Contrasting effects of sleep fragmentation and angiotensin-II treatment upon pro-inflammatory responses of mice

Disordered sleep promotes inflammation in brain and peripheral tissues, but the mechanisms that regulate these responses are poorly understood. One hypothesis is that activation of the sympathetic nervous system (SNS) from sleep loss elevates blood pressure to promote vascular sheer stress leading to inflammation. As catecholamines produced from SNS activation can directly regulate inflammation, we pharmacologically altered blood pressure using an alternative approach-manipulation of the renin-angiotensin system (RAS). Male C57BL6/J mice were treated with angiotensin or captopril to elevate and reduce blood pressure, respectively and then exposed to 24-h of sleep fragmentation (SF) or allowed to sleep (control). Pro- and anti-inflammatory cytokine gene expression and as endothelial adhesion gene expression as well as serum glucocorticoids (corticosterone) were measured. RAS manipulation elevated cytokines and endothelial adhesion expression in heart and aorta while SF increased cytokine expression in peripheral tissues, but not brain. However, there were interactive effects of angiotensin-II and SF upon cytokine gene expression in hippocampus and hypothalamus, but not prefrontal cortex. SF, but not RAS manipulation, elevated serum corticosterone concentration. These findings highlight the contrasting effects of RAS manipulation and SF, implying that inflammation from SF is acting on different pathways that are largely independent of RAS manipulation

Antiproliferative effect of immunoliposomes containing 5-fluorodeoxyuridine-dipalmitate on colon cancer cells

We have investigated the antiproliferative action towards CC531 colon adenocarcinoma cells of target cell-specific immunoliposomes containing the amphiphilic dipalmitoyl derivative of 5-fluorodeoxyuridine (FUdR-dP). FUdR-dP incorporated in immunoliposomes caused a 13-fold stronger inhibition of CC531 cell growth in vitro, during a 72-h treatment, than FUdR-dP in liposomes without antibody, demonstrating that the prodrug is efficiently hydrolysed to yield the active drug, FUdR, intracellularly. The intracellular release of active FUdR was confirmed by determining the fate of H-3-labelled immunoliposomal FUdR-dP. Treatments shorter than 72 h with FUdR-dP in immunoliposomes resulted in anti-tumour activities comparable to, or even higher than, that of free FUdR. The shorter treatments reflect more closely the in vivo situation and illustrate the potential advantage of the use of immunoliposomes over non-targeted liposomal FUdR-dP or free FUdR. Association of tumour cell-specific immunoliposomes with CC531 cells was up to tenfold higher than that of liposomes without antibody or with irrelevant IgG coupled, demonstrating a specific interaction between liposomes and target cells which causes an efficient intracellular delivery of the drug. Since biochemical evidence indicates a lack of internalization or degradation of the liposomes as such; we postulate that entry of the drug most likely involves the direct transfer of the prodrug from the immunoliposome to the cell membrane during its antigen-specific interaction with the cells. followed by hydrolysis of FUdR-dP leading to relatively high intracellular FUdR-levels. In conclusion, we describe a targeted liposomal formulation for the anticancer drug FUdR, which is able to deliver the active drug to colon carcinoma cells with high efficiency, without the need for the cells to internalize the liposomes as such

Legionella Metaeffector Exploits Host Proteasome to Temporally Regulate Cognate Effector

Pathogen-associated secretion systems translocate numerous effector proteins into eukaryotic host cells to coordinate cellular processes important for infection. Spatiotemporal regulation is therefore important for modulating distinct activities of effectors at different stages of infection. Here we provide the first evidence of “metaeffector,” a designation for an effector protein that regulates the function of another effector within the host cell. Legionella LubX protein functions as an E3 ubiquitin ligase that hijacks the host proteasome to specifically target the bacterial effector protein SidH for degradation. Delayed delivery of LubX to the host cytoplasm leads to the shutdown of SidH within the host cells at later stages of infection. This demonstrates a sophisticated level of coevolution between eukaryotic cells and L. pneumophila involving an effector that functions as a key regulator to temporally coordinate the function of a cognate effector protein

Proteomic Analysis of Growth Phase-Dependent Expression of Legionella pneumophila Proteins Which Involves Regulation of Bacterial Virulence Traits

Legionella pneumophila, which is a causative pathogen of Legionnaires' disease, expresses its virulent traits in response to growth conditions. In particular, it is known to become virulent at a post-exponential phase in vitro culture. In this study, we performed a proteomic analysis of differences in expression between the exponential phase and post-exponential phase to identify candidates associated with L. pneumophila virulence using 2-Dimentional Fluorescence Difference Gel Electrophoresis (2D-DIGE) combined with Matrix-Assisted Laser Desorption/Ionization–Mass Spectrometry (MALDI-TOF-MS). Of 68 identified proteins that significantly differed in expression between the two growth phases, 64 were up-regulated at a post-exponential phase. The up-regulated proteins included enzymes related to glycolysis, ketone body biogenesis and poly-3-hydroxybutyrate (PHB) biogenesis, suggesting that L. pneumophila may utilize sugars and lipids as energy sources, when amino acids become scarce. Proteins related to motility (flagella components and twitching motility-associated proteins) were also up-regulated, predicting that they enhance infectivity of the bacteria in host cells under certain conditions. Furthermore, 9 up-regulated proteins of unknown function were found. Two of them were identified as novel bacterial factors associated with hemolysis of sheep red blood cells (SRBCs). Another 2 were found to be translocated into macrophages via the Icm/Dot type IV secretion apparatus as effector candidates in a reporter assay with Bordetella pertussis adenylate cyclase. The study will be helpful for virulent analysis of L. pneumophila from the viewpoint of physiological or metabolic modulation dependent on growth phase

Satellite-based terrestrial production efficiency modeling

Production efficiency models (PEMs) are based on the theory of light use efficiency (LUE) which states that a relatively constant relationship exists between photosynthetic carbon uptake and radiation receipt at the canopy level. Challenges remain however in the application of the PEM methodology to global net primary productivity (NPP) monitoring. The objectives of this review are as follows: 1) to describe the general functioning of six PEMs (CASA; GLO-PEM; TURC; C-Fix; MOD17; and BEAMS) identified in the literature; 2) to review each model to determine potential improvements to the general PEM methodology; 3) to review the related literature on satellite-based gross primary productivity (GPP) and NPP modeling for additional possibilities for improvement; and 4) based on this review, propose items for coordinated research

Same data, different analysts: Variation in effect sizes due to analytical decisions in ecology and evolutionary biology

Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small “many analyst” study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future