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Open AccessArticle
A Simple, Reusable and Low-Cost LVDT-Based in Situ Bolt Preload Monitoring System during Fastening for a Truck Wheel Assembly
Metals 2019, 9(3), 336; https://doi.org/10.3390/met9030336 (registering DOI) -
Abstract
The aim of this study is to design and test a new, simple, and reusable linear variable differential transformer (LVDT)-based in situ bolt preload monitoring system (L-PMS) during fastening of a truck wheel assembly. Instead of measuring the elongation of a bolt, the [...] Read more.
The aim of this study is to design and test a new, simple, and reusable linear variable differential transformer (LVDT)-based in situ bolt preload monitoring system (L-PMS) during fastening of a truck wheel assembly. Instead of measuring the elongation of a bolt, the distance between the end surfaces of both the bolt and nut was monitored via the L-PMS. The distance obtained from the L-PMS was experimentally correlated with the actual preload measured by a washer-type load cell. Since the variation of the distance is related to the stiffness of the bolt and clamped parts, a finite element analysis was also conducted to predict the sensitivity of L-PMS. There was a strong linear relationship between the distance and bolt preload after the bolt and nut were fully snugged. However, a logarithm-shaped nonlinear relationship was irregularly observed before getting snugged, making it difficult to define a clear relationship. In order to tackle this issue, an arc-shaped conductive line was screen-printed onto the surface of the clamped parts using a conductive carbon paste. The results show that a resistance variation of the conductive line during fastening enables to determine the snug point, so the L-PMS combined with resistance measurement results in an approximately ±6% error in the measurement of bolt preload. The proposed L-PMS offers a simple but highly reliable way for measuring bolt preload during fastening, which could be utilized in a heavy-truck production line. Full article
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<p>Schematic diagrams of (<bold>a</bold>) the common bolted joint and (<bold>b</bold>) the concept of L-PMS.</p>
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<p>Materials used in this work: (<bold>a</bold>) a bolt and nut, (<bold>b</bold>) a hub assembly with six bolts, and (<bold>c</bold>) a truck wheel placed onto the hub assembly in (<bold>b</bold>).</p>
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<p>(<bold>a</bold>) Components of the L-PMS: LVDT, customized jig, and ring magnet, (<bold>b</bold>) assembled L-PMS, (<bold>c</bold>) experimental setup, and (<bold>d</bold>) bolt numbering.</p>
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<p>DAQ system for measuring both the displacement from LVDT and the preload from a washer-type load cell.</p>
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<p>Relationship between externally applied torques and measured preloads.</p>
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<p>Experimental results: (<bold>a</bold>) variation of distance as a function of preload, (<bold>b</bold>) comparison of the sensitivity of L-PMS from the region A for each bolt (the same color corresponds to the same set of test), and (<bold>c</bold>) histogram for the sensitivities from all the bolts.</p>
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<p>Variation of <italic>D</italic><sub>bn</sub> after mounting and dismounting L-PMS.</p>
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<p>(<bold>a</bold>) Analysis model for the bolt, (<bold>b</bold>) one-sixth analysis model for the clamped parts, (<bold>c</bold>) axial deformation of the bolt under 1 kN tensile force, and (<bold>d</bold>) axial deformation of the clamped parts under 1 kN compressive force. Unit for (<bold>c</bold>) and (<bold>d</bold>) is μm.</p>
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<p>Conductive line screen-printed near the nut bearing surface using carbon paste.</p>
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<p>(<bold>a</bold>) Variation in the resistance change ratio with the distance <italic>D</italic><sub>bn</sub> and (<bold>b</bold>) relationship between corresponding preload and the distance <italic>D</italic><sub>bn</sub>.</p>
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<p>(<bold>a</bold>) Comparison between estimated preloads from the L-PMS and actual preloads measured by the washer-type load cell: (<bold>a</bold>) a bar chart near the actual preload of 60 kN and (<bold>b</bold>) a scatter plot for the actual preloads from 45 kN to 60 kN.</p>
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Open AccessArticle
Selenium Biofortification Differentially Affects Sulfur Metabolism and Accumulation of Phytochemicals in Two Rocket Species (Eruca Sativa Mill. and Diplotaxis Tenuifolia) Grown in Hydroponics
Plants 2019, 8(3), 68; https://doi.org/10.3390/plants8030068 (registering DOI) -
Abstract
Biofortification can be exploited to enrich plants in selenium (Se), an essential micronutrient for humans. Selenium as selenate was supplied to two rocket species, Eruca sativa Mill. (salad rocket) and Diplotaxis tenuifolia (wild rocket), at 0–40 μM in hydroponics and its effects on [...] Read more.
