[go: up one dir, main page]

 
 
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

Article Types

Countries / Regions

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

Search Results (2,398)

Search Parameters:
Keywords = growth and reproduction

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
19 pages, 2944 KiB  
Article
Dynamic Membrane Lipid Changes in Physcomitrium patens Reveal Developmental and Environmental Adaptations
by Deepshila Gautam, Jyoti R. Behera, Suhas Shinde, Shivakumar D. Pattada, Mary Roth, Libin Yao, Ruth Welti and Aruna Kilaru
Biology 2024, 13(9), 726; https://doi.org/10.3390/biology13090726 - 16 Sep 2024
Abstract
Membrane lipid composition is critical for an organism’s growth, adaptation, and functionality. Mosses, as early non-vascular land colonizers, show significant adaptations and changes, but their dynamic membrane lipid alterations remain unexplored. Here, we investigated the temporal changes in membrane lipid composition of the [...] Read more.
Membrane lipid composition is critical for an organism’s growth, adaptation, and functionality. Mosses, as early non-vascular land colonizers, show significant adaptations and changes, but their dynamic membrane lipid alterations remain unexplored. Here, we investigated the temporal changes in membrane lipid composition of the moss Physcomitrium patens during five developmental stages and analyzed the acyl content and composition of the lipids. We observed a gradual decrease in total lipid content from the filamentous protonema stage to the reproductive sporophytes. Notably, we found significant levels of very long-chain polyunsaturated fatty acids, particularly arachidonic acid (C20:4), which are not reported in vascular plants and may aid mosses in cold and abiotic stress adaptation. During vegetative stages, we noted high levels of galactolipids, especially monogalactosyldiacylglycerol, associated with chloroplast biogenesis. In contrast, sporophytes displayed reduced galactolipids and elevated phosphatidylcholine and phosphatidic acid, which are linked to membrane integrity and environmental stress protection. Additionally, we observed a gradual decline in the average double bond index across all lipid classes from the protonema stage to the gametophyte stage. Overall, our findings highlight the dynamic nature of membrane lipid composition during moss development, which might contribute to its adaptation to diverse growth conditions, reproductive processes, and environmental challenges. Full article
(This article belongs to the Special Issue Lipid Metabolism in Plant Growth and Development)
Show Figures

Figure 1

Figure 1
<p>Fatty acid profile of different developmental stages in <span class="html-italic">P. patens</span>. (<b>A</b>) Visual representation of five developmental stages of the moss; protonema (PN), early gametophore (EG), mid-gametophore (MG), late gametophore (LG) and sporophyte (SP); (<b>B</b>) Total lipid content of each developmental stage in mg/g fresh weight (FW) of tissue; (<b>C</b>) Distribution of fatty acids (FAs) in the sporophyte; (<b>D</b>) Major fatty acid (top) and minor fatty acid levels in <span class="html-italic">P. patens</span> during developmental stages.</p>
Full article ">Figure 2
<p>Major and minor lipid classes of the moss. Lipid content in various lipid classes at different developmental stages; protonema (PN), early gametophore (EG), mid-gametophore (MG), late gametophore (LG) and sporophyte (SP).</p>
Full article ">Figure 3
<p>Distribution of lipid classes. The percentage of each lipid class is shown for various developmental stages of the moss (protonema (PN), early gametophore (EG), mid-gametophore (MG), late gametophore (LG) and sporophyte (SP)), Selaginella (Sm), Arabidopsis (At) seedlings and seeds [<a href="#B27-biology-13-00726" class="html-bibr">27</a>], and mouse [<a href="#B28-biology-13-00726" class="html-bibr">28</a>]. The graphs indicate percentage of the total lipid weight for each lipid class.</p>
Full article ">Figure 4
<p>The acyl composition of galactolipids. Acyl composition of MGDG and DGDG in various developmental stages of the moss (protonema (PN), early gametophore (EG), mid-gametophore (MG), late gametophore (LG) and sporophyte (SP) and comparison with Selaginella (Sm) and Arabidopsis (At) 8-day seedlings [<a href="#B27-biology-13-00726" class="html-bibr">27</a>].</p>
Full article ">Figure 5
<p>Acyl composition of PC. The acyl composition of various developmental stages (various developmental stages of the moss (protonema (PN), early gametophore (EG), mid-gametophore (MG), late gametophore (LG) and sporophyte (SP)) of the moss, Selaginella (Sm) [<a href="#B27-biology-13-00726" class="html-bibr">27</a>].</p>
Full article ">Figure 6
<p>Acyl composition of PE. The acyl composition of PE in developmental stages of the moss (protonema (PN), early gametophore (EG), mid-gametophore (MG), late gametophore (LG) and sporophyte (SP) and comparison with Selaginella (Sm).</p>
Full article ">Figure 7
<p>Double bond index (DBI) of major and minor lipid classes during <span class="html-italic">P. patens</span> development. The values are shown as heat map with red being the highest, cyan showing the lowest, and yellow representing the mid-range value. The highest DBI values in each growth stages are shown as highlighted red color text. Monogalactosyl diacylglycerol (MGDG), digalactosyl diacylglycerol (DGDG), phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), phosphatidylinositol (PI), phosphatidylserine (PS), phosphatidic acid (PA), lysophosphatidylglycerol (LysoPG), lysophosphatidylcholine (LysoPC), and lysophosphatidylethanolamine (LysoPE).</p>
Full article ">
25 pages, 6783 KiB  
Article
Metabolomic and Transcriptomic Analyses Reveal the Potential Mechanisms of Dynamic Ovarian Development in Goats during Sexual Maturation
by Yanyan Wang, Tianle Chao, Qing Li, Peipei He, Lu Zhang and Jianmin Wang
Int. J. Mol. Sci. 2024, 25(18), 9898; https://doi.org/10.3390/ijms25189898 (registering DOI) - 13 Sep 2024
Viewed by 314
Abstract
The ovary is a crucial reproductive organ in mammals, and its development directly influences an individual’s sexual maturity and reproductive capacity. To comprehensively describe ovarian sexual maturation in goats, we integrated phenotypic, hormonal, metabolomic, and transcriptomic data from four specific time points: after [...] Read more.
The ovary is a crucial reproductive organ in mammals, and its development directly influences an individual’s sexual maturity and reproductive capacity. To comprehensively describe ovarian sexual maturation in goats, we integrated phenotypic, hormonal, metabolomic, and transcriptomic data from four specific time points: after birth (D1), at 2 months old (M2), at 4 months old (M4), and at 6 month old (M6). The study showed that during the early stage (D1–M2), ovarian growth was the most rapid, with weight and morphology increasing by 284% and 65%, respectively, and hormone levels rose significantly, with estradiol increasing by 57%. Metabolomic analysis identified 1231 metabolites, primarily lipids, lipid molecules, and organic acids, which can support hormone balance and follicle development by providing energy and participating in signaling transduction. Transcriptomic analysis identified 543 stage-specific differentially expressed genes, mainly enriched in steroid biosynthesis, amino acid metabolism, and the PI3K/AKT pathway, which are key factors influencing ovarian cell proliferation, apoptosis, hormone secretion, and metabolism. The integrated analysis revealed the key processes in the ovarian steroid hormone biosynthesis pathway and gene/metabolite networks associated with ovarian phenotypes and hormone levels, ultimately highlighting scavenger receptor class B type 1 (SCARB1), Cytochrome P450 Family 1 Subfamily A Member 1 (CYP11A1), 3beta-hydroxysteroid dehydrogenase (3BHSD), progesterone, estradiol, and L-phenylalanine as key regulators of ovarian morphological and functional changes at different developmental stages. This study is the first to reveal the metabolic changes and molecular regulatory mechanisms during ovarian sexual maturation in goats, providing valuable insights for understanding reproductive system development and optimizing reproductive performance and breeding efficiency. Full article
(This article belongs to the Section Molecular Endocrinology and Metabolism)
Show Figures

