DSM (Netherlands)

One of the most common post-translational modifications occurring in bacteria is proteolytic cleavage. Particularly, in secretory proteins this cleavage is observed for specific protein segments, named pro-peptides, which are frequently located between the signal peptide and the mature protein. Pro-peptides are known to serve two main functions, namely a chaperone function in protein folding and an enzyme-inhibiting function. To date, the contribution of pro-peptides to protein secretion is not fully understood. Therefore, the present study was aimed at assessing possible roles of the N-terminal pro-peptides from the S8 class subtilisin-like serine proteases Bpr, Vpr and WprA of Bacillus subtilis in protein secretion. To this end, the signal peptides of these proteases were fused to the alkaline phosphatase PhoA of Escherichia coli with or without the cognate pro-peptides. Subsequently, secretion of the PhoA protein and its activity were assessed in the B. subtilis type strain 168 or the genome-reduced strain IIG-Bs27-39 and correlated to the respective pro-peptide structure as predicted by AlphaFold. Altogether, the obtained results suggest that the contributions of the investigated pro-peptides to protein secretion in B. subtilis are related to a combination of their functions in protein folding, their own folding state, and the applied strain.

Seismic analysis of a PWR reactor building has been performed with different hypothesis for the soil structure interaction ; the model was an axisymmetric finite element one. A first calculation with springs and dampers for the SSI, applied on a circle has shown a great deformation of the basemat. Then a stiffness matrix, equivalent to the SSI was established and introduced in the axisymmetric model. The paper will describe calculation methodology of this impedance matrix and results of modal analysis.

Molecular dynamics (MD) simulations were carried out to determine the impact of the cationic valence on the thermodynamic properties of the MOX (Mixed Oxide) samples. It has been found recently that U and Pu cations can coexist with various valences in near-stoichiometric and hypostoichiometric samples. Moreover, the CALPHAD method predicts a variation of valence concentrations with increasing temperature. As a consequence, this work investigates the effect of pentavalent and trivalent cations on the thermodynamic properties, i.e., lattice parameter, thermal expansion, and heat capacity. Stoichiometric conditions in MOX systems were studied by considering pentavalent and trivalent cation concentrations between 0 and 0.08. In addition, two charge compensation mechanisms arising under hypostoichiometric conditions─particularly those involving U5+ and Pu3+, together with the resulting variation in O/M─were examined. This work highlights for the first time the influence of these elements on MOX fuel properties, in particular, on the Bredig transition. The lattice parameters, linear thermal expansion coefficients, and heat capacities were calculated.

SummaryThe “Integrative Metabolic-Flux Platform for Analysis, Contextualization, and Targeting” (IMPACT) is a comprehensive, fully modular platform designed for both targeted and untargeted metabolomics analysis in stable-isotope labeling experiments. It facilitates the accurate calculation of mass isotopomer distributions (MIDs) and the annotation of unknown metabolites with a contextualization algorithm, addressing the challenges in metabolomics research. IMPACT integrates an entire preprocessing pipeline for LC-MS data, including peak picking, feature grouping, and peak filling, along with advanced features for isotope detection, MID calculation, and its core feature contextualization, enabling metabolite integration into biological pathway networks. The platform supports various file formats and offers user-friendly online access, making it accessible for researchers seeking to elucidate metabolic pathways and networks with precision and reliability.Availability and implementationIMPACT is implemented in Python 3.9 and R 4.3.2, with a front-end in Javascript utilizing the Cytoscape.js library for data visualization. It is available as a docker container and can be accessed online at https://impact.bioinfo.nat.tu-bs.de, providing a user-friendly interface for metabolomics data analysis.

Polysaccharides occur throughout all kingdoms of life including plants, microorganisms and animals. They differ profoundly in structure and function ranging from heparin that serves as an anti-coagulant to pectins that are used to modify food properties. Mass spectrometry (MS) is a powerful technique for the characterization of polysaccharides and is often applied in conjunction with chemical or enzymatic degradation. Here, we show that wide mass range ultrahigh-resolution matrix-assisted laser desorption/ionization (MALDI) in-source decay (ISD) Fourier transform ion cyclotron resonance (FTICR) MS is suitable as a generic and robust method for the analysis of polysaccharides at the level of large fragments. We successfully analyzed polysaccharides that differ widely regarding composition and origin. The ISD fragment ions conferred information on monosaccharide composition as well as modifications including sulfation, amidation and methoxylation. Additionally, we could determine the pectins' degree of methoxylation from these ISD fragment ions. Performing MS2 of the ISD fragments confirmed the proposed identity and provided additional structural characteristics. We conclude that MALDI-ISD-MS is a fast and robust method for the characterization of polysaccharides complementing other analytical techniques. Specifically, the method may be evaluated for a rapid determination of differences between polysaccharide variants and batches, for establishing structure-function relationships.

With the vast advances in computing technology, computational (or in silico) modelling has emerged as a transformative tool in dermatology. These findings can provide novel insights into complex biological processes and aid in the development of innovative therapeutic and regenerative strategies for the skin. Modelling combines experimental data and knowledge across multiple disciplines, serving as a common framework to elucidate the workings of the skin. From a biomedical perspective, the mechanisms of skin diseases can be studied by simulating cellular interactions and signalling pathways. Computational investigations of these mechanisms can be categorised into two distinct approaches: data-driven and model-based. Data-driven approaches allow the diagnosis of skin diseases on the basis of data collection via imaging or feedback from portable sensors, often yielding performance exceeding that of their human counterparts. Model-based methods are well suited to address topics such as skin cell biology and biomechanics, contributing to wound healing and skin cancer research. Furthermore, such modelling has found utility in the development of virtual skin models and skin-on-chip devices, enabling the prediction of skin responses to various substances, including cosmetics and drugs. In the realm of dermatological surgery, computational tools have been instrumental in optimizing surgical planning and improving clinical outcomes. While significant advancements have been made, challenges such as data availability, model validation, and interdisciplinary collaboration persist. This review highlights the current state-of-the-art in computational modeling in dermatology, identifies key challenges, and outlines its prospects.

This research introduces a concept for predicting elemental contaminants, namely, chlorine, oxygen, nitrogen, silicon, bromine, and fluorine, in household plastic packaging waste as potential feedstock for pyrolysis. Existing predictive models primarily focus on broad concentration estimates based on common polymer types, often neglecting the complexity and variability of real plastic packaging waste streams. In contrast, this study presents a detailed object-level categorization framework, classifying contaminants by hierarchical levels (primary, secondary, and tertiary) and sources (packaging material, contained product, and unintended material). By mapping these contamination pathways, the model also distinguishes between organic and inorganic elemental contaminants, offering a deeper understanding of their presence in feedstock. Validation through comprehensive elemental analyses confirmed the predictive accuracy of the model, thus demonstrating its potential utility as a decision-support tool to enhance feedstock composition, optimize pyrolysis process, and mitigate downstream risks.