ESCRT machinery mediates selective microautophagy of endoplasmic reticulum in yeast

ER-phagy, the selective autophagy of endoplasmic reticulum (ER), safeguards organelle homeostasis by eliminating misfolded proteins and regulating ER size. ER-phagy can occur by macroautophagic and microautophagic mechanisms. While dedicated machinery for macro-ER-phagy has been discovered, the molecules and mechanisms mediating micro-ER-phagy remain unknown. Here, we first show that micro-ER-phagy in yeast involves the conversion of stacked cisternal ER into multilamellar ER whorls during microautophagic uptake into lysosomes. Second, we identify the conserved Nem1-Spo7 phosphatase complex and the ESCRT machinery as key components for micro-ER-phagy. Third, we demonstrate that macro- and micro-ER-phagy are parallel pathways with distinct molecular requirements. Finally, we provide evidence that the ESCRT machinery directly functions in scission of the lysosomal membrane to complete the microautophagic uptake of ER. These findings establish a framework for a mechanistic understanding of micro-ER-phagy and, thus, a comprehensive appreciation of the role of autophagy in ER homeostasis

Inverse Problems in a Bayesian Setting

In a Bayesian setting, inverse problems and uncertainty quantification (UQ) --- the propagation of uncertainty through a computational (forward) model --- are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.Comment: arXiv admin note: substantial text overlap with arXiv:1312.504

Parameter Identification in a Probabilistic Setting

Parameter identification problems are formulated in a probabilistic language, where the randomness reflects the uncertainty about the knowledge of the true values. This setting allows conceptually easily to incorporate new information, e.g. through a measurement, by connecting it to Bayes's theorem. The unknown quantity is modelled as a (may be high-dimensional) random variable. Such a description has two constituents, the measurable function and the measure. One group of methods is identified as updating the measure, the other group changes the measurable function. We connect both groups with the relatively recent methods of functional approximation of stochastic problems, and introduce especially in combination with the second group of methods a new procedure which does not need any sampling, hence works completely deterministically. It also seems to be the fastest and more reliable when compared with other methods. We show by example that it also works for highly nonlinear non-smooth problems with non-Gaussian measures.Comment: 29 pages, 16 figure

A Deterministic Filter for Non-Gaussian Bayesian Estimation

We present a fully deterministic method to compute sequential updates for stochastic state estimates of dynamic models from noisy measurements. It does not need any assumptions about the type of distribution for either data or measurement — in particular it does not have to assume any of them as Gaussian. It is based on a polynomial chaos expansion (PCE) of the stochastic variables of the model. We use a minimum variance estimator that combines an a priori state estimate and noisy measurements in a Bayesian way. For computational purposes, the update equation is projected onto a finite-dimensional PCE-subspace. The resulting Kalman-type update formula for the PCE coefficients can be efficiently computed solely within the PCE. As it does not rely on sampling, the method is deterministic, robust, and fast. In this paper we discuss the theory and practical implementation of the method. The original Kalman filter is shown to be a low-order special case. In a first experiment, we perform a bi-modal identification using noisy measurements. Additionally, we provide numerical experiments by applying it to the well known Lorenz-84 model and compare it to a related method, the ensemble Kalman filter

Direct Bayesian Update of Polynomial Chaos Representations

We present a fully deterministic approach to a probabilistic interpretation of inverse problems in which unknown quantities are represented by random fields or processes, described by a non-Gaussian prior distribution. The description of the introduced random fields is given in a ``white noise'' framework, which enables us to solve the stochastic forward problem through Galerkin projection onto polynomial chaos. With the help of such representation, the probabilistic identification problem is cast in a polynomial chaos expansion setting and the linear Bayesian form of updating. This representation leads to a corresponding new formulation of the minimum squared error estimator, obtained by its additional projection onto the polynomial chaos basis. By introducing the Hermite algebra this becomes a direct, purely algebraic way of computing the posterior, which is inexpensive to evaluate. In addition, we show that the well-known Kalman filter method is the low order part of this update. The proposed method has been tested on a stationary diffusion equation with prescribed source terms, characterised by an uncertain conductivity parameter which is then identified from limited and noisy data obtained by a measurement of the diffusing quantity

Anthracyclines, proteasome activity and multi-drug-resistance

BACKGROUND: P-glycoprotein is responsible for the ATP-dependent export of certain structurally unrelated compounds including many chemotherapeutic drugs. Amplification of P-glycoprotein activity can result in multi-drug resistance and is a common cause of chemotherapy treatment failure. Therefore, there is an ongoing search for inhibitors of P-glycoprotein. Observations that cyclosporin A, and certain other substances, inhibit both the proteasome and P-glycoprotein led us to investigate whether anthracyclines, well known substrates of P-gp, also inhibit the function of the proteasome. METHODS: Proteasome function was measured in cell lysates from ECV304 cells incubated with different doses of verapamil, doxorubicin, daunorubicin, idarubicin, epirubicin, topotecan, mitomycin C, and gemcitabine using a fluorogenic peptide assay. Proteasome function in living cells was monitored using ECV304 cells stably transfected with the gene for an ubiquitin/green fluorescent protein fusion protein. The ability of the proteasome inhibitor MG-132 to affect P-glycoprotein function was monitored by fluorescence due to accumulation of daunorubicin in P-glycoprotein overexpressing KB 8-5 cells. RESULTS: Verapamil, daunorubicin, doxorubicin, idarubicin, and epirubicin inhibited 26S chymotrypsin-like function in ECV304 extracts in a dose-dependent fashion. With the exception of daunorubicin, 20S proteasome function was also suppressed. The proteasome inhibitor MG-132 caused a dose-dependent accumulation of daunorubicin in KB 8-5 cells that overexpress P-glycoprotein, suggesting that it blocked P-glycoprotein function. CONCLUSION: Our data indicate that anthracyclines inhibit the 26S proteasome as well as P-glycoprotein. Use of inhibitors of either pathway in cancer therapy should take this into consideration and perhaps use it to advantage, for example during chemosensitization by proteasome inhibitors

The effects of tea extracts on proinflammatory signaling

BACKGROUND: Skin toxicity is a common side effect of radiotherapy for solid tumors. Its management can cause treatment gaps and thus can impair cancer treatment. At present, in many countries no standard recommendation for treatment of skin during radiotherapy exists. In this study, we explored the effect of topically-applied tea extracts on the duration of radiation-induced skin toxicity. We investigated the underlying molecular mechanisms and compared effects of tea extracts with the effects of epigallocatechin-gallate, the proposed most-active moiety of green tea. METHODS: Data from 60 patients with cancer of the head and neck or pelvic region topically treated with green or black tea extracts were analyzed retrospectively. Tea extracts were compared for their ability to modulate IL-1β, IL-6, IL-8, TNFα and PGE(2 )release from human monocytes. Effects of tea extracts on 26S proteasome function were assessed. NF-κB activity was monitored by EMSAs. Viability and radiation response of macrophages after exposure to tea extracts was measured by MTT assays. RESULTS: Tea extracts supported the restitution of skin integrity. Tea extracts inhibited proteasome function and suppressed cytokine release. NF-κB activity was altered by tea extracts in a complex, caspase-dependent manner, which differed from the effects of epigallocatechin-gallate. Additionally, both tea extracts, as well as epigallocatechin-gallate, slightly protected macrophages from ionizing radiation CONCLUSION: Tea extracts are an efficient, broadly available treatment option for patients suffering from acute radiation-induced skin toxicity. The molecular mechanisms underlying the beneficial effects are complex, and most likely not exclusively dependent on effects of tea polyphenols such as epigallocatechin-gallate

NF-κB inhibition impairs the radioresponse of hypoxic EMT-6 tumour cells through downregulation of inducible nitric oxide synthase

Hypoxic EMT-6 tumour cells displayed a high level of inducible nitric oxide synthase (iNOS) and an increased radiosensitivity after a 16 h exposure to lipopolysaccharide, a known activator of nuclear factor-κB (NF-κB). Both iNOS activation and radioresponse were impaired by the NF-κB inhibitors phenylarsine oxide and lactacystin. Contrasting to other studies, our data show that inhibition of NF-κB may impair the radioresponse of tumour cells through downregulation of iNOS. © 2003 Cancer Research UK.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Oxygen Levels Do Not Determine Radiation Survival of Breast Cancer Stem Cells

For more than a century oxygen has been known to be one of the most powerful radiosensitizers. However, despite decades of preclinical and clinical research aimed at overcoming tumor hypoxia, little clinical progress has been made so far. Ionizing radiation damages DNA through generation of free radicals. In the presence of oxygen these lesions are chemically modified, and thus harder to repair while hypoxia protects cells from radiation (Oxygen enhancement ratio (OER)). Breast cancer stem cells (BSCSs) are protected from radiation by high levels of free radical scavengers even in the presence of oxygen. This led us to hypothesize that BCSCs exhibit an OER of 1. Using four established breast cancer cell lines (MCF-7, T47D, MDA-MB-231, SUM159PT) and primary breast cancer samples, we determined the number of BCSCs using cancer stem cell markers (ALDH1, low proteasome activity), compared radiation clonogenic survival and mammosphere formation under normoxic and hypoxic conditions, and correlated these results to the expression levels of key members of the free radical scavenging systems. The number of BCSCs increased with increased aggressiveness of the cancer. This correlated with increased radioresistance (SF8Gy), and decreasing OERs. When cultured as mammospheres, breast cancer cell lines and primary samples were highly radioresistant and not further protected by hypoxia (OER∼1)