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Search Results (12,077)

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21 pages, 757 KiB  
Review
The Need to Increase Strain-Specific DNA Information from the Invasive Cyanobacteria Sphaerospermopsis aphanizomenoides and Cuspidothrix issatschenkoi
by Daniela R. de Figueiredo
Water 2025, 17(4), 579; https://doi.org/10.3390/w17040579 - 17 Feb 2025
Abstract
Climate change is promoting the occurrence of Harmful Cyanobacterial Blooms (HCBs) across freshwaters, posing serious risks for the ecosystems and human health. Under these warmer conditions, particularly blooms of invasive Aphanizomenon-like species such as Cuspidothrix issatschenkoi and Sphaerospermopsis aphanizomenoides (previously known as [...] Read more.
Climate change is promoting the occurrence of Harmful Cyanobacterial Blooms (HCBs) across freshwaters, posing serious risks for the ecosystems and human health. Under these warmer conditions, particularly blooms of invasive Aphanizomenon-like species such as Cuspidothrix issatschenkoi and Sphaerospermopsis aphanizomenoides (previously known as Aphanizomenon issatschenkoi and Aphanizomenon/Anabaena aphanizomenoides, respectively) have been reported to spread to higher latitudes, leading to increased toxic risks. Aphanizomenon and Anabaena genera have undergone several taxonomical revisions in recent years due to their morphological ambiguity, also corroborated by a high phylogenetic diversity. Furthermore, there is also a high phenotypic and genotypic variability within each one of these species, leading to diverse physiological and ecological traits. Therefore, DNA-based information is crucial not only to overcome possible species misidentifications, but also to provide information at the strain level. However, for the invasive Cuspidothrix issatschenkoi and Sphaerospermopsis aphanizomenoides, there is still a lack of geographically dispersed strains with available nucleotide sequences in databases, limiting deeper ecological studies to better understand their ecology and invasive trend. This review aimed to compile and discuss the geographical distribution of Cuspidothrix issatschenkoi and Sphaerospermopsis aphanizomenoides strains found in the NCBI nucleotide database and make some recommendations on the need to increase these numbers under the exponential inputs from DNA-metabarcoding. The integration of DNA-based information in water quality monitoring programmes is crucial to identify reoccurring bloom-forming strains and better understand their physiology and invasive ecology, ultimately leading to their effective forecast, and mitigation of their potential massive growth in target freshwater bodies. Full article
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Figure 1
<p>Number of publications in the Scopus database after a search using “aphanizomenoides” (<span class="html-italic">n</span> = 53, in blue) or “issatschenkoi” (<span class="html-italic">n</span> = 60, in orange). (The Scopus search aimed for the article title, abstract, and keywords).</p>
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18 pages, 1111 KiB  
Article
DNA Metabarcoding Using Indexed Primers: Workflow to Characterize Bacteria, Fungi, Plants, and Arthropods from Environmental Samples
by Teresa M. Tiedge, Jorden T. Rabasco and Kelly A. Meiklejohn
Diversity 2025, 17(2), 137; https://doi.org/10.3390/d17020137 - 17 Feb 2025
Abstract
Environmental DNA from bulk materials can be analyzed to gain an understanding of the bacterial, fungal, plant, and/or arthropod communities present. DNA metabarcoding is widely used to characterize these biological communities, by amplifying “barcode” regions and sequencing these amplicons via next-generation sequencing. The [...] Read more.
Environmental DNA from bulk materials can be analyzed to gain an understanding of the bacterial, fungal, plant, and/or arthropod communities present. DNA metabarcoding is widely used to characterize these biological communities, by amplifying “barcode” regions and sequencing these amplicons via next-generation sequencing. The Earth Microbiome Project (EMP) adopted the use of indexed primers, PCR primers containing Illumina® adapter sequences and a unique 12-nucleotide Golay barcode to simplify the identification of bacterial taxa via the 16S barcode. We sought to develop a wet laboratory workflow utilizing indexed primers that could cost-effectively reduce bench time while simultaneously targeting multiple DNA barcode regions to characterize bacterial (16S), fungal (ITS1), plant (ITS2, trnL p6 loop), and arthropod (COI) communities. The EMP primer constructs for 16S were modified to accommodate our DNA barcode regions of interest while also permitting successful demultiplexing following sequencing. A single indexed primer pair was designed for ITS1 and trnL p6 loop, and two primer pairs were developed for ITS2 and COI. To test the workflow, a total of 648 soil and 336 dust samples were processed, with key steps including DNA isolation, total DNA quantification, amplification with indexed primers, library purification and quantification, and Illumina MiSeq sequencing. Based on raw read counts and analysis of positive controls, the trnL p6 loop and ITS2 a primer pairs performed comparably to the originally designed 16S primers. Both COI primers pairs, ITS1 and ITS2 b primers, had lower raw reads compared to the other three primer pairs. The combination of the three plant targets successfully recovered all plant taxa in the positive controls except for Nephrolepis exaltata [Nephrolepidaceae] and the COI primers recovered all arthropod taxa except for the beetle. Notably, none of the taxa in the fungal positive control were recovered using ITS1. For environmental samples, sequencing was successful for all primers except COI c, and primer biases were observed for all three plant primers, in which a small number of families were uniquely amplified for each primer pair. This workflow can be applied to many disciplines that utilize DNA metabarcoding given its customizability and flexibility with Illumina sequencing chemistry. Full article
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Figure 1

Figure 1
<p>Indexed Primer and Library Constructs Following the Earth Microbiome Project’s Design. (<b>A</b>) Indexed forward primers contain the sequences for the Illumina p5 adapter (orange), Golay barcode (blue), primer pad and linker (light green) and the target-specific PCR forward primer (yellow). (<b>B</b>) Indexed reverse primers contain the sequences for the Illumina p7 adapter (pink), primer pad and linker (light gray), and the target-specific reverse PCR primer (red). (<b>C</b>) The full library construct and locations of where the sequencing primers anneal is indicated with the three black primer boxes. Arrows indicate the direction of sequencing. Figure made in BioRender.</p>
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<p>Positive Control Composition and Recovery. Known taxa were combined into a single pool to make up the positive control (PC) that was included on every amplification plate and sequencing run (<span class="html-italic">n</span>, 12). Each taxon is represented by a single color, with taxa not purposefully added to the controls indicated in black (abundance &lt; 1%). Using 16S primers for bacteria, six species were prevalent at 1% or greater in the positive controls and were designated as “high-non” taxa and colored gray (<b>A</b>). Target taxa recovered using the <span class="html-italic">COI</span> d for arthropods (<b>B</b>), <span class="html-italic">COI</span> c for arthropods (<b>C</b>), ITS2 a for plants (<b>D</b>), ITS2 b for plants (<b>E</b>), and <span class="html-italic">trnL</span> for plants (<b>F</b>), primer pairs are also displayed. Taxa that were unable to be recovered were not plotted for a given sequencing run. ASVs within a single taxon are plotted within each panel and outlined in black. Depending on the primer pair and taxa recovered, more than one ASV may have been assigned to a given taxon.</p>
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22 pages, 3141 KiB  
Article
Areca catechu L. Extract Inhibits Colorectal Cancer Tumor Growth by Modulating Cell Apoptosis and Autophagy
by Meng-Hsiu Tsai, Chang-Han Chen, Chien-Lin Chen, Mei-Hsien Lee, Li-Ching Wu, Yi-Chiung Hsu, Chao-Yang Hsiao, Chang-Ti Lee, Kuo-Li Pi and Li-Jen Su
Curr. Issues Mol. Biol. 2025, 47(2), 128; https://doi.org/10.3390/cimb47020128 - 17 Feb 2025
Viewed by 50
Abstract
Colorectal cancer (CRC) is a common cancer globally, and chemotherapy often causes severe complications, necessitating effective drugs with minimal side effects. As Areca catechu L. extract (ACE) is a Traditional Chinese Medicine that contains numerous active compounds with anticancer effects, in this study, [...] Read more.
Colorectal cancer (CRC) is a common cancer globally, and chemotherapy often causes severe complications, necessitating effective drugs with minimal side effects. As Areca catechu L. extract (ACE) is a Traditional Chinese Medicine that contains numerous active compounds with anticancer effects, in this study, the Cell Counting Kit-8 (CCK-8) assay was used to determine ACE’s effect on CRC cell lines, revealing that it significantly inhibits CoLo320DM and HCT116 cells. In vivo experiments with NU-Foxn1nu mice indicated that ACE inhibits tumor growth, while a flow cytometry assay revealed that higher ACE concentrations increased cell apoptosis and ROS levels. Next-generation sequencing (NGS) showed that ACE increases the fold changes in apoptosis, DNA damage, and autophagy-related genes while inhibiting the fold changes in cell proliferation and Wnt signaling pathway genes. We conducted Western blotting to confirm these findings. Overall, ACE demonstrates potential as a drug candidate by promoting apoptosis and autophagy, and significantly reducing cell viability and tumor growth, thus offering a new approach for effective colorectal cancer treatment with minimal side effects. Full article
(This article belongs to the Special Issue Biochemical Composition and Activity of Medicinal Plants and Food)
25 pages, 18941 KiB  
Article
The Gut Microbiota Metabolite Butyrate Modulates Acute Stress-Induced Ferroptosis in the Prefrontal Cortex via the Gut–Brain Axis
by Zhen Wang, Xiaoying Ma, Weibo Shi, Weihao Zhu, Xiaowei Feng, Hongjian Xin, Yifan Zhang, Bin Cong and Yingmin Li
Int. J. Mol. Sci. 2025, 26(4), 1698; https://doi.org/10.3390/ijms26041698 - 17 Feb 2025
Viewed by 17
Abstract
Stress has been implicated in the onset of mental disorders such as depression, with the prefrontal cortex (PFC) playing a crucial role. However, the underlying mechanisms remain to be fully elucidated. Metabolites secreted by intestinal flora can enter the bloodstream and exert regulatory [...] Read more.
