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18 pages, 9670 KiB  
Article
Genome-Wide Characterization and Analysis of the bHLH Gene Family in Perilla frutescens
by Jiankang Chen, Jiayi Xu, Ping Wang, Yihan Wang, Yumeng Wang, Junmei Lian, Yan Yan, Lin Cheng, Yingping Wang and Peng Di
Int. J. Mol. Sci. 2024, 25(24), 13717; https://doi.org/10.3390/ijms252413717 (registering DOI) - 22 Dec 2024
Abstract
Perilla frutescens (L.) Britt. is a traditional medicinal and culinary plant with a long history of cultivation and significant potential for broader utilization. The basic helix-loop-helix (bHLH) gene family is essential for regulating plant growth, development, stress responses, and secondary metabolism. [...] Read more.
Perilla frutescens (L.) Britt. is a traditional medicinal and culinary plant with a long history of cultivation and significant potential for broader utilization. The basic helix-loop-helix (bHLH) gene family is essential for regulating plant growth, development, stress responses, and secondary metabolism. However, the bHLH gene family in P. frutescens has not yet been characterized. In this study, a total of 205 bHLH genes were identified in P. frutescens through genome mining and analysis. Phylogenetic analysis classified these PfbHLH genes into 23 distinct subfamilies. Promoter analysis revealed an enrichment of cis-acting elements linked to plant hormone signaling and stress responses, suggesting their potential regulatory roles in development, growth, and stress adaptation. Expression profiling based on publicly available RNA-seq data demonstrated tissue-specific expression patterns of PfbHLH genes in roots, stems, and leaves. Four PfbHLH genes (PfbHLH66, PfbHLH45, PfbHLH13, and PfbHLH5) showed significant responses to methyl jasmonate (MeJA) induction. Yeast one-hybrid assays confirmed that these PfbHLH proteins could bind to the cis-acting G-box (CACGTG) element. This study offers new perspectives on the evolution, regulatory mechanisms, and functional roles of the bHLH gene family in P. frutescens. The findings deepen our understanding of the functional diversity within this gene family and establish a foundation for genetic enhancement and the biosynthesis of medicinal compounds in the species. Full article
(This article belongs to the Special Issue Plant Responses to Biotic and Abiotic Stresses)
14 pages, 1152 KiB  
Article
An Investigation of the Saccharides Profile and Metabolic Gene Expression in Muskrat Scented Glands in Different Secretion Seasons
by Juntong Zhou, Defu Hu, Nuannuan Feng, Shuqiang Liu and Junqing Li
Animals 2024, 14(24), 3705; https://doi.org/10.3390/ani14243705 (registering DOI) - 22 Dec 2024
Abstract
The adult male muskrat has a pair of scented glands, which show clear seasonal changes in their developmental status between the secretion season and non-secretion season. During the secretion season, the scented glands are much larger than in the non-secretion season, with the [...] Read more.
The adult male muskrat has a pair of scented glands, which show clear seasonal changes in their developmental status between the secretion season and non-secretion season. During the secretion season, the scented glands are much larger than in the non-secretion season, with the metabolism of glandular cells increasing and a large amount of musk being produced. In this work, the blood, musk, and scented gland tissue were collected from three healthy adult male muskrats during secretion season (September). And the blood and scented gland tissue from another three healthy adult male muskrats during the non-secretion season (November) were also sampled. The saccharides from blood and musk were detected by liquid chromatography–mass spectrometry (LC-MS), indicating the saccharides are concentrated in the scented glands during the secretion season. What is more, transcriptome analysis was employed to investigate the expression patterns of saccharides’ pathways, suggesting some saccharides’ metabolism-related genes undergo significant seasonal changes. Above all, scented gland saccharides’ metabolism displays seasonal differences, and the enhancement in saccharides’ metabolic activity during the secretion phase maintains glandular proliferation and secretion function. Full article
(This article belongs to the Section Mammals)
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Figure 1

Figure 1
<p>The result of OPLS-DA. (<b>a</b>) The differential analysis of saccharide metabolism in serum during the secretion and non-secretion season. (<b>b</b>) The differential analysis of saccharide metabolites in the blood serum and musk during the secretion and season. (The horizontal axis represents the score Tp of the main components in the OSC process. The vertical axis represents the score values TOR2X and R2Y of the orthogonal components in the OSC process, respectively, indicating the explanatory power of the constructed model on the X and Y matrices. The X matrix is the model input, i.e., the metabolite quantification matrix, the Y matrix is the model output, i.e., the sample grouping matrix, and Q2 represents the predictive ability of the model, i.e., whether the constructed model can distinguish the correct sample grouping based on metabolic expression levels. When Q2 &gt; 0.5, it can be considered an effective model).</p>
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<p>The abundance of saccharide substances (saccharides with significant changes are marked by ‘*’).</p>
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<p>The gene expression of the transcriptome related to saccharide metabolism. (<b>a</b>) Starch and sucrose metabolism (ko00050); (<b>b</b>) galactose metabolism (ko00052); (<b>c</b>) fructose and mannose metabolism (ko00051); (<b>d</b>) pentose phosphate pathway (ko00030); (<b>e</b>) amino sugar and nucleotide sugar metabolism (ko00520); (<b>f</b>) glycosaminoglycan degradation (ko00531); (<b>g</b>) pentose and glucuronate interconversions (ko00040); (<b>h</b>) glycolysis/gluconeogenesis (ko00010). Data are presented as means + SD (<span class="html-italic">n</span> = 9). FDR &lt; 0.01 and fold change ≥ 2. FDR, false discovery rate. FPKM, fragments per kilobase of transcript per million mapped reads.</p>
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15 pages, 4045 KiB  
Article
Mulberry Branch Extracts Enhance the Antioxidant Capacity of Broiler Breast Muscle by Activating the Nrf2 and Cytochrome P450 Signaling Pathway
by Xiang Shi, Wei Qian, Xinlan Wei, Xiaoqing Qin, Jinyan Han, Chao Su and Lijun Bao
Animals 2024, 14(24), 3702; https://doi.org/10.3390/ani14243702 (registering DOI) - 22 Dec 2024
Abstract
Mulberry branch extracts (MBEs) have garnered significant attention as natural feed additives and antioxidants; however, their antioxidant properties in meat post-slaughter and their influence on muscle-related metabolic processes remain largely unexplored. Herein, we evaluated the effects of MBEs on the antioxidant capacity and [...] Read more.
Mulberry branch extracts (MBEs) have garnered significant attention as natural feed additives and antioxidants; however, their antioxidant properties in meat post-slaughter and their influence on muscle-related metabolic processes remain largely unexplored. Herein, we evaluated the effects of MBEs on the antioxidant capacity and metabolic processes of breast muscle in yellow-feather broilers by adding 0 g/kg, 1.5 g/kg, 3.0 g/kg, and 4.5 g/kg of MBEs to their diets. The results demonstrate that MBEs enhanced the activity of antioxidant enzymes in muscle tissue. Specifically, a real-time quantitative PCR analysis revealed that MBEs increased the expression of antioxidant enzyme genes in a dose-dependent manner, activated the Nrf2 signaling pathway, and upregulated the expression of the Nrf2 gene and its downstream targets at doses of up to 3.0 g/kg. Furthermore, the results of widely targeted metabolomics indicate that the dietary supplementation of MBEs changed the amino acid profile of the muscle, increasing the levels of amino acids and small peptides that contribute to antioxidant properties while reducing the contents of oxidized lipids and carnitine (C5:1) and partially reducing the content of lysophosphatidylcholine (LPC). Notably, at doses of up to 3 g/kg, the levels of five signature bile acids increased in correlation with the added dose. A KEGG analysis indicated that the differential metabolites were predominantly enriched in the metabolism of xenobiotics by cytochrome P450, suggesting that the function of MBEs may be associated with the expression of P450 enzymes. In summary, this study demonstrates that MBEs are effective, safe, and natural antioxidants, offering a viable solution to mitigating oxidative stress in the yellow-feather broiler farming industry and even in livestock farming. Full article
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Figure 1
<p>Effect of MBEs on antioxidant capacity in breast muscle of broilers. (<b>A</b>) Catalase, CAT. (<b>B</b>) Glutathione peroxidase, GSH-Px. (<b>C</b>) Total superoxide dismutase, T-SOD. (<b>D</b>) Total antioxidant capacity, T-AOC. (<b>E</b>) MDA. CK: control group; Treat-1500: basal diet containing 1.5 g/kg; Treat-3000: basal diet containing 3.0 g/kg; Treat-4500: basal diet containing 4.5 g/kg. <sup>a,b,c,d</sup> Different letters indicate significant differences for interaction effect (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of MBEs on relative mRNA expression levels of antioxidant-related genes. (<b>A</b>) Effects of MBEs on relative expression of antioxidant enzyme genes. <span class="html-italic">GSH-Px</span>: glutathione peroxidase; <span class="html-italic">SOD1</span>: superoxide dismutase 1. <span class="html-italic">CAT</span>: catalase. (<b>B</b>) Effects of MBEs on relative mRNA expression levels of Nrf2 signaling pathway. <span class="html-italic">Nrf2</span>: nuclear factor erythroid 2-related factor 2; <span class="html-italic">HO-1</span>: heme oxygenase-1; <span class="html-italic">NQO-1</span>: NAD(P)H quinone oxidoreductase-1; <span class="html-italic">GCLC</span>: glutamyl–cysteine ligase; <span class="html-italic">GCLM</span>: glutamate–cysteine ligase modifier subunit. <sup>a,b,c,d</sup> Different letters indicate significant differences for interaction effect (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effect of MBEs on overall metabolites in broiler breast muscle. (<b>A</b>) Ring chart of proportion of all metabolite classes. (<b>B</b>) Heat map analysis of all metabolites.</p>
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<p>Effect of different levels of MBEs added to diet on metabolites of broiler breast muscle compared to control group. OPLS-DA analysis between CK and Treat-1500 (<b>A</b>), CK and Treat-3000 (<b>B</b>), and CK and Treat-4500 groups (<b>C</b>). Volcano plot analysis between CK and Treat-1500 (<b>D</b>), CK and Treat-3000 (<b>E</b>), and CK and Treat-4500 groups (<b>F</b>). Heat map analysis between CK and Treat-1500 (<b>G</b>), CK and Treat-3000 (<b>H</b>), and CK and Treat-4500 groups (<b>I</b>).</p>
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<p>Top 10 metabolites with largest multiplicative upregulated and down-regulated adjustments. (<b>A</b>) Control group vs. Treat-1500 group. (<b>B</b>) Control group vs. Treat-3000 group. (<b>C</b>) Control group vs. Treat-4500 group. Red bars: up-regulation; Green bars: down-regulation.</p>
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<p>K-means clustering of differential metabolite profiles.</p>
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<p>KEGG metabolic pathway enrichment analysis based on significant differential metabolites. (<b>A</b>) Control group vs. Treat-1500 group. (<b>B</b>) Control group vs. Treat-3000 group. (<b>C</b>) Control group vs. Treat-4500 group.</p>
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<p>Schematic plot of MBEs ameliorating antioxidant capacity of broiler breast muscle. XRE: xenobiotic response element; AhR: aryl hydrocarbon receptor; ARE: antioxidant response element. Red arrows: up-regulation; Green arrows: down- regulation.</p>
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13 pages, 1666 KiB  
Article
The Use of a Penta-Deuterophenyl Substituent to Improve the Metabolic Stability of a Tyrosine Kinase Inhibitor
by Júlia Dulsat, Raimon Puig de la Bellacasa and José I. Borrell
Molecules 2024, 29(24), 6042; https://doi.org/10.3390/molecules29246042 (registering DOI) - 22 Dec 2024
Viewed by 127
Abstract
In cases in which a rapid metabolism is the cause of an unfavorable pharmacokinetic profile, it is important to determine the Sites of Metabolism (SoMs) of a molecule to introduce the necessary modifications to improve the stability of the compound. The substitution of [...] Read more.
