Renal microRNA- and RNA-profiles in progressive chronic kidney disease.

BACKGROUND: MicroRNAs (miRNAs) contribute to chronic kidney disease (CKD) progression via regulating mRNAs involved in renal homeostasis. However, their association with clinical outcome remains poorly understood. MATERIALS AND METHODS: We performed miRNA and mRNA expression profiling on renal biopsy sections by qPCR (miRNA) and microarrays (mRNA) in a discovery (n = 43) and in a validation (n = 29) cohort. miRNAs differentiating stable and progressive cases were inversely correlated with putative target mRNAs, which were further characterized by pathway analysis using KEGG pathways. RESULTS: miR-30d, miR-140-3p, miR-532-3p, miR-194, miR-190, miR-204 and miR-206 were downregulated in progressive cases. These seven miRNAs correlated with upregulated 29 target mRNAs involved in inflammatory response, cell-cell interaction, apoptosis and intra-cellular signalling. In particular, miR-206 and miR-532-3p were associated with distinct biological processes via the expression of their target mRNAs: Reduced expression of miR-206 in progressive disease correlated with the upregulation of target mRNAs participating in inflammatory pathways (CCL19, CXCL1, IFNAR2, NCK2, PTK2B, PTPRC, RASGRP1 and TNFRSF25). Progressive cases also showed a lower expression of miR-532-3p and an increased expression of target transcripts involved in apoptosis pathways (MAP3K14, TNFRSF10B/TRAIL-R2, TRADD and TRAF2). In the validation cohort, we confirmed the decreased expression of miR-206 and miR-532-3p, and the inverse correlation of these miRNAs with the expression of nine of the 12 target genes. The levels of the identified miRNAs and the target mRNAs correlated with clinical parameters and histological damage indices. CONCLUSIONS: These results suggest the involvement of specific miRNAs and mRNAs in biological pathways associated with the progression of CKD.

Metallothioneins and renal ageing.

BACKGROUND: Human lifespan is increasing continuously and about one-third of the population >70 years of age suffers from chronic kidney disease. The pathophysiology of the loss of renal function with ageing is unclear. METHODS: We determined age-associated gene expression changes in zero-hour biopsies of deceased donor kidneys without laboratory signs of impaired renal function, defined as a last serum creatinine >0.96 mg/dL in females and >1.18 mg/dL in males, using microarray technology and the Significance Analysis of Microarrays routine. Expression changes of selected genes were confirmed by quantitative polymerase chain reaction and in situ hybridization and immunohistochemistry for localization of respective mRNA and protein. Functional aspects were examined in vitro. RESULTS: Donors were classified into three age groups (<40, 40-59 and >59 years; Groups 1, 2 and 3, respectively). In Group 3 especially, genes encoding for metallothionein (MT) isoforms were more significantly expressed when compared with Group 1; localization studies revealed predominant staining in renal proximal tubular cells. RPTEC/TERT1 cells overexpressing MT2A were less susceptible towards cadmium chloride-induced cytotoxicity and hypoxia-induced apoptosis, both models for increased generation of reactive oxygen species. CONCLUSIONS: Increased expression of MTs in the kidney with ageing might be a protective mechanism against increased oxidative stress, which is closely related to the ageing process. Our findings indicate that MTs are functionally involved in the pathophysiology of ageing-related processes.

Molecular disease presentation in diabetic nephropathy.

Diabetic nephropathy, as the most prevalent chronic disease of the kidney, has also become the primary cause of end-stage renal disease with the incidence of kidney disease in type 2 diabetics continuously rising. As with most chronic diseases, the pathophysiology is multifactorial with a number of deregulated molecular processes contributing to disease manifestation and progression. Current therapy mainly involves interfering in the renin-angiotensin-aldosterone system using angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers. Better understanding of molecular processes deregulated in the early stages and progression of disease hold the key for development of novel therapeutics addressing this complex disease. With the advent of high-throughput omics technologies, researchers set out to systematically study the disease on a molecular level. Results of the first omics studies were mainly focused on reporting the highest deregulated molecules between diseased and healthy subjects with recent attempts to integrate findings of multiple studies on the level of molecular pathways and processes. In this review, we will outline key omics studies on the genome, transcriptome, proteome and metabolome level in the context of DN. We will also provide concepts on how to integrate findings of these individual studies (i) on the level of functional processes using the gene-ontology vocabulary, (ii) on the level of molecular pathways and (iii) on the level of phenotype molecular models constructed based on protein-protein interaction data.

Disease map-based biomarker selection and pre-validation for bladder cancer diagnostic.

