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17 pages, 3623 KiB  
Article
Two Novel Mouse Models of Duchenne Muscular Dystrophy with Similar Dmd Exon 51 Frameshift Mutations and Varied Phenotype Severity
by Iuliia P. Baikova, Leonid A. Ilchuk, Polina D. Safonova, Ekaterina A. Varlamova, Yulia D. Okulova, Marina V. Kubekina, Anna V. Tvorogova, Daria M. Dolmatova, Zanda V. Bakaeva, Evgenia N. Kislukhina, Natalia V. Lizunova, Alexandra V. Bruter and Yulia Yu. Silaeva
Int. J. Mol. Sci. 2025, 26(1), 158; https://doi.org/10.3390/ijms26010158 - 27 Dec 2024
Abstract
Duchenne muscular dystrophy (DMD) is a severe X-linked genetic disorder caused by an array of mutations in the dystrophin gene, with the most commonly mutated regions being exons 48–55. One of the several existing approaches to treat DMD is gene therapy, based on [...] Read more.
Duchenne muscular dystrophy (DMD) is a severe X-linked genetic disorder caused by an array of mutations in the dystrophin gene, with the most commonly mutated regions being exons 48–55. One of the several existing approaches to treat DMD is gene therapy, based on alternative splicing and mutant exon skipping. Testing of such therapy requires animal models that carry mutations homologous to those found in human patients. Here, we report the generation of two genetically modified mouse lines, named “insT” and “insG”, with distinct mutations at the same position in exon 51 that lead to a frameshift, presumably causing protein truncation. Hemizygous males of both lines exhibit classical signs of muscular dystrophy in all muscle tissues except for the cardiac tissue. However, pathological changes are more pronounced in one of the lines. Membrane localization of the protein is reduced to the point of absence in one of the lines. Moreover, an increase in full-length isoform mRNA was detected in diaphragms of insG line mice. Although further work is needed to qualify these mutations as sole origins of dissimilarity, both genetically modified mouse lines are suitable models of DMD and can be used to test gene therapy based on alternative splicing. Full article
(This article belongs to the Special Issue CRISPR-Cas Systems and Genome Editing—2nd Edition)
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Figure 1

Figure 1
<p>Overall statistics of histopathology, lifespan, and <span class="html-italic">Dmd</span> expression in mutant lines (insT, blue and insG, red) and wild-type (wt, black/white) animals. (<b>A</b>) Frequency distributions of minimal Feret diameter of fibers in the three corresponding muscular tissues, error bars—95% CI, (<b>B</b>) mean coefficient of variation of minimal Feret’s diameter in different tissues, %, error bars—sd, (<b>C</b>) percentage of fibers with centrally located nuclei in sections of corresponding muscle tissues (five random animals per group × all nuclei in five sections), (<b>D</b>) diaphragm width (five sections per 25 animals in each set), (<b>E</b>) survival curves of mutant lines, (<b>F</b>) foldchange of dystrophin isoform mRNA level relative to level in wild-type animals. Wild-type sibling animals were pooled to form a single group in all panels. * <span class="html-italic">p</span> &lt; 0.03, ** <span class="html-italic">p</span> &lt; 0.002, *** <span class="html-italic">p</span> &lt; 0.0002.</p>
Full article ">Figure 2
<p>Cross-sections of diaphragms (<b>A</b>–<b>C</b>) and intercostal muscles (<b>D</b>–<b>F</b>), H&amp;E staining. (<b>A</b>,<b>D</b>)—wild type mouse; (<b>B</b>,<b>E</b>)—insT mutant mouse; (<b>C</b>,<b>F</b>)—insG mutant mouse. Black arrows—central nuclei; red arrows—fibrosis and necrotic muscle fibers; green arrow—adipose tissue. Scale bar: 100 μm.</p>
Full article ">Figure 3
<p>Cross-sections and longitudinal sections of skeletal muscles (gastrocnemius muscles as an example), H&amp;E staining. (<b>A</b>,<b>B</b>)—wild type mouse; (<b>C</b>,<b>D</b>)—insT mutant mouse; (<b>E</b>,<b>F</b>)—insG mutant mouse. Black arrows—central nuclei; red arrows—fibrosis and necrotic muscle fibers. Scale bar: 100 μm.</p>
Full article ">Figure 4
<p>Longitudinal sections of myocardium. (<b>A</b>,<b>D</b>)—wild-type mouse. (<b>B</b>,<b>E</b>)—insT mutant mouse. (<b>C</b>,<b>F</b>)—insG mutant mouse. (<b>A</b>–<b>C</b>)—Regaud’s iron hematoxylin staining, (<b>D</b>–<b>F</b>)—HBFP staining. Scale bar: 100 μm.</p>
Full article ">Figure 5
<p>Anti-N-terminal and anti-C-terminal dystrophin immunofluorescence staining on Formalin-Fixed Paraffin-Embedded tissues, in combination with Alexa Fluor<sup>®</sup> 488. Nuclear staining: Hoechst 33342. Scale bar: 50 μm.</p>
Full article ">
23 pages, 3866 KiB  
Article
Effects of Marquandomyces marquandii SGSF043 on the Germination Activity of Chinese Cabbage Seeds: Evidence from Phenotypic Indicators, Stress Resistance Indicators, Hormones and Functional Genes
by Xu Zheng, Yuxia Huang, Xinpeng Lin, Yuanlong Chen, Haiyan Fu, Chunguang Liu, Dong Chu and Fengshan Yang
Plants 2025, 14(1), 58; https://doi.org/10.3390/plants14010058 - 27 Dec 2024
Abstract
In this study, the effect of Metarhizium spp. M. marquandii on the seed germination of cabbage, a cruciferous crop, was investigated. The effects of this strain on the seed germination vigor, bud growth and physiological characteristics of Chinese cabbage were analyzed by a [...] Read more.
In this study, the effect of Metarhizium spp. M. marquandii on the seed germination of cabbage, a cruciferous crop, was investigated. The effects of this strain on the seed germination vigor, bud growth and physiological characteristics of Chinese cabbage were analyzed by a seed coating method. The results showed the following: (1) The coating agent M. marquandii SGSF043 could significantly improve the germination activity of Chinese cabbage seeds. (2) The strain concentration in the seed coating agent had different degrees of regulation on the antioxidase system of the buds, indicating that it could activate the antioxidant system and improve the antioxidant ability of the buds. (3) When the concentration of M. marquandii SGSF043 was 5.6 × 106 CFU/mL (average per grain), the effect of M. marquandii SGSF043 on the leaf hormones Indole Acetic Acid (IAA), Gibberellic Acid (GA) and Abscisic Acid (ABA) of Chinese cabbage seedlings was significantly higher than that of other treatment groups, indicating that the strain could optimize the level of plant hormones. (4) M. marquandii SGSF043 could induce the expression of stress-resistance-related genes in different tissue parts of Chinese cabbage and improve the growth-promoting stress resistance of buds. This study showed that M. marquandii SGSF043 could not only improve the germination vitality of Chinese cabbage seeds but also enhance the immunity of young buds. The results provide a theoretical basis for the application potential of Metarhizium marquandii in agricultural production. Full article
(This article belongs to the Section Plant Protection and Biotic Interactions)
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<p>The whole process of cabbage seed germination. (<b>A</b>) and the effect of <span class="html-italic">M. marquandii</span> SGSF043 on germination of cabbage (<b>B</b>–<b>D</b>). Note: (<b>A</b>) (<b>a</b>) The imbibition stage, (<b>b</b>) the germination stage, (<b>c</b>,<b>d</b>) the germination stage, (<b>e</b>) the budding stage. (<b>B</b>–<b>D</b>) LM: the coating treatment of <span class="html-italic">M. marquandii</span> SGSF043 with 1 × 10<sup>6</sup> CFU/mL; MM: the coating treatment of <span class="html-italic">M. marquandii</span> SGSF043 with 1 × 10<sup>7</sup> CFU/mL; HM: the coating treatment of <span class="html-italic">M. marquandii</span> SGSF043 with 1 × 10<sup>8</sup> CFU/mL. Control: PDB liquid medium was coated with blank control. * indicates a difference between different inoculation treatments (** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001). a–d represent significant differences between different treatment groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 2
<p>Effect of <span class="html-italic">M. marquandii</span> SGSF043 on secondary roots and the seedling biomass of the cabbage after 96 h. (<b>A</b>) Stem diameter of buds. (<b>B</b>) Number of secondary roots. (<b>C</b>) Root-shoot ratio (Aeria part weight/Root weight). (<b>D</b>) Total fresh weight of buds. (<b>E</b>) Fresh weight of aerial parts of buds. (<b>F</b>) Fresh weight of roots of buds. The result is the mean ± standard deviation (<span class="html-italic">n</span> = 30). * indicates a difference between different inoculation treatments (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001). (<b>G</b>) Correlation heat map of each index. BH is the bud height, BL is bud root length, ST is stem diameter, SRN is number of secondary roots, AFW is above-ground fresh weight, UFW is below-ground fresh weight, TF is total fresh weight and RTR is root–shoot ratio (below-ground dry weight/above-ground dry weight). The result is the mean ± standard deviation (<span class="html-italic">n</span> = 30). * indicates a difference between different inoculation treatments (* <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.001).</p>
