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Int. J. Mol. Sci., Volume 18, Issue 12 (December 2017) – 280 articles

Cover Story (view full-size image): The unprecedented crystal structure of Mycobacterium tuberculosis O6-alkylguanine-DNA alkyltransferase (OGT) in complex, with a modified double-stranded DNA (dsDNA) reveals molecular details of the cooperative DNA binding mechanism of this suicidal enzyme. A peculiar supramolecular assembly can be observed in the OGT–dsDNA crystal lattice in which three different protein units are oriented onto the same DNA molecule. In particular, one protein unit binds a modified guanine base, while both additional protein monomers flip out a deoxyadenosine residue. View this paper
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2082 KiB  
Article
Leptin Stimulates Prolactin mRNA Expression in the Goldfish Pituitary through a Combination of the PI3K/Akt/mTOR, MKK3/6/p38MAPK and MEK1/2/ERK1/2 Signalling Pathways
by Aifen Yan, Yanfeng Chen, Shuang Chen, Shuisheng Li, Yong Zhang, Jirong Jia, Hui Yu, Lian Liu, Fang Liu, Chaoqun Hu, Dongsheng Tang and Ting Chen
Int. J. Mol. Sci. 2017, 18(12), 2781; https://doi.org/10.3390/ijms18122781 - 20 Dec 2017
Cited by 14 | Viewed by 6633
Abstract
Leptin actions at the pituitary level have been extensively investigated in mammalian species, but remain insufficiently characterized in lower vertebrates, especially in teleost fish. Prolactin (PRL) is a pituitary hormone of central importance to osmoregulation in fish. Using goldfish as a model, we [...] Read more.
Leptin actions at the pituitary level have been extensively investigated in mammalian species, but remain insufficiently characterized in lower vertebrates, especially in teleost fish. Prolactin (PRL) is a pituitary hormone of central importance to osmoregulation in fish. Using goldfish as a model, we examined the global and brain-pituitary distribution of a leptin receptor (lepR) and examined the relationship between expression of lepR and major pituitary hormones in different pituitary regions. The effects of recombinant goldfish leptin-AI and leptin-AII on PRL mRNA expression in the pituitary were further analysed, and the mechanisms underlying signal transduction for leptin-induced PRL expression were determined by pharmacological approaches. Our results showed that goldfish lepR is abundantly expressed in the brain-pituitary regions, with highly overlapping PRL transcripts within the pituitary. Recombinant goldfish leptin-AI and leptin-AII proteins could stimulate PRL mRNA expression in dose- and time-dependent manners in the goldfish pituitary, by both intraperitoneal injection and primary cell incubation approaches. Moreover, the PI3K/Akt/mTOR, MKK3/6/p38MAPK, and MEK1/2/ERK1/2—but not JAK2/STAT 1, 3 and 5 cascades—were involved in leptin-induced PRL mRNA expression in the goldfish pituitary. Full article
(This article belongs to the Section Biochemistry)
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Graphical abstract

Graphical abstract
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<p>In vivo and in vitro effects of leptin-AI and leptin-AII treatment on <span class="html-italic">PRL</span> transcripts in goldfish. Dose- (<b>A</b>) and time-dependent (<b>B</b>) effects of leptin-AI or leptin-AII IP injection on <span class="html-italic">PRL</span> mRNA expression in the pituitary. For time-course experiments, goldfish were IP injected with leptin-AI or leptin-AII at 100 ng/g for 0, 3, 6, 12, 24 and 48 h. For dose-dependence experiments, the goldfish were IP-injected with leptin-AI or leptin-AII for 24 h with increasing doses (1–100 ng/g). Dose- (<b>C</b>) and time-dependent (<b>D</b>) effects of leptin-AI or leptin-AII incubation on <span class="html-italic">PRL</span> mRNA expression in primary cultured pituitary cells. For time-course experiments, goldfish pituitary cells were incubated for 3, 6, 12, 24, and 48 h with leptin-AI or leptin-AII, at a concentration of 100 nM. For dose-dependence experiments, goldfish pituitary cells were incubated with leptin-AI or leptin-AII for 24 h with increasing doses (0.01–100 nM). In these studies, data are expressed as the mean ± standard error (SE, <span class="html-italic">n</span> = 10 for the in vivo study, and <span class="html-italic">n</span> = 4 for the in vitro study). The same letter represents a similar level of transcriptional expression (<span class="html-italic">p</span> &gt; 0.05), and a different letter represents significant difference in levels of transcriptional expression between two groups (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>A</b>) Expression profiles of goldfish <span class="html-italic">lepR</span> in various tissues and brain regions, including brain (Br), gill (Gi), heart (Ht), intestine (In), liver (Lv), spleen (Sp), kidney (Kn), muscle (Ms), Fat (Fa), testis (Ts), ovary (Ov), the olfactory bulb (OB), telencephalon (Te), optic tectum (OT), cerebellum (Ce), medulla oblongata (MO), spinal cord (SC), hypothalamus (Hy), and pituitary (Pi), as assessed by real-time quantitative (qPCR); (<b>B</b>) Expression pattern of major hormones, including <span class="html-italic">GH</span>, <span class="html-italic">PRL</span>, <span class="html-italic">POMC</span>, <span class="html-italic">GTH-α</span>, <span class="html-italic">SL-α</span>, <span class="html-italic">SL-β</span>, <span class="html-italic">leptin</span>-AI, <span class="html-italic">leptin</span>-AII and <span class="html-italic">lepR,</span> in different regions of pituitary assessed by qPCR. T-P indicates total pituitary; A-P indicates anterior pituitary, corresponding to rostral pars distalis (RPD) in mammalian pituitary; M-P indicates medium pituitary, corresponding to proximal pars distalis (PPD) in mammalian pituitary; P-P indicates posterior pituitary corresponding to neurointermediate lobe (NIL), consisting of neurohypophysis (NHP) and pars intermedia (PI) in mammalian pituitary; (<b>C</b>) diagram showing the goldfish brain regions; (<b>D</b>) diagram showing different parts of the goldfish pituitary. In these studies, data are expressed as the mean ± SE (<span class="html-italic">n</span> = 3 for testis and ovary, and <span class="html-italic">n</span> = 6 for other tissue and pituitary samples).</p>
Full article ">Figure 3
<p>Effects of JAK/STAT signal pathway blockers on leptin-stimulated <span class="html-italic">PRL</span> mRNA levels in primary cultured goldfish pituitary cells. Goldfish pituitary cells were treated with leptin-AI (100 nM) or leptin-AII (100 nM), in the presence or absence of JAK2 inhibitor AG490 (50 μM, <b>A</b>), STAT1 inhibitor FA (10 μM, <b>B</b>), STAT3 inhibitor DPP (500 nM, <b>C</b>), and STAT5 inhibitor IQDMA (20 μM, <b>D</b>) for 24 h. In the present study, the data are expressed as mean ± SE (<span class="html-italic">n</span> = 4). The same letter represents a similar level of transcriptional expression (<span class="html-italic">p</span> &gt; 0.05), and a different letter represents significant difference in levels of transcriptional expression between two groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 4
<p>Effects of PI3K/Akt/mTOR signal pathway blockers on leptin-stimulated <span class="html-italic">PRL</span> mRNA levels in primary cultured goldfish pituitary cells. Goldfish pituitary cells were treated with leptin-AI (100 nM) or leptin-AII (100 nM) in the presence or absence of PI3K inhibitors LY294002 (10 μM, <b>A</b>) and wortmannin (100 nM, <b>B</b>), Akt inhibitor API2 (100 nM, <b>C</b>), and mTOR inhibitor rapamycin (20 nM, <b>D</b>) for 24 h. In the present study, the data are expressed as mean ± SE (<span class="html-italic">n</span> = 4). The same letter represents a similar level of transcriptional expression (<span class="html-italic">p</span> &gt; 0.05), and a different letter represents significant difference in levels of transcriptional expression between two groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 5
<p>Effects of MAPK signal pathway blockers on leptin-stimulated <span class="html-italic">PRL</span> mRNA levels in primary cultured goldfish pituitary cells. Goldfish pituitary cells were treated with leptin-AI (100 nM) or leptin-AII (100 nM) in the presence or absence of p<sup>38</sup>MAPK inhibitors PD169316 (100 nM, <b>A</b>) and SB02190 (100 nM, <b>B</b>), MEK<sub>1/2</sub> inhibitors PD98059 (10 μM, <b>C</b>) and U0126 (200 nM, <b>D</b>), for 24 h. In the present study, the data are expressed as mean ± SE (<span class="html-italic">n</span> = 4). The same letter represents a similar level of transcriptional expression (<span class="html-italic">p</span> &gt; 0.05), and a different letter represents significant difference in levels of transcriptional expression between the two groups (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Working model for signal transduction mechanisms involved in leptin stimulation of <span class="html-italic">PRL</span> gene expression via lepR in the goldfish pituitary.</p>
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1047 KiB  
Article
Evaluation of Biosynthesis, Accumulation and Antioxidant Activityof Vitamin E in Sweet Corn (Zea mays L.) during Kernel Development
by Lihua Xie, Yongtao Yu, Jihua Mao, Haiying Liu, Jian Guang Hu, Tong Li, Xinbo Guo and Rui Hai Liu
Int. J. Mol. Sci. 2017, 18(12), 2780; https://doi.org/10.3390/ijms18122780 - 20 Dec 2017
Cited by 28 | Viewed by 4927
Abstract
Sweet corn kernels were used in this research to study the dynamics of vitamin E, by evaluatingthe expression levels of genes involved in vitamin E synthesis, the accumulation of vitamin E, and the antioxidant activity during the different stage of kernel development. Results [...] Read more.
Sweet corn kernels were used in this research to study the dynamics of vitamin E, by evaluatingthe expression levels of genes involved in vitamin E synthesis, the accumulation of vitamin E, and the antioxidant activity during the different stage of kernel development. Results showed that expression levels of ZmHPT and ZmTC genes increased, whereas ZmTMT gene dramatically decreased during kernel development. The contents of all the types of vitamin E in sweet corn had a significant upward increase during kernel development, and reached the highest level at 30 days after pollination (DAP). Amongst the eight isomers of vitamin E, the content of γ-tocotrienol was the highest, and increased by 14.9 folds, followed by α-tocopherolwith an increase of 22 folds, and thecontents of isomers γ-tocopherol, α-tocotrienol, δ-tocopherol,δ-tocotrienol, and β-tocopherol were also followed during kernel development. The antioxidant activity of sweet corn during kernel development was increased, and was up to 101.8 ± 22.3 μmol of α-tocopherol equivlent/100 g in fresh weight (FW) at 30 DAP. There was a positive correlation between vitamin E contents and antioxidant activity in sweet corn during the kernel development, and a negative correlation between the expressions of ZmTMT gene and vitamin E contents. These results revealed the relations amongst the content of vitamin E isomers and the gene expression, vitamin E accumulation, and antioxidant activity. The study can provide a harvesting strategy for vitamin E bio-fortification in sweet corn. Full article
(This article belongs to the Special Issue Molecular Transformations of Natural Products)
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Graphical abstract

Graphical abstract
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<p>Relative expression of genes involved in vitamin E synthesis during sweet corn kernel development (mean ± SE). <span class="html-italic"><sub>Zm</sub>HPT</span> = homogentisate phytyltransferase; <span class="html-italic"><sub>Zm</sub>TC</span> = tocopherol cyclase; <span class="html-italic"><sub>Zm</sub>TMT</span> = γ-tocopherol methyltransferase; DAP = days after pollination. Bars with no letters in common are significantly different (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 2
<p>NP-HPLC chromatogram of vitamin E standard and extracts in sweet corn during kernel development (numbers 1–7 represent α-T, α-T3, β-T, γ-T, γ-T3, δ-T, δ-T3, respectively, T means tocopherol, and T3 means tocotrienol). (<b>a</b>) is the chromatograms of vitamin E standards; (<b>b</b>–<b>f</b>) are the chromatograms of sweet corn extracts from 10 DAP to 30 DAP. EU = Emission Unit.</p>
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<p>Total tocopherols (T), tocotrienols (T3) and vitamin E contents of sweet corn during kernel development (mean ± SD). Bars with no letters in common are significantly different (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">
7626 KiB  
Article
Anomalous Behavior of Hyaluronan Crosslinking Due to the Presence of Excess Phospholipids in the Articular Cartilage System of Osteoarthritis
by Piotr Bełdowski, Piotr Weber, Tomasz Andrysiak, Wayne K. Augé II, Damian Ledziński, Tristan De Leon and Adam Gadomski
Int. J. Mol. Sci. 2017, 18(12), 2779; https://doi.org/10.3390/ijms18122779 - 20 Dec 2017
Cited by 14 | Viewed by 4193
Abstract
Lubrication of articular cartilage is a complex multiscale phenomenon in synovial joint organ systems. In these systems, synovial fluid properties result from synergistic interactions between a variety of molecular constituent. Two molecular classes in particular are of importance in understanding lubrication mechanisms: hyaluronic [...] Read more.
Lubrication of articular cartilage is a complex multiscale phenomenon in synovial joint organ systems. In these systems, synovial fluid properties result from synergistic interactions between a variety of molecular constituent. Two molecular classes in particular are of importance in understanding lubrication mechanisms: hyaluronic acid and phospholipids. The purpose of this study is to evaluate interactions between hyaluronic acid and phospholipids at various functionality levels during normal and pathological synovial fluid conditions. Molecular dynamic simulations of hyaluronic acid and phospholipids complexes were performed with the concentration of hyaluronic acid set at a constant value for two organizational forms, extended (normal) and coiled (pathologic). The results demonstrated that phospholipids affect the crosslinking mechanisms of hyaluronic acid significantly and the influence is higher during pathological conditions. During normal conditions, hyaluronic acid and phospholipid interactions seem to have no competing mechanism to that of the interaction between hyaluronic acid to hyaluronic acid. On the other hand, the structures formed under pathologic conditions were highly affected by phospholipid concentration. Full article
(This article belongs to the Section Molecular Biophysics)
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Figure 1

Figure 1
<p>Artistic depiction of simplified composition articular cartilage in synovial joint organ system at different levels of functionality. The articular cartilage surfaces are depicted as multilamellar bilayers of the surface active phospholipid layer without the underlying cartilage zones. Micellar structures are shown between the articulating surfaces in various states of micellization and hyaluronan is shown as molecular chains (green) in various states of crosslinking and network formation. Donut-like structures represent PL vesicles. PL can create cylinder like structures surrounding HA chains and form more complex networks. Lubricin, water and other SF components have not been depicted in the picture.</p>
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<p>The structures of HA and PL at the end of the simulation: (<b>A</b>,<b>C</b>,<b>E</b>,<b>G</b>) results for a normal network; and (<b>B</b>,<b>D</b>,<b>F</b>,<b>H</b>) and for a pathological network. From (<b>A</b>,<b>B</b>) to (<b>G</b>,<b>H</b>), the number of phospholipids increases. Carbon atoms are depicted as blue; hydrogen as white; and nitrogen as yellow. Starting from the second row, HA is depicted as green for better visualization of PL orientation towards the HA network.</p>
Full article ">Figure 2 Cont.
<p>The structures of HA and PL at the end of the simulation: (<b>A</b>,<b>C</b>,<b>E</b>,<b>G</b>) results for a normal network; and (<b>B</b>,<b>D</b>,<b>F</b>,<b>H</b>) and for a pathological network. From (<b>A</b>,<b>B</b>) to (<b>G</b>,<b>H</b>), the number of phospholipids increases. Carbon atoms are depicted as blue; hydrogen as white; and nitrogen as yellow. Starting from the second row, HA is depicted as green for better visualization of PL orientation towards the HA network.</p>
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<p>Total hydrogen bond energy in normal HA networks with increasing concentration of phospholipids: (<b>A</b>) intra-molecular H-bond energy is depicted; and (<b>B</b>) inter-molecular bond energy.</p>
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<p>Total hydrogen bond energy in pathologic HA networks with increasing concentration of phospholipids: (<b>A</b>) intra-molecular H-bond energy is depicted; and (<b>B</b>) inter-molecular bond energy.</p>
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<p>Total number of hydrophobic interactions for normal networks: (<b>A</b>) intra-molecular hydrophobic contacts are depicted; and (<b>B</b>) inter-molecular contacts.</p>
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<p>Total number of hydrophobic interactions for pathologic networks: (<b>A</b>) intra-molecular hydrophobic contacts are depicted; and (<b>B</b>) inter-molecular contacts.</p>
Full article ">Figure 7
<p>Normalized H-bond energy and hydrophobic interactions between HA and PL per one phospholipid: (<b>A</b>,<b>B</b>) inter-molecular networks are depicted; and (<b>C</b>,<b>D</b>) intra-molecular networks.</p>
Full article ">Figure 8
<p>Radius of gyration of an average HA chain: (<b>A</b>) inter-molecular networks are depicted; and (<b>B</b>) intra-molecular networks.</p>
Full article ">Figure 9
<p>Solvent accessible surface of HA networks: (<b>A</b>) inter-molecular networks are depicted; and (<b>B</b>) intra-molecular networks.</p>
Full article ">Figure 10
<p>Linear fit to MSD, according to sample MSD method: <math display="inline"> <semantics> <msub> <mi>c</mi> <mn>1</mn> </msub> </semantics> </math> (<b>A</b>,<b>C</b>); and <math display="inline"> <semantics> <msub> <mi>c</mi> <mn>4</mn> </msub> </semantics> </math> (<b>B</b>,<b>D</b>); and (<b>A</b>,<b>B</b>) normal structure; and (<b>C</b>,<b>D</b>) abnormal structure.</p>
Full article ">Figure 11
<p>Linear fit to inter-molecular H-bond energies for HA according to sample MSD method. Two pictures in left column correspond to <math display="inline"> <semantics> <msub> <mi>c</mi> <mn>1</mn> </msub> </semantics> </math> concentration and pictures in right column correspond to <math display="inline"> <semantics> <msub> <mi>c</mi> <mn>4</mn> </msub> </semantics> </math> concentration. Two pictures in first row correspond to normal physiological structure. In the second row - pathological structure.</p>
Full article ">Figure 12
<p>Final structure of 16 HA chains with PL in concentration <math display="inline"> <semantics> <mrow> <msub> <mi>c</mi> <mn>5</mn> </msub> <mo>=</mo> <mn>2</mn> <mo>×</mo> <msub> <mi>c</mi> <mn>4</mn> </msub> </mrow> </semantics> </math>. As one can see, PL can penetrate HA network and create micelle-like structures.</p>
Full article ">Figure 13
<p>Initial structures of HA chains (water molecules and PL are not depicted): (<b>A</b>) a parallel (normal, inter-molecular) network is depicted; and (<b>B</b>) a coiled network (abnormal, intra-molecular) is depicted.</p>
Full article ">
711 KiB  
Review
Iron Overload and Chelation Therapy in Non-Transfusion Dependent Thalassemia
by Rayan Bou-Fakhredin, Abdul-Hamid Bazarbachi, Bachar Chaya, Joseph Sleiman, Maria Domenica Cappellini and Ali T. Taher
Int. J. Mol. Sci. 2017, 18(12), 2778; https://doi.org/10.3390/ijms18122778 - 20 Dec 2017
Cited by 25 | Viewed by 6421
Abstract
Iron overload (IOL) due to increased intestinal iron absorption constitutes a major clinical problem in patients with non-transfusion-dependent thalassemia (NTDT), which is a cumulative process with advancing age. Current models for iron metabolism in patients with NTDT suggest that suppression of serum hepcidin [...] Read more.
Iron overload (IOL) due to increased intestinal iron absorption constitutes a major clinical problem in patients with non-transfusion-dependent thalassemia (NTDT), which is a cumulative process with advancing age. Current models for iron metabolism in patients with NTDT suggest that suppression of serum hepcidin leads to an increase in iron absorption and subsequent release of iron from the reticuloendothelial system, leading to depletion of macrophage iron, relatively low levels of serum ferritin, and liver iron loading. The consequences of IOL in patients with NTDT are multiple and multifactorial. Accurate and reliable methods of diagnosis and monitoring of body iron levels are essential, and the method of choice for measuring iron accumulation will depend on the patient’s needs and on the available facilities. Iron chelation therapy (ICT) remains the backbone of NTDT management and is one of the most effective and practical ways of decreasing morbidity and mortality. The aim of this review is to describe the mechanism of IOL in NTDT, and the clinical complications that can develop as a result, in addition to the current and future therapeutic options available for the management of IOL in NTDT. Full article
(This article belongs to the Special Issue Thalassemia in 2017)
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Figure 1
<p>Iron overload mechanism in non-transfusion-dependent thalassemia. GDF-15: growth differentiation factor-15; TWGF-1: twisted gastrulation factor-1; HIFs: hypoxia inducible transcription factors; TMPRSS6: transmembrane protease, serine 6. (↑: increase; ↓: decrease).</p>
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<p>Iron overload screening, diagnosis and treatment algorithms in non-transfusion-dependent thalassemia [<a href="#B1-ijms-18-02778" class="html-bibr">1</a>,<a href="#B37-ijms-18-02778" class="html-bibr">37</a>]. SF: serum ferritin; LIC: liver iron concentration in mg Fe/g dry weight; FU: follow-up.</p>
Full article ">
2158 KiB  
Article
Lipopolysaccharide-Induced Acute Kidney Injury Is Dependent on an IL-18 Receptor Signaling Pathway
by Yuji Nozaki, Shoichi Hino, Jinhai Ri, Kenji Sakai, Yasuaki Nagare, Mai Kawanishi, Kaoru Niki, Masanori Funauchi and Itaru Matsumura
Int. J. Mol. Sci. 2017, 18(12), 2777; https://doi.org/10.3390/ijms18122777 - 20 Dec 2017
Cited by 20 | Viewed by 6295
Abstract
The proinflammatory cytokine interleukin (IL)-18 is an important mediator of the organ failure induced by endotoxemia. IL-18 (known as an interferon-gamma (IFN-γ) inducing factor), and other inflammatory cytokines have important roles in lipopolysaccharide (LPS)-induced acute kidney injury (AKI). We investigated the effect of [...] Read more.
