The combination of stable isotope labeling (SIL) with mass spectrometry (MS) allows comparison of the abundance of thousands of proteins in complex mixtures. However, interpretation of the large data sets generated by these techniques remains a challenge because appropriate statistical standards are lacking. Here, we present a generally applicable model that accurately explains the behavior of data obtained using current SIL approaches, including 18O, iTRAQ, and SILAC labeling, and different MS instruments. The model decomposes the total technical variance into the spectral, peptide, and protein variance components, and its general validity was demonstrated by confronting 48 experimental distributions against 18 different null hypotheses. In addition to its general applicability, the performance of the algorithm was at least similar than that of other existing methods. The model also provides a general framework to integrate quantitative and error information fully, allowing a comparative analysis of the results obtained from different SIL experiments. The model was applied to the global analysis of protein alterations induced by low H2O2 concentrations in yeast, demonstrating the increased statistical power that may be achieved by rigorous data integration. Our results highlight the importance of establishing an adequate and validated statistical framework for the analysis of high-throughput data.

SUMMARY RAP1 is part of shelterin, the protective complex at telomeres. RAP1 also binds along chromosome arms, where it is proposed to regulate gene expression. To investigate the nontelomeric roles of RAP1 in vivo, we generated a RAP1 whole-body knockout mouse. These mice show early onset of obesity, which is more severe in females than in males. Rap1-deficient mice show accumulation of abdominal fat, hepatic steatosis, and high-fasting plasma levels of insulin, glucose, cholesterol, and alanine aminotransferase. Gene expression analyses of liver and visceral white fat from Rap1-deficient mice before the onset of obesity show deregulation of metabolic programs, including fatty acid, glucose metabolism, and PPARα signaling. We identify Pparα and Pgc1α as key factors affected by Rap1 deletion in the liver. We show that RAP1 binds to Pparα and Pgc1α loci and modulates their transcription. These findings reveal a role for a telomere-binding protein in the regulation of metabolism.

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Flocculation studies of precipitated calcium carbonate induced by cationic polyacrylamides (C-PAMs) were carried out using light diffraction scattering (LDS). The effect of both polymer charge density and concentration on the flocculation process and on flocs density was investigated. As expected, results show that high charge density C-PAM induces flocculation by bridging and patching mechanisms simultaneously, while medium charge density C-PAM acts mainly according to the bridging mechanism. Consequently, the mass fractal dimensions of the flocs produced by high charge density C-PAM are higher. Results also show the effect of flocculant concentration: flocculation rate decreases and denser flocs are obtained as flocculant concentration increases. The results obtained so far allowed a preliminary quantitative evaluation of flocculation kinetics. In the flocculation curve, two regions corresponding to different kinetics were identified: a first region dominated by particle aggregation and a second region dominated by flocs stabilization. Therefore, LDS is considered a useful tool to evaluate flocculants performance. A strategy was developed that resulted in the use of LDS to retrieve, in a single test, information on the evolution with time of flocs dimension and structure, flocs resistance and flocculation kinetics. All the tests were performed under turbulent conditions similar to the ones prevailing in process equipment.