Figure 1

Figure 1
<p>Analysis of ovarian phenotypic characteristics and serum hormone levels at four developmental stages. (<b>A</b>) Analysis of the ovarian weight (OW), ovarian length (OL), ovarian width (OD), and ovarian thickness (OT). (<b>B</b>) Levels of gonadotropin-releasing hormone (GnRH), luteinizing hormone (LH), follicle-stimulating hormone (FSH), progesterone (PROG) and estradiol (E2) in serum. D1: after birth, M2: at 2 months old, M4: at 4 months old, M6: at 6 months old. Values represent mean ± standard error. Different lowercase letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). * <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 = not statistically significant.</p>
Full article ">Figure 2
<p>Basic metabolomics analysis of ovarian tissues at different developmental stages. (<b>A</b>) Principal component analysis of metabolites at four developmental stages. (<b>B</b>) Top 10 classes of metabolites identified during ovarian development. (<b>C</b>) The number of DAMs in pairwise comparisons. (<b>D</b>) The Venn diagram of DAMs in pairwise comparisons. (<b>E</b>) The heatmap of DAMs during ovarian development.</p>
Full article ">Figure 3
<p>The expression patterns and functional enrichment analysis of DAMs. (<b>A</b>) Expression pattern analysis of DAMs. (<b>B</b>) KEGG pathway enrichment analysis of all DAMs.</p>
Full article ">Figure 4
<p>Basic transcriptome analysis of ovarian tissues at different developmental stages. (<b>A</b>) Venn diagram of the number of mRNA in ovary tissues at different developmental stages. (<b>B</b>) Validation of transcriptome data by qRT-PCR. (<b>C</b>) The number of DAMs in pairwise comparisons. (<b>D</b>) The Upset Plot for DEGs in different comparison groups.</p>
Full article ">Figure 5
<p>The expression patterns and functional enrichment analysis of DEGs. (<b>A</b>) The heatmap of DEGs during ovarian development. (<b>B</b>) KEGG pathway enrichment analysis of all DEGs. (<b>C</b>) Expression pattern analysis of DEGs.</p>
Full article ">Figure 6
<p>The integrated metabolomic and transcriptomic analysis of ovarian tissues at different developmental stages. (<b>A</b>) The O2PLS analysis of DAMs and DEGs. (<b>B</b>) Bar plot of the top 20 transcriptomics and metabolites pq1.</p>
Full article ">Figure 7
<p>The pathway related to steroid hormone synthesis and the heatmap of changes in metabolites and genes. (<b>A</b>) The steroid biosynthesis pathway. (<b>B</b>) The ovarian steroid synthesis pathway. (<b>C</b>) Correlation analysis of DAMs and DEGs involved in steroid synthesis. Blue indicates a negative correlation and red indicates a positive correlation. The “*” and “**” represent significant differences at levels of <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01 according to Pearson’s correlation coefficient. (<b>D</b>) The estrogen signaling pathway during ovarian development.</p>
Full article ">Figure 8
<p>Analysis of the WGCNA of the ovarian metabolome at different developmental stages. (<b>A</b>) The clustering dendrogram of all metabolites. The color rows below the dendrogram show module assignments based on the dynamic hybrid branch cutting method. Each color represents a different module of co-expressed metabolites, while the “Merged dynamic” row indicates merged modules, with gray representing unassigned metabolites. The y-axis (Height) represents the dissimilarity between metabolites, with higher values indicating less similarity. (<b>B</b>) Heatmap of the correlations between modules and phenotypic traits. Modules are color-coded on the left, and phenotypic traits are listed below. The numbers in the cells represent correlation coefficients, with red for positive correlations and green for negative correlations. Color intensity indicates correlation strength. (<b>C</b>,<b>D</b>) Visualization of connections of metabolites in pink (<b>C</b>) and brown (<b>D</b>) modules. According to the connection weight value, only the first 100 functional relationships are shown in the figures. Yellow nodes represent hub metabolites with high connectivity, while blue nodes represent others. The thickness of the edges between nodes reflects the strength of their connections, with thicker edges indicating stronger associations, highlights key metabolites in the modules. OW: ovarian weight. OL: ovarian length. OD: ovarian width. OT: ovarian thickness. E2: estradiol. PROG: progesterone.</p>
Full article ">Figure 9
<p>Analysis of the WGCNA of ovarian transcriptome at different developmental stages. (<b>A</b>) The clustering dendrogram of all genes. The color rows below the dendrogram show module assignments based on the dynamic hybrid branch cutting method. Each color represents a different module of co-expressed genes, while the “Merged dynamic” row indicates merged modules, with gray representing unassigned metabolites. The y-axis (Height) represents the dissimilarity between genes, with higher values indicating less similarity. (<b>B</b>) Heatmap of the correlations between modules and phenotypic traits. The modules are color-coded on the left, and phenotypic traits are listed below. The numbers in the cells represent correlation coefficients, with red for positive correlations and green for negative correlations. Color intensity indicates correlation strength. (<b>C</b>) KEGG enrichment analysis of genes in the yellow module. The y-axis shows enriched biological pathways, and the x-axis (Count) indicates the number of genes involved. (<b>D</b>) Network visualization of the top 50 genes in the yellow module, based on connection weights. Yellow nodes represent hub genes with high connectivity, while blue nodes represent other genes. Edge thickness reflects connection strength, with thicker edges indicating stronger associations. OL: ovarian length. OD: ovarian width. OT: ovarian thickness. E2: estradiol. PROG: progesterone.</p>
Full article ">
12 pages, 261 KiB  
Article
Information of Growth Traits Is Helpful for Genetic Evaluation of Litter Size in Pigs
by Hui Yang, Lei Yang, Jinhua Qian, Lei Xu, Li Lin and Guosheng Su
Animals 2024, 14(18), 2669; https://doi.org/10.3390/ani14182669 - 13 Sep 2024
Viewed by 170
Abstract
Litter size is an important trait in pig production. But selection accuracy for this trait is relatively low, compared with production traits. This study, for the first time, investigated the improvement of genetic evaluation of reproduction traits such as litter size in pigs [...] Read more.
Litter size is an important trait in pig production. But selection accuracy for this trait is relatively low, compared with production traits. This study, for the first time, investigated the improvement of genetic evaluation of reproduction traits such as litter size in pigs using data of production traits as an additional information source. The data of number of piglets born alive per litter (NBA), age at 100 kg of body weight (Age100), and lean meet percentage (LMP) in a Yorkshire population were analyzed, using either a single-trait model or the multitrait model that allows us to account for environmental correlation between reproduction and production traits in the situation that one individual has only one record for a production trait while multiple records for a reproduction trait. Accuracy of genetic evaluation using single-trait and multitrait models were assessed by model-based accuracy (Rm) and validation accuracy (Rv). Two validation scenarios were considered. One scenario (Valid_r1) was that the individuals did not have a record of NBA, but Age100 and LMP. The other (Valid_r2) was that the individuals did not have a record for all the three traits. The estimate of heritability was 0.279 for Age100, 0.371 for LMP, and 0.076 for NBA. Genetic correlation was 0.308 between Age100 and LMP, 0.369 between Age100 and NBA, and 0.022 between LMP and NBA. Compared with the single-trait model, the multitrait model including Age100 increased prediction accuracy for NBA by 3.6 percentage points in Rm and 5.9 percentage points in Rv for the scenario of Valid_r1. The increase was 1.8 percentage points in Rm and 3.8 percentage points in Rv for the scenario of Valid_r2. Age100 also gained in the multitrait model but was smaller than NBA. However, LMP did not benefit from a multitrait model and did not have a positive contribution to genetic evaluation for NBA. In addition, the multitrait model, in general, slightly reduced level bias but not dispersion bias of genetic evaluation. According to these results, it is recommended to predict breeding values using a multitrait model including growth and reproduction traits. Full article
(This article belongs to the Special Issue Genetic Improvement in Pigs)
17 pages, 7033 KiB  
Article
RNA Sequencing Reveals a Strong Predominance of THRA Splicing Isoform 2 in the Developing and Adult Human Brain
by Eugenio Graceffo, Robert Opitz, Matthias Megges, Heiko Krude and Markus Schuelke
Int. J. Mol. Sci. 2024, 25(18), 9883; https://doi.org/10.3390/ijms25189883 - 13 Sep 2024
Viewed by 262
Abstract
Thyroid hormone receptor alpha (THRα) is a nuclear hormone receptor that binds triiodothyronine (T3) and acts as an important transcription factor in development, metabolism, and reproduction. In mammals, THRα has two major splicing isoforms, THRα1 and THRα2. The better-characterized isoform, THRα1, is a [...] Read more.
Thyroid hormone receptor alpha (THRα) is a nuclear hormone receptor that binds triiodothyronine (T3) and acts as an important transcription factor in development, metabolism, and reproduction. In mammals, THRα has two major splicing isoforms, THRα1 and THRα2. The better-characterized isoform, THRα1, is a transcriptional stimulator of genes involved in cell metabolism and growth. The less-well-characterized isoform, THRα2, lacks the ligand-binding domain (LBD) and is thought to act as an inhibitor of THRα1 activity. The ratio of THRα1 to THRα2 splicing isoforms is therefore critical for transcriptional regulation in different tissues and during development. However, the expression patterns of both isoforms have not been studied in healthy human tissues or in the developing brain. Given the lack of commercially available isoform-specific antibodies, we addressed this question by analyzing four bulk RNA-sequencing datasets and two scRNA-sequencing datasets to determine the RNA expression levels of human THRA1 and THRA2 transcripts in healthy adult tissues and in the developing brain. We demonstrate how 10X Chromium scRNA-seq datasets can be used to perform splicing-sensitive analyses of isoforms that differ at the 3′-end. In all datasets, we found a strong predominance of THRA2 transcripts at all examined stages of human brain development and in the central nervous system of healthy human adults. Full article
Show Figures

