[go: up one dir, main page]

 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

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

Article Types

Countries / Regions

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

Search Results (57,708)

Search Parameters:
Keywords = RNA

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
28 pages, 1008 KiB  
Review
Decoding Cancer through Silencing the Mitochondrial Gatekeeper VDAC1
by Tasleem Arif, Anna Shteinfer-Kuzmine and Varda Shoshan-Barmatz
Biomolecules 2024, 14(10), 1304; https://doi.org/10.3390/biom14101304 (registering DOI) - 15 Oct 2024
Abstract
Mitochondria serve as central hubs for regulating numerous cellular processes that include metabolism, apoptosis, cell cycle progression, proliferation, differentiation, epigenetics, immune signaling, and aging. The voltage-dependent anion channel 1 (VDAC1) functions as a crucial mitochondrial gatekeeper, controlling the flow of ions, such as [...] Read more.
Mitochondria serve as central hubs for regulating numerous cellular processes that include metabolism, apoptosis, cell cycle progression, proliferation, differentiation, epigenetics, immune signaling, and aging. The voltage-dependent anion channel 1 (VDAC1) functions as a crucial mitochondrial gatekeeper, controlling the flow of ions, such as Ca2+, nucleotides, and metabolites across the outer mitochondrial membrane, and is also integral to mitochondria-mediated apoptosis. VDAC1 functions in regulating ATP production, Ca2+ homeostasis, and apoptosis, which are essential for maintaining mitochondrial function and overall cellular health. Most cancer cells undergo metabolic reprogramming, often referred to as the “Warburg effect”, supplying tumors with energy and precursors for the biosynthesis of nucleic acids, phospholipids, fatty acids, cholesterol, and porphyrins. Given its multifunctional nature and overexpression in many cancers, VDAC1 presents an attractive target for therapeutic intervention. Our research has demonstrated that silencing VDAC1 expression using specific siRNA in various tumor types leads to a metabolic rewiring of the malignant cancer phenotype. This results in a reversal of oncogenic properties that include reduced tumor growth, invasiveness, stemness, epithelial–mesenchymal transition. Additionally, VDAC1 depletion alters the tumor microenvironment by reducing angiogenesis and modifying the expression of extracellular matrix- and structure-related genes, such as collagens and glycoproteins. Furthermore, VDAC1 depletion affects several epigenetic-related enzymes and substrates, including the acetylation-related enzymes SIRT1, SIRT6, and HDAC2, which in turn modify the acetylation and methylation profiles of histone 3 and histone 4. These epigenetic changes can explain the altered expression levels of approximately 4,000 genes that are associated with reversing cancer cells oncogenic properties. Given VDAC1’s critical role in regulating metabolic and energy processes, targeting it offers a promising strategy for anti-cancer therapy. We also highlight the role of VDAC1 expression in various disease pathologies, including cardiovascular, neurodegenerative, and viral and bacterial infections, as explored through siRNA targeting VDAC1. Thus, this review underscores the potential of targeting VDAC1 as a strategy for addressing high-energy-demand cancers. By thoroughly understanding VDAC1’s diverse roles in metabolism, energy regulation, mitochondrial functions, and other cellular processes, silencing VDAC1 emerges as a novel and strategic approach to combat cancer. Full article
13 pages, 816 KiB  
Article
Impact of Dupilumab on Skin Surface Lipid-RNA Profile in Severe Asthmatic Patients
by Yoshihiko Sato, Hitoshi Sasano, Sumiko Abe, Yuuki Sandhu, Shoko Ueda, Sonoko Harada, Yuki Tanabe, Kyoko Shima, Tetsuya Kuwano, Yuya Uehara, Takayoshi Inoue, Ko Okumura, Kazuhisa Takahashi and Norihiro Harada
Curr. Issues Mol. Biol. 2024, 46(10), 11425-11437; https://doi.org/10.3390/cimb46100680 (registering DOI) - 15 Oct 2024
Abstract
The analysis of skin surface lipid-RNAs (SSL-RNAs) provides a non-invasive method for understanding the molecular pathology of atopic dermatitis (AD), but its relevance to asthma remains uncertain. Although dupilumab, a biologic drug approved for both asthma and AD, has shown efficacy in improving [...] Read more.
The analysis of skin surface lipid-RNAs (SSL-RNAs) provides a non-invasive method for understanding the molecular pathology of atopic dermatitis (AD), but its relevance to asthma remains uncertain. Although dupilumab, a biologic drug approved for both asthma and AD, has shown efficacy in improving symptoms for both conditions, its impact on SSL-RNAs is unclear. This study aimed to investigate the impact of dupilumab treatment on SSL-RNA profiles in patients with severe asthma. Methods: An SSL-RNA analysis was performed before and after administering dupilumab to asthma patients requiring this intervention. Skin samples were collected non-invasively from patients before and after one year of dupilumab treatment. Although 26 patients were enrolled, an SSL-RNA analysis was feasible in only 7 due to collection challenges. After dupilumab treatment, improvements were observed in asthma symptoms, exacerbation rates, and lung function parameters. Serum levels of total IgE and periostin decreased. The SSL-RNA analysis revealed the differential expression of 218 genes, indicating significant down-regulation of immune responses, particularly those associated with type 2 inflammation, suggesting potential improvement in epithelial barrier function. Dupilumab treatment may not only impact type 2 inflammation but also facilitate the normalization of the skin. Further studies are necessary to fully explore the potential of SSL-RNA analysis as a non-invasive biomarker for evaluating treatment response in asthma. Full article
17 pages, 905 KiB  
Review
Advances in Plant GABA Research: Biological Functions, Synthesis Mechanisms and Regulatory Pathways
by Yixuan Hu, Xin Huang, Qinglai Xiao, Xuan Wu, Qi Tian, Wenyi Ma, Noman Shoaib, Yajie Liu, Hui Zhao, Zongyun Feng and Guowu Yu
Plants 2024, 13(20), 2891; https://doi.org/10.3390/plants13202891 (registering DOI) - 15 Oct 2024
Abstract
The γ-aminobutyric acid (GABA) is a widely distributed neurotransmitter in living organisms, known for its inhibitory role in animals. GABA exerts calming effects on the mind, lowers blood pressure in animals, and enhances stress resistance during the growth and development of plants. Enhancing [...] Read more.
The γ-aminobutyric acid (GABA) is a widely distributed neurotransmitter in living organisms, known for its inhibitory role in animals. GABA exerts calming effects on the mind, lowers blood pressure in animals, and enhances stress resistance during the growth and development of plants. Enhancing GABA content in plants has become a focal point of current research. In plants, GABA is synthesized through two metabolic pathways, the GABA shunt and the polyamine degradation pathway, with the GABA shunt being the primary route. Extensive studies have investigated the regulatory mechanisms governing GABA synthesis. At the genetic level, GABA production and degradation can be modulated by gene overexpression, signaling molecule-induced expression, transcription factor regulation, and RNA interference. Additionally, at the level of transporter proteins, increased activity of GABA transporters and proline transporters enhances the transport of glutamate and GABA. The activity of glutamate decarboxylase, a key enzyme in GABA synthesis, along with various external factors, also influences GABA synthesis. This paper summarizes the biological functions, metabolic pathways, and regulatory mechanisms of GABA, providing a theoretical foundation for further research on GABA in plants. Full article
14 pages, 3764 KiB  
Article
Evaluating Native Bacillus Strains as Potential Biocontrol Agents against Tea Anthracnose Caused by Colletotrichum fructicola
by Meixia Chen, Hui Lin, Weifan Zu, Lulu Wang, Wenbo Dai, Yulin Xiao, Ye Zou, Chengkang Zhang, Wei Liu and Xiaoping Niu
Plants 2024, 13(20), 2889; https://doi.org/10.3390/plants13202889 (registering DOI) - 15 Oct 2024
Abstract
Anthracnose of the tea plant (Camellia sinensis), caused by Colletotrichum spp., poses a significant threat to both the yield and quality of tea production. To address this challenge, researchers have looked to the application of endophytic bacteria as a natural alternative [...] Read more.
Anthracnose of the tea plant (Camellia sinensis), caused by Colletotrichum spp., poses a significant threat to both the yield and quality of tea production. To address this challenge, researchers have looked to the application of endophytic bacteria as a natural alternative to the use chemical pesticides, offering potential for enhancing disease resistance and abiotic stress tolerance in tea plants. This study focused on identifying effective microbial agents to combat tea anthracnose caused by Colletotrichum fructicola. A total of 38 Bacillus-like strains were isolated from the tea rhizosphere, with 8 isolates showing substantial inhibitory effects against the mycelial growth of C. fructicola, achieving an average inhibition rate of 60.68%. Among these, strain T3 was particularly effective, with a 69.86% inhibition rate. Through morphological, physiological, and biochemical characterization, along with 16S rRNA gene phylogenetics analysis, these strains were identified as B. inaquosorum (T1 and T2), B. tequilensis (T3, T5, T7, T8, and T19), and B. spizizenii (T6). Biological and molecular assays confirmed that these strains could induce the expression of genes associated with antimicrobial compounds like iturin, fengycin, subtilosin, and alkaline protease, which effectively reduced the disease index of tea anthracnose and enhanced tea plant growth. In conclusion, this study demonstrates that B. inaquosorum, B. tequilensis, and B. spizizenii strains are promising biocontrol agents for managing tea anthracnose. Full article
(This article belongs to the Section Plant Protection and Biotic Interactions)
Show Figures

