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

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

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (1,654)

Search Parameters:
Keywords = glutamine

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
9 pages, 504 KiB  
Article
THOP1 Is Entailed in a Genetic Fingerprint Associated with Late-Onset Alzheimer’s Disease
by Sharlee Climer
Biomolecules 2025, 15(3), 337; https://doi.org/10.3390/biom15030337 - 26 Feb 2025
Viewed by 59
Abstract
In a systematic explorative study of genetic patterns on chromosome 19, we discovered a pattern comprising 23 SNP alleles that is significantly associated with late-onset Alzheimer’s disease (AD). This association was validated using two independent datasets. The pattern includes thimet oligopeptidase (THOP1 [...] Read more.
In a systematic explorative study of genetic patterns on chromosome 19, we discovered a pattern comprising 23 SNP alleles that is significantly associated with late-onset Alzheimer’s disease (AD). This association was validated using two independent datasets. The pattern includes thimet oligopeptidase (THOP1), which has a long and disputatious relationship with AD. It also spans solute carrier family 39 member 3 (SLC39A3) and small glutamine-rich tetratricopeptide repeat co-chaperone alpha (SGTA) and is upstream from DIRAS family GTPase 1 (DIRAS1). We utilized population data to observe the frequencies of this genetic pattern for 11 different ancestries and noted that it is highly common for Europeans and relatively infrequent for Africans. This research provides a distinct genetic signature for AD risk, as well as insights into the complicated relationship between this disease and THOP1. Full article
(This article belongs to the Section Molecular Biomarkers)
Show Figures

Figure 1

Figure 1
<p>Relative genomic locations of the 23 single-nucleotide polymorphisms (SNPs) included in the associated pattern. See <a href="#app1-biomolecules-15-00337" class="html-app">Supplementary Table S1</a> for precise locations.</p>
Full article ">
23 pages, 3859 KiB  
Article
Deciphering Colorectal Cancer–Hepatocyte Interactions: A Multiomics Platform for Interrogation of Metabolic Crosstalk in the Liver–Tumor Microenvironment
by Alisa B. Nelson, Lyndsay E. Reese, Elizabeth Rono, Eric D. Queathem, Yinjie Qiu, Braedan M. McCluskey, Alexandra Crampton, Eric Conniff, Katherine Cummins, Ella Boytim, Senali Dansou, Justin Hwang, Sandra E. Safo, Patrycja Puchalska, David K. Wood, Kathryn L. Schwertfeger and Peter A. Crawford
Int. J. Mol. Sci. 2025, 26(5), 1976; https://doi.org/10.3390/ijms26051976 - 25 Feb 2025
Viewed by 74
Abstract
Metabolic reprogramming is a hallmark of cancer, enabling tumor cells to adapt to and exploit their microenvironment for sustained growth. The liver is a common site of metastasis, but the interactions between tumor cells and hepatocytes remain poorly understood. In the context of [...] Read more.
Metabolic reprogramming is a hallmark of cancer, enabling tumor cells to adapt to and exploit their microenvironment for sustained growth. The liver is a common site of metastasis, but the interactions between tumor cells and hepatocytes remain poorly understood. In the context of liver metastasis, these interactions play a crucial role in promoting tumor survival and progression. This study leverages multiomics coverage of the microenvironment via liquid chromatography and high-resolution, high-mass-accuracy mass spectrometry-based untargeted metabolomics, 13C-stable isotope tracing, and RNA sequencing to uncover the metabolic impact of co-localized primary hepatocytes and a colon adenocarcinoma cell line, SW480, using a 2D co-culture model. Metabolic profiling revealed disrupted Warburg metabolism with an 80% decrease in glucose consumption and 94% decrease in lactate production by hepatocyte–SW480 co-cultures relative to SW480 control cultures. Decreased glucose consumption was coupled with alterations in glutamine and ketone body metabolism, suggesting a possible fuel switch upon co-culturing. Further, integrated multiomics analysis indicates that disruptions in metabolic pathways, including nucleoside biosynthesis, amino acids, and TCA cycle, correlate with altered SW480 transcriptional profiles and highlight the importance of redox homeostasis in tumor adaptation. Finally, these findings were replicated in three-dimensional microtissue organoids. Taken together, these studies support a bioinformatic approach to study metabolic crosstalk and discovery of potential therapeutic targets in preclinical models of the tumor microenvironment. Full article
(This article belongs to the Special Issue Research Progress of Metabolomics in Health and Disease)
Show Figures

Figure 1

Figure 1
<p>Multiomics study of co-cultures of primary hepatocytes and SW480s. (<b>A</b>) Scheme of 2D co-culture system and timeline for cell collection. After growth arrest, 3T3J2s were plated with freshly thawed primary rat hepatocytes for 7 days, and then a subset of these plates received SW480s. After 3 days of co-culturing, all media and cells were collected for analysis. (<b>B</b>) Omics coverage of co-cultured groups included broad metabolomic coverage utilizing NMR- and LC-MS/MS-based approaches and bulk RNA sequencing. These data were integrated using univariate approaches to interrogate the impact of metabolomic changes on tumor transcriptional profiles. Abbreviations: HJ, hepatocyte+3T3-J2 co-culture; SJH, SW480+3T3-J2+hepatocyte co-culture; SJ, SW480+3T3-J2 co-culture; NMR, nuclear magnetic resonance; LC-MS/MS, liquid chromatography hyphenated with tandem mass spectrometry.</p>
Full article ">Figure 2
<p>Fuel utilization in 2-dimensional co-cultures. (<b>A</b>) Glucose consumption and lactate production in moles per ng total DNA per day. (<b>B</b>) Lactate/glucose ratio of media concentration after 24 h. (<b>C</b>) Concentration of acetoacetate (AcAc), β-hydroxybutryrate (βOHB) and total ketone bodies (TKB) in mmol/g DNA measured in media after 24 h of incubation. (<b>D</b>) Relative abundance of glutamine in total ion counts after normalization to total ng DNA. Volcano plot showing upregulated and downregulated metabolites in SJH compared to (<b>E</b>) HJ control cultures and (<b>F</b>) SJ control cultures; positive log2FC = up in SJH. Significance tested using unpaired <span class="html-italic">t</span>-test, comparison HJ vs. SJH or SJ vs. SJH, and corrected for multiple comparisons using Benjamini–Hochberg method. * <span class="html-italic">p</span> adj. &lt; 0.05, ** <span class="html-italic">p</span> adj. &lt; 0.01, *** <span class="html-italic">p</span> adj. &lt; 0.001, and **** <span class="html-italic">p</span> adj. &lt; 0.0001. Abbreviations: HJ, hepatocyte+3T3-J2 co-culture; SJH, SW480+3T3-J2+hepatocyte co-culture; SJ, SW480+3T3-J2 co-culture.</p>
Full article ">Figure 3
<p>Analytical dilution of co-culture controls reveals metabolic adaptation in SJH co-cultures. (<b>A</b>) Schematic of analytical dilution of HJ and SJ controls to form a 1-to-1 ratio (1T1) after metabolite extraction. (<b>B</b>) Volcano plot upregulated and downregulated metabolites in SJH compared to 1T1 ion counts; positive log2FC = up in SJH. SJH, SW480+3T3-J2+hepatocyte co-culture; 1T1, 1-to-1 ratio of SJ to HJ metabolite extractant.</p>
Full article ">Figure 4
<p>Metabolic interactions of biosynthetic pathways in SJH co-cultures. Fold change of metabolite abundance after 24 h co-culture relative to time point 0 in (<b>A</b>) media abundance of purine metabolism products, hypoxanthine and uric acid; (<b>B</b>) intracellular inosine pools; (<b>D</b>) media abundance of pyrimidine biosynthesis intermediates, uridine and orotic acid; and (<b>E</b>) intracellular pyrimidine intermediates and substrates, aspartate, carbamoyl aspartate, orotic acid, UDP, and uridine. (<b>C</b>) <sup>13</sup>C-enrichment of intracellular inosine pools from 22 mM [U-<sup>13</sup>C<sub>6</sub>]glucose in 3D microtissue organoids. Statistical comparison by unpaired <span class="html-italic">t</span>-test; letters indicate significance in comparison to HJ controls (“a”) or SJ controls (“b”). * <span class="html-italic">p</span> adj. &lt; 0.05. HJ, hepatocyte+3T3-J2 co-culture; SJH, SW480+3T3-J2+hepatocyte co-culture; SJ, SW480+3T3-J2 co-culture; UDP, uridine diphosphate.</p>
Full article ">Figure 5
<p>Discriminant ITUM analysis of SJH co-cultures. (<b>A</b>) Biplot of first two principal components (PC1 and PC2) of PCA of HJ, SJH, and SJ co-cultures by <sup>13</sup>C-glucose-enriched isotopologues. Black-filled circles represent samples. Spheres show co-culture groups. Blue directed vectors show isotopologue loadings for PC1 and PC2. (<b>B</b>) Hierarchical clustering of ITUM SJH vs. SJ Pearson correlation matrix. White triangle with “1” label indicates control cluster of isotopologues; yellow shapes with “2” and “3” labels correspond to cluster of isotopologues from region of strongly co-enriched isotopologues in response to co-culture; and black shapes with “4” label correspond to cluster of isotopologues with weak co-enrichment in response to co-culture. Red arrow, U_M6 positive correlation with unenriched M+0 isotopologues in Region 1; blue arrow, U_M6 negative correlation with multiple isotopologues, including GSH_M3, in Region 2; and yellow arrow, GSH_M4 positive correlation with metabolic precursor E_M4 in Region 3. Abbreviations: HJ, hepatocyte+3T3-J2 co-culture; SJH, SW480+3T3-J2+hepatocyte co-culture; SJ, SW480+3T3-J2 co-culture; .M#, isotopologue representing number of heavy carbons present in the molecule (i.e., M1 indicates presence of 1 heavy 13-carbon); aKG, alpha-ketoglutarate; S, serine; M, malate; L, lactate; C, citrate; ATP, adenosine triphosphate; U, uridine diphosphate N-acetylglucosamine; G, glycine; D, aspartate; E, glutamate; GSH, glutathione; Sc, succinate; GPI, glycerophosphoinositol.</p>
Full article ">Figure 6
<p>Transcriptional profiling of tumor cells in 3D microtissues identifies alterations in metabolic pathways upon exposure to hepatocytes. (<b>A</b>) Three-dimensional microtissue organoid scheme. (<b>B</b>) Volcano plot showing significantly upregulated and downregulated genes in samples from SJH cultures compared with SJ cultures. Positive logFC indicates up in SJH cultures. (<b>C</b>) Gene ontology analysis using DEG as input. (<b>D</b>) GSEA hallmark analysis of SJ compared with SJH. A positive NES score indicates gene profiles that are enriched in tumor cells from the SJH condition compared with the SJ condition. All shown pathways adjusted FDR &lt; 0.05. (<b>E</b>) Expression patterns of core enriched genes associated with Myc pathway and two metabolic pathways, oxidative phosphorylation and glutathione metabolism, that are positively enriched in the SJH condition and heat maps. (<b>F</b>) GSEA Oncogenic analysis of SJ compared with SJH. A negative NES score indicates gene profiles that are enriched in tumor cells from the SJ condition compared with the SJH condition. All shown pathways adjusted FDR &lt; 0.05. HJ, hepatocyte+3T3-J2 co-culture; SJH, SW480+3T3-J2+hepatocyte co-culture; SJ, SW480+3T3-J2 co-culture.</p>
Full article ">Figure 7
<p>Multiomics pathway analysis of metabolic adaptation to hepatocytes. (<b>A</b>) Correlation network of differentially expressed genes (DEGs) and metabolites in SJH co-cultures compared to SJ control cultures. Gene names filtered from full bulk RNA-sequencing DEGs for significance of correlation to metabolites of interest (<span class="html-italic">p</span> &lt; 0.001). Red lines indicate strong positive associations, and blue represent strong negative associations (R &gt; |0.98|). (<b>B</b>) Hierarchical clustering of Pearson correlation matrix of transcripts highly correlated with glutamyl-glycine (Glu-Gly). (<b>C</b>) Gene counts with functional group membership of 98 transcripts were found to correlate strongly with glutamyl-glycine (Glu-Gly). Abbreviations: Glu-Gly, glutamyl-glycine dipeptide; UMP, uridine monophosphate.</p>
Full article ">
18 pages, 656 KiB  
Article
Exploring the Relationship Between Brain Neurochemistry, Cervical Impairments and Pain Sensitivity in People with Migraine, Whiplash-Headache, Low Back Pain and Healthy Controls: A Secondary Analysis of a Cross-Sectional Case-Control Study
by Aimie L. Peek, Zhiqi Liang, Julia Treleaven and Trudy Rebbeck
J. Clin. Med. 2025, 14(5), 1510; https://doi.org/10.3390/jcm14051510 - 24 Feb 2025
Viewed by 261
Abstract
Background/Objectives: Gamma-Aminobutyric Acid (GABA) and glutamate are the main inhibitory and excitatory neurochemicals of the central nervous system. Recently, increased GABA+ (GABA+ macromolecules) and Glx (glutamate and glutamine) levels have been reported in migraine. Conversely, decreased GABA+ and Glx levels have been [...] Read more.
Background/Objectives: Gamma-Aminobutyric Acid (GABA) and glutamate are the main inhibitory and excitatory neurochemicals of the central nervous system. Recently, increased GABA+ (GABA+ macromolecules) and Glx (glutamate and glutamine) levels have been reported in migraine. Conversely, decreased GABA+ and Glx levels have been reported in conditions such as chronic musculoskeletal pain and other chronic widespread pain conditions. This has led to the hypothesis that unique neurochemical profiles may underpin different headache and pain conditions. What is currently unknown is how neurochemical levels correlate with different clinical presentations of local and widespread pain sensitivity. The aims of this study were therefore to (i) explore the relationship between brain neurochemicals and clinical presentations of different headache and pain conditions and (ii) use a novel approach to explore how participants cluster based on their neurochemical profiles and explore the clinical characteristics of the participants in these neurochemical clusters. Methods: In this exploratory secondary analysis of a cross-sectional study, participants with migraine (n = 20), whiplash-headache (n = 20), and low back pain (n = 20), and healthy controls (n = 21) completed pain, disability and psychological distress questionnaires, received Magnetic Resonance Spectroscopy (MEGAPRESS), and underwent cervical musculoskeletal and quantitative sensory testing. Participants were classified based on cervical musculoskeletal impairment, increased cervical pain sensitivity, and central sensitization. Correlations between neurochemical levels and clinical classifications were explored. Cluster analysis was used to determine how participants grouped based on their neurochemical profiles. Pain, disability and psychological distress scores and clinical classifications were then compared between the resultant clusters. Post hoc testing explored increased cervical pain sensitivity within the clusters. Results: GABA+ levels moderately correlated with increased cervical pain sensitivity (r2 = 0.31, p = 0.006), with no other significant correlations. Cluster analysis revealed three neurochemical profiles, Cluster 1 (Low GABA+ levels) had moderate disability, Cluster 2 (highest Glx levels) had the lowest pain and disability, and Cluster 3 (highest GABA+ levels) had the highest pain and disability. Post hoc testing demonstrated that the cluster with the highest GABA+ levels (Cluster 3) had the most cervical pain sensitivity. Conclusions: This study suggests that considering the pain condition or presence of central sensitization alone is not sufficient to explain GABA+ and Glx levels. Our findings suggest that increased cervical pain sensitivity might be more reflective of GABA+ levels than pain condition or central sensitization and would benefit from further investigation to further elucidate the relationship between brain neurochemicals and clinical characteristics of pain sensitivity. Full article
(This article belongs to the Special Issue Neck Pain: Advancements in Assessment and Contemporary Management)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p><b>Post Hoc Analysis:</b> Scatter graph of individuals GABA+ and Glx levels in relation to the presence of increased cervical pain sensitivity (Cerv Sens). Each individual participant is represented as a point on the plot in relation to their Glutamate level (IU) y axis, plotted against their GABA+ level x axis. Cluster 1 (Low GABA, Low Glx) is represented by green, Cluster 2 (High Glx, Low GABA) by blue, and Cluster 3 (High GABA, Low Glx) by purple. Those with increased cervical sensitivity are indicated with a dark fill, those without cervical sensitivity have a light fill. The overlayed circles demonstrate the position of each of the clusters on the graph. This graph shows the majority of those with increased cervical pain sensitivity have similar GABA+ and Glx levels to those in Cluster 3.</p>
Full article ">Figure 2
<p>Infographic demonstrating prevalence of Increased cervical pain sensitivity (Cervical pain Sens) and central sensitization (Cent Sens) within the three clusters. Each cluster is represented by a colour (Cluster 1 Green, Cluster 2 Blue, Cluster 3 Purple. The inner ring shows the distribution of participants within each cluster. The middle ring shows the number of participants with central sensitization (dark colour) and the number without central sensitization (light colour). The outer ring shows the number of participants with increased cervical pain sensitivity (dark colour) and without cervical pain sensitivity (light colour). The boxes demonstrate the neurochemical profile of that cluster.</p>
Full article ">
16 pages, 702 KiB  
Review
Glucose Metabolism and Tumor Microenvironment: Mechanistic Insights and Therapeutic Implications
by Wiktoria Andryszkiewicz, Julia Gąsiorowska, Maja Kübler, Karolina Kublińska, Agata Pałkiewicz, Adam Wiatkowski, Urszula Szwedowicz and Anna Choromańska
Int. J. Mol. Sci. 2025, 26(5), 1879; https://doi.org/10.3390/ijms26051879 - 22 Feb 2025
Viewed by 270
Abstract
Metabolic reprogramming in cancer cells involves changes in glucose metabolism, glutamine utilization, and lipid production, as well as promoting increased cell proliferation, survival, and immune resistance by altering the tumor microenvironment. Our study analyzes metabolic reprogramming in neoplastically transformed cells, focusing on changes [...] Read more.
Metabolic reprogramming in cancer cells involves changes in glucose metabolism, glutamine utilization, and lipid production, as well as promoting increased cell proliferation, survival, and immune resistance by altering the tumor microenvironment. Our study analyzes metabolic reprogramming in neoplastically transformed cells, focusing on changes in glucose metabolism, glutaminolysis, and lipid synthesis. Moreover, we discuss the therapeutic potential of targeting cancer metabolism, focusing on key enzymes involved in glycolysis, the pentose phosphate pathway, and amino acid metabolism, including lactate dehydrogenase A, hexokinase, phosphofructokinase and others. The review also highlights challenges such as metabolic heterogeneity, adaptability, and the need for personalized therapies to overcome resistance and minimize adverse effects in cancer treatment. This review underscores the significance of comprehending metabolic reprogramming in cancer cells to engineer targeted therapies, personalize treatment methodologies, and surmount challenges, including metabolic plasticity and therapeutic resistance. Full article
(This article belongs to the Special Issue Molecular Research of Cancer Metabolism and Biomarkers)
Show Figures

