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Biology, Volume 9, Issue 5 (May 2020) – 24 articles

Cover Story (view full-size image): 5-FU-based chemotherapy remains the mainstay for colorectal cancer (CRC) treatment. However, cancer cells acquire chemoresistance, causing treatment failure. Understanding the molecular mechanism of resistance will uncover new avenues for curing CRC. One mechanism of acquired chemoresistance is defective drug metabolism. 5-FU is converted into various metabolites, causing DNA and RNA damage and inducing apoptosis. An HRMS-based method was used to extract and quantify levels of 5-FU metabolites from parental and resistant CRC cell lysates and media. Results show the reduced levels of 5-FU metabolites in resistant cells. Moreover, treatment of CRC cells with FdUMP, an active metabolite of 5-FU, induced resistant cell death. Overall, an effective and efficient protocol was developed for the comparative quantitation of polar compounds from cells. FdUMP is also highlighted as an alternative CRC therapy. [...] Read more.
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18 pages, 2745 KiB  
Article
A New IL6 Isoform in Chinese Soft-Shelled Turtle (Pelodiscus sinesis) Discovered: Its Regulation during Cold Stress and Infection
by Zuobing Zhang, Miao Tian, Ruxin Song, Xiao Xing, Yong Fan, Lan Wang, Cuijuan Niu and Roy A. Dalmo
Biology 2020, 9(5), 111; https://doi.org/10.3390/biology9050111 - 25 May 2020
Cited by 6 | Viewed by 3011
Abstract
The Chinese soft-shelled turtle (Pelodiscus sinesis) is a widely cultured commercial species in East and Southeast Asian countries. The turtles frequently suffer from acute cold stress during farming in China. Stress-induced factor such as Interleukin-6 (IL6) is a multifunctional molecule that [...] Read more.
The Chinese soft-shelled turtle (Pelodiscus sinesis) is a widely cultured commercial species in East and Southeast Asian countries. The turtles frequently suffer from acute cold stress during farming in China. Stress-induced factor such as Interleukin-6 (IL6) is a multifunctional molecule that plays important roles in innate and adaptive immune response. In the present study, we found that the turtle possessed two IL6 transcripts, where one IL6 transcript contained a signal peptide sequence (psIL6), while the other IL6 transcript (psIL6ns) possessed no such signal peptide gene. To test any differential expression of the two isoforms during temperature and microbial stress, turtles were adapted to optimal environmental water temperature (25 °C), stressed by acute cooling for 24 h, followed with the challenge of Aeromonas hydrophila (1.8 × 108 CFU) or Staphylococcus aureus (5.8 × 108 CFU). Gene characterization revealed that psIL6ns, a splicer without codons encoding a signal peptide and identical to the one predicted from genomic sequence, and psIL6, a splicer with codons encoding a signal peptide, were both present. Inducible expression was documented in primary spleen cells stimulated with ConA and poly I: C. The splenic and intestinal expression of psIL6ns and psIL6 was increased in response to temperature stress and bacterial infection. Full article
(This article belongs to the Section Evolutionary Biology)
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Figure 1

Figure 1
<p>Nucleotide and deduced amino acid sequences of <span class="html-italic">psIL6</span> and <span class="html-italic">psIL6ns</span>. Additional nucleotides of <span class="html-italic">psIL6ns</span> are in italic and shaded. Translation starting sites for <span class="html-italic">psIL6</span> and <span class="html-italic">psIL6ns</span> are in uppercases. Signal peptide of <span class="html-italic">psIL6</span> is boxed. The IL6-superfamily domain has a single strikethrough. The IL6/G-CSF/MGF family signature characteristic (C-X(9)-C-X(6)-G-L-X(2)-Y/F-X(3)-L) is shaded. Instability motifs (attta) are underlined and polyadenylation signal (aataaa) is above the dashed line.</p>
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<p>Multiple sequence alignment of the deduced IL6 in the Chinese soft-shelled turtle and other selected animals. The consensus residues are shaded. In the consensus line, asterisks (*) resemble completely identical residues in all selected species, and dots (.), and colons (:) represent similarity. MUSCLE program was used for the alignment. Accession numbers of genes are supplied in <a href="#app1-biology-09-00111" class="html-app">Supplementary Table S4</a> and the first two letters of the sequence name represent the initial letters of species’ Latin name. Signal peptide, Helix A-E, and AB loop of human IL6 are clearly denoted. Conserved cysteines are indicated with triangles and putative disulfide bonds are linked with single lines. Critical amino acid residues in the IL6/G-CSF/MGF family signature characteristics were labeled with solid arrows.</p>
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<p>Phylogenetic tree showing the relationship between the turtle <span class="html-italic">IL6</span> gene and genes of <span class="html-italic">IL6</span> families in other selected vertebrate species. The phylogram was constructed on MUSCLE and MEGA X. The neighbor-joining method was used. Johns–Taylor–Thornton model with Gamma value of 1.676610112 and bootstrap values of 1000 replications were adopted. Accession numbers are supplied in <a href="#app1-biology-09-00111" class="html-app">Supplementary Figure S4</a>. <span class="html-italic">psIL6</span> and <span class="html-italic">psIL6ns</span> are labeled with a filled square.</p>
Full article ">Figure 4
<p>The constitutive expression of <span class="html-italic">psIL6</span> and <span class="html-italic">IL6ns</span> mRNA was determined by real-time PCR in seven tissues from eight turtles. The results were calculated in a relative expression method, and presented as mean + SD. <span class="html-italic">ef1α</span> was chosen as the reference gene. If there is not any same letter (uppercased vs. uppercased, lowercased vs. lowercased) in any two different groups, it represents that there is a statistical significance (<span class="html-italic">p</span> &lt; 0.05); ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001. One-way ANOVA analysis was performed to analyze the data in different tissues and Tukey’s method was applied as a post hoc test. Unpaired T test was used to compare <span class="html-italic">psIL6</span> and <span class="html-italic">IL6ns</span> transcript levels in the same tissues.</p>
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<p>Transcript levels of <span class="html-italic">psIL6</span> and <span class="html-italic">psIL6ns</span> in primary spleen cells upon stimulation. The relative expression method was used in the calculation with <span class="html-italic">ef1α</span> as the reference gene. In the <span class="html-italic">X</span>-axis, stimulation times (h) are listed. (<b>a</b>) <span class="html-italic">psIL6</span> and (<b>b</b>) <span class="html-italic">psIL6ns</span> expression after poly I: C (5 μg mL<sup>−1</sup>) stimulation, (<b>c</b>) <span class="html-italic">psIL6</span> and (<b>d</b>) <span class="html-italic">psIL6ns</span> expression after ConA (25 μg mL<sup>−1</sup>) stimulation. The data are presented as mean + SD (<span class="html-italic">n</span> = 6). Different letters above the bars represent statistical significance between the time-points (<span class="html-italic">p</span> &lt; 0.05); ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001. Two-way ANOVA analysis was carried out followed by Bonferroni’s multiple comparison test for the multiple comparison. When a non-parametric method was found applicable, Kruskal–Wallis analysis was first used and when there was a significance, the Mann–Whitney U test was used as a post hoc test.</p>
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<p>Semi-quantitative RT-PCR results of <span class="html-italic">psIL6</span> and <span class="html-italic">psIL6ns</span> expression in the brain, spleen, distal ileum, and large intestine after oral administration of <span class="html-italic">A. hydrophila. ef1α</span> was chosen as the reference gene. C-RT: PBS-treated turtle; T-RT: <span class="html-italic">A. hydrophila</span> treated turtle. + represents the addition of reverse-transcriptase when running the reverse-transcription; —represents the replacement of reverse-transcriptase with H<sub>2</sub>O when running the reverse-transcription. H<sub>2</sub>O means the template was H<sub>2</sub>O.</p>
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<p>Expression of <span class="html-italic">psiIL6</span> (<b>a</b>) and <span class="html-italic">psIL6ns</span> (<b>b</b>) in the spleen after <span class="html-italic">S. aureus</span> and <span class="html-italic">A. hydrophila</span> in vivo challenge within 7 days after cold stress. The relative expression method was applied in the calculation with <span class="html-italic">ef1α</span> as the reference gene. The data are presented as mean + SD (<span class="html-italic">n</span> = 6). Capitalized and small letters: Statistical comparison between time points within a treatment group. Different letter denotes statistically significant difference (<span class="html-italic">p</span> &lt; 0.05, capitalized vs. capitalized, small letters vs. small letters). (<b>a</b>) <span class="html-italic">psIL6</span> and (<b>b</b>) <span class="html-italic">psIL6ns</span> expression. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001. ns: No statistically significant difference between the treatment groups. Two-way ANOVA analysis was carried out followed by Bonferroni’s multiple comparison test for multiple comparison. When a non-parametric method was found applicable, Kruskal–Wallis analysis was first used and when there was a significance, the Mann–Whitney U test was used as a post hoc test.</p>
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<p>Expression of the <span class="html-italic">psIL6</span> and <span class="html-italic">psIL6ns</span> gene in the intestine (distal ileum) after <span class="html-italic">S. aureus and A. hydrophila</span> in vivo infection within 7 days after acute cold stress. Capitalized and small letters: Statistical comparison between time points within a treatment group. Different letter denotes statistically significant difference (<span class="html-italic">p</span> &lt; 0.05, capitalized vs. capitalized, small letters vs. small letters). (<b>a</b>) <span class="html-italic">psIL6</span> and (<b>b</b>) <span class="html-italic">psIL6ns</span> expression. The data are presented as mean + SD (n = 6). * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001. ns: No statistically significant difference between the treatment groups. Two-way ANOVA analysis was carried out, followed by Bonferroni’s multiple comparison test in the multiple comparison. When a non-parametric method was found applicable, Kruskal–Wallis analysis was first used and when there was a significance, the Mann–Whitney U test was used as a post hoc test.</p>
Full article ">
15 pages, 1884 KiB  
Article
In Silico Identification of Type III PKS Chalcone and Stilbene Synthase Homologs in Marine Photosynthetic Organisms
by Daniele De Luca and Chiara Lauritano
Biology 2020, 9(5), 110; https://doi.org/10.3390/biology9050110 - 22 May 2020
Cited by 10 | Viewed by 4577
Abstract
Marine microalgae are photosynthetic microorganisms at the base of the marine food webs. They are characterized by huge taxonomic and metabolic diversity and several species have been shown to have bioactivities useful for the treatment of human pathologies. However, the compounds and the [...] Read more.
Marine microalgae are photosynthetic microorganisms at the base of the marine food webs. They are characterized by huge taxonomic and metabolic diversity and several species have been shown to have bioactivities useful for the treatment of human pathologies. However, the compounds and the metabolic pathways responsible for bioactive compound synthesis are often still unknown. In this study, we aimed at analysing the microalgal transcriptomes available in the Marine Microbial Eukaryotic Transcriptome Sequencing Project (MMETSP) database for an in silico search of polyketide synthase type III homologs and, in particular, chalcone synthase (CHS) and stilbene synthase (STS), which are often referred to as the CHS/STS family. These enzymes were selected because they are known to produce compounds with biological properties useful for human health, such as cancer chemopreventive, anti-inflammatory, antioxidant, anti-angiogenic, anti-viral and anti-diabetic. In addition, we also searched for 4-Coumarate: CoA ligase, an upstream enzyme in the synthesis of chalcones and stilbenes. This study reports for the first time the occurrence of these enzymes in specific microalgal taxa, confirming the importance for microalgae of these pathways and giving new insights into microalgal physiology and possible biotechnological applications for the production of bioactive compounds. Full article
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<p>Enzymatic reactions catalysed by type III PKSs for the production of naringenin chalcone and resveratrol.</p>
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<p>Maximum likelihood phylogenetic tree of chalcone/stilbene synthases. Numbers in parentheses after strain denomination refer to the last three codes of transcripts. Support to nodes was inferred using the Shimodaira-Hasegawa-like test.</p>
Full article ">Figure 3
<p>Maximum likelihood phylogenetic tree of 4-Coumarate:CoA ligase. Numbers in parentheses after strain denomination refer to the last three codes of transcripts. Support to nodes was inferred using the Shimodaira-Hasegawa-like test.</p>
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<p>Localisation and annotation of chalcone/stilbene synthase domains in strains containing multiple transcripts: <span class="html-italic">Chattonella subsalsa</span> CCMP2191 (<b>a</b>), <span class="html-italic">Heterosigma akashiwo</span> CCMP2393 (<b>b</b>), <span class="html-italic">Ochromas</span> sp. CCMP1393 (<b>c</b>), and <span class="html-italic">Pyramimonas parkeae</span> CCMP726 (<b>d</b>). Green bars refer to N-terminus domains, whereas orange bars to C-terminus ones. Transcript codes (CAMPEP) are as in <a href="#app1-biology-09-00110" class="html-app">File S1</a>.</p>
Full article ">
14 pages, 4367 KiB  
Review
Brain Control Reproduction by the Endocrine System of Female Blue Gourami (Trichogaster trichopterus)
by Gad Degani
Biology 2020, 9(5), 109; https://doi.org/10.3390/biology9050109 - 21 May 2020
Cited by 6 | Viewed by 4058
Abstract
Blue gourami belongs to the Labyrinithici fish and the Anabantiform order. It is characterized by a specific organ located above its gills for the respiration of atmospheric oxygen. This specific adaptation to low oxygen levels affects reproduction that is controlled by the brain, [...] Read more.
