Concentration-dependent alterations in the human plasma proteome following controlled exposure to diesel exhaust

Traffic-related air pollution (TRAP) exposure is associated with systemic health effects, which can be studied using blood-based markers. Although we have previously shown that high TRAP concentrations alter the plasma proteome, the concentration-response relationship between blood proteins and TRAP is unexplored in controlled human exposure studies. We aimed to identify concentration-dependent plasma markers of diesel exhaust (DE), a model of TRAP. Fifteen healthy non-smokers were enrolled into a double-blinded, crossover study where they were exposed to filtered air (FA) and DE at 20, 50 and 150 μg/m3 PM2.5 for 4h, separated by ≥ 4-week washouts. We collected blood at 24h post-exposure and used label-free mass spectrometry to quantify proteins in plasma. Proteins exhibiting a concentration-response, as determined by linear mixed effects models (LMEMs), were assessed for pathway enrichment using WebGestalt. Top candidates, identified by sparse partial least squares discriminant analysis and LMEMs, were confirmed using enzyme-linked immunoassays. Thereafter, we assessed correlations between proteins that showed a DE concentration-response and acute inflammatory endpoints, forced expiratory volume in 1 s (FEV1) and methacholine provocation concentration causing a 20% drop in FEV1 (PC20). DE exposure was associated with concentration-dependent alterations in 45 proteins, which were enriched in complement pathways. Of the 9 proteins selected for confirmatory immunoassays, based on complementary bioinformatic approaches to narrow targets and availability of high-quality assays, complement factor I (CFI) exhibited a significant concentration-dependent decrease (-0.02 μg/mL per μg/m3 of PM2.5, p = 0.04). Comparing to FA at discrete concentrations, CFI trended downward at 50 (-2.14 ± 1.18, p = 0.08) and significantly decreased at 150 μg/m3 PM2.5 (-2.93 ± 1.18, p = 0.02). CFI levels were correlated with FEV1, PC20 and nasal interleukin (IL)-6 and IL-1β. This study details concentration-dependent alterations in the plasma proteome following DE exposure at concentrations relevant to occupational and community settings. CFI shows a robust concentration-response and association with established measures of airway function and inflammation.

Uncovering Molecular Heterogeneity in the Kidney With Spatially Targeted Mass Spectrometry

The kidney functions through the coordination of approximately one million multifunctional nephrons in 3-dimensional space. Molecular understanding of the kidney has relied on transcriptomic, proteomic, and metabolomic analyses of kidney homogenate, but these approaches do not resolve cellular identity and spatial context. Mass spectrometry analysis of isolated cells retains cellular identity but not information regarding its cellular neighborhood and extracellular matrix. Spatially targeted mass spectrometry is uniquely suited to molecularly characterize kidney tissue while retaining in situ cellular context. This review summarizes advances in methodology and technology for spatially targeted mass spectrometry analysis of kidney tissue. Profiling technologies such as laser capture microdissection (LCM) coupled to liquid chromatography tandem mass spectrometry provide deep molecular coverage of specific tissue regions, while imaging technologies such as matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) molecularly profile regularly spaced tissue regions with greater spatial resolution. These technologies individually have furthered our understanding of heterogeneity in nephron regions such as glomeruli and proximal tubules, and their combination is expected to profoundly expand our knowledge of the kidney in health and disease.

Mannitol and renal graft injury in patients undergoing deceased donor renal transplantation - a randomized controlled clinical trial

Background

Ischaemia/reperfusion (I/R) injury is associated with renal tissue damage during deceased donor renal transplantation. The effect of mannitol to reduce I/R injury during graft reperfusion in renal transplant recipients is based on weak evidence. We evaluated the effect of mannitol to reduce renal graft injury represented by 16 serum biomarkers, which are indicators for different important pathophysiological pathways. Our primary outcome were differences in biomarker concentrations between the mannitol and the placebo group 24 h after graft reperfusion. Additionally, we performed a linear mixed linear model to account biomarker concentrations before renal transplantation.

