Nothing Special   »   [go: up one dir, main page]

You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (4,493)

Search Parameters:
Keywords = transport stress

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
16 pages, 1614 KiB  
Article
Biogenic ZnO Nanoparticles Effectively Alleviate Cadmium-Induced Stress in Durum Wheat (Triticum durum Desf.) Plants
by Eleonora Coppa, Giulia Quagliata, Samuela Palombieri, Chiara Iavarone, Francesco Sestili, Daniele Del Buono and Stefania Astolfi
Environments 2024, 11(12), 285; https://doi.org/10.3390/environments11120285 - 12 Dec 2024
Abstract
This study investigated the potential of biogenic ZnO nanoparticles (ZnO-NPs) to alleviate cadmium (Cd) toxicity in durum wheat plants exposed for 14 days to 25 μM CdSO4. By applying ZnO-NPs at two different concentrations (25 and 50 mg L−1), we [...] Read more.
This study investigated the potential of biogenic ZnO nanoparticles (ZnO-NPs) to alleviate cadmium (Cd) toxicity in durum wheat plants exposed for 14 days to 25 μM CdSO4. By applying ZnO-NPs at two different concentrations (25 and 50 mg L−1), we observed increased chlorophyll content, beneficially impacting the photosynthetic efficiency, and enhanced sulfur, zinc, and iron accumulation. Moreover, the ZnO-NP treatment reduced the Cd accumulation in shoots, mitigating leaf chlorosis and oxidative damage. This response was clearly mediated by the increased thiol and phytochelatin production, as well as the enhanced sulfate uptake rate, with TdSultr1.3 as the most responsive gene coding for high-affinity transporter to Cd stress. In conclusion, the application of biogenic ZnO-NPs appears to be a promising approach for reducing the uptake of heavy metals by plants. In addition, it could be successfully used in combination with contamination prevention measures and/or remediation of contaminated sites to remove and mitigate the harmful effects of Cd on the environment and human health. Full article
Show Figures

Figure 1

Figure 1
<p>Effect of foliar application of ZnO-NPs at the concentration of 0, 25, and 50 ppm on shoot and root biomass (FW, fresh weight), shoot-to-root ratio (insert), and chlorophyll content, measured as SPAD units, of durum wheat plants grown in the absence (C) or presence (Cd) of 25 μM Cd. Data are reported as the mean of three biological replicates ± SD (n = 3). Different lower case letters indicate statistically significant differences among the growth conditions (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 2
<p>Oxidative damage expressed as changes in MDA accumulation in shoot (<b>A</b>) and root (<b>B</b>) tissues of durum wheat plants grown in the absence (C) or presence (Cd) of 25 μM Cd and treated foliarly with ZnO-NPs at the concentration of 0, 25, and 50 ppm. Statistics as in <a href="#environments-11-00285-f001" class="html-fig">Figure 1</a>.</p>
Full article ">Figure 3
<p>Effect of foliar application of ZnO-NPs at the concentration of 0, 25, and 50 ppm on non-protein thiols concentration in the shoot (<b>A</b>) and root (<b>B</b>) tissues of durum wheat plants grown in the absence (C) or presence (Cd) of 25 μM Cd. Statistics as in <a href="#environments-11-00285-f001" class="html-fig">Figure 1</a>.</p>
Full article ">Figure 4
<p>Changes in Cd, Zn, and Fe concentration (from left to right) in the shoot and root tissues of durum wheat plants grown in the absence (C) or presence (Cd) of 25 μM Cd and treated foliarly with ZnO-NPs at the concentration of 0, 25, and 50 ppm. The translocation rate, a measure of a plant’s ability to move Cd from roots to shoots, was calculated as the percentage ratio of shoot-to-root Cd concentration. Statistics as in <a href="#environments-11-00285-f001" class="html-fig">Figure 1</a>.</p>
Full article ">Figure 5
<p>Effect of foliar application of ZnO-NPs at the concentration of 0, 25 and 50 ppm on total S concentration (<b>on the left</b>) in shoot (<b>A</b>) and root (<b>B</b>) tissues and on the relative expression levels by qRT-PCR of the genes encoding high-affinity sulfate transporters (<span class="html-italic">TdSultr1.1</span> and <span class="html-italic">TdSultr1.3</span>) (<b>on the right</b>) in roots of durum wheat plants grown in the absence (C) or presence (Cd) of 25 μM Cd. Statistics as in <a href="#environments-11-00285-f001" class="html-fig">Figure 1</a>.</p>
Full article ">Figure 6
<p>Correlation networks of durum wheat (<b>A</b>) shoots and (<b>B</b>) roots. The blue and red lines represent the positive and negative correlations, respectively. Abbreviations are as follows: Cd, cadmium concentration; Fe, iron concentration; Zn, zinc concentration; S, total S concentration; Thiols, non-protein thiols concentration; MDA, malondialdehyde concentration; TR, Cd translocation rate; and ZnO-NPs, the concentration of biogenic ZnO-NPs applied as foliar spray.</p>
Full article ">
21 pages, 4296 KiB  
Article
Comparative Analysis of the Effects of Maternal Hypoxia and Placental Ischemia on HIF1-Dependent Metabolism and the Glucocorticoid System in the Embryonic and Newborn Rat Brain
by Oleg Vetrovoy, Sofiya Potapova, Viktor Stratilov and Ekaterina Tyulkova
Int. J. Mol. Sci. 2024, 25(24), 13342; https://doi.org/10.3390/ijms252413342 - 12 Dec 2024
Abstract
Prenatal hypoxia, often accompanied by maternal glucocorticoid stress, can predispose offspring to neurological disorders in adulthood. If placental ischemia (PI) primarily reduces fetal oxygen supply, the maternal hypoxia (MH) model also elicits a pronounced fetal glucocorticoid exposure. Here, we compared MH and PI [...] Read more.
Prenatal hypoxia, often accompanied by maternal glucocorticoid stress, can predispose offspring to neurological disorders in adulthood. If placental ischemia (PI) primarily reduces fetal oxygen supply, the maternal hypoxia (MH) model also elicits a pronounced fetal glucocorticoid exposure. Here, we compared MH and PI in rats to distinguish their unique and overlapping effects on embryonic and newborn brain development. We analyzed glucocorticoid transport into the developing brain, glucocorticoid receptor (GR) expression, and GR-dependent transcription, along with key enzymes regulating glucocorticoid metabolism in maternal (MP) and fetal placentas (FP) and in the brain. Additionally, we examined hypoxia-inducible factor 1-alpha (HIF1α) and its downstream genes, as well as glycolysis and the pentose phosphate pathway, both associated with the transport of substrates essential for glucocorticoid synthesis and degradation. Both MH and PI induced HIF1-dependent metabolic alterations, enhancing glycolysis and transiently disrupting redox homeostasis. However, only MH caused a maternal glucocorticoid surge that altered early fetal brain glucocorticoid responsiveness. Over time, these differences may lead to distinct long-term outcomes in neuronal structure and function. This work clarifies the individual contributions of hypoxic and glucocorticoid stresses to fetal brain development, suggesting that combining the MH and PI models could provide valuable insights for future investigations into the mechanisms underlying developmental brain pathologies, including non-heritable psychoneurological and neurodegenerative disorders. Full article
Show Figures