In cases in which a rapid metabolism is the cause of an unfavorable pharmacokinetic profile, it is important to determine the Sites of Metabolism (SoMs) of a molecule to introduce the necessary modifications to improve the stability of the compound. The substitution of hydrogen atoms by deuterium atoms has been proposed to ameliorate such properties due to the greater stability of the C-D bonds. IQS016, bearing a 2-phenylamino substituent, is a compound previously described by our group with good biological activity as a discoidin domain receptor (DDR2) inhibitor but suffers from low metabolic stability determined in a test with rat-liver microsomes (less than 50% of the initial compound after 60 min). We have obtained the corresponding 2-(penta-deuterophenyl) analog (IQS016-d5) from aniline-2,3,4,5,6-d5 showing that it has a better metabolic stability than IQS016 and a higher inhibitory effect on isolated tyrosine kinase receptors but not a better 2D in vitro effect. Full article
(This article belongs to the Special Issue Design, Synthesis and Biological Evaluation of Heterocyclic Compounds)
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Figure 1
<p>Structures of <b>IQS016</b> and the corresponding deuterated analog <b>IQS016-<span class="html-italic">d<sub>5</sub></span></b>.</p>
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<p>Graphical representation of the drug–enzyme interaction between <b>IQS016-<span class="html-italic">d<sub>5</sub></span></b> and the heme group of the 2C8CYP enzyme obtained from the Molecular Forecaster software.</p>
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<p><sup>13</sup>C-NMR spectra (D<sub>2</sub>O) of aniline-2,3,4,5,6-<span class="html-italic">d<sub>5</sub></span> (<b>2</b>) and 1-(phenyl-<span class="html-italic">d<sub>5</sub></span>)guanidine hydrogen sulfite (<b>3</b>).</p>
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<p>Metabolic stability of <b>IQS016</b> (n = 2) and <b>IQS016-<span class="html-italic">d<sub>5</sub></span></b> (n = 2) using the rat-liver microsome incubation protocol.</p>
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<p>Cell viability (%) in a 2D culture of PANC-1 (blue), BxPC-3 (green), and hNDF (purple) after 72 h of incubation with <b>IQS016</b> and <b>IQS016-<span class="html-italic">d<sub>5</sub></span></b> (n = 3).</p>
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<p>Synthetic route for <b>IQS016-<span class="html-italic">d<sub>5</sub></span></b>: (a) MeOH, MW irradiation, 65 °C, 2 min, 79%; (b) NaOMe, MeOH, 5 h, reflux, Ar atmosphere, 99%; (c) (1) <b>3</b>, anhydrous 1,4-dioxane, NaOMe, 65 °C, 15 min and (2) addition of <b>6</b>, 140 °C, 40 min, 79%; (d) NaOMe, MeOH, 140 °C, 40 min, 78%; (e) anhydrous DMSO, NaH, 10 min room temp., 100 °C for 4 h, 79%; (f) NaH, anhydrous DMSO, MeI, room temp., overnight, 68%.</p>
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13 pages, 2168 KiB  
Article
Carrot-Derived Rhamnogalacturonan-I Consistently Increases the Microbial Production of Health-Promoting Indole-3-Propionic Acid Ex Vivo
by Annick Mercenier, Lam Dai Vu, Jonas Poppe, Ruud Albers, Sue McKay and Pieter Van den Abbeele
Metabolites 2024, 14(12), 722; https://doi.org/10.3390/metabo14120722 (registering DOI) - 21 Dec 2024
Viewed by 319
Abstract
Background: Using dietary interventions to steer the metabolic output of the gut microbiota towards specific health-promoting metabolites is often challenging due to interpersonal variation in treatment responses. Methods: In this study, we combined the ex vivo SIFR® (Systemic Intestinal Fermentation Research) technology [...] Read more.
Background: Using dietary interventions to steer the metabolic output of the gut microbiota towards specific health-promoting metabolites is often challenging due to interpersonal variation in treatment responses. Methods: In this study, we combined the ex vivo SIFR® (Systemic Intestinal Fermentation Research) technology with untargeted metabolite profiling to investigate the impact of carrot-derived rhamnogalacturonan-I (cRG-I) on ex vivo metabolite production by the gut microbiota of 24 human adults. Results: The findings reveal that at a dose equivalent to 1.5 g/d, cRG-I consistently promoted indole-3-propionic acid (IPA) production (+45.8% increase) across all subjects. At a dose equivalent to 0.3 g/d, increased IPA production was also observed (+14.6%), which was comparable to the effect seen for 1.5 g/d inulin (10.6%). IPA has been shown to provide protection against diseases affecting the gut and multiple organs. The Pearson correlation analysis revealed a strong correlation (R = 0.65, padjusted = 6.1 × 10−16) between the increases in IPA levels and the absolute levels of Bifidobacterium longum, a producer of indole-3-lactic acid (ILA), an intermediate in IPA production. Finally, the community modulation score, a novel diversity index, demonstrated that cRG-I maintained a high α-diversity which has previously been linked to elevated IPA production. Conclusions: The results from the ex vivo SIFR® experiment mirrored clinical outcomes and provided novel insights into the impact of cRG-I on the gut microbiome function. Importantly, we demonstrated that cRG-I promotes tryptophan conversion into IPA via gut microbiome modulation, thus conferring benefits via amino acid derived metabolites extending beyond those previously reported for short chain fatty acids (SCFA) resulting from carbohydrate fermentation. Full article
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<p>Study design using the ex vivo SIFR<sup>®</sup> technology to assess the impact of cRG-I, IN and XA on the human gut microbiota. (<b>A</b>) Reactor design using the ex vivo SIFR<sup>®</sup> technology to test the impact of the fibers with different specificities at a dose equivalent to 0.3 g/d (cRG-I_L) or 1.5 g/d (cRG-I_H, IN and XA), compared to a no-substrate control (NSC) in fecal samples of 24 human adults in parallel. (<b>B</b>) Timeline and analyses at 0 h and 48 h. Analysis of key fermentation parameters and microbial composition was reported earlier by Van den Abbeele et al., 2023 [<a href="#B15-metabolites-14-00722" class="html-bibr">15</a>].</p>
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<p>cRG-I, XA and IN stimulated the microbial production of different metabolites. The heat map displays the impact of a dose equivalent to 0.3 g/d carrot-derived rhamnogalacturonan-I (cRG-I_L) or 1.5 g/d carrot-derived rhamnogalacturonan-I (cRG-I_H), inulin (IN) and xanthan (XA) on a selection of metabolites identified at level 1 and 2a, as quantified Via untargeted LC-MS after 48 h of incubation. Colonic fermentation was simulated using SIFR<sup>®</sup> technology for healthy adults (<span class="html-italic">n</span> = 24). The reported metabolites were significantly affected by the treatments (FDR &lt; 0.20). Significant differences are indicated in bold of the log<sub>2</sub>-transformed average fold change (abundance treatment/abundance NSC). Metabolite classes and subclasses (based on the precursor amino acids or nucleobases) are indicated on the left side of the heat map. cRG-I: carrot-derived rhamnogalacturonan-I, IN: inulin, XA: xanthan.</p>
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<p>cRG-I enhanced the microbial production of health-related metabolites and reduced the production of harmful linoleic acid derivatives. (<b>a</b>) The bar chart showing level 1/2a metabolites that were significantly affected (highlighted by asterisks) by an equivalent dose of 0.3 and 1.5 g/d carrot-derived rhamnogalacturonan-I (cRG-I_L and cRG_H, respectively), after 48 h of SIFR<sup>®</sup> colonic incubation for healthy adults (<span class="html-italic">n</span> = 24). The data are presented as log<sub>2</sub>-transformed average fold change (abundance treatment/abundance NSC). Potentially beneficial and harmful metabolites are highlighted in green and yellow, respectively, while metabolites in gray are not discussed with respect to health benefits. (<b>b</b>) log2-transformed fold change versus NSC for a selection of health-related metabolites promoted by cRG-I. (<b>c</b>) Disease-associated linoleic acid derivatives that were reduced by cRG-I.</p>
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<p>The fermentation of cRG-I promoted indole-3-propionic acid (IPA) production consistently across 24 donors, correlating with the consistent increase in <span class="html-italic">Bifidobacterium longum</span> (OTU10). (<b>a</b>) Absolute IPA levels (area under curve, AUC) and (<b>b</b>) log<sub>2</sub>-transformed fold change versus NSC, as quantified Via LC-MS after 48 h SIFR<sup>®</sup> colonic fermentation of carrot-derived rhamnogalacturonan-I (cRG-I), inulin (IN) and xanthan (XA) by the gut microbiota of 24 healthy adults. (<b>c</b>) Absolute levels (cells/mL) and (<b>d</b>) log<sub>2</sub>-transformed fold change in <span class="html-italic">B. longum</span> (OTU10). (<b>e</b>) The Pearson correlation analysis between <span class="html-italic">B. longum</span> (OTU3) and IPA across all study arms. The Pearson correlation coefficient (R) and corrected <span class="html-italic">p</span>-value indicating the significance of the correlation are presented. (<b>f</b>) Schematic presentation of reductive conversion of tryptophan into IPA Via indole-3-pyruvic acid (IPyA) and indole-3-lactic acid (ILA). cRG-I likely promotes IPA Via stimulation of ILA-producing <span class="html-italic">B. longum</span>. Competing pathways that convert tryptophan to indole-3-acetic acid (IAA) and indole are shown in gray. Interactions between <span class="html-italic">B. thetaiotaomicron</span> and <span class="html-italic">E. coli</span> that suppress indole biosynthesis upon pectin supplementation are also shown [<a href="#B19-metabolites-14-00722" class="html-bibr">19</a>].</p>
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23 pages, 3717 KiB  
Article
Influence of Yeast Interactions on the Fermentation Process and Aroma Production in Synthetic Cocoa Pulp vs. Real Mucilage Media
by Lydie Besançon, Da Lorn, Christelle Kouamé, Joël Grabulos, Marc Lebrun, Angélique Fontana, Sabine Schorr-Galindo, Renaud Boulanger, Caroline Strub and Alexandre Colas de la Noue
Fermentation 2024, 10(12), 662; https://doi.org/10.3390/fermentation10120662 (registering DOI) - 21 Dec 2024
Viewed by 378
Abstract
Cocoa fermentation plays a key role in defining chocolate’s flavor, with yeasts being central to this process. This study aimed to explore intraspecific genetic diversity of major indigenous yeasts (i.e., Saccharomyces cerevisiae and Pichia kudriavzevii), and their potential interaction in the cocoa [...] Read more.
