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15 pages, 281 KiB  
Article
TMEM132C rs7296262 Single-Nucleotide Polymorphism Is Significantly Associated with Nausea Induced by Opioids Administered for Cancer Pain and Postoperative Pain
by Yuna Kang, Daisuke Nishizawa, Seii Ohka, Takeshi Terui, Kunihiko Ishitani, Ryozo Morino, Miyuki Yokota, Junko Hasegawa, Kyoko Nakayama, Yuko Ebata, Kyotaro Koshika, Tatsuya Ichinohe and Kazutaka Ikeda
Int. J. Mol. Sci. 2024, 25(16), 8845; https://doi.org/10.3390/ijms25168845 (registering DOI) - 14 Aug 2024
Abstract
Opioids are almost mandatorily used for analgesia for cancer pain and postoperative pain. Opioid analgesics commonly induce nausea as a side effect. However, the genetic factors involved are still mostly unknown. To clarify the genetic background of individual differences in the occurrence of [...] Read more.
Opioids are almost mandatorily used for analgesia for cancer pain and postoperative pain. Opioid analgesics commonly induce nausea as a side effect. However, the genetic factors involved are still mostly unknown. To clarify the genetic background of individual differences in the occurrence of nausea during opioid administration, the incidence of nausea was investigated in 331 patients (Higashi-Sapporo Hospital [HS] group) who received morphine chronically for cancer pain treatment and in 2021 patients (Cancer Institute Hospital [CIH] group) who underwent elective surgery under general anesthesia. We conducted a genome-wide association study of nausea in HS samples. Among the top 20 candidate single-nucleotide polymorphisms (SNPs), we focused on the TMEM132C rs7296262 SNP, which has been reportedly associated with psychiatric disorders. The rs7296262 SNP was significantly associated with nausea in both the HS and CIH groups (TT+TC vs. CC; HS group, p = 0.0001; CIH group, p = 0.0064). The distribution of nausea-prone genotypes for the rs7296262 SNP was reversed between HS and CIH groups. These results suggest that the TMEM132C rs7296262 SNP is significantly associated with nausea during opioid use, and the effect of the SNP genotype on nausea is reversed between chronic and acute phases of opioid use. Full article
(This article belongs to the Special Issue Recent Progress of Opioid Research, 2nd Edition)
14 pages, 6491 KiB  
Article
Effect of Synthetic Vitreous Fiber Exposure on TMEM16A Channels in a Xenopus laevis Oocyte Model
by Martina Zangari, Giuliano Zabucchi, Martina Conti, Paola Lorenzon, Violetta Borelli, Andrew Constanti, Francesco Dellisanti, Sara Leone, Lisa Vaccari and Annalisa Bernareggi
Int. J. Mol. Sci. 2024, 25(16), 8661; https://doi.org/10.3390/ijms25168661 - 8 Aug 2024
Viewed by 256
Abstract
Many years ago, asbestos fibers were banned and replaced by synthetic vitreous fibers because of their carcinogenicity. However, the toxicity of the latter fibers is still under debate, especially when it concerns the early fiber interactions with biological cell membranes. Here, we aimed [...] Read more.
Many years ago, asbestos fibers were banned and replaced by synthetic vitreous fibers because of their carcinogenicity. However, the toxicity of the latter fibers is still under debate, especially when it concerns the early fiber interactions with biological cell membranes. Here, we aimed to investigate the effects of a synthetic vitreous fiber named FAV173 on the Xenopus laevis oocyte membrane, the cell model we have already used to characterize the effect of crocidolite asbestos fiber exposure. Using an electrophysiological approach, we found that, similarly to crocidolite asbestos, FAV173 was able to stimulate a chloride outward current evoked by step membrane depolarizations, that was blocked by the potent and specific TMEM16A channel antagonist Ani9. Exposure to FAV173 fibers also altered the oocyte cell membrane microvilli morphology similarly to crocidolite fibers, most likely as a consequence of the TMEM16A protein interaction with actin. However, FAV173 only partially mimicked the crocidolite fibers effects, even at higher fiber suspension concentrations. As expected, the crocidolite fibers’ effect was more similar to that induced by the co-treatment with (Fe3+ + H2O2), since the iron content of asbestos fibers is known to trigger reactive oxygen species (ROS) production. Taken together, our findings suggest that FAV173 may be less harmful that crocidolite but not ineffective in altering cell membrane properties. Full article
(This article belongs to the Section Molecular Biophysics)
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<p>(<b>a</b>) Example of SEM images of Croc and FAV173 fibers. (<b>b</b>) Vitelline membrane of non-treated (Ctrl), Croc-treated, and ground FAV173-treated oocytes. Note, in (<b>b</b>), a Croc fiber partially inserted into the pore of the vitelline membrane. Croc = 15 μg/mL, FAV173 = 200 μg/mL.</p>
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<p>Dose–response effect of FAV173 fiber exposure (from 15 to 400 μg/mL) on resting potential (RP) in (<b>a</b>) and membrane resistance (R<sub>m</sub>) in (<b>b</b>). In (<b>a</b>,<b>b</b>), the green bars show the effect of 15 μg/mL of Croc for comparison. The effect of FAV173 and Croc is expressed as % of decrease with respect to untreated cells (Ctrl) from the same donor. * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001 fibers vs. Ctrl (unpaired <span class="html-italic">t</span>-test). In (<b>c</b>), the time course effect of Croc and FAV treatments on RP (<span class="html-italic">n</span> ≥ 3 donors). <sup>§§§</sup> <span class="html-italic">p</span> &lt; 0.001 FAV173 vs. Croc, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 Croc (15–20) vs. Croc (5–10), (unpaired <span class="html-italic">t</span>-test). In (<b>d</b>), the effect on RP was irreversible only at higher fiber concentrations in Croc-treated cells (120 min, Croc: 45 μg/mL; FAV173: 600 μg/mL). *** <span class="html-italic">p</span> &lt; 0.001 Croc vs. Ctrl (one-way ANOVA with Tukey’s post hoc).</p>
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<p>In (<b>a</b>), examples of current traces recorded by stepping the membrane potential from −80 to +40 mV (10 mV steps, 3 s, V<sub>h</sub> = −40 mV) in non-treated (Ctrl, black), Croc-treated (green), and FAV173-treated (red) cells. (<b>b</b>) I–V relationships from Ctrl, Croc-treated, and FAV173-treated cells (same batch). Note the outward rectification in treated cells. (<b>c</b>) Percentage increase in the evoked current amplitude measured at −80 mV and +40 mV and normalized to their respective Ctrl (of the same donor). *** <span class="html-italic">p</span> &lt; 0.001, Fiber-treatments vs. Ctrl; <sup>§§</sup> <span class="html-italic">p</span> &lt; 0.001, Croc vs. FAV173 (unpaired <span class="html-italic">t</span>-test). Croc: 15 μg/mL; FAV173: 200 μg/mL.</p>
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<p>(<b>a</b>, <b>left</b>) Examples of recording traces from Ctrl cells in normal bathing solution, in presence of [Ca<sup>2+</sup>]<sub>e</sub> = 11 mM (Ca11) and Ca11 + Ani9 (1 μM). (<b>a</b>, <b>right</b>) I–V relationships in Ctrl (normal bathing solution), Ca11, and Ca11 + Ani9 (1 μM) conditions. Note that, in Ca11, the currents at negative potentials increased with respect to those recorded in normal bathing solution, and the evident blocking effect of Ani9. (<b>b</b>, <b>left</b>) Evoked currents from FAV173-treated (200 μg/mL) cells in presence of Ca11 and Ca11 + Ani9 (1 μM). (<b>b</b>, <b>right</b>) The I–V relationships revealed a significant increase in the evoked currents at positive and negative potentials in presence of Ca11 and a partial blocking effect induced by Ani9. Ctrl: <span class="html-italic">n</span> = 5; FAV173: <span class="html-italic">n</span> = 7, same donor. * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, Fibers vs. Ctrl (unpaired <span class="html-italic">t</span>-test); <sup>§</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>§§</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>§§§</sup> <span class="html-italic">p</span> &lt; 0.001, Ca11 vs. Ca11 + Ani9 (paired <span class="html-italic">t</span>-test).</p>
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<p>Example of SEM images of microvilli in (<b>a</b>) Ctrl oocyte, and oocytes in Ca11 bath condition in the absence and in the presence of FAV173 (200 μg/mL). In the inset of Ctrl, the vitelline membrane (VM) above the microvilli (MV) is shown. The FAV173 effect on the diameter (<b>b</b>) and density (<b>c</b>) of the microvilli (<b>c</b>), *** <span class="html-italic">p</span> &lt; 0.001 vs. Ca11 (unpaired <span class="html-italic">t</span>-test). In (<b>d</b>) microvilli of Croc and (Fe<sup>3+</sup> + H<sub>2</sub>O<sub>2</sub>)-treated oocytes. Comparison of microvilli diameter (<b>e</b>) and density (<b>f</b>) values measured under the 3 test conditions (* <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001 vs. Ctrl, <sup>§</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>§§§</sup> <span class="html-italic">p</span> &lt; 0.001 (Fe<sup>3+</sup> + H<sub>2</sub>O<sub>2</sub>) vs. Croc, One-Way ANOVA with Tukey’s post hoc). Cells were treated for 60 min. (values are in the text). Scale bar 1 μm.</p>
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<p>(<b>a</b>) Comparison of RP values in oocytes in the presence of CyTD alone (<span class="html-italic">n</span> = 5), or with FAV173 (<span class="html-italic">n</span> = 13) and Ani9 (<span class="html-italic">n</span> = 13), Croc (<span class="html-italic">n</span> = 12) and Ani9 (<span class="html-italic">n</span> = 15), (Fe<sup>3+</sup> + H<sub>2</sub>O<sub>2</sub>) (<span class="html-italic">n</span> = 12), and Ani9 (<span class="html-italic">n</span> = 14). * <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 vs. CyTD, <sup>§§§</sup> <span class="html-italic">p</span> &lt; 0.001 CyTD + Croc vs. CyTD + Croc + Ani9 (ANOVA with Tukey’s post hoc). (<b>b</b>) Examples of SEM images of the plasmalemma from CyTD, CyTD + Croc, and (Fe<sup>3+</sup> + H<sub>2</sub>O<sub>2</sub>) + CyTD-treated oocytes. In both cases, damage on the plasmalemma are visible (red asterisks). In (<b>c</b>), the presence of Ani9 fully recovered the membrane damage in the Croc-treated cells, while it is still visible in (Fe<sup>3+</sup> + H<sub>2</sub>O<sub>2</sub>)-treated cells (white asterisk). Scale bar: 1 μm.</p>
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5 pages, 1047 KiB  
Proceeding Paper
Lung Cancer Biomarker Identification from Differential Expression Analysis Using RNA-Seq Data for Designing Multitargeted Drugs
by Syed Naseer Ahmad Shah and Rafat Parveen
Biol. Life Sci. Forum 2024, 35(1), 2; https://doi.org/10.3390/blsf2024035002 - 7 Aug 2024
Viewed by 165
Abstract
Lung cancer presents a global health challenge, demanding exploration of its molecular intricacies for treatment targets. The goal is to delay progression and intervene early, reducing patient burden. Novel biomarkers are urgently needed for early diagnosis. We analysed RNA sequencing on lung cancer [...] Read more.
