Genomic biomarkers for chronic kidney disease

The Michigan O'Brien Kidney Research Center: transforming translational kidney research through systems biology

Research on kidney diseases is being transformed by the rapid expansion and innovations in omics technologies. The analysis, integration, and interpretation of big data, however, have been an impediment to the growing interest in applying these technologies to understand kidney function and failure. Targeting this urgent need, the University of Michigan O'Brien Kidney Translational Core Center (MKTC) and its Administrative Core established the Applied Systems Biology Core. The Core provides need-based support for the global kidney community centered on enabling incorporation of systems biology approaches by creating web-based, user-friendly analytic and visualization tools, like Nephroseq and Nephrocell, guiding with experimental design, and processing, analysis, and integration of large data sets. The enrichment core supports systems biology education and dissemination through workshops, seminars, and individualized training sessions. Meanwhile, the Pilot and Feasibility Program of the MKTC provides pilot funding to both early-career and established investigators new to the field, to integrate a systems biology approach into their research projects. The relevance and value of the portfolio of training and services offered by MKTC are reflected in the expanding community of young investigators, collaborators, and users accessing resources and engaging in systems biology-based kidney research, thereby motivating MKTC to persevere in its mission to serve the kidney research community by enabling access to state-of-the-art data sets, tools, technologies, expertise, and learning opportunities for transformative basic, translational, and clinical studies that will usher in solutions to improve the lives of people impacted by kidney disease.

OMICS in Chronic Kidney Disease: Focus on Prognosis and Prediction

Chronic kidney disease (CKD) patients are characterized by a high residual risk for cardiovascular (CV) events and CKD progression. This has prompted the implementation of new prognostic and predictive biomarkers with the aim of mitigating this risk. The ‘omics’ techniques, namely genomics, proteomics, metabolomics, and transcriptomics, are excellent candidates to provide a better understanding of pathophysiologic mechanisms of disease in CKD, to improve risk stratification of patients with respect to future cardiovascular events, and to identify CKD patients who are likely to respond to a treatment. Following such a strategy, a reliable risk of future events for a particular patient may be calculated and consequently the patient would also benefit from the best available treatment based on their risk profile. Moreover, a further step forward can be represented by the aggregation of multiple omics information by combining different techniques and/or different biological samples. This has already been shown to yield additional information by revealing with more accuracy the exact individual pathway of disease.

Serum elabela and apelin levels during different stages of chronic kidney disease

PURPOSE

The association of serum elabela (ELA) and apelin with the progression of chronic kidney disease (CKD) is unknown. We determined if serum ELA and apelin levels were associated with CKD stage.

METHODS

This observational study involved 60 CKD patients and 20 healthy, age-, race-, and gender-matched controls. The participants were grouped according to renal function as follows: normal control group, CKD1 group (stage-1 CKD, 20 patients), CKD3 group (stage-3 CKD, 20 patients), and CKD5 group (stage-5 CKD, 20 patients) in accordance with the Kidney Disease Outcomes - Quality Initiative criteria. We recorded the demographic, clinical, and biochemical data of all participants. Serum ELA and apelin levels were measured using commercially available enzyme-linked immunosorbent assays.

RESULTS

Serum ELA levels gradually and significantly declined with decreases in the estimated glomerular filtration rate (eGFR). Serum ELA showed significant negative correlations with serum creatinine (r = -0.529, p < .001), blood urea nitrogen (r = -0.575, p < .001), systolic blood pressure (r = -0.455, p < .001), and diastolic blood pressure (r = -0.450, p < .001), and significant positive correlations with hemoglobin (r = 0.523, p < .001) and eGFR (r = 0.728, p < .001). Multiple regression analysis showed that eGFR independently influenced serum ELA levels. No significant association was found between serum apelin levels and CKD progression.

CONCLUSION

In CKD patients, serum ELA levels decreased with decreasing eGFR. This finding may provide a new target for the prediction, diagnosis, and staging of CKD.

