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21 pages, 4921 KiB  
Article
Preclinical Efficacy and Proteomic Prediction of Molecular Targets for s-cal14.1b and s-cal14.2b Conotoxins with Antitumor Capacity in Xenografts of Malignant Pleural Mesothelioma
by Angélica Luna-Nophal, Fernando Díaz-Castillo, Vanessa Izquierdo-Sánchez, Jesús B. Velázquez-Fernández, Mario Orozco-Morales, Luis Lara-Mejía, Johana Bernáldez-Sarabia, Noemí Sánchez-Campos, Oscar Arrieta, José Díaz-Chávez, Jorge-Ismael Castañeda-Sánchez, Alexei-Fedorovish Licea-Navarro and Saé Muñiz-Hernández
Mar. Drugs 2025, 23(1), 32; https://doi.org/10.3390/md23010032 (registering DOI) - 10 Jan 2025
Abstract
Malignant pleural mesothelioma (MPM) is a rare neoplasm with increasing incidence and mortality rates. Although recent advances have improved the overall prognosis, they have not had an important impact on survival of patients with MPM, such that more effective treatments are needed. Some [...] Read more.
Malignant pleural mesothelioma (MPM) is a rare neoplasm with increasing incidence and mortality rates. Although recent advances have improved the overall prognosis, they have not had an important impact on survival of patients with MPM, such that more effective treatments are needed. Some species of marine snails have been demonstrated to be potential sources of novel anticancer molecules. This study analyzed the anticancer effects in vitro and in vivo of two peptides found in C. californicus. The effects of s-cal14.1b and s-cal14.2b on cell proliferation, apoptosis, and cytotoxicity were evaluated in 2D and 3D cultures of MPM-derived cells. Proteomics analysis of 3D cultures treated with conotoxins was performed to examine changes in expression or abundance. And the therapeutic effects of both conotoxins were evaluated in MPM mouse xenografts. s-cal14.1b and s-cal14.2b induced apoptosis and cytotoxicity in 2D and 3D cultures. However, only s-cal14.1b modified spheroid growth. Approximately 600 proteins exhibited important differential expression, which was more heterogeneous in H2452 vs MSTO-211H spheroids. The in silico protein functional analysis showed modifications in the biological pathways associated with carcinogenesis. CAPN1, LIMA1, ANXA6, HUWE1, PARP1 or PARP4 proteins could be potential cell targets for conotoxins and serve as biomarkers in MPM. Finally, we found that both conotoxins reduced the tumor mass in MPM xenografts; s-cal14.1b reached statistical significance. Based on these results, s-cal14.1b and s-cal14.2b conotoxins could be potential therapeutic drugs for MPM neoplasms with no apparent side effects on normal cells. Full article
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Graphical abstract
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<p>(<b>A</b>) Percentages of proliferation obtained at 24 h and 48 h post-exposition with s-cal14.1b and s-cal12.2b toxin in monolayers cultures. * The point where <span class="html-italic">p</span> = 0.05, compared the treated vs untreated monolayer. (<b>B</b>) At the top are present plots representative of apoptosis acquisition and at the bottom are present apoptosis percentage graphs. MRC-5 cell lines do not have a statistically significant induction of apoptosis. Percentage of apoptosis induced in H2452 and MSTO-211H cell lines reaches a statistical significance. * Statistical significance <span class="html-italic">p</span> ≤ 0.05.</p>
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<p>Final volume (mm<sup>3</sup>) of spheroids during formation and growth period; arrows indicate the day in which conotoxins were added to culture. * Statistical difference vs control cultures unexposed; <span class="html-italic">p</span> ≤ 0.05.</p>
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<p>The spheroid morphology during formation and growth was monitored for a long time. * The day conotoxins were added to culture. Panels titled PC correspond to phase contrast and FL to fluorescent mark (green) of cytotoxic effects; all images were taken under the same epifluorescence microscopy parameters. Barr = 100 μm.</p>
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<p>Heat maps representing the differential expression according to experimental condition; control refers to proteins in untreated 3D culture (they were previously compared to 2D culture). All treatments’ expressions were compared to the corresponding 3D control. The expression value rang is indicated by color, green for upregulated and red for downregulated.</p>
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<p>Biological process most significant expressed by 3D cultures according to the experimental condition. Proteins are grouped according to their participation in several processes. F and G, after the toxin’s name, represent the formation or growth period, respectively.</p>
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<p>Interaction between proteins in MSTO-211H or H2452 3D cultures. Red squares indicate downregulated proteins and green circles upregulated ones. Interactions between proteins are indicate by black lines. The relationship was generated by Cytoscape software.</p>
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<p>Tumor growth in xenograft model was inhibited by s-cal14.1b; volume is presented as mean ± standard deviation, the follow up was seven weeks. s-cal14.1b showed statistical difference with respect at untreated group mice (blue vs. black line). Right panel, representative images of tumor mass, after sacrifice of the mice. Barr = 15 mm.</p>
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22 pages, 1478 KiB  
Article
Proteomic Profiling of Extracellular Vesicles in Inflammatory Bowel Diseases
by Montse Baldán-Martín, Mikel Azkargorta, Ainhoa Lapitz, Lorena Ortega Moreno, Ibon Iloro, Samuel Fernández-Tomé, Ander Arbelaiz, Iraide Escobes, Alicia C. Marín, David Bernardo, Luis Bujanda, Jesús M. Bañales, Felix Elortza, Javier P. Gisbert and María Chaparro
Int. J. Mol. Sci. 2025, 26(2), 526; https://doi.org/10.3390/ijms26020526 - 9 Jan 2025
Abstract
Background: The proteomic analysis of serum extracellular vesicles (EVs) could be a useful tool for studying the pathophysiology of Crohn’s disease (CD) and ulcerative colitis (UC), as well as for biomarker discovery. Aims: To characterize the proteomic composition of serum EVs in patients [...] Read more.
Background: The proteomic analysis of serum extracellular vesicles (EVs) could be a useful tool for studying the pathophysiology of Crohn’s disease (CD) and ulcerative colitis (UC), as well as for biomarker discovery. Aims: To characterize the proteomic composition of serum EVs in patients with CD and UC to identify biomarkers and molecular pathways associated with pathogenesis and activity. Methods: Serum EVs were enriched and analyzed in patients with quiescent CD, active CD (aCD), quiescent UC, active UC (aUC), and healthy controls (HCs) (n = 30 per group). All groups were matched for age and sex. Disease activity was assessed by ileocolonoscopy and categorized based on the SES-CD (CD) and the endoscopic sub-score of the Mayo Score (UC). EVs were enriched by ultracentrifugation, and their size and concentration were determined by nanoparticle tracking analysis. The expression of CD63, CD81, and CD9 was determined using Western blotting. Proteomic analysis was performed by label-free nano-LC MS/MS. Results: A total of 324 proteins were identified; 60 showed differential abundance in CD-HC, 34 in UC-HC, and 21 in CD-UC. Regarding disease activity, the abundance of 58 and 32 proteins was altered in aCD-HC and aUC-HC, respectively. Functional analyses revealed that proteins associated with aCD were involved in immune regulation, whereas those linked to aUC were enriched in oxidative stress. Conclusions: We have identified expressed proteins between EVs from patients with CD and UC, depending on the presence of disease, the disease type, and the disease activity. These proteins are potential candidates as disease biomarkers and open new research avenues to better understand these conditions. Full article
(This article belongs to the Special Issue Inflammatory Bowel Diseases: Molecular Mechanism and Therapeutics)
21 pages, 4964 KiB  
Article
Proteomic Characterization of Liver Cancer Cells Treated with Clinical Targeted Drugs for Hepatocellular Carcinoma
by Hezhou Long, Jiafu Zhou, Changxia Zhou, Shuyu Xie, Jingling Wang, Minjia Tan and Junyu Xu
Biomedicines 2025, 13(1), 152; https://doi.org/10.3390/biomedicines13010152 (registering DOI) - 9 Jan 2025
Abstract
Background/Objectives: Hepatocellular carcinoma (HCC) remains a significant global health concern, primarily due to the limited efficacy of targeted therapies, which are often compromised by drug resistance and adverse side effects. Methods: In this study, we utilized a Tandem Mass Tag (TMT)-based [...] Read more.
Background/Objectives: Hepatocellular carcinoma (HCC) remains a significant global health concern, primarily due to the limited efficacy of targeted therapies, which are often compromised by drug resistance and adverse side effects. Methods: In this study, we utilized a Tandem Mass Tag (TMT)-based quantitative proteomic approach to analyze global protein expression and serine/threonine/tyrosine (S/T/Y) phosphorylation modifications in HepG2 cells following treatment with three clinically relevant hepatocellular carcinoma-targeted agents: apatinib, regorafenib, and lenvatinib. Results: Utilizing KEGG pathway enrichment analysis, biological process enrichment analysis, and protein interaction network analysis, we elucidated the common and specific metabolic pathways, biological processes, and protein interaction regulatory networks influenced by three liver cancer therapeutics. The study additionally proposed potential combinational treatment strategies, highlighting a possible synergistic interaction between HCC-targeted drugs and the DNA methyltransferase inhibitor. Furthermore, through the integration of clinical phosphorylation site data, we identified several phosphorylation sites that exhibited higher abundance in tumor tissues compared to adjacent non-tumor tissues. These sites were associated with poor prognosis and elevated functional scores. Conclusions: In summary, this study conducted an in-depth analysis of the molecular alterations in proteins and phosphorylation modifications induced by clinical HCC-targeted drugs, predicting drug combination strategies and therapeutic targets. Full article
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Figure 1
<p>Drug-perturbated proteomics in liver cancer HepG2 cell line. (<b>a</b>) Workflow for TMT-based proteomic analysis with apatinib, regorafenib, and lenvatinib treatments in the HepG2 cells. (<b>b</b>) Results from the principal component analysis of proteomic data. (<b>c</b>) Overview of protein expression after treatment with three liver cancer-targeted drugs, showing the dynamics of protein abundance (log2 ratio). (<b>d</b>) Volcanic map of differentially expressed proteins in HepG2 cells treated with apatinib, regorafenib, and lenvatinib.</p>
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<p>Functional enrichment analysis of changed proteins treated with different drugs. KEGG pathway enrichment analysis of upregulated and downregulated proteins in (<b>a</b>) apatinib-treated, (<b>b</b>) regorafenib-treated, and (<b>c</b>) lenvatinib-treated groups. Biological process analysis of differentially expressed proteins in (<b>d</b>) apatinib-treated, (<b>e</b>) regorafenib-treated, and (<b>f</b>) lenvatinib-treated groups.</p>
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<p>The protein–protein interaction network under the treatment of regorafenib. The protein–protein interaction network of upregulated proteins (<b>a</b>) and downregulated proteins (<b>b</b>) in the regorafenib-treated group.</p>
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<p>Proteomic analysis predicting potential drug combination strategy. (<b>a</b>) Workflow of combination therapy prediction using published proteome profiling data. (<b>b</b>) Overview of potential drug combination of three liver cancer-targeted drugs identified through published proteomics data. (<b>c</b>) Scatter plots illustrating previously reported drug combinations of three liver cancer-targeted drugs.</p>
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<p>Characterization of the dynamic phosphorylation level after treatment with the three drugs in the HepG2 Cell Line. (<b>a</b>) The Western blot analysis of global phosphorylation in HepG2 cells treated with apatinib, regorafenib, and lenvatinib. (<b>b</b>) Volcano plots of differential phosphorylation sites based on protein expression profile data for three liver cancer-targeted drugs. Biological process analysis of proteins with significantly altered phosphorylation sites in (<b>c</b>) apatinib- and (<b>d</b>) regorafenib-treated groups. Protein–protein interaction network of proteins with significantly altered phosphorylation sites in (<b>e</b>) apatinib- and (<b>f</b>) regorafenib-treated groups.</p>
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<p>Functional exploration of the identified phosphorylated substrates. (<b>a</b>) Overview of functional phosphorylation sites for three liver cancer-targeted drugs based on integrated clinical phosphorylation data. (<b>b</b>) Overview of functional phosphorylation sites based on phosphorylation site functionality scoring. (<b>c</b>) Visualization of phosphorylation site in the TK1: S13 protein structure model. (<b>d</b>) Box plot illustrating differential expression of TK1: S13 in tumors versus NATs. (<b>e</b>) Survival probability analysis of phosphorylation site TK1: S13 associated with poor prognosis.</p>
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18 pages, 4143 KiB  
Article
Proteomic Analysis of the Murine Liver Response to Oral Exposure to Aflatoxin B1 and Ochratoxin A: The Protective Role to Bioactive Compounds
by Silvia Trombetti, Alessandra Cimbalo, Michela Grosso, Pilar Vila-Donat, Jordi Mañes and Lara Manyes
Toxins 2025, 17(1), 29; https://doi.org/10.3390/toxins17010029 - 9 Jan 2025
Viewed by 133
Abstract
Aflatoxin B1 (AFB1) and Ochratoxin A (OTA) are considered the most important mycotoxins in terms of food safety. The aim of this study was to evaluate the hepatotoxicity of AFB1 and OTA exposure in Wistar rats and to assess the beneficial effect of [...] Read more.