CONTEXT: Urinary biomarkers are promising as simple alternatives to cystoscopy for the diagnosis of de novo and recurrent bladder cancer. OBJECTIVE: To identify a highly sensitive and specific biomarker candidate set with potential clinical utility in bladder cancer. MATERIALS AND METHODS: Urinary biomarker concentrations were determined by ELISA. The performance of individual markers and marker combinations was assessed using ROC analysis. RESULTS: A five-biomarker panel (IL8, MMP9, VEGFA, PTGS2 and EN2) was defined from the candidate set. DISCUSSION AND CONCLUSION: This panel showed a better overall performance than the best individual marker. Further validation studies are needed to evaluate its clinical utility in bladder cancer.

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.

A combined transcriptome and bioinformatics approach to unilateral ureteral obstructive uropathy in the fetal sheep model.

PURPOSE: Fetal obstructive uropathy is a leading cause of loss of renal function. Characterizing the molecular fingerprint of cellular responses to obstruction in a fetal model of complete unilateral ureteral obstruction may help elucidate the activated mechanisms and suggest new therapeutic interventions. MATERIAL AND METHODS: Unilateral ureteral obstruction was created in 3 sheep fetuses at day 60 of gestation. For transcriptome analysis total RNA was extracted from vital renal biopsies 2 weeks after intervention from obstructed kidneys and from control kidneys of untreated twins. cDNA preparation, hybridization to the GeneChip® Bovine Genome Array and array scanning were done according to manufacturer protocols. Bioinformatics analysis was used to derive functional biological processes linked to obstructive uropathy. Quantitative reverse-transcriptase-polymerase chain reaction and immunohistochemistry were used to validate microarray results. RESULTS: Seven biological processes were identified as significantly affected by differentially regulated features that characterize unilateral ureteral obstruction, namely protein metabolism and modification, other metabolism, neuronal activity, ligand mediated signaling, amino acid metabolism, coenzyme/prosthetic group metabolism and rRNA metabolism. Literature mining identified 17 candidate genes previously reported as key in the context of unilateral ureteral obstruction, related pathological mechanisms or other kidney diseases. CONCLUSIONS: Combined transcriptome and bioinformatics analysis allowed the identification of enriched processes in the fetal sheep model of unilateral ureteral obstruction that are likely associated with renal damage but to our knowledge have not been previously identified. Future clarification of these molecular fingerprints may eventually provide therapeutic targets and early predictive markers involved in the pathogenesis of fetal uropathy.

Linking transcriptomic and proteomic data on the level of protein interaction networks.

Integration and joint analysis of omics profiles derived on the genome, transcriptome, proteome and metabolome levels is a natural next step in realizing a Systems Biology view of cellular processes. However, merging, e.g. mRNA concentration and protein abundance profiles, is not straightforward, as a direct overlap of differentially regulated/abundant features, resulting from transcriptomics and proteomics, is for various reasons limited. We present the procedures for integrating omics profiles at the level of protein interaction networks, exemplified by using transcriptomic and proteomic data sets characterizing chronic kidney disease. On the level of direct feature overlap, only a limited number of genes and proteins were found to be significantly affected following a separate transcript and protein profile analysis, including a collagen subtype and uromodulin, both being described in the context of renal failure. On the level of protein pathway and process categories, this minor overlap increases substantially, identifying cell structure, cell adhesion, as well as immunity and defense mechanisms as jointly populated with features individually identified as relevant in transcriptomics and proteomics experiments. Mapping diverse data sources characterizing a given phenotype under the analysis on directed and also undirected protein interaction networks serves in joint functional interpretation of omics data sets.

Impaired metabolism in donor kidney grafts after steroid pretreatment.

Summary We recently showed in a randomized control trial that steroid pretreatment of the deceased organ donor suppressed inflammation in the transplant organ but did not reduce the rate or duration of delayed graft function (DGF). This study sought to elucidate such of those factors that caused DGF in the steroid-treated subjects. Genome-wide gene expression profiles were used from 20 steroid-pretreated donor-organs and were analyzed on the level of regulatory protein-protein interaction networks. Significance analysis of microarrays (SAM) yielded 63 significantly down-regulated sequences associated with DGF that could be functionally categorized according to Protein ANalysis THrough Evolutionary Relationships ontologies into two main biologic processes: transport (P < 0.001) and metabolism (P < 0.001). The identified genes suggest hypoxia as the cause of DGF, which cannot be counterbalanced by steroid treatment. Our data showed that molecular pathways affected by ischemia such as transport and metabolism are associated with DGF. Potential interventional targeted therapy based on these findings includes peroxisome proliferator-activated receptor agonists or caspase inhibitors.

