PEtab—Interoperable specification of parameter estimation problems in systems biology

Reproducibility and reusability of the results of data-based modeling studies are essential. Yet, there has been—so far—no broadly supported format for the specification of parameter estimation problems in systems biology. Here, we introduce PEtab, a format which facilitates the specification of parameter estimation problems using Systems Biology Markup Language (SBML) models and a set of tab-separated value files describing the observation model and experimental data as well as parameters to be estimated. We already implemented PEtab support into eight well-established model simulation and parameter estimation toolboxes with hundreds of users in total. We provide a Python library for validation and modification of a PEtab problem and currently 20 example parameter estimation problems based on recent studies.     

The cell death protease Kex1p is essential for hypochlorite-induced apoptosis in yeast

Following microbial pathogen invasion, the human immune system of activated phagocytes generates and releases the potent oxidant hypochlorous acid (HOCl), which contributes to the killing of menacing microorganisms. Though tightly controlled, HOCl generation by the myeloperoxidase-hydrogen peroxide-chloride system of neutrophils/monocytes may occur in excess and lead to tissue damage. It is thus of marked importance to delineate the molecular pathways underlying HOCl cytotoxicity in both microbial and human cells. Here, we show that HOCl induces the generation of reactive oxygen species (ROS), apoptotic cell death and the formation of specific HOCl-modified epitopes in the budding yeast Saccharomyces cerevisiae. Interestingly, HOCl cytotoxicity can be prevented by treatment with ROS scavengers, suggesting oxidative stress to mediate the lethal effect. The executing pathway involves the pro-apoptotic protease Kex1p, since its absence diminishes HOCl-induced production of ROS, apoptosis and protein modification. By characterizing HOCl-induced cell death in yeast and identifying a corresponding central executor, these results pave the way for the use of Saccharomyces cerevisiae in HOCl research, not least given that it combines both being a microorganism as well as a model for programmed cell death in higher eukaryotes.     

Membrane protein shaving with thermolysin can be used to evaluate topology predictors

Topology analysis of membrane proteins can be obtained by enzymatic shaving in combination with MS identification of peptides. Ideally, such analysis could provide quite detailed information about the membrane spanning regions. Here, we examine the ability of some shaving enzymes to provide large‐scale analysis of membrane proteome topologies. To compare different shaving enzymes, we first analyzed the detected peptides from two over‐expressed proteins. Second, we analyzed the peptides from non‐over‐expressed Escherichia coli membrane proteins with known structure to evaluate the shaving methods. Finally, the identified peptides were used to test the accuracy of a number of topology predictors. At the end we suggest that the usage of thermolysin, an enzyme working at the natural pH of the cell for membrane shaving, is superior because: (i) we detect a similar number of peptides and proteins using thermolysin and trypsin; (ii) thermolysin shaving can be run at a natural pH and (iii) the incubation time is quite short. (iv) Fewer detected peptides from thermolysin shaving originate from the transmembrane regions. Using thermolysin shaving we can also provide a clear separation between the best and the less accurate topology predictors, indicating that using data from shaving can provide valuable information when developing new topology predictors.     

Transcriptional Cofactor TBLR1 Controls Lipid Mobilization in White Adipose Tissue

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Highlights? TBLR1 controls cAMP-dependent lipolysis in adipocytes ? Adipocyte-specific deletion of TBLR1 in mice impairs fasting-induced lipolysis ? Lack of TBLR1 in adipocytes aggravates diet-induced obesity and metabolic dysfunction ? TBLR1 mRNA levels in WAT are elevated under lipolytic conditions in mice and humans     

Seminal roots of wild and cultivated barley differentially respond to osmotic stress in gene expression,suberization, and hydraulic conductivity

Wild barley, Hordeum vulgare spp. spontaneum, has a wider genetic diversity than its cultivated progeny, Hordeum vulgare spp. vulgare. Osmotic stress leads to a series of different responses in wild barley seminal roots, ranging from no changes in suberization to enhanced endodermal suberization of certain zones and the formation of a suberized exodermis, which was not observed in the modern cultivars studied so far. Further, as a response to osmotic stress, the hydraulic conductivity of roots was not affected in wild barley, but it was 2.5-fold reduced in cultivated barley. In both subspecies, osmotic adjustment by increasing proline concentration and decreasing osmotic potential in roots was observed. RNA-sequencing indicated that the regulation of suberin biosynthesis and water transport via aquaporins were different between wild and cultivated barley. These results indicate that wild barley uses different strategies to cope with osmotic stress compared with cultivated barley. Thus, it seems that wild barley is better adapted to cope with osmotic stress by maintaining a significantly higher hydraulic conductivity of roots during water deficit.     

Field evaluation of abundance estimates under binomial and multinomial N-mixture models

Assessing and modelling abundance from animal count data is a very common task in ecology and management. Detection is arguably never perfect, but modern hierarchical models can incorporate detection probability and yield abundance estimates that are corrected for imperfect detection. Two variants of these models rely on counts of unmarked individuals, or territories (binomial N-mixture models, or binmix), and on detection histories based on territory-mapping data (multinomial N-mixture models or multimix). However, calibration studies which evaluate these two N-mixture model approaches are needed. We analysed conventional territory-mapping data (three surveys in 2014 and four in 2015) using both binmix and multimix models to estimate abundance for two common avian cavity-nesting forest species (Great Tit Parus major and Eurasian Blue Tit Cyanistes caeruleus). In the same study area, we used two benchmarks: occupancy data from a dense nestbox scheme and total number of detected territories. To investigate variance in estimates due to the territory assignment, three independent ornithologists conducted territory assignments. Nestbox occupancy yields a minimum number of territories, as some natural cavities may have been used, and binmix model estimates were generally higher than this benchmark. Estimates using the multimix model were slightly more precise than binmix model estimates. Depending on the person assigning the territories, the multimix model estimates became quite different, either overestimating or underestimating the ‘truth’. We conclude that N-mixture models estimated abundance reliably, even for our very small sample sizes. Territory-mapping counts depended on territory assignment and this carried over to estimates under the multimix model. This limitation has to be taken into account when abundance estimates are compared between sites or years. Whenever possible, accounting for such hidden heterogeneity in the raw data of bird surveys, via including a ‘territory editor’ factor, is recommended. Distributing the surveys randomly (in time and space) to editors may also alleviate this problem.     

Polymerase manager protein UmuD directly regulates Escherichia coli DNA polymerase III α binding to ssDNA

Replication by Escherichia coli DNA polymerase III is disrupted on encountering DNA damage. Consequently, specialized Y-family DNA polymerases are used to bypass DNA damage. The protein UmuD is extensively involved in modulating cellular responses to DNA damage and may play a role in DNA polymerase exchange for damage tolerance. In the absence of DNA, UmuD interacts with the α subunit of DNA polymerase III at two distinct binding sites, one of which is adjacent to the single-stranded DNA-binding site of α. Here, we use single molecule DNA stretching experiments to demonstrate that UmuD specifically inhibits binding of α to ssDNA. We predict using molecular modeling that UmuD residues D91 and G92 are involved in this interaction and demonstrate that mutation of these residues disrupts the interaction. Our results suggest that competition between UmuD and ssDNA for α binding is a new mechanism for polymerase exchange.