Aflatoxin B1 (AFB1) and Ochratoxin A (OTA) are considered the most important mycotoxins in terms of food safety. The aim of this study was to evaluate the hepatotoxicity of AFB1 and OTA exposure in Wistar rats and to assess the beneficial effect of fermented whey (FW) and pumpkin (P) as functional ingredients through a proteomic approach. For the experimental procedures, rats were fed AFB1 and OTA individually or in combination, with the addition of FW or a FW-P mixture during 28 days. For proteomics analysis, peptides were separated using a LC-MS/MS-QTOF system and differentially expressed proteins (DEPs) were statistically filtered (p < 0.05) distinguishing males from females. Gene ontology visualization allowed the identification of proteins involved in important biological processes such as the response to xenobiotic stimuli and liver development. Likewise, KEGG pathway analysis reported the metabolic routes as the most affected, followed by carbon metabolism and biosynthesis of amino acids. Overall, the results highlighted a strong downregulation of DEPs in the presence of AFB1 and OTA individually but not with the mixture of both, suggesting a synergistic effect. However, FW and P have helped in the mitigation of processes triggered by mycotoxins. Full article
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Figure 1
<p>Venn diagram representation of common DEPs for male (<b>A</b>) and female (<b>B</b>) rats exposed to mycotoxins versus the control. <span class="html-italic">p</span> &lt; 0.05 were significantly different from the control.</p>
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<p>Venn diagram representation of common DEPs for male (<b>A</b>) and female (<b>B</b>) rats exposed to FW and mycotoxins versus the corresponding mycotoxin. <span class="html-italic">p</span> &lt; 0.05 were significantly different from mycotoxins group.</p>
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<p>Venn diagram representation of common DEPs for male (<b>A</b>) and female (<b>B</b>) rats exposed to FW + P and mycotoxins versus the corresponding mycotoxin. <span class="html-italic">p</span> &lt; 0.05 were significantly different from mycotoxins group.</p>
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<p>Gene ontology (GO) functional annotation of differentially expressed proteins for biological processes and molecular functions of male (<b>A</b>,<b>C</b>) and female (<b>B</b>,<b>D</b>) rats exposed to FW + AFB1, FW + OTA, and FW + AFB1 + OTA compared with respective mycotoxins without functional ingredient.</p>
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<p>Gene ontology (GO) functional annotation of differentially expressed proteins for biological processes and molecular functions of male (<b>A</b>,<b>C</b>) and female (<b>B</b>,<b>D</b>) rats exposed to FW + P + AFB1, FW + P + OTA, and FW + P + AFB1 + OTA compared with respective mycotoxins without functional ingredients.</p>
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<p>Heatmap representation on the expression of DEPs involved in the main biological processes after AFB1, OTA, and the combination (AFB1 + OTA) exposure in presence of FW or FW + P in male (<b>A</b>) and female (<b>B</b>) rats compared to control. The red-to-green gradient represents the logarithmic fold change value for upregulation (Log2FC &gt; 0) and downregulation (Log2FC &lt; 0), respectively. Black box is log2FC = 0. <span class="html-italic">p</span> &lt; 0.05 significantly different from the mycotoxin groups.</p>
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<p>KEGG pathway visualization of significant signaling pathways in rats exposed to mycotoxins in combination with fermented whey (FW) (<b>A</b>) or fermented whey + pumpkin (FW + P) (<b>B</b>) feed related to the number of proteins involved compared with the exposure without functional ingredients.</p>
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<p>Heatmap representation of the expression of DEPs involved in the main signaling pathways after AFB1, OTA, and the combination (AFB1 + OTA) exposure in presence of FW or FW + P in male (<b>A</b>) and female (<b>B</b>) rat livers compared with the expression after exposure to mycotoxins without functional ingredients. The red-to-green gradient represents the logarithmic fold change value for upregulation (LogFC &gt; 0) and downregulation (LogFC &lt; 0), respectively. Black box is log2FC = 0. <span class="html-italic">p</span> &lt; 0.05 significantly different from the mycotoxin’s groups.</p>
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<p>KEGG pathway visualization showing key molecular events involved in the development of hepatocellular carcinoma (HCC). The diagram highlights the critical signaling pathways, including those related to cell cycle regulation, apoptosis, and metabolic alterations, which contribute to the initiation and progression of liver cancer. Red stars indicate DEPs found in this study after AFB1 and OTA exposure and bioactive ingredients.</p>
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18 pages, 7349 KiB  
Article
Pseudomonas aeruginosa pqs Quorum Sensing Mediates Interaction with Mycobacterium abscessus In Vitro
by Yun Long, Zhi Li, Menglu Li, Peiyi Lu, Yujia Deng, Pengyao Wu, Xue Li, Gangjian Qin, Jiamin Huang, Wenying Gao, Guobao Li, Tianyuan Jia and Liang Yang
Microorganisms 2025, 13(1), 116; https://doi.org/10.3390/microorganisms13010116 - 8 Jan 2025
Viewed by 353
Abstract
Pseudomonas aeruginosa and Mycobacterium abscessus are opportunistic pathogens that cause severe infections in hospitals, and their co-infections are increasingly reported. The interspecies interactions between these two bacterial species and their potential impacts on infections are largely unexplored. In this study, we first demonstrated [...] Read more.
Pseudomonas aeruginosa and Mycobacterium abscessus are opportunistic pathogens that cause severe infections in hospitals, and their co-infections are increasingly reported. The interspecies interactions between these two bacterial species and their potential impacts on infections are largely unexplored. In this study, we first demonstrated that P. aeruginosa inhibits the growth of M. abscessus by iron chelating via pqs quorum sensing. Next, through proteomic analysis, we discovered that the PQS molecule significantly changed a large amount of protein expression in M. abscessus, including proteins involved in the type VII secretion system and iron homeostasis. Furthermore, we revealed that PQS significantly enhanced the production of bacterial membrane vesicles (MVs) by M. abscessus. Our study suggests that the P. aeruginosa PQS can serve as an interspecies signaling molecule to communicate with Mycobacterium and affect their physiology and virulence. Full article
(This article belongs to the Section Medical Microbiology)
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Figure 1
<p>Interaction between <span class="html-italic">M. abscessus</span> and <span class="html-italic">P. aeruginosa</span>. (<b>A</b>) Pairwise proximity assays showing the interaction between <span class="html-italic">M. abscessus</span> 19977 (Mab) and <span class="html-italic">P. aeruginosa</span> PAO1 (Pa) at varying distances (15 mm, 20 mm, 25 mm, 30 mm). (<b>B</b>) Magnified view of <span class="html-italic">M. abscessus</span> colonies with the measurement method indicated. (<b>C</b>) Quantitative analysis of <span class="html-italic">M. abscessus</span> displacement in relation to the distance from <span class="html-italic">P. aeruginosa</span>. Data are expressed as Mean ± SD (n = 3). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Proteomic analyses of <span class="html-italic">M. abscessus</span> after PQS treatment. The heatmap (<b>A</b>) and the volcano plot (<b>B</b>) of significantly differentially expressed proteins in the <span class="html-italic">M. abscessus</span> 19977 strain (Mab) + PQS proteome data compared to the Mab group. The top five upregulated and five downregulated DEPs are indicated in the volcano plot. (<b>C</b>) Gene Ontology (GO) annotation classification analysis of DEPs. (<b>D</b>) KEGG pathway analysis of DEPs in Mab + PQS compared to Mab group.</p>
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<p>PQS increased MV productions in <span class="html-italic">M. abscessus</span> 19977 strain. (<b>A</b>) The image of the appearance of <span class="html-italic">M. abscessus</span> 19977 strain and Mab + PQS liquid cultures. (<b>B</b>) Growth curves of <span class="html-italic">M. abscessus</span> 19977 strain with 250 ng/mL DMSO and PQS, respectively. (<b>C</b>) Precipitation image of MVs via centrifugation separation. (<b>D</b>) Quantification of protein concentration in MVs. (<b>E</b>) Electron microscopy images of MVs in Mab and Mab + PQS groups. (<b>F</b>) MV concentration was measured by NTA in Mab and Mab + PQS groups (n = 3, Mann–Whitney <span class="html-italic">U</span> test). *** <span class="html-italic">p</span> &lt; 0.001 compared with the Mab group. (<b>G</b>) MV particle size distribution measured by NTA in Mab and Mab + PQS groups. Data are expressed as mean ± SD (n = 3, Mann–Whitney <span class="html-italic">U</span> test). *** <span class="html-italic">p</span> &lt; 0.001 compared with the Mab group. Data are expressed as mean ± SD.</p>
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<p>The impact of PQS exposure on the secretion of MVs and morphological changes in <span class="html-italic">M. abscessus</span>. (<b>A</b>) SEM images showing the surface of <span class="html-italic">M. abscessus</span> cells with and without exposure to PQS. MVs are indicated with arrows and the lower panels show the solid squares at higher magnification. (<b>B</b>) TEM images, following negative staining, illustrate the presence of MVs around <span class="html-italic">M. abscessus</span> cells. The lower panels show the solid squares at higher magnification.</p>
Full article ">Figure 5
<p>Quantitative real-time PCR (qPCR) analysis of select genes to verify proteomic results. (<b>A</b>) Relative mRNA expressions of the iron acquisition-related genes in PQS-treated <span class="html-italic">M. abscessus</span>. (<b>B</b>) Relative mRNA expressions of the type VII secretion system-related genes in PQS-treated <span class="html-italic">M. abscessus</span>. Values are expressed as relative expression with respect to the endogenous control gene, <span class="html-italic">sigA</span>. Data are expressed as Mean ± SD (n = 3). ns: not significant, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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16 pages, 2193 KiB  
Article
Comprehensive Analysis of the Proteome of S. cerevisiae Wild-Type and pdr5Δ Cells in Response to Bisphenol A (BPA) Exposure
by Valentina Rossio and Joao A. Paulo
Microorganisms 2025, 13(1), 114; https://doi.org/10.3390/microorganisms13010114 - 8 Jan 2025
Viewed by 260
Abstract
Bisphenol A, an endocrine-disrupting compound, is widely used in the industrial production of plastic products. Despite increasing concerns about its harmful effects on human health, animals, and the environment, the use of BPA has been banned only in infant products, and its effects [...] Read more.
