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Int. J. Mol. Sci., Volume 25, Issue 14 (July-2 2024) – 502 articles

Cover Story (view full-size image): Bioluminescence, the light produced by biochemical reactions involving luciferases in living organisms, has been extensively investigated for various applications. It has attracted particular interest as an internal light source for theranostic applications due to its safe and efficient characteristics, particularly overcoming the limited penetration of conventional external light sources. This comprehensive review presents the fundamental concepts of bioluminescence and explores its recent applications across diverse fields. Moreover, it discusses future research directions based on the current status of bioluminescent systems for further expansion of their potential. View this paper
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12 pages, 4021 KiB  
Case Report
Acute Erythroid Leukemia Post-Chemo-Radiotherapy and Autologous Stem Cell Transplantation Due to Multiple Myeloma: Tracing the Paths to Leukemic Transformation
by Gábor Méhes, Attila Mokánszki, Anikó Ujfalusi, Zsuzsa Hevessy, Zsófia Miltényi, Lajos Gergely and Judit Bedekovics
Int. J. Mol. Sci. 2024, 25(14), 8003; https://doi.org/10.3390/ijms25148003 - 22 Jul 2024
Viewed by 1160
Abstract
The clinical impact of therapy-related acute leukemias is increasing with the extension of cancer-related survival; however, the origins remain largely unknown. Acute erythroleukemia (AEL), a rare unfavorable type of myeloid neoplasia, may also develop secondary to cytotoxic therapy. The disorder is featured by [...] Read more.
The clinical impact of therapy-related acute leukemias is increasing with the extension of cancer-related survival; however, the origins remain largely unknown. Acute erythroleukemia (AEL), a rare unfavorable type of myeloid neoplasia, may also develop secondary to cytotoxic therapy. The disorder is featured by specific genetic alterations, most importantly multi-allelic mutations of the TP53 gene. While AEL might appear as a part of the therapy-related MDS/AML, spectrum information regarding the genetic complexity and progression is largely missing. We present two AEL cases arising after cytotoxic therapy and melphalan-based myeloablation/autologous peripheral stem cell transplantation due to multiple myeloma (MM). As stated, multiple pathogenic TP53 variants were present unrelated to preexisting MM, in parallel with uninvolved/wild-type hemopoiesis. Potential mechanisms of leukemic transformation are discussed, which include (1) preexisting preneoplastic hemopoietic stem cells (HSC) serving as the common origin for both MM and AEL, (2) the generation and intramedullary survival of p53-deficient post-chemotherapy HSCs, (3) reinoculation of mobilized autologous TP53 mutated HSCs, and (4) melphalan treatment-related late-onset myelodysplasia/leukemia with newly acquired TP53 mutations. Full article
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Figure 1
<p>Bone marrow biopsy morphology (Case 1). Key features are hypercellular parenchyma with maturation defect and up to 80% of early erythroid precursors by conventional HE staining (<b>top left</b>), suppression of the myeloid lineage and a lack of terminal granulopoieses (in red, NASD histochemistry, (<b>top right</b>)), dysplastic megakaryocytes ((<b>bottom left</b>), CD61 IHC), and a few mature plasmocytes ((<b>bottom right</b>), CD138 IHC) (×400 virtual magnification, scale bar = 100 µm).</p>
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<p>Bone marrow IHC showing limited CD34 labeling (up to 5%, with blast morphology) (<b>top left</b>) and approximately 15% CD117+ cells, mostly with proerythroblast morphology (<b>top right</b>). The great majority of cells (80%) presented with CD71 expression (<b>bottom left</b>); up to 30% of the cells presented with E-cadherin positivity and proerythroblast morphology (<b>bottom right</b>) (×400 virtual magnification, scale bar = 100 µm).</p>
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<p>p53 labeling was restricted to the erythroid lineage. CD34/p53 double-IHC presenting generally with CD34 (violet)-negative and p53-positive (brown) blast cells (<b>top</b>), CD71/p53 double-IHC displaying CD71+ (violet) erythroblasts with and without p53 labeling (brown) (<b>bottom</b>). Strong nuclear p53 positivity refers to mutant <span class="html-italic">TP53</span> status in approximately 50% of the erythroblasts (×400 virtual magnification, scale bar = 100 µm).</p>
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<p>Chromosome karyotyping presented individual subclones with two unrelated chromosome 17p alterations from the same bone marrow sample with AEL diagnosis (Case 1). Subclone 1 was highlighted by the karyotype 45,XY,-7,add(17)(p13.?3),-18,-21,+2mar[5] (<b>left</b>); one of the marker chromosomes is a potential derivate of chromosome 18 (not further analyzed), in contrast to subclone 2, with the karyotype 45,XY,-5,-7,add(11)(p15),del(17)(p13.?1),+mar[3] (<b>right</b>). A significant portion of dividing cells presented with the normal karyotype 46,XY[12].</p>
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<p>Bone marrow chromosome analysis presented the complex karyotype of 51,XY,+Y,del(5)(q12q3?5),+10,+11,+19,-20,+21,+mar[20] at the time of the AEL diagnosis (Case 2).</p>
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<p>Potential evolution leukemic transformation and the development of pCT AEL following HDM-APSCT due MM. Chemo-radiotherapy and delayed APSCT were effective in treating MM in both presented cases (green line illustrates declining myeloma tumor burden). Secondary AEL showing characteristic <span class="html-italic">TP53</span> mutations in immature erythroblasts as a unique feature could be the result of the transforming effect of VTD chemotherapy, with or without residual mutant HSCs, or of the HDM therapy (red line). Clonal aberrations in prior treatments, as well as in the pheresis product, could not be observed via NGS; thus, HDM-induced genotoxicity is favored as the most likely mechanism of leukemogenesis. (symbolic appearance of preleukemic/leukemic TP53 mutated myeloid clone in red, disappearance of the myeloma clone in green, dashed line represents lack of evident involvement in the presented case studies).</p>
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15 pages, 8324 KiB  
Article
Overexpression of BnaA10.WRKY75 Decreases Cadmium and Salt Tolerance via Increasing ROS Accumulation in Arabidopsis and Brassica napus L.
by Xiaoke Ping, Qianjun Ye, Mei Yan, Jia Wang, Taiyuan Zhang, Sheng Chen, Kadambot H. M. Siddique, Wallace A. Cowling, Jiana Li and Liezhao Liu
Int. J. Mol. Sci. 2024, 25(14), 8002; https://doi.org/10.3390/ijms25148002 - 22 Jul 2024
Cited by 3 | Viewed by 1052
Abstract
Soil is indispensable for agricultural production but has been seriously polluted by cadmium and salt in recent years. Many crops are suffering from this, including rapeseed, the third largest global oilseed crop. However, genes simultaneously related to both cadmium and salt stress have [...] Read more.
Soil is indispensable for agricultural production but has been seriously polluted by cadmium and salt in recent years. Many crops are suffering from this, including rapeseed, the third largest global oilseed crop. However, genes simultaneously related to both cadmium and salt stress have not been extensively reported yet. In this study, BnaA10.WRKY75 was screened from previous RNA-seq data related to cadmium and salt stress and further analyses including sequence comparison, GUS staining, transformation and qRT-PCR were conducted to confirm its function. GUS staining and qRT-PCR results indicated BnaA10.WRKY75 was induced by CdCl2 and NaCl treatment. Sequence analysis suggested BnaA10.WRKY75 belongs to Group IIc of the WRKY gene family and transient expression assay showed it was a nuclear localized transcription factor. BnaA10.WRKY75-overexpressing Arabidopsis and rapeseed plants accumulated more H2O2 and O2 and were more sensitive to CdCl2 and NaCl treatment compared with untransformed plants, which may be caused by the downregulation of BnaC03.CAT2. Our study reported that BnaA10.WRKY75 increases sensitivity to cadmium and salt stress by disrupting the balance of reactive oxygen species both in Arabidopsis and rapeseed. The results support the further understanding of the mechanisms underlying cadmium and salt tolerance and provide BnaA10.WRKY75 as a valuable gene for rapeseed abiotic stress breeding. Full article
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<p>Response of <span class="html-italic">WRKY</span> transcription factors to cadmium stress. (<b>a</b>) Expression levels of 75 differently expressed <span class="html-italic">WRKY</span> transcription factors as revealed by RNA-seq. Expression levels were described by fold change and (Cd<sup>2+</sup> 0 h) was used as the control. Four <span class="html-italic">BnaWRKY75s</span> and <span class="html-italic">BnaA10.WRKY75</span> were indicated by line and star, respectively. (<b>b</b>) Expression levels of four <span class="html-italic">BnaWRKY75s</span> under cadmium stress. (<b>c</b>) GUS staining results of <span class="html-italic">Arabidopsis</span> transgenic plants expressing <span class="html-italic">pBnaA10.WRKY75::GUS</span>. White arrows indicate the difference in GUS signal between cadmium treated and untreated plants. Bars: 1 cm.</p>
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<p>Protein sequences analysis and cis-element identification of four <span class="html-italic">BnaWRKY75s</span>. (<b>a</b>) Multiple sequences alignment. Two domains and representative amino acids were marked by line and star, respectively. (<b>b</b>) A phylogenetic tree including four BnaWRKY75s and 7 AtWRKY proteins from Group IIc. The green shading indicates the proteins that are closely related to BnaA10.WRKY75. (<b>c</b>) Genomic location of cis-element in <span class="html-italic">BnaWRKY75s</span> promoter.</p>
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<p>Localization of BnaA10.WRKY75 in tobacco epidermal cells.</p>
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<p>Expression of <span class="html-italic">BnaA10.WRKY75</span> in tissues detected by GUS staining: (<b>a</b>) 5 and (<b>b</b>) 14-day-old seedling; (<b>c</b>) 30-day-old leaf; (<b>d</b>) 10-day-old leaf; (<b>e</b>) stem; (<b>f</b>) flower; (<b>g</b>–<b>i</b>) silique at 1, 7 and 14 days after flowering. Bar: 1 cm.</p>
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<p>The effects of cadmium stress on wild-type and <span class="html-italic">BnaA10.WRKY75</span>-overexpressing plants. (<b>a</b>,<b>b</b>) Root length performance of seedlings grown on MS medium with or without CdCl<sub>2</sub> added for three weeks. (<b>c</b>,<b>d</b>) Performance of leaves and (<b>e</b>) H<sub>2</sub>O<sub>2</sub> and O<sub>2</sub><sup>−</sup> accumulation of plants irrigated by 500 μM CdCl<sub>2</sub> solution for 7 d. White arrows in (<b>c</b>) indicate the difference in leaves between <span class="html-italic">BnaA10.WRKY75</span> overexpressing and Col-0 seedlings. (<b>f</b>,<b>g</b>) The performance of rapeseed seedlings irrigated by 1000 μM CdCl<sub>2</sub> solution for 10 d and white arrows indicate the difference in leaves between <span class="html-italic">BnaA10.WRKY75</span> overexpressing and J9709 seedlings. (<b>h</b>) DAB and NBT staining results of rapeseed plants irrigated by 1000 μM CdCl<sub>2</sub> solution. Values in (<b>b</b>) are the mean ± SD of three replications and differences in comparisons were revealed by student’s <span class="html-italic">t</span>-test. **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001. Bars: (<b>a</b>,<b>c</b>–<b>e</b>,<b>h</b>) 1 cm; (<b>f</b>,<b>g</b>) 2 cm.</p>
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<p>Response of <span class="html-italic">BnaWRKY75s</span> to stresses as obtained from BnIR database. (<b>a</b>,<b>b</b>) Expression patterns of <span class="html-italic">BnaWRKY75s</span> in leaves and roots, respectively. Gray shadings in (<b>a</b>,<b>b</b>) indicate significant upregulation of <span class="html-italic">BnaA10.WRKY75</span>.</p>
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<p>Response of <span class="html-italic">BnaA10.WRKY75</span> to three types of abiotic stress. (<b>a</b>) qRT-PCR result in rapeseed cv. J9709; (<b>b</b>,<b>c</b>) GUS staining results of transgenic <span class="html-italic">Arabidopsis</span> plants expressing <span class="html-italic">pBnaA10.WRKY75::GUS</span> under control and 100 mM NaCl treatments. Values in (<b>a</b>) are the mean ± SD of three replications. Bars: (<b>b</b>,<b>c</b>) 1 cm.</p>
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<p>The effects of salt stress on wild-type and <span class="html-italic">BnaA10.WRKY75</span>-overexpressing plants. (<b>a</b>) Comparison of plant performance under salt treatment. (<b>b</b>–<b>e</b>) Differences in fresh weight, chlorophyll, proline and MDA content between Col-0 and transgenic <span class="html-italic">Arabidopsis</span> plants. (<b>f</b>) DAB and NBT staining revealed H<sub>2</sub>O<sub>2</sub> and O<sub>2</sub><sup>−</sup> accumulation in leaves of <span class="html-italic">Arabidopsis</span> plants under salt treatment. (<b>g</b>) Performance of hydroponic rapeseed seedlings treated with salt solution for 10 d. (<b>h</b>) DAB and NBT staining revealed H<sub>2</sub>O<sub>2</sub> and O<sub>2</sub><sup>−</sup> accumulation in leaves of rapeseed plants under salt treatment. Values in (<b>b</b>–<b>e</b>) are the mean ± SD of three replications and differences in comparisons were revealed by student’s <span class="html-italic">t</span>-test. **, <span class="html-italic">p</span> &lt; 0.01. Bars: (<b>f</b>,<b>h</b>) 1 cm; (<b>a</b>,<b>g</b>) 2 cm.</p>
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<p>BnaA10.WRKY75 regulates the expression of genes related to cadmium and salt stress. (<b>a</b>) <span class="html-italic">BnaC03.HMA4c</span>, (<b>b</b>,<b>c</b>) <span class="html-italic">AtSOS1</span>, (<b>d</b>) <span class="html-italic">BnaCAT2s</span>, (<b>e</b>) <span class="html-italic">AtCAT2</span> and (<b>f</b>) <span class="html-italic">BnaC03.CAT2</span>. Values in (<b>a</b>–<b>f</b>) are the mean ± SD of three replications and differences in comparisons were revealed by student’s <span class="html-italic">t</span>-test. *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ns, no significance.</p>
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<p>The role and working frame of <span class="html-italic">BnaA10.WRKY75</span> in response to cadmium and salt stress. Green lines mean promotion and red lines mean inhibition. Solid and dashed lines represent determined and undetermined regulatory relationships, respectively. <span class="html-italic">BnaA10.WRKY75</span> was induced by cadmium and salt stress then repressed <span class="html-italic">BnaC03.CAT2</span>, which is responsible for ROS scavenging. <span class="html-italic">BnaA10.WRKY75</span> also promotes the expression of <span class="html-italic">BnaC03.HMA4c</span> and increases Cd<sup>2+</sup> transport.</p>
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13 pages, 2712 KiB  
Article
The REPLUMLESS Transcription Factor Controls the Expression of the RECEPTOR-LIKE CYTOPLASMIC KINASE VI_A2 Gene Involved in Shoot and Fruit Patterning of Arabidopsis thaliana
by Erzsébet Kenesi, Orsolya Beöthy-Fehér, Réka Szőllősi, Ildikó Domonkos, Ildikó Valkai and Attila Fehér
Int. J. Mol. Sci. 2024, 25(14), 8001; https://doi.org/10.3390/ijms25148001 - 22 Jul 2024
Viewed by 696
Abstract
The promoter of the RECEPTOR-LIKE CYTOPLASMIC KINASE VI_A2 (RLCK VI_A2) gene contains nine binding sites for the REPLUMLESS (RPL) transcription factor. In agreement, the expression of the kinase gene was strongly downregulated in the rpl-4 mutant. Comparing phenotypes of loss-of-function mutants, [...] Read more.
The promoter of the RECEPTOR-LIKE CYTOPLASMIC KINASE VI_A2 (RLCK VI_A2) gene contains nine binding sites for the REPLUMLESS (RPL) transcription factor. In agreement, the expression of the kinase gene was strongly downregulated in the rpl-4 mutant. Comparing phenotypes of loss-of-function mutants, it was revealed that both genes are involved in stem growth, phyllotaxis, organization of the vascular tissues, and the replum, highlighting potential functional interactions. The expression of the RLCKVI_A2 gene from the constitutive 35S promoter could not complement the rpl-4 phenotypes but exhibited a dominant positive effect on stem growth and affected vascular differentiation and organization. The results also indicated that the number of vascular bundles is regulated independently from stem thickness. Although our study cannot demonstrate a direct link between the RPL and RLVKVI_A2 genes, it highlights the significance of the proper developmental regulation of the RLCKVI_A2 promoter for balanced stem development. Full article
(This article belongs to the Special Issue Modern Plant Cell Biotechnology: From Genes to Structure, 2nd Edition)
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Figure 1
<p>Comparison of the expression of the <span class="html-italic">At2G18890</span> (RLCKVI_A2) promoter driving the <span class="html-italic">GUS</span> marker gene in wild-type (<b>A</b>,<b>C</b>,<b>E</b>,<b>G</b>,<b>I</b>,<b>K</b>,<b>M</b>) and <span class="html-italic">rpl-4</span> mutant (<b>B</b>,<b>D</b>,<b>F</b>,<b>H</b>,<b>J</b>,<b>L</b>,<b>N</b>) genetic backgrounds.</p>
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<p>The phyllotactic pattern of siliques of wild-type (wt) (<b>A</b>,<b>D</b>,<b>G</b>), <span class="html-italic">rpl-4</span> (<b>B</b>,<b>E</b>,<b>H</b>), and <span class="html-italic">rlckvi_a2</span> (<b>C</b>,<b>F</b>,<b>I</b>) mutants without (<b>A</b>–<b>C</b>) and with (<b>D</b>–<b>I</b>) ectopic expression (OX1 and OX2) of the <span class="html-italic">RLCKVI_A2</span> gene under the control of the 35S promoter, respectively, is shown. The divergence angles of two successive siliques on the stem were determined for a minimum of ten T4 generation plants at the developmental stage 8 [<a href="#B28-ijms-25-08001" class="html-bibr">28</a>]. The distribution of measured angles falling into the indicated angle size categories is shown according to [<a href="#B29-ijms-25-08001" class="html-bibr">29</a>].</p>
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<p>The replum of wild-type (wt), <span class="html-italic">rpl-4</span>, and <span class="html-italic">rlckvi_a2</span> fruits. Scanning electron microscopy of the fruit surface (<b>A</b>–<b>C</b>) and cross-sections (<b>D</b>–<b>F</b>) verified a thinner visible (indicated by ] in (<b>A</b>–<b>C</b>)) and smaller inner (<b>D</b>–<b>G</b>) replum for the <span class="html-italic">rpl-4</span> mutant compared to the other two lines. Overlaying cross-sections for three replums per line shows that the inner replum of the <span class="html-italic">rlckvi_a2</span> mutant is larger than that of the wild-type (<b>G</b>), although the visible replum of these two lines is about the same size (<b>A</b>,<b>C</b>).</p>
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<p>Stem thickness and vascular organization of wild-type (wt), <span class="html-italic">rlckvi_a2</span>, and <span class="html-italic">rpl-4</span> mutant plants. Cross-sections of the inflorescence stems of the three investigated lines (<b>A</b>) were analyzed for stem cross-sectional area (<b>B</b>), the number of vascular bundles per stem (<b>C</b>), xylem size ((<b>D</b>); see dashed lines in (<b>G</b>)), the number of xylem vessels per xylem (<b>E</b>), and the size of xylem vessels (<b>F</b>). Stem parameters were determined for ten plants per line. Xylem parameters were measured for all xylems in four plants per line. The distribution of the measured values is represented as a box plot for each line. Significant differences from the wild-type were determined by Student’s <span class="html-italic">t</span>-test (<span class="html-italic">p</span> &lt; 0.05 = *; <span class="html-italic">p</span> &lt; 0.01 = **). The organization of vascular bundles is exemplified in (<b>G</b>). vb—vascular bundle; xv—xylem vessel; pc—arc of procambium.</p>
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<p>Effect of ectopic RLCKVI_A2 expression on stem thickness and the vasculature. Stem cross-sectional area (<b>A</b>,<b>D</b>,<b>G</b>); the number of vascular bundles per stem (<b>B</b>,<b>E</b>,<b>H</b>); and the average size of xylems (<b>C</b>,<b>F</b>,<b>I</b>) were determined for wild-type (wt; (<b>A</b>–<b>C</b>)), rpl-4 (<b>D</b>–<b>F</b>), or rlckvi_a2 (<b>G</b>–<b>I</b>) plants without and with overexpression (OX1 and OX2) of the <span class="html-italic">35S:RLCKVI_A2</span> gene. Ten plants per line were measured. The distribution of the measured values is represented as a box plot for each line. The data of the overexpressor lines (OX1 and OX2) were compared to their respective control using Student’s <span class="html-italic">t</span>-test (<span class="html-italic">p</span> &lt; 0.01 = **).</p>
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<p>Stem cross-sections of wild-type (wt) and mutant plants (<span class="html-italic">rpl-4</span> or <span class="html-italic">rlckvi_a2</span>) without and with overexpression (OX1 and OX2) of the <span class="html-italic">35S:RLCKVI_A2</span> gene. Red asterisks indicate closely placed/clustered vascular bundles.</p>
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25 pages, 7766 KiB  
Article
A Systematic Hierarchical Virtual Screening Model for RhlR Inhibitors Based on PCA, Pharmacophore, Docking, and Molecular Dynamics
by Jiarui Du, Jiahao Li, Juqi Wen, Jun Liu, Haichuan Xiao, Antian Zhang, Dongdong Yang, Pinghua Sun, Haibo Zhou and Jun Xu
Int. J. Mol. Sci. 2024, 25(14), 8000; https://doi.org/10.3390/ijms25148000 - 22 Jul 2024
Viewed by 882
Abstract
RhlR plays a key role in the quorum sensing of Pseudomonas aeruginosa. The current structure–activity relationship (SAR) studies of RhlR inhibitors mainly focus on elucidating the functional groups. Based on a systematic review of previous research on RhlR inhibitors, this study aims [...] Read more.