Figure 1

Figure 1
<p>Diagram illustrating the Warburg effect. The Warburg effect, also known as aerobic glycolysis, involves the conversion of glucose into lactate while bypassing the mitochondria and avoiding oxidative phosphorylation. This process is regulated by factors such as pyruvate kinase M2 (PKM2), lactate dehydrogenase A (LDHA), hypoxia-inducible factor-1 (HIF-1), and the tumor suppressor protein p53. Created in BioRender. Szwedowicz, U. (2025) [<a href="#B8-ijms-26-01879" class="html-bibr">8</a>,<a href="#B9-ijms-26-01879" class="html-bibr">9</a>,<a href="#B10-ijms-26-01879" class="html-bibr">10</a>,<a href="#B11-ijms-26-01879" class="html-bibr">11</a>,<a href="#B12-ijms-26-01879" class="html-bibr">12</a>,<a href="#B13-ijms-26-01879" class="html-bibr">13</a>].</p>
Full article ">
24 pages, 1074 KiB  
Review
The Impact of Immunomodulatory Components Used in Clinical Nutrition—A Narrative Review
by Aleksandra Raczyńska, Teresa Leszczyńska, Piotr Skotnicki and Aneta Koronowicz
Nutrients 2025, 17(5), 752; https://doi.org/10.3390/nu17050752 - 21 Feb 2025
Viewed by 293
Abstract
Background: Malnutrition is a clinical condition that leads to unfavourable changes in health. It affects 35–55% of hospitalized patients, and in the case of cancer, this prevalence rises to 40–90% of patients. Screening nutritional status is essential for preventing undernutrition, which is crucial [...] Read more.
Background: Malnutrition is a clinical condition that leads to unfavourable changes in health. It affects 35–55% of hospitalized patients, and in the case of cancer, this prevalence rises to 40–90% of patients. Screening nutritional status is essential for preventing undernutrition, which is crucial as its treatment. Undernutrition in patients after severe injuries significantly increases catabolic changes. Cytokines and hormones, such as epinephrine, glucagon, and cortisol, are released, which can increase energy expenditure by 50%. Properly conducted nutritional treatment aims to maintain or improve the nutritional status of patients whose nutrition with a natural diet is insufficient, moreover, in some cases, treatment of the underlying disease. Methods: This study is a narrative review focused on immunonutrition. The search for source articles, mainly from the last 10 years, was conducted in the PubMed and Google Schoolar databases, as well as in printed books. The key words used were “malnutrition”, “inflammation”, “clinical nutrition”, “immunomodulatory components”, “nutritional status assessment”, “enteral nutrition”, “parenteral nutrition”, and their combinations. Results: Providing substances such as omega-3 fatty acids, glutamine, arginine, nucleotides, antioxidants, and prebiotic fiber has a beneficial impact on immunological and anti-inflammatory pathways. The above-mentioned ingredients may inhibit the secretion of pro-inflammatory cytokines, activate anti-inflammatory cytokines, stimulate immune cells, and have a beneficial effect in allergic diseases, respiratory infections, or wound healing. Conslusion: Immunonutrition can be administrated via oral, enteral, and parenteral routes. It is crucial to highlight the importance of proper nutritional status in patients. The relationship between inflammation and malnutrition creates a vicious cycle, where one negatively affects the other due to increased metabolic demand, loss of appetite, weakened immune system, and gut dysbiosis. Full article
(This article belongs to the Special Issue The Effect of Bioactive Compounds in Anti-inflammation)
Show Figures

Figure 1

Figure 1
<p>Algorithm of action in clinical nutrition [<a href="#B3-nutrients-17-00752" class="html-bibr">3</a>].</p>
Full article ">Figure 2
<p>Simplified impact of immunonutrition. Immunonutrition plays a key role in modulating the immune response and reducing inflammation, which translates into improved clinical parameters for patients. Nutrients such as glutamine, arginine, omega-3 fatty acids, vitamins (E, D, and C), nucleotides, and microelements (selenium, zinc, and magnesium) have significant immunomodulatory effects, influencing the functioning of immune cells and inflammatory processes. Omega-3 fatty acids and vitamins E, D, C have the ability to reduce (as indicated by arrows) the activity of pro-inflammatory cytokines. Reducing inflammation through appropriate nutritional intervention translates into numerous clinical benefits, including reduced infection rates, improved immunological parameters in surgical patients, increased efficacy of anticancer therapies (radiotherapy and chemotherapy), and shortened hospitalization time. Consequently, immunonutrition is an important element of supportive therapy that can significantly improve treatment outcomes and quality of life of patients [<a href="#B68-nutrients-17-00752" class="html-bibr">68</a>,<a href="#B69-nutrients-17-00752" class="html-bibr">69</a>,<a href="#B70-nutrients-17-00752" class="html-bibr">70</a>,<a href="#B71-nutrients-17-00752" class="html-bibr">71</a>,<a href="#B72-nutrients-17-00752" class="html-bibr">72</a>,<a href="#B73-nutrients-17-00752" class="html-bibr">73</a>,<a href="#B74-nutrients-17-00752" class="html-bibr">74</a>,<a href="#B75-nutrients-17-00752" class="html-bibr">75</a>,<a href="#B76-nutrients-17-00752" class="html-bibr">76</a>].</p>
Full article ">
14 pages, 3142 KiB  
Review
Metabolism of Tryptophan, Glutamine, and Asparagine in Cancer Immunotherapy—Synergism or Mechanism of Resistance?
by Kajetan Kiełbowski, Estera Bakinowska, Rafał Becht and Andrzej Pawlik
Metabolites 2025, 15(3), 144; https://doi.org/10.3390/metabo15030144 - 21 Feb 2025
Viewed by 230
Abstract
Amino acids are crucial components of proteins, key molecules in cellular physiology and homeostasis. However, they are also involved in a variety of other mechanisms, such as energy homeostasis, nitrogen exchange, further synthesis of bioactive compounds, production of nucleotides, or activation of signaling [...] Read more.
Amino acids are crucial components of proteins, key molecules in cellular physiology and homeostasis. However, they are also involved in a variety of other mechanisms, such as energy homeostasis, nitrogen exchange, further synthesis of bioactive compounds, production of nucleotides, or activation of signaling pathways. Moreover, amino acids and their metabolites have immunoregulatory properties, significantly affecting the behavior of immune cells. Immunotherapy is one of the oncological treatment methods that improves cytotoxic properties of one’s own immune system. Thus, enzymes catalyzing amino acid metabolism, together with metabolites themselves, can affect immune antitumor properties and responses to immunotherapy. In this review, we will discuss the involvement of tryptophan, glutamine, and asparagine metabolism in the behavior of immune cells targeted by immunotherapy and summarize results of the most recent investigations on the impact of amino acid metabolites on immunotherapy. Full article
(This article belongs to the Special Issue Amino Acid Metabolism and Function in Human Diseases)
Show Figures

