Retinal degeneration depends on Bmi1 function and reactivation of cell cycle proteins.

The epigenetic regulator Bmi1 controls proliferation in many organs. Reexpression of cell cycle proteins such as cyclin-dependent kinases (CDKs) is a hallmark of neuronal apoptosis in neurodegenerative diseases. Here we address the potential role of Bmi1 as a key regulator of cell cycle proteins during neuronal apoptosis. We show that several cell cycle proteins are expressed in different models of retinal degeneration and required in the Rd1 photoreceptor death process. Deleting E2f1, a downstream target of CDKs, provided temporary protection in Rd1 mice. Most importantly, genetic ablation of Bmi1 provided extensive photoreceptor survival and improvement of retinal function in Rd1 mice, mediated by a decrease in cell cycle markers and regulators independent of p16(Ink4a) and p19(Arf). These data reveal that Bmi1 controls the cell cycle-related death process, highlighting this pathway as a promising therapeutic target for neuroprotection in retinal dystrophies.

Epigenetic regulation of the bacterial cell cycle.

N(6)-methyl-adenines can serve as epigenetic signals for interactions between regulatory DNA sequences and regulatory proteins that control cellular functions, such as the initiation of chromosome replication or the expression of specific genes. Several of these genes encode master regulators of the bacterial cell cycle. DNA adenine methylation is mediated by Dam in gamma-proteobacteria and by CcrM in alpha-proteobacteria. A major difference between them is that CcrM is cell cycle regulated, while Dam is active throughout the cell cycle. In alpha-proteobacteria, GANTC sites can remain hemi-methylated for a significant period of the cell cycle, depending on their location on the chromosome. In gamma-proteobacteria, most GATC sites are only transiently hemi-methylated, except regulatory GATC sites that are protected from Dam methylation by specific DNA-binding proteins.

Hematopoietic stem cells reversibly switch from dormancy to self-renewal during homeostasis and repair.

Bone marrow hematopoietic stem cells (HSCs) are crucial to maintain lifelong production of all blood cells. Although HSCs divide infrequently, it is thought that the entire HSC pool turns over every few weeks, suggesting that HSCs regularly enter and exit cell cycle. Here, we combine flow cytometry with label-retaining assays (BrdU and histone H2B-GFP) to identify a population of dormant mouse HSCs (d-HSCs) within the lin(-)Sca1+cKit+CD150+CD48(-)CD34(-) population. Computational modeling suggests that d-HSCs divide about every 145 days, or five times per lifetime. d-HSCs harbor the vast majority of multilineage long-term self-renewal activity. While they form a silent reservoir of the most potent HSCs during homeostasis, they are efficiently activated to self-renew in response to bone marrow injury or G-CSF stimulation. After re-establishment of homeostasis, activated HSCs return to dormancy, suggesting that HSCs are not stochastically entering the cell cycle but reversibly switch from dormancy to self-renewal under conditions of hematopoietic stress.

Reflections on Partial Least Squares Path Modeling

The purpose of the present article is to take stock of a recent exchange in Organizational Research Methods between critics (Rönkkö & Evermann, 2013) and proponents (Henseler et al., 2014) of partial least squares path modeling (PLS-PM). The two target articles were centered around six principal issues, namely whether PLS-PM: (1) can be truly characterized as a technique for structural equation modeling (SEM); (2) is able to correct for measurement error; (3) can be used to validate measurement models; (4) accommodates small sample sizes; (5) is able to provide null hypothesis tests for path coefficients; and (6) can be employed in an exploratory, model-building fashion. We summarize and elaborate further on the key arguments underlying the exchange, drawing from the broader methodological and statistical literature in order to offer additional thoughts concerning the utility of PLS-PM and ways in which the technique might be improved. We conclude with recommendations as to whether and how PLS-PM serves as a viable contender to SEM approaches for estimating and evaluating theoretical models.

