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13 pages, 3489 KiB  
Article
The LIFR Inhibitor EC359 Effectively Targets Type II Endometrial Cancer by Blocking LIF/LIFR Oncogenic Signaling
by Nicole Spencer, Alondra Lee Rodriguez Sanchez, Rahul Gopalam, Panneerdoss Subbarayalu, Daisy M. Medina, Xue Yang, Paulina Ramirez, Lois Randolph, Emily Jean Aller, Bindu Santhamma, Manjeet K. Rao, Rajeshwar Rao Tekmal, Hareesh B. Nair, Edward R. Kost, Ratna K. Vadlamudi and Suryavathi Viswanadhapalli
Int. J. Mol. Sci. 2023, 24(24), 17426; https://doi.org/10.3390/ijms242417426 - 13 Dec 2023
Cited by 2 | Viewed by 1475
Abstract
Endometrial cancer (ECa) is the most common female gynecologic cancer. When comparing the two histological subtypes of endometrial cancer, Type II tumors are biologically more aggressive and have a worse prognosis than Type I tumors. Current treatments for Type II tumors are ineffective, [...] Read more.
Endometrial cancer (ECa) is the most common female gynecologic cancer. When comparing the two histological subtypes of endometrial cancer, Type II tumors are biologically more aggressive and have a worse prognosis than Type I tumors. Current treatments for Type II tumors are ineffective, and new targeted therapies are urgently needed. LIFR and its ligand, LIF, have been shown to play a critical role in the progression of multiple solid cancers and therapy resistance. The role of LIF/LIFR in the progression of Type II ECa, on the other hand, is unknown. We investigated the role of LIF/LIFR signaling in Type II ECa and tested the efficacy of EC359, a novel small-molecule LIFR inhibitor, against Type II ECa. The analysis of tumor databases has uncovered a correlation between diminished survival rates and increased expression of leukemia inhibitory factor (LIF), suggesting a potential connection between altered LIF expression and unfavorable overall survival in Type II ECa. The results obtained from cell viability and colony formation assays demonstrated a significant decrease in the growth of Type II ECa LIFR knockdown cells in comparison to vector control cells. Furthermore, in both primary and established Type II ECa cells, pharmacological inhibition of the LIF/LIFR axis with EC359 markedly decreased cell viability, long-term cell survival, and invasion, and promoted apoptosis. Additionally, EC359 treatment reduced the activation of pathways driven by LIF/LIFR, such as AKT, mTOR, and STAT3. Tumor progression was markedly inhibited by EC359 treatment in two different patient-derived xenograft models in vivo and patient-derived organoids ex vivo. Collectively, these results suggest LIFR inhibitor EC359 as a possible new small-molecule therapeutics for the management of Type II ECa. Full article
(This article belongs to the Section Molecular Oncology)
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Figure 1

Figure 1
<p>LIF is overexpressed in Type II ECa and Knockdown (KD) of LIFR reduced ECa progression in vitro. (<b>A</b>) Association of LIF expression with overall survival of ECa patients (cBioportal). (<b>B</b>) Patients with altered LIF expression in overall survival patient cohort. (<b>C</b>) Expression of LIF in normal (<span class="html-italic">n</span> = 126) and uterine carcinosarcoma (<span class="html-italic">n</span> = 57) from TCGA patient cohort (OncoDB). (<b>D</b>) Levels of LIFR in siRNA-mediated KD patient-derived ECa cells were measured via Western blotting. Densitometric analysis of Western blots was performed using ImageJ software Version 1.51 and the fold change was calculated using total LIFR protein over GAPDH protein expression. (<b>E</b>) The effect of LIFR-KD on cell viability in ECa-47 cells was measured using CellTiter-Glo assays. (<b>F</b>) The effect of LIFR-KD on ECa-47 cell survival was measured using colony formation assays. Quantitation is shown in the right panel (<b>G</b>). Data in (<b>D</b>–<b>G</b>) are representative of three independent experiments (<span class="html-italic">n</span> = 3). The <span class="html-italic">p</span>-values in (<b>C</b>,<b>E</b>,<b>G</b>) were calculated using one-way ANOVA. Data are represented as mean ± SE. * <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 2
<p>LIFR inhibitor EC359 reduced cell viability, colony formation and invasion of Type II ECa cells. (<b>A</b>) Effect of EC359 on cell viability of established and patient-derived primary ECa cells was determined using MTT assay. (<b>B</b>) Effect of EC359 on cell survival of ECa cells was measured using colony formation assay. (<b>C</b>) Quantitation of the percentage of colonies is shown. The effect of EC359 on cell invasion of Type II ECa cells was determined using Boyden chamber assays. Images were shown in panel (<b>D</b>) and quantitation of the percentage of cells invaded is shown in panel (<b>E</b>). Data are representative of three independent experiments (<span class="html-italic">n</span> = 3). Data are represented as mean ± SE. Scale bar represents 100 µm. In (<b>C</b>,<b>E</b>), <span class="html-italic">p</span>-values were calculated using one-way ANOVA. * <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>
Full article ">Figure 3
<p>LIFR inhibitor EC359 promoted apoptosis of Type II ECa cells and reduced the growth of PDOs. (<b>A</b>–<b>F</b>) Effect of EC359 (100 nmol/L) on apoptosis of patient-derived type II ECa cells (<span class="html-italic">n</span> = 3) was determined using Annexin V staining. (<b>G</b>) Effect of various doses of EC359 treatment in patient-derived organoids (PDOs) was measured using CellTiter-Glo luminescent cell viability assay. (<b>H</b>) PDO images were shown. Scale bar represents 100 µm. Data are representative of three independent experiments (<span class="html-italic">n</span> = 3). Data are represented as mean ± SE. In (<b>A</b>–<b>F</b>), <span class="html-italic">p</span>-values were calculated using student’s <span class="html-italic">t</span>-test. *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 4
<p>EC359 inhibits LIFR downstream signaling in Type II ECa cells. (<b>A</b>) ECa-47, and ECa-81 cells stably expressing STAT3-luc reporter were treated with EC359 (50 nmol/L) and reporter activity was measured after 18 h. (<b>B</b>) Effect of EC359 (100 nmol/L) treatment (6 h) on STAT3 target genes was measured using RT-qPCR analysis (<span class="html-italic">n</span> = 3) in ECa-78 cells. (<b>C</b>) ECa-47 and KLE cells were treated with EC359 (100 nmol/L) for 6 h and the status of LIFR downstream signaling was measured using Western blotting. Densitometric analysis of Western blots were performed using ImageJ software Version 1.51 and the fold change was calculated using the phosphorylated protein over total protein expression of each protein. Data are representative of three independent experiments (<span class="html-italic">n</span> = 3). Data are represented as mean ± SE. In (<b>A</b>,<b>B</b>), <span class="html-italic">p</span>-values were calculated using student’s <span class="html-italic">t</span>-test and one-way ANOVA, respectively. * <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>
Full article ">Figure 5
<p>EC359 inhibits the growth of Type II ECa xenograft tumors. Patient-derived xenograft (PDX) tumors were treated with vehicle or EC359 (5 mg/kg/3 days/week/ip for ECa-15-PDX and 10 mg/kg/3 days/week/oral for ECa-81-PDX). EC359 was administered intraperitoneally (i.p.) to ECa-15 xenograft tumor-bearing mice and orally (gavage) to ECa-81 xenograft tumor-bearing mice. Tumor volumes and tumor weights and body weights of ECa-15 (<span class="html-italic">n</span> = 7 tumors) (<b>A</b>–<b>C</b>) and ECa-81 (<span class="html-italic">n</span> = 5 tumors) (<b>D</b>–<b>F</b>) are shown. (<b>G</b>) Ki-67 expression as a marker of proliferation was analyzed via IHC and quantitated (<b>H</b>). Data are represented as mean ± SE. <span class="html-italic">p</span>-values were calculated using student’s <span class="html-italic">t</span>-test (<b>B</b>,<b>E</b>,<b>H</b>) and two-way ANOVA (<b>A</b>,<b>D</b>). * <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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26 pages, 4472 KiB  
Article
Heterogeneity in the Metastatic Microenvironment: JunB-Expressing Microglia Cells as Potential Drivers of Melanoma Brain Metastasis Progression
by Orit Adir, Orit Sagi-Assif, Tsipi Meshel, Shlomit Ben-Menachem, Metsada Pasmanik-Chor, Dave S. B. Hoon, Isaac P. Witz and Sivan Izraely
Cancers 2023, 15(20), 4979; https://doi.org/10.3390/cancers15204979 - 13 Oct 2023
Cited by 1 | Viewed by 1418
Abstract
Reciprocal signaling between melanoma brain metastatic (MBM) cells and microglia reprograms the phenotype of both interaction partners, including upregulation of the transcription factor JunB in microglia. Here, we aimed to elucidate the impact of microglial JunB upregulation on MBM progression. For molecular profiling, [...] Read more.
