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Curr. Issues Mol. Biol., Volume 47, Issue 1 (January 2025) – 34 articles

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13 pages, 438 KiB  
Review
The Development of Methods of BLOTCHIP®-MS for Peptidome: Small Samples in Tuberous Sclerosis
by Kunio Yui, George Imataka, Kotaro Yuge, Hitomi Sasaki, Tadashi Shiohama, Kyoichi Asada and Hidehisa Tachiki
Curr. Issues Mol. Biol. 2025, 47(1), 34; https://doi.org/10.3390/cimb47010034 - 7 Jan 2025
Abstract
Abstract: Mutations in TSC1 or TSC2 in axons induce tuberous sclerosis complex. Neurological manifestations mainly include epilepsy and autism spectrum disorder (ASD). ASD is the presenting symptom (25–50% of patients). ASD was observed at significantly higher frequencies in participants with TSC2 than [...] Read more.
Abstract: Mutations in TSC1 or TSC2 in axons induce tuberous sclerosis complex. Neurological manifestations mainly include epilepsy and autism spectrum disorder (ASD). ASD is the presenting symptom (25–50% of patients). ASD was observed at significantly higher frequencies in participants with TSC2 than those with TSC1 mutations. The occurrence of TSC2 mutations is about 50% larger than TSC1. Therefore, ASD may develop due to TSC2 deficiency. TSC2 regulates microRNA biogenesis and Microprocessor activity via GSK3β. Of reference, everolimus has the best treatment target because of the higher potency of interactions with mTORC2 rather than rapamycin. Mutations in the TSC1 and TSC2 genes result in the constitutive hyperactivation of the mammalian target of the rapamycin (mTOR) pathway, contributing to the growth of benign tumors or hamartomas in various organs. TSC2 mutations were associated with a more severe phenotypic spectrum than TSC1 mutations because of the inhibition of the mTOR cascade. There are few studies on the peptide analysis of this disorder in relation to everolimus. Only one study reported that, in ten plasma samples, pre-melanosome protein (PMEL) and S-adenosylmethionine (SAM) were significantly changed as diagnostic prognostic effects. Our study on peptide analysis in Protosera Inc (Osaka, Japan) revealed that three peptides that were related to inflammation in two patients with tuberous sclerosis, who showed a 30% decrease in ASD symptoms following everolimus treatment. TSC2 mutations were associated with a more severe phenotypic spectrum due to the inhibition of the mTOR cascade. PMEL and SAM were significantly changed as diagnostic effects. Full article
26 pages, 15907 KiB  
Article
Antiedemic Effect of the Myosin Light Chain Kinase Inhibitor PIK7 in the Rat Model of Myocardial Ischemia Reperfusion Injury
by Dmitry L. Sonin, Mikhail S. Medved, Asker Y. Khapchaev, Maria V. Sidorova, Marina E. Palkeeva, Olga A. Kazakova, Garry V. Papayan, Daniil A. Mochalov, Sarkis M. Minasyan, Ilya E. Anufriev, Daria V. Mukhametdinova, Natalia M. Paramonova, Ksenia M. Balabanova, Anastasia S. Lopatina, Ilia V. Aleksandrov, Natalya Yu. Semenova, Anna A. Kordyukova, Kirill V. Zaichenko, Vladimir P. Shirinsky and Michael M. Galagudza
Curr. Issues Mol. Biol. 2025, 47(1), 33; https://doi.org/10.3390/cimb47010033 - 6 Jan 2025
Viewed by 245
Abstract
Myocardial ischemia-reperfusion injury increases myocardial microvascular permeability, leading to enhanced microvascular filtration and interstitial fluid accumulation that is associated with greater microvascular obstruction and inadequate myocardial perfusion. A burst of reactive oxygen species and inflammatory mediators during reperfusion causes myosin light chain kinase [...] Read more.
Myocardial ischemia-reperfusion injury increases myocardial microvascular permeability, leading to enhanced microvascular filtration and interstitial fluid accumulation that is associated with greater microvascular obstruction and inadequate myocardial perfusion. A burst of reactive oxygen species and inflammatory mediators during reperfusion causes myosin light chain kinase (MLCK)-dependent endothelial hyperpermeability, which is considered a preventable cause of reperfusion injury. In the present study, a single intravenous injection of MLCK peptide inhibitor PIK7 (2.5 mg/kg or 40 mg/kg) was found to suppress the vascular hyperpermeability caused by ischemia/reperfusion injury in an in vivo rat model. The antiedemic effect of PIK7 is transient and ceases within 90 min of reperfusion. The early no-reflow detected for the first time after 30 min ischemia in this model of myocardial infarction reduces the area accessible for PIK7. Electron microscopy has shown membrane-bound blebs of endotheliocytes, which partially or completely obturate the capillary lumen, and few capillaries with signs of intercellular gap formation in samples obtained from the center of the early no-reflow zone in control and PIK7-injected rats. Co-injection of PIK7 with NO donor sodium nitroprusside (SNP) increases blood flow in the zone of early no-reflow, while reducing the increased vascular permeability caused by SNP. Full article
(This article belongs to the Section Molecular Medicine)
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Figure 1
<p>Protocol of the experimental study.</p>
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<p>Effects of 100 µM PIK7 on the thrombin-induced EA.hy926 endothelial monolayer hyperpermeability and myosin RLC phosphorylation in EA.hy926 endothelial cells. (<b>a</b>) The levels of the thrombin-induced myosin regulatory light chain (RLC) phosphorylation in EA.hy926 cells were calculated based on sequential visualization of myosin RLC monophosphorylated at Ser19 (P-RLC), diphosphorylated at Thr18/Ser19 (PP-RLC), and total RLC on Western blots of endothelial cell samples using an external standard mixture containing RLC/P-RLC/PP-RLC, as described in [<a href="#B23-cimb-47-00033" class="html-bibr">23</a>]. Representative Western blots are shown. Bars below represent the sum of mono- and di-phosphorylated (activated) myosin RLC before (0 min) or 20 min after thrombin (Thr) administration normalized by the total RLC content in each sample. Data are presented as means ± SD, <span class="html-italic">n</span> = 4. (<b>b</b>) Effect of PIK7 on the thrombin-stimulated 70 kDa FITC-dextran permeability across the EA.hy926 endothelial cell monolayer. Cultured cells were preincubated or not with PIK7 for 60 min before 100 nM thrombin was added at 0 min. Cells in control were left untreated by PIK7 or thrombin. Data are presented as means ± SD, <span class="html-italic">n</span> = 5. <span class="html-italic">p</span> &lt; 0.05 for all PIK7 data points within the 20–180 min range as compared to the thrombin-stimulated cells in the absence of PIK7.</p>
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<p>Dynamics of the level of mean arterial pressure during the experiment. Data are presented as median (Me) and interquartile range (Q1; Q3).</p>
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<p>Dynamics of heart rate level during the experiment. Data are presented as median (Me) and interquartile range (Q1; Q3).</p>
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<p>Effect of PIK7 on mean arterial pressure.</p>
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<p>Duration of intravenous administration of sodium nitroprusside (60 μg/kg).</p>
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<p>Myocardial infarction size. Sizes of area at risk (<b>a</b>) and area of necrosis (<b>b</b>).</p>
Full article ">Figure 8
<p>Comparison of sizes of no-reflow zones at the beginning and at the end of reperfusion in the same heart in the CNT and PIK7 2.5 groups. Representative images of ICG-fluorescence (ICG-0′) and ThS-fluorescence (ThS) in transverse sections of hearts from CNT (<b>a</b>) and PIK7 2.5 (<b>b</b>) groups. Dotted lines are the boundaries of the no-reflow (NR) zone. (<b>c</b>) Comparison of sizes of no-reflow zones obtained by planimetric analysis of fluorescence images in transverse heart slices. Comparative analysis of ICG- and ThS-fluorescence images in the SNP group revealed a trend toward an increase in the size of the no-reflow zone between the beginning of reperfusion (ICG-0′) and the end (ThS-120′). (<b>d</b>) Representative images of apical slices of the same heart stained with Evans blue: in white light (left) and in ThS-fluorescence (right). ThS was injected at the second minute of reperfusion (ThS-2′); planimetric analysis allows confirmation of the primary no-reflow observed by ICG staining. Scale bar: 1 mm.</p>
Full article ">Figure 9
<p>Comparison of no-reflow zone area sizes between groups with late (ICG-90′) ICG administration. Representative images of ICG-fluorescence (ICG-90′) and ThS-fluorescence (ThS) in transverse sections of hearts from CNT (<b>a</b>) and PIK7 2.5 (<b>b</b>) groups. Dotted lines are the boundaries of the no-reflow (NR) zone. (<b>c</b>) Comparison of no-reflow zone sizes at the first minutes of reperfusion (ICG-0′ subgroup) and 90 min of reperfusion (ICG-90′ subgroup) obtained by ICG-fluorescence image analysis. (<b>d</b>) Comparison of no-reflow zone sizes at the end of the second hour of reperfusion (ThS) obtained by analyzing ThS-fluorescence images. Scale bar: 1 mm.</p>
Full article ">Figure 10
<p>Effect of sodium nitroprusside administered during the first minutes of reperfusion on ICG-0′ fluorescence intensity in the myocardial infarction zone. Representative images of ICG-fluorescence (ICG-0′) and ThS-fluorescence (ThS) in transverse slices of hearts from CNT (<b>a</b>) and SNP (<b>b</b>) groups. (<b>b</b>) The area between the dashed lines bounding the anatomical risk zone is divided into 6 equal sectors: two border sectors (BZ-1 and BZ-2) and four inner sectors (S1–S4). Each sector is divided into three grid cells: (1) subepicardial (Subep.), (2) intramural (Intr.), and (3) subendocardial (Suben.). Ref. is the red reference line in the reference sector plotted at an equal distance from the border sectors. (<b>c</b>) and (<b>d</b>) ICG- and ThS-fluorescence intensity plots along scan line 3 from epicardium to endocardium and reference lines from CNT (<b>a</b>) and SNP (<b>b</b>) slice images, respectively. The gray “White” line is drawn from TTC-stained slice images. The three arrows in the two graphs indicate the fluorescence intensity levels (ICG or ThS) in the three myocardial layers and the “+” or “−” signs indicate the ratio to the reference fluorescence intensity level (ICG ref. or ThS ref.). NR—no-reflow, SF—slow-flow. Scale bar: 1 mm.</p>
Full article ">Figure 11
<p>Comparison of contrast between sectors of the intramural layer of the left ventricular wall in the risk zone and the remote zone (interventricular septum) at different time points. (<b>a</b>,<b>b</b>) ICG-fluorescence intensity in the intramural layer of apical (<b>a</b>) and midline (<b>b</b>) slices in groups with early ICG administration (ICG-0′). (<b>c</b>,<b>d</b>) Intensity of ThS-fluorescence in the intramural layer of apical (<b>c</b>) and medial (<b>d</b>) slices in groups with early ICG administration (ICG-0′). (<b>e</b>,<b>f</b>) ICG-fluorescence intensity in the intramural layer of apical (<b>e</b>) and midline (<b>f</b>) slices in groups with delayed administration of ICG (ICG-90′). (<b>g</b>,<b>h</b>) Intensity of ThS-fluorescence in the intramural layer of apical (<b>g</b>) and medial (<b>h</b>) slices in groups with late ICG administration (ICG-90′). BZ-1 and BZ-2 are border sectors of the risk zone (<a href="#cimb-47-00033-f009" class="html-fig">Figure 9</a>b); S1, S2, S3, and S4 are inner sectors. *—statistically significant difference (<span class="html-italic">p</span> &lt; 0.05) with the same cell in the control group.</p>
Full article ">Figure 12
<p>Representative electronograms of myocardial samples taken from the intramural layer of the central sectors of the zone at risk from control rats (<b>a</b>,<b>c</b>,<b>e</b>) and PIK7 2.5 rats (<b>b</b>,<b>d</b>,<b>f</b>) at 10 min of reperfusion. (<b>a</b>) Black arrows show mitochondria with rupture of the outer membrane. (<b>c</b>,<b>d</b>) Arrows point to the extravasated erythrocyte; short thick arrows—basement membrane. (<b>e</b>) Arrows point to the open interendothelial space with a membrane bleb (mb) adjacent to it. (<b>f</b>) Two pentagonal arrows point to protrusions of edematous endothelium into the capillary lumen containing two erythrocytes: Ec—endotheliocyte; thin arrow—capillary lumen (the lumen of a capillary in which two erythrocytes are stuck together); Er—erythrocytes; PV—pinocytic vesicles, short arrows; short thick arrows—basement membrane; M—mitochondria; cf—collagen fibers; CMc—cardiomyocyte. Electronograms were taken with a transmission electron microscope HITACHI7800 at magnifications of 52,000 (<b>a</b>), 65,000 (<b>b</b>), 20,000 (<b>c</b>), 16,000 (<b>d</b>), 52,000 (<b>e</b>) and 39,000 (<b>f</b>).</p>
Full article ">Figure 12 Cont.
<p>Representative electronograms of myocardial samples taken from the intramural layer of the central sectors of the zone at risk from control rats (<b>a</b>,<b>c</b>,<b>e</b>) and PIK7 2.5 rats (<b>b</b>,<b>d</b>,<b>f</b>) at 10 min of reperfusion. (<b>a</b>) Black arrows show mitochondria with rupture of the outer membrane. (<b>c</b>,<b>d</b>) Arrows point to the extravasated erythrocyte; short thick arrows—basement membrane. (<b>e</b>) Arrows point to the open interendothelial space with a membrane bleb (mb) adjacent to it. (<b>f</b>) Two pentagonal arrows point to protrusions of edematous endothelium into the capillary lumen containing two erythrocytes: Ec—endotheliocyte; thin arrow—capillary lumen (the lumen of a capillary in which two erythrocytes are stuck together); Er—erythrocytes; PV—pinocytic vesicles, short arrows; short thick arrows—basement membrane; M—mitochondria; cf—collagen fibers; CMc—cardiomyocyte. Electronograms were taken with a transmission electron microscope HITACHI7800 at magnifications of 52,000 (<b>a</b>), 65,000 (<b>b</b>), 20,000 (<b>c</b>), 16,000 (<b>d</b>), 52,000 (<b>e</b>) and 39,000 (<b>f</b>).</p>
Full article ">Figure 12 Cont.
<p>Representative electronograms of myocardial samples taken from the intramural layer of the central sectors of the zone at risk from control rats (<b>a</b>,<b>c</b>,<b>e</b>) and PIK7 2.5 rats (<b>b</b>,<b>d</b>,<b>f</b>) at 10 min of reperfusion. (<b>a</b>) Black arrows show mitochondria with rupture of the outer membrane. (<b>c</b>,<b>d</b>) Arrows point to the extravasated erythrocyte; short thick arrows—basement membrane. (<b>e</b>) Arrows point to the open interendothelial space with a membrane bleb (mb) adjacent to it. (<b>f</b>) Two pentagonal arrows point to protrusions of edematous endothelium into the capillary lumen containing two erythrocytes: Ec—endotheliocyte; thin arrow—capillary lumen (the lumen of a capillary in which two erythrocytes are stuck together); Er—erythrocytes; PV—pinocytic vesicles, short arrows; short thick arrows—basement membrane; M—mitochondria; cf—collagen fibers; CMc—cardiomyocyte. Electronograms were taken with a transmission electron microscope HITACHI7800 at magnifications of 52,000 (<b>a</b>), 65,000 (<b>b</b>), 20,000 (<b>c</b>), 16,000 (<b>d</b>), 52,000 (<b>e</b>) and 39,000 (<b>f</b>).</p>
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31 pages, 6747 KiB  
Article
Prognostic Value of PSMB5 and Correlations with LC3II and Reactive Oxygen Species Levels in the Bone Marrow Mononuclear Cells of Bortezomib-Resistant Multiple Myeloma Patients
by Eva Plakoula, Georgios Kalampounias, Spyridon Alexis, Evgenia Verigou, Alexandra Kourakli, Kalliopi Zafeiropoulou and Argiris Symeonidis
Curr. Issues Mol. Biol. 2025, 47(1), 32; https://doi.org/10.3390/cimb47010032 - 6 Jan 2025
Viewed by 194
Abstract
Proteasome inhibitors (PIs) constitute the most common type of induction treatment for multiple myeloma. Interactions between the proteasome, autophagy, and reactive oxygen species (ROS) have been shown in the past, thus emphasizing the need for a better understanding of the underlying pathophysiology. For [...] Read more.
