The proteasome biogenesis regulator Rpn4 cooperates with the unfolded protein response to promote ER stress resistance

Misfolded proteins in the endoplasmic reticulum (ER) activate the unfolded protein response (U PR), which enhances protein folding to restore homeostasis. Additional pathways respond to ER stress, but how they help counteract protein misfolding is incompletely understood. Here, we develop a titratable system for the induction of ER stress in yeast to enable a genetic screen for factors that augment stress resistance independently of the UPR. We identify the proteasome biogenesis regulator Rpn4 and show that it cooperates with the UPR. Rpn4 abundance increases during ER stress, first by a post-transcriptional, then by a transcriptional mechanism. Induction of RPN4 transcription is triggered by cytosolic mislocalization of secretory proteins, is mediated by multiple signaling pathways and accelerates clearance of misfolded proteins from the cytosol. Thus, Rpn4 and the UPR are complementary elements of a modular cross-compartment response to ER stress

The Pairwise Peculiar Velocity Dispersion of Galaxies: Effects of the Infall

We study the reliability of the reconstruction method which uses a modelling of the redshift distortions of the two-point correlation function to estimate the pairwise peculiar velocity dispersion of galaxies. In particular, the dependence of this quantity on different models for the infall velocity is examined for the Las Campanas Redshift Survey. We make extensive use of numerical simulations and of mock catalogs derived from them to discuss the effect of a self-similar infall model, of zero infall, and of the real infall taken from the simulation. The implications for two recent discrepant determinations of the pairwise velocity dispersion for this survey are discussed.Comment: minor changes in the discussion; accepted for publication in ApJ; 8 pages with 2 figures include

A Simple Model for Pulse Profiles from Precessing Pulsars, with Special Application to Relativistic Binary PSR B1913+16

We study the observable pulse profiles that can be generated from precessing pulsars. A novel coordinate system is defined to aid visualization of the observing geometry. Using this system we explore the different families of profiles that can be generated by simple, circularly symmetric beam shapes. An attempt is then made to fit our model to the observations of relativistic binary PSR B1913+16. It is found that while qualitatively similar pulse profiles can be produced, this minimal model is insufficient for an accurate match to the observational data. Consequently, we confirm that the emission beam of PSR B1913+16 must deviate from circular symmetry, as first reported by Weisberg and Taylor. However, the approximate fits obtained suggest that it may be sufficient to consider only minimal deviations from a circular beam in order to explain the data. We also comment on the applicability of our analysis technique to other precessing pulsars, both binary and isolated.Comment: 35 pages and 8 figures. Published versio

Role of Programmed Proteolysis During Meiosis

Meiosis is the process which forms gametes and spores for reproduction in eukaryotic cells. During the pachytene phase of meiosis I, a protein structure, called the Synaptonemal Complex (SC), forms between homologous chromosomes and creates a scaffold for genetic recombination. In yeast, the Zip1 protein is a major structural component of the SC. At restrictive temperature for meiosis, ZIP1 is required for completion of meiotic divisions. At permissive temperature ZIP1 is required for proper chromosome segregation. We observed that chemical inhibition of the proteasome, with MG132, results in arrest at prophase of meiosis I. Based on these results, we questioned whether there is a regulatory relationship between the SC and the proteasome. Our findings demonstrate the localization of the proteasome along the SC, consistent with proteolysis of SC proteins by the proteasome. Furthermore, lack of double-strand breaks, lack of SC and lack of recombination proteins, result in failed proteasome recruitment to chromosomes during meiosis I. This implies that the proteasome plays not only a role in proper meiotic division, but also double-strand break repair and chromosomal recombination. Fluorescent microscopy techniques were applied to determine the chromosomal localization of the proteasome. Epitope tagged recombination proteins (ZIP1, ZIP3 and MSH4) were utilized along with tagged proteasome components to determine the pattern of proteasome localization to meiotic chromosomes. This is significant as a clearer, fundamental understanding of the proteasome’s role in meiosis may serve to illuminate the causation of many birth-defects, miscarriages and stillbirths.https://engagedscholarship.csuohio.edu/u_poster_2013/1023/thumbnail.jp

The global cropland footprint of the non-food bioeconomy

A rapidly growing share of global agricultural areas is devoted to the production of biomass for non-food purposes. The derived products include, for example, biofuels, textiles, detergents or cosmetics. Given the far-reaching global implications of an expanding non-food bioeconomy, an assessment of the bioeconomy’s resource use from a footprint perspective is urgently needed. We determine the global cropland footprint of non-food products with a hybrid land flow accounting model combining data from the Food and Agriculture Organization and the multi-regional input-output model EXIOBASE. The globally interlinked model covers all cropland areas used for the production of crop- and animal-based non-food commodities for the years from 1995 to 2010. We analyse global patterns of raw material producers, processers and consumers of bio-based non-food products, with a particular focus on the European Union. Results illustrate that the EU is a major processer and the number one consumer region of non-food cropland, despite being only the fifth largest producing region. Two thirds of the cropland required to satisfy EU non-food consumption are located in other world regions, giving rise to a significant dependency on imported products and to potential impacts on distant ecosystems. With almost 29% in 2010, oilseed production, used to produce, for example, biofuels, detergents and polymers, represents the dominant share in the EU’s non-food cropland footprint. There is also a significant contribution of more traditional non-food biomass uses such as fibre crops (for textiles) and animal hides and skins (for leather products). Our study emphasises the importance of comprehensively assessing the implications of the non-food bioeconomy expansion as envisaged in various policy strategies, such as the Bioeconomy Strategy of the European Commission

