Bioactives

Hornstedtia scyphifera (J.Koenig) Steud. represents a lesser-known member of the ginger family (Zingiberaceae) that is used in Malaysia as spice and traditional medicine. The phytochemical investigation of leaves from this species utilizing diverse analytical methods has provided comprehensive insights into its chemical profile for the first time. Headspace gas chromatography-mass spectrometry (HS-GCMS) and GCMS analyses of essential oil and nonpolar extracts verified α-pinene, camphene, p-cymene, and camphor as main volatile compounds. Metabolite profiling of the crude extract by ultra-high-performance-liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) unveiled terpenoids, flavonoids and other phenolics as major compound classes. Isolation and follow-up structure elucidation, involving 1D and 2D NMR, HRMS, UV and CD analysis, yielded two new sesquiterpenoids, (1R,5S,6S,7R,10R)-mustak-14-oic acid (1) and (1R,6S,7S,10R)-6-hydroxy-anhuienosol (2), along with 24 known compounds (seven terpenoids, seven flavonoids, ten phenolics), 21 of these never reported for H. scyphifera. Additionally, the crude extract and fractions from the purification process were screened for antibacterial and antifungal activity. This is supplemented by an extensive literature research for described bioactivities of all isolated compounds. Our results support and explain previously detected antimicrobial, antifungal and neuroprotective effects of H. scyphifera extracts and provide evidence for its potential pharmacological importance.

A bottleneck in the development of new anti-cancer drugs is the recognition of their mode of action (MoA). We combined metabolomics and machine learning to predict MoAs of novel anti-proliferative drug candidates, focusing on human prostate cancer cells (PC-3). As proof of concept, we studied 38 drugs with known effects on 16 key processes of cancer metabolism, profiling low molecular weight intermediates of the central carbon and cellular energy metabolism (CCEM) by LC-MS/MS. These metabolic patterns unveiled distinct MoAs, enabling accurate MoA predictions for novel agents by machine learning. We validate the transferability of MoA predictions from PC-3 to two other cancer cell models and show that correct predictions are still possible, but at the expense of prediction quality. Furthermore, metabolic profiles of treated cells yield insights into intracellular processes, exemplified for drugs inducing different types of mitochondrial dysfunction. Specifically, we predict that pentacyclic triterpenes inhibit oxidative phosphorylation and affect phospholipid biosynthesis, as supported by respiration parameters, lipidomics, and molecular docking. Using biochemical insights from individual drug treatments, our approach offers new opportunities, including the optimization of combinatorial drug applications.

Introduction: Climate change poses various threats to marine life, particularly in shallow tropical waters. Objective: The impact of increased temperature and ultraviolet (UV) exposure on two photosymbiotic cnidarians, a common bubble-tip anemone and an upside-down jellyfish, was investigated. Methods: To illustrate the response of aquatic organisms, the metabolomes of unstressed Entacmaea quadricolor and Cassiopea andromeda were compared for detailed metabolite profiling. UHPLC-MS cou-pled with chemometrics and GNPS molecular networking was employed for sample classification and identification of markers unique to stress responses in each organism. Results: Several compounds with bioactive functions, including peptides and terpenoids, were reported for the first time in both organisms, viz. cyclic tetraglutamate, campestriene, and ceramide aminoethyl phosphonate (CEAP d18:2/16:0). Both anemone and jellyfish were subjected to either elevated UV-B light intensity up to 6.6 KJ m-2 or increased temperatures (28°C, 30°C, 32°C, and 34°C) over 4 days. Phospholipids, steroids, and ceramides emerged as chief markers of both types of stress, as revealed by the multivariate data analysis. Lysophosphatidylcholine (LPC 16:0), LPC (18:0/0:0), and echinoclasterol sulfate appeared as markers in both UV and thermal stress models of the anemone, whereas methyl/pro-pyl cholestane-hexa-ol were discriminatory in the UV stress model only. In the case of jellyfish, nonpolar glycosyl ceramide GlcCer (d14:1/28:6) served as a marker for UV stress, whereas polar peptides were ele-vated in the thermal stress model. Interestingly, both models of jellyfish share a phospholipid, lysophos-phatidylethanolamine (LPE 20:4), as a distinctive marker for stress, reported to be associated indirectly with the activity of innate immune response within other photosymbiotic Cnidaria such as corals and appears to be a fundamental stress response in marine organisms. Conclusion: This study presents several bioinformatic tools for the first time in two cnidarian organisms to provide not only a broader coverage of their metabolome but also broader insights into cnidarian bleaching in response to different stressors, i.e., heat and UV light, by comparing their effects in anemone versus jellyfish.

