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Search Results (3,624)

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22 pages, 2852 KiB  
Article
Influence of Supercritical Fluid Extraction Process on Techno-Functionality of Enzymatically Derived Peptides from Filter-Pressed Shrimp Waste
by Narjes Badfar, Ali Jafarpour, Federico Casanova, Lucas Sales Queiroz, Adane Tilahun Getachew, Charlotte Jacobsen, Flemming Jessen and Nina Gringer
Mar. Drugs 2025, 23(3), 122; https://doi.org/10.3390/md23030122 - 11 Mar 2025
Abstract
This study explored how combining supercritical fluid extraction (SFE) and enzymatic hydrolysis influences the structure and functionality of peptides recovered from filter-pressed shrimp waste. Freeze-dried press cake (PC) was defatted via SFE and hydrolyzed using Alcalase (ALC) and trypsin (TRYP). ALC-treated PC achieved [...] Read more.
This study explored how combining supercritical fluid extraction (SFE) and enzymatic hydrolysis influences the structure and functionality of peptides recovered from filter-pressed shrimp waste. Freeze-dried press cake (PC) was defatted via SFE and hydrolyzed using Alcalase (ALC) and trypsin (TRYP). ALC-treated PC achieved the highest protein recovery (63.49%), extraction yield (24.73%), and hydrolysis degree (18.10%) (p < 0.05). SFE-treated hydrolysates showed higher zeta potential (−47.23 to −49.93 mV) than non-SFE samples (−25.15 to −38.62 mV) but had larger droplet sizes, indicating lower emulsion stability. SC-ALC displayed reduced fluorescence intensity and a red shift in maximum wavelength. TRYP hydrolysates reduced interfacial tension (20 mN/m), similar to sodium caseinate (Na-Cas, 13 mN/m), but with lesser effects. Dilatational rheology showed TRYP hydrolysates formed stronger, solid-like structures. These results emphasize protease efficacy over SFE for extracting functional compounds, enhancing shrimp waste valorization. Full article
(This article belongs to the Special Issue Marine-Derived Ingredients for Functional Foods)
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Figure 1

Figure 1
<p>Size exclusion chromatograms illustrating the profiles of shrimp shell hydrolysates (SPHs) obtained without or after supercritical fluid extraction (SFE). (<span style="color:red">—</span>PC-ALC <span style="color:#FFC000">—</span>SC-ALC, <span style="color:#0070C0">—</span>PC-TRYP <span style="color:#00B050">—</span>SC-TRYP).</p>
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<p>Intrinsic fluorescence of SPHs without and after SFE. (<span style="color:red">—</span>PC-ALC <span style="color:#FFC000">—</span>SC-ALC, <span style="color:#0070C0">—</span>PC-TRYP <span style="color:#00B050">—</span>SC-TRYP). The normalized version of Intrinsic fluorescence is available in the <a href="#app1-marinedrugs-23-00122" class="html-app">supplementary file as Figure S1</a>.</p>
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<p>Interfacial tension (IFT) of shrimp shell protein hydrolysates (SPHs). (<span style="color:red">—</span>PC-ALC <span style="color:#F4EE00">—</span>SC-ALC, <span style="color:#0070C0">—</span>PC-TRYP <span style="color:#00B050">—</span>SC-TRYP), sodium caseinate (<span style="color:#ED7D31">—</span>Na-Cas) as positive control and (<span style="color:#00B0F0">—</span>W/O) water in oil droplet.</p>
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<p>Amplitude sweep test of shrimp shell hydrolysates (SPHs) and controls. (<span style="color:red">—</span>PC-ALC <span style="color:#F4EE00">—</span>SC-ALC <span style="color:#0070C0">—</span>PC-TRYP <span style="color:#00B050">—</span>SC-TRYP), sodium caseinate (<span style="color:#ED7D31">—</span>Na-Cas) as positive control and (<span style="color:#00B0F0">—</span>W/O) water in oil droplet. Elastic modulus (E′) is represented with symbol line and viscous modulus (E″) is represented with dotted line.</p>
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<p>Phase angle of shrimp shell hydrolysates (SPHs) and controls. (<span style="color:red">—</span>PC-ALC <span style="color:#F4EE00">—</span>SC-ALC, <span style="color:#0070C0">—</span>PC-TRYP <span style="color:#00B050">—</span>SC-TRYP), sodium caseinate (<span style="color:#ED7D31">—</span>Na-Cas) as positive control and (<span style="color:#00B0F0">—</span>W/O) water in oil droplet.</p>
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<p>Frequency sweep test of shrimp shell hydrolysates (SPHs). (<span style="color:red">—</span>PC-ALC <span style="color:#F4EE00">—</span>SC-ALC, <span style="color:#0070C0">—</span>PC-TRYP <span style="color:#00B050">—</span>SC-TRYP), sodium caseinate (<span style="color:#ED7D31">—</span>Na-Cas) as positive control and (<span style="color:#00B0F0">—</span>W/O) water in oil droplet. Elastic modulus (E′) is represented with symbol line and viscous modulus (E″) is represented with dotted line.</p>
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<p>Lissajous plots of 0.1% SPHs, Na-CAS solution (positive control), and water-in-oil droplet (W/O) under 5, 13.75, 22.5, 31.25, and 40% amplitude.</p>
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<p>Creaming index of emulsions stabilized with 0.2% protein of SPHs and Na-Cas. (<span style="color:red">—</span>PC-ALC <span style="color:#F4EE00">—</span>SC-ALC, <span style="color:#0070C0">—</span>PC-TRYP <span style="color:#00B050">—</span>SC-TRYP <span style="color:#ED7D31">—</span>Na-Cas).</p>
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16 pages, 704 KiB  
Review
Anti-ADAMTS13 Autoantibodies in Immune-Mediated Thrombotic Thrombocytopenic Purpura
by Michael R. Snyder and Robert W. Maitta
Antibodies 2025, 14(1), 24; https://doi.org/10.3390/antib14010024 - 10 Mar 2025
Viewed by 161
Abstract
Autoantibodies to ADAMTS13 are at the center of pathology of the immune-mediated thrombotic thrombocytopenic purpura. These autoantibodies can be either inhibitory (enzymatic function) or non-inhibitory, resulting in protein depletion. Under normal physiologic conditions, antibodies are generated in response to foreign antigens, which can [...] Read more.
Autoantibodies to ADAMTS13 are at the center of pathology of the immune-mediated thrombotic thrombocytopenic purpura. These autoantibodies can be either inhibitory (enzymatic function) or non-inhibitory, resulting in protein depletion. Under normal physiologic conditions, antibodies are generated in response to foreign antigens, which can include infectious agents; however, these antibodies may at times cross-react with self-epitopes. This is one of the possible mechanisms mediating formation of anti-ADAMTS13 autoantibodies. The process known as “antigenic mimicry” may be responsible for the development of these autoantibodies that recognize and bind cryptic epitopes in ADAMTS13, disrupting its enzymatic function over ultra large von Willebrand factor multimers, forming the seeds for platelet activation and microthrombi formation. In particular, specific amino acid sequences in ADAMTS13 may lead to conformational structures recognized by autoantibodies. Generation of these antibodies may occur more frequently among patients with a genetic predisposition. Conformational changes in ADAMTS13 between open and closed states can also constitute the critical change driving either interactions with autoantibodies or their generation. Nowadays, there is a growing understanding of the role that autoantibodies play in ADAMTS13 pathology. This knowledge, especially of functional qualitative differences among antibodies and the ADAMTS13 sequence specificity of such antibodies, may make possible the development of targeted therapeutic agents to treat the disease. This review aims to present what is known of autoantibodies against ADAMTS13 and how their structure and function result in disease. Full article
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Figure 1
<p>A working model of anti-ADAMTS13 autoantibody formations and mechanisms of binding in iTTP. (<b>1</b>) Under physiologic conditions, ADAMTS13 circulates in a folded or “closed” conformation mediated by interactions between its central spacer domain and CUB domains. Initial autoantibody generation is thought to occur due to “molecular mimicry”. This process occurs when T lymphocytes are primed by antigenic determinants of pathogens (green triangle, green circle), which cross-react with peptides/epitopes from ADAMTS13 (red triangle, blue circle), conformationally similar in amino acid composition or in structure to pathogen-derived peptide sequences. (<b>2</b>) Under pathologic conditions, possibly triggered by re-exposure to antigenic determinants later during infection, inflammation, or a yet-to-be-defined stressor, non-inhibitory autoantibodies may be generated and bind to the distal C-terminal domain or other domains. This forces a conformational change in ADAMTS13 into its less stable “open” conformation, which alone may be sufficient to cause ADAMTS13 dysfunction and disease in some patients. (<b>3</b>) In some patients, exposure to cryptic epitopes (red triangle) in the central spacer domain during this “open” state facilitates the binding of specifically neutralizing antibodies, which directly inhibit ADAMTS13 enzymatic function. Decreased ADAMTS13 activity leads to a buildup of vWF multimers along endothelial walls, resulting in the formation of platelet-rich microthrombi. M = metalloprotease domain; D = disintegrin-like domain; T1-8 = thrombospondin type 1 repeats; C = cysteine-rich region; Spacer = spacer domain; CUB 1 and 2 = C1r/C1s-Uegf-Bmp1 domains.</p>
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24 pages, 5371 KiB  
Article
Selenium-Enriched Polysaccharides from Lentinula edodes Mycelium: Biosynthesis, Chemical Characterisation, and Assessment of Antioxidant Properties
by Eliza Malinowska, Grzegorz Łapienis, Agnieszka Szczepańska and Jadwiga Turło
Polymers 2025, 17(6), 719; https://doi.org/10.3390/polym17060719 - 9 Mar 2025
Viewed by 332
Abstract
Selenium–polysaccharides possess antioxidant properties, making them promising materials for functional foods, pharmaceuticals, and clinical applications. This study examines the incorporation of selenium into polysaccharides via mycelial biosynthesis and its effects on structure and antioxidant activity. Polysaccharides obtained from Lentinula edodes-submerged cultures grown [...] Read more.