RhlR plays a key role in the quorum sensing of Pseudomonas aeruginosa. The current structure–activity relationship (SAR) studies of RhlR inhibitors mainly focus on elucidating the functional groups. Based on a systematic review of previous research on RhlR inhibitors, this study aims to establish a systematic, hierarchical screening model for RhlR inhibitors. We initially established a database and utilized principal component analysis (PCA) to categorize the inhibitors into two classes. Based on the training set, pharmacophore models were established to elucidate the structural characteristics of ligands. Subsequently, molecular docking, molecular dynamics simulations, and the calculation of binding free energy and strain energy were performed to validate the crucial interactions between ligands and receptors. Then, the screening criteria for RhlR inhibitors were established hierarchically based on ligand structure characteristics, ligand–receptor interaction, and receptor affinity. Test sets were finally employed to validate the hierarchical virtual screening model by comparing it with the current SAR studies of RhlR inhibitors. The hierarchical screening model was confirmed to possess higher accuracy and a true positive rate, which holds promise for subsequent screening and the discovery of active RhlR inhibitors. Full article
(This article belongs to the Section Molecular Informatics)
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<p>(<b>a</b>) Principal Components (PCs) contribution (<b>b</b>) Classification results of PCA (dim: dimension, PCA: principal component analysis).</p>
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<p>(<b>a</b>) Systematic review of Class 1; (<b>b</b>) Systematic review of Class 2.</p>
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<p>(<b>a</b>) The match between the pharmacophore AAADR_1 and active compounds in Class 1; (<b>b</b>) The match between the pharmacophore AHHR_2 and active compounds in Class 2 (the red sphere represents the hydrogen bond acceptor, the blue sphere represents the hydrogen bond donor, the green sphere represents the hydrophobic feature, and the orange circle represents the aromatic ring).</p>
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<p>Diagram of molecular docking of Class 1. (<b>a</b>)The 3D docking diagram of compounds in protein pocket. (<b>b</b>) Diagram of key interaction sites for docking. (<b>c</b>)The box plot of docking scores. (<b>d</b>) The box plot of strain energy (in <a href="#ijms-25-08000-f004" class="html-fig">Figure 4</a>c,d, the yellow bars represent active compounds, while the blue bars represent the inactive).</p>
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<p>Diagram of molecular dynamics of Class 1 (<b>a</b>) RMSD and RMSF plots (<b>b</b>) Interaction histograms of compounds (<b>c</b>) The contact sites of interactions for compounds (<b>d</b>) The box plot of binding free energy (<b>e</b>) The bar chart of key hydrogen bonds (<b>f</b>) The bar chart of key hydrophobic interactions (in <a href="#ijms-25-08000-f005" class="html-fig">Figure 5</a>d–f, the yellow bars represent active compounds, while the blue bars represent the inactive).</p>
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<p>Diagram of molecular docking of Class 2 (<b>a</b>) The 3D docking diagram of compounds in protein pocket (<b>b</b>) Diagram of key interaction sites for docking (<b>c</b>) The box plot of docking scores (<b>d</b>) The box plot of strain energy (in <a href="#ijms-25-08000-f006" class="html-fig">Figure 6</a>c,d, the green bars represent active compounds, while the purple bars represent the inactive).</p>
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<p>Diagram of molecular dynamics of Class 2 (<b>a</b>) RMSD and RMSF plots (<b>b</b>) Interaction histograms of compounds (<b>c</b>) The contact sites of interactions for compounds (<b>d</b>) The box plot of binding free energy (<b>e</b>) The bar chart of key hydrogen bonds (<b>f</b>) The bar chart of key hydrophobic interactions (in <a href="#ijms-25-08000-f007" class="html-fig">Figure 7</a>d–f, the green bars represent active compounds, while the purple bars represent the inactive).</p>
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<p>(<b>a</b>) Pharmacophore ADRH established based on the existing SAR (<b>b</b>) Pharmacophore AADH established based on the existing SAR (<b>c</b>) Pharmacophore AHHR established based on the existing SAR (the red sphere represents the hydrogen bond acceptor, the blue sphere represents the hydrogen bond donor, the green sphere represents the hydrophobic feature, and the orange circle represents the aromatic ring).</p>
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<p>(<b>a</b>) The match between test set of Class 1 and the pharmacophore AAADR_1 (<b>b</b>) The match between test set of Class 2 and the pharmacophore AHHR_2 (<b>c</b>) The top-ranked compounds in test sets of Class 1 based on pharmacophore screening (the yellow bars represent the pharmacophore scoring of different compounds, while the gray bars represent the number of matches with pharmacophore elements) (<b>d</b>) The top-ranked compounds in test sets of Class 2 based on pharmacophore screening (the green bars represent the pharmacophore scoring of different compounds, while the gray bars represent the number of matches with pharmacophore elements).</p>
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<p>(<b>a</b>) The 3D docking diagram of test set of Class 1 (<b>b</b>) RMSD and RMSF plots of test set (<b>c</b>) Interaction histograms of test set (<b>d</b>) The ranking of compounds in Class 1 based on docking scores (<b>e</b>) Strain energy of test set of Class 1 (<b>f</b>) Binding free energy of test set of Class 1 (<b>g</b>) The matches of interactions with the standard of Class 1 (the blocks with yellow represent hydrogen bonding, while orange signify hydrophobic interactions) (<b>h</b>) The summary of screening results for test set of Class 1 (the blocks with yellow represents compliance with standards, while the absence of a color indicates non-compliance) (<b>i</b>) The screening performance of the model was validated by the test set of Class 1.</p>
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<p>(<b>a</b>) The 3D docking diagram of test set of Class 2 (<b>b</b>) RMSD and RMSF plots of test set (<b>c</b>) Interaction histograms of test set (<b>d</b>) The ranking of compounds in Class 2 based on docking scores (<b>e</b>) Strain energy of test set of Class 2 (<b>f</b>) Binding free energy of test set of Class 2 (<b>g</b>) The matches of interactions with the standard of Class 2 (the blocks with green represent hydrogen bonding, while blue signify hydrophobic interactions) (<b>h</b>) The summary of screening results for test set of Class 2 (the blocks with green represents compliance with standards, while the absence of a color indicates non-compliance) (<b>i</b>) The screening performance of the model was validated by the test set of Class 2.</p>
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<p>(<b>a</b>) The comparison of different screening criteria for Class 1, with yellow bars presenting the number of true positive compounds, while blue representing the number of false positives. (Article 1: [<a href="#B25-ijms-25-08000" class="html-bibr">25</a>]; Article 2: [<a href="#B23-ijms-25-08000" class="html-bibr">23</a>].) (<b>b</b>) The comparison of different screening criteria for Class 2, with green bars presenting the number of true positive compounds, while purple representing the number of false positives. (Article 3: [<a href="#B27-ijms-25-08000" class="html-bibr">27</a>]).</p>
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18 pages, 758 KiB  
Review
Opioid Use and Gut Dysbiosis in Cancer Pain Patients
by Flaminia Coluzzi, Maria Sole Scerpa, Chiara Loffredo, Marina Borro, Joseph V. Pergolizzi, Jo Ann LeQuang, Elisa Alessandri, Maurizio Simmaco and Monica Rocco
Int. J. Mol. Sci. 2024, 25(14), 7999; https://doi.org/10.3390/ijms25147999 - 22 Jul 2024
Viewed by 1067
Abstract
Opioids are commonly used for the management of severe chronic cancer pain. Their well-known pharmacological effects on the gastrointestinal system, particularly opioid-induced constipation (OIC), are the most common limiting factors in the optimization of analgesia, and have led to the wide use of [...] Read more.
Opioids are commonly used for the management of severe chronic cancer pain. Their well-known pharmacological effects on the gastrointestinal system, particularly opioid-induced constipation (OIC), are the most common limiting factors in the optimization of analgesia, and have led to the wide use of laxatives and/or peripherally acting mu-opioid receptor antagonists (PAMORAs). A growing interest has been recently recorded in the possible effects of opioid treatment on the gut microbiota. Preclinical and clinical data, as presented in this review, showed that alterations of the gut microbiota play a role in modulating opioid-mediated analgesia and tolerability, including constipation. Moreover, due to the bidirectional crosstalk between gut bacteria and the central nervous system, gut dysbiosis may be crucial in modulating opioid reward and addictive behavior. The microbiota may also modulate pain regulation and tolerance, by activating microglial cells and inducing the release of inflammatory cytokines and chemokines, which sustain neuroinflammation. In the subset of cancer patients, the clinical meaning of opioid-induced gut dysbiosis, particularly its possible interference with the efficacy of chemotherapy and immunotherapy, is still unclear. Gut dysbiosis could be a new target for treatment in cancer patients. Restoring the physiological amount of specific gut bacteria may represent a promising therapeutic option for managing gastrointestinal symptoms and optimizing analgesia for cancer patients using opioids. Full article
(This article belongs to the Section Biochemistry)
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Figure 1
<p>Mechanisms of opioid-mediated modifications of the gut microbiota. Opioids ensure analgesia and cause constipation through their activity on mu-opioid receptors respectively in the central and enteric nervous system. Leaky-gut has been recognized as the main mechanism of bacterial translocation, which activates enteric glia, leading to the massive release of pro-inflammatory mediators. The resulting altered gut microbiota has been implicated in most of the challenging conditions related to chronic opioid use, such as tolerance, addiction, and reward. The bidirectional relationship between the gut microbiota and the brain play a key role in the well-known gut–brain axis.</p>
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21 pages, 6015 KiB  
Article
AdNAC20 Regulates Lignin and Coumarin Biosynthesis in the Roots of Angelica dahurica var. formosana
by Wenjie Qu, Wenjuan Huang, Chen Chen, Jinsong Chen, Lin Zhao, Yijie Jiang, Xuan Du, Renlang Liu, Yinyin Chen, Kai Hou, Dongbei Xu and Wei Wu
Int. J. Mol. Sci. 2024, 25(14), 7998; https://doi.org/10.3390/ijms25147998 - 22 Jul 2024
Viewed by 802
Abstract
Angelica dahurica var. formosana (ADF), which belongs to the Umbelliferae family, is one of the original plants of herbal raw material Angelicae Dahuricae Radix. ADF roots represent an enormous biomass resource convertible for disease treatment and bioproducts. But, early bolting of [...] Read more.
Angelica dahurica var. formosana (ADF), which belongs to the Umbelliferae family, is one of the original plants of herbal raw material Angelicae Dahuricae Radix. ADF roots represent an enormous biomass resource convertible for disease treatment and bioproducts. But, early bolting of ADF resulted in lignification and a decrease in the coumarin content in the root, and roots lignification restricts its coumarin for commercial utility. Although there have been attempts to regulate the synthesis ratio of lignin and coumarin through biotechnology to increase the coumarin content in ADF and further enhance its commercial value, optimizing the biosynthesis of lignin and coumarin remains challenging. Based on gene expression analysis and phylogenetic tree profiling, AdNAC20 as the target for genetic engineering of lignin and coumarin biosynthesis in ADF was selected in this study. Early-bolting ADF had significantly greater degrees of root lignification and lower coumarin contents than that of the normal plants. In this study, overexpression of AdNAC20 gene plants were created using transgenic technology, while independent homozygous transgenic lines with precise site mutation of AdNAC20 were created using CRISPR/Cas9 technology. The overexpressing transgenic ADF plants showed a 9.28% decrease in total coumarin content and a significant 12.28% increase in lignin content, while knockout mutant plants showed a 16.3% increase in total coumarin content and a 33.48% decrease in lignin content. Furthermore, 29,671 differentially expressed genes (DEGs) were obtained by comparative transcriptomics of OE-NAC20, KO-NAC20, and WT of ADF. A schematic diagram of the gene network interacting with AdNAC20 during the early-bolting process of ADF was constructed by DEG analysis. AdNAC20 was predicted to directly regulate the transcription of several genes with SNBE-like motifs in their promoter, such as MYB46, C3H, and CCoAOMT. In this study, AdNAC20 was shown to play a dual pathway function that positively enhanced lignin formation but negatively controlled coumarin formation. And the heterologous expression of the AdNAC20 gene at Arabidopsis thaliana proved that the AdNAC20 gene also plays an important role in the process of bolting and flowering. Full article
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Figure 1

Figure 1
<p>Physiological changes during early bolting of <span class="html-italic">ADF</span>. (<b>A</b>) Development of flowering and bolting. (<b>B</b>) Root phloem/xylem ratio of early-bolting and normal plants. (<b>C</b>) The right panel shows early-bolting plants, and the left panel shows normal plants. (<b>D</b>) Lignin content of early-bolting and normal plants. (<b>E</b>) Coumarin content of early-bolting and normal plants. EB, early-bolting <span class="html-italic">ADF</span>; NB, non-bolting <span class="html-italic">ADF</span>. * Indicates extremely significant difference (<span class="html-italic">p</span> &lt; 0.05); ** indicates extremely significant difference (<span class="html-italic">p</span> &lt; 0.01).</p>
Full article ">Figure 2
<p>Sequence analysis of <span class="html-italic">AdNAC20. (</span><b>A</b>) Homology analysis of the protein encoded by the <span class="html-italic">AdNAC20</span> gene. (<b>B</b>) Protein structure predicted by SWISS-MODEL homologous modeling. (<b>C</b>) Subcellular localization analysis of AdNAC20 in <span class="html-italic">N. benthamiana</span>. (<b>D</b>) Subcellular localization analysis of AdNAC20 in <span class="html-italic">ADF</span> leaves. (<b>E</b>) Tissue expression analysis of <span class="html-italic">AdNAC20</span>. GFP, blank control; PR mCherry, nuclear localization marker; TD, bright; merged, combination map; marker, nuclear and cytomembrane localization marker. * Indicates extremely significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Instantaneous transformation of the <span class="html-italic">ADF</span> root system by <span class="html-italic">Agrobacterium</span>-mediated root leaching. (<b>A</b>) Instantaneous conversion fluorescence map after root immersion. (<b>B</b>) Lignin and total coumarin contents in roots after instantaneous transformation of each bacterial solution. OE-<span class="html-italic">NAC20</span>, <span class="html-italic">AdNAC20</span>-overexpressing plants of <span class="html-italic">ADF</span>; KO-<span class="html-italic">NAC20, AdNAC20</span> mutant plants of <span class="html-italic">ADF</span>; CK, wild-type plants of <span class="html-italic">ADF</span>.</p>
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<p>Expression of <span class="html-italic">AdNAC20</span> in <span class="html-italic">A. thaliana</span> and its phenotype. (<b>A</b>) <span class="html-italic">A. thaliana</span> plants overexpressing <span class="html-italic">AdNAC20</span> at the early-flowering stage on day 33. (<b>B</b>) Phenotypes of <span class="html-italic">A. thaliana</span> plants overexpressing <span class="html-italic">AdNAC20</span> at the late-flowering stage of 44 days. (<b>C</b>) Statistics of flowering days for bolting <span class="html-italic">A. thaliana</span> plants overexpressing <span class="html-italic">AdNAC20</span>. (<b>D</b>) The early-flowering phenotype of <span class="html-italic">A. thaliana</span> plants with <span class="html-italic">AdNAC20</span> deletion on day 33. (<b>E</b>) Phenotypes of <span class="html-italic">A. thaliana</span> plants with <span class="html-italic">AdNAC20</span> deletion at the late-flowering stage of 44 days. (<b>F</b>) Statistics of flowering days for bolting <span class="html-italic">A. thaliana</span> plants with the <span class="html-italic">AdNAC20</span> gene deletion mutant. (<b>G</b>) The <span class="html-italic">AdNAC20</span> deletion mutant supplemented the early-flowering phenotype of <span class="html-italic">A. thaliana</span> at 33 days. (<b>H</b>) The phenotype of <span class="html-italic">A. thaliana</span> plants at the late-flowering stage 44 days after <span class="html-italic">AdNAC20</span> deletion. (<b>I</b>) Statistics on flowering days of bolting <span class="html-italic">A. thaliana</span> plants supplemented with the <span class="html-italic">AdNAC20</span> gene deletion mutant. (<b>J</b>) Root weight statistics and root scanning of <span class="html-italic">A. thaliana</span> seedlings after 60 days. (** indicates highly significant differences.) (<b>K</b>) Observation of lignin content in <span class="html-italic">A. thaliana</span> after 60 days of seeding and root cross-cut saffron–solid green staining. Red indicates lignified cell tissue. Green indicates cellulose cell tissue. 35S:<span class="html-italic">AdNAC20</span>, <span class="html-italic">AdNAC20</span> overexpression lines of <span class="html-italic">A. thaliana</span> plants; RC <span class="html-italic">AdNAC20</span>, <span class="html-italic">AdNAC20</span> reverse-complemented lines of <span class="html-italic">A. thaliana</span> plants; <span class="html-italic">At nac20</span>, <span class="html-italic">AdNAC20</span> mutant lines of <span class="html-italic">A. thaliana</span> plants; col, wild-type of <span class="html-italic">A. thaliana</span> plants (Col-0). (** indicates highly significant differences.)</p>
Full article ">Figure 4 Cont.
<p>Expression of <span class="html-italic">AdNAC20</span> in <span class="html-italic">A. thaliana</span> and its phenotype. (<b>A</b>) <span class="html-italic">A. thaliana</span> plants overexpressing <span class="html-italic">AdNAC20</span> at the early-flowering stage on day 33. (<b>B</b>) Phenotypes of <span class="html-italic">A. thaliana</span> plants overexpressing <span class="html-italic">AdNAC20</span> at the late-flowering stage of 44 days. (<b>C</b>) Statistics of flowering days for bolting <span class="html-italic">A. thaliana</span> plants overexpressing <span class="html-italic">AdNAC20</span>. (<b>D</b>) The early-flowering phenotype of <span class="html-italic">A. thaliana</span> plants with <span class="html-italic">AdNAC20</span> deletion on day 33. (<b>E</b>) Phenotypes of <span class="html-italic">A. thaliana</span> plants with <span class="html-italic">AdNAC20</span> deletion at the late-flowering stage of 44 days. (<b>F</b>) Statistics of flowering days for bolting <span class="html-italic">A. thaliana</span> plants with the <span class="html-italic">AdNAC20</span> gene deletion mutant. (<b>G</b>) The <span class="html-italic">AdNAC20</span> deletion mutant supplemented the early-flowering phenotype of <span class="html-italic">A. thaliana</span> at 33 days. (<b>H</b>) The phenotype of <span class="html-italic">A. thaliana</span> plants at the late-flowering stage 44 days after <span class="html-italic">AdNAC20</span> deletion. (<b>I</b>) Statistics on flowering days of bolting <span class="html-italic">A. thaliana</span> plants supplemented with the <span class="html-italic">AdNAC20</span> gene deletion mutant. (<b>J</b>) Root weight statistics and root scanning of <span class="html-italic">A. thaliana</span> seedlings after 60 days. (** indicates highly significant differences.) (<b>K</b>) Observation of lignin content in <span class="html-italic">A. thaliana</span> after 60 days of seeding and root cross-cut saffron–solid green staining. Red indicates lignified cell tissue. Green indicates cellulose cell tissue. 35S:<span class="html-italic">AdNAC20</span>, <span class="html-italic">AdNAC20</span> overexpression lines of <span class="html-italic">A. thaliana</span> plants; RC <span class="html-italic">AdNAC20</span>, <span class="html-italic">AdNAC20</span> reverse-complemented lines of <span class="html-italic">A. thaliana</span> plants; <span class="html-italic">At nac20</span>, <span class="html-italic">AdNAC20</span> mutant lines of <span class="html-italic">A. thaliana</span> plants; col, wild-type of <span class="html-italic">A. thaliana</span> plants (Col-0). (** indicates highly significant differences.)</p>
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<p>Identification of <span class="html-italic">AdNAC20 ADF</span> mutant plants via CRISPR/Cas9 mutagenesis. (<b>A</b>) Selection of sgRNAs for gene editing of <span class="html-italic">AdNAC20</span>. The gray boxes represent conserved structural domains; the straight lines extending out the region are the sgRNAs, and the red letters indicate the CGGs in the PAM sequence. (<b>B</b>) DNA identification of <span class="html-italic">AdNAC20</span> mutants in <span class="html-italic">ADF</span>. (<b>C</b>) Translated amino acid sequence of the mutant. (<b>D</b>) Expression of the <span class="html-italic">AdNAC20</span> gene in plants overexpressing this gene. (<b>E</b>) Phenotypes of the <span class="html-italic">AdNAC20</span>-modified lines. (<b>G</b>) Coumarin content in the roots and leaves of gene-edited <span class="html-italic">ADF</span> and the control. (<b>F</b>) Effective lignin extraction in the <span class="html-italic">AdNAC20</span>-modified lines. Section observation of root lignin phloroglucinol staining of <span class="html-italic">ADF</span> roots. Phloroglucinol redifies lignin, and the darker the color is, the greater the degree of lignification. OE-<span class="html-italic">NAC20</span>, <span class="html-italic">AdNAC20</span>-overexpressing plants of <span class="html-italic">ADF</span>; KO-<span class="html-italic">NAC20, AdNAC20</span> mutant plants of <span class="html-italic">ADF</span>; WT, wild-type plants of <span class="html-italic">ADF</span>. (** indicates highly significant differences.)</p>
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<p><span class="html-italic">AdNAC20</span> upregulated <span class="html-italic">lignin</span>-related enzyme-encoding genes and downregulated coumarin-related enzyme-encoding genes in the phloem. Red font indicates up, blue font indicates down, and black font indicates no significant change. (<b>A</b>) <span class="html-italic">AdNAC20</span> upregulated lignin-related enzyme-encoding genes in the phloem. (<b>B</b>) <span class="html-italic">AdNAC20</span> downregulates coumarin-related enzyme-encoding genes in the xylem. PAL, phenylalanine ammonia-lyase; CYP73A, trans-cinnamate 4-monooxygenase; C4H, cinnamate-4-hydroxylase; 4CL, 4-coumarate--CoA ligase; CSE, caffeoylshikimate esterase; C3H, coumarale-3-hydroxylase; COMT, caffeic acid 3-O-methyltransferase; CCoAOMT1, caffeoyl-CoA 5-O-methyltransfenase; CAD, cinnamyl-alcohol dehydrogenase; CCR, cinnamoyl-CoA reductase; REF1, coniferyl-aldehyde dehydrogenase; F5H, ferulate-5-hydroxylase; PT, prenyltransferase; PS, psoralen synthase; BMT, bergaptol O-methyltransferase; OMT, O-methyl-transferase; CYP82C4, 5-hydroxy-8-methoxypsoralen; C2H, coumarale-2-hydroxylase; COSY, coumarin synthase; S8H, scopoletin 8-hydroxylase; HCT, shikimate O-hydroxycinnamoyltransferase. (Red font indicates up-regulation of expression, blue font indicates down-regulation of expression, black font indicates no significant change in expression.)</p>
Full article ">Figure 7
<p>Expression levels of related genes in <span class="html-italic">ADF</span> xylem. * Indicates a significant difference, and ** indicates an extremely significant difference. OE, <span class="html-italic">AdNAC20</span>-overexpressing plants of <span class="html-italic">ADF</span>; KO, <span class="html-italic">AdNAC20</span> mutant plants of <span class="html-italic">ADF</span>; WT, wild-type plants of <span class="html-italic">ADF</span>; PAL, phenylalanine ammonia-lyase; PT, prenyltransferase; PS, psoralen synthase; CCR, cinnamoyl-CoA reductase; BMT, bergaptol O-methyltransferase; CSE, caffeoylshikimate esterase; CYP82C4, 5-hydroxy-8-methoxypsoralen; SVP, short vegetative phase.</p>
Full article ">Figure 8
<p>DEGs with opposite expression patterns in the xylem and phloem of <span class="html-italic">AdNAC20</span> transgenic plants analyzed via a Venn diagram. (<b>A</b>) DEGs of 16 genes divided into four groups, Groups A~D. (<b>B</b>) Heatmap of DEGs of the four groups and <span class="html-italic">AdNAC20</span> as Group E. OE_PH, root phloem of <span class="html-italic">AdNAC20</span>-overexpressing plants of <span class="html-italic">ADF</span>; KO_PH, root phloem of <span class="html-italic">AdNAC20</span> mutant plants of <span class="html-italic">ADF</span>; WY_PH, root phloem of wild−type plants of <span class="html-italic">ADF</span>; OE_XY, root xylem of <span class="html-italic">AdNAC20</span>-overexpressing plants of <span class="html-italic">ADF</span>; KO_XY, root xylem of <span class="html-italic">AdNAC20</span> mutant plants of <span class="html-italic">ADF</span>; WY_XY, root xylem of wild-type plants of <span class="html-italic">ADF</span>.</p>
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<p>Protein–protein interactions of proteins predicted using STRING 12.0 software based on the <span class="html-italic">Daucus carota</span> L. var. <span class="html-italic">sativa</span> protein database. (<b>A</b>) Interaction network of DCAR 005363, a homologous protein of <span class="html-italic">AdNAC20</span>. (<b>B</b>) Interaction network of DCAR_027802, a homologous protein of <span class="html-italic">TRINITY_DN89720_c1_g2</span> (<span class="html-italic">NAC7-like</span>).</p>
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<p>Schematic diagram of the gene network interacting with <span class="html-italic">AdNAC20</span> during early-bolting of <span class="html-italic">ADF</span>. <span class="html-italic">AdNAC20</span> may be involved in the transcriptional network of lignin biosynthesis controlled by auxin and may also regulate lignin and coumarin biosynthesis via the phenylpropanoid pathway. SNBE-like indicates that a gene regulates the expression of the next gene by recognizing this motif in the promoter. Green arrows represent the predicted direct transcriptional regulation of <span class="html-italic">AdNAC20</span> in this study. Red font represents upregulated genes. The blue font represents downregulated genes. The pink background represents the lignin biosynthesis pathway. The blue background represents the coumarin biosynthetic pathway. AUX, auxin response factors; IAAs, indole-3-acetic acid induced proteins; VND4, vascular-related NAC domain protein 6; VND7, vascular-related NAC domain protein 7; PID, protein kinase PINOID 2-like; ARF, auxin response factor; PAL, phenylalanine ammonia-lyase; CYP73A, trans-cinnamate 4-monooxygenase; C4H, cinnamate-4-hydroxylase; 4CL, 4-coumarate--CoA ligase; CSE, caffeoylshikimate esterase; C3H, coumarale-3-hydroxylase; COMT, caffeic acid 3-O-methyltransferase; CCoAOMT1, caffeoyl-CoA 5-O-methyltransfenase; CAD, cinnamyl-alcohol dehydrogenase; CCR, cinnamoyl-CoA reductase; REF1, coniferyl-aldehyde dehydrogenase; F5H, ferulate-5-hydroxylase; PT, prenyltransferase; PS, psoralen synthase; BMT, bergaptol O-methyltransferase; OMT, O-methyl-transferase; CYP82C4, 5-hydroxy-8-methoxypsoralen; C2H, coumarale-2-hydroxylase; COSY, coumarin synthase; S8H, scopoletin 8-hydroxylase; HCT, shikimate O-hydroxycinnamoyltransferase. (Red font indicates up-regulation of expression, blue font indicates down-regulation of expression.)</p>
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22 pages, 3610 KiB  
Article
Functional Activity of Isoform 2 of Human eRF1
by Alexey Shuvalov, Alexandr Klishin, Nikita Biziaev, Ekaterina Shuvalova and Elena Alkalaeva
Int. J. Mol. Sci. 2024, 25(14), 7997; https://doi.org/10.3390/ijms25147997 - 22 Jul 2024
Viewed by 1467
Abstract
Eukaryotic release factor eRF1, encoded by the ETF1 gene, recognizes stop codons and induces peptide release during translation termination. ETF1 produces several different transcripts as a result of alternative splicing, from which two eRF1 isoforms can be formed. Isoform 1 codes well-studied canonical [...] Read more.
