How are “Atypical” Sulfite Dehydrogenases Linked to Cell Metabolism? Interactions between the SorT Sulfite Dehydrogenase and Small Redox Proteins

Sulfite dehydrogenases (SDHs) are enzymes that catalyze the oxidation of the toxic and mutagenic compound sulfite to sulfate, thereby protecting cells from adverse effects associated with sulfite exposure. While some bacterial SDHs that have been characterized to date are able to use cytochrome c as an electron acceptor, the majority of these enzymes prefer ferricyanide as an electron acceptor and have therefore been termed “atypical” SDHs. Identifying the natural electron acceptor of these enzymes, however, is crucial for understanding how the “atypical” SDHs are integrated into cell metabolism. The SorT sulfite dehydrogenase from Sinorhizobium meliloti is a representative of this enzyme type and we have investigated the interactions of SorT with two small redox proteins, a cytochrome c and a Cu containing pseudoazurin, that are encoded in the same operon and are co-transcribed with the sorT gene. Both potential acceptor proteins have been purified and characterized in terms of their biochemical and electrochemical properties, and interactions and enzymatic studies with both the purified SorT sulfite dehydrogenase and components of the respiratory chain have been carried out. We were able to show for the first time that an “atypical” sulfite dehydrogenase can couple efficiently to a cytochrome c isolated from the same organism despite being unable to efficiently reduce horse heart cytochrome c, however, at present the role of the pseudoazurin in SorT electron transfer is unclear, but it is possible that it acts as an intermediate electron shuttle between. The SorT system appears to couple directly to the respiratory chain, most likely to a cytochrome oxidase

Bioelectrocatalysis of sulfite dehydrogenase from Sinorhizobium meliloti with its physiological cytochrome electron partner

We demonstrate electrochemically driven catalytic voltammetry of the Mo-dependent sulfite dehydrogenase (SorT) from the α-Proteobacterium Sinorhizobium meliloti with its physiological electron acceptor, the c-type cytochrome (SorU), with both proteins co-adsorbed on a chemically modified Au working electrode. Both SorT and SorU were constrained under a perm-selective dialysis membrane with the biopolymer chitosan as a co-adsorbate, while the electrode was modified with a 3-mercaptopropionate self-assembled monolayer cast on the Au electrode. Cyclic voltammetry of the SorU protein reveals a well-defined quasireversible Fe redox couple at +130 mV versus NHE in 100 mM phosphate buffer solution (pH 7.0). Introduction of wild-type sulfite dehydrogenase (SorT) and sulfite transforms this transient SorU voltammetric response into a sigmoidal catalytic wave, which increases with sulfite concentration before eventually saturating. In addition to the wild-type enzyme, the variants SorT, SorT, and SorT were also examined electrochemically in an effort to better understand the role of amino acid residue Arg78, which is in the vicinity of the Mo active site of SorT

Modeling antibiotic and cytotoxic effects of the dimeric isoquinoline IQ-143 on metabolism and its regulation in Staphylococcus aureus, Staphylococcus epidermidis and human cells

Background: Xenobiotics represent an environmental stress and as such are a source for antibiotics, including the isoquinoline (IQ) compound IQ-143. Here, we demonstrate the utility of complementary analysis of both host and pathogen datasets in assessing bacterial adaptation to IQ-143, a synthetic analog of the novel type N,C-coupled naphthyl-isoquinoline alkaloid ancisheynine. Results: Metabolite measurements, gene expression data and functional assays were combined with metabolic modeling to assess the effects of IQ-143 on Staphylococcus aureus, Staphylococcus epidermidis and human cell lines, as a potential paradigm for novel antibiotics. Genome annotation and PCR validation identified novel enzymes in the primary metabolism of staphylococci. Gene expression response analysis and metabolic modeling demonstrated the adaptation of enzymes to IQ-143, including those not affected by significant gene expression changes. At lower concentrations, IQ-143 was bacteriostatic, and at higher concentrations bactericidal, while the analysis suggested that the mode of action was a direct interference in nucleotide and energy metabolism. Experiments in human cell lines supported the conclusions from pathway modeling and found that IQ-143 had low cytotoxicity. Conclusions: The data suggest that IQ-143 is a promising lead compound for antibiotic therapy against staphylococci. The combination of gene expression and metabolite analyses with in silico modeling of metabolite pathways allowed us to study metabolic adaptations in detail and can be used for the evaluation of metabolic effects of other xenobiotics

The Disulfide Stress Response and Protein S-thioallylation Caused by Allicin and Diallyl Polysulfanes in Bacillus subtilis as Revealed by Transcriptomics and Proteomics

Garlic plants (Allium sativum L.) produce antimicrobial compounds, such as diallyl thiosulfinate (allicin) and diallyl polysulfanes. Here, we investigated the transcriptome and protein S-thioallylomes under allicin and diallyl tetrasulfane (DAS4) exposure in the Gram-positive bacterium Bacillus subtilis. Allicin and DAS4 caused a similar thiol-specific oxidative stress response, protein and DNA damage as revealed by the induction of the OhrR, PerR, Spx, YodB, CatR, HypR, AdhR, HxlR, LexA, CymR, CtsR, and HrcA regulons in the transcriptome. At the proteome level, we identified, in total, 108 S-thioallylated proteins under allicin and/or DAS4 stress. The S-thioallylome includes enzymes involved in the biosynthesis of surfactin (SrfAA, SrfAB), amino acids (SerA, MetE, YxjG, YitJ, CysJ, GlnA, YwaA), nucleotides (PurB, PurC, PyrAB, GuaB), translation factors (EF-Tu, EF-Ts, EF-G), antioxidant enzymes (AhpC, MsrB), as well as redox-sensitive MarR/OhrR and DUF24-family regulators (OhrR, HypR, YodB, CatR). Growth phenotype analysis revealed that the low molecular weight thiol bacillithiol, as well as the OhrR, Spx, and HypR regulons, confer protection against allicin and DAS4 stress. Altogether, we show here that allicin and DAS4 cause a strong oxidative, disulfide and sulfur stress response in the transcriptome and widespread S-thioallylation of redox-sensitive proteins in B. subtilis. The results further reveal that allicin and polysulfanes have similar modes of actions and thiol-reactivities and modify a similar set of redox-sensitive proteins by S-thioallylation

