Species-Specific Differences in the Susceptibility of Fungi to the Antifungal Protein AFP Depend on C-3 Saturation of Glycosylceramides

AFP is an antimicrobial peptide (AMP) produced by the filamentous fungus Aspergillus giganteus and is a very potent inhibitor of fungal growth that does not affect the viability of bacteria, plant, or mammalian cells. It targets chitin synthesis and causes plasma membrane permeabilization in many human- and plant-pathogenic fungi, but its exact mode of action is not known. After adoption of the “damage-response framework of microbial pathogenesis” regarding the analysis of interactions between AMPs and microorganisms, we have recently proposed that the cytotoxic capacity of a given AMP depends not only on the presence/absence of its target(s) in the host and the AMP concentration applied but also on other variables, such as microbial survival strategies. We show here using the examples of three filamentous fungi (Aspergillus niger, Aspergillus fumigatus, and Fusarium graminearum) and two yeasts (Saccharomyces cerevisiae and Pichia pastoris) that the important parameters defining the AFP susceptibilities of these fungi are (i) the presence/absence of glycosylceramides, (ii) the presence/absence of Δ3(E) desaturation of the fatty acid chain therein, and (iii) the (dis)ability of these fungi to respond to AFP inhibitory effects with the fortification of their cell walls via increased chitin and β-(1,3)-glucan synthesis. These observations support the idea of the adoption of the damage-response framework to holistically understand the outcome of AFP inhibitory effects.TU Berlin, Open-Access-Mittel - 201

Screening for Incidental Sars-Cov-2 Infection in a Neurocritical Care Unit: A Longitudinal Diagnostic Prediction Model

Background: Rapid diagnosis of SARS-CoV-2 infection in patients not primarily assigned with the diagnosis of COVID-19 is highly relevant to effectively rule out virus transmission among patients and medical staff. The purpose is to develop a model for the prediction of the actual presence of a SARS-CoV-2 infection before a valid test result is available and to avoid unnecessary testing in Critical Care Units. Methods: Datasets of laboratory and blood gas analysis tests were collected retrospectively for the development and subsequent validation of machine learning (ML) based models. The data set was composed of 1. 254 SARS-CoV-2 positive cases, collected in an ICU dedicated to patients with COVID-19 pneumonia, 2a. 914 SARS-CoV-2 negative patients treated in a Neurocritical Care Unit and 2b. 32 patients treated for severe influenza pneumonia in a Medical ICU at the same hospital. The models were subsequently validated on a dataset collected from the Neurocritical Care Unit that consisted of data from 7 positive and 42 negative patients. Models were adapted to newly available laboratory values throughout their ICU stay. Extremely Randomized Trees (ERT) and Random Forest (RF) models were evaluated. A baseline model comprising fully grown trees, an optimized model including optimal values for the maximum depth, and a simplified model that only uses the 6 most important features were trained. Results: The overall best model, evaluated via crossvalidation on the development set, is an optimized ERT model with a ROC AUC value of 0.946. The model performance on the validation set is best for the simplified RF model achieving a ROC AUC value of 0.701. Gini feature and permutation importance for the simplified RF model revealed hemoglobin, procalcitonin, C-reactive protein, glomerular filtration rate based on CKD-EPI equation, creatinine, and urea as the most important input features. Using the simplified RF model and a threshold of 0.012 for the probability, a sensitivity above 80% with a specificity of 43% is achieved. Compared to a hypothetical daily testing regimen, using a threshold of 0.145, the simplified RF model detects all positive cases, and, with a false positive rate of 35%, daily tests might be reduced by two thirds. Conclusions: The model developed may support the medical staff in the ICUs by enabling faster and more reliable recognition of COVID-19. Unnecessary serial test sampling might be reduced. To ensure the quality of the model before clinical use, it should be further validated in prospective patient cohorts

Updating genome annotation for the microbial cell factory Aspergillus niger using gene co-expression networks