Figure 1

Figure 1
<p>A summary of selected immunotherapeutic agents targeting PD-1/PD-L1 and CTLA-4. Created in BioRender. Kiełbowski, K. (2025) <a href="https://BioRender.com/a49x953" target="_blank">https://BioRender.com/a49x953</a>.</p>
Full article ">Figure 2
<p>Derivatives of tryptophan. Created in BioRender. Kiełbowski, K. (2025) <a href="https://BioRender.com/y78q273" target="_blank">https://BioRender.com/y78q273</a>.</p>
Full article ">Figure 3
<p>The activity of tryptophan metabolites is associated with tumor progression. Tryptophan can be metabolized into indoles by the microbiome or into kynurenine by IDO enzymes. These ligands activate the Ahr receptor and stimulate pathways and responses associated with cancer progression. These mechanisms provide a background for the development of agents targeting Trp metabolism. Created in BioRender. Kiełbowski, K. (2025) <a href="https://BioRender.com/d52f610" target="_blank">https://BioRender.com/d52f610</a>.</p>
Full article ">Figure 4
<p>Enzymatic and non-enzymatic activity of IDO1 were both correlated with tumor progression. Red dots highlight potential targets for future cancer treatment and for improving the efficacy of immunotherapy. Created in BioRender. Kiełbowski, K. (2025) <a href="https://BioRender.com/z73b504" target="_blank">https://BioRender.com/z73b504</a>.</p>
Full article ">Figure 5
<p>Glutamine is metabolized to glutamate and eventually to α-ketoglutarate, which enters the TCA cycle and takes part in the production of ATP molecules. Studies demonstrated that elements of the glutamine metabolism pathway are involved in the regulation of the tumor microenvironment, thus affecting cancer growth. Created in BioRender. Kiełbowski, K. (2025) <a href="https://BioRender.com/z58j607" target="_blank">https://BioRender.com/z58j607</a>.</p>
Full article ">Figure 6
<p>Asparagine is metabolized to aspartate, and eventually to oxaloacetate, which takes part in the production of ATP molecules in the TCA cycle. Studies demonstrated that elements of the asparagine metabolism pathway are involved in the regulation of CD8+ T cell responses and signaling pathways associated with tumorigenesis and autophagy, and thus have a complex role in cancer pathogenesis. Created in BioRender. Kiełbowski, K. (2025) <a href="https://BioRender.com/n01h835" target="_blank">https://BioRender.com/n01h835</a>.</p>
Full article ">
19 pages, 6699 KiB  
Article
Influence of Electron Beam Irradiation and RPMI Immersion on the Development of Magnesium-Doped Hydroxyapatite/Chitosan Composite Bioactive Layers for Biomedical Applications
by Andreea Groza, Maria E. Hurjui, Sasa A. Yehia-Alexe, Cornel Staicu, Coralia Bleotu, Simona L. Iconaru, Carmen S. Ciobanu, Liliana Ghegoiu and Daniela Predoi
Polymers 2025, 17(4), 533; https://doi.org/10.3390/polym17040533 - 18 Feb 2025
Viewed by 275
Abstract
Magnesium-doped hydroxyapatite/chitosan composite coatings produced by the radio-frequency magnetron sputtering technique were exposed to 5 MeV electron beams of 8 and 30 Gy radiation doses in a linear electron accelerator. The surfaces of unirradiated layers are smooth, while the irradiated ones exhibit nano-structures [...] Read more.
Magnesium-doped hydroxyapatite/chitosan composite coatings produced by the radio-frequency magnetron sputtering technique were exposed to 5 MeV electron beams of 8 and 30 Gy radiation doses in a linear electron accelerator. The surfaces of unirradiated layers are smooth, while the irradiated ones exhibit nano-structures with sizes that increase from 60 nm at a 8 Gy dose to 200 nm at a 30 Gy dose. Young’s modulus and the stiffness of the layers decrease from 58.9 GPa and 10 µN/nm to 5 GPa and 2.2 µN/nm, respectively, when the radiation doses are increased from 0 to 30 Gy. These data suggest the diminishing of the contribution of the chitosan to the elasticity of the magnesium-doped hydroxyapatite/chitosan composite layers after electron beam irradiation. The biological capabilities of the coatings were assessed before and after their immersion in RPMI-1640 cell culture medium for 7 and 14 days, respectively, and further cultured with a MG63 cell line (ATCC CRL1427) in Dulbecco’s Modified Eagle Medium supplemented with fetal bovine serum, penicillin–streptomycin, and L-glutamine. Thus, 1 µm spherical structures were developed on the surfaces of the layers exposed to a 30 Gy radiation dose and immersed for 14 days in the RPMI-1640 biological medium. The molecular structures of all the RPMI-1640 immersed samples were modified by the growth of a carbonated hydroxyapatite layer characterized by a B-type substitution, as Fourier Transform Infrared Spectroscopy revealed. The biological assay proved the increased biocompatibility of the layers kept in RPMI-1640 medium and enhanced MG63 cell attachment and proliferation. Atomic force microscopy analysis indicated the elongated fibroblastic cell morphology of MG63 cells with minor alteration at 30 Gy irradiation doses as a result of layer biocompatibility modifications. Full article
Show Figures

Figure 1

Figure 1
<p>FTIR spectra: (<b>a</b>) MgHApCs layers (black line) and RPMI medium (red line); (<b>b</b>) MgHApCs-7D (black line) and MgHApCs-14D (red line) samples; (<b>c</b>) MgHApCs-8 Gy-7D (black line) and MgHApCs-8Gy-14D (red line) samples; (<b>d</b>) MgHApCs-30 Gy-7D (black line) and MgHApCs-30 Gy-14D (red line) samples.</p>
Full article ">Figure 2
<p>FTIR spectra in 950–800 cm<sup>−1</sup>: (<b>a</b>) unirradiated layers; (<b>b</b>) 8 Gy irradiated layers; (<b>c</b>) 30 Gy irradiated layers.</p>
Full article ">Figure 3
<p>SEM images: (<b>a</b>) MgHApCs, (<b>c</b>) MgHApCs-7D, and (<b>e</b>) MgHApCs-14D layers. Three-dimensional surface plot of SEM images: (<b>b</b>) MgHApCs, (<b>d</b>) MgHApCs-7D, and (<b>f</b>) MgHApCs-14D layers. EDS spectra: (<b>g</b>) MgHApCs, (<b>h</b>) MgHApCs-7D, and (<b>i</b>) MgHApCs-14D layers.</p>
Full article ">Figure 4
<p>SEM images: (<b>a</b>) MgHApCs-8Gy, (<b>c</b>) MgHApCs-8Gy-7D, and (<b>e</b>) MgHApCs-8Gy-14D layers. Three-dimensional surface plot of SEM images: (<b>b</b>) MgHApCs-8Gy, (<b>d</b>) MgHApCs-8Gy-7D, and (<b>f</b>) MgHApCs-8Gy-14D layers. EDS spectra: (<b>g</b>) MgHApCs-8Gy, (<b>h</b>) MgHApCs-8Gy-7D, and (<b>i</b>) MgHApCs-8Gy-14D layers.</p>
Full article ">Figure 5
<p>SEM images: (<b>a</b>) MgHApCs-30Gy, (<b>c</b>) MgHApCs-30Gy-7D, and (<b>e</b>) MgHApCs-30Gy-14D layers. Three-dimensional surface plot of SEM images: (<b>b</b>) MgHApCs-30Gy, (<b>d</b>) MgHApCs-30Gy-7D, (<b>f</b>) MgHApCs-30Gy-14D layers. EDS spectra: (<b>g</b>) MgHApCs-30Gy, (<b>h</b>) MgHApCs-30Gy-7D, and (<b>i</b>) MgHApCs-30Gy-14D layers.</p>
Full article ">Figure 6
<p>MTT assay of MG63 cells incubated for 24 and 48 h: (<b>a</b>) MgHApCs layers unirradiated and immersed in RPMI for 7 and 14 days; (<b>b</b>) MgHApCs layers irradiated with 8 Gy and immersed in RPMI for 7 and 14 days; (<b>c</b>) MgHApCs layers irradiated with 30 Gy and immersed in RPMI for 7 and 14 days. The results are represented as mean ± standard deviation (SD) and are expressed as percentages of control (100% viability). The statistical analysis was conducted using one-way ANOVA. The <span class="html-italic">p</span>-values indicated are the following: * <span class="html-italic">p</span> ≤ 0.03, ** <span class="html-italic">p</span> ≤ 0.002, and *** <span class="html-italic">p</span> ≤ 0.001.</p>
Full article ">Figure 7
<p>Two-dimensional AFM topographies of MG63 cells after 48 h of incubation with MgHApCs unirradiated layers (<b>a</b>) and immersed in RPMI for 7 days (<b>c</b>) and 14 days (<b>e</b>), as well as their 3D representations (<b>b</b>,<b>d</b>,<b>f</b>).</p>
Full article ">Figure 8
<p>Two-dimensional AFM topographies of MG63 cells after 48 h of incubation with MgHApCs layers irradiated with 8 Gy (<b>a</b>) and immersed in RPMI for 7 days (<b>c</b>) and 14 days (<b>e</b>), as well as their 3D representations (<b>b</b>,<b>d</b>,<b>f</b>).</p>
Full article ">Figure 9
<p>Two-dimensional AFM topographies of MG63 cells after 48 h of incubation with MgHApCs layers irradiated with 30 Gy (<b>a</b>) and immersed in RPMI for 7 days (<b>c</b>) and 14 days (<b>e</b>), as well as their 3D representations (<b>b</b>,<b>d</b>,<b>f</b>).</p>
Full article ">
21 pages, 5323 KiB  
Article
Mixed Ammonium-Nitrate Nutrition Regulates Enzymes, Gene Expression, and Metabolic Pathways to Improve Nitrogen Uptake, Partitioning, and Utilization Efficiency in Rice
by Xianting Fan, Chusheng Lu, Zaid Khan, Zhiming Li, Songpo Duan, Hong Shen and Youqiang Fu
Plants 2025, 14(4), 611; https://doi.org/10.3390/plants14040611 - 18 Feb 2025
Viewed by 247
Abstract
Ammonium and nitrate nitrogen are the two main forms of inorganic nitrogen (N) available to crops. However, it is not clear how mixtures of ammonium and nitrate N affect N uptake and partitioning in major rice cultivars in southern China. This study investigated [...] Read more.
Ammonium and nitrate nitrogen are the two main forms of inorganic nitrogen (N) available to crops. However, it is not clear how mixtures of ammonium and nitrate N affect N uptake and partitioning in major rice cultivars in southern China. This study investigated the effects of different ammonium nitrogen and nitrate nitrogen mixture treatments (100:0, 75:25, 50:50, 25:75, and 0:100) on the growth, photosynthetic characteristics, nitrogen uptake, gene expression, and yield of different rice cultivars (Mei Xiang Zhan NO. 2: MXZ2; Nan Jing Xiang Zhan: NJXZ). Rice root biomass, tiller number, and yield were increased by 69.5%, 42.5%, and 46.8%, respectively, in the 75:25 ammonium-nitrate mixed treatment compared to the 100:0 ammonium-nitrate mixed treatment. The nitrogen content in rice roots, stems, leaves, and grains increased by 69.5%, 64.0%, 65.5%, and 17.5%, respectively. In addition, compared with MXZ2, NJXZ had a greater proportion of N allocated to leaves and grains. Analysis of root enzyme activities revealed that the 75:25 ammonium-nitrate mixed nutrient treatment increased rice root glutamine synthetase activity by an average of 35.0% and glutamate synthetase activity by an average of 52.0%. Transcriptome analysis revealed that the 75:25 mixed ammonium-nitrate nutrient treatment upregulated the expression of genes related to the nitrogen metabolism transporter pathway. Weighted correlation network analysis revealed that some differentially expressed genes (HISX and RPAB5) regulated the activities of nitrogen-metabolizing enzymes in rice and some (SAT2, CYSKP, SYIM, CHI1, and XIP1) modulated amino acid synthesis; greater expression of these genes was detected in the 75:25 ammonium-nitrate mixed nutrient treatment. The expression characteristics of the above genes were further confirmed by RT‒qPCR. Interestingly, the expression levels of the above genes were significantly correlated with the glutamate synthase activity, photosynthetic rate, and root volume. It is noteworthy that increasing the expression of the aforementioned genes coupled with nitrogen uptake was observed in the three main rice cultivars. These results suggest that the 75:25 ammonium-nitrate mixture may have increased nitrogen-metabolizing enzyme activities and promoted nitrogen uptake through the upregulated expression of nitrogen metabolism-related genes, thereby increasing tiller number and improving rice yield. Full article
Show Figures

