Cells

19 pages, 7241 KiB

Open AccessArticle

Novel Drug Delivery Particles Can Provide Dual Effects on Cancer “Theranostics” in Boron Neutron Capture Therapy

by Abdul Basith Fithroni, Haruki Inoue, Shengli Zhou, Taufik Fatwa Nur Hakim, Takashi Tada, Minoru Suzuki, Yoshinori Sakurai, Manabu Ishimoto, Naoyuki Yamada, Rani Sauriasari, Wolfgang A. G. Sauerwein, Kazunori Watanabe, Takashi Ohtsuki and Eiji Matsuura

Cells 2025, 14(1), 60; https://doi.org/10.3390/cells14010060 - 6 Jan 2025

Viewed by 1138

Abstract

Boron (B) neutron capture therapy (BNCT) is a novel non-invasive targeted cancer therapy based on the nuclear capture reaction ¹⁰B (n, alpha) ⁷Li that enables the death of cancer cells without damaging neighboring normal cells. However, the development of clinically approved [...] Read more.

Boron (B) neutron capture therapy (BNCT) is a novel non-invasive targeted cancer therapy based on the nuclear capture reaction ¹⁰B (n, alpha) ⁷Li that enables the death of cancer cells without damaging neighboring normal cells. However, the development of clinically approved boron drugs remains challenging. We have previously reported on self-forming nanoparticles for drug delivery consisting of a biodegradable polymer, namely, “AB-type” Lactosome^® nanoparticles (AB-Lac particles)- highly loaded with hydrophobic B compounds, namely o-Carborane (Carb) or 1,2-dihexyl-o-Carborane (diC6-Carb), and the latter (diC6-Carb) especially showed the “molecular glue” effect. Here we present in vivo and ex vivo studies with human pancreatic cancer (AsPC-1) cells to find therapeutically optimal formulas and the appropriate treatment conditions for these particles. The biodistribution of the particles was assessed by the tumor/normal tissue ratio (T/N) in terms of tumor/muscle (T/M) and tumor/blood (T/B) ratios using near-infrared fluorescence (NIRF) imaging with indocyanine green (ICG). The in vivo and ex vivo accumulation of B delivered by the injected AB-Lac particles in tumor lesions reached a maximum by 12 h post-injection. Irradiation studies conducted both in vitro and in vivo showed that AB-Lac particles-loaded with either ¹⁰B-Carb or ¹⁰B-diC6-Carb significantly inhibited the growth of AsPC-1 cancer cells or strongly inhibited their growth, with the latter method being significantly more effective. Surprisingly, a similar in vitro and in vivo irradiation study showed that ICG-labeled AB-Lac particles alone, i.e., without any ¹⁰B compounds, also revealed a significant inhibition. Therefore, we expect that our ICG-labeled AB-Lac particles-loaded with ¹⁰B compound(s) may be a novel and promising candidate for providing not only NIRF imaging for a practical diagnosis but also the dual therapeutic effects of induced cancer cell death, i.e., “theranostics”. Full article

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22 pages, 11367 KiB

Open AccessArticle

Nuclear N-WASP Induces Actin Polymerization in the Nucleus with Cortactin as an Essential Factor

by Xin Jiang, Purusottam Mohapatra, Maria Rossing, Wenqian Zheng, Olga Zbodakova, Jayashree Vijay Thatte, Claus Storgaard Sørensen, Thu Han Le Phan and Cord Brakebusch

Cells 2025, 14(1), 59; https://doi.org/10.3390/cells14010059 - 6 Jan 2025

Viewed by 894

Abstract

Nuclear actin polymerization was reported to control different nuclear processes, but its regulation is poorly understood. Here, we show that N-WASP can trigger the formation of nuclear N-WASP/F-actin nodules. While a cancer hotspot mutant of N-WASP lacking the VCA domain (V418fs) had a [...] Read more.

