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22 pages, 7159 KiB  
Article
Sucrose Promotes the Proliferation and Differentiation of Callus by Regulating ROS Intensity in Agapanthus praecox
by Jianhua Yue, Yan Dong, Changmei Du, Chaoxin Li, Xinyi Wang and Yan Zhang
Horticulturae 2024, 10(12), 1350; https://doi.org/10.3390/horticulturae10121350 - 16 Dec 2024
Abstract
The proliferation and differentiation of callus is the foundation for plant regeneration and propagation. The type of carbon sources in the medium significantly influences the efficacy of callus proliferation and differentiation in plants in vitro. Our study performed transcriptomic and physiological analyses utilizing [...] Read more.
The proliferation and differentiation of callus is the foundation for plant regeneration and propagation. The type of carbon sources in the medium significantly influences the efficacy of callus proliferation and differentiation in plants in vitro. Our study performed transcriptomic and physiological analyses utilizing sucrose, glucose, and maltose to understand the physiological and molecular characteristics of the proliferation and differentiation potential affected by carbon sources in Agapanthus praecox. Differentially expressed genes were notably associated with plant hormone signal transduction, glycolysis/gluconeogenesis, and MAPK signaling in the proliferation and differentiation of callus. The physiological indicators suggest glucose enhanced both callus and cell size by increasing endogenous indole-3-acetic acid (IAA), cytokinin, brassinosteroid, gibberellin (GAs), starch, and glucose levels, while concurrently reducing levels of reactive oxygen species (ROS) such as hydrogen peroxide (H2O2) and hydroxyl radical (·OH). Conversely, sucrose treatment promoted differentiation potential by elevating IAA oxidase activity alongside stress-related hormones such as abscisic acid and ethylene levels. Additionally, sucrose treatment led to increased accumulation of sucrose, fructose, H2O2, and ·OH within the callus tissue. Furthermore, sucrose influenced the regenerative capacity by modulating glycometabolism and osmoregulation. Our study posits that glucose facilitates callus proliferation via diminished ROS intensity while sucrose promotes callus differentiation by maintaining moderate ROS levels. Altogether, our results suggest carbon sources affected the regenerative capabilities of callus by regulating plant hormone signal and ROS intensity in A. praecox. Full article
(This article belongs to the Special Issue Plant Tissue and Organ Cultures for Crop Improvement in Omics Era)
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Figure 1

Figure 1
<p>Morphological and transcriptomic differences of callus cultured by sucrose, glucose, and maltose. (<b>A</b>) Morphology of samples including callus cultured by sucrose, glucose, and maltose; the bar represents 1.0 cm. The white arrow indicates an adventitious bud and a hairy root. (<b>B</b>) Cell micromorphology of callus, bar = 100 μm. (<b>C</b>) Correlation analysis among samples; the horizontal axis represents the sample clusters, and colors from blue to red indicate the correlation index from low to high. (<b>D</b>) Venn diagram of DEGs among three compared pairs, including Suc/Mal, Suc/Glu, and Glu/Mal. The red arrows indicate upregulation, and the green arrows indicate downregulation. (<b>E</b>) KEGG pathway enrichment of the comparison of Suc/Glu. The bubble size represents the number of members detected in the KEGG pathway, and the color of the bubble represents the <span class="html-italic">p</span>-value, the same as below. (<b>F</b>) KEGG pathway enrichment of the comparison of Suc/Mal. (<b>G</b>) KEGG pathway enrichment of the comparison of Glu/Mal.</p>
Full article ">Figure 2
<p>Hierarchical clustering analyses of DEGs among samples of sucrose, glucose, maltose, and IEC. (<b>A</b>) Hierarchical clustering analyses of DEGs between samples including Suc, Glu, Mal, and IEC. (<b>B</b>) Hierarchical clustering analyses of DEGs in subcluster 1. (<b>C</b>) Hierarchical clustering analyses of DEGs in subcluster 2. (<b>D</b>) Hierarchical clustering analyses of DEGs in subcluster 3. (<b>E</b>) Hierarchical clustering analyses of DEGs in subcluster 4.</p>
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<p>Differential analyses of plant hormone signal transduction and metabolism. (<b>A</b>) Analyses of the contents and enzymatic activity of plant hormones. The data are means, <span class="html-italic">n</span> = 3. Means marked by the same letter in the column are not significantly different according to Duncan’s multiple range test at <span class="html-italic">p</span> &lt; 0.05. Table marked in red, yellow, and green indicating high, middle, and low values with different carbon source treatments. (<b>B</b>) DEGs with higher expression levels with sucrose. (<b>C</b>) DEGs with lower expression levels with sucrose. (<b>D</b>) Hierarchical clustering analyses of DEGs. (<b>E</b>) Size of callus treated by PIC, <span class="html-italic">n</span> = 3. Means marked by the same letter on the bar are not significantly different according to Duncan’s multiple range test at <span class="html-italic">p</span> &lt; 0.05, and the same hereinafter. (<b>F</b>) Size of callus treated by GA<sub>4</sub>. (<b>G</b>) Size of callus treated by homobrassinolide (HBL). (<b>H</b>) Size of callus treated by ABA. Abbreviations: BIN2: brassinosteroid insensitive2; TGA: TGACG binding TFs; IAA: auxin/indole-3-acetic acid; PP2C: type 2C protein phosphatases; JAZ: jasmonate ZIM domain protein; SAUR: small auxin-up RNA; ARR-A: type-A authentic response regulator; ARR-B: type-B authentic response regulator; NPR1: nonexpressor of pathogenesis-related genes 1; BZR1_2: brassinosteroid-resistant 1/2; AHP: histidine-containing phosphotransfer protein; PIF3: phytochrome-interacting factor 3; SNRK2: sucrose nonfermenting 1-related protein kinase 2; EIN3: ethylene-insensitive protein 3; TIR1: transport inhibitor response 1; TCH4: xyloglucan: xyloglucosyl transferase TOUCH4; BSK: BR-signaling kinase; PIF4: phytochrome-interacting factor 4; GH3: Gretchen Hagen 3; AHK2_3_4: Arabidopsis histidine kinase 2/3/4 (cytokinin receptor); PR1: pathogenesis-related protein 1; DELLA: DELLA transcriptional regulatory proteins; ABF: ABA responsive element binding factor; CTR1: constitutive triple response1; AUX1, LAX: auxin influx carrier (AUX1/LAX family); ARF: auxin response factor; CYCD3: cyclin D3; PYL: pyrabactin resistance 1-like protein; GID1: gibberellin insensitive dwarf1; MPK6: mitogen-activated protein kinase 6; BRI1: brassinosteroid insensitive 1.</p>
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<p>Differential analyses of starch and sucrose metabolism. (<b>A</b>) Analyses of the contents of starch and soluble sugars. The data are means, <span class="html-italic">n</span> = 3. Means marked by the same letter in the column are not significantly different according to Duncan’s multiple range test at <span class="html-italic">p</span> &lt; 0.05. Table marked in red, yellow, and green indicates high, middle, and low values with different carbon source treatments. (<b>B</b>) DEGs with higher expression levels in Suc/Glu and Suc/Mal. (<b>C</b>) DEGs with lower expression levels in Suc/Glu and higher expression levels in Glu/Mal. (<b>D</b>) DEGs with lower expression levels in Suc/Glu and higher expression levels in Glu/Mal. (<b>E</b>) Hierarchical clustering analyses of DEGs involved in starch and sucrose metabolism. (<b>F</b>) DEGs with lower expression levels in Suc/Glu and Suc/Mal. (<b>G</b>) Size of callus treated by different concentrations of sucrose, <span class="html-italic">n</span> = 3. Means marked by the same letter on the bar are not significantly different according to Duncan’s multiple range test at <span class="html-italic">p</span> &lt; 0.05, and the same hereinafter. (<b>H</b>) Size of callus treated by the combination of sucrose and glucose. (<b>I</b>) Size of callus treated by the combination of sucrose and fructose. (<b>J</b>) Size of callus treated by the combination of sucrose and maltose. Abbreviations: GBE1, glgB: 1,4-alpha-glucan branching enzyme; SUS: sucrose synthase; glgC: glucose-1-phosphate adenylyltransferase; INV, sacA: beta-fructofuranosidase; PYG, glgP: glycogen phosphorylase; scrK: fructokinase; malZ: alpha-glucosidase; TREH, treA, treF: alpha-trehalase; TPS: trehalose 6-phosphate synthase/phosphatase; ISA, treX: isoamylase; otsB: trehalose 6-phosphate phosphatase; GPl, pgi: glucose-6-phosphate isomerase; ENPP1_3, CD203: ectonucleotide pyrophosphatase/phosphodiesterase family member 1/3; malQ: 4-alpha-glucanotransferase; UGP2, galU, galF: UTP--glucose-1-phosphate uridylyltransferase; SPP: sucrose-6-phosphatase; HK: hexokinase; NV, sacA: beta-fructofuranosidase; TREH, treA, treF: alpha,alpha-trehalase.</p>
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<p>Differential analyses of MAPK signaling pathway. (<b>A</b>) Analyses of the contents and enzymatic activity involved in ROS metabolism. The data are means, <span class="html-italic">n</span> = 3. Means marked by the same letter in the column are not significantly different according to Duncan’s multiple range test at <span class="html-italic">p</span> &lt; 0.05. Table marked in red, yellow, and green indicates high, middle, and low values with different carbon source treatments. (<b>B</b>) DEGs with higher expression levels in Suc/Glu and Suc/Mal. (<b>C</b>) DEGs with higher expression levels in Suc/Glu and lower expression levels in Glu/Mal. (<b>D</b>) DEGs with lower expression levels in Suc/Glu and higher expression levels in Glu/Mal. (<b>E</b>) DEGs with lower expression levels in Suc/Glu and Suc/Mal. (<b>F</b>) Size of callus treated by H<sub>2</sub>O<sub>2</sub>, <span class="html-italic">n</span> = 3. Means marked by the same letter on the bar are not significantly different according to Duncan’s multiple range test at <span class="html-italic">p</span> &lt; 0.05, and the same hereinafter. (<b>G</b>) Size of callus treated by 2, 4-D. (<b>H</b>) Size of callus treated by PEG 6000. (<b>I</b>) Size of callus treated by Ac-DEVD-CHO (CHO) and carbonyl cyanide m-chlorophenylhydrazone (CCCP). Abbreviations: MAPK7: mitogen-activated protein kinase 7; RBOH: respiratory burst oxidase; IRAK1: interleukin-1 receptor-associated kinase 1; CALM: calmodulin; WRKY33: WRKY DNA-binding protein 33; CTSL: cathepsin L; IDH1, IDH2, icd: isocitrate dehydrogenase; CYC: cytochrome c; PR1: pathogenesis-related protein 1; ACsL, fadD: long-chain acyl-CoA synthetase; CTSH: cathepsin H; PEX12, PAF3: peroxin-12; HAO: (S)-2-hydroxy-acid oxidase; VIP1: transcription factor VIP1; ACAA1: acetyl-CoA acyltransferase 1; PXMP2, PMP22: peroxisomal membrane protein 2; PEX10: peroxin-10; FLS2: LRR receptor-like serine/threonine-protein kinase FLS2; EIN3: ethylene-insensitive protein 3; FBXL2_20: F-box and leucine-rich repeat protein 2/20; PP2C: type 2C protein phosphatases; PYL: abscisic acid receptor PYR/PYL family; NRK2: sucrose nonfermenting 1-related protein kinase 2; ANP1: mannan polymerase II complex ANP1 subunit; copA, ATP7: P-type Cu+ transporter; katE, CAT, catB, srpA: catalase; MKK9: mitogen-activated protein kinase kinase 9; PARP: poly (ADP-ribose) polymerases; ATF4, CREB2: cyclic AMP-dependent transcription factor ATF-4; EIF2S1: translation initiation factor 2 subunit 1.</p>