Methods

Thirty-four patients undergoing deceased donor renal transplantation were randomly assigned to receive either 20% mannitol or 0.9% NaCl placebo solution before, during, and after graft reperfusion. Sixteen serum biomarkers (MMP1, CHI3L1, CCL2, MMP8, HGF, GH, FGF23, Tie2, VCAM1, TNFR1, IGFBP7, IL18, NGAL, Endostatin, CystC, KIM1) were measured preoperatively and 24 h after graft reperfusion using Luminex assays and ELISA.

Results

Sixteen patients in each group were analysed. Tie2 differed 24 h after graft reperfusion between both groups (p = 0.011). Change of log2 transformed concentration levels over time differed significantly in four biomarkers (VCAM1,Endostatin, KIM1, GH; p = 0.007; p = 0.013; p = 0.004; p = 0.033; respectively) out of 16 between both groups.

Conclusion

This study showed no effect of mannitol on I/R injury in patients undergoing deceased renal transplantation. Thus, we do not support the routinely use of mannitol to attenuate I/R injury.

Trial registration

NCT02705573. Registered on 10th March 2016.

Integrative analysis of prognostic biomarkers derived from multiomics panels helps discrimination of chronic kidney disease trajectories in people with type 2 diabetes

Clinical risk factors explain only a fraction of the variability of estimated glomerular filtration rate (eGFR) decline in people with type 2 diabetes. Cross-omics technologies by virtue of a wide spectrum screening of plasma samples have the potential to identify biomarkers for the refinement of prognosis in addition to clinical variables. Here we utilized proteomics, metabolomics and lipidomics panel assay measurements in baseline plasma samples from the multinational PROVALID study (PROspective cohort study in patients with type 2 diabetes mellitus for VALIDation of biomarkers) of patients with incident or early chronic kidney disease (median follow-up 35 months, median baseline eGFR 84 mL/min/1.73 m², urine albumin-to-creatinine ratio 8.1 mg/g). In an accelerated case-control study, 258 individuals with a stable eGFR course (median eGFR change 0.1 mL/min/year) were compared to 223 individuals with a rapid eGFR decline (median eGFR decline -6.75 mL/min/year) using Bayesian multivariable logistic regression models to assess the discrimination of eGFR trajectories. The analysis included 402 candidate predictors and showed two protein markers (KIM-1, NTproBNP) to be relevant predictors of the eGFR trajectory with baseline eGFR being an important clinical covariate. The inclusion of metabolomic and lipidomic platforms did not improve discrimination substantially. Predictions using all available variables were statistically indistinguishable from predictions using only KIM-1 and baseline eGFR (area under the receiver operating characteristic curve 0.63). Thus, the discrimination of eGFR trajectories in patients with incident or early diabetic kidney disease and maintained baseline eGFR was modest and the protein marker KIM-1 was the most important predictor.

Construction and analysis of a diabetic nephropathy related protein-protein interaction network reveals nine critical and functionally associated genes

Diabetic nephropathy (DN) is one of the common diabetic complications, but the mechanisms are still largely unknown. In this study, we constructed a DN related protein-protein interaction network (DNPPIN) on the basis of RNA-seq analysis of renal cortices of DN and normal mice, and the STRING database. We analyzed DNPPIN in detail revealing nine critical proteins which are central in DNPPIN, and contained in one network module which is functionally enriched in ribosome, nucleic acid binding and metabolic process. Overall, this study identified nine critical and functionally associated protein-coding genes concerning DN. These genes could be a starting point of future research towards the goal of elucidating the mechanisms of DN pathogenesis and progression.