Figure 1

Figure 1
<p>Screening of <span class="html-italic">bacillus</span> isolates against tea anthracnose pathogen <span class="html-italic">C. fructicola</span> strain N. (<b>A</b>,<b>B</b>) Symptoms of anthracnose disease on leaves of tea plant in the field. (<b>C</b>,<b>D</b>) Upper and reverse view of tea anthracnose pathogen <span class="html-italic">C. fructicola</span> strain N on PDA plate at 37 °C for three days. (<b>E</b>,<b>F</b>) Screening of <span class="html-italic">Bacillus</span> isolates from the rhizosphere soil of tea plants. Morphological characteristics of isolates (<b>E</b>) and their antagonistic effect against tea anthracnose pathogen <span class="html-italic">C. fructicola</span> on PDA plates at 37 °C for two days (<b>F</b>).</p>
Full article ">Figure 2
<p>Morphological characteristics of the <span class="html-italic">Bacillus</span> isolates. Gram staining (<b>A</b>,<b>C</b>) and spore staining (<b>B</b>,<b>D</b>) of eight biocontrol strains. Size bars = 10 μm.</p>
Full article ">Figure 3
<p>Maximum likelihood phylogenetic tree for eight strains and <span class="html-italic">Bacillus</span> spp. inferred from 16S rRNA gene sequences using MEGA 11. The numbers at the nodes indicate the bootstrap support calculated from 1000 replications, with values ≥ 70% displayed.</p>
Full article ">Figure 4
<p>Inhibitory activity of <span class="html-italic">Bacillus</span> isolates against the <span class="html-italic">C. fructicola</span> strains. The inhibitory effects of eight <span class="html-italic">Bacillus</span> isolates on <span class="html-italic">C. fructicola</span> strain N1<sup>#</sup> (<b>A</b>,<b>B</b>), <span class="html-italic">C. fructicola</span> strain N5<sup>#</sup> (<b>C</b>,<b>D</b>), and <span class="html-italic">C. fructicola</span> strain N8<sup>#</sup> (<b>E</b>,<b>F</b>). The photograph on the left of each panel represents the mock control (CK), and the images on the right display the inhibitory effects of <span class="html-italic">Bacillus</span> isolate treatment.</p>
Full article ">Figure 5
<p>The PCR amplification of genes involved in the production of antimicrobial compounds using genomic DNA of 8 <span class="html-italic">Bacillus</span> isolates was verified by a 1% agarose gel. Bands corresponding to the <span class="html-italic">fenA</span> (<b>A</b>) and <span class="html-italic">fenD</span> (<b>B</b>) genes were associated with fengycin, <span class="html-italic">aprE</span> (<b>C</b>) with alkaline protease, <span class="html-italic">sboA</span> (<b>D</b>) with subtilosin, and <span class="html-italic">ituC</span> (<b>E</b>) and <span class="html-italic">ituA</span> (<b>F</b>) with iturin. M denotes the size marker, while numbers 1–8 correspond to strains T1, T2, T3, T5, T6, T8, and T19, respectively. + represents positive control, using DNA from <span class="html-italic">Bacillus subtilis</span> as a template, and − represents negative control, using ddH<sub>2</sub>O as a template.</p>
Full article ">Figure 6
<p>Biocontrol effect of <span class="html-italic">Bacillus</span> strain <span class="html-italic">B. inaquosorum</span> T1, <span class="html-italic">B. tequilensis</span> T3, and <span class="html-italic">B. spizizenii</span> T6 treatments on tea anthracnose on the tea leaves caused by <span class="html-italic">C. fructicola.</span> (<b>A</b>–<b>D</b>) Disease incidence of four leaf groups inoculated with <span class="html-italic">C. fructicola</span> and treated with different <span class="html-italic">Bacillus</span> strains. (<b>E</b>) Disease lesion areas and (<b>F</b>) control efficacy were calculated after 10 days of inoculation with the pathogen. *** indicates significant differences between treatments, according to one-way ANOVA (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">
17 pages, 1211 KiB  
Article
Three-Year Monitoring of Microorganisms’ Composition and Concentration in Atmospheric Aerosols of Novosibirsk City and Suburbs
by Irina Andreeva, Aleksandr Safatov, Olga Totmenina, Sergei Olkin, Maxim Rebus, Galina Buryak, Tatiana Alikina, Olga Baturina and Marsel Kabilov
Microorganisms 2024, 12(10), 2068; https://doi.org/10.3390/microorganisms12102068 (registering DOI) - 15 Oct 2024
Abstract
The atmospheric environment is formed under the influence of local and distant sources as a result of horizontal and vertical transport. In the present work, microbiological analysis of 604 samples of atmospheric aerosol collected in the period from September 2020 to September 2023 [...] Read more.
The atmospheric environment is formed under the influence of local and distant sources as a result of horizontal and vertical transport. In the present work, microbiological analysis of 604 samples of atmospheric aerosol collected in the period from September 2020 to September 2023 at four sites differing in anthropogenic load, located in Novosibirsk and the region, was carried out. Day and night aerosol samples were collected during 12 h every two weeks by filtration using Sartorius reinforced Teflon membranes, then sown on a set of nutrient media. The taxonomic affiliation of the isolated microbial isolates was determined based on phenotypic characteristics and analysis of 16S rRNA gene nucleotide sequences. Changes in the composition and concentration of culturable microorganisms depending on the season, time of day, and site of aerosol sampling were observed. In winter, lower fungi and bacteria of the genera Bacillus, Staphylococcus, Micrococcus dominated with an average concentration from zero to 12.5 CFU/m3 of aerosol. In the warm period, the concentration and diversity of cocci, spore-forming and non-spore-forming bacteria, actinomycetes, and fungi (up to 1970 CFU/m3), among which pathogenic microorganisms were found, increased sharply in aerosols. The use of 16S metabarcoding techniques has greatly expanded the range of aerosols’ microbial diversity detectable. Full article
(This article belongs to the Section Environmental Microbiology)
22 pages, 1264 KiB  
Article
The Association between Gut Microbiota and Serum Biomarkers in Children with Atopic Dermatitis
by Irina G. Kalashnikova, Alexandra I. Nekrasova, Anna V. Korobeynikova, Maria M. Bobrova, German A. Ashniev, Sirozhdin Yu. Bakoev, Angelica V. Zagainova, Mariya V. Lukashina, Larisa R. Tolkacheva, Ekaterina S. Petryaikina, Alexander S. Nekrasov, Sergey I. Mitrofanov, Tatyana A. Shpakova, Lidiya V. Frolova, Natalya V. Bulanova, Ekaterina A. Snigir, Vladimir E. Mukhin, Vladimir S. Yudin, Valentin V. Makarov, Anton A. Keskinov and Sergey M. Yudinadd Show full author list remove Hide full author list
Biomedicines 2024, 12(10), 2351; https://doi.org/10.3390/biomedicines12102351 (registering DOI) - 15 Oct 2024
Abstract
Background. Currently, it is known that the gut microbiota plays an important role in the functioning of the immune system, and a rebalancing of the bacterial community can arouse complex immune reactions and lead to immune-mediated responses in an organism, in particular, the [...] Read more.
Background. Currently, it is known that the gut microbiota plays an important role in the functioning of the immune system, and a rebalancing of the bacterial community can arouse complex immune reactions and lead to immune-mediated responses in an organism, in particular, the development of atopic dermatitis (AD). Cytokines and chemokines are regulators of the innate and adaptive immune response and represent the most important biomarkers of the immune system. It is known that changes in cytokine profiles are a hallmark of many diseases, including atopy. However, it remains unclear how the bacterial imbalance disrupts the function of the immune response in AD. Objectives. We attempted to determine the role of gut bacteria in modulating cytokine pathways and their role in atopic inflammation. Methods. We sequenced the 16S rRNA gene from 50 stool samples of children aged 3–12 years who had confirmed atopic dermatitis, and 50 samples from healthy children to serve as a control group. To evaluate the immune status, we conducted a multiplex immunofluorescence assay and measured the levels of 41 cytokines and chemokines in the serum of all participants. Results. To find out whether changes in the composition of the gut microbiota were significantly associated with changes in the level of inflammatory cytokines, a correlation was calculated between each pair of bacterial family and cytokine. In the AD group, 191 correlations were significant (Spearman’s correlation coefficient, p ≤ 0.05), 85 of which were positive and 106 which were negative. Conclusions. It has been demonstrated that intestinal dysbiosis is associated with alterations in cytokine profiles, specifically an increase in proinflammatory cytokine concentrations. This may indicate a systemic impact of these conditions, leading to an imbalance in the immune system’s response to the Th2 type. As a result, atopic conditions may develop. Additionally, a correlation between known AD biomarkers (IL-5, IL-8, IL-13, CCL22, IFN-γ, TNF-α) and alterations in the abundance of bacterial families (Pasteurellaceae, Barnesiellaceae, Eubacteriaceae) was observed. Full article
22 pages, 2402 KiB  
Article
Chronic Low-Dose-Rate Radiation-Induced Persistent DNA Damage and miRNA/mRNA Expression Changes in Mouse Hippocampus and Blood
by Hong Wang, Salihah Lau, Amanda Tan and Feng Ru Tang
Cells 2024, 13(20), 1705; https://doi.org/10.3390/cells13201705 (registering DOI) - 15 Oct 2024
Abstract
Our previous study demonstrated that the acute high-dose-rate (3.3 Gy/min) γ-ray irradiation (γ-irradiation) of postnatal day-3 (P3) mice with 5 Gy induced depression and drastic neuropathological changes in the dentate gyrus of the hippocampus of adult mice. The present study investigated the effects [...] Read more.
Our previous study demonstrated that the acute high-dose-rate (3.3 Gy/min) γ-ray irradiation (γ-irradiation) of postnatal day-3 (P3) mice with 5 Gy induced depression and drastic neuropathological changes in the dentate gyrus of the hippocampus of adult mice. The present study investigated the effects of chronic low-dose-rate (1.2 mGy/h) γ-irradiation from P3 to P180 with a cumulative dose of 5 Gy on animal behaviour, hippocampal cellular change, and miRNA and mRNA expression in the hippocampus and blood in female mice. The radiation exposure did not significantly affect the animal’s body weight, and neuropsychiatric changes such as anxiety and depression were examined by neurobehavioural tests, including open field, light-dark box, elevated plus maze, tail suspension, and forced swim tests. Immunohistochemical staining did not detect any obvious loss of mature and immature neurons (NeuN and DCX) or any inflammatory glial response (IBA1, GFAP, and PDGFRα). Nevertheless, γH2AX foci in the stratum granulosum of the dentate gyrus were significantly increased, suggesting the chronic low-dose-rate irradiation induced persistent DNA damage foci in mice. miRNA sequencing and qRT-PCR indicated an increased expression of miR-448-3p and miR-361-5p but decreased expression of miR-193a-3p in the mouse hippocampus. Meanwhile, mRNA sequencing and qRT-PCR showed the changed expression of some genes, including Fli1, Hs3st5, and Eif4ebp2. Database searching by miRDB and TargetScan predicted that Fli1 and Hs3st5 are the targets of miR-448-3p, and Eif4ebp2 is the target of miR-361-5p. miRNA/mRNA sequencing and qRT-PCR results in blood showed the increased expression of miR-6967-3p and the decreased expression of its target S1pr5. The interactions of these miRNAs and mRNAs may be related to the chronic low-dose-rate radiation-induced persistent DNA damage. Full article
(This article belongs to the Section Cells of the Nervous System)
Show Figures

