From acute injury to chronic disease: pathophysiological hypothesis of an epithelial/mesenchymal crosstalk alteration in CKD

Observational clinical studies link acute kidney injury to chronic kidney disease (CKD) progression. The pathophysiological mechanisms that underlie this process are currently unknown but recently published papers suggest that tubular epithelial cells and interstitial mesenchymal cells emerge as a single unit, and their integrity alteration as a whole might lead to renal fibrosis and CKD. The present article reviews the biological findings supporting the hypothesis of an altered epithelial/mesenchymal crosstalk in fibrosis development and progression toward CK

Acute impairment of rat renal function by L -NAME as measured using dynamic MRI.

OBJECTIVE: To assess the feasibility of utilizing dynamic contrast enhanced (DCE)-MRI for depicting the effects of N (G)-nitro-L -arginine methylester (L -NAME), a nitric oxide synthase (NOS) inhibitor, on glomerular filtration rate (GFR) in rats. Since Gd-DTPA is mainly cleared through the kidneys, a first-order kinetic model was used to estimate GFR based on a clearance index (k ( cl )) that describes the tracer transport rate from the renal cortex to the outer medulla. MATERIALS AND METHODS: Normotensive Sprague-Dawley rats were infused with either vehicle (0.9% NaCl) or one of three doses of L -NAME (1, 3 or 10 mg/kg) for 30 min prior to imaging. In a separate set of animals, systolic blood pressure (SBP) was measured for all treatment groups. RESULTS: L -NAME caused a significant increase in SBP at all doses when compared to pre-dose values and at the two highest doses, post-infusion, when compared to vehicle. Administration of L -NAME also led to dose-dependent changes in the rate of Gd-DTPA uptake and tracer concentrations reached in selected regions of the kidney. The k ( cl ) measurements indicated a significant impairment of GFR following NOS blockade at the highest dose of L -NAME. CONCLUSION: DCE-MRI method detected changes in GFR in response to NO inhibition with L -NAME. This non-invasive technique could be used in longitudinal studies in preclinical and clinical settings offering a rapid assessment of single-kidney function

The kidney as a target organ in pharmaceutical research

Kidney diseases are a major source of morbidity and mortality in humans. In developed countries, mortality owing to chronic kidney disease (CKD) terminating in end-stage renal failure is comparable with that associated with cancer. A full understanding of the mechanisms implicated in the progression of CKD is needed to achieve its prevention and to delay the need for support strategies based on dialysis and transplantation. Renal fibrosis is the unifying feature of progressive renal alterations. In this review, we discuss the current status of possible mechanisms, tools and targets in CKD. Pathophysiological compound identification, biomarker discovery and accurate selection of clinical validation criteria appear to be three key elements needed to develop a successful innovative pharmaceutical approach to treating kidney diseases

Epithelial-mesenchymal crosstalk alteration in kidney fibrosis

The incidence of chronic kidney diseases (CKD) is constantly rising, reaching epidemic proportions in the western world and leading to an enormous threat, even to modern health-care systems, in industrialized countries. Therapies of CKD have greatly improved following the introduction of drugs targeting the renin-angiotensin system (RAAS) but even this refined pharmacological approach has failed to stop progression to end-stage renal disease (ESRD) in many individuals. In vitro historical data and recent new findings have suggested that progression of renal fibrosis might occur as a result of an altered tubulo-interstitial microenvironment and, more specifically, as a result of an altered epithelial-mesenchymal crosstalk. Here we the review biological findings that support the hypothesis of an altered cellular crosstalk in an injured local tubulo-interstitial microenvironment leading to renal disease progression. Copyright © 2012 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd

Epithelial cells as active player in fibrosis: findings from an in vitro model.

Kidney fibrosis, a scarring of the tubulo-interstitial space, is due to activation of interstitial myofibroblasts recruited locally or systemically with consecutive extracellular matrix deposition. Newly published clinical studies correlating acute kidney injury (AKI) to chronic kidney disease (CKD) challenge this pathological concept putting tubular epithelial cells into the spotlight. In this work we investigated the role of epithelial cells in fibrosis using a simple controlled in vitro system. An epithelial/mesenchymal 3D cell culture model composed of human proximal renal tubular cells and fibroblasts was challenged with toxic doses of Cisplatin, thus injuring epithelial cells. RT-PCR for classical fibrotic markers was performed on fibroblasts to assess their modulation toward an activated myofibroblast phenotype in presence or absence of that stimulus. Epithelial cell lesion triggered a phenotypical modulation of fibroblasts toward activated myofibroblasts as assessed by main fibrotic marker analysis. Uninjured 3D cell culture as well as fibroblasts alone treated with toxic stimulus in the absence of epithelial cells were used as control. Our results, with the caveats due to the limited, but highly controllable and reproducible in vitro approach, suggest that epithelial cells can control and regulate fibroblast phenotype. Therefore they emerge as relevant target cells for the development of new preventive anti-fibrotic therapeutic approaches