A systems pharmacology workflow with experimental validation to assess the potential of anakinra for treatment of focal and segmental glomerulosclerosis

Focal and Segmental Glomerulosclerosis (FSGS) is a severe glomerulopathy that frequently leads to end stage renal disease. Only a subset of patients responds to current therapies, making it important to identify alternative therapeutic options. The interleukin (IL)-1 receptor antagonist anakinra is beneficial in several diseases with renal involvement. Here, we evaluated the potential of anakinra for FSGS treatment. Molecular process models obtained from scientific literature data were used to build FSGS pathology and anakinra mechanism of action models by exploiting information on protein interactions. These molecular models were compared by statistical interference analysis and expert based molecular signature matching. Experimental validation was performed in Adriamycin- and lipopolysaccharide (LPS)-induced nephropathy mouse models. Interference analysis (containing 225 protein coding genes and 8 molecular process segments) of the FSGS molecular pathophysiology model with the drug mechanism of action of anakinra identified a statistically significant overlap with 43 shared molecular features that were enriched in pathways relevant in FSGS, such as plasminogen activating cascade, inflammation and apoptosis. Expert adjudication of molecular signature matching, focusing on molecular process segments did not suggest a high therapeutic potential of anakinra in FSGS. In line with this, experimental validation did not result in altered proteinuria or significant changes in expression of the FSGS-relevant genes COL1A1 and NPHS1. In summary, an integrated bioinformatic and experimental workflow showed that FSGS relevant molecular processes can be significantly affected by anakinra beyond the direct drug target IL-1 receptor type 1 (IL1R1) context but might not counteract central pathophysiology processes in FSGS. Anakinra is therefore not suggested for extended preclinical trials.

Multimarker Panels in Diabetic Kidney Disease: The Way to Improved Clinical Trial Design and Clinical Practice?

Diabetic kidney disease (DKD) is a complex and multifactorial disorder associated with deregulations in a large number of different biological pathways on the molecular level. Using the 2 established biomarkers, estimated glomerular filtration rate (eGFR) and albuminuria will not allow allocating patients to tailored therapy. Molecular multimarker panels as sensors for the deregulation of the various disease mechanisms combined with a better understanding of how investigational as well as approved drugs interfere with these disease processes forms the basis for platform trials in DKD. In these platform trials, patients with DKD are assigned to the most suitable treatment arm based on their molecular marker profile. Close monitoring of biomarkers after treatment initiation together with assessment of renal function and "off-target" effects will allow identification of therapy responders, with nonresponders shifted to the next-best treatment arm based on their molecular profile. In this viewpoint article, we summarize evidence on the variation of DKD disease progression as well as the response to therapy and outline procedures to model disease pathophysiology supporting biomarker panel construction. Finally, the use of biomarkers in clinical trial setup is discussed.

Validation of systems biology derived molecular markers of renal donor organ status associated with long term allograft function

Donor organ quality affects long term outcome after renal transplantation. A variety of prognostic molecular markers is available, yet their validity often remains undetermined. A network-based molecular model reflecting donor kidney status based on transcriptomics data and molecular features reported in scientific literature to be associated with chronic allograft nephropathy was created. Significantly enriched biological processes were identified and representative markers were selected. An independent kidney pre-implantation transcriptomics dataset of 76 organs was used to predict estimated glomerular filtration rate (eGFR) values twelve months after transplantation using available clinical data and marker expression values. The best-performing regression model solely based on the clinical parameters donor age, donor gender, and recipient gender explained 17% of variance in post-transplant eGFR values. The five molecular markers EGF, CD2BP2, RALBP1, SF3B1, and DDX19B representing key molecular processes of the constructed renal donor organ status molecular model in addition to the clinical parameters significantly improved model performance (p-value = 0.0007) explaining around 33% of the variability of eGFR values twelve months after transplantation. Collectively, molecular markers reflecting donor organ status significantly add to prediction of post-transplant renal function when added to the clinical parameters donor age and gender.

Mathematical and Computational Modeling in Complex Biological Systems

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.