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<p>Analyses of the effects of carbon source combination on the proliferation and differentiation of callus. (<b>A</b>) Morphological differences of callus treated by the combination of sucrose and the hydrolysate of sucrose (glucose and fructose). The bars in the morphology and micromorphology represent 1.0 cm and 100 μm, respectively, and the same hereinafter. (<b>B</b>) Size of callus (left Y-axis) and single cell (right Y-axis) treated by the combination of sucrose and hydrolysate of sucrose. Data on the bars marked without the same lowercase letter indicate significant differences at <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">n</span> = 3, and the same hereinafter. (<b>C</b>) Morphological differences of callus treated by the combination of sucrose and glucose. (<b>D</b>) Size of callus (left Y-axis) and single cell (right Y-axis) treated by the combination of sucrose and glucose. (<b>E</b>) Morphological differences of callus treated by the combination of sucrose and fructose. (<b>F</b>) Size of callus (left Y-axis) and single cell (right Y-axis) treated by the combination of sucrose and fructose. (<b>G</b>) Morphological differences of callus treated by the combination of hydrolysate of sucrose and glucose. (<b>H</b>) Size of callus (left Y-axis) and single cell (right Y-axis) treated by the combination of hydrolysate of sucrose and glucose. (<b>I</b>) Morphological differences of callus treated by the combination of hydrolysate of sucrose and fructose. (<b>J</b>) Size of callus (left Y-axis) and single cell (right Y-axis) treated by the combination of hydrolysate of sucrose and fructose. (<b>K</b>) Morphological differences of callus treated by the combination of hydrolysate of sucrose and maltose. (<b>L</b>) Size of callus (left Y-axis) and single cell (right Y-axis) treated by the combination of hydrolysate of sucrose and maltose.</p>
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<p>Analyses of the effects of osmotic regulatory substance (ORS) on the proliferation and differentiation of callus. (<b>A</b>) Morphological differences of callus treated by the combination of sucrose and PEG. The bars in the morphology and micromorphology represent 1.0 cm and 100 μm, respectively, and the same hereinafter. (<b>B</b>) Size of callus (left Y-axis) and single cell (right Y-axis) treated by the combination of sucrose and PEG. Data on the bars marked without the same lowercase letter indicate significant differences at <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">n</span> = 3, and the same hereinafter. (<b>C</b>) Morphological differences of callus treated by the combination of glucose and PEG. (<b>D</b>) Size of callus (left Y-axis) and single cell (right Y-axis) treated by the combination of glucose and PEG. (<b>E</b>) Morphological differences of callus treated by the combination of fructose and PEG. (<b>F</b>) Size of callus (left Y-axis) and single cell (right Y-axis) treated by the combination of fructose and PEG. (<b>G</b>) Morphological differences of callus treated by the combination of glucose, fructose, and PEG. (<b>H</b>) Size of callus (left Y-axis) and single cell (right Y-axis) treated by the combination of glucose, fructose, and PEG. (<b>I</b>) Morphological differences of callus treated by the combination of sucrose, glucose, and PEG. (<b>J</b>) Size of callus (left Y-axis) and single cell (right Y-axis) treated by the combination of sucrose, glucose, and PEG. (<b>K</b>) Morphological differences of callus treated by the combination of sucrose, fructose, and PEG. (<b>L</b>) Size of callus (left Y-axis) and single cell (right Y-axis) treated by the combination of sucrose, fructose, and PEG.</p>
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<p>Hypothesized model diagram of the acquisition of regenerative potential induced by carbon sources in <span class="html-italic">A. praecox</span>. (<b>A</b>) Carbon sources affected the proliferation and differentiation of callus, and the intensity of ROS determined the cell fate of callus. (<b>B</b>) Schematic diagram about the influence of carbon sources on hormone metabolism, sugar content, ROS, and protective enzymes. Since maltose treatment usually results in moderate levels of physiological indicators, maltose is used as a control. The short horizontal line indicate control, the red arrows indicate a significant increase, and the green arrows a indicate significant decrease.</p>
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17 pages, 4274 KiB  
Article
Neurotransmitter Metabolic Disturbance in Methamphetamine Abusers: Focus on Tryptophan and Tyrosine Metabolic Pathways
by Xi Wang, Weilan Wu, Jing Liu, Miaoyang Hu, Jie Cheng, Jianping Xiong, Xufeng Chen, Rong Gao and Jun Wang
Toxics 2024, 12(12), 912; https://doi.org/10.3390/toxics12120912 (registering DOI) - 16 Dec 2024
Viewed by 178
Abstract
Methamphetamine (METH) abuse disrupts the homeostasis of neurotransmitter (NT) metabolism, contributing to a wide range of neurological and psychological disorders. However, the specific effects of METH on NT metabolism, particularly for the tryptophan (TRP) and tyrosine (TYR) metabolic pathways, remain poorly understood. In [...] Read more.
Methamphetamine (METH) abuse disrupts the homeostasis of neurotransmitter (NT) metabolism, contributing to a wide range of neurological and psychological disorders. However, the specific effects of METH on NT metabolism, particularly for the tryptophan (TRP) and tyrosine (TYR) metabolic pathways, remain poorly understood. In this study, serum samples from 78 METH abusers and 79 healthy controls were analyzed using Ultra-High-Performance Liquid Chromatography with Tandem Mass Spectrometry (UHPLC-MS/MS). A total of 41 substances, primarily from the TRP and TYR metabolic pathways, were detected and subjected to multivariate analysis. Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) revealed a significant separation of serum metabolites between METH abusers and controls, encompassing the disturbance of serotonergic, kynurenic, and microbial metabolism. In the serotonergic pathway, METH significantly reduced melatonin (MLT) levels and impaired the conversion of serotonin (5-HT) to N-acetylserotonin (NAS), a key precursor of MLT. In the kynurenic pathway, METH promoted a shift to the toxic metabolic pathway, evidenced by elevated levels of 3-hydroxykynurenine (3-HK) and quinolinic acid (QA). Furthermore, microbial metabolic pathway-related indole and its derivatives were markedly suppressed in METH abusers. Gender-specific differences were also observed, with NT metabolism in TRP and TYR pathways showing more pronounced alterations in male or female subgroups. Therefore, the current study provides a comprehensive overview of the disturbance in TRP- and TYR-associated NT metabolism caused by METH abuse and highlights NT metabolism as a promising therapeutic target for METH-induced neural and psychiatric disorders. Full article
(This article belongs to the Section Neurotoxicity)
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Graphical abstract

Graphical abstract
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<p>PCA of the NTs and their metabolites in the serum of METH abusers and healthy controls. (<b>A</b>) Nonsupervised PCA score plot. (<b>B</b>) PLS-DA score plot. (<b>C</b>) OPLS-DA score plot. Red triangles represent the METH abusers’ serum metabolomes; blue dots represent the healthy controls’ serum metabolomes. (<b>D</b>) Permutation test. It verifies the prediction ability of the OPLS-DA model. <span class="html-italic">p</span> values of &lt;0.05 were considered statistically significant. The permutation number was set to 100. (<b>E</b>) VIP plot of the serum metabolomes. VIP &gt; 0.8. (<b>F</b>) Volcano plot. Red dots: upregulated metabolites; blue dots: downregulated metabolites; gray dots: no obviously affected metabolites.</p>
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<p>The changed serum NTs and the dynamic metabolism of the TRP–kynurenic pathway in METH abusers. (<b>A</b>) Pathway schematic of TRP–kynurenic metabolism. Black arrows show the host pathway; blue arrows show the microbial pathway. Host enzymes with genomic evidence are marked in red; microbial enzymes with genomic evidence are marked in green. (<b>B</b>) Differences in absolute concentrations (ng/mL) of TRP–kynurenic metabolites and the statistically significant ratios in the TRP–kynurenic pathway. C, healthy controls. M, METH abusers. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>The changed serum NTs and the dynamic metabolism of the TRP–serotonergic pathway in METH abusers. (<b>A</b>) Pathway schematic of TRP–serotonergic metabolism. Black arrows indicate the host degradation pathway; host enzymes with genomic evidence are marked in red. (<b>B</b>) Differences in absolute concentrations (ng/mL) of TRP–serotonergic metabolites and the statistically significant ratios in the TRP–serotonergic pathway. C, healthy controls. M, METH abusers. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>The changed serum NTs and the dynamic metabolism of the TRP–microbial metabolic pathway in METH abusers. (<b>A</b>) Pathway schematic of TRP–microbial metabolism. Black arrows show the host pathway; blue arrows show the microbial pathway. Host enzymes with genomic evidence are marked in red; microbial enzymes with genomic evidence are marked in green. (<b>B</b>) Differences in absolute concentrations (ng/mL) of TRP–microbial metabolism and the statistically significant ratios in the TRP–microbial metabolic pathway. C, healthy controls. M, METH abusers. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>The changed serum NTs and the dynamic metabolism of the TYR–dopamine pathway in METH abusers. (<b>A</b>) Pathway schematic of TYR–dopamine metabolism. Black arrows indicate the host degradation pathway; host enzymes with genomic evidence are marked in red. (<b>B,C</b>) Differences in absolute concentrations (ng/mL) of TYR–dopamine metabolism, other amino acid NTs, and the statistically significant ratios. C, healthy controls. M, METH abusers. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>ROC curves distinguishing the controls and the METH abusers. Model 1 contained the TRP–microbial metabolic pathway. Model 2 contained the TRP–kynurenic pathway. Model 3 contained the TRP–serotonergic pathway.</p>
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<p>Alterations in NTs and neuroactive metabolites from the <span class="html-italic">t</span>-test in METH abusers of different genders. (<b>A,B</b>) Heatmaps of statistically significant metabolites from the <span class="html-italic">t</span>-test in male and female METH abusers. C, healthy controls. M, METH abusers. hrM, METH abusers of higher exposure. hM, METH abusers of high exposure. mM, METH abusers of medium exposure. lM, METH abusers of low exposure. (<b>C</b>) Venn plot that illustrates differences in serum metabolites between male and female METH abusers, with overlapping areas indicating shared changes caused by METH use in both genders.</p>
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23 pages, 8488 KiB  
Article
Pan-Cancer Insights: A Study of Microbial Metabolite Receptors in Malignancy Dynamics
by Nikolas Dovrolis, Michail Spathakis, Alexandra R. Collins, Varun Kumar Pandey, Muhammad Ikhtear Uddin, Donald D. Anderson, Tetiana Kaminska, Vasilis Paspaliaris and George Kolios
Cancers 2024, 16(24), 4178; https://doi.org/10.3390/cancers16244178 (registering DOI) - 15 Dec 2024
Viewed by 416
Abstract
Background/Objectives: The role of the gut microbiome in cancer biology has become an increasingly prominent area of research, particularly regarding the role of microbial metabolites and their receptors (MMRs). These metabolites, through the various gut–organ axes, have been proven to influence several pathogenetic [...] Read more.