Figure 1

Figure 1
<p>Weight measurement indicates that chronic irradiation with a dose rate of 1.2 mGy/h did not affect weight gain from 2 weeks during irradiation until 64 weeks after the first irradiation started. Animal weight gain increased significantly during the first week of irradiation. The Student’s <span class="html-italic">t</span>-test was used to compare the body weight between control and exp groups (1.2 mGy/h) at different time points. * <span class="html-italic">p</span> &lt; 0.05. Control: n = 9; Exp (1.2 mGy/h): n = 15. W: week; C: control; E: exp (1.2 mGy/h); 1W-C means control mice in 1 week.</p>
Full article ">Figure 2
<p>Neurobehavioural tests did not show chronic irradiation-induced anxiety and depression behavioral changes. Time spent (<b>A</b>) and distance travelled (<b>B</b>) in each area in the open field test; time spent (<b>C</b>) in the light and dark box and distance travelled (<b>D</b>) in the light box in the light–dark box test; time spent (<b>E</b>) and distance travelled (<b>F</b>) in three areas in the elevated plus maze; (<b>G</b>) time immobile in the tail suspension test and forced swim test. TST: tail suspension test; FST: forced swim test. The Student’s <span class="html-italic">t</span>-test was used to compare the data between the control and exp groups (1.2 mGy/h). Control: n = 9; exp group: n = 15.</p>
Full article ">Figure 3
<p>Immunohistochemical staining of the dentate gyrus of the hippocampus in the control and experiment mice. (<b>A1</b>–<b>A3</b>): NeuN immunopositive mature neurons (arrow); (<b>B1</b>–<b>B3</b>): GFAP immunopositive astrocytes (arrow); (<b>C1</b>–<b>C3</b>): PDGFRα immunopositive oligodendrocyte precursor cells (arrow) in the hilus; (<b>D1</b>–<b>D3</b>): IBA1 immunopositive microglia (arrow) in the hilus and the granule cell layer; (<b>E1</b>–<b>E3</b>): DCX immunopositive immature neurons (arrow) in the subgranular zone; (<b>F1</b>–<b>F3</b>): γH2AX immunostaining shows DNA damage foci in the granule cells. Scale bar = 100 μm in (<b>A1</b>) applies to (<b>B1</b>–<b>E1</b>) and (<b>A2</b>–<b>E2</b>). Scale bar = 50 μm in (<b>F1</b>) applies to (<b>F2</b>). (<b>A3</b>–<b>F3</b>): statistical results. <span class="html-italic">* p</span> &lt; 0.05. Control: n = 8; exp group: n = 7.</p>
Full article ">Figure 3 Cont.
<p>Immunohistochemical staining of the dentate gyrus of the hippocampus in the control and experiment mice. (<b>A1</b>–<b>A3</b>): NeuN immunopositive mature neurons (arrow); (<b>B1</b>–<b>B3</b>): GFAP immunopositive astrocytes (arrow); (<b>C1</b>–<b>C3</b>): PDGFRα immunopositive oligodendrocyte precursor cells (arrow) in the hilus; (<b>D1</b>–<b>D3</b>): IBA1 immunopositive microglia (arrow) in the hilus and the granule cell layer; (<b>E1</b>–<b>E3</b>): DCX immunopositive immature neurons (arrow) in the subgranular zone; (<b>F1</b>–<b>F3</b>): γH2AX immunostaining shows DNA damage foci in the granule cells. Scale bar = 100 μm in (<b>A1</b>) applies to (<b>B1</b>–<b>E1</b>) and (<b>A2</b>–<b>E2</b>). Scale bar = 50 μm in (<b>F1</b>) applies to (<b>F2</b>). (<b>A3</b>–<b>F3</b>): statistical results. <span class="html-italic">* p</span> &lt; 0.05. Control: n = 8; exp group: n = 7.</p>
Full article ">Figure 4
<p>Low-dose-rate irradiation-induced hippocampal mRNA changes: (<b>A</b>) Heatmap of mRNA changes from mRNA sequencing in the control and experiment (Exp) mice. (<b>B</b>) qRT-PCR indicates a significant down-regulation of <span class="html-italic">Ccn1</span>, <span class="html-italic">Fli1</span>, <span class="html-italic">Fosb</span>, <span class="html-italic">Ets1</span>, <span class="html-italic">Hs3st5,</span> and <span class="html-italic">Eif4ebp2</span> genes, and up-regulation of <span class="html-italic">Cort</span>, <span class="html-italic">Foxh1</span>, and <span class="html-italic">Opalin</span> genes. * <span class="html-italic">p</span> &lt; 0.05. Control: n = 3; Exp group: n = 3. FH: female hippocampus; FH1, FH2, FH3 are controls; FH4, FH5, FH6 are exp mice (1.2 mGy/h); TPM: transcript per million.</p>
Full article ">Figure 5
<p>Low-dose-rate irradiation-induced hippocampal miRNA changes: (<b>A</b>) Heatmap of miRNA changes from miRNA sequencing in the control and experiment mice; (<b>B</b>) qRT-PCR indicates a significant down-regulation of miR-193a-3p and up-regulation of miR-448-3p and miR-361-5p in the irradiated mice (* <span class="html-italic">p</span> &lt; 0.05), but no changes for other miRNA investigated (<span class="html-italic">p</span> &gt; 0.05). Control: n = 3; Exp group: n = 3. FH: female hippocampus; FH1, FH2, FH3 are controls; FH4, FH5, FH6 are exp (irradiated with 1.2 mGy/h).</p>
Full article ">Figure 6
<p>Low-dose-rate irradiation-induced blood mRNA changes: (<b>A</b>) Heatmap of mRNA sequencing results in the blood of control and experiment mice; (<b>B</b>) qRT-PCR indicates a significant down-regulation of <span class="html-italic">Tppp3</span>, <span class="html-italic">S1pr5</span>, and <span class="html-italic">Rbpms</span> and up-regulation of <span class="html-italic">Lepr</span> genes (* <span class="html-italic">p</span> &lt; 0.05) but no changes of other mRNAs in the blood of the control and irradiated mice. Control: n = 3; exp group: n = 3. FB: female blood; FB1, FB2, and FB3 are controls; FB4, FB5, and FB6 are exp mice (1.2 mGy/h); TPM: transcript per million.</p>
Full article ">Figure 7
<p>Low-dose-rate irradiation-induced blood miRNA changes: (<b>A</b>) Heatmap of miRNA sequencing results in the blood of control and experiment mice; (<b>B</b>) qRT-PCR indicates a significant down-regulation of miR-296-5p and up-regulation of miR-6967-3p (* <span class="html-italic">p</span> &lt; 0.05) but no changes of other miRNAs in the blood of the control and irradiated mice. The Student’s <span class="html-italic">t</span>-test was used to compare the data between control and exp mice (1.2 mGy/h). Control: n = 3; exp group: n = 3. FB: female blood; FB1, FB2, FB3 are controls; FB4, FB5, FB6 are exp (1.2 mGy/h).</p>
Full article ">Figure 8
<p>Low-dose-rate irradiation-induced differentially expressed miRNAs and mRNAs in both the blood and hippocampus. (<b>A</b>,<b>B</b>): Venn diagram of 18 differentially expressed miRNAs (<b>A</b>) and two mRNAs (<b>B</b>) in both the blood and hippocampus of irradiated mice compared to the control; (<b>C</b>) table list of 18 miRNAs differentially expressed in both the blood and hippocampus; (<b>D</b>) table list of two mRNAs differentially expressed in both the blood and hippocampus.</p>
Full article ">
20 pages, 1762 KiB  
Article
Temporal Changes in Jejunal and Ileal Microbiota of Broiler Chickens with Clinical Coccidiosis (Eimeria maxima)
by Katarzyna B. Miska, Philip M. Campos, Sara E. Cloft, Mark C. Jenkins and Monika Proszkowiec-Weglarz
Animals 2024, 14(20), 2976; https://doi.org/10.3390/ani14202976 (registering DOI) - 15 Oct 2024
Abstract
Coccidiosis in broiler chickens continues to be a major disease of the gastrointestinal tract, causing economic losses to the poultry industry worldwide. The goal of this study was to generate a symptomatic Eimeria maxima (1000 oocysts) infection to determine its effect on the [...] Read more.
Coccidiosis in broiler chickens continues to be a major disease of the gastrointestinal tract, causing economic losses to the poultry industry worldwide. The goal of this study was to generate a symptomatic Eimeria maxima (1000 oocysts) infection to determine its effect on the luminal and mucosal microbiota populations (L and M) in the jejunum and ileum (J and IL). Samples were taken from day 0 to14 post-infection, and sequencing of 16S rRNA was performed using Illumina technology. Infected birds had significantly (p < 0.0001) lower body weight gain (BWG), higher feed conversion ratio (FCR) (p = 0.0015), increased crypt depth, and decreased villus height (p < 0.05). The significant differences in alpha and beta diversity were observed primarily at height of infection (D7). Analysis of taxonomy indicated that J-L and M were dominated by Lactobacillus, and in IL-M, changeover from Candidatus Arthromitus to Lactobacillus as the major taxon was observed, which occurred quicky in infected animals. LEfSe analysis found that in the J-M of infected chickens, Lactobacillus was significantly more abundant in infected (IF) chickens. These findings show that E. maxima infection affects the microbiota of the small intestine in a time-dependent manner, with different effects on the luminal and mucosal populations. Full article
41 pages, 1833 KiB  
Review
Current State of Therapeutics for HTLV-1
by Tiana T. Wang, Ashley Hirons, Marcel Doerflinger, Kevin V. Morris, Scott Ledger, Damian F. J. Purcell, Anthony D. Kelleher and Chantelle L. Ahlenstiel
Viruses 2024, 16(10), 1616; https://doi.org/10.3390/v16101616 (registering DOI) - 15 Oct 2024
Abstract
Human T cell leukaemia virus type-1 (HTLV-1) is an oncogenic retrovirus that causes lifelong infection in ~5–10 million individuals globally. It is endemic to certain First Nations populations of Northern and Central Australia, Japan, South and Central America, Africa, and the Caribbean region. [...] Read more.
Human T cell leukaemia virus type-1 (HTLV-1) is an oncogenic retrovirus that causes lifelong infection in ~5–10 million individuals globally. It is endemic to certain First Nations populations of Northern and Central Australia, Japan, South and Central America, Africa, and the Caribbean region. HTLV-1 preferentially infects CD4+ T cells and remains in a state of reduced transcription, often being asymptomatic in the beginning of infection, with symptoms developing later in life. HTLV-1 infection is implicated in the development of adult T cell leukaemia/lymphoma (ATL) and HTLV-1-associated myelopathies (HAM), amongst other immune-related disorders. With no preventive or curative interventions, infected individuals have limited treatment options, most of which manage symptoms. The clinical burden and lack of treatment options directs the need for alternative treatment strategies for HTLV-1 infection. Recent advances have been made in the development of RNA-based antiviral therapeutics for Human Immunodeficiency Virus Type-1 (HIV-1), an analogous retrovirus that shares modes of transmission with HTLV-1. This review highlights past and ongoing efforts in the development of HTLV-1 therapeutics and vaccines, with a focus on the potential for gene therapy as a new treatment modality in light of its successes in HIV-1, as well as animal models that may help the advancement of novel antiviral and anticancer interventions. Full article
(This article belongs to the Special Issue HIV and HTLV Infections and Coinfections)
22 pages, 19388 KiB  
Article
Network Pharmacology Approaches Used to Identify Therapeutic Molecules for Chronic Venous Disease Based on Potential miRNA Biomarkers
by Oscar Salvador Barrera-Vázquez, Juan Luis Escobar-Ramírez and Gil Alfonso Magos-Guerrero
J. Xenobiot. 2024, 14(4), 1519-1540; https://doi.org/10.3390/jox14040083 (registering DOI) - 15 Oct 2024
Abstract
Chronic venous disease (CVD) is a prevalent condition in adults, significantly affecting the global elderly population, with a higher incidence in women than in men. The modulation of gene expression through microRNA (miRNA) partly regulated the development of cardiovascular disease (CVD). Previous research [...] Read more.
Chronic venous disease (CVD) is a prevalent condition in adults, significantly affecting the global elderly population, with a higher incidence in women than in men. The modulation of gene expression through microRNA (miRNA) partly regulated the development of cardiovascular disease (CVD). Previous research identified a functional analysis of seven genes (CDS2, HDAC5, PPP6R2, PRRC2B, TBC1D22A, WNK1, and PABPC3) as targets of miRNAs related to CVD. In this context, miRNAs emerge as essential candidates for CVD diagnosis, representing novel molecular and biological knowledge. This work aims to identify, by network analysis, the miRNAs involved in CVD as potential biomarkers, either by interacting with small molecules such as toxins and pollutants or by searching for new drugs. Our study shows an updated landscape of the signaling pathways involving miRNAs in CVD pathology. This latest research includes data found through experimental tests and uses predictions to propose both miRNAs and genes as potential biomarkers to develop diagnostic and therapeutic methods for the early detection of CVD in the clinical setting. In addition, our pharmacological network analysis has, for the first time, shown how to use these potential biomarkers to find small molecules that may regulate them. Between the small molecules in this research, toxins, pollutants, and drugs showed outstanding interactions with these miRNAs. One of them, hesperidin, a widely prescribed drug for treating CVD and modulating the gene expression associated with CVD, was used as a reference for searching for new molecules that may interact with miRNAs involved in CVD. Among the drugs that exhibit the same miRNA expression profile as hesperidin, potential candidates include desoximetasone, curcumin, flurandrenolide, trifluridine, fludrocortisone, diflorasone, gemcitabine, floxuridine, and reversine. Further investigation of these drugs is essential to improve the treatment of cardiovascular disease. Additionally, supporting the clinical use of miRNAs as biomarkers for diagnosing and predicting CVD is crucial. Full article
Show Figures

