[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
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

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 (10,711)

Search Parameters:
Keywords = metabolomics

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
17 pages, 3720 KiB  
Article
Effects of Different Stocking Densities on Snail Bellamya purificata Foot Muscle Nutritional Quality and Metabolic Function
by Yingyue Lou, Rui Jia, Bing Li, Linjun Zhou, Jian Zhu and Yiran Hou
Animals 2024, 14(24), 3618; https://doi.org/10.3390/ani14243618 (registering DOI) - 15 Dec 2024
Abstract
Snail Bellamya purificata is not only useful for bioremediation, purifying aquaculture environments, but it is also a commercially valuable and nutritionally rich aquatic product. To analyze the effect of various stocking densities on the muscle nutritional quality and metabolic functions of B. purificata [...] Read more.
Snail Bellamya purificata is not only useful for bioremediation, purifying aquaculture environments, but it is also a commercially valuable and nutritionally rich aquatic product. To analyze the effect of various stocking densities on the muscle nutritional quality and metabolic functions of B. purificata. The transcriptome and metabolome were analyzed and set up three different density groups—low (LD, 234.38 g/m2), medium (MD, 468.75 g/m2), and high (HD, 937.5 g/m2). The results of the study showed that the weight gain (WG) and specific growth rate (SGR) of B. purificata in the MD and HD groups were significantly lower compared to the LD group. High stocking density significantly reduced the oleic acid (C18:1n9c), linoleic acid (C18:2n6c), alpha-linolenic acid (C18:3n3), eicosadienoic acid (C20:2), erucic acid (C22:1n9), docosahexaenoic acid (DHA, C22:6n3), and lignoceric acid (C24:0) levels within snail foot muscle. Most of the identified differentially expressed genes (DEGs) were categorized as Signal transduction, according to the Kyoto Encyclopedia of Genes and Genomes (KEGG); these genes were categorized into Transport and catabolism, Endocrine system, and Immune system. A total of 11 upregulated DEGs and 19 downregulated DEGs were identified and confirmed to be associated with density stress. The identified metabolites were mainly enriched in the Metabolism category, with 620 differential metabolites identified in positive ion (POS) mode and 265 differential metabolites identified in the negative ion (NEG) mode among different stocking density groups. The differential metabolites affected by stocking density were primarily amino acids, nucleic acids, vitamins, and lipid metabolites. There were 8 upregulated differential metabolites and 14 downregulated differential metabolites identified and confirmed to be associated with density stress. These findings elucidated the response mechanisms of B. purificata to adverse stocking density conditions and provide data and a theoretical basis for selecting appropriate stocking densities for B. purificata. Full article
(This article belongs to the Special Issue Recent Research on Shellfish Aquaculture and Reproduction)
Show Figures

Figure 1

Figure 1
<p>Differences in the alanine (Ala), arginine (Arg), asparagine (Asp), cystine (Cys), glutamic acid (Glu), glycine (Gly), histidine (His), isoleucine (Ile), leucine (Leu), lysine (Lys), methionine (Met), phenylalanine (Phe), proline (Pro), serine (Ser), threonine (Thr), tyrosine (Tyr), and valine (Val) within snail <span class="html-italic">Bellamya purificata</span> foot muscle between the three groups. Identical lowercase letters indicate that there is no significant difference between the three groups (<span class="html-italic">p</span> &gt; 0.05).</p>
Full article ">Figure 2
<p>Differences in the myristic acid (C14:0), pentadecanoic acid (C15:0), palmitic acid (C16:0), palmitoleic acid (C16:1), margaric acid (C17:0), stearic acid (C18:0), cis-9-octadecenoic acid (C18:1n-9c), linoleic acid (C18:2n6c), alpha-linolenic acid (C18:3n3), eicosadienoic acid (C20:2), arachidonic acid (C20:4n6), eicosapentaenoic acid (EPA, C20:5n3), heneicosanoic acid (C21:0), erucic acid (C22:1n9), docosahexaenoic acid (DHA, C22:6n3), and lignoceric acid (C24:0) within snail <span class="html-italic">Bellamya purificata</span> foot muscle between the three groups. Different lowercase letters indicate significant differences between the three groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 3
<p>Functional annotations for the transcriptome sequencing in foot muscles of snail <span class="html-italic">Bellamya purificata</span>. (<b>a</b>) KEGG for annotation of snail <span class="html-italic">Bellamya purificata</span> foot muscle transcriptome sequencing. (<b>b</b>) COG annotations for the transcriptome sequencing across snails <span class="html-italic">B. purificata</span> foot muscles.</p>
Full article ">Figure 4
<p>Differences in the functional gene expression within foot muscles of snail <span class="html-italic">Bellamya purificata</span> between the three groups. (<b>a</b>) PCA evaluating the differences in the gene expression profiles of snail foot muscle among the three groups. (<b>b</b>) Numbers of the DEGs in the three groups comparisons. (<b>c</b>) Venn diagram identifying the specific DEGs shared by three comparisons: HD vs. LD, HD vs. MD, and MD vs. LD.</p>
Full article ">Figure 5
<p>Functional annotations for the metabolites in foot muscles of snail <span class="html-italic">Bellamya purificata</span>. (<b>a</b>) KEGG annotations for the metabolites in foot muscles of snail <span class="html-italic">Bellamya purificata</span>. (<b>b</b>) PCA assessed the differences in the metabolomics of the snail foot muscles in three groups.</p>
Full article ">Figure 6
<p>Differences in metabolites in foot muscles of snails from group LD, group MD and group HD. (<b>a</b>) The volcano plot for the differential metabolites under POS and NEG between the three groups. (<b>b</b>) Venn diagram identifying the specific differential metabolites shared by three comparisons: HD vs. LD, HD vs. MD, and MD vs. LD.</p>
Full article ">
23 pages, 1457 KiB  
Article
Beneficial Effects of Maternal Supplementation of Yeast Single-Cell Protein on Suckling Piglets by Altering Sow Gut Microbiome and Milk Metabolome
by Zhongping Chen, Biao Li, Yong Zhuo, Yonggang Zhang and Guoshun Chen
Fermentation 2024, 10(12), 643; https://doi.org/10.3390/fermentation10120643 (registering DOI) - 15 Dec 2024
Viewed by 71
Abstract
This study aimed to assess the impact of yeast single-cell protein (YP) supplementation in diets from late gestation through lactation on sow reproductive performance and the associated gut microbiome and metabolomic changes in milk. A total of 172 sows, at 103 days of [...] Read more.
This study aimed to assess the impact of yeast single-cell protein (YP) supplementation in diets from late gestation through lactation on sow reproductive performance and the associated gut microbiome and metabolomic changes in milk. A total of 172 sows, at 103 days of gestation, were randomly assigned to four treatment groups: a control group receiving 2% fishmeal and three groups receiving 0.5%, 1.0%, and 2.0% YP, replacing 0.3%, 0.6%, and 1.5% fishmeal, respectively. No significant effects were observed on litter performance in sows. The inclusion of 2% YP displayed an augmented litter weight gain and piglet weight gain during lactation. Microbial sequencing revealed a marked decrease in Enterobacteriaceae abundance in sow feces at day 113 of gestation following dietary YP supplementation. Moreover, it led to a notable reduction of microbial-associated lipids, such as endotoxin, in serum and milk. In summary, YP supplementation in sow diets reduced gut pathogenic microbiota and their components, contributing to enhanced growth performance in suckling piglets. Full article
Show Figures

Figure 1

Figure 1
<p>Effects of 2% dietary YP supplementation on the microbial diversity at the genus level on day 113 of gestation (<b>A</b>) and day 18 of lactation (<b>B</b>). Control denotes the control group, while YP indicates the diet supplemented with 2% yeast single-cell protein.</p>
Full article ">Figure 2
<p>Effects of dietary 2% yeast hydrolysate supplementation on the endotoxin levels in serum, colostrum, and milk. (<b>A</b>) and (<b>B</b>) denote serum endotoxin level on G113 and L18, respectively. (<b>C</b>) and (<b>D</b>) denote endotoxin level in colostrum and milk, respectively. G113, day 113 of gestation; L18, day 18 of lactation. Control denotes the control group, while YP indicates the diet supplemented with 2% yeast single-cell protein. * denotes <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 3
<p>Volcano plots of differentially altered metabolites. Panels A and B depict the volcano plots for the different metabolites with positive ions (<b>A</b>) and negative ions (<b>B</b>) in the colostrum, respectively. (<b>C</b>,<b>D</b>) show the volcano plots for the differential metabolites with positive and negative ions in milk collected on day 18 of lactation. CON denotes the control group, while YP indicates the diet supplemented with 2% yeast single-cell protein.</p>
Full article ">
19 pages, 5168 KiB  
Article
Pleozymes: Pleiotropic Oxidized Carbon Nanozymes Enhance Cellular Metabolic Flexibility
by Anh T. T. Vo, Karthik Mouli, Anton V. Liopo, Philip Lorenzi, Lin Tan, Bo Wei, Sara A. Martinez, Emily A. McHugh, James M. Tour, Uffaf Khan, Paul J. Derry and Thomas A. Kent
Nanomaterials 2024, 14(24), 2017; https://doi.org/10.3390/nano14242017 (registering DOI) - 15 Dec 2024
Viewed by 214
Abstract
Our group has synthesized a pleiotropic synthetic nanozyme redox mediator we term a “pleozyme” that displays multiple enzymatic characteristics, including acting as a superoxide dismutase mimetic, oxidizing NADH to NAD+, and oxidizing H2S to polysulfides and thiosulfate. Benefits have [...] Read more.
Our group has synthesized a pleiotropic synthetic nanozyme redox mediator we term a “pleozyme” that displays multiple enzymatic characteristics, including acting as a superoxide dismutase mimetic, oxidizing NADH to NAD+, and oxidizing H2S to polysulfides and thiosulfate. Benefits have been seen in acute and chronic neurological disease models. The molecule is sourced from coconut-derived activated charcoal that has undergone harsh oxidization with fuming nitric acid, which alters the structure and chemical characteristics, yielding 3–8 nm discs with broad redox potential. Prior work showed pleozymes localize to mitochondria and increase oxidative phosphorylation and glycolysis. Here, we measured cellular NAD+ and NADH levels after pleozyme treatment and observed increased total cellular NADH levels but not total NAD+ levels. A 13C-glucose metabolic flux analysis suggested pleozymes stimulate the generation of pyruvate and lactate glycolytically and from the tricarboxylic acid (TCA) cycle, pointing to malate decarboxylation. Analysis of intracellular fatty acid abundances suggests pleozymes increased fatty acid β-oxidation, with a concomitant increase in succinyl- and acetyl-CoA. Pleozymes increased total ATP, potentially via flexible enhancement of NAD+-dependent catabolic pathways such as glycolysis, fatty acid β-oxidation, and metabolic flux through the TCA cycle. These effects may be favorable for pathologies that compromise metabolism such as brain injury. Full article
(This article belongs to the Special Issue Carbon-Based Nanomaterials for Biomedicine Applications)
Show Figures

