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Search Results (1,616)

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27 pages, 979 KiB  
Review
The Role of the Chronotype in Developing an Excessive Body Weight and Its Complications—A Narrative Review
by Marta Pelczyńska, Małgorzata Moszak, Julita Wojciechowska, Anita Płócienniczak, Jan Potocki, Joanna Blok, Julia Balcerzak, Mikołaj Zblewski and Paweł Bogdański
Nutrients 2025, 17(1), 80; https://doi.org/10.3390/nu17010080 (registering DOI) - 28 Dec 2024
Abstract
The chronotype, the personal predisposition towards morning or evening activities, significantly influences health conditions, sleep, and eating regulations. Individuals with evening chronotypes are often at a higher risk for weight gain due to misalignment between their natural tendencies of functioning and social schedules, [...] Read more.
The chronotype, the personal predisposition towards morning or evening activities, significantly influences health conditions, sleep, and eating regulations. Individuals with evening chronotypes are often at a higher risk for weight gain due to misalignment between their natural tendencies of functioning and social schedules, resulting in insufficient sleep, disruptions in eating habits, and decreased physical activity levels. Often, impaired glucose tolerance and changes in melatonin, adiponectin, and leptin secretion, along with alterations in the clock gene functions in subjects with evening preferences, may be predisposed to obesity. These disturbances contribute to metabolic dysregulation, which may lead to the subsequent onset of obesity complications, such as hypertension, type 2 diabetes, sleep apnea, and liver diseases. Targeting critical components of the circadian system and synchronizing people’s chronotypes with lifestyle conditions could deliver potential strategies for preventing and treating metabolic disorders. Thus, it is recommended to take a personalized chronobiological approach to maintain a normal body weight and metabolic health. Nevertheless, future studies are needed to identify the clear mechanisms between the chronotype and human health. This article provides a narrative review and discussion of recent data to summarize studies on the circadian rhythm in the context of obesity. The manuscript represents a comprehensive overview conducted between August and November 2024 using the National Library of Medicine browser (Medline, Pub-Med, Web of Science). Full article
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<p>Comparison of features characterizing evening and morning chronotypes. This figure was generated using the program <a href="http://www.canva.com" target="_blank">www.canva.com</a> (accessed on 16 October 2024).</p>
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<p>The differences in physical activity between morning and evening chronotypes. Abbreviations: EC—evening chronotype; HR—heart rate; MC—morning chronotype; PA—physical activity. This figure was made using the program <a href="http://www.canva.com" target="_blank">www.canva.com</a> (accessed on 24 October 2024).</p>
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21 pages, 3895 KiB  
Article
Transcriptomic Analysis of Wheat Under Multi LED Light Conditions
by Lei Sun, Ding Li, Chunhong Ma, Bo Jiao, Jiao Wang, Pu Zhao, Fushuang Dong and Shuo Zhou
Plants 2025, 14(1), 46; https://doi.org/10.3390/plants14010046 - 27 Dec 2024
Viewed by 184
Abstract
Light is a vital environmental cue that profoundly influences the development of plants. LED lighting offers significant advantages in controlled growth environments over fluorescent lighting. Under monochromatic blue LED light, wheat plants exhibited reduced stature, accelerated spike development, and a shortened flowering period [...] Read more.
Light is a vital environmental cue that profoundly influences the development of plants. LED lighting offers significant advantages in controlled growth environments over fluorescent lighting. Under monochromatic blue LED light, wheat plants exhibited reduced stature, accelerated spike development, and a shortened flowering period with increased blue light intensity promoting an earlier heading date. In this study, we conducted a comprehensive transcriptome analysis to investigate the molecular mechanisms underlying wheat plants’ response to varying light conditions. We identified 34 types of transcription factors (TFs) and highlighted the dynamic changes of key families such as WRKY, AP2/ERF, MYB, bHLH, and NAC, which play crucial roles in light-induced gene regulation. Additionally, this study revealed differential effects of blue and red light on the expression levels of genes related to hormones such as cytokinin (CK) and salicylic acid (SA) synthesis as well as significant changes in pathways such as flavonoid biosynthesis, circadian rhythms, chlorophyll synthesis, and flowering. Particularly, blue light upregulated genes involved in chlorophyll synthesis, contrasting with the downregulation observed under red light. Furthermore, blue light enhanced the expression of anthocyanin synthesis-related genes, such as CHS, underscoring its role in promoting anthocyanin accumulation. These findings provide valuable insights into how light quality impacts crop growth and development. Full article
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<p>Effects of different light treatments on plant growth. (<b>A</b>) Development of spikes under blue light and white light treatments, (a) white light; (b) blue light; (<b>B</b>) height of the plants; and (<b>C</b>) development of spikes under varying light intensities, (a) 56.7 μmol/m<sup>2</sup> s; (b) 49.3 μmol/m<sup>2</sup> s; (c) 35.6 μmol/m<sup>2</sup> s; and (d) 23.7 μmol/m<sup>2</sup> s. (<b>D</b>,<b>E</b>) Statistics of spike length under blue and white light conditions and varying light intensities, respectively.</p>
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<p>Global view of transcriptome expression and differential gene expression. (<b>A</b>) The experimental design schematic. The experiment included four light treatments: blue light, red light, white light, and a 1:1 mixture of red and blue light. Exposure was initiated from dark conditions, and samples were taken at three time points: 1 h (1 h), 6 h (6 h), and 14 days (14 d). Differentially expressed genes (DEGs) were identified by comparing the gene expression profiles at each time point under the respective light treatments with those under white light conditions. (<b>B</b>) Spearman correlation coefficient (SCC) of gene expression profiles between samples; (<b>C</b>) principal component analysis (PCA) of samples distinguished by different colors with three biological repeats; (<b>D</b>) petal plot, where each petal represents the number of uniquely expressed genes during that time period; and (<b>E</b>) number of differentially expressed genes over time under white light conditions.</p>
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<p>Transcription factor ridge plot showing the changes in number and types of transcription factors under different light conditions compared to white light at 1 h, 6 h, and 14 d.</p>
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<p>Expression changes in hormone synthesis-related genes under different light treatments. Heatmaps represent the log2 fold change (log2FC) values of gene expression levels involved in the GA, SA, ABA, CK, JA, and ethylene synthesis pathways compared to white light conditions. Each time point is represented by three treatments in three colors: blue for blue light, red for red light, and gray for a 1:1 mixture of blue and red light.</p>
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<p>Analysis of pathway enrichment in response to different light conditions. (<b>A</b>) KEGG enrichment analysis. KEGG pathway enrichment analysis was performed on differentially expressed genes (DEGs) identified by comparing the gene expression profiles under each light treatment with those under white light conditions at the same time points. The pathways were ranked according to the total number of enriched genes across all conditions, and the results are visualized using a heatmap, with the specific differentially expressed genes and their enrichment analysis under various light conditions relative to white light. (<b>B</b>) GO enrichment analysis of unique DEGs. By comparing samples from each light condition with those from white light, genes that were significantly differentially expressed under each light condition were filtered, then unique condition-specific differential DEGs were screened for GO enrichment analysis. Left and right panels are GO enrichment analysis of unique DEGs in white light compared to blue and red light at 14 d, separately. (<b>C</b>) Changes in chlorophyll content after treatment under blue and red light.</p>
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<p>Genes associated with the circadian clock. (<b>A</b>) Partial and core genes in the circadian rhythm plant pathway. (<b>B</b>) Heatmap showing the expression levels of clock-related genes. (<b>C</b>) Heatmap illustrating the expression levels of VRN family genes, supplemented with qRT-PCR validation results. (<b>D</b>) qRT-PCR validation and RNA-seq expression profiles for selected genes.</p>
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<p>Gene co-expression network analysis. (<b>A</b>) Gene module classification heatmap showing the standardized log(fpkm) values of genes, which can be categorized into three classes based on their expression patterns. (<b>B</b>) Schematic diagram of selected gene network interactions. (<b>C</b>) Expression patterns of modules 3 and 7. (<b>D</b>) Expression patterns of four bHLH genes among 404 neighbor genes.</p>
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28 pages, 10378 KiB  
Article
Effects of Long-Term Fasting on Gut Microbiota, Serum Metabolome, and Their Association in Male Adults
by Feng Wu, Yaxiu Guo, Yihua Wang, Xiukun Sui, Hailong Wang, Hongyu Zhang, Bingmu Xin, Chao Yang, Cheng Zhang, Siyu Jiang, Lina Qu, Qiang Feng, Zhongquan Dai, Chunmeng Shi and Yinghui Li
Nutrients 2025, 17(1), 35; https://doi.org/10.3390/nu17010035 - 26 Dec 2024
Viewed by 296
Abstract
Background: Long-term fasting demonstrates greater therapeutic potential and broader application prospects in extreme environments than intermittent fasting. Method: This pilot study of 10-day complete fasting (CF), with a small sample size of 13 volunteers, aimed to investigate the time-series impacts on gut microbiome, [...] Read more.