Figure 1

Figure 1
<p>Network analysis of CVD-associated miRNAs with their expression, sources, countries of origin, and detection methods. The network depicts the interconnected structure of miRNAs derived from CVD patients, organized by their expression, sources, countries of origin, and detection methods. In the network, miRNAs are denoted as blue (upregulated) or red (downregulated) nodes, green nodes represent countries, yellow nodes represent sources, and gray nodes represent detection methods. The connections between the nodes signify the frequency of independent study reports. The three most outstanding sources were the proximal part of the significant saphenous vein tissue, vein tissues, and peripheral blood mononuclear cells. China was the country with the most available finds from miRNAs. Microarrays and RT-PCR are the most effective methods for diagnosing CVD. At least two tissues are expected to contain five specific miRNAs: miR-34a, miR-34c, miR-202-3, miR-1202, and miR-130a. The network, constructed using Cytoscape software (v.3.10.2), comprises 78 nodes and 193 edges, with a diameter and a network density of 6 and 0.106, respectively. Please refer to the <a href="#app1-jox-14-00083" class="html-app">Supplementary Materials (Figure S1)</a> for a better image resolution.</p>
Full article ">Figure 2
<p>Network analysis of miRNAs associated with CVD and their predicted targets. (<b>A</b>) The bar plot visually presents the number of targetable genes in the miRNA curated dataset. In the plot, gray bars represent upregulated genes, blue bars denote downregulated genes, and orange bars indicate genes with undetermined expression (ND). (<b>B</b>) We constructed a structural network using reported and predicted interactions between miRNAs and their targeted genes. This network consists of 1882 nodes and 5267 edges, with a diameter and a network density of 12 and 0.001. The network was created using Cytoscape software (v.3.10.2). Please refer to the <a href="#app1-jox-14-00083" class="html-app">Supplementary Materials (Figure S2)</a> for a better image resolution.</p>
Full article ">Figure 3
<p>The structural network represents the ten most connected nodes, including miRNAs and targets. Nodes are color-coded from orange to yellow based on their degree of connection, representing the most connected genes and miRNAs in the network. The most relevant nodes in this network are WNK-1, hsa-miR-106b-3p, IL1NR, hsa-miR-92a-3p, PPP6R2, hsa-miR-454-3p, PRRC2B, hsa-miR-548ac, hsa-miR-128-3p, and ADIPOQ. The network consists of 921 nodes and 1256 edges, with a diameter and a network density of 7 and 0.003, respectively. This network was created using Cytoscape software (v.3.10.2). Please refer to the <a href="#app1-jox-14-00083" class="html-app">Supplementary Materials (Figure S3)</a> for a better image resolution.</p>
Full article ">Figure 4
<p>The structural network of small molecules, phlebotonic, and miRNAs. The structural network depicts the targeted miRNAs (green nodes) between small molecules (yellow nodes) and the phlebotonic hesperidin (pink node). It was observed that curcumin exhibited the highest connection of miRNAs with the reported phlebotonic hesperidin. The network involves 246 nodes and 1153 edges, with a diameter and a network density of 6 and 0.038, respectively. The network construction utilized Cytoscape software (v.3.10.2). Please refer to the <a href="#app1-jox-14-00083" class="html-app">Supplementary Materials (Figure S4)</a> for a better image resolution.</p>
Full article ">Figure 5
<p>Structural network of hesperidin-specific miRNAs that are also altered by small molecules. This structural network depicts the miRNA profiles shared by both miRNAs that are specifically upregulated or downregulated by the reference compound hesperidin and the small molecules studied in this work. These shared miRNA profiles between hesperidin and small molecules allow the selection of potential candidates for CVD treatment. Downregulated miRNAs are shown in red, upregulated in blue, and those shared with small molecules in orange. The network involves 93 nodes and 320 edges, with a diameter and a density of 4 and 0.075, respectively. This network was created using Cytoscape software (v.3.10.2). Please refer to the <a href="#app1-jox-14-00083" class="html-app">Supplementary Materials (Figure S5)</a> for a better image resolution.</p>
Full article ">Scheme 1
<p>Flow diagram of the bibliographical screening performed for this research.</p>
Full article ">
29 pages, 3558 KiB  
Article
Genome-Wide Transcriptional Response of Avocado to Fusarium sp. Infection
by Michel Pale, Claudia-Anahí Pérez-Torres, Catalina Arenas-Huertero, Emanuel Villafán, Diana Sánchez-Rangel and Enrique Ibarra-Laclette
Plants 2024, 13(20), 2886; https://doi.org/10.3390/plants13202886 (registering DOI) - 15 Oct 2024
Abstract
The avocado crop is relevant for its economic importance and because of its unique evolutionary history. However, there is a lack of information regarding the molecular processes during the defense response against fungal pathogens. Therefore, using a genome-wide approach in this work, we [...] Read more.
The avocado crop is relevant for its economic importance and because of its unique evolutionary history. However, there is a lack of information regarding the molecular processes during the defense response against fungal pathogens. Therefore, using a genome-wide approach in this work, we investigated the transcriptional response of the Mexican horticultural race of avocado (Persea americana var. drymifolia), including miRNAs profile and their possible targets. For that, we established an avocado–Fusarium hydroponic pathosystem and studied the response for 21 days. To guarantee robustness in the analysis, first, we improved the avocado genome assembly available for this variety, resulting in 822.49 Mbp in length with 36,200 gene models. Then, using an RNA-seq approach, we identified 13,778 genes differentially expressed in response to the Fusarium infection. According to their expression profile across time, these genes can be clustered into six groups, each associated with specific biological processes. Regarding non-coding RNAs, 8 of the 57 mature miRNAs identified in the avocado genome are responsive to infection caused by Fusarium, and the analysis revealed a total of 569 target genes whose transcript could be post-transcriptionally regulated. This study represents the first research in avocados to comprehensively explore the role of miRNAs in orchestrating defense responses against Fusarium spp. Also, this work provides valuable data about the genes involved in the intricate response of the avocado during fungal infection. Full article
(This article belongs to the Special Issue Molecular Biology and Genomics of Plant-Pathogen Interactions)
Show Figures

