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Kontiolahti, Finland

Staedtler F.,Novartis | Hartmann N.,Novartis | Letzkus M.,Novartis | Bongiovanni S.,Novartis | And 4 more authors.
Biomarkers | Year: 2013

Context: Correct gender assignment in humans at the molecular level is crucial in many scientific disciplines and applied areas. Materials and methods: Candidate gender markers were identified through supervised statistical analysis of genome wide microarray expression data from human blood samples (N=123, 58 female, 65 male) as a training set. The potential of the markers to predict undisclosed tissue donor gender was tested on microarray data from 13 healthy and 11 cancerous human tissue collections (internal) and external datasets from samples of varying tissue origin. The abundance of some genes in the marker panel was quantified by RT-PCR as alternative analytical technology. Results: We identified and qualified predictive, gender-specific transcript markers based on a set of five genes (RPS4Y1, EIF1AY, DDX3Y, KDM5D and XIST). Conclusion: Gene expression marker panels can be used as a robust tissue- and platform-independent predictive approach for gender determination. © 2013 Informa UK Ltd. All rights reserved: reproduction in whole or part not permitted.


Scherer A.,Spheromics | Gunther O.P.,James Hogg Center | Balshaw R.F.,University of British Columbia | Hollander Z.,University of British Columbia | And 9 more authors.
BMC Medical Genomics | Year: 2013

Background: End-stage renal failure is associated with profound changes in physiology and health, but the molecular causation of these pleomorphic effects termed "uremia" is poorly understood. The genomic changes of uremia were explored in a whole genome microarray case-control comparison of 95 subjects with end-stage renal failure (n = 75) or healthy controls (n = 20). Methods. RNA was separated from blood drawn in PAXgene tubes and gene expression analyzed using Affymetrix Human Genome U133 Plus 2.0 arrays. Quality control and normalization was performed, and statistical significance determined with multiple test corrections (qFDR). Biological interpretation was aided by knowledge mining using NIH DAVID, MetaCore and PubGene. Results: Over 9,000 genes were differentially expressed in uremic subjects compared to normal controls (fold change: -5.3 to +6.8), and more than 65% were lower in uremia. Changes appeared to be regulated through key gene networks involving cMYC, SP1, P53, AP1, NFkB, HNF4 alpha, HIF1A, c-Jun, STAT1, STAT3 and CREB1. Gene set enrichment analysis showed that mRNA processing and transport, protein transport, chaperone functions, the unfolded protein response and genes involved in tumor genesis were prominently lower in uremia, while insulin-like growth factor activity, neuroactive receptor interaction, the complement system, lipoprotein metabolism and lipid transport were higher in uremia. Pathways involving cytoskeletal remodeling, the clathrin-coated endosomal pathway, T-cell receptor signaling and CD28 pathways, and many immune and biological mechanisms were significantly down-regulated, while the ubiquitin pathway and certain others were up-regulated. Conclusions: End-stage renal failure is associated with profound changes in human gene expression which appears to be mediated through key transcription factors. Dialysis and primary kidney disease had minor effects on gene regulation, but uremia was the dominant influence in the changes observed. This data provides important insight into the changes in cellular biology and function, opportunities for biomarkers of disease progression and therapy, and potential targets for intervention in uremia. © 2013 Scherer et al.; licensee BioMed Central Ltd.


Rodder S.,University of Bern | Scherer A.,Spheromics | Korner M.,University of Bern | Marti H.-P.,University of Bern
Virchows Archiv | Year: 2011

Metzincins and functionally related genes play important roles in extracellular matrix remodeling both in healthy and fibrotic conditions. We recently presented a transcriptomic classifier consisting of 19 metzincins and related genes (MARGS) discriminating biopsies from renal transplant patients with or without interstitial fibrosis/tubular atrophy (IF/TA) by virtue of gene expression measurement (Roedder et al., Am J Transplant 9:517-526, 2009). Here we demonstrate that the same algorithm has diagnostic value in non-transplant solid organ fibrosis. We used publically available microarray datasets of 325 human heart, liver, lung, kidney cortex, and pancreas microarray samples (265 with fibrosis, 60 healthy controls). Expression of nine commonly differentially expressed genes was confirmed by TaqMan low-density arrays (Applied Biosystems, USA) in 50 independent archival tissue specimens with matched histological diagnoses to microarray patients. In separate and in combined, integrated microarray data analyses of five datasets with 325 samples, the previously published MARGS classifier for renal post-transplant IF/TA had a mean AUC of 87% and 82%, respectively. These data demonstrate that the MARGS gene panel classifier not only discriminates IF/TA from normal renal transplant tissue, but also classifies solid organ fibrotic conditions of human pancreas, liver, heart, kidney, and lung tissue samples with high specificity and accuracy, suggesting that the MARGS classifier is a cross-platform, cross-organ classifier of fibrotic conditions of different etiologies when compared to normal tissue. © Springer-Verlag 2011.