Eukaryotic release factor eRF1, encoded by the ETF1 gene, recognizes stop codons and induces peptide release during translation termination. ETF1 produces several different transcripts as a result of alternative splicing, from which two eRF1 isoforms can be formed. Isoform 1 codes well-studied canonical eRF1, and isoform 2 is 33 amino acid residues shorter than isoform 1 and completely unstudied. Using a reconstituted mammalian in vitro translation system, we showed that the isoform 2 of human eRF1 is also involved in translation. We showed that eRF1iso2 can interact with the ribosomal subunits and pre-termination complex. However, its codon recognition and peptide release activities have decreased. Additionally, eRF1 isoform 2 exhibits unipotency to UGA. We found that eRF1 isoform 2 interacts with eRF3a but stimulated its GTPase activity significantly worse than the main isoform eRF1. Additionally, we studied the eRF1 isoform 2 effect on stop codon readthrough and translation in a cell-free translation system. We observed that eRF1 isoform 2 suppressed stop codon readthrough of the uORFs and decreased the efficiency of translation of long coding sequences. Based on these data, we assumed that human eRF1 isoform 2 can be involved in the regulation of translation termination. Moreover, our data support previously stated hypotheses that the GTS loop is important for the multipotency of eRF1 to all stop codons. Whereas helix α1 of the N-domain eRF1 is proposed to be involved in conformational rearrangements of eRF1 in the A-site of the ribosome that occur after GTP hydrolysis by eRF3, which ensure hydrolysis of peptidyl-tRNA at the P site of the ribosome. Full article
(This article belongs to the Special Issue Structure and Function of Ribosomal Proteins 2024)
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Figure 1
<p>Expression of isoforms of human eRF1. (<b>A</b>) Amino acid sequence and structure comparison of isoforms of human eRF1. Isoform 2 of eRF1 is truncated for the first 33 amino acid residues, which include a α1-helix and important for stop codon recognition GTS motif. PDB: 1DT9. (<b>B</b>) Visualization of data on ribosomal profiling of human eRF1 transcripts (ENST00000360541–iso1; ENST00000499810–iso2) in the Trips-viz browser (URL (accessed on 26 May 2024): <a href="https://trips.ucc.ie/" target="_blank">https://trips.ucc.ie/</a>) [<a href="#B45-ijms-25-07997" class="html-bibr">45</a>,<a href="#B46-ijms-25-07997" class="html-bibr">46</a>]. Combined data show translation of the first 33 codons, unique to isoform 1, while the expression level of isoform 2 cannot be assessed. However, analysis of ribosomal profiling of human embryonic stem (hES) cells shows a reduced density of ribosome distribution in the first 33 codons and a significantly higher level of representation of ribosomal complexes on the common sequence for two isoforms.</p>
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<p>eRF1iso2 bound to different ribosomes. Western blot analysis of ribosomal subunits isolated from placenta, HeLa, RRL, and Krebs-2 lysate. Antibodies raised against eRF1 (sc-365686) were used for detection. Ctrl—mixture of recombinant eRF1iso1 and eRF1iso2. The first and second lanes contain recombinant eRF1iso1 and eRF1iso2, 0.005 and 0.0025 pmol, respectively. All other control lanes (6, 10, 13) contain 0.005 pmol of recombinant eRF1iso1 and eRF1iso2.</p>
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<p>eRF1iso2 binding with purified 80S ribosomes and ribosomal subunits. Western blot analysis of 80S ribosomes resolved by sucrose density gradients and 40S and 60S ribosomal subunits after incubation with eRF1iso1 or eRF1iso2 in the presence and absence of eRF3a. Antibodies raised against eRF1, eRF3a, rpL9, rpS15 were used for detection. The fractions of the SDG are indicated above the Western blots; fraction 1 corresponds to the top of the gradient and fraction 14 to the bottom. Blue lines indicate the junction of Western blot images after Protein Marker lane cutting, which was performed to facilitate comparison of matched samples (raw data available). 80S ribosomes were assembled from 40S and 60S subunits purified from RRL.</p>
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<p>eRF1iso2 binding with the preTC. Western blot analysis of SDG of the preTC after incubation with eRF1iso1 or eRF1iso2 in the presence of eRF3a and GTP or GDPCP. Antibodies raised against eRF1, eRF3a, and rpL9 were used for detection. The fractions of the SDG are indicated above the Western blots; fraction 1 corresponds to the top of the gradient and fraction 14 to the bottom.</p>
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<p>Functional activity of eRF1iso2 in translation termination. (<b>A</b>) Termination complex formation efficiency by eRF1iso1 and eRF1iso2 in the presence and absence of eRF3a. Toe-print analysis of TC formed at UAA stop codon in the various concentrations of eRF1 isoforms (n = 3). (<b>B</b>) Hydrolysis of peptidyl-tRNA induced by eRF1iso1, eRF1iso2, and eRF3a. Termiluc assay on the preTCs assembled at UAA, UAG, and UGA stop codons. v<sub>0</sub> is the initial termination rate (RLU/min) (n = 3). (<b>C</b>) GTPase activity of eRF3a in the presence of eRF1iso1 and eRF1iso2 (n = 3). The error bars represent the standard deviation. Background activity measured in the presence of all components except eRF1 was subtracted. Asterisks indicate statistically significant differences (*, <span class="html-italic">p</span> &lt; 0.05; **; <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Binding of eRF1iso2 with eRF3a in solution. (<b>A</b>) Representative figure of the binding of eRF1iso1 and eRF1iso2 to eRF3a determined by pull-down of His-SUMO-eRF3a in the presence of eRF1 isoforms. Protein samples before loading onto the Ni-NTA resin (ctrl) or after cleavage by Ulp1 protease were analyzed by SDS electrophoresis. All experiments were carried out in three replicates. (<b>B</b>) The intensities of the eRF1 bands were normalized to the intensity of the eRF3a band. Histogram data are presented as mean relative intensity ± standard error of the mean. The difference was considered significant when <span class="html-italic">p</span> value (two-tailed <span class="html-italic">t</span>-test) was less than 0.05. n.s., non-significant difference (<span class="html-italic">p</span> ≥ 0.05).</p>
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<p>Effect of eRF1iso2 on the stop codon readthrough. (<b>A</b>) Effect of excess of eRF1 isoforms on the stop codon readthrough in HEK293 lysate on Fluc reporter mRNAs with different PTCs. (<b>B</b>) Effect of excess of eRF1 isoforms on the stop codon readthrough in HeLa lysate on the Nluc reporter mRNAs with different leader sequences. All experiments were carried out in three replicates. Histogram data are presented as mean relative intensity (readthrough, %) ± standard error of the mean. The difference was considered significant when <span class="html-italic">p</span> value (two-tailed <span class="html-italic">t</span>-test) was less than 0.05 (*). n.s., non-significant difference (<span class="html-italic">p</span> ≥ 0.05). Asterisks indicate statistically significant differences (*, <span class="html-italic">p</span> &lt; 0.05; **; <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Effect of eRF1 isoforms on translation. (<b>A</b>) Translation of Nluc mRNAs with various CDS lengths in the presence of eRF1 isoforms in HeLa lysate. (<b>B</b>) Translation of Fluc mRNA in the presence of eRF1 isoforms in HEK293 lysate. (<b>C</b>) Translation of Nluc mRNAs with the additional sense codons in the presence of eRF1 isoforms in HEK293 lysate. 5x UXX–repeat of 5 identical codons, each of UGG, UCG, UUG, UCU, or UUA. All experiments were carried out in three replicates. Histogram data are presented as mean relative intensity (relative translation efficiency, %) ± standard error of the mean. The difference was considered significant when <span class="html-italic">p</span> value (two-tailed <span class="html-italic">t</span>-test) was less than 0.05 (*). Asterisks indicate statistically significant differences (*, <span class="html-italic">p</span> &lt; 0.05; **; <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001; ****, <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Effect of additional factors on translation termination induced by eRF1iso2. (<b>A</b>) Hydrolysis of peptidyl-tRNA induced by eRF1iso2 in the presence of PDCD4, eIF3j, DDX19, Nsp1, eRF3a, and PABP. Termiluc assay on the preTCs assembled at UAA stop codon. v<sub>0</sub> is the initial termination rate (RLU/min) (n = 3). The red color corresponds to eRF1iso2 data, and the blue color corresponds to eRF1iso1 data. (<b>B</b>) Hydrolysis of peptidyl-tRNA induced by eRF1iso1 and eRF1iso2 in the presence of PABP and eRF3a. Termi-luc assay on the preTCs assembled at UAA (n = 3). The red color corresponds to eRF1iso2 data, and the blue color corresponds to eRF1iso1 data. (<b>C</b>) GTPase activity of eRF3a in the presence of eRF1iso1, eRF1iso2, and PABP (n = 3). Background activity measured in the presence of all components except eRF1 was subtracted. The error bars represent the standard deviation. n.s., non-significant difference (<span class="html-italic">p</span> ≥ 0.05). Asterisks indicate statistically significant differences (*, <span class="html-italic">p</span> &lt; 0.05; **; <span class="html-italic">p</span> &lt; 0.01; ****, <span class="html-italic">p</span> &lt; 0.0001).</p>
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29 pages, 17140 KiB  
Article
The Integrated Bioinformatic Approach Reveals the Prognostic Significance of LRP1 Expression in Ovarian Cancer
by Tesfaye Wolde, Vipul Bhardwaj, Md. Reyad-ul-Ferdous, Peiwu Qin and Vijay Pandey
Int. J. Mol. Sci. 2024, 25(14), 7996; https://doi.org/10.3390/ijms25147996 - 22 Jul 2024
Viewed by 1520
Abstract
A hyperactive tumour microenvironment (TME) drives unrestricted cancer cell survival, drug resistance, and metastasis in ovarian carcinoma (OC). However, therapeutic targets within the TME for OC remain elusive, and efficient methods to quantify TME activity are still limited. Herein, we employed an integrated [...] Read more.
A hyperactive tumour microenvironment (TME) drives unrestricted cancer cell survival, drug resistance, and metastasis in ovarian carcinoma (OC). However, therapeutic targets within the TME for OC remain elusive, and efficient methods to quantify TME activity are still limited. Herein, we employed an integrated bioinformatics approach to determine which immune-related genes (IRGs) modulate the TME and further assess their potential theragnostic (therapeutic + diagnostic) significance in OC progression. Using a robust approach, we developed a predictive risk model to retrospectively examine the clinicopathological parameters of OC patients from The Cancer Genome Atlas (TCGA) database. The validity of the prognostic model was confirmed with data from the International Cancer Genome Consortium (ICGC) cohort. Our approach identified nine IRGs, AKT2, FGF7, FOS, IL27RA, LRP1, OBP2A, PAEP, PDGFRA, and PI3, that form a prognostic model in OC progression, distinguishing patients with significantly better clinical outcomes in the low-risk group. We validated this model as an independent prognostic indicator and demonstrated enhanced prognostic significance when used alongside clinical nomograms for accurate prediction. Elevated LRP1 expression, which indicates poor prognosis in bladder cancer (BLCA), OC, low-grade gliomas (LGG), and glioblastoma (GBM), was also associated with immune infiltration in several other cancers. Significant correlations with immune checkpoint genes (ICGs) highlight the potential importance of LRP1 as a biomarker and therapeutic target. Furthermore, gene set enrichment analysis highlighted LRP1’s involvement in metabolism-related pathways, supporting its prognostic and therapeutic relevance also in BLCA, OC, low-grade gliomas (LGG), GBM, kidney cancer, OC, BLCA, kidney renal clear cell carcinoma (KIRC), stomach adenocarcinoma (STAD), and stomach and oesophageal carcinoma (STES). Our study has generated a novel signature of nine IRGs within the TME across cancers, that could serve as potential prognostic predictors and provide a valuable resource to improve the prognosis of OC. Full article
(This article belongs to the Special Issue New Insights in Translational Bioinformatics)
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<p>The identification and functional categorisation of differentially expressed IRGs in OC patients. (<b>A</b>). The volcano plot illustrates the differences in gene expression between tumour and normal samples. The vertical axis (y-axis) represents the mean value of −log10 (<span class="html-italic">p</span>-value), indicating the significance of the gene expression changes, while the horizontal axis (x-axis) represents the log fold change (<span class="html-italic">logFC</span>), showing the magnitude of the expression differences. Red dots denote upregulated genes, blue dots represent downregulated genes, and grey dots indicate genes with unchanged expression levels. (<b>B</b>). Venn diagram visualises the overlap between different sets of genes. Light blue indicates differentially expressed genes in patients with active OC, dark yellow represents immune genes in OC patients, and red denotes differentially expressed immune genes specific to OC patients. This helps to identify and distinguish the IRGs that are differentially expressed in the context of OC. (<b>C</b>). KEGG pathway enrichment analysis categorises the differentially expressed immune-linked genes according to their involvement in various biological pathways. The horizontal axis represents the degree value of each target, with ‘hsa’ indicating Homo sapiens (human). The red colour in the KEGG pathway indicates an increased <span class="html-italic">z-score</span>, suggesting pathway activation, while white indicates a decreased <span class="html-italic">z-score</span>. Blue dots represent downregulated genes (<span class="html-italic">logFC</span>), and red dots indicate upregulated genes (<span class="html-italic">logFC</span>), providing a comprehensive view of the pathways affected by the differentially expressed immune genes. (<b>D</b>). The protein–protein interaction (PPI) network visualises the interactions between the differentially expressed IRGs. The y-axis lists the genes, while the x-axis shows the number of adjacent nodes in increasing order, from SDC4 to LRP1. This network helps to identify key regulatory genes and their interaction partners, shedding light on the molecular mechanisms underlying immune responses in OC.</p>
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<p>The analysis of prognostic IRGs signature and independent prognostic analysis involves evaluating the characteristics and predictive power of a set of nine prognostic genes. (<b>A</b>). Case distribution and grouping based on risk score in the test set: This chart displays the distribution of cases in the test set, categorised according to their risk scores. Patients are grouped into high-risk and low-risk categories based on their calculated risk scores, allowing for the assessment of the prognostic value of the IRGs signature. (<b>B</b>). Kaplan–Meier (K–M) curves for overall survival (OS) in high- and low-risk groups in the test set: K–M survival curves compare the OS between high-risk and low-risk groups in the test set. These curves help visualise the survival probability over time, demonstrating the impact of the risk score on patient prognosis. (<b>C</b>). Receiver operating characteristic (ROC) curve of risk score at 1, 3, and 5 years for OS in the test set: The ROC curves evaluate the predictive accuracy of the risk scores at 1, 3, and 5 years for OS. The area under the curve (AUC) indicates the effectiveness of the risk scores in predicting patient outcomes, with higher AUC values signifying better predictive performance. (<b>D</b>). Univariate independent prognostic analysis: This analysis assesses the prognostic significance of individual factors, including the nine prognostic IRGs. By evaluating each factor independently, this analysis identifies which genes or clinical variables are significantly associated with OS. (<b>E</b>). Calibration curve of nomogram at 1 year: The calibration curve at 1 year compares the predicted probabilities of OS with the actual observed outcomes. This visualisation assesses the accuracy of the nomogram in predicting 1-year survival rates, indicating how well the model’s predictions align with real-world data. (<b>F</b>). Calibration curve of nomogram at 3 years: Similar to the 1-year calibration curve, this graph compares the predicted and observed probabilities of OS at 3 years. It helps evaluate the long-term predictive accuracy of the nomogram for medium-term prognosis. (<b>G</b>). Calibration curve of nomogram at 5 years: This calibration curve extends the assessment to 5 years, comparing predicted and observed survival probabilities to evaluate the model’s accuracy for long-term prognosis. It provides insight into the reliability of the nomogram for extended survival predictions.</p>
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<p>The construction of OS risk prognostic models in OC involves a series of analytical steps to evaluate and validate the prognostic significance of specific gene signatures. (<b>A</b>). Univariate Cox regression analysis of the risk signatures for OS: This analysis examines the relationship between each risk signature and OS in OC patients. By evaluating each gene or risk factor separately, the analysis identifies which signatures are significantly associated with patient survival outcomes. (<b>B</b>). LASSO Cox regression model for OS: The LASSO Cox regression model is used to refine the selection of prognostic genes. In this model, the x-axis represents the logarithm of the penalty parameter (<span class="html-italic">log-lambda</span>), while the y-axis denotes the partial likelihood of deviance. This model helps to minimise overfitting by applying a penalty to the number of variables, ensuring that only the most significant genes are included in the final prognostic model. (<b>C</b>). LASSO Cox regression model analysis of nine prognostic IRGs: This step involves applying the LASSO Cox regression method specifically to the nine identified prognostic genes. The model helps determine the optimal set of genes that contribute most significantly to OS, providing a robust predictive signature for OC prognosis. (<b>D</b>). Multivariate Cox risk signatures for expression of nine IRGs in normal, tumour, and metastatic groups: This analysis evaluates the expression levels of the nine model IRGs across different sample groups—normal, tumour, and metastatic tissues. The fold changes are calculated for the following: FC_TvsN, fold change between tumour and normal tissues; FC_MvsT, fold change between metastatic and tumour tissues; and FC_MvsN, fold change between metastatic and normal tissues. The analysis also incorporates the Least Absolute Shrinkage and Selection Operator (LASSO) method to further refine the gene selection.</p>
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<p>Estimation of the Immunoreactivity. (<b>A</b>). Expression of PD1 in high- and low-risk groups: The expression levels of PD1 (Programmed cell death protein 1) are measured in log2(FPKM + 1). The x-axis categorises the expression levels into three groups: normal (crimson), low (blue), and high (red) compared to OC-free groups. (<b>B</b>). Expression of CTLA4 in high- and low-risk groups: The CTLA4 (Cytotoxic T-lymphocyte-associated protein 4) expression levels are also measured in log2(FPKM + 1). The x-axis categorises the expression levels into normal (crimson), low (blue), and high (red) compared to OC-free groups. (<b>C</b>). Expression of PDL1 in high- and low-risk groups: PDL1 (Programmed death-ligand 1) expression levels, shown in log2(FPKM + 1), are categorised similarly on the x-axis into normal (crimson), low (blue), and high (red) compared to OC-free groups. (<b>D</b>). Scores of IPS and IPS + PD1 in high- and low-risk groups: The y-axis indicates the Immunophenoscore (IPS) scores, with categories on the x-axis for high-risk (red) and low-risk (blue) groups. This helps assess the immune response potential in both risk groups when considering PD1 expression. (<b>E</b>). Scores of IPS and IPS + CTLA4 in high- and low-risk groups: The IPS scores are again shown on the y-axis, with the x-axis differentiating high-risk (red) and low-risk (blue) groups, now considering CTLA4 expression. (<b>F</b>). Scores of IPS, IPS + PD1 + CTLA4 in high- and low-risk groups: The y-axis shows the IPS scores, while the x-axis indicates high-risk (red) and low-risk (blue) groups considering the combined expression of PD1 and CTLA4. (<b>G</b>). Relation of risk score and expression of PDL1 in the high-risk group: This panel depicts the correlation between risk scores (ranging from −7 to −2) and PDL1 expression levels in the high-risk group. (<b>H</b>). Relation of risk score and expression of PD1 in the high-risk group: This panel shows the correlation between risk scores (ranging from −7 to −2) and PD1 expression levels in the high-risk group. (<b>I</b>). Relation of risk score and expression of CTLA4 in the high-risk group: This panel illustrates the correlation between risk scores (ranging from −7 to −2) and CTLA4 expression levels in the high-risk group. These analyses comprehensively examine the expression of immune checkpoint genes (PD1, CTLA4, and PDL1) across different risk groups and their correlation with risk scores.</p>
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<p>The predictive power of nine IRGs in OC was assessed using receiver operating characteristic (ROC) curve analysis, with areas under the curve (AUC) and <span class="html-italic">p</span>-values indicated. The ROC curve evaluates the diagnostic value of each gene by measuring its sensitivity and specificity, thereby determining its effectiveness in distinguishing between different states of the disease. (<b>A</b>). AUC of risk signature with diagnostic value of LRP1: The AUC for LRP1 (Low-density lipoprotein receptor-related protein 1) demonstrates its diagnostic value in OC. A higher AUC value indicates a stronger predictive ability for patient outcomes based on LRP1 expression levels. (<b>B</b>). AUC of risk signature with diagnostic value of FGF7: The diagnostic value of FGF7 (Fibroblast Growth Factor 7) is evaluated through its AUC, reflecting its accuracy in predicting OC presence and progression. (<b>C</b>). AUC of risk signature with diagnostic value of OBP2A: The AUC for OBP2A (Odorant Binding Protein 2A) indicates how well this gene can discriminate between OC states, contributing to its potential use as a diagnostic marker. (<b>D</b>). AUC of risk signature with diagnostic value of IL27RA: IL27RA (Interleukin 27 Receptor Subunit Alpha) is assessed for its diagnostic value, with the AUC showing its capability to predict OC outcomes effectively. (<b>E</b>). AUC of risk signature with diagnostic value of PI3: The AUC for PI3 (Peptidase Inhibitor 3) measures its diagnostic power in OC, indicating its potential role as a predictive biomarker. (<b>F</b>). AUC of risk signature with diagnostic value of FOS: The diagnostic value of FOS (Fos Proto-Oncogene) is evaluated through its AUC, highlighting its effectiveness in predicting disease states in OC. (<b>G</b>). AUC of risk signature with diagnostic value of PDGFRA: PDGFRA (Platelet-Derived Growth Factor Receptor Alpha) is assessed for its diagnostic potential, with the AUC demonstrating its predictive accuracy for OC. (<b>H</b>). AUC of risk signature with diagnostic value of PAEP: The AUC for PAEP (Progestagen Associated Endometrial Protein) indicates its diagnostic value, reflecting its sensitivity and specificity in OC prediction. (<b>I</b>). AUC of risk signature with diagnostic value of AKT2: The diagnostic value of AKT2 (AKT Serine/Threonine Kinase 2) is evaluated, showing significant sensitivity and specificity across two databases.</p>
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<p>Drug susceptibility testing results. (<b>A</b>). Lower sensitivity to Cisplatin: In high-risk groups, chemotherapeutics with lower sensitivity were screened by estimating the IC<sub>50</sub> values for Cisplatin. The results indicated a higher IC<sub>50</sub>, signifying reduced susceptibility to this drug. (<b>B</b>). Lower sensitivity to Etoposide: Similarly, lower sensitivity to Etoposide was observed in high-risk groups, as evidenced by higher IC<sub>50</sub> values, suggesting decreased effectiveness of this chemotherapeutic agent. (<b>C</b>). Lower sensitivity to Methotrexate: Methotrexate also demonstrated lower sensitivity in high-risk groups, with higher IC<sub>50</sub> values indicating diminished drug efficacy. (<b>D</b>). Lower sensitivity to MK-2206: The IC<sub>50</sub> estimation for MK-2206 revealed lower sensitivity in high-risk groups, marking it as less effective for these patients. (<b>E</b>). Lower sensitivity to Lenalidomide: Lenalidomide showed higher IC<sub>50</sub> values in high-risk groups, denoting lower sensitivity and reduced therapeutic potential. (<b>F</b>). Higher sensitivity to Erlotinib: Conversely, Erlotinib was found to be more effective in high-risk groups, with lower IC<sub>50</sub> values indicating higher sensitivity to this chemotherapeutic. (<b>G</b>). Higher sensitivity to Imatinib: Imatinib also exhibited higher sensitivity in high-risk groups, as reflected by its lower IC<sub>50</sub> values, suggesting better efficacy. (<b>H</b>). Higher sensitivity to Nilotinib: Lastly, Nilotinib showed greater effectiveness in high-risk groups, with lower IC<sub>50</sub> values indicating increased sensitivity to this drug. These drug susceptibility testing results provide critical insights into the varying effectiveness of different chemotherapeutics in high-risk groups. Statistical significance: * indicates <span class="html-italic">p</span>-value &lt; 0.05.</p>
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<p>The expression of LRP1 across various cancers. (<b>A</b>). Differential expression analysis using TCGA database: The expression levels of LRP1 were compared between cancerous and normal tissues across twenty different types of cancers using data from The Cancer Genome Atlas (TCGA). This analysis provided insights into the differential expression patterns of <span class="html-italic">LRP1</span>, revealing significant variations in its expression between tumours and normal tissues. (<b>B</b>). Integrated analysis using TCGA and GTEx datasets: A comprehensive analysis incorporating data from both the TCGA and the Genotype-Tissue Expression (GTEx) projects was conducted for thirty-four cancer types. The results indicated that LRP1 was highly expressed in tumour samples of KIPAN (kidney cancers), HNSC (head and neck squamous cell carcinoma), KIRC (kidney renal clear cell carcinoma), OC (ovarian cancer), and PAAD (pancreatic adenocarcinoma). In contrast, <span class="html-italic">LRP1</span> expression was lower in lung squamous cell carcinoma (LUSC) tumour samples compared to normal tissues in both datasets. In the visual representation, red denotes LRP1 expression in tumour groups and blue represents its expression in normal groups. Statistical significance: * indicates <span class="html-italic">p</span>-value &lt; 0.05; ** indicates <span class="html-italic">p</span>-value &lt; 0.01; and *** indicates <span class="html-italic">p</span>-value &lt; 0.001.</p>
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<p>Association of high LRP1 expression with poor prognosis in cancers. (<b>A</b>). Analysis of OS using the Cox regression model: A forest plot was generated to analyse the impact of LRP1 expression on OS across 39 different cancer types. This statistical analysis provided insights into the association between high <span class="html-italic">LRP1</span> expression levels and worse OS outcomes in various cancers. (<b>B</b>). Analysis of disease-free survival (DFS) using the Cox regression model: Similarly, a forest plot table was constructed to examine the effect of <span class="html-italic">LRP1</span> expression on DFS across the same 39 cancer types. This analysis allowed for the assessment of the relationship between elevated <span class="html-italic">LRP1</span> expression and reduced DFS rates in different cancer cohorts. (<b>C</b>–<b>J</b>). Kaplan–Meier curve analysis of high <span class="html-italic">LRP1</span> expression: K–M curve analysis was performed to further elucidate the prognostic significance of high <span class="html-italic">LRP1</span> expression. The analysis revealed that elevated <span class="html-italic">LRP1</span> expression predicted poor prognosis in several cancer types, including bladder urothelial carcinoma (BLCA), ovarian cancer (OC), lower-grade glioma (LGG), glioblastoma multiforme (GBM), kidney renal clear cell carcinoma (KIRC), thyroid carcinoma (THCA), stomach adenocarcinoma (STAD), and stomach and oesophageal carcinoma (STES).</p>
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<p>The flowchart provides a schematic overview of the study design, illustrating the impact of immune-associated gene expression on the prognostic, therapeutic, and diagnostic identification, and validation of these genes in ovarian cancer (OC). The study integrates several analytical approaches and databases to achieve a comprehensive understanding of the immune landscape in OC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses are utilised to categorise and map the functional roles and pathways of the differentially expressed immune-related genes (IRGs). Tumour mutation burden (TMB) and microsatellite instability (MSI) metrics are assessed to evaluate the genetic alterations and their implications on immune responses. The TIMER (Tumor Immune Estimation Resource) and TISIDIB (tumour–immune system interaction) databases are utilised to estimate the extent of immune cell infiltration within the tumour microenvironment. The Sanger Box 3 databases are referenced for further insights into immune cell infiltration patterns. This integrative approach facilitates a robust evaluation of the prognostic potential of IRGs, enhances diagnostic accuracy, and identifies potential therapeutic targets in OC.</p>
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27 pages, 2669 KiB  
Article
Transcriptomic Analyses Reveal That Coffea arabica and Coffea canephora Have More Complex Responses under Combined Heat and Drought than under Individual Stressors
by Isabel Marques, Isabel Fernandes, Octávio S. Paulo, Dora Batista, Fernando C. Lidon, Ana P. Rodrigues, Fábio L. Partelli, Fábio M. DaMatta, Ana I. Ribeiro-Barros and José C. Ramalho
Int. J. Mol. Sci. 2024, 25(14), 7995; https://doi.org/10.3390/ijms25147995 - 22 Jul 2024
Cited by 1 | Viewed by 947
Abstract
Increasing exposure to unfavorable temperatures and water deficit imposes major constraints on most crops worldwide. Despite several studies regarding coffee responses to abiotic stresses, transcriptome modulation due to simultaneous stresses remains poorly understood. This study unravels transcriptomic responses under the combined action of [...] Read more.