Cocoa fermentation plays a key role in defining chocolate’s flavor, with yeasts being central to this process. This study aimed to explore intraspecific genetic diversity of major indigenous yeasts (i.e., Saccharomyces cerevisiae and Pichia kudriavzevii), and their potential interaction in the cocoa pulp environment. Their metabolic intraspecific diversity was characterized in synthetic cocoa pulp medium. Then, Saccharomyces cerevisiae, Pichia kudriavzevii, and other strains were introduced to each other to evaluate their potential negative interaction. Interesting strain associations were selected to further explore their interaction in synthetic cocoa pulp medium as well as real fresh cocoa pulp. From a fermentation campaign in Ivory Coast, a set of Saccharomyces (S.) cerevisiae and Pichia (P.) kudriavzevii strains were isolated from batches classified according to their chocolate quality (i.e., standard, intermediate, or premium chocolate). Less abundant species (i.e., Torulaspora franciscae, Kluyveromyces marxianus) were also isolated and tested for their potential negative interactions with S. cerevisiae and P. kudriavzevii. A set of strains were selected and cultured in single and in co-culture in a minimal cocoa pulp synthetic medium and in fresh cocoa pulp to highlight potential positive and/or negative interactions regarding fermentative aroma profile (i.e., higher alcohols, acetate esters, medium-chain fatty acids, and ethyl esters). The results highlighted the dominance of S. cerevisiae in fermentation kinetics and medium- to long-chain ester production, contrasted with P. kudriavzevii’s efficiency in short-chain ester synthesis. Intraspecific aroma profile variations can be pointed out. The co-cultures of P. kudriavzevii and S. cerevisiae strains isolated from the premium chocolate batch had a positive impact on the fermented pulp aroma profile. Negative interactions were observed with Torulaspora franciscae, which eliminated P. kudriavzevii’s aroma expression. Finally, the comparison of the data obtained for the minimal cocoa pulp synthetic medium compared to the cocoa pulp allowed us to draw conclusions about the use of synthetic media for studying cocoa fermentation. These findings emphasize the complex microbial interactions in cocoa fermentation that could shape future cocoa bean aroma. Full article
(This article belongs to the Special Issue Development and Application of Starter Cultures, 2nd Edition)
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Figure 1
<p>Cluster analysis showing the intraspecies relationship of the isolates of the 19 strains of <span class="html-italic">S. cerevisiae</span> (<b>a</b>) and the 19 strains of <span class="html-italic">P. kudriavzevii</span> (<b>b</b>) by (GTG)5-rep-PCR fingerprinting. The strains selected for the next steps of analysis are indicated by an *. Solid-line frames (―) indicate the premium-quality chocolate batch, dotted-line frames (- -) indicate the intermediate-quality batch, and unframed isolates indicate the standard-quality batch. (<b>c</b>) Maximum CO<sub>2</sub> release rate (g/L.h<sup>−1</sup>) distribution between the 19 strains of <span class="html-italic">S. cerevisiae</span> and the 19 strains of <span class="html-italic">P. kudriavzevii</span> in glucose-rich YPD medium in static conditions at 30 °C for 7 days.</p>
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<p>Illustration of the inhibitory effect observed during killer assay between (<b>a</b>,<b>b</b>) <span class="html-italic">S. cerevisiae</span> S56 and <span class="html-italic">K. marxianus</span> N22, and (<b>c</b>,<b>d</b>) <span class="html-italic">P.kudriavzevii</span> P66 and <span class="html-italic">T. franciscea</span> N5. YPD medium acidified with citrate phosphate buffer at pH 3.7 (<b>b</b>,<b>d</b>) and pH 4.5 (<b>a</b>,<b>c</b>) with methylene blue. A similar behaviour (<b>c</b>,<b>d</b>) was observed between N5 and all <span class="html-italic">P. kudriavzevii</span> strains. For <span class="html-italic">S. cerevisiae</span>, only strains S56, S59, S60, and S77 showed negative interactions with N22. No other interactions were observed between all the strains.</p>
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<p>Heatmap (z-scores, with blue indicating lower values and red indicating higher values) and boxplot summary (z-scores) of the aroma profile of minimal cocoa pulp synthetic medium (MPS) fermented by single <span class="html-italic">S. cerevisiae</span> (S), <span class="html-italic">P. kudriavzevii</span> (P), and non-<span class="html-italic">S. cerevisiae</span> (N) strains after 5 days of fermentation (30 °C). Clustering was performed using the Euclidean distance metric for both yeast strains (columns) and aromatic compounds (row), grouping similar profiles together. Abbreviations: S77, single culture of <span class="html-italic">S. cerevisiae</span> S77; P76, single culture of <span class="html-italic">P. kudriavzevii</span> P76; N22, single culture of <span class="html-italic">K. marxianus</span> N22. Solid-line frames (―) indicate the premium-quality chocolate batch, dotted-line frames (- -) indicate the intermediate-quality batch, and unframed match indicate the standard-quality batch.</p>
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<p>Radar chart representation of standardized aroma z-scores for individual yeast strains and their respective co-cultures in the MPS medium after 5 days of fermentation (30 °C). Each line represents the aroma profile of a particular yeast strain or co-culture (P36, S4, S35, S77, N5, N22, S77+N22, N5+P36, S35+P36, and P36+S4) based on the distinct aroma profiles of (<b>a</b>–<b>d</b>) <span class="html-italic">P. kudriavzevii</span> and (<b>e</b>–<b>h</b>) <span class="html-italic">S. cerevisiae.</span> The strains selected illustrate a range of fermentation and aroma production capabilities: P36, the top <span class="html-italic">P. kudriavzevii</span> strain aroma producer from the premium batch; S35, highly active <span class="html-italic">S. cerevisiae</span> strain with strong aroma potential from the premium batch; S4, the control with intermediate fermentation performance and low aroma potential; N5 and N22, non-<span class="html-italic">S. cerevisiae</span> strains with inhibitory impact; and S77, a <span class="html-italic">S. cerevisiae</span> strain known for its interaction with N22.</p>
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<p>Fermentation kinetics and yeast viability during cocoa pulp fermentation (30 °C): (<b>a</b>) total yeast biomass for <span class="html-italic">S. cerevisiae</span> (S35), <span class="html-italic">P. kudriavzevii</span> (P36), and <span class="html-italic">T. franciscae</span> (N5) in single cultures; (<b>b</b>) yeast biomass distribution in co-cultures (S35+P36); (<b>c</b>) yeast biomass distribution in co-cultures (N5+P36); (<b>d</b>–<b>f</b>) sugar consumption (sucrose, glucose, and fructose); (<b>g</b>) CO<sub>2</sub> release; (<b>h</b>) ethanol production throughout fermentation.</p>
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<p>Principal component analysis (PCA) based on the main volatile compounds (esters and higher alcohols) produced by single and co-cultures of <span class="html-italic">S. cerevisiae</span> S35, <span class="html-italic">P. kudriavzevii</span> P36, and non-<span class="html-italic">S. cerevisiae</span> N5 in the cocoa pulp medium at different times of fermentation, from 0 h to 120 h. (<b>a</b>) Loading plot and (<b>b</b>) score plot showing the influence of the strains (colors) and the fermentation time in hours (shapes). Single and co-culture strains and species details are explained in the legend of the figure. Abbreviations: S35, single culture of <span class="html-italic">S. cerevisiae</span> S35; P36, single culture of <span class="html-italic">P. kudriavzevii</span> P36; N5, single culture of <span class="html-italic">T. franciscae</span> N5; S35+P36, co-culture of S35 and P36; N5+P36, co-culture of N5 and P36.</p>
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15 pages, 3475 KiB  
Article
NMR and LC-MS-Based Metabolomics to Study the Effect of Surfactin on the Metabolome of Flax
by Omar Abdelaziz Benamar, Mathie Craquelin, Damien Herfurth, Roland Molinié, Jean-Xavier Fontaine, Akeapot Srifa, Marc Ongena, François Mesnard and Ophélie Fliniaux
Appl. Sci. 2024, 14(24), 11999; https://doi.org/10.3390/app142411999 (registering DOI) - 21 Dec 2024
Viewed by 425
Abstract
Flax (Linum usitatissimum) is a versatile plant used in a range of applications, from textiles to nutrition. Surfactin, a cyclic lipopeptide biosurfactant produced by bacteria such as Bacillus subtilis, has potential as a biocontrol agent or as a plant defense inducer [...] Read more.