Lung cancer presents a global health challenge, demanding exploration of its molecular intricacies for treatment targets. The goal is to delay progression and intervene early, reducing patient burden. Novel biomarkers are urgently needed for early diagnosis. We analysed RNA sequencing on lung cancer samples from NCBI’s SRA database. Using Bioconductor in R, we identified key genes, including hub genes TOP2A and TMEM100, crucial for cellular processes. Additionally, FDA-approved drugs are repurposed as multitargeted inhibitors against upregulated genes, validated through simulations. This approach aims to inhibit the function of crucial genes, potentially offering effective treatment for lung cancer within a comprehensive strategy. Full article
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<p>Flow chart of the study.</p>
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13 pages, 837 KiB  
Review
Exploring Candidate Gene Studies and Alexithymia: A Systematic Review
by Yazmín Hernández-Díaz, Alma Delia Genis-Mendoza, Thelma Beatriz González-Castro, Ana Fresán, Carlos Alfonso Tovilla-Zárate, María Lilia López-Narváez, Isela Esther Juárez-Rojop and Humberto Nicolini
Genes 2024, 15(8), 1025; https://doi.org/10.3390/genes15081025 - 4 Aug 2024
Viewed by 473
Abstract
Background: Alexithymia is a trait involving difficulties in processing emotions. Genetic association studies have investigated candidate genes involved in alexithymia’s pathogenesis. Therefore, the aim of the present study was to perform a systematic review of the genetic background associated with alexithymia. Methods: A [...] Read more.
Background: Alexithymia is a trait involving difficulties in processing emotions. Genetic association studies have investigated candidate genes involved in alexithymia’s pathogenesis. Therefore, the aim of the present study was to perform a systematic review of the genetic background associated with alexithymia. Methods: A systematic review of genetic studies of people with alexithymia was conducted. Electronic databases including PubMed, Scopus, and Web of Science were searched for the study purpose. We used the words “Alexithymia”, “gene”, “genetics”, “variants”, and “biomarkers”. The present systematic review was performed following the Preferred Reporting Items for Systematic reviews and Meta-Analyses statement. We found only candidate gene studies. A total of seventeen studies met the eligibility criteria, which comprised 22,361 individuals. The candidate genes associated with alexithymia were the serotoninergic pathway genes solute carrier family 6 member 4 (SLC6A4), serotonin 1A receptor (HTR1A), and serotonin 1A receptor (HTR2A); the neurotransmitter metabolism genes dopamine receptor D2 (DRD2), ankyrin repeat and kinase domain containing 1 (ANKK1), catechol-o-methyltransferase (COMT), brain-derived neurotrophic factor (BDNF), and oxytocin receptor (OXTR); and other pathway genes, vitamin D-binding protein (VDBP), tumor protein P53 regulated apoptosis inducing protein 1 (TP53AIP1), Rho GTPase Activating Protein 32 (ARHGAP32), and transmembrane protein 88B (TMEM88B). Conclusion: The results of this study showed that only case–control gene studies have been performed in alexithymia. On the basis of our findings, the majority of alexithymia genes and polymorphisms in this study belong to the serotoninergic pathway and neurotransmitter metabolism genes. These data suggest a role of serotoninergic neurotransmission in alexithymia. Nevertheless, more and future research is required to learn about the role of these genes in alexithymia. Full article
(This article belongs to the Special Issue Genetics and Genomics of Psychiatric Disorders)
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<p>Flow diagram of the selection criteria for this systematic review.</p>
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<p>Main candidate genes associated with alexithymia.</p>
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16 pages, 4106 KiB  
Article
Hypomyelination Leukodystrophy 16 (HLD16)-Associated Mutation p.Asp252Asn of TMEM106B Blunts Cell Morphological Differentiation
by Sui Sawaguchi, Miki Ishida, Yuki Miyamoto and Junji Yamauchi
Curr. Issues Mol. Biol. 2024, 46(8), 8088-8103; https://doi.org/10.3390/cimb46080478 - 27 Jul 2024
Viewed by 275
Abstract
Transmembrane protein 106B (TMEM106B), which is a type II transmembrane protein, is believed to be involved in intracellular dynamics and morphogenesis in the lysosome. TMEM106B is known to be a risk factor for frontotemporal lobar degeneration and has been recently identified as the [...] Read more.
Transmembrane protein 106B (TMEM106B), which is a type II transmembrane protein, is believed to be involved in intracellular dynamics and morphogenesis in the lysosome. TMEM106B is known to be a risk factor for frontotemporal lobar degeneration and has been recently identified as the receptor needed for the entry of SARS-CoV-2, independently of angiotensin-converting enzyme 2 (ACE2). A missense mutation, p.Asp252Asn, of TMEM106B is associated with hypomyelinating leukodystrophy 16 (HLD16), which is an oligodendroglial cell-related white matter disorder causing thin myelin sheaths or myelin deficiency in the central nervous system (CNS). However, it remains to be elucidated how the mutated TMEM106B affects oligodendroglial cells. Here, we show that the TMEM106B mutant protein fails to exhibit lysosome distribution in the FBD-102b cell line, an oligodendroglial precursor cell line undergoing differentiation. In contrast, wild-type TMEM106B was indeed localized in the lysosome. Cells harboring wild-type TMEM106B differentiated into ones with widespread membranes, whereas cells harboring mutated TMEM106B failed to differentiate. It is of note that the output of signaling through the lysosome-resident mechanistic target of rapamycin (mTOR) was greatly decreased in cells harboring mutated TMEM106B. Furthermore, treatment with hesperetin, a citrus flavonoid known as an activator of mTOR signaling, restored the molecular and cellular phenotypes induced by the TMEM106B mutant protein. These findings suggest the potential pathological mechanisms underlying HLD16 and their amelioration. Full article
(This article belongs to the Special Issue Molecules at Play in Neurological Diseases 2024)
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<p>TMEM106B protein with the HLD16-associated D252N mutation is widely distributed throughout the cytoplasmic regions. (<b>A</b>) FBD-102b cells (surrounded by white dotted lines) were transfected with the plasmid encoding wild-type (WT) TMEM106B tagged with EGFP at its C-terminus or EGFP-tagged TMEM106B with the D252N mutation. Transfected cells were stained with DAPI for nuclear staining. Scan plots were created along the white dotted lines in the direction of the arrows in the images. (<b>B</b>) Graphs showing fluorescence intensities (F.I., arbitrary units) along the white dotted lines in the direction of the arrows are presented at the bottom of the representative fluorescence images. (<b>C</b>) Cells with abnormal, widely distributed structures were counted and statistically depicted (** <span class="html-italic">p</span> &lt; 0.01; <span class="html-italic">n</span> = 10 fields).</p>
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<p>Mutated TMEM106B is present in the lysosome. (<b>A</b>) Cells (surrounded by white dotted lines) were transfected with the plasmid encoding mutated TMEM106B (D252N). Transfected cells were stained with the respective antibodies against ER-specific KDEL, Golgi body-specific GM130, and lysosome-resident LAMP1. Scan plots were created along the white dotted lines in the direction of the arrows in the images. (<b>B</b>) Graphs showing fluorescence intensities (F.I., arbitrary units) along the white dotted lines in the direction of the arrows are presented at the bottom of the representative fluorescence images. (<b>C</b>) The respective merged percentages are depicted in bar graphs (<span class="html-italic">n</span> = 3 fields).</p>