Figure 1

Figure 1
<p>Schematic of PEG-OAC (pleozyme) synthesis. The synthesis starts by oxidizing a medicinal ingredient, coconut shell activated charcoal, with fuming nitric acid (90% HNO<sub>3</sub>) at 100 °C for 6 h in a round-bottom flash. The products are subsequently purified using water bath dialysis for 7 days and filtered through a 0.22 μm membrane to collect oxidized activated charcoal (OACs). Next, OACs are left to react with 5 kDa polyethylene glycol (PEG) in DIC and DMF for 48 h at room temperature. Subsequently, bath dialysis and filtration are performed to produce sterile PEG-OACs (pleozymes). Figure created using <a href="http://BioRender" target="_blank">BioRender</a>.</p>
Full article ">Figure 2
<p>Pleozymes have no effect on NAD<sup>+</sup>- and NADH-associated luminescence. Briefly, 100 µM stocks of NAD<sup>+</sup> and NADH are prepared in PBS and subsequently diluted in PBS to 200, 100, 50, and 25 nM NAD<sup>+</sup> and NADH, separately, with and without 0.4 µg/mL pleozymes. The luminescence is recorded following the protocol provided with the NAD<sup>+</sup>/NADH Glo Assay (Promega). N = 1 experiment, each point represents the luminescence mean of two wells and its standard deviation (both directions).</p>
Full article ">Figure 3
<p>Pleozymes increase intracellular NADH. (<b>A</b>) Luminescence assays reveal that pleozymes have no statistical effect on NAD<sup>+</sup>, (<b>B</b>) increase NADH, and (<b>C</b>) decrease the NAD<sup>+</sup>/NADH ratio. Mean + standard deviation (both directions). N = 5 independent assays. Friedman test. * represents <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 4
<p>Schematic of <sup>13</sup>C-glucose isotope tracing metabolic flux analysis. bEnd.3 cells were cultured in media containing 1,2-<sup>13</sup>C-glucose for 24 h. Diagrams indicating isotope-labeled and -unlabeled carbons included for glycolytic intermediates that were analyzed using ion chromatography-mass spectrometry (IC-MS). For steps following the cleavage of fructose-1,6,-bisphosphate to glyceraldehyde-3-phosphate, two 3-carbon molecules are generated per intermediate (highlighted in green), with one containing both <sup>13</sup>C atoms from the glucose tracer. Figure created using BioRender. (<a href="https://BioRender.com/f03b209" target="_blank">https://BioRender.com/f03b209</a>, accessed on 29 November 2024).</p>
Full article ">Figure 5
<p>Pleozyme treatment of glycolytically active bEnd.3 murine endothelial cells decreased isotopic enrichment of pyruvate. Columns show metabolite enrichment values from phosphate buffered saline- (control) and pleozyme-treated bEnd.3 cells. Colors represent isotopic enrichment of each metabolite, computed from <sup>13</sup>C tracing mass spectrometric metabolic flux analysis. Colors are scaled linearly, with higher and lower enrichment values represented by redder and bluer shades, respectively. Basally elevated isotopic enrichment of glycolytic metabolites suggests a higher rate of generation (i.e., increased glycolytic flux). Decreased pyruvate enrichment (blue arrow) in the context of increased glycolytic flux suggests hypothetically increased downstream metabolism of pyruvate with pleozyme treatment. Lactate enrichment (red arrow) remained unaffected with pleozymes. Dendrogram indicates hierarchical clusters of metabolites with similar expression patterns, generated using Ward’s algorithm. N = 1 experiment.</p>
Full article ">Figure 6
<p>Pleozyme treatment of bEnd.3 cells decreased the intracellular abundance of glycolytically derived pyruvate and increased lactate. Colors represent mean DNA-normalized mass spectrometric abundances of M + 2 isotope-labeled glycolytic intermediates and lactate at baseline (“Control, 24 h”) and following pleozyme treatment. Isotope-labeled intermediates are derived from glycolytic metabolism of 1,2-<sup>13</sup>C-glucose tracer, suggesting that pleozyme treatment hypothetically increased the generation of lactate from glycolytic precursors (blue and red arrows). Decreased fructose-6-phosphate suggests increased metabolism of early glycolytic intermediates toward lactate generation with pleozyme treatment. Colors scaled following quantile binning, with an equal number of data points per bin. N = 1 experiment.</p>
Full article ">Figure 7
<p>Pleozyme treatment of bEnd.3 cells increased intracellular abundances of non-glycolytically derived pyruvate and lactate. Colors represent mean DNA-normalized mass spectrometric abundances of M + 0 unlabeled glycolytic intermediates and lactate at baseline (“Control, 24 h”) and following pleozyme treatment. Unlabeled intermediates are not derived from glycolytic metabolism of 1,2-<sup>13</sup>C-glucose tracer, suggesting that pleozyme treatment hypothetically increased the generation of lactate from precursors aside from glucose (blue and red arrows). Decreased glucose- and fructose-6-phosphate suggest increased metabolism of residual unlabeled early glycolytic intermediates (present intracellularly prior to tracer glucose treatment) toward lactate generation with pleozyme treatment. Colors scaled following quantile binning, with an equal number of data points per bin. N = 1 experiment.</p>
Full article ">Figure 8
<p>Pleozyme treatment of bEnd.3 cells decreased abundances of TCA cycle intermediates, glutamate, and aspartate, while increasing levels of 2-oxoglutarate. Colors represent mean DNA-normalized mass spectrometric abundances of M + 0 unlabeled TCA cycle intermediates and the amino acids glutamate and aspartate at baseline (“Control, 24 h”) and following pleozyme treatment. A trend, albeit relatively small of decreased TCA metabolite abundances including citrate, isocitrate, succinate, fumarate, and malate may suggest that pleozyme treatment induces the diversion of TCA cycle intermediates toward other biosynthetic pathways. Decreased abundance of glutamate may correspond with an increase abundance of 2-oxoglutarate entering the TCA cycle, which, together with a decreased abundance of aspartate, may indicate increased replenishment of TCA cycle intermediates (anaplerosis) through amino acid transamination. Colors scaled following quantile binning, with an equal number of data points per bin. N = 1 experiment.</p>
Full article ">Figure 9
<p>Pleozyme treatment of bEnd.3 cells increased abundances of medium chain fatty acids and myristic acid. Colors represent mean DNA-normalized mass spectrometric intensities of intracellular fatty acid metabolites (N = 3 technical replicates of one experiment). Increased mass spectrometric abundances of capric, lauric, and myristic acids with a concurrent trend toward decreased long- and very long-chain fatty acids (i.e., pentadecylic and behenic acids) suggests hypothetically increased fatty acid oxidation with pleozyme treatment. Colors scaled following quantile binning, with an equal number of data points per bin.</p>
Full article ">Figure 10
<p>Pleozyme treatment of bEnd.3 cells increased abundances of acetyl- and succinyl-CoA. Colors represent mean DNA-normalized mass spectrometric intensities of intracellular fatty acid metabolites (N = 3 technical replicates of one experiment). Increased mass spectrometric abundances of acetyl- and succinyl-CoA suggests increased higher rate of generation with pleozyme treatment. Pleozymes induce a minor decrease in levels of the fatty acid synthesis precursor malonyl-CoA, suggesting limited metabolic flux through this anabolic pathway. Colors scaled following quantile binning, with an equal number of data points per bin.</p>
Full article ">Figure 11
<p>Pleozymes increase intracellular ATP levels after 2 and 24 h of treatment. bEnd.3 murine brain endothelial cells are treated with pleozymes for 2 and 24 h. Then, cell lysate is collected, and ATP levels are measured using a luciferase-coupled luminescence assay. (<b>A</b>) Pleozymes at a hypothetical intracellular level of 0.4 µg/mL do not affect ATP-associated luminescence. N = 1 experiment, points represent the mean luminescence of two technical replicates and its standard deviation (ATP in blue, top direction and ATP+pleozymes in red, bottom direction). (<b>B</b>) Pleozyme treatment trends toward an increase in intracellular ATP levels at both treatment durations, and this trend is statistically significant at 2 h in two independent assays and at 24 h in one assay. N = 4–8 technical replicates, mean ± standard deviation (both directions), Student’s t test, * represents <span class="html-italic">p</span> &lt; 0.05, *** represents <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 12
<p>Pleozymes enhance intracellular and mitochondrial energetic pathways and catalytically supply metabolic reactions with NAD<sup>+</sup> regeneration activities. Localizing in the cytoplasm and mitochondria, pleozymes catalytically accelerate NAD<sup>+</sup> regeneration from NADH and subsequent ATP production, leading to an increase in the NAD<sup>±</sup>-dependent metabolic rates. Pleozymes accelerate intracellular energy metabolisms through glycolysis and lactate generation. Pleozyme treatment also increases fatty acid β-oxidation and supplies acetyl- and succinyl-CoA to the TCA cycle, driving the cycling reactions and replenishing the lactate precursor, pyruvate, from malate. Figure created using BioRender (<a href="https://BioRender.com/d80a234" target="_blank">https://BioRender.com/d80a234</a>, accessed on 29 November 2024).</p>
Full article ">
13 pages, 5185 KiB  
Article
A Comprehensive Metabolomic and Microbial Analysis Following Dietary Amino Acid Reduction in Mice
by Raghad Khalid Al-Ishaq, Carmen R. Ferrara, Nisha Stephan, Jan Krumsiek, Karsten Suhre and David C. Montrose
Metabolites 2024, 14(12), 706; https://doi.org/10.3390/metabo14120706 (registering DOI) - 14 Dec 2024
Viewed by 345
Abstract
Introduction: Nutritional metabolomics provides a comprehensive overview of the biochemical processes that are induced by dietary intake through the measurement of metabolite profiles in biological samples. However, there is a lack of deep phenotypic analysis that shows how dietary interventions influence the metabolic [...] Read more.
Introduction: Nutritional metabolomics provides a comprehensive overview of the biochemical processes that are induced by dietary intake through the measurement of metabolite profiles in biological samples. However, there is a lack of deep phenotypic analysis that shows how dietary interventions influence the metabolic state across multiple physiologic sites. Dietary amino acids have emerged as important nutrients for physiology and pathophysiology given their ability to impact cell metabolism. Methods: The aim of the current study is to evaluate the effect of modulating amino acids in diet on the metabolome and microbiome of mice. Here, we report a comprehensive metabolite profiling across serum, liver, and feces, in addition to gut microbial analyses, following a reduction in either total dietary protein or diet-derived non-essential amino acids in mice. Results: We observed both distinct and overlapping patterns in the metabolic profile changes across the three sample types, with the strongest signals observed in liver and serum. Although amino acids and related molecules were the most commonly and strongly altered group of metabolites, additional small molecule changes included those related to glycolysis and the tricarboxylic acid cycle. Microbial profiling of feces showed significant differences in the abundance of select species across groups of mice. Conclusions: Our results demonstrate how changes in dietary amino acids influence the metabolic profiles across organ systems and the utility of metabolomic profiling for assessing diet-induced alterations in metabolism. Full article
(This article belongs to the Special Issue Nutritional Metabolomics in Cancer)
Show Figures