Blue gourami belongs to the Labyrinithici fish and the Anabantiform order. It is characterized by a specific organ located above its gills for the respiration of atmospheric oxygen. This specific adaptation to low oxygen levels affects reproduction that is controlled by the brain, which integrates different effects on reproduction mainly through two axes—the gonadotropic brain pituitary gonad axis (BPG) and the hypothalamic-pituitary-somatotropic axis (HPS axis), including the interactions between them. This brain control reproduction of the Anabantoidei suborder summarizes information that has been published on the hormones involved in controlling the reproduction system of a model female blue gourami fish (Trichogaster trichopterus), including unpublished data. In the whole-brain transcriptome of blue gourami, 17 transcription genes change during vitellogenesis in the brain. The hormones involved in reproduction in blue gourami described in the present paper include: Kisspeptin 2 (Kiss 2) and its receptors 1 and 2 (KissR 1 and 2); gonadotropin-releasing hormone 1, 2 and 3 (GnRH1, 2 and 3); GnRH receptor; pituitary adenylate cyclase-activating polypeptide (PACAP) and its related peptide (PRP); somatolactin (SL); follicle-stimulating hormone (FSH); luteinizing hormone (LH); growth hormone (GH); prolactin (PRL), 17β-estradiol (E2); testosterone (T); vitellogenesis (VTL); and 17α,20β- dihydroxy-4-pregnen-3-one (17,20P). A proposed quality model is presented regarding the brain control oogenesis in blue gourami that has a Labyrinth organ about which relatively little information has been published. This paper summarizes the complex various factors involved in the interactions between external and internal elements affecting the brain of fish reproduction in the Anabantiform order. It is suggested to study in the future the involvement of receptors of hormones, pheromones, and genome changes in various organs belonging to the reproduction system during the reproduction cycles about which little is known. Full article
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<p>The relationship between sexual behavior, oogenesis and nursing in blue gourami [<a href="#B1-biology-09-00109" class="html-bibr">1</a>]. (<b>A</b>) High density—no reproduction occurs. (<b>B</b>) The male builds a bubble nest. (<b>C</b>) Sexual behavior under the nest. (<b>D</b>) The male wraps his body around the female and the female spawns eggs that will be deposited in the nest. (<b>E</b>) The bubble nest with eggs in it. (<b>F</b>) Oocytes in pre-vitellogenesis. (<b>G</b>) Oocytes in vitellogenesis. (<b>H</b>) Oocytes in maturation. (<b>I</b>) Oocytes in ovulation. (<b>J</b>) Fry hatches in the nest [<a href="#B9-biology-09-00109" class="html-bibr">9</a>,<a href="#B10-biology-09-00109" class="html-bibr">10</a>].</p>
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<p>Factors affecting the hormones of female reproduction. The environments [<a href="#B19-biology-09-00109" class="html-bibr">19</a>,<a href="#B32-biology-09-00109" class="html-bibr">32</a>], sexual behavior [<a href="#B11-biology-09-00109" class="html-bibr">11</a>] and pheromones [<a href="#B12-biology-09-00109" class="html-bibr">12</a>], Kisspeptin (Kiss 1 and 2) [<a href="#B18-biology-09-00109" class="html-bibr">18</a>], gonadotropin-releasing hormone (GnRH1 and 3) [<a href="#B20-biology-09-00109" class="html-bibr">20</a>], follicle-stimulating hormone (FSH), luteinizing hormone (LH) [<a href="#B39-biology-09-00109" class="html-bibr">39</a>,<a href="#B42-biology-09-00109" class="html-bibr">42</a>], pituitary adenylate cyclase-activating polypeptide (PACAP) and its related peptide (PRP) [<a href="#B20-biology-09-00109" class="html-bibr">20</a>], growth hormone (GH) [<a href="#B50-biology-09-00109" class="html-bibr">50</a>], prolactin (PRL) [<a href="#B35-biology-09-00109" class="html-bibr">35</a>]. The FSH and LH act on the ovary to synthesize steroids, 17β-estradiol (E2) [<a href="#B51-biology-09-00109" class="html-bibr">51</a>,<a href="#B52-biology-09-00109" class="html-bibr">52</a>], testosterone (T) [<a href="#B51-biology-09-00109" class="html-bibr">51</a>,<a href="#B52-biology-09-00109" class="html-bibr">52</a>], and 17α,20β- dihydroxy-4-pregnen-3-one (17,20P) [<a href="#B51-biology-09-00109" class="html-bibr">51</a>,<a href="#B52-biology-09-00109" class="html-bibr">52</a>], and in the liver, synthesis vitellogenin (VTG) [<a href="#B45-biology-09-00109" class="html-bibr">45</a>] and insulin-like growth factor 1 (IGF1) [<a href="#B29-biology-09-00109" class="html-bibr">29</a>].</p>
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<p>mRNA levels (based on RNA-Seq) of transcripts associated with known genes (<a href="#biology-09-00109-t001" class="html-table">Table 1</a>) representing the differences in edgeR analysis (<span class="html-italic">p</span> &lt; 0.001) between pre-vitellogenesis (<b>A</b>) and vitellogenesis (<b>B</b>) in female blue gourami brains [<a href="#B55-biology-09-00109" class="html-bibr">55</a>].</p>
Full article ">Figure 4
<p>Proposed sketch of hormones secretion from the brain and pituitary of female blue gourami s based on gene expression in various studies [<a href="#B10-biology-09-00109" class="html-bibr">10</a>,<a href="#B17-biology-09-00109" class="html-bibr">17</a>,<a href="#B18-biology-09-00109" class="html-bibr">18</a>,<a href="#B20-biology-09-00109" class="html-bibr">20</a>,<a href="#B30-biology-09-00109" class="html-bibr">30</a>,<a href="#B31-biology-09-00109" class="html-bibr">31</a>,<a href="#B34-biology-09-00109" class="html-bibr">34</a>,<a href="#B36-biology-09-00109" class="html-bibr">36</a>,<a href="#B55-biology-09-00109" class="html-bibr">55</a>]. OB—Olfactory bulb, OT—Optic tectum, CER—Carpus cerebelii, PPa—Nucleus preopticus parvicellularis posterioris, PI—Pituitary gland, HY—Hypothalamus, Kiss2—Kisspeptin 2, GnRH1, 2 and 3—Gonadotropin-releasing hormone 1, 2 and 3, PACAP38—Pituitary adenylate cyclase activating polypeptide, FSH—Follicle-stimulating hormone, LH—Luteinizing hormone.</p>
Full article ">Figure 5
<p>Comparing the gene transcriptions of Kiss2, KissR1, and KissR2 in the brain of female blue gourami during oogenesis [<a href="#B18-biology-09-00109" class="html-bibr">18</a>].</p>
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<p>The relationship between the transcription of Kisspeptin (Kiss) 1 and 2 in the brain and the mRNA level of gonadotropin-releasing hormone (GnRH1, 2, 3), pituitary adenylate cyclase-activating polypeptide (PACAP) in various tissues of female blue gourami is presented [<a href="#B17-biology-09-00109" class="html-bibr">17</a>,<a href="#B18-biology-09-00109" class="html-bibr">18</a>,<a href="#B20-biology-09-00109" class="html-bibr">20</a>,<a href="#B31-biology-09-00109" class="html-bibr">31</a>,<a href="#B55-biology-09-00109" class="html-bibr">55</a>].</p>
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<p>Proposed quality model showing the relationship between gonadotropins levels and steroids during the gonadal cycle, low vitellogenesis (LV), high vitellogenesis (HV), and maturation (MS). The steroids are 17β-estradiol (E2), testosterone (T), 17α,20β- dihydroxy-4-pregnen-3-one (17,20-P) [<a href="#B10-biology-09-00109" class="html-bibr">10</a>,<a href="#B23-biology-09-00109" class="html-bibr">23</a>,<a href="#B24-biology-09-00109" class="html-bibr">24</a>,<a href="#B39-biology-09-00109" class="html-bibr">39</a>].</p>
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<p>The transcription of ovary aromatase (CYP19a) and brain aromatase (CYP19a) at various stages of oocytes in vitellogenesis and maturation of female blue gourami [<a href="#B37-biology-09-00109" class="html-bibr">37</a>].</p>
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<p>Variations in GH and prolactin mRNA levels during the gonadal cycle (X 8.5 × 10<sup>−6</sup>): pre-vitellogenesis (PV), low vitellogenesis (LV), high vitellogenesis (HV), and maturation (MS). Each histogram represents the average of five independent measurements (mean ± SE) [<a href="#B35-biology-09-00109" class="html-bibr">35</a>,<a href="#B50-biology-09-00109" class="html-bibr">50</a>].</p>
Full article ">Figure 10
<p>The interactions between the two axes, BPG and BPLB, of female blue gourami control oogenesis based on the review and unpublished data [<a href="#B9-biology-09-00109" class="html-bibr">9</a>,<a href="#B10-biology-09-00109" class="html-bibr">10</a>,<a href="#B11-biology-09-00109" class="html-bibr">11</a>,<a href="#B12-biology-09-00109" class="html-bibr">12</a>,<a href="#B13-biology-09-00109" class="html-bibr">13</a>,<a href="#B17-biology-09-00109" class="html-bibr">17</a>,<a href="#B18-biology-09-00109" class="html-bibr">18</a>,<a href="#B19-biology-09-00109" class="html-bibr">19</a>,<a href="#B20-biology-09-00109" class="html-bibr">20</a>,<a href="#B21-biology-09-00109" class="html-bibr">21</a>,<a href="#B22-biology-09-00109" class="html-bibr">22</a>,<a href="#B23-biology-09-00109" class="html-bibr">23</a>,<a href="#B24-biology-09-00109" class="html-bibr">24</a>,<a href="#B25-biology-09-00109" class="html-bibr">25</a>,<a href="#B26-biology-09-00109" class="html-bibr">26</a>,<a href="#B27-biology-09-00109" class="html-bibr">27</a>,<a href="#B28-biology-09-00109" class="html-bibr">28</a>,<a href="#B29-biology-09-00109" class="html-bibr">29</a>,<a href="#B30-biology-09-00109" class="html-bibr">30</a>,<a href="#B31-biology-09-00109" class="html-bibr">31</a>,<a href="#B32-biology-09-00109" class="html-bibr">32</a>,<a href="#B33-biology-09-00109" class="html-bibr">33</a>,<a href="#B34-biology-09-00109" class="html-bibr">34</a>,<a href="#B35-biology-09-00109" class="html-bibr">35</a>,<a href="#B36-biology-09-00109" class="html-bibr">36</a>,<a href="#B37-biology-09-00109" class="html-bibr">37</a>,<a href="#B38-biology-09-00109" class="html-bibr">38</a>,<a href="#B39-biology-09-00109" class="html-bibr">39</a>,<a href="#B40-biology-09-00109" class="html-bibr">40</a>,<a href="#B41-biology-09-00109" class="html-bibr">41</a>,<a href="#B42-biology-09-00109" class="html-bibr">42</a>,<a href="#B43-biology-09-00109" class="html-bibr">43</a>,<a href="#B44-biology-09-00109" class="html-bibr">44</a>,<a href="#B45-biology-09-00109" class="html-bibr">45</a>,<a href="#B46-biology-09-00109" class="html-bibr">46</a>,<a href="#B47-biology-09-00109" class="html-bibr">47</a>,<a href="#B48-biology-09-00109" class="html-bibr">48</a>,<a href="#B49-biology-09-00109" class="html-bibr">49</a>]. Gonadotropin-releasing hormone 1 and 3 (GnRH1 and 3), follicle-stimulating hormone (FSH), luteinizing hormone (LH), pituitary adenylate cyclase-activating polypeptide (PACAP) and its related peptide (PRP), growth hormone (GH), prolactin (PRL), 17β-estradiol (E2), testosterone (T), 17α,20β- dihydroxy-4-pregnen-3-one (17,20P), vitellogenin (VTG), insulin-like growth factor 1 (IGF1).</p>
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17 pages, 2144 KiB  
Review
Clinical Genetics of Prolidase Deficiency: An Updated Review
by Marta Spodenkiewicz, Michel Spodenkiewicz, Maureen Cleary, Marie Massier, Giorgos Fitsialos, Vincent Cottin, Guillaume Jouret, Céline Poirsier, Martine Doco-Fenzy and Anne-Sophie Lèbre
Biology 2020, 9(5), 108; https://doi.org/10.3390/biology9050108 - 21 May 2020
Cited by 34 | Viewed by 5661
Abstract
Prolidase is a ubiquitous enzyme that plays a major role in the metabolism of proline-rich proteins. Prolidase deficiency is a rare autosomal recessive inborn metabolic and multisystemic disease, characterized by a protean association of symptoms, namely intellectual disability, recurrent infections, splenomegaly, skin lesions, [...] Read more.
Prolidase is a ubiquitous enzyme that plays a major role in the metabolism of proline-rich proteins. Prolidase deficiency is a rare autosomal recessive inborn metabolic and multisystemic disease, characterized by a protean association of symptoms, namely intellectual disability, recurrent infections, splenomegaly, skin lesions, auto-immune disorders and cytopenia. To our knowledge, no published review has assembled the different clinical data and research studies over prolidase deficiency. The aim of this study is to summarize the actual state of the art from the descriptions of all the patients with a molecular diagnosis of prolidase deficiency reported to date regarding the clinical, biological, histopathological features, therapeutic options and functional studies. Full article
(This article belongs to the Section Genetics and Genomics)
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<p>Clinical and biological features reported in prolidase deficiency (PD) patients 1. (<b>a</b>) Main clinical features of PD patients. (<b>b</b>) Age of onset of the first symptoms. (<b>c</b>) Other dermatological lesions. (<b>d</b>) Biological analysis.</p>
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<p>Crystal Structure of one subunit of wild-type Human prolidase dimer as a ribbon representation with reported missense and nonsense variants in patients with PD. The N-terminal domain is colored in green, and the catalytic C-terminal domain in orange. Mn<sup>2+</sup> ions are represented in dotted, violet spheres and Pro ligand with blue sticks to indicate the location of the active sites of the prolidase dimer. Variants are represented as red spheres. The figure performed using PYMOL (the PyMOL Molecular Graphics System, Version 1.7, Schrodinger, LLC, New York, NY, USA) and human protein database (5M4Q) [<a href="#B70-biology-09-00108" class="html-bibr">70</a>].</p>
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<p>Schematic representation of the <span class="html-italic">PEPD</span> with the reported variants in patients with PD. The 15 exons are represented as blue boxes, introns as blue lines. The green box represents the region encoding for the proteic Nt domain, and the orange box represents the region encoding for the Ct domain. The underscored variant is reported for the first time in this study.</p>
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<p>Different mechanisms may be involved in the pathophysiology of PD. Imidodipeptides, as glycylproline, are split out by prolidase. In the representation of the enzymatic reaction, glycine is colored in blue, proline in black. Prolidase activity participates to the recycling of imidopeptide-containing proteins. As reported by previous studies, other cellular factors and receptors are dependent on, or are regulated by, prolidase activity or expression (IGFR, HIF-1α, TGFβ1 and NK-κB). Insulin growth factor and β1 integrin receptor signaling upregulate prolidase activity [<a href="#B2-biology-09-00108" class="html-bibr">2</a>,<a href="#B95-biology-09-00108" class="html-bibr">95</a>,<a href="#B96-biology-09-00108" class="html-bibr">96</a>,<a href="#B97-biology-09-00108" class="html-bibr">97</a>]. Inhibitors of prolidase activity induce a decrease of TGFβ 1 and its receptor [<a href="#B100-biology-09-00108" class="html-bibr">100</a>] and upregulates NF-κB expression [<a href="#B2-biology-09-00108" class="html-bibr">2</a>]. HIF-1α expression was shown to be prolidase-dependent [<a href="#B2-biology-09-00108" class="html-bibr">2</a>,<a href="#B105-biology-09-00108" class="html-bibr">105</a>].</p>
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14 pages, 834 KiB  
Article
Novel Dynamic Structures of 2019-nCoV with Nonlocal Operator via Powerful Computational Technique
by Wei Gao, P. Veeresha, D. G. Prakasha and Haci Mehmet Baskonus
Biology 2020, 9(5), 107; https://doi.org/10.3390/biology9050107 - 21 May 2020
Cited by 138 | Viewed by 4556
Abstract
In this study, we investigate the infection system of the novel coronavirus (2019-nCoV) with a nonlocal operator defined in the Caputo sense. With the help of the fractional natural decomposition method (FNDM), which is based on the Adomian decomposition and natural transform methods, [...] Read more.
In this study, we investigate the infection system of the novel coronavirus (2019-nCoV) with a nonlocal operator defined in the Caputo sense. With the help of the fractional natural decomposition method (FNDM), which is based on the Adomian decomposition and natural transform methods, numerical results were obtained to better understand the dynamical structures of the physical behavior of 2019-nCoV. Such behaviors observe the general properties of the mathematical model of 2019-nCoV. This mathematical model is composed of data reported from the city of Wuhan, China. Full article
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<p>Flow chart of the above-described model [<a href="#B20-biology-09-00107" class="html-bibr">20</a>].</p>
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<p>Behavior of results obtained for <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>a</mi> </mstyle> <mo>)</mo> </mrow> <mo> </mo> <msub> <mi>S</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>b</mi> </mstyle> <mo>)</mo> </mrow> <msub> <mi>E</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>c</mi> </mstyle> <mo>)</mo> </mrow> <msub> <mi>I</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>d</mi> </mstyle> <mo>)</mo> </mrow> <msub> <mi>A</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>e</mi> </mstyle> <mo>)</mo> </mrow> <msub> <mi>R</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>f</mi> </mstyle> <mo>)</mo> </mrow> <mo> </mo> <mi>M</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> for distinct fractional order <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi>α</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>Behavior of results obtained for <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>a</mi> </mstyle> <mo>)</mo> </mrow> <mo> </mo> <msub> <mi>S</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>b</mi> </mstyle> <mo>)</mo> </mrow> <msub> <mi>E</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>c</mi> </mstyle> <mo>)</mo> </mrow> <msub> <mi>I</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>d</mi> </mstyle> <mo>)</mo> </mrow> <msub> <mi>A</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>e</mi> </mstyle> <mo>)</mo> </mrow> <msub> <mi>R</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>f</mi> </mstyle> <mo>)</mo> </mrow> <mo> </mo> <mi>M</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> for distinct fractional order <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi>α</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
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10 pages, 1321 KiB  
Article
Evolution of Hemoglobin Genes in a Subterranean Rodent Species (Lasiopodomys mandarinus)
by Hong Sun, Kaihong Ye, Denghui Liu, Dan Pan, Shiming Gu and Zhenlong Wang
Biology 2020, 9(5), 106; https://doi.org/10.3390/biology9050106 - 20 May 2020
Cited by 5 | Viewed by 3482
Abstract
The Mandarin vole (Lasiopodomys mandarinus), a typical subterranean rodent, has undergone hematological adaptations to tolerate the hypoxic/hypercapnic underground environment. Hemoglobin (Hb) genes encode respiratory proteins functioning principally in oxygen binding and transport to various tissues and organs. To investigate the evolution [...] Read more.