Background: Long-term fasting demonstrates greater therapeutic potential and broader application prospects in extreme environments than intermittent fasting. Method: This pilot study of 10-day complete fasting (CF), with a small sample size of 13 volunteers, aimed to investigate the time-series impacts on gut microbiome, serum metabolome, and their interrelationships with biochemical indices. Results: The results show CF significantly affected gut microbiota diversity, composition, and interspecies interactions, characterized by an expansion of the Proteobacteria phylum (about six-fold) and a decrease in Bacteroidetes (about 50%) and Firmicutes (about 34%) populations. Notably, certain bacteria taxa exhibited complex interactions and strong correlations with serum metabolites implicated in energy and amino acid metabolism, with a particular focus on fatty acylcarnitines and tryptophan derivatives. A key focus of our study was the effect of Ruthenibacterium lactatiformans, which was highly increased during CF and exhibited a strong correlation with fat metabolic indicators. This bacterium was found to mitigate high-fat diet-induced obesity, glucose intolerance, dyslipidemia, and intestinal barrier dysfunction in animal experiments. These effects suggest its potential as a probiotic candidate for the amelioration of dyslipidemia and for mediating the benefits of fasting on fat metabolism. Conclusions: Our pilot study suggests that alterations in gut microbiota during CF contribute to the shift of energy metabolic substrate and the establishment of a novel homeostatic state during prolonged fasting. Full article
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<p>The impact of 10-day complete fasting on human gut microbiota’s diversity, difference and composition. (<b>A</b>) A schematic of the study design and the time points of sample collection. (<b>B</b>) Comparison of alpha-diversity based on Shannon index in the gut microbiota at different time points. (<b>C</b>) Principal coordinate analysis (PCoA) plot of the gut microbiota during complete fasting experiment based on the Bray–Curtis distances. (<b>D</b>) The distribution of Bray–Curtis distances from samples among the different courses in the complete fasting experiment based on the abundance. (<b>E</b>,<b>G</b>,<b>H</b>) The stacked bar plot showed the relative abundance of the gut microbiota at the phylum (<b>E</b>), genus (<b>G</b>), and species (<b>H</b>) levels, respectively. Each bar represents the mean of all detected samples at each time point. (<b>F</b>) The Firmicutes to Bacteroidetes ratio at each time point. Boxes and whiskers showed quartiles with outliers as individual points. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001; Significant difference (<span class="html-italic">p</span> &lt; 0.05) determined by Wilcox test. (<b>B</b>,<b>D</b>,<b>F</b>), PERMANOVA test. (<b>C</b>) BF: before fasting; CF3<sup>:</sup> 3rd day of complete fasting; CF9<sup>:</sup> 9th day of complete fasting; CR3<sup>:</sup> 3rd day of calorie restriction; FR5<sup>:</sup> 5th day of full recovery. Sample number is 13 at BF3 and FR5, 7 at CF3, 6 at CF9, and 12 at CR3.</p>
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<p>Ten-day complete fasting induced the different change profiles of human gut species. (<b>A</b>) Clustered profiles of changed species across the complete fasting times inferred by STEM analysis. Statistically significant profiles (<span class="html-italic">p</span> &lt; 0.05) are represented in color. Similar colors represented the same type of change profile. The upper left number is the profile ID, and the lower left number presents the species count in each box. (<b>B</b>) Heatmap of the relative abundance of the species with significant difference (based on the Permutation test) using log10(X + min(X [X! = 0]) (X: the relative abundance of the species)) by R with colors gradually changing from blue to red, corresponding to low and high relative abundance, respectively, and trend (based on the STEM analysis) (<span class="html-italic">p</span> &lt; 0.05) during the complete fasting experiment. (<b>C</b>) The ridgeline plot shows the top 10 most abundant species in the heatmap. BF: before fasting; CF3: 3rd day of complete fasting; CF9: 9th day of complete fasting; CR3: 3rd day of calorie restriction; FR5: 5th day of full recovery.</p>
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<p>Ten-day CF impacted the correlation of the human gut microbiota. (<b>A</b>) Network analysis of the interactions between the differential species based on the Spearman correlation coefficients (|r| ≥ 0.8 and <span class="html-italic">p</span> &lt; 0.05). The fill color of the circles and diamonds represented the corresponding phylum. <span class="html-italic">Ethanoligenens harbinense</span> and <span class="html-italic">Intestinimonas butyriciproducens</span> connected with more species. (<b>B</b>) The relative abundance fluctuations of the species in the correlation network over the five time points. The node size positively correlated with its relative abundance. (<b>C</b>) The ridgeline plot shows the network topological parameters. BF: before fasting; CF3: 3rd day of complete fasting; CF9: 9th day of complete fasting; CR3: 3rd day of calorie restriction; FR5: 5th day of full recovery.</p>
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<p>Ten-day complete fasting restructured serum metabolome. (<b>A</b>) The principal component analysis (PCA) plot of serum metabolites during the CF experiments based on Bray–Curtis distances. (<b>B</b>) Clustered results in the serum metabolites with K-Means. (<b>C</b>) Significant change profiles (<span class="html-italic">p</span> &lt; 0.05) of serum metabolites in ESI+ and ESI− modes across the different time points by STEM analysis. The upper left number was the profile ID, and the lower left number presented the metabolite count in each box. (<b>D</b>) Metabolite set enrichment analysis (MSEA) of the significantly enriched and affected metabolic pathways for the serum metabolites (both ESI+ and ESI−) with an increasing trend. (<b>E</b>,<b>G</b>,<b>H</b>) The top 25 enriched metabolic pathways of differential metabolites at CF3 (<b>E</b>), CF6 (<b>G</b>), and CF9 (<b>H</b>) using the MetaboAnalyst metabolic pathway analysis tool. (<b>F</b>) Venn diagram of the number of enriched metabolic pathways among CF3, CF6, and CF9. The 12 common pathways were highlighted in Subfigure (<b>E</b>) with a red underline. ESI+: positive electrospray ionization; ESI−: negative electrospray ionization; BF: before fasting; CF3: 3rd day of complete fasting; CF9: 9th day of complete fasting; CR3: 3rd day of calorie restriction; FR5: 5th day of full recovery.</p>
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<p>The relationship between differential metabolites and gut microbiota during a 10-day complete fast. (<b>A</b>) Network diagram of the correlation between gut microbiota and serum metabolites. The circle and diamond represented the bacteria species and metabolites, respectively. The node size positively correlates with relative abundance. The fill color of the circle represented its corresponding phylum. The line thickness was proportional to the absolute value of the correlation coefficient (|r| &gt; 0.8 and <span class="html-italic">p</span> &lt; 0.05). The red line means a positive correlation, and the blue line means a negative correlation. (<b>B</b>) The relative abundance fluctuations of gut microbiota and serum metabolites in the correlation network over the five time points. The node size positively correlates with relative abundance. (<b>C</b>) Venn diagram of the metabolite number from host or bacteria. BF: before fasting; CF3: 3rd day of complete fasting; CF9: 9th day of complete fasting; CR3: 3rd day of calorie restriction; FR5: 5th day of full recovery.</p>
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<p>Correlation analysis between serum differential metabolites or fat metabolism relative biochemical indexes (BCIs) and differential gut microbiota during the 10-day complete fasting. (<b>A</b>) The percent of gut microbiota counts with |r| &gt; 0.6 and <span class="html-italic">p</span> &lt; 0.05 with serum metabolites. The correlation diagram between <span class="html-italic">Ruthenibacterium lactatiformans</span> and fat acylcarnitine (<b>B</b>) Hexadec-2-enoylcarnitine, (<b>C</b>) vaccenylcarnitine, (<b>D</b>) L-palmitoylcarnitine, (<b>E</b>) L-acetylcarnitine, and (<b>F</b>) 5-tetradecenoylcarnitine) with |r| &gt; 0.7 and its relative abundance changes of <span class="html-italic">Ruthenibacterium lactatiformans</span> (<b>G</b>). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, VS. BF, n = 13. (<b>H</b>) The percent of gut microbiota counts with |r| &gt; 0.6 and <span class="html-italic">p</span> &lt; 0.05 with fat metabolism relative to BCIs. (<b>I</b>) The percent of fat metabolism relative BCIs counts with |r| &gt; 0.6 and <span class="html-italic">p</span> &lt; 0.05 with gut microbiota. (<b>J</b>,<b>K</b>) Correlation diagram of low-density lipoprotein cholesterol (LDL-C) and total cholesterol (CHOL) with <span class="html-italic">Intestinimonas butyriciproducens</span>. (<b>L</b>,<b>M</b>) The redundancy analysis (RDA) between differential gut microbiota and BCIs during fasting. BF: before fasting; CF3: 3rd day of complete fasting; CF9: 9th day of complete fasting; CR3: 3rd day of calorie restriction; FR5: 5th day of full recovery.</p>
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<p>Serum level changes of tryptophan derivative metabolites and enzymes during 10-day complete fasting. (<b>A</b>) Serotonin detected by ELISA; (<b>B</b>,<b>C</b>,<b>E</b>,<b>G</b>–<b>I</b>) The relative abundance changes original from metabolome. (<b>D</b>,<b>F</b>) The correction between indolelactic acid, indoline, and <span class="html-italic">Ruthenibacterium lactatiformans</span>. (<b>J</b>,<b>K</b>) The relative abundance of Tryptophan metabolic enzymes original from metagenome sequencing. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, VS. BF. BF: before fasting (n = 13); CF3: 3rd day of complete fasting (n = 13 for serum and n = 7 for fecal); CF6: 6th day of complete fasting (n = 13); CF9: 9th day of complete fasting (n = 13 for serum and n = 6 for fecal); CR3: 3rd day of calorie restriction (n = 13 for serum and n = 12 for fecal); FR5: 5th day of full recovery (n = 13).</p>
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<p>The protective effects of <span class="html-italic">Ruthenibacterium lactatiformans</span> and ILA on HFD-induced obesity and metabolic abnormalities. Mice were fed an HFD or co-administrated <span class="html-italic">Ruthenibacterium lactatiformans</span> or ILA alternately every other day for 9 weeks. (<b>A</b>) body weight every week. (<b>B</b>) body weight gain percent at 9th week. The weight of epididymal fat pad (<b>C</b>) and liver (<b>D</b>). The HE staining (<b>E</b>) of epididymal fat and adipocyte size analysis (<b>F</b>). The serum concentration of CHOL (<b>G</b>), triglycerides (<b>H</b>), low-density lipoprotein (<b>I</b>), high-density lipoprotein (<b>J</b>), D-Lactate (<b>L</b>) and Diamine oxidase (<b>M</b>). (<b>K</b>) intraperitoneal glucose tolerance test. CN: with control diet, HFD: high-fat diet, ILA: indolelactic acid, RL: <span class="html-italic">Ruthenibacterium lactatiformans,</span> CHOL: total cholesterol, TG: triglycerides, LDL: low-density lipoprotein, HDL: high-density lipoprotein. AUC: area under curve, IGTT: intraperitoneal glucose tolerance test. ## <span class="html-italic">p</span> &lt; 0.01, vs. HFD; * <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, n = 6 for CN group and n = 8 for other groups.</p>
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13 pages, 2063 KiB  
Article
Intake of S-Methylmethionine Alters Glucose Metabolism and Hepatic Gene Expression in C57BL/6J High-Fat-Fed Mice
by Mariana Buranelo Egea, Gavin Pierce and Neil Shay
Foods 2025, 14(1), 34; https://doi.org/10.3390/foods14010034 - 26 Dec 2024
Viewed by 258
Abstract
A diet containing foods that are sources of S-methylmethionine (SMM), and its use as a dietary supplement, have demonstrated beneficial health effects. Thus, the objective of this work was to evaluate the inclusion of SMM as a dietary supplement in C57BL/6J high-fat-fed mice [...] Read more.
