Adverse left ventricular remodeling by glycoprotein nonmetastatic melanoma protein B in myocardial infarction

Järve, Anne; Mühlstedt, Silke; Qadri, Fatimunnisa; Nickl, Bernadette; Schulz, Herbert; Hübner, Norbert; Özcelik, Cemil; Bäder, Michael

doi:10.1096/fj.201600613r

Cited by 21 publications

(20 citation statements)

References 64 publications

(98 reference statements)

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“…A subtle difference could be attributed to strain, age, sex, or housing-condition differences (see Transparent Methods ). The validity of the datasets was further confirmed by the regulated expressions of the known disease marker genes ( Bosworth and de Boer, 2013 , Cheng et al., 2015 , Hiratsuka et al., 2006 , Jarve et al., 2017 , Pabla et al., 2015 , Port et al., 2011 , Ramesh and Reeves, 2002 , Reed et al., 2011 , Sehl et al., 2000 , Suyama et al., 2012 , Tonne et al., 2013 , Wada et al., 2016 ) ( Figure 2 ). To evaluate how broadly the gene expressions are altered across various organs, we compared both model and sham control with WT control datasets independently using DESeq2 analysis.…”

Section: Resultsmentioning

confidence: 76%

The Body-wide Transcriptome Landscape of Disease Models

et al. 2018

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SummaryVirtually all diseases affect multiple organs. However, our knowledge of the body-wide effects remains limited. Here, we report the body-wide transcriptome landscape across 13–23 organs of mouse models of myocardial infarction, diabetes, kidney diseases, cancer, and pre-mature aging. Using such datasets, we find (1) differential gene expression in diverse organs across all models; (2) skin as a disease-sensor organ represented by disease-specific activities of putative gene-expression network; (3) a bone-skin cross talk mediated by a bone-derived hormone, FGF23, in response to dysregulated phosphate homeostasis, a known risk-factor for kidney diseases; (4) candidates for the signature activities of many more putative inter-organ cross talk for diseases; and (5) a cross-species map illustrating organ-to-organ and model-to-disease relationships between human and mouse. These findings demonstrate the usefulness and the potential of such body-wide datasets encompassing mouse models of diverse disease types as a resource in biological and medical sciences. Furthermore, the findings described herein could be exploited for designing disease diagnosis and treatment.

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Section: Resultsmentioning

confidence: 76%

The Body-wide Transcriptome Landscape of Disease Models

et al. 2018

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“…This protein, also called osteoactivin, DC-HIL, or hematopoietic growth factor inducible neurokinin-1, has been studied extensively in many contexts including cancer, kidney injury, obesity, non-alcoholic steatohepatitis, Parkinson disease, osteoarthritis, lysosome storage disorders, and heart failure; and, in most of these contexts expression of GPNMB is induced by the related pathology, likely in response to lysosomal stress[12, 22–27]. However, loss of Gpnmb expression in DBA/2J mice is associated with preserved cardiac function after myocardial infarction[28]. Thus, there is much interest in Gpnmb and the role it plays in a multitude of diseases and in normal physiology.…”

Section: Discussionmentioning

confidence: 99%

QTL analysis of macrophages from an AKR/JxDBA/2J intercross identified the Gpnmb gene as a modifier of lysosome function