Figure 1

Figure 1
<p><b>Study overview.</b> (<b>a</b>) Schematic representation of the 3′-ends of the <span class="html-italic">THRA</span> isoform 1 and 2 mRNAs encoding THR<span class="html-italic">α</span>1 and THR<span class="html-italic">α</span>2, respectively. The orange spheres represent the T3 ligand, and the solid rectangles represent the exons. The schematic highlights that T3 can bind to THR<span class="html-italic">α</span>1 but not to THR<span class="html-italic">α</span>2. (<b>b</b>) Schematic representation of local T3-responsive gene expression based on the abundance of THR<span class="html-italic">α</span>1 and THR<span class="html-italic">α</span>2. In the presence of the same amount of local T3, cell types that synthesize more THR<span class="html-italic">α</span>1 will have greater T3-responsive gene expression compared with cell types that synthesize more THR<span class="html-italic">α</span>2. (<b>c</b>) Overview of the datasets used in this study. Datasets with a <b><span style="color:#205493">blue</span></b> background are bulk RNA-seq datasets, while datasets with a <b><span style="color:#7F6000">beige</span></b> background are single-cell RNA-seq datasets, [<a href="#B9-ijms-25-09883" class="html-bibr">9</a>,<a href="#B10-ijms-25-09883" class="html-bibr">10</a>,<a href="#B11-ijms-25-09883" class="html-bibr">11</a>]. (<b>d</b>) Schematic representation of the developmental stages covered by the datasets in relation to human development. Datasets with a <b><span style="color:#205493">blue</span></b> background are bulk RNA-seq datasets, while datasets with a <b><span style="color:#7F6000">beige</span></b> background are single-cell RNA-seq datasets. (<b>e</b>) Bioinformatic pipelines used to analyze the <b><span style="color:#205493">bulk RNA-seq datasets</span></b> and the <b><span style="color:#7F6000">single-cell RNA-seq datasets</span></b>.</p>
Full article ">Figure 2
<p><b><span class="html-italic">THRA</span> isoform expression pattern of the D1 GSE224153 dataset in transcripts per million (TPM)</b>. The graph shows that all the nervous system samples expressed high levels of total <span class="html-italic">THRA</span> (full bar length), with a predominance of the <span class="html-italic">THRA2</span> isoform (dark blue). Samples are sorted in decreasing order based on the difference between <span class="html-italic">THRA2</span> and <span class="html-italic">THRA1</span> (light blue). Samples above the red line express more <span class="html-italic">THRA2</span> than <span class="html-italic">THRA1</span>. Data from ~62 M uniquely mapped reads.</p>
Full article ">Figure 3
<p><b><span class="html-italic">THRA</span> isoform expression pattern of the D2 E-MTAB-8325 dataset in transcripts per million (TPM)</b>. (<b>a</b>) TPM calculated with StringTie (full transcript length) showing an increase in total <span class="html-italic">THRA</span> expression (full bar height) over time and a strong predominance of <span class="html-italic">THRA2</span> (dark blue) at all time points. Bar plots show the mean ± SEM; n = 3. (<b>b</b>) TPM calculated using exon 9b as a proxy for <span class="html-italic">THRA1</span> (light blue) and exon 10 as a proxy for <span class="html-italic">THRA2</span> (dark blue). The increased TPM values in (<b>b</b>) compared with (<b>a</b>) agree with the fact that TPM calculations normalize against transcript length; hence, using the shorter exon instead of the full transcript results in higher TPM values. The plots show a similar expression pattern as in (<b>a</b>), indicating that exons 9b and 10 can be used as proxies for isoform expression. Bar plots show the mean ± SEM; n = 3. Data from ~51 M uniquely mapped reads.</p>
Full article ">Figure 4
<p><b><span class="html-italic">THRA</span> isoform expression pattern of dataset D3 GSE250144 in transcripts per million (TPM)</b>. (<b>a</b>) Graph showing an increase in total <span class="html-italic">THRA</span> (full bar length) expression over time and a strong predominance of <span class="html-italic">THRA2</span> (dark blue) over <span class="html-italic">THRA1</span> (light blue) at all time points. Bar plots show the mean ± SEM; n ≥ 2. (<b>b</b>) Expression levels of total <span class="html-italic">THRA</span> (black) showing no difference between controls and samples treated with 50 nM T3 for 48 h before collection (orange). ns = not significant; mean ± SEM; one-way ANOVA test; n ≥ 2. (<b>c</b>) Expression levels of <span class="html-italic">THRA1</span> (light blue) showing no difference between the controls and samples treated with 50 nM T3 for 48 h before collection (orange). ns = not significant; error bars represent the mean ± SEM; one-way ANOVA test; n ≥ 2. (<b>d</b>) Expression levels of <span class="html-italic">THRA2</span> (dark blue) showing no difference between the controls and samples treated with 50 nM T3 for 48 h before collection (orange). ns = not significant; error bars represent the mean ± SEM; one-way ANOVA test; n ≥ 2; data from ~78 M uniquely mapped reads.</p>
Full article ">Figure 5
<p><b><span class="html-italic">THRA</span> isoform expression pattern of the D4 GSE153076 dataset in transcripts per million (TPM)</b>. (<b>a</b>) Graph showing an increase in total <span class="html-italic">THRA</span> expression (full bar length) over time and a strong predominance of <span class="html-italic">THRA2</span> (dark blue) over <span class="html-italic">THRA1</span> (light blue) at all time points in <span class="html-italic">Homo sapiens</span> samples. Bar plots show the mean ± SEM; n = 3. (<b>b</b>) Graph showing an increase in total <span class="html-italic">THRA</span> expression (full bar length) over time and a strong predominance of <span class="html-italic">THRA2</span> (dark blue) over <span class="html-italic">THRA1</span> (light blue) at all time points in <span class="html-italic">Gorilla gorilla</span> samples. Bar plots show the mean ± SEM; n = 3. (<b>c</b>) Graph comparing the expression levels of <span class="html-italic">THRA1</span> (light blue) and <span class="html-italic">THRA2</span> (dark blue) in <span class="html-italic">Homo sapiens</span> (solid line) vs. <span class="html-italic">Gorilla gorilla</span> (dashed line); error bars show the mean ± SEM; n = 3. Data from ~20 M uniquely mapped reads.</p>
Full article ">Figure 6
<p><b><span class="html-italic">THRA</span> isoform expression pattern of human cortical organoids from a single-cell dataset (D5 E-MTAB-8337)</b>. (<b>a</b>) Schematic of the edits made to the human reference genome annotation to detect <span class="html-italic">THRA</span> isoforms in 10X Chromium scRNA-seq datasets. Exons up to exon 9a [chr17:40,089,33] were considered <span class="html-italic">THRA</span> (in <b><span style="color:#434343">gray</span></b>), exon 9b and its 3′-UTR were specifically mapped to <span class="html-italic">THRA1</span> (light blue), and exon 10 and its 3′-UTR were specifically mapped to <span class="html-italic">THRA2</span> (dark blue). (<b>b</b>) UMAP plot showing the cell types identified by manual curation. These include a group of (i) progenitor radial glial cells (RG-1, RG-2, RG-enriched ribosome and proliferating RG), (ii) outer radial glial cells (oRG-1 to oRG-5), (iii) intermediate neuronal precursor cells (NPCs-1 and NPCs-2), (iv) early differentiated neurons (Neurons-1 to Neurons-4 and Neurons—ribosome enriched), (v) early astrocyte precursor cells (APCs), (vi) early oligodendrocyte precursor cells (OPCs), and, finally, (vii) choroid plexus TTR<sup>+</sup> cells (choroid plexus cells). (<b>c</b>) Dot plot showing the relative expression levels of the gene markers used to identify each population. Markers: <span class="html-italic">VIM</span> for radial glia, <span class="html-italic">RPL26</span> for ribosome enrichment, <span class="html-italic">UBE2C</span> for proliferating radial glia; <span class="html-italic">FABP7</span> and <span class="html-italic">PTN</span> for outer radial glia; <span class="html-italic">PLP1</span> for oligodendrocytic lineage; <span class="html-italic">APOE</span> for astrocytic lineage; <span class="html-italic">TTR</span> for choroid plexus cells; and <span class="html-italic">STMN2</span>, <span class="html-italic">SYT1</span>, <span class="html-italic">DCX</span>, and <span class="html-italic">GAP43</span> for neurons. (<b>d</b>) Dot plot showing the relative expression levels of <span class="html-italic">THRA1</span> and <span class="html-italic">THRA2</span> isoforms in each cell population (all n = 7 samples and time points combined). (<b>e</b>) Dot plot showing the relative expression levels of <span class="html-italic">THRA1</span> and <span class="html-italic">THRA2</span> isoforms in each cell population separated by the time point of analysis. NPC, neural precursor cell; APC, astrocyte precursor cell; OPC, oligodendrocyte precursor cell; oRG, outer radial glia; RG, radial glia; UMAP, Uniform Manifold Approximation and Projection.</p>
Full article ">Figure 7
<p><b><span class="html-italic">THRA</span> isoform expression pattern of a human embryo single-cell dataset (D6 GSE157329)</b>. (<b>a</b>) UMAP plot showing the cell types identified by the authors of the dataset. The dashed rectangle highlights the four cell populations that were sub-clustered and used for further downstream analysis. (<b>b</b>) Dot plot showing the relative expression levels of gene markers in the identified cell populations. Markers: <span class="html-italic">VIM</span>, <span class="html-italic">SOX2</span>, and <span class="html-italic">PAX6</span> for neural progenitors; <span class="html-italic">SOX10</span>, <span class="html-italic">MPZ</span>, and <span class="html-italic">PLP1</span> for Schwann cells; <span class="html-italic">POU4F1</span> for sensory neurons; and <span class="html-italic">STMN2</span>, <span class="html-italic">GAP43</span>, and <span class="html-italic">DCX</span> for neurons. (<b>c</b>) Dot plot showing the relative expression levels of <span class="html-italic">THRA1</span> and <span class="html-italic">THRA2</span> isoforms in each cell population (data from all n = 6 embryos of Carnegie stages 12–16 combined). UMAP, uniform manifold approximation and projection.</p>
Full article ">
20 pages, 8029 KiB  
Article
Impact of Temperature Elevation on Microbial Communities and Antibiotic Degradation in Cold Region Soils of Northeast China
by Zijun Ni, Xiaorong Zhang, Shuhai Guo, Huaqi Pan and Zongqiang Gong
Toxics 2024, 12(9), 667; https://doi.org/10.3390/toxics12090667 - 13 Sep 2024
Viewed by 239
Abstract
This study systematically investigated the effects of temperature changes on the degradation of antibiotics in soil, as well as the alterations in microbial community structure and aggregation, through a field warming experiment in a greenhouse. Compared to non-warming soil, the warming treatment significantly [...] Read more.
This study systematically investigated the effects of temperature changes on the degradation of antibiotics in soil, as well as the alterations in microbial community structure and aggregation, through a field warming experiment in a greenhouse. Compared to non-warming soil, the warming treatment significantly accelerated the degradation rate of tetracyclines during soil freezing and mitigated the impact of environmental fluctuations on soil microbial communities. The greenhouse environment promoted the growth and reproduction of a wide range of microbial taxa, but the abundance of Myxococcota was positively correlated with antibiotic concentrations in both treatments, suggesting a potential specific association with antibiotic degradation processes. Long-term warming in the greenhouse led to a shift in the assembly process of soil microbial communities, with a decrease in dispersal limitation and an increase in the drift process. Furthermore, co-occurrence network analysis revealed a more loosely structured microbial community in the greenhouse soil, along with the emergence of new characteristic taxa. Notably, more than 60% of the key taxa that connected the co-occurrence networks in both groups belonged to rare taxa, indicating that rare taxa play a crucial role in maintaining community structure and function. Full article
(This article belongs to the Section Toxicity Reduction and Environmental Remediation)
Show Figures