Self-potentials in partially saturated media: the importance of explicit modeling of electrode effects

Self-potential (SP) data are of interest to vadose zone hydrology because of their direct sensitivity to water flow and ionic transport. There is unfortunately little consensus in the literature about how to best model SP data under partially saturated conditions, and different approaches (often supported by one laboratory data set alone) have been proposed. We argue that this lack of agreement can largely be traced to electrode effects that have not been properly taken into account. A series of drainage and imbibition experiments were considered in which we found that previously proposed approaches to remove electrode effects were unlikely to provide adequate corrections. Instead, we explicitly modeled the electrode effects together with classical SP contributions using a flow and transport model. The simulated data agreed overall with the observed SP signals and allowed decomposing the different signal contributions to analyze them separately. After reviewing other published experimental data, we suggest that most of them include electrode effects that have not been properly taken into account. Our results suggest that previously presented SP theory works well when considering the modeling uncertainties presently associated with electrode effects. Additional work is warranted to not only develop suitable electrodes for laboratory experiments but also to assure that associated electrode effects that appear inevitable in longer term experiments are predictable, so that they can be incorporated into the modeling framework.

In vivo 13C NMR spectroscopy and metabolic modeling in the brain: a practical perspective.

In vivo 13C NMR spectroscopy has the unique capability to measure metabolic fluxes noninvasively in the brain. Quantitative measurements of metabolic fluxes require analysis of the 13C labeling time courses obtained experimentally with a metabolic model. The present work reviews the ingredients necessary for a dynamic metabolic modeling study, with particular emphasis on practical issues.

Fluorodeoxyuridine mediated cell cycle synchronization in S-phase increases the Auger radiation cell killing with 125I-iododeoxyuridine.

Aim: 125I-iododeoxyuridine is a potential Auger radiation therapy agent. Its incorporation in DNA of proliferating cells is enhanced by fluorodeoxyuridine. Here, we evaluated therapeutic activities of 125I-iododeoxyuridine in an optimized fluorodeoxyuridine pre-treatment inducing S-phase synchronization. Methods: After S-phase synchronization by fluorodeoxyuridine, cells were treated with 125I-iododeoxyuridine. Apoptosis analysis and S-phase synchronization were studied by flow cytometry. Cell survival was determined by colony-forming assay. Based on measured growth parameters, the number of decays per cell that induced killing was extrapolated. Results: Treatment experiments showed that 72 to 91% of synchronized cells were killed after 0.8 and 8 kBq/ml 125I-iododeoxyuridine incubation, respectively. In controls, only 8 to 38% of cells were killed by corresponding 125I-iododeoxyuridine activities alone and even increasing the activity to 80 kBq/ml gave only 42 % killing. Duplicated treatment cycles or repeated fluorodeoxyuridine pre-treatment allowed enhancing cell killing to >95 % at 8 kBq/ml 125I-iododeoxyuridine. About 50 and 160 decays per S-phase cells in controls and S-phase synchronization, respectively, were responsible for the observed cell killing at 0.8 kBq/ml radio-iododeoxyuridine. Conclusion: These data show the successful application of fluorodeoxyuridine that provided increased 125I-iododeoxyuridine Auger radiation cell killing efficacy through S-phase synchronization and high DNA incorporation of radio-iododeoxyuridine.

Biological exposure indicators: quantification of biological variability using toxicokinetic modeling

Compartmental and physiologically based toxicokinetic modeling coupled with Monte Carlo simulation were used to quantify the impact of biological variability (physiological, biochemical, and anatomic parameters) on the values of a series of bio-indicators of metal and organic industrial chemical exposures. A variability extent index and the main parameters affecting biological indicators were identified. Results show a large diversity in interindividual variability for the different categories of biological indicators examined. Measurement of the unchanged substance in blood, alveolar air, or urine is much less variable than the measurement of metabolites, both in blood and urine. In most cases, the alveolar flow and cardiac output were identified as the prime parameters determining biological variability, thus suggesting the importance of workload intensity on absorbed dose for inhaled chemicals.

Passive immunization with neutralizing antibodies interrupts the mouse mammary tumor virus life cycle.