Figure 1

Figure 1
<p>The effect of MH and PI on HIF1α protein expression levels in the MP (<b>a</b>) and FP (<b>b</b>) at e15 and e20, detected by Western blotting. (<b>a</b>) HIF1α protein levels: e20: ** <span class="html-italic">p</span> &lt; 0.01 vs. control (Student’s test).</p>
Full article ">Figure 2
<p>The effect of MH and PI on mRNA levels of the glucocorticoid receptor (<span class="html-italic">nr3c1</span>) in the MP (<b>a</b>) and FP (<b>b</b>) at e15 and e20, detected by RT PCR. The effect of MH and PI on GR protein expression levels in the MP (<b>c</b>) and FP (<b>d</b>) at e15 and e20, detected by Western blotting. The effect of MH and PI on mRNA levels of the glucocorticoid-dependent genes <span class="html-italic">ztb16</span>, <span class="html-italic">dusp1</span>, and <span class="html-italic">fkbp5</span> in the MP (<b>e</b>) and FP (<b>f</b>) at e15 and e20, detected by RT PCR. (<b>e</b>) mRNA levels of <span class="html-italic">dusp1</span>: e15: * <span class="html-italic">p</span> &lt; 0.05 vs. control (Student’s test). mRNA levels of <span class="html-italic">fkbp5</span>: e15: ** <span class="html-italic">p</span> &lt; 0.01 vs. control (Kruskal-Wallis test, Dunn’s test). e20 ** <span class="html-italic">p</span> &lt; 0.01 vs. control (one-way ANOVA, Tukey’s test), &amp;&amp; <span class="html-italic">p</span> &lt; 0.01 between MH and PI (one-way ANOVA, Tukey’s test).</p>
Full article ">Figure 3
<p>The effect of MH and PI on mRNA levels of the <span class="html-italic">hsd11b1</span> and <span class="html-italic">hsd11b2</span> in the MP (<b>a</b>) and FP (<b>b</b>) at e15 and e20, detected by RT PCR. The effect of MH and PI on the protein expression levels of HSD11B1 and HSD11B2 in the MP (<b>c</b>) and HSD11B2 in the FP (<b>d</b>) at e15 and e20, detected by Western blotting. (<b>b</b>) mRNA levels of <span class="html-italic">hsd11b2</span>: e15: ** <span class="html-italic">p</span> &lt; 0.01 vs. control (Welch ANOVA, Dunnett’s test). (<b>c</b>) HSD11B2 protein levels: e20: ** <span class="html-italic">p</span> &lt; 0.01 vs. control (Student’s test).</p>
Full article ">Figure 4
<p>The effect of MH and PI on the corticosterone levels (<b>a</b>) in the brain at e15, e16, e17, e20 and p1, detected by ELISA. The effect of MH and PI on mRNA levels of the glucocorticoid receptor (<span class="html-italic">nr3c1</span>) (<b>b</b>) in the brain at e15, e16, e17, e20 and p1, detected by RT PCR. The effect of MH and PI on GR protein expression levels (<b>c</b>) in the brain at e15, e16, e17, e20 and p1, detected by Western blotting. The effect of MH and PI on mRNA levels of the glucocorticoid-dependent genes <span class="html-italic">ztb16</span>, <span class="html-italic">dusp1</span>, and <span class="html-italic">fkbp5</span> (<b>d</b>) in the brain at e15, e16, e17, e20 and p1, detected by RT PCR. (<b>a</b>) Corticosterone levels: e15, * <span class="html-italic">p</span> &lt; 0.05 vs. control (one-way ANOVA, Tukey’s test); &amp; <span class="html-italic">p</span> &lt; 0.05 between MH and PI (one-way ANOVA, Tukey’s test). e16, * <span class="html-italic">p</span> &lt; 0.05 vs. control (one-way ANOVA, Tukey’s test); ** <span class="html-italic">p</span> &lt; 0.01 vs. control (one-way ANOVA, Tukey’s test). (<b>d</b>) mRNA levels of <span class="html-italic">zbtb16</span>: e20, * <span class="html-italic">p</span> &lt; 0.05 vs. control (Kruskal-Wallis, Dunn’s test); ** <span class="html-italic">p</span> &lt; 0.01 vs. control (Kruskal-Wallis, Dunn’s test). mRNA levels of <span class="html-italic">dusp1</span>: e20, * <span class="html-italic">p</span> &lt; 0.05 vs. control (Kruskal-Wallis, Dunn’s test); ** <span class="html-italic">p</span> &lt; 0.01 vs. control (Kruskal-Wallis, Dunn’s test). mRNA levels of <span class="html-italic">fkbp5</span>: e16, * <span class="html-italic">p</span> &lt; 0.05 vs. control (Kruskal-Wallis, Dunn’s test); p1, * <span class="html-italic">p</span> &lt; 0.05 vs. control (Kruskal-Wallis, Dunn’s test); &amp;&amp; <span class="html-italic">p</span> &lt; 0.01 between MH and PI (Kruskal-Wallis, Dunn’s test).</p>
Full article ">Figure 5
<p>The effect of MH and PI on mRNA levels of <span class="html-italic">hsd11b1</span> and <span class="html-italic">hsd11b2</span> (<b>a</b>) in the brain at e15, e16, e17, e20 and p1, detected by RT PCR. The effect of MH and PI on the protein expression levels of the HSD11B2 (<b>b</b>) in the brain at e15, e16, e17, e20 and p1, detected by Western blotting. (<b>a</b>) mRNA levels of <span class="html-italic">hsd11b1</span>: e17, * <span class="html-italic">p</span> &lt; 0.05 vs. control (Kruskal-Wallis, Dunn’s test); p1, *** <span class="html-italic">p</span> &lt; 0.001 vs. control (Kruskal-Wallis, Dunn’s test). mRNA levels of <span class="html-italic">hsd11b2</span>: e16, ** <span class="html-italic">p</span> &lt; 0.01 vs. control (one-way ANOVA, Tukey’s test); e20, * <span class="html-italic">p</span> &lt; 0.05 vs. control (Kruskal-Wallis, Dunn’s test); &amp; <span class="html-italic">p</span> &lt; 0.05 between MH and PI (Kruskal-Wallis, Dunn’s test); p1, * <span class="html-italic">p</span> &lt; 0.05 vs. control (Welch ANOVA, Dunnett’s test); &amp; <span class="html-italic">p</span> &lt; 0.05 between MH and PI (Welch ANOVA, Dunnett’s test). (<b>b</b>) HSD11B2 protein levels: e17, * <span class="html-italic">p</span> &lt; 0.05 vs. control (Student’s test), ** <span class="html-italic">p</span> &lt; 0.01 vs. corresponding control (Student’s test); e20, ** <span class="html-italic">p</span> &lt; 0.01 vs. control (Mann-Whitney’s test).</p>
Full article ">Figure 6
<p>The effect of MH and PI on mRNA levels of <span class="html-italic">hif1α</span> (<b>a</b>) in the brain at e15, e16, e17, e20 and p1, detected by RT PCR. The effect of MH and PI on HIF1α protein expression levels (<b>b</b>) in the brain at e15, e16, e17, e20 and p1, detected by Western blotting. The effect of MH and PI on mRNA levels of the HIF1-dependent genes <span class="html-italic">glut1</span>, <span class="html-italic">hk1</span>, <span class="html-italic">pfkb3</span>, <span class="html-italic">ldha</span>, <span class="html-italic">pdk1</span>, and <span class="html-italic">mct4</span> (<b>c</b>) in the brain at e15, e16, e17, e20 and p1, detected by RT PCR. (<b>b</b>) HIF1α protein levels: e15: ** <span class="html-italic">p</span> &lt; 0.01 vs. control (Student’s test). (<b>c</b>) mRNA levels of <span class="html-italic">glut1</span>: e16, ** <span class="html-italic">p</span> &lt; 0.01 vs. control (one-way ANOVA, Tukey’s test), *** <span class="html-italic">p</span> &lt; 0.001 vs. control (one-way ANOVA, Tukey’s test); e20, * <span class="html-italic">p</span> &lt; 0.05 vs. control (Welch ANOVA, Dunnett’s test); p1, * <span class="html-italic">p</span> &lt; 0.05 PI vs. control (Welch ANOVA, Dunnett’s test). mRNA levels of <span class="html-italic">hk1</span>: e15, ** <span class="html-italic">p</span> &lt; 0.01 vs. control (one-way ANOVA, Tukey’s test); &amp;&amp;&amp; <span class="html-italic">p</span> &lt; 0.001 between MH and PI (one-way ANOVA, Tukey’s test); e16, * <span class="html-italic">p</span> &lt; 0.05 vs. control (Welch ANOVA, Dunnett’s test). mRNA levels of <span class="html-italic">pfkb3</span>: e16, * <span class="html-italic">p</span> &lt; 0.05 vs. control (Kruskal-Wallis, Dunn’s test), ** <span class="html-italic">p</span> &lt; 0.01 vs. control (Kruskal-Wallis, Dunn’s test); e20, ** <span class="html-italic">p</span> &lt; 0.01 vs. control (Kruskal-Wallis, Dunn’s test); p1, * <span class="html-italic">p</span> &lt; 0.05 vs. control (Kruskal-Wallis, Dunn’s test). mRNA levels of <span class="html-italic">ldha</span>: e20, * <span class="html-italic">p</span> &lt; 0.05 vs. control (one-way ANOVA, Tukey’s test); p1, * <span class="html-italic">p</span> &lt; 0.05 vs. control (Welch ANOVA, Dunnett’s test). mRNA levels of <span class="html-italic">pdk1</span>: p1, * <span class="html-italic">p</span> &lt; 0.05 vs. control (one-way ANOVA, Tukey’s test); &amp; <span class="html-italic">p</span> &lt; 0.05 between MH and PI (one-way ANOVA, Tukey’s test). mRNA levels of <span class="html-italic">mct4</span>: e15, * <span class="html-italic">p</span> &lt; 0.05 vs. control (one-way ANOVA, Tukey’s test); &amp; <span class="html-italic">p</span> &lt; 0.05 between MH and PI (one-way ANOVA, Tukey’s test).</p>
Full article ">Figure 7
<p>The effect of MH and PI on LDHA protein expression levels (<b>a</b>) in the brain at e15, e16, e17, e20 and p1, detected by Western blotting. The effect of MH and PI on LDH activity (<b>b</b>), lactate (<b>c</b>) and pyruvate (<b>d</b>) levels in the brain at e15, e16, e17, e20 and p1, detected by colorimetric tests. (<b>a</b>) LDHA protein levels: e15, ** <span class="html-italic">p</span> &lt; 0.01 MH vs. control (Student’s test), ** <span class="html-italic">p</span> &lt; 0.01 PI vs. control (Student’s test). (<b>b</b>) LDH activity: e15, ** <span class="html-italic">p</span> &lt; 0.01 vs. control (one-way ANOVA, Tukey’s test), *** <span class="html-italic">p</span> &lt; 0.001 vs. control (one-way ANOVA, Tukey’s test). (<b>c</b>) Lactate levels: e15, * <span class="html-italic">p</span> &lt; 0.05 vs. control (one-way ANOVA, Tukey’s test), ** <span class="html-italic">p</span> &lt; 0.01 vs. control (one-way ANOVA, Tukey’s test).</p>
Full article ">Figure 8
<p>The effect of MH and PI on mRNA levels of <span class="html-italic">g6pd</span> (<b>a</b>) in the brain at e15, e16, e17, e20 and p1, detected by RT PCR. The effect of MH and PI on G6PD activity (<b>b</b>), NADPH (<b>c</b>), GSHred (<b>d</b>) and MDA (<b>e</b>) levels in the brain at e15, e16, e17, e20 and p1, detected by colorimetric tests. (<b>a</b>) mRNA levels of <span class="html-italic">g6pd</span>: e15, * <span class="html-italic">p</span> &lt; 0.05 vs. control (Welch ANOVA, Dunnett’s test); e16, ** <span class="html-italic">p</span> &lt; 0.01 vs. control (one-way ANOVA, Tukey’s test); p1, * <span class="html-italic">p</span> &lt; 0.05 vs. control (Kruskal-Wallis, Dunn’s test); &amp; <span class="html-italic">p</span> &lt; 0.05 between MH and PI (Kruskal-Wallis, Dunn’s test). (<b>b</b>) G6PD activity: e15, * <span class="html-italic">p</span> &lt; 0.05 vs. control (one-way ANOVA, Tukey’s test); e16, * <span class="html-italic">p</span> &lt; 0.05 vs. control (Welch ANOVA, Dunnett’s test); e17, * <span class="html-italic">p</span> &lt; 0.05 vs. control (one-way ANOVA, Tukey’s test), *** <span class="html-italic">p</span> &lt; 0.001 vs. control (one-way ANOVA, Tukey’s test). (<b>c</b>) NADPH levels: e15, ** <span class="html-italic">p</span> &lt; 0.01 vs. control (one-way ANOVA, Tukey’s test). (<b>d</b>) GSHred levels: e15: * <span class="html-italic">p</span> &lt; 0.05 vs. control (one-way ANOVA, Tukey’s test); &amp; <span class="html-italic">p</span> &lt; 0.05 between MH and PI (one-way ANOVA, Tukey’s test). e17: * <span class="html-italic">p</span> &lt; 0.05 vs. control (one-way ANOVA, Tukey’s test).</p>
Full article ">Figure 9
<p>Schematic outline of the experimental study design. MH, maternal hypoxia; PI, placental ischemia; e0–e20, embryonic days; p0, day of birth; p1, first postnatal day.</p>
Full article ">
20 pages, 2484 KiB  
Article
Bacterial Tolerance to 1-Butanol and 2-Butanol: Quantitative Assessment and Transcriptomic Response
by Alexander Arsov, Penka Petrova, Maria Gerginova, Lidia Tsigoriyna, Nadya Armenova, Ina Ignatova and Kaloyan Petrov
Int. J. Mol. Sci. 2024, 25(24), 13336; https://doi.org/10.3390/ijms252413336 - 12 Dec 2024
Abstract
The unique fuel characteristics of butanol and the possibility of its microbial production make it one of the most desirable environmentally friendly substitutes for petroleum fuels. However, the highly toxic nature of 1-butanol to the bacterial strains makes it unprofitable for commercial production. [...] Read more.
The unique fuel characteristics of butanol and the possibility of its microbial production make it one of the most desirable environmentally friendly substitutes for petroleum fuels. However, the highly toxic nature of 1-butanol to the bacterial strains makes it unprofitable for commercial production. By comparison, 2-butanol has similar fuel qualities, and despite the difficulties in its microbial synthesis, it holds promise because it may be less toxic. This paper is the first comprehensive study to compare bacterial tolerance to different butanol isomers by examining the growth of 31 bacterial strains under 1-butanol and 2-butanol stress conditions. The presented results reveal that all tested strains showed a higher tolerance to 2-butanol than to 1-butanol at each solvent concentration (1%, 2%, and 3% v/v). Moreover, with an increased solvent concentration, bacterial cells lost their resistance to 1-butanol more rapidly than to 2-butanol. A comparison of the transcriptome profiles of the reference strains Bacillus subtilis ATCC 168 and E. coli ATCC 25922 disclosed a specific response to butanol stress. Most notably, in the presence of 2-butanol E. coli ATCC 25922 showed a reduced expression of genes for chaperones, efflux pumps, and the flagellar apparatus, as well as an enhancement of membrane and electron transport. B. subtilis, with 2-butanol, did not perform emergency sporulation or escape, as some global transcriptional stress response regulators were downregulated. The overexpression of ribosomal RNAs, pyrimidine biosynthesis genes, and DNA- and RNA-binding proteins such as pcrA and tnpB was crucial in the response. Full article
(This article belongs to the Special Issue Microbial Omics)
Show Figures

