological CDK inhibitors have extended the repertoire of potential drugs for CLL. Multi-targeted CDK inhibitors affect CDKs involved in regulating both cell cycle progression and transcription. Their interference with transcriptional elongation represses anti-apoptotic proteins and, thus, promotes the induction of apoptosis. Importantly, there is evidence that treatment with CDK inhibitors can overcome resistance to therapy. The pharmacological CDK inhibitors have great potential for use in combination with other therapeutics and represent promising tools for the development of new curative treatments for CLL.

Biomarker robustness reveals the PDGF network as driving disease outcome in ovarian cancer patients in multiple studies

Background

Ovarian cancer causes more deaths than any other gynecological cancer. Identifying the molecular mechanisms that drive disease progress in ovarian cancer is a critical step in providing therapeutics, improving diagnostics, and affiliating clinical behavior with disease etiology. Identification of molecular interactions that stratify prognosis is key in facilitating a clinical-molecular perspective.

Results

The Cancer Genome Atlas has recently made available the molecular characteristics of more than 500 patients. We used the TCGA multi-analysis study, and two additional datasets and a set of computational algorithms that we developed. The computational algorithms are based on methods that identify network alterations and quantify network behavior through gene expression.

We identify a network biomarker that significantly stratifies survival rates in ovarian cancer patients. Interestingly, expression levels of single or sets of genes do not explain the prognostic stratification. The discovered biomarker is composed of the network around the PDGF pathway. The biomarker enables prognosis stratification.

Conclusion

The work presented here demonstrates, through the power of gene-expression networks, the criticality of the PDGF network in driving disease course. In uncovering the specific interactions within the network, that drive the phenotype, we catalyze targeted treatment, facilitate prognosis and offer a novel perspective into hidden disease heterogeneity.

From systems biology to systems biomedicine

Systems Biology is about combining theory, technology, and targeted experiments in a way that drives not only data accumulation but knowledge as well. The challenge in Systems Biomedicine is to furthermore translate mechanistic insights in biological systems to clinical application, with the central aim of improving patients' quality of life. The challenge is to find theoretically well-chosen models for the contextually correct and intelligible representation of multi-scale biological systems. In this review, we discuss the current state of Systems Biology, highlight the emergence of Systems Biomedicine, and highlight some of the topics and views that we think are important for the efficient application of Systems Theory in Biomedicine.

Urine proteomics in kidney and urogenital diseases: Moving towards clinical applications

To date, multiple biomarker discovery studies in urine have been conducted. Nevertheless, the rate of progression of these biomarkers to qualification and even more clinical application is extremely low. The scope of this article is to provide an overview of main clinically relevant proteomic findings from urine focusing on kidney diseases, bladder and prostate cancers. In addition, approaches for promoting the use of urine in clinical proteomics including potential means to facilitate the validation of existing promising findings (biomarker candidates identified from previous studies) and to increase the chances for success for the identification of new biomarkers are discussed.

Proteomics in chronic kidney disease: The issues clinical nephrologists need an answer for

A growing number of patients are recognised to have chronic kidney disease (CKD). However, only a minority will progress to end-stage renal disease requiring dialysis or transplantation. Currently available diagnostic and staging tools frequently fail to identify those at higher risk of progression or death. Furthermore within specific disease entities there are shortcomings in the prediction of the need for therapeutic interventions or the response to different forms of therapy. Kidney and urine proteomic biomarkers are considered as promising diagnostic tools to predict CKD progression early in diabetic nephropathy, facilitating timely and selective intervention that may reduce the related health-care expenditures. However, independent groups have not validated these findings and the technique is not currently available for routine clinical care. Furthermore, there are gaps in our understanding of predictors of progression or need for therapy in non-diabetic CKD. Presumably, a combination of tissue and urine biomarkers will be more informative than individual markers. This review identifies clinical questions in need of an answer, summarises current information on proteomic biomarkers and CKD, and describes the European Kidney and Urine Proteomics initiative that has been launched to carry out a clinical study aimed at identifying urinary proteomic biomarkers distinguishing between fast and slow progressors among patients with biopsy-proven primary glomerulopathies.

Mapping of molecular pathways, biomarkers and drug targets for diabetic nephropathy

PURPOSE

For diseases with complex phenotype such as diabetic nephropathy (DN), integration of multiple Omics sources promises an improved description of the disease pathophysiology, being the basis for novel diagnostics and therapy, but equally important personalization aspects.