Proteasome inhibitors (PIs) constitute the most common type of induction treatment for multiple myeloma. Interactions between the proteasome, autophagy, and reactive oxygen species (ROS) have been shown in the past, thus emphasizing the need for a better understanding of the underlying pathophysiology. For this study, bone marrow mononuclear cells from 110 myeloma patients were collected at different disease stages. PSMB5 and LC3I/II protein levels were determined using Western blot, proteasome proteolytic activity (PPA) with spectrofluorometry, and ROS with flow cytometry. PSMB5 accumulation was found to diminish after PI treatment (p-value = 0.014), and the same pattern was observed in PPA (p-value < 0.001). Conversely, LC3II protein levels were elevated at both remission and relapse compared to baseline levels (p-value = 0.041). Patients with a baseline PSMB5 accumulation lower than 1.06 units had longer disease-free survival compared to those with values above 1.06 units (12.0 ± 6.7 vs. 36 ± 12.1 months; p-value < 0.001). Median ROS levels in plasma cells were significantly higher at relapse compared to both baseline and remission levels (p-value < 0.001), implying poor prognosis. Overall, post-treatment PSMB5 reduction could indicate a shift from proteasomal to autophagic degradation as a main proteostatic mechanism, thus explaining resistance. The elevated oxidative stress in PI-treated patients could possibly serve as an additional compensatory mechanism. Full article
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Figure 1

Figure 1
<p>Protein accumulation assay of PSMB5 and LC3II in BMMCs. (<b>a</b>,<b>c</b>) Western blot analysis for the detection of PSMB5 and LC3II in the BMMCs of representative MM patients at baseline, first remission, and relapse and of controls. Beta-actin was used as a loading control to verify equal protein mountings. All immunoblotting experiments were conducted in triplicate for each subject (<span class="html-italic">n</span> = 110). (<b>b</b>,<b>d</b>) Protein levels of PSMB5 and LC3II of all available MM patients at baseline (<span class="html-italic">n</span> = 30), first remission (<span class="html-italic">n</span> = 30), and relapse (<span class="html-italic">n</span> = 30) and of controls (<span class="html-italic">n</span> = 17). Each bar corresponds to the mean signal intensity per mg of total protein; the error bars correspond to the standard deviation. * to a <span class="html-italic">p</span>-value &lt; 0.05; ** corresponds to a <span class="html-italic">p</span>-value &lt; 0.01.</p>
Full article ">Figure 2
<p>Proteasome proteolytic activity (PPA) determination of BMMCs by spectrofluorometry in MM patients at baseline (<span class="html-italic">n</span> = 30), first remission (<span class="html-italic">n</span> = 30), and relapse (<span class="html-italic">n</span> = 30) and of controls (<span class="html-italic">n</span> = 17). (<b>a</b>) One-way ANOVA confirmed that all groups exhibited a statistically significant difference (<span class="html-italic">p</span>-value &lt; 0.0001). (<b>b</b>) PPA of a subgroup of MM patients (<span class="html-italic">n</span> = 7) at baseline and at their first remission. Patients in remission exhibited significantly decreased PPA (<span class="html-italic">p</span>-value &lt; 0.0001). (<b>c</b>) PPA of a subgroup of MM patients (<span class="html-italic">n</span> = 7) at baseline and at relapse. Again, patients at relapse showed significantly decreased values (<span class="html-italic">p</span>-value &lt; 0.0001). (<b>a</b>–<b>c</b>) Each bar corresponds to the mean fluorescence intensity per mg of total protein, and the error bars correspond to the standard deviation. **** corresponds to a <span class="html-italic">p</span>-value &lt; 0.0001.</p>
Full article ">Figure 3
<p>Estimation of reactive oxygen species levels. BMMCs were incubated with H<sub>2</sub>DCFDA. (<b>a</b>–<b>e</b>) Flow cytometry data of representative patients from each patient group: (<b>a</b>) A typical scattergram of BMMCs from a control subject; (<b>b</b>) ROS levels of a patient at baseline; (<b>c</b>) ROS levels of a patient in remission; (<b>d</b>) ROS levels of a patient in relapse. (<b>e</b>) Histogram of H<sub>2</sub>DCFDA fluorescence intensity of BMMCs from all groups. (<b>f</b>) Bar chart where the mean values from each patient group are compared. Each bar represents the mean of all median values. The number of patients is annotated, as is the sample’s mean. * corresponds to a <span class="html-italic">p</span>-value &lt; 0.05.</p>
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<p>Estimation of reactive oxygen species levels in the CD138+/CD38+ subpopulation (plasma cells) of BMMCs. (<b>a</b>–<b>e</b>) Flow cytometry data of representative patients from each patient group: (<b>a</b>) A typical scattergram of BMMCs from a control subject, (<b>b</b>) scattergram of a patient at baseline, (<b>c</b>) scattergram of a patient in his first remission, and (<b>d</b>) scattergram of a patient during first relapse. (<b>e</b>) Histogram of H<sub>2</sub>DCFDA fluorescence intensity of CD138+/CD38+ BMMCs from all groups. (<b>f</b>) Bar chart where the mean values from each patient group are compared. Each bar represents the mean of all median values. The number of patients is annotated, as is the sample’s mean. **** corresponds to a <span class="html-italic">p</span>-value &lt; 0.0001.</p>
Full article ">Figure 5
<p>Protein accumulation assay of MnSOD. (<b>a</b>) Western blot analysis for the detection of MnSOD in the BMMCs of representative MM patients at baseline, first remission, and relapse and of controls. All immunoblotting experiments were conducted in triplicate for each subject (<span class="html-italic">n</span> = 24). (<b>b</b>) Protein levels of MnSOD of all available MM patients at baseline (<span class="html-italic">n</span> = 6), first remission (<span class="html-italic">n</span> = 6), and relapse (<span class="html-italic">n</span> = 12) and of controls (<span class="html-italic">n</span> = 4). Each bar corresponds to the mean signal intensity per mg of total protein, and the error bars correspond to the standard deviation. * corresponds to a <span class="html-italic">p</span>-value &lt; 0.05.</p>
Full article ">Figure 6
<p>Protein accumulation of PSMB5 and LC3II in BMMCs of patients in remission. (<b>a</b>) Western blot analysis for the detection of PSMB5 of a representative MM patient at baseline and at first remission. (<b>b</b>) Western blot analysis for the detection of LC3II of a representative MM patient at baseline and at remission. (<b>c</b>) Protein levels of PSMB5 at both time points of the whole patient cohort (<span class="html-italic">n</span> = 7) and of the controls. (<b>d</b>) Protein levels of LC3II at both time points of the whole patient cohort and of the controls. (<b>c</b>,<b>d</b>) Each bar corresponds to the mean signal intensity per mg of total protein of each patient group, and the error bars correspond to the standard deviation. “<span class="html-italic">ns</span>” corresponds to non-significant; ** corresponds to a <span class="html-italic">p</span>-value &lt; 0.01.</p>
Full article ">Figure 7
<p>Protein accumulation of PSMB5 and LC3II in BMMCs of patients at relapse. (<b>a</b>) Western blot analysis for the detection of PSMB5 of a representative MM patient at baseline and at relapse. (<b>b</b>) Western blot analysis for the detection of LC3II of a representative MM patient at baseline and relapse. (<b>c</b>) Protein levels of PSMB5 at both time points of the whole patient cohort (<span class="html-italic">n</span> = 7) and of the controls. (<b>d</b>) Protein levels of LC3II at both time points of the whole patient cohort and of the controls (<b>c</b>,<b>d</b>) Each bar corresponds to the mean signal intensity per mg of total protein of each patient group, and the error bars correspond to the standard deviation. * corresponds to a <span class="html-italic">p</span>-value &lt; 0.05.</p>
Full article ">Figure 8
<p>Kaplan–Meier estimates for disease-free survival in multiple myeloma patients. Patients were subcategorized into two groups according to their baseline PSMB5 protein accumulation level: red ribbon: patients with PSMB5 levels higher than 1.06; green ribbon: patients with PSMB5 levels at baseline lower than 1.06. A statistically significant difference was detected, and the group with a higher PSMB5 accumulation had a shorter disease-free survival than patients with PSMB5 accumulation lower than 1.06 (<span class="html-italic">p</span>-value &lt; 0.0001). **** corresponds to a <span class="html-italic">p</span>-value &lt; 0.0001.</p>
Full article ">Figure A1
<p>Pearson’s correlations of PSMB5 with PPA/LC3II. (<b>a</b>) PSMB5 and PPA coefficient at baseline, (<b>b</b>) at remission, and (<b>c</b>) at relapse. (<b>d</b>) PSMB5 and LC3 at baseline, (<b>e</b>) at remission, and (<b>f</b>) at relapse. (<b>a</b>–<b>f</b>) Pearson’s coefficient is annotated as (r), the correlation coefficient is annotated as (R<sup>2</sup>), and the <span class="html-italic">p</span>-values correspond to the linear regression analysis.</p>
Full article ">Figure A2
<p>Pearson’s correlations. (<b>a</b>) PSMB5 and ROS coefficient at baseline, (<b>b</b>) at remission, and (<b>c</b>) at relapse. (<b>d</b>) PPA and ROS at baseline, (<b>e</b>) at remission, and (<b>f</b>) at relapse. (<b>g</b>) Correlation of LC3 and ROS level at baseline, (<b>h</b>) at remission, and (<b>i</b>) at relapse. (<b>a</b>–<b>i</b>) Pearson’s coefficient is annotated as (r), the correlation coefficient is annotated as (R<sup>2</sup>), and the <span class="html-italic">p</span>-values correspond to the linear regression analysis.</p>
Full article ">Figure A2 Cont.
<p>Pearson’s correlations. (<b>a</b>) PSMB5 and ROS coefficient at baseline, (<b>b</b>) at remission, and (<b>c</b>) at relapse. (<b>d</b>) PPA and ROS at baseline, (<b>e</b>) at remission, and (<b>f</b>) at relapse. (<b>g</b>) Correlation of LC3 and ROS level at baseline, (<b>h</b>) at remission, and (<b>i</b>) at relapse. (<b>a</b>–<b>i</b>) Pearson’s coefficient is annotated as (r), the correlation coefficient is annotated as (R<sup>2</sup>), and the <span class="html-italic">p</span>-values correspond to the linear regression analysis.</p>
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14 pages, 2385 KiB  
Article
The Effect of Autologous Dendritic Cell Immunotherapy on Kidney Function and Endothelial Dysfunction of Patients with Diabetic Kidney Disease (DKD): An Open Label Clinical Trial
by Martina Lily Yana, Enda Cindylosa Sitepu, Jonny, Linda Chiuman, I Nyoman Ehrich Lister and Terawan Agus Putranto
Curr. Issues Mol. Biol. 2025, 47(1), 31; https://doi.org/10.3390/cimb47010031 - 6 Jan 2025
Viewed by 193
Abstract
This study aimed to evaluate the effects of autologous dendritic cell (DC) immunotherapy on clinical outcomes (glomerular filtration rate/GFR and urine creatinine albumin ratio/UACR) and endothelial dysfunction (ICAM, VCAM, VEGF) in patients with diabetic kidney disease (DKD). Endothelial dysfunction induced by inflammation is [...] Read more.
This study aimed to evaluate the effects of autologous dendritic cell (DC) immunotherapy on clinical outcomes (glomerular filtration rate/GFR and urine creatinine albumin ratio/UACR) and endothelial dysfunction (ICAM, VCAM, VEGF) in patients with diabetic kidney disease (DKD). Endothelial dysfunction induced by inflammation is one of the key factors in the pathogenesis of DKD. In this one-group pretest–posttest quasi-experimental study, 69 subjects with DKD were administered a single dose of autologous DC immunotherapy ex vivo. UACR was measured at baseline and at weeks 1, 2, 3, and 4, while ICAM, VCAM, VEGF, and GFR were measured at baseline and at week 4 post-immunotherapy. The results showed a significant reduction in median UACR from 250 (IQR 71–668) mg/g at baseline to 164 (IQR 49–576) mg/g at week 4 (p < 0.05). GFR did not show any significant changes after immunotherapy. HbA1c (B = −33.270, p = 0.021) and baseline UACR (B = −0.185, p < 0.001) were identified as significant predictors of UACR change. Although there were no significant changes in ICAM, VCAM, and VEGF, subgroup analysis revealed a decrease in VCAM in macroalbuminuria patients and an increase in those with good glycemic control, suggesting differing endothelial responses. In conclusion, autologous DC immunotherapy effectively reduced UACR in DKD patients, and significant VCAM changes were found in macroalbuminuria and good glycemic control subjects. Further research is needed to understand the mechanisms behind UACR reduction and the long-term impact of this therapy. Full article
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<p>Weekly comparison of urine albumine–creatinine ratio (UACR). * <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>-value was calculated in comparison to baseline (week 0) using the Wilcoxon sign rank test. Data were presented asa median ± interquartile range (IQR). All subjects <span class="html-italic">n</span> = 69, microalbuminuria <span class="html-italic">n</span> = 36, macroalbuminuria <span class="html-italic">n</span> = 36.</p>
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<p>Regression coefficients with standard errors for predictors of ΔUACR. ΔUACR was calculated from the difference between UACR at week four and baseline. Independent variables significantly predict ΔUACR [F(5, 63) = 12.97; <span class="html-italic">p</span> &lt; 0.001]. This model explains 50.7% of variance in ΔUACR (R<sup>2</sup> = 0.507). Constant (β<sub>0</sub>) in this model is 292.27 ± 255.90. * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, eGFR: estimated glomerular filtration rate, HbA1c: A1c hemoglobin, UACR: urine albumin–creatinine ratio.</p>
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<p>VCAM pre- and post intervention with sub-group analysis. * <span class="html-italic">p</span> &lt; 0.05; hypothesis testing was conducted using the Wilcoxcon sign rank test. Microalbuminuria: UACR 30–300 mg/g at baseline; macroalbuminuria: UACR &gt; 300 mg/g at baseline; good glycemic control: HbA1c ≤ 7.00; poor glycemic control: HbA1c &gt; 7.00. VCAM: vascular cell adhesion molecule; total subjects n = 69, microalbuminuria n = 36, macroalbuminuria n = 36, good glycemic control n = 18, poor glycemic control n = 51.</p>
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<p>Correlation matrix of kidney function and endothelial dysfunction biomarkers. * <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; Correlational analysis was conducted using Spearman correlation. ICAM: intercellular adhesion molecule; VCAM: vascular cell adhesion molecule; VEGF: vasoendothelial growth factor.</p>
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28 pages, 6624 KiB  
Review
Phytochemicals in Breast Cancer Prevention and Treatment: A Comprehensive Review
by Adil Farooq Wali, Jayachithra Ramakrishna Pillai, Sirajunisa Talath, Pooja Shivappa, Sathvik Belagodu Sridhar, Mohamed El-Tanani, Imran Rashid Rangraze, Omnia Ibrahim Mohamed and Nowar Nizar Al Ani
Curr. Issues Mol. Biol. 2025, 47(1), 30; https://doi.org/10.3390/cimb47010030 - 6 Jan 2025
Viewed by 220
Abstract
Extensive investigation has been conducted on plant-based resources for their pharmacological usefulness, including various cancer types. The scope of this review is wider than several studies with a particular focus on breast cancer, which is an international health concern while studying sources of [...] Read more.
