Chronic exposure to simulated space conditions predominantly affects cytoskeleton remodeling and oxidative stress response in mouse fetal fibroblasts

Microgravity and cosmic rays as found in space are difficult to recreate on earth. However, ground-based models exist to simulate space flight experiments. In the present study, an experimental model was utilized to monitor gene expression changes in fetal skin fibroblasts of murine origin. Cells were continuously subjected for 65 h to a low dose. (55 mSv) of ionizing radiation (IR), comprising a mixture of high-linear energy transfer (LET) neutrons and low-LET gamma-rays, and/or simulated microgravity using the random positioning machine (RPM), after which microarrays were performed. The data were analyzed both by gene set enrichment analysis (GSEA) and single gene analysis (SGA). Simulated microgravity affected fetal murine fibroblasts by inducing oxidative stress responsive genes. Three of these genes are targets of the nuclear factor-erythroid 2 p45-related factor 2 (Nrf2), which may play a role in the cell response to simulated microgravity. In addition, simulated gravity decreased the expression of genes involved in cytoskeleton remodeling, which may have been caused by the downregulation of the serum response factor (SRF), possibly through the Rho signaling pathway. Similarly, chronic exposure to low-dose IR caused the downregulation of genes involved in cytoskeleton remodeling, as well as in cell cycle regulation and DNA damage response pathways. Many of the genes or gene sets that were altered in the individual treatments (RPM or IR) were not altered in the combined treatment (RPM and IR), indicating a complex interaction between RPM and IR

Loperamide, pimozide, and STF-62247 trigger autophagy-dependent cell death in glioblastoma cells

Autophagy is a well-described degradation mechanism that promotes cell survival upon nutrient starvation and other forms of cellular stresses. In addition, there is growing evidence showing that autophagy can exert a lethal function via autophagic cell death (ACD). As ACD has been implicated in apoptosis-resistant glioblastoma (GBM), there is a high medical need for identifying novel ACD-inducing drugs. Therefore, we screened a library containing 70 autophagy-inducing compounds to induce ATG5-dependent cell death in human MZ-54 GBM cells. Here, we identified three compounds, i.e. loperamide, pimozide, and STF-62247 that significantly induce cell death in several GBM cell lines compared to CRISPR/Cas9-generated ATG5- or ATG7-deficient cells, pointing to a death-promoting role of autophagy. Further cell death analyses conducted using pharmacological inhibitors revealed that apoptosis, ferroptosis, and necroptosis only play minor roles in loperamide-, pimozide- or STF-62247-induced cell death. Intriguingly, these three compounds induce massive lipidation of the autophagy marker protein LC3B as well as the formation of LC3B puncta, which are characteristic of autophagy. Furthermore, loperamide, pimozide, and STF-62247 enhance the autophagic flux in parental MZ-54 cells, but not in ATG5 or ATG7 knockout (KO) MZ-54 cells. In addition, loperamide- and pimozide-treated cells display a massive formation of autophagosomes and autolysosomes at the ultrastructural level. Finally, stimulation of autophagy by all three compounds is accompanied by dephosphorylation of mammalian target of rapamycin complex 1 (mTORC1), a well-known negative regulator of autophagy. In summary, our results indicate that loperamide, pimozide, and STF-62247 induce ATG5- and ATG7-dependent cell death in GBM cells, which is preceded by a massive induction of autophagy. These findings emphasize the lethal function and potential clinical relevance of hyperactivated autophagy in GBM

Mutant IDH1 Differently Affects Redox State and Metabolism in Glial Cells of Normal and Tumor Origin

IDH1R132H (isocitrate dehydrogenase 1) mutations play a key role in the development of low-grade gliomas. IDH1wt converts isocitrate to α-ketoglutarate while reducing nicotinamide adenine dinucleotide phosphate (NADP+), whereas IDH1R132H uses α-ketoglutarate and NADPH to generate the oncometabolite 2-hydroxyglutarate (2-HG). While the effects of 2-HG have been the subject of intense research, the 2-HG independent effects of IDH1R132H are still ambiguous. The present study demonstrates that IDH1R132H expression but not 2-HG alone leads to significantly decreased tricarboxylic acid (TCA) cycle metabolites, reduced proliferation, and enhanced sensitivity to irradiation in both glioblastoma cells and astrocytes in vitro. Glioblastoma cells, but not astrocytes, showed decreased NADPH and NAD+ levels upon IDH1R132H transduction. However, in astrocytes IDH1R132H led to elevated expression of the NAD-synthesizing enzyme nicotinamide phosphoribosyltransferase (NAMPT). These effects were not 2-HG mediated. This suggests that IDH1R132H cells utilize NAD+ to restore NADP pools, which only astrocytes could compensate via induction of NAMPT. We found that the expression of NAMPT is lower in patient-derived IDH1-mutant glioma cells and xenografts compared to IDH1-wildtype models. The Cancer Genome Atlas (TCGA) data analysis confirmed lower NAMPT expression in IDH1-mutant versus IDH1-wildtype gliomas. We show that the IDH1 mutation directly affects the energy homeostasis and redox state in a cell-type dependent manner. Targeting the impairments in metabolism and redox state might open up new avenues for treating IDH1-mutant gliomas.publishedVersio

Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles

<div><p>Changes in gene expression programs play a central role in cancer. Chromosomal aberrations such as deletions, duplications and translocations of DNA segments can lead to highly significant positive correlations of gene expression levels of neighboring genes. This should be utilized to improve the analysis of tumor expression profiles. Here, we develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in tumor. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the simultaneous usage of higher-order state-transitions and autoregressive emissions as novel model features. We apply autoregressive higher-order HMMs to the analysis of breast cancer and glioma gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other related methods. The performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions. This benefit could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumors independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles. Moreover, we reveal interesting novel details of systematic alterations of gene expression levels in known cancer signaling pathways distinguishing oligodendrogliomas, astrocytomas and glioblastomas. An implementation is available under <a href="http://www.jstacs.de/index.php/ARHMM" target="_blank">www.jstacs.de/index.php/ARHMM</a>.</p></div

Genes overexpressed in the most discriminative pathways distinguishing different types of gliomas.

<p>Overexpressed genes representing the most discriminative cancer signaling pathways distinguishing oligodendrogliomas (OD), astrocytomas (AS) and glioblastomas (GBM) at the level of the top 300 genes (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100295#pone-0100295-g006" target="_blank">Figure 6</a>). Genes identified as overexpressed in a specific glioma type are indicated by ‘1’, otherwise ‘0’. The column ‘Signaling Pathways’ represents the corresponding membership of each gene in one or more of these pathways.</p

Selected state space representations of models included in the novel model class of autoregressive higher-order HMMs.

<p>State space representations of selected models included in the class of autoregressive higher-order HMMs. Hidden states are denoted by and emissions are denoted by for . Arrows between nodes define modeled statistical dependencies. <b>a</b>) Standard mixture model (AR()-HMM()). <b>b</b>) Mixture model with second-order autoregressive emissions (AR()-HMM()). <b>c</b>) Standard HMM with first-order state-transitions (AR()-HMM()). <b>d</b>) Standard higher-order HMM with second-order state-transitions (AR()-HMM()). <b>e</b>) Autoregressive higher-order HMM with second-order state-transitions and second-order autoregressive emissions (AR()-HMM()).</p

Local chromosomal dependencies of gene expression levels in different types of cancer.

<p>Spatial correlations of expression levels of genes in increasing chromosomal order up to ten were quantified by an average autocorrelation function that considers each chromosome-specific expression profile in each individual tumor sample. The autocorrelation function quantifies the similarity of gene expression levels of neighboring genes on a chromosome in a fixed distance. Corresponding average autocorrelation functions are shown for three types of cancer (i) different types of gliomas (red) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100295#pone.0100295-Madhavan1" target="_blank">[33]</a>, (ii) breast cancer expression profiles (orange) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100295#pone.0100295-Pollack1" target="_blank">[3]</a> and (iii) glioblastoma expression profiles (grey) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100295#pone.0100295-deTayrac1" target="_blank">[4]</a>. Additionally, the green curve represents the average autocorrelation function of normal brain reference gene expression profiles taken from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100295#pone.0100295-Madhavan1" target="_blank">[33]</a>. Due to chromosomal aberrations in gliomas, expression levels of genes in close chromosomal proximity tend to show greater similarity in gliomas (red) than in corresponding normal brain tissues (green). Moreover, the black curve represents mean values and standard deviations of the average autocorrelation function for randomly permuted glioma gene expression profiles from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100295#pone.0100295-Madhavan1" target="_blank">[33]</a> across 100 repeats. The observation of significant local chromosomal dependencies in tumor expression profiles compared to permuted expression profiles motivates the development of autoregressive higher-order HMMs for the analysis of tumor expression profiles.</p