Selenium–polysaccharides possess antioxidant properties, making them promising materials for functional foods, pharmaceuticals, and clinical applications. This study examines the incorporation of selenium into polysaccharides via mycelial biosynthesis and its effects on structure and antioxidant activity. Polysaccharides obtained from Lentinula edodes-submerged cultures grown in Se-supplemented and non-supplemented media were analysed for Se content (RP-HPLC/FLD), structure (FT-IR, HPLC, and HPGPC-ELSD), and antioxidant activity (DPPH scavenging, reducing power, and Fe2+ chelation). Two low-molecular-weight Se–heteropolysaccharides (Se-FE-1.1 and Se-FE-1.2) containing ~80 and 125 µg/g Se were isolated, primarily composed of glucose, mannose, and galactose with β-glycosidic linkages. Se incorporation into polysaccharides selectively enhanced their antioxidant activity in the DPPH radical scavenging assay, with minimal effects observed in iron chelation and reducing power assays. Crude Se–polysaccharides displayed the highest antioxidant activity, suggesting an additional contribution from protein components. Our findings demonstrate that Se is effectively incorporated into polysaccharides, altering monosaccharide composition while preserving glycosidic linkages. The selective enhancement of radical scavenging suggests that selenium plays a specific role in antioxidant activity, primarily influencing radical scavenging mechanisms rather than interactions with metal ions. Further research is needed to clarify the mechanisms of selenium incorporation, the nature of its bonding within the polysaccharide molecule, and its impact on biological activity. Full article
(This article belongs to the Special Issue Optimization, Properties and Application of Polysaccharides)
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Graphical abstract

Graphical abstract
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<p>Schematic representation of the isolation and purification process for Se-enriched polysaccharides and their non-selenised analogues. *discarded due to insufficient yield.</p>
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<p>(<b>a</b>) Stepwise ion-exchange chromatography profile of the crude Se-FE-1 polysaccharide fraction on DEAE-Sephadex A-50 resin. The three distinct peaks correspond to polysaccharide fractions Se-FE-1.1, Se-FE-1.2, and Se-FE-1.3 eluted with Tris-HCl buffer at increasing ionic strength (0–1 M NaCl). (<b>b</b>–<b>d</b>) Elution profiles of Se-FE-1.1, Se-FE-1.2, and Se-FE-1.3 during purification on a Sephadex G-25 desalting column.</p>
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<p>HPGPC chromatograms of the FE and Se-FE fractions. Solid lines represent (1) FE-1.1 and (2) FE-1.2. Dashed lines represent (3) Se-FE-1.1 and (4) Se-FE-1.2. The inset figure shows a fragment of the FE-1.2 chromatogram with increased intensity.</p>
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<p>Positions of FE and Se-FE fractions on the calibration line based on β-glucans (HPGPC chromatograms). The symbol ○ denotes β-glucan standards.</p>
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<p>HPLC analysis of the monosaccharide composition of Se-enriched polysaccharides and their non-Se-enriched reference fractions: (<b>a</b>) standard sample, (<b>b</b>) FE-1.1, (<b>c</b>) FE-1.2, (<b>d</b>) Se-FE-1.1, and (<b>e</b>) Se-FE-1.2. Peaks: (1) mannose, (2) glucosamine, (3) ribose, (4) rhamnose, (5) glucuronic acid, (6) galactosamine, (7) glucose, (8) galactose, (9) xylose, and (10) fucose.</p>
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<p>FT-IR spectra of the crude Se-FE-1 polysaccharide fraction; purified Se-enriched polysaccharides, Se-FE-1.1 and Se-FE-1.2; and their non-Se-enriched analogues.</p>
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<p>DPPH radical scavenging ability of purified Se-enriched polysaccharides, Se-FE-1.1 and Se-FE-1.2, compared with their unmodified non-Se-enriched analogues, FE-1.1 and FE-1.2; the crude Se-FE-1 fraction; and reference antioxidants. Each value represents the mean ± standard deviation (n = 3).</p>
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<p>Reducing power of purified Se-enriched polysaccharide fractions, Se-FE-1.1 and Se-FE-1.2, compared with their unmodified non-Se-enriched analogues, FE-1.1 and FE-1.2; the crude Se-FE-1 fraction; and reference antioxidants. Each value represents the mean ± standard deviation (n = 3).</p>
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<p>Fe<sup>2+</sup> chelating ability of purified Se-enriched polysaccharide fractions, Se-FE-1.1 and Se-FE-1.2, compared with their unmodified non-Se-enriched analogues, FE-1.1 and FE-1.2; the crude Se-FE-1 fraction; and reference antioxidants. Each value represents the mean ± standard deviation (n = 3).</p>
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18 pages, 874 KiB  
Article
Kinetic Description of Viral Capsid Self-Assembly Using Mesoscopic Non-Equilibrium Thermodynamics
by Jason Peña, Leonardo Dagdug and David Reguera
Entropy 2025, 27(3), 281; https://doi.org/10.3390/e27030281 - 8 Mar 2025
Viewed by 124
Abstract
The self-assembly mechanisms of various complex biological structures, including viral capsids and carboxysomes, have been theoretically studied through numerous kinetic models. However, most of these models focus on the equilibrium aspects of a simplified kinetic description in terms of a single reaction coordinate, [...] Read more.
The self-assembly mechanisms of various complex biological structures, including viral capsids and carboxysomes, have been theoretically studied through numerous kinetic models. However, most of these models focus on the equilibrium aspects of a simplified kinetic description in terms of a single reaction coordinate, typically the number of proteins in a growing aggregate, which is often insufficient to describe the size and shape of the resulting structure. In this article, we use mesoscopic non-equilibrium thermodynamics (MNET) to derive the equations governing the non-equilibrium kinetics of viral capsid formation. The resulting kinetic equation is a Fokker–Planck equation, which considers viral capsid self-assembly as a diffusive process in the space of the relevant reaction coordinates. We discuss in detail the case of the self-assembly of a spherical (icosahedral) capsid with a fixed radius, which corresponds to a single degree of freedom, and indicate how to extend this approach to the self-assembly of spherical capsids that exhibit radial fluctuations, as well as to tubular structures and systems with higher degrees of freedom. Finally, we indicate how these equations can be solved in terms of the equivalent Langevin equations and be used to determine the rate of formation and size distribution of closed capsids, opening the door to the better understanding and control of the self- assembly process. Full article
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Figure 1
<p>Schematic representation of a partial capsid having <span class="html-italic">n</span> CBBs (top left corner) and the free energy landscape generated by <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>G</mi> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> </semantics></math> in Equation (<a href="#FD2-entropy-27-00281" class="html-disp-formula">2</a>) as a function of the number of CBBs, <span class="html-italic">n</span>. The self-assembly of viral capsids is described from left to right, while the disassembly of closed capsids occurs from right to left. The nucleation barrier towards assembly, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msup> <mi>G</mi> <mo>∗</mo> </msup> </mrow> </semantics></math>, differs from the nucleation barrier driving the disassembly of closed capsids, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>G</mi> <mrow> <mi>dis</mi> </mrow> <mo>∗</mo> </msubsup> </mrow> </semantics></math>, reflecting the system’s hysteresis.</p>
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26 pages, 2988 KiB  
Article
A Multi-Input Neural Network Model for Accurate MicroRNA Target Site Detection
by Mohammad Mohebbi, Amirhossein Manzourolajdad, Ethan Bennett and Phillip Williams
Non-Coding RNA 2025, 11(2), 23; https://doi.org/10.3390/ncrna11020023 - 7 Mar 2025
Viewed by 141
Abstract
(1) Background: MicroRNAs are non-coding RNA sequences that regulate cellular functions by targeting messenger RNAs and inhibiting protein synthesis. Identifying their target sites is vital to understanding their roles. However, it is challenging due to the high cost and time demands of experimental [...] Read more.