A diet containing foods that are sources of S-methylmethionine (SMM), and its use as a dietary supplement, have demonstrated beneficial health effects. Thus, the objective of this work was to evaluate the inclusion of SMM as a dietary supplement in C57BL/6J high-fat-fed mice to verify whether this compound alone would be responsible for these positive effects. Mice were divided into three groups: LF (low-fat diet), HF (high-fat diet), and HF+SMM (high-fat diet plus SMM), and maintained for 10 weeks with water and food provided ad libitum. Body weight and food intake were measured weekly, and food efficiency was calculated. In addition, at week 9, fasting glucose was measured and, after necropsy, at week 10, liver, inguinal adipose, and kidney weights were measured; triglycerides, histology, liver gene expression, serum insulin, and MCP-1 levels were also determined. Final body weight, average weight gain, and the liver/body weight of the SMM group showed a significant difference with the LF group. HF+SMM-fed mice show improved regulation in glucose metabolism, demonstrated by the assessment of fasting glucose, insulin concentration, and HOMA-IR, compared with the HF-fed group. Liver triglycerides and MCP-1 levels showed no significant differences between fed groups. By the positive gene regulation of Sult1e1, Phlda1, and Ciart, we hypothesized that SMM administration to mice may have regulated xenobiotic, glucose, and circadian rhythm pathways. Full article
(This article belongs to the Special Issue Bioactive Compounds in Food: From Molecule to Biological Function)
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<p>Final body weight (<b>A</b>), Average weight gain (<b>B</b>), Food efficiency (<b>C</b>), Liver /body weight (<b>D</b>), Kidney/body weight (<b>E</b>), Adipose/body weight (<b>F</b>) in male C57BL/6J mice fed either a low-fat (LF) diet, a high-fat (HF) diet, or HF plus DL-methionine methylsulfonium chloride (HF+SMM) after 10 weeks. Groups not sharing the same lowercase letters indicate that one-way ANOVA found significant differences between groups (<span class="html-italic">p</span> &lt; 0.05). Values shown are the average (<span class="html-italic">n</span> = 12 for control groups and <span class="html-italic">n</span> = eight for the experimental group) ± SEM.</p>
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<p>Fasting glucose at week 9 (<b>A</b>), serum insulin concentration (<b>B</b>), homeostasis model assessment of insulin resistance (HOMA-IR) (<b>C</b>), and homeostatic model assessment of β-cell function (HOMA-%B) (<b>D</b>) in male C57BL/6J mice fed either a low fat (LF) diet, a high fat (HF) diet, and HF plus DL-methionine methylsulfonium chloride (HF+SMM) after 10 weeks. Groups not sharing the same lowercase letters indicate that one-way ANOVA found significant differences between groups (<span class="html-italic">p</span> &lt; 0.05). Values shown are averages (<span class="html-italic">n</span> = 12 for control groups and <span class="html-italic">n</span> = 8 for the experimental group) ± SEM.</p>
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<p>Serum Monocyte Chemoattractant Protein-1 (MCP-1) concentration (B) in male C57BL/6J mice fed either a low-fat (LF) diet, a high-fat (HF) diet, or HF plus DL-methionine methylsulfonium chloride (HF+SMM) after 10 weeks. One-way ANOVA indicated no significant differences between diet groups (<span class="html-italic">p</span> &lt; 0.05). Values shown are averages (<span class="html-italic">n</span> = 12 for control groups and <span class="html-italic">n</span> = 8 for the experimental group) ± SEM.</p>
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<p>Total triacylglycerol (TAG) assay (<b>A</b>) and a study of hematoxylin-eosin-stained liver cross-sections of male C57BL/6J mice fed a LF diet (<b>B</b>), a HF diet (<b>C</b>), and HF+SMM (<b>D</b>) for 10 weeks. Slides were observed under 400 magnification (40× objective), using an Olympus IX71 light microscope (Olympus, PA, USA). (a,b) Groups not sharing the same lowercase letters indicate that one-way ANOVA found significant differences between groups (<span class="html-italic">p</span> &lt; 0.05). Average (<span class="html-italic">n</span> = 12 for control groups and <span class="html-italic">n</span> = 8 for the experimental group) ± SEM.</p>
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<p>Venn diagram with gene expression of liver of male C57BL/6J mice fed either a LF diet or HF+SMM diet.</p>
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14 pages, 984 KiB  
Review
Insufficient Sleep Syndrome in Childhood
by Teruhisa Miike
Children 2025, 12(1), 19; https://doi.org/10.3390/children12010019 - 26 Dec 2024
Viewed by 177
Abstract
Sleep disorders in children have a negative impact on mental and physical development, and a lack of sleep is one of the most important problems in infancy. At the age when naps are commonly accepted, the judgment of whether the amount of sleep [...] Read more.
Sleep disorders in children have a negative impact on mental and physical development, and a lack of sleep is one of the most important problems in infancy. At the age when naps are commonly accepted, the judgment of whether the amount of sleep is adequate has been based on the total amount of sleep per day. In other words, the idea is that even if the amount of sleep at night is insufficient, it is not considered insufficient if it is compensated for by taking a long nap or sleeping late on weekend mornings. However, these lifestyle habits disrupt the circadian rhythm and cause social jet lag, which is not appropriate for healthy mental and physical development. Therefore, in this review, I present the average required nighCime basic sleep duration (NBSD) of 10 h for Japanese and 11 h for Caucasian children as a judgment standard. (1) If the child sleeps less than 8 h at night, and (2) if the child sleeps less than 9 h at night or 30 to 60 min less than the required NBSD, immediate treatment is recommended. I also discuss briefly how to address sleep insufficiency in childhood. Full article
(This article belongs to the Special Issue Insufficient Sleep Syndrome in Children and Adolescents)
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<p>Nocturnal sleep duration and daytime sleep duration of various age groups on weekdays and weekends [<a href="#B3-children-12-00019" class="html-bibr">3</a>].</p>
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<p>Night bedtime and morning wake time for each age group on weekdays and at weekends [<a href="#B3-children-12-00019" class="html-bibr">3</a>].</p>
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21 pages, 7437 KiB  
Article
Transcriptomic Insights into Higher Anthocyanin Accumulation in ‘Summer Black’ Table Grapes in Winter Crop Under Double-Cropping Viticulture System
by Congqiao Wang, Chengyue Li, Youhuan Li, Yue Zeng, Jie Jiang, Linhui Wu, Siyu Yang, Dan Yuan, Lifang Chen, Zekang Pei, Viola Kayima, Haidi Liu, Zhipeng Qiu and Dongliang Qiu
Plants 2025, 14(1), 26; https://doi.org/10.3390/plants14010026 - 25 Dec 2024
Viewed by 47
Abstract
Anthocyanins are responsible for grape (Vitis vinifera L.) skin color. To obtain a more detailed understanding of the anthocyanin regulatory networks across’ the summer and winter seasons in grapes under a double-cropping viticulture system, the transcriptomes of ‘Summer Black’ grapes were analyzed [...] Read more.
