Aspergillus protein degradation pathways with different secreted protease sets at neutral and acidic pH.

Aspergillus fumigatus grows well at neutral and acidic pH in a medium containing protein as the sole nitrogen source by secreting two different sets of proteases. Neutral pH favors the secretion of neutral and alkaline endoproteases, leucine aminopeptidases (Laps) which are nonspecific monoaminopeptidases, and an X-prolyl dipeptidase (DppIV). Acidic pH environment promotes the secretion of an aspartic endoprotease of pepsin family (Pep1) and tripeptidyl-peptidases of the sedolisin family (SedB and SedD). A novel prolyl peptidase, AfuS28, was found to be secreted in both alkaline and acidic conditions. In previous studies, Laps were shown to degrade peptides from their N-terminus until an X-Pro sequence acts as a stop signal. X-Pro sequences can be then removed by DppIV, which allows Laps access to the following residues. We have shown that at acidic pH Seds degrade large peptides from their N-terminus into tripeptides until Pro in P1 or P'1 position acts as a stop for these exopeptidases. However, X-X-Pro and X-X-X-Pro sequences can be removed by AfuS28 thus allowing Seds further sequential proteolysis. In conclusion, both alkaline and acidic sets of proteases contain exoprotease activity capable of cleaving after proline residues that cannot be removed during sequential digestion by nonspecific exopeptidases.

Accelerated Microstructure Imaging via Convex Optimization (AMICO) in crossing fibers

Mapping the microstructure properties of the local tissues in the brain is crucial to understand any pathological condition from a biological perspective. Most of the existing techniques to estimate the microstructure of the white matter assume a single axon orientation whereas numerous regions of the brain actually present a fiber-crossing configuration. The purpose of the present study is to extend a recent convex optimization framework to recover microstructure parameters in regions with multiple fibers.

Accelerated Microstructure Imaging via Convex Optimisation for regions with multiple fibres (AMICOx)

This paper reviews and extends our previous work to enable fast axonal diameter mapping from diffusion MRI data in the presence of multiple fibre populations within a voxel. Most of the existing mi-crostructure imaging techniques use non-linear algorithms to fit their data models and consequently, they are computationally expensive and usually slow. Moreover, most of them assume a single axon orientation while numerous regions of the brain actually present more complex configurations, e.g. fiber crossing. We present a flexible framework, based on convex optimisation, that enables fast and accurate reconstructions of the microstructure organisation, not limited to areas where the white matter is coherently oriented. We show through numerical simulations the ability of our method to correctly estimate the microstructure features (mean axon diameter and intra-cellular volume fraction) in crossing regions.