Flax (Linum usitatissimum) is a versatile plant used in a range of applications, from textiles to nutrition. Surfactin, a cyclic lipopeptide biosurfactant produced by bacteria such as Bacillus subtilis, has potential as a biocontrol agent or as a plant defense inducer in agriculture. This work aims to determine the effects of surfactin treatment at two kinetic points on the metabolism of flax hydroponic cultures, using advanced metabolomic techniques, including 1H NMR and LC-MS analyses. Surfactin, detected in the roots, has a significant local impact on the metabolic profiles of flax roots, leading mainly to a higher content of cyanogenic compounds and amino acids and a lower content of carbohydrates. Surfactin, which is not detected in the aerial parts, also induces contrasted changes in amino acids, sugars, and secondary metabolite accumulation between stems and leaves. Surfactin treatment of flax leads to both a local and systemic effect on flax metabolism. These changes suggest that plant response to surfactin treatment could induce an enhanced plant defense. This could suggest potential applications of surfactin in the agricultural field as a biostimulant or biocontrol agent, to limit the use of chemical compounds in culture, and to limit their negative impact on both health and the environment. Full article
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<p>Flax plants cultivated in hydroponic devices. Photos taken before the 56 h harvest (<b>left</b>) and the 10 D harvest (<b>right</b>); for each kinetic point, the left rack shows the control plants, and the right rack shows the surfactin-treated plants. Wooden stick height: 30 cm.</p>
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<p>Comparison of the dry weight (mg) of each organ in both surfactin-treated (blue) and control (green) plants for both kinetic points harvesting: (<b>A</b>) roots, (<b>B</b>) stems, and (<b>C</b>) leaves, harvested (56 h, 10 D). For each organ, different letters indicate significant differences determined with the Kruskal–Wallis test.</p>
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<p>Signals of surfactin detected (<b>A</b>) by NMR analysis; doublet (δ 0.85 ppm, J = 6.7 Hz), present in standard (red) and 56 h surfactin-treated flax root extract (blue) or absent in 56 h control flax root extract (green); (<b>B</b>) by LC-MS analysis; peaks at 6.27, 6.35, and 6.43 min present in 56 h surfactin-treated flax root extract (blue) or absent in 56 h control flax root extract (green), (<b>C</b>) with the corresponding <span class="html-italic">m</span>/<span class="html-italic">z</span> of [M-H]<sup>−</sup> below.</p>
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<p>OPLS-DA score plot derived from <sup>1</sup>H NMR (<b>left</b>) and LC-MS (<b>right</b>) datasets: control flax roots (green) versus surfactin-treated flax roots.</p>
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<p>Heatmap of the discriminant metabolites differently accumulated in flax root extracts between surfactin-treated and control conditions. The scale bar (from red to blue) on the right represents the ratio of metabolite content in surfactin-treated flax roots to that in control flax roots. The column on the left indicates the kinetic point at 56 h and the column on the right indicates the kinetic point at 10 D. The analytical methods used to determine discriminant metabolites are indicated on the left of the graph; the red color corresponds to the combination of NMR and LC-MS, the blue color corresponds to LC-MS, and the purple color corresponds to NMR.</p>
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<p>The OPLS-DA score plot derived from <sup>1</sup>H NMR (<b>left</b>) and LC-MS (<b>right</b>) datasets obtained from stem extracts (<b>upper part</b>) or leaf extracts (<b>lower part</b>); control flax plants (green) versus surfactin-treated flax plants (blue).</p>
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<p>Heatmap of the discriminant metabolites differently accumulated in the flax stem (<b>left</b>) and leave (<b>right</b>) extracts between the surfactin-treated and control conditions. The scale bar (from red to blue) on the right represents the ratio of metabolite content in surfactin-treated flax aerial parts to that in control flax aerial parts. For each organ, the column on the left indicates the kinetic point at 56 h, and the column on the right indicates the kinetic point at 10 D. The analytical methods used to determine discriminant metabolites are indicated on the left of the graph; the red color corresponds to the combination of NMR and LC-MS, the blue color corresponds to LC-MS, and the purple color corresponds to NMR.</p>
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22 pages, 2813 KiB  
Article
A Proteomic Examination of Plasma Extracellular Vesicles Across Colorectal Cancer Stages Uncovers Biological Insights That Potentially Improve Prognosis
by Abidali Mohamedali, Benjamin Heng, Ardeshir Amirkhani, Shivani Krishnamurthy, David Cantor, Peter Jun Myung Lee, Joo-Shik Shin, Michael Solomon, Gilles J. Guillemin, Mark S. Baker and Seong Beom Ahn
Cancers 2024, 16(24), 4259; https://doi.org/10.3390/cancers16244259 (registering DOI) - 21 Dec 2024
Viewed by 311
Abstract
Background: Recent advancements in understanding plasma extracellular vesicles (EVs) and their role in disease biology have provided additional unique insights into the study of Colorectal Cancer (CRC). Methods: This study aimed to gain biological insights into disease progression from plasma-derived extracellular vesicle proteomic [...] Read more.
Background: Recent advancements in understanding plasma extracellular vesicles (EVs) and their role in disease biology have provided additional unique insights into the study of Colorectal Cancer (CRC). Methods: This study aimed to gain biological insights into disease progression from plasma-derived extracellular vesicle proteomic profiles of 80 patients (20 from each CRC stage I–IV) against 20 healthy age- and sex-matched controls using a high-resolution SWATH-MS proteomics with a reproducible centrifugation method to isolate plasma EVs. Results: We applied the High-Stringency Human Proteome Project (HPP) guidelines for SWATH-MS analysis, which refined our initial EV protein identification from 1362 proteins (10,993 peptides) to a more reliable and confident subset of 853 proteins (6231 peptides). In early-stage CRC, we identified 11 plasma EV proteins with differential expression between patients and healthy controls (three up-regulated and eight down-regulated), many of which are involved in key cancer hallmarks. Additionally, within the same cohort, we analysed EV proteins associated with tumour recurrence to identify potential prognostic indicators for CRC. A subset of up-regulated proteins associated with extracellular vesicle formation (GDI1, NSF, and TMED9) and the down-regulation of TSG101 suggest that micro-metastasis may have occurred earlier than previously anticipated. Discussion: By employing stringent proteomic analysis and a robust SWATH-MS approach, we identified dysregulated EV proteins that potentially indicate early-stage CRC and predict recurrence risk, including proteins involved in metabolism, cytoskeletal remodelling, and immune response. While our findings underline discrepancies with other studies due to differing isolation and stringency parameters, they provide valuable insights into the complexity of the EV proteome, emphasising the need for standardised protocols and larger, well-controlled studies to validate potential biomarkers. Full article
(This article belongs to the Special Issue Plasma Proteomics Analysis Predicts Cancer Biomarkers)
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<p>The systematic application of high-stringency criteria to the identification of proteins for this study, resulting in a dataset of 853 proteins of high confidence. We observed a reduction of 37% in the number of proteins and 43% in that of peptides compared to the default settings. Proteolytic peptides are defined as those that are consistently identified by MS and uniquely identify each protein. A nested peptide is an identified peptide sequence that is fully subsumed within another identified peptide sequence. <a href="#app1-cancers-16-04259" class="html-app">Supplementary Table S2</a> details the peptides identified for each protein across different stringency levels.</p>
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<p>CRC/healthy plasma extracellular vesicle (EV) protein identification. Venn diagram comparisons between (<b>a</b>) EV proteins identified from our study and extracellular vesicle protein databases from ExoCarta, Vesiclepedia, and Human EV PeptideAtlas [<a href="#B27-cancers-16-04259" class="html-bibr">27</a>] and (<b>b</b>) EV proteins and top 100 extracellular vesicle protein markers from ExoCarta and Vesiclepedia. (<b>c</b>) Protein markers identified from our study represent components of EVs, including the apoptotic body, microvesicle, and exosome [<a href="#B24-cancers-16-04259" class="html-bibr">24</a>]. (<b>d</b>) Cellular component Gene Ontology (GO) analysis of identified EV proteins. ExoCarta and Vesiclepedia EV protein databases downloaded from <a href="http://www.exocarta.org/" target="_blank">http://www.exocarta.org/</a> (accessed on 17 December 2024) and <a href="http://microvesicles.org/" target="_blank">http://microvesicles.org/</a> (accessed on 17 December 2024), respectively. <a href="#app1-cancers-16-04259" class="html-app">Supplementary Table S3</a> provides detailed information, including the lists of proteins in each database, the detection methodologies employed, and the data accessed dates.</p>
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<p>Plasma EV protein quantification in early stage I of CRC vs. healthy controls. (<b>a</b>) Volcano plot representations on differentially expressed proteins (FC &gt; 1.5, <span class="html-italic">p</span>-value &lt; 0.05) between stage I and healthy controls. Blue dots indicate up-regulated proteins and red dots indicate down-regulated proteins in stage I compared to controls. (<b>b</b>) Box plots illustrate the protein expression patterns between control, stage I, and stage II. *: <span class="html-italic">p</span>-value &lt; 0.05, **: <span class="html-italic">p</span>-value &lt; 0.01.</p>
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<p>Plasma EV protein quantification comparing the non-recur group (47 CRC patients in stages I/II/III without tumour recurrence within 5 years of primary tumour resection) and recur group (13 CRC patients in stages I/II/III with tumour recurrence within 5 years). (<b>a</b>) Volcano plot representations of differentially expressed proteins (FC &gt; 1.5, <span class="html-italic">p</span>-value &lt; 0.05) between non-recurred and recurred patient groups. Blue dots indicate up-regulated proteins and red dots indicate down-regulated proteins in recurred compared to non-recurred (i.e., cured). (<b>b</b>) Box plots illustrate the protein expression patterns between the non-recur and recur groups. *: <span class="html-italic">p</span>-value &lt; 0.05, **: <span class="html-italic">p</span>-value &lt; 0.01.</p>
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15 pages, 2286 KiB  
Article
Digital-Tier Strategy Improves Newborn Screening for Glutaric Aciduria Type 1
by Elaine Zaunseder, Julian Teinert, Nikolas Boy, Sven F. Garbade, Saskia Haupt, Patrik Feyh, Georg F. Hoffmann, Stefan Kölker, Ulrike Mütze and Vincent Heuveline
Int. J. Neonatal Screen. 2024, 10(4), 83; https://doi.org/10.3390/ijns10040083 (registering DOI) - 21 Dec 2024
Viewed by 173
Abstract
Glutaric aciduria type 1 (GA1) is a rare inherited metabolic disease increasingly included in newborn screening (NBS) programs worldwide. Because of the broad biochemical spectrum of individuals with GA1 and the lack of reliable second-tier strategies, NBS for GA1 is still confronted with [...] Read more.
