Marker-free CRISPR-Cas9 based genetic engineering of the phytopathogenic fungus, Penicillium expansum.

Filamentous fungi are prolific producers of secondary metabolites (SecMets), including compounds with antibiotic properties, like penicillin, that allows the producing fungus to combat competitors in a shared niche. However, the biological function of the majority of these small complex metabolites for the producing fungi remains unclear (Macheleidt et al., 2016). In an effort to address this lack of knowledge, we have chosen to study the microbial community of moldy apples in the hope of shedding more light on the role of SecMets for the dynamics of the microbial community. Penicillium expansum is one of the prevalent fungal species in this system, and in co-culture experiments with other apple fungal pathogens, we have observed up- and downregulation of several SecMets when compared to monocultures. However, molecular genetic dissection of the observed changes is challenging, and new methodologies for targeted genetic engineering in P. expansum are needed. In the current study, we have established a CRISPR-Cas9 dependent genetic engineering toolbox for the targeted genetic manipulation of P. expansum to allow for single-step construction of marker-free strains. The method and effect of different combinations of a Cas9-sgRNA expressing plasmids and repair template substrates in the NHEJ-proficient WT strain is tested by targeted deletion of melA, encoding a PKS responsible for pigment formation, which upon deletion resulted in white mutants. Co-transformation with a linear double-stranded DNA fragment consisting of two 2 kb homology arms flanking the PKS gene proved to be the most efficient strategy with 100% confirmed deletions by diagnostic PCR. Shorter homology arms (500-1000 bp) resulted in 20-30% deletion efficiency. Furthermore, we demonstrate the application of the CRISPR-Cas9 method for targeted deletion of biosynthetic genes without a visible phenotype, insertion of a visual reporter-encoding gene (mRFP), and overexpression of biosynthetic genes. Combined, these tools will advance in enabling the deciphering of SecMet biosynthetic pathways, provide in situ insight into when and where SecMets are produced, and provide an avenue to study the role of P. expansum SecMets in shaping the microbial community development on moldy apples via marker-free targeted genetic engineering of P. expansum.

Rapid eye movement sleep behavior disorder as an outlier detection problem.

OBJECTIVE: Idiopathic rapid eye movement (REM) sleep behavior disorder is a strong early marker of Parkinson's disease and is characterized by REM sleep without atonia and/or dream enactment. Because these measures are subject to individual interpretation, there is consequently need for quantitative methods to establish objective criteria. This study proposes a semiautomatic algorithm for the early detection of Parkinson's disease. This is achieved by distinguishing between normal REM sleep and REM sleep without atonia by considering muscle activity as an outlier detection problem. METHODS: Sixteen healthy control subjects, 16 subjects with idiopathic REM sleep behavior disorder, and 16 subjects with periodic limb movement disorder were enrolled. Different combinations of five surface electromyographic channels, including the EOG, were tested. A muscle activity score was automatically computed from manual scored REM sleep. This was accomplished by the use of subject-specific features combined with an outlier detector (one-class support vector machine classifier). RESULTS: It was possible to correctly separate idiopathic REM sleep behavior disorder subjects from healthy control subjects and periodic limb movement subjects with an average validation area under the receiver operating characteristic curve of 0.993 when combining the anterior tibialis with submentalis. Additionally, it was possible to separate all subjects correctly when the final algorithm was tested on 12 unseen subjects. CONCLUSIONS: Detection of idiopathic REM sleep behavior disorder can be regarded as an outlier problem. Additionally, the EOG channels can be used to detect REM sleep without atonia and is discriminative better than the traditional submentalis. Furthermore, based on data and methodology, arousals and periodic limb movements did only have a minor influence on the quantification of the muscle activity. Analysis of muscle activity during nonrapid eye movement sleep may improve the separation even further.