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<p>Cells harboring mutated TMEM106B show decreased cell differentiation abilities. (<b>A</b>) Cells harboring wild-type (WT) or mutated (D252N) TMEM106B were allowed to differentiate for 0 or 5 days. Cells surrounded with dotted red lines in the middle panels are magnified in the right panels. The cell surrounded by a white dotted line is a typically differentiated one with widespread membranes. (<b>B</b>) Differentiated cells are statistically depicted (** <span class="html-italic">p</span> &lt; 0.01; <span class="html-italic">n</span> = 10 fields). (<b>C</b>) The lysates of cells at 5 days following the induction of differentiation were immunoblotted with the respective antibodies against differentiation markers PLP1 and MBP, cell lineage marker Sox10, and internal control actin. (<b>D</b>) Quantification of immunoreactive bands, using control immunoreactive bands as 100%, is depicted in the respective graphs of PLP1, MBP, Sox10, and actin (** <span class="html-italic">p</span> &lt; 0.01; <span class="html-italic">n</span> = 3 blots).</p>
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<p>Cells harboring mutated TMEM106B show decreased phosphorylation levels of ribosomal S6 and translational 4E-BP1 proteins. (<b>A</b>) The lysates of cells at 5 days following the induction of differentiation were immunoblotted with the respective antibodies against phosphorylated ribosomal S6 and translational 4E-BP1 proteins (pS6 and p4E-BP1). Total ribosomal S6 and translational 4E-BP1 protein (S6 and 4E-BP1) bands are also presented. (<b>B</b>) Quantification of immunoreactive bands, using control immunoreactive bands as 100%, is depicted in the respective graphs of pS6, S6, p4E-BP1, and 4E-BP1 (** <span class="html-italic">p</span> &lt; 0.01; <span class="html-italic">n</span> = 3 blots).</p>
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<p>Hesperetin recovers phenotypes of cells harboring mutated TMEM106B. (<b>A</b>) Cells harboring mutated TMEM106B were allowed to differentiate for 0 or 5 days in the presence or absence of 10 μm hesperetin (DMSO as the vehicle). Cells surrounded with dotted red lines in the middle panels are magnified in the right panels. The cell surrounded by a white dotted line is a typically differentiated one with widespread membranes. (<b>B</b>) Differentiated cells are statistically depicted (** <span class="html-italic">p</span> &lt; 0.01; <span class="html-italic">n</span> = 10 fields). (<b>C</b>) The lysates of cells at 5 days following the induction of differentiation were immunoblotted with the respective antibodies against differentiation markers PLP1 and MBP, cell lineage marker Sox10, and internal control actin. (<b>D</b>) Quantification of immunoreactive bands, using hesperetin plus immunoreactive bands as 100%, is depicted in the respective graphs of PLP1, MBP, Sox10, and actin (** <span class="html-italic">p</span> &lt; 0.01; <span class="html-italic">n</span> = 3 blots).</p>
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<p>Hesperetin recovers decreased phosphorylation levels of ribosomal S6 and translational 4E-BP1 proteins in cells harboring mutated TMEM106B. (<b>A</b>) The lysates of cells at 5 days following the induction of differentiation in the presence or absence of hesperetin were immunoblotted with the respective antibodies against phosphorylated ribosomal S6 and translational 4E-BP1 proteins (pS6 and p4E-BP1). Total ribosomal S6 and translational 4E-BP1 protein (S6 and 4E-BP1) bands are also presented. (<b>B</b>) Quantification of immunoreactive bands, using hesperetin plus immunoreactive bands as 100%, is depicted in the respective graphs of pS6, S6, p4E-BP1, and 4E-BP1 (** <span class="html-italic">p</span> &lt; 0.01; <span class="html-italic">n</span> = 3 blots).</p>
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17 pages, 2027 KiB  
Review
Genetic Deficiencies of Hyaluronan Degradation
by Stephen P. Fink and Barbara Triggs-Raine
Cells 2024, 13(14), 1203; https://doi.org/10.3390/cells13141203 - 16 Jul 2024
Viewed by 462
Abstract
Hyaluronan (HA) is a large polysaccharide that is broadly distributed and highly abundant in the soft connective tissues and embryos of vertebrates. The constitutive turnover of HA is very high, estimated at 5 g per day in an average (70 kg) adult human, [...] Read more.
Hyaluronan (HA) is a large polysaccharide that is broadly distributed and highly abundant in the soft connective tissues and embryos of vertebrates. The constitutive turnover of HA is very high, estimated at 5 g per day in an average (70 kg) adult human, but HA turnover must also be tightly regulated in some processes. Six genes encoding homologues to bee venom hyaluronidase (HYAL1, HYAL2, HYAL3, HYAL4, HYAL6P/HYALP1, SPAM1/PH20), as well as genes encoding two unrelated G8-domain-containing proteins demonstrated to be involved in HA degradation (CEMIP/KIAA1199, CEMIP2/TMEM2), have been identified in humans. Of these, only deficiencies in HYAL1, HYAL2, HYAL3 and CEMIP have been identified as the cause or putative cause of human genetic disorders. The phenotypes of these disorders have been vital in determining the biological roles of these enzymes but there is much that is still not understood. Deficiencies in these HA-degrading proteins have been created in mice and/or other model organisms where phenotypes could be analyzed and probed to expand our understanding of HA degradation and function. This review will describe what has been found in human and animal models of hyaluronidase deficiency and discuss how this has advanced our understanding of HA’s role in health and disease. Full article
(This article belongs to the Special Issue Role of Hyaluronan in Human Health and Disease)
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<p>Organization of genes encoding HA degrading proteins in humans and mice. The organization and sizes of the human and mouse genes are based on assemblies NC_000003.12 and NC_000007.14 (human) or NC_00075.7 and NC_000072.7 (mouse). The sizes of the genes are shown above the gene in kilobase pairs. The scaling is unique for each map to allow all hyaluronidase genes in a chromosomal region to be displayed.</p>
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<p>Expression of genes encoding HA-degrading proteins in human tissues. Estimates of gene expression were determined by RNA-Seq in a previous study [<a href="#B51-cells-13-01203" class="html-bibr">51</a>]. Mean ± standard deviation is shown for the major tissues where RNA-Seq data were available from at least 3 different biological replicates. Results are graphed for <span class="html-italic">HYAL1</span>, <span class="html-italic">HYAL2</span>, <span class="html-italic">HYAL3</span>, <span class="html-italic">HYAL4</span>, <span class="html-italic">SPAM1</span>, <span class="html-italic">CEMIP1</span> and <span class="html-italic">CEMIP2</span> using GraphPad Software Version 10 (Boston, MA, USA). To facilitate viewing, some error bars are not fully graphed and are indicated by * (SD = 9.81), ** (SD = 1.84), and *** (SD = 21.8).</p>
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22 pages, 3132 KiB  
Article
Neurosteroid [3α,5α]-3-Hydroxy-pregnan-20-one Enhances the CX3CL1-CX3CR1 Pathway in the Brain of Alcohol-Preferring Rats with Sex-Specificity
by Irina Balan, Adelina Grusca, Samantha Lucenell Chéry, Baylee R. Materia, Todd K. O’Buckley and A. Leslie Morrow
Life 2024, 14(7), 860; https://doi.org/10.3390/life14070860 - 9 Jul 2024
Cited by 1 | Viewed by 561
Abstract
This study investigates the impact of allopregnanolone ([3α,5α]3-hydroxypregnan-20-one or 3α,5α-tetrahydroprogesterone (3α,5α-THP); 10 mg/kg, IP) on fractalkine/CX3-C motif chemokine ligand 1 (CX3CL1) levels, associated signaling components, and markers for microglial and astrocytic cells in the nucleus accumbens (NAc) of male and female alcohol-preferring (P) [...] Read more.