(1) Background: MicroRNAs are non-coding RNA sequences that regulate cellular functions by targeting messenger RNAs and inhibiting protein synthesis. Identifying their target sites is vital to understanding their roles. However, it is challenging due to the high cost and time demands of experimental methods and the high false-positive rates of computational approaches. (2) Methods: We introduce a Multi-Input Neural Network (MINN) algorithm that integrates diverse biologically relevant features, including the microRNA duplex structure, substructures, minimum free energy, and base-pairing probabilities. For each feature derived from a microRNA target-site duplex, we create a corresponding image. These images are processed in parallel by the MINN algorithm, allowing it to learn a comprehensive and precise representation of the underlying biological mechanisms. (3) Results: Our method, on an experimentally validated test set, detects target sites with an AUPRC of 0.9373, Precision of 0.8725, and Recall of 0.8703 and outperforms several commonly used computational methods of microRNA target-site predictions. (4) Conclusions: Incorporating diverse biologically explainable features, such as duplex structure, substructures, their MFEs, and binding probabilities, enables our model to perform well on experimentally validated test data. These features, rather than nucleotide sequences, enhance our model to generalize beyond specific sequence contexts and perform well on sequentially distant samples. Full article
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Figure 1
<p>Threshold optimization for our multi-input neural network target-site detection model. Precision, Recall, and Specificity curves are shown for a range of threshold values (0 to 1) to find the optimal threshold to separate target and non-target sites. The optimal threshold was determined by locating the intersection of the Precision and Recall curves, ensuring a balance between these metrics. This figure shows the optimal threshold for our model and the respective Precision, Recall, and Specificity values at the threshold point.</p>
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<p>Precision–Recall (PR) curves for our model and the compared microRNA target prediction methods. The curves are generated by sliding a threshold from 1 to 0 in steps of −0.01. The figure illustrates the superior performance of our proposed model compared to others. Notably, energy-based methods exhibit similar performance in the mid-range, while Mimosa, though less effective, still rank significantly above the random classifier line.</p>
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<p>Neural network architecture developed to learn the base pair (BP) preferences of microRNA–target-site duplex structures. This model, with a single output neuron, is constructed using features that represent all possible canonical base pairs (single, double, and triple) between microRNA and target-site nucleotides. The network weights, after training, provide BP preferences in the structure of microRNA–target-site duplex. The weights, resulted from training the model on experimental samples, represent the BP preferences underlying microRNA targeting mechanisms.</p>
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<p>Heatmap of base-pairing weights extracted from the optimal model: This figure shows the model-learned base-pairing weights, indicating a higher preference for pairings in some areas more than others, for example, between microRNA nucleotides 1–7 and target-site positions 0–7. Additionally, nucleotides 22, 23, and 24 of microRNA demonstrate a notable tendency to bind to the target site, which aligns with the experimental findings indicating that base pairing at the microRNA end can compensate for mismatches in the seed region [<a href="#B48-ncrna-11-00023" class="html-bibr">48</a>].</p>
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<p>Dynamic programming algorithm for predicting microRNA–target-site duplex structures. (<b>A</b>) Scoring table with cumulative weights for microRNA and target-site base pairs, demonstrating how pairing weights accumulate across sequence positions. (<b>B</b>) Backtracking table used to trace the optimal base-pairing path, enabling the reconstruction of the predicted duplex structure. (<b>C</b>) Dynamic programming rules defining weights for specific base pairs and in particular indices; as an example, rule (1,1,AU) means when microRNA[1] is A, and target site[1] is U, and the weight of such pairing is 0.2. Note that the weights in (<b>C</b>) are provided as examples and are not actual values, intended to make the algorithm tables and the figure easier to understand. (<b>D</b>) Predicted secondary structure for a microRNA and target-site pair, showing specific nucleotide bindings and the total calculated weight of the structure, reflecting binding preferences based on learned model weights.</p>
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<p>Multi-input neural network (MINN) architecture for detecting microRNA target sites. The model comprises four parallel CNN branches, each processing one of the input matrices. Each CNN has three convolutional layers with filter sizes of 32, 64, and 128, all with a 3 × 3 kernel size. ReLU activation is used in each convolutional layer, and then max-pooling and dropout regularization are applied to avoid overfitting. The outputs from these CNNs are flattened and merged into a single feature vector. This vector is passed through two fully connected layers with 128 and 64 neurons, and one dropout layer with a rate of 0.25. The final layer is a single neuron with sigmoid activation that provides a probability score (between 0 and 1) for the binding chance between microRNA and CTS sequences. This architecture effectively combines multiple inputs to enhance prediction performance.</p>
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<p>Threshold optimization for RNAhybrid in target-site classification. This figure illustrates the Precision, Recall, and Specificity curves for a range of threshold values (0 to 1) to distinguish target from non-target-sites. Following the same threshold optimization approach as with our model, the optimal threshold for RNAhybrid was identified by locating the intersection of the Precision and Recall curves. Precision, Recall, and Specificity values at this optimal threshold highlight the RNAhybrid’s performance in detecting microRNA target-sites.</p>
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<p>Radar chart illustrating the comparative performance of different methods across key evaluation metrics, including AUPRC, precision (PPV), recall (Rec.), F1 score, accuracy (Acc.), specificity (Spec.), and negative predictive value (NPV). The chart highlights the strengths and weaknesses of each method in predicting microRNA target sites. Note that AUPRC value for each method is placed next to the method name.</p>
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18 pages, 8753 KiB  
Article
Enhanced Protein Separation Performance of Cellulose Acetate Membranes Modified with Covalent Organic Frameworks
by Shurui Shao, Maoyu Liu, Baifu Tao, Kayode Hassan Lasisi, Wenqiao Meng, Xing Wu and Kaisong Zhang
Membranes 2025, 15(3), 84; https://doi.org/10.3390/membranes15030084 - 6 Mar 2025
Viewed by 204
Abstract
As a porous crystalline material, covalent organic frameworks (COFs) have attracted significant attention due to their extraordinary features, such as an ordered pore structure and excellent stability. Synthesized through the aldehyde amine condensation reaction, TpPa-1 COFs (Triformylphloroglucinol-p-Phenylenediamine-1 COFs) were blended with cellulose acetate [...] Read more.
As a porous crystalline material, covalent organic frameworks (COFs) have attracted significant attention due to their extraordinary features, such as an ordered pore structure and excellent stability. Synthesized through the aldehyde amine condensation reaction, TpPa-1 COFs (Triformylphloroglucinol-p-Phenylenediamine-1 COFs) were blended with cellulose acetate (CA) to form a casting solution. The TpPa-1 COF/CA ultrafiltration membrane was then prepared using the non-solvent-induced phase inversion (NIPS) method. The influence of TpPa-1 COFs content on the hydrophilicity, stability and filtration performance of the modified membrane was studied. Due to the hydrophilic groups in TpPa-1 COFs and the network structure formed by covalent bonds, the modified CA membranes exhibited higher hydrophilicity and lower protein adsorption compared with the pristine CA membrane. The porous crystalline structure of TpPa-1 COFs increased the water permeation path in the CA membrane, improving the permeability of the modified membrane while maintaining an outstanding bovine serum albumin (BSA) rejection. Furthermore, the addition of TpPa-1 COFs reduced protein adsorption on the CA membrane and overcame the trade-off between permeability and selectivity in CA membrane bioseparation applications. This approach provides a sustainable method for enhancing membrane performance while enhancing the application of membranes in protein purification. Full article
(This article belongs to the Special Issue Membrane Separation and Water Treatment: Modeling and Application)
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Figure 1
<p>The synthetic schematic diagram of TpPa-1 COFs (<b>A</b>) and preparation process of TpPa-1 COF/CA membranes (<b>B</b>).</p>
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<p>Characterization of TpPa-1 COFs: (<b>A</b>) FE-SEM and EDS; (<b>B</b>) FTIR; (<b>C</b>–<b>E</b>) XPS and the detailed spectral analysis of XPS; (<b>F</b>) XRD; (<b>G</b>) the particle size distribution.</p>
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<p>The SEM images of prepared membranes. (Red squares and straight lines represent the position of the third column of pictures in the membranes).</p>
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<p>The AFM images of prepared membranes.</p>
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<p>The water contact angle of the prepared membranes.</p>
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<p>The thermogravimetric analysis and derivative thermogravimetry of M0–M5.</p>
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<p>(<b>A</b>) The pure water flux of M0–M5; (<b>B</b>) the flux and protein rejection of M0–M5; (<b>C</b>) the molecular weight cut-off of M0–M5; (<b>D</b>) the anti-fouling performance of M0 and M3.</p>
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<p>(<b>A</b>–<b>D</b>) The fluorescence images of the membrane surfaces; (<b>E</b>,<b>F</b>) simulation system for BSA adsorption of membranes without and with TpPa-1 COFs: BSA molecule, yellow; C, gray; O, red; N, blue; H, white; (<b>G</b>) the protein adsorption mass of membranes; (<b>H</b>) the interaction energy between membranes and BSA; (<b>I</b>) the comparison of TpPa-1 COF/CA membranes and other CA membranes.</p>
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<p>(<b>A</b>–<b>D</b>) The fluorescence images of the membrane surfaces; (<b>E</b>,<b>F</b>) simulation system for BSA adsorption of membranes without and with TpPa-1 COFs: BSA molecule, yellow; C, gray; O, red; N, blue; H, white; (<b>G</b>) the protein adsorption mass of membranes; (<b>H</b>) the interaction energy between membranes and BSA; (<b>I</b>) the comparison of TpPa-1 COF/CA membranes and other CA membranes.</p>
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18 pages, 6149 KiB  
Article
Identification of Aldehyde Dehydrogenase Gene Family in Glycyrrhiza uralensis and Analysis of Expression Pattern Under Drought Stress
by Mengyuan He, Xu Ouyang, Linyuan Cheng, Yuetao Li, Nana Shi, Hongxia Ma, Yu Sun, Hua Yao and Haitao Shen
Int. J. Mol. Sci. 2025, 26(5), 2333; https://doi.org/10.3390/ijms26052333 - 5 Mar 2025
Viewed by 191
Abstract
Aldehyde dehydrogenases (ALDHs) are a gene family that relies on NAD +/NADP + proteins to oxidize toxic aldehydes to non-toxic carboxylic acids, and they play a crucial role in the growth and development of plants, as well as in their ability [...] Read more.
