Environmental Factors Affecting the Expression of pilAB as Well as the Proteome and Transcriptome of the Grass Endophyte Azoarcus sp. Strain BH72

gene encoding the structural protein of type IV pili that are essential for plant colonization appears to be regulated in a population density-dependent manner.. The expression of one of them was shown to be induced in plant roots. sp. to analyze mechanisms and molecules involved in the population-dependent gene expression in this endophyte in future

Comparative genome analysis of Burkholderia phytofirmans PsJN reveals a wide spectrum of endophytic lifestyles based on interaction strategies with host plants

Burkholderia phytofirmans PsJN is a naturally occurring plant-associated bacterial endophyte that effectively colonizes a wide range of plants and stimulates their growth and vitality. Here we analyze whole genomes, of PsJN and of eight other endophytic bacteria. This study illustrates that a wide spectrum of endophytic life styles exists. Although we postulate the existence of typical endophytic traits, no unique gene cluster could be exclusively linked to the endophytic lifestyle. Furthermore, our study revealed a high genetic diversity among bacterial endophytes as reflected in their genotypic and phenotypic features. B. phytofirmans PsJN is in many aspects outstanding among the selected endophytes. It has the biggest genome consisting of two chromosomes and one plasmid, well-equipped with genes for the degradation of complex organic compounds and detoxification, e.g., 24 glutathione-S-transferase (GST) genes. Furthermore, strain PsJN has a high number of cell surface signaling and secretion systems and harbors the 3-OH-PAME quorum-sensing system that coordinates the switch of free-living to the symbiotic lifestyle in the plant-pathogen R. solanacearum. The ability of B. phytofirmans PsJN to successfully colonize such a wide variety of plant species might be based on its large genome harboring a broad range of physiological functions

Reports about 8 selected benchmark cases of model hierarchies : Deliverable number: D5.1 - Version 0.1

Based on the multitude of industrial applications, benchmarks for model hierarchies will be created that will form a basis for the interdisciplinary research and for the training programme. These will be equipped with publically available data and will be used for training in modelling, model testing, reduced order modelling, error estimation, efficiency optimization in algorithmic approaches, and testing of the generated MSO/MOR software. The present document includes the description about the selection of (at least) eight benchmark cases of model hierarchies.EC/H2020/765374/EU/Reduced Order Modelling, Simulation and Optimization of Coupled Systems/ROMSO

Neratinib protects pancreatic beta cells in diabetes

The loss of functional insulin-producing β-cells is a hallmark of diabetes. Mammalian sterile 20-like kinase 1 (MST1) is a key regulator of pancreatic β-cell death and dysfunction; its deficiency restores functional β-cells and normoglycemia. The identification of MST1 inhibitors represents a promising approach for a β-cell-protective diabetes therapy. Here, we identify neratinib, an FDA-approved drug targeting HER2/EGFR dual kinases, as a potent MST1 inhibitor, which improves β-cell survival under multiple diabetogenic conditions in human islets and INS-1E cells. In a pre-clinical study, neratinib attenuates hyperglycemia and improves β-cell function, survival and β-cell mass in type 1 (streptozotocin) and type 2 (obese Leprdb/db) diabetic mouse models. In summary, neratinib is a previously unrecognized inhibitor of MST1 and represents a potential β-cell-protective drug with proof-of-concept in vitro in human islets and in vivo in rodent models of both type 1 and type 2 diabetes

Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma

Accurate and fast assessment of resection margins is an essential part of a dermatopathologist’s clinical routine. In this work, we successfully develop a deep learning method to assist the dermatopathologists by marking critical regions that have a high probability of exhibiting pathological features in whole slide images (WSI). We focus on detecting basal cell carcinoma (BCC) through semantic segmentation using several models based on the UNet architecture. The study includes 650 WSI with 3443 tissue sections in total. Two clinical dermatopathologists annotated the data, marking tumor tissues’ exact location on 100 WSI. The rest of the data, with ground-truth sectionwise labels, are used to further validate and test the models. We analyze two different encoders for the first part of the UNet network and two additional training strategies: (a) deep supervision, (b) linear combination of decoder outputs, and obtain some interpretations about what the network’s decoder does in each case. The best model achieves over 96%, accuracy, sensitivity, and specificity on the Test set

Multimodal Lung Cancer Subtyping Using Deep Learning Neural Networks on Whole Slide Tissue Images and MALDI MSI