A systematic review and participant-level meta-analysis found little association of retinal microvascular caliber with reduced kidney function

Previously, variation in retinal vascular caliber has been reported in association with chronic kidney disease (CKD) but findings remain inconsistent. To help clarify this we conducted individual participant data meta-analysis and aggregate data meta-analysis on summary estimates to evaluate cross-sectional associations between retinal vascular caliber and CKD. A systematic review was performed using Medline and EMBASE for articles published until October 2018. The aggregate analysis used a two-stage approach combining summary estimates from eleven studies (44,803 patients) while the individual participant analysis used a one-stage approach combining raw data from nine studies (33,222 patients). CKD stages 3-5 was defined as an estimated glomerular filtration rate under 60 mL/min/1.73m2. Retinal arteriolar and venular caliber (central retinal arteriolar and venular equivalent) were assessed from retinal photographs using computer-assisted methods. Logistic regression estimated relative risk of CKD stages 3-5 associated with a 20 μm decrease (approximately one standard deviation) in central retinal arteriolar and venular equivalent. Prevalence of CKD stages 3-5 was 11.2% of 33,222 and 11.3% of 44,803 patients in the individual participant and aggregate data analysis, respectively. No significant associations were detected in adjusted analyses between central retinal arteriolar and venular equivalent and CKD stages 3-5 in the aggregate analysis for central retinal arteriolar relative risk (0.98, 95% confidence interval 0.94-1.03); venular equivalent (0.99, 0.95-1.04) or individual participant central retinal arteriolar (0.99, 0.95-1.04) or venular equivalent (1.01, 0.97-1.05). Thus, meta-analysis provided little evidence to suggest that cross sectional direct measurements of retinal vascular caliber was associated with CKD stages 3-5 in the general population. Hence, meta-analyses of longitudinal studies evaluating the association between retinal parameters and CKD stages 3-5 may be warranted.

Diabetes induced renal complications by leukocyte activation of nuclear factor κ-B and its regulated genes expression

Type 2 diabetes mellitus (T2D) is a metabolic disorder characterized by inappropriate insulin function. Despite wide progress in genome studies, defects in gene expression for diabetes prognosis still incompletely identified. Prolonged hyperglycemia activates NF-κB, which is a main player in vascular dysfunctions of diabetes. Activated NF-κB, triggers expression of various genes that promote inflammation and cell adhesion process. Alteration of pro-inflammatory and profibrotic gene expression contribute to the irreversible functional and structural changes in the kidney resulting in diabetic nephropathy (DN). To identify the effect of some important NF-κB related genes on mediation of DN progression, we divided our candidate genes on the basis of their function exerted in bloodstream into three categories (Proinflammatory; NF-κB, IL-1B, IL-6, TNF-α and VEGF); (Profibrotic; FN, ICAM-1, VCAM-1) and (Proliferative; MAPK-1 and EGF). We analyzed their expression profile in leukocytes of patients and explored their correlation to diabetic kidney injury features. Our data revealed the overexpression of both proinflammatory and profibrotic genes in DN group when compared to T2D group and were associated positively with each other in DN group indicating their possible role in DN progression. In DN patients, increased expression of proinflammatory genes correlated positively with glycemic control and inflammatory markers indicating their role in DN progression. Our data revealed that the persistent activation NF-κB and its related genes observed in hyperglycemia might contribute to DN progression and might be a good diagnostic and therapeutic target for DN progression. Large-scale studies are needed to evaluate the potential of these molecules to serve as disease biomarkers.

Transcriptome analysis of primary podocytes reveals novel calcium regulated regulatory networks

Podocytes are pivotal in establishing the selective permeability of the glomerular filtration barrier. Recently, we showed that an increase of the intracellular calcium ion concentration [Ca²⁺ ] causes a rapid and transient actin reset (CaAR) measurable through live imaging microscopy using lifeact-mCherry as an actin dye in different cell types including the podocyte. This and other studies show the critical role [Ca²⁺ ] and the actin cytoskeleton play in podocyte homeostasis. To further investigate the role of [Ca²⁺ ] and the actin cytoskeleton in podocytes, we used a double fluorescent reporter mouse model to establish a primary podocyte culture system. We treated these podocytes temporarily with a Calcium Ionophore and facultatively with Latrunculin A, an inhibitor of actin polymerization. Unbiased genome wide transcriptional analysis identified a transcriptional response in podocytes to elevated [Ca²⁺ ] levels, affecting mRNA levels of PDGF-BB, RICTOR, and MIR17HG as mediators of Ca²⁺ -signaling. Comparison of the ex vivo transcriptional response from the primary podocyte culture with glomerular transcripts across a wide spectrum of CKD disease confirmed co-regulation of transcript sets, establishing the disease relevance of the model system. Our findings demonstrate novel [Ca²⁺ ] regulated gene networks in podocytes deepening our understanding of podocyte biology and disease.