This study investigates the impact of allopregnanolone ([3α,5α]3-hydroxypregnan-20-one or 3α,5α-tetrahydroprogesterone (3α,5α-THP); 10 mg/kg, IP) on fractalkine/CX3-C motif chemokine ligand 1 (CX3CL1) levels, associated signaling components, and markers for microglial and astrocytic cells in the nucleus accumbens (NAc) of male and female alcohol-preferring (P) rats. Previous research suggested that 3α,5α-THP enhances anti-inflammatory interleukin-10 (IL-10) cytokine production in the brains of male P rats, with no similar effect observed in females. This study reveals that 3α,5α-THP elevates CX3CL1 levels by 16% in the NAc of female P rats, with no significant changes observed in males. The increase in CX3CL1 levels induced by 3α,5α-THP was observed in females across multiple brain regions, including the NAc, amygdala, hypothalamus, and midbrain, while no significant effect was noted in males. Additionally, female P rats treated with 3α,5α-THP exhibited notable increases in CX3CL1 receptor (CX3CR1; 48%) and transforming growth factor-beta 1 (TGF-β1; 24%) levels, along with heightened activation (phosphorylation) of signal transducer and activator of transcription 1 (STAT1; 85%) in the NAc. Conversely, no similar alterations were observed in male P rats. Furthermore, 3α,5α-THP decreased glial fibrillary acidic protein (GFAP) levels by 19% in both female and male P rat NAc, without affecting microglial markers ionized calcium-binding adaptor molecule 1 (IBA1) and transmembrane protein 119 (TMEM119). These findings indicate that 3α,5α-THP enhances the CX3CL1/CX3CR1 pathway in the female P rat brain but not in males, primarily influencing astrocyte reactivity, with no observed effect on microglial activation. Full article
(This article belongs to the Section Physiology and Pathology)
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<p>(<b>a</b>) 3α,5α-THP upregulates the levels of CX3-C motif chemokine ligand 1/fractalkine (CX3CL1) in the nucleus accumbens (NAc) of female, but not male alcohol-preferring (P) rats. Male and female P rats (n = 10/group) were treated intraperitoneally with 3α,5α-THP (10 mg/kg) or vehicle (45% <span class="html-italic">w</span>/<span class="html-italic">v</span> 2-hydroxypropyl-β-cyclodextrin) control. After 60 min, the nucleus accumbens (NAc) was examined using ELISA to determine CX3CL1 expression. In females, the administration of 3α,5α-THP resulted in a significant increase in CX3CL1 levels within the NAc (<span class="html-italic">p</span> = 0.02). However, in male rats, 3α,5α-THP treatment did not lead to a notable change in CX3CL1 expression within the NAc (<span class="html-italic">p</span> = 0.91). In the graphs, every column, along with its error bar, represents the mean ± SEM, expressed in pg/mg of total protein level. Each circle represents an individual CX3CL1 value for vehicle-treated rats, while the black squares indicate the CX3CL1 values for the 3α,5α-THP-treated rats. * <span class="html-italic">p</span> &lt; 0.05. (<b>b</b>) Qualitative evaluation of 3α,5α-THP’s impact on the intracellular distribution of CX3CL1 in the NAc of female P rats. Double-immunofluorescent staining was conducted using antibodies targeting CX3CL1 alongside NeuN (a neuronal marker), TMEM119 (a microglial marker) or GFAP (an astrocyte marker). In vehicle control, CX3CL1 was observed to localize within NeuN-positive neuronal cells while not co-localizing with TMEM119-positive microglial cells or GFAP-positive astrocytic cells. Treatment with 3α,5α-THP did not induce any evident alterations in CX3CL1’s intracellular localization. Scale bar is 50 µm.</p>
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<p>3α,5α-THP enhances CX3CR1 levels in the nucleus accumbens (NAc) of female P rats, but not in males. Male and female alcohol-preferring (P) rats (n = 10/group) were treated intraperitoneally with 3α,5α-THP (10 mg/kg) or vehicle (45% <span class="html-italic">w</span>/<span class="html-italic">v</span> 2-hydroxypropyl-β-cyclodextrin) control. After 60 min, the NAc was examined using immunoblotting assays to determine CX3CR1 expression. Specifically, in females, administering 3α,5α-THP resulted in a significant elevation of CX3CR1 levels within the NAc (<span class="html-italic">p</span> = 0.0006). Conversely, in male rats, treatment with 3α,5α-THP did not induce a noteworthy alteration in CX3CR1 expression within the NAc (<span class="html-italic">p</span> = 0.98). In the graphs, every column, along with its error bar, represents the mean ± SEM, expressed as a percentage relative to the average value of the vehicle control. Each circle represents an individual CX3CR1 value, normalized to β-Actin for vehicle-treated rats, while the black squares indicate the corresponding values for the 3α,5α-THP-treated rats. *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>3α,5α-THP upregulates the levels of pSTAT1 in the nucleus accumbens (NAc) of female, but not male P rats. Male and female alcohol-preferring (P) rats (n = 10/group) were treated intraperitoneally with 3α,5α-THP (10 mg/kg) or vehicle (45% <span class="html-italic">w</span>/<span class="html-italic">v</span> 2-hydroxypropyl-β-cyclodextrin) control. After 60 min, the NAc was examined using immunoblotting assays to determine pSTAT1 expression. In females, the administration of 3α,5α-THP resulted in a significant increase in pSTAT1 levels within the NAc (<span class="html-italic">p</span> = 0.0002). However, in male rats, 3α,5α-THP treatment did not lead to a notable change in pSTAT1 expression within the NAc (<span class="html-italic">p</span> = 0.08). In the graphs, every column, along with its error bar, represents the mean ± SEM, expressed as a percentage relative to the average value of the vehicle control. Each circle represents an individual pSTAT1 value, normalized to β-Actin for vehicle-treated rats, while the black squares indicate the corresponding values for the 3α,5α-THP-treated rats. *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>3α,5α-THP upregulates the levels of TGF-β1 in the nucleus accumbens (NAc) of female, but not male P rats. Male and female alcohol-preferring (P) rats (n = 10/group) were treated intraperitoneally with 3α,5α-THP (10 mg/kg) or vehicle (45% <span class="html-italic">w</span>/<span class="html-italic">v</span> 2-hydroxypropyl-β-cyclodextrin) control. After 60 min, the NAc was examined using immunoblotting assays to determine TGF-β1 expression. In females, the administration of 3α,5α-THP (10 mg/kg, IP) resulted in a significant increase in TGF-β1 levels within the NAc (<span class="html-italic">p</span> = 0.04). However, in male rats, 3α,5α-THP treatment did not lead to a notable change in TGF-β1 expression within the NAc (<span class="html-italic">p</span> = 0.21). In the graphs, every column, along with its error bar, represents the mean ± SEM, expressed as a percentage relative to the average value of the vehicle control. Each circle represents an individual TGF-β1 value, normalized to β-Actin for vehicle-treated rats, while the black squares indicate the corresponding values for the 3α,5α-THP-treated rats. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>3α,5α-THP downregulates the levels of the astrocytic marker GFAP in the nucleus accumbens (NAc) of female and male P rats. Male and female alcohol-preferring (P) rats (n = 10/group) were treated intraperitoneally with 3α,5α-THP (10 mg/kg) or vehicle (45% <span class="html-italic">w</span>/<span class="html-italic">v</span> 2-hydroxypropyl-β-cyclodextrin) control. After 60 min, the NAc was examined using immunoblotting assays to determine GFAP expression. In both females (<span class="html-italic">p</span> = 0.02) and males (<span class="html-italic">p</span> = 0.03), administering 3α,5α-THP led to a significant decrease in GFAP levels within the NAc. In the graphs, every column, along with its error bar, represents the mean ± SEM, expressed as a percentage relative to the average value of the vehicle control. Each circle represents an individual GFAP value, normalized to β-Actin for vehicle-treated rats, while the black squares indicate the corresponding values for the 3α,5α-THP-treated rats. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Schematic of 3α,5α-THP (allopregnanolone) actions on CX3CL1/CX3CR1 signaling in the brain of female ethanol-naïve alcohol-preferring (P) rats. Initially, activation of inflammatory TLR pathways leads to the upregulation of inflammatory factors, resulting in decreased levels (red arrows down) of CX3CL1 in neurons and CX3CR1 in glia, thereby reducing (black double-ended arrows crossed by a red zigzag) CX3CL1/CX3CR1 signaling between neurons and glia. Concurrently, GFAP upregulation (red arrow up) in astrocytes also contributes to disrupted neuron–glial communication. Upon administration of 3α,5α-THP, inflammatory TLR pathways are inhibited, resulting in the downregulation of inflammatory factors. Subsequently, CX3CL1 levels in neurons increase (green arrow up) through the activation (phosphorylation) of STAT1 (pSTAT1 upregulation is represented by a green arrow up), while CX3CR1 levels in glia are upregulated (green arrow up) via TGF-β1 (TGF-β1 upregulation is represented by a green arrow up) involvement. Additionally, GFAP levels in astrocytes decrease (green arrow down). These changes ultimately lead to enhanced neuron–glial communication (black double-ended arrows). Created with BioRender.com.</p>
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17 pages, 12794 KiB  
Article
TMEM176B Promotes EMT via FGFR/JNK Signalling in Development and Tumourigenesis of Lung Adenocarcinoma
by Ping-Hui Sun, Siyu Xia, Runzhu Yuan, Bin Zhang and Guangsuo Wang
Cancers 2024, 16(13), 2447; https://doi.org/10.3390/cancers16132447 - 3 Jul 2024
Viewed by 638
Abstract
Lung cancer, the leading cause of cancer-related incidence and mortality worldwide, is characterised by high invasiveness and poor prognosis. Novel therapeutic targets are required, especially for patients with inoperable metastatic disease requiring systemic therapies to improve patients’ welfare. Recently, studies indicated that TMEM176B [...] Read more.