Reciprocal signaling between melanoma brain metastatic (MBM) cells and microglia reprograms the phenotype of both interaction partners, including upregulation of the transcription factor JunB in microglia. Here, we aimed to elucidate the impact of microglial JunB upregulation on MBM progression. For molecular profiling, we employed RNA-seq and reverse-phase protein array (RPPA). To test microglial JunB functions, we generated microglia variants stably overexpressing JunB (JunBhi) or with downregulated levels of JunB (JunBlo). Melanoma-derived factors, namely leukemia inhibitory factor (LIF), controlled JunB upregulation through Janus kinase (JAK)/signal transducer and activator of transcription 3 (STAT3) signaling. The expression levels of JunB in melanoma-associated microglia were heterogeneous. Flow cytometry analysis revealed the existence of basal-level JunB-expressing microglia alongside microglia highly expressing JunB. Proteomic profiling revealed a differential protein expression in JunBhi and JunBlo cells, namely the expression of microglia activation markers Iba-1 and CD150, and the immunosuppressive molecules SOCS3 and PD-L1. Functionally, JunBhi microglia displayed decreased migratory capacity and phagocytic activity. JunBlo microglia reduced melanoma proliferation and migration, while JunBhi microglia preserved the ability of melanoma cells to proliferate in three-dimensional co-cultures, that was abrogated by targeting leukemia inhibitory factor receptor (LIFR) in control microglia–melanoma spheroids. Altogether, these data highlight a melanoma-mediated heterogenous effect on microglial JunB expression, dictating the nature of their functional involvement in MBM progression. Targeting microglia highly expressing JunB may potentially be utilized for MBM theranostics. Full article
(This article belongs to the Special Issue Microenvironment and Cancer Progression 2.0)
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Figure 1

Figure 1
<p>Melanoma-secreted factors reprogram gene expression of microglia. (<b>A</b>–<b>C</b>) mRNA samples of microglia cells treated with MCM for 3 or 24 h were sequenced on an Illumina NextSeq 550. (<b>A</b>) Genes differentially expressed in microglia treated with DP.CB2 CM (<b>left</b>) or M12.CB3 CM (<b>right</b>) at 3 h (<b>top</b> charts) or 24 h (<b>bottom</b> charts) (pAdj &lt; 0.05 and FC ≤ − 2 or FC ≥ 2) were classified and selected biological processes using the DAVID database are shown. (<b>B</b>) Venn diagrams comparing genes differentially expressed in microglia treated with either DP.CB2 CM or M12.CB3 CM for 3 and 24 h. (<b>C</b>) Volcano plots showing gene expression of microglia treated with DP.CB2 or M12.CB3 CM for 3 h. Upregulated genes (pAdj &lt; 0.05 and FC ≥ 2) are shown in red, downregulated genes (pAdj &lt; 0.05 and FC ≤ −2) are shown in blue, and genes that did not significantly change are shown in black. Selected genes are highlighted. (<b>D</b>) Western blot analysis of JunB and β-tubulin expression in microglia cells treated with MCM for 3 h. Representative blot and quantification of JunB expression are presented. Data are shown as mean expression + SEM of biological replicates. * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.005. (<b>E</b>) YDFR.CB3, DP.CB2, M12.CB3, and M16.CB3 cells were intracardially inoculated into BALB/c nude mice, and the brains were harvested after 4–6 weeks. Brain sections were stained for melanoma (green), JunB (red), and Iba-1 (cyan). Cell nuclei were stained with DAPI (blue). Yellow arrows indicate co-expression of Iba-1 and JunB. Magnification: ×40, scale bar: 25 μm.</p>
Full article ">Figure 2
<p>MBM-derived leukemia inhibitory factor (LIF) upregulates JunB expression in microglia via JAK/STAT3 signaling. (<b>A</b>) Western blot analysis of JunB and β-tubulin in microglia cells treated with MCM combined with STAT3 (S727) inhibitor (zoledronic acid), JAK inhibitor (baricitinib), JNK inhibitor (SP600125), MEK/ERK inhibitor (U0126), and DMSO as a control for 3 h. Representative blot and quantification of JunB expression are presented (normalized to cells treated with MCM + DMSO). Data are shown as mean expression + SEM of biological replicates. * <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; 0.005. (<b>B</b>) Western blot analysis of JunB and β-tubulin expression in microglia cells treated with LIF (25 ng/mL), OSM (50 ng/mL), IL-4 (20 ng/mL), IL-6 (20 ng/mL), IL-15 (50 ng/mL), and IL-27 (20 ng/mL) for 3 h. Representative blot and quantification of JunB expression are presented. Data are shown as mean expression + SEM of biological replicates. * <span class="html-italic">p</span> &lt; 0.05. (<b>C</b>) ELISA measurement of LIF in MCM. The bars represent the average cytokine concentration + SEM. (<b>D</b>) Western blot analysis of p-STAT3 (Tyr705) and STAT3 expression in microglia cells treated with LIF (25 ng/mL) for 10 min. Representative blot and quantification of p-STAT3 (Tyr705), STAT3, and β-tubulin expression are presented. Data are shown as mean expression + SEM of biological replicates. * <span class="html-italic">p</span> &lt; 0.05. (<b>E</b>) Western blot analysis of JunB and β-tubulin expression in microglia cells treated with LIF (25 ng/mL) with or without baricitinib for 3 h. Representative blot and quantification of JunB expression are presented. Data are shown as mean expression + SEM of biological replicates. * <span class="html-italic">p</span> &lt; 0.05. (<b>F</b>) Flow cytometry analysis of LIFR and gp130 expression in microglia cells. (<b>G</b>, 1–4) Western blot analysis of JunB and β-tubulin in microglia cells treated with or without MCM with LIFR inhibitor (EC359) or DMSO as control for 3 h. Representative blot and quantification of JunB expression are presented. Data are shown as mean expression + SEM of biological replicates. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. In all the experiments, unless indicated otherwise, microglia cells grown in starvation medium served as controls.</p>
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<p>JunB regulates the molecular phenotype of microglia cells. (<b>A</b>) Flow cytometry analysis of intracellular JunB expression in microglia cells treated with MCM for 3 h. The colors used refer to the density of the cells relative to JunB expression. Blue and green correspond to lower cell density. Yellow is mid-range cell density. Red and orange correspond to higher cell density. The left rectangles mark the expression range of JunB in ~95% of control microglia and the right rectangles mark the expression range of JunB in ~5% of control microglia. The bars on the graph represent the mean % of cells expressing basal JunB levels (blue bars) and the mean % of cells expressing high JunB levels (red bars) + SEM, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. (<b>B</b>) Protein lysates of JunB<sup>hi</sup> and JunB<sup>lo</sup> and matching control microglia cells were analyzed for the expression of 492 proteins using RPPA. The tables list the differentially expressed proteins (<span class="html-italic">p</span> &lt; 0.05, FC ≤ −1.25 or FC ≥ 1.25) in JunB<sup>hi</sup> (<b>top</b>) and JunB<sup>lo</sup> (<b>bottom</b>) microglia cells compared to their controls. (<b>C</b>) Iba-1, CD14, CD16, CD150, and CD163 expression was determined using flow cytometry in JunB<sup>hi</sup> and JunB<sup>lo</sup> microglia cells and their controls. Representative flow cytometry histograms of marker expression are shown. The bars represent the mean percentage of positive cells + SEM. * <span class="html-italic">p</span> &lt; 0.05. (<b>D</b>) PDL-1 expression was determined using flow cytometry in JunB<sup>hi</sup> and JunB<sup>lo</sup> microglia cells and their controls. Representative flow cytometry histograms are shown. The bars represent the mean percentage of positive cells + SEM. ** <span class="html-italic">p</span> &lt; 0.001. (<b>E</b>,<b>F</b>) The relative expression of SOCS3 (<b>E</b>) and SERPINE1 (<b>F</b>) mRNA in JunB<sup>hi</sup> and JunB<sup>lo</sup> microglia cells and their controls was detected by RT-qPCR. RS9 was used for gene expression normalization. The bars represent the mean expression of SOCS3 (normalized to control cells) + SEM, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 4
<p>JunB impacts the functional phenotype of microglia cells. (<b>A</b>,<b>B</b>) JunB<sup>hi</sup> and JunB<sup>lo</sup> microglia cells and their controls were seeded in a monolayer. Upon confluence, a scratch was performed in each well and cells were allowed to migrate. Images were acquired every 3 h for 48 h using Incucyte S3. (<b>A</b>) Representative images of wound-healing assay at 27 h. Scale bar: 500 μm. (<b>B</b>) Analysis of the mean <span class="underline">+</span> SEM of wound confluence (%) was obtained from the Incucyte S3 software (v2019A). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. (<b>C</b>) Phagocytosis of fluorescent beads as determined by flow cytometry analysis. JunB<sup>hi</sup> and JunB<sup>lo</sup> microglia cells and their controls were incubated with fluorescent latex beads for 4 h. The bars represent the mean percentage of phagocytic (fluorescence positive) cells (normalized to control) + SEM, * <span class="html-italic">p</span> &lt; 0.05. (<b>D</b>) JunB<sup>hi</sup> and JunB<sup>lo</sup> microglia cells and their controls were seeded in 96-well plate and treated with concentrated MCM (×30) for 24 h, after which the nitric oxide (NO) concentration in the medium was measured by Griess reagent, as well as the NO concentration in the concentrated MCM as control. The bars represent the relative NO concentration (NO concentration measured 24 h after treatment divided by NO concentration of the initial concentrated MCM) + SEM, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Microglial JunB promotes the malignant phenotype of MBM cells. (<b>A</b>) Viability of MBM cells treated with CM of JunB<sup>hi</sup> and JunB<sup>lo</sup> microglia cells and their controls for 48 h was measured by XTT. The bars represent the mean viability of melanoma cells treated with CM of JunB<sup>hi</sup> and JunB<sup>lo</sup> microglia cells normalized to cells treated with CM of control microglia + SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. (<b>B</b>) MBM cells were seeded in a monolayer. Upon confluence, cells were treated with mitomycin C for 3 h, then a scratch was performed in each well, CM of JunB<sup>hi</sup>, JunB<sup>lo</sup>, and matching control microglia cells was added, and cells were allowed to migrate. Images were acquired every 3 h for 48 h using Incucyte S3. Analysis of the mean <span class="underline">+</span> SEM of wound confluence (%) was obtained from the Incucyte S3 software. * <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; 0.005. (<b>C</b>–<b>E</b>) The relative expression of ALDOC (<b>C</b>), NQO1 (<b>D</b>), and SOCS3 (<b>E</b>) mRNA in MBM cells treated with starvation medium or with CM of JunB<sup>hi</sup> and JunB<sup>lo</sup> microglia cells and their controls for 24 h was detected by RT-qPCR. RS9 was used for gene expression normalization. The bars represent mean expression (normalized to MG con<sup>hi</sup> or MG con<sup>lo</sup> cells) + SEM, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. (<b>F</b>, 1–3) Spheroid formation assay of mCherry-labeled MBM cells and GFP-labeled microglia cells (1:1) with EC359 or DMSO, imaged for 48 h using the IncuCyte system. Representative images of the wells at the beginning and end point of the experiment are presented. Mean mCherry integrated intensity (RCU×µM<sup>2</sup>) and GFP integrated intensity (GCU×µM<sup>2</sup>) ± SEM are presented in the graphs. * <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; 0.005. Experiments were performed at least three times, in 4–6 replicates. Scale bar: 500 μm.</p>
Full article ">Figure 5 Cont.
<p>Microglial JunB promotes the malignant phenotype of MBM cells. (<b>A</b>) Viability of MBM cells treated with CM of JunB<sup>hi</sup> and JunB<sup>lo</sup> microglia cells and their controls for 48 h was measured by XTT. The bars represent the mean viability of melanoma cells treated with CM of JunB<sup>hi</sup> and JunB<sup>lo</sup> microglia cells normalized to cells treated with CM of control microglia + SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. (<b>B</b>) MBM cells were seeded in a monolayer. Upon confluence, cells were treated with mitomycin C for 3 h, then a scratch was performed in each well, CM of JunB<sup>hi</sup>, JunB<sup>lo</sup>, and matching control microglia cells was added, and cells were allowed to migrate. Images were acquired every 3 h for 48 h using Incucyte S3. Analysis of the mean <span class="underline">+</span> SEM of wound confluence (%) was obtained from the Incucyte S3 software. * <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; 0.005. (<b>C</b>–<b>E</b>) The relative expression of ALDOC (<b>C</b>), NQO1 (<b>D</b>), and SOCS3 (<b>E</b>) mRNA in MBM cells treated with starvation medium or with CM of JunB<sup>hi</sup> and JunB<sup>lo</sup> microglia cells and their controls for 24 h was detected by RT-qPCR. RS9 was used for gene expression normalization. The bars represent mean expression (normalized to MG con<sup>hi</sup> or MG con<sup>lo</sup> cells) + SEM, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. (<b>F</b>, 1–3) Spheroid formation assay of mCherry-labeled MBM cells and GFP-labeled microglia cells (1:1) with EC359 or DMSO, imaged for 48 h using the IncuCyte system. Representative images of the wells at the beginning and end point of the experiment are presented. Mean mCherry integrated intensity (RCU×µM<sup>2</sup>) and GFP integrated intensity (GCU×µM<sup>2</sup>) ± SEM are presented in the graphs. * <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; 0.005. Experiments were performed at least three times, in 4–6 replicates. Scale bar: 500 μm.</p>
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21 pages, 1517 KiB  
Article
A Clinical Qualification Protocol Highlights Overlapping Genomic Influences and Neuro-Autonomic Mechanisms in Ehlers–Danlos and Long COVID-19 Syndromes
by Golder N. Wilson
Curr. Issues Mol. Biol. 2023, 45(7), 6003-6023; https://doi.org/10.3390/cimb45070379 - 17 Jul 2023
Cited by 3 | Viewed by 3073
Abstract
A substantial fraction of the 15% with double-jointedness or hypermobility have the traditionally ascertained joint-skeletal, cutaneous, and cardiovascular symptoms of connective tissue dysplasia and its particular manifestation as Ehlers–Danlos syndrome (EDS). The holistic ascertainment of 120 findings in 1261 EDS patients added neuro-autonomic [...] Read more.