Stress has been implicated in the onset of mental disorders such as depression, with the prefrontal cortex (PFC) playing a crucial role. However, the underlying mechanisms remain to be fully elucidated. Metabolites secreted by intestinal flora can enter the bloodstream and exert regulatory effects on the body. Consequently, this study aims to investigate the molecular mechanisms by which gut flora influences ferroptosis in PFC neurons, thereby affecting depression-like behavioral changes in mice subjected to acute stress. Initially, we established a mouse model of acute restraint stress (3-day duration) and verified that stress-induced ferroptosis of PFC neurons contributed to depression-like behavioral alterations in mice, as evidenced by morphological, behavioral, and molecular biology assessments. Subsequently, through fecal microbiota transplantation (FMT) experiments, we established a significant correlation between gut microbiota and ferroptosis of PFC neurons in acute stress-exposed mice. 16S rDNA sequencing identified butyric acid-producing bacteria, specifically g_Butyricimonas and its primary metabolite, butyric acid, as critical regulators of ferroptosis in PFC neurons in acutely stressed mice. Furthermore, the intervention of butyrate demonstrated its potential to ameliorate damage to the intestinal and blood–brain barriers in these mice. This intervention also mitigated depression-like behaviors induced by ferroptosis of PFC neurons by alleviating systemic inflammatory responses. The findings of this study indicate that acute stress-induced ferroptosis of PFC neurons plays a critical role in depression-like behavioral changes in mice. Additionally, the gut microbiota metabolite butyrate can modulate ferroptosis and depression-like behavioral changes through the gut–brain axis. Full article
(This article belongs to the Section Molecular Microbiology)
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Figure 1

Figure 1
<p>Acute stress induced depressive-like behavior and PFC neuronal damage in mice. (<b>A</b>) Body weight of the mice in the CON and RS groups over the initial three-day period. Data are expressed as mean ± SEM, with statistical significance assessed via two-factor repeated measures ANOVA. (<b>B</b>) Serum corticosterone levels in the two groups of mice. (<b>C</b>) Representative movement trajectories of the two groups of mice during the open field test. Red lines show the mice’s movement in the central zone, and brown lines indicate its trajectory in the peripheral zone. (<b>D</b>,<b>E</b>) In the open field test, the percentage of movement distance within the central area (<b>D</b>) and the percentage of residence time in the central area (<b>E</b>). (<b>F</b>) The percentage of immobile time in the tail suspension test. <span class="html-italic">n</span> = 6. (<b>G</b>,<b>H</b>) Representative images of HE staining (<b>G</b>) and Nissl staining (<b>H</b>) of the PFC in the two groups of mice. Scale bars: 200 μm and 50 μm. (<b>I</b>,<b>J</b>) The relative protein expression levels of TF and TFR in the PFC. <span class="html-italic">n</span> = 6. (<b>K</b>–<b>M</b>) The MDA content (<b>K</b>), Fe<sup>2+</sup> content (<b>L</b>), and GSH content (<b>M</b>) in the PFC of the two groups of mice. <span class="html-italic">n</span> = 4. Data were presented as mean ± SEM. Statistical significance between the two groups was assessed using Student’s <span class="html-italic">t</span>-test for normally distributed data, and the Mann–Whitney rank sum test for data that did not follow a normal distribution. Significance levels were denoted as * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Acute stress-induced ferroptosis of PFC neurons is implicated in depression-like behavior in mice. (<b>A</b>) Schematic representation of the experimental protocol for administering the ferroptosis inhibitors. The figure was drawn by Figdraw, ID:RUUAO2fbf0. (<b>B</b>–<b>D</b>) Relative protein expression levels of TF (<b>C</b>) and TFR (<b>D</b>) in the PFC. <span class="html-italic">n</span> = 3. (<b>E</b>–<b>G</b>) MDA content (<b>E</b>), Fe<sup>2+</sup> content (<b>F</b>), and GSH content (<b>G</b>) in the PFC of each group of mice. <span class="html-italic">n</span> = 4. (<b>H</b>,<b>I</b>) Representative histological images of HE staining (<b>H</b>) and Nissl staining (<b>I</b>) in the PFC of mice from the respective groups. Scale bars: 200 μm and 50 μm. (<b>J</b>) Body weight data of mice in each group were recorded over the first three days. The data are presented as mean ± SEM, and statistical significance was assessed using a two-factor repeated measures ANOVA. (<b>K</b>) Representative movement trajectories of each group of mice were captured during the open field test. Red lines show the mice’s movement in the central zone, and brown lines indicate its trajectory in the peripheral zone. (<b>L</b>,<b>M</b>) In the open field test, the percentage of movement distance in the central area (<b>L</b>) and the percentage of time spent in the central area (<b>M</b>). (<b>N</b>) The percentage of immobile time for each group of mice was determined during the tail suspension test. <span class="html-italic">n</span> = 6. Data are expressed as mean ± SEM. Statistical significance among multiple groups was assessed using one-way ANOVA. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>The gut microbiota is implicated in ferroptosis within the PFC of acutely stressed mice. (<b>A</b>) Experimental flowchart of FMT. The figure was drawn by Figdraw, ID:RWTIU78278. (<b>B</b>–<b>E</b>) Alpha diversity indices (Chao1, Faith’s_pd, Pielou_e, Shannon) of gut microbiota in mice before and after antibiotic treatment. (<b>F</b>) The beta diversity analysis of gut microbiota in mice before and after antibiotic treatment, utilizing PCoA analysis based on the Bray–Curtis distance. The Adonis test was employed to identify statistical differences between the two groups. (<b>G</b>,<b>H</b>) The relative abundance of gut microbiota at the phylum level before and after antibiotic treatment. <span class="html-italic">n</span> = 6. (<b>I</b>,<b>J</b>) The relative protein expression levels of TF and TFR in the PFC of mice from the FMT-C and FMT-S groups. (<b>K</b>–<b>M</b>) The MDA content ((<b>K</b>), <span class="html-italic">n</span> = 6), Fe<sup>2+</sup> content ((<b>L</b>), <span class="html-italic">n</span> = 6), and GSH content ((<b>M</b>), <span class="html-italic">n</span> = 5) in the PFC of the two groups of mice. (<b>N</b>,<b>O</b>) Representative histological images of HE staining (<b>N</b>) and Nissl staining (<b>O</b>) in the PFC of mice from the FMT-C and FMT-S groups. Scale bars: 200 μm and 50 μm. (<b>P</b>) Monitoring of body weight changes in mice from the FMT-C and FMT-S groups over a 15-day period following fecal microbiota transplantation. Data are presented as mean ± SEM, and statistical significance was assessed using two-factor repeated measures ANOVA. (<b>Q</b>) Representative movement trajectories for each group of mice in the open field test. Red lines show the mice’s movement in the central zone, and brown lines indicate its trajectory in the peripheral zone. (<b>R</b>,<b>S</b>) In the open field test, the percentage of movement distance (<b>R</b>) and the percentage of residence time (<b>S</b>) in the central area. (<b>T</b>) The percentage of immobile time for each group of mice during the tail suspension test. <span class="html-italic">n</span> = 7. Data are expressed as mean ± SEM. The Student’s <span class="html-italic">t</span>-test was employed to assess the statistical significance between the two groups when the data followed a normal distribution. Conversely, the Mann–Whitney test was utilized to evaluate the statistical differences between the groups when the data deviated from a normal distribution. * <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. Pre: before antibiotic treatment. Post: after antibiotic treatment.</p>
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<p>Butyric acid-producing bacteria are the primary enterobacteria implicated in the ferroptosis of PFC neurons in acutely stressed mice. (<b>A</b>,<b>B</b>) Alpha diversity indices of gut microbiota, including Chao1 (<b>A</b>) and Pielou_e (<b>B</b>). (<b>C</b>) Beta diversity analysis of gut microbiota across different groups of mice. (<b>D</b>) Relative abundance of gut microbiota at the phylum level in each group. (<b>E</b>) Ratio of the relative abundance of <span class="html-italic">p_Firmicutes</span> to <span class="html-italic">p_Bacteroidetes</span> in each group. CON, RS, <span class="html-italic">n</span> = 8; FMT-C, FMT-S, <span class="html-italic">n</span> = 7. (<b>F</b>) Random forest analysis was conducted to compare the gut microbiota between the mice in CON and RS groups. <span class="html-italic">n</span> = 8. (<b>G</b>) Random forest analysis was conducted to compare the gut microbiota between the mice in the FMT-C and FMT-S groups. <span class="html-italic">n</span> = 7. The key intestinal flora exhibiting consistent trends in both the RS and FMT-S groups are highlighted within a box. Blue indicates the intestinal flora that were reduced in the RS and FMT-S groups, while red indicates the intestinal flora that were increased in these groups. (<b>H</b>–<b>L</b>) The bar plots show the relative abundances of <span class="html-italic">g_Butyricimonas</span>, <span class="html-italic">g_[Prevotella]</span>, <span class="html-italic">g_Coprococcus</span>, <span class="html-italic">g_Dorea</span>, and <span class="html-italic">g_Streptococcus</span> in each group, based on the results of analyses (<b>F</b>) and (<b>G</b>). CON, RS, <span class="html-italic">n</span> = 8; FMT-C, FMT-S, <span class="html-italic">n</span> = 7. (<b>M</b>) Heat map illustrating the correlation analysis between key gut microbiota and indicators related to stress or ferroptosis. (<b>N</b>) Network diagram depicting the correlation analysis between key gut microbiota and indicators associated with stress or ferroptosis, <span class="html-italic">n</span> = 6. Data are presented as mean ± SEM. Statistical significance between the two groups was determined using Student’s <span class="html-italic">t</span>-test for normally distributed data, and the Mann–Whitney rank sum test for data that did not follow a normal distribution. * <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, ns = non significant. |Weight|: absolute value of the weight difference between the first day and last day treatment. CD/TD: percentage of distance moved in the central area of the open field experiment. CT: Percentage of time spent in the central area of the open field experiment. IT: Percentage of stationary immobility time in tail suspension experiments.</p>
