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17 pages, 2166 KiB  
Article
Immunogenic Cell Death Traits Emitted from Chronic Lymphocytic Leukemia Cells Following Treatment with a Novel Anti-Cancer Agent, SpiD3
by Elizabeth Schmitz, Abigail Ridout, Audrey L. Smith, Alexandria P. Eiken, Sydney A. Skupa, Erin M. Drengler, Sarbjit Singh, Sandeep Rana, Amarnath Natarajan and Dalia El-Gamal
Biomedicines 2024, 12(12), 2857; https://doi.org/10.3390/biomedicines12122857 - 16 Dec 2024
Viewed by 1007
Abstract
Background: Targeted therapies (e.g., ibrutinib) have markedly improved chronic lymphocytic leukemia (CLL) management; however, ~20% of patients experience disease relapse, suggesting the inadequate depth and durability of these front-line strategies. Moreover, immunotherapeutic success in CLL has been stifled by its pro-tumor microenvironment milieu [...] Read more.
Background: Targeted therapies (e.g., ibrutinib) have markedly improved chronic lymphocytic leukemia (CLL) management; however, ~20% of patients experience disease relapse, suggesting the inadequate depth and durability of these front-line strategies. Moreover, immunotherapeutic success in CLL has been stifled by its pro-tumor microenvironment milieu and low mutational burden, cultivating poor antigenicity and limited ability to generate anti-tumor immunity through adaptive immune cell engagement. Previously, we have demonstrated how a three-carbon-linker spirocyclic dimer (SpiD3) promotes futile activation of the unfolded protein response (UPR) in CLL cells through immense misfolded-protein mimicry, culminating in insurmountable ER stress and programmed CLL cell death. Method: Herein, we used flow cytometry and cell-based assays to capture the kinetics and magnitude of SpiD3-induced damage-associated molecular patterns (DAMPs) in CLL cell lines and primary samples. Result: SpiD3 treatment, in vitro and in vivo, demonstrated the capacity to propagate immunogenic cell death through emissions of classically immunogenic DAMPs (CALR, ATP, HMGB1) and establish a chemotactic gradient for bone marrow-derived dendritic cells. Conclusions: Thus, this study supports future investigation into the relationship between novel therapeutics, manners of cancer cell death, and their contributions to adaptive immune cell engagement as a means for improving anti-cancer therapy in CLL. Full article
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<p>CLL cells display ecto-CALR following SpiD3 treatment. HG-3 ((<b>A</b>,<b>B</b>); n = 3); OSU-CLL ((<b>C</b>,<b>D</b>); n = 3); or patient-derived CLL ((<b>E</b>,<b>F</b>); n = 5) cells were treated with vehicle (Veh), SpiD3 (0.25–2 µM), FeCl<sub>2</sub> (160 μM), or the positive control, etoposide (Etop; 20 µM) for the indicated durations. Viable cells were analyzed by flow cytometry for changes in surface CALR expression (ecto-CALR). Primary patient-derived CLL cells were additionally designated as CD19+/CD5+ by flow cytometry. Data are presented as mean ± SEM. Comparisons across treatment groups were analyzed with respect to the vehicle by one-way ANOVA. Asterisks denote magnitude of significance: * <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>SpiD3 treatment evokes extracellular ATP release. HG-3 (<b>A</b>); and OSU-CLL (<b>B</b>) cells were treated over 24 h (n = 3) with vehicle (Veh), SpiD3 (0.5–2 µM), or the positive control, etoposide (Etop; 20 µM). Extracellular ATP measurements at 8, 16, and 24 h were parsed out to evaluate the average extracellular ATP measured at these timepoints in comparison to the matched timepoint vehicle. Data are presented as mean ± SEM. Comparisons across treatment groups were analyzed with respect to the matched timepoint average vehicle by one-way ANOVA. Asterisks denote magnitude of significance: * <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>SpiD3-treated cells release extracellular HMGB1. Supernatant from HG-3 ((<b>A</b>,<b>B</b>); n = 3); OSU-CLL ((<b>C</b>,<b>D</b>); n = 3); and primary CLL ((<b>E</b>); n = 10) cells were evaluated for extracellular HMGB1 after 24 h or 48 h of treatment with the vehicle (Veh), SpiD3 (0.5–2 µM), ibrutinib (1 µM), or positive control, etoposide (Etop; 20 µM). Data are presented as mean ± SEM. Comparisons across treatment groups were analyzed with respect to the vehicle by one-way ANOVA. Asterisks denote magnitude of significance: * <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>Chemotactic potential of SpiD3-treated cell supernatants. Bone marrow dendritic cells (BMDCs) were allowed to migrate for 6 h toward supernatant collected from HG-3 (<b>A</b>); and OSU-CLL (<b>B</b>) cells after 24 h treatment with the vehicle (Veh), SpiD3 (0.5–2 µM), or the positive control, etoposide (Etop; 20 µM). GM-CSF (20 ng/mL) stimulated media, and supernatant derived from heat-shocked CLL cells (HS) served as positive chemotactic controls. The number of migrated BMDCs were counted via flow cytometry analysis (n = 3). The chemotactic index is a comparison of the migrated events observed from treatment conditions to that of the vehicle condition. Data are represented as mean ± SEM. Comparisons across treatment groups were analyzed with respect to the vehicle by one-way ANOVA. Asterisks denote magnitude of significance: * <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><span class="html-italic">In vivo</span> SpiD3 treatment yields an immunostimulatory response. (<b>A</b>) Schematic of experiment design: Eµ-TCL1 mice with comparable leukemia burden were treated intravenously with SpiD3 prodrug (SpiD3_AP, 10 mg/kg; n = 6) or equivalent vehicle (Veh; 50% PEG400, 10% DMSO, 40% water; n = 5) once daily for 3 days, as previously reported [<a href="#B20-biomedicines-12-02857" class="html-bibr">20</a>]. Following treatment, spleen cells were collected for flow cytometry analysis and plasma was isolated from murine blood; (<b>B</b>) leukemic (CD19+/CD5+) cells from murine spleens were analyzed by flow cytometry for changes in surface CALR expression (ecto-CALR) and compared to the percentage of leukemic cells detected in spleens of the same mice (as reported in Eiken, et al. [<a href="#B20-biomedicines-12-02857" class="html-bibr">20</a>]). The concentrations of plasma inflammatory cytokines and chemokines were assessed using Mouse Anti-Virus Response (<b>C</b>,<b>E</b>); and Mouse Pro-Inflammatory Chemokine (<b>D</b>,<b>F</b>) LEGENDplex™ flow cytometry-based multiplex immunoassays. (<b>C</b>,<b>D</b>) Heatmaps display fold change in the plasma analyte concentration compared to the average of vehicle-treated mice. Columns represent individual mice per treatment group. (<b>E</b>,<b>F</b>) Raw plasma analyte concentration and correlation with the percentage of CD19+/CD5+ spleen-derived cells are shown for select analytes. Individual data points (Veh = black circles; SpiD3_AP = blue triangles) in addition to summary statistics (mean ± SEM) are shown. Comparisons between treatment groups were analyzed by unpaired <span class="html-italic">t</span>-test. Asterisks denote magnitude of significance: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Illustrative summary of SpiD3 anti-leukemic activity. CLL cell cytotoxicity via SpiD3 is demonstrated by: (i) inhibition of NF-κB signaling; and (ii) accumulation of unfolded proteins, promoting ER stress, activating a futile UPR and, subsequently, the associated programmed cell death pathways. ER stress is a proposed prerequisite for immunogenic DAMP emissions; we hypothesize it is this facet of SpiD3-associated effects that result in detectable hallmarks of immunogenic cell death from CLL cells. This diagram is adapted from Eiken, et al. CLL, chronic lymphocytic leukemia; DC, dendritic cell; iDAMP, immunogenic damage-associated molecular pattern; ER, endoplasmic reticulum; UPR, unfolded protein response.</p>
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17 pages, 2474 KiB  
Article
Molecular Mimicry between Toxoplasma gondii B-Cell Epitopes and Neurodevelopmental Proteins: An Immunoinformatic Approach
by Karla F. Meza-Sosa, David Valle-Garcia, Hugo González-Conchillos, Tonali Blanco-Ayala, Alelí Salazar, Itamar Flores, Saúl Gómez-Manzo, Dinora Fabiola González Esquivel, Gonzalo Pérez de la Cruz, Benjamín Pineda and Verónica Pérez de la Cruz
Biomolecules 2024, 14(8), 933; https://doi.org/10.3390/biom14080933 - 1 Aug 2024
Viewed by 1575
Abstract
Epidemiological studies and meta-analyses have shown a strong association between high seroprevalence of Toxoplasma gondii (T. gondii) and schizophrenia. Schizophrenic patients showed higher levels of anti-Toxoplasma immunoglobulins M and G (IgM and IgG) when compared to healthy controls. Previously, in a [...] Read more.
Epidemiological studies and meta-analyses have shown a strong association between high seroprevalence of Toxoplasma gondii (T. gondii) and schizophrenia. Schizophrenic patients showed higher levels of anti-Toxoplasma immunoglobulins M and G (IgM and IgG) when compared to healthy controls. Previously, in a rat model, we demonstrated that the progeny of mothers immunized with T. gondii lysates before gestation had behavioral and social impairments during adulthood. Therefore, we suggested that T. gondii infection can trigger autoreactivity by molecularly mimicking host brain proteins. Here, we aimed to identify the occurrence of antigenic mimicry between T. gondii epitopes and host brain proteins. Using a bioinformatic approach, we predicted T. gondii RH-88 B cell epitopes and compared them to human cell-surface proteins involved in brain development and differentiation (BrainS). Five different algorithms for B-cell-epitope prediction were used and compared, resulting in 8584 T. gondii epitopes. We then compared T. gondii predicted epitopes to BrainS proteins by local sequence alignments using BLASTP. T. gondii immunogenic epitopes significantly overlapped with 42 BrainS proteins. Among these overlapping proteins essential for brain development and differentiation, we identified HSP90 and NOTCH receptors as the proteins most likely to be targeted by the maternally generated pathogenic antibodies due to their topological overlap at the extracellular region of their sequence. This analysis highlights the relevance of pregestational clinical surveillance and screening for potential pathogenic anti-T. gondii antibodies. It also identifies potential targets for the design of vaccines that could prevent behavioral and cognitive impairments associated with pre-gestational T. gondii exposure. Full article
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<p>Analysis scheme: A global scheme of our bioinformatic pipeline is shown. Briefly, potential epitopes from <span class="html-italic">Toxoplasma gondii</span> (<span class="html-italic">T. gondii</span>) proteins were predicted bioinformatically. Epitopes were merged within and between prediction methods. Matches from those epitopes with human surface proteins expressed during brain differentiation (neurodevelopment) were searched. Transmembranal regions for those matching proteins were predicted to find high-confidence predictions of molecular mimicry. aa—amino acids.</p>
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<p>Human cell-surface proteins matching <span class="html-italic">T. gondii</span> epitopes. (<b>A</b>) Venn diagram showing the overlap between <span class="html-italic">T. gondii</span> epitopes (blue, left) and human cell-surface proteins involved in neurodevelopment (BrainS proteins; orange, right). (<b>B</b>) Venn diagram showing the average overlap between <span class="html-italic">T. gondii</span> epitopes (blue, left) and randomly selected human cell-surface proteins not involved in neurodevelopment (non-BrainS proteins; gray, right). The random selection was repeated 100 times, as depicted.</p>
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<p>Proteins matching <span class="html-italic">T. gondii</span> epitopes by cell type. In the second row, the total number of proteins in the BrainS dataset is indicated for each cell type. The next row shows the number of proteins in the BrainS dataset that matched <span class="html-italic">T. gondii</span> epitope(s) for the indicated cell types. The percentage of matches compared to the total number of proteins is shown at the bottom row.</p>
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<p>Protein–protein interaction network and expression profiles of genes encoding human proteins with the potential for molecular mimicry. (<b>A</b>) Protein–protein interaction network of identified candidates. The ball color indicates whether they are involved in neurogenesis (red), axonogenesis (blue-purpleish) or gliogenesis (yellow) according to Gene Ontology (GO). Edge thickness between proteins indicates the level of confidence for that interaction. (<b>B</b>) Heatmaps showing normalized expression levels of the 20 identified candidates at the transcript level (messenger RNA) in three mouse-brain regions (forebrain, midbrain, and hindbrain) at different embryonic (E) days, which are indicated above each column.</p>
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<p>Prediction of protein topology and overlap with <span class="html-italic">T. gondii</span>. (<b>A</b>) Structural overlap between human heat shock protein 90 (HSP90AA1 or HSP90α, green) and <span class="html-italic">T. gondii</span> putative HSP90 protein (red). (<b>B</b>) Overlap between human NOTCH1 receptor protein (green) and <span class="html-italic">T. gondii</span> EGF-Like domain-containing protein (red). The extracellular regions of BrainS proteins (HSP90AA1 and NOTCH receptor) are highlighted in green, while transmembrane and intracellular regions were removed.</p>
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16 pages, 1408 KiB  
Review
A Comprehensive Review on the Intricate Interplay between COVID-19 Immunization and the New Onset of Pemphigus Foliaceus
by Beatrice Bălăceanu-Gurău, Adrian Dumitrascu, Călin Giurcăneanu, Raluca Tatar, Cristian-Dorin Gurău and Olguța Anca Orzan
Vaccines 2024, 12(8), 857; https://doi.org/10.3390/vaccines12080857 - 30 Jul 2024
Viewed by 1481
Abstract
Autoimmune bullous diseases (AIBDs) are characterized by the formation of vesicles, bullous lesions, and mucosal erosions. The autoantibodies target the cellular anchoring structures from the surface of epidermal keratinocyte named desmosomes, leading to a loss of cellular cohesion named acantholysis. AIBDs are classified [...] Read more.