Bisphenol A, an endocrine-disrupting compound, is widely used in the industrial production of plastic products. Despite increasing concerns about its harmful effects on human health, animals, and the environment, the use of BPA has been banned only in infant products, and its effects on cellular processes are not fully understood. To investigate the impact of BPA on eukaryotic cells, we analyzed the proteome changes of wild-type and PDR5-deleted S. cerevisiae strains exposed to different doses of BPA using sample multiplexing-based proteomics. We found that the ABC multidrug transporter Pdr5 plays an important role in protecting yeast cells from BPA toxicity, with its absence significantly sensitizing cells to BPA. BPA inhibited yeast growth in a dose-dependent manner, with a more pronounced effect in PDR5-deleted cells. Proteomic analysis revealed that BPA induces widespread dose-dependent changes in protein abundance, including the upregulation of metabolic pathways such as arginine biosynthesis and the downregulation of mitochondrial proteins. Additionally, we observed markers of cellular stress induced by BPA by identifying multiple stress-induced proteins that were upregulated by this compound. As cellular processes affected by BPA have been shown to be evolutionarily conserved, these insights can advance our understanding of BPA’s cellular impact and its broader effects on human health. Full article
(This article belongs to the Section Molecular Microbiology and Immunology)
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<p>Experimental workflow, dataset summary, and the effect of bisphenol A (BPA) on cellular growth after six-hour treatment. (<b>A</b>) Wild-type and <span class="html-italic">pdr5</span>∆ <span class="html-italic">S. cerevisiae</span> cells were grown in duplicate to exponential phase (24 °C) and treated with the indicated BPA concentrations or ethanol (EtOH) as a control for six hours. (<b>B</b>) Cells were harvested and processed for mass spectrometry analysis. In brief, yeast cells were lysed, and total protein was extracted and digested. The subsequent peptides were labeled with tandem mass tag (TMTpro) reagents, as indicated, pooled 1:1, and fractionated by basic pH reversed-phase (BPRP) HPLC prior to mass spectrometry analysis. This panel was assembled, in part, using Biorender.com. (<b>C</b>) Percentage of cells at 6 h treated with the indicated BPA concentration compared to EtOH-treated cells. (<b>D</b>) Dataset summary.</p>
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<p>Principal component analysis (PCA), hierarchical clustering analysis (HCA), and differentially abundant proteins (DAPs) in wild-type and <span class="html-italic">pdr5</span>Δ cells treated with BPA. (<b>A</b>) PCA of the dataset illustrates the clustering of the replicates. (<b>B</b>) HCA of the TMT relative abundance (TMT RA) for the 4687 proteins quantified across the 16 TMT channels. Duplicates of each condition are indicated as A and B. (<b>C</b>) The table summarizes the differentially abundant proteins (DAPs) in the two yeast strains, wt and <span class="html-italic">pdr5</span>Δ, treated with the indicated BPA concentrations compared to the control (EtOH-treated) strains.</p>
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<p>Proteome-wide profiling of differentially abundant proteins in wild-type cells after treatment with 300 mg/mL BPA. (<b>A</b>) The volcano plot illustrates differentially abundant proteins (i.e., |log<sub>2</sub> ratio| &gt; 0.5, and <span class="html-italic">p</span>-value &lt; 0.05) in wild-type cells after treatment with 300 mg/mL BPA. Proteins highlighted in (<b>C</b>,<b>E</b>) are labeled. (<b>B</b>) The top gene ontology (GO) biological processes (BP) terms associated with the proteins that are increasing in (<b>A</b>). (<b>C</b>) Bar graphs illustrate the TMT relative abundance (RA) of the classes of proteins in (<b>B</b>). (<b>D</b>) The top GO cellular component (CC) terms associated with proteins with decreased abundance after BPA treatment. (<b>E</b>) TMT relative abundance measurements of mitochondrial proteins with decreased abundance in (<b>A</b>).</p>
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<p>Proteins changing with lower doses of BPA in wild-type cells and proteome-wide profiling of differentially abundant proteins in <span class="html-italic">pdr5</span>∆ cells treated with 150 mg/mL BPA. (<b>A</b>) The table summarizes the differentially abundant proteins in wild-type cells treated with 50 and 150 mg/mL of BPA compared to the EtOH condition. (<b>B</b>) The volcano plot illustrates differentially abundant proteins (i.e., |log<sub>2</sub> ratio| &gt; 0.5, and <span class="html-italic">p</span>-value &lt; 0.05) in <span class="html-italic">pdr5</span>Δ cells after treatment with 150 mg/mL BPA compared to <span class="html-italic">pdr5</span>Δ cells treated with EtOH. (<b>C</b>) The top gene ontology (GO) biological processes (BP) terms associated with the proteins increasing after BPA treatment in <span class="html-italic">pdr5</span>Δ cells. Bar graphs illustrate the TMT relative abundance (RA) of proteins increasing in <span class="html-italic">pdr5</span>Δ cells involved in (<b>D</b>) methionine metabolism and of (<b>E</b>) the protein Ykl071 (Osi1, oxidative stress-induced protein 1).</p>
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19 pages, 5944 KiB  
Article
A Comparative Transcriptome and Proteome Analysis of the Molecular Mechanism Underlying Anterior to Dorsal Eye Rotation in the Celestial-Eye Goldfish (Carassius auratus)
by Rongni Li and Yansheng Sun
Int. J. Mol. Sci. 2025, 26(2), 466; https://doi.org/10.3390/ijms26020466 - 8 Jan 2025
Viewed by 182
Abstract
Goldfish (Carassius auratus), subjected to millennia of artificial selection and breeding, have diversified into numerous ornamental varieties, such as the celestial-eye (CE) goldfish, noted for its unique dorsal eye rotation. Previous studies have primarily focused on anatomical modifications in CE goldfish [...] Read more.
Goldfish (Carassius auratus), subjected to millennia of artificial selection and breeding, have diversified into numerous ornamental varieties, such as the celestial-eye (CE) goldfish, noted for its unique dorsal eye rotation. Previous studies have primarily focused on anatomical modifications in CE goldfish eyes, yet the molecular underpinnings of their distinctive eye orientation remain poorly understood. This study employed high-throughput transcriptome and proteome sequencing on 110-day-old full-sibling CE goldfish, which displayed either anterior or upward eye rotations. Verification of these findings was conducted using quantitative PCR (qPCR) for transcriptomic data and parallel reaction monitoring (PRM) for proteomic analysis. Our research identified 73,685 genes and 7717 proteins, pinpointing 8 common differentially expressed genes (DEGs) and proteins (DEPs) implicated in cytoskeleton remodeling, cell adhesion, apoptosis, and optic nerve regeneration. Enrichment analyses further delineated pathways associated with apoptosis, necroptosis, and cell adhesion molecules. The results indicated a significant role for genes involved in cytoskeletal dynamics, nervous system function, and apoptotic processes in the dorsal eye rotation of CE goldfish. Analyses of abnormalities in ocular membrane structures, along with disturbances in lipid and protein synthesis metabolism and energy metabolism during developmental stages, provided compelling evidence for the potential use of CE goldfish as a model organism in studying human eye-related disorders. This investigation provided the first comprehensive transcriptomic and proteomic overview of eye rotation in CE goldfish, offering insights crucial for the genetic breeding of new ornamental fish varieties. Full article
(This article belongs to the Special Issue Molecular Research on Embryo Developmental Potential)
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<p>FPKM values’ violin plot.</p>
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<p>A volcano map of DEGs identified with the eye rotating forward and upward in CEs. Note: The x-axis represents log2(Fold Change), and the y-axis indicates −log10(<span class="html-italic">p</span>adj value); −log10<span class="html-italic">p</span>adj = 1.301 and <span class="html-italic">p</span>adj = 0.05 are considered significant differences. The vertical dashed line in the figure represents log2(Fold Change) threshold, and the horizontal dashed line represents the <span class="html-italic">p</span>-value = 0.05 threshold. Red plots indicate upregulated genes, and green indicate downregulated genes when comparing T with C groups.</p>
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<p>The GO analysis bar graph (T vs. C). Note: The figure displays the top 10 GO terms with the smallest <span class="html-italic">p</span>adj-value, which are the most significantly enriched terms, selected from each GO category. The x-axis represents the GO enrichment term descriptions, and the y-axis represents the −log10(<span class="html-italic">p</span>adj-value) for each term’s enrichment. The colors red, green, and blue represent the biological process (BP), cellular component (CC), and molecular function (MF), respectively.</p>
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<p>The KEGG pathway enrichment scatter plot of DEGs (T vs. C). Note: In total, 26 pathways comprised the union of the top 20 enriched pathways from T vs. C. The x-axis represents the enrichment generationof each pathway, and the y-axis shows the enrichment pathway. The color and sizesof dots indicate the <span class="html-italic">p</span>adj value and the number of DEGs assigned to the corresponding pathway, respectively. <span class="html-italic">p</span>adj &lt; 0.05 is considered significantly enriched.</p>
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<p>Expression trends of DEGs in RNA-seq and RT-qPCR in the CE groups (T vs. C).</p>
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<p>Boxplot of TMT proteomic data.</p>
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<p>Heatmap and clustering analysis of DEPs.</p>
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<p>The volcano plot of DEPs identified with the eye rotating forward and upward in CEs. Note: The volcano plot of a total of 7717 proteins identified in T and C groups.The x-axis represents log2(Fold Change), and the y-axis represents −log10(<span class="html-italic">p</span>-value). The red dots indicate 191 significantly upregulated proteins (<span class="html-italic">p</span> &lt; 0.05 and FC &gt; 1.2), and the green dots indicate 95 significantly downregulated proteins (<span class="html-italic">p</span> &lt; 0.05 and FC &lt; 0.83). The gray dots represent proteins withnon-significant (<span class="html-italic">p</span> &gt; 0.05 or 0.83 &lt; FC &lt; 1.2) differences in expression.</p>
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<p>The top ten most significantly enriched GO terms for each category analysis with T vs. C. Note: The x-axis represents the enrichment ratio (−Log10<span class="html-italic">p</span>), and the y-axis represents the enrichment GO terms. The dot sizes represent the number of DEPs.</p>
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<p>The top 11 most significantly enriched KEGG pathway analysis of DEPs with T vs. C. Note: The x-axis represents the enrichment ratio (−Log<sub>10</sub>(<span class="html-italic">p</span>)), and the y-axis represents the enrichment KEGG terms. The dot sizes represent the number of DEPs.</p>
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<p>Venn diagram comparing DEGs/DEPs in the T vs. C transcriptome and proteome.</p>
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<p>The cluster analysis of common significantly DEGs andDEPs between T and C groups in the combined with transcriptome and proteome data. Note: The x-axis represents the samples of T and C groups with research methods containing transcriptome and proteome, respectively, and the y-axis represents the clustering genes.</p>
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<p>The bubble plot of common KEGG pathways between T and C groups in the combined with transcriptome and proteome data. Note: The color represents the enrichment ratio (−Log10<span class="html-italic">p</span>), and the dot sizes represent the number of DEGs or DEPs.</p>
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<p>The mid and late stages of eye development in celestial-eye goldfish. Note: (<b>Left</b>): CE goldfish with forward eyes (T group), (<b>Right</b>): CE goldfish with upward eyes (C group).</p>
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23 pages, 2972 KiB  
Article
The Protective Effect of Docosahexaenoic Acid on Mitochondria in SH-SY5Y Model of Rotenone-Induced Toxicity
by Britta Eggers, Jennifer Stepien, Anne-Katrin Reker, Svenja Esser, Kathy Pfeiffer, Magdalena Pawlas, Katalin Barkovits and Katrin Marcus
Metabolites 2025, 15(1), 29; https://doi.org/10.3390/metabo15010029 - 8 Jan 2025
Viewed by 200
Abstract
Background: Polyunsaturated fatty acids in particular omega-3 fatty acids, such as docosahexaenoic acid (DHA), are essential nutrients and components of the plasma membrane. They are involved in various processes, including synaptic development, functionality, integrity, and plasticity, and are therefore thought to have general [...] Read more.