Figure 1

Figure 1
<p>Effect of different ammonium-nitrate nutrient mixtures on rice phenology, biomass, and yield. The figure shows (<b>A</b>) rice growth phenotype, (<b>B</b>) root biomass, (<b>C</b>) stem biomass, (<b>D</b>) leaf biomass, (<b>E</b>) tiller number per plant, and (<b>F</b>) grain yield per plant. According to Fisher’s protected LSD test (<span class="html-italic">p</span> &lt; 0.05), different lowercase letters indicate significant differences between treatments of the same cultivar. 100:0 ammonium-nitrate mixed nutrient, 75:25 ammonium-nitrate mixed nutrient, 50:50 ammonium-nitrate mixed nutrient, 25:75 ammonium-nitrate mixed nutrient, 0:100 ammonium-nitrate mixed nutrient.</p>
Full article ">Figure 2
<p>Rice root growth under different ammonium–nitrate nutrient mixtures. (<b>a</b>–<b>j</b>) represent root RGB images of different rice cultivars under different ammonium-nitrate nutrient mixtures, and (<b>a’</b>–<b>j’</b>) represent root cross sections after toluidine blue staining. St, stele. Ae, aerenchyma. Ep, epidermis. Scale bars = 4 cm (<b>a</b>–<b>e</b>), 5 cm (<b>f</b>–<b>j</b>), and 100 µm (<b>a’</b>–<b>j’</b>). According to Fisher’s protected LSD test <span class="html-italic">p</span> &lt; 0.05), different lowercase letters indicate significant differences between treatments of the same cultivar.</p>
Full article ">Figure 3
<p>Effects of different ammonium-nitrate nutrient mixtures on the activities of key enzymes involved in nitrogen metabolism in rice. The figure shows the (<b>A</b>) glutamine synthetase activity and (<b>B</b>) glutamate synthase activity. According to Fisher’s protected LSD test (<span class="html-italic">p</span> &lt; 0.05), different lowercase letters indicate significant differences between treatments of the same cultivar.</p>
Full article ">Figure 4
<p>Effect of different ammonium-nitrate mixtures on the photosynthetic characteristics of rice leaves. The figure shows the (<b>A</b>) chlorophyll a, (<b>B</b>) chlorophyll b, (<b>C</b>) carotenoid, (<b>D</b>) and total chlorophyll levels, (<b>E</b>) net photosynthetic rate, (<b>F</b>) stomatal conductance, (<b>G</b>) transpiration rate, and (<b>H</b>) intercellular CO<sub>2</sub> concentration. According to Fisher’s protected LSD test (<span class="html-italic">p</span> &lt; 0.05), different lowercase letters indicate significant differences between treatments of the same cultivar.</p>
Full article ">Figure 5
<p>Effect of different ammonium-nitrate nutrient mixtures on the nitrogen content of rice tissues. (<b>A</b>) rice cultivar Mei Xiang Zhan No.2; (<b>B</b>) rice cultivar Nan Jing Xiang Zhan. Common lowercase letters indicate significant differences between treatments according to Fisher’s protected LSD test (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 6
<p>Venn diagrams showing significant up- and downregulated genes in rice roots under different ammonium-nitrate mixed nutrient conditions. As indicated by (<b>A</b>), there is up-regulated gene expression between different treatments of the MXZ2 rice cultivar. As indicated by (<b>B</b>), there is down-regulated gene expression between different treatments of the MXZ2 rice cultivar. As indicated by (<b>C</b>), there is up-regulated gene expression between different treatments of the NJXZ rice cultivar. As indicated by (<b>D</b>), there is down-regulated gene expression between different treatments of the NJXZ rice cultivar.</p>
Full article ">Figure 7
<p>(<b>A</b>) visualization of the first 30 genes in the black module, (<b>B</b>) visualization of the first 30 genes in the green module, and (<b>C</b>) visualization of the first 30 genes in the purple module.</p>
Full article ">Figure 7 Cont.
<p>(<b>A</b>) visualization of the first 30 genes in the black module, (<b>B</b>) visualization of the first 30 genes in the green module, and (<b>C</b>) visualization of the first 30 genes in the purple module.</p>
Full article ">Figure 8
<p>Correlation of the normalized fold change with RNA-seq results after RT‒qPCR validation of the seven key hubs. (<b>A</b>) RT‒qPCR analysis of <span class="html-italic">HISX</span>, (<b>B</b>) RT‒qPCR analysis of <span class="html-italic">RPAB5</span>, (<b>C</b>) RT‒qPCR analysis of <span class="html-italic">SAT2</span>, (<b>D</b>) RT‒qPCR analysis of <span class="html-italic">SYIM</span>, (<b>E</b>) RT‒qPCR analysis of <span class="html-italic">CYSKP</span>, (<b>F</b>) RT‒qPCR analysis of <span class="html-italic">CHI1</span>, (<b>G</b>) RT‒qPCR analysis of <span class="html-italic">XIP1</span>, and (<b>H</b>) correlation analysis of normalized fold changes from RT‒qPCR data and RNA‒seq data. Different lowercase letters indicate significant differences between treatments at <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">
85 pages, 24685 KiB  
Review
Adaptogens in Long-Lasting Brain Fatigue: An Insight from Systems Biology and Network Pharmacology
by Alexander Panossian, Terrence Lemerond and Thomas Efferth
Pharmaceuticals 2025, 18(2), 261; https://doi.org/10.3390/ph18020261 - 15 Feb 2025
Viewed by 462
Abstract
Long-lasting brain fatigue is a consequence of stroke or traumatic brain injury associated with emotional, psychological, and physical overload, distress in hypertension, atherosclerosis, viral infection, and aging-related chronic low-grade inflammatory disorders. The pathogenesis of brain fatigue is linked to disrupted neurotransmission, the glutamate-glutamine [...] Read more.
Long-lasting brain fatigue is a consequence of stroke or traumatic brain injury associated with emotional, psychological, and physical overload, distress in hypertension, atherosclerosis, viral infection, and aging-related chronic low-grade inflammatory disorders. The pathogenesis of brain fatigue is linked to disrupted neurotransmission, the glutamate-glutamine cycle imbalance, glucose metabolism, and ATP energy supply, which are associated with multiple molecular targets and signaling pathways in neuroendocrine-immune and blood circulation systems. Regeneration of damaged brain tissue is a long-lasting multistage process, including spontaneously regulating hypothalamus-pituitary (HPA) axis-controlled anabolic–catabolic homeostasis to recover harmonized sympathoadrenal system (SAS)-mediated function, brain energy supply, and deregulated gene expression in rehabilitation. The driving mechanism of spontaneous recovery and regeneration of brain tissue is a cross-talk of mediators of neuronal, microglia, immunocompetent, and endothelial cells collectively involved in neurogenesis and angiogenesis, which plant adaptogens can target. Adaptogens are small molecules of plant origin that increase the adaptability of cells and organisms to stress by interaction with the HPA axis and SAS of the stress system (neuroendocrine-immune and cardiovascular complex), targeting multiple mediators of adaptive GPCR signaling pathways. Two major groups of adaptogens comprise (i) phenolic phenethyl and phenylpropanoid derivatives and (ii) tetracyclic and pentacyclic glycosides, whose chemical structure can be distinguished as related correspondingly to (i) monoamine neurotransmitters of SAS (epinephrine, norepinephrine, and dopamine) and (ii) steroid hormones (cortisol, testosterone, and estradiol). In this narrative review, we discuss (i) the multitarget mechanism of integrated pharmacological activity of botanical adaptogens in stress overload, ischemic stroke, and long-lasting brain fatigue; (ii) the time-dependent dual response of physiological regulatory systems to adaptogens to support homeostasis in chronic stress and overload; and (iii) the dual dose-dependent reversal (hormetic) effect of botanical adaptogens. This narrative review shows that the adaptogenic concept cannot be reduced and rectified to the various effects of adaptogens on selected molecular targets or specific modes of action without estimating their interactions within the networks of mediators of the neuroendocrine-immune complex that, in turn, regulates other pharmacological systems (cardiovascular, gastrointestinal, reproductive systems) due to numerous intra- and extracellular communications and feedback regulations. These interactions result in polyvalent action and the pleiotropic pharmacological activity of adaptogens, which is essential for characterizing adaptogens as distinct types of botanicals. They trigger the defense adaptive stress response that leads to the extension of the limits of resilience to overload, inducing brain fatigue and mental disorders. For the first time, this review justifies the neurogenesis potential of adaptogens, particularly the botanical hybrid preparation (BHP) of Arctic Root and Ashwagandha, providing a rationale for potential use in individuals experiencing long-lasting brain fatigue. The review provided insight into future research on the network pharmacology of adaptogens in preventing and rehabilitating long-lasting brain fatigue following stroke, trauma, and viral infections. Full article
(This article belongs to the Section Natural Products)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Simplified pattern of adaptive stress response over time (blue color dot line) and stress-protective effect of an adaptogen (red color line) increasing resistance and decreasing sensitivity to a conditional stressor, extending the duration of the resistance anabolic phase to fatigue, and preventing the onset of the exhaustion phase (distress), leading to adaptation to stress and increasing the level of homeostasis. AU—an arbitrary unit of an outcome measure of a stress response, e.g., increasing the number of errors over the time detected in Conner’s computerized cognitive performance test for attention and impulsivity, biochemical markers (cortisol, nitric oxide, etc.). It is modified from [<a href="#B31-pharmaceuticals-18-00261" class="html-bibr">31</a>,<a href="#B32-pharmaceuticals-18-00261" class="html-bibr">32</a>,<a href="#B88-pharmaceuticals-18-00261" class="html-bibr">88</a>,<a href="#B89-pharmaceuticals-18-00261" class="html-bibr">89</a>].</p>
Full article ">Figure 2
<p>Symbolic representation of three typical scenarios of the degree of brain fatigue over time after a head injury, emotional strain-induced stroke, or viral infection: a—adaptive reversible recovery when the patient returns to work and social life (green line), b—for others, the mental energy does not return as expected, but the person gets on with the job, even if the energy is not quite enough. It works for a while, but after maybe a few months or half a year, the person has run out of energy, and brain fatigue increases (solid blue line); c—irreversible damage if some patients still have severe distressing and long-lasting brain fatigue can make work impossible and daily lifestyle at home be sufficiently exhausting (red line) [<a href="#B5-pharmaceuticals-18-00261" class="html-bibr">5</a>].</p>
Full article ">Figure 3
<p>Glutamate–glutamine cycle in nerve cells [<a href="#B156-pharmaceuticals-18-00261" class="html-bibr">156</a>,<a href="#B157-pharmaceuticals-18-00261" class="html-bibr">157</a>,<a href="#B158-pharmaceuticals-18-00261" class="html-bibr">158</a>,<a href="#B159-pharmaceuticals-18-00261" class="html-bibr">159</a>,<a href="#B160-pharmaceuticals-18-00261" class="html-bibr">160</a>]. Neurotransmission between two neurons’ synapses and interactions with a nearby astrocyte, where glutamate is the signal substance between nerve cells. Glutamate is released from the presynaptic terminal, and after exerting its effect on the recipient neuron, the postsynaptic membrane is taken up by the astrocytes’ discharge and converted to glutamine, which is then transported back to the nerve cells to form new glutamate. Glutamate inside the astrocytes also signals that glucose is taken from the blood into the astrocytes and onto the nerve cells as new energy. In distress or brain damage-induced neuroinflammation, see the SOS sign in red; the astrocytes’ capacity to convert glutamate into glutamine is reduced due to a lack of energy supply mediated by reduced ATP. More glutamate accumulates around the nerve cells, and increasing amounts accumulate in the synapse areas, making signaling less specific. If mental activity is high in this situation, there is a risk that nerve cell signaling will fail due to decreased amounts of glucose/energy. The astrocytes take up less glucose, and less energy and glutamate are available in the neurons. Modified from [<a href="#B5-pharmaceuticals-18-00261" class="html-bibr">5</a>,<a href="#B6-pharmaceuticals-18-00261" class="html-bibr">6</a>].</p>
Full article ">Figure 4
<p>Simplified overview of the stress system (central nervous system, CNS, and peripheral tissues/organs in the periphery) and reciprocal connections of elements of the neuroendocrine-immune complex to mobilize an adaptive response against the stressor. The brain and spinal cord comprise the CNS, the cerebral cortex—glutamatergic pyramidal and GABA-ergic interneurons, and glial cells, including astrocytes, oligodendrocytes, and microglia. The forebrain includes dorsal glutamatergic neurons, ventral GABAergic interneurons, and locus coeruleus (LC) neurons. The peripheral components of the stress system include the hypothalamic–pituitary–adrenal axis (HPA), the autonomic nervous system (ANS) comprising the sympathetic nervous system (SNS) secreting mainly norepinephrine (NE) and acetylcholine (AcCh), and the sympathy–adrenomedullary (SAM) system, and (ii) the parasympathetic nervous system (PNS) secreting AcCh. Two key end hormones, cortisol and epinephrine, regulate metabolism, circulation, and blood homeostasis. The abbreviations of hormones and neurotransmitters are as follows: Hypothalamic hormones: CRH, corticotropin-releasing hormone; GnRH, gonadotropin-releasing hormone; and dopamine. Pituitary hormones: ACTH, adrenocorticotropic hormone; AVP, arginine vasopressin; FSH, follicle-stimulating hormone; GH, growth hormone; LH, luteinizing hormone; Oxt, oxytocin; PRL, prolactin; and TSH, thyroid-stimulating hormone. Adrenal cortex hormones: steroid hormones—corticosteroids (cortisol), mineralocorticoids, and androgens. Adrenal hormone: E, epinephrine. Pineal gland hormone: melatonin. Other peripheral hormones: testosterone, T; estrogens, Es; thyroxin, T4; triiodothyronine, T3; somatomedins, IGF; angiotensin II; erythropoietin; calcitriol; somatostatin; glucagon; insulin; parathyroid hormone; and calcitonin. Neurotransmitters: Neuropeptide Y; substance P; GABA; serotonin; dopamine; acetylcholine; norepinephrine; and epinephrine.</p>
Full article ">Figure 5