EXPERIMENTAL DESIGN

Molecular features on DN were retrieved from public domain Omics studies and by mining scientific literature, patent text and clinical trial specifications. Molecular feature sets were consolidated on a human protein interaction network and interpreted on the level of molecular pathways in the light of the pathophysiology of the disease and its clinical context defined as associated biomarkers and drug targets.

RESULTS

About 1000 gene symbols each could be assigned to the pathophysiological description of DN and to the clinical context. Direct feature comparison showed minor overlap, whereas on the level of molecular pathways, the complement and coagulation cascade, PPAR signaling, and the renin-angiotensin system linked the disease descriptor space with biomarkers and targets.

CONCLUSION AND CLINICAL RELEVANCE

Only the combined molecular feature landscapes closely reflect the clinical implications of DN in the context of hypertension and diabetes. Omics data integration on the level of interaction networks furthermore provides a platform for identification of pathway-specific biomarkers and therapy options.

Urinary proteome analysis enables assessment of renoprotective treatment in type 2 diabetic patients with microalbuminuria

Background

Previously the angiotensin II receptor blocker Irbesartan has been demonstrated to reduce the risk for progression from microalbuminuria to macroalbuminuria in type 2 diabetic patients. The purpose of this study was to evaluate the effect of treatment with Irbesartan in type 2 diabetic patients with microalbuminuria on the urinary proteome.

Methods

High-resolution capillary-electrophoresis coupled to mass-spectrometry (CE-MS) was used to profile the low-molecular-weight proteome in urine of a subgroup of patients from a two year randomized irbesartan versus placebo therapy trial, which included hypertensive type 2 diabetic patients with microalbuminuria on ongoing antihypertensive medication (IRMA2-substudy).

Results

We demonstrate that the therapy with 300 mg Irbesartan daily over a period of two years results in significant changes of the urinary proteome. Both, a classifier developed previously that consists of urinary peptides indicative of chronic kidney disease, as well as several individual peptides changed significantly after treatment. These changes were not observed in the placebo-treated individuals. Most prominent are changes of urinary collagen fragments associated with progression of diabetic nephropathy, indicating normalization in urinary peptides.

Conclusion

CE-MS analysis of urine enabled identification of peptides as potential surrogate markers for renoprotection in microalbuminuric type 2 diabetic patients, which show persistent improvement after longterm treatment with Irbesartan. The results suggest that a major benefit of treatment by Irbesartan may be improvement of collagen turnover, reduction of fibrosis. They further suggest that urinary proteome analysis could be utilized to assess potential benefit of therapeutic intervention, providing statistically significant results even on a small population.

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

Background

The pathogenesis of diabetes mellitus (DM) is variable, comprising different inflammatory and immune responses. Proteome analysis holds the promise of delivering insight into the pathophysiological changes associated with diabetes. Recently, we identified and validated urinary proteomics biomarkers for diabetes. Based on these initial findings, we aimed to further validate urinary proteomics biomarkers specific for diabetes in general, and particularity associated with either type 1 (T1D) or type 2 diabetes (T2D).

Methodology/Principal Findings

Therefore, the low-molecular-weight urinary proteome of 902 subjects from 10 different centers, 315 controls and 587 patients with T1D (n = 299) or T2D (n = 288), was analyzed using capillary-electrophoresis mass-spectrometry. The 261 urinary biomarkers (100 were sequenced) previously discovered in 205 subjects were validated in an additional 697 subjects to distinguish DM subjects (n = 382) from control subjects (n = 315) with 94% (95% CI: 92–95) accuracy in this study. To identify biomarkers that differentiate T1D from T2D, a subset of normoalbuminuric patients with T1D (n = 68) and T2D (n = 42) was employed, enabling identification of 131 biomarker candidates (40 were sequenced) differentially regulated between T1D and T2D. These biomarkers distinguished T1D from T2D in an independent validation set of normoalbuminuric patients (n = 108) with 88% (95% CI: 81–94%) accuracy, and in patients with impaired renal function (n = 369) with 85% (95% CI: 81–88%) accuracy. Specific collagen fragments were associated with diabetes and type of diabetes indicating changes in collagen turnover and extracellular matrix as one hallmark of the molecular pathophysiology of diabetes. Additional biomarkers including inflammatory processes and pro-thrombotic alterations were observed.

Conclusions/Significance

These findings, based on the largest proteomic study performed to date on subjects with DM, validate the previously described biomarkers for DM, and pinpoint differences in the urinary proteome of T1D and T2D, indicating significant differences in extracellular matrix remodeling.