The Mandarin vole (Lasiopodomys mandarinus), a typical subterranean rodent, has undergone hematological adaptations to tolerate the hypoxic/hypercapnic underground environment. Hemoglobin (Hb) genes encode respiratory proteins functioning principally in oxygen binding and transport to various tissues and organs. To investigate the evolution of α- and β-hemoglobin (Hb) in subterranean rodent species, we sequenced Hb genes of the Mandarin vole and the related aboveground Brandt’s vole (L. brandtii). Sequencing showed that in both voles, α-globin was encoded by a cluster of five functional genes in the following linkage order: HBZ, HBA-T1, HBQ-T1, HBA-T2, and HBQ-T2; among these, HBQ-T2 is a pseudogene in both voles. The β-globin gene cluster in both voles also included five functional genes in the following linkage order: HBE, HBE/HBG, HBG, HBB-T1, and HBB-T2. Phylogenetic analysis revealed that the Mandarin vole underwent convergent evolution with its related aboveground species (Brandt’s vole) but not with other subterranean rodent species. Selection pressure analyses revealed that α- and β-globin genes are under strong purifying selection (ω < 1), and branch-site analyses identified positive selection sites on HBAT-T1 and HBB-T1 in different subterranean rodent species. This suggests that the adaptive evolution of these genes enhanced the ability of Hb to store and transport oxygen in subterranean rodent species. Our findings highlight the critical roles of Hb genes in the evolution of hypoxia tolerance in subterranean rodent species. Full article
(This article belongs to the Section Evolutionary Biology)
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<p>Genomic structure of the α and β-globin gene family in the Mandarin vole.</p>
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<p>Bayesian tree of rodent species and the genomic structures of their α-globin genes. Numerals indicate Bayesian posterior probability values; colored text indicates subterranean rodent species.</p>
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<p>Bayesian phylogenetic tree of rodent species and the genomic structures of their β-globin genes. Numerals indicate Bayesian posterior probability values; colored text indicates subterranean rodent species.</p>
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12 pages, 2574 KiB  
Article
Period of Boar Ejaculate Collection Contributes to the Yearly Intra-Male Variability of Seminal Plasma Cytokines
by Lorena Padilla, Xiomara Lucas, Inmaculada Parrilla, Cristina Perez-Patiño, Heriberto Rodriguez-Martinez, Jordi Roca and Isabel Barranco
Biology 2020, 9(5), 105; https://doi.org/10.3390/biology9050105 - 20 May 2020
Cited by 4 | Viewed by 2831
Abstract
The concentrations of cytokines in seminal plasma (SP) fluctuate over time in healthy males, weakening their practical usefulness as diagnostic tools. This study evaluated the relevance of intra-male variability in SP cytokines and to what extent the period of the year when ejaculate [...] Read more.
The concentrations of cytokines in seminal plasma (SP) fluctuate over time in healthy males, weakening their practical usefulness as diagnostic tools. This study evaluated the relevance of intra-male variability in SP cytokines and to what extent the period of the year when ejaculate is collected contributes to such variability. Thirteen cytokines (GM-CSF, IFNγ, IL-1α, IL-1β, IL-1ra, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IL-18, and TNFα) were measured using the Luminex xMAP® technology for 180 SP samples of ejaculate collected over a year from nine healthy and fertile boars. The SP samples were grouped into two annual periods according to decreasing or increasing daylight and ambient temperature. Intra-male variability was higher than inter-male variability for all cytokines. All SP cytokines showed concentration differences between the two periods of the year, showing the highest concentration during the increasing daylength/temperature period, irrespective of the male. Similarly, some cytokines showed differences between daylength/temperature periods when focusing on their total amount in the ejaculate. No strong relationship (explaining more than 50% of the total variance) was found between annual fluctuations in SP-cytokine levels and semen parameters. In conclusion, the period of the year during which ejaculates were collected helps explain the intra-male variability of SP-cytokine levels in breeding boars. Full article
(This article belongs to the Section Immunology)
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<p>Violin plots representing the concentrations of granulocyte macrophage colony-stimulating factor (GM-CSF), interferon-gamma (IFNγ), interleukin (IL)-1α, IL-1β, IL-1ra, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IL-18, and tumor necrosis factor-α (TNFα) in seminal plasma of nine boars (20 ejaculates per boar). The dashed line represents the median and the dotted lines the 25% and 75% quartiles. All cytokines showed differences among boars (<span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Intraclass correlation coefficient (ICC 3,1) values in terms of single measures (dot) and 95% confidence intervals (bars) of cytokine concentrations in 180 boar seminal plasma samples. Cytokines: granulocyte-macrophage colony-stimulating factor (GM-CSF), interferon-gamma (IFNγ), interleukin (IL)-1α, IL-1β, IL-1ra, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IL-18, and tumor necrosis factor-α (TNFα).</p>
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<p>Variation pattern of cytokine concentrations in seminal plasma from ejaculates collected from nine boars (20 ejaculates per boar) during the times of year when increasing (I, January to June) or decreasing daylight/temperature (D, July to December) dominated. Grey box indicates no differences between periods, while red and green boxes indicate a relationship between dominating seasonal parameters with high and low cytokine concentrations (<span class="html-italic">p</span> &lt; 0.05), respectively. Cytokines: Granulocyte macrophage colony-stimulating factor (GM-CSF), interferon-gamma (IFNγ), interleukin (IL)-1α, IL-1β, IL-1ra, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IL-18, and tumor necrosis factor-α (TNFα).</p>
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<p>Variation pattern in the total amount of seminal plasma cytokines in ejaculates collected from nine boars (20 ejaculates per boar) during the periods of increasing (I, January to June) or decreasing daylight/temperature (D, July to December). Grey box indicates no differences between periods, while red and green boxes indicate a relationship between dominating seasonal parameters with high and low total cytokine amounts (<span class="html-italic">p</span> &lt; 0.05), respectively. Cytokines: Granulocyte macrophage colony-stimulating factor (GM-CSF), interferon-gamma (IFNγ), interleukin (IL)-1α, IL-1β, IL-1ra, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IL-18, and tumor necrosis factor-α (TNFα).</p>
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14 pages, 1374 KiB  
Article
The Utility of Genomic and Transcriptomic Data in the Construction of Proxy Protein Sequence Databases for Unsequenced Tree Nuts
by Cary Pirone-Davies, Melinda A. McFarland, Christine H. Parker, Yoko Adachi and Timothy R. Croley
Biology 2020, 9(5), 104; https://doi.org/10.3390/biology9050104 - 19 May 2020
Cited by 3 | Viewed by 3307
Abstract
As the apparent incidence of tree nut allergies rises, the development of MS methods that accurately identify tree nuts in food is critical. However, analyses are limited by few available tree nut protein sequences. We assess the utility of translated genomic and transcriptomic [...] Read more.
As the apparent incidence of tree nut allergies rises, the development of MS methods that accurately identify tree nuts in food is critical. However, analyses are limited by few available tree nut protein sequences. We assess the utility of translated genomic and transcriptomic data for library construction with Juglans regia, walnut, as a model. Extracted walnuts were subjected to nano-liquid chromatography–mass spectrometry (n-LC-MS/MS), and spectra were searched against databases made from a six-frame translation of the genome (6FT), a transcriptome, and three proteomes. Searches against proteomic databases yielded a variable number of peptides (1156–1275), and only ten additional unique peptides were identified in the 6FT database. Searches against a transcriptomic database yielded results similar to those of the National Center for Biotechnology Information (NCBI) proteome (1200 and 1275 peptides, respectively). Performance of the transcriptomic database was improved via the adjustment of RNA-Seq read processing methods, which increased the number of identified peptides which align to seed allergen proteins by ~20%. Together, these findings establish a path towards the construction of robust proxy protein databases for tree nut species and other non-model organisms. Full article
(This article belongs to the Special Issue Foodomics: Food Authentication, Processing and Nutrition)
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<p>Histogram of the sequence lengths of the NCBI proteome and the transcriptome. The transcriptome is represented in blue, the proteome in red.</p>
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<p>Venn diagrams comparing the total number of peptides identified in five databases. Number of unique peptides listed, along with the number of peptides shared by all databases. (<b>A</b>). NCBI, Maker, and Braker databases, (<b>B</b>). NCBI and 6FT databases, (<b>C</b>). NCBI and Transcriptomic databases.</p>
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<p>Plot of the mean number of peptides identified across eight transcriptomic databases constructed using different read processing conditions in walnut (<span class="html-italic">n</span> = 4). Bars represent standard deviations, sd = max_pct_stdev.</p>
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<p>Alignment of sequences from a cluster of related sulfur-rich seed storage sequences from a parsimonious comparison (<b>A</b>). Comparison of the NCBI proteome (XP 018824007.1) and the transcriptome assembled under published conditions. (<b>B</b>). Comparison of the NCBI proteome and the improved transcriptome assembled using Rcorrector and max_pct_stdev = 100.</p>
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13 pages, 995 KiB  
Review
Clinical Features of Parkinson’s Disease: The Evolution of Critical Symptoms
by Csaba Váradi
Biology 2020, 9(5), 103; https://doi.org/10.3390/biology9050103 - 19 May 2020
Cited by 99 | Viewed by 22704
Abstract
Parkinson’s disease (PD) is a multi-attribute neurodegenerative disorder combining motor and nonmotor symptoms without well-defined diagnostic clinical markers. The presence of primary motor features (bradykinesia, rest tremor, rigidity and loss of postural reflexes) are the most characteristic signs of PD that are also [...] Read more.
Parkinson’s disease (PD) is a multi-attribute neurodegenerative disorder combining motor and nonmotor symptoms without well-defined diagnostic clinical markers. The presence of primary motor features (bradykinesia, rest tremor, rigidity and loss of postural reflexes) are the most characteristic signs of PD that are also utilized to identify patients in current clinical practice. The successful implementation of levodopa treatment revealed that nonmotor features are the main contributors of patient disability in PD, and their occurrence might be earlier than motor symptoms during disease progression. Targeted detection of prodromal PD symptoms can open up new possibilities in the identification of PD patients and provide potential patient populations for developing novel neuroprotective therapies. In this review, the evolution of critical features in PD diagnosis is described with special attention to nonmotor symptoms and their possible detection. Full article
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<p>The diverse nature of nonmotor symptoms affecting Parkinson’s disease (PD) patients.</p>
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<p>Timeline of clinical signs expressed throughout PD.</p>
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21 pages, 2194 KiB  
Review
Magnetotactic Bacteria and Magnetosomes as Smart Drug Delivery Systems: A New Weapon on the Battlefield with Cancer?
by Danuta Kuzajewska, Agata Wszołek, Wojciech Żwierełło, Lucyna Kirczuk and Agnieszka Maruszewska
Biology 2020, 9(5), 102; https://doi.org/10.3390/biology9050102 - 19 May 2020
Cited by 38 | Viewed by 7199
Abstract
An important direction of research in increasing the effectiveness of cancer therapies is the design of effective drug distribution systems in the body. The development of the new strategies is primarily aimed at improving the stability of the drug after administration and increasing [...] Read more.
An important direction of research in increasing the effectiveness of cancer therapies is the design of effective drug distribution systems in the body. The development of the new strategies is primarily aimed at improving the stability of the drug after administration and increasing the precision of drug delivery to the destination. Due to the characteristic features of cancer cells, distributing chemotherapeutics exactly to the microenvironment of the tumor while sparing the healthy tissues is an important issue here. One of the promising solutions that would meet the above requirements is the use of Magnetotactic bacteria (MTBs) and their organelles, called magnetosomes (BMs). MTBs are commonly found in water reservoirs, and BMs that contain ferromagnetic crystals condition the magnetotaxis of these microorganisms. The presented work is a review of the current state of knowledge on the potential use of MTBs and BMs as nanocarriers in the therapy of cancer. The growing amount of literature data indicates that MTBs and BMs may be used as natural nanocarriers for chemotherapeutics, such as classic anti-cancer drugs, antibodies, vaccine DNA, and siRNA. Their use as transporters increases the stability of chemotherapeutics and allows the transfer of individual ligands or their combinations precisely to cancerous tumors, which, in turn, enables the drugs to reach molecular targets more effectively. Full article
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<p>Schematic illustration of the hypothesized mechanism of magnetosome formation (MamA, -B, -E, -K, -L, -M, -N, -O, -Q, -R, -S, -T; Mms6; MagA—proteins involved in formation and maturation of magnetosomes).</p>
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<p>Properties of <span class="html-italic">Magnetotactic</span> bacteria and magnetosomes as advantages and disadvantages in drug-carriers design.</p>
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<p><span class="html-italic">Magnetotactic</span> bacteria as potential drug-carriers capable of penetrating the tumor.</p>
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<p>Application of magnetosomes for drug delivery in cancer therapy.</p>
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<p>Proposed mechanisms of drug docking on bacterial magnetosomes for: (<b>a</b>) doxorubicin, (<b>b</b>) cytarabine, (<b>c</b>) antibodies, (<b>d</b>) vaccine DNA plasmids, (<b>e</b>) siRNA, and (<b>f</b>) complex of two different drugs in combined therapy (GP–genipin, NHS–N-hydroxysuccinimidyl, PAMAM–polyamidoamine dendrimers, PEI–polyethyleneimine, PLGA–poly-L-glutamic acid, SANH–succinimidyl 6-hydrazinonicotinate acetone hydrazone).</p>
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15 pages, 2720 KiB  
Article
Comparative Analysis of Porcine Follicular Fluid Proteomes of Small and Large Ovarian Follicles
by Victor M. Paes, José R. de Figueiredo, Peter L. Ryan, Scott T. Willard and Jean M. Feugang
Biology 2020, 9(5), 101; https://doi.org/10.3390/biology9050101 - 17 May 2020
Cited by 12 | Viewed by 3722
Abstract
Ovarian follicular fluid is widely used for in vitro oocyte maturation, but its in-depth characterization to extract full beneficial effects remains unclear. Here, we performed both shotgun (nanoscale liquid chromatography coupled to tandem mass spectrometry or nanoLC-MS/MS) and gel-based (two dimension-differential in-gel electrophoresis [...] Read more.
Ovarian follicular fluid is widely used for in vitro oocyte maturation, but its in-depth characterization to extract full beneficial effects remains unclear. Here, we performed both shotgun (nanoscale liquid chromatography coupled to tandem mass spectrometry or nanoLC-MS/MS) and gel-based (two dimension-differential in-gel electrophoresis or 2D-DIGE) proteomics, followed by functional bioinformatics to compare the proteomes of follicular fluids collected from small (<4 mm) and large (>6–12 mm) follicles of pig ovaries. A total of 2321 unique spots were detected with the 2D-DIGE across small and large follicles, while 2876 proteins with 88% successful annotations were detected with the shotgun approach. The shotgun and 2D-DIGE approaches revealed about 426 and 300 proteins that were respectively common across samples. Six proteins detected with both technical approaches were significantly differently expressed between small and large follicles. Pathways such as estrogen and PI3K-Akt signaling were significantly enriched in small follicles while the complement and coagulation cascades pathways were significantly represented in large follicles. Up-regulated proteins in small follicles were in favor of oocyte maturation, while those in large follicles were involved in the ovulatory process preparation. Few proteins with potential roles during sperm–oocyte interactions were especially detected in FF of large follicles and supporting the potential role of the ovarian FF on the intrafallopian sperm migration and interaction with the oocyte. Full article
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<p>Total protein detection (<b>A</b>) and annotations (<b>B</b>). Venn diagram of porcine follicular fluid was constructed with tools available at the Bioinformatics and Evolutionary Genomics, Ghent, Belgium. SNA: small non-atretic; LNA: large non-atretic; NCBI: National Center for Biotechnology Information. P &lt; 0.05 indicates significant difference.</p>
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<p>Protein-to-protein interaction networks of up-regulated proteins in small non-atretic (SNA: <b>A</b>) and large non-atretic (LNA: <b>B</b>) datasets.</p>
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<p>Protein–protein interaction (PPI) networks in dataset specifics to small non-atretic (SNA: <b>A</b>) and large non-atretic (LNA: <b>B</b>). The figure shows the overview dynamic pattens of PPI networks in the porcine FF during follicle growth. The LNA dataset has more interaction networks than the SNA.</p>
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<p>Two dimension-differential in gel electrophoresis (2D-DIGE) of protein in SNA (green spots) and LNA (red spots) samples.</p>
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14 pages, 3967 KiB  
Article
De-Escalation by Reversing the Escalation with a Stronger Synergistic Package of Contact Tracing, Quarantine, Isolation and Personal Protection: Feasibility of Preventing a COVID-19 Rebound in Ontario, Canada, as a Case Study
by Biao Tang, Francesca Scarabel, Nicola Luigi Bragazzi, Zachary McCarthy, Michael Glazer, Yanyu Xiao, Jane M. Heffernan, Ali Asgary, Nicholas Hume Ogden and Jianhong Wu
Biology 2020, 9(5), 100; https://doi.org/10.3390/biology9050100 - 16 May 2020
Cited by 33 | Viewed by 12317
Abstract
Since the beginning of the COVID-19 pandemic, most Canadian provinces have gone through four distinct phases of social distancing and enhanced testing. A transmission dynamics model fitted to the cumulative case time series data permits us to estimate the effectiveness of interventions implemented [...] Read more.