Background: Polyunsaturated fatty acids in particular omega-3 fatty acids, such as docosahexaenoic acid (DHA), are essential nutrients and components of the plasma membrane. They are involved in various processes, including synaptic development, functionality, integrity, and plasticity, and are therefore thought to have general neuroprotective properties. Considerable research evidence further supports the beneficial effects of omega-3 fatty acids, specifically on mitochondria, through their antioxidant and anti-apoptotic properties, making them an attractive addition in treatment options for neurodegenerative disorders in which mitochondrial alterations are commonly observed. However, precise information on the underlying protective mechanisms is still lacking. Methods: We utilized the most common neuronal cell line (SH-SY5Y) and induced mitochondrial oxidative stress through the addition of rotenone. To study the potential protective effect of DHA, the cells were additionally pre-treated with DHA prior to rotenone administration. By combining SILAC labeling, mitochondria enrichment, and subsequent proteomic analyses, we aimed to determine the capacity of DHA to alleviate mitochondrial oxidative stress in vitro and further shed light on the molecular mechanisms contributing to the proposed neuroprotective effect. Results: We confirmed a reduced cell viability and an increased abundance of reactive oxygen species upon rotenone treatment, DHA pre-treatment was shown to decrease said species. Additionally proteomic analysis revealed an increased expression of mitochondrial proteins in DHA pre-treated cells. Conclusions: With our study, we were able to define a potential compensatory mechanism by which the inhibition of complex I is overcome by an increased activity of the fatty acid beta oxidation in response to DHA. Full article
(This article belongs to the Section Cell Metabolism)
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<p>Morphological and metabolic changes of differentiated SH-SY5Y cells. (<b>A</b>) Differentiated (diff) SH-SY5Y cells display an increased number of neuronal (N) cells (adjusted one-way analysis of variance (ANOVA) <span class="html-italic">p</span>-value ** &lt; 0.01) and a decreased number of Schwann cell-like (S) cells (adjusted ANOVA <span class="html-italic">p</span>-value *** &lt; 0.001) than undifferentiated (undiff) cells. Five 500 × 500 μm sections of (<b>C</b>) undiff and (<b>D</b>) diff SH-SY5Y (20× magnification, scale 50 µm) distributed across the cell population, were acquired for manual counting of cell subtypes present in the SH-SY5Y cell population that showed immunoreactivity using tubulin β-3 chain (TUBB3, in pink, marker for diff neurons) and vimentin (VIM, in green marker for Schwann cells) antibodies. The relative proportion of TUBB3- and VIM-positive cells was determined from the total number of cell bodies labeled with DAPI (approx. 650 cells). (<b>B</b>) Diff cells exhibit a significant increase in neurite length (<span class="html-italic">p</span>-value * &lt; 0.05) compared to undiff cells. (<b>E</b>) SH-SY5Y cells (40× magnification, scale 50 µm) show significantly elongated and branched neurites visualized by TUBB3 (in pink, marked with arrows, Student’s <span class="html-italic">t</span>-test <span class="html-italic">p</span>-value &lt; 0.05). (<b>F</b>–<b>H</b>) Quantification of protein markers during differentiation of SH-SY5Y cells (n = 3). Abundance of the neuronal marker proteins (<b>F</b>) tubulin β-3 chain (TUBB3), (<b>G</b>) and microtubule-associated protein-2 (MAP2), as well as H. dopamine beta-hydroxylase (DBH) is measured by LC-MS/MS. Plots display log2 iBAQ intensities of all replicates and the mean value is marked. After differentiation, the level of all neuronal markers increased significantly. Statistical analysis was performed by <span class="html-italic">t</span>-test and Benjamini-Hochberg correction. <span class="html-italic">p</span>-value *** &lt; 0.001. (<b>I</b>) Calibration curve of synthetic dopamine (DA). Concentration of synthetic DA with 0.005, 0.01, 0.05, 0.1, 0.5, 1, and 5 ng/µL measured in triplicates using HPLC-based detection. R<sup>2</sup> is 0.9978. Based on the chromatograms ((<b>J</b>) DA in undifferentiated (n = 3) and (<b>K</b>) in differentiated SH-SY5Y cells (n = 3)). (<b>L</b>) DA concentration of undiff and diff SH-SY5Y cells was determined with the equation of the regression line of the DA calibration curve displayed in (<b>F</b>). Measured concentrations, retention time (RT) for DA on HPLC, and protein concentrations (Bradford) as means of three replicates are given in the table. The DA quantity was further normalised in relation to the protein concentration and revealed a substantial increase in DA in differentiated cells.</p>
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<p>Effect of Rot and DHA treatment on cell viability and ROS production. To identify significant differences between experimental groups, one-way analysis of variance (ANOVA) and a subsequent Dunnett’s multiple comparisons test was used to compare the differences between each group. For all comparisons, <span class="html-italic">p</span> &lt; 0.05 was considered statistically significant. SH-SY5Y cells were treated with Rot (for (<b>A</b>) 72 h (<b>B</b>) 48 h (<b>C</b>) 24 h) or DHA (for (<b>E</b>) 24 h (<b>F</b>) 48 h) in concentrations from 5 to 100 µM (**** <span class="html-italic">p</span> &lt; 0.0001 vs. Ctrl) (for all treatment groups n = 6). The results are displayed as the percentage of cell viability of the untreated control group (Ctrl) in a bar chart representing the mean ± SD (n = 6). The Rot treatment resulted in a dose-depending decrease in cell viability compared to Ctrl in all tested concentrations already after 24 h (**** <span class="html-italic">p</span> &lt; 0.0001). (<b>D</b>) Dose–response curve of Rot concentrations from 0 to 100 µM during treatment time of 24 h (n = 8). The calculated LC<sub>50</sub> value is 5.4 µM for Rot (<b>E</b>) DHA treatment showed no significant effect after 24 h. (<b>F</b>) An increase in cell viability by 25% was observed already at a concentration of 10 µM after 48 h. (<b>G</b>) Pre-treatment with DHA led to a reduced Rot-induced toxicity (5.4 µM Rot for 24 h) for all tested DHA concentrations (10 µM, 25 µM, 50 µM, and 100 µM DHA increased cell viability to 73% (<span class="html-italic">p</span> &lt; 0.001), 80% (<span class="html-italic">p</span> &lt; 0.001), 75% (<span class="html-italic">p</span> &lt; 0.01) and 71% (<span class="html-italic">p</span> &lt; 0.001), respectively). Bar chart represents mean ± SD, vs. Ctrl/Rot *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001 vs. Rot (n = 6). (<b>H</b>) ROS production. SH-SY5Y cells were treated with 5.4 µM Rot for 24 h (Rot), 25 µM DHA for 48 h (DHA), 25 µM DHA for 48 h, or 5.4 µM Rot for 24 h (DHA + Rot), as well as 55 mM TBHP for 4 h as a positive control (pos. Ctrl). ROS production was determined by DCFDA / H2DCFDA assay. The determined average Ctrl value was set to 1. Bar chart represents mean ± SD, **** <span class="html-italic">p</span> &lt; 0.001 and ** <span class="html-italic">p</span> &lt; 0.01, vs. Ctrl (n = 6) or not significant (ns). Rot-treated cells showed a significant increase in ROS content, while a pre-treatment with DHA reduced the ROS level. DHA treatment alone caused no change in ROS content.</p>
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<p>Quantitative assessment of proteomics changes. (<b>A</b>) Principal component analysis of enriched mitochondrial fractions (n = 4) of control cells (Ctrl, blue circle), DHA pre-treated + rotenone-treated cells (DHA + Rot, green square), and rotenone-treated cells (Rot, orange diamond). Grouping of samples can be observed for Component 1 (highlighted in a black border) indicating technically derived differences between the different density gradient enrichment replicates. On Component 2, the grouping of samples into experimental groups is highlighted. Ctrl cells form a distinct cluster (light blue circle) apart from DHA + Rot and Rot cells (light orange circle). (<b>B</b>) Ranking plots of mitochondrial protein abundances from Rot- and DHA + Rot-treated SH-SY5Y cells. Plots show ranked normalized intensities of Rot/Ctrl and DHA + Rot/Ctrl of all identified mitochondrial proteins. Proteins from the Rot treatment are displayed in orange, and DHA + Rot treatment in green points. The comparison revealed that most mitochondrial proteins are higher in DHA + Rot-treated cells than in solely Rot-treated cells. (<b>C</b>) Volcano Plot displaying differentially regulated proteins (Student’s <span class="html-italic">t</span>-test, two-tailed, unpaired, adjustment according to Benjamini–Hochberg (q-value), differentially regulated proteins are defined by q-value &lt; 0.05) between Rot (orange) and Ctrl (blue). (<b>D</b>) Volcano Plot displaying differentially regulated proteins (Student’s <span class="html-italic">t</span>-test, two-tailed, unpaired, adjustment according to Benjamini–Hochberg (q-value), differentially regulated proteins are defined by q-value &lt; 0.05) between DHA + Rot (green) and Ctrl (blue).</p>
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<p>Functional assessment of differential proteins. (<b>A</b>) Overlap analysis of proteins being of higher abundance in Rot-stressed cells (n = 4, in red) and DHA + Rot-treated cells (n = 4, in green) compared to the Ctrl (n = 4) (Student’s <span class="html-italic">t</span>-test, two-tailed, unpaired, adjustment according to Benjamini–Hochberg (q-value), differentially regulated proteins are defined by q-value &lt; 0.05). Common proteins are displayed in grey. (<b>B</b>) GO Term enrichment analysis based on cellular compartments of common upregulated proteins in both treatment groups compared to Ctrl. (<b>C</b>) GO Term enrichment analysis based on cellular compartments of uniquely upregulated proteins in Rot-treated cells compared to Ctrl. (<b>D</b>) GO Term enrichment analysis based on cellular compartments of uniquely upregulated proteins in DHA + Rot-treated cells compared to Ctrl.</p>