Nuclear actin polymerization was reported to control different nuclear processes, but its regulation is poorly understood. Here, we show that N-WASP can trigger the formation of nuclear N-WASP/F-actin nodules. While a cancer hotspot mutant of N-WASP lacking the VCA domain (V418fs) had a dominant negative function on nuclear F-actin, an even shorter truncation mutant found in melanoma (R128*) strongly promoted nuclear actin polymerization. Nuclear localization of N-WASP was not regulated by the cell cycle and increasing nuclear F-actin formation by N-WASP had no obvious influence on replication. However, nuclear N-WASP/F-actin nodules colocalized partially with RNA Pol II clusters. N-WASP-dependent actin polymerization promoted the maturation of RNA Pol II clusters, with the short truncation mutant R128* unexpectedly showing the strongest effect. Nuclear N-WASP nodules including V418fs colocalized with WIP and cortactin. Importantly, cortactin binding was essential but not sufficient for F-actin formation, while WIP binding was required for actin polymerization by R128*. These data reveal a cortactin-dependent role for N-WASP in the regulation of nuclear F-actin and indicate contrasting nuclear effects for N-WASP mutants found in cancer. Full article

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31 pages, 2904 KiB

Open AccessReview

The Estrogen–Immune Interface in Endometriosis

by Emily Greygoose, Pat Metharom, Hakan Kula, Timur K. Seckin, Tamer A. Seckin, Ayse Ayhan and Yu Yu

Cells 2025, 14(1), 58; https://doi.org/10.3390/cells14010058 - 6 Jan 2025

Viewed by 1299

Abstract

Endometriosis is a gynecologic condition characterized by the growth of endometrium-like stroma and glandular elements outside of the uterine cavity. The involvement of hormonal dysregulation, specifically estrogen, is well established in the initiation, progression, and maintenance of the condition. Evidence also highlights the [...] Read more.

Endometriosis is a gynecologic condition characterized by the growth of endometrium-like stroma and glandular elements outside of the uterine cavity. The involvement of hormonal dysregulation, specifically estrogen, is well established in the initiation, progression, and maintenance of the condition. Evidence also highlights the association between endometriosis and altered immune states. The human endometrium is a highly dynamic tissue that undergoes frequent remodeling in response to hormonal regulation during the menstrual cycle. Similarly, endometriosis shares this propensity, compounded by unclear pathogenic mechanisms, presenting unique challenges in defining its etiology and pathology. Here, we provide a lens to understand the interplay between estrogen and innate and adaptive immune systems throughout the menstrual cycle in the pathogenesis of endometriosis. Estrogen is closely linked to many altered inflammatory and immunomodulatory states, affecting both tissue-resident and circulatory immune cells. This review summarizes estrogenic interactions with specific myeloid and lymphoid cells, highlighting their implications in the progression of endometriosis. Full article

(This article belongs to the Special Issue Cellular and Molecular Mechanisms in Gynecological Disorders)