Figure 1

Figure 1
<p>Location and experimental layout of the antibiotics contaminate farmland.</p>
Full article ">Figure 2
<p>Changes in antibiotic concentration (<b>a</b>) and soil temperature (<b>b</b>) in In and Outside treatments across twelve months. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001, NS, not significant.</p>
Full article ">Figure 3
<p>Diversity analysis of In and Outside treatments. Venn diagrams of the abundant (<b>a</b>) and rare (<b>b</b>) communities in both treatments, along with Shannon and Chao1 indices at four time points. (<b>c</b>) NMDS analysis of community structure based on Bray–Curtis distances for both abundant and rare categories. *** <span class="html-italic">p</span> &lt; 0.001, a, b, c, and d reflect the 5% significance level.</p>
Full article ">Figure 4
<p>Analysis of the correlation between dominant phyla and families with antibiotics. Abundance changes in the top ten phyla and families in the abundant and rare communities of the In and Outside treatments at four time points (<b>a</b>), and the Spearman correlation with six antibiotics (<b>b</b>). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 5
<p>Contribution of community assembly to community aggregation in different treatments. The relative importance of different ecological processes for the rare and abundant communities at four time points (<b>a</b>), and the 121 bins (<b>b</b>) in In and Outside treatments.</p>
Full article ">Figure 6
<p>Co-occurrence networks and module abundance analysis. (<b>a</b>) Microbial community network co-occurrence analysis for In (<b>a</b>) and Outside (<b>b</b>) treatments. Two nodes with Spearman’s r &gt; 0.8 and <span class="html-italic">p</span> &lt; 0.05 were connected as edges, displaying edges with positive correlations. Nodes connected by edges of the same color belong to the same module. (<b>c</b>) Relative abundance changes and ASV numbers of different abundance categories in modules at four time points.</p>
Full article ">Figure 7
<p>Screening of key taxa in bacterial communities. Zi-Pi plots for In (<b>a</b>) and Outside (<b>b</b>) treatments. (<b>c</b>) The top 20 taxa ranked by feature importance in the random forest model in the two treatments and the correlation with antibiotics. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">
24 pages, 2773 KiB  
Review
Epigenetic Regulation of Fungal Secondary Metabolism
by Yufei Zhang, Wenbin Yu, Yi Lu, Yichuan Wu, Zhiwei Ouyang, Yayi Tu and Bin He
J. Fungi 2024, 10(9), 648; https://doi.org/10.3390/jof10090648 - 13 Sep 2024
Viewed by 315
Abstract
Secondary metabolism is one of the important mechanisms by which fungi adapt to their living environment and promote survival and reproduction. Recent studies have shown that epigenetic regulation, such as DNA methylation, histone modifications, and non-coding RNAs, plays key roles in fungal secondary [...] Read more.
Secondary metabolism is one of the important mechanisms by which fungi adapt to their living environment and promote survival and reproduction. Recent studies have shown that epigenetic regulation, such as DNA methylation, histone modifications, and non-coding RNAs, plays key roles in fungal secondary metabolism and affect fungal growth, survival, and pathogenicity. This review describes recent advances in the study of epigenetic regulation of fungal secondary metabolism. We discuss the way in which epigenetic markers respond to environmental changes and stimulate the production of biologically active compounds by fungi, and the feasibility of these new findings applied to develop new antifungal strategies and optimize secondary metabolism. In addition, we have deliberated on possible future directions of research in this field. A deeper understanding of epigenetic regulatory networks is a key focus for future research. Full article
(This article belongs to the Special Issue Recent Advances in Fungal Secondary Metabolism, 2nd Edition)
Show Figures

Figure 1

Figure 1
<p>Epigenetic modifications at different sites on histones.</p>
Full article ">Figure 2
<p>Regulatory mechanisms of DNA methylation: (<b>A</b>) Directly preventing transcription factor binding: DNA methylation can prevent transcription factors from binding to DNA, thus preventing the transcription of genes. Transcription factors normally bind to unmethylated DNA sequences to initiate the transcription process of a gene. However, when DNA methylation occurs in the vicinity of a transcription factor binding site, the methylated base pair prevents transcription factors from binding to the DNA, resulting in the repression of gene transcription. (<b>B</b>) The formation of inactive chromatin structures can affect gene transcription. (<b>C</b>) Methylated DNA can also recruit proteins of the DNA methylation-binding protein family to bind to methylated DNA, forming DNA methylation complexes. These complexes can further interact with co-transcriptional repressor complexes, which together maintain the shutdown state of genes, thereby repressing gene transcription.</p>
Full article ">Figure 3
<p>The regulatory process of histone methylation: Gene expression is regulated through the synergistic action of methylation enzymes and demethylation enzymes. Methylases are able to transfer methyl groups to lysine residues of histones, causing methylation of gene regions. The addition of methylated groups leads to the tightening of chromatin, making it difficult for the relevant gene regions to be bound by transcription factors, RNA polymerases and other proteins, thus inhibiting the transcriptional activity of genes.</p>
Full article ">Figure 4
<p>The regulatory process of histone acetylation: Histone acetyltransferases (HATs) transfer acetyl groups to histone proteins for regulation. HATs bind to chromatin-opening-associated proteins and relax chromatin structure by acetylating lysine residues of histones, allowing transcription factors to bind more readily to DNA and facilitating gene transcription. Deacetylases (HDACs) inhibit chromatin acetylation levels by removing acetyl groups from histones. The deacetylation process can further change the structure of chromatin and affect gene transcription. The activity and localisation of HDACs are regulated by many other factors such as cofactors, transcription factors and non-coding RNAs. These regulatory factors can affect the binding and catalytic activity of HDACs and thus the acetylation status of histones.</p>
Full article ">
12 pages, 257 KiB  
Article
Effects of Nutrient Manipulation during Peripartum and Suckling Period on Productivity of Hanwoo Cows and Offspring
by Gi-Hwal Son, Na-Hui Kim, So-Hee Lee, Young-Lae Kim, Jun-Sang Ahn, Min-Ji Kim, Jong-Suh Shin and Byung-Ki Park
Animals 2024, 14(18), 2633; https://doi.org/10.3390/ani14182633 - 11 Sep 2024
Viewed by 229
Abstract
This study investigated the effects of nutrient manipulation during the peripartum and suckling periods on the productivity of Hanwoo cows and their offspring. A total of 183 pregnant cows and their 180 offspring were randomly assigned to either a control group, fed a [...] Read more.
This study investigated the effects of nutrient manipulation during the peripartum and suckling periods on the productivity of Hanwoo cows and their offspring. A total of 183 pregnant cows and their 180 offspring were randomly assigned to either a control group, fed a formula feed with 13.5% crude protein (CP) and 70.5% total digestible nutrients (TDN), or a treatment group, fed nutrient-enriched formula feed with 18.0% CP and 72.5% TDN. Offspring were similarly divided and fed either 17.0% CP and 69.5% TDN (control) or 21.5% CP and 72.5% TDN (treatment). Results showed that body weight recovery was higher in the treatment group, although wither height, body length, and body condition scores were similar between groups. The treatment group exhibited increased chest girth, reduced intervals for first return to estrus, and shorter days open compared to the control group. Plasma non-esterified fatty acids, albumin, and progesterone concentrations of Hanwoo cows varied between groups at the 3 months before and after calving. Offspring in the treatment group had higher body weight and average daily gain at birth, three and six months of age, with higher dry matter intake. These findings suggest that nutrient-enriched formula feed positively influences the reproductive efficiency of Hanwoo cows and the growth performance of their offspring. Full article
15 pages, 5040 KiB  
Article
Transcriptome Analysis Reveals the Effect of Oyster Mushroom Spherical Virus Infection in Pleurotus ostreatus
by Yifan Wang, Junjie Yan, Guoyue Song, Zhizhong Song, Matthew Shi, Haijing Hu, Lunhe You, Lu Zhang, Jianrui Wang, Yu Liu, Xianhao Cheng and Xiaoyan Zhang
Int. J. Mol. Sci. 2024, 25(17), 9749; https://doi.org/10.3390/ijms25179749 - 9 Sep 2024
Viewed by 305
Abstract
Oyster mushroom spherical virus (OMSV) is a mycovirus that inhibits mycelial growth, induces malformation symptoms, and decreases the yield of fruiting bodies in Pleurotus ostreatus. However, the pathogenic mechanism of OMSV infection in P. ostreatus is poorly understood. In this study, RNA [...] Read more.
Oyster mushroom spherical virus (OMSV) is a mycovirus that inhibits mycelial growth, induces malformation symptoms, and decreases the yield of fruiting bodies in Pleurotus ostreatus. However, the pathogenic mechanism of OMSV infection in P. ostreatus is poorly understood. In this study, RNA sequencing (RNA-seq) was conducted, identifying 354 differentially expressed genes (DEGs) in the mycelium of P. ostreatus during OMSV infection. Verifying the RNA-seq data through quantitative real-time polymerase chain reaction on 15 DEGs confirmed the consistency of gene expression trends. Both Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses highlighted the pivotal role of primary metabolic pathways in OMSV infection. Additionally, significant changes were noted in the gene expression levels of carbohydrate-active enzymes (CAZymes), which are crucial for providing the carbohydrates needed for fungal growth, development, and reproduction by degrading renewable lignocellulose. The activities of carboxymethyl cellulase, laccase, and amylase decreased, whereas chitinase activity increased, suggesting a potential mechanism by which OMSV influenced mycelial growth through modulating CAZyme activities. Therefore, this study provided insights into the pathogenic mechanisms triggered by OMSV in P. ostreatus. Full article
(This article belongs to the Special Issue Advances in Plant Virus Diseases and Virus-Induced Resistance)
Show Figures