Figure 1

Figure 1
<p>Relative growth rates of Gram-positive and Gram-negative genera in media with different concentrations of 1-butanol and 2-butanol. (<b>a</b>) 1% <span class="html-italic">v</span>/<span class="html-italic">v</span>; (<b>b</b>) 2% <span class="html-italic">v</span>/<span class="html-italic">v</span>; (<b>c</b>) 3% <span class="html-italic">v</span>/<span class="html-italic">v</span>.</p>
Full article ">Figure 2
<p>Volcano plots showing significant differentially expressed genes in the bacterial cells grown on 2-butanol vs. 1-butanol. Log2 fold change (FC) and <span class="html-italic">p</span>-value are presented. (<b>a</b>) <span class="html-italic">E. coli</span> ATCC 25922; (<b>b</b>) <span class="html-italic">B. subtilis</span> ATCC 168. Designations: yellow—FC ≥ 2; blue—FC ≤ (−2); raw <span class="html-italic">p</span>-value &lt; 0.05.</p>
Full article ">Figure 3
<p>Differentially expressed genes (DEGs) in <span class="html-italic">E. coli</span> ATCC 25922, subjected to butanol stress (2% <span class="html-italic">v</span>/<span class="html-italic">v</span>). Fold change, as log2 FC, is estimated as the expression levels on 2-butanol vs. on 1-butanol. (<b>a</b>) Upregulated: <span class="html-italic">tdcC</span>—threonine/serine transporter; <span class="html-italic">nikB</span>, <span class="html-italic">nikC</span>, <span class="html-italic">nikA</span>—nickel ABC transporter permease subunits; <span class="html-italic">sgcB</span>—PTS sugar transporter IIB SgcB; <span class="html-italic">glpT</span>—glycerol-3-phosphate transporter; <span class="html-italic">treB</span>—PTS trehalose transporter IIBC; <span class="html-italic">nikD</span>—nickel import ATP-binding protein; <span class="html-italic">gatB</span>—PTS galactitol transporter IIB; <span class="html-italic">dctA</span>, <span class="html-italic">dcuA</span>, <span class="html-italic">dcuB</span>—C4-dicarboxylate transporters; <span class="html-italic">nikE</span>—nickel import ATP-binding protein; <span class="html-italic">alsB</span>—D-allose transporter; <span class="html-italic">focA</span>—formate transporter; <span class="html-italic">nirC</span>—nitrite transporter; <span class="html-italic">mdtH</span>—multidrug efflux MFS transporter MdtH; <span class="html-italic">yqcE</span>—MFS transporter; <span class="html-italic">exuT</span>—hexuronate transporter; <span class="html-italic">yicL</span>—carboxylate/amino acid/amine transporter. (<b>b</b>) Downregulated: <span class="html-italic">mdtA</span>, <span class="html-italic">mdtB</span>, <span class="html-italic">mdtC</span>, <span class="html-italic">mdtD</span>, <span class="html-italic">emrY</span>—multidrug efflux RND transporter permease subunits; <span class="html-italic">phnC</span>, <span class="html-italic">phnD</span>, <span class="html-italic">phone</span>—phosphonate ABC transporter proteins; <span class="html-italic">artJ</span>—arginine ABC transporter substrate-binding protein; <span class="html-italic">pstA</span>—phosphate permease; <span class="html-italic">pstB</span>, <span class="html-italic">pstC</span>, <span class="html-italic">pstS</span>—phosphate ABC transporter proteins; <span class="html-italic">ptsG</span>—PTS glucose transporter IIBC; <span class="html-italic">emrK</span>—multidrug efflux MFS transporter periplasmic adaptor; <span class="html-italic">ybbA</span>—ABC transporter ATP-binding protein—<span class="html-italic">Sbp</span>, sulfate/thiosulfate transporter; <span class="html-italic">rbsC</span>—ribose ABC transporter permease.</p>
Full article ">Figure 4
<p>Differentially expressed genes encoding porins in <span class="html-italic">E. coli</span> ATCC 25922; log2 FC of 2-butanol versus 1-butanol is presented. Genes and proteins: <span class="html-italic">ompW</span>—outer membrane protein OmpW; <span class="html-italic">ompN</span>—porin OmpN; <span class="html-italic">phoE</span>—phosphoporin PhoE; <span class="html-italic">ompF</span>—porin OmpF; <span class="html-italic">ompR</span>—two-component system response regulator OmpR; <span class="html-italic">lamb</span>—maltoporin LamB; <span class="html-italic">uidC</span>—glucuronide uptake porin UidC; <span class="html-italic">ompX</span>—outer membrane protein OmpX; <span class="html-italic">chiP</span>—chitoporin; <span class="html-italic">aqpZ</span>—aquaporin Z; <span class="html-italic">ompC</span>—porin OmpC; <span class="html-italic">ompA</span>—porin OmpA; <span class="html-italic">ompD</span>—porin OmpD.</p>
Full article ">Figure 5
<p>Differentially expressed genes for membrane transporters in <span class="html-italic">B. subtilis</span> ATCC 168; log2 FC applies to 2-butanol versus 1-butanol (1% <span class="html-italic">v</span>/<span class="html-italic">v</span>).</p>
Full article ">Figure 6
<p>DEGs of <span class="html-italic">tdcABCDE</span> operon of <span class="html-italic">E. coli</span> ATCC 25922; log2 FC applies to 2-butanol versus 1-butanol (2% <span class="html-italic">v</span>/<span class="html-italic">v</span>).</p>
Full article ">Figure 7
<p>Colibactin biosynthesis gene cluster in <span class="html-italic">E. coli</span> ATCC 25922. Fold change of differentially expressed genes with 2-butanol versus 1-butanol (2% <span class="html-italic">v</span>/<span class="html-italic">v</span>). The genes encode the following proteins: <span class="html-italic">clbR</span>—LuxR family transcriptional regulator; <span class="html-italic">clbC</span>, <span class="html-italic">clbI</span>—polyketide synthases; <span class="html-italic">clbJ</span>, <span class="html-italic">clbH</span>—peptide synthetases; <span class="html-italic">clbD</span>, <span class="html-italic">clbF</span>—dehydrogenases; <span class="html-italic">clbA</span>—transferase; <span class="html-italic">clbS</span>—self-protection protein; <span class="html-italic">clbG</span>—acyltransferase; <span class="html-italic">clbE</span>—amino malonyl-acyl carrier.</p>
Full article ">Figure 8
<p>Regulated genes for tRNA ligases in <span class="html-italic">B. subtilis</span> ATCC 168.</p>
Full article ">Figure 9
<p>A comparative scheme of the effects of 1-butanol and 2-butanol on the microbial cell, as revealed by transcriptomic results.</p>
Full article ">
13 pages, 1988 KiB  
Article
Effects of Exogenous Tryptophan in Alleviating Transport Stress in Pearl Gentian Grouper (Epinephelus fuscoguttatus ♀ × E. lanceolatus ♂)
by Jie Cao, Dan Fang, Weiqiang Qiu and Jing Xie
Animals 2024, 14(24), 3583; https://doi.org/10.3390/ani14243583 - 12 Dec 2024
Viewed by 200
Abstract
Live fish transportation plays a crucial role in the commercial fish trade. Consequently, mitigating stress during transportation is essential for enhancing the survival rate of fish and reducing potential financial losses. In this study, the effectiveness was evaluated of exogenous tryptophan in reducing [...] Read more.
Live fish transportation plays a crucial role in the commercial fish trade. Consequently, mitigating stress during transportation is essential for enhancing the survival rate of fish and reducing potential financial losses. In this study, the effectiveness was evaluated of exogenous tryptophan in reducing transport stress in hybrid grouper, Epinephelus fuscoguttatus ♀ × E. lanceolatus ♂. Firstly, the groupers were divided into the following five experimental groups: 40 mg/L MS-222 group, 30 mg/L tryptophan, 50 mg/L tryptophan, 70 mg/L tryptophan, and the control group without additives. Followed by transportation simulation, the fish samples were collected before and after transportation for the determination of antioxidant enzyme activities, apoptosis gene, and inflammatory gene expressions. The results indicated that the superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px) activities and malondialdehyde (MDA) levels in all groups were significantly increased, while they were lower in the 50 mg/L Trp treated group compared to the control group (p < 0.05). Compared with the control group in the 50 mg/L Trp, 70 mg/L Trp, and 40 mg/L MS-222-treated groups, serum cortisol and blood glucose levels were significantly increased (p < 0.05), and anti-inflammatory factor (IL-10) gene expression was upregulated and pro-inflammatory factor (IL-1β) gene expression was decreased (p < 0.05). In addition, it was found that the 30 mg/L Trp, 50 mg/L Trp, and 40 mg/L MS-222 treatment groups had less green fluorescence than the control group by measuring the mitochondrial membrane potential, and 50 mg/L Trp and MS-222 showed more red fluorescence in fluorescence images than the other samples at the same sampling time. Therefore, in this study, it was demonstrated that the tryptophan could be used as a new anti-stress agent for hybrid groupers during transport, and additional research is required to identify the specific conditions that yield the best outcomes. Full article
(This article belongs to the Special Issue Research Progress in Growth, Health and Metabolism of Fishes)
Show Figures