Autoimmune bullous diseases (AIBDs) are characterized by the formation of vesicles, bullous lesions, and mucosal erosions. The autoantibodies target the cellular anchoring structures from the surface of epidermal keratinocyte named desmosomes, leading to a loss of cellular cohesion named acantholysis. AIBDs are classified into intraepidermal or subepidermal types based on clinical features, histological characteristics, and immunofluorescence patterns. Pemphigus foliaceus (PF) is an acquired, rare, autoimmune skin condition associated with autoantibodies that specifically target desmoglein-1, leading to a clinical presentation characterized by delicate cutaneous blisters, typically sparing the mucous membranes. Several factors, including genetic predisposition, environmental triggers, malignancies, medication use, and vaccination (for influenza, hepatitis B, rabies, tetanus, and more recently, severe acute respiratory syndrome Coronavirus 2 known as SARS-CoV-2), can potentially trigger the onset of pemphigus. With the advent of vaccines playing a pivotal role in combatting the 2019 coronavirus disease (COVID-19), extensive research has been conducted globally to ascertain their efficacy and potential cutaneous adverse effects. While reports of AIBDs post-COVID-19 vaccination exist in the medical literature, instances of PF following vaccination have been less commonly reported worldwide. The disease’s pathophysiology is likely attributed to the resemblance between the ribonucleic acid (RNA) antigen present in these vaccines and cellular nuclear matter. The protein produced by the BNT-162b2 messenger ribonucleic acid (mRNA) vaccine includes immunogenic epitopes that could potentially trigger autoimmune phenomena in predisposed individuals through several mechanisms, including molecular mimicry, the activation of pattern recognition receptors, the polyclonal stimulation of B cells, type I interferon production, and autoinflammation. In this review, we present a comprehensive examination of the existing literature regarding the relationship between COVID-19 and PF, delving into their intricate interactions. This exploration improves the understanding of both pemphigus and mRNA vaccine mechanisms, highlighting the importance of close monitoring for PF post-immunization. Full article
(This article belongs to the Special Issue 2nd Edition: Safety and Autoimmune Response to SARS-CoV-2 Vaccination)
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<p>Crusted erosions localized on an erythematous base on the upper trunk.</p>
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<p>Crusted erosions placed on an erythematous base on the scalp.</p>
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<p>Transient small flaccid blister located on the left breast.</p>
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<p>Histopathological examination reveals detachment of the upper layers of the epidermis from the lower ones, forming a suprabasal bulla with infiltrating neutrophils and focal detachment of rounded keratinocytes from the upper layers (acantholysis at the granular layer).</p>
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10 pages, 731 KiB  
Brief Report
Autoimmunity against Nucleus Ambiguous Is Putatively Possible in Both Long-COVID-19 and Vaccinated Subjects: Scientific Evidence and Working Hypothesis
by Silvestro Ennio D’Anna, Alessandra Maria Vitale, Giuseppa D’Amico, Celeste Caruso Bavisotto, Pasquale Ambrosino, Francesco Cappello, Mauro Maniscalco and Antonella Marino Gammazza
Biology 2024, 13(6), 359; https://doi.org/10.3390/biology13060359 - 21 May 2024
Cited by 1 | Viewed by 1627
Abstract
As reported by the World Health Organization (WHO), about 10–20% of people have experienced mid- to long-term effects following SARS-CoV-2 infection, collectively referred to as post-COVID-19 condition or long-COVID, including some neurovegetative symptoms. Numerous findings have suggested that the onset of these neurovegetative [...] Read more.
As reported by the World Health Organization (WHO), about 10–20% of people have experienced mid- to long-term effects following SARS-CoV-2 infection, collectively referred to as post-COVID-19 condition or long-COVID, including some neurovegetative symptoms. Numerous findings have suggested that the onset of these neurovegetative symptoms upon viral infection may be caused by the production of autoantibodies through molecular mimicry phenomena. Accordingly, we had previously demonstrated that 22 of the human proteins sharing putatively immunogenic peptides with SARS-CoV-2 proteins are expressed in the dorsal motor nucleus and nucleus ambiguous. Therefore, if molecular mimicry occurs following severe forms of COVID-19, there could be transitory or permanent damage in some vagal structures, resulting in a lower vagal tone and all the related clinical signs. We investigated the presence of autoantibodies against two proteins of vagal nuclei sharing a peptide with SARS-CoV-2 spike glycoprotein using an immunoassay test on blood obtained from patients with cardiorespiratory symptoms in patients affected by ongoing symptomatic COVID-19 (long-COVID), subjects vaccinated without a history of SARS-CoV-2 infection, and subjects not vaccinated without a history of SARS-CoV-2 infection. Interestingly, putative autoantibodies were present in both long-COVID-19 and vaccinated groups, opening interesting questions about pathogenic mechanisms of the disease. Full article
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Graphical abstract
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<p>Representative dot-blot immunoassay of the reaction between long-COVID, vaccinated, and healthy control serum with corticotropin-releasing factor receptor 2 (Pcort) and calcitonin gene type 1 receptor (Pcalc)-related peptide. The original dot-blot full image is provided in the <a href="#app1-biology-13-00359" class="html-app">Supplementary Materials (Figures S1 and S2)</a>.</p>
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<p>In this draft, we illustrate our hypothesis about the triggering of autoimmunity in subjects exposed to SARS-CoV-2 without having been vaccinated (unvaccinated) or after having been vaccinated, i.e., subjects exposed to spike proteins by vaccination. The latter probably avoids the overcoming of an infective threshold (in terms of viral load) beyond which autoimmunity phenomena may become manifested with clinical signs and symptoms. In other terms, vaccination predisposes the subject to react better to the infection and therefore to contain its spread to other organs other than those of the upper airways (respiratory mucosa, tonsils, etc.).</p>
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25 pages, 2929 KiB  
Article
Mining Autoimmune-Disorder-Linked Molecular-Mimicry Candidates in Clostridioides difficile and Prospects of Mimic-Based Vaccine Design: An In Silico Approach
by Saleh Alshamrani, Mutaib M. Mashraqi, Ahmad Alzamami, Norah A. Alturki, Hassan H. Almasoudi, Mohammed Abdulrahman Alshahrani and Zarrin Basharat
Microorganisms 2023, 11(9), 2300; https://doi.org/10.3390/microorganisms11092300 - 12 Sep 2023
Cited by 1 | Viewed by 2147
Abstract
Molecular mimicry, a phenomenon in which microbial or environmental antigens resemble host antigens, has been proposed as a potential trigger for autoimmune responses. In this study, we employed a bioinformatics approach to investigate the role of molecular mimicry in Clostridioides difficile-caused infections [...] Read more.
Molecular mimicry, a phenomenon in which microbial or environmental antigens resemble host antigens, has been proposed as a potential trigger for autoimmune responses. In this study, we employed a bioinformatics approach to investigate the role of molecular mimicry in Clostridioides difficile-caused infections and the induction of autoimmune disorders due to this phenomenon. Comparing proteomes of host and pathogen, we identified 23 proteins that exhibited significant sequence homology and were linked to autoimmune disorders. The disorders included rheumatoid arthritis, psoriasis, Alzheimer’s disease, etc., while infections included viral and bacterial infections like HIV, HCV, and tuberculosis. The structure of the homologous proteins was superposed, and RMSD was calculated to find the maximum deviation, while accounting for rigid and flexible regions. Two sequence mimics (antigenic, non-allergenic, and immunogenic) of ≥10 amino acids from these proteins were used to design a vaccine construct to explore the possibility of eliciting an immune response. Docking analysis of the top vaccine construct C2 showed favorable interactions with HLA and TLR-4 receptor, indicating potential efficacy. The B-cell and T-helper cell activity was also simulated, showing promising results for effective immunization against C. difficile infections. This study highlights the potential of C. difficile to trigger autoimmunity through molecular mimicry and vaccine design based on sequence mimics that trigger a defensive response. Full article
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<p>Superposed structures of the <span class="html-italic">C. difficile</span> and human homologous proteins. (<b>A</b>) Molecular chaperone DnaK; (<b>B</b>) Translation elongation factor 4; (<b>C</b>) Uracil-DNA glycosylase; (<b>D</b>) Acetyl-CoA C-acetyltransferase; (<b>E</b>) 3-oxoacid CoA-transferase subunit B; (<b>F</b>) UDP-glucose 4-epimerase GalE; (<b>G</b>) V-type proton ATPase subunit B; (<b>H</b>) V-type ATP synthase catalytic unit A; (<b>I</b>) Phosphopyruvate hydratase; (<b>J</b>) ATP-dependent Clp endopeptidase proteolytic subunit ClpP; (<b>K</b>) ATP-dependent Clp endopeptidase proteolytic subunit ClpP; (<b>L</b>) F0F1 ATP synthase subunit beta. Due to space constraints, the first 12 (<a href="#microorganisms-11-02300-t001" class="html-table">Table 1</a>) of the 23 proteins are shown here. Human homologs are shown in brown and bacterial proteins are shown in blue.</p>
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<p>Conservation of sequence mimics from (<b>A</b>) phosphoribosylaminoimidazolecarboxamide formyltransferase and (<b>B</b>) adenylosuccinate lyase used for vaccine design underlined by red (and star symbol). Yellow color indicates insufficient data for conservation inference.</p>
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<p>The 5083 bp cloned vector of the vaccine construct.</p>
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<p>Immune system cells released after the C2 vaccine and <span class="html-italic">C. difficile</span> protein (phosphoribosylaminoimidazolecarboxamide formyltransferase and adenylosuccinate lyase) stress, including (<b>A</b>) immunoglobulins, (<b>B</b>) B cells, (<b>C</b>) TH cells, (<b>D</b>) TC cells, and (<b>E</b>) NK cells.</p>
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<p>Vaccine construct (shown in green) interaction with (<b>A</b>) HLA-A, (<b>B</b>) HLA-B, and (<b>C</b>) TLR-4. Receptors are shown in cyan. (<b>D</b>) Control Tumor necrosis factor ligand superfamily member 11 (RANK-L) and 11A (RANK) from <span class="html-italic">Mus musculus</span>.</p>
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17 pages, 2844 KiB  
Article
SARS-CoV-2 Gut-Targeted Epitopes: Sequence Similarity and Cross-Reactivity Join Together for Molecular Mimicry
by Aaron Lerner, Carina Benzvi and Aristo Vojdani
Biomedicines 2023, 11(7), 1937; https://doi.org/10.3390/biomedicines11071937 - 7 Jul 2023
Cited by 3 | Viewed by 2976
Abstract
The gastrointestinal tract can be heavily infected by SARS-CoV-2. Being an auto-immunogenic virus, SARS-CoV-2 represents an environmental factor that might play a role in gut-associated autoimmune diseases. However, molecular mimicry between the virus and the intestinal epitopes is under-investigated. The present study aims [...] Read more.