Extensive investigation has been conducted on plant-based resources for their pharmacological usefulness, including various cancer types. The scope of this review is wider than several studies with a particular focus on breast cancer, which is an international health concern while studying sources of flavonoids, carotenoids, polyphenols, saponins, phenolic compounds, terpenoids, and glycosides apart from focusing on nursing. Important findings from prior studies are synthesized to explore these compounds’ sources, mechanisms of action, complementary and synergistic effects, and associated side effects. It was reviewed that the exposure to certain doses of catechins, piperlongumine, lycopene, isoflavones and cucurbitacinfor a sufficient period can provide profound anticancer benefits through biological events such as cell cycle arrest, cells undergoing apoptosis and disruption of signaling pathways including, but not limited to JAK-STAT3, HER2-integrin, and MAPK. Besides, the study also covers the potential adverse effects of these phytochemicals. Regarding mechanisms, the widest attention is paid to Complementary and synergistic strategies are discussed which indicate that it would be realistic to alter the dosage and delivery systems of liposomes, nanoparticles, nanoemulsions, and films to enhance efficacy. Future research directions include refining these delivery approaches, further elucidating molecular mechanisms, and conducting clinical trials to validate findings. These efforts could significantly advance the role of phytocompounds in breast cancer management. Full article
(This article belongs to the Special Issue Phytochemicals and Cancer, 2nd Edition)
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<p>Molecular mechanisms of phytochemicals in cancer treatment. It demonstrates their roles in cellular proliferation, apoptosis, and cell cycle. Phytochemicals prevent angiogenesis, metastasis, oxidative stress, and inflammation and regulate redox signaling. They inhibit enzymes, modulate the mammosphere formation, and enhance immune system activity. The anti-apoptotic modulation and targeting of breast cancer cells and preventing the rate of perfusion look positive due to the antioxidative nature of the flavonoids.</p>
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<p>Key signaling pathways involved in cancer progression and drug resistance: (<b>A</b>) Akt/PI3K/mTOR Pathway: Growth factors activate receptor tyrosine kinases (RTKs), stimulating PI3K and Akt, leading to mTORC1 activation. This regulates cell growth, proliferation, apoptosis, metastasis, and drug resistance by modulating PTEN expression, miR-21 levels, 4E-BP1, and S6k1. (<b>B</b>) MAPK Pathway: RTK activation triggers RAS, leading to RAF and MEK activation. Downstream effectors (JNK, p38, ERK 1/2) influence similar cellular processes. (<b>C</b>) JAK/STAT Pathway: Cytokines stimulate JAK, phosphorylating STATs, which translocate to the nucleus, driving genes involved in growth and drug resistance.</p>
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<p>Major sources of phytochemicals from food.</p>
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15 pages, 6407 KiB  
Article
Identification of Potential Selective PAK4 Inhibitors Through Shape and Protein Conformation Ensemble Screening and Electrostatic-Surface-Matching Optimization
by Xiaoxuan Zhang, Meile Zhang, Yihao Li and Ping Deng
Curr. Issues Mol. Biol. 2025, 47(1), 29; https://doi.org/10.3390/cimb47010029 - 6 Jan 2025
Viewed by 228
Abstract
P21-activated kinase 4 (PAK4) plays a crucial role in the proliferation and metastasis of various cancers. However, developing selective PAK4 inhibitors remains challenging due to the high homology within the PAK family. Therefore, developing highly selective PAK4 inhibitors is critical to overcoming the [...] Read more.
P21-activated kinase 4 (PAK4) plays a crucial role in the proliferation and metastasis of various cancers. However, developing selective PAK4 inhibitors remains challenging due to the high homology within the PAK family. Therefore, developing highly selective PAK4 inhibitors is critical to overcoming the limitations of existing inhibitors. We analyzed the structural differences in the binding pockets of PAK1 and PAK4 by combining cross-docking and molecular dynamics simulations to identify key binding regions and unique structural features of PAK4. We then performed screening using shape and protein conformation ensembles, followed by a re-evaluation of the docking results with deep-learning-driven GNINA to identify the candidate molecule, STOCK7S-56165. Based on this, we applied a fragment-replacement strategy under electrostatic-surface-matching conditions to obtain Compd 26. This optimization significantly improved electrostatic interactions and reduced binding energy, highlighting its potential for selectivity. Our findings provide a novel approach for developing selective PAK4 inhibitors and lay the theoretical foundation for future anticancer drug design. Full article
(This article belongs to the Special Issue New Insight: Enzymes as Targets for Drug Development, 2nd Edition)
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<p>Overall workflow.</p>
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<p>Structural comparison of the binding cavities in PAK1 and PAK4 with their ligands. (<b>a</b>) Crystal structure of PAK4 (PDB ID: 7CP4) and (<b>b</b>) crystal structure of PAK1 (PDB ID: 5DEY). The hydrophobic surfaces of the binding cavities are visualized using color shading. The secondary structures of the receptors, including the α-helices, are highlighted: PAK1 is shown in blue (<b>c</b>) and PAK4 is shown in pink (<b>d</b>).</p>
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<p>Chemical structures of representative inhibitors targeting PAK1 and PAK4.</p>
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<p>Binding cavities of PAK1 and PAK4. Left: Crystal structure of the PAK4 (PDB ID: 7CP4). Right: Crystal structure of the PAK1 (PDB ID: 5DEY). The binding cavities of both receptors are highlighted using electrostatic potential coloring, with key binding site residues labeled in blue.</p>
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<p>Heatmap of the free energy decomposition for PAK1 and PAK4 systems. Red indicates the interactions of inhibitors with key residues around the binding site in PAK1, while blue indicates the interactions of inhibitors with key residues around the binding site in PAK4. Key residues are highlighted in yellow boxes.</p>
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<p>(<b>a</b>) The chemical structure of STOCK7S-56165, (<b>b</b>) the chemical structure of Compd 26, and (<b>c</b>) binding free energy contributions of Compd 55, STOCK7S-56165, and Compd 26 to PAK4 (energy unit: kcal/mol). The result for Compd 26 is the average value of stable 100-250 ns from three independent replicate simulations.</p>
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<p>(<b>a</b>) RMSD analysis of Compd 26 during MD simulations from three independent replicate calculations, (<b>b</b>) 3D binding pose of Compd 26 with PAK4, where hydrogen bonds and hydrophobic interactions are represented by green and pink lines, respectively, and (<b>c</b>) IGMH analysis of the interaction between Compd 26 and PAK4; the green color block indicates that the main interaction is van der Waals interaction.</p>
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<p>ESP surface of PAK4-binding site (PDB ID: 7CP4), Compd 55, STOCK7S-56165, and Compd 26.</p>
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17 pages, 930 KiB  
Review
Molecular Findings Before Vision Loss in the Streptozotocin-Induced Rat Model of Diabetic Retinopathy
by Mădălina Moldovan, Roxana-Denisa Capraș, Raluca Paşcalău and Gabriela Adriana Filip
Curr. Issues Mol. Biol. 2025, 47(1), 28; https://doi.org/10.3390/cimb47010028 - 4 Jan 2025
Viewed by 299
Abstract
The streptozotocin-induced rat model of diabetic retinopathy presents similarities to the disease observed in humans. After four weeks following the induction of diabetes, the rats experience vision impairment. During this crucial four-week period, significant changes occur, with vascular damage standing out as a [...] Read more.
The streptozotocin-induced rat model of diabetic retinopathy presents similarities to the disease observed in humans. After four weeks following the induction of diabetes, the rats experience vision impairment. During this crucial four-week period, significant changes occur, with vascular damage standing out as a clinically significant factor, alongside neovascularization. While redox imbalance, activation of microglia, secretion of pro-inflammatory cytokines, and neuronal cell death are also observed, the latter remains an emerging hypothesis requiring further exploration. This review is a comprehensive and up-to-date chronological depiction of the progression of diabetic retinopathy within the initial four weeks of hyperglycemia, which precede the onset of vision loss. The data are structured in weekly changes. In the first week, oxidative stress triggers the activation of retinal microglia, which produces inflammation, leading to altered neurotransmission. The second week is characterized by leukostasis, which promotes ischemia, while neural degeneration begins and is accompanied by a simultaneous increase in vessel permeability. The progression of redox and inflammatory imbalances characterized the third week. Finally, in the fourth week, significant developments occur as vessels dilate and become tortuous, neovascularization develops, and retinal thickness diminishes, ultimately leading to vision loss. Through this clearly structured outline, this review aims to delineate a framework for the progression of streptozotocin-induced diabetic retinopathy. Full article
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<p>Graphical representation of main events that occur in the streptozotocin-induced rat model of diabetic retinopathy, before onset of vision loss.</p>
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<p>PRISMA flowchart.</p>
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20 pages, 1692 KiB  
Article
Serum hsa-miR-22-3p, hsa-miR-885-5p, Lipase-to-Amylase Ratio, C-Reactive Protein, CA19-9, and Neutrophil-to-Lymphocyte Ratio as Prognostic Factors in Advanced Pancreatic Ductal Adenocarcinoma
by Jakub Wnuk, Dorota Hudy, Joanna Katarzyna Strzelczyk, Łukasz Michalecki, Kamil Dybek and Iwona Gisterek-Grocholska
Curr. Issues Mol. Biol. 2025, 47(1), 27; https://doi.org/10.3390/cimb47010027 - 3 Jan 2025
Viewed by 311
Abstract
Pancreatic cancer (PC) is the seventh most common cause of cancer-related death worldwide. The low survival rate may be due to late diagnosis and asymptomatic early-stage disease. Most patients are diagnosed at an advanced stage of the disease. The search for novel prognostic [...] Read more.
Pancreatic cancer (PC) is the seventh most common cause of cancer-related death worldwide. The low survival rate may be due to late diagnosis and asymptomatic early-stage disease. Most patients are diagnosed at an advanced stage of the disease. The search for novel prognostic factors is still needed. Two miRNAs, miR-22-3p and miR-885-5p, which show increased expression in PC, were selected for this study. The aim of this study was to evaluate the utility of these miRNAs in the prognosis of PC. Other prognostic factors such as lipase-to-amylase ratio (LAR), neutrophil-to-lymphocyte ratio (NLR), and carbohydrate antigen 19-9 (CA19-9) were also evaluated in this study. This study was conducted in 50 patients previously diagnosed with pancreatic ductal adenocarcinoma in clinical stage (CS) III and IV. All patients underwent a complete medical history, physical examination, and routine laboratory tests including a complete blood count, C-reactive protein (CRP), CA19-9, lipase, and amylase. Two additional blood samples were taken from each patient to separate plasma and serum. Isolation of miRNA was performed using TRI reagent with cel-miR-39-3p as a spike-in control. Reverse transcription of miRNA was performed using a TaqMan Advanced miRNA cDNA Synthesis Kit. The relative expression levels of miR-22-3p and miR-885-5p were measured using RT-qPCR. Serum hsa-miR-22-3p was detected in 22 cases (44%), while hsa-miR-885-5p was detected in 33 cases (66%). There were no statistically significant differences in serum or plasma miRNA expression levels between patient groups based on clinical stage, gender, or BMI. There were no statistically significant differences in LAR between patients with different CS. For NLR, CRP and CA19-9 thresholds were determined using ROC analysis (6.63, 24.7 mg/L and 4691 U/mL, respectively). Cox’s F test for overall survival showed statistically significant differences between groups (p = 0.002 for NLR, p = 0.007 for CRP and p = 0.007 for CA19-9). Utility as prognostic biomarkers was confirmed in univariate and multivariate analysis for CA19-9, CRP, and NLR. The selected miRNAs and LAR were not confirmed as reliable prognostic markers in PC. Full article
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<p>Probability of survival according to clinical stage (Cox’s F test <span class="html-italic">p</span> = 0.043). CS—clinical stage.</p>
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<p>Probability of survival based on an age threshold of 72 years (Cox’s F test <span class="html-italic">p</span> &gt; 0.05; 0.07).</p>
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<p>Probability of survival based on CA19-9 levels (threshold: 4619 U/mL).</p>
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<p>Probability of survival based on C-reactive protein (CRP) level (threshold 24.7 mg/L).</p>
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<p>Probability of survival based on NLR values (threshold: 6.63).</p>
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7 pages, 1271 KiB  
Case Report
1q21.1 Duplication Syndrome and Anorectal Malformations: A Literature Review and a New Case
by Maria Minelli, Chiara Palka Bayard de Volo, Melissa Alfonsi, Serena Capanna, Elisena Morizio, Maria Enrica Miscia, Gabriele Lisi, Liborio Stuppia and Valentina Gatta
Curr. Issues Mol. Biol. 2025, 47(1), 26; https://doi.org/10.3390/cimb47010026 - 3 Jan 2025
Viewed by 235
Abstract
Background: Anorectal malformations (ARMs) are a common pediatric surgical problem with an incidence of 1:1500 to 1:5000 live births. The phenotypical spectrum extends from anal stenosis to imperforate anus with or without anal fistula to persistent cloaca. They can manifest as either non-syndromic [...] Read more.
Background: Anorectal malformations (ARMs) are a common pediatric surgical problem with an incidence of 1:1500 to 1:5000 live births. The phenotypical spectrum extends from anal stenosis to imperforate anus with or without anal fistula to persistent cloaca. They can manifest as either non-syndromic or syndromic conditions. Various environmental and genetic risk factors have been elucidated. The widespread use of genetic screening tests for the investigation of developmental disorders increased the recognition of copy number variants (CNVs) of the 1q21.1 region. Duplications have also been associated with a multitude of congenital anomalies, such as heart disease, short stature, scoliosis, urogenital, and ARMs, and they have also been found in healthy individuals. The aim of this manuscript is to contribute to the definition of the phenotype associated with 1q21.1 duplications. Case presentation: The present case describes a male, referred to us for an ARM, in whom array—comparative genomic hybridization (array-CGH) identified 1q21.1 duplication inherited from his healthy mother. No other genetic test was performed on the patient. Conclusions: We propose considering genetic evaluation and analysis in patients with only one congenital malformation in order to eventually make an early diagnosis and a better quality of treatments. Full article
(This article belongs to the Special Issue Genomic Analysis of Common Disease)
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<p>An overview of the clinical manifestations and genetic causes of ARMs.</p>
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<p>The figure shows, below the ideogram of chromosome 1, the partial duplication of the long arm in the q21.1 region detected in the proband and in his mother.</p>
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<p>Algorithm for the clinical evaluation of a patient diagnosed with ARM (created in BioRender. Minelli, M. (2024); <a href="https://BioRender.com/h05w499" target="_blank">https://BioRender.com/h05w499</a>, accessed on 14 November 2024).</p>
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<p>“STRING” reproduction of the ACP6 pattern of interactions.</p>
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17 pages, 11241 KiB  
Article
Expression Analysis and Functional Validation of DcTPSb1 in Terpene Synthesis of Dendrobium chrysotoxum
by Yuxuan Jin, Shuting Zhou, Zhihui Du, Weize Wang and Zhilin Chen
Curr. Issues Mol. Biol. 2025, 47(1), 25; https://doi.org/10.3390/cimb47010025 - 3 Jan 2025
Viewed by 335
Abstract
Terpenes are critical components of the floral fragrance component in Dendrobium chrysotoxum, synthesized by terpene synthase (TPS). Analysis of the D. chrysotoxum genome and transcriptional data revealed that the gene DcTPSb1 was significantly up-regulated during flowering periods, showing a strong correlation with [...] Read more.