The proinflammatory cytokine interleukin (IL)-18 is an important mediator of the organ failure induced by endotoxemia. IL-18 (known as an interferon-gamma (IFN-γ) inducing factor), and other inflammatory cytokines have important roles in lipopolysaccharide (LPS)-induced acute kidney injury (AKI). We investigated the effect of inflammatory cytokines and Toll-like receptor 4 (TLR4) expression, an event that is accompanied by an influx of monocytes, including CD4+ T cells and antigen-presenting cells (APCs) in IL-18Rα knockout (KO) mice and wild-type (WT) mice after LPS injection. In the acute advanced phase, the IL-18Rα KO mice showed a higher survival rate and a suppressed increase of blood urea nitrogen, increased levels of proinflammatory cytokines such as IFN-γ and IL-18, the infiltration of CD4+ T cells and the expression of kidney injury molecule-1 as an AKI marker. In that phase, the renal mRNA expression of the M1 macrophage phenotype and C-C chemokine receptor type 7 as the maturation marker of dendritic cells (DCs) was also significantly decreased in the IL-18Rα KO mice, although there were small numbers of F4/80+ cells and DCs in the kidney. Conversely, there were no significant differences in the expressions of mRNA and protein TLR4 after LPS injection between the WT and IL-18Rα KO groups. Our results demonstrated that the IL-18Rα-mediated signaling pathway plays critical roles in CD4+ T cells and APCs and responded more quickly to IFN-γ and IL-18 than TLR4 stimulation in the pathogenesis of LPS-induced AKI. Full article
(This article belongs to the Special Issue Signaling Pathway of Immune Cells and Immune Disorder)
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Figure 1
<p>Lipopolysaccharide (LPS) affects the expression levels of interleukin (IL)-18, IL-18Rα and IL-18Rβ in the mouse kidney. C57BL/6 mice were injected intraperitoneally with 30 mg/kg LPS and sacrificed at 18 and 120 h after LPS injection. At 18 and 120 h, CD4<sup>+</sup> T cells and F4/80<sup>+</sup> cells were isolated using a BD FACSAria<sup>TM</sup> special-order research product with purities of 90–95% from splenocytes in C57BL/6 WT mice. Gene expressions of IL-18, IL-18Rα and IL-18Rβ were measured by real-time PCR. In each experiment, the expression levels were normalized to the expression of 18SrRNA and were expressed relative to the values of saline-treated control mice. The data are the mean fold-increase ± SEM: ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001, the mice without and with LPS injection at 0 and 120 h (<span class="html-italic">n</span> = 4 and <span class="html-italic">n</span> = 6) vs. 18 h (<span class="html-italic">n</span> = 4) after LPS injection.</p>
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<p>Effect of IL-18Rα on the survival and blood urea nitrogen (BUN) level in the development of AKI after LPS injection. (<b>A</b>) Survival of WT and IL-18Rα KO mice subjected to sepsis by LPS injection. Mice were evaluated 2×/day until 120 h post-LPS injection (WT <span class="html-italic">n</span> = 13; IL-18Rα KO mice <span class="html-italic">n</span> = 18; **** <span class="html-italic">p</span> &lt; 0.001). (<b>B</b>) Renal function of WT and IL-18Rα KO mice at 0, 18 and 120 h before and after LPS injection, assessed by BUN levels. The data are the mean values ± SEM (** <span class="html-italic">p</span> &lt; 0.01, WT vs. IL-18Rα KO mice).</p>
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<p>Effect of IL-18Rα on the production of inflammatory cytokines after LPS injection. Serum TNF, IFN-γ, IL-6, IL-10, IL-12p40 and IL-18 productions were measured as biomarkers of AKI at 0 (KO and WT, <span class="html-italic">n</span> = 3), 18 (<span class="html-italic">n</span> = 11 and <span class="html-italic">n</span> = 10) and 120 h (<span class="html-italic">n</span> = 16 and <span class="html-italic">n</span> = 6) before and after LPS injection. The data are the mean values ± SEM (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, WT vs. IL-18Rα KO mice).</p>
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<p>Effect of IL-18Rα on the accumulation of inflammation cells in the glomerulus and interstitium after LPS injection The accumulation of CD4<sup>+</sup> T cells, F4/80<sup>+</sup>, CD68<sup>+</sup> and CD11c<sup>+</sup> cells in the glomerulus and interstitium at 0 (KO and WT, <span class="html-italic">n</span> = 3), 18 (<span class="html-italic">n</span> = 11 and <span class="html-italic">n</span> = 10) and 120 h (<span class="html-italic">n</span> = 16 and <span class="html-italic">n</span> = 6) before and after LPS injection. The data are the mean ± SEM (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, the cell numbers in IL-18Rα KO vs. WT mice).</p>
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<p>The effect of IL-18Rα on the Kim-1 expression after LPS injection. No tubular Kim-1 was observed in the WT or IL-18Rα KO mice without LPS injection (<b>A</b>,<b>D</b>; <span class="html-italic">n</span> = 3). At 18 h post-LPS injection, there were more tubular Kim-1<sup>+</sup> cells (indicated by <b>red arrows</b> at high power, ×400) in the WT mice (<b>B</b>, <span class="html-italic">n</span> = 11) than in the IL-18Rα KO mice (<b>E</b>, <span class="html-italic">n</span> = 10). At 120 h after LPS injection, few tubular Kim-1 cells are present in the WT and IL-18Rα KO mice (<b>C</b>, <span class="html-italic">n</span> = 16; <b>F</b>, <span class="html-italic">n</span> = 6). In the IL-18Rα KO mice, there were significantly fewer Kim-1<sup>+</sup> proximal tubules (<b>G</b>). At 18 h, the intrarenal Kim-1 mRNA expression was decreased in the IL-18Rα KO mice compared to the WT mice (WT, <span class="html-italic">n</span> = 11; KO, <span class="html-italic">n</span> = 10). However, the Kim-1 mRNA expression was increased in the IL-18Rα KO mice compared to the WT mice at 120 h after LPS injection (<b>H</b>, <span class="html-italic">n</span> = 16 and <span class="html-italic">n</span> = 6). Photomicrographs were taken at ×400. Values are the mean ± SEM (* <span class="html-italic">p</span> &lt; 0.05 and *** <span class="html-italic">p</span> &lt; 0.005). Scale bar, 50 μm.</p>
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<p>Effect of IL-18Rα on TLR4 in LPS-induced AKI. The gene expression (<b>A</b>), protein levels (<b>B</b>) and tubular staining of TLR4 (<b>D</b>) in WT and IL-18Rα KO mice at 18 (<span class="html-italic">n</span> = 11, <span class="html-italic">n</span> = 10) and 120 h (<span class="html-italic">n</span> = 16, <span class="html-italic">n</span> = 6) after LPS injection were assayed by real-time PCR, Western blotting and immunohistochemistry. (<b>C</b>) A representative band of TLR4 and β-actin. In the immunohistochemical staining (<b>D</b>), TLR4<sup>+</sup> cells were present in the dilated tubules, indicated by <b>red arrow</b><b>s</b> (×400) in the WT and IL-18Rα KO mice at 18 and 120 h after LPS injection. The data are the mean ± SEM. Photomicrographs: ×400. Scale bar, 50 μm.</p>
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<p>Effect of splenocyte transfer on renal function and survival rates. (<b>A</b>) CD4<sup>+</sup> T cells and F4/80<sup>+</sup> cells (2 × 10<sup>6</sup>/mouse) from splenocytes of C57BL/6 WT mice were injected intravenously into each IL-18Rα KO mouse two days before LPS injection. Mice were sacrificed at 0 (each group; <span class="html-italic">n</span> = 3), 18 (<span class="html-italic">n</span> = 5 and <span class="html-italic">n</span> = 9) and 120 h (<span class="html-italic">n</span> = 5 and <span class="html-italic">n</span> = 7) after LPS injection. The data are the mean ± SEM (* <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.005). (<b>B</b>) The survival of the WT mice, the IL-18Rα KO mice and the IL-18Rα KO mice that received a transfer of CD4<sup>+</sup> T cells or F4/80<sup>+</sup> cells subjected to sepsis by LPS injection. Mice were evaluated 2×/day until 120 h (*** <span class="html-italic">p</span> &lt; 0.005). As controls, IL-18Rα KO and WT mice were also culled at 0 (each group; <span class="html-italic">n</span> = 3), 18 (<span class="html-italic">n</span> = 10 and <span class="html-italic">n</span> = 11) and 120 h (<span class="html-italic">n</span> = 5, <span class="html-italic">n</span> = 4), and we determined the survival rate at 120 h as an endpoint.</p>
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2793 KiB  
Article
The Role of PAR2 in TGF-β1-Induced ERK Activation and Cell Motility
by Hendrik Ungefroren, David Witte, Christian Fiedler, Thomas Gädeken, Roland Kaufmann, Hendrik Lehnert, Frank Gieseler and Bernhard H. Rauch
Int. J. Mol. Sci. 2017, 18(12), 2776; https://doi.org/10.3390/ijms18122776 - 20 Dec 2017
Cited by 19 | Viewed by 5983
Abstract
Background: Recently, the expression of proteinase-activated receptor 2 (PAR2) has been shown to be essential for activin receptor-like kinase 5 (ALK5)/SMAD-mediated signaling and cell migration by transforming growth factor (TGF)-β1. However, it is not known whether activation of non-SMAD TGF-β signaling (e.g., RAS–RAF–MEK–extracellular [...] Read more.
Background: Recently, the expression of proteinase-activated receptor 2 (PAR2) has been shown to be essential for activin receptor-like kinase 5 (ALK5)/SMAD-mediated signaling and cell migration by transforming growth factor (TGF)-β1. However, it is not known whether activation of non-SMAD TGF-β signaling (e.g., RAS–RAF–MEK–extracellular signal-regulated kinase (ERK) signaling) is required for cell migration and whether it is also dependent on PAR2. Methods: RNA interference was used to deplete cells of PAR2, followed by xCELLigence technology to measure cell migration, phospho-immunoblotting to assess ERK1/2 activation, and co-immunoprecipitation to detect a PAR2–ALK5 physical interaction. Results: Inhibition of ERK signaling with the MEK inhibitor U0126 blunted the ability of TGF-β1 to induce migration in pancreatic cancer Panc1 cells. ERK activation in response to PAR2 agonistic peptide (PAR2–AP) was strong and rapid, while it was moderate and delayed in response to TGF-β1. Basal and TGF-β1-dependent ERK, but not SMAD activation, was blocked by U0126 in Panc1 and other cell types indicating that ERK activation is downstream or independent of SMAD signaling. Moreover, cellular depletion of PAR2 in HaCaT cells strongly inhibited TGF-β1-induced ERK activation, while the biased PAR2 agonist GB88 at 10 and 100 µM potentiated TGF-β1-dependent ERK activation and cell migration. Finally, we provide evidence for a physical interaction between PAR2 and ALK5. Our data show that both PAR2–AP- and TGF-β1-induced cell migration depend on ERK activation, that PAR2 expression is crucial for TGF-β1-induced ERK activation, and that the functional cooperation of PAR2 and TGF-β1 involves a physical interaction between PAR2 and ALK5. Full article
(This article belongs to the Special Issue TGF-beta Family in Fibrosis and Cancer)
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<p>PAR2–AP- and TGF-β1-driven random cell migration are dependent on ERK activation. Panc1 cells (left-hand graph) and Colo357 cells (right-hand graph) were subjected to a cell migration assay (chemokinesis setup) in the presence of vehicle (0.1% dimethylsulfoxide, DMSO) or 20 µM U0126 and either PAR2–AP (15 µM 2-furoyl-LIGRLO-NH<sub>2</sub> (2f-LI), left-hand graph) or TGF-β1 (5 ng/mL, right-hand graph). In the left graph, differences are significant (<span class="html-italic">p</span> &lt; 0.05, unpaired Student’s <span class="html-italic">t</span>-test) between vehicle + PAR2–AP treated cells (blue curve, tracing B) and U0126 + PAR2–AP treated cells (magenta curve, tracing D) at 4:00 and all later time points, and between vehicle treated cells (red curve, tracing A) and U0126 treated cells (green curve, tracing C) at 6:00 and all later time points. In the right-hand graph, differences are significant between vehicle + TGF-β1 treated cells (blue curve, tracing B) and U0126 + TGF-β1 treated cells (magenta curve, tracing D) at 12:00 and all later time points. For a color-independent identification of the curves, see letters to the right of each graph. In each graph, data are shown from one representative experiment; three experiments were performed in total.</p>
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<p>Kinetics of ERK activation in response to PAR2–AP or TGF-β1 in various cell types. (<b>a</b>) Panc1 (left) and HaCaT (right) cells were stimulated with 2f-LI (15 µM) for 2–240 min and subsequently analyzed by phospho-immunoblotting for phospho-ERK1/2 (p-ERK1/2). Following removal of the p-ERK1/2 antibody, the blot was incubated with antibodies to total ERK1/2 and to HSP90 as a loading control; (<b>b</b>) Panc1 and Colo357 cells were stimulated with TGF-β1 (5 ng/mL) for 1–12 h and subjected to immunoblot analysis for p-ERK1/2, ERK, and HSP90 as described in (<b>a</b>). The graphs below the immunoblots show the results from the densitometric analysis of four independent experiments (mean ± SD). The asterisks indicate significant differences relative to the untreated control, <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>ERK1/2 but not SMAD3 activation in response to TGF-β1 was blocked by the MEK inhibitor U0126. The indicated cell lines were grown to confluence, starved for 24 h in medium containing 0.1% bovine serum albumin, and treated for the indicated times with TGF-β1 (T, 5 ng/mL) in the absence or presence of either vehicle (V, 0.2% DMSO), the MEK inhibitor U0126 (U, 20 µM), or the Rac1 inhibitor NSC23766 (N, 200 µM) as negative control. Cells were subjected to immunoblotting for p-ERK1/2, ERK1/2, p-SMAD3C, and SMAD3 and for HSP90 to control for equal loading. The total forms of ERK1/2 and SMAD3 were not different between the various time points and treatments. The functionality of U0126 was confirmed by its ability to block ERK1/2 activation after a 5 min challenge of cells with EGF (E, 10 ng/mL).</p>
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<p>Both PAR2–AP- and TGF-β1-induced ERK activation are dependent on PAR2 protein expression. (<b>a</b>) Panc1 cells were transiently transfected with 50 nM of either control (Co) siRNA or siRNA specific to PAR2 (PAR2). Following stimulation with PAR2–AP (P2-AP), PAR1–AP (P1-AP) for 5 min or TGF-β (T) for 1 h, cells were subjected to immunoblotting for p-ERK1/2 and ERK1/2, and for HSP90 as a loading control. The graph below the blot shows densitometric data (mean ± SD) of underexposed bands derived from three parallel wells. One representative experiment is shown out of three performed in total. Asterisks indicate significance <span class="html-italic">p</span> &lt; 0.05; (<b>b</b>) HaCaT cells were transfected with 50 nM of either Co siRNA or PAR2 siRNA, stimulated for the indicated times with TGF-β1, and processed for immunoblotting of p-ERK1/2 and ERK1/2. The graphs below the blots show results from densitometry-based quantification of three experiments, mean ± SD. The asterisk indicates significance <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>GB88 increases basal and TGF-β1-induced ERK activation and cell migration. (<b>a</b>) Panc1 cells were serum starved (1% fetal bovine serum, FBS) for 20 h prior to treatment for the indicated times with vehicle (V) or GB88 at concentrations (conc.) of either 10 µM or 100 µM. Crude cellular lysates were immunoblotted for p-ERK1/2, ERK1/2, and HSP90, and underexposed replicas subjected to densitometric analysis. Data in the graph represent the mean ± SD of three bands derived from cells from three parallel wells. One representative experiment is shown out of three performed in total. Asterisks indicate significance <span class="html-italic">p</span> &lt; 0.05; (<b>b</b>) Panc1 cells were subjected to real-time cell migration assays in the presence of vehicle (0.1% DMSO) and either 10 µM of GB88 (left-hand graph) or GB110 (right-hand graph). In the left-hand graph, differences are significant (<span class="html-italic">p</span> &lt; 0.05, unpaired Student’s <span class="html-italic">t</span>-test) between vehicle + TGF-β1 treated cells (blue curve, tracing B) and GB88 + TGF-β1 treated cells (magenta curve, tracing D) at 8:00 and all later time points. In the right-hand graph, differences are significant between vehicle + TGF-β1 treated cells (blue curve, tracing B) and GB110 + TGF-β1 treated cells (magenta curve, tracing D) at 4:00 and all later time points. In each graph, data are shown from one representative experiment out of three experiments performed in total.</p>
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<p>PAR2 and activin receptor-like kinase 5 (ALK5) can be co-immunoprecipitated. Panc1 cells were transfected with empty vector, PAR2-Myc-DKK, or ALK5-HA, alone or in combination, as indicated. Two days later, total cell lysates (20 µg each) were sequentially immunoblotted (IB) with IgG<sub>2a</sub> as negative control (left blot), and with anti-Myc tag (middle blot) and anti-ALK5 antibodies (right blot) for detection of transfected PAR2 and transfected (and endogenous) ALK5 protein, respectively. The appearance of bands for endogenous ALK5 indicates equal protein loading. Subsequently, anti-Myc or anti-His microbead-based IP was used on ~1 mg of lysate (duplicate samples) to precipitate PAR2–Myc–DKK along with associated proteins followed by immunoblotting for ALK5 (upper panel) and anti-Myc tag (lower panel). Numbers next to the molecular weight marker (M) lanes indicate the molecular mass (in kDa). Note that PAR2–Myc–DKK migrates as a complex of diffuse bands between ~100 and &gt;250 kDa because of heavy glycosylation [<a href="#B35-ijms-18-02776" class="html-bibr">35</a>,<a href="#B36-ijms-18-02776" class="html-bibr">36</a>]. Data shown are from a representative experiment out of five experiments performed in total.</p>
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6165 KiB  
Article
Evaluation of the Expression of Amine Oxidase Proteins in Breast Cancer
by Woo Young Sun, Junjeong Choi, Yoon Jin Cha and Ja Seung Koo
Int. J. Mol. Sci. 2017, 18(12), 2775; https://doi.org/10.3390/ijms18122775 - 20 Dec 2017
Cited by 29 | Viewed by 6496
Abstract
We aimed to evaluate the expression of amine oxidase proteins in breast cancer and their clinical implications. We performed immunohistochemical staining of amine oxidase proteins (LOX, lysyl oxidase, AOC3, amine oxidase, MAOA, monoamine oxidase A, MAOB, monoamine oxidase B). Based on their hormone [...] Read more.
We aimed to evaluate the expression of amine oxidase proteins in breast cancer and their clinical implications. We performed immunohistochemical staining of amine oxidase proteins (LOX, lysyl oxidase, AOC3, amine oxidase, MAOA, monoamine oxidase A, MAOB, monoamine oxidase B). Based on their hormone receptors, such as estrogen receptor (ER) and progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), and Ki-67 immunohistochemical staining, breast cancer was divided into four molecular subtypes: luminal A, luminal B, HER-2 type, and triple-negative breast cancer (TNBC). Luminal A was observed in 380 cases (49.4%), luminal B in 224 (29.1%), HER-2 type in 68 (8.8%), and TNBC in 98 (12.7%). Stromal AOC3, MAO-A, and MAO-B expression varied according to molecular subtypes. Stromal AOC3 expression was high in luminal B and HER-2 type and MAO-A expression was high in luminal A and luminal B (p < 0.001). MAO-B expression was higher in TNBC than in other subtypes (p = 0.020). LOX positivity was associated with high histological grade (p < 0.001) and high Ki-67 labeling index (LI) (p = 0.009), and stromal AOC3 positivity was associated with high histological grade (p = 0.001), high Ki-67 LI (p < 0.001), and HER-2 positivity (p = 0.002). MAO-A positivity was related to low histological grade (p < 0.001), ER positivity, PR positivity (p < 0.001), and low Ki-67 LI (p < 0.001). In univariate analysis, MAO-A positivity was related to short disease-free survival in HER-2 type (p = 0.013), AOC3 negativity was related to short disease-free survival and overall survival in ER-positive breast cancer, PR-positive breast cancer, HER-2-negative breast cancer, and lymph node metastasis. In conclusion, the expression of amine oxidase proteins varies depending on the molecular subtype of breast cancer. Stromal AOC3 expression was high in luminal B and HER-2 type, and MAO-A expression was high in luminal A and luminal B. Full article
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<p>Heat map of amine oxidase in breast cancer molecular subtype. LOX, lysyl oxidase, AOC3, amine oxidase, MAOA, monoamine oxidase A, MAOB, monoamine oxidase B, TNBC, triple negative breast cancer, S, stroma, green, positive, red, negative.</p>
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<p>Differential expression of amine oxidase in different tumor subtypes. The expression of stromal AOC3, MAO-A, and MAO-B; high expression of stromal AOC3 in luminal B and HER-2-type breast cancers; and high MAO-A expression in luminal A and luminal B (<span class="html-italic">p</span> &lt; 0.001). MAO-B expression was higher in TNBC than that in other proteins (<span class="html-italic">p</span> = 0.020).</p>
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<p>Correlation between the expression of amine oxidase and clinicopathological characteristics. LOX positivity was associated with a high histological grade (<span class="html-italic">p</span> &lt; 0.001) and high Ki-67 LI (<span class="html-italic">p</span> = 0.009). Stromal AOC3 positivity was associated with a high histological grade (<span class="html-italic">p</span> = 0.001), high Ki-67 LI (<span class="html-italic">p</span> &lt; 0.001), and HER-2 positivity (<span class="html-italic">p</span> = 0.002). MAO-A positivity was associated with a low histological grade (<span class="html-italic">p</span> &lt; 0.001), estrogen receptor (ER) positivity (<span class="html-italic">p</span> &lt; 0.001), PR positivity (<span class="html-italic">p</span> &lt; 0.001), and low Ki-67 LI (<span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Functional analysis using STRING database. Using “Mutual Exclusivity” function, 3 significant gene pairs with mutually exclusive alteration were identified (<span class="html-italic">ERBB2-PGR</span>, <span class="html-italic">AOC3-ERBB2</span>, <span class="html-italic">MKI67-ESR1</span>) and 1 gene pair with concurrent alteration (<span class="html-italic">AOC3-MAOA</span>). The response to hormone stimulus (red) and the response to lipids (blue) include Lox and MAOB together with ESR1, suggestive of the presence of common activator of estrogen positive type of breast cancer.</p>
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<p>Effect of the expression of amine oxidase on survival in breast cancer. MAO-A positivity was associated with short DFS in HER-2-type breast cancer (<b>a</b>) (<span class="html-italic">p</span> = 0.013), luminal A was associated with short OS (<b>b</b>) (<span class="html-italic">p</span> = 0.047). In ER-positive breast cancer, AOC3 negativity was associated with short DFS (<b>c</b>) and short OS (<b>d</b>) (<span class="html-italic">p</span> = 0.013 and <span class="html-italic">p</span> = 0.037, respectively). AOC3 negativity was associated with short DFS (<b>e</b>) and short OS (<b>f</b>) in PR-positive cancer (<span class="html-italic">p</span> = 0.028 and <span class="html-italic">p</span> = 0.012, respectively). In HER-2-negative breast cancer, AOC3 negativity was associated with short DFS (<b>g</b>) and short OS (<b>h</b>) (<span class="html-italic">p</span> = 0.026 and <span class="html-italic">p</span> = 0.037, respectively), and in the breast cancer showing lymph node metastasis, AOC3 negativity was associated with short DFS (<b>i</b>) and short OS (<b>j</b>) (<span class="html-italic">p</span> = 0.038 and <span class="html-italic">p</span> = 0.012, respectively).</p>
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756 KiB  
Review
Molecular Determinants of Malignant Brain Cancers: From Intracellular Alterations to Invasion Mediated by Extracellular Vesicles
by Gabriella Schiera, Carlo Maria Di Liegro and Italia Di Liegro
Int. J. Mol. Sci. 2017, 18(12), 2774; https://doi.org/10.3390/ijms18122774 - 20 Dec 2017
Cited by 20 | Viewed by 6712
Abstract
Malignant glioma cells invade the surrounding brain parenchyma, by migrating along the blood vessels, thus promoting cancer growth. The biological bases of these activities are grounded in profound alterations of the metabolism and the structural organization of the cells, which consequently acquire the [...] Read more.