<p>This figure presents a hypothetical representation of the adaptogenic effect of adaptogens on adaptive homeostasis. Adaptive homeostasis refers to the transient reversible adjustments of the homeostatic range in response to exposure to challenging signaling molecules or events. Any biological function or measurement oscillates around a mean or median within a homeostatic range considered ’normal’ or physiological. Adaptogens, as shown in the figure, increase the normal homeostatic thresholds (adaptive homeostasis) to a pathological state, thereby enhancing resilience to stress within the adaptive stress system. This system regulates various bodily functions, including the neuroendocrine-immune complex, blood circulatory and digestive systems, organismal development, and aging.</p>
Full article ">Figure 6
<p>Adaptive stress response factors, mediators, and effectors and the effect of adaptogens in stress and inflammaging-induced aging-related diseases. The adaptive stress response involves the activation of intracellular and extracellular signaling pathways and increased expression of antiapoptotic proteins, neuropeptides, antioxidant enzymes, and the defense response of an organism, resulting in increased survival. One primary mechanism of adaptogens’ action is that they trigger adaptive cellular stress response pathways in human brain cells, similar to exercise, dietary restriction, and cognitive stimulation, which may exert their health benefits. Each of these environmental factors induces a mild stress response in nerve cells in the brain, increasing the expression of stress resistance proteins such as heat-shock protein 70 (HSP-70), Hsp32, and nerve cell growth factors, preventing the degeneration of neurons during aging, enhancing learning and memory, and exerting beneficial effects on many different organ systems, including the cardiovascular and glucose-regulating systems. These endogenous cellular defense pathways, including NRF2 signaling pathways, integrate adaptive stress responses to prevent neurodegenerative disease.</p>
Full article ">Figure 7
<p>Chronic stress-induced symptoms and the effect of adaptogens on key mediators and effectors of adaptive stress response and effectors induce neuroprotection, resulting in increased cognitive function and mental and physical performance. Brain cells respond adaptively by enhancing their ability to function and resist stress, as shown by an update from the authors’ free access publication [<a href="#B25-pharmaceuticals-18-00261" class="html-bibr">25</a>] and authors’ drawings.</p>
Full article ">Figure 8
<p>The hypothetic molecular mechanisms and modes of the pharmacological action of adaptogen are updated from the authors’ free access publication [<a href="#B66-pharmaceuticals-18-00261" class="html-bibr">66</a>] and authors’ drawings. Effects of adaptogens on key mediators of neuroendocrine-immune complex, cardiovascular, and detoxifying systems that regulate adaptive stress response to stressors/pathogens in stress and aging-induced diseases and disorders. CRH- and ACTH-induced stimulation of GPCR receptors activates the cAMP-dependent protein kinase (PKA) signaling pathway in the regulation of energy balance and metabolism across multiple systems, including adipose tissue (lipolysis), liver (gluconeogenesis, glucose tolerance), pancreas, gut (insulin exocytosis and sensitivity), etc. The key molecules involved in the PI3K-Akt signaling pathway are receptor tyrosine kinases (RTKs). Activating the PI3K-Akt signaling pathway promotes cell proliferation and growth, stimulates cell cycle vascular remodeling and cell survival, and inhibits cell apoptosis in response to extracellular signals. The nonspecific antiviral action of ginseng is associated with the activation of innate immunity by upregulation of the expression of the pathogen’s pattern recognition receptors, specifically toll-like receptors and TLR-mediated signaling pathways. The protein kinase C (PKC) family of enzymes with isoforms plays an essential cell-type-specific role, particularly in the immune system, through phosphorylation of CARD-CC family proteins and subsequent NF-κB activation. Three stress-activated MAPK signaling pathways playing crucial roles in cell proliferation, differentiation, survival, and death have been implicated in the pathogenesis of many human diseases, including Alzheimer’s disease, Parkinson’s disease, and cancer. (1) The stress factors inducing the activation of the c-Jun N-terminal kinase (JNK)/stress-activated protein kinase (SAPK)-mediated adaptive signaling pathway are heat shock, irradiation, reactive oxygen species, cytotoxic drugs, inflammatory cytokines, hormones, growth factors, and other stresses. Activating the JNK/MAPK10 signaling pathway promotes cell death and apoptosis via the upregulation of proapoptotic genes. (2) The activation of the extracellular-signal-regulated kinase (ERK) pathway is initiated by hormones and stresses to trigger endothelial cell proliferation during angiogenesis, T cell activation, long-term potentiation in hippocampal neurons, phosphorylation of the transcription factor p53, activation of phospholipase A2 in mast cells, followed by activation of biosynthesis leukotrienes and inflammation/allergy, etc. (3) The third major stress-activated p38 signaling pathway contributes to the control of inflammation, the release of cytokines by macrophages and neutrophils, apoptosis, cell differentiation, and cell cycle regulation. Activation is shown in red, while the inhibition is in blue color cycles/ellipses (effect of ginseng/ginsenosides), arrows, and clouds. BDNF, brain-derived neurotrophic factor; cAMP, cyclic adenosine monophosphate; CREB, cAMP-responsive element-binding protein; ERK, extracellular signal-regulated kinase; GSK-3, glycogen synthase kinase-3; JNK, the c-Jun N-terminal kinase (JNK)/stress-activated protein kinase (SAPK MAPK, mitogen-activated protein kinase); NF-κB, nuclear factor-kappa B; Nrf2, nuclear factor E2-related factor 2; PI3K, phosphatidylinositol 3-kinase; PKA, protein kinase A; PKB, protein kinase B; PLC, phospholipase C.</p>
Full article ">Figure 9
<p>Effect of <span class="html-italic">Rhodiola</span> extracts on the eicosanoid signaling pathway. Arachidonic acid (AA) was released from membrane phospholipids by phospholipase A2 (PLA2) and then converted to eicosanoids by two types of enzymes: (i) prostaglandin endoperoxide synthases (PTGS), commonly referred to as cyclooxygenases (COX-1 and COX-2), catalyze the key step in the synthesis of prostaglandin H2 (PGH2), which is converted into pro-inflammatory thromboxanes (TXs), prostaglandins (PGE, PGF, and PGD), and prostacyclins (PGI). (ii) The lipoxygenases include ALOX5, ALOX12, and ALOX15. ALOX5 catalyzes the key step in the conversion of AA to pro-inflammatory leukotriene A4, B4, and C4. ALOX12 synthesizes pro-inflammatory 12(S)-HETE [12(S)-hydroxyeicosatetraenoic acid]. ALOX15, in concert with ALOX5, is involved in forming anti-inflammatory lipoxins A4 and B4. Eicosanoid receptors belong to the family of G-protein-coupled receptors. Some of these receptors include BLT-1,-2 CYSLTR1, and CYSLTR2 for pro-inflammatory leukotrienes; PTGERs for prostaglandin E2; PTGFR for pro-inflammatory prostaglandin F2; PTGDR for prostaglandin D2; and TBXA2R for pro-inflammatory thromboxane A2. Eicosanoids transduce signals via their membrane receptors and mediate complex biological processes like inflammation, vascular permeability, allergic reactions, labor induction, and carcinogenesis. Downstream effect analysis reveals predicted pharmacological effects of <span class="html-italic">Rhodiola</span> mediated by eicosanoid signaling pathways [<a href="#B68-pharmaceuticals-18-00261" class="html-bibr">68</a>].</p>
Full article ">Figure 10
<p>Simplified hypothetic representation of the hormetic dose–response relationship of toxic hormetins and adaptogens; figures adapted from free access publication [<a href="#B30-pharmaceuticals-18-00261" class="html-bibr">30</a>,<a href="#B220-pharmaceuticals-18-00261" class="html-bibr">220</a>,<a href="#B269-pharmaceuticals-18-00261" class="html-bibr">269</a>].</p>
Full article ">Figure 11
<p>(<b>a</b>) The total number of genes deregulated by ginsenoside Rg5 in concentrations ranging from 1 μM to 1 aM; (<b>b</b>) ginsenoside Rg5 concentration-dependent fold change expression of selected differentially regulated genes in the hippocampal neuronal cell line HT22; (<b>c</b>) Venn diagram of genes deregulated by ginsenoside Rg5 at concentrations 1 μM, 1 nM, 1 pM, 1 fM, and 1 aM.</p>
Full article ">Figure 12
<p>CRH signaling pathways are differently regulated by <span class="html-italic">Withania somnifera</span> extract at a concentration of 1.5 mg/L (corresponding to the dose of 90 mg in humans), WSL (<b>a</b>), and 5 mg/L (corresponding to the dose of 300 mg in humans), WS (<b>b</b>) in cultivated neuroglial cells. <a href="#pharmaceuticals-18-00261-f012" class="html-fig">Figure 12</a>a shows the inhibition of the CRH receptor-related intracellular signal transduction pathway, while <a href="#pharmaceuticals-18-00261-f012" class="html-fig">Figure 12</a>b shows the predicted activation of this pathway. At a concentration of 5 mg/L, corresponding to a human daily dose of 300 mg, WS extract did not affect the expression of CRH, AP-1 transcription factor subunit (FOS), CACNA1E, CACNG6, or CACNA2D3 encoding calcium voltage-gated channel auxiliary subunits as it did at a lower concentration of 1.6 mg/L. Protein kinase C η and ζ encoding genes. PRKCH and PRKCZ were downregulated, and guanylate cyclase 1 soluble subunit α and β (GUCY1A3, GUCY1B3) and prostaglandin-endoperoxide synthase 2/COX-2 (PTGS2) genes were upregulated [<a href="#B67-pharmaceuticals-18-00261" class="html-bibr">67</a>].</p>
Full article ">Figure 13
<p>Glucocorticoid signaling pathways are differently regulated by <span class="html-italic">Withania somnifera</span> extract at a concentration of 1.5 mg/L (corresponding to the dose of 90 mg in humans), WSL (<b>a</b>), and 5 mg/L (corresponding to the dose of 300 mg in humans), WS (<b>b</b>) in cultivated neuroglial cells [<a href="#B67-pharmaceuticals-18-00261" class="html-bibr">67</a>].</p>
Full article ">Figure 14
<p>Effect of Withania extracts in two doses on the eicosanoid signaling pathway. The details are the legends of <a href="#pharmaceuticals-18-00261-f005" class="html-fig">Figure 5</a> [<a href="#B67-pharmaceuticals-18-00261" class="html-bibr">67</a>].</p>
Full article ">Figure 15
<p>The mediators and interactions between vascular and brain cells in poststroke adult neurogenesis and angiogenesis: BDNF, brain-derived neurotrophic factor; IFN-γ, interferon-gamma; IL-4, interleukin-4; iNOS, inducible nitric oxide synthase; MMP-3, matrix metalloproteinase-3; NGF, nerve growth factor; TGF-β, transforming growth factor-β; TNFα, tumor necrosis factor-alpha; VEGF, vascular endothelial growth factor (Modified from [<a href="#B12-pharmaceuticals-18-00261" class="html-bibr">12</a>]).</p>
Full article ">Figure 16
<p>The effects of RR-WS (Adaptra<sup>®</sup>) on gene expression in human T98G neuroglia cells and the predicted activation of the development of neurons. The authors’ drawings were adapted from a free access publication [<a href="#B69-pharmaceuticals-18-00261" class="html-bibr">69</a>]. The synergy effects (red arrows) of hybridization of a combination of Rhodiola with Withania on neurogenesis signaling pathways in isolated neuroglia cells. The intensity of green and red squares indicates fold changes compared to control, where green means down- and red means upregulation. Synergistic or antagonistic effects on gene expression were observed by comparison of the impact of the BHP Adaptra = combination of RR-WS (sample A1) with a lack of the impact of individual extracts RR (<span class="html-italic">R. rosea</span>), WS (<span class="html-italic">Withania somnifera</span>), and WSL <span class="html-italic">Withania somnifera</span> low dose, correspondingly samples A2, A3, and A7) at a significance level of <span class="html-italic">p</span> &lt; 0.05 (log = 1.3) and a z-score &gt; 2. The symbolic interpretation of synergy and antagonism by the image of a hybrid creature from Greek mythology, the Sphinx of Lanuvium, with a human head and a lion’s body-derived wing due to their synergistic and antagonistic (e.g., lack of human legs) interactions. The image of two kinds of hybrid creatures from ancient mythology is a visual analogy of botanical hybrid preparations (BHP) symbolizing synergy, e.g., wings of a sphinx, a mythical hybrid creature with the head of a human, the body of a lion, and the wings derived due to the synergy effect. Sphinx of Lanuvium. Near Rome. Roman, about AD 120–140. British Museum. It was found at Monte Cagnolo, outside Lanuvium, near Rome. Source: [<a href="#B417-pharmaceuticals-18-00261" class="html-bibr">417</a>].</p>
Full article ">
19 pages, 5421 KiB  
Article
Effects of Oasis Evolution on Soil Microbial Community Structure and Function in Arid Areas
by Junhu Tang, Haiqiang Zhu, Xinyu Ma, Zhaolong Ding, Yan Luo, Xiaofei Wang, Rui Gao and Lu Gong
Forests 2025, 16(2), 343; https://doi.org/10.3390/f16020343 - 14 Feb 2025
Viewed by 310
Abstract
Soil is an important link in the cycling of carbon, nitrogen, and other elements. The soil environment, especially the soil water, nutrients, and salts, undergoes profound changes in the process of oasis evolution. As a key component of the soil ecosystem in an [...] Read more.
Soil is an important link in the cycling of carbon, nitrogen, and other elements. The soil environment, especially the soil water, nutrients, and salts, undergoes profound changes in the process of oasis evolution. As a key component of the soil ecosystem in an oasis, soil microbial communities are strongly influenced by environmental factors and have feedback effects on them. However, the response of the soil microbial community structure and function to the process of oasis evolution and its mechanism is still unclear. In this study, the effects of different land-use types, including cotton field (CF), orchard (OR), forest land (FL), waste land (WL) and sand land (SL), on the soil microbial community structure and function were analyzed by metagenomic sequencing. The results showed that the cotton field had the highest soil water content, showing a significant difference compared with the other land-use types. Forest land had the highest soil pH, also showing a significant difference compared with the other land-use types. Among the land-use types with different degrees of oasis evolution, Pseudarthrobacter and Actinomycetota were the dominant phyla, with higher relative abundance. The main metabolic pathways in the cotton field, sand land, and waste land were L-glutamine biosynthesis, ornithine cycle, and nitrate reduction V. The soil total salt, moisture content, pH, and available potassium were the important soil physicochemical factors influencing soil microorganisms. This study will deepen our understanding of the role of soil microbial communities in the process of oasis evolution and provide a scientific basis for ecological restoration and desertification control in arid areas. Full article
(This article belongs to the Special Issue Elemental Cycling in Forest Soils)
Show Figures