Lung cancer, the leading cause of cancer-related incidence and mortality worldwide, is characterised by high invasiveness and poor prognosis. Novel therapeutic targets are required, especially for patients with inoperable metastatic disease requiring systemic therapies to improve patients’ welfare. Recently, studies indicated that TMEM176B is a positive regulator in breast and gastric cancers, and it could be a potential target for treatment. In this study, we used single-cell sequencing, proteomics, Co-IP, and in vivo and in vitro experimental models to investigate the role of TMEM176B in lung adenocarcinoma development. Our study indicated that TMEM176B expression was enhanced in lung adenocarcinoma tissues, and it was associated with shorter overall survival (OS). TMEM176B promoted cellular functions, including cell proliferation, invasion, migration and adhesion in vitro and tumour growth in vivo. Moreover, the tube formation ability of endothelial cells was enhanced by treating with the tumour cell-conditioned medium. We have also demonstrated that TMEM176B regulated EMT via the FGFR1/JNK/Vimentin/Snail signalling cascade. Overall, our study suggests TMEM176B could be a potential therapeutic target in lung adenocarcinoma. Full article
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<p>Expression of TMEM176B in lung cancer and clinical outcomes. (<b>A</b>) Tissue array IHC staining (400×, scale bar = 20 μm). The relative intensity of IHC staining was analysed using ImageJ software for comparisons with (<b>B</b>) tumour and adjacent normal tissues, (<b>C</b>) different tumour stages and (<b>D</b>) patients who survived or died from the disease. (<b>E</b>) TMEM176B expression in LUAD and LUSC (<a href="http://gepia2.cancer-pku.cn" target="_blank">http://gepia2.cancer-pku.cn</a>, accessed on 3 December 2023). Overall survival of TMEM176B in (<b>F</b>) NSCLC, (<b>G</b>) LUAD and (<b>H</b>) LUSC (<a href="https://kmplot.com/analysis/" target="_blank">https://kmplot.com/analysis/</a>, accessed on 4 December 2023). **, <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.</p>
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<p>TMEM176B overexpression in in vitro and in vivo experiments. Examining TMEM176B expression in different cell lines using (<b>A</b>) PCR and (<b>B</b>) qPCR. Verification of TMEM176B overexpression using (<b>C</b>) qPCT and (<b>D</b>) Western blot. In vitro cell function assays for (<b>E</b>) cell proliferation, (<b>F</b>) cell invasion, (<b>G</b>) cell migration and (<b>H</b>) cell adhesion; overexpression of TMEM176B promoted all cell functions significantly. Establishing in vivo CDX mouse models (<span class="html-italic">n</span> = 10 per group) using (<b>I</b>) PC9 cells (control vs. overexpression: <span class="html-italic">n</span> = 10/10 vs. <span class="html-italic">n</span> = 10/10) and (<b>J</b>) A549 cells (control vs. overexpression: <span class="html-italic">n</span> = 8/10 vs. <span class="html-italic">n</span> = 9/10); overexpression of TMEM176B increased tumour growth in both cell lines significantly. (<b>K</b>) Tube formation assay of HUVEC cells was enhanced when the cells were treated with conditioned medium collected from PC9 or A549 cell lines. *, <span class="html-italic">p</span> &lt; 0.05, **, <span class="html-italic">p</span> &lt; 0.01 and ****, <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Cell population of co-cultured PC9 and HUVEC cells. (<b>A</b>) UMAP of total cells. (<b>B</b>) Percentage of cell compositions. (<b>C</b>) TMEM176B expression patterns in Case and Cont. (<b>D</b>) Subgroup of epithelial cells and their major GO term (<b>E</b>). (<b>F</b>) Composition percentage of each subgroup. (<b>G</b>) TMEM176B expression in the subgroup of epithelial cells. (<b>H</b>) The expression percentage of TMEM176B in the epithelial subgroup. ****, <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Ligand–receptor interactions between cancer cells and endothelial cells. (<b>A</b>) Overall interaction. Ligand–receptor interactions among endothelial cells and 5 subgroups of cancer cells in Cont (<b>B</b>) and Case (<b>C</b>). (<b>D</b>) The strength of interactions. (<b>E</b>) Alternative genes of interactions and their expression (<b>F</b>). Ligand–receptor pairs between the CXCL8 subgroup and endothelial cells were up-regulated (<b>H</b>) and down-regulated (<b>G</b>).</p>
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<p>FGFR1 and Vimentin interacted with TMEM176B. (<b>A</b>) FGFR1 interacted with TMEM176B using Co-IP. (<b>B</b>) TMEM176B interacted with 342 proteins using mass spectrometry. (<b>C</b>) GO enrichment analysis of interacted proteins. Principal component analysis of proteomics in PC9 (<b>D</b>) and A549 (<b>E</b>) cells. Volcano plot analysis of proteomics in PC9 (<b>F</b>) and A549 (<b>G</b>) cells. (<b>H</b>) GO enrichment analysis for DEGs in PC9 cells.</p>
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<p>Overexpression of TMEM176B-enhanced cell functions in lung adenocarcinoma through FGFR and JNK. Cell function assays for PC9 and A549 treated with FGFR, JNK, ERK, p38, AKT or PI3K inhibitors; cell proliferation (<b>A</b>), cell invasion (<b>B</b>), cell migration (<b>C</b>) and cell adhesion (<b>D</b>). A decreasing trend was observed for all cell functions using FGFR inhibitors; there was also a significant reduction in cell functions when cells were treated with the JNK inhibitor. *, <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.</p>
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<p>Overexpression of TMEM176B-enhanced EMT in lung adenocarcinoma via FGFR/JNK/VIM/Snail signalling cascade. Western blot and relative intensity of bands for PC9 (<b>A</b>) and A549 (<b>B</b>) cells. (<b>C</b>) IHC staining using mouse CDX tumour tissues (200× and 400×, scale bar = 50 μm). *, <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.</p>
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18 pages, 2714 KiB  
Article
Unexpected Genetic Twists in Patients with Cardiac Devices
by Emilia-Violeta Goanta, Cristina Vacarescu, Georgica Tartea, Adrian Ungureanu, Sebastian Militaru, Alexandra Muraretu, Adelina-Andreea Faur-Grigori, Lucian Petrescu, Radu Vătăsescu and Dragos Cozma
J. Clin. Med. 2024, 13(13), 3801; https://doi.org/10.3390/jcm13133801 - 28 Jun 2024
Viewed by 445
Abstract
Objective: To assess the frequency and types of genetic mutations in patients with arrhythmias who underwent cardiac device implantation. Methods: Retrospective observational study, including 38 patients with different arrhythmias and cardiac arrest as a first cardiac event. Treatment modalities encompass pacemakers, transvenous defibrillators, [...] Read more.
Objective: To assess the frequency and types of genetic mutations in patients with arrhythmias who underwent cardiac device implantation. Methods: Retrospective observational study, including 38 patients with different arrhythmias and cardiac arrest as a first cardiac event. Treatment modalities encompass pacemakers, transvenous defibrillators, loop recorders, subcutaneous defibrillators, and cardiac resynchronization therapy. All patients underwent genetic testing, using commercially available panels (106–174 genes). Outcome measures include mortality, arrhythmia recurrence, and device-related complications. Results: Clinical parameters revealed a family history of sudden cardiac death in 19 patients (50%), who were predominantly male (58%) and had a mean age of 44.5 years and a mean left ventricle ejection fraction of 40.3%. Genetic testing identified mutations in various genes, predominantly TMEM43 (11%). In two patients (3%) with arrhythmogenic cardiomyopathy, complete subcutaneous defibrillator extraction with de novo transvenous implantable cardioverter-defibrillator implantation was needed. The absence of multiple associations among severe gene mutations was crucial for cardiac resynchronization therapy response. Mortality in this group was around 3% in titin dilated cardiomyopathy patients. Conclusions: Integration of genetic testing into the decision-making process for patients with electronic devices represents a paradigm shift in personalized medicine. By identifying genetic markers associated with arrhythmia susceptibility, heart failure etiology, and cardiac resynchronization therapy response, clinicians can tailor device choices to optimize patient outcomes. Full article
(This article belongs to the Special Issue Clinical Management of Patients with Heart Failure)
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<p>Distribution of positive genetic test (<b>A</b>)/variant of unknown significance or negative test (<b>B</b>) in the overall study cohort.</p>
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<p>Prevalence of genetic mutations.</p>
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<p>The evolution of a patient with a <span class="html-italic">TNNI3K</span> gene mutation after proper positioning of the LV lead in the posterolateral wall.</p>
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<p>The cases of two young patients with S-ICD who experienced early battery depletion and under-sensing due to low voltage surface QRS. A transvenous ICD (T-ICD) was recommended with complete removal of the S-ICD system. The red circles on the ECG are highlighting QRS underdetection by the S-ICD.</p>
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<p>Correlations between left ventricular ejection fraction (LVEF) and age of all patients in the study (<b>A</b>), between LVEF and age of patients with positive genetic tests (<b>B</b>), and between LVEF and age of patients with a variant of unknown significance or a negative test (<b>C</b>).</p>
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10 pages, 1435 KiB  
Communication
Genome-Wide Association Analysis and Genetic Parameters for Egg Production Traits in Peking Ducks
by Jun Zhou, Jiang-Zhou Yu, Mei-Yi Zhu, Fang-Xi Yang, Jin-Ping Hao, Yong He, Xiao-Liang Zhu, Zhuo-Cheng Hou and Feng Zhu
Animals 2024, 14(13), 1891; https://doi.org/10.3390/ani14131891 - 27 Jun 2024
Viewed by 479
Abstract
Egg production traits are crucial in the poultry industry, including age at first egg (AFE), egg number (EN) at different stages, and laying rate (LR). Ducks exhibit higher egg production capacity than other poultry species, but the genetic mechanisms are still poorly understood. [...] Read more.