Figure 1

Figure 1
<p>The mycelium growth of OMSV-free (Mock) and OMSV−infected (OMSV) <span class="html-italic">P. ostreatus</span> strains. (<b>a</b>) The mycelium growth on PDA plates after seven days of cultivation. (<b>b</b>) RT-PCR detection of OMSV. Lane M, DNA Marker2000. Numbers 1–3 represent three biological replicates.</p>
Full article ">Figure 2
<p>Gene expression analysis of OMSV-free (Mock) and OMSV-infected (OMSV) strains of <span class="html-italic">P. ostreatus</span>. (<b>a</b>) Principal component analysis (PCA) of each sample. The FPKM values of each sample were used to perform PCA. (<b>b</b>) The FPKM box plots of each sample. The horizontal axis represents the sample names, while the vertical axis displays the log10 (FPKM) values. Different colors denote distinct samples.</p>
Full article ">Figure 3
<p>Differential gene analysis of OMSV-free (Mock) and OMSV-infected (OMSV) <span class="html-italic">P. ostreatus</span> strains. (<b>a</b>) Volcano plot of all identified genes. Genes with upregulated expression are indicated by red dots, those with downregulated expression by blue dots, and genes not differentially expressed by gray dots. (<b>b</b>) The statistical map of DEGs. The horizontal axis indicates the comparison names, and the vertical axis indicates the number of DEGs. (<b>c</b>) Heatmap displaying the clustering analysis of all DEGs for the OMSV-free and OMSV-infected strains of <span class="html-italic">P. ostreatus</span>. The expression levels of DEGs were normalized using the log10 FPKM method. Each row represents a single gene, and each column corresponds to a sample group. The color gradient from blue to red indicates that the FPKM value ranges from low to high.</p>
Full article ">Figure 4
<p>KOG analysis of the DEGs of OMSV-free and OMSV-infected <span class="html-italic">P. ostreatus</span> strains. The vertical axis indicates the number of DEGs within a specific functional cluster, while the horizontal axis represents the functional classes.</p>
Full article ">Figure 5
<p>GO and KEGG function classification of DEGs of OMSV-free and OMSV-infected <span class="html-italic">P. ostreatus</span> strains. (<b>a</b>) GO pathway-annotated genes. The vertical axis represented the name of the enriched GO term, while the horizontal axis indicated the number of DEGs within the corresponding term. Different colors represent three different categories. (<b>b</b>) KEGG pathway-annotated genes. Different colors represent four different categories. (<b>c</b>) Statistics of KEGG enrichment.</p>
Full article ">Figure 5 Cont.
<p>GO and KEGG function classification of DEGs of OMSV-free and OMSV-infected <span class="html-italic">P. ostreatus</span> strains. (<b>a</b>) GO pathway-annotated genes. The vertical axis represented the name of the enriched GO term, while the horizontal axis indicated the number of DEGs within the corresponding term. Different colors represent three different categories. (<b>b</b>) KEGG pathway-annotated genes. Different colors represent four different categories. (<b>c</b>) Statistics of KEGG enrichment.</p>
Full article ">Figure 6
<p>Validation of selected DEGs of OMSV-free (Mock) and OMSV−infected (OMSV) strains of <span class="html-italic">P. ostreatus</span> using qRT-PCR. The statistical analysis included a one-way analysis of variance (ANOVA) and <span class="html-italic">t</span>-tests, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 7
<p>The enzyme activity of OMSV-free (Mock) and OMSV−infected (OMSV) <span class="html-italic">P. ostreatus</span> strains during mycelial growth. (<b>a</b>) Laccase activity; (<b>b</b>) amylase activity; (<b>c</b>) CMCase activity; (<b>d</b>) chitinase activity. Numbers 5–8 denote the days of mycelial growth in liquid medium. The statistical analysis included a one-way analysis of variance (ANOVA) and <span class="html-italic">t</span>-tests, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">
23 pages, 1762 KiB  
Review
Review of Liquid Vitamin A and E Formulations in Veterinary and Livestock Production: Applications and Perspectives
by Yauheni Shastak and Wolf Pelletier
Vet. Sci. 2024, 11(9), 421; https://doi.org/10.3390/vetsci11090421 - 9 Sep 2024
Viewed by 1166
Abstract
Vitamins A and E are vital fat-soluble micronutrients with distinct yet intertwined roles in various biological processes. This review delves into their functions, nutritional requirements across different animal species, the consequences of deficiencies, and the impact of liquid formulations on veterinary medicine and [...] Read more.
Vitamins A and E are vital fat-soluble micronutrients with distinct yet intertwined roles in various biological processes. This review delves into their functions, nutritional requirements across different animal species, the consequences of deficiencies, and the impact of liquid formulations on veterinary medicine and livestock production. Vitamin A exists in multiple forms, essential for vision, immunity, and growth, while vitamin E acts primarily as an antioxidant, safeguarding cell membranes from oxidative damage. Hypovitaminosis in these vitamins can lead to severe health consequences, affecting vision, immunity, growth, reproduction, and neurological functions. Hence, supplementation, particularly through innovative liquid formulations, becomes pivotal in addressing deficiencies and enhancing overall animal health and productivity. Injectable forms of vitamins A and E show promise in enhancing reproductive performance, growth, and immune function in livestock. Administering these vitamins through drinking water offers a convenient way to enhance livestock health and productivity, particularly during times of stress or increased nutritional needs. Liquid vitamin A and E drops offer a flexible and effective solution in veterinary practice, allowing precise dosing and easy administration, particularly for companion animals. Future research may aim to optimize formulations and explore targeted therapies and precision feeding via nutrigenomics, promising advancements in veterinary medicine and livestock production. Full article
Show Figures