Eikrem O.,University of Bergen | Beisland C.,University of Bergen | Hjelle K.,University of Bergen | Flatberg A.,Norwegian University of Science and Technology | And 6 more authors.
PLoS ONE | Year: 2016

Formalin-fixed, paraffin-embedded (FFPE) tissues are an underused resource for molecular analyses. This proof of concept study aimed to compare RNAseq results from FFPE biopsies with the corresponding RNAlater1 (Qiagen, Germany) stored samples from clear cell renal cell carcinoma (ccRCC) patients to investigate feasibility of RNAseq in archival tissue. From each of 16 patients undergoing partial or full nephrectomy, four core biopsies, such as two specimens with ccRCC and two specimens of adjacent normal tissue, were obtained with a 16g needle. One normal and one ccRCC tissue specimen per patient was stored either in FFPE or RNAlater1. RNA sequencing libraries were generated applying the new Illumina TruSeq1 Access library preparation protocol. Comparative analysis was done using voom/Limma R-package. The analysis of the FFPE and RNAlater1 datasets yielded similar numbers of detected genes, differentially expressed transcripts and affected pathways. The FFPE and RNAlater datasets shared 80% (n = 1106) differentially expressed genes. The average expression and the log2 fold changes of these transcripts correlated with R2 = 0.97, and R2 = 0.96, respectively. Among transcripts with the highest fold changes in both datasets were carbonic anhydrase 9 (CA9), neuronal pentraxin-2 (NPTX2) and uromodulin (UMOD) that were confirmed by immunohistochemistry. IPA revealed the presence of gene signatures of cancer and nephrotoxicity, renal damage and immune response. To simulate the feasibility of clinical biomarker studies with FFPE samples, a classifier model was developed for the FFPE dataset: expression data for CA9 alone had an accuracy, specificity and sensitivity of 94%, respectively, and achieved similar performance in the RNAlater dataset. Transforming growth factor-β1 (TGFB1)-regulated genes, epithelial to mesenchymal transition (EMT) and NOTCH signaling cascade may support novel therapeutic strategies. In conclusion, in this proof of concept study, RNAseq data obtained from FFPE kidney biopsies are comparable to data obtained from fresh stored material, thereby expanding the utility of archival tissue specimens. © 2016 Eikrem et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Marti H.-P.,University of Bergen | Fuscoe J.C.,National Center for Toxicological Research (NCTR) | Kwekel J.C.,National Center for Toxicological Research (NCTR) | Anagnostopoulou A.,University of Bern | Scherer A.,Spheromics
Nephrology Dialysis Transplantation | Year: 2014

Background. We have previously described a transcriptomic classifier consisting of metzincins and related genes (MARGS) discriminating kidneys and other organs with or without fibrosis from human biopsies. We now apply our MARGS-based algorithm to a rat model of age-associated interstitial renal fibrosis. Methods. Untreated Fisher 344 rats (n = 76) were sacrificed between 2 to 104 weeks of age. For gene expression studies, we used single colour (Cy3) Agilent Whole Rat Genome 4 × 44k microarrays; 4-5 animals of each sex were profiled at each of the following ages: 2, 5, 6, 8, 15, 21, 78 and 104 weeks. Intensity data were subjected to variance stabilization (www.Partek.com). Data were analysed with ANOVA and other statistical methods. Results. Sixty MARGS were differentially expressed across age groups. More MARGS were differentially expressed in older males than in older females. Principal component analysis showed gene expression induced segregation of age groups by sex from 6 to 104 weeks of age. The expression level of MMP7 correlated best with fibrosis grade. Severity of fibrosis was determined in 20 animals at 78 and 104 weeks of age. Expression values of 15 of 19 genes of the original classifier present on the Agilent array, in conjunction with linear discriminant analysis, was sufficient to correctly classify these 20 samples into non-fibrosis and fibrosis. Overrepresentation of MMP2 protein and CD44 protein in fibrosis was confirmed by immunofluorescence. Conclusions. Based on these results and our previous work, the MARGS classifier represents a cross-organ and crossspecies classifier of fibrosis irrespective of aetiology. This finding provides evidence for a common pathway leading to fibrosis and will help to design a PCR-based clinical test. © 2014 The Author 2014. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

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