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15 pages, 2350 KiB  
Review
The Role of CXCL4 in Systemic Sclerosis: DAMP, Auto-Antigen and Biomarker
by Silvia Porreca, Anna Mennella and Loredana Frasca
Int. J. Mol. Sci. 2025, 26(6), 2421; https://doi.org/10.3390/ijms26062421 - 7 Mar 2025
Abstract
Systemic sclerosis (SSc) is an autoimmune disease characterized by specific autoantibodies, vasculopathy and fibrosis of the skin and internal organs. In SSc, chronic activation of the immune system is largely sustained by endogenous inflammatory mediators that act as damage-associated molecular patterns (DAMPs), which [...] Read more.
Systemic sclerosis (SSc) is an autoimmune disease characterized by specific autoantibodies, vasculopathy and fibrosis of the skin and internal organs. In SSc, chronic activation of the immune system is largely sustained by endogenous inflammatory mediators that act as damage-associated molecular patterns (DAMPs), which activate Toll-like receptors (TLRs). Major autoantigens are nucleic acids or molecules that are able to bind nucleic acids. It is important to identify solid and predictive biomarkers of both disease activity and disease subtype. CXCL4 has been regarded as a new biomarker for early SSc in recent years, and here, we discuss its modulation over the course of a disease and after pharmacological interventions. Moreover, we provide evidence that CXCL4, in addition to being a biomarker of SSc subtypes and a prognostic marker of disease severity, has a dual pathogenic role in SSc: on the one hand, in complex with self-nucleic acids, CXCL4 acts as a DAMP for IFN-I and pro-inflammatory cytokines’ release by innate immune cells (such as dendritic cells); on the other hand, CXCL4 is a target of both antibodies and T cells, functioning as an autoantigen. CXCL4 is certainly an interesting molecule in inflammation and autoimmunity, not only in SSc, and it may also be considered as a therapy target. Full article
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Figure 1

Figure 1
<p>Pleiotropic functions of CXCL4. CXCL4 is a member of kinocidins, with direct capacity for bacterial killing. The image illustrates the most important functions of CXCL4, which are also relevant in autoimmunity and in SSc.</p>
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<p>CXCL4 organizes dsDNA into immunogenic liquid–crystalline complexes suitable for TLR9-mediated IFN-α production. Inter-DNA spacing close to the steric size of TLR9 (d = 3–4 nm) allows the optimal binding of columnar DNA lattices to TLR9-clustered arrays. CXCL4 organizes DNA into liquid–crystalline columnar lattices at an inter-DNA spacing compatible with TLR9 amplification. DNA fragmentation increases the total number of discrete DNA fragments for the same mass of DNA, with optimal close packing of DNA ligands [<a href="#B38-ijms-26-02421" class="html-bibr">38</a>].</p>
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<p>CXCL4–DNA and CXCL4–RNA complexes activate immune cells via TLRs. As with other well-known DAMPs like Tenascin-C, acting via TLR4 [<a href="#B49-ijms-26-02421" class="html-bibr">49</a>], fibronectin, S100A8 and S100A9 (alarmins), when CXCL4 binds to DNA/RNA, it acts as a DAMP and participates in the inflammation process.</p>
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<p>Model for B-cell activation via CXCL4–DNA/RNA complexes. After stimulation through TLR7/8/9 by CXCL4–DNA/RNA complexes, pDCs become activated and start to produce IFN-I, which, together with BAFF, IL-15, IL-2, help plasma cell differentiation (pDCs can also produce BAFF, [<a href="#B58-ijms-26-02421" class="html-bibr">58</a>], whereas IL-15 and IL-2 can be produced by T cells but also by stroll cells, monocytes, etc. [<a href="#B59-ijms-26-02421" class="html-bibr">59</a>]). Afterward, B-cell transition and differentiation in antibody-secreting plasma cells occurs. In SSc, activated B cells start to produce antibodies, among which there could be anti-CXCL4 antibodies.</p>
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<p>CXCL4 as an autoantigen in SSc. When complexed with nucleic acids, CXCL4 stimulates pDCs and mDCs via TLRs, which activates both cell types and induces the production of IFN-I, IL-12, IL-23 and TNF-α. IFN-I released by pDCs implements the antigen-presenting cell capacity of mDCs, whereas pro-inflammatory cytokines can activate CD4 T cells (including Th17 cells and perhaps CD8 T cells [<a href="#B64-ijms-26-02421" class="html-bibr">64</a>]). T cells specific to CXCL4 are likely to provide B-cell help for autoantibody production [<a href="#B41-ijms-26-02421" class="html-bibr">41</a>] or directly induce an inflammation that favor fibrosis. An algorithm that predicts the binding capacity of CXCL4-derived epitopes to HLA molecules showed that the sequence of CXCL4 possesses “binding motifs” for several HLA-DR alleles represented in Caucasians (DR4, DR1 and DR11), which may explain the recognition by CD4 T cells [<a href="#B41-ijms-26-02421" class="html-bibr">41</a>].</p>
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21 pages, 8256 KiB  
Perspective
Zα and Zβ Localize ADAR1 to Flipons That Modulate Innate Immunity, Alternative Splicing, and Nonsynonymous RNA Editing
by Alan Herbert, Oleksandr Cherednichenko, Terry P. Lybrand, Martin Egli and Maria Poptsova
Int. J. Mol. Sci. 2025, 26(6), 2422; https://doi.org/10.3390/ijms26062422 - 7 Mar 2025
Abstract
The double-stranded RNA editing enzyme ADAR1 connects two forms of genetic programming, one based on codons and the other on flipons. ADAR1 recodes codons in pre-mRNA by deaminating adenosine to form inosine, which is translated as guanosine. ADAR1 also plays essential roles in [...] Read more.
The double-stranded RNA editing enzyme ADAR1 connects two forms of genetic programming, one based on codons and the other on flipons. ADAR1 recodes codons in pre-mRNA by deaminating adenosine to form inosine, which is translated as guanosine. ADAR1 also plays essential roles in the immune defense against viruses and cancers by recognizing left-handed Z-DNA and Z-RNA (collectively called ZNA). Here, we review various aspects of ADAR1 biology, starting with codons and progressing to flipons. ADAR1 has two major isoforms, with the p110 protein lacking the p150 Zα domain that binds ZNAs with high affinity. The p150 isoform is induced by interferon and targets ALU inverted repeats, a class of endogenous retroelement that promotes their transcription and retrotransposition by incorporating Z-flipons that encode ZNAs and G-flipons that form G-quadruplexes (GQ). Both p150 and p110 include the Zβ domain that is related to Zα but does not bind ZNAs. Here we report strong evidence that Zβ binds the GQ that are formed co-transcriptionally by ALU repeats and within R-loops. By binding GQ, ADAR1 suppresses ALU-mediated alternative splicing, generates most of the reported nonsynonymous edits and promotes R-loop resolution. The recognition of the various alternative nucleic acid conformations by ADAR1 connects genetic programming by flipons with the encoding of information by codons. The findings suggest that incorporating G-flipons into editmers might improve the therapeutic editing efficacy of ADAR1. Full article
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Figure 1
<p>Zα and RNA editing in innate immunity. (<b>A</b>) Zα binds in a structure-specific fashion to Z-DNA with high affinity. The space-filling representations of P193 and N173 highlight their interaction with ZNA. Loss of function P193A and N175S variants are causal for the Aicardi–Goutières type 6 interferonopathy. (<b>B</b>) The only other protein in the human genome with a Zα domain is ZBP1. It activates inflammatory cell death in response to Z-RNA produced by viruses and endogenous retroelements (EREs) that amplify themselves by RdRp. Z-RNA also forms in stress granules. ADAR1 suppresses the Z-RNA-dependent activation of ZBP1. ADAR1 also prevents the dsRNA activation of PKR (protein kinase R encoded by EIF2AKA). PKR inhibits EIF2α (Eukaryotic Initiation Factor 2 alpha) dependent translation, leading to stress granule formation. ZBP1 interfaces with effector pathways through RHIMs (receptor-interacting protein homotypic interaction motif). (<b>C</b>) Most ALU elements are dimeric with a left and right arm. A- and B-boxes enable transcription by RNA Polymerase 3. The Z-Box present in ALU, as indicated by the red outline in (<b>D</b>), has one guanosine to cytosine substitution disrupting the expected pyrimidine/purine alternation (as indicated by the dot above the residue), The sequence is colored coded by base. The flip of the ALU Z-Box to Z-RNA helps identify transcripts as of host origin as pathogens lack this class of Z-forming element.</p>
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<p>Zβ and RNA Editing. (<b>A</b>) ADAR1 p110 isoform domains. (<b>B</b>) Schematic of the Zβ winged helix-turn-helix domain, with the α-helices highlighted in blue and the β-sheet strands in yellow. The Y→I substitution that greatly diminishes binding to Z-DNA relative to Zα and the location of R328 in the α3 helix are shown with bold type face. (<b>C</b>) The docking of Zβ to a parallel strand GQ with U4 loops, modeled by Molecular Dynamics Simulation. R328 makes key contacts between the protein and DNA/RNA. (<b>D</b>) The sets of G-tetrad pairs that can form RNA-GQ are underlined. The right and left arms correspond to those in <a href="#ijms-26-02422-f001" class="html-fig">Figure 1</a>C. (<b>E</b>) A model of how Zβ enables the loading of p110 first onto a GQ formed during transcription, then onto a folded dsRNA (<b>F</b>) to enable engagement of the deaminase domain (<b>G</b>). (<b>H</b>) ADAR1 p110 and p150 are localized to ALU inverted repeats by different flipon conformations. (<b>I</b>) The sites of NSE are not associated with ALU elements but rather with GQs that are in close proximity. (<b>J</b>) GQ motifs on the antisense strand of some ALU families are close to a cluster of 3′ “AG” splice acceptor sites. Two of these “AG” sites change the reading frame and the third introduces a UAG stop codon into a reading frame. The coding potential of isoforms is further varied by Zβ-dependent editing of the “AG” splice sites and exonic NSE. (<b>K</b>) Through selection, ALU exonization, alternative splicing, and NSE can increase phenotypic diversity. The GQ and ALU elements may be incorporated into an exon or downstream of a splice junction. In the latter case, the GQ promotes editing of the exon when the downstream exon contains an exon complementary site (ECS), while the remnants of an ALU inverted repeat promote folding of the dsRNA editing substrate. In other cases, an ALU-derived GQ may be associated with the 3′ splice junction. Editing of the “AG” acceptor site would result in exon skipping. (<b>L</b>) Molecular Dynamics Simulation of the ADAR1 Zβ domain complexed with rGQ (pink surface) stacked on dGQ (green surface). (<b>M</b>) Close-up view of the interface between Zβ and rGQ. The Arginine 33 side chain penetrates deeply into the rGQ molecule, forming multiple hydrogen bonds and favorable van der Waals contacts with three guanine residues and the RNA backbone. Lysine 31 (corresponding to K326 in human ADAR1 p150) forms hydrogen bonds and a favorable ion-pair interaction with the RNA backbone. Eight water molecules within the interface form hydrogen bonding networks that bridge the protein backbone and amino acid sidechains with the RNA backbones and bases.</p>
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<p>Examples of the GQ forming sequences associated with RNA editing. (<b>A</b>) ALU-associated editing of the RHOA transcript. The red boxes highlight the GQ. (<b>B</b>) The long dsRNA editing substrate formed by the fold back of the two ALU inverted repeats displayed in (<b>C</b>). The red box shows the position of the RHOA exon relative to the ALU elements. (<b>D</b>) The well-characterized K242R edit of NEIL1 (endonuclease VIII-like DNA glycosylase) is associated with a GQ. (<b>E</b>) The NEIL1 editing substrate with the edits indicated by red arrows. (<b>F</b>) The edits and various RNA isoforms of TNFRSF14 pre-mRNA are associated with a GQ motif. (<b>G</b>) Location of the edits in a dsRNA substrate formed by the TNFRSF14 transcript. (<b>H</b>) The FLG (filaggrin) RNA is associated with both a GQ and the antisense CCDST (cervical cancer associated DHX9 suppressive transcript) that could form a dsRNA substrate. (<b>I</b>) The FLG pre-mRNA by itself also folds into a dsRNA substrate.</p>
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19 pages, 2176 KiB  
Article
Evaluating the Immunogenicity of an Intranasal Microparticle Combination Vaccine for COVID-19 and Influenza
by Sharon Vijayanand, Smital Patil, Priyal Bagwe, Revanth Singh, Emmanuel Adediran and Martin J. D’Souza
Vaccines 2025, 13(3), 282; https://doi.org/10.3390/vaccines13030282 - 7 Mar 2025
Abstract
Background: Infectious respiratory pathogens like SARS-CoV-2 and influenza frequently mutate, leading to the emergence of variants. This necessitates continuous updates to FDA-approved vaccines with booster shots targeting the circulating variants. Vaccine hesitancy and needle injections create inconvenience and contribute to reduced global vaccination [...] Read more.