Figure 1

Figure 1
<p>The study area.</p>
Full article ">Figure 2
<p>Physicochemical properties of different soil layers under different land-use types. (<b>a</b>) The soil water content; (<b>b</b>) the soil bulk density; (<b>c</b>) the soil pH value; (<b>d</b>) the total salt; (<b>e</b>) the soil total nitrogen; (<b>f</b>) the soil available phosphorus; (<b>g</b>) the soil available potassium; and (<b>h</b>) the soil microbial biomass carbon. Note: Different lowercase letters indicate a significant difference between the different soil depths of a land-use type and the different uppercase letters indicate a significant difference in the physicochemical properties at a soil depth between the different land-use types.</p>
Full article ">Figure 2 Cont.
<p>Physicochemical properties of different soil layers under different land-use types. (<b>a</b>) The soil water content; (<b>b</b>) the soil bulk density; (<b>c</b>) the soil pH value; (<b>d</b>) the total salt; (<b>e</b>) the soil total nitrogen; (<b>f</b>) the soil available phosphorus; (<b>g</b>) the soil available potassium; and (<b>h</b>) the soil microbial biomass carbon. Note: Different lowercase letters indicate a significant difference between the different soil depths of a land-use type and the different uppercase letters indicate a significant difference in the physicochemical properties at a soil depth between the different land-use types.</p>
Full article ">Figure 3
<p>Soil microbial community compositions in different land-use types. (<b>a</b>) Composition of the soil microbial communities in the different land use types at the phylum level; and (<b>b</b>) composition of the soil microbial communities in the different land-use types at the genus level.</p>
Full article ">Figure 3 Cont.
<p>Soil microbial community compositions in different land-use types. (<b>a</b>) Composition of the soil microbial communities in the different land use types at the phylum level; and (<b>b</b>) composition of the soil microbial communities in the different land-use types at the genus level.</p>
Full article ">Figure 4
<p>Soil microbial taxonomic characteristics in different land-use types. (<b>a</b>) The NMDS analysis of the soil microbes of the different land-use types; and (<b>b</b>) the LEfSe analysis of the soil microbes of the different land-use types.</p>
Full article ">Figure 4 Cont.
<p>Soil microbial taxonomic characteristics in different land-use types. (<b>a</b>) The NMDS analysis of the soil microbes of the different land-use types; and (<b>b</b>) the LEfSe analysis of the soil microbes of the different land-use types.</p>
Full article ">Figure 5
<p>Soil microbial metabolic pathways under different land-use types.</p>
Full article ">Figure 6
<p>Taxonomic characteristics of soil microbial metabolic pathways in different land-use types. (<b>a</b>) The RDA analysis of the soil microbial metabolic pathways of the different land-use types; and (<b>b</b>) the LEfSe analysis of the soil microbial metabolic pathways of the different land-use types.</p>
Full article ">Figure 7
<p>Two-dimensional ranking plot and matrix of correlation coefficients between soil microorganisms and soil properties. (<b>a</b>) The RDA analysis of the soil microbiology and soil properties; and (<b>b</b>) the heatmap of the soil microbiology and soil properties. ** indicates highly significant correlation (<span class="html-italic">p</span> &lt; 0.01), * indicates significant correlation (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">
14 pages, 2224 KiB  
Article
Application of 12C6 Heavy Ion-Irradiated BHK-21 Cells in Production of Foot-and-Mouth Disease Vaccine
by Xiangdong Song, Shiyu Tao, Fanglan An, Xiaoming Li, Jingcai Yang, Yan Cui and Xuerong Liu
Vet. Sci. 2025, 12(2), 167; https://doi.org/10.3390/vetsci12020167 - 13 Feb 2025
Viewed by 405
Abstract
FMD poses a significant threat to animal husbandry and public health security. This study aims to investigate an innovative method for producing FMD vaccines. Wild-type BHK-21 cells were subjected to heavy ion irradiation. Following the optimization of irradiation parameters, the mutant cell line [...] Read more.
FMD poses a significant threat to animal husbandry and public health security. This study aims to investigate an innovative method for producing FMD vaccines. Wild-type BHK-21 cells were subjected to heavy ion irradiation. Following the optimization of irradiation parameters, the mutant cell line BHK-7 was selected using the limited dilution method. The concentration of FMDV 146S in the BHK-7 cells was markedly elevated, significantly enhancing FMDV replication. The suspension culture and domestication experiments demonstrated that BHK-7 exhibited characteristics like those of the control BHK-21 cells, thereby improving production efficiency and reducing costs. The metabolic analysis of the BHK-7 suspension cultures indicated that glutamine (GLN) may play a crucial role in FMDV replication, with the addition of an appropriate amount of GLN enhancing viral replication levels. Ten successive generations of BHK-7 cells showed stability in FMDV replication post-domestication, indicating good genetic stability. In this study, we obtained a mutant somatic cell line, BHK-7, which promotes FMDV replication through heavy ion irradiation technology. Through suspension culture domestication and metabolic analysis, this study provides a novel approach and concept for FMD vaccine production, as well as a reference for the development of other vaccine cell lines. Full article
(This article belongs to the Special Issue Viral Infections in Wild and Domestic Animals)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) BHK-21 cells were induced with 12C6 heavy ions at doses of 0 Gy, 5 Gy, 10 Gy, 15 Gy, 20 Gy, and 25 Gy. Cell viability was assessed via trypan blue staining and detected via Count STAR (Countstar Rigel S2, RY074B2001), and mortality rate was calculated. Data represent means and standard errors from six independent experiments. (<b>b</b>) After BHK-21 cells were induced with different doses, foot- and -mouth disease virus (FMDV) was used to infect cells at infection multiplicity (MOI) of 0.1 for 16 h. (<b>c</b>) After researchers exposed BHK-21 cells to irradiation doses of 5 Gy, 10 Gy, and 15 Gy, mutant cells obtained through monoclonal screening were infected with FMDV at multiplicity of infection (MOI) of 0.1 for 16 h, resulting in alterations in antigen content, specifically in the 146S component. Experiments were conducted in triplicate as biological replicates. Data are presented as means ± SDs from three independent experiments and were analyzed via Student’s two-tailed unpaired <span class="html-italic">t</span>-tests. *, <span class="html-italic">p</span> &lt; 0.05; ***, <span class="html-italic">p</span> &lt; 0.001. The orange bar graph represents the 146s content after FMDV infection in the mutant cell group, and the white bar graph represents the 146s content after FMDV infection in control BHK-21 cells.</p>
Full article ">Figure 2
<p>(<b>a</b>) Shows expression level of FMDV genes in mutant BHK-7 and control BHK-21 cells infected with FMDV at multiplicity of infection (MOI) of 0.1 for 16 h. (<b>b</b>) Shows change in 146S antigen content in mutant BHK-7 and control BHK-21 cells infected with FMDV at multiplicity of infection (MOI) of 0.1 for 16 h. (<b>c</b>) Shows change in TCID50 after mutant BHK-7 and control BHK-21 cells are infected with FMDV. (<b>d</b>) Shows growth curves of mutant BHK-7 and control BHK-21 cells. Experimental data are all from three biological replicates. Data are presented as means ± SDs from three independent experiments and were analyzed via Student’s two-tailed unpaired <span class="html-italic">t</span>-tests.; ****, <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 3
<p>(<b>a</b>) BHK-7-FC suspension cells were stained with trypan blue for viability assessment and bright-field imaging. (<b>b</b>) BHK-21 suspension cells were stained with trypan blue for viability assessment and bright-field imaging. (<b>c</b>) Proliferation curve of BHK-7-FC cells in suspension culture is presented. (<b>d</b>) Short-term viability of BHK-7-FC cells in suspension culture over time was evaluated. Data are presented as means ± SDs from three independent experiments. Red circles represent clumped cells.</p>
Full article ">Figure 4
<p>(<b>a</b>) Temporal variations in GLN content following FMDV infection in BHK-7-FC suspension cultures. (<b>b</b>) Changes in NH<sub>4</sub><sup>+</sup> levels at various time points post-FMDV infection in BHK-7-FC suspension cultures. (<b>c</b>) Temporal changes in LAC content following FMDV infection in BHK-7-FC suspension cultures. (<b>d</b>) Variations in GLUC content over time after FMDV infection in BHK-7-FC suspension cultures. (<b>e</b>) Effect of supplementing different concentrations of GLN on 146S antigen content 16 h post-FMDV infection; (<b>f</b>) Impact of adding 1 mmol/L GLN to both BHK-7-FC and control BHK-21 cells on 146S antigen content 16 h post-FMDV infection. All experiments were conducted with three biological replicates. Data are presented as means ± SDs from three independent experiments and were analyzed via Student’s two-tailed unpaired <span class="html-italic">t</span>-tests. *, <span class="html-italic">p</span> &lt; 0.05; **, Black and white dots represent the scatter points on the column, indicating the specific data of each sample within the group.</p>
Full article ">Figure 5
<p>(<b>a</b>) The BHK-7-FC cell line was passaged for 10 successive generations. Following FMDV infection, the 146S antigen content was assessed every three generations compared with the control BHK-21 cells. Additionally, the replication stability of the mutant BHK-7 cells after suspension culture adaptation was evaluated. (<b>b</b>) BHK-7-FC cells were passaged for 10 consecutive generations, and their TCID<sub>50</sub> was measured every three generations after FMDV infection, in comparison with control BHK-21 cells. The data are presented as the means ± SDs from three independent experiments and were analyzed via Student’s two-tailed unpaired <span class="html-italic">t</span>-tests. ***, <span class="html-italic">p</span> &lt; 0.001; ****, <span class="html-italic">p</span> &lt; 0.0001. T3, represents the third passage of cells, T6, represents the sixth passage of cells, and T9 represents the ninth passage of cells.</p>
Full article ">
15 pages, 3129 KiB  
Article
Improved Natamycin Production in Streptomyces gilvosporeus Through Mutagenesis and Enhanced Nitrogen Metabolism
by Liang Wang, Wen Xiao, Hongjian Zhang, Jianhua Zhang and Xusheng Chen
Microorganisms 2025, 13(2), 390; https://doi.org/10.3390/microorganisms13020390 - 10 Feb 2025
Viewed by 690
Abstract
Natamycin is a polyene macrocyclic antibiotic extensively used in food, medical, and agricultural industries. However, its high production cost and low synthetic efficiency fail to meet the growing market demand. Therefore, enhancing the production of natamycin-producing strains is crucial for achieving its industrial-scale [...] Read more.
Natamycin is a polyene macrocyclic antibiotic extensively used in food, medical, and agricultural industries. However, its high production cost and low synthetic efficiency fail to meet the growing market demand. Therefore, enhancing the production of natamycin-producing strains is crucial for achieving its industrial-scale production. This study systematically evaluated 16 mutagenesis methods and identified atmospheric and room temperature plasma mutagenesis combined with 2-deoxyglucose tolerance screening as the optimal strategy for enhancing natamycin production. A high-yield mutant strain, AG-2, was obtained, achieving an 80% increase in natamycin production (1.53 g/L) compared to the original strain. Metabolic analysis revealed that glycolysis and the pentose phosphate pathway were enhanced in AG-2, while the tricarboxylic acid cycle was weakened, significantly increasing the supply of precursors such as acetyl-CoA, methylmalonyl-CoA, and the reducing power of NADPH. Additionally, overexpression of the nitrogen metabolism regulatory gene glnR promoted the supply of glutamate and glutamine, further increasing natamycin production in AG-2 to 1.85 g/L. In a 5 L fermenter, the engineered strain AG-glnR achieved a final natamycin production of 11.50 g/L, 1.67 times higher than the original strain. This study is the first to combine mutagenesis with nitrogen metabolism regulation, effectively enhancing natamycin production and providing a novel approach for the efficient synthesis of other polyene antibiotics. Full article
(This article belongs to the Special Issue Microbial Manufacture of Natural Products)
Show Figures