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<p>Intensities of pathway-associated proteins. Heat map displaying the difference of log 2 normalized mean protein intensities of DHA + Rot- vs. Rot-treated cells (n = 4 each) for proteins of the fatty acid beta oxidation and the citric acid cycle, based on MitoCarta 3.0. Differences in mean intensities are highlighted utilizing a color gradient and were sorted, respectively, (high differences in mean values in red, small differences in mean values highlighted in blue).</p>
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<p>Proposed effect of DHA on rotenone stressed mitochondria. Supplementation of DHA results in its break down into succinyl-CoA via the fatty acid beta oxidation. In the citric acid cycle, succinyl-CoA is conserved into succinate by succinate-CoA ligases (SUCLG1/2) and subsequently converted into fumarate by succinate-dehydrogenase, which is both active in the citric acid cycle and the respiratory chain, functioning as complex II. (This figure was partially created using Servier Medical Art templates, which are licensed under a Creative Commons Attribution 3.0 Unported License; <a href="https://smart.servier.com" target="_blank">https://smart.servier.com</a>, accessed on 29 November 2021).</p>
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17 pages, 717 KiB  
Review
A Sequencing Overview of Malignant Peripheral Nerve Sheath Tumors: Findings and Implications for Treatment
by Kangwen Xiao, Kuangying Yang and Angela C. Hirbe
Cancers 2025, 17(2), 180; https://doi.org/10.3390/cancers17020180 - 8 Jan 2025
Viewed by 234
Abstract
Malignant peripheral nerve sheath tumors (MPNSTs) are rare but aggressive malignancies with a low 5-year survival rate despite current treatments. MPNSTs frequently harbor mutations in key genes such as NF1, CDKN2A, TP53, and PRC2 components (EED or SUZ12) [...] Read more.
Malignant peripheral nerve sheath tumors (MPNSTs) are rare but aggressive malignancies with a low 5-year survival rate despite current treatments. MPNSTs frequently harbor mutations in key genes such as NF1, CDKN2A, TP53, and PRC2 components (EED or SUZ12) across different disease stages. With the rapid advancement of high-throughput sequencing technologies, the molecular characteristics driving MPNST development are becoming clearer. This review summarizes recent sequencing studies on peripheral nerve sheath tumors, including plexiform neurofibromas (PNs), atypical neurofibromatous neoplasm with uncertain biologic potential (ANNUBP), and MPNSTs, highlighting key mutation events in tumor progression from the perspectives of epigenetics, transcriptomics, genomics, proteomics, and metabolomics. We also discuss the therapeutic implications of these genomic findings, focusing on preclinical and clinical trials targeting these alterations. Finally, we conclude that overcoming tumor resistance through combined targeted therapies and personalized treatments based on the molecular characteristics of MPNSTs will be a key direction for future treatment strategies. Full article
(This article belongs to the Special Issue Sarcoma: Clinical Trials and Management)
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<p>Summary of key findings from recent sequencing technologies (created by Biorender).</p>
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24 pages, 1857 KiB  
Article
Responsivity of Two Pea Genotypes to the Symbiosis with Rhizobia and Arbuscular Mycorrhiza Fungi—A Proteomics Aspect of the “Efficiency of Interactions with Beneficial Soil Microorganisms” Trait
by Andrej Frolov, Julia Shumilina, Sarah Etemadi Afshar, Valeria Mashkina, Ekaterina Rhomanovskaya, Elena Lukasheva, Alexander Tsarev, Anton S. Sulima, Oksana Y. Shtark, Christian Ihling, Alena Soboleva, Igor A. Tikhonovich and Vladimir A. Zhukov
Int. J. Mol. Sci. 2025, 26(2), 463; https://doi.org/10.3390/ijms26020463 - 8 Jan 2025
Viewed by 167
Abstract
It is well known that individual pea (Pisum sativum L.) cultivars differ in their symbiotic responsivity. This trait is typically manifested with an increase in seed weights, due to inoculation with rhizobial bacteria and arbuscular mycorrhizal fungi. The aim of this study [...] Read more.
It is well known that individual pea (Pisum sativum L.) cultivars differ in their symbiotic responsivity. This trait is typically manifested with an increase in seed weights, due to inoculation with rhizobial bacteria and arbuscular mycorrhizal fungi. The aim of this study was to characterize alterations in the root proteome of highly responsive pea genotype k-8274 plants and low responsive genotype k-3358 ones grown in non-sterile soil, which were associated with root colonization with rhizobial bacteria and arbuscular mycorrhizal fungi (in comparison to proteome shifts caused by soil supplementation with mineral nitrogen salts). Our results clearly indicate that supplementation of the soil with mineral nitrogen-containing salts switched the root proteome of both genotypes to assimilation of the available nitrogen, whereas the processes associated with nitrogen fixation were suppressed. Surprisingly, inoculation with rhizobial bacteria had only a minor effect on the root proteomes of both genotypes. The most pronounced response was observed for the highly responsive k-8274 genotype inoculated simultaneously with rhizobial bacteria and arbuscular mycorrhizal fungi. This response involved activation of the proteins related to redox metabolism and suppression of excessive nodule formation. In turn, the low responsive genotype k-3358 demonstrated a pronounced inoculation-induced suppression of protein metabolism and enhanced diverse defense reactions in pea roots under the same soil conditions. The results of the study shed light on the molecular basis of differential symbiotic responsivity in different pea cultivars. The raw data are available in the PRIDE repository under the project accession number PXD058701 and project DOI 10.6019/PXD058701. Full article
(This article belongs to the Section Molecular Microbiology)
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<p>The numbers of non-redundant proteins (protein groups), identified in the <span class="html-italic">P. sativum</span> genotypes k-8274 (<b>a</b>) and k-3358 (<b>b</b>) plants grown in the absence of soil supplementations (controls), plants grown with supplementation of mineral nitrogen salts in the absence of microorganism complementation (mineral nutrition, MN), plants inoculated with rhizobia (nodule bacteria, NB), and plants inoculated with rhizobia and arbuscular mycorrhizal fungi (NB+AMF).</p>
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<p>The most represented functional groups (<b>a</b>) and prediction of sub-cellular localization (<b>b</b>) for the proteins identified as differentially abundant in the roots of the k-3358 and k-8274 <span class="html-italic">P. sativum</span> plants. The individual proteins comprising each functional group (as well as all related information) are listed in <a href="#app1-ijms-26-00463" class="html-app">Supplementary Tables S4-1, S4-2, S5-1 and S5-2</a>.</p>
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<p>Top five functional groups (bins) representing the proteins identified as differentially abundant in <span class="html-italic">P.sativum</span> genotypes k-3358 (<b>a</b>,<b>c</b>,<b>d</b>) and k-8274 (<b>b</b>,<b>e</b>) under different soil supplementation conditions—external mineral nitrogen nutrition (MN, (<b>a</b>,<b>b</b>)), symbiotic rhizobia (nodule bacteria—NB, (<b>c</b>), combined complementation of arbuscular mycorrhiza and nodule bacteria (AMF+NB, (<b>d</b>,<b>e</b>)). Numerical values indicate the numbers of proteins constituting individual up- (green) or down-regulated (red) functional classes. The individual proteins comprising each functional group (in addition to all related information) are listed in <a href="#app1-ijms-26-00463" class="html-app">Supplementary Information S1, Figures S1-5–S1-10 and Supplementary Information S4</a>.</p>
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<p>Prediction of the sub-cellular localization of proteins identified as up- and down-regulated in <span class="html-italic">P. sativum</span> genotypes k-3358 (panels (<b>a</b>,<b>c</b>,<b>d</b>)) and K 8274 (<b>b</b>,<b>e</b>) under external mineral nitrogen nutrition conditions (MN, panels (<b>a</b>,<b>b</b>)), upon inoculation with nodule bacteria (NB, panel (<b>c</b>)), upon the combined inoculation with arbuscular mycorrhiza and nodule bacteria (AMF+NB, panels (<b>d</b>,<b>e</b>)). The individual proteins annotated to specific predicted compartments are listed in <a href="#app1-ijms-26-00463" class="html-app">Supplementary information S5</a>. SM—symbiosome membrane, EPR—endoplasmic reticulum, PM—plasma membrane.</p>
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23 pages, 11223 KiB  
Review
Proximity Labeling: Precise Proteomics Technology for Mapping Receptor Protein Neighborhoods at the Cancer Cell Surface
by Saman Rahmati and Andrew Emili
Cancers 2025, 17(2), 179; https://doi.org/10.3390/cancers17020179 - 8 Jan 2025
Viewed by 236
Abstract
Cell surface receptors are pivotal to cancer cell transformation, disease progression, metastasis, early detection, targeted therapy, drug responses, and clinical outcomes. Since they coordinate complex signaling communication networks in the tumor microenvironment, mapping the physical interaction partners of cell surface receptors in vivo [...] Read more.