Background: Infectious respiratory pathogens like SARS-CoV-2 and influenza frequently mutate, leading to the emergence of variants. This necessitates continuous updates to FDA-approved vaccines with booster shots targeting the circulating variants. Vaccine hesitancy and needle injections create inconvenience and contribute to reduced global vaccination rates. To address the burden of frequent painful injections, this manuscript explores the potential of non-invasive intranasal (IN) vaccine administration as an effective alternative to intramuscular (IM) shots. Further, as a proof-of-concept, an inactivated combination vaccine for COVID-19 and influenza was tested to eliminate the need for separate vaccinations. Methods: The methods involved encapsulating antigens and adjuvants in poly(lactic-co-glycolic acid) (PLGA) polymer matrices, achieving over 85% entrapment. The vaccine was evaluated in vitro for cytotoxicity and immunogenicity before being administered to 6–8-week-old Swiss Webster mice at weeks 0, 3, and 6. The mice were then assessed for antibody levels and cellular responses. Results: The intranasal microparticle (IN-MP) vaccine induced an innate immune response, autophagy, and were non-cytotoxic in vitro. In vivo, the vaccine led to high levels of virus-specific serum IgM, IgG, and IgA binding antibodies, as well as elevated IgG and IgA levels in the lung wash samples. The antibodies generated demonstrated neutralizing activity against the SARS-CoV-2 pseudovirus. Furthermore, the IN-MP vaccine prompted increased antigen-specific CD4+ and CD8+ T-cell responses in the vaccinated mice. Conclusions: The IN-MP combination vaccine produced immune responses comparable to or higher than the IM route, indicating its potential as an alternative to IM injections. Full article
(This article belongs to the Special Issue Innovating Vaccine Research in Mucosal Vaccines)
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<p>Preclinical vaccine study timeline and dosing regimen. 6–8-week-old mice were immunized with three doses at weeks 0, 3, and 6. The mice were bled 2 weeks after each dose at weeks 3, 5, 8, and 10 to evaluate serum antibody levels. The mice were challenged intranasally with 50 μL of an 0.5 × LD<sub>50</sub> dose of Influenza A Virus, A/Puerto Rico/8-WG/1934 (H1N1) at week 10 and sacrificed 14 days after challenge (week 12). Terminal blood, lymph nodes, spleen, and lungs were isolated and processed for evaluation of immune responses.</p>
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<p>In vitro nitrite release and cytotoxicity assessment. (<b>A</b>). Nitrite released by DCs upon stimulation with various treatment groups. Cell density was adjusted to 3 × 10<sup>4</sup> cells/well. The nitrite release in the supernatant was assessed using the Griess’s assay. NO plays a role in fighting invading pathogens and activation of signaling molecules in innate immunity. The MP groups produced increased levels of nitrite compared to the suspension vaccine. Addition of adjuvants potentiated the response of the MP vaccine. The (iSCoV-2 + IIV + adjuvants) MP group received only half the dose of unadjuvanted (iSCoV-2 + IIV) MP group (listed in <a href="#vaccines-13-00282-t001" class="html-table">Table 1</a>) and produced response comparable to the unadjuvanted MP combination vaccine. Data are expressed as mean ± SEM (<span class="html-italic">n</span> = 3), One-way ANOVA test, Post hoc Tukey’s multiple comparisons test, ns (non-significant), ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001, and **** <span class="html-italic">p</span> ≤ 0.001. (<b>B</b>,<b>C</b>). Percent cell viability of DCs pulsed with varying concentrations of IIV MP (B) and CPG 7909 MP (<b>C</b>). Cell density was adjusted to 1 × 10<sup>4</sup> cells/well. The cells were treated with two-fold serial dilutions of the corresponding MP groups at three concentrations, 31.25 µg/mL, 62.5 µg/mL, 125 µg/mL in cDMEM (100 μL/well) for 24 h. 25% <span class="html-italic">v</span>/<span class="html-italic">v</span> DMSO was used as a -ve control, and cells only were used as a +ve control. The % cell viability was &gt;95% for all concentrations tested. Data are expressed as mean ± SEM (<span class="html-italic">n</span> = 3). One-way ANOVA test, Post hoc Dunnett’s multiple comparison test, **** <span class="html-italic">p</span> ≤ 0.0001, ns, non-significant.</p>
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<p>Assessing autophagy in DCs treated with different groups A-D. Fluorescent microscope imaging. (<b>A</b>). Cells only (−ve control). (<b>B</b>). Antigen suspension. (<b>C</b>). Vaccine MP. (<b>D</b>). Vaccine + Adjuvants in MP. (<b>E</b>). Flow cytometry analysis of autophagy. The vaccine MP induces autophagy in DCs which is significantly higher than the antigen suspension group (<b>E</b>). The anitgen suspension induces autophagy to a lesser extent (<b>B</b>). Data expressed as mean ± SEM, n = 4, one-way ANOVA, post hoc Tukey’s multiple comparisons test. *** <span class="html-italic">p</span> ≤ 0.001.</p>
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<p>Virus-specific antibody levels in the serum and lung wash of vaccinated mice. The serum antibodies were assessed for their ability to bind to inactivated SARS-CoV-2 or inactivated influenza A H1N1 using ELISA. The functional activity of the serum antibodies was assessed for SARS-CoV-2 using a pseudovirus neutralization assay. IgG and IgA levels in the lung wash of the vaccination mice were assessed at week 12 using ELISA. (<b>A</b>). SARS-CoV-2-specific IgM. (<b>B</b>). Influenza A H1N1-specific IgM. Serum IgM levels in vaccinated mice peaked at week 2 after the prime dose and subsequently decreased in the following weeks. (<b>C</b>). % neutralization of SARS-CoV-2 pseudovirus by serum antibodies was assessed at weeks 5 and 12. The neutralizing capacity of the antibodies varied for each animal and was found to range between 47 and 99% at week 5, and between 50% and 90% at week 12 for the IN-MP vaccine group. (<b>D</b>). SARS-CoV-2-specific IgG. (<b>E</b>). Influenza A H1N1-specific IgG. The serum IgG levels increased after the prime dose and remained high until week 12. The IN-MP vaccine group exhibited higher antibody levels compared to the IM suspension group. SARS-CoV-2-specific antibody levels dominated over the influenza-specific antibody levels. (<b>F</b>). Virus-specific IgG in lung wash samples. The IN-MP vaccine induced significant IgG levels in lungs. (<b>G</b>). SARS-CoV-2-specific IgA. (<b>H</b>). Influenza A H1N1-specific IgA. The serum IgA levels of the IN-MP vaccine were higher than the IM suspension vaccine for both viruses. (<b>I</b>). Virus-specific IgA in lung wash samples. The IN-MP vaccine induced significant IgA responses in the lung wash samples of vaccinated mice. Responses obtained are compared to no treatment (control) and IM suspension (control) group. Data are expressed as individual values, n = 6. Two-way ANOVA, post hoc Tukey’s multiple comparisons test. ns, non-significant, * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001, **** <span class="html-italic">p</span> ≤ 0.0001.</p>
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<p>Virus-specific IgG1 and IgG2a levels and IgG1/IgG2a ratio in the serum of vaccinated mice. (<b>A</b>). SARS-CoV-2-specific IgG1. (<b>B</b>). Influenza A H1N1-specific IgG1. (<b>C</b>). SARS-CoV-2-specific IgG2a. (<b>D</b>). Influenza A H1N1-specific IgG2a. (<b>E</b>). SARS-CoV-2-specific IgG1/IgG2a. The SARS-CoV-2-specific IgG1/IgG2a ratio was evaluated which indicated that the mean value was 3.6 for the IN-MP vaccine and 6.2 for IM suspension vaccine. (<b>F</b>). Influenza A H1N1-specific IgG1/IgG2a. For influenza, the mean IgG1/IgG2a was 5.4 for the IN-MP vaccine and 7.9 for the IM suspension vaccine. The Responses obtained are compared to no treatment (control) and IM suspension (control) group. Data are expressed as individual values, n = 6. Two-way ANOVA, post hoc Tukey’s multiple comparisons test. ns, non-significant, * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001, **** <span class="html-italic">p</span> ≤ 0.0001.</p>
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<p>SARS-CoV-2-specific and Influenza A H1N1-specific CD4+ and CD8+ T-cells in the splenocytes and lymphocytes of vaccinated mice. The antigen-specific responses were evaluated by indirectly stimulating the cells with the respective antigen (5 μg/mL) and IL-2 (100 IU/mL) to activate antigen-primed T-cells in vaccinated mice. The % cells expressing CD4 and CD8 on the T-cell surface was quantified using flow cytometry. The IN-MP vaccine induced high percentages of antigen-specific T-cells expressing CD4 and CD8 levels in the lymphocytes and only CD4+ T cells in the spleen. (<b>A</b>). CD4+ T-cells in the lymphocytes. (<b>B</b>). CD8+ T-cells in the lymphocytes. (<b>C</b>). CD4+ T-cells in the splenocytes (<b>D</b>). CD8+ T-cells in the splenocytes. Responses obtained are compared to no treatment (control) and the IM suspension (control) group. Data are expressed as individual values, n = 6, Two-way ANOVA, post hoc Tukey’s multiple comparisons test. ns, non-significant, * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001, **** <span class="html-italic">p</span> ≤ 0.0001.</p>
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24 pages, 4533 KiB  
Article
Anti-Tumor Effects of Cecropin A and Drosocin Incorporated into Macrophage-like Cells Against Hematopoietic Tumors in Drosophila mxc Mutants
by Marina Hirata, Tadashi Nomura and Yoshihiro H. Inoue
Cells 2025, 14(6), 389; https://doi.org/10.3390/cells14060389 - 7 Mar 2025
Viewed by 158
Abstract
Five major antimicrobial peptides (AMPs) in Drosophila are induced in multiple sex combs (mxc) mutant larvae harboring lymph gland (LG) tumors, and they exhibit anti-tumor effects. The effects of other well-known AMPs, Cecropin A and Drosocin, remain unexplored. We investigated the [...] Read more.