Malignant glioma cells invade the surrounding brain parenchyma, by migrating along the blood vessels, thus promoting cancer growth. The biological bases of these activities are grounded in profound alterations of the metabolism and the structural organization of the cells, which consequently acquire the ability to modify the surrounding microenvironment, by altering the extracellular matrix and affecting the properties of the other cells present in the brain, such as normal glial-, endothelial- and immune-cells. Most of the effects on the surrounding environment are probably exerted through the release of a variety of extracellular vesicles (EVs), which contain many different classes of molecules, from genetic material to defined species of lipids and enzymes. EV-associated molecules can be either released into the extracellular matrix (ECM) and/or transferred to neighboring cells: as a consequence, both deep modifications of the recipient cell phenotype and digestion of ECM components are obtained, thus causing cancer propagation, as well as a general brain dysfunction. In this review, we first analyze the main intracellular and extracellular transformations required for glioma cell invasion into the brain parenchyma; then we discuss how these events may be attributed, at least in part, to EVs that, like the pawns of a dramatic chess game with cancer, open the way to the tumor cells themselves. Full article
(This article belongs to the Special Issue Glioma Cell Invasion)
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<p>Cross-talk between glioma cells (<b>A</b>) and other cells (<b>B</b>,<b>C</b>), embedded in the extracellular matrix (ECM). The glioma cells have acquired the ability to move through the brain parenchyma, along the blood vessels (<b>D</b>), in small groups (guerrilla war) [<a href="#B23-ijms-18-02774" class="html-bibr">23</a>]; their invasiveness is mostly due to the extension of invadopodia (inv) and to the release of different kinds of extracellular vesicles: (i) membrane vesicles (MVs), light grey, which originate by directly budding from the plasma membrane and (ii) exosomes, blue, which are released after fusion with the plasma membrane of multivesicular bodies (MVB), components of the endosomal compartment. Both kinds of vesicles are equipped with different molecules (lipids, proteins and RNAs od different classes), which can be directly released into ECM if the vesicles break outside the cells (<b>a</b>). Alternatively, EVs can be bound by receptors present on the recipient cells (<b>b</b>), or fuse with the plasma membrane of these cells (<b>c</b>). Cells that receive information from glioma cells can, in turn, produce MVs, light yellow (<b>d</b>) and exosomes, dark yellow (<b>e</b>), which contain factors able to further stimulate glioma cell proliferation and invasion. In a normal astrocyte (<b>C</b>) AQP4 forms orthogonal arrays of particles (OAPs), localized in the cell endfeet (groups of small ovals drawn in red). In the glioma cell, AQP4 (red circles) is neither included in OAPs, nor localized; in addition, AQP4 levels are upregulated.</p>
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3978 KiB  
Article
Lym-1 Chimeric Antigen Receptor T Cells Exhibit Potent Anti-Tumor Effects against B-Cell Lymphoma
by Long Zheng, Peisheng Hu, Brandon Wolfe, Caryn Gonsalves, Luqing Ren, Leslie A. Khawli, Harvey R. Kaslow and Alan L. Epstein
Int. J. Mol. Sci. 2017, 18(12), 2773; https://doi.org/10.3390/ijms18122773 - 20 Dec 2017
Cited by 3 | Viewed by 8631
Abstract
T cells expressing chimeric antigen receptors (CARs) recognizing CD19 epitopes have produced remarkable anti-tumor effects in patients with B-cell malignancies. However, cancer cells lacking recognized epitopes can emerge, leading to relapse and death. Thus, CAR T cells targeting different epitopes on different antigens [...] Read more.
T cells expressing chimeric antigen receptors (CARs) recognizing CD19 epitopes have produced remarkable anti-tumor effects in patients with B-cell malignancies. However, cancer cells lacking recognized epitopes can emerge, leading to relapse and death. Thus, CAR T cells targeting different epitopes on different antigens could improve immunotherapy. The Lym-1 antibody targets a conformational epitope of Human Leukocyte Antigen-antigen D Related (HLA-DR) on the surface of human B-cell lymphomas. Lym-1 CAR T cells were thus generated for evaluation of cytotoxic activity towards lymphoma cells in vitro and in vivo. Human T cells from healthy donors were transduced to express a Lym-1 CAR, and assessed for epitope-driven function in culture and towards Raji xenografts in NOD-scidIL2Rgammanull (NSG) mice. Lym-1 CAR T cells exhibited epitope-driven activation and lytic function against human B-cell lymphoma cell lines in culture and mediated complete regression of Raji/Luciferase-Green fluorescent protein (Raji/Luc-GFP) in NSG mice with similar or better reactivity than CD19 CAR T cells. Lym-1 CAR transduction of T cells is a promising immunotherapy for patients with Lym-1 epitope positive B-cell malignancies. Full article
(This article belongs to the Special Issue Chimeric Antigen Receptor (CAR) T Cell Therapy)
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<p>Schematic representation of Lym-1 CAR and CD19 (FMC 63) CAR constructs.</p>
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<p>Efficient Lym-1 CAR expression on human primary T cells. (<b>A</b>) Cell numbers were recorded after mock or virus transduction. (<span class="html-italic">n</span> = 3 replicates per point; representative of three donors); (<b>B</b>) At day 10, 10<sup>6</sup> T cells were labeled with 2 μg biotin-protein L, followed by detection with Allophycocyanin (APC)-streptavidin. Mock-transduced T cells served as a negative control. (<span class="html-italic">n</span> = 6); (<b>C</b>) After expansion, the CD4/CD8 ratio of the T-cell preparations shown in Panel B were analyzed for CD4 and CD8 expression (representative of three donors).</p>
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<p>Detection of Lym-1 and CD19 epitopes on Daudi and Raji cells, but not K562 cells. Cell surface epitope intensity was detected by incubation with Dylight 650 conjugated chLym-1 antibody or APC conjugated anti-CD19 antibody.</p>
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<p>Epitope-driven upregulation of CD107a on Lym-1 and CD19 CAR T cells. Lym-1 and CD19 CAR T cells were detected by protein L and APC-streptavidin flow cytometry. Mock transduced T cells were added to each preparation to adjust the CAR T cell fraction to 30%. T cells (2 × 10<sup>5</sup>) were then incubated with 10<sup>5</sup> Raji or Daudi cells. Mock transduced T cells alone and CAR transduced T cells incubated with epitope-negative K562 cells served as negative controls. An anti-CD107a antibody and monensin were then added to the wells soon after. After a 5 h incubation, cells were labeled with PE-anti-CD3 antibody to differentiate tumor and T cells using flow cytometry. (<b>Top panel</b>) examples of data; (<b>Bottom panel</b>): data from <span class="html-italic">n</span> = 3 (ns, not significant; ** = <span class="html-italic">p</span> &lt; 0.01; compared to CD107a level when co-incubated with K562).</p>
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<p>Epitope-driven cytotoxicity of Lym-1 and CD19 CAR T cells. T cells (control or 30% CAR positive) were cultured overnight with 2 × 10<sup>4</sup> K562, Raji, or Daudi cells at indicated ratio. Supernatants were processed to measure cytotoxicity. Data from one donor is shown; similar results were obtained from a second donor. For each donor, three independent transductions were each assessed using triplicate determinations. ** = <span class="html-italic">p</span> &lt; 0.01; **** = <span class="html-italic">p</span> &lt; 0.001 compared to % lysis in mock-transduced T cells at the same E/T ratio.</p>
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<p>Epitope-driven release of cytokines from Lym-1 and CD19 CAR T cells. The percentage of CAR-transduced T cells was adjusted to 30%. Cells (2 × 10<sup>5</sup>) were then incubated with 10<sup>5</sup> K562, Raji, Daudi, or no target cells. Representative cytokine release levels from two donors are shown.</p>
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<p>Epitope-driven proliferation of Lym-1 and CD19 CAR T cells. Lym-1 or CD19 CAR T cells (10<sup>6</sup>) were labeled with CSFE-Far-red dye and co-cultured with 10<sup>6</sup> irradiated K562, Daudi, or Raji cells in the absence of exogenous cytokines. CAR T cells cultured alone served as a negative control. After five days of co-culture, cells were labeled with PE-anti CD3 and analyzed by flow cytometry.</p>
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<p>Lym-1 and CD19 CAR T cells eradicate Raji/Luc-GFP xenograft tumors in vivo. (<b>A</b>) Ventral and dorsal bioluminescence imaging of tumor burden in control and treated mice. Raji/Luc-GFP cells (10<sup>6</sup>) were injected intravenously into 8–10 week old male NSG mice (day 0). Luciferase bioluminescence was measured at day 6 to assess pre-treatment tumor burden. On day 7, 10<sup>7</sup> mock-transduced T cells, CD19 CAR T cells, or Lym-1 CAR T cells in 100 μL phosphate buffered saline (PBS) were injected intravenously (<span class="html-italic">n</span> = 5)<span class="html-italic">.</span> For the chLym-1 antibody group, 100 μg chLym-1 was injected intravenously in 100 μl PBS on days 7, 9, and 11. (<b>B</b>) Quantification of bioluminescence shown in (<b>A</b>). (<b>C</b>) Kaplan–Meier plot of survival of mice. * = <span class="html-italic">p</span> &lt; 0.01, compared to mock T cells group.</p>
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3442 KiB  
Review
Nrf2, the Master Regulator of Anti-Oxidative Responses
by Sandra Vomund, Anne Schäfer, Michael J. Parnham, Bernhard Brüne and Andreas Von Knethen
Int. J. Mol. Sci. 2017, 18(12), 2772; https://doi.org/10.3390/ijms18122772 - 20 Dec 2017
Cited by 480 | Viewed by 15670
Abstract
Tight regulation of inflammation is very important to guarantee a balanced immune response without developing chronic inflammation. One of the major mediators of the resolution of inflammation is the transcription factor: the nuclear factor erythroid 2-like 2 (Nrf2). Stabilized following oxidative stress, Nrf2 [...] Read more.
Tight regulation of inflammation is very important to guarantee a balanced immune response without developing chronic inflammation. One of the major mediators of the resolution of inflammation is the transcription factor: the nuclear factor erythroid 2-like 2 (Nrf2). Stabilized following oxidative stress, Nrf2 induces the expression of antioxidants as well as cytoprotective genes, which provoke an anti-inflammatory expression profile, and is crucial for the initiation of healing. In view of this fundamental modulatory role, it is clear that both hyper- or hypoactivation of Nrf2 contribute to the onset of chronic diseases. Understanding the tight regulation of Nrf2 expression/activation and its interaction with signaling pathways, known to affect inflammatory processes, will facilitate development of therapeutic approaches to prevent Nrf2 dysregulation and ameliorate chronic inflammatory diseases. We discuss in this review the principle mechanisms of Nrf2 regulation with a focus on inflammation and autophagy, extending the role of dysregulated Nrf2 to chronic diseases and tumor development. Full article
(This article belongs to the Special Issue Nrf2 in Redox Signaling: A Double Edged Sword)
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Figure 1
<p>Regulation of Nrf2 expression. (<b>A</b>) Domain structure of Nrf2; (<b>B</b>) Keap1-dependent degradation of Nrf2 (mod. from [<a href="#B8-ijms-18-02772" class="html-bibr">8</a>]), T-bar = inhibition of Nrf2 degradation, consequently blocking Keap1 release; (<b>C</b>) Keap1-independent mechanism of Nrf2 degradation.</p>
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<p>Regulation of Nrf2 expression. (<b>A</b>) Domain structure of Nrf2; (<b>B</b>) Keap1-dependent degradation of Nrf2 (mod. from [<a href="#B8-ijms-18-02772" class="html-bibr">8</a>]), T-bar = inhibition of Nrf2 degradation, consequently blocking Keap1 release; (<b>C</b>) Keap1-independent mechanism of Nrf2 degradation.</p>
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<p>Positive feedback-loop of Nrf2 activation by <span class="html-italic">p62/SQSTM1</span>. (<b>A</b>) Domain structure of p62/SQSTM1; (<b>B</b>) p62/SQSTM1 is an important protein for selective autophagy, binds to Keap1 and other long-lived proteins and forms polyubiquitinated protein aggregates. Furthermore, it binds to the autophagy marker LC3 within the autophagosome, thereby leading the aggregated proteins into the autophagosome. After fusion with a lysosome, proteins and organelles, such as mitochondria, are degraded within the autophagosome. By binding to Keap1, p62/SQSTM1 stabilizes Nrf2 and enhances its translocation into the nucleus, where Nrf2 activates its target genes (<span style="color:red">↑</span> = upregulation of Nrf2 target genes). One of these genes is <span class="html-italic">p62/SQSTM1</span>.</p>
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<p>Acetaminophen (APAP) metabolism within the liver. Entering hepatocytes, APAP is metabolized &gt;80% by glucuronyltransferases and sulfotransferases to soluble conjugates, which are excreted into the urine. CYP P450 monooxygenases can metabolize APAP to the electrophilic reactive metabolite NAPQI that is detoxified by glutathione <span class="html-italic">S</span>-transferase (Gst) using glutathione (GSH). If GSH stocks are exhausted, NAPQI oxidizes liver proteins, especially mitochondrial proteins by covalent binding. This induces ROS generation and consequently oxidative stress, which can lead to hepatocyte necrosis and apoptosis. To counteract cell death, cytoprotective signaling via Nrf2 activation and stabilization is induced by oxidative stress. Thereby, Keap1 releases Nrf2 which translocates into the nucleus, induces cytoprotective gene expression and replenishes the GSH stores. APAP—acetaminophen; CYP—cytochrome P450; GSH—glutathione; Gst—glutathione S-transferase; mito—mitochondria; Keap1—Kelch-like ECH associated protein; NAPQI—<span class="html-italic">N</span>-acetyl-p-benzoquinone imine; Nrf2—NF-E2 p45-related factor 2; ROS—reactive oxygen species (adapted from [<a href="#B85-ijms-18-02772" class="html-bibr">85</a>]).</p>
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<p>Influence of Nrf2 activation on COPD. Air pollutants, cigarette smoke and bacterial or viral infection cause oxidative stress and inflammation in the lung. Nrf2 activation induces cytoprotective gene expression to counteract the toxic effect of ROS. Moreover, Nrf2 inhibits transcription of proinflammatory cytokines, especially in macrophages, to reduce the recruitment of inflammatory cells into the lung. If Nrf2 is reduced, inflammation and ROS lead to cell death of lung epithelial cells and consequently mediate emphysema. CD4/CD8—effector/cytotoxic T-cells; IL—interleukins; —NF-E2 p45-related factor 2; ROS—reactive oxygen species, <span style="color:#00B050">↑</span>/<span style="color:#00B050">↓</span> = up-/downregulation of expression with a positive effect on disease progression, <span style="color:#00B050">T-bars</span>/<span style="color:red">T-bars</span> = inhibition with a <span style="color:#00B050">positive</span>/<span style="color:red">negative</span> effect on disease progression.</p>
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<p>Roles of Nrf2 in tumorigenesis. Activated Nrf2 can prevent onset of cancer by protecting the cell from environmental stressors, like ROS and xenobiotics which can cause DNA damage. Low levels of Nrf2-target genes (reducing ROS levels and eliminating xenobiotics) can lead to tumorigenesis upon stress. In contrast, during tumor progression, Nrf2 activity can contribute to chemoresistance and the ability of tumor cells to circumvent apoptosis, whereas the inhibition of Nrf2 provokes cancer drug susceptibility and the loss of antiapoptotic signals.</p>
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Addendum
Addendum: Cechová, M. et al. Towards Better Understanding of Pea Seed Dormancy Using Laser Desorption/Ionization Mass Spectrometry. Int. J. Mol. Sci. 2017, 18, 2196
by Monika Cechová, Markéta Válková, Iveta Hradilová, Anna Janská, Aleš Soukup, Petr Smýkal and Petr Bednář
Int. J. Mol. Sci. 2017, 18(12), 2771; https://doi.org/10.3390/ijms18122771 - 20 Dec 2017
Viewed by 3007
Abstract
It has been brought to our attention that one funding project of Ministry of Education, Youth and Sports of the Czech Republic (LO1417) was missing in the Acknowledgement section of our published paper [1], and therefore we would like to add it and [...] Read more.
It has been brought to our attention that one funding project of Ministry of Education, Youth and Sports of the Czech Republic (LO1417) was missing in the Acknowledgement section of our published paper [1], and therefore we would like to add it and report the Acknowledgements as follows [...] Full article
(This article belongs to the Special Issue Metabolomics in the Plant Sciences 2017)
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Review
ω-3 Long Chain Polyunsaturated Fatty Acids as Sensitizing Agents and Multidrug Resistance Revertants in Cancer Therapy
by Paola Antonia Corsetto, Irma Colombo, Joanna Kopecka, Angela Maria Rizzo and Chiara Riganti
Int. J. Mol. Sci. 2017, 18(12), 2770; https://doi.org/10.3390/ijms18122770 - 20 Dec 2017
Cited by 48 | Viewed by 7641
Abstract
Chemotherapy efficacy is strictly limited by the resistance of cancer cells. The ω-3 long chain polyunsaturated fatty acids (ω-3 LCPUFAs) are considered chemosensitizing agents and revertants of multidrug resistance by pleiotropic, but not still well elucidated, mechanisms. Nowadays, it is accepted that alteration [...] Read more.
Chemotherapy efficacy is strictly limited by the resistance of cancer cells. The ω-3 long chain polyunsaturated fatty acids (ω-3 LCPUFAs) are considered chemosensitizing agents and revertants of multidrug resistance by pleiotropic, but not still well elucidated, mechanisms. Nowadays, it is accepted that alteration in gene expression, modulation of cellular proliferation and differentiation, induction of apoptosis, generation of reactive oxygen species, and lipid peroxidation are involved in ω-3 LCPUFA chemosensitizing effects. A crucial mechanism in the control of cell drug uptake and efflux is related to ω-3 LCPUFA influence on membrane lipid composition. The incorporation of docosahexaenoic acid in the lipid rafts produces significant changes in their physical-chemical properties affecting content and functions of transmembrane proteins, such as growth factors, receptors and ATP-binding cassette transporters. Of note, ω-3 LCPUFAs often alter the lipid compositions more in chemoresistant cells than in chemosensitive cells, suggesting a potential adjuvant role in the treatment of drug resistant cancers. Full article
(This article belongs to the Special Issue Omega-3 Fatty Acids in Health and Disease: New Knowledge)
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<p>Proposed mechanisms for ω-3 LCPUFA anticancer effects. LCPUFA—long chain polyunsaturated fatty acids; EPA—eicosapentaenoic acid; DHA—docosahexaenoic acid.</p>
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<p>Overview of the key COX, LOX, and CYP-derived metabolites from EPA and DHA. COX—cyclooxygenases; LOX—lipoxygenases; CYP—cytochrome P450; PG—prostaglandin; Tx—thromboxane; HpETE—hydroperoxy eicosatetraenoic acid; HpEPE—hydroperoxy eicosapentaenoic acid; EpETE—epoxy eicosatetraenoic acid; DiHETE—dihydroxy eicosatetraenoic acid; HEPE—hydroxy eicosapentaenoic acid; HpDHA—hydroperoxy docosahexaenoic acid; HDHA—hydroxy docosahexaenoic acid; EpDPE—epoxy docosapentaenoic acid; DiHDPE—dihydroxy docosapentaenoic acid; Lx—lipoxin; LT—leukotriene; Mar—maresin, PD—protectin; RvD—D series resolvins; RvE—E series resolvins.</p>
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<p>DHA impact on lipid raft structure. DHA incorporation in membrane affects lipid raft organization inducing a shift from cholesterol/saturated fatty acid-rich domains to ω-3 LCPUFA-rich/cholesterol-poor domains, which exhibit different height ranges.</p>
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<p>ω-3 LCPUFAs reverse chemoresistance induced by P-glycoprotein by modulating cholesterol synthesis and altering membrane lipid microenvironment. (<b>A</b>) MDR cells have a high synthesis of cholesterol, owing to the constitutive over-expression of the enzyme 3-hydroxy-3-methlyglutaryl coenzyme A reductase (HMGCoAR). This is independent of the activation of the transcription factor sterol regulatory binding protein-2 (SREBP2) but is due to the lower activity of the E3-ubiquitin ligase Trc8. A high cholesterol content in the plasma membrane sustains the activity of P-glycoprotein (Pgp), which effluxes several chemotherapeutic drugs (d); (<b>B</b>) DHA and EPA are allosteric activators of Trc8 and increase the Trc8-mediated ubiquitination (Uq) of HMGCoAR, reducing cholesterol synthesis. Such cholesterol depletion, together with the incorporation of DHA/EPA in plasma membrane, alters the cholesterol rich/saturated fatty acids rich lipid micro-domains such as lipid rafts, reduces Pgp surface level and activity. As a result, ω-3 LCPUFAs increase the intracellular retention of Pgp substrates, chemosensitizing resistant cells.</p>
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Article
Genome-Wide Identification, Evolutionary and Expression Analyses of the GALACTINOL SYNTHASE Gene Family in Rapeseed and Tobacco
by Yonghai Fan, Mengna Yu, Miao Liu, Rui Zhang, Wei Sun, Mingchao Qian, Huichun Duan, Wei Chang, Jinqi Ma, Cunmin Qu, Kai Zhang, Bo Lei and Kun Lu
Int. J. Mol. Sci. 2017, 18(12), 2768; https://doi.org/10.3390/ijms18122768 - 20 Dec 2017
Cited by 28 | Viewed by 5400
Abstract
Galactinol synthase (GolS) is a key enzyme in raffinose family oligosaccharide (RFO) biosynthesis. The finding that GolS accumulates in plants exposed to abiotic stresses indicates RFOs function in environmental adaptation. However, the evolutionary relationships and biological functions of GolS family in rapeseed ( [...] Read more.