Full article ">Figure 3
<p>Effect of <span class="html-italic">M. marquandii</span> SGSF043 on chlorophyll, MDA contents and antioxidant enzyme activities of the cabbage buds after 96 h. The result is the mean ± standard deviation of three repeats (<span class="html-italic">n</span> = 3). (<b>A</b>) ChlorophyII content of leaves in different treatment groups. (<b>B</b>) MDA content in the aerial parts and roots of buds. (<b>C</b>) SOD activity in the aerial parts and roots of buds. (<b>D</b>) POD activity in the aerial parts and roots of buds. (<b>E</b>) CAT activity in the aerial parts and roots of buds. a–d represent significant differences between different treatment groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 4
<p>Effect of <span class="html-italic">M. marquandii</span> SGSF043 on the seedling phytohormone of the cabbage after 96 h. The result is the mean ± standard deviation (<span class="html-italic">n</span> = 3). (<b>A</b>) Spermidine content in the aerial parts and roots of buds. (<b>B</b>) GA content in the aerial parts and roots of buds. (<b>C</b>) JA content in the aerial parts and roots of buds. (<b>D</b>) IAA content in the aerial parts and roots of buds. (<b>E</b>) ABA content in the aerial parts and roots of buds. a–c represent significant differences between different treatment groups (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 5
<p>Expression of functional genes related to Chinese cabbage after 96 h. The result is the mean ± standard deviation of three repeats (<span class="html-italic">n</span> = 3). (CK) Expression of genes in control group without any treatment. (HM) Expression of genes in high concentration treatment group with <span class="html-italic">M. marquandii</span> SGSF043 at a concentration of 10<sup>8</sup> CFU/mL. * indicates a difference between different inoculation treatments when the same gene is treated (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001).</p>
Full article ">Figure 6
<p>Principal component analysis of cabbage sprouts after 96 h.</p>
Full article ">Figure 7
<p>Preparation and budding process of coating agent <span class="html-italic">M. marquandii</span> SGSF043.</p>
Full article ">
6 pages, 956 KiB  
Communication
OsBBX2 Delays Flowering by Repressing Hd3a Expression Under Long-Day Conditions in Rice
by Yusi Yang, Jiaming Wei, Xiaojie Tian, Changhua Liu, Xiufeng Li and Qingyun Bu
Plants 2025, 14(1), 48; https://doi.org/10.3390/plants14010048 - 27 Dec 2024
Viewed by 53
Abstract
Members of the B-Box (BBX) family of proteins play crucial roles in the growth and development of rice. Here, we identified a rice BBX protein, Oryza sativa BBX2 (OsBBX2), which exhibits the highest expression in the root. The transcription of OsBBX2 follows a [...] Read more.
Members of the B-Box (BBX) family of proteins play crucial roles in the growth and development of rice. Here, we identified a rice BBX protein, Oryza sativa BBX2 (OsBBX2), which exhibits the highest expression in the root. The transcription of OsBBX2 follows a diurnal rhythm under photoperiodic conditions, peaking at dawn. Functional analysis revealed that OsBBX2 possesses transcriptional repression activity. The BBX2 was overexpressed in the rice japonica cultivar Longjing 11 (LJ11), in which Ghd7 and PRR37 were non-functional or exhibited weak functionality. The overexpression of OsBBX2 (OsBBX2 OE) resulted in a delayed heading date under a long-day (LD) condition, whereas the bbx2 mutant exhibited flowering patterns similar to the wild type (WT). Additionally, transcripts of Ehd1, Hd3a, and RFT1 were downregulated in the OsBBX2 OE lines under the LD condition. OsBBX2 interacted with Hd1 (BBX18), and the bbx2 hd1 double mutant displayed a late flowering phenotype comparable to that of hd1. Furthermore, OsBBX2 enhanced the transcriptional repression of Hd3a through its interaction with Hd1, as demonstrated in the protoplast-based assay. Taken together, these findings suggest that the OsBBX2 delays flowering by interacting with Hd1 and co-repressing Hd3a transcription. Full article
(This article belongs to the Special Issue Crop Functional Genomics and Biological Breeding)
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<p><span class="html-italic">OsBBX2</span> delays flowering by repressing <span class="html-italic">Hd3a</span> expression. (<b>A</b>) Representative image of LJ11 and <span class="html-italic">BBX OE</span> plants grown under LD conditions at the heading stage. (<b>B</b>) Flowering time of LJ11 and <span class="html-italic">BBX OE</span> under LD conditions. Data are means ± standard error (SE; <span class="html-italic">n</span> = 20). <span class="html-italic">p</span> values were calculated by Student’s <span class="html-italic">t</span> test compared to LJ11; **: <span class="html-italic">p</span> &lt; 0.01. (<b>C</b>) Schematic diagrams of the reporter plasmids used in the rice protoplast transient assay. REN, Renilla luciferase; LUC, firefly luciferase. (<b>D</b>) The LUC activity in rice protoplasts with indicated reporter plasmids. Data are means ± SE (<span class="html-italic">n</span> = 3). Statistically significant differences are indicated by different lowercase letters (<span class="html-italic">p</span> &lt; 0.05, one-way ANOVA with Tukey’s significant difference test). (<b>E</b>) The yeast two-hybrid assay showed that BBX2 interacts with Hd1. Yeast grew at 30 °C for 3 days. Empty vectors were used as the negative controls. AD, activation domain. BD, binding domain. (<b>F</b>) The LCI assay of the <span class="html-italic">BBX2</span> interaction with <span class="html-italic">Hd1</span> in <span class="html-italic">N. benthamiana</span> leaves. The co-transformation of cLUC-<span class="html-italic">Hd1</span> and nLUC-<span class="html-italic">BBX2</span> led to the re-constitution of the LUC signal, whereas no signal was detected when cLUC-<span class="html-italic">Hd1</span> and nLUC, cLUC and nLUC-<span class="html-italic">BBX2</span>, and cLUC and nLUC were co-expressed. In each experiment, at least five independent <span class="html-italic">N. benthamiana</span> leaves were infiltrated and analyzed. (<b>G</b>–<b>I</b>) A representative image of LJ11, <span class="html-italic">bbx2</span> (<b>G</b>), <span class="html-italic">hd1</span> (<b>H</b>), and <span class="html-italic">bbx2 hd1</span> (<b>I</b>) mutants grown under the LD condition at the heading stage. (<b>J</b>) The flowering time of LJ11, <span class="html-italic">bbx2</span>, <span class="html-italic">hd1</span>, and <span class="html-italic">bbx2 hd1</span> mutants under LD conditions. Data are means ± standard error (SE; <span class="html-italic">n</span> = 20). Statistically significant differences are indicated by different lowercase letters (<span class="html-italic">p</span> &lt; 0.05, one-way ANOVA with Tukey’s significant difference test). (<b>K</b>) Schematic diagrams of the reporter plasmids used in the rice protoplast transient assay. <span class="html-italic">35S<sub>Pro</sub>:GFP</span> was used as the control and <span class="html-italic">35S<sub>Pro</sub>:BBX2</span>, <span class="html-italic">35S<sub>Pro</sub>:Hd1</span>, and <span class="html-italic">Hd3a<sub>Pro</sub>:LUC</span> were used as the effectors and reporters. (<b>L</b>) Relative LUC activity expressed with reporters and effectors. The expression level of Renilla (REN) was used as an internal control. The LUC/REN ratio represents the relative activity of the <span class="html-italic">Hd3a</span> promoter. Data are shown as means ± SE (<span class="html-italic">n</span> = 3). Statistically significant differences are indicated by different lowercase letters (<span class="html-italic">p</span> &lt; 0.05, one-way ANOVA with Tukey’s significant difference test).</p>
Full article ">
9 pages, 232 KiB  
Communication
Bacterial Multiresistance and Microbial Diversity of Milk Received by a University Hospital Milk Bank
by Dayane da Silva Zanini, Benedito Donizete Menozzi, Wanderson Sirley Reis Teixeira, Felipe Fornazari, Gismelli Cristiane Angeluci, Raquel Cuba Gaspar, Lucas Franco Miranda Ribeiro, Carlos Eduardo Fidelis, Marcos Veiga dos Santos, Juliano Gonçalves Pereira and Helio Langoni
Microorganisms 2025, 13(1), 28; https://doi.org/10.3390/microorganisms13010028 - 27 Dec 2024
Viewed by 81
Abstract
Breastfeeding is fundamental for the development and protection of the newborn, and microorganisms present in breast milk are associated with the development of the infant’s intestinal microbiota. However, there are factors that interfere with breastfeeding, resulting in the need to supply donated milk [...] Read more.