Biofortification can be exploited to enrich plants in selenium (Se), an essential micronutrient for humans. Selenium as selenate was supplied to two rocket species, Eruca sativa Mill. (salad rocket) and Diplotaxis tenuifolia (wild rocket), at 0–40 μM in hydroponics and its effects on the content and profile of sulphur (S)-compounds and other phytochemicals was evaluated. D. tenuifolia accumulated more total Se and selenocysteine than E. sativa, concentrating up to ~300 mg Se kg−1 dry weight from 10–40 μM Se. To ensure a safe and adequate Se intake, 30 and 4 g fresh leaf material from E. sativa grown with 5 and 10–20 μM Se, respectively or 4 g from D. tenuifolia supplied with 5 μM Se was estimated to be optimal for consumption. Selenium supplementation at or above 10 μM differentially affected S metabolism in the two species in terms of the transcription of genes involved in S assimilation and S-compound accumulation. Also, amino acid content decreased with Se in E. sativa but increased in D. tenuifolia and the amount of phenolics was more reduced in D. tenuifolia. In conclusion, selenate application in hydroponics allowed Se enrichment of rocket. Furthermore, Se at low concentration (5 μM) did not significantly affect accumulation of phytochemicals and plant defence S-metabolites. Full article
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<p>Fresh weight (FW) of leaves (<bold>A</bold>) and roots (<bold>B</bold>) of <italic>E. sativa</italic> and <italic>D. tenuifolia</italic> plants grown in hydroponics with 0–40 μM selenate. The FW reported is the average FW of each leaf (±SD, <italic>n</italic> = 9). Different letters in bold above bars indicate significant differences between the means (<italic>p</italic> &lt; 0.05) of values referred to <italic>E. sativa</italic>, while different letters not bolded indicate significant differences between the means (<italic>p</italic> &lt; 0.05) of values referred to <italic>D. tenuifolia</italic>.</p>
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<p>Total Se concentration in leaves (<bold>A</bold>) and roots (<bold>B</bold>) of <italic>E. sativa</italic> and <italic>D. tenuifolia</italic> plants grown in hydroponics with 0–40 μM selenate. Content of total Se per plant (relatively to the leaf edible part) (<bold>C</bold>) and translocation factor, TF (calculated as the Se shoot:Se root ratio) (<bold>D</bold>). Data shown are the mean ± SD of three replicates. Different letters in bold above bars indicate significant differences between the means (<italic>p</italic> &lt; 0.05) of values referred to <italic>E. sativa</italic>, while different letters not bolded indicate significant differences between the means (<italic>p</italic> &lt; 0.05) of values referred to <italic>D. tenuifolia</italic>.</p>
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<p>Total S concentration in leaves (<bold>A</bold>) and roots (<bold>B</bold>) of <italic>E. sativa</italic> and <italic>D. tenuifolia</italic> plants grown in hydroponics with 0–40 μM selenate. Data shown are the mean ± SD of three replicates. Different letters in bold above bars indicate significant differences between the means (<italic>p</italic> &lt; 0.05) of values referred to <italic>E. sativa</italic>, while different letters not bolded indicate significant differences between the means (<italic>p</italic> &lt; 0.05) of values referred to <italic>D. tenuifolia</italic>. Se:S ratio in leaves (<bold>C</bold>) and roots (<bold>D</bold>) of the two species.</p>
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<p>Content of cysteine (Cys, <bold>A</bold>), total glutathione (GSH, <bold>B</bold>), methionine (Met, <bold>C</bold>) and glucosinolates (GLS, <bold>D</bold>) in leaves of <italic>E. sativa</italic> and <italic>D. tenuifolia</italic> plants grown in hydroponics with 0–40 μM selenate. Data shown are the mean ± SD of three replicates. Different letters in bold above bars indicate significant differences between the means (<italic>p</italic> &lt; 0.05) of values referred to <italic>E. sativa</italic>, while different letters not bolded indicate significant differences between the means (<italic>p</italic> &lt; 0.05) of values referred to <italic>D. tenuifolia</italic>.</p>
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<p>Expression profiling by real-time RT-PCR of sulphate transporter genes coding for SULTR1;1, SULTR1;2 and SULTR2;1 in roots (<bold>A</bold>) and SULTR2;1 in leaves (<bold>B</bold>) of <italic>E. sativa</italic> and <italic>D. tenuifolia</italic> plants grown in hydroponics with 0–40 μM selenate. Data shown are the mean ± SD of three replicates. Different letters above bars indicate significant differences between the means (<italic>p</italic> &lt; 0.05). n.s. = not significant differences between means.</p>
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<p>Expression profiling by real-time RT-PCR of genes coding for ATP sulfurylase isoforms (ATPS1, ATPS2, ATPS4), MYB28, BCAT, MAM1, UGT74b1, MYR in leaves of <italic>E. sativa</italic> plants grown in hydroponics with 0–40 μM selenate. Data shown are the mean ± SD of three replicates. Different letters above bars indicate significant differences between the means (<italic>p</italic> &lt; 0.05). n.s. = not significant differences between means.</p>
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<p>Expression profiling by real-time RT-PCR of genes coding for ATP sulfurylase isoforms (ATPS1, ATPS2, ATPS4), MYB28, BCAT, MAM1, UGT74b1, MYR in leaves of <italic>D. tenuifolia</italic> plants grown in hydroponics with 0–40 μM selenate. Data shown are the mean ± SD of three replicates. Different letters above bars indicate significant differences between the means (<italic>p</italic> &lt; 0.05). n.s. = not significant differences between means.</p>
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<p>Content of total amino acids (<bold>A</bold>) and phenolics (<bold>B</bold>) in leaves of <italic>E. sativa</italic> and <italic>D. tenuifolia</italic> plants grown in hydroponics with 0–40 μM Se. Data are the mean ± SD of three replicates. Different letters in bold above bars indicate significant differences between the means (<italic>p</italic> &lt; 0.05) of values referred to <italic>E. sativa</italic>, while different letters not bolded indicate significant differences between the means (<italic>p</italic> &lt; 0.05) of values referred to <italic>D. tenuifolia</italic>.</p>
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Open AccessFeature PaperReview
Role of Natural Products in Modulating Histone Deacetylases in Cancer
Molecules 2019, 24(6), 1047; https://doi.org/10.3390/molecules24061047 (registering DOI) -
Abstract
Histone deacetylases (HDACs) are enzymes that can control transcription by modifying chromatin conformation, molecular interactions between the DNA and the proteins as well as the histone tail, through the catalysis of the acetyl functional sites removal of proteins from the lysine residues. Also, [...] Read more.