The gastrointestinal tract can be heavily infected by SARS-CoV-2. Being an auto-immunogenic virus, SARS-CoV-2 represents an environmental factor that might play a role in gut-associated autoimmune diseases. However, molecular mimicry between the virus and the intestinal epitopes is under-investigated. The present study aims to elucidate sequence similarity between viral antigens and human enteric sequences, based on known cross-reactivity. SARS-CoV-2 epitopes that cross-react with human gut antigens were explored, and sequence alignment was performed against self-antigens implicated in enteric autoimmune conditions. Experimental SARS-CoV-2 epitopes were aggregated from the Immune Epitope Database (IEDB), while enteric antigens were obtained from the UniProt Knowledgebase. A Pairwise Local Alignment tool, EMBOSS Matcher, was employed for the similarity search. Sequence similarity and targeted cross-reactivity were depicted between 10 pairs of immunoreactive epitopes. Similar pairs were found in four viral proteins and seven enteric antigens related to ulcerative colitis, primary biliary cholangitis, celiac disease, and autoimmune hepatitis. Antibodies made against the viral proteins that were cross-reactive with human gut antigens are involved in several essential cellular functions. The relationship and contribution of those intestinal cross-reactive epitopes to SARS-CoV-2 or its potential contribution to gut auto-immuno-genesis are discussed. Full article
(This article belongs to the Special Issue Molecular Mechanism for Coronavirus Infection)
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<p>A graphical representation of the workflow. <b>Data Aggregation</b>: SARS-CoV-2 epitopes were extracted from IEDB, and human antigens that are implicated in enteric ADs were depicted. UniProt was searched to retrieve proteins sequences of the enteric self-antigens. <b>Sequence Alignment</b>: Emboss Matcher was employed; 58 Similar Sequences were found with a cut-off of at least 6 identical AAs and peptide length &gt; 7. <b>Data Validation</b>: IEDB was searched to validate that the assayed enteric epitopes harbor those peptide sequences. Out of those, 10 were part of antigens that were previously identified to cross-react with SARS-CoV-2 antigens.</p>
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<p>Schematic presentation of sequence similarity that leads to cross-reactivity between SARS-CoV-2 and gut-associated ADs. (<b>A</b>) Sequence similarity between a SARS-CoV-2 epitope and a Human epitope. (<b>B</b>) Interaction of viral antigens with immune cells and activating an adaptive immune system. (<b>C</b>) Cross-reactivity at B cell level when clonal antibodies bind to viral epitopes and to similar self-epitopes. (<b>D</b>) Cross-reactivity at the T cell level involves the recognition of viral epitopes and similar self-epitopes by the same CD4 T cell. CD4 T cells initiate an immune response against SARS-CoV-2 when APC present viral epitopes on HLA-II, but the same T cells have autoreactive potential when these epitopes are similar to self-epitopes, and an immune response will be directed against host-antigens as well. (<b>E</b>) Autoreactive CD8 T cells that recognize viral antigens through MHC-I may directly cause tissue damage when these epitopes are similar to self-epitopes.</p>
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<p>Schematic presentation of sequence similarity leading to cross-reactivity between SARS-CoV-2 and self-antigens implicated in gut-associated ADs. (<b>A</b>) During homeostasis, the epithelial cells interact together and maintain tight junction functional integrity. (<b>B</b>) Epithelial cells that express ACE2 are exposed to SARS-CoV-2 infection. Upon cellular penetration, intestinal homeostasis is compromised. The damaged epithelium enhances intestinal permeability and increases local and systemic inflammation, resulting in hyperstimulation of the immune responses. (<b>C</b>) Viral antigens are detected and processed by the immune cells, and antigen-presenting cells deliver them to lymphoid tissues. (<b>D</b>) When adaptive immune cells are activated, they proliferate and migrate to the site of infection, trying to eliminate the invasive pathogen. (<b>E</b>) Cross-reactivity at the T cell level involves the recognition of viral epitopes and similar self-epitopes by the same CD4/CD8 T cell. (<b>F</b>) Cross-reactivity at the B cell level occurs when clonal antibodies bind to viral epitopes that are similar to self-epitopes. (<b>G</b>) As the viral infection spreads into enteric organs, effector T and B cells extend their defense as well. Thus, increasing their likelihood to encounter self-antigens with similar epitopes and invoking an auto-reactive attack.</p>
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19 pages, 794 KiB  
Article
Molecular Mimicry and HLA Polymorphisms May Drive Autoimmunity in Recipients of the BNT-162b2 mRNA Vaccine: A Computational Analysis
by Rossella Talotta
Microorganisms 2023, 11(7), 1686; https://doi.org/10.3390/microorganisms11071686 - 28 Jun 2023
Cited by 9 | Viewed by 2098
Abstract
Background: After the start of the worldwide COVID-19 vaccination campaign, there were increased reports of autoimmune diseases occurring de novo after vaccination. This in silico analysis aimed to investigate the presence of protein epitopes encoded by the BNT-162b2 mRNA vaccine, one of the [...] Read more.
Background: After the start of the worldwide COVID-19 vaccination campaign, there were increased reports of autoimmune diseases occurring de novo after vaccination. This in silico analysis aimed to investigate the presence of protein epitopes encoded by the BNT-162b2 mRNA vaccine, one of the most widely administered COVID-19 vaccines, which could induce autoimmunity in predisposed individuals. Methods: The FASTA sequence of the protein encoded by the BNT-162b2 vaccine served as the key input to the Immune Epitope Database and Analysis Resource. Linear peptides with 90% BLAST homology were selected, and T-cell, B-cell, and MHC-ligand assays without MHC restriction were searched and analyzed. HLA disease associations were screened on the HLA-SPREAD platform by selecting only positive markers. Results: By 7 May 2023, a total of 5693 epitopes corresponding to 21 viral but also human proteins were found. The latter included CHL1, ENTPD1, MEAF6, SLC35G2, and ZFHX2. Importantly, some autoepitopes may be presented by HLA alleles positively associated with various immunological diseases. Conclusions: The protein product of the BNT-162b2 mRNA vaccine contains immunogenic epitopes that may trigger autoimmune phenomena in predisposed individuals through a molecular mimicry mechanism. Genotyping for HLA alleles may help identify individuals at risk. However, further wet-lab studies are needed to confirm this hypothesis. Full article
(This article belongs to the Special Issue Microbial Infections and Rheumatic Diseases)
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<p>Percentage of BNT-162b2 vaccine-90% BLAST-homolog epitopes found in the spike protein of different SARS-CoV-2 strains according to IEDB analysis (last update on 7 May 2023).</p>
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<p>Potential scenario explaining some of the immune-mediated side effects following BNT-162b2 mRNA vaccine administration. Cross-reactivity between the epitopes present in the encoded spike protein and self-epitopes of proteins mostly involved in nervous system function and control of inflammation may account for cases of fatigue, neuroinflammation, neuronal dysfunction, central and peripheral pain, and systemic inflammation. Abbreviations: CHL1, neural cell adhesion molecule L1-like protein; ENTPD1, ectonucleoside triphosphate diphosphohydrolase 1; MEAF6, MYST/Esa1-associated factor 6; ZFHX2, zinc finger homeobox 2. The figure was created with BioRender.com, accessed on 18 June 2023.</p>
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16 pages, 2696 KiB  
Article
Molecular Mimicry Mapping in Streptococcus pneumoniae: Cues for Autoimmune Disorders and Implications for Immune Defense Activation
by Mutaib M. Mashraqi, Ahmad Alzamami, Norah A. Alturki, Saleh Alshamrani, Mousa M. Alshahrani, Hassan H. Almasoudi and Zarrin Basharat
Pathogens 2023, 12(7), 857; https://doi.org/10.3390/pathogens12070857 - 21 Jun 2023
Cited by 2 | Viewed by 2298
Abstract
Streptococcus pneumoniae contributes to a range of infections, including meningitis, pneumonia, otitis media, and sepsis. Infections by this bacterium have been associated with the phenomenon of molecular mimicry, which, in turn, may contribute to the induction of autoimmunity. In this study, we utilized [...] Read more.
Streptococcus pneumoniae contributes to a range of infections, including meningitis, pneumonia, otitis media, and sepsis. Infections by this bacterium have been associated with the phenomenon of molecular mimicry, which, in turn, may contribute to the induction of autoimmunity. In this study, we utilized a bioinformatics approach to investigate the potential for S. pneumoniae to incite autoimmunity via molecular mimicry. We identified 13 S. pneumoniae proteins that have significant sequence similarity to human proteins, with 11 of them linked to autoimmune disorders such as psoriasis, rheumatoid arthritis, and diabetes. Using in silico tools, we predicted the sequence as well as the structural homology among these proteins. Database mining was conducted to establish links between these proteins and autoimmune disorders. The antigenic, non-allergenic, and immunogenic sequence mimics were employed to design and validate an immune response via vaccine construct design. Mimic-based vaccine construct can prove effective for immunization against the S. pneumoniae infections. Immune response simulation and binding affinity was assessed through the docking of construct C8 to human leukocyte antigen (HLA) molecules and TLR4 receptor, with promising results. Additionally, these mimics were mapped as conserved regions on their respective proteins, suggesting their functional importance in S. pneumoniae pathogenesis. This study highlights the potential for S. pneumoniae to trigger autoimmunity via molecular mimicry and the possibility of vaccine design using these mimics for triggering defense response. Full article
(This article belongs to the Special Issue The Biology of Streptococcus and Streptococcal Infection)
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<p>The 3D superposed structures of <span class="html-italic">S. pneumoniae</span> homologs (represented in blue) with similar human proteins (represented in brown): (<b>A</b>) 6-phosphogluconate dehydrogenase (human: brown, bacteria: blue), (<b>B</b>) Chaperone DnaK, (<b>C</b>) Methionine adenosyltransferase, (<b>D</b>) Uracil-DNA glycosylase, (<b>E</b>) GTP-binding protein LepA, (<b>F</b>) V-type ATP synthase alpha chain, (<b>G</b>) V-type ATP synthase beta chain, (<b>H</b>) Translation–elongation factor Tu, and (<b>I</b>) ATP synthase F1, alpha subunit.</p>
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<p>ConSurf-mined evolutionary conservation of (<b>A</b>) DnaK and (<b>B</b>) UDP-glucose epimerase. Epitopes are underlined in red and those used for vaccine design are depicted by a red star. HLA binding alleles for MHC-I were determined as HLA-A*02:03, HLA-B*44:03, HLA-B*44:02, HLA-A*68:01, HLA-A*02:06, HLA-DQA1*01:01/DQB1*05:01, and HLA-DRB4*01:01 for MHC-II. Eight vaccine constructs were created (<a href="#app1-pathogens-12-00857" class="html-app">Supplementary Table S1</a>), and the one having the highest antigenicity and other desirable properties (C8) was selected for downstream processing.</p>
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<p>(<b>A</b>) The 3D structure of C8, (<b>B</b>) C8 docked with TLR4 human receptor, (<b>C</b>) C8 docked with HLA-A, and (<b>D</b>) C8 docked with HLA-B. All human receptors are depicted by orange and vaccine construct (ligand) is depicted by blue.</p>
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<p>(<b>A</b>) Antigen count for the response initiated by C8. (<b>B</b>) B-cell population response of C8 after immunization.</p>
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<p>Cloned vaccine construct C8 shown in red in the <span class="html-italic">E. coli</span> expression vector.</p>
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20 pages, 2675 KiB  
Article
Idiotope-Driven T-Cell/B-Cell Collaboration-Based T-Cell Epitope Prediction Using B-Cell Receptor Repertoire Sequences in Infectious Diseases
by Yukio Nakamura, Meng Ling Moi, Takashi Shiina, Tadasu Shin-I and Ryuji Suzuki
Viruses 2023, 15(5), 1186; https://doi.org/10.3390/v15051186 - 17 May 2023
Cited by 2 | Viewed by 3838
Abstract
T-cell recognition of antigen epitopes is a crucial step for the induction of adaptive immune responses, and the identification of such T-cell epitopes is, therefore, important for understanding diverse immune responses and controlling T-cell immunity. A number of bioinformatic tools exist that predict [...] Read more.