Aldehyde dehydrogenases (ALDHs) are a gene family that relies on NAD +/NADP + proteins to oxidize toxic aldehydes to non-toxic carboxylic acids, and they play a crucial role in the growth and development of plants, as well as in their ability to withstand stress. This study identified 26 ALDH genes from six Glycyrrhiza uralensis gene families distributed on six chromosomes. By analyzing the phylogeny, gene structure, conserved motifs, cis-regulatory elements, collinearity of homologs, evolutionary patterns, differentiation patterns, and expression variations under drought stress, we found that the ALDH gene is involved in phytohormones and exhibits responsiveness to various environmental stressors by modulating multiple cis-regulatory elements. In addition, GuALDH3H1, GuALDH6B1, GuALDH12A2, and GuALDH12A1 have been identified as playing a crucial role in the response to drought stress. By analyzing the expression patterns of different tissues under drought stress, we discovered that GuALDH3I2 and GuALDH2B2 exhibited the most pronounced impact in relation to the drought stress response, which indicates that they play a positive role in the response to abiotic stress. These findings provide a comprehensive theoretical basis for the ALDH gene family in Glycyrrhiza uralensis and enhance our understanding of the molecular mechanisms underlying ALDH genes in licorice growth, development, and adaptation to drought stress. Full article
(This article belongs to the Section Molecular Plant Sciences)
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<p>The sequence alignment of the <span class="html-italic">ALDH</span> conserved structural domain of the <span class="html-italic">GuALDH</span> proteins. The conserved <span class="html-italic">ALDH</span> domain (PF00171) of all <span class="html-italic">GuALDH</span> proteins was analyzed. The purple frame indicates the conserved glutamic acid active site (PS00687), and the purple label indicates the conserved active site.</p>
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<p>Phylogenetic analysis of <span class="html-italic">ALDH</span> members. <span class="html-italic">ALDH</span> proteins were aligned using ClustalW, and phylogenetic analysis was performed with MEGA11 based on maximum likelihood. Resulting tree was categorized into six families, with each background color representing different family and specific name of each <span class="html-italic">ALDH</span> family labeled accordingly. Green star indicates <span class="html-italic">Glycyrrhiza uralensis</span>, and red star indicates <span class="html-italic">Arabidopsis thaliana</span>.</p>
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<p>The structure and protein structure of the <span class="html-italic">ALDH</span> gene family in <span class="html-italic">Glycyrrhiza uralensis</span>. (<b>A</b>) The phylogenetic tree was created using MEGA11. (<b>B</b>) A structural analysis of the exons/introns of <span class="html-italic">GuALDH</span> genes. The green box indicates the exon, and the blue box indicates the 3′ or 5′ UTRs (untranslated regions). (<b>C</b>) The motif composition of the <span class="html-italic">ALDH</span> gene in <span class="html-italic">Glycyrrhiza uralensis</span>. Different colored boxes represent different motifs.</p>
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<p>Chromosome distribution of <span class="html-italic">GuALDHs</span> in <span class="html-italic">Glycyrrhiza uralensis</span>.</p>
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<p>The distribution of the <span class="html-italic">GuALDH</span> gene chromosomes and the interchromosomal connections. The connection between duplicated genes in <span class="html-italic">GuALDHs</span> is represented by a red line.</p>
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<p>The evolutionary relationship between the <span class="html-italic">ALDH</span> gene in <span class="html-italic">Glycyrrhiza uralensis</span> and different species of <span class="html-italic">Arabidopsis thaliana</span>, soybean, rice, and alfalfa.</p>
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<p>An analysis of cis-acting elements on the promoters of the <span class="html-italic">GuALDH</span> gene family in <span class="html-italic">Glycyrrhiza uralensis</span>. The sequence 2000 bp upstream of the ATG in <span class="html-italic">GuALDHs</span> was analyzed for cis-element responsiveness. The heatmap illustrates the quantity of cis-elements, with higher counts represented in red and lower counts in gray.</p>
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<p>Expression patterns of <span class="html-italic">GuALDHs</span> in different tissues. Based on RNA-sequencing (RNA-seq) data of <span class="html-italic">Glycyrrhiza uralensis</span>, the expression patterns were analyzed. A hierarchical clustering heatmap was drawn based on the Fragments Per Kilobase of exon model per Million mapped fragments (FPKM) value. The three expression pattern groups are represented by distinct colors: red indicates a high expression level, while blue indicates a low expression level.</p>
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<p>The regulatory network of co-expression for <span class="html-italic">GuALDH</span> in <span class="html-italic">Glycyrrhiza uralensis</span>.</p>
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<p>Patterns of expression for <span class="html-italic">GuALDH</span> genes in response to drought stress. The relative expression levels of <span class="html-italic">GuALDH</span> genes were examined in <span class="html-italic">Glycyrrhiza uralensis</span> seedlings after being treated with PEG for durations of 2 h and 24 h. The tap water seedlings were used as a reference for the expression of <span class="html-italic">GuALDHs</span> genes at each time point. The data are presented as the mean ± standard deviation. ** <span class="html-italic">p</span> &lt; 0.01 indicate statistically significant differences between the two conditions.</p>
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<p>Changes in malondialdehyde content in three kinds of licorice at different times under PEG stress.</p>
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22 pages, 3903 KiB  
Article
Ultrasound-Assisted Extraction, Characterization, and Antioxidant Activities of the Polysaccharides from Fermented Astragalus membranaceus
by Jingyan Zhang, Zijing Liang, Kang Zhang, Xi Tang, Lei Wang, Xueyan Gu, Huub F. J. Savelkoul and Jianxi Li
Molecules 2025, 30(5), 1159; https://doi.org/10.3390/molecules30051159 - 4 Mar 2025
Viewed by 308
Abstract
This study aimed to optimize the ultrasound-assisted extraction (UAE) of polysaccharides from fermented Astragalus membranaceus (FAPS) and to investigate the physicochemical properties and antioxidant activities of the extracted polysaccharides. Using a combination of single-factor experiments and response surface methodology based on a Box–Behnken [...] Read more.
This study aimed to optimize the ultrasound-assisted extraction (UAE) of polysaccharides from fermented Astragalus membranaceus (FAPS) and to investigate the physicochemical properties and antioxidant activities of the extracted polysaccharides. Using a combination of single-factor experiments and response surface methodology based on a Box–Behnken design, we improved the extraction of crude FAPS without deproteinization. Under optimal conditions (50 °C, 60 min, 8 mL/g, 480 W), the yield of crude FAPS obtained by UAE (7.35% ± 0.08) exceeded the yield from convectional hot water extraction (6.95% ± 0.24). After protein removal, the FAPS was subjected to comprehensive chemical analyses, including HPLC, HPGPC, FT-IR, UV spectroscopy, and a Congo red assay. The results showed that FAPS had a significantly higher carbohydrate content compared to the non-fermented group (95.38% ± 6.20% vs. 90.938% ± 3.80%), while the protein content was significantly lower than that of the non-fermented Astragalus polysaccharides (APS) group (1.26% ± 0.34% vs. 6.76% ± 0.87%). In addition, FAPS had a higher average molecular weight and a lower Mw/Mn ratio compared to APS. The primary monosaccharides in FAPS were identified as Glc, Ara, Gal and GalA, with a molar ratio of 379.72:13.26:7.75:6.78, and FAPS lacked a triple helix structure. In vitro, antioxidant assays showed that FAPS possessed superior antioxidant properties compared to APS. These results emphasize the significant potential of FAPS as an antioxidant, possibly superior to that of APS. The results of this study suggest that fermentation and UAE offer promising applications for the development and utilization of Astragalus membranaceus for human and animal health. Full article
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<p>Flow chart of UAE and antioxidant activity analysis of FAPS.</p>
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<p>Effects of different extraction temperatures (<b>A</b>), extraction times (<b>B</b>), ratios of water to material (<b>C</b>) and extraction powers (<b>D</b>) on the yield of crude FAPS (CFAPS).</p>
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<p>The three dimensional response surface plots and two dimensional contour plots show the interaction effects between water to material ratio and extraction time (<b>A</b>,<b>D</b>), extraction power and extraction time (<b>B</b>,<b>E</b>), extraction power and the ratio of material on the yield of CFAPS (<b>C</b>,<b>F</b>).</p>
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<p>The HPLC chromatograms of monosaccharide standards (<b>A</b>), APS (<b>B</b>) and FAPS (<b>C</b>).</p>
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<p>The HPGPC spectra of APS (<b>A</b>) and FAPS (<b>B</b>).</p>
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<p>Molecular weight distribution of FAPS and APS.</p>
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<p>FT-IR spectrum of APS (<b>A</b>) and FAPS (<b>B</b>).</p>
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<p>UV spectrum of FAPS and APS (<b>A</b>). Changes in absorption wavelength maximum of mixture of Congo red, FAPS and APS at various concentrations of NaOH (<b>B</b>).</p>
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<p>Scavenging effects of APS and FAPS at different concentrations on DPPH radical scavenging assay (<b>A</b>), hydroxyl radical scavenging assay (<b>B</b>), ABTS radical scavenging assay (<b>C</b>) and ferric reducing power assay (<b>D</b>).</p>
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11 pages, 643 KiB  
Review
IgG Biomarkers in Multiple Sclerosis: Deciphering Their Puzzling Protein A Connection
by Leonard Apeltsin and Xiaoli Yu
Biomolecules 2025, 15(3), 369; https://doi.org/10.3390/biom15030369 - 4 Mar 2025
Viewed by 334
Abstract
Identifying reliable biomarkers in peripheral blood is critical for advancing the diagnosis and management of multiple sclerosis (MS), particularly given the invasive nature of cerebrospinal fluid (CSF) sampling. This review explores the role of B cells and immunoglobulins (Igs), particularly IgG and IgM, [...] Read more.
Identifying reliable biomarkers in peripheral blood is critical for advancing the diagnosis and management of multiple sclerosis (MS), particularly given the invasive nature of cerebrospinal fluid (CSF) sampling. This review explores the role of B cells and immunoglobulins (Igs), particularly IgG and IgM, as biomarkers for MS. B cell oligoclonal bands (OCBs) in the CSF are well-established diagnostic tools, yet peripheral biomarkers remain underdeveloped. Emerging evidence highlights structural and functional variations in immunoglobulin that may correlate with disease activity and progression. A recent novel discovery of blood IgG aggregates in MS patients that fail to bind Protein A reveals promising diagnostic potential and confirms previous findings of the unique features of immunoglobulin G in MS and the potential link between the superantigen Protein A and MS. These aggregates, enriched in IgG1 and IgG3 subclasses, exhibit unique structural properties, including mutations in the framework region 3 (FR3) of IGHV3 genes, and are associated with complement-dependent neuronal apoptosis. Data based on ELISA have demonstrated that IgG aggregates in plasma can distinguish MS patients from healthy controls and other central nervous system (CNS) disorders with high accuracy and differentiate between disease subtypes. This suggests a role for IgG aggregates as non-invasive biomarkers for MS diagnosis and monitoring. Full article
(This article belongs to the Collection Feature Papers in Molecular Biomarkers)
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<p>Graphic overview depicting the antibody structure, the arrangement of CDRs and their framework in heavy and light chains, and the binding of Protein A, resulting in the enrichment of IgG aggregates. (<b>A</b>) An antibody is composed of a heavy and light chain with Fab and Fc fragments. (<b>B</b>) CDR1-CDR3 and the framework segments in heavy and light chains. (<b>C</b>) The binding of Protein A resulted in the enrichment of IgG aggregates, which can be used as MS biomarkers.</p>
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<p>Structure of a Protein A domain (magenta) binding to a germline <span class="html-italic">IGHV3</span> variable chain (blue). Three FR3 residues (Gln-H81, Asn-H82a, Ser-H82b) that mediate the binding are highlighted as red atomic spheres. These residues are impacted by indel mutation, observed exclusively in MS patients, which disrupts Protein A binding. Here, Protein A is shown to bind to the region near the CDR, whereas in most figures, it is bound to CH2.</p>
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32 pages, 4595 KiB  
Article
Integrative In Silico Analysis to Identify Functional and Structural Impacts of nsSNPs on Programmed Cell Death Protein 1 (PD-1) Protein and UTRs: Potential Biomarkers for Cancer Susceptibility
by Hakeemah Al-Nakhle, Retaj Al-Shahrani, Jawanah Al-Ahmadi, Wesal Al-Madani and Rufayda Al-Juhani
Genes 2025, 16(3), 307; https://doi.org/10.3390/genes16030307 - 4 Mar 2025
Viewed by 360
Abstract
Background: Programmed cell death protein 1 (PD-1), encoded by the PDCD1 gene, is critical in immune checkpoint regulation and cancer immune evasion. Variants in PDCD1 may alter its function, impacting cancer susceptibility and disease progression. Objectives: This study evaluates the structural, functional, and [...] Read more.
