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18 pages, 24028 KiB  
Article
Retinol-Binding Protein 4 as a Biomarker in Cancer: Insights from a Pan-Cancer Analysis of Expression, Immune Infiltration, and Methylation
by Jia Zhao, Yaxin Liu, Lingqin Zhou and Yi Liu
Genes 2025, 16(2), 150; https://doi.org/10.3390/genes16020150 - 25 Jan 2025
Viewed by 510
Abstract
Background: Retinol-binding protein 4 (RBP4) is primarily recognized for its role in retinoid transport, but has recently been implicated in cancer progression and prognosis. However, a comprehensive pan-cancer analysis of RBP4’s expression, prognostic significance, and functional associations across various cancers is lacking. Methods: [...] Read more.
Background: Retinol-binding protein 4 (RBP4) is primarily recognized for its role in retinoid transport, but has recently been implicated in cancer progression and prognosis. However, a comprehensive pan-cancer analysis of RBP4’s expression, prognostic significance, and functional associations across various cancers is lacking. Methods: We conducted a pan-cancer analysis of RBP4 using data from public databases. RBP4 expression levels were examined in 33 tumor types, and correlations with clinical outcomes, immune cell infiltration, DNA methylation, and gene mutations were assessed. Enrichment analyses of RBP4 and its co-expressed genes were performed to explore associated biological pathways. Additionally, in vitro experiments were conducted to assess the effects of RBP4 on cell migration and proliferation. Results: RBP4 showed differential expression between tumor and normal tissues, with downregulation in 21 cancer types and upregulation in 6. High expression levels of RBP4 were associated with poor overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in specific cancers, notably in BRCA, HNSC, and STAD, whereas it was a favorable prognostic factor in cancers such as KIRP and MESO. RBP4 expression was also associated with immune cell infiltration, particularly with CD4+ Th2 cells and immune checkpoint genes. DNA methylation analysis suggested that the methylation of RBP4 may play a role in its regulatory mechanisms across cancer types. Enrichment analyses revealed that RBP4 and its co-expressed genes are involved in metabolism-related pathways and immune regulation. Functional assays indicated that RBP4 knockdown promoted tumor cell migration and proliferation. Conclusions: This study provides a comprehensive pan-cancer analysis of RBP4, identifying its prognostic potential and possible involvement in tumor immunity and metabolism. Our findings suggest that RBP4 could serve as a novel biomarker and therapeutic target in cancer, although further experimental studies are required to elucidate its precise mechanisms in specific cancer types. Full article
(This article belongs to the Special Issue Advances in Bioinformatics of Human Diseases)
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Figure 1

Figure 1
<p>Analysis of RBP4 expression in various types of cancer: (<b>A</b>) The differential mRNA expression levels of RBP4 across cancer types. (<b>B</b>) The IHC images shows protein expression levels of RBP4in KIRC, LIHC, COAD, and STAD, obtained from the HPA database (*, <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 indicates no statistical significance).</p>
Full article ">Figure 2
<p>The expression profiles of the RBP4 gene in stages I, II, III, and IV of diverse cancer types derived from the TCGA database (*, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001; ****, <span class="html-italic">p</span> &lt; 0.0001, ns indicate no statistical significance).</p>
Full article ">Figure 3
<p>The relationship between RBP4 expression and patient outcomes was analyzed as follows: (<b>A</b>) Forest plot illustrating the associations between RBP4 expression and overall survival (OS) across 33 cancer types (risk factor: <span class="html-italic">p</span> &lt; 0.05 and Ln HR &gt; 0, protective factor: <span class="html-italic">p</span> &lt; 0.05 and Ln HR &lt; 0). (<b>B</b>) Kaplan–Meier survival analysis was conducted to illustrate the relationship between RBP4 expression and OS. (<b>C</b>) Forest plot displaying the associations between RBP4 expression and disease-specific survival (DSS) across 33 cancer types. (<b>D</b>) The association between RBP4 expression and DSS were showed by Kaplan–Meier analysis.</p>
Full article ">Figure 4
<p>The relationship between RBP4 expression and the progression-free interval (PFI) was analyzed as follows: (<b>A</b>) Forest plot illustrating the associations between RBP4 expression and PFI across 33 cancer types (risk factor: <span class="html-italic">p</span> &lt; 0.05 and Ln HR &gt; 0, protective factor: <span class="html-italic">p</span> &lt; 0.05 and Ln HR &lt; 0). (<b>B</b>) Kaplan–Meier survival analysis was conducted to illustrate the association between RBP4 expression and PFI.</p>
Full article ">Figure 5
<p>The relationship between RBP4 expression and immune cell infiltration across different cancers was examined as follows: (<b>A</b>) Heatmap illustrating the correlation between RBP4 expression and 37 types of immune cells. (<b>B</b>) Correlation analysis between RBP4 expression levels and CD4+ Th2 cells in specific cancers. (<b>C</b>) Analysis of the relationship between RBP4 expression and immune checkpoint genes. (<b>D</b>) The correlation between RBP4 expression and immune checkpoints was showed by heatmap. (*, <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>
Full article ">Figure 6
<p>The genetic alterations of RBP4 were illustrated as follows: (<b>A</b>) The alteration frequency and mutation types of RBP4 across various cancers, as obtained from cBioPortal. (<b>B</b>) A summary of RBP4’s structural variants, mutations, and copy-number alterations in pan-cancer. (<b>C</b>) Identification of specific sites associated with different mutation types of RBP4.</p>
Full article ">Figure 7
<p>The relationship between RBP4 expression and DNA methylation was examined as follows: (<b>A</b>) Analysis of DNA methylation across 33 cancer types. (<b>B</b>) Examination of the correlation between RBP4 expression and DNA methylation at the cg13228314 site in BRCA, CHOL, COAD, KIRC, KIRP, LAML, LGG, LIHC, PAAD, PRAD, READ, SARC, SKCM, and STAD.</p>
Full article ">Figure 8
<p>The functional annotation of RBP4 in pan-cancer was conducted as follows: (<b>A</b>) PPI network illustrating RBP4 and its forty co-expressed genes. (<b>B</b>) GO enrichment analysis of RBP4-related genes, plotting biological processes, cellular components, and molecular functions associated with RBP4. (<b>C</b>) KEGG enrichment analysis of RBP4-related genes, identifying key pathways involved in RBP4 function.</p>
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<p>The effect of RBP4 on the migration and proliferation of Huh7 cells: (<b>A</b>,<b>B</b>) Wound healing assay results indicating that siRBP4 treatment influenced Huh7 cell migration. (<b>C</b>,<b>D</b>) The expression levels of relevant proteins in Huh7 cells were shown by Western blot analysis. (<b>E</b>) Cell proliferation analysis conducted using the CCK-8 assay to evaluate the proliferation capacity of Huh7 cells (*, <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>
Full article ">
19 pages, 2242 KiB  
Article
A Computational Recognition Analysis of Promising Prognostic Biomarkers in Breast, Colon and Lung Cancer Patients
by Tala Bakheet, Nada Al-Mutairi, Mosaab Doubi, Wijdan Al-Ahmadi, Khaled Alhosaini and Fahad Al-Zoghaibi
Int. J. Mol. Sci. 2025, 26(3), 1017; https://doi.org/10.3390/ijms26031017 - 25 Jan 2025
Viewed by 504
Abstract
Breast, colon, and lung carcinomas are classified as aggressive tumors with poor relapse-free survival (RFS), progression-free survival (PF), and poor hazard ratios (HRs) despite extensive therapy. Therefore, it is essential to identify a gene expression signature that correlates with RFS/PF and HR status [...] Read more.
Breast, colon, and lung carcinomas are classified as aggressive tumors with poor relapse-free survival (RFS), progression-free survival (PF), and poor hazard ratios (HRs) despite extensive therapy. Therefore, it is essential to identify a gene expression signature that correlates with RFS/PF and HR status in order to predict treatment efficiency. RNA-binding proteins (RBPs) play critical roles in RNA metabolism, including RNA transcription, maturation, and post-translational regulation. However, their involvement in cancer is not yet fully understood. In this study, we used computational bioinformatics to classify the functions and correlations of RBPs in solid cancers. We aimed to identify molecular biomarkers that could help predict disease prognosis and improve the therapeutic efficiency in treated patients. Intersection analysis summarized more than 1659 RBPs across three recently updated RNA databases. Bioinformatics analysis showed that 58 RBPs were common in breast, colon, and lung cancers, with HR values < 1 and >1 and a significant Q-value < 0.0001. RBP gene clusters were identified based on RFS/PF, HR, p-value, and fold induction. To define union RBPs, common genes were subjected to hierarchical clustering and were classified into two groups. Poor survival was associated with high genes expression, including CDKN2A, MEX3A, RPL39L, VARS, GSPT1, SNRPE, SSR1, and TIA1 in breast and colon cancer but not with lung cancer; and poor survival was associated with low genes expression, including PPARGC1B, EIF4E3, and SMAD9 in breast, colon, and lung cancer. This study highlights the significant contribution of PPARGC1B, EIF4E3, and SMAD9 out of 11 RBP genes as prognostic predictors in patients with breast, colon, and lung cancers and their potential application in personalized therapy. Full article
(This article belongs to the Special Issue Molecular Pathways and New Therapies for Breast Cancer)
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Figure 1

Figure 1
<p>Study layout shows steps of the union RBPs list compilation. (<b>A</b>) Master list of 1659 genes was intersected out of 3302 compiled genes from three databases including RBPome, Census and RBPDB. (<b>B</b>) In total, 58 common RBP gene signatures out of 1659 genes were segregated across breast, colon and lung cancers and exposed to further filtration along with HR values (&gt;1 and &lt;1) and <span class="html-italic">p</span>-value &lt; 0.05 and Q-value &lt; 0.001. (<b>C</b>) In total, 11 union RBP gene signatures were filtered out of 4 hierarchical clusters.</p>
Full article ">Figure 2
<p>Hierarchical clustering heat map graphs. The common RBPs signature (58 genes) expression values, HR and <span class="html-italic">p</span>-values were subjected to be clustered into 6 clusters for (<b>A</b>) breast cancer, (<b>B</b>) colon and (<b>C</b>) lung cancer.</p>
Full article ">Figure 3
<p>Functional analysis in human cells: (<b>A</b>) Pie chart representing the consensus targets of the RBPs master genes. (<b>B</b>) Pie chart representing the domain consensus of the union RBP genes list. (<b>C</b>) The union RBP genes’ functional classifications including molecular, biological process and cellular component. (<b>D</b>) Network representing the current protein–protein interaction of the union RBP genes, the promising prognostic biomarkers were circled with red line.</p>
Full article ">Figure 4
<p>Cross correlations between the union RBP genes signature across multiple genes were determined for; (<b>A</b>) breast, (<b>B</b>) colon and (<b>C</b>) lung cancer. Volcano plot is shown by plotting the R-values on the <span class="html-italic">x</span>-axis and their significance <span class="html-italic">p</span>-values as corresponding –Log<sub>10</sub> (<span class="html-italic">p</span>-values) on the <span class="html-italic">y</span>-axis.</p>
Full article ">Figure 5
<p>Kaplan–Meier plots representing relapse-free survival (RFS) of breast and colon cancer patients and progression-free survival (PF) of lung cancer patients across the union RBP genes signature subgroups. (<b>A</b>,<b>D</b>,<b>G</b>) All up and downregulated genes, (<b>B</b>,<b>E</b>,<b>H</b>) the upregulated genes and (<b>C</b>,<b>F</b>,<b>I</b>) downregulated genes across breast, colon and lung cancers, respectively.</p>
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28 pages, 11515 KiB  
Article
A VersaTile Approach to Reprogram the Specificity of the R2-Type Tailocin Towards Different Serotypes of Escherichia coli and Klebsiella pneumoniae
by Dorien Dams, Célia Pas, Agnieszka Latka, Zuzanna Drulis-Kawa, Lars Fieseler and Yves Briers
Antibiotics 2025, 14(1), 104; https://doi.org/10.3390/antibiotics14010104 - 18 Jan 2025
Viewed by 941
Abstract
Background: Phage tail-like bacteriocins, or tailocins, provide a competitive advantage to producer cells by killing closely related bacteria. Morphologically similar to headless phages, their narrow target specificity is determined by receptor-binding proteins (RBPs). While RBP engineering has been used to alter the target [...] Read more.
