Bacterial porin disrupts mitochondrial membrane potential and sensitizes host cells to apoptosis

The bacterial PorB porin, an ATP-binding beta-barrel protein of pathogenic Neisseria gonorrhoeae, triggers host cell apoptosis by an unknown mechanism. PorB is targeted to and imported by host cell mitochondria, causing the breakdown of the mitochondrial membrane potential (delta psi m). Here, we show that PorB induces the condensation of the mitochondrial matrix and the loss of cristae structures, sensitizing cells to the induction of apoptosis via signaling pathways activated by BH3-only proteins. PorB is imported into mitochondria through the general translocase TOM but, unexpectedly, is not recognized by the SAM sorting machinery, usually required for the assembly of beta-barrel proteins in the mitochondrial outer membrane. PorB integrates into the mitochondrial inner membrane, leading to the breakdown of delta psi m. The PorB channel is regulated by nucleotides and an isogenic PorB mutant defective in ATP-binding failed to induce delta psi m loss and apoptosis, demonstrating that dissipation of delta psi m is a requirement for cell death caused by neisserial infection

Reverse Engineering Time Discrete Finite Dynamical Systems: A Feasible Undertaking?

With the advent of high-throughput profiling methods, interest in reverse engineering the structure and dynamics of biochemical networks is high. Recently an algorithm for reverse engineering of biochemical networks was developed by Laubenbacher and Stigler. It is a top-down approach using time discrete dynamical systems. One of its key steps includes the choice of a term order, a technicality imposed by the use of Gröbner-bases calculations. The aim of this paper is to identify minimal requirements on data sets to be used with this algorithm and to characterize optimal data sets. We found minimal requirements on a data set based on how many terms the functions to be reverse engineered display. Furthermore, we identified optimal data sets, which we characterized using a geometric property called “general position”. Moreover, we developed a constructive method to generate optimal data sets, provided a codimensional condition is fulfilled. In addition, we present a generalization of their algorithm that does not depend on the choice of a term order. For this method we derived a formula for the probability of finding the correct model, provided the data set used is optimal. We analyzed the asymptotic behavior of the probability formula for a growing number of variables n (i.e. interacting chemicals). Unfortunately, this formula converges to zero as fast as , where and . Therefore, even if an optimal data set is used and the restrictions in using term orders are overcome, the reverse engineering problem remains unfeasible, unless prodigious amounts of data are available. Such large data sets are experimentally impossible to generate with today's technologies

Bivariate random-effects meta-analysis and the estimation of between-study correlation

BACKGROUND: When multiple endpoints are of interest in evidence synthesis, a multivariate meta-analysis can jointly synthesise those endpoints and utilise their correlation. A multivariate random-effects meta-analysis must incorporate and estimate the between-study correlation (ρ(B)). METHODS: In this paper we assess maximum likelihood estimation of a general normal model and a generalised model for bivariate random-effects meta-analysis (BRMA). We consider two applied examples, one involving a diagnostic marker and the other a surrogate outcome. These motivate a simulation study where estimation properties from BRMA are compared with those from two separate univariate random-effects meta-analyses (URMAs), the traditional approach. RESULTS: The normal BRMA model estimates ρ(B )as -1 in both applied examples. Analytically we show this is due to the maximum likelihood estimator sensibly truncating the between-study covariance matrix on the boundary of its parameter space. Our simulations reveal this commonly occurs when the number of studies is small or the within-study variation is relatively large; it also causes upwardly biased between-study variance estimates, which are inflated to compensate for the restriction on [Formula: see text] (B). Importantly, this does not induce any systematic bias in the pooled estimates and produces conservative standard errors and mean-square errors. Furthermore, the normal BRMA is preferable to two normal URMAs; the mean-square error and standard error of pooled estimates is generally smaller in the BRMA, especially given data missing at random. For meta-analysis of proportions we then show that a generalised BRMA model is better still. This correctly uses a binomial rather than normal distribution, and produces better estimates than the normal BRMA and also two generalised URMAs; however the model may sometimes not converge due to difficulties estimating ρ(B). CONCLUSION: A BRMA model offers numerous advantages over separate univariate synthesises; this paper highlights some of these benefits in both a normal and generalised modelling framework, and examines the estimation of between-study correlation to aid practitioners

Systematical Detection of Significant Genes in Microarray Data by Incorporating Gene Interaction Relationship in Biological Systems

Many methods, including parametric, nonparametric, and Bayesian methods, have been used for detecting differentially expressed genes based on the assumption that biological systems are linear, which ignores the nonlinear characteristics of most biological systems. More importantly, those methods do not simultaneously consider means, variances, and high moments, resulting in relatively high false positive rate. To overcome the limitations, the SWang test is proposed to determine differentially expressed genes according to the equality of distributions between case and control. Our method not only latently incorporates functional relationships among genes to consider nonlinear biological system but also considers the mean, variance, skewness, and kurtosis of expression profiles simultaneously. To illustrate biological significance of high moments, we construct a nonlinear gene interaction model, demonstrating that skewness and kurtosis could contain useful information of function association among genes in microarrays. Simulations and real microarray results show that false positive rate of SWang is lower than currently popular methods (T-test, F-test, SAM, and Fold-change) with much higher statistical power. Additionally, SWang can uniquely detect significant genes in real microarray data with imperceptible differential expression but higher variety in kurtosis and skewness. Those identified genes were confirmed with previous published literature or RT-PCR experiments performed in our lab

