Hans-Peter Marti1, James C Fuscoe2, Joshua C Kwekel2, Aikaterini Anagnostopoulou3, Andreas Scherer4. 1. Department of Clinical Medicine, University of Bergen, Bergen, Norway. 2. Division of Systems Biology, National Center for Toxicological Research, FDA, Jefferson, AR, USA. 3. Institute of Anatomy, University of Bern, Bern, Switzerland. 4. Spheromics, Kontiolahti, Finland.
Abstract
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 cross-species 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.
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 cross-species 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.
Authors: Joshua C Kwekel; Vikrant Vijay; Varsha G Desai; Carrie L Moland; James C Fuscoe Journal: Biol Sex Differ Date: 2015-01-28 Impact factor: 5.027
Authors: Merina Elahi; Noha Eshera; Nkosazana Bambata; Helen Barr; Beverly Lyn-Cook; Julie Beitz; Maria Rios; Deborah R Taylor; Marilyn Lightfoote; Nada Hanafi; Lowri DeJager; Paddy Wiesenfeld; Pamela E Scott; Emmanuel O Fadiran; Marsha B Henderson Journal: J Womens Health (Larchmt) Date: 2016-02-12 Impact factor: 2.681
Authors: Hans-Peter Marti; Aaron Jeffs; Andreas Scherer; John Leader; Catherine Leader; Jennifer Bedford; Robert Walker Journal: PLoS One Date: 2016-12-21 Impact factor: 3.240
Authors: Lea Landolt; Øystein Eikrem; Philipp Strauss; Andreas Scherer; David H Lovett; Christian Beisland; Kenneth Finne; Tarig Osman; Mohammad M Ibrahim; Gro Gausdal; Lavina Ahmed; James B Lorens; Jean Paul Thiery; Tuan Zea Tan; Miroslav Sekulic; Hans-Peter Marti Journal: Physiol Rep Date: 2017-06
Authors: Inger T Enoksen; Dmitri Svistounov; Jon V Norvik; Vidar T N Stefansson; Marit D Solbu; Bjørn O Eriksen; Toralf Melsom Journal: Nephrol Dial Transplant Date: 2022-08-22 Impact factor: 7.186