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A summary of the hormonal changes in endometrial tissue, with reference to the proportional abundance of immune cells between the healthy endometrium versus eutopic endometrium in endometriosis. The menstrual cycle (0 to 28 days) is annotated to scale given the expected endometrial thickness, with corresponding estrogen (E2, pink), progesterone (P4, orange), luteinizing hormone (LH, green), and follicle-stimulating hormone (FSH, blue) levels overlaid. Wider lines indicate hormonal variance, while dashed lines to fade show uncertainty in hormonal crossovers in reference to the 28-day cycle. Proportional Increases were calculated from statistics derived from works reported in Huang, et al. [<a href="#B7-cells-14-00058" class="html-bibr">7</a>]. Full article ">Figure 2
Immune cell dynamics and local estrogen concentrations in endometriosis. (A) Comparative immune cell profiles in the healthy endometrium, eutopic endometrium, and peritoneal lesions: pro-inflammatory vs. immune-suppressive microenvironments. The corresponding endometriosis patient panels, although not depicted directly, are under the pretext of a dysregulated endocrine milieu of estrogen dominance and progesterone resistance. The exact proportion and distribution of immune cell subtypes is still debated; however, there remain notable differences in activation states and function. ‘^’ indicates an elevation of relative cell abundance or activity. Key acronyms: macrophage phenotypes 1 or 2 (M1 and M2); T helper (Th); T regulatory (Treg). (B) Local E2 concentration in women with and without endometriosis. Local (intratissue) E2 concentrations derived from Huhtinen et al., 2012 [<a href="#B24-cells-14-00058" class="html-bibr">24</a>]. Tissue-specific E2 levels illustrate the differences in focal concentrations between endometriotic lesions and control tissues. Notable differences in E2 concentrations across endometriotic subtypes are also evident. These differences in E2 concentrations highlight the importance of investigating the constitutive or induced expression profiles of specific estrogen receptors in focal immune cells. Full article ">Figure 3
Structural summary of ER isoforms and their role in transcriptional regulation: differences between ERα and ERβ in AF1 and AF2 activity. The general structure of the ER includes the transactivation domain containing activation-function 1 (AF1), the DNA-binding domain (DBD), hinge region, ligand-binding domain (LBD), and activation-function 2 (AF2). ERs exist in two isoforms, ERα and ERβ, which form functionally distinct homo- and heterodimers (αα, αβ, and ββ) that play non-redundant roles in transcriptional regulation. The primary differences between ERα and ERβ are in the N-terminal domain, which contains the ligand-independent AF1, with a 16% similarity between the isoforms. The AF2 at the COOH-terminal ligand-binding domain (LBD) exhibits a 59% amino acid sequence similarity between the isoforms. Upon E2 binding, conformational change ensues to the receptor, enabling the recruitment of coregulators to the activation-function domains. The AF1 region, differing between ERα and ERβ, results in distinct transcriptional activities. In contrast, AF2 activity is similar in both isoforms. Full article ">Figure 4
Effects of high and low estradiol (E2) on estrogen receptor expression and their respective roles in immune cells: A generalized snapshot of the current literature derived from endometrium and endometriosis tissue experiments. This figure provides a generalized overview of the effects of E2 on immune cells derived from endometrium and endometriosis tissues. The figure highlights how high and low E2 concentrations influence the expression of estrogen receptor alpha (Erα), beta (Erβ), and G protein-coupled estrogen receptor (GPER), as well as summarizes the resulting functions. Figure legend: E2 concentrations: high (red) and low (green); Gene/protein expression (respective to Erα/Erβ/GPER): unclear/limited information (‘?’); limited/sparse data with minimal findings (‘?’ alongside ‘+’ OR ‘−’); upregulated (+); downregulated (−). Note: up/downregulation predominantly refers to compared tissue regions inclusive of the internal (matched) endometrium and the healthy control endometrium. Estrogen receptor effect/function: enhanced (^‘Cell Name’); hindered (~‘Cell Name’). Full article ">

13 pages, 1977 KiB

Open AccessArticle

The Aryl Hydrocarbon Receptor (AhR) Is a Novel Gene Involved in Proper Physiological Functions of Pancreatic β-Cells

by Shuhd Bin Eshaq, Jalal Taneera, Shabana Anjum, Abdul Khader Mohammed, Mohammad H. Semreen, Karem H. Alzoubi, Mohamed Eladl, Yasser Bustanji, Eman Abu-Gharbieh and Waseem El-Huneidi

Cells 2025, 14(1), 57; https://doi.org/10.3390/cells14010057 - 6 Jan 2025

Viewed by 1019

Abstract

The Kynurenine pathway is crucial in metabolizing dietary tryptophan into bioactive compounds known as kynurenines, which have been linked to glucose homeostasis. The aryl hydrocarbon receptor (AhR) has recently emerged as the endogenous receptor for the kynurenine metabolite, kynurenic acid (KYNA). However, the [...] Read more.

