Attenuated Induction of the Unfolded Protein Response in Adult Human Primary Astrocytes in Response to Recurrent Low Glucose

This is the final version. Available on open access from Frontiers Media via the DOI in this record. The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.AIMS/HYPOTHESIS: Recurrent hypoglycaemia (RH) is a major side-effect of intensive insulin therapy for people with diabetes. Changes in hypoglycaemia sensing by the brain contribute to the development of impaired counterregulatory responses to and awareness of hypoglycaemia. Little is known about the intrinsic changes in human astrocytes in response to acute and recurrent low glucose (RLG) exposure. METHODS: Human primary astrocytes (HPA) were exposed to zero, one, three or four bouts of low glucose (0.1 mmol/l) for three hours per day for four days to mimic RH. On the fourth day, DNA and RNA were collected. Differential gene expression and ontology analyses were performed using DESeq2 and GOseq, respectively. DNA methylation was assessed using the Infinium MethylationEPIC BeadChip platform. RESULTS: 24 differentially expressed genes (DEGs) were detected (after correction for multiple comparisons). One bout of low glucose exposure had the largest effect on gene expression. Pathway analyses revealed that endoplasmic-reticulum (ER) stress-related genes such as HSPA5, XBP1, and MANF, involved in the unfolded protein response (UPR), were all significantly increased following low glucose (LG) exposure, which was diminished following RLG. There was little correlation between differentially methylated positions and changes in gene expression yet the number of bouts of LG exposure produced distinct methylation signatures. CONCLUSIONS/INTERPRETATION: These data suggest that exposure of human astrocytes to transient LG triggers activation of genes involved in the UPR linked to endoplasmic reticulum (ER) stress. Following RLG, the activation of UPR related genes was diminished, suggesting attenuated ER stress. This may be a consequence of a successful metabolic adaptation, as previously reported, that better preserves intracellular energy levels and a reduced necessity for the UPR.Diabetes UKJDRF postdoctoral fellowshipMedical Research Council (MRC)Wellcome TrustBiotechnology & Biological Sciences Research Council (BBSRC)Novo Nordisk UK Research FoundationMary Kinross Charitable TrustEuropean Foundation for the Study of Diabetes/Novo Nordisk Programm

The impact of formative testing on study behaviour and study performance of (bio)medical students: a smartphone application intervention study.

BACKGROUND: Formative testing can increase knowledge retention but students often underuse available opportunities. Applying modern technology to make the formative tests more attractive for students could enhance the implementation of formative testing as a learning tool. This study aimed to determine whether formative testing using an internet-based application ("app") can positively affect study behaviour as well as study performance of (bio)medical students. METHODS: A formative testing app "Physiomics, to the next level" was introduced during a 4-week course to a large cohort (n = 461) of Dutch first year (bio)medical students of the Radboud University. The app invited students to complete 7 formative tests throughout the course. Each module was available for 3-4 days to stimulate the students to distribute their study activities throughout the 4-week course. RESULTS: 72% of the students used the app during the course. Study time significantly increased in intensive users (p < 0.001), while no changes were observed in moderate (p = 0.07) and non-users (p = 0.25). App-users obtained significantly higher grades during the final exam of the course (p < 0.05). Non-users more frequently failed their final exam (34%, OR 3.6, 95% CI: 2.0-6.4) compared to moderate users (19%) and intensive users (12%). Students with an average grade <6.5 during previous courses benefitted most from the app, as intensive (5.8 ± 0.9 / 36%) and moderate users (5.8 ± 0.9 / 33%) obtained higher grades and failed their exam less frequently compared to non-users (5.2 ± 1.1 / 61%). The app was also well appreciated by students; students scored the app with a grade of 7.3 ± 1.0 out of 10 and 59% of the students indicated that they would like the app to be implemented in future courses. CONCLUSIONS: A smartphone-based application of formative testing is an effective and attractive intervention to stimulate study behaviour and improve study performance in (bio) medical students

Novel epigenetic clock for fetal brain development predicts prenatal age for cellular stem cell models and derived neurons

Induced pluripotent stem cells (iPSCs) and their differentiated neurons (iPSC-neurons) are a widely used cellular model in the research of the central nervous system. However, it is unknown how well they capture age-associated processes, particularly given that pluripotent cells are only present during the earliest stages of mammalian development. Epigenetic clocks utilize coordinated age-associated changes in DNA methylation to make predictions that correlate strongly with chronological age. It has been shown that the induction of pluripotency rejuvenates predicted epigenetic age. As existing clocks are not optimized for the study of brain development, we developed the fetal brain clock (FBC), a bespoke epigenetic clock trained in human prenatal brain samples in order to investigate more precisely the epigenetic age of iPSCs and iPSC-neurons. The FBC was tested in two independent validation cohorts across a total of 194 samples, confirming that the FBC outperforms other established epigenetic clocks in fetal brain cohorts. We applied the FBC to DNA methylation data from iPSCs and embryonic stem cells and their derived neuronal precursor cells and neurons, finding that these cell types are epigenetically characterized as having an early fetal age. Furthermore, while differentiation from iPSCs to neurons significantly increases epigenetic age, iPSC-neurons are still predicted as being fetal. Together our findings reiterate the need to better understand the limitations of existing epigenetic clocks for answering biological research questions and highlight a limitation of iPSC-neurons as a cellular model of age-related diseases

