Functional Analysis of the Phycomyces carRA Gene Encoding the Enzymes Phytoene Synthase and Lycopene Cyclase

Phycomyces carRA gene encodes a protein with two domains. Domain R is characterized by red carR mutants that accumulate lycopene. Domain A is characterized by white carA mutants that do not accumulate significant amounts of carotenoids. The carRA-encoded protein was identified as the lycopene cyclase and phytoene synthase enzyme by sequence homology with other proteins. However, no direct data showing the function of this protein have been reported so far. Different Mucor circinelloides mutants altered at the phytoene synthase, the lycopene cyclase or both activities were transformed with the Phycomyces carRA gene. Fully transcribed carRA mRNA molecules were detected by Northern assays in the transformants and the correct processing of the carRA messenger was verified by RT-PCR. These results showed that Phycomyces carRA gene was correctly expressed in Mucor. Carotenoids analysis in these transformants showed the presence of ß-carotene, absent in the untransformed strains, providing functional evidence that the Phycomyces carRA gene complements the M. circinelloides mutations. Co-transformation of the carRA cDNA in E. coli with different combinations of the carotenoid structural genes from Erwinia uredovora was also performed. Newly formed carotenoids were accumulated showing that the Phycomyces CarRA protein does contain lycopene cyclase and phytoene synthase activities. The heterologous expression of the carRA gene and the functional complementation of the mentioned activities are not very efficient in E. coli. However, the simultaneous presence of both carRA and carB gene products from Phycomyces increases the efficiency of these enzymes, presumably due to an interaction mechanism

Structured Dictionaries for Ischemia Estimation in Cardiac BOLD MRI at Rest

Cardiac Phase-resolved Blood-Oxygen-Level-Dependent (CP–BOLD) MRI examines changes in myocardial oxygenation in response to ischemia without contrast and stress agents. Since signal intensity changes are subtle, quantitative approaches are necessary to examine variations in myocardial BOLD signals and identify ischemic myocardial territories. Here, using data from animal studies, we extract myocardial time series (BOLD signal as a function of cardiac phase) and explore such variations using a structured dictionary-learning framework, considering shift-invariant learning and spatial priors. We use it: to learn a model of baseline (absence of disease) myocardial time series; and in datasets where disease is assumed, to obtain a spatial map of ischemia presence, identifying myocardial time series from ischemic territories in an unsupervised fashion, by exploiting structural properties, or the lack thereof, in the data. By providing new visualization and quantification approaches, we hope to accelerate the clinical translation of cardiac BOLD MRI for noninvasive ischemia assessment