Figure 1

Figure 1
<p>Global metabolite shifts occur in response to modifying dietary amino acids. Principal component analysis (PCA) was conducted on the metabolite profiles of livers (<b>A</b>), serum (<b>B</b>), feces (<b>C</b>) or all three matrices combined (<b>D</b>) from mice fed control (CL), reduced protein (RP) or reduced NEAA (RN) diets for two weeks.</p>
Full article ">Figure 2
<p>Reducing protein or non-essential amino acids in diet induces significant changes in the levels of metabolites in the liver, serum and feces. Small molecules that were significantly changed (<span class="html-italic">p</span> adj &lt; 0.05) in abundance in the livers (<b>A</b>), serum (<b>B</b>), feces (<b>C</b>) or all three matrices combined (<b>D</b>) from mice fed a reduced protein (RP) or reduced NEAA (RN) diet compared to mice given a control diet (CL) are shown as heat maps. Red color indicates increased abundance; blue color indicates decreased abundance.</p>
Full article ">Figure 3
<p>Non-essential amino acid levels change in multiple biological matrices following consumption of amino acid-modified diets. (<b>A</b>) The abundance of non-essential amino acids in each of the study diets is shown. (<b>B</b>) The relative levels of non-essential amino acids in the liver, serum and feces from mice fed a reduced protein (RP) or reduced NEAA (RN) diet compared to mice given a control diet (CL) are shown (* <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 4
<p>Essential amino acid levels change in multiple biological matrices following consumption of amino acid-modified diets. (<b>A</b>) The abundance of essential amino acids in each of the study diets is shown. (<b>B</b>) The relative levels of essential amino acids in the liver, serum and feces from mice fed a reduced protein (RP) or reduced NEAA (RN) diet compared to mice given a control diet (CL) are shown (* <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>Reducing amino acid content of diet induces a modest shift in bacterial populations. 16S rRNA profiling of bacteria was carried out on feces from mice fed control (CL), reduced protein (RP) or reduced NEAA (RN) diets for two weeks. (<b>A</b>) Data are shown as principal coordinate analysis. (<b>B</b>) Diversity of bacteria type, as determined by Shannon Index, is shown for each group. Black line indicates average value for each group. (<b>C</b>,<b>D</b>) Relative abundance of bacteria in each group is shown at the phyla (<b>C</b>) and species levels (<b>D</b>). (<b>E</b>) Those species that were higher than 1% of overall abundance in any one group and significantly different in at least one group are shown as log abundance. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">
43 pages, 26465 KiB  
Article
Exploring the Physiological and Molecular Mechanisms by Which Potassium Regulates Low-Temperature Tolerance of Coconut (Cocos nucifera L.) Seedlings
by Lilan Lu, Yuping Wang, Md. Abu Sayed, Amjad Iqbal and Yaodong Yang
Agronomy 2024, 14(12), 2983; https://doi.org/10.3390/agronomy14122983 (registering DOI) - 14 Dec 2024
Viewed by 239
Abstract
Coconut holds significant importance as a fruit and oilseed crop in tropical and subtropical regions. However, low-temperature (LT) stress has caused substantial reductions in yield and economics and impedes coconut production, therefore constraining its widespread cultivation and utilization. The appropriate application of potassium [...] Read more.
Coconut holds significant importance as a fruit and oilseed crop in tropical and subtropical regions. However, low-temperature (LT) stress has caused substantial reductions in yield and economics and impedes coconut production, therefore constraining its widespread cultivation and utilization. The appropriate application of potassium (K) has the potential to enhance the cold tolerance of crops and mitigate cold damage, but the regulatory mechanisms by which K improves coconut adaptability to cold stress remain poorly understood. Transcriptome and metabolomic analyses were performed on coconut seedlings treated with LT (5 °C) and room temperature (25 °C) under various K conditions: K0 (0.1 mM KCL), KL (2 mM KCL), KM (4 mM KCL), and KH (8 mM KCL). Correlation analysis with physiological indicators was also conducted. The findings indicated that K absorption, nutrient or osmotic regulation, accumulation of substances, photosynthesis, hormone metabolism, and reactive oxygen species (ROS) clearance pathways played crucial roles in the adaptation of coconut seedlings to LT stress. LT stress disrupted the homeostasis of hormones, antioxidant enzyme activity, chlorophyll, K, and the regulation of nutrients and osmolytes. This stress also leads to the downregulation of genes and metabolites related to K transporters, hormone metabolism, transcription factors, and the metabolism of nutrients and osmolytes. Applying K helped maintain the homeostasis of hormones, antioxidant enzyme activity, chlorophyll, K, and the regulation of nutrients and osmolytes, promoted the removal of ROS, and reduced malondialdehyde, consequently diminishing the damage caused by LT stress to coconut seedlings. Furthermore, the comprehensive analysis of metabolomics and transcriptomics highlighted the importance of carbohydrate metabolism, biosynthesis of other secondary metabolites, amino acid metabolism, lipid metabolism, and ABC transporters in K’s role in improving coconut seedlings’ tolerance to LT stress. This study identified the pivotal biological pathways, regulatory genes, and metabolites implicated in K regulation of coconut seedlings to acclimate to LT stress. Full article
(This article belongs to the Special Issue Application of Multi-Omics and Systems Biology in Crop Breeding)
14 pages, 6506 KiB  
Article
Comparison of Fecal Microbiota and Metabolites Between Captive and Grazing Male Reindeer
by Fei Zhao, Quanmin Zhao, Songze Li, Yuhang Zhu, Huazhe Si, Jiang Feng and Zhipeng Li
Animals 2024, 14(24), 3606; https://doi.org/10.3390/ani14243606 (registering DOI) - 14 Dec 2024
Viewed by 220
Abstract
The reindeer (Rangifer tarandus) is a circumpolar member of the Cervidae family, and has adapted to a harsh environment. Summer is a critical period for reindeer, with peak digestibility facilitating body fat accumulation. The gut microbiota plays a pivotal role in [...] Read more.
The reindeer (Rangifer tarandus) is a circumpolar member of the Cervidae family, and has adapted to a harsh environment. Summer is a critical period for reindeer, with peak digestibility facilitating body fat accumulation. The gut microbiota plays a pivotal role in nutrient metabolism, and is affected by captivity. However, differences in the composition of the gut microbiota and metabolites between captive and grazing reindeer during summer remain poorly understood. Here, we conducted a comparative study of the fecal microbiota and metabolites between captive (n = 6) and grazing (n = 6) male reindeer, using full-length 16S rRNA gene sequencing and gas chromatography–time-of-flight mass spectrometry, respectively. Our results indicated that Prevotella, Phocaeicola, Papillibacter, Muribaculum, and Bacteroides were the predominant genera in the feces of reindeer. However, microbial diversity was significantly higher in captive reindeer compared to their grazing counterparts. Principal coordinate analysis revealed significant differences in the fecal microbiota between captive and grazing reindeer. In captive reindeer, the relative abundances of the genera Clostridium, Paraprevotella, Alistipes, Paludibacter, Lentimicrobium, Paraclostridium, and Anaerovibrio were significantly higher, while those of the genera Prevotella, Phocaeicola, Pseudoflavonifractor, and Lactonifactor were significantly lower. A comparison of predicted functions indicated that pathways involved in fat digestion and absorption, histidine metabolism, lysine biosynthesis, and secondary bile acid biosynthesis were more abundant in captive reindeer, whereas the pathways of fructose and mannose metabolism and propanoate metabolism were less abundant. An untargeted metabolomic analysis revealed that 624 metabolites (e.g., amino acids, lipids, fatty acids, and bile acids) and 645 metabolites (e.g., carbohydrates and purines) were significantly increased in the feces of captive and grazing reindeer, respectively. In conclusion, we unveiled significant differences in fecal microbiota and metabolites between captive and grazing male reindeer, with the results suggesting a potentially enhanced ability to utilize plant fibers in grazing reindeer. Full article
Show Figures

