Prion protein cleavage fragments regulate adult neural stem cell quiescence through redox modulation of mitochondrial fission and SOD2 expression

Neurogenesis continues in the post-developmental brain throughout life. The ability to stimulate the production of new neurones requires both quiescent and actively proliferating pools of neural stem cells (NSCs). Actively proliferating NSCs ensure that neurogenic demand can be met, whilst the quiescent pool makes certain NSC reserves do not become depleted. The processes preserving the NSC quiescent pool are only just beginning to be defined. Herein, we identify a switch between NSC proliferation and quiescence through changing intracellular redox signalling. We show that N-terminal post-translational cleavage products of the prion protein (PrP) induce a quiescent state, halting NSC cellular growth, migration, and neurite outgrowth. Quiescence is initiated by the PrP cleavage products through reducing intracellular levels of reactive oxygen species. First, inhibition of redox signalling results in increased mitochondrial fission, which rapidly signals quiescence. Thereafter, quiescence is maintained through downstream increases in the expression and activity of superoxide dismutase-2 that reduces mitochondrial superoxide. We further observe that PrP is predominantly cleaved in quiescent NSCs indicating a homeostatic role for this cascade. Our findings provide new insight into the regulation of NSC quiescence, which potentially could influence brain health throughout adult life.     

Microbes,mathematics, and models

Microbial model systems have a long history of fruitful use in fields that include evolution and ecology. In order to develop further insight into modelling practice, we examine how the competitive exclusion and coexistence of competing species have been modelled mathematically and materially over the course of a long research history. In particular, we investigate how microbial models of these dynamics interact with mathematical or computational models of the same phenomena. Our cases illuminate the ways in which microbial systems and equations work as models, and what happens when they generate inconsistent findings about shared targets. We reveal an iterative strategy of comparative modelling in different media, and suggest reasons why microbial models have a special degree of epistemic tractability in multimodel inquiry.     

Comparative genetic differentiation study of three coexisting mangrove crabs in western Atlantic

ABSTRACT

A multispecies approach may reveal the factors influencing the genetic differentiation of coexisting species along a wide geographic area. Our aim was to compare the genetic differentiation of mangrove crabs coexisting along the western Atlantic in order to verify if there are common barriers, using two mitochondrial DNA markers to quantify genetic differentiation, variance and diversity. In addition, we included a mismatch distribution to check demographic history. Our data revealed that species with similar pelagic larval duration had either high genetic differentiation or the absence of genetic structure. These results can be explained by factors that contribute to genetic differentiation, such as the presence of large estuarine areas (acting as barriers), ocean currents, and larval behaviour. Also, historical events that promoted the isolation of some areas in the past, such as glacial cycles during the Pleistocene, could have caused the observed high levels of genetic divergence in some species.     

Forecasting Longevity Gains Using a Seemingly Unrelated Time Series Model

In this paper a multivariate time series model using the seemingly unrelated time series equation (SUTSE) framework is proposed to forecast longevity gains. The proposed model is represented in state space form and uses Kalman filtering to estimate the unobservable components and fixed parameters. We apply the model both to male mortality rates in Portugal and the USA. Our results compare favorably, in terms of mean absolute percentage error, in‐sample and out‐of‐sample, to those obtained by the Lee–Carter method and some of its extensions. Copyright © 2015 John Wiley & Sons, Ltd.     

Degradation of the mitochondrial complex I assembly factor TMEM126B under chronic hypoxia

Cell stress such as hypoxia elicits adaptive responses, also on the level of mitochondria, and in part is mediated by the hypoxia-inducible factor (HIF) 1α. Adaptation of mitochondria towards acute hypoxic conditions is reasonably well understood, while regulatory mechanisms, especially of respiratory chain assembly factors, under chronic hypoxia remains elusive. One of these assembly factors is transmembrane protein 126B (TMEM126B). This protein is part of the mitochondrial complex I assembly machinery. We identified changes in complex I abundance under chronic hypoxia, in association with impaired substrate-specific mitochondrial respiration. Complexome profiling of isolated mitochondria of the human leukemia monocytic cell line THP-1 revealed HIF-1α-dependent deficits in complex I assembly and mitochondrial complex I assembly complex (MCIA) abundance. Of all mitochondrial MCIA members, we proved a selective HIF-1-dependent decrease of TMEM126B under chronic hypoxia. Mechanistically, HIF-1α induces the E3-ubiquitin ligase F-box/WD repeat-containing protein 1A (β-TrCP1), which in turn facilitates the proteolytic degradation of TMEM126B. Attenuating a functional complex I assembly appears critical for cellular adaptation towards chronic hypoxia and is linked to destruction of the mitochondrial assembly factor TMEM126B.     

Une controverse entre Émile Picard et Leopold Kronecker

Archive for History of Exact Sciences - Leopold Kronecker constructs in two articles published in 1869 and 1878, a theory which has its roots in Sturm’s work on the determination of the...     

Spatial dependence model with feature difference

This paper presents a new spatial dependence model with an adjustment of feature difference. The model accounts for the spatial autocorrelation in both the outcome variables and residuals. The feature difference adjustment in the model helps to emphasize feature changes across neighboring units, while suppressing unobserved covariates that are present in the same neighborhood. The prediction at a given unit incorporates components that depend on the differences between the values of its main features and those of its neighboring units. In contrast to conventional spatial regression models, our model does not require a comprehensive list of global covariates necessary to estimate the outcome variable at the unit, as common macro-level covariates are differenced away in the regression analysis. Using the real estate market data in Hong Kong, we applied Gibbs sampling to determine the posterior distribution of each model parameter. The result of our empirical analysis confirms that the adjustment of feature difference with an inclusion of the spatial error autocorrelation produces better out-of-sample prediction performance than other conventional spatial dependence models. In addition, our empirical analysis can identify components with more significant contributions.