Cell surface receptors are pivotal to cancer cell transformation, disease progression, metastasis, early detection, targeted therapy, drug responses, and clinical outcomes. Since they coordinate complex signaling communication networks in the tumor microenvironment, mapping the physical interaction partners of cell surface receptors in vivo is vital for understanding their roles, functional states, and suitability as therapeutic targets. Yet traditional methods like immunoprecipitation and affinity purification–mass spectrometry often fail to detect key but weak or transient receptor–protein interactions. Proximity labeling, a cutting-edge proteomics technology, addresses these technical challenges by enabling precise mapping of protein neighborhoods around a receptor target on the cell surface of cancer cells. This technique has been successfully applied in vitro and in vivo for proteomic mapping across various model systems. This review explores the fundamental principles, technologies, advantages, limitations, and applications of proximity labeling in cancer biology, focusing on mapping receptor microenvironments. By advancing mechanistic insights into cancer cell receptor signaling mechanisms, proximity labeling is poised to transform cancer research, improve targeted therapies, and illuminate avenues to overcome drug resistance. Full article
(This article belongs to the Special Issue Mass Spectrometry-Based “Omics” Approaches in Cancer Research)
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<p>Schematics illustrating the biophysical principles of various proximity labeling strategies for mapping receptor protein interactions on cancer cells. (<b>A</b>) Biotin ligase (BioID, TurboID, or miniTurbo) fusion proteins utilize ATP and biotin to catalyze the formation of reactive biotin-5′-AMP, which diffuses and covalently labels proximal proteins. (<b>B</b>) Peroxidase-based enzymes (APEX/APEX2) use exogenous hydrogen peroxide to oxidize biotin-phenol into reactive phenoxyl radicals that preferentially label nearby proteins. (<b>C</b>) Peroxidase-based strategies facilitate selective labeling of the extracellular protein environment surrounding a specific antibody-bound target. (<b>D</b>) Recombinant ProtA-TurboID fusion enables the labeling of proteins in proximity to an antibody-bound target of interest. (<b>E</b>) Light-activated labeling: Upon blue light illumination, photocatalyst-conjugated antibodies label receptor proteins on the cell surface with high spatial precision. (<b>F</b>) TurboID-based organelle labeling: TurboID allows targeted labeling of localized proteins or trafficking to specific organelles (e.g., ER secretome cargo), enabling detailed mapping of subcellular environments. Collectively, each strategy provides unique capabilities for studying receptor protein interaction dynamics with distinct spatial and functional specificity.</p>
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<p>Generalized overview of proximity labeling workflow as applied to mapping receptor protein interactions on cancer cells. The process begins with proximity labeling of cell surface receptors or other targets on cultured cancer cells or heterogeneous tumor samples expressing the protein of interest. Following labeling, the cells are lysed, and biotinylated proteins are isolated using streptavidin beads. The captured proteins are enzymatically digested with trypsin and analyzed using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Data analysis and bioinformatic filtering then generate a scored protein interaction network, revealing potential unknown interaction partners. By characterizing receptor protein neighborhoods on cancer cells, this workflow can reveal fundamental mechanisms driving pathology, identify prognostic biomarkers, and uncover novel therapeutic targets, including for biologics such as antibodies, antibody–drug conjugates, engineered Chimeric Antigen Receptor (CAR) T-cells, and classical small-molecule inhibitors.</p>
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<p>Utility of proximity labeling for the development of therapeutics for cancer. Proximity labeling is a powerful tool for developing new cancer treatments by enabling precise mapping of receptor-associated protein interactomes at spatial scales to improve understanding of drug mechanisms of action or to discover potentially actionable new targets. Enzyme-based methods target intracellular receptor domains, while antibody-based photocatalytic techniques label extracellular regions; antibody–drug conjugates (ADCs) can also define target functionality and specificity. Mass spectrometry data from labeled samples can then be integrated with other datasets, such as spatial proteomics or genomics, transcriptomics, and multiplex imaging, using machine learning algorithms to construct predictive models and identify markers of clinical response and targets in the cancer receptor microenvironment. This comprehensive approach can inform the development of innovative therapies, including biologics, bispecific antibodies, ADCs, and small-molecule inhibitors, advancing precision medicine to improve cancer treatment.</p>
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20 pages, 11188 KiB  
Article
Changes to the Autophagy-Related Muscle Proteome Following Short-Term Treatment with Ectoine in the Duchenne Muscular Dystrophy Mouse Model mdx
by Eulàlia Gómez Armengol, Caroline Merckx, Hanne De Sutter, Jan L. De Bleecker and Boel De Paepe
Int. J. Mol. Sci. 2025, 26(2), 439; https://doi.org/10.3390/ijms26020439 - 7 Jan 2025
Viewed by 281
Abstract
The most severe form of muscular dystrophy (MD), known as Duchenne MD (DMD), remains an incurable disease, hence the ongoing efforts to develop supportive therapies. The dysregulation of autophagy, a degradative yet protective mechanism activated when tissues are under severe and prolonged stress, [...] Read more.
The most severe form of muscular dystrophy (MD), known as Duchenne MD (DMD), remains an incurable disease, hence the ongoing efforts to develop supportive therapies. The dysregulation of autophagy, a degradative yet protective mechanism activated when tissues are under severe and prolonged stress, is critically involved in DMD. Treatments that harness autophagic capacities therefore represent a promising therapeutic approach. Osmolytes are protective organic molecules that regulate osmotic pressure and cellular homeostasis and may support tissue-repairing autophagy. We therefore explored the effects of the osmolyte ectoine in the standard mouse model of DMD, the mdx, focusing on the autophagy-related proteome. Mice were treated with ectoine in their drinking water (150 mg/kg) or through daily intraperitoneal injection (177 mg/kg) until they were 5.5 weeks old. Hind limb muscles were dissected, and samples were prepared for Western blotting for protein quantification and for immunofluorescence for an evaluation of tissue distribution. We report changes in the protein levels of autophagy-related 5 (ATG5), Ser366-phosphorylated sequestosome 1 (SQSTM1), heat shock protein 70 (HSP70), activated microtubule-associated protein 1A/1B-light chain 3 (LC3 II) and mammalian target of rapamycin (mTOR). Most importantly, ectoine significantly improved the balance between LC3 II and SQSTM1 levels in mdx gastrocnemius muscle, and LC3 II immunostaining was most pronounced in muscle fibers of the tibialis anterior from treated mdx. These findings lend support for the further investigation of ectoine as a potential therapeutic intervention for DMD. Full article
(This article belongs to the Special Issue Molecular Insights into Muscular Dystrophy)
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<p>Protein levels in extensor digitorum longus muscle of 26-week-old mice. Total protein samples were prepared from four individual healthy control (BL 10 1–4) and four individual mdx (1–4) mice. A 20 µL sample (30 µg protein) was loaded in each lane. (<b>A</b>) Protein bands for autophagy-related 5 (ATG5), autophagy-related 7 (ATG7), microtubule-associated protein 1A/1B-light chain 3 (LC3), parkin (PRKN) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as a loading control are shown. (<b>B</b>) Protein levels are reported as relative values to GAPDH. A significant difference can be observed in the ratio of phosphatidylethanolamine-conjugated LC3 (LC3 II) over unconjugated LC3 (LC3 I); <span class="html-italic">p</span> = 0.03 (*) between the BL 10 and mdx group, tested with an unpaired <span class="html-italic">t</span> test. Other protein levels are not significantly different. Graphs and statistical analyses were generated with GraphPad Prism 10.2.3. Full blots are provided as a supplement (<a href="#app1-ijms-26-00439" class="html-app">Figure S1</a>).</p>
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<p>Immunofluorescence staining of mdx soleus muscle of 4-week-old mice (<b>A</b>) and tibialis anterior muscle of 12-week-old mice (<b>C</b>,<b>D</b>). (<b>A</b>) Staining for autophagy-related 16-like 1 (ATG16L1; AlexaFluor 488, green) and neural cell adhesion molecule (NCAM; CY3, red) in a BL 10 control (<b>A</b>) and an mdx mouse (<b>B</b>) shows strong sarcoplasmic staining in the majority of muscle fibers. Strong NCAM staining is present in a cluster of small perifascicular muscle fibers in the mdx section, indicating that these are regenerating muscle fibers, and from these muscle fibers, ATG16L1 staining is absent. Staining for microtubule-associated protein 1A/1B-light chain 3 conjugated to phosphatidylethanolamine (LC3 II; CY3, red) and macrophages (F4-80; AlexaFluor 488, green) are shown in a BL 10 control (<b>C</b>) and an mdx mouse (<b>D</b>). (<b>C</b>) In a healthy control, LC3 II staining is mostly absent, and a few scattered macrophages can be observed. (<b>D</b>) In an mdx section, sarcolemmal LC3 II staining localizes to muscle fibers in perifascicular regions. Macrophages are abundant in this microscopic field, indicated by bright green fluorescence. The diffuse and weaker staining that can be observed in the connective tissue accumulating in the perifascicular region can be considered nonspecific. Scale bars = 100 µM.</p>
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<p>Protein levels in extensor digitorum longus muscle of untreated and ectoine-treated mice. Two total protein samples were prepared per group by pooling muscles from two individual mice. A 20 µL sample (30 µg protein) was loaded in each lane. (<b>A</b>) Protein bands for autophagy-related 5 (ATG5), autophagy-related 7 (ATG7), microtubule-associated protein 1A/1B-light chain 3 conjugated to phosphatidylethanolamine (LC3 II), sequestosome 1 (SQSTM1), parkin (PRKN) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as a loading control are shown in control black 10 mice (BL 10 1–4), and mdx treated with ectoine in their drinking water (ECT DW 1–4), ectoine administered via intraperitoneal injection (ECT IP 1–4), and intraperitoneal saline (SAL IP 1–4), serving as a treatment control. (<b>B</b>) Protein levels are reported as values relative to glyceraldehyde-3-phosphate dehydrogenase (GAPDH). Significant changes can be observed for ATG5 levels, with an adjusted <span class="html-italic">p</span> = 0.02 between BL 10 and mdx DW ECT (*), and <span class="html-italic">p</span> = 0.04 between mdx IP ECT and mdx DW ECT (*), tested with the Brown–Forsythe test and Welch ANOVA for multiple comparisons. Graphs and statistical analyses were generated with GraphPad Prism 10.2.3. Full blots are provided as a supplement (<a href="#app1-ijms-26-00439" class="html-app">Figure S1</a>).</p>