Figure 1

Figure 1
<p>Changes in DO (<b>A</b>), TAN (<b>B</b>), and pH (<b>C</b>) in water during transportation. “*” indicates significant differences (<span class="html-italic">p</span> &lt; 0.05) between the treatment groups.</p>
Full article ">Figure 2
<p>Oxidative stress parameters ((<b>A</b>), superoxide dismutase; (<b>B</b>), catalase; (<b>C</b>), glutathione reductase; (<b>D</b>), malondialdehyde) of pearl gentian grouper in each experimental group during the simulated transportation. Letters a~e indicate significant differences (<span class="html-italic">p</span>  &lt;  0.05) between the treatment groups, the same below.</p>
Full article ">Figure 3
<p>Serum biochemical parameters ((<b>A</b>), cortisol; (<b>B</b>), glucose; (<b>C</b>), aspartate transaminase; (<b>D</b>), alanine aminotransferase) of pearl gentian grouper in each experimental group during the simulated transportation. Letters a~d indicate signifcant diferences (<span class="html-italic">p</span> &lt; 0.05) between the treatment groups”.</p>
Full article ">Figure 4
<p>Mitochondrial confocal images of pearl gentian grouper transported in different treatment groups.</p>
Full article ">Figure 5
<p>Apoptosis gene (<b>A</b>), <span class="html-italic">bax</span>; (<b>B</b>), <span class="html-italic">bcl-2</span>; (<b>C</b>) <span class="html-italic">caspase 3</span>; (<b>D</b>), <span class="html-italic">caspase 9</span>) and inflammatory gene (<b>E</b>), <span class="html-italic">IL-10</span>; (<b>F</b>), <span class="html-italic">IL-1β</span>) expressions of pearl gentian grouper transported in different treatment groups. Letters a~d indicate signifcant diferences (<span class="html-italic">p</span> &lt; 0.05) between the treatment groups.</p>
Full article ">
22 pages, 5373 KiB  
Article
A Predictive Compact Model of Effective Travel Time Considering the Implementation of First-Mile Autonomous Mini-Buses in Smart Suburbs
by Andres Udal, Raivo Sell, Krister Kalda and Dago Antov
Smart Cities 2024, 7(6), 3914-3935; https://doi.org/10.3390/smartcities7060151 - 11 Dec 2024
Viewed by 283
Abstract
An important development task for the suburbs of smart cities is the transition from rigid and economically inefficient public transport to the flexible order-based service with autonomous vehicles. The article proposes a compact model with a minimal input data set to estimate the [...] Read more.
An important development task for the suburbs of smart cities is the transition from rigid and economically inefficient public transport to the flexible order-based service with autonomous vehicles. The article proposes a compact model with a minimal input data set to estimate the effective daily travel time (EDTT) of an average resident of a suburban area considering the availability of the first-mile autonomous vehicles (AVs). Our example case is the Järveküla residential area beyond the Tallinn city border. In the model, the transport times of the whole day are estimated on the basis of the forenoon outbound trips. The one-dimensional distance-based spatial model with 5 residential origin zones and 6 destination districts in the city is applied. A crucial simplification is the 3-parameter sub-model of the distribution of distances on the basis of the real mobility statistics. Effective travel times, optionally completed with psycho-physiological stress factors and psychologically perceived financial costs, are calculated for all distances and transportation modes using the characteristic speeds of each mode of transport. A sub-model of switching from 5 traditional transport modes to two AV-assisted modes is defined by an aggregated AV acceptance parameter ‘a’ based on resident surveys. The main output of the model is the EDTT, dependent on the value of the parameter a. Thanks to the compact and easily adjustable set of input data, the main values of the presented model are its generalizability, predictive ability, and transferability to other similar suburban use cases. Full article
(This article belongs to the Special Issue Cost-Effective Transportation Planning for Smart Cities)
Show Figures

Figure 1

Figure 1
<p>Growth of annual number of publications dedicated to application of autonomous vehicles in future transportation.</p>
Full article ">Figure 2
<p>General structure of the calculation model. The upper corner numbers of the blocks correspond to the subsections in the paper text.</p>
Full article ">Figure 3
<p>Explanation of example suburban transport task: (<b>a</b>) Location of Järveküla residential area (purple rectangle) in Rae municipality beyond the southern border of Tallinn city (red line). The blue line marks the major public transportation bus line 132 to Tallinn center; (<b>b</b>) Current development stage of Järveküla residential area of approx. 200 houses; (<b>c</b>) Pilot AV shuttle minibus designed for first-mile transport service in residential area.</p>
Full article ">Figure 4
<p>Selection of two reference areas within the city limits of Tallinn (Mõigu and Kakumäe-Tiskre), for which the trip length distribution functions were found.</p>
Full article ">Figure 5
<p>Summary of trip distance statistics of daily outbound trips for two example residential areas of Tallinn city on basis of the synthetic population database of Tallinn: (<b>a1</b>) Differential distributions with 1 km step for Mõigu area; (<b>b1</b>) The integrated cumulative distributions for Mõigu area; (<b>a2</b>) Differential distributions with 1 km step for Kakumäe-Tiskre area; (<b>b2</b>) The integrated cumulative distributions for Kakumäe-Tiskre area.</p>
Full article ">Figure 6
<p>Results of RMS-fitting of the statistics of forenoon outbound trips by the 2-parameter sigmoid curves for Kakumäe-Tiskre and Mõigu districts.</p>
Full article ">Figure 7
<p>The constructed 3-parameter model of distribution of trip distances combining the initial short-distance contribution and the smooth sigmoid step for lengthier distances. Parameter values are estimated to represent the Järveküla example area.</p>
Full article ">Figure 8
<p>One-dimensional distances-based spatial model of transportation task: (<b>a</b>) an abstract map of residential area housing with local institutions, transport artery, and public transport stops on one edge; (<b>b</b>) distances-based concept of destination districts in metropolitan areas; (<b>c</b>) the simplified one-dimensional spatial scheme of origin zones and destination districts.</p>
Full article ">Figure 9
<p>Explanation of concept of three-dimensional modality-origin-destination matrix used to sum up the daily transport times. Matrix defines 5 origin zones, 6 destinations districts, and 5 + 2 transportation modes. Each cell of MOD matrix is characterized by transport time with optional psych-physiological and economical extra terms and weight factors of distance and transport mode.</p>
Full article ">Figure 10
<p>Explanation of the two-stage concept of outbound trips and input parameter set for calculation of effective transportation time costs.</p>
Full article ">Figure 11
<p>Explanation of 2-stage effective trip times methodology with actual numerical values of input parameters.</p>
Full article ">Figure 12
<p>The main output of the model: daily effective transportation times of an average suburban resident versus the aggregated parameter of autonomous vehicle acceptance <math display="inline"><semantics> <mi>α</mi> </semantics></math>.</p>
Full article ">
25 pages, 5613 KiB  
Article
Regulatory Role of Vacuolar Calcium Transport Proteins in Growth, Calcium Signaling, and Cellulase Production in Trichoderma reesei
by Letícia Harumi Oshiquiri, Lucas Matheus Soares Pereira, David Batista Maués, Elizabete Rosa Milani, Alinne Costa Silva, Luiz Felipe de Morais Costa de Jesus, Julio Alves Silva-Neto, Flávio Protásio Veras, Renato Graciano de Paula and Roberto Nascimento Silva
J. Fungi 2024, 10(12), 853; https://doi.org/10.3390/jof10120853 - 11 Dec 2024
Viewed by 299
Abstract
Recent research has revealed the calcium signaling significance in the production of cellulases in Trichoderma reesei. While vacuoles serve as the primary calcium storage within cells, the function of vacuolar calcium transporter proteins in this process remains unclear. In this study, we [...] Read more.
Recent research has revealed the calcium signaling significance in the production of cellulases in Trichoderma reesei. While vacuoles serve as the primary calcium storage within cells, the function of vacuolar calcium transporter proteins in this process remains unclear. In this study, we conducted a functional characterization of four vacuolar calcium transport proteins in T. reesei. This was accomplished by the construction of the four mutant strains ∆trpmc1, ∆tryvc1, ∆tryvc3, and ∆tryvc4. These mutants displayed enhanced growth when subjected to arabinose, xylitol, and xylose. Furthermore, the mutants ∆trpmc1, ∆tryvc1, and ∆tryvc4 showed a reduction in growth under conditions of 100 mM MnCl2, implying their role in manganese resistance. Our enzymatic activity assays revealed a lack of the expected augmentation in cellulolytic activity that is typically seen in the parental strain following the introduction of calcium. This was mirrored in the expression patterns of the cellulase genes. The vacuolar calcium transport genes were also found to play a role in the expression of genes involved with the biosynthesis of secondary metabolites. In summary, our research highlights the crucial role of the vacuolar calcium transporters and, therefore, of the calcium signaling in orchestrating cellulase and hemicellulase expression, sugar utilization, and stress resistance in T. reesei. Full article
(This article belongs to the Special Issue Omics Approaches in Trichoderma Research)
Show Figures