Robinet

Ritchey

Alzayed

et al. 2019

Preprint

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Our prior studies found differences in the AKR/J and DBA/2J strains in regard to atherosclerosis and macrophage phenotypes including cholesterol ester loading, cholesterol efflux, and autolysosome formation. The goal of this study was to determine if there were differences in macrophage lysosome function, and if so to use quantitative trait locus (QTL) analysis to identify the causal gene. Lysosome function was measured by incubation with an exogenous double-labeled ovalbumin indicator sensitive to proteolysis. DBA/2J vs. AKR/J bone marrow macrophages had significantly decreased lysosome function. Macrophages were cultured from 120 mice derived from an AKR/JxDBA/2J F4 intercross. We measured lysosome function and performed a high density genome scan. QTL analysis yielded two genome wide significant loci on chromosomes 6 and 17, called macrophage lysosome function modifier (Mlfm) loci Mlfm1 and Mlfm2. After adjusting for Mlfm1, two additional loci were identified. Based on proximity to the Mlfm1 peak, macrophage mRNA expression differences with AKR/J >> DBA/2J, and a protein coding nonsense variant in DBA/2J, the Gpnmb gene, encoding a lysosomal membrane protein, was our top candidate. To test this candidate, Gpnmb expression was knocked down with siRNA in AKR/J macrophages; and, to express the wildtype Gpnmb in DBA/2J macrophages, we obtained a DBA/2 substrain, DBA/2J-Gpnmb+/SjJ, which was isolated from the parental strain prior to its acquiring the nonsense mutation, and subsequently back crossed to the modern DBA/2J background. Knockdown of Gpnmb in AKR/J macrophages decreased lysosome function, while restoration of the wildtype Gpnmb allele in DBA/2J macrophages increased lysosome function. However, this modifier of lysosome function was not responsible for the strain differences in macrophage cholesterol ester loading or cholesterol efflux. In conclusion, we identified the Gpnmb gene as the major modifier of lysosome function and we showed that the ‘QTL in a dish’ strategy is efficient in identifying modifier genes.Author SummaryInbred strains of mice differ in both their genetic backgrounds as well as in many traits; and, classical mouse genetics allows the mapping of genes responsible for these traits. We identified many traits that differ between the inbred strains AKR/J and DBA/2J, including atherosclerosis susceptibility, macrophage cholesterol metabolism, and in the current study, macrophage protein degradation via an organelle called the lysosome. Using mouse genetic mapping and bioinformatics we identified a candidate gene, called Gpnmb, responsible for modifying lysosome function; and, the DBA/2J strain carries a mutation in this gene. Here we demonstrate that the Gpnmb gene is a modifier of lysosome function by either correcting this Gpnmb mutation in DBA/2J macrophages, or by knocking down Gpnmb expression in AKR/J macrophages. This study is noteworthy as the human GPNMB gene has been implicated in many diseases including cancer, kidney injury, obesity, non-alcoholic steatohepatitis, Parkinson disease, osteoarthritis, and lysosome storage disorders.

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“…Previous studies using different cardiac injury models have shown that cardiac tissue levels of GPNMB generally increased in response to stress. These include the desmin knockout mouse model ( Psarras, Mavroidis et al 2012 ), the Theiler’s murine encephalomyelitis virus-induced acute viral myocarditis model ( Omura, Kawai et al 2014 ), and the myocardial infarction rat and mouse models ( Järve et al 2017 ). In the myocardial infarction model, GPNMB mRNA transcript was up-regulated 17-fold in the peri-infarct (PI) area in the rat and 300-fold in the mouse at 24 hr and 7 days after myocardial infarction, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…We investigate a novel potential HF biomarker, Glycoprotein NMB (GPNMB), in both mice and humans. GPNMB is a type 1 transmembrane protein also known as osteoactivin ( Selim 2009 ) that has been recently involved in inflammation, fibrosis and myocardial remodeling ( Järve et al 2017 ).…”

mentioning

confidence: 99%

Systems Genetics Approach to Biomarker Discovery: GPNMB and Heart Failure in Mice and Humans

Lin

Chang²,

O’Hearn³

et al. 2018

G3 Genes|Genomes|Genetics

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We describe a simple bioinformatics method for biomarker discovery that is based on the analysis of global transcript levels in a population of inbred mouse strains showing variation for disease-related traits. This method has advantages such as controlled environment and accessibility to heart and plasma tissue in the preclinical selection stage. We illustrate the approach by identifying candidate heart failure (HF) biomarkers by overlaying mouse transcriptome and clinical traits from 91 Hybrid Mouse Diversity Panel (HMDP) inbred strains and human HF transcriptome from the Myocardial Applied Genomics Network (MAGNet) consortium. We found that some of the top differentially expressed genes correlated with known human HF biomarkers, such as galectin-3 and tissue inhibitor of metalloproteinase 1. Using ELISA assays, we investigated one novel candidate, Glycoprotein NMB, in a mouse model of chronic β-adrenergic stimulation by isoproterenol (ISO) induced HF. We observed significantly lower GPNMB plasma levels in the ISO model compared to the control group (p-value = 0.007). In addition, we assessed GPNMB plasma levels among 389 HF cases and controls from the METabolic Syndrome In Men (METSIM) study. Lower levels of GPNMB were also observed in patients with HF from the METSIM study compared to non-HF controls (p-value < 0.0001). In summary, we have identified several candidate biomarkers for HF using the cardiac transcriptome data in a population of mice that may be directly relevant and applicable to human populations.

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Adverse left ventricular remodeling by glycoprotein nonmetastatic melanoma protein B in myocardial infarction

Cited by 21 publications

References 64 publications

The Body-wide Transcriptome Landscape of Disease Models

The Body-wide Transcriptome Landscape of Disease Models

QTL analysis of macrophages from an AKR/JxDBA/2J intercross identified the Gpnmb gene as a modifier of lysosome function

Systems Genetics Approach to Biomarker Discovery: GPNMB and Heart Failure in Mice and Humans

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