Anthocyanins are responsible for grape (Vitis vinifera L.) skin color. To obtain a more detailed understanding of the anthocyanin regulatory networks across’ the summer and winter seasons in grapes under a double-cropping viticulture system, the transcriptomes of ‘Summer Black’ grapes were analyzed using RNA sequencing. The average daily temperature during the harvest stage in the summer crop, ranging from 26.18 °C to 32.98 °C, was higher than that in the winter crop, ranging from 11.03 °C to 23.90 °C. Grapes from the winter crop accumulated a greater content of anthocyanins than those from the summer crop, peaking in the harvest stage (E-L38) with 207.51 mg·100 g−1. Among them, malvidin-3-O-glucoside (Mv-3-G) had the highest monomer content, accounting for 32%. The content of Cy-3-G during winter increased by 55% compared to summer. KEGG analysis indicated that the flavonoid biosynthesis and circadian rhythm—plant pathways are involved in the regulation of anthocyanin biosynthesis during fruit development. Pearson’s coefficient showed significant positive correlations between anthocyanin content and the VvDFR, VvUFGT, VvOMT, VvMYB, and VvbHLH genes in the winter crop; at full veraison stage, their expressions were 1.34, 1.98, 1.28, 1.17, and 1.34 times greater than in summer, respectively. The higher expression of VvUFGT and VvOMT led to higher contents of Cy-3-G and Mv-3-G in the winter berries, respectively. Full article
(This article belongs to the Special Issue Horticultural Plant Cultivation and Fruit Quality Enhancement)
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<p>The mean daily temperature throughout the growth of grape berries. (<b>a</b>) Summer crop daily average temperature trends. (<b>b</b>) Winter crop daily average temperature trends. Developmental stages: pruning (E-L 1); 5 leaves separated (E-L 12); berry pea size (E-L 31); green berries (E-L 33); beginning of veraison (E-L 35); full veraison (E-L 36); end of veraison (E-L 37); and harvest stage (E-L 38). Points a, b, and c denote measurements at the front, middle, and back of the greenhouses for the summer crop. Points d, e, and f denote measurements at the front, middle, and back of the greenhouses for the winter crop.</p>
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<p>The effect of seasonal treatment on the content of anthocyanins in ‘Summer Black’ grape berries. (<b>a</b>) Grape berries in different developmental stages in winter and summer crops. (<b>b</b>) Total anthocyanin content in winter and summer crops. (<b>c</b>) Del-phinidin-3-O-glucoside (Dp-3-G) content in winter and summer crops. (<b>d</b>) Cyanidin-3-O-glucoside (Cy-3-G) content in winter and summer crops. (<b>e</b>) Petunidin-3-O-glucoside (Pt-3-G) content in winter and summer crops. (<b>f</b>) Peonidin-3-O-glucoside (Pn-3-G) content in winter and summer crops. (<b>g</b>) Malvidin-3-O-glucoside (Mv-3-G) content in winter and summer crops. Developmental stages: green berries (E-L 33); beginning of veraison (E-L 35); full veraison (E-L 36); end of veraison (E-L 37); and harvest stage (E-L 38). The vertical bars indicate the standard error of the mean (SEM). Statistical significance is denoted by distinct letters (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Analysis of gene expression profiles in the transcriptome. (<b>a</b>) Pearson correlation matrix of samples using transcriptome data. (<b>b</b>) Comparison analysis of gene expression levels across different experimental settings. (<b>c</b>) Principal component analysis (PCA) using gene expression levels. (<b>d</b>) Number of DEGs identified from seasonal comparisons. (<b>e</b>) Top 20 KEGG enrichment bubble map for DEGs between WE-L 38 and SE-L 38. Treatments: green berries from summer crop (SE-L33); full veraison in summer crop (SE-L36); harvest stage in summer crop (SE-L38); green berries from winter crop (WE-L33); full veraison in winter crop (WE-L36); and harvest stage in winter crop (WE-L38).</p>
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<p>Differentially expressed genes (DEGs) in the circadian rhythm—plant pathway and flavonoid biosynthesis pathway under double-cropping cultivation. (<b>a</b>) DEGs in the circadian rhythm—plant pathway. (<b>b</b>) DEGs in the flavonoid biosynthesis pathway. Treatments: green berries from the summer crop (SE-L33) and full veraison in summer crop (SE-L36). (<b>c</b>) RT-qPCR validation of DEGs identified in RNA-seq analysis. (<b>d</b>) Pearson’s coefficient for summer crop. (<b>e</b>) Pearson’s coefficient for winter crop. Treatments: harvest stage in summer crop (SE-L38); green berries from winter crop (WE-L33); full veraison in winter crop (WE-L36); and harvest stage in winter crop (WE-L38). Orange boxes represent upregulated genes, while dark blue boxes represent downregulated genes. The red frame represents the summer crop, and the blue frame represents the winter crop. *, ** and *** indicate <span class="html-italic">p</span> ≤ 0.05, <span class="html-italic">p</span> ≤ 0.01, <span class="html-italic">p</span> ≤ 0.001, respectively.</p>
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<p>The effect of berry thinning (BT), basal leaf removal (LR), and their combination on the content of anthocyanins in ‘Summer Black’ grape berries in the harvest stage (E-L 38). (<b>a</b>) Visual appearance of representative bunches of ‘Summer Black’ table grapes harvested from vines subjected to treatments of berry thinning (BT), basal leaf removal (LR), and their combination. (<b>b</b>) BT+LR treatment anthocyanin content. (<b>c</b>) BT treatment anthocyanin content. (<b>d</b>) LR treatment anthocyanin content. (<b>e</b>) Control treatment anthocyanin content. (<b>f</b>) BT+LR treatment anthocyanin-related gene expression. (<b>g</b>) BT treatment anthocyanin-related gene expression. (<b>h</b>) LR treatment anthocyanin-related gene expression. (<b>i</b>) Control treatment anthocyanin-related gene expression. (<b>j</b>) Test of intersubjective effects of seasonal treatments and pruning techniques on anthocyanin content. (<b>k</b>) Test of intersubjective effects of seasonal treatments and pruning techniques on related gene expression. Developmental stages: green berries (E-L 33); full veraison (E-L 36); and harvest stage (E-L 38). The vertical bar denotes the standard error of the mean (SEM). Statistical significance is denoted by different letters (<span class="html-italic">p</span> &lt; 0.05). *, ** and *** indicate <span class="html-italic">p</span> ≤ 0.05, <span class="html-italic">p</span> ≤ 0.01, <span class="html-italic">p</span> ≤ 0.001, respectively.</p>
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<p>The molecular mechanism of the regulation of grape anthocyanin metabolism: cyanidin-3-O-glucoside (Cy-3-G), peonidin-3-O-glucoside (Pn-3-G), malvidin-3-O-glucoside (Mv-3-G), delphinidin-3-O-glucoside (Dp-3-G), and petunidin-3-O-glucoside (Pt-3-G).</p>
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13 pages, 2818 KiB  
Article
Two-Dimensional Transition Metal Dichalcogenide: Synthesis, Characterization, and Application in Candlelight OLED
by Dipanshu Sharma, Sanna Gull, Anbalagan Ramakrishnan, Sushanta Lenka, Anil Kumar, Krishan Kumar, Pin-Kuan Lin, Ching-Wu Wang, Sinn-Wen Chen, Saulius Grigalevicius and Jwo-Huei Jou
Molecules 2025, 30(1), 27; https://doi.org/10.3390/molecules30010027 - 25 Dec 2024
Viewed by 230
Abstract
Low-color-temperature candlelight organic light-emitting diodes (OLEDs) offer a healthier lighting alternative by minimizing blue light exposure, which is known to disrupt circadian rhythms, suppress melatonin, and potentially harm the retina with prolonged use. In this study, we explore the integration of transition metal [...] Read more.
Low-color-temperature candlelight organic light-emitting diodes (OLEDs) offer a healthier lighting alternative by minimizing blue light exposure, which is known to disrupt circadian rhythms, suppress melatonin, and potentially harm the retina with prolonged use. In this study, we explore the integration of transition metal dichalcogenides (TMDs), specifically molybdenum disulfide (MoS2) and tungsten disulfide (WS2), into the hole injection layers (HILs) of OLEDs to enhance their performance. The TMDs, which are known for their superior carrier mobility, optical properties, and 2D layered structure, were doped at levels of 0%, 5%, 10%, and 15% in PEDOT:PSS-based HILs. Our findings reveal that OLEDs doped with 10% MoS2 exhibit notable enhancements in power efficacy (PE), current efficacy (CE), and external quantum efficiency (EQE) of approximately 39%, 21%, and 40%, respectively. In comparison, OLEDs incorporating 10% of WS2 achieve a PE of 28%, a CE of 20%, and an EQE of 35%. The enhanced performance of the MoS2-doped devices is attributed to their superior hole injection and balanced carrier transport properties, resulting in more efficient operation. These results highlight the potential of incorporating 2D TMDs, especially MoS2, into OLED technology as a promising strategy to enhance energy efficiency. This approach aligns with environmental, social, and governance (ESG) goals by emphasizing reduced environmental impact and promoting ethical practices in technology development. The improved performance metrics of these TMD-doped OLEDs suggest a viable path towards creating more energy-efficient and health-conscious lighting solutions. Full article
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<p>Structural and morphological characterization of MoS<sub>2</sub> and WS<sub>2</sub>. (<b>a</b>) PXRD patterns show characteristic diffraction peaks for (bulk) MoS<sub>2</sub> and WS<sub>2</sub>. (<b>b</b>) Raman spectra provide insight into the structural properties of both materials (bulk). SEM images show the morphology of (<b>c</b>) bulk and (<b>e</b>) exfoliated MoS<sub>2</sub>, highlighting the transformation into smaller nanosheets. (<b>d</b>,<b>f</b>) show the corresponding SEM images for bulk and exfoliated WS<sub>2</sub>, respectively.</p>
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<p>AFM images of WS<sub>2</sub> (<b>a</b>–<b>c</b>) and MoS<sub>2</sub> (<b>d</b>–<b>f</b>) doped in PEDOT:PSS at a concentration of 5%, 10%, and 15%, respectively.</p>
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<p>Performance of hole-only devices (HOD), (<b>a</b>) device structure, and (<b>b</b>) current density vs. voltage plot of different MoS<sub>2</sub> and WS<sub>2</sub> concentrations, showing the effect on hole transport properties. (<b>c</b>) Energy level diagram illustrating the alignment of energy levels in hybrid PEDOT:PSS, highlighting its impact on hole transport for OLED applications.</p>
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<p>Effects of different hole injection layers (HILs) on the (<b>a</b>) power efficacy vs. luminance, (<b>b</b>) current efficacy vs. luminance, (<b>c</b>) EQE vs. luminance, and (<b>d</b>) luminance vs. voltage (inset: glowing device) of candlelight OLED devices.</p>
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<p>CIE coordinate diagram (<b>a</b>) illustrating the blackbody radiation curve, and (<b>b</b>) the normalized intensity vs. wavelength curve of the OLED device, with a color temperature of 1900 K, indicating alignment with natural light, which is beneficial for health and the environment.</p>
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14 pages, 2501 KiB  
Article
Urolithin A Modulates PER2 Degradation via SIRT1 and Enhances the Amplitude of Circadian Clocks in Human Senescent Cells
by Rassul Kuatov, Jiro Takano, Hideyuki Arie, Masaru Kominami, Norifumi Tateishi, Ken-ichi Wakabayashi, Daisuke Takemoto, Takayuki Izumo, Yoshihiro Nakao, Wataru Nakamura, Kazuyuki Shinohara and Yasukazu Nakahata
Nutrients 2025, 17(1), 20; https://doi.org/10.3390/nu17010020 - 25 Dec 2024
Viewed by 200
Abstract
Background/Objectives: Circadian clocks are endogenous systems that regulate numerous biological, physiological, and behavioral events in living organisms. Aging attenuates the precision and robustness of circadian clocks, leading to prolonged and dampened circadian gene oscillation rhythms and amplitudes. This study investigated the effects of [...] Read more.