Egg production traits are crucial in the poultry industry, including age at first egg (AFE), egg number (EN) at different stages, and laying rate (LR). Ducks exhibit higher egg production capacity than other poultry species, but the genetic mechanisms are still poorly understood. In this study, we collected egg-laying data of 618 Peking ducks from 22 to 66 weeks of age and genotyped them by whole-genome resequencing. Genetic parameters were calculated based on SNPs, and a genome-wide association study (GWAS) was performed for these traits. The SNP-based heritability of egg production traits ranged from 0.09 to 0.54. The GWAS identified nine significant SNP loci associated with AFE and egg number from 22 to 66 weeks. These loci showed that the corresponding alleles were positively correlated with a decrease in the traits. Moreover, three potential candidate genes (ENSAPLG00020011445, ENSAPLG00020012564, TMEM260) were identified. Functional enrichment analyses suggest that specific immune responses may have a critical impact on egg production capacity by influencing ovarian function and oocyte maturation processes. In conclusion, this study deepens the understanding of egg-laying genetics in Peking duck and provides a sound theoretical basis for future genetic improvement and genomic selection strategies in poultry. Full article
(This article belongs to the Section Animal Genetics and Genomics)
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<p>Genetic parameters of age at first egg, egg number, and egg laying rate based on SNP information. The diagonal displays the estimated results of heritability for each trait, the upper triangle shows the estimated results of genetic correlation coefficients between each trait, and the lower triangle shows the estimated results of phenotypic correlation coefficients between each trait. In the correlation coefficients between two traits, “**” indicates <span class="html-italic">p</span> &lt; 0.01, and “*” signifies 0.01 &lt; <span class="html-italic">p</span> &lt; 0.05. AFE, age at first egg; EN1, egg number in the pre-peak laying period from 22 to 30 weeks of age; EN2, egg number in the peak laying period from 31 to 55 weeks of age; EN3, egg number in the persistent laying period from 56 to 66 weeks of age; EN22-36WK, egg number from 22 to 36 weeks of age; EN22-51WK, egg number from 22 to 51 weeks of age; EN22-66WK, egg number from 22 to 66 weeks of age; LR, egg laying rate from 28 to 56 weeks of age.</p>
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<p>Manhattan plot of genome-wide association study for egg production traits; statistical comparison of genotype and phenotype for the most significant SNPs, and gene functional enrichment analysis. (<b>a</b>): AFE, age at first egg. (<b>b</b>): EN22-66WK, egg number from 22 to 66 weeks of age. Each dot represents an SNP in the dataset. The horizontal gray line and gray dashed line indicate the genome-wise significance threshold (<span class="html-italic">p</span>-value = 1.17 × 10<sup>−6</sup>) and genome-wise suggestive significance threshold (<span class="html-italic">p</span>-value = 2.35 × 10−5), respectively. (<b>c</b>): AFE and three genotypes of the SNP Chr2:89099404 (C/A). (<b>d</b>): AFE and two genotypes of the SNP Chr5:4239638 (T/C). (<b>e</b>): EN22-66WK and three genotypes of the SNP Chr24:4432581 (C/G). “**” indicates that there is a significant phenotypic difference between different genotypes according to the non-parametric test (<span class="html-italic">p</span> &lt; 0.01). (<b>f</b>): Functional enrichment analysis was conducted on the candidate genes annotated from significant SNPs in different egg production traits.</p>
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22 pages, 880 KiB  
Review
Pathological Functions of Lysosomal Ion Channels in the Central Nervous System
by Jianke Cen, Nan Hu, Jiawen Shen, Yongjing Gao and Huanjun Lu
Int. J. Mol. Sci. 2024, 25(12), 6565; https://doi.org/10.3390/ijms25126565 - 14 Jun 2024
Viewed by 904
Abstract
Lysosomes are highly dynamic organelles that maintain cellular homeostasis and regulate fundamental cellular processes by integrating multiple metabolic pathways. Lysosomal ion channels such as TRPML1-3, TPC1/2, ClC6/7, CLN7, and TMEM175 mediate the flux of Ca2+, Cl, Na+, [...] Read more.
Lysosomes are highly dynamic organelles that maintain cellular homeostasis and regulate fundamental cellular processes by integrating multiple metabolic pathways. Lysosomal ion channels such as TRPML1-3, TPC1/2, ClC6/7, CLN7, and TMEM175 mediate the flux of Ca2+, Cl, Na+, H+, and K+ across lysosomal membranes in response to osmotic stimulus, nutrient-dependent signals, and cellular stresses. These ion channels serve as the crucial transducers of cell signals and are essential for the regulation of lysosomal biogenesis, motility, membrane contact site formation, and lysosomal homeostasis. In terms of pathophysiology, genetic variations in these channel genes have been associated with the development of lysosomal storage diseases, neurodegenerative diseases, inflammation, and cancer. This review aims to discuss the current understanding of the role of these ion channels in the central nervous system and to assess their potential as drug targets. Full article
(This article belongs to the Special Issue Ion Channels in the Nervous System)
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<p><b>Function and regulation of lysosomes.</b> Receiving inputs from both endocytic and autophagic pathways, lysosomes play a fundamental role in cellular housekeeping and metabolism, as well as critical and sophisticated roles in the immune and nervous systems. Lysosomal ion channels, by mediating ion gradients and intracellular signaling pathways, participate in various lysosomal functions, including lysosomal membrane trafficking, catabolite export, nutrient sensing, and mTOR signaling. The dynamic interplay of these ion channels not only underscores the complexity of lysosomal regulation but also highlights their indispensability in maintaining cellular health and responding to physiological challenges. (Ion concentrations in cytosol: [K<sup>+</sup>]<sub>Cytosol</sub> = 150–180 mM [<a href="#B43-ijms-25-06565" class="html-bibr">43</a>], [Na<sup>+</sup>]<sub>Cytosol</sub> = 10–110 mM [<a href="#B44-ijms-25-06565" class="html-bibr">44</a>,<a href="#B45-ijms-25-06565" class="html-bibr">45</a>], [Ca<sup>2+</sup>]<sub>Cytosol</sub> = 100 nM [<a href="#B46-ijms-25-06565" class="html-bibr">46</a>], [H<sup>+</sup>] <sub>Cytosol</sub> = 40–50 nM [<a href="#B47-ijms-25-06565" class="html-bibr">47</a>], [Cl<sup>−</sup>]<sub>Cytosol</sub> = 25 ± 15 mM [<a href="#B48-ijms-25-06565" class="html-bibr">48</a>,<a href="#B49-ijms-25-06565" class="html-bibr">49</a>]. Ion concentrations in lysosome: [K<sup>+</sup>]<sub>L</sub> = 2–50 mM [<a href="#B41-ijms-25-06565" class="html-bibr">41</a>,<a href="#B50-ijms-25-06565" class="html-bibr">50</a>], [Na<sup>+</sup>]<sub>L</sub> = 80 ± 60 mM [<a href="#B41-ijms-25-06565" class="html-bibr">41</a>,<a href="#B50-ijms-25-06565" class="html-bibr">50</a>], [Ca<sup>2+</sup>]<sub>L</sub> = 500 ± 100 μM [<a href="#B51-ijms-25-06565" class="html-bibr">51</a>], [H<sup>+</sup>]<sub>L</sub> = 25 μM [<a href="#B52-ijms-25-06565" class="html-bibr">52</a>], [Cl<sup>−</sup>]<sub>L</sub>= 90 ± 30 mM [<a href="#B53-ijms-25-06565" class="html-bibr">53</a>,<a href="#B54-ijms-25-06565" class="html-bibr">54</a>]. Lysosomal membrane potential varies from 20 to 114 mV (luminal-side positive) [<a href="#B39-ijms-25-06565" class="html-bibr">39</a>,<a href="#B40-ijms-25-06565" class="html-bibr">40</a>,<a href="#B41-ijms-25-06565" class="html-bibr">41</a>]. The figure was created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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26 pages, 13349 KiB  
Article
Anomaly Detection and Artificial Intelligence Identified the Pathogenic Role of Apoptosis and RELB Proto-Oncogene, NF-kB Subunit in Diffuse Large B-Cell Lymphoma
by Joaquim Carreras and Rifat Hamoudi
BioMedInformatics 2024, 4(2), 1480-1505; https://doi.org/10.3390/biomedinformatics4020081 - 7 Jun 2024
Viewed by 899
Abstract
Background: Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent lymphomas. DLBCL is phenotypically, genetically, and clinically heterogeneous. Aim: We aim to identify new prognostic markers. Methods: We performed anomaly detection analysis, other artificial intelligence techniques, and conventional statistics using gene [...] Read more.