Since the beginning of the COVID-19 pandemic, most Canadian provinces have gone through four distinct phases of social distancing and enhanced testing. A transmission dynamics model fitted to the cumulative case time series data permits us to estimate the effectiveness of interventions implemented in terms of the contact rate, probability of transmission per contact, proportion of isolated contacts, and detection rate. This allows us to calculate the control reproduction number during different phases (which gradually decreased to less than one). From this, we derive the necessary conditions in terms of enhanced social distancing, personal protection, contact tracing, quarantine/isolation strength at each escalation phase for the disease control to avoid a rebound. From this, we quantify the conditions needed to prevent epidemic rebound during de-escalation by simply reversing the escalation process. Full article
(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)
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<p>The flowchart of the transmission dynamics model, where the population is stratified by susceptible, exposed, asymptomatic infectious, symptomatic infectious, and recovered status, and by quarantine and isolation status.</p>
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<p>Data fitting and projection. (<b>A</b>) The red circles represent the cumulative confirmed cases in Ontario, Canada. (<b>B</b>–<b>D</b>) Based on the mean curve of the 500 times estimation, the population of <math display="inline"><semantics> <mrow> <mi>I</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>A</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>D</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> reach their peak times around April 12, April 16 and April 21 while the peak values are around 1538, 1469 and 2444, respectively. The final cumulative confirmed cases projected by the model is about 23,940.</p>
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<p>Estimated effective reproduction number <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>t</mi> </msub> </mrow> </semantics></math> in Ontario, Canada as a function of time (mean value and 95% confidence interval).</p>
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<p>Values of <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>c</mi> </msub> </mrow> </semantics></math> in the plane <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mi>q</mi> <mo>,</mo> <mn>1</mn> <mo>/</mo> <msub> <mi>δ</mi> <mi>I</mi> </msub> <mo stretchy="false">)</mo> </mrow> </semantics></math>, with baseline parameters estimated on March 24 (top-left panel), and under different scenarios of contact rate reduction (other panels). The red line corresponds to <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> (with dashed lines showing confidence intervals in the baseline scenario). The black crosses correspond to the parameters (max/min) estimated after March 24 (last day of the estimation period is April 21).</p>
Full article ">Figure 5
<p>Same as <a href="#biology-09-00100-f004" class="html-fig">Figure 4</a>, with baseline parameters estimated in the period March 14–18.</p>
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<p>Same as <a href="#biology-09-00100-f004" class="html-fig">Figure 4</a>, with baseline parameters estimated in the period before March 14.</p>
Full article ">Figure 7
<p>Values of <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>c</mi> </msub> </mrow> </semantics></math> in the plane <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mrow> <mi>q</mi> <mo>,</mo> <mi>β</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>, with baseline parameters estimated on March 24 (top-left panel), and under different scenarios of contact rate reduction (other panels). The red line corresponds to <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> (with dashed lines showing confidence intervals in the baseline scenario). The black crosses correspond to the parameters (max/min) estimated after March 24 (last day of the estimation period is April 21).</p>
Full article ">Figure 8
<p>Same as <a href="#biology-09-00100-f007" class="html-fig">Figure 7</a>, with baseline parameters estimated in the period March 14–18.</p>
Full article ">Figure 9
<p>Same as <a href="#biology-09-00100-f007" class="html-fig">Figure 7</a>, with baseline parameters estimated in the period before March 14.</p>
Full article ">Figure 10
<p>Values of <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>c</mi> </msub> </mrow> </semantics></math> in the plane <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mrow> <mi>q</mi> <mo>,</mo> <mi>c</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> for different values of the time for diagnosis (<math display="inline"><semantics> <mrow> <mn>1</mn> <mo>/</mo> <msub> <mi>δ</mi> <mi>I</mi> </msub> </mrow> </semantics></math>). The red line corresponds to <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> (with dashed lines showing confidence intervals).</p>
Full article ">Figure 11
<p>Values of <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>c</mi> </msub> </mrow> </semantics></math> in the plane <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mrow> <mi>q</mi> <mo>,</mo> <msub> <mi>δ</mi> <mi>I</mi> </msub> <mo>/</mo> <mo stretchy="false">(</mo> <msub> <mi>δ</mi> <mi>I</mi> </msub> <mo>+</mo> <mi>α</mi> <mo>+</mo> <msub> <mi>γ</mi> <mi>I</mi> </msub> </mrow> <mo>)</mo> </mrow> <mo stretchy="false">)</mo> </mrow> </semantics></math>, for different values of the contact rate. The red line corresponds to <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> (dashed lines showing confidence intervals).</p>
Full article ">Figure 12
<p>Left panel: values of <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>c</mi> </msub> </mrow> </semantics></math> in the plane <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mrow> <mi>α</mi> <mo>,</mo> <mi>β</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>. Right panel: values of <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>c</mi> </msub> </mrow> </semantics></math> in the plane <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mrow> <msub> <mi>γ</mi> <mi>I</mi> </msub> <mo>,</mo> <mi>β</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>. The other parameters are those estimated on March 24. The red line corresponds to <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> (dashed lines showing confidence intervals).</p>
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10 pages, 3281 KiB  
Article
Rigosertib-Activated JNK1/2 Eliminate Tumor Cells through p66Shc Activation
by Julia K. Günther, Aleksandar Nikolajevic, Susanne Ebner, Jakob Troppmair and Sana Khalid
Biology 2020, 9(5), 99; https://doi.org/10.3390/biology9050099 - 15 May 2020
Cited by 5 | Viewed by 3386
Abstract
Rigosertib, via reactive oxygen species (ROS), stimulates cJun N-terminal kinases 1/2 (JNK1/2), which inactivate RAS/RAF signaling and thereby inhibit growth and survival of tumor cells. JNK1/2 are not only regulated by ROS—they in turn can also control ROS production. The prooxidant and cell [...] Read more.
Rigosertib, via reactive oxygen species (ROS), stimulates cJun N-terminal kinases 1/2 (JNK1/2), which inactivate RAS/RAF signaling and thereby inhibit growth and survival of tumor cells. JNK1/2 are not only regulated by ROS—they in turn can also control ROS production. The prooxidant and cell death function of p66Shc requires phosphorylation by JNK1/2. Here, we provide evidence that establishes p66Shc, an oxidoreductase, as a JNK1/2 effector downstream of Rigosertib-induced ROS production, DNA damage, and cell death. This may provide a common pathway for suppression of tumor cell growth by Rigosertib. Full article
(This article belongs to the Section Cell Biology)
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Figure 1

Figure 1
<p>Effect of Rigosertib on MAPK signaling. Representative Western blots show an increase in cJun N-terminal kinases 1/2 (JNK1/2) (<b>A</b>) and a decrease in ERK1/2 activity (<b>B</b>) in MCF7, PC3, and DU-145 cells stimulated with 50 μM Rigosertib for 18 h. Numbers on top of the blot indicate the fold change in protein phosphorylation upon Rigosertib treatment (normalized to loading control) relative to DMSO (solvent)-treated control samples. All experiments have been repeated at least three times with consistent results. A representative blot is shown.</p>
Full article ">Figure 2
<p>Rigosertib increases p66Shc activity and cell death in tumor cell lines. Representative Western blots show an increase in p66Shc activity (<b>A</b>), γH2AX phosphorylation (<b>B</b>), and PARP cleavage (<b>C</b>) in MCF-7, PC3, and DU-145 cells stimulated with 50 μM Rigosertib for 18 h. Numbers on top of the blot indicate the fold change in protein phosphorylation upon Rigosertib treatment (normalized to loading control) relative to DMSO (solvent)-treated control samples. For PARP, the ratio of cleaved and intact protein is shown. MCF-7, PC3, and DU-145 were imaged in phase contrast to detect cellular morphology (<b>D</b>) and analyzed for cell death by Annexin/PI after treating cells with either Rigosertib (50 μM) or DMSO for 96 h. Results are presented as % of Annexin V-positive and PI-positive cells (<b>E</b>) and scattered plots (<b>F</b>). The drug-containing medium was refreshed after 48 h during 96 h incubation time. All experiments have been repeated at least three times with consistent results, except the Annexin/PI analysis for DU-145, which has been repeated twice. Values shown are mean ± S.D. A representative blot is shown. Scale bar size: 100 µm.</p>
Full article ">Figure 2 Cont.
<p>Rigosertib increases p66Shc activity and cell death in tumor cell lines. Representative Western blots show an increase in p66Shc activity (<b>A</b>), γH2AX phosphorylation (<b>B</b>), and PARP cleavage (<b>C</b>) in MCF-7, PC3, and DU-145 cells stimulated with 50 μM Rigosertib for 18 h. Numbers on top of the blot indicate the fold change in protein phosphorylation upon Rigosertib treatment (normalized to loading control) relative to DMSO (solvent)-treated control samples. For PARP, the ratio of cleaved and intact protein is shown. MCF-7, PC3, and DU-145 were imaged in phase contrast to detect cellular morphology (<b>D</b>) and analyzed for cell death by Annexin/PI after treating cells with either Rigosertib (50 μM) or DMSO for 96 h. Results are presented as % of Annexin V-positive and PI-positive cells (<b>E</b>) and scattered plots (<b>F</b>). The drug-containing medium was refreshed after 48 h during 96 h incubation time. All experiments have been repeated at least three times with consistent results, except the Annexin/PI analysis for DU-145, which has been repeated twice. Values shown are mean ± S.D. A representative blot is shown. Scale bar size: 100 µm.</p>
Full article ">Figure 3
<p>JNK1/2 regulation of p66Shc activity in tumor cell lines upon Rigosertib treatment. Representative Western blots demonstrating the effects of SP600125 (20 μM, JNK inhibitor) treatment before Rigosertib application (50 μM for 18 h) on cJun (<b>A</b>), ERK1/2 (<b>B</b>), and p66ShcS36 phosphorylation (<b>C</b>) in MCF7, PC3, and DU-145 cells. Numbers on top of the blot indicate the fold change in protein phosphorylation upon Rigosertib treatment (normalized to loading control) relative to DMSO (solvent)-treated control samples. All experiments have been repeated at least three times with consistent results. A representative blot is shown.</p>
Full article ">Figure 4
<p>Rigosertib-mediated tumor cell killing is reduced by JNK1/2 inhibition. Representative Western blots documenting the effect of SP600125 treatment (20 μM, added one h before Rigosertib) on γH2AX phosphorylation (<b>A</b>) and PARP cleavage (<b>B</b>) in MCF7, PC3 and DU-145 cells. Rigosertib was applied for 18 h at a concentration of 50 µM). Numbers on top of the blot indicate the fold change in protein phosphorylation upon Rigosertib treatment (normalized to loading control) relative to DMSO (solvent) treated control samples. All experiments have been repeated at least three times with consistent results. A representative blot is shown.</p>
Full article ">Figure 5
<p>During the submission of the revised manuscrip an incorrect version of this Figure has been submitted, The figure inserted now is as requested by the reviewers. p66Shc is necessary for Rigosertib-mediated tumor cell killing. Representative Western blots demonstrate γH2AX phosphorylation and PARP cleavage in p66Shc-HA-His transiently transfected MCF7 cells treated with Rigosertib (50 μM) or DMSO for 18 h. All experiments have been repeated at least three times with consistent results. A representative blot is shown.</p>
Full article ">Figure 6
<p>Rigosertib-mediated cell death pathways (modified from Ritt et al. [<a href="#B7-biology-09-00099" class="html-bibr">7</a>]). Work by Ritt et al. identified the effect of Rigosertib-induced JNK activation on the suppression of the RAS signaling pathway and consequent blunting survival and growth signals. Our data point to an additional mechanism, which may be even more common, linking activated JNK1/2 to the activation of p66Shc, which has been implicated in ROS-mediated cell death under various conditions [<a href="#B12-biology-09-00099" class="html-bibr">12</a>].</p>
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19 pages, 1934 KiB  
Article
A Comparative Study of Rat Urine 1H-NMR Metabolome Changes Presumably Arising from Isoproterenol-Induced Heart Necrosis Versus Clarithromycin-Induced QT Interval Prolongation
by Matthieu Dallons, Manon Delcourt, Corentin Schepkens, Manuel Podrecca and Jean-Marie Colet
Biology 2020, 9(5), 98; https://doi.org/10.3390/biology9050098 - 13 May 2020
Cited by 1 | Viewed by 4288
Abstract
Cardiotoxicity remains a challenging concern both in drug development and in the management of various clinical situations. There are a lot of examples of drugs withdrawn from the market or stopped during clinical trials due to unpredicted cardiac adverse events. Obviously, current conventional [...] Read more.