A significant challenge in our understanding of biological systems is the high number of genes with unknown function in many genomes. The fungal genus Aspergillus contains important pathogens of humans, model organisms, and microbial cell factories. Aspergillus niger is used to produce organic acids, proteins, and is a promising source of new bioactive secondary metabolites. Out of the 14,165 open reading frames predicted in the A. niger genome only 2% have been experimentally verified and over 6,000 are hypothetical. Here, we show that gene co-expression network analysis can be used to overcome this limitation. A meta-analysis of 155 transcriptomics experiments generated co-expression networks for 9,579 genes (∼65%) of the A. niger genome. By populating this dataset with over 1,200 gene functional experiments from the genus Aspergillus and performing gene ontology enrichment, we could infer biological processes for 9,263 of A. niger genes, including 2,970 hypothetical genes. Experimental validation of selected co-expression sub-networks uncovered four transcription factors involved in secondary metabolite synthesis, which were used to activate production of multiple natural products. This study constitutes a significant step towards systems-level understanding of A. niger, and the datasets can be used to fuel discoveries of model systems, fungal pathogens, and biotechnology.DFG, 325093850, Open Access Publizieren 2017 - 2018 / Technische Universität BerlinEC/FP7/607332/EU/Quantitative Biology for Fungal Secondary Metabolite Producers/QuantFun

Direct subthalamic nucleus stimulation influences speech and voice quality in Parkinson's disease patients

BACKGROUND DBS of the subthalamic nucleus (STN) considerably ameliorates cardinal motor symptoms in PD. Reported STN-DBS effects on secondary dysarthric (speech) and dysphonic symptoms (voice), as originating from vocal tract motor dysfunctions, are however inconsistent with rather deleterious outcomes based on post-surgical assessments. OBJECTIVE To parametrically and intra-operatively investigate the effects of deep brain stimulation (DBS) on perceptual and acoustic speech and voice quality in Parkinson's disease (PD) patients. METHODS We performed an assessment of instantaneous intra-operative speech and voice quality changes in PD patients (n = 38) elicited by direct STN stimulations with variations of central stimulation features (depth, laterality, and intensity), separately for each hemisphere. RESULTS First, perceptual assessments across several raters revealed that certain speech and voice symptoms could be improved with STN-DBS, but this seems largely restricted to right STN-DBS. Second, computer-based acoustic analyses of speech and voice features revealed that both left and right STN-DBS could improve dysarthric speech symptoms, but only right STN-DBS can considerably improve dysphonic symptoms, with left STN-DBS being restricted to only affect voice intensity features. Third, several subareas according to stimulation depth and laterality could be identified in the motoric STN proper and close to the associative STN with optimal (and partly suboptimal) stimulation outcomes. Fourth, low-to-medium stimulation intensities showed the most optimal and balanced effects compared to high intensities. CONCLUSIONS STN-DBS can considerably improve both speech and voice quality based on a carefully arranged stimulation regimen along central stimulation features

Malignant Keratitis Caused by a Highly-Resistant Strain of Fusarium Tonkinense from the Fusarium Solani Complex

Fusarium spp. are moulds ubiquitously distributed in nature and only occasionally pathogenic for humans. Species of the Fusarium solani complex are the predominant keratitis inducing pathogens, because they are endowed with proper virulence factors. These fungi can adhere to the cornea creating a biofilm and, with the help of enzymes and cytotoxins, penetrate the cornea. Whereas an intact cornea is hardly able to be invaded by Fusarium spp. in spite of appropriate virulence factors, these opportunistic fungi may profit from predisposing conditions, for example mechanical injuries. This can lead to a progressive course of corneal infection and may finally affect the whole eye up to the need for enucleation. Here, we present and discuss the clinical, microbiological and histopathological aspects of a particular case due to Fusarium tonkinense of the Fusarium solani complex with severe consequences in a patient without any obvious predisposing factors. A broad portfolio of antifungal agents was applied, both topically and systemically as well as two penetrating keratoplasties were performed. The exact determination of the etiologic agent of the fungal infection proved likewise to be very challenging

Influence of Encapsulation Process Temperature on the Performance of Perovskite Mini Modules