Background: Phage tail-like bacteriocins, or tailocins, provide a competitive advantage to producer cells by killing closely related bacteria. Morphologically similar to headless phages, their narrow target specificity is determined by receptor-binding proteins (RBPs). While RBP engineering has been used to alter the target range of a selected R2 tailocin from Pseudomonas aeruginosa, the process is labor-intensive, limiting broader application. Methods: We introduce a VersaTile-driven R2 tailocin engineering and screening platform to scale up RBP grafting. Results: This platform achieved three key milestones: (I) engineering R2 tailocins specific to Escherichia coli serogroups O26, O103, O104, O111, O145, O146, and O157; (II) reprogramming R2 tailocins to target, for the first time, the capsule and a new species, specifically the capsular serotype K1 of E. coli and K11 and K63 of Klebsiella pneumoniae; (III) creating the first bivalent tailocin with a branched RBP and cross-species activity, effective against both E. coli K1 and K. pneumoniae K11. Over 90% of engineered tailocins were effective, with clear pathways for further optimization identified. Conclusions: This work lays the groundwork for a scalable platform for the development of engineered tailocins, marking an important step towards making R2 tailocins a practical therapeutic tool for targeted bacterial infections. Full article
(This article belongs to the Section Bacteriophages)
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Figure 1

Figure 1
<p>Pipeline for tailocin engineering and production. (<b>A</b>) Modular build-up of the R2 tailocin receptor-binding protein (RBP). (<b>B</b>) Establishment of a tile repository consisting of anchor and receptor-binding domain (RBD) tiles, flanked by position tags (P<sub>start</sub>, P<sub>mid</sub> and P<sub>end</sub>), sourced from the R2 tailocin and phages infecting <span class="html-italic">Escherichia coli</span> or <span class="html-italic">Klebsiella pneumoniae</span> and cloned in the pVTE entry vector using VersaTile cloning. (<b>C</b>) The use of VersaTile assembly to combine the anchor and RBD tiles in a predefined order in the expression vector. (<b>D</b>) The production of engineered tailocins in the producer host PAO1 Δ<span class="html-italic">prf15</span> upon induction of the SOS response with mitomycin C to produce the RBP-deficient mutant R2Δ<span class="html-italic">prf15</span> and in trans expression of the chimeric RBP with IPTG. (<b>E</b>) Results of the spot assay with visible zones at different concentrations of the spotted R2 wild-type (R2-WT) tailocin and its derivates, namely with the in trans expression of the chimeric RBP (R2-WT-trans), the in trans expression of the VersaTile assembled RBP (R2-WT-VT), and the expression of the deficient RBP (R2Δ<span class="html-italic">prf15</span>). (<b>F</b>) Overview of the results of the spot assay for all R2 wild-type tailocin derivatives. Lower concentrations indicate a higher R2 tailocin activity, and the lowest concentrations at which visible clearance was observed with the naked eye on a bacterial lawn of the <span class="html-italic">P. aeruginosa</span> target strain CF510 are displayed.</p>
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<p>Results of the survival assay of the R2 wild-type tailocin derivatives R2-WT, R2-trans, and R2-VT. The red asterisk of R2-WT-VT indicates the presence of the six-nucleotide junction between the anchor and RBD introduced by the VersaTile technique. Each R2 tailocin was tested on the susceptible target strain <span class="html-italic">P. aeruginosa</span> CF510. Significant differences are shown by asterisks (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.005, *** <span class="html-italic">p</span> &lt; 0.001). Each graph presents the bacterial colony count in function of the concentration of the added R2 tailocin (derivative). The value of each biological replicate is displayed using open circles and the mean values are shown as full circles.</p>
Full article ">Figure 3
<p>Spot assay of all engineered R2 tailocins tested against all available <span class="html-italic">Escherichia coli</span> strains of different serogroups. The lowest concentrations at which clearance was observed on bacterial lawns of <span class="html-italic">E. coli</span> target strains are displayed. Lower concentrations indicate a higher R2 tailocin activity. The engineered R2 tailocins and target strains are organized and colored according to O-antigen serogroups of the phage host donating the RBD and the target <span class="html-italic">E. coli</span> strain.</p>
Full article ">Figure 4
<p>Survival and growth inhibition assays of the different engineered <span class="html-italic">Escherichia coli</span> O-antigen-targeting R2 tailocins. One example of each targeted O-antigen serotype is given in this figure. A full version of this figure covering all strains tested can be found in <a href="#app1-antibiotics-14-00104" class="html-app">Supplementary Figure S4</a>. Each R2 tailocin was tested on their susceptible <span class="html-italic">E. coli</span> target strains. Significant differences are shown by asterisks (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.005, *** <span class="html-italic">p</span> &lt; 0.001). Reference (ref) indicates the value that was used as a reference for statistical comparison (untreated sample or control B). (<b>A</b>) Results of the survival assay. One plot is shown for each R2 tailocin construct, showing the bacterial colony count in function of the concentration of the added R2 tailocin. The value of each biological replicate is displayed using open circles and the mean values are shown as filled circles. (<b>B</b>) Growth inhibition assay results at 8 h are shown per R2 tailocin construct. The relative OD<sub>600</sub> of each biological replicate is displayed using open circles, and the mean relative OD<sub>600</sub> is shown as full circles. Two additional controls were performed, one containing the R2 tailocin but without the bacterial strain (Control A) and one containing a receptor-binding protein (RBP)-lacking mutant R2 tailocin particle (R2Δ<span class="html-italic">prf15</span>) (Control B). Both controls were added at the highest available R2 tailocin concentration (220–250 µg/mL).</p>
Full article ">Figure 4 Cont.
<p>Survival and growth inhibition assays of the different engineered <span class="html-italic">Escherichia coli</span> O-antigen-targeting R2 tailocins. One example of each targeted O-antigen serotype is given in this figure. A full version of this figure covering all strains tested can be found in <a href="#app1-antibiotics-14-00104" class="html-app">Supplementary Figure S4</a>. Each R2 tailocin was tested on their susceptible <span class="html-italic">E. coli</span> target strains. Significant differences are shown by asterisks (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.005, *** <span class="html-italic">p</span> &lt; 0.001). Reference (ref) indicates the value that was used as a reference for statistical comparison (untreated sample or control B). (<b>A</b>) Results of the survival assay. One plot is shown for each R2 tailocin construct, showing the bacterial colony count in function of the concentration of the added R2 tailocin. The value of each biological replicate is displayed using open circles and the mean values are shown as filled circles. (<b>B</b>) Growth inhibition assay results at 8 h are shown per R2 tailocin construct. The relative OD<sub>600</sub> of each biological replicate is displayed using open circles, and the mean relative OD<sub>600</sub> is shown as full circles. Two additional controls were performed, one containing the R2 tailocin but without the bacterial strain (Control A) and one containing a receptor-binding protein (RBP)-lacking mutant R2 tailocin particle (R2Δ<span class="html-italic">prf15</span>) (Control B). Both controls were added at the highest available R2 tailocin concentration (220–250 µg/mL).</p>
Full article ">Figure 5
<p>Spot assay of all engineered capsule-targeting R2 tailocins tested against <span class="html-italic">Klebsiella pneumoniae</span> and <span class="html-italic">Escherichia coli</span> capsular serotypes. The lowest concentrations at which clearance was observed on bacterial lawns of <span class="html-italic">K. pneumoniae</span> and <span class="html-italic">E. coli</span> target strains are displayed. Lower concentrations indicate a higher R2 tailocin activity. Engineered R2 tailocins that were not spotted against certain strains were indicated as not determined (ND). <span class="html-italic">E. coli</span> strain ECOR28 has an unknown capsular serotype, as indicated by a question mark. The engineered R2 tailocins and target strains are organized and colored according to K-antigen serogroups of the phage host donating the RBD and the target <span class="html-italic">K. pneumoniae</span> or <span class="html-italic">E. coli</span> strain.</p>
Full article ">Figure 6
<p>Survival and growth inhibition assays of the capsule-targeting R2 tailocins. One example of each targeted capsular serotype is given in this figure. A full version of this figure covering all strains tested can be found in <a href="#app1-antibiotics-14-00104" class="html-app">Supplementary Figure S4</a>. Each R2 tailocin was tested on its susceptible <span class="html-italic">E. coli</span> and <span class="html-italic">K. pneumoniae</span> target strain. Significant differences are shown by asterisks (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.005, *** <span class="html-italic">p</span> &lt; 0.001). Reference (ref) indicates the value that was used as a reference for statistical comparison (untreated sample or control B). (<b>A</b>) Results of the survival assay. One plot is shown for each R2 tailocin construct, showing the bacterial colony count in function of the concentration of the added R2 tailocin. The value of each biological replicate is displayed using open circles and the mean values are shown as full circles. (<b>B</b>) Results of the growth inhibition assay at 8 h are shown per R2 tailocin construct. The relative OD<sub>600</sub> of each biological replicate is displayed using open circles, and the mean relative OD<sub>600</sub> is shown as full circles. Two additional controls were performed, one containing R2 tailocin but lacking the bacterial strain (Control A) and one containing an RBP-lacking mutant R2 tailocin particle (R2Δ<span class="html-italic">prf15</span>) instead of the engineered R2 tailocin of interest (Control B). Both controls were added at the same concentration as the highest available R2 tailocin concentration (100–250 µg/mL).</p>
Full article ">Figure 7
<p>Analysis of bivalent R2 tailocin R2-K11-K1. (<b>A</b>) Modular build-up of R2 tailocin R2-K11-K1 with a dual receptor-binding protein (RBP) system with a branched anchor structure. The branched RBP system consists of four building blocks: (I) the N-terminal R2 anchor for attachment of the tailocin baseplate; (II) the RBD with specificity towards <span class="html-italic">Klebsiella</span> capsular serotype K11, sourced from phage K11gp17, including the T4gp10-like branching domain; (III) the conserved peptide (CP) sourced from phage KP32 to attach the second RBP to the branching domain of the first RBP; and (IV) the K1 RBD sourced from phage K1F. (<b>B</b>) The four tiles were assembled in the shuttle expression vector pVTD29 in a predefined order using VersaTile. (<b>C</b>) Results of the survival assay. The bacterial colony count is shown in function of the concentration of the added R2 tailocin. The value of each biological replicate is displayed using open circles and the mean values are shown as filled circles. (<b>D</b>) Growth inhibition assay results at 8 h, of the engineered bivalent R2 tailocin targeting both <span class="html-italic">K. pneumoniae</span> and <span class="html-italic">E. coli</span> capsular serotypes. The relative OD<sub>600</sub> of each biological replicate is displayed using open circles and the mean relative OD<sub>600</sub> is shown as full circles. Two additional controls were performed, one containing R2 tailocin, but lacking the bacterial strain (Control A), and one containing an RBP-lacking mutant R2 tailocin particle (R2Δ<span class="html-italic">prf15</span>) (Control B). For both the survival and growth inhibition assays, the R2 tailocin was tested on its susceptible <span class="html-italic">E. coli</span> and <span class="html-italic">K. pneumoniae</span> target strains, which are indicated in vertical, gray-colored headings on the left side of the figure. Significant differences are shown by asterisks (* <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001). Reference (ref) indicates the value that was used as a reference for statistical comparison (untreated sample or control B).</p>
Full article ">
18 pages, 4209 KiB  
Article
Tailoring Structural, Emulsifying, and Interfacial Properties of Rice Bran Protein Through Limited Enzymatic Hydrolysis After High-Hydrostatic-Pressure Pretreatment
by Shirang Wang, Zhen Hua, Tengyu Wang, Guoping Yu and Yu Sun
Foods 2025, 14(2), 292; https://doi.org/10.3390/foods14020292 - 17 Jan 2025
Viewed by 556
Abstract
We carried out limited enzymatic hydrolysis with trypsin on rice bran protein (RBP) pretreated by high hydrostatic pressure (HHP) in this study. The effects of the degree of hydrolysis (DH) on the structural and emulsifying properties were investigated. The results indicated that the [...] Read more.
We carried out limited enzymatic hydrolysis with trypsin on rice bran protein (RBP) pretreated by high hydrostatic pressure (HHP) in this study. The effects of the degree of hydrolysis (DH) on the structural and emulsifying properties were investigated. The results indicated that the molecular structure of RBP changed after limited enzymatic hydrolysis. The rice bran protein hydrolysate (RBPH, DH8) exhibited a better molecular distribution, a smaller particle size (200.4 nm), a better emulsifying activity index (31.82 m2/g), and an improved emulsifying stability index (24.69 min). RBPH emulsions with different DH (0–12) values were prepared. The interfacial properties, such as particle size, the ζ-potential, and the interfacial tension of the emulsions, were measured. Compared to the control, the interfacial properties of the RBPH emulsions were significantly improved after limited enzymatic hydrolysis. The RBPH emulsion at DH8 showed better stability with a smaller emulsion droplet size (2.31 μm), a lower ζ-potential (−25.56 mV), and a lower interfacial tension. This study can provide a theoretical basis for the application of RBP as the plant protein-based emulsifier in the beverage industry. Full article
(This article belongs to the Section Food Engineering and Technology)
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Figure 1

Figure 1
<p>Relationship between DH and hydrolysis time.</p>
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<p>The effect of limited enzymatic hydrolysis on the particle size of RBPH. The error bars indicate the standard deviation obtained from triplicate determinations. Different letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The effect of limited enzymatic hydrolysis on the ζ-potential of RBPH. The error bars indicate the standard deviation obtained from triplicate determinations. Different letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The effect of limited enzymatic hydrolysis on the SDS-PAGE of RBPH.</p>
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<p>The effect of limited enzymatic hydrolysis on the secondary structure of RBPH. (<b>A</b>) FTIR spectra; (<b>B</b>) secondary structure content.</p>
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<p>The effect of limited enzymatic hydrolysis on the fluorescence intensity of RBPH.</p>
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<p>The effect of limited enzymatic hydrolysis on the microstructure of RBPH.</p>
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<p>The effect of limited enzymatic hydrolysis on the molecular-weight distribution profiles of RBPH.</p>
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<p>The effect of limited enzymatic hydrolysis on the EAI and ESI values of RBPH. The error bars indicate the standard deviation obtained from triplicate determinations. Different letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The effect of limited enzymatic hydrolysis on the droplet size and ζ-potential of the RBPH emulsion. The error bars indicate the standard deviation obtained from triplicate determinations. Different letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The effect of limited enzymatic hydrolysis on the turbidity of RBPH emulsion. The error bars indicate the standard deviation obtained from triplicate determinations. Different letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The effect of limited enzymatic hydrolysis on the contact angle (<b>A</b>) and interfacial tension (<b>B</b>) of RBPH emulsions.</p>
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<p>The effect of limited enzymatic hydrolysis on the microscopic morphology of RBPH emulsion.</p>
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17 pages, 16109 KiB  
Article
Effect of High Percentages of Coated Recycled Aggregates on the Flexural Behavior of Reinforced Concrete Beams
by Catalina Martínez, Viviana Letelier and Bruno Wenzel
Appl. Sci. 2025, 15(2), 829; https://doi.org/10.3390/app15020829 - 16 Jan 2025
Viewed by 440
Abstract
Currently, the use of recycled aggregates (RA) in new concrete is allowed by several international regulations, although their replacement is limited to low percentages of the coarse fraction. In order to increase the percentage of RA, several authors have studied different processes to [...] Read more.