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<p>Protein levels in gastrocnemius muscle of untreated and ectoine-treated mice. Total protein samples were prepared from two sets of three individual mice per group: control black 10 mice (BL 10 1–3, 4–6), mdx administered with saline intraperitoneally (SAL IP 1–3, 4–6) and mdx administered with ectoine intraperitoneally (SAL IP 1–3, 4–6). A 20 µL sample (30 µg protein) was loaded in each lane. (<b>A</b>,<b>C</b>) Protein bands for heat shock protein 70 (HSP70), voltage-dependent anion channels (VDACs), parkin (PRKN), microtubule-associated protein 1A/1B-light chain 3 conjugated to phosphatidylethanolamine (LC3 II), sequestosome 1 (SQSTM1), SQSTM1 phosphorylated on its Ser366 residue (p-SQSTM1), peroxisome proliferator-activated receptor-gamma coactivator 1α (PGC-1α), mammalian target of rapamycin (mTOR) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as a loading control are shown (<b>B</b>–<b>D</b>). Protein levels are reported as relative values to GAPDH. In the first sample set, significant changes can be observed between BL 10 and mdx IP ECT in HSP70 (adjusted <span class="html-italic">p</span> = 0.009 **) and p-SQSTM1 levels (adjusted <span class="html-italic">p</span> = 0.003); the latter remains significantly different when calculated as a ratio of phosphorylated over total SQSTM1 protein levels (adjusted <span class="html-italic">p</span> = 0.006 **). In the second sample set, mammalian target of rapamycin (mTOR) levels are significantly higher in IP ECT compared to BL 10 (adjusted <span class="html-italic">p</span> = 0.02 *). A significant increase in pSQSTM1 cannot be confirmed; however, the levels of the proteins p-SQSTM1 and LC3 II do display a tendency to increase. Graphs and statistical analyses with the Brown–Forsythe test and Welch ANOVA for multiple comparisons were generated with GraphPad Prism 10.2.3. Full blots are provided as a supplement (<a href="#app1-ijms-26-00439" class="html-app">Figure S1</a>).</p>
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<p>Autophagic efficiency responds to ectoine treatment in mdx. Theoretical autophagic efficiency is calculated as the ratio of microtubule-associated protein 1A/1B-light chain 3 conjugated to phosphatidylethanolamine (LC3 II) over sequestosome 1 (SQSTM1) as relative to the ratio observed in healthy control mice (n = 6) set to 1.0, presented as boxplots showing the median and interquartile range. In comparison to control mdx treated with intraperitoneal saline (IP SAL n = 6), autophagic efficiency is significantly higher in mdx treated with ectoine (IP ECT n = 6), as demonstrated by performing an unpaired <span class="html-italic">t</span>-test (<span class="html-italic">p</span> = 0.003 **). Graphs and statistical analyses were generated with GraphPad Prism 10.2.3.</p>
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<p>Immunofluorescence co-staining with F4/80 antibody, which stains macrophages (AlexaFluor 488, green), and 1A/1B-light chain 3 conjugated to phosphatidylethanolamine (LC3 II, CY3, red) in tibialis anterior muscle. In a healthy control (<b>A</b>) and mdx treated with intraperitoneal saline (<b>B</b>), infrequent partial sarcolemmal LC3 II staining is observed on certain muscle fibers. (<b>C</b>) In an mdx treated with intraperitoneal ectoine, macrophage staining localizes a necrotic invaded muscle fiber, which is LC3 II-negative (asterisk). (<b>D</b>) In an mdx treated with ectoine in its drinking water, macrophages are abundant, and a small muscle fiber surrounded by an inflammatory infiltrate is strongly LC 3 II-positive (arrow). Scale bars = 50 µm.</p>
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<p>Mitochondrial protein levels in soleus muscle of untreated and ectoine-treated mice. Two mitochondria-enriched protein samples were prepared per group by pooling soleus muscle from four individual mice (except for the first BL 10 sample, which was prepared from three mice). (<b>A</b>) Protein bands for heat shock protein 70 (HSP70), voltage-dependent anion channels (VDACs), parkin (PRKN), microtubule-associated protein 1A/1B-light chain 3 (LC3) and the corresponding total protein stain are shown in control black 10 mice (BL 10 1–7 and in mdx) intraperitoneally administered with saline (SAL IP 1–8) or ectoine (ECT IP 1–8), or ectoine in the drinking water (ECT DW 1–8). (<b>B</b>) Protein levels are reported as relative values normalized to total protein content. Significant changes can be observed for LC3 II levels, with an adjusted <span class="html-italic">p</span> = 0.01 between mdx IP ECT and mdx DW ECT (*); significance does not remain when calculated as a ratio of LC3 II over I. Graphs and statistical analyses with the Brown–Forsythe test and Welch ANOVA for multiple comparisons were generated with GraphPad Prism 10.2.3. Full blots are provided as a supplement (<a href="#app1-ijms-26-00439" class="html-app">Figure S1</a>).</p>
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18 pages, 2245 KiB  
Article
Helicobacter pylori HP0135 Is a Small Lipoprotein That Has a Role in Outer Membrane Stability
by Doreen Nguyen, Rachel G. Ivester, Kyle Rosinke and Timothy R. Hoover
Molecules 2025, 30(2), 204; https://doi.org/10.3390/molecules30020204 - 7 Jan 2025
Viewed by 372
Abstract
Helicobacter pylori is a Gram-negative bacterium and human pathogen that is linked to various gastric diseases, including peptic ulcer disease, chronic gastritis, and gastric cancer. The filament of the H. pylori flagellum is surrounded by a membranous sheath that is contiguous with the [...] Read more.
Helicobacter pylori is a Gram-negative bacterium and human pathogen that is linked to various gastric diseases, including peptic ulcer disease, chronic gastritis, and gastric cancer. The filament of the H. pylori flagellum is surrounded by a membranous sheath that is contiguous with the outer membrane. Proteomic analysis of isolated sheathed flagella from H. pylori B128 identified the lipoprotein HP0135 as a potential component of the flagellar sheath. HP0135 is a small protein, with the mature HP0135 lipoprotein only 28 amino acid residues in length. Deletion of hp0135 in H. pylori B128 resulted in morphological abnormalities that included extensive formation of outer membrane vesicles and increased frequency of mini-cells. Introducing a plasmid-borne copy of hp0135 into the H. pylori Δhp0135 mutant suppressed the morphological abnormalities. The phenotype of the Δhp0135 mutant suggests HP0135 has roles in stabilizing the cell envelope and cell division. Full article
(This article belongs to the Section Macromolecular Chemistry)
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<p>Alignment of the amino acid sequences of HP0135 homologs and the predicted tertiary structure of HP0135. (<b>A</b>) Amino acid sequences of HP0135 homologs were aligned using the Clustal Omega (<a href="http://www.ebi.ac.uk/jdispatcher/msa/clustalo" target="_blank">www.ebi.ac.uk/jdispatcher/msa/clustalo</a> (accessed on 10 September 2024)) sequence analysis tool [<a href="#B38-molecules-30-00204" class="html-bibr">38</a>]. HP0135 homologs are from the following species: <span class="html-italic">Helicobacter acinonychis</span> (Hac), <span class="html-italic">H. pylori</span> (Hpy), <span class="html-italic">Helicobacter</span> sp. 219-2 (219-2), <span class="html-italic">Helicobacter cetorum</span> (Hce), <span class="html-italic">Helicobacter suis</span> (Hsu), <span class="html-italic">Helicobacter ailurogastricus</span> (Hai), <span class="html-italic">Helicobacter</span> sp. NHP19-012 (19-012), <span class="html-italic">Helicobacter</span> sp. NHP22-001 (22-001), <span class="html-italic">Helicobacter heilmannii</span> ASB1.4 (Hhe), <span class="html-italic">Helicobacter</span> sp. NHP21005 (21005), <span class="html-italic">Helicobacter</span> sp. NHP19-003 (19-003), <span class="html-italic">Helicobacter bizzozeronii</span> (Hbi), <span class="html-italic">Helicobacter mehlei</span> (Hme), <span class="html-italic">Helicobacter salmonis</span> (Hsa), <span class="html-italic">Helicobacter baculiformis</span> (Hba), <span class="html-italic">Helicobacter felis</span> (Hfe), <span class="html-italic">Helicobacter cynogastricus</span> (Hcy), <span class="html-italic">Helicobacter</span> sp. L8 (L8), <span class="html-italic">Helicobacter vulpis</span> (Hvu), <span class="html-italic">Helicobacter</span> sp. 13S00477-4 (77-4), <span class="html-italic">Helicobacter</span> sp. 12S02232-10 (32-10), and <span class="html-italic">Helicobacter</span> sp. 11S02596-1 (96-1). For each amino acid position, identical amino acids are indicated with an asterisk and similar amino acids are indicated with one dot. Hydrophobic, polar, basic, and acidic amino acid residues are indicated in red, green, magenta, and blue, respectively. The predicted signal peptide cleavage site is indicated by the red arrow. (<b>B</b>) Tertiary structure of HP0135 prolipoprotein predicted by AlphaFold2 [<a href="#B39-molecules-30-00204" class="html-bibr">39</a>]. Regions in blue indicate a high confidence for predicted structure, while regions in yellow indicate a lower confidence for predicted structure. The position of Cys-17, which is the site of cleavage, is indicated. The position of Ser-31, which is the last amino acid residue within a well-ordered tertiary structure, is also indicated.</p>
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<p>TEM images of wild-type <span class="html-italic">H. pylori</span> B128, the original Δ<span class="html-italic">hp0135</span> mutant, and the complemented Δ<span class="html-italic">hp0135</span> strain. (<b>A</b>) TEM field showing examples of <span class="html-italic">H. pylori</span> Δ<span class="html-italic">hp0135</span> mutant cells. The black arrows indicate cells in the field that have OMVs. The yellow arrow indicates a cell that appears to be undergoing lysis. (<b>B</b>) An example of a mini-cell formed by the <span class="html-italic">H. pylori</span> Δ<span class="html-italic">hp0135</span> mutant is indicated by the white arrow. The black arrow indicates a normal-length cell that displays OMVs at the cell pole. (<b>C</b>) TEM field showing wild-type <span class="html-italic">H. pylori</span> B128 cells. Polar flagella are indicated by the black arrows. (<b>D</b>) TEM field showing examples of cells of Δ<span class="html-italic">hp0135</span> mutant that was complemented with a plasmid-borne copy of <span class="html-italic">hp0135</span>. The arrow indicates a cell that has OMVs. (<b>E</b>) Proportion of cells with OMVs in Δ<span class="html-italic">hp0135</span> mutant, wild-type <span class="html-italic">H. pylori</span> B128, and Δ<span class="html-italic">hp0135</span>/pHP0135 strain. Cells were counted from two biological replicates for each strain, and the number of cells counted for the Δ<span class="html-italic">hp0135</span> mutant, wild type, and Δ<span class="html-italic">hp0135</span>/pHP0135 strain were 139, 78, and 60, respectively. The proportion of cells with OMVs in the Δ<span class="html-italic">hp0135</span> mutant was significantly higher compared to the wild type and the complemented strain as determined by Fisher’s exact test (<span class="html-italic">p</span>-value &lt; 0.00001).</p>
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<p>TEM images of strains RIH3A and RIH3C. (<b>A</b>,<b>B</b>) TEM images of strain RIH3A. The black arrows indicate flagellated cells. (<b>C</b>,<b>D</b>) TEM images of strain RIH3C. The black arrows indicate flagellated cells.</p>
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21 pages, 6032 KiB  
Article
Developmental Proteomics Reveals the Dynamic Expression Profile of Global Proteins of Haemaphysalis longicornis (Parthenogenesis)
by Min-Xuan Liu, Xiao-Pei Xu, Fan-Ming Meng, Bing Zhang, Wei-Gang Li, Yuan-Yuan Zhang, Qiao-Ying Zen and Wen-Ge Liu
Life 2025, 15(1), 59; https://doi.org/10.3390/life15010059 - 6 Jan 2025
Viewed by 287
Abstract
H. longicornis is used as an experimental animal model for the study of three-host ticks due to its special life cycle and easy maintenance in the laboratory and in its reproduction. The life cycle of H. longicornis goes through a tightly regulated life [...] Read more.