Cardiotoxicity remains a challenging concern both in drug development and in the management of various clinical situations. There are a lot of examples of drugs withdrawn from the market or stopped during clinical trials due to unpredicted cardiac adverse events. Obviously, current conventional methods for cardiotoxicity assessment suffer from a lack of predictivity and sensitivity. Therefore, there is a need for developing new tools to better identify and characterize any cardiotoxicity that can occur during the pre-clinical and clinical phases of drug development as well as after marketing in exposed patients. In this study, isoproterenol and clarithromycin were used as prototypical cardiotoxic agents in rats in order to evaluate potential biomarkers of heart toxicity at very early stages using 1H-NMR-based metabonomics. While isoproterenol is known to cause heart necrosis, clarithromycin may induce QT interval prolongation. Heart necrosis and QT prolongation were validated by histological analysis, serum measurement of lactate dehydrogenase/creatine phosphate kinase and QTc measurement by electrocardiogram (ECG). Urine samples were collected before and repeatedly during daily exposure to the drugs for 1H-NMR based-metabonomics investigations. Specific metabolic signatures, characteristic of each tested drug, were obtained from which potential predictive biomarkers for drug-induced heart necrosis and drug-induced QT prolongation were retrieved. Isoproterenol-induced heart necrosis was characterized by higher levels of taurine, creatine, glucose and by lower levels of Krebs cycle intermediates, creatinine, betaine/trimethylamine N-oxide (TMAO), dimethylamine (DMA)/sarcosine. Clarithromycin-induced QT prolongation was characterized by higher levels of creatinine, taurine, betaine/TMAO and DMA/sarcosine and by lower levels of Krebs cycle intermediates, glucose and hippurate. Full article
(This article belongs to the Special Issue Molecular Targets and Targeting in Biomedical Sciences)
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Figure 1

Figure 1
<p>Validation of the considered cardiotoxicological mechanism for each experiment. (<b>A</b>) Transversal slide of heart from rat daily exposed to isoproterenol (ISO) at the end of the exposure period (3 days) and from control rat. Arrows indicate early stage of cardiomyocytes necrosis. Lens 250×. (<b>B</b>) Relative means ± SEM of lactate dehydrogenase (LDH) and creatine phosphate kinase (CPK) <span class="html-italic">serum</span> concentrations from rats before the exposures and at the end of ISO and clarithromycin (CLAR) exposures. Paired t-Test: * <span class="html-italic">p</span>-value &lt; 0.05; ** <span class="html-italic">p</span>-value &lt; 0.01. (<b>C</b>): Rat electrocardiogram (ECG) before the daily CLAR exposure and at the end of the exposure period (7 days). (<b>D</b>) Means ± SEM of corrected QT interval (QTc) measured on ECG from rats before the daily CLAR exposure and at days 1, 4 and 7 of the exposure period. Paired t-test: * <span class="html-italic">p</span>-value &lt; 0.05.</p>
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<p>500 MHz <sup>1</sup>H-NMR urine spectra from Wistar Han rats. (<b>A</b>) One day before any exposure. (<b>B</b>) First day of the ISO exposure. (<b>C)</b> Third day of the CLAR exposure. Metabolite assignments: A—Succinate; B—α-Ketoglutarate; C—Citrate; D—Dimethylamine (DMA) or Sarcosine; E—Creatinine; F—Betaine or Trimethylamine N-oxide (TMAO); G—trans-Aconitate; H—Hippurate; I—Allantoin; J—Creatine; K—Taurine; L—Glucose.</p>
Full article ">Figure 3
<p>Projection to latent structure discriminant analysis (PLS-DA) modeling of metabonomic study performed on urine samples from rats daily exposed to ISO. (<b>A</b>) Scores plot from <sup>1</sup>H-NMR spectra of rat urine samples at different time points: One day before the exposure (•) vs. day 1 of the exposure period (<span style="color:#0432FF">•</span>) vs. days 2 and 3 of the exposure period (<span style="color:#8064A2">•</span>). R<sup>2</sup><sub>cum</sub> = 0.84; Q<sup>2</sup><sub>cum</sub> = 0.8; Hotelling’s T2 = 0.95; <span class="html-italic">p</span>-value of analysis of variance of cross-validated residuals (CV-ANOVA) &lt; 0.001. Arrows indicate the direction of the metabolic changes. (<b>B</b>) Cross-validation plot (R<sup>2</sup> in green, Q<sup>2</sup> in blue) with a permutation test repeated 200 times. The Y axis intercepts were R<sup>2</sup> = (0.0; 0.118) and Q<sup>2</sup> = (0.0; −0.22). (<b>C</b>) Loadings plot from <sup>1</sup>H-NMR spectra of rat urine samples at different time points with corresponding identified metabolites.</p>
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<p>Metabolite Set Enrichment Analysis performed on <sup>1</sup>H-NMR metabonomic data of ISO exposure, using MetaboAnalyst 4.0 online software. 3 × 10<sup>3</sup>.</p>
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<p>PLS-DA modeling of metabonomic study performed on urine samples from rats daily exposed to CLAR. (<b>A</b>) Scores plot from <sup>1</sup>H-NMR spectra of rat urine samples at different time points One day before the exposure (•) vs. days 1 and 2 of the exposure period (<span style="color:#00B050">•</span>) vs. day 3 of the exposure period (<span style="color:red">•</span>) vs. days 6 and 7 of the exposure period (<span style="color:#FED507">•</span>). R<sup>2</sup><sub>cum</sub> = 0.65; Q<sup>2</sup><sub>cum</sub> = 0.44; Hotelling’s T2 = 0.95; <span class="html-italic">p</span>-value (CV-ANOVA) &lt; 0.01. Arrows indicate the direction of the metabolic changes. (<b>B</b>) Cross-validation plot if days -1, 1, 2, 6 and 7 are grouped in the same class (R<sup>2</sup> in green, Q<sup>2</sup> in blue) with a permutation test repeated 200 times. The Y axis intercepts were R<sup>2</sup> = (0.0; 0.356) and Q<sup>2</sup> = (0.0; −0.291). (<b>C</b>) Loadings plot from <sup>1</sup>H-NMR spectra of rat urine samples at different time points with corresponding identified metabolites.</p>
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<p>Metabolite Set Enrichment Analysis performed on <sup>1</sup>H-NMR metabonomic data of CLAR exposure, using MetaboAnalyst 4.0 online software.</p>
Full article ">Figure 7
<p>PLS-DA modeling of metabonomic study performed on urine samples from rats daily exposed to ISO or CLAR. (<b>A</b>) Scores plot from <sup>1</sup>H-NMR spectra of rat urine samples at different time points: one day before the exposure (•) vs. days 1, 2 and 3 of the ISO exposure period (<span style="color:#0432FF">•</span>) vs. day 3 of the CLAR exposure period (<span style="color:red">•</span>). R<sup>2</sup><sub>cum</sub> = 0.88; Q<sup>2</sup><sub>cum</sub> = 0.81; Hotelling’s T2 = 0.95; <span class="html-italic">p-value</span> (CV-ANOVA) &lt; 0.001. Arrows indicate the direction of the metabolic changes. (<b>B</b>) Cross-validation plot (R<sup>2</sup> in green, Q<sup>2</sup> in blue) with a permutation test repeated 200 times. The Y axis intercepts were R<sup>2</sup> = (0.0; 0.2) and Q<sup>2</sup> = (0.0; −0.467). (<b>C</b>) Heatmap constructed from relative means of discriminant metabolites normalized integrals for ISO and CLAR exposures.</p>
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10 pages, 783 KiB  
Article
Using Early Data to Estimate the Actual Infection Fatality Ratio from COVID-19 in France
by Lionel Roques, Etienne K Klein, Julien Papaïx, Antoine Sar and Samuel Soubeyrand
Biology 2020, 9(5), 97; https://doi.org/10.3390/biology9050097 - 8 May 2020
Cited by 60 | Viewed by 17967
Abstract
The number of screening tests carried out in France and the methodology used to target the patients tested do not allow for a direct computation of the actual number of cases and the infection fatality ratio (IFR). The main objective of this work [...] Read more.
The number of screening tests carried out in France and the methodology used to target the patients tested do not allow for a direct computation of the actual number of cases and the infection fatality ratio (IFR). The main objective of this work is to estimate the actual number of people infected with COVID-19 and to deduce the IFR during the observation window in France. We develop a ‘mechanistic-statistical’ approach coupling a SIR epidemiological model describing the unobserved epidemiological dynamics, a probabilistic model describing the data acquisition process and a statistical inference method. The actual number of infected cases in France is probably higher than the observations: we find here a factor ×8 (95%-CI: 5–12) which leads to an IFR in France of 0.5% (95%-CI: 0.3–0.8) based on hospital death counting data. Adjusting for the number of deaths in nursing homes, we obtain an IFR of 0.8% (95%-CI: 0.45–1.25). This IFR is consistent with previous findings in China (0.66%) and in the UK (0.9%) and lower than the value previously computed on the Diamond Princess cruse ship data (1.3%). Full article
(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)
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Figure 1

Figure 1
<p>Expected number of observed cases associated with the MLE vs. number of cases actually detected (total cases). The curve corresponds to cumulated values of the expected observation <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mi>t</mi> </msub> <mspace width="0.166667em"/> <msubsup> <mi>p</mi> <mi>t</mi> <mo>∗</mo> </msubsup> </mrow> </semantics></math> given by the model, and the crosses correspond to the data (cumulated values of <math display="inline"><semantics> <msub> <mover accent="true"> <mi>δ</mi> <mo>^</mo> </mover> <mi>t</mi> </msub> </semantics></math>).</p>
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<p>Distribution of the cumulated number of infected cases (<math display="inline"><semantics> <mrow> <mi>I</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>+</mo> <mi>R</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>) across time. Solid line: average value obtained from the posterior distribution of the parameters. Dotted curves: 0.025 and 0.975 pointwise posterior quantiles. Blue crosses: data (cumulated values of <math display="inline"><semantics> <msub> <mover accent="true"> <mi>δ</mi> <mo>^</mo> </mover> <mi>t</mi> </msub> </semantics></math>).</p>
Full article ">Figure A1
<p>Joint posterior distributions of <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>α</mi> <mo>,</mo> <mi>κ</mi> <mo>)</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mn>0</mn> </msub> <mo>,</mo> <mi>α</mi> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mn>0</mn> </msub> <mo>,</mo> <mi>κ</mi> <mo>)</mo> </mrow> </semantics></math>.</p>
Full article ">Figure A1 Cont.
<p>Joint posterior distributions of <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>α</mi> <mo>,</mo> <mi>κ</mi> <mo>)</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mn>0</mn> </msub> <mo>,</mo> <mi>α</mi> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mn>0</mn> </msub> <mo>,</mo> <mi>κ</mi> <mo>)</mo> </mrow> </semantics></math>.</p>
Full article ">Figure A2
<p>Joint posterior distributions of <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>α</mi> <mo>,</mo> <mi>κ</mi> <mo>)</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mn>0</mn> </msub> <mo>,</mo> <mi>α</mi> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mn>0</mn> </msub> <mo>,</mo> <mi>κ</mi> <mo>)</mo> </mrow> </semantics></math> obtained with a more informative prior.</p>
Full article ">Figure A3
<p>Dynamics of the IFR in France. Solid line: average value obtained from the posterior distribution of the parameters. Dotted curves: 0.025 and 0.975 pointwise quantiles.</p>
Full article ">Figure A4
<p>Posterior distribution of the basic reproduction number <math display="inline"><semantics> <msub> <mi>R</mi> <mn>0</mn> </msub> </semantics></math> in France.</p>
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24 pages, 3477 KiB  
Article
A High-Resolution Mass Spectrometry-Based Quantitative Metabolomic Workflow Highlights Defects in 5-Fluorouracil Metabolism in Cancer Cells with Acquired Chemoresistance
by Sanjay Shahi, Ching-Seng Ang and Suresh Mathivanan
Biology 2020, 9(5), 96; https://doi.org/10.3390/biology9050096 - 6 May 2020
Cited by 4 | Viewed by 4928
Abstract
Currently, 5-fluorouracil (5-FU)-based combination chemotherapy is the mainstay in the treatment of metastatic colorectal cancer (CRC), which benefits approximately 50% of the patients. However, these tumors inevitably acquire chemoresistance resulting in treatment failure. The molecular mechanisms driving acquired chemotherapeutic drug resistance in CRC [...] Read more.
Currently, 5-fluorouracil (5-FU)-based combination chemotherapy is the mainstay in the treatment of metastatic colorectal cancer (CRC), which benefits approximately 50% of the patients. However, these tumors inevitably acquire chemoresistance resulting in treatment failure. The molecular mechanisms driving acquired chemotherapeutic drug resistance in CRC is fundamental for the development of novel strategies for circumventing resistance. However, the specific phenomenon that drives the cancer cells to acquire resistance is poorly understood. Understanding the molecular mechanisms that regulate chemoresistance will uncover new avenues for the treatment of CRC. Among the various mechanisms of acquired chemoresistance, defects in the drug metabolism pathways could play a major role. In the case of 5-FU, it gets converted into various active metabolites, which, directly or indirectly, interferes with the replication and transcription of dividing cells causing DNA and RNA damage. In this project, we developed a high-resolution mass spectrometry-based method to effectively extract and quantify levels of the 5-FU metabolites in cell lysates and media of parental and 5-FU resistant LIM1215 CRC cells. The analysis highlighted that the levels of 5-FU metabolites are significantly reduced in 5-FU resistant cells. Specifically, the level of the nucleotide fluorodeoxyuridine monophosphate (FdUMP) is reduced with treatment of 5-FU clarifying the compromised 5-FU metabolism in resistant cells. Corroborating the metabolomic analysis, treatment of the resistant cells with FdUMP, an active metabolite of 5-FU, resulted in effective killing of the resistant cells. Overall, in this study, an effective protocol was developed for comparative quantitation of polar metabolites and nucleotide analogues from the adherent cells efficiently. Furthermore, the utility of FdUMP as an alternative for CRC therapy is highlighted. Full article
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Figure 1
<p>Colorectal cancer (CRC) cells gain resistance to 5-fluorouracil (5-FU) with long term exposure to 5-FU. (<b>A</b>) For developing 5-FU resistance in LIM1215 cells, parental cells were cultured continuously until they got fully confluent with increasing concentrations of 5-FU (1, 5, 10, 25 to 50 µm). (<b>B</b>) FACS apoptosis assay performed with LIM1215 parental and resistant cells following treatment with 50 µm 5-FU for 72 h showed that resistant cells are protected from 5-FU mediated cell death. ns; not significant. Data is presented as mean ± s.e.m.; significance determined by Student’s t-test (n = 3).</p>
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<p>Generation of 5-FU and FdUMP calibration curve. Lowest limit of detection and quantification are two key factors for the accurate results. To determine the limits of detection and for the relative comparison of the results, the calibration curve was generated using the peak area obtained from serially diluted 5-FU (<b>A</b>) and FdUMP (<b>B</b>) spiked into conditioned media from the parental cell culture. The green dot from the curve represents the peak area of the 0.780 ng/µL concentration of FdUMP, which was selected for the relative comparison of the detected compounds.</p>
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<p>Development of quantitative metabolomics workflow to analyze 5-FU metabolism in CRC cells. (<b>A</b>) 5-Fluorouracil (5-FU) is an uracil analogue and within the cells it is rapidly converted into different metabolites. The active metabolites of 5-FU directly or indirectly affect replication and transcription leading the cells to undergo apoptosis. Following entry into the cells, 5-FU gets converted into fluorouridine (FURD), fluorouridine monophosphtate (FUMP) and fluorodeoxyuridine (FdURD). FURD gets converted into FUMP then to fluorouridine diphosphate (FUDP), which either subsequently forms fluorouridine triphosphate (FUTP) or gets converted into fluorodeoxyuridine diphosphate (FdUDP) and gets converted into FdUTP. FdUDP and the FdUDR gets converted into fluorodeoxyuridine monophosphate (FdUMP). FdUMP is a thymidylate synthase (TS) inhibitor that inhibits the synthesis of deoxythymidine triphosphate (dTTP) from deoxyuridine monophosphate (dUMP); hence, dUMP, in turn, can get converted into deoxyuridine triphosphate (dUTP). The FUTP, FdUTP and dUTP so formed gets incorporated into newly synthesizing DNA and RNA, leading to DNA and RNA damage. (<b>B</b>) Determination of changes in 5-FU and its metabolites in parental and 5-FU resistant LIM1215 cells was performed by quantitative metabolomic analysis. Parental and resistant cells were cultured until 70% confluency was reached and were treated with 50 µm 5-FU for 72 h. A comparative analysis needed the development of a protocol to effectively and efficiently extract 5-FU and its metabolites from within the cells as well as from the conditioned media with strategy for normalization of the results. The protocol shown was used to culture and treat the cells with equal volume of media with equal concentration of 5-FU. Cells were trypsinized, counted and extraction of compounds was performed following which the samples were freeze dried (<b>C</b>). (<b>D</b>) MS was coupled with F5 phase ultra high performance liquid chromatography (UHPLC) and data analysis was done using bioinformatics tools.</p>
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<p>Relative quantitation of 5-FU and its metabolites.</p>
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<p>Isotope pattern analysis and fragmentation tree analysis using SIRIUS. To further validate the results, the experimental prediction was compared with theoretical prediction and database searches using Sirius software. The m/z for both precursor and the product ions were subjected to analysis using SIRIUS, which compared the data to the PubChem as well as all the molecular formula of similar masses to provide an identification score. Example shown here was using FdUMP standard and FURD, whose standard was not available. The chromatograms depicting precursors and fragment ions peaks for FdUMP standard and FURD from test sample (<b>A</b>). (<b>B</b>) Result obtained from SIRIUS software following the comparison of the MS1 and MS2 spectrum derived from Skyline with PubChem only for FdUMP and PubChem and all possible molecular formula for FURD (MS2). Both comparisons from MS1 and MS2 gave positive score predicting correct identification for all compounds and all the results were within the acceptable mass deviation of 5 ppm. (<b>C</b>) The tool also predicts the fragment ions that could have been generated from the predicted molecular formula and represented as a fragmentation tree that explains the molecular formula for the selected fragment ions. ‘<span style="color:#00B050">*</span>’ represents the ion fragments that were used for the identification of the compounds that were successfully predicted with SIRIUS 4.0.1 as the fragment ion generated from dissociation of the precursor compound.</p>
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<p>5-FU metabolism is dysregulated in resistant cells. To analyze the metabolism pathway of 5-FU in parental and resistant cells, LIM1215 cells were cultured in media with and without 50 µM 5-FU for 72 h. Media and cells were collected, and the metabolites were extracted. Trypan blue cell counting was used to determine the cell concentration and an equal number of cells were resuspended in methanol and mechanically lysed to extract the metabolites. Samples were subjected to tandem mass spectrometry coupled to F5 phased UHPLC. Raw data were processed and analyzed using bioinformatics tools, particularly, Skyline. Quantitation was based on the peak area obtained from Skyline and the comparison was normalized relative to the peak area of 0.780 ng/µL FdUMP standard. The graphs represent levels based on the peak area ratio of 5-FU and reduced levels of metabolites in resistant cells compared to parental cell lysates (<b>A</b>) and media (<b>B</b>), revealing that the 5-FU metabolism pathway is impaired in resistant cells. ns; not significant. Data is presented as mean ± s.e.m. determined by Student’s t-test (n = 10).</p>
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<p>Resistant cells are sensitive to active metabolite FdUMP.</p>
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16 pages, 3201 KiB  
Article
3-Iodothyronamine Affects Thermogenic Substrates’ Mobilization in Brown Adipocytes
by Manuela Gencarelli, Annunziatina Laurino, Elisa Landucci, Daniela Buonvicino, Costanza Mazzantini, Grazia Chiellini and Laura Raimondi
Biology 2020, 9(5), 95; https://doi.org/10.3390/biology9050095 - 4 May 2020
Cited by 5 | Viewed by 3400
Abstract
We investigated the effect of 3-iodothyronamine (T1AM) on thermogenic substrates in brown adipocytes (BAs). BAs isolated from the stromal fraction of rat brown adipose tissue were exposed to an adipogenic medium containing insulin in the absence (M) or in the presence of 20 [...] Read more.