SiDroForest: a comprehensive forest inventory of Siberian boreal forest investigations including drone-based point clouds, individually labeled trees, synthetically generated tree crowns, and Sentinel-2 labeled image patches

The SiDroForest (Siberian drone-mapped forest inventory) data collection is an attempt to remedy the scarcity of forest structure data in the circumboreal region by providing adjusted and labeled tree-level and vegetation plot-level data for machine learning and upscaling purposes. We present datasets of vegetation composition and tree and plot level forest structure for two important vegetation transition zones in Siberia, Russia; the summergreen–evergreen transition zone in Central Yakutia and the tundra–taiga transition zone in Chukotka (NE Siberia). The SiDroForest data collection consists of four datasets that contain different complementary data types that together support in-depth analyses from different perspectives of Siberian Forest plot data for multi-purpose applications. i. Dataset 1 provides unmanned aerial vehicle (UAV)-borne data products covering the vegetation plots surveyed during fieldwork (Kruse et al., 2021, https://doi.org/10.1594/PANGAEA.933263). The dataset includes structure-from-motion (SfM) point clouds and red–green–blue (RGB) and red–green–near-infrared (RGN) orthomosaics. From the orthomosaics, point-cloud products were created such as the digital elevation model (DEM), canopy height model (CHM), digital surface model (DSM) and the digital terrain model (DTM). The point-cloud products provide information on the three-dimensional (3D) structure of the forest at each plot.ii. Dataset 2 contains spatial data in the form of point and polygon shapefiles of 872 individually labeled trees and shrubs that were recorded during fieldwork at the same vegetation plots (van Geffen et al., 2021c, https://doi.org/10.1594/PANGAEA.932821). The dataset contains information on tree height, crown diameter, and species type. These tree and shrub individually labeled point and polygon shapefiles were generated on top of the RGB UVA orthoimages. The individual tree information collected during the expedition such as tree height, crown diameter, and vitality are provided in table format. This dataset can be used to link individual information on trees to the location of the specific tree in the SfM point clouds, providing for example, opportunity to validate the extracted tree height from the first dataset. The dataset provides unique insights into the current state of individual trees and shrubs and allows for monitoring the effects of climate change on these individuals in the future.iii. Dataset 3 contains a synthesis of 10 000 generated images and masks that have the tree crowns of two species of larch (Larix gmelinii and Larix cajanderi) automatically extracted from the RGB UAV images in the common objects in context (COCO) format (van Geffen et al., 2021a, https://doi.org/10.1594/PANGAEA.932795). As machine-learning algorithms need a large dataset to train on, the synthetic dataset was specifically created to be used for machine-learning algorithms to detect Siberian larch species.iv. Dataset 4 contains Sentinel-2 (S-2) Level-2 bottom-of-atmosphere processed labeled image patches with seasonal information and annotated vegetation categories covering the vegetation plots (van Geffen et al., 2021b, https://doi.org/10.1594/PANGAEA.933268). The dataset is created with the aim of providing a small ready-to-use validation and training dataset to be used in various vegetation-related machine-learning tasks. It enhances the data collection as it allows classification of a larger area with the provided vegetation classes. The SiDroForest data collection serves a variety of user communities. The detailed vegetation cover and structure information in the first two datasets are of use for ecological applications, on one hand for summergreen and evergreen needle-leaf forests and also for tundra–taiga ecotones. Datasets 1 and 2 further support the generation and validation of land cover remote-sensing products in radar and optical remote sensing. In addition to providing information on forest structure and vegetation composition of the vegetation plots, the third and fourth datasets are prepared as training and validation data for machine-learning purposes. For example, the synthetic tree-crown dataset is generated from the raw UAV images and optimized to be used in neural networks. Furthermore, the fourth SiDroForest dataset contains S-2 labeled image patches processed to a high standard that provide training data on vegetation class categories for machine-learning classification with JavaScript Object Notation (JSON) labels provided. The SiDroForest data collection adds unique insights into remote hard-to-reach circumboreal forest regions.</p

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Frühneuzeitliche Pflanzen- und pharmazeutische Keramikfunde in der Osnabrücker Innenstadt

In der Innenstadt Osnabrück wurde bei Bauarbeiten am Domhof eine bruchsteingemauerte Abfallgrube mit Funden aus dem 16. und 17. Jahrhundert gefunden. Angefüllt war die Grube überwiegend mit zerscherbten Keramik- bzw. Glasgefäßen für den Labor- und Apothekerbedarf, sowie organischen Makroresten. Dabei sind besonders die Samenfunde von Interesse. Auf den Nutzen der gefundenen Pflanzenarten wird näher eingegangen.During works in the city ot Osnabrück, asewer from the 16th and 17th century was found. It contained ceramies and glass bottles used in pharmacies. Also a lot of seeds and other organic material were found. This study concerns the use of the plants as officinal herbs, legumes and fruits