The nephrologist of tomorrow: towards a kidney-omic future

Omics refers to the collective technologies used to explore the roles and relationships of the various types of molecules that make up the phenotype of an organism. Systems biology is a scientific discipline that endeavours to quantify all of the molecular elements of a biological system. Therefore, it reflects the knowledge acquired by omics in a meaningful manner by providing insights into functional pathways and regulatory networks underlying different diseases. The recent advances in biotechnological platforms and statistical tools to analyse such complex data have enabled scientists to connect the experimentally observed correlations to the underlying biochemical and pathological processes. We discuss in this review the current knowledge of different omics technologies in kidney diseases, specifically in the field of pediatric nephrology, including biomarker discovery, defining as yet unrecognized biologic therapeutic targets and linking omics to relevant standard indices and clinical outcomes. We also provide here a unique perspective on the field, taking advantage of the experience gained by the large-scale European research initiative called "Systems Biology towards Novel Chronic Kidney Disease Diagnosis and Treatment" (SysKid). Based on the integrative framework of Systems biology, SysKid demonstrated how omics are powerful yet complex tools to unravel the consequences of diabetes and hypertension on kidney function.

Systems Biology-Derived Biomarkers to Predict Progression of Renal Function Decline in Type 2 Diabetes

OBJECTIVE

Chronic kidney disease (CKD) in diabetes has a complex molecular and likely multifaceted pathophysiology. We aimed to validate a panel of biomarkers identified using a systems biology approach to predict the individual decline of estimated glomerular filtration rate (eGFR) in a large group of patients with type 2 diabetes and CKD at various stages.

RESEARCH DESIGN AND METHODS

We used publicly available "omics" data to develop a molecular process model of CKD in diabetes and identified a representative parsimonious set of nine molecular biomarkers: chitinase 3-like protein 1, growth hormone 1, hepatocyte growth factor, matrix metalloproteinase (MMP) 2, MMP7, MMP8, MMP13, tyrosine kinase, and tumor necrosis factor receptor-1. These biomarkers were measured in baseline serum samples from 1,765 patients recruited into two large clinical trials. eGFR decline was predicted based on molecular markers, clinical risk factors (including baseline eGFR and albuminuria), and both combined, and these predictions were evaluated using mixed linear regression models for longitudinal data.

RESULTS

The variability of annual eGFR loss explained by the biomarkers, indicated by the adjusted R² value, was 15% and 34% for patients with eGFR ≥60 and <60 mL/min/1.73 m², respectively; variability explained by clinical predictors was 20% and 31%, respectively. A combination of molecular and clinical predictors increased the adjusted R² to 35% and 64%, respectively. Calibration analysis of marker models showed significant (all P < 0.0001) but largely irrelevant deviations from optimal calibration (calibration-in-the-large: -1.125 and 0.95; calibration slopes: 1.07 and 1.13 in the two groups, respectively).

CONCLUSIONS

A small set of serum protein biomarkers identified using a systems biology approach, combined with clinical variables, enhances the prediction of renal function loss over a wide range of baseline eGFR values in patients with type 2 diabetes and CKD.

Recent Advances in the Diagnosis and Management of Bladder Cancer

The most common malignancy of urinary tract is bladder cancer. It is one of the most widespread cancers of the world and ranks nine among frequent malignancies existing in world. The only solution to above burning problem is timely diagnosis at earlier stage, and the cancer research is being forwarded in this direction. There are various prominent gene modifications responsible for growth of bladder cancer. The present review is focused on recent advances in the field of cancer makers involving, genetic, urinary, pathological, etc., approaches to contain the deadly process of carcinogenesis. The present review provides an insight on the emerging biomarkers that could be developed to boost current bladder cancer detection strategies. This shall help timely diagnosis of this deadly disease at early stage, thereby, helping bladder cancer patients to fight against this iniquity of life.

Boolean modeling techniques for protein co-expression networks in systems medicine

INTRODUCTION

Application of systems biology/systems medicine approaches is promising for proteomics/biomedical research, but requires selection of an adequate modeling type.