Currently, the use of recycled aggregates (RA) in new concrete is allowed by several international regulations, although their replacement is limited to low percentages of the coarse fraction. In order to increase the percentage of RA, several authors have studied different processes to improve the microstructure of its surface. Therefore, it is necessary to analyze whether the current standards simulate the structural behavior of concretes with high percentages of RA. For this purpose, beams with 0%, 50% and 100% RA replacement coated with recycled binder paste (RBP) were made and their behavior was compared with the equations of the Eurocode 2 and ACI 318-19 code. As a result, we found that when 100% coated RA was used, the reduction in compressive strength was only 12.73%, with similar cracking patterns observed in RA beams across all series. In addition, the load capacity of the beams with RA was higher than the theoretical values provided by the codes. Finally, the experimental critical deflection was higher than that calculated by the code equations. Thus, it is recommended that these higher deflections be taken into account at the time of design. Full article
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<p>Particle size distribution of the aggregates (i.e., NA and RA).</p>
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<p>Recycled aggregate without treatment (<b>right</b>) and coated recycled aggregate (<b>left</b>).</p>
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<p>(<b>a</b>) Details of the reinforced beams and (<b>b</b>) loading frame.</p>
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<p>Cracking patterns and failure modes of the tested beams.</p>
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<p>Load–deflection curves for the tested beams.</p>
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<p>Relationship between the experimental and theoretical cracking moments (<math display="inline"><semantics> <msub> <mi>M</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> </semantics></math>).</p>
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<p>Relationship between the experimental and theoretical ultimate moments (<math display="inline"><semantics> <msub> <mi>M</mi> <mi>u</mi> </msub> </semantics></math>).</p>
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<p>Relationship between experimental and theoretical cracking deflection values (<math display="inline"><semantics> <msub> <mi>δ</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> </semantics></math>).</p>
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<p>Relationship between experimental and theoretical ultimate deflection values (<math display="inline"><semantics> <msub> <mi>δ</mi> <mi>u</mi> </msub> </semantics></math>).</p>
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27 pages, 1958 KiB  
Review
Host RNA-Binding Proteins as Regulators of HIV-1 Replication
by Sebastian Giraldo-Ocampo, Fernando Valiente-Echeverría and Ricardo Soto-Rifo
Viruses 2025, 17(1), 43; https://doi.org/10.3390/v17010043 - 31 Dec 2024
Viewed by 902
Abstract
RNA-binding proteins (RBPs) are cellular factors involved in every step of RNA metabolism. During HIV-1 infection, these proteins are key players in the fine-tuning of viral and host cellular and molecular pathways, including (but not limited to) viral entry, transcription, splicing, RNA modification, [...] Read more.
RNA-binding proteins (RBPs) are cellular factors involved in every step of RNA metabolism. During HIV-1 infection, these proteins are key players in the fine-tuning of viral and host cellular and molecular pathways, including (but not limited to) viral entry, transcription, splicing, RNA modification, translation, decay, assembly, and packaging, as well as the modulation of the antiviral response. Targeted studies have been of paramount importance in identifying and understanding the role of RNA-binding proteins that bind to HIV-1 RNAs. However, novel approaches aimed at identifying all the proteins bound to specific RNAs (RBPome), such as RNA interactome capture, have also contributed to expanding our understanding of the HIV-1 replication cycle, allowing the identification of RBPs with functions not only in viral RNA metabolism but also in cellular metabolism. Strikingly, several of the RBPs found through interactome capture are not canonical RBPs, meaning that they do not have conventional RNA-binding domains and are therefore not readily predicted as being RBPs. Further studies on the different cellular targets of HIV-1, such as subtypes of T cells or myeloid cells, or on the context (active replication versus reactivation from latency) are needed to fully elucidate the host RBPome bound to the viral RNA, which will allow researchers and clinicians to discover new therapeutic targets during active replication and provirus reactivation from latency. Full article
(This article belongs to the Special Issue Regulation of the Virus Lifecycle by Cellular RNA-Binding Proteins)
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<p>Overview of the HIV-1 replication cycle and the involvement of viral (red) and host (black) RNA-binding proteins in each step as described by targeted studies. After viral entry, viral RNA is converted into a proviral DNA integrated into the host genome. RNA polymerase ll mediates proviral DNA transcription to produce a 9 kb transcript, which can be fully spliced, partially spliced, or unspliced to produce viral proteins or packaged as genomic RNA (in the case of the unspliced RNA). RBPs are involved in every step of the viral cycle, including reverse transcription, transcription or latency, nuclear export or retention, translation, RNA modification, and packaging. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>Approaches used to study the HIV-1 RBPome in different models. In vitro-transcribed fragments of HIV-1 RNA tagged with biotin, MS2-binding sites, or an aptamer are incubated with a cell lysate (<b>a</b>); or U2OS cells containing a provirus encoding truncated fragments of HIV-1 RNA tagged with MS2-binding sites are transfected with a Tat-encoding plasmid to induce provirus reactivation and with an MS2-encoding plasmid to produce flag-tagged MS2, and cells are lysed (<b>b</b>). In both cases, the viral RNAs bound to the RBPs are captured and purified using magnetic beads coupled to streptavidin (for biotin-tagged transcripts) or an anti-flag antibody (for flag–MS2 bound transcripts), or captured by an MS2–maltose-binding-protein (MBP) fusion protein or a tobramycin-coupled agarose matrix. After capture/purification, samples are eluted and analyzed by mass spectrometry. Alternatively, cells are infected for a pre-determined time prior to RNA–protein cross-linking by UV light or formaldehyde, and cross-linked RNPs are hybridized with specific HIV-1 RNA biotin-labeled antisense probes and captured/purified with streptavidin-coupled magnetic beads or hybridized and captured with oligo(dT)-coupled magnetic beads. Captured RNPs are eluted and analyzed by mass spectrometry (<b>c</b>). Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>The HIV-1 transactivation response (TAR) element is a hotspot for host RNA-binding proteins. Several host proteins bind to the HIV-1 5′UTR, many of which mediate important functions by binding to the TAR region. These proteins include TAR RNA-binding protein (TRBP), which enhances viral mRNA translation, inhibits PKR activation, and recruits FTSJ3, which deposits the epitranscriptomic mark 2′-O-methylation, allowing the vRNA to be recognized by the cell as a self-molecule. NSUN1 binds to the TAR region, blocking Tat binding, which reduces HIV-1 transcription and promotes latency, and deposits the m<sup>5</sup>C mark, likely leading to transcript degradation. TAF7, a host protein similar to Tat, promotes the nuclear export and translation of vRNA. The microprocessor complex Drosha/Dgcr8, when bound to TAR, leads to the recruitment of RNA decay machinery and vRNA degradation. SRSF1 also reduces Tat binding and promotes US RNA splicing. Once the vRNA reaches the cytoplasm, DDX3X unfolds the highly structured TAR region, allowing vRNA translation. Finally, PKR binds to the TAR region, where it could lead to global translation arrest, but is counteracted by ADAR1, TRBP, and PACT. Instead, low-level PKR binding to the TAR region likely leads to vRNA splicing enhancement through eIF2α phosphorylation. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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11 pages, 2323 KiB  
Article
PTHrP Promotes RBP4 Expression Under the Control of PPARγ in the Kidney
by María Paz Nieto-Bona, Almudena G. Carrasco, Gema Medina-Gomez, Ricardo J. Bosch and Adriana Izquierdo-Lahuerta
Int. J. Mol. Sci. 2025, 26(1), 142; https://doi.org/10.3390/ijms26010142 - 27 Dec 2024
Viewed by 546
Abstract
Parathyroid hormone-related protein (PTHrP) and retinol-binding protein 4 (RBP4) have been associated with a worse prognosis of kidney disease. Recently, the direct interconnection between PTHrP and the peroxisome proliferator-activated receptor gamma (PPARγ), a nuclear receptor whose activation is nephroprotective, has been discovered. The [...] Read more.
Parathyroid hormone-related protein (PTHrP) and retinol-binding protein 4 (RBP4) have been associated with a worse prognosis of kidney disease. Recently, the direct interconnection between PTHrP and the peroxisome proliferator-activated receptor gamma (PPARγ), a nuclear receptor whose activation is nephroprotective, has been discovered. The aim of this study was to analyze the relationship between PTHrP, PPARγ, and RBP4. For this purpose, we analyzed the levels of these proteins, which were studied in the kidneys of five experimental groups of mice at 6 weeks of age: controls, diabetics, insulin-treated diabetics, transgenic mice overexpressing PTHrP at the renal level, and the latter mice that were also induced with diabetes. In addition, we also analyzed the expression levels of these molecules in two mouse podocyte cell lines, controls and PPARγKO, subjected to a lipotoxic insult by palmitic acid. We found that RBP4 and PTHrP are increased in the kidney in pathological conditions and that insulin and PPARγ act regulating PTHrP and RBP4 expression, suggesting that the regulation of this system is critical for the maintenance of renal homeostasis and how it becomes imbalanced in different pathophysiological conditions. Full article
(This article belongs to the Special Issue The Role of Cytokines in Diseases)
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<p>RBP4 expression is incremented in diabetic kidney and renal PTHrP overexpression promotes increased RBP4 expression. Representative micrographs of the immunofluorescence for RBP4 (green) in kidney of mice: control, diabetic, diabetic treated with insulin, Tg PTHrP, and Tg PTHrP diabetic. PTHrP: parathyroid hormone-related protein; RBP4: retinol-binding protein 4; Tg PTHrP: transgenic overexpression of PTHrP. Magnification 400×.</p>
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<p>RBP4 and PTHrP expression is incremented in diabetic kidney and insulin treatment prevent this increase, while PPARγ is not changed at this age. (<b>A</b>) Representative western blot of RBP4, PTHrP, and PPARγ in kidney of diabetic mice of 2, 4, and 6 weeks of age; (<b>B</b>) representative western blot of RBP4, PTHrP, and PPARγ in kidney of control mice, diabetic mice, and diabetic mice treated with insulin at 6 weeks. (<b>C</b>) Quantifications of protein expression in kidney of mice of different groups at 6 weeks. (<b>D</b>) Pearson correlation between PTHrP and RBP4 protein levels expression in mice. N = 6–8 animals/group; PPARγ: peroxisome proliferator-activated receptor gamma; PTHrP: parathyroid hormone-related protein; RBP4: retinol-binding protein 4; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Expression of RBP4, PTHrP, and PPARγ in control and PPARγKO podocytes. (<b>A</b>) Relative mRNA expression of and protein expression of RBP4; (<b>B</b>) relative mRNA expression of PTHrP; (<b>C</b>) relative mRNA expression of PPARγ; (<b>D</b>) Pearson correlation between PTHrP and RBP4 mRNA levels expression in control podocytes; (<b>E</b>) RBP4 detection in supernatant of control and PPARγKO podocytes cultures. Data are shown as mean ± SEM (n = 3 experiments). * <span class="html-italic">p &lt;</span> 0.05 versus Vh control podocytes; # <span class="html-italic">p &lt;</span> 0.05 versus Vh PPARγKO podocytes. Vh: vehicle; PA: palmitic acid. N = 6–8 animals/group; PPARγ: peroxisome proliferator-activated receptor gamma; PTHrP: parathyroid hormone-related protein; RBP4: retinol-binding protein 4.</p>
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<p>Relation between PTHrP and PPARγ in the kidney, and implications of RBP4 in physiological and pathophysiological conditions. PPARγ: peroxisome proliferator-activated receptor gamma; PTHrP: parathyroid hormone-related protein; RBP4: retinol-binding protein 4.</p>
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13 pages, 5884 KiB  
Article
Strategic Optimization of the Middle Domain IIIA in RBP-Albumin IIIA-IB Fusion Protein to Enhance Productivity and Thermostability
by Myungho Sohn, Sanggil Kim, Hyeon Ju Jeong, In Young Ko, Ji Wook Moon, Dowon Lee and Junseo Oh
Int. J. Mol. Sci. 2025, 26(1), 137; https://doi.org/10.3390/ijms26010137 - 27 Dec 2024
Viewed by 577
Abstract
The protein therapeutics market, including antibody and fusion proteins, has experienced steady growth over the past decade, underscoring the importance of optimizing amino acid sequences. In our previous study, we developed a fusion protein, R31, which combines retinol-binding protein (RBP) with albumin domains [...] Read more.