T-cell recognition of antigen epitopes is a crucial step for the induction of adaptive immune responses, and the identification of such T-cell epitopes is, therefore, important for understanding diverse immune responses and controlling T-cell immunity. A number of bioinformatic tools exist that predict T-cell epitopes; however, many of these methods highly rely on evaluating conventional peptide presentation by major histocompatibility complex (MHC) molecules, but they ignore epitope sequences recognized by T-cell receptor (TCR). Immunogenic determinant idiotopes are present on the variable regions of immunoglobulin molecules expressed on and secreted by B-cells. In idiotope-driven T-cell/B-cell collaboration, B-cells present the idiotopes on MHC molecules for recognition by idiotope-specific T-cells. According to the idiotype network theory formulated by Niels Jerne, such idiotopes found on anti-idiotypic antibodies exhibit molecular mimicry of antigens. Here, by combining these concepts and defining the patterns of TCR-recognized epitope motifs (TREMs), we developed a T-cell epitope prediction method that identifies T-cell epitopes derived from antigen proteins by analyzing B-cell receptor (BCR) sequences. This method allowed us to identify T-cell epitopes that contain the same TREM patterns between BCR and viral antigen sequences in two different infectious diseases caused by dengue virus and SARS-CoV-2 infection. The identified epitopes were among the T-cell epitopes detected in previous studies, and T-cell stimulatory immunogenicity was confirmed. Thus, our data support this method as a powerful tool for the discovery of T-cell epitopes from BCR sequences. Full article
(This article belongs to the Special Issue Virus Bioinformatics 2023)
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<p>Two forms of T-B collaboration and molecular mimicry. (<b>A</b>) Infecting antigens or antigens taken up by phagocytosis in a professional antigen-presenting cell (pAPC) are processed and presented as peptides (red) on the MHC molecule to be recognized by specific T-cells. The activated T-cells recognize a peptide (T-cell epitope) on a target cell or B-cell and exert effector functions as cytotoxic T lymphocytes (CTLs) in cellular immunity and as helper T-cells both in cellular and humoral immunity, respectively. In T-B collaboration, the helper T-cells recognize an antigen-derived peptide (red) and promote the production of antibodies against the antigen, while in idiotope (id)-driven T-B collaboration, they recognize an idiotypic peptide (red) and help B-cells produce antibodies containing ids (red). (<b>B</b>) Molecular mimicry. Antibody 1 idiotypic antibody (Ab1) is specific for an antigen (Ag). Anti-idiotypic antibody Ab2 will be induced as a consequence of idiotypic interactions and will recognize Ab1. Ab2 is specific for the paratope of Ab1, and thus, exhibits molecular mimicry with the Ag. Analysis of Ab2 sequences will thus identify Ag-derived sequences.</p>
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<p>T-cell epitope motif patterns in TREM and MAM. (<b>A</b>) Fraction of atomic contacts in TCR–peptide binding was determined per position of the peptide (see <a href="#app1-viruses-15-01186" class="html-app">Supplementary Data File S1</a> for details). The fraction of contacts with TCR relative to the total number of TCR–peptide binding contacts is shown with fractions of over 10%, highlighted in red, and the other positions with less contact with TCR are shown in blue. For each class of TCR–peptide binding, the total frequencies for the five positions with fractions of over 10% are indicated on the upper right. (<b>B</b>) One 9-mer class I and two 15-mer class II peptides (IIa and IIb) show distinct motif patterns in contact with TCR (TREM) and MHC (MAM). In the class I peptide, the central residues (P4–8) are recognized by the TCR, and the other positions are in contact with the MHC (MAM I). By contrast, for class II peptides, non-continuous amino acid residues in a peptide are exposed to the TCR (P2,3,5,7,8 for TREM IIa, and P-1,3,5,7,8 for TREM IIb), and the other positions show fewer contacts contributed to the interaction with MHC, such as MAM IIa and MAM IIb.</p>
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<p>The scheme for T-cell epitope prediction. (<b>A</b>) An overview of the T-cell epitope prediction process. (<b>B</b>) A workflow of the pipeline steps of the T-cell epitope prediction process. (<b>C</b>) A workflow of the full-length sequence analysis algorithm.</p>
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<p>Expansion of IgG and IgM clones in response to DENV infection. The number of BCR clones (IgG, upper and IgM, lower) were compared between healthy donor controls and DENV infection for each of the frequencies over 0.1%, 0.5%, and 1%. Black bars indicate the mean number of clones. Statistical significance was analyzed using the Mann–Whitney U 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, ns: not significant.</p>
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<p>Predicted TREMs in DENV infection. (<b>A</b>) Summary of the number of peptides resulting from each step of the epitope prediction for DENV. The number of clones or peptides includes all samples examined, and peptides were selected and narrowed down using Vietnam isolates as primary reference sequences at Step 8. A total of 49 TREMs were identified by analyzing 1427 clones with a frequency of ≥1% from 18 DENV patients. (<b>B</b>) Top 14 TREMs shared by more than five individuals (see also <a href="#app1-viruses-15-01186" class="html-app">Supplementary Data File S2</a> for details of all 49 TREMs). Among them, 46 TREMs were class II epitopes (IIa or IIb). TREMs shared among individuals tend to germline-typed sequences, whereas patient-unique TREMs contain SHMs and are found in CDR3 overlapping regions. A total of 43 out of 49 showed B-cell linear epitope potential. A total of 22 TREMs were found in T-cell epitopes that have previously been reported to induce T-cell activity (<a href="#app1-viruses-15-01186" class="html-app">Supplementary Data File S2</a>). Note that SHMs are also found in MAM sequences.</p>
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<p>Characterization and T-cell immunogenicity of TREM/MAM epitopes from DENV-responsive BCR clones. (<b>A</b>) Overlap of TREM sequences among DENV-1, germline IGHV, and DENV-responsive BCR clones. (<b>B</b>) Distribution of identified TREMs in the variable region of the immunoglobulin sequence. (<b>C</b>) MA plot showing alternation of MHC binding affinity (IC<sub>50</sub>) from germline MAM sequences to mutated sequences in BCR clones. M: log<sub>2</sub>(IC<sub>50</sub> of BCR peptide sequences with MAM mutated/IC<sub>50</sub> of germline BCR peptide sequences). A: 1/2(log<sub>2</sub>(IC<sub>50</sub> of BCR peptide sequences with MAM mutated) and log<sub>2</sub>(IC<sub>50</sub> of germline BCR peptide sequences). Blue (up) and red (down) points indicate increased and reduced HLA binding affinity by SHM, respectively. (<b>D</b>) IL-4-producing spot-forming cell (SFC) counts following stimulation with eight different peptides in cultured ELISPOT assays. Details of the peptides are shown in <a href="#viruses-15-01186-t003" class="html-table">Table 3</a>. PBMCs derived from two different donors were tested. Peptide sequences with TREM in bold and underlined text are indicated. (<b>E</b>) Representative cultured ELISPOT assays of T-cell responses with control (left) and DENV no. 2 peptide (HQLWATLLSLTFIKT, right). The representative images and spot counts are shown for IFN-γ and IL-4, with the mean number of spots of duplicate wells shown in parenthesis. (<b>F</b>) Ex vivo ELISPOT assays showing T-cell stimulation with a pool of eight identified peptides in PBMCs from cases with a history of DENV infection (right) but not in DMSO controls (left). The representative images and spot counts are shown for IFN-γ and IL-4.</p>
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<p>Predicted SARS-CoV-2 T-cell epitopes with previously confirmed T-cell activity. (<b>A</b>) Summary of the number of peptides resulting from each step of the epitope prediction for COVID. The number of clones or peptides include all samples examined, and peptides were selected and narrowed down using Vietnam isolates as primary reference sequences at Step 8. (<b>B</b>) A total of 26 out of 72 TREMs that were identified by analyzing 128 clones with a frequency ≥1% from 20 COVID-19 patients were among the T-cell epitopes that had previously been reported to induce T-cell activity (see also <a href="#app1-viruses-15-01186" class="html-app">Supplementary Data File S3</a> for details), and conserved across SARS-CoV-2 variants and other coronaviruses (denoted as “+”).</p>
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26 pages, 1883 KiB  
Review
Mimicry of Tumour-Associated Carbohydrates: Is It a Promising Option for Cancer Treatment?
by Valeria Inés Segatori, Gretel Magalí Ferreira, Selene Rojo, Aylen Camila Nogueira, Jeremías Omar Castillo, Cynthia Antonella Gulino and Mariano Rolando Gabri
Immuno 2023, 3(2), 122-147; https://doi.org/10.3390/immuno3020009 - 23 Mar 2023
Cited by 1 | Viewed by 3800
Abstract
Modulation of the immune system has been demonstrated as a powerful approach to treating cancer. Immunotherapies are generally classified as active or passive according to their ability to trigger the immune system. During the last decades, information regarding the relevance of aberrant glycosylation [...] Read more.