The Kynurenine pathway is crucial in metabolizing dietary tryptophan into bioactive compounds known as kynurenines, which have been linked to glucose homeostasis. The aryl hydrocarbon receptor (AhR) has recently emerged as the endogenous receptor for the kynurenine metabolite, kynurenic acid (KYNA). However, the specific role of AhR in pancreatic β-cells remains largely unexplored. This study aimed to investigate the expression of AhR in human pancreatic islets using publicly available RNA-sequencing (RNA-seq) databases and to explore its correlations with various metabolic parameters and key β-cell markers. Additionally, functional experiments were conducted in INS-1 cells, a rat β-cell line, to elucidate the role of Ahr in β-cell biology. RNA-seq data analysis confirmed the expression of AHR in human islets, with elevated levels observed in pancreatic islets obtained from diabetic and obese donors compared to non-diabetic or lean donors. Furthermore, AHR expression showed an inverse correlation with the expression of key β-cell functional genes, including insulin, PDX-1, MAFA, KCNJ11, and GCK. Silencing Ahr expression using siRNA in INS-1 cells decreased insulin secretion, insulin content, and glucose uptake efficiency, while cell viability, apoptosis rate, and reactive oxygen species (ROS) production remained unaffected. Moreover, Ahr silencing led to the downregulation of major β-cell regulator genes, Ins1, Ins2, Pdx-1, and Glut2, at both the mRNA and protein levels. In summary, this study provides novel insights into the role of AhR in maintaining proper β-cell function. These findings suggest that AhR could be a potential target for future therapeutic strategies in treating type 2 diabetes (T2D). Full article

(This article belongs to the Special Issue Molecular Mechanisms of Signal Transduction in the Islet Cells)

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Expression analysis of AHR in human pancreatic islets. (A) RNA-seq expression of AHR, KCNJ11, PDX1, INSR, MAFA, GCK, and GLUT1 in human islets obtained from non-diabetic donors (n = 49). (B). AHR expression levels in human islets obtained from diabetic/hyperglycemic donors (n = 25) compared to nondiabetic/normoglycemic islet (n = 49) donors. (C) AHR expression levels in human islets obtained from lean donors (n = 18; BMI below 24) compared to obese donors (n = 19; BMI above 29). (D) AHR expression levels in human islets obtained from male donors (n = 53) compared to female donors (n = 33). Correlation of AHR expression with HbA1c% (n = 66) (E), BMI (n = 87) (F), or age (n = 87) (G). *; p > 0.05, ns; not significant. Bars represent mean ± SEM. Nonparametric Mann–Whitney t-tests were used in (B–D). Nonparametric Spearman’s correlation test was used in (E–G). Full article ">Figure 2
Expression correlations of AHR with key pancreatic β-cell markers. Expression of AHR was correlated with INS (A), PDX1 (B), MAFA (C), KCNJ11 (D), GCK (E), and GLUT1 (F) using nonparametric Spearman’s correlation. (G) Expression levels of AHR in human fat tissue (n = 12), pancreatic islets (n = 12), liver (n = 12), and skeletal muscle tissues (n = 12), obtained from the same donors. (H) Expression levels of AHR in sorted pancreatic cells, ductal, acinar, or PSC as obtained from Islet Gene View (IGV) web tool. Full article ">Figure 3
Silencing of Ahr and its impact on INS-1 cell function. (A) Analysis of mRNA expression of AhR 48 h after siRNA transfection as determined by qPCR in Ahr-silenced or control cells. (B) Confocal images of immunofluorescent staining of AHR protein in INS-1 with or without Ahr silencing. Blue is DAPI nuclear staining and green is AHR protein staining. The overlay of two markers is shown in the merged image. Magnification 60× (n. of experiments = 1). (C) Cell viability assay using MTT test. (D,E) Apoptosis analyzed by flow cytometry analysis using Annexin V-PI staining (D). The left panel denotes a summary of the apoptosis results (E). (F) ROS production measurements determined by luminescence-based analysis. (G) Normalized insulin secretion was stimulated in control or Ahr-silenced cells at 2.8 mM glucose, 16.7 mM glucose, and 2.8 mM glucose with KCL or αKIC. (H) Insulin content measurements relative to the total protein concentration. (I) Glucose uptake efficiency evaluated by flow cytometry. Data were acquired from three independent experiments unless otherwise mentioned. Bars display the mean ± SD. * p < 0.05, ** p < 0.01, *** p < 0.001, ns; not significant. Full article ">Figure 4
Impact of Ahr silencing on key β-cell function genes. (A) mRNA expression of Ins1, Ins2, Glut2, Insrβ, Pdx-1, and Gck in Ahr-silenced cells compared to control cells. Protein expression of (B) Pro/Insulin, (C) PDX-1, (D) GLUT2, (E) GCK, and (F) INSRβ relative to β-actin endogenous protein. Bars indicate mean ± SD fold changes in protein expression from three independent experiments. * p < 0.05, ** p < 0.01, ns; not significant. Full article ">