TAK1 Is Required for Survival of Mouse Fibroblasts Treated with TRAIL, and Does So by NF-κB Dependent Induction of cFLIPL

Tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) is known as a “death ligand”—a member of the TNF superfamily that binds to receptors bearing death domains. As well as causing apoptosis of certain types of tumor cells, TRAIL can activate both NF-κB and JNK signalling pathways. To determine the role of TGF-β-Activated Kinase-1 (TAK1) in TRAIL signalling, we analyzed the effects of adding TRAIL to mouse embryonic fibroblasts (MEFs) derived from TAK1 conditional knockout mice. TAK1−/− MEFs were significantly more sensitive to killing by TRAIL than wild-type MEFs, and failed to activate NF-κB or JNK. Overexpression of IKK2-EE, a constitutive activator of NF-κB, protected TAK1−/− MEFs against TRAIL killing, suggesting that TAK1 activation of NF-κB is critical for the viability of cells treated with TRAIL. Consistent with this model, TRAIL failed to induce the survival genes cIAP2 and cFlipL in the absence of TAK1, whereas activation of NF-κB by IKK2-EE restored the levels of both proteins. Moreover, ectopic expression of cFlipL, but not cIAP2, in TAK1−/− MEFs strongly inhibited TRAIL-induced cell death. These results indicate that cells that survive TRAIL treatment may do so by activation of a TAK1–NF-κB pathway that drives expression of cFlipL, and suggest that TAK1 may be a good target for overcoming TRAIL resistance

From Poverty to Disaster and Back: a Review of the Literature

Poor people are disproportionally affected by natural hazards and disasters. This paper provides a review of the multiple factors that explain why this is the case. It explores the role of exposure (often, but not always, poor people are more likely to be affected by hazards), vulnerability (when they are affected, poor people tend to lose a larger fraction of their wealth), and socio-economic resilience (poor people have a lower ability to cope with and recover from disaster impacts). Finally, the paper highlights the vicious circle between poverty and disaster losses: poverty is a major driver of people’s vulnerability to natural disasters, which in turn increase poverty in a measurable and significant way. The main policy implication is that poverty reduction can be considered as disaster risk management, and disaster risk management can be considered as poverty reduction

Nociceptors: a phylogenetic view

The ability to react to environmental change is crucial for the survival of an organism and an essential prerequisite is the capacity to detect and respond to aversive stimuli. The importance of having an inbuilt “detect and protect” system is illustrated by the fact that most animals have dedicated sensory afferents which respond to noxious stimuli called nociceptors. Should injury occur there is often sensitization, whereby increased nociceptor sensitivity and/or plasticity of nociceptor-related neural circuits acts as a protection mechanism for the afflicted body part. Studying nociception and nociceptors in different model organisms has demonstrated that there are similarities from invertebrates right through to humans. The development of technology to genetically manipulate organisms, especially mice, has led to an understanding of some of the key molecular players in nociceptor function. This review will focus on what is known about nociceptors throughout the Animalia kingdom and what similarities exist across phyla; especially at the molecular level of ion channels

Mutations in SLC29A3, Encoding an Equilibrative Nucleoside Transporter ENT3, Cause a Familial Histiocytosis Syndrome (Faisalabad Histiocytosis) and Familial Rosai-Dorfman Disease

The histiocytoses are a heterogeneous group of disorders characterised by an excessive number of histiocytes. In most cases the pathophysiology is unclear and treatment is nonspecific. Faisalabad histiocytosis (FHC) (MIM 602782) has been classed as an autosomal recessively inherited form of histiocytosis with similarities to Rosai-Dorfman disease (RDD) (also known as sinus histiocytosis with massive lymphadenopathy (SHML)). To elucidate the molecular basis of FHC, we performed autozygosity mapping studies in a large consanguineous family and identified a novel locus at chromosome 10q22.1. Mutation analysis of candidate genes within the target interval identified biallelic germline mutations in SLC29A3 in the FHC kindred and in two families reported to have familial RDD. Analysis of SLC29A3 expression during mouse embryogenesis revealed widespread expression by e14.5 with prominent expression in the central nervous system, eye, inner ear, and epithelial tissues including the gastrointestinal tract. SLC29A3 encodes an intracellular equilibrative nucleoside transporter (hENT3) with affinity for adenosine. Recently germline mutations in SLC29A3 were also described in two rare autosomal recessive disorders with overlapping phenotypes: (a) H syndrome (MIM 612391) that is characterised by cutaneous hyperpigmentation and hypertrichosis, hepatomegaly, heart anomalies, hearing loss, and hypogonadism; and (b) PHID (pigmented hypertrichosis with insulin-dependent diabetes mellitus) syndrome. Our findings suggest that a variety of clinical diagnoses (H and PHID syndromes, FHC, and familial RDD) can be included in a new diagnostic category of SLC29A3 spectrum disorder

Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018.

Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field