Background/Objectives: Circadian clocks are endogenous systems that regulate numerous biological, physiological, and behavioral events in living organisms. Aging attenuates the precision and robustness of circadian clocks, leading to prolonged and dampened circadian gene oscillation rhythms and amplitudes. This study investigated the effects of food-derived polyphenols such as ellagic acid and its metabolites (urolithin A, B, and C) on the aging clock at the cellular level using senescent human fibroblast cells, TIG-3 cells. Methods: Lentivirus-infected TIG-3 cells expressing Bmal1-luciferase were used for real-time luciferase monitoring assays. Results: We revealed that urolithins boosted the amplitude of circadian gene oscillations at different potentials; urolithin A (UA) amplified the best. Furthermore, we discovered that UA unstabilizes PER2 protein while stabilizing SIRT1 protein, which provably enhances BMAL1 oscillation. Conclusions: The findings suggest that urolithins, particularly UA, have the potential to modulate the aging clock and may serve as therapeutic nutraceuticals for age-related disorders associated with circadian dysfunction. Full article
(This article belongs to the Section Nutrition and Public Health)
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<p>Effects of ellagic acid and urolithin A on the circadian clock of senescent cells. (<b>A</b>) Representative circadian oscillation patterns of DMSO-, ellagic acid (EA; 33.1 μM)-, or urolithin A (UA; 13.1 μM)-treated luciferase driven by <span class="html-italic">Bmal1</span> promoter were shown. (<b>B</b>,<b>C</b>) The period lengths and relative amplitudes were analyzed by the cosinor method using the data from (<b>A</b>). Each sample number was 6 or 7. The value of DMSO was set to 1 for the relative amplitude. ANOVA followed by Dunnett’s post-hoc test was analyzed. Statistical significance compared with the control “DMSO” is indicated as * <span class="html-italic">p</span> &lt; 0.05, or ** <span class="html-italic">p</span> &lt; 0.005.</p>
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<p>Effects of ellagic acid and urolithin A on the circadian clock of proliferative cells. (<b>A</b>) Representative circadian oscillation patterns of DMSO-, EA (33.1 μM)-, or UA (30 μM)-treated luciferase driven by <span class="html-italic">Bmal1</span> promoter were shown. (<b>B</b>,<b>C</b>) The period lengths and relative amplitudes were analyzed by the cosinor method using the data from (<b>A</b>). Each sample number was 5 or 6. The value of DMSO was set to 1 for the relative amplitude. ANOVA followed by Dunnett’s post-hoc test was analyzed. Statistical significance compared with the control “DMSO” is indicated as *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Effects of EA and its derivatives on the circadian clock of senescent cells. (<b>A</b>) Ellagic acid and its metabolites are shown. (<b>B</b>) Representative circadian oscillation patterns of EA-, UA-, UB, or UC-treated luciferase driven by <span class="html-italic">Bmal1</span> promoter were shown. (<b>C</b>) Amplitudes were analyzed with the cosinor method using the data from (<b>B</b>), and the amplitude of 0 mM for each metabolite was set to 1. Each sample number was 5 to 8. Values are presented as the mean ± SEM. ANOVA followed by Dunnett’s post-hoc test was analyzed. Statistical significance compared with the control “0” is indicated as * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, or *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Effects of UA on the circadian clock gene expressions in senescent cells. Circadian gene expression levels after the UA treatment were quantified by qPCR. Each sample was normalized by the amount of <span class="html-italic">18S rRNA</span>. Each gene expression level in DMSO was set to 1. Each sample number was 5 to 9. Values are presented as the mean ± SEM. The Student’s two-tailed <span class="html-italic">t</span>-tests were performed. Statistical significance compared with the control “DMSO” is indicated as * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of UA on Per2 protein stability in senescent cells. (<b>A</b>) The protein stability of luciferase-fused Per2 protein (Per2-luc) or luciferase alone (luc) was measured using the real-time monitoring system. (<b>B</b>) Effects of UA on protein stability were analyzed. Values indicate the percentages of t<sub>1/2</sub> of the UA-treated condition divided by t<sub>1/2</sub> of the DMSO-treated condition. Each sample number was 4. Values are presented as the mean ± SEM. The Student’s two-tailed <span class="html-italic">t</span>-tests were performed. Statistical significance compared with the control “luc” is indicated as ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Effects of UA on Sirt1 amount in senescent cells. (<b>A</b>) <span class="html-italic">SIRT1</span> expression levels after the UA treatment were quantified by qPCR. Samples were normalized by the amount of <span class="html-italic">18S rRNA</span>. <span class="html-italic">SIRT1</span> expression level in DMSO was set to 1. The sample numbers were 9. Values are presented as the mean ± SEM. The Student’s two-tailed <span class="html-italic">t</span>-tests were performed. Statistical significance compared with the control “DMSO” is indicated as ** <span class="html-italic">p</span> &lt; 0.01. (<b>B</b>) SIRT1 (upper panel) and a-TUBLIN (bottom panel) protein levels under indicated conditions were detected. The arrowhead indicates non-specific bands.</p>
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<p>Scheme of how UA amplifies circadian gene expression in senescent cells. UA treatment increases the SIRT1 protein amount, which may promote its deacetylation activity and subsequent degradation of PER2, thereby releasing CLOCK/BMAL1 repression by PER/CRY. This enhances oscillatory <span class="html-italic">REV-ERB</span> and thereby <span class="html-italic">BMAL1</span> gene expression.</p>
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32 pages, 2169 KiB  
Review
Circadian Influences on Brain Lipid Metabolism and Neurodegenerative Diseases
by Yusuf Hussain, Mohammad Irfan Dar and Xiaoyue Pan
Metabolites 2024, 14(12), 723; https://doi.org/10.3390/metabo14120723 - 22 Dec 2024
Viewed by 377
Abstract
Circadian rhythms are intrinsic, 24 h cycles that regulate key physiological, mental, and behavioral processes, including sleep–wake cycles, hormone secretion, and metabolism. These rhythms are controlled by the brain’s suprachiasmatic nucleus, which synchronizes with environmental signals, such as light and temperature, and consequently [...] Read more.
Circadian rhythms are intrinsic, 24 h cycles that regulate key physiological, mental, and behavioral processes, including sleep–wake cycles, hormone secretion, and metabolism. These rhythms are controlled by the brain’s suprachiasmatic nucleus, which synchronizes with environmental signals, such as light and temperature, and consequently maintains alignment with the day–night cycle. Molecular feedback loops, driven by core circadian “clock genes”, such as Clock, Bmal1, Per, and Cry, are essential for rhythmic gene expression; disruptions in these feedback loops are associated with various health issues. Dysregulated lipid metabolism in the brain has been implicated in the pathogenesis of neurological disorders by contributing to oxidative stress, neuroinflammation, and synaptic dysfunction, as observed in conditions such as Alzheimer’s and Parkinson’s diseases. Disruptions in circadian gene expression have been shown to perturb lipid regulatory mechanisms in the brain, thereby triggering neuroinflammatory responses and oxidative damage. This review synthesizes current insights into the interconnections between circadian rhythms and lipid metabolism, with a focus on their roles in neurological health and disease. It further examines how the desynchronization of circadian genes affects lipid metabolism and explores the potential mechanisms through which disrupted circadian signaling might contribute to the pathophysiology of neurodegenerative disorders. Full article
(This article belongs to the Special Issue Cellular Metabolism in Neurological Disorders)
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<p>Circadian clock gene regulation in brain. The central circadian clock in the SCN regulates body rhythms and sends signals to peripheral clocks. The CLOCK/BMAL1 complex binds E-box elements on target genes (CRY1/2, PER1/2/3, REV-ERBα/β, and RORα). PER and CRY proteins form cytoplasmic heterodimers that shuttle to the nucleus. After phosphorylation by CK1δ and CK1ε, PER/CRY suppress E-box gene transcription via CLOCK/BMAL1.</p>
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<p>Lipid trafficking in the brain. Astrocytes synthesize cholesterol via HMG-CoA reductase (HMG-CoAR), package it into ApoE lipoproteins, and export it via ABCA1. Neurons receive APOE/C1 lipoprotein for neurite growth, synaptogenesis, or conversion to 24-OHC by cholesterol 24-hydroxylase (CYP46). Stimulated or stressed neurons release FAs in APOE particle lipoprotein to astrocytes for degradation or storage in lipid droplets. Lipids for oligodendrocyte myelination/remyelination are synthesized by both astrocytes and oligodendrocytes, and ApoE aids in astrocyte-to-oligodendrocyte transport. Also, neurons can transfer cholesterol-rich lipoproteins to oligodendrocytes through low-density lipoprotein receptor (LDLr) on oligodendrocytes. Excess saturated FAs from astrocytes can cause oligodendrocyte death via lipoapoptosis. Lipids from myelin debris activate triggering receptor expressed on myeloid cells 2 (also called TREM2) signaling. TREM2 expression triggers spleen tyrosine kinase (Syk) activation, triggering lipid metabolism genes that aid in lipid droplet breakdown and lipid efflux, and is also known to participate in neurological disorders [<a href="#B97-metabolites-14-00723" class="html-bibr">97</a>,<a href="#B98-metabolites-14-00723" class="html-bibr">98</a>,<a href="#B99-metabolites-14-00723" class="html-bibr">99</a>,<a href="#B100-metabolites-14-00723" class="html-bibr">100</a>,<a href="#B101-metabolites-14-00723" class="html-bibr">101</a>].</p>
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<p>Dysregulated circadian rhythm and lipid metabolism drive neuronal insult in Huntington’s disease. Environmental factors downregulate circadian genes, thereby disrupting neuronal lipid homeostasis. This disturbance promotes increased lipid uptake by neurons, and results in lipid accumulation and subsequent downregulation of ABC transporters, including ABCA1, ABCA4, and ABCG5, which are critical for phospholipid and cholesterol transport. Diminished ABC transporter activity exacerbates lipid aggregation, impairs lipophagy, and hinders the clearance of excess lipids. This lipid overload contributes to septin dysregulation, which in turn impairs nerve conduction, and ultimately triggers neuroinflammation and neuronal degeneration associated with HD.</p>
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<p>Hippocampal and entorhinal cortex insult in Alzheimer’s disease neurodegeneration. Primarily, the hippocampus and entorhinal cortex two are key regions for learning, memory, and spatial orientation. Genetic and environmental factors compromise circadian rhythm, followed by lipid metabolism. Loss of central circadian rhythms disrupts amyloid β fluid oscillations, speeding up amyloid plaque buildup. For example, downregulation of Bmal1 in the brain parenchyma elevates ApoE expression and encourages fibrillar plaque buildup [<a href="#B55-metabolites-14-00723" class="html-bibr">55</a>]. Combined with Aβ plaque buildup and tau tangles, circadian disruption worsens neuroinflammation and impairs synaptic plasticity [<a href="#B212-metabolites-14-00723" class="html-bibr">212</a>]. Subsequently, abnormal autophagy leads to BBB breakdown, and a loss of pericyte functioning aggravates the condition [<a href="#B215-metabolites-14-00723" class="html-bibr">215</a>]. As circadian rhythm disturbances compound this molecular damage, neurodegeneration accelerates and drives AD progression.</p>
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<p>Pathophysiology of Parkinson’s disease: Under physiological conditions, α-synuclein exists as a soluble random coil. Pathological conditions are caused by abnormal lipid metabolism and desynchronized circadian rhythm. α-synuclein is misfolded, thus forming toxic dimers, trimers, and oligomers. These misfolded forms aggregate into protofibrils, intermediates, and amyloid fibrils. Aggregates form Lewy bodies or Lewy neurites and deposited over dopaminergic neurons and lead to neuroinflammation, neuronal death, and subsequently degradation of the substabtia nigra.</p>
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9 pages, 725 KiB  
Article
Impact of Long-Term Cannabidiol (CBD) Treatment on Mouse Kidney Transcriptome
by Mikołaj Rokicki, Jakub Żurowski, Sebastian Sawicki, Ewa Ocłoń, Tomasz Szmatoła, Igor Jasielczuk, Karolina Mizera-Szpilka, Ewelina Semik-Gurgul and Artur Gurgul
Genes 2024, 15(12), 1640; https://doi.org/10.3390/genes15121640 - 21 Dec 2024
Viewed by 329
Abstract
Background: Cannabidiol, which is one of the main cannabinoids present in Cannabis sativa plants, has been shown to have therapeutic properties, including anti-inflammatory and antioxidant effects that may be useful for treatment of various kidney conditions. Objectives: This article investigates the effect of [...] Read more.