The protein therapeutics market, including antibody and fusion proteins, has experienced steady growth over the past decade, underscoring the importance of optimizing amino acid sequences. In our previous study, we developed a fusion protein, R31, which combines retinol-binding protein (RBP) with albumin domains IIIA and IB, linked by a sequence (AAAA), and includes an additional disulfide bond (N227C-V254C) in IIIA. This fusion protein effectively inhibited hepatic stellate cell activation. In this study, we further optimized the sequence. The G176K mutation at the C-terminus of RBP altered the initiation site of the first α-helix in domain IIIA, shifting it from P182 to K176, and promoted polar interactions between K176 and adjacent residues, enhancing the rigidity of the RBP/IIIA interface. The introduction of an additional disulfide bond (V231C/Y250C) connecting helices 3 and 4 in IIIA resulted in a three-fold increase in productivity and a 2 °C improvement in thermal stability compared to R31. Furthermore, combining the G176K mutation with V231C/Y250C further enhanced both productivity and anti-fibrotic activity. These findings suggest that the enhanced stability of domain IIIA, conferred by V231C/Y250C, along with the increased rigidity of the RBP/IIIA interface, optimizes interdomain distance and alignment, facilitating proper protein folding. Full article
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<p>The predicted structures of R31 (<b>A</b>) and R31-G176K (<b>B</b>) generated using AlphaFold2. In these models, the RBP domain is shown in orange and the albumin domains IIIA and IB are depicted in cyan and magenta, respectively. The region linking the RBP and IIIA domains is marked with a black circle (<b>A</b>). Specific residues are highlighted as follows: C70 and C174 in blue, P182 in black, and 176 in red.</p>
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<p>The AlphaFold2-predicted structure of the wild-type residue G176 (<b>A</b>) and the mutant K176 residue (<b>B</b>), with interaction analysis conducted using the Schrödinger Suite Release 2024-2. In these structures, G176K is highlighted in green, interacting residues in yellow, and surrounding residues in light blue. In both panels, oxygen and nitrogen atoms are marked in red and blue, respectively. Polar interactions are represented by red dashed lines.</p>
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<p>Expression level of the fusion proteins R31 and R31-G176K in Expi293 cells. Expi293 cells were transiently transfected with a plasmid encoding the fusion protein R31 and R31-G176K. The culture supernatant was analyzed by SDS-PAGE with 10 mM DTT and then subjected to Western blotting using an anti-His tag antibody. M denotes the molecular weight marker.</p>
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<p>The predicted structure of R31 generated using AlphaFold2. In this model, the RBP domain is shown in orange and the albumin domains IIIA and IB are depicted in cyan and magenta, respectively. The native C235/C246 bond and N227C/V254C are colored in red and blue, respectively. Specific residues V231 and Y250 are highlighted in yellow.</p>
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<p>Comparison of expression levels of R31 variants in Expi293 cells. (<b>A</b>) Expi293 cells were transiently transfected with a plasmid encoding the fusion proteins R31, R31-G176K, R31-V231C/Y250C, and R31-G176K-V231C/Y250C. The culture supernatant was analyzed by SDS-PAGE with 10 mM DTT and then subjected to Western blotting using an anti-His tag antibody. M denotes the molecular weight marker. (<b>B</b>) Western blot results were quantified using ImageJ (version 1.53m). The quantitative densitometric data represent the means ± SD from two independent experiments.</p>
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<p>Expression and purification of R31 variants in Expi293 cells. Expi293 cells were transiently transfected with a plasmid encoding the fusion proteins R31, R31-G176K, R31-V231C/Y250C, and R31-G176K-V231C/Y250C. The resulting fusion proteins were purified using Ni Sepharose, followed by size exclusion chromatography. (<b>A</b>) SDS-PAGE analysis of the purified proteins, with (+) and without (-) 10 mM DTT, shown in the panel. M denotes the molecular weight marker. (<b>B</b>) The size exclusion chromatography profiles of the purified proteins.</p>
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<p>Protein thermal shift (PTS) assay results for R31 variants, R31 (<b>A</b>), R31-G176K (<b>B</b>), R31-V231C/Y250C (<b>C</b>), and R31-G176K-V231C/Y250C (<b>D</b>), in PBS (pH 7.4). The melting temperature (Tm) for each variant is as follows: R31 (74.06 °C), R31-G176K (74.15 °C), R31-V231C/Y250C (76.24 °C), and R31-G176K-V231C/Y250C (76.10 °C). The different shades of blue represent the results from three independent experimental replicates, while the blue line indicates the graph derived from the averaged values. The central dotted line represents the calculated thermostability temperature.</p>
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<p>Anti-fibrotic effects of fusion proteins on hepatic stellate cells (HSCs). HSCs after passage 1 were treated with purified fusion proteins (0.75 μM), R31 (black), R31-G176K (hatched), R31-V231C/Y250C (light grey), or R31-G176K-V231C/Y250C (dark gray), for 16 h. The expression levels of alpha-smooth muscle actin (α-SMA) (<b>A</b>) and collagen type I (<b>B</b>) were assessed using real-time PCR. Statistical significance was determined by paired <span class="html-italic">t</span>-test (n = 3), and values are indicated as significant at * <span class="html-italic">p</span> &lt; 0.05 or ** <span class="html-italic">p</span> &lt; 0.01 compared to the untreated control cells.</p>
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<p>Fusion proteins induced phenotypic changes in hepatic stellate cells (HSCs). HSCs after passage 1 were treated with purified fusion proteins (0.75 μM), R31, R31-G176K, R31-V231C/Y250C, or R31-G176K-V231C/Y250C, for 16 h. Morphological changes were then assessed using a light microscope. Scale bar = 30 μm.</p>
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14 pages, 1038 KiB  
Article
Profiling of snoRNAs in Exosomes Secreted from Cells Infected with Influenza A Virus
by Wojciech Rozek, Malgorzata Kwasnik, Wojciech Socha, Bartosz Czech and Jerzy Rola
Int. J. Mol. Sci. 2025, 26(1), 12; https://doi.org/10.3390/ijms26010012 - 24 Dec 2024
Viewed by 706
Abstract
Small nucleolar RNAs (snoRNAs) are non-coding RNAs (ncRNAs) that regulate many cellular processes. Changes in the profiles of cellular ncRNAs and those secreted in exosomes are observed during viral infection. In our study, we analysed differences in expression profiles of snoRNAs isolated from [...] Read more.
Small nucleolar RNAs (snoRNAs) are non-coding RNAs (ncRNAs) that regulate many cellular processes. Changes in the profiles of cellular ncRNAs and those secreted in exosomes are observed during viral infection. In our study, we analysed differences in expression profiles of snoRNAs isolated from exosomes of influenza (IAV)-infected and non-infected MDCK cells using high-throughput sequencing. The analysis revealed 133 significantly differentially regulated snoRNAs (131 upregulated and 2 downregulated), including 93 SNORD, 38 SNORA, and 2 SCARNA. The most upregulated was SNORD58 (log2FoldChange = 9.61), while the only downregulated snoRNAs were SNORD3 (log2FC = −2.98) and SNORA74 (log2FC = −2.67). Several snoRNAs previously described as involved in viral infections were upregulated, including SNORD27, SNORD28, SNORD29, SNORD58, and SNORD44. In total, 533 interactors of dysregulated snoRNAs were identified using the RNAinter database with an assigned confidence score ≥ 0.25. The main groups of predicted interactors were transcription factors (TFs, 169 interactors) and RNA-binding proteins (RBPs, 130 interactors). Among the most important were pioneer TFs such as POU5F1, SOX2, CEBPB, and MYC, while in the RBP category, notable interactors included Polr2a, TNRC6A, IGF2BP3, and FMRP. Our results suggest that snoRNAs are involved in pro-viral activity, although follow-up studies including experimental validation would be beneficial. Full article
(This article belongs to the Special Issue Exosomes and Non-Coding RNA Research in Health and Disease)
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<p>List of the 50 most upregulated snoRNAs, ranked by their log2Fold Change values. Colours indicate the classification of upregulated snoRNAs into specific types: C/D box snoRNAs (SNORD)—blue, H/ACA box snoRNAs (SNORA)—red, and SCARNAs—yellow.</p>
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<p>Graphical representation of snoRNA interactions: (<b>A</b>). Main categories of predicted interactors for dysregulated snoRNAs. Groups of interactors within each category are marked with colours: transcription factors (TF)—blue, RNA-binding proteins (RBP)—orange, microRNAs (miRNA)—grey, messenger RNAs (mRNA)—yellow, long non-coding RNAs (lncRNA)—light blue, histone modifications—green, snoRNAs—dark blue, and others—dark orange. (<b>B</b>). A circus plot illustrating the proportion of predicted interactions between various types of snoRNAs and the main categories of interactors. The width of the lines connecting the two halves of the plot represents the number of interactions. In the lower half of the plot, specific types of snoRNAs are indicated by distinct colours: SNORA—red, SNORD—blue, and SCARNA—green. In the upper half of the plot, the main categories of interactors are represented by the following colours: transcription factors (TF)—grey, histone modifications—orange, long non-coding RNAs (lncRNA)—light blue, microRNAs (miRNA)—light green, messenger RNAs (mRNA)—black, RNA-binding proteins (RBP)—dark red, and others—pink.</p>
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38 pages, 12587 KiB  
Article
Employing Neural Networks, Fuzzy Logic, and Weibull Analysis for the Evaluation of Recycled Brick Powder in Concrete Compositions
by Mohammad Mohtasham Moein, Komeil Rahmati, Ali Mohtasham Moein, Ashkan Saradar, Sam E. Rigby and Amin Akhavan Tabassi
Buildings 2024, 14(12), 4062; https://doi.org/10.3390/buildings14124062 - 21 Dec 2024
Viewed by 635
Abstract
Using construction and demolition (C&D) waste in concrete production is a promising step toward environmental resilience amid the construction industry’s ecological footprint. The extensive history of using bricks in the construction of buildings has resulted in a considerable amount of waste associated with [...] Read more.
