Biobyte Solutions (Germany)

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.

SummaryLipoproteins (LPP) and extracellular vesicles are carriers of extracellular small RNA, with potential applications both in the areas of diagnostics and therapeutics. Lipid nanoparticles overlap across a range of densities and sizes in plasma, making them difficult to isolate intact and without contamination from other plasma components. Accurate characterization of their cargo through efficient isolation from other plasma components is required to understand their function. Here we describe the simultaneous separation of LPP classes using sequential flotation ultracentrifugation followed by size exclusion chromatography from 500μL of starting plasma. Using western blot, denaturing and non-denaturing gel electrophoresis, nuclear magnetic resonance, and electron microscopy, we demonstrate separation of the LPP classes with minimal contamination. We also show unique lipidomic, proteomic and small RNA signatures for each LPP class, including, for the first time, very high density high density lipoprotein particles in the density range of 1.21–1.25 g/mL.

Background: Following the COVID-19 pandemic, a significant rise in multidrug-resistant pathogens such as Acinetobacter, Enterobacter, Escherichia, Klebsiella, Providencia, Pseudomonas and Serratia spp. was observed in Minas Gerais, Brazil. Diagnostic disparities and antimicrobial overuse for COVID-19-associated pneumonia likely contributed to resistance gene spread. Methods: This retrospective, cross-sectional, descriptive study analysed 13,012 Gram-negative bacterial isolates collected from hospitals across Minas Gerais between 2016 and 2022. Samples, including urine, tracheal secretion, blood and surgical wound exudate, were referred to the state public health laboratory (LACEN-MG) based on resistance patterns detected via antibiograms. Genotypic analysis involved PCR screening for resistance genes (blaKPC, blaOXA, blaNDM, blaSPM, blaVIM, blaIMP), categorized by pandemic period (pre-, during and post-COVID-19). The presence of at least one gene indicated multidrug resistance. Results: Among isolates, 70.8% were fermenting bacilli, mainly Klebsiella spp. (73.8%), 29.2% were non-fermenting, mostly Acinetobacter spp. (62.1%) and Pseudomonas spp. (37.9%). Post-pandemic resistance increased significantly (p<0.05). blaKPC declined in relative frequency (66.3% pre-pandemic to 44.4% post-pandemic) but absolute numbers rose (n=2,906). blaOXA and blaNDM increased to 41.1% and 13.5%, respectively, with blaNDM increasing over sixfold. Mcr-1 appeared exclusively post-pandemic in four isolates. blaSPM, blaVIM and blaIMP remained rare; blaIMP was detected only post-pandemic (0.24%, n=16). Conclusions: Post-pandemic, resistance gene profiles diversified, with expansion of blaOXA, blaNDM and emergence of mcr-1. Strengthened molecular surveillance, equitable diagnostic access and antimicrobial stewardship are urgently needed in Brazil.

Genistein (GEN) and daidzein (DAI) are soy isoflavones known to bind to estrogen receptors. Overall health effects of GEN and DAI in humans exhibit a dual nature, presenting both health benefits and concerns related to their interaction with the estrogen receptor. The metabolomes of these isoflavones were determined in 28-day oral studies in male and female Wistar rats to elucidate (1) metabolites changes, (2) compare their metabolomes with other compounds and (3) identify toxicological modes of action (MoA). Dose levels for GEN were 1000 and 300 mg/kg bw by gavage and 1000 and 300 ppm (via diet). DAI gavage dose levels were 1000 and 100 mg/kg bw. Results were evaluated using the MetaMap®Tox data base. Both compounds demonstrated metabolome profiles which were associated with estrogenic profiles and compounds, predominantly in females. However, the metabolomes were compound specific with relatively few common metabolite changes. There were no relevant matches between any GEN and any DAI treatment group indicating that both compounds are substantially different from metabolome perspective. Ranking of the metabolome patters for GEN and DAI with ≥ 1000 compounds in the MetaMap®Tox database revealed correlations with estrogenic and other hormonally active compounds. GEN-treated females correlated best with Cabergoline, a dopamine D2 receptor agonist, DAI females with tamoxifen and diethylstilbestrol, suggesting that even their estrogenic activity may be different. Beyond estrogenic effects, the high dose (HD) DAI metabolome indicated altered fatty acid metabolism associated with PPAR-alpha activation. For GEN, there was an indication of ethanolamine-like liver effects. Dose levels without estrogenic effects for GEN were 1000 and 100 mg/kg bw for males and females respectively, there were no estrogenic effects in the feeding studies. For DAI males, the no estrogenic effect level was 300 mg/kg bw, for females < 100 mg/kg bw, suggesting that DAI may be a more potent estrogen than GEN in rats.

