Description: Prostaglandins (PGs) are ubiquitous membrane-derived, lipid-signaling molecules with wide ranging effects throughout the body. In the brain, PGE 2 is the key regulator of fever after inflammation but is also implicated in neural development and synaptic plasticity. The steroid hormone estradiol is also a key regulator of neural development and synaptic plasticity. Recently, we showed that administering cyclooxygenase (COX) inhibitors to block PGE 2 production increased the total length of Purkinje cell dendrites, the number of dendritic spines, and the level of spinophilin protein, which is enriched in dendritic spines. Correspondingly, PGE 2 administration into the cerebellum decreased spinophilin protein content. We now report that PGE 2 stimulates estradiol synthesis in the immature rat cerebellum via enhanced activity of the aromatase enzyme. Treatment with cyclooxygenase inhibitors reduced cerebellar aromatase activity and estradiol content whereas PGE 2 administration increased both. Treatment with either PGE 2 or estradiol stunted Purkinje neuron dendritic length and complexity and produced a corresponding reduction in spinophilin content. Treatment with formestane to inhibit aromatase activity led to excessive sprouting of the dendritic tree, whereas elevated estradiol had the opposite effect. Electrophysiological measurements from Purkinje neurons revealed novel sex differences in input resistance and membrane capacitance that were abolished by estradiol exposure, whereas a sex difference in the amplitude of the afterhyperpolarization after an action potential was not. Correlated changes in action potential threshold suggest that prolonged alterations in neuronal firing activity could be a consequence of increased estradiol content during the second week of life. These findings reveal a previously unappreciated role for PG-stimulated steroidogenesis in the developing brain and a new potential route for inflammation-mediated disruption of neuronal maturation.

Description: Motivation Single cell transcriptional profiling opens up a new avenue in studying the functional role of cell-to-cell variability in physiological processes. The analysis of single cell expression profiles creates new challenges due to the distributive nature of the data and the stochastic dynamics of gene transcription process. The reconstruction of gene regulatory networks (GRNs) using single cell transcriptional profiles is particularly challenging, especially when directed gene-gene relationships are desired. Results We developed SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS) for the inference of GRNs from single cell transcriptional profiles. We focused on time-stamped cross-sectional expression data, commonly generated from transcriptional profiling of single cells collected at multiple time points after cell stimulation. SINCERITIES recovers directed regulatory relationships among genes by employing regularized linear regression (ridge regression), using temporal changes in the distributions of gene expressions. Meanwhile, the modes of the gene regulations (activation and repression) come from partial correlation analyses between pairs of genes. We demonstrated the efficacy of SINCERITIES in inferring GRNs using in silico time-stamped single cell expression data and single cell transcriptional profiles of THP-1 monocytic human leukemia cells. The case studies showed that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3. Moreover, SINCERITIES has a low computational complexity and is amenable to problems of extremely large dimensionality. Finally, an application of SINCERITIES to single cell expression data of T2EC chicken erythrocytes pointed to BATF as a candidate novel regulator of erythroid development. Availability and implementation MATLAB and R version of SINCERITIES are freely available from the following websites: http://www.cabsel.ethz.ch/tools/sincerities.html and https://github.com/CABSEL/SINCERITIES . The single cell THP-1 and T2EC transcriptional profiles are available from the original publications ( Kouno et al. , 2013 ; Richard et al. , 2016 ). The in silico single cell data are available on SINCERITIES websites. Contact rudi.gunawan@chem.ethz.ch Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.