Background: Cannabidiol, which is one of the main cannabinoids present in Cannabis sativa plants, has been shown to have therapeutic properties, including anti-inflammatory and antioxidant effects that may be useful for treatment of various kidney conditions. Objectives: This article investigates the effect of long-term cannabidiol (CBD) treatment on changes in the renal transcriptome in a mouse model. The main hypothesis was that systematic CBD treatment would affect gene expression associated with those processes in the kidney. Methods: The study was conducted on male C57BL/6J mice. Mice in the experimental groups received daily intraperitoneal injections of CBD at doses of 10 mg/kg or 20 mg/kg body weight (b.w.) for 28 days. After the experiment, kidney tissues were collected, RNA was isolated, and RNA-Seq sequencing was performed. Results: The results show CBD’s effects on changes in gene expression, including the regulation of genes related to circadian rhythm (e.g., Ciart, Nr1d1, Nr1d2, Per2, and Per3), glucocorticoid receptor function (e.g., Cyp1b1, Ddit4, Foxo3, Gjb2, and Pck1), lipid metabolism (e.g., Cyp2d22, Cyp2d9, Decr2 Hacl1, and Sphk1), and inflammatory response (e.g., Cxcr4 and Ccl28). Conclusions: The obtained results suggest that CBD may be beneficial for therapeutic purposes in treating kidney disease, and its effects should be further analyzed in clinical trials. Full article
(This article belongs to the Section Molecular Genetics and Genomics)
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<p>Results of RNA-Seq data analysis. (<b>A</b>) Principal components analysis for all samples; (<b>B</b>) number of genes altered with both CBD treatments; (<b>C</b>) MA plots; (<b>D</b>) heatmaps for genes significantly altered using CBD treatments in kidneys.</p>
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17 pages, 1467 KiB  
Article
Sex- and Substance-Specific Associations of Circadian-Related Genes with Addiction in the UK Biobank Cohort Implicate Neuroplasticity Pathways
by Ayub Khan, Mete Minbay, Ziad Attia, Ahmet Ali Ay and Krista K. Ingram
Brain Sci. 2024, 14(12), 1282; https://doi.org/10.3390/brainsci14121282 - 20 Dec 2024
Viewed by 523
Abstract
Background/Objectives: The circadian clockwork is implicated in the etiology of addiction, with circadian rhythm disruptions bidirectionally linked to substance abuse, but the molecular mechanisms that underlie this connection are not well known. Methods: Here, we use machine learning to reveal sex- and substance-specific [...] Read more.
Background/Objectives: The circadian clockwork is implicated in the etiology of addiction, with circadian rhythm disruptions bidirectionally linked to substance abuse, but the molecular mechanisms that underlie this connection are not well known. Methods: Here, we use machine learning to reveal sex- and substance-specific associations with addiction in variants from 51 circadian-related genes (156,702 SNPs) in 98,800 participants from a UK Biobank cohort. We further analyze SNP associations in a subset of the cohort for substance-specific addictions (alcohol, illicit drugs (narcotics), and prescription drugs (opioids)). Results: We find robust (OR > 10) and novel sex-specific and substance-specific associations with variants in synaptic transcription factors (ZBTB20, CHRNB3) and hormone receptors (RORA), particularly in individuals addicted to narcotics and opioids. Circadian-related gene variants associated with male and female addiction were non-overlapping; variants in males primarily involve dopaminergic pathways, while variants in females factor in metabolic and inflammation pathways, with a novel gene association of female addiction with DELEC1, a gene of unknown function. Conclusions: Our findings underscore the complexity of genetic pathways associated with addiction, involving core clock genes and circadian-regulated pathways, and reveal novel circadian-related gene associations that will aid the development of targeted, sex-specific therapeutic interventions for substance abuse. Full article
(This article belongs to the Section Computational Neuroscience and Neuroinformatics)
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<p>Associations of SNP-SNP epistatic interactions with addiction from overall, female, and male groups. Associations were calculated using logistic regression results in (<b>a</b>) overall, (<b>b</b>) female, and (<b>c</b>) male population samples, and were visualized using Gephi software. SNP nodes are represented by colored circles, where the node size is relative to odds ratios of the individual SNPs. Colors represent the risk categories of the variants according to the following key: gray = insignificant; light red = low risk; dark red = high risk; light blue = low protection; and dark blue = high protection. “Risk” indicates an odds ratio above 1, and “protection” refers to an odds ratio below 1. The “low” category indicates that the SNP’s association with addiction was not significant after <span class="html-italic">p</span> value correction, whereas “high” means the association remained significant after <span class="html-italic">p</span> value correction. Edges representing epistatic interactions are shown with the same color and size coding. Non-significant interactions (indicated by gray edges) were excluded. Mapping from the SNP label to rsIDs is given in <a href="#app1-brainsci-14-01282" class="html-app">Supplementary Table S5</a>.</p>
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<p>Association rule learning (ARL) networks. Networks show the co-occurrence of genes and chronotype in addiction risk “rules” within the UK Biobank population samples: (<b>a</b>) overall, (<b>b</b>) female, and (<b>c</b>) male. Genes or chronotype are represented as nodes, while the co-occurrence of these genes within rules is shown as edges connecting the nodes. The frequency of rule observations is reflected by the sizes of nodes and edges: larger nodes indicate items observed more frequently in rules, and larger edges signify pairs of items that commonly co-occur. The depth of node or edge color represents the average lift value of the rules containing that node or edge. Numbers in parentheses indicate the count of variants from a given gene that are present in the rules and contribute to the average lift. Lift serves as a measure of the increased likelihood of observing addiction in individuals whose genotype adheres to a specific rule, compared to a randomly selected individual.</p>
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10 pages, 247 KiB  
Article
Anticipatory Behaviour During the Approach to Feeding Times as a Measure of Horse Welfare
by Fernando Mata, Georgina Boyton and Tamsin Young
Animals 2024, 14(24), 3677; https://doi.org/10.3390/ani14243677 - 20 Dec 2024
Viewed by 300
Abstract
Anticipatory behaviour is increasingly being recognised as a measure of animal welfare. This behaviour is linked to reward sensitivity, which reflects the balance of positive and negative experiences. This study examined anticipatory behaviour in horses fed either ad libitum or rationed diets, aiming [...] Read more.
Anticipatory behaviour is increasingly being recognised as a measure of animal welfare. This behaviour is linked to reward sensitivity, which reflects the balance of positive and negative experiences. This study examined anticipatory behaviour in horses fed either ad libitum or rationed diets, aiming to identify differences in behaviour patterns during the periods of one hour immediately before and after feeding. Behavioural data were collected via video surveillance over five days, focusing on the pre- and post-feeding periods of stabled horses. The data were successfully fit to Poisson models. The results indicate that ration-fed horses exhibit more stereotypic and arousal behaviours, with anticipatory behaviour intensifying closer to feeding times. These findings suggest a potential link between feeding stress and anticipatory behaviour, especially in horses on rationed diets. This study underscores the importance of considering feeding practices in horse welfare management and highlights anticipatory behaviour as a valuable indicator for assessing animal well-being. Such assessments, rooted in circadian rhythms, offer a less resource-intensive means for ongoing welfare monitoring in animal care settings. Full article
(This article belongs to the Section Animal Welfare)
21 pages, 4283 KiB  
Article
Modeling Floral Induction in the Narrow-Leafed Lupin Lupinus angustifolius Under Different Environmental Conditions
by Maria A. Duk, Vitaly V. Gursky, Mikhail P. Bankin, Elena A. Semenova, Maria V. Gurkina, Elena V. Golubkova, Daisuke Hirata, Maria G. Samsonova and Svetlana Yu. Surkova
Plants 2024, 13(24), 3548; https://doi.org/10.3390/plants13243548 - 19 Dec 2024
Viewed by 271
Abstract
Flowering is initiated in response to environmental cues, with the photoperiod and ambient temperature being the main ones. The regulatory pathways underlying floral transition are well studied in Arabidopsis thaliana but remain largely unknown in legumes. Here, we first applied an in silico [...] Read more.