32 pages, 9144 KiB

Open AccessArticle

Small Extracellular Vesicles Promote Axon Outgrowth by Engaging the Wnt-Planar Cell Polarity Pathway

by Samar Ahmad, Tania Christova, Melanie Pye, Masahiro Narimatsu, Siyuan Song, Jeffrey L. Wrana and Liliana Attisano

Cells 2025, 14(1), 56; https://doi.org/10.3390/cells14010056 - 6 Jan 2025

Viewed by 1041

Abstract

In neurons, the acquisition of a polarized morphology is achieved upon the outgrowth of a single axon from one of several neurites. Small extracellular vesicles (sEVs), such as exosomes, from diverse sources are known to promote neurite outgrowth and thus may have therapeutic [...] Read more.

In neurons, the acquisition of a polarized morphology is achieved upon the outgrowth of a single axon from one of several neurites. Small extracellular vesicles (sEVs), such as exosomes, from diverse sources are known to promote neurite outgrowth and thus may have therapeutic potential. However, the effect of fibroblast-derived exosomes on axon elongation in neurons of the central nervous system under growth-permissive conditions remains unclear. Here, we show that fibroblast-derived sEVs promote axon outgrowth and a polarized neuronal morphology in mouse primary embryonic cortical neurons. Mechanistically, we demonstrate that the sEV-induced increase in axon outgrowth requires endogenous Wnts and core PCP components including Prickle, Vangl, Frizzled, and Dishevelled. We demonstrate that sEVs are internalized by neurons, colocalize with Wnt7b, and induce relocalization of Vangl2 to the distal axon during axon outgrowth. In contrast, sEVs derived from neurons or astrocytes do not promote axon outgrowth, while sEVs from activated astrocytes inhibit elongation. Thus, our data reveal that fibroblast-derived sEVs promote axon elongation through the Wnt-PCP pathway in a manner that is dependent on endogenous Wnts. Full article

(This article belongs to the Section Cell Signaling)

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15 pages, 5336 KiB

Open AccessArticle

Trehalose Ameliorates Zebrafish Emotional and Social Deficits Caused by CLN8 Dysfunction

by Rosario Licitra, Stefania Della Vecchia, Lorenzo Santucci, Rachele Vivarelli, Sara Bernardi, Filippo M. Santorelli and Maria Marchese

Cells 2025, 14(1), 55; https://doi.org/10.3390/cells14010055 - 5 Jan 2025

Viewed by 941

Abstract

CLN8 and other neuronal ceroid lipofuscinoses (NCLs) often lead to cognitive decline, emotional disturbances, and social deficits, worsening with disease progression. Disrupted lysosomal pH, impaired autophagy, and defective dendritic arborization contribute to these symptoms. Using a cln8^−/− zebrafish model, we identified significant [...] Read more.

CLN8 and other neuronal ceroid lipofuscinoses (NCLs) often lead to cognitive decline, emotional disturbances, and social deficits, worsening with disease progression. Disrupted lysosomal pH, impaired autophagy, and defective dendritic arborization contribute to these symptoms. Using a cln8^−/− zebrafish model, we identified significant impairments in locomotion, anxiety, and aggression, along with subtle deficits in social interactions, positioning zebrafish as a useful model for therapeutic studies in NCL. Our findings show that trehalose, an autophagy enhancer, ameliorates anxiety, and modestly improves social behavior and predator avoidance in mutant zebrafish. This finding aligns animal models with clinical reports suggestive of behavioral improvements in NCL patients. Trehalose holds promise as a therapeutic agent for CLN8, warranting further research into its neuroprotective mechanisms and clinical applications. Full article

(This article belongs to the Special Issue Advances in Zebrafish Disease Models)