Figure 1

Figure 1
<p>Microbial community composition and diversity in the feces of captive and grazing reindeer. Microbial community composition in the feces of the Cap and Gra groups at the phylum (<b>A</b>) and genus (<b>B</b>) levels. (<b>C</b>) A comparison of alpha-diversity indices in feces between the Cap and Gra groups. (<b>D</b>) PCoA illustrating the differences in microbial community membership and structure in reindeer feces between the Cap and Gra groups at the OTU level, based on Bray–Curtis dissimilarity, Unweighted UniFrac distance, and Weighted UniFrac distance. * <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 2
<p>The significantly different genera in the feces of captive and grazing reindeer. (<b>A</b>) A Venn diagram illustrating genera that were common and unique to the Cap and Gra groups. (<b>B</b>) A heatmap depicting the significantly different genera in feces between the Cap and Gra groups. Individuals are shaded from blue to red to represent relative abundances (low to high). * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 3
<p>A comparison of the potential functions of microbes in the feces of captive and grazing reindeer. (<b>A</b>) PCoA illustrating the variation in microbial functions at KEGG level 3, based on the Bray–Curtis dissimilarity matrix, in feces between the Cap and Gra groups. (<b>B</b>) A heatmap showing the significantly different metabolic pathways of fecal microbiota between the Cap and Gra groups. Individuals are shaded from blue to red to indicate relative abundances (low to high). * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 4
<p>Differences in fecal metabolites between captive and grazing reindeer. (<b>A</b>) A pie chart illustrating the classification of identified metabolites in feces. (<b>B</b>) PCA and PLS-DA plots highlighting the differences in fecal metabolites between the Cap (blue) and Gra (red) groups. (<b>C</b>) A comparison of the total concentrations of lipids, fatty acids, bile acids, carbohydrates, purines, pyrimidines, and amino acids between the Cap and Gra groups. (<b>D</b>) Volcano plots depicting the significantly different metabolites in feces between the Cap and Gra groups. (<b>E</b>) A heatmap showing the significantly different metabolites in reindeer feces when comparing the Gra group to the Cap group. Individuals are shaded from yellow to purple to indicate concentrations (low to high). (<b>F</b>) A lollipop chart displaying the enriched metabolic pathways of significantly different metabolites. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 5
<p>The co-occurrence of significantly different microbiota and metabolites in the feces of captive (<b>A</b>) and grazing (<b>B</b>) reindeer. The Spearman correlation coefficient (|rho| &gt; 0.8 and <span class="html-italic">p</span> ≤ 0.05) was calculated from the abundances of microbiota and the concentrations of metabolites. Node colors indicate microbiota and metabolites, with yellow and blue edges representing positive and negative correlations, respectively.</p>
Full article ">
25 pages, 8251 KiB  
Article
Effects of Far-Red Light and Ultraviolet Light-A on Growth, Photosynthesis, Transcriptome, and Metabolome of Mint (Mentha haplocalyx Briq.)
by Lishu Yu, Lijun Bu, Dandan Li, Kaili Zhu, Yongxue Zhang, Shaofang Wu, Liying Chang, Xiaotao Ding and Yuping Jiang
Plants 2024, 13(24), 3495; https://doi.org/10.3390/plants13243495 (registering DOI) - 14 Dec 2024
Viewed by 232
Abstract
To investigate the effects of different light qualities on the growth, photosynthesis, transcriptome, and metabolome of mint, three treatments were designed: (1) 7R3B (70% red light and 30% blue light, CK); (2) 7R3B+ far-red light (FR); (3) 7R3B+ ultraviolet light A (UVA). The [...] Read more.
To investigate the effects of different light qualities on the growth, photosynthesis, transcriptome, and metabolome of mint, three treatments were designed: (1) 7R3B (70% red light and 30% blue light, CK); (2) 7R3B+ far-red light (FR); (3) 7R3B+ ultraviolet light A (UVA). The results showed that supplemental FR significantly promoted the growth and photosynthesis of mint, as evidenced by the increase in plant height, plant width, biomass, effective quantum yield of PSII photochemistry (Fv’/Fm’), maximal quantum yield of PSII (Fv/Fm), and performance index (PI). UVA and CK exhibited minimal differences. Transcriptomic and metabolomic analysis indicated that a total of 788 differentially expressed genes (DEGs) and 2291 differential accumulated metabolites (DAMs) were identified under FR treatment, mainly related to plant hormone signal transduction, phenylpropanoid biosynthesis, and flavonoid biosynthesis. FR also promoted the accumulation of phenylalanine, sinapyl alcohol, methylchavicol, and anethole in the phenylpropanoid biosynthesis pathway, and increased the levels of luteolin and leucocyanidin in the flavonoid biosynthesis pathway, which may perhaps be applied in practical production to promote the natural antibacterial and antioxidant properties of mint. An appropriate increase in FR radiation might alter transcript reprogramming and redirect metabolic flux in mint, subsequently regulating its growth and secondary metabolism. Our study uncovered the regulation of FR and UVA treatments on mint in terms of growth, physiology, transcriptome, and metabolome, providing reference for the cultivation of mint and other horticultural plants. Full article
(This article belongs to the Section Plant Physiology and Metabolism)
Show Figures