Glutaric aciduria type 1 (GA1) is a rare inherited metabolic disease increasingly included in newborn screening (NBS) programs worldwide. Because of the broad biochemical spectrum of individuals with GA1 and the lack of reliable second-tier strategies, NBS for GA1 is still confronted with a high rate of false positives. In this study, we aim to increase the specificity of NBS for GA1 and, hence, to reduce the rate of false positives through machine learning methods. Therefore, we studied NBS profiles from 1,025,953 newborns screened between 2014 and 2023 at the Heidelberg NBS Laboratory, Germany. We identified a significant sex difference, resulting in twice as many false-positives male than female newborns. Moreover, the proposed digital-tier strategy based on logistic regression analysis, ridge regression, and support vector machine reduced the false-positive rate by over 90% compared to regular NBS while identifying all confirmed individuals with GA1 correctly. An in-depth analysis of the profiles revealed that in particular false-positive results with high associated follow-up costs could be reduced significantly. In conclusion, understanding the origin of false-positive NBS and implementing a digital-tier strategy to enhance the specificity of GA1 testing may significantly reduce the burden on newborns and their families from false-positive NBS results. Full article
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<p>False-positive newborn screening results for GA1. Sex-specific differences in false-positive newborn screening results for GA1 from 2014 to 2021.</p>
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<p>Further analysis of 485 false-positive screening results in GA1. (<b>A</b>) Reports of kidney insufficiency (<span class="html-italic">n</span> = 10) and transfusion, medication, or sepsis (<span class="html-italic">n</span> = 23) in false-positive newborn screening results for GA1. (<b>B</b>) Evaluation of urinary 3-OH-GA analysis in false-positive newborn screening results for GA1 including 34 (7%) newborns with elevated 3-OH-GA.</p>
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<p>Detailed analysis of classification by LR algorithm of subgroups of false-positive newborn screening results in the training and validation data set (<span class="html-italic">n</span> = 485) and the test data set (<span class="html-italic">n</span> = 235) for GA1 for newborns with kidney insufficiency (<span class="html-italic">n</span> = 10 patients with kidney insufficiency in the training and validation data set, <span class="html-italic">n</span> = 3 patients with kidney insufficiency in the test data set).</p>
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<p>Detailed analysis of classification by LR algorithm of subgroups of false-positive newborn screening results in the training and validation data set (<span class="html-italic">n</span> = 485) and the test data set (<span class="html-italic">n</span> = 235) for GA1. Evaluation for newborns with elevated urinary 3-OH-GA (<span class="html-italic">n</span> = 34) in the training and validation data set and patients with with elevated urinary 3-OH-GA (<span class="html-italic">n</span> = 5) in the test data set).</p>
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13 pages, 1359 KiB  
Article
Radiomic Consensus Clustering in Glioblastoma and Association with Gene Expression Profiles
by Tadeusz H. Wroblewski, Mert Karabacak, Carina Seah, Raymund L. Yong and Konstantinos Margetis
Cancers 2024, 16(24), 4256; https://doi.org/10.3390/cancers16244256 (registering DOI) - 21 Dec 2024
Viewed by 241
Abstract
Background/Objectives: Glioblastoma (GBM) is the most common malignant primary central nervous system tumor with extremely poor prognosis and survival outcomes. Non-invasive methods like radiomic feature extraction, which assess sub-visual imaging features, provide a potentially powerful tool for distinguishing molecular profiles across groups of [...] Read more.
Background/Objectives: Glioblastoma (GBM) is the most common malignant primary central nervous system tumor with extremely poor prognosis and survival outcomes. Non-invasive methods like radiomic feature extraction, which assess sub-visual imaging features, provide a potentially powerful tool for distinguishing molecular profiles across groups of patients with GBM. Using consensus clustering of MRI-based radiomic features, this study aims to investigate differential gene expression profiles based on radiomic clusters. Methods: Patients from the TCGA and CPTAC datasets (n = 114) were included in this study. Radiomic features including T1, T1 with contrast, T2, and FLAIR MRI sequences were extracted using PyRadiomics. Selected radiomic features were then clustered using ConsensusClusterPlus (k-means base algorithm and Euclidean distance), which iteratively subsamples and clusters 80% of the data to identify stable clusters by calculating the frequency in which each patient is a member of a cluster across iterations. Gene expression data (available for n = 69 patients) was analyzed using differential gene expression (DEG) and gene set enrichment (GSEA) approaches, after batch correction using ComBat-seq. Results: Three distinct clusters were identified based on the relative consensus matrix and cumulative distribution plots (Cluster 1, n = 25; Cluster 2, n = 46; Cluster 3, n = 43). No significant differences in patient demographic characteristics, MGMT methylation status, tumor location, or overall survival were identified across clusters. Differentially expressed genes were identified in Cluster 1, which have been previously associated with GBM prognosis, recurrence, and treatment sensitivity. GSEA of Cluster 1 showed an enrichment of genes upregulated for immune-related and DNA metabolism pathways and genes downregulated in pathways associated with protein and histone deacetylation. Clusters 2 and 3 exhibited fewer DEGs which failed to reach significance after multiple testing corrections. Conclusions: Consensus clustering of radiomic features revealed unique gene expression profiles in the GBM cohort which likely represent subtle differences in tumor biology and radiosensitivity that are not visually discernible, underscoring the potential of radiomics to serve as a non-invasive alternative for identifying GBM molecular heterogeneity. Further investigation is still required to validate these findings and their clinical implications. Full article
(This article belongs to the Section Cancer Informatics and Big Data)
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<p>Diagram of radiomic feature extraction workflow. MRI = Magnetic resonance imaging.</p>
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<p>Differentially expressed genes (DEG) across radiomic consensus clusters. Volcano plots representing DEGs for (<b>A</b>) Cluster 1, (<b>B</b>) Cluster 2, and (<b>C</b>) Cluster 3. Horizontal dashed line represents <span class="html-italic">p</span> = 0.01 and vertical line represents absolute logFC = 0.5. Nominally significant represents un-adjusted <span class="html-italic">p</span> &lt; 0.01 with absolute logFC &gt; 0.5. Points representing nominally significant genes that are upregulated in red, and downregulated in blue. Significance determined as <span class="html-italic">p</span>-adjusted &lt; 0.05 (FDR) with logFC (absolute log<sub>2</sub> fold change) &gt; 0.5.</p>
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<p>Upregulated pathways associated with differentially expressed genes in GBM. Top Gene Ontology biological process pathways for each radiomic consensus cluster (<b>A</b>–<b>C</b>). Size of each point indicates the number of genes (N Genes) represented in each gene set and color represents the normalized enrichment score (NES). The dashed vertical line represents <span class="html-italic">p</span>-adjusted = 0.05.</p>
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13 pages, 2315 KiB  
Systematic Review
Anti-Platelet Therapy with Cangrelor in Cardiogenic Shock Patients: A Systematic Review and Single-Arm Meta-Analysis
by Jacopo D’Andria Ursoleo, Luca Baldetti, Marina Pieri, Pasquale Nardelli, Savino Altizio, Silvia Ajello and Anna Mara Scandroglio
Medicina 2024, 60(12), 2092; https://doi.org/10.3390/medicina60122092 (registering DOI) - 21 Dec 2024
Viewed by 300
Abstract
Background and Objectives: Percutaneous coronary intervention (PCI) is a proven therapy for acute myocardial infarction (AMI) cardiogenic shock (CS). Dual anti-platelet therapy (i.e., aspirin plus an oral P2Y12 inhibitor) is recommended in patients treated with PCI. However, CS patients present severe hemodynamic instability, [...] Read more.