Five major antimicrobial peptides (AMPs) in Drosophila are induced in multiple sex combs (mxc) mutant larvae harboring lymph gland (LG) tumors, and they exhibit anti-tumor effects. The effects of other well-known AMPs, Cecropin A and Drosocin, remain unexplored. We investigated the tumor-elimination mechanism of these AMPs. A half-dose reduction in either the Toll or Imd gene reduced the induction of these AMPs and enhanced tumor growth in mxcmbn1 mutant larvae, indicating that their anti-tumor effects depend on the innate immune pathway. Overexpression of these AMPs in the fat body suppressed tumor growth without affecting cell proliferation. Apoptosis was promoted in the mutant but not in normal LGs. Conversely, knockdown of them inhibited apoptosis and enhanced tumor growth; therefore, they inhibit LG tumor growth by inducing apoptosis. The AMPs from the fat body were incorporated into the hemocytes of mutant but not normal larvae. Another AMP, Drosomycin, was taken up via phagocytosis factors. Enhanced phosphatidylserine signals were observed on the tumor surface. Inhibition of the signals exposed on the cell surface enhanced tumor growth. AMPs may target phosphatidylserine in tumors to induce apoptosis and execute their tumor-specific effects. AMPs could be beneficial anti-cancer drugs with minimal side effects for clinical development. Full article
(This article belongs to the Special Issue Drosophila as a Model for Understanding Human Disease)
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Figure 1
<p>The expression of <span class="html-italic">Dro-GFP</span> and <span class="html-italic">CecA1-GFP</span> reporters in the fat body (FB) of <span class="html-italic">mxc<sup>mbn1</sup></span> mutant larvae: (<b>a</b>,<b>b</b>) Bright-field (BF) stereomicroscopic images of the FB of mature third-instar larvae carrying the <span class="html-italic">Drosocin</span> (<span class="html-italic">Dro</span>)-<span class="html-italic">GFP</span> reporter. Scale bars: 500 µm. (<b>a′</b>,<b>b′</b>) Green fluorescent protein (GFP) fluorescence images of the FB of mature third-instar larvae with the <span class="html-italic">Dro</span>-<span class="html-italic">GFP</span> reporter. (<b>c</b>,<b>d</b>) BF stereomicroscopic images of the FB in a mature third-instar larva carrying the <span class="html-italic">Cecropin A1</span> (<span class="html-italic">CecA1</span>)-<span class="html-italic">GFP</span> reporter. (<b>c′</b>,<b>d′</b>) GFP fluorescence images of the FB of the larvae with the <span class="html-italic">CecA1</span>-<span class="html-italic">GFP</span> reporter. (<b>a</b>,<b>c</b>) Normal control (<span class="html-italic">w/Y</span>) and (<b>b</b>,<b>d</b>) <span class="html-italic">mxc<sup>mbn1</sup></span> mutant (<span class="html-italic">mxc<sup>mbn1</sup>/Y</span>) larvae. (<b>e</b>,<b>f</b>) mRNA quantification of <span class="html-italic">Dro</span> and <span class="html-italic">CecA1</span> using quantitative reverse transcription-PCR (qRT-PCR). The X-axis of each graph shows the mRNA levels of the normal control (<span class="html-italic">w/Y</span>) and <span class="html-italic">mxc<sup>mbn1</sup></span> (<span class="html-italic">mxc<sup>mbn1</sup>/Y</span>) larvae from left to right; the Y-axis shows the mRNA levels of the target gene relative to the endogenous control gene (<span class="html-italic">Rp49</span>). (<b>e</b>,<b>f</b>) mRNA levels of the <span class="html-italic">Dro</span> (<b>e</b>) and <span class="html-italic">CecA1</span> (<b>f</b>) genes. Significant differences between the experimental groups were determined using Welch′s <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05, ns: not significant). The error bars indicate the standard error of the mean (SEM).</p>
Full article ">Figure 2
<p>The mRNA levels of <span class="html-italic">Dro</span> and <span class="html-italic">CecA</span> genes in the fat body and the LG tumor size of <span class="html-italic">mxc<sup>mbn1</sup></span> larvae heterozygous for mutations of the genes encoding the factors in innate immune pathways: (<b>a</b>,<b>b</b>) Quantification of mRNA levels of the <span class="html-italic">Dro</span> gene encoding Drosocin and the <span class="html-italic">CecA1</span> gene encoding Cecropin A using qRT-PCR. X-axis of each graph shows mRNA levels of <span class="html-italic">mxc<sup>mbn1</sup></span> larvae, mutant larvae heterozygous for <span class="html-italic">Toll<sup>1-RXA</sup></span> mutation, and mutant larvae heterozygous for <span class="html-italic">imd<sup>1</sup></span> mutation from left to right. Y-axis shows relative mRNA level of each target gene ((<b>a</b>) <span class="html-italic">Dro</span>, or (<b>b</b>) <span class="html-italic">CecA1</span>) to an endogenous control gene (<span class="html-italic">Rp49</span>). Significant differences between the groups were determined via one-way ANOVA for multiple comparisons (** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001, <span class="html-italic">n</span> = 3). The error bars indicate SEM. (<b>c</b>–<b>f</b>) DAPI-stained images of lymph glands (LGs) excised from male mature third-instar larvae. Shown are (<b>c</b>) normal control larvae, (<b>d</b>) <span class="html-italic">mxc<sup>mbn1</sup></span> larvae, and (<b>e</b>,<b>f</b>) mutant larvae heterozygous for <span class="html-italic">Toll<sup>1-RXA</sup></span> (<b>e</b>) and <span class="html-italic">imd<sup>1</sup></span> (<b>f</b>) mutations, respectively. Scale bars: 100 µm. (<b>g</b>) Quantification graph indicates LG size of larvae with each genotype. Significant differences between the groups were determined using one-way ANOVA for multiple comparisons (*** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001). The red lines indicate mean LG size; the error bars indicate SEM.</p>
Full article ">Figure 3
<p>Observation of lymph glands (LGs) from <span class="html-italic">mxc<sup>mbn1</sup></span> larvae and quantification of their size via induction of <span class="html-italic">Dro</span> or <span class="html-italic">CecA1</span> overexpression (OE) in a fat body (FB)-specific manner: (<b>a</b>–<b>f</b>) Fluorescence images of DAPI-stained LGs collected from mature third-instar larvae. (<b>a</b>) Pair of LGs from a normal control larva (<span class="html-italic">w/Y</span>; <span class="html-italic">r4&gt;+</span>). (<b>b</b>) LG from control larvae with FB-specific overexpression of <span class="html-italic">Dro</span> (<span class="html-italic">w/Y</span>; <span class="html-italic">r4&gt;Dro</span>) or (<b>c</b>) <span class="html-italic">CecA1</span> (<span class="html-italic">w/Y</span>; <span class="html-italic">r4&gt;CecA1</span>). Pair of LGs from (<b>d</b>) <span class="html-italic">mxc<sup>mbn1</sup></span> larva (<span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">r4&gt;+</span>), (<b>e</b>) <span class="html-italic">mxc<sup>mbn1</sup></span> larvae with FB-specific expression of <span class="html-italic">Dro</span> (<span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">r4&gt;Dro</span>) or (<b>f</b>) <span class="html-italic">CecA1</span> (<span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">r4&gt;CecA1</span>). Scale bars: 100 µm. (<b>g</b>) LG size quantification in larvae with FB-specific expression of <span class="html-italic">Dro</span> (<span class="html-italic">w/Y</span>; <span class="html-italic">r4&gt;+</span> (<span class="html-italic">n</span> = 20), <span class="html-italic">w/Y</span>; <span class="html-italic">r4&gt;Dro</span> (<span class="html-italic">n</span> = 20), <span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">r4&gt;+</span> (<span class="html-italic">n</span> = 20), <span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">r4&gt;Dro</span> (<span class="html-italic">n</span> = 20)), and <span class="html-italic">CecA1</span> ((<span class="html-italic">w/Y</span> (<span class="html-italic">n</span> = 20), <span class="html-italic">w/Y</span>; <span class="html-italic">r4&gt;CecA1</span> (<span class="html-italic">n</span> = 20), <span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">r4&gt;+</span> (<span class="html-italic">n</span> = 20), <span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">r4&gt;CecA1</span> (<span class="html-italic">n</span> = 20)). Significant differences between the groups were determined using one-way ANOVA for multiple comparisons (**** <span class="html-italic">p</span> &lt; 0.0001, ns: not significant). The red lines indicate the mean LG size; the error bars indicate SEM.</p>
Full article ">Figure 4
<p>Observation and quantification of apoptosis areas in LGs of <span class="html-italic">mxc<sup>mbn1</sup></span> larvae with FB (FB)-specific overexpression (OE) of <span class="html-italic">Dro</span> or <span class="html-italic">CecA1</span>: (<b>a</b>–<b>f</b>) Immunostaining of LGs with anti-cDcp1 antibody that recognizes apoptotic cells in LGs from the third instar-stage mature larvae. (<b>a</b>) Pair of LGs from control larvae (<span class="html-italic">w/Y</span>; <span class="html-italic">r4&gt;+</span>). (<b>b</b>) Control larvae overexpressing <span class="html-italic">Dro</span> (<span class="html-italic">w/Y</span>; <span class="html-italic">r4&gt;Dro</span>), or (<b>c</b>) <span class="html-italic">CecA1</span> (<span class="html-italic">w/Y</span>; <span class="html-italic">r4&gt;CecA1</span>) specifically in FB. (<b>d</b>) Anterior lobes of pair of LGs from <span class="html-italic">mxc<sup>mbn1</sup></span> larvae (<span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">r4&gt;+</span>). (<b>e</b>) <span class="html-italic">mxc<sup>mbn1</sup></span> larvae overexpressing <span class="html-italic">Dro</span> (<span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">r4&gt;Dro</span>) or (<b>f</b>) <span class="html-italic">CecA1</span> (<span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">r4&gt;CecA1</span>). Blue indicates DNA staining; green in (<b>a</b>–<b>f</b>) and (<b>a′</b>–<b>f′</b>) indicates anti-cDcp1 immunostaining signals. Scale bars: 100 µm. (<b>g</b>) Percentage of areas occupied by apoptotic cells in lobe regions of LGs from larvae with FB-specific <span class="html-italic">Dro</span> overexpression (<span class="html-italic">n</span> = 21 LGs from 11 larvae) or <span class="html-italic">CecA1</span> (<span class="html-italic">n</span> = 24 LGs from 12 larvae). Significant differences between the groups were determined using one-way ANOVA for multiple comparisons (* <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001, ns: not significant). Red line indicates the mean percentage of apoptosis. The error bars indicate SEM.</p>
Full article ">Figure 5
<p>Quantification of LG sizes in <span class="html-italic">mxc<sup>mbn1</sup></span> larvae with FB-specific knockdown of <span class="html-italic">Dro</span> or <span class="html-italic">CecA1:</span> (<b>a</b>–<b>f</b>) DAPI-stained images of LGs from mature third-instar larvae. (<b>a</b>–<b>c</b>) LGs expressing dsRNAs against mRNAs for (<b>a</b>) <span class="html-italic">GFP</span> (<span class="html-italic">w/Y</span>; <span class="html-italic">r4&gt;GFPRNAi</span>) (control), (<b>b</b>) <span class="html-italic">Dro</span> (<span class="html-italic">w/Y</span>; <span class="html-italic">r4&gt;DroRNAi</span>), or (<b>c</b>) <span class="html-italic">CecA1</span> (<span class="html-italic">w/Y</span>; <span class="html-italic">r4&gt;CecA1RNAi</span>) specifically in FB are shown. (<b>d</b>–<b>f</b>) LGs expressing dsRNAs against (<b>d</b>) <span class="html-italic">GFP</span> in FB of <span class="html-italic">mxc<sup>mbn1</sup></span> larvae (<span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">r4&gt;GFPRNAi</span>), (<b>e</b>) <span class="html-italic">Dro</span> (<span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">r4&gt;DroRNAi</span>) or (<b>f</b>) <span class="html-italic">CecA1</span> (<span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">r4&gt;CecA1RNAi</span>). Scale bars: 100 µm. (<b>g</b>) Quantification graphs of the LG size in larvae of each genotype have. LG size of larvae with <span class="html-italic">DroRNAi</span> (<span class="html-italic">n</span> = 15 LGs from 8 larvae) and <span class="html-italic">CecA1RNAi</span> (<span class="html-italic">n</span> = 13 LGs from 7 larvae). Significant differences between the groups were determined using one-way ANOVA for multiple comparisons (** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001, ns: not significant). The red lines indicate mean LG size. The error bars indicate SEM.</p>
Full article ">Figure 6
<p>Apoptosis observation and quantification in LGs of <span class="html-italic">mxc<sup>mbn1</sup></span> larvae with FB-specific knockdown of <span class="html-italic">Dro</span> or <span class="html-italic">CecA1</span>: (<b>a</b>–<b>f</b>) Immunostaining of LGs with anti-cDcp1 antibody that recognizes apoptotic cells. LGs expressing dsRNA against mRNAs for (<b>a</b>) <span class="html-italic">GFP</span> (<span class="html-italic">w/Y</span>; <span class="html-italic">r4&gt;GFPRNAi</span>), (<b>b</b>) <span class="html-italic">Dro</span> (<span class="html-italic">w/Y</span>; <span class="html-italic">r4&gt;DroRNAi</span>), or (<b>c</b>) <span class="html-italic">CecA1</span> (<span class="html-italic">w/Y</span>; <span class="html-italic">r4&gt;CecA1RNAi</span>) specifically in FB are shown. (<b>d</b>–<b>f</b>) LG expressing dsRNA against (<b>d</b>) <span class="html-italic">GFP</span> specifically in FB of <span class="html-italic">mxc<sup>mbn1</sup></span> larvae (<span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">r4&gt;GFPRNAi</span>), (<b>e</b>) <span class="html-italic">Dro</span> (<span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">r4&gt;DroRNAi</span>), or (<b>f</b>) <span class="html-italic">CecA1</span> (<span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">r4&gt;CecA1RNAi</span>) are shown. Blue indicates DNA staining; green in (<b>a</b>–<b>f</b>) and (<b>a′</b>–<b>f′</b>) indicates anti-cDcp1 immunostaining signals. Scale bars: 100 µm. (<b>g</b>) Graphs indicate percentage of apoptotic cells in LG lobe regions of larvae with FB-specific depletion of <span class="html-italic">Dro</span> (<span class="html-italic">n</span> = 15 LGs from 8 larvae) or <span class="html-italic">CecA1</span> (<span class="html-italic">n</span> = 13 LGs from 7 larvae). Significant differences between the groups were determined using one-way ANOVA for multiple comparisons (**** <span class="html-italic">p</span> &lt; 0.0001, ns: not significant). The red line indicates the mean percentage of apoptosis. The error bars indicate SEM.</p>
