Targeting autophagy for drug-induced hepatotoxicity

Autophagy is a lysosomal degradation pathway for bulk cytosolic proteins and damaged organelles, and is well known to act as a cell survival mechanism. Acetaminophen (APAP) overdose can cause liver injury in animals and humans by inducing necrosis due to mitochondrial damage. We recently found that pharmacological induction of autophagy by rapamycin protects against, whereas pharmacological suppression of autophagy by chloroquine exacerbates, APAP-induced liver injury in mice. Autophagy is induced to remove APAP-induced damaged mitochondria and thus attenuates APAP-induced hepatocyte necrosis. To our surprise, we found that liver-specific Atg5 knockout mice are not more susceptible, but are resistant to APAP-induced liver injury due to compensatory effects. Our work suggests that pharmacological modulation of autophagy is a novel therapeutic approach to ameliorate APAP-induced liver injury. Moreover, our work also suggests that caution needs to be exercised when using genetic autophagy gene knockout mice for pathophysiological studies.     

Targeting autophagy for drug-induced hepatotoxicity

Autophagy is a lysosomal degradation pathway for bulk cytosolic proteins and damaged organelles, and is well known to act as a cell survival mechanism. Acetaminophen (APAP) overdose can cause liver injury in animals and humans by inducing necrosis due to mitochondrial damage. We recently found that pharmacological induction of autophagy by rapamycin protects against, whereas pharmacological suppression of autophagy by chloroquine exacerbates, APAP-induced liver injury in mice. Autophagy is induced to remove APAP-induced damaged mitochondria and thus attenuates APAP-induced hepatocyte necrosis. To our surprise, we found that liver-specific Atg5 knockout mice are not more susceptible, but are resistant to APAP-induced liver injury due to compensatory effects. Our work suggests that pharmacological modulation of autophagy is a novel therapeutic approach to ameliorate APAP-induced liver injury. Moreover, our work also suggests that caution needs to be exercised when using genetic autophagy gene knockout mice for pathophysiological studies.     

Prediction of hepatotoxicity for drugs using human pluripotent stem cell-derived hepatocytes

Drug-induced liver toxicity is a main reason for withdrawals of new drugs in late clinical phases and post-launch of the drugs. Thus, hepatotoxicity screening of drug candidates in pre-clinical stage is important for reducing drug attrition rates during the clinical development process. Here, we show commercially available hepatocytes that could be used for early toxicity evaluation of drug candidates. From our hepatic differentiation technology, we obtained highly pure (≥98%) hepatocytes from human embryonic stem cells (hESCs) having mature phenotypes and similar gene expression profiles with those of primary human tissues. Furthermore, we optimized 96-well culture condition of hESC-derived hepatocytes suitable for toxicity tests in vitro. To this end, we demonstrated the efficacy of our optimized hepatocyte model for predicting hepatotoxicity against the Chinese herbal medicines and showed that toxicity patterns from our hepatocyte model was similar to those of human primary cultured hepatocytes. We conclude that toxicity test using our hepatocyte model could be a good alternative cell source for pre-clinical study to predict potential hepatotoxicity in drug discovery industries.     

Evaluation of HepaRG cells for the assessment of indirect drug-induced hepatotoxicity using INH as a model substance

HepaRG cells are widely used as an in vitro model to assess drug-induced hepatotoxicity. However, only few studies exist so far regarding their suitability to detect the effects of drugs requiring a preceding activation via the cytochrome P450 (CYP) system. A prototypic substance is the anti-tuberculosis agent INH, which is metabolized into N-acetylhydrazine, which then triggers hepatotoxicity. Therefore, the aim of the present study was to test if this effect can also be detected in HepaRG cells and if it can be counteracted by the known hepatoprotectant silibinin. For this purpose, differentiated HepaRG cells were treated with increasing concentrations of INH (0.1–100 mM) or 10 mM INH plus escalating concentrations of silibinin (1–100 µM). After 48 h of treatment, cell morphology and parameters indicating cell vitality, oxidative stress, and liver cell function were assessed. High concentrations of INH led to severe histopathological changes, reduced cell vitality and glutathione content, increased LDH and ASAT release into the medium, enhanced lipid peroxidation, and elevated cleaved caspase-3 expression. Additionally, glycogen depletion and reduced biotransformation capacity were seen at high INH concentrations, whereas at low concentrations an induction of biotransformation enzymes was noticed. Silibinin caused clear-cut protective effects, but with few parameters INH toxicity was even aggravated, most probably due to increased metabolization of INH into its toxic metabolite. In conclusion, HepaRG cells are excellently suited to evaluate the effects of substances requiring prior toxification via the CYP system, such as INH. They additionally enable the identification of complex substance interactions.     