Figure 1

Figure 1
<p>Effects of different light qualities on mint growth morphology. CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A.</p>
Full article ">Figure 2
<p>Effects of different light qualities on mint growth. (<b>A</b>) Plant height. (<b>B</b>) Plant width. (<b>C</b>) Plant height (d34). (<b>D</b>) Plant width (d34). CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times, and different letters indicated significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 3
<p>Effects of different light qualities on mint biomass. (<b>A</b>) Shoot fresh weight. (<b>B</b>) Root fresh weight. (<b>C</b>) Shoot dry weight. (<b>D</b>) Root dry weight. CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times, and different letters indicated significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 4
<p>Effects of different light qualities on mint light response curves. CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A.</p>
Full article ">Figure 5
<p>Effects of different light qualities on mint gas exchange parameters. (<b>A</b>) Net photosynthetic rate (P<sub>n</sub>). (<b>B</b>) Intercellular CO<sub>2</sub> concentration (C<sub>i</sub>). (<b>C</b>) Stomatal conductance (G<sub>s</sub>). (<b>D</b>) Transpiration rate (T<sub>r</sub>). (<b>E</b>) Water use efficiency (WUE). CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times, and different letters indicated significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 6
<p>Effects of different light qualities on chlorophyll fluorescence parameters. (<b>A</b>) Actual photochemical efficiency of PSII (ΦPSII). (<b>B</b>) Electron transport rate (ETR). (<b>C</b>) Photochemical quenching coefficient (qP). (<b>D</b>) Effective quantum yield of PSII photochemistry (F<sub>v</sub>’/F<sub>m</sub>’). (<b>E</b>) Maximal quantum yield of PSII (F<sub>v</sub>/F<sub>m</sub>). (<b>F</b>) Performance index (PI). CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times, and different letters indicated significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 7
<p>Effects of different light qualities on gene expression. (<b>A</b>) PCA of transcriptome samples under different light qualities. (<b>B</b>) Number of DEGs detected in FR vs. CK and UVA vs. CK. (<b>C</b>) Venn diagram of DEGs in FR vs. CK and UVA vs. CK. CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times.</p>
Full article ">Figure 8
<p>Effects of different light qualities on KEGG pathway enrichment of DEGs. (<b>A</b>) KEGG pathway enrichment of DEGs between FR and CK treatments. (<b>B</b>) KEGG pathway enrichment of DEGs between UVA and CK treatments. CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times.</p>
Full article ">Figure 9
<p>Venn diagram of DAMs in FR vs. CK and UVA vs. CK. CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times.</p>
Full article ">Figure 10
<p>Effects of different light qualities on KEGG pathway enrichment of DAMs. (<b>A</b>) KEGG annotation of DAMs. (<b>B</b>) The top ten up and down-regulated DAMs of fold change between FR and CK treatments. (<b>C</b>) The top ten up and down-regulated DAMs of fold change between UVA and CK treatments. (<b>D</b>) KEGG pathway enrichment of DAMs between FR and CK treatments. (<b>E</b>) KEGG pathway enrichment of DAMs between UVA and CK treatments. CK: 7R3B, FR: 7R3B + far-red light, UVA: 70% red light and 30% blue light (7R3B) + ultraviolet light A. Treatments were replicated three times.</p>
Full article ">Figure 11
<p>The DEGs and DAMs involved in plant hormone signal transduction pathway in response to different light qualities. The color in the rectangle represents the genes or metabolites that were regulated under different light qualities (red indicated up-regulation; yellow indicated non-significant; blue indicated down-regulation). CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times.</p>
Full article ">Figure 12
<p>The DEGs and DAMs involved in phenylpropanoid biosynthesis pathway in response to different light qualities. The color in the rectangle represents the genes or metabolites that were regulated under different light qualities (red indicated up-regulation; yellow indicated non-significant; blue indicated down-regulation). CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times.</p>
Full article ">Figure 13
<p>The DEGs and DAMs involved in flavonoid biosynthesis pathway in response to different light qualities. The color in the rectangle represents the genes or metabolites that were regulated under different light qualities (red indicated up-regulation; yellow indicated non-significant; blue indicated down-regulation). CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times.</p>
Full article ">Figure 14
<p>(<b>A</b>) qRT-PCR analysis of the gene expression patterns and FPKM expression level in mint seedlings under different light qualities. (<b>B</b>) Log<sub>2</sub> Fold Change of RNA-seq and qRT-PCR analysis of <span class="html-italic">AUX/IAA</span>, <span class="html-italic">DELLA</span>, and <span class="html-italic">POD</span>. CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A. Treatments were replicated three times. “*” indicates a significant correlation at <span class="html-italic">p</span> ≤ 0.05 and “**” indicates a significant correlation at <span class="html-italic">p</span> ≤ 0.01.</p>
Full article ">Figure 15
<p>Correlation analysis between different results. The upper right ellipse represents the correlation between different parameters, and the lower left numbers represent the correlation coefficients, with red being a positive correlation and blue being a negative correlation. “*” indicates a significant correlation at <span class="html-italic">p</span> ≤ 0.05 and “**” indicates a significant correlation at <span class="html-italic">p</span> ≤ 0.01.</p>
Full article ">Figure 16
<p>PCA of different results. Arrow direction and length indicate correlation and strength, respectively.</p>
Full article ">Figure 17
<p>(<b>A</b>) Seedling rack for conducting experiments. (<b>B</b>) Spectral graphs of different treatments. CK: 70% red light and 30% blue light (7R3B), FR: 7R3B + far-red light, UVA: 7R3B + ultraviolet light A.</p>
Full article ">Figure 18
<p>The frame diagram depicting the addition of FR to red and blue light on the growth, photosynthesis, transcriptome, and metabolome of mint. The arrow “↑” indicates that the indicator was up-regulated under FR. FR: 70% red light and 30% blue light (7R3B) + far-red light.</p>
Full article ">
25 pages, 9716 KiB  
Article
From Tea to Functional Foods: Exploring Caryopteris mongolica Bunge for Anti-Rheumatoid Arthritis and Unraveling Its Potential Mechanisms
by Xin Dong, Zhi Wang, Yao Fu, Yuxin Tian, Peifeng Xue, Yuewu Wang, Feiyun Yang, Guojing Li and Ruigang Wang
Nutrients 2024, 16(24), 4311; https://doi.org/10.3390/nu16244311 - 13 Dec 2024
Viewed by 249
Abstract
Background: Caryopteris mongolica Bunge (CM) shows promising potential for managing rheumatoid arthritis (RA) and digestive disorders, attributed to its rich content of bioactive compounds such as polyphenols and flavonoids. Despite its common use in herbal tea, the specific mechanisms underlying CM’s anti-inflammatory and [...] Read more.
Background: Caryopteris mongolica Bunge (CM) shows promising potential for managing rheumatoid arthritis (RA) and digestive disorders, attributed to its rich content of bioactive compounds such as polyphenols and flavonoids. Despite its common use in herbal tea, the specific mechanisms underlying CM’s anti-inflammatory and joint-protective effects remain unclear, limiting its development as a functional food. This study investigated the effects of aqueous CM extract on RA in collagen-induced arthritis (CIA) rats and explored the underlying mechanisms. Methods: Forty-eight female Sprague-Dawley rats were randomly assigned to six groups (n = 8): normal control, CIA model, methotrexate (MTX), and CM high-, middle-, and low-dose groups. Anti-inflammatory and joint-protective effects were evaluated using biochemical and histological analyses. To elucidate the mechanisms, we applied metabolomics, network pharmacology, and transcriptomics approaches. Results: The results demonstrated that CM extract effectively suppressed synovial inflammation in CIA rats, reducing joint degradation. CM’s anti-inflammatory effects were mediated through the TNF signaling pathway, modulating glycerophospholipid and amino acid metabolism, including reduced levels of tryptophan, LysoPC, and asparagine. Molecular docking identified scutellarin and apigenin as key bioactive compounds. Additionally, immunofluorescence analysis revealed CM’s therapeutic effects via TNF signaling inhibition and suppression of M1 macrophage polarization. Conclusions: These findings highlight the therapeutic potential of CM for RA and support its development as a functional food or pharmaceutical product. Full article
24 pages, 11790 KiB  
Article
Intercropping Shapes the Metabolome and Microbiome of Medicinal Giant Lily (Cardiocrinum giganteum) in Bamboo, Chinese Fir, and Mixed Forests
by Jie Zhang, Yilin Ning, Haoyu Wu, Guibin Gao, Zhizhuang Wu, Yuwen Peng, Zhiyuan Huang and Xiaoping Zhang
Forests 2024, 15(12), 2201; https://doi.org/10.3390/f15122201 - 13 Dec 2024
Viewed by 232
Abstract
Intercropping is a promising strategy for sustainable medicinal plant cultivation, but its impact on plant–microbe interactions remains poorly understood. This study investigated the influence that intercropping giant lily (Cardiocrinum giganteum) with bamboo (BG), Chinese fir (FG), and mixed forests (MG) had [...] Read more.
Intercropping is a promising strategy for sustainable medicinal plant cultivation, but its impact on plant–microbe interactions remains poorly understood. This study investigated the influence that intercropping giant lily (Cardiocrinum giganteum) with bamboo (BG), Chinese fir (FG), and mixed forests (MG) had on the giant lily metabolome and microbiome compared to a monoculture control (GG). Metabolomic analysis revealed that BG significantly increased the accumulation of terpenoids (e.g., yucalexin B22, 19.39-fold), alkaloids (e.g., anabasine, 2.97-fold), and steroids (e.g., O-acetyl-lariciresinol, 4.49-fold), while MG induced the production of stress-related metabolites (e.g., aflatoxin G2, 128.62-fold), and FG enhanced nitrogen metabolism (e.g., putrescine, 2.47-fold). Intercropping altered the rhizosphere and endophytic microbial communities, with BG enriching beneficial bacteria (e.g., Acidobacteria and Alphaproteobacteria) and FG promoting symbiotic fungi (e.g., Serendipita and Xylariales). Network analysis revealed strong correlations between specific microbial taxa (e.g., Bacillus and Ceratobasidiaceae) and key metabolites (e.g., norpandamarilactonine A, methylgingerol), indicating their potential roles in shaping the metabolic profiles of giant lily. These findings highlight the complex interplay between intercropping systems, microbial communities, and medicinal plant metabolism and provide a basis for developing targeted cultivation strategies to enhance the production of bioactive compounds in giant lily and other medicinal plants. Full article
23 pages, 11258 KiB  
Article
Interaction Between Liver Metabolism and Gut Short-Chain Fatty Acids via Liver–Gut Axis Affects Body Weight in Lambs
by Haibo Wang, Jinshun Zhan, Shengguo Zhao, Haoyun Jiang, Haobin Jia, Yue Pan, Xiaojun Zhong and Junhong Huo
Int. J. Mol. Sci. 2024, 25(24), 13386; https://doi.org/10.3390/ijms252413386 - 13 Dec 2024
Viewed by 252
Abstract
The gut–liver axis and its interactions are essential for host physiology. Thus, we examined the jejunal microbiota, fermentation parameters, digestive enzymes, morphology, and liver metabolic profiles in different growth development lambs to investigate the liver–gut axis’s role in their development. One hundred male [...] Read more.
The gut–liver axis and its interactions are essential for host physiology. Thus, we examined the jejunal microbiota, fermentation parameters, digestive enzymes, morphology, and liver metabolic profiles in different growth development lambs to investigate the liver–gut axis’s role in their development. One hundred male Hu lambs of similar birth weight and age were raised under the same conditions until they reached 180 days of age. Subsequently, the eight lambs with the highest (HADG) and lowest (LADG) average daily weight gains were slaughtered for index assessment. The study indicates that the body weight, carcass weight, propanoic acid, butyric acid, propanoic acid ratio, butyric acid ratio, and digestive enzymes (beta-glucosidase, microcrystalline cellulase, xylanase, and carboxymethyl cellulase) were significantly higher in HDAG lambs than in LADG lambs (p < 0.05). Additionally, there were no significant differences in the jejunal microbiota’s structure and function among lambs at different growth development stages (p > 0.05). Overall, our analysis revealed that HADG lambs compared to LADG lambs exhibited an up-regulation of metabolites (such as spermine, cholic acid, succinic acid, betaine, etc.) that were positively correlated with the butyric acid ratio, propanoic acid ratio, propanoic acid, xylanase, microcrystalline cellulase, beta-glucosidase, amylase, carboxymethyl cellulase, carcass weight, and body weight, while these metabolites were negatively correlated with the kidney, acetic acid, acetic acid/ propanoic acid, and acetic acid ratio. Furthermore, there was a significant correlation between liver metabolism and jejunal microbiota. This study revealed significant differences in hepatic metabolites and jejunal fermentation among lambs at different growth stages, which may inform targeted regulation strategies to enhance lamb productivity. Full article
(This article belongs to the Section Molecular Endocrinology and Metabolism)
Show Figures