Terpenes are critical components of the floral fragrance component in Dendrobium chrysotoxum, synthesized by terpene synthase (TPS). Analysis of the D. chrysotoxum genome and transcriptional data revealed that the gene DcTPSb1 was significantly up-regulated during flowering periods, showing a strong correlation with the accumulation of aromatic monoterpenes in the floral components of Dendrobium chrysotoxum. Consequently, the DcTPSb1 gene was selected for further analysis. DcTPSb1 exhibited elevated expression levels in flowers among four organs (roots, stems, leaves, flowers) of D. chrysotoxum, with the highest expression observed during the blooming phase, which aligned with the accumulation of volatile terpenes during flowering. DcTPSb1, located in the chloroplasts, was identified as a member of the TPS-b subfamily associated with monoterpenes synthesis, showing close phylogenetic relationships with homologous proteins in related plant species. An analysis of the promoter region of DcTPSb1 indicated that it may be regulated by methyl jasmonate (MeJA) responsiveness. Functionally, DcTPSb1 was shown to catalyze the conversion of geranyl diphosphate (GPP) to linalool, ocimene, and (-)-α-pinitol in vitro. Overexpression of DcTPSb1 in tobacco resulted in a significant increase in terpenoid release during the blooming stage; however, the up-regulated substances did not include their catalytic products. The classification of DcTPSb1 as a terpene synthase capable of producing multiple products provides valuable insights into the complex biosynthesis of terpenes in orchids. These findings enhance our understanding of the functional diversity of DcTPSb1 and the processes involved in terpene biosynthesis in orchids. Full article
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<p>Comparison of amino acid sequence of DoTPSb1 protein in <span class="html-italic">Dendrobium chrysotoxum</span> with four high similarity sequences from NCBI (PKU83075.1, Alpha-terpineol synthase of <span class="html-italic">Dendrobium catenatum</span>; KAI0507613.1, hypothetical proteins KFK09_013739 of <span class="html-italic">Dendrobium nobile</span>; XP_020590622.1, terpene synthase 10-like of <span class="html-italic">Phalaenopsis equestris</span>; QIN90833.1, terpenoid synthase of the <span class="html-italic">Oncidium hybrid cultivar</span>). Meanwhile, three motifs critical for monoterpene synthesis, including two motifs rich in aspartic acid residues, DDxxD and NSE/DTE, and the arginine–tryptophan motif, RRX8W, are marked out in the above image. Completely identical sequences are marked in black, four identical amino acids are marked with boxes, and the rest of the alignments are not marked. Related detailed information is shown in <a href="#app1-cimb-47-00025" class="html-app">Table S3</a>.</p>
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<p>The evolutionary tree was formed based on the neighbor-joining method in the MEGA 11.0 tool. All sequences belonging to several different plants were obtained from NCBI. Seven TPS subfamilies were shown in six colors, in which DcTPSb1 protein from <span class="html-italic">D. chrysotoxum</span> was distributed in the largest blue area of the TPS-b family (marked with *). All sequence information can be found in <a href="#app1-cimb-47-00025" class="html-app">Supplementary Table S3</a>.</p>
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<p>Graph of the result of an experiment on subcellular localization of DcTPSb1 from <span class="html-italic">D. chrysotoxum</span>. Red represents chloroplast autofluorescence; green represents GFP display fluorescence; yellow represents fluorescence of the fusion protein connecting GFP and DcTPSb1, i.e., indicating cellular localization of the DcTPSb1 protein.</p>
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<p>Different tissues of <span class="html-italic">D. chrysotoxum</span>, include leaf, stem, root (<b>a</b>) and three developmental stages of flowers (<b>b</b>). (<b>c</b>) Expression levels of <span class="html-italic">DcTPSb1</span> in different <span class="html-italic">D. chrysotoxum</span> tissues 18S served as the internal standard. HB, bud stage; BK, half blooming stage; SK, full blooming stage. Each bar represents the mean ± standard error of three independent biological replicates. The one-way ANOVA was performed to identify the differences among experimental groups, which indicated significant differences <span class="html-italic">p</span> &lt; 0.001 (lowercase letters).</p>
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<p>Functional characterization of DcTPSb1. (<b>a</b>) Purification of DcTPSb1 recombinant protein. M, marker; S, supernatant; R, rinse solution. The red frame indicates purified target protein; Eluted protein represents GST-DcTPSb1 protein with 89.2 KDa. (<b>b</b>) Gas chromatograms of three monoterpenes generated by DcTPSb1 when GPP (Geranyl pyrophosphate) was used as a substrate. Control added high-temperature inactivated DcTPSb1 protein, in which no products were generated.</p>
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<p>The distributions, categories, and numbers of twenty cis-acting elements in 2000 bp upstream region of the initiation codon (ATG) of <span class="html-italic">DcTPSb1</span> in <span class="html-italic">D. chrysotoxum</span>. The above figure represents a wide distribution throughout the entire region of 20 cis-acting elements (represented by 20 types of blocks, respectively) distributed throughout the entire region with three categories in three colors. The following figure shows 20 types of cis-acting components belonging to three categories, represented by three colors. The detailed distribution of motifs is shown in <a href="#app1-cimb-47-00025" class="html-app">Table S4</a>.</p>
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<p>Analysis of volatile metabolome results of GC-MS analysis between DcTPSb1-overexpressed tobacco and wild type. (<b>a</b>) Ring diagram of metabolite class composition. Each color represents a metabolite category, and the area of the color block indicates the proportion of that category; (<b>b</b>) Heat map of differential metabolite clustering. On the left side of the figure is the sample clustering line, and on the top is the sample clustering line. Class is the first level classification of substances. Different colors are the colors filled with different values obtained after standardizing the relative content. Red represents high content, and green represents low content; (<b>c</b>) Dynamic distribution of differences in levels of differential metabolites. Each point represents a substance, with green points representing the top ten substances ranked downwards and red points representing the top ten substances ranked upwards.</p>
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20 pages, 3255 KiB  
Article
Elucidating the Mechanisms of Acquired Palbociclib Resistance via Comprehensive Metabolomics Profiling
by Lulu Yang, Yajun Yue, Zhendong Wang, You Jiang, Zhichao Xue and Yongzhuo Zhang
Curr. Issues Mol. Biol. 2025, 47(1), 24; https://doi.org/10.3390/cimb47010024 - 2 Jan 2025
Viewed by 362
Abstract
Palbociclib is a cyclin-dependent kinase 4/6 inhibitor and a commonly used antitumor drug. Many cancers are susceptible to palbociclib resistance, however, the underlying metabolism mechanism and extent of resistance to palbociclib are unknown. In this study, LC-MS metabolomics was used to investigate the [...] Read more.
Palbociclib is a cyclin-dependent kinase 4/6 inhibitor and a commonly used antitumor drug. Many cancers are susceptible to palbociclib resistance, however, the underlying metabolism mechanism and extent of resistance to palbociclib are unknown. In this study, LC-MS metabolomics was used to investigate the metabolite changes of colorectal cancer SW620 cells that were resistant to palbociclib. The study indicated that there were 76 metabolite expression differences between SW620 cells with palbociclib resistance and the parental SW620 cells involving amino acids, glutathione, ABC transporters, and so on. MetaboAnalyst 6.0 metabolic pathway analysis showed that arginine synthesis, β-alanine metabolism, and purine metabolism were disrupted. These results may provide potential clues to the metabolism mechanism of drug resistance in cancer cells that are resistant to palbociclib. Our study has the potential to contribute to the study of anti-palbociclib resistance. Full article
(This article belongs to the Section Molecular Pharmacology)
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<p>Growth inhibition of SW620 cells and resistant SW620 cells exposed to different concentrations of palbociclib for 2 days, 3 days, 4 days, and 5 days. Blue represents SW620 cells, and red represents SW620 PD_R cells.</p>
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<p>Flowchart of experimental operation. There were three experimental groups. The first experimental group was SW620 cells without any treatment; the second experimental group was SW620 cells treated once with 1 μM palbociclib; and the third experimental group was drug-resistant SW620 cells treated with 1 μM palbociclib for 6 months.</p>
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<p>Score plots of the PCA (<b>A</b>) and PLS-DA (<b>B</b>) models for the metabolome data obtained by LC-MS showing the metabolic profile differences between the control and palbociclib-treated groups. Red cycle: control group; green cycle: SW620 + 1 μM PD group; blue cycle: SW620 PD_R group.</p>
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<p>Volcano plot analysis of differential metabolites in drug-resistant SW620 cells. The x-axis represents log2 (fold change), while the y-axis represents the <span class="html-italic">p</span>-value in −log10 scale. The significantly upregulated metabolites are indicated in orange polka dots and those downregulated in blue polka dots (<span class="html-italic">p</span> &lt; 0.05 and fold change &gt; 1.5 or &lt;0.75).</p>
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<p>Significantly altered metabolites among the control and the palbociclib-treated groups. The box and whisker plots on the left show the original peak intensity values (mean ± SD). The box and whisker plots on the right summarize the normalized values. Red represents the expression level of metabolites in SW620 cells, green represents the expression level of metabolites in SW620 + 1 μM PD cells, and blue represents the expression level of metabolites in SW620 PD_R cells.</p>
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<p>Hierarchical clustering heat map of the top 25 differential metabolites selected based on t-tests/ANOVA, with the degree of change marked with red (upregulation) and blue (downregulation). The distance measure was set to “Euclidean”, and the clustering algorithm was set to “Ward”. (<b>A</b>) Control group, SW620 + 1 μM PD group, and SW620 PD_R group. (<b>B</b>) Control group and SW620 PD_R group. (<b>C</b>) Control group and SW620 + 1 μM PD group.</p>
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<p>Schematic diagram of regulated metabolites and potential dysregulated metabolic pathways. Detected upregulated metabolites are shown with an orange background; downregulated metabolites are shown with a blue background; a blank background indicates no statistical significance or undetected. Abbreviations: CoA, coenzyme A; IMP, hypoxanthine; GMP, guanosine monophosphate; ADP, adenosine 3′,5′-diphosphate; UMP, uridine 5′-monophosphate; UTP, uridine triphosphate; UDP, uridine 5′-diphosphate; CDP, cytidine diphosphate; CTP, cytidine triphosphate.</p>
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3 pages, 174 KiB  
Editorial
Common Genetic Variants in Rare Disorders: Hematology and Beyond
by Paschalis Evangelidis, Maria Gavriilaki, Nikolaos Kotsiou and Eleni Gavriilaki
Curr. Issues Mol. Biol. 2025, 47(1), 23; https://doi.org/10.3390/cimb47010023 - 1 Jan 2025
Viewed by 474
Abstract
Emerging evidence suggests that common genetic variants play a significant role in various rare but life-threatening hematological and non-hematological conditions [...] Full article
18 pages, 1984 KiB  
Review
Advances in RNA-Based Therapeutics: Challenges and Innovations in RNA Delivery Systems
by Yuxuan Liu, Yaohui Ou and Linlin Hou
Curr. Issues Mol. Biol. 2025, 47(1), 22; https://doi.org/10.3390/cimb47010022 - 31 Dec 2024
Viewed by 442
Abstract
Nucleic acids, as carriers of genetic information, have found wide applications in both medical and research fields, including gene editing, disease diagnostics, and drug development. Among various types of nucleic acids, RNA offers greater versatility compared to DNA due to its single-stranded structure, [...] Read more.
Nucleic acids, as carriers of genetic information, have found wide applications in both medical and research fields, including gene editing, disease diagnostics, and drug development. Among various types of nucleic acids, RNA offers greater versatility compared to DNA due to its single-stranded structure, ability to directly encode proteins, and high modifiability for targeted therapeutic and regulatory applications. Despite its promising potential in biomedicine, RNA-based medicine still faces several challenges. Notably, one of the most significant technical hurdles is achieving efficient and targeted RNA delivery while minimizing immune responses. Various strategies have been developed for RNA delivery, including viral vectors, virus-like particles (VLPs), lipid nanoparticles (LNPs), and extracellular vesicles (EVs). In this review, we explore the applications of these delivery methods, highlight their advantages and limitations, and discuss recent research advancements, providing insights for the future of RNA-based therapeutics. Full article
(This article belongs to the Section Biochemistry, Molecular and Cellular Biology)
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<p>Schematic showing the components of SEND. SEND is a delivery platform that integrates an endogenous Gag homolog, a fusogen (glycoprotein), and cargo mRNA, allowing customization for specific applications.</p>
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25 pages, 3859 KiB  
Article
Polydatin-Induced Shift of Redox Balance and Its Anti-Cancer Impact on Human Osteosarcoma Cells
by Alessio Cimmino, Magda Gioia, Maria Elisabetta Clementi, Isabella Faraoni, Stefano Marini and Chiara Ciaccio
Curr. Issues Mol. Biol. 2025, 47(1), 21; https://doi.org/10.3390/cimb47010021 - 31 Dec 2024
Viewed by 329
Abstract
Cancer cells demonstrate remarkable resilience by adapting to oxidative stress and undergoing metabolic reprogramming, making oxidative stress a critical target for cancer therapy. This study explores, for the first time, the redox-dependent anticancer effects of Polydatin (PD), a glucoside derivative of resveratrol, on [...] Read more.
Cancer cells demonstrate remarkable resilience by adapting to oxidative stress and undergoing metabolic reprogramming, making oxidative stress a critical target for cancer therapy. This study explores, for the first time, the redox-dependent anticancer effects of Polydatin (PD), a glucoside derivative of resveratrol, on the human Osteosarcoma (OS) cells SAOS-2 and U2OS. Using cell-based biochemical assays, we found that cytotoxic doses of PD (100–200 µM) promote ROS production, deplete glutathione (GSH), and elevate levels of both total iron and intracellular malondialdehyde (MDA), which are key markers of ferroptosis. Notably, the ROS scavenger N-acetylcysteine (NAC) and the ferroptosis inhibitor ferrostatin-1 (Fer-1) partially reverse PD’s cytotoxic effects. Interestingly, PD’s ability to hinder cell adhesion and migration appears independent of its pro-oxidant effect. Analysis of the oxidative stress regulators SIRT1 and Nrf2 at the gene and protein levels using real-time PCR and Western blot indicates an early oxidative response to PD treatment. PD remains effective under tumor-like conditions of hypoxia and serum starvation, and sensitizes OS cells to ROS-inducing chemotherapeutics like doxorubicin (DOX) and cisplatin (CIS). Importantly, PD exhibits minimal toxicity to non-tumorigenic cells (hFOB), suggesting a favorable therapeutic profile. Overall, our findings underscore that PD-induced redox imbalance plays a crucial role in its anti-OS effects, warranting further exploration into the molecular mechanisms behind its pro-oxidant activity. Full article
(This article belongs to the Special Issue Phytochemicals and Cancer, 2nd Edition)
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<p>Effects of PD on growth and proliferation of SAOS-2, U2OS, and hFOB 1.19 cells. (<b>A</b>) The viability of SAOS-2, U2OS, and hFOB 1.19 cell lines was assessed by the MTT assay after treatment with PD (25–200 µM) or DMSO vehicle for the indicated time points. The percentage of cell viability of PD-treated cultures was calculated by normalizing their O.D. value to that of DMSO control cultures. (<b>B</b>) The proliferation of SAOS-2, U2OS, and hFOB 1.19 cell lines on type-I-collagen-pre-coated wells was evaluated by measuring live cell confluence after exposure to PD (25–200 µM) for 24, 48, and 72 h, using the confluence function of the Spark microplate reader. The percentage of cell confluence was calculated by normalizing values recorded for PD-treated cells to those of DMSO control cultures. All results are given as the mean ± SD of three independent experiments performed in triplicate. Statistical analysis was performed using one-way ANOVA followed by Tukey’s post hoc test, with significance levels indicated as * <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 compared with vehicle group.</p>
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<p>Effects of PD treatment on SAOS-2 and U2OS cell migration and adhesion/detachment capacity. (<b>A</b>) Comparison of cell migration between PD-treated cells (25 and 50 µM) and vehicle controls (DMSO ≤ 0.05%) using a scratch test, at the indicated time points. Quantitative analysis was performed by calculating the percentage change in the wound space after scratching (see <a href="#sec2-cimb-47-00021" class="html-sec">Section 2</a> for further details). Left panels: Representative images of migrated SAOS-2 and U2OS cells recorded using the Tecan Spark instrument. Right panels: Histograms showing the percentage of wound closure. (<b>B</b>) Cell adhesion assays after 24 h incubation on pre-coated plates with PD (25 and 50 μM) or vehicle control. The attachment data were quantified as the percentage of attached cells in PD-treated samples compared to that in DMSO-treated samples. Left panel: Representative microscopical images of cell adhesion assays (scale bar: 100 μm), taken by an inverted light microscope (OLIMPUS EP 50). Right panels: The attachment data are quantified as the percentage of attached cells in PD-treated samples compared to that in DMSO-treated samples. (<b>C</b>) Cell detachment assays after 24 h incubation on pre-coated plates with PD (25 and 50 μM) or DMSO as control. Left panel: Representative microscopical pictures (scale bar: 100 μm). Right panels: Cell detachment data expressed as the percentage of attached cells after shaking vs. the initial cell number. All the results are given as the mean ± SD (n = 3). Statistical significance between treated and control groups was determined using one-way ANOVA followed by Tukey’s post hoc test with significance levels indicated as * <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>PD-induced changes in ROS and GSH levels in OS cells and their effect on cell viability and proliferation. SAOS-2 and U2OS cells were treated with PD 100 μM (+) and 200 μM (++) for 24 h (<b>A</b>,<b>B</b>) or 48 h (<b>C</b>,<b>D</b>). (<b>A</b>) DCF fluorescence assays showing the intracellular ROS production in SAOS-2 and U2OS cells, with or without pre-incubation with NAC (10 mM, 1 h prior to PD treatment). ROS are expressed as percentage of DCFDA fluorescence intensity compared to DMSO-treated cells arbitrarily set at 100%. (<b>B</b>) Cellular GSH/GSSG ratio measured by fluorometric microplate format. A quantity of 1 mM NAC was added or not to the growth medium for 1 h prior to PD treatment. Ratios are reported as percentage change compared to vehicle control. (<b>C</b>) Effects of ROS inhibition on cell viability assessed by the MTT assay after 48 h of treatment. The absorbance values at 570 nm were converted into percentage absorbance with respect to DMSO control. (<b>D</b>) Proliferation of SAOS-2 and U2OS cells on type-I-collagen-pre-coated wells, with or without pre-incubation with 10 mM NAC. Top panel: Representative images of SAOS-2 and U2OS cells stained with crystal violet after 48 h of treatment with PDIC50 (120 µM and 160 µM for SAOS-2 and U2OS, respectively) or DMSO; scale bar: 100 µm. Bottom panel: The percentage of cell live confluence of PD-treated cells at 48 h was measured using the confluence function of the Spark microplate reader and is reported as percentage vs. DMSO control cultures. All data are presented as the mean ± SD of three independent experiments with triplicate sets in each assay. Statistical significance between treatment groups and controls was determined using one-way ANOVA followed by Tukey’s post hoc test (** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001). Comparisons between PD-treated samples (100 or 200 µM) and their respective NAC-treated samples were analyzed using one-way ANOVA followed by Tukey’s post hoc test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Impact of PD exposure on intracellular iron levels and MDA accumulation in OS cells. Total iron and oxidized Fe(II) and reduced Fe(III) in the cytosol of SAOS-2 and U2OS cells (<b>A</b>) and MDA levels (<b>B</b>) after 24 h treatment with PDIC50 values (120 µM and 160 µM for SAOS-2 and U2OS, respectively) or DMSO as control. Statistical significance was assessed using umpired <span class="html-italic">t</span>-tests (* <span class="html-italic">p</span> &lt; 0.05 vs. DMSO). (<b>C</b>) Effects of Fer-1 on cell viability of PD-treated cells, assessed by the MTT assay. Cells were incubated with PD at their respective IC50 values, or vehicle control, in the presence or absence of Fer-1 (1 µM) for 48 h. One-way ANOVA followed by Tukey’s post hoc test was adopted to calculate the significant difference (** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001). All data are given as the mean ± SD of two independent experiments performed in triplicate.</p>