Modulation of the immune system has been demonstrated as a powerful approach to treating cancer. Immunotherapies are generally classified as active or passive according to their ability to trigger the immune system. During the last decades, information regarding the relevance of aberrant glycosylation as a major player in tumour biology encouraged expectations for the development of new therapeutic strategies directed at glycans. Several tumour-associated carbohydrate antigens (TACAs) have been identified and validated as suitable immunotherapeutic targets, leading to promising therapeutic developments. It is known that TACAs are poorly immunogenic since they are unable to trigger a proper immune response. Given that they are not presented by major histocompatibility complex (MHC) molecules and that they induce immune tolerance, the development of active immunotherapeutic strategies against TACAs is a real challenge. However, antitumor strategies based on mimetics of TACAs have been developed and show promising results. Active immunotherapies based on TACAs mimicry can currently be grouped into strategies based on the use of mimetic peptides and anti-idiotype (Id) antibodies. In this review, we discussed the scientific basis on which these strategies are based and the available therapeutic options that have shown the best results in preclinical studies and in clinical practice. Full article
(This article belongs to the Section Cancer Immunology and Immunotherapy)
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<p>Schematic representation of the altered glycan expression that occurs during tumour progression. (<bold>a</bold>) Aberrant glycosylation in malignant transformation. Tumour cells show high expression of sialylated and fucosylated glycans, overexpress glycosphingolipids, and demonstrate increased expression of immature truncated O-glycans, which are almost absent in normal cells. (<bold>b</bold>) Tumour-associated carbohydrate antigens (TACAs) can be divided into five groups according to their structural similarities: (1) The globo-series family includes Globo-H, SSEA-3, and SSEA-4. (2) Gangliosides are glycosphingolipids with at least one sialic acid as a terminal glycan. The main family members are GD2, GD3, GM2, NeuGcGM3, and FucGM1. (3) Lewis antigen structures comprise terminal Lex, SLex, Slea, and Ley. (4) Truncated O-glycans include Tn, STn, and TF antigens. (5) Polysialic acid glycans are also expressed in tumour cells.</p>
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<p>Cytotoxic cellular responses are induced after protein and glycan presentation on antigen-presenting cells. To induce an effective cytotoxic response directed at tumour cells, antigen-presenting cells process and present antigens to T and/or invariant natural killer T (iNKT) cells. T cell-dependent or thymus-dependent antigens are processed and presented on (a) major histocompatibility complex class I (MHC I) or (b) class II (MHC II) molecules to CD8+ or CD4+ T cells, respectively, depending on whether they are endogenous or extracellular proteins. CD8+ T cells will mediate the lysis of tumour or pathogen-infected cells, while the CD4+ T cell subpopulation will induce B cell differentiation into memory or antibody-secreting plasma cells, enabling the generation of antibodies and eventually a long-lasting immune response. (c) Conversely, gangliosides and glycolipids in general are presented via the CD1d receptor expressed by antigen-presenting cells. CD1d antigen presentation will further activate the iNKT cell subpopulation.</p>
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<p>Schematic representation of the active immunotherapies directed at TACAs that have reached preclinical or clinical stages so far. (<bold>a</bold>) Therapeutic approaches based on anti-idiotype (Id) antibodies. Ab1, which recognises GD3, GD2, and NeuGc-containing glycoconjugates was used to develop BEC2, ganglidiomab, TriGem, and racotumomab anti-Id antibodies. When administered in patients formulated with their specific adjuvants, all of these anti-Id antibodies demonstrated the induction of specific Ab3 able to recognize the nominal TACA. (<bold>b</bold>) Mimetic peptide-based therapeutic strategies. GRL and DGG are circularised decamers that mimic GD2 ganglioside. Each decamer is conjugated to KLH and finally adjuvanted in aluminium hydroxide (alum). The administration of the DNA vaccine 47-LDA induces the synthesis of a GD2 mimetic peptide when combined with an additional plasmid containing IL-15 and IL-21 sequences as immunostimulators. The mimetic peptide GD3P4 resembles the GD3 ganglioside structure. When formulated in MONTANIDETM ISA 51, is able to induce an anti-GD3 humoral response. Regarding immature truncated O-glycans, the so-called D2 strategy is based on 15-mer peptides in a single molecule, which are recognized by a TF-specific lectin. Its final formulation with alum and inactivated B. pertussis induces anti-TF antibodies. Finally, the mimetic peptide P10s conjugated with the pan-T cell carrier PADRE and MONTANIDETM ISA 51 as an adjuvant induces an immune response mainly against GD2 and Ley when administered to patients. P10s-PADRE is the first mimetic peptide to reach clinical trials and show promising results since it also induces a cellular immune response.</p>
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9 pages, 1709 KiB  
Article
Molecular Mimicry between SARS-CoV-2 and Human Endocrinocytes: A Prerequisite of Post-COVID-19 Endocrine Autoimmunity?
by Leonid P. Churilov, Muslimbek G. Normatov and Vladimir J. Utekhin
Pathophysiology 2022, 29(3), 486-494; https://doi.org/10.3390/pathophysiology29030039 - 25 Aug 2022
Cited by 27 | Viewed by 4055
Abstract
Molecular mimicry between human and microbial/viral/parasite peptides is common and has long been associated with the etiology of autoimmune disorders provoked by exogenous pathogens. A growing body of evidence accumulated in recent years suggests a strong correlation between SARS-CoV-2 infection and autoimmunity. The [...] Read more.
Molecular mimicry between human and microbial/viral/parasite peptides is common and has long been associated with the etiology of autoimmune disorders provoked by exogenous pathogens. A growing body of evidence accumulated in recent years suggests a strong correlation between SARS-CoV-2 infection and autoimmunity. The article analyzes the immunogenic potential of the peptides shared between the SARS-CoV-2 spike glycoprotein (S-protein) and antigens of human endocrinocytes involved in most common autoimmune endocrinopathies. A total of 14 pentapeptides shared by the SARS-CoV-2 S-protein, thyroid, pituitary, adrenal cortex autoantigens and beta-cells of the islets of Langerhans were identified, all of them belong to the immunoreactive epitopes of SARS-CoV-2. The discussion of the findings relates the results to the clinical correlates of COVID-19-associated autoimmune endocrinopathies. The most common of these illnesses is an autoimmune thyroid disease, so the majority of shared pentapeptides belong to the marker autoantigens of this disease. The most important in pathogenesis of severe COVID-19, according to the authors, may be autoimmunity against adrenals because their adequate response prevents excessive systemic action of the inflammatory mediators causing cytokine storm and hemodynamic shock. A critique of the antigenic mimicry concept is given with an assertion that peptide sharing is not a guarantee but only a prerequisite for provoking autoimmunity based on the molecular mimicry. The latter event occurs in carriers of certain HLA haplotypes and when a shared peptide is only used in antigen processing Full article
(This article belongs to the Special Issue Mosaic of Autoimmunity)
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<p>Konstantin Sergeevich Merezhkovsky (aka: Mereschkowski, Mereschkowsky), an originator of the symbiogenetic theory and antigen mimicry ideas (Photo – public domain. Original portrait of 1885 is preserved at Saint Petersburg State University Zoological Museum).</p>
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<p>Molecular models showing the location of the identified shared pentapeptides (according to AlphaFold and PDB databases). Pentapeptides are shown in frames: (<b>A</b>) LPPLL; (<b>B</b>) GYQPY; (<b>C</b>) LDPLS; (<b>D</b>) AGAAL; (<b>E</b>) VGYQP; (<b>F</b>) SALLA; (<b>G</b>) LQDVV; (<b>H</b>) RAAEI; (<b>I</b>) ICGDS; (<b>J</b>) FNFSQ; (<b>K</b>) SAIGK; (<b>L</b>) SNLLL.</p>
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11 pages, 1408 KiB  
Article
Antibodies towards TVLLPVIFF Amino Acid Sequence of TNF Receptor Induced by Helicobacter pylori in Patients with Coronary Heart Disease
by Weronika Gonciarz, Agata Tomaszewska, Agnieszka Krupa, Tomasz Rechciński, Maciej Chałubiński, Marlena Broncel and Magdalena Chmiela
J. Clin. Med. 2022, 11(9), 2545; https://doi.org/10.3390/jcm11092545 - 1 May 2022
Cited by 5 | Viewed by 2196
Abstract
Background: Molecular mimicry between Helicobacter pylori (Hp) and the host components resulting in induction of cross-reacting antibodies has been suggested as accessory mechanism in the development of coronary heart disease (CHD). A potential target for antibodies induced during Hp infection by the components [...] Read more.
Background: Molecular mimicry between Helicobacter pylori (Hp) and the host components resulting in induction of cross-reacting antibodies has been suggested as accessory mechanism in the development of coronary heart disease (CHD). A potential target for antibodies induced during Hp infection by the components of these bacteria might be amino acid sequence TVLLPVIFF (P1) of tumor necrosis factor receptor (TNFR), which is exposed on vascular endothelium and immunocompetent cells, driving inflammation. Aim: To examine whether anti-P1 IgG are induced during Hp infection in CHD patients. Methods: Sera from CHD patients infected with Hp (54) vs. sera of uninfected healthy donors (22) were tested by the ELISA for anti-H. pylori antibodies, anti-P1 IgG, and for antibodies towards control sequence IAKEGFEKIS (P2). Sera of Caviae porcellus infected experimentally with Hp (30) or uninfected (10) were included into this study. The same serum samples, which were positive for anti-P1 IgG, were adsorbed with Hp and then subjected to the ELISA. The biological activity of anti-P1 IgG was assessed in complement (C1q) binding assay. Results: Sera of 43 CHD patients seropositive for anti-Hp IgG contained anti-P1 IgG binding C1q. Additionally, 10 serum samples of animals seropositive for anti-Hp IgG contained anti-P1 IgG. Anti-P1 IgG in tested sera were neutralized by their adsorption with Hp. Conclusion: In CHD patients infected with Hp, antibodies cross-reacting with TNFR common sequence are produced. Further studies are necessary to define immunogenic Hp determinants and to confirm possible cellular effects of cross-reacting antibodies. Full article
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<p>The prevalence and the level of antibodies in human sera. (<b>A</b>) IgG antibodies towards <span class="html-italic">H. pylori</span> glycine extract (GE) (<b>i</b>) Healthy donors (HD) seronegative for anti-GE IgG–HD Hp- (n = 22), patients with coronary heart disease (CHD) seropositive for anti-GE IgG–CHD Hp+ (n = 54); (<b>B</b>) anti-P1 IgG (<b>i</b>) and anti-P2 IgG (<b>ii</b>); the level of anti-P1 IgG before and after the adsorption of sera from CHD Hp+ patients with heat-inactivated <span class="html-italic">H. pylori</span> (<b>iii</b>). Shown are mean values ± standard deviation (SD). P1, synthetic peptide with the amino acid sequence (TVLLPLVIFF) present in human tumor necrosis factor receptor (TNFR); P2, control peptide (IAKEGFEKIS). The dot in the figure represents an individual data. Statistical significance: CHD(Hp+) vs. HD (Hp-) (Ai,Bi), CHD(Hp+) sera before vs. sera after adsorption with <span class="html-italic">H. pylori</span>, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>The prevalence and the level of antibody production in <span class="html-italic">Caviae porcellus</span> model. In <span class="html-italic">Caviae porcellus</span> infected experimentally with <span class="html-italic">H. pylori,</span> the enzyme-linked immunosorbent assay (ELISA) was used to examine serum samples for anti-GE IgG (<b>Ai</b>) and anti-P1/P2 IgG (<b>Bi</b>,<b>Bii</b>). Anti-P1 antibodies in serum samples of <span class="html-italic">H. pylori</span> infected animals, 7 and 28 days from inoculation, non-adsorbed or adsorbed with these bacteria (<b>Biii</b>). Sera were collected from control animals noninfected with <span class="html-italic">H. pylori</span>, n = 10, and from animals infected with <span class="html-italic">H. pylori</span>, 7 days, n = 10 or 28 days, n = 20, after the last inoculation with <span class="html-italic">H. pylori</span>. Shown are mean values ± standard deviation (SD). P1, synthetic peptide with the amino acid sequence (TVLLPLVIFF) of <span class="html-italic">Caviae porcellus</span> tumor necrosis factor receptor (TNFR); P2, control synthetic peptide (IAKEGFEKIS). GE, glycine acid extract from the reference <span class="html-italic">H. pylori</span> strain. The dot, square or triangle in the figure represent an individual data. Statistical significance: sera from <span class="html-italic">Caviae porcellus</span> infected with <span class="html-italic">H. pylori</span> vs. sera from non-infected animals (<b>Ai</b>,<b>Bi</b>,<b>Biii</b>) sera before and after adsorption * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Detection of anti-P1-antibodies, which bind C1q subunit of complement, by the ELISA in serum samples of <span class="html-italic">Caviae porcellus</span> infected with <span class="html-italic">H. pylori.</span> Serum samples were added to microtiter plates coated with P1 peptide, and after binding with anti-P1 antibodies, the plates were incubated with C1q. The P1-anti-P1 IgG-C1q complexes were detected using anti-C1q antibodies labeled with horseradish peroxidase (HRP). The sera were collected from control animals noninfected with <span class="html-italic">H. pylori</span>, n = 10, or infected with these bacteria, 7 days, n = 10 or 28 days, n = 20, after the last inoculation with <span class="html-italic">H. pylori</span>. Shown are mean values ± standard deviation (SD). P1, synthetic peptide containing the amino acid sequence (TVLLPLVIFF) of <span class="html-italic">Caviae porcellus</span> tumor necrosis factor receptor (TNFR). The square in the figure represents an individual data. Statistical significance: * <span class="html-italic">p</span> &lt; 0.05.</p>
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18 pages, 2229 KiB  
Article
Organ Specific Copy Number Variations in Visceral Metastases of Human Melanoma
by Orsolya Papp, Viktória Doma, Jeovanis Gil, György Markó-Varga, Sarolta Kárpáti, József Tímár and Laura Vízkeleti
Cancers 2021, 13(23), 5984; https://doi.org/10.3390/cancers13235984 - 28 Nov 2021
Cited by 7 | Viewed by 3085
Abstract
Malignant melanoma is one of the most aggressive skin cancers with high potential of visceral dissemination. Since the information about melanoma genomics is mainly based on primary tumors and lymphatic or skin metastases, an autopsy-based visceral metastasis biobank was established. We used copy [...] Read more.