A substantial fraction of the 15% with double-jointedness or hypermobility have the traditionally ascertained joint-skeletal, cutaneous, and cardiovascular symptoms of connective tissue dysplasia and its particular manifestation as Ehlers–Danlos syndrome (EDS). The holistic ascertainment of 120 findings in 1261 EDS patients added neuro-autonomic symptoms like headaches, muscle weakness, brain fog, chronic fatigue, dyspnea, and bowel irregularity to those of arthralgia and skin laxity, 15 of these symptoms shared with those of post-infectious SARS-CoV-2 (long COVID-19). Underlying articulo-autonomic mechanisms guided a clinical qualification protocol that qualified DNA variants in 317 genes as having diagnostic utility for EDS, six of them identical (F2-LIFR-NLRP3-STAT1-T1CAM1-TNFRSF13B) and eighteen similar to those modifying COVID-19 severity/EDS, including ADAMTS13/ADAMTS2-C3/C1R-IKBKG/IKBKAP-PIK3C3/PIK3R1-POLD4/POLG-TMPRSS2/TMPRSS6-WNT3/WNT10A. Also, contributing to EDS and COVID-19 severity were forty and three genes, respectively, impacting mitochondrial functions as well as parts of an overlapping gene network, or entome, that are hypothesized to mediate the cognitive–behavioral, neuro-autonomic, and immune-inflammatory alterations of connective tissue in these conditions. The further characterization of long COVID-19 natural history and genetic predisposition will be necessary before these parallels to EDS can be carefully delineated and translated into therapies. Full article
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Figure 1

Figure 1
<p>Clinical protocol for DNA variant qualification. Clinical DNA variant (column 4) and 1–4 + medical diagnostic utilities (last column) are added to consensus qualifications (column 2) as discussed in the text; DNA/protein change and gene abbreviations except for <span class="html-italic">MTHFR</span> (methylene tetrahydrofolate reductase) and <span class="html-italic">HBB</span> (beta-globin) are explained in <a href="#app1-cimb-45-00379" class="html-app">Tables S2 and S3</a>; single amino acid codes (A—alanine, D—aspartate, E—glutamate, I—isoleucine, L—leucine, M—methionine, P—proline, Q—glutamine, R—arginine, S—serine, T—threonine, X—stop, V—valine) used here; fs, frame-shift.</p>
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<p>Genes relevant to EDS or COVID-19 infection by tissue element or product type. (<b>A</b>) Connective tissue element/process relations (box, <a href="#cimb-45-00379-f002" class="html-fig">Figure 2</a>A bottom) are from associated diseases (<a href="#app1-cimb-45-00379" class="html-app">Tables S2 and S3</a>). COVID-19 percentages are those of 83 genes after 21 impacting viral-related processes were subtracted. (<b>B</b>) Gene product functions are explained in the legend to <a href="#app1-cimb-45-00379" class="html-app">Table S2</a>. COVID-19 percentages are of all 104 genes listed in <a href="#app1-cimb-45-00379" class="html-app">Table S3</a> (the <span class="html-italic">PNPLA3</span> gene associated with gastrointestinal disease is not listed). Colors indicate relative proportions for EDS (blue) and COVID19 (red). Significantly (<span class="html-italic">p</span> &lt; 0.05) lower X/ higher ↑ proportions (see Methods).</p>
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<p>Genes and symptoms related to EDS and COVID-19. Genes related to EDS (<a href="#app1-cimb-45-00379" class="html-app">Table S2</a>) and COVID-19 infection (<a href="#app1-cimb-45-00379" class="html-app">Table S3</a>) are envisioned as overlapping parts of a network (rhizome below) connected through pathogenic mechanisms (trunk sap, phloem) to common symptoms of EDS (<a href="#app1-cimb-45-00379" class="html-app">Table S1</a>) and long COVID-19 (canopy above). EDS symptom ranges are for females over age 10.5 years from the EDS1261database; long COVID percentages and ranges are taken from <a href="#cimb-45-00379-f002" class="html-fig">Figure 2</a> of the work by Deer et al. [<a href="#B37-cimb-45-00379" class="html-bibr">37</a>].</p>
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26 pages, 5208 KiB  
Article
Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis
by Zeenat Mirza, Md Shahid Ansari, Md Shahid Iqbal, Nesar Ahmad, Nofe Alganmi, Haneen Banjar, Mohammed H. Al-Qahtani and Sajjad Karim
Cancers 2023, 15(12), 3237; https://doi.org/10.3390/cancers15123237 - 18 Jun 2023
Cited by 8 | Viewed by 4323
Abstract
Background: Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed [...] Read more.
Background: Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed genes (DEGs) for BC diagnosis and prognosis. Methods: A cohort of 701 samples from 11 GEO BC microarray datasets was used for the identification of significant DEGs. Seven ML methods, including RFECV-LR, RFECV-SVM, LR-L1, SVC-L1, RF, and Extra-Trees were applied for gene reduction and the construction of a diagnostic model for cancer classification. Kaplan–Meier survival analysis was performed for prognostic signature construction. The potential biomarkers were confirmed via qRT-PCR and validated by another set of ML methods including GBDT, XGBoost, AdaBoost, KNN, and MLP. Results: We identified 355 DEGs and predicted BC-associated pathways, including kinetochore metaphase signaling, PTEN, senescence, and phagosome-formation pathways. A hub of 28 DEGs and a novel diagnostic nine-gene signature (COL10A, S100P, ADAMTS5, WISP1, COMP, CXCL10, LYVE1, COL11A1, and INHBA) were identified using stringent filter conditions. Similarly, a novel prognostic model consisting of eight-gene signatures (CCNE2, NUSAP1, TPX2, S100P, ITM2A, LIFR, TNXA, and ZBTB16) was also identified using disease-free survival and overall survival analysis. Gene signatures were validated by another set of ML methods. Finally, qRT-PCR results confirmed the expression of the identified gene signatures in BC. Conclusion: The ML approach helped construct novel diagnostic and prognostic models based on the expression profiling of BC. The identified nine-gene signature and eight-gene signatures showed excellent potential in BC diagnosis and prognosis, respectively. Full article
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<p>Boxplot showing the expression distribution for dataset GSE7904. (<b>A</b>) Raw (un-normalized) expression distribution with log2 scale in the range of −200 to 400. (<b>B</b>) Normalized intensities showing almost similar distributions of expression intensities, with the log2 scale in the range of 0 to 12.</p>
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<p>Volcano plot showing differentially expressed genes: (i) the majority were non-significant (black), (ii) upregulated DEGs (red), and (iii) downregulated DEGs (blue).</p>
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<p>Canonical pathways derived using the IPA tool. (<b>A</b>) Kinetochore metaphase signaling pathway, (<b>B</b>) PTEN pathway overlapped with breast cancer associated genes.</p>
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<p>(<b>A</b>) Venn diagram showing 28 hub genes derived from the intersection of DEGs &gt; 3 ML and DEGs &gt; 4 datasets. (<b>B</b>) Unsupervised hierarchical clustering: heatmap of 701 samples, including 356 breast tumor (BT, cyan) and 345 normal breast (NB, pink) tissues, showing the gene expression pattern of 28 hub genes, including diagnostic and prognostic gene signatures. Upregulated genes are shown in red and downregulated genes are in blue.</p>
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<p>K-nearest neighbors (KNN)-based ML model for diagnostic gene signature showing the mean ROC (AUC 0.989 ± 0.013).</p>
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<p>PCA plot showing an overall distribution of the samples (n = 701), including breast tumor (blue) and normal breast tissue (red) based on transcriptomics profiles: (<b>A</b>) 54,675 probes, (<b>B</b>) 355 DEGs, (<b>C</b>) 28 hub genes, and (<b>D</b>) diagnostic nine-gene signature.</p>
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<p>KM plot based on the relapse-free survival analysis of eight individual genes (mRNA, gene-chip) of prognostic gene signature. The <span class="html-italic">X</span>-axis and <span class="html-italic">Y</span>-axis represent time in months and the probability of the survival of patients, respectively. The impact of the high and low expression of the gene on patient survival is shown in red and black lines, respectively.</p>
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<p>KM plot based on the overall survival analysis of eight individual genes (mRNA, gene-chip) of prognostic gene signature. The <span class="html-italic">X</span>-axis and <span class="html-italic">Y</span>-axis represent time in months and the probability of the survival of patients, respectively. The impact of the high and low expression of the gene on patient survival is shown in red and black lines, respectively.</p>
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<p>RFS and OS analyses and the validation of upregulated (<span class="html-italic">CCNE2, NUSAP1, TPX2</span>, and <span class="html-italic">S100P</span>), and downregulated (<span class="html-italic">ITM2A, LIFR, TNXA,</span> and <span class="html-italic">ZBTB16</span>) gene groups (mRNA, RNA seq) of the prognostic gene signature. The <span class="html-italic">X</span>-axis and <span class="html-italic">Y</span>-axis represent time in months and the probability of the survival of patients. The impact of the high and low expression of the gene on patient survival is shown in red and black lines, respectively.</p>
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<p>Gradient-boosting decision trees (GBDT) based on the ML model for the prognostic gene signature showing the mean ROC (AUC 0.993 ± 0.006).</p>
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<p>qRT-PCR results showing overexpression of <span class="html-italic">COL10A</span>, <span class="html-italic">S100P</span>, <span class="html-italic">WISP1</span>, <span class="html-italic">COMP</span>, <span class="html-italic">CXCL10</span>, <span class="html-italic">COL11A1</span>, <span class="html-italic">INHBA</span>; <span class="html-italic">CCNE2</span>, <span class="html-italic">NUSAP1</span>, <span class="html-italic">TPX2</span>, and <span class="html-italic">S100P</span> genes, and under-expression of <span class="html-italic">ADAMTS5</span>, <span class="html-italic">LYVE1</span>, <span class="html-italic">ITM2A</span>, <span class="html-italic">LIFR</span>, <span class="html-italic">TNXA</span>, and <span class="html-italic">ZBTB16</span> genes.</p>
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12 pages, 2452 KiB  
Article
Global Transcriptional Profiling of Granulosa Cells from Polycystic Ovary Syndrome Patients: Comparative Analyses of Patients with or without History of Ovarian Hyperstimulation Syndrome Reveals Distinct Biomarkers and Pathways
by Maha H. Daghestani, Huda A. Alqahtani, AlBandary AlBakheet, Mashael Al Deery, Khalid A. Awartani, Mazin H. Daghestani, Namik Kaya, Arjumand Warsy, Serdar Coskun and Dilek Colak
J. Clin. Med. 2022, 11(23), 6941; https://doi.org/10.3390/jcm11236941 - 25 Nov 2022
Cited by 3 | Viewed by 2371
Abstract
Ovarian hyperstimulation syndrome (OHSS) is often a complication of polycystic ovarian syndrome (PCOS), the most frequent disorder of the endocrine system, which affects women in their reproductive years. The etiology of OHSS is multifactorial, though the factors involved are not apparent. In an [...] Read more.
Ovarian hyperstimulation syndrome (OHSS) is often a complication of polycystic ovarian syndrome (PCOS), the most frequent disorder of the endocrine system, which affects women in their reproductive years. The etiology of OHSS is multifactorial, though the factors involved are not apparent. In an attempt to unveil the molecular basis of OHSS, we conducted transcriptome analysis of total RNA extracted from granulosa cells from PCOS patients with a history of OHSS (n = 6) and compared them to those with no history of OHSS (n = 18). We identified 59 significantly dysregulated genes (48 down-regulated, 11 up-regulated) in the PCOS with OHSS group compared to the PCOS without OHSS group (p-value < 0.01, fold change >1.5). Functional, pathway and network analyses revealed genes involved in cellular development, inflammatory and immune response, cellular growth and proliferation (including DCN, VIM, LIFR, GRN, IL33, INSR, KLF2, FOXO1, VEGF, RDX, PLCL1, PAPPA, and ZFP36), and significant alterations in the PPAR, IL6, IL10, JAK/STAT and NF-κB signaling pathways. Array findings were validated using quantitative RT-PCR. To the best of our knowledge, this is the largest cohort of Saudi PCOS cases (with or without OHSS) to date that was analyzed using a transcriptomic approach. Our data demonstrate alterations in various gene networks and pathways that may be involved in the pathophysiology of OHSS. Further studies are warranted to confirm the findings. Full article
(This article belongs to the Special Issue Hot Topics in Reproductive Medicine Research)
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<p>Global transcriptional changes associated with history of OHSS. (<b>A</b>) The unsupervised principal component analysis (PCA) and (<b>B</b>) two-dimensional hierarchical clustering analysis clearly distinguished individuals with PCOS with a positive history of OHSS from those without OHSS (<b>A</b>,<b>B</b>, respectively). The expression level of each gene across the samples is normalized to [−3, 3]. Hierarchical clustering was performed using Pearson’s correlation with average linkage clustering. Pink spheres indicate OHSS, blue spheres indicate PCOS (without OHSS). Red and green in the heatmap denote highly and weakly expressed genes, respectively. (<b>C</b>) Over-represented biological functions and (<b>D</b>) significantly altered canonical pathways associated with DEG (up- or down-regulated) in OHSS patients. <span class="html-italic">X</span>-axis indicates the significance (−log <span class="html-italic">p</span>-value) of the functional/pathway association that is dependent on the number of genes in a class as well as biological relevance. The threshold line represents a <span class="html-italic">p</span>-value of 0.05.</p>
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<p>Gene interaction network analysis of significantly dysregulated genes in OHSS group. Top-scoring gene interaction networks with high relevancy scores are shown. Nodes represent genes and the edges indicate biological relationship between the nodes. Straight and dashed lines represent direct or indirect gene-to-gene interactions, respectively. The functional class of the gene product are represented with different shapes (see legend). Red/green indicated up- (down-) regulated in OHSS compared to PCOS group. The color intensity is correlated with fold change.</p>
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23 pages, 4101 KiB  
Article
Repositioning Mifepristone as a Leukaemia Inhibitory Factor Receptor Antagonist for the Treatment of Pancreatic Adenocarcinoma
by Cristina Di Giorgio, Antonio Lupia, Silvia Marchianò, Martina Bordoni, Rachele Bellini, Carmen Massa, Ginevra Urbani, Rosalinda Roselli, Federica Moraca, Valentina Sepe, Bruno Catalanotti, Elva Morretta, Maria Chiara Monti, Michele Biagioli, Eleonora Distrutti, Angela Zampella and Stefano Fiorucci
Cells 2022, 11(21), 3482; https://doi.org/10.3390/cells11213482 - 3 Nov 2022
Cited by 7 | Viewed by 2835
Abstract
Pancreatic cancer is a leading cause of cancer mortality and is projected to become the second-most common cause of cancer mortality in the next decade. While gene-wide association studies and next generation sequencing analyses have identified molecular patterns and transcriptome profiles with prognostic [...] Read more.