The cross-correlation between galaxies of different luminosities and Colors

We study the cross-correlation between galaxies of different luminosities and colors, using a sample selected from the SDSS Dr 4. Galaxies are divided into 6 samples according to luminosity, and each of these samples is divided into red and blue subsamples. Projected auto-correlation and cross-correlation is estimated for these subsample. At projected separations r_p > 1\mpch, all correlation functions are roughly parallel, although the correlation amplitude depends systematically on luminosity and color. On r_p < 1\mpch, the auto- and cross-correlation functions of red galaxies are significantly enhanced relative to the corresponding power laws obtained on larger scales. Such enhancement is absent for blue galaxies and in the cross-correlation between red and blue galaxies. We esimate the relative bias factor on scales r > 1\mpch for each subsample using its auto-correlation function and cross-correlation functions. The relative bias factors obtained from different methods are similar. For blue galaxies the luminosity-dependence of the relative bias is strong over the luminosity range probed (-23.0<M_r < -18.0),but for red galaxies the dependence is weaker and becomes insignificant for luminosities below L^*. To examine whether a significant stochastic/nonlinear component exists in the bias relation, we study the ratio R_ij= W_{ii}W_{jj}/W_{ij}^2, where W_{ij} is the projected correlation between subsample i and j. We find that the values of R_ij are all consistent with 1 for all-all, red-red and blue-blue samples, however significantly larger than 1 for red-blue samples. For faint red - faint blue samples the values of R_{ij} are as high as ~ 2 on small scales r_p < 1 \mpch and decrease with increasing r_p.Comment: 25 pages, 18 figures, Accepted for publication in Ap

Protocol

The Perseus software provides a comprehensive framework for the statistical analysis of large-scale quantitative proteomics data, also in combination with other omics dimensions. Rapid developments in proteomics technology and the ever-growing diversity of biological studies increasingly require the flexibility to incorporate computational methods designed by the user. Here, we present the new functionality of Perseus to integrate self-made plugins written in C#, R, or Python. The user-written codes will be fully integrated into the Perseus data analysis workflow as custom activities. This also makes language-specific R and Python libraries from CRAN (cran.r-project.org), Bioconductor (bioconductor.org), PyPI (pypi.org), and Anaconda (anaconda.org) accessible in Perseus. The different available approaches are explained in detail in this article. To facilitate the distribution of user-developed plugins among users, we have created a plugin repository for community sharing and filled it with the examples provided in this article and a collection of already existing and more extensive plugins. © 2020 The Authors. Basic Protocol 1: Basic steps for R plugins Support Protocol 1: R plugins with additional arguments Basic Protocol 2: Basic steps for python plugins Support Protocol 2: Python plugins with additional arguments Basic Protocol 3: Basic steps and construction of C# plugins Basic Protocol 4: Basic steps of construction and connection for R plugins with C# interface Support Protocol 4: Advanced example of R Plugin with C# interface: UMAP Basic Protocol 5: Basic steps of construction and connection for python plugins with C# interface Support Protocol 5: Advanced example of python plugin with C# interface: UMAP Support Protocol 6: A basic workflow for the analysis of label-free quantification proteomics data using perseus. © 2020 The Authors

Automated Plankton Classification With a Dynamic Optimization and Adaptation Cycle

With recent advances in Machine Learning techniques based on Deep Neural Networks (DNNs), automated plankton image classification is becoming increasingly popular within the marine ecological sciences. Yet, while the most advanced methods can achieve human-level performance on the classification of everyday images, plankton image data possess properties that frequently require a final manual validation step. On the one hand, this is due to morphological properties manifesting in high intra-class and low inter-class variability, and, on the other hand is due to spatial-temporal changes in the composition and structure of the plankton community. Composition changes enforce a frequent updating of the classifier model via training with new user-generated training datasets. Here, we present a Dynamic Optimization Cycle (DOC), a processing pipeline that systematizes and streamlines the model adaptation process via an automatic updating of the training dataset based on manual-validation results. We find that frequent adaptation using the DOC pipeline yields strong maintenance of performance with respect to precision, recall and prediction of community composition, compared to more limited adaptation schemes. The DOC is therefore particularly useful when analyzing plankton at novel locations or time periods, where community differences are likely to occur. In order to enable an easy implementation of the DOC pipeline, we provide an end-to-end application with graphical user interface, as well as an initial dataset of training images. The DOC pipeline thus allows for high-throughput plankton classification and quick and systematized model adaptation, thus providing the means for highly-accelerated plankton analysis