Histone deacetylases (HDACs) are enzymes that can control transcription by modifying chromatin conformation, molecular interactions between the DNA and the proteins as well as the histone tail, through the catalysis of the acetyl functional sites removal of proteins from the lysine residues. Also, HDACs have been implicated in the post transcriptional process through the regulation of the proteins acetylation, and it has been found that HDAC inhibitors (HDACi) constitute a promising class of pharmacological drugs to treat various chronic diseases, including cancer. Indeed, it has been demonstrated that in several cancers, elevated HDAC enzyme activities may be associated with aberrant proliferation, survival and metastasis. Hence, the discovery and development of novel HDACi from natural products, which are known to affect the activation of various oncogenic molecules, has attracted significant attention over the last decade. This review will briefly emphasize the potential of natural products in modifying HDAC activity and thereby attenuating initiation, progression and promotion of tumors. Full article
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<p>Structures of selected HDACi inhibitors from natural products.</p>
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Open AccessReview
Ion Channels: New Actors Playing in Chemotherapeutic Resistance
Cancers 2019, 11(3), 376; https://doi.org/10.3390/cancers11030376 (registering DOI) -
Abstract
In the battle against cancer cells, therapeutic modalities are drastically limited by intrinsic or acquired drug resistance. Resistance to therapy is not only common, but expected: if systemic agents used for cancer treatment are usually active at the beginning of therapy (i.e., 90% [...] Read more.
In the battle against cancer cells, therapeutic modalities are drastically limited by intrinsic or acquired drug resistance. Resistance to therapy is not only common, but expected: if systemic agents used for cancer treatment are usually active at the beginning of therapy (i.e., 90% of primary breast cancers and 50% of metastases), about 30% of patients with early-stage breast cancer will have recurrent disease. Altered expression of ion channels is now considered as one of the hallmarks of cancer, and several ion channels have been linked to cancer cell resistance. While ion channels have been associated with cell death, apoptosis and even chemoresistance since the late 80s, the molecular mechanisms linking ion channel expression and/or function with chemotherapy have mostly emerged in the last ten years. In this review, we will highlight the relationships between ion channels and resistance to chemotherapy, with a special emphasis on the underlying molecular mechanisms. Full article
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<p>Chemoresistance pathways involving Orai calcium channels. Orai calcium channels are able to modulate sensitivity to chemotherapy. Processes known to alter chemoresistance are highlighted in bold. These include calcium overload, MultiDrug Resistance (MDR), autophagy, modulation of signalling pathways (MAPK and PI3K-Akt/Sgk), transcription factors (NF-κB, c-myc, p53), and EMT (Epithelial to Mesenchymal Transition). Processes initiated by Orai1 and Orai3 are summarized by purple and blue arrows, respectively. The dashed arrows are not different from plain arrows, except that they cross other arrows.</p>
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<p>Schematic representation of intracellular Ca<sup>2+</sup> pathways involved in chemoresistance. Anti-apoptotic Bcl-2 protein enhances chemoresistance by a dual role at both the Endoplasmic Reticulum (ER), by inhibiting Ca<sup>2+</sup> depletion through Inositol 1,4,5-trisphosphate Receptors (IP<sub>3</sub>Rs), and the mitochondria by inhibiting apoptotic complex Bax/Bak formation. TAT-fused inositol 1,4,5-trisphosphate receptor-derived peptide (TAT-IDP<sup>S</sup>), by inhibiting BH4 domain, enhances Ca<sup>2+</sup> efflux from the ER through IP<sub>3</sub>Rs to mitochondrial Voltage-Dependent Anion Channel (VDAC).</p>
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<p>Chemoresistance pathways involving K<sup>+</sup> channels. K<sup>+</sup> channels could modulate the sensitivity to different chemotherapies by regulating pro-survival pathways (intrinsec or extrinsec apoptosis pathways, volume regulation, Akt/RAS/MAPK pathways, interacting with receptors or transporters). MDR: Multidrug Resistance Protein; EGFR: Epidermal Growth Factor Receptor; CXCR4: C-X-C Chemokine Receptor type 4; TRAILR: TRAIL Receptor; Cyt C: Cytochrome C; AIF: Apoptosis Inducing Factor; SMAC: Second Mitochondria-derived Activator of Caspases; RVD: Regulatory Volume Decrease.</p>
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<p>Multidrug resistance is associated with an increase of intracellular free Mg<sup>2+</sup>. Intracellular free Mg<sup>2+</sup> could promote multidrug resistance through three main potential mechanisms: ① Mg<sup>2+</sup> ?binds to MDR1 leading to drug extrusion; ② Mg<sup>2+</sup> enters into the mitochondria through Mrs2 channel leading to Bax inhibition and resistance to apoptosis, ③ Mg<sup>2+</sup> activates the nuclear PPM1D phosphatase which alters p53 stabilization and protects cancer cells against apoptosis. Chemoresistant cancer cells have a lower Mg<sup>2+</sup> influx and lower amounts of TRPM6 and TRPM7 channels at the plasma membrane.</p>
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<p>Chemoresistance pathways involving Cl<sup>?</sup> channels. Cl<sup>?</sup> channels can modulate the sensitivity to different chemotherapies by inhibiting the AVD or by modulating the prosurvival pathways and the drugs availabilities. The dashed line separates two virtual cells presenting either reduction activity of Cl<sup>?</sup> channels (left part) or an upregulation of Cl<sup>?</sup> channels (right part). MDR1: Multidrug Resistance 1; VRAC: Volume Regulated Chloride Channel; AVD: Apoptosis Volume Decrease; HER2: Human Epidermal growth factor Receptor 2; MRP1: Multidrug associated Resistance Protein 1; EGFR: Epidermal Growth Factor Receptor.</p>
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<p>Putative channel associations involved in chemoresistance. Different ion channels could form complexes and modulate chemoresistance. In each grey box, the current description of channel associated-chemoresistance is presented in the light part, and examples of putative ion channels associations are suggested in the dark part.</p>
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Open AccessReview
Bacillus velezensis: A Valuable Member of Bioactive Molecules within Plant Microbiomes
Molecules 2019, 24(6), 1046; https://doi.org/10.3390/molecules24061046 (registering DOI) -
Abstract
Bacillus velezensis is an aerobic, gram-positive, endospore-forming bacterium that promotes plant growth. Numerous strains of this species have been reported to suppress the growth of microbial pathogens, including bacteria, fungi, and nematodes. Based on recent phylogenetic analysis, several Bacillus species have been reclassified [...] Read more.
