Endocytosis and lysosomal degradation of GluA2/3 AMPARs in response to oxygen/glucose deprivation in hippocampal but not cortical neurons

Abstract Global cerebral ischemia results in oxygen and glucose deprivation (OGD) and consequent delayed cell death of vulnerable neurons, with hippocampal CA1 neurons more vulnerable than cortical neurons. Most AMPA receptors (AMPARs) are heteromeric complexes of subunits GluA1/GluA2 or GluA2/GluA3, and the presence of GluA2 renders AMPARs Ca2+-impermeable. In hippocampal CA1 neurons, OGD causes the synaptic expression of GluA2-lacking Ca2+-permeable AMPARs, contributing to toxic Ca2+ influx. The loss of synaptic GluA2 is caused by rapid trafficking of GluA2-containing AMPARs from the cell surface, followed by a delayed reduction in GluA2 mRNA expression. We show here that OGD causes endocytosis, lysosomal targeting and consequent degradation of GluA2- and GluA3-containing AMPARs, and that PICK1 is required for both OGD-induced GluA2 endocytosis and lysosomal sorting. Our results further suggest that GluA1-containing AMPARs resist OGD-induced endocytosis. OGD does not cause GluA2 endocytosis in cortical neurons, and we show that PICK1 binding to the endocytic adaptor AP2 is enhanced by OGD in hippocampal, but not cortical neurons. We propose that endocytosis of GluA2/3, caused by a hippocampal-specific increase in PICK1-AP2 interactions, followed by PICK1-dependent lysosomal targeting, are critical events in determining changes in AMPAR subunit composition in the response to ischaemia

Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm

<div><p>Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (<i>n</i> = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.</p></div

Many different datasets can lead to the same bar graph.

<p>The full data may suggest different conclusions from the summary statistics. The means and SEs for the four example datasets shown in Panels B–E are all within 0.5 units of the means and SEs shown in the bar graph (Panel A). <i>p</i>-values were calculated in R (version 3.0.3) using an unpaired t-test, an unpaired t-test with Welch’s correction for unequal variances, or a Wilcoxon rank sum test. In Panel B, the distribution in both groups appears symmetric. Although the data suggest a small difference between groups, there is substantial overlap between groups. In Panel C, the apparent difference between groups is driven by an outlier. Panel D suggests a possible bimodal distribution. Additional data are needed to confirm that the distribution is bimodal and to determine whether this effect is explained by a covariate. In Panel E, the smaller range of values in group two may simply be due to the fact that there are only three observations. Additional data for group two would be needed to determine whether the groups are actually different.</p

Correction: From Static to Interactive: Transforming Data Visualization to Improve Transparency

<p>Correction: From Static to Interactive: Transforming Data Visualization to Improve Transparency</p

Many different datasets can lead to the same line graph.

<p>The line graph (mean ± standard error) provides no information about whether changes are consistent across individuals (Panel A). The scatterplots shown in the Panels B–D reveal very different patterns of change, even though the means and standard errors differ by less than 0.3 units. The lower scatterplots showing the differences between measurements allow readers to quickly assess the direction, magnitude, and distribution of the changes. The solid lines show the median difference. In Panel B, values for every subject are higher in the second condition. In Panel C, there are no consistent differences between the two conditions. Panel D suggests that there may be distinct subgroups of “responders” and “nonresponders.” Adapted from Weissgerber et al. [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002484#pbio.1002484.ref001" target="_blank">1</a>].</p

Bridging the gap between informatics and medicine upon medical school entry: Implementing a course on the Applicative Use of ICT

<div><p>Education is undergoing profound changes due to permanent technological innovations. This paper reports the results of a pilot study aimed at developing, implementing and evaluating the course, "Applicative Use of Information and Communication Technologies (ICT) in Medicine," upon medical school entry. The Faculty of Medicine, University of Belgrade, introduced a curriculum reform in 2014 that included the implementation of the course, “Applicative Use of ICT in Medicine” for first year medical students. The course was designed using a blended learning format to introduce the concepts of Web-based learning environments. Data regarding student knowledge, use and attitudes towards ICT were prospectively collected for the classes of 2015/16 and 2016/17. The teaching approach was supported by multimedia didactic materials using Moodle LMS. The overall quality of the course was also assessed. The five level Likert scale was used to measure attitudes related to ICT. In total, 1110 students were assessed upon medical school entry. A small number of students (19%) had previous experience with e-learning. Students were largely in agreement that informatics is needed in medical education, and that it is also useful for doctors (4.1±1.0 and 4.1±0.9, respectively). Ability in informatics and use of the Internet in education in the adjusted multivariate regression model were significantly associated with positive student attitudes toward ICT. More than 80% of students stated that they had learned to evaluate medical information and would use the Internet to search medical literature as an additional source for education. The majority of students (77%) agreed that a blended learning approach facilitates access to learning materials and enables time independent learning (72%). Implementing the blended learning course, "Applicative Use of ICT in Medicine," may bridge the gap between medicine and informatics upon medical school entry. Students displayed positive attitudes towards using ICT and gained adequate skills necessary to function effectively in an information-rich environment.</p></div

Self-reported ability in informatics and computers for two student classes upon medical school entry.

<p>Self-reported ability in informatics and computers for two student classes upon medical school entry.</p

Reinventing Biostatistics Education for Basic Scientists

<div><p>Numerous studies demonstrating that statistical errors are common in basic science publications have led to calls to improve statistical training for basic scientists. In this article, we sought to evaluate statistical requirements for PhD training and to identify opportunities for improving biostatistics education in the basic sciences. We provide recommendations for improving statistics training for basic biomedical scientists, including: 1. Encouraging departments to require statistics training, 2. Tailoring coursework to the students’ fields of research, and 3. Developing tools and strategies to promote education and dissemination of statistical knowledge. We also provide a list of statistical considerations that should be addressed in statistics education for basic scientists.</p></div

Evaluation of the course–both cohorts.

<p>Evaluation of the course–both cohorts.</p

Student demographic data and response rates per academic school year.

<p>Student demographic data and response rates per academic school year.</p