Background: Programmed cell death protein 1 (PD-1), encoded by the PDCD1 gene, is critical in immune checkpoint regulation and cancer immune evasion. Variants in PDCD1 may alter its function, impacting cancer susceptibility and disease progression. Objectives: This study evaluates the structural, functional, and regulatory impacts of non-synonymous single-nucleotide polymorphisms (nsSNPs) in the PDCD1 gene, focusing on their pathogenic and oncogenic roles. Methods: Computational tools, including PredictSNP1.0, I-Mutant2.0, MUpro, HOPE, MutPred2, Cscape, Cscape-Somatic, GEPIA2, cBioPortal, and STRING, were used to analyze 695 nsSNPs in the PD1 protein. The analysis covered structural impacts, stability changes, regulatory effects, and oncogenic potential, focusing on conserved domains and protein–ligand interactions. Results: The analysis identified 84 deleterious variants, with 45 mapped to conserved regions like the Ig V-set domain essential for ligand-binding interactions. Stability analyses identified 78 destabilizing variants with significant protein instability (ΔΔG values). Ten nsSNPs were identified as potential cancer drivers. Expression profiling showed differential PDCD1 expression in tumor versus normal tissues, correlating with improved survival in skin melanoma but limited value in ovarian cancer. Regulatory SNPs disrupted miRNA-binding sites and transcriptional regulation, affecting PDCD1 expression. STRING analysis revealed key PD-1 protein partners within immune pathways, including PD-L1 and PD-L2. Conclusions: This study highlights the significance of PDCD1 nsSNPs as potential biomarkers for cancer susceptibility, advancing the understanding of PD-1 regulation. Experimental validation and multi-omics integration are crucial to refine these findings and enhance theraputic strategies. Full article
(This article belongs to the Special Issue Molecular Diagnostic and Prognostic Markers of Human Cancers)
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<p>A workflow representing all of the in silico tools utilized in this study.</p>
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<p>The schematic diagram illustrates the domain architecture of the PD-1 protein. The protein consists of the following regions: SP (signal peptide, orange), N-loop, Ig-like V type domain (blue), stalk (green), TM (transmembrane domain, pink), and CR (cytoplasmic region, gray).</p>
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<p>The PD-1 protein structure’s schematic representation illustrates key domains, post-translational modifications, and nsSNPs. Loss of N-linked glycosylation is indicated at residues N49, N58, and N116 (blue text). The nsSNPs associated with cancer driver mutations are shown as red arrows, highlighting critical residues affected by these variants. Gain of ADP-ribosylation is observed at residues R86/R112 (orange text). PTMs include loss of sulfation or phosphorylation at residue Y223 (red text) and gain of sulfation at residue Y248 (green text). Glycosylation sites, which play an essential role in PD-1 folding and ligand interaction, are marked by “G” symbols.</p>
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<p>Differential expression of <span class="html-italic">PDCD1</span> in (<b>A</b>) ovarian cancer (OV) and (<b>B</b>) skin cutaneous melanoma (SKCM). Expression levels are shown as log2(TPM + 1) for tumor (T) and normal (N) samples. Significant upregulation of <span class="html-italic">PDCD1</span> was observed in SKCM tumor tissues compared to normal tissues (* <span class="html-italic">p</span> &lt; 0.05), while differences in OV were less pronounced.</p>
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<p>Kaplan–Meier survival curves of (<b>A</b>) ovarian serous cystadenocarcinoma (OV) and (<b>B</b>) skin cutaneous melanoma (SKCM) patients stratified by <span class="html-italic">PDCD1</span> expression levels. In OV, the red line represents the high-<span class="html-italic">PDCD1</span>-expression group (<span class="html-italic">n</span> = 210), and the blue line represents the low-expression group (<span class="html-italic">n</span> = 212). No significant survival difference was observed between the two groups (log-rank <span class="html-italic">p</span> = 0.25; HR = 0.87; <span class="html-italic">p</span>(HR) = 0.24). The dotted lines denote the 95% confidence intervals for each group. In SKCM, the red line represents the high-<span class="html-italic">PDCD1</span>-expression group (<span class="html-italic">n</span> = 229), and the blue line represents the low-expression group (<span class="html-italic">n</span> = 229). High <span class="html-italic">PDCD1</span> expression was significantly associated with improved overall survival (log-rank <span class="html-italic">p</span> = 3.8 × 10<sup>−5</sup>; HR = 0.57; <span class="html-italic">p</span>(HR) = 4.8 × 10<sup>−5</sup>). The dotted lines similarly indicate the 95% confidence intervals for each group.</p>
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<p>Protein–protein interaction network of <span class="html-italic">PDCD1</span> and its interacting partners, constructed using the STRING database. Key interacting proteins include CD274, CD80, CD86, CTLA4, LAG3, PTPN6, PTPN11, and LGALS9, among others. The network highlights the complex interactions between immune checkpoint molecules and signaling pathways, which play a crucial role in immune regulation. Colored lines between nodes represent various types of evidence for interactions, including known interactions (experimental and database-derived), predicted interactions, and text mining.</p>
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<p>KEGG pathway enrichment analysis illustrating pathways (<span class="html-italic">y</span>-axis) ranked by their enrichment strength (<span class="html-italic">x</span>-axis). The size of each bubble represents the number of genes associated with the corresponding pathway, with larger bubbles indicating a higher gene count. The color gradient of the bubbles denotes the False Discovery Rate (FDR), reflecting statistical significance; darker colors correspond to lower FDR values (higher significance), while lighter colors indicate higher FDR values (lower significance). Horizontal bars behind the bubbles highlight the similarity between gene sets, with brighter bars representing greater similarity. Key pathways include “Cell adhesion molecules”, “Intestinal immune network for IgA production”, and others, showcasing their overrepresentation in the analysis.</p>
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<p>Biological Process (Gene Ontology) enrichment analysis showing key biological processes (<span class="html-italic">y</span>-axis) ranked by their statistical significance (−log (FDR) on the <span class="html-italic">x</span>-axis). Bubble size represents the gene count associated with each process, with larger bubbles indicating more genes involved. The color gradient of the bubbles reflects the False Discovery Rate (FDR), where darker colors denote lower FDR values (higher statistical significance) and lighter colors indicate higher FDR values (lower statistical significance). Horizontal bars behind the bubbles represent gene set similarity, with brighter bars indicating higher similarity among gene sets. Key processes include “Regulation of leukocyte cell-cell adhesion”, “Regulation of T-cell activation”, and “T-cell costimulation”, emphasizing their overrepresentation in the analysis.</p>
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17 pages, 5075 KiB  
Article
Insight into the Role of the Aryl Hydrocarbon Receptor in Bovine Coronavirus Infection by an Integrated Approach Combining In Vitro and In Silico Methods
by Luca Del Sorbo, Clementina Acconcia, Maria Michela Salvatore, Giovanna Fusco, Violetta Vasinioti, Maria Stella Lucente, Liqian Zhu, Annamaria Pratelli, Luigi Russo, Anna Andolfi, Rosa Iacovino and Filomena Fiorito
Microorganisms 2025, 13(3), 579; https://doi.org/10.3390/microorganisms13030579 - 4 Mar 2025
Viewed by 258
Abstract
It is well known that the host response to different human and animal coronaviruses infection is regulated by the aryl hydrocarbon receptor, a ligand-activated transcription factor. The present study investigates the expression of the aryl hydrocarbon receptor during bovine coronavirus infection, through in [...] Read more.