Full article ">Figure 7
<p>Apoptosis area quantification in <span class="html-italic">mxc<sup>mbn1</sup></span> larvae LGs after synthetic cecropin A peptide injection: (<b>a</b>–<b>d</b>) Immunostaining of LGs in control (<b>a</b>,<b>c</b>) and <span class="html-italic">mxc<sup>mbn1</sup></span> (<b>b</b>,<b>d</b>) larvae with anti-cDcp1 antibody that recognizes apoptotic cells. Third-instar larvae injected with PBS (control; (<b>a</b>,<b>b</b>)) or synthetic cecropin A (<b>c</b>,<b>d</b>) dissolved in PBS. Green in (<b>a”</b>–<b>d”</b>) indicates signal of anti-cDcp1 immunostaining, and blue (white in (<b>a′</b>–<b>d′</b>)) indicates DNA staining. Scale bars: 100 µm. (<b>e</b>) Quantification graphs indicate percentage of apoptotic cells in LG lobe regions after injecting PBS (<span class="html-italic">n</span> = 5 LGs from 3 <span class="html-italic">w/Y</span> and <span class="html-italic">n</span> = 22 LGs from 11 <span class="html-italic">mxc<sup>mbn1</sup>/Y</span> larvae), and cecropin A (<span class="html-italic">n</span> = 7 LGs from 4 <span class="html-italic">w/Y</span> and <span class="html-italic">n</span> = 8 LGs from 4 <span class="html-italic">mxc<sup>mbn1</sup>/Y</span> larvae). Significant differences were determined using one-way ANOVA for multiple comparisons (** <span class="html-italic">p</span> &lt; 0.01, ns: not significant). The red line indicates mean percentage of apoptosis. The error bars indicate SEM.</p>
Full article ">Figure 8
<p>Observation of circulating hemocytes containing HA-tagged Cecropin A produced in the FB in control and <span class="html-italic">mxc<sup>mbn1</sup></span> larvae: (<b>a</b>,<b>b</b>) Merged images of anti-HA immunostaining and DNA staining of circulating hemocytes in normal (<span class="html-italic">w/Y</span>; <span class="html-italic">r4&gt;CecA1-HA</span>) (<b>a</b>) and <span class="html-italic">mxc<sup>mbn1</sup></span> larvae (<span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">r4&gt;CecA1-HA</span>) (<b>b</b>) expressing Cecropin A-HA in the FB. Green in (<b>a</b>,<b>b</b>,<b>a″</b>,<b>b″</b>), fluorescence of anti-HA immunostaining; magenta in (<b>a</b>,<b>b</b>), DNA staining (white in <b>a′</b>,<b>b′</b>). Magnified image of hemocyte indicated with an arrow is presented in insets in (<b>b″</b>). Bright-field (BF) images (<b>a‴</b>,<b>b‴</b>). Scale bars: 10 µm. (<b>c</b>) Percentages of hemocytes harboring HA-tagged Cecropin A in control and <span class="html-italic">mxc<sup>mbn1</sup></span> larvae. Significant differences were determined using Welch′s <span class="html-italic">t</span>-test (**** <span class="html-italic">p</span> &lt; 0.0001, <span class="html-italic">n</span> = 20). The error bars indicate SEM.</p>
Full article ">Figure 9
<p>Observation and quantification of hemocytes in which GFP-tagged Drosomycin was incorporated in control and <span class="html-italic">mxc<sup>mbn1</sup></span> larvae: (<b>a</b>,<b>b</b>) GFP fluorescence of circulating hemocytes to detect GFP-tagged Drosomycin (<b>a</b>,<b>b</b>), induced in the FB of control (<span class="html-italic">w</span>, <span class="html-italic">Drs::GFP/Y</span>) (<b>a</b>) and <span class="html-italic">mxc<sup>mbn1</sup> (mxc<sup>mbn1</sup></span>, <span class="html-italic">Drs::GFP/Y</span>) (<b>b</b>) larvae. (<b>c</b>,<b>d</b>) GFP fluorescence indicating GFP-tagged Drosomycin in circulating hemocytes of mutant larvae with hemocyte-specific knockdown of <span class="html-italic">draper</span> (<span class="html-italic">mxc<sup>mbn1</sup></span>, <span class="html-italic">Drs::GFP/Y</span>; <span class="html-italic">He&gt;drprRNAi</span>) (<b>c</b>), or <span class="html-italic">shark</span> (<span class="html-italic">mxc<sup>mbn1</sup></span>, <span class="html-italic">Drs::GFP/Y</span>; <span class="html-italic">He&gt;sharkRNAi</span>) (<b>d</b>). Circulating hemocytes with GFP-tagged Drosomycin (Drs::GFP) are colored in green in (<b>a</b>–<b>d</b>,<b>a″</b>–<b>d″</b>). DNA is magenta in (<b>a</b>–<b>d</b>) (white in (<b>a′</b>–<b>d′</b>)). Bright-field (BF) images (<b>a‴</b>–<b>d‴</b>). Scale bars: 10 µm. (<b>e</b>) Percentages of hemocytes with GFP-tagged Drosomycin in control and <span class="html-italic">mxc<sup>mbn1</sup></span> larvae. X-axis from left to right: control larvae expressing GFP-tagged Drosomycin under its promoter (<span class="html-italic">w</span>, <span class="html-italic">Drs::GFP/Y</span> (<span class="html-italic">n</span> = 374 hemocytes (6 larvae)), <span class="html-italic">mxc<sup>mbn1</sup></span>, <span class="html-italic">Drs::GFP/Y</span> (<span class="html-italic">n</span> = 1021 (8)), <span class="html-italic">mxc<sup>mbn1</sup></span>, <span class="html-italic">Drs::GFP/Y</span>; <span class="html-italic">He&gt;drprRNAi</span> (<span class="html-italic">n</span> = 2098 (8)), and <span class="html-italic">mxc<sup>mbn1</sup></span>, <span class="html-italic">Drs::GFP/Y</span>; <span class="html-italic">He&gt;sharkRNAi</span> (<span class="html-italic">n</span> = 1193 (6)). Significant differences were determined using one-way ANOVA for multiple comparisons (**** <span class="html-italic">p</span> &lt; 0.0001). The error bars indicate SEM.</p>
Full article ">Figure 10
<p>Detection of phosphatidylserine (PS) exposed on cell surface of lymph gland (LG) tumors in control and <span class="html-italic">mxc<sup>mbn1</sup></span> larvae: (<b>a</b>,<b>b</b>) DAPI-stained fluorescence images of LGs from larvae at the third instar stage: (<b>a</b>) normal control; (<b>b</b>) <span class="html-italic">mxc<sup>mbn1</sup></span> mutant. Blue in a,b (white in (<b>a′</b>,<b>b′</b>)) indicates DNA staining and green in (<b>a</b>,<b>b</b>) and (<b>a″</b>,<b>b″</b>) indicates Annexin V-GFP signal. Scale bars: 100 µm. (<b>c</b>) Quantification graph indicating percentage of GFP fluorescent regions in LGs, indicative of Annexin V binding. Significant differences were determined using Welch’s <span class="html-italic">t</span>-test (**** <span class="html-italic">p</span> &lt; 0.0001, <span class="html-italic">n</span> = 16). The red line indicates mean percentage. The error bars indicate SEM.</p>
Full article ">Figure 11
<p>Loss of PS on the surface of LG cells via Xkr scramblase knockdown and its influence on LG hyperplasia in <span class="html-italic">mxc<sup>mbn1</sup></span> larvae: (<b>a</b>–<b>c</b>) DAPI-stained anterior lobes and fluorescence indicating Alexa 594-Annexin V binding to PS on the LG lobes in normal control (<span class="html-italic">w/Y</span>) larvae (<b>a</b>), <span class="html-italic">mxc<sup>mbn1</sup></span> larvae with the ectopic expression of control dsRNA in the medulla zone in primary lobes of the LG (<span class="html-italic">mxc<sup>mbn1</sup></span>/<span class="html-italic">Y</span>; <span class="html-italic">upd3&gt;GFPRNAi</span>) (<b>b</b>), <span class="html-italic">mxc<sup>mbn1</sup></span> larvae with the depletion of <span class="html-italic">xkr</span> mRNA (<span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">upd3&gt;xkrRNAi</span>), and (<b>c</b>) larvae at the third instar stage. DNA is stained in blue in (<b>a</b>–<b>c</b>) (white in (<b>a′</b>–<b>c′</b>)), and Alexa594-Annexin-V is in magenta in (<b>a</b>–<b>c</b>,<b>a″</b>–<b>c″</b>). Scale bars: 100 μm. (<b>d</b>) Quantification of the LG size of <span class="html-italic">mxc<sup>mbn1</sup></span> larvae with <span class="html-italic">xkr</span> depletion in LG tumor cells. The average LG size was calculated among the controls (<span class="html-italic">w/Y</span>) (<span class="html-italic">n</span> = 9 LGs (5 larvae)), <span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">upd3&gt;GFPRNAi</span> (<span class="html-italic">n</span> = 16 (8)), and <span class="html-italic">mxc<sup>mbn1</sup>/Y</span>; <span class="html-italic">upd3&gt;xkrRNAi</span> (<span class="html-italic">n</span> = 28 (14)). Significant differences were determined using one-way ANOVA for multiple comparisons (* <span class="html-italic">p</span> &lt; 0.05). The red lines indicate the mean percentage of apoptosis or the mean LG size. The error bars indicate SEM.</p>
Full article ">
36 pages, 1252 KiB  
Review
The Role of Inflammation in the Pathogenesis of Comorbidity of Chronic Obstructive Pulmonary Disease and Pulmonary Tuberculosis
by Stanislav Kotlyarov and Dmitry Oskin
Int. J. Mol. Sci. 2025, 26(6), 2378; https://doi.org/10.3390/ijms26062378 - 7 Mar 2025
Viewed by 47
Abstract
The comorbid course of chronic obstructive pulmonary disease (COPD) and pulmonary tuberculosis is an important medical and social problem. Both diseases, although having different etiologies, have many overlapping relationships that mutually influence their course and prognosis. The aim of the current review is [...] Read more.
The comorbid course of chronic obstructive pulmonary disease (COPD) and pulmonary tuberculosis is an important medical and social problem. Both diseases, although having different etiologies, have many overlapping relationships that mutually influence their course and prognosis. The aim of the current review is to discuss the role of different immune mechanisms underlying inflammation in COPD and pulmonary tuberculosis. These mechanisms are known to involve both the innate and adaptive immune system, including various cellular and intercellular interactions. There is growing evidence that immune mechanisms involved in the pathogenesis of both COPD and tuberculosis may jointly contribute to the tuberculosis-associated obstructive pulmonary disease (TOPD) phenotype. Several studies have reported prior tuberculosis as a risk factor for COPD. Therefore, the study of the mechanisms that link COPD and tuberculosis is of considerable clinical interest. Full article
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Figure 1

Figure 1
<p>Lung tuberculosis (<b>A</b>) and COPD (<b>B</b>), having some common risk factors and mechanisms of development associated with inflammation, form a separate clinical phenotype in which the polymorphism of morphologic, radiologic, and clinical manifestations of tuberculosis plays an important role due to variability in immune response, structural changes in lung tissue, and individual features of disease pathogenesis. Note: A. Respiratory tuberculosis is an infectious disease caused by Mycobacterium tuberculosis. The main morphological manifestation of tuberculosis is the formation of specific granulomas with a characteristic histological structure: the presence of central caseous necrosis surrounded by a shaft of epithelioid cells, macrophages, lymphocytes, and giant Langhans cells, which reflects a cell-mediated immune response to the penetration of the pathogen. At first exposure to mycobacteria, more often in childhood, primary tuberculosis develops, which manifests itself by the formation of a primary affect (Ghon complex) in the lungs. The primary affect is characterized by an area of caseous tissue degeneration in the area of initial pathogen inoculation (predominantly subpleurally in the lower/middle lobes), tuberculous lymphangitis with caseous lymphadenitis of regional nodes, and often reactive pleurisy. The course of primary tuberculosis varies from asymptomatic with subsequent self-healing and calcification of the focus to manifest progressive tuberculosis with the risk of dissemination, development of serious complications and tuberculous meningitis. Secondary tuberculosis develops due to reactivation of latent tuberculosis infection or exogenous MBT superinfection and is characterized by polymorphism of morphological forms, localized mainly in the apical parts of the lungs. Focal tuberculosis is usually represented by single dense fibrous foci (up to 10 mm) within 1–2 segments with minimal perifocal inflammation and a clinically asymptomatic course. In contrast, infiltrative tuberculosis is characterized by predominantly exudative-necrotic changes with extensive areas of caseification surrounded by inflammatory infiltration, with a tendency to destruction of lung tissue and formation of decay cavities. In disseminated tuberculosis, there is hematogenous or lymphogenous spread of the pathogen with the formation of multiple foci and foci of infiltrative character. Tuberculoma is a form of secondary tuberculosis, which is an encapsulated focus of caseosis ranging in size from 1–2 to 5 cm or more, usually clinically silent and often detected during prophylactic radiologic examinations. Fibrotic cavernous tuberculosis is the final stage of development of all forms of respiratory tuberculosis with the formation of thick-walled cavities (caverns) with a three-layer wall (pyogenic, granulation, fibrous zones), which are surrounded by massive pericavitary sclerosis, bronchial deformation and destruction of parenchyma. In fibrotic cavernous tuberculosis, there is a high risk of multidrug-resistant and extensively drug-resistant MBT. B. Chronic obstructive pulmonary disease (COPD) is associated with chronic exposure to inhaled particles and gases, leading to the development of inflammation in the bronchi involving various cells. Prolonged inflammation leads to symptoms, development of bronchial obstruction, and emphysema. Neutrophils and macrophages are involved in bronchial remodeling through the production of elastases, matrix metalloproteinases. Disturbances in pro- and anti-inflammatory polarization of macrophages leads to chronicization of inflammation and development of systemic inflammation.</p>
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<p>Macrophages play an important role in the pathogenesis of both COPD and tuberculosis.</p>
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11 pages, 1353 KiB  
Article
Inflammatory Bowel Disease from the Perspective of Newer Innate Immune System Biomarkers
by Martin Tobi, Fadi Antaki, MaryAnn Rambus, Jason Hellman, James Hatfield, Suzanne Fligiel and Benita McVicker
Gastrointest. Disord. 2025, 7(1), 22; https://doi.org/10.3390/gidisord7010022 - 6 Mar 2025
Viewed by 154
Abstract
Background: The perspective of inflammatory bowel disease (IBD) has changed radically since the first decade of the 21st century, and the formerly monolithic components of IBD, ulcerative colitis (UC), and Crohn’s disease (CD) have undergone a fundamental convergence, with realization that there is [...] Read more.