Increasing exposure to unfavorable temperatures and water deficit imposes major constraints on most crops worldwide. Despite several studies regarding coffee responses to abiotic stresses, transcriptome modulation due to simultaneous stresses remains poorly understood. This study unravels transcriptomic responses under the combined action of drought and temperature in leaves from the two most traded species: Coffea canephora cv. Conilon Clone 153 (CL153) and C. arabica cv. Icatu. Substantial transcriptomic changes were found, especially in response to the combination of stresses that cannot be explained by an additive effect. A large number of genes were involved in stress responses, with photosynthesis and other physiologically related genes usually being negatively affected. In both genotypes, genes encoding for protective proteins, such as dehydrins and heat shock proteins, were positively regulated. Transcription factors (TFs), including MADS-box genes, were down-regulated, although responses were genotype-dependent. In contrast to Icatu, only a few drought- and heat-responsive DEGs were recorded in CL153, which also reacted more significantly in terms of the number of DEGs and enriched GO terms, suggesting a high ability to cope with stresses. This research provides novel insights into the molecular mechanisms underlying leaf Coffea responses to drought and heat, revealing their influence on gene expression. Full article
(This article belongs to the Special Issue Plants Responses to Climate Change)
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<p>Total number of expressed genes in <span class="html-italic">Coffea arabica</span> cv. Icatu and <span class="html-italic">C. canephora</span> cv. Conilon Clone 153 (CL153) plants grown under well-watered (WW; light colors) and control temperature (25 °C; blue) conditions before gradual exposure to severe water deficit (SWD; dark colors). Afterward, WW and SWD plants were additionally exposed to increased temperatures of 37 °C (green) and 42 °C (orange), followed by a 2-week recovery period (REC14, yellow) with full rewatering and a temperature of 25 °C.</p>
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<p>Differentially expressed genes (DEGs) relative to initial control conditions (25 °C, WW, light colors) in <span class="html-italic">Coffea arabica</span> cv. Icatu and <span class="html-italic">C. canephora</span> cv. Conilon Clone 153 (CL153) plants after gradual exposure to severe water deficit (SWD, dark colors) and to increased temperatures of 37 °C (green) and 42 °C (red), followed by a 2-week recovery period recovery (REC14, yellow) with full rewatering and a temperature of 25 °C. Intersections between 37 °C and 42 °C (gray), 42 °C and REC14 (pink), 37 °C and REC14 (ashy), and 42 °C and REC14 (violet) are shown.</p>
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<p>Proportion of significantly up- (red) and down-regulated (blue) DEGs associated with antioxidant activities, lipid metabolism, photosynthesis, and respiration in Icatu (<b>A</b>) and CL153 (<b>B</b>). Treatments are as explained in <a href="#ijms-25-07995-f002" class="html-fig">Figure 2</a>.</p>
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<p>Over-representation analysis of Gene Ontology (GO) terms performed with gProfiler against functional annotations in Icatu (<b>A</b>) and CL153 (<b>B</b>). GO terms are grouped by main category—Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). Counts (size) indicate the number of DEGs annotated with each GO term, and dots are colored by the adjusted <span class="html-italic">p</span>-value (red: up-regulated DEGs; blue: down-regulated DEG). Treatments are as explained in <a href="#ijms-25-07995-f002" class="html-fig">Figure 2</a>.</p>
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<p>Correlation results between RNA-seq data and qRT–PCR expression levels considering the following selected genes: <span class="html-italic">PP2C51</span>, protein phosphatase 2C 51-like; <span class="html-italic">LEADC3</span>, late embryogenesis abundant protein Dc3-like; <span class="html-italic">DH1a</span>, dehydrin DH1a; <span class="html-italic">SUS2</span>, sucrose synthase 2-like; <span class="html-italic">PIP2-2</span>, aquaporin PIP2-2-like; <span class="html-italic">XTH6</span>, xyloglucan endotransglucosylase/hydrolase protein 6; <span class="html-italic">GOLS2</span>, galactinol synthase 2-like; <span class="html-italic">CuSOD1</span>, superoxide dismutase [Cu-Zn]; <span class="html-italic">APXChl</span>, chloroplast ascorbate peroxidase.</p>
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20 pages, 2526 KiB  
Review
An Overview on the Adhesion Mechanisms of Typical Aquatic Organisms and the Applications of Biomimetic Adhesives in Aquatic Environments
by Jiani Liu, Junyi Song, Ling Zeng and Biru Hu
Int. J. Mol. Sci. 2024, 25(14), 7994; https://doi.org/10.3390/ijms25147994 - 22 Jul 2024
Cited by 1 | Viewed by 1312
Abstract
Water molecules pose a significant obstacle to conventional adhesive materials. Nevertheless, some marine organisms can secrete bioadhesives with remarkable adhesion properties. For instance, mussels resist sea waves using byssal threads, sandcastle worms secrete sandcastle glue to construct shelters, and barnacles adhere to various [...] Read more.
Water molecules pose a significant obstacle to conventional adhesive materials. Nevertheless, some marine organisms can secrete bioadhesives with remarkable adhesion properties. For instance, mussels resist sea waves using byssal threads, sandcastle worms secrete sandcastle glue to construct shelters, and barnacles adhere to various surfaces using their barnacle cement. This work initially elucidates the process of underwater adhesion and the microstructure of bioadhesives in these three exemplary marine organisms. The formation of bioadhesive microstructures is intimately related to the aquatic environment. Subsequently, the adhesion mechanisms employed by mussel byssal threads, sandcastle glue, and barnacle cement are demonstrated at the molecular level. The comprehension of adhesion mechanisms has promoted various biomimetic adhesive systems: DOPA-based biomimetic adhesives inspired by the chemical composition of mussel byssal proteins; polyelectrolyte hydrogels enlightened by sandcastle glue and phase transitions; and novel biomimetic adhesives derived from the multiple interactions and nanofiber-like structures within barnacle cement. Underwater biomimetic adhesion continues to encounter multifaceted challenges despite notable advancements. Hence, this work examines the current challenges confronting underwater biomimetic adhesion in the last part, which provides novel perspectives and directions for future research. Full article
(This article belongs to the Special Issue Biomimetic Materials Applied in the Analytical and Biomedical Fields)
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<p>Microscopic images of mussel byssal threads, sandcastle glue, and barnacle cement (<b>A</b>–<b>C</b>). Macroscopic and microscopic structures of mussel byssal threads. (<b>A</b>) Mussel byssal threads adhere to the surface of organic glass. (<b>B</b>) μ-CT images of iodine-stained mussel byssal threads on the ventral surface show the internal gland and groove structures near the middle and distal depressions of the foot, respectively. (<b>C</b>) Schematic diagram of a single byssal thread, with scanning electron microscope images showing the morphology of the cuticle (scale bar: 3 μm), core (scale bar: 5 μm), and plaque (scale bar: 50 μm) [<a href="#B30-ijms-25-07994" class="html-bibr">30</a>]. Copyright 2004, Wiley-VCH. (<b>D</b>–<b>G</b>) Morphology of sandcastle glue. (<b>D</b>) Laboratory conditions: Sandcastle worms construct shelters using zirconia beads. (<b>E</b>,<b>F</b>) Fine gaps among particles inhale sandcastle glue to form concave capillary bridges. (<b>G</b>) Scanning electron microscope images of fractured glue after freeze-drying [<a href="#B46-ijms-25-07994" class="html-bibr">46</a>]. Copyright 2013, American Chemical Society. (<b>H</b>–<b>K</b>) Nanofiber structure of barnacle cement. (<b>H</b>) Thick and opaque barnacle cement. (<b>I</b>) Scanning electron microscopy reveals abundant small nanofibers within (<b>H</b>). (<b>J</b>) Barnacles are placed on a substrate containing numerous glass beads for one day. (<b>K</b>) Abundant barnacle secretions are found among glass beads [<a href="#B55-ijms-25-07994" class="html-bibr">55</a>]. Copyright 2017, Elsevier.</p>
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<p>Key aspects of adhesives in mussel plaques, sandcastle worm glue, and barnacle cement: (<b>A</b>) Cohesion and adhesion principles of mussel byssal proteins. (<b>B</b>) Underwater adhesion process and mechanism of sandcastle worms [<a href="#B7-ijms-25-07994" class="html-bibr">7</a>]. (<b>C</b>) Distribution and amino acid preferences of barnacle cement proteins.</p>
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18 pages, 1710 KiB  
Article
Mouse Model of Parkinson’s Disease with Bilateral Dorsal Striatum Lesion with 6-Hydroxydopamine Exhibits Cognitive Apathy-like Behavior
by Masato Okitsu, Masayo Fujita, Yuki Moriya, Hiroko Kotajima-Murakami, Soichiro Ide, Rika Kojima, Kazunari Sekiyama, Kazushi Takahashi and Kazutaka Ikeda
Int. J. Mol. Sci. 2024, 25(14), 7993; https://doi.org/10.3390/ijms25147993 - 22 Jul 2024
Viewed by 1016
Abstract
Among the symptoms of Parkinson’s disease (PD), apathy comprises a set of behavioral, affective, and cognitive features that can be classified into several subtypes. However, the pathophysiology and brain regions that are involved in these different apathy subtypes are still poorly characterized. We [...] Read more.
Among the symptoms of Parkinson’s disease (PD), apathy comprises a set of behavioral, affective, and cognitive features that can be classified into several subtypes. However, the pathophysiology and brain regions that are involved in these different apathy subtypes are still poorly characterized. We examined which subtype of apathy is elicited in a mouse model of PD with 6-hydroxydopamine (6-OHDA) lesions and the behavioral symptoms that are exhibited. Male C57/BL6J mice were allocated to sham (n = 8) and 6-OHDA (n = 13) groups and locally injected with saline or 4 µg 6-OHDA bilaterally in the dorsal striatum. We then conducted motor performance tests and apathy-related behavioral experiments. We then pathologically evaluated tyrosine hydroxylase (TH) immunostaining. The 6-OHDA group exhibited significant impairments in motor function. In the behavioral tests of apathy, significant differences were observed between the sham and 6-OHDA groups in the hole-board test and novelty-suppressed feeding test. The 6-OHDA group exhibited impairments in inanimate novel object preference, whereas social preference was maintained in the three-chamber test. The number of TH+ pixels in the caudate putamen and substantia nigra compacta was significantly reduced in the 6-OHDA group. The present mouse model of PD predominantly showed dorsal striatum dopaminergic neuronal loss and a decrease in novelty seeking as a symptom that is related to the cognitive apathy component. Full article
(This article belongs to the Special Issue Animal Research Model for Neurological Diseases)
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<p>Experimental design. (<b>A</b>) Concept of this study, with correspondence between subdomains of apathy, human symptoms, and behavioral experiments in rodents. No previous studies have reported direct correspondence between human apathy subtypes and specific behavioral experiments in animal. However, a study described human clinical symptoms according to each of the apathy subtypes [<a href="#B12-ijms-25-07993" class="html-bibr">12</a>], and a mini-review proposed human clinical symptoms (not categorized by subtype) that are associated with apathy and behavioral experiments in animals to assess them [<a href="#B26-ijms-25-07993" class="html-bibr">26</a>]. We combined these reports to form the concept of the present study. Using a mouse model of PD with 6-hydroxydopamine lesions in the bilateral dorsal striatum, we examined which subtype of apathy is caused by the model and evaluated behavioral symptoms that are elicited in apathy-related behavioral experiments. (<b>B</b>) Experimental flow of the present study. After 2 weeks of habituation, three motor performance tests were conducted before 6-OHDA lesioning (pretest). A total of 21 mice were assigned to the sham (n = 8) and 6-OHDA lesion (n = 13) groups, and the 6-OHDA lesioning procedure was then performed. After 3 weeks, motor performance tests were conducted again (posttest), and then seven tests that are related to apathy-like behavior were conducted. Finally, we performed tyrosine hydroxylase (TH) immunostaining. †: euthanasia.</p>
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<p>Pathological analysis. (<b>A</b>) Representative immunohistological images of tyrosine hydroxylase (TH) staining. For assessment of the caudate putamen (CPu) and nucleus accumbens (NAcc), bregma +0.86 mm levels were taken as the cephalic slice (left column). The CPu is outlined in blue, and the NAcc is outlined in yellow. For assessment of the substantia nigra compacta (SNc) and ventral tegmental area (VTA), bregma −3.08 mm levels were taken as the caudal slice (right column). The SNc is outlined in green, and the VTA is outlined in pink. In 6-hydroxydopamine (6-OHDA)-lesioned individuals (upper row), less TH staining was visible in the lateral, dorsal part of the CPu and substantia nigra compacta (SNc). Scale bar = 500 µm. (<b>B</b>,<b>C</b>) Number of TH+ pixels in the CPu and SNc. (<b>D</b>,<b>E</b>) Cell counts of TH+ cells in the NAcc and VTA. The significant loss of TH-positive pixels in the CPu and cell counts in the SNc were observed in the 6-OHDA group compared with the sham group. In the NAcc and VTA, there was no significant reduction in pixels or cell counts in the 6-OHDA group compared with the sham group. The data are expressed as the mean ± SEM with data point overlap. *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>The motor performance tests. (<b>A</b>) Accelerating rotarod test. The mean latency to fall from the rotating rod was recorded in each trial. (<b>B</b>) Pole test. Two parameters were measured: time required for the mice to orient themselves in a downward direction (T<sub>turn</sub>) and time to descend to the base of the pole (T<sub>down</sub>). The average of five trials was measured for each individual. (<b>C</b>) Balance beam test. The mean time to cross the beam in three trials was measured. On the horizontal axis (x-axis) of the graph, Pre represents the results before 6-hydroxydopamine (6-OHDA) administration, and 3w represents the results 3 weeks after 6-OHDA administration. Except for T<sub>turn</sub> in the pole test, all parameters showed significant deterioration in the 6-OHDA group compared with the sham group 3 weeks after 6-OHDA administration. In a pre- and post-treatment comparison within the same group, there was significant deterioration in the 6-OHDA group after treatment in the rotarod test. The data are expressed as the mean ± SEM with data point overlap. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Behavioral tests of anhedonia. (<b>A</b>) Sucrose preference test (SPT). Sucrose preference (expressed as a percentage) was calculated according to the following formula: sucrose solution intake/(sucrose solution + tap water intake). (<b>B</b>) Novelty-suppressed feeding test (NSFT). The latency to take a bite of the pellet that was placed in the center while holding it with the forelimbs was recorded. In the SPT, the sucrose preference was not different between the sham and 6-OHDA groups. In the NSFT, the mean latency was significantly longer in the 6-OHDA group. The data are expressed as the mean ± SEM, with data point overlap. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Behavioral tests of anxiety and novelty-seeking behavior. (<b>A</b>) Open-field test (OFT). (<b>B</b>) Hole-board test (HBT). The number of times each mouse entered the four center square zone (left), the duration each mouse was in the four center square zone (middle), and the total distance traveled during the test (right) were measured. None of these parameters were different between the sham and 6-OHDA groups. In the HBT, the number of times the mice introduced their head in the holes was measured and significantly decreased in the 6-OHDA group compared with the sham group. The data are expressed as the mean ± SEM with data point overlap. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Behavioral tests of self-care behavior. (<b>A</b>) Nest-building test (NBT). (<b>B</b>) Splash test (ST). Nest-building activity was assessed by scoring the formation of a nest according to a six-point (0–5) scale, and 0.5 was added to the integers for intermediate cases. There was a nonsignificant trend toward a decrease in the nest-building score in the 6-OHDA group compared with the sham group. The ST was started by spraying a 10% sucrose solution twice on the dorsal coat of the mouse, and the following behaviors were videotaped for 5 min: latency to the first bout of grooming behavior and total number of grooming bouts. The latency to the first grooming bout increased but not significantly in the 6-OHDA group compared with the sham group. The total number of grooming behaviors was also not significantly different between the two groups. The data are expressed as the mean ± SEM with data point overlap.</p>
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<p>Three-chamber test. (<b>A</b>) Results of session 1, sociability test. (<b>B</b>) Results of session 2, social novelty preference. In small round wire cages in the left and right of three chambers, a 25 mm sponge cube (novel object) or a 14-week-old male C57BL/6J mouse with no prior contact with the test mouse was enclosed, respectively (session 1). The test mice were placed to the middle chamber and allowed to explore it for 10 min. To determine the interaction time, the total duration that the head of the test mouse was within the zone that was set at 30 mm around each of the cages was automatically counted using animal behavior analysis software. The interaction time for the novel object significantly decreased in the 6-OHDA group compared with the sham group, but there was no significant difference for the social object. After the session, the 14-week-old male C57BL/6J mouse was again placed in the holding cage (familiar), and a second unfamiliar mouse (stranger) was enclosed in the other wire cage on the opposite side (session 2). The rest of the procedure was conducted in the same way as mentioned above for session 1. The interaction time for either the familiar or stranger mouse did not significantly differ between the two groups. The data are expressed as the mean ± SEM with data point overlap. * <span class="html-italic">p</span> &lt; 0.05.</p>
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16 pages, 498 KiB  
Review
Biomarkers in the Diagnosis and Prediction of Medication Response in Depression and the Role of Nutraceuticals
by Cristina Beer, Fiona Rae, Annalese Semmler and Joanne Voisey
Int. J. Mol. Sci. 2024, 25(14), 7992; https://doi.org/10.3390/ijms25147992 - 22 Jul 2024
Viewed by 1028
Abstract
Depression continues to be a significant and growing public health concern. In clinical practice, it involves a clinical diagnosis. There is currently no defined or agreed upon biomarker/s for depression that can be readily tested. A biomarker is defined as a biological indicator [...] Read more.
Depression continues to be a significant and growing public health concern. In clinical practice, it involves a clinical diagnosis. There is currently no defined or agreed upon biomarker/s for depression that can be readily tested. A biomarker is defined as a biological indicator of normal physiological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention that can be objectively measured and evaluated. Thus, as there is no such marker for depression, there is no objective measure of depression in clinical practice. The discovery of such a biomarker/s would greatly assist clinical practice and potentially lead to an earlier diagnosis of depression and therefore treatment. A biomarker for depression may also assist in determining response to medication. This is of particular importance as not all patients prescribed with medication will respond, which is referred to as medication resistance. The advent of pharmacogenomics in recent years holds promise to target treatment in depression, particularly in cases of medication resistance. The role of pharmacogenomics in routine depression management within clinical practice remains to be fully established. Equally so, the use of pharmaceutical grade nutrients known as nutraceuticals in the treatment of depression in the clinical practice setting is largely unknown, albeit frequently self-prescribed by patients. Whether nutraceuticals have a role in not only depression treatment but also in potentially modifying the biomarkers of depression has yet to be proven. The aim of this review is to highlight the potential biomarkers for the diagnosis, prediction, and medication response of depression. Full article
(This article belongs to the Section Molecular Pharmacology)
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<p>Biomarkers for depression. Biomarkers identified before treatment initiation are classified as diagnostic or predictive. Diagnostic markers identify a patient with depression and predictive markers determine overall likelihood of response to medication. Mediators are biomarkers used after medication initiation and help predict overall likelihood of response/remission.</p>
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14 pages, 4396 KiB  
Article
Theoretical and Experimental Study on Carbodiimide Formation
by Marcell Dániel Csécsi, Virág Kondor, Edina Reizer, Renáta Zsanett Boros, Péter Tóth, László Farkas, Béla Fiser, Zoltán Mucsi, Miklós Nagy and Béla Viskolcz
Int. J. Mol. Sci. 2024, 25(14), 7991; https://doi.org/10.3390/ijms25147991 - 22 Jul 2024
Viewed by 1294
Abstract
Carbodiimides are important crosslinkers in organic synthesis and are used in the isocyanate industry as modifier additives. Therefore, the understanding of their formation is of high importance. In this work, we present a theoretical B3LYP/6-31G(d) and SMD solvent model and experimental investigation of [...] Read more.
Carbodiimides are important crosslinkers in organic synthesis and are used in the isocyanate industry as modifier additives. Therefore, the understanding of their formation is of high importance. In this work, we present a theoretical B3LYP/6-31G(d) and SMD solvent model and experimental investigation of the formation of diphenylcarbodiimide (CDI) from phenyl isocyanate using a phosphorus-based catalyst (MPPO) in ortho-dichlorobenzene (ODCB) solvent. Kinetic experiments were based on the volumetric quantitation of CO2 evolved, at different temperatures between 40 and 80 °C. Based on DFT calculations, we managed to construct a more detailed reaction mechanism compared to previous studies which is supported by experimental results. DFT calculations revealed that the mechanism is composed of two main parts, and the rate determining step of the first part, controlling the CO2 formation, is the first transition state with a 52.9 kJ mol−1 enthalpy barrier. The experimental activation energy was obtained from the Arrhenius plot (ln k vs. 1/T) using the observed second-order kinetics, and the obtained 55.8 ± 2.1 kJ mol−1 was in excellent agreement with the computational one, validating the complete mechanism, giving a better understanding of carbodiimide production from isocyanates. Full article
(This article belongs to the Section Physical Chemistry and Chemical Physics)
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<p>Timeline of carbodiimide research including the most relevant articles, patents, and books between 1950 and 2023 (Khorana [<a href="#B13-ijms-25-07991" class="html-bibr">13</a>], Weith [<a href="#B12-ijms-25-07991" class="html-bibr">12</a>], Sheehan [<a href="#B11-ijms-25-07991" class="html-bibr">11</a>], Neumann [<a href="#B14-ijms-25-07991" class="html-bibr">14</a>], Campbell [<a href="#B5-ijms-25-07991" class="html-bibr">5</a>,<a href="#B15-ijms-25-07991" class="html-bibr">15</a>], Monagle [<a href="#B16-ijms-25-07991" class="html-bibr">16</a>,<a href="#B17-ijms-25-07991" class="html-bibr">17</a>], Appleman [<a href="#B18-ijms-25-07991" class="html-bibr">18</a>], Kurzer [<a href="#B19-ijms-25-07991" class="html-bibr">19</a>], Smeltz [<a href="#B20-ijms-25-07991" class="html-bibr">20</a>], Sandler [<a href="#B21-ijms-25-07991" class="html-bibr">21</a>], Hansen [<a href="#B22-ijms-25-07991" class="html-bibr">22</a>], Williams [<a href="#B9-ijms-25-07991" class="html-bibr">9</a>], Mikołajczyk [<a href="#B23-ijms-25-07991" class="html-bibr">23</a>], Pankratov [<a href="#B8-ijms-25-07991" class="html-bibr">8</a>], Ulrich [<a href="#B1-ijms-25-07991" class="html-bibr">1</a>], Savino [<a href="#B2-ijms-25-07991" class="html-bibr">2</a>], Damrauer [<a href="#B24-ijms-25-07991" class="html-bibr">24</a>], and Waleed [<a href="#B25-ijms-25-07991" class="html-bibr">25</a>]).</p>
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<p>The overall reaction of the formation of diphenylcarbodiimide (CDI) using phenyl isocyanate (NCO) and 3-methyl-1-phenyl-2-phospholene-l-oxide (MPPO) catalyst.</p>
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<p>Potential energy surface (PES) of the overall reaction coordinate, representing the intermediates (<b>IMs</b>), adducts (<b>ADs</b>), complexes (<b>RC</b>, <b>PC</b>), and transition states (<b>TSs</b>), and the calculated relative enthalpies (Δ<span class="html-italic">H</span>) using the B3LYP/6-31G(d) level of theory at 298.15 K and 1 atm with the SMD solvent model in ODCB. Bold and dashed lines refer to the PES, the red and blue lines describe the first and second subprocess, respectively.</p>
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<p>Conversion of the isocyanate versus time at different temperatures.</p>
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<p>(<b>a</b>) Kinetic diagram of the initial part of measurements, using second-order, linearized kinetic equation, where regression constants (<span class="html-italic">R</span><sup>2</sup>) are at least 0.99. (<b>b</b>) Arrhenius plot of the five measurements shows the rate constant versus temperature; the fitted linear equation: <span class="html-italic">y</span> = −6709.3 <span class="html-italic">x</span> + 12.67.</p>
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<p>The experimental apparatus. A,B,A′,B′—thermostats; C—thermometer; D—reflux condenser; E—inlets for NCO and nitrogen input; F—reaction vessel; G—magnetic stirrer; H—3-way stopcock; I—atmospheric equilibrium flask; J—gas burette; K—level vessel; L—laboratory scissor jack.</p>
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<p>(<b>a</b>) Detailed reaction mechanism of the first subprocess (formation of phosphine imide) with the intermediates and transition state structures. Optimized 3D structures along the reaction pathway with the relevant bond lengths in Angstrom, computed with the B3LYP/6-31G(d) level of theory at 298.15 K and 1 atm in ODCB solvent. <b>NCO</b>—phenyl isocyanate; <b>MPPO</b>—3-methyl-1-phenyl-2-phospholene-l-oxide catalyst; <b>R</b>—reactant; <b>RC</b>—reactant complex; <b>TS</b>—transition state; <b>AD</b>—intermediate adduct; <b>IM</b>—intermediate product; <b>IM1</b>—intermediate complex. (<b>b</b>) Detailed reaction mechanism of the second subprocess (formation of diphenylcarbodiimide). Optimized 3D structures along the reaction pathway with the relevant bond lengths in Angstrom, computed with the B3LYP/6-31G(d) level of theory at 298.15 K and 1 atm in ODCB solvent. <b>NCO</b>—phenyl isocyanate; <b>MPPO</b>—3-methyl-1-phenyl-2-phospholene-l-oxide catalyst; R—reactant; <b>RC</b>—reactant complex; <b>TS</b>—transition state; <b>AD</b>—intermediate adduct; <b>IM</b>—intermediate product; <b>IM1</b>, <b>IM2</b>—intermediate complexes; <b>PC</b>—product complex; <b>P</b>—reaction product; <b>CDI</b>—diphenylcarbodiimide.</p>
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<p>The pseudorotation in the first (AD1 → TS2 → AD2) and second subprocesses (AD3 → TS5 → AD4) and the calculated NICS values (<span class="html-italic">δ</span><sub>0</sub> in ppm, this value is −9.6 ppm for benzene ring), which refer to the slightly aromatic character of the P–O–C–N rings. Purple bond lengths and the same colored ‘<b>a</b>’ refer to the axial groups, while blue bonds and black colored ‘<b>e</b>’ refer to the equatorial groups.</p>
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26 pages, 2404 KiB  
Review
Glioma and Peptidergic Systems: Oncogenic and Anticancer Peptides
by Manuel Lisardo Sánchez, Arturo Mangas and Rafael Coveñas
Int. J. Mol. Sci. 2024, 25(14), 7990; https://doi.org/10.3390/ijms25147990 - 22 Jul 2024
Cited by 2 | Viewed by 929
Abstract
Glioma cells overexpress different peptide receptors that are useful for research, diagnosis, management, and treatment of the disease. Oncogenic peptides favor the proliferation, migration, and invasion of glioma cells, as well as angiogenesis, whereas anticancer peptides exert antiproliferative, antimigration, and anti-angiogenic effects against [...] Read more.