Figure 1

Figure 1
<p>Cladogram of calcium transport proteins. We used sequences of characterized proteins present in the cell membrane, vacuoles, endoplasmic reticulum, and Golgi apparatus together with <span class="html-italic">T. reesei</span> proteins of unknown localization identified in this study. The support values from 1000 resamples are indicated by the black circles, varying from 0 to 1. The figure only shows values from 0.8 to 1, from smaller to larger size.</p>
Full article ">Figure 2
<p>Comparative expression analysis of <span class="html-italic">T. reesei</span> putative vacuolar calcium transport proteins, homologous to <span class="html-italic">S. cerevisiae’</span>s YVC1 and PMC1. The RNA-Seq data were obtained from studies [<a href="#B6-jof-10-00853" class="html-bibr">6</a>,<a href="#B17-jof-10-00853" class="html-bibr">17</a>,<a href="#B18-jof-10-00853" class="html-bibr">18</a>,<a href="#B19-jof-10-00853" class="html-bibr">19</a>,<a href="#B20-jof-10-00853" class="html-bibr">20</a>,<a href="#B48-jof-10-00853" class="html-bibr">48</a>]. (<b>a</b>) <span class="html-italic">yvc1</span> expression in commonly used parental strains and (<b>b</b>) the mutant strains lacking <span class="html-italic">Xyr1</span>, <span class="html-italic">Azf1</span>, <span class="html-italic">Cre1</span>, <span class="html-italic">Tmk1</span>, <span class="html-italic">Tmk2</span> and <span class="html-italic">Epl2</span>, respectively. (<b>c</b>,<b>d</b>) <span class="html-italic">pmc1</span> expression under the same conditions. Cel = cellulose, Glu = glucose, Soph = sophorose, SCB = sugarcane bagasse, Gly = glycerol.</p>
Full article ">Figure 3
<p>Growth of strains QM6a∆<span class="html-italic">tmus53</span>∆<span class="html-italic">pyr4</span> (parental), ∆<span class="html-italic">trpmc1</span>, ∆<span class="html-italic">tryvc1</span>, ∆<span class="html-italic">tryvc3</span>, and ∆<span class="html-italic">tryvc</span>4 in minimal media in the presence of 25 mM of different carbon sources for 72 h, with and without 10 mM CaCl<sub>2</sub> supplementation. The values represent the absorbance readings at 750 nm.</p>
Full article ">Figure 4
<p>Growth of strains QM6a∆<span class="html-italic">tmus53</span>∆<span class="html-italic">pyr4</span> (parental), ∆<span class="html-italic">trpmc1</span>, ∆<span class="html-italic">tryvc1</span>, ∆<span class="html-italic">tryvc3</span>, and ∆<span class="html-italic">tryvc</span>4 in 25 mM xylose minimal media for 48 or 72 h in the presence of 10, 100, or 300 mM of the metals calcium (<b>a</b>,<b>b</b>), copper (<b>c</b>,<b>d</b>), manganese (<b>e</b>,<b>f</b>), cobalt (<b>g</b>,<b>h</b>), zinc (<b>i</b>,<b>j</b>), iron (<b>k</b>,<b>l</b>), magnesium (<b>m</b>,<b>n</b>) or aluminum (<b>o</b>,<b>p</b>). Significance levels are indicated as follows: * <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 in relation to the parental strain (Student’s <span class="html-italic">t</span>-test). The control condition (gray bars) was calculated for each microplate.</p>
Full article ">Figure 5
<p>Growth of strains QM6a∆<span class="html-italic">tmus53</span>∆<span class="html-italic">pyr4</span> (parental), ∆<span class="html-italic">trpmc1</span>, ∆<span class="html-italic">tryvc1</span>, ∆<span class="html-italic">tryvc3</span>, and ∆<span class="html-italic">tryvc</span>4 in 25 mM xylose Minimal media in the presence of 0.5 M NaCl (<b>a</b>–<b>c</b>) or 0.5 M KCl (<b>d</b>–<b>f</b>) and in presence or absence of 10 mM CaCl<sub>2</sub>. * <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 in relation to the parental strain (two-way ANOVA, followed by Tukey’s multiple comparisons). Non-significant results are indicated as ‘ns’. The control condition (gray bars) was calculated for each microplate.</p>
Full article ">Figure 6
<p>Growth analysis of strains QM6a∆<span class="html-italic">tmus53</span>∆<span class="html-italic">pyr4</span> (parental), ∆<span class="html-italic">trpmc1</span>, ∆<span class="html-italic">tryvc1</span>, ∆<span class="html-italic">tryvc3</span>, and ∆<span class="html-italic">tryvc</span>4 in MA with 1% carboxymethylcellulose (CMC) for 5 days or 2% glucose + 100 mM Congo red (CR) for 3 days. (<b>a</b>) growth in plates, (<b>b</b>) growth measurements in 1% CMC and (<b>c</b>) 100 mM CR. Significance levels are indicated as follows: * <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 (Student’s <span class="html-italic">t</span>-test). Non-significant results are indicated as ‘ns’.</p>
Full article ">Figure 7
<p>CMCase activity of parental and mutant strains grown for 24 h in MA media with 1% cellulose with or without 10 mM CaCl<sub>2</sub> supplementation for 24 h (<b>a</b>), 48 h (<b>b</b>), 72 h (<b>c</b>), and 96 h (<b>d</b>), and gene expression of endoglucanase (<span class="html-italic">cel7b</span>) (<b>e</b>), cellobiohydrolases (<span class="html-italic">cel7a</span> and <span class="html-italic">cel6a</span>) (<b>f</b>,<b>g</b>) and beta-glucosidase (<span class="html-italic">cel3a</span>) (<b>h</b>). Significance levels are indicated as follows: * <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 (Student’s <span class="html-italic">t</span>-test). Non-significant results are indicated as ‘ns’.</p>
Full article ">Figure 8
<p>Gene expression of transcription factors and calcium signaling components of parental and mutant strains grown on MA media with 1% cellulose for 24 h. Gene expression of <span class="html-italic">xyr1</span> (<b>a</b>), <span class="html-italic">ace3</span> (<b>b</b>), <span class="html-italic">hac1a</span> (<b>c</b>), <span class="html-italic">cre1</span> (<b>d</b>), <span class="html-italic">crz1</span> (<b>e</b>), <span class="html-italic">cam</span> (<b>f</b>), and <span class="html-italic">cna1</span> (<b>g</b>). * <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 (Student’s <span class="html-italic">t</span>-test). Non-significant results are indicated as ‘ns’.</p>
Full article ">Figure 9
<p>Gene expression of xylose metabolism enzymes (<b>a</b>–<b>c</b>), calcium signaling components (<b>d</b>–<b>f</b>), xylanases (<b>g</b>,<b>h</b>), and secondary metabolites components (<b>i</b>–<b>k</b>) of parental and mutant strains grown on MA media with 25 mM xylose for 48 h. * <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 (Student’s <span class="html-italic">t</span>-test). Non-significant results are indicated as ‘ns’.</p>
Full article ">Figure 10
<p>Confocal microscopy and analysis of cell wall thickness. (<b>a</b>) the strains were grown in MA media with 25 mM xylose supplemented with 10 mM CaCl<sub>2</sub> and stained with 5 µM Fluo-4/AM. (<b>b</b>) the strains were grown in MEX media supplemented with 0 or 10 mM CaCl<sub>2</sub> for 1 day in a microscope slide and stained with 0.001% calcofluor white. (<b>c</b>) measurements of cell wall thickness. ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001 (Student’s <span class="html-italic">t</span>-test). Non-significant results are indicated as ‘ns’.</p>
Full article ">Figure 11
<p>Putative model of the function of the vacuolar calcium transport proteins YVC1 and PMC1 of <span class="html-italic">T. reesei</span>. The deletion of these proteins impairs the dynamics of calcium transport in the vacuoles, therefore, when extracellular calcium enters the cells, the calcium-mediated signaling does not occur properly, as the expression of the transcription factors ACE3, CRZ1, CRE1, YPR1, and YPR2 are increased/decreased, causing a reduction in cellulases/hemicellulases expression, manganese and osmotic stress tolerance and an increase in xylose assimilation and in cell wall thickness. red arrows down—down-regulation effect and blue arrows up—up-regulation effect.</p>
Full article ">
17 pages, 735 KiB  
Review
Stress Biomarkers in Pigs: Current Insights and Clinical Application
by Vasileios G. Papatsiros, Georgios Maragkakis and Georgios I. Papakonstantinou
Vet. Sci. 2024, 11(12), 640; https://doi.org/10.3390/vetsci11120640 - 10 Dec 2024
Viewed by 431
Abstract
Our study aimed to contribute to the understanding of the stress process in pigs to better assess and control their stress levels. Nowadays, pigs in intensive farming are exposed to several stress factors, such as weaning, transportation, diseases and vaccinations. As a result, [...] Read more.
Our study aimed to contribute to the understanding of the stress process in pigs to better assess and control their stress levels. Nowadays, pigs in intensive farming are exposed to several stress factors, such as weaning, transportation, diseases and vaccinations. As a result, the animals experience significant stress responses and inflammatory reactions that affect their health, growth and productivity. Therefore, it is crucial to assess their stress levels, and the use of stress biomarkers could be useful in their evaluation. An up-to-date overview of the different biomarkers that can be used for the assessment of stress is given. It also discusses the methods used to investigate these biomarkers, particularly non-invasive approaches, such as saliva sampling, as practical tools for monitoring animal welfare. In conclusion, our study highlights the importance of using multiple biomarkers for a comprehensive evaluation of stress and points to the need for further research to standardize the sampling procedures and improve stress management in pig farming. Full article
Show Figures

Figure 1

Figure 1
<p>Main causes and consequences of stress in pigs.</p>
Full article ">
18 pages, 4051 KiB  
Article
Photosynthetic Efficiency of Plants as an Indicator of Tolerance to Petroleum-Contaminated Soils
by Piotr Dąbrowski, Ilona Małuszyńska, Marcin J. Małuszyński, Bogumiła Pawluśkiewicz, Tomasz Gnatowski, Aneta H. Baczewska-Dąbrowska and Hazem M. Kalaji
Sustainability 2024, 16(24), 10811; https://doi.org/10.3390/su162410811 - 10 Dec 2024
Viewed by 379
Abstract
Significant efforts have been made to develop environmentally friendly remediation methods to restore petroleum-damaged ecosystems. One such approach is cultivating plant species that exhibit high resistance to contamination. This study aimed to assess the impact of petroleum-derived soil pollutants on the photosynthetic performance [...] Read more.
Significant efforts have been made to develop environmentally friendly remediation methods to restore petroleum-damaged ecosystems. One such approach is cultivating plant species that exhibit high resistance to contamination. This study aimed to assess the impact of petroleum-derived soil pollutants on the photosynthetic performance of selected plant species used in green infrastructure development. A pot experiment was conducted using both contaminated and uncontaminated soils to grow six plant species under controlled conditions. Biometric parameters and chlorophyll a fluorescence measurements were taken, followed by statistical analyses to compare plant responses under stress and control conditions. This study is the first to simultaneously analyze PF, DF, and MR820 signals in plant species exposed to petroleum contamination stress. The results demonstrated that petroleum exposure reduced the activity of both PSII and PSI, likely due to increased nonradiative energy dissipation in PSII antenna chlorophylls, decreased antenna size, and/or damage to the photosynthetic apparatus. Additionally, petroleum contamination affected the electron transport chain efficiency, limiting electron flow between PSII and PSI. The most resistant species to petroleum-induced stress were Lolium perenne, Poa pratensis, and Trifolium repens. Full article
(This article belongs to the Section Pollution Prevention, Mitigation and Sustainability)
Show Figures