Bacillus velezensis is an aerobic, gram-positive, endospore-forming bacterium that promotes plant growth. Numerous strains of this species have been reported to suppress the growth of microbial pathogens, including bacteria, fungi, and nematodes. Based on recent phylogenetic analysis, several Bacillus species have been reclassified as B. velezensis. However, this information has yet to be integrated into a well-organized resource. Genomic analysis has revealed that B. velezensis possesses strain-specific clusters of genes related to the biosynthesis of secondary metabolites, which play significant roles in both pathogen suppression and plant growth promotion. More specifically, B. velezensis exhibits a high genetic capacity for synthesizing cyclic lipopeptides (i.e., surfactin, bacillomycin-D, fengycin, and bacillibactin) and polyketides (i.e., macrolactin, bacillaene, and difficidin). Secondary metabolites produced by B. velezensis can also trigger induced systemic resistance in plants, a process by which plants defend themselves against recurrent attacks by virulent microorganisms. This is the first study to integrate previously published information about the Bacillus species, newly reclassified as B. velezensis, and their beneficial metabolites (i.e., siderophore, bacteriocins, and volatile organic compounds). Full article
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<p><italic>Bacillus velezensis</italic> is the conspecific species integrating <italic>B. amyloliquefaciens</italic> subsp. <italic>plantarum</italic> and <italic>B. methylotrophicus</italic> (adapted by Dunlap et al. [<xref ref-type="bibr" rid="B22-molecules-24-01046">22</xref>]). The significance of the numbers are explained at the bottom of the same column.</p>
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<p>Phylogenetic tree constructed from the <italic>rpoB</italic> gene sequences of type strains of species from the “<italic>B. subtilis</italic> species complex” by the neighbor-joining method (using MEGA software). Bootstrap values (%) are given at the nodes obtained by repeating the analysis 1000 times. The scale bar indicates 0.02 nucleotide substitutions per nucleotide position.</p>
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<p>Molecular structure of ribosomal and nonribosomal bioactive compounds synthesized by <italic>B. velezensis</italic>.</p>
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<p>Signal transduction pathway of induced systemic resistance stimulated by <italic>B. velezensis</italic>. NPR1: non-expressor of PR1; JA/ET: the jasmonic acid/ethylene signaling pathways; SA: Salicylic Acid.</p>
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Open AccessFeature PaperReview
Drugging the Small GTPase Pathways in Cancer Treatment: Promises and Challenges
Cells 2019, 8(3), 255; https://doi.org/10.3390/cells8030255 (registering DOI) -
Abstract
Small GTPases are a family of low molecular weight GTP-hydrolyzing enzymes that cycle between an inactive state when bound to GDP and an active state when associated to GTP. Small GTPases regulate key cellular processes (e.g., cell differentiation, proliferation, and motility) as well [...] Read more.
Small GTPases are a family of low molecular weight GTP-hydrolyzing enzymes that cycle between an inactive state when bound to GDP and an active state when associated to GTP. Small GTPases regulate key cellular processes (e.g., cell differentiation, proliferation, and motility) as well as subcellular events (e.g., vesicle trafficking), making them key participants in a great array of pathophysiological processes. Indeed, the dysfunction and deregulation of certain small GTPases, such as the members of the Ras and Arf subfamilies, have been related with the promotion and progression of cancer. Therefore, the development of inhibitors that target dysfunctional small GTPases could represent a potential therapeutic strategy for cancer treatment. This review covers the basic biochemical mechanisms and the diverse functions of small GTPases in cancer. We also discuss the strategies and challenges of inhibiting the activity of these enzymes and delve into new approaches that offer opportunities to target them in cancer therapy. Full article
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<p>Role of small GTPases in human cells. Most small GTPases are implied in the regulation of protein secretion, endocytosis and vesicle trafficking. For instance, Ran-activation gradient controls both the export and import of macromolecules between the nucleus and the cytoplasm. Additionally, Rab1 is responsible for regulation of vesicle trafficking between the endoplasmic reticulum and the Golgi apparatus, whereas Rab6 modulates the reverse transport, as well as through the different Golgi apparatus vesicles. Arf1 is implied in intra-Golgi transport, but also enables the accumulation of fatty acids inside the lipid droplets. Otherwise, Rab5 regulates endosome coating. The control of secretory vesicle formation is mainly mediated by Rab11. The products resulting from phagosome digestion can be carried to Golgi apparatus in a Rab9-dependent process, or return to the extracellular matrix in a Rab11-dependent mechanism. Arf6, which is associated with the plasma membrane when inactive, works as a master regulator of vesicle processes. On the other hand, other small GTPases are involved in the maintenance of cell shape and movement, such as Rac, which promotes the generation of lamellipodia, or Cdc42, which promotes the formation of filopodia. RhoA induces the formation of actin filaments in response to cellular stresses. Otherwise, Ras induces the phosphorylation and activation of MAPK, inducing prosurvival responses, such as cell proliferation and cell cycle progression, as well as limiting prodeath signals, such as apoptosis.</p>
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<p>New strategies to target small GTPases in human cancers. To improve the therapeutic efficacy of inhibitors of small GTPases, new approaches have been developed by different strategies. Red boxes represent the inhibitor of GTPases in each of the strategies. Those include generation of new molecules that can fill the specific GEF binding site in GTPases, disruption of GEF-mediated guanine nucleotide exchange, filling of nucleotide binding pocket of small GTPases, impairing nucleotide attachment, and the stimulation of GAP proteins. Given that most of small GTPases need to be attached to the organelle membrane to exert their actions, the development of novel molecules with the ability to abolish this binding has arisen recently as an innovative strategy to inhibit these molecules. Finally, the development of some drugs that interfere with these could also be great to inhibit small GTPases. A brief table situated next to each section of the graphic indicates the small GTPase inhibitors that work through that mechanism.</p>
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Open AccessFeature PaperArticle
Analysis of the Zn-Binding Domains of TRIM32, the E3 Ubiquitin Ligase Mutated in Limb Girdle Muscular Dystrophy 2H
Cells 2019, 8(3), 254; https://doi.org/10.3390/cells8030254 (registering DOI) -
Abstract
Members of the tripartite motif family of E3 ubiquitin ligases are characterized by the presence of a conserved N-terminal module composed of a RING domain followed by one or two B-box domains, a coiled-coil and a variable C-terminal region. The RING and B-box [...] Read more.