Full article ">Figure 5
<p>The gut microbiota metabolite butyrate modulates ferroptosis in PFC neurons of acutely stressed mice. (<b>A</b>,<b>B</b>) Quantification of butyric acid levels in serum and PFC tissues of mice across the CON, RS, FMT-C, and FMT-S groups. <span class="html-italic">n</span> = 4. (<b>C</b>) Schematic representation of the experimental design for the butyric acid treatment. The figure was drawn by Figdraw, ID:IRPWW4b47d. (<b>D</b>,<b>E</b>) Measurement of butyric acid concentrations in serum and PFC tissues of mice subjected to the butyrate treatment. (<b>F</b>–<b>H</b>) Analysis of relative protein expression levels of TF (<b>G</b>) and TFR (<b>H</b>) in the PFC across different groups. (<b>I</b>–<b>K</b>) The MDA content (<b>I</b>), Fe<sup>2+</sup> content (<b>J</b>), and GSH content (<b>K</b>) in the PFC of each group of mice. <span class="html-italic">n</span> = 4. (<b>L</b>) Body weight measurements of mice subjected to the butyrate treatment across the first three days. <span class="html-italic">n</span> = 6. Data are expressed as mean ± SEM, with statistical significance assessed via two-factor repeated measures ANOVA. (<b>M</b>,<b>N</b>) Representative histological images of HE staining (<b>M</b>) and Nissl staining (<b>N</b>) of the PFC for each group of mice are provided. Scale bars: 200 μm and 50 μm. (<b>O</b>) Representative movement trajectories of each group of mice during the open field test. Red lines show the mice’s movement in the central zone, and brown lines indicate its trajectory in the peripheral zone. (<b>P</b>,<b>Q</b>) In the open field test, the percentage of movement distance within the central area (<b>P</b>) and the percentage of residence time in the central area (<b>Q</b>). (<b>R</b>) The percentage of immobile time in the tail suspension test. <span class="html-italic">n</span> = 6. Data are presented as mean ± SEM. Statistical significance across multiple groups was assessed using one-way ANOVA, with significance levels indicated as follows: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Acute stress promotes ferroptosis by inducing inflammation in the PFC. (<b>A</b>) Representative images of Alcian blue staining of colon tissues from mice in the CON group and RS group. Scale bars: 400 μm and 100 μm. (<b>B</b>) Quantification of Alcian blue-positive cells in colon tissues across the two groups. (<b>C</b>,<b>D</b>) Relative expression of tight junction proteins Claudin-5 and Occludin in colon tissues from the two groups. <span class="html-italic">n</span> = 6. (<b>E</b>–<b>H</b>) Levels of IL-1β (<b>E</b>), IL-6 (<b>F</b>), IL-4 (<b>G</b>), and IL-10 (<b>H</b>) in the serum of mice in the two groups. <span class="html-italic">n</span> = 4. (<b>I</b>) Quantification of Evans blue content in the PFC of CON and RS mice. <span class="html-italic">n</span> = 4. (<b>J</b>,<b>K</b>) Relative expression of tight junction proteins Claudin-5 and Occludin in the PFC from mice in the two groups. <span class="html-italic">n</span> = 6. (<b>L</b>–<b>P</b>) Assessment of relative mRNA expression of inflammatory cytokines IL-1β (<b>L</b>), IL-6 (<b>M</b>), TNF-α (<b>N</b>), IL-4 (<b>O</b>), and IL-10 (<b>P</b>) in the PFC of mice from the CON and RS groups. <span class="html-italic">n</span> = 6. Data are presented as mean ± SEM. The Student’s <span class="html-italic">t</span>-test was employed to assess statistical significance between the two groups when the data adhered to a normal distribution. Conversely, the Mann–Whitney rank sum test was utilized to evaluate statistical differences between the two groups when the data did not conform to a normal distribution. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 7
<p>Butyrate mitigates ferroptosis in prefrontal cortex neurons of mice subjected to acute stress by modulating inflammation along the gut–brain axis. (<b>A</b>) Representative images of Alcian blue staining in colonic tissues from mice in the C-NS, C-BA, S-NS, and S-BA groups. Scale bars: 400 μm and 100 μm. (<b>B</b>) Quantification of Alcian blue-stained positive cells in colonic tissues across the four groups. (<b>C</b>–<b>E</b>) Relative expression of tight junction proteins Claudin-5 and Occludin in colonic tissues of the four groups. (<b>F</b>–<b>I</b>) Serum levels of IL-1β (<b>F</b>), IL-6 (<b>G</b>), IL-4 (<b>H</b>), and IL-10 (<b>I</b>) in the four groups. <span class="html-italic">n</span> = 4. (<b>J</b>–<b>L</b>) Relative expression of tight junction proteins Claudin-5 and Occludin in the PFC of the four groups. <span class="html-italic">n</span> = 3. (<b>M</b>–<b>Q</b>) Relative mRNA expression of inflammatory cytokines IL-1β (<b>M</b>), IL-6 (<b>N</b>), TNF-α (<b>O</b>), IL-4 (<b>P</b>), and IL-10 (<b>Q</b>) in the PFC of mice from the four groups. <span class="html-italic">n</span> = 4. Data are presented as means ± SEM. Statistical significance was determined using one-way ANOVA across multiple groups, with significance thresholds set at * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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23 pages, 4150 KiB  
Article
Data-Driven Identification of Early Cancer-Associated Genes via Penalized Trans-Dimensional Hidden Markov Models
by Saeedeh Hajebi Khaniki and Farhad Shokoohi
Biomolecules 2025, 15(2), 294; https://doi.org/10.3390/biom15020294 - 16 Feb 2025
Viewed by 134
Abstract
Colorectal cancer (CRC) is a significant worldwide health problem due to its high prevalence, mortality rates, and frequent diagnosis at advanced stages. While diagnostic and therapeutic approaches have evolved, the underlying mechanisms driving CRC initiation and progression are not yet fully understood. Early [...] Read more.
Colorectal cancer (CRC) is a significant worldwide health problem due to its high prevalence, mortality rates, and frequent diagnosis at advanced stages. While diagnostic and therapeutic approaches have evolved, the underlying mechanisms driving CRC initiation and progression are not yet fully understood. Early detection is critical for improving patient survival, as initial cancer stages often exhibit epigenetic changes—such as DNA methylation—that regulate gene expression and tumor progression. Identifying DNA methylation patterns and key survival-related genes in CRC could thus enhance diagnostic accuracy and extend patient lifespans. In this study, we apply two of our recently developed methods for identifying differential methylation and analyzing survival using a sparse, finite mixture of accelerated failure time regression models, focusing on key genes and pathways in CRC datasets. Our approach outperforms two other leading methods, yielding robust findings and identifying novel differentially methylated cytosines. We found that CRC patient survival time follows a two-component mixture regression model, where genes CDH11, EPB41L3, and DOCK2 are active in the more aggressive form of CRC, whereas TMEM215, PPP1R14A, GPR158, and NAPSB are active in the less aggressive form. Full article
(This article belongs to the Section Molecular Genetics)
12 pages, 959 KiB  
Article
Pharmacogenetics and Pharmacokinetics of Moxifloxacin in MDR-TB Patients in Indonesia: Analysis for ABCB1 and SLCO1B1
by Nurul Annisa, Nadiya N. Afifah, Prayudi Santoso, Vycke Yunivita, Lindsey H. M. te Brake, Rob E. Aarnoutse, Melisa I. Barliana and Rovina Ruslami
Antibiotics 2025, 14(2), 204; https://doi.org/10.3390/antibiotics14020204 - 16 Feb 2025
Viewed by 262
Abstract
Background/Objectives: Studies show that SNPs in ABCB1 rs2032582 and SLCO1B1 rs4149015 affect the PK profile of moxifloxacin, a key drug for MDR-TB. This study aimed to assess the genotype frequencies of ABCB1 rs2032582 and SLCO1B1 rs4149015; describe moxifloxacin AUC0–24 and C [...] Read more.
Background/Objectives: Studies show that SNPs in ABCB1 rs2032582 and SLCO1B1 rs4149015 affect the PK profile of moxifloxacin, a key drug for MDR-TB. This study aimed to assess the genotype frequencies of ABCB1 rs2032582 and SLCO1B1 rs4149015; describe moxifloxacin AUC0–24 and Cmax; and evaluate the association between genotype variations and moxifloxacin AUC0–24 and Cmax, corrected for the effect of other determinants in MDR-TB patients in Indonesia. Methods: The genotypes were identified using DNA sequencing. Plasma samples for PK analysis were collected at either two or four timepoints post-dose, at steady state. AUC0–24 values were assessed with a limited sampling formula. A multivariate linear regression analysis identified the determinants for moxifloxacin AUC0–24 and Cmax. Results: We recruited 204 MDR-TB patients for PG analysis, with 80 providing PK samples. The majority of the ABCB1 and SLCO1B1 genotypes were wildtype (GG), 41.7% and 93.6%, respectively. The geometric mean AUC0–24 for moxifloxacin was 78.6 mg·h/L and that for Cmax was 6.1 mg/L. No statistically significant difference in exposure to moxifloxacin could be shown between the genotypes. Sex, age, and dose in mg/kg/body weight were significant determinants of the AUC0–24 of moxifloxacin. Conclusions: The major genotype of ABCB1 rs2032582 and SLCO1B1 rs4149015 was wildtype, and the exposure to moxifloxacin was high but not related to the studied genotype in an Indonesian population. Full article
(This article belongs to the Section Pharmacokinetics and Pharmacodynamics of Drugs)
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<p>Patient tree.</p>
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<p>(<b>a</b>) Boxplot <span class="html-italic">ABCB1</span> rs2032582 genotype with moxifloxacin geometric mean AUC<sub>0–24</sub>; (<b>b</b>) boxplot <span class="html-italic">ABCB1</span> rs2032582 genotype with moxifloxacin geometric mean C<sub>max</sub>.</p>
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<p>(<b>a</b>) Boxplot <span class="html-italic">SLCO1B1</span> rs4149015 genotype with moxifloxacin geometric mean AUC<sub>0–24</sub>; (<b>b</b>) boxplot <span class="html-italic">SLCO1B1</span> rs4149015 genotype with moxifloxacin geometric mean C<sub>max</sub>.</p>
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19 pages, 1922 KiB  
Article
Characterization of the Complete Mitochondrial Genome of Dwarf Form of Purpleback Flying Squid (Sthenoteuthis oualaniensis) and Phylogenetic Analysis of the Family Ommastrephidae
by Wenjuan Duo, Lei Xu, Mohd Johari Mohd Yusof, Yingmin Wang, Seng Beng Ng and Feiyan Du
Genes 2025, 16(2), 226; https://doi.org/10.3390/genes16020226 - 15 Feb 2025
Viewed by 161
Abstract
Background: The Ommastrephidae family of cephalopods is important in marine ecosystems as both predators and prey. Species such as Todarodes pacificus, Illex argentinus, and Dosidicus gigas are economically valuable but are threatened by overfishing and environmental changes. The genus Sthenoteuthis, [...] Read more.