Using construction and demolition (C&D) waste in concrete production is a promising step toward environmental resilience amid the construction industry’s ecological footprint. The extensive history of using bricks in the construction of buildings has resulted in a considerable amount of waste associated with this commonly used material. This study aimed to assess the quality of concrete by examining the effect of replacing cement with varying percentages of recycled brick powder (RBP—0% to 50%). The primary objectives include evaluating the mechanical properties of concrete and establishing the feasibility of using RBP as a partial cement substitute. The investigation of target concrete can be divided into two phases: (i) laboratory investigation, and (ii) numerical investigation. In the laboratory phase, the performance of concrete with RBP was assessed under short-term dynamic and various static loads. The drop-weight test recommended by the ACI 544 committee was used to assess the short-term dynamic behavior (352 concrete discs). Furthermore, the behavior under static load was analyzed through compressive, flexural, and tensile strength tests. During the numerical phase, artificial neural network models (ANN) and fuzzy logic models (FL) were used to predict the results of 28-day compressive strength. The impact life with different failure probabilities was predicted based on the impact resistance results, by combining the Weibull distribution model. Additionally, an impact damage evolution equation was presented for mixtures containing RBP. The results show that the use of RBP up to 15% caused a slight decrease in compressive, flexural, and tensile strength (about 3–5%). Also, by replacing RBP up to 15%, the first crack strength decreased by 7.15% and the failure strength decreased by 6.46%. The average error for predicting 28-day compressive strength by FL and ANN models was recorded as 4.66% and 0.87%, respectively. In addition, the results indicate that the impact data follow the two-parameter Weibull distribution, and the R2 value for different mixtures was higher than 0.9275. The findings suggest that incorporating RBP in concrete can contribute to sustainable construction practices by reducing the reliance on cement and utilizing waste materials. This approach not only addresses environmental concerns but also enhances the quality assessment of concrete, offering potential cost savings and resource efficiency for the construction industry. Real-world applications include using RBP-enhanced concrete in non-structural elements, such as pavements, walkways, and landscaping features, where high strength is not the primary requirement. Full article
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<p>Methodology.</p>
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<p>Brick powder production process.</p>
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<p>Brick powder.</p>
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<p>Number of samples and concrete discs in the RDWI test.</p>
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<p>RDWI test device.</p>
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<p>Specifications and details of the RDWI test device.</p>
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<p>28-day compressive strength prediction models: (<b>a</b>) Artificial Neural Network (ANN); (<b>b</b>) Fuzzy Logic (FL).</p>
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<p>Average results of 28-day compressive strength.</p>
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<p>28-day compressive strength changes compared to the control mix.</p>
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<p>Prior research on compressive strength [<a href="#B23-buildings-14-04062" class="html-bibr">23</a>,<a href="#B26-buildings-14-04062" class="html-bibr">26</a>,<a href="#B27-buildings-14-04062" class="html-bibr">27</a>,<a href="#B46-buildings-14-04062" class="html-bibr">46</a>,<a href="#B47-buildings-14-04062" class="html-bibr">47</a>,<a href="#B103-buildings-14-04062" class="html-bibr">103</a>,<a href="#B104-buildings-14-04062" class="html-bibr">104</a>,<a href="#B105-buildings-14-04062" class="html-bibr">105</a>,<a href="#B106-buildings-14-04062" class="html-bibr">106</a>]: (<b>a</b>) Impact of brick powder; (<b>b</b>) Regression analysis of compressive strength versus varying brick powder doses.</p>
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<p>Average results of 28-day flexural strength.</p>
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<p>28-day flexural strength changes compared to the control mix.</p>
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<p>Prior research on flexural strength [<a href="#B26-buildings-14-04062" class="html-bibr">26</a>,<a href="#B27-buildings-14-04062" class="html-bibr">27</a>,<a href="#B47-buildings-14-04062" class="html-bibr">47</a>,<a href="#B48-buildings-14-04062" class="html-bibr">48</a>,<a href="#B49-buildings-14-04062" class="html-bibr">49</a>,<a href="#B89-buildings-14-04062" class="html-bibr">89</a>,<a href="#B102-buildings-14-04062" class="html-bibr">102</a>,<a href="#B108-buildings-14-04062" class="html-bibr">108</a>]: (<b>a</b>) Impact of brick powder; (<b>b</b>) Regression analysis of flexural strength versus varying brick powder doses.</p>
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<p>The intensity of changes in compressive and flexural strength.</p>
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<p>Examining the severity of changes in compressive and flexural strength in previous studies [<a href="#B26-buildings-14-04062" class="html-bibr">26</a>,<a href="#B27-buildings-14-04062" class="html-bibr">27</a>].</p>
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<p>Average 28-day tensile strength results and changes compared to the control mixture.</p>
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<p>Prior research on tensile strength [<a href="#B49-buildings-14-04062" class="html-bibr">49</a>,<a href="#B50-buildings-14-04062" class="html-bibr">50</a>,<a href="#B101-buildings-14-04062" class="html-bibr">101</a>,<a href="#B102-buildings-14-04062" class="html-bibr">102</a>]: (<b>a</b>) Impact of brick powder; (<b>b</b>) Regression analysis of tensile strength versus varying brick powder doses.</p>
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<p>Average of impact strength: (<b>a</b>) First crack strength; (<b>b</b>) Failure strength.</p>
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<p>Percentage changes in impact strength.</p>
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<p>The INPB results.</p>
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<p>Impact energy and Impact ductility index results.</p>
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<p>The comparisons of the measured and predicted compressive strengths with FL and ANN.</p>
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<p>Prediction error by ANN and FL model compared to reality.</p>
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<p>The correlation of the measured and predicted compressive strengths: (<b>a</b>) FL model; (<b>b</b>) ANN model.</p>
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<p>The performance of the ANN model: (<b>a</b>) the training; (<b>b</b>) the validation; (<b>c</b>) the test; (<b>d</b>) all datasets.</p>
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<p>Weibull lines for first crack strength.</p>
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<p>Weibull lines for failure strength.</p>
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<p>Impact strength corresponding to reliability level: (<b>a</b>) control; (<b>b</b>) RB5; (<b>c</b>) RB10; (<b>d</b>) RB15; (<b>e</b>) RB20; (<b>f</b>) RB25; (<b>g</b>) RB30; (<b>h</b>) RB35; (<b>i</b>) RB40; (<b>j</b>) RB45; (<b>k</b>) RB50.</p>
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<p>Impact strength corresponding to reliability level: (<b>a</b>) control; (<b>b</b>) RB5; (<b>c</b>) RB10; (<b>d</b>) RB15; (<b>e</b>) RB20; (<b>f</b>) RB25; (<b>g</b>) RB30; (<b>h</b>) RB35; (<b>i</b>) RB40; (<b>j</b>) RB45; (<b>k</b>) RB50.</p>
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<p>Different stages of an impact–damage–evolution diagram.</p>
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<p>Impact–damage–evolution diagram for mixtures containing brick powder compared to the control mixture: (<b>a</b>) RB5 &amp; RB10; (<b>b</b>) RB15 &amp; RB20; (<b>c</b>) RB25 &amp; RB30; (<b>d</b>) RB35 &amp; RB40; (<b>e</b>) RB45 &amp; RB50.</p>
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19 pages, 7612 KiB  
Article
Identification of a Potential PGK1 Inhibitor with the Suppression of Breast Cancer Cells Using Virtual Screening and Molecular Docking
by Xianghui Chen, Zanwen Zuo, Xianbin Li, Qizhang Li and Lei Zhang
Pharmaceuticals 2024, 17(12), 1636; https://doi.org/10.3390/ph17121636 - 5 Dec 2024
Viewed by 924
Abstract
Background/Objectives: Breast cancer is the second most common malignancy worldwide and poses a significant threat to women’s health. However, the prognostic biomarkers and therapeutic targets of breast cancer are unclear. A prognostic model can help in identifying biomarkers and targets for breast cancer. [...] Read more.
Background/Objectives: Breast cancer is the second most common malignancy worldwide and poses a significant threat to women’s health. However, the prognostic biomarkers and therapeutic targets of breast cancer are unclear. A prognostic model can help in identifying biomarkers and targets for breast cancer. In this study, a novel prognostic model was developed to optimize treatment, improve clinical prognosis, and screen potential phosphoglycerate kinase 1 (PGK1) inhibitors for breast cancer treatment. Methods: Using data from the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) were identified in normal individuals and breast cancer patients. The biological functions of the DEGs were examined using bioinformatics analysis. A novel prognostic model was then constructed using the DEGs through LASSO and multivariate Cox regression analyses. The relationship between the prognostic model, survival, and immunity was also evaluated. In addition, virtual screening was conducted based on the risk genes to identify novel small molecule inhibitors of PGK1 from Chemdiv and Targetmol libraries. The effects of the potential inhibitors were confirmed through cell experiments. Results: A total of 230 up- and 325 down-regulated DEGs were identified in HER2, LumA, LumB, and TN breast cancer subtypes. A new prognostic model was constructed using ten risk genes. The analysis from The Cancer Genome Atlas (TCGA) indicated that the prognosis was poorer in the high-risk group compared to the low-risk group. The accuracy of the model was confirmed using the ROC curve. Furthermore, functional enrichment analyses indicated that the DEGs between low- and high-risk groups were linked to the immune response. The risk score was also correlated with tumor immune infiltrates. Moreover, four compounds with the highest score and the lowest affinity energy were identified. Notably, D231-0058 showed better inhibitory activity against breast cancer cells. Conclusions: Ten genes (ACSS2, C2CD2, CXCL9, KRT15, MRPL13, NR3C2, PGK1, PIGR, RBP4, and SORBS1) were identified as prognostic signatures for breast cancer. Additionally, results showed that D231-0058 (2-((((4-(2-methyl-1H-indol-3-yl)-1,3-thiazol-2-yl)carbamoyl)methyl)sulfanyl)acetic acid) may be a novel candidate for treating breast cancer. Full article
(This article belongs to the Section Pharmacology)
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<p>Identification and functional enrichment analysis of DEGs. (<b>A</b>–<b>D</b>) Top 20 up-regulated and down-regulated genes in HER2 (<b>A</b>), LumA (<b>B</b>), LumB (<b>C</b>), and TN (<b>D</b>) subtype tumor samples from GSE29431, GSE38959, GSE45827, GSE65194, and GSE115275 datasets. The red color represents up-regulated genes, while green indicates down-regulated genes. The numbers shown in the figure represent the log fold change (logFC) of genes in each dataset. The cutoff criteria are <span class="html-italic">p</span> &lt; 0.05 and |logFC| &gt; 0.5. (<b>E</b>) The Venn diagram of DEGs of HER2, LumA, LumB, and TN subtype tumor samples from GSE29431, GSE38959, GSE45827, GSE65194, and GSE115275 datasets. (<b>F</b>) The bar plot of GO functional enrichment analysis. The top 10 terms of biological process, cellular component, and molecular function are shown. (<b>G</b>) The bar plot illustrates the results of KEGG functional enrichment analysis.</p>
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<p>Analysis of the prognostic model in BC. (<b>A</b>) Forest plot of the signature risk model. (<b>B</b>) Lasso model for screening the key genes. (<b>C</b>) Multivariate Cox analysis confirming hub genes for risk model. (<b>D</b>) The expression levels of ten hub genes in breast cancer tissues compared to normal tissues. (<b>E</b>,<b>G</b>,<b>I</b>) Kaplan–Meier analysis of survival differences between high-risk and low-risk groups in training (<b>E</b>), test (<b>G</b>), and entire (<b>I</b>) sets. (<b>F</b>,<b>H</b>,<b>J</b>) Receiver operating characteristic (ROC) curve analysis on the ten model gene signatures in the training (<b>F</b>), test (<b>H</b>), and entire (<b>J</b>) sets. AUC, the area under the curve. These curves are performed by R package survival ROC. (<b>K</b>) Univariate Cox analysis of risk score and clinicopathological features in the entire set. (<b>L</b>) Multivariate Cox analysis of clinicopathological features and risk score in the entire set. (<b>M</b>) The ROC curve of the risk score and clinical characteristics. (<b>N</b>) The ROC curve and AUC values for the predictive signature at 1-year, 3-year, and 5-year survival rates.</p>
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<p>Analysis of the relationships between risk score and clinical characteristics of breast cancer in the TCGA cohort. (<b>A</b>) Heat map of ten model genes and clinical characteristics in the high- and low-risk groups. *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; and ***, <span class="html-italic">p</span> &lt; 0.001. (<b>B</b>) Analysis of overall survival in TCGA-BC patients based on clinical stratification, focusing on high- and low-risk groups by age, clinical stage, N stage, and T stage.</p>
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<p>The nomogram in predicting overall survival of breast cancer. (<b>A</b>) The nomogram predicts 1-, 3-, and 5-year overall survival. (<b>B</b>) Calibration maps were utilized to predict survival rates at 1, 3, and 5 years.</p>
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<p>Analysis of functional enrichment across different risk groups. (<b>A</b>) Volcano chart of differentially expressed genes; (<b>B</b>) GO analysis explored the potential function in terms of biological process (BP), cellular component (CC), and molecular function (MF); (<b>C</b>) KEGG analysis showed the potential pathway enrichment; (<b>D</b>) GSEA analysis demonstrated the potential activated and suppressed pathway enrichment in the high-risk group compared with the low-risk group.</p>
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<p>Immune features analysis in risk groups. (<b>A</b>,<b>B</b>) ssGSEA (single-sample gene set enrichment analysis) scores for immune cells (<b>A</b>) and immune function (<b>B</b>) in TCGA cohort. (<b>C</b>) The expression of immune checkpoint-related genes and the correlation between risk scores. aDCs, activated dendritic cells; APC, antigen-presenting cell; CCR, chemokine receptor; HLA, human leukocyte antigen; iDCs, immature dendritic cells; IFN, interferon; MHC, major histocompatibility complex; NK, natural killer; pDCs, plasmacytoid dendritic cells; Tfh, T follicular helper; Th, T helper cell; TIL, tumor-infiltrating lymphocyte; Treg, T regulatory cell. * <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.</p>
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<p>Three-dimensional interaction between PGK1 (2X13) and D715-2871 (<b>A</b>), Y040-8304 (<b>B</b>), D715-0344 (<b>C</b>), and D231-0058 (<b>D</b>). Yellow dotted lines represent hydrogen bonds, pinkish-red dotted lines represent salt bridges, and green balls depict magnesium ions.</p>
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<p>Inhibitory activity of D715-2871, Y040-8304, D715-0344, and D231-0058 against breast cancer cells T-47D and MCF-7. (<b>A</b>) CCK8 assay for cell viability. Cancer cells were treated with D715-2871, Y040-8304, D715-0344, or D231-0058 (0, 0.1, 1, 10, and 100 μg/mL) for 24 h. Data were presented as mean ± SD (<span class="html-italic">n</span> = 6). (<b>B</b>) CCK8 assay for cell viability. Cancer cells were treated with D231-0058 (0, 1, 3, 10, 30, and 100 μg/mL) for 24 and 48 h. Data were presented as mean ± SD (<span class="html-italic">n</span> = 6). (<b>C</b>) Microscopic observation of the cells treated with D231-0058 (0, 1, 3, 10, 30, and 100 μg/mL) for 24 h.</p>
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16 pages, 4036 KiB  
Article
Decoding the Molecular Grammar of TIA1-Dependent Stress Granules in Proteostasis and Welander Distal Myopathy Under Oxidative Stress
by Isabel Alcalde-Rey, Beatriz Ramos Velasco, José Alcalde and José M. Izquierdo
Cells 2024, 13(23), 1961; https://doi.org/10.3390/cells13231961 - 27 Nov 2024
Viewed by 846
Abstract
T-cell intracellular antigen 1 (TIA1) is an RNA-binding protein (RBP) that plays a multifunctional role in RNA metabolism. TIA1 has three RNA-Recognition Motifs (RRMs) and a prion-like carboxyl C-terminal domain (LCD) with intrinsically disordered regions (IDR) implicated in the dynamics (i.e., formation, assembly, [...] Read more.