Breastfeeding is fundamental for the development and protection of the newborn, and microorganisms present in breast milk are associated with the development of the infant’s intestinal microbiota. However, there are factors that interfere with breastfeeding, resulting in the need to supply donated milk to milk banks for these children. Even though there is a restriction on medications prescribed for pregnant and breastfeeding women, some antimicrobials are accepted, as long as they are used correctly and as they can increase the selection pressure for resistant bacteria. The microorganisms present in breast milk from a human milk bank were evaluated and the resistance of the isolates to antimicrobials was phenotypically characterized. In total, 184 microbial isolates were identified by mass spectrometry, of 12 bacterial genera and 1 yeast genus. There was a high prevalence of bacteria of the genus Staphylococcus, mainly S. epidermidis (33%). Resistance to antimicrobials varied among species, with a higher percentage of isolates resistant to penicillins and macrolides. Multidrug resistance was identified in 12.6% of 143 isolates. Breast milk contains a wide variety of microorganisms, mainly those of the Staphylococcus and Enterobacter genera. There was a high percentage of resistant isolates, and multidrug resistance in Klebsiella oxytoca (66.7%; 4/6) and S. epidermidis (15.0%; 9/60) isolates, which increases the public health concern. Full article
(This article belongs to the Section Food Microbiology)
24 pages, 3622 KiB  
Article
Antimicrobial Resistance of Waste Water Microbiome in an Urban Waste Water Treatment Plant
by Zvezdimira Tsvetanova and Rosen Boshnakov
Water 2025, 17(1), 39; https://doi.org/10.3390/w17010039 - 27 Dec 2024
Viewed by 123
Abstract
Waste water treatment plants (WWTP) are considered as a hotspot for the acquisition and dissemination of antimicrobial resistance (AMR). The present study aimed to assess the AMR rate of the waste water microbiome in a selected WWTP and the treatment efficiency. Culture-dependent and [...] Read more.
Waste water treatment plants (WWTP) are considered as a hotspot for the acquisition and dissemination of antimicrobial resistance (AMR). The present study aimed to assess the AMR rate of the waste water microbiome in a selected WWTP and the treatment efficiency. Culture-dependent and PCR methods were used in the AMR study of raw and treated waste water (TWW) microbiomes. The population proportion of heterotrophic plate count (HPC) bacteria resistant to five antibiotic classes was assessed, as well as the AMR phenotype of a total of 238 Enterobacteriaceae and 259 Enterococcus spp. strains. Waste water treatment increased tetracycline- and ciprofloxacin-resistant bacteria by 67% and 61%, as well as the incidence of Enterobacteriaceae resistant to ciprofloxacin, co-trimoxazole, and cephalosporins. Multiple resistance increased, and 8.8% of TWW isolates exhibited an ESBL-producing phenotype, most often encoded by blaTEM and blaCTX-M genes. The most common resistance among Enterococcus spp. was to erythromycin and tetracycline, and despite the increased AMR rate among TWW isolates, only the increase in tetracycline resistance and the decrease in high-level gentamicin resistance were significant. All parameters analysed demonstrated limited removal of resistant HPC or faecal indicator bacteria in the studied WWTP and a positive selective effect towards some of them, most often to ciprofloxacin. Full article
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<p>Relative population proportion of the HPC bacteria resistant to the tested antibiotics in untreated (UWW) and treated (TWW) wastewater. Antibiotics: <span class="html-italic">Amp</span>—ampicillin; <span class="html-italic">TE</span>—tetracycline; <span class="html-italic">C</span>—chloramphenicol; <span class="html-italic">CIP</span>—ciprofloxacin; <span class="html-italic">Sul</span>—sulfamethoxazole.</p>
Full article ">Figure 2
<p>Dynamics of resistant HPC bacteria in the studied WWTP: (<b>a</b>) in the untreated wastewater (UWW); (<b>b</b>) in the treated wastewater (TWW). Antibiotics: <span class="html-italic">Amp</span>—ampicillin; <span class="html-italic">TE</span>—tetracycline; <span class="html-italic">C</span>—chloramphenicol; <span class="html-italic">CIP</span>—ciprofloxacin; <span class="html-italic">Sul</span>—sulfamethoxazole; *—significant difference between UWW and TWW values (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 3
<p>AMR phenotypes of <span class="html-italic">Enterobacteriaceae</span>, isolated from untreated wastewater (UWW) and treated wastewater (TWW). Tested antibiotics: <span class="html-italic">Amp</span>—ampicillin; <span class="html-italic">AUG</span>—amoxicillin/clavulanic acid; <span class="html-italic">Pi</span>—piperacillin; <span class="html-italic">PTZ</span>—piperacillin/tazobactam; <span class="html-italic">CTX</span>—cefotaxime; <span class="html-italic">CTR</span>—ceftriaxone; <span class="html-italic">CPM</span>—cefepime; <span class="html-italic">Cx</span>—cefoxitin; <span class="html-italic">CIP</span>—ciprofloxacin; <span class="html-italic">C</span>—chloramphenicol; <span class="html-italic">COT</span>—co-trimoxazole; <span class="html-italic">GEN</span>—gentamicin; <span class="html-italic">IMI</span>—imipenem; <span class="html-italic">S</span>—streptomycin; <span class="html-italic">TE</span>—tetracycline.</p>
Full article ">Figure 4
<p>AMR phenotype of <span class="html-italic">Enterobacteriaceae</span> isolated from waste water: (<b>a</b>) untreated, UWW or (<b>b</b>) treated, TWW. AMR-1 or AMR-2—expressed resistant phenotype to one or two classes of antibiotics; MAR-3 to MAR-6—multiple resistance towards the specified number of AB classes.</p>
Full article ">Figure 5
<p>AMR phenotype of ESBL-producing <span class="html-italic">Enterobacteriaceae</span> strains, isolated from untreated (UWW) and treated (TWW) waste water. Tested antibiotics: <span class="html-italic">Amp</span>—ampicillin; <span class="html-italic">AUG</span>—amoxicillin/clavulanic acid; <span class="html-italic">CAZ</span>—ceftazidime; <span class="html-italic">CTX</span>—cefotaxime; <span class="html-italic">CPM</span>—cefepime; <span class="html-italic">Cx</span>—cefoxitin; <span class="html-italic">CIP</span>—ciprofloxacin; <span class="html-italic">C</span>—chloramphenicol; <span class="html-italic">COT</span>—co-trimoxazole; <span class="html-italic">GEN</span>—gentamicin; <span class="html-italic">IMI</span>—imipenem; <span class="html-italic">MEM</span>—meropenem; <span class="html-italic">MOX</span>—moxifloxacin; <span class="html-italic">NA</span>—nalidixic acid; <span class="html-italic">TE</span>—tetracycline.</p>
Full article ">Figure 6
<p>AMR of ESBL-producing <span class="html-italic">Enterobacteriaceae</span> isolates from untreated (<b>a</b>) and treated (<b>b</b>) wastewater.</p>
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<p>Prevalence of AMR among <span class="html-italic">Enterococcus</span> spp. isolated from treated (TWW) and untreated (UWW) waste water. Tested antibiotics: <span class="html-italic">E</span>—erythromycin; <span class="html-italic">Amp</span>—ampicillin; <span class="html-italic">IMI</span>—imipenem; <span class="html-italic">CIP</span>—ciprofloxacin; <span class="html-italic">C</span>—chloramphenicol; <span class="html-italic">COT</span>—co-trimoxazole; <span class="html-italic">HLG</span>—high level gentamicin; <span class="html-italic">TE</span>—tetracycline; <span class="html-italic">TEI</span>—teicoplanin; <span class="html-italic">TGC</span>—tigecycline; <span class="html-italic">LZ</span>—linezolid; <span class="html-italic">VA</span>—vancomycin; *—significant difference (<span class="html-italic">p</span> &lt; 0.05) by Fisher’s exact test.</p>
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<p>Prevalence of AMR among enterococcal isolates from untreated waste water (<b>a</b>) and treated waste water (<b>b</b>). MAR—multiple resistance to three or more classes of ABs.</p>
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<p>Incidence of the examined antibiotic resistance genes (ARGs) among the isolates from both wastewater types. UWW—untreated waste water; TWW—treated waste water.</p>
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13 pages, 1725 KiB  
Article
Intra-Cardiac Kinetic Energy and Ventricular Flow Analysis in Bicuspid Aortic Valve: Impact on Left Ventricular Function, Dilation Severity, and Surgical Referral
by Ali Fatehi Hassanabad and Julio Garcia
Fluids 2025, 10(1), 5; https://doi.org/10.3390/fluids10010005 - 27 Dec 2024
Viewed by 136
Abstract
Intra-cardiac kinetic energy (KE) and ventricular flow analysis (VFA), as derived from 4D-flow MRI, can be used to understand the physiological burden placed on the left ventricle (LV) due to bicuspid aortic valve (BAV). Our hypothesis was that the KE of each VFA [...] Read more.