Figure 1

Figure 1
<p>Weather conditions during the experiment period.</p>
Full article ">Figure 2
<p>Induction curves of chlorophyll <span class="html-italic">a</span> fluorescence of investigated plants under control soil and petroleum-contaminated soil. <span class="html-italic">Dactylis glomerata</span> L. var Amba (DGA), <span class="html-italic">Lolium perenne</span> L. var. Maja (LPM) and Nira (LPN), <span class="html-italic">Poa pretensis</span> L. var. Appalachian (PPA), <span class="html-italic">Phleum pretense</span> L. var. Egida (PPE), <span class="html-italic">Trifolium repens</span> L. var Grass. Huia (TRH), relative units, n = 8.</p>
Full article ">Figure 3
<p>Delayed fluorescence induction curves of investigated plants under control soil and petroleum-contaminated soil. <span class="html-italic">Dactylis glomerata</span> L. var Amba (DGA), <span class="html-italic">Lolium perenne</span> L. var. Maja (LPM) and Nira (LPN), <span class="html-italic">Poa pretensis</span> L. var. Appalachian (PPA), <span class="html-italic">Phleum pretense</span> L. var. Egida (PPE), <span class="html-italic">Trifolium repens</span> L. var Grass. Huia (TRH), relative units, n = 8.</p>
Full article ">Figure 4
<p>Modulated light reflection at 820 nm of investigated plants under control soil and petroleum-contaminated soil. <span class="html-italic">Dactylis glomerata</span> L. var Amba (DGA), <span class="html-italic">Lolium perenne</span> L. var. Maja (LPM) and Nira (LPN), <span class="html-italic">Poa pretensis</span> L. var. Appalachian (PPA), <span class="html-italic">Phleum pretense</span> L. var. Egida (PPE), <span class="html-italic">Trifolium repens</span> L. var Grass. Huia (TRH), relative units, n = 8.</p>
Full article ">Figure 5
<p>Principal component analysis for control experiment (<b>a</b>) and petroleum contamination soil (<b>b</b>). <span class="html-italic">Dactylis glomerata</span> L. var Amba (DGA), <span class="html-italic">Lolium perenne</span> L. var. Maja (LPM) and Nira (LPN), <span class="html-italic">Poa pretensis</span> L. var. Appalachian (PPA), <span class="html-italic">Phleum pretense</span> L. var. Egida (PPE), <span class="html-italic">Trifolium repens</span> L. var Grass. Huia (TRH).</p>
Full article ">Figure 6
<p>T-Student test results of all measured parameters of tested plant species. <span class="html-italic">Dactylis glomerata</span> L. var Amba (DGA), <span class="html-italic">Lolium perenne</span> L. var. Maja (LPM) and Nira (LPN), <span class="html-italic">Poa pretensis</span> L. var. Appalachian (PPA), <span class="html-italic">Phleum pretense</span> L. var. Egida (PPE), <span class="html-italic">Trifolium repens</span> L. var Grass. Huia (TRH). The ns refer to non-significant differences, and asterisks *, **, ***, **** refer to different significance levels represented by <span class="html-italic">p</span>-values lower or equal to 0.05, 0.01, 0.001 and 0.0001 respectively.</p>
Full article ">Figure 7
<p>Boxplot results of the parameters Area (<b>a</b>), MRmin (<b>b</b>), and I2 (<b>c</b>) indicating statistically significant (or no-significant) differences in the fluorescence variables enable the impact of the contamination treatment on the state of the analyzed species. <span class="html-italic">Dactylis glomerata</span> L. var Amba (DGA), <span class="html-italic">Lolium perenne</span> L. var. Maja (LPM) and Nira (LPN), <span class="html-italic">Poa pretensis</span> L. var. Appalachian (PPA), <span class="html-italic">Phleum pretense</span> L. var. Egida (PPE), <span class="html-italic">Trifolium repens</span> L. var Grass. Huia (TRH). The blue box and circles regard control experiment, whereas the green box and triangles indicate the Petroleum contamination experiment.</p>
Full article ">
19 pages, 707 KiB  
Review
Promoters, Key Cis-Regulatory Elements, and Their Potential Applications in Regulation of Cadmium (Cd) in Rice
by Xinxin Xu, Qingxian Mo, Zebin Cai, Qing Jiang, Danman Zhou and Jicai Yi
Int. J. Mol. Sci. 2024, 25(24), 13237; https://doi.org/10.3390/ijms252413237 - 10 Dec 2024
Viewed by 301
Abstract
Rice (Oryza sativa), a globally significant staple crop, is crucial for ensuring human food security due to its high yield and quality. However, the intensification of industrial activities has resulted in escalating cadmium (Cd) pollution in agricultural soils, posing a substantial [...] Read more.
Rice (Oryza sativa), a globally significant staple crop, is crucial for ensuring human food security due to its high yield and quality. However, the intensification of industrial activities has resulted in escalating cadmium (Cd) pollution in agricultural soils, posing a substantial threat to rice production. To address this challenge, this review comprehensively analyzes rice promoters, with a particular focus on identifying and characterizing key cis-regulatory elements (CREs) within them. By elucidating the roles of these CREs in regulating Cd stress response and accumulation in rice, we aim to establish a scientific foundation for developing rice varieties with reduced Cd accumulation and enhanced tolerance. Furthermore, based on the current understanding of plant promoters and their associated CREs, our study identifies several critical research directions. These include the exploration of tissue-specific and inducible promoters, as well as the discovery of novel CREs specifically involved in the mechanisms of Cd uptake, transport, and detoxification in rice. Our findings not only contribute to the existing knowledge base on genetic engineering strategies for mitigating Cd contamination in rice but pave the way for future research aimed at enhancing rice’s resilience to Cd pollution, ultimately contributing to the safeguarding of global food security. Full article
(This article belongs to the Section Molecular Plant Sciences)
Show Figures

Figure 1

Figure 1
<p>A proposed model for utilizing promoters and Cd-responsive <span class="html-italic">cis</span>-regulatory elements (CREs) to regulate Cd tolerance and accumulation in rice.</p>
Full article ">
20 pages, 7393 KiB  
Article
Stage-Specific Effects of Silver Nanoparticles on Physiology During the Early Growth Stages of Rice
by Ruxue Pan, Zailin Zhang, Ya Li, Sihong Zhu, Sumera Anwar, Jiaquan Huang, Chuanling Zhang and Liyan Yin
Plants 2024, 13(23), 3454; https://doi.org/10.3390/plants13233454 - 9 Dec 2024
Viewed by 405
Abstract
Silver nanoparticles (AgNPs), widely utilized nanomaterials, can negatively affect crop growth and development. However, it remains unclear whether crops exhibit similar responses to AgNPs stress at seed germination and seedling stages. In this study, rice seeds and seedlings were exposed to AgNPs, and [...] Read more.
Silver nanoparticles (AgNPs), widely utilized nanomaterials, can negatively affect crop growth and development. However, it remains unclear whether crops exhibit similar responses to AgNPs stress at seed germination and seedling stages. In this study, rice seeds and seedlings were exposed to AgNPs, and their growth, photosynthetic efficiency, and antioxidant systems were recorded. demonstrated significant AgNPs accumulation in rice tissues, with notable higher accumulation in seedlings exposed to AgNPs after germination compared to AgNPs exposure during germination. The roots exhibited greater AgNPs accumulation than shoots across both stages. Exposure to AgNPs during the seed germination stage, even at concentrations up to 2 mg/L, did not significantly affect growth, physiological indices, or oxidative stress. In contrast, seedlings exposed to 1 and 2 mg/L AgNPs showed significant reductions in shoot length, biomass, nutrient content, and photosynthetic efficiency. At low AgNPs concentrations, the maximum relative electron transport rate (rETRmax) was significantly reduced, while the higher concentrations caused pronounced declines in the chlorophyll a fluorescence transient curves (OJIP) compared to the control group. Antioxidant enzyme activities increased in both leaves and roots in a dose-dependent manner, with roots exhibiting significantly higher activity, suggesting that roots are the primary site of AgNPs stress responses. In conclusion, rice responds differently to AgNPs exposure at distinct developmental stages, with the seedling stage being more susceptible to AgNPs-induced stress than the seed germination stage. These findings underscore the importance of considering growth stages when assessing the food safety and environmental risks associated with AgNPs exposure. Full article
(This article belongs to the Special Issue Aquatic Plants and Wetland)
Show Figures

Figure 1

Figure 1
<p>Ag accumulation in rice tissues exposed to AgNPs at the seed germination and seedling stages. AgNPs exposure at the seed germination stage: (<b>A</b>) Ag content in the shoots and (<b>B</b>) Ag content in the roots. For AgNPs exposure at the seedling stage: (<b>C</b>) Ag content in the shoots and (<b>D</b>) Ag content in the roots. Data are presented as mean ± SD. According to Tukey’s test (<span class="html-italic">p</span> &lt; 0.05), different lowercase letters indicate significant differences among treatments (<span class="html-italic">n</span> = 3).</p>
Full article ">Figure 2
<p>Growth phenotypes of rice seedlings exposed to AgNPs at seed germination and seedling stages. AgNPs exposure at the seed germination stage: (<b>A</b>,<b>C</b>). AgNPs exposure at the seedling stage: (<b>B</b>,<b>D</b>).</p>
Full article ">Figure 3
<p>Effects of AgNPs exposure at seed germination and the seedling stages on rice growth parameters. AgNPs exposure at the seed germination stage: (<b>A</b>) shoot length and root length, (<b>B</b>) shoot fresh weight and root fresh weight, and (<b>C</b>) shoot dry weight and root dry weight. AgNPs exposure at the seedling stage: (<b>D</b>) shoot length and root length, (<b>E</b>) shoot fresh weight and root fresh weight, and (<b>F</b>) shoot dry weight and root dry weight. Data are presented as mean ± SD. According to Tukey’s test (<span class="html-italic">p</span> &lt; 0.05), different lowercase letters indicate significant differences among treatments, <span class="html-italic">n</span> ≥ 4.</p>
Full article ">Figure 4
<p>Effects of AgNPs exposure at seed germination and seedling stages on leaf photosynthetic parameters. AgNPs exposure at the seed germination stage: (<b>A</b>) maximum relative electron transport rate (rETR<sub>max</sub>) and (<b>B</b>) chlorophyll a fluorescence transient curves (OJIP). AgNPs exposure at the seedling stage: (<b>C</b>) rETR<sub>max</sub> and (<b>D</b>) OJIP curves. Data are presented as mean ± SD. According to Tukey’s test (<span class="html-italic">p</span> &lt; 0.05), different lowercase letters indicate significant differences among treatments, <span class="html-italic">n</span> ≥ 4.</p>
Full article ">Figure 5
<p>Effects of AgNPs exposure at seed germination and seedling stages on antioxidant substances in rice leaves. AgNPs exposure at the seed germination stage: (<b>A</b>) 3,3′-diaminobenzidine (DAB) staining image and (<b>B</b>) malondialdehyde (MDA) content. AgNPs exposure at the seedling stage: (<b>C</b>) DAB staining image and (<b>D</b>) MDA content. The MDA data are presented as mean ± SD. According to Tukey’s test (<span class="html-italic">p</span> &lt; 0.05), different lowercase letters indicate significant differences among treatments, <span class="html-italic">n</span> ≥ 3.</p>
Full article ">Figure 6
<p>Effects of AgNPs exposure at seed germination and seedling stages on antioxidant substances in rice roots. AgNPs exposure at the seed germination stage: (<b>A</b>) MDA content and (<b>B</b>) ROS fluorescence intensity. AgNPs exposure at the seedling stage: (<b>C</b>) MDA content and (<b>D</b>) ROS fluorescence intensity. Data are presented as mean ± SD. According to Tukey’s test (<span class="html-italic">p</span> &lt; 0.05), different lowercase letters indicate significant differences among treatments, <span class="html-italic">n</span> ≥ 3.</p>
Full article ">Figure 7
<p>Effects of AgNPs exposure at seed germination and seedling stages on antioxidant enzyme activities in rice leaves. AgNPs exposure at the seed germination stage: (<b>A</b>) protein content, (<b>B</b>) SOD activity, (<b>C</b>) POD activity, and (<b>D</b>) CAT activity. AgNPs exposure at the seedling stage: (<b>E</b>) protein content, (<b>F</b>) SOD activity, (<b>G</b>) POD activity, and (<b>H</b>) CAT activity. Data are mean ± SD. According to Tukey’s test (<span class="html-italic">p</span> &lt; 0.05), different lowercase letters indicate significant differences among treatments, <span class="html-italic">n</span> ≥ 3.</p>
Full article ">Figure 8
<p>Effects of AgNPs exposure at seed germination and seedling stages on antioxidant enzyme activities in rice roots. AgNPs exposure at the seed germination stage: (<b>A</b>) protein content, (<b>B</b>) SOD activity, (<b>C</b>) POD activity, (<b>D</b>) CAT activity, (<b>E</b>) APX activity, and (<b>F</b>) GSH content. AgNPs exposure at the seedling stage: (<b>G</b>) protein content, (<b>H</b>) SOD activity, (<b>I</b>) POD activity, (<b>J</b>) CAT activity, (<b>K</b>) APX activity, and (<b>L</b>) GSH content. Data are mean ± SD. According to Tukey’s test (<span class="html-italic">p</span> &lt; 0.05), different lowercase letters indicate significant differences among treatments, <span class="html-italic">n</span> ≥ 3.</p>
Full article ">
17 pages, 7298 KiB  
Article
Temperature-Dependent Raman Scattering and Correlative Investigation of AlN Crystals Prepared Using a Physical Vapor Transport (PVT) Method
by Zhe Chuan Feng, Manika Tun Nafisa, Yao Liu, Li Zhang, Yingming Wang, Xiaorong Xia, Ze Tao, Chuanwei Zhang, Jeffrey Yiin, Benjamin Klein and Ian Ferguson
Photonics 2024, 11(12), 1161; https://doi.org/10.3390/photonics11121161 - 9 Dec 2024
Viewed by 360
Abstract
Ultrawide bandgap (UWBG) AlN c- and m-face crystals have been prepared using the physical vapor transport (PVT) method and studied penetratively using temperature-dependent (TD) Raman scattering (RS) measurements under both visible (457 nm) and DUV (266 nm) excitations in 80–870 K, plus correlative [...] Read more.
Ultrawide bandgap (UWBG) AlN c- and m-face crystals have been prepared using the physical vapor transport (PVT) method and studied penetratively using temperature-dependent (TD) Raman scattering (RS) measurements under both visible (457 nm) and DUV (266 nm) excitations in 80–870 K, plus correlative atomic force microscopy (AFM) and variable-angle (VA) spectroscopic ellipsometry (SE). VASE identified their band gap energy as 6.2 eV, indicating excellent AlN characteristics and revealing Urbach energy levels of about 85 meV. Raman analyses revealed the residual tensile stress. TDRS shows that the E2(high) phonon lifetime decayed gradually in the 80–600 K range. Temperature has the greater influence on the stress of m-face grown AlN crystal. The influence of low temperature on the E2(high) phonon lifetime of m-plane AlN crystal is greater than that of the high-temperature region. By way of the LO-phonon and plasma coupling (LOPC), simulations of A1(LO) modes and carrier concentrations along different faces and depths in AlN crystals are determined. These unique and significant findings provide useful references for the AlN crystal growth and deepen our understanding on the UWBG AlN materials. Full article
(This article belongs to the Special Issue Research, Development and Application of Raman Scattering Technology)
Show Figures

