An Overview of the Latest Metabolomics Studies on Atopic Eczema with New Directions for Study.

Atopic eczema (AE) is an inflammatory skin disorder affecting approximately 20% of children worldwide and early onset can lead to asthma and allergies. Currently, the mechanisms of the disease are not fully understood. Metabolomics, the analysis of small molecules in the skin produced by the host and microbes, opens a window to observe the mechanisms of the disease which then may lead to new drug targets for AE treatment. Here, we review the latest advances in AE metabolomics, highlighting both the lipid and non-lipid molecules, along with reviewing the metabolites currently known to reside in the skin.

Ragweed plants grown under elevated CO2 levels produce pollen which elicit stronger allergic lung inflammation.

BACKGROUND: Common ragweed has been spreading as a neophyte in Europe. Elevated CO2 levels, a hallmark of global climate change, have been shown to increase ragweed pollen production, but their effects on pollen allergenicity remain to be elucidated. METHODS: Ragweed was grown in climate-controlled chambers under normal (380 ppm, control) or elevated (700 ppm, based on RCP4.5 scenario) CO2 levels. Aqueous pollen extracts (RWE) from control- or CO2 -pollen were administered in vivo in a mouse model for allergic disease (daily for 3-11 days, n = 5) and employed in human in vitro systems of nasal epithelial cells (HNECs), monocyte-derived dendritic cells (DCs), and HNEC-DC co-cultures. Additionally, adjuvant factors and metabolites in control- and CO2 -RWE were investigated using ELISA and untargeted metabolomics. RESULTS: In vivo, CO2 -RWE induced stronger allergic lung inflammation compared to control-RWE, as indicated by lung inflammatory cell infiltrate and mediators, mucus hypersecretion, and serum total IgE. In vitro, HNECs stimulated with RWE increased indistinctively the production of pro-inflammatory cytokines (IL-8, IL-1ß, and IL-6). In contrast, supernatants from CO2 -RWE-stimulated HNECs, compared to control-RWE-stimulated HNECS, significantly increased TNF and decreased IL-10 production in DCs. Comparable results were obtained by stimulating DCs directly with RWEs. The metabolome analysis revealed differential expression of secondary plant metabolites in control- vs CO2 -RWE. Mixes of these metabolites elicited similar responses in DCs as compared to respective RWEs. CONCLUSION: Our results indicate that elevated ambient CO2 levels elicit a stronger RWE-induced allergic response in vivo and in vitro and that RWE increased allergenicity depends on the interplay of multiple metabolites.

Current and future approaches for in vitro hit discovery in diabetes mellitus.

Type 2 diabetes mellitus (T2DM) is a serious public health problem. In this review, we discuss current and promising future drugs, targets, in vitro assays and emerging omics technologies in T2DM. Importantly, we open the perspective to image-based high-content screening (HCS), with the focus of combining it with metabolomics or lipidomics. HCS has become a strong technology in phenotypic screens because it allows comprehensive screening for the cell-modulatory activity of small molecules. Metabolomics and lipidomics screen for perturbations at the molecular level. The combination of these data-intensive comprehensive technologies is enabled by the rapid development of artificial intelligence. It promises a deep cellular and molecular phenotyping directly linked to chemical information about the applied drug candidates or complex mixtures.

Unleashing high content screening in hit detection - Benchmarking AI workflows including novelty detection.

Complex mixtures containing natural products are still an interesting source of novel drug candidates. High content screening (HCS) is a popular tool to screen for such. In particular, multiplexed HCS assays promise comprehensive bioactivity profiles, but generate also high amounts of data. Yet, only some machine learning (ML) applications for data analysis are available and these usually require a profound knowledge of the underlying cell biology. Unfortunately, there are no applications that simply predict if samples are biologically active or not (any kind of bioactivity). Within this work, we benchmark ML algorithms for binary classification, starting with classical ML models, which are the standard classifiers of the scikit-learn library or ensemble models of these classifiers (a total of 92 models tested). Followed by a partial least square regression (PLSR)-based classification (44 tested models in total) and simple artificial neural networks (ANNs) with dense layers (72 tested models in total). In addition, a novelty detection (ND) was examined, which is supposed to handle unknown patterns. For the final analysis the models, with and without upstream ND, were tested with two independent data sets. In our analysis, a stacking model, an ensamble model of class ML algorithms, performed best to predict new and unknown data. ND improved the predictions of the models and was useful to handle unknown patterns. Importantly, the classifier presented here can be easily rebuilt and be adapted to the data and demands of other groups. The hit detector (ND + stacking model) is universal and suitable for a broader application to support the search for new drug candidates.