Background: The Ommastrephidae family of cephalopods is important in marine ecosystems as both predators and prey. Species such as Todarodes pacificus, Illex argentinus, and Dosidicus gigas are economically valuable but are threatened by overfishing and environmental changes. The genus Sthenoteuthis, especially S. oualaniensis, shows significant morphological and genetic variation, including medium-sized and dwarf forms found in the South China Sea. Methods: Specimens of S. oualaniensis were collected from the South China Sea, their genomic DNA sequenced, and phylogenetic relationships analyzed using mitochondrial genomes from various Ommastrephidae species. Results: The study presents the complete mitochondrial genome of the dwarf form of S. oualaniensis (20,320 bp) and compares it with the medium-sized form, revealing a typical vertebrate structure with 13 protein-coding genes, 21 tRNA genes, and 2 rRNA genes, along with a strong AT bias. Nucleotide composition analysis shows a 12% genetic divergence between the two forms, suggesting a recent common ancestor and potential cryptic speciation, with all protein-coding genes exhibiting purifying selection based on Ka/Ks ratios below 1. Conclusions: The mitochondrial genome of the dwarf form of S. oualaniensis shows a close evolutionary relationship with the medium-sized form and a 12% genetic divergence, suggesting potential cryptic speciation. These findings underscore the importance of mitochondrial analysis in understanding speciation and guiding future conservation efforts. Full article
19 pages, 4929 KiB  
Review
Essays on the Binary Representations of the DNA Data
by Evgeny V. Mavrodiev and Nicholas E. Mavrodiev
DNA 2025, 5(1), 10; https://doi.org/10.3390/dna5010010 - 14 Feb 2025
Viewed by 285
Abstract
The advancement of modern genomics has led to the large-scale industrial production of molecular data and scientific outcomes. Simultaneously, conventional DNA character alignments (sequence alignments) are utilized for DNA-based phylogenetic analyses without further recoding procedures or any a priori determination of character polarity, [...] Read more.
The advancement of modern genomics has led to the large-scale industrial production of molecular data and scientific outcomes. Simultaneously, conventional DNA character alignments (sequence alignments) are utilized for DNA-based phylogenetic analyses without further recoding procedures or any a priori determination of character polarity, contrary to the requirements of foundations of phylogenetic systematics. These factors are the primary reasons why the binary perspective has not been implemented in modern molecular phylogenetics. In this study, we demonstrate how to recode conventional DNA data into various types of binary matrices, either unpolarized or with established polarity. Despite its historical foundation, our analytical approach to DNA sequence data has not been adequately explored since the inception of the molecular age. Binary representations of conventional DNA alignments allow for the analysis of molecular data from a purely comparative or static perspective. Furthermore, we show that the binarization of DNA data possesses broad mathematical and cultural connotations, making them intriguing regardless of their applications to different phylogenetic procedures. Full article
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Figure 1
<p>(<b>A</b>) Results of maximum parsimony (MP hereinafter) analyses of the conventional plastid genomic DNA matrix of the bamboos (Arundinarieae, Poaceae, flowering plants) from [<a href="#B28-dna-05-00010" class="html-bibr">28</a>]. Final trees were rooted relative to <span class="html-italic">Dendrocalamus latiflorus</span> Munro [<a href="#B28-dna-05-00010" class="html-bibr">28</a>]. The cladogram represents the median consensus tree based on Robinson–Foulds (RF) distance (with the best score found = 8837) of 184 shortest output trees of length = 5019 (CI = 0.89, RI = 0.91). The number of taxa = 157. All constant characters from the original alignment are excluded from the analysis. The number of variable characters = 4304, number of parsimony-informative characters = 2003. * nodes received MP Jackknife (JK) support &gt; 50% after 20,000 fast JK replicates; ! nodes recovered MP Bootstrap support in the analysis from [<a href="#B28-dna-05-00010" class="html-bibr">28</a>] (200 full heuristic replicates). (<b>B</b>) Results of MP of the binary representation of the conventional DNA matrix from A., re-coded following the proposed <span class="html-italic">1001</span> Method 1. Initial binary data were polarized before analysis relative to <span class="html-italic">D. latiflorus</span>, assumed as an outgroup [<a href="#B28-dna-05-00010" class="html-bibr">28</a>]. The cladogram represents the majority-rule consensus of 191 shortest output trees of length = 10,014 (CI = 0.88, RI = 0.89). The number of taxa = 157. The number of binary characters = 8783, number of parsimony-informative characters = 4088. * nodes received MP Jackknife (JK) support &gt; 50% after 20,000 fast JK replicates. (<b>C</b>) Results of MP analyses of the binary representation of the conventional DNA matrix from A., re-coded following the proposed <span class="html-italic">1001</span> Method 2. Data polarized before analysis relative to <span class="html-italic">D. latiflorus</span>, assumed as an out-group based on the previous results of [<a href="#B28-dna-05-00010" class="html-bibr">28</a>]. The cladogram represents the majority-rule consensus of 139 shortest output trees of length = 4993 (CI = 0.89, RI = 0.91). The number of taxa = 157. The number of binary characters = 4993, number of parsimony-informative characters = 2027. * nodes received MP Jackknife (JK) support &gt; 50% after 20,000 fast JK replicates. All MP analyses were conducted using program PAUPrat [<a href="#B29-dna-05-00010" class="html-bibr">29</a>,<a href="#B30-dna-05-00010" class="html-bibr">30</a>,<a href="#B31-dna-05-00010" class="html-bibr">31</a>] as implemented in CIPRES [<a href="#B32-dna-05-00010" class="html-bibr">32</a>] following 200 ratchet replicates with no more than 10 trees of length greater than or equal to 1 saved in each replicate, and the TBR branch swapping/MulTrees option in effect; -pct = 20%, all characters weighted uniformly, and gaps were treated as ‘‘missing”. MP jackknifing [<a href="#B33-dna-05-00010" class="html-bibr">33</a>] was conducted using PAUP* version 4.a168 [<a href="#B31-dna-05-00010" class="html-bibr">31</a>] (PAUP* hereinafter) as implemented in CIPRES [<a href="#B32-dna-05-00010" class="html-bibr">32</a>]. Robinson–Foulds consensus [<a href="#B14-dna-05-00010" class="html-bibr">14</a>,<a href="#B34-dna-05-00010" class="html-bibr">34</a>] calculated using RFS version 2.0 [<a href="#B34-dna-05-00010" class="html-bibr">34</a>]. Majority-rule consensus calculated in PAUP* [<a href="#B31-dna-05-00010" class="html-bibr">31</a>]. Branches with a minimum length of zero collapsed. All gaps and ambiguities of the conventional DNA matrix (<b>A</b>) were recoded as missing data (“?”) before binary permutations. Roman numerals correspond to the “major lineages” of Arundinarieae [<a href="#B28-dna-05-00010" class="html-bibr">28</a>].</p>
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<p>The results of two three-taxon statement analyses (3TA hereinafter) of Clades 1 and 2 (<a href="#dna-05-00010-f001" class="html-fig">Figure 1</a>). The DNA alignments have been polarized following <span class="html-italic">1001</span> Method 2 and subsequently established as binary three-taxon matrices using TAXODIUM version 1.2 [<a href="#B18-dna-05-00010" class="html-bibr">18</a>] (TAXODIUM hereinafter). Following the results of the previous analyses (<a href="#dna-05-00010-f001" class="html-fig">Figure 1</a>), <span class="html-italic">Indocalamus wilsonii</span> (Rendle) C.S.Chao and C.D.Chu (Clade 1) and <span class="html-italic">Bergbambos tessellata</span> (Nees) Stapleton (Clade 2) were assumed to be outgroup taxa before Method 2 was applied to the DNA characters. (<b>A</b>) The results of the first 3TA (Clade 1). Majority-rule consensus of 193 shortest output trees of length = 527,046 (CI = 0.92, RI = 0.91). The number of taxa in the 487168 character–3TA matrix is 72. All 487,168 3TSs are parsimony-informative and weighted uniformly. (<b>B</b>) The results of the second 3TA (Clade 2). Majority-rule consensus of 201 shortest output trees of length = 187,857 (CI = 0.86, RI = 0.83). The number of taxa in the 161,027 character–3TA matrix is 80. All 1,610,278 3TSs are parsimony-informative and weighted uniformly. For the meaning of Roman numerals and the details of the MP analyses, see the legend of <a href="#dna-05-00010-f001" class="html-fig">Figure 1</a>.</p>