Intra-cardiac kinetic energy (KE) and ventricular flow analysis (VFA), as derived from 4D-flow MRI, can be used to understand the physiological burden placed on the left ventricle (LV) due to bicuspid aortic valve (BAV). Our hypothesis was that the KE of each VFA component would impact the surgical referral outcome depending on LV function decrement, BAV phenotype, and aortic dilation severity. A total of 11 healthy controls and 49 BAV patients were recruited. All subjects underwent cardiac magnetic resonance imaging (MRI) examination. The LV mass was inferior in the controls than in the BAV patients (90 ± 26 g vs. 45 ± 17 g, p = 0.025), as well as the inferior ascending aorta diameter indexed (15.8 ± 2.5 mm/m2 vs. 19.3 ± 3.5 mm/m2, p = 0.005). The VFA KE was higher in the BAV group; significant increments were found for the maximum KE and mean KE in the VFA components (p < 0.05). A total of 14 BAV subjects underwent surgery after the scans. When comparing BAV nonsurgery vs. surgery-referred cohorts, the maximum KE and mean KE were elevated (p < 0.05). The maximum and mean KE were also associated with surgical referral (r = 0.438, p = 0.002 and r = 0.371, p = 0.009, respectively). In conclusion, the KE from VFA components significantly increased in BAV patients, including in BAV patients undergoing surgery. Full article
(This article belongs to the Special Issue Recent Advances in Cardiovascular Flows)
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<p>Acquisition and analysis of 4D-flow MRI. (<b>a</b>) Acquisition planning of 4D-flow MRI requires a respiratory navigator and whole-heart coverage using sagittal planes. (<b>b</b>) Valve tracking of the aortic and mitral valves requires standard three-chamber acquisitions for automated detection and tracking of the valves. A co-registration to the 4D-flow volume facilitates with plane location for flow component analysis. (<b>c</b>) Illustration of the visualization of intra-cardiac pathlines used for the assessment of ventricular flow analysis and its corresponding components.</p>
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<p>Correlation of maximum kinetic energy for delayed ejection (Max KE DE). LVEDVi: left ventricular end-diastolic volume indexed; LVESVi: left ventricular end-systolic volume indexed.</p>
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<p>Correlations of mean kinetic energy (KE) for ventricular flow components with left ventricular end-diastolic volume indexed (LVEDVi). (<b>a</b>) Scatter plot showing the correlation of mean KE from direct flow (DF) and LVEDVi; (<b>b</b>) Scatter plot showing the correlation of mean KE from delayed ejection flow (DE) and LVEDVi; (<b>c</b>) Scatter plot showing the correlation of mean KE from retained inflow (RI) and LVEDVi; (<b>d</b>) Scatter plot showing the correlation of mean KE from residual volume (RV) and LVEDVi.</p>
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<p>Correlations of mean kinetic energy (KE) for ventricular flow components with left ventricular end-systolic volume indexed (LVESVi). (<b>a</b>) Scatter plot showing the correlation of mean KE from direct flow (DF) and LVESVi; (<b>b</b>) Scatter plot showing the correlation of mean KE from delayed ejection flow (DE) and LVESVi; (<b>c</b>) Scatter plot showing the correlation of mean KE from retained inflow (RI) and LVESVi; (<b>d</b>) Scatter plot showing the correlation of mean KE from residual volume (RV) and LVESVi.</p>
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17 pages, 7728 KiB  
Article
Evaluation of Lonicera caerulea Cultivar Diversity Based on Phenotypic Traits and Nutrient Composition
by Yu Qi, Chunming Li, Xueying Liang, Anqi Chen, Guimei Zhao, Hui Bai, Haixia Li, Zhaoning Wang, Wenzhe Han, Yuandong Ma, Linping Tian, Yanmin Wang and Huanzhen Liu
Forests 2025, 16(1), 25; https://doi.org/10.3390/f16010025 - 26 Dec 2024
Viewed by 166
Abstract
Lonicera caerulea L. has high nutritional and health value, and it is an important emerging small berry tree species. In this study, the morphology and nutrient composition of 60 cultivars were used to analyze and evaluate the diversity of the genus. Morphological analysis [...] Read more.
Lonicera caerulea L. has high nutritional and health value, and it is an important emerging small berry tree species. In this study, the morphology and nutrient composition of 60 cultivars were used to analyze and evaluate the diversity of the genus. Morphological analysis showed that the phenotypic traits of different cultivars had significant differences (p < 0.01). The phenotypic coefficient of variation (PCV) of each trait was 12.42%~84.06%, and the coefficient of genetic variation (GCV) was between 7.07%~71.72%. The analysis of nutrient content showed significant differences among the cultivars (p < 0.01). The PCV of each trait was 3.95%~96.10%, and the GCV was 0.13%~32.83%. Based on breeding objectives, cultivars with excellent growth and leaf quantitative traits, fruit quantitative traits and nutrient contents were selected through the method of comprehensive analysis of multiple characters. Traits of the selected varieties were all above average, and specific genetic gain was higher. At the same time, the selection of varieties was carried out according to flowering and fruiting phenology, which provided an indication for the breeding of improved varieties. In this study, growth, leaf and fruit quantitative traits, phenological period and nutrient components of different cultivars provided valuable information for the breeding of improved varieties. Full article
(This article belongs to the Section Forest Biodiversity)
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<p>Correlation analysis of phenotypic traits of different <span class="html-italic">Lonicera caerulea</span> cultivars; description of traits is the same as in <a href="#forests-16-00025-t002" class="html-table">Table 2</a>. * is significantly correlated, ** is very significant correlation.</p>
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<p>Correlation between phenology and fruit quantity traits of different cultivars. For phenological traits, please refer to <a href="#forests-16-00025-t001" class="html-table">Table 1</a>. * is significantly correlated, ** is very significant correlation.</p>
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<p>Correlation analysis of phenology and nutrient composition of fruit of different cultivars; note: SSD, soluble solids; SSR, soluble sugar; TAD, titratable acid; SAR, the sugar–acid ratio; TPL, total phenols; FLA, a flavonoid; ATD, anthocyanidin. For phenological traits, please refer to <a href="#forests-16-00025-t001" class="html-table">Table 1</a>. * is significantly correlated, ** is very significant correlation.</p>
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<p>Cluster analysis of different cultivars carried out based on three growth traits.</p>
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<p>Cluster analysis of different cultivars of <span class="html-italic">Lonicera caerulea</span> based on five quantitative leaf traits.</p>
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<p>Cluster analysis of different cultivars of <span class="html-italic">Lonicera caerulea</span> based on four fruit quantity traits.</p>
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<p>Cluster analysis of <span class="html-italic">Lonicera caerulea</span> cultivars based on seven nutrients.</p>
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<p>Evaluation of multiple traits and the trait gain of the selected groups. The trait parameters are the same as above. The percentages in the graph are genetic gains. (<b>A</b>) shows the genetic gain of growth traits, (<b>B</b>,<b>C</b>) show the genetic gain of leaf traits of the selected varieties, (<b>D</b>,<b>E</b>) show the genetic gain of fruit phenotypic traits of the selected varieties, and (<b>F</b>–<b>H</b>) show the genetic gain of the nutrients of the selected varieties.</p>
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<p>Cultivars were screened based on flowering and fruiting phenology. Note: Horizontal dashed lines in A and C are averages of each phenological stage; the vertical dashed line represents the selected cultivars according to the breeding objectives and the time of each phenological period. The ordinate numbers in (<b>A</b>,<b>B</b>) refer to the period from 1 May to 31 May. The numbers in (<b>C</b>,<b>D</b>) refer to the period from 1 May to 30 June.</p>
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12 pages, 1306 KiB  
Article
Sleep Breathing Disorders’ Screening Among Children Approaching Orthodontic Evaluation: A Preliminary Study
by Marco Storari, Francesca Stramandinoli, Maurizio Ledda, Alberto Verlato, Alessio Verdecchia and Enrico Spinas
Appl. Sci. 2025, 15(1), 101; https://doi.org/10.3390/app15010101 - 26 Dec 2024
Viewed by 264
Abstract
Background: The orthodontist can play an important role in the early detection of sleep-disordered breathing (SDB), aiding in the prevention of dentoskeletal complications and systemic issues. Early intervention supports proper pediatric development, emphasizing the need for SDB screening in orthodontics. SDB involves abnormal [...] Read more.