Two connected histopathological hallmarks of Alzheimer’s disease (AD) are chronic neuroinflammation and synaptic dysfunction. The accumulation of the most prevalent posttranslationally modified form of Aβ1–42, pyroglutamylated amyloid-β (Aβ3(pE)-42) in astrocytes is directly linked to glial activation and the release of proinflammatory cytokines that in turn contribute to early synaptic dysfunction in AD. At present, the mechanisms of Aβ3(pE)-42 uptake to astrocytes are unknown and pharmacological interventions that interfere with this process are not available. Here we developed a simple screening assay to identify substances from a plant extract library that prevent astroglial Aβ3(pE)-42 uptake. We first show that this approach yields valid and reproducible results. Second, we show endocytosis of Aβ3(pE)-42 oligomers by astrocytes and that quercetin, a plant flavonol, is effective to specifically block astrocytic buildup of oligomeric Aβ3(pE)-42. Importantly, quercetin does not induce a general impairment of endocytosis. However, it efficiently protects against early synaptic dysfunction following exogenous Aβ3(pE)-42 application.

The mature seed in legumes consists of an embryo and seed coat. In contrast to knowledge about the embryo, we know relatively little about the seed coat. We analyzed the gene expression during seed development using a panel of cultivated and wild pea genotypes. Gene co‐expression analysis identified gene modules related to seed development, dormancy, and domestication. Oxidoreductase genes were found to be important components of developmental and domestication processes. Proteomic and metabolomic analysis revealed that domestication favored proteins involved in photosynthesis and protein metabolism at the expense of seed defense. Seed coats of wild peas were rich in cell wall‐bound metabolites and the protective compounds predominated in their seed coats. Altogether, we have shown that domestication altered pea seed development and modified (mostly reduced) the transcripts along with the protein and metabolite composition of the seed coat, especially the content of the compounds involved in defense. We investigated dynamic profiles of selected identified phenolic and flavonoid metabolites across seed development. These compounds usually deteriorated the palatability and processing of the seeds. Our findings further provide resources to study secondary metabolism and strategies for improving the quality of legume seeds which comprise an important part of the human protein diet.

Various cyclic lipopeptides (CLPs, 23 compounds) were tested for their antitumor potential against human cervix adenocarcinoma HeLa cells. From the fast screening (tested concentrations: 0.01 and 10 μM) compound 10 ((12S,6S,10S,13S)-6-((R)-sec-butyl)-7-(2-(dodecylamino)-2-oxoethyl)-13-isopropyl- 82-nitro-2,5,12,15-tetraoxo-4,7,11,14-tetraaza-1(1,2)-pyrrolidina-8(1,4)-benzenacyclopentadecaphane- 10-carboxamide) was identified as active against HeLa cell line. The MTT 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide and CV (crystal violet) assays revealed at least five times higher cytotoxic potential of 10 (IC50 = 12.3±1.8 μM, MTT; 9.4±1.5 μM; CV) in comparison to control drug natural occurring CLP surfactin (IC50 = 64.9±0.8 μM, MTT; 76.2±1.6 μM; CV). The cell cycle analysis performed by DAPI (4\',6-diamidino- 2-phenylindole) assay indicated the involvement of apoptosis in HeLa cell death upon treatment with 10, which was confirmed by apoptosis assay (annexin V/PI). Furthermore, during this process caspase activation could be detected (ApoStat assay, immunocytochemistry caspase-3 analysis). The flow cytometry analysis did not display induction of autophagy as a possible death mechanism in HeLa cells upon 10 treatment. The current findings could be used to design more effective CLPs based on 10 structure as potential anticancer agents.

Flavor is the main factor driving consumers acceptance of food products. However, tracking the biochemistry of flavor is a formidable challenge due to the complexity of food composition. Current methodologies for linking individual molecules to flavor in foods and beverages are expensive and time-consuming. Predictive models based on machine learning (ML) are emerging as an alternative to speed up this process. Nonetheless, the optimal approach to predict flavor features of molecules remains elusive. In this work we present FlavorMiner, an ML-based multilabel flavor predictor. FlavorMiner seamlessly integrates different combinations of algorithms and mathematical representations, augmented with class balance strategies to address the inherent class of the input dataset. Notably, Random Forest and K-Nearest Neighbors combined with Extended Connectivity Fingerprint and RDKit molecular descriptors consistently outperform other combinations in most cases. Resampling strategies surpass weight balance methods in mitigating bias associated with class imbalance. FlavorMiner exhibits remarkable accuracy, with an average ROC AUC score of 0.88. This algorithm was used to analyze cocoa metabolomics data, unveiling its profound potential to help extract valuable insights from intricate food metabolomics data. FlavorMiner can be used for flavor mining in any food product, drawing from a diverse training dataset that spans over 934 distinct food products.Scientific Contribution FlavorMiner is an advanced machine learning (ML)-based tool designed to predict molecular flavor features with high accuracy and efficiency, addressing the complexity of food metabolomics. By leveraging robust algorithmic combinations paired with mathematical representations FlavorMiner achieves high predictive performance. Applied to cocoa metabolomics, FlavorMiner demonstrated its capacity to extract meaningful insights, showcasing its versatility for flavor analysis across diverse food products. This study underscores the transformative potential of ML in accelerating flavor biochemistry research, offering a scalable solution for the food and beverage industry.

