Biology subjects

Dittmar, G.

Publications and source records attributed to Dittmar, G..

5 recordsLinked to original sources

bioRxiv2018

SorCS2 controls functional expression of amino acid transporter EAAT3 to protect neurons from oxidative stress and epilepsy-induced pathology

The family of VPS10P domain receptors emerges as central regulator of intracellular protein sorting in neurons with relevance for various brain pathologies. Here, we identified a unique role for the family member SorCS2 in protection of neurons from oxidative stress and from epilepsy-induced cell death. We show that SorCS2 acts as sorting receptor that targets the neuronal amino acid transporter EAAT3 to the plasma membrane to facilitate import of cysteine, required for synthesis of the reactive oxygen species scavenger glutathione. Absence of SorCS2 activity causes aberrant transport of EAAT3 to lysosome for catabolism and impairs cysteine uptake. As a consequence, SorCS2-deficient mice exhibit oxidative brain damage that coincides with enhanced neuronal cell death and increased mortality during epilepsy. Our findings highlight a protective role for SorCS2 in neuronal stress response and provide an explanation for upregulation of the receptor seen in surviving neurons of the human epileptic brain.

neuroscience↗

bioRxiv2018

Independent component analysis provides clinically relevant insights into the biology of melanoma patients

The integration of publicly available and new patient-derived transcriptomic datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels. Here we present a methodology that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large reference datasets. The approach is based on independent component analysis (ICA) - an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes merged new and reference datasets into signals with minimal mutual dependency. By applying the method to a small cohort of primary melanoma and control samples combined with a large public melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. Cancer subtypes, patient survival and activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation were characterized. Additionally, through integration of transcriptomes and miRNomes, the method identified biological functions of miRNAs, which would otherwise not be possible.

genomics↗

bioRxiv2018

An eicosanoid protects from statin-induced myopathic changes in primary human cells

Statin-related muscle side effects are a constant healthcare problem since patient compliance is dependent on side effects. Statins reduce plasma cholesterol levels and can prevent secondary cardiovascular disease. Although statin-induced muscle damage has been studied, preventive or curative therapies are yet to be reported.\n\nWe exposed primary human muscle cell populations (n=25) to a lipophilic (simvastatin) and a hydrophilic (rosuvastatin) statin and analyzed their expressome. Data and pathway analyses included GOrilla, Reactome and DAVID. We measured mevalonate intracellularly and analyzed eicosanoid profiles secreted by human muscle cells. Functional assays included proliferation and differentiation quantification.\n\nMore than 1800 transcripts and 900 proteins were differentially expressed after exposure to statins. Simvastatin had a stronger effect on the expressome than rosuvastatin, but both statins influenced cholesterol biosynthesis, fatty acid metabolism, eicosanoid synthesis, proliferation, and differentiation of human muscle cells. Cultured human muscle cells secreted {omega}-3 and {omega}-6 derived eicosanoids and prostaglandins. The {omega}-6 derived metabolites were found at higher levels secreted from simvastatin-treated primary human muscle cells. Eicosanoids rescued muscle cell differentiation.\n\nOur data suggest a new aspect on the role of skeletal muscle in cholesterol metabolism. For clinical practice, the addition of omega-n fatty acids could be suitable to prevent or treat statin-myopathy.

molecular biology↗

bioRxiv2017

Protein Interaction Screen on Peptide Matrix (PRISMA) reveals interaction footprints and the PTM-dependent interactome of intrinsically disordered C/EBPβ

CCAAT enhancer binding protein beta (C/EBP{beta}) is a pioneer transcription factor that specifies cell differentiation. C/EBP{beta} is intrinsically unstructured, a molecular feature common to many proteins involved in signal processing and epigenetics. The structure of C/EBP{beta} differs depending on alternative translation start site usage and multiple post-translational modifications (PTM). Mutation of distinct PTM sites in C/EBP{beta} alters designated protein interactions and cell differentiation, suggesting a C/EBP{beta} PTM indexing code determines epigenetic outcomes. Herein, we systematically explored the interactome of C/EBP{beta} using an array of spot-synthesised C/EBP{beta}-derived linear tiling peptides with and without PTM, combined with mass spectrometric proteomic analysis of protein interactions. We identified interaction footprints of ~1300 proteins in nuclear cell extracts, many with chromatin modifying, remodelling and RNA processing functions. The results suggest C/EBP{beta} acts as a multi-tasking molecular switchboard, integrating signal-dependent modifications and structural plasticity to orchestrate interactions with numerous protein complexes directing cell fate and function.\n\nHighlightsO_LIPeptide array based interaction proteomics map SLiM and PTM dependent C/EBP{beta} interactome\nC_LIO_LINovel links between C/EBP{beta}, RNA processing, transcription elongation, MLL, NuRD were revealed\nC_LIO_LIC/EBP{beta} structure organizes modular hub function for gene regulatory machinery\nC_LIO_LIPRISMA is suitable to resolve protein interactions and networks based on intrinsically disordered proteins\nC_LI

biochemistry↗

bioRxiv2017

Dr.Paso: Drug response prediction and analysis system for oncology research

The prediction of anticancer drug response is crucial for achieving a more effective and precise treatment of patients. Models based on the analysis of large cell line collections have shown potential for investigating drug efficacy in a clinically-meaningful, cost-effective manner. Using data from thousands of cancer cell lines and drug response experiments, we propose a drug sensitivity prediction system based on a 47-gene expression profile, which was derived from an unbiased transcriptomic network analysis approach. The profile reflects the molecular activity of a diverse range of cancer-relevant processes and pathways. We validated our model using independent datasets and comparisons with published models. A high concordance between predicted and observed drug sensitivities was obtained, including additional validated predictions for four glioblastoma cell lines and four drugs. Our approach can accurately predict anti-cancer drug sensitivity and will enable further pre-clinical research. In the longer-term, it may benefit patient-oriented investigations and interventions.

bioinformatics↗