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<p>(<b>A</b>) The simplified phylogeny of flowering plants and outgroups resulted from the MP analysis of the 38,553 bp cpDNA alignment from [<a href="#B35-dna-05-00010" class="html-bibr">35</a>]. The general strategy of the analysis is described in [<a href="#B18-dna-05-00010" class="html-bibr">18</a>]. The heuristic search for the most parsimonious tree was performed with the implied weights [<a href="#B36-dna-05-00010" class="html-bibr">36</a>] included in the search procedure, and the value of the <span class="html-italic">k</span>-function was assigned as three. The phylogeny is established as a single phylogram. Goloboff fit = −10,023.39940, with the actual length of the tree equal to 48186, CI = 0.55, RI = 0.61. The number of informative characters is equal to 13,328. (<b>B</b>) The most parsimonious hierarchy of patterns was obtained from the MP analysis of the same strategy as in (<b>A</b>). The latter was based on the polarized binary matrix recoded from the conventional cpDNA alignment (<b>A</b>) following <span class="html-italic">1001</span> Method 1, with <span class="html-italic">Cryptomeria</span> (Cupressaceae Bartlett, gymnosperms) assumed as the best outgroup. The hierarchy of patterns is established as a single cladogram. Goloboff fit = −24165.80162 with the actual length of the tree equal to 102,724, CI = 0.49, RI = 0.60. The number of informative characters equals 32,141. (<b>C</b>). The most parsimonious hierarchy of patterns resulted from the MP analysis, which followed the same strategy as in A (see above) but without implied weights [<a href="#B36-dna-05-00010" class="html-bibr">36</a>] included in the search procedure. The analysis was based on the polarized binary matrix recoded from the conventional cpDNA alignment (<b>A</b>) following <span class="html-italic">1001</span> Method 2, assuming <span class="html-italic">Cryptomeria</span> as the best outgroup. The hierarchy of patterns is established as a single cladogram of the length 48,552, CI = 0.56, RI = 0.62. The number of informative characters equals 15,653. (<b>D</b>) The single most parsimonious hierarchy of patterns resulted from the MP analysis, which followed the same strategy as in (<b>A</b>) (see above) but without implied weights [<a href="#B36-dna-05-00010" class="html-bibr">36</a>] included in the search procedure. The analysis was based on the three-taxon statement matrix with 1,652,888 fractionally weighted [<a href="#B4-dna-05-00010" class="html-bibr">4</a>,<a href="#B12-dna-05-00010" class="html-bibr">12</a>,<a href="#B14-dna-05-00010" class="html-bibr">14</a>,<a href="#B18-dna-05-00010" class="html-bibr">18</a>] three-taxon statements calculated by TAXODIUM [<a href="#B18-dna-05-00010" class="html-bibr">18</a>]. This matrix is derived from the polarized binary representation (<span class="html-italic">1001</span> Method 2) of the 28,196 bp largest clique, estimated by PHYLIP version 3.695 [<a href="#B19-dna-05-00010" class="html-bibr">19</a>] based on a 38,553 bp cpDNA alignment (<b>A</b>). <span class="html-italic">Cryptomeria</span> is assumed to be the best outgroup. The hierarchy of patterns is established as a cladogram of the length of 230,181.7318, CI = 0.99, RI = 0.99. The number of informative characters (three-taxon statements) equals 1 652 888.</p>
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<p>(<b>A</b>) An unrooted simplified molecular phylogeny of <span class="html-italic">Ceratophyllum</span> (Ceratophyllaceae A. Gray, flowering plants) [<a href="#B37-dna-05-00010" class="html-bibr">37</a>], showing the ambiguous placement of <span class="html-italic">C. echinatum</span> [<a href="#B37-dna-05-00010" class="html-bibr">37</a>]. (<b>B</b>) A summary of the cladistic analyses [<a href="#B38-dna-05-00010" class="html-bibr">38</a>], demonstrating that <span class="html-italic">C. echinatum</span> is a sister group to the narrowly defined genus <span class="html-italic">Ceratophyllum</span>. All analyses (<b>B</b>) were based on the binary ’presence–absence’ representation of the molecular data from [<a href="#B37-dna-05-00010" class="html-bibr">37</a>], adding an artificial all-zero outgroup. As a result of the cladistic analyses of the binary recoded DNA sequence data [<a href="#B37-dna-05-00010" class="html-bibr">37</a>,<a href="#B38-dna-05-00010" class="html-bibr">38</a>], <span class="html-italic">C. echinatum</span> was defined as a sister group of the narrowly circumscribed genus <span class="html-italic">Ceratophyllum</span> [<a href="#B38-dna-05-00010" class="html-bibr">38</a>] and transferred to the newly established genus <span class="html-italic">Fassettia</span> based on the obtained phylogenetic placement [<a href="#B38-dna-05-00010" class="html-bibr">38</a>]. See [<a href="#B38-dna-05-00010" class="html-bibr">38</a>] for details of the cladistic analyses and taxonomic treatment. Clade “<span class="html-italic">Ceratophyllum</span>” is marked with an asterisk (*). This figure also shows the ‘presence–absence’ binary coding (<b>B</b>) of the DNA sequence data (<b>A</b>), as implemented in <span class="html-italic">1001</span>.</p>
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<p>Leibniz’s original four-digit binary representation of Arabic numbers one, two, four, and eight (indicated by exclamation marks, added by us). In the third column of this table, Leibniz himself linked this representation with the combination of solid and dotted lines, each corresponding to one of the four <span class="html-italic">T’ai Hsüan Ching</span> tetragrams (indicated by exclamation marks, added by us), namely the tetragrams <span class="html-italic">Penetration</span>, <span class="html-italic">Legion</span>, <span class="html-italic">Fullness</span>, and <span class="html-italic">Law</span> (<span class="html-italic">Model</span>) [<a href="#B73-dna-05-00010" class="html-bibr">73</a>]. Reproduced from Leibniz’s manuscript <span class="html-italic">De Dyadics</span>, as interpreted and translated by Yakovlev [<a href="#B72-dna-05-00010" class="html-bibr">72</a>], see pp. 195, 201, and 202.</p>
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16 pages, 654 KiB  
Article
Adiponectin C1Q and Collagen Domain Containing rs266729, Cyclin-Dependent Kinase Inhibitor 2A and 2B rs10811661, and Signal Sequence Receptor Subunit 1 rs9505118 Polymorphisms and Their Association with Gestational Diabetes Mellitus: A Case-Control Study in a Romanian Population
by Mihai Muntean, Claudiu Mărginean, Elena Silvia Bernad, Claudia Bănescu, Victoria Nyulas, Irina Elena Muntean and Vladut Săsăran
Int. J. Mol. Sci. 2025, 26(4), 1654; https://doi.org/10.3390/ijms26041654 - 14 Feb 2025
Viewed by 227
Abstract
Gestational diabetes mellitus (GDM) and type 2 diabetes mellitus (T2DM) are public health concerns worldwide. These two diseases share the same pathophysiological and genetic similarities. This study aimed to investigate the T2DM known single nucleotide polymorphisms (SNPs) of the adiponectin C1Q and collagen [...] Read more.
Gestational diabetes mellitus (GDM) and type 2 diabetes mellitus (T2DM) are public health concerns worldwide. These two diseases share the same pathophysiological and genetic similarities. This study aimed to investigate the T2DM known single nucleotide polymorphisms (SNPs) of the adiponectin C1Q and collagen domain containing (ADIPOQ), cyclin-dependent kinase inhibitor 2A and 2B (CDKN2A/2B), and signal sequence receptor subunit 1 (SSR1) genes in a cohort of Romanian GDM pregnant women and perinatal outcomes. DNA was isolated from the peripheral blood of 213 pregnant women with (n = 71) or without (n = 142) GDM. Afterward, ADIPOQ (rs266729), CDKN2A/2B (rs10811661), and SSR1 (rs9505118) gene polymorphisms were genotyped using TaqMan Real-Time PCR analysis. Women with GDM had a higher pre-pregnancy body mass index (BMI) (p < 0.0001), higher BMI (p < 0.0001), higher insulin resistance homeostatic model assessment (IR-HOMA) (p = 0.0002), higher insulin levels (p = 0.003), and lower adiponectin levels (p = 0.004) at birth compared to pregnant women with normoglycemia. GDM pregnant women had gestational hypertension (GH) more frequently during pregnancy (p < 0.0001), perineal lacerations more frequently during vaginal birth (p = 0.03), and more macrosomic newborns (p < 0.0001) than pregnant women from the control group. We did not find an association under any model (allelic, genotypic, dominant, or recessive) of ADIPOQ rs266729, CDKN2A/2B rs10811661, and SSR1 rs9505118 polymorphisms and GDM. In correlation analysis, we found a weak positive correlation (r = 0.24) between the dominant model GG + CG vs. CC of rs266729 and labor induction failure. In the dominant model TT vs. CC + CT of rs10811661, we found a weak negative correlation between this model and perineal lacerations. Our results suggest that the ADIPOQ rs266729, the CDKN2A/2B rs10811661, and the SSR1 rs9505118 gene polymorphisms are not associated with GDM in a cohort of Romanian pregnant women. Full article
(This article belongs to the Special Issue Molecular Therapeutics for Diabetes and Related Complications)
19 pages, 8079 KiB  
Article
Perioperative Multi-Kingdom Gut Microbiota Alters in Coronary Artery Bypass Grafting
by Zhou Fu, Yanxiong Jia, Jing Zhao, Yulin Guo, Boqia Xie, Kun An, Wen Yuan, Yihang Chen, Jiuchang Zhong, Zhaohui Tong, Xiaoyan Liu and Pixiong Su
Biomedicines 2025, 13(2), 475; https://doi.org/10.3390/biomedicines13020475 - 14 Feb 2025
Viewed by 229
Abstract
Background: Coronary artery bypass grafting (CABG) is one of the main treatments for coronary heart disease (CHD). Gut microbiota, including bacteria, fungi, archaea, and virus, has been reported to be associated with CHD. However, the changes in the multi-kingdom gut microbiota after [...] Read more.