Artificial intelligence (AI) has shown potential for facilitating the detection and classification of tumors. In patients with non-small cell lung cancer, distinguishing between the most common subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SqCC), is crucial for the development of an effective treatment plan. This task, however, may still present challenges in clinical routine. We propose a two-modality, AI-based classification algorithm to detect and subtype tumor areas, which combines information from matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) data and digital microscopy whole slide images (WSIs) of lung tissue sections. The method consists of first detecting areas with high tumor cell content by performing a segmentation of the hematoxylin and eosin-stained (H&E-stained) WSIs, and subsequently classifying the tumor areas based on the corresponding MALDI MSI data. We trained the algorithm on six tissue microarrays (TMAs) with tumor samples from N = 232 patients and used 14 additional whole sections for validation and model selection. Classification accuracy was evaluated on a test dataset with another 16 whole sections. The algorithm accurately detected and classified tumor areas, yielding a test accuracy of 94.7% on spectrum level, and correctly classified 15 of 16 test sections. When an additional quality control criterion was introduced, a 100% test accuracy was achieved on sections that passed the quality control (14 of 16). The presented method provides a step further towards the inclusion of AI and MALDI MSI data into clinical routine and has the potential to reduce the pathologist’s work load. A careful analysis of the results revealed specific challenges to be considered when training neural networks on data from lung cancer tissue

Population density-dependent expression of <i>pilAB</i> of <i>Azoarcus</i> sp.

<p>(A) Population density-dependent induction of <i>pilAB</i> gene expression in <i>Azoarcus</i> sp. BH72 wild type background and BHΔ<i>pilS</i> mutant background. Cultures were grown in liquid aerobic culture (VM-Ethanol), and samples were taken at certain time points to measure β-glucuronidase activity (<i>pilAB</i>::<i>uidA</i>-fusion, left axis). The optical densities at these time points are shown in the inlay. The results are representative of three independent experiments. Error bars indicate standard deviations. Increase of the expression levels at exponential in comparison to stationary growth phase (last time point) was significant for wild type (BH72::pJBLP14, black triangles, <i>P</i><0.001), and highly significant for <i>pilS</i> mutant cells (BHD<i>pilS</i>::pJBLP14, black squares, <i>P</i><0.0001, unpaired t-test). (B) PilA protein abundance in <i>Azoarcus</i> sp. BH72 wild type and mutant background. Stationary phase cultures (OD 2.5) of <i>Azoarcus</i> wild type (wt) and Δ<i>pilS</i> mutant cells were compared. Western blot of whole-cell protein extracts with antiserum against PilA. Equal amounts of protein were loaded (8 µg).</p

Expression of transcriptional gene fusions in conditioned supernatant.

<p>(A–F) Gene expression determined by ß-glucuronidase activities from respective transcriptional reporter gene fusions with <i>uidA</i>, in VM-ethanol medium. (A) Induction of <i>pilAB</i> gene expression in BHΔ<i>pilS</i>::pJBLP14 by supernatants of <i>Azoarcus</i> sp. BH72, <i>Azoarcus communis</i>, <i>Chromobacterium violaceum</i>, <i>Pseudomonas stutzeri</i> and <i>Azospirillum brasilense</i> obtained by supernatant bioassays after four hours of incubation. (B, C, D, E) Induction of <i>azo1544</i> (B), <i>azo1684</i> (C), <i>azo2876</i> (D) and <i>azo3874</i> (E) gene expression in the respective strains by supernatants of <i>Azoarcus</i> sp. BH72, <i>Azoarcus communis</i> and <i>Azospirillum brasilense</i> obtained by supernatant bioassays after four hours of incubation. (F) Fold changes of induction for all tested genes (<i>pilAB</i>, <i>azo1544</i>, <i>azo1684</i>, <i>azo2876</i> and <i>azo3874</i>) in comparison to each other. For all experiments, fresh medium was used instead of supernatant as negative control, and the values were set to one for calculation of fold changes. Standard deviation was calculated from at least three independent experiments. Stars indicate significance (at least <i>P<0.05</i>) as determined by unpaired t-test analyses.</p

Expression of a transcriptional <i>azo2876::gfp</i> fusion in pure culture or during interaction with rice roots.

<p>Phase contrast (A–C) and corresponding fluorescence micrographs (E–G), of strain <i>Azoarcus</i> sp. BH<i>azo2876</i> expressing a transcriptional <i>2876::gfp</i> fusion in pure culture, or in infected rice roots (fluorescence micrographs D, H). Cells grown in VM-ethanol medium at exponential (A, E) or stationary growth phase (C, G), or in conditioned supernatant for 4 h (B, F); all fluorescence images taken with the same setting of the video camera. (D, H) Roots of rice seedlings 13 d after inoculation; bacterial GFP fluorescence at emergence points of lateral roots (D, with close-up in right corner, and in epidermal root cell (H). Bars correspond to 7 µm (A–C, E–G), 10 µm (D) and 20 µM (H).</p