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<p>PD-induced effects on oxidative stress-related gene expression and protein levels. (<b>A</b>) The mRNA levels of SIRT-1 and Nrf-2 in SAOS-2 and U2OS cells were measured by real-time PCR after 24 and 48 h of treatment with PDIC50 values (120 µM and 160 µM for SAOS-2 and U2OS, respectively) or DMSO. The results are shown as means ± SD from two independent experiments with at least three technical replicates per condition. Significance between PD-treated and DMSO-treated samples was determined using one-way ANOVA, followed by Tukey’s post hoc test, with significance levels indicated as * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. (<b>B</b>) Western blot analysis. Left Panel: Blots probed with specific antibodies for SIRT1 (75 kDa), Nrf2 (68 kDa), and GAPDH (37 kDa). GAPDH was used as the loading control. Right Panel: The ratios were calculated following densitometric analysis of the bands from two independent experiments and expressed as mean ± SD.</p>
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<p>Effect of PD under conditions of hypoxia and serum starvation. (<b>A</b>) Effect of PD on the hypoxia-enhanced proliferation of SAOS-2 and U2OS cells. SAOS-2 and U2OS cells were incubated under normoxic or hypoxic conditions (37 °C, 5% CO<sub>2</sub>, 3% O<sub>2</sub>) for 24 h, either without or with the indicated concentrations of PD (3-h pre-treatment; see <a href="#sec2-cimb-47-00021" class="html-sec">Section 2</a> for further details). Hypoxic cell culture conditions were maintained using the gas and humidity control function of the Tecan Spark instruments. Top Panel: Representative images of SAOS-2 and U2OS cells acquired under hypoxic condition, following 24 h of PD treatment (100 and 200 µM). Scale bar: 100 µm. Bottom Panel: The percentage of cell live confluence was measured using the same instruments. The results are shown as the mean ± SD of three independent experiments. Statistical significance was determined using one-way ANOVA followed by Tukey’s post hoc test (* <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). (<b>B</b>) Effects of PD treatment in OS cells grown under serum starvation conditions. SAOS-2 and U2OS were incubated in serum-reduced medium (0.5% FBS) with or without PD 100 and 200 µM, at 24, 48, and 72 h, prior to the MTT assay. The cell viability index was derived by dividing the O.D. at 570 nm at a given time over the O.D. recorded for the control cells (which were incubated without PD). Data shown are means ± SD of three independent experiments. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, treated vs. untreated.</p>
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<p>Combined effects of PD and chemotherapeutic agents, DOX and CIS, on OS cells growth. (<b>A</b>,<b>B</b>) SAOS-2 and U2OS were preliminarily treated with DOX (0.5–25 µM) or CIS (1.5–60 µM) for 48 h to determine the IC50 value for each drug. Cell viability is reported as absorbance results of MTT assay (Mean ± SD, n = 3). (<b>C</b>,<b>E</b>) Cytotoxicity effects of PD (100 or 200 µM) in combination with DOXIC50 (10 µM) or CISIC50 (20 µM) for 48 h, assessed by MTT assay. The data are reported as percentage values, based on optical density (O.D.) at 570 nm for cells treated with PD, DOX/CIS or DOX/CIS-PD co-treated, relative to the untreated control groups (set to 100%). Values represent mean ± SD of three independent experiments. Significance between different groups were determined using one-way ANOVA followed by Tukey’s post hoc test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.001, *** <span class="html-italic">p</span> &lt; 0.001). (<b>D</b>,<b>F</b>) SAOS-2 and U2OS cells were pre-treated with 10 mM NAC for 1h before being exposed to PDIC50 (120 µM and 160 µM for SAOS-2 and U2OS, respectively), DOXIC50 or CISIC50 either individually or in combination with PDIC50/DOXIC50 or PDIC50/CISIC50, for 48 h. Data were analyzed using one-way ANOVA followed by Tukey’s multiple comparison test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.001, *** <span class="html-italic">p</span> &lt; 0.001). Results are presented as the mean ± SD from three independent experiments. The untreated control group was set to 100%. Data for NAC-treated samples, previously reported in <a href="#cimb-47-00021-f003" class="html-fig">Figure 3</a>, were omitted from the current graph.</p>
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22 pages, 8118 KiB  
Article
The Association of Cell-Free LncH19 and miR-29b Expression with the PI3K/AKT/HIF-1/VEGF Pathway in Patients with Diabetic Nephropathy: In Silico Prediction and Clinical Validation
by Noha M. Abd El-Fadeal, Basma Osman Sultan, Asmaa K. K. AbdelMaogood, Essam Al Ageeli, Fatma Tohamy Mekhamer, Sherihan Rohayem, Ahmed Shahidy, Nora Hosny, Manal S. Fawzy, Mohammed M. Ismail and Hidi A. A. Abdellatif
Curr. Issues Mol. Biol. 2025, 47(1), 20; https://doi.org/10.3390/cimb47010020 - 31 Dec 2024
Viewed by 340
Abstract
Diabetic nephropathy (DN) affects about one-third of patients with diabetes and can lead to end-stage renal disease despite numerous trials aimed at improving diabetic management. Non-coding RNAs (ncRNAs) represent a new frontier in DN research, as increasing evidence suggests their involvement in the [...] Read more.
Diabetic nephropathy (DN) affects about one-third of patients with diabetes and can lead to end-stage renal disease despite numerous trials aimed at improving diabetic management. Non-coding RNAs (ncRNAs) represent a new frontier in DN research, as increasing evidence suggests their involvement in the occurrence and progression of DN. A growing body of evidence suggests that long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in DN signaling pathways might serve as novel biomarkers or therapeutic targets, although this remains to be fully explored. Our study included four groups, each comprising 40 adults: patients with diabetes (a) without albuminuria, (b) with microalbuminuria, (c) with macroalbuminuria, and a control group. All participants underwent history-taking and clinicolaboratory assessments, including CBC, fasting blood sugar, HbA1c, lipid profile, liver function, and renal function tests. Additionally, expressions of lncRNA H19, miRNA-29b, PI3K, AKT, mTOR, and HIF-1 alpha were assessed using qPCR. lncRNA H19 expression was upregulated in patients with albuminuria compared to the DM group. Furthermore, based on qPCR, the level of lncRNA H19 was negatively correlated with eGFR and miRNA-29b expression. On the other hand, the lncRNA H19 level was positively correlated with PI3K, AKT, mTOR, and HIF-1 alpha levels. We also found that the lncH19/miRNA-29b ratio was significantly increased in patients with DN and macroalbuminuria. In conclusion, lncRNA H19 was upregulated in patients with DN, and this increase was associated with miRNA29b downregulation. Therefore, our study suggests a novel link between the lncH19/miRNA-29b ratio and DN, indicating that it might serve as a potential biomarker for the dynamic monitoring of DN. Full article
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<p>A flow chart of in silico data analysis for the miRNA target prediction enrichment pathway analysis and miRNA–disease interaction.</p>
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<p>miR-29–lncH19 pairing showing the target binding sites. Source: starBase v2. database (<a href="http://starbase.sysu.edu.cn/" target="_blank">http://starbase.sysu.edu.cn/</a>, accessed 2 January 2024).</p>
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<p>MicroRNA-29B and lncH19 gene structural analysis. (<b>A</b>) miRNA-29b chromosomal location and (<b>B</b>) miR-29b secondary structure, (<b>C</b>) lncH19 chromosomal location, and (<b>D</b>) lncH19 secondary structure. Source: <a href="https://useast.ensembl.org/Homo_sapiens/Gene/" target="_blank">https://useast.ensembl.org/Homo_sapiens/Gene/</a>; last accessed 25 December 2024).</p>
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<p>MiR-29b-3p, lncH19, and VEGF evidence of cross interactions. (<b>A</b>) miR-2b with VEGF; (<b>B</b>) lncH19 loop interaction with miR-29b and VEGF.</p>
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<p>MiR-29b-3p, lncH19, and VEGF evidence of cross interactions. (<b>A</b>) miR-2b with VEGF; (<b>B</b>) lncH19 loop interaction with miR-29b and VEGF.</p>
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<p>Interaction analysis for “PI3K/AKT/HIF-1” pathway. (<b>A</b>) Target pathways and genes (red boxes) in enriched KEGG pathway. The original pathway is adopted with permission (<a href="https://www.kegg.jp/pathway/map04066" target="_blank">https://www.kegg.jp/pathway/map04066</a>) (last accessed 20 November 2024) [<a href="#B23-cimb-47-00020" class="html-bibr">23</a>], (<b>B</b>) protein–protein interaction and clusters, and (<b>C</b>) coexpression analysis (Data sources: STRING database).</p>
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<p>Gene expression among the four investigated groups, including the DM (green), DM with microalbuminuria (blue), and DM with macroalbuminuria (red) for lncH19 (panel <b>A</b>), miR-29b (panel <b>B</b>), their ratio (panel <b>C</b>) and target genes (i.e., panel <b>D</b>: PI3K, panel <b>E</b>: AKT, panel <b>F</b>: mTOR, and panel <b>G</b>: HIF-1). Data are presented as median and interquartile range; Kruskal–Wallis was employed to calculate the <span class="html-italic">p</span>-values, where * denotes a significant difference vs. the control group, # indicates a significant difference vs. the DM group, and <span>$</span> indicates significant differences vs. the microalbuminuria group at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>VEGFA protein analysis. (<b>A</b>) Circulating levels of serum VEGFA in the four studied groups. (<b>B</b>) The correlation between eGFR and the serum level of VEGFA. (Data were presented as median and interquartile range. Kruskal–Wallis tests were used to calculate the <span class="html-italic">p</span>-value where * denotes a statistically significant difference vs. the control group, # indicates a significant difference vs. the diabetic group, and <span>$</span> indicates a significant difference vs. the microalbuminuria group at <span class="html-italic">p</span> &lt; 0.05. R<sup>2</sup> indicates the results of the Spearman correlation test, and the significance was determined at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>An illustration of the correlation matrix showing the connection between lncH19 level and various study variables. The blue color represents positive correlations, while the red color represents negative correlations. The value of R represents the strength of the association. Statistical significance was determined if <span class="html-italic">p</span>-value &lt; 0.05.</p>
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<p>VEGF and lncH19 as an indicator for prognosis. ROC curve analysis for VEGF and lncH19 distinguishes patients with DN from those without DN. AUC: area under the curve, CI: Confidence Interval. * Statistical significance at <span class="html-italic">p</span>-value less than 0.05.</p>
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13 pages, 1398 KiB  
Article
Do Salivary Cullin7 Gene Expression and Protein Levels Provide Advantages over Plasma Levels in Diagnosing Breast Cancer?
by Ceren Tilgen Yasasever, Derya Duranyıldız, Süleyman Bademler and Hilal Oğuz Soydinç
Curr. Issues Mol. Biol. 2025, 47(1), 19; https://doi.org/10.3390/cimb47010019 - 31 Dec 2024
Viewed by 390
Abstract
In addition to the tumor suppressor role of Cullin 7 (Cul7), one of the proteins belonging to the Cullin (Cul) family, studies have also suggested that Cul7 may act as an oncogene under certain conditions. The role of the Cul7 molecule in breast [...] Read more.
In addition to the tumor suppressor role of Cullin 7 (Cul7), one of the proteins belonging to the Cullin (Cul) family, studies have also suggested that Cul7 may act as an oncogene under certain conditions. The role of the Cul7 molecule in breast cancer is still unclear, and understanding its function could have significant implications for identifying novel therapeutic targets or improving diagnostic strategies in breast cancer management. In this study, the levels of the Cul7 molecule in plasma and noninvasive material saliva were investigated, and its possibility as a marker for breast cancer was discussed. Protein levels of blood and saliva samples taken from breast cancer patients and a healthy control group were measured by the ELISA (Enzyme-Linked Immunosorbent Assay) method. Gene expression levels between the two groups were analyzed by the qPCR (quantitative Polymerase Chain Reaction) method. In our study, Cul7 mRNA and protein expression levels were examined in 60 breast cancer patients and 20 healthy female controls, and a statistically insignificant difference was found between the patient and control groups in both plasma and saliva samples (p > 0.05). No correlation was found between the clinical characteristics of the patients and plasma and saliva Cul7 gene expression and protein levels (p > 0.05). Considering the possibility of Cul7 being a biomarker at the protein and mRNA levels, plasma is thought to be a better study material for Cul7. Our findings suggest that in the context of a study on salivary material, the expression of Cul7 at the mRNA level may have better potential utility as a biomarker. Full article
(This article belongs to the Section Molecular Medicine)
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<p>Gene expression of plasma (<b>a</b>) and saliva (<b>b</b>) <span class="html-italic">Cul7</span> median levels of breast cancer patients and healthy controls.</p>
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<p>ROC analysis of gene expression tests.</p>
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<p>Box plots of Cul7 protein in plasma (<b>a</b>) and saliva (<b>b</b>) samples of breast cancer patients and healthy controls.</p>
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<p>ROC analysis of plasma and saliva Cul7 protein tests.</p>
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24 pages, 4068 KiB  
Review
Research Progress on the Chemical Constituents and Pharmacological Effects of Houttuynia cordata Thunb and a Predictive Analysis of Quality Markers
by Zhuo Yang, Peng Ji, Chenchen Li, Fanlin Wu, Yongli Hua, Yanming Wei and Yuxia Cao
Curr. Issues Mol. Biol. 2025, 47(1), 18; https://doi.org/10.3390/cimb47010018 - 31 Dec 2024
Viewed by 419
Abstract
Houttuynia cordata (H. cordata) is widely used in respiratory disease control as an important heat-clearing and detoxifying traditional Chinese medicine. It effectively clears away heat and toxins, eliminates carbuncles, and drains pus, and it is diuretic and detoxicating. The aim of [...] Read more.
Houttuynia cordata (H. cordata) is widely used in respiratory disease control as an important heat-clearing and detoxifying traditional Chinese medicine. It effectively clears away heat and toxins, eliminates carbuncles, and drains pus, and it is diuretic and detoxicating. The aim of this study is to review the botany, chemical composition, pharmacological effects, and quality control of H. cordata to establish a better-quality evaluation system. Google Scholar, Baidu Scholar, PubMed, ScienceDirect, Web of Science, and multiple databases, including China National Knowledge Infrastructure (CNKI) and Wanfang Data, were searched. A structural diagram of the compound was drawn using ChemDraw software. H. cordata contains volatile oils, flavonoids, and alkaloids. It has antibacterial, anti-inflammatory, antiviral, antioxidant, antitumor, and immunity-enhancing pharmacological effects. By analyzing the literature, it was predicted that Houttuynia sodium, methyl nonyl ketone, quercetin, and quercitrin could be used as the quality markers (Q-marker) of H. cordata. This provides a basis for further research into the applications of H. cordata. Full article
(This article belongs to the Section Molecular Pharmacology)
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<p>Main compounds and constituents of essential oil of <span class="html-italic">H. cordata</span>.</p>
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<p>Molecular structures of some compounds.</p>
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<p>Structures of some alkaloids.</p>
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<p>Structures of some flavonoids.</p>
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<p>Structures of some organic acids.</p>
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<p>Diagram of inhibitory mechanisms.</p>
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<p>Diagram of the anti-inflammatory mechanism of action of SH.</p>
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<p>Mechanism of HCP inhibition of LPS-induced chronic vascular inflammation in rats.</p>
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<p>Diagram of the antiviral mechanism.</p>
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<p>Antitumor mechanism of action diagram.</p>
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<p>Association diagram of chemical composition and pharmacological action of <span class="html-italic">Houttuynia cordata</span>.</p>
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9 pages, 6190 KiB  
Communication
‘Two in One’ Cloning Vector Applied for Blunt-End and T-A Cloning with One-Step Digestion–Ligation and Screening of Positive Recombinants by Unaided Eyes
by Xingli Zhang, Chong Teng, Kaidi Lyu, Shanhua Lyu and Yinglun Fan
Curr. Issues Mol. Biol. 2025, 47(1), 17; https://doi.org/10.3390/cimb47010017 - 31 Dec 2024
Viewed by 373
Abstract
To clone DNA sequences quickly and precisely into plasmids is essential for molecular biology studies. Some cloning vectors have been developed for the cloning of PCR products, including blunt-end and T-A cloning. However, different plasmids are required for the cloning of PCR products [...] Read more.