Background: The orthodontist can play an important role in the early detection of sleep-disordered breathing (SDB), aiding in the prevention of dentoskeletal complications and systemic issues. Early intervention supports proper pediatric development, emphasizing the need for SDB screening in orthodontics. SDB involves abnormal breathing during sleep, with obstructive sleep apnea (OSA) in children presenting unique diagnostic challenges compared to adults. Aim: This study aimed to identify children at risk for SDB through a validated screening questionnaire during orthodontic evaluations. Methods: This prospective study recruited children under 12 years of age between July 2023 and July 2024. The Sleep Clinical Record was used to screen for SDB indicators. Results: Among the 48 participants (31 females, 17 males) aged 5–12 years, 69% were identified as being at risk for SDB. Risk factors included oral breathing, nasal obstruction, tonsillar hypertrophy, malocclusion, high Friedman scores, narrow palates, and positive Brouillette phenotypes, all showing significant correlations (p < 0.05). Conclusion: The findings underline the critical importance of early SDB screening in orthodontic settings. These preliminary results encourage further research on larger cohorts to refine diagnostic tools and interventions. Early recognition and management of SDB can significantly enhance systemic health and craniofacial outcomes in pediatric patients. Full article
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<p>Sleep Clinical Record, the questionnaire used in this preliminary prospective trial. The Sleep Clinical Record consists of three items: physical examination, subjective symptoms, and clinical history.</p>
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<p>Kruskal–Wallis equality-of-populations rank test. Oral breathing, usual nasal obstruction, tonsillar hypertrophy, malocclusion, Friedman score, narrow palate, facial phenotype, and positive Brouillette score are statistically significant.</p>
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17 pages, 2980 KiB  
Article
Mapping and Validation of Quantitative Trait Loci on Yield-Related Traits Using Bi-Parental Recombinant Inbred Lines and Reciprocal Single-Segment Substitution Lines in Rice (Oryza sativa L.)
by Ghulam Ali Manzoor, Changbin Yin, Luyan Zhang and Jiankang Wang
Plants 2025, 14(1), 43; https://doi.org/10.3390/plants14010043 - 26 Dec 2024
Viewed by 167
Abstract
Yield-related traits have higher heritability and lower genotype-by-environment interaction, making them more suitable for genetic studies in comparison with the yield per se. Different populations have been developed and employed in QTL mapping; however, the use of reciprocal SSSLs is limited. In this [...] Read more.
Yield-related traits have higher heritability and lower genotype-by-environment interaction, making them more suitable for genetic studies in comparison with the yield per se. Different populations have been developed and employed in QTL mapping; however, the use of reciprocal SSSLs is limited. In this study, three kinds of bi-parental populations were used to investigate the stable and novel QTLs on six yield-related traits, i.e., plant height (PH), heading date (HD), thousand-grain weight (TGW), effective tiller number (ETN), number of spikelets per panicle (NSP), and seed set percentage (SS). Two parental lines, i.e., japonica Asominori and indica IR24, their recombinant inbred lines (RILs), and reciprocal single-segment substitution lines (SSSLs), i.e., AIS and IAS, were genotyped by SSR markers and phenotyped in four environments with two replications. Broad-sense heritability of the six traits ranged from 0.67 to 0.94, indicating their suitability for QTL mapping. In the RIL population, 18 stable QTLs were identified for the six traits, 4 for PH, 6 for HD, 5 for TGW, and 1 each for ETN, NSP, and SS. Eight of them were validated by the AIS and IAS populations. The results indicated that the allele from IR24 increased PH, and the alternative allele from Asominori reduced PH at qPH3-1. AIS18, AIS19, and AIS20 were identified to be the donor parents which can be used to increase PH in japonica rice; on the other hand, IAS14 and IAS15 can be used to reduce PH in indica rice. The allele from IR24 delayed HD, and the alternative allele reduced HD at qHD3-1. AIS14 and AIS15 were identified to be the donor parents which can be used to delay HD in japonica rice; IAS13 and IAS14 can be used to reduce HD in indica rice. Reciprocal SSSLs not only are the ideal genetic materials for QTL validation, but also provide the opportunity for fine mapping and gene cloning of the validated QTLs. Full article
(This article belongs to the Special Issue Genetic Analysis of Quantitative Traits in Plants)
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<p>Frequency distributions of six yield-related traits in the rice (<span class="html-italic">Oryza sativa</span> L.) bi-parental RIL population. The two parents are represented by symbols IR24 and ASO at the top of each histogram. The six traits are denoted by PH: plant height; HD: heading date; TGW: thousand-grain weight; ETN: effective tiller number; NSP: number of spikelets per panicle; SS: seed set percentage. The four environments and BLUE values are denoted by GL: Guilin; GY: Guiyang: NC: Nanchang; NJ: Nanjing; and BL: BLUE.</p>
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<p>Validation of two QTLs on chromosome 3, one for PH and one for HD, i.e., <span class="html-italic">qPH3-1</span> (<b>A</b>) and <span class="html-italic">qHD3-1</span> (<b>B</b>), by reciprocal SSSLs. Indicated on the left side are phenotypic values; on the right side are marker genotypes. Chromosomal segments of Asominori are denoted by 0, and IR24 denoted by 2.</p>
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<p>The development diagram of the RIL and reciprocal SSSL populations from the same two parents.</p>
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14 pages, 5431 KiB  
Article
Transcriptional Changes Associated with Amyoplasia
by Artem E. Komissarov, Olga E. Agranovich, Ianina A. Kuchinskaia, Irina V. Tkacheva, Olga I. Bolshakova, Evgenia M. Latypova, Sergey F. Batkin and Svetlana V. Sarantseva
Int. J. Mol. Sci. 2025, 26(1), 124; https://doi.org/10.3390/ijms26010124 - 26 Dec 2024
Viewed by 180
Abstract
Arthrogryposis, which represents a group of congenital disorders, includes various forms. One such form is amyoplasia, which most commonly presents in a sporadic form in addition to distal forms, among which hereditary cases may occur. This condition is characterized by limited joint mobility [...] Read more.