It is well known that the host response to different human and animal coronaviruses infection is regulated by the aryl hydrocarbon receptor, a ligand-activated transcription factor. The present study investigates the expression of the aryl hydrocarbon receptor during bovine coronavirus infection, through in vitro and in silico investigations. The in vitro studies demonstrate that the aryl hydrocarbon receptor and as well as its targets, CYP1A1 and CYP1B1, were significantly activated by bovine coronavirus infection in bovine cells (MDBK). During infection, the pretreatment of cells with non-cytotoxic doses of CH223191, a selective inhibitor of the aryl hydrocarbon receptor, resulted in a significant reduction in virus yield and a downregulation in the viral spike protein expression. These findings occurred in the presence of the inhibition of aryl hydrocarbon receptor signaling. Our results reveal that the bovine coronavirus acts on viral replication, upregulating the aryl hydrocarbon receptor and its downstream target proteins, CYP1A1 and CYP1B1. In addition, following the in silico studies, the three-dimensional structural model of the bovine aryl hydrocarbon receptor in complex with the antagonist CH223191 indicates that the molecular mechanism, by which the PASB and TAD domains of the receptor interact with the inhibitor, is mainly driven by an extensive network of hydrophobic interactions, with a series of hydrogen bonds contributing to stabilizing the complex. Interestingly, bioinformatic analyses revealed that the PASB and TAD domains in the human and bovine aryl hydrocarbon receptor present high similarity at the primary sequence and three-dimensional structure levels. Taken together, these findings represent a fundamental step for the development of innovative drugs targeting AhR as a potential object for CoVs therapy. Full article
(This article belongs to the Special Issue Viral Diseases: Current Research and Future Directions)
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<p>Chemical structure of the AhR inhibitor CH223191.</p>
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<p>The AhR inhibitor CH223191 at the concentration of 2 µM induces no significant (<span class="html-italic">p</span> &gt; 0.5) differences in MDBK cell viability after 24 h of pretreatment. (<b>A</b>) Microscopic MDBK cells treated with DMSO or with CH223191 at different concentrations and stained with TB while cells were attached to wells. Scale bar 100 µm. (<b>B</b>). Identification of the IC<sub>50</sub> of CH223191 inhibitor by using different concentrations (2, 5, 10, and 20 μM) and development of dose–response curve in MDBK cells after 24 h of pretreatment. Cell viability was assessed by TB staining and scored by an automated cell counter. Significant differences between DMSO and CH223191-treated cells are indicated by probability <span class="html-italic">p</span>. ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>AhR inhibitor CH223191 increases cell viability during BCoV infection. (<b>A</b>) MDBK cells pretreated or not with CH223191 at 2 µM and infected with BCoV. At 24 h p.i., cells were stained with TB while cells were attached to wells and observed under a light microscope. Scale bar = 100 µm. (<b>B</b>) Dose–response curve of MDBK cells pretreated with CH223191 at 2 μM and infected with BCoV. After 24 h of infection, cell viability was determined by TB staining and scored by automated cell counter. Significant differences between BCoV+DMSO and BCoV+CH223191-treated cells are indicated by probability <span class="html-italic">p</span>. *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>AhR inhibitor CH223191 reduced morphological signs of cell death during BCoV infection in MDBK cells. Cells pretreated or not with CH223191 were infected with BCoV. At 24 h p.i., cells were stained with (<b>A</b>) Giemsa and analyzed under a light microscope. Morphological features of cell death, such as cellular shrinkage (arrowhead) and pyknosis and chromatin condensation (arrow) were mainly reduced in the CH223191-treated infected groups. (<b>B</b>) In AO/PI panels, PI fluorescent cells, indicating dead and/or dying cells, were mainly detected in BCoV-infected cells compared to CH213191-treated infected cells. Scale bar 100 µm. The results of one experiment representative of three independent experiments were reported.</p>
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<p>AhR inhibitor CH223191 induces a reduction in virus yield during BCoV infection in MDBK cells. Cells pretreated or not with AhR inhibitor CH223191 were infected with BCoV at 24 h p.i. (<b>A</b>) Virus yield was assessed by the TCID<sub>50</sub> method and reported as Log TCID<sub>50</sub>/mL. Significant differences between BCoV-infected cells and CH223191-treated infected cells are indicated by probability <span class="html-italic">p</span>. *** <span class="html-italic">p</span> &lt; 0.001. (<b>B</b>) CPE by crystal violet staining was detected by the ZOE Cell Imager. Scale bar 100 µm. The results of one experiment representative of three independent experiments were reported.</p>
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<p>AhR is expressed in MDBK cells. AhR inhibitor CH223191 significantly induced a reduction in AhR expression in MDBK cells. BCoV activates the expression of AhR, and the AhR inhibitor (CH223191) downregulates both AhR and S protein expression during BCoV infection in MDBK cells. (<b>A</b>) In CH223191-treated and untreated uninfected cells, as well as in CH223191-treated and untreated BCoV-infected cells, immunofluorescence staining was performed to assess AhR and S protein expression. Scale bar = 25 µm. (<b>B</b>,<b>C</b>) Bars are the mean ratio generated from the integrated density (product of area and mean fluorescence intensity) of the AhR and S protein expression during BCoV infection. Significant differences between control (DMSO-treated) and BCoV-infected cells, as well as between BCoV-infected cells and AhR-inhibitor-treated infected cells for both AhR and S proteins, are indicated by probability <span class="html-italic">p</span>. * <span class="html-italic">p</span> &lt; 0.05 and *** <span class="html-italic">p</span> &lt; 0.001. The integrated density was measured by ImageJ. Error bars represent standard deviation measurement. The results of one experiment representative of three independent experiments were reported.</p>
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<p>BCoV activates the expression of both CYP1A1 and CYP1B1 (AhR signaling) during infection in MDBK cells. MDBK cells, pretreated or not with AhR inhibitor, were infected with BCoV at an MOI of 0.5 for 24 h. Then, immunofluorescence staining with antibodies recognizing (<b>A</b>) CYP1A1 and (<b>B</b>) CYP1B1 was performed. Scale bar = 50 µm. (<b>C</b>,<b>D</b>) Bars are the mean ratio generated from the integrated density (product of the area and mean fluorescence intensity) of the CYP1A1 and CYP1B1 expression during BCoV infection. Significant differences between DMSO and BCoV-infected cells, as well as between BCoV-infected cells and AhR-inhibitor-treated infected cells for both CYP1A1 and CYP1B1 proteins, are indicated by probability <span class="html-italic">p</span>. * <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span>. ** <span class="html-italic">p</span> &lt; 0.01. The integrated density was measured by ImageJ. Error bars represent standard deviation measurement. The results of one experiment representative of three independent experiments were reported.</p>
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<p>Recognition mechanism of CH223191 at the bAhR. (<b>A</b>) Structural representation of the full-length b AhR r, highlighting its four key domains: the bHLH (basic Helix-Loop-Helix) domain, spanning residues 27–80; the PAS A (Per Arnt Sim A) domain, spanning residues 111–181; the PAS B (Per Arnt Sim B) domain, spanning residues 275–342; and the TAD (Transactivation Domain), spanning residues 348–386. Each domain plays a crucial role in the function of the receptor and ligand interaction, providing a detailed understanding of the structural organization of the receptor. (<b>B</b>) 3D model of the bAhR (residues 1–400) predicted by AlphaFold, showing the folded regions containing the bHLH, PAS A, and PAS B domains. (<b>C</b>) Docking model of the CH223191 ligand bound to the bAhR. The figure highlights two key hydrogen bonds formed with residues Gln382 and Ser345, a π–π interaction with the aromatic side chain of Phe294, and several hydrophobic interactions with surrounding residues of the receptor, all of which are illustrated in the figure. These interactions contribute to the stable binding of CH223191 within the ligand-binding domain of the receptor.</p>
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12 pages, 8950 KiB  
Article
First Detection and Molecular Characterization of Peach Latent Mosaic Viroid (PLMVd) in Kazakhstan
by Gulshan E. Stanbekova, Leila T. Nadirova, Ruslan V. Kryldakov, Bulat K. Iskakov and Andrey V. Zhigailov
Pathogens 2025, 14(3), 243; https://doi.org/10.3390/pathogens14030243 - 3 Mar 2025
Viewed by 168
Abstract
Viroids represent obligate plant pathogens composed exclusively of non-protein coding small single-stranded RNAs that cause high economic losses worldwide. A field survey was carried out to assess the incidence of the peach latent mosaic viroid (PLMVd) in southeastern Kazakhstan, the region of the [...] Read more.
Viroids represent obligate plant pathogens composed exclusively of non-protein coding small single-stranded RNAs that cause high economic losses worldwide. A field survey was carried out to assess the incidence of the peach latent mosaic viroid (PLMVd) in southeastern Kazakhstan, the region of the country where fruit trees are mainly grown. Of 246 stone fruit trees, 20 (8.13%) were infected with the PLMVd. The incidence of the PLMVd in the peach (19.23%; 15/78) was significantly higher than that in the apricot (6.76%; 5/74; p = 0.0234). Eight of the detected viroids were cloned and used for full-genome sequencing. The nucleotide sequence similarity of the selected isolates found in Kazakhstan was 83.9–100%. A phylogenetic analysis indicated three clusters for the Kazakhstani isolates of the PLMVd. Three groups of Kazakhstani viroids differed in their predicted secondary structure. During the survey, the PLMVd was detected and genetically characterized for the first time in Kazakhstan. The obtained results indicate the need to develop state control measures for the PLMVd, including regular monitoring surveys. We identified several SNPs of the PLMVd that had not been previously described. The results may be useful in optimizing diagnostic approaches for detecting stone fruit viroids and preventing their spread through propagation material. Full article
(This article belongs to the Section Viral Pathogens)
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<p>Sampling sites in southeastern Kazakhstan included in this survey.</p>
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<p>The alignment of cloned whole-genome sequences of the PLMVd from southeastern Kazakhstan. The PLMVd reference sequence (GenBank: M83545) [<a href="#B19-pathogens-14-00243" class="html-bibr">19</a>] is shown for comparative purposes. Nucleotides involved in the formation of plus and minus hammerhead structures are boxed [<a href="#B20-pathogens-14-00243" class="html-bibr">20</a>]. Nucleotide variations identified only in Kazakhstani isolates are indicated with gray boxes. Nucleotide variations that disrupt the basic structures P1–P11 of the PLMVd are highlighted in red. Nucleotide variations that restore or enhance the basic structures of the PLMVd are highlighted in blue. Nucleotide variations that do not affect the formation of secondary structures of the PLMVd are highlighted in green. Nucleotide variations that disrupt the basic structures of the PLMVd but contribute to the formation of other secondary structures are highlighted in light brown.</p>
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<p>Phylogenetic analysis based on the full-genome sequences of PLMVd isolates. The neighbor-joining phylogenetic tree is constructed in MEGA-X from alignments of eight complete PLMVd sequences generated in this study and 27 database sequences. The tree is drawn to scale, with the branch lengths representing the numbers of substitutions per site. The percentage of trees in which the associated taxa clustered is shown next to the branches. The GenBank accession numbers are shown in parentheses. The Kazakhstani PLMVd isolates determined in this study (marked with a black circle) are in blue, orange, or green rectangles, depending on their grouping. The red asterisk is the reference genome (GenBank: M83545) [<a href="#B19-pathogens-14-00243" class="html-bibr">19</a>].</p>
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<p>Nucleotide sequences of the Kazakhstani PLMVd variants and the reference isolate of PLMVd folded in the secondary structure of the lowest free energy predicted by the RNA-structure prediction tool [<a href="#B18-pathogens-14-00243" class="html-bibr">18</a>]. (<b>a</b>) The PLMVd reference structure (GenBank: M83545) [<a href="#B19-pathogens-14-00243" class="html-bibr">19</a>]; (<b>b</b>) Group 1 Kazakhstani PLMVd isolates (GenBank: PP857833, PV034720, PV034722-PV034724); (<b>c</b>) Group 2 Kazakhstani PLMVd isolates (GenBank: PP857834, PV034721); (<b>d</b>) Group 3 Kazakhstani PLMVd isolates (GenBank: PV034725). Nucleotide variations that disrupt the basic structures of the PLMVd are highlighted in red. Nucleotide variations that restore or enhance the basic structures of the PLMVd are highlighted in blue. Nucleotide variations that do not affect the formation of secondary structures of the PLMVd are highlighted in green. Nucleotide variations that disrupt the basic structures of the PLMVd but contribute to the formation of other secondary structures are highlighted in light brown.</p>
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15 pages, 5847 KiB  
Article
Integrative Analysis Provides Insights into Genes Encoding LEA_5 Domain-Containing Proteins in Tigernut (Cyperus esculentus L.)
by Zhi Zou, Xiaowen Fu, Xiaoping Yi, Chunqiang Li, Jiaquan Huang and Yongguo Zhao
Plants 2025, 14(5), 762; https://doi.org/10.3390/plants14050762 - 1 Mar 2025
Viewed by 364
Abstract
LEA_5 domain-containing proteins constitute a small family of late embryogenesis-abundant proteins that are essential for seed desiccation tolerance and dormancy. However, their roles in non-seed storage organs such as underground tubers are largely unknown. This study presents the first genome-scale analysis of the [...] Read more.