Perovskite-on-silicon tandem solar cells are a promising candidate to significantly increase the efficiency of PV modules. Despite the fast research progress on material and solar cells aspects, there is still a lack of processes for an industrial module integration of these devices. One aspect hereby is the adaption of encapsulation materials and processes to the requirements of perovskite materials. Process temperatures of about 150 °C are necessary to use well proven, in silicon PV commonly applied encapsulation materials with a high reliability. However, perovskites start to decompose into their components at high temperatures. This limits the encapsulation process temperature, which in turn constraints the choice of encapsulation materials. This work presents an encapsulation process for methylammonium lead iodide (MAPhb) single junction perovskite solar cells (PSCs) with conventional production tools in glass-glass modules that serves as a model system for perovskite tandem applications. We evaluate the influence of the encapsulation process temperature between 120 °C and 160 °C on the performance of mini modules. The UV-absorbing encapsulation material is processable over the whole investigated temperature regime. We observe a difference in the IV-characteristics between the PSCs encapsulated in the temperature range of 120 °C - 140 °C to those processed at 160 °C. At lower encapsulation temperatures the IV-curves taken 1 h after encapsulation show a pronounced S-shape and no degradation of Foe. In contrast, the PSCs encapsulated at 160 °C exhibit a Foe decrease of up to 29% compared to the initial measurement shortly after PSC fabrication and no significant S-shape. Both, the S-shape that occurs at low encapsulation temperatures and the Foe loss after encapsulation at 160 °C, are no longer significant after one week of module storage under dark conditions. The presented encapsulation process therefore does not permanently damage the MAPbb PSCs even at a standard encapsulation temperature of 160 °C. To ensure long-term operation, we test the fabricated mini modules in a damp heat test at 85 °C and a relative humidity of 85%. We find no significant additional degradation caused by damp heat in 1250 h test duration compared to a reference module stored in ambient air

Efficient Large Scale Electromagnetics Simulations Using Dynamically Adapted Meshes with the Discontinuous Galerkin Method

A framework for performing dynamic mesh adaptation with the discontinuous Galerkin method (DGM) is presented. Adaptations include modifications of the local mesh step size (h-adaptation) and the local degree of the approximating polynomials (p-adaptation) as well as their combination. The computation of the approximation within locally adapted elements is based on projections between finite element spaces (FES), which are shown to preserve an upper limit of the electromagnetic energy. The formulation supports high level hanging nodes and applies precomputation of surface integrals for increasing computational efficiency. Error and smoothness estimates based on interface jumps are presented and applied to the fully hp-adaptive simulation of two examples in one-dimensional space. A full wave simulation of electromagnetic scattering form a radar reflector demonstrates the applicability to large scale problems in three-dimensional space.Comment: 33 pages, 8 figures, submitted to Journal of Computational and Applied Mathematic

Identification of dihydromyricetin as a natural DNA methylation inhibitor with rejuvenating activity in human skin

Changes in DNA methylation patterning have been reported to be a key hallmark of aged human skin. The altered DNA methylation patterns are correlated with deregulated gene expression and impaired tissue functionality, leading to the well-known skin aging phenotype. Searching for small molecules, which correct the aged methylation pattern therefore represents a novel and attractive strategy for the identification of anti-aging compounds. DNMT1 maintains epigenetic information by copying methylation patterns from the parental (methylated) strand to the newly synthesized strand after DNA replication. We hypothesized that a modest inhibition of this process promotes the restoration of the ground-state epigenetic pattern, thereby inducing rejuvenating effects. In this study, we screened a library of 1800 natural substances and 640 FDA-approved drugs and identified the well-known antioxidant and anti-inflammatory molecule dihydromyricetin (DHM) as an inhibitor of the DNA methyltransferase DNMT1. DHM is the active ingredient of several plants with medicinal use and showed robust inhibition of DNMT1 in biochemical assays. We also analyzed the effect of DHM in cultivated keratinocytes by array-based methylation profiling and observed a moderate, but significant global hypomethylation effect upon treatment. To further characterize DHM-induced methylation changes, we used published DNA methylation clocks and newly established age predictors to demonstrate that the DHM-induced methylation change is associated with a reduction in the biological age of the cells. Further studies also revealed re-activation of age-dependently hypermethylated and silenced genes in vivo and a reduction in age-dependent epidermal thinning in a 3-dimensional skin model. Our findings thus establish DHM as an epigenetic inhibitor with rejuvenating effects for aged human skin