To clone DNA sequences quickly and precisely into plasmids is essential for molecular biology studies. Some cloning vectors have been developed for the cloning of PCR products, including blunt-end and T-A cloning. However, different plasmids are required for the cloning of PCR products with blunt ends and 3′ A overhang ends. Here, a novel cloning vector, pYFRed, which is based on the pUC19 backbone, has emerged and can be applied in both blunt-end and T-A cloning. PCR products can be cloned into the pYFRed by a one-step digestion–ligation reaction in a tube. The endonuclease recognition sequences of SmaI, Eco53kI, EcoRV, PmeI, and SwaI for blunt-end cloning and XcmI for T-A cloning were designed and added between the lac promoter and the starting codon ATG of the mScarlet-I gene of pYFRed. The ligation efficiency was significantly higher because the restriction enzyme sites utilized were removed from the vector after being successfully constructed. The mScarlet-I gene was introduced into the pYFRed for the screening of the positive recombinants by the unaided eye without the need for additional reagents/equipment. pYFRed is easy to construct in an ordinary laboratory, which facilitates researchers to develop their cloning vector without purchasing commercial cloning vectors. Full article
(This article belongs to the Section Molecular Microbiology)
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<p>Transformed <span class="html-italic">E. coli</span> in LB medium without IPTG. (<b>a</b>): transformed clones under natural light; (<b>b</b>): detected with Tanon-5200Multi machine (Tanon Co., Ltd., Shanghai, China) with green excitation at 540 nm and emission at 600 nm; (<b>c</b>): agarose gel electrophoresis image with five red-colored clones (Lane 1–5) and white-colored clone (Lane 6) digestion with <span class="html-italic">Hin</span>dIII and <span class="html-italic">Xcm</span>I. Lane M: DL5000 DNA marker.</p>
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<p>A diagrammatic presentation of the pYFRed cloning vector. The basic skeleton, including the AmpR-ori-<span class="html-italic">lac</span> promoter cassette, of the vector comes from the pUC19 vector. The blunt-end restriction endonuclease recognition sites are shown in green and were used for blunt-end cloning. Two <span class="html-italic">Xcm</span>I digested sites were used for T-A cloning. The <span class="html-italic">mScarlet-I</span> gene is marked in red.</p>
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<p>The cloning site regions of pYFRed. (<b>a</b>): the restriction sites for blunt-end cloning are indicated with green letters, and the restriction sites for T-A cloning are shown with blue letters. (<b>b</b>): pYFRed will be digested by <span class="html-italic">Xcm</span>I, and the produced linear DNA molecule would have a T-overhang in the 3′-end (the yellow-highlighted letter T in the first <span class="html-italic">Xcm</span>I digested site will be retained and the yellow-highlighted letter A in the second <span class="html-italic">Xcm</span>I digested site will be removed in this strand). The blue arrows show the start (ATG with blue highlight), and stop (TAA with grey highlight) codons of <span class="html-italic">mScarlet-I</span>.</p>
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<p>Enzyme digestion with <span class="html-italic">Hin</span>dIII of recombinants. M: DL5000 DNA marker; CK1: pUC19; CK2: pYFRed; R1-R8: white clones of recombinant transformants.</p>
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<p>PCR assay of transformed clones. M: a DL2000 DNA marker; 1–15: white clones; ck+: positive control (soybean DNA); ck−: negative control (red clone).</p>
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<p>Cloning strategy of PCR product-cloning into pYFRed vector. DNA fragment of interest and pYFRed vector are mixed in one tube together with blunt-end restriction endonuclease or <span class="html-italic">Xcm</span>I and T4 ligase. The one-step digestion–ligation mixture is transformed into <span class="html-italic">E. coli</span>. All white clones are positive clones.</p>
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9 pages, 1666 KiB  
Article
The Electric Field Guided HaCaT Cell Migration Through the EGFR/p38 MAPK/Akt Pathway
by Huajian Zhou, Shihao Zhang, Xiaoli Jin, Chunxian A, Peng Gong and Sanjun Zhao
Curr. Issues Mol. Biol. 2025, 47(1), 16; https://doi.org/10.3390/cimb47010016 - 31 Dec 2024
Viewed by 368
Abstract
Previous studies have shown that the endogenous electric field (EF) is an overriding cure in guiding cell migration toward the wound center to promote wound healing, but the mechanism underlying is unclear. In this study, we investigated the molecular mechanism of electric field-guided [...] Read more.
Previous studies have shown that the endogenous electric field (EF) is an overriding cure in guiding cell migration toward the wound center to promote wound healing, but the mechanism underlying is unclear. In this study, we investigated the molecular mechanism of electric field-guided cell migration in human keratinocyte HaCaT cells. Our results showed that HaCaT cells migrate toward the anode under EFs. The phosphorylation levels of p38 MAPK and Akt were obviously elevated in the EF. Knocking down p38 MAPK obviously abolished directed migration of HaCaT cells under the EFs. Inhibiting p38 MAPK by SB203580 impaired the EF-guided cell migration. The electric field may guide HaCaT cell migration through the EGFR/p38 MAPK/Akt pathway. Full article
(This article belongs to the Special Issue The 25th Anniversary of CIMB: Perspectives in Molecular Biology)
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<p>HaCaT cell electrotaxis and increased phosphorylation levels of p38 MAPK and Akt. (<b>A</b>) The migration trajectory of HaCaT cells under a 100 mV EF (<span class="html-italic">n</span> = 200). (<b>B</b>) HaCaT cell migration direction (cosθ), speed (μm/min), and Euclidian persistence and displacement (μm/min) in the EF (<span class="html-italic">n</span> = 200). (<b>C</b>) The phosphorylation levels of p38 MAPK and Akt were increased in the EF group. The data were obtained from three independent experiments and are presented as the mean ± s.e.m. *** <span class="html-italic">p</span> &lt; 0.001 (two-tailed unpaired Student’s <span class="html-italic">t</span>-test).</p>
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<p>Knockdown of p38 MAPK obviously abolished the EF-guided cell migration. (<b>A</b>) Western blotting confirmed the knockdown of p38 MAPK by RNAi. (<b>B</b>) Knocking down p38 MAPK obviously abolished EF guided HaCaT cell migration (<span class="html-italic">n</span> = 300). The data were obtained from three independent experiments and are presented as the mean ± s.e.m. *** <span class="html-italic">p</span> &lt; 0.001 (two-tailed unpaired Student’s <span class="html-italic">t</span>-test).</p>
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<p>Inhibition of p38 MAPK impaired EF-guided HaCaT cell migration. (<b>A</b>) The migration direction (cosθ), speed (μm/min), Euclidian persistence, and velocity (μm/min) of HaCaT cells were obviously impaired by treatment with SB203580 (<span class="html-italic">n</span> = 300). (<b>B</b>) The phosphorylation of Akt was obviously impaired when the activation of p38 MAPK was inhibited by SB203580. The data were obtained from three independent experiments and are presented as the mean ± s.e.m. *** <span class="html-italic">p</span> &lt; 0.001 (two-tailed unpaired Student’s <span class="html-italic">t</span>-test).</p>
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<p>The electric field guided HaCaT cell migration through the EGFR/p38 MAPK/Akt pathway. (<b>A</b>) Blocking EGFR (AG1478) and Akt (MK-2206) impaired EF guided HaCaT cell migration (<span class="html-italic">n</span> = 300). (<b>B</b>) Blocking the activation of Akt with MK-2206 had no effect on the activation of EGFR or p38 MAPK. (<b>C</b>) Blocking EGFR resulted in decreased phosphorylation of p38 MAPK and Akt. Blocking p38 MAPK resulted in a decreased phosphorylation level of Akt. The data were obtained from three independent experiments and are presented as the mean ± s.e.m. *** <span class="html-italic">p</span> &lt; 0.001 (two-tailed unpaired Student’s <span class="html-italic">t</span>-test).</p>
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14 pages, 14809 KiB  
Article
Construction of Shoot Apical Meristem cDNA Yeast Library of Brassica napus L. and Screening of Proteins That Interact with the Inflorescence Regulatory Factors BnTFL1s
by Lingxiong Zan, Haidong Liu, Xutao Zhao, Dezhi Du and Kaixiang Li
Curr. Issues Mol. Biol. 2025, 47(1), 15; https://doi.org/10.3390/cimb47010015 - 30 Dec 2024
Viewed by 294
Abstract
The determinate inflorescence trait of Brassica napus L. is associated with various desirable agricultural characteristics. BnTFL1s (BnaA10.TFL1 and BnaC09.TFL1), which encode the transcription factor TERMINAL FLOWER 1 (TFL1), have previously been identified as candidate genes controlling this trait through map-based cloning. [...] Read more.
The determinate inflorescence trait of Brassica napus L. is associated with various desirable agricultural characteristics. BnTFL1s (BnaA10.TFL1 and BnaC09.TFL1), which encode the transcription factor TERMINAL FLOWER 1 (TFL1), have previously been identified as candidate genes controlling this trait through map-based cloning. However, the mechanism underlying the effects of the BnTFL1 proteins remains unclear. Further, proteins generally interact with each other to fulfill their biological functions. The objective of this study was to construct a cDNA library of the shoot apical meristem (SAM) of B. napus and screen for proteins that interact with BnTFL1s, to better understand its mechanism of action. The recombination efficiency of the yeast two-hybrid (Y2H) library that we constructed was 100%, with insertion fragment lengths ranging from 750 to 2000 bp and a capacity of approximately 1.44 × 107 CFUs (colony-forming units), sufficient for screening protein interactions. Additionally, the bait vector pGBKT7-BnTFL1s was transformed into yeast cells alongside positive and negative controls, demonstrating no toxicity to the yeast cells and no self-activation. This bait was used to screen the SAM cDNA library of B. napus, ultimately identifying two BnTFL1s-interacting proteins: 14-3-3-like protein GF14 omega GRF2. These interactions were verified through one-to-one interaction experiments. This study provides a foundation for further research on the biological functions of the BnTFL1s genes and their regulatory role in inflorescence formation in B. napus, while providing a reference for studying similar mechanisms in other plants. Full article
(This article belongs to the Section Molecular Plant Sciences)
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<p>Process of total RNA extraction, mRNA purification, and double-stranded-complementary DNA (ds-cDNA) synthesis. (<b>A</b>) Total RNA was extracted from the shoot apical meristem (SAM) of <span class="html-italic">B. napus</span>. (<b>B</b>) mRNA was isolated from total RNA. (<b>C</b>) ds-cDNA was synthesized. (<b>D</b>) DL 2000 DNA Marker, 1 and 2: RNA, mRNA or cDNA from <span class="html-italic">B. napus</span> SAM.</p>
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<p>Identification of library capacity and insert length for yeast two-hybrid (Y2H) libraries. (<b>A</b>) Identification of primary library capacity. (<b>B</b>) Identification of insert length and recombination rate for primary library. M: DL 2000 DNA Marker; 1–24: PCR products of 24 colonies. (<b>C</b>) Identification of secondary library capacity. (<b>D</b>) Identification of insert length and recombination rate for secondary library. M: DL 2000 DNA Marker; 1–24: PCR products of 24 colonies.</p>
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<p>Construction of the bait vector pGBKT7-BnTFL1s. (<b>A</b>) Amplification of <span class="html-italic">BnTFL1s</span> genes from the cDNA of the <span class="html-italic">B. napus</span> shoot apical meristem (SAM); lanes 1–8: PCR products from eight individual colonies. (<b>B</b>) Results of double enzyme digestion of the pGBKT7 vector; lane 1: circular pGBKT7 vector; lane 2–11: pGBKT7 vector digested with EcoRI and BamHI. (<b>C</b>) Double digestion results of the bait vector pGBKT7-BnTFL1s; lane 1: bait vector pGBKT7-BnTFL1s; lane 2: bait vector pGBKT7-BnTFL1s digested with EcoRI; and BamHI.</p>
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<p>The bait plasmid pGBKT7-BnTFL1s does not exhibit self-activation. (<b>A</b>) Co-transformed pGBKT7–53 and pGADT7-T as a positive control. (<b>B</b>) Co-transformed pGBKT7-Lam and pGADT7-T as a negative control. (<b>C</b>) Co-transformed pGBKT7-BnTFL1s and pGADT7-T for self-activation verification. DDO/X: SD/-Trp/-leu/X-α-gal culture media; QDO/X/A: SD/-Trp/-leu/-His/-Ade/X-α-gal/AbA culture media.</p>
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<p>A total of 62 clones can grow normally and produce blue color on QDO/X/A plates. (<b>A</b>) Result of initial screening of positive clones on DDO/X plates. (<b>B</b>) Growth of 92 blue clones on QDO/X/A plates. The symbols ‘+’ and ‘−’ indicate the positive and negative controls, respectively.</p>
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<p>Results of partial one-to-one interaction verification of candidate proteins interacting with pGBKT7-BnTFL1s.</p>
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<p>One-to-one interaction validation. (<b>A</b>) AD-8 interacted with pGBKT7-BnTFL1s in one-to-one interaction validation. (<b>B</b>) AD-10 interacted with pGBKT7-BnTFL1s in one-to-one interaction validation.</p>
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<p>TFL1 interacts with 14-3-3 in both <span class="html-italic">Brassica rapa</span> L. and <span class="html-italic">Brassica oleracea</span> L. (<b>A</b>) BraA10.TFL1 interacted with BraA07.14-3-3 in <span class="html-italic">B. rapa.</span> (<b>B</b>) BOC06.14-3-3 interacted with BOC09.TFL1 in <span class="html-italic">B. oleracea.</span></p>
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3 pages, 159 KiB  
Editorial
Editorial for the Special Issue ‘Molecular Mechanisms of Leukemia’
by Jungeun An and Myunggon Ko
Curr. Issues Mol. Biol. 2025, 47(1), 14; https://doi.org/10.3390/cimb47010014 - 30 Dec 2024
Viewed by 241
Abstract
Leukemia encompasses a diverse and intricate group of hematological malignancies that arise from hematopoietic stem and progenitors (HSPCs) in the bone marrow [...] Full article
(This article belongs to the Special Issue Molecular Mechanisms of Leukemia)
15 pages, 3167 KiB  
Article
Identification of Olfactory Receptors Responding to Androstenone and the Key Structure Determinant in Domestic Pig
by Peidong Yang, Tingting Luo, Shuqi Yang, Anjing Zhang, Yuan Tang, Li Chen, Jinyong Wang, Yongju Zhao, Zhining Zhong, Xuemin Li, Ziyin Han, Yupei Zhang, Yue Tang, Jideng Ma, Long Jin, Keren Long, Mingzhou Li and Lu Lu
Curr. Issues Mol. Biol. 2025, 47(1), 13; https://doi.org/10.3390/cimb47010013 - 30 Dec 2024
Viewed by 298
Abstract
Olfactory receptors (ORs) are members of the transmembrane G protein-coupled receptor superfamily, playing a crucial role in odor recognition, which further mediates crucial biological processes in mammals. In sows, androstenone can trigger sexual behaviors through olfaction, but the underlying mechanism remains to be [...] Read more.