T-cell intracellular antigen 1 (TIA1) is an RNA-binding protein (RBP) that plays a multifunctional role in RNA metabolism. TIA1 has three RNA-Recognition Motifs (RRMs) and a prion-like carboxyl C-terminal domain (LCD) with intrinsically disordered regions (IDR) implicated in the dynamics (i.e., formation, assembly, and disassembly) of transient RNA-protein aggregates known as stress granules (SGs). A protein related to TIA1 is its paralog TIA1-related/like protein (TIAR/TIAL1), whose amino acid sequence, structural organisation, and molecular and cellular functions are highly conserved with TIA1. Both proteins are the main components of SGs, which are non-membranous RNA-protein condensates formed under stress to promote cell survival. Welander distal myopathy (WDM) is a late-onset muscular dystrophy that has been linked to a single-nucleotide substitution (c.1362G>A; p.E384K) in the gene encoding the TIA1 protein, which impacts TIA1-dependent SGs dynamics. Herein, we have analysed cellular and molecular aspects by targeting mutagenesis to position 384 to understand its molecular grammar in an amino acid/proteinogenic-dependent or -independent manner under oxidative stress. The observations suggest differential, even opposing, behaviours between TIA1 and TIAR in the presence of specific amino acids with negative and positive charges, and also uncharged acids, at equivalent positions of TIA1 and TIAR, respectively. Collectively, these findings illustrate a characteristic molecular grammar of TIAR- and TIA1-dependent SGs under oxidative conditions, suggesting a gain of versatility between two structurally and functionally highly conserved/related proteins. Full article
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<p>Collection of amino acid variants at position 384 of human TIA1. (<b>A</b>) Schematic 3D structure of the human TIA1 protein with the in silico AlphaFold tool. The image shows the first and the last residues, the RRMs 1-3, and the C-terminal LCD of human TIA1. The encircled area indicates a C-terminal extension with the amino acid at position 384 of TIA1 highlighted in red. (<b>B</b>) Images of automated protein structure prediction and structure-based function annotation of structural models of WT and WDM TIA1 variants. The image series illustrates the WT (upper pictures) and WDM (lower pictures) 3D TIA1 structural models for the top five options estimated by the in silico iTASSER tool [<a href="#B43-cells-13-01961" class="html-bibr">43</a>]. Green and blue structures indicate the RRM 1-3 and the yellow/red one shows the LCD terminal domain. (<b>C</b>) Generation of the entire collection of TIA1 mutants dependent on residue 384. Schematic of the sequence of the last eight amino acid residues of the human TIA1 protein with each of the substitutions (blue bold type), nucleotide triplets (highlighting in bold those containing the substitution/mutation), and the families or categories into which the TIA1 mutants were grouped. The asterisk identifies the nonsense or stop codon. The controls (WT/E) and mutant (WDM/K) are boxed in green. (<b>D</b>) Expression analysis of TIA1 mutants using the Western blot technique. Western blot of protein extracts from HEK-Flp cells transfected with the entire collection of GFP-TIA1 mutants at position 384 with the corresponding substitutions. The monoclonal antibodies used were anti-TIA1, with anti-HuR as a loading control marker. Molecular weight markers (kDa) and proteins identified are indicated on the left and right, respectively.</p>
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<p>Differential dynamics of stress granules dependent on TIA1 variants at position 384. Fluorescence microscopy images of HEK-Flp cells transfected with GFP-TIA1 plasmids and their variants, identified in the legend at the top of this panel. Expression of the GFP-TIA1 fusion protein is shown in green, the G3BP1 antibody in red, and “Merge” corresponds to the combination of the three channels: GFP (green), G3BP1 (red), and To-Pro3 (grey-stained nuclei). The white arrows indicate TIA1-SGs. Images are shown both in the absence and presence of sodium arsenite treatment for each mutation examined. Scale bar, 10 μm.</p>
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<p>Alternative dynamics of stress granules dependent on TIA1 variants at position 384. Fluorescence microscopy images of HEK-Flp cells transfected with GFP-TIA1 plasmids and their variants, identified in the legend at the top of this panel. Expression of the GFP-TIA1 fusion protein is shown in green, the G3BP1 antibody in red, and “Merge” corresponds to a combination of the three channels: GFP (green), G3BP1 (red), and To-Pro3 (grey-stained nuclei). The white arrows illustrate TIA1-SGs. Images are shown both in the absence and presence of sodium arsenite treatment for each mutation examined. Scale bar, 10 μm.</p>
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<p>Quantification of the relative number and size of TIA1 variant-dependent SGs described in <a href="#cells-13-01961-f002" class="html-fig">Figure 2</a> and <a href="#cells-13-01961-f003" class="html-fig">Figure 3</a>. Data represent the mean ± standard error of the mean (n = 18–114 cells for each condition; * <span class="html-italic">p</span> &lt; 0.5; ** <span class="html-italic">p</span> &lt; 0.1; *** <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Collection of amino acid variants at position 373 of human TIAR. (<b>A</b>) Schematic 3D structure of the human TIAR protein by the in silico AlphaFold tool. The image shows the first and last residues, the RRMs 1–3, and the C-terminal LCD of human TIAR. The encircled area indicates a C-terminal extension with the amino acid located at position 373 in TIAR highlighted in red. (<b>B</b>) Images of automated protein structure prediction and structure-based function annotation of structural models of TIAR<sup>Q</sup> (WT) and TIAR<sup>K</sup> variants. The image series illustrates the WT (upper pictures) and TIAR<sup>K</sup> (lower pictures) 3D TIAR structural models for the top five options estimated by the in silico iTASSER tool. Green and blue structures indicate the RRM 1-3 and the yellow/red one shows the LCD terminal domain. (<b>C</b>) Generation of the collection of TIAR mutants dependent on Q residue. Schematic of the sequence of the last eight amino acid residues of the human TIAR protein with each of the substitutions (blue bold type), and nucleotide triplets (highlighting in bold those containing the substitution/mutation). The asterisk identifies the nonsense or stop codon. Controls (WT/TIAR<sup>Q</sup>) are boxed in green. (<b>D</b>) Expression analysis of TIAR mutants using the Western blot technique. Western blot of protein extracts from HEK-Flp cells transfected with the entire collection of GFP-TIAR mutants with the corresponding substitutions. The monoclonal antibodies used were anti-TIA1, with anti-HuR used as a loading control marker. Molecular weight markers (kDa) and proteins identified are indicated on the left and right, respectively.</p>
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<p>Differential dynamics of stress granules dependent on TIAR variants. (<b>A</b>) Fluorescence microscopy images of HEK-Flp cells transfected with GFP-TIAR and their variants, identified in the legend at the top of this panel. Expression and distribution/location of the GFP-TIAR fusion protein are shown in green, the G3BP1 antibody in red, and “Merge” corresponds to the combination of the three channels: GFP, G3BP1, and To-Pro3 (grey-stained nuclei). The white arrows show TIAR-SGs. Images are shown both in the absence and presence of sodium arsenite treatment for each mutation studied. Scale bar, 10 μm. (<b>B</b>) Quantification of SG number and size under the experimental described in (<b>A</b>). Data represent the mean ± standard error of the mean (n = 10–30 cells for each condition; * <span class="html-italic">p</span> &lt; 0.5; ** <span class="html-italic">p</span> &lt; 0.1; *** <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Summary of the impact of TIA1 variants at position 384 on the dynamics of TIA1-dependent stress granules. WT represents wild-type TIA1, WDM illustrates TIA1 containing the p.E384K mutation associated with Welander distal myopathy, and P indicates proline residue-associated behaviour, where the number of SGs is much lower, in transfected FT293 cells with TIA1 variants under oxidative stress conditions (sodium arsenite). This figure was created with BioRender.com. [<a href="#B47-cells-13-01961" class="html-bibr">47</a>].</p>
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17 pages, 2761 KiB  
Article
Generation of Transcript Length Variants and Reprogramming of mRNA Splicing During Atherosclerosis Progression in ApoE-Deficient Mice
by Miguel Hueso, Adrián Mallén and Estanis Navarro
Biomedicines 2024, 12(12), 2703; https://doi.org/10.3390/biomedicines12122703 - 26 Nov 2024
Viewed by 705
Abstract
Background. Variant 3′UTRs provide mRNAs with different binding sites for miRNAs or RNA-binding proteins (RBPs) allowing the establishment of new regulatory environments. Regulation of 3′UTR length impacts on the control of gene expression by regulating accessibility of miRNAs or RBPs to homologous sequences [...] Read more.
Background. Variant 3′UTRs provide mRNAs with different binding sites for miRNAs or RNA-binding proteins (RBPs) allowing the establishment of new regulatory environments. Regulation of 3′UTR length impacts on the control of gene expression by regulating accessibility of miRNAs or RBPs to homologous sequences in mRNAs. Objective. Studying the dynamics of mRNA length variations in atherosclerosis (ATS) progression and reversion in ApoE-deficient mice exposed to a high-fat diet and treated with an αCD40-specific siRNA or with a sequence-scrambled siRNA as control. Methods. We gathered microarray mRNA expression data from the aortas of mice after 2 or 16 weeks of treatments, and used these data in a Bioinformatics analysis. Results. Here, we report the lengthening of the 5′UTR/3′UTRs and the shortening of the CDS in downregulated mRNAs during ATS progression. Furthermore, treatment with the αCD40-specific siRNA resulted in the partial reversion of the 3′UTR lengthening. Exon analysis showed that these length variations were actually due to changes in the number of exons embedded in mRNAs, and the further examination of transcripts co-expressed at weeks 2 and 16 in mice treated with the control siRNA revealed a process of mRNA isoform switching in which transcript variants differed in the patterns of alternative splicing or activated latent/cryptic splice sites. Conclusion. We document length variations in the 5′UTR/3′UTR and CDS of mRNAs downregulated during atherosclerosis progression and suggest a role for mRNA splicing reprogramming and transcript isoform switching in the generation of disease-related mRNA sequence diversity and variability. Full article
(This article belongs to the Special Issue Impact of 3'UTR Variants on mRNA Stability)
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<p>Pipeline of the experimental approach followed. Female ApoE<sup>-/-</sup> mice were treated twice weekly for 16 weeks (arrowheads) with an intraperitoneal administration of 50 μg of an αCD40 siRNA (treatment group, T) or 50 μg of a scrambled sequence siRNA (control group, C). Aortic tissue was extracted at weeks 8 (basal) and 10 and 24 for both experimental groups (C10, C24, T10 and T24). Total RNA was extracted and used for a microarray experiment in which expression data were normalized to the basal levels at week 8. Only downregulated transcripts of the C and T groups were used in this analysis of length dynamics (see text). In parenthesis is the number of transcripts from each group. Transcripts simultaneously expressed in two experimental conditions (C10/C24 and T10/T24) were identified and used for bioinformatic analysis of the mechanisms regulating transcript length variation.</p>
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<p>Analysis of 3′UTR length variations. Shown are the length distribution of transcripts downregulated in ATS progression (C10 to C24, panels <b>A</b>) and treatment-induced regression (T10 vs. T24, panels <b>B</b>). In all cases, the plots show the distribution of the 3′UTRs of the transcripts as transcript density vs. length in bp. The pink line refers to the distribution of the mouse reference exome, while the blue line shows that of the query population of transcripts. Blue arrows show the displacements of the blue line from the mouse reference transcriptome distribution. Dotted blue lines are drawn to facilitate comparison of peaks. Blue circles highlight differences in the second peak. Numbers inside the graphics show the statistical significance of the comparison (from Student’s <span class="html-italic">t</span>-test in the ShinyGO webtool; statistical significance as follows, * <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.0005). In parenthesis, the number of transcripts used for the analysis in each group is noted.</p>
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<p>Analysis of 5′UTR and CDS length variations in ATS progression (C10 to C24). Length distribution of the 5′UTRs (<b>A</b>) or CDS (<b>B</b>) of the transcripts downregulated in ATS progression (C10 to C24) and plotted as transcript density vs. length in bp. The pink line plots the length distribution of the mouse reference exome, while the blue line shows that of the query population of transcripts. Blue arrows show the displacements of the blue line from the mouse reference exome distribution. Dotted red line centers the peak of the mouse reference exomes to facilitate comparison among plots. Numbers inside the graphics show the statistical significance of the comparison (from Student’s <span class="html-italic">t</span>-test in the ShinyGO webtool; statistical significance as follows, * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.005). In parenthesis, the number of transcripts used for the analysis in each group is noted.</p>
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<p>Analysis of 5′UTR and CDS length variations in ATS regression (T24 vs. C24). Length distribution of the 5′UTRs (<b>A</b>) or CDS (<b>B</b>) of the transcripts downregulated in ATS regression (T24 vs. C24) and plotted as transcript density vs. length in bp. The pink line plots the length distribution of the mouse reference exome, while the blue line shows that of the query population of transcripts. Blue arrows show the displacements of the blue line from the mouse reference exome distribution. Dotted red line centers the peak of the mouse reference exomes to facilitate comparison among plots. Numbers inside the graphics show the statistical significance of the comparison (from Student’s <span class="html-italic">t</span>-test in the ShinyGO webtool; statistical significance as follows, * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.005). In parenthesis, the number of transcripts used for the analysis in each group is noted.</p>
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<p>Variations in the number of exons in the groups tested compared with expected values from mouse reference transcriptome. Red bars correspond to the query group while grey bars indicate the expected values. Decimal values refer to the average number of exons across transcript variants of a gene with multiple alternatively spliced isoforms (Chi-squared test in ShinyGO).</p>
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<p>Alternative 5′ splicing event at the terminal region of the Mta3 transcript variant 1. (<b>A</b>) Diagram of four terminal exons (grey boxes) of the Mta3 gene. Shown are the last two exons of the transcript variant 3 (NM_001171053, blue at the top of the boxes) and the last four of transcript variant 1 (NM_001171052, red at the bottom of the boxes). Numbers state the beginning/end of each exon and dotted lines the splicing events linking exons. Bold numbers refer to the positions in the genomic sequence NC_000083, which includes the Mta3 gene. Shown are also the positions of the stop codons (red dots) and the poly-A tails (An). The cryptic splicing event links position 1614 in at the middle of exon 13 of variant 1 with position 1615 at exon 14 of variant 1. Exons E12–E15 are numbered (E12–E15) according to their positions in the genomic sequence NC_000083. Not drawn to scale. (<b>B</b>) Donor/acceptor splicing signals at exons E12/E13 (upper) and E13/E14 (alternative cryptic splicing, lower) of transcript variant 1 and comparison with the canonical signals. Coincident positions are signaled with a vertical bar. In bold, exonic sequences.</p>
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<p>Graphical summary of the findings of this work. Graphical representation of the functional regions (5′UTR, CDS, 3′UTR and poly-A tail) of a model mRNA and their length dynamics during ATS progression and upon treatment with the αCD40 siRNA. Arrows and headings indicate the sense of the length variations. Designed from data in <a href="#biomedicines-12-02703-f002" class="html-fig">Figure 2</a>, <a href="#biomedicines-12-02703-f003" class="html-fig">Figure 3</a> and <a href="#biomedicines-12-02703-f004" class="html-fig">Figure 4</a>. Not drawn to scale.</p>
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24 pages, 752 KiB  
Review
MicroRNAs and RNA-Binding Protein-Based Regulation of Bone Metastasis from Hepatobiliary Cancers and Potential Therapeutic Strategies
by Sharmila Fagoonee and Ralf Weiskirchen
Cells 2024, 13(23), 1935; https://doi.org/10.3390/cells13231935 - 21 Nov 2024
Cited by 1 | Viewed by 912
Abstract
Hepatobiliary cancers, such as hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), are among the deadliest malignancies worldwide, leading to a significant number of cancer-related deaths. While bone metastases from these cancers are rare, they are highly aggressive and linked to poor prognosis. This review [...] Read more.