Background: The perspective of inflammatory bowel disease (IBD) has changed radically since the first decade of the 21st century, and the formerly monolithic components of IBD, ulcerative colitis (UC), and Crohn’s disease (CD) have undergone a fundamental convergence, with realization that there is likely an element of shared pathogenesis. The ground shift began with genomic revelation but with the current emergence of the innate immune system (InImS) as a key player, allowing for improved understanding of the associations between the immune underpinnings of IBD. Methods: Using unique ferritin/fecal p87 (FERAD) or using colonoscopic effluent as denominator (FEREFF) and other ratios to test this hypothesis, we prospectively enrolled 2185 patients with increased risk of colorectal cancer, of whom 31 had UC and 18 CD, with 2136 controls and brought to bear in a convenient measure for the InImS, the FERAD ratio. The FERAD, FEREFF, and NLR ratios have been shown to be effective measures of the InImS in COVID-19 and various cancers. p87 is expressed in gut Paneth cells known to modulate the microbiome by secretion of alpha-defensins, a natural antibiotic. Other related parameters were also evaluated. Results: There was no significant difference between the FERAD ratio in UC and CD. However, differences between IBD entities and controls were substantial. Conclusions: InImS settings in IBD are similar between CD and UC. p87 tissue immunohistochemistry (IHC) is also shared. Other InImS markers, such as the absolute neutrophil/lymphocyte ratio, are also confluent between the two IBD forms. Full article
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<p>A flow diagram showing the total number and those of the IBD patients and controls.</p>
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<p>Bar diagram depicting the comparison of FERAD levels in IBD and controls.</p>
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<p>Bar diagram showing mean levels of colonic p87 expression in disease versus controls.</p>
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<p>The crypts are well-oriented and likely represent inactive UC. The Adnab-9 antibody was used in a concentration of 1:50 dilution and developed using the ABC kit (see <a href="#sec3-gastrointestdisord-07-00022" class="html-sec">Section 3</a> below). The magnification in <a href="#gastrointestdisord-07-00022-f004" class="html-fig">Figure 4</a> is 20×.</p>
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<p>A direct correlation exists in IBD patients between initial and final ferritin determinations.</p>
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17 pages, 3522 KiB  
Article
Differential Responses of Pediatric and Adult Primary Epithelial Cells to Human Metapneumovirus and Respiratory Syncytial Virus Infection
by Pius I. Babawale and Antonieta Guerrero-Plata
Viruses 2025, 17(3), 380; https://doi.org/10.3390/v17030380 - 6 Mar 2025
Viewed by 164
Abstract
Human metapneumovirus (HMPV) and respiratory syncytial virus (RSV) are pneumoviruses causing lower respiratory tract infections, primarily in infants and children rather than in healthy adults. Human bronchial epithelial cells serve as a viral replication target and source of the innate immune response to [...] Read more.
Human metapneumovirus (HMPV) and respiratory syncytial virus (RSV) are pneumoviruses causing lower respiratory tract infections, primarily in infants and children rather than in healthy adults. Human bronchial epithelial cells serve as a viral replication target and source of the innate immune response to these viruses. To better understand the immune responses induced by RSV and HMPV in the pediatric airway epithelium, we comparatively studied pediatric and adult epithelial responses. We used normal human bronchial epithelial (NHBE) cells cultured in an air–liquid interface culture system (ALI), which helps to mimic the architecture of the human lower respiratory tract epithelium. Our results demonstrate differential viral replication patterns and reduced interferons; and inflammatory cytokines’ expression in pediatric cells compared to adult cells. However, pediatric epithelial cells expressed an increased mucus response and induced a stronger pro-inflammatory response in monocyte-derived dendritic cells. These findings reveal age-dependent immune epithelial responses that may contribute to more severe infections by HMPV and RSV. Full article
(This article belongs to the Section Human Virology and Viral Diseases)
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<p>Age-related differences in NHBE cell susceptibility to HMPV and RSV infection. NHBE cells from pediatric and adult donors cultured at the air–liquid interface (ALI) and infected with RSV or HMPV. (<b>A</b>) After 7 days of HMPV and RSV infection, cells were stained with H&amp;E staining to assess cell morphology; Scale bar = 50 μm. (<b>B</b>) Kinetics of viral copy numbers. Data represent mean ± SEM from three donors for each age group. Statistical significance was determined using two-way ANOVA with Šídák’s multiple comparisons test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>IFN responses of pediatric and adult NHBE cells to RSV and HMPV infection. NHBE cells were infected with RSV or HMPV. Gene expression of (<b>A</b>) type I IFNs (IFN-α2, IFN-β, IFN-ε, and IFN-ω) and (<b>B</b>) type III IFNs (IFN-λ1 and IFN-λ2/3) was assessed by RT-qPCR at different time points. Statistical significance was determined using two-way ANOVA with Šídák’s multiple comparisons test (* <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). Non-significant (ns).</p>
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<p>Differential expression of ISGs by pediatric and adult NHBE cells. Adult and pediatric NHBE cells were differentiated in ALI culture and infected with RSV and HMPV. RNA samples were collected at different time points and analyzed for expression of key ISGs (IFIT1, IFIT2, IFIT3, OAS1, MX1, and ISG15) by RT-qPCR. Statistical significance was determined using two-way ANOVA with Šídák’s multiple comparisons test (* <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>Cytokine responses in NHBE cells infected with HMPV or RSV. Adult and pediatric NHBE cells were differentiated in ALI culture and infected with RSV or HMPV. RNA samples were collected at different time points and analyzed by RT-qPCR for the expression of (<b>A</b>) pro-inflammatory cytokines and (<b>B</b>) epithelial alarmins. Statistical significance was determined using two-way ANOVA with Šídák’s multiple comparisons test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Cytokine release from human epithelial cells infected with HMPV or RSV. Pediatric and adult NHBE cells were grown in ALI culture and infected with HMPV or RSV. Apical washes were collected at different time points after infection, and concentration of cytokines was determined by LEGENDplex multiplex immunoassay. Statistical significance was determined using two-way ANOVA with Dunnett’s multiple comparisons test (* <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Mucin expression induced by RSV and HMPV in pediatric and adult NHBE cells. NHBE cells were differentiated in ALI culture and infected with RSV and HMPV. (<b>A</b>) Cells were stained with PAS histological staining on day 7 after infection. Scale bar = 50 μm. (<b>B</b>) Further analysis by RT-qPCR assessed the expression of <span class="html-italic">MUC5AC</span> and <span class="html-italic">MUC5B</span> levels. Statistical significance was determined using two-way ANOVA with Šídák’s multiple comparisons test (** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001. Non-significant (ns).</p>
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<p>Induction of inflammatory cytokines in mo-DCs co-cultured with either pediatric or adult NHBE cells. mo-DCs were co-cultured with NHBE cells from either pediatric or adult donors infected with HMPV or RSV. (<b>A</b>) Schematic representation of the co-culture setup: Fully differentiated NHBE cells were infected with HMPV or RSV and cultured with mo-DCs for 3 days. mo-DCs were analyzed for the expression of (<b>B</b>) IL-6, (<b>C</b>) TNF-α, and (<b>D</b>) IL-1β by RT-qPCR. Data represent mean ± SEM, <span class="html-italic">n</span> = 4–6. Statistical significance was determined using the Kruskal–Wallis test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01); non-significant (ns).</p>
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15 pages, 2229 KiB  
Article
Resveratrol Upregulates Antioxidant Factors Expression and Downmodulates Interferon-Inducible Antiviral Factors in Aging
by Iara Grigoletto Fernandes, Luana de M. Oliveira, Milena M. de Souza Andrade, Ricardo W. Alberca, Júlia Cataldo Lima, Emanuella Sarmento Alho de Sousa, Anna Julia Pietrobon, Nátalli Zanete Pereira, Anna Cláudia Calvielli Castelo Branco, Alberto José da Silva Duarte and Maria Notomi Sato
Int. J. Mol. Sci. 2025, 26(5), 2345; https://doi.org/10.3390/ijms26052345 - 6 Mar 2025
Viewed by 82
Abstract
Immunosenescence, a process with a dysfunctional immune response that may favor infection is associated with an increase in inflammatory responses mediated by proinflammatory cytokines, characteristic of inflammaging. Aging and immunosenescence have a relationship relating to oxidative stress and inflammaging. Therefore, natural antioxidant compounds [...] Read more.
Immunosenescence, a process with a dysfunctional immune response that may favor infection is associated with an increase in inflammatory responses mediated by proinflammatory cytokines, characteristic of inflammaging. Aging and immunosenescence have a relationship relating to oxidative stress and inflammaging. Therefore, natural antioxidant compounds could be candidates for the control of the oxidative process. Our purpose was to evaluate the effect of resveratrol (Resv) on the antioxidant, antiviral, and anti-inflammatory responses induced by toll-like receptors (TLRs) 3, 4, and 7/8 agonists stimulation on peripheral blood mononuclear cells (PBMCs) of elderly and healthy female individuals (63–82 years old) and young and healthy female individuals (21–31 years old). Our data show that Resv may upregulate antioxidant factor expression, such as catalase (CAT) and SIRT1, in response to TLR4 and TLR7/8 agonists, similarly in both young and aged groups. Moreover, the Resv anti-inflammatory effect was detected by inhibiting IL-1β, TNF-α, and IL-10 secretion levels, as well as by the chemokines CCL2 and CCL5, induced by TLR4 and TLR7/8 stimulation. Curiously, Resv decreased antiviral genes, such as MxA, STING, and IRF7 expression, possibly by reducing the inflammatory effects of interferon-induced genes. Taken together, our results demonstrate the ability of Resv to stimulate antioxidant factors, leading to a downmodulation of the inflammatory response induced by innate immune stimulation. These findings point out Resv as a strategy to control the upregulation of inflammatory response, even in elderly individuals. Full article
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<p>Resv downmodulates antiviral factors upon TLR3 activation. (<b>A</b>) Heatmap of antioxidant and antiviral factors expressed by PBMC stimulated with TLR agonist 3 (POLY(I:C)) and addition of Resv. The young group is colored in green, and the elderly group is colored in yellow. Red shade gene expression means above the row average, and blue shade expression means below the average. Z-score represents subtract mean, divided by standard deviation. (<b>B</b>) Comparison of the constitutive gene expression of young healthy volunteers and elderly healthy volunteers by qPCR. The relative expression of the targets was calculated in comparison to the amplification of the constitutive gene, GAPDH, and in comparison, to the non-stimulated situation. N = 9–10 individuals per group. Data are expressed as median and interquartile range. Paired Wilcoxon test: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.001 (stimulated vs. unstimulated).</p>
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<p>Resv upregulates CAT/SIRT1 expressions while decreasing antiviral factors upon TLR4 activation. (<b>A</b>) Heatmap of antioxidant and antiviral factors expressed by PBMCs stimulated with TLR agonist 4 (LPS) and addition of Resv. The young group is colored in green, and the elderly group is colored in yellow. Red shade gene expression means above the row average, and blue shade expression means below the average. Z-score represents subtract mean, divided by standard deviation. (<b>B</b>) Comparison of the constitutive gene expression of young healthy volunteers and elderly healthy volunteers by qPCR. The relative expression of the targets was calculated in comparison to the amplification of the constitutive gene, GAPDH, and in comparison to the non-stimulated situation. N = 9–10 individuals per group. Data are expressed as median and interquartile range. Paired Wilcoxon test: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.001 (stimulated vs. unstimulated).</p>
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<p>Resv upregulates CAT expressions while decreasing antiviral factors upon TLR7/8 activation. (<b>A</b>) Heatmap of antioxidant and antiviral factors expressed by PBMC stimulated with TLR agonists 7 and 8 (CL097) and addition of Resv (N = 20). The young group is colored in green, and the elderly group is colored in yellow. Red shade gene expression means above the row average, and blue shade expression means below the average. Z-score represents subtract mean, divided by standard deviation. (<b>B</b>) Comparison of the constitutive gene expression of young healthy volunteers and elderly healthy volunteers by qPCR. The relative expression of the targets was calculated in comparison to the amplification of the constitutive gene, GAPDH, and in comparison to the non-stimulated situation. N = 9–10 individuals per group. Data are expressed as median and interquartile range. Paired Wilcoxon test: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.001 (stimulated vs. unstimulated).</p>
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<p>Proinflammatory cytokines induced by TLR4 and TLR7/8 stimulation were inhibited by Resv. PBMCs were incubated for 24 h with CL097 (2.5 µg/mL), LPS (1 µg/mL), POLY(I:C) (10 µg/mL), and Resv (100 µM). The production of cytokines IL-1β, TNF-α, IFN-γ, and IL-10 was assessed by flow cytometry. N = 9–10 individuals per group. Data expressed in the median and interquartile range. Unpaired Mann–Whitney test: # <span class="html-italic">p</span> &lt; 0.05 (young vs. elderly). Wilcoxon paired test: * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01 (stimulated vs. unstimulated).</p>
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<p>CCL2 and CCL5 induced by TLR4 and TLR3 stimulation were inhibited by Resv. PBMCs were incubated for 24 h with CL097 (2.5 µg/mL), LPS (1 µg/mL), and POLY(I:C) (10 µg/mL) in the presence of Resv (100 µM). CCL2 and CCL5 chemokines were assessed by flow cytometry. N = 9–10 individuals per group. Data expressed as median and interquartile range. One-way ANOVA test: * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01. Unpaired Mann Whitney test: # <span class="html-italic">p</span> &lt; 0.05 (stimulated vs. unstimulated).</p>
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19 pages, 1133 KiB  
Review
Polysaccharides with Arabinose: Key Players in Reducing Chronic Inflammation and Enhancing Immune Health in Aging
by Patricia Pantoja Newman, Brenda Landvoigt Schmitt, Rafael Moura Maurmann and Brandt D. Pence
Molecules 2025, 30(5), 1178; https://doi.org/10.3390/molecules30051178 - 6 Mar 2025
Viewed by 190
Abstract
Aging is associated with a decline in physiological performance leading to increased inflammation and impaired immune function. Polysaccharides (PLs) found in plants, fruits, and fungi are emerging as potential targets for therapeutic intervention, but little is known about their effects on chronic inflammation [...] Read more.