AREAS COVERED

This article reviews the existing Boolean network modeling approaches, which provide in comparison with alternative modeling techniques several advantages for the processing of proteomics data. Application of methods for inference, reduction and validation of protein co-expression networks that are derived from quantitative high-throughput proteomics measurements is presented. It's also shown how Boolean models can be used to derive system-theoretic characteristics that describe both the dynamical behavior of such networks as a whole and the properties of different cell states (e.g. healthy or diseased cell states). Furthermore, application of methods derived from control theory is proposed in order to simulate the effects of therapeutic interventions on such networks, which is a promising approach for the computer-assisted discovery of biomarkers and drug targets. Finally, the clinical application of Boolean modeling analyses is discussed. Expert commentary: Boolean modeling of proteomics data is still in its infancy. Progress in this field strongly depends on provision of a repository with public access to relevant reference models. Also required are community supported standards that facilitate input of both proteomics and patient related data (e.g. age, gender, laboratory results, etc.).

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.

BcCluster: A Bladder Cancer Database at the Molecular Level

BACKGROUND

Bladder Cancer (BC) has two clearly distinct phenotypes. Non-muscle invasive BC has good prognosis and is treated with tumor resection and intravesical therapy whereas muscle invasive BC has poor prognosis and requires usually systemic cisplatin based chemotherapy either prior to or after radical cystectomy. Neoadjuvant chemotherapy is not often used for patients undergoing cystectomy. High-throughput analytical omics techniques are now available that allow the identification of individual molecular signatures to characterize the invasive phenotype. However, a large amount of data produced by omics experiments is not easily accessible since it is often scattered over many publications or stored in supplementary files.

OBJECTIVE

To develop a novel open-source database, BcCluster (http://www.bccluster.org/), dedicated to the comprehensive molecular characterization of muscle invasive bladder carcinoma.

MATERIALS

A database was created containing all reported molecular features significant in invasive BC. The query interface was developed in Ruby programming language (version 1.9.3) using the web-framework Rails (version 4.1.5) (http://rubyonrails.org/).

RESULTS

BcCluster contains the data from 112 published references, providing 1,559 statistically significant features relative to BC invasion. The database also holds 435 protein-protein interaction data and 92 molecular pathways significant in BC invasion. The database can be used to retrieve binding partners and pathways for any protein of interest. We illustrate this possibility using survivin, a known BC biomarker.

CONCLUSIONS

BcCluster is an online database for retrieving molecular signatures relative to BC invasion. This application offers a comprehensive view of BC invasiveness at the molecular level and allows formulation of research hypotheses relevant to this phenotype.

Integrative Biology of Diabetic Kidney Disease

BACKGROUND

The leading cause of ESRD in the U.S. is diabetic kidney disease (DKD). Despite significant efforts to improve outcomes in DKD, the impact on disease progression has been disappointing. This has prompted clinicians and researchers to search for alternative approaches to identify persons at risk, and to search for more effective therapies to halt progression of DKD. Identification of novel therapies is critically dependent on a more comprehensive understanding of the pathophysiology of DKD, specifically at the molecular level. A more expansive and exploratory view of DKD is needed to complement more traditional research approaches that have focused on single molecules.

SUMMARY

In recent years, sophisticated research methodologies have emerged within systems biology that should allow for a more comprehensive disease definition of DKD. Systems biology provides an inter-disciplinary approach to describe complex interactions within biological systems including how these interactions influence systems' functions and behaviors. Computational modeling of large, system-wide, quantitative data sets is used to generate molecular interaction pathways, such as metabolic and cell signaling networks.

KEY MESSAGES

Importantly, interpretation of data generated by systems biology tools requires integration with enhanced clinical research data and validation using model systems. Such an integrative biological approach has already generated novel insights into pathways and molecules involved in DKD. In this review, we highlight recent examples of how combining systems biology with traditional clinical and model research efforts results in an integrative biology approach that has significantly added to the understanding of the complex pathophysiology of DKD.