Hepatobiliary cancers, such as hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), are among the deadliest malignancies worldwide, leading to a significant number of cancer-related deaths. While bone metastases from these cancers are rare, they are highly aggressive and linked to poor prognosis. This review focuses on RNA-based molecular mechanisms that contribute to bone metastasis from hepatobiliary cancers. Specifically, the role of two key factors, microRNAs (miRNAs) and RNA-binding proteins (RBPs), which have not been extensively studied in the context of HCC and CCA, is discussed. These molecules often exhibit abnormal expression in hepatobiliary tumors, influencing cancer cell spread and metastasis by disrupting bone homeostasis, thereby aiding tumor cell migration and survival in the bone microenvironment. This review also discusses potential therapeutic strategies targeting these RNA-based pathways to reduce bone metastasis and improve patient outcomes. Further research is crucial for developing effective miRNA- and RBP-based diagnostic and prognostic biomarkers and treatments to prevent bone metastases in hepatobiliary cancers. Full article
(This article belongs to the Special Issue Molecular Mechanism of Bone Disease)
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<p>Most common functions of RBPs under physiological conditions or in cancer. In healthy cells, RBPs can post-transcriptionally regulate RNA metabolism, including alternative splicing, RNA modification, mRNA stability, and translation. This is represented in a simplified version in the figure. Under pathological conditions, increased expression of RBPs can lead to aberrant RNP formation through recruitment of oncogenic proteins, RBPs, and diverse RNA species, leading to dysregulated mRNA stability, translation, and alternative splicing, as well as alterations in cellular localizations of RNAs.</p>
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44 pages, 45485 KiB  
Review
A Critical Review of the Technical Characteristics of Recycled Brick Powder and Its Influence on Concrete Properties
by Jinkang Hu, Wisal Ahmed and Dengwu Jiao
Buildings 2024, 14(11), 3691; https://doi.org/10.3390/buildings14113691 - 20 Nov 2024
Viewed by 1662
Abstract
This paper presents a systematic overview of the applications of RBP as a substitute for cement. Initially, the fundamental properties of RBP, including physical properties, chemical compositions, and morphology, are discussed. Subsequently, the effects of RBP on various aspects of cement-based materials, such [...] Read more.
This paper presents a systematic overview of the applications of RBP as a substitute for cement. Initially, the fundamental properties of RBP, including physical properties, chemical compositions, and morphology, are discussed. Subsequently, the effects of RBP on various aspects of cement-based materials, such as fresh properties, shrinkage behavior, hydration, microstructure, strength development, and durability, are thoroughly reviewed. The findings of this study reveal that waste brick powder exhibits pozzolanic activity and can be used to partially replace cement in concrete formulations. However, its relatively high water absorption and irregular shape increase the water demand and, thus, reduce the rheological properties. The incorporation of RBP with 10–20% or finer particle sizes can refine the pore structure and promote the formation of hydration products. However, replacements of RBP greater than 25% can lead to adverse effects on the mechanical properties, frost resistance, and carbonation resistance of cementitious composites. Therefore, to enhance the effectiveness of RBP, measures such as improving fineness, incorporating mineral admixtures, adjusting curing conditions, and applying nano- or chemical modifications are necessary. This study provides valuable technical support for promoting the sustainable preparation of construction materials, which holds important environmental and economic implications. Full article
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Figure 1

Figure 1
<p>Generation of construction and demolition waste (data derived from [<a href="#B6-buildings-14-03691" class="html-bibr">6</a>,<a href="#B7-buildings-14-03691" class="html-bibr">7</a>]).</p>
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<p>Density map of the keywords (Colors from red to blue indicate the frequency of keywords from high to low).</p>
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<p>Median diameter (D50) for RBP and Portland cement (data derived from [<a href="#B10-buildings-14-03691" class="html-bibr">10</a>,<a href="#B40-buildings-14-03691" class="html-bibr">40</a>,<a href="#B44-buildings-14-03691" class="html-bibr">44</a>,<a href="#B45-buildings-14-03691" class="html-bibr">45</a>,<a href="#B46-buildings-14-03691" class="html-bibr">46</a>]).</p>
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<p>Typical XRD patterns of RBP (data derived from (<b>a</b>) [<a href="#B71-buildings-14-03691" class="html-bibr">71</a>] and (<b>b</b>) [<a href="#B67-buildings-14-03691" class="html-bibr">67</a>]).</p>
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<p>SEM images of RBP with various particle sizes (data derived from [<a href="#B43-buildings-14-03691" class="html-bibr">43</a>]): (<b>a</b>) D50 = 27.1 μm; (<b>b</b>) D50 = 15.8 μm; (<b>c</b>) D50 = 10.5 μm; (<b>d</b>) D50 = 3.4 μm.</p>
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<p>SEM images of RBP after (<b>a</b>) grinding for 30 min and (<b>b</b>) calcination at 600 °C (data derived from [<a href="#B75-buildings-14-03691" class="html-bibr">75</a>]).</p>
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<p>Typical water demand of cementitious materials containing RBP (data derived from (<b>a</b>) [<a href="#B39-buildings-14-03691" class="html-bibr">39</a>] and (<b>b</b>) [<a href="#B43-buildings-14-03691" class="html-bibr">43</a>]): (<b>a</b>) relationship between relative water demand and RBP replacement ratio; (<b>b</b>) relationship between the variation in water demand and RBP size.</p>
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<p>The impact of the particle sizes of RBP on the static and dynamic yield stresses (data derived from [<a href="#B96-buildings-14-03691" class="html-bibr">96</a>]): (<b>a</b>) static yield stress of concrete containing RBP; (<b>b</b>) dynamic yield stress of concrete containing RBP.</p>
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<p>Relationship between dynamic yield stress and mean interparticle distance (data derived from [<a href="#B95-buildings-14-03691" class="html-bibr">95</a>]).</p>
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<p>Relationship between the replacement ratio and the relative slump of cementitious materials containing RBP with various D50 (data derived from [<a href="#B5-buildings-14-03691" class="html-bibr">5</a>,<a href="#B42-buildings-14-03691" class="html-bibr">42</a>,<a href="#B98-buildings-14-03691" class="html-bibr">98</a>,<a href="#B101-buildings-14-03691" class="html-bibr">101</a>,<a href="#B102-buildings-14-03691" class="html-bibr">102</a>]).</p>
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<p>Drying shrinkage of mortar with RBP at various replacement ratios (data derived from (<b>a</b>) [<a href="#B39-buildings-14-03691" class="html-bibr">39</a>] and (<b>b</b>) [<a href="#B120-buildings-14-03691" class="html-bibr">120</a>]): (<b>a</b>) 7.5–30%; (<b>b</b>) 30–70%.</p>
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<p>Self-shrinkage of cementitious materials containing RBP (data derived from (<b>a</b>) [<a href="#B125-buildings-14-03691" class="html-bibr">125</a>] and (<b>b</b>) [<a href="#B102-buildings-14-03691" class="html-bibr">102</a>]): (<b>a</b>) impact of the replacement ratio; (<b>b</b>) impact of the particle size.</p>
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<p>Chemical shrinkage of concrete containing RBP (data derived from [<a href="#B69-buildings-14-03691" class="html-bibr">69</a>]): (<b>a</b>) early 24 h; (<b>b</b>) early 168 h.</p>
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<p>Hydration heat release rate of cement paste with RBP (data derived from [<a href="#B92-buildings-14-03691" class="html-bibr">92</a>]).</p>
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<p>Cumulative hydration heat with various RBP replacement ratios over a 72 h period (data derived from [<a href="#B10-buildings-14-03691" class="html-bibr">10</a>,<a href="#B26-buildings-14-03691" class="html-bibr">26</a>,<a href="#B43-buildings-14-03691" class="html-bibr">43</a>,<a href="#B53-buildings-14-03691" class="html-bibr">53</a>,<a href="#B64-buildings-14-03691" class="html-bibr">64</a>,<a href="#B75-buildings-14-03691" class="html-bibr">75</a>,<a href="#B92-buildings-14-03691" class="html-bibr">92</a>,<a href="#B125-buildings-14-03691" class="html-bibr">125</a>,<a href="#B135-buildings-14-03691" class="html-bibr">135</a>,<a href="#B136-buildings-14-03691" class="html-bibr">136</a>]).</p>
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<p>Cumulative hydration heat with various particle sizes over a 72 h period (data derived from [<a href="#B10-buildings-14-03691" class="html-bibr">10</a>,<a href="#B92-buildings-14-03691" class="html-bibr">92</a>]).</p>
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<p>Peak value and cumulative hydration heat of cement paste containing RBP and Nano silicon over a 72 h period (data derived from [<a href="#B135-buildings-14-03691" class="html-bibr">135</a>]).</p>
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<p>TG analysis of cement paste with various RBP replacement ratios at 28 days (data derived from [<a href="#B64-buildings-14-03691" class="html-bibr">64</a>]).</p>
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<p>XRD patterns of cement paste with different RBP replacement ratios (data derived from [<a href="#B39-buildings-14-03691" class="html-bibr">39</a>]).</p>
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<p>XRD patterns of cement paste containing RBP with different particle sizes—M0 (reference), M1 (D50 = 27.1 μm), M2 (D50 = 15.8 μm), M3 (D50 = 10.5 μm), and M4 (D50 = 3.4 μm) (data derived from [<a href="#B43-buildings-14-03691" class="html-bibr">43</a>]): (<b>a</b>) curing duration 28 days; (<b>b</b>) curing duration 90 days.</p>
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<p>XRD patterns of different cement pastes containing RBP under different curing conditions, where S is SiO<sub>2</sub>, CH is Ca(OH)<sub>2</sub>, E is ettringite, UCs is unhydrated compounds, A-RBP is RP with a specific surface area of 460 m<sup>2</sup>/ kg, and B-RBP is RBP with a specific surface area of 632 m<sup>2</sup>/kg (data derived from [<a href="#B140-buildings-14-03691" class="html-bibr">140</a>]): (<b>a</b>) standard curing; (<b>b</b>) steam curing.</p>
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<p>Variation of XRD pattern before and after the initial carbonation of cement paste with RBP (data derived from [<a href="#B77-buildings-14-03691" class="html-bibr">77</a>]).</p>
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<p>SEM images of (<b>a</b>) cement paste with 20% RBP at 1 day and 7 days; (<b>b</b>) cement paste with 40% RBP at 7 days (data derived from (<b>a</b>) [<a href="#B141-buildings-14-03691" class="html-bibr">141</a>] and (<b>b</b>) [<a href="#B106-buildings-14-03691" class="html-bibr">106</a>]).</p>
Full article ">Figure 23 Cont.