Members of the tripartite motif family of E3 ubiquitin ligases are characterized by the presence of a conserved N-terminal module composed of a RING domain followed by one or two B-box domains, a coiled-coil and a variable C-terminal region. The RING and B-box are both Zn-binding domains but, while the RING is found in a large number of proteins, the B-box is exclusive to the tripartite motif (TRIM) family members in metazoans. Whereas the RING has been extensively characterized and shown to possess intrinsic E3 ligase catalytic activity, much less is known about the role of the B-box domains. In this study, we adopted an in vitro approach using recombinant point- and deletion-mutants to characterize the contribution of the TRIM32 Zn-binding domains to the activity of this E3 ligase that is altered in a genetic form of muscular dystrophy. We found that the RING domain is crucial for E3 ligase activity and E2 specificity, whereas a complete B-box domain is involved in chain assembly rate modulation. Further, in vitro, the RING domain is necessary to modulate TRIM32 oligomerization, whereas, in cells, both the RING and B-box cooperate to specify TRIM32 subcellular localization, which if altered may impact the pathogenesis of diseases. Full article
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<p>Schematic diagram showing the domain structure of TRIM32 and the deletion/point mutants used in this study.</p>
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<p>TRIM32 carrying mutations or deletion of the B-box shows activity similar to the wild-type protein. Increasing doses of recombinant full-length TRIM32 (FL), ΔRING (ΔR), ΔB-box (ΔBB) or C100A/C103A mutants were used as E3 ligase in an in vitro reaction with the UbE2D1 conjugating enzyme. Proteins were resolved by SDS-PAGE and poly-ubiquitin chains detected with anti-ubiquitin antibody.</p>
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<p>Deletion of the TRIM32 B-box domain affects the rate of poly-ubiquitin chain formation. Comparable amounts of full-length TRIM32 (<bold>A</bold>), C100A/C103A (<bold>B</bold>) or ΔB-box (<bold>C</bold>) mutants were used in an in vitro ubiquitination reaction with the UbE2N/V2-conjugating enzymes. Samples were taken at the indicated time points and proteins were resolved by SDS-PAGE. Poly-ubiquitin chains were detected by anti-ubiquitin immunoblot (top panels) followed by stripping and reprobing of the membrane with anti-TRIM32 antibody (bottom panels). (<bold>D</bold>) Poly-ubiquitin chains were quantified, normalized to the amount of E3 used in the assay and plotted (solid lines). The observed reaction rate was calculated and plotted (dashed lines). Equations showing the calculated reaction rate for each construct are shown. Average and standard error of five independent experiments are represented.</p>
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<p>Deletion of the B-box does not prevent TRIM32 catalytic activity and does not change UbE2 specificity. Recombinant full-length TRIM32 (FL), ΔRING (ΔR) or ΔB-box (ΔBB) was used as E3 ligase in in vitro ubiquitination reactions with a panel of UbE2-conjugating enzymes as indicated. In each panel, the first lane is the reaction with no E3 (?). Control reactions without the addition of E2 enzymes are shown (No E2). Proteins were resolved by SDS-PAGE and membranes incubated with anti-ubiquitin (top panels) or anti-TRIM32 (bottom panels). Asterisks indicate SDS-resistant high molecular weight species likely representing TRIM32 oligomers (see text below).</p>
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<p>TRIM32 RING domain is necessary for oligomerization. Recombinant full-length TRIM32, ΔRING, and ΔB-box mutants were run on native PAGE (top panel) or in denaturing conditions (SDS-PAGE, bottom panel) and detected by anti-TRIM32 immunoblot.</p>
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<p>TRIM32’s RING and B-box domains contribute to determine TRIM32 subcellular localization. Representative images of C2C12 myoblasts expressing exogenous GFP-tagged full-length TRIM32 or ΔRING, ΔB-box and C100A/C103A mutants. Localization was assessed by fluorescence imaging with nuclei counterstaining (DAPI) at 63× and 100× magnification as indicated.</p>
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<p>Co-localization of TRIM32 mutants. Representative images of C2C12 myoblasts co-expressing exogenous HA-ΔB-box (red signal) and GFP-tagged full-length TRIM32 or ΔRING, ΔB-box mutants (green signal). Localization was assessed by fluorescence imaging with nuclei counterstaining (DAPI) at 40× magnification.</p>
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Open AccessEditorial
Recent Advances in Urban Ventilation Assessment and Flow Modelling
Atmosphere 2019, 10(3), 144; https://doi.org/10.3390/atmos10030144 (registering DOI) -
Abstract
The Atmosphere Special Issue “Recent Advances in Urban Ventilation Assessment and Flow
Modelling” collects twenty-one original papers and one review paper published in 2017[...] Full article
Open AccessArticle
The Effects of Pertuzumab and Its Combination with Trastuzumab on HER2 Homodimerization and Phosphorylation
Cancers 2019, 11(3), 375; https://doi.org/10.3390/cancers11030375 (registering DOI) -
Abstract
Pertuzumab (Perjeta) is an anti-HER2 monoclonal antibody that is used for treatment of HER2-positive breast cancers in combination with trastuzumab (Herceptin) and docetaxel and showed promising clinical outcomes. Pertuzumab is suggested to block heterodimerization of HER2 with EGFR and HER3 that abolishes canonical [...] Read more.