Glioma cells overexpress different peptide receptors that are useful for research, diagnosis, management, and treatment of the disease. Oncogenic peptides favor the proliferation, migration, and invasion of glioma cells, as well as angiogenesis, whereas anticancer peptides exert antiproliferative, antimigration, and anti-angiogenic effects against gliomas. Other peptides exert a dual effect on gliomas, that is, both proliferative and antiproliferative actions. Peptidergic systems are therapeutic targets, as peptide receptor antagonists/peptides or peptide receptor agonists can be administered to treat gliomas. Other anticancer strategies exerting beneficial effects against gliomas are discussed herein, and future research lines to be developed for gliomas are also suggested. Despite the large amount of data supporting the involvement of peptides in glioma progression, no anticancer drugs targeting peptidergic systems are currently available in clinical practice to treat gliomas. Full article
(This article belongs to the Special Issue Current Research on Cancer Biology and Therapeutics: 2nd Edition)
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<p>Oncogenic and anticancer peptides involved in glioma development.</p>
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<p>Oncogenic peptides favoring (red rectangles) and anticancer peptides counteracting (green rectangles) the hallmarks (proliferative signaling maintenance, replicative immortality, invasion and metastasis activation, angiogenesis promotion, cell death resistance, immune destruction evasion, and energy metabolism reprogramming) responsible for glioma development.</p>
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<p>Anti-glioma therapeutic strategies: peptides, monoclonal antibodies, peptide receptor knockdown, peptide receptor antagonists, miR upregulation, drugs, and inhibitors.</p>
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<p>Chemical structures of peptide receptor antagonists showing anti-glioma effects. These structures were illustrated using KingDraw free software [<a href="#B142-ijms-25-07990" class="html-bibr">142</a>], except for VIPhyp, which was illustrated using the PepDraw program [<a href="#B143-ijms-25-07990" class="html-bibr">143</a>].</p>
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22 pages, 3560 KiB  
Article
Phytochemical Analysis and Antioxidant and Antifungal Activities of Powders, Methanol Extracts, and Essential Oils from Rosmarinus officinalis L. and Thymus ciliatus Desf. Benth.
by Noui Hendel, Djamel Sarri, Madani Sarri, Edoardo Napoli, Antonio Palumbo Piccionello and Giuseppe Ruberto
Int. J. Mol. Sci. 2024, 25(14), 7989; https://doi.org/10.3390/ijms25147989 - 22 Jul 2024
Cited by 3 | Viewed by 1374
Abstract
Chemical residues in food pose health risks such as cancer and liver issues. This has driven the search for safer natural alternatives to synthetic fungicides and preservatives. The aim of this study was to characterize the chemical composition of the essential oils (EO), [...] Read more.
Chemical residues in food pose health risks such as cancer and liver issues. This has driven the search for safer natural alternatives to synthetic fungicides and preservatives. The aim of this study was to characterize the chemical composition of the essential oils (EO), determine the polyphenolic contents, and evaluate the in vitro antioxidant and antifungal activities of methanol extracts (ME), essential oils (EO), and powders from Rosmarinus officinalis L. (rosemary) and Thymus ciliatus (Desf) Benth. (thyme) from the M’sila region, Algeria. The chemical composition of the EOs was determined by GC-MS. R. officinalis EO was composed of 31 components, mainly camphor (41.22%), camphene (18.14%), and α-pinene (17.49%); T. ciliatus EO was composed of 58 components, mainly, in percentage, α-pinene (22.18), myrcene (13.13), β-pinene (7.73), β-caryophyllene (10.21), and germacrene D (9.90). The total phenols and flavonoids were determined spectrophotometrically, and the rosemary ME was found to possess the highest polyphenolic content (127.1 ± 2.40 µg GAE/mg), while the thyme ME had the highest flavonoid content (48.01 ± 0.99 µg QE/mg). The antioxidant activity was assessed using three methods: rosemary ME was the most potent, followed by DPPH (IC50 = 13.43 ± 0.14 µg/mL), β-carotene/linoleic acid (IC50 = 39.01 ± 2.16 μg/mL), and reducing power (EC50 = 15.03 ± 1.43 µg/mL). Antifungal activity was assessed for 32 pathogenic and foodborne fungi. Four methods were applied to the solid medium. Incorporating the powdered plant into the culture medium (at 10%) reduced the fungal growth to greater than 50% in 21.88% and 6.25% of all fungal isolates, for R. officinalis and T. ciliatus, respectively. The ME, applied by the well diffusion method (0.1 g/mL), was less effective. Different concentrations of EO were tested. Incorporating the EO into the culture medium (1500 μL/L) inhibited 50% of the molds to levels of 50 and 75% for R. officinalis and T. ciliatus, respectively, with the complete inhibition of four fungi. Fumigated EO (15 μL) inhibited 65% of the molds to levels of 65 and 81.25% for R. officinalis and T. ciliatus, respectively, with the complete inhibition of five fungi. There was little to no sporulation in conjunction with the inhibition. Our results revealed some of the potential of the studied plants to fight foodborne molds and presented their promising characteristics as a source of alternatives to chemical pesticides and synthetic preservatives. Further studies are needed to find adequate application techniques in the food safety area. Full article
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<p>Effect of powders and methanol extracts of <span class="html-italic">R. officinalis</span> and <span class="html-italic">T. ciliatus</span> on the radial growth of tested molds grown on Potato Dextrose Agar (PDA). (<b>A</b>) Powder (10%, <span class="html-italic">w</span>/<span class="html-italic">v</span>); (<b>B</b>) methanol extract (0.1 g/mL). The data are represented as the average ± SD (<span class="html-italic">n</span> = 3). Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) between the two tested plants on each mold, according to Sidak’s multiple comparisons test.</p>
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<p>Effect of the powdered plant on the radial growth of some tested fungi; 1—<span class="html-italic">A. ochraceus</span>, 2—<span class="html-italic">A. parasiticus</span>, 3—<span class="html-italic">B. aclada</span>, 4—<span class="html-italic">F. oxysporum</span>, 5—<span class="html-italic">P. expansum</span>; (a) control, (b) PDA medium supplemented with <span class="html-italic">R. officinalis</span>, and (c) PDA medium supplemented with <span class="html-italic">T. ciliatus</span>.</p>
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<p>Effect of the plant ME on the radial growth of some tested fungi; 1—<span class="html-italic">P. digitatum</span>, 2—<span class="html-italic">F. graminearum</span>, 3—<span class="html-italic">A. alternata</span>, 4—<span class="html-italic">F. oxysporum</span>, 5—<span class="html-italic">F. proliferatum</span>; (a) control, (b) PDA medium supplemented with <span class="html-italic">R. officinalis</span>, (c) PDA medium supplemented with <span class="html-italic">T. ciliatus</span>.</p>
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<p>Effect of EOs of <span class="html-italic">R. officinalis</span> and <span class="html-italic">T. ciliatus</span> on the radial growth of the tested molds grown on PDA by direct contact method of (<b>A</b>) 500, (<b>B</b>) 1000, and (<b>C</b>) 1500 μL/L. The data are represented as the average ± SD (<span class="html-italic">n</span> = 3). Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) between the two tested plants on each mold, according to Sidak’s multiple comparisons test.</p>
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<p>Effect of EOs of <span class="html-italic">R. officinalis</span> and <span class="html-italic">T. ciliatus</span> on the radial growth of the tested molds grown on PDA by direct contact method of (<b>A</b>) 5, (<b>B</b>) 10, and (<b>C</b>) 15 μL. The data are represented as the average ± SD (<span class="html-italic">n</span> = 3). Different letters indicate significant differences (<span class="html-italic">p &lt;</span> 0.05) between the two tested plants on each mold, according to Sidak’s multiple comparisons test.</p>
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<p>Effect of direct contact with EO on the radial growth of (1) <span class="html-italic">M. suaveolens</span>, (2) <span class="html-italic">F. culmorum</span>, (3) <span class="html-italic">P. griseofulvum,</span> and (4) <span class="html-italic">P. expansum</span>; a: Control; b,c: mold exposed to concentrations of 1000 and 1500 µL/mL of <span class="html-italic">R. officinalis</span> EO, respectively; d–f: mold exposed to concentrations of 500, 1000, and 1500 µL/mL of <span class="html-italic">T. ciliatus</span> EO, respectively.</p>
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<p>Effect of EO fumigation on the radial growth of (1) <span class="html-italic">B. cinerea</span>, (2) <span class="html-italic">A. alternata</span>, (3) <span class="html-italic">F. graminearum,</span> and (4) <span class="html-italic">A. flavus</span>; a: Control; b,c: mold exposed to fumigation of 10 and 15 µL of <span class="html-italic">R. officinalis</span> EO, respectively; d–f: mold exposed to fumigation of 5, 10, and 15 µL of <span class="html-italic">T. ciliatus</span> EO, respectively.</p>
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17 pages, 4354 KiB  
Review
Impact of Different Anti-Hyperglycaemic Treatments on Bone Turnover Markers and Bone Mineral Density in Type 2 Diabetes Mellitus Patients: A Systematic Review and Meta-Analysis
by Md Sadman Sakib Saadi, Rajib Das, Adhithya Mullath Ullas, Diane E. Powell, Emma Wilson, Ioanna Myrtziou, Chadi Rakieh and Ioannis Kanakis
Int. J. Mol. Sci. 2024, 25(14), 7988; https://doi.org/10.3390/ijms25147988 - 22 Jul 2024
Viewed by 1606
Abstract
Diabetic bone disease (DBD) is a frequent complication in patients with type 2 diabetes mellitus (T2DM), characterised by altered bone mineral density (BMD) and bone turnover marker (BTMs) levels. The impact of different anti-diabetic medications on the skeleton remains unclear, and studies have [...] Read more.
Diabetic bone disease (DBD) is a frequent complication in patients with type 2 diabetes mellitus (T2DM), characterised by altered bone mineral density (BMD) and bone turnover marker (BTMs) levels. The impact of different anti-diabetic medications on the skeleton remains unclear, and studies have reported conflicting results; thus, the need for a comprehensive systematic review is of paramount importance. A systematic search was conducted in PubMed and the Cochrane Library. The primary outcomes assessed were changes in BMD in relation to different anatomical sites and BTMs, including mainly P1NP and CTX as well as OPG, OCN, B-ALP and RANK-L. Risk of bias was evaluated using the JADAD score. The meta-analysis of 19 randomised controlled trials comprising 4914 patients showed that anti-diabetic medications overall increased BMD at the lumbar spine (SMD: 0.93, 95% CI [0.13, 1.73], p = 0.02), femoral neck (SMD: 1.10, 95% CI [0.47, 1.74], p = 0.0007) and in total hip (SMD: 0.33, 95% CI [−0.25, 0.92], p = 0.27) in comparison with placebo, but when compared with metformin, the overall effect favoured metformin over other treatments (SMD: −0.23, 95% CI [−0.39, −0.07], p = 0.004). GLP-1 receptor agonists and insulin analogues seem to improve BMD compared to placebo, while SGLT2 inhibitors and thiazolidinediones (TZDs) showed no significant effect, although studies’ number cannot lead to safe conclusions. For BTMs, TZDs significantly increased P1NP levels compared to placebo. However, no significant differences were observed for CTX, B-ALP, OCN, OPG, and RANK-L between anti-diabetic drugs and metformin or placebo. High heterogeneity and diverse follow-up durations among studies were evident, which obscures the validity of the results. This review highlights the variable effects of anti-diabetic drugs on DBD in T2DM patients, emphasising the need for long-term trials with robust designs to better understand these relationships and inform clinical decisions. Full article
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Figure 1
<p>PRISMA flow diagram of the selection process for the included articles.</p>
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<p>Standardised mean differences in BMD measurements based on different anatomical sites in T2DM patients receiving different classes of anti-diabetic drugs versus placebo. SMD in subgroups as well as total effect are presented with 95% confidence intervals using the random effects model [<a href="#B36-ijms-25-07988" class="html-bibr">36</a>,<a href="#B44-ijms-25-07988" class="html-bibr">44</a>,<a href="#B46-ijms-25-07988" class="html-bibr">46</a>,<a href="#B48-ijms-25-07988" class="html-bibr">48</a>,<a href="#B50-ijms-25-07988" class="html-bibr">50</a>].</p>
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<p>Mean differences in BMD measurements based on different anatomical sites in T2DM patients receiving different classes of anti-diabetic drugs versus metformin. Mean differences in subgroups as well as total effect are presented with 95% confidence intervals using the random effects model [<a href="#B53-ijms-25-07988" class="html-bibr">53</a>,<a href="#B57-ijms-25-07988" class="html-bibr">57</a>,<a href="#B58-ijms-25-07988" class="html-bibr">58</a>,<a href="#B61-ijms-25-07988" class="html-bibr">61</a>].</p>
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<p>Mean differences in serum P1NP levels receiving various anti-diabetic drugs versus placebo. Mean differences in the studies as well as total effect are presented with 95% confidence intervals using the random effects model [<a href="#B36-ijms-25-07988" class="html-bibr">36</a>,<a href="#B45-ijms-25-07988" class="html-bibr">45</a>,<a href="#B46-ijms-25-07988" class="html-bibr">46</a>,<a href="#B47-ijms-25-07988" class="html-bibr">47</a>,<a href="#B48-ijms-25-07988" class="html-bibr">48</a>,<a href="#B50-ijms-25-07988" class="html-bibr">50</a>].</p>
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<p>SMD in serum P1NP levels in patients receiving various anti-diabetic drugs versus metformin. Meta-regression was performed by applying the random effects model [<a href="#B51-ijms-25-07988" class="html-bibr">51</a>,<a href="#B52-ijms-25-07988" class="html-bibr">52</a>,<a href="#B53-ijms-25-07988" class="html-bibr">53</a>,<a href="#B54-ijms-25-07988" class="html-bibr">54</a>].</p>
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<p>SMD in serum CTX levels comparing various anti-diabetic drugs versus placebo. Meta-regression was performed by applying the random effects model [<a href="#B36-ijms-25-07988" class="html-bibr">36</a>,<a href="#B45-ijms-25-07988" class="html-bibr">45</a>,<a href="#B46-ijms-25-07988" class="html-bibr">46</a>,<a href="#B47-ijms-25-07988" class="html-bibr">47</a>,<a href="#B48-ijms-25-07988" class="html-bibr">48</a>,<a href="#B50-ijms-25-07988" class="html-bibr">50</a>].</p>
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<p>SMD in serum CTX levels in the comparison of anti-diabetic drugs versus metformin. Meta-regression was performed using the random effects model [<a href="#B51-ijms-25-07988" class="html-bibr">51</a>,<a href="#B52-ijms-25-07988" class="html-bibr">52</a>,<a href="#B53-ijms-25-07988" class="html-bibr">53</a>,<a href="#B54-ijms-25-07988" class="html-bibr">54</a>].</p>
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16 pages, 5376 KiB  
Article
Transcriptome-Wide Identification of m6A Writers, Erasers and Readers and Their Expression Profiles under Various Biotic and Abiotic Stresses in Pinus massoniana Lamb.
by Sheng Yao, Yidan Song, Xiang Cheng, Dengbao Wang, Qianzi Li, Jingjing Zhang, Qingyang Chen, Qiong Yu and Kongshu Ji
Int. J. Mol. Sci. 2024, 25(14), 7987; https://doi.org/10.3390/ijms25147987 - 22 Jul 2024
Viewed by 871
Abstract
N6-methyladenosine (m6A) RNA modification is the most prevalent form of RNA methylation and plays a crucial role in plant development. However, our understanding of m6A modification in Masson pine (Pinus massoniana Lamb.) remains limited. In this [...] Read more.
N6-methyladenosine (m6A) RNA modification is the most prevalent form of RNA methylation and plays a crucial role in plant development. However, our understanding of m6A modification in Masson pine (Pinus massoniana Lamb.) remains limited. In this study, a complete analysis of m6A writers, erasers, and readers in Masson pine was performed, and 22 m6A regulatory genes were identified in total, including 7 m6A writers, 7 m6A erases, and 8 readers. Phylogenetic analysis revealed that all m6A regulators involved in Masson pine could be classified into three distinct groups based on their domains and motifs. The tissue expression analysis revealed that the m6A regulatory gene may exert a significant influence on the development of reproductive organs and leaves in Masson pine. Moreover, the results from stress and hormone expression analysis indicated that the m6A regulatory gene in Masson pine might be involved in drought stress response, ABA-signaling-pathway activation, as well as resistance to Monochamus alternatus. This study provided valuable and anticipated insights into the regulatory genes of m6A modification and their potential epigenetic regulatory mechanisms in Masson pine. Full article
(This article belongs to the Section Molecular Plant Sciences)
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<p>Phylogenetic analysis of m<sup>6</sup>A writers from <span class="html-italic">A. thaliana</span>, <span class="html-italic">Populus trichocarpa</span>, nine <span class="html-italic">Rosaceae plants</span> and <span class="html-italic">P. massoniana</span>.</p>
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<p>Phylogenetic analysis of ALKB family from <span class="html-italic">A. thaliana</span>, <span class="html-italic">P. trichocarpa</span>, nine <span class="html-italic">Rosaceae plants</span>, and <span class="html-italic">P. massoniana</span>. A, B, C, and D represent different subgroups.</p>
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<p>Phylogenetic analysis of YTH family from <span class="html-italic">A. thaliana</span>, <span class="html-italic">P. trichocarpa</span>, nine Rosaceae plants, and <span class="html-italic">P. massoniana</span>.</p>
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<p>Schematics of the conserved motifs (<b>A</b>) and functional domains (<b>B</b>) of m<sup>6</sup>A regulators in Masson pine.</p>
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<p>Predictions of PrLDs and disordered regions made by the PLAAC (accessed on 15 March 2024). (<b>A</b>): m<sup>6</sup>A writers, (<b>B</b>): m<sup>6</sup>A erasers, (<b>C</b>): m<sup>6</sup>A readers. The black line represents the background and the red line is the prediction of the prion structure region. If the red line is in the non-baseline region, it indicates that the prion structure region is at that location and the phase transition is highly likely.</p>
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<p>Heat map of RNA-Seq expression of m<sup>6</sup>A regulators in different tissues of Masson pine. (<b>A</b>): The organization diagram of Masson pine. (<b>B</b>): Heat map for tissue specific analysis.</p>
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<p>Relative expression levels of m<sup>6</sup>A genes during conifer development based on qRT-PCR analysis. (<b>A</b>): m<sup>6</sup>A writers, (<b>B</b>): m<sup>6</sup>A erasers, (<b>C</b>): m<sup>6</sup>A readers. The relative expression level was measured with the expression level of “New needle” as the control. Different numbers of “*” indicate significant differences (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01), ns indicate no significant difference. Data are shown as mean ± SE, with three biological replicates and three technical replicates.</p>
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<p>Relative expression levels of m<sup>6</sup>A genes under ABA treatment based on qRT-PCR analysis. (<b>A</b>): m6A writers, (<b>B</b>): m6A erasers, (<b>C</b>): m6A readers. Different numbers of “*” indicate significant differences (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01), ns indicate no significant difference. Data are shown as mean ± SE, with three biological replicates and three technical replicates.</p>
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<p>Expression pattern of m<sup>6</sup>A regulatory genes under biotic and abiotic stress conditions. Note: (<b>A</b>): Heat map of expression of Masson pine under drought stress. The soil moisture content for the growth of Masson pine was set to four gradients: A (80 ± 5)%, B (65 ± 5)%, C (50 ± 5)%, and D (35 ± 5)%, respectively. They were placed at 75% humidity for 60 d and subsequently sequenced. Fragments per kilobase of exon model per million mapped fragments (FPKM) values were computed to assess the expression level of m<sup>6</sup>A regulators. (<b>B</b>): The Masson pine clones MK27-1 and MK30-1 exhibited relatively low resistance to <span class="html-italic">Monochamus alternatus</span>, whereas the MK94-1 clones demonstrated comparatively high resistance against <span class="html-italic">M. alternatus</span>. Transcripts Per Kilobase of exonmodel per Million mapped reads (TPM) values were computed to assess the expression level of m<sup>6</sup>A regulators.</p>
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<p>Block diagram of the research stepwise procedure.</p>
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36 pages, 8923 KiB  
Article
Discovery of Cell-Permeable Allosteric Inhibitors of Liver Pyruvate Kinase: Design and Synthesis of Sulfone-Based Urolithins
by Shazia Iqbal, Md. Zahidul Islam, Sajda Ashraf, Woonghee Kim, Amal A. AL-Sharabi, Mehmet Ozcan, Essam Hanashalshahaby, Cheng Zhang, Mathias Uhlén, Jan Boren, Hasan Turkez and Adil Mardinoglu
Int. J. Mol. Sci. 2024, 25(14), 7986; https://doi.org/10.3390/ijms25147986 - 22 Jul 2024
Viewed by 1101
Abstract
Metabolic dysfunction-associated fatty liver disease (MAFLD) presents a significant global health challenge, characterized by the accumulation of liver fat and impacting a considerable portion of the worldwide population. Despite its widespread occurrence, effective treatments for MAFLD are limited. The liver-specific isoform of pyruvate [...] Read more.