Microarray and bioinformatics analysis of gene expression in experimental membranous nephropathy.

BACKGROUND: Passive Heymann nephritis (PHN), the best characterized animal model of experimental membranous nephropathy, is characterized by subepithelial immune deposits, podocyte foot processes effacement and massive proteinuria beginning 4 days following disease induction. Although single genes involved in PHN have been studied, no whole genome-wide expression analysis of kidney tissue has been performed. METHODS: Microarray analysis was performed to identify gene expression changes in PHN rat kidneys during the onset of proteinuria. RESULTS: Our results showed that 234 transcripts were differentially expressed in diseased animals compared to controls. Genes exclusively upregulated in diseased animals were mainly required for cell structure and motility, immunity and defense, cell cycle, and developmental processes. The single most increased gene was transgelin (Tagln) showing a 70-fold upregulation in animals with PHN. Protein-protein interaction analysis revealed the following four processes of major relevance in disease manifestation: (i) DNA damage and repair; (ii) changes in the extracellular matrix; (iii) deregulation of cytokines and growth factors, as well as (iv) rearrangements of the cytoskeleton. CONCLUSION: We show for the first time the complex interplay between multiple different genes in experimental membranous nephropathy, supporting a role for genomic approaches to better understanding and defining specific disease processes.

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.

A fetal sheep model for studying compensatory mechanisms in the healthy contralateral kidney after unilateral ureteral obstruction.

INTRODUCTION: Fetal unilateral ureteral obstruction (UUO) triggers complex pathophysiology involving not only the affected organ but also the contralateral kidney, which undergoes evident compensatory changes. OBJECTIVE: We hypothesized that it would be possible to characterize a transcriptomic fingerprint and selected molecular mechanisms for compensatory growth of contralateral kidneys in UUO, specifically focusing on mediators, carriers, membrane transport, and organ crosstalk in an ovine fetal UUO model. STUDY DESIGN: A fetal ovine model of complete UUO was created on the 60th day of gestation. For transcriptomics profiling, total RNA was extracted from vital renal biopsies of contralateral (non-obstructed) kidneys harvested on the 80th day of gestation, and kidneys of untreated fetuses served as controls. Statistical analysis provided the set of differentially regulated genes further forwarded to bioinformatics analysis for identification of eventual compensatory molecular mechanisms. Histological analysis was performed with hematoxylin and eosin and periodic acid-Schiff stains. RESULTS: Contralateral kidneys showed compensatory hypertrophic renal growth, represented on the molecular side by 324 protein coding genes differentially regulated compared with the control kidney samples. Bioinformatics analysis identified an interactome (Figure) consisting of 102 genes with 108 interactions mainly involving transporters (protein transport and protein localization as well as in protein degradation), signaling molecules, DNA/nucleotide/RNA processing, and components of catabolism and cell cycle regulation. Within the interactome, nine receptors were identified as differentially regulated on the contralateral kidney, involving potential renoprotective ligands of the prostaglandin and the bradykinin receptor, arginine vasopressin receptor 1B, and integrin beta 4. Interestingly, a broad range of molecules found differentially expressed, has been previously described in stress response, renoprotection and repair (e.g., MAPK3, MCP1, DICER1, and others). DISCUSSION: The compensatory renal growth interactome provides a network of transcripts significantly altered in the contralateral kidney, potentially allowing novel insights into mechanisms, interactions, and signaling pathways associated with compensatory growth, and renal protection and repair. Interestingly, the finding of an embedded gene signature reflecting signaling and communication suggests a key role of these processes in CRG either by crosstalk, soluble substances, carriers, or membrane signaling. CONCLUSIONS: Using a transcriptomics approach, it was possible to identify a gene expression fingerprint of contralateral renal growth in a fetal UUO model. Further studies are warranted to validate those processes and to allow incorporation of this knowledge in new fetal diagnostic or even therapeutic strategies.

Functional molecular units for guiding biomarker panel design.

The field of biomarker research has experienced a major boost in recent years, and the number of publications on biomarker studies evaluating given, but also proposing novel biomarker candidates is increasing rapidly for numerous clinically relevant disease areas. However, individual markers often lack sensitivity and specificity in the clinical context, resting essentially on the intra-individual phenotype variability hampering sensitivity, or on assessing more general processes downstream of the causative molecular events characterizing a disease term, in consequence impairing disease specificity. The trend to circumvent these shortcomings goes towards utilizing multimarker panels, thus combining the strength of individual markers to further enhance performance regarding both sensitivity and specificity. A way of identifying the optimal composition of individual markers in a panel approach is to pick each marker as representative for a specific pathophysiological (mechanistic) process relevant for the disease under investigation, hence resulting in a multimarker panel for covering the set of pathophysiological processes underlying the frequently multifactorial composition of a clinical phenotype.Here we outline a procedure of identifying such sets of disease-specific pathophysiological processes (units) delineated on the basis of disease-associated molecular feature lists derived from literature mining as well as aggregated, publicly available Omics profiling experiments. With such molecular units in hand, providing an improved reflection of a specific clinical phenotype, biomarker candidates can then be assigned to or novel candidates are to be selected from these units, subsequently resulting in a multimarker panel promising improved accuracy in disease diagnosis as well as prognosis.