Figure 1

Figure 1
<p>Structural formula for all-trans-retinol.</p>
Full article ">Figure 2
<p>ATRA (all-trans-retinoic acid) as a modulator of T cell immunity [<a href="#B36-vetsci-11-00421" class="html-bibr">36</a>].</p>
Full article ">Figure 3
<p>Structural formula for α-tocopherol.</p>
Full article ">Figure 4
<p>Vitamin A bioavailability in chickens 14 days post-parenteral administration [<a href="#B11-vetsci-11-00421" class="html-bibr">11</a>].</p>
Full article ">Figure 5
<p>Average latissimus dorsi muscle fiber diameter at harvest (adapted from Wang et al., [<a href="#B23-vetsci-11-00421" class="html-bibr">23</a>]). Angus steer calves were administered with 0 (control) or 150,000 IU vitamin A (retinyl palmitate) per calf at birth and 1 month of age. The resulting steers were harvested at 14 months of age. * <span class="html-italic">p</span> &lt; 0.05; Mean, <span class="html-italic">n</span> = 9.</p>
Full article ">
15 pages, 777 KiB  
Review
Effects of Heat Stress-Induced Sex Hormone Dysregulation on Reproduction and Growth in Male Adolescents and Beneficial Foods
by Seong-Hee Ko
Nutrients 2024, 16(17), 3032; https://doi.org/10.3390/nu16173032 - 8 Sep 2024
Viewed by 579
Abstract
Heat stress due to climate warming can significantly affect the synthesis of sex hormones in male adolescents, which can impair the ability of the hypothalamus to secrete gonadotropin-releasing hormone on the hypothalamic–pituitary–gonadal axis, which leads to a decrease in luteinizing hormone and follicle-stimulating [...] Read more.
Heat stress due to climate warming can significantly affect the synthesis of sex hormones in male adolescents, which can impair the ability of the hypothalamus to secrete gonadotropin-releasing hormone on the hypothalamic–pituitary–gonadal axis, which leads to a decrease in luteinizing hormone and follicle-stimulating hormone, which ultimately negatively affects spermatogenesis and testosterone synthesis. For optimal spermatogenesis, the testicular temperature should be 2–6 °C lower than body temperature. Heat stress directly affects the testes, damaging them and reducing testosterone synthesis. Additionally, chronic heat stress abnormally increases the level of aromatase in Leydig cells, which increases estradiol synthesis while decreasing testosterone, leading to an imbalance of sex hormones and spermatogenesis failure. Low levels of testosterone in male adolescents lead to delayed puberty and incomplete sexual maturation, negatively affect height growth and bone mineral density, and can lead to a decrease in lean body mass and an increase in fat mass. In order for male adolescents to acquire healthy reproductive capacity, it is recommended to provide sufficient nutrition and energy, avoid exposure to heat stress, and provide foods and supplements to prevent or repair testosterone reduction, germ cell damage, and sperm count reduction caused by heat stress so that they can enter a healthy adulthood. Full article
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Regulation of sex hormone production. GnRH: gonadotropin-releasing hormone, LH: luteinizing hormone, FSH: follicle-stimulating hormone, DHT: 5α-dihydrotestosterone.</p>
Full article ">Figure 2
<p>Effects of heat stress on the hypothalamic–pituitary–gonadal axis during puberty. GnRH: gonadotropin-releasing hormone, GH: growth hormone, GHRH: growth hormone-releasing hormone, LH: luteinizing hormone, FSH: follicle-stimulating hormone.</p>
Full article ">
21 pages, 3481 KiB  
Article
Does Nitrogen Fertilization Improve Nitrogen-Use Efficiency in Spring Wheat?
by Aixia Xu, Yafei Chen, Xuexue Wei, Zechariah Effah, Lingling Li, Junhong Xie, Chang Liu and Sumera Anwar
Agronomy 2024, 14(9), 2049; https://doi.org/10.3390/agronomy14092049 - 7 Sep 2024
Viewed by 364
Abstract
To investigate the effects and mechanism of prolonged inorganic nitrogen (N) fertilization on the N-use efficiency of spring wheat (Triticum aestivum L.), a long-term study initiated in 2003 was conducted. The study analyzed how N fertilization affects dry matter translocation, N translocation, [...] Read more.
To investigate the effects and mechanism of prolonged inorganic nitrogen (N) fertilization on the N-use efficiency of spring wheat (Triticum aestivum L.), a long-term study initiated in 2003 was conducted. The study analyzed how N fertilization affects dry matter translocation, N translocation, soil NO3-N, and N-use efficiency. Five different N-fertilizer rate treatments were tested: N0, N52.5, N105, N157.5, and N210, corresponding to annual N fertilizer doses of 0, 52.5, 105.0, 157.5, and 210.0 kg N ha−1, respectively. Results showed that increasing N-fertilizer rates significantly enhanced the two-year average dry matter accumulation amount (DMA) at maturity by 22.97–56.25% and pre-flowering crop growth rate (CGR) by 17.11–92.85%, with no significant increase beyond 105 kg N ha−1. However, no significant correlation was observed between the dry matter translocation efficiency (DTE) and wheat grain yield. Both insufficient and excessive N applications resulted in an imbalanced N distribution favoring vegetative growth over reproductive growth, thus negatively impacting N-use efficiency. At maturity, the N-fertilized treatments significantly increased the two-year average N accumulation amount (NAA) by 52.04–129.98%, with no further increase beyond 105 kg N ha−1. N fertilization also improved the two-year average N translocation efficiency (NTE) by 56.89–63.80% and the N contribution proportion (NCP) of wheat vegetative organs by 27.79–57.83%, peaking in the lower-N treatment (N52.5). However, high-N treatment (N210) led to an increase in NO3-N accumulation in the 0–100 cm soil layer, with an increase of 26.27% in 2018 and 122.44% in 2019. This higher soil NO3-N accumulation in the 0–100 cm layer decreased NHI, NUE, NAE, NPFP, and NMB. Additionally, N fertilization significantly reduced the two-year average N harvest index (NHI) by 9.89–12.85% and N utilization efficiency (NUE) by 11.14–20.79%, both decreasing with higher N application rates. The NAA followed the trend of anthesis > maturity > jointing. At the 105 kg N ha−1 rate, the highest N agronomic efficiency (NAE) (9.31 kg kg−1), N recovery efficiency (NRE) (38.32%), and N marginal benefit (NMB) (10.67 kg kg−1) were observed. Higher dry matter translocation amount (DTA) and N translocation amount (NTA) reduced NHI and NUE, whereas higher NTE improved NHI, NUE, and N partial factor productivity (NPFP). Overall, N fertilization enhanced N-use efficiency in spring wheat by improving N translocation rather than dry matter translocation. Full article
(This article belongs to the Section Agroecology Innovation: Achieving System Resilience)
Show Figures

Figure 1

Figure 1
<p>Monthly rainfall and temperature for the experimental years.</p>
Full article ">Figure 2
<p>The dry matter translocation amount (DTA) in total aboveground (<b>A</b>) and different tissues (<b>D</b>), dry matter translocation efficiency (DTE) of total aboveground (<b>B</b>) and different tissues (<b>E</b>), and dry matter contribution proportion (DCP) of total aboveground (<b>C</b>) and different tissues (<b>F</b>) of wheat according to N fertilizer supply (N0, N52.5, N105, N157.5, and N210 represent annual N-fertilizer rates of 0, 52.5, 105.0, 157.5, and 210.0 kg N ha<sup>−1</sup>, respectively). Vertical bars represent standard errors, and columns with different letters indicate statistically significant differences according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
Full article ">Figure 3
<p>Nitrogen (N) accumulation (mg stem<sup>−1</sup>) in various wheat tissues according to N fertilizer supply (N0, N52.5, N105, N157.5, and N210 represent annual N-fertilizer rate of 0, 52.5, 105.0, 157.5, and 210.0 kg N ha<sup>−1</sup>, respectively) at (<b>A</b>) anthesis in 2018, (<b>B</b>) anthesis in 2019, (<b>C</b>) anthesis as a two-year average, (<b>D</b>) maturity in 2018, (<b>E</b>) maturity in 2019, and (<b>F</b>) maturity as a two-year average. Vertical bars represent standard errors, and columns with different letters indicate statistically significant differences according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
Full article ">Figure 4
<p>Nitrogen translocation and contribution to grain with different nitrogen (N) treatments (mg stem<sup>−1</sup>). The N translocation amount of wheat vegetative organs (<b>A</b>) and various tissues (<b>D</b>), N translocation efficiency of vegetative organs (<b>B</b>) and various tissues (<b>E</b>), and N contribution proportion of wheat vegetative organs (<b>C</b>) and different tissues (<b>F</b>) according to N fertilizer supply (N0, N52.5, N105, N157.5, and N210 represent annual N-fertilizer rates of 0, 52.5, 105.0, 157.5, and 210.0 kg N ha<sup>−1</sup>, respectively). Vertical bars represent standard errors, and columns with different letters indicate statistically significant differences according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
Full article ">Figure 5
<p>NO<sub>3</sub>–N accumulation (kg ha<sup>−1</sup>) in the 0–100 cm soil layers at maturity of wheat in 2018 (<b>A</b>) and 2019 (<b>B</b>) (N0, N52.5, N105, N157.5, and N210 represent annual N-fertilizer rates of 0, 52.5, 105.0, 157.5, and 210.0 kg N ha<sup>−1</sup>, respectively). Vertical bars represent standard errors, and columns with different letters indicate statistically significant differences according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
Full article ">Figure 6
<p>(<b>A</b>) Nitrogen (N) harvest index (NHI), (<b>B</b>) N utilization efficiency (NUE, kg kg<sup>−1</sup>), (<b>C</b>) N agronomic efficiency (NAE, kg kg<sup>−1</sup>), (<b>D</b>) N recovery efficiency (NRE, %), (<b>E</b>) N partial factor productivity (NPFP, kg kg<sup>−1</sup>), and (<b>F</b>) N marginal benefit (NMB, kg kg<sup>−1</sup>) according to N fertilizer supply (N0, N52.5, N105, N157.5, and N210 represent annual N-fertilizer rates of 0, 52.5, 105.0, 157.5, and 210.0 kg N ha<sup>−1</sup>, respectively). Vertical bars represent standard errors, and columns with different letters indicate statistically significant differences according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
Full article ">Figure 7
<p>Correlation coefficients among nitrogen (N) harvest index (NHI), N utilization efficiency (NUE, kg kg<sup>−1</sup>), N agronomic efficiency (NAE, kg kg<sup>−1</sup>), N recovery efficiency (NRE, %), N partial factor productivity (NPFP, kg kg<sup>−1</sup>), N marginal benefit (NMB, kg kg<sup>−1</sup>), dry matter translocation amount (DTA, mg stem<sup>−1</sup>), dry matter translocation efficiency (DTE, %), N translocation amount (NTA, kg ha<sup>−1</sup>), and N translocation efficiency (NTE, %) across N fertilizer treatments. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">
10 pages, 1252 KiB  
Article
Effect of Different Post-Flowering Photoperiods on Main Agronomic Traits of Strawberry (Fragaria × ananassa Duch. cv. Akihime)
by Cai Ren, Lamei Jiang, Weizhi Chen and Ziyi Wang
Agronomy 2024, 14(9), 2039; https://doi.org/10.3390/agronomy14092039 - 6 Sep 2024
Viewed by 431
Abstract
Reproductive growth is one of the most important stages in the life history of plants and is regulated by photoperiod. However, the effect of different photoperiods after flowering on the reproductive growth stages of different plants and their roles is still unclear. For [...] Read more.
Reproductive growth is one of the most important stages in the life history of plants and is regulated by photoperiod. However, the effect of different photoperiods after flowering on the reproductive growth stages of different plants and their roles is still unclear. For this reason, this study took the short-day plant strawberry (Fragaria × ananassa Duch. cv. Akihime) as the research object, performed different photoperiod treatments (ND: natural daylight; SD: short daylight; LD: long daylight) after flowering, and studied the effects of photoperiod on fruit growth period, fruit quality, flower opening, and plant height in different inflorescence of fruits. The results showed that different photoperiods had significantly different effects on the growth and development of strawberries after flowering, and LD and SD had opposite effects: (1) Under the condition of SD, the fruit matured after 17 days of treatment, while the LD and ND advanced this by 6 and 5 days. LD could significantly delay the development of the first inflorescence of fruits, resulting in longer ripening period and fruit appearance, and the quality traits were better. (2) The number of flowers in the secondary inflorescence and the development process was effectively accelerated by LD, and the total number of flowers under the long-day treatment was significantly more than that under the short-day treatment and the natural condition from 12 to 25 days after the end of the flowering period. Under the condition of LD, the fruits matured after 53 days of treatment, which was 5 days earlier than the other two treatments, and the period from flowering to maturity was shortened. (3) The effect of different photoperiods on the final plant height of strawberries after flowering had no significant difference (p < 0.05). In conclusion, this study found that photoperiod could effectively regulate the reproductive growth stage of strawberry after flowering, which enriched the experimental material and theoretical basis for studying the photoperiod as a mechanism for regulating plant growth and development, providing technical guidance for artificial regulation of strawberry growth period and fruit quality. Full article
(This article belongs to the Section Horticultural and Floricultural Crops)
Show Figures