We investigated the effect of 3-iodothyronamine (T1AM) on thermogenic substrates in brown adipocytes (BAs). BAs isolated from the stromal fraction of rat brown adipose tissue were exposed to an adipogenic medium containing insulin in the absence (M) or in the presence of 20 nM T1AM (M+T1AM) for 6 days. At the end of the treatment, the expression of p-PKA/PKA, p-AKT/AKT, p-AMPK/AMPK, p-CREB/CREB, p-P38/P38, type 1 and 3 beta adrenergic receptors (β1–β3AR), GLUT4, type 2 deiodinase (DIO2), and uncoupling protein 1 (UCP-1) were evaluated. The effects of cell conditioning with T1AM on fatty acid mobilization (basal and adrenergic-mediated), glucose uptake (basal and insulin-mediated), and ATP cell content were also analyzed in both cell populations. When compared to cells not exposed, M+T1AM cells showed increased p-PKA/PKA, p-AKT/AKT, p-CREB/CREB, p-P38/P38, and p-AMPK/AMPK, downregulation of DIO2 and β1AR, and upregulation of glycosylated β3AR, GLUT4, and adiponectin. At basal conditions, glycerol release was higher for M+T1AM cells than M cells, without any significant differences in basal glucose uptake. Notably, in M+T1AM cells, adrenergic agonists failed to activate PKA and lipolysis and to increase ATP level, but the glucose uptake in response to insulin exposure was more pronounced than in M cells. In conclusion, our results suggest that BAs conditioning with T1AM promote a catabolic condition promising to fight obesity and insulin resistance. Full article
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<p>The lipid content of cells with medium (M) and M+T1AM cells. M and M+T1AM cells were obtained as described in the Materials and Methods, and their lipid droplets were visualized (Panel a) and quantified (Panel c) by Oil-red O staining. A representative picture of a cell stained directly in culture wells taken with an inverted microscope is shown in Panel (<b>b</b>). The absorbance at 510 nm of the dye extracted from M and M+T1AM cells with isopropyl alcohol is reported in Panel (<b>c</b>). The results on the histogram represented the mean ± standard error of the mean (SEM) of the absorbance measured from three different cell preparations (* <span class="html-italic">p</span> &lt; 0.05, vs. M cells). (<b>a</b>) The image magnification is 40×; scale bar 100 µm.</p>
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<p>Differentiation marker levels in M and M+T1AM cells. M and M+T1AM cells, obtained as described in the Material and Methods, were analyzed for the expression levels of UCP-1, type 2 deiodinase (DIO2), GLUT4, adiponectin, and type 1 and type 3 beta adrenergic receptors β1- and β3-AR by Western-blot analysis or semi-quantitative PCR, as described in the “Materials and Methods”. In Panels (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>,<b>i,m</b>), representative experiments are shown. Each gel was loaded with the cDNA or with proteins obtained from two different cell preparations. (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>l</b>,<b>n</b>). Densitometric analysis is reported as the mean ± standard error of the mean (SEM; n = 4 cell preparations) of arbitrary units (AU; see the Methods; * <span class="html-italic">p</span>&lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001 vs. M cells).</p>
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<p>The basal and adrenergic mediated lipolysis in M and M+T1AM cells. The glycerol accumulated in M and M+T1AM cell medium in the absence (basal) or in the presence a not selective beta adrenergic agonist, 1 µM Isoproterenol (ISO), a selective β3AR agonist, a selective β3AR antagonist 0.1 µM BRL37344 (BRL), 0.1 µM SR 59230A (SR), or 0.1 µM BRL and 1 µM SR was measured fluorometrically as described in the Materials and Methods. Results on the histogram are presented as arbitrary units of fluorescence (AU)/10<sup>5</sup> cells and represented as the mean ± standard error mean (SEM) values from three different cell preparations with each point run in triplicate (*** <span class="html-italic">p</span> &lt; 0.001 vs. basal release of M cells; §§ <span class="html-italic">p</span> &lt; 0.01 vs. BRL treatment of M cells; ### <span class="html-italic">p</span> &lt; 0.001 vs. basal release of M+T1AM cells).</p>
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<p>M+T1AM cells show activation of PKA, CREB, and P38: the effect of adrenergic agonists on PKA activation. p-PKA/PKA was measured in M and M+T1AM cells not exposed (basal) or exposed for 15 min to 100 nM insulin (Ins) or 0.1 µM BRL37344 (BRL) as described in the Materials and Methods. A representative experiment is shown in Panel (<b>a</b>). Each gel was loaded with proteins prepared from two different cell preparations. Results on the histogram in Panel (<b>b</b>) are presented as arbitrary units (AU) and represent the mean ± standard error mean (SEM) values from two different cell preparations (*** <span class="html-italic">p</span>&lt;0.001 vs. basal M cells; * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 vs. basal M cells). The expression levels of p-CREB/CREB and p-P38 in M and M+T1AM cells were also examined. Representative experiments are shown in Panels (<b>c</b>,<b>e</b>). Results on the histograms in Panels (<b>d</b>,<b>f</b><span class="html-italic">)</span> report the mean ± standard error of the SEM (n = 4 cell preparations) of AU (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01 vs. M cells).</p>
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<p>The insulin-stimulated glucose uptake in M+T1AM and M cells: the effect at the AKT activation. The radiochemical determination of basal and insulin-stimulated glucose uptake in M and M+T1AM was carried out as described in the Materials and Methods. (<b>a</b>) Results on the histogram represent the mean ± standard error of the mean (SEM) of the radioactivity, expressed as DPM/10<sup>5</sup> cells, recovered in cells from three different preparations with each point run in triplicate (* <span class="html-italic">p</span> &lt; 0.05 and *** <span class="html-italic">p</span> &lt; 0.01 vs. basal release of M cells; §§§ <span class="html-italic">p</span> &lt; 0.001 vs. insulin effect in M cells). M and M+T1AM cells were also analyzed to determine the p-AKT/AKT at basal and after insulin exposure conditions, as described in the Materials and Methods. (<b>b</b>) A representative experiment is shown. The gel was loaded with proteins derived from two different cell preparations (<b>c</b>). The densitometric analysis of p-AKT/AKT in M and M+T1AM cells is reported as arbitrary units (AU, see the Methods). Results on the histogram represent the mean ± standard error of the mean (SEM; n = 4 cell preparations) of AU (see Methods; * <span class="html-italic">p</span> &lt; 0.05, <sup>**</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>***</sup> <span class="html-italic">p</span> &lt; 0.001 vs. basal M cells; <sup>§</sup> <span class="html-italic">p</span> &lt; 0.05 vs. basal M+T1AM cells).</p>
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<p>ATP and p-AMPK cell levels in M and M+T1AM cells. M and M+T1AM cells were analyzed for ATP content and p-AKT/AKT expression as described in the Materials and Methods. Panel (<b>a</b>) shows ATP cellular content in M and M+T1AM cells at basal conditions and after 3 h incubation with 0.1 µM BRL37344 (BRL; * <span class="html-italic">p</span> &lt; 0.05, §§ <span class="html-italic">p</span> &lt; 0.001 vs. basal). Western blot analysis of p-AKT/AKT in M and M+T1AM cells is reported as arbitrary units (AU); see the Methods. A representative experiment is shown The gel was loaded with proteins derived from two different cell preparations (<b>b</b>) The histogram in Panel (<b>c</b>) represents the mean ± standard error of the mean (SEM; n = 4 cell preparations) of AU (see the Methods; * <span class="html-italic">p</span> &lt; 0.05, <sup>**</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>§§</sup> <span class="html-italic">p</span> &lt; 0.001 vs. basal).</p>
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10 pages, 1545 KiB  
Article
Temperature Decreases Spread Parameters of the New Covid-19 Case Dynamics
by Jacques Demongeot, Yannis Flet-Berliac and Hervé Seligmann
Biology 2020, 9(5), 94; https://doi.org/10.3390/biology9050094 - 3 May 2020
Cited by 104 | Viewed by 11088
Abstract
(1) Background: The virulence of coronavirus diseases due to viruses like SARS-CoV or MERS-CoV decreases in humid and hot weather. The putative temperature dependence of infectivity by the new coronavirus SARS-CoV-2 or covid-19 has a high predictive medical interest. (2) Methods: External temperature [...] Read more.
(1) Background: The virulence of coronavirus diseases due to viruses like SARS-CoV or MERS-CoV decreases in humid and hot weather. The putative temperature dependence of infectivity by the new coronavirus SARS-CoV-2 or covid-19 has a high predictive medical interest. (2) Methods: External temperature and new covid-19 cases in 21 countries and in the French administrative regions were collected from public data. Associations between epidemiological parameters of the new case dynamics and temperature were examined using an ARIMA model. (3) Results: We show that, in the first stages of the epidemic, the velocity of contagion decreases with country- or region-wise temperature. (4) Conclusions: Results indicate that high temperatures diminish initial contagion rates, but seasonal temperature effects at later stages of the epidemy remain questionable. Confinement policies and other eviction rules should account for climatological heterogeneities, in order to adapt the public health decisions to possible geographic or seasonal gradients. Full article
(This article belongs to the Special Issue Theories and Models on COVID-19 Epidemics)
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<p>Left: Start of covid-19 epidemic in countries with various climates. Right: Daily number of new cases from 25 January until 14 March 2020 in France.</p>
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<p>(<b>a</b>) Virulence of covid-19 in liquids and secretions (from [<a href="#B27-biology-09-00094" class="html-bibr">27</a>]); (<b>b</b>) Linear regression of negative initial autocorrelation slope on mean weather temperature of six countries, France, UK, Spain, Italy, China and Chile (Pearson correlation coefficient R = 0.97, one-tailed <span class="html-italic">p</span> = 0.001). (<b>c</b>) Autocorrelation function A for three countries, France, Spain and Chile showing during February until 14 March 2020 a decrease in the positive correlation duration and the negative initial slope of the auto-correlation curve when the mean temperature of the country increases.</p>
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<p>Daily increase in confirmed COVID-19 cases for administrative regions of France on 6 March 2020 (filled symbols, dotted line, log regression model) and on 15 March 2020 (circles, interrupted line, exponential regression model).</p>
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<p>Slope of exponential model fitted to data in <a href="#biology-09-00094-t003" class="html-table">Table 3</a> as a function of mean annual temperature in that country. The Pearson correlation coefficient is R = −0.568, one-tailed <span class="html-italic">p</span> = 0.0036.</p>
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11 pages, 1846 KiB  
Communication
The Role of the Histone Methyltransferase EZH2 in Liver Inflammation and Fibrosis in STAM NASH Mice
by Seul Lee, Dong-Cheol Woo, Jeeheon Kang, Moonjin Ra, Ki Hyun Kim, Seoung Rak Lee, Dong Kyu Choi, Heejin Lee, Ki Bum Hong, Sang-Hyun Min, Yongjun Lee and Ji Hoon Yu
Biology 2020, 9(5), 93; https://doi.org/10.3390/biology9050093 - 2 May 2020
Cited by 22 | Viewed by 5312
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a leading form of chronic liver disease, with few biomarkers and treatment options currently available. Non-alcoholic steatohepatitis (NASH), a progressive disease of NAFLD, may lead to fibrosis, cirrhosis, and hepatocellular carcinoma. Epigenetic modification can contribute to the [...] Read more.
Non-alcoholic fatty liver disease (NAFLD) is a leading form of chronic liver disease, with few biomarkers and treatment options currently available. Non-alcoholic steatohepatitis (NASH), a progressive disease of NAFLD, may lead to fibrosis, cirrhosis, and hepatocellular carcinoma. Epigenetic modification can contribute to the progression of NAFLD causing non-alcoholic steatohepatitis (NASH), in which the exact role of epigenetics remains poorly understood. To identify potential therapeutics for NASH, we tested small-molecule inhibitors of the epigenetic target histone methyltransferase EZH2, Tazemetostat (EPZ-6438), and UNC1999 in STAM NASH mice. The results demonstrate that treatment with EZH2 inhibitors decreased serum TNF-alpha in NASH. In this study, we investigated that inhibition of EZH2 reduced mRNA expression of inflammatory cytokines and fibrosis markers in NASH mice. In conclusion, these results suggest that EZH2 may present a promising therapeutic target in the treatment of NASH. Full article
(This article belongs to the Section Cell Biology)
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<p>Treatment with Enhancer of Zeste Homolog 2 (EZH2) inhibitors reduces liver steatosis in STAM NASH mice. (<b>A</b>) NASH STAM mice experimental design. STAM-Vehicle (Control) were injected with streptozotocin (STZ) on day 2 to induce a diabetic state. Mice were fed a high-fat diet from four weeks. Mice were dosed with either vehicle, Obeticholic acid, UNC1999, or EPZ6438 once a day from 6–9 weeks for the study. (<b>B</b>) Representative hematoxylin and eosin (H&amp;E) stained (200x) liver sections from healthy (Normal), STAM-Vehicle (Control), STAM-Obeticholic acid (Obeticholic acid), STAM-UNC1999 (UNC1999), and STAM-EZH2 (EPZ6438) groups in the NASH mice. (<b>C</b>) Representative Oil Red O stained (200x) liver sections from healthy (Normal), STAM-Vehicle (Control), STAM-Obeticholic acid (Obeticholic acid), STAM-UNC1999 (UNC1999), and STAM-EZH2 (EPZ6438) groups in the NASH mice. Healthy (Normal), n = 5; Vehicle (Control), n = 5; STAM-Obeticholic acid (Obeticholic acid), n = 5; STAM-UNC1999 (UNC1999) n = 5; STAM-EZH2 (EPZ6438), n = 5.</p>
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<p>Treatment with EZH2 inhibitors has no effect on body weight in STAM NASH mice. (<b>A</b>) Effect of EZH2 inhibitors (UNC1999, EPZ-6438) and obeticholic acid on body weight. (<b>B</b>) Effect of EZH2 inhibitors on the liver, kidney, and white adipose tissue relative weight. Relative organ weight was measured as the weight of the organ divided by the body weight. Data are shown as mean ± standard deviation (n = 5). Abbreviations: WAT, white adipose tissue.</p>
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<p>Treatment with EZH2 inhibitors attenuates liver inflammation in STAM NASH mice. (<b>A</b>) Effect of EZH2 inhibitors (UNC1999, EPZ-6438) treatment on the serum levels of tumor necrosis factor-α (TNF-α). (<b>B</b>) Effects of EZH2 inhibitors and obeticholic acid treatment on the serum levels of alanine aminotransferase (ALT) and glucose. Statistically significant differences versus NASH group and EZH2 inhibitors treatment group are marked as * <span class="html-italic">p</span> &lt; 0.05, respectively.</p>
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<p>Treatment with EZH2 inhibitors inhibits ezh2 target Runx3 gene and liver inflammation-related IFN-γ gene in STAM NASH mice. Gene expression of EZH2 target and inflammatory marker from Real-Time PCR. Expression of genes in healthy (Normal), STAM-Vehicle (Control), STAM-UNC1999 (UNC1999), and STAM-EZH2 (EPZ6438) groups in the NASH mice. The final concentration of UNC1999 and EPZ6438 is 10 mg/kg. Statistically significant differences versus the STAM-Vehicle group and EZH2 inhibitors treatment group are marked as * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01, respectively.</p>
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<p>Treatment with EZH2 inhibitors inhibits liver inflammation and fibrosis-related genes in STAM NASH mice. Gene expression of inflammatory and fibrosis markers from Real-Time PCR. Expression of marker genes in healthy (Normal), STAM-Vehicle (Control), STAM-Obeticholic acid (Obeticholic acid), STAM-UNC1999 (UNC1999) and STAM-EZH2 (EPZ6438) groups in the NASH mice. The final concentration of Obeticholic acid, UNC1999 and EPZ6438 is 10 mg/kg. Statistically significant differences versus the STAM-Vehicle group and EZH2 inhibitors treatment group are marked as * <span class="html-italic">p</span> &lt; 0.05, respectively.</p>
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25 pages, 8194 KiB  
Article
Curcumin Sensitizes Kidney Cancer Cells to TRAIL-Induced Apoptosis via ROS Mediated Activation of JNK-CHOP Pathway and Upregulation of DR4
by Ismael Obaidi, Hilary Cassidy, Verónica Ibáñez Gaspar, Jasmin McCaul, Michael Higgins, Melinda Halász, Alison L. Reynolds, Breandan N. Kennedy and Tara McMorrow
Biology 2020, 9(5), 92; https://doi.org/10.3390/biology9050092 - 1 May 2020
Cited by 20 | Viewed by 6073
Abstract
Tumour necrosis factor-related apoptosis-inducing ligand (TRAIL), is a selective anticancer cytokine capable of exerting a targeted therapy approach. Disappointingly, recent research has highlighted the development of TRAIL resistance in cancer cells, thus minimising its usefulness in clinical settings. However, several recent studies have [...] Read more.