Arthrogryposis, which represents a group of congenital disorders, includes various forms. One such form is amyoplasia, which most commonly presents in a sporadic form in addition to distal forms, among which hereditary cases may occur. This condition is characterized by limited joint mobility and muscle weakness, leading to limb deformities and various clinical manifestations. At present, the pathogenesis of this disease is not clearly understood, and its diagnosis is often complicated due to significant phenotypic diversity, which can result in delayed detection and, consequently, limited options for symptomatic treatment. In this study, a transcriptomic analysis of the affected muscles from patients diagnosed with amyoplasia was performed, and more than 2000 differentially expressed genes (DEGs) were identified. A functional analysis revealed disrupted biological processes, such as vacuole organization, cellular and aerobic respiration, regulation of mitochondrion organization, cellular adhesion, ATP synthesis, and others. The search for key nodes (hubs) in protein–protein interaction networks allowed for the identification of genes involved in mitochondrial processes. Full article
(This article belongs to the Special Issue Genes and Human Diseases 2.0)
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<p>(<b>A</b>) List of muscle biopsy samples subjected to RNA-seq; and (<b>B</b>) heatmap displaying the Spearman correlations between samples.</p>
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<p>RNA-seq analysis showing differential gene expression in amyoplasia muscle and control muscle. Volcano plots showing −log (adjusted <span class="html-italic">p</span>-value) vs. log2 (fold change) for “lower” (<b>A</b>), “upper+lower” (<b>B</b>), and “upper” (<b>C</b>) groups. Dashed vertical lines mark log2 (fold change) &gt; |4|. Dashed horizontal line marks adjusted <span class="html-italic">p</span>-value &lt; 0.001. Blue dots represent downregulated genes and red dots represent upregulated genes. Venn plots illustrating the intersection of (<b>D</b>) downregulated DEGs and (<b>E</b>) upregulated DEGs between “lower”, “lower+upper”, and “upper” groups.</p>
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<p>Gene Ontology function and pathway enrichment analysis of downregulated (<b>A</b>) and upregulated (<b>B</b>) DEGs in the “lower” group. Dotplots for each of the GO analysis categories (biological processes, molecular function and cellular component) are presented.</p>
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<p>Gene Ontology function and pathway enrichment analysis of downregulated (<b>A</b>) and upregulated (<b>B</b>) DEGs in the “lower+upper” group. Dotplots for each of the GO analysis categories (biological processes, molecular function and cellular component) are presented.</p>
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<p>Gene Ontology function and pathway enrichment analysis of downregulated (<b>A</b>) and upregulated (<b>B</b>) DEGs in the “upper” group. Dotplots for each of the GO analysis categories (biological processes, molecular function and cellular component) are presented.</p>
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<p>Analysis of PPI networks for “lower” group sample: (<b>A</b>) MCODE-clustered subnetwork for downregulated DEGs; (<b>B</b>) MCODE-clustered subnetwork for upregulated DEGs. Hub genes identified by cytoHubba. (<b>C</b>) Hub genes of the PPI network for downregulated DEGs; (<b>D</b>) Hub genes of the PPI network for upregulated DEGs. Enrichment analysis of MCODE-clustered subnetwork by Metascape. (<b>E</b>) Enrichment analysis of downregulated DEGs; (<b>F</b>) Enrichment analysis of upregulated DEGs.</p>
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<p>Analysis of PPI networks for “upper+lower” group sample. (<b>A</b>) MCODE-clustered subnetwork for downregulated DEGs; (<b>B</b>) MCODE-clustered subnetwork for upregulated DEGs. Hub genes identified by cytoHubba. (<b>C</b>) Hub genes of the PPI network for downregulated DEGs; (<b>D</b>) Hub genes of the PPI network for upregulated DEGs. Enrichment analysis of MCODE-clustered subnetwork by Metascape. (<b>E</b>) Enrichment analysis of downregulated DEGs; (<b>F</b>) Enrichment analysis of upregulated DEGs.</p>
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<p>Analysis of PPI networks for “upper” group sample. (<b>A</b>) MCODE-clustered subnetwork for downregulated DEGs; (<b>B</b>) MCODE-clustered subnetwork for upregulated DEGs. Hub genes identified by cytoHubba. (<b>C</b>) Hub genes of the PPI network for downregulated DEGs; (<b>D</b>) Hub genes of the PPI network for upregulated DEGs. Enrichment analysis of MCODE-clustered subnetwork by Metascape. (<b>E</b>) Enrichment analysis of downregulated DEGs; (<b>F</b>) Enrichment analysis of upregulated DEGs.</p>
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9 pages, 852 KiB  
Communication
The Combinational Effect of Enhanced Infection Control Measures and Targeted Clinical Metagenomics Surveillance on the Burden of Endemic Carbapenem and Other β-Lactam Resistance Among Severely Ill Pediatric Patients
by Athina Giampani, Maria Simitsopoulou, Maria Sdougka, Christos Paschaloudis, Emmanuel Roilides and Elias Iosifidis
Biomedicines 2025, 13(1), 31; https://doi.org/10.3390/biomedicines13010031 - 26 Dec 2024
Viewed by 190
Abstract
Background: Antimicrobial resistance (AMR) is recognized as one of the most important global public health threats. There is an urgent need to reduce the spread of these multidrug-resistant bacteria (MDR-B), particularly in extremely vulnerable patients. The aim of this study was to investigate [...] Read more.
Background: Antimicrobial resistance (AMR) is recognized as one of the most important global public health threats. There is an urgent need to reduce the spread of these multidrug-resistant bacteria (MDR-B), particularly in extremely vulnerable patients. The aim of this study was to investigate whether targeted gene amplification performed directly on clinical samples can be used simultaneously with a bundle of enhanced infection control measures in a Pediatric Intensive Care Unit (PICU) endemic to MDR-B. Methods: This study had three phases: (1) the baseline phase was performed prior to intervention when first screening and sample collection were performed; (2) the intervention phase was performed when various enhanced infection control measures (EICM) were applied; and (3) the maintenance phase occurred when EICMs were combined with the implementation of targeted molecular surveillance. The presence of four carbapenemase genes, blaKPC, blaOXA-48-like, blaVIM, and blaNDM, as well as the β-lactamase genes blaTEM and blaSHV, was evaluated by PCR after DNA isolation directly from stool samples. The results were compared to culture-based phenotypic analysis. Results and Conclusions: The implementation of EICM appeared to reduce the resistance burden in this sample endemic to an MDR-B clinical setting. The direct implementation of a targeted and customized rapid molecular detection assay to clinical samples seems to be an effective clinical tool for the evaluation of EICM measures. Full article
(This article belongs to the Section Microbiology in Human Health and Disease)
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<p>Compliance of applied enhanced infection control measurements. (<b>a</b>) Percentage of hand hygiene compliance; (<b>b</b>) CLABSI rate per 1000 central line days; (<b>c</b>) total antibiotic consumption by daily defined doses per 100 bed-days; (<b>d</b>) Carbapenem consumption by daily defined doses per 100 bed-days. EICM: enhanced infection control measures. Baseline phase: 1–5 months; intervention phase: 6–13 months; and maintenance phase: 14–18 months.</p>
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<p>Presence/absence of carbapenemase and other β-lactamase genes of <span class="html-italic">bla</span><sub>KPC</sub>, <span class="html-italic">bla</span><sub>VIM</sub>, <span class="html-italic">bla</span><sub>NDM</sub>, <span class="html-italic">bla</span><sub>OXA-48-like</sub>, <span class="html-italic">bla</span><sub>TEM</sub> and <span class="html-italic">bla</span><sub>SHV</sub>: (<b>a</b>) before intervention; (<b>b</b>) negative and positive patients with the presence of at least one carbapenemase before intervention; (<b>c</b>) after intervention; and (<b>d</b>) negative and positive patients for the presence of at least one carbapenemase gene after intervention.</p>
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16 pages, 1075 KiB  
Review
The Intersection of Epigenetics and Senolytics in Mechanisms of Aging and Therapeutic Approaches
by Daiana Burdusel, Thorsten R. Doeppner, Roxana Surugiu, Dirk M. Hermann, Denissa Greta Olaru and Aurel Popa-Wagner
Biomolecules 2025, 15(1), 18; https://doi.org/10.3390/biom15010018 - 26 Dec 2024
Viewed by 188
Abstract
The biological process of aging is influenced by a complex interplay of genetic, environmental, and epigenetic factors. Recent advancements in the fields of epigenetics and senolytics offer promising avenues for understanding and addressing age-related diseases. Epigenetics refers to heritable changes in gene expression [...] Read more.