<p>SEM images of (<b>a</b>) cement paste with 20% RBP at 1 day and 7 days; (<b>b</b>) cement paste with 40% RBP at 7 days (data derived from (<b>a</b>) [<a href="#B141-buildings-14-03691" class="html-bibr">141</a>] and (<b>b</b>) [<a href="#B106-buildings-14-03691" class="html-bibr">106</a>]).</p>
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<p>SEM image of cementitious materials containing 0%, 20%, and 50% RBP (data derived from (<b>a</b>) [<a href="#B147-buildings-14-03691" class="html-bibr">147</a>], (<b>b</b>) [<a href="#B151-buildings-14-03691" class="html-bibr">151</a>], and (<b>c</b>) [<a href="#B143-buildings-14-03691" class="html-bibr">143</a>]).</p>
Full article ">Figure 24 Cont.
<p>SEM image of cementitious materials containing 0%, 20%, and 50% RBP (data derived from (<b>a</b>) [<a href="#B147-buildings-14-03691" class="html-bibr">147</a>], (<b>b</b>) [<a href="#B151-buildings-14-03691" class="html-bibr">151</a>], and (<b>c</b>) [<a href="#B143-buildings-14-03691" class="html-bibr">143</a>]).</p>
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<p>SEM images of OPC paste and cement paste containing RBP with D50 = 2.7 μm at curing times of (<b>a</b>) 3 days and (<b>b</b>) 90 days (data derived from [<a href="#B10-buildings-14-03691" class="html-bibr">10</a>]).</p>
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<p>Porosity of cement paste containing 30% RBP with different maximum diameters (data derived from [<a href="#B153-buildings-14-03691" class="html-bibr">153</a>]).</p>
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<p>Compressive strength of cementitious materials containing RBP (data derived from [<a href="#B5-buildings-14-03691" class="html-bibr">5</a>,<a href="#B28-buildings-14-03691" class="html-bibr">28</a>,<a href="#B42-buildings-14-03691" class="html-bibr">42</a>,<a href="#B44-buildings-14-03691" class="html-bibr">44</a>,<a href="#B45-buildings-14-03691" class="html-bibr">45</a>,<a href="#B46-buildings-14-03691" class="html-bibr">46</a>,<a href="#B48-buildings-14-03691" class="html-bibr">48</a>,<a href="#B49-buildings-14-03691" class="html-bibr">49</a>,<a href="#B84-buildings-14-03691" class="html-bibr">84</a>,<a href="#B100-buildings-14-03691" class="html-bibr">100</a>,<a href="#B101-buildings-14-03691" class="html-bibr">101</a>,<a href="#B102-buildings-14-03691" class="html-bibr">102</a>,<a href="#B159-buildings-14-03691" class="html-bibr">159</a>,<a href="#B162-buildings-14-03691" class="html-bibr">162</a>,<a href="#B163-buildings-14-03691" class="html-bibr">163</a>,<a href="#B164-buildings-14-03691" class="html-bibr">164</a>]).</p>
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<p>Effect of various factors on the compressive strength of cementitious materials containing RBP (data derived from [<a href="#B5-buildings-14-03691" class="html-bibr">5</a>,<a href="#B10-buildings-14-03691" class="html-bibr">10</a>,<a href="#B42-buildings-14-03691" class="html-bibr">42</a>,<a href="#B44-buildings-14-03691" class="html-bibr">44</a>,<a href="#B45-buildings-14-03691" class="html-bibr">45</a>,<a href="#B46-buildings-14-03691" class="html-bibr">46</a>,<a href="#B102-buildings-14-03691" class="html-bibr">102</a>,<a href="#B164-buildings-14-03691" class="html-bibr">164</a>]).</p>
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<p>Effects of the RBP replacement ratio on the compressive strength of cementitious materials (data derived from (<b>a</b>) [<a href="#B5-buildings-14-03691" class="html-bibr">5</a>,<a href="#B28-buildings-14-03691" class="html-bibr">28</a>,<a href="#B42-buildings-14-03691" class="html-bibr">42</a>,<a href="#B44-buildings-14-03691" class="html-bibr">44</a>,<a href="#B45-buildings-14-03691" class="html-bibr">45</a>,<a href="#B46-buildings-14-03691" class="html-bibr">46</a>,<a href="#B47-buildings-14-03691" class="html-bibr">47</a>,<a href="#B48-buildings-14-03691" class="html-bibr">48</a>,<a href="#B49-buildings-14-03691" class="html-bibr">49</a>,<a href="#B84-buildings-14-03691" class="html-bibr">84</a>,<a href="#B100-buildings-14-03691" class="html-bibr">100</a>,<a href="#B101-buildings-14-03691" class="html-bibr">101</a>,<a href="#B102-buildings-14-03691" class="html-bibr">102</a>,<a href="#B159-buildings-14-03691" class="html-bibr">159</a>,<a href="#B162-buildings-14-03691" class="html-bibr">162</a>,<a href="#B163-buildings-14-03691" class="html-bibr">163</a>,<a href="#B164-buildings-14-03691" class="html-bibr">164</a>,<a href="#B165-buildings-14-03691" class="html-bibr">165</a>,<a href="#B166-buildings-14-03691" class="html-bibr">166</a>,<a href="#B167-buildings-14-03691" class="html-bibr">167</a>,<a href="#B168-buildings-14-03691" class="html-bibr">168</a>,<a href="#B169-buildings-14-03691" class="html-bibr">169</a>] and (<b>b</b>) [<a href="#B5-buildings-14-03691" class="html-bibr">5</a>,<a href="#B10-buildings-14-03691" class="html-bibr">10</a>,<a href="#B42-buildings-14-03691" class="html-bibr">42</a>,<a href="#B44-buildings-14-03691" class="html-bibr">44</a>,<a href="#B45-buildings-14-03691" class="html-bibr">45</a>,<a href="#B46-buildings-14-03691" class="html-bibr">46</a>,<a href="#B102-buildings-14-03691" class="html-bibr">102</a>,<a href="#B164-buildings-14-03691" class="html-bibr">164</a>]): (<b>a</b>) correlation between the RBP replacement ratio and relative compressive strength; (<b>b</b>) effects of the RBP replacement ratio on the relative compressive strength.</p>
Full article ">Figure 30
<p>Effects of the curing duration on the compressive strength of cementitious materials with 30% RBP (data derived from [<a href="#B10-buildings-14-03691" class="html-bibr">10</a>,<a href="#B43-buildings-14-03691" class="html-bibr">43</a>,<a href="#B44-buildings-14-03691" class="html-bibr">44</a>,<a href="#B45-buildings-14-03691" class="html-bibr">45</a>,<a href="#B46-buildings-14-03691" class="html-bibr">46</a>,<a href="#B48-buildings-14-03691" class="html-bibr">48</a>,<a href="#B102-buildings-14-03691" class="html-bibr">102</a>,<a href="#B162-buildings-14-03691" class="html-bibr">162</a>,<a href="#B164-buildings-14-03691" class="html-bibr">164</a>]).</p>
Full article ">Figure 31
<p>Effects of the particle size on the compressive strength of cementitious materials with 30% RBP (data derived from (<b>a</b>) [<a href="#B10-buildings-14-03691" class="html-bibr">10</a>,<a href="#B43-buildings-14-03691" class="html-bibr">43</a>,<a href="#B96-buildings-14-03691" class="html-bibr">96</a>,<a href="#B102-buildings-14-03691" class="html-bibr">102</a>,<a href="#B140-buildings-14-03691" class="html-bibr">140</a>,<a href="#B153-buildings-14-03691" class="html-bibr">153</a>,<a href="#B162-buildings-14-03691" class="html-bibr">162</a>,<a href="#B164-buildings-14-03691" class="html-bibr">164</a>] and (<b>b</b>) [<a href="#B10-buildings-14-03691" class="html-bibr">10</a>]): (<b>a</b>) relative compressive strength; (<b>b</b>) compressive strength.</p>
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<p>Flexural strength of cementitious materials containing RBP (data derived from [<a href="#B19-buildings-14-03691" class="html-bibr">19</a>,<a href="#B28-buildings-14-03691" class="html-bibr">28</a>,<a href="#B42-buildings-14-03691" class="html-bibr">42</a>,<a href="#B46-buildings-14-03691" class="html-bibr">46</a>,<a href="#B49-buildings-14-03691" class="html-bibr">49</a>,<a href="#B83-buildings-14-03691" class="html-bibr">83</a>,<a href="#B84-buildings-14-03691" class="html-bibr">84</a>,<a href="#B100-buildings-14-03691" class="html-bibr">100</a>,<a href="#B102-buildings-14-03691" class="html-bibr">102</a>,<a href="#B159-buildings-14-03691" class="html-bibr">159</a>,<a href="#B175-buildings-14-03691" class="html-bibr">175</a>,<a href="#B177-buildings-14-03691" class="html-bibr">177</a>]).</p>
Full article ">Figure 33
<p>Effect of various factors on the flexural strength of cementitious materials containing RBP (data derived from [<a href="#B19-buildings-14-03691" class="html-bibr">19</a>,<a href="#B40-buildings-14-03691" class="html-bibr">40</a>,<a href="#B46-buildings-14-03691" class="html-bibr">46</a>,<a href="#B49-buildings-14-03691" class="html-bibr">49</a>,<a href="#B83-buildings-14-03691" class="html-bibr">83</a>,<a href="#B84-buildings-14-03691" class="html-bibr">84</a>,<a href="#B102-buildings-14-03691" class="html-bibr">102</a>].</p>
Full article ">Figure 34
<p>Correlation between the RBP replacement ratio and relative flexural strength of cementitious materials containing RBP (data derived from (<b>a</b>) [<a href="#B19-buildings-14-03691" class="html-bibr">19</a>,<a href="#B28-buildings-14-03691" class="html-bibr">28</a>,<a href="#B42-buildings-14-03691" class="html-bibr">42</a>,<a href="#B46-buildings-14-03691" class="html-bibr">46</a>,<a href="#B49-buildings-14-03691" class="html-bibr">49</a>,<a href="#B83-buildings-14-03691" class="html-bibr">83</a>,<a href="#B84-buildings-14-03691" class="html-bibr">84</a>,<a href="#B100-buildings-14-03691" class="html-bibr">100</a>,<a href="#B102-buildings-14-03691" class="html-bibr">102</a>,<a href="#B159-buildings-14-03691" class="html-bibr">159</a>,<a href="#B175-buildings-14-03691" class="html-bibr">175</a>,<a href="#B177-buildings-14-03691" class="html-bibr">177</a>] and (<b>b</b>) [<a href="#B19-buildings-14-03691" class="html-bibr">19</a>,<a href="#B42-buildings-14-03691" class="html-bibr">42</a>,<a href="#B46-buildings-14-03691" class="html-bibr">46</a>,<a href="#B49-buildings-14-03691" class="html-bibr">49</a>,<a href="#B83-buildings-14-03691" class="html-bibr">83</a>,<a href="#B84-buildings-14-03691" class="html-bibr">84</a>,<a href="#B102-buildings-14-03691" class="html-bibr">102</a>,<a href="#B159-buildings-14-03691" class="html-bibr">159</a>,<a href="#B179-buildings-14-03691" class="html-bibr">179</a>]): (<b>a</b>) correlation between the RBP alternative ratio and relative flexural strength; (<b>b</b>) effects of the RBP replacement ratio on the relative flexural strength.</p>
Full article ">Figure 35
<p>Relationship between the relative flexural strength and the curing duration of cementitious materials with 30% RBP (data derived from [<a href="#B10-buildings-14-03691" class="html-bibr">10</a>,<a href="#B46-buildings-14-03691" class="html-bibr">46</a>,<a href="#B83-buildings-14-03691" class="html-bibr">83</a>,<a href="#B102-buildings-14-03691" class="html-bibr">102</a>]).</p>
Full article ">Figure 36
<p>Relationship between the relative flexural strength and particle size (data derived from [<a href="#B10-buildings-14-03691" class="html-bibr">10</a>,<a href="#B102-buildings-14-03691" class="html-bibr">102</a>]).</p>
Full article ">Figure 37
<p>Elastic modulus of cementitious materials containing RBP (data derived from [<a href="#B101-buildings-14-03691" class="html-bibr">101</a>,<a href="#B102-buildings-14-03691" class="html-bibr">102</a>]).</p>
Full article ">Figure 38
<p>The charge passed through cementitious materials containing RBP with various replacement ratio at 28 days (data derived from [<a href="#B137-buildings-14-03691" class="html-bibr">137</a>,<a href="#B138-buildings-14-03691" class="html-bibr">138</a>,<a href="#B185-buildings-14-03691" class="html-bibr">185</a>]).</p>
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<p>Relationship between the Cl<sup>−</sup> diffusion coefficient and curing time (data derived from [<a href="#B72-buildings-14-03691" class="html-bibr">72</a>]).</p>
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<p>Weight variation of mortars containing RBP in H<sub>2</sub>SO<sub>4</sub> solution (data derived from [<a href="#B55-buildings-14-03691" class="html-bibr">55</a>]).</p>
Full article ">Figure 41
<p>Mass loss and dynamic elastic modulus of mortars containing RBP after freeze–thaw cycles (data derived from [<a href="#B189-buildings-14-03691" class="html-bibr">189</a>]): (<b>a</b>) mass losses of mortars containing RBP after freeze–thaw cycles; (<b>b</b>) relative dynamic elastic modulus of mortars containing RBP.</p>
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