Figure 1

Figure 1
<p>Symptoms of fusariosis in seedlings of avocado var. <span class="html-italic">drymifolia</span> at 30 dpi. Photography triptych on the left: leaf, stem, and root of uninfected plants (Control). Photography triptych on the right: leaf, stem, and root of infected seedlings. Infected plants were root inoculated with 1 × 10<sup>6</sup> water-conidia suspension; control plants were treated with sterile water.</p>
Full article ">Figure 2
<p>Schematic representation of some relevant genome metrics of avocado var. <span class="html-italic">drymifolia</span> genome. (<b>a</b>) Available assembled genome harbor 822.49 Mbp contained in a total of 7159 scaffolds. Input data from generating this new version were previously reported from Rendón-Anaya et al. [<a href="#B34-plants-13-02886" class="html-bibr">34</a>] and were downloaded from GenBank. In total, 60.77% of the whole genome sequence (totaling 822.49 Mbp) was successfully anchored to the genetic map. A pie chart was used to visualize this information. (<b>b</b>) The gene set, which was predicted in both anchored and not anchored genomic sequences, comprises a total of 36,200 genes (25,959 and 10,241, respectively). (<b>c</b>) The anchored genome sequences to the genetic map are shown in a chromosome-scale graph. (<b>d</b>) Completeness estimated based on single copy orthologs shared between flowering plants from the dicotyledon clade (n = 1375). The bar’s colors represent the classes resulting from the BUSCO assessment.</p>
Full article ">Figure 3
<p>Genes of avocado var. <span class="html-italic">drymifolia</span> identified as differentially expressed (DE) in response to <span class="html-italic">Fusarium</span> sp. infection. (<b>a</b>) Heatmap of expression profiles showing differentially expressed genes (DEGs), (<b>b</b>) Hierarchical clustering tree that shows closeness (or similarity) between the distinct sampling points included in differential expression analysis, (<b>c</b>) Venn diagram which show DEGs identified on each sampling point. In parentheses, the percentage of the total represented by those DEGs shared or not, between each sampling point.</p>
Full article ">Figure 4
<p>Clusters of DEGs formed based on their expression profiles and GO enrichment analysis, which shows the most representative functional categories for each cluster (six in total; C1–C6, respectively). (<b>a</b>) Clusters of DEGs with similar expression patterns responsive to <span class="html-italic">Fusarium</span> sp. infection. (<b>b</b>) Representative biological processes for each cluster generated).</p>
Full article ">Figure 5
<p>Main enriched hormonal processes in response to <span class="html-italic">Fusarium</span> sp. The figure shows the main phytohormones involved in the pathogenesis process and the number of genes involved in the regulated associated processes, which are biosynthesis, metabolism, transport, signaling, and regulation of SAR responses. Gray bars show those genes shared in multiple biological processes.</p>
Full article ">Figure 6
<p>DEmiRNAs responsive to <span class="html-italic">Fusarium</span> sp. infection and the biological processes (BP) regulated by them. (<b>a</b>) UpSet plot of identified DEmiRNAs representing the number of target genes associated with each of them. (<b>b</b>) Bubble plot representing the main BP in which the associated targets of each identified DEmiRNA could intervene. The figure in the (<b>b</b>) panel was generated only considering the annotated target genes.</p>
Full article ">Figure 7
<p>Involvement of DEmiRNAs in phytohormone regulation. The bubble plot illustrates the primary phytohormones and their respective roles, including biosynthesis, transport, metabolism, and involvement in SAR responses. It also indicates how the identified DEmiRNAs might intervene in these processes.</p>
Full article ">Figure 8
<p>A schematic representation of the innate immune system model by which avocado var. <span class="html-italic">drymifolia</span> seeks to counteract the <span class="html-italic">Fusarium</span> sp. infection. In avocado defense responses initiated after <span class="html-italic">Fusarium</span> sp. recognition. Genes associated with signal transduction activation, which are responsive to the recognition of elicitor molecules, such as <span class="html-italic">LYK3</span> and <span class="html-italic">RLK1</span>, were evidenced. The recognition is also mediated by <span class="html-italic">R</span> genes, exemplified by <span class="html-italic">RPP13</span>. This recognition and signaling cascades allow the accumulation of reactive oxygen species (ROS) and the activation of various transcription factors (TFs). Activation of these TFs facilitates the involvement of four main processes: microRNA expression, phenylpropanoids biosynthesis, biosynthesis and involvement of phytohormones, and expression of different genes. The main phytohormones involved in pathogenesis responses are AUX, ET, JA, SA, and ABA, the latter being the most represented in hormone-mediated signaling process. ABA can negatively regulate ET and AUX activity by suppressing genes such as <span class="html-italic">YUCCA4</span> and <span class="html-italic">EIN3</span> and intervene in JA signaling by regulating the <span class="html-italic">MYC2</span> gene. ET and JA act synergistically with the involvement of <span class="html-italic">WRKY33</span> gene. AUX, on the other hand, is involved, like phenylpropanoids biosynthesis, in root development, where AUX transporter activity is represented by ABC, PIN, and AUX. One process represented is SAR response, which is mediated by crosstalk between phytohormones such as ET, JA, and SA, in addition to the involvement of different genes considered important for optimal SAR responses, such as <span class="html-italic">ELP2</span>, <span class="html-italic">FLD</span>, <span class="html-italic">FVE</span>, and <span class="html-italic">FMO1</span>, as well as transporters like EDS5, highlighting the importance of this process as a primary response during the pathogenesis event. The regulatory involvement of microRNAs is reflected at different levels of regulation process. They can intervene in pathogen recognition regulation, as in the case of the miRNA/gene pair <span class="html-italic">miR157</span>/<span class="html-italic">LYK3</span> and <span class="html-italic">Ctg0854_RaGOO_5920</span>/<span class="html-italic">RLK1</span>. They can also be involved in phenylpropanoids biosynthesis, as in the case of <span class="html-italic">miR166</span>/<span class="html-italic">MYB4</span>, and regulate AUX activity with the action of <span class="html-italic">chr11_RaGOO_17754</span> on the <span class="html-italic">YUC5</span> gene. Both <span class="html-italic">miR166b</span> and <span class="html-italic">chr11_RaGOO_17754</span> may regulate root development. Finally, SAR may be regulated by the activity of miR166 on the <span class="html-italic">JAR1</span> gene and <span class="html-italic">chr4_RaGOO_33952</span> on the <span class="html-italic">PEN3</span> gene.</p>
Full article ">
16 pages, 3474 KiB  
Article
Quantitative Trait Locus Mapping Combined with RNA Sequencing Identified Candidate Genes for Resistance to Powdery Mildew in Bitter Gourd (Momordica charantia L.)
by Rukui Huang, Jiazuo Liang, Xixi Ju, Yuhui Huang, Xiongjuan Huang, Xiaofeng Chen, Xinglian Liu and Chengcheng Feng
Int. J. Mol. Sci. 2024, 25(20), 11080; https://doi.org/10.3390/ijms252011080 (registering DOI) - 15 Oct 2024
Abstract
Improving the powdery mildew resistance of bitter gourd is highly important for achieving high yield and high quality. To better understand the genetic basis of powdery mildew resistance in bitter gourd, this study analyzed 300 lines of recombinant inbred lines (RILs) formed by [...] Read more.
Improving the powdery mildew resistance of bitter gourd is highly important for achieving high yield and high quality. To better understand the genetic basis of powdery mildew resistance in bitter gourd, this study analyzed 300 lines of recombinant inbred lines (RILs) formed by hybridizing the powdery mildew-resistant material MC18 and the powdery mildew-susceptible material MC402. A high-density genetic map of 1222.04 cM was constructed via incorporating 1,996,505 SNPs generated by resequencing data from 180 lines, and quantitative trait locus (QTL) positioning was performed using phenotypic data at different inoculation stages. A total of seven QTLs related to powdery mildew resistance were identified on four chromosomes, among which qPm-3-1 was detected multiple times and at multiple stages after inoculation. By selecting 18 KASP markers that were evenly distributed throughout the region, 250 lines and parents were genotyped, and the interval was narrowed to 207.22 kb, which explained 13.91% of the phenotypic variation. Through RNA-seq analysis of the parents, 11,868 differentially expressed genes (DEGs) were screened. By combining genetic analysis, gene coexpression, and sequence comparison analysis of extreme materials, two candidate genes controlling powdery mildew resistance in bitter gourd were identified (evm.TU.chr3.2934 (C3H) and evm.TU.chr3.2946 (F-box-LRR)). These results represent a step forward in understanding the genetic regulatory network of powdery mildew resistance in bitter gourd and lay a molecular foundation for the genetic improvement in powdery mildew resistance. Full article
(This article belongs to the Section Molecular Plant Sciences)
Show Figures