Tumour necrosis factor-related apoptosis-inducing ligand (TRAIL), is a selective anticancer cytokine capable of exerting a targeted therapy approach. Disappointingly, recent research has highlighted the development of TRAIL resistance in cancer cells, thus minimising its usefulness in clinical settings. However, several recent studies have demonstrated that cancer cells can be sensitised to TRAIL through the employment of a combinatorial approach, utilizing TRAIL in conjunction with other natural or synthetic anticancer agents. In the present study, the chemo-sensitising effect of curcumin on TRAIL-induced apoptosis in renal carcinoma cells (RCC) was investigated. The results indicate that exposure of kidney cancer ACHN cells to curcumin sensitised the cells to TRAIL, with the combination treatment of TRAIL and curcumin synergistically targeting the cancer cells without affecting the normal renal proximal tubular epithelial cells (RPTEC/TERT1) cells. Furthermore, this combination treatment was shown to induce caspase-dependent apoptosis, inhibition of the proteasome, induction of ROS, upregulation of death receptor 4 (DR4), alterations in mitogen-activated protein kinase (MAPK) signalling and induction of endoplasmic reticulum stress. An in vivo zebrafish embryo study demonstrated the effectiveness of the combinatorial regime to inhibit tumour formation without affecting zebrafish embryo viability or development. Overall, the results arising from this study demonstrate that curcumin has the ability to sensitise TRAIL-resistant ACHN cells to TRAIL-induced apoptosis. Full article
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<p>Chemical structure of curcumin [<a href="#B18-biology-09-00092" class="html-bibr">18</a>].</p>
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<p>Effects of curcumin on cellular viability of cancerous ACHN and normal epithelial RPTEC/TERT1 cells. Cell viability was assessed in both (<b>a</b>) cancerous ACHN cells cultured for 24 h before treatment and (<b>b</b>) RPTEC/TERT1 cells cultured and treated 10 days post-confluency. Both cell lines were treated with curcumin (0, 5, 15 or 25 µM) for 4 h before exposure to TRAIL (0, 10, 50 or 200 ng/mL) until 24 h. Cell viability was assessed using the FluoroFire-Blue ProViaTox Resazurin Fluorescent assay, with the results expressed as the mean resorufin fluorescence (% of control) ± SEM of three independent experiments. ** and *** indicate statistically significant difference at <span class="html-italic">p</span> &lt; 0.01 and <span class="html-italic">p</span> &lt; 0.001, respectively. Two-way ANOVA was used to analyse the data (<b>c</b>) combination index blot and data show the interactions between TRAIL and curcumin based on medium or 50% effect level. The line indicates an additive effect, whereas values below are synergistic and the above are antagonistic. The degree of synergy is determined based on the calculated effect by CompuSyn software; less than 0.5 determined to have a higher degree of synergy while low synergy can be observed above than 0.5. Accordingly, a very strong synergistic interaction can be noticed between 25 µM curcumin with 200 or 50 ng/mL TRAIL.</p>
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<p>Effects of curcumin and TRAIL on the cell morphology (<b>a</b>) ACHN cells were treated with (<b>i</b>) culture medium contains 0.05% DMSO, (<b>ii</b>) 50 ng/mL TRAIL, (<b>iii</b>) 25 µM curcumin or (<b>iv</b>) 50 ng/mL TRAIL plus 25 μM Curcumin. ACHN cellular morphology was assessed using phase contrast microscopy under 100× magnification (scale 100 µM). (<b>v</b>) Higher magnification (identified bleb-like protrusions or “apoptotic bodies” (scale 50 µM). (<b>b</b>) RPTEC/TERT1 cells were cultured on six-well plates for 10 days to allow differentiation before treatment with (<b>i</b>) culture medium contains 0.05% DMSO only or with (<b>ii</b>) 50 ng/mL TRAIL plus 25 μM curcumin. The effects on cellular morphology were assessed. The arrows indicate the presence of fluid-filled dooms which reflect the establishment of an intact and functioning transport system within the cells and acts as an indicator of the overall RPTEC/TERT1 health (scale 200 µM).</p>
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<p>Curcumin or curcumin+TRAIL induced apoptotic cell death. ACHN cells were cultured on six-well plates and exposed to 25 µM curcumin for 4 h. Following this, the cells were incubated with 0.05% DMSO in culture medium or 50 ng/mL TRAIL for 8 or 24 h (<b>a</b>). The treated cells were labelled with YO-PRO-1 (100 µM in DMSO) and propidium iodide (10 µg/mL) immediately prior to flow cytometry analysis. (<b>a</b>) Representative scatter plots show and compare the effects of the treatments at two different time points (8 and 24 h). (<b>b</b>) Results are presented as the mean of the percentage of early apoptosis, late apoptosis, and DNA fragmented cells induced by a specific treatment ± SEM of three independent experiments. *, ** and *** indicate statistically significant difference from respective control at <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01, and <span class="html-italic">p</span> &lt; 0.001, respectively. Two-way ANOVA was used to analyse the data.</p>
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<p>Curcumin/TRAIL combination treatment induced apoptosis via induction of caspases activation (<b>a</b>) ACHN cells were cultured in six-well plates for 24 h. The cells were then incubated with culture medium containing 0.5% DMSO or 25 µM curcumin for 4 h, followed by a further incubation vehicle or 50 ng/mL TRAIL for a further 2, 4, 6, 8 and 20 h. Fluorescence was kinetically detected by a scanning fluorescent microplate reader for a period of 120 min (120 cycles, one measurement per minute) at 37 °C at an emission and excitation of 400 and 505 nm, respectively. The results were normalized against protein content and presented as mean ± SEM of five independent experiments. One-way ANOVA was used to analyse the data (<b>b</b>) ACHN cells were cultured on six-well plates and treated with culture medium containing 0.5% DMSO, 50 ng/mL TRAIL, 25 µM curcumin or a co-treatment of 50 ng/mL TRAIL/25 μM curcumin. Whole cell protein was extracted at 8 and 24 h with RIPA buffer. Equal amounts of protein were separated by SDS-PAGE electrophoresis, transferred to nitrocellulose and indirectly probed for PARP using monoclonal antibodies. GAPDH was employed as a loading control. A representative blot is shown from three independent experiments. (<b>c</b>) ACHN cells were cultured on six-well plates either pre-treated with vehicle or 200 µM z-VAD-fmk for 1 h, then incubated with 25 µM curcumin for 4 h, followed by a further incubation with 50 ng/mL TRAIL for up to 24 h. Cell viability was assessed using FluoroFire-Blue ProViaTox Resazurin Fluorescent assay. *, ** and *** indicate statistically significant differences from control at <span class="html-italic">p</span> &lt; 0.05, 0.01, and 0.001, respectively. Independent <span class="html-italic">t</span>-test was employed to analyse the data.</p>
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<p>The effect of curcumin, TRAIL, and curcumin/TRAIL combination on the expression of pro-and anti-apoptotic target proteins in ACHN cells. Cells were cultured on six-well plates and treated with vehicle control, 50 ng/mL TRAIL, 25 µM curcumin or 50 ng/mL TRAIL/25 µM curcumin combination treatment. After exposure for 8- and 24-h whole cell lysates were prepared with RIPA buffer. Equal amounts of proteins were separated by SDS-PAGE electrophoresis, transferred to nitrocellulose and indirectly probed for (Bax, BCL-2, P53, FADD, cFLIP and CDK1primary antibodies. GAPDH was employed as a loading control. A representative blot is shown from three independent experiments.</p>
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<p>Effect of curcumin, TRAIL, and curcumin + TRAIL combination on proteasome activity in ACHN cells. ACHN cells were cultured on six-well plates incubated with culture medium containing 0.05% DMSO or 25 µM curcumin for 4 h. Following this, cells were either analysed or further incubated with culture medium or 50 ng/mL TRAIL for 4, 8, 12 and 24 h. following the incubation period, cells were broken in lysis buffer. Lysates were incubated for 1 h with 100 µM of SUC-LLVY-AMC fluorogenic substrates specific for 20S subunit of proteasome. The release of (7-amino-4-methyl-coumarin) AMC was fluorometrically measured using an excitation wavelength of 360 nm and an emission wavelength of 475 nm. The assay was run over a period of 60 min (60 cycles, one measurement per min) at 37 °C. To ensure that the observed activity was indeed proteasome derived, 10 µM MG-132 was added to another technical replicates of the vehicle- treated cell lysate, which then served as a positive control for the assay. The results were normalized for protein contents using BCA protein assay and presented as mean ± SEM of three independent experiments. *, ** and *** indicate statistically significant differences relative to the control at <span class="html-italic">p</span> &lt; 0.05, 0.01 and 0.001, respectively. One-way ANOVA was used to analyse the data.</p>
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<p>ROS induction by curcumin/TRAIL combination. (<b>a</b>) ACHN cells were cultured on six-well plates and treated with vehicle control or 25 μM curcumin for 4 h, followed by a further incubation with 50 ng/mL TRAIL for an additional 2, 4, 8 and 20 h. Therefore, the final exposure length was (<b>i</b>) 6, (<b>ii</b>) 8, (<b>iii</b>) 12 and (<b>iv</b>) 24 h. ROS was measured using Carboxy-H2DFFDA probe and analysed by flow cytometry. Negative control cells were incubated with 0.05% DMSO containing complete culture medium only. Arrows indicate a high ROS level following curcumin + TRAIL compared to the other treatments. (<b>b</b>) Flow cytometry data was presented as mean relative fluorescent intensity ± SEM of three independent experiments. Two-way ANOVA was used to analyse the data.</p>
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<p>Death receptor 4 upregulation by curcumin/TRAIL combination. (<b>a</b>) ACHN cells were cultured on six-well plates and treated with vehicle control or 25 μM curcumin for 4 h, followed by a further incubation with 50 ng/mL TRAIL for up to 24 h. Cells were incubated with (<b>i</b>) APC- or (<b>ii</b>) PE-conjugated monoclonal anti-human DR4 and DR5 antibodies, respectively. Independent t-test was used to analyse the data. (<b>b</b>) Following cell treatment, RNA was isolated and cDNA was synthesized. qRT-PCR was then performed using a TaqMan-based primer specific to DR4. Results were normalized to GAPDH. The figure is a representative of 3 independent experiments. One-way ANOVA was used to analyse the data.</p>
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<p>NAC pre-treatment abrogates CHOP and DR4 activation by curcumin or curcumin/TRAIL combination. ACHN cells were pre-treated with 0.5% DMSO-containing culture medium, 50 ng/mL TRAIL, 25 µM curcumin or a co-treatment of 50 ng/mL TRAIL + 25 μM curcumin. The cells were pre-treated with 10 mM N-acetyl cysteine (NAC) for 1 h to block ROS activity prior to exposure to the various treatments for 24 h. Whole cell protein was extracted at 24 h with RIPA buffer. Equal amounts of protein were separated by SDS-PAGE electrophoresis, transferred to nitrocellulose and indirectly probed for (<b>a</b>) DR4 and (<b>b</b>) CHOP using monoclonal antibodies and an ECL detection system. GAPDH was used as a loading control. *, ** and *** indicate statistically significant differences from respective controls at <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01 and <span class="html-italic">p</span> &lt; 0.01. One-way ANOVA was used to analyse the data.</p>
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<p>NAC pre-treatment abolished curcumin or curcumin/TRAIL combination-induced MAPK dysregulation ACHN cells were pre-treated with control medium (0.05% DMSO), 50 ng/mL TRAIL, 25 µM curcumin or 50 ng/mL TRAIL/25 μM curcumin combination treatment for further 24 h. Whole cell lysates were prepared in RIPA buffer. Equal amounts of protein were separated by SDS-PAGE electrophoresis, transferred to nitrocellulose and indirectly probed for p-JNK, JNK p-P38, P38, and p-ERK and ERK using monoclonal antibodies and ECL detection system. To assess the effect of pre-treatment with an antioxidant the ACHN cells were incubated with (<b>bi–iii</b>) or without (<b>ai–iii</b>) 10 mM NAC for 1 h prior to treatment of the cells as previously described. Whole cells lysates were assessed by Western blot indirectly probing for the aforementioned primary antibodies. One-way ANOVA was used to analyse the data.</p>
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<p>CHOP, but not DR4, was activated by JNK following curcumin/TRAIL combination treatment ACHN cells were either pre-treated with DMSO or 10 μM JNK inhibitor (SP600125) for 1 h. Cells were then treated with vehicle control, 50 ng/mL TRAIL, 25 µM curcumin or 50 ng TRAIL/25 μM curcumin. Whole cell protein was extracted at 24 h with RIPA buffer. Equal amounts of protein were separated by SDS-PAGE electrophoresis, transferred to nitrocellulose and indirectly probed for (<b>a</b>) P-JNK, (<b>b</b>) CHOP and (<b>c</b>) DR4 using a mAb and ECL detection system. GAPDH was used as a loading control. Representative blots are shown from 3 independent experiments. ** = <span class="html-italic">p</span> &lt; 0.01 and *** = <span class="html-italic">p</span> &lt; 0.001. One-way ANOVA was used to analyse the data.</p>
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<p>In vivo testing of the treatments using zebrafish embryos. Adult wild fish were set in breeding tanks. Next day, the eggs were collected, washed, inspected and placed in embryo Danieau’s medium. (<b>a</b>) 48 h post-fertilization (48 hpf), at least, 10 embryos were placed in each well of a 48-well plate containing 400 µL embryo medium. The treatments, which included 25 µM curcumin, 50 ng/mL TRAIL or 25 µM curcumin + 50 ng/mL TRAIL, were added to the embryonic medium for 72 h. Post-treatment, the fish were anesthetized using 0.002% tricain and inspected under a microscope for any abnormalities or death. Representative light microscopy images were taken using an Olympus SZX10 microscope. (<b>b</b>) At least 25 larvae were injected with the ACHN cells labelled with vibrant Dil (red) in their yolk sac using a SYS-PV830 microinjector system. At 72 h post-injection, the larvae were anesthetized with 0.002% tricain then placed on a glass slide and imaged using a fluorescent Olympus SZX10 microscope. BF: bright field channel, RFP1: red channel, Scale bar is 2 mm, n = 3. (<b>c</b>) Statistical analysis using independent t-test to compare between the fluorescent intensity of embryos injected with untreated and pre-treated cells. N = 3 and *** indicates a significance level at <span class="html-italic">p</span> &lt; 0.001.</p>
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13 pages, 1527 KiB  
Article
Epigenetic Changes in Host Ribosomal DNA Promoter Induced by an Asymptomatic Plant Virus Infection
by Miryam Pérez-Cañamás, Elizabeth Hevia and Carmen Hernández
Biology 2020, 9(5), 91; https://doi.org/10.3390/biology9050091 - 28 Apr 2020
Cited by 7 | Viewed by 4596
Abstract
DNA cytosine methylation is one of the main epigenetic mechanisms in higher eukaryotes and is considered to play a key role in transcriptional gene silencing. In plants, cytosine methylation can occur in all sequence contexts (CG, CHG, and CHH), and its levels are [...] Read more.