MiRNAs as potential biomarker of kidney diseases: A review

MicroRNAs (miRNAs) are 22 nucleotides short, non-coding and tissue-specific single-stranded RNA which modulates target gene expression. Presently, shreds of evidence confirmed that miRNAs play a key role in kidney pathophysiology. The objectives of the present review are to summarize new research data towards the latest developments in the potential use of miRNAs as a diagnostic biomarker for kidney diseases. This holistic information will update the existing knowledge of kidney disease biomarkers. "miRNA profile for Diabetic Kidney disease, Acute kidney injury, Renal fibrosis, hemodialysis, transplants, FSGS, IgAN, etc." are the search keywords which have been used in this review. The search outcome gave an exciting insightful perception of miRNAs competence as a biomarker. Also it is observed that various samples as plasma, urine and biopsies were used for profiling the miRNA expression. The miRNAs were not only used for diagnostic biomarkers but also for therapeutic targets. Each kidney disease showed different miRNAs expression profile and few miRNAs quite common with some kidney diseases. miRNAs are simple and efficient diagnostic biomarkers for kidney diseases.

Clinical decision support system to predict chronic kidney disease: A fuzzy expert system approach

BACKGROUND AND OBJECTIVES

Diagnosis and early intervention of chronic kidney disease are essential to prevent loss of kidney function and a large amount of financial resources. To this end, we developed a fuzzy logic-based expert system for diagnosis and prediction of chronic kidney disease and evaluate its robustness against noisy data.

METHODS

At first, we identified the diagnostic parameters and risk factors through a literature review and a survey of 18 nephrologists. Depending on the features selected, a set of fuzzy rules for the prediction of chronic kidney disease was determined by reviewing the literature, guidelines and consulting with nephrologists. Fuzzy expert system was developed using MATLAB software and Mamdani Inference System. Finally, the fuzzy expert system was evaluated using data extracted from 216 randomly selected medical records of patients with and without chronic kidney disease. We added noisy data to our dataset and compare the performance of the system on original and noisy datasets.

RESULTS

We selected 16 parameters for the prediction of chronic kidney disease. The accuracy, sensitivity, and specificity of the final system were 92.13 %, 95.37 %, and 88.88 %, respectively. The area under the curve was 0.92 and the Kappa coefficient was 0.84, indicating a very high correlation between the system diagnosis and the final diagnosis recorded in the medical records. The performance of the system on noisy input variables indicated that in the worse scenario, the accuracy, sensitivity, and specificity of the system decreased only by 4.43 %, 7.48 %, and 5.41 %, respectively.

CONCLUSION

Considering the desirable performance of the proposed expert system, the system can be useful in the prediction of chronic kidney disease.

Systems biology approaches toward autosomal dominant polycystic kidney disease (ADPKD)

Background

Autosomal dominant polycystic kidney disease (ADPKD), a common of monogenetic disorder caused by the polycystic kidney disease-1 (PKD1) or PKD2 genes deficiency. In this study, we have re-analyzed a microarray dataset to generate a holistic view of this disease.

Methodology

GSE7869, an expression profiling dataset was downloaded from the Gene Expression Omnibus (GEO) database. After quality control assessment, using GEO2R tool of GEO, genes with adjusted p-value ≤ 0.05 were determined as differentially expressed (DE). The expression profiles from ADPKD samples in different sizes were compared. Using CluePedia plugin of Cytoscape software, the protein–protein interaction (PPI) networks were constructed and analyzed by Cytoscape NetworkAnalyzer tool and MCODE application. Pathway enrichment analysis of clustered genes by MCODE with the high centrality parameters in PPI networks was performed using Cytoscape ClueGO plugin. Moreover, by Enrichr database, microRNAs (miRNAs) and transcription factors (TFs) targeted DE genes were identified.