LEA_5 domain-containing proteins constitute a small family of late embryogenesis-abundant proteins that are essential for seed desiccation tolerance and dormancy. However, their roles in non-seed storage organs such as underground tubers are largely unknown. This study presents the first genome-scale analysis of the LEA_5 family in tigernut (Cyperus esculentus L.), a Cyperaceae plant producing desiccation-tolerant tubers. Four LEA_5 genes identified from the tigernut genome are twice of two present in model plants Arabidopsis thaliana and Oryza sativa. A comparison of 86 members from 34 representative plant species revealed the monogenic origin and lineage-specific family evolution in Poales, which includes the Cyperaceae family. CeLEA5 genes belong to four out of five orthogroups identified in this study, i.e., LEA5a, LEA5b, LEA5c, and LEA5d. Whereas LEA5e is specific to eudicots, LEA5b and LEA5d appear to be Poales-specific and LEA5c is confined to families Cyperaceae and Juncaceae. Though no syntenic relationship was observed between CeLEA5 genes, comparative genomics analyses indicated that LEA5b and LEA5c are more likely to arise from LEA5a via whole-genome duplication. Additionally, local duplication, especially tandem duplication, also played a role in the family expansion in Juncus effuses, Joinvillea ascendens, and most Poaceae plants examined in this study. Structural variation (e.g., fragment insertion) and expression divergence of LEA_5 genes were also observed. Whereas LEA_5 genes in A. thaliana, O. sativa, and Zea mays were shown to be preferentially expressed in seeds/embryos, CeLEA5 genes have evolved to be predominantly expressed in tubers, exhibiting seed desiccation-like accumulation during tuber maturation. Moreover, CeLEA5 orthologs in C. rotundus showed weak expression in various stages of tuber development, which may explain the difference in tuber desiccation tolerance between these two close species. These findings highlight the lineage-specific evolution of the LEA_5 family, which facilitates further functional analysis and genetic improvement in tigernut and other species. Full article
(This article belongs to the Special Issue Tempo and Mode of Diversification in Plant Evolution)
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<p>Structural and phylogenetic analyses of <span class="html-italic">LEA_5</span> family genes in <span class="html-italic">C. esculentus</span>. (<b>A</b>) Kyte–Doolittle hydrophobicity plots of CeLEA5 proteins using ProtScale (v1). (<b>B</b>) Amino acid composition of CeLEA5 proteins. (<b>C</b>) Multiple sequence alignment of CeLEA5 proteins using MUSCLE (v5.1). Identical and similar amino acids are highlighted in black or dark grey, respectively, whereas conserved LEA_5 domains are boxed in red. (<b>D</b>) An unrooted phylogenetic tree resulting from full-length Ce/Os/AtLEA5 proteins with RAxML (maximum likelihood method and bootstrap of 1000 replicates), where the distance scale denotes the number of amino acid substitutions per site. The name of each clade (i.e., I and II) is indicated next to the corresponding group. (<b>E</b>) The exon-intron structures. “1” represents the intron phase that is located between the first and second bases of a codon. (<b>F</b>) The distribution of conserved motifs among Ce/Os/AtLEA5 proteins, where different motifs are represented by different color blocks as indicated and the same color block in different proteins indicates a certain motif. (At: <span class="html-italic">A. thaliana</span>; Ce: <span class="html-italic">C. esculentus</span>; LEA: Late embryogenesis abundant; Os: <span class="html-italic">Oryza sativa</span>).</p>
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<p>Species-specific distribution of five orthogroups in 34 representative plant species. The species tree is referred to NCBI Taxonomy (<a href="https://www.ncbi.nlm.nih.gov/taxonomy" target="_blank">https://www.ncbi.nlm.nih.gov/taxonomy</a>, accessed on 20 November 2024) and well-established recent WGDs are marked: γ represents the whole-genome triplication event shared by all core eudicots; β and α represent two WGDs that are specific to Brassicaceae; β″ and α″ represent two Araceae-specific WGDs; τ represents the WGD shared by all core monocots; p represents the Arecaceae-specific WGD; σ represents the Poales-specific WGD; and ρ represents the Poaceae-specific WGD. Names of tested plant families are indicated next to the corresponding branches. (LEA: Late embryogenesis abundant; WGD: whole-genome duplication).</p>
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<p>Synteny analyses within and between <span class="html-italic">C. esculentus</span> and representative plant species. (<b>A</b>) Chromosomal localization and duplication events of the <span class="html-italic">LEA_5</span> family genes in <span class="html-italic">C. esculentus</span> and <span class="html-italic">R. breviuscula</span>. (<b>B</b>) Synteny analyses within and between <span class="html-italic">C. esculentus</span>, <span class="html-italic">C. littledalei</span>, <span class="html-italic">C. scoparia</span>, and <span class="html-italic">R. breviuscula</span>. (<b>C</b>) Synteny analyses within and between <span class="html-italic">C. esculentus</span>, <span class="html-italic">J. effusus</span>, <span class="html-italic">S. stoloniferum</span>, <span class="html-italic">A. comosus</span>, and <span class="html-italic">J. ascendens</span>. (<b>D</b>) Synteny analyses within and between <span class="html-italic">C. esculentus</span>, <span class="html-italic">E. guineensis</span>, <span class="html-italic">A. officinalis</span>, and <span class="html-italic">D. alata</span>. (<b>E</b>) Synteny analyses within and between <span class="html-italic">C. esculentus</span>, <span class="html-italic">A. gramineus</span>, <span class="html-italic">A. trichopoda</span>, <span class="html-italic">A. thaliana</span>, and <span class="html-italic">R. communis</span>. (<b>F</b>) Synteny analyses within and between <span class="html-italic">J. ascendens</span>, <span class="html-italic">P. latifolius</span>, <span class="html-italic">O. sativa</span>, and <span class="html-italic">S. bicolor</span>. Shown are <span class="html-italic">LEA_5</span> gene-encoding chromosomes/scaffolds and only syntenic blocks containing <span class="html-italic">LEA_5</span> genes are marked, where red and purple lines indicate intra- and inter-species, respectively. The scale is in Mb. (Ac: <span class="html-italic">A. comosus</span>; Ag: <span class="html-italic">A. gramineus</span>; Ao: <span class="html-italic">A. officinalis</span>; At: <span class="html-italic">A. thaliana</span>; Atr: <span class="html-italic">A. trichopoda</span>; Bd: <span class="html-italic">B. distachyon</span>; Ce: <span class="html-italic">C. esculentus</span>; Cl: <span class="html-italic">C. littledalei</span>; Cs: <span class="html-italic">C. scoparia</span>; <span class="html-italic">Da</span>: <span class="html-italic">D. alata</span>; Eg: <span class="html-italic">E. guineensis</span>; Ja: <span class="html-italic">J. ascendens</span>; Je: <span class="html-italic">J. effuses</span>; Mb: megabase; Os: <span class="html-italic">O. sativa</span>; <span class="html-italic">Pl: P. latifolius</span>; Rb: <span class="html-italic">R. breviuscula</span>; Rc: <span class="html-italic">R. communis</span>; Sb: <span class="html-italic">S. bicolor</span>; Ss: <span class="html-italic">S. stoloniferum</span>).</p>
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<p>Expression profiles of <span class="html-italic">Ce/CrLEA5</span> genes. (<b>A</b>) Tissue-specific expression profiles of five <span class="html-italic">CeLEA5</span> genes. (<b>B</b>) Expression profiles of <span class="html-italic">Ce/CrLEA5</span> genes at three representative stages of tuber development. (<b>C</b>) Expression profiles of <span class="html-italic">CeLEA5-1</span>, <span class="html-italic">-2</span>, <span class="html-italic">-3</span>, and <span class="html-italic">-4</span> at different stages of tuber development. The heatmap was generated using the R package (v2) implemented with a row-based standardization. Color scale represents FPKM normalized log<sub>2</sub> transformed counts, where blue indicates low expression and red indicates high expression. Bars indicate SD (N = 3) and uppercase letters indicate difference significance tested following Duncan’s one-way multiple-range post hoc ANOVA (<span class="html-italic">p</span> &lt; 0.01). (Ce: <span class="html-italic">C. esculentus</span>; Cr: <span class="html-italic">C. rotundus</span>; DAI: days after tuber initiation; DAS: days after sowing; FPKM: Fragments per kilobase of exon per million fragments mapped).</p>
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17 pages, 3997 KiB  
Article
Bioinformatics and Expression Analysis of CHI Gene Family in Sweet Potato
by Yaqin Wu, Xiaojie Jin, Lianjun Wang, Chong Wang, Jian Lei, Shasha Chai, Wenying Zhang, Xinsun Yang and Rui Pan
Plants 2025, 14(5), 752; https://doi.org/10.3390/plants14050752 - 1 Mar 2025
Viewed by 203
Abstract
Chalcone isomerase (CHI) is not only an enzyme related to flavonoid biosynthesis, but also one of the key enzymes in the flavonoid metabolic pathway. In this study, members of the CHI gene family were identified in the whole genome of sweet potato. Bioinformatics [...] Read more.