Galactinol synthase (GolS) is a key enzyme in raffinose family oligosaccharide (RFO) biosynthesis. The finding that GolS accumulates in plants exposed to abiotic stresses indicates RFOs function in environmental adaptation. However, the evolutionary relationships and biological functions of GolS family in rapeseed (Brassica napus) and tobacco (Nicotiana tabacum) remain unclear. In this study, we identified 20 BnGolS and 9 NtGolS genes. Subcellular localization predictions showed that most of the proteins are localized to the cytoplasm. Phylogenetic analysis identified a lost event of an ancient GolS copy in the Solanaceae and an ancient duplication event leading to evolution of GolS4/7 in the Brassicaceae. The three-dimensional structures of two GolS proteins were conserved, with an important DxD motif for binding to UDP-galactose (uridine diphosphate-galactose) and inositol. Expression profile analysis indicated that BnGolS and NtGolS genes were expressed in most tissues and highly expressed in one or two specific tissues. Hormone treatments strongly induced the expression of most BnGolS genes and homologous genes in the same subfamilies exhibited divergent-induced expression. Our study provides a comprehensive evolutionary analysis of GolS genes among the Brassicaceae and Solanaceae as well as an insight into the biological function of GolS genes in hormone response in plants. Full article
(This article belongs to the Section Biochemistry)
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<p>Phylogenetic, gene structure and conserved motifs of GolS proteins in <span class="html-italic">A. thaliana</span>, <span class="html-italic">B. napus</span> and <span class="html-italic">N. tabacum</span>. (<b>A</b>) Amino acid sequences of AtGolS, BnGolS and NtGolS were aligned using MUSCLE. The phylogenetic tree was constructed with the online PhyML server with bootstrap analysis (100 replicates) and displayed using FigTree v1.4.0. The 36 GolS proteins from <span class="html-italic">A. thaliana</span>, <span class="html-italic">B. napus</span> and <span class="html-italic">N. tabacum</span> (GolS in <span class="html-italic">A. trichopoda</span> as an outgroup) clustered into four distinct groups; (<b>B</b>) Gene structures were generated by the Gene Structure Display Server. Exons (CDS) and introns are shown with green wedges and black lines, respectively. Numbers 0, 1 and 2 represent the introns in phases 0, 1 and 2, respectively. The scale bar represents 1.0 kb. At: <span class="html-italic">A. thaliana</span>; Bn: <span class="html-italic">B. napus</span>; Nt: <span class="html-italic">N. tabacum</span>; AmTr: <span class="html-italic">A. trichopoda</span>. (<b>C</b>) Conserved motifs in AtGolS, BnGolS and NtGolS proteins were identified by MEME. A colored box indicates the different motifs that are numbered along the bottom.</p>
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<p>Phylogenetic relationship among GolS proteins in plants. The ML tree was generated with bootstrap analysis (100 replicates) using aligned GolS protein sequences from <span class="html-italic">A. thaliana</span>, <span class="html-italic">B. napus</span>, <span class="html-italic">B. rapa</span>, <span class="html-italic">B. oleracea</span>, <span class="html-italic">N. tabacum</span>, <span class="html-italic">S. lycopersicum</span>, <span class="html-italic">S. tuberosum</span>, <span class="html-italic">O. sativa</span> and <span class="html-italic">Z. mays</span> (GolS in <span class="html-italic">A. trichopoda</span> as an outgroup) using the online PhyML server. The tree was displayed with FigTree v1.4.0. GolS proteins in the phylogenetic tree clustered into four groups (Group 1, Group 2, Group 3 and Group 4). At: <span class="html-italic">A. thaliana</span>; Bn: <span class="html-italic">B. napus</span>; Bra: <span class="html-italic">B. rapa</span>; Bol: <span class="html-italic">B. oleracea</span>; Nt: <span class="html-italic">N. tabacum</span>; Sl: <span class="html-italic">S. lycopersicum;</span> St: <span class="html-italic">S. tuberosum</span>; Os: <span class="html-italic">O. sativa</span>; Zm: <span class="html-italic">Z. mays</span>; AmTr: <span class="html-italic">A. trichopoda</span><span class="html-italic">.</span></p>
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<p>Inferred origin and evolutionary relationship of <span class="html-italic">GolS</span> genes and their copy number change among nine plants. The digits represent the number of <span class="html-italic">GolS</span> genes in plants species. Triangle represents the original genes of <span class="html-italic">GolS</span> in plants, while the circle represents the duplicated genes. The blue line indicates that <span class="html-italic">B. napus</span> is formed by <span class="html-italic">B. rape</span> and <span class="html-italic">B. oleracea</span>.</p>
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<p>Distribution of <span class="html-italic">BnGolS</span> gene family members along the <span class="html-italic">B. napus</span> chromosomes and synteny map of <span class="html-italic">GolS</span> genes from <span class="html-italic">A. thaliana B. napus</span>, <span class="html-italic">B. rapa</span> and <span class="html-italic">B. oleracea.</span> (<b>A</b>) Chromosomal information for <span class="html-italic">BnGolS</span> genes was obtained from the <span class="html-italic">Brassica</span> database and was mapped to <span class="html-italic">B. napus</span> chromosomes. Syntenic relationships are indicated with connecting lines; (<b>B</b>) Genes located within the <span class="html-italic">B. napus</span> genome that are syntenic with genes of <span class="html-italic">A. thaliana</span>, <span class="html-italic">B. rapa</span> and <span class="html-italic">B. oleracea</span> are indicated by connecting lines.</p>
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<p>3D Structure predictions for BnGolS and NtGolS. BnGolS1-2 and NtGolS1-1 were selected as the representative GolS proteins from <span class="html-italic">B. napus</span> and <span class="html-italic">N. tabacum</span>, respectively. The models were predicted by I-TASSER and the rabbit muscle glycogenin structure (PDB ID1ll0) was used as the template for the 3D structure predication. The conserved DXD and HxxGxxPW motifs are marked on the 3D structure in red. Green represents α-helices, yellow represents β-strands and navy blue represents random coils. (<b>A</b>) Modeled3D structure of BnGolS1-2; (<b>B</b>) Modeled 3D structure of NtGolS1-1; (<b>C</b>) Template model of 1ll0B. Structural images were generated with Chimera 1.2.</p>
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<p>Docking of ligands onto the modeled BnGolS1-2 protein structure. (<b>A</b>) Docking results with inositol; (<b>B</b>) Docking results with UDP-galactose; (<b>C</b>,<b>D</b>) Surface representation of BnGolS1-2 showing that the ligands are buried deep in the binding pocket. In BnGolS1-2 proteins, the binding positions for UDP-galactose and inositol are close to the DxD motif. The inositol and UDP-galactose binding sites are represented in blue and the DXD and HxxGxxPW motifs are marked on the surface in red. Images were generated with Chimera 1.2.</p>
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<p>Tissue-specific and hormone-induced expression patterns of <span class="html-italic">BnGolS</span> and <span class="html-italic">NtGolS</span> genes. (<b>A</b>) Expression profiles of <span class="html-italic">BnGolS</span> genes in <span class="html-italic">B. napus</span>; (<b>B</b>) Expression profiles of <span class="html-italic">NtGolS</span> genes in <span class="html-italic">N. tabacum</span>; (<b>C</b>) Expression profiles of <span class="html-italic">BnGolS</span> genes in response to hormone treatments. The color bar to the right of the figures represents the log<sub>2</sub> expression value and the green color represents the low or no expression in (<b>A</b>) and (<b>B</b>) while it represents the down-regulation in hormone treatment in (<b>C</b>).</p>
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Article
High-Throughput RNA-Seq Data Analysis of the Single Nucleotide Polymorphisms (SNPs) and Zygomorphic Flower Development in Pea (Pisum sativum L.)
by Keyuan Jiao, Xin Li, Wuxiu Guo, Shihao Su and Da Luo
Int. J. Mol. Sci. 2017, 18(12), 2710; https://doi.org/10.3390/ijms18122710 - 20 Dec 2017
Cited by 7 | Viewed by 5373
Abstract
Pea (Pisum sativum L.) is a model plant that has been used in classical genetics and organ development studies. However, its large and complex genome has hindered research investigations in pea. Here, we generated transcriptomes from different tissues or organs of three [...] Read more.
Pea (Pisum sativum L.) is a model plant that has been used in classical genetics and organ development studies. However, its large and complex genome has hindered research investigations in pea. Here, we generated transcriptomes from different tissues or organs of three pea accessions using next-generation sequencing to assess single nucleotide polymorphisms (SNPs), and further investigated petal differentially expressed genes to elucidate the mechanisms regulating floral zygomorphy. Eighteen samples were sequenced, which yielded a total of 617 million clean reads, and de novo assembly resulted in 87,137 unigenes. A total of 9044 high-quality SNPs were obtained among the three accessions, and a consensus map was constructed. We further discovered several dorsoventral asymmetrically expressed genes that were confirmed by qRT-PCR among different petals, including previously reported three CYC-like proliferating cell factor (TCP) genes. One MADS-box gene was highly expressed in dorsal petals, and several MYB factors were predominantly expressed among dorsal, lateral, and/or ventral petals, together with a ventrally expressed TCP gene. In sum, our comprehensive database complements the existing resources for comparative genetic mapping and facilitates future investigations in legume zygomorphic flower development. Full article
(This article belongs to the Section Molecular Plant Sciences)
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<p>Length distribution of unigenes identified in our de novo assembly Total number (87,137), Total length (91,915,266 bp), Mean length (1054.84 bp), Maximum length (15,111 bp), Minimum length (201 bp), N50 length (1796 bp), N90 length (425 bp), Number of unigenes with &gt;N50 (16,181).</p>
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<p>A comparative map between pea and <span class="html-italic">Medicago.</span> (<b>A</b>) Eight chromosomes of Medicago (Mt). (<b>B</b>) 324 unigenes that are located on the consensus map and can be successfully mapped to Mt genome. (<b>C</b>) 10,472 unigenes matching to the unique region of the <span class="html-italic">Medicago</span> reference genome. (<b>D</b>) SNPs among the JI2822, JI992, and Terese pea accessions. Different colors represent various Mt Chrs and PsLGs in (<b>A</b>,<b>B</b>). Dots in (<b>D</b>) represent SNPs among pea accessions JI2822, JI992, and Terese from 324 unigenes in (<b>B</b>).</p>
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<p>A heat map showing the expression patterns of DEGs in three types of petals. Dp, dorsal petals; Lp, lateral petals; Vp, ventral petals.</p>
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<p>A Neighbor-joining consensus tree and the expression patterns of TCP genes in pea. (<b>a</b>) An unrooted protein tree for the TCP proteins using MEGA 5.0.1. The tree summarizes the evolutionary relationships among the 24 AtTCPs and 22 PsTCPs. Bootstrap values are denoted above the nodes. (<b>b</b>) The expression patterns of the TCP genes among different organs in pea. VA, Vegetative shoot apices; RA, Reproductive shoot apices; Small, 2-mm small buds; Big, 5-mm big buds; Dp, Dorsal petals; Lp, Lateral petals; Vp, Ventral petals.</p>
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<p>A neighbor-joining consensus tree and the expression patterns of the pea AP1/FUL clade MADS-box genes. (<b>a</b>) An unrooted protein tree for the AP1/FUL clade MADS-box genes using MEGA 5.0.1. Bootstrap values are denoted above the nodes. (<b>b</b>) The expression patterns of the AP1/FUL clade genes in different organs in pea. VA, Vegetative shoot apices; RA, Reproductive shoot apices; Small, 2-mm small buds; Big, 5-mm big buds; Dp, Dorsal petals; Lp, Lateral petals; Vp, Ventral petals.</p>
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<p>qPCR validation of selected genes with a DV asymmetrical expression patterns in three types of pea petals. <span class="html-italic">PsCYC1</span> (Unigene0018426), <span class="html-italic">PsCYC2</span> (Unigene0018425), <span class="html-italic">PsFULa</span> (Unigene0011778), <span class="html-italic">dpMYB</span> (Unigene0031178), <span class="html-italic">PsCYC3</span> (Unigene0048351), <span class="html-italic">dlpMYB</span> (Unigene0005297), <span class="html-italic">lpMYB</span> (Unigene0082746), and <span class="html-italic">vpTCP</span> (Unigene0087108). Error bars indicate ±SD.</p>
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Article
Differential Expression of VvLOXA Diversifies C6 Volatile Profiles in Some Vitis vinifera Table Grape Cultivars
by Xu Qian, Lei Sun, Xiao-Qing Xu, Bao-Qing Zhu and Hai-Ying Xu
Int. J. Mol. Sci. 2017, 18(12), 2705; https://doi.org/10.3390/ijms18122705 - 20 Dec 2017
Cited by 25 | Viewed by 5391
Abstract
C6 volatiles are synthesized through lipoxygenase-hydroperoxide lyase (LOX-HPL) pathway and these volatiles play important roles in the aromatic quality of grape berries. This study investigated the evolution of both C6 volatiles and the key genes in the LOX-HPL pathway in different table grape [...] Read more.
C6 volatiles are synthesized through lipoxygenase-hydroperoxide lyase (LOX-HPL) pathway and these volatiles play important roles in the aromatic quality of grape berries. This study investigated the evolution of both C6 volatiles and the key genes in the LOX-HPL pathway in different table grape cultivars during the berry development period, and further assessed the correlation between the accumulation of C6 volatiles and the expression of these genes in these cultivars. Results showed that hexanal, (E)-2-hexenal, (E)-2-hexen-1-ol and (Z)-3-hexen-1-ol were found to be the dominant C6 volatiles in these ripened grape cultivars under two consecutive vintages, and their flavor notes were incorporated in the overall aroma of these cultivars. The cultivar “Xiangfei” showed the most abundant level of C6 aldehydes and C6 acid, whereas the cultivar “Tamina” and “Moldova” possessed the highest C6 alcohol content. The “Muscat of Alexandria” cultivar was found to contain the highest level of C6 esters. C6 volatiles were grouped into three evolutionary patterns in these cultivars during berry development, and their evolution was consistent with the evolution of the LOX-HPL pathway genes’ expression. Pearson’s correlation analysis indicated that the LOX-HPL-pathway-related genes were correlated to the accumulation of C6 volatiles in these cultivars, and VvLOXA appeared to be an important gene that regulated the synthesis of all C6 volatiles. Full article
(This article belongs to the Section Molecular Plant Sciences)
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<p>Concentrations of volatile compounds and their odor threshold values in different table grape cultivars at harvest. (<b>a</b>) C6 aldehydes; (<b>b</b>) C6 alcohols; (<b>c</b>) C6 acid; and (<b>d</b>) C6 esters. Different letters mean significant differences according to the Duncan test (<span class="html-italic">p</span> &lt; 0.05) in individual vintages. “Zmgx” represents the “Zaomeiguixiang” cultivar; “Alexandria” represents the “Muscat of Alexandria” cultivar; OTV is the odor threshold value.</p>
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<p>Heat maps of C6 volatiles in different table grape cultivars during berry development in 2013 and 2014. “Zmgx” represents the “Zaomeiguixiang” cultivar. “Alexandria” represents the “Muscat of Alexandria” cultivar.</p>
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<p>Transcript levels of key genes in LOX/HPL pathways in different table grape cultivars during berry development in 2014. GenBank accession numbers are as follows: <span class="html-italic">VvLOXA</span> (FJ858255), VvLOXO (FJ858257), <span class="html-italic">VvHPL1</span> (HM627632), <span class="html-italic">VvADH1</span> (AF194173), <span class="html-italic">VvADH2</span> (AF194174) and <span class="html-italic">VvAAT</span> (AAW22989). E-L 35 (early veraison), E-L 36 (mid-ripening stage), E-L 37 (end of veraison), and E-L 38 (harvest). “Zmgx” represents “Zaomeiguixiang” cultivar and “Alexandria” represents “Muscat of Alexandria” cultivar. Expression levels of each gene were expressed as a ratio relative to the E-L 35 stage of “Xiangfei” cultivar, which was set at 1.</p>
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Article
Comparative Analysis of Fruit Ripening-Related miRNAs and Their Targets in Blueberry Using Small RNA and Degradome Sequencing
by Yanming Hou, Lulu Zhai, Xuyan Li, Yu Xue, Jingjing Wang, Pengjie Yang, Chunmei Cao, Hongxue Li, Yuhai Cui and Shaomin Bian
Int. J. Mol. Sci. 2017, 18(12), 2767; https://doi.org/10.3390/ijms18122767 - 19 Dec 2017
Cited by 36 | Viewed by 5112
Abstract
MicroRNAs (miRNAs) play vital roles in the regulation of fruit development and ripening. Blueberry is an important small berry fruit crop with economical and nutritional value. However, nothing is known about the miRNAs and their targets involved in blueberry fruit ripening. In this [...] Read more.
MicroRNAs (miRNAs) play vital roles in the regulation of fruit development and ripening. Blueberry is an important small berry fruit crop with economical and nutritional value. However, nothing is known about the miRNAs and their targets involved in blueberry fruit ripening. In this study, using high-throughput sequencing of small RNAs, 84 known miRNAs belonging to 28 families and 16 novel miRNAs were identified in white fruit (WF) and blue fruit (BF) libraries, which represent fruit ripening onset and in progress, respectively. Among them, 41 miRNAs were shown to be differentially expressed during fruit maturation, and 16 miRNAs representing 16 families were further chosen to validate the sRNA sequencing data by stem-loop qRT-PCR. Meanwhile, 178 targets were identified for 41 known and 7 novel miRNAs in WF and BF libraries using degradome sequencing, and targets of miR160 were validated using RLM-RACE (RNA Ligase-Mediated (RLM)-Rapid Amplification of cDNA Ends) approach. Moreover, the expression patterns of 6 miRNAs and their targets were examined during fruit development and ripening. Finally, integrative analysis of miRNAs and their targets revealed a complex miRNA-mRNA regulatory network involving a wide variety of biological processes. The findings will facilitate future investigations of the miRNA-mediated mechanisms that regulate fruit development and ripening in blueberry. Full article
(This article belongs to the Section Molecular Plant Sciences)
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<p>Venn diagram of blueberry miRNAs in the libraries of white (WF) and blue fruits (BF).</p>
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<p>Phylogenetic analysis of known miRNA mature sequences of blueberry (<span class="html-italic">Vaccinium corymbosum</span>) and grape (<span class="html-italic">Vitis vinifera</span>) obtained from the sequencing data (this work) and miRbase 21.0, respectively. The miRNAs were clustered into 10 classes (I–X). The miRNAs in light blue background were from blueberry and the ones in dark grey were from grape in the inner ring. The middle ring shows the miRNA families in different colors. The outer ring shows the length of mature miRNAs.</p>
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<p>Expression analysis of known and novel miRNAs from blueberry obtained from the sequencing data. miRNAs were grouped into 3 classes (I–III) based on their accumulation patterns. WF and BF refer to white fruit and blue fruit, respectively.</p>
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<p>Expression comparison of selected miRNAs between WF and BF stages in blueberry by qRT-PCR and deep sequencing. Gray line represents transcript abundance changes calculated. Green bar indicates relative expression level determined by qRT-PCR analysis. The standard error of the mean is represented by an error bar. WF and BF refer to white fruit and blue fruit, respectively.</p>
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<p>Gene ontology classification of target genes for differentially expressed known miRNAs between white fruit stage and mature fruit stage libraries in blueberry.</p>
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<p>miRNA-mRNA regulatory network involved in blueberry fruit ripening. <span class="html-italic">TCP</span>, <span class="html-italic">TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING CELL NUCLEAR ANTIGEN FACTOR</span>; <span class="html-italic">AP2</span>, <span class="html-italic">APETALA 2</span>; <span class="html-italic">SAR1A</span>, <span class="html-italic">GTP-BINDING PROTEIN SAR1A-LIKE</span>; <span class="html-italic">PP2-A12</span>, <span class="html-italic">PHLOEM PROTEIN 2-A12</span>; <span class="html-italic">AGO2</span>, <span class="html-italic">ARGONAUTE 2</span>; <span class="html-italic">NRT1/PTR</span>, <span class="html-italic">NITRATE TRANSPORTER 1/PEPTIDE TRANSPORTER</span>; <span class="html-italic">GLD</span>, <span class="html-italic">GLUTAREDOXIN-C10</span>; <span class="html-italic">UCTH9</span>, <span class="html-italic">UBIQUITIN CARBOXYL-TERMINAL HYDROLASE 9</span>; <span class="html-italic">PPR</span>, <span class="html-italic">PENTATRICOPEPTIDE REPEAT-CONTAINING PROTEIN</span>; <span class="html-italic">SPL</span>, <span class="html-italic">SQUAMOSA PROMOTER-BINDING-LIKE PROTEIN</span>; <span class="html-italic">IAA27</span>, <span class="html-italic">INDOLE-3-ACETIC ACID 27</span>; <span class="html-italic">RER4</span>, <span class="html-italic">RETICULATA-RELATED 4</span>; <span class="html-italic">MOT2</span>, <span class="html-italic">MOLYBDATE TRANSPORTER 2</span>; <span class="html-italic">GAMYB</span>, <span class="html-italic">GIBBERELLIC ACID-REGULATED MYB</span>; <span class="html-italic">GRF1</span>, <span class="html-italic">GROWTH-REGULATING FACTOR 1</span>; <span class="html-italic">HA11</span>, <span class="html-italic">H(+)-ATPASE 11-LIKE</span>; <span class="html-italic">BTAF1</span>, <span class="html-italic">TATA-BINDING PROTEIN-ASSOCIATED FACTOR BTAF1</span>; <span class="html-italic">UCTH24</span>, <span class="html-italic">UBIQUITIN CARBOXYL-TERMINAL HYDROLASE 24</span>; <span class="html-italic">CYP</span>, <span class="html-italic">CYCLOPHILIN; FTSZ, FILAMENTING TEMPERATURE SENSITIVE Z</span>; <span class="html-italic">NH2L</span>, <span class="html-italic">NUDIX HYDROLASE 2-LIKE</span>; <span class="html-italic">CHER</span>, <span class="html-italic">CALCIUM HOMEOSTASIS ENDOPLASMIC RETICULUM PROTEIN-LIKE</span>; <span class="html-italic">SSP</span>, <span class="html-italic">SEED STORAGE PROTEIN</span>; <span class="html-italic">IQD14</span>, <span class="html-italic">IQ-DOMAIN14</span>; <span class="html-italic">CDase</span>, <span class="html-italic">NEUTRAL CERAMIDASE-LIKE</span>; <span class="html-italic">ELP6</span>, <span class="html-italic">ELONGATOR COMPLEX PROTEIN 6</span>; <span class="html-italic">AFB2</span>, <span class="html-italic">AUXIN SIGNALING F-BOX 2</span>; <span class="html-italic">TIR1</span>, <span class="html-italic">TRANSPORT INHIBITOR RESPONSE 1</span>; <span class="html-italic">ARF</span>, <span class="html-italic">AUXIN RESPONSE FACTOR</span>; <span class="html-italic">CLIP1</span>, <span class="html-italic">CAP-GLY DOMAIN-CONTAINING LINKER PROTEIN 1</span>; <span class="html-italic">NAC100</span>, <span class="html-italic">NAC DOMAIN-CONTAINING PROTEIN 100</span>; <span class="html-italic">PAT8</span>, <span class="html-italic">PROTEIN S-ACYLTRANSFERASE 8</span>; <span class="html-italic">APS2</span>, <span class="html-italic">ATP SULFURYLASE</span>; <span class="html-italic">UBC 24</span>, <span class="html-italic">UBIQUITIN-CONJUGATING ENZYME E2 24</span>; <span class="html-italic">RBP2I</span>, <span class="html-italic">RNA POLYMERASE II SECOND LARGEST SUBUNIT</span>; <span class="html-italic">HSP70</span>, <span class="html-italic">HEAT SHOCK 70 kDa PROTEIN 15-LIKE</span>; <span class="html-italic">RPP13</span>, <span class="html-italic">DISEASE RESISTANCE RPP13-LIKE PROTEIN 1</span>.</p>
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<p>Expression patterns of the known and novel vco-miRNAs identified in this work and their target genes during fruit development and ripening. The accumulation patterns of vco-miR160b and 2 target genes (<b>A</b>), vco-miR172a-3p and its target gene (<b>B</b>); vco-miR319b and its target gene (<b>C</b>); vco-miR396a-5p and 3 target genes (<b>D</b>); vco-miR403a and its target gene (<b>E</b>); vco-miR_n10 and its target gene (<b>F</b>). The expression level in GP was set as 1. <span class="html-italic">U6</span> and <span class="html-italic">ACTIN</span> genes were used as the internal control for miRNA expression and target genes expression, respectively. Error bars indicate standard error of three biological and technical replicates. The different letters (a–d) indicate significant differences at <span class="html-italic">p</span> &lt; 0.01 according to Duncan’s multiple range tests. GP, green pad; GC, green cup; WF, white fruit; PF, pink fruit; BF, blue fruit.</p>
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<p>A schematic model showing the proposed roles of miRNAs involved in blueberry fruit development and ripening. T bar and arrow refer to negative and positive effect on downstream effector or biological process, respectively.</p>
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11119 KiB  
Article
Effects and Mechanisms of Total Flavonoids from Blumea balsamifera (L.) DC. on Skin Wound in Rats
by Yuxin Pang, Yan Zhang, Luqi Huang, Luofeng Xu, Kai Wang, Dan Wang, Lingliang Guan, Yingbo Zhang, Fulai Yu, Zhenxia Chen and Xiaoli Xie
Int. J. Mol. Sci. 2017, 18(12), 2766; https://doi.org/10.3390/ijms18122766 - 19 Dec 2017
Cited by 40 | Viewed by 7389
Abstract
Chinese herbal medicine (CHM) evolved through thousands of years of practice and was popular not only among the Chinese population, but also most countries in the world. Blumea balsamifera (L.) DC. as a traditional treatment for wound healing in Li Nationality Medicine has [...] Read more.