Background and Objectives: Percutaneous coronary intervention (PCI) is a proven therapy for acute myocardial infarction (AMI) cardiogenic shock (CS). Dual anti-platelet therapy (i.e., aspirin plus an oral P2Y12 inhibitor) is recommended in patients treated with PCI. However, CS patients present severe hemodynamic instability, deranged hemostatic balance, and the need for invasive mechanical circulatory support (MCS) alongside invasive procedures, resulting in an increased risk of both bleeding and thrombotic complications, leaving uncertainty about the best anti-thrombotic treatment. Recently, the parenteral short-acting P2Y12 inhibitor has been increasingly used in the acute cardiac care setting, mainly in light of its favourable pharmacokinetic profile and organ-independent metabolism. Materials and Methods: In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we performed a systematic review and single-arm meta-analysis of the safety and efficacy outcomes (i.e., rates of major bleeding, occurrence of stent/any thrombosis, and hospital survival) of all existing original studies reporting on the intravenous administration of cangrelor in AMI-CS patients. Results: Ten studies (678 patients with CS) published between 2017 and 2023 were included in the present review: nine were observational and one had a randomized design. Percutaneous revascularization was performed in >80% of patients across the studies. Moreover, 26% of patients were treated with temporary MCS, and in all studies, concomitant systemic anticoagulation was performed. Cangrelor was administered intravenously at the dosage of 4 mcg/kg/min in 57% of patients, 0.75 mcg/kg/min in 37% of patients, and <0.75 mcg/kg/min in 6%. The pooled rate of major bleeding was 17% (11–23%, confidence interval [CI]), and the pooled rate of stent thrombosis and any thrombosis were 1% (0.3–2.3% CI) and 3% (0.4–7% CI), respectively. Pooled hospital survival was 66% (59–73% CI). Conclusions: Cangrelor administration in AMI-CS patients was feasible and safe with a low rate of thromboembolic complications. Haemorrhagic complications were more frequent than thrombotic events. Nevertheless, to date, the optimal dosage of cangrelor in this clinical context still remains not universally recognized. Full article
(This article belongs to the Section Pulmonology)
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<p>Visual abstract presenting main article structure, objective, research methodology, and results. AMI-CS: acute myocardial infarction–cardiogenic shock; tMCS: temporary mechanical circulatory support.</p>
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<p>Flowchart of the studies selection and identification process.</p>
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<p>Dose of cangrelor in <span class="html-italic">n</span> = 281 patients from included studies (<span class="html-italic">n</span> = 10) (<b>A</b>). Therapeutic regimen of cangrelor in <span class="html-italic">n</span> = 120 patients from included studies (<span class="html-italic">n</span> = 10) (<b>B</b>). BRIDGE: The Bridging Antiplatelet Therapy with Cangrelor in Patients Undergoing Cardiac Surgery Trial; CHAMPION: Cangrelor Versus Standard Therapy to Achieve Optimal Management of Platelet Inhibition Trial; DAPT: dual antiplatelet therapy; ASA: aspirin; SAPT: single anti-platelet therapy.</p>
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<p>Type and strategy (single- or multi-device) of mechanical circulatory support (MCS) in <span class="html-italic">n</span> = 173 patients from included studies (<span class="html-italic">n</span> = 10). VA-ECMO: venoarterial extracorporeal membrane oxygenation; IABP: intra-aortic balloon pump.</p>
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<p>The effect of cangrelor on the rate of major bleeding (<b>A</b>) [<a href="#B9-medicina-60-02092" class="html-bibr">9</a>,<a href="#B21-medicina-60-02092" class="html-bibr">21</a>,<a href="#B24-medicina-60-02092" class="html-bibr">24</a>,<a href="#B25-medicina-60-02092" class="html-bibr">25</a>,<a href="#B26-medicina-60-02092" class="html-bibr">26</a>,<a href="#B27-medicina-60-02092" class="html-bibr">27</a>,<a href="#B28-medicina-60-02092" class="html-bibr">28</a>,<a href="#B29-medicina-60-02092" class="html-bibr">29</a>,<a href="#B30-medicina-60-02092" class="html-bibr">30</a>,<a href="#B31-medicina-60-02092" class="html-bibr">31</a>], hospital survival (<b>B</b>) [<a href="#B21-medicina-60-02092" class="html-bibr">21</a>,<a href="#B25-medicina-60-02092" class="html-bibr">25</a>,<a href="#B26-medicina-60-02092" class="html-bibr">26</a>,<a href="#B28-medicina-60-02092" class="html-bibr">28</a>,<a href="#B29-medicina-60-02092" class="html-bibr">29</a>,<a href="#B30-medicina-60-02092" class="html-bibr">30</a>,<a href="#B31-medicina-60-02092" class="html-bibr">31</a>], thrombosis (<b>C</b>) [<a href="#B9-medicina-60-02092" class="html-bibr">9</a>,<a href="#B21-medicina-60-02092" class="html-bibr">21</a>,<a href="#B24-medicina-60-02092" class="html-bibr">24</a>,<a href="#B25-medicina-60-02092" class="html-bibr">25</a>,<a href="#B26-medicina-60-02092" class="html-bibr">26</a>,<a href="#B27-medicina-60-02092" class="html-bibr">27</a>,<a href="#B28-medicina-60-02092" class="html-bibr">28</a>,<a href="#B29-medicina-60-02092" class="html-bibr">29</a>,<a href="#B30-medicina-60-02092" class="html-bibr">30</a>], and any thrombotic event (<b>D</b>) [<a href="#B9-medicina-60-02092" class="html-bibr">9</a>,<a href="#B21-medicina-60-02092" class="html-bibr">21</a>,<a href="#B24-medicina-60-02092" class="html-bibr">24</a>,<a href="#B25-medicina-60-02092" class="html-bibr">25</a>,<a href="#B26-medicina-60-02092" class="html-bibr">26</a>,<a href="#B27-medicina-60-02092" class="html-bibr">27</a>,<a href="#B29-medicina-60-02092" class="html-bibr">29</a>,<a href="#B30-medicina-60-02092" class="html-bibr">30</a>].</p>
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25 pages, 1591 KiB  
Article
Assessment of Immobilized Lacticaseibacillus rhamnosus OLXAL-1 Cells on Oat Flakes for Functional Regulation of the Intestinal Microbiome in a Type-1 Diabetic Animal Model
by Grigorios Nelios, Ioanna Prapa, Gregoria Mitropoulou, Vasiliki Kompoura, Evangelos Balafas, Nikolaos Kostomitsopoulos, Amalia E. Yanni and Yiannis Kourkoutas
Foods 2024, 13(24), 4134; https://doi.org/10.3390/foods13244134 (registering DOI) - 20 Dec 2024
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Abstract
The aim of this study was to examine the effect of free or immobilized Lacticaseibacillus rhamnosus OLXAL-1 cells on oat flakes on the gut microbiota and metabolic and inflammatory markers in a streptozotocin (STZ)-induced Type-1 Diabetes Mellitus (T1DM) animal model. Forty-eight male Wistar [...] Read more.
The aim of this study was to examine the effect of free or immobilized Lacticaseibacillus rhamnosus OLXAL-1 cells on oat flakes on the gut microbiota and metabolic and inflammatory markers in a streptozotocin (STZ)-induced Type-1 Diabetes Mellitus (T1DM) animal model. Forty-eight male Wistar rats were assigned into eight groups (n = 6): healthy or diabetic animals that received either a control diet (CD and DCD), an oat-supplemented diet (OD and DOD), a diet supplemented with free L. rhamnosus OLXAL-1 cells (CFC and DFC), or a diet supplemented with immobilized L. rhamnosus OLXAL-1 cells on oat flakes (CIC and DIC). Neither L. rhamnosus OLXAL-1 nor oat supplementation led to any significant positive effects on body weight, insulin levels, plasma glucose concentrations, or lipid profile parameters. L. rhamnosus OLXAL-1 administration resulted in a rise in the relative abundances of Lactobacillus and Bifidobacterium, as well as increased levels of lactic, acetic, and butyric acids in the feces of the diabetic animals. Additionally, supplementation with oat flakes significantly reduced the microbial populations of E. coli, Enterobacteriaceae, coliforms, staphylococci, and enterococci and lowered IL-1β levels in the blood plasma of diabetic animals. These findings suggested that probiotic food-based strategies could have a potential therapeutic role in managing dysbiosis and inflammation associated with T1DM. Full article
(This article belongs to the Section Nutraceuticals, Functional Foods, and Novel Foods)
19 pages, 2933 KiB  
Article
Metabolic Profiling of ‘Elstar’ and ‘Nicoter’ Apples: Impact of Storage Time, Dynamic Controlled Atmosphere and 1-MCP Treatment
by Fabio Rodrigo Thewes, Felix Büchele, Lilian Osmari Uhlmann, Adriana Lugaresi, Daiane Quadros de Oliveria Neuwald, Auri Brackmann, Vanderlei Both, Roger Wagner and Daniel Alexandre Neuwald
Horticulturae 2024, 10(12), 1372; https://doi.org/10.3390/horticulturae10121372 - 20 Dec 2024
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Abstract
The aim of this work was to evaluate the effect of CA and DCA on sugars, tricarboxylic acid cycle (TCA), anaerobic metabolism and some volatile compounds of ‘Elstar’ and ‘Nicoter’ apples. This study also aimed to evaluate the effect of ethylene action blocking [...] Read more.