Background/Objectives: The role of the gut microbiome in cancer biology has become an increasingly prominent area of research, particularly regarding the role of microbial metabolites and their receptors (MMRs). These metabolites, through the various gut–organ axes, have been proven to influence several pathogenetic mechanisms. This study conducted a comprehensive pan-cancer analysis of MMR transcriptomic profiles across twenty-three cancer types, exploring the mechanisms through which they can influence cancer development and progression. Methods: Utilizing both cancer cell lines from CCLE (Cancer Cell Line Encyclopedia) and human tumor samples from TCGA (The Cancer Gene Atlas), we analyzed 107 MMRs interacting with microbial metabolites such as short-chain fatty acids, bile acids, indole derivatives, and others while studying their interactions with key known cancer genes. Results: Our results revealed that certain MMRs, such as GPR84 and serotonin receptors, are consistently upregulated in various malignancies, while others, like ADRA1A, are frequently downregulated, suggesting diverse roles in cancer pathophysiology. Furthermore, we identified significant correlations between MMR expression and cancer hallmark genes and pathways, including immune evasion, proliferation, and metastasis. Conclusions: These findings suggest that the interactions between microbial metabolites and MMRs may serve as potential biomarkers for cancer diagnosis, prognosis, and therapy, highlighting their therapeutic potential. This study underscores the significance of the microbiota–cancer axis and provides novel insights into microbiome-based strategies for cancer treatment. Full article
(This article belongs to the Special Issue Human Microbiome, Diet and Cancerogenesis)
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Figure 1
<p>Microbial metabolite receptor (MMR) expression in the Cancer Cell Line Encyclopedia (CCLE) dataset. (<b>A</b>) MMR with the highest expression per tissue-specific cell line. (<b>B</b>) MMRs summarized by their ligand, showing which tissue-specific cell line they are highly expressed in.</p>
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<p>Differential expression of microbial metabolite receptors (MMRs) in a pan-cancer setting. (<b>A</b>) Each MMR’s dysregulation versus control samples per studied cancer type. (<b>B</b>) MMR expression dysregulation summarized by ligand type per studied cancer type. Red color signifies upregulation and blue downregulation.</p>
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<p>Top dysregulated microbial metabolite receptors (MMRs) per studied cancer type. X-axis represents log2FoldChange values, while the dashed red lines signify log<sub>2</sub>FoldChange of ±1.</p>
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<p>Spearman Correlation Heatmap showcasing the relationship between cancer types based on MMR expression profiles. Red indicates a positive correlation, while blue highlights inverse correlations. (* <span class="html-italic">p</span>-value &lt; 0.05; ** <span class="html-italic">p</span>-value &lt; 0.01; *** <span class="html-italic">p</span>-value &lt; 0.001).</p>
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<p>(<b>A</b>) Pairwise Spearman correlation of cancer hallmark genes (CHGs) and microbial metabolite receptors (MMRs) based on their expression. All correlations exhibit Spearman’s rho lower than −0.8 or higher than 0.8 and <span class="html-italic">p</span>-values lower than 10<sup>−10</sup>. (<b>B</b>) Pairwise Spearman correlation of MMRs when the CHGs are aggregated into cancer hallmark pathways (CHPs) (<b>C</b>) Summary of the strongest correlations between MMRs and CHPs.</p>
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13 pages, 1039 KiB  
Article
The Role of Guilt Feelings in the Development of the Burnout Process: The Influence on Psychosomatic Problems
by Pedro Gil-LaOrden, Mary Sandra Carlotto and Pedro R. Gil-Monte
Behav. Sci. 2024, 14(12), 1196; https://doi.org/10.3390/bs14121196 - 13 Dec 2024
Viewed by 334
Abstract
Burnout is a psychological consequence of prolonged work-related stress. Previous studies have concluded that guilt feelings could explain the development of the burnout process and its relationship with other health disorders. The aim of this study was to evaluate the mediating role of [...] Read more.
Burnout is a psychological consequence of prolonged work-related stress. Previous studies have concluded that guilt feelings could explain the development of the burnout process and its relationship with other health disorders. The aim of this study was to evaluate the mediating role of guilt feelings in the relationship between burnout and psychosomatic problems. The sample comprised 714 Brazilian teachers (82.10% women). Burnout was assessed using the Spanish Burnout Inventory (SBI). The hypotheses were evaluated together using a path model to test the mediating role of guilt feelings in the development of burnout and its relationship with psychosomatic problems. Two models were constructed: the hypothesized model (i.e., indolence → guilt → psychosomatic problems) vs. the alternative model (i.e., indolence → psychosomatic problems → guilt). According to the results, the hypothesized model obtained a satisfactory fit to the data, whereas the alternative model’s fit was found to be inadequate. We concluded that the hypothesized model was a good representation of the relationship among burnout, guilt feelings and psychosomatic problems. We recommend taking into consideration feelings of guilt to improve the diagnosis of burnout. Full article
(This article belongs to the Special Issue Promoting Behavioral Change to Improve Health Outcomes)
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Figure 1
<p>Hypothesized model.</p>
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<p>Standardized coefficients for the hypothesized model revised. Note. All relationships were significant at <span class="html-italic">p</span> ≤ 0.001.</p>
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<p>Standardized coefficients for the alternative model revised.</p>
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10 pages, 2282 KiB  
Article
Bioactive Secondary Metabolites from Harposporium anguillulae Against Meloidogyne incognita
by Dong Li, Ling-Feng Bao, Hong-Mei Lei, Guang-Ke Zhang, Guo-Hong Li and Pei-Ji Zhao
Microorganisms 2024, 12(12), 2585; https://doi.org/10.3390/microorganisms12122585 - 13 Dec 2024
Viewed by 254
Abstract
Root-knot nematodes (RKNs) are pathogens that endanger a wide range of crops and cause serious global agricultural losses. In this study, we investigated metabolites of the endoparasitic fungus Harposporium anguillulae YMF1.01751, with the expectation of discovering valuable Meloidogyne incognita biocontrol compounds. Based on [...] Read more.
Root-knot nematodes (RKNs) are pathogens that endanger a wide range of crops and cause serious global agricultural losses. In this study, we investigated metabolites of the endoparasitic fungus Harposporium anguillulae YMF1.01751, with the expectation of discovering valuable Meloidogyne incognita biocontrol compounds. Based on results obtained by a liquid chromatograph coupled to a mass spectrometer (LC-MS) of crude extracts under five culture conditions and their nematicidal activity against M. incognita, corn meal agar (CMA) medium was determined as the scale-up fermentation medium. Twelve metabolites (112) were isolated from the fermentation products, and compound 1 was identified to be a new cyclic tetrapeptide. The activity assay results showed that phenylacetic acid (11) had good nematicidal activity at 400 μg/mL, and the mortalities of M. incognita were 89.76% and 96.05% at 12 and 24 h, respectively, while the mortality of canthin-6-one (2) against M. incognita was 44.26% at 72 h. In addition, the results of chemotaxis activity showed that 1-(1H-indol-3-yl)ethanone (10) possessed attraction activity towards M. incognita. At the tested concentrations, cyclo-(Arg-Pro) (4) and cyclo-(Val-Ile) (7) showed an avoidant response to M. incognita. This study provides insight into the nematode-active compounds of H. anguillulae origin and offers new opportunities for the development of RKN biocontrol products. Full article
(This article belongs to the Special Issue Secondary Metabolism of Microorganisms, 3rd Edition)
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<p>The LC-MS detection of extracts produced by <span class="html-italic">H. anguillulae</span> YMF1.01751 in five media. (<b>A</b>) Principal component analysis of the metabolites from <span class="html-italic">H. anguillulae</span> YMF1.01751 in different media. (<b>B</b>) LC-MS profiles of metabolites extracted from <span class="html-italic">H. anguillulae</span> YMF1.01751 in total ion chromatography.</p>
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<p>The COSY and key HMBC correlations of <b>1</b>.</p>
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<p>The metabolites isolated from <span class="html-italic">H. anguillulae</span> YMF1.01751.</p>
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<p>The nematicidal activity of compounds against <span class="html-italic">M. incognita</span>. In the same time period of data, two way-ANOVA statistical analysis indicates significant differences (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.005; *** <span class="html-italic">p</span> &lt; 0.003; **** <span class="html-italic">p</span> &lt; 0.001) compared with the control.</p>
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<p>The chemotactic activity of compounds towards <span class="html-italic">M. incognita</span>. (<b>A</b>) Schematic diagram of chemotactic activity. (<b>B</b>) The chemotactic activity of compounds. In the same time period of data, two way-ANOVA statistical analysis indicates significant differences (* <span class="html-italic">p</span> &lt; 0.05) compared with the control.</p>
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45 pages, 16580 KiB  
Review
Bisindole Compounds—Synthesis and Medicinal Properties
by Maria Marinescu
Antibiotics 2024, 13(12), 1212; https://doi.org/10.3390/antibiotics13121212 - 13 Dec 2024
Viewed by 753
Abstract
The indole nucleus stands out as a pharmacophore, among other aromatic heterocyclic compounds with remarkable therapeutic properties, such as benzimidazole, pyridine, quinoline, benzothiazole, and others. Moreover, a series of recent studies refer to strategies for the synthesis of bisindole derivatives, with various medicinal [...] Read more.