Animal models of drug-induced pulmonary fibrosis: an overview of molecular mechanisms and characteristics

Cell Biology and Toxicology - Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease characterized by progressive loss of pulmonary function. Drug-induced interstitial lung...     

Low-cost image processing system for analyzing molecular RNA fingerprints.

A complete image digitizing and processing system is described for capturing, enhancing and analyzing molecular fingerprints. The low-cost, high-resolution system features a Motorola 68000 processor, multi-tasking, a separate video coprocessor, and color or gray scale processing. Thousands of manipulations are possible using functions which include histographic equalization, edge detection, filtering, overlays, false coloring, zoom, pan and print. All operations are initiated and controlled with a mouse. Techniques for enhancing, scaling and comparing molecular fingerprints are described. The techniques all involve using a graphical interface to select and manipulate the various processes. The system has been used successfully for about 1.5 years, and it has been ideal for our application which requires human judgment at many steps between processing and which probably would not lend itself to a completely automated analysis. Similar techniques could probably be used with this system on many other applications.     

Prediction of protein subcellular localization by weighted gene ontology terms

We develop a new weighting approach of gene ontology (GO) terms for predicting protein subcellular localization. The weights of individual GO terms, corresponding to their contribution to the prediction algorithm, are determined by the term-weighting methods used in text categorization. We evaluate several term-weighting methods, which are based on inverse document frequency, information gain, gain ratio, odds ratio, and chi-square and its variants. Additionally, we propose a new term-weighting method based on the logarithmic transformation of chi-square. The proposed term-weighting method performs better than other term-weighting methods, and also outperforms state-of-the-art subcellular prediction methods. Our proposed method achieves 98.1%, 99.3%, 98.1%, 98.1%, and 95.9% overall accuracies for the animal BaCelLo independent dataset (IDS), fungal BaCelLo IDS, animal Höglund IDS, fungal Höglund IDS, and PLOC dataset, respectively. Furthermore, the close correlation between high-weighted GO terms and subcellular localizations suggests that our proposed method appropriately weights GO terms according to their relevance to the localizations.     

Prediction of nuclear proteins using SVM and HMM models

Background  

The nucleus, a highly organized organelle, plays important role in cellular homeostasis. The nuclear proteins are crucial for chromosomal maintenance/segregation, gene expression, RNA processing/export, and many other processes. Several methods have been developed for predicting the nuclear proteins in the past. The aim of the present study is to develop a new method for predicting nuclear proteins with higher accuracy.     

Testing macro-evolutionary models using incomplete molecular phylogenies.

Phylogenies reconstructed from gene sequences can be used to investigate the tempo and mode of species diversification. Here we develop and use new statistical methods to infer past patterns of speciation and extinction from molecular phylogenies. Specifically, we test the null hypothesis that per-lineage speciation and extinction rates have remained constant through time. Rejection of this hypothesis may provide evidence for evolutionary events such as adaptive radiations or key adaptations. In contrast to previous approaches, our methods are robust to incomplete taxon sampling and are conservative with respect to extinction. Using simulation we investigate, first, the adverse effects of failing to take incomplete sampling into account and, second, the power and reliability of our tests. When applied to published phylogenies our tests suggest that, in some cases, speciation rates have decreased through time.     

Refinement of comparative models of protein structure by using multicanonical molecular dynamics simulations

Comparative modelling is a powerful method that easily predicts a considerably accurate structure of a protein by using a template structure having a similar amino-acid sequence to the target protein. However, in the region where the amino-acid sequence is different between the target and the template, the predicted structure remains unreliable. In such a case, the model has to be refined. In the present study, we explored the possibility of a molecular dynamics-based method, using the human SAP Src Homology 2 (SH2) domain as the modelling target. The multicanonical method was used to alleviate the multiple-minima problem and the generalised Born/surface area model was used to reduce the computational cost. In addition, position restraints were imposed on the atoms in the reliable regions to avoid unnecessary conformational sampling. We analyzed the conformational distribution of the ligand-recognition loop of the domain and found that the most populated conformational clusters in the ensemble of the model agreed well with one of the two major clusters in the ensemble of the reference simulation starting from the crystal structure. This demonstrates that the current refinement method can significantly improve the accuracy of an unreliable region in a comparative model.     