The biological process of aging is influenced by a complex interplay of genetic, environmental, and epigenetic factors. Recent advancements in the fields of epigenetics and senolytics offer promising avenues for understanding and addressing age-related diseases. Epigenetics refers to heritable changes in gene expression without altering the DNA sequence, with mechanisms like DNA methylation, histone modification, and non-coding RNA regulation playing critical roles in aging. Senolytics, a class of drugs targeting and eliminating senescent cells, address the accumulation of dysfunctional cells that contribute to tissue degradation and chronic inflammation through the senescence-associated secretory phenotype. This scoping review examines the intersection of epigenetic mechanisms and senolytic therapies in aging, focusing on their combined potential for therapeutic interventions. Senescent cells display distinct epigenetic signatures, such as DNA hypermethylation and histone modifications, which can be targeted to enhance senolytic efficacy. Epigenetic reprogramming strategies, such as induced pluripotent stem cells, may further complement senolytics by rejuvenating aged cells. Integrating epigenetic modulation with senolytic therapy offers a dual approach to improving healthspan and mitigating age-related pathologies. This narrative review underscores the need for continued research into the molecular mechanisms underlying these interactions and suggests future directions for therapeutic development, including clinical trials, biomarker discovery, and combination therapies that synergistically target aging processes. Full article
(This article belongs to the Section Molecular Medicine)
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<p>The primary epigenetic mechanisms involved in aging include DNA methylation, histone modifications, chromatin remodeling, and non-coding RNAs. During aging, there is a general trend of genome-wide hypomethylation, though specific regions may undergo either hypermethylation or hypomethylation. Aged cells also exhibit heterochromatin loss, which is reflected in changes to histone content and modification patterns. Additionally, the formation of senescence-associated heterochromatin foci (SAHFs) is a notable feature of cellular aging. Finally, miRNA deregulation, driven by impaired miRNA biogenesis, is observed across various species and tissues as part of the aging process. Abbreviations: DNMT, DNA methyltransferase; HAT, histone acetyltransferase.</p>
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<p>The action of senolytic agents on senescent cells. The schematic illustrates the transition from normal to senescent cells and the impact of senolytic agents on aging and healthspan. On the left, various normal cell types (e.g., neurons, fibroblasts, epithelial cells) maintain tissue function. As aging progresses, these cells accumulate damage and enter a state of senescence. Senescent cells exhibit the senescence-associated secretory phenotype (SASP), releasing inflammatory cytokines, matrix metalloproteinases (MMPs), and chemokines, which contribute to tissue dysfunction and promote age-related diseases. Senolytic agents target these dysfunctional senescent cells, as shown in the lower right section, selectively inducing apoptosis and reducing their burden. This “hit-and-run” approach helps decrease SASP factors and supports tissue homeostasis, ultimately extending healthspan by reducing inflammation and delaying the onset of age-related diseases.</p>
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13 pages, 1680 KiB  
Article
Identification of Genetic Markers of APOM and CYP7A1 Genes Affecting Milk Production Traits in Chinese Holstein
by Yanan Liu, Zijiao Guo, Junqing Ni, Chendong Yang, Bo Han, Yabin Ma, Jianming Li, Guie Jiang, Weijie Zheng and Dongxiao Sun
Agriculture 2025, 15(1), 33; https://doi.org/10.3390/agriculture15010033 - 26 Dec 2024
Viewed by 189
Abstract
Our previous study identified the apolipoprotein M (APOM) and cytochrome P450 family 7 subfamily A polypeptide 1 (CYP7A1) genes as candidates for milk traits in dairy cattle, which were significantly up-regulated in liver tissue of Holstein cows between the [...] Read more.
Our previous study identified the apolipoprotein M (APOM) and cytochrome P450 family 7 subfamily A polypeptide 1 (CYP7A1) genes as candidates for milk traits in dairy cattle, which were significantly up-regulated in liver tissue of Holstein cows between the dry and lactation periods. The two genes play critical roles in the peroxisome proliferator-activated receptor (PPAR) pathway. In this study, we further confirmed whether the APOM and CYP7A1 genes had significant genetic impacts on milk production traits in a Chinese Holstein population. By dual-direction sequencing of the polymerase chain reaction (PCR) products of the complete coding sequences and 2000 bp of the 5′ and 3′ flanking regions on pooled DNA sample, seven and three single nucleotide polymorphisms (SNPs) were identified in APOM and CYP7A1, respectively. With SAS 9.2, phenotype-genotype association analysis revealed such SNPs were significantly associated with at least one of the milk production traits, including 305-day milk yield, milk fat yield, milk fat percentage, milk protein yield, and milk protein percentage in the first and second lactations (p = <0.01~0.04). With Haploview 4.2, we further found that six SNPs in APOM and thee SNPs in CYP7A1 formed one haplotype, respectively. The haplotypes were significantly associated with at least one of milk production traits as well (p = <0.01~0.02). Of note, we found the SNPs in the 5′ regulatory region, rs209293266 and rs110721287 in APOM and rs42765359 in CYP7A1, significantly impacted the gene transcriptional activity after mutation (p < 0.01) through changing the transcription factor binding sites by using luciferase assay experiments. Additionally, with RNAfold Web Server, rs110098953 and rs378530166 changed the mRNA secondary structures of APOM and CYP7A1 genes, respectively. In summary, our research is the first to demonstrate that APOM and CYP7A1 genes have significantly genetic effects on milk yield and composition traits, and the identified SNPs may serve as available genetic markers for genomic selection program in dairy cattle. Full article
(This article belongs to the Section Farm Animal Production)
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<p>Sketches of recombinant plasmids. The red text refers to the different alleles. CG, GG, and CT: <span class="html-italic">APOM</span> gene sketches of recombinant plasmids. C and A: <span class="html-italic">CYP7A1</span> gene sketches of recombinant plasmids.</p>
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<p>LD extension among the identified SNPs in <span class="html-italic">APOM</span> and <span class="html-italic">CYP7A1</span>. The text above the haplotype blocks contains the SNP names. The numbers in the haplotype blocks represent D′, the redder the haplotype blocks, the stronger of LD. (<b>A</b>) LD among the 7 SNPs in <span class="html-italic">APOM</span>. (<b>B</b>) LD among the 3 SNPs in <span class="html-italic">CYP7A1</span>.</p>
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<p>Luciferase assay results in HEK293 cells. pGL4.14 + pRL-TK: Empty vector. (<b>A</b>) Luciferase activity analysis of rs209293266 and rs110721287 in <span class="html-italic">APOM</span> gene. (<b>B</b>) Luciferase activity analysis of rs42765359 in <span class="html-italic">CYP7A1</span> gene. **: <span class="html-italic">p</span> &lt; 0.01. *: <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>mRNA secondary structure prediction for SNPs in exon and UTR regions of <span class="html-italic">APOM</span> and <span class="html-italic">CYP7A1</span>. (<b>A</b>) The mRNA secondary structure of rs110098953 mutation A in <span class="html-italic">APOM</span>. (<b>B</b>) The mRNA secondary structure of rs110098953 mutation G in <span class="html-italic">APOM</span>. (<b>C</b>) The mRNA secondary structure of rs378530166 mutation A in <span class="html-italic">CYP7A1</span>. (<b>D</b>) The mRNA secondary structure of rs378530166 mutation G in <span class="html-italic">CYP7A1</span>. Different colors represent the probability of complementary base pairing in RNA sequences, with the probability ranging from 0 to 1 using blue to red.</p>
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<p>mRNA secondary structure prediction for SNPs in exon and UTR regions of <span class="html-italic">APOM</span> and <span class="html-italic">CYP7A1</span>. (<b>A</b>) The mRNA secondary structure of rs110098953 mutation A in <span class="html-italic">APOM</span>. (<b>B</b>) The mRNA secondary structure of rs110098953 mutation G in <span class="html-italic">APOM</span>. (<b>C</b>) The mRNA secondary structure of rs378530166 mutation A in <span class="html-italic">CYP7A1</span>. (<b>D</b>) The mRNA secondary structure of rs378530166 mutation G in <span class="html-italic">CYP7A1</span>. Different colors represent the probability of complementary base pairing in RNA sequences, with the probability ranging from 0 to 1 using blue to red.</p>
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11 pages, 561 KiB  
Article
Animal as the Solution II: Phenotyping for Low Milk Urea Nitrogen A1PF Dairy Cows
by Fabiellen C. Pereira, Sagara Kumara, Anita Fleming, Shu Zhan Lai, Ella Wilson and Pablo Gregorini
Animals 2025, 15(1), 32; https://doi.org/10.3390/ani15010032 - 26 Dec 2024
Viewed by 211
Abstract
The societal pressure on intensive pastoral dairying demands the search for strategies to reduce the amount of N flowing through and excreted by dairy cows. One of the strategies that is being currently explored focuses on the animal as a solution, as there [...] Read more.