The indole nucleus stands out as a pharmacophore, among other aromatic heterocyclic compounds with remarkable therapeutic properties, such as benzimidazole, pyridine, quinoline, benzothiazole, and others. Moreover, a series of recent studies refer to strategies for the synthesis of bisindole derivatives, with various medicinal properties, such as antimicrobial, antiviral, anticancer, anti-Alzheimer, anti-inflammatory, antioxidant, antidiabetic, etc. Also, a series of natural bisindole compounds are mentioned in the literature for their various biological properties and as a starting point in the synthesis of other related bisindoles. Drawing from these data, we have proposed in this review to provide an overview of the synthesis techniques and medicinal qualities of the bisindolic compounds that have been mentioned in recent literature from 2010 to 2024 as well as their numerous uses in the chemistry of materials, nanomaterials, dyes, polymers, and corrosion inhibitors. Full article
(This article belongs to the Section Novel Antimicrobial Agents)
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Graphical abstract

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<p>Material applications of indole and bisindole compounds with medicinal properties reported in the literature [<a href="#B24-antibiotics-13-01212" class="html-bibr">24</a>,<a href="#B25-antibiotics-13-01212" class="html-bibr">25</a>,<a href="#B26-antibiotics-13-01212" class="html-bibr">26</a>,<a href="#B27-antibiotics-13-01212" class="html-bibr">27</a>,<a href="#B28-antibiotics-13-01212" class="html-bibr">28</a>,<a href="#B29-antibiotics-13-01212" class="html-bibr">29</a>,<a href="#B30-antibiotics-13-01212" class="html-bibr">30</a>,<a href="#B31-antibiotics-13-01212" class="html-bibr">31</a>,<a href="#B32-antibiotics-13-01212" class="html-bibr">32</a>,<a href="#B33-antibiotics-13-01212" class="html-bibr">33</a>,<a href="#B34-antibiotics-13-01212" class="html-bibr">34</a>,<a href="#B35-antibiotics-13-01212" class="html-bibr">35</a>,<a href="#B36-antibiotics-13-01212" class="html-bibr">36</a>,<a href="#B37-antibiotics-13-01212" class="html-bibr">37</a>,<a href="#B38-antibiotics-13-01212" class="html-bibr">38</a>].</p>
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<p>Natural bisindolic compounds with various therapeutic properties.</p>
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<p>Medicinal properties of bisindolic compounds.</p>
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<p>The structures of <span class="html-italic">bis</span>(indolyl)methanes <b>62</b>–<b>67</b> with antimicrobial properties.</p>
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<p>The structures of <span class="html-italic">bis</span>(indolyl)methanes <b>68</b>–<b>70</b> with antimicrobial properties.</p>
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<p>The structures of bisindoles <b>76</b>–<b>84</b> with antimicrobial properties.</p>
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<p>The structures of bisindoles <b>86</b>–<b>87</b> with antimicrobial properties.</p>
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<p>The structures of bisindole compounds <b>94</b>–<b>98</b> with antimicrobial properties.</p>
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<p>Binding mode of compound <b>100e</b> and active site residue in isoform 1 of Bcl-2. Adapted from [<a href="#B149-antibiotics-13-01212" class="html-bibr">149</a>].</p>
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<p>The structures of bisindole compounds <b>102</b>–<b>105</b> with antitubercular properties.</p>
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<p>The structures of bisindole compounds <b>106</b>–<b>111</b> with antimalarial properties.</p>
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<p>The structures of bisindole compounds <b>112</b>–1<b>38</b> with antileishmanial properties.</p>
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<p>(<b>a</b>) Binding mode of the most active compounds in pteridine reductase active site. (<b>b</b>) Binding mode of compound <b>119</b> (green color) in comparison with pentamidine (blue color). Adapted from [<a href="#B161-antibiotics-13-01212" class="html-bibr">161</a>].</p>
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<p>Bisindoles with antiviral properties.</p>
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<p>Docked pose of <b>142</b> in the hydrophobic pocket of gp41 (PDB 2xra), allowing movement of side chains of Gln 575 and Trp 571. A salt bridge from one carbonyl oxygen on the ligand to Lys574-εNH<sub>2</sub> is shown as an orange dotted line, and a hydrogen bond is predicted from the second carbonyl oxygen to the lysine eNH<sub>2</sub>. Adapted from [<a href="#B167-antibiotics-13-01212" class="html-bibr">167</a>].</p>
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<p>Bisindoles with anticancer properties.</p>
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<p>Possible binding mode of the most potent compounds and target proteins. Binding modes of the cocrystallized ligand Ibrutinib (orange), compound <b>152</b> (green), and <b>153</b> (magenta) against the anticancer target EGFR (PDB ID: 5YU9). Adapted from [<a href="#B178-antibiotics-13-01212" class="html-bibr">178</a>].</p>
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<p>Dock pose of indole derivatives <b>154</b> and <b>155</b> with Bcr-Abl and GSK-3β proteins. Adapted from [<a href="#B179-antibiotics-13-01212" class="html-bibr">179</a>].</p>
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<p>Bisindoles <b>157</b>–<b>162</b> with anticancer properties.</p>
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<p>Molecular orbital energy-generated 2D interaction plot of bisindoles (<b>a</b>) <b>161</b>; (<b>b</b>) <b>162</b>. Adapted from [<a href="#B160-antibiotics-13-01212" class="html-bibr">160</a>].</p>
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<p>Bisindoles <b>168</b>–<b>179</b> as MARK4 inhibitors.</p>
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<p>Bisindoles <b>182</b>–<b>193</b> with anti-inflammatory properties.</p>
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<p>Bisindoles <b>194</b>–<b>197</b> with anti-inflammatory activity.</p>
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<p>Bisindoles <b>200</b>–<b>197</b> with anti-Alzheimer properties.</p>
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<p>(<b>a</b>) A 2D interaction diagram of compound <b>205</b> within the binding site of MAO-A (PDB ID = 2Z5X); (<b>b</b>) 2D interaction diagram of compound <b>205</b> within the binding site of MAO-B (PDB ID = 2V5Z). Adapted from [<a href="#B195-antibiotics-13-01212" class="html-bibr">195</a>].</p>
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<p>Bisindole <b>209</b> with antioxidant properties.</p>
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<p>Bisindoles <b>212</b>–<b>219</b> with antidiabetic properties.</p>
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<p>Bisindoles <b>221a</b> and <b>221b</b> as carbonic anhydrase II inhibitors.</p>
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<p>Synthesis of bis(indolyl)methanes <b>3</b> using different catalysts.</p>
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<p>Synthesis of bis(indolyl)methanes <b>6</b> by LiO<span class="html-italic">t</span>-Bu-promoted alkylation of indoles <b>4</b>.</p>
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<p>Synthesis of bis(indolyl)methane phosphonates <b>9</b> using In(OTf)<sub>3</sub> as catalyst.</p>
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<p>Synthesis of 3,3-bis(indol-3-yl)propanoates <b>12</b> FeCl<sub>3</sub>/AgOTf as catalyst.</p>
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<p>Synthesis of bis(indolyl)methanes <b>14</b> from indole <b>1</b> and ketones [<a href="#B107-antibiotics-13-01212" class="html-bibr">107</a>,<a href="#B108-antibiotics-13-01212" class="html-bibr">108</a>,<a href="#B109-antibiotics-13-01212" class="html-bibr">109</a>].</p>
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<p>Synthesis of bis(indolyl)methanes <b>16</b> from indole <b>1</b> and electron-deficient alkenes <b>15</b>.</p>
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<p>Synthesis of bis(indolyl)methanes <b>19</b> using a domino reaction.</p>
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<p>Synthesis of <span class="html-italic">homo</span>-bisindolylmethanes <b>22</b> using RMgX <b>21</b> as reactant.</p>
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<p>Synthesis of <span class="html-italic">homo</span>-bis(indolyl)methanes <b>25</b> using RLi <b>24</b> as reactant.</p>
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<p>Synthesis of <span class="html-italic">homo</span>-bis(indolylmethanes) <b>28</b> using alkynyl lithium reagents <b>27</b> as reactants.</p>
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<p>Synthesis of bis(indolyl) oximes <b>31</b> via hetero-Diels-Alder reaction.</p>
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<p>Synthesis of bis(indolyl) hydrazones <b>34</b> via hetero-Diels-Alder reaction.</p>
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<p>Synthesis of bisindoles <b>37</b> using a multicomponent reaction.</p>
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<p>Synthesis of 3,3-bis(1<span class="html-italic">H</span>-indol-3-yl)indolin-2-ones <b>40</b>.</p>
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<p>Synthesis of isatin bisindoles <b>43</b>.</p>
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<p>Synthesis of acenaphthene bisindoles <b>46</b>.</p>
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<p>Synthesis of acenaphthene bisindoles <b>48</b>.</p>
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<p>Synthesis of acenaphthene bisindoles <b>51</b>.</p>
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<p>Synthesis of amide bisindoles <b>54</b>, <b>55</b>, and <b>57</b>.</p>
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<p>Synthesis of O,O′-dimethyl scalaridine A <b>62</b>.</p>
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<p>Synthesis of bisindoles <b>74</b> and <b>75</b>.</p>
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<p>Synthesis of antimicrobial bisindoles <b>93</b>.</p>
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<p>Synthesis of antileishmanial bisindoles <b>112</b>–<b>138</b>.</p>
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<p>Synthesis of antileishmanial seleno-bisindole <b>139</b>.</p>
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<p>Synthesis of anti-HIV-1 bisindoles <b>140a</b>–<b>140m</b>.</p>
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<p>Synthesis of anticancer bisindoles <b>148</b> and <b>149</b>.</p>
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<p>Synthesis of anticancer bisindoles <b>154</b>–<b>156</b>.</p>
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<p>Synthesis of anticancer bisindoles <b>180</b>–<b>181</b>.</p>
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<p>Synthesis of anti-Alzheimer bisindole <b>204</b>.</p>
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<p>Synthesis of antioxidant bisindole <b>210</b>.</p>
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<p>Synthesis of antidiabetic bisindole <b>211</b>.</p>
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<p>Synthesis of antidiabetic bisindole <b>213</b>.</p>
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<p>Synthesis of analgesic bisindoles <b>220a</b> and <b>220b</b>.</p>
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18 pages, 8857 KiB  
Article
De Novo Regeneration of Cannabis sativa cv. Cheungsam and Evaluation of Secondary Metabolites of Its Callus
by S. M. Ahsan, Da Bin Kwon, Md. Injamum-Ul-Hoque, Md. Mezanur Rahman, Inhwa Yeam and Hyong Woo Choi
Horticulturae 2024, 10(12), 1331; https://doi.org/10.3390/horticulturae10121331 - 12 Dec 2024
Viewed by 343
Abstract
Cannabis sativa L. cv. ‘Cheungsam’ is an industrial hemp plant of Republic of Korea origin, primarily cultivated for fiber and seed production. In vitro seed germination and tissue culture are valuable tools for developing various biotechnological techniques. In the present study, we aimed [...] Read more.