DNA cytosine methylation is one of the main epigenetic mechanisms in higher eukaryotes and is considered to play a key role in transcriptional gene silencing. In plants, cytosine methylation can occur in all sequence contexts (CG, CHG, and CHH), and its levels are controlled by multiple pathways, including de novo methylation, maintenance methylation, and demethylation. Modulation of DNA methylation represents a potentially robust mechanism to adjust gene expression following exposure to different stresses. However, the potential involvement of epigenetics in plant-virus interactions has been scarcely explored, especially with regard to RNA viruses. Here, we studied the impact of a symptomless viral infection on the epigenetic status of the host genome. We focused our attention on the interaction between Nicotiana benthamiana and Pelargonium line pattern virus (PLPV, family Tombusviridae), and analyzed cytosine methylation in the repetitive genomic element corresponding to ribosomal DNA (rDNA). Through a combination of bisulfite sequencing and RT-qPCR, we obtained data showing that PLPV infection gives rise to a reduction in methylation at CG sites of the rDNA promoter. Such a reduction correlated with an increase and decrease, respectively, in the expression levels of some key demethylases and of MET1, the DNA methyltransferase responsible for the maintenance of CG methylation. Hypomethylation of rDNA promoter was associated with a five-fold augmentation of rRNA precursor levels. The PLPV protein p37, reported as a suppressor of post-transcriptional gene silencing, did not lead to the same effects when expressed alone and, thus, it is unlikely to act as suppressor of transcriptional gene silencing. Collectively, the results suggest that PLPV infection as a whole is able to modulate host transcriptional activity through changes in the cytosine methylation pattern arising from misregulation of methyltransferases/demethylases balance. Full article
(This article belongs to the Special Issue Plant-Pathogen Interaction)
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<p>PLPV infection induces changes in the methylation pattern of rDNA promoter in <span class="html-italic">N. benthamiana</span>. (<b>A</b>) Diagram of the 45S rRNA transcriptional unit with the transcription start site indicated by an arrow. Stretch selected for analysis by bisulfite sequencing is detailed at the bottom, with the potential methylated cytosines labelled in pink (CG), purple (CHG), and orange (CHH). (<b>B</b>) Differential DNA methylation levels between mock-inoculated (green) and PLPV-infected plants (red) in the CG, CHG, and CHH sequence contexts. Error bars depict the standard deviations from two independent biological replicates and the statistical significance was tested using a paired <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05). (<b>C</b>) Position-specific methylation levels in the analyzed samples of rDNA.</p>
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<p>Expression levels of DNA methylation/demethylation genes during a PLPV infection. RT-qPCR analysis of mRNA levels of the selected genes in systemic leaves from mock-inoculated (green) and PLPV-infected plants (red) harvested at 34 d.p.i. Bars depict standard deviations from three independent biological replicates and the statistical significance was tested using a paired <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Relative accumulation of pre-rRNA. (<b>A</b>) Fragment of the rRNA unit. Blue arrows below depict the position of the primers used in the analysis. (<b>B</b>) RT-qPCR analysis of systemic leaves from mock-inoculated (green) and PLPV-infected (red) plants harvested at 34 d.p.i. Bars depict standard deviations from three independent biological replicates and the statistical significance was tested using a paired <span class="html-italic">t</span>-test (** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Analysis of the potential implication of the VSR p37 in rDNA promoter hypomethylation in <span class="html-italic">N. benthamiana</span> plants. (<b>A</b>) Differential DNA methylation levels between mock (green) and p37 (red) agroinfiltrated plants in the CG, CHG, and CHH sequence contexts. Error bars depict the standard deviations from two independent biological replicates. (<b>B</b>) RT-qPCR to estimate the relative expression levels of the genes involved in methylation/demethylation with imbalances in PLPV-infected plants. Data are representative of three biological replicates. The statistical significance was tested using a paired <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01).</p>
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10 pages, 896 KiB  
Article
The Water Content Drives the Susceptibility of the Lichen Evernia prunastri and the Moss Brachythecium sp. to High Ozone Concentrations
by Andrea Vannini, Giulia Canali, Mario Pica, Cristina Nali and Stefano Loppi
Biology 2020, 9(5), 90; https://doi.org/10.3390/biology9050090 - 27 Apr 2020
Cited by 9 | Viewed by 3014
Abstract
The aim of this study was to evaluate the tolerance of lichens (Evernia prunastri) and mosses (Brachythecium sp.) to short-term (1 h), acute (1 ppm) O3 fumigation under different hydration states (dry, <10% water content, metabolism almost inactive; wet, [...] Read more.
The aim of this study was to evaluate the tolerance of lichens (Evernia prunastri) and mosses (Brachythecium sp.) to short-term (1 h), acute (1 ppm) O3 fumigation under different hydration states (dry, <10% water content, metabolism almost inactive; wet, >200% water content, metabolism fully active). We hypothesized that stronger damage would occur following exposure under wet conditions. In addition, we checked for the effect of recovery (1 week) after the exposure. Ozone fumigation negatively affected the content of chlorophyll only in wet samples, but in the moss, such a difference was no longer evident after one week of recovery. Photosynthetic efficiency was always impaired by O3 exposure, irrespective of the dry or wet state, and also after one week of recovery, but the effect was much stronger in wet samples. The antioxidant power was increased in wet moss and in dry lichen, while a decrease was found for wet lichens after 1 week. Our results confirm that the tolerance to O3 of lichens and mosses may be determined by their low water content, which is the case during the peaks of O3 occurring during the Mediterranean summer. The role of antioxidant power as a mechanism of resistance to high O3 concentrations needs to be further investigated. Full article
(This article belongs to the Section Plant Science)
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<p>OJIP fluorescence transients of the lichen <span class="html-italic">Evernia prunastri</span> (up) and the moss <span class="html-italic">Brachythecium sp.</span> (down) after the O<sub>3</sub> fumigation (left) and the recovery time (right). Legend: (○) control samples (mean of dry and wet), (∆) dry fumigated samples, (◊) wet fumigated samples.</p>
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17 pages, 2154 KiB  
Article
Next-Generation Sequencing and MALDI Mass Spectrometry in the Study of Multiresistant Processed Meat Vancomycin-Resistant Enterococci (VRE)
by Carolina Sabença, Telma de Sousa, Soraia Oliveira, Didier Viala, Laetitia Théron, Christophe Chambon, Michel Hébraud, Racha Beyrouthy, Richard Bonnet, Manuela Caniça, Patrícia Poeta and Gilberto Igrejas
Biology 2020, 9(5), 89; https://doi.org/10.3390/biology9050089 - 27 Apr 2020
Cited by 16 | Viewed by 4414
Abstract
Vancomycin-resistant enterococci (VRE), due to their intrinsic resistance to various commonly used antibiotics and their malleable genome, make the treatment of infections caused by these bacteria less effective. The aims of this work were to characterize isolates of Enterococcus spp. that originated from [...] Read more.
Vancomycin-resistant enterococci (VRE), due to their intrinsic resistance to various commonly used antibiotics and their malleable genome, make the treatment of infections caused by these bacteria less effective. The aims of this work were to characterize isolates of Enterococcus spp. that originated from processed meat, through phenotypic and genotypic techniques, as well as to detect putative antibiotic resistance biomarkers. The 19 VRE identified had high resistance to teicoplanin (89%), tetracycline (94%), and erythromycin (84%) and a low resistance to kanamycin (11%), gentamicin (11%), and streptomycin (5%). Based on a Next-Generation Sequencing NGS technique, most isolates were vanA-positive. The most prevalent resistance genes detected were erm(B) and aac(6’)-Ii, conferring resistance to the classes of macrolides and aminoglycosides, respectively. MALDI-TOF mass spectrometry (MS) analysis detected an exclusive peak of the Enterococcus genus at m/z (mass-to-charge-ratio) 4428 ± 3, and a peak at m/z 6048 ± 1 allowed us to distinguish Enterococcus faecium from the other species. Several statistically significant protein masses associated with resistance were detected, such as peaks at m/z 6358.27 and m/z 13237.3 in ciprofloxacin resistance isolates. These results reinforce the relevance of the combined and complementary NGS and MALDI-TOF MS techniques for bacterial characterization. Full article
(This article belongs to the Section Microbiology)
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<p>Representative spectrum of <span class="html-italic">Enterococcus</span> spp. without antibiotic.</p>
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<p><span class="html-italic">Enterococcus faecium</span> spectrum showing the peak m/z 6048 ± 1 (<span class="html-italic">p</span>-value is 0.00001) characteristic of these strains.</p>
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<p>Peaks obtained for tetracycline. From the analysis of the ClinProTools software, the control spectrum without antibiotics (red) and the spectrum with the action of tetracycline (green) were obtained. The results show statistically significant peaks, with a <span class="html-italic">p</span>-value of 0.0000001, such as masses m/z 4424.01 and m/z 4526.45 noted by arrows.</p>
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<p>The red spectrum represents the control and the green spectrum was obtained by the action of teicoplanin. The arrow shows a peak at m/z 4652.66 with greater intensity in the presence of the antibiotic.</p>
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<p>The red spectrum represents the control, and the green spectrum was obtained by the action of ciprofloxacin. The arrow shows the peak at m/z 4652.66 with greater intensity in the presence of the antibiotic. Among the 27 specific peaks detected in the isolates resistant to ampicillin, only the peak with mass m/z 6338 was identified by the three classification algorithms, with an area under the curve (AUC) value of 0.89. This peak was also identified in erythromycin-resistant isolates. In contrast, three other peaks, m/z 2361.38, m/z 3304.92, and m/z 7240.29, proved to be exclusive to these isolates, which means they are potential biomarkers of ampicillin resistance.</p>
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<p>The red spectrum represents the control, and the green spectrum was obtained by the action of ampicillin. The arrow shows the peak at m/z 3304.92 that was only detected in the presence of the antibiotic.</p>
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<p>AUC value for the peak at m/z 4898.64. The value of the area under the ROC curve is 0.99, indicating a high-test pass for detecting the peak at m/z 4898.64.</p>
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19 pages, 2733 KiB  
Article
Footprints of a Singular 22-Nucleotide RNA Ring at the Origin of Life
by Jacques Demongeot and Alexandra Henrion-Caude
Biology 2020, 9(5), 88; https://doi.org/10.3390/biology9050088 - 25 Apr 2020
Cited by 5 | Viewed by 5340
Abstract
(1) Background: Previous experimental observations and theoretical hypotheses have been providing insight into a hypothetical world where an RNA hairpin or ring may have debuted as the primary informational and functional molecule. We propose a model revisiting the architecture of RNA-peptide interactions at [...] Read more.
(1) Background: Previous experimental observations and theoretical hypotheses have been providing insight into a hypothetical world where an RNA hairpin or ring may have debuted as the primary informational and functional molecule. We propose a model revisiting the architecture of RNA-peptide interactions at the origin of life through the evolutionary dynamics of RNA populations. (2) Methods: By performing a step-by-step computation of the smallest possible hairpin/ring RNA sequences compatible with building up a variety of peptides of the primitive network, we inferred the sequence of a singular docosameric RNA molecule, we call the ALPHA sequence. Then, we searched for any relics of the peptides made from ALPHA in sequences deposited in the different public databases. (3) Results: Sequence matching between ALPHA and sequences from organisms among the earliest forms of life on Earth were found at high statistical relevance. We hypothesize that the frequency of appearance of relics from ALPHA sequence in present genomes has a functional necessity. (4) Conclusions: Given the fitness of ALPHA as a supportive sequence of the framework of all existing theories, and the evolution of Archaea and giant viruses, it is anticipated that the unique properties of this singular archetypal ALPHA sequence should prove useful as a model matrix for future applications, ranging from synthetic biology to DNA computing. Full article
(This article belongs to the Special Issue Perspectives of Theoretical Medicine)
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<p>Computation of the Archetypal Loop AL. (<b>a</b>) Hamiltonian path in the graph having amino acids as vertices; (<b>b</b>) AL ring AUGGUACUGCCAUUCAAGAUG; (<b>c</b>) Optimal hairpin form of AL; (<b>d</b>) Urancestral tRNA-Gly [<a href="#B6-biology-09-00088" class="html-bibr">6</a>], where nucleotides common with AL are indicated in black. (<b>e</b>) GlytRNA<sup>GCC</sup> of <span class="html-italic">Œnothera coquimbensis</span> [<a href="#B53-biology-09-00088" class="html-bibr">53</a>], whose loops (D-, anti-codon, articulation and T<sub>Ψ</sub>-loops) fit quasi-perfectly AL; (<b>f</b>) pentamer frequencies in whole human genome [<a href="#B42-biology-09-00088" class="html-bibr">42</a>] and percentages of tRNAs containing TGGTA and TTCNA in their D- and T<sub>Ψ</sub>-loops, among tRNAs having NTGCCAN as an anticodon loop in different species of the tRNADB-CE database [<a href="#B51-biology-09-00088" class="html-bibr">51</a>].</p>
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<p>Giant viruses classification tree. The numbers at the periphery of the circular tree (from [<a href="#B58-biology-09-00088" class="html-bibr">58</a>]) indicate the ALPHA pentamer-proximity P<sub>22</sub> of the Giant viruses genomes to the ALPHA sequence.</p>
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<p>Archaea classification tree. The numbers at the periphery of the circular tree (from [<a href="#B55-biology-09-00088" class="html-bibr">55</a>,<a href="#B56-biology-09-00088" class="html-bibr">56</a>]) indicate the ALPHA pentamer-proximity P<sub>9</sub> of the Archaea genomes to the subset of nine pentamers from ALPHA: {ATTCA, TTCAA, TCAAG, CAAGA, AAGAT, AGATG, GATGA, ATGAA, TGAAT}.</p>
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<p>Bacteria phylogeny (from [<a href="#B60-biology-09-00088" class="html-bibr">60</a>]) based on the sequences of the 16S ribosomal RNA genes. The numbers at the periphery of the phylogeny indicate the ALPHA pentamer-proximity P<sub>22</sub> of the 5S ribosomal RNAs (in red).</p>
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<p>Lupine mitochondrial tRNA-Gly [<a href="#B61-biology-09-00088" class="html-bibr">61</a>].</p>
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<p>tRNA-Gly sequences from different living realms [<a href="#B42-biology-09-00088" class="html-bibr">42</a>,<a href="#B43-biology-09-00088" class="html-bibr">43</a>,<a href="#B44-biology-09-00088" class="html-bibr">44</a>,<a href="#B45-biology-09-00088" class="html-bibr">45</a>,<a href="#B46-biology-09-00088" class="html-bibr">46</a>,<a href="#B47-biology-09-00088" class="html-bibr">47</a>,<a href="#B48-biology-09-00088" class="html-bibr">48</a>,<a href="#B49-biology-09-00088" class="html-bibr">49</a>,<a href="#B50-biology-09-00088" class="html-bibr">50</a>,<a href="#B51-biology-09-00088" class="html-bibr">51</a>,<a href="#B52-biology-09-00088" class="html-bibr">52</a>].</p>
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<p>ALPHA ring as ribosome ancestor. (<b>a</b>) Primitive translation machinery centered on ALPHA ring with synthesis of Ala-Phe peptide on UGGUAA RNA guide [<a href="#B78-biology-09-00088" class="html-bibr">78</a>] or on its complement in ALPHA GCCAUU. (<b>b</b>) ALPHA can be divided into six sub-sequences corresponding to the codons classes candidates for the second step of the descending partition, which follows (<b>c</b>) the min-max principle: “mean mutation error M equals information I” [<a href="#B79-biology-09-00088" class="html-bibr">79</a>] and gives at step 4 (<b>d</b>) the “wobble” partition coding for the 11 early assigned amino acids plus a group of codons assigned to late amino acids [<a href="#B80-biology-09-00088" class="html-bibr">80</a>,<a href="#B81-biology-09-00088" class="html-bibr">81</a>,<a href="#B82-biology-09-00088" class="html-bibr">82</a>].</p>
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