Results

In this study to explore the molecular pathogenesis of kidney in ADPKD, mRNA expression profiles of cysts from patients in different sizes were re-analyzed. The comparisons were performed between normal with minimally cystic tissue (MCT) samples, MCTs with small cysts, and small cysts with large cysts. 512, 7024, and 655 DE genes were determined, respectively. The top central genes, e.g. END1, EGFR, and FOXO1 were identified with topology and clustering analysis. DE genes that were significantly enriched in PPI networks are critical genes and their roles in ADPKD remain to be assessed in future experimental studies beside miRNAs and TFs predicted. Furthermore, the functional analysis resulted in which most of them are expected to be associated with ADPKD pathogenesis, such as signal pathways that involved in cell growth, inflammation, and cell polarity.

Conclusion

We have here explored systematic approaches for molecular mechanisms assay of ADPKD as a monogenic disease, which may also be used for other monogenetic diseases beside complex diseases to provide suitable therapeutic targets.

A Multi-layered Quantitative In Vivo Expression Atlas of the Podocyte Unravels Kidney Disease Candidate Genes

Damage to and loss of glomerular podocytes has been identified as the culprit lesion in progressive kidney diseases. Here, we combine mass spectrometry-based proteomics with mRNA sequencing, bioinformatics, and hypothesis-driven studies to provide a comprehensive and quantitative map of mammalian podocytes that identifies unanticipated signaling pathways. Comparison of the in vivo datasets with proteomics data from podocyte cell cultures showed a limited value of available cell culture models. Moreover, in vivo stable isotope labeling by amino acids uncovered surprisingly rapid synthesis of mitochondrial proteins under steady-state conditions that was perturbed under autophagy-deficient, disease-susceptible conditions. Integration of acquired omics dimensions suggested FARP1 as a candidate essential for podocyte function, which could be substantiated by genetic analysis in humans and knockdown experiments in zebrafish. This work exemplifies how the integration of multi-omics datasets can identify a framework of cell-type-specific features relevant for organ health and disease.

•

Deep proteome and transcriptome analyses of native podocytes unravel druggable targets

•

Static and dynamic proteomics uncover features of podocyte identity and proteostasis

•

Candidate genes for nephrotic syndrome were predicted based on multi-omic integration

•

FARP1 is a previously unreported candidate gene for human proteinuric kidney disease

A network-based variable selection approach for identification of modules and biomarker genes associated with end-stage kidney disease

AIMS

Intervention for end-stage kidney disease (ESKD), which is associated with adverse prognoses and major economic burdens, is challenging due to its complex pathogenesis. The study was performed to identify biomarker genes and molecular mechanisms for ESKD by bioinformatics approach.

METHODS

Using the Gene Expression Omnibus dataset GSE37171, this study identified pathways and genomic biomarkers associated with ESKD via a multi-stage knowledge discovery process, including identification of modules of genes by weighted gene co-expression network analysis, discovery of important involved pathways by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, selection of differentially expressed genes by the empirical Bayes method, and screening biomarker genes by the least absolute shrinkage and selection operator (Lasso) logistic regression. The results were validated using GSE70528, an independent testing dataset.

RESULTS

Three clinically important gene modules associated with ESKD, were identified by weighted gene co-expression network analysis. Within these modules, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed important biological pathways involved in ESKD, including transforming growth factor-β and Wnt signalling, RNA-splicing, autophagy and chromatin and histone modification. Furthermore, Lasso logistic regression was conducted to identify five final genes, namely, CNOT8, MST4, PPP2CB, PCSK7 and RBBP4 that are differentially expressed and associated with ESKD. The accuracy of the final model in distinguishing the ESKD cases and controls was 96.8% and 91.7% in the training and validation datasets, respectively.

CONCLUSION

Network-based variable selection approaches can identify biological pathways and biomarker genes associated with ESKD. The findings may inform more in-depth follow-up research and effective therapy.

Two subgroups in systemic lupus erythematosus with features of antiphospholipid or Sjögren's syndrome differ in molecular signatures and treatment perspectives

Background

Previous studies and own clinical observations of patients with systemic lupus erythematosus (SLE) suggest that SLE harbors distinct immunophenotypes. This heterogeneity might result in differences in response to treatment in different subgroups and obstruct clinical trials. Our aim was to understand how SLE subgroups may differ regarding underlying pathophysiology and characteristic biomarkers.