Metabolic dysfunction-associated fatty liver disease (MAFLD) presents a significant global health challenge, characterized by the accumulation of liver fat and impacting a considerable portion of the worldwide population. Despite its widespread occurrence, effective treatments for MAFLD are limited. The liver-specific isoform of pyruvate kinase (PKL) has been identified as a promising target for developing MAFLD therapies. Urolithin C, an allosteric inhibitor of PKL, has shown potential in preliminary studies. Expanding upon this groundwork, our study delved into delineating the structure-activity relationship of urolithin C via the synthesis of sulfone-based urolithin analogs. Our results highlight that incorporating a sulfone moiety leads to substantial PKL inhibition, with additional catechol moieties further enhancing this effect. Despite modest improvements in liver cell lines, there was a significant increase in inhibition observed in HepG2 cell lysates. Specifically, compounds 15d, 9d, 15e, 18a, 12d, and 15a displayed promising IC50 values ranging from 4.3 µM to 18.7 µM. Notably, compound 15e not only demonstrated a decrease in PKL activity and triacylglycerol (TAG) content but also showed efficient cellular uptake. These findings position compound 15e as a promising candidate for pharmacological MAFLD treatment, warranting further research and studies. Full article
(This article belongs to the Section Biochemistry)
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Graphical abstract

Graphical abstract
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<p>Metabolic Pathway and Key Players Involved in MAFLD.</p>
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<p>Structural basis of ellagic acid (1), urolithin C (2), urolithin D (3), and their synthetic derivative (4), along with the proposed design of sulfonamides and sultam derivatives featuring a substituted benzylamine ‘handle’ for the development of PKL inhibitors.</p>
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<p>Synthesis of sulfone-based urolithin C compounds.</p>
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<p>Synthesis of sulfone-based urolithin C analogs.</p>
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<p>(<b>a</b>) PKM CRISPR knock-out HepG2 WT cells were designed. HepG2 PKM CRISPR KO cells have PKL only. (<b>b</b>) Pyruvate kinase activity assay on HepG2 KO cells (PKL only) protein lysate. Generated pyruvate (O.D. value at 570 nm) was calculated as a percentile (%). (<b>c</b>) IC<sub>50</sub> value of PKL enzymatic activity inhibition for most active compounds. 30 µM, 10 µM, 3 µM, 1 µM, 300 nM, 30 nM, and untreated groups were tested on HepG2 KO cell lysate. (<b>d</b>) Pyruvate kinase activity assay on HepG2 KO cells after 20 µM compounds for 4 hr treatment. Generated pyruvate (O.D. value at 570 nm) was calculated as a percentile (%). Data are represented as mean ± SD, * <span class="html-italic">p</span> &lt; 0.05, Student’s <span class="html-italic">t</span>-test.</p>
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<p>(<b>a</b>) Pyruvate kinase activity on cells. PK activities were tested on 20 µM <b>15e</b> for 4 h treated HepG2 CRISPR PKM KO cells (<b>left</b>) and HepG2 WT (<b>right</b>). The generated pyruvate during the assay was visualized in the histogram. (<b>b</b>) CETSA assay for PKM2 and PKL. <b>15e</b> 2 h treated HepG2 WT cells had heat shock at 60 °C for 5 min, and soluble proteins were analyzed using western blot. (<b>c</b>) Cell permeable PKL inhibitor <b>15e</b> was used to treat the HepG2 WT DNL steatosis model for one week at 5 µM, 2.5 µM, 1.25 µM, and 0.625 µM. After one week, TAG contents and cell viability were measured. (<b>d</b>) Western blot analysis for <b>15e</b> treated one-week HepG2 WT steatosis model. Compound <b>15e</b> was used to treat the HepG2 DNL steatosis model at a 5 µM concentration. The band intensity of DNL-involved steatosis proteins was measured. Arrows indicate FASN and PKL, which decreased protein expression. Data are represented as mean ± SD, * <span class="html-italic">p</span> &lt; 0.05, Student’s <span class="html-italic">t</span>-test.</p>
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<p>Protein-ligand interaction diagram of compounds (<b>a</b>) <b>15d</b>, (<b>b</b>) <b>12d</b>, (<b>c</b>) <b>9d</b>, (<b>d</b>) <b>15e</b>, (<b>e</b>) <b>18a</b>, and (<b>f</b>) <b>15a</b> bound to the allosteric interface of PKLR; (<b>g</b>) presenting the docking pose of the above-mentioned six urolithin D derivatives.</p>
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<p>Protein-ligand interaction diagram of compounds (<b>a</b>) <b>15d</b>, (<b>b</b>) <b>12d</b>, (<b>c</b>) <b>9d</b>, (<b>d</b>) <b>15e</b>, (<b>e</b>) <b>18a</b>, and (<b>f</b>) <b>15a</b> bound to the allosteric interface of PKLR; (<b>g</b>) presenting the docking pose of the above-mentioned six urolithin D derivatives.</p>
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<p>Structural representation of biaryl sulfonamide (linear) analogs for SAR.</p>
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<p>Structural representation of Sultam (cyclic) analogs of urolithic C for SAR.</p>
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<p>Shows the SAR study, identifying the pharmacophoric features of urolithin C and newly designed molecules, along with their IC<sub>50</sub> values.</p>
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<p>Presents a concise summary of the observed structure-activity relationship (SAR) of the PKL inhibitors investigated in this study.</p>
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<p>Synthesis of biaryl sulfonamides (with a benzyl group); reagents and conditions: (a) HSO<sub>3</sub>Cl, 0 °C, 15 min, 82%; (b) DIPEA, CH<sub>2</sub>Cl<sub>2</sub>, r.t., 1 h, 90%; (c) Pd (PPh<sub>3</sub>)<sub>4</sub>, toluene:EtOH:water/5:2:1, 120 °C, MW, 1 h, 72–92%; (d) BBr<sub>3</sub>, DCM, r.t., o/n, 52–75%.</p>
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<p>Synthesis of biaryl sulfonamides (with a fluorobenzyl group); reagents and conditions: (a) HSO<sub>3</sub>Cl, 0 °C, 15 min, 82%; (b) DIPEA, CH<sub>2</sub>Cl<sub>2</sub>, r.t., 1 h, 82%; (c) Pd (PPh<sub>3</sub>)<sub>4</sub>, toluene:EtOH:water/5:2:1, 120 °C, MW, 1 h, 67–88%; (d) BBr<sub>3</sub>, DCM, r.t., o/n, 52–72%.</p>
Full article ">Scheme 3
<p>Synthesis of sultam derivatives of urolithin C (with benzyl group); Reagents and conditions: (a) DIPEA, CH<sub>2</sub>Cl<sub>2</sub>, r.t., 1 h, 90%; (b) Pd (PPh<sub>3</sub>)<sub>4</sub>, toluene:EtOH:water/5:2:1, 120 °C, MW, 1 h, 72–92%; (c) PIDA, I<sub>2</sub>, K<sub>2</sub>CO<sub>3</sub>, CH<sub>2</sub>Cl<sub>2</sub>, 35 °C, 30 min–3 h, 45–79%; (d) BBr<sub>3</sub>, DCM, r.t., o/n, 52–71%.</p>
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<p>Synthesis of biaryl sulfonamides (with a fluorobenzyl group); reagents and conditions: (a) DIPEA, CH<sub>2</sub>Cl<sub>2</sub>, r.t., 1 h, 85%; (b) Pd (PPh<sub>3</sub>)<sub>4</sub>, toluene:EtOH:water/5:2:1, 120 °C, MW, 1 h, 67–88%; (c) PIDA, I<sub>2</sub>, K<sub>2</sub>CO<sub>3</sub>, CH<sub>2</sub>Cl<sub>2</sub>, 35 °C, 30 min–3 h, 55–70%; (d) BBr<sub>3</sub>, DCM, r.t., o/n, 55–68%.</p>
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<p>Synthesis of sultam derivatives of urolithin C (without benzyl group); reagents and conditions: (a) DIPEA, CH<sub>2</sub>Cl<sub>2</sub>, r.t., 1 h, 90%; (b) Pd(PPh<sub>3</sub>)<sub>4</sub>, toluene:EtOH:water/5:2:1, 120 °C, MW, 1 h; (c) PIDA, I<sub>2</sub>, K<sub>2</sub>CO<sub>3</sub>, CH<sub>2</sub>Cl<sub>2</sub>, 35 °C, 30 min–3 h; (d) BBr<sub>3</sub>, DCM, r.t., o/n, and MsOH, r.t., 1 h.</p>
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19 pages, 10535 KiB  
Article
Ribosome Pausing Negatively Regulates Protein Translation in Maize Seedlings during Dark-to-Light Transitions
by Mingming Hou, Wei Fan, Deyi Zhong, Xing Dai, Quan Wang, Wanfei Liu and Shengben Li
Int. J. Mol. Sci. 2024, 25(14), 7985; https://doi.org/10.3390/ijms25147985 - 22 Jul 2024
Viewed by 963
Abstract
Regulation of translation is a crucial step in gene expression. Developmental signals and environmental stimuli dynamically regulate translation via upstream small open reading frames (uORFs) and ribosome pausing. Recent studies have revealed many plant genes that are specifically regulated by uORF translation following [...] Read more.
Regulation of translation is a crucial step in gene expression. Developmental signals and environmental stimuli dynamically regulate translation via upstream small open reading frames (uORFs) and ribosome pausing. Recent studies have revealed many plant genes that are specifically regulated by uORF translation following changes in growth conditions, but ribosome-pausing events are less well understood. In this study, we performed ribosome profiling (Ribo-seq) of etiolated maize (Zea mays) seedlings exposed to light for different durations, revealing hundreds of genes specifically regulated at the translation level during the early period of light exposure. We identified over 400 ribosome-pausing events in the dark that were rapidly released after illumination. These results suggested that ribosome pausing negatively regulates translation from specific genes, a conclusion that was supported by a non-targeted proteomics analysis. Importantly, we identified a conserved nucleotide motif downstream of the pausing sites. Our results elucidate the role of ribosome pausing in the control of gene expression in plants; the identification of the cis-element at the pausing sites provides insight into the mechanisms behind translation regulation and potential targets for artificial control of plant translation. Full article
(This article belongs to the Special Issue New Insights in Translational Bioinformatics)
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Figure 1
<p>Genomic distribution, length, and 3 nt periodicity of identified ribosome-protected fragments (RPFs): (<b>A</b>) Distribution of RPFs across different features of the maize genome. Annotated coding sequences (CDSs), upstream open reading frame (uORF), downstream ORF (dORF), overlapped ORF, and novel coding regions are indicated with different colors. The numbers out and in parentheses indicate the gene number and the percentage of total reads, respectively. (<b>B</b>) Meta-gene analysis of the 29-nucleotide (nt) RPFs near the annotated translation start and stop sites in the maize genome. The red, blue, and orange bars represent the three possible open reading frames. E, P, and A indicate the aminoacyl-tRNA entry site, the P-site (peptidyl-tRNA formation site), and the E-site (uncharged tRNA exit site) in ribosomes, respectively. The numbers in the drawn ribosomes indicate the number of nucleotides protected by ribosomes upstream of the start codon and downstream of the stop codon.</p>
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<p>Translational regulation mainly occurs in the early stages of light exposure. (<b>A</b>–<b>E</b>) Differentially translational regulated genes (DTGs) following different durations of light exposure ((<b>A</b>), 0–0.5 h; (<b>B</b>), 0.5–1 h; (<b>C</b>), 1–2 h; (<b>D</b>), 2–4 h; (<b>E</b>), 0.5–4 h). Upregulated and downregulated DTGs are indicated with red and blue dots, respectively. (<b>F</b>) Venn diagrams showing the extent of overlap between differentially expressed genes (DEGs) and DTGs responsive to light exposure. DTGs and DEGs are indicated by the blue and yellow circles, respectively. The numbers represent the specific and common DEGs and DTGs.</p>
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<p>Expression patterns of photomorphogenesis-related transcripts at different time points: (<b>A</b>–<b>D</b>) Scatterplots of the fold-changes in RNA or RPF abundance following various durations of light exposure: (<b>A</b>), 0–0.5 h; (<b>B</b>), 0.5–1 h; (<b>C</b>), 1–2 h; (<b>D</b>), 2–4 h. All values are log2-normalized fold-changes between the listed time points. (<b>E</b>–<b>H</b>) Scatterplots of the translation efficiency (TE) following various durations of light exposure: (<b>E</b>), 0–0.5 h; (<b>F</b>), 0.5–1 h; (<b>G</b>), 1–2 h; (<b>H</b>), 2–4 h. (<b>I</b>–<b>L</b>) Scatterplots of log2-normalized fold-changes in TE or RPF abundance following various durations of light exposure: (<b>I</b>), 0–0.5 h; (<b>J</b>), 0.5–1 h; (<b>K</b>), 1–2 h; (<b>L</b>), 2–4 h. Positive and negative regulators of photomorphogenesis are indicated with red and blue triangles, respectively. Chloroplast transcripts are indicated in green rectangles. Upregulated, downregulated, and unchanged transcripts are marked with pink, cyan, and gray dots, respectively.</p>
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<p>Genome-wide light-responsive ribosome-pausing events in maize-etiolated seedlings: (<b>A</b>) Venn diagram showing the extent of overlap between significant ribosome-pausing events identified at each of the time points of illumination. The numbers indicate pausing events specific to each sample or common to different samples. (<b>B</b>) Clustering analysis of transcripts showing ribosome-pausing events defining five clusters. The color scale indicates the strength of ribosome pausing. (<b>C</b>) Gene ontology (GO) term enrichment analysis (biology processes) of the genes whose transcripts belong to one of the five clusters defined above. The size of the circles indicates the number of genes; the color indicates the P value.</p>
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<p>Ribosome pausing negatively regulates translation in maize. (<b>A</b>) The Wilcoxon test was used to assess significant differences in TE for transcripts showing ribosome pausing at different time points of light exposure. Significant mark * for <span class="html-italic">p</span> value &lt; 0.05. (<b>B</b>) Distribution and coverage of RPFs along five randomly chosen transcripts showing ribosome pausing at different time points of light exposure. β-tubulin 6b is a non-pausing control. (<b>C</b>) Translation intensity (TI) for transcripts with high pausing scores at 0 or 2 h into light exposure. Significant mark *** for <span class="html-italic">p</span> value &lt; 0.001. (<b>D</b>) Protein abundance, based on mass spectrometry analysis, translated from transcripts with high pausing scores at 0 or 2 h into light exposure. Significant mark *** for <span class="html-italic">p</span> value &lt; 0.001.</p>
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<p>Sequence features of ribosome-pausing sites: (<b>A</b>) Meta-analysis showing the distribution of ribosome-pausing sites along different regions of maize transcripts. UTR, untranslated region. (<b>B</b>) Sequence logo of the region upstream of ribosome-pausing sites. The height of each letter indicates their probability at the corresponding positions. The positions along the <span class="html-italic">x</span>-axis are relative to the ribosome-pausing sites. The –1 position indicates the upstream 1 nt to the ribosome-pausing site. (<b>C</b>) Metaplot of GC content around ribosome-pausing sites (blue) and random RPFs (green). The GC content is over 500 bp on either side of the ribosome-pausing sites. (<b>D</b>) GC content over full-length transcripts with ribosome pausing and random transcripts. The lengths of transcripts have been normalized.</p>
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<p>(<b>A</b>) A repeated CGC motif appears in the region downstream of ribosome-pausing sites. The height of each letter indicates the probability at the corresponding position. The positions along the <span class="html-italic">x</span>-axis are the length of the motif. (<b>B</b>) Maximum ribosome-pausing scores between transcripts with or without CGC motifs in all ribosome-paused transcripts. Significant mark *** for <span class="html-italic">p</span> value &lt; 0.001. (<b>C</b>) Predicted secondary structure of the CGC motif. The possibilities of base-pairing are indicated as a gradient from blue (0%) to red (100%). (<b>D</b>) GO term enrichment analysis of genes whose transcripts contain the CGC motif.</p>
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14 pages, 1518 KiB  
Brief Report
Investigating Expression Dynamics of miR-21 and miR-10b in Glioblastoma Cells In Vitro: Insights into Responses to Hypoxia and Secretion Mechanisms
by Hanna Charbit and Iris Lavon
Int. J. Mol. Sci. 2024, 25(14), 7984; https://doi.org/10.3390/ijms25147984 - 22 Jul 2024
Cited by 1 | Viewed by 939
Abstract
Glioblastoma poses significant challenges in oncology, with bevacizumab showing promise as an antiangiogenic treatment but with limited efficacy. microRNAs (miRNAs) 10b and 21 have emerged as potential biomarkers for bevacizumab response in glioblastoma patients. This study delves into the expression dynamics of miR-21 [...] Read more.
Glioblastoma poses significant challenges in oncology, with bevacizumab showing promise as an antiangiogenic treatment but with limited efficacy. microRNAs (miRNAs) 10b and 21 have emerged as potential biomarkers for bevacizumab response in glioblastoma patients. This study delves into the expression dynamics of miR-21 and miR-10b in response to hypoxia and explores their circulation mechanisms. In vitro experiments exposed glioma cells (A172, U87MG, U251) and human umbilical vein endothelial cells (HUVEC) to hypoxic conditions (1% oxygen) for 24 h, revealing heightened levels of miR-10b and miR-21 in glioblastoma cells. Manipulating miR-10b expression in U87MG, demonstrating a significant decrease in VEGF alpha (VEGFA) following miR-10b overexpression under hypoxic conditions. Size exclusion chromatography illustrated a notable shift towards miR-21 and miR-10b exosomal packaging during hypoxia. A proposed model suggests that effective bevacizumab treatment reduces VEGFA levels, heightening hypoxia and subsequently upregulating miR-21 and miR-10b expression. These miRNAs, released via exosomes, might impact various cellular processes, with miR-10b notably contributing to VEGFA level reduction. However, post-treatment increases in miR-10b and miR-21 could potentially restore cells to normoxic conditions through the downregulation of VEGF. This study highlights the intricate feedback loop involving miR-10b, miR-21, and VEGFA in glioblastoma treatment, underscoring the necessity for personalized therapeutic strategies. Further research should explore clinical implications for personalized glioma treatments. Full article
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Figure 1
<p>Impact of hypoxic conditions on miR-10b and miR-21 expression in glioblastoma and endothelial cell line. A172 (black bars), U87MG (gray bars), and U251 (light gray bars) glioblastoma cell lines, along with the endothelial cell line HUVECs (white bars), were exposed to 1% oxygen for 24 h, followed by RNA extraction for qPCR analysis. Quantification of both miRNAs was determined in each cell line relative to the respective cell line cultured under normoxic conditions. The asterisk indicates a significant increase under hypoxic conditions compared to normoxic conditions, with (*) denoting <span class="html-italic">p</span> &lt; 0.05 and (**) denoting <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Relative quantification of VEGFA in U87MG cells following the overexpression of miR-10b under normoxic and hypoxic conditions. U87MG glioblastoma cells were transfected with various plasmids as indicated on the x-axis and incubated under either normoxic conditions (black bars) or hypoxic conditions (light gray bars). VEGFA expression was assessed by qPCR after 24 h of incubation. The asterisk indicates a significant expression following hypoxia induction in cells containing the empty plasmid compared to cells in normoxic conditions. Additionally, it denotes a significant expression under hypoxia in cells transfected with the miR-10b plasmid compared to cells transfected with the empty plasmid. Furthermore, it indicates a significant expression in cells under hypoxia co-transfected with the plasmid containing miR-10b and its competitive inhibitor compared to cells transfected with miR-10b alone. (*) represents <span class="html-italic">p</span> &lt; 0.05 and (**) represents <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Quantification of miR-10b and miR-21 in size chromatography fractions from U87MG cell culture medium. U87MG cells were cultured under normoxic or hypoxic conditions for 24 h (as indicated), and their medium was processed using size exclusion columns. qPCR was then used to measure miR-10b (gray columns) and miR-21 (light gray columns) levels in each fraction. Exosomes, identified as large particles, are eluted in fractions 7–11, while proteins and molecules with a diameter smaller than 75 nm are eluted in fractions 12–20. The asterisk indicates a significant miR-10b and miR-21 quantification under normoxic conditions, particularly in the later eluted fractions (fractions 12–20), compared to the earlier eluted fractions (fractions 7–11). Additionally, it denotes a significant miRNA quantification following 24 h of hypoxia, specifically within the exosomal fractions (fractions 7–11) compared to the same fractions under normoxic conditions. (**) represents <span class="html-italic">p</span> &lt; 0.01.</p>
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21 pages, 6272 KiB  
Article
Variation of Cyclodextrin (CD) Complexation with Biogenic Amine Tyramine: Pseudopolymorphs of β-CD Inclusion vs. α-CD Exclusion, Deep Atomistic Insights
by Thammarat Aree
Int. J. Mol. Sci. 2024, 25(14), 7983; https://doi.org/10.3390/ijms25147983 - 22 Jul 2024
Viewed by 777
Abstract
Tyramine (TRM) is a biogenic catecholamine neurotransmitter, which can trigger migraines and hypertension. TRM accumulated in foods is reduced and detected using additive cyclodextrins (CDs) while their association characteristics remain unclear. Here, single-crystal X-ray diffraction and density functional theory (DFT) calculation have been [...] Read more.
Tyramine (TRM) is a biogenic catecholamine neurotransmitter, which can trigger migraines and hypertension. TRM accumulated in foods is reduced and detected using additive cyclodextrins (CDs) while their association characteristics remain unclear. Here, single-crystal X-ray diffraction and density functional theory (DFT) calculation have been performed, demonstrating the elusive pseudopolymorphs in β-CD inclusion complexes with TRM base/HCl, β-CD·0.5TRM·7.6H2O (1) and β-CD·TRM HCl·4H2O (2) and the rare α-CD·0.5(TRM HCl)·10H2O (3) exclusion complex. Both 1 and 2 share the common inclusion mode with similar TRM structures in the round and elliptical β-CD cavities, belong to the monoclinic space group P21, and have similar herringbone packing structures. Furthermore, 3 differs from 2, as the smaller twofold symmetry-related, round α-CD prefers an exclusion complex with the twofold disordered TRM–H+ sites. In the orthorhombic P21212 lattice, α-CDs are packed in a channel-type structure, where the column-like cavity is occupied by disordered water sites. DFT results indicate that β-CD remains elliptical to suitably accommodate TRM, yielding an energetically favorable inclusion complex, which is significantly contributed by the β-CD deformation, and the inclusion complex of α-CD with the TRM aminoethyl side chain is also energetically favorable compared to the exclusion mode. This study suggests the CD implications for food safety and drug/bioactive formulation and delivery. Full article
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Figure 1
<p>Structure overlays of (<b>a</b>) β-CD, (<b>b</b>) α-CD, and (<b>c</b>) TRM molecules in the inclusion complexes β-CD–TRM base (<b>1</b>), β-CD–TRM HCl (<b>2</b>), and the exclusion complex α-CD–TRM HCl (<b>3</b>), in comparison to the uncomplexed β-CD·12H<sub>2</sub>O (<b>i</b>; [<a href="#B23-ijms-25-07983" class="html-bibr">23</a>]) and α-CD·6H<sub>2</sub>O (<b>ii</b>; [<a href="#B24-ijms-25-07983" class="html-bibr">24</a>]), viewed with respect to the CD O4 plane and the TRM aromatic ring (AR). Only non-H atoms of the CD backbones (excluding O6) and the TRM rigid portion (excluding C8 and N1) are considered for the rms fit calculations (insets). Note the elliptical β-CD in <b>2</b> (light-orange line). Two torsion angles describing the TRM flexibility in <b>1</b>–<b>3</b> are listed (inset).</p>
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<p>Radar plots showing the variations in (<b>a</b>,<b>e</b>) tilt angles, (<b>b</b>,<b>f</b>) O3(<span class="html-italic">n</span>)⋯O2(<span class="html-italic">n</span> + 1) distances, (<b>c</b>) O4(<span class="html-italic">n</span>) deviations, and (<b>d</b>) O4(<span class="html-italic">n</span>)⋯centroid distances in the glucose units G1–G6/G7 of α-/β-CD upon complexation with TRM base (<b>1</b>) and TRM HCl (<b>2</b>,<b>3</b>). For comparison, data from the inclusion complexes β-CD–(–)-epicatechin(EC) [<a href="#B14-ijms-25-07983" class="html-bibr">14</a>], α-CD–ferulic acid(FEA) [<a href="#B6-ijms-25-07983" class="html-bibr">6</a>], and the uncomplexed β-CD·12H<sub>2</sub>O [<a href="#B23-ijms-25-07983" class="html-bibr">23</a>], α-CD·6H<sub>2</sub>O [<a href="#B24-ijms-25-07983" class="html-bibr">24</a>] are included, and their glucose units are renumbered for the best fit; see also <a href="#app1-ijms-25-07983" class="html-app">Table S1</a>. In (<b>c</b>), the gray area indicates that the glucose units have O4 below their mean plane (negative values). Angles and distances are in ° and Å.</p>
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<p>(Left) similar inclusion geometries of (<b>a</b>) a half-occupied TRM base in the round β-CD (<b>1</b>) and (<b>b</b>) a fully occupied TRM–H<sup>+</sup> in the elliptical β-CD (<b>2</b>) in the crystalline state at 296 K. For clarity, TRM molecules are shown with a space-filling model and water sites with occupancies of 0.2–0.6 are disordered with 0.5 TRM inside and near the β-CD cavity are omitted (<b>1</b>). (<b>c</b>) The α-CD·0.5(TRM–H<sup>+</sup>Cl<sup>−</sup>) exclusion complex (<b>3</b>) in the solid state at 296 K; 10 water molecules distributed over 16 sites inside and outside the α-CD cavity are shown in orange balls. The intramolecular, interglucose O2/O3∙∙∙O2/O3 H-bonds within CD and intermolecular N/O–H∙∙∙O H-bonds are indicated by blue and magenta connecting lines, respectively. (Right) intermolecular N/O–H∙∙∙O/Cl H-bonds stabilizing TRM base (<b>1</b>) and TRM–H<sup>+</sup> (<b>2</b>) in the distinct β-CD cavities (magenta lines). The OH groups and water molecules of the adjacent asymmetric units are labeled in italics. (Middle inset) inclusion geometries in <b>1</b> and <b>2</b> are shown with the β-CD O4-plane centroid relative to the TRM aromatic ring (AR) centroid and their interplanar angles. ORTEP diagrams are drawn at the 30% probability level.</p>
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<p>Crystal packing in a herringbone fashion of inclusion complexes (<b>a</b>) β-CD–TRM base (<b>1</b>) and (<b>b</b>) β-CD–TRM HCl (<b>2</b>), both in the monoclinic space group <span class="html-italic">P</span>2<sub>1</sub>. (<b>c</b>) Channel-type packing of the exclusion complex α-CD–TRM HCl (<b>3</b>), in the orthorhombic space group <span class="html-italic">P</span>2<sub>1</sub>2<sub>1</sub>2. CD macrocycles, TRM, water, and chloride are shown with the wireframe and space-filling models: C, cyan; O, red; N, purple; Cl, green; and H, light gray. In <b>1</b> and <b>3</b>, the disordered water sites are omitted for clarity. In <b>1</b> and <b>2</b>, the intramolecular, interglucose O2/O3∙∙∙O2/O3 H-bonds within CD and intermolecular N/O–H∙∙∙O/Cl H-bonds are indicated by blue and magenta connecting lines, respectively. In <b>3</b>, the intra- and intermolecular interactions are shown with magenta lines, the CD columns at <span class="html-italic">b</span> = 0.5 are marked in blue, and the twofold rotation symmetry along the <span class="html-italic">c</span>-axis is also labeled (<span class="html-fig-inline" id="ijms-25-07983-i002"><img alt="Ijms 25 07983 i002" src="/ijms/ijms-25-07983/article_deploy/html/images/ijms-25-07983-i002.png"/></span>, <span class="html-fig-inline" id="ijms-25-07983-i001"><img alt="Ijms 25 07983 i001" src="/ijms/ijms-25-07983/article_deploy/html/images/ijms-25-07983-i001.png"/></span>). The twofold-symmetry, half-occupied TRM–H<sup>+</sup> is explained in the inset. The intermolecular C–H···O interactions are not shown (<a href="#app1-ijms-25-07983" class="html-app">Tables S2–S4</a>).</p>
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<p>Structure superpositions of (<b>a</b>) β-CD(r)−TRM, (<b>b</b>) β-CD(e)−TRM and β-CD(e)−TRM(f), and (<b>c</b>) α-CD−TRM(excl) and α-CD−TRM(incl) complexes derived from X-ray analysis (blue lines) and DFT full-geometry optimization in the gas phase (red and black lines); see also <a href="#ijms-25-07983-f006" class="html-fig">Figure 6</a> and <a href="#app1-ijms-25-07983" class="html-app">Tables S5–S7</a>. The rms fits are calculated for non-H atoms of the CD skeletons, excluding TRM.</p>
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<p>DFT-optimized structures of inclusion complexes (<b>a</b>) β-CD(r)−TRM, (<b>b</b>) β-CD(e)−TRM, (<b>c</b>) β-CD(e)−TRM(f), (<b>e</b>) α-CD−TRM(incl), and exclusion complex (<b>d</b>) α-CD−TRM(excl), shown only from side view; see also <a href="#ijms-25-07983-f005" class="html-fig">Figure 5</a> and <a href="#app1-ijms-25-07983" class="html-app">Figure S1</a>. The stabilization, interaction, and deformation energies (Δ<span class="html-italic">E</span><sub>stb</sub>, Δ<span class="html-italic">E</span><sub>int</sub>, Δ<span class="html-italic">E</span><sub>def1</sub>, and Δ<span class="html-italic">E</span><sub>def2</sub>) are summarized in the inset; see also <a href="#app1-ijms-25-07983" class="html-app">Tables S5–S7</a>. The intramolecular O–H∙∙∙O H-bonds within CDs and intermolecular N/O–H∙∙∙O/N H-bonds and O–H∙∙∙π interactions are indicated by blue and magenta connecting lines, respectively. The intramolecular, interglucose C6–H···O5 H-bonds and host–guest C–H···O/N interactions are not shown (<a href="#app1-ijms-25-07983" class="html-app">Tables S5 and S6</a>).</p>
Full article ">Scheme 1
<p>(<b>a</b>) Chemical structures and atom numbering schemes of tyramine (TRM) base/HCl and cyclodextrins (CDs). (<b>b</b>) X-ray-derived molecular structures of the inclusion complexes β-CD–TRM base (<b>1</b>), β-CD–TRM HCl (<b>2</b>), and the exclusion complex α-CD–TRM HCl (<b>3</b>); ORTEP plot at 30% probability level. In <b>2</b>, TRM base is protonated (TRM–H<sup>+</sup>) and counterbalanced by a fully occupied Cl<sup>–</sup>; its position is arbitrarily shown. Doubly disordered O64−H group of β-CD in <b>2</b> is indicated by letters A, B; both sites have a half occupancy factor. The connecting blue lines indicate the intramolecular, interglucose O2/O3∙∙∙O2/O3 H-bonds that stabilize the CD conformation. In <b>3</b>, one α-CD and 0.5 TRM–H<sup>+</sup> are on the twofold-symmetry positions, respectively, at the O4 centroid and the bond midpoints of C2–C2(−<span class="html-italic">x</span> + 1, −<span class="html-italic">y</span> + 1, <span class="html-italic">z</span>) and C4–C4(−<span class="html-italic">x</span> + 1, −<span class="html-italic">y</span> + 1, <span class="html-italic">z</span>), along the <span class="html-italic">c</span>-axis (marked with <span class="html-fig-inline" id="ijms-25-07983-i001"><img alt="Ijms 25 07983 i001" src="/ijms/ijms-25-07983/article_deploy/html/images/ijms-25-07983-i001.png"/></span>, <span class="html-fig-inline" id="ijms-25-07983-i002"><img alt="Ijms 25 07983 i002" src="/ijms/ijms-25-07983/article_deploy/html/images/ijms-25-07983-i002.png"/></span>). The doubly disordered TRM–H<sup>+</sup> with equal occupancies of 0.25 is counterbalanced by 0.25 Cl<sup>−</sup> in the intermolecular spaces.</p>
Full article ">Scheme 1 Cont.
