Discovering study-specific gene regulatory networks

This article has been made available through the Brunel Open Access Publishing Fund.Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets

Protein Diffusion in Mammalian Cell Cytoplasm

We introduce a new method for mesoscopic modeling of protein diffusion in an entire cell. This method is based on the construction of a three-dimensional digital model cell from confocal microscopy data. The model cell is segmented into the cytoplasm, nucleus, plasma membrane, and nuclear envelope, in which environment protein motion is modeled by fully numerical mesoscopic methods. Finer cellular structures that cannot be resolved with the imaging technique, which significantly affect protein motion, are accounted for in this method by assigning an effective, position-dependent porosity to the cell. This porosity can also be determined by confocal microscopy using the equilibrium distribution of a non-binding fluorescent protein. Distinction can now be made within this method between diffusion in the liquid phase of the cell (cytosol/nucleosol) and the cytoplasm/nucleoplasm. Here we applied the method to analyze fluorescence recovery after photobleach (FRAP) experiments in which the diffusion coefficient of a freely-diffusing model protein was determined for two different cell lines, and to explain the clear difference typically observed between conventional FRAP results and those of fluorescence correlation spectroscopy (FCS). A large difference was found in the FRAP experiments between diffusion in the cytoplasm/nucleoplasm and in the cytosol/nucleosol, for all of which the diffusion coefficients were determined. The cytosol results were found to be in very good agreement with those by FCS

Associations of physical activity, sedentary time, and diet quality with biomarkers of inflammation in children

We investigated the associations of physical activity (PA), sedentary time (ST), and diet quality with biomarkers of inflammation in 390 children (192 girls, 198 boys) aged 6–8 years. PA energy expenditure (PAEE), light PA, moderate PA (MPA), vigorous PA (VPA), moderate-to-vigorous PA (MVPA), and ST were assessed by combined movement and heart rate sensor. Finnish Children Healthy Eating Index was calculated using data from 4 d food records. Body fat percentage (BF%) was measured by dual-energy X-ray absorptiometry. High-sensitivity C-reactive protein (Hs-CRP), leptin, interleukin-6 (IL-6), adiponectin, tumour necrosis factor-α, and glycoprotein acetyls were measured from fasting blood samples. PAEE, MPA, VPA, and MVPA were inversely associated with hs-CRP (β=−191 to −139, 95% CI=−0.294 to −0.024), leptin (β=−0.409 to −0.301, 95% CI=−0.499 to −0.107), IL-6 (β=−0.136 to −0.104, 95% CI=−0.240 to −0.001) and PAEE, MPA, and MVPA were inversely associated with glycoprotein acetyls (β=−0.117 to −0.103, 95% CI=−0.213 to −0.001). ST was directly associated with hs-CRP (β=0.170, 95% CI=0.070–0.269), leptin (β=0.355, 95% CI=0.265–0.445), and IL-6 (β=0.105, 95% CI=0.005–0.205). VPA was inversely associated with hs-CRP, leptin, and IL-6 in children with higher BF% (β=−0.344 to −0.181, 95% CI=−0.477 to −0.033) but not among children with lower BF% (β=−0.007–0.033, 95% CI=−0.183–0.184). In conclusion, PA was inversely and ST directly associated with circulating levels of biomarkers of inflammation among children. Furthermore, we observed that PA was inversely associated with these biomarkers for inflammation in children with a higher BF%. Highlights Systemic inflammation, as indicated by increased circulating concentrations of biomarkers for inflammation, may be important in causal pathways leading to insulin resistance, sub-clinical atherosclerosis, and eventually clinical manifestations of cardiovascular diseases. Higher levels of physical activity and lower levels of sedentary time were associated with more favourable inflammatory profile. Body fat percentage modified these associations and especially vigorous intensity physical activity was inversely associated with biomarkers of inflammation on children with higher body fat percentage but not in children with lower body fat percentage.