MS4A15 drives ferroptosis resistance through calcium-restricted lipid remodeling.

Ferroptosis is an iron-dependent form of cell death driven by biochemical processes that promote oxidation within the lipid compartment. Calcium (Ca2+) is a signaling molecule in diverse cellular processes such as migration, neurotransmission, and cell death. Here, we uncover a crucial link between ferroptosis and Ca2+ through the identification of the novel tetraspanin MS4A15. MS4A15 localizes to the endoplasmic reticulum, where it blocks ferroptosis by depleting luminal Ca2+ stores and reprogramming membrane phospholipids to ferroptosis-resistant species. Specifically, prolonged Ca2+ depletion inhibits lipid elongation and desaturation, driving lipid droplet dispersion and formation of shorter, more saturated ether lipids that protect phospholipids from ferroptotic reactive species. We further demonstrate that increasing luminal Ca2+ levels can preferentially sensitize refractory cancer cell lines. In summary, MS4A15 regulation of anti-ferroptotic lipid reservoirs provides a key resistance mechanism that is distinct from antioxidant and lipid detoxification pathways. Manipulating Ca2+ homeostasis offers a compelling strategy to balance cellular lipids and cell survival in ferroptosis-associated diseases.

Enhanced Access to the Health-Related Skin Metabolome by Fast, Reproducible and Non-Invasive WET PREP Sampling.

Our skin influences our physical and mental health, and its chemical composition can reflect environmental and disease conditions. Therefore, through sampling the skin metabolome, we can provide a promising window into the mechanisms of the body. However, the broad application of skin metabolomics has recently been hampered by a lack of easy and widely applicable sampling methods. Here, we present a novel rapid, simple, and, most importantly, painless and non-invasive sampling technique suitable for clinical studies of fragile or weakened skin. The method is called WET PREP and is simply a lavage of the skin which focuses on capturing the metabolome. We systematically evaluate WET PREPs in comparison with the non-invasive method of choice in skin metabolomics, swab collection, using ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS2) on two complementary chromatographic columns (C18 reversed phase and hydrophilic interaction chromatography). We also integrate targeted analyses of key metabolites of skin relevance. Overall, WET PREP provides a strikingly more stable shared metabolome across sampled individuals, while also being able to capture unique individual metabolites with a high consistency in intra-individual reproducibility. With the exception of (phospho-)lipidomic studies, we recommend WET PREPs as the preferred skin metabolome sampling technique due to the quick preparation time, low cost, and gentleness for the patient.

Alumina-reinforced zirconia implants: survival rate and fracture strength in a masticatory simulation trial.

BACKGROUND: Alumina-toughened zirconia (ATZ) is a possible alternative material to titanium for oral implants. No data are available on the fracture strength of ATZ oral implants. PURPOSE: The purpose of this study was to examine one-piece implants made of ATZ ceramic under artificial loading conditions and to compare the fracture strength of these implants with implants fabricated from tetragonal zirconium dioxide poylcrystal (TZP)-A. MATERIALS AND METHODS: A total of 72 implants, 48 ATZ implants (groups A and B) and 24 TZP-A implants (group C), were investigated. A chamfer preparation at the implant heads was performed on all implants of groups B and C. Eight implants of each group underwent 1.2 or five million thermomechanical loading cycles in the chewing simulator (load value: 98 N). Further eight implants of each group were not cyclic loaded. Finally, the fracture strength of all implants was determined using a universal testing machine. RESULTS: No implant fractured during loading in the chewing simulator. All implants were placed in the universal testing machine to evaluate fracture strength. The mean fracture strength values±standard deviations for the implants without artificial loading were 1734±165 N (ATZ, no preparation), 1220±85 N (ATZ, with preparation), and 578±49 N (TZP-A, with preparation); 1489±190 N (ATZ, no preparation), 1064±121 N (ATZ, with preparation), and 607±57 N (TZP-A, with preparation) with 1.2 million loading cycles; and 1358±187 N (ATZ, no preparation), 1098±97 N (ATZ, with preparation), and 516±45 N (TZP-A, with preparation) with five million cycles. The ATZ implants showed significantly higher mean fracture strengths compared with the TZP-A implants. Modification of the implant head using diamond burs and increased loading time also led to a significant decrease in fracture strength. CONCLUSIONS: The ATZ implants showed an increased mechanical stability compared with the TZP-A. Modification of the implant head resulted in a decrease in fracture strength. However, within the limits of this in vitro investigation it can be concluded that ATZ implants will withstand functional loading over an estimated period of 20 years.