<p>(<b>a</b>) Chemical structures and atom numbering schemes of tyramine (TRM) base/HCl and cyclodextrins (CDs). (<b>b</b>) X-ray-derived molecular structures of the inclusion complexes β-CD–TRM base (<b>1</b>), β-CD–TRM HCl (<b>2</b>), and the exclusion complex α-CD–TRM HCl (<b>3</b>); ORTEP plot at 30% probability level. In <b>2</b>, TRM base is protonated (TRM–H<sup>+</sup>) and counterbalanced by a fully occupied Cl<sup>–</sup>; its position is arbitrarily shown. Doubly disordered O64−H group of β-CD in <b>2</b> is indicated by letters A, B; both sites have a half occupancy factor. The connecting blue lines indicate the intramolecular, interglucose O2/O3∙∙∙O2/O3 H-bonds that stabilize the CD conformation. In <b>3</b>, one α-CD and 0.5 TRM–H<sup>+</sup> are on the twofold-symmetry positions, respectively, at the O4 centroid and the bond midpoints of C2–C2(−<span class="html-italic">x</span> + 1, −<span class="html-italic">y</span> + 1, <span class="html-italic">z</span>) and C4–C4(−<span class="html-italic">x</span> + 1, −<span class="html-italic">y</span> + 1, <span class="html-italic">z</span>), along the <span class="html-italic">c</span>-axis (marked with <span class="html-fig-inline" id="ijms-25-07983-i001"><img alt="Ijms 25 07983 i001" src="/ijms/ijms-25-07983/article_deploy/html/images/ijms-25-07983-i001.png"/></span>, <span class="html-fig-inline" id="ijms-25-07983-i002"><img alt="Ijms 25 07983 i002" src="/ijms/ijms-25-07983/article_deploy/html/images/ijms-25-07983-i002.png"/></span>). The doubly disordered TRM–H<sup>+</sup> with equal occupancies of 0.25 is counterbalanced by 0.25 Cl<sup>−</sup> in the intermolecular spaces.</p>
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20 pages, 5554 KiB  
Article
Identification and Validation of New DNA-PKcs Inhibitors through High-Throughput Virtual Screening and Experimental Verification
by Liujiang Dai, Pengfei Yu, Hongjie Fan, Wei Xia, Yaopeng Zhao, Pengfei Zhang, John Z. H. Zhang, Haiping Zhang and Yang Chen
Int. J. Mol. Sci. 2024, 25(14), 7982; https://doi.org/10.3390/ijms25147982 - 22 Jul 2024
Cited by 1 | Viewed by 1210
Abstract
DNA-PKcs is a crucial protein target involved in DNA repair and response pathways, with its abnormal activity closely associated with the occurrence and progression of various cancers. In this study, we employed a deep learning-based screening and molecular dynamics (MD) simulation-based pipeline, identifying [...] Read more.
DNA-PKcs is a crucial protein target involved in DNA repair and response pathways, with its abnormal activity closely associated with the occurrence and progression of various cancers. In this study, we employed a deep learning-based screening and molecular dynamics (MD) simulation-based pipeline, identifying eight candidates for DNA-PKcs targets. Subsequent experiments revealed the effective inhibition of DNA-PKcs-mediated cell proliferation by three small molecules (5025-0002, M769-1095, and V008-1080). These molecules exhibited anticancer activity with IC50 (inhibitory concentration at 50%) values of 152.6 μM, 30.71 μM, and 74.84 μM, respectively. Notably, V008-1080 enhanced homology-directed repair (HDR) mediated by CRISPR/Cas9 while inhibiting non-homologous end joining (NHEJ) efficiency. Further investigations into the structure-activity relationships unveiled the binding sites and critical interactions between these small molecules and DNA-PKcs. This is the first application of DeepBindGCN_RG in a real drug screening task, and the successful discovery of a novel DNA-PKcs inhibitor demonstrates its efficiency as a core component in the screening pipeline. Moreover, this study provides important insights for exploring novel anticancer therapeutics and advancing the development of gene editing techniques by targeting DNA-PKcs. Full article
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Figure 1
<p>The virtual screening procedure integrates DeepBindGCN models with other methods to identify highly reliable drug candidates for DNA-PKcs. (<b>a</b>) The screening inhibitors against the Chemdiv dataset for DNA-PKcs using a combination of DeepBindGCN_BC/RG, Schrödinger docking, MD simulation, and experimental methods. (<b>b</b>) The last frame from the 40 ns pocket MD simulation of the three identified active compounds, showing both 3D and 2D interaction details.</p>
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<p>The calculated free energy landscape from metadynamics simulation for those candidates with favorable binding with DNA-PKcs. (<b>a</b>) The calculated free energy landscape for DNA-PKcs with candidates 4290-0112, 5025-0002, 5795-0108, 7238-1541, and 8601-0106. (<b>b</b>) The calculated free energy landscape for DNA-PKcs with candidates C163-0038, C163-0039, C163-0087, C200-6885, and C684-0025. (<b>c</b>) The calculated free energy landscape for DNA-PKcs with candidates E208-0020, G744-0225, L102-0385, M769-1095 and S431-0991. (<b>d</b>) The calculated free energy landscape for DNA-PKcs with candidates SA50-0140, V001-2119, V008-1080 and V014-8131.</p>
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<p>The RMSD value and number of hydrogen bonds of selected compounds with DNA-PKcs during the MD simulation. (<b>a</b>) The RMSD value over the 40 ns MD simulation for the eight selected protein–compound complexes. (<b>b</b>) The number of hydrogen bonds between DNA-PKcs and the eight selected compounds.</p>
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<p>DNA-PKcs inhibitors induce proliferation inhibition in 786-O RCC cells. (<b>a</b>,<b>b</b>) 786-O RCC cells were treated with M3814, other small molecules (10 μM, 100 μM) or vehicle control (0.1% DMSO) for applied time; cell inhibition was analyzed by CCK8 assay. (<b>c</b>–<b>o</b>) the cell viability IC<sub>50</sub> values of different small molecules against 786-O cells. All <span class="html-italic">p</span>-values were obtained by comparing to the DMSO group at the same concentration. ns denotes not significant, * denotes <span class="html-italic">p</span> &lt; 0.05, ** denotes <span class="html-italic">p</span> &lt; 0.01, *** denotes <span class="html-italic">p</span> &lt; 0.001, **** denotes <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Evaluation of HDR-mediated gene targeting efficiency by treatment with different small molecules. (<b>a</b>) Schematics of the donor plasmid and targeting strategy for HDR-mediated knock-in of the ires-GFP reporter at GAPDH 3-UTR. (<b>b</b>,<b>c</b>) FACS analysis of HEK293T cells treated with different small molecules or vehicles showing HDR-mediated integration of ires-GFP in the presence of RNP mixture and ires-GFP donor. The cells were co-transfected with donor/RNP by nucleofection and analyzed two days post transfection.</p>
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<p>Evaluation of NHEJ efficiency in HEK293T cells treated with different small molecules using modified TLR reporter. (<b>a</b>) Schematic depicting the outcome after the induction of a site-specific double-strand break (DSB). If the break undergoes NHEJ mediated repair, eGFP will be translated out of frame and iRFP will be expressed, producing far-red fluorescent cells. (<b>b</b>) HEK293T cells overexpressing the TLR reporter (BFP positive) were nucleofected with Cas9 and sgRNA plasmids, then treated with different small molecules or vehicle. NHEJ efficiency was evaluated by detecting iRFP expression 48 h post-treatment using a flow cytometer.</p>
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<p>Interaction analysis of DNA-PKcs with newly discovered and known active compounds with docked conformation. (<b>A</b>) Compound 5025-0002 binding to the DNA-PKcs pocket, displaying both 3D detailed interactions above and 2D representation below. (<b>B</b>) Interaction between M769-1095 and DNA-PKcs pocket. (<b>C</b>) Interaction between V008-1080 and DNA-PKcs pocket. (<b>D</b>) Interaction between M3814 and DNA-PKcs pocket. (<b>E</b>) Interaction between NU7441 and DNA-PKcs pocket. Residues in all 3D visualizations are color-coded by B-Factor in PyMOL, with distances for crucial interactions (depicted with dotted lines) measured. The 2D diagrams employ colors and symbols as standardized by the Schrödinger 2D interaction plots.</p>
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12 pages, 2259 KiB  
Article
A Comparative Investigation of the Pulmonary Vasodilating Effects of Inhaled NO Gas Therapy and Inhalation of a New Drug Formulation Containing a NO Donor Metabolite (SIN-1A)
by Attila Oláh, Bálint András Barta, Mihály Ruppert, Alex Ali Sayour, Dávid Nagy, Tímea Bálint, Georgina Viktória Nagy, István Puskás, Lajos Szente, Levente Szőcs, Tamás Sohajda, Endre Zima, Béla Merkely and Tamás Radovits
Int. J. Mol. Sci. 2024, 25(14), 7981; https://doi.org/10.3390/ijms25147981 - 22 Jul 2024
Viewed by 916
Abstract
Numerous research projects focused on the management of acute pulmonary hypertension as Coronavirus Disease 2019 (COVID-19) might lead to hypoxia-induced pulmonary vasoconstriction related to acute respiratory distress syndrome. For that reason, inhalative therapeutic options have been the subject of several clinical trials. In [...] Read more.
Numerous research projects focused on the management of acute pulmonary hypertension as Coronavirus Disease 2019 (COVID-19) might lead to hypoxia-induced pulmonary vasoconstriction related to acute respiratory distress syndrome. For that reason, inhalative therapeutic options have been the subject of several clinical trials. In this experimental study, we aimed to examine the hemodynamic impact of the inhalation of the SIN-1A formulation (N-nitroso-N-morpholino-amino-acetonitrile, the unstable active metabolite of molsidomine, stabilized by a cyclodextrin derivative) in a porcine model of acute pulmonary hypertension. Landrace pigs were divided into the following experimental groups: iNO (inhaled nitric oxide, n = 3), SIN-1A-5 (5 mg, n = 3), and SIN-1A-10 (10 mg, n = 3). Parallel insertion of a PiCCO system and a pulmonary artery catheter (Swan-Ganz) was performed for continuous hemodynamic monitoring. The impact of iNO (15 min) and SIN-1A inhalation (30 min) was investigated under physiologic conditions and U46619-induced acute pulmonary hypertension. Mean pulmonary arterial pressure (PAP) was reduced transiently by both substances. SIN-1A-10 had a comparable impact compared to iNO after U46619-induced pulmonary hypertension. PAP and PVR decreased significantly (changes in PAP: −30.1% iNO, −22.1% SIN-1A-5, −31.2% SIN-1A-10). While iNO therapy did not alter the mean arterial pressure (MAP) and systemic vascular resistance (SVR), SIN-1A administration resulted in decreased MAP and SVR values. Consequently, the PVR/SVR ratio was markedly reduced in the iNO group, while SIN-1A did not alter this parameter. The pulmonary vasodilatory impact of inhaled SIN-1A was shown to be dose-dependent. A larger dose of SIN-1A (10 mg) resulted in decreased PAP and PVR in a similar manner to the gold standard iNO therapy. Inhalation of the nebulized solution of the new SIN-1A formulation (stabilized by a cyclodextrin derivative) might be a valuable, effective option where iNO therapy is not available due to dosing difficulties or availability. Full article
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<p>Pharmacodynamics of inhaled nitric oxide (iNO) and inhaled SIN-1A formulation at different doses (SIN-1A-5 5 mg, SIN-1A-10 10 mg). PAP: pulmonary artery pressure.</p>
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<p>Alterations in systemic and pulmonary circulation during administration of inhaled nitric oxide (iNO) and inhaled SIN-1A formulation at different doses (SIN-1-5 5 mg, SIN-1-10 10 mg). PAP: pulmonary artery pressure, PVR: pulmonary vascular resistance, SAP: systemic arterial pressure, SVR: systemic vascular resistance. * <span class="html-italic">p</span> &lt; 0.05 vs. iNO baseline vs. baseline + inhalation or U46619 vs. U46619 + inhalation. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 vs. SIN-1A-5 baseline vs. baseline + inhalation or U46619 vs. U46619 + inhalation. <sup>##</sup> <span class="html-italic">p</span> &lt; 0.05 vs. SIN-1A-10 baseline vs. baseline + inhalation or U46619 vs. U46619 + inhalation.</p>
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<p>Experimental setting: preparation steps, hemodynamic monitoring, and induction of acute pulmonary hypertension (PH). Dosing of inhaled nitric oxide (iNO) and inhaled SIN-1A formulation at different doses (SIN-1A-5 5 mg, SIN-1A-10 10 mg inhalation). CO: cardiac output, HR: heart rate, PAP: pulmonary artery pressure, PCWP: pulmonary wedge pressure, SAP: systemic arterial pressure. iNO, n = 3; SIN-1A 5 mg (SIN-1A-5), n = 3; SIN-1A 10 mg (SIN-1A-10), n = 3.</p>
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<p>Metabolism and NO release of molsidomine. * is a chemical common sign.</p>
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11 pages, 1145 KiB  
Article
Hypersensitivity of Intrinsically Photosensitive Retinal Ganglion Cells in Migraine Induces Cortical Spreading Depression
by Eiichiro Nagata, Motoharu Takao, Haruki Toriumi, Mari Suzuki, Natsuko Fujii, Saori Kohara, Akio Tsuda, Taira Nakayama, Ayana Kadokura and Manaka Hadano
Int. J. Mol. Sci. 2024, 25(14), 7980; https://doi.org/10.3390/ijms25147980 - 22 Jul 2024
Viewed by 1047
Abstract
Migraine is a complex disorder characterized by episodes of moderate-to-severe, often unilateral headaches and generally accompanied by nausea, vomiting, and increased sensitivity to light (photophobia), sound (phonophobia), and smell (hyperosmia). Photophobia is considered the most bothersome symptom of migraine attacks. Although the underlying [...] Read more.
Migraine is a complex disorder characterized by episodes of moderate-to-severe, often unilateral headaches and generally accompanied by nausea, vomiting, and increased sensitivity to light (photophobia), sound (phonophobia), and smell (hyperosmia). Photophobia is considered the most bothersome symptom of migraine attacks. Although the underlying mechanism remains unclear, the intrinsically photosensitive retinal ganglion cells (ipRGCs) are considered to be involved in photophobia associated with migraine. In this study, we investigated the association between the sensitivity of ipRGCs and migraines and cortical spreading depression (CSD), which may trigger migraine attacks. The pupillary responses closely associated with the function of ipRGCs in patients with migraine who were irradiated with lights were evaluated. Blue (486 nm) light irradiation elicited a response from ipRGCs; however, red light (560 nm) had no such effect. Melanopsin, a photosensitive protein, phototransduces in ipRGCs following blue light stimulation. Hypersensitivity of ipRGCs was observed in patients with migraine. CSD was more easily induced with blue light than with incandescent light using a mouse CSD model. Moreover, CSD was suppressed, even in the presence of blue light, after injecting opsinamide, a melanopsin inhibitor. The hypersensitivity of ipRGCs in patients with migraine may induce CSD, resulting in migraine attacks. Full article
(This article belongs to the Special Issue Novel Therapeutic Approaches for Migraine Headaches)
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<p>The representative alterations in pupil diameters in patients with migraine and controls. The upper panels show the alterations in pupil diameter in the controls, whereas the lower panels show the alterations in pupil diameter in the patients with migraine. Irradiation with light induced miosis (downward direction), whereas discontinuation of irradiation resulted in mydriasis (upper direction). When irradiation with blue-LED light was discontinued, the time required for patients with migraine to return to their baseline pupil diameter was longer than that in controls. Diagonal boxes: red-LED light irradiation time; black boxes: blue-LED light irradiation time; LED: light-emitting diode.</p>
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<p>The attenuation rate of pupils in patients with migraine and controls. (<b>a</b>) The changes in pupil diameter. The pupil constricts when irradiated with light (miosis: down direction). The pupil dilates when irradiation is discontinued (mydriasis: upper direction). The time required for the pupil diameter to return to baseline after pupil miosis was divided into two parts: the first half and the second half. (<b>b</b>) Under blue-LED light irradiation, the attenuation rate was significantly increased during both the first and second halves in the patients with migraine compared with that in controls. However, no significant difference was observed between the two groups under red-LED irradiation. Attenuation rate = pupil contraction integral OFF (0–0.17.5 s)/contraction integral ON (20 s). LED: light-emitting diode.</p>
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<p>Representative recording of CSD induction in mice. The upper graph shows the changes in direct current (DC) potential, whereas the lower graph shows the changes in regional cerebral blood flow (rCBF). Black inverted triangle, KCl addition; white triangle, opsinamide intraperitoneal injection; white circle, incandescent light is on; diagonal circle, blue LED light is on. The DC potential decreased when cortical spreading depression (CSD) was induced; at the same time, CBF increased. CSD was induced using 0.225 M KCl after 15 min in a dark room. Under incandescent light, CSD was induced using 0.2 M KCl. Under blue LED light, CSD was induced using 0.175 M KCl. Moreover, when opsinamide was injected intraperitoneally into the mice, the CSD threshold increased under blue LED light. LED: light-emitting diode.</p>
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<p>Summary of the CSD threshold related to KCl concentrations. The cortical spreading depression (CSD) threshold under blue LED light was lower than that under incandescent light irradiation. The addition of opsinamide under these conditions increased the CSD threshold. LED: light-emitting diode.</p>
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<p>A diagram depicting the experimental setting for inducing CSD. CSD, cortical spreading depression; rCBF: regional cerebral blood flow.</p>
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23 pages, 2244 KiB  
Review
Glioma Stem Cells as Promoter of Glioma Progression: A Systematic Review of Molecular Pathways and Targeted Therapies
by Edoardo Agosti, Sara Antonietti, Tamara Ius, Marco Maria Fontanella, Marco Zeppieri and Pier Paolo Panciani
Int. J. Mol. Sci. 2024, 25(14), 7979; https://doi.org/10.3390/ijms25147979 - 22 Jul 2024
Cited by 2 | Viewed by 1881
Abstract
Gliomas’ aggressive nature and resistance to therapy make them a major problem in oncology. Gliomas continue to have dismal prognoses despite significant advancements in medical science, and traditional treatments like surgery, radiation (RT), and chemotherapy (CT) frequently prove to be ineffective. After glioma [...] Read more.
Gliomas’ aggressive nature and resistance to therapy make them a major problem in oncology. Gliomas continue to have dismal prognoses despite significant advancements in medical science, and traditional treatments like surgery, radiation (RT), and chemotherapy (CT) frequently prove to be ineffective. After glioma stem cells (GSCs) were discovered, the traditional view of gliomas as homogeneous masses changed. GSCs are essential for tumor growth, treatment resistance, and recurrence. These cells’ distinct capacities for differentiation and self-renewal are changing our knowledge of the biology of gliomas. This systematic literature review aims to uncover the molecular mechanisms driving glioma progression associated with GSCs. The systematic review adhered to PRISMA guidelines, with a thorough literature search conducted on PubMed, Ovid MED-LINE, and Ovid EMBASE. The first literature search was performed on 1 March 2024, and the search was updated on 15 May 2024. Employing MeSH terms and Boolean operators, the search focused on molecular mechanisms associated with GCSs-mediated glioma progression. Inclusion criteria encompassed English language studies, preclinical studies, and clinical trials. A number of 957 papers were initially identified, of which 65 studies spanning from 2005 to 2024 were finally included in the review. The main GSC model distribution is arranged in decreasing order of frequency: U87: 20 studies (32.0%); U251: 13 studies (20.0%); A172: 4 studies (6.2%); and T98G: 2 studies (3.17%). From most to least frequent, the distribution of the primary GSC pathway is as follows: Notch: 8 studies (12.3%); STAT3: 6 studies (9.2%); Wnt/β-catenin: 6 studies (9.2%); HIF: 5 studies (7.7%); and PI3K/AKT: 4 studies (6.2%). The distribution of molecular effects, from most to least common, is as follows: inhibition of differentiation: 22 studies (33.8%); increased proliferation: 18 studies (27.7%); enhanced invasive ability: 15 studies (23.1%); increased self-renewal: 5 studies (7.7%); and inhibition of apoptosis: 3 studies (4.6%). This work highlights GSC heterogeneity and the dynamic interplay within the glioblastoma microenvironment, underscoring the need for a tailored approach. A few key pathways influencing GSC behavior are JAK/STAT3, PI3K/AKT, Wnt/β-catenin, and Notch. Therapy may target these pathways. This research urges more study to fill in knowledge gaps in the biology of GSCs and translate findings into useful treatment approaches that could improve GBM patient outcomes. Full article
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<p>The Modified NOS.</p>
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<p>PRISMA flow chart.</p>
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<p>The PRISMA-ScR checklist. Abbreviations: JBI = Joanna Briggs Institute; PRISMA-ScR = Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. * Where sources of evidence (see second footnote) are compiled from, such as bibliographic databases, social media platforms, and web sites. † A more inclusive/heterogeneous term used to account for the different types of evidence or data sources (e.g., quantitative and/or qualitative research, expert opinion, and policy documents) that may be eligible in a scoping review as opposed to only studies. This is not to be confused with information sources (see first footnote). ‡ The frameworks by Arksey and O’Malley (6), Levac and colleagues (7), and the JBI guidance (4, 5) refer to the process of data extraction in a scoping review as data charting. § The process of systematically examining research evidence to assess its validity, results, and relevance before using it to inform a decision. This term is used for items 12 and 19 instead of “risk of bias” (which is more applicable to systematic reviews of interventions) to include and acknowledge the various sources of evidence that may be used in a scoping review (e.g., quantitative and/or qualitative research, expert opinion, and policy document).</p>
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14 pages, 6340 KiB  
Article
Computational Insights into Reproductive Toxicity: Clustering, Mechanism Analysis, and Predictive Models
by Huizi Cui, Qizheng He, Wannan Li, Yuying Duan and Weiwei Han
Int. J. Mol. Sci. 2024, 25(14), 7978; https://doi.org/10.3390/ijms25147978 - 22 Jul 2024
Viewed by 1105
Abstract
Reproductive toxicity poses significant risks to fertility and progeny health, making its identification in pharmaceutical compounds crucial. In this study, we conducted a comprehensive in silico investigation of reproductive toxic molecules, identifying three distinct categories represented by Dimethylhydantoin, Phenol, and Dicyclohexyl phthalate. Our [...] Read more.