Background: Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent lymphomas. DLBCL is phenotypically, genetically, and clinically heterogeneous. Aim: We aim to identify new prognostic markers. Methods: We performed anomaly detection analysis, other artificial intelligence techniques, and conventional statistics using gene expression data of 414 patients from the Lymphoma/Leukemia Molecular Profiling Project (GSE10846), and immunohistochemistry in 10 reactive tonsils and 30 DLBCL cases. Results: First, an unsupervised anomaly detection analysis pinpointed outliers (anomalies) in the series, and 12 genes were identified: DPM2, TRAPPC1, HYAL2, TRIM35, NUDT18, TMEM219, CHCHD10, IGFBP7, LAMTOR2, ZNF688, UBL7, and RELB, which belonged to the apoptosis, MAPK, MTOR, and NF-kB pathways. Second, these 12 genes were used to predict overall survival using machine learning, artificial neural networks, and conventional statistics. In a multivariate Cox regression analysis, high expressions of HYAL2 and UBL7 were correlated with poor overall survival, whereas TRAPPC1, IGFBP7, and RELB were correlated with good overall survival (p < 0.01). As a single marker and only in RCHOP-like treated cases, the prognostic value of RELB was confirmed using GSEA analysis and Kaplan–Meier with log-rank test and validated in the TCGA and GSE57611 datasets. Anomaly detection analysis was successfully tested in the GSE31312 and GSE117556 datasets. Using immunohistochemistry, RELB was positive in B-lymphocytes and macrophage/dendritic-like cells, and correlation with HLA DP-DR, SIRPA, CD85A (LILRB3), PD-L1, MARCO, and TOX was explored. Conclusions: Anomaly detection and other bioinformatic techniques successfully predicted the prognosis of DLBCL, and high RELB was associated with a favorable prognosis. Full article
(This article belongs to the Special Issue Feature Papers in Applied Biomedical Data Science)
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<p>Histological heterogeneity of DLBCL. Despite the fact that DLBCL is a unique lymphoma subtype, its morphological characteristics are heterogeneous, including the neoplastic B lymphocytes and variable content of the tumor immune microenvironment. Hematoxylin and eosin stain (scale bar = 50 μm). The histological cases were retrieved from the lymphoma database of the Department of Pathology, Tokai University, School of Medicine.</p>
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<p>Types of artificial intelligence methods.</p>
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<p>Types of machine learning methods for predictive data analysis. In addition to anomaly detection analysis, there are many other types of machine learning that can be classified as supervised (<b>A</b>), unsupervised (<b>B</b>), and reinforcement learning (<b>C</b>). Of note, this figure includes methods usually used in predictive data analysis, but it does not focus on deep learning and reinforcement learning (please refer to popular deep learning frameworks such as tensorflow, keras, and pytorch, for documentation).</p>
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<p>Segmentation analysis. This figure shows example images of the K-Means cluster (<b>A</b>), Kohonen clustering analysis (<b>B</b>), and anomaly detection (<b>C</b>).</p>
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<p>Aim and methodology. The discovery set was the Lymphoma/Leukemia Molecular Profiling Project (LLMPP) GSE10846 gene expression dataset (last update 25 March 2019) of 414 cases.</p>
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<p>Anomaly index values. Anomaly detection analysis identifies outliners, or unusual cases, in the data. It records information on what normal behavior looks like and identifies outliers even if they do not conform to any known pattern. It is an unsupervised method that examines large numbers of variables to identify clusters or peer groups. Then, each record is compared to others in its peer group to identify possible anomalies. Each record (blue circle) is assigned an abnormality index. High index implies a higher average of the case than the average. In the setup, several options can be specified, such as the adjustment of coefficient, number of peer groups, noise level, and noise ratio.</p>
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<p>Machine learning and artificial neural networks using the LLMPP gene expression dataset. Abnormality detection analysis identified 12 genes. The prognostic value of these genes for overall survival was tested using several artificial intelligence analysis techniques. XGBoost tree (<b>A</b>), random forest (<b>B</b>), C5 tree (<b>C</b>), and neural network (<b>D</b>). Of note, the prognostic value of <span class="html-italic">RELB</span> was confirmed in the RCHOP-like cases of the LLMPP series using conventional overall survival analysis of Kaplan–Meier with log-rank tests (<b>E</b>). High gene expression of <span class="html-italic">RELB</span> was associated with favorable overall survival (<b>E</b>).</p>
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<p>Protein−protein interaction analysis and gene set enrichment analysis (GSEA) of <span class="html-italic">RELB</span> gene and pathway. First, a functional network association analysis (protein−protein interaction network) focused on RELB created a pathway. Later, this RELB pathway was used in the GSEA analysis. The GSEA analysis confirmed the association of the RELB gene and pathway with a favorable overall survival of patients with DLBCL treated with R-CHOP therapy. Functional network association analysis (<b>A</b>), GSEA (<b>B</b>).</p>
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<p>Immunohistochemical analysis of RELB in reactive tonsils and DLBCL. The protein expression of RELB was analyzed in 10 reactive tonsils (tissue control) and 30 cases of DLBCL not otherwise specified (NOS). In reactive tonsils, RELB expression was mainly present in the germinal centers of the follicles, with strong staining in macrophage/dendritic cells and weak in the B-lymphocytes. In DLBCL NOS, the staining was heterogeneous, ranging from 0 to 3+, and expressed by neoplastic B-lymphocytes and cells of the microenvironment.</p>
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<p>Immunohistochemical analysis of RELB in relationship with other immune microenvironment markers in DLBCL NOS. The expression of RELB in DLBCL was heterogeneous, with a pattern compatible with mixture of macrophage/dendritic cells and B-lymphocytes. Correlation with other macrophage-associated and immune microenvironment/immune checkpoint markers was performed using HLA DP-DR, SIRPA, CD85A, PD-L1, MARCO, and TOX (TOX1). Original magnification 400×.</p>
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<p>Validation of the association between <span class="html-italic">RELB</span> gene expression and overall survival in other series.</p>
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19 pages, 1514 KiB  
Article
A Comprehensive Analysis of Non-Desmosomal Rare Genetic Variants in Arrhythmogenic Cardiomyopathy: Integrating in Padua Cohort Literature-Derived Data
by Maria Bueno Marinas, Marco Cason, Riccardo Bariani, Rudy Celeghin, Monica De Gaspari, Serena Pinci, Alberto Cipriani, Ilaria Rigato, Alessandro Zorzi, Stefania Rizzo, Gaetano Thiene, Martina Perazzolo Marra, Domenico Corrado, Cristina Basso, Barbara Bauce and Kalliopi Pilichou
Int. J. Mol. Sci. 2024, 25(11), 6267; https://doi.org/10.3390/ijms25116267 - 6 Jun 2024
Viewed by 702
Abstract
Arrhythmogenic cardiomyopathy (ACM) is an inherited myocardial disease at risk of sudden death. Genetic testing impacts greatly in ACM diagnosis, but gene-disease associations have yet to be determined for the increasing number of genes included in clinical panels. Genetic variants evaluation was undertaken [...] Read more.
Arrhythmogenic cardiomyopathy (ACM) is an inherited myocardial disease at risk of sudden death. Genetic testing impacts greatly in ACM diagnosis, but gene-disease associations have yet to be determined for the increasing number of genes included in clinical panels. Genetic variants evaluation was undertaken for the most relevant non-desmosomal disease genes. We retrospectively studied 320 unrelated Italian ACM patients, including 243 cases with predominant right-ventricular (ARVC) and 77 cases with predominant left-ventricular (ALVC) involvement, who did not carry pathogenic/likely pathogenic (P/LP) variants in desmosome-coding genes. The aim was to assess rare genetic variants in transmembrane protein 43 (TMEM43), desmin (DES), phospholamban (PLN), filamin c (FLNC), cadherin 2 (CDH2), and tight junction protein 1 (TJP1), based on current adjudication guidelines and reappraisal on reported literature data. Thirty-five rare genetic variants, including 23 (64%) P/LP, were identified in 39 patients (16/243 ARVC; 23/77 ALVC): 22 FLNC, 9 DES, 2 TMEM43, and 2 CDH2. No P/LP variants were found in PLN and TJP1 genes. Gene-based burden analysis, including P/LP variants reported in literature, showed significant enrichment for TMEM43 (3.79-fold), DES (10.31-fold), PLN (117.8-fold) and FLNC (107-fold). A non-desmosomal rare genetic variant is found in a minority of ARVC patients but in about one third of ALVC patients; as such, clinical decision-making should be driven by genes with robust evidence. More than two thirds of non-desmosomal P/LP variants occur in FLNC. Full article
(This article belongs to the Special Issue Novel Biomarkers for Cardiovascular Diseases)
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<p>Schematic representation of protein structure and aminoacidic variants localization. (<b>A</b>) Transmembrane protein 43 (TMEM43). (<b>B</b>) Desmin (DES). (<b>C</b>) Filamin C (FLNC). (<b>D</b>) Cadherin 2 (CDH2). Variants found in our cohort are highlighted in red. (<b>E</b>) Plot with amino acid sequences running on the x-axis and the selective pressure represented by Ka/Ks ratio on the y-axis. Red dots represent amino acidic changes found in our cohort or reported in literature.</p>
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<p>Clinical setting of 17-year-old <span class="html-italic">DES</span> carrier affected by left-dominant ACM. Clinical–pathological correlation in 17-year-old male (#9) affected by ACM with left-dominant pattern who underwent cardiac transplantation for refractory heart failure (missense variant c.346A&gt;G carrier). (<b>A</b>) 12-leads ECG showing the typical epsilon waves in right precordial leads and T wave inversion on V1–V2. (<b>B</b>) Transthoracic echocardiogram with evidence of enlargement of right ventricular outflow tract (white arrow). (<b>C</b>) Cine-CMR confirming the dilatation (white asterisk). (<b>D</b>,<b>E</b>) Short-axis CE-CMR with LGE detectable on the entire RV wall (white arrow) and on the right side of the interventricular septum (white arrowhead). LV involvement in the inferior wall is also detectable (black arrow). (<b>F</b>) RV EMB demonstrating areas of replacement-type fibrosis and endocardial thickening (Heidenhain trichrome stain, panoramic view, scale bar 200 µm). (<b>G</b>) Gross view of the explanted heart with biventricular scars corresponding at histology (<b>H</b>) to transmural fibrosis of the RV free wall, subepicardial fibrosis in the LV and septal involvement on the RV-side (Heidenhaim trichrome stain, panoramic view, scale bar 5 mm).</p>
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<p>Flow diagram summarizing the literature review and inclusion/exclusion process.</p>
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13 pages, 3345 KiB  
Article
An Exercise Immune Fitness Test to Unravel Disease Mechanisms—A Proof-of-Concept Heart Failure Study
by Galyna Bondar, Abhinandan Das Mahapatra, Tra-Mi Bao, Irina Silacheva, Adrian Hairapetian, Thomas Vu, Stephanie Su, Ananya Katappagari, Liana Galan, Joshua Chandran, Ruben Adamov, Lorenzo Mancusi, Isabel Lai, Anca Rahman, Tristan Grogan, Jeffrey J. Hsu, Monica Cappelletti, Peipei Ping, David Elashoff, Elaine F. Reed and Mario C. Dengadd Show full author list remove Hide full author list
J. Clin. Med. 2024, 13(11), 3200; https://doi.org/10.3390/jcm13113200 - 29 May 2024
Viewed by 1064
Abstract
Background: Cardiorespiratory fitness positively correlates with longevity and immune health. Regular exercise may provide health benefits by reducing systemic inflammation. In chronic disease conditions, such as chronic heart failure and chronic fatigue syndrome, mechanistic links have been postulated between inflammation, muscle weakness, [...] Read more.
