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“Once considered divine retribution for sins, comorbidities of obesity (metabolic syndrome) are today attributed to obesity-induced metabolic defects. Here, we propose that obesity and hyperleptinemia protect lipid-intolerant nonadipose organs
against lipotoxic lipid spillover during sustained caloric surplus. Metabolic syndrome is ascribed to lipotoxicity caused by age-related resistance to antilipotoxic protection by leptin.”
“Amide proton NMR signals from the N- terminal domain of monomeric a- synuclein ( aS) are lost when the sample temperature is raised from 10 C to 35 C at pH 7.4. Although the temperature- induced effects have been attributed to conformational exchange caused DNA Damage inhibitor by an increase in a- helix structure, we show that the loss of signals is due to fast amide proton exchange. At low ionic strength, hydrogen exchange rates are faster for the N- terminal segment of aS than for the acidic C- terminal domain. When the salt concentration is raised to 300 mM, exchange rates increase throughout the protein and become
similar for the N- and C- terminal domains. This indicates that the enhanced protection of amide protons from the C- terminal domain at low salt is electrostatic in nature. Ca chemical shift data point to < 10% residual a- helix structure at 10 C and 35 C. Conformational exchange contributions to R2 are negligible at both temperatures. In contrast to the situation
in vitro, the majority of amide protons YAP-TEAD Inhibitor 1 supplier are observed at 37 C Selleck OTX015 in 1 H- 15 N HSQC spectra of aS encapsulated within living Escherichia coli cells. Our finding that temperature effects on aS NMR spectra can be explained by hydrogen exchange obviates the need to invoke special cellular factors. The retention of signals is likely due to slowed hydrogen exchange caused by the lowered intracellular pH of high- density E. coli cultures. Taken together, our results emphasize that aS remains predominantly unfolded at physiological temperature and pH – an important conclusion for mechanistic models of the association of aS with membranes and fibrils.”
“Neuroimaging data are high dimensional and thus cumbersome to analyze. Manifold learning is a technique to find a low dimensional representation for high dimensional data. With manifold learning, data analysis becomes more tractable in the low dimensional space. We propose a novel shape quantification method based on a manifold learning method, ISOMAP, for brain MRI. Existing work applied another manifold learning method, multidimensional scaling (MDS), to quantify shape information for distinguishing Alzheimer’s disease (AD) from normal. We enhance the existing methodology by (1) applying it to distinguish mild cognitive impairment (MCI) from normal, (2) adopting a more advanced manifold learning technique.