Pancreatic cancer is a leading cause of cancer mortality and is projected to become the second-most common cause of cancer mortality in the next decade. While gene-wide association studies and next generation sequencing analyses have identified molecular patterns and transcriptome profiles with prognostic relevance, therapeutic opportunities remain limited. Among the genes that are upregulated in pancreatic ductal adenocarcinomas (PDAC), the leukaemia inhibitory factor (LIF), a cytokine belonging to IL-6 family, has emerged as potential therapeutic candidate. LIF is aberrantly secreted by tumour cells and promotes tumour progression in pancreatic and other solid tumours through aberrant activation of the LIF receptor (LIFR) and downstream signalling that involves the JAK1/STAT3 pathway. Since there are no LIFR antagonists available for clinical use, we developed an in silico strategy to identify potential LIFR antagonists and drug repositioning with regard to LIFR antagonists. The results of these studies allowed the identification of mifepristone, a progesterone/glucocorticoid antagonist, clinically used in medical abortion, as a potent LIFR antagonist. Computational studies revealed that mifepristone binding partially overlapped the LIFR binding site. LIF and LIFR are expressed by human PDAC tissues and PDAC cell lines, including MIA-PaCa-2 and PANC-1 cells. Exposure of these cell lines to mifepristone reverses cell proliferation, migration and epithelial mesenchymal transition induced by LIF in a concentration-dependent manner. Mifepristone inhibits LIFR signalling and reverses STAT3 phosphorylation induced by LIF. Together, these data support the repositioning of mifepristone as a potential therapeutic agent in the treatment of PDAC. Full article
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<p>LIF and LIFR show an opposite regulation in human PDAC. RNA-seq analysis of non-neoplastic and neoplastic mucosa of PDAC from GSE196009 repository. Each dot represents a patient. Data shown represent the gene profile expression of (<b>A</b>) LIFR, (<b>B</b>) LIF, chemokine receptor type 4 (<b>C</b>) CXCR4. Results are the mean ± SEM of 6 (Non-neoplastic) and 13 (Neoplastic) samples per group. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>LIF/LIFR expression is modulated in MIA PaCa-2 and PANC-1 cells and LIF induces cell proliferation. Relative mRNA expression of (<b>A</b>) LIFR and (<b>B</b>) LIF in MIA PaCa-2 (green) and PANC-1 (light blue) cell lines. (<b>C</b>) Immunofluorescence analysis of LIFR expression in PANC-1 and MIA PaCa-2 cell lines (magnification 100× and 500×). Dose-response curve of LIF (5, 10, 25, 50, 100 ng/mL) determined using MTS assay on (<b>D</b>) MIA PaCa-2 and (<b>E</b>) PANC-1 cell lines. Each value is expressed relative to the non-treated (NT) value, which is arbitrarily set to 1. Results are the mean<tt> </tt>±<tt> </tt>SEM of 10 samples per group. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>LIFR inhibition reverses pancreatic cancer cell proliferation and EMT process promoted by LIF. MIA PaCa-2 cells were serum-starved and primed with LIF (10 ng/mL) alone or in combination with increasing concentrations of LIFR antagonist, EC359 (25, 50,100, 1000 nM). Data shown are (<b>A</b>) dose-response curve of EC359 (25, 50, 100, 1000 nM) determined using MTS assay on cells. Each value is expressed relative to the non-treated (NT) value, which is arbitrarily set to 1. Results are the mean ± SEM of 10 samples per group. (<b>B</b>) Relative mRNA expression of the proliferation marker <span class="html-italic">C-Myc</span>; the EMT markers <span class="html-italic">VIM</span> and <span class="html-italic">SNAIL-</span>1; and <span class="html-italic">CXCR4</span>. Each value is normalized to <span class="html-italic">GAPDH</span> and is expressed relative to those of positive controls, which are arbitrarily set to 1. Results are the mean ± SEM of five samples per group (* represents statistical significance versus NT, and # versus LIF, <span class="html-italic">p</span> &lt; 0.05). Panel (<b>C</b>) shows changes in vimentin expression assessed by immunofluorescence analysis in MIA PaCa-2 cells triggered with LIF (10 ng/mL) alone or in combination with EC359 25 nM.</p>
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<p>Modelling. (<b>A</b>) <span class="html-italic">h</span>LIFR-ID01 complex. The protein backbone is displayed in ribbon and coloured by distinguishing the five domains (D1–D5). In the zoom view, (I) the ID01 binding mode of mifepristone (displayed as yellow ball and stick), highlighting the “T-inverted” shape; (II) the “T-inverted” shape well-fit with <span class="html-italic">h</span>LIFR surface; (III) the main residues involved in the interactions with the mifepristone; (IV) the hydrophobic (yellow) and hydrogen bond acceptor (red) maps fit with mifepristone. The principal residues are labelled and highlighted in wireframe or CPK(A3), while hydrogen bonds are in dashed black lines. (<b>B</b>) Superimposition between the ID01 best MM/GBSA value pose (black) and the <span class="html-italic">m</span>LIFR-<span class="html-italic">h</span>LIF X-ray (PDB ID: 2Q7N) (cyan). The three loops L1 (255-VSASSG-260), L2 (303-NPGRVTALVGPRAT-316), and L3 (332-KRAEAPTNES-341) are highlighted in yellow, blue, and green, respectively. The red rectangle highlights the clash with the propyne moiety of mifepristone and <span class="html-italic">h</span>LIF. (<b>C</b>) Two-dimensional structures of mifepristone. (<b>D</b>) The root means square fluctuation (RMSF) plot of the three compared systems (ID01 complex, <span class="html-italic">h</span>LIFR-<span class="html-italic">h</span>LIF, and <span class="html-italic">h</span>LIFR) during 100 ns of MDs.</p>
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<p>Mifepristone antagonizes LIFR and reduces STAT3 activation. (<b>A</b>) Mifepristone inhibition activity of LIFR/LIF binding accessed by a cell-free AlphaScreen assay. (<b>B</b>) STAT3 transactivation on HepG2 cells. Results are expressed as mean ± SEM of five samples per group.</p>
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<p>Mifepristone reduces MIA PaCa-2 cell proliferation and reverts EMT process. (<b>A</b>) Dose-response curve of mifepristone (0.1, 1, 10, 20, 50 µM) was determined using MTS assay on MIA PaCa-2 cells (n = 10). (<b>B</b>) Immunofluorescence analysis of Ki-67 positive MIA PaCa-2 cells left untreated or challenged with LIF (10 ng/mL) alone or in combination with mifepristone 10 µM; and estimated number of Ki-67 positive cells. MIA PaCa-2 cells were serum-starved and challenged with a vehicle or LIF 10 ng/mL alone or in combination with mifepristone (10, 20 µM) for 24 h. Cell cycle phase analysis was performed by Ki-67/7-AAD staining through IC-FACS. (<b>C</b>) Representative IC-FACS shows cell cycle fraction in each experimental group. (<b>D</b>) Data shown are frequencies of cells in the G0-G1 phase and S-G2-M phase. (<b>E</b>) Representative IC-FACS shows cell cycle fraction in each experimental group. (<b>F</b>) Data shown are frequencies of Annexin V<sup>+</sup> single cells. Each value is normalized to untreated cells, expressed relative to those of controls, which are arbitrarily set to 1. Results are the mean ± SEM of three samples for group (* represents statistical significance versus NT, and # versus LIF, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Mifepristone inhibits in vitro migration in STAT3-dependent signalling. (<b>A</b>) Relative mRNA expression of Vimentin and CXCR4. (<b>B</b>) Immunofluorescence analysis of Vimentin expression. (<b>C</b>) Scratch wound healing assay. MIA PaCa-2 cell monolayers were scraped in a straight line using a p200 pipette tip; then, they were left untreated or primed with LIF 10 ng/mL alone or in combination with mifepristone 10 or 20 µM and EC359 25 nM. The wound generated was captured at 0 and 48 h of incubation with the compounds above described. The images show cell migration at the two times point indicated. (<b>D</b>) Images of obtained points were analysed, measuring scraped area and its closure vs. the first time point at 0 h. Results are the mean ± SEM of three samples per group (* represents statistical significance versus NT, and # versus LIF, <span class="html-italic">p</span> &lt; 0.05). (<b>E</b>) Analysis of STAT3 signalling pathway. Representative Western blot analysis of STAT3 and phospho-STAT3, proteins in MIA-PaCa-2 cells exposed to LIF (10 nM) alone or in combination with increasing concentration of mifepristone (10, 20, 50 µM) for 20 min. (<b>F</b>) Densitometric analysis demonstrating phospho-STAT3/STAT3 ratio.</p>
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<p>Analysis of mifepristone effects on MIA PaCa-2 cells challenged with LIF by RNAseq. (<b>A</b>) Heterogeneity characterization of the three experimental groups as shown by principal component analysis (PCA) plot. (<b>B</b>) Venn diagram of differentially expressed genes showing the overlapping region between the three experimental groups. (<b>C</b>) Volcano plots of transcripts differentially expressed between different experimental groups (fold change &lt;−2 or &gt;+2, <span class="html-italic">p</span> value &lt; 0.05). Red dots represent significantly upregulated genes, and green dots represent significantly downregulated genes. (<b>D</b>) Table showing genes modulated by LIF/mifepristone versus LIF.</p>
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13 pages, 3959 KiB  
Article
Inhibition of LIFR Blocks Adiposity-Driven Endometrioid Endometrial Cancer Growth
by Logan Blankenship, Uday P. Pratap, Xue Yang, Zexuan Liu, Kristin A. Altwegg, Bindu Santhamma, Kumaraguruparan Ramasamy, Swapna Konda, Yidong Chen, Zhao Lai, Siyuan Zheng, Gangadhara R. Sareddy, Philip T. Valente, Edward R. Kost, Hareesh B. Nair, Rajeshwar R. Tekmal, Ratna K. Vadlamudi and Suryavathi Viswanadhapalli
Cancers 2022, 14(21), 5400; https://doi.org/10.3390/cancers14215400 - 2 Nov 2022
Cited by 5 | Viewed by 2185
Abstract
Endometrial cancer (EC) is the fourth most common cancer in women, and half of the endometrioid EC (EEC) cases are attributable to obesity. However, the underlying mechanism(s) of obesity-driven EEC remain(s) unclear. In this study, we examined whether LIF signaling plays a role [...] Read more.