Malignant melanoma is one of the most aggressive skin cancers with high potential of visceral dissemination. Since the information about melanoma genomics is mainly based on primary tumors and lymphatic or skin metastases, an autopsy-based visceral metastasis biobank was established. We used copy number variation arrays (N = 38 samples) to reveal organ specific alterations. Results were partly completed by proteomic analysis. A significant increase of high-copy number gains was found in an organ-specific manner, whereas copy number losses were predominant in brain metastases, including the loss of numerous DNA damage response genes. Amplification of many immune genes was also observed, several of them are novel in melanoma, suggesting that their ectopic expression is possibly underestimated. This “immunogenic mimicry” was exclusive for lung metastasis. We also provided evidence for the possible autocrine activation of c-MET, especially in brain and lung metastases. Furthermore, frequent loss of 9p21 locus in brain metastases may predict higher metastatic potential to this organ. Finally, a significant correlation was observed between BRAF gene copy number and mutant allele frequency, mainly in lung metastases. All of these events may influence therapy efficacy in an organ specific manner, which knowledge may help in alleviating difficulties caused by resistance. Full article
(This article belongs to the Special Issue Metastatic Progression of Human Melanoma)
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Figure 1
<p>CNV landscape of examined melanoma samples: (<b>A</b>) primary tumors vs. distant melanoma metastases, (<b>B</b>) distinct distant metastatic sites (brain vs. liver vs. lung). Blue and red colors indicate copy number gains and losses, respectively.</p>
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<p>Distribution of CNV types between primary tumors and distant metastases from distinct sites. Asterisk means the level of significance (* <span class="html-italic">p</span> ≤ 0.05). Kruskal–Wallis test was used for the multiple group comparisons, and Mann–Whitney–Wilcoxon test was applied for the primary vs. all metastasis analysis. Abbreviations: CNG, copy number gain (CN &gt; 2); CNL, copy number loss (CN &lt; 2); LOH, loss of heterozygosity; hCNG, high-copy number gain (CN ≥ 4); lCNG, low-copy number gain (4 &gt; CN &gt; 2); hoCNL, homozygous copy number loss (CN = 0); heCNL, heterozygous copy number loss (2 &gt; CN &gt; 0).</p>
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<p>Copy number loss (CNL) frequency in different melanoma metastases affecting genes coding proteins in DDR subpathways. Radar chart represents the CNL frequency in primary melanomas and visceral metastases. In brackets, we indicated the count of samples resected from primary tumors and a given metastatic site. The vertical axis represents the number of genes altered in any of the DDR subpathways. Table shows the frequency (percentage) of the TOP6 DDR genes affected in melanoma. Abbreviations: DDR, DNA damage repair; BER, base excision repair; HR, homologous recombination; MMR, mismatch repair, NER, nucleotide excision repair.</p>
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<p>Shared and specific chromosomal regions distorted by high-copy number gains (CN ≥ 4) and homozygous losses. Blue and red arrows represent copy number gains and number losses, respectively.</p>
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<p>Relative abundance levels of the transcripts differentially expressed in at least one group of metastases relative to primary melanomas. Asterix represents the level of significance (* <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) using analysis of variance (ANOVA) test adjusted with the Benjamini–Hochberg method and an additional Tukey–Kramer post hoc test for each ANOVA analysis.</p>
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<p>Relative abundance levels of proteins with significant differences between sample origin-based groups in the proteomics cohorts. (<b>A</b>) Proteins with significant differences between at least two groups from the prospective cohort. (<b>B</b>) Proteins with significant differences between at least two groups from the postmortem cohort. Asterisks represent the level of significance (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01) using Analysis of Variance (ANOVA) test adjusted with the Benjamini–Hochberg method and an additional Tukey–Kramer post hoc test for each ANOVA analysis.</p>
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19 pages, 4039 KiB  
Article
Commensal Bifidobacterium Strains Enhance the Efficacy of Neo-Epitope Based Cancer Vaccines
by Michele Tomasi, Mattia Dalsass, Francesco Beghini, Ilaria Zanella, Elena Caproni, Laura Fantappiè, Assunta Gagliardi, Carmela Irene, Enrico König, Luca Frattini, Giulia Masetti, Samine Jessica Isaac, Federica Armanini, Fabio Cumbo, Aitor Blanco-Míguez, Alberto Grandi, Nicola Segata and Guido Grandi
Vaccines 2021, 9(11), 1356; https://doi.org/10.3390/vaccines9111356 - 18 Nov 2021
Cited by 14 | Viewed by 4304
Abstract
A large body of data both in animals and humans demonstrates that the gut microbiome plays a fundamental role in cancer immunity and in determining the efficacy of cancer immunotherapy. In this work, we have investigated whether and to what extent the gut [...] Read more.
A large body of data both in animals and humans demonstrates that the gut microbiome plays a fundamental role in cancer immunity and in determining the efficacy of cancer immunotherapy. In this work, we have investigated whether and to what extent the gut microbiome can influence the antitumor activity of neo-epitope-based cancer vaccines in a BALB/c-CT26 cancer mouse model. Similarly to that observed in the C57BL/6-B16 model, Bifidobacterium administration per se has a beneficial effect on CT26 tumor inhibition. Furthermore, the combination of Bifidobacterium administration and vaccination resulted in a protection which was superior to vaccination alone and to Bifidobacterium administration alone, and correlated with an increase in the frequency of vaccine-specific T cells. The gut microbiome analysis by 16S rRNA gene sequencing and shotgun metagenomics showed that tumor challenge rapidly altered the microbiome population, with Muribaculaceae being enriched and Lachnospiraceae being reduced. Over time, the population of Muribaculaceae progressively reduced while the Lachnospiraceae population increased—a trend that appeared to be retarded by the oral administration of Bifidobacterium. Interestingly, in some Bacteroidales, Prevotella and Muribaculacee species we identified sequences highly homologous to immunogenic neo-epitopes of CT26 cells, supporting the possible role of “molecular mimicry” in anticancer immunity. Our data strengthen the importance of the microbiome in cancer immunity and suggests a microbiome-based strategy to potentiate neo-epitope-based cancer vaccines. Full article
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<p>Effect of peptide-adsorbed OMV vaccination and Bifidobacterium administration on tumor growth inhibition. (<b>A</b>) Schematic representation of immunization schedule. Four groups of BALB/c mice (5 mice/group, two independent experiments) were injected with 1.5 × 10<sup>5</sup> CT26 cells (“C”) into the right flank. The day after challenge, two groups were vaccinated (“I”) every three days with 20 μg OMVs while the other two groups were injected with OMVs + pentatope (20 μg OMVs + M03, M20, M27, M68, M26 peptides, 20 μg each). At day 7 and 14 from tumor challenge, mice from one group immunized with OMVs and one group immunized with OMVs + Pentatope were given two oral gavages (“G”) of Bifidobacterium cocktail. X, end of the experiment. (<b>B</b>) Analysis of tumor development. The tumor growth in mice treated as described in (<b>A</b>) was followed by measuring the tumor volume (mean ± s.e.m.) over a period of 22 days from challenge. (<b>C</b>) Frequency of pentatope-specific CD4<sup>+</sup> and CD8<sup>+</sup> T cells. At the end of the experiment, spleens were collected from each mouse and the frequency of IFN-γ-producing, pentatope-specific CD4<sup>+</sup> and CD8<sup>+</sup> T cells was determined by flow cytometry after splenocyte stimulation with pentatope peptides (see Materials and Methods for details). Statistical analysis was performed using ANOVA and unpaired, one-tailed Student’s <span class="html-italic">t</span>-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. ns, not significant.</p>
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<p>Effect of engineered OMV vaccination and Bifidobacterium administration on tumor growth inhibition. (<b>A</b>) SDS-PAGE analysis of engineered OMVs. M03, M20, M27, M68, M26 peptides were fused to the C-terminus of FhuD2 and the plasmids expressing the fusion proteins were used to transform <span class="html-italic">E. coli</span> BL21(DE3)ΔompA strain. OMVs were purified from the supernatant of the recombinant clones and 20 μg of each OMV preparation were analyzed by SDS-PAGE. Stars indicate the bands corresponding to each fusion protein. (<b>B</b>) Schematic representation of animal treatment. Four groups of BALB/c mice (5 mice/group, two independent experiments) were injected with 1.5 × 10<sup>5</sup> CT26 cells (“C”) into the right flank. The day after challenge, two groups were vaccinated (“I”) every three days with 20 μg of OMVs while the other two groups were injected with a mixture (4 μg each) of the five engineered OMVs (20 μg in total). At day 7 and 14 from tumor challenge, mice from one group immunized (“I”) with OMVs and one group immunized with engineered OMVs were given two oral gavages of Bifidobacterium cocktail. T1-7 correspond to the days when fecal samples were collected for microbiome analysis (see text and <a href="#vaccines-09-01356-f003" class="html-fig">Figure 3</a>). X, end of the experiment. (<b>C</b>) Analysis of tumor development. The tumor growth in mice treated as described in (<b>A</b>) was followed by measuring the tumor volume (mean ± s.e.m.) over a period of 22 days from challenge. Statistical analysis was performed using ANOVA and unpaired, one-tailed Student’s <span class="html-italic">t</span>-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. ns, not significant.</p>
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<p>Microbiome variations identified by 16s rRNA sequencing. (<b>A</b>–<b>C</b>) Alteration of microbiome composition at tumor injection (T1/T2 vs. T3) as measured by alpha diversity index Chao1 (<b>A</b>), multidimensional scaling (MDS) on samples using the Weighted UniFrac measure (<span class="html-italic">p</span> = 0.001, PERMANOVA with 999 permutations) (<b>B</b>) and relative abundance distribution of key bacteria (<b>C</b>). This later analysis shows an increase in relative abundance of bacteria belonging to the <span class="html-italic">Muribaculaceae</span> group (<span class="html-italic">q</span>-value = 4.2 × 10<sup>−5</sup> Wald test) and a decrease in the genus <span class="html-italic">Lachnospiraceae NK4A136</span> (<span class="html-italic">q</span>-value = 0.026 Wald test). (<b>D</b>–<b>E</b>) Comparison of gut microbiome in mice at T3 and T7. Multidimensional scaling (MDS) on samples using the Weighted UniFrac measure showed a statistically significant difference (<span class="html-italic">p</span> = 0.001, PERMANOVA with 999 permutations). (<b>D</b>) Differentially abundant species between T3 and T7 (Wilcoxon test). Only species found to be significantly differentially abundant after false discovery-rate error correction (<span class="html-italic">p<sub>adj</sub></span> &lt; 0.05) and with a median relative abundance &gt;0.01 are reported. (<b>E</b>,<b>F</b>) Boxplot of relative abundances of <span class="html-italic">Muribaculaceae</span> across timepoints and separated by treatment. <span class="html-italic">Muribaculaceae</span> was altered after administrating the <span class="html-italic">Bifidobacterium</span> in mice vaccinated with nondecorated vesicles. VPB: Pentatope OMVs + <span class="html-italic">Bifidobacterium</span> gavage; VPP: pentatope OMVs; VVB: empty OMVs + <span class="html-italic">Bifidobacterium</span> gavage; VVP: empty OMVs.</p>
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<p>Microbiome variations identified by shotgun metagenomic sequencing. (<b>A</b>) Alteration of microbiome composition at tumor injection (T1/T2 vs. T3) as measured by multidimensional scaling (MDS) on samples using Bray–Curtis distance. (<b>B</b>) Comparison of gut microbiome in mice at T3 and T7. Multidimensional scaling (MDS) on samples using Bray–Curtis distance. (<b>C</b>) Differentially abundant species between T3 and T7 (Wilcoxon test). Only species found to be significantly differentially abundant after false discovery rate error correction (<span class="html-italic">p<sub>adj</sub></span> &lt; 0.05) and with a median relative abundance &gt; 0.01 are reported. VPB: Pentatope OMVs + <span class="html-italic">Bifidobacterium</span> gavage; VPP: pentatope OMVs; VVB: empty OMVs + <span class="html-italic">Bifidobacterium</span> gavage; VVP: empty OMVs.</p>
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<p>Heatmap representing abundances of 4 MMEs and bacterial species in which each MME is present. Each MME was selected for the presence of the CD8 CT26 tumor mutation, presence in at least 5 mice, with a predicted MHC I-binding percentile rank less than 0.5 and an identity higher than 60% against the CT26 CD8 neo-epitopes. On the left side are reported MME’s abundances expressed as reads per million. On the right side are reported the species in which that specific MME was found (black) inside the assembled metagenome. VPB: Pentatope OMVs + Bifidobacterium gavage; VPP: pentatope OMVs; VVB: empty OMVs + Bifidobacterium gavage; VVP: empty OMVs.</p>
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23 pages, 3610 KiB  
Article
All-Trans Retinoic Acid Stimulates Viral Mimicry, Interferon Responses and Antigen Presentation in Breast-Cancer Cells
by Marco Bolis, Gabriela Paroni, Maddalena Fratelli, Arianna Vallerga, Luca Guarrera, Adriana Zanetti, Mami Kurosaki, Silvio Ken Garattini, Maurizio Gianni’, Monica Lupi, Linda Pattini, Maria Monica Barzago, Mineko Terao and Enrico Garattini
Cancers 2020, 12(5), 1169; https://doi.org/10.3390/cancers12051169 - 6 May 2020
Cited by 19 | Viewed by 4326
Abstract
All-trans retinoic acid (ATRA), a recognized differentiating agent, has significant potential in the personalized/stratified treatment of breast cancer. The present study reports on the molecular mechanisms underlying the anti-tumor activity of ATRA in breast cancer. The work is based on transcriptomic experiments performed [...] Read more.