Chinese herbal medicine (CHM) evolved through thousands of years of practice and was popular not only among the Chinese population, but also most countries in the world. Blumea balsamifera (L.) DC. as a traditional treatment for wound healing in Li Nationality Medicine has a long history of nearly 2000 years. This study was to evaluate the effects of total flavonoids from Blumea balsamifera (L.) DC. on skin excisional wound on the back of Sprague-Dawley rats, reveal its chemical constitution, and postulate its action mechanism. The rats were divided into five groups and the model groups were treated with 30% glycerol, the positive control groups with Jing Wan Hong (JWH) ointment, and three treatment groups with high dose (2.52 g·kg−1), medium dose (1.26 g·kg−1), and low dose (0.63 g·kg−1) of total flavonoids from B. balsamifera. During 10 consecutive days of treatment, the therapeutic effects of rates were evaluated. On day 1, day 3, day 5, day 7, and day 10 after treatment, skin samples were taken from all the rats for further study. Significant increases of granulation tissue, fibroblast, and capillary vessel proliferation were observed at day 7 in the high dose and positive control groups, compared with the model group, with the method of 4% paraformaldehyde for histopathological examination and immunofluorescence staining. To reveal the action mechanisms of total flavonoids on wound healing, the levels of CD68, vascular endothelial growth factor (VEGF), transforming growth factor-β1 (TGF-β1), and hydroxyproline were measured at different days. Results showed that total flavonoids had significant effects on rat skin excisional wound healing compared with controls, especially high dose ones (p < 0.05). Furthermore, the total flavonoid extract was investigated phytochemically, and twenty-seven compounds were identified from the total flavonoid sample by ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry/diode array detector (UPLC-Q-TOF-MS/DAD), including 16 flavonoid aglucons, five flavonoid glycosides (main peaks in chromatogram), five chlorogenic acid analogs, and 1 coumarin. Reports show that flavonoid glycoside possesses therapeutic effects of curing wounds by inducing neovascularization, and chlorogenic acid also has anti-inflammatory and wound healing activities; we postulated that all the ingredients in total flavonoids sample maybe exert a synergetic effect on wound curing. Accompanied with detection of four growth factors, the upregulation of these key growth factors may be the mechanism of therapeutic activities of total flavonoids. The present study confirmed undoubtedly that flavonoids were the main active constituents that contribute to excisional wound healing, and suggested its action mechanism of improving expression levels of growth factors at different healing phases. Full article
(This article belongs to the Special Issue Novel Biomaterials for Tissue Engineering 2018)
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<p>UPLC chromatograms at 254 nm of total flavonoids sample in positive ion modes analyzed by UPLC-Q-TOF/MS/DAD.</p>
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<p>Recovery of wounds at different times. JWH, Jing Wan Hong.</p>
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<p>Effects of total flavonoids from <span class="html-italic">B. balsamifera</span> on wound healing rate of rats. Values are expressed as mean ± SD (<span class="html-italic">n</span> = 6) as compared to the control group, * <span class="html-italic">p</span> &lt; 0.05, <sup>∆</sup> <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Effects of total flavonoids from <span class="html-italic">B. balsamifera</span> on CD68 levels in wound tissues of rats. Values are expressed as mean ± SD (<span class="html-italic">n</span> = 6) as compared to control group, * <span class="html-italic">p</span> &lt; 0.05, <sup>∆</sup> <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Effects of total flavonoids from <span class="html-italic">B. balsamifera</span> on vascular endothelial growth factor (VEGF) levels in wound tissues of rats. Values are expressed as mean ± SD (<span class="html-italic">n</span> = 6) as compared to the control group, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of total flavonoids from <span class="html-italic">B. balsamifera</span> on TGF-<span class="html-italic">β</span><sub>1</sub> levels in wound tissues of rats. Values are expressed as mean ± SD (<span class="html-italic">n</span> = 6) as compared to the control group, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of total flavonoids from <span class="html-italic">B. balsamifera</span> on hydroxyproline level in wound tissues of rats. Values are expressed as mean ± SD (<span class="html-italic">n</span> = 6) as compared to the control group, * <span class="html-italic">p</span> &lt; 0.05, <sup>∆</sup> <span class="html-italic">p</span> &lt; 0.01.</p>
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1234 KiB  
Article
Overexpression and Down-Regulation of Barley Lipoxygenase LOX2.2 Affects Jasmonate-Regulated Genes and Aphid Fecundity
by Aleksandra Losvik, Lisa Beste, Robert Glinwood, Emelie Ivarson, Jennifer Stephens, Li-Hua Zhu and Lisbeth Jonsson
Int. J. Mol. Sci. 2017, 18(12), 2765; https://doi.org/10.3390/ijms18122765 - 19 Dec 2017
Cited by 28 | Viewed by 5366
Abstract
Aphids are pests on many crops and depend on plant phloem sap as their food source. In an attempt to find factors improving plant resistance against aphids, we studied the effects of overexpression and down-regulation of the lipoxygenase gene LOX2.2 in barley ( [...] Read more.
Aphids are pests on many crops and depend on plant phloem sap as their food source. In an attempt to find factors improving plant resistance against aphids, we studied the effects of overexpression and down-regulation of the lipoxygenase gene LOX2.2 in barley (Hordeum vulgare L.) on the performance of two aphid species. A specialist, bird cherry-oat aphid (Rhopalosiphum padi L.) and a generalist, green peach aphid (Myzus persicae Sulzer) were studied. LOX2.2 overexpressing lines showed up-regulation of some other jasmonic acid (JA)-regulated genes, and antisense lines showed down-regulation of such genes. Overexpression or suppression of LOX2.2 did not affect aphid settling or the life span on the plants, but in short term fecundity tests, overexpressing plants supported lower aphid numbers and antisense plants higher aphid numbers. The amounts and composition of released volatile organic compounds did not differ between control and LOX2.2 overexpressing lines. Up-regulation of genes was similar for both aphid species. The results suggest that LOX2.2 plays a role in the activation of JA-mediated responses and indicates the involvement of LOX2.2 in basic defense responses. Full article
(This article belongs to the Special Issue Plant Defense Genes Against Biotic Stresses)
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<p>Relative transcript abundance of <span class="html-italic">LOX2.2</span> and a selection of other jasmonic acid (JA)-regulated genes in OeLOX2.2 and antiLOX2.2 lines, their respective controls, and Hsp5 plants. Error bars indicate SE. Letters indicate significant difference between lines for a particular gene (Kruskal-Wallis test at <span class="html-italic">p</span> &lt; 0.05). Samples were from leaves of nine-day old plants (Hsp5, primary leaf) or 14-day old plants (all other genotypes, second leaf). Reference genes: <span class="html-italic">Hsp70</span> and <span class="html-italic">Tubulin</span>.</p>
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<p>Volatile profiles of control and OeLOX2.2 lines. (<b>a</b>) Uninfested plants, (<b>b</b>) bird cherry-oat aphid (BCA)-infested plants. White bars represent the control plants and black bars the overexpressing line OeLOX2.2. Average amounts in ng (±SE) in 48 h volatile collection from 15 plants (<span class="html-italic">n</span> = 6).</p>
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<p>Aphid numbers on control and transgenic lines, five days after infestation with twenty adult apterous aphids. (<b>a</b>) Numbers of bird cherry-oat aphid (BCA), (<b>b</b>) numbers of green peach aphid (GPA). White and black bars indicate, respectively, control and transgenic lines. Bars indicate the average (±SE). <span class="html-italic">n</span> = 12 for BCA and <span class="html-italic">n</span> = 22 for GPA. Asterisks indicate significant differences after aphid infestation between control and transgenic plants (<span class="html-italic">t</span>-test, * <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01).</p>
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<p>Relative transcript abundance in control and transgenic lines without aphids and infested with BCA for five days. (<b>a</b>) Control and OeLOX2.2 plants, (<b>b</b>) control and antiLOX2.2 plants. White and light grey bars represent control plants, with and without aphids; dark grey and black bars represent transgenic lines with and without aphids. The transcript abundance is relative to reference genes <span class="html-italic">Hsp70</span> and <span class="html-italic">Tubulin</span> (±SE). Different letters indicate significant differences between the infested lines, asterisks indicate significant differences for the same line with and without BCA (* <span class="html-italic">p</span> &lt; 0.05 Mann-Whitney test; <span class="html-italic">n</span> = 3 for plants without aphids, <span class="html-italic">n</span> = 4 for infested plants; three technical replicates).</p>
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<p>Relative transcript abundance in control and transgenic lines without aphids and infested with GPA for five days. (<b>a</b>) Control and OeLOX2.2 plants, (<b>b</b>) control and antiLOX2.2 plants. White and light grey bars represent control plants, with and without aphids; dark grey and black bars represent transgenic lines with and without aphids. The transcript abundance is relative to reference genes <span class="html-italic">Hsp70</span> and <span class="html-italic">Tubulin</span> (±SE). Different letters indicate significant differences between the infested lines, asterisks indicate significant differences for the same line with and without GPA (* <span class="html-italic">p</span> &lt; 0.05 Mann-Whitney test; <span class="html-italic">n</span> = 3 for plants without aphids, <span class="html-italic">n</span> = 4 for infested plants; three technical replicates).</p>
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2834 KiB  
Article
Vitamin D and Its Analogues Decrease Amyloid-β (Aβ) Formation and Increase Aβ-Degradation
by Marcus O. W. Grimm, Andrea Thiel, Anna A. Lauer, Jakob Winkler, Johannes Lehmann, Liesa Regner, Christopher Nelke, Daniel Janitschke, Céline Benoist, Olga Streidenberger, Hannah Stötzel, Kristina Endres, Christian Herr, Christoph Beisswenger, Heike S. Grimm, Robert Bals, Frank Lammert and Tobias Hartmann
Int. J. Mol. Sci. 2017, 18(12), 2764; https://doi.org/10.3390/ijms18122764 - 19 Dec 2017
Cited by 72 | Viewed by 7909
Abstract
Alzheimer’s disease (AD) is characterized by extracellular plaques in the brain, mainly consisting of amyloid-β (Aβ), as derived from sequential cleavage of the amyloid precursor protein. Epidemiological studies suggest a tight link between hypovitaminosis of the secosteroid vitamin D and AD. Besides decreased [...] Read more.
Alzheimer’s disease (AD) is characterized by extracellular plaques in the brain, mainly consisting of amyloid-β (Aβ), as derived from sequential cleavage of the amyloid precursor protein. Epidemiological studies suggest a tight link between hypovitaminosis of the secosteroid vitamin D and AD. Besides decreased vitamin D level in AD patients, an effect of vitamin D on Aβ-homeostasis is discussed. However, the exact underlying mechanisms remain to be elucidated and nothing is known about the potential effect of vitamin D analogues. Here we systematically investigate the effect of vitamin D and therapeutically used analogues (maxacalcitol, calcipotriol, alfacalcidol, paricalcitol, doxercalciferol) on AD-relevant mechanisms. D2 and D3 analogues decreased Aβ-production and increased Aβ-degradation in neuroblastoma cells or vitamin D deficient mouse brains. Effects were mediated by affecting the Aβ-producing enzymes BACE1 and γ-secretase. A reduced secretase activity was accompanied by a decreased BACE1 protein level and nicastrin expression, an essential component of the γ-secretase. Vitamin D and analogues decreased β-secretase activity, not only in mouse brains with mild vitamin D hypovitaminosis, but also in non-deficient mouse brains. Our results further strengthen the link between AD and vitamin D, suggesting that supplementation of vitamin D or vitamin D analogues might have beneficial effects in AD prevention. Full article
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<p>Chemical structure of 25(OH) vitamin D<sub>3</sub> and different vitamin D analogues. Structural changes between the analogues are highlighted in red or with red cycles.</p>
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<p>Effect of vitamin D and analogues on Aβ generation. Cells were treated with 25(OH) vitamin D<sub>3</sub> (calcifediol), the vitamin D<sub>3</sub> analogues maxacalcitol, calcipotriol, alfacalcidol, the vitamin D<sub>2</sub> analogues paricalcitol, doxercalciferol in a final concentration of 100 nM or solvent control (EtOH). (<b>A</b>) Total secreted Aβ level in SH-SY5Y APP695 overexpressing cells (<span class="html-italic">n</span> = 3). Aβ of the conditioned media was analyzed by immunoprecipitation and Western Blot (WB) analysis. Using Post Hoc analysis, no significant differences between calcifediol and analogues were found in respect to their potential to reduce Aβ level. (<b>B</b>) Determination of α-secretase activity in living SH-SY5Y wt cells (<span class="html-italic">n</span> = 7). (<b>C</b>) Analysis of β-secretase activity in isolated membranes of SH-SY5Y wt cells (<span class="html-italic">n</span> = 7) and in living cells (<span class="html-italic">n</span> = 5). (<b>D</b>) β-secretase activity in three wt mouse brain and five vitamin D deficient mouse brain homogenates (<span class="html-italic">n</span> = 3). Vitamin D and analogues influence β-secretase activity in wt mouse brains and in vitamin D deficient mouse brains. (<b>E</b>) RT-PCR analysis of <span class="html-italic">BACE1</span> in SH-SY5Y wt cells (<span class="html-italic">n</span> = 3). (<b>F</b>) Determination of BACE1 protein level in cell lysates of SH-SY5Y wt cells by WB analysis (<span class="html-italic">n</span> = 3). Control conditions were set to 100% and illustrated as a line in the graphic. Error bars represent the standard error of the mean. Asteriks show the statistical significance calculated by unpaired Student’s <span class="html-italic">t</span> test (* <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.001).</p>
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<p>Effect of vitamin D and analogues on Aβ generation. Cells were treated with 25(OH) vitamin D<sub>3</sub> (calcifediol), the vitamin D<sub>3</sub> analogues maxacalcitol, calcipotriol, alfacalcidol, the vitamin D<sub>2</sub> analogues paricalcitol, doxercalciferol in a final concentration of 100 nM or solvent control (EtOH). (<b>A</b>) Total secreted Aβ level in SH-SY5Y APP695 overexpressing cells (<span class="html-italic">n</span> = 3). Aβ of the conditioned media was analyzed by immunoprecipitation and Western Blot (WB) analysis. Using Post Hoc analysis, no significant differences between calcifediol and analogues were found in respect to their potential to reduce Aβ level. (<b>B</b>) Determination of α-secretase activity in living SH-SY5Y wt cells (<span class="html-italic">n</span> = 7). (<b>C</b>) Analysis of β-secretase activity in isolated membranes of SH-SY5Y wt cells (<span class="html-italic">n</span> = 7) and in living cells (<span class="html-italic">n</span> = 5). (<b>D</b>) β-secretase activity in three wt mouse brain and five vitamin D deficient mouse brain homogenates (<span class="html-italic">n</span> = 3). Vitamin D and analogues influence β-secretase activity in wt mouse brains and in vitamin D deficient mouse brains. (<b>E</b>) RT-PCR analysis of <span class="html-italic">BACE1</span> in SH-SY5Y wt cells (<span class="html-italic">n</span> = 3). (<b>F</b>) Determination of BACE1 protein level in cell lysates of SH-SY5Y wt cells by WB analysis (<span class="html-italic">n</span> = 3). Control conditions were set to 100% and illustrated as a line in the graphic. Error bars represent the standard error of the mean. Asteriks show the statistical significance calculated by unpaired Student’s <span class="html-italic">t</span> test (* <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.001).</p>
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<p>Effect of vitamin D and analogues on γ-secretase. (<b>A</b>) Analysis of γ-secretase activity in living SH-SY5Y wt cells (<span class="html-italic">n</span> ≥ 5). (<b>B</b>) mRNA level of the γ-secretase component nicastrin determined by RT-PCR analysis (<span class="html-italic">n</span> = 3). Error bars represent the standard error of the mean. Asteriks show the statistical significance calculated by unpaired Student’s <span class="html-italic">t</span> test (* <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.001).</p>
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<p>Effect of vitamin D and analogues on Aβ catabolism. Vitamin D and its analogues influence Aβ-degradation. Total Aβ-degradation in (<b>A</b>) mouse neuroblastoma N2a wt cells (<span class="html-italic">n</span> = 3) and (<b>B</b>) vitamin D deficient mouse brains (<span class="html-italic">n</span> = 5). Calcifediol and its analogues increased the Aβ-degradation compared to solvent control. (<b>C</b>) RT-PCR analysis of <span class="html-italic">NEP</span> expression in SH-SY5Y wt cells (<span class="html-italic">n</span> = 3). (<b>D</b>) NEP activity in N2a cells (<span class="html-italic">n</span> = 8). Error bars represent the standard error of the mean. Asteriks show the statistical significance calculated by unpaired Student’s <span class="html-italic">t</span> test (* <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.001).</p>
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<p>Effect of vitamin D and analogues on Interleukin-1β level. Interleukin-1β (IL-1β) level was determined by enzyme-linked immunosorbent assay (ELISA) technique (<span class="html-italic">n</span> ≥ 3). IL-1β was analyzed in SH-SY5Y wt cells incubated with 100 nM calcifediol or analogues compared to cells treated with the solvent control. Error bars represent the standard error of the mean. Asteriks show the statistical significance calculated by unpaired Student’s <span class="html-italic">t</span> test (* <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01).</p>
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<p>Model of the pleiotropic effects of vitamin D and analogues on Aβ-homeostasis. Vitamin D and analogues decrease amyloidogenic amyloid precursor protein (APP) processing by affecting β- and γ-secretase activity. The reduction of β-secretase activity is caused by a direct effect of vitamin D and its analogues on β-secretase activity combined with indirect effects on <span class="html-italic">BACE1</span> gene expression and total BACE1 protein level. The γ-secretase activity is reduced by decreased gene expression of nicastrin responsible for the maturation of the heterotetrameric γ-secretase complex. A stimulation of the non-amyloidogenic α-secretase processing of APP was found for 25(OH) vitamin D<sub>3</sub> and the vitamin D<sub>2</sub> analogue paricalcitol. Total Aβ level in presence of vitamin D and analogues are further reduced by increased Aβ-degradation.</p>
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3186 KiB  
Article
A Genome-Wide Association Study and Complex Network Identify Four Core Hub Genes in Bipolar Disorder
by Zengyan Xie, Xianyan Yang, Xiaoya Deng, Mingyue Ma and Kunxian Shu
Int. J. Mol. Sci. 2017, 18(12), 2763; https://doi.org/10.3390/ijms18122763 - 19 Dec 2017
Cited by 10 | Viewed by 5874
Abstract
Bipolar disorder is a common and severe mental illness with unsolved pathophysiology. A genome-wide association study (GWAS) has been used to find a number of risk genes, but it is difficult for a GWAS to find genes indirectly associated with a disease. To [...] Read more.
Bipolar disorder is a common and severe mental illness with unsolved pathophysiology. A genome-wide association study (GWAS) has been used to find a number of risk genes, but it is difficult for a GWAS to find genes indirectly associated with a disease. To find core hub genes, we introduce a network analysis after the GWAS was conducted. Six thousand four hundred fifty eight single nucleotide polymorphisms (SNPs) with p < 0.01 were sifted out from Wellcome Trust Case Control Consortium (WTCCC) dataset and mapped to 2045 genes, which are then compared with the protein–protein network. One hundred twelve genes with a degree >17 were chosen as hub genes from which five significant modules and four core hub genes (FBXL13, WDFY2, bFGF, and MTHFD1L) were found. These core hub genes have not been reported to be directly associated with BD but may function by interacting with genes directly related to BD. Our method engenders new thoughts on finding genes indirectly associated with, but important for, complex diseases. Full article
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<p>Results of the genome wide association study (GWAS). The horizontal axis represents 22 chromosomes and the vertical axis represents the negative logarithm with base 10 of GWAS <span class="html-italic">p</span>-value for each SNP. Red line: canonical 5 × 10<sup>−8</sup> cutoff. Blue line: 0.01 cutoff used in this study.</p>
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<p>Overlapped genes associated with four mental illnesses.</p>
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<p>BD risk gene interaction network. Only the nodes with a degree ≥4 are shown. Green balls are BD risk genes identified in the GWAS with <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>The topology properties of the network. (<b>a</b>) The distribution of number of nodes with different degrees. (<b>b</b>) Frequency distribution of shortest paths. (<b>c</b>) The relationship between topological coefficients and the number of node neighbors. (<b>d</b>) The relationship between betweenness and the number of node neighbors.</p>
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<p>Gene clusters identified with Cytoscape. Yellow nodes are core hub genes. No core hub gene is found in Cluster 4 (<b>d</b>). (<b>a</b>) Cluster 1; (<b>b</b>) Cluster 2; (<b>c</b>) Cluster 3; (<b>d</b>) Cluster 4; (<b>e</b>) Cluster 5.</p>
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8205 KiB  
Article
Potent Inhibition of miR-34b on Migration and Invasion in Metastatic Prostate Cancer Cells by Regulating the TGF-β Pathway
by Li-li Fang, Bao-fei Sun, Li-rong Huang, Hai-bo Yuan, Shuo Zhang, Jing Chen, Zi-jiang Yu and Heng Luo
Int. J. Mol. Sci. 2017, 18(12), 2762; https://doi.org/10.3390/ijms18122762 - 19 Dec 2017
Cited by 38 | Viewed by 4506
Abstract
The importance of miRNAs in the progression of prostate cancer (PCa) has further been supported by the finding that miRNAs have been identified as potential oncogenes or tumor suppressors in PCa. Indeed, in eukaryotes, miRNAs have been found to regulate and control gene [...] Read more.