Figure 1

Figure 1
<p>Distribution of bin markers on bitter gourd chromosomes.</p>
Full article ">Figure 2
<p>Chromosomal distribution of PM-resistant QTLs in a bitter gourd RIL population.</p>
Full article ">Figure 3
<p>Fine mapping of <span class="html-italic">qPm-3-1</span>.</p>
Full article ">Figure 4
<p>(<b>a</b>) Number of upregulated and downregulated DEGs within the materials. (<b>b</b>) Venn diagram of DEGs within the materials. (<b>c</b>) Number of upregulated and downregulated DEGs at different stages within the materials. (<b>d</b>) Venn diagram of DEGs at different stages within the materials.</p>
Full article ">Figure 5
<p>(<b>a</b>) GO enrichment analysis of all DEGs. (<b>b</b>) KEGG enrichment analysis of all DEGs.</p>
Full article ">Figure 6
<p>(<b>a</b>) Hierarchical clustering tree of genes identified via coexpression network analysis. (<b>b</b>) Heatmap of significant correlations between modules and different inoculation periods. (<b>c</b>) Gene coexpression network within the red, green, pink, and tan modules.</p>
Full article ">Figure 7
<p>(<b>a</b>) Process of identifying candidate genes in the <span class="html-italic">qPm-3-1</span> interval by combining QTL mapping, fine mapping, differential expression analysis, coexpression network, and sequence comparison analysis. (<b>b</b>) qRT–PCR detection of candidate gene expression, n = 3, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. (<b>c</b>) SNPs or Indels in <span class="html-italic">evm.TU.chr3.2934</span> between parents and extreme materials and the difference between the disease severity rates (DSRs) of the two genotypes. (<b>d</b>) SNPs or Indels in <span class="html-italic">evm.TU.chr3.2946</span> between parents and extreme materials and the difference between the DSR of the two genotypes.</p>
Full article ">
20 pages, 1646 KiB  
Article
Comparative Analysis of Hepatic Gene Expression Profiles in Murine and Porcine Sepsis Models
by Fëllanza Halimi, Tineke Vanderhaeghen, Steven Timmermans, Siska Croubels, Claude Libert and Jolien Vandewalle
Int. J. Mol. Sci. 2024, 25(20), 11079; https://doi.org/10.3390/ijms252011079 (registering DOI) - 15 Oct 2024
Abstract
Sepsis remains a huge unmet medical need for which no approved drugs, besides antibiotics, are on the market. Despite the clinical impact of sepsis, its molecular mechanism remains inadequately understood. Recent insights have shown that profound hepatic transcriptional reprogramming, leading to fatal metabolic [...] Read more.
Sepsis remains a huge unmet medical need for which no approved drugs, besides antibiotics, are on the market. Despite the clinical impact of sepsis, its molecular mechanism remains inadequately understood. Recent insights have shown that profound hepatic transcriptional reprogramming, leading to fatal metabolic abnormalities, might open a new avenue to treat sepsis. Translation of experimental results from rodents to larger animal models of higher relevance for human physiology, such as pigs, is critical and needs exploration. We performed a comparative analysis of the transcriptome profiles in murine and porcine livers using the following sepsis models: cecal ligation and puncture (CLP) in mice and fecal instillation (FI) in pigs, both of which induce polymicrobial septic peritonitis, and lipopolysaccharide (LPS)-induced endotoxemia in pigs, inducing sterile inflammation. Using bulk RNA sequencing, Metascape pathway analysis, and HOMER transcription factor motif analysis, we were able to identify key genes and pathways affected in septic livers. Conserved upregulated pathways in murine CLP and porcine LPS and FI generally comprise typical inflammatory pathways, except for ER stress, which was only found in the murine CLP model. Conserved pathways downregulated in sepsis comprise almost exclusively metabolic pathways such as monocarboxylic acid, steroid, biological oxidation, and small-molecule catabolism. Even though the upregulated inflammatory pathways were equally induced in the two porcine models, the porcine FI model more closely resembles the metabolic dysfunction observed in the CLP liver compared to the porcine LPS model. This comprehensive comparison focusing on the hepatic responses in mouse CLP versus LPS or FI in pigs shows that the two porcine sepsis models generally resemble quite well the mouse CLP model, with a typical inflammatory signature amongst the upregulated genes and metabolic dysfunction amongst the downregulated genes. The hepatic ER stress observed in the murine model could not be replicated in the porcine models. When studying metabolic dysfunction in the liver upon sepsis, the porcine FI model more closely resembles the mouse CLP model compared to the porcine LPS model. Full article
(This article belongs to the Special Issue Molecular Mechanisms and Pathophysiology of Sepsis)
Show Figures

Figure 1

Figure 1
<p>Genome-wide overview of the gene expression profile of the liver in CLP-induced polymicrobial sepsis in mice. (<b>A</b>) Male C57BL/6J mice were subjected to a sham or CLP procedure, and the livers were isolated 24 h after surgery for a genome-wide transcriptomics analysis via bulk RNA-seq. (<b>B</b>) Volcano plot depicting the differentially expressed genes (<span class="html-italic">p</span> ≤ 0.05, LFC ≤ −1 or LFC ≥ 1) affected by CLP compared to sham. Genes with a −log10 <span class="html-italic">p</span>-value exceeding 80 were excluded for improved plot clarity. (<b>C</b>,<b>D</b>) Top ten enriched gene ontology (GO) terms for genes that are upregulated (<span class="html-italic">p</span> ≤ 0.05, LFC ≥ 1) (<b>C</b>) or downregulated (<span class="html-italic">p</span> ≤ 0.05, LFC ≤ −1) (<b>D</b>) in CLP mice compared to sham controls. Analyses were performed using the Metascape analysis interface. (<b>E</b>,<b>F</b>) HOMER motif analysis of CLP-induced genes (<b>E</b>) or genes downregulated by CLP (<b>F</b>) compared to sham controls (start offset: −1 kb, end offset: 50 bp downstream TSS) (<span class="html-italic">p</span> ≤ 0.05). Enriched motifs with their name and <span class="html-italic">p</span>-value (<span class="html-italic">p</span>-val) are displayed. #mots = number of motifs found amongst the downregulated genes (absolute (relative %)); #background = number of motifs found amongst background genes (absolute (relative %)).</p>
Full article ">Figure 2
<p>Comparative analysis of the gene expression profile in the liver of CLP-induced polymicrobial sepsis in mice and FI-induced polymicrobial sepsis in pigs. (<b>A</b>) Experimental set-up. Mice were subjected to a sham or CLP procedure, and pigs were subjected to a fecal peritonitis (FI) or control procedure. Livers were isolated, respectively, 24 h and 18 h after initiation for genome-wide transcriptomics analysis using bulk RNA-seq. (<b>B</b>) Scatter plot demonstrating LFC of all differentially expressed genes in CLP mice (<span class="html-italic">p</span> ≤ 0.05, LFC ≤ −1 or LFC ≥ 1) versus the LFC of the orthologues genes in FI pigs. r = 1 depicts the slope if no difference in LFC can be observed between CLP or FI. The red dotted line indicates the real slope of the data. Data were analyzed with linear regression. (<b>C</b>) Venn diagram depicting the overlap between the upregulated DEGs in murine CLP versus porcine FI. Selected condition-specific enriched Metascape pathways and biological functions are noted. For the separate DEGs in CLP or FI, only the unique pathways and biological functions are depicted. (<b>D</b>) Venn diagram depicting the overlap between the downregulated DEGs in murine CLP versus porcine FI. Selected condition-specific enriched Metascape pathways and biological functions are noted. For the separate DEGs in CLP or FI, only the unique pathways and biological functions are depicted. (<b>E</b>) HOMER motif analysis of the shared up- and downregulated DEGs in CLP mice and FI pigs compared to their appropriate controls (start offset: −1 kb, end offset: 50 bp downstream TSS) (<span class="html-italic">p</span> ≤ 0.05 and genes with mouse orthologue). Enriched motifs with their name and <span class="html-italic">p</span>-value are depicted. #mots = number of motifs found amongst the downregulated genes (absolute (relative %)); #background = number of motifs found amongst background genes (absolute (relative %)).</p>
Full article ">Figure 3
<p>Comparative analysis of the gene expression profile in the liver of CLP-induced polymicrobial sepsis in mice and LPS-induced endotoxemia in pigs. (<b>A</b>) Experimental set-up. Mice were subjected to a sham or CLP procedure, and pigs were subjected to a LPS infusion or control procedure. Livers were isolated, respectively, 24 h and 15 h after initiation for genome-wide transcriptomics analysis using bulk RNA-seq. (<b>B</b>) Scatter plot demonstrating LFC of all differentially expressed genes in CLP mice (<span class="html-italic">p</span> ≤ 0.05, LFC ≤ −1 or LFC ≥ 1) versus the LFC of these orthologues genes in LPS pigs. r = 1 depicts the slope if no difference in LFC can be observed between CLP or LPS. The red dotted line indicates the real slope of the data. Data were analyzed with linear regression. (<b>C</b>) Venn diagram depicting the overlap between the upregulated DEGs in murine CLP versus porcine LPS. Selected condition-specific enriched Metascape pathways and biological functions are noted. For the separate DEGs in CLP or LPS, only the unique pathways and biological functions are depicted. (<b>D</b>) Venn diagram depicting the overlap between the downregulated DEGs in murine CLP versus porcine LPS. Selected condition-specific enriched Metascape pathways and biological functions are noted. For the separate DEGs in CLP or LPS, only the unique pathways and biological functions are depicted. (<b>E</b>) HOMER motif analysis of the shared up- and downregulated DEGs in murine CLP versus porcine LPS compared to their appropriate controls (start offset: −1 kb, end offset: 50 bp downstream TSS) (<span class="html-italic">p</span> ≤ 0.05 and genes with mouse orthologue). Enriched motifs with their name and <span class="html-italic">p</span>-value are depicted. #mots = number of motifs found amongst the downregulated genes (absolute (relative %)); #background = number of motifs found amongst background genes (absolute (relative %)).</p>
Full article ">Figure 4
<p>Side-by-side comparison of affected pathways based on target gene expression in the murine CLP and porcine LPS and FI model. (<b>A</b>) The top five affected pathways retrieved from the upregulated DEGs of the murine CLP model are depicted. The average LFC of genes belonging to the specified pathway is plotted for each sepsis model. (<b>B</b>) The top five affected pathways retrieved from the downregulated DEGs of the murine CLP model are depicted. The average LFC of genes belonging to the specified pathway is plotted for each sepsis model. (<b>C</b>) Heat map of selected HNF4α-dependent genes showing the LFC and significance of the specified gene compared to the respective control group for each animal model. (<b>D</b>) Heat map of selected PPARα-dependent genes showing the LFC and significance of the specified gene compared to the respective control group for each animal model. (<b>E</b>) Heat map of selected ER stress-related genes showing the LFC and significance of the specified gene compared to the respective control group for each animal model. (<b>F</b>) Heat map of selected genes related to “vascular process in circulatory system” showing the LFC and significance of the specified gene compared to the respective control group for each animal model. **** <span class="html-italic">p</span> ≤ 0.0001; *** <span class="html-italic">p</span> ≤ 0.001; ** <span class="html-italic">p</span> ≤ 0.01; * <span class="html-italic">p</span> ≤ 0.05. ns, not significant.</p>
Full article ">
16 pages, 6874 KiB  
Article
Genome-Wide Identification of the RALF Gene Family and Expression Pattern Analysis in Zea mays (L.) under Abiotic Stresses
by Baoping Xue, Zicong Liang, Yue Liu, Dongyang Li and Chang Liu
Plants 2024, 13(20), 2883; https://doi.org/10.3390/plants13202883 (registering DOI) - 15 Oct 2024
Abstract
Rapid Alkalization Factor (RALF) is a signaling molecule in plants that plays a crucial role in growth and development, reproductive processes, and responses to both biotic and abiotic stresses. Although RALF peptides have been characterized in Arabidopsis and rice, a comprehensive bioinformatics analysis [...] Read more.
Rapid Alkalization Factor (RALF) is a signaling molecule in plants that plays a crucial role in growth and development, reproductive processes, and responses to both biotic and abiotic stresses. Although RALF peptides have been characterized in Arabidopsis and rice, a comprehensive bioinformatics analysis of the ZmRALF gene family in maize is still lacking. In this study, we identified 20 RALF genes in the maize genome. Sequence alignment revealed significant structural variation among the ZmRALF family genes. Phylogenetic analysis indicates that RALF proteins from Arabidopsis, rice, and maize can be classified into four distinct clades. Duplication events suggest that the expansion of the RALF gene family in maize primarily relies on whole-genome duplication. ZmRALF genes are widely expressed across various tissues; ZmRALF1/15/18/19 are highly expressed in roots, while ZmRALF6/11/14/16 are predominantly expressed in anthers. RNA-seq and RT-qPCR demonstrated that the expression levels of ZmRALF7, ZmRALF9, and ZmRALF13 were significantly up-regulated and down-regulated in response to PEG and NaCl stresses, respectively. Overall, our study provides new insights into the role of the RALF gene family in abiotic stress. Full article
(This article belongs to the Collection Exploration and Application of Useful Agricultural Genes)
Show Figures