Endometrial cancer (EC) is the fourth most common cancer in women, and half of the endometrioid EC (EEC) cases are attributable to obesity. However, the underlying mechanism(s) of obesity-driven EEC remain(s) unclear. In this study, we examined whether LIF signaling plays a role in the obesity-driven progression of EEC. RNA-seq analysis of EEC cells stimulated by adipose conditioned medium (ADP-CM) showed upregulation of LIF/LIFR-mediated signaling pathways including JAK/STAT and interleukin pathways. Immunohistochemistry analysis of normal and EEC tissues collected from obese patients revealed that LIF expression is upregulated in EEC tissues compared to the normal endometrium. Treatment of both primary and established EEC cells with ADP-CM increased the expression of LIF and its receptor LIFR and enhanced proliferation of EEC cells. Treatment of EEC cells with the LIFR inhibitor EC359 abolished ADP-CM induced colony formation andcell viability and decreased growth of EEC organoids. Mechanistic studies using Western blotting, RT-qPCR and reporter assays confirmed that ADP-CM activated LIF/LIFR downstream signaling, which can be effectively attenuated by the addition of EC359. In xenograft assays, co-implantation of adipocytes significantly enhanced EEC xenograft tumor growth. Further, treatment with EC359 significantly attenuated adipocyte-induced EEC progression in vivo. Collectively, our data support the premise that LIF/LIFR signaling plays an important role in obesity-driven EEC progression and the LIFR inhibitor EC359 has the potential to suppress adipocyte-driven tumor progression. Full article
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<p>Global RNA-seq analysis of RL95-2 cells treated with ADP-CM identified unique pathways. (<b>A</b>) Heatmap showing differentially expressed genes upon ADP-CM treatment with |FC | &gt; 1.5. (<b>B</b>–<b>D</b>) GSEA showing positively enriched pathways in ADP-CM-treated cells. GSEA plots show JAK-STAT pathway (<b>C</b>) and interferon alpha (<b>D</b>) response gene signatures were positively enriched in ADP-CM-treated group. (<b>E</b>) Heatmap shows the regulation of several known STAT3 target genes in ADP-CM-treated cells. (<b>F</b>) Ingenuity pathway analysis shows the top 10 pathways upregulated in ADP-CM-treated cells. (<b>G</b>) Validation of LIFR target genes by RT-qPCR. Data are represented as mean ± SE. **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>LIF is overexpressed in EEC. (<b>A</b>) Tissue microarray consisting of samples from patients with EEC (n = 46) and from individuals with normal endometrial tissue (n = 33) were evaluated for LIF expression, and representative IHC images are shown with LIF intensity score 0–3. (<b>B</b>) Quantitation of expression of LIF in normal and EEC tissue microarray is shown. Data are represented as mean ± SE. * <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>Adipocytes induced LIFR signaling in EEC cells. (<b>A</b>) Schematic of ADP-CM treatment of EEC cells. EEC cells were treated with ADP-CM for 24 h, and the expression of LIFR, pSTAT3, STAT3 (<b>B</b>) and LIFR downstream signaling (<b>C</b>) was determined by Western blotting. (<b>D</b>) Effect of adipose conditions on EEC cell proliferation was determined by MTT cell viability assay. (<b>E</b>) LIFR gene expression in established and primary EEC cells was analyzed by RT-qPCR. (<b>F</b>) Ishikawa cells were incubated with ADP-CM for 24 h, and the expression of LIFR target genes was analyzed by RT-qPCR. (<b>G</b>) Schematic of transwell co-culture system. (<b>H</b>) Adipocytes were indirectly co-cultured with primary EEC model cells (EC16) using a transwell culture system for 24 h, and the expression of LIFR target genes was analyzed by RT-qPCR. Data are represented as mean ± SE. ** <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>EC359 reduced adiposity-induced cell viability, colony formation and LIFR downstream signaling. (<b>A</b>) Established and primary endometrial cancer cells were incubated with ADP-CM with or without EC359 and cell proliferation was determined by MTT cell viability assay. (<b>B</b>) Effect of EC359 on adiposity-induced cell survival of EEC cells was measured using colony formation assays, and quantitation is shown on the right panel. (<b>C</b>) Representative images of PDOs cultured in ADP-CM in the presence or absence of EC359 are shown. (<b>D</b>) Effect of EC359 on adiposity-induced cell viability of organoids was measured using CellTiter-Glo 3D-Superior Cell Viability Assay. (<b>E</b>) Ishikawa and primary EEC (EC14) cells were treated with ADP-CM, and expression of LIFR and downstream signaling proteins were analyzed using Western blot analysis. (<b>F</b>) EEC cells stably expressing STAT3-luc were treated with ADP-CM in the presence or absence of EC359, and STAT3 reporter activity was measured after 24 h. (<b>G</b>) Primary EEC cells (EC16) incubated with ADP-CM with or without EC359, and the expression of LIFR target genes was analyzed by RT-qPCR. Data are represented as mean ± SE. ** <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>EC359 reduced LIFR downstream signaling in EEC cells co-cultured with adipocytes. (<b>A</b>,<b>B</b>), primary EEC (EC16) (<b>A</b>) and RL95-2 (<b>B</b>) cells were co-cultured with adipocytes (ADP-CC1 and ADP-CC2) for 24 h with or without EC359, and the expression of LIFR and downstream signaling proteins were analyzed using Western blot analysis. (<b>C</b>,<b>D</b>) Effect of EC359 treatment using RL95-2 cells co-cultured with ADP-CC1 (<b>C</b>) and ADP-CC2 (<b>D</b>) for 24 h on LIFR-targeted genes was measured using RT-qPCR. Data are represented as mean ± SE. **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>EC359 treatment inhibits adiposity-induced in vivo xenograft tumor growth. A-C, RL95-2 cells were injected along with mature adipocytes and treated with or without EC359. RL95-2 cells injected along with mature adipocytes served as a vehicle. Tumor volume was measured twice a week. Tumor volume (<b>A</b>), tumor images (<b>B</b>), tumor weights (<b>C</b>) and IHC of Ki67 (<b>D</b>) are shown. (<b>E</b>) Schematic representation: Obesity conditions activate LIF/LIFR signaling, and this promotes EC progression via activation of STAT3, mTOR, AKT, MAPK signaling. Data are represented as mean ± SE. * <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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15 pages, 2598 KiB  
Article
RNA Modification-Related Genetic Variants in Genomic Loci Associated with Bone Mineral Density and Fracture
by Limin Han, Jingyun Wu, Mimi Wang, Zhentao Zhang, Dian Hua, Shufeng Lei and Xingbo Mo
Genes 2022, 13(10), 1892; https://doi.org/10.3390/genes13101892 - 18 Oct 2022
Cited by 5 | Viewed by 2455
Abstract
Genome-wide association studies (GWASs) have identified more than 500 loci for bone mineral density (BMD), but functional variants in these loci are less known. The aim of this study was to identify RNA modification-related SNPs (RNAm-SNPs) for BMD in GWAS loci. We evaluated [...] Read more.
Genome-wide association studies (GWASs) have identified more than 500 loci for bone mineral density (BMD), but functional variants in these loci are less known. The aim of this study was to identify RNA modification-related SNPs (RNAm-SNPs) for BMD in GWAS loci. We evaluated the association of RNAm-SNPs with quantitative heel ultrasound BMD (eBMD) in 426,824 individuals, femoral neck (FN) and lumbar spine (LS) BMD in 32,961 individuals and fracture in ~1.2 million individuals. Furthermore, we performed functional enrichment, QTL and Mendelian randomization analyses to support the functionality of the identified RNAm-SNPs. We found 300 RNAm-SNPs significantly associated with BMD, including 249 m6A-, 28 m1A-, 3 m5C-, 7 m7G- and 13 A-to-I-related SNPs. m6A-SNPs in OP susceptibility genes, such as WNT4, WLS, SPTBN1, SEM1, FUBP3, LRP5 and JAG1, were identified and functional enrichment for m6A-SNPs in the eBMD GWAS dataset was detected. eQTL signals were found for nearly half of the identified RNAm-SNPs, and the affected gene expression was associated with BMD and fracture. The RNAm-SNPs were also associated with the plasma levels of proteins in cytokine-cytokine receptor interaction, PI3K-Akt signaling, NF-kappa B signaling and MAPK signaling pathways. Moreover, the plasma levels of proteins (CCL19, COL1A1, CTSB, EFNA5, IL19, INSR, KDR, LIFR, MET and PLXNB2) in these pathways were found to be associated with eBMD in Mendelian randomization analysis. This study identified functional variants and potential causal genes for BMD and fracture in GWAS loci and suggested that RNA modification may play an important role in osteoporosis. Full article
(This article belongs to the Section Human Genomics and Genetic Diseases)
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<p>Genome-wide distribution of the identified eBMD-associated m<sup>6</sup>A-SNPs. The Manhattan plot shows the associations between m<sup>6</sup>A-SNPs and eBMD. The x-axis indicates chromosome positions. The y-axis indicates −log<sub>10</sub><span class="html-italic">p</span> values of the associations. The red line indicates the genome-wide significance level of 5.0 × 10<sup>−</sup><sup>8</sup>.</p>
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<p>Association between the <span class="html-italic">IDUA</span> gene and eBMD. The m<sup>6</sup>A-SNP rs6815946 in the <span class="html-italic">IDUA</span> gene was associated with eBMD and fracture. The expression levels of the <span class="html-italic">FGFRL1</span> gene in skeletal muscle were associated with eBMD and fracture.</p>
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<p>Association signals of m<sup>6</sup>A-SNPs with eBMD. The six regional association plots show the associations between m<sup>6</sup>A-SNPs in key OP susceptibility genes and eBMD. The m<sup>6</sup>A-SNPs in each gene locus are annotated in the plot.</p>
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<p>Association between the <span class="html-italic">SPTBN1</span> gene and eBMD. The m<sup>6</sup>A-SNP rs2229503 in the <span class="html-italic">SPTBN1</span> gene was associated with eBMD. The expression levels of the <span class="html-italic">SPTBN1</span> gene in adipose tissue and whole blood were associated with eBMD.</p>
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<p>Pathway enrichment of the identified proteins. The upper panel shows the pathways for proteins affected by RNAm-SNPs identified in pQTL analysis; the lower panel shows the pathways for potential causal proteins identified in MR analysis.</p>
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6 pages, 212 KiB  
Editorial
OSM/OSMR and Interleukin 6 Family Cytokines in Physiological and Pathological Condition
by Francesca Lantieri and Tiziana Bachetti
Int. J. Mol. Sci. 2022, 23(19), 11096; https://doi.org/10.3390/ijms231911096 - 21 Sep 2022
Cited by 7 | Viewed by 1842
Abstract
Oncostatin M (OSM) is a member of the interleukin-6 (IL-6) family of cytokines and can bind two different receptors, Leukemia inhibitory factor receptor (LIFR) and Oncostatin M receptor (OSMR), through a complex containing the common glycoprotein 130 (gp130) subunit [...] Full article
17 pages, 1334 KiB  
Review
Regulation of Embryonic Stem Cell Self-Renewal
by Guofang Chen, Shasha Yin, Hongliang Zeng, Haisen Li and Xiaoping Wan
Life 2022, 12(8), 1151; https://doi.org/10.3390/life12081151 - 29 Jul 2022
Cited by 7 | Viewed by 4878
Abstract
Embryonic stem cells (ESCs) are a type of cells capable of self-renewal and multi-directional differentiation. The self-renewal of ESCs is regulated by factors including signaling pathway proteins, transcription factors, epigenetic regulators, cytokines, and small molecular compounds. Similarly, non-coding RNAs, small RNAs, and microRNAs [...] Read more.
Embryonic stem cells (ESCs) are a type of cells capable of self-renewal and multi-directional differentiation. The self-renewal of ESCs is regulated by factors including signaling pathway proteins, transcription factors, epigenetic regulators, cytokines, and small molecular compounds. Similarly, non-coding RNAs, small RNAs, and microRNAs (miRNAs) also play an important role in the process. Functionally, the core transcription factors interact with helper transcription factors to activate the expression of genes that contribute to maintaining pluripotency, while suppressing the expression of differentiation-related genes. Additionally, cytokines such as leukemia suppressor factor (LIF) stimulate downstream signaling pathways and promote self-renewal of ESCs. Particularly, LIF binds to its receptor (LIFR/gp130) to trigger the downstream Jak-Stat3 signaling pathway. BMP4 activates the downstream pathway and acts in combination with Jak-Stat3 to promote pluripotency of ESCs in the absence of serum. In addition, activation of the Wnt-FDZ signaling pathway has been observed to facilitate the self-renewal of ESCs. Small molecule modulator proteins of the pathway mentioned above are widely used in in vitro culture of stem cells. Multiple epigenetic regulators are involved in the maintenance of ESCs self-renewal, making the epigenetic status of ESCs a crucial factor in this process. Similarly, non-coding RNAs and cellular energetics have been described to promote the maintenance of the ESC’s self-renewal. These factors regulate the self-renewal and differentiation of ESCs by forming signaling networks. This review focused on the role of major transcription factors, signaling pathways, small molecular compounds, epigenetic regulators, non-coding RNAs, and cellular energetics in ESC’s self-renewal. Full article
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<p>LIF-mediated signaling pathways regulate mESCs pluripotency.</p>
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<p>The activation of the Wnt signaling promotes ES cell self-renewal.</p>
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<p>Cellular energetics in stem cell self-renewal.</p>
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15 pages, 7211 KiB  
Article
Ciliary Neurotrophic Factor (CNTF) and Its Receptors Signal Regulate Cementoblasts Apoptosis through a Mechanism of ERK1/2 and Caspases Signaling
by Jiawen Yong, Sabine Groeger, Julia von Bremen and Sabine Ruf
Int. J. Mol. Sci. 2022, 23(15), 8335; https://doi.org/10.3390/ijms23158335 - 28 Jul 2022
Cited by 6 | Viewed by 2142
Abstract
Ciliary neurotrophic factor (CNTF) was identified as a survival factor in various types of peripheral and central neurons, glia and non-neural cells. At present, there is no available data on the expression and localization of CNTF-receptors in cementoblasts as well as on the [...] Read more.