A 3-biomarker-panel predicts renal outcome in patients with proteinuric renal diseases.

BACKGROUND: Clinical and histological parameters are valid prognostic markers in renal disease, although they may show considerable interindividual variability and sometimes limited prognostic value. Novel molecular markers and pathways have the potential to increase the predictive prognostic value of the so called "traditional markers". METHODS: Transcriptomics profiles from laser-capture microdissected proximal tubular epithelial cells from routine kidney biopsies were correlated with a chronic renal damage index score (CREDI), an inflammation score (INSCO), and clinical parameters. We used data from 20 renal biopsies with various proteinuric renal diseases with a median follow-up of 49 months (discovery cohort). For validation we performed microarrays from whole kidney biopsies from a second cohort consisting of 16 patients with a median follow-up time of 28 months (validation cohort). RESULTS: 562 genes correlated with the CREDI score and 285 genes correlated with the INSCO panel, respectively. 39 CREDI and 90 INSCO genes also correlated with serum creatinine at follow-up. After hierarchical clustering we identified 5 genes from the CREDI panel, and 10 genes from the INSCO panel, respectively, which showed kidney specific gene expression. After exclusion of genes, which correlated to each other by > 50% we identified VEGF-C from the CREDI panel and BMP7, THBS1, and TRIB1 from the INSCO panel. Traditional markers for chronic kidney disease progression and inflammation score predicted 44% of the serum creatinine variation at follow-up. VEGF-C did not further enhance the predictive value, but BMP7, THBS1 and TRIB1 together predicted 94% of the serum creatinine at follow up (p < 0.0001). The model was validated in a second cohort of patients yielding also a significant prediction of follow up creatinine (48%, p = 0.0115). CONCLUSION: We identified and validated a panel of three genes in kidney biopsies which predicted serum creatinine at follow-up and therefore might serve as biomarkers for kidney disease progression.

Molecular pathways and crosstalk characterizing the cardiorenal syndrome.

The risk of developing cardiovascular diseases (CVD) is dramatically increased in patients with chronic kidney diseases (CKD). Mechanisms leading to this cardiorenal syndrome (CRS) are multifactorial, and combined analyses of both failing organs may provide routes toward developing strategies for early risk assessment, prognosis, and consequently effective therapy. In order to identify molecular mechanisms involved in the crosstalk between the diseased cardiovascular system and kidney, we analyzed tissue specific transcriptomics profiles on atherosclerosis and diabetic nephropathy together with gene sets associated with cardiovascular and chronic kidney diseases that derived from a literature mining approach. We focused on enriched molecular pathways and highlight molecular interactions found within as well as between affected pathways identified for the two organs. Analysis on the level of molecular pathways pointed out the role of PPAR signaling, coagulation, inflammation, and focal adhesion pathways in formation and progression of the CRS. The proteins apolipoprotein A1 (APOA1) and albumin (ALB) turned out to be of particular importance in the context of dyslipidemia, one of the major risk factors for the development of CVD. In summary, our analyses highlight mechanisms associated with dyslipidemia, hemodynamic regulation, and inflammation on the interface between the cardiovascular and the renal system.

Computational analysis workflows for Omics data interpretation.

Progress in experimental procedures has led to rapid availability of Omics profiles. Various open-access as well as commercial tools have been developed for storage, analysis, and interpretation of transcriptomics, proteomics, and metabolomics data. Generally, major analysis steps include data storage, retrieval, preprocessing, and normalization, followed by identification of differentially expressed features, functional annotation on the level of biological processes and molecular pathways, as well as interpretation of gene lists in the context of protein-protein interaction networks. In this chapter, we discuss a sequential transcriptomics data analysis workflow utilizing open-source tools, specifically exemplified on a gene expression dataset on familial hypercholesterolemia.

Linking molecular feature space and disease terms for the immunosuppressive drug rapamycin.