Background: Coronary artery bypass grafting (CABG) is one of the main treatments for coronary heart disease (CHD). Gut microbiota, including bacteria, fungi, archaea, and virus, has been reported to be associated with CHD. However, the changes in the multi-kingdom gut microbiota after CABG are not yet clear. This study aimed to explore the changes in multi-kingdom gut microbiota during the early postoperative period of CABG. Methods: We collected fecal samples from 40 patients before and 1 week after CABG surgery. Metagenomic sequencing was used to detect the microbial spectrum and gene functions in the patients’ fecal samples. Results: Post-CABG patients exhibited significant changes in the composition of multi-kingdom gut microbiota and gene functions. Among bacteria, beneficial species such as Bifidobacterium, Bacteroides, and Blautia were significantly reduced after CABG, while the harmful species Enterococcus was significantly increased. In fungi, Schizosaccharomyces pombe was significantly decreased in the postoperative group, while Saccharomyces cerevisiae and Aspergillus chevalieri were significantly increased postoperatively. Spearman correlation analysis indicated that Schizosaccharomyces pombe had positive interactions with beneficial bacteria such as Lachnospiraceae, Ruminococcus, and Blautia. Among archaea, the preoperatively enriched Methanomethylovorans-SGB40959 was significantly reduced postoperatively, and Spearman correlation analysis showed a significant positive interaction with probiotics Ruminococcus and Dorea. In viruses, the phage Enterococcus virus EFP01, which infects Enterococcus, was significantly increased postoperatively and showed a significant positive interaction with Enterococcus. Additionally, postoperative dysregulation of gene functions such as the Phosphoenolpyruvate-dependent Sugar Phosphotransferase System (PTS), Transposition, DNA-mediated, and Transposase Activity was observed, and Spearman correlation analysis indicated significant correlations between the dysregulated gene functions and the microbial communities. Conclusions: This study comprehensively revealed the changes in multi-kingdom species post-CABG. The reduction of beneficial microorganisms and the increase of harmful microorganisms after surgery are of significant clinical importance for understanding the overall health status of post-CABG patients and for optimizing postoperative treatment plans. Future research needs to further explore how to improve the prognosis of post-CABG patients by modulating the gut microbiota. Full article
(This article belongs to the Section Microbiology in Human Health and Disease)
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Figure 1
<p>Postoperative changes in Alpha diversity. Alpha diversity of multi-kingdom microbiota before and after surgery, as represented by Shannon index, Simpson index, inverse Simpson index, species richness, and evenness. (<b>a</b>–<b>e</b>) Bacteria; (<b>f</b>–<b>j</b>) Fungi; (<b>k</b>–<b>o</b>) Archaea; (<b>p</b>–<b>t</b>) Virus. * <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; 0.0001.</p>
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<p>Postoperative Changes in Abundance of Bacteria. (<b>a</b>–<b>c</b>) Relative abundance of the top ten most common bacterial species at the phylum, genus, and species levels before (T1) and after surgery (T2). The vertical axis shows species abundance, and the horizontal axis shows the preoperative (T1) and postoperative (T2) groups. (<b>d</b>) Results from Linear Discriminant Analysis (LefSe), highlighting bacterial species with an LDA score greater than 3.5 at various taxonomic levels (phylum to species) between preoperative and postoperative groups.</p>
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<p>Postoperative changes in abundance of fungi. (<b>a</b>–<b>c</b>) Relative abundance of the top ten most common fungal species at the phylum, genus, and species levels before (T1) and after surgery (T2). The vertical axis shows species abundance, and the horizontal axis shows preoperative (T1) and postoperative (T2) groups. (<b>d</b>) Linear Discriminant Analysis (LefSe) highlighting fungal species with an LDA score greater than 3.5, comparing the preoperative and postoperative groups across taxonomic levels from phylum to species.</p>
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<p>Postoperative changes in abundance of archaea. (<b>a</b>–<b>c</b>) Relative abundance of the top ten most common archaeal species at the phylum, genus, and species levels before (T1) and after surgery (T2). The vertical axis shows species abundance, and the horizontal axis shows preoperative (T1) and postoperative (T2) groups. (<b>d</b>) Linear Discriminant Analysis (LefSe) highlighting archaeal species with an LDA score greater than 3.3, comparing the preoperative and postoperative groups across taxonomic levels from phylum to species.</p>
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<p>(<b>a</b>–<b>c</b>) Relative abundance of the top ten viral taxa at the family, genus, and species levels before (T1) and after surgery (T2). The vertical axis represents the relative abundance of taxa, and the horizontal axis represents the preoperative (T1) and postoperative (T2) groups. (<b>d</b>) Linear Discriminant Analysis (LefSe) highlighting differential viral taxa with LDA scores greater than 3.3, comparing preoperative and postoperative groups across taxonomic levels from phylum to species.</p>
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<p>Differences in GO genes, MetaCyc pathways, and CARD genes between T1 (preoperative) and T2 (postoperative) groups. (<b>a</b>–<b>c</b>) Principal coordinates analysis (PCoA) based on Bray–Curtis distances showing differences in GO genes, MetaCyc pathways, and CARD genes between T1 and T2. PCoA1 and PCoA2 represent the two components with the greatest variance, and the percentages indicate their contribution to total variance. (<b>d</b>–<b>f</b>) Non-metric multidimensional scaling (NMDS) showing differences in GO terms, MetaCyc pathways, and CARD genes between T1 and T2.</p>
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<p>Correlations among multi-kingdom species. (<b>a</b>) Correlations between differentially enriched bacteria, fungi, archaea, and viruses in the pre-surgery group; this panel shows the correlations between pre-surgery and post-surgery enriched differential bacteria, fungi, archaea, and viruses in the pre-surgery group. (<b>b</b>) Correlations between differentially enriched bacteria, fungi, archaea, and viruses in the post-surgery group: this panel shows the correlations between pre-surgery and post-surgery enriched differential bacteria, fungi, archaea, and viruses in the post-surgery group. (Pre represents species enriched before surgery, Po represents species enriched after surgery, calculated using Spearman correlation). * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>The correlation between multi-kingdom species and Metacyc and CARD. (<b>a</b>) Correlation between multi-kingdom species and GO genes (Pre represents species or genes enriched before surgery, Po represents species or genes enriched after surgery). (<b>b</b>) Correlation between multi-kingdom species and metabolic pathways (Metacyc) (Pre represents species or metabolic pathways enriched before surgery, Po represents species or metabolic pathways enriched after surgery). (<b>c</b>) Correlation between multi-kingdom species and resistance genes (CARD) (Pre represents species or resistance genes enriched before surgery, Po represents species or resistance genes enriched after surgery). * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Postoperative Changes in Beta diversity. Changes in beta diversity at phylum, genus, species levels of bacteria, fungi, archaea and family, genus, species levels of virus based on principal component analysis (PCA) and non-metric multidimensional scaling (NMDS). (<b>a</b>–<b>f</b>) Bacteria; (<b>g</b>–<b>l</b>) Fungi; (<b>m</b>–<b>r</b>) Archaea; (<b>s</b>–<b>x</b>) Virus. PCA: The x–axis and y–axis represent the first and second principal components, respectively, with percentages indicating their contribution to the total variance between samples. Each point represents an individual sample, and samples from the same group are color-coded. Bray–Curtis distances were used to calculate Adonis R<sup>2</sup> and <span class="html-italic">p</span> values. NMDS: Each point represents a sample, and the distance between points reflects the degree of dissimilarity between samples. Samples from the same group are color-coded. A stress value of less than 0.2 indicates acceptable reliability for the NMDS results. T1 represents preoperative group and T2 represents postoperative group.</p>
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<p>LEfSe Analysis of GO Terms, MetaCyc Pathways, and CARD Genes. (<b>a</b>) LEfSe analysis (LDA &gt; 2.5) showing differentially expressed GO terms between T1 (preoperative) and T2 (postoperative) groups, covering biological processes (BP), cellular components (CC), and molecular functions (MF). (<b>b</b>) LEfSe analysis (LDA &gt; 2.5) showing differentially expressed metabolic pathways between T1 and T2 groups. (<b>c</b>) LEfSe analysis (LDA &gt; 2.5) identifying differentially expressed antibiotic resistance genes between T1 and T2 groups. Orange indicates genes enriched and upregulated post-surgery, while blue indicates genes enriched pre-surgery and downregulated post-surgery.</p>
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22 pages, 27088 KiB  
Article
Integrated Analysis of Somatic DNA Variants and DNA Methylation of Tumor Suppressor Genes in Colorectal Cancer
by Hisashi Nishiki, Hiroki Ura, Sumihito Togi, Hisayo Hatanaka, Hideto Fujita, Hiroyuki Takamura and Yo Niida
Int. J. Mol. Sci. 2025, 26(4), 1642; https://doi.org/10.3390/ijms26041642 - 14 Feb 2025
Viewed by 209
Abstract
DNA methylation of tumor suppressor genes in cancer is known to be a mechanism for silencing gene expression, but much remains unknown about its extent and relationship to somatic variants at the DNA sequence level. In this study, we comprehensively analyzed DNA methylation [...] Read more.