The aim of this work was to evaluate the effect of CA and DCA on sugars, tricarboxylic acid cycle (TCA), anaerobic metabolism and some volatile compounds of ‘Elstar’ and ‘Nicoter’ apples. This study also aimed to evaluate the effect of ethylene action blocking by 1-MCP (0.650 ppm). The storage conditions tested for both cultivars were (1) CA; (2) DCA-CF; (3) DCA-RQ 1.3; (4) DCA-RQ 1.5; (5) DCA-CD 1.1; and (6) DCA-CD 1.3. The lowest oxygen limit (LOL) was higher for the ‘Nicoter’ apples, and the three DCA methods were able to detect this difference between the cultivars. Sorbitol had a trend of accumulation when the fruit was stored under DCA-RQ and DCA-CD, especially in higher RQ and CD, showing a negative Pearson correlation with the oxygen partial pressure over the storage period. The 1-MCP treatment induced sorbitol accumulation even when the fruit was stored under CA. The TCA intermediaries, such as citrate, 2-oxoglutarate, succinate, fumarate and oxaloacetate, were the most affected by the atmosphere conditions and the 1-MCP treatment for both cultivars. Malic acid was more affected by the storage time than the atmosphere conditions. Succinate and fumarate had an accumulation trend when the fruit was stored under DCA-RQ. Full article
(This article belongs to the Special Issue Advanced Postharvest Technology in Processed Horticultural Products)
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<p>Oxygen setpoint variation for fruit stored under controlled atmosphere (CA) and dynamic controlled atmosphere based on chlorophyll fluorescence (DCA-CF), respiratory quotient (DCA-RQ) and carbon dioxide (DCA-CD) over 9 months at a temperature of 1 °C for ‘Elstar’ and 3 °C for ‘Nicoter’.</p>
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<p>Heat map showing the relative changes for ethylene production and respiration rates of ‘Elstar’ and ‘Nicoter’ apples after harvest, at 6 and 9 months of storage under controlled atmosphere (CA) and dynamic controlled atmosphere monitored by chlorophyll fluorescence (DCA-CF), respiratory quotient (DCA-RQ) and carbon dioxide (DCA-CD), either without or with 1-MCP treatment (650 ppb), plus 7 d of shelf life at 20 °C.</p>
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<p>Heat map showing the relative changes of sugars, organic acids and some volatile compounds of ‘Elstar’ apples after harvest, at 6 and 9 months of storage under controlled atmosphere (CA) and dynamic controlled atmosphere monitored by chlorophyll fluorescence (DCA-CF), respiratory quotient (DCA-RQ) and carbon dioxide (DCA-CD), either without or with 1-MCP treatment (650 ppb), plus 7 d of shelf life at 20 °C.</p>
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<p>Heat map showing the relative change (compared to CA without 1-MCP) of sugars, organic acids and some volatile compounds of ‘Nicoter’ apples after harvest, at 6 and 9 months of storage under controlled atmosphere (CA) and dynamic controlled atmosphere monitored by chlorophyll fluorescence (DCA-CF), respiratory quotient (DCA-RQ) and carbon dioxide (DCA-CD), either without or with 1-MCP treatment (650 ppb), plus 7 d of shelf life at 20 °C.</p>
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<p>Correlation network for ‘Elstar’ apples after 6 months of storage under controlled atmosphere (CA) and dynamic controlled atmosphere monitored by chlorophyll fluorescence (DCA-CF), respiratory quotient (DCA-RQ) and carbon dioxide (DCA-CD), either without or with 1-MCP treatment (650 ppb), plus 7 d of shelf life at 20 °C. Blue and red lines show negative and positive correlation, respectively. The sizes of the lines show the correlation magnitude (all correlations shown are higher than 0.5). Suc: sucrose; Glu: glucose; Fru: fructose; Sor: sorbitol; Pir: pyruvate; Ace: acetaldehyde; EtOh: ethanol; EtAC: ethyl acetate; AA: acetic acid; CA: citrate; 2OA: 2-oxoglutarate; GA: glutamate; SA: succinate; FA: fumarate; MA: malate; OAA: oxaloacetate; OA: oxalate; 2metp: 2-methylpropanol; 2meta: 2-methylpropyl acetate; CR: carbon release.</p>
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<p>Correlation network for ‘Elstar’ apples after 9 months of storage under controlled atmosphere (CA) and dynamic controlled atmosphere monitored by chlorophyll fluorescence (DCA-CF), respiratory quotient (DCA-RQ) and carbon dioxide (DCA-CD), either without or with 1-MCP treatment (650 ppb), plus 7 d of shelf life at 20 °C. Blue and red lines show negative and positive correlation, respectively. The sizes of the lines show the correlation magnitude (all correlations shown are higher than 0.5). Suc: sucrose; Glu: glucose; Fru: fructose; Sor: sorbitol; Pir: pyruvate; Ace: acetaldehyde; EtOH: ethanol; EtAC: ethyl acetate; AA: acetic acid; CA: citrate; 2OA: 2-oxoglutarate; GA: glutamate; SA: succinate; FA: fumarate; MA: malate; OAA: oxaloacetate; OA: oxalate; 2metp: 2-methylpropanol; 2meta: 2-methylpropyl acetate; CR: carbon release.</p>
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<p>Correlation network for ‘Nicoter’ apples after 6 months of storage under controlled atmosphere (CA) and dynamic controlled atmosphere monitored by chlorophyll fluorescence (DCA-CF), respiratory quotient (DCA-RQ) and carbon dioxide (DCA-CD), either without or with 1-MCP treatment (650 ppb), plus 7 d of shelf life at 20 °C. Blue and red lines show negative and positive correlation, respectively. The sizes of the lines show the correlation magnitude (all correlations shown are higher than 0.5). Suc: sucrose; Glu: glucose; Fru: fructose; Sor: sorbitol; Pir: pyruvate; Ace: acetaldehyde; EtOH: ethanol; EtAC: ethyl acetate; AA: acetic acid; CA: citrate; 2OA: 2-oxoglutarate; GA: glutamate; SA: succinate; FA: fumarate; MA: malate; OAA: oxaloacetate; OA: oxalate; 2metp: 2-methylpropanol; 2meta: 2-methylpropyl acetate; CR: carbon release.</p>
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<p>Correlation network for ‘Nicoter’ apples after 9 months of storage under controlled atmosphere (CA) and dynamic controlled atmosphere monitored by chlorophyll fluorescence (DCA-CF), respiratory quotient (DCA-RQ) and carbon dioxide (DCA-CD), either without or with 1-MCP treatment (650 ppb), plus 7 d of shelf life at 20 °C. Blue and red lines show negative and positive correlation, respectively. The sizes of the lines show the correlation magnitude (all correlations shown are higher than 0.5). Suc: sucrose; Glu: glucose; Fru: fructose; Sor: sorbitol; Pir: pyruvate; Ace: acetaldehyde; EtOH: ethanol; EtAC: ethyl acetate; AA: acetic acid; CA: citrate; 2OA: 2-oxoglutarate; GA: glutamate; SA: succinate; FA: fumarate; MA: malate; OAA: oxaloacetate; OA: oxalate; 2metp: 2-methylpropanol; 2meta: 2-methylpropyl acetate; CR: carbon release.</p>
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17 pages, 2859 KiB  
Article
The Tumor Metabolite 5′-Deoxy-5′Methylthioadenosine (MTA) Inhibits Maturation and T Cell-Stimulating Capacity of Dendritic Cells
by Christina Brummer, Katrin Singer, Frederik Henrich, Katrin Peter, Carolin Strobl, Bernadette Neueder, Christina Bruss, Kathrin Renner, Tobias Pukrop, Wolfgang Herr, Michael Aigner and Marina Kreutz
Cells 2024, 13(24), 2114; https://doi.org/10.3390/cells13242114 - 20 Dec 2024
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Abstract
Metabolite accumulation in the tumor microenvironment fosters immune evasion and limits the efficiency of immunotherapeutic approaches. Methylthioadenosine phosphorylase (MTAP), which catalyzes the degradation of 5′-deoxy-5′methylthioadenosine (MTA), is downregulated in many cancer entities. Consequently, MTA accumulates in the microenvironment of MTAP-deficient tumors, where it [...] Read more.