Cannabis sativa L. cv. ‘Cheungsam’ is an industrial hemp plant of Republic of Korea origin, primarily cultivated for fiber and seed production. In vitro seed germination and tissue culture are valuable tools for developing various biotechnological techniques. In the present study, we aimed to develop a tissue culture process for hemp plants using Cheungsam as a model plant and examine the secondary metabolites produced from its callus. We also developed a method to prepare pathogen-free seedlings from field-derived seeds using hydrogen peroxide (H2O2) solution as a liquid germination medium. Treating seedlings with removed seed coat in 3% H2O2 significantly reduced the contamination rate. Callus formation and de novo organogenesis of shoots and roots from callus were successfully achieved using cotyledon and leaf tissues prepared from the pathogen-free seedlings. The most effective in vitro regeneration results were obtained using the Murashige and Skoog (MS) medium supplemented with certain targeted growth regulators. An optimal combination of 0.5 mg/L thidiazuron (TDZ) and 1.0 mg/L 1-naphthalene acetic acid proved highly effective for callus induction. The addition of 0.5 mg/L TDZ in the MS medium significantly stimulated shoot proliferation, while robust root development was best supported by MS medium supplemented with 2.5 mg/L indole-3-butyric acid for both cotyledon and leaf explants. Finally, gas chromatography–mass spectrometry (GC–MS) analysis of ethanol extract from Cheungsam leaf callus revealed the presence of different secondary metabolites, including 9-octadecenamide, methyl salicylate, dodecane, tetradecane, and phenol, 2,4-bis-(1,1-dimethylethyl). This study provides a comprehensive de novo regeneration protocol for Cheungsam plants and insight into the secondary metabolite profiles of its callus. Full article
(This article belongs to the Special Issue Innovative Micropropagation of Horticultural and Medicinal Plants)
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<p>Pathogen-free in vitro seedling preparation from Cheungsam seeds. (<b>A</b>) Enhanced germination rates of Cheungsam seeds germinated in different volumes of 1% H<sub>2</sub>O<sub>2</sub>. (<b>B</b>) Reduced contamination rates of seedlings germinated in different volumes of 1% H<sub>2</sub>O<sub>2</sub>. Contamination rates were assessed 2 weeks after transferring the sterilized seedlings to MS medium. (<b>C</b>) Reduced contamination rates of seedlings additionally sterilized with different volumes of 1% H<sub>2</sub>O<sub>2</sub> after removing the seed coat. (<b>D</b>) Reduced contamination rates of seedlings with removed seed coat, additionally sterilized with 3% H<sub>2</sub>O<sub>2</sub> for different durations (1, 2, 3, and 4 h). (<b>E</b>) Representative images of seedlings with removed seed coat sterilized with 3% H<sub>2</sub>O<sub>2</sub> for different durations (1, 2, 3, and 4 h). Pictures were taken 2 weeks after transferring the sterilized seedlings to MS medium. CON: water control (Scale bar in all panels = 2 cm). Different letters above the bars indicate a statistically significant difference at <span class="html-italic">p</span> &lt; 0.05 according to the Tukey HSD test.</p>
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<p>Callus formation and de novo organogenesis of the excised leaf of Cheungsam plants. (<b>A</b>) Pathogen-free seedlings germinated in H<sub>2</sub>O<sub>2</sub> solution as a liquid germination medium and grown on MS medium. Red circle: Leaf tissue used for callus formation. Scale bar = 0.2 cm. (<b>B</b>–<b>D</b>) Callus induced from leaf explants. Scale bar = 0.2 cm. Initial swelling and formation of callus from leaf explants (4-week-old, (<b>B</b>)). Green nodular photosynthetic and friable callus (red arrows, 5-week-old, (<b>C</b>)). The initial stage of shoot regeneration from photosynthetic callus (red arrow, 6-week-old). (<b>D</b>). Scale bar = 0.2 cm. (<b>E</b>,<b>F</b>) De Novo shoot morphogenesis from leaf-induced callus on shoot induction medium (SIM) containing 0.5 mg/L TDZ. Scale bar = 0.2 cm. (<b>G</b>) De Novo root morphogenesis from leaf-induced callus on root induction medium (RIM) containing 2.5 mg/L IBA. Scale bar = 1 cm. Regenerated roots are indicated by the red arrow.</p>
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<p>Callus formation and de novo organogenesis of excised cotyledon tissues from Cheungsam plants. (<b>A</b>) Pathogen-free seedlings germinated in H<sub>2</sub>O<sub>2</sub> solution as a liquid germination medium and grown on MS medium. Red circle: Cotyledon tissue used for callus formation. Scale bar = 0.2 cm. (<b>B</b>) Callus induced from the cotyledon explants 7 days after incubation on CIM. Initial swelling and formation of callus from cotyledon explant. Green nodular photosynthetic and friable calli are indicated by red arrows. Scale bar = 0.2 cm. (<b>C</b>,<b>D</b>). De Novo shoot morphogenesis from cotyledon-induced callus on SIM containing 0.5 mg/L TDZ. Scale bar = 0.2 cm. (<b>E</b>) De Novo root morphogenesis from cotyledon-induced callus on RIM containing 2.5 mg/L IBA. Regenerated roots are indicated by the red arrow. Scale bar = 1 cm.</p>
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<p>GC–MS total ion chromatogram (TIC) obtained from the analyses of ethanol extract of Cheungsam callus. TIC was generated from the analysis of ethanol extract of one-month-old Cheungsam leaf callus.</p>
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21 pages, 3792 KiB  
Review
MetAP2 as a Therapeutic Target for Obesity and Type 2 Diabetes: Structural Insights, Mechanistic Roles, and Inhibitor Development
by Dong Oh Moon
Biomolecules 2024, 14(12), 1572; https://doi.org/10.3390/biom14121572 - 10 Dec 2024
Viewed by 435
Abstract
Type 2 Diabetes Mellitus (T2DM) and obesity are globally prevalent metabolic disorders characterized by insulin resistance, impaired glucose metabolism, and excessive adiposity. Methionine aminopeptidase 2 (MetAP2), an intracellular metalloprotease, has emerged as a promising therapeutic target due to its critical role in regulating [...] Read more.
Type 2 Diabetes Mellitus (T2DM) and obesity are globally prevalent metabolic disorders characterized by insulin resistance, impaired glucose metabolism, and excessive adiposity. Methionine aminopeptidase 2 (MetAP2), an intracellular metalloprotease, has emerged as a promising therapeutic target due to its critical role in regulating lipid metabolism, energy balance, and protein synthesis. This review provides a comprehensive analysis of MetAP2, including its structural characteristics, catalytic mechanism, and functional roles in the pathophysiology of T2DM and obesity. The unique architecture of MetAP2’s active site and its interactions with substrates are examined to elucidate its enzymatic function. The review also explores the development of MetAP2 inhibitors, focusing on their mechanisms of action, preclinical and clinical findings, and therapeutic potential. Special emphasis is placed on docking studies to analyze the binding interactions of six key inhibitors (fumagillin, TNP-470, beloranib, ZGN-1061, indazole, and pyrazolo[4,3-b]indole) with MetAP2, revealing their structural determinants for efficacy and specificity. These findings underscore the potential of MetAP2 as a therapeutic target and provide valuable insights for the rational design of next-generation inhibitors to address obesity and T2DM. Full article
(This article belongs to the Special Issue New Insights into Cardiometabolic Diseases)
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<p>Structure and function of MetAP2 in ribosomal interaction and catalytic mechanism. (<b>A</b>) 3D structure of MetAP2. The structure of human MetAP2 (composed of 478 amino acids) features a central β-sheet core surrounded by α-helices (α1–α7), providing structural stability necessary for its catalytic function. The conserved “pita bread” fold within the C-terminal domain forms a deep cleft that houses the active site. The 3D structure of MetAP2 was obtained from the Protein Data Bank (PDB ID: 1BOA, <a href="https://www.rcsb.org/structure/1BOA" target="_blank">https://www.rcsb.org/structure/1BOA</a>, accessed on 11 November 2024). (<b>B</b>) Active site and catalytic mechanism of MetAP2. The active site, located within the deep pocket of the pita bread fold, binds two metal ions, typically cobalt but potentially manganese under physiological conditions. These metal ions are stabilized by conserved residues, including Asp251, Asp262, His331, Glu364, and Glu459. Asp262 and Glu459 act as bidentate ligands, coordinating both metal ions, while Asp251, Glu364, and His331 each coordinate one of the metal ions individually. The metal ions activate a bridging water molecule (H<sub>2</sub>O), converting it into a nucleophilic hydroxide ion (OH<sup>−</sup>) through deprotonation. This hydroxide ion then attacks the carbonyl carbon of the N-terminal methionine peptide bond, leading to peptide bond cleavage and the removal of the initiator methionine from the nascent polypeptide. (<b>C</b>) MetAP2 binds to the large ribosomal subunit at the tunnel exit, where it specifically interacts with the expansion segment ES27L of the rRNA. This interaction positions MetAP2 to efficiently engage with the emerging nascent polypeptide, allowing it to cleave the N-terminal methionine as the polypeptide exits the ribosome. The figure illustrates MetAP2’s location relative to the mRNA and tRNA during translation, with ES27L providing an anchor point that stabilizes MetAP2 at the ribosome. This precise positioning enables MetAP2 to facilitate the maturation of newly synthesized proteins by selectively removing the initiator methionine from nascent polypeptides.</p>
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<p>A model of MetAP2-dependent N-myristoylation and its role in T2DM. (<b>A</b>) Adipocyte: The model illustrates the effects of MetAP2-dependent N-myristoylation on cAMP signaling and lipid metabolism within adipocytes. The myristoylation of Gαi enables its membrane localization, where it suppresses adenylate cyclase activity, reducing cAMP levels. MetAP2 inhibition prevents Gαi membrane anchoring, resulting in elevated cAMP levels. This activates PKA, which phosphorylates hormone-sensitive lipase (HSL), promoting triglyceride breakdown into glycerol and free fatty acids. Concurrently, cAMP activates CREB, enhancing the transcription of thermogenic genes, such as UCP1, to increase mitochondrial thermogenesis and energy expenditure, reducing fat accumulation and aiding in obesity management. (<b>B</b>) Hepatocyte: The panel highlights the role of myristoylated PKCε in promoting hepatic insulin resistance. Elevated fatty acid levels increase diacylglycerol (DAG) accumulation, activating PKCε, which is stabilized on the membrane through myristoylation. Activated PKCε inhibits IRS-1/IRS-2 phosphorylation, impairing PI3K/AKT signaling, reducing glycogen synthesis, and increasing gluconeogenesis. MetAP2 inhibition could modulate PKCε activity by preventing its myristoylation, offering potential therapeutic effects against lipid-induced hepatic insulin resistance. (<b>C</b>) Macrophage and skeletal muscle cell: The model illustrates the importance of TRAM’s N-myristoylation in TLR4-mediated inflammatory signaling. Upon LPS binding, TLR4 dimerizes, and myristoylated TRAM colocalizes with TLR4 on the membrane. PKCε phosphorylates TRAM at Ser-16, causing its dissociation from the membrane to interact with TRIF. This activates NF-κB and IRF3 pathways, inducing TNF-alpha expression. Secreted TNF-alpha exacerbates insulin resistance by activating JNK, which disrupts insulin signaling in skeletal muscle cells. TNF-alpha activates JNK, which inhibits IRS-1 phosphorylation and downstream PI3K/AKT signaling. This impairs GLUT4 translocation to the plasma membrane, reducing glucose uptake and contributing to insulin resistance.</p>
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<p>Docking results of Fumagillin, TNP-470, and Beloranib with MetAP2. The docking results of fumagillin (<b>A</b>), TNP-470 (<b>B</b>), and beloranib (<b>C</b>) with MetAP2, performed using CB-Dock2 (<a href="https://cadd.labshare.cn/cb-dock2/" target="_blank">https://cadd.labshare.cn/cb-dock2/</a>, accessed on 14 November 2024) and visualized with the Biovia Discovery Studio Visualizer (version 21.1.0.20298), are presented. The MetAP2 structure (PDB ID: 1BOA) was obtained from the Protein Data Bank, and ligand structures (SDF files) were downloaded from PubChem. The docking process was conducted using CB-Dock2, which predicted binding poses and calculated binding scores (in kcal/mol). The binding scores for fumagillin, TNP-470, and beloranib were −6.7, −6.8, and −6.2 kcal/mol, respectively. Key contact residues involved in hydrogen bonding and hydrophobic interactions are displayed in the 2D interaction diagrams, including His231, Tyr444, Leu447, and His382. These residues are critical for stabilizing the inhibitors within the MetAP2 active site. The docking study highlights the covalent and hydrophobic interactions, particularly with His231, which is essential for MetAP2 inhibition.</p>
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<p>Docking results of ZGN-1061, Indazole, and Pyrazolo[4,3-b]indole with MetAP2. The docking results of ZGN-1061 (<b>A</b>), indazole (<b>B</b>), and pyrazolo[4,3-b]indole (<b>C</b>) with MetAP2 were obtained using CB-Dock2 (<a href="https://cadd.labshare.cn/cb-dock2/" target="_blank">https://cadd.labshare.cn/cb-dock2/</a>, accessed on 14 November 2024) and visualized with the Biovia Discovery Studio Visualizer (version 21.1.0.20298). The MetAP2 structure (PDB ID: 1BOA) was obtained from the Protein Data Bank, and ligand structures (SDF files) were retrieved from PubChem. CB-Dock2 was employed to predict the binding sites and calculate binding scores (in kcal/mol), which were −7.2, −8.3, and −8.1 kcal/mol for ZGN-1061, indazole, and pyrazolo[4,3-b]indole, respectively. The 2D interaction diagrams depict the binding interactions with key residues such as His231, His339, Tyr444, and Leu447. Both indazole and pyrazolo[4,3-b]indole formed strong non-covalent interactions with the active site metal ions, while ZGN-1061 demonstrated a balanced binding profile with hydrophobic contacts in the adjacent pocket. The results emphasize the importance of nitrogen-containing warheads and hydrophobic substituents at the 6- and 7-positions for maximizing the binding affinity and stability.</p>
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18 pages, 9437 KiB  
Article
Seed Dressing Containing Gibberellic Acid, Indole-3-Acetic Acid, and Brassinolide Improves Maize Seed Germination and Seedling Growth Under Cold Stress
by Jingjing Cui, Liqiang Zhang, Qianqian Li, Yuan Qi, Jiajun Ma, Danyang Guo, Pengyu Zhang, Yujie Xu, Yan Gu and Hongyu Wang
Agronomy 2024, 14(12), 2933; https://doi.org/10.3390/agronomy14122933 - 9 Dec 2024
Viewed by 493
Abstract
Chemical products, such as seed dressings, are often used to regulate crop growth and development and improve yields. In this study, we investigated a seed dressing containing 0.136% gibberellic acid (GA), indole-3-acetic acid (IAA)-, and brassinolide (BL) as a wettable powder (WP), hereafter [...] Read more.