Next to development of novel drugs also drug repositioning appears promising for tackling unmet clinical needs. Here Omics provided the ground for novel analysis strategies for linking drug and disease by integrating profiles on the molecular as well as the clinical data level. We developed a workflow for linking drugs and diseases for identifying repositioning options, and exemplify the procedure for the immunosuppressive drug rapamycin. Our strategy rests on delineating a drug-specific molecular profile by combining Omics data reflecting the drug's impact on the cellular status as well as drug-associated molecular features extracted from the scientific literature. For rapamycin the respective profile held 905 unique molecular features reflecting defined molecular processes as identified by molecular pathway and process enrichment analysis. Literature mining identified 419 diseases significantly associated with this rapamycin molecular feature list, and transforming the significance of gene-disease associations into a continuous score allowed us to compute ROC and precision-recall for comparing this disease list with diseases already undergoing clinical trials utilizing rapamycin. The AUC of this assignment was computed as 0.84, indicating excellent recovery of relevant disease terms solely based on the drug molecular feature profile. We verified relevant indications by comparing molecular feature sets characteristic for the identified diseases to the drug molecular feature profile, demonstrating highly significant overlaps. The presented workflow allowed positive identification of diseases associated with rapamycin utilizing the drug-specific molecular feature profile, and may be well applicable to other drugs of interest.

Integrative bioinformatics analysis of proteins associated with the cardiorenal syndrome.

The cardiorenal syndrome refers to the coexistence of kidney and cardiovascular disease, where cardiovascular events are the most common cause of death in patients with chronic kidney disease. Both, cardiovascular as well as kidney diseases have been extensively analyzed on a molecular level, resulting in molecular features and associated processes indicating a cross-talk of the two disease etiologies on a pathophysiological level. In order to gain a comprehensive picture of molecular factors contributing to the bidirectional interplay between kidney and cardiovascular system, we mined the scientific literature for molecular features reported as associated with the cardiorenal syndrome, resulting in 280 unique genes/proteins. These features were then analyzed on the level of molecular processes and pathways utilizing various types of protein interaction networks. Next to well established molecular features associated with the renin-angiotensin system numerous proteins involved in signal transduction and cell communication were found, involving specific molecular functions covering receptor binding with natriuretic peptide receptor and ligands as well known example. An integrated analysis of identified features pinpointed a protein interaction network involving mediators of hemodynamic change and an accumulation of features associated with the endothelin and VEGF signaling pathway. Some of these features may function as novel therapeutic targets.

Biomarkers in renal transplantation ischemia reperfusion injury.

Ischemia reperfusion injury (IRI) is a choreographed process leading to delayed graft function (DGF) and reduced long-term patency of the transplanted organ. Early identification of recipients of grafts at risk would allow modification of the posttransplant management, and thereby potentially improve short- and long-term outcomes. The recently emerged "omics" technologies together with bioinformatics workup have allowed the integration and analysis of IRI-associated molecular profiles in the context of DGF. Such a systems biological approach promises qualitative information about interdependencies of complex processes such as IRI regulation, rather than offering descriptive tables of differentially regulated features on a transcriptome, proteome, or metabolome level leaking the functional, biological framework. In deceased-donor kidney transplantation as the primary causative factor resulting in IRI and DGF, a distinct signature and choreography of molecular events in the graft before harvesting seems to be associated with subsequent DGF. A systems biological assessment of these molecular changes suggests that processes along inflammation are of pivotal importance for the early stage of IRI. The causal proof of this association has been tested by a double-blinded, randomized, controlled trial of steroid or placebo infusion into deceased donors before the organs were harvested. Thorough systems biological analysis revealed a panel of biomarkers with excellent discrimination. In summary, integrated analysis of omics data has brought forward biomarker candidates and candidate panels that promise early assessment of IRI. However, the clinical utility of these markers still needs to be established in prospective trials in independent patient populations.

A dependency graph approach for the analysis of differential gene expression profiles.

A central aim of differential gene expression profile analysis is to provide an interpretation of given data at the level of biological processes and pathways. However, traversing descriptive data into context is not straightforward. We present a gene-centric dependency graph approach supporting an interpretation of omics profiles at the level of affected networks. The core of our dependency graph comprises data objects encoding the functional categorization of a particular gene, its tissue-specific reference gene expression, as well as known interactions and subcellular location of assigned proteins. On the basis of these genome, transcriptome, and proteome data we compute pair-wise object (gene) dependencies and interpret them as weighted edges in a dependency graph. Mapping of omics profiles on this graph can be used to identify connectors linking features of the omics list, in turn providing the basis for identification of subgraphs and motifs characterizing the cellular state under analysis. We exemplify this approach by analyzing differential gene expression data characterizing B-cell lymphoma and demonstrate the identification of B-cell lymphoma associated subgraphs.