Olfactory receptors (ORs) are members of the transmembrane G protein-coupled receptor superfamily, playing a crucial role in odor recognition, which further mediates crucial biological processes in mammals. In sows, androstenone can trigger sexual behaviors through olfaction, but the underlying mechanism remains to be explored. To efficiently and accurately screen pig olfactory receptors responding to androstenone and the key structure determinant, we adapted the high-throughput RNA-seq strategy to screen the altered genes upon androstenone treatment in the olfactory epithelium of pigs, yielding 1397 downregulated genes. Of which, 15 OR genes and 49 OR-like genes were candidate androstenone-responsive genes, and 5 ORs (OR2D2, OR8D1, OR8D2, OR10Z1 and OR7D4) were proven as responsible for androstenone-mediated olfaction in vitro. Among the five ORs, pig OR7D4 has the highest level of androstenone response. To further find the structural determinant, we performed ligand-binding cavity analysis on pig OR7D4 with androstenone, predicted seven potential structural sites and further confirmed that F178 and T203 are the key sites for recognizing androstenone. Nevertheless, the natural non-synonymous mutation M133V (rs696400829) of pig OR7D4 was proven to significantly impair the respondence to androstenone. This is the first time the ORs responding to androstenone in pigs and the key structural determinant of pig OR7D4 were identified, which highlights the significance of investigating the role of OR7D4 in pig reproduction performance in the future. Full article
(This article belongs to the Section Biochemistry, Molecular and Cellular Biology)
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<p>Transcriptomic change in pigs’ olfactory epithelium upon androstenone treatment. (<b>a</b>) Flowchart of sampling and transcriptome sequencing. (<b>b</b>) tSNE clustering of RNA-seq samples; red and blue represent samples from the control group and the androstenone treatment group, respectively. (<b>c</b>) Volcano plot of DEGs. (<b>d</b>) Top 10 GO terms of the up-DEGs. (<b>e</b>) Top 10 GO terms of the down-DEGs.</p>
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<p>Response of the olfactory receptors to different concentrations of androstenone. (<b>a</b>) Flowchart of the heterologous expression and functional assay of ORs. (<b>b</b>) Response of the control group (not transfected with ORs) to androstenone. (<b>c</b>) Response of OR2D2 to androstenone. (<b>d</b>) Response of OR6X1 to androstenone. (<b>e</b>) Response of OR7D4 to androstenone. (<b>f</b>) Response of OR8D1 to androstenone. (<b>g</b>) Response of OR8D2 to androstenone. (<b>h</b>) Response of OR10V1 to androstenone. (<b>i</b>) Response of OR10Z1 to androstenone. The different colours of columns represent the different concentrations of adrostenone. The error bar represents SEM, * 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>Potential key sites mediating the response to androstenone of pig OR7D4. (<b>a</b>) Molecular docking plot. Simulated docking results of pig OR7D4 with androstenone molecules marked in red. Binding cavity predicted are marked in blue. (<b>b</b>) Alignment of androstenone ORs of pigs. Relatively conserved sites in the binding cavity of pig OR7D4 are marked by arrows. Highly conserved loci are marked in red, with 10 loci between each black dot. Secondary structures are shown above the sequence.</p>
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<p>Evaluation of potential key sites mediating the response to androstenone of pig OR7D4. (<b>a</b>) Response of pig OR7D4-F178A compared to that of pig OR7D4 wild type. (<b>b</b>) Response of pig OR7D4-N195A compared to that of pig OR7D4 wild type. (<b>c</b>) Response of pig OR7D4-L199A compared to that of pig OR7D4 wild type. (<b>d</b>) Response of pig OR7D4-T203A compared to that of pig OR7D4 wild type. (<b>e</b>) Response of pig OR7D4-P210A compared to that of pig OR7D4 wild type. (<b>f</b>) Response of pig OR7D4-Y278A compared to that of pig OR7D4 wild type. (<b>g</b>) Response of pig OR7D4-T279A compared to that of pig OR7D4 wild type. The column height represents the response level of androstenone in the wild type control group (blue) and different mutation groups (red) when simulated with 200 μM androstenone, and the values of each group are presented as the ratio of the values detected relative to the control group. The error bar represents SEM. ** represents <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>The effect of polymorphism sites caused by different non-synonymous SNPs on the response level of pig OR7D4 to androstenone. (<b>a</b>) Response of pig OR7D4-P79L compared to that of pig OR7D4 wild type. (<b>b</b>) Response of pig OR7D4-M105G compared to that of pig OR7D4 wild type. (<b>c</b>) Response of pig OR7D4-V108F compared to that of pig OR7D4 wild type. (<b>d</b>) Response of pig OR7D4-M133V compared to that of pig OR7D4 wild type. (<b>e</b>) Response of pig OR7D4-A202T compared to that of pig OR7D4 wild type. (<b>f</b>) Response of pig OR7D4-A202V compared to that of pig OR7D4 wild type. The column height represents the response level of androstenone in the wild type control group (blue) and different mutation groups (red) when simulated with 200 μM androstenone, and the values of each group are presented as the ratio of the values detected relative to the control group. The error bar represents SEM. ** represents <span class="html-italic">p</span> &lt; 0.01.</p>
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13 pages, 8941 KiB  
Article
Genetic Variation for Wild Populations of the Rare and Endangered Plant Glyptostrobus pensilis Based on Double-Digest Restriction Site-Associated DNA Sequencing
by Yongrong Huang, Yu Li, Xiaojie Hong, Suzhen Luo, Dedan Cai, Xiangxi Xiao, Yunpeng Huang and Yushan Zheng
Curr. Issues Mol. Biol. 2025, 47(1), 12; https://doi.org/10.3390/cimb47010012 - 30 Dec 2024
Viewed by 307
Abstract
Glyptostrobus pensilis is an endangered tree species, and detecting its genetic diversity can reveal the mechanisms of endangerment, providing references for the conservation of genetic resources. Samples of 137 trees across seven populations within Fujian Province were collected and sequenced using double-digest restriction [...] Read more.
Glyptostrobus pensilis is an endangered tree species, and detecting its genetic diversity can reveal the mechanisms of endangerment, providing references for the conservation of genetic resources. Samples of 137 trees across seven populations within Fujian Province were collected and sequenced using double-digest restriction site-associated DNA (ddRAD-seq). A total of 3,687,189 single-nucleotide polymorphisms (SNPs) were identified, and 15,158 high-quality SNPs were obtained after filtering. The genetic diversity in the populations was found to be low (Ho = 0.08630, He = 0.03475, π = 0.07239), with a high genetic differentiation coefficient (Fst). When K = 4, the coefficient of variation (CV) error value was minimized, suggesting that the 137 individuals could be divided into four groups, with frequent gene flow between them. Principal component analysis (PCA) divided the seven populations into two major categories based on their north–south geographic location. The clustering was consistent with those obtained from the PCA. The main reasons for the endangerment of G. pensilis are likely to be poor natural regeneration, human disturbances, and climatic factors. It is recommended that methods such as in situ conservation, ex situ conservation, and the establishment of germplasm banks be implemented to maintain the genetic diversity of G. pensilis populations. Full article
(This article belongs to the Section Molecular Plant Sciences)
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<p>Sampling locations of seven populations of <span class="html-italic">Glyptostrobus pensilis</span> in Fujian Province. ND: Ningde; NP: Nanping; FZ: Fuzhou; SM: Sanming; PT: Putian; QZ: Quanzhou; LY: Longyan; XPX: Xiapuxian; FAS: Fu’anshi; ZNX: Zhouningxian; JCQ: Jiaochengqu; PNX: Pingnanxian; GTX: Gutianxian; PCX: Puchengxian; ZHX: Zhenghexian; JOS: Jian’oushi; SWS: Shaowuxian; LYX: Luoyuanxian; MHX: Minhouxian; YTX: Yongtaixian; XYX: Xianyouxian; DHX: Dehuaxian; YCX: Yongchunxian; ZPS: Zhangpingshi.</p>
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<p>A <span class="html-italic">G. pensilis</span> population from Ningde (PNX).</p>
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<p>(<b>A</b>) Admixture validation error values corresponding to different <span class="html-italic">K</span> values. (<b>B</b>) Genetic structure of <span class="html-italic">G. pensilis</span> populations.</p>
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<p>(<b>A</b>) The principal component analysis (PCA) of <span class="html-italic">G. pensilis</span> populations. The PCA plots for the 137 samples were created based on Principal Component 1 (horizontal axis) and Principal Component 2 (vertical axis). (<b>B</b>) Cluster analysis of 137 <span class="html-italic">G. pensilis</span> genotypes based on the approximate maximum likelihood method under the GTR model. ND: Ningde; NP: Nanping; FZ: Fuzhou; SM: Sanming; PT: Putian; QZ: Quanzhou; LY: Longyan.</p>
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12 pages, 7832 KiB  
Article
CRISPR/cas9 Allows for the Quick Improvement of Tomato Firmness Breeding
by Qihong Yang, Liangyu Cai, Mila Wang, Guiyun Gan, Weiliu Li, Wenjia Li, Yaqin Jiang, Qi Yuan, Chunchun Qin, Chuying Yu and Yikui Wang
Curr. Issues Mol. Biol. 2025, 47(1), 9; https://doi.org/10.3390/cimb47010009 - 29 Dec 2024
Viewed by 352
Abstract
Fruit firmness is crucial for storability, making cultivating varieties with higher firmness a key target in tomato breeding. In recent years, tomato varieties primarily rely on hybridizing ripening mutants to produce F1 hybrids to enhance firmness. However, the undesirable traits introduced by [...] Read more.
Fruit firmness is crucial for storability, making cultivating varieties with higher firmness a key target in tomato breeding. In recent years, tomato varieties primarily rely on hybridizing ripening mutants to produce F1 hybrids to enhance firmness. However, the undesirable traits introduced by these mutants often lead to a decline in the quality of the varieties. CRISPR/Cas9 has emerged as a crucial tool in accelerating plant breeding and improving specific target traits as technology iterates. In this study, we used a CRISPR/Cas9 system to simultaneously knock out two genes, FIS1 and PL, which negatively regulate firmness in tomato. We generated single and double gene knockout mutants utilizing the tomato genetic transformation system. The fruit firmness of all knockout mutants exhibited a significant enhancement, with the most pronounced improvement observed in the double mutant. Furthermore, we assessed other quality-related traits of the mutants; our results indicated that the fruit quality characteristics of the gene-edited lines remained statistically comparable to those of the wild type. This approach enabled us to create transgenic-free mutants with diverse genotypes across fewer generations, facilitating rapid improvements in tomato firmness. This study offers significant insights into molecular design breeding strategies for tomato. Full article
(This article belongs to the Section Molecular Plant Sciences)
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<p>Rapid improvement of tomato firmness using the CRISPR/Cas9 technology. (<b>a</b>) The schematic showing the gene structure of <span class="html-italic">FIS1</span> and <span class="html-italic">PL</span>; four target sgRNAs were designed for gene edit. The red arrows represent the four target sgRNAs, and the black arrows represent the corresponding detection primers. (<b>b</b>) The detail sequences of gene editing lines. (<b>c</b>) The compression resistance of wild-type and mutant fruits. Significant differences are calculated using the Tukey–Kramer test, with different letters indicating a highly significant difference (<span class="html-italic">p</span> &lt; 0.05). Error bars, mean ± SD. <span class="html-italic">n</span> = three biological replicates.</p>
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<p>Appearance changes and water loss rate of wild-type and mutant fruits during storage. (<b>a</b>) Appearance changes in wild-type and mutant fruits during storage at the red ripening stage. (<b>b</b>) Water loss rate of wild-type and mutant tomato fruits.</p>
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<p>Microscopic observation of pericarp in wild type and mutant. (<b>a</b>) Comparison of pericarp thickness. (<b>b</b>) Observation of pericarp cells, scale bar = 500 μm. (<b>c</b>) The cell area of pericarp in wild type and mutant. Significant differences are calculated using the Tukey–Kramer test, with different letters indicating a highly significant difference (<span class="html-italic">p</span> &lt; 0.05). Error bars, mean ± SD. <span class="html-italic">n</span> = three biological replicates.</p>
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<p>Thickness of the cuticle on the epidermis in wild type and mutants. (<b>a</b>) Cuticle sections stained with Sudan IV to visualize the cutinization of epidermal cell walls. (<b>b</b>) The thickness of cuticle and the degree of invagination. Red arrows indicate the degree of invagination of the cutinization, while black arrows indicate the thickness of the cuticular layer. Scale bar = 25 μm. Significant differences are calculated using the Tukey–Kramer test, with different letters indicating a highly significant difference (<span class="html-italic">p</span> &lt; 0.05). Error bars, mean ± SD. <span class="html-italic">n</span> = three biological replicates.</p>
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<p>The appearance and quality of fruits in wild type and mutants. (<b>a</b>) The appearance of fruits in wild type and mutants. (<b>b</b>) The weight of wild-type and mutant fruit. (<b>c</b>) The soluble solids content of wild-type and mutant fruit. FW, fruit weight. TSS, soluble solids. Significant differences are calculated using the Tukey–Kramer test (<span class="html-italic">p</span> &lt; 0.05). Error bars, mean ± SD. n = three biological replicates. NS, no significance. Scale bar = 2 cm.</p>
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4 pages, 183 KiB  
Editorial
Editorial for the Special Issue ‘Molecular Breeding and Genetics Research in Plants’
by Shimeles Tilahun
Curr. Issues Mol. Biol. 2025, 47(1), 11; https://doi.org/10.3390/cimb47010011 - 29 Dec 2024
Viewed by 489
Abstract
Despite significant advancements in plant breeding research, the challenges posed by a growing global population, the impact of abiotic and biotic stresses, and the uncertainties of climate change necessitate continued focus and innovation in plant breeding and genetic studies [...] Full article
(This article belongs to the Special Issue Molecular Breeding and Genetics Research in Plants)
18 pages, 9762 KiB  
Article
Transcriptional Profiling of Testis Development in Pre-Sexually-Mature Hezuo Pig
by Zunqiang Yan, Qiaoli Yang, Pengfei Wang and Shuangbao Gun
Curr. Issues Mol. Biol. 2025, 47(1), 10; https://doi.org/10.3390/cimb47010010 - 29 Dec 2024
Viewed by 424
Abstract
Spermatogenesis is an advanced biological process, relying on intricate interactions between somatic and germ cells in testes. Investigating various cell types is challenging because of cellular heterogeneity. Single-cell RNA sequencing (scRNA-seq) offers a method to analyze cellular heterogeneity. In this research, we performed [...] Read more.
Spermatogenesis is an advanced biological process, relying on intricate interactions between somatic and germ cells in testes. Investigating various cell types is challenging because of cellular heterogeneity. Single-cell RNA sequencing (scRNA-seq) offers a method to analyze cellular heterogeneity. In this research, we performed 10× Genomics scRNA-seq to conduct an unbiased single-cell transcriptomic analysis in Hezuo pig (HZP) testis at one month of age during prepuberty. We collected 14,276 cells and identified 8 cell types (including 2 germ cells types and 6 somatic cell types). Pseudo-timing analysis demonstrated that Leydig cells (LCs) and myoid cells (MCs) originated from a shared progenitor cell lineage. Moreover, the functional enrichment analyses showed that the genes of differential expression were enriched in spermatogonia (SPG) and were enriched in the cell cycle, reproduction, and spermatogenesis. Expressed genes in spermatocytes (SPCs) were enriched in the cAMP, cell cycle, male gamete generation, reproductive system development, and sexual reproduction, while growth hormone synthesis, gamete generation, reproductive process, and spermine synthase activity were enriched in Sertoli cells (SCs). Additionally, chemokine, B cell receptor, activation of immune response, and enzyme binding were enriched in macrophages. Our study investigated transcriptional alterations across different cell types during spermatogenesis, yielding new understandings of spermatogenic processes and cell development. This research delivers an exploration of spermatogenesis and testicular cell biology in HZP, establishing the groundwork for upcoming breeding initiatives. Full article
(This article belongs to the Special Issue Reproductive Biology and Germ Cell Development, 2nd Edition)
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<p>(<b>A</b>) A schematic representation of the experimental workflow. (<b>B</b>) Histological examination. Left panel: magnified 100×; right panel: magnified 400×.</p>
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<p>Data quality overview. (<b>A</b>) Visualization of effective cell detection. (<b>B</b>) Multicellular clustering displayed using the t-distributed stochastic neighbor embedding (t-SNE) diagram. (<b>C</b>) Sequencing saturation depicted on a map. (<b>D</b>) Median gene count per cell. (<b>E</b>) Distribution of detected gene numbers. (<b>F</b>) Distribution of total unique molecular identifier (UMI) counts. (<b>G</b>) Percentage of mitochondrial gene expression across individual cells.</p>
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<p>Transcriptome profile and cluster analysis of testicular cells. (<b>A</b>) t-SNE plot showcasing the clustering of unselected spermatogenic cells. (<b>B</b>) Uniform manifold approximation and projection (UMAP) plot displaying the profiling of spermatogenic cells. (<b>C</b>) Stacked bar chart indicating cell counts in each cluster. (<b>D</b>) Bar chart representing the proportion of cells across 16 clusters. (<b>E</b>) t-SNE and (<b>F</b>) UMAP plots displaying transcript expression level via UMIs.</p>
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<p>Analysis of differentially expressed genes (DEGs). (<b>A</b>) The count of DEGs identified within every cluster. (<b>B</b>) Heatmap illustrating a total of 80 DEGs across the various clusters. (<b>C</b>,<b>D</b>) Violin plots depicting the expression trend of <span class="html-italic">LHX9</span> and <span class="html-italic">RDH16</span> genes.</p>
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<p>Detection of cell types. (<b>A</b>) t-SNE plot showing cell-kind identification. (<b>B</b>–<b>O</b>) Violin plots presenting the expression of cell type-specific genes in various clusters. (<b>P</b>,<b>Q</b>,<b>W</b>,<b>X</b>) t-SNE plots showing the expression pattern of <span class="html-italic">STAR</span>, <span class="html-italic">INSL3</span>, <span class="html-italic">CD163</span> and <span class="html-italic">C1QA</span> gene across different clusters.</p>
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<p>Dot plots displaying the expression pattern of cell-specific genes in testicular cells.</p>
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<p>Pseudo-time analysis of Leydig cells (LCs) and myoid cells (MCs). Pseudo-time data (<b>A</b>) and differentiation status (<b>B</b>) of clusters 5, 8, and 13 suggested a shared progenitor for the LC and MC lineages. The pseudo-time scale represents the developmental progression, where lower values correspond to earlier stages. Different colors highlight distinct stages of differentiation.</p>
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<p>Functional enrichment analysis of spermatogonia (SPG) and spermatocytes (SPCs). (<b>A</b>,<b>B</b>) represent the top 20 GO terms for SPG and SPCs DEGs, while (<b>C</b>,<b>D</b>) illustrate the top 20 KEGG pathways for SPG and SPCs DEGs.</p>
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<p>Functional enrichment analysis of Sertoli cells (SCs) and LCs was performed. The top 20 GO terms associated with DEGs in SCs (<b>A</b>) and LCs (<b>B</b>) are presented. Additionally, the top 20 KEGG pathways linked to DEGs in SCs (<b>C</b>) and LCs (<b>D</b>) are identified.</p>
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17 pages, 744 KiB  
Review
Mechanisms and Emerging Regulators of Neuroinflammation: Exploring New Therapeutic Strategies for Neurological Disorders
by Mi Eun Kim and Jun Sik Lee
Curr. Issues Mol. Biol. 2025, 47(1), 8; https://doi.org/10.3390/cimb47010008 - 26 Dec 2024
Viewed by 537
Abstract
Neuroinflammation is a complex and dynamic response of the central nervous system (CNS) to injury, infection, and disease. While acute neuroinflammation plays a protective role by facilitating pathogen clearance and tissue repair, chronic and dysregulated inflammation contributes significantly to the progression of neurodegenerative [...] Read more.