Figure 1

Figure 1
<p>Chromosome distribution of <span class="html-italic">ZmRALF</span> genes. The distribution of 20 <span class="html-italic">ZmRALFs</span> genes on ten maize chromosomes.</p>
Full article ">Figure 2
<p>Multiple sequence alignment of ZmRALF proteins. Multiple sequence alignment was carried out using DNAman 8. The red triangles represent cysteine residues. The scissors represent the RRXL protease recognition site. The first red box represents the RRXL cleavage site and the second red box represents the YISY conserved motif. The blue lines represent GASYY conserved motif.</p>
Full article ">Figure 3
<p>Phylogenetic tree of 133 proteins from ZmRALF, AtRALF, SiRALF, SbRALF, and OsRALF proteins. These protein sequences were aligned using MUSCLE, then a phylogenetic tree was constructed using the neighbor-joining (N-J) method with 1000 bootstraps in MEGA 7.0. The picture was created with the online tool iTOL 6.9.1 (<a href="https://itol.embl.de/" target="_blank">https://itol.embl.de/</a>, accessed on 20 July 2024). Different colors indicate different RALF subgroups.</p>
Full article ">Figure 4
<p>The gene structure and protein conserved motifs of ZmRALF. (<b>A</b>) The phylogenetic tree was constructed using MEGA 7.0 software (method: neighbor-joining; bootstrap: 1000). (<b>B</b>) The gene structure was analyzed using GFF files. (<b>C</b>) Protein conserved motifs were analyzed by MEME. (<b>D</b>) Protein sequence analysis of ZmRALF motifs.</p>
Full article ">Figure 5
<p>The duplication events analysis of <span class="html-italic">ZmRALF</span> genes. Names in red name represent tandem replication genes, while blue lines represent whole-genome duplication genes.</p>
Full article ">Figure 6
<p>Collinear gene pair analysis of <span class="html-italic">ZmRALF</span> between maize and rice and <span class="html-italic">Arabidopsis</span>. The blue line represents collinear gene pairs. The pink color represents the maize chromosome, the green represents the rice chromosome, and the purple represents the <span class="html-italic">Arabidopsis</span> chromosome.</p>
Full article ">Figure 7
<p>Analysis of cis-elements of <span class="html-italic">ZmRALF</span> gene promoter sequences. (<b>A</b>) This evolutionary tree was generated by MEGA 7.0 software (method: neighbor-joining; bootstrap: 1000). (<b>B</b>) Cis-elements in the 2 kb promoter sequences of <span class="html-italic">ZmRALF</span> genes were predicted. These cis-elements include hormone-related, abiotic and biotic-related, growth and development-related, light-related, and transcription factor binding sites-related.</p>
Full article ">Figure 8
<p>The tissue expression pattern of <span class="html-italic">ZmRALF</span> genes in different tissues including root, endosperm, leaf base, ear, embryo, anther, leaf tip, shoot, and leaf. Red and blue boxes indicate high and low expression levels of <span class="html-italic">ZmRALF</span> genes.</p>
Full article ">Figure 9
<p>The expression pattern analysis of <span class="html-italic">ZmRALF</span> under different abiotic stresses including drought, heat, salt, and cold. Red and blue boxes indicate high and low expression levels of <span class="html-italic">ZmRALF</span> genes.</p>
Full article ">Figure 10
<p>The relative expression patterns of <span class="html-italic">ZmRALF</span> genes under 20% PEG6000 and 200 mM NaCl stresses, as determined by RT-qPCR. (<b>A</b>–<b>C</b>) Expression levels of <span class="html-italic">ZmRALF7</span>, <span class="html-italic">ZmRALF9</span>, and <span class="html-italic">RALF13</span> genes in response to 20% PEG6000 treatment. (<b>D</b>–<b>F</b>) Expression levels of <span class="html-italic">ZmRALF7</span>, <span class="html-italic">ZmRALF9</span>, and <span class="html-italic">RALF13</span> genes in response to 200 mM NaCl treatment. The expression levels at subsequent time points were calculated relative to the 0 h measurement. The data include the standard error (SE) based on three replicates. Different letters indicate significant differences determined by one-way analysis of variance (ANOVA). RT-qPCR analysis utilized the <span class="html-italic">Zm00001d013367</span> gene as an internal control.</p>
Full article ">
18 pages, 30188 KiB  
Article
Intestinal Region-Dependent Impact of NFκB-Nrf Crosstalk in Myenteric Neurons and Adjacent Muscle Cells in Type 1 Diabetic Rats
by Bence Pál Barta, Benita Onhausz, Abigél Egyed-Kolumbán, Afnan AL Doghmi, János Balázs, Zita Szalai, Ágnes Ferencz, Edit Hermesz, Mária Bagyánszki and Nikolett Bódi
Biomedicines 2024, 12(10), 2347; https://doi.org/10.3390/biomedicines12102347 (registering DOI) - 15 Oct 2024
Abstract
Background/Objectives: Type 1 diabetes affects cytokines as potential inducers of NFκB signalling involved in inflammation and neuronal survival. Our goal was to assess the expression of NFκB p65 and its negative regulator, Nrf2, in myenteric neurons and adjacent smooth muscle of different gut [...] Read more.
Background/Objectives: Type 1 diabetes affects cytokines as potential inducers of NFκB signalling involved in inflammation and neuronal survival. Our goal was to assess the expression of NFκB p65 and its negative regulator, Nrf2, in myenteric neurons and adjacent smooth muscle of different gut segments after chronic hyperglycaemia and immediate insulin treatment. Methods: After ten weeks of hyperglycaemia, intestinal samples of control, streptozotocin-induced diabetic and insulin-treated diabetic rats were prepared for fluorescent immunohistochemistry, immunogold electron microscopy, ELISA and qPCR. Results: In the diabetic rats, the proportion of NFκB p65-immunoreactive myenteric neurons decreased significantly in the duodenum and increased in the ileum. The density of NFκB p65-labelling gold particles increased in the ileal but remained unchanged in the duodenal ganglia. Meanwhile, both total and nuclear Nrf2 density increased in the myenteric neurons of the diabetic duodenum. In smooth muscle, NFκB p65 and Nrf2 density increased in the small intestine of diabetic rats. While on the mRNA level, NFκB p65 and Nrf2 were induced, on the protein level, NFκB p65 increased and Nrf2 decreased in muscle/myenteric plexus homogenates. Insulin treatment had protective effects. Conclusions: Our findings reveal a segment-specific NFκB and Nrf expression in myenteric neurons and ganglionic muscular environments, which may contribute to regional neuronal survival and motility disturbances in diabetes. Full article
(This article belongs to the Special Issue Inflammation and Peripheral Nervous System)
Show Figures