In Vietnam, the stems and roots of the Rutaceous plant Paramignya trimera (Oliv.) Burkill (known locally as “Xáo tam phân”) are widely used to treat liver diseases such as viral hepatitis and acute and chronic cirrhosis. In an effort to search for Vietnamese natural compounds capable of inhibiting coronavirus based on molecular docking screening, two new dimeric coumarin glycosides, namely cis-paratrimerin B (1) and cis-paratrimerin A (2), and two previously identified coumarins, the trans-isomers paratrimerin B (3) and paratrimerin A (4), were isolated from the roots of P. trimera and tested for their anti-ACE-2 inhibitory properties in vitro. It was discovered that ACE-2 enzyme was inhibited by cis-paratrimerin B (1), cis-paratrimerin A (2), and trans-paratrimerin B (3), with IC50 values of 28.9, 68, and 77 μM, respectively. Docking simulations revealed that four biscoumarin glycosides had good binding energies (ΔG values ranging from −10.6 to −14.7 kcal/mol) and mostly bound to the S1′ subsite of the ACE-2 protein. The key interactions of these natural ligands include metal chelation with zinc ions and multiple H-bonds with Ser128, Glu145, His345, Lys363, Thr371, Glu406, and Tyr803. Our findings demonstrated that biscoumarin glycosides from P. trimera roots occur naturally in both cis- and trans- diastereomeric forms. The biscoumarin glycosides Lys363, Thr371, Glu406, and Tyr803. Our findings demonstrated that biscoumarin glycosides from P. trimera roots hold potential for further studies as natural ACE-2 inhibitors for preventing SARS-CoV-2 infection.

Background: The diagnosis of Alzheimer’s disease (AD) relies on core cerebrospinal fluid (CSF) biomarkers, amyloid beta (Aβ) and tau. As the brain is then already damaged, researchers still strive to discover earlier biomarkers of disease onset and the progression of AD. Glycation, advanced glycation end products (AGEs) and oxidative modifications on proteins in CSF mirror the underlying biological mechanisms that contribute to early AD pathology. However, analyzing free AGEs in the body fluids of AD patients has led to controversial results. Thus, this pilot study aimed to test the feasibility of detecting, identifying and quantifying differentially glycated, AGE or oxidatively modified peptides in CSF proteins of AD patients (n = 5) compared to a control group (n = 5). Methods: To this end, we utilized a data-dependent (DDA) nano liquid chromatography (LC) linear ion trap-Orbitrap tandem mass spectrometry (MS/MS) ) approach and database search that included over 30 glycative and oxidative modifications in four search nodes to analyze endogenous modifications on individual peptides. Furthermore, we quantified candidate peptide abundance using LC Quan. Results: We identified 299 sites of early and advanced glycation and 53 sites of oxidatively modified tryptophan. From those, we identified 17 promising candidates as putative biomarkers (receiver operating curve-area under the curve (ROC-AUC) > 0.8), albeit without statistical significance. Conclusions: The potential candidates with higher discrimination power showed correlations with established diagnostic markers, thus hinting toward the potential of those peptides as biomarkers.

Diterpenoids form a diverse group of natural products, many of which are or could become pharmaceuticals or industrial chemicals. The modular character of diterpene biosynthesis and the promiscuity of the enzymes involved make combinatorial biosynthesis a promising approach to generate libraries of diverse diterpenoids. Here, we report on the combinatorial assembly in yeast of ten diterpene synthases producing (+)-copalyldiphosphate-derived backbones and four cytochrome P450 oxygenases (CYPs) in diverse combinations. This resulted in the production of over 200 diterpenoids. Based on literature and chemical database searches, 162 of these compounds can be considered new-to-Nature. The CYPs accepted most substrates they were given but remained regioselective with few exceptions. Our results provide the basis for the systematic exploration of the diterpenoid chemical space in yeast using sequence databases.