Why is 11beta-hydroxysteroid dehydrogenase type 1 facing the endoplasmic reticulum lumen? Physiological relevance of the membrane topology of 11beta-HSD1

11Beta-hydroxysteroid dehydrogenase type 1 (11beta-HSD1) is essential for the local activation of glucocorticoid receptors (GR). Unlike unliganded cytoplasmic GR, 11beta-HSD1 is an endoplasmic reticulum (ER)-membrane protein with lumenal orientation. Cortisone might gain direct access to 11beta-HSD1 by free diffusion across membranes, indirectly via intracellular binding proteins or, alternatively, by insertion into membranes. Membranous cortisol, formed by 11beta-HSD1 at the ER-lumenal side, might then activate cytoplasmic GR or bind to ER-lumenal secretory proteins. Compartmentalization of 11beta-HSD1 is important for its regulation by hexose-6-phosphate dehydrogenase (H6PDH), which regenerates cofactor NADPH in the ER lumen and stimulates oxoreductase activity. ER-lumenal orientation of 11beta-HSD1 is also essential for the metabolism of the alternative substrate 7-ketocholesterol (7KC), a major cholesterol oxidation product found in atherosclerotic plaques and taken up from processed cholesterol-rich food. An 11beta-HSD1 mutant adopting cytoplasmic orientation efficiently catalyzed the oxoreduction of cortisone but not 7KC, indicating access to cortisone from both sides of the ER-membrane but to 7KC only from the lumenal side. These aspects may be relevant for understanding the physiological role of 11beta-HSD1 and for developing therapeutic interventions to control glucocorticoid reactivation.

Correspondence establishment in statistical modeling of shapes with arbitrary topology

Correspondence establishment is a key step in statistical shape model building. There are several automated methods for solving this problem in 3D, but they usually can only handle objects with simple topology, like that of a sphere or a disc. We propose an extension to correspondence establishment over a population based on the optimization of the minimal description length function, allowing considering objects with arbitrary topology. Instead of using a fixed structure of kernel placement on a sphere for the systematic manipulation of point landmark positions, we rely on an adaptive, hierarchical organization of surface patches. This hierarchy can be built on surfaces of arbitrary topology and the resulting patches are used as a basis for a consistent, multi-scale modification of the surfaces' parameterization, based on point distribution models. The feasibility of the approach is demonstrated on synthetic models with different topologies.

Parameter-free extraction of the functional network topology from intracranial EEG recordings: a time-resolved study of graph properties in focal onset seizures

Rationale: Focal onset epileptic seizures are due to abnormal interactions between distributed brain areas. By estimating the cross-correlation matrix of multi-site intra-cerebral EEG recordings (iEEG), one can quantify these interactions. To assess the topology of the underlying functional network, the binary connectivity matrix has to be derived from the cross-correlation matrix by use of a threshold. Classically, a unique threshold is used that constrains the topology [1]. Our method aims to set the threshold in a data-driven way by separating genuine from random cross-correlation. We compare our approach to the fixed threshold method and study the dynamics of the functional topology. Methods: We investigate the iEEG of patients suffering from focal onset seizures who underwent evaluation for the possibility of surgery. The equal-time cross-correlation matrices are evaluated using a sliding time window. We then compare 3 approaches assessing the corresponding binary networks. For each time window: * Our parameter-free method derives from the cross-correlation strength matrix (CCS)[2]. It aims at disentangling genuine from random correlations (due to finite length and varying frequency content of the signals). In practice, a threshold is evaluated for each pair of channels independently, in a data-driven way. * The fixed mean degree (FMD) uses a unique threshold on the whole connectivity matrix so as to ensure a user defined mean degree. * The varying mean degree (VMD) uses the mean degree of the CCS network to set a unique threshold for the entire connectivity matrix. * Finally, the connectivity (c), connectedness (given by k, the number of disconnected sub-networks), mean global and local efficiencies (Eg, El, resp.) are computed from FMD, CCS, VMD, and their corresponding random and lattice networks. Results: Compared to FMD and VMD, CCS networks present: *topologies that are different in terms of c, k, Eg and El. *from the pre-ictal to the ictal and then post-ictal period, topological features time courses that are more stable within a period, and more contrasted from one period to the next. For CCS, pre-ictal connectivity is low, increases to a high level during the seizure, then decreases at offset. k shows a ‘‘U-curve’’ underlining the synchronization of all electrodes during the seizure. Eg and El time courses fluctuate between the corresponding random and lattice networks values in a reproducible manner. Conclusions: The definition of a data-driven threshold provides new insights into the topology of the epileptic functional networks.