Chalcone isomerase (CHI) is not only an enzyme related to flavonoid biosynthesis, but also one of the key enzymes in the flavonoid metabolic pathway. In this study, members of the CHI gene family were identified in the whole genome of sweet potato. Bioinformatics methods were used to analyze the physical and chemical properties, systematic evolution, conserved domain, chromosome location, cis-acting elements of the promoter, and so on, of CHI gene family members. In addition, the tissue site-specific expression of CHI gene family members and their expression patterns under three kinds of abiotic stress were analyzed. The results showed that five members of IbCHI gene family were identified in sweet potato, which were unevenly distributed on four chromosomes. The protein secondary structure and tertiary structure were consistent, and there was a conservative domain related to chalcone isomerase. The prediction of subcellular localization showed that it was mainly located in cytoplasm and chloroplast. Systematic evolution showed that the members of sweet potato CHI gene family could be divided into Type I-IV, and the Type I gene IbCHI1 showed CHI catalytic activity in transgenic callus. The collinearity gene pairs were identified between sweet potato and allied species. Its promoter contains light response elements, hormone response elements, and stress response elements. The results of real-time fluorescence quantitative PCR (qRT-PCR) analysis showed that the expression of the IbCHI gene was tissue-specific and that the catalytic genes IbCHI1 and IbCHI5 serve as primary responders to abiotic stress, while the non-catalytic members IbCHI3 and IbCHI4 may fine-tune metabolic flux or participate in low-temperature, salt, and drought stress signaling. This study can provide a theoretical basis for a follow-up functional genomics study of the chalcone isomerase gene family in sweet potato. Full article
(This article belongs to the Special Issue Cell Physiology and Stress Adaptation of Crops)
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<p>Protein tertiary structure prediction of <span class="html-italic">IbCHI</span>.</p>
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<p>Chromosome localization of <span class="html-italic">CHI</span> gene family in <span class="html-italic">Ipomoea batatas</span>. The heatmap represents the gene distribution density on chromosomes.</p>
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<p>Phylogenetic and functional analysis of <span class="html-italic">CHI</span> gene families in <span class="html-italic">Ipomoea batatas</span>. (<b>a</b>) Phylogenetic analysis of CHI gene family members in <span class="html-italic">Ipomoea batatas</span>, <span class="html-italic">Arabidopsis thaliana</span>, <span class="html-italic">Glycine max</span>, <span class="html-italic">Oryza sativa</span>, and <span class="html-italic">Solanum lycopersicum.</span> I, II, III, IV represent type I, type II, type III and type IV CHI respectively. (<b>b</b>) Total flavonoid content quercetin equivalents in the <span class="html-italic">IbCHI1</span>-<span class="html-italic">IbCHI5</span>-overexpression calluses. * and ** stands for significance level at <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.01, respectively.</p>
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<p>Analysis of <span class="html-italic">CHI</span> gene family evolution, conserved motif, protein conserved domain, gene structure, and promoter elements in sweet potato. (<b>a</b>) <span class="html-italic">CHI</span> gene family conserved motif, protein conserved domain, and gene structure. (<b>b</b>) Promoter element analysis. The darker the red, the greater the number.</p>
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<p>Collinearity analysis of sweet potato <span class="html-italic">CHI</span> gene. (<b>a</b>) Sweet potato and <span class="html-italic">Arabidopsis CHI</span> gene collinearity. (<b>b</b>) Sweet potato and <span class="html-italic">Ipomoea triloba CHI</span> gene collinearity. (<b>c</b>) Sweet potato and <span class="html-italic">Ipomoea trifida CHI</span> gene collinearity.</p>
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<p>Tissue-specific expression analysis of <span class="html-italic">CHI</span> gene family in sweet potato. The different lowercase letters stants for the significant level at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Expression analysis of flavonoid synthesis pathway genes in sweet potato in response to low-temperature stress. (<b>a</b>) The expression level in transcriptome analysis (Cold stress for 12 h); (<b>b</b>) time-dynamic expression level of CHI genes in sweet potato. “LT_12h” stands for low-temperature treatment for 12 h. The different lowercase letters stants for the significant level at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Expression analysis of flavonoid synthesis pathway genes in sweet potato in response to high-salt stress. (<b>a</b>) The expression level in transcriptome analysis (Salt stress for 24 h); (<b>b</b>) time-dynamic expression level of CHI genes in sweet potato. “ST_24h” stands for salt treatment for 12 h. The different lowercase letters stants for the significant level at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Expression analysis of flavonoid synthesis pathway genes in sweet potato in response to drought stress. (<b>a</b>) The expression level in transcriptome analysis (drought stress for 24 h); (<b>b</b>) time-dynamic expression level of CHI genes in sweet potato. “DT_24h” stands for drought treatment for 24 h. The different lowercase letters stants for the significant level at <span class="html-italic">p</span> &lt; 0.05.</p>
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18 pages, 6647 KiB  
Article
Genome-Wide Identification and Functional Characterization of the Glycosyltransferase 43 (GT43) Gene Family in Sorghum bicolor for Biofuel Development: A Comprehensive Study
by Rehana Rehana, Muhammad Anwar, Sarmad Frogh Arshad, Muhammad Usman and Imran Ahmad Khan
Processes 2025, 13(3), 709; https://doi.org/10.3390/pr13030709 - 28 Feb 2025
Viewed by 234
Abstract
Sorghum (Sorghum bicolor) is an essential bioenergy crop. Cellulosic and non-cellulosic polysaccharides, which can be transformed into biofuels, comprise most of its biomass. Many glycosyltransferases (GT) families, including GT43, are involved in the biosynthesis of xylan in plants’ [...] Read more.
Sorghum (Sorghum bicolor) is an essential bioenergy crop. Cellulosic and non-cellulosic polysaccharides, which can be transformed into biofuels, comprise most of its biomass. Many glycosyltransferases (GT) families, including GT43, are involved in the biosynthesis of xylan in plants’ primary and secondary cells. In this study, the GT43 gene family was identified, and its secondary structure and a three-dimensional (3D) model were constructed. Additionally, subcellular localization, detection of motifs, and analyses of its phylogenetic tree, physiochemical properties, protein–protein interaction network, gene structure, functional domain, gene duplication, Cis-acting elements, sequence logos, multiple sequence alignment, and gene expression profiles were performed based on RNA-sequence analyses. As a result, eleven members of the GT43 gene family were identified, and the phylogenetic tree of the GT43 gene family showed that all GT43 genes had evolutionary relationships with sorghum. Analyses of gene structure, motifs, sequence logos, and multiple sequence alignment showed that all members of the GT43 protein family were highly conserved. Subcellular localization showed all members of the GT43 protein family were localized in different compartments of sorghum. The secondary structure of the GT43 genes comprised different percentages of α-helices, random coils, β-turns, and extended strands. The tertiary structure model showed that all GT43 proteins had similar 3D structures. The results of the current study indicated that members of the GT43 gene family (Sobic.010G238800, Sobic.003G254700, and Sobic.001G409100) were highly expressed in internodes of the sorghum plant, based on RNA-Sequencing. The framework used in this study will be valuable for advancing research aligned with modern technology requirements and for enhancing understanding of the relationships among GT43 genes in Sorghum bicolor. Full article
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<p>(<b>a</b>) Phylogenetic tree of <span class="html-italic">GT43</span> proteins in <span class="html-italic">Sorghum bicolor</span>. (<b>b</b>): Maximum likelihood tree was constructed for <span class="html-italic">GT43</span> genes in <span class="html-italic">Sorghum bicolor</span>, <span class="html-italic">Arabidopsis thaliana</span>, and <span class="html-italic">Oryza sativa</span> using MEGA 6.0 program.</p>
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<p>Subcellular localization of <span class="html-italic">GT43</span> gene family in <span class="html-italic">Sorghum bicolor</span>, shown by heat map.</p>
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<p>Using the STRING database, we built a protein-protein interaction network to study the interactions between the sorghum <span class="html-italic">GT43</span> genes. The colored nodes show proteins, and the lines between them show the interactions between the proteins, as recorded by the database references for functional enrichment.</p>
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<p>Co-expression was observed in <span class="html-italic">Sorghum bicolor</span> and other organisms like <span class="html-italic">O. sativa</span>, <span class="html-italic">P. trichocarpa</span>, and <span class="html-italic">A. thaliana</span>.</p>
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<p>Conserved domains of <span class="html-italic">Sorghum bicolor GT43</span> protein. Colored boxes serve as indicators for each site. Measurement bar represents 600 amino acids.</p>
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<p>Motif analysis of <span class="html-italic">GT43</span> gene family.</p>
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<p>Sequence logos of <span class="html-italic">GT43</span> motifs 1–3 in Sorghum.</p>
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<p>Schematic diagram representing structures of <span class="html-italic">GT43</span> genes of sorghum. Exons are characterized by yellow boxes and introns by black lines. Intron phase numbers 0 and 1 are also displayed at beginning of introns. All dimensions are accurate in this diagram.</p>
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<p>The <span class="html-italic">GT43s</span> proteins are represented in three dimensions (3D). A similar protein modeling technique on the SWISS-MODEL website was used to generate the 3D model of the <span class="html-italic">GT43</span> protein. The bottom of each 3D model displays distinct colored proteins from the various subfamilies.</p>
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<p><span class="html-italic">Cis</span>-elements in promotor region. Different colored wedges represent different cis-elements. Length and position of each <span class="html-italic">GT43</span> gene are drawn to scale. Scale bar indicates DNA sequence length.</p>
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<p>(<b>a</b>): Conserved region (amino acid residue) sequence logos for (<b>a</b>) <span class="html-italic">Sorghum bicolor</span>, (<b>b</b>) <span class="html-italic">Oryza sativa</span>, and (<b>c</b>) <span class="html-italic">Arabidopsis thaliana</span>.</p>
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<p>Expression profiles of <span class="html-italic">GT43</span> in internodes of <span class="html-italic">Sorghum bicolor</span>. Gene is shown to right, and tissues or treatment are shown at bottom.</p>
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<p>Multiple sequence alignment between <span class="html-italic">GT43</span> proteins.</p>
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