The importance of miRNAs in the progression of prostate cancer (PCa) has further been supported by the finding that miRNAs have been identified as potential oncogenes or tumor suppressors in PCa. Indeed, in eukaryotes, miRNAs have been found to regulate and control gene expression by degrading mRNA at the post-transcriptional level. In this study, we investigated the expression of miR-34 family members, miR-34b and miR-34c, in different PCa cell lines, and discussed the molecular mechanism of miR-34b in the invasion and migration of PCa cells in vitro. The difference analyses of the transcriptome between the DU145 and PC3 cell lines demonstrated that both miR-34b and -34c target critical pathways that are involved in metabolism, such as proliferation, and migration, and invasion. The molecular expression of miR-34b/c were lower in PC3 cells. Moreover, over-expression of miR-34b/c in PC3 cells caused profound phenotypic changes, including decreased cell proliferation, migration and invasion. Moreover, the players that regulate expression levels of transforming growth factor-β (TGF-β), TGF-β receptor 1 (TGF-βR1), and p53 or phosphorylation levels of mothers against decapentaplegic 3 (SMAD3) in the TGF-β/Smad3 signaling pathway have yet to be elucidated, and will provide novel tools for diagnosis and treatment of metastatic PCa. Full article
(This article belongs to the Section Biochemistry)
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<p>Comparison of migration and invasion capacity of prostate cancer cells. (<b>A</b>) Representative images of the invasion and migration of DU145 and PC3 cells taken by an inverted microscope (20× objective); (<b>B</b>) Quantitative analysis of cell migration (2 h) and invasion (4 h) in DU145 and PC3 cells. * <span class="html-italic">p</span> &lt; 0.05 ** <span class="html-italic">p</span> &lt; 0.01. Per condition, three independent experiments were performed.</p>
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<p>Expression levels of miR-34a, miR-34b, and miR-34c in prostate cancer cell lines DU145 and PC3. (<b>A</b>) Heat-map of miRNAs with differential expression comparing DU145 and PC3 cells. Up-regulated miRNAs are in red, whereas down-regulated genes are shown in green. Expression of miR-34b and miR-34c is up-regulated in DU145 cells and downregulated in PC3 cells; (<b>B</b>) Expression of miR-34a, miR-34b and miR-34c in DU145 and PC3 cells by miRNA sequencing analysis; (<b>C</b>) The relative expression of miR-34a, miR-34b, and miR-34c in DU145 and PC3 cells by qRT-PCR analysis. ** <span class="html-italic">p</span> &lt; 0.01. Per condition, three independent experiments were performed.</p>
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<p>Both miR-34b and miR-34c suppresses prostate cancer cell proliferation. (<b>A</b>,<b>B</b>) 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay of PC3 cells transfected with miR-34b/c mimic (50 nM) or mimic negative control (NC) (50 nM) and DU145 cells transfected with miR-34b/c inhibitor (100 nM) or inhibitor NC (100 nM); (<b>C</b>,<b>D</b>) Effects on cell growth with ectopic expression of miR-34b/c in DU145 and PC3 by live cells counting compared to the negative control. Per condition, three independent experiments were performed.</p>
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<p>Flow cytometric data of apoptotic PC3 cells, 48 h after transfection with miR-34b/c mimic, mimic NC or without transfection (Blank). (<b>A</b>) Cells were harvested, stained with annexin-V-FITC and PI, and analyzed by flow cytometry. (<b>B</b>) Data showing the percentages of early and late apoptotic cells (right quadrants).</p>
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<p>(<b>A</b>,<b>B</b>) Relative expression of miR-34b and miR-34c in PC3 cells transfected with miR-34b/c mimic or mimic negative control (NC) or without transfection (Blank) by qRT-PCR analysis. ** <span class="html-italic">p</span> &lt; 0.01 vs. mimic NC or blank group; (<b>C</b>) qRT-PCR analysis showing the relative expression of miR-34b and miR-34c in DU-145 cells transfected with miR-34b/c inhibitor or inhibitor NC or without transfection (Blank). * <span class="html-italic">p</span> &lt; 0.05 vs. inhibitor NC or blank group.</p>
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<p>Random migration by a wound healing/scratch assay. (<b>A</b>,<b>B</b>) Shown are representative images of the scratch assay in DU145 and PC3 cells at 0, 6, 12, 24 and 36 h after the scratch was created; (<b>C</b>,<b>D</b>) The rate of migration calculated by the width of the wound in DU145 and PC3 cells after ectopic expression of miR-34b/c by Image-Pro Plus software (Media Cybernetics, MD, USA).</p>
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<p>The capacity of migration and invasion of PC3 cells after transfection with miR-34b/c at 2, 4, 8, 12 h respectively. (<b>A</b>,<b>B</b>) Representative images of the invasion and migration of PC3 cells taken by an inverted microscope (20× objective); (<b>C</b>,<b>D</b>) Quantitative analysis of cell migration and invasion. * <span class="html-italic">p</span> &lt; 0.05 vs. mimic NC or blank group (basal migration without transfection). Per condition, three independent experiments were performed.</p>
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<p>The migration and invasion capacity after transfection with miR-34b/c in DU-145 cells at 2, 4, 8, 12 h respectively. (<b>A</b>,<b>B</b>) Representative images showing the invasion and migration of DU-145 cells transfected with miR-34b/c inhibitor or inhibitor NC or without transfection (20× objective); (<b>C</b>,<b>D</b>) Quantitative analysis of cell migration and invasion. * <span class="html-italic">p</span> &lt; 0.05 vs. inhibitor NC or blank group. Per condition, three independent experiments were performed.</p>
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<p>Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicating the pathways affected by miR-34b in prostate cancer cells. The size of the dots indicates the number of differential genes in the pathway, whereas colors represent the significant <span class="html-italic">p</span>-value of the pathway. The 20 most significantly different pathways are shown.</p>
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<p>Transcript levels of TGF-β1, TGFβ-R1, SMAD3, p53 and RAS after treatment with 50 nM miR-34b mimic, NC mimic in PC3 cells by PCR (<b>A</b>) and qRT-PCR (<b>B</b>) β-actin was used as an internal control. The mRNA expression of genes was not significantly different in PC3 cells treated with or without miR-34b mimics. Per condition, three independent experiments were performed. TGF: transforming growth factor; SMAD: mothers against decapentaplegic; p53: a phosphorylated protein with a molecular weight of 53,000; RAS: rat sarcoma.</p>
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<p>Western blot analysis of TGF-β1, TGFβ-R1, SMAD3, p-SMAD3, p53, and RAS after ectopic expression of miR-34b/c in PC3 cells. (<b>A</b>) Protein levels of TGF-β1, TGFβ-R1, and p53 and phosphorylation of SMAD3 in miR-34b mimic-treated PC3 cells were lower compared to that in mimic NC or blank control groups. Protein levels of RAS were not significantly different between groups; (<b>B</b>) In miR-34c mimic-treated PC3 cells, protein levels of TGF-β, TGFβ-R, p53 and phosphorylation of SMAD3 were not significantly different when compared to control groups; (<b>C</b>,<b>D</b>) The relative protein levels were calculated by Image J software (Rawak Software, Inc., Dresden, Germany), and β-actin was used as a loading control. ** <span class="html-italic">p</span> &lt; 0.01 * <span class="html-italic">p</span> &lt; 0.05 vs. mimic NC or blank group. Per condition, three independent experiments were performed.</p>
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<p>TGF-β signaling pathway. TGF-β superfamily of cytokines bind to type I receptors and type II receptors at the cell surface, thereby forming a tetrameric complex. Binding of TGF-β1 to its receptor II activated the TGF-β receptor type I kinase, resulting in phosphorylation of the downstream signal transducer Smad2/3(R-SMADs). Phosphorylated R-SMADs forms a complex with a common mediator, Smad4 (Co-SMAD). This complex is translocated into the nucleus, where it binds DNA and regulates transcription of many genes by interacting with transcriptional cofactors. SMAD7 represses signaling by other SMADs to down-regulate the system. Other signaling pathways, including the MAPK-ERK cascade are activated by TGF-β signaling, and modulate SMAD activation.</p>
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Article
Functional Analysis of Human Hub Proteins and Their Interactors Involved in the Intrinsic Disorder-Enriched Interactions
by Gang Hu, Zhonghua Wu, Vladimir N. Uversky and Lukasz Kurgan
Int. J. Mol. Sci. 2017, 18(12), 2761; https://doi.org/10.3390/ijms18122761 - 19 Dec 2017
Cited by 84 | Viewed by 6630
Abstract
Some of the intrinsically disordered proteins and protein regions are promiscuous interactors that are involved in one-to-many and many-to-one binding. Several studies have analyzed enrichment of intrinsic disorder among the promiscuous hub proteins. We extended these works by providing a detailed functional characterization [...] Read more.
Some of the intrinsically disordered proteins and protein regions are promiscuous interactors that are involved in one-to-many and many-to-one binding. Several studies have analyzed enrichment of intrinsic disorder among the promiscuous hub proteins. We extended these works by providing a detailed functional characterization of the disorder-enriched hub protein-protein interactions (PPIs), including both hubs and their interactors, and by analyzing their enrichment among disease-associated proteins. We focused on the human interactome, given its high degree of completeness and relevance to the analysis of the disease-linked proteins. We quantified and investigated numerous functional and structural characteristics of the disorder-enriched hub PPIs, including protein binding, structural stability, evolutionary conservation, several categories of functional sites, and presence of over twenty types of posttranslational modifications (PTMs). We showed that the disorder-enriched hub PPIs have a significantly enlarged number of disordered protein binding regions and long intrinsically disordered regions. They also include high numbers of targeting, catalytic, and many types of PTM sites. We empirically demonstrated that these hub PPIs are significantly enriched among 11 out of 18 considered classes of human diseases that are associated with at least 100 human proteins. Finally, we also illustrated how over a dozen specific human hubs utilize intrinsic disorder for their promiscuous PPIs. Full article
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<p>Enrichment in intrinsic disorder of human hub proteins and their interactors. The <span class="html-italic">x</span>- and <span class="html-italic">y</span>-axis show the amount of disorder content of the hubs and hub interactors, respectively. Each protein-protein interaction (PPI) is mapped into this two-dimensional plane and the density of these hub-interactor pairs is represented by green isolines. For instance, 40% of these pairs occupy the lower left corner where the disorder content of both hubs and interactors is below 0.25. The density was modelled with the Epanechnikov kernel function using Mathematica software. Next, we simulated a randomized PPI network that follows the same distribution of node density, i.e., we randomly assigned interactions between the human proteins to maintain the same density profile as in the true PPI network. Coloring of the inside of the two-dimensional plane reflects a relative ratio between the density of true (<span class="html-italic">d</span><sub>n</sub>) and randomized (<span class="html-italic">d</span><sub>r</sub>) interactions in the PPI networks calculated as [<span class="html-italic">d</span><sub>n</sub>(x,y)-<span class="html-italic">d</span><sub>r</sub>(x,y)]/<span class="html-italic">d</span><sub>r</sub>(x,y). The color scale given on the right defines values of the ratio, e.g., orange corresponds to PPIs which are 0.5 times more frequent in the true PPI network compared to the random network.</p>
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<p>Number of disulfide bonds panel (<b>A</b>) and disorder content panel (<b>B</b>) in specific subcellular locations. We report the values for all human proteins (black bars), hubs (solid blue), hub interactors (solid red), and hub interactors that exclude hubs (solid green) that are associated with the disorder-enriched hub PPIs vs the remaining hubs (blue horizontal stripes), hub interactors (red horizontal stripes), and hub interactors that exclude hubs (green horizontal stripes), respectively, for each location. We consider all locations that include at least 10 proteins for each of the seven protein sets. The locations in panel (<b>A</b>,<b>B</b>) are sorted in descending order by the values of the number of disulfide bonds (disorder content) for all proteins (black bars).</p>
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<p>Number of disulfide bonds panel (<b>A</b>) and disorder content panel (<b>B</b>) in specific subcellular locations. We report the values for all human proteins (black bars), hubs (solid blue), hub interactors (solid red), and hub interactors that exclude hubs (solid green) that are associated with the disorder-enriched hub PPIs vs the remaining hubs (blue horizontal stripes), hub interactors (red horizontal stripes), and hub interactors that exclude hubs (green horizontal stripes), respectively, for each location. We consider all locations that include at least 10 proteins for each of the seven protein sets. The locations in panel (<b>A</b>,<b>B</b>) are sorted in descending order by the values of the number of disulfide bonds (disorder content) for all proteins (black bars).</p>
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<p>Significance of the differences in the functional and structural characteristics between hubs (on the left), all hub interactors, and hub interactors that exclude hubs (on the right) that are associated with the disorder-enriched hub PPIs when compared to the remaining hubs (on the left) and the corresponding remaining interactors (on the right). Panel (<b>A</b>) summarizes the results concerning structural characteristics, functional regions and motifs, evolutionary conservation and the overall abundance of PTMs. Panel (<b>B</b>) gives detailed results for specific types of PTMs. We reported relative differences and their statistical significance. The characteristics are sorted in descending order by their relative differences for the hubs. The characteristics are color-coded as follows: green for large (relative difference &gt; 20%) and statistically significant (<span class="html-italic">p</span>-value &lt; 0.001) enrichment; red for large (relative difference &lt; −20%) and statistically significant (<span class="html-italic">p</span>-value &lt; 0.001) depletion; and blue for lack of large and significant differences (|relative difference| &lt; 20% or <span class="html-italic">p</span>-value over 0.001). Abbreviations: Eukaryotic linear motif (ELM); molecular recognition feature (MoRF; short disordered protein binding region); and posttranslational modification (PTM).</p>
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<p>Intrinsic disorder levels in the disordered human hubs characterized by the highest levels of disorder (<b>A</b>–<b>E</b>), highly disordered hubs characterized by the highest levels of interactability (<b>F</b>–<b>J</b>), and ordered hubs with the highest interactability levels (<b>K</b>–<b>O</b>). The disorder was annotated using the MobiDB platform [<a href="#B141-ijms-18-02761" class="html-bibr">141</a>,<a href="#B142-ijms-18-02761" class="html-bibr">142</a>,<a href="#B143-ijms-18-02761" class="html-bibr">143</a>]; disorder content is shown in red font. Each plot represents disorder tendencies in two forms—by bar plots showing location of IDPRs and by area plots showing sequence distribution of consensus disorder scores evaluated by MobiDB lite disorder predictor [<a href="#B143-ijms-18-02761" class="html-bibr">143</a>]. (<b>A</b>) Thyroid hormone receptor-associated protein 3 (UniProt ID: Q9Y2W1). (<b>B</b>) Zinc finger CCCH domain-containing protein 18 (UniProt ID: Q86VM9). (<b>C</b>) Scaffold attachment factor B1 (UniProt ID: Q15424). (<b>D</b>) Intracellular hyaluronan-binding protein 4 (UniProt ID: Q5JVS0). (<b>E</b>) TATA-binding protein-associated factor 2N (UniProt ID: Q92804). (<b>F</b>) BAG family molecular chaperone regulator 3 (UniProt ID: O95817). (<b>G</b>) CREB-binding protein (UniProt ID: Q92793). (<b>H</b>) RNA-binding protein EWS (UniProt ID: Q01844). (<b>I</b>) Cyclin-dependent kinase inhibitor 1 (UniProt ID: P38936). (<b>J</b>) Mediator of DNA damage checkpoint protein 1 (UniProt ID: Q14676). (<b>K</b>) Ubiquitin (UniProt ID: P0CG48; 8548 interactors). (<b>L</b>) Growth factor receptor-bound protein 2 (UniProt ID: P62993; 804 interactors). (<b>M</b>) Actin (UniProt ID: P60709; 263 interactors). (<b>N</b>) Protection of telomeres protein 1 (UniProt ID: Q9NUX5; 200 interactors). (<b>O</b>) Protein mago nashi homolog (UniProt ID: P61326; 190 interactors).</p>
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<p>Structural characterization of highly connected ordered hubs. (<b>A</b>) Solution NMR structure of human ubiquitin (PDB ID: 1XQQ) [<a href="#B207-ijms-18-02761" class="html-bibr">207</a>]. (<b>B</b>) Solution NMR structure of a complex between the N-terminal SH3 domain of GRB2 (residues 1–56, red ribbons) and a peptide from SOS (blue ribbons) (PDB ID: 1AZE) [<a href="#B210-ijms-18-02761" class="html-bibr">210</a>]. (<b>C</b>) Minimized mean solution NMR structure of a complex between the SH2 domain of human GRB2 (residues 49–168, red ribbon) and a KPFY*VNVEF peptide (blue ribbon) (PDB ID: 1BMB) [<a href="#B211-ijms-18-02761" class="html-bibr">211</a>]. (<b>D</b>) Minimized mean solution NMR structure of a complex between the C-terminal SH3 domain of human GRB2 (residues 159–215, red ribbon) and a ligand peptide (blue ribbon) (PDB ID: 1IO6). (<b>E</b>) Crystal structure of a telomeric shelterin complex between the POT1 C-terminal domain (POT1C, residues 330–634, red ribbon) and POT1-binding region (residues 254–336, blue ribbon) of the adrenocortical dysplasia protein homolog (PDB ID: 5JUN7) [<a href="#B212-ijms-18-02761" class="html-bibr">212</a>]. (<b>F</b>) Crystal structure of a core EJC complex containing the complex of MAGOH (full length, dark orange ribbon), Y14 (residues 66–174, light orange ribbon), eIF4AIII (full length, red ribbon), Btz (the SELOR domain, residues 137–286, two blue ribbons), a non-hydrolyzable ATP analog (AMPPNP, bound to eIF4AIII), and U<sub>15</sub> RNA (yellow ribbon) (PDB ID: 2J0Q) [<a href="#B213-ijms-18-02761" class="html-bibr">213</a>].</p>
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<p>Connectivity of proteins in the human PPI network. Panel (<b>A</b>) summarizes the fraction of proteins (nodes in the PPI network) with a given number of PPI interactions (degree). Panel (<b>B</b>) gives the cumulative fraction of proteins having a degree less than the corresponding value on the <span class="html-italic">x</span>-axis. The circles (crosses) show the results for the original PPI network collected from mentha (the network where proteins were mapped to UniProt). The dashed vertical line in panel (<b>B</b>) shows the degree that demarcates hubs, which is defined as the 20% of the most connected nodes.</p>
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2063 KiB  
Review
Galectin-7 in Epithelial Homeostasis and Carcinomas
by Tamara Advedissian, Frédérique Deshayes and Mireille Viguier
Int. J. Mol. Sci. 2017, 18(12), 2760; https://doi.org/10.3390/ijms18122760 - 19 Dec 2017
Cited by 30 | Viewed by 6325
Abstract
Galectins are small unglycosylated soluble lectins distributed both inside and outside the cells. They share a conserved domain for the recognition of carbohydrates (CRD). Although galectins have a common affinity for β-galatosides, they exhibit different binding preferences for complex glycans. First described twenty [...] Read more.
Galectins are small unglycosylated soluble lectins distributed both inside and outside the cells. They share a conserved domain for the recognition of carbohydrates (CRD). Although galectins have a common affinity for β-galatosides, they exhibit different binding preferences for complex glycans. First described twenty years ago, galectin-7 is a prototypic galectin, with a single CRD, able to form divalent homodimers. This lectin, which is mainly expressed in stratified epithelia, has been described in epithelial tissues as being involved in apoptotic responses, in proliferation and differentiation but also in cell adhesion and migration. Most members of the galectins family have been associated with cancer biology. One of the main functions of galectins in cancer is their immunomodulating potential and anti-angiogenic activity. Indeed, galectin-1 and -3, are already targeted in clinical trials. Another relevant function of galectins in tumour progression is their ability to regulate cell migration and cell adhesion. Among these galectins, galectin-7 is abnormally expressed in various cancers, most prominently in carcinomas, and is involved in cancer progression and metastasis but its precise functions in tumour biology remain poorly understood. In this issue, we will focus on the physiological functions of galectin-7 in epithelia and present the alterations of galectin-7 expression in carcinomas with the aim to describe its possible functions in tumour progression. Full article
(This article belongs to the Special Issue Galectins in Cancer and Translational Medicine)
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<p>(<b>a</b>) Representation of the crystal structure of galectin-7 in complex with LacNAc (PBD 4XBQ) [<a href="#B30-ijms-18-02760" class="html-bibr">30</a>]. Carbohydrates are recognized by the residues H49, N51, R53, N62, W69, E72 and R74 forming the CRD. (<b>b</b>) Crystal structure of homodimeric galectin-7 (PBD 1BKZ) [<a href="#B18-ijms-18-02760" class="html-bibr">18</a>] illustrating the “back-to-back” arrangement of galectin-7 dimers with the two CRD orientated in the opposite direction. Structures obtained from <a href="http://www.rcsb.org" target="_blank">www.rcsb.org</a>.</p>
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<p>Schematic representation of known and putative functions of galectin-7. In addition to the functions of galectin-7 in cell proliferation, apoptosis, differentiation, migration and adhesion described in this issue, few evidence highlights other functions of galectin-7 in epithelia. As an illustration, galectin-7 has been shown to interfere with Transforming Growth factor β (TGFβ) signalling in response to Hepatocyte Growth Factor (HGF) by promoting smad3 export from the nucleus and thus preventing liver fibrosis occurrence [<a href="#B45-ijms-18-02760" class="html-bibr">45</a>]. In addition, the commensal bacteria <span class="html-italic">Finegoldia magna</span> has been described to adhere to the upper layers of the epidermis through binding of the adhesion bacterial protein <span class="html-italic">F. magna</span> Adhesion Factor (FAF) to galectin-7, indicating that galectin-7 can bind to ligands from microbial origin [<a href="#B46-ijms-18-02760" class="html-bibr">46</a>].</p>
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<p>Galectin-7 is recruited at damaged lysosomes. Twelve minutes incubation with the lysosome-damaging agent GPN (glycyl-<span class="html-small-caps">l</span>-phenylalanine 2-naphthylamide) induces intracellular accumulation of galectin-7 at damaged lysosomes in HaCaT cells. Scale bar = 10 μm.</p>
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1091 KiB  
Review
Control of Nucleotide Metabolism Enables Mutant p53’s Oncogenic Gain-of-Function Activity
by Valentina Schmidt, Rachana Nagar and Luis A. Martinez
Int. J. Mol. Sci. 2017, 18(12), 2759; https://doi.org/10.3390/ijms18122759 - 19 Dec 2017
Cited by 12 | Viewed by 6812
Abstract
Since its discovery as an oncoprotein in 1979, investigation into p53’s many identities has completed a full circle and today it is inarguably the most extensively studied tumor suppressor (wild-type p53 form or WTp53) and oncogene (mutant p53 form or mtp53) in cancer [...] Read more.
Since its discovery as an oncoprotein in 1979, investigation into p53’s many identities has completed a full circle and today it is inarguably the most extensively studied tumor suppressor (wild-type p53 form or WTp53) and oncogene (mutant p53 form or mtp53) in cancer research. After the p53 protein was declared “Molecule of the Year” by Science in 1993, the p53 field exploded and a plethora of excellent reviews is now available on every aspect of p53 genetics and functional repertoire in a cell. Nevertheless, new functions of p53 continue to emerge. Here, we discuss a novel mechanism that contributes to mtp53’s Gain of Functions GOF (gain-of-function) activities and involves the upregulation of both nucleotide de novo synthesis and nucleoside salvage pathways. Full article
(This article belongs to the Special Issue Emerging Non-Canonical Functions and Regulation of p53)
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<p>An overview of nucleotide synthesis. PRPP = 5-phospho ribosyl pyro phosphate; ATASE = amidophosphoribosyltransferase; IMP = inosine mono phosphate; AMP = adenosine mono phosphate; GMP = guanosine mono phosphate; CMP = cytidine mono phosphate; GMPS = guanosine mono phosphate synthetase; UMP = uridine mono phosphate; TS = thymidine synthetase; dATP = deoxyadenosine tri phosphate; dGTP = deoxyguanosine tri phosphate; dCTP = deoxycytosine tri phosphate; dTMP = deoxythymidine mono phosphate; APRT = adenine phosphoribosyl transferases; HGPRT = hypoxanthine-guanine phosphoribosyl transferases; dCK = deoxycytidine kinase; dGK = deoxyguanosine kinase. The dotted line represents the multiple steps.</p>
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<p>Schematic showing the mechanisms of cancer cells metastasis in brain. Adapted and modified from Chen et al. [<a href="#B90-ijms-18-02759" class="html-bibr">90</a>].</p>
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Article
Microsomal Prostaglandin E Synthase-1 Facilitates an Intercellular Interaction between CD4+ T Cells through IL-1β Autocrine Function in Experimental Autoimmune Encephalomyelitis
by Takako Takemiya, Chisen Takeuchi and Marumi Kawakami
Int. J. Mol. Sci. 2017, 18(12), 2758; https://doi.org/10.3390/ijms18122758 - 19 Dec 2017
Cited by 7 | Viewed by 5995
Abstract
Microsomal prostaglandin synthetase-1 (mPGES-1) is an inducible terminal enzyme that produces prostaglandin E2 (PGE2). In our previous study, we investigated the role of mPGES-1 in the inflammation and demyelination observed in experimental autoimmune encephalomyelitis (EAE), an animal model of multiple [...] Read more.