DNA methylation of tumor suppressor genes in cancer is known to be a mechanism for silencing gene expression, but much remains unknown about its extent and relationship to somatic variants at the DNA sequence level. In this study, we comprehensively analyzed DNA methylation and somatic variants of all gene regions across the genome of the major tumor suppressor genes, APC, TP53, SMAD4, and mismatch repair genes in colorectal cancer using a novel next-generation sequencing-based analysis method. The Targeted Methyl Landscape (TML) shows that DNA hypermethylation patterns of these tumor suppressor genes in colorectal cancer are more complex and widespread than previously thought. Extremely high levels of DNA methylation were observed in relatively long regions around exon 1A of APC and exon 1 and surrounding region of MLH1. DNA hypermethylation occurred whether or not somatic DNA variants were present in the tumor. Even in tumors where the loss of heterozygosity has been demonstrated by somatic variants alone, additional methylation of the same gene can occur. Our data demonstrate that somatic variants and hypermethylation of these tumor suppressor genes were considered independent, parallel events, not exclusive of each other or having one event affecting the other. Full article
(This article belongs to the Section Molecular Oncology)
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Figure 1
<p>An example of an IGV image of vLAS, TgCap and TML (ST-57 tumor DNA). The CpG methylations shown in the TML graph have gaps corresponding to the promoter regions of each gene (white arrows), indicating hypomethylation of CpG islands. The black arrows show the orientation of each gene on the genome.</p>
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<p>IGV image of TML CpG methylation ratio indicated by Bismark bedGraph. (<b>a</b>) <span class="html-italic">APC</span> promotor exon 1A. (<b>b</b>) <span class="html-italic">APC</span> promotor exon 1B. ND; Normal colon tissue DNA, ND_Σ; Sum of the CpG methylation ratios of ND in the target region, TD; colon tumor DNA, TD_Σ; Sum of the CpG methylation ratios of TD in the target region, TD_Z; Methylation Z score of TD. (<b>c</b>) <span class="html-italic">TP53</span> exon 1~intron 1. (<b>d</b>) <span class="html-italic">SMAD4</span> exon 1~intron 1. (<b>e</b>) <span class="html-italic">EPCAM MSH2</span> intervening region. (<b>f</b>) <span class="html-italic">MSH2</span> intron 1~2. (<b>g</b>) <span class="html-italic">MSH6</span> exon 1~intron 1. (<b>h</b>) <span class="html-italic">MLH1</span> 5′UTR~exon 1.</p>
Full article ">Figure 2 Cont.
<p>IGV image of TML CpG methylation ratio indicated by Bismark bedGraph. (<b>a</b>) <span class="html-italic">APC</span> promotor exon 1A. (<b>b</b>) <span class="html-italic">APC</span> promotor exon 1B. ND; Normal colon tissue DNA, ND_Σ; Sum of the CpG methylation ratios of ND in the target region, TD; colon tumor DNA, TD_Σ; Sum of the CpG methylation ratios of TD in the target region, TD_Z; Methylation Z score of TD. (<b>c</b>) <span class="html-italic">TP53</span> exon 1~intron 1. (<b>d</b>) <span class="html-italic">SMAD4</span> exon 1~intron 1. (<b>e</b>) <span class="html-italic">EPCAM MSH2</span> intervening region. (<b>f</b>) <span class="html-italic">MSH2</span> intron 1~2. (<b>g</b>) <span class="html-italic">MSH6</span> exon 1~intron 1. (<b>h</b>) <span class="html-italic">MLH1</span> 5′UTR~exon 1.</p>
Full article ">Figure 2 Cont.
<p>IGV image of TML CpG methylation ratio indicated by Bismark bedGraph. (<b>a</b>) <span class="html-italic">APC</span> promotor exon 1A. (<b>b</b>) <span class="html-italic">APC</span> promotor exon 1B. ND; Normal colon tissue DNA, ND_Σ; Sum of the CpG methylation ratios of ND in the target region, TD; colon tumor DNA, TD_Σ; Sum of the CpG methylation ratios of TD in the target region, TD_Z; Methylation Z score of TD. (<b>c</b>) <span class="html-italic">TP53</span> exon 1~intron 1. (<b>d</b>) <span class="html-italic">SMAD4</span> exon 1~intron 1. (<b>e</b>) <span class="html-italic">EPCAM MSH2</span> intervening region. (<b>f</b>) <span class="html-italic">MSH2</span> intron 1~2. (<b>g</b>) <span class="html-italic">MSH6</span> exon 1~intron 1. (<b>h</b>) <span class="html-italic">MLH1</span> 5′UTR~exon 1.</p>
Full article ">Figure 2 Cont.
<p>IGV image of TML CpG methylation ratio indicated by Bismark bedGraph. (<b>a</b>) <span class="html-italic">APC</span> promotor exon 1A. (<b>b</b>) <span class="html-italic">APC</span> promotor exon 1B. ND; Normal colon tissue DNA, ND_Σ; Sum of the CpG methylation ratios of ND in the target region, TD; colon tumor DNA, TD_Σ; Sum of the CpG methylation ratios of TD in the target region, TD_Z; Methylation Z score of TD. (<b>c</b>) <span class="html-italic">TP53</span> exon 1~intron 1. (<b>d</b>) <span class="html-italic">SMAD4</span> exon 1~intron 1. (<b>e</b>) <span class="html-italic">EPCAM MSH2</span> intervening region. (<b>f</b>) <span class="html-italic">MSH2</span> intron 1~2. (<b>g</b>) <span class="html-italic">MSH6</span> exon 1~intron 1. (<b>h</b>) <span class="html-italic">MLH1</span> 5′UTR~exon 1.</p>
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<p>Library preparation methods of vLAS, TgCap and TML.</p>
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11 pages, 2843 KiB  
Article
Genetic Diversity of the Traditional Economic Green Alga Capsosiphon fulvescens in Republic of Korea
by Soon Jeong Lee, Eun-Young Lee and Sang-Rae Lee
Diversity 2025, 17(2), 132; https://doi.org/10.3390/d17020132 - 14 Feb 2025
Viewed by 234
Abstract
The taxonomic position of the green alga Capsosiphon fulvescens was first reported from Northern Europe and has since been reported from all over the world, including Korea. In Korea, C. fulvescens has been used as an essential edible economic alga for approximately 570 [...] Read more.
The taxonomic position of the green alga Capsosiphon fulvescens was first reported from Northern Europe and has since been reported from all over the world, including Korea. In Korea, C. fulvescens has been used as an essential edible economic alga for approximately 570 years, from the time of the Joseon Dynasty to the present, and is currently under development as a new aquaculture strain. Therefore, examining the taxonomic relationships between the European and Korean C. fulvescens is important. In this study, we analyzed nuclear 18S rDNA and ITS regions and compared them with the DNA sequences of authentic materials of North Atlantic C. fulvescens. Additionally, rbcL and tufA genes were sequenced to analyze genetic variations among populations. The results showed that the Korean and European C. fulvescens were different species. Moreover, the Korean C. fulvescens was distantly related to the North Atlantic C. fulvescens at the order level. Moreover, the Korean C. fulvescens formed a sister group with the North Pacific Pseudothrix borealis. Cryptic genetic diversity was observed at the intraspecific level among the Korean populations. These findings will help in tracing the origin of the Korean C. fulvescens and provide new genetic insights into this species. Full article
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<p>Aquaculture farm showing the cultivation of <span class="html-italic">Capsosiphon fulvescens</span> in Korea (Jangheung, Republic of Korea; 30 April 2014).</p>
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<p>A herbarium specimen (National Institute of Biological Resources [NIBR]) examined in this study (Wando, Republic of Korea, 15 March 2022; NIBR accession number: NIBRCL0000115865).</p>
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<p>Morphological features of vegetative cells of Korean <span class="html-italic">Capsosiphon fulvescens</span>. Scale bar: (1) 1 cm, (2) 1 mm, (3) 100 μm, and (4) 100 μm.</p>
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<p>Neighbor-joining tree constructed using sequences of the 18S rDNA region by applying Kimura’s two-parameter model. The bootstrap test was conducted with 2000 replicates.</p>
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<p>Neighbor-joining tree constructed using sequences of the internal transcribed spacer (ITS) region by applying Kimura’s two-parameter model. The bootstrap test was conducted with 2000 replicates.</p>
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21 pages, 6511 KiB  
Article
Bacterial Community Composition and Diversity of Soils from Different Geographical Locations in the Northeastern USA
by Luis Jimenez
Microbiol. Res. 2025, 16(2), 47; https://doi.org/10.3390/microbiolres16020047 - 14 Feb 2025
Viewed by 244
Abstract
Soil is the most dynamic matrix in the environment and where biogeochemical cycles take place through the activities of microorganisms such as bacteria. A 16S rRNA sequence analysis of seven different soil samples from different geographical locations in the northeastern part of the [...] Read more.
Soil is the most dynamic matrix in the environment and where biogeochemical cycles take place through the activities of microorganisms such as bacteria. A 16S rRNA sequence analysis of seven different soil samples from different geographical locations in the northeastern part of the United States of America was conducted in order to determine bacterial community composition and diversity and whether geographical distance affects community composition. Microbial DNA was extracted from each soil sample and next generation sequencing was performed. Overall, the predominant bacterial phyla with high relative abundance in each soil were found to be members of Pseudomonadota, Actinomycetota, Acidobacteriota, Chloroflexota, and Bacteroidota which comprised the core microbiome in all 7 soils analyzed. At the order level, the top four bacteria belonged to Rhizobiales, Actinomycetales, Gaiellales, and Solirubrobacterales. Bacterial identification at the genus level were predominantly unclassified with an average of 58%. However, when identification was possible, the most abundant genera detected were Bradyrhizobium and Rhodoplanes. Surface soil samples from the states of New York, Maryland, and Delaware showed the lowest bacterial diversity when compared to suburban soil samples from the state of New Jersey. Similarity between bacterial communities decreased with increasing distance, indicating the dispersal limitations of some bacteria to colonize different habitats where some types show high relative abundance and others did not. However, in some samples, deterministic factors such as land management and possible vehicle emissions probably affected the assemblage and diversity of bacterial communities. Stochastic and deterministic processes might have determined the biogeographical distribution of bacteria in soils influencing the community structure and diversity. Full article
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<p>Soil sample locations. Maryland (MD), Delaware (DE), Lodi (L), Weehawken (W), Fair Lawn (FL), Fort Lee (FTL), New York (NY). (Source: [<a href="#B24-microbiolres-16-00047" class="html-bibr">24</a>]).</p>
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<p>Rarefaction measure: observed OTU species over sequences per sample.</p>
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<p>Relative abundance of dominant bacterial phyla in soils.</p>
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<p>Venn diagram of common bacterial phyla. (<b>a</b>) Lodi, Weehawken, Fort Lee, Fair Lawn. (<b>b</b>) Fair Lawn, Maryland, Delaware, New York. (<b>c</b>) Lodi, Maryland, Delaware, New York. (<b>d</b>) Fort Lee, Maryland, Delaware, New York. (<b>e</b>) Weehawken, Maryland, Delaware, New York. Numbers represent common bacterial phyla between sites. Numbers in parentheses represent percentage of common bacterial phyla between sites.</p>
Full article ">Figure 4 Cont.