Flowering is initiated in response to environmental cues, with the photoperiod and ambient temperature being the main ones. The regulatory pathways underlying floral transition are well studied in Arabidopsis thaliana but remain largely unknown in legumes. Here, we first applied an in silico approach to infer the regulatory inputs of four FT-like genes of the narrow-leafed lupin Lupinus angustifolius. We studied the roles of FTc1, FTc2, FTa1, and FTa2 in the activation of meristem identity gene AGL8 in response to 8 h and 16 h photoperiods, vernalization, and the circadian rhythm. We developed a set of regression models of AGL8 regulation by the FT-like genes and fitted these models to the recently published gene expression data. The importance of the input from each FT-like gene or their combinations was estimated by comparing the performance of models with one or few FT-like genes turned off, thereby simulating loss-of-function mutations that were yet unavailable in L. angustifolius. Our results suggested that in the early flowering Ku line and intermediate Pal line, the FTc1 gene played a major role in floral transition; however, it acted through different mechanisms under short and long days. Turning off the regulatory input of FTc1 resulted in substantial changes in AGL8 expression associated with vernalization sensitivity and the circadian rhythm. In the wild ku line, we found that both FTc1 and FTa1 genes had an essential role under long days, which was associated with the vernalization response. These results could be applied both for setting up new experiments and for data analysis using the proposed modeling approach. Full article
(This article belongs to the Section Plant Modeling)
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Figure 1
<p>A general scheme of flowering initiation in <span class="html-italic">Arabidopsis thaliana</span> and a putative network in the narrow-leafed lupin <span class="html-italic">Lupinus angustifolius</span>. In <span class="html-italic">Arabidopsis</span>, the expression of the <span class="html-italic">FT</span> gene is activated in the leaves by the photoperiod and vernalization pathways. Next, the FT protein becomes expressed in the shoot apical meristem, where in complex with the transcription factor FD, it activates meristem identity genes, including <span class="html-italic">AP1</span> and <span class="html-italic">FUL</span>. Meristem identity genes, in turn, activate pathways responsible for the formation of floral organs. <span class="html-italic">L. angustifolius</span> has four <span class="html-italic">FT</span> gene orthologues, which are <span class="html-italic">FTc1</span>, <span class="html-italic">FTc2</span>, <span class="html-italic">FTa1</span>, and <span class="html-italic">FTa2</span>. The mechanisms of <span class="html-italic">FT</span>-like gene activation by environmental signals and the involvement of each <span class="html-italic">FT</span>-like gene in the regulation of meristem identity genes are still unknown (shown in the blue dotted box). <span class="html-italic">AGL8</span> is the <span class="html-italic">L. angustifolius</span> orthologue of the <span class="html-italic">Arabidopsis AP1</span> and <span class="html-italic">FUL</span> genes and a putative target of <span class="html-italic">FT</span>-like genes.</p>
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<p>Data fitting results for Models 1 and 3, which show the lowest values of the cost function. Averaged dynamics and standard deviation of the experimental data are shown in red, and the model solutions (averaged over 1000 runs) are shown in black. Green dots represent the simulation results from 10 randomly chosen runs of the minimization process. “N” and “V” stand for non-vernalized and vernalized data, respectively. “9 A.M.” and “3 P.M.” are the times of the day when the data were collected. T1–T4 stand for sampling terms [<a href="#B21-plants-13-03548" class="html-bibr">21</a>].</p>
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<p>Number (#) of free parameters (red), minimal cost function value F<sub>min</sub> (blue), and AIC value (green) for Models 1–3. The blue and green dotted lines correspond to the minimum values of the cost function and AIC, respectively.</p>
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<p>Cost function values (F) from 1000 minimization runs in <b>Model 1</b> under hypotheses H0–5 for three <span class="html-italic">L. angustifolius</span> lines. Asterisks indicate statistically significant differences in the mean F between Hi (i = 1…5) and H0 (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01). The labels 8 h and 16 h are SD and LD photoperiods, respectively.</p>
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<p>Expression dynamics in <b>Models H4, H5, and H0</b> compared to experimental data for the 8 h photoperiod. Averaged dynamics of the experimental data are shown in red, and the model solution (average of 1000 runs) is shown in blue. Green dots represent the simulation results from 10 random runs of the minimization process. Models showing specific defects in solutions compared to H0 are marked with brown frames. “N” and “V” stand for non-vernalized and vernalized data, respectively. The labels “9 A.M.” and “3 P.M.” are the times of the day when the data were collected. T1–T4 stand for sampling terms [<a href="#B21-plants-13-03548" class="html-bibr">21</a>].</p>
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<p>Expression dynamics in Models H4, H5, and H0 compared to experimental data for the 16 h photoperiod. Averaged dynamics of the experimental data are shown in red, and the model solution (average of 1000 runs) is shown in blue. Green dots represent the simulation results from 10 random runs of the minimization process. Models showing specific defects in solutions compared to H0 are marked with brown frames. “N” and “V” stand for non-vernalized and vernalized data; “7 A.M.” and “6 P.M.” are the times of data collection during LD. T1–T4 stand for sampling terms [<a href="#B21-plants-13-03548" class="html-bibr">21</a>].</p>
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<p>Cost function values (F) for 1000 minimization runs of <b>Model 1</b> and <b>Models 4–7</b> for three <span class="html-italic">L. angustifolius</span> lines. Asterisks indicate statistically significant differences in the mean F between Model i (i = 4…7) and <b>Model 1</b> (** <span class="html-italic">p</span> &lt; 0.01). The labels 8 h and 16 h are SD and LD photoperiods.</p>
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<p>The roles of <span class="html-italic">FT</span>-like genes in <span class="html-italic">AGL8</span> regulation in <span class="html-italic">L. angustifolius</span> models. The figure summarizes the regulatory effects of the exclusion of one or several <span class="html-italic">FT</span>-like genes from <span class="html-italic">AGL8</span> regulation under 8 and 16 h photoperiods. Circles of different sizes show an effect of <span class="html-italic">FT</span>-like gene exclusion on the cost function values of <b>Model 1</b> under hypotheses H1–H5. <span class="html-italic">FT</span>-like genes excluded from each model are specified in the top panel and crossed out in red. The larger the circle, the stronger the influence of regulators on <span class="html-italic">AGL8</span> expression. In models with the smallest circles, cost function values did not show statistically significant differences from model H0, where <span class="html-italic">AGL8</span> was regulated by all four <span class="html-italic">FT</span>-like genes (<span class="html-italic">FTa1</span>, <span class="html-italic">FTa2</span>, <span class="html-italic">FTc1</span>, and <span class="html-italic">FTc2</span>) (<a href="#plants-13-03548-f004" class="html-fig">Figure 4</a>). Cost function values in the models with middle and large circles had statistically significant differences from model H0. However, only models with large circles exhibited patterning defects and/or changes in regulatory parameters. The association of changes in the regulatory parameters with vernalization and circadian rhythms are indicated by different colors, according to the key at the bottom panel. The <span class="html-italic">c</span><sub>1</sub> constant presents the regulatory input of <span class="html-italic">FT</span>-like genes, while <span class="html-italic">c</span><sub>0</sub> reflects the regulation of <span class="html-italic">AGL8</span> by other factors (<a href="#app1-plants-13-03548" class="html-app">Supplementary Tables S1 and S2</a>).</p>
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<p>Experimental data on the expression dynamics of <span class="html-italic">AGL8</span>, <span class="html-italic">FTc1</span>, <span class="html-italic">FTa1</span>, <span class="html-italic">FTa2,</span> and <span class="html-italic">FTc2</span> genes over the 8 h (SD) and 16 h (LD) photoperiods [<a href="#B21-plants-13-03548" class="html-bibr">21</a>]. The data were obtained with qRT-PCR. “N” and “V” stand for non-vernalized and vernalized data; “9 A.M.” and “3 P.M.” are the times of the day when the data were collected during SD, while “7 A.M.” and “6 P.M.” are the times of data collection for LD. T1–T4 stand for sampling terms [<a href="#B21-plants-13-03548" class="html-bibr">21</a>].</p>
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21 pages, 1015 KiB  
Review
Chronobiology in Paediatric Neurological and Neuropsychiatric Disorders: Harmonizing Care with Biological Clocks
by Gabriele Giannotta, Marta Ruggiero and Antonio Trabacca
J. Clin. Med. 2024, 13(24), 7737; https://doi.org/10.3390/jcm13247737 - 18 Dec 2024
Viewed by 442
Abstract
Background: Chronobiology has gained attention in the context of paediatric neurological and neuropsychiatric disorders, including migraine, epilepsy, autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and post-traumatic stress disorder (PTSD). Disruptions in circadian rhythms are associated with key symptoms such as sleep disturbances, [...] Read more.