H. longicornis is used as an experimental animal model for the study of three-host ticks due to its special life cycle and easy maintenance in the laboratory and in its reproduction. The life cycle of H. longicornis goes through a tightly regulated life cycle to adapt to the changing host and environment, and these stages of transition are also accompanied by proteome changes in the body. Here, we used the isobaric tags for a relative and absolute quantification (iTRAQ) technique to systematically describe and analyze the dynamic expression of the protein and the molecular basis of the proteome of H. longicornis in seven differential developmental stages (eggs, unfed larvae, engorged larvae, unfed nymphs, engorged nymphs unfed adults, and engorged adults). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the differentially expressed proteins (DEPs) were used. In our study, A total of 2044 proteins were identified, and their expression profiles were classified at different developmental stages. In addition, it was found that tissue and organ development-related proteins and metabolism-related proteins were involved in different physiological processes throughout the life cycle through the GO and KEGG analysis of DEPs. More importantly, we found that the up-regulated proteins of engorged adult ticks were mainly related to yolk absorption, degradation, and ovarian development-related proteins. The abundance of the cuticle proteins in the unfed stages was significantly higher compared with those of the engorged ticks in the previous stages. We believe that our study has made a significant contribution to the research on H. longicornis, which is an important vector of SFTSV. In this study, we identified changes in the proteome throughout the H. longicornis development, and functional analysis highlighted the important roles of many key proteins in developmental events (ovarian development, the molting process, the development of midgut, the development and degeneration of salivary glands, etc.). The revelation of this data will provide a reference proteome for future research on tick functional proteins and candidate targets for elucidating H. longicornis development and developing new tick control strategies. Full article
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<p>(<b>A</b>) Wayne diagram of the total proteins identified by three repeated experiments on <span class="html-italic">H. longicornis</span>. R1, repeat 1; R2, repeat 2; and R3, repeat 3. (<b>B</b>) Distribution of the specific peptides and (<b>C</b>) protein coverage distribution.</p>
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<p>Comparative analysis of the differentially expressed proteins (DEPs) in the different developmental stages of <span class="html-italic">H. longicornis</span>. EE, egg; UL, unfed larva; FL, engorged larva; UN, unfed nymph.</p>
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<p>The heat map represents the proteome analysis results of six genes compared across different developmental stages, while the bar graph of (<b>A</b>–<b>F</b>) displays the RT-qPCR analysis results for CRK, flotillin, Mo-25, dystrophin, septin-1, and septin-2, respectively). EE, egg; UL, unfed larva; FL, engorged larva; UN, unfed nymph; FN, engorged nymph; UA, unfed adult; FA, engorged adult.</p>
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<p>Chitin-binding proteins. EE, egg; UL, unfed larva; FL, engorged larva; UN, unfed nymph; FN, engorged nymph; UA, unfed adult; FA, engorged adult.</p>
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<p>Digestion-related proteins. EE, egg; UL, unfed larva; FL, engorged larva; UN, unfed nymph; FN, engorged nymph; UA, unfed adult; FA, engorged adult.</p>
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<p>Vitellogenin (Vg)-related proteins. EE, egg; UL, unfed larva; FL, engorged larva; UN, unfed nymph; FN, engorged nymph; UA, unfed adult; FA, engorged adult.</p>
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<p>Cuticle-related proteins. EE, egg; UL, unfed larva; FL, engorged larva; UN, unfed nymph; FN, engorged nymph; UA, unfed adult; FA, engorged adult.</p>
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<p>Membrane proteins. EE, egg; UL, unfed larva; FL, engorged larva; UN, unfed nymph; FN, engorged nymph; UA, unfed adult; FA, engorged adult.</p>
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<p>Salivary proteins. EE, egg; UL, unfed larva; FL, engorged larva; UN, unfed nymph; FN, engorged nymph; UA, unfed adult; FA, engorged adult.</p>
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<p>Secreted proteins. EE, egg; UL, unfed larva; FL, engorged larva; UN, unfed nymph; FN, engorged nymph; UA, unfed adult; FA, engorged adult.</p>
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<p>Gene Ontology (GO) enrichment for the differentially expressed proteins (DEPs) (<span class="html-italic">p</span> &lt; 0.05) of the different life stages of <span class="html-italic">H. longicornis</span>. (<b>A</b>) Unfed larva vs. egg, (<b>B</b>) engorged larva vs. unfed larva, and (<b>C</b>) unfed nymph vs. engorged larva. GO functional annotations in the three main categories: molecular function, cellular component, and biological process.</p>
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<p>Gene Ontology (GO) enrichment for the differentially expressed proteins (DEPs) (<span class="html-italic">p</span> &lt; 0.05) of the different life stages of <span class="html-italic">H. longicornis</span>. (<b>A</b>) Engorged nymph vs. unfed nymph, (<b>B</b>) unfed adult vs. engorged nymph, and (<b>C</b>) engorged adult vs. unfed adult. The GO functional annotations are in three main categories: molecular function, cellular component, and biological process.</p>
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<p>Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the differentially expressed proteins (DEPs) (<span class="html-italic">p</span> &lt; 0.05) of the different life stages of <span class="html-italic">H. longicornis</span>. <span class="html-italic">p</span> &lt; 0.05 indicates significant enrichment in the development-related pathways. The top 20 pathways are shown. (<b>A</b>) Unfed larva vs. egg, (<b>B</b>) engorged larva vs. unfed larva, and (<b>C</b>) unfed nymph vs. engorged larva. The KEGG enrichment was measured by the Rich factor, <span class="html-italic">q</span>-value, and the number of genes enriched in this pathway. The colors and sizes of the spots represent the <span class="html-italic">q</span>-values and the number of target genes, respectively. EE, egg; UL, unfed larva; FL, engorged larva; UN, unfed nymph.</p>
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<p>Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the differentially expressed proteins (DEPs) (<span class="html-italic">p</span> &lt; 0.05) of the different life stages of <span class="html-italic">H. longicornis</span>. <span class="html-italic">p</span> &lt; 0.05 indicates significant enrichment in the development-related pathways. The top 20 pathways are shown. (<b>A</b>) engorged nymph vs. unfed nymph, (<b>B</b>) unfed adult vs. engorged nymph, and (<b>C</b>) engorged adult vs. unfed adult. The KEGG enrichment was measured by the Rich factor, <span class="html-italic">q</span>-value, and number of genes enriched in this pathway. The colors and sizes of the spots represent the <span class="html-italic">q</span>-values and the number of target genes, respectively. UN, unfed nymph; FN, engorged nymph; UA, unfed adult; FA, engorged adult.</p>
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13 pages, 2247 KiB  
Article
Characterizing Antimicrobial Effects of Radiation and Antibiotic Interactions on Staphylococcus aureus and Escherichia coli Using MALDI-TOF MS
by Ali Haider, Renáta Homlok, Csilla Mohácsiné Farkas and Tamás Kocsis
Antibiotics 2025, 14(1), 41; https://doi.org/10.3390/antibiotics14010041 - 6 Jan 2025
Viewed by 418
Abstract
Background/Objectives: Antibiotic-resistant bacteria are becoming a major challenge in human and veterinary medicine, as well as in food processing. Methods: In this study, the protein diversity in antibiotic-sensitive and -resistant strains of Staphylococcus aureus and Escherichia coli was investigated by exposing them to [...] Read more.
Background/Objectives: Antibiotic-resistant bacteria are becoming a major challenge in human and veterinary medicine, as well as in food processing. Methods: In this study, the protein diversity in antibiotic-sensitive and -resistant strains of Staphylococcus aureus and Escherichia coli was investigated by exposing them to varying doses of gamma irradiation, with and without antibiotic presence. Changes in bacterial protein profiles were characterized using MALDI-TOF MS to reveal dose-dependent adaptations and potentiation effects under combined irradiation and antibiotic treatments. Results: The results indicate that MALDI-TOF MS effectively differentiates between sensitive and resistant strains, particularly at lower radiation doses (0, 0.2, and 0.4 kGy), with distinct separation in protein spectra. However, at 0.6 kGy, protein profiles plateaued, suggesting a potential threshold effect in radiation response. In 24-h cultures from irradiated Staphylococcus aureus, significant differences emerged in the resistant strain at 0.6 kGy in the presence of antibiotics, with further generational divergence dependent on initial antibiotic exposure. In the case of the sensitive strain, profiles were notably distinct at the 0.4 and 0.6 kGy doses, revealing dose- and treatment-specific responses. For Escherichia coli, generational differences between resistant and sensitive strains were apparent, though antibiotic effects on protein profiles were limited to the 0.6 kGy dose. Conclusions: The results underscore a potentiation interaction between irradiation and antibiotic exposure, affecting protein diversity and adaptation. Sensitive strains displayed heightened proteomic responses to minor treatment variations, while resistant strains exhibited more stable profiles across conditions. The findings highlight MALDI-TOF MS as a valuable tool in detecting proteomic biomarkers linked to bacterial resistance and stress adaptation. Full article
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<p>Comparison of resistant (B.02174) and sensitive (B.01755) <span class="html-italic">Staphylococcus aureus</span> in the presence of piperacillin (total variance: 93.505%; PC1: 83.622%; PC2: 9.882%).</p>
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<p>Separation of resistant <span class="html-italic">Staphylococcus aureus</span> (B.02174) based on radiation dose in the presence and absence of piperacillin (total variance: 97.160%; PC1: 93.991%; PC2: 3.169%).</p>
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<p>Separating sensitive <span class="html-italic">Staphylococcus aureus</span> (B.01755) based on radiation dose, in the presence and absence of piperacillin (total variance: 96.815%; PC1: 92.271%; PC2: 4.544%).</p>
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<p>Comparison of resistant (B.02357) and sensitive (B01748) <span class="html-italic">Escherichia coli</span> stains in the presence and absence of piperacillin (total variance: 89.800%; PC1: 33.319%; PC2: 6.481%).</p>
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<p>Separation of resistant <span class="html-italic">Escherichia coli</span> (B.02357) based on radiation dose, in the presence and absence of piperacillin (total variance: 92.138%; PC1: 82.330%; PC2: 9.808%).</p>
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<p>Separating sensitive <span class="html-italic">Escherichia coli</span> (B01748) based on radiation dose, in the presence and absence of piperacillin (total variance: 97.931%; PC1: 91.661%; PC2: 6.270%).</p>
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