Aging is associated with a decline in physiological performance leading to increased inflammation and impaired immune function. Polysaccharides (PLs) found in plants, fruits, and fungi are emerging as potential targets for therapeutic intervention, but little is known about their effects on chronic inflammation and aging. This review aims to highlight the current advances related to the use of PLs, with the presence of arabinose, to attenuate oxidative stress and chronic and acute inflammation, and their immunomodulatory effects associated with antioxidant status in monocytes, macrophages, and neutrophil infiltration, and leukocyte rolling adhesion in neutrophils. In addition, recent studies have shown the importance of investigating the ‘major’ monosaccharide, such as arabinose, present in several of these polysaccharides, and with described effects on gut microbiome, glucose, inflammation, allergy, cancer cell proliferation, neuromodulation, and metabolic stress. Perspectives and opportunities for further investigation are provided. By promoting a balanced immune response and reducing inflammation, PLs with arabinose or even arabinose per se may alleviate the immune dysregulation and inflammation seen in the elderly, therefore providing a promising strategy to mitigate a variety of diseases. Full article
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<p>Effect of arabinose in different models.</p>
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20 pages, 1300 KiB  
Article
Venomous Cargo: Diverse Toxin-Related Proteins Are Associated with Extracellular Vesicles in Parasitoid Wasp Venom
by Jennifer Chou, Michael Z. Li, Brian Wey, Mubasshir Mumtaz, Johnny R. Ramroop, Shaneen Singh and Shubha Govind
Pathogens 2025, 14(3), 255; https://doi.org/10.3390/pathogens14030255 - 5 Mar 2025
Viewed by 188
Abstract
Unusual membrane-bound particles are present in the venom of the parasitoid wasps that parasitize Drosophila melanogaster. These venom particles harbor about 400 proteins and suppress the encapsulation of a wasp egg. Whereas the proteins in the particles of Leptopilina boulardi venom modify host hemocyte [...] Read more.
Unusual membrane-bound particles are present in the venom of the parasitoid wasps that parasitize Drosophila melanogaster. These venom particles harbor about 400 proteins and suppress the encapsulation of a wasp egg. Whereas the proteins in the particles of Leptopilina boulardi venom modify host hemocyte properties, those in L. heterotoma kill host hemocytes. The mechanisms underlying this differential effect are not well understood. The proteome of the L. heterotoma venom particles has been described before, but that of L. boulardi has not been similarly examined. Using sequence-based programs, we report the presence of conserved proteins in both proteomes with strong enrichment in the endomembrane and exosomal cell components. Extracellular vesicle markers are present in both proteomes, as are numerous toxins. Both proteomes also contain proteins lacking any annotation. Among these, we identified the proteins with structural similarity to the ADP-ribosyltransferase enzymes involved in bacterial virulence. We propose that invertebrate fluids like parasitoid venom contain functional extracellular vesicles that deliver toxins and virulence factors from a parasite to a host. Furthermore, the presence of such vesicles may not be uncommon in the venom of other animals. An experimental verification of the predicted toxin functions will clarify the cellular mechanisms underlying successful parasitism. Full article
(This article belongs to the Special Issue Computational Approaches in Mechanisms of Pathogenesis)
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<p>Venom gland morphology. (<b>A</b>) The entire venom gland complex is composed of the long gland (LG, anterior-most); the connecting duct (CD); the reservoir (R); and the ovipositor (Ovi). The ovipositor enters the host larva during egg laying, and the venom contents are deposited into the host at the same time. (<b>B</b>–<b>E</b>) The long glands from <span class="html-italic">Lb17</span> (<b>B</b>,<b>C</b>) and <span class="html-italic">Lh14</span> wasps (<b>D</b>,<b>E</b>), stained with Hoechst 33258 and rhodamine phalloidin. For all samples, the anterior end of the long gland is placed in the top left. N = nose; L = long gland lumen; * = secretory cells; triangle = canals. (<b>C</b>,<b>E</b>) Only the red channel is shown to highlight the F-actin-rich canals. Select Z-stack images were assembled from either 9 (for <span class="html-italic">Lb</span>), or 10 (for <span class="html-italic">Lh</span>) optical sections to visualize the 3D views of canal organization.</p>
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<p>Characterization of <span class="html-italic">Lb</span> and <span class="html-italic">Lh</span> EV proteins. (<b>A</b>,<b>B</b>) Enrichment analysis of <span class="html-italic">Lb</span> (<b>A</b>) and <span class="html-italic">Lh</span> (<b>B</b>) EV proteins. The specific cellular compartments found in Vesiclepedia are shown on the X-axis. The primary Y-axis indicates the percentage of genes, calculated by FunRich, as the number of genes within the provided dataset (for <span class="html-italic">Lb</span> or <span class="html-italic">Lh</span>) that are associated with the listed cellular compartment (i.e., plasma membrane, nucleus, etc.) divided by the total number of genes within the provided dataset found within the FunRich/Vesiclepedia database (see Methods). The secondary Y-axis shows the −log10 (<span class="html-italic">p</span>-value). In both species, there is significant enrichment in the exosomes, lysosomes, mitochondria, ER–Golgi compartment, and ribosomes (<span class="html-italic">p</span> &lt; 0.01). The Bonferroni correction was used by the FunRich program to calculate the <span class="html-italic">p</span>-values.</p>
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<p>Pie charts showing the proportions of various classes of toxin-related protein domains. (<b>A</b>,<b>B</b>) The data presented are from 23 <span class="html-italic">Lb</span> (<b>A</b>) and 30 <span class="html-italic">Lh</span> (<b>B</b>) proteins. Seven toxin categories common to both species are shown in the same color. Species-specific toxin categories are italicized. The number of proteins in each category is indicated. See <a href="#app1-pathogens-14-00255" class="html-app">Tables S2 and S3</a> for more details.</p>
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<p>A conserved ART fold and R-S-E motif in wasp sequences. (<b>A</b>) Structural superposition of <span class="html-italic">Lb_284</span> (XP_051165226.1; tv_blue; RMSD: 1.84 Å)<span class="html-italic">, Lb_316</span> (XP_051176700.1; slate blue; RMSD: 1.61Å), and <span class="html-italic">Lb_340</span> (XP_051161330.1; marine blue; RMSD: 1.46 Å) with <span class="html-italic">Bacillus cereus</span> C3 exoenzyme (PDB: 4XSH; red). (<b>B</b>) Structural superposition of <span class="html-italic">Lh_005</span> (XP_043484983.1; slate blue; RMSD: 2.40 Å) with <span class="html-italic">Bacillus cereus</span> C3 exoenzyme (PDB: 4XSH; red). (<b>C</b>) Multiple sequence alignment of three <span class="html-italic">Lb</span> and one <span class="html-italic">Lh</span> putative ARTs visualized in ESPript. Residues shaded red denote identity matches while residues colored red and boxed in blue show similarity matches. Residues colored blue represent NAD<sup>+</sup> -interacting residues and those that are shaded in magenta show NAD<sup>+</sup>-interacting residues that form a putative R-S-E motif. The secondary structure descriptions for the <span class="html-italic">Lb</span> sequences are shown above and those for the <span class="html-italic">Lh</span> sequence are shown below the alignment. See <a href="#app1-pathogens-14-00255" class="html-app">Table S4 and Figures S2–S5</a>.</p>
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32 pages, 10662 KiB  
Article
Characterization of Exhausted T Cell Signatures in Pan-Cancer Settings
by Rifat Tasnim Juthi, Saiful Arefeen Sazed, Manvita Mareboina, Apostolos Zaravinos and Ilias Georgakopoulos-Soares
Int. J. Mol. Sci. 2025, 26(5), 2311; https://doi.org/10.3390/ijms26052311 - 5 Mar 2025
Viewed by 181
Abstract
T cells play diverse roles in cancer immunology, acting as tumor suppressors, cytotoxic effectors, enhancers of cytotoxic T lymphocyte responses and immune suppressors; providing memory and surveillance; modulating the tumor microenvironment (TME); or activating innate immune cells. However, cancer cells can disrupt T [...] Read more.
T cells play diverse roles in cancer immunology, acting as tumor suppressors, cytotoxic effectors, enhancers of cytotoxic T lymphocyte responses and immune suppressors; providing memory and surveillance; modulating the tumor microenvironment (TME); or activating innate immune cells. However, cancer cells can disrupt T cell function, leading to T cell exhaustion and a weakened immune response against the tumor. The expression of exhausted T cell (Tex) markers plays a pivotal role in shaping the immune landscape of multiple cancers. Our aim was to systematically investigate the role of known T cell exhaustion (Tex) markers across multiple cancers while exploring their molecular interactions, mutation profiles, and potential implications for immunotherapy. The mRNA expression profile of six Tex markers, LAG-3, PDCD1, TIGIT, HAVCR2, CXCL13, and LAYN was investigated in pan-cancer. Utilizing data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), The Cancer Proteome Atlas (TCPA), and other repositories, we characterized the differential expression of the Tex markers, their association with the patients’ survival outcome, and their mutation profile in multiple cancers. Additionally, we analyzed the effects on cancer-related pathways and immune infiltration within the TME, offering valuable insights into mechanisms of cancer immune evasion and progression. Finally, the correlation between their expression and sensitivity to multiple anti-cancer drugs was investigated extensively. Differential expression of all six markers was significantly associated with KIRC and poor prognosis in several cancers. They also played a potential activating role in apoptosis, EMT, and hormone ER pathways, as well as a potential inhibitory role in the DNA damage response and RTK oncogenic pathways. Infiltration of different immune cells was also found to be associated with the expression of the Tex-related genes in most cancer types. These findings underline that the reviving of exhausted T cells can be used to enhance the efficacy of immunotherapy in cancer patients. Full article
(This article belongs to the Special Issue Big Data in Multi-Omics)
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<p>(<b>A</b>) Bubble plot illustrating the fold change of six Tex marker genes across 14 cancer types. (<b>B</b>) Scattered boxplots showing differential expression of Tex mRNA expression in kidney tumors (KIRC) compared to normal kidney tissues. (<b>C</b>) The boxplots summarize the trend of the Tex mRNA expression from early to late stage KIRC. * <span class="html-italic">p</span> &lt; 0.05, **** <span class="html-italic">p</span> &lt; 0.0001, ns—not significant. (<b>D</b>) The bubble plots illustrate the difference between high and low mRNA expression of the Tex marker genes in different cancer types.</p>
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<p>(<b>A</b>) Bubble plot illustrating the fold change of six Tex marker genes across 14 cancer types. (<b>B</b>) Scattered boxplots showing differential expression of Tex mRNA expression in kidney tumors (KIRC) compared to normal kidney tissues. (<b>C</b>) The boxplots summarize the trend of the Tex mRNA expression from early to late stage KIRC. * <span class="html-italic">p</span> &lt; 0.05, **** <span class="html-italic">p</span> &lt; 0.0001, ns—not significant. (<b>D</b>) The bubble plots illustrate the difference between high and low mRNA expression of the Tex marker genes in different cancer types.</p>
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<p>(<b>A</b>) Survival outcome difference between the high and low expression group of the Tex marker genes. (<b>B</b>) Survival contribution (OS, DSS, and DFS) map of hazard ratio (HR) of the Tex marker genes in pan-cancer. Estimation was conducted using the Mantel–Cox test. Red block, higher risk; blue block, lower risk; darkened outline, significant prognosis. (<b>C</b>) Kaplan–Meier overall survival (OS) plots for high and low expression signatures of the Tex marker genes in uveal melanoma (UVM) and skin melanoma (SKCM). Red and blue dotted line represent 95% confidence interval (CI) for each group.</p>
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<p>(<b>A</b>) Survival outcome difference between the high and low expression group of the Tex marker genes. (<b>B</b>) Survival contribution (OS, DSS, and DFS) map of hazard ratio (HR) of the Tex marker genes in pan-cancer. Estimation was conducted using the Mantel–Cox test. Red block, higher risk; blue block, lower risk; darkened outline, significant prognosis. (<b>C</b>) Kaplan–Meier overall survival (OS) plots for high and low expression signatures of the Tex marker genes in uveal melanoma (UVM) and skin melanoma (SKCM). Red and blue dotted line represent 95% confidence interval (CI) for each group.</p>
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<p>(<b>A</b>) Cancers percentage in which the mRNA expression of the six Tex marker genes has a potential effect on the activity of 10 cancer-related pathways. Blue color depicts the shifting of the effect toward inhibition; red color depicts the shifting of the effect toward activation. Each cell contains a percentage (%) representing the proportion of cancer types in which each gene demonstrated a significant association (either inducing or inhibitory) with a specific pathway in pan-cancer. (<b>B</b>) PAS of high and low Tex genes’ mRNA expression in breast cancers (BRCA).</p>
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<p>(<b>A</b>) Heatmap representing mutation frequency of SNV across pan-cancer. (<b>B</b>) Oncoplot representing the frequency of mutation of Tex marker genes in 314 cases and their distribution across selected cancers. Percentage indicates the ratio of genetically altered tumor samples to the total no. of samples. (<b>C</b>) Percentage distribution of amplification and deletion of Tex marker genes. (<b>D</b>) Pie plot summarizing the CNV of Tex marker genes in the few cancer types. (<b>E</b>) Heterozygous CNV profile of Tex marker genes in pan-cancers. (<b>F</b>) Homozygous CNV profile of Tex marker genes in pan-cancers. (<b>G</b>) Methylation difference of Tex marker genes in selected cancers. (<b>H</b>) Methylation and mRNA expression correlation of Tex marker genes in pan-cancers.</p>
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<p>(<b>A</b>) Heatmap representing mutation frequency of SNV across pan-cancer. (<b>B</b>) Oncoplot representing the frequency of mutation of Tex marker genes in 314 cases and their distribution across selected cancers. Percentage indicates the ratio of genetically altered tumor samples to the total no. of samples. (<b>C</b>) Percentage distribution of amplification and deletion of Tex marker genes. (<b>D</b>) Pie plot summarizing the CNV of Tex marker genes in the few cancer types. (<b>E</b>) Heterozygous CNV profile of Tex marker genes in pan-cancers. (<b>F</b>) Homozygous CNV profile of Tex marker genes in pan-cancers. (<b>G</b>) Methylation difference of Tex marker genes in selected cancers. (<b>H</b>) Methylation and mRNA expression correlation of Tex marker genes in pan-cancers.</p>
Full article ">Figure 4 Cont.
