Cellular localization and associations of the major lipolytic proteins in human skeletal muscle at rest and during exercise

Lipolysis involves the sequential breakdown of fatty acids from triacylglycerol and is increased during energy stress such as exercise. Adipose triglyceride lipase (ATGL) is a key regulator of skeletal muscle lipolysis and perilipin (PLIN) 5 is postulated to be an important regulator of ATGL action of muscle lipolysis. Hence, we hypothesized that non-genomic regulation such as cellular localization and the interaction of these key proteins modulate muscle lipolysis during exercise. PLIN5, ATGL and CGI-58 were highly (>60%) colocated with Oil Red O (ORO) stained lipid droplets. PLIN5 was significantly colocated with ATGL, mitochondria and CGI-58, indicating a close association between the key lipolytic effectors in resting skeletal muscle. The colocation of the lipolytic proteins, their independent association with ORO and the PLIN5/ORO colocation were not altered after 60 min of moderate intensity exercise. Further experiments in cultured human myocytes showed that PLIN5 colocation with ORO or mitochondria is unaffected by pharmacological activation of lipolytic pathways. Together, these data suggest that the major lipolytic proteins are highly expressed at the lipid droplet and colocate in resting skeletal muscle, that their localization and interactions appear to remain unchanged during prolonged exercise, and, accordingly, that other post-translational mechanisms are likely regulators of skeletal muscle lipolysis

The X-ray spectrum of the Seyfert I galaxy Markarian 766: Dusty warm absorber or relativistic emission lines?

Competing models for broad spectral features in the soft X-ray spectrum of the Seyfert I galaxy Mrk 766 are tested against data from a 130 ks XMM-Newton observation. A model including relativistically broadened Lyalpha emission lines of O VIII N VII and C VI is a better fit to 0.3-2 keV XMM RGS data than a dusty warm absorber. Moreover, the measured depth of neutral iron absorption lines in the spectrum is inconsistent with the magnitude of the iron edge required to produce the continuum break at 17-18 Angstrom in the dusty warm absorber model. The relativistic emission line model can reproduce the broadband (0.1-12 keV) XMM EPIC data with the addition of a fourth line to represent emission from ionized iron at 6.7 keV and an excess due to reflection at energies above the iron line. The pro le of the 6.7 keV iron line is consistent with that measured for the low-energy lines. There is evidence in the RGS data, at the 3sigma level, of spectral features that vary with source flux. The covering fraction of warm absorber gas is estimated to be 12%. Iron in the warm absorber is found to be overabundant with respect to CNO, compared to solar values

How robust are recommended waiting times to pacing after cardiac surgery that are derived from observational data?

AIMS: For bradycardic patients after cardiac surgery, it is unknown how long to wait before implanting a permanent pacemaker (PPM). Current recommendations vary and are based on observational studies. This study aims to examine why this variation may exist. METHODS AND RESULTS: We conducted first a study of patients in our institution and second a systematic review of studies examining conduction disturbance and pacing after cardiac surgery. Of 5849 operations over a 6-year period, 103 (1.8%) patients required PPM implantation. Only pacing dependence at implant and time from surgery to implant were associated with 30-day pacing dependence. The only predictor of regression of pacing dependence was time from surgery to implant. We then applied the conventional procedure of receiver operating characteristic (ROC) analysis, seeking an optimal time point for decision-making. This suggested the optimal waiting time was 12.5 days for predicting pacing dependence at 30 days for all patients (area under the ROC curve (AUC) 0.620, P = 0.031) and for predicting regression of pacing dependence in patients who were pacing-dependent at implant (AUC 0.769, P < 0.001). However, our systematic review showed that recommended optimal decision-making time points were strongly correlated with the average implant time point of those individual studies (R = 0.96, P < 0.001). We further conducted modelling which revealed that in any such study, the ROC method is strongly biased to indicate a value near to the median time to implant as optimal. CONCLUSION: When commonly used automated statistical methods are applied to observational data with the aim of defining the optimal time to pacing after cardiac surgery, the suggested answer is likely to be similar to the average time to pacing in that cohort