All-trans retinoic acid (ATRA), a recognized differentiating agent, has significant potential in the personalized/stratified treatment of breast cancer. The present study reports on the molecular mechanisms underlying the anti-tumor activity of ATRA in breast cancer. The work is based on transcriptomic experiments performed on ATRA-treated breast cancer cell-lines, short-term tissue cultures of patient-derived mammary-tumors and a xenograft model. ATRA upregulates gene networks involved in interferon-responses, immune-modulation and antigen-presentation in retinoid-sensitive cells and tumors characterized by poor immunogenicity. ATRA-dependent upregulation of these gene networks is caused by a viral mimicry process, involving the activation of endogenous retroviruses. ATRA induces a non-canonical type of viral mimicry, which results in increased expression of the IRF1 (Interferon Responsive Factor 1) transcription factor and the DTX3L (Deltex-E3-Ubiquitin-Ligase-3L) downstream effector. Functional knockdown studies indicate that IRF1 and DTX3L are part of a negative feedback loop controlling ATRA-dependent growth inhibition of breast cancer cells. The study is of relevance from a clinical/therapeutic perspective. In fact, ATRA stimulates processes controlling the sensitivity to immuno-modulatory drugs, such as immune-checkpoint-inhibitors. This suggests that ATRA and immunotherapeutic agents represent rational combinations for the personalized treatment of breast cancer. Remarkably, ATRA-sensitivity seems to be relatively high in immune-cold mammary tumors, which are generally resistant to immunotherapy. Full article
(This article belongs to the Special Issue Targeted Cancer Therapy)
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<p>Characteristics and all-trans retinoic acid (ATRA)-sensitivity of the breast cancer cell-lines (<b>A</b>) The dendrogram illustrates the unsupervised hierarchical clustering of the indicated 16 breast cancer cell-lines based on the constitutive whole-genome gene-expression profiles determined by RNA-sequencing (<span class="html-italic">RNA-seq</span>) analysis. The cell-lines are also classified according to the estrogen receptor, <span class="html-italic">HER2</span> receptor (<span class="html-italic">ERBB2</span>, erb-b2 receptor tyrosine kinase 2) and morphological characteristics into 4 groups: <span class="html-italic">ER<sup>+</sup></span> = estrogen receptor positive, <span class="html-italic">HER2<sup>+</sup></span> = HER2 positive, <span class="html-italic">TNBC</span> = triple-negative breast cancer, <span class="html-italic">TNBC-mes</span> = triple-negative breast cancer with a mesenchymal phenotype. (<b>B</b>) The indicated cell-lines are ranked according to their sensitivity to the anti-proliferative action of ATRA using the <span class="html-italic">ATRA-score</span> index. The higher the <span class="html-italic">ATRA-score</span> value, the higher the sensitivity of the cell-line to ATRA. Basal cell-lines are indicated with a square, while luminal cell-lines are indicated with a circle. Cell-lines are classified according to a high, intermediate and low sensitivity to ATRA, as shown.</p>
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<p>Interaction networks of the genes up- and downregulated by ATRA in the retinoid-sensitive cell-lines. The 754 genes whose up- or downregulation is proportional to ATRA-sensitivity were used to construct an interaction network based on the encoded proteins (STRING database, <a href="https://string-db.org" target="_blank">https://string-db.org</a>). The 2 upregulated modules (<span class="html-italic">MHC Class-I Loading and presentation</span>; <span class="html-italic">Interferon Response</span>) can be further condensed into a single group, as indicated by a red circle. The single genes belonging to this module are indicated by red dots. The higher the average upregulation afforded by ATRA, the larger the size of each red dot. The 4 downregulated modules (<span class="html-italic">Cell-cycle</span>, <span class="html-italic">Histone-cluster I</span>, <span class="html-italic">Chromatin-packaging/organization</span>, <span class="html-italic">DNA-repair</span>) can be further clustered into a single group marked by a blue circle. The single genes belonging to this module are indicated by blue dots. The lower the fold-change observed following ATRA treatment, the larger the size of each blue dot. The heat-maps of the ATRA/DMSO fold changes observed for the genes belonging to the up- (right) and downregulated (left) interaction networks in each cell-line are shown. The cell-lines are ranked from left to right according to their decreasing sensitivity to ATRA (decreasing <span class="html-italic">ATRA-score</span> value). Luminal cell-lines are indicated in red, while basal cell-lines are marked in blue. The <span class="html-italic">IRF1</span> and <span class="html-italic">DTX3L</span> mRNAs are marked with a red circle.</p>
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<p>Gene set enrichment analysis of the RNA-seq results obtained in breast cancer cell-lines and <span class="html-italic">HCC-1599</span> xenografts. (<b>A</b>) Gene set enrichment analysis was performed on the genes whose up- or downregulation is proportional to ATRA-sensitivity using the HALLMARK gene sets. The top 18 enriched gene sets are shown. The blue and red columns indicate the gene sets collectively down- and upregulated by ATRA, respectively. (<b>B</b>) <span class="html-italic">HCC-1599</span> cells were transplanted subcutaneously in nude mice. Xenografted animals were treated with two daily doses of vehicle or ATRA (15 mg/kg) orally according to the scheme illustrated in [<a href="#B9-cancers-12-01169" class="html-bibr">9</a>]. Twenty-four h following the last treatment, the <span class="html-italic">HCC-1599</span> xenografts of 3 ATRA-treated and 3 vehicle-treated animals were subjected to whole-genome gene-expression microarray analysis. Genes significantly up- and downregulated by ATRA were subjected to gene set enrichment analysis using the HALLMARK gene sets. The blue and red columns indicate the gene sets collectively down- and upregulated by ATRA, respectively. The horizontal line indicates the FDR (False Discovery Rate) threshold value considered. Green = Gene sets involved in the control of proliferation/cell-cycle; Orange = Gene sets involved in the control of the Myc-pathway; Violet = Gene sets involved in interferon/inflammatory responses.</p>
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<p>ATRA-related upregulation of antigen-presentation and interferon-α genes in breast cancer. (<b>A</b>) The left graph indicates the enrichment of the two indicated gene networks. The horizontal line indicates the FDR (False Discovery Rate) threshold value. The heat-map on the right illustrates the effects of ATRA on the “<span class="html-italic">MHC1-APPL</span>” (<span class="html-italic">Antigen-Presentation Folding Assembly and Peptide-Loading of Class-I MHC</span>) gene set. The cell-lines are ordered according to the <span class="html-italic">ATRA-score</span> from left to right. The <span class="html-italic">ATRA-score</span> values are shown above the heat-map. Luminal and basal cell-lines are marked in red and blue, respectively. (<b>B</b>) The left graph illustrates the ATRA-dependent enrichment of “<span class="html-italic">IFNα-Response</span>” and “<span class="html-italic">MHC1-APPL</span>” gene sets in the xenografts of <span class="html-italic">HCC-1599</span> cells. The two heat-maps on the right compare the effects of ATRA on the “<span class="html-italic">MHC1-APPL</span>” gene set in <span class="html-italic">HCC-1599</span> cell-cultures and xenografts. (<b>C</b>) Using the CCLE (Cancer Cell-Line Encyclopedia) <span class="html-italic">RNA-seq</span> data, the constitutive expression levels of the “<span class="html-italic">IFNα-Response</span>”, “<span class="html-italic">MHC1-APPL</span>” and <span class="html-italic">“Fatty Acid Metabolism”</span> (negative control) gene sets were evaluated using the Single-Sample Gene Set Enrichment Analysis (<span class="html-italic">ssGSEA-score</span>) on 47 breast cancer cell-lines (<a href="#app1-cancers-12-01169" class="html-app">Supplementary Figure S8</a>). The data are shown following clustering of the cell-lines in two groups characterized by <span class="html-italic">ATRA-scores</span> above (HIGH-ATRA-sensitivity) and below (LOW-ATRA-sensitivity) a 0.17 threshold value (<a href="#app1-cancers-12-01169" class="html-app">Supplementary Figure S8</a>). Statistical significance was calculated with the pairwise <span class="html-italic">t</span>-test corrected for multiple comparisons (<span class="html-italic">n</span> = 3), using the Benjamini–Hochberg correction method. (<b>D</b>) The panel illustrates the whole-genome gene-expression results of patient-derived tumor tissue slices exposed to ATRA (1 μM) or vehicle (DMSO) for 48 h. The column graph on the left illustrates the ATRA-dependent enrichment of “<span class="html-italic">IFNα-Response</span>” and “<span class="html-italic">MHC1-APPL</span>” gene sets in the tissue slices. The horizontal line indicates the FDR threshold value. The heatmap on the right illustrates the ATRA-dependent expression of the <span class="html-italic">“IFNα-Response”</span> and <span class="html-italic">“MHC1-APPL”</span> genes. The response to the anti-proliferative effects of ATRA was determined by measurement of the <span class="html-italic">Ki-67</span> proliferation marker in the tissue slices exposed to DMSO/ATRA and the results obtained are illustrated by the column bar-graph shown above the heatmap on the right. ** Significantly different relative to the corresponding DMSO-treated control value (<span class="html-italic">p</span> &lt; 0.01 following a two-way <span class="html-italic">t</span>-test analysis). * Significantly different relative to the corresponding DMSO-treated control value (<span class="html-italic">p</span> &lt; 0.05 following a two-way <span class="html-italic">t</span>-test analysis). When tumor cells are characterized by a luminal phenotype, the patient number (No.) is marked in red. Conversely, the patient No. is marked in blue if tumor cells are characterized by a basal phenotype. The following breast cancer subtypes were diagnosed by the pathologist: Patient 36 = Luminal B, Patient 64 = Luminal B, Patient 31 = triple-negative, Patient 67 = HER2-positive (positive for <span class="html-italic">ERBB2</span>, erb-b2 receptor tyrosine kinase 2), Patient 23 = triple-negative, Patient 41 = Luminal A, Patient 61 = Luminal B, Patient 44 = Luminal A, Patient 22 = triple-negative. (<b>E</b>) Using the <span class="html-italic">RNA-seq</span> data of the breast cancer cases of the TCGA dataset, we predicted ATRA-sensitivity using the <span class="html-italic">ATRA-21</span> model. The diagrams illustrate the correlations between the <span class="html-italic">ATRA-21</span> values and the <span class="html-italic">ssGSEA-scores</span>.</p>