<p>(<b>A</b>) Heatmap representing mutation frequency of SNV across pan-cancer. (<b>B</b>) Oncoplot representing the frequency of mutation of Tex marker genes in 314 cases and their distribution across selected cancers. Percentage indicates the ratio of genetically altered tumor samples to the total no. of samples. (<b>C</b>) Percentage distribution of amplification and deletion of Tex marker genes. (<b>D</b>) Pie plot summarizing the CNV of Tex marker genes in the few cancer types. (<b>E</b>) Heterozygous CNV profile of Tex marker genes in pan-cancers. (<b>F</b>) Homozygous CNV profile of Tex marker genes in pan-cancers. (<b>G</b>) Methylation difference of Tex marker genes in selected cancers. (<b>H</b>) Methylation and mRNA expression correlation of Tex marker genes in pan-cancers.</p>
Full article ">Figure 4 Cont.
<p>(<b>A</b>) Heatmap representing mutation frequency of SNV across pan-cancer. (<b>B</b>) Oncoplot representing the frequency of mutation of Tex marker genes in 314 cases and their distribution across selected cancers. Percentage indicates the ratio of genetically altered tumor samples to the total no. of samples. (<b>C</b>) Percentage distribution of amplification and deletion of Tex marker genes. (<b>D</b>) Pie plot summarizing the CNV of Tex marker genes in the few cancer types. (<b>E</b>) Heterozygous CNV profile of Tex marker genes in pan-cancers. (<b>F</b>) Homozygous CNV profile of Tex marker genes in pan-cancers. (<b>G</b>) Methylation difference of Tex marker genes in selected cancers. (<b>H</b>) Methylation and mRNA expression correlation of Tex marker genes in pan-cancers.</p>
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<p>(<b>A</b>) Association between Tex mRNA expression and immune infiltrates in OV and UCEC. (<b>B</b>) Correlation between the GSVA score and immune cell infiltration in pan-cancer. *: <span class="html-italic">p</span> value ≤ 0.05; #: FDR ≤ 0.05. (<b>C</b>) Difference of immune cell infiltration between Tex marker WT and mutants in UCEC. (<b>D</b>) Disparity of immune cell infiltration between gene set SNV groups in UCEC. (<b>E</b>) Correlation between Tex marker CNVs and immune infiltration in BRCA. (<b>F</b>) Difference of immune infiltration between gene set CNV groups in PAAD. (<b>G</b>) Correlation between methylated Tex markers and immune infiltration in the HNSC.</p>
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<p>(<b>A</b>) Association between Tex mRNA expression and immune infiltrates in OV and UCEC. (<b>B</b>) Correlation between the GSVA score and immune cell infiltration in pan-cancer. *: <span class="html-italic">p</span> value ≤ 0.05; #: FDR ≤ 0.05. (<b>C</b>) Difference of immune cell infiltration between Tex marker WT and mutants in UCEC. (<b>D</b>) Disparity of immune cell infiltration between gene set SNV groups in UCEC. (<b>E</b>) Correlation between Tex marker CNVs and immune infiltration in BRCA. (<b>F</b>) Difference of immune infiltration between gene set CNV groups in PAAD. (<b>G</b>) Correlation between methylated Tex markers and immune infiltration in the HNSC.</p>
Full article ">Figure 5 Cont.
<p>(<b>A</b>) Association between Tex mRNA expression and immune infiltrates in OV and UCEC. (<b>B</b>) Correlation between the GSVA score and immune cell infiltration in pan-cancer. *: <span class="html-italic">p</span> value ≤ 0.05; #: FDR ≤ 0.05. (<b>C</b>) Difference of immune cell infiltration between Tex marker WT and mutants in UCEC. (<b>D</b>) Disparity of immune cell infiltration between gene set SNV groups in UCEC. (<b>E</b>) Correlation between Tex marker CNVs and immune infiltration in BRCA. (<b>F</b>) Difference of immune infiltration between gene set CNV groups in PAAD. (<b>G</b>) Correlation between methylated Tex markers and immune infiltration in the HNSC.</p>
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<p>(<b>A</b>) Correlation between Tex marker gene expression and IC50 across pan-cancer. (<b>B</b>) The ROC plot shows the relationship between Tex mRNA expression and sensitivity in chemotherapy in BRCA. (<b>C</b>) The ROC plot showing relationship between Tex mRNA expression and sensitivity in chemotherapy in OV. (<b>D</b>) The ROC plot shows the relationship between Tex mRNA expression and sensitivity in chemotherapy in GBM. (<b>E</b>) Drug sensitivity analysis of particular Tex marker genes. (<b>F</b>) The regulator prioritization clustering heatmap shows the association of Tex with immunosuppression indicators.</p>
Full article ">Figure 6 Cont.
<p>(<b>A</b>) Correlation between Tex marker gene expression and IC50 across pan-cancer. (<b>B</b>) The ROC plot shows the relationship between Tex mRNA expression and sensitivity in chemotherapy in BRCA. (<b>C</b>) The ROC plot showing relationship between Tex mRNA expression and sensitivity in chemotherapy in OV. (<b>D</b>) The ROC plot shows the relationship between Tex mRNA expression and sensitivity in chemotherapy in GBM. (<b>E</b>) Drug sensitivity analysis of particular Tex marker genes. (<b>F</b>) The regulator prioritization clustering heatmap shows the association of Tex with immunosuppression indicators.</p>
Full article ">Figure 6 Cont.
<p>(<b>A</b>) Correlation between Tex marker gene expression and IC50 across pan-cancer. (<b>B</b>) The ROC plot shows the relationship between Tex mRNA expression and sensitivity in chemotherapy in BRCA. (<b>C</b>) The ROC plot showing relationship between Tex mRNA expression and sensitivity in chemotherapy in OV. (<b>D</b>) The ROC plot shows the relationship between Tex mRNA expression and sensitivity in chemotherapy in GBM. (<b>E</b>) Drug sensitivity analysis of particular Tex marker genes. (<b>F</b>) The regulator prioritization clustering heatmap shows the association of Tex with immunosuppression indicators.</p>
Full article ">Figure 6 Cont.
<p>(<b>A</b>) Correlation between Tex marker gene expression and IC50 across pan-cancer. (<b>B</b>) The ROC plot shows the relationship between Tex mRNA expression and sensitivity in chemotherapy in BRCA. (<b>C</b>) The ROC plot showing relationship between Tex mRNA expression and sensitivity in chemotherapy in OV. (<b>D</b>) The ROC plot shows the relationship between Tex mRNA expression and sensitivity in chemotherapy in GBM. (<b>E</b>) Drug sensitivity analysis of particular Tex marker genes. (<b>F</b>) The regulator prioritization clustering heatmap shows the association of Tex with immunosuppression indicators.</p>
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16 pages, 3455 KiB  
Article
Genome-Wide Identification and Expression Analysis of CrRLK1-like Gene Family in Potatoes (Solanum tuberosum L.) and Its Role in PAMP-Triggered Immunity
by Yazhou Bao, Ru Zhao, Sixian Hu, Xiaoli Li, Like Wang, Ji Wang, Junbin Ji, Weiduo Wang, Changqing Zhu, Jiajia Chen, Ailing Ben, Jinfeng Peng and Tingli Liu
Genes 2025, 16(3), 308; https://doi.org/10.3390/genes16030308 - 4 Mar 2025
Viewed by 186
Abstract
Background: The Catharanthus roseus receptor-like kinase 1-like (CrRLK1L) subfamily, a specialized group within receptor-like kinases (RLKs), was initially identified in C. roseus cell cultures. CrRLK1L plays an important role in the growth, development and stress response of plants. Although CrRLK1L genes have been [...] Read more.
Background: The Catharanthus roseus receptor-like kinase 1-like (CrRLK1L) subfamily, a specialized group within receptor-like kinases (RLKs), was initially identified in C. roseus cell cultures. CrRLK1L plays an important role in the growth, development and stress response of plants. Although CrRLK1L genes have been characterized across multiple plant species, their biological and genetic functions in potatoes (Solanum tuberosum) remains poorly elucidated. Methods: a genome-wide investigation, phylogenetic analysis, chromosome localization, exon–intron structure, conserved motifs, stress-responsive cis-elements, tissue-specific expression patterns, and their effects on pathogen associated molecular patterns (PAMPs) induced reactive oxygen species (ROS) production were analyzed. Results: A total of 29 CrRLK1L genes were identified in the S. tuberosum genome, unevenly distributed across 10 chromosomes and divided into three groups. Tissue-specific expression analysis revealed seven genes highly expressed in all tissues, while CrRLK1L13 was specific to stamens and flowers. Under stress conditions (mannitol, salt, hormone, and heat), StCrRLK1L genes exhibited diverse expression patterns. Functional characterization in Nicotiana benthamiana identified seven ROS suppressors and four ROS enhancers, implicating their roles in PAMP-triggered immunity. Conclusions: This study provides valuable insights into the StCrRLK1L gene family, enhancing our understanding of its functions, particularly in plant innate immunity. Full article
(This article belongs to the Section Plant Genetics and Genomics)
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<p>Chromosomal locations of potato StCrRLK1L gene family members. Twenty-nine genes are mapped to 10 chromosomes based on Phytozome annotations.</p>
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<p>A phylogenetic tree of CrRLK1L-related proteins from <span class="html-italic">A. thaliana</span> (blue circles) and <span class="html-italic">S. tuberosum</span> (red circles). The tree was constructed using ClustalW alignment and MEGA X with 1000 bootstrap replicates.</p>
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<p>Structural and evolutionary analysis of StCrRLK1Ls. (<b>A</b>) Maximum-likelihood phylogenetic tree of 29 StCrRLK1Ls grouped into three subclasses. (<b>B</b>) Exon–intron architecture: green (UTRs), yellow (exons), gray line (introns). (<b>C</b>) Conserved motif distribution, colored by position in protein sequence.</p>
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<p><span class="html-italic">Cis</span>-element distribution in the StCrRLK1L promoters. Element positions are scaled relative to the translation start site (ATG).</p>
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<p>Heat map of StCrRLK1L expression across tissues in DM potatoes. Color intensity reflects log2-transformed FPKM values.</p>
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<p>Expression dynamics of the StCrRLK1L genes under stress conditions. The color scale was plotted using the log2 mean of FPKM of each gene.</p>
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<p>The Modulation of PAMP-triggered ROS by StCrRLK1Ls. The indicated constructs were transiently expressed by agrobacterium-mediated transient expression for 2 days and subjected to flg22-induced ROS examination (mean ± SD, <span class="html-italic">n</span> ≥ 8, and one-way ANOVA followed by Tukey’s post hoc test; different letters indicate significant difference at <span class="html-italic">p</span> &lt; 0.01). The protein expression is shown in <a href="#app1-genes-16-00308" class="html-app">Figure S1</a>.</p>
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20 pages, 2766 KiB  
Review
Biological Effects of Micro-/Nano-Plastics in Macrophages
by Massimiliano G. Bianchi, Lavinia Casati, Giulia Sauro, Giuseppe Taurino, Erika Griffini, Christian Milani, Marco Ventura, Ovidio Bussolati and Martina Chiu
Nanomaterials 2025, 15(5), 394; https://doi.org/10.3390/nano15050394 - 4 Mar 2025
Viewed by 168
Abstract
The environmental impact of plastics is worsened by their inadequate end-of-life disposal, leading to the ubiquitous presence of micro- (MPs) and nanosized (NPs) plastic particles. MPs and NPs are thus widely present in water and air and inevitably enter the food chain, with [...] Read more.