Chemical products, such as seed dressings, are often used to regulate crop growth and development and improve yields. In this study, we investigated a seed dressing containing 0.136% gibberellic acid (GA), indole-3-acetic acid (IAA)-, and brassinolide (BL) as a wettable powder (WP), hereafter referred to as GA-IAA-BL WP. This product is a new plant growth regulator of plant origin that can improve crop stress resistance and yield. However, its effect on maize seed germination and seedling growth under low-temperature stress is unclear. In this study, GA-IAA-BL WP was applied to maize ‘Liukexing 99’ seeds at 50, 100, 150, or 200 mg mL−1, and seeds were germinated in an artificial climatic chamber at 10, 15, or 25 °C for 14 d. Application at 100 mg mL−1 significantly increased the germination rate as well as seedling shoot and root length and dry and fresh weight at all three temperatures. This application rate also increased the contents of proline, malondialdehyde, soluble sugars, and soluble proteins; the activities of catalase, superoxide dismutase, and peroxidase; and root vigor. Our results demonstrate that GA-IAA-BL WP can reduce the negative impacts of low-temperature stress on seed germination and seedling growth. Full article
(This article belongs to the Section Plant-Crop Biology and Biochemistry)
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<p>Maize was grown in plastic containers in a climate chamber. (<b>a</b>) Aerial view of the container; (<b>b</b>,<b>c</b>) side view of the container in a climate chamber.</p>
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<p>Effects of GA-IAA-BL WP on the germination rate at different temperatures. The bars represent the mean ± SE, <span class="html-italic">n</span> = 50 replicates. The A, B, and C lettering indicates differences between temperatures under the same treatment (<span class="html-italic">p</span> &lt; 0.05). Lowercase letters (a and b) indicate significant differences between GA-IAA-BL WP treatments at the same temperature (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effect of GA-IAA-BL WP on maize seedling growth at different temperatures.</p>
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<p>Effect of GA-IAA-BL WP on maize seedling shoot (<b>a</b>) and root (<b>b</b>) growth at different temperatures. The bars represent the mean ± SE, <span class="html-italic">n</span> = 50 replicates. The A, B, and C lettering indicates differences between temperatures under the same treatment (<span class="html-italic">p</span> &lt; 0.05). Lowercase letters (a, b, c, and d) indicate significant differences between GA-IAA-BL WP treatments at the same temperature (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effect of temperature and GA-IAA-BL WP seed dressing on the root–shoot ratio of maize seedlings. The bars represent the mean ± SE, <span class="html-italic">n</span> = 50 replicates. The A, B, and C lettering indicates differences between temperatures under the same treatment (<span class="html-italic">p</span> &lt; 0.05). Lowercase letters (a, b, and c) indicate differences between GA-IAA-BL WP treatments at the same temperature (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effect of temperature and GA-IAA-BL WP seed dressing on dry and fresh weights of maize seedling shoots (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>,<b>i</b>,<b>k</b>) and roots (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j</b>,<b>l</b>). The bars represent the mean ± SE, <span class="html-italic">n</span> = 50 replicates. Lowercase letters (a, b, c, and d) indicate differences between GA-IAA-BL WP treatments at the same temperature (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effect of temperature and GA-IAA-BL WP on enzymes. Superoxide dismutase (SOD) (<b>a</b>–<b>c</b>), catalase (CAT) (<b>d</b>–<b>f</b>), and peroxidase (POD) (<b>g</b>–<b>i</b>) activity in maize seedling shoots and roots. The bars represent the mean ± SE, <span class="html-italic">n</span> = 50 replicates. Lowercase letters (a, b, c, and d) indicate differences between GA-IAA-BL WP treatments at the same temperature (<span class="html-italic">p</span> &lt; 0.05). A0–A4 indicate the GA-IAA-BL WP concentration: 0, 50, 100, 150, and 200 mg mL<sup>−1</sup>, respectively.</p>
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<p>Effect of temperature and GA-IAA-BL WP on proline (Pro) (<b>a</b>–<b>c</b>) and malondialdehyde (MDA) (<b>d</b>–<b>f</b>) in maize seedling shoots and roots. The bars represent the mean ± SE, <span class="html-italic">n</span> = 50 replicates. Lowercase letters (a, b, c, and d) indicate differences between GA-IAA-BL WP treatments at the same temperature (<span class="html-italic">p</span> &lt; 0.05). A0–A4 indicate the GA-IAA-BL WP concentration: 0, 50, 100, 150, and 200 mg mL<sup>−1</sup>, respectively.</p>
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<p>Effect of temperature and GA-IAA-BL WP on root soluble sugar concentration (<b>a</b>), root soluble protein concentration (<b>b</b>), and root vigor (TTC) (<b>c</b>) of maize seedlings. The bars represent the mean ± SE, <span class="html-italic">n</span> = 50 replicates. The A, B, and C lettering indicates differences between temperatures under the same treatment (<span class="html-italic">p</span> &lt; 0.05). Lowercase letters (a, b, c, and d) indicate differences between GA-IAA-BL WP treatments at the same temperature (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Structural equation modeling (SEM) of the effect of GA-IAA-BL WP and temperature on maize seed germination and seedling growth. Blue arrows indicate negative correlations, and red arrows indicate positive correlations between variables (* <span class="html-italic">p</span> &lt; 0.05). The black dotted line indicates no significant correlation (<span class="html-italic">p</span> &gt; 0.05). A1–A4 indicate the GA-IAA-BL WP concentration: 50, 100, 150, and 200 mg mL<sup>−1</sup>, respectively. Organic matter accumulation includes the dry and fresh weights of maize seedling shoots and roots. Antioxidant enzyme activity includes catalase, superoxide dismutase, and peroxidase.</p>
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18 pages, 2317 KiB  
Article
Improving the Anti-Tumor Effect of Indoleamine 2,3-Dioxygenase Inhibitor CY1-4 by CY1-4 Nano-Skeleton Drug Delivery System
by Hui Li, Junwei Liu, Jingru Wang, Zhuoyue Li, Jianming Yu, Xu Huang, Bingchuan Wan, Xiangbao Meng and Xuan Zhang
J. Funct. Biomater. 2024, 15(12), 372; https://doi.org/10.3390/jfb15120372 - 9 Dec 2024
Viewed by 446
Abstract
Background: CY1-4, 9-nitropyridine [2′,3′:4,5] pyrimido [1,2-α] indole -5,11- dione, is an indoleamine 2,3-dioxygenase (IDO) inhibitor and a poorly water-soluble substance. It is very important to increase the solubility of CY1-4 to improve its bioavailability and therapeutic effect. In this study, the mesoporous silica [...] Read more.