Ciliary neurotrophic factor (CNTF) was identified as a survival factor in various types of peripheral and central neurons, glia and non-neural cells. At present, there is no available data on the expression and localization of CNTF-receptors in cementoblasts as well as on the role of exogenous CNTF on this cell line. The purpose of this study was to determine if cementoblasts express CNTF-receptors and analyze the mechanism of its apoptotic regulation effects on cementoblasts. OCCM-30 cementoblasts were cultivated and stimulated kinetically using CNTF protein (NBP2-35168, Novus Biologicals). Quantified transcriptional (RT-qPCR) and translational (WB) products of CNTFRα, IL-6Rα (CD126), LIFR, p-GP130, GP130, p-ERK1/2, ERK1/2, Caspase-8, -9, -3 and cleaved-caspase-3 were evaluated. Immunofluorescence (IF) staining was applied to visualize the localization of the CNTF-receptors within cells. The apoptosis ratio was measured with an Annexin-V FITC/PI kit. The ERK1/2 antagonist (FR180204, Calbiochem) was added for further investigation by flow cytometry analysis. The CNTF-receptor complex (CNTFRα, LIFR, GP130) was functionally up-regulated in cementoblasts while cultivated with exogenous CNTF. CNTF significantly attenuated cell viability and proliferation for long-term stimulation. Flow cytometry analysis shows that CNTF enhanced the apoptosis after prolonged duration. However, after only a short-term period, CNTF halts the apoptosis of cementoblasts. Further studies revealed that CNTF activated phosphorylated GP130 and the anti-apoptotic molecule ERK1/2 signaling to participate in the regulation of the apoptosis ratio of cementoblasts. In conclusion, CNTF elicited the cellular functions through a notable induction of its receptor complex in cementoblasts. CNTF has an inhibitory effect on the cementoblast homeostasis. These data also elucidate a cellular mechanism for an exogenous CNTF-triggered apoptosis regulation in a mechanism of ERK1/2 and caspase signaling and provides insight into the complex cellular responses induced by CNTF in cementoblasts. Full article
(This article belongs to the Special Issue Cell Apoptosis)
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<p>The tripartite CNTF-receptor complex is up-regulated by ciliary neurotrophic factor in cementoblasts. (<b>A</b>,<b>B</b>) WB showed protein expression of the CNTF-receptors (CNTFRα, LIFR and IL-6Rα) in OCCM-30 cells induced by CNTF protein (400 ng/mL) for various periods. Internal β-actin serves as loading control. (<b>C</b>) The expression of mRNAs encoding the CNTF-receptors were quantified by RT-qPCR. The relative mRNA expression of each gene was obtained through normalizing to internal <span class="html-italic">PPIB</span>. The statistical significance was determined by student <span class="html-italic">t</span>-test (<span class="html-italic">n</span> = 3 for each group). (<b>D</b>,<b>E</b>) The CNTF-receptors immunofluorescent localization showed the expression of CNTFRα (red arrow), LIFR (yellow arrow) and IL-6Rα (orange arrow) in CNTF-treated OCCM-30 cells. Nuclei are stained with DAPI (blue). Scale bar: 100 μm (image magnification: 40×); 50 μm (image magnification: 60×). Bar indicates values ± standard deviation (SD) which represent three independent experiments. Statistically significant differences (indicated by asterisks) are shown as follows (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.005).</p>
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<p>Ciliary neurotrophic factor triggers GP130 protein expression and phosphorylated GP130 in cementoblasts. (<b>A</b>,<b>B</b>) The protein expression of GP130 and phosphorylated GP130 were determined by WB. Internal β-actin serve as loading control. The line chart shows the densitometric analysis of p-GP130 expression related to total GP-130 expression. (<b>C</b>) RT-qPCR quantification of <span class="html-italic">GP130</span> (<span class="html-italic">IL-6st</span>) gene expression in OCCM-30 cells when treated with CNTF (400 ng/mL) for indicated time. The relative mRNA expression was obtained through normalizing to internal <span class="html-italic">PPIB</span>. (<b>D</b>,<b>E</b>) IF staining of subcellular localization of GP130 (white arrow) as well as p-GP130 (grey arrow) in non-stimulated cells (negative control) and CNTF-stimulated OCCM-30 cells. Nuclei are stained with DAPI (blue). Individual and merged images of GP130 and p-GP130 are shown. Scale bar: 100 μm (image magnification: 40×); 50 μm (image magnification: 60×). Bar indicates values ± standard deviation (SD) which represent three independent experiments. Statistically significant differences (indicated by asterisks) are shown as follows (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.005; *** <span class="html-italic">p</span> &lt; 0.0005).</p>
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<p>Ciliary neurotrophic factor impairs OCCM-30 homeostasis and activates the expression of ERK1/2 MAPK signaling. (<b>A</b>,<b>B</b>) Immunofluorescence microscopy images show representative proliferation markers Ki-67 expression. OCCM-30 cells were exposed to CNTF (400 ng/mL) and then underwent immunofluorescence staining for Ki-67 to visualize cells in the proliferation stage. The proportion of proliferating cells for each group was quantified according to Ki-67 positive cells (Ki-67<sup>+</sup>)/total cell counting (DAPI). (<b>C</b>) Cell viability assay was performed by MTS assay. IL-6 cytokine served as positive control. (<b>D</b>,<b>E</b>) Representative immunoblot of p-ERK1/2 protein expression in the presence of CNTF (400 ng/mL) at different time points. Internal β-actin serves as loading control. Densitometric immunoblot analysis of bands indicated the enhanced p-ERK1/2 expression relative to that of the control group. Densitometric results are showed as fold change. Bar indicates values ± standard deviation (SD) which represent three independent experiments. Statistically significant differences (indicated by asterisks) are shown as follows (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.005; *** <span class="html-italic">p</span> &lt; 0.0005).</p>
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<p>Ciliary neurotrophic factor regulates apoptosis rate and triggers the caspases signaling. (<b>A</b>,<b>B</b>) Representative plots from Annexin-V FITC and PI staining by flow cytometry analysis performed in triplicate are shown. Apoptotic cells (Annexin-V FITC<sup>+</sup>/PI<sup>+</sup>) are shown in the upper right quadrant. Graphics show the percentages of apoptotic cells exposed to CNTF (400 ng/mL) at different time periods. (<b>C</b>,<b>D</b>) Representative immunoblot showed that the protein expression of Caspase-8, -9 and -3 as well as cleaved-caspase-3 in response to CNTF (400 ng/mL) in a time-dependent manner. β-actin was loaded as an internal control. (<b>E</b>) mRNA expression of <span class="html-italic">Caspase-8</span>, <span class="html-italic">-9</span> and <span class="html-italic">-3</span> in response to CNTF (400 ng/mL) stimulation at indicated time period. Bar indicates values ± standard deviation (SD) which represent three independent experiments. Statistically significant differences (indicated by asterisks) are shown as follows (ns, no significant difference; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.005; *** <span class="html-italic">p</span> &lt; 0.0005).</p>
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<p>ERK1/2 signal is involved in the regulation of apoptosis of cementoblasts and the caspases pathway. (<b>A</b>,<b>B</b>) Graphics show the percentages of apoptotic cells exposed to ERK1/2 inhibitor (1.0 μg/mL, FR180204) as well as co-stimulation with CNTF (400 ng/mL). (<b>C</b>) The scheme summarizes the mode of CNTF action in cementoblasts: CNTF activated the tripartite CNTF-receptor complex targets and phosphorylated GP130 protein, which recruits ERK1/2 signaling and caspases signaling expression. FR180204 promotes apoptosis in OCCM-30 cells and CNTF addition suppressed the ERK1/2 inhibitor-induced apoptosis within a short-term period. Bar indicates values ± standard deviation (SD) which represent three independent experiments. Statistically significant differences (indicated by asterisks) are shown as follows (** <span class="html-italic">p</span> &lt; 0.005; *** <span class="html-italic">p</span> &lt; 0.0005).</p>
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14 pages, 2806 KiB  
Article
Transcriptome Sequencing to Identify Important Genes and lncRNAs Regulating Abdominal Fat Deposition in Ducks
by Chunyan Yang, Zhixiu Wang, Qianqian Song, Bingqiang Dong, Yulin Bi, Hao Bai, Yong Jiang, Guobin Chang and Guohong Chen
Animals 2022, 12(10), 1256; https://doi.org/10.3390/ani12101256 - 13 May 2022
Cited by 3 | Viewed by 2430
Abstract
Abdominal fat deposition is an important trait in meat-producing ducks. F2 generations of 304 Cherry Valley and Runzhou Crested White ducks were studied to identify genes and lncRNAs affecting abdominal fat deposition. RNA sequencing was used to study abdominal fat tissue of four [...] Read more.
Abdominal fat deposition is an important trait in meat-producing ducks. F2 generations of 304 Cherry Valley and Runzhou Crested White ducks were studied to identify genes and lncRNAs affecting abdominal fat deposition. RNA sequencing was used to study abdominal fat tissue of four ducks each with high or low abdominal fat rates. In all, 336 upregulated and 297 downregulated mRNAs, and 95 upregulated and 119 downregulated lncRNAs were identified. Target gene prediction of differentially expressed lncRNAs identified 602 genes that were further subjected to Gene Ontology and KEGG pathway analysis. The target genes were enriched in pathways associated with fat synthesis and metabolism and participated in biological processes, including Linoleic acid metabolism, lipid storage, and fat cell differentiation, indicating that these lncRNAs play an important role in abdominal fat deposition. PPAPA, FOXO3, FASN, PNPLA2, FKBP5, TCF7L2, BMP2, FGF2, LIFR, ZBTB16, SIRT, GYG2, NCOR1, and NR3C1 were involved in the regulation of abdominal fat deposition. PNPLA2, TCF7L2, FGF2, LIFR, BMP2, FKBP5, GYG2, and ZBTB16 were regulated by the lncRNAs TCONS_00038080, TCONS_0033547, TCONS_00066773, XR_001190174.3, XR_003492471.1, XR_003493494.1, XR_001192142.3, XR_002405656.2, XR_002401822.2, XR_003497063.1, and so on. This study lays foundations for exploring molecular mechanisms underlying the regulation of abdominal fat deposition in ducks and provides a theoretical basis for breeding high-quality meat-producing ducks. Full article
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<p>Identification and analysis of lncRNA. Note: (<b>A</b>) Venn diagram of annotation results of CPC, CNCI, and Swissprot. (<b>B</b>) Statistical chart of new lncRNA transcript types.</p>
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<p>The expression of differential mRNA (DEGs) and differential lncRNA (DELs) in the high and low abdominal fat rate groups. Note: (<b>A</b>) Statistics on the number of DEGs in the high and low abdominal fat rate groups; (<b>B</b>) statistics on the number of DELs in the high and low abdominal fat rate groups; (<b>C</b>) volcano map of DEGs, the <span class="html-italic">y</span> axis is the value of −log10 (<span class="html-italic">p</span> Value), the <span class="html-italic">x</span> axis is the value of log2 (FC), and the two threshold lines respectively represent <span class="html-italic">p</span> = 0.01 and FC = 2; (<b>D</b>) volcano map of DELs, the <span class="html-italic">y</span> axis is the value of −log10 (<span class="html-italic">p</span> Value), and the <span class="html-italic">x</span> axis is the value of log2 (FC). The two threshold lines respectively represent FDR = 0.05 and FC = 2.</p>
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<p>Analysis of Gene Ontology and KEGG Pathway Enrichment of differential mRNAs. Note: (<b>A</b>) Gene Ontology of differential mRNAs. Blue column represents BP, red column represents CC and green column represents MF; (<b>B</b>) KEGG Pathway Enrichment of differential mRNAs.</p>
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<p>Analysis of Gene Ontology and KEGG Pathway Enrichment of target genes of differential lncRNAs. Note: (<b>A</b>) Gene Ontology of target genes of differential lncRNAs. Blue column represents BP, red column represents CC, and green column represents MF; (<b>B</b>) KEGG Pathway Enrichment of target genes of differential lncRNAs.</p>
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<p>The interaction between DE lncRNA and its target genes as well as DEGs. Note: (<b>A</b>) Protein–protein interactions of genes and target genes of differential lncRNAs. Note: (<b>B</b>) Interactions between lncRNAs and target genes.</p>
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<p>qRT-PCR verification of differentially expressed genes/lncRNA results. Red represents HL and blue represents LF (0.01 &lt; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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24 pages, 8655 KiB  
Review
The Role of Oncostatin M and Its Receptor Complexes in Cardiomyocyte Protection, Regeneration, and Failure
by Thomas Kubin, Praveen Gajawada, Peter Bramlage, Stefan Hein, Benedikt Berge, Ayse Cetinkaya, Heiko Burger, Markus Schönburg, Wolfgang Schaper, Yeong-Hoon Choi and Manfred Richter
Int. J. Mol. Sci. 2022, 23(3), 1811; https://doi.org/10.3390/ijms23031811 - 5 Feb 2022
Cited by 13 | Viewed by 6633
Abstract
Oncostatin M (OSM), a member of the interleukin-6 family, functions as a major mediator of cardiomyocyte remodeling under pathological conditions. Its involvement in a variety of human cardiac diseases such as aortic stenosis, myocardial infarction, myocarditis, cardiac sarcoidosis, and various cardiomyopathies make the [...] Read more.