Quantitative in vitro to in vivo extrapolation of tissues toxicity

Predicting repeated-dosing in vivo drug toxicity from in vitro testing and omics data gathering requires significant support in bioinformatics, mathematical modeling and statistics. We present here the major aspects of the work devoted within the framework of the European integrated Predict-IV to pharmacokinetic modeling of in vitro experiments, physiologically based pharmacokinetic (PBPK) modeling, mechanistic models of toxicity for the kidney and brain, large scale dose-response analyses methods and biomarker discovery tools. All of those methods have been applied to various extent to the drug datasets developed by the project's partners. Our approach is rather generic and could be adapted to other drugs or drug candidates. It marks a successful integration of the work of the different teams toward a common goal of predictive quantitative in vitro to in vivo extrapolation.

Protein interactome of muscle invasive bladder cancer

Muscle invasive bladder carcinoma is a complex, multifactorial disease caused by disruptions and alterations of several molecular pathways that result in heterogeneous phenotypes and variable disease outcome. Combining this disparate knowledge may offer insights for deciphering relevant molecular processes regarding targeted therapeutic approaches guided by molecular signatures allowing improved phenotype profiling. The aim of the study is to characterize muscle invasive bladder carcinoma on a molecular level by incorporating scientific literature screening and signatures from omics profiling. Public domain omics signatures together with molecular features associated with muscle invasive bladder cancer were derived from literature mining to provide 286 unique protein-coding genes. These were integrated in a protein-interaction network to obtain a molecular functional map of the phenotype. This feature map educated on three novel disease-associated pathways with plausible involvement in bladder cancer, namely Regulation of actin cytoskeleton, Neurotrophin signalling pathway and Endocytosis. Systematic integration approaches allow to study the molecular context of individual features reported as associated with a clinical phenotype and could potentially help to improve the molecular mechanistic description of the disorder.

From molecular signatures to predictive biomarkers: modeling disease pathophysiology and drug mechanism of action

Omics profiling significantly expanded the molecular landscape describing clinical phenotypes. Association analysis resulted in first diagnostic and prognostic biomarker signatures entering clinical utility. However, utilizing Omics for deepening our understanding of disease pathophysiology, and further including specific interference with drug mechanism of action on a molecular process level still sees limited added value in the clinical setting. We exemplify a computational workflow for expanding from statistics-based association analysis toward deriving molecular pathway and process models for characterizing phenotypes and drug mechanism of action. Interference analysis on the molecular model level allows identification of predictive biomarker candidates for testing drug response. We discuss this strategy on diabetic nephropathy (DN), a complex clinical phenotype triggered by diabetes and presenting with renal as well as cardiovascular endpoints. A molecular pathway map indicates involvement of multiple molecular mechanisms, and selected biomarker candidates reported as associated with disease progression are identified for specific molecular processes. Selective interference of drug mechanism of action and disease-associated processes is identified for drug classes in clinical use, in turn providing precision medicine hypotheses utilizing predictive biomarkers.

Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development

Biomarker research is continuously expanding in the field of clinical proteomics. A combination of different proteomic-based methodologies can be applied depending on the specific clinical context of use. Moreover, current advancements in proteomic analytical platforms are leading to an expansion of biomarker candidates that can be identified. Specifically, mass spectrometric techniques could provide highly valuable tools for biomarker research. Ideally, these advances could provide with biomarkers that are clinically applicable for disease diagnosis and/ or prognosis. Unfortunately, in general the biomarker candidates fail to be implemented in clinical decision making. To improve on this current situation, a well-defined study design has to be established driven by a clear clinical need, while several checkpoints between the different phases of discovery, verification and validation have to be passed in order to increase the probability of establishing valid biomarkers. In this review, we summarize the technical proteomic platforms that are available along the different stages in the biomarker discovery pipeline, exemplified by clinical applications in the field of bladder cancer biomarker research.

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.