Background: CY1-4, 9-nitropyridine [2′,3′:4,5] pyrimido [1,2-α] indole -5,11- dione, is an indoleamine 2,3-dioxygenase (IDO) inhibitor and a poorly water-soluble substance. It is very important to increase the solubility of CY1-4 to improve its bioavailability and therapeutic effect. In this study, the mesoporous silica nano-skeleton carrier material Sylysia was selected as the carrier to load CY1-4, and then the CY1-4 nano-skeleton drug delivery system (MSNM@CY1-4) was prepared by coating the hydrophilic polymer material Hydroxypropyl methylcellulose (HPMC) and the lipid material Distearoylphosphatidyl-ethanolamine-poly(ethylene glycol)2000 (DSPE-PEG2000) to improve the anti-tumor effect of CY1-4. Methods: The solubility and dissolution of MSNM@CY1-4 were investigated, and its bioavailability, anti-tumor efficacy, IDO inhibitory ability and immune mechanism were evaluated in vivo. Results: CY1-4 was loaded in MSNM@CY1-4 in an amorphous form, and MSNM@CY1-4 could significantly improve the solubility (up to about 200 times) and dissolution rate of CY1-4. In vivo studies showed that the oral bioavailability of CY1-4 in 20 mg/kg MSNM@CY1-4 was about 23.9-fold more than that in 50 mg/kg CY1-4 suspension. In B16F10 tumor-bearing mice, MSNM@CY1-4 significantly inhibited tumor growth, prolonged survival time, significantly inhibited IDO activity in blood and tumor tissues, and reduced Tregs in tumor tissues and tumor-draining lymph nodes to improve anti-tumor efficacy. Conclusions: The nano-skeleton drug delivery system (MSNM@CY1-4) constructed in this study is a potential drug delivery platform for improving the anti-tumor effect of oral poorly water-soluble CY1-4. Full article
(This article belongs to the Section Biomaterials for Drug Delivery)
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<p>The chemical structural formula of CY1-4.</p>
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<p>In vitro release of CY1-4 from MSNM@CY1-4 in (<b>a</b>) artificial gastric fluid (pH 1.2) and (<b>b</b>) artificial intestinal fluid (pH 6.8).</p>
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<p>In vivo plasma concentration–time curve of CY1-4 after oral administration of MSNM@CY1-4.</p>
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<p>In vivo anti-tumor study of MSNM@CY1-4 in B16F10 tumor-bearing C57BL/6 mice. (<b>a</b>) Schematic graph of the experimental design. (<b>b</b>) Tumor volume curves of tumor-bearing mice received different treatments. (<b>c</b>) Body weight changes of the tumor-bearing mice. (<b>d</b>) Survival curve of tumor-bearing mice received different treatments. Data are shown as mean ± SD, n = 6 (** <span class="html-italic">p</span> &lt; 0.01 vs. control; <span>$</span> <span class="html-italic">p</span> &lt; 0.05, <span>$</span><span>$</span> <span class="html-italic">p</span> &lt; 0.01 vs. 50 mg/kg CY1-4 suspension; # <span class="html-italic">p</span> &lt; 0.05 vs. 10 mg/kg MSNM@CY1-4).</p>
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<p>In vivo IDO inhibition study of MSNM@CY1-4 in B16F10 tumor-bearing C57BL/6 mice. (<b>a</b>) Schematic graph of the experimental design. (<b>b</b>) The Kyn/Trp ratio in (<b>b<sub>1</sub></b>) blood and (<b>b<sub>2</sub></b>) tumor following various treatments. (<b>c</b>) The photograph images (<b>c<sub>1</sub></b>) and weight (<b>c<sub>2</sub></b>) of tumors collected from mice on the 14th day after tumor inoculation. Data are shown as mean ± SD, n = 3 (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 vs. control; <span>$</span> <span class="html-italic">p</span> &lt; 0.05, <span>$</span><span>$</span> <span class="html-italic">p</span> &lt; 0.01 vs. CY1-4 suspension).</p>
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<p>In vivo IDO inhibition study of MSNM@CY1-4 in B16F10 tumor-bearing C57BL/6 mice. (<b>a</b>) Schematic graph of the experimental design. (<b>b</b>) The Kyn/Trp ratio in (<b>b<sub>1</sub></b>) blood and (<b>b<sub>2</sub></b>) tumor following various treatments. (<b>c</b>) The photograph images (<b>c<sub>1</sub></b>) and weight (<b>c<sub>2</sub></b>) of tumors collected from mice on the 14th day after tumor inoculation. Data are shown as mean ± SD, n = 3 (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 vs. control; <span>$</span> <span class="html-italic">p</span> &lt; 0.05, <span>$</span><span>$</span> <span class="html-italic">p</span> &lt; 0.01 vs. CY1-4 suspension).</p>
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<p>In vivo anti-tumor immunity response of MSNM@CY1-4 in B16F10 tumor-bearing C57BL/6 mice. (<b>a</b>) Schematic illustration of the experimental design. (<b>b</b>) Infiltration of (<b>b<sub>1</sub></b>) Tregs, (<b>b<sub>2</sub></b>) CD4<sup>+</sup> T cells, and (<b>b<sub>3</sub></b>) CD8<sup>+</sup> T cells in tumors through the flow cytometric examination. (<b>c</b>) Tumor-draining lymph node infiltration of (<b>c<sub>1</sub></b>) Tregs, (<b>c<sub>2</sub></b>) CD4<sup>+</sup> T cells, and (<b>c<sub>3</sub></b>) CD8<sup>+</sup> T cells through flow cytometric examination. Data are shown as mean ± SD, n = 3 (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 vs. control).</p>
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16 pages, 343 KiB  
Review
A Diagnostic Approach in Large B-Cell Lymphomas According to the Fifth World Health Organization and International Consensus Classifications and a Practical Algorithm in Routine Practice
by Magda Zanelli, Francesca Sanguedolce, Maurizio Zizzo, Stefano Ricci, Alessandra Bisagni, Andrea Palicelli, Valentina Fragliasso, Benedetta Donati, Giuseppe Broggi, Ioannis Boutas, Nektarios Koufopoulos, Moira Foroni, Francesca Coppa, Andrea Morini, Paola Parente, Valeria Zuccalà, Rosario Caltabiano, Massimiliano Fabozzi, Luca Cimino, Antonino Neri and Stefano Ascaniadd Show full author list remove Hide full author list
Int. J. Mol. Sci. 2024, 25(23), 13213; https://doi.org/10.3390/ijms252313213 - 9 Dec 2024
Viewed by 369
Abstract
In this article, we provide a review of large B-cell lymphomas (LBCLs), comparing the recently published fifth edition of the WHO classification and the International Consensus Classification (ICC) on hematolymphoid tumors. We focus on updates in the classification of LBCL, an heterogeneous group [...] Read more.
In this article, we provide a review of large B-cell lymphomas (LBCLs), comparing the recently published fifth edition of the WHO classification and the International Consensus Classification (ICC) on hematolymphoid tumors. We focus on updates in the classification of LBCL, an heterogeneous group of malignancies with varying clinical behaviors and different pathological and molecular features, providing a comparison between the two classifications. Besides the well-recognized diagnostic role of clinical, morphological and immunohistochemical data, both classifications recognize the ever-growing impact of molecular data in the diagnostic work-up of some entities. The main aim is to offer a guide for clinicians and pathologists on how the new classifications can be applied to LBCL diagnosis in routine practice. In the first part of the paper, we review the following categories: LBLs transformed from indolent B-cell lymphomas, diffuse large B-cell lymphoma, not otherwise specified (DLBCL, NOS), double-hit/triple-hit lymphomas (DH/TH), high-grade large B-cell lymphoma, not otherwise specified (HGBCL, NOS), LBCL with IRF4 rearrangement, Burkitt lymphoma (BL) and HGBCL/LBCL with 11q aberration, focusing on the differences between the two classifications. In the second part of the paper, we provide a practical diagnostic algorithm when facing LBCLs in routine daily practice. Full article
(This article belongs to the Special Issue From Diagnosis to Treatment of Haematological Neoplasms)
16 pages, 2195 KiB  
Article
Diastereomeric N,S-Dialkyl Dithiocarbamates Derived from (E)-Chalcones and ʟ-Tryptophan: Microwave-Assisted Synthesis and In Vitro Studies Against Fusarium oxysporum
by Natalia Agudelo-Ibañez, Sergio Torres-Cortés, Ericsson Coy-Barrera, Ivon Buitrago and Diego Quiroga
Organics 2024, 5(4), 598-613; https://doi.org/10.3390/org5040031 - 9 Dec 2024
Viewed by 782
Abstract
The synthesis of indole phytoalexin-like analogs related to alkyl (((1-(4-substitutedphenyl)-3-oxo-3-phenylpropyl)thio)carbonothioyl)-ʟ-tryptophanate 1ad and the evaluation of their antifungal activity against the phytopathogen Fusarium oxysporum is reported. The target compounds were synthesized in the following two stages: (1) the initial esterification of ʟ-tryptophan, [...] Read more.
The synthesis of indole phytoalexin-like analogs related to alkyl (((1-(4-substitutedphenyl)-3-oxo-3-phenylpropyl)thio)carbonothioyl)-ʟ-tryptophanate 1ad and the evaluation of their antifungal activity against the phytopathogen Fusarium oxysporum is reported. The target compounds were synthesized in the following two stages: (1) the initial esterification of ʟ-tryptophan, which reacted with trimethyl silane chloride and simple aliphatic alcohols (R = Me, Et) under microwave irradiation (MWI) at 100 °C to obtain the respective alkyl ester 2ab; (2) the resulting mixture of ʟ-tryptophanates 2ab with carbon disulfide and (E)-chalcone 3ab under MWI at 50 °C during 60 min, followed by purification through classical column chromatography (55–76% yields). The products were obtained as mixtures of (S,R) and (S,S) diastereoisomers. An LC-DAD-MS analysis allowed us to establish the ratio of these diastereoisomers, and subsequent DFT/B3LYP-based computational calculations of the NMR 1H chemical shifts suggested that the major diastereoisomer involved an (S,R) absolute configuration, comprising more than 60% of the mixture. The compounds 1ad were subjected to an antifungal activity test against the phytopathogen F. oxysporum using an amended medium-based assay. Compound series 1 showed inhibition percentages of 80% at the first concentration and IC50 values between 0.33 and 5.71 mM, demonstrating greater potential as antifungal agents compared to other ʟ-tryptophan derivatives like alkyl (2S)-3-(1H-indol-3-yl)-2-{[(1Z)-3-oxobut-1-en-1-yl]amino}propanoate, which presented lower inhibition percentages. In summary, phytoalexin analogs derived from ʟ-tryptophan and (E)-chalcones significantly inhibited the mycelial growth of Fusarium oxysporum, indicating their potential as effective antifungal agents. Full article
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<p>Chemical structure of the (<span class="html-italic">S</span>,<span class="html-italic">R</span>)-diastereoisomers of alkyl (((3-oxo-1,3-diphenylpropyl)thio)carbonothioyl)-ʟ-tryptophanates previously analyzed [<a href="#B15-organics-05-00031" class="html-bibr">15</a>].</p>
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<p>Chromatographic profiles for the crude reaction mixtures obtained after the synthesis of compounds <b>1a</b>–<b>d</b>. Lowercase numbers for each signal assignment (i.e., <b>1a<sub>1</sub></b>–<b>d<sub>1</sub></b> and <b>1a<sub>2</sub></b>–<b>d<sub>2</sub></b>) indicate the discrimination of two diastereoisomers ((<span class="html-italic">S</span>,<span class="html-italic">S</span>) or (<span class="html-italic">S</span>,<span class="html-italic">R</span>)). Each plot pinpointed to a specific diastereoisomer corresponds to its respective mass spectrum, which displays <span class="html-italic">m</span>/<span class="html-italic">z</span> signals for the ESI-MS adducts [M + H]<sup>+</sup> (the more intense MS signal) and [M + MeOH + H]<sup>+</sup> (lesser intense, final MS signal).</p>
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<p>(<b>a</b>) DFT-derived models for diastereomers of compounds <b>1c</b>–<b>d</b>; (<b>b</b>) <sup>1</sup>H and <sup>13</sup>C calculated chemical shift for the diastereoisomers (<span class="html-italic">S</span>,<span class="html-italic">R</span>)-<b>1c</b>, (<span class="html-italic">S</span>,<span class="html-italic">S</span>)-<b>1c</b>, (<span class="html-italic">S</span>,<span class="html-italic">R</span>)-<b>1d</b>, and (<span class="html-italic">S</span>,<span class="html-italic">S</span>)-<b>1d</b>.</p>
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<p>Two-dimensional NMR experiments for compound <b>1d</b>. (<b>a</b>) HMQC, (<b>b</b>) HMBC, (<b>c</b>) COSY, and (<b>d</b>) NOESY.</p>
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<p>Synthesis of compounds <b>1a</b>–<b>d</b> from ʟ-tryptophan and 4-substituted (<span class="html-italic">E</span>)-chalcones <b>3a</b>–<b>b</b>.</p>
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15 pages, 1569 KiB  
Article
Prediagnostic Plasma Nutrimetabolomics and Prostate Cancer Risk: A Nested Case–Control Analysis Within the EPIC Study
by Enrique Almanza-Aguilera, Miriam Martínez-Huélamo, Yamilé López-Hernández, Daniel Guiñón-Fort, Anna Guadall, Meryl Cruz, Aurora Perez-Cornago, Agnetha L. Rostgaard-Hansen, Anne Tjønneland, Christina C. Dahm, Verena Katzke, Matthias B. Schulze, Giovanna Masala, Claudia Agnoli, Rosario Tumino, Fulvio Ricceri, Cristina Lasheras, Marta Crous-Bou, Maria-Jose Sánchez, Amaia Aizpurua-Atxega, Marcela Guevara, Kostas K. Tsilidis, Anastasia Chrysovalantou Chatziioannou, Elisabete Weiderpass, Ruth C. Travis, David S. Wishart, Cristina Andrés-Lacueva and Raul Zamora-Rosadd Show full author list remove Hide full author list
Cancers 2024, 16(23), 4116; https://doi.org/10.3390/cancers16234116 - 8 Dec 2024
Viewed by 646
Abstract
Background and Objective: Nutrimetabolomics may reveal novel insights into early metabolic alterations and the role of dietary exposures on prostate cancer (PCa) risk. We aimed to prospectively investigate the associations between plasma metabolite concentrations and PCa risk, including clinically relevant tumor subtypes. [...] Read more.