Oncostatin M (OSM), a member of the interleukin-6 family, functions as a major mediator of cardiomyocyte remodeling under pathological conditions. Its involvement in a variety of human cardiac diseases such as aortic stenosis, myocardial infarction, myocarditis, cardiac sarcoidosis, and various cardiomyopathies make the OSM receptor (OSMR) signaling cascades a promising therapeutic target. However, the development of pharmacological treatment strategies is highly challenging for many reasons. In mouse models of heart disease, OSM elicits opposing effects via activation of the type II receptor complex (OSMR/gp130). Short-term activation of OSMR/gp130 protects the heart after acute injury, whereas chronic activation promotes the development of heart failure. Furthermore, OSM has the ability to integrate signals from unrelated receptors that enhance fetal remodeling (dedifferentiation) of adult cardiomyocytes. Because OSM strongly stimulates the production and secretion of extracellular proteins, it is likely to exert systemic effects, which in turn, could influence cardiac remodeling. Compared with the mouse, the complexity of OSM signaling is even greater in humans because this cytokine also activates the type I leukemia inhibitory factor receptor complex (LIFR/gp130). In this article, we provide an overview of OSM-induced cardiomyocyte remodeling and discuss the consequences of OSMR/gp130 and LIFR/gp130 activation under acute and chronic conditions. Full article
(This article belongs to the Special Issue Cytokine Receptors In Development, Homeostasis & Disease)
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<p>Simplified scheme illustrating the members of the interleukin-6 family of cytokines and their receptor complexes involved in adult cardiomyocyte remodeling and the differential formation of receptor complexes by OSM in the human, rat, and mouse. IL-6, IL-11, CT-1, LIF, and OSM elicit receptor complex activation in cardiomyocytes. Amongst them, OSM showed the strongest morphological effect on cultured cardiomyocytes, whereas the other members exerted a strong and comparable effect, except for IL-6, which was less effective because the weakly expressed IL-6 receptor probably needs trans-signaling events. The common co-receptor gp130 may explain the similar morphological responses of cardiomyocytes exposed to the IL-6 family. IL-6 and IL-11 activate two homodimeric gp130 receptor complexes containing the IL-6 receptor and the IL-11 receptor, respectively. In contrast, CT-1, LIF, and OSM do not require an additional non-signaling receptor to form functional complexes because they signal through a complex consisting of gp130 and the LIFR. OSM is unique in terms of receptor binding in that it can bind to both the type I (LIFR/gp130) receptor complex and type II (OSMR/gp130) receptor complex in humans and rats, whereas it binds only to the type I complex in mice.</p>
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<p>Schematic representation of morphological effects of activated type I and type II receptor complexes on cultured adult rat cardiomyocytes and IL-6 secretion. In all our screens, we utilized 20 ng/mL of albumin (Con), rat leukemia inhibitory factor (LIF, activates the rat LIFR/gp130 complex), mouse oncostatin M (mOSM, activates the rat OSMR/gp130 receptor complex), rat oncostatin M (rOSM, activates the rat LIFR/gp130 and the rat OSMR/gp130 complex), cardiotrophin-1 (CT1), transforming growth factor-β (TGFβ), tumor necrosis factor-α (TNFα), interleukin-1α (IL-1α), and fibroblast growth factor-2 (FGF2). (<b>A</b>) IL-6 ELISA showing concentrations of IL-6 in the cardiomyocyte culture supernatants 36 h after stimulation with albumin (<span class="html-italic">n</span> = 8), OSM (<span class="html-italic">n</span> = 8), LIF (<span class="html-italic">n</span> = 6), CT-1 (<span class="html-italic">n</span> = 6), TGFß (<span class="html-italic">n</span> = 4), TNFα (<span class="html-italic">n</span> = 4), and IL-1α (<span class="html-italic">n</span> = 2). Data represent the mean ± SEM. Statistical analysis was performed through an unpaired t test with Welch’s correction showing significances between Con and OSM **** <span class="html-italic">p</span> &lt; 0.0001 and between Con and IL-1α ** <span class="html-italic">p</span> &lt; 0.002. (<b>B</b>) Bright-field micrograph of a freshly isolated rat cardiomyocyte showing a complex three-dimensional structure with typical cross-striation. (<b>C</b>) Intercalated discs (stained with connexin-43 in red) are organized laterally (yellow arrows) and at the cell ends (white arrows). White ovals indicate nuclei and the green color marks sarcomeres (stained with sarcomeric α-actinin) in a freshly isolated cardiomyocyte. (<b>D</b>) Scheme summarizing the main morphological effects of rOSM, mOSM, LIF, and FGF2 after 5–7 days in culture. Cellular elongation and formation of multiple extensions were the most obvious changes after oncostatin M treatment. While elongation after rOSM stimulation was dominant, a certain amount of spreading might have become visible with increased culture time when cellular contacts were reestablished. It is important to note that serum (fetal calf serum, FCS) was only utilized for the initial plating of cardiomyocytes and then the cultures were kept without serum or any further growth enhancer/stimulant in these studies. For comparison, FGF-2-stimulated cultures show an increase in surface area but comparatively little elongation or formation of extensions.</p>
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<p>The architecture of intercalated discs is changed in the diseased myocardium. (<b>A</b>) Connexin 43, which marks intercalated discs in the normal heart, is downregulated/rearranged in cardiomyocytes of the border and remote zone in the infarcted mouse myocardium. (<b>B</b>) P-ERM (a marker of activated Ezrin/Radixin/Moesin proteins) characterizes the formation of cardiomyocyte extensions (yellow arrows) in the infarcted mouse myocardium. (<b>C</b>) Similarly, connexin 43 is downregulated in patients with ischemic (ICM) and dilated (DCM) cardiomyopathy, indicating remodeling of intercalated discs. CON represents human control tissue.</p>
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<p>Infiltration of OSM-releasing cells is associated with fetal remodeling, increased expression of the OSMR, and secreted proteins in the human myocardium. MyoC indicates myocarditis, CS is cardiac sarcoidosis, ICM means ischemic cardiomyopathy, and CON represents human control tissue. Actn1 is non-muscle α-actinin-1 (Actn1). (<b>A</b>) Increases in OSM-positive infiltrates (mainly macrophages) are consistent with an increased expression of the OSMR in cardiomyocytes. (<b>B</b>) Increased dedifferentiation characterizes fetal remodeling (image is modified from [<a href="#B13-ijms-23-01811" class="html-bibr">13</a>]). Note the strong and thin cardiomyocyte elongation. (<b>C</b>) Both single FGF23-positive and clusters of positive cardiomyocytes can be identified. OSM is currently the only known cytokine that induces FGF23 expression in cardiomyocytes.</p>
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<p>Localization of the interleukin-1 receptor antagonist (IL-1ra) is influenced by the underlying disease. (<b>A</b>) IL-1ra in cardiomyocytes of a patient with ischemic cardiomyopathy. (<b>B</b>) IL-1ra in the granuloma of a patient with cardiac sarcoidosis.</p>
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<p>Hypothetical overall model of OSM-driven protection, regeneration, and failure of the heart. (<b>A</b>) The development of hypertrophy is initiated by activation of the OSMR in patients with aortic stenosis. When activation of the OSMR cascades decreases, the amount of hypertrophic signals increases (FGFs, IGFs). (<b>B</b>) After a cardiac injury such as acute myocardial infarction, OSM-releasing infiltrates reduce damage/infarct expansion and extension by inducing fetal remodeling of cardiomyocytes. Macrophage infiltration is controlled by various chemokine families (Reg3, IL-7, MCPs). (<b>C</b>) Cardiomyocytes form extensions and restore cell-cell contacts. (<b>D</b>) Infiltration and inflammatory processes are downregulated by anti-inflammatory molecules (IL-1ra, TGF-β) and hypertrophic pathways are activated (FGFs, IGFs). (<b>E</b>) Fetal remodeling is downregulated and myocytes undergo hypertrophic remodeling. Surviving cardiomyocytes adapt to the increased workload by enlarging and accumulating sarcomeres. (<b>F</b>) If infiltration and inflammatory processes persist, chronically activated OSM receptors cause degeneration of cardiomyocytes. (<b>G</b>) Dying cells and elongation of surviving cardiomyocytes lead to dilatation of the myocardium.</p>
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23 pages, 3578 KiB  
Review
Leukemia Inhibitory Factor: An Important Cytokine in Pathologies and Cancer
by Megan M. Jorgensen and Pilar de la Puente
Biomolecules 2022, 12(2), 217; https://doi.org/10.3390/biom12020217 - 27 Jan 2022
Cited by 25 | Viewed by 7819
Abstract
Leukemia Inhibitory Factor (LIF) is a member of the IL-6 cytokine family and is expressed in almost every tissue type within the body. Although LIF was named for its ability to induce differentiation of myeloid leukemia cells, studies of LIF in additional diseases [...] Read more.
Leukemia Inhibitory Factor (LIF) is a member of the IL-6 cytokine family and is expressed in almost every tissue type within the body. Although LIF was named for its ability to induce differentiation of myeloid leukemia cells, studies of LIF in additional diseases and solid tumor types have shown that it has the potential to contribute to many other pathologies. Exploring the roles of LIF in normal physiology and non-cancer pathologies can give important insights into how it may be dysregulated within cancers, and the possible effects of this dysregulation. Within various cancer types, LIF expression has been linked to hallmarks of cancer, such as proliferation, metastasis, and chemoresistance, as well as overall patient survival. The mechanisms behind these effects of LIF are not well understood and can differ between different tissue types. In fact, research has shown that while LIF may promote malignancy progression in some solid tumors, it can have anti-neoplastic effects in others. This review will summarize current knowledge of how LIF expression impacts cellular function and dysfunction to help reveal new adjuvant treatment options for cancer patients, while also revealing potential adverse effects of treatments targeting LIF signaling. Full article
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<p>LIF expression in human tissues. LIF expression measured in a wide variety of tissue types based on Immunohistochemical staining. Figure available from Human Protein Atlas, LIF Tissue Atlas, Protein Expression Overview. <a href="http://v20.proteinatlas.org/ENSG00000128342-LIF/tissue" target="_blank">v20.proteinatlas.org/ENSG00000128342-LIF/tissue</a> (accessed on 2 May 2021).</p>
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<p>LIF expression in normal tissue and cancer. LIF expression in normal tissue compared to tumor tissue across many tissue types. Significant differences between normal and tumor tissue determined by Mann–Whitney U test are marked with red*. Figure generated from <a href="https://www.tnmplot.com/" target="_blank">https://www.tnmplot.com/</a> (accessed on 3 May 2021), Copyright ©: Department of Bioinformatics, Semmelweis University 2020. Data used in figure gathered from RNAseq data from GTex, TCGA, and TARGET.</p>
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<p>Regulators of LIF Expression. Up-Regulators and Down-Regulators of LIF expression, and the tissue types this effect is seen in.</p>
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<p>LIFR and its cytokines. The LIFR consists of LIFRβ and gp130. While LIF is the primary ligand of the LIFR, other cytokines such as OSM, CT-1, CNTF, CRLF1, and CLCF1 can also bind to and signal through LIFR. Figure created with BioRender.com.</p>
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<p>LIFR expression in normal tissue and cancer. LIFR expression in normal tissue compared to tumor tissue across many tissue types. Figure generated from <a href="https://www.tnmplot.com/" target="_blank">https://www.tnmplot.com/</a> (accessed on 3 May 2021), Copyright ©: Department of Bioinformatics, Semmelweis University 2020. Significant differences by Mann–Whitney U test are marked with red *. Data from figure gathered from RNAseq data from GTex, TCGA, and TARGET.</p>
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<p>Downstream LIF Signaling Pathways. LIF binding to the LIFRβ/gp130 receptor complex activates tyrosine kinase pathways, such as JAK/STAT and PI3K, and downstream signaling molecules lead to activation of transcription factors able to alter gene expression in a cell-type specific manner. Figure created with BioRender.com.</p>
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<p>Downstream Actions of LIF. Within cancers, LIF can be seen to activate pathways such as JAK/STAT, PI3K/Akt, Ras/Raf, and MAPK. LIF expression has been linked to downregulation of p53, Hippo/YAP, IL-6, E-Cadherin, and TIMP-1. LIF expression has been seen to upregulate MDM2, IL-6R, SOCS3, IL-6, IL-1β, IL-8, GRB2, Caspase-4, STAT91, pp120, RAF-1, SOD, TFE3, TFEB, PLC-γ, PTP1D, SHC, and mTORC1. Figure created with BioRender.com.</p>
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<p>LIF in Cancers Summary. LIF, an IL-6 type cytokine, has potential as a cancer biomarker, and has roles in tumor progression, metastasis and cancer stem cells, treatment resistance, and cachexia. Figure created with BioRender.com.</p>
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19 pages, 4420 KiB  
Article
Concomitant Activation of OSM and LIF Receptor by a Dual-Specific hlOSM Variant Confers Cardioprotection after Myocardial Infarction in Mice
by Holger Lörchner, Juan M. Adrian-Segarra, Christian Waechter, Roxanne Wagner, Maria Elisa Góes, Nathalie Brachmann, Krishnamoorthy Sreenivasan, Astrid Wietelmann, Stefan Günther, Nicolas Doll, Thomas Braun and Jochen Pöling
Int. J. Mol. Sci. 2022, 23(1), 353; https://doi.org/10.3390/ijms23010353 - 29 Dec 2021
Cited by 7 | Viewed by 3352
Abstract
Oncostatin M (OSM) and leukemia inhibitory factor (LIF) signaling protects the heart after myocardial infarction (MI). In mice, oncostatin M receptor (OSMR) and leukemia inhibitory factor receptor (LIFR) are selectively activated by the respective cognate ligands while OSM activates both the OSMR and [...] Read more.