<p>Venn diagram of common bacterial phyla. (<b>a</b>) Lodi, Weehawken, Fort Lee, Fair Lawn. (<b>b</b>) Fair Lawn, Maryland, Delaware, New York. (<b>c</b>) Lodi, Maryland, Delaware, New York. (<b>d</b>) Fort Lee, Maryland, Delaware, New York. (<b>e</b>) Weehawken, Maryland, Delaware, New York. Numbers represent common bacterial phyla between sites. Numbers in parentheses represent percentage of common bacterial phyla between sites.</p>
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<p>Jaccard similarity index of bacterial phyla between soil samples.</p>
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<p>Relative abundance of predominant bacterial genera in soils.</p>
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<p>Venn diagram of common bacterial genera. (<b>a</b>) Lodi, Weehawken, Fort Lee, Fair Lawn. (<b>b</b>) Fair Lawn, Maryland, Delaware, New York. (<b>c</b>) Lodi, Maryland, Delaware, New York. (<b>d</b>) Fort Lee, Maryland, Delaware, New York. (<b>e</b>) Weehawken, Maryland, Delaware, New York. Numbers represent common bacterial genera between sites. Numbers in parentheses represent percentage of common bacterial genera between sites.</p>
Full article ">Figure 7 Cont.
<p>Venn diagram of common bacterial genera. (<b>a</b>) Lodi, Weehawken, Fort Lee, Fair Lawn. (<b>b</b>) Fair Lawn, Maryland, Delaware, New York. (<b>c</b>) Lodi, Maryland, Delaware, New York. (<b>d</b>) Fort Lee, Maryland, Delaware, New York. (<b>e</b>) Weehawken, Maryland, Delaware, New York. Numbers represent common bacterial genera between sites. Numbers in parentheses represent percentage of common bacterial genera between sites.</p>
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<p>Jaccard similarity index of bacterial genera between soil samples.</p>
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<p>Relative abundance (percentage) of predominant bacterial orders in soil samples.</p>
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<p>Venn diagram of common bacterial orders. (<b>a</b>) Lodi, Weehawken, Fort Lee, Fair Lawn. (<b>b</b>) Fair Lawn, Maryland, Delaware, New York. (<b>c</b>) Lodi, Maryland, Delaware, New York. (<b>d</b>) Fort Lee, Maryland, Delaware, New York. (<b>e</b>) Weehawken, Maryland, Delaware, New York. Numbers represent common bacterial orders between sites. Numbers in parentheses represent percentage of common bacterial orders between sites.</p>
Full article ">Figure 10 Cont.
<p>Venn diagram of common bacterial orders. (<b>a</b>) Lodi, Weehawken, Fort Lee, Fair Lawn. (<b>b</b>) Fair Lawn, Maryland, Delaware, New York. (<b>c</b>) Lodi, Maryland, Delaware, New York. (<b>d</b>) Fort Lee, Maryland, Delaware, New York. (<b>e</b>) Weehawken, Maryland, Delaware, New York. Numbers represent common bacterial orders between sites. Numbers in parentheses represent percentage of common bacterial orders between sites.</p>
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<p>Jaccard similarity index of bacterial orders between soil samples.</p>
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23 pages, 2969 KiB  
Review
Small Interfering RNAs as Critical Regulators of Plant Life Process: New Perspectives on Regulating the Transcriptomic Machinery
by Marta Puchta-Jasińska, Paulina Bolc, Aleksandra Pietrusińska-Radzio, Adrian Motor and Maja Boczkowska
Int. J. Mol. Sci. 2025, 26(4), 1624; https://doi.org/10.3390/ijms26041624 - 14 Feb 2025
Viewed by 160
Abstract
Small interfering RNAs (siRNAs) are a distinct class of regulatory RNAs in plants and animals. Gene silencing by small interfering RNAs is one of the fundamental mechanisms for regulating gene expression. siRNAs are critical regulators during developmental processes. siRNAs have similar structures and [...] Read more.
Small interfering RNAs (siRNAs) are a distinct class of regulatory RNAs in plants and animals. Gene silencing by small interfering RNAs is one of the fundamental mechanisms for regulating gene expression. siRNAs are critical regulators during developmental processes. siRNAs have similar structures and functions to small RNAs but are derived from double-stranded RNA and may be involved in directing DNA methylation of target sequences. siRNAs are a less well-studied class than the miRNA group, and researchers continue to identify new classes of siRNAs that appear at specific developmental stages and in particular tissues, revealing a more complex mode of siRNA action than previously thought. This review characterizes the siRNA classes and their biogenesis process and focuses on presenting their known functions in the regulation of plant development and responses to biotic and abiotic stresses. The review also highlights the exciting potential for future research in this field, proposing methods for detecting plant siRNAs and a bioinformatic pathway for identifying siRNAs and their functions. Full article
(This article belongs to the Special Issue Signaling and Stress Adaptation in Plants)
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<p>The plant RNA universe: messenger RNA (mRNA), non-coding RNA (ncRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), small nuclear RNA (snRNA), and small nucleolar RNA (snoRNA), long noncoding RNA (lncRNA), intermediate-size ncRNA (im-ncRNA), micro RNA (miRNA), small interfering RNA (siRNA), natural antisense siRNA (natsiRNA), trans-acting siRNA (ta-siRNA), heterochromatic siRNA (hcsiRNA), phase-acting siRNA (phasiRNA).</p>
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<p>Biogenesis of siRNAs, ta-siRNAs and phasiRNAs in plants. Biogenesis involves the transcription of the <span class="html-italic">TAS</span> and <span class="html-italic">PHAS</span> loci, cleavage of primary transcripts involving miRNAs or AGO proteins, and the production of mature siRNAs incorporated into the RISC complex with AGO proteins.</p>
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<p>Mechanisms of the transcriptional gene silencing (TGS) or post-transcriptional gene silencing (PTGS) of siRNA-directed genes in plants.</p>
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<p>General experimental methods for analyzing siRNAs.</p>
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<p>General protocol for siRNA identification.</p>
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11 pages, 2751 KiB  
Article
Stage- and Tissue-Specific Expression of MET1 and CMT2 Genes During Germination in Abies koreana E.H.Wilson
by Sun-cheon Hong, Koeun Jeon and Kyu-suk Kang
Forests 2025, 16(2), 337; https://doi.org/10.3390/f16020337 - 14 Feb 2025
Viewed by 246
Abstract
Abies koreana E.H.Wilson (Korean fir), an endangered high-altitude conifer native to South Korea, is facing severe population decline due to climate change and low germination rates. While ecological factors have been studied, the genetic and epigenetic mechanisms underlying its seed development are still [...] Read more.
Abies koreana E.H.Wilson (Korean fir), an endangered high-altitude conifer native to South Korea, is facing severe population decline due to climate change and low germination rates. While ecological factors have been studied, the genetic and epigenetic mechanisms underlying its seed development are still poorly understood. DNA methylation, regulated by MET1 and CMT2, plays a critical role in the stability of gene expression during seed development. This study investigates the expression patterns of MET1 and CMT2 across 12 developmental stages, from pre-germination to post-germination, with a focus on shoot and root tissues. RNA-seq data were analyzed to identify MET1 and CMT2, and expression patterns were validated using RT-qPCR. MET1 showed high sequence conservation with conifers such as Pinus sylvestris, indicating potential conservation of CG methylation mechanisms among conifer species. CMT2 showed lower sequence conservation across species, indicating reduced evolutionary conservation compared to MET1. Tissue-specific analysis showed MET1 being predominantly active in shoots during cotyledon development, while CMT2 was upregulated in roots at later stages. These findings highlight the dynamic and tissue-specific roles of DNA methylation in the seed development of A. koreana, contributing to a better understanding of the genetic and epigenetic mechanisms involved in its germination and early growth. Full article
(This article belongs to the Special Issue Forest Tree Breeding, Testing, and Selection)
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<p>Materials of <span class="html-italic">A. koreana</span> E.H.Wilson: (<b>A</b>) mature cone, (<b>B</b>) dried cone, and (<b>C</b>) embryo dissected from a seed for RNA extraction and gene expression analysis.</p>
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<p>Phylogenetic tree of <span class="html-italic">MET1</span> gene in <span class="html-italic">A. koreana</span>.</p>
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<p>Fold change in gene expression in the shoot tissue for each developmental stage determined via RT-qPCR analysis. (<b>a</b>) <span class="html-italic">MET1</span> and (<b>b</b>) <span class="html-italic">CMT2</span>. Different lowercase letters indicate statistically significant differences among developmental stages, as determined by Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Fold change in gene expression in the root tissue for each = developmental stage determined via RT-qPCR analysis. (<b>a</b>) <span class="html-italic">MET1</span> and (<b>b</b>) <span class="html-italic">CMT2</span>. Different lowercase letters indicate statistically significant differences among developmental stages, as determined by Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Differences in fold change in the gene expression of <span class="html-italic">MET1</span> and <span class="html-italic">CMT2</span> by tissue within the same stage. (<b>A</b>) <span class="html-italic">MET1</span> in Stage 8, (<b>B</b>) <span class="html-italic">CMT2</span> in Stage 3, (<b>C</b>) <span class="html-italic">CMT2</span> in Stage 4, (<b>D</b>) <span class="html-italic">CMT2</span> in Stage 5, (<b>E</b>) <span class="html-italic">CMT2</span> in Stage 7, and (<b>F</b>) <span class="html-italic">CMT2</span> in Stage 10. Asterisks (*) indicate statistically significant differences between groups (<span class="html-italic">p</span> ≤ 0.05, <span class="html-italic">t</span>-test).</p>
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