Chromosomal-level genome of velvet bean (Mucuna pruriens) provides resources for L-DOPA synthetic research and development.

Mucuna pruriens, commonly called velvet bean, is the main natural source of levodopa (L-DOPA), which has been marketed as a psychoactive drug for the clinical management of Parkinson's disease and dopamine-responsive dystonia. Although velvet bean is a very important plant species for food and pharmaceutical manufacturing, the lack of genetic and genomic information about this species severely hinders further molecular research thereon and biotechnological development. Here, we reported the first velvet bean genome, with a size of 500.49 Mb and 11 chromosomes encoding 28,010 proteins. Genomic comparison among legume species indicated that velvet bean speciated ∼29 Ma from soybean clade, without specific genome duplication. Importantly, we identified 21 polyphenol oxidase coding genes that catalyse l-tyrosine to L-DOPA in velvet bean, and two subfamilies showing tandem expansion on Chr3 and Chr7 after speciation. Interestingly, disease-resistant and anti-pathogen gene families were found contracted in velvet bean, which might be related to the expansion of polyphenol oxidase. Our study generated a high-quality genomic reference for velvet bean, an economically important agricultural and medicinal plant, and the newly reported L-DOPA biosynthetic genes could provide indispensable information for the biotechnological and sustainable development of an environment-friendly L-DOPA biosynthesis processing method.

[Spine disc MR image analysis using improved independent component analysis based active appearance model and Markov random field].

As there are not many research reports on segmentation and quantitative analysis of soft tissues in lumbar medical images, this paper presents an algorithm for segmenting and quantitatively analyzing discs in lumbar Magnetic Resonance Imaging (MRI). Vertebrae are first segmented using improved Independent component analysis based active appearance model (ICA-AAM), and lumbar curve is obtained with Minimum Description Length (MDL); based on these results, fast and unsupervised Markov Random Field (MRF) disc segmentation combining disc imaging features and intensity profile is further achieved; finally, disc herniation is quantitatively evaluated. The experiment proves that the proposed algorithm is fast and effective, thus providing doctors with aid in diagnosing and curing lumbar disc herniation.