Microsomal prostaglandin synthetase-1 (mPGES-1) is an inducible terminal enzyme that produces prostaglandin E2 (PGE2). In our previous study, we investigated the role of mPGES-1 in the inflammation and demyelination observed in experimental autoimmune encephalomyelitis (EAE), an animal model of multiple sclerosis, using mPGES-1-deficient (mPGES-1−/−) and wild-type (wt) mice. We found that mPGES-1 facilitated inflammation, demyelination, and paralysis and was induced in vascular endothelial cells and macrophages and microglia around inflammatory foci. Here, we investigated the role of interleukin-1β (IL-1β) in the intercellular mechanism stimulated by mPGES-1 in EAE spinal cords in the presence of inflammation. We found that the area invaded by CD4-positive (CD4+) T cells was extensive, and that PGE2 receptors EP1–4 were more induced in activated CD4+ T cells of wt mice than in those of mPGES-1−/− mice. Moreover, IL-1β and IL-1 receptor 1 (IL-1r1) were produced by 65% and 48% of CD4+ T cells in wt mice and by 44% and 27% of CD4+ T cells in mPGES-1−/− mice. Furthermore, interleukin-17 (IL-17) was released from the activated CD4+ T cells. Therefore, mPGES-1 stimulates an intercellular interaction between CD4+ T cells by upregulating the autocrine function of IL-1β in activated CD4+ T cells, which release IL-17 to facilitate axonal and myelin damage in EAE mice. Full article
(This article belongs to the Special Issue Advances in Multiple Sclerosis 2017)
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<p>Expression of microsomal PGE<sub>2</sub> synthase-1 (mPGES-1) and the effect of mPGES-1 on experimental autoimmune encephalomyelitis (EAE) paralysis. Immunohistochemistry image of mPGE-1 (green) with CD31 (<b>A</b>,<b>B</b>), CD11b (<b>C</b>,<b>D</b>), and CD4 (<b>E</b>,<b>F</b>) in the inflammatory region of spinal cords of wt EAE mice (<b>A</b>,<b>C</b>,<b>E</b>) and ko EAE (<b>B</b>,<b>D</b>,<b>F</b>) mice. Onset of symptoms (<b>G</b>) and the EAE score at day 19 (<b>H</b>) in EAE development. Scale bars (20 μm) for all images. wt, wild-type; ko, <span class="html-italic">mPGES-1<sup>−/−</sup></span>. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Distribution of CD4<sup>+</sup> T cells in EAE spinal cords. Immunohistochemistry image showing CD4 in the inflammatory region of the spinal cord in wt EAE (<b>A</b>,<b>B</b>), ko EAE (<b>C</b>,<b>D</b>), wt control (<b>G</b>), and ko control (<b>H</b>) mice. Measurement of half size area in the spinal cord (<b>E</b>). Percentage of the CD4<sup>+</sup> area in the EAE spinal cord half (<b>F</b>). Scale bar (20 μm) for (<b>A</b>,<b>C</b>,<b>G</b>,<b>H</b>), and (200 μm) for (<b>B</b>,<b>D</b>,<b>E</b>). wt, wild-type; ko, <span class="html-italic">mPGES-1<sup>−/−</sup></span>; cont, naïve mice; EAE, EAE mice. * <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Expression of EP receptors and interleukin-1β (IL-1β) in CD4<sup>+</sup> T cells in EAE spinal cords. Immunohistochemistry image showing EP1–4 (green), CD4 (red), and IL-1β (blue) in the inflammatory region of the spinal cords of EAE wild-type mice (<b>A</b>) and EAE <span class="html-italic">mPGES-1<sup>−/−</sup></span> mice (<b>B</b>). Scale bar (50 μm) for all images.</p>
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<p>Expression of IL-1β in CD4<sup>+</sup> T cells in the spinal cords of EAE and control mice. Immunohistochemistry image showing IL-1β (green) and CD4 (red) in the inflammatory region of the spinal cords of wt EAE (<b>A</b>), wt control (<b>B</b>), ko EAE (<b>C</b>), and ko control (<b>D</b>) mice. Percentage of the IL-1β<sup>+</sup> CD4<sup>+</sup> area in the CD4<sup>+</sup> area of EAE spinal cords (<b>E</b>). Scale bar (50 μm) for upper panels and (20 μm) for lower panels of (<b>A</b>–<b>D</b>). wt, wild-type; ko, <span class="html-italic">mPGES-1<sup>−/−</sup></span>; cont, naïve mice; EAE, EAE mice. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.005, *** <span class="html-italic">p</span> &lt; 0.0005.</p>
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<p>Expression of IL-1β in CD11b<sup>+</sup> macrophages and microglia in the spinal cords of EAE and control mice. Immunohistochemistry image showing IL-1β (green) and CD11b (red) in the inflammatory region of the spinal cords of wt EAE (<b>A</b>), wt control (<b>B</b>), ko EAE (<b>C</b>), and ko control (<b>D</b>) mice. Percentage of the IL-1β<sup>+</sup> CD11b<sup>+</sup> area in the CD11b<sup>+</sup> area of EAE spinal cords (<b>E</b>). Scale bar (50 μm) for upper panels and (20 μm) for lower panels of (<b>A</b>–<b>D</b>). wt, wild-type; ko, <span class="html-italic">mPGES-1<sup>−/−</sup></span>; cont, naïve mice; EAE, EAE mice. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Expression of interleukin-1 receptor 1 (IL-1r1) in CD4<sup>+</sup> T cells in the spinal cords of EAE and control mice. Immunohistochemistry image showing IL-1r1 (green) and CD4 (red) in the inflammatory region of the spinal cords of wt EAE (<b>A</b>), wt control (<b>B</b>), ko EAE (<b>C</b>), and ko control (<b>D</b>) mice. Percentage of the IL-1r1<sup>+</sup> CD4<sup>+</sup> area in the CD4<sup>+</sup> area of EAE spinal cords (<b>E</b>). Scale bar (50 μm) for upper panels and (20 μm) for lower panels of (<b>A</b>–<b>D</b>). wt, wild-type; ko, <span class="html-italic">mPGES-1<sup>−/−</sup></span>; cont, naïve mice; EAE, EAE mice. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Expression of interleukin-17 (IL-17) and interferon-γ (IFN-γ) in CD4<sup>+</sup> T cells in the spinal cords of EAE mice. Immunohistochemistry image showing IL-17 (green) and CD4 (red) in the inflammatory region of the spinal cords of wt EAE (<b>A</b>) and ko EAE (<b>B</b>) mice; and IFN-γ (green) and CD4 (red) in the inflammatory region of the spinal cords of wt EAE (<b>C</b>) and ko EAE (<b>D</b>) mice. Scale bar (50 μm) for upper panels and (20 μm) for lower panels of A–D. wt, wild-type; ko, <span class="html-italic">mPGES-1<sup>−/−</sup></span>.</p>
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<p>PGE<sub>2</sub> synthesized by mPGES-1 facilitates the invasion of CD4<sup>+</sup> T cells by inducing EP and IL-1r1 receptors, and potentiates IL-1β production, which exerts an autocrine signaling through IL-1r1 in wt EAE mice. This intercellular interaction affects IL-17 production, which finally aggravates inflammation and demyelination. Red arrows show production and secretion of PGE<sub>2</sub>, blue arrows show IL-1β, green arrows show IL-17, and gray arrows show cells infiltration from blood to spinal cord.</p>
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<p>In contrast, in <span class="html-italic">mPGES-1<sup>−/−</sup></span> mice, CD4<sup>+</sup> T cell invasion is suppressed along with EP receptor appearance, and, subsequently, IL-1r1 appearance and the production of IL-1β and IL-17 are reduced. Red arrows show production and secretion of PGE<sub>2</sub>, blue arrows show IL-1β, green arrows show IL-17, and gray arrows show cells infiltration from blood to spinal cord.</p>
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Article
Biopersistence of NiO and TiO2 Nanoparticles Following Intratracheal Instillation and Inhalation
by Takako Oyabu, Toshihiko Myojo, Byeong-Woo Lee, Takami Okada, Hiroto Izumi, Yukiko Yoshiura, Taisuke Tomonaga, Yun-Shan Li, Kazuaki Kawai, Manabu Shimada, Masaru Kubo, Kazuhiro Yamamoto, Kenji Kawaguchi, Takeshi Sasaki and Yasuo Morimoto
Int. J. Mol. Sci. 2017, 18(12), 2757; https://doi.org/10.3390/ijms18122757 - 19 Dec 2017
Cited by 28 | Viewed by 4433
Abstract
The hazards of various types of nanoparticles with high functionality have not been fully assessed. We investigated the usefulness of biopersistence as a hazard indicator of nanoparticles by performing inhalation and intratracheal instillation studies and comparing the biopersistence of two nanoparticles with different [...] Read more.
The hazards of various types of nanoparticles with high functionality have not been fully assessed. We investigated the usefulness of biopersistence as a hazard indicator of nanoparticles by performing inhalation and intratracheal instillation studies and comparing the biopersistence of two nanoparticles with different toxicities: NiO and TiO2 nanoparticles with high and low toxicity among nanoparticles, respectively. In the 4-week inhalation studies, the average exposure concentrations were 0.32 and 1.65 mg/m3 for NiO, and 0.50 and 1.84 mg/m3 for TiO2. In the instillation studies, 0.2 and 1.0 mg of NiO nanoparticles and 0.2, 0.36, and 1.0 mg of TiO2 were dispersed in 0.4 mL water and instilled to rats. After the exposure, the lung burden in each of five rats was determined by Inductively Coupled Plasma-Atomic Emission Spectrometer (ICP-AES) from 3 days to 3 months for inhalation studies and to 6 months for instillation studies. In both the inhalation and instillation studies, NiO nanoparticles persisted for longer in the lung compared with TiO2 nanoparticles, and the calculated biological half times (BHTs) of the NiO nanoparticles was longer than that of the TiO2 nanoparticles. Biopersistence also correlated with histopathological changes, inflammatory response, and other biomarkers in bronchoalveolar lavage fluid (BALF) after the exposure to nanoparticles. These results suggested that the biopersistence is a good indicator of the hazards of nanoparticles. Full article
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<p>Biopersistence of NiO and TiO<sub>2</sub> nanoparticles in rat lungs in inhalation studies. (<b>A</b>) NiO nanoparticles; (<b>B</b>) TiO<sub>2</sub> nanoparticles.</p>
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<p>Biopersistence of NiO and TiO<sub>2</sub> nanoparticles in rat lungs in intratracheal instillation studies. (<b>A</b>) NiO nanoparticles; (<b>B</b>) TiO<sub>2</sub> nanoparticles.</p>
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<p>Relationship between lung burden and biological half time in inhalation and instillation studies.</p>
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<p>Cells in bronchoalveolar lavage fluid (BALF) at 3 days after the inhalation. (<b>A</b>) ② NiO-IH-H; (<b>B</b>) ⑥ TiO<sub>2</sub>-IH-H.</p>
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<p>Relationship between biological half time and the other biomarkers. (Mean total cell count (TTC), polymorphonuclear neutrophils (PMN), lactate dehydrogenase (LDH), chemokine-induced neutrophil chemoattractant (CINC-1), and hemo oxygenase-1 (HO-1) at 1 month after the exposure).</p>
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<p>Determination method of NiO or TiO<sub>2</sub> in lung.</p>
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Review
Adjunctive Therapy Approaches for Ischemic Stroke: Innovations to Expand Time Window of Treatment
by Talia Knecht, Jacob Story, Jeffrey Liu, Willie Davis, Cesar V. Borlongan and Ike C. Dela Peña
Int. J. Mol. Sci. 2017, 18(12), 2756; https://doi.org/10.3390/ijms18122756 - 19 Dec 2017
Cited by 41 | Viewed by 7291
Abstract
Tissue plasminogen activator (tPA) thrombolysis remains the gold standard treatment for ischemic stroke. A time-constrained therapeutic window, with the drug to be given within 4.5 h after stroke onset, and lethal side effects associated with delayed treatment, most notably hemorrhagic transformation (HT), limit [...] Read more.
Tissue plasminogen activator (tPA) thrombolysis remains the gold standard treatment for ischemic stroke. A time-constrained therapeutic window, with the drug to be given within 4.5 h after stroke onset, and lethal side effects associated with delayed treatment, most notably hemorrhagic transformation (HT), limit the clinical use of tPA. Co-administering tPA with other agents, including drug or non-drug interventions, has been proposed as a practical strategy to address the limitations of tPA. Here, we discuss the pharmacological and non-drug approaches that were examined to mitigate the complications—especially HT—associated with delayed tPA treatment. The pharmacological treatments include those that preserve the blood-brain barrier (e.g., atovarstatin, batimastat, candesartan, cilostazol, fasudil, minocycline, etc.), enhance vascularization and protect the cerebrovasculature (e.g., coumarin derivate IMM-H004 and granulocyte-colony stimulating factor (G-CSF)), and exert their effects through other modes of action (e.g., oxygen transporters, ascorbic acid, etc.). The non-drug approaches include stem cell treatments and gas therapy with multi-pronged biological effects. Co-administering tPA with the abovementioned therapies showed promise in attenuating delayed tPA-induced side effects and stroke-induced neurological and behavioral deficits. Thus, adjunctive treatment approach is an innovative therapeutic modality that can address the limitations of tPA treatment and potentially expand the time window for ischemic stroke therapy. Full article
(This article belongs to the Special Issue Molecular Research on Neurodegenerative Diseases)
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<p>Proposed molecular targets of adjunctive treatments to enhance therapeutic window of tissue plasminogen activator (tPA) treatment. Acute stroke may cause injury to endothelial cells causing release of free radicals and pro-inflammatory cytokines. The signaling actions of tPA on the neurovascular unit may also increase blood-brain barrier (BBB) leakage, neurovascular cell death, and hemorrhagic transformation (HT). Moreover, the HT that ensues after delayed tPA treatment has been attributed to increased reperfusion and the effect of tPA on metalloproteinase (MMP) activity and other signaling pathways, including lipoprotein receptor-related protein (LRP), protease-activated receptor (PAR1), and PDGRF-α signaling. Ascorbic acid, normobaric oxygen (NBO) attenuates delayed tPA-induced complications in preclinical stroke models via inhibition of ROS production and BBB protection. Atovarstatin, minocycline, cilostazol, GM6001, fasudil, candesartan, bryostatin, and IMM-H004 reduces the HT by preserving the BBB through their actions on various MMPs and tight junction proteins. Granulocyte-colony stimulating factor (G-CSF) and IMM-H004 may reduce the HT by enhancing neurovascularization in addition to restoring BBB integrity. Imatinib reduces HT through the PDGRF-α receptor, while atovarstatin exerts its therapeutic benefits via inhibition of PAR1. Stem cells may also exert multi-pronged effects including BBB protection via its actions on various matrix metalloproteinases (MMPs). Abbreviations: EPC, endothelial progenitor cell; G-CSF, granulocyte-colony stimulating factor; HMGB1, high-mobility-group-box-1; ROS, reactive oxygen species; LRP, lipoprotein receptor-related protein; PAR1, protease-activated receptor; PDGFR-α, platelet-derived growth factor α-receptor (PDGFR-α); NBO, normobaric oxygen.</p>
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Review
Metabolic Pathways of the Warburg Effect in Health and Disease: Perspectives of Choice, Chain or Chance
by Jorge S. Burns and Gina Manda
Int. J. Mol. Sci. 2017, 18(12), 2755; https://doi.org/10.3390/ijms18122755 - 19 Dec 2017
Cited by 135 | Viewed by 14439
Abstract
Focus on the Warburg effect, initially descriptive of increased glycolysis in cancer cells, has served to illuminate mitochondrial function in many other pathologies. This review explores our current understanding of the Warburg effect’s role in cancer, diabetes and ageing. We highlight how it [...] Read more.
Focus on the Warburg effect, initially descriptive of increased glycolysis in cancer cells, has served to illuminate mitochondrial function in many other pathologies. This review explores our current understanding of the Warburg effect’s role in cancer, diabetes and ageing. We highlight how it can be regulated through a chain of oncogenic events, as a chosen response to impaired glucose metabolism or by chance acquisition of genetic changes associated with ageing. Such chain, choice or chance perspectives can be extended to help understand neurodegeneration, such as Alzheimer’s disease, providing clues with scope for therapeutic intervention. It is anticipated that exploration of Warburg effect pathways in extreme conditions, such as deep space, will provide further insights crucial for comprehending complex metabolic diseases, a frontier for medicine that remains equally significant for humanity in space and on earth. Full article
(This article belongs to the Special Issue Oxidative Stress and Space Biology: An Organ-Based Approach)
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<p>Glucose metabolism in cells. Extracellular glucose enters the cell via GLUT, a glucose transporter protein, that facilitates transport through the lipid bilayer plasma membrane. Subsequently, glucose is metabolized by pathway enzymes including HK, hexokinase; G6PD, glucose-6-phospahate dehydrogenase; PGI, phosphoglucose isomerase; PFK, phosphofructokinase; ALDO, aldolase; TPI, triose phosphate isomerase; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; PGK, phosphoglycerate kinase; PGAM, phosphoglycerate mutase; ENO, enolase; PK, pyruvate kinase. Mitochondrial metabolism regulatory enzyme PDK, Pyruvate dehydrogenase kinase, phosphorylates and inactivates pyruvate dehydrogenase, the first component of PDC, the pyruvate dehydrogenase complex converting Pyruvate to Acetyl-CoA. Oxidation of Acetyl-CoA by TCA, Tricarboxylic acid cycle chemical reactions in the matrix of the mitochondria releases stored energy. The TCA metabolite citrate can be exported outside mitochondria to be broken down into oxaloacetate and acetyl-CoA by the enzyme ACL, ATP citrate lyase. Cytosolic acetyl-CoA serves as a central intermediate in lipid metabolism. When oxygen is in short supply, LDH, lactate dehydrogenase converts pyruvate, the final product of glycolysis, to lactate. MCT, monocarboxylate transporter proteins allow lactate to traverse cell membranes. Solid purple arrows, metabolite transition pathways; thin solid black arrow, coenzyme transition; Dotted T-bar, inhibition.</p>
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<p>Molecular changes driving the Warburg effect. The shift to aerobic glycolysis in tumor cells reflects multiple oncogenic signaling pathways. Downstream from an active RTK, receptor tyrosine kinase, PI3K, Phosphatidylinositol 3-kinase activates AKT, protein kinase B/Akt strain transforming, stimulating glycolysis by directly regulating glycolytic enzymes. It also activates mechanistic target of rapamycin, mTOR, a protein kinase altering metabolism in various ways, including enhancement of three key transcription factors; the avian Myelocytomatosis viral oncogene proto-oncogene homologue (MYC); Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB); and the hypoxia-inducible factor 1 alpha (HIF-1α), to promote a hypoxia-adaptive metabolism. NF-κB subunits bind and activate the <span class="html-italic">HIF-1α</span> gene. HIF-1α increases expression of glucose transporters, GLUT, glycolytic enzymes and pyruvate dehydrogenase kinase, PDK, that blocks pyruvate dehydrogenase complex, PDC driven entry of pyruvate into the tricarboxylic acid, TCA cycle. Transcription factor MYC cooperates with HIF-1α to activate several genes encoding glycolytic proteins, yet also stimulates mitochondrial biogenesis whilst inhibiting mitochondrial respiration, favoring substrates for macromolecular synthesis in dividing cells. Tumor suppressor p53 ordinarily opposes the glycolytic phenotype via Phosphatase and Tensin homolog, PTEN but loss of p53 function (dashed line) is frequent in tumor cells. Octamer binding protein 1 (OCT1) activates transcription of genes that drive glycolysis and suppress oxidative phosphorylation. Change to the pyruvate kinase M2, PKM2 isoform affects glycolysis by slowing the pyruvate kinase reaction, diverting substrates into an alternative biosynthetic and reduced nicotinamide adenine dinucleotide phosphate, (NADPH)-generating pentose phosphate pathway, PPP. Phosphoglycerate mutase, PGAM and α-enolase, ENO1 are commonly upregulated in cancer as is the pyruvate kinase M2 isozyme, PKM2 allowing a high rate of nucleic acid synthesis, especially in tumor cells. Lactate dehydrogenase, LDH enhances production of lactate, a signaling molecule that can stabilize HIF-1α and accumulate in the tumor microenvironment via Monocarboxylate Transporters, MCT, nourishing adjacent aerobic tumor cells that convert lactate to pyruvate for further metabolic processing. Solid black arrows, influenced targets; solid purple arrows, metabolite transition pathways; dotted purple arrows, reduced metabolite transition pathways; thin solid black arrow, coenzyme transition; dotted T-bar, inhibition.</p>
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<p>Glutamine metabolism: The influence of anaplerosis (entry of glutamine into the TCA cycle), cataplerosis (removal of glutamine as malate) and glyceroneogenesis (marked red). Mechanisms by which glutamine is metabolized for energy involve entry of glutamine into the TCA cycle (anaplerosis), balanced by its removal (cataplerosis) as malate. Malate can be subsequently converted to oxaloacetate (OAA) and then to phosphoenolpyruvate (PEP) via the cytosolic enzyme phosphenolpyruvate carboxylase (PEPCK). PEP can be converted to pyruvate by pyruvate kinase for entry into the TCA cycle as acetyl-CoA or transamination to alanine. The pathway of glyceroneogenesis in which carbon from sources other than glucose or glycerol contributes to the formation of <span class="html-small-caps">l</span>-glycerol-3-phosphate (3-Glycerol-P) for conversion to triglycerides, involves a balance of anaplerosis (entry of OAA synthesized from pyruvate via pyruvate carboxylase) and cataplerosis (removal of intermediates to support the synthesis of glyceride-glycerol). Reduction of glycolysis-derived dihydroxyacetone phosphate (DHAP) to 3-Glycerol-P provides cells with the activated glycerol backbone needed to synthesize new triglycerides with fatty acids (FA). Solid arrows, anaplerosis pathways; red arrows, glyceroneogenesis pathway; dotted arrow, mitochondrial cataplerosis pathway.</p>
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<p>Mechanisms influencing the mitochondrial metabolism and the Warburg effect. (<b>A</b>) Environmentally driven metabolic “choice”. In cells proliferating under hypoxic pressure with activated HIF-1, the electron transport chain is inhibited because of the lack of oxygen as electron acceptor. HIF-1-induced inactivation of PDH-1 helps ensure that glucose is diverted away from mitochondrial acetyl-CoA-mediated citrate production. The alternative pathway for maintaining citrate synthesis involves reductive carboxylation, thought to rely on a reverse flux of glutamine-derived α-ketogluturate via isocytrate dehydrogenase-2 (IDH2). The reverse flux in mitochondria can be maintained by NADH conversion to NADPH by the mitochondrial transhydrogenase, with the resulting NADPH driving α-ketoglutarate carboxylation. Citrate/isocitrate exported to the cytosol may be metabolized oxidatively by isocytrate dehydrogenase-1 (IDH1), and contributes to the production of cytosolic NADPH. (<b>B</b>) Mutational events that “chain” cells to particular metabolic pathways. Oncogenic IDH1 and IDH2 mutations can cause gain of function in cancer cells. Somatic mutation at a crucial arginine residue in cytoplasmic IDH1 or mitochondrial IDH2 are frequent early mutations in glioma and acute myeloid leukaemia which cause an unusual gain of novel enzymatic activity. Instead of isocitrate being converted to α-ketoglutarate, with production of NADPH, α-ketoglutarate is metabolised to 2-hydroxyglutarate with the consumption of NADPH. 2-hydroxyglutarate can compete for α-ketoglutarate-dependent enzymes including histone demethylases and DNA hydroxylases, thereby altering both metabolism and epigenetic phenotypes. (<b>C</b>) “Chance” deterioration events in ageing. SIRT3 regulates multiple pathways involved in energy and ROS production. Some mitochondrial metabolic processes activated by SIRT3 include acetate metabolism-mediated direct deacetylation of acetyl-CoA synthetase (AceCS2), the TCA cycle via direct deacetylation of SDH, and ROS production induced by direct acetylation of the respiratory chain complexes I, II and III. Thin solid black arrow, alternative reductive carboxylation pathway; Solid black arrow, TCA cycle pathway; dotted arrow, alternative TCA cycle pathways; red dotted arrow ROS production pathway.</p>
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<p>Mechanisms influencing the mitochondrial metabolism and the Warburg effect. (<b>A</b>) Environmentally driven metabolic “choice”. In cells proliferating under hypoxic pressure with activated HIF-1, the electron transport chain is inhibited because of the lack of oxygen as electron acceptor. HIF-1-induced inactivation of PDH-1 helps ensure that glucose is diverted away from mitochondrial acetyl-CoA-mediated citrate production. The alternative pathway for maintaining citrate synthesis involves reductive carboxylation, thought to rely on a reverse flux of glutamine-derived α-ketogluturate via isocytrate dehydrogenase-2 (IDH2). The reverse flux in mitochondria can be maintained by NADH conversion to NADPH by the mitochondrial transhydrogenase, with the resulting NADPH driving α-ketoglutarate carboxylation. Citrate/isocitrate exported to the cytosol may be metabolized oxidatively by isocytrate dehydrogenase-1 (IDH1), and contributes to the production of cytosolic NADPH. (<b>B</b>) Mutational events that “chain” cells to particular metabolic pathways. Oncogenic IDH1 and IDH2 mutations can cause gain of function in cancer cells. Somatic mutation at a crucial arginine residue in cytoplasmic IDH1 or mitochondrial IDH2 are frequent early mutations in glioma and acute myeloid leukaemia which cause an unusual gain of novel enzymatic activity. Instead of isocitrate being converted to α-ketoglutarate, with production of NADPH, α-ketoglutarate is metabolised to 2-hydroxyglutarate with the consumption of NADPH. 2-hydroxyglutarate can compete for α-ketoglutarate-dependent enzymes including histone demethylases and DNA hydroxylases, thereby altering both metabolism and epigenetic phenotypes. (<b>C</b>) “Chance” deterioration events in ageing. SIRT3 regulates multiple pathways involved in energy and ROS production. Some mitochondrial metabolic processes activated by SIRT3 include acetate metabolism-mediated direct deacetylation of acetyl-CoA synthetase (AceCS2), the TCA cycle via direct deacetylation of SDH, and ROS production induced by direct acetylation of the respiratory chain complexes I, II and III. Thin solid black arrow, alternative reductive carboxylation pathway; Solid black arrow, TCA cycle pathway; dotted arrow, alternative TCA cycle pathways; red dotted arrow ROS production pathway.</p>
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Article
Flavonoids of Kudzu Root Fermented by Eurtotium cristatum Protected Rat Pheochromocytoma Line 12 (PC12) Cells against H2O2-Induced Apoptosis
by Bo Zhang, Wen Li and Mingsheng Dong
Int. J. Mol. Sci. 2017, 18(12), 2754; https://doi.org/10.3390/ijms18122754 - 19 Dec 2017
Cited by 29 | Viewed by 4595
Abstract
Novel bioactive components have greatly attracted attention as they demonstrate health benefits. Reversed-phase high performance liquid chromatography (RP-HPLC) showed that isoflavonoid compounds of kudzu root (Pueraria lobata) fermented by Eurtotium cristatum and extracted using de-ionized water were higher active compared with [...] Read more.