Estimation of progression of multi-state chronic disease using the Markov model and prevalence pool concept

<p>Abstract</p> <p>Background</p> <p>We propose a simple new method for estimating progression of a chronic disease with multi-state properties by unifying the prevalence pool concept with the Markov process model.</p> <p>Methods</p> <p>Estimation of progression rates in the multi-state model is performed using the E-M algorithm. This approach is applied to data on Type 2 diabetes screening.</p> <p>Results</p> <p>Good convergence of estimations is demonstrated. In contrast to previous Markov models, the major advantage of our proposed method is that integrating the prevalence pool equation (that the numbers entering the prevalence pool is equal to the number leaving it) into the likelihood function not only simplifies the likelihood function but makes estimation of parameters stable.</p> <p>Conclusion</p> <p>This approach may be useful in quantifying the progression of a variety of chronic diseases.</p

A Bayesian approach to quantifying the effects of mass poultry vaccination upon the spatial and temporal dynamics of H5N1 in Northern Vietnam.

Published versio

Generalized linear model for interval mapping of quantitative trait loci

We developed a generalized linear model of QTL mapping for discrete traits in line crossing experiments. Parameter estimation was achieved using two different algorithms, a mixture model-based EM (expectation–maximization) algorithm and a GEE (generalized estimating equation) algorithm under a heterogeneous residual variance model. The methods were developed using ordinal data, binary data, binomial data and Poisson data as examples. Applications of the methods to simulated as well as real data are presented. The two different algorithms were compared in the data analyses. In most situations, the two algorithms were indistinguishable, but when large QTL are located in large marker intervals, the mixture model-based EM algorithm can fail to converge to the correct solutions. Both algorithms were coded in C++ and interfaced with SAS as a user-defined SAS procedure called PROC QTL

Body fat measurement by bioelectrical impedance and air displacement plethysmography: a cross-validation study to design bioelectrical impedance equations in Mexican adults

<p>Abstract</p> <p>Background</p> <p>The study of body composition in specific populations by techniques such as bio-impedance analysis (BIA) requires validation based on standard reference methods. The aim of this study was to develop and cross-validate a predictive equation for bioelectrical impedance using air displacement plethysmography (ADP) as standard method to measure body composition in Mexican adult men and women.</p> <p>Methods</p> <p>This study included 155 male and female subjects from northern Mexico, 20–50 years of age, from low, middle, and upper income levels. Body composition was measured by ADP. Body weight (BW, kg) and height (Ht, cm) were obtained by standard anthropometric techniques. Resistance, R (ohms) and reactance, Xc (ohms) were also measured. A random-split method was used to obtain two samples: one was used to derive the equation by the "all possible regressions" procedure and was cross-validated in the other sample to test predicted versus measured values of fat-free mass (FFM).</p> <p>Results and Discussion</p> <p>The final model was: FFM (kg) = 0.7374 * (Ht²/R) + 0.1763 * (BW) - 0.1773 * (Age) + 0.1198 * (Xc) - 2.4658. R²was 0.97; the square root of the mean square error (SRMSE) was 1.99 kg, and the pure error (PE) was 2.96. There was no difference between FFM predicted by the new equation (48.57 ± 10.9 kg) and that measured by ADP (48.43 ± 11.3 kg). The new equation did not differ from the line of identity, had a high R²and a low SRMSE, and showed no significant bias (0.87 ± 2.84 kg).</p> <p>Conclusion</p> <p>The new bioelectrical impedance equation based on the two-compartment model (2C) was accurate, precise, and free of bias. This equation can be used to assess body composition and nutritional status in populations similar in anthropometric and physical characteristics to this sample.</p

Adaptation of mammalian host-pathogen interactions in a changing arctic environment

Many arctic mammals are adapted to live year-round in extreme environments with low winter temperatures and great seasonal variations in key variables (e.g. sunlight, food, temperature, moisture). The interaction between hosts and pathogens in high northern latitudes is not very well understood with respect to intra-annual cycles (seasons). The annual cycles of interacting pathogen and host biology is regulated in part by highly synchronized temperature and photoperiod changes during seasonal transitions (e.g., freezeup and breakup). With a warming climate, only one of these key biological cues will undergo drastic changes, while the other will remain fixed. This uncoupling can theoretically have drastic consequences on host-pathogen interactions. These poorly understood cues together with a changing climate by itself will challenge host populations that are adapted to pathogens under the historic and current climate regime. We will review adaptations of both host and pathogens to the extreme conditions at high latitudes and explore some potential consequences of rapid changes in the Arctic

The contribution of genetic variants to disease depends on the ruler

Our understanding of the genetic basis of disease has evolved from descriptions of overall heritability or familiality to the identification of large numbers of risk loci. One can quantify the impact of such loci on disease using a plethora of measures, which can guide future research decisions. However, different measures can attribute varying degrees of importance to a variant. In this Analysis, we consider and contrast the most commonly used measures-specifically, the heritability of disease liability, approximate heritability, sibling recurrence risk, overall genetic variance using a logarithmic relative risk scale, the area under the receiver-operating curve for risk prediction and the population attributable fraction-and give guidelines for their use that should be explicitly considered when assessing the contribution of genetic variants to disease