Figure 1

Figure 1
<p>Analysis of the microbiota diversity of the jejuna of HADG and LADG lambs. (<b>A</b>) ASV Venn diagram analysis of HADG and LADG lambs. (<b>B</b>) PCoA analysis of HADG and LADG lambs. (<b>C</b>) NMDS analysis of HADG and LADG lambs.</p>
Full article ">Figure 2
<p>Analysis of the microbiota composition and function prediction in the jejuna of HADG and LADG lambs. HADG and LADG lamb jejunal microbiota phylum (<b>A</b>) and genus (<b>B</b>) level top 10 stacking diagrams. (<b>C</b>) LEfSe analysis jejunal biomarkers in HADG and LADG lambs. Network diagrams of LADG (<b>D</b>) and HADG (<b>E</b>) microbiota correlations at the top 80 genus level are constructed. HADG and LADG lamb jejunal microbiota functional enrichment of class 1 (<b>F</b>), class 2 (<b>G</b>), and class 3 (<b>H</b>) analyzed using PICRUSt2.</p>
Full article ">Figure 2 Cont.
<p>Analysis of the microbiota composition and function prediction in the jejuna of HADG and LADG lambs. HADG and LADG lamb jejunal microbiota phylum (<b>A</b>) and genus (<b>B</b>) level top 10 stacking diagrams. (<b>C</b>) LEfSe analysis jejunal biomarkers in HADG and LADG lambs. Network diagrams of LADG (<b>D</b>) and HADG (<b>E</b>) microbiota correlations at the top 80 genus level are constructed. HADG and LADG lamb jejunal microbiota functional enrichment of class 1 (<b>F</b>), class 2 (<b>G</b>), and class 3 (<b>H</b>) analyzed using PICRUSt2.</p>
Full article ">Figure 3
<p>Correlation analysis of jejunal microbiota and host phenotypes. (<b>A</b>) Heat map analysis of microbiota (genus level) correlation with phenotype of HADG and LADG lambs. (<b>B</b>) Mantel’s r analysis of microbiota correlation with phenotype of HADG and LADG lambs. The edge width corresponds to the Mantel’s r statistic for the corresponding distance correlations, and the edge color indicates the significance of the Mantel’s <span class="html-italic">p</span> statistic, with a gray line indicating <span class="html-italic">p</span> &gt; 0.05. The type of line denotes a positive (solid) or negative (dashed) correlation. Note: the values in the correlation heat map indicate the correlation coefficients. * In the correlation heat map, we indicate <span class="html-italic">p</span> &lt; 0.05. Abbreviations: acetic acid (AA), propionic acid (PA), butyric acid (BA), total volatile fatty acids (TVFAs), acetic acid/propionic acid (AA:PA), acetic acid ratio (AAR), propionic acid ratio (PAR), butyric acid ratio (BAR), beta-glucosidase (GLU), microcrystalline cellulose (MCC), lipase, xylanase (Xyl), amylase (AMS), carboxymethyl cellulose (CMC), body weight (BW), carcass weight (CW), and dressing percentage (DP).</p>
Full article ">Figure 4
<p>Overall analysis of liver metabolites of HADG and LADG lambs. (<b>A</b>) Liver metabolism PCoA analysis of HADG and LADG lambs. (<b>B</b>) Plotting of OPLS-DA model scores of LADG and HADG lambs. (<b>C</b>) Plotting of volcano diagram of LADG vs. HADG lambs. KEGG enrichment sites (<b>D</b>) and classification map (<b>E</b>) of liver top 20 differential metabolites. The different colored entries in the figure represent the hierarchical classification of KEGG pathway annotations, corresponding to KO pathway level 2 and the KEGG pathway name. Additionally, the lengths and values of the bars indicate the number and proportion of differential metabolites associated with that pathway. (<b>F</b>) Correlation analysis between HADG and LADG lamb phenotypes and the liver differential enrichment metabolites from the two most abundant pathways. The values in the correlation heat map indicate the correlation coefficients. * In the correlation heat map, we indicate <span class="html-italic">p</span> &lt; 0.05. Abbreviations: acetic acid (AA), propionic acid (PA), butyric acid (BA), total volatile fatty acids (TVFAs), acetic acid/propionic acid (AA:PA), acetic acid ratio (AAR), propionic acid ratio (PAR), butyric acid ratio (BAR), beta-glucosidase (GLU), microcrystalline cellulose (MCC), lipase, xylanase (Xyl), amylase (AMS), carboxymethyl cellulose (CMC), body weight (BW), carcass weight (CW), and dressing percentage (DP).</p>
Full article ">Figure 4 Cont.
<p>Overall analysis of liver metabolites of HADG and LADG lambs. (<b>A</b>) Liver metabolism PCoA analysis of HADG and LADG lambs. (<b>B</b>) Plotting of OPLS-DA model scores of LADG and HADG lambs. (<b>C</b>) Plotting of volcano diagram of LADG vs. HADG lambs. KEGG enrichment sites (<b>D</b>) and classification map (<b>E</b>) of liver top 20 differential metabolites. The different colored entries in the figure represent the hierarchical classification of KEGG pathway annotations, corresponding to KO pathway level 2 and the KEGG pathway name. Additionally, the lengths and values of the bars indicate the number and proportion of differential metabolites associated with that pathway. (<b>F</b>) Correlation analysis between HADG and LADG lamb phenotypes and the liver differential enrichment metabolites from the two most abundant pathways. The values in the correlation heat map indicate the correlation coefficients. * In the correlation heat map, we indicate <span class="html-italic">p</span> &lt; 0.05. Abbreviations: acetic acid (AA), propionic acid (PA), butyric acid (BA), total volatile fatty acids (TVFAs), acetic acid/propionic acid (AA:PA), acetic acid ratio (AAR), propionic acid ratio (PAR), butyric acid ratio (BAR), beta-glucosidase (GLU), microcrystalline cellulose (MCC), lipase, xylanase (Xyl), amylase (AMS), carboxymethyl cellulose (CMC), body weight (BW), carcass weight (CW), and dressing percentage (DP).</p>
Full article ">Figure 5
<p>Liver differential metabolite KEGG enrichment and host phenotype correlation analysis of HADG and LADG lambs. (<b>A</b>) Network map of differential metabolites in the liver KEGG enriched top five pathways. Moreover, the size of the yellowish nodes in the graph corresponds to the quantity of enriched differential metabolites, while the smaller nodes connected to them represent the specific metabolites that have been annotated to the pathway, and the change in color indicates that the fold of differences takes the value of log2. Figure note size represents the number of enriched different metabolite. The categorization of the top 20 KEGG pathways that up-regulate (<b>B</b>) and down-regulate (<b>C</b>) liver metabolites by HADG compared to LADG. The different colored entries in the figure represent the hierarchical classification of KEGG pathway annotations, corresponding to KO pathway level 2 and the KEGG pathway name. Additionally, the lengths and values of the bars indicate the number and proportion of differential metabolites associated with that pathway.</p>
Full article ">Figure 5 Cont.
<p>Liver differential metabolite KEGG enrichment and host phenotype correlation analysis of HADG and LADG lambs. (<b>A</b>) Network map of differential metabolites in the liver KEGG enriched top five pathways. Moreover, the size of the yellowish nodes in the graph corresponds to the quantity of enriched differential metabolites, while the smaller nodes connected to them represent the specific metabolites that have been annotated to the pathway, and the change in color indicates that the fold of differences takes the value of log2. Figure note size represents the number of enriched different metabolite. The categorization of the top 20 KEGG pathways that up-regulate (<b>B</b>) and down-regulate (<b>C</b>) liver metabolites by HADG compared to LADG. The different colored entries in the figure represent the hierarchical classification of KEGG pathway annotations, corresponding to KO pathway level 2 and the KEGG pathway name. Additionally, the lengths and values of the bars indicate the number and proportion of differential metabolites associated with that pathway.</p>
Full article ">Figure 6
<p>Mantel’s r analysis of the liver KEGG enrichment pathway in correlation with the phenotype of HADG and LADG lambs. The edge width corresponds to the Mantel’s r statistic for the corresponding distance correlations, and the edge color indicates the significance of the Mantel’s <span class="html-italic">p</span> statistic, with a turquoise line indicating <span class="html-italic">p</span> ≤ 0.001, magenta line indicating 0.001 &lt; <span class="html-italic">p</span> ≤ 0.01, green line indicating 0.01 &lt; <span class="html-italic">p</span> ≤ 0.05, and gray line indicating <span class="html-italic">p</span> &gt; 0.05. The type of line denotes a positive (solid) or negative (dashed) correlation. Note: the values in the correlation heat map indicate the correlation coefficients. * In the correlation heat map, we indicate <span class="html-italic">p</span> &lt; 0.05. Abbreviations: acetic acid (AA), propionic acid (PA), butyric acid (BA), total volatile fatty acids (TVFAs), acetic acid/propionic acid (AA:PA), acetic acid ratio (AAR), propionic acid ratio (PAR), butyric acid ratio (BAR), beta-glucosidase (GLU), microcrystalline cellulose (MCC), lipase, xylanase (Xyl), amylase (AMS), carboxymethyl cellulose (CMC), body weight (BW), carcass weight (CW), and dressing percentage (DP).</p>
Full article ">
13 pages, 3689 KiB  
Article
Integrated Analysis of Transcriptome and Metabolome in the Brain After Cold Stress of Red Tilapia During Overwintering
by Chenxi Zhu, Haoran Yang, Wenbin Zhu, Qichen Jiang, Zaijie Dong and Lanmei Wang
Int. J. Mol. Sci. 2024, 25(24), 13372; https://doi.org/10.3390/ijms252413372 - 13 Dec 2024
Viewed by 212
Abstract
Cold stress during overwintering is considered a bottleneck problem limiting the development of the red tilapia (Oreochromis spp.) industry, and the regulation mechanism is currently not well understood. In this study, the fish (initial weight: 72.71 ± 1.32 g) were divided into [...] Read more.
Cold stress during overwintering is considered a bottleneck problem limiting the development of the red tilapia (Oreochromis spp.) industry, and the regulation mechanism is currently not well understood. In this study, the fish (initial weight: 72.71 ± 1.32 g) were divided into the cold stress group (cold) and the control (normal) group. In the control group, the water temperature was maintained at 20 °C, which is basically consistent with the overwintering water temperature in greenhouses of local areas. In the cold group, the water temperature decreased from 20 °C to 8 °C by 2 °C per day during the experiment. At the end of the experiment, the levels of fish serum urea nitrogen, glucose, norepinephrine, alkaline phosphatase, total bilirubin, and total cholesterol in the cold group changed significantly compared with that in the control group (P < 0.05). Then transcriptome sequencing and LC–MS metabolome of brain tissue were further employed to obtain the mRNA and metabolite datasets. We found that the FoxO signaling pathway and ABC transporters played an important role by transcriptome–metabolome association analysis. In the FoxO signaling pathway, the differentially expressed genes were related to cell cycle regulation, apoptosis and immune-regulation, and oxidative stress resistance and DNA repair. In the ABC transporters pathway, the ATP-binding cassette (ABC) subfamily abca, abcb, and abcc gene expression levels, and the deoxycytidine, L-lysine, L-glutamic acid, L-threonine, ornithine, and uridine metabolite contents changed. Our results suggested that the cold stress may promote apoptosis through regulation of the FoxO signaling pathway. The ABC transporters may respond to cold stress by regulating amino acid metabolism. The results provided a comprehensive understanding of fish cold stress during overwintering, which will facilitate the breeding of new cold-resistant varieties of red tilapia in the future. Full article
Show Figures

