Novel insights into the unfolded protein response using Pichia pastoris specific DNA microarrays

Background DNA Microarrays are regarded as a valuable tool for basic and applied research in microbiology. However, for many industrially important microorganisms the lack of commercially available microarrays still hampers physiological research. Exemplarily, our understanding of protein folding and secretion in the yeast Pichia pastoris is presently widely dependent on conclusions drawn from analogies to Saccharomyces cerevisiae. To close this gap for a yeast species employed for its high capacity to produce heterologous proteins, we developed full genome DNA microarrays for P. pastoris and analyzed the unfolded protein response (UPR) in this yeast species, as compared to S. cerevisiae. Results By combining the partially annotated gene list of P. pastoris with de novo gene finding a list of putative open reading frames was generated for which an oligonucleotide probe set was designed using the probe design tool TherMODO (a thermodynamic model-based oligoset design optimizer). To evaluate the performance of the novel array design, microarrays carrying the oligo set were hybridized with samples from treatments with dithiothreitol (DTT) or a strain overexpressing the UPR transcription factor HAC1, both compared with a wild type strain in normal medium as untreated control. DTT treatment was compared with literature data for S. cerevisiae, and revealed similarities, but also important differences between the two yeast species. Overexpression of HAC1, the most direct control for UPR genes, resulted in significant new understanding of this important regulatory pathway in P. pastoris, and generally in yeasts. Conclusion The differences observed between P. pastoris and S. cerevisiae underline the importance of DNA microarrays for industrial production strains. P. pastoris reacts to DTT treatment mainly by the regulation of genes related to chemical stimulus, electron transport and respiration, while the overexpression of HAC1 induced many genes involved in translation, ribosome biogenesis, and organelle biosynthesis, indicating that the regulatory events triggered by DTT treatment only partially overlap with the reactions to overexpression of HAC1. The high reproducibility of the results achieved with two different oligo sets is a good indication for their robustness, and underlines the importance of less stringent selection of regulated features, in order to avoid a large number of false negative results

Novel insights into the unfolded protein response using Pichia pastoris specific DNA microarrays

Background DNA Microarrays are regarded as a valuable tool for basic and applied research in microbiology. However, for many industrially important microorganisms the lack of commercially available microarrays still hampers physiological research. Exemplarily, our understanding of protein folding and secretion in the yeast Pichia pastoris is presently widely dependent on conclusions drawn from analogies to Saccharomyces cerevisiae. To close this gap for a yeast species employed for its high capacity to produce heterologous proteins, we developed full genome DNA microarrays for P. pastoris and analyzed the unfolded protein response (UPR) in this yeast species, as compared to S. cerevisiae. Results By combining the partially annotated gene list of P. pastoris with de novo gene finding a list of putative open reading frames was generated for which an oligonucleotide probe set was designed using the probe design tool TherMODO (a thermodynamic model-based oligoset design optimizer). To evaluate the performance of the novel array design, microarrays carrying the oligo set were hybridized with samples from treatments with dithiothreitol (DTT) or a strain overexpressing the UPR transcription factor HAC1, both compared with a wild type strain in normal medium as untreated control. DTT treatment was compared with literature data for S. cerevisiae, and revealed similarities, but also important differences between the two yeast species. Overexpression of HAC1, the most direct control for UPR genes, resulted in significant new understanding of this important regulatory pathway in P. pastoris, and generally in yeasts. Conclusion The differences observed between P. pastoris and S. cerevisiae underline the importance of DNA microarrays for industrial production strains. P. pastoris reacts to DTT treatment mainly by the regulation of genes related to chemical stimulus, electron transport and respiration, while the overexpression of HAC1 induced many genes involved in translation, ribosome biogenesis, and organelle biosynthesis, indicating that the regulatory events triggered by DTT treatment only partially overlap with the reactions to overexpression of HAC1. The high reproducibility of the results achieved with two different oligo sets is a good indication for their robustness, and underlines the importance of less stringent selection of regulated features, in order to avoid a large number of false negative results

BibGlimpse: The case for a light-weight reprint manager in distributed literature research

Background While text-mining and distributed annotation systems both aim at capturing knowledge and presenting it in a standardized form, there have been few attempts to investigate potential synergies between these two fields. For instance, distributed annotation would be very well suited for providing topic focussed, expert knowledge enriched text corpora. A key limitation for this approach is the availability of literature annotation systems that can be routinely used by groups of collaborating researchers on a day to day basis, not distracting from the main focus of their work. Results For this purpose, we have designed BibGlimpse. Features like drop-to-file, SVM based automated retrieval of PubMed bibliography for PDF reprints, and annotation support make BibGlimpse an efficient, light-weight reprint manager that facilitates distributed literature research for work groups. Building on an established open search engine, full-text search and structured queries are supported, while at the same time making shared collections of annotated reprints accessible to literature classification and text-mining tools. Conclusion BibGlimpse offers scientists a tool that enhances their own literature management. Moreover, it may be used to create content enriched, annotated text corpora for research in text-mining

Bayesian latent class metric conjoint analysis. A case study from the Austrian mineral water market.