Figure 1

Figure 1
<p>Relative levels of NFκB p65 mRNA. (<b>a</b>) Relative levels of NFκB p65 mRNA in tissue homogenates from different gut segments of control rats. The expression level of NFκB p65 mRNA was nearly three times higher in the colon than in the small intestinal segments of controls. Data were expressed as means ± SEM. ** <span class="html-italic">p</span> &lt; 0.01 (compare with CD); <sup>oo</sup> <span class="html-italic">p</span> &lt; 0.01 (between CI and CC). CD—control duodenum, CI—control ileum, CC—control colon. (<b>b</b>) Effects of long-lasting hyperglycaemia and insulin treatment on the relative level of NFκB p65 mRNA in tissue homogenates from different gut segments. In the diabetics, the relative level of NFκB p65 mRNA displayed a more than 3-fold increase in the tissue homogenates of the colon and ileum, which was prevented by insulin treatment. Data were expressed as means ± SEM. ** <span class="html-italic">p</span> &lt; 0.01 (compare to C); <sup>oo</sup> <span class="html-italic">p</span> &lt; 0.01 (between D and ID). C—controls, D—diabetics, ID—insulin-treated diabetics.</p>
Full article ">Figure 2
<p>Tissue levels of NFκB p65. (<b>a</b>) Tissue levels of NFκB p65 in intestinal smooth muscle/myenteric plexus homogenates from different gut segments of control rats. The tissue level of NFκB p65 displayed a significant decrease from the duodenum to the colon of controls. Data were expressed as means ± SEM. * <span class="html-italic">p</span> &lt; 0.05 (compare with CD). CD—control duodenum, CI—control ileum, CC—control colon. (<b>b</b>) Effects of long-lasting hyperglycaemia and insulin treatment on the tissue levels of NFκB p65 in smooth muscle/myenteric plexus homogenates from different gut segments. In the diabetic rats, the NFκB p65 level was doubled in the duodenum and tripled in the ileum, while it did not change in the colon. Immediate insulin treatment was completely protective against diabetes-related changes. Data were expressed as means ± SEM. * <span class="html-italic">p</span> &lt; 0.05 (compare with C); <sup>o</sup> <span class="html-italic">p</span> &lt; 0.05 (between D and ID). C—controls, D—diabetics, ID—insulin-treated diabetics.</p>
Full article ">Figure 3
<p>Representative fluorescent micrographs of whole-mount preparations of myenteric ganglia from the duodenum and ileum of control, diabetic and insulin-treated diabetic rats after NFκB p65-HuC/HuD double-labelling immunohistochemistry. HuC/HuD as a pan-neuronal marker was applied to label myenteric neurons. CD—control duodenum, CI—control ileum, DD—diabetic duodenum, DI—diabetic ileum, IDD—insulin-treated diabetic duodenum, IDI—insulin-treated diabetic ileum, arrows—NFκB p65-immunoreactive myenteric neurons. Scale bars: 20 μm.</p>
Full article ">Figure 3 Cont.
<p>Representative fluorescent micrographs of whole-mount preparations of myenteric ganglia from the duodenum and ileum of control, diabetic and insulin-treated diabetic rats after NFκB p65-HuC/HuD double-labelling immunohistochemistry. HuC/HuD as a pan-neuronal marker was applied to label myenteric neurons. CD—control duodenum, CI—control ileum, DD—diabetic duodenum, DI—diabetic ileum, IDD—insulin-treated diabetic duodenum, IDI—insulin-treated diabetic ileum, arrows—NFκB p65-immunoreactive myenteric neurons. Scale bars: 20 μm.</p>
Full article ">Figure 4
<p>Proportion of NFκB p65-immunoreactive myenteric neurons in the duodenum and ileum of control, diabetic and insulin-treated diabetic rats. In the diabetics, the proportion of NFκB p65-immunoreactive myenteric neurons was significantly decreased in the duodenum and increased in the ileum, which was prevented by immediate insulin treatment. Data were expressed as mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 (compare with C); <sup>o</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>oooo</sup> <span class="html-italic">p</span> &lt; 0.0001 (between D and ID). C—controls, D—diabetics, ID—insulin-treated diabetics.</p>
Full article ">Figure 5
<p>Representative electron micrographs of portions of the perikaryon and nuclei of myenteric neurons from ileum and intestinal smooth muscle cells from duodenum of control, diabetic and insulin-treated diabetic rats after NFκB p65 post-embedding immunohistochemistry. CD—control duodenum, CI—control ileum, DD—diabetic duodenum, DI—diabetic ileum, IDD—insulin-treated diabetic duodenum, IDI—insulin-treated diabetic ileum, arrows—18 nm gold particles’ labelling NFκB p65. Scale bars: 250 nm.</p>
Full article ">Figure 6
<p>Quantification of gold particles’ labelling NFκB p65 in myenteric ganglia (<b>a</b>) and intestinal smooth muscle (<b>b</b>) from different gut segments of control, diabetic and insulin-treated diabetic rats. The number of NFκB p65-labelling gold particles increased in the ileal myenteric ganglia and intestinal smooth muscle of both the duodenum and the ileum of diabetic animals relative to the controls, which was prevented by insulin. Data were expressed as means ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 (compare with C); <sup>o</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>oo</sup> <span class="html-italic">p</span> &lt; 0.01 (between D and ID). C—controls, D—diabetics, ID—insulin-treated diabetics.</p>
Full article ">Figure 7
<p>Relative levels of Nrf2 mRNA. (<b>a</b>) Relative levels of Nrf2 mRNA in tissue homogenates from different gut segments of control rats. The expression level of Nrf2 mRNA was multiple times higher in the colon than in the small intestinal segments of the controls. Data were expressed as means ± SEM. ** <span class="html-italic">p</span> &lt; 0.01 (compare with CD); <sup>oooo</sup> <span class="html-italic">p</span> &lt; 0.0001 (between CI and CC). CD—control duodenum, CI—control ileum, CC—control colon. (<b>b</b>) Effects of long-lasting hyperglycaemia and insulin treatment on the relative level of Nrf2 mRNA in tissue homogenates from different gut segments. In the diabetics, the relative level of Nrf2 mRNA displayed a robust increase in all segments along the duodenum–ileum–colon axis, which was prevented by insulin treatment. Data were expressed as means ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 (compare with C); <sup>oo</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>ooo</sup> <span class="html-italic">p</span> &lt; 0.001 (between D and ID). C—controls, D—diabetics, ID—insulin-treated diabetics.</p>
Full article ">Figure 8
<p>Tissue levels of Nrf2. (<b>a</b>) Tissue levels of Nrf2 in intestinal smooth muscle/myenteric plexus homogenates from different gut segments of control rats. The tissue levels of Nrf2 were significantly higher in the small intestine than the colon of the controls. Data were expressed as means ± SEM. ** <span class="html-italic">p</span> &lt; 0.01 (compare with CD); <sup>o</sup> <span class="html-italic">p</span> &lt; 0.05 (between CI and CC). CD—control duodenum, CI—control ileum, CC—control colon. (<b>b</b>) Effects of long-lasting hyperglycaemia and insulin treatment on the tissue levels of Nrf2 in smooth muscle/myenteric plexus homogenates from different gut segments. In the diabetic rats, the Nrf2 level was decreased in the duodenum and ileum, while it did not change in the colon. Immediate insulin treatment was protective against diabetes-related changes. Data were expressed as means ± SEM. ** <span class="html-italic">p</span> &lt; 0.01 (compare with C); <sup>o</sup> <span class="html-italic">p</span> &lt; 0.05 (between D and ID). C—controls, D—diabetics, ID—insulin-treated diabetics.</p>
Full article ">Figure 9
<p>Representative fluorescent micrograph of a whole-mount preparation of myenteric ganglia from the ileum of a control rat after NFκB p65-Nrf2-Peripherin triple-labelling immunohistochemistry (<b>a</b>). Pan-neuronal Peripherin was used to label myenteric neurons. Representative electron micrograph of a portion of the perikaryon and nucleus of a myenteric neuron from duodenum of a control rat after NFκB p65-Nrf2 post-embedding immunohistochemistry (<b>b</b>). Red circles—10 nm gold particles’ labelling NFκB p65, arrows—18 nm gold particles’ labelling Nrf2. CI—control ileum, CD—control duodenum. Scale bars: 20 µm (<b>a</b>), 250 nm (<b>b</b>).</p>
Full article ">Figure 10
<p>Representative electron micrographs of portions of the perikaryon and nuclei of myenteric neurons (<b>a</b>) and intestinal smooth muscle cells (<b>b</b>) from the duodenum of control and diabetic rats after Nrf2 post-embedding immunohistochemistry. CD—control duodenum, DD—diabetic duodenum. Arrows—18 nm gold particles’ labelling Nrf2 in myenteric neurons, arrowheads—18 nm gold particles’ labelling Nrf2 in muscle cells. Scale bars: 250 nm.</p>
Full article ">Figure 11
<p>Quantification of gold particles’ labelling Nrf2 in myenteric ganglia (<b>a</b>) and intestinal smooth muscle (<b>b</b>) from the duodenum of control, diabetic and insulin-treated diabetic rats. The number of Nrf2-labelling gold particles increased in the myenteric ganglia and smooth muscle of diabetic duodenum relative to controls, which was partially prevented by insulin treatment. Data were expressed as means ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 (compare with C); <sup>oo</sup> <span class="html-italic">p</span> &lt; 0.01 (between D and ID). C—controls, D—diabetics, ID—insulin-treated diabetics.</p>
Full article ">
Back to TopTop