Mouse mammary tumor virus (MMTV) infects the host via mucosal surfaces and exploits the host immune system for systemic spread and chronic infection. We have tested a neutralizing rat monoclonal antibody specific for the retroviral envelope glycoprotein gp52 for its efficiency in preventing acute and chronic mucosal and systemic infection. The antibody completely inhibits the superantigen response and chronic viral infection following systemic or nasal infection. Surprisingly however, the antibody only partially inhibits the early infection of antigen-presenting cells in the draining lymph node. Despite this initially inefficient protection from infection, superantigen-specific B- and T-cell responses and systemic viral spread are abolished, leading to complete clearance of the retroviral infection and hence interruption of the viral life cycle. In conclusion, systemic neutralizing monoclonal antibodies can provide an efficient protection against chronic retroviral amplification and persistence.

Distribution of metals exposure and associations with cardiometabolic risk factors in the "Modeling the Epidemiologic Transition Study".

BACKGROUND: Metals are known endocrine disruptors and have been linked to cardiometabolic diseases via multiple potential mechanisms, yet few human studies have both the exposure variability and biologically-relevant phenotype data available. We sought to examine the distribution of metals exposure and potential associations with cardiometabolic risk factors in the "Modeling the Epidemiologic Transition Study" (METS), a prospective cohort study designed to assess energy balance and change in body weight, diabetes and cardiovascular disease risk in five countries at different stages of social and economic development. METHODS: Young adults (25-45 years) of African descent were enrolled (N = 500 from each site) in: Ghana, South Africa, Seychelles, Jamaica and the U.S.A. We randomly selected 150 blood samples (N = 30 from each site) to determine concentrations of selected metals (arsenic, cadmium, lead, mercury) in a subset of participants at baseline and to examine associations with cardiometabolic risk factors. RESULTS: Median (interquartile range) metal concentrations (μg/L) were: arsenic 8.5 (7.7); cadmium 0.01 (0.8); lead 16.6 (16.1); and mercury 1.5 (5.0). There were significant differences in metals concentrations by: site location, paid employment status, education, marital status, smoking, alcohol use, and fish intake. After adjusting for these covariates plus age and sex, arsenic (OR 4.1, 95% C.I. 1.2, 14.6) and lead (OR 4.0, 95% C.I. 1.6, 9.6) above the median values were significantly associated with elevated fasting glucose. These associations increased when models were further adjusted for percent body fat: arsenic (OR 5.6, 95% C.I. 1.5, 21.2) and lead (OR 5.0, 95% C.I. 2.0, 12.7). Cadmium and mercury were also related with increased odds of elevated fasting glucose, but the associations were not statistically significant. Arsenic was significantly associated with increased odds of low HDL cholesterol both with (OR 8.0, 95% C.I. 1.8, 35.0) and without (OR 5.9, 95% C.I. 1.5, 23.1) adjustment for percent body fat. CONCLUSIONS: While not consistent for all cardiometabolic disease markers, these results are suggestive of potentially important associations between metals exposure and cardiometabolic risk. Future studies will examine these associations in the larger cohort over time.

The estimation of PMP and PMF on alpine basins in Switzerland

This paper presents a very fine grid hydrological model based on the spatiotemporal repartition of precipitation and on the topography. The goal is to estimate the flood on a catchment area, using a Probable Maximum Precipitation (PMP) leading to a Probable Maximum Flood (PMF). The spatiotemporal distribution of the precipitation was realized using six clouds modeled by the advection-diffusion equation. The equation shows the movement of the clouds over the terrain and also gives the evolution of the rain intensity in time. This hydrological modeling is followed by a hydraulic modeling of the surface and subterranean flows, done considering the factors that contribute to the hydrological cycle, such as the infiltration, the exfiltration and the snowmelt. This model was applied to several Swiss basins using measured rain, with results showing a good correlation between the simulated and observed flows. This good correlation proves that the model is valid and gives us the confidence that the results can be extrapolated to phenomena of extreme rainfall of PMP type. In this article we present some results obtained using a PMP rainfall and the developed model.

Copeptin is not associated with menstrual cycle hormone.