Neuroinflammation is a complex and dynamic response of the central nervous system (CNS) to injury, infection, and disease. While acute neuroinflammation plays a protective role by facilitating pathogen clearance and tissue repair, chronic and dysregulated inflammation contributes significantly to the progression of neurodegenerative disorders such as Alzheimer’s disease, Parkinson’s disease, and Multiple Sclerosis. This review explores the cellular and molecular mechanisms underlying neuroinflammation, focusing on the roles of microglia, astrocytes, and peripheral immune cells. Key signaling pathways, including NF-κB, JAK-STAT, and the NLRP3 inflammasome, are discussed alongside emerging regulators such as non-coding RNAs, epigenetic modifications, and the gut–brain axis. The therapeutic landscape is evolving, with traditional anti-inflammatory drugs like NSAIDs and corticosteroids offering limited efficacy in chronic conditions. Immunomodulators, gene and RNA-based therapeutics, and stem cell methods have all shown promise for more specific and effective interventions. Additionally, the modulation of metabolic states and gut microbiota has emerged as a novel strategy to regulate neuroinflammation. Despite significant progress, challenges remain in translating these findings into clinically viable therapies. Future studies should concentrate on integrated, interdisciplinary methods to reduce chronic neuroinflammation and slowing the progression of neurodegenerative disorders, providing opportunities for revolutionary advances in CNS therapies. Full article
(This article belongs to the Special Issue The Role of Neuroinflammation in Neurodegenerative Diseases)
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<p>The major molecular pathways driving neuroinflammation in Alzheimer’s disease, highlighting their interconnected roles in perpetuating chronic inflammation. At the center of the diagram is neuroinflammation, which is fueled by four key pathways. The NF-κB pathway, activated by amyloid beta (Aβ) and tau proteins, promotes the production of pro-inflammatory cytokines such as TNF-α and IL-6, amplifying the inflammatory response. The NLRP3 inflammasome, triggered by Aβ, reactive oxygen species (ROS), and mitochondrial dysfunction, leads to the release of IL-1β and IL-18, further escalating neuroinflammation. The JAK-STAT pathway, induced by cytokines like IL-6, drives the activation of neurotoxic astrocytes, which contribute to neuronal damage. Finally, the MAPK pathway, stimulated by Aβ, tau, and oxidative stress, enhances ROS production and cytokine release, exacerbating oxidative damage and inflammation. Together, these pathways form a complex network that underpins the inflammatory processes observed in Alzheimer’s disease.</p>
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18 pages, 1401 KiB  
Review
Advanced In Vitro Models for Preclinical Drug Safety: Recent Progress and Prospects
by Dileep G. Nair and Ralf Weiskirchen
Curr. Issues Mol. Biol. 2025, 47(1), 7; https://doi.org/10.3390/cimb47010007 - 26 Dec 2024
Viewed by 721
Abstract
The majority of drugs are typically orally administered. The journey from drug discovery to approval is often long and expensive, involving multiple stages. A major challenge in the drug development process is drug-induced liver injury (DILI), a condition that affects the liver, the [...] Read more.
The majority of drugs are typically orally administered. The journey from drug discovery to approval is often long and expensive, involving multiple stages. A major challenge in the drug development process is drug-induced liver injury (DILI), a condition that affects the liver, the organ responsible for metabolizing most drugs. Traditionally, identifying DILI risk has been difficult due to the poor correlation between preclinical animal models and in vitro systems. Differences in physiology between humans and animals or cell lines contribute to the failure of many drug programs during clinical trials. The use of advanced in vitro systems that closely mimic human physiology, such as organ-on-a-chip models like gut–liver-on-a-chip, can be crucial in improving drug efficacy while minimizing toxicity. Additionally, the adaptation of these technologies has the potential to significantly reduce both the time and cost associated with obtaining safe drug approvals, all while adhering to the 3Rs principle (replacement, reduction, refinement). In this review, we discuss the significance, current status, and future prospects of advanced platforms, specifically organ-on-a-chip models, in supporting preclinical drug discovery. Full article
(This article belongs to the Special Issue Advances in Molecular Biology Methods in Hepatology Research)
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<p>Schematic representation of the drug discovery and development pipeline. The first step is target identification, followed by target validation. Lead discovery is the next step and is generally pursued using high-throughput screening. Lead optimization is carried out before preclinical studies. The drug candidates selected from preclinical studies are taken to clinical trials, and a successful drug is approved for clinical usage.</p>
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<p>Uses of intestinal organoids. Intestinal organoids have a variety of common applications, including replacing or compensating for functional structures, mimicking host–microbe interactions, studying interactions with immune cell types, and investigating drug toxicity or developmental biology.</p>
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<p>Clinically relevant in vitro models that demonstrate increased correlation to human biology. This progression is evident through the advancement from 2D cell culture models to 3D models like spheroids or organoids and ultimately to organ-on-a-chip models.</p>
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<p>Gut–liver axis during normal conditions compared to conditions with metabolic dysfunction-associated fatty liver disease (MAFLD). (<b>A</b>) In normal conditions, there is bidirectional communication between the intestines and liver, known as the gut–liver axis, which helps maintain homeostasis. (<b>B</b>) However, under MAFLD conditions, factors originating in the gut, such as dysbiosis, leaky gut, and inflammatory cytokines, can impact the liver. Additionally, altered bile acid metabolites can affect gut function. An efficient organ-on-a-chip model can be used to represent this scenario [<a href="#B50-cimb-47-00007" class="html-bibr">50</a>].</p>
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27 pages, 2314 KiB  
Review
Anticancer Ribosomally Synthesized and Post-Translationally Modified Peptides from Plants: Structures, Therapeutic Potential, and Future Directions
by Hyeon-Jeong Hwang, Youngsang Nam, Chanhee Jang, Eun La Kim, Eun Seo Jang, Yeo Jin Lee and Seoung Rak Lee
Curr. Issues Mol. Biol. 2025, 47(1), 6; https://doi.org/10.3390/cimb47010006 - 26 Dec 2024
Viewed by 494
Abstract
Cancer remains a significant medical challenge, necessitating the discovery of novel therapeutic agents. Ribosomally synthesized and post-translationally modified peptides (RiPPs) from plants have emerged as a promising source of anticancer compounds, offering unique structural diversity and potent biological activity. This review identifies and [...] Read more.
Cancer remains a significant medical challenge, necessitating the discovery of novel therapeutic agents. Ribosomally synthesized and post-translationally modified peptides (RiPPs) from plants have emerged as a promising source of anticancer compounds, offering unique structural diversity and potent biological activity. This review identifies and discusses cytotoxic RiPPs across various plant families, focusing on their absolute chemical structures and reported cytotoxic activities against cancer cell lines. Notably, plant-derived RiPPs such as rubipodanin A and mallotumides A–C demonstrated low nanomolar IC50 values against multiple cancer cell types, highlighting their therapeutic potential. By integrating traditional ethnobotanical knowledge with modern genomic and bioinformatic approaches, this study underscores the importance of plant RiPPs as a resource for developing innovative cancer treatments. These findings pave the way for further exploration of plant RiPPs, emphasizing their role in addressing the ongoing challenges in oncology and enhancing the repertoire of effective anticancer therapies. Full article
(This article belongs to the Special Issue Phytochemicals in Cancer Chemoprevention and Treatment)
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<p>Typical biosynthetic pathway of RiPPs. The RiPP-related gene in DNA is transcribed into mRNA, and the mRNA is subsequently translated by ribosomes, resulting in the formation of a precursor peptide. (<b>A</b>) In most cases, precursor peptides are single-core form, containing a leader and core peptide, with the follower peptide being an optional component. (<b>B</b>) While less prevalent, some precursor peptides are multi-core form, consisting of two or more core peptides linked by recognition sequences. The precursor peptide undergoes post-translational modification by specific enzymes, which introduce modifications such as cyclization, dehydration, and methylation to the core peptide. Once these modifications are complete, proteases remove the leader and other peptides, converting the modified core peptide into a mature RiPP. At this stage, a variety of mature RiPPs can be produced from the multi-core precursor peptide.</p>
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<p>Precursor peptides of several plant-derived RiPPs. Single-core precursor peptides containing one core peptide. The core peptides of Oak1 and SgA1 are subsequently processed into kalata B1 and segetalin A, respectively. Multi-core precursor peptides containing multiple core peptides. TIPTOP2 contains six core peptides, which correspond to four types of core peptides processed into MCoTI-I, MCoTI-II, MCoTI-IV, and MCoTI-V. LbaLycA contains twelve core peptides, composed of three types that will mature into lyciumin A, lyciumin B, and lyciumin D. RS: Recognition sequence.</p>
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<p>Diagram of proteolysis mechanisms in the biosynthetic pathways of plant RiPPs. (<b>A</b>) One-step removal of peptides. In this mechanism, the peptide sequence upstream or downstream of the core peptide is removed in a single step by one enzyme during maturation into the final RiPP product. (<b>B</b>) Two-step removal of peptides. In this mechanism, peptide sequences are removed through a multi-step process, either by the sequential activity of different enzymes or by repeated actions of the same enzyme.</p>
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<p>Scheme of biosynthetic pathways for a typical cyclotide (<b>A</b>) and the orbitide, segetalin A (<b>B</b>). (<b>A</b>) Cyclotide biosynthesis. The cyclotide precursor peptide is transported to the ER via an N-terminal signal sequence. Disulfide bonds are first formed by a disulfide isomerase. Subsequently, N-terminal processing proteases remove all peptides upstream of the core peptide, including the leader peptide. Finally, the head-to-tail cyclization of the core peptide is facilitated by an AEP-like ligase, which also removes the downstream peptide sequences, yielding the mature cyclotide. (<b>B</b>) Segetalin A biosynthesis. The precursor peptide for segetalin A first undergoes leader peptide removal by OLP1, resulting in presegetalin A1. Subsequently, PCY1 catalyzes macrocylization to produce the mature segetalin A.</p>
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<p>Chemical structures of compounds <b>1</b>–<b>6</b>.</p>
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<p>Chemical structures of cyclic peptides <b>7</b>–<b>21</b>.</p>
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<p>Chemical structures of cyclic peptides <b>22</b>–<b>25</b>.</p>
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<p>Chemical structures of cyclic peptides <b>26</b>–<b>30</b>.</p>
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<p>Chemical structures of peptides <b>31</b>–<b>33</b>.</p>
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17 pages, 2724 KiB  
Article
Whole Genome Identification and Integrated Analysis of Long Non-Coding RNAs Responding ABA-Mediated Drought Stress in Panax ginseng C.A. Meyer
by Peng Chen, Cheng Chang and Lingyao Kong
Curr. Issues Mol. Biol. 2025, 47(1), 5; https://doi.org/10.3390/cimb47010005 - 25 Dec 2024
Viewed by 383
Abstract
Panax ginseng C.A. Meyer is a perennial herb that is used worldwide for a number of medical purposes. Long non-coding RNAs (lncRNAs) play a crucial role in diverse biological processes but still remain poorly understood in ginseng, which has limited the application of [...] Read more.
Panax ginseng C.A. Meyer is a perennial herb that is used worldwide for a number of medical purposes. Long non-coding RNAs (lncRNAs) play a crucial role in diverse biological processes but still remain poorly understood in ginseng, which has limited the application of molecular breeding in this plant. In this study, we identified 17,478 lncRNAs and 3106 novel mRNAs from ginseng by high-throughput illumine sequencing. 50 and 257 differentially expressed genes (DEGs) and DE lncRNAs (DELs) were detected under drought + ABA vs. drought conditions, respectively. The DEGs and DELs target genes main enrichment is focused on the “biosynthesis of secondary metabolites”, “starch and sucrose metabolism”, and “carbon metabolism” pathways under drought + ABA vs. drought conditions according to KEGG pathway enrichment analysis, suggesting that these secondary metabolites biosynthesis pathways might be crucial for ABA-mediated drought stress response in ginseng. Together, we identified drought stress response lncRNAs in ginseng for the first time and found that the target genes of these lncRNAs mainly regulate the biosynthesis of secondary metabolites pathway to response to drought stress. These findings also open up a new visual for molecular breeding in ginseng. Full article
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<p>The RNA sequencing and bioinformatic analysis workflow in this study. The orange box shows the process of sequencing and bioinformatic analysis. The green box illustrates the data source and criteria quality control. LncRNAs and mRNAs identification were showed in the yellow box. Differential expression analysis and function enrichment analysis are displayed in the purple box.</p>
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<p>A comprehensive analysis of lncRNAs and mRNAs in ginseng roots. (<b>A</b>) Pie diagram shows the counts of different kind of lncRNAs. (<b>B</b>–<b>D</b>) Comparison of exon number, transcript length and ORF length between lncRNAs and mRNAs.</p>
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<p>Differentially expressed genes (DEGs) at the ginseng root tissue under different treatment conditions. D + A, represented drought + ABA treatment. (<b>A</b>) Volcano plots of DEGs in drought vs. CK. (<b>B</b>) Volcano plots of DEGs in D + A vs. CK. (<b>C</b>) Volcano plots of DEGs in D + A vs. drought. Upregulated, down-regulated, and non-differentially expressed genes are represented by red, green, and blue dots, respectively. (<b>D</b>) Heatmap and cluster analysis of the expression level of DEGs. CK1, CK2, CK3, D1, D2, D3, D + A1, D + A2, and D + A3 represent three repetitions of control, drought, and drought +ABA treatment, respectively.</p>
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<p>Comparison of Gene Ontology (GO) classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially expressed genes (DEGs). (<b>A</b>–<b>C</b>) GO analysis of DEGs in drought vs. control, drought + ABA vs. control and drought + ABA vs. drought, respectively. (<b>D</b>) KEGG pathway enrichment analysis of DEGs in drought + ABA vs. drought. The size of the dot indicates the number of DEGs in the corresponding pathway. BP, CC, and MF represent biological processes (BP), cellular components (CC), and molecular functions (MF), respectively.</p>
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<p>Differentially expressed lncRNAs (DELs) at the ginseng root tissue under different treatment conditions. D + A, represented drought + ABA treatment. (<b>A</b>) Volcano plots of DELs in drought vs. CK. (<b>B</b>) Volcano plots of DELs in D + A vs. CK. (<b>C</b>) Volcano plots of DELs in D + A vs. drought. Up-regulated, down-regulated, and non-differentially expressed lncRNAs are represented by red, green, and blue dots, respectively. (<b>D</b>) Heatmap and cluster analysis of the expression level of DELs. CK1, CK2, CK3, D1, D2, D3, D + A1, D + A2, and D + A3 represent three repetitions of control, drought, and drought +ABA treatment, respectively.</p>
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<p>Comparison of Gene Ontology (GO) classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the target genes of differentially expressed lnCRNAs (DELs). (<b>A</b>–<b>C</b>) GO analysis of these target genes in drought vs. control, drought + ABA vs. control and drought + ABA vs. drought, respectively. (<b>D</b>) KEGG pathway enrichment analysis of target genes of lncRNAs in drought + ABA vs. drought treatment. The size of the dot indicates the number of target genes in the corresponding pathway. BP, CC, and MF represent biological processes (BP), cellular components (CC), and molecular functions (MF), respectively.</p>
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