This paper presents the fully Bayesian analysis of the latent class model using a new approach towards MCMC estimation in the context of mixture models. The approach starts with estimating unidentified models for various numbers of classes. Exact Bayes' factors are computed by the bridge sampling estimator to compare different models and select the number of classes. Estimation of the unidentified model is carried out using the random permutation sampler. From the unidentified model estimates for model parameters that are not class specific are derived. Then, the exploration of the MCMC output from the unconstrained model yields suitable identifiability constraints. Finally, the constrained version of the permutation sampler is used to estimate group specific parameters. Conjoint data from the Austrian mineral water market serve to illustrate the method. (author's abstract)Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science

Enhanced Data Return from Lunar Farside using RF-Optical TT and C

Science return and high bandwidth communications are key issues to support the foreseen endeavours on spaceflights to the Moon and beyond. For a given mass, power consumption and volume, laser communications can offer an increase in TM bandwidth over classical RF technology allowing for a variety of new options, specifically to missions that require very large distances, such as to the Moon and even beyond. This increase in TM data rate allows for more raw scientific data to be sent back to Earth where data processing can be performed on ground. Enhanced sensing techniques could be used that generate more science data and access during flight could be faster. This paper presents an overview of proof of concept test results obtained for optical telemetry return in a field test campaign together with ESA in October 2007. Based on the results obtained, a sample mission is outlined that shows an optical relay on a HALO trajectory at lunar farside, using an integrated RF-optical TT&C transponder concept

Fully Bayesian Analysis of Multivariate Latent Class Models with an Application to Metric Conjoint Analysis

In this paper we head for a fully Bayesian analysis of the latent class model with a priori unknown number of classes. Estimation is carried out by means of Markov Chain Monte Carlo (MCMC) methods. We deal explicitely with the consequences the unidentifiability of this type of model has on MCMC estimation. Joint Bayesian estimation of all latent variables, model parameters, and parameters determining the probability law of the latent process is carried out by a new MCMC method called permutation sampling. In a first run we use the random permutation sampler to sample from the unconstrained posterior. We will demonstrate that a lot of important information, such as e.g. estimates of the subject-specific regression coefficients, is available from such an unidentified model. The MCMC output of the random permutation sampler is explored in order to find suitable identifiability constraints. In a second run we use the permutation sampler to sample from the constrained posterior by imposing identifiablity constraints. The unknown number of classes is determined by formal Bayesian model comparison through exact model likelihoods. We apply a new method of computing model likelihoods for latent class models which is based on the method of bridge sampling. The approach is applied to simulated data and to data from a metric conjoint analysis in the Austrian mineral water market. (author's abstract)Series: Forschungsberichte / Institut für Statisti

The Heterogeneity Model and its Special Cases. An Illustrative Comparison.

In this paper we carry out fully Bayesian analysis of the general heterogeneity model, which is a mixture of random effects model, and its special cases, the random coefficient model and the latent class model. Our application comes from Conjoint analysis and we are especially interested in what is gained by the general heterogeneity model in comparison to the other two when modeling consumers' heterogeneous preferences. (author's abstract)Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science

A Fully Bayesian Analysis of Multivariate Latent Class Models with an Application to Metric Conjoint Analysis

In this paper we head for a fully Bayesian analysis of the latent class model with a priori unknown number of classes. Estimation is carried out by means of Markov Chain Monte Carlo (MCMC) methods. We deal explicitely with the consequences the unidentifiability of this type of model has on MCMC estimation. Joint Bayesian estimation of all latent variables, model parameters, and parameters determining the probability law of the latent process is carried out by a new MCMC method called permutation sampling. In a first run we use the random permutation sampler to sample from the unconstrained posterior. We will demonstrate that a lot of important information, such as e.g. estimates of the subject-specific regression coefficients, is available from such an unidentified model. The MCMC output of the random permutation sampler is explored in order to find suitable identifiability constraints. In a second run we use the permutation sampler to sample from the constrained posterior by imposing identifiablity constraints. The unknown number of classes is determined by formal Bayesian model comparison through exact model likelihoods. We apply a new method of computing model likelihoods for latent class models which is based on the method of bridge sampling. The approach is applied to simulated data and to data from a metric conjoint analysis in the Austrian mineral water market. (author's abstract)Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science

Bayesian analysis of the heterogeneity model

In the present paper we consider Bayesian estimation of a finite mixture of models with random effects which is also known as the heterogeneity model. First, we discuss the properties of various MCMC samplers that are obtained from full conditional Gibbs sampling by grouping and collapsing. Whereas full conditional Gibbs sampling turns out to be sensitive to the parameterization chosen for the mean structure of the model, the alternative sampler is robust in this respect. However, the logical extension of the approach to the sampling of the group variances does not further increase the efficiency of the sampler. Second, we deal with the identifiability problem due to the arbitrary labeling within the model. Finally, a case study involving metric Conjoint analysis serves as a practical illustration. (author's abstract)Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science