The societal pressure on intensive pastoral dairying demands the search for strategies to reduce the amount of N flowing through and excreted by dairy cows. One of the strategies that is being currently explored focuses on the animal as a solution, as there are differences in N metabolism between cows even within the same herd. This work was conducted to explore such an approach in A1PF herds in New Zealand and the possibility of identifying A1PF cows that are divergent for milk urea nitrogen (MUN) concentration through phenotyping as a potential viable strategy to reduce N leaching and emissions from temperate dairy systems. Three herd tests were conducted to select a population sample of 200 cows (exhibiting the lowest 100 and highest 100 MUN concentrations). Milk samples were collected from the 200 cows during mid and late lactation to test for milk solids content and MUN. From the 200 cows, urine for urinary N concentration (UN), blood for plasma urea N, total antioxidants (TAS), and glutathione peroxidase (GPx) were collected from the 20 extremes (the lowest 10 and highest 10 MUN concentrations). Milk urea N was greater in cows selected as high-MUN cows (16.2 vs. 14.32 ± 0.23 mg/dL) and greater during late lactation (16.9 vs. 13.0 ± 0.19 mg/dL). Milk solids and fat content were 38% and 20% greater in cows selected as low-MUN cows than in high-MUN cows during mid lactation (p < 0.001). Low-MUN cows had lower UN than high-MUN cows during mid lactation (0.64 vs. 0.88 ± 0.11%). The N concentration in the plasma (p = 0.01) and Tas (p = 0.06) were greater during late lactation. There was a positive relationship between the MUN concentration phenotype used for selection and the MUN concentration for the trial period and MUN concentration and UN concentration during mid and late lactation (p < 0.001). Our results suggest that A1PF cows within a commercial herd can be phenotyped and selected for low-MUN, which may be potentially a viable strategy to reduce N losses to the environment and create healthier systems. Following genetic tracking, those cows can be bred to further promote low-MUN A1PF herds. Full article
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<p>The milk urea N (MUN) values from cows during sampling points at mid and late lactation are affected by the cow MUN value at the phenotyping. For every one-unit increase in MUN at the phenotyping, there was a subsequent 0.35 mg increase in N from urea per dL of milk (<span class="html-italic">p</span> &lt; 0.0001). The sampling period affected the intercept of the regression line (<span class="html-italic">p</span> &lt; 0.0001). The regression model’s adjusted R<sup>2</sup> = 0.32.</p>
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22 pages, 13566 KiB  
Article
In Silico Analysis Revealed Marco (SR-A6) and Abca1/2 as Potential Regulators of Lipid Metabolism in M1 Macrophage Hysteresis
by Yubo Zhang, Wenbo Yang, Yutaro Kumagai, Martin Loza, Yitao Yang, Sung-Joon Park and Kenta Nakai
Int. J. Mol. Sci. 2025, 26(1), 111; https://doi.org/10.3390/ijms26010111 - 26 Dec 2024
Viewed by 201
Abstract
Macrophages undergo polarization, resulting in distinct phenotypes. These transitions, including de-/repolarization, lead to hysteresis, where cells retain genetic and epigenetic signatures of previous states, influencing macrophage function. We previously identified a set of interferon-stimulated genes (ISGs) associated with high lipid levels in macrophages [...] Read more.
Macrophages undergo polarization, resulting in distinct phenotypes. These transitions, including de-/repolarization, lead to hysteresis, where cells retain genetic and epigenetic signatures of previous states, influencing macrophage function. We previously identified a set of interferon-stimulated genes (ISGs) associated with high lipid levels in macrophages that exhibited hysteresis following M1 polarization, suggesting potential alterations in lipid metabolism. In this study, we applied weighted gene co-expression network analysis (WGCNA) and conducted comparative analyses on 162 RNA-seq samples from de-/repolarized and lipid-loaded macrophages, followed by functional exploration. Our results demonstrate that during M1 hysteresis, the sustained high expression of Marco (SR-A6) enhances lipid uptake, while the suppression of Abca1/2 reduces lipid efflux, collectively leading to elevated intracellular lipid levels. This accumulation may compensate for reduced cholesterol biosynthesis and provide energy for sustained inflammatory responses and interferon signaling. Our findings elucidate the relationship between M1 hysteresis and lipid metabolism, contributing to understanding the underlying mechanisms of macrophage hysteresis. Full article
(This article belongs to the Section Molecular Immunology)
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<p>Flowchart illustrating the study design. Workflow for data processing and quality control, weighted gene co-expression network analysis (WGCNA), functional analysis, and downstream analysis in this study.</p>
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<p>Batch effect correction and quality control in the macrophage gene expression data. (<b>A</b>) Relative gene expression levels (Z-score) among the RNA-seq data before and after the batch effect correction and (<b>B</b>) trajectory PCA plot of macrophage RNA-seq expression across all phenotypes. The colored dots represent macrophages in different phenotypes and arrows represent polarization trajectory. The points within the red dashed box represent lipid-loaded samples. (<b>C</b>) Hierarchical clustering based on RNA expression of all samples. The red arrow indicates the M1-like phenotype direction, while the blue arrow indicates the M2-like direction. Lipid-loaded macrophage samples are enclosed in a red dashed box.</p>
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<p>Re-grouping of samples based on macrophage stimulation time. The horizontal axis of the table represents the grouping of macrophages based on different polarization stimulation times and types, while the vertical axis displays the transcriptome distribution tree using hierarchical clustering for all samples. The red and blue arrows in the table indicate trends toward M1-like and M2-like macrophages, respectively. The red arrows below represent M1 repolarization stimulation, the blue arrows represent M2 repolarization stimulation, and the gray arrows represent depolarization. The points on the scale below indicate the approximate positions of each depolarized/repolarized sample, with the M0, M1, and M2 phenotypes serving as critical points.</p>
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<p>Co-expression network analysis based on WGCNA. (<b>A</b>) Analysis of scale-free fit index and mean connectivity for best parameter screening, the numbers represent different parameters. (<b>B</b>) Cluster dendrogram of co-expression genes with co-expression modules, the colors represent different gene modules. (<b>C</b>) Heatmap of associations among module eigengenes with all identified co-expression modules, the colors represent different gene modules. On the right side, bar plots are used to display the correlation coefficients between MEgreen, MEblack, and MEbrown with all phenotypes. (<b>D</b>) The heatmap shows the expression of MEbrown genes and MEblue genes across all samples. The portion highlighted by the red dashed circle indicates the occurrence of a late on-site phenomenon for brown genes in the 4_96M1 group and the 26_96reM1_M2 group.</p>
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<p>(<b>A</b>) GO and (<b>B</b>) KEGG analysis of all identified gene modules.</p>
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<p>Identifying core sub-network and hub genes for module MEbrown. (<b>A</b>) Scatter plot of module membership and gene significance in the brown module to 24_48h_deM0_M1 and reM2_M1 phenotypes, the dots represent all genes in gene module. (<b>B</b>) Venn diagram representing number of hub genes of MEbrown to 24_48h_deM0_M1 and reM2_M1 and their common hub genes. (<b>C</b>) Pairwise correlation analysis of 22 common hub genes shows a significant positive or negative correlation between each hub gene in heatmap. (<b>D</b>) Protein–protein interaction (PPI) network of hub genes was identified. Yellow nodes represent uncommon hub genes and red nodes represent common hub genes. (<b>E</b>) Experimental based pairwise gene co-expression correlation identified in the STRING database. (<b>F</b>) Bar plots illustrating differences in RNA expression (TPM) of hub genes after treated by AcLDL, GW3965, and KLA. The asterisks indicate that the differences are statistically significant. ‘*’ <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. ‘-’ not significant.</p>
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<p>Strong gene expression correlation between ISGs and hub genes. (<b>A</b>) Correlation coefficients between ISGs and hub genes selected from MEbrowm. The values and transparency represent Pearson’s correlation coefficients of each gene pair. (<b>B</b>) Correlation coefficients between ISGs and lipid biosynthesis-related genes. Heatmap of top 30 genes with the highest connectivity identified in MEmagenta and MEred across all samples. (<b>C</b>–<b>H</b>) Gene expression levels of the ISGs, negative correlation hub genes, and positive correlation hub genes under (<b>C</b>–<b>E</b>) M0 → M1 → M0 and (<b>F</b>–<b>H</b>) M0 → M1 → M2 from 0 h to 96 h.</p>
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<p>Strong gene expression correlation between ISGs and hub genes. (<b>A</b>–<b>D</b>) Gene expression levels of (<b>A</b>) Lox-1, Cd68, SR-A1, and Cd36; (<b>B</b>) Marco (SR-A6); (<b>C</b>) Lxra, Abca1, and Abca2; and (<b>D</b>) Cd5l and Apoc1 under M0 → M1 → M0 between 0 and 96 h. (<b>E</b>) Schematic representation of the relationship between macrophage M1 hysteresis and macrophage lipid metabolism with the order of protentional underlying mechanism (1–5).</p>
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