Pertuzumab (Perjeta) is an anti-HER2 monoclonal antibody that is used for treatment of HER2-positive breast cancers in combination with trastuzumab (Herceptin) and docetaxel and showed promising clinical outcomes. Pertuzumab is suggested to block heterodimerization of HER2 with EGFR and HER3 that abolishes canonical function of HER2. However, evidence on the exact mode of action of pertuzumab in homodimerization of HER2 are limited. In this study, we investigated the effect of pertuzumab and its combination with trastuzumab on HER2 homodimerization, phosphorylation and whole gene expression profile in Chinese hamster ovary (CHO) cells stably overexpressing human HER2 (CHO-K6). CHO-K6 cells were treated with pertuzumab, trastuzumab, and their combination, and then HER2 homodimerization and phosphorylation at seven pY sites were investigated. The effects of the monoclonal antibodies on whole gene expression and the expression of cell cycle stages, apoptosis, autophagy, and necrosis were studied by cDNA microarray. Results showed that pertuzumab had no significant effect on HER2 homodimerization, however, trastuzumab increased HER2 homodimerization. Interestingly, pertuzumab increased HER2 phosphorylation at Y1127, Y1139, and Y1196 residues, while trastuzumab increased HER2 phosphorylation at Y1196. More surprisingly, combination of pertuzumab and trastuzumab blocked the phosphorylation of Y1005 and Y1127 of HER2. Our results also showed that pertuzumab, but not trastuzumab, abrogated the effect of HER2 overexpression on cell cycle in particular G1/S transition, G2/M transition, and M phase, whereas trastuzumab abolished the inhibitory effect of HER2 on apoptosis. Our findings confirm that pertuzumab is unable to inhibit HER2 homodimerization but induces HER2 phosphorylation at some pY sites that abolishes HER2 effects on cell cycle progress. These data suggest that the clinical effects of pertuzumab may mostly through the inhibition of HER2 heterodimers, rather than HER2 homodimers and that pertuzumab binding to HER2 may inhibit non-canonical HER2 activation and function in non-HER-mediated and dimerization-independent pathway(s). Full article
Open AccessArticle
Isoform-Specific NO Synthesis by Arabidopsis thaliana Nitrate Reductase
Plants 2019, 8(3), 67; https://doi.org/10.3390/plants8030067 (registering DOI) -
Abstract
Nitrate reductase (NR) is important for higher land plants, as it catalyzes the rate-limiting step in the nitrate assimilation pathway, the two-electron reduction of nitrate to nitrite. Furthermore, it is considered to be a major enzymatic source of the important signaling molecule nitric [...] Read more.
Nitrate reductase (NR) is important for higher land plants, as it catalyzes the rate-limiting step in the nitrate assimilation pathway, the two-electron reduction of nitrate to nitrite. Furthermore, it is considered to be a major enzymatic source of the important signaling molecule nitric oxide (NO), that is produced in a one-electron reduction of nitrite. Like many other plants, the model plant Arabidopsis thaliana expresses two isoforms of NR (NIA1 and NIA2). Up to now, only NIA2 has been the focus of detailed biochemical studies, while NIA1 awaits biochemical characterization. In this study, we have expressed and purified functional fragments of NIA1 and subjected them to various biochemical assays for comparison with the corresponding NIA2-fragments. We analyzed the kinetic parameters in multiple steady-state assays using nitrate or nitrite as substrate and measured either substrate consumption (nitrate or nitrite) or product formation (NO). Our results show that NIA1 is the more efficient nitrite reductase while NIA2 exhibits higher nitrate reductase activity, which supports the hypothesis that the isoforms have special functions in the plant. Furthermore, we successfully restored the physiological electron transfer pathway of NR using reduced nicotinamide adenine dinucleotide (NADH) and nitrate or nitrite as substrates by mixing the N-and C-terminal fragments of NR, thus, opening up new possibilities to study NR activity, regulation and structure. Full article
Figures

Figure 1

Figure 1