Background: Chronobiology has gained attention in the context of paediatric neurological and neuropsychiatric disorders, including migraine, epilepsy, autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and post-traumatic stress disorder (PTSD). Disruptions in circadian rhythms are associated with key symptoms such as sleep disturbances, mood dysregulation, and cognitive impairments, suggesting a potential for chronobiology-based therapeutic approaches. Methods: This narrative review employs a systematic approach to identify relevant studies through searches of three major scientific databases, NCBI/PubMed, ScienceDirect, and Scopus, up to July 2024. We used a combination of broad and condition-specific keywords, such as “chronobiology”, “biorhythm”, “pediatric”, “epilepsy”, “ADHD”, and “ASD”, among others. Articles in English that focused on clinical features, treatments, or outcomes related to circadian rhythms in paediatric populations were included, while non-peer-reviewed articles and studies lacking original data were excluded. Rayyan software was used for article screening, removing duplicates, and facilitating consensus among independent reviewers. Results: A total of 87 studies were included in the analysis. Findings reveal a consistent pattern of circadian rhythm disruptions across the disorders examined. Specifically, dysregulation of melatonin and cortisol secretion is observed in children with ASD, ADHD, and PTSD, with altered circadian timing contributing to sleep disturbances and mood swings. Alterations in core clock genes (CLOCK, BMAL1, PER, and CRY) were also noted in children with epilepsy, which was linked to seizure frequency and timing. Chronotherapy approaches showed promise in managing these disruptions: melatonin supplementation improved sleep quality and reduced ADHD symptoms in some children, while light therapy proved effective in stabilizing sleep–wake cycles in ASD and ADHD patients. Additionally, behaviour-based interventions, such as the Early Start Denver Model, showed success in improving circadian alignment in children with ASD. Conclusions: This review highlights the significant role of circadian rhythm disruptions in paediatric neurological and neuropsychiatric disorders, with direct implications for treatment. Chronobiology-based interventions, such as melatonin therapy, light exposure, and individualized behavioural therapies, offer potential for improving symptomatology and overall functioning. The integration of chronotherapy into clinical practice could provide a paradigm shift from symptom management to more targeted, rhythm-based treatments. Future research should focus on understanding the molecular mechanisms behind circadian disruptions in these disorders and exploring personalized chronotherapeutic approaches tailored to individual circadian patterns. Full article
(This article belongs to the Section Clinical Pediatrics)
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<p>Potential consequences of biorhythm dysregulation in paediatric neurological and neuropsychiatric disorders. Legend: ASD: autism spectrum disorder; ADHD: attention-deficit/hyperactivity disorder; PTSD: Post-traumatic stress disorder.</p>
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<p>Interdependent chronobiological factors to consider in chronotherapy.</p>
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20 pages, 12418 KiB  
Article
LncRNA-MSTRG.19083.1 Targets NTRK2 as a miR-429-y Sponge to Regulate Circadian Rhythm via the cAMP Pathway in Yak Testis and Cryptorchidism
by Tianan Li, Qiu Yan, Jinghong Nan, Xue Huang, Ruiqing Wang, Yong Zhang, Xingxu Zhao and Qi Wang
Int. J. Mol. Sci. 2024, 25(24), 13553; https://doi.org/10.3390/ijms252413553 - 18 Dec 2024
Viewed by 245
Abstract
Long noncoding RNAs (LncRNAs) play essential roles in numerous biological processes in mammals, such as reproductive physiology and endocrinology. Cryptorchidism is a common male reproductive disease. Circadian rhythms are actively expressed in the reproductive system. In this study, a total of 191 LncRNAs [...] Read more.
Long noncoding RNAs (LncRNAs) play essential roles in numerous biological processes in mammals, such as reproductive physiology and endocrinology. Cryptorchidism is a common male reproductive disease. Circadian rhythms are actively expressed in the reproductive system. In this study, a total of 191 LncRNAs were obtained from yak testes and cryptorchids. Then, we identified NTRK2’s relationship to circadian rhythm and behavioral processes. Meanwhile, the ceRNA (LncRNA-MSTRG.19083.1/miR-429-y/NTRK2) network was constructed, and its influence on circadian rhythm was revealed. The results showed that NTRK2 and LncRNA-MSTRG.19083.1 were significantly upregulated, and miR-429-y was obviously decreased in cryptorchid tissue; NTRK2 protein was mainly distributed in the Leydig cells of the testis. In addition, the upregulation of the expression level of miR-429-y resulted in the significant downregulation of LncRNA and NTRK2 levels, while the mRNA and protein levels of CREB, CLOCK, and BMAL1 were significantly upregulated; the knockdown of miR-429-y resulted in the opposite changes. Our findings suggested that LncRNA-MSTRG.19083.1 competitively binds to miR-429-y to target NTRK2 to regulate circadian rhythm through the cAMP pathway. Taken together, the results of our study provide a comprehensive understanding of how the LncRNA-miRNA-mRNA networks operate when yak cryptorchidism occurs. Knowledge of circadian-rhythm-associated mRNAs and LncRNAs could be useful for better understanding the relationship between circadian rhythm and reproduction. Full article
(This article belongs to the Section Molecular Genetics and Genomics)
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<p>Analysis of differentially expressed LncRNA: (<b>A</b>) LncRNA distribution in the testis group and the cryptorchidism group; (T, testis; C, cryptorchidism); (<b>B</b>) number of LncRNA types; (<b>C</b>) total number of LncRNAs that were differentially expressed in the testis and the cryptorchidism group; the red color represents the number of LncRNAs upregulated in cryptorchidism compared to normal testes, the green color represents the number of LncRNAs decreased. (<b>D</b>) cluster heat map analysis of testis and cryptorchidism; (<b>E</b>) LncRNA differentially expressed volcano map; (<b>F</b>) GO analysis of 191 differentially expressed LncRNAs; (<b>G</b>) KEGG enrichment analysis.</p>
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<p>GO and KEGG analysis of differentially expressed genes (circadian rhythms): (<b>A</b>) genes involved in circadian activity were analyzed in testis and cryptorchids; (<b>B</b>) analysis of differentially expressed genes involved in circadian rhythms in testicular and cryptorchidism; (<b>C</b>) biological process analysis of circadian rhythm-related differential genes; (<b>D</b>) screening for circadian rhythm-related differential genes using Veen map; (<b>E</b>) interaction gene analysis of NTRK2; (<b>F</b>) functional analysis of NTRK2 involved in the GO enrichment process; (<b>G</b>) NTRK2 is involved in the enrichment signaling pathway.</p>
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<p>Verification of differentially expressed LncRNAs in the testis and cryptorchids: (<b>A</b>) differentially expressed LncRNAs were detected with qRT-PCR, <span class="html-italic">n</span> = 3, mean ± SD, <span class="html-italic">** p</span> &lt; 0.01; (<b>B</b>) LncRNA sequencing analysis; (<b>C</b>) differentially expressed miRNAs were detected with qRT-PCR, <span class="html-italic">n</span> = 3, mean ± SD, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Differential expression of genes was verified in the testis and cryptorchids of the yaks: (<b>A</b>) differentially expressed mRNAs were detected with qRT-PCR, <span class="html-italic">n</span> = 3, mean ± SD, <span class="html-italic">** p</span> &lt; 0.01; (<b>B</b>) mRNA sequencing analysis.</p>
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<p>NTRK2 expression pattern analysis in testis and cryptorchids: (<b>A</b>–<b>C</b>) mRNA and protein expression levels of NTRK2 were analyzed using qPCR and Western blot, <span class="html-italic">n</span> = 3, mean ± SD, <span class="html-italic">** p</span> &lt; 0.01; (<b>D</b>) H&amp;E staining was used to analyze the morphology and structure of testis and cryptorchids; (<b>E</b>,<b>F</b>) protein distribution of NTRK2 was stained by immunohistochemistry and immunofluorescence in testis and cryptorchids. LC: Leydig cells, SC: Sertoli cells, SP: spermatogonium, PS: primary spermatocyte, ST: seminiferous tubule, PMC: peritubular myoid cells.</p>
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<p>Targeting relationship between <span class="html-italic">NTRK2</span> and LncRNAs/miRNAs: (<b>A</b>) the scatter plot revealed the expression level of LncRNAs; (<b>B</b>,<b>C</b>) the network map and mulberry map reveal the targeting relationship between NTRK2 and LncRNAs and miRNAs. –– means Gene unknown.</p>
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<p>Between target gene NTRK2 and LncRNA and miR-429-y, (<b>A</b>) IF staining identified isolated yak LCs using antibodies against HSD3β (green) and β-tubulin (red), with magnification, 20×; (<b>B</b>,<b>C</b>) binding site of LncRNA-MSTRG.19083.1, NTRK2, and miRNA-429-y; (<b>D</b>) Luciferase activity in 293T cells after co-transfection with mimics of miRNA-429-y (100 nM) or mimic NC (100 nM) and pmirGLO-LncRNA-MSTRG.19083.1/NTRK2 3′-UTR-WT (400 ng) or pmirGLO-LncRNA-MSTRG.19 083.1/NTRK2 3′-UTR-MUT (400 ng). Values represent mean ± SD; <span class="html-italic">n</span> = 3, <span class="html-italic">** p</span> &lt; 0.01.</p>
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<p>Validation of the targeting relationship between miR-429-y and LncRNA-MSTRG.19083.1 and NTRK2. (<b>A</b>,<b>B</b>) Localization of NTRK2 protein after transfection with mimic-miR-429-y/inhibitor-miR-429-y in LCs was analyzed by immunofluorescence staining. NTRK2 was colored green, HSD3β is shown in red, and nuclei were counterstained with DAPI (blue); magnification, 20×. (<b>C</b>–<b>F</b>) mRNA expression of miR-429-y after transfection of 100 nM mimic/inhibitor into LCs for 48 h. Values represent mean ± SD; <span class="html-italic">n</span> = 3. ** <span class="html-italic">p</span> &lt; 0.01. (<b>D</b>,<b>E</b>,<b>G</b>,<b>H</b>) the mRNA expression of LncRNA-MSTRG.19083.1 and NTRK2 after transfection of 100 nM mimic/inhibitor into LCs for 48 h. ** <span class="html-italic">p</span> &lt; 0.01. (<b>I</b>–<b>K</b>) Protein expression of NTRK2 was assessed by Western blotting after transfection of 100 nM mimic/inhibitor into LCs for 48 h (<span class="html-italic">n</span> = 3). ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>LncRNA-MSTRG.19083.1/miR-429-y targets NTRK2 to mediate the cAMP signaling pathway to regulate circadian rhythm: (<b>A</b>–<b>E</b>) mRNA and protein expression of CREB, CLOCK, and BAML1 after transfection of 100 nM mimic into LCs for 48 h, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01; (<b>F</b>–<b>J</b>) mRNA and protein expression of CREB, CLOCK, and BAML1 after transfection of 100 nM inhibitor into LCs for 48 h, <span class="html-italic">** p</span> &lt; 0.01; (<b>K</b>) process model diagram of regulatory mechanism.</p>
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