Background: Cardiorespiratory fitness positively correlates with longevity and immune health. Regular exercise may provide health benefits by reducing systemic inflammation. In chronic disease conditions, such as chronic heart failure and chronic fatigue syndrome, mechanistic links have been postulated between inflammation, muscle weakness, frailty, catabolic/anabolic imbalance, and aberrant chronic activation of immunity with monocyte upregulation. We hypothesize that (1) temporal changes in transcriptome profiles of peripheral blood mononuclear cells during strenuous acute bouts of exercise using cardiopulmonary exercise testing are present in adult subjects, (2) these temporal dynamic changes are different between healthy persons and heart failure patients and correlate with clinical exercise-parameters and (3) they portend prognostic information. Methods: In total, 16 Heart Failure (HF) patients and 4 healthy volunteers (HV) were included in our proof-of-concept study. All participants underwent upright bicycle cardiopulmonary exercise testing. Blood samples were collected at three time points (TP) (TP1: 30 min before, TP2: peak exercise, TP3: 1 h after peak exercise). We divided 20 participants into 3 clinically relevant groups of cardiorespiratory fitness, defined by peak VO2: HV (n = 4, VO2 ≥ 22 mL/kg/min), mild HF (HF1) (n = 7, 14 < VO2 < 22 mL/kg/min), and severe HF (HF2) (n = 9, VO2 ≤ 14 mL/kg/min). Results: Based on the statistical analysis with 20–100% restriction, FDR correction (p-value 0.05) and 2.0-fold change across the three time points (TP1, TP2, TP3) criteria, we obtained 11 differentially expressed genes (DEG). Out of these 11 genes, the median Gene Expression Profile value decreased from TP1 to TP2 in 10 genes. The only gene that did not follow this pattern was CCDC181. By performing 1-way ANOVA, we identified 8/11 genes in each of the two groups (HV versus HF) while 5 of the genes (TTC34, TMEM119, C19orf33, ID1, TKTL2) overlapped between the two groups. We found 265 genes which are differentially expressed between those who survived and those who died. Conclusions: From our proof-of-concept heart failure study, we conclude that gene expression correlates with VO2 peak in both healthy individuals and HF patients, potentially by regulating various physiological processes involved in oxygen uptake and utilization during exercise. Multi-omics profiling may help identify novel biomarkers for assessing exercise capacity and prognosis in HF patients, as well as potential targets for therapeutic intervention to improve VO2 peak and quality of life. We anticipate that our results will provide a novel metric for classifying immune health. Full article
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<p>Study Design.</p>
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<p>Average expression of 11 genes across all three time points and 20 subjects. Color-coding identifies upper quartile (blue) and lower quartile (red).</p>
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<p>Gene Expression of 11 genes at Time Points 1, 2, and 3. The left columns represent the normalized signal values for Time Point 1, the middle columns for Time Point 2, and the right columns for Time Point 3.</p>
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<p>The Venn diagram depicts the 11 genes described in <a href="#jcm-13-03200-f003" class="html-fig">Figure 3</a> and <a href="#app1-jcm-13-03200" class="html-app">Table S1</a>. While 5 of the 11 genes overlap between the group HV and HF, 6 of the 11 genes are specific to either HV (<span class="html-italic">n</span> = 4) or HF (<span class="html-italic">n</span> = 16).</p>
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<p>Cluster entities diagram for all 20 subjects and TP1, TP2 and TP3 with FC cut-off 2.0 and 11 genes. Top to bottom: <span class="html-italic">TTC34</span>, <span class="html-italic">DPYD-AS1</span>, <span class="html-italic">CCDC181</span>, <span class="html-italic">TKTL2</span>, <span class="html-italic">THSD7A</span>, <span class="html-italic">TMEML19</span>, <span class="html-italic">TNRFSF12A</span>, <span class="html-italic">CD300LD</span>, <span class="html-italic">MGAT5B</span>, <span class="html-italic">C19orf33</span>, <span class="html-italic">ID1</span>.</p>
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<p>Covariate regression analysis shows an expression of the 11 genes across all three TPs for HV and HF groups in correlation to VO2max and % predicted oxygen uptake. The color coding depicts a positive (red) and a negative (blue) correlation. The darker the color the stronger the respective correlation. Top to bottom: <span class="html-italic">TTC34</span>, <span class="html-italic">DPYD-AS1</span>, <span class="html-italic">CCDC181</span>, <span class="html-italic">TKTL2</span>, <span class="html-italic">THSD7A</span>, <span class="html-italic">TMEM119</span>, <span class="html-italic">TNFRSF12A</span>, <span class="html-italic">CD300LD</span>, <span class="html-italic">MGAT5B</span>, <span class="html-italic">C19orf33</span>, <span class="html-italic">ID1</span>.</p>
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<p>HF group (n-9), analyzed by time-point (TP1, TP2, TP3) and as a shared gene set. Comparison of Z test results for death-associated heart failure group and three time points separately.</p>
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12 pages, 870 KiB  
Review
Ca2+-Dependent Cl Channels in Vascular Tone Regulation during Aging
by Miriam Petrova, Monika Lassanova, Jana Tisonova and Silvia Liskova
Int. J. Mol. Sci. 2024, 25(10), 5093; https://doi.org/10.3390/ijms25105093 - 7 May 2024
Viewed by 902
Abstract
Identifying alterations caused by aging could be an important tool for improving the diagnosis of cardiovascular diseases. Changes in vascular tone regulation involve various mechanisms, like NO synthase activity, activity of the sympathetic nervous system, production of prostaglandin, endothelium-dependent relaxing, and contracting factors, [...] Read more.
Identifying alterations caused by aging could be an important tool for improving the diagnosis of cardiovascular diseases. Changes in vascular tone regulation involve various mechanisms, like NO synthase activity, activity of the sympathetic nervous system, production of prostaglandin, endothelium-dependent relaxing, and contracting factors, etc. Surprisingly, Ca2+-dependent Cl channels (CaCCs) are involved in all alterations of the vascular tone regulation mentioned above. Furthermore, we discuss these mechanisms in the context of ontogenetic development and aging. The molecular and electrophysiological mechanisms of CaCCs activation on the cell membrane of the vascular smooth muscle cells (VSMC) and endothelium are explained, as well as the age-dependent changes that imply the activation or inhibition of CaCCs. In conclusion, due to the diverse intracellular concentration of chloride in VSMC and endothelial cells, the activation of CaCCs depends, in part, on intracellular Ca2+ concentration, and, in part, on voltage, leading to fine adjustments of vascular tone. The activation of CaCCs declines during ontogenetic development and aging. This decline in the activation of CaCCs involves a decrease in protein level, the impairment of Ca2+ influx, and probably other alterations in vascular tone regulation. Full article
(This article belongs to the Special Issue Aging in Cardiovascular Diseases)
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<p>The activation of CaCCs during agonist (norepinephrine)–induced contraction in the arteries of young (<b>A</b>) and aged (<b>B</b>) animals. (<b>1</b>) The release of Ca<sup>2+</sup> from sarcoplasmic reticulum through IP<sub>3</sub>R leads to activation of CaCCs. (<b>2</b>) The activation of CaCCs, together with the opening of L–VDCC (positive feedback, red arrow), causing fast depolarization of VSMC via the Cl<sup>−</sup> efflux and Ca<sup>2+</sup> influx (<b>A</b>). Age-dependent changes involve a decrease in the activity of CaCCs and L–VDCC, causing slower depolarization of VSMC (<b>B</b>). (<b>3</b>) Elevated [Ca<sup>2+</sup>]i activates CICR, and Ca<sup>2+</sup> sparks provide additional stimulus for CaCCs activation in VSMC. (<b>4</b>) Cl<sup>−</sup> efflux and Ca<sup>2+</sup> influx increase depolarization and cause VSMC contraction. CaCCs and L–VDCC are inactivated through CaMKII. (<b>5</b>) Depending on the [Cl<sup>−</sup>]i, the activation of CaCCs on endothelium causes depolarization that increases NO release, or hyperpolarization that decreases NO release. L–VDCC are not expressed on endothelial cells (indicated by dashed lines), but the Ca<sup>2+</sup> entry through L–VDCC in VSMC can pass to the endothelium through positions aligned with holes in the internal elastic lamina in amounts sufficient to activate Ca<sup>2+</sup> signaling in endothelial cells [<a href="#B30-ijms-25-05093" class="html-bibr">30</a>]. TRPV4 channels are localized in nanoscale proximity of CaCCs and are activated together with CaCCs, leading to increased Ca<sup>2+</sup> influx into the endothelial cell [<a href="#B31-ijms-25-05093" class="html-bibr">31</a>]. (<b>6</b>) The downregulation of CaCCs contributes to enhanced proliferation of VSMC. Solid arrows indicate stimulation, dashed arrows indicate inhibition, red arrow indicates positive feedback, purple arrows indicate Ca<sup>2+</sup> currents, and orange arrows indicate Cl<sup>−</sup> currents. Abbreviations: AC, adenylate cyclase; AR, adrenergic receptor; CaCCs, Ca<sup>2+</sup>–dependent Cl<sup>−</sup> channels; cAMP, cyclic adenosine monophosphate; CIRC, Ca<sup>2+</sup>–induced Ca<sup>2+</sup> release; CaMKII, Ca<sup>2+</sup>/calmodulin–dependent protein kinase II; EC, endothelial cell; eNOS, endothelial NO synthase; Gi, G protein–coupled receptor with αi subunit; Gq, G protein–coupled receptor with αq subunit; Gs, G protein–coupled receptor with αs subunit; IP<sub>3</sub>, inositol triphosphate; IP<sub>3</sub>R, IP<sub>3</sub> receptor; NO, nitric oxide; L–VDCC, L–type voltage–dependent Ca<sup>2+</sup> channels; PKA, protein kinase A; PKG, protein kinase G; PLC, phospholipase C; RyR, ryanodine receptor; TRPV4, transient receptor potential cation channel subfamily V member 4; VSMC, vascular smooth muscle cell.</p>
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