Figure 1

Figure 1
<p>AFM surface morphologies of AlN crystals: (<b>a</b>) AlNa, (<b>b</b>) AlNb, and (<b>c</b>) AlNc, respectively.</p>
Full article ">Figure 2
<p>VASE Psi and Delta spectra vs. wavelength (nm) from an AlN crystal of AlNa, with a 60° incident angle, measured at RT. Dotted lines are fitted results.</p>
Full article ">Figure 3
<p>(<b>a</b>) The n~λ (red line) and k~λ (green line) curves and (<b>b</b>) the α~λ (red line) relation of an AlN crystal of AlNa, respectively.</p>
Full article ">Figure 4
<p>The absorption coefficient α vs. energy (eV) of three AlN crystals of AlNa, AlNb, and AlNc, respectively.</p>
Full article ">Figure 5
<p>The relationships of (αhv)<sup>2</sup> vs. energy (eV), i.e., Tauc plots, of three AlN crystals of AlNa, AlNb, and AlNc, respectively, in which the band gap of each sample is acquired: E<sub>g</sub> (AlNa) = 6.207 eV, E<sub>g</sub> (AlNb) = 6.217 eV, and E<sub>g</sub> (AlNc) = 6.209 eV, respectively.</p>
Full article ">Figure 6
<p>The relationship of Ln(α) vs. energy (eV) of three AlN crystals of AlNa, AlNb, and AlNc, respectively.</p>
Full article ">Figure 7
<p>The relationship of Ln(α) vs. energy (eV) of three AlN crystals of AlNa, AlNb, and AlNc, respectively.</p>
Full article ">Figure 8
<p>RT Raman spectra from three AlN crystals of AlNa, AlNb, and AlNC, under excitations of (<b>a</b>) visible 457 nm diode laser and (<b>b</b>) UV 325 nm HeCd laser, respectively.</p>
Full article ">Figure 9
<p>Temperature-dependent Raman scattering (TDRS) measurements under DUV 266 nm excitation for two AlN crystals of (<b>a</b>) AlNa and (<b>b</b>) AlNb, respectively.</p>
Full article ">Figure 10
<p>Temperature dependences of the E<sub>2</sub>(high) Raman Shift/FWHM and fits by applying Equations (5)–(8) for three AlN crystals of (<b>a</b>) AlNa, (<b>b</b>) AlNb, and (<b>c</b>) AlNc, respectively, with symbols for the data values and solid lines for fits based upon Equations (4)–(7).</p>
Full article ">Figure 11
<p>The temperature relationships of the biaxial stress, calculated by Equation (3), of three AlN crystals of AlNa, AlNb, and AlNc, respectively.</p>
Full article ">Figure 12
<p>The temperature relationship between the E<sub>2</sub>(high) phonon lifetime and FWHM for three AlN crystals of (<b>a</b>) AlNa, (<b>b</b>) AlNb, and (<b>c</b>) AlNc, respectively.</p>
Full article ">Figure 13
<p>TDRS measurements in 80 K–600 K under visible (457 nm) excitation for an AlN sample AlNa with (<b>a</b>) 3D display and (<b>b</b>) only E<sub>2</sub>(high) mode.</p>
Full article ">Figure 14
<p>Fitted AlN A<sub>1</sub>(LO) modes from Raman scattering data (ex. 266 nm) of ANa measured at different Ts of 80–200–300–400–500–700 K {100 K data like 80 K}.</p>
Full article ">Figure 15
<p>Fitted AlN A<sub>1</sub>(LO) modes from Raman scattering data (ex. 266 nm) of AlNb measured at different Ts of 80–300–800 K and 80–90 to 110–200–250–300–500–800 K graphs.</p>
Full article ">Figure 16
<p>Fitted AlN A<sub>1</sub>(LO) modes from Raman scattering data (ex. 457 nm) of ANa measured at different Ts of 80–300–800 K and 80–90–110–200–250–300–500–800 K graphs.</p>
Full article ">Figure 17
<p>The temperature dependencies of (<b>a</b>) A<sub>1</sub>(LO) peak frequency, (<b>b</b>) A<sub>1</sub>(LO) peak FWHM, and (<b>c</b>) carrier concentration, in a temperature ranging from 80 K to 600 K or 800 K, respectively. Each T-dependence is fitted by a second-order polynomial individually and listed near each set of data points inside three graphs.</p>
Full article ">
18 pages, 7020 KiB  
Article
Genome-Wide Identification of the ALMT Gene Family in Nine Rosaceae Species and Functional Analysis Associated with Organic Acid Accumulation in Prunus mume
by Ximeng Lin, Pengyu Zhou, Yin Wu, Ziqi Wang, Yuying Lu, Silas Segbo, Feng Gao, Chengdong Ma, Xiao Huang, Zhaojun Ni, Ting Shi and Zhihong Gao
Horticulturae 2024, 10(12), 1305; https://doi.org/10.3390/horticulturae10121305 - 7 Dec 2024
Viewed by 362
Abstract
ALMT (aluminum-activated malate transporter) proteins play crucial roles in the transport of organic acids and have significant implications for plant stress responses and development. While extensively studied in some plants, the characteristics and functional divergence of the ALMT gene family have not yet [...] Read more.
ALMT (aluminum-activated malate transporter) proteins play crucial roles in the transport of organic acids and have significant implications for plant stress responses and development. While extensively studied in some plants, the characteristics and functional divergence of the ALMT gene family have not yet been thoroughly explored in Prunus mume and some other Rosaceae species. In this study, we systematically analyzed the ALMT gene family across nine Rosaceae species to explore their evolutionary relationships, structural characteristics, and functional roles. A total of 138 ALMT genes were identified and categorized into four groups based on a phylogenetic analysis. The motif analysis confirmed the accuracy of the phylogenetic grouping. The collinearity analysis indicated that whole-genome duplication events were the primary drivers of ALMT gene expansion in these species. Furthermore, the cis-acting element analysis revealed diverse regulatory elements associated with environmental responses, including abscisic acid, light, and jasmonic acid. The gene expression correlation analysis showed that PmALMT1 is primarily associated with malic acid accumulation, whereas PmALMT8 is related to citric acid accumulation. Further transient expression in Nicotiana benthamiana validated the above conclusion. This comprehensive analysis provides valuable insights into the evolution, function, and regulation of the ALMT gene family in Rosaceae species. Full article
(This article belongs to the Section Genetics, Genomics, Breeding, and Biotechnology (G2B2))
Show Figures