Figure 1

Figure 1
<p>Day length of different photoperiod treatments. ND: natural daylight (ND), plants treated 118 days (20 November 2020–17 March 2021); SD: short daylight (SD), plants treated 58 days (20 January 2021–17 March 2021); LD: long daylight (LD), plants treated 58 days (20 January 2021–17 March 2021).</p>
Full article ">Figure 2
<p>Effects of different photoperiod treatments on the first inflorescence of strawberry fruit size (data are mean ± SD of 7 fruits). (Note: Different lowercase letters indicate significant differences in strawberry size between different treatments (<span class="html-italic">p</span> &lt; 0.05)).</p>
Full article ">Figure 3
<p>Effects of different photoperiod treatments on the first inflorescence of strawberry fruit appearance.</p>
Full article ">Figure 4
<p>Effects of different photoperiod treatments on the secondary inflorescence of total flowers per strawberry plant (Note: Data are the means of six plants. Different lowercase letters indicate significant differences in the number of flowers between different treatments).</p>
Full article ">
11 pages, 584 KiB  
Article
Dietary Supplementation with 25-Hydroxyvitamin D3 on Reproductive Performance and Placental Oxidative Stress in Primiparous Sows during Mid-to-Late Gestation
by Jing Li, Qingyue Bi, Yu Pi, Xianren Jiang, Yanpin Li and Xilong Li
Antioxidants 2024, 13(9), 1090; https://doi.org/10.3390/antiox13091090 - 6 Sep 2024
Viewed by 393
Abstract
The placenta plays a crucial role in nutrient transport and waste exchange between the dam and fetus, sustaining fetal growth. While the positive effects of 25-hydroxyvitamin D3 (25-OH-D3) on animal performance have been reported, its impact on placental function remains [...] Read more.
The placenta plays a crucial role in nutrient transport and waste exchange between the dam and fetus, sustaining fetal growth. While the positive effects of 25-hydroxyvitamin D3 (25-OH-D3) on animal performance have been reported, its impact on placental function remains largely unknown. Therefore, this study aimed to investigate the effects of supplementing 25-OH-D3 in the diet of primiparous sows on reproductive performance, antioxidant capacity, placental oxidative stress, nutrient transport, and inflammatory response during mid-to-late gestation. A total of 45 healthy Landrace × Yorkshire primiparous sows on day 60 of gestation were selected and randomly allocated to three treatment groups based on body weight and backfat thickness: the control group (corn-soybean meal basal diet), the VD3 group (basal diet + 2000 IU VD3), and the 25-OH-D3 group (basal diet + 50 μg/kg 25-OH-D3). The results demonstrated that supplementation with 25-OH-D3 in the diet enhanced sows’ average litter weight and birth weight during mid-to-late gestation. Additionally, plasma malondialdehyde (MDA) concentrations in sows significantly decreased in the VD3 and 25-OH-D3 groups (p < 0.05). Furthermore, lower gene expressions of placental HO-1, GPX2, IL-8, and IL-6 were found in the VD3 or 25-OH-D3 groups (p < 0.05 or p < 0.10), while higher gene expressions of GLUT1 and SNAT2 in the placenta of sows were observed in the VD3 and 25-OH-D3 groups, respectively (p < 0.05). These findings indicate that the supplementation of VD3 and 25-OH-D3 in the diet of sows can improve their plasma oxidative stress status, enhance placental antioxidant capacity and nutrient transport, and reduce placental inflammatory responses, with more pronounced improvements in sow performance observed in sows fed diets supplemented with 25-OH-D3. Full article
(This article belongs to the Special Issue Oxidative Stress in Reproduction of Mammals)
Show Figures

Figure 1

Figure 1
<p>The effect of supplementing VD<sub>3</sub> and 25-OH-D<sub>3</sub> to the sow diet on the mRNA expression of placental antioxidant-related genes during mid-to-late gestation. The results are presented as mean ± SEM, n = 8. * represents a significant difference (<span class="html-italic">p</span> &lt; 0.05). CT = control group fed with the basal diet; VD<sub>3</sub> = VD<sub>3</sub> group fed with the supplementation of 2000 IU/kg Vitamin D<sub>3</sub> in the basal diet; 25-OH-D<sub>3</sub> = 25-OH-D<sub>3</sub> group fed with the supplementation of 50 µg/kg 25-OH-D<sub>3</sub> in the basal diet. HO-1 = heme oxygenase-1; Nrf2 = nuclear factor-erythroid 2-related factor 2; SOD1 = superoxide dismutase 1; SOD2 = superoxide dismutase 2; CAT = catalase; GPX1 = glutathione peroxidase 1; GPX2 = glutathione peroxidase 2.</p>
Full article ">Figure 2
<p>The effect of supplementing VD<sub>3</sub> and 25-OH-D<sub>3</sub> into the sow diet on the mRNA expression of placental nutrient transport (<b>a</b>) and immune-related genes (<b>b</b>) during mid-to-late gestation. The results are presented as mean ± SEM, <span class="html-italic">n</span> = 8. * represents a significant difference (<span class="html-italic">p</span> &lt; 0.05). CT = control group fed with the basal diet; VD<sub>3</sub> = VD<sub>3</sub> group fed with the supplementation of 2000 IU/kg Vitamin D<sub>3</sub> in the basal diet; 25-OH-D<sub>3</sub> = 25-OH-D<sub>3</sub> group fed with the supplementation of 50 µg/kg 25-OH-D<sub>3</sub> in the basal diet. <span class="html-italic">GLUT1</span> = glucose transporter type 1; <span class="html-italic">SNAT2</span> = sodium coupled neutral amino acid transporter 2; <span class="html-italic">SNAT1</span> = sodium coupled neutral amino acid transporter 1; <span class="html-italic">VEGFA</span> = vascular endothelial growth factor A; <span class="html-italic">IL-8</span> = interleukin-8; <span class="html-italic">IL-6</span> = interleukin-6; <span class="html-italic">IL-1β</span> = interleukin-1β; <span class="html-italic">TNF-α</span> = tumor necrosis factor-α.</p>
Full article ">
10 pages, 1869 KiB  
Article
Metarhizium-Inoculated Coffee Seeds Promote Plant Growth and Biocontrol of Coffee Leaf Miner
by Jéssica Letícia Abreu Martins, Mayara Loss Franzin, Douglas da Silva Ferreira, Larissa Cristina Rocha Magina, Elem Fialho Martins, Laís Viana Paes Mendonça, Wânia dos Santos Neves, Angelo Pallini, Fernando Hercos Valicente, Jason M. Schmidt, Simon Luke Elliot and Madelaine Venzon
Microorganisms 2024, 12(9), 1845; https://doi.org/10.3390/microorganisms12091845 - 6 Sep 2024
Viewed by 369
Abstract
Metarhizium (Hypocreales: Clavicipitaceae) has a multifunctional life cycle, establishing as a plant endophyte and acting as entomopathogenic fungi. Metarhizium robertsii and Metarhizium brunneum can be associated with coffee plants and provide enhanced protection against a major pest of coffee, the coffee leaf miner [...] Read more.
Metarhizium (Hypocreales: Clavicipitaceae) has a multifunctional life cycle, establishing as a plant endophyte and acting as entomopathogenic fungi. Metarhizium robertsii and Metarhizium brunneum can be associated with coffee plants and provide enhanced protection against a major pest of coffee, the coffee leaf miner (CLM) (Leucoptera coffeella). This association would be an easily deployable biological control option. Here we tested the potential of inoculating coffee seeds with M. robertsii and M. brunneum collected from the soil of coffee crops in the Cerrado (Brazil) for control of the CLM and the enhancement of plant growth with a commonly used fungicide. We conducted the experiment in a greenhouse and after the seedlings grew, we placed them in a cage with two couples of CLMs. We evaluated the CLM development time, reproduction, and plant growth traits. We observed a longer development time of CLMs when fed on plants inoculated with both isolates. In addition, the CLMs laid fewer eggs compared to those fed on plants without fungal inoculation. Plant growth was promoted when seeds were inoculated with fungi, and the fungicide did not affect any evaluated parameter. Coffee seed inoculation with M. robertsii and M. brunneum appears to provide protection against CLMs and promote growth improvement. Full article
Show Figures