Advanced identification of global bioactivity hotspots via screening of the metabolic fingerprint of entire ecosystems.

Natural products (NP) are a valuable drug resource. However, NP-inspired drug leads are declining, among other reasons due to high re-discovery rates. We developed a conceptual framework using the metabolic fingerprint of entire ecosystems (MeE) to facilitate the discovery of global bioactivity hotspots. We assessed the MeE of 305 sites of diverse aquatic ecosystems, worldwide. All samples were tested for antiviral effects against the human immunodeficiency virus (HIV), followed by a comprehensive screening for cell-modulatory activity by High-Content Screening (HCS). We discovered a very strong HIV-1 inhibition mainly in samples taken from fjords with a strong terrestrial input. Multivariate data integration demonstrated an association of a set of polyphenols with specific biological alterations (endoplasmic reticulum, lysosomes, and NFkB) caused by these samples. Moreover, we found strong HIV-1 inhibition in one unrelated oceanic sample closely matching to HIV-1-inhibitory drugs on a cytological and a chemical level. Taken together, we demonstrate that even without physical purification, a sophisticated strategy of differential filtering, correlation analysis, and multivariate statistics can be employed to guide chemical analysis, to improve de-replication, and to identify ecosystems with promising characteristics as sources for NP discovery.

Titanium Implant Characteristics After Implantoplasty: An In Vitro Study on Two Different Kinds of Instrumentation.

PURPOSE: To assess surface characteristics and implant stability after implantoplasty performed by two different instrument sequences regarding material loss, surface roughness, and fracture load resistance. Additionally, operators' subjective experience during instrumentation and the damage to neighboring teeth were evaluated. MATERIALS AND METHODS: Titanium implants were placed in the position of both first maxillary molars in models exposing 6 mm of their surface. Implantoplasty was performed in phantom heads: Exposed surfaces were instrumented with diamonds and Arkansas stones or abrasive stones and silicone polishers. Operators reported on abrasion, gloss, effectiveness, and tactility using a visual analog scale (VAS). Residual wall thickness of implants was measured on radiographs, material abrasion using three-dimensional (3D) scans, and surface roughness by contact profilometry. Maximum bending moments were measured. RESULTS: Residual thickness and weight loss were comparable after both treatments (0.3 ± 0.1 and 0.25 ± 0.07 mm and 0.22 ± 0.01 g, and 0.03 ± 0.01 mm and 0.02 ± 0.01 g, respectively, P > .05). Mean surface roughness was lower (P = .0001) for the group with the silicone polishers (0.4 ± 0.2 µm) compared with the group employing diamonds (0.8 ± 0.1 µm). Maximum bending moments showed neither intergroup differences nor stability loss compared with untreated implants. The stone-and-silicone polisher group showed less abrasion (4.6 ± 2.2) and higher gloss values (8.1 ± 1.4) than the diamond-and-Arkansas group (3.1 ± 1.3 and 4.1 ± 2.1, respectively). Superficial tooth injuries at proximal neighbor teeth were common (73% and 80%). CONCLUSION: Implantoplasty did not weaken implant stability. The use of silicone polishers revealed lower surface roughness. Regarding surface smoothness, the instrumentation sequence employing silicon carbide and Arkansas stones followed by silicone polishers seems to be superior to the combination of diamond and Arkansas stones.