A new method for taking fingerprints using photographic film

A new method for obtaining dermatoglyphic prints is described. Photographic film is used enabling prints to be obtained directly, and at any enlargement. The sweat-pores can be shown in detail, and counted.     

Prediction of siRNA knockdown efficiency using artificial neural network models

Selective knockdown of gene expression by short interference RNAs (siRNAs) has allowed rapid validation of gene functions and made possible a high throughput, genome scale approach to interrogate gene function. However, randomly designed siRNAs display different knockdown efficiencies of target genes. Hence, various prediction algorithms based on siRNA functionality have recently been constructed to increase the likelihood of selecting effective siRNAs, thereby reducing the experimental cost. Toward this end, we have trained three Back-propagation and Bayesian neural network models, previously not used in this context, to predict the knockdown efficiencies of 180 experimentally verified siRNAs on their corresponding target genes. Using our input coding based primarily on RNA structure thermodynamic parameters and cross-validation method, we showed that our neural network models outperformed most other methods and are comparable to the best predicting algorithm thus far published. Furthermore, our neural network models correctly classified 74% of all siRNAs into different efficiency categories; with a correlation coefficient of 0.43 and receiver operating characteristic curve score of 0.78, thus highlighting the potential utility of this method to complement other existing siRNA classification and prediction schemes.     

Mechanical resistance of proteins explained using simple molecular models

Recent experiments have demonstrated that proteins unfold when two atoms are mechanically pulled apart, and that this process is different to when heated or when a chemical denaturant is added to the solution. Experiments have also shown that the response of proteins to external forces is very diverse, some of them being "hard," and others "soft." Mechanical resistance originates from the presence of barriers on the energy landscape; together, experiment and simulation have demonstrated that unfolding occurs through alternative pathways when different pairs of atoms undergo mechanical extension. Here we use simulation to probe the mechanical resistance of six structurally diverse proteins when pulled in different directions. For this, we use two very different models: a detailed, transferable one, and a coarse-grained, structure-based one. The coarse-grained model gives results that are surprisingly similar to the detailed one and qualitatively agree with experiment; i.e., the mechanical resistance of different proteins or of a single protein pulled in different directions can be predicted by simulation. The results demonstrate the importance of pulling direction relative to the local topology in determining mechanical stability, and rationalize the effect of the location of importation/degradation tags on the rates of mitochondrial import or protein degradation in vivo.     

Bayesian hierarchically weighted finite mixture models for samples of distributions

Finite mixtures of Gaussian distributions are known to provide an accurate approximation to any unknown density. Motivated by DNA repair studies in which data are collected for samples of cells from different individuals, we propose a class of hierarchically weighted finite mixture models. The modeling framework incorporates a collection of k Gaussian basis distributions, with the individual-specific response densities expressed as mixtures of these bases. To allow heterogeneity among individuals and predictor effects, we model the mixture weights, while treating the basis distributions as unknown but common to all distributions. This results in a flexible hierarchical model for samples of distributions. We consider analysis of variance-type structures and a parsimonious latent factor representation, which leads to simplified inferences on non-Gaussian covariance structures. Methods for posterior computation are developed, and the model is used to select genetic predictors of baseline DNA damage, susceptibility to induced damage, and rate of repair.     

Prediction of the stability of coiled coils using molecular dynamics simulations

We have performed 40–80 ns-long molecular dynamics (MD) simulations of the GCN4 leucine zipper and synthetic coiled coils using the GROMOS96 (43a2) and OPLS-AA force fields, with the aim of predicting coiled coil stability. Starting with an initial configuration of two peptides placed in an ideal coiled coil configuration, we find that changing the amino acid sequence modestly or decreasing peptide length can lead to a decrease in the final α-helicity of coiled coils, although for peptides as long or longer than 16 residues, the values of helicity do not decrease to the low values seen in the experimental results of Lumb et al. (Biochemistry. 1994, 33, 7361–7367) or of Su et al. (Biochemistry. 1994, 33, 15501–15510), presumably because the simulations are not long enough. We find, however, that helicity correlates positively with the number of close hydrophobic interactions between the two peptides, showing that stable coiled coils in the simulations are tightly packed. The minimum interhelical distances are 0.50–0.66 nm for charged groups, indicating that favorable charge interactions are also important for the stability of the coiled coil.     