Background and Objective: Nutrimetabolomics may reveal novel insights into early metabolic alterations and the role of dietary exposures on prostate cancer (PCa) risk. We aimed to prospectively investigate the associations between plasma metabolite concentrations and PCa risk, including clinically relevant tumor subtypes. Methods: We used a targeted and large-scale metabolomics approach to analyze plasma samples of 851 matched PCa case–control pairs from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Associations between metabolite concentrations and PCa risk were estimated by multivariate conditional logistic regression analysis. False discovery rate (FDR) was used to control for multiple testing correction. Results: Thirty-one metabolites (predominately derivatives of food intake and microbial metabolism) were associated with overall PCa risk and its clinical subtypes (p < 0.05), but none of the associations exceeded the FDR threshold. The strongest positive and negative associations were for dimethylglycine (OR = 2.13; 95% CI 1.16–3.91) with advanced PCa risk (n = 157) and indole-3-lactic acid (OR = 0.28; 95% CI 0.09–0.87) with fatal PCa risk (n = 57), respectively; however, these associations did not survive correction for multiple testing. Conclusions: The results from the current nutrimetabolomics study suggest that apart from early metabolic deregulations, some biomarkers of food intake might be related to PCa risk, especially advanced and fatal PCa. Further independent and larger studies are needed to validate our results. Full article
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<p>Volcano plot showing the magnitude of associations (odds ratio [OR]) and statistical significance (−log10 <span class="html-italic">p</span> values) for plasma metabolites and overall prostate cancer risk in the nested case–control within the EPIC cohort. Significant associations (above the dotted horizontal line) were set at −log10 <span class="html-italic">p</span>-values ≥ −1.30 (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Volcano forest plot showing odds ratios and 95% CI for the associations between plasma metabolites and five relevant clinical tumor subtypes of prostate cancer in the nested case–control study within the EPIC cohort. Only significant associations at <span class="html-italic">p</span> &lt; 0.05 (not FDR adjusted) are shown. Abbreviations: 3-MPPAG = 3-(3′-methoxyphenyl)propanoic acid-4′-glucuronide; 3-MPPAS = 3-(3′-methoxyphenyl)propanoic acid-4′-sulfate; 4-HHPPA = (R/S)-2-hydroxy-3-(4′-hydroxyphenyl)propanoic acid; 6-AMMU = 6-amino-5-(N-methylformylamino)-1-methyluracil; CMPF = 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid; DHPPA = 3-(3′,4′-dihydroxyphenyl)propanoic acid; DHSBA = 3,5-dihydroxy-4-(sulfooxy)benzoic acid; HPAA sulfate = N-(2-hydroxyphenyl)-acetamide sulfate; TMAO = trimethylamine N-oxide.</p>
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<p>Heatmap showing significant (<span class="html-italic">p</span> &lt; 0.05) Spearman’s correlations between plasma concentrations of 31 metabolites and the habitual intake of selected foods and food groups in the nested prostate cancer case–control study within the EPIC cohort. Full data on rho coefficients and statistical significance for each pair of correlations are shown in <a href="#app1-cancers-16-04116" class="html-app">Supplementary Table S2</a>. Abbreviations: 3-MPPAG = 3-(3′-methoxyphenyl)propanoic acid-4′-glucuronide; 3-MPPAS = 3-(3′-methoxyphenyl)propanoic acid-4′-sulfate; 4-HHPPA = (R/S)-2-hydroxy-3-(4′-hydroxyphenyl)propanoic acid; 6-AMMU = 6-amino-5-(N-methylformylamino)-1-methyluracil; CMPF = 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid; DHPPA = 3-(3′,4′-dihydroxyphenyl)propanoic acid; DHSBA = 3,5-dihydroxy-4-(sulfooxy)benzoic acid; HPAA sulfate = N-(2-hydroxyphenyl)-acetamide sulfate; TMAO = trimethylamine N-oxide.</p>
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13 pages, 3533 KiB  
Article
Low-Molecular-Weight Organic Acid as an Alternative to Promote the Rooting of Persimmon Rootstock Shoot Cuttings
by Jingjing Geng, Chi Zhang, Shaoning Deng, Bowei Liu, Mengye Cheng, Xiuhong An, Hongxia Wang and Wenjiang Wang
Plants 2024, 13(23), 3440; https://doi.org/10.3390/plants13233440 - 8 Dec 2024
Viewed by 386
Abstract
Organic acids are naturally present in plants and exert a positive influence on plant development, which justifies surveying their potential effect on adventitious root (AR) formation. In this study, 0.0298 mol/L (4000 mg/L) of malic acid and 0.0267 mol/L (4000 mg/L) of tartaric [...] Read more.
Organic acids are naturally present in plants and exert a positive influence on plant development, which justifies surveying their potential effect on adventitious root (AR) formation. In this study, 0.0298 mol/L (4000 mg/L) of malic acid and 0.0267 mol/L (4000 mg/L) of tartaric acid were used to explore the effects of low-molecular-weight organic acid on the rooting of persimmon rootstock Diospyros lotus L. during cutting propagation. After organic acid treatment, the rooting percentage and the survival rate significantly increased, accompanied by a greater development of lateral roots. Anatomical analysis revealed that Diospyros lotus L. exhibits characteristics that induce root primordia, and organic acid treatment can enhance the differentiation of root primordia. Furthermore, treatment with organic acid led to a substantial decrease in soluble sugar and starch contents, along with a slight increase in soluble protein content during early cutting stages. Additionally, the indole-3-acetic acid (IAA) content peaked in the early stages of AR formation and was significantly higher than that of the control, while abscisic acid (ABA) levels exhibited the opposite trend. Comparatively, gibberellic acid (GA3) remained at extremely low levels throughout the rooting process in the organic acid groups compared to the control. In conclusion, the current study uncovers the anatomical structure over time during AR formation, revealing the dynamic changes in the related main nutrients and hormones and providing new ideas and a new practical approach for improving root regeneration in persimmon rootstock cuttings. Full article
(This article belongs to the Section Plant Development and Morphogenesis)
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Figure 1

Figure 1
<p>Low-molecular-weight organic acid treatment promotes the rooting of persimmon rootstock L938. (<b>A</b>). The morphological changes in adventitious roots of L938 cuttings under different treatments. (<b>B</b>). Rooting rate (% of cuttings that developed at least one root). (<b>C</b>). Survival rate (% of live cuttings). There were 33 plants per treatment and three replications. Values are the means of three replicates and the error bars represent the standard error. Asterisks indicate a significant difference between the CK and the organic acid treatment in the rooting rate and the survival rate (*** means <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Histological observations of pre-adventitious root initiation stages of L938 cutting under 4000 mg/L malic acid treatment. (<b>A</b>). 0 d. (<b>B</b>). 10 d. (<b>C</b>). 20 d. (<b>D</b>). 30 d. (<b>E</b>). 40 d. (<b>F</b>). 50 d. Rp: root primordium; Ar: adventitious root; Pe: epidermis; Co: cortex; Ph: phloem; ve: vessel; Xy: xylem; Vc: vascular cambium; Pi: pith; Ra: pith ray. Scale bar: 100 μm.</p>
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<p>Histological observations of pre-adventitious root initiation stages of L938 cuttings under 4000 mg/L tartaric acid treatments. (<b>A</b>). 0 d. (<b>B</b>). 10 d. (<b>C</b>). 20 d. (<b>D</b>). 30 d. (<b>E</b>). 40 d. (<b>F</b>). 50 d. Rp: root primordium; Ar: adventitious root; Pe: epidermis; Co: cortex; Ph: phloem; Ve: vessel; Xy: xylem; Vc: vascular cambium; Pi: pith; Ra: pith ray. Scale bar: 100 μm.</p>
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<p>Histological observations of pre-adventitious root initiation stages of L938 cutting under water condition (CK). (<b>A</b>). 0 d. (<b>B</b>). 10 d. (<b>C</b>). 20 d. (<b>D</b>). 30 d. (<b>E</b>). 40 d. (<b>F</b>). 50 d. Rp: root primordium; Ar: adventitious root; Pe: epidermis; Co: cortex; Ph: phloem; Ve: vessel; Xy: xylem; Vc: vascular cambium; Pi: pith; Ra: pith ray. Scale bar: 100 μm.</p>
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<p>Dynamic changes in the contents of soluble sugar (<b>A</b>), starch (<b>B</b>), and soluble protein (<b>C</b>) during the rooting process of cuttings. Values are means of three replicates, and the error bars represent the standard error. Asterisks indicate significant difference between CK and organic acid treatments (* means <span class="html-italic">p</span> &lt; 0.05, ** means <span class="html-italic">p</span> &lt; 0.01, *** means <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Dynamic changes in the contents of four endogenous hormones during the rooting process of cuttings. (<b>A</b>). ZT content. (<b>B</b>). ABA content. (<b>C</b>). IAA content. (<b>D</b>). GA<sub>3</sub> content. Values are means of three replicates, and the error bars represent standard errors. Asterisks indicate significant differences between CK and organic acid treatments (* means <span class="html-italic">p</span> &lt; 0.05, ** means <span class="html-italic">p</span> &lt; 0.01, *** means <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Cutting process of date plum L938. (<b>A</b>). Date plum L938 plants used for cutting. (<b>B</b>). The substrate was sprayed with 1000 × carbendazim for disinfection. (<b>C</b>,<b>D</b>). The planted cuttings of date plum L938 placed in the full-light misty seedbed.</p>
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6 pages, 851 KiB  
Communication
Cyclization Modes in Anilides of N-Protected 3-Oxo-4-phenylaminobutyric Acid Under Knorr Conditions
by Plamen Angelov and Yordanka Mollova-Sapundzhieva
Molbank 2024, 2024(4), M1933; https://doi.org/10.3390/M1933 - 6 Dec 2024
Viewed by 313
Abstract
Anilides of 3-oxo-4-phenylaminobutyric acid with Troc or COOEt protection at the phenylamino group undergo competing cyclization processes in neat polyphosphoric acid at 80 °C. Depending on the protecting group and the duration of the process, three main products in different ratios are formed. [...] Read more.
Anilides of 3-oxo-4-phenylaminobutyric acid with Troc or COOEt protection at the phenylamino group undergo competing cyclization processes in neat polyphosphoric acid at 80 °C. Depending on the protecting group and the duration of the process, three main products in different ratios are formed. Along with the quinolin-2-ones, resulting from the classic Knorr cyclization, an indole derivative and a spirocyclic product have also been obtained from the COOEt-protected substrate. It has been demonstrated that the obtained indole derivative is capable of further dearomative spirocyclization under the studied conditions. Full article
(This article belongs to the Section Organic Synthesis and Biosynthesis)
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Scheme 1

Scheme 1
<p>Possible modes of cyclization in anilides of <span class="html-italic">N</span>-protected 3-oxo-4-phenylaminobutyric acid.</p>
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<p>Products obtained from anilides of <span class="html-italic">N</span>-protected 3-oxo-4-phenylaminobutyric acid under Knorr conditions.</p>
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<p>Alternative pathways to the spirocyclic product <b>4a</b>.</p>
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<p>Protonation of quinolones <b>3</b>.</p>
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