Methods

In a cross-sectional study, including 378 well-characterized SLE patients and 316 individually matched population controls, we defined subgroups based on the patients’ autoantibody profile at inclusion. We selected a core of an antiphospholipid syndrome-like SLE (aPL+ group; positive in the lupus anticoagulant (LA) test and negative for all three of SSA (Ro52 and Ro60) and SSB antibodies) and a Sjögren’s syndrome-like SLE (SSA/SSB+ group; positive for all three of SSA (Ro52 and Ro60) and SSB antibodies but negative in the LA test). We applied affinity-based proteomics, targeting 281 proteins, together with well-established clinical biomarkers and complementary immunoassays to explore the difference between the two predefined SLE subgroups.

Results

The aPL+ group comprised 66 and the SSA/SSB+ group 63 patients. The protein with the highest prediction power (receiver operating characteristic (ROC) area under the curve = 0.89) for separating the aPL+ and SSA/SSB+ SLE subgroups was integrin beta-1 (ITGB1), with higher levels present in the SSA/SSB+ subgroup. Proteins with the lowest p values comparing the two SLE subgroups were ITGB1, SLC13A3, and CERS5. These three proteins, rheumatoid factor, and immunoglobulin G (IgG) were all increased in the SSA/SSB+ subgroup. This subgroup was also characterized by a possible activation of the interferon system as measured by high KRT7, TYK2, and ETV7 in plasma. In the aPL+ subgroup, complement activation was more pronounced together with several biomarkers associated with systemic inflammation (fibrinogen, α-1 antitrypsin, neutrophils, and triglycerides).

Conclusions

Our observations indicate underlying pathogenic differences between the SSA/SSB+ and the aPL+ SLE subgroups, suggesting that the SSA/SSB+ subgroup may benefit from IFN-blocking therapies while the aPL+ subgroup is more likely to have an effect from drugs targeting the complement system. Stratifying SLE patients based on an autoantibody profile could be a way forward to understand underlying pathophysiology and to improve selection of patients for clinical trials of targeted treatments.

Electronic supplementary material

The online version of this article (10.1186/s13075-019-1836-8) contains supplementary material, which is available to authorized users.

BDNF: mRNA expression in urine cells of patients with chronic kidney disease and its role in kidney function

Podocyte loss and changes to the complex morphology are major causes of chronic kidney disease (CKD). As the incidence is continuously increasing over the last decades without sufficient treatment, it is important to find predicting biomarkers. Therefore, we measured urinary mRNA levels of podocyte genes NPHS1, NPHS2, PODXL and BDNF, KIM‐1, CTSL by qRT‐PCR of 120 CKD patients. We showed a strong correlation between BDNF and the kidney injury marker KIM‐1, which were also correlated with NPHS1, suggesting podocytes as a contributing source. In human biopsies, BDNF was localized in the cell body and major processes of podocytes. In glomeruli of diabetic nephropathy patients, we found a strong BDNF signal in the remaining podocytes. An inhibition of the BDNF receptor TrkB resulted in enhanced podocyte dedifferentiation. The knockdown of the orthologue resulted in pericardial oedema formation and lowered viability of zebrafish larvae. We found an enlarged Bowman's space, dilated glomerular capillaries, podocyte loss and an impaired glomerular filtration. We demonstrated that BDNF is essential for glomerular development, morphology and function and the expression of BDNF and KIM‐1 is highly correlated in urine cells of CKD patients. Therefore, BDNF mRNA in urine cells could serve as a potential CKD biomarker.

The Warburg Effect in Diabetic Kidney Disease

Diabetic kidney disease (DKD) is the leading cause of morbidity and mortality in diabetic patients. Defining risk factors for DKD using a reductionist approach has proven challenging. Integrative omics-based systems biology tools have shed new insights in our understanding of DKD and have provided several key breakthroughs for identifying novel predictive and diagnostic biomarkers. In this review, we highlight the role of the Warburg effect in DKD and potential regulating factors such as sphingomyelin, fumarate, and pyruvate kinase muscle isozyme M2 in shifting glucose flux from complete oxidation in mitochondria to the glycolytic pathway and its principal branches. With the development of highly sensitive instruments and more advanced automatic bioinformatics tools, we believe that omics analyses and imaging techniques will focus more on singular-cell-level studies, which will allow in-depth understanding of DKD and pave the path for personalized kidney precision medicine.