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Schematic representation of the experimental trial. Full article ">Figure 2
Novel tank test behavioral results before (A) and after (B) the trehalose treatment on cln8−/− and WT fish (n = 12). Data are represented as individual values (lines indicate means ± SEM). Statistical analyses showed increasing anxiety in cln8−/− compared to WT before the trehalose feed supplementation (* p ≤ 0.05; ** p ≤ 0.01). The images on the left are representative of the heatmap locomotor activity during the test, the range of color is from dark blue (low activity) to red (high activity). Full article ">Figure 3
Shoaling test behavioral results before (A) and after (B) the trehalose treatment on cln8−/− and WT fish (n = 20). Data are represented as individual values (lines indicate means ± SEM). Statistical analyses showed impairments in swimming performances and shoal cohesion in cln8−/− compared to WT both before and after the trehalose feed supplementation (except for distance between subjects mitigated by trehalose treatment) (* p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001). Full article ">Figure 4
Social preference test behavioral results before (A) and after (B) the trehalose treatment on cln8−/− and WT fish (n = 10). Data are represented as individual values (lines indicate means ± SEM) and expressed in terms of cumulative duration. Statistical analyses showed impairments in sociality in cln8−/− compared to WT before the trehalose feed supplementation (* p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001). The images on the left are representative of the heatmap locomotor activity during the test; the range of color is from dark blue (low activity) to red (high activity). Full article ">Figure 5
Aggression test results before (A) and after (B) trehalose treatment on cln8−/− and WT fish (n = 12). Data are represented as individual values (lines indicate means ± SEM). Statistical analyses showed higher aggressiveness in cln8−/− compared to WT before the trehalose feed supplementation (except for females, which proved to be more aggressive even after treatment) (** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001). The images on the left are representative of the heatmap locomotor activity during the test; the range of color is from dark blue (low activity) to red (high activity). Full article ">Figure 6
Predator avoidance test results before (A) and after (B) trehalose treatment on cln8−/− and WT fish (n = 10). Data are represented as individual values (lines indicate means ± SEM) and expressed in terms of cumulative duration. Statistical analyses showed impairments in predator avoidance in cln8−/− compared to WT before the trehalose feed supplementation (* p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001). The images on the left are representative of the heatmap locomotor activity during the test; the range of color is from dark blue (low activity) to red (high activity). Full article ">Figure 7
Novel object tests behavioral results before (A) and after (B) the trehalose treatment on cln8−/− and WT fish (n = 12). Data are represented as individual values (lines indicate means ± SEM). Statistical analyses showed impairments in cognitive abilities in cln8−/− compared to WT both before and after the trehalose feed supplementation (* p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001). Full article ">

32 pages, 5152 KiB

Open AccessReview

From Homeostasis to Neuroinflammation: Insights into Cellular and Molecular Interactions and Network Dynamics

by Ludmila Müller, Svetlana Di Benedetto and Viktor Müller

Cells 2025, 14(1), 54; https://doi.org/10.3390/cells14010054 - 5 Jan 2025

Viewed by 1332

Abstract

Neuroinflammation is a complex and multifaceted process that involves dynamic interactions among various cellular and molecular components. This sophisticated interplay supports both environmental adaptability and system resilience in the central nervous system (CNS) but may be disrupted during neuroinflammation. In this article, we [...] Read more.

Neuroinflammation is a complex and multifaceted process that involves dynamic interactions among various cellular and molecular components. This sophisticated interplay supports both environmental adaptability and system resilience in the central nervous system (CNS) but may be disrupted during neuroinflammation. In this article, we first characterize the key players in neuroimmune interactions, including microglia, astrocytes, neurons, immune cells, and essential signaling molecules such as cytokines, neurotransmitters, extracellular matrix (ECM) components, and neurotrophic factors. Under homeostatic conditions, these elements promote cellular cooperation and stability, whereas in neuroinflammatory states, they drive adaptive responses that may become pathological if dysregulated. We examine how neuroimmune interactions, mediated through these cellular actors and signaling pathways, create complex networks that regulate CNS functionality and respond to injury or inflammation. To further elucidate these dynamics, we provide insights using a multilayer network (MLN) approach, highlighting the interconnected nature of neuroimmune interactions under both inflammatory and homeostatic conditions. This perspective aims to enhance our understanding of neuroimmune communication and the mechanisms underlying shifts from homeostasis to neuroinflammation. Applying an MLN approach offers a more integrative view of CNS resilience and adaptability, helping to clarify inflammatory processes and identify novel intervention points within the layered landscape of neuroinflammatory responses. Full article