Figure 1

Figure 1
<p>The chemical structure of natamycin.</p>
Full article ">Figure 2
<p>Different strategies for screening natamycin high-producing mutants. (<b>a</b>) Strategies based on UV mutagenesis; (<b>b</b>) strategies based on NTG mutagenesis; (<b>c</b>) strategies based on DES mutagenesis; and (<b>d</b>) strategies based on ARTP mutagenesis. In each method, the strains were mutagenized and then coated onto plates containing varying concentrations of streptomycin, 2-DG, KH<sub>2</sub>PO<sub>4</sub>, and LiCl. The plates were then incubated at 28 °C for 8 d. Natamycin production and dry cell weight (DCW) of mutants were measured after 96 h of shake flask fermentation. Each fermentation experiment was performed in triplicate, and the results were expressed as the mean of three independent replicates, with error bars representing the standard deviation. * indicates significance at <span class="html-italic">p</span> &lt; 0.05, ** indicates significance at <span class="html-italic">p</span> &lt; 0.01, and *** indicates significance at <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 3
<p>Screening for natamycin-producing strains using ARTP mutagenesis combined with 2-DG. (<b>a</b>) Workflow of ARTP mutagenesis combined with 2-DG tolerance screening; (<b>b</b>) agar diffusion method to assess the antimicrobial activity of the mutants; (<b>c</b>) natamycin production and dry cell weight (DCW) of the five highest-yielding mutants obtained on plates with different phosphate concentrations; (<b>d</b>) comparison of the inhibition zone diameter between <span class="html-italic">S. gilvosporeus</span> ATCC 13326 and <span class="html-italic">S. gilvosporeus</span> AG-2; and (<b>e</b>) HPLC analysis of natamycin production in <span class="html-italic">S. gilvosporeus</span> ATCC 13326 and <span class="html-italic">S. gilvosporeus</span> AG-2. Fermentation was repeated three times. Data are presented as the mean of three independent cultures, with error bars representing standard deviation.</p>
Full article ">Figure 4
<p>Physiological differences between <span class="html-italic">S. gilvosporeus</span> ATCC 13326 and <span class="html-italic">S. gilvosporeus</span> AG-2. (<b>a</b>) Mycelial morphology; (<b>b</b>) natamycin production; (<b>c</b>) dry cell weight (DCW); (<b>d</b>) specific production rate; and (<b>e</b>) productivity.</p>
Full article ">Figure 5
<p>Key genes and metabolic changes in the natamycin biosynthetic pathway in the natamycin high-producing strain AG-2. Red indicates upregulated genes/metabolites, while blue indicates downregulated genes/metabolites. The red numbers represent the fold changes in upregulated genes, and the blue numbers represent the fold changes in downregulated genes.</p>
Full article ">Figure 6
<p>Effect of nitrogen metabolism on natamycin production by <span class="html-italic">S. gilvosporeus</span> AG2. (<b>a</b>) Exogenous addition of glutamate at 24, 36, and 48 h on natamycin production of AG-2; (<b>b</b>) exogenous addition of glutamine at 24, 36, and 48 h on natamycin production of AG-2. DCW: dry cell weight.</p>
Full article ">Figure 7
<p>Enhancement of nitrogen metabolism increases natamycin production in AG2. (<b>a</b>) Effect of GlnR overexpression and inhibition on natamycin production. (<b>b</b>) Comparison of natamycin production between <span class="html-italic">S. gilvosporeus</span> AG2-<span class="html-italic">glnR</span> and the parental strain <span class="html-italic">S. gilvosporeus</span> ATCC 13326 in 5 L bioreactors. (<b>c</b>) Comparison of dry cell weight (DCW) between <span class="html-italic">S. gilvosporeus</span> AG2-<span class="html-italic">glnR</span> and the parental strain <span class="html-italic">S. gilvosporeus</span> ATCC 13326 in 5 L bioreactors.</p>
Full article ">
17 pages, 2207 KiB  
Article
Advanced Machine Learning for Comparative Synovial Fluid Analysis in Osteoarthritis and Rheumatoid Arthritis
by Karolina Krystyna Kopeć, Gabrieleanselmo Uccheddu, Paweł Chodnicki, Antonio Noto, Cristina Piras, Martina Spada, Luigi Atzori and Vassilios Fanos
Metabolites 2025, 15(2), 112; https://doi.org/10.3390/metabo15020112 - 10 Feb 2025
Viewed by 442
Abstract
Osteoarthritis (OA) and rheumatoid arthritis (RA) are joint diseases that share similar clinical features but have different etiologies, making a differential diagnosis particularly challenging. Background/Objectives: Utilizing advanced machine learning (ML) techniques on metabolomic data, this study aimed to identify key metabolites in [...] Read more.
Osteoarthritis (OA) and rheumatoid arthritis (RA) are joint diseases that share similar clinical features but have different etiologies, making a differential diagnosis particularly challenging. Background/Objectives: Utilizing advanced machine learning (ML) techniques on metabolomic data, this study aimed to identify key metabolites in synovial fluid (SF) that could aid in distinguishing between OA and RA. Methods: Metabolite data from the MetaboLights database (MTBLS564), analyzed using nuclear magnetic resonance (NMR), were processed using normalization, a principal component analysis (PCA), and a partial least squares discriminant analysis (PLS-DA) to reveal prominent clustering. Results: Decision forests and random forest classifiers, optimized using genetic algorithms (GAs), highlighted a selection of a few metabolites—primarily glutamine, pyruvate, and proline—with significant discriminative power. A Shapley additive explanations (SHAP) analysis confirmed these metabolites to be pivotal predictors, offering a streamlined approach for clinical diagnostics. Conclusions: Our findings suggest that a minimal set of key metabolites can effectively be relied upon to distinguish between OA and RA, supported by an optimized ML model achieving high accuracy. This workflow could streamline diagnostic efficiency and enhance clinical decision-making in rheumatology. Full article
(This article belongs to the Collection Metabolome Mining)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>A schematic overview of the study’s diagnostic workflow, integrating a decision stump, reverse feature extraction via support vector machines (SVMs), and a random forest to identify critical metabolite bins distinguishing the disease. The workflow also incorporates a genetic algorithm to optimize the selection of model hyperparameters.</p>
Full article ">Figure 2
<p>A 3D principal component analysis (PCA) of metabolite data differentiating osteoarthritis (OA) and rheumatoid arthritis (RA) samples.</p>
Full article ">Figure 3
<p>The 1D scatter plots of the most discriminative metabolite bins for differentiating osteoarthritis (OA) and rheumatoid arthritis (RA) samples. Each plot displays the distribution of metabolite levels, normalized using min–max scaling.</p>
Full article ">Figure 4
<p>Pair plots of metabolite distributions and relationships by cluster. Diagonal plots show the distribution of each metabolite bin within the respective clusters (OA and RA), while the off-diagonal plots display pairwise relationships and distributions of metabolites, categorized by their assigned clusters. Each scatter plot highlights the separability and potential correlations between metabolites. Clustering was performed based on scaled data.</p>
Full article ">Figure 5
<p>SHAP analysis of the top 20 most key metabolite bins for disease classification in a random forest. Positive SHAP values indicate a higher likelihood of the patient being classified as having RA, while negative values suggest a higher likelihood of classification as OA. Metabolites with higher concentrations that promote RA are marked in red, while those promoting OA are marked in blue.</p>
Full article ">Figure 6
<p>Venn diagrams illustrating key metabolites identified for the diagnosis of OA (<b>top</b>) and RA (<b>bottom</b>) using both PLS-DA and machine-learning-based approaches. The blue color indicates a metabolite ambiguously associated with both diseases.</p>
Full article ">
12 pages, 1610 KiB  
Article
The Mechanism of Ammonia-Assimilating Bacteria Promoting the Growth of Oyster Mushrooms (Pleurotus ostreatus)
by Rui Li, Qi Zhang, Yuannan Chen, Yuqian Gao, Yanqing Yang, Qin Liu, Weili Kong, Haopeng Chai, Bingke Sun, Yanan Li and Liyou Qiu
J. Fungi 2025, 11(2), 130; https://doi.org/10.3390/jof11020130 - 9 Feb 2025
Viewed by 412
Abstract
Oyster mushrooms (Pleurotus ostreatus) are one of the most commonly grown edible mushrooms using compost, which contains high concentrations of ammonia. In this study, inoculation of the oyster mushroom culture substrate with ammonia-assimilating bacterium Enterobacter sp. B12, either before or after [...] Read more.
Oyster mushrooms (Pleurotus ostreatus) are one of the most commonly grown edible mushrooms using compost, which contains high concentrations of ammonia. In this study, inoculation of the oyster mushroom culture substrate with ammonia-assimilating bacterium Enterobacter sp. B12, either before or after composting, reduced the ammonia nitrogen content, increased the total nitrogen content of the compost, and enhanced the mushroom yield. Co-cultivation with P. ostreatus mycelia on potato dextrose agar (PDA) plates containing 200 mM NH4+, B12 reduced reactive oxygen species (ROS) accumulation in the mycelia and downregulated the expression of the ROS-generating enzymes NADPH oxidase A (NOXA) and the stress hormone ethylene synthase 1-aminocyclopropane-1-carboxylate oxidase (ACO). It also downregulated the expression of the ammonia-assimilating related genes in the mycelia, such as glutamate dehydrogenase (GDH), glutamate synthase (GOGAT), glutamine synthetase (GS), ammonia transporter protein (AMT), and amino acid transporter protein (AAT), while upregulating its own ammonia-assimilation genes. These findings suggest that the mechanism by which B12 promoted oyster mushroom growth was that B12 assimilated ammonia, alleviated ammonia stress, mitigated ROS accumulation in the mycelia, and supplied ammonia and amino acids to the mycelia. To our knowledge, ammonia-assimilating bacteria are a novel type of mushroom growth promoter (MGP). Full article
(This article belongs to the Special Issue Edible and Medicinal Macrofungi, 3rd Edition)
Show Figures