<p>Nitrate reduction by NR-Mo-heme proteins. (<bold>A</bold>) Anaerobic Michaelis–Menten kinetics of NIA1-Mo-heme (red) and NIA2-Mo-heme (black) measured with the MV:nitrate assay. (<bold>B</bold>) Kinetic parameters of multiple batches of NIA1-Mo-heme (red) and NIA2-Mo-heme (black) determined in the MV:nitrate assay. The K<sub>M</sub> and <italic>k<sub>cat</sub></italic> for NIA1-Mo-heme and NIA2-Mo-heme were compared via unpaired t-test (GraphPad Prism 5). The means ± SEM of <italic>n</italic> = 33 kinetic series for NIA1-Mo-heme (made with 23 protein batches) and <italic>n</italic> = 13 kinetic series for NIA2-Mo-heme (eight protein batches used) are shown. <italic>p</italic>-value: *** &lt; 0.001 &lt; ** &lt; 0.01 &lt; * &lt; 0.05.</p>
Full article ">Figure 2
<p>Re-constituted nitrate reductase activity. (<bold>A</bold>) Cartoon representation of the re-constitution of full-length NR activity by combination of the separate NR-Mo-heme and NR-FAD fragments in vitro. (<bold>B</bold>) Anaerobic NADH:nitrate assay of NR-Mo-heme (NIA1 red, NIA2 black) combined with increasing ratios of NR-FAD fragment. Increasing nitrate reductase activity was observed with increasing ratio of FAD-fragment. (<bold>C</bold>) Steady-state NADH:nitrate kinetics of re-constituted NIA1 (red) and NIA2 (black) activities.</p>
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<p>Nitrite reduction by NIA1 and NIA2. (<bold>A</bold>) Anaerobic Michaelis–Menten kinetics of 50 nM NIA1-Mo-heme (red) and 1 μM NIA2-Mo-heme (black) measured with the BV:nitrite assay. A higher concentration of NIA2-Mo-heme enzyme than NIA1-Mo-heme was needed to obtain reaction velocities in a similar order of magnitude. (<bold>B</bold>) Kinetic parameters of NIA1-Mo-heme (red) and NIA2-Mo-heme (black) determined in the BV:nitrite assay. The K<sub>M</sub> and <italic>k<sub>cat</sub></italic> for the NIA1-Mo-heme (red) and NIA2-Mo-heme (black) are compared via unpaired t-test (GraphPad Prism 5). The means ± SEM of <italic>n</italic> = 21 kinetic series for NIA1-Mo-heme (made with 12 protein batches) and <italic>n</italic> = 10 kinetic series for NIA2-Mo-heme (made with eight protein batches) are shown. <italic>p</italic>-value: *** &lt; 0.001 &lt; **&lt; 0.01 &lt; * &lt; 0.05. (<bold>C</bold>,<bold>D</bold>) Nitrite reductase activity by re-constituted NIA1 (<bold>C</bold>) and NIA2 (<bold>D</bold>) measured using an NO-analyzer at different nitrite concentrations (indicated by the numbers, μM). (<bold>E</bold>,<bold>F</bold>) Hyperbolic curve fit of the assays from (<bold>C</bold>,<bold>D</bold>).</p>
Full article ">Figure 3 Cont.
<p>Nitrite reduction by NIA1 and NIA2. (<bold>A</bold>) Anaerobic Michaelis–Menten kinetics of 50 nM NIA1-Mo-heme (red) and 1 μM NIA2-Mo-heme (black) measured with the BV:nitrite assay. A higher concentration of NIA2-Mo-heme enzyme than NIA1-Mo-heme was needed to obtain reaction velocities in a similar order of magnitude. (<bold>B</bold>) Kinetic parameters of NIA1-Mo-heme (red) and NIA2-Mo-heme (black) determined in the BV:nitrite assay. The K<sub>M</sub> and <italic>k<sub>cat</sub></italic> for the NIA1-Mo-heme (red) and NIA2-Mo-heme (black) are compared via unpaired t-test (GraphPad Prism 5). The means ± SEM of <italic>n</italic> = 21 kinetic series for NIA1-Mo-heme (made with 12 protein batches) and <italic>n</italic> = 10 kinetic series for NIA2-Mo-heme (made with eight protein batches) are shown. <italic>p</italic>-value: *** &lt; 0.001 &lt; **&lt; 0.01 &lt; * &lt; 0.05. (<bold>C</bold>,<bold>D</bold>) Nitrite reductase activity by re-constituted NIA1 (<bold>C</bold>) and NIA2 (<bold>D</bold>) measured using an NO-analyzer at different nitrite concentrations (indicated by the numbers, μM). (<bold>E</bold>,<bold>F</bold>) Hyperbolic curve fit of the assays from (<bold>C</bold>,<bold>D</bold>).</p>
Full article ">Figure 4
<p>Inhibition of nitrite reductase activity by nitrate. Using re-constituted NR-activity, NO production by NIA1 (red) or NIA2 (black) was monitored on the NO analyzer with 400 μM nitrite. Increasing concentrations of nitrate were added simultaneously with the nitrite, and NO production decreased. The % residual activity was fitted with a hyperbolic curve (GraphPad Prism 7) and IC<sub>50</sub> and I<sub>max</sub> for nitrate determined.</p>
Full article ">
Open AccessReview
Centromere Repeats: Hidden Gems of the Genome
Genes 2019, 10(3), 223; https://doi.org/10.3390/genes10030223 (registering DOI) -
Abstract
Satellite DNAs are now regarded as powerful and active contributors to genomic and chromosomal evolution. Paired with mobile transposable elements, these repetitive sequences provide a dynamic mechanism through which novel karyotypic modifications and chromosomal rearrangements may occur. In this review, we discuss the [...] Read more.
