BackgroundAggressive lipid lowering with high doses of statins increases the risk of statin-induced myopathy. However, the cellular mechanisms leading to muscle damage are not known and sensitive biomarkers are needed to identify patients at risk of developing statin-induced serious side effects.MethodologyWe performed bioinformatics analysis of whole genome expression profiling of muscle specimens and UPLC/MS based lipidomics analyses of plasma samples obtained in an earlier randomized trial from patients either on high dose simvastatin (80 mg), atorvastatin (40 mg), or placebo.Principal FindingsHigh dose simvastatin treatment resulted in 111 differentially expressed genes (1.5-fold change and p-value<0.05), while expression of only one and five genes was altered in the placebo and atorvastatin groups, respectively. The Gene Set Enrichment Analysis identified several affected pathways (23 gene lists with False Discovery Rate q-value<0.1) in muscle following high dose simvastatin, including eicosanoid synthesis and Phospholipase C pathways. Using lipidomic analysis we identified previously uncharacterized drug-specific changes in the plasma lipid profile despite similar statin-induced changes in plasma LDL-cholesterol. We also found that the plasma lipidomic changes following simvastatin treatment correlate with the muscle expression of the arachidonate 5-lipoxygenase-activating protein.ConclusionsHigh dose simvastatin affects multiple metabolic and signaling pathways in skeletal muscle, including the pro-inflammatory pathways. Thus, our results demonstrate that clinically used high statin dosages may lead to unexpected metabolic effects in non-hepatic tissues. The lipidomic profiles may serve as highly sensitive biomarkers of statin-induced metabolic alterations in muscle and may thus allow us to identify patients who should be treated with a lower dose to prevent a possible toxicity.

Our results suggest that maintenance of high mitochondrial transcription and lack of inflammation in SAT are associated with low liver fat and MHO.

BackgroundCoordinated efforts to collect large-scale data sets provide a basis for systems level understanding of complex diseases. In order to translate these fragmented and heterogeneous data sets into knowledge and medical benefits, advanced computational methods for data analysis, integration and visualization are needed.MethodsWe introduce a novel data integration framework, Anduril, for translating fragmented large-scale data into testable predictions. The Anduril framework allows rapid integration of heterogeneous data with state-of-the-art computational methods and existing knowledge in bio-databases. Anduril automatically generates thorough summary reports and a website that shows the most relevant features of each gene at a glance, allows sorting of data based on different parameters, and provides direct links to more detailed data on genes, transcripts or genomic regions. Anduril is open-source; all methods and documentation are freely available.ResultsWe have integrated multidimensional molecular and clinical data from 338 subjects having glioblastoma multiforme, one of the deadliest and most poorly understood cancers, using Anduril. The central objective of our approach is to identify genetic loci and genes that have significant survival effect. Our results suggest several novel genetic alterations linked to glioblastoma multiforme progression and, more specifically, reveal Moesin as a novel glioblastoma multiforme-associated gene that has a strong survival effect and whose depletion in vitro significantly inhibited cell proliferation. All analysis results are available as a comprehensive website.ConclusionsOur results demonstrate that integrated analysis and visualization of multidimensional and heterogeneous data by Anduril enables drawing conclusions on functional consequences of large-scale molecular data. Many of the identified genetic loci and genes having significant survival effect have not been reported earlier in the context of glioblastoma multiforme. Thus, in addition to generally applicable novel methodology, our results provide several glioblastoma multiforme candidate genes for further studies.Anduril is available at http://csbi.ltdk.helsinki.fi/anduril/The glioblastoma multiforme analysis results are available at http://csbi.ltdk.helsinki.fi/anduril/tcga-gbm/