Figure 1

Figure 1
<p>Effect of inoculation of oyster mushroom culture substrate with <span class="html-italic">Enterobacter</span> sp. B12 on ammonia nitrogen, total nitrogen in compost, and mushroom yield. (<b>a</b>) Ammonia nitrogen and total nitrogen content in compost. (<b>b</b>) Fresh weight of the first-crop mushrooms. − −, control without B12 inoculum before and after composting; + −, inoculation of B12 inoculum only before composting; − +, inoculation of B12 inoculum only after composting; + +, inoculation of B12 inoculum before and after composting. AN, ammonia nitrogen; TN, total nitrogen; same color bars with different lowercase letters indicated significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 2
<p>Effects of <span class="html-italic">Enterobacter</span> sp. B12 on the growth of <span class="html-italic">P. ostreatus</span> mycelia co-cultured on PDA plates with varying concentrations of ammonia. Ctr, control; B12, <span class="html-italic">Enterobacter</span> sp. B12. Bars marked with different lowercase letters showed a significant difference (<span class="html-italic">p</span> &lt; 005).</p>
Full article ">Figure 3
<p>Activities of ROS-scavenging enzymes in <span class="html-italic">P. ostreatus Po</span>164 mycelia and mycelia–B12 co-culture system growing on PDA plates containing 0 mM and 200 mM NH<sub>4</sub><sup>+</sup>Cl. (<b>a</b>) SOD activity. (<b>b</b>) CAT activity. (<b>c</b>) POD activity. Ctr, control; B12, <span class="html-italic">Enterobacter</span> sp. B12. Bars marked with different lowercase letters showed a significant difference (<span class="html-italic">p</span> &lt; 005).</p>
Full article ">Figure 4
<p>Activities of ammonia assimilating enzymes in <span class="html-italic">P. ostreatus Po</span>164 mycelia and the mycelia–B12 co-culture system growing on PDA plates containing 0 mM and 200 mM NH<sub>4</sub>Cl. (<b>a</b>) GDH activity. (<b>b</b>) GS activity. (<b>c</b>) GOGAT activity. Ctr, control; B12, <span class="html-italic">Enterobacter</span> sp. B12. Bars marked with different lowercase letters showed a significant difference (<span class="html-italic">p</span> &lt; 005).</p>
Full article ">Figure 5
<p>ROS levels in <span class="html-italic">P. ostreatus</span> mycelia induced by ammonia. Ctr, control; B12, <span class="html-italic">Enterobacter</span> sp. B12. Scale bars are 100 µm.</p>
Full article ">Figure 6
<p>Effect of co-culture on the gene expression in <span class="html-italic">P. ostreatus</span> mycelia and <span class="html-italic">Enterobacter</span> sp. B12 on PDA plates with 200 mM NH<sub>4</sub>Cl. (<b>a</b>) The gene expression level in mycelia was related to the mycelia growing alone in the plates without NH<sub>4</sub>Cl. Ctr, control; B12, mycelia co-cultured with <span class="html-italic">Enterobacter</span> sp. B12. Bars marked # indicated a significant difference (<span class="html-italic">p</span> &lt; 0.05) to the gene expression level in mycelia growing alone in the plates without NH<sub>4</sub>Cl; bars marked * showed a significant difference (<span class="html-italic">p</span> &lt; 0.05) to the gene expression level in mycelia growing alone in the plates with 200 mM NH<sub>4</sub>Cl. (<b>b</b>) The gene expression level was related to B12 culturing on the plates without NH<sub>4</sub>Cl. B12, <span class="html-italic">Enterobacter</span> sp. B12 growing alone; B12 + <span class="html-italic">Po</span>164, <span class="html-italic">Enterobacter</span> sp. B12 co-cultured with <span class="html-italic">Po</span>164 mycelia. Bars marked # indicated a significant difference (<span class="html-italic">p</span> &lt; 0.05) to the gene expression level in B12 growing alone on the plates without NH<sub>4</sub>Cl; bars marked * showed a significant difference (<span class="html-italic">p</span> &lt; 005) to the gene expression level in B12 growing alone on the plates with 200 mM NH<sub>4</sub>Cl. (<b>c</b>) The gene expression level was related to the mycelia growing alone on the plates without NH<sub>4</sub>Cl. Ctr, control; B12, mycelia co-cultured with <span class="html-italic">Enterobacter</span> sp. B12. Bars marked # indicated a significant difference at <span class="html-italic">p</span> &lt; 005 to the gene expression level in the mycelia growing alone in the plates without NH<sub>4</sub>Cl; bars marked * showed a significant difference at <span class="html-italic">p</span> &lt; 005 to the gene expression level in the mycelia growing alone on the plates with 200 mM NH<sub>4</sub>Cl.</p>
Full article ">
22 pages, 4057 KiB  
Article
Evaluation of the Effects of Epicoccum nigrum on the Olive Fungal Pathogens Verticillium dahliae and Colletotrichum acutatum by 1H NMR-Based Metabolic Profiling
by Federica Angilè, Mario Riolo, Santa Olga Cacciola, Francesco Paolo Fanizzi and Elena Santilli
J. Fungi 2025, 11(2), 129; https://doi.org/10.3390/jof11020129 - 8 Feb 2025
Viewed by 458
Abstract
Olive trees are a cornerstone of Mediterranean agriculture but face significant threats from diseases such as Verticillium wilt and olive anthracnose. These diseases, caused by Verticillium dahliae and Colletotrichum spp., respectively, result in significant economic losses and degrade olive oil quality. While traditional [...] Read more.
Olive trees are a cornerstone of Mediterranean agriculture but face significant threats from diseases such as Verticillium wilt and olive anthracnose. These diseases, caused by Verticillium dahliae and Colletotrichum spp., respectively, result in significant economic losses and degrade olive oil quality. While traditional chemical treatments present environmental risk, sustainable alternatives such as biological control agents (BCAs) are gaining attention. Epicoccum nigrum, an antagonistic fungus, has shown potential as a BCA due to its production of antimicrobial secondary metabolites. This study aimed to observe whether E. nigrum has an antagonistic ability against V. dahliae and C. acutatum, and to elucidate the metabolic interactions between these fungi using NMR-based metabolomics. E. nigrum showed inhibitory effects on the growth of C. acutatum and V. dahlia of 44.97% and 38.73% respectively. Metabolomic profiling revealed distinct biochemical responses in E. nigrum, V. dahliae, and C. acutatum under mono- and dual-culture. Multivariate statistical analysis highlighted the metabolic shifts in mycelia and identified the primary metabolites, such as glutamine, 4-aminobutyrate, and phenylalanine that are involved in adaption for survival in stress conditions such as the presence of a competitor. The results could be important for a better understanding of the primary fungal metabolism, which is still poorly characterized. Further investigation is needed, but these results suggest that E. nigrum could serve as a BCA, offering a more sustainable approach to managing olive diseases. Full article
Show Figures

Figure 1

Figure 1
<p>Phylogenetic trees of <span class="html-italic">Verticillium dahliae</span> (<b>A</b>) and <span class="html-italic">Epicoccum nigrum</span> (<b>B</b>) based on the internal transcribed spacer (ITS) and, for <span class="html-italic">E. nigrum</span>, also on the β-tubulin (Tub2) gene. In panel (<b>A</b>), the phylogenetic tree for <span class="html-italic">V. dahliae</span> was constructed using only ITS sequences, with <span class="html-italic">Gibellulopsis nigrescens</span> as the outgroup and a log likelihood of −1029.50. In panel (<b>B</b>), the multilocus phylogenetic tree of <span class="html-italic">E. nigrum</span> was developed using both ITS and Tub2 sequences, with <span class="html-italic">Epicoccum dendrobii</span> as the outgroup and a log likelihood of −1560.69. Maximum likelihood estimation was used to infer the trees, and the Tamura–Nei model was applied.</p>
Full article ">Figure 2
<p>(<b>A</b>) Petri dish cultures: C9D2C + RD4C, dual-culture of <span class="html-italic">C. acutatum</span> and <span class="html-italic">E nigrum</span>; RD4C, mono-culture of <span class="html-italic">E nigrum;</span> ER 1357 + RD4C, dual-culture of <span class="html-italic">V. dahliae</span> and <span class="html-italic">E nigrum;</span> ER 1357, mono-culture of <span class="html-italic">V. dahliae.</span> (<b>B</b>) Percentage of mycelial growth inhibition of <span class="html-italic">Colletotrichum acutatum</span> (C9D2C) and <span class="html-italic">Verticillium dahliae</span> (ER 1357) observed in the dual-culture test against <span class="html-italic">Epicoccum nigrum</span> (RD4C). Inhibition values were calculated after 10 days for <span class="html-italic">C. acutatum</span> and 15 days for <span class="html-italic">V. dahliae</span> following incubation.</p>
Full article ">Figure 3
<p>Typical <sup>1</sup>H-NMR ZGCPPR spectra of aqueous extracts of three pathogens: <span class="html-italic">E. nigrum</span> (<b>bottom</b>), <span class="html-italic">V. dahliae</span> (<b>middle</b>) and <span class="html-italic">C. acutatum</span> (<b>top</b>); a representative expansion of aromatic region is also shown.</p>
Full article ">Figure 4
<p>Typical <sup>1</sup>H NMR spectra of lipid extracts of three pathogens: <span class="html-italic">E. nigrum</span> (<b>bottom</b>), <span class="html-italic">V. dahliae</span> (<b>middle</b>) and <span class="html-italic">C. acutatum</span> (<b>top</b>).</p>
Full article ">Figure 5
<p>(<b>a</b>) OPLS-DA t[1]/t[2] score plot performed on aqueous extracts of <span class="html-italic">E. nigrum</span> cultivated in mono- and dual-assay; (<b>b</b>) loading plot for the model, colored according to the correlation-scaled vector (p(corr)). The variables indicate the chemical shift value (ppm) in the <sup>1</sup>H NMR spectra.</p>
Full article ">Figure 6
<p>(<b>a</b>) OPLS-DA t[1]/to [1] score plot performed on <span class="html-italic">V. dahliae</span> aqueous extracts cultivated in mono- and dual-assay; mono, mono-culture assay (<span class="html-italic">V. dahliae</span>); dual, dual-culture assay (<span class="html-italic">V. dahliae + E. nigrum</span>); (<b>b</b>) loading plot for the OPLS-DA model; (<b>c</b>) OPLS-DA t[1]/to [1] score plot performed on <span class="html-italic">C. acutatum</span> aqueous extracts cultivated in mono- and dual-assay; mono, mono-culture assay (<span class="html-italic">C. acutatum</span>); dual, dual-culture assay (<span class="html-italic">C. acutatum + E. nigrum)</span>; (<b>d</b>) loading plot for the model, colored according to the correlation-scaled vector (p(corr)). The variables indicate the chemical shift value (ppm) in the <sup>1</sup>H NMR spectra.</p>
Full article ">Figure 7
<p>Variation in discriminating metabolite content between mono- and dual-culture assay for aqueous extract; (<b>a</b>) <span class="html-italic">E. nigrum</span>; (<b>b</b>) <span class="html-italic">V. dahliae</span>; (<b>c</b>) <span class="html-italic">C. acutatum</span>. Signif. codes ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05.</p>
Full article ">Figure 8
<p>(<b>a</b>) OPLS-DA t[1]/t[2] score plot performed on lipid extracts of <span class="html-italic">E. nigrum</span> cultivated in mono- and dual-assay; (<b>b</b>) loading plot for the model, colored according to the correlation-scaled vector (p(corr)). The variables indicated the chemical shift value (ppm) in the <sup>1</sup>H NMR spectra.</p>
Full article ">Figure 9
<p>(<b>a</b>) OPLS-DA t[1]/to [1] score plot performed on <span class="html-italic">V. dahliae</span> lipid extracts cultivated in mono- and dual-assay; mono, mono-culture assay (<span class="html-italic">V. dahliae</span>); dual, dual-culture assay (<span class="html-italic">V. dahliae + E. nigrum</span>); (<b>b</b>) loading plot for the OPLS-DA model; (<b>c</b>) OPLS-DA t[1]/to [1] score plot performed on <span class="html-italic">C. acutatum</span> lipid extracts cultivated in mono- and dual-assay; mono, mono-culture assay (<span class="html-italic">C. acutatum</span>); dual, dual-culture assay (<span class="html-italic">C. acutatum + E. nigrum)</span>; (<b>d</b>) loading plot for the model, colored according to the correlation-scaled vector (p(corr)). The variables indicate the chemical shift value (ppm) in the <sup>1</sup>H NMR spectra.</p>
Full article ">Figure 10
<p>Variation in discriminating fatty acid contents between mono- and dual-culture assay for aqueous extracts; (<b>a</b>) <span class="html-italic">E. nigrum</span>; (<b>b</b>) <span class="html-italic">V. dahliae</span>; (<b>c</b>) <span class="html-italic">C. acutatum</span>. Signif. codes ‘***’ 0.001 ‘**’ 0.01.</p>
Full article ">
Back to TopTop