The change in the skin microbiome as individuals age is only partially known. To provide a better understanding of the impact of aging, whole-genome sequencing analysis was performed on facial skin swabs of 100 healthy female Caucasian volunteers grouped by age and wrinkle grade. Volunteers' metadata were collected through questionnaires and non-invasive biophysical measurements. A simple model and a biological statistical model were used to show the difference in skin microbiota composition between the two age groups. Taxonomic and non-metric multidimensional scaling analysis showed that the skin microbiome was more diverse in the older group (≥55 yo). There was also a significant decrease in Actinobacteria, namely in Cutibacterium acnes, and an increase in Corynebacterium kroppenstedtii. Some Streptococcus and Staphylococcus species belonging to the Firmicutes phylum and species belonging to the Proteobacteria phylum increased. In the 18-35 yo younger group, the microbiome was characterized by a significantly higher proportion of Cutibacterium acnes and Lactobacillus, most strikingly, Lactobacillus crispatus. The functional analysis using GO terms revealed that the young group has a higher significant expression of genes involved in biological and metabolic processes and in innate skin microbiome protection. The better comprehension of age-related impacts observed will later support the investigation of skin microbiome implications in antiaging protection.

While grouping/read-across is widely used to fill data gaps, chemical registration dossiers are often rejected due to weak category justifications based on structural similarity only. Metabolomics provides a route to robust chemical categories via evidence of shared molecular effects across source and target substances. To gain international acceptance, this approach must demonstrate high reliability, and best-practice guidance is required. The MetAbolomics ring Trial for CHemical groupING (MATCHING), comprising six industrial, government and academic ring-trial partners, evaluated inter-laboratory reproducibility and worked towards best-practice. An independent team selected eight substances (WY-14643, 4-chloro-3-nitroaniline, 17α-methyl-testosterone, trenbolone, aniline, dichlorprop-p, 2-chloroaniline, fenofibrate); ring-trial partners were blinded to their identities and modes-of-action. Plasma samples were derived from 28-day rat tests (two doses per substance), aliquoted, and distributed to partners. Each partner applied their preferred liquid chromatography–mass spectrometry (LC–MS) metabolomics workflows to acquire, process, quality assess, statistically analyze and report their grouping results to the European Chemicals Agency, to ensure the blinding conditions of the ring trial. Five of six partners, whose metabolomics datasets passed quality control, correctly identified the grouping of eight test substances into three categories, for both male and female rats. Strikingly, this was achieved even though a range of metabolomics approaches were used. Through assessing intrastudy quality-control samples, the sixth partner observed high technical variation and was unable to group the substances. By comparing workflows, we conclude that some heterogeneity in metabolomics methods is not detrimental to consistent grouping, and that assessing data quality prior to grouping is essential. We recommend development of international guidance for quality-control acceptance criteria. This study demonstrates the reliability of metabolomics for chemical grouping and works towards best-practice.

The antimicrobial resistance catastrophe is a growing global health threat and predicted to be worse in developing countries. Phages for Global Health (PGH) is training scientists in these regions to isolate relevant therapeutic phages for pathogenic bacteria within their locality, and thus contributing to making phage technology universally available. During the inaugural PGH workshop in East Africa, samples from Ugandan municipal sewage facilities were collected and two novel Escherichia coli lytic phages were isolated and characterized. The phages, UP19 (capsid diameter ∼100 nm, contractile tail ∼120/20 nm) and UP30 (capsid diameter ∼70 nm, noncontractile tail of ∼170/20 nm), lysed ∼82% and ∼36% of the 11 clinical isolates examined, respectively. The genomes of UP19 (171.402 kb, 282 CDS) and UP30 (49.834 kb, 75 CDS) closely match the genera Dhakavirus and Tunavirus, respectively. The phages isolated have therapeutic potential for further development against E. coli infections.

Understanding the mode and site of action of a herbicide is key for its efficient development, the evaluation of its toxicological risk, efficient weed control and resistance management. Recently, the mode of action (MoA) of the herbicide cinmethylin was identified in lipid biosynthesis with acyl-ACP thioesterase (FAT) as the site of action (SoA). Cinmethylin was registered for selective use in cereal crops for the control of grass weeds in 2020. Here, we present a high-resolution co-crystal structure of FAT in complex with cumyluron identified by a high throughput crystallization screen. We show binding to and inhibition of FAT by cumyluron. Furthermore, in an array of experiments consisting of FAT binding assays, FAT inhibition assays, physiological and metabolic profiling, we tested compounds that are structurally related to cumyluron and identified the commercial herbicides oxaziclomefone, methyldymron, tebutam and bromobutide, with so far unknown sites of action, as FAT inhibitors. Additionally, we show that the previously described FAT inhibitors cinmethylin and methiozolin bind to FAT in a nanomolar range, inhibit FAT enzymatic activity and lead to similar metabolic changes. Based on presented data, we corroborate cinmethylin and methiozolin as potent FAT inhibitors and identify FAT as the SoA of the herbicides cumyluron, oxaziclomefone, bromobutide, methyldymron and tebutam. © 2022 Society of Chemical Industry.