Satellite DNAs are now regarded as powerful and active contributors to genomic and chromosomal evolution. Paired with mobile transposable elements, these repetitive sequences provide a dynamic mechanism through which novel karyotypic modifications and chromosomal rearrangements may occur. In this review, we discuss the regulatory activity of satellite DNA and their neighboring transposable elements in a chromosomal context with a particular emphasis on the integral role of both in centromere function. In addition, we discuss the varied mechanisms by which centromeric repeats have endured evolutionary processes, producing a novel, species-specific centromeric landscape despite sharing a ubiquitously conserved function. Finally, we highlight the role these repetitive elements play in the establishment and functionality of de novo centromeres and chromosomal breakpoints that underpin karyotypic variation. By emphasizing these unique activities of satellite DNAs and transposable elements, we hope to disparage the conventional exemplification of repetitive DNA in the historically-associated context of ‘junk’. Full article
Figures

Figure 1

Figure 1
<p>Overview of satellite DNA structure in a human centromere/pericentromere. (<bold>a</bold>) α satellite monomers (colored solid arrows) are organized into a repeating unit, called a higher order repeat (HOR) (red dashed arrows). In this example, 10 monomers are in each HOR (10-mers). HOR units are repeated in a chromosome-specific manner 100–1000 s of times within a functional centromere core. Within a single HOR, monomers share anywhere from 50–80% sequence identity with one another. The same monomer within different HORs in the same array may share up to 99% identity. Solo monomers (solid arrows) are found in the pericentromeric region and are highly variable in terms of sequence and orientation. Within the centromere, transposable elements (TE) insertions typically include recently active or active (hot) elements, while the TE insertions found in the pericentromere are older, inactive elements. (<bold>b</bold>) The core centromere structure (red dot, chromosome schematic) of human chromosomes (a generic chromosome ideogram is indicated, middle) consists of different α satellite arrays arranged in HORs (dashed arrows). Each HOR array may contain a different monomer number; in this example, the functional centromere (i.e., assembles CENP-A nucleosomes) at a 10-mer HOR (red dashed arrows). A 7-mer HOR is found nearby but is an inactive epiallele. Both HORs are separated by non-centromeric DNA, which may contain genes. α satellites are also found throughout the pericentromere (bottom schematic, different colored blocks). (<bold>c</bold>) Representative cladogram of the phylogenetic relationship of the non-HOR α monomers shown in (b). In this example, strata of newer satellites are closer to the HOR arrays, while older satellites are found more distally. Relative age of satellites is indicated by tree branch length; shorter branches are younger elements and deeper branches are older.</p>
Full article ">Figure 2
<p>Chromosome 17 epialleles. (<bold>a</bold>) Ideogram of chromosome 17 (top). Zoom inset of epialleles showing monomer number for HORs and orientation. D17Z1B HORs carry 14 monomers, as do D17Z1C HORs. D17Z1 HORs are variable in the human population, with wild type epialleles containing 16-mer, 15-mer, and 14-mer HORs (pink) and variant epialleles containing wild type HORs in addition to 13-mer and 12-mer HORs (green). (<bold>b</bold>) Variation of the D17Z1 epiallele is linked to centromere activity. When the variation in D17Z1 increases, CENP-A nucleosomes (red) decrease; when variation exceeds 80%, the centromere assembles on the D17Z1B epiallele.</p>
Full article ">Figure 3
<p>Schematic of the evolutionary mechanisms that impact centromere repeats. (<bold>a</bold>) Two models for the derivation of species-specific satellites are shown: (left) A satellite array evolves from a library of satellites, culminating in a dominant satellite; (right) TE insertion(s) followed by mutations, such as deletions, lead to the evolution of new satellites. In both cases, a homogenized array evolves through molecular drive mechanisms, such as intra-array concerted evolution. Stabilization of the arrays into HOR arrays defines the active centromere core, where CENP-A nucleosomes (red) are assembled. Other events, such as inter-array conversion, can lead to the spread of new HORs or changes in HOR copy number (bottom). (<bold>b</bold>) Two homologous chromosomes share the same satellite repeat (red), but one homolog experiences an expansion of that repeat through de novo mutations. During female meiosis, the larger centromere attracts more microtubules, resulting in the loss of the homolog with the weaker centromere into the polar body during meiosis I. The larger centromere is preferentially driven to the viable egg following unequal distribution of chromatids during meiosis II.</p>
Full article ">Figure 4
<p>The hypothetical evolution of new centromeres. The ancestral chromosome in this example is submetacentric (the centromere is indicated with red ‘nucleosomes’). The active locus (black dot) carries satellite arrays. Some individual(s) in a population experience the destabilization of the active centromere and formation of a neocentromere, perhaps through the activation of a new TE, resulting in a centric shift (CS). The new centromere is indicated with a black dot, while the latent centromere is indicated with an open circle. The new centromere becomes fixed in a population and eventually gains new satellite arrays (orange), either by interchromosomal seeding from the old centromere (grey) or from the TE itself. Over time, the latent centromere loses its HORs while the new centromere becomes stabilized. In some cases, the ENC can lead to a new species karyotype.</p>
Full article ">
Open AccessArticle
A Statistical and Spatial Analysis of Portuguese Forest Fires in Summer 2016 Considering Landsat 8 and Sentinel 2A Data
Environments 2019, 6(3), 36; https://doi.org/10.3390/environments6030036 (registering DOI) -
Abstract
Forest areas in Portugal are often affected by fires. The objective of this work was to analyze the most fire-affected areas in Portugal in the summer of 2016 for two municipalities considering data from Landsat 8 OLI and Sentinel 2A MSI (prefire and [...] Read more.
Forest areas in Portugal are often affected by fires. The objective of this work was to analyze the most fire-affected areas in Portugal in the summer of 2016 for two municipalities considering data from Landsat 8 OLI and Sentinel 2A MSI (prefire and postfire data). Different remote sensed data-derived indices, such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR), could be used to identify burnt areas and estimate the burn severity. In this work, NDVI was used to evaluate the area burned, and NBR was used to estimate the burn severity. The results showed that the NDVI decreased considerably after the fire event (2017 images), indicating a substantial decrease in the photosynthesis activity in these areas. The results also indicate that the NDVI differences (dNDVI) assumes the highest values in the burned areas. The results achieved for both sensors regarding the area burned presented differences from the field data no higher than 13.3% (for Sentinel 2A, less than 7.8%). We conclude that the area burned estimated using the Sentinel 2A data is more accurate, which can be justified by the higher spatial resolution of this data. Full article

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