Background: Copeptin (CP), a derivate from the antidiuretic hormone (ADH) precursor pre-pro-vasopressin, stochiometrically mirrors ADH secretion. CP is increasingly evaluated as a diagnostic and prognostic biomarker in different diseases. It is therefore important to recognize possible confounding factors when interpreting CP levels. In healthy regularly menstruating women, there is a small but measurable physiological variability of hormones involved in fluid regulation. ADH plasma levels have been found to be lowest at menstruation, increasing during the follicular phase with a peak at ovulation and a drop in the luteal phase. We investigated the variability of CP during the menstrual cycle (MC) and its correlation to MC hormones. Methods: In total, 15 healthy women with regular MC (from 26 to 33 days) were included in this study. Ovulation was confirmed by progesterone (prog) levels on day 21 of the MC before entering the study and during the study. Blood collection was performed on days 3, 5, 8-16, 18, 21, 24 and 27 of their MC. Serums were assayed for prog, estradiol (E2), LH, and CP. Mixed linear regression analysis for repeated measures was performed to study the changes of CP, prog, E2 and LH during the MC, and to test the correlation of CP with sex hormones during the MC. Results: Mean MC length in all subjects was 28.5±2.2 d. E2, prog, and LH exhibited characteristic changes during the MC (all P< 0.05). All cycles were ovulatory (peak prog 54±15 nmol/l). CP levels did not change significantly throughout the MC, and were not associated with changes in prog, E2 or LH-levels (all P=ns). Conclusion: CP levels remain stable during the MC and are not influenced by changes in sex hormones. This implicates that it is not necessary to consider MC phases when using CP as a biomarker in premenopausal women.

Qualitative modeling identifies IL-11 as a novel regulator in maintaining self-renewal in human pluripotent stem cells.

Pluripotency in human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) is regulated by three transcription factors-OCT3/4, SOX2, and NANOG. To fully exploit the therapeutic potential of these cells it is essential to have a good mechanistic understanding of the maintenance of self-renewal and pluripotency. In this study, we demonstrate a powerful systems biology approach in which we first expand literature-based network encompassing the core regulators of pluripotency by assessing the behavior of genes targeted by perturbation experiments. We focused our attention on highly regulated genes encoding cell surface and secreted proteins as these can be more easily manipulated by the use of inhibitors or recombinant proteins. Qualitative modeling based on combining boolean networks and in silico perturbation experiments were employed to identify novel pluripotency-regulating genes. We validated Interleukin-11 (IL-11) and demonstrate that this cytokine is a novel pluripotency-associated factor capable of supporting self-renewal in the absence of exogenously added bFGF in culture. To date, the various protocols for hESCs maintenance require supplementation with bFGF to activate the Activin/Nodal branch of the TGFβ signaling pathway. Additional evidence supporting our findings is that IL-11 belongs to the same protein family as LIF, which is known to be necessary for maintaining pluripotency in mouse but not in human ESCs. These cytokines operate through the same gp130 receptor which interacts with Janus kinases. Our finding might explain why mESCs are in a more naïve cell state compared to hESCs and how to convert primed hESCs back to the naïve state. Taken together, our integrative modeling approach has identified novel genes as putative candidates to be incorporated into the expansion of the current gene regulatory network responsible for inducing and maintaining pluripotency.

Hierarchical modeling gave plausible estimates of associations between metabolic syndrome and components of antiretroviral therapy.

OBJECTIVE: Hierarchical modeling has been proposed as a solution to the multiple exposure problem. We estimate associations between metabolic syndrome and different components of antiretroviral therapy using both conventional and hierarchical models. STUDY DESIGN AND SETTING: We use discrete time survival analysis to estimate the association between metabolic syndrome and cumulative exposure to 16 antiretrovirals from four drug classes. We fit a hierarchical model where the drug class provides a prior model of the association between metabolic syndrome and exposure to each antiretroviral. RESULTS: One thousand two hundred and eighteen patients were followed for a median of 27 months, with 242 cases of metabolic syndrome (20%) at a rate of 7.5 cases per 100 patient years. Metabolic syndrome was more likely to develop in patients exposed to stavudine, but was less likely to develop in those exposed to atazanavir. The estimate for exposure to atazanavir increased from hazard ratio of 0.06 per 6 months' use in the conventional model to 0.37 in the hierarchical model (or from 0.57 to 0.81 when using spline-based covariate adjustment). CONCLUSION: These results are consistent with trials that show the disadvantage of stavudine and advantage of atazanavir relative to other drugs in their respective classes. The hierarchical model gave more plausible results than the equivalent conventional model.