Metabolite accumulation in the tumor microenvironment fosters immune evasion and limits the efficiency of immunotherapeutic approaches. Methylthioadenosine phosphorylase (MTAP), which catalyzes the degradation of 5′-deoxy-5′methylthioadenosine (MTA), is downregulated in many cancer entities. Consequently, MTA accumulates in the microenvironment of MTAP-deficient tumors, where it is known to inhibit tumor-infiltrating T cells and NK cells. However, the impact of MTA on other intra-tumoral immune cells has not yet been fully elucidated. To study the effects of MTA on dendritic cells (DCs), human monocytes were maturated into DCs with (MTA-DC) or without MTA (co-DC) and analyzed for activation, differentiation, and T cell-stimulating capacity. MTA altered the cytokine secretion profile of monocytes and impaired their maturation into dendritic cells. MTA-DCs produced less IL-12 and showed a more immature-like phenotype characterized by decreased expression of the co-stimulatory molecules CD80, CD83, and CD86 and increased expression of the monocyte markers CD14 and CD16. Consequently, MTA reduced the capability of DCs to stimulate T cells. Mechanistically, the MTA-induced effects on monocytes and DCs were mediated by a mechanism beyond adenosine receptor signaling. These results provide new insights into how altered polyamine metabolism impairs the maturation of monocyte-derived DCs and impacts the crosstalk between T and dendritic cells. Full article
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<p>MTA alters the cytokine secretion profile of human monocytes. Unstimulated human monocytes from healthy donors were incubated for 24 h in the absence or presence of MTA. (<b>A</b>) The cytokine secretion profile of monocytes, including TNF, IL-6, IL-1β, and IL-10, incubated without (control, black) or with MTA (250 µM, red) was analyzed. Results represent the mean of n = 4–6 independent experiments. Cytokine levels are shown as log [pg/mL]. (<b>B</b>,<b>C</b>) Concentrations of (<b>B</b>) IL-6 and (<b>C</b>) IL-10 secreted by monocytes incubated without (co, black) or with increasing concentrations (1, 10, 100, or 250 µM) of MTA (red). Results represent the mean + SD of n = 4–6. Statistical analysis was performed via the Friedman test followed by the post hoc Dunn’s test. Significance is indicated for <span class="html-italic">p</span> &lt; 0.05 (*).</p>
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<p>MTA alters the differentiation of monocyte-derived dendritic cells. (<b>A</b>) Monocytes were differentiated (medium + 144 U/mL IL-4 + 225 U/mL GM-CSF d0-d7) into immature (iDC, without LPS) or mature (mDC, + 10 mM LPS d5-d7) dendritic cells according to the depicted protocol in the absence (co, −, black) or presence (+, red) of 15 µM or 150 µM MTA (d0–7). Fractions of monocyte-derived (<b>B</b>) iDCs and, respectively, (<b>C</b>) mDCs harvested after seven days of differentiation are shown as a percentage of control DCs. Results represent the mean + SD of n = 3 (iDC) and n = 10–15 (mDC). Statistical analysis was performed via the Friedman test (iDC, paired) or Kruskal–Wallis test (mDC, unpaired) followed by the post hoc Dunn’s test. Significance is indicated for <span class="html-italic">p</span> &lt; 0.05 (*) and <span class="html-italic">p</span> &lt; 0.001 (***).</p>
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<p>DCs maturated under MTA show a more immature phenotype and an altered cytokine production profile. (<b>A</b>,<b>B</b>) Surface marker profile of (<b>A</b>) iDCs and (<b>B</b>) mDCs differentiated with 150 µM MTA in comparison to control DCs. Results represent the mean of n = 6 (iDC) and, respectively, n = 4–17 (mDC) and are shown as MFI fold expression in relation to control DCs (set as 1) differentiated without MTA. Increased expression is depicted in green, and decreased in red. Surface marker expression with and without MTA was compared via the Wilcoxon test (iDC, paired) and Mann–Whitney U test (mDC, unpaired). Significance is indicated for <span class="html-italic">p</span> &lt; 0.05 (*), <span class="html-italic">p</span> &lt; 0.01 (**), <span class="html-italic">p</span> &lt; 0.001 (***); # represents <span class="html-italic">p</span> = 0.05, ns = not significant. (<b>C</b>,<b>D</b>) Concentrations of IL-12 secreted by iDCs (<b>C</b>) and mDCs (<b>D</b>) incubated without (co, black) or with (red) increasing concentrations (15 and 150 µM) of MTA for 7 days. Results represent the mean + SD of n = 6. Statistical analysis was performed via the Friedman test followed by the post hoc Dunn’s test. Significance is indicated for <span class="html-italic">p</span> &lt; 0.05 (*).</p>
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<p>MTA impairs the T cell stimulation capacity of DCs. (<b>A</b>) mDCs were maturated from human monocytes without (co, black) or with (red) the addition of 15 or 150 µM MTA (d0–7). After maturation, mDCs were co-cultured in fixed ratios (100:1, 50:1, or 25:1) with allogeneic T cells for 5 days in a mixed lymphocyte reaction. On day 12, <sub>3</sub>H-Thymidin was added. Radioactivity, as a marker of T cell proliferation, was measured on d13. Results represent the mean + SD of n = 5 healthy donors. Comparisons between mDC MTA and control groups were performed via the Friedman multiple comparison test and the post hoc Dunn’s test. Significance is indicated for <span class="html-italic">p</span> &lt; 0.05 (*) and <span class="html-italic">p</span> &lt; 0.01 (**). (<b>B</b>,<b>C</b>) Peptide-loaded (CMVpp65<sub>495–503</sub>) and protein-loaded (CMVpp65) mDCs maturated without (co-DC) or with 150 µM MTA (MTA-DC) were co-cultured with autologous CD8+ T lymphocytes (ratio 5:1) from the same donor for 11 days. On day 7, CD8+ T cells were restimulated with freshly maturated peptide- or protein-loaded mDCs. On day 11, the fraction of positive antigen-specific (peptid or protein) CD8+ T cells was analyzed using flow cytometry. Unstimulated CD8+ T cells were used as the negative control. (<b>B</b>) INF-y-positive antigen-specific CD8 T cells stimulated by MTA-DCs are shown as fold changes of CD8+ T cells stimulated by co-DCs (=1). The mean + SD of n = 3–4 is shown. Co-DC and MTA-DC groups were statistically compared via the Mann–Whitney U test. Significance is indicated for <span class="html-italic">p</span> &lt; 0.05 (*), ns = not significant. (<b>C</b>) Antigen-specific CD8+ T cells stimulated by co-DCs or MTA-DCs were analyzed using flow cytometry regarding INF-y and IL-2. One representative experiment is shown.</p>
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<p>MTA-induced effects on monocytes are mediated by a mechanism beyond adenosine receptor signaling. (<b>A</b>) Immunohistochemical staining of adenosine receptors A1, A2A, A2B, and A3 on monocytes. Staining for CD45 and isotype is shown as the positive and, respectively, negative control. One representative experiment is shown. (<b>B</b>) Monocytes were incubated with or without 10, 100, or 1000 µM MTA for 60 min and lysed. Intracellular cAMP levels were measured using an immunoassay kit. Forskolin (50 µM) was used as a positive control. (<b>C</b>) Monocytes were incubated with (+) or without (−) 250 µM MTA, the A1 antagonist (A1i) 8-Cyclopentyl-1,3Dipropylxanthin (CPCPX), the A2a antagonist (A2Ai) 8-3-Chlorostyryl-coffeine (CSC), the A2B antagonist (A2Bi) alloxazin, or the A3 antagonist (A3) MRS1292 for 20 h. IL-6 concentrations of cell culture supernatants were measured by ELISA. Results represent the mean of n = 4–6 and are shown as the mean + SD. Significance was determined using one-way ANOVA and post hoc Dunn’s multiple comparisons tests (** <span class="html-italic">p</span> &lt; 0.01, ns = not significant).</p>
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<p>MTA-induced effects on DC maturation can be partly reproduced by PRMT5 inhibition. (<b>A</b>) Surface marker profiles of DCs differentiated from MNCs in the presence of 150 µM MTA or the PRMT5 inhibitor EPZ-0015666 (10 µM) in comparison to control and DMSO-treated DCs were analyzed by flow cytometry. Results represent the mean of n = 3. Surface markers are shown as the MFI fold expression relative to control DCs. Histogram overlays of one example donor are shown. Significance was determined using one-way ANOVA and post hoc Dunnett’s multiple comparisons tests (* <span class="html-italic">p</span> &lt; 0.05). (<b>B</b>) Graphical illustration of MTA-induced effects on the crosstalk between DCs and CD8+ T cells. Red arrows indicate downregulation by MTA. The figure was created with BioRender.</p>
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14 pages, 2614 KiB  
Article
Identification of Bioactive Compounds from the Roots of Rehmannia glutinosa and Their In Silico and In Vitro AMPK Activation Potential
by Hwaryeong Lee, Isoo Youn, Sang Gyun Noh, Hyun Woo Kim, Eunhye Song, Sang-Jip Nam, Hae Young Chung and Eun Kyoung Seo
Molecules 2024, 29(24), 6009; https://doi.org/10.3390/molecules29246009 - 20 Dec 2024
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Abstract
Rehmannia glutinosa Libosch., which belongs to the Orobanchaceae family, is a perennial herb found in China, Japan, and Korea. In traditional medicine, it is used to cool the body, improve water metabolism in the kidney, and provide protection from metabolic diseases such as [...] Read more.
Rehmannia glutinosa Libosch., which belongs to the Orobanchaceae family, is a perennial herb found in China, Japan, and Korea. In traditional medicine, it is used to cool the body, improve water metabolism in the kidney, and provide protection from metabolic diseases such as type 2 diabetes mellitus (T2DM) and obesity. In this study, three new compounds were isolated from the roots of R. glutinosa, along with eighteen known compounds. Structure elucidation was performed with spectroscopic analyses including nuclear magnetic resonance (NMR) and circular dichroism (CD) spectroscopy. As the AMP-activated protein kinase (AMPK) signaling pathway is reportedly related to metabolic diseases, AMPK activation studies were conducted using in silico simulations and in vitro assays. Among the isolated compounds, 1 showed a potential as an AMPK activator in both in silico simulations and in vitro experiments. Our findings expand the chemical profiles of the plant R. glutinosa and suggest that one newly found compound (1) activates AMPK. Full article
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<p>Structures of the isolated compounds <b>1</b>–<b>21</b>.</p>
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<p>COSY and HMBC key correlations of compounds <b>1</b>, <b>2</b>, and <b>8</b>.</p>
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<p>Nuclear Overhauser effect correlations of compounds <b>1</b>, <b>2</b>, and <b>8</b>.</p>
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<p>Binding interactions of (<b>a</b>) 5-amino-4-imidazolecarboxamide ribonucleoside (AICAR) and (<b>b</b>) compound <b>1</b> with AMP-activated protein kinase (AMPK) in silico docking simulations in Autodock 4.2. Green arrow: hydrogen bond (H-bond) donor; red arrow: H-bond acceptor; yellow interaction: hydrophobic interaction or van der Waals force; blue arrow: aromatic interaction. (ARG, arginine; HIS, histidine; ILE, isoleucine; LYS, lysine; PHE, phenylalanine; THR, threonine; VAL, valine).</p>
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<p>Molecular dynamics study of AICAR and compound <b>1</b> against the AMPK receptor. (<b>a</b>) Root-mean-square deviation (RMSD) plots of the target protein (AMPK) and ligands (AICAR or <b>1</b>) complex over 100 ns MD simulation. A red circle indicates an unstable region. (<b>b</b>) Root-mean-square fluctuation (RMSF) plots of AMPK and the ligands (AICAR and <b>1</b>). (<b>c</b>) Number of hydrogen bonds of the ligands (AICAR and <b>1</b>) with AMPK.</p>
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<p>AMPK activation by AICAR (the control) and compound <b>1</b>. The AMPK activation was evaluated by measuring AMPK phosphorylation (pAMPK) in the presence of AICAR and compound <b>1</b> using Western blotting. (<b>a</b>) The concentration of AICAR was 200 and 400 μM. The concentrations of compound <b>1</b> were 5, 10, and 20 μM. The ratio of p-AMPK/AMPK was measured after normalization of p-AMPK and AMPK to GAPDH, respectively. (<b>b</b>) The concentration of AICAR was 500 μM, and the concentrations of compound <b>1</b> were 2, 4, and 8 μM. p-AMPK: phosphorylated AMPK. CPT1A: carnitine palmitoyl transferase 1A.</p>
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