Figure 1

Figure 1
<p>Phylogenetic analysis of ALMT genes. The ML tree was constructed by IQ-tree from nine Rosaceae species and Arabidopsis, with 1000 bootstrapping replicates. The <span class="html-italic">ALMT</span> genes were divided into four groups (Group A, Group B, Group C, and Group D). Different background colors indicate the different groups of the ALMT proteins and the colors in the different nodes represent different species.</p>
Full article ">Figure 2
<p>Motif, transmembrane domain, and structural analyses. (<b>a</b>) Subgroup classification. ALMT tree constructed by IQ-tree with 138 <span class="html-italic">ALMT</span> genes. The background color indicates the 4 subgroups of the <span class="html-italic">ALMT</span> genes. (<b>b</b>) Motif analysis. The conserved motifs of the <span class="html-italic">ALMT</span> genes and the 15 motifs are displayed in different colors. (<b>c</b>) Transmembrane domain analysis. The transmembrane regions in ALMT proteins are presented in blue. (<b>d</b>) Gene structural analysis. The gene elements sizes were comparable to their sequence lengths, and exons of the genes are displayed in green, while untranslated regions (UTRs) are shown in yellow.</p>
Full article ">Figure 3
<p>Protein properties of ALMTs, including the molecular weight (<b>a</b>), instability index (<b>b</b>), isoelectric points (<b>c</b>) and grand average of hydropathicity (<b>d</b>).</p>
Full article ">Figure 4
<p>Synteny of <span class="html-italic">ALMTs</span> in the nine Rosaceae species. Gray lines represent syntenic regions between adjacent species. Homologous <span class="html-italic">ALMTs</span> located within syntenic regions are connected by lines of different colors other than gray.</p>
Full article ">Figure 5
<p>Localization and synteny of <span class="html-italic">ALMT</span> genes in Prunus mume. (Ⅰ) Gene density expressed as a percentage within a 100 kb window. (Ⅱ) GC content expressed as a percentage within a 10 kb window. (Ⅲ) Nitrogen content expressed as a percentage within a 10 kb window. (Ⅳ) NGS coverage for fruit within a 100 kb window. (Ⅴ) NGS coverage for leaf tissue within a 100 kb window. (Ⅵ) Positions of genes on the genomic landscape.</p>
Full article ">Figure 6
<p>The cis-acting element analysis of 11 <span class="html-italic">PmALMT</span> genes. (<b>a</b>) Localization of 16 identified cis-acting elements in the promoters of <span class="html-italic">PmALMT</span> in Prunus mume. Different colors represent different cis-acting elements. (<b>b</b>) The number of 16 cis-acting elements in the promoters of <span class="html-italic">PmALMT</span> genes.</p>
Full article ">Figure 7
<p>The expression levels of <span class="html-italic">PmALMTs</span>. (<b>a</b>) Expression levels of <span class="html-italic">PmALMT</span> genes in roots, stems, leaves, flowers, and fruits, measured in FPKMs. (<b>b</b>) Expression pattern of <span class="html-italic">PmALMT1</span> during fruit development. (<b>c</b>) Expression pattern of <span class="html-italic">PmALMT8</span> during fruit development. S1 corresponds to 33 DAF (days after flowering), S2 to 48 DAF, S3 to 62 DAF, S4 to 77 DAF, and S5 to 92 DAF. Different letters (a, b, c, d, e) indicate statistical significance differences between groups.</p>
Full article ">Figure 8
<p>Subcellular localization of <span class="html-italic">PmALMT1</span> and <span class="html-italic">PmALMT8</span>. (<b>a</b>) Tobacco leaves were transiently transformed with mCherry and EGFP empty vectors as controls. (<b>b</b>) Tobacco leaves transiently co-expressed the fusion plasmid (35S:PmALMT1-EGFP) and the membrane marker (35S:PIP2A-mCherry). (<b>c</b>) Tobacco leaves transiently co-expressed the fusion plasmid (35S:PmALMT8-EGFP) and the membrane marker (35S:PIP2A-mCherry).</p>
Full article ">Figure 9
<p>Transient expression of <span class="html-italic">PmALMT1</span> and <span class="html-italic">PmALMT8</span>. (<b>a</b>) Overexpression of <span class="html-italic">PmALMT1</span> in <span class="html-italic">Nicotiana benthamiana</span>. The left leaf was photographed in bright field, while the right leaf was photographed under UV light. (<b>b</b>) Relative expression level of <span class="html-italic">PmALMT1</span> in <span class="html-italic">Nicotiana benthamiana</span> leaves. (<b>c</b>) Overexpression of <span class="html-italic">PmALMT8</span> in <span class="html-italic">Nicotiana benthamiana</span>. The left leaf was photographed in bright field, while the right leaf was photographed under UV light. (<b>d</b>) Relative expression level of <span class="html-italic">PmALMT8</span> in <span class="html-italic">Nicotiana benthamiana</span> leaves. (<b>e</b>) The malic acid contents of <span class="html-italic">PmALMT1</span>-CK and <span class="html-italic">PmALMT1</span>-OE. (<b>f</b>) The citric acid contents of <span class="html-italic">PmALMT1</span>-CK and <span class="html-italic">PmALMT1</span>-OE. (<b>g</b>) The malic acid contents of <span class="html-italic">PmALMT8</span>-CK and <span class="html-italic">PmALMT8</span>-OE. (<b>h</b>) The citric acid contents of <span class="html-italic">PmALMT8</span>-CK and <span class="html-italic">PmALMT8</span>-OE. Asterisks (**) indicate significant differences between groups (<span class="html-italic">p</span> &lt; 0.01).</p>
Full article ">
14 pages, 2080 KiB  
Article
A XGBoost-Based Prediction Method for Meat Sheep Transport Stress Using Wearable Photoelectric Sensors and Infrared Thermometry
by Ruiqin Ma, Runqing Chen, Buwen Liang and Xinxing Li
Sensors 2024, 24(23), 7826; https://doi.org/10.3390/s24237826 - 7 Dec 2024
Viewed by 357
Abstract
Transportation pressure poses a serious threat to the health of live sheep and the quality of their meat. So, the edible Hu sheep was chosen as the research object for meat sheep. We constructed a systematic biosignal detecting, processing, and modeling method. The [...] Read more.
Transportation pressure poses a serious threat to the health of live sheep and the quality of their meat. So, the edible Hu sheep was chosen as the research object for meat sheep. We constructed a systematic biosignal detecting, processing, and modeling method. The biosignal sensing was performed with wearable sensors (photoelectric sensor and infrared temperature measurement) for physiological dynamic sensing and continuous monitoring of the transport environment of meat sheep. Core waveform extraction and modern spectral estimation methods are used to determine and strip out the target signal waveform from it for the purpose of accurate sensing and the acquisition of key transport parameters. Subsequently, we built a qualitative stress assessment method based on external manifestations with reference to the Karolinska drowsiness scale to establish stage classification rules for monitoring data in the transportation environment of meat sheep. Finally, machine learning algorithms such as Gaussian Naive Bayes (GaussianNB), Passive-Aggressive Aggregative Classifier (PAC), Nearest Centroid (NC), K-Nearest Neighbor Classification (KNN), Random Forest (RF), Support Vector Classification (SVC), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGB) were established to predict the classification models of transportation stress in meat sheep. Their classification results were compared. The results show that SVC and GBDT algorithms are more effective and the overall model classification accuracy reached 86.44% and 91.53%. XGB has the best results. The accuracy of the assessment of the transport stress state of meat sheep after the optimization of three parameters was 100%, 90.91%, and 93.33%, and the classification accuracy of the overall model reached 94.92%. The final results achieved improve transport reliability, reduce transport risk, and solve the problems of inefficient meat sheep transport supervision and quality control. Full article
(This article belongs to the Section Biosensors)
Show Figures

Figure 1

Figure 1
<p>Flow chart of pulse wave sensing signal processing and transmission.</p>
Full article ">Figure 2
<p>Reconstruct the signal to obtain the PPG signal.</p>
Full article ">Figure 3
<p>Comparison figure of classification accuracy of different machine learning algorithms for optimization level.</p>
Full article ">
12 pages, 267 KiB  
Review
The Role of Dietary Magnesium in Cardiovascular Disease
by Forrest H. Nielsen
Nutrients 2024, 16(23), 4223; https://doi.org/10.3390/nu16234223 - 6 Dec 2024
Viewed by 741
Abstract
In the past 20 years, a large number of epidemiological studies, randomized controlled trials, and meta-analyses have found an inverse relationship between magnesium intake or serum magnesium and cardiovascular disease, indicating that low magnesium status is associated with hypertension, coronary artery calcification, stroke, [...] Read more.
In the past 20 years, a large number of epidemiological studies, randomized controlled trials, and meta-analyses have found an inverse relationship between magnesium intake or serum magnesium and cardiovascular disease, indicating that low magnesium status is associated with hypertension, coronary artery calcification, stroke, ischemic heart disease, atrial fibrillation, heart failure, and cardiac mortality. Controlled metabolic unit human depletion–repletion experiments found that a mild or moderate magnesium deficiency can cause physiological and metabolic changes that respond to magnesium supplementation, which indicates that these types of deficiencies or chronic latent magnesium deficiency are contributing factors to the occurrence and severity of cardiovascular disease. Mechanisms through which a mild or moderate magnesium deficiency can contribute to this risk include inflammatory stress, oxidative stress, dyslipidemia and deranged lipid metabolism, endothelial dysfunction, and dysregulation of cellular ion channels, transporters, and signaling. Based on USA official DRIs or on suggested modified DRIs based on body weight, a large number of individuals routinely consume less magnesium than the EAR. This especially occurs in populations that do not consume recommended amounts of whole grains, pulses, and green vegetables. Thus, inadequate magnesium status contributing to cardiovascular disease is widespread, making magnesium a nutrient of public health concern. Full article
(This article belongs to the Special Issue The Role of Magnesium Status in Human Health)
14 pages, 2814 KiB  
Article
Elastic Recovery In-Die During Cyclic Loading of Solid Anaerobic Digestate
by Grzegorz Łysiak and Ryszard Kulig
Materials 2024, 17(23), 5976; https://doi.org/10.3390/ma17235976 - 6 Dec 2024
Viewed by 280
Abstract
Anaerobic digestate represents a valuable organic by-product, with one of the main challenges being its enhanced utilization. Pelletization offers potential benefits by improving the digestate’s storability, facilitating transport, and significantly expanding its application as a fertilizer or biofuel. Understanding the mechanisms of densification [...] Read more.
Anaerobic digestate represents a valuable organic by-product, with one of the main challenges being its enhanced utilization. Pelletization offers potential benefits by improving the digestate’s storability, facilitating transport, and significantly expanding its application as a fertilizer or biofuel. Understanding the mechanisms of densification and their impact on the final product quality is essential and served as the inspiration for this research. Its primary focus was stress relaxation and the subsequent elongation of pellets within the compaction chamber (in-die). It investigated the hypothesis that elastic recovery, resulting from internal stress relaxation once the compressive force is removed, has direct implications for pellet quality. The investigations were conducted using a Zwick universal machine. Samples of digestate with varied moisture levels, i.e., 10, 13, 16, 19, and 22%, were loaded with amplitudes of 8, 11, 14, 17, and 20 kN. Ten loading and unloading cycles were employed. Elastic recovery (in-die) (ERin-die) in the investigated digestate increased with rising MC and compaction pressure but decreased with increasing cycle number. There was little correlation between ERin-die and pellet strength. Permanent strain energy exerted the greatest influence on pellet quality. Permanent strain energy had the greatest influence on pellet quality. Examining hysteresis loop behavior emerged as a promising area for further research to better understand springback phenomena. Full article
Show Figures

Figure 1

Figure 1
<p>A schematic diagram for determining the in-die elastic response, ER<sub>in-die</sub>.</p>
Full article ">Figure 2
<p>Effect of moisture content (<b>a</b>), applied pressure (<b>b</b>), and cycle number (<b>c</b>) of SAD on elastic recovery (ER<sub>in-die</sub>—elastic recovery, MC—moisture content, P—pressure, CN—cycle number).</p>
Full article ">Figure 3
<p>The interaction effects of moisture content with cycle number (<b>a</b>) and pressure (<b>b</b>) on the elastic recovery of SAD.</p>
Full article ">Figure 4
<p>Relationships between elastic recovery in-die and out-of-die.</p>
Full article ">Figure 5
<p>Hysteresis loops for 10 and 22% levels of moisture content of SAD.</p>
Full article ">Figure 6
<p>Effect of moisture content (<b>a</b>) and cycle number (<b>b</b>) on the area bounded between loading and unloading curves (without cycle 1).</p>
Full article ">Figure 7
<p>Strength index of pellets for variable pressure values and moisture contents (<b>a</b>), (<b>b</b>) effect of elastic recovery in-die ER<sub>in-die</sub> on SI values.</p>
Full article ">
Back to TopTop