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<p>Effects of ATRA on the immunophenograms, endogenous retroviral elements and viral mimicry upstream regulators of breast cancer cell-lines. (<b>A</b>) The indicated cell-lines were treated with vehicle (DMSO) or ATRA (1 μM) for 24 h. The circle plots show the effects exerted by ATRA on the immunophenograms derived from the RNA-seq data. The <span class="html-italic">ATRA-score</span> values are shown in parenthesis. (<b>B</b>,<b>C</b>) The indicated breast cancer cell-lines were exposed to vehicle (DMSO) or ATRA (1 μM) for 24 h. (<b>B</b>) The box-plots show the effects exerted by ATRA on the expression of individual RNAs deriving from endogenous retroviruses (<span class="html-italic">ENV</span>). For each cell-line, the points represent the mean (<span class="html-italic">n</span> = 3) ATRA/DMSO fold-change values of the single RNAs determined. Within the luminal and basal groups, cell-lines are positioned according to a decreasing <span class="html-italic">ATRA-score</span> from left to right, as indicated. * Significant ATRA-dependent up- or downregulation (<span class="html-italic">p</span>-value &lt; 0.1); *** Significant ATRA-dependent upregulation (<span class="html-italic">p</span>-value &lt; 0.001). (<b>C</b>) The diagrams illustrate the correlations between the effects exerted by ATRA on <span class="html-italic">ENV</span>-derived mRNAs and the <span class="html-italic">ATRA-scores</span> determined in luminal and basal cell-lines. ATRA/DMSO fold-change <span class="html-italic">ENV</span> RNAs = fold-change of the median expression values of the 42 classes of <span class="html-italic">ENV</span> RNAs determined in each cell-line following treatment with ATRA (1 μM) or vehicle (DMSO) for 24 h. The median fold-changes are expressed in linear values and were calculated from the logarithmic values shown in (<b>B</b>) and <a href="#app1-cancers-12-01169" class="html-app">Supplementary Table S5</a>. The R<sup>2</sup> values of the correlations are indicated. (<b>D</b>) Left: <span class="html-italic">SK-BR-3</span> cells were treated with DMSO or ATRA (1 μM) for 24 h. Cell extracts were subjected to Western blot analysis for TBK1, Serine/172 phosphorylated TBK1 (TBK1<sup>S172</sup>), p65 and Serine/536 phosphorylated p65 (p65<sup>S536</sup>). Right: <span class="html-italic">SK-BR-3</span> cells were treated with DMSO or ATRA (1 μM) for the indicated amount of time. Total cell extracts were subjected to Western blot analysis for TBK1, TBK1<sup>S172</sup> and tubulin (TUB) as a loading control. TBK1<sup>S172</sup>/TBK1 = ratio of the densitometric results obtained for the phosphorylated TBK1 and total TBK1 bands (p65<sup>S536</sup>/p65 = ratio of the densitometric results obtained for the phosphorylated p65 and total p65 bands). (<b>E</b>) <span class="html-italic">SK-BR-3</span> cells were treated with the TBK1 inhibitor, MRT67307, in the presence/absence of ATRA at the indicated concentrations for 24 h. Cells were pre-treated with the TBK1 inhibitor for 1 h. Total cell extracts were subjected to Western blot analysis for IRF1, DTX3L and tubulin (TUB). IRF1/TUB = ratio of the densitometric results obtained for the <span class="html-italic">IRF1</span> and Tubulin bands, DTX3L/TUB = ratio of the densitometric results obtained for the <span class="html-italic">DTX3L</span> and Tubulin bands. For the whole Western blot figures, please see <a href="#app1-cancers-12-01169" class="html-app">Figure S19</a>.</p>
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<p>Expression of the IRF1 and DTX3L proteins/mRNAs in breast cancer cell-lines and tissues. (<b>A</b>) <span class="html-italic">IRF1</span> (Interferon-Responsive-Factor-1) and <span class="html-italic">DTX3L</span> (Deltex-E3-Ubiquitin-Ligase-3L) mRNAs were measured by PCR (Polymerase Chain Reaction) in the indicated 41 cell-lines (column graphs). Each value is the mean ± standard deviation (SD) of 3 replicate cultures and the results are normalized for the 18S mRNA. The Western blots illustrate the levels of IRF1 and DTX3L proteins. The 16 cell-lines belonging to our experimental panel are indicated in blue (basal cell-lines) and red (luminal cell-lines). The basal or luminal phenotype of all the other cell-lines is indicated by blue and red dots, respectively. Each well contains an equivalent amount of protein. <span class="html-italic">CAL-851</span> and <span class="html-italic">BT474</span> cells (internal loading controls) are marked in green. The asterisk underneath the indicated Western blots marks the cell-lines for which bands are not detectable. The positivity of each cell-line to HER2 (<span class="html-italic">HER2<sup>+</sup></span>) or ER (<span class="html-italic">ER<sup>+</sup></span>) as well as the negativity of each cell line to HER2, ER and PR (progesterone receptor) is indicated by the <span class="html-italic">Triple-neg</span> (Triple-negative) designation. For the whole Western blot figures, please see <a href="#app1-cancers-12-01169" class="html-app">Supplementary Information</a>. (<b>B</b>) and (<b>C</b>) The <span class="html-italic">IRF1</span> as well as <span class="html-italic">DTX3L</span> mRNA and protein levels are compared following clustering of the cell-lines according to the luminal (<span class="html-italic">Lum</span>) and basal (<span class="html-italic">Bas</span>) phenotype or the presence/absence of the <span class="html-italic">ER</span> (Estrogen Receptor) and <span class="html-italic">HER2</span> proteins as defined in (<b>A</b>). Each point represents a single cell-line. The quantitative protein data were obtained from the densitometric analysis of the Western blots shown in (<b>A</b>), following normalization with the internal loading controls. * Significantly different following the Student’s <span class="html-italic">t</span>-test, corrected for multiple testing (Benjamini–Hochberg) (<span class="html-italic">p</span> &lt; 0.05). ** (<span class="html-italic">p</span> &lt; 0.01). (<b>D</b>) and (<b>E</b>) The Box Plots show the <span class="html-italic">IRF1</span> and <span class="html-italic">DTX3L</span> mRNA levels of breast cancer tissue samples determined from the <span class="html-italic">RNA-seq</span> data available in the TCGA database (1108 cases). The breast cancer cases are classified according to the PAM50 algorithm into 5 distinct groups: Luminal A = <span class="html-italic">LumA</span>, <span class="html-italic">HER2</span>-positive = <span class="html-italic">HER2<sup>+</sup></span>, Luminal B = <span class="html-italic">LumB</span>, Normal-like = <span class="html-italic">Nor-like</span>, Basal = <span class="html-italic">Bas</span>. Each point represents an individual case. * Significantly different following pairwise <span class="html-italic">t</span>-test comparison adjusted for multiple testing according to the Benjamini–Hochberg correction method (<span class="html-italic">p</span> &lt; 0.05). ** (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Effects of ATRA and derived retinoids on IRF1 and DTX3L in breast cancer cell-lines and functional significance of the two proteins in ATRA-dependent growth inhibition. (<b>A</b>) IRF1 and (<b>B</b>) DTX3L protein and mRNA levels were determined in vehicle (DMSO)- or ATRA (1 μM)-treated cell-lines (24 h). IRF1 and DTX3L proteins were determined in the nuclear and cytosolic fraction by Western blot analysis. The column graphs above the Western blots show the densitometric results obtained. The column graphs underneath the Western blots show the effect exerted by ATRA on the expression levels of the <span class="html-italic">IRF1</span> and <span class="html-italic">DTX3L</span> mRNAs (Taqman assays). (<b>C</b>) <span class="html-italic">SK-BR-3</span> and (<b>D</b>) <span class="html-italic">HCC-1599</span> cells were treated with vehicle and the indicated RAR agonist (1 μM), the γ-secretase inhibitors, DAPT [(2S)-N-[(3,5-Difluorophenyl)acetyl]-L-alanyl-2-phenyl]glycine-1,1-dimethylethyl-ester] (1 μM) and PF-3084014 [(2S)-2-[[(2S)-6,8-Difluoro-1,2,3,4-tetrahydro-2-naphthalenyl]amino]-N-[1-[2-[(2,2-dimethylpropyl)amino]-1,1-dimethylethyl]-1H-imidazol-4-yl]-pentanamide-dihydrobromide] (1 μM), PTX (paclitaxel, 0.01 μM), DOXO (doxorubicin, 0.2 μM) and VP16 (etoposide, 2.0 and 20 μM) for 24 h. Cellular extracts were subjected to Western blot analysis for IRF1, DTX3L and tubulin (TUB, loading control). IRF1/TUB = ratio of the densitometric results obtained for the IRF1 and Tubulin bands, DTX3L/TUB = ratio of the densitometric results obtained for the DTX3L and Tubulin bands. (<b>E</b>) <span class="html-italic">SK-BR-3</span> cells were transfected with the small interfering RNAs, siIRF1(a), siIRF1(b), or the scrambled siNC control. 24 h following transfection, cells were treated with vehicle (DMSO) or the indicated concentrations of ATRA for another 96 h. Cell-growth was evaluated with sulforhodamine assays and the results are expressed as the % growth inhibition relative to the corresponding siNC control (lower panel). Values are the mean ± SD of 3 replicate cultures. * significantly different (Student’s <span class="html-italic">t</span>-test, <span class="html-italic">p</span> &lt; 0.05; red asterisks: siIRF1(a) versus siNC, green asterisks: siIRF1(b) versus siNC). ** significantly different (Student’s <span class="html-italic">t</span>-test, <span class="html-italic">p</span> &lt; 0.01; red asterisks: siIRF1(a) versus siNC, green asterisks: siIRF1(b) versus siNC). The effect of silencing on the <span class="html-italic">IRF1</span> protein was evaluated after 24-h treatment with vehicle or ATRA (1 μM). Cell extracts were subjected to Western blot analysis for <span class="html-italic">IRF1</span> and Tubulin (upper panel). IRF1/TUB = ratio of the densitometric results obtained for the <span class="html-italic">IRF1</span> and Tubulin bands, DTX3L/TUB = ratio of the densitometric results obtained for the <span class="html-italic">DTX3L</span> and Tubulin bands. (<b>F</b>) <span class="html-italic">SK-BR-3</span> cells were stably infected with shDTX3L(a), shDTX3L(b), the void lentiviral vector (shVOID) and a scrambled shRNA (shNC). Following antibiotic selection and isolation, the indicated cell populations were treated (24 h) with vehicle (DMSO) or ATRA (1 μM). Total cell extracts were subjected to Western blot analysis for DTX3L and TUB (upper panel). DTX3L/TUB = ratio of the densitometric results obtained for the <span class="html-italic">DTX3L</span> and Tubulin bands. Cell-growth (lower panel) was evaluated as in (<b>E</b>). Each value is the mean ± SD of 3 replicate cultures. <span class="html-italic">SK-BR-3</span> = parental cell-line. * significantly different from shNC and shVOID (Student’s <span class="html-italic">t</span>-test, <span class="html-italic">p</span> &lt; 0.05). ** significantly different from shNC and shVOID (Student’s <span class="html-italic">t</span>-test, <span class="html-italic">p</span> &lt; 0.01). For the whole Western blot figures, please see <a href="#app1-cancers-12-01169" class="html-app">Figure S19</a>.</p>
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