Reproductive toxicity poses significant risks to fertility and progeny health, making its identification in pharmaceutical compounds crucial. In this study, we conducted a comprehensive in silico investigation of reproductive toxic molecules, identifying three distinct categories represented by Dimethylhydantoin, Phenol, and Dicyclohexyl phthalate. Our analysis included physicochemical properties, target prediction, and KEGG and GO pathway analyses, revealing diverse and complex mechanisms of toxicity. Given the complexity of these mechanisms, traditional molecule-target research approaches proved insufficient. Support Vector Machines (SVMs) combined with molecular descriptors achieved an accuracy of 0.85 in the test dataset, while our custom deep learning model, integrating molecular SMILES and graphs, achieved an accuracy of 0.88 in the test dataset. These models effectively predicted reproductive toxicity, highlighting the potential of computational methods in pharmaceutical safety evaluation. Our study provides a robust framework for utilizing computational methods to enhance the safety evaluation of potential pharmaceutical compounds. Full article
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<p>An overview of the study workflow.</p>
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<p>(<b>A</b>) Chemical space of reproductive toxic and non-toxic molecules. (<b>B</b>) Clustering results for reproductive toxic molecules.</p>
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<p>Physicochemical properties of three representative molecules. (<b>A</b>) Dimethylhydantoin. (<b>B</b>) Phenol. (<b>C</b>) Dicyclohexyl phthalate.</p>
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<p>Distribution of top 50 targets for the three representative molecules. (<b>A</b>) Dimethylhydantoin. (<b>B</b>) Phenol. (<b>C</b>) Dicyclohexyl phthalate. (<b>D</b>) Intersection of targets.</p>
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<p>Analysis of representative molecule Dimethylhydantoin. (<b>A</b>) GO analysis. (<b>B</b>) KEGG analysis.</p>
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<p>Analysis of representative molecule Phenol. (<b>A</b>) GO analysis. (<b>B</b>) KEGG analysis.</p>
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<p>Analysis of representative molecule Dicyclohexyl phthalate. (<b>A</b>) GO analysis. (<b>B</b>) KEGG analysis.</p>
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<p>The architecture of the deep learning model.</p>
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13 pages, 2057 KiB  
Article
Exploring the Effects of an Alfalfa Leaf-Derived Adsorbent on Microbial Community, Ileal Morphology, Barrier Function, and Immunity in Turkey Poults during Chronic Aflatoxin B1 Exposure
by María de Jesús Nava-Ramírez, Jing Liu, Juan Omar Hernández-Ramírez, Xochitl Hernandez-Velasco, Juan D. Latorre, Alma Vázquez-Durán, Guolong Zhang, Roberto Senas-Cuesta, Sergio Gómez-Rosales, Andressa Stein, Billy M. Hargis, Guillermo Téllez-Isaías, Abraham Méndez-Albores and Jesús A. Maguey-González
Int. J. Mol. Sci. 2024, 25(14), 7977; https://doi.org/10.3390/ijms25147977 - 22 Jul 2024
Viewed by 939
Abstract
This article follows-up on our recently published work, which evaluated the impact of the addition of an alfalfa leaf-derived adsorbent in the aflatoxin B1 (AFB1)-contaminated diet in regard to the production parameters, blood cell count, serum biochemistry, liver enzymes, and [...] Read more.
This article follows-up on our recently published work, which evaluated the impact of the addition of an alfalfa leaf-derived adsorbent in the aflatoxin B1 (AFB1)-contaminated diet in regard to the production parameters, blood cell count, serum biochemistry, liver enzymes, and liver histology of turkey poults. This paper presents complementary results on microbial community, ileal morphology, barrier function, and immunity. For this purpose, 350 1-day-old female turkey poults were randomly distributed into five groups: (1) Control, AFB1-free diet; (2) AF, AFB1-contaminated diet at 250 ng/g; (3) alfalfa, AFB1-free diet + 0.5% (w/w) adsorbent; (4) alfalfa + AF, AFB1-contaminated diet at 250 ng/g + 0.5% (w/w) adsorbent; and (5) YCW + AF, AFB1-contaminated diet at 250 ng/g + 0.5% (w/w) commercial yeast cell wall-based adsorbent (reference group). In general, in the AF group, the growth of opportunistic pathogens was promoted, which lead to gut dysbacteriosis, mainly influenced by Streptococcus lutetiensis. Conversely, a significant increase in beneficial bacteria (Faecalibacterium and Coprococcus catus) was promoted by the addition of the plant-based adsorbent. Moreover, the AF group had the lowest villus height and a compromised barrier function, as evidenced by a significant (p < 0.05) increase in fluorescein isothiocyanate dextran (FITC-d), but these negative effects were almost reversed by the addition of the alfalfa adsorbent. Furthermore, the AF + YCW and alfalfa + AF groups exhibited a significant increase in the cutaneous basophil hypersensitivity response compared to the rest of the experimental groups. Taken together, these results pointed out that the alfalfa counteracts the adverse effects of AFB1 in poults, facilitating the colonization of beneficial bacteria and improving the barrier function of the turkey poults. Full article
(This article belongs to the Section Molecular Microbiology)
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<p>Alpha and beta diversities of the cecal microbiota among different experimental groups. The cecal contents (<span class="html-italic">n</span> = 7/treatment) were subjected to 16S rRNA gene sequencing. Observed ASV, Pielou’s Evenness, and Shannon Index were calculated to measure the α-diversity of the cecal microbiota. Kruskal–Wallis test was used for statistical significance determination. The β-diversity-weighted UniFrac and unweighted UniFrac distances were used to generate the principal coordinates analysis (PCoA) plots. Permutational multivariate analysis of variance (PERMANOVA) was used for statistical significance determination. Control, AFB<sub>1</sub>-free diet; AF, AFB<sub>1</sub>-contaminated diet at 250 ng/g; alfalfa, AFB<sub>1</sub>-free diet + 0.5% (<span class="html-italic">w</span>/<span class="html-italic">w</span>) adsorbent; alfalfa + AF, AFB<sub>1</sub>-contaminated diet at 250 ng/g + 0.5% (<span class="html-italic">w</span>/<span class="html-italic">w</span>) adsorbent; and YCW + AF, AFB<sub>1</sub>-contaminated diet at 250 ng/g + 0.5% (<span class="html-italic">w</span>/<span class="html-italic">w</span>) commercial yeast cell wall-based adsorbent (reference group).</p>
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<p>Differential enrichment of bacterial ASVs between different experimental groups (<span class="html-italic">n</span> = 7/treatment) was determined using linear discriminant analysis (LDA) effect size (LEfSe), with the all-against-all multiclass analysis, <span class="html-italic">p</span> &lt; 0.05, and a logarithmic LDA threshold of 3.0. Control, AFB<sub>1</sub>-free diet; AF, AFB<sub>1</sub>-contaminated diet at 250 ng/g; alfalfa, AFB<sub>1</sub>-free diet + 0.5% (<span class="html-italic">w</span>/<span class="html-italic">w</span>) adsorbent; alfalfa + AF, AFB<sub>1</sub>-contaminated diet at 250 ng/g + 0.5% (<span class="html-italic">w</span>/<span class="html-italic">w</span>) adsorbent; and YCW + AF, AFB<sub>1</sub>-contaminated diet at 250 ng/g + 0.5% (<span class="html-italic">w</span>/<span class="html-italic">w</span>) commercial yeast cell wall-based adsorbent (reference group).</p>
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<p>Relative abundances of differentially enriched bacterial ASVs (<span class="html-italic">n</span> = 7/treatment). Significance was calculated using Kruskal–Wallis test. <sup>a,b</sup> indicates significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05). Control, AFB<sub>1</sub>-free diet; AF, AFB<sub>1</sub>-contaminated diet at 250 ng/g; alfalfa, AFB<sub>1</sub>-free diet + 0.5% (<span class="html-italic">w</span>/<span class="html-italic">w</span>) adsorbent; alfalfa + AF, AFB<sub>1</sub>-contaminated diet at 250 ng/g + 0.5% (<span class="html-italic">w</span>/<span class="html-italic">w</span>) adsorbent; and YCW + AF, AFB<sub>1</sub>-contaminated diet at 250 ng/g + 0.5% (<span class="html-italic">w</span>/<span class="html-italic">w</span>) commercial yeast cell wall-based adsorbent (reference group).</p>
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<p>Morphometric analysis of the ileum in turkey poults fed a diet containing AFB<sub>1</sub> and adsorbent materials. Histological images were taken using a 4× objective on H&amp;E-stained tissue sections. (<b>A</b>) Control, AFB<sub>1</sub>-free diet; (<b>B</b>) AF, AFB<sub>1</sub>-contaminated diet at 250 ng/g; (<b>C</b>) alfalfa, AFB<sub>1</sub>-free diet + 0.5% (<span class="html-italic">w</span>/<span class="html-italic">w</span>) adsorbent; (<b>D</b>) alfalfa + AF, AFB<sub>1</sub>-contaminated diet at 250 ng/g + 0.5% (<span class="html-italic">w</span>/<span class="html-italic">w</span>) adsorbent; and (<b>E</b>) YCW + AF, AFB<sub>1</sub>-contaminated diet at 250 ng/g + 0.5% (<span class="html-italic">w</span>/<span class="html-italic">w</span>) commercial yeast cell wall-based adsorbent (reference group). L = Large (µm); W = Width (µm); A = Area (µm<sup>2</sup>).</p>
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16 pages, 4092 KiB  
Article
Evaluation of SARS-CoV-2-Specific IgY Antibodies: Production, Reactivity, and Neutralizing Capability against Virus Variants
by Jacob Schön, Andrea Aebischer, Nico Joël Halwe, Lorenz Ulrich, Donata Hoffmann, Sven Reiche, Martin Beer and Christian Grund
Int. J. Mol. Sci. 2024, 25(14), 7976; https://doi.org/10.3390/ijms25147976 - 21 Jul 2024
Viewed by 1334
Abstract
The emergence of SARS-CoV-2 in late 2019 initiated a global pandemic, which led to a need for effective therapeutics and diagnostic tools, including virus-specific antibodies. Here, we investigate different antigen preparations to produce SARS-CoV-2-specific and virus-neutralizing antibodies in chickens (n = 3/antigen) and [...] Read more.
The emergence of SARS-CoV-2 in late 2019 initiated a global pandemic, which led to a need for effective therapeutics and diagnostic tools, including virus-specific antibodies. Here, we investigate different antigen preparations to produce SARS-CoV-2-specific and virus-neutralizing antibodies in chickens (n = 3/antigen) and rabbits (n = 2/antigen), exploring, in particular, egg yolk for large-scale production of immunoglobulin Y (IgY). Reactivity profiles of IgY preparations from chicken sera and yolk and rabbit sera were tested in parallel. We compared three types of antigens based on ancestral SARS-CoV-2: an inactivated whole-virus preparation, an S1 spike-protein subunit (S1 antigen) and a receptor-binding domain (RBD antigen, amino acids 319–519) coated on lumazine synthase (LS) particles using SpyCather/SpyTag technology. The RBD antigen proved to be the most efficient immunogen, and the resulting chicken IgY antibodies derived from serum or yolk, displayed strong reactivity with ELISA and indirect immunofluorescence and broad neutralizing activity against SARS-CoV-2 variants, including Omicron BA.1 and BA.5. Preliminary in vivo studies using RBD–lumazine synthase yolk preparations in a hamster model showed that local application was well tolerated and not harmful. However, despite the in vitro neutralizing capacity, this antibody preparation did not show protective effect. Further studies on galenic properties seem to be necessary. The RBD–lumazine antigen proved to be suitable for producing SARS-CoV-2 specific antibodies that can be applied to such therapeutic approaches and as reference reagents for SARS-CoV-2 diagnostics, including virus neutralization assays. Full article
(This article belongs to the Special Issue COVID-19 Pandemic: Therapeutic Strategies and Vaccines: 2nd Edition)
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<p>Immune response of chickens and rabbits following immunization with three different SARS-CoV-2 antigen preparations, the RBD antigen (SARS-CoV-2 receptor-binding-domain coupled to LS-particles), the S1 antigen (S1 subunit of the SARS-CoV-2 spike protein coupled to LS-particles) and an inactivated SARS-CoV-2 full-virus preparation. (<b>A</b>) The immunization and sampling scheme for the different antigens. (<b>B</b>) All sera (dilution factor 1:100) from immunized chickens and reference rabbits were tested by RBD-specific ELISA, and samples from animals immunized with the full-virus preparation were also tested with N-specific ELISA. The mean with SEM of individual animals is depicted for the sera. (<b>C</b>) Yolk samples of the chicken were analyzed by RBD-specific ELISA and for the whole-virus group with the N-specific ELISA, in addition. The single values for the weekly yolk-pool samples are shown.</p>
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<p>Reactivity of antibody preparations from chickens immunized with either the RBD antigen (SARS-CoV-2 receptor-binding-domain coupled to LS-particles), the S1 antigen (S1 subunit of the SARS-CoV-2 spike protein coupled to LS-particles) or an inactivated SARS-CoV-2 full-virus preparation tested in immunofluorescence assay. (<b>A</b>) Serum and (<b>B</b>) yolk preparations from chickens are reactive with RBD antigen on immunofluorescence plates showing fluorescence signal of antibody preparations raised against recombinant RBD, S1 or whole-virus preparations. Bar 20 µm. (<b>C</b>) Yolk from the RBD group was also positive against the Delta and Omicron BA.1 variant. Bar 100 µm.</p>
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<p>SARS-CoV-2 live-virus neutralization titers (VNT<sub>100</sub> (titer-neutralizing 100 TCID<sub>50</sub>)) of sera and yolk samples from either the RBD antigen (SARS-CoV-2 receptor-binding-domain coupled to LS-particles), the S1 antigen (S1 subunit of the SARS-CoV-2 spike protein coupled to LS-particles) or an inactivated SARS-CoV-2 full-virus preparation-immunized chickens and rabbits. The course of the neutralizing immune response of chickens in (<b>A</b>) sera (mean with SEM) and (<b>B</b>) yolk samples (individual values) tested against the sequence-homologous WT variant. Neutralizing reactivity of (<b>C</b>) chicken and rabbit sera (mean with SEM) and (<b>D</b>) yolk preparations (individual values) were additionally tested against Alpha, Beta, Delta and Omicron BA.1 and BA.5 variants.</p>
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<p>Intranasal application of RBD-yolk preparation in a prophylactic, therapeutic or post-exposure experimental setup in Syrian hamsters. (<b>A</b>) Experimental setup. (<b>B</b>) Relative body weight. (<b>C</b>) Virus genome (genome copies per mL) in nasal washing samples and (<b>D</b>) 5 dpc organ samples. (<b>E</b>) Infectious virus quantified in conchae, lung (cranial) and nasal washing samples of 3 dpc. Statistical differences were calculated by two-way ANOVA with Tukey´s multiple comparison test.</p>
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41 pages, 1964 KiB  
Review
Histidine Phosphorylation: Protein Kinases and Phosphatases
by Jia Ning, Margaux Sala, Jeffrey Reina, Rajasree Kalagiri, Tony Hunter and Brandon S. McCullough
Int. J. Mol. Sci. 2024, 25(14), 7975; https://doi.org/10.3390/ijms25147975 - 21 Jul 2024
Cited by 1 | Viewed by 1790
Abstract
Phosphohistidine (pHis) is a reversible protein post-translational modification (PTM) that is currently poorly understood. The P-N bond in pHis is heat and acid-sensitive, making it more challenging to study than the canonical phosphoamino acids pSer, pThr, and pTyr. As advancements in the development [...] Read more.
Phosphohistidine (pHis) is a reversible protein post-translational modification (PTM) that is currently poorly understood. The P-N bond in pHis is heat and acid-sensitive, making it more challenging to study than the canonical phosphoamino acids pSer, pThr, and pTyr. As advancements in the development of tools to study pHis have been made, the roles of pHis in cells are slowly being revealed. To date, a handful of enzymes responsible for controlling this modification have been identified, including the histidine kinases NME1 and NME2, as well as the phosphohistidine phosphatases PHPT1, LHPP, and PGAM5. These tools have also identified the substrates of these enzymes, granting new insights into previously unknown regulatory mechanisms. Here, we discuss the cellular function of pHis and how it is regulated on known pHis-containing proteins, as well as cellular mechanisms that regulate the activity of the pHis kinases and phosphatases themselves. We further discuss the role of the pHis kinases and phosphatases as potential tumor promoters or suppressors. Finally, we give an overview of various tools and methods currently used to study pHis biology. Given their breadth of functions, unraveling the role of pHis in mammalian systems promises radical new insights into existing and unexplored areas of cell biology. Full article
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Figure 1
<p>Comparison of NME family members: (<b>A</b>) Evolutionary tree of the sequence alignment of NME1–10 based on their amino acid sequences obtained from UniProt. (<b>B</b>) Main differences between Group I and Group II family members. Image created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>Known subcellular localization of NME family members in eukaryotic cells. Image created with <a href="http://Biorender.com" target="_blank">Biorender.com</a>.</p>
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<p>Histidine protein kinases and phosphatases in eukaryotic cells. NME2 and PHPT1 reversibly dephosphorylate His358 and His711 of the ion channel proteins KCa3.1 and TRPV5, respectively. TRPC4 is another ion channel protein histidine dephosphorylated by PHPT1 on His912. NME2 and PHPT1 phosphorylate and dephosphorylate His266 of the G beta subunit of G-protein coupled receptors (GPCR). ACLY is a cell metabolism protein reversibly phosphorylated on His760 by NME1 and PHPT1. Succinyl-CoA synthetase alpha (SCSα) and ALDOC are two other metabolic enzymes that are phosphorylated by NME1 on His299 and Asp319, respectively. NME1 can also phosphorylate the kinase suppressor of Ras (KSR) on Ser392 and Annexin A1. LHPP dephosphorylates NME1 and NME2 on His118, and PGAM5 dephosphorylates NME2 on His118. Image created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>Effects of PTMs and natural inhibitors on oligomerization, structure, and enzyme activity of NME1. NME1 hexamers are shown in each panel. For clarity, only two monomers are shown in colors (green and blue), and the rest are all in grey. Cysteines are represented by spheres, while the Kpn loop (residues 109–114) in chains A (the green chain) is in red. A 6-triangle cartoon model is shown at the bottom-right corner of each panel to show the topology of the oligomers, where red lines represent inter- or intra-molecular disulfides, and yellow crosses represent non-covalent CoA inhibitors: (<b>A</b>) Native state (PDB 2HVD). (<b>B</b>) Oxidized state (PDB 4ENO). (<b>C</b>) A model of dissociated NME1 adapted from panel B (PDB 4ENO). (<b>D</b>) CoA-bound NME1 (PDB 7ZTK). (<b>E</b>) A covalent CoAylation is modeled onto C109 (PDB 2HVD).</p>
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67 pages, 2808 KiB  
Review
Circulating Liquid Biopsy Biomarkers in Glioblastoma: Advances and Challenges
by Attila A. Seyhan
Int. J. Mol. Sci. 2024, 25(14), 7974; https://doi.org/10.3390/ijms25147974 - 21 Jul 2024
Cited by 1 | Viewed by 2477
Abstract
Gliomas, particularly glioblastoma (GBM), represent the most prevalent and aggressive tumors of the central nervous system (CNS). Despite recent treatment advancements, patient survival rates remain low. The diagnosis of GBM traditionally relies on neuroimaging methods such as magnetic resonance imaging (MRI) or computed [...] Read more.
Gliomas, particularly glioblastoma (GBM), represent the most prevalent and aggressive tumors of the central nervous system (CNS). Despite recent treatment advancements, patient survival rates remain low. The diagnosis of GBM traditionally relies on neuroimaging methods such as magnetic resonance imaging (MRI) or computed tomography (CT) scans and postoperative confirmation via histopathological and molecular analysis. Imaging techniques struggle to differentiate between tumor progression and treatment-related changes, leading to potential misinterpretation and treatment delays. Similarly, tissue biopsies, while informative, are invasive and not suitable for monitoring ongoing treatments. These challenges have led to the emergence of liquid biopsy, particularly through blood samples, as a promising alternative for GBM diagnosis and monitoring. Presently, blood and cerebrospinal fluid (CSF) sampling offers a minimally invasive means of obtaining tumor-related information to guide therapy. The idea that blood or any biofluid tests can be used to screen many cancer types has huge potential. Tumors release various components into the bloodstream or other biofluids, including cell-free nucleic acids such as microRNAs (miRNAs), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), proteins, extracellular vesicles (EVs) or exosomes, metabolites, and other factors. These factors have been shown to cross the blood-brain barrier (BBB), presenting an opportunity for the minimally invasive monitoring of GBM as well as for the real-time assessment of distinct genetic, epigenetic, transcriptomic, proteomic, and metabolomic changes associated with brain tumors. Despite their potential, the clinical utility of liquid biopsy-based circulating biomarkers is somewhat constrained by limitations such as the absence of standardized methodologies for blood or CSF collection, analyte extraction, analysis methods, and small cohort sizes. Additionally, tissue biopsies offer more precise insights into tumor morphology and the microenvironment. Therefore, the objective of a liquid biopsy should be to complement and enhance the diagnostic accuracy and monitoring of GBM patients by providing additional information alongside traditional tissue biopsies. Moreover, utilizing a combination of diverse biomarker types may enhance clinical effectiveness compared to solely relying on one biomarker category, potentially improving diagnostic sensitivity and specificity and addressing some of the existing limitations associated with liquid biomarkers for GBM. This review presents an overview of the latest research on circulating biomarkers found in GBM blood or CSF samples, discusses their potential as diagnostic, predictive, and prognostic indicators, and discusses associated challenges and future perspectives. Full article
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<p>Updated WHO classification of tumors of the CNS. The 2021 WHO classification of CNS tumors introduced significant changes, such as limiting the diagnosis of GBM to only <span class="html-italic">IDH</span> wild type tumors, reclassifying previously diagnosed <span class="html-italic">IDH</span>-mutated GBMs as astrocytomas, <span class="html-italic">IDH</span>-mutated, and grade 4, and requiring the presence of <span class="html-italic">IDH</span> mutations for tumors to be classified as astrocytomas or oligodendrogliomas. For the abbreviations, go to the abbreviations list at the end of the text. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a> (accessed on 6 March 2023).</p>
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<p>Examples of biomarkers measured in circulation, along with the advantages and disadvantages of liquid biopsy versus tumor tissue biopsy, are shown. This schematic representation illustrates circulating biomarkers released from the tumor into the bloodstream through the partially disrupted BBB. These biomarkers may also be directly secreted into the CSF. In patients with GBM, a compromised BBB allows circulating biomarkers such as ctDNAs, miRNAs, EVs, CTCs, proteins, and metabolites to enter the bloodstream or CSF. These biomarkers can be collected through blood or CSF draws and subsequently analyzed. The illustration provides a breakdown of tumoral components within the circulatory system. Various analytical methods, including PCR, qRT-PCR, NGS, WGS, immunoaffinity capture, ELISA, mass spectrometry, chemiluminescent immunoassay, and density gradient centrifugation, have been used to detect circulating analytes. Each circulating analyte can be assessed for tumor-specific changes such as various types of mutations, epigenetic modifications, DNA fragmentation patterns, nucleosome patterning, chromosomal aberrations, and the presence, absence, or changes in levels of ctDNAs, miRNAs (and other noncoding RNAs as well as mRNAs), CTCs, proteins, cytokines, metabolites, EVs, or exosomes, along with post-translational modifications. Each type of biomarker detection method, whether blood- or CSF-based or tissue-based, has unique advantages and disadvantages in diagnosing and monitoring GBM patients. Abbreviations: BBB, blood–brain barrier; CSF, cerebrospinal fluid; CTCs, circulating tumor cells; ctDNA, circulating tumor DNA; DNA, deoxyribonucleic acid; EVs, extracellular vesicles; NGS, next-generation sequencing; PCR, polymerase chain reaction; RNA, ribonucleic acid. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a> (accessed on 11 July 2024).</p>
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<p>Comparison of examples of liquid biopsy techniques, highlighting their capabilities, shortcomings, and available technologies. Abbreviations: CNA, copy number alterations; CTC, circulating tumor cells; ctDNA, circulating tumor DNA; DNA, deoxyribonucleic acid; ddPCR, droplet digital PCR; miRNA, microRNA; NGS, next-generation sequencing; PCR, polymerase chain reaction; RNA, ribonucleic acid. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a> (accessed on 11 July 2024).</p>
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