Novel bioactive components have greatly attracted attention as they demonstrate health benefits. Reversed-phase high performance liquid chromatography (RP-HPLC) showed that isoflavonoid compounds of kudzu root (Pueraria lobata) fermented by Eurtotium cristatum and extracted using de-ionized water were higher active compared with non-fermented. A model of H2O2-inducd cell damage was built using rat pheochromocytoma line 12 (PC12) cell to observe the protective effect of non-fermented kudzu root (Pueraria lobata) (NFK) and fermented kudzu root (Pueraria lobata) (FK). Cell viability and apoptosis were analyzed through inverted microscopy and flow cytometry. The level of lactate dehydrogenase, catalase activity, superoxide dismutase, glutathione, and reactive oxygen species (ROS) were evaluated. Results showed that NFK and FK could significantly protect PC12 cell against damage caused by H2O2-induced oxidative stress. The intracellular antioxidant system was increased, protected the cell membrane inhibit H2O2-induced apoptosis by scavenging of ROS. Moreover, NFK and FK regulated the cell cycle to prevent cell apoptosis. Isoflavonoid from the kudzu root especially fermented kudzu root with E. cristatum are potentially therapeutic drugs against diseases induced by oxidative damage. Full article
(This article belongs to the Section Biochemistry)
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<p>Reversed-phase high performance liquid chromatography (RP-HPLC) chromatogram analyzed the isoflavones of seven standard, non-fermented kudzu root (NFK), fermented kudzu root (FK). (<b>a</b>) including (1) Puerarin; (2) Daidzin; (3) Glycitin; (4) Genistin; (5) Ferulic acid; (6) Daidzein; (7) Glycitein; (8) Genistein; (<b>b</b>) NFK; (<b>c</b>) FK.</p>
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<p>Effects of NFK and FK on PC12 cell damaged by 0.1 mM H<sub>2</sub>O<sub>2</sub>. (<b>a</b>) Cell microscope observed under an inverted microscope, (<b>a1</b>) CK; (<b>a2</b>) damage group; and (<b>a3</b>,<b>a4</b>) sample group (scale: 100 µm); (<b>b</b>) Cytotoxicity of NFK (10 mg/mL) and FK (10 mg/mL) on PC12 cells. Data are presented as mean ± S.D. (<span class="html-italic">n</span> = 3). <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 indicates significant differ between the control group and damage group, ** <span class="html-italic">p</span> &lt; 0.01 indicates significant difference between sample group and damage group.</p>
Full article ">Figure 3
<p>(<b>a</b>) The different concentration of NFK and FK on PC12 cells viability. Each value is expressed as the mean ± SD (<span class="html-italic">n</span> = 3). In a column, the same superscript letters indicates that the difference between NFK and FK is not significant (<span class="html-italic">p</span> &gt; 0.05); (<b>b</b>) The oxidant model was evaluated by different concentration of H<sub>2</sub>O<sub>2</sub> on PC12 cells, which treated for 0.5 h exposed H<sub>2</sub>O<sub>2</sub>. Each value is expressed as the mean ± SD (<span class="html-italic">n</span> = 3). Within a column, values with the different superscript letters are significantly different from each other at <span class="html-italic">p</span> &lt; 0.05, line indicated the IC<sub>50</sub> value.</p>
Full article ">Figure 4
<p>Intracellular ROS level of NFK (10 mg/mL) and FK (10 mg/mL) in PC 12 cells. The fluorescent probe DCFH-DA was used to the cell ROS level. Data are presented as mean ± S.D., (<span class="html-italic">n</span> = 3). <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 compared with the control cells; ** <span class="html-italic">p</span> &lt; 0.01 compared with the damage group.</p>
Full article ">Figure 5
<p>NFK (10 mg/mL) and FK (10 mg/mL) prevent H<sub>2</sub>O<sub>2</sub>-induced PC12 cell apoptosis. The probe Annexin V-FITC/PI was used to determine the cell apoptosis. Data are presented as mean ± S.D., (<span class="html-italic">n</span> = 3). <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 compared with the control cells; ** <span class="html-italic">p</span> &lt; 0.01 compared with the damage group.</p>
Full article ">Figure 6
<p>Cell apoptosis was measured by labeling the cells with Annexin-V-FITC and counterstaining with PI. (<b>a</b>) CK; (<b>b</b>) damage group; and (<b>c</b>,<b>d</b>) sample group. The numbers indicate the percentage of cells in each quadrant (lower left: FITC−/PI−, intact cells; lower right: FITC+/PI−, apoptotic cells; upper left: FITC−/PI+, necrotic cells; and upper right: FITC+/PI+, late apoptotic cells).</p>
Full article ">Figure 7
<p>Effects of NFK (10 mg/mL) and FK (10 mg/mL) on H<sub>2</sub>O<sub>2</sub> induced PC12 cell cycle as determined through flow cytometry.</p>
Full article ">Figure 8
<p>Cell cycle distribution of PC12 in (<b>a</b>) CK; (<b>b</b>) damage group and (<b>c</b>,<b>d</b>) sample group were analyzed through flow cytometry (<span style="color:#80EE80">■</span> G1, <span style="color:#66FFFF">■</span> G2, <span style="color:#CCCC00">■</span> S).</p>
Full article ">Figure 9
<p>Effects of caspase-3 activity of NFK (10 mg/mL) and FK (10 mg/mL) on H<sub>2</sub>O<sub>2</sub>-treated PC12 cells. Data are presented as mean ± S.D., (<span class="html-italic">n</span> = 3). <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 compared with the control cells; ** <span class="html-italic">p</span> &lt; 0.01 compared with the H<sub>2</sub>O<sub>2</sub> damage group.</p>
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5647 KiB  
Article
Sulfuretin Attenuates MPP+-Induced Neurotoxicity through Akt/GSK3β and ERK Signaling Pathways
by Ramesh Pariyar, Ramakanta Lamichhane, Hyun Ju Jung, Sung Yeon Kim and Jungwon Seo
Int. J. Mol. Sci. 2017, 18(12), 2753; https://doi.org/10.3390/ijms18122753 - 19 Dec 2017
Cited by 23 | Viewed by 9181
Abstract
Parkinson’s disease (PD) is the second most common neurodegenerative disease. It is caused by the death of dopaminergic neurons in the substantia nigra pars compacta. Oxidative stress and mitochondrial dysfunction contribute to the loss of dopaminergic neurons in PD. Sulfuretin is a potent [...] Read more.
Parkinson’s disease (PD) is the second most common neurodegenerative disease. It is caused by the death of dopaminergic neurons in the substantia nigra pars compacta. Oxidative stress and mitochondrial dysfunction contribute to the loss of dopaminergic neurons in PD. Sulfuretin is a potent antioxidant that is reported to be beneficial in the treatment of neurodegenerative diseases. In this study, we examined the protective effect of sulfuretin against 1-methyl-4-phenyl pyridinium (MPP+)-induced cell model of PD in SH-SY5Y cells and the underlying molecular mechanisms. Sulfuretin significantly decreased MPP+-induced apoptotic cell death, accompanied by a reduction in caspase 3 activity and polyADP-ribose polymerase (PARP) cleavage. Furthermore, it attenuated MPP+-induced production of intracellular reactive oxygen species (ROS) and disruption of mitochondrial membrane potential (MMP). Consistently, sulfuretin decreased p53 expression and the Bax/Bcl-2 ratio. Moreover, sulfuretin significantly increased the phosphorylation of Akt, GSK3β, and ERK. Pharmacological inhibitors of PI3K/Akt and ERK abolished the cytoprotective effects of sulfuretin against MPP+. An inhibitor of GSK3β mimicked sulfuretin-induced protection against MPP+. Taken together, these results suggest that sulfuretin significantly attenuates MPP+-induced neurotoxicity through Akt/GSK3β and ERK signaling pathways in SH-SY5Y cells. Our findings suggest that sulfuretin might be one of the potential candidates for the treatment of PD. Full article
(This article belongs to the Special Issue Neuroprotective Strategies 2017)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Sulfuretin protects SH-SY5Y cells against MPP<sup>+</sup>-induced cytotoxicity. Cells were pretreated with different doses of sulfuretin (10–40 μM) for 2 h and then exposed to MPP<sup>+</sup> (1 mM) for 2 h. (<b>A</b>) After treatment, morphological changes were observed under a light microscope. Scale bar = 50 μm. Representative images are shown (<span class="html-italic">n</span> = 3). (<b>B</b>) Cell viability was measured using MTT assay. (<b>C</b>) Cytotoxicity was determined by measuring LDH release into the medium. Values are calculated using the equation as shown in Materials and Methods and presented relative to control as mean percentage change ± standard deviation (S.D.) (<span class="html-italic">n</span> = 5). Differences are statistically significant at ** <span class="html-italic">p</span> ˂ 0.01 and *** <span class="html-italic">p</span> ˂ 0.001 vs. the control group and ## <span class="html-italic">p</span> &lt; 0.01 and ### <span class="html-italic">p</span> ˂ 0.001 vs. the MPP<sup>+</sup> group.</p>
Full article ">Figure 1 Cont.
<p>Sulfuretin protects SH-SY5Y cells against MPP<sup>+</sup>-induced cytotoxicity. Cells were pretreated with different doses of sulfuretin (10–40 μM) for 2 h and then exposed to MPP<sup>+</sup> (1 mM) for 2 h. (<b>A</b>) After treatment, morphological changes were observed under a light microscope. Scale bar = 50 μm. Representative images are shown (<span class="html-italic">n</span> = 3). (<b>B</b>) Cell viability was measured using MTT assay. (<b>C</b>) Cytotoxicity was determined by measuring LDH release into the medium. Values are calculated using the equation as shown in Materials and Methods and presented relative to control as mean percentage change ± standard deviation (S.D.) (<span class="html-italic">n</span> = 5). Differences are statistically significant at ** <span class="html-italic">p</span> ˂ 0.01 and *** <span class="html-italic">p</span> ˂ 0.001 vs. the control group and ## <span class="html-italic">p</span> &lt; 0.01 and ### <span class="html-italic">p</span> ˂ 0.001 vs. the MPP<sup>+</sup> group.</p>
Full article ">Figure 2
<p>Sulfuretin suppresses MPP<sup>+</sup>-induced apoptosis, caspase-3 activity, and PARP proteolysis. SH-SY5Y cells were pretreated with sulfuretin for 2 h and then treated with MPP<sup>+</sup> (1 mM) for 24 h. (<b>A</b>) Apoptosis was evaluated by annexin V and PI staining. Flow cytometric profile represents annexin V-FITC on the <span class="html-italic">x</span>-axis and PI on the <span class="html-italic">y</span>-axis. (<b>B</b>) Caspase 3 activity was measured by using colorimetric caspase-3 assay kit. (<b>C</b>) Protein levels of PARP were measured by western blot analysis. Representative blots and their densitometric quantification are shown. Values are presented relative to control as mean fold change ± S.D. (<span class="html-italic">n</span> = 3). Differences are statistically significant at ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001 vs. the control group and # <span class="html-italic">p</span> ˂ 0.05 and ### <span class="html-italic">p</span> ˂ 0.001 vs. the MPP<sup>+</sup> group.</p>
Full article ">Figure 3
<p>Sulfuretin reverses MPP<sup>+</sup>-induced intracellular accumulation of ROS and reduction in MMP. SH-SY5Y cells were pretreated with sulfuretin (20 or 40 μM) for 2 h and then exposed to MPP<sup>+</sup> (1 mM) for 24 h. Further, the cells were incubated for 30 min at 37 °C with 2,7-dichlorofluorescein diacetate (DCFH-DA) (10 μM). (<b>A</b>) Representative images of the cells under a fluorescence microscope are shown. DCFH-DA oxidation by ROS is indicated in green colour. Scale bar = 50 μM. (<b>B</b>) DCFH-DA fluorescence intensities were measured by fluorimetry with a plate reader at ex/em: 485/535 nm. (<b>C</b>) MMP was measured by fluorimetry with a plate reader at ex/em: 549/575 nm. (<b>D</b>) Protein levels of p53, Bax, and Bcl-2 were measured by Western blot analysis. Representative blots and their densitometric quantification are shown. Values are presented relative to control as mean fold change ± S.D. (<span class="html-italic">n</span> = 3). Differences are statistically significant at ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 vs. the control group and ## <span class="html-italic">p</span> &lt; 0.01, ### <span class="html-italic">p</span> &lt; 0.001 vs. the MPP<sup>+</sup> group.</p>
Full article ">Figure 3 Cont.
<p>Sulfuretin reverses MPP<sup>+</sup>-induced intracellular accumulation of ROS and reduction in MMP. SH-SY5Y cells were pretreated with sulfuretin (20 or 40 μM) for 2 h and then exposed to MPP<sup>+</sup> (1 mM) for 24 h. Further, the cells were incubated for 30 min at 37 °C with 2,7-dichlorofluorescein diacetate (DCFH-DA) (10 μM). (<b>A</b>) Representative images of the cells under a fluorescence microscope are shown. DCFH-DA oxidation by ROS is indicated in green colour. Scale bar = 50 μM. (<b>B</b>) DCFH-DA fluorescence intensities were measured by fluorimetry with a plate reader at ex/em: 485/535 nm. (<b>C</b>) MMP was measured by fluorimetry with a plate reader at ex/em: 549/575 nm. (<b>D</b>) Protein levels of p53, Bax, and Bcl-2 were measured by Western blot analysis. Representative blots and their densitometric quantification are shown. Values are presented relative to control as mean fold change ± S.D. (<span class="html-italic">n</span> = 3). Differences are statistically significant at ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 vs. the control group and ## <span class="html-italic">p</span> &lt; 0.01, ### <span class="html-italic">p</span> &lt; 0.001 vs. the MPP<sup>+</sup> group.</p>
Full article ">Figure 4
<p>MPP<sup>+</sup> decreases Akt/GSK3β and ERK phosphorylation and increases p53 expression, whereas sulfuretin reverses its effect. SH-SY5Y cells were pretreated with sulfuretin for 2 h and then treated with MPP<sup>+</sup> for 24 h. After cell lysis, the extracted proteins were subjected to Western blot analysis using specific antibodies. Protein levels of (<b>A</b>) p-Akt, Akt, p-GSK3β, GSK3β, p-CREB, and GAPDH; (<b>B</b>) p-ERK, ERK, p-p38, p38, p-JNK, and JNK were determined. Representative blots and their densitometric quantification are shown. Values are presented relative to control as mean fold change ± S.D. (<span class="html-italic">n</span> = 3). Differences are statistically significant at * <span class="html-italic">p &lt;</span> 0.05, ** <span class="html-italic">p &lt;</span> 0.01, and *** <span class="html-italic">p &lt;</span> 0.001 vs. the control group and ## <span class="html-italic">p &lt;</span> 0.01, and ### <span class="html-italic">p &lt;</span> 0.001 vs. the MPP<sup>+</sup> group.</p>
Full article ">Figure 4 Cont.
<p>MPP<sup>+</sup> decreases Akt/GSK3β and ERK phosphorylation and increases p53 expression, whereas sulfuretin reverses its effect. SH-SY5Y cells were pretreated with sulfuretin for 2 h and then treated with MPP<sup>+</sup> for 24 h. After cell lysis, the extracted proteins were subjected to Western blot analysis using specific antibodies. Protein levels of (<b>A</b>) p-Akt, Akt, p-GSK3β, GSK3β, p-CREB, and GAPDH; (<b>B</b>) p-ERK, ERK, p-p38, p38, p-JNK, and JNK were determined. Representative blots and their densitometric quantification are shown. Values are presented relative to control as mean fold change ± S.D. (<span class="html-italic">n</span> = 3). Differences are statistically significant at * <span class="html-italic">p &lt;</span> 0.05, ** <span class="html-italic">p &lt;</span> 0.01, and *** <span class="html-italic">p &lt;</span> 0.001 vs. the control group and ## <span class="html-italic">p &lt;</span> 0.01, and ### <span class="html-italic">p &lt;</span> 0.001 vs. the MPP<sup>+</sup> group.</p>
Full article ">Figure 5
<p>LY294002 suppresses sulfuretin-induced protection against MPP<sup>+</sup>, whereas SB415286 reverses MPP<sup>+</sup>-induced cytotoxicity. SH-SY5Y cells were pretreated with or without LY294002 (10 μM) for 2 h, followed by treatment with or without sulfuretin (40 μM) for 2 h and exposed to MPP<sup>+</sup> (1 mM) for 24 h. (<b>A</b>) Cell viability was measured by MTT assay. Values are presented relative to control as mean percentage change ± S.D. (<span class="html-italic">n</span> = 3). (<b>B</b>) Protein levels of p-Akt, Akt, p-GSK3β, GSK3β, p-ERK, ERK, and GAPDH were determined by Western blot analysis. Representative blots and their densitometric quantification are shown. Values are presented relative to control as mean fold change ± S.D. (<span class="html-italic">n</span> = 3). (<b>C</b>) SH-SY5Y cells were pretreated with or without SB415286 (20 μM) for 2 h, and then exposed to MPP<sup>+</sup> (1 mM) for 24 h. Cell viability was measured by MTT assay. Values are presented relative to control as mean percentage change ± S.D. (<span class="html-italic">n</span> = 3). Differences are statistically significant at ** <span class="html-italic">p &lt;</span> 0.01, *** <span class="html-italic">p &lt;</span> 0.001 vs. the control group, ## <span class="html-italic">p &lt;</span> 0.01, ### <span class="html-italic">p &lt;</span> 0.001 vs. the MPP<sup>+</sup> group, and <span>$</span><span>$</span> <span class="html-italic">p &lt;</span> 0.01, <span>$</span><span>$</span><span>$</span> <span class="html-italic">p &lt;</span> 0.001 vs. the MPP<sup>+</sup> and sulfuretin co-treated group.</p>
Full article ">Figure 5 Cont.
<p>LY294002 suppresses sulfuretin-induced protection against MPP<sup>+</sup>, whereas SB415286 reverses MPP<sup>+</sup>-induced cytotoxicity. SH-SY5Y cells were pretreated with or without LY294002 (10 μM) for 2 h, followed by treatment with or without sulfuretin (40 μM) for 2 h and exposed to MPP<sup>+</sup> (1 mM) for 24 h. (<b>A</b>) Cell viability was measured by MTT assay. Values are presented relative to control as mean percentage change ± S.D. (<span class="html-italic">n</span> = 3). (<b>B</b>) Protein levels of p-Akt, Akt, p-GSK3β, GSK3β, p-ERK, ERK, and GAPDH were determined by Western blot analysis. Representative blots and their densitometric quantification are shown. Values are presented relative to control as mean fold change ± S.D. (<span class="html-italic">n</span> = 3). (<b>C</b>) SH-SY5Y cells were pretreated with or without SB415286 (20 μM) for 2 h, and then exposed to MPP<sup>+</sup> (1 mM) for 24 h. Cell viability was measured by MTT assay. Values are presented relative to control as mean percentage change ± S.D. (<span class="html-italic">n</span> = 3). Differences are statistically significant at ** <span class="html-italic">p &lt;</span> 0.01, *** <span class="html-italic">p &lt;</span> 0.001 vs. the control group, ## <span class="html-italic">p &lt;</span> 0.01, ### <span class="html-italic">p &lt;</span> 0.001 vs. the MPP<sup>+</sup> group, and <span>$</span><span>$</span> <span class="html-italic">p &lt;</span> 0.01, <span>$</span><span>$</span><span>$</span> <span class="html-italic">p &lt;</span> 0.001 vs. the MPP<sup>+</sup> and sulfuretin co-treated group.</p>
Full article ">Figure 6
<p>PD98059 suppresses sulfuretin-induced protection against MPP<sup>+</sup>. SH-SY5Y cells were pretreated with or without PD98059 (10 μM) for 2 h, followed by treatment with or without sulfuretin (40 μM) for 2 h, and exposed to MPP<sup>+</sup> (1 mM) for 24 h. (<b>A</b>) Cell viability was measured by MTT assay. Values are presented relative to control as mean percentage change ± S.D. (<span class="html-italic">n</span> = 3). (<b>B</b>) Protein levels of p-ERK, ERK, p-Akt, Akt, p-GSK3β, GSK3β, and GAPDH were determined by Western blot analysis. Representative blots and their densitometric quantification are shown. Values are presented relative to control as mean fold change ± S.D. (<span class="html-italic">n</span> = 3). Differences are statistically significant at * <span class="html-italic">p &lt;</span> 0.05, ** <span class="html-italic">p &lt;</span> 0.01, *** <span class="html-italic">p &lt;</span> 0.001 vs. the control group, ### <span class="html-italic">p &lt;</span> 0.001 vs. the MPP<sup>+</sup> group, and <span>$</span><span>$</span> <span class="html-italic">p &lt;</span> 0.01, <span>$</span><span>$</span><span>$</span> <span class="html-italic">p &lt;</span> 0.001 vs. the MPP<sup>+</sup> and sulfuretin pretreated group.</p>
Full article ">Figure 6 Cont.
<p>PD98059 suppresses sulfuretin-induced protection against MPP<sup>+</sup>. SH-SY5Y cells were pretreated with or without PD98059 (10 μM) for 2 h, followed by treatment with or without sulfuretin (40 μM) for 2 h, and exposed to MPP<sup>+</sup> (1 mM) for 24 h. (<b>A</b>) Cell viability was measured by MTT assay. Values are presented relative to control as mean percentage change ± S.D. (<span class="html-italic">n</span> = 3). (<b>B</b>) Protein levels of p-ERK, ERK, p-Akt, Akt, p-GSK3β, GSK3β, and GAPDH were determined by Western blot analysis. Representative blots and their densitometric quantification are shown. Values are presented relative to control as mean fold change ± S.D. (<span class="html-italic">n</span> = 3). Differences are statistically significant at * <span class="html-italic">p &lt;</span> 0.05, ** <span class="html-italic">p &lt;</span> 0.01, *** <span class="html-italic">p &lt;</span> 0.001 vs. the control group, ### <span class="html-italic">p &lt;</span> 0.001 vs. the MPP<sup>+</sup> group, and <span>$</span><span>$</span> <span class="html-italic">p &lt;</span> 0.01, <span>$</span><span>$</span><span>$</span> <span class="html-italic">p &lt;</span> 0.001 vs. the MPP<sup>+</sup> and sulfuretin pretreated group.</p>
Full article ">
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