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.

Potential diagnostic biomarkers for chronic kidney disease of unknown etiology (CKDu) in Sri Lanka: a pilot study

Background

In Sri Lanka, there exists chronic kidney disease of both known (CKD) and unknown etiologies (CKDu). Identification of novel biomarkers that are customized to the specific causative factors would lead to early diagnosis and clearer prognosis of the diseases. This study aimed to find genetic biomarkers in blood to distinguish and identify CKDu from CKD as well as healthy populations from CKDu endemic and non-endemic areas of Sri Lanka.

Methods

The expression patterns of a selected panel of 12 potential genetic biomarkers were analyzed in blood using RT-qPCR. Fold changes of gene expressions in early and late stages of CKD and CKDu patients, and an apparently healthy population of a CKDu endemic area, Girandurukotte (GH) were calculated relative to apparently healthy volunteers from a CKDu non-endemic area, Kandy (KH) of Sri Lanka, using the comparative CT method.

Results

Significant differences were observed between KH and early stage CKDu for both the insulin-like growth factor binding protein 1 (IGFBP1; p = 0.012) and kidney injury molecule-1 (KIM1; p = 0.003) genes, and KH and late stage CKD and CKDu for the glutathione-S-transferase mu 1 (GSTM1; p < 0.05) gene. IGFBP1 and KIM1 genes showed significant difference between the early and late stage CKDu (p < 0.01). The glutamate cysteine ligase catalytic subunit (GCLC) gene had significantly different expression between KH and all the other study groups (p < 0.01). The GH group was significantly different from the KH group for the oxidative stress related genes, G6PD, GCLC and GSTM1 (p < 0.01), and also the KIM1 gene (p = 0.003). IGFBP1, insulin-like growth factor binding protein 3 (IGFBP3), fibronectin 1 (FN1) and KIM1 showed significant correlations with serum creatinine, and IGFBP1, KIM1 and kallikrein 1 (KLK1) with eGFR (p < 0.05).

Conclusion

A panel consisting of IGFBP1, KIM1, GCLC and GSTM1 genes could be used in combination for early screening of CKDu, whereas these genes in addition with FN1, IGFBP3 and KLK1 could be used to monitor progression of CKDu. The regulation of these genes has to be studied on larger populations to validate their efficiency for further clinical use.

RNA expression signatures and posttranscriptional regulation in diabetic nephropathy

In the last decade, the integration of molecular approaches including transcriptome and miRNome analyses uncovered pathological mechanisms involved in the progression of diabetic nephropathy (DN). Using these techniques, molecular marker candidates [both messenger RNA (mRNA) and miRNA] have also been identified which may enable the characterization of patients at high risk for progression to end-stage renal disease. The results of such studies are urgently needed for a molecular definition of DN and for targeted treatment to improve patient care. The heterogeneity of kidney tissue and the minute amounts of RNA isolated from renal biopsies remain a challenge for omics-studies. Nevertheless, several studies have succeeded in the identification of RNA expression signatures in patients with diabetes and kidney disease. These studies show a reduced expression of growth factors such as VEGF and EGF, and an increased expression of matrix components and matrix-modulating enzymes, an activation of specific NF-κB modules, inflammatory pathways and the complement system. microRNAs are involved in the fine-tuning of mRNA abundance by binding to the 3' untranslated region of a target mRNA, which leads in most cases to translational repression or mRNA cleavage and a decrease in protein output. Here, we review the platforms used for miRNA expression profiling and ways to predict miRNA targets and functions. Several miRNAs have been shown to be involved in the pathogenesis of DN (e.g. miR-21, miR-192, miR-215, miR-216a, miR-29, let-7, miR-25, miR-93, etc.). Functional studies provide evidence that miRNAs are not only diagnostic tools but also represent potential therapeutic targets in DN.

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.