The environmental impact of plastics is worsened by their inadequate end-of-life disposal, leading to the ubiquitous presence of micro- (MPs) and nanosized (NPs) plastic particles. MPs and NPs are thus widely present in water and air and inevitably enter the food chain, with inhalation and ingestion as the main exposure routes for humans. Many recent studies have demonstrated that MPs and NPs gain access to several body compartments, where they are taken up by cells, increase the production of reactive oxygen species, and lead to inflammatory changes. In most tissues, resident macrophages engage in the first approach to foreign materials, and this interaction largely affects the subsequent fate of the material and the possible pathological outcomes. On the other hand, macrophages are the main organizers and controllers of both inflammatory responses and tissue repair. Here, we aim to summarize the available information on the interaction of macrophages with MPs and NPs. Particular attention will be devoted to the consequences of this interaction on macrophage viability and functions, as well as to possible implications in pathology. Full article
(This article belongs to the Section Biology and Medicines)
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<p>The main steps of the interaction of MPs/NPs with macrophages. The figure represents a unitary vision of the interaction of MPs/NPs with macrophages, from changes occurring before the exposure (environmental weathering) to organ disease, as the result of alterations of macrophages at the cell level. For polarization, the solid line represents the favored drive towards M1 polarization, while the dotted line represents the less common drive towards M2 polarization. See text for a more detailed description. Created in BioRender. Bianchi, M. n80u669.</p>
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12 pages, 1504 KiB  
Article
The Role of NFAT5 in Immune Response and Antioxidant Defense in the Thick-Shelled Mussel (Mytilus coruscus)
by Yijiang Bei, Xirui Si, Wenjun Ma, Pengzhi Qi and Yingying Ye
Animals 2025, 15(5), 726; https://doi.org/10.3390/ani15050726 - 4 Mar 2025
Viewed by 188
Abstract
Nuclear Factor of Activated T Cells 5 (NFAT5) is a transcription factor that plays a pivotal role in immune regulation. While its functions have been extensively studied in mammalian immune systems, its role in marine invertebrates, particularly in bivalves, remains largely [...] Read more.
Nuclear Factor of Activated T Cells 5 (NFAT5) is a transcription factor that plays a pivotal role in immune regulation. While its functions have been extensively studied in mammalian immune systems, its role in marine invertebrates, particularly in bivalves, remains largely unexplored. This study provides the first characterization of the NFAT5 gene in the thick-shelled mussel (Mytilus coruscus), investigating its evolutionary characteristics and immunological functions. Using direct RNA sequencing, McNFAT5 was comprehensively analyzed, revealing its critical involvement in the innate immune response of M. coruscus to Vibrio alginolyticus challenge. Differential expression patterns of McNFAT5 were observed across various tissues with the highest expression detected in hemolymphs. The knockdown of McNFAT5 using small interfering RNA (siRNA) led to a significant reduction in the activities of superoxide dismutase (SOD), Na+/K+-ATPase, and antioxidant enzymes compared to levels observed post-infection. These findings highlight the central role of McNFAT5 in modulating antioxidant defense mechanisms. In conclusion, McNFAT5 is a key regulatory factor in the innate immune system of M. coruscus, providing valuable insights into the immune adaptive mechanisms and evolutionary mechanisms of bivalve immunity. This study contributes to a deeper understanding of the immune regulatory networks in marine invertebrates. Full article
(This article belongs to the Section Animal Physiology)
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<p>Molecular characterization of <span class="html-italic">McNFAT5</span> (<b>A</b>) SMART-predicted schematic of <span class="html-italic">McNFAT5</span> functional domains. (<b>B</b>) SWISS-MODEL-generated three-dimensional structure of <span class="html-italic">McNFAT5</span>. (<b>C</b>) Phylogenetic analysis of <span class="html-italic">McNFAT5</span> with selected species using the neighbor-joining method in MEGA 7.0 with 5000 bootstrap replications. Different colors indicate distinct taxa. The species in red font, <span class="html-italic">Mytilus coruscus</span>, represents the focus of our study.</p>
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<p><span class="html-italic">McNFAT5</span> responds to pathogen stimulation. (<b>A</b>) Agarose gel electrophoresis validation of <span class="html-italic">McNFAT5</span> expression in various tissues of <span class="html-italic">M. coruscus</span>, including foot, gill, mantle, hemolymph, gonad and digestive gland. The presence of clear bands in all tested tissues confirms the expression of <span class="html-italic">McNFAT5</span> across different tissue types. (<b>B</b>) Immunofluorescence showing translocation of <span class="html-italic">NFAT5</span> to the nucleus after <span class="html-italic">V. alginolyticus</span> infection. Red: <span class="html-italic">McNFAT5</span> protein, Blue: DAPI-stained nuclei. Scale bars = 20. Each sample was analyzed in triplicate. Vertical error bars represent the mean ± standard error (SE, n = 3). (<b>C</b>) The β-actin gene of <span class="html-italic">M. coruscus</span> was used as an internal reference to normalize the cDNA templates across all samples. Differential expression across the six tissues was analyzed using one-way ANOVA. Distinct letters indicate statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) in the relative expression levels of <span class="html-italic">McNFAT5</span> mRNA.</p>
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<p>Temporal expression patterns of <span class="html-italic">McNFAT5</span> after infection. <span class="html-italic">McNFAT5</span> mRNA expression was assessed via qRT-PCR at 6 h, 12 h, 24 h, and 48 h post-<span class="html-italic">V. alginolyticus</span> infection. Data are shown as mean ± SD (n = 3) with different letters indicating significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The impact of <span class="html-italic">NFAT5</span> knockdown on the activity of three key enzymes and antioxidant capacity. (<b>A</b>) The expression of <span class="html-italic">McNFAT5</span> was downregulated by Si-<span class="html-italic">McNFAT5</span>. (<b>B</b>) Na<sup>+</sup>, K<sup>+</sup>-ATPase. (<b>C</b>) SOD. (<b>D</b>) T-AOC. The vertical bars represent the mean ± SD (n = 3). Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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26 pages, 1442 KiB  
Article
The Association of Toll-like Receptor-9 Gene Single-Nucleotide Polymorphism and AK155(IL-26) Serum Levels with Chronic Obstructive Pulmonary Disease Exacerbation Risk: A Case-Controlled Study with Bioinformatics Analysis
by Entsar R. Mokhtar, Salwa I. Elshennawy, Heba Elhakeem, Rayyh A. M. Saleh, Sawsan Bakr Elsawy, Khadiga S. M. Salama, Maha Fathy Mohamed, Rania Hamid Bahi, Hayam H. Mansour, Sammar Ahmed Kasim Mahmoud, Marwa M. Hassan, Sara M. Elhadad, Hanaa Mohammed Eid El Sayed, Aliaa N. Mohamed and Nadia M. Hamdy
Biomedicines 2025, 13(3), 613; https://doi.org/10.3390/biomedicines13030613 - 3 Mar 2025
Viewed by 267
Abstract
Background: A crucial challenge is the determination of chronic obstructive pulmonary disease (COPD) immune-related mechanisms, where one of the important components of the inflammatory axes in COPD is Toll-like receptor-9 (TLR9) and interleukin-26 AK155(IL-26). Aim: To examine the relation between TLR9 (T1237C) SNP [...] Read more.
Background: A crucial challenge is the determination of chronic obstructive pulmonary disease (COPD) immune-related mechanisms, where one of the important components of the inflammatory axes in COPD is Toll-like receptor-9 (TLR9) and interleukin-26 AK155(IL-26). Aim: To examine the relation between TLR9 (T1237C) SNP rs5743836 and serum levels of AK155(IL-26) with the exacerbation of COPD. Subjects: A total of 96 COPD patients sub-classified into two groups. Materials: DNA was purified from blood samples of stable COPD patients (n = 48) vs. exacerbated COPD patients (n = 48) as well as 42 age- and sex-matched healthy smokers and passive smokers as a control group. Methods: Genotyping for TLR9 rs5743836 (T1237C) polymorphism was performed using real time polymerase chain reaction (RT-PCR). AK155(IL-26) serum levels were determined using ELISA. Results: There is a significantly higher frequency of the mutant homozygous genotype (C/C) and the mutated C allele of TLR9 rs5743836 (T1237C) in COPD patients and in the exacerbated group when compared with the control group and stable COPD patients, respectively, with OR 31.98, 1.8 to 57.7, and OR 3.64, 0.98 to 13.36, respectively. For the mutated C allele, the OR was 3.57, 1.94 to 6.56, p = 0.001, OR 1.83, 1.02 to 3.27, p = 0.041, respectively. In the exacerbated COPD group, there was a significant association between TLR9 rs5743836 SNP and BMI and the lung vital function measures, CRP, and AK155(IL-26). The exacerbated COPD group has higher serum levels of AK155(IL-26) compared with the stable group or when compared with the control group (p = 0.001) for both. AK155(IL-26) serum levels have a positive significant correlation with CRP and BMI and a significant negative correlation with FEV1% and FEV1/FVC in exacerbated COPD patients. Conclusions: Our results demonstrated a relation linking TLR-9 rs5743836 (T1237C) expression and the risk of COPD development and its exacerbation, indicating that dysfunctional polymorphisms of the innate immune genes can affect COPD development and its exacerbation. AK155(IL-26) upregulation was related to decreased lung functionality, systematic inflammatory disease, and COPD exacerbation. Full article
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Graphical abstract
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<p>ROC curve analysis of AK155(IL-26) (pg/mL) between COPD patients and control group (<b>A</b>) and exacerbated and stable COPD patients (<b>B</b>). [Sens.: sensitivity; Spec.: specificity; PPV: positive predictive value; NPV: negative predictive value; AUD: Area Under the Curve; <span class="html-italic">p</span> &lt; 0.001 is highly significant].</p>
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<p>Genes’ relative expression heatmap: (<b>A</b>) <span class="html-italic">AK155(IL-26)</span> and (<b>B</b>) <span class="html-italic">TLR9</span>. Legend is 0.0., with yellowish (low expression) through red to dark red color indicating highly expressed gene. <a href="http://husch.comp-genomics.org/#/info_tissue/Lung" target="_blank">http://husch.comp-genomics.org/#/info_tissue/Lung</a> (accessed on 5 November 2024).</p>
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<p>(<b>A</b>) The receptor combination of IL10RB and IL20RA is unique and specific for AK155(IL26), presented as pathway members <a href="https://omim.org/entry/605679?search=il26&amp;highlight=il26" target="_blank">https://omim.org/entry/605679?search=il26&amp;highlight=il26</a> and <a href="http://signalink.org/node/Q9NPH9" target="_blank">http://signalink.org/node/Q9NPH9</a>, as well as <a href="https://signor.uniroma2.it/relation_result.php?id=Q9NPH9" target="_blank">https://signor.uniroma2.it/relation_result.php?id=Q9NPH9</a>. (<b>B</b>) <a href="https://www.proteinatlas.org/ENSG00000239732-TLR9/structure+interaction#interaction" target="_blank">https://www.proteinatlas.org/ENSG00000239732-TLR9/structure+interaction#interaction</a> and according to <a href="https://signor.uniroma2.it/relation_result.php?id=Q9NR96" target="_blank">https://signor.uniroma2.it/relation_result.php?id=Q9NR96</a> TLR9 activates myeloid differentiation primary protein 88 (MyD88) and a Toll/Interleukin receptor protein domain. (<b>C</b>) The direct link between AK155(IL-26) and TLR-9 evidenced via the STRING version 12.0 database <a href="https://string-db.org/cgi/network?taskId=b6gWhpi8e53F&amp;sessionId=b4XNW7ObLS7n" target="_blank">https://string-db.org/cgi/network?taskId=b6gWhpi8e53F&amp;sessionId=b4XNW7ObLS7n</a>. (<b>D</b>) List 1, with violet color to the left, is AK155(IL-26), and list 2 indicates TLR9, yellow-colored to the right, showing 5 common regulators between AK155(IL-26) as “List 1” and TLR-9 as “List 2,” which are ETS1, SMARCA4, CEBPA, CTCF, and EGR1. This result is evident when interacting via the STRING, as well as protein interaction via STITCH database. (<b>E</b>,<b>F</b>) <a href="https://genome.ucsc.edu/cgi-bin/hgGeneGraph?gene=IL26&amp;1=OK&amp;supportLevel=text&amp;hideIndirect=on&amp;geneCount=20&amp;geneAnnot=drugbank&amp;1=OK&amp;geneCount=20" target="_blank">https://genome.ucsc.edu/cgi-bin/hgGeneGraph?gene=IL26&amp;1=OK&amp;supportLevel=text&amp;hideIndirect=on&amp;geneCount=20&amp;geneAnnot=drugbank&amp;1=OK&amp;geneCount=20</a>. (<b>G</b>) <a href="https://genome.ucsc.edu/cgi-bin/hgGeneGraph?gene=TLR9&amp;1=OK&amp;supportLevel=text&amp;hideIndirect=on&amp;geneCount=20&amp;geneCount=15&amp;geneAnnot=drugbank&amp;1=OK" target="_blank">https://genome.ucsc.edu/cgi-bin/hgGeneGraph?gene=TLR9&amp;1=OK&amp;supportLevel=text&amp;hideIndirect=on&amp;geneCount=20&amp;geneCount=15&amp;geneAnnot=drugbank&amp;1=OK</a> (all accessed on 5 November 2024) [black-colored genes denote treatment hits by Drug Bank; continuous gray line for results indicates dataset interaction curated from source document, and no text-mining line indicates no curated information where text mining is evident; blue continuous line indicates interaction from several datasets with text mining]. Accessed on 5 November 2024.</p>
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