Oncostatin M (OSM) and leukemia inhibitory factor (LIF) signaling protects the heart after myocardial infarction (MI). In mice, oncostatin M receptor (OSMR) and leukemia inhibitory factor receptor (LIFR) are selectively activated by the respective cognate ligands while OSM activates both the OSMR and LIFR in humans, which prevents efficient translation of mouse data into potential clinical applications. We used an engineered human-like OSM (hlOSM) protein, capable to signal via both OSMR and LIFR, to evaluate beneficial effects on cardiomyocytes and hearts after MI in comparison to selective stimulation of either LIFR or OSMR. Cell viability assays, transcriptome and immunoblot analysis revealed increased survival of hypoxic cardiomyocytes by mLIF, mOSM and hlOSM stimulation, associated with increased activation of STAT3. Kinetic expression profiling of infarcted hearts further specified a transient increase of OSM and LIF during the early inflammatory phase of cardiac remodeling. A post-infarction delivery of hlOSM but not mOSM or mLIF within this time period combined with cardiac magnetic resonance imaging-based strain analysis uncovered a global cardioprotective effect on infarcted hearts. Our data conclusively suggest that a simultaneous and rapid activation of OSMR and LIFR after MI offers a therapeutic opportunity to preserve functional and structural integrity of the infarcted heart. Full article
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<p>Species-specific binding differences of LIF and OSM to OSMR and LIFR in human and mice. (<b>A</b>) Schematic illustration of species-specific binding properties of human OSM (hOSM), human LIF (hLIF), murine OSM (mOSM) and murine LIF (mLIF). Note that the generation of a human-like OSM (hlOSM) mutant mimics the binding properties of hOSM with the OSMR and LIFR in mice [<a href="#B17-ijms-23-00353" class="html-bibr">17</a>]. (<b>B</b>) Three-dimensional model of mOSM, mLIF and hlOSM. The AB loop and D–helix of each molecule constitute structural determinants of their species-specific receptor binding properties. The hlOSM protein contains the human AB loop sequence, which is highlighted in green.</p>
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<p>Transcriptome and gene set enrichment analysis characterizes STAT3, STAT5 and c–Myc as major common signaling molecules downstream of OSMR and LIFR activation in cardiomyocytes. (<b>A</b>) Experimental set-up for transcriptome analysis of primary murine cardiomyocytes after treatment with mLIF, mOSM and hlOSM (each at 20 ng mL<sup>−1</sup>) for 24 h (<span class="html-italic">n</span> = 3). Addition of equivalent volumes of sterile PBS served as control. (<b>B</b>) Principal component analysis of cultured cardiomyocytes treated with PBS, mLIF, mOSM and hlOSM. (<b>C</b>) Number of differentially up- and downregulated genes in mLIF–, mOSM– and hlOSM–treated versus PBS-treated cardiomyocytes. (<b>D</b>,<b>E</b>) Venn diagram of down- and up-regulated differentially expressed genes (DEG) in mLIF–, mOSM– and hlOSM–treated cardiomyocytes. (<b>F</b>) Gene set enrichment analysis (GSEA) of mLIF–, mOSM– and hlOSM–treated cardiomyocytes. GSEA was performed by pairwise comparisons of all cytokine-treated versus control samples. Gene sets are ranked by a normalized enrichment score.</p>
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<p>OSMR- and LIFR-mediated activation of STAT3 but not STAT5 and c–Myc coincides with an increased survival of cultured cardiomyocytes under hypoxic conditions. Representative images and semi-quantitative analysis of immunoblots of (<b>A</b>,<b>B</b>) phosphorylated (p–) STAT3 (Tyr705), (<b>C</b>,<b>D</b>) p–STAT5 (Tyr694) and (<b>E</b>,<b>F</b>) p–c–Myc (Ser62) in cardiomyocytes cultured under normoxic and hypoxic conditions (<span class="html-italic">n</span> = 5). Statistical analysis was performed by one-way ANOVA together with Bonferroni post-test comparisons to assess differences under normoxic and hypoxic conditions. Asterisks indicate Bonferroni post–hoc test significances between cytokine-treated versus PBS-treated cardiomyocytes with ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 and **** <span class="html-italic">p</span> &lt; 0.0001. Data represent ratios (FC) of expression in cytokine-treated versus PBS-treated cardiomyocytes. Total STAT3, STAT5 and c–Myc expression is shown below. (<b>G</b>,<b>H</b>) Representative immunofluorescent images and quantitative analysis of Calcein<sup>pos</sup> viable cardiomyocytes (green) cultured under normoxic and hypoxic conditions (Normoxia +PBS, <span class="html-italic">n</span> = 2; Hypoxia +PBS, +mLIF, +mOSM and +hlOSM, <span class="html-italic">n</span> = 4). Hoechst 33342 (blue) visualizes nuclei of cardiomyocytes. (<b>I</b>) Statistical analysis of lactate dehydrogenase release by hypoxic cardiomyocytes was performed by one-way ANOVA (<span class="html-italic">n</span> = 8). Asterisks indicate Bonferroni post-hoc test significances between cytokine-treated versus PBS-treated cardiomyocytes as well as between PBS-treated cardiomyocytes under normoxic and hypoxic conditions with * <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 and **** <span class="html-italic">p</span> &lt; 0.0001. Data are presented as mean ± sem.</p>
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<p>Kinetic expression pattern of OSM, LIF and their corresponding receptors OSMR and LIFR in cardiac tissue after the onset of myocardial infarction in mice. (<b>A</b>) Schematic illustration of kinetic expression profiling. Cardiac tissue was harvested at indicated time points post-MI and fractionated into a non-infarcted remote zone (RZ) and infarction zone (IZ). Expression of OSMR, OSM, LIFR and LIF in RZ and IZ was analyzed by immunoblotting (<span class="html-italic">n</span> = 6 at Day 1; <span class="html-italic">n</span> = 5 at Day 2; <span class="html-italic">n</span> = 7 at Day 4; <span class="html-italic">n</span> = 5 at Day 7; <span class="html-italic">n</span> = 5 at Day 14 post-MI). (<b>B</b>) Two representative immunoblots of OSMR, OSM, LIFR and LIF in RZ and IZ at indicated time points post-MI. (<b>C</b>–<b>F</b>) Semi-quantitative analysis of immunoblots shown in (<b>B</b>) based on the adjusted mean volume pixel density of bands. Statistical analysis was performed by two-way ANOVA. Asterisks indicate Bonferroni post-hoc test significances between RZ and IZ at individual time points as well as between RZ and IZ throughout the period of observation with * <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 and **** <span class="html-italic">p</span> &lt; 0.0001. Data are presented as mean ± sem. Immunofluorescence analysis of (<b>G</b>) OSMR and (<b>H</b>) LIFR in infarcted hearts of mice 7 days after MI. F-actin: grey. OSMR: red. LIFR: blue. IZ: infarction zone. RZ: remote zone. RV: right ventricle. LV: left ventricle. Scale bars, 500 µm and 100 µm in magnified sections.</p>
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<p>Post-infarction administration of mLIF, mOSM and hlOSM modulates the activation of STAT3, STAT5 and c–Myc at distinct sites of the myocardium. (<b>A</b>) Schematic illustration of systemic post-infarction administration of mLIF, mOSM and hlOSM in mice during the first three days of cardiac remodeling. At Day 4 post-MI, hearts were harvested, fractionated into a non-infarcted remote zone (RZ), border zone (BZ) and an infarction zone (IZ) to perform spatial expression analysis of signaling molecules via immunoblotting. Two representative images and semi-quantitative analysis of immunoblots of (<b>B</b>,<b>C</b>) p–STAT3 (Tyr705), (<b>D</b>,<b>E</b>) p–STAT5 (Tyr694) and (<b>F</b>,<b>G</b>) p–c–Myc (Ser62) in RZ, BZ and IZ of mice (<span class="html-italic">n</span> = 4 for all groups). Semi-quantitative analysis of immunoblots is based on the adjusted mean volume pixel density of bands. Statistical analysis was performed by one-way ANOVA together with Bonferroni post-test comparisons in order to monitor myocardial site-specific effects upon administration of PBS, mOSM, mLIF and hlOSM. Asterisks indicate Bonferroni post-hoc test significances between cytokine- and PBS-treated mice in RZ, BZ and IZ with * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001 and **** <span class="html-italic">p</span> &lt; 0.0001. Data are presented as mean ± sem.</p>
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<p>Simultaneous activation of the OSMR and LIFR by hlOSM preserves cardiac architecture and contractility after the onset of myocardial infarction. (<b>A</b>) Schematic illustration of the different global myocardial strain directions of the left ventricle: longitudinal shortening, circumferential shortening and radial thickening. Statistical analysis of magnetic resonance imaging-based (<b>B</b>) global longitudinal strain, (<b>C</b>) global circumferential strain and (<b>D</b>) global radial strain of the left ventricle at Day 28 post-MI following systemic injections of 100 ng recombinant mLIF, mOSM and hlOSM per gram of body weight at Days 0, 1, 2 and 3. Injections of equivalent volumes of sterile PBS, i.e., 100 μL per mouse, served as controls (+PBS, <span class="html-italic">n</span> = 8; +mLIF, <span class="html-italic">n</span> = 9, +mOSM, <span class="html-italic">n</span> = 8 and +hlOSM, <span class="html-italic">n</span> = 9). (<b>E</b>) Schematic illustration of the subdivision of the left ventricle into basal, midventricular, and apical segments used for regional circumferential strain (RCS) analyses. Statistical analysis of (<b>F</b>) basal RCS, (<b>G</b>) midventricular RCS and (<b>H</b>) apical RCS. Statistical analysis was performed by one-way ANOVA. Asterisks indicate Bonferroni post-hoc test significances between cytokine- and PBS–treated mice with * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01. Data are presented as mean ± sem. (<b>I</b>) Representative bull’s-eye plots illustrating the distribution of left ventricular regional circumferential strain values in PBS–, mLIF–, mOSM– and hlOSM–treated mice. The outer circle corresponds to basal, the middle circle to midventricular, and the inner circle to apical areas. Blue colors display negative strain values. Green colored areas reflect positive strain values.</p>
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