Figure 1

Figure 1
<p>DEGs identification and functional enrichment. (<b>A</b>) PCA analysis, PC1 represents the first principal component, and the percentage in brackets represents the contribution rate of the first principal component to the sample difference; PC2 represents the second principal component; (<b>B</b>) volcano plot, each point in the volcano plot represents a gene. The <span class="html-italic">x</span>-axis represents the logarithm of the fold change in a gene expression in two samples, and the <span class="html-italic">y</span>-axis represents the negative logarithm of the FDR (false discovery rate). The greater the absolute value of the <span class="html-italic">x</span>-axis, the greater the fold change in the gene expression between the two samples. The larger the ordinate value, the more significant the differential expression, and the more reliable the differentially expressed genes (DEGs). The green dots represent down-regulated DEGs, the red dots represent up-regulated DEGs, and the black dots represent non-DEGs; (<b>C</b>) KEGG DEGs pathway enrichment scatter plot, the abscissa of the scatterplot is the enrichment factor, which represents the ratio of the number of target genes divided by the number of all the genes in each pathway. The larger the enrichment factor, the more significant the enrichment level of DEGs in this pathway. The color of the dot represents the q value, and the size of the dot represents the number of DEGs in the pathway; (<b>D</b>) KEGG DEGs classification map. The vertical coordinate (left) is the name of KEGG secondary metabolic pathway, the vertical coordinate (right) is the name of KEGG primary metabolic pathway, and the horizontal coordinate is the number of genes annotated to this pathway and their proportion to the total number of genes annotated.</p>
Full article ">Figure 2
<p>Differential metabolites (DMs) identification and functional enrichment, (<b>A</b>) orthogonal projections to latent structures-discriminant analysis score scatter plot of NB vs. CB. The horizontal coordinate t[1]p represents the predicted principal component score of the first principal component, and the vertical coordinate t[1]o represents the orthogonal principal component score, and the scatter shape and color represent different experimental groups. It can be seen that the difference between the two groups of samples is very significant, and all samples are within a 95% confidence interval (Hotelling’s T-squared ellipse). (<b>B</b>) Screening volcano plot of differential metabolites. Each point in the volcano plot represents a metabolite. The <span class="html-italic">x</span>-axis represents the fold change in the relative substances in the group (taking the logarithm of base 2), the <span class="html-italic">y</span>-axis represents the <span class="html-italic">p</span>-value of the Student’s <span class="html-italic">t</span> test (taking the logarithm of base 10), and the size of the scatter point represents the VIP value of the OPLS-DA model. The larger the scatter point, the larger the VIP value. Scatter colors represent the final screening results, with significantly upregulated metabolites shown in red, significantly downregulated metabolites shown in blue, and non-significantly differentiated metabolites shown in gray. (<b>C</b>) Top 20 VIP bubble map of differential metabolites. Each point in the bubble represents a metabolite. The <span class="html-italic">x</span>-axis represents the VIP scores of the top 20 differential metabolite variables, and the <span class="html-italic">x</span>-axis represents the top 20 differential metabolites. The scatter color represents the VIP value, and the scatter size represents the <span class="html-italic">p</span> value. (<b>D</b>) Differential metabolite clustering heatmap. The <span class="html-italic">x</span>-axis represents the sample name and the clustering result of the sample, and the <span class="html-italic">y</span>-axis represents the clustering result of the differential metabolites and substances. Different columns in the diagram represent different samples, and different rows represent different metabolites. The color represents the relative levels of metabolites in the sample. (<b>E</b>) KEGG DMs pathway enrichment scatter plot. Each row in the plot represents a KEGG pathway. The horizontal coordinate is the enrichment factor, and the larger the enrichment factor, the more significant the enrichment level of differential metabolites in this pathway. The color of the dots represents the <span class="html-italic">p</span> value, and the bubble size represents the number of differentiated metabolites annotated in the pathway.</p>
Full article ">Figure 3
<p>(<b>A</b>) KEGG pathway enrichment scatter plot of co-analysis of DEGs and DMs in NB vs. CB; (<b>B</b>) comparison of expression between qRT-PCR validation and Illumina sequencing. Log-fold changes are expressed as the ratio of gene expression after normalization to <span class="html-italic">β-actin</span>.</p>
Full article ">Figure 4
<p>(<b>A</b>) FoxO signaling KEGG pathway; (<b>B</b>) ABC transporters KEGG pathway. Red indicates upregulation, green indicates downregulation, blue indicates both upregulation and downregulation.</p>
Full article ">
16 pages, 6403 KiB  
Article
Integrated Transcriptome and Metabolome Analysis Reveals Mechanism of Flavonoid Synthesis During Low-Temperature Storage of Sweet Corn Kernels
by Jingyan Liu, Yingni Xiao, Xu Zhao, Jin Du, Jianguang Hu, Weiwei Jin and Gaoke Li
Foods 2024, 13(24), 4025; https://doi.org/10.3390/foods13244025 - 12 Dec 2024
Viewed by 492
Abstract
Sweet corn is a globally important food source and vegetable renowned for its rich nutritional content. However, post-harvest quality deterioration remains a significant challenge due to sweet corn’s high sensitivity to environmental factors. Currently, low-temperature storage is the primary method for preserving sweet [...] Read more.
Sweet corn is a globally important food source and vegetable renowned for its rich nutritional content. However, post-harvest quality deterioration remains a significant challenge due to sweet corn’s high sensitivity to environmental factors. Currently, low-temperature storage is the primary method for preserving sweet corn; however, the molecular mechanisms involved in this process remain unclear. In this study, kernels stored at different temperatures (28 °C and 4 °C) for 1, 3, and 5 days after harvest were collected for physiological and transcriptomic analysis. Low temperature storage significantly improved the PPO and SOD activity in sweet corn kernels compared to storage at a normal temperature. A total of 1993 common differentially expressed genes (DEGs) were identified in kernels stored at low temperatures across all three time points. Integrated analysis of transcriptomic and previous metabolomic data revealed that low temperature storage significantly affected flavonoid biosynthesis. Furthermore, 11 genes involved in flavonoid biosynthesis exhibited differential expression across the three storage periods, including CHI, HCT, ANS, F3′H, F3′5′H, FLS, and NOMT, with Eriodictyol, Myricetin, and Hesperetin-7-O-glucoside among the key flavonoids. Correlation analysis revealed three AP2/ERF-ERF transcription factors (EREB14, EREB182, and EREB200) as potential regulators of flavonoid biosynthesis during low temperature treatment. These results enhance our understanding of the mechanisms of flavonoid synthesis in sweet corn kernels during low-temperature storage. Full article
(This article belongs to the Section Foodomics)
Show Figures