Figure 1

Figure 1
<p>Developmental time from egg to adult of <span class="html-italic">Leucoptera coffeella</span> from coffee seeds inoculated with the treatments: C1 (untreated seeds); C2 (fungicide-treated seeds); T1 (untreated seeds plus <span class="html-italic">M. robertsii</span>); T2 (fungicide-treated seeds plus <span class="html-italic">M. robertsii</span>); T3 (untreated seeds plus <span class="html-italic">M. brunneum</span>); and T4 (fungicide-treated seeds plus <span class="html-italic">M. brunneum</span>).</p>
Full article ">Figure 2
<p>Survival of adult females (<b>A</b>) and males (<b>B</b>) of <span class="html-italic">Leucoptera coffeella</span> when fed on coffee plants following the treatments: C1 (untreated seeds); C2 (fungicide-treated seeds); T1 (untreated seeds plus <span class="html-italic">M. robertsii</span>); T2 (fungicide-treated seeds plus <span class="html-italic">M. robertsii</span>); T3 (untreated seeds plus <span class="html-italic">M. brunneum</span>); and T4 (fungicide-treated seeds plus <span class="html-italic">M. brunneum</span>).</p>
Full article ">Figure 3
<p>Number of eggs per female of <span class="html-italic">Leucoptera coffeella</span> that emerged from coffee seedlings grown from seeds inoculated with the treatments: C1 (untreated seeds); C2 (fungicide-treated seeds); T1 (untreated seeds plus <span class="html-italic">M. robertsii</span>); T2 (fungicide-treated seeds plus <span class="html-italic">M. robertsii</span>); T3 (untreated seeds plus <span class="html-italic">M. brunneum</span>); and T4 (fungicide-treated seeds plus <span class="html-italic">M. brunneum</span>). Bars with the same letters are not statistically different via the Tukey method (<span class="html-italic">p</span> &lt; 0.001).</p>
Full article ">Figure 4
<p>Growth variables of plants from coffee seeds: C1 (untreated seeds); C2 (fungicide-treated seeds); T1 (untreated seeds plus <span class="html-italic">M. robertsii</span>); T2 (fungicide-treated seeds plus <span class="html-italic">M. robertsii</span>); T3 (untreated seeds plus <span class="html-italic">M. brunneum</span>); and T4 (fungicide-treated seeds plus <span class="html-italic">M. brunneum</span>). (<b>A</b>) root length; (<b>B</b>) shoot system height; (<b>C</b>) shoot system fresh mass; (<b>D</b>) root system fresh mass; (<b>E</b>) root system dry mass; and (<b>F</b>) shoot system dry mass. Bars with the same letters are not statistically different via the Tukey method (<span class="html-italic">p</span> &lt; 0.001).</p>
Full article ">
19 pages, 5676 KiB  
Article
The Promising Role of Synthetic Flavors in Advancing Fish Feeding Strategies: A Focus on Adult Female Zebrafish (Danio rerio) Growth, Welfare, Appetite, and Reproductive Performances
by Federico Conti, Ike Olivotto, Nico Cattaneo, Massimiliano Pavanello, İdris Şener, Matteo Antonucci, Giulia Chemello, Giorgia Gioacchini and Matteo Zarantoniello
Animals 2024, 14(17), 2588; https://doi.org/10.3390/ani14172588 - 5 Sep 2024
Viewed by 558
Abstract
The present study aimed to test over a six-month period different synthetic flavors in zebrafish (Danio rerio) as an experimental model. Specifically, two attractive and one repulsive synthetic flavors were added (1% w/w) to a specific zebrafish diet, [...] Read more.
The present study aimed to test over a six-month period different synthetic flavors in zebrafish (Danio rerio) as an experimental model. Specifically, two attractive and one repulsive synthetic flavors were added (1% w/w) to a specific zebrafish diet, which was administered to the fish during the whole life cycle (from larvae to adults), to evaluate their physiological responses, emphasizing fish welfare, feed intake, growth, reward mechanisms, and reproductive performances. Fish welfare was not affected by all tested flavors, while both attractive flavors promoted fish feed ingestion and growth. The results were supported by both molecular and immunohistochemical analyses on appetite-regulating neurohormonal signals, along with the influence of the feed hedonic properties induced by the brain reward sensation, as demonstrated by the dopamine receptor gene expression. Finally, the present study demonstrated that a higher feed intake also had positive implications on fish reproductive performances, suggesting a promising role of synthetic flavors for the aquaculture industry. In conclusion, the results highlighted the potential of synthetic flavors to improve fish feeding strategies by providing a consistent and effective alternative to traditional stimulants, thereby reducing dependence on natural sources. Full article
(This article belongs to the Section Aquatic Animals)
Show Figures

Figure 1

Figure 1
<p>Final body weight (mg) (<b>left</b>) and survival rate (%) (<b>right</b>) of adult zebrafish fed the experimental diets. Boxplot shows minimum and maximum (whiskers), first quartile, median, and third quartile (box). <sup>a–d</sup> Different letters indicate statistically significant differences among the experimental groups (<span class="html-italic">p</span> &lt; 0.05). Values are presented as mean ± SD (<span class="html-italic">n</span> = 3); ns, no significant differences.</p>
Full article ">Figure 2
<p>Percentage of feed ingested by adult zebrafish, calculated 15 min post dietary administration. Results are expressed as mean + SD (<span class="html-italic">n</span> = 3). <sup>a,b</sup> Different letters indicate statistically significant differences among the experimental groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 3
<p>Representative figures of intestine and liver parenchyma of adult zebrafish (<b>a</b>,<b>b</b>) from the present study: (<b>a</b>) details on mucosal folds (double-headed arrow: mucosal folds height); (<b>b</b>) liver parenchyma. Scale bars: 50 μm.</p>
Full article ">Figure 4
<p>Relative mRNA abundance of genes involved in growth analyzed in liver samples from adult zebrafish. (<b>a</b>) <span class="html-italic">igf1</span> and (<b>b</b>) <span class="html-italic">mstnb</span>. Results are expressed as mean + SD (<span class="html-italic">n</span> = 5). <sup>a,b</sup> Different letters denote statistically significant differences among the experimental groups; ns, no significant differences.</p>
Full article ">Figure 5
<p>Relative mRNA abundance of genes involved in appetite regulation analyzed in brain (<span class="html-italic">npy</span>), intestine (<span class="html-italic">ghrl</span>), and liver (<span class="html-italic">lepa</span>) samples from adult zebrafish. (<b>a</b>) <span class="html-italic">npy</span>, (<b>b</b>) <span class="html-italic">ghrl</span>, and (<b>c</b>) <span class="html-italic">lepa</span>. Results are expressed as mean + SD (<span class="html-italic">n</span> = 5). <sup>a–c</sup> Different letters indicate statistically significant differences among the experimental groups.</p>
Full article ">Figure 6
<p>Relative mRNA abundance of <span class="html-italic">drd2a</span> involved in brain reward system analyzed in brain samples from adult zebrafish. Results are expressed as mean + SD (<span class="html-italic">n</span> = 5). <sup>a,b</sup> Different letters denote statistically significant differences among the experimental groups.</p>
Full article ">Figure 7
<p>Relative mRNA abundance of <span class="html-italic">nr3c1</span> involved in stress response analyzed in liver samples from adult zebrafish. Results are expressed as mean + SD (<span class="html-italic">n</span> = 5); ns, no significant differences.</p>
Full article ">Figure 8
<p>Neuropeptide Y (NPY) expression in the olfactory epithelium of zebrafish fed the different experimental diets. (<b>a</b>) Zebrafish olfactory organ, highlighting the morphology of epithelium arranged in lamellae. The central and medial region of each lamella includes a continuous sensory area, in which different olfactory sensory neurons (OSNs) are located, as well as a lateral non-sensory area (illustration by Conti F.); (<b>b</b>,<b>c</b>) DAPI (blue) correspond to nuclei while green (Alexa Fluor<sup>®</sup> 488) correspond to NPY (CTRL group); (<b>d</b>) representative transverse section of zebrafish olfactory epithelium from CTRL group showing NPY-expressing cells (arrowheads); (<b>e</b>) representative transverse section of zebrafish olfactory epithelium from F35 (caramel) group showing NPY-expressing cells (arrowheads). Scale bar = 50 µm.</p>
Full article ">Figure 9
<p>Oocyte developmental stages in zebrafish ovary. PreV, pre-vitellogenic stage; PostV, post-vitellogenic stage; N, nucleus. Scale bar: 200 µm.</p>
Full article ">Figure 10
<p>Total number of spawned eggs per day and percentage of hatching rate observed in fish fed the different experimental diets. (<b>a</b>) Average daily number of eggs laid within a 10-day period. (<b>b</b>) Hatching rate expressed as percentage. Boxplots show minimum and maximum (whiskers), first quartile, median, and third quartile (boxes). <sup>a–c</sup> Different letters indicate statistically significant differences between experimental groups (<span class="html-italic">p</span> &lt; 0.05). Values are presented as mean ± SD (<span class="html-italic">n</span> = 10 spawning days, <span class="html-italic">n</span> = 9 for hatching rate).</p>
Full article ">Figure 11
<p>Relative mRNA abundance of genes involved in vitellogenin production analyzed in liver from adult female zebrafish. (<b>a</b>) <span class="html-italic">vtg1</span>, (<b>b</b>) <span class="html-italic">vtg2</span>, and (<b>c</b>) <span class="html-italic">vtg3</span>. Results are expressed as mean + SD (<span class="html-italic">n</span> = 5). <sup>a,b</sup> Different letters indicate statistically significant differences among the experimental groups.</p>
Full article ">
Back to TopTop