(This article belongs to the Special Issue Behind and beyond Neuroinflammation: State of the Art and New Perspectives)

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Microglia in the CNS transition between dynamic states in response to environmental cues. In a resting state (blue area), they exhibit a ramified morphology, low activation markers, and perform surveillance, supporting neuronal health, synaptic pruning, and immune defense. Pro-inflammatory stimuli (e.g., LPS and IFN-γ) shift microglia to an “M1” state (red area), with an amoeboid shape, high activation marker expression, and characterized by the production of pro-inflammatory cytokines, driving neuroinflammation. Anti-inflammatory signals (e.g., IL-4, IL-10) promote an “M2” state (green area), associated with tissue repair and anti-inflammatory roles, marked by the expression of Arg1 and CD206. These functional states underscore microglial adaptability in maintaining CNS homeostasis and responding to pathological changes. IL: interleukin; TNF: tumor necrosis factor; ↑: increase; ↓: decrease; CD: cellular debris; IFN: interferon; NTF: neurotrophic factors; TGF: tumor growth factor; CD: cluster of differentiation; MHC: major histocompatibility complex; Arg1: arginase 1. Full article ">Figure 2
Astrocytes exhibit three distinct phenotypes that play unique roles in CNS homeostasis and neuroinflammation. In a quiescent state (blue area), astrocytes support synaptic function, regulate neurotransmitter levels, and provide metabolic support, releasing molecules like glutamine and D-serine. They also promote synaptogenesis and modulate the ECM to maintain a stable neural environment. During neuroinflammation, astrocytes can transition to either a pro-inflammatory (A1) or an anti-inflammatory (A2) reactive state. A1 astrocytes (red area), activated by signals from microglia or neurons, upregulate complement cascade proteins, release neurotoxic factors, and contribute to neuroinflammation and neuronal damage. Morphologically hypertrophic, they express markers like GFAP and secrete cytokines (e.g., IL-6, CCL2), forming glial scars to surround damaged areas. In contrast, A2 astrocytes (green area), induced by anti-inflammatory signals like IL-4 or IL-10, facilitate tissue repair and inflammation resolution. They express neurotrophic factors (e.g., GDNF and BDNF), anti-inflammatory cytokines, and detoxifying enzymes, promoting neuroprotection and neuronal survival. These astrocytic phenotypes underscore the diverse roles astrocytes play in both protective and pathological responses in the CNS. N: neuron; GFAP: glial fibrillary acidic protein; NT: neurotransmitter; LPS: lipopolysaccharides; IFN: interferon; ECM: extracellular matrix; IL: interleukin; ↑: increase; ↓: decrease; CCL2: CC-chemokine ligand 2; GS: glial scar; NTF: neurotrophic factors; BDNF: brain-derived neurotrophic factor; GDNF: glial cell line-derived neurotrophic factor. Full article ">Figure 3
In the CNS, T cells and macrophages perform essential, adaptable roles in both health and disease states. In a healthy CNS, T cells (left) patrol the brain’s borders, including the meninges and choroid plexus, where they maintain immune surveillance and prevent excessive infiltration that could disrupt neural function. Regulatory T cells (Tregs) release anti-inflammatory cytokines like IL-10 and TGF-β, helping to preserve neuronal health by preventing excessive immune activation. Macrophages (right) at CNS borders serve as immune sentinels, clearing antigens and supporting blood–brain barrier integrity. During neuroinflammation, T cells become more prominent within the CNS, with pro-inflammatory Th1 cells releasing cytokines like IFN-γ and TNF, which can exacerbate neuronal stress. Conversely, Th2 cells release IL-4 and IL-10 to help moderate inflammation and promote tissue repair. Macrophages respond by adopting either an inflammatory M1-like phenotype, which releases TNF-α and IL-1β, or a reparative M2-like phenotype, which aids in tissue repair and resolves inflammation. Both T cells and macrophages are also modulated by neurotransmitters like dopamine and serotonin, aligning their immune activity with CNS signaling and ensuring a balance between protection and neuronal preservation. Tregs: regulatory T cells; Th1: T-