Figure 1

Figure 1
<p>Effects of low temperature on sweet corn kernels at different storage times. (<b>A</b>) Appearance changes of kernels between low temperature (down) and control temperature (up). D1, D3, and D5 represent days of storage (1, 3, and 5). F and N represent low and normal temperatures, respectively. Scale bar = 1 cm. (<b>B</b>) Changes of enzyme activities at low temperatures in sweet corn kernels. Asterisks indicate significant differences between two temperatures based on two-tailed Student’s <span class="html-italic">t</span>-test (** <span class="html-italic">p</span> &lt; 0.01, “ns” indicates no significant difference).</p>
Full article ">Figure 2
<p>An overview of transcriptomic profiles in sweet corn kernels. (<b>A</b>) PCA of gene expression levels (FPKM) in sweet corn kernels at different storage temperatures. Each dot represents an independent experimental repeat, with three biological replicates. (<b>B</b>) A Venn diagram showing the distribution of expressed genes at different storage temperatures. The special and core genes are shown in the diagram.</p>
Full article ">Figure 3
<p>A summary of all differentially expressed genes (DEGs) between low temperatures and normal temperatures in sweet corn kernels during storage. (<b>A</b>) A heatmap of all the DEGs. The red and blue blocks indicate the high abundance and low abundance genes, respectively. (<b>B</b>) A Venn diagram showing the distribution of the DEGs at different storage times. (<b>C</b>–<b>E</b>) Volcano plots showing DEGs at different storage times. Red and green dots represented upregulated and downregulated genes, respectively. (<b>F</b>) Expression patterns of DEGs by K-means clustering analysis.</p>
Full article ">Figure 4
<p>Enrichment analysis of common DEGs and common DAMs in sweet corn kernels during storage. (<b>A</b>) GO enrichment analysis of common DEGs. The dot size represented the number of genes in each pathway. The Padj represented the adjusted <span class="html-italic">p</span>-value of the enrichment analysis. (<b>B</b>) KEGG enrichment analysis combined common DEGs and common DAMs. The top 20 terms were selected based on transcriptome analysis. The triangles represent the transcriptome analysis, while the dots represented the metabolome analysis. The size represents the number of metabolites detected in this study and the color represents the <span class="html-italic">p</span>-value of enrichment analysis.</p>
Full article ">Figure 5
<p>Integrated transcriptomic and metabolomics data reveal the changes in the flavonoid biosynthesis pathway in sweet corn kernels during low-temperature storage. The red and blue chemicals represent the up-accumulated and the down-accumulated metabolites, respectively. Eleven DEGs are highlighted in the green box. Red represents upregulated genes, while blue represents downregulated genes. More detailed information for these genes is listed in <a href="#app1-foods-13-04025" class="html-app">Table S6</a>.</p>
Full article ">Figure 6
<p>The potential gene regulation network of the flavonoid biosynthesis pathway in sweet corn kernels during low-temperature storage. (<b>A</b>) The construction of a regulation module for 11 DEGs’ expression using weighted correlation network analysis (WGCNA). Each row represents a module eigengene, while the column represents the gene expression pattern. (<b>B</b>) The networks were established from the Pearson correlation coefficient (PCC) correlation among metabolites, genes, and transcription factors of the flavonoid biosynthesis pathway. The thickness of the line represents the correlation value, while the solid and dotted lines represent positive and negative correlations, respectively. (<b>C</b>) The relative expression of marker genes in sweet corn during different storage times. F and N represent low and normal temperatures, respectively. Asterisks indicate differences in gene expression between two temperatures based on a two-tailed Student’s <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001, “ns” indicates no significant difference).</p>
Full article ">
15 pages, 1151 KiB  
Study Protocol
Investigating the Impact of Glycogen-Depleting Exercise Combined with Prolonged Fasting on Autophagy and Cellular Health in Humans: A Randomised Controlled Crossover Trial
by Andrius Masedunskas, Isabella de Ciutiis, Leanne K. Hein, Anjie Ge, Yvonne X. Kong, Miao Qi, Drishya Mainali, Lara Rogerson-Wood, Cynthia M. Kroeger, Yvonne A. Aguirre Candia, Maria L. Cagigas, Tian Wang, David Hutchinson, Angelo Sabag, Freda H. Passam, Laura Piccio, Timothy J. Sargeant and Luigi Fontana
Nutrients 2024, 16(24), 4297; https://doi.org/10.3390/nu16244297 - 12 Dec 2024
Viewed by 368
Abstract
Importance: Although prolonged fasting has become increasingly popular, the favourable biological adaptations and possible adverse effects in humans have yet to be fully elucidated. Objective: To investigate the effects of a three-day water-only fasting, with or without exercise-induced glycogen depletion, on autophagy activation [...] Read more.
Importance: Although prolonged fasting has become increasingly popular, the favourable biological adaptations and possible adverse effects in humans have yet to be fully elucidated. Objective: To investigate the effects of a three-day water-only fasting, with or without exercise-induced glycogen depletion, on autophagy activation and the molecular pathways involved in cellular damage accumulation and repair in healthy humans. Design: A randomised, single-centre, two-period, two-sequence crossover trial. The primary outcome is autophagic activity, assessed as flux in peripheral blood mononuclear cells (PBMCs) measured in the context of whole blood. Secondary outcomes include changes in body composition, heart rate variability, endothelial function, and genomic, epigenomic, metabolomic, proteomic, and metagenomic adaptations to fasting in plasma, platelets, urine, stools, and PBMCs. Detailed profiling of circulating immune cell populations and their functional states will be assessed by flow cytometry. Setting: All clinical investigations will be undertaken at the Charles Perkins Centre Royal Prince Alfred Hospital clinic, University of Sydney, Australia. Participants: Twenty-four individuals aged 18 to 70 years, with a BMI of 20–40 kg/m2, free of major health conditions other than obesity. Discussion: While autophagic flux induction through fasting has garnered interest, there is a notable lack of human studies on this topic. This trial aims to provide the most detailed and integrated analysis of how three days of prolonged water-only fasting, combined with glycogen-depleting exercise, affects autophagy activation and other crucial metabolic and molecular pathways linked to cellular, metabolic, and immune health. Insights from this study may pave the way for safe and effective strategies to induce autophagy, offering potential preventive interventions for a range of chronic conditions. Full article
(This article belongs to the Section Carbohydrates)
Show Figures

Figure 1

Figure 1
<p>Experimental Design of the PROFASTA Trial. This randomised, two-period, two-sequence crossover trial involves 20 participants who are assigned to two interventions: Intervention A consists of a 3-day water-only fast, while Intervention B involves a 3-day water-only fast preceded by a single bout of glycogen-depleting endurance exercise performed on the first day of fasting. Each participant will complete both interventions, allowing for a comprehensive assessment of the effects of fasting and exercise on autophagic flux and other metabolic adaptations.</p>
Full article ">Figure 2
<p>Intervention week and sequence of blood sample collection. This diagram outlines the schedule for blood sample collection during the intervention week (clinic visits 3–5 and 7–9). The intervention week includes the start of fasting, the end of fasting, and the end of refeeding visits and does not include the baseline visits or the final follow-up visit.</p>
Full article ">
4 pages, 183 KiB  
Editorial
COVIDomics: Metabolomic Views on COVID-19
by Armando Cevenini, Lucia Santorelli and Michele Costanzo
Metabolites 2024, 14(12), 702; https://doi.org/10.3390/metabo14120702 - 12 Dec 2024
Viewed by 311
Abstract
During the COVID-19 pandemic, omics-based methodologies were extensively used to study the pathological mechanisms of SARS-CoV-2 infection and replication in human cells at a large scale [...] Full article
(This article belongs to the Special Issue COVIDomics: Metabolomic Views on COVID-19 and Related Diseases)
20 pages, 5085 KiB  
Article
Antioxidant Effects and Potential Mechanisms of Citrus reticulata ‘Chachi’ Components: An Integrated Approach of Network Pharmacology and Metabolomics
by Jiahao Xiao, Tian Sun, Shengyu Jiang, Zhiqiang Xiao, Yang Shan, Tao Li, Zhaoping Pan, Qili Li and Fuhua Fu
Foods 2024, 13(24), 4018; https://doi.org/10.3390/foods13244018 - 12 Dec 2024
Viewed by 467
Abstract
Citrus reticulata ‘Chachi’ (CRC), recognized for its considerable edible and medicinal significance, is a valuable source of metabolites beneficial to human health. This research investigates the metabolic distinctions and antioxidant properties across four different parts of CRC, using multivariate statistical analysis to interpret [...] Read more.
Citrus reticulata ‘Chachi’ (CRC), recognized for its considerable edible and medicinal significance, is a valuable source of metabolites beneficial to human health. This research investigates the metabolic distinctions and antioxidant properties across four different parts of CRC, using multivariate statistical analysis to interpret metabolomic data and network pharmacology to identify potential antioxidant targets and relevant signaling pathways. The results indicate considerable metabolic differences in different parts of the sample, with 1622 metabolites showing differential expression, including 816 secondary metabolites, primarily consisting of terpenoids (31.02%) and flavonoids (25.22%). The dried mature citrus peel (CP) section demonstrates the highest level of total phenolics (6.8 mg/g), followed by the pulp without seed (PU) (4.52 mg/g), pulp with seed (PWS) (4.26 mg/g), and the seed (SE) (2.16 mg/g). Interestingly, targeted high-performance liquid chromatography of flavonoids reveals the highest level of nobiletin and tangeretin in CP, whereas PU has the highest level of hesperidin, narirutin, and didymin. Furthermore, all four sections of CRC exhibit robust antioxidant properties in in vitro assessments (CP > PU > PWS > SE). Lastly, the network pharmacology uncovered potential antioxidant mechanisms in CRC. This research offers deeper insights into the development and utilization of byproducts in the CRC processing industry. Full article
Show Figures

Figure 1

Figure 1
<p>(<b>A</b>) Classification of primary metabolites. (<b>B</b>) Classification of secondary metabolites. (<b>C</b>) Three-dimensional PCA score plot. (<b>D</b>) PLS-DA score plot. (<b>E</b>) Permutation test plot with 200 permutations. (<b>F</b>) Sample correlation heat map.</p>
Full article ">Figure 2
<p>(<b>A</b>–<b>C</b>) OPLS-DA score plots. (<b>A</b>) CP and PU; (<b>B</b>) PWS and PU; (<b>C</b>) SE and PU. (<b>D</b>–<b>F</b>) Volcano plots of differential metabolite expression levels. (<b>D</b>) CP and PU; (<b>E</b>) PWS and PU; (<b>F</b>) SE and PU.</p>
Full article ">Figure 3
<p>(<b>A</b>) Venn diagram of differential metabolites; (<b>B</b>) Classification of 816 secondary differential metabolites.</p>
Full article ">Figure 4
<p>Heat maps display the levels of secondary differentially expressed metabolites in four parts of CRC. (<b>A</b>) Flavonoids; (<b>B</b>) Terpenoids; (<b>C</b>) Phenolic acids and derivatives; (<b>D</b>) Steroids and steroid derivatives.</p>
Full article ">Figure 5
<p>Heat maps display the levels of secondary differentially expressed metabolites in four parts of CRC. (<b>A</b>) Coumarins and derivatives; (<b>B</b>) Organic acids and derivatives; (<b>C</b>) Alkaloids and derivatives; (<b>D</b>) Others.</p>
Full article ">Figure 6
<p>Bubble chart of KEGG enrichment pathways for secondary differential metabolites.</p>
Full article ">Figure 7
<p>HPLC chromatograms of 16 flavonoid standards (1, Verbascoside; 2, Taxifolin; 3, Narirutin; 4, Naringin; 5, Hesperidin; 6, Neohesperidin; 7, Rutin; 8, Rhoifolin; 9, Diosmin; 10, Didymin; 11, Hesperetin; 12, Luteolin; 13, Diosmetin; 14, Sinensetin; 15, Nobiletin; 16, Tangeretin) and samples. (<b>A</b>) Flavonoid standard mixture, 283 nm. (<b>B</b>) Flavonoid standard mixture, 330 nm. (<b>C</b>) PU, 283 nm. (<b>D</b>) CP, 330 nm. (<b>E</b>) PWS, 283 nm. (<b>F</b>) SE, 283 nm.</p>
Full article ">Figure 8
<p>(<b>A</b>) Venn diagram of overlapping targets between flavonoid active components and oxidative damage. (<b>B</b>,<b>C</b>) Protein–protein interaction (PPI) analysis network diagram. (<b>C</b>) Top 10 GO enrichment analysis bar chart. (<b>D</b>) Top 20 KEGG enrichment analysis bubble chart of signaling pathways.</p>
Full article ">
Back to TopTop