Recent advances in renal interstitial fibrosis and tubular atrophy after kidney transplantation

Although kidney transplantation has been an important means for the treatment of patients with end stage of renal disease, the long-term survival rate of the renal allograft remains a challenge. The cause of late renal allograft loss, once known as chronic allograft nephropathy, has been renamed “interstitial fibrosis and tubular atrophy” (IF/TA) to reflect the histologic pattern seen on biopsy. The mechanisms leading to IF/TA in the transplanted kidney include inflammation, activation of renal fibroblasts, and deposition of extracellular matrix proteins. Identifying the mediators and factors that trigger IF/TA may be useful in early diagnosis and development of novel therapeutic strategies for improving long-term renal allograft survival and patient outcomes. In this review, we highlight the recent advances in our understanding of IF/TA from three aspects: pathogenesis, diagnosis, and treatment.

Genetics and primary care: where are we headed?

Since first sequencing the human genome in 2003, emerging genetic/genomic technologies have ushered in a revolutionary era of medicine that purports to bridge molecular biology and clinical care. The field of translational medicine is charged with mediating this revolution. Sequencing innovations are far outpacing guidelines intended to ease their practice-based applications, including in primary care. As a result, genomic medicine’s full integration in primary care settings especially, has been slow to materialize. Researchers and clinicians alike face substantial challenges in navigating contentious ethical issues raised in translation and implementation, namely preserving the spirit of whole-person approaches to care; maintaining respect for persons and communities; and translating genetic risk into clinical actionability. This commentary therefore explores practical barriers to, and ethical implications of, incorporating genomic technologies in the primary care sector. These ethical challenges are both philosophical and infrastructural. From a primary care perspective, the commentary further reviews the ethical, legal and social implications of the Center for Disease Control’s proposed model for assessing the validity and utility of genomic testing and family health history applications. Lastly, the authors provide recommendations for future translational initiatives that aim to maximize the capacities of genomic medicine, without compromising primary care philosophies and foundations of practice.

The urine microRNA profile may help monitor post-transplant renal graft function

Noninvasive, cost-effective biomarkers that allow accurate monitoring of graft function are needed in kidney transplantation. Since microRNAs (miRNAs) have emerged as promising disease biomarkers, we sought to establish an miRNA signature in urinary cell pellets comparing kidney transplant patients diagnosed with chronic allograft dysfunction (CAD) with interstitial fibrosis and tubular atrophy and those recipients with normal graft function. Overall, we evaluated 191 samples from 125 deceased donor primary kidney transplant recipients in the discovery, initial validation, and the longitudinal validation studies for noninvasive monitoring of graft function. Of 1733 mature miRNAs studied using microarrays, 22 were found to be differentially expressed between groups. Ontology and pathway analyses showed inflammation as the principal biological function associated with these miRNAs. Twelve selected miRNAs were longitudinally evaluated in urine samples of an independent set of 66 patients, at two time points after kidney transplant. A subset of these miRNAs was found to be differentially expressed between groups early after kidney transplant before histological allograft injury was evident. Thus, a panel of urine miRNAs was identified as potential biomarkers for monitoring graft function and anticipating progression to CAD in kidney transplant patients.

MTHFR, MTR and MTRR polymorphisms and risk of chronic kidney disease in Japanese: cross-sectional data from the J-MICC Study

PURPOSE

Chronic kidney disease (CKD) is well known as a strong risk factor for both of end-stage renal disease and cardiovascular disease. To clarify the associations of MTHFR, MTR, and MTRR polymorphisms with the risk of CKD in Japanese, we examined this association among Japanese subjects using cross-sectional data.

METHODS

The subjects for this analysis were 3,318 participants consecutively selected from the Japan Multi-institutional Collaborative Cohort (J-MICC) Study. The polymorphisms were genotyped by a multiplex polymerase chain reaction-based Invader assay. Age- and sex-adjusted odds ratio (aOR) of CKD with stage 3-5 was calculated for each genotype.