Detect tissue heterogeneity in gene expression data with BioQC

Abstract Background Gene expression data can be compromised by cells originating from other tissues than the target tissue of profiling. Failures in detecting such tissue heterogeneity have profound implications on data interpretation and reproducibility. A computational tool explicitly addressing the issue is warranted. Results We introduce BioQC, a R/Bioconductor software package to detect tissue heterogeneity in gene expression data. To this end BioQC implements a computationally efficient Wilcoxon-Mann-Whitney test and provides more than 150 signatures of tissue-enriched genes derived from large-scale transcriptomics studies. Simulation experiments show that BioQC is both fast and sensitive in detecting tissue heterogeneity. In a case study with whole-organ profiling data, BioQC predicted contamination events that are confirmed by quantitative RT-PCR. Applied to transcriptomics data of the Genotype-Tissue Expression (GTEx) project, BioQC reveals clustering of samples and suggests that some samples likely suffer from tissue heterogeneity. Conclusions Our experience with gene expression data indicates a prevalence of tissue heterogeneity that often goes unnoticed. BioQC addresses the issue by integrating prior knowledge with a scalable algorithm. We propose BioQC as a first-line tool to ensure quality and reproducibility of gene expression data

Epithelial cell injury characterization (upper panel) and fibroblast activation (lower panel) in an <i>in vitro</i> reconstructed microenvironment.

<p>(<b>A</b>) Scheme of the reconstructed microenvironment and workflow analysis of the cisplatin-injured proximal tubular epithelial cells HKC-8 cells and of the WS-1 dermal fibroblasts. (<b>B</b>) Cell viability and (<b>C</b>) apoptosis analysis. Cisplatin-treated proximal tubular epithelial cells HKC-8 cells showed decreased cell viability and increased apoptosis. (<b>D</b>). Cell cycle analysis showed that HKC-8 cells treated with cisplatin high dose (40 µM) were blocked in G2/M phase at 24, 48 and 72 h, whereas cells treated with the low dose (20 µM) reverted at 72 h to a condition similar to control. Cytokine release analysis with (<b>E</b>) IL-6 and (<b>F</b>) RANTES levels. Cisplatin-treated HKC-8 cells produced increased amounts of IL-6 and RANTES. (<b>G</b>) Gene-level analysis results for selected genes showing a stronger response to Ciplatin high dose (CisHigh) than to Ciplatin low dose (CisLow). Expression levels on a logarithmic scale are shown as a heat map: no detectable expression is indicated by black color, increasing expression levels are indicated by brighter shades of yellow. Note that several genes show up twice in the figure because they are represented by multiple probes on the Illumina chip. While the measured values do not necessarily agree, the overall trend of up-regulation is the same. (<b>H</b>) Gene-level analysis was complemented by a network-level approach using Gene Set Enrichment Analysis against the Pathway Commons collection of gene regulatory networks (<a href="http://www.pathwaycommons.org" target="_blank">www.pathwaycommons.org</a>). Cisplatin treated cells (L: low, H: high) were compared to controls (C), and renal clear cell carcinoma (RCC) cells were compared to “normal adjacent” tissue (GEO accession number GSE781; as this data set is based on a different expression array technology, we did not compare expression levels of individual genes for this analysis). The heat map shows FDR-corrected q values on a logarithmic scale for up-regulated (red shades) and down-regulated networks (green shades), black indicating no change. An FDR-corrected q value of 0.01 corresponds to an absolute score of 4.6 on this scale. Please, note that the RCC dataset (last column) does not imply any involvement of the networks shown here. (<b>I–L</b>) RT-PCR analysis and mRNA levels of the (<b>I</b>) <i>Acta2</i> gene (encoding alpha smooth muscle actin) (<b>J</b>) <i>TGF-b1</i>gene (encoding transforming growth factor beta 1), (<b>K</b>) <i>COL1A1</i> gene (encoding collagen-1α1) and (<b>L</b>) ID-1 gene (encoding Inhibitor of differentiation 1). Retrieved WS-1 dermal fibroblasts showed increased level for key fibrotic markers α-SMA, TGF-β1 and Collagen 1α1 and decreased level of ID-1 when epithelial cells HK-C8 cells were layered on top. Gene expression profile for the same gene in absence of HK-C8 cells can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056575#pone.0056575.s002" target="_blank">Figure S2B</a>-E. n.s. = not statistically different, * = p<0.05, ** = p<0.001.</p