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

ROC curves of ln FGF23, ln PTH, T₅₀, proteinuria and eGFR in predicting fibrosis ≤20%.

<p>ROC curve analysis of ln FGF23 (Fig 2A), ln PTH (Fig 2B), T₅₀ (Fig 2C), proteinuria (Fig 2D), eGFR (Fig 2E) for the prediction of fibrosis ≤20%. (2F): ROC cuvre analysis of ln FGF23, ln PTH and T₅₀ combined for the prediction of fibrosis ≤20%. As T₅₀ and eGFR are markers that are negatively associated with fibrosis, we used the opposite values of those markers. AUC: Area Under the Curve; eGFR: estimated Glomerular Filtration Rate; FGF23: Fibroblast growth factor 23; Ln: log-transformed; PTH: parathyroid hormone; ROC: Receiver Operating Characteristic; T₅₀: Calcification propensity.</p

Associations of T₅₀ with vascular lesions in kidney allograft recipients (n = 129), univariate and multivariate linear regression analysis.

<p>Associations of T₅₀ with vascular lesions in kidney allograft recipients (n = 129), univariate and multivariate linear regression analysis.</p

ROC curves of T₅₀, 25D, PTH, proteinuria and eGFR in predicting Fibrosis >40%.

<p>As T₅₀, vitamin D and eGFR are markers that are negatively associated with fibrosis, we used the opposite values of those markers. (<b>3A-B-C-D-E)</b> Separate ROC curves for ln PTH, T₅₀, 25D, proteinuria and eGFR (<b>3F</b>) ROC curve for ln PTH, 25D and T₅₀ combined. 25D: 25-hydroxyvitamin D; AUC: Area Under the Curve; eGFR: estimated Glomerular Filtration Rate; Ln: log-transformed; PTH: parathyroid hormone; ROC: Receiver Operating Characteristic; T₅₀: Calcification propensity.</p

Correlations between phosphocalcic biomarkers, T₅₀ and interstitial fibrosis in renal allograft recipients (n = 129).

<p>Scatter plot graphs of (A) calcium, (B) phosphate, (C) vitamin D, (D) ln PTH, (E) ln FGF23, (F) Klotho, (G) T₅₀, (H) eGFR versus interstitial fibrosis. Each symbol represents one patient. The continuous line indicates least-square linear regression. eGFR: estimated Glomerular Filtration Rate; FGF23: Fibroblast growth factor 23; PTH: parathyroid hormone; T₅₀: Calcification propensity; Ln: log-transformed. </p

Phosphocalcic biomarkers and T₅₀ associate with chronic vascular lesions as assessed by banff (ah+aah+cv) in renal allograft recipients.

<p>Scatter plot graphs of (A) calcium, (B) phosphate, (C) vitamin D, (D) ln PTH, (E) ln FGF23, (F) Klotho, (G) T_50, (H) eGFR versus vascular lesions. Each symbol represents one patient. The continuous line indicates least-square linear regression. Ah: arteriolar hyaline thickening; aah: circumferential hyaline arteriolar thickening; cv: vascular fibrous intimal thickening; eGFR: estimated Glomerular Filtration Rate; Ln: log-transformed; PTH: parathyroid hormone; T₅₀: Calcification propensity.</p

ROC curves of ln FGF23, ln PTH, T₅₀, proteinuria and eGFR in predicting fibrosis ≤20%.

<p>ROC curve analysis of ln FGF23 (Fig 2A), ln PTH (Fig 2B), T₅₀ (Fig 2C), proteinuria (Fig 2D), eGFR (Fig 2E) for the prediction of fibrosis ≤20%. (2F): ROC cuvre analysis of ln FGF23, ln PTH and T₅₀ combined for the prediction of fibrosis ≤20%. As T₅₀ and eGFR are markers that are negatively associated with fibrosis, we used the opposite values of those markers. AUC: Area Under the Curve; eGFR: estimated Glomerular Filtration Rate; FGF23: Fibroblast growth factor 23; Ln: log-transformed; PTH: parathyroid hormone; ROC: Receiver Operating Characteristic; T₅₀: Calcification propensity.</p

Baseline characteristics of the study population (n = 129): clinical parameters, medication and laboratory measurements.

<p>Baseline characteristics of the study population (n = 129): clinical parameters, medication and laboratory measurements.</p

Baseline characteristics of the study population (n = 129): biopsy indication, biopsy diagnosis and chronic histological lesions.

<p>Baseline characteristics of the study population (n = 129): biopsy indication, biopsy diagnosis and chronic histological lesions.</p

Associations of serum biomarkers with interstitial fibrosis in kidney allograft recipients (n = 129).

<p>Univariate and multivariate linear regression analysis.</p