RESULTS

When those with MTHFR C677T C/C were defined as references, those with MTHFR C677T C/T and T/T demonstrated the aORs for CKD of 1.14 (95 % CI 0.93-1.40) and 1.39 (1.06-1.82), respectively. Marginally significantly decreased risk of CKD with increasing number of MTR A2756G G allele (p = 0.058) was observed. Stratified analyses by plasma folate low (<7.4 ng/ml) or high (≥7.4 ng/ml) suggested significantly higher OR of CKD for those with MTHFR C677T T/T and low serum folate with the aOR of 2.07 (95 % CI 1.30-3.31) compared with that for those with MTHFR C677T T/T and high serum folate.

CONCLUSIONS

The present study found a significant association between the subjects with the T/T genotype of MTHFR C677T polymorphism and the elevated risk of CKD, which may suggest the possibility of the risk evaluation and prevention of this potentially life-threatening disease based on genetic traits in the near future.

Chip-based nLC-TOF-MS is a highly stable technology for large-scale high-throughput analyses

Many studies focused on the discovery of novel biomarkers for the diagnosis and treatment of disease states are facilitated by mass spectrometry-based technology. HPLC coupled to mass spectrometry is widely used; miniaturization of this technique using nano-liquid chromatography (LC)-mass spectrometry (MS) usually results in better sensitivity, but is associated with limited repeatability. The recent introduction of chip-based technology has significantly improved the stability of nano-LC-MS, but no substantial studies to verify this have been performed. To evaluate the temporal repeatability of chip-based nano-LC-MS analyses, N-glycans released from a serum sample were repeatedly analyzed using nLC-PGC-chip-TOF-MS on three non-consecutive days. With an average inter-day coefficient of variation of 4 %, determined on log10-transformed integrals, the repeatability of the system is very high. Overall, chip-based nano-LC-MS appears to be a highly stable technology, which is suitable for the profiling of large numbers of clinical samples for biomarker discovery.

Perspectives on systems biology applications in diabetic kidney disease

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

Current drug development challenges in chronic kidney disease (CKD)--identification of individualized determinants of renal progression and premature cardiovascular disease (CVD)

Chronic kidney disease (CKD) and end-stage renal disease (ESRD) are currently considered as major health burdens. Notably, CKD can be regarded as an interesting clinical model of accelerated cardiovascular disease (CVD) and ageing, which offers exciting new perspectives and challenges for pharmaceutical drug development. However, during the last decades, therapeutic advances to slow down the progression of CKD and reduce CVD risk have largely failed due to several possible reasons including (i) the lack of profound understanding of the pathophysiology of chronic renal damage and its associated CVD; (ii) an inadequate characterization of molecular mechanisms of currently approved therapies such as renin-angiotensin-aldosterone-system (RAAS) blockade; (iii) the unclear biochemical property needs required for novel therapeutic approaches; (iv) the missing quantity and quality of clinical trials in the nephrology field; and, most importantly, (v) the absence of prognostic renal biomarkers that reflect the severity of the structural organ damage and predict ESRD as well as CVD mortality. There is clearly an insufficient understanding of why a significant proportion of CKD patients progress to ESRD and/or die from CVD whereas others rather remain stable. In this article, we urge renal researchers to develop novel experimental and clinical tools for rational and translational drug discovery. Identification of individualized determinants of CKD progression and/or premature CVD will enable personalized medicine and lead to novel innovative nephro- and/or cardioprotective pharmacological treatment in these high-risk patients.

Biomarkers: hopes and challenges in the path from discovery to clinical practice

Biomarkers are objectively measured indicators of normal or pathological processes that may be helpful in diagnosis, staging, monitoring treatment, or prognostic evaluation of a disease. Although development of genomic, metabolomic and proteomic technologies has contributed to an explosion in identification of candidate analytes, validation remains expensive and challenging, and successful introduction of new biomarkers to clinical practice occurs at a very slow pace. The goal of this introductory overview is to provide the context for a series of review manuscripts published in the special issue on biomarkers. The promises and challenges of biomarker discovery are highlighted. Discovery and implementation of transformative new biomarkers in clinical practice requires close collaborations between scientists, clinicians and industry. High throughput technologies can identify a myriad of promising candidates but cannot predict their clinical value. In addition to rapid effective and systematic approaches for clinical validation, there is a need to study and establish links between the purported biomarker and the pathophysiologic basis of the disease of interest. Biomarkers are most informative when they provide insights into activation of specific pathways, thus serving as windows into the molecular basis of the disease.