Description: Background: Images depicting morbidly obese models [i.e. body mass index (BMI) 〉40] often accompany media articles about the health risks of being overweight (i.e. BMI 25–30). Little is known about the effect of this mismatch on people’s understanding of risk, and perceptions of message relevance. Methods: In total, 563 participants (291 overweight/obese and 272 healthy weight) were randomly allocated to read a health message about the risk of heart disease posed by being overweight, presented alongside (i) a photo of an overweight model, (ii) a morbidly obese model or (iii) no photo. Between-group differences in the primary outcomes of message relevance, and the body size perceived to be ‘at risk’, were assessed, and the potential moderating effects of motivation, weight concern and existing risk knowledge explored. Results: Overweight and obese participants in the exaggerated (morbidly obese) image condition interpreted health risks to relate to a larger body size than those who saw no image ( F (2, 290) = 4.06, P = 0.02). There was no experimental effect on perceived personal relevance ( F (2, 290) = 0.25, P = 0.38). No significant moderation effects were detected, and there was no effect of study condition in healthy weight participants for either outcome. Conclusion: The findings suggest that the use of morbidly obese models in messages regarding the health risks of being overweight may undermine the impact of these messages among those who they most aim to reach; the reader may perceive a reduced risk of being ‘only’ overweight, and that a higher weight is needed for the negative effects of excess weight to occur.

Description: Background: Images depicting morbidly obese models [i.e. body mass index (BMI) 〉40] often accompany media articles about the health risks of being overweight (i.e. BMI 25–30). Little is known about the effect of this mismatch on people’s understanding of risk, and perceptions of message relevance. Methods: In total, 563 participants (291 overweight/obese and 272 healthy weight) were randomly allocated to read a health message about the risk of heart disease posed by being overweight, presented alongside (i) a photo of an overweight model, (ii) a morbidly obese model or (iii) no photo. Between-group differences in the primary outcomes of message relevance, and the body size perceived to be ‘at risk’, were assessed, and the potential moderating effects of motivation, weight concern and existing risk knowledge explored. Results: Overweight and obese participants in the exaggerated (morbidly obese) image condition interpreted health risks to relate to a larger body size than those who saw no image ( F (2, 290) = 4.06, P = 0.02). There was no experimental effect on perceived personal relevance ( F (2, 290) = 0.25, P = 0.38). No significant moderation effects were detected, and there was no effect of study condition in healthy weight participants for either outcome. Conclusion: The findings suggest that the use of morbidly obese models in messages regarding the health risks of being overweight may undermine the impact of these messages among those who they most aim to reach; the reader may perceive a reduced risk of being ‘only’ overweight, and that a higher weight is needed for the negative effects of excess weight to occur.

Description: Motivation: We addressed the problem of inferring gene regulatory network (GRN) from gene expression data of knockout (KO) experiments. This inference is known to be underdetermined and the GRN is not identifiable from data. Past studies have shown that suboptimal design of experiments (DOE) contributes significantly to the identifiability issue of biological networks, including GRNs. However, optimizing DOE has received much less attention than developing methods for GRN inference. Results: We developed REDuction of UnCertain Edges (REDUCE) algorithm for finding the optimal gene KO experiment for inferring directed graphs (digraphs) of GRNs. REDUCE employed ensemble inference to define uncertain gene interactions that could not be verified by prior data. The optimal experiment corresponds to the maximum number of uncertain interactions that could be verified by the resulting data. For this purpose, we introduced the concept of edge separatoid which gave a list of nodes (genes) that upon their removal would allow the verification of a particular gene interaction. Finally, we proposed a procedure that iterates over performing KO experiments, ensemble update and optimal DOE. The case studies including the inference of Escherichia coli GRN and DREAM 4 100-gene GRNs, demonstrated the efficacy of the iterative GRN inference. In comparison to systematic KOs, REDUCE could provide much higher information return per gene KO experiment and consequently more accurate GRN estimates. Conclusions: REDUCE represents an enabling tool for tackling the underdetermined GRN inference. Along with advances in gene deletion and automation technology, the iterative procedure brings an efficient and fully automated GRN inference closer to reality. Availability and implementation: MATLAB and Python scripts of REDUCE are available on www.cabsel.ethz.ch/tools/REDUCE . Contact: rudi.gunawan@chem.ethz.ch Supplementary information: Supplementary data are available at Bioinformatics online.