Resveratrol depletes mitochondrial DNA and inhibition of autophagy enhances resveratrol-induced caspase activation

We recently demonstrated that resveratrol induces caspase-dependent apoptosis in multiple cancer cell types. Whether apoptosis is also regulated by other cell death mechanisms such as autophagy is not clearly defined. Here we show that inhibition of autophagy enhanced resveratrol-induced caspase activation and apoptosis. Resveratrol inhibited colony formation and cell proliferation in multiple cancer cell types. Resveratrol treatment induced accumulation of LC3-II, which is a key marker for autophagy. Pretreatment with 3-methyladenine (3-MA), an autophagy inhibitor, increased resveratrol-mediated caspase activation and cell death in breast and colon cancer cells. Inhibition of autophagy by silencing key autophagy regulators such as ATG5 and Beclin-1 enhanced resveratrol-induced caspase activation. Mechanistic analysis revealed that Beclin-1 did not interact with proapoptotic proteins Bax and Bak; however, Beclin-1 was found to interact with p53 in the cytosol and mitochondria upon resveratrol treatment. Importantly, resveratrol depleted ATPase 8 gene, and thus, reduced mitochondrial DNA (mtDNA) content, suggesting that resveratrol induces damage to mtDNA causing accumulation of dysfunctional mitochondria triggering autophagy induction. Together, our findings indicate that induction of autophagy during resveratrol-induced apoptosis is an adaptive response.     

Integrating multi-platform genomic data using hierarchical Bayesian relevance vector machines

Background

Recent advances in genome technologies and the subsequent collection of genomic information at various molecular resolutions hold promise to accelerate the discovery of new therapeutic targets. A critical step in achieving these goals is to develop efficient clinical prediction models that integrate these diverse sources of high-throughput data. This step is challenging due to the presence of high-dimensionality and complex interactions in the data. For predicting relevant clinical outcomes, we propose a flexible statistical machine learning approach that acknowledges and models the interaction between platform-specific measurements through nonlinear kernel machines and borrows information within and between platforms through a hierarchical Bayesian framework. Our model has parameters with direct interpretations in terms of the effects of platforms and data interactions within and across platforms. The parameter estimation algorithm in our model uses a computationally efficient variational Bayes approach that scales well to large high-throughput datasets.

Results

We apply our methods of integrating gene/mRNA expression and microRNA profiles for predicting patient survival times to The Cancer Genome Atlas (TCGA) based glioblastoma multiforme (GBM) dataset. In terms of prediction accuracy, we show that our non-linear and interaction-based integrative methods perform better than linear alternatives and non-integrative methods that do not account for interactions between the platforms. We also find several prognostic mRNAs and microRNAs that are related to tumor invasion and are known to drive tumor metastasis and severe inflammatory response in GBM. In addition, our analysis reveals several interesting mRNA and microRNA interactions that have known implications in the etiology of GBM.

Conclusions

Our approach gains its flexibility and power by modeling the non-linear interaction structures between and within the platforms. Our framework is a useful tool for biomedical researchers, since clinical prediction using multi-platform genomic information is an important step towards personalized treatment of many cancers. We have a freely available software at: http://odin.mdacc.tmc.edu/~vbaladan.

     

Comparative stability of ammonium- and nitrate-induced nitrate reductase activity in maize leaves

Ammonium sulfate (5 mM) had no effect on nitrate reductase activity during a 3 hr dark incubation, but the enzyme was increased 2.5-fold during a subsequent 24 hr incubation of the maize leaves in light. The enzyme activity induced by ammonium ion declined at a slower rate under non-inducing conditions than that induced by nitrate. The decline in ammonium stimulated enzyme activity in the dark was also slower than that with nitrate. Further. cycloheximide accelerated the dark inactivation of the ammonium-enzyme while it had no effect on the nitrate-enzyme. The experiments demonstrate that increase in nitrate reductase activity by ammonium ion is different from the action of nitrate action.