Nutritional upgrading for omnivorous carpenter ants by the endosymbiont Blochmannia

<p>Abstract</p> <p>Background</p> <p>Carpenter ants (genus <it>Camponotus</it>) are considered to be omnivores. Nonetheless, the genome sequence of <it>Blochmannia floridanus</it>, the obligate intracellular endosymbiont of <it>Camponotus floridanus</it>, suggests a function in nutritional upgrading of host resources by the bacterium. Thus, the strongly reduced genome of the endosymbiont retains genes for all subunits of a functional urease, as well as those for biosynthetic pathways for all but one (arginine) of the amino acids essential to the host.</p> <p>Results</p> <p>Nutritional upgrading by <it>Blochmannia </it>was tested in 90-day feeding experiments with brood-raising in worker-groups on chemically defined diets with and without essential amino acids and treated or not with antibiotics. Control groups were fed with cockroaches, honey water and Bhatkar agar. Worker-groups were provided with brood collected from the queenright mother-colonies (45 eggs and 45 first instar larvae each). Brood production did not differ significantly between groups of symbiotic workers on diets with and without essential amino acids. However, aposymbiotic worker groups raised significantly less brood on a diet lacking essential amino acids. Reduced brood production by aposymbiotic workers was compensated when those groups were provided with essential amino acids in their diet. Decrease of endosymbionts due to treatment with antibiotic was monitored by qRT-PCR and FISH after the 90-day experimental period. Urease function was confirmed by feeding experiments using ¹⁵N-labelled urea. GC-MS analysis of ¹⁵N-enrichment of free amino acids in workers revealed significant labelling of the non-essential amino acids alanine, glycine, aspartic acid, and glutamic acid, as well as of the essential amino acids methionine and phenylalanine.</p> <p>Conclusion</p> <p>Our results show that endosymbiotic <it>Blochmannia </it>nutritionally upgrade the diet of <it>C. floridanus </it>hosts to provide essential amino acids, and that it may also play a role in nitrogen recycling via its functional urease. <it>Blochmannia </it>may confer a significant fitness advantage via nutritional upgrading by enhancing competitive ability of <it>Camponotus </it>with other ant species lacking such an endosymbiont. Domestication of the endosymbiont may have facilitated the evolutionary success of the genus <it>Camponotus</it>.</p

Robust Inverse Modeling of Growing Season Net Ecosystem Exchange in a Mountainous Peatland: Influence of Distributional Assumptions on Estimated Parameters and Total Carbon Fluxes

While boreal lowland bogs have been extensively studied using the eddy‐covariance (EC) technique, less knowledge exists on mountainous peatlands. Hence, half‐hourly CO2 fluxes of an ombrotrophic peat bog in the Harz Mountains, Germany, were measured with the EC technique during a growing season with exceptionally dry weather spells. A common biophysical process model for net ecosystem exchange was used to describe measured CO2 fluxes and to fill data gaps. Model parameters and uncertainties were estimated by robust inverse modelling in a Bayesian framework using a population‐based Markov Chain Monte Carlo sampler. The focus of this study was on the correct statistical description of error, i.e. the differences between the measured and simulated carbon fluxes, and the influence of distributional assumptions on parameter estimates, cumulative carbon fluxes, and uncertainties. We tested the Gaussian, Laplace, and Student's t distribution as error models. The t‐distribution was identified as best error model by the deviance information criterion. Its use led to markedly different parameter estimates, a reduction of parameter uncertainty by about 40%, and, most importantly, to a 5% higher estimated cumulative CO2 uptake as compared to the commonly assumed Gaussian error distribution. As open‐path measurement systems have larger measurement error at high humidity, the standard deviation of the error was modeled as a function of measured vapor pressure deficit. Overall, this paper demonstrates the importance of critically assessing the influence of distributional assumptions on estimated model parameters and cumulative carbon fluxes between the land surface and the atmosphere