Skin provides the first defense against pathogenic micro-organisms and is also colonized by a diverse microbiota. Phylogenetic analysis of whole skin microbiome at different skin sites in health and disease has generated important insights on possible microbial involvement in modulating skin health. However, functional roles of the skin microbial community remain unclear. The most common sebaceous skin commensal yeasts are the basidiomycetes, Malassezia. Here, we characterized the dominant secreted Malassezia globosa protease in culture and subsequently named it Malassezia globosa Secreted Aspartyl Protease 1 (MgSAP1). We defined recombinant MgSAP1's substrate cleavage profile using an unbiased, mass-spectrometry-based technique. We show that this enzyme is physiologically relevant as mgsap1 expression was detected on at least one facial skin site of 17 healthy human volunteers. In addition, we demonstrated that this protease rapidly hydrolyzes Staphylococcus aureus protein A, an important S. aureus virulence factor involved in immune evasion and biofilm formation. We further observed that MgSAP1 has anti-biofilm properties against S. aureus. Taken together, our study defines a role for the skin fungus Malassezia in inter-kingdom interactions and suggests that this fungus and the enzymes it produces may be beneficial for skin health.

BackgroundRecombinant protein production in the methylotrophic yeast Pichia pastoris largely relies on integrative vectors. Although the stability of integrated expression cassettes is well appreciated for most applications, the availability of reliable episomal vectors for this host would represent a useful tool to expedite cloning and high-throughput screening, ameliorating also the relatively high clonal variability reported in transformants from integrative vectors caused by off-target integration in the P. pastoris genome. Recently, heterologous and endogenous autonomously replicating sequences (ARS) were identified in P. pastoris by genome mining, opening the possibility of expanding the available toolbox to include efficient episomal plasmids. The aim of this technical report is to validate a 452-bp sequence (“panARS”) in context of P. pastoris expression vectors, and to compare their performance to classical integrative plasmids. Moreover, we aimed to test if such episomal vectors would be suitable to sustain in vivo recombination, using fragments for transformation, directly in P. pastoris cells.ResultsA panARS-based episomal vector was evaluated using blue fluorescent protein (BFP) as a reporter gene. Normalized fluorescence from colonies carrying panARS-BFP outperformed the level of signal obtained from integrative controls by several-fold, whereas endogenous sequences, identified from the P. pastoris genome, were not as efficient in terms of protein production. At the single cell level, panARS-BFP clones showed lower interclonal variability but higher intraclonal variation compared to their integrative counterparts, supporting the idea that heterologous protein production could benefit from episomal plasmids. Finally, efficiency of 2-fragment and 3-fragment in vivo recombination was tested using varying lengths of overlapping regions and molar ratios between fragments. Upon optimization, minimal background was obtained for in vivo assembled vectors, suggesting this could be a quick and efficient method to generate of episomal plasmids of interest.ConclusionsAn expression vector based on the panARS sequence was shown to outperform its integrative counterparts in terms of protein productivity and interclonal variability, facilitating recombinant protein expression and screening. Using optimized fragment lengths and ratios, it was possible to perform reliable in vivo recombination of fragments in P. pastoris. Taken together, these results support the applicability of panARS episomal vectors for synthetic biology approaches.

We describe a new approach to the analysis of gene expression data using Associative Clustering Neural Network (ACNN). ACNN dynamically evaluates similarity between any two gene samples through the interactions of a group of gene samples. It has feasibility to more robust performance than those similarities evaluated by direct distances. The clustering performance of ACNN has been tested on the Leukemias data set. The experimental results demonstrate that ACNN can achieve superior performance in high dimensional data ( 7129 genes). The performance can be further enhanced when some useful feature selection methodologies are incorporated. The study has shown ACNN can achieve 98.61% accuracy on clustering the Leukemias data set with correlation analysis.