Generalized mixture models for molecular phylogenetic estimation

The rapidly growing availability of multigene sequence data during the past decade has enabled phylogeny estimation at phylogenomic scales. However, dealing with evolutionary process heterogeneity across the genome becomes increasingly challenging. Here we develop a mixture model approach that uses reversible jump Markov chain Monte Carlo (MCMC) estimation to permit as many distinct models as the data require. Each additional model considered may be a fully parametrized general time-reversible model or any of its special cases. Furthermore, we expand the usual proposal mechanisms for topology changes to permit hard polytomies (i.e., zero-length internal branches). This new approach is implemented in the Crux software toolkit. We demonstrate the feasibility of using reversible jump MCMC on mixture models by reexamining a well-known 44-taxon mammalian data set comprising 22 concatenated genes. We are able to reproduce the results of the original analysis (with respect to bipartition support) when we make identical assumptions, but when we allow for polytomies and/or use data-driven mixture model estimation, we infer much lower bipartition support values for several key bipartitions.     

基于地理加权回归克里格的日平均气温插值

气温是大量农业、水文、气候、生态模型的输入变量.在地形复杂的区域,考虑气温与环境变量的线性回归关系和残差的自相关性的方法(如回归克里格法,regression Kriging,RK)是目前气温插值的主要方法.但此类方法多使用基于普通最小二乘的全局回归技术,没有顾及回归关系的空间非平稳性.地理加权回归克里格(geographically weighted regression-Kriging,GWRK)是一种既能顾及回归关系的空间非平稳性、又能考虑残差的自相关性的一种插值方法.本文用RK和GWRK对海南岛2013年12月18日的日平均气温进行插值并进行比较研究.依相关性分析和逐步回归分析的结果,采用RK1(以海拔为辅助变量)、GWRK1(以海拔为辅助变量)、RK2(以纬度、海拔、海陆距离为辅助变量)和GWRK2(以海拔、海陆距离为辅助变量)4种模型进行研究,并用80个验证站评估4种模型的精度.结果表明:GWRK1模型的最大正误差、最大负误差、平均绝对误差、均方根误差均最接近于0.从最大正误差、平均绝对误差、均方根误差3个指标看,考虑更多辅助变量的RK2、GWRK2模型反而不及只考虑海拔的RK1、GWRK1模型,表明RK2、GWRK2模型中辅助变量之间的相关性对插值结果有较大影响.     

How to get more out of molecular fingerprints: practical tools for microbial ecology

Community-level molecular techniques are widely used in comparative microbial ecology to assess the diversity of microbial communities and their response to changing environments. These include among others denaturing and temperature gradient gel electrophoresis (DGGE/TGGE), single-strand conformation polymorphism (SSCP), length heterogeneity-PCR (LH-PCR), terminal-restriction fragment length polymorphism (tRFLP) and 16S rRNA gene clone libraries. The amount of data derived from these techniques available in literature is continuously increasing and the lack of a universal way to interpret the raw fingerprint itself makes it difficult to compare between different results. Taking the DGGE technique as an example, we propose a setting-independent theoretical interpretation of the DGGE pattern, based on a straightforward processing on three levels of analysis: (i) the range-weighted richness (Rr) reflecting the carrying capacity of the system, (ii) the dynamics (Dy) reflecting the specific rate of species coming to significance, and (iii) functional organization (Fo), defined through a relation between the structure of a microbial community and its functionality. These Rr, Dy and Fo values, each representing a score to describe a microbial community, can be plotted in a 3D graph. The latter represents a visual ecological interpretation of the initial raw fingerprinting pattern.     

Prediction of antigenic determinants of trichosanthin by molecular modeling

The antigenic determinants of trichosanthin were predicted by molecular modeling.First,the threedimensional structure model of the antigen-binding fragment of anti-trichosanthin immunoglobulin E was built on the basis of its amino-acid sequence and the known three-dimensional structure of an antibody with similar sequence.Secondly,the preferable antigen-antibody interactions were obtained based on the known three-dimensional structure of trichosanthin and of the hypervariable regions of anti-trichosanthin immunoglobulin E.Two regions in the molecular surface of trichosanthin were found to form extensive interactions with the hypervariable regions of the antibody and have been predicted to be the possible antigenic determinants:one is composed of two polypeptide segments,Ile201-Glu210 and Ile225-Asp229,which are close to each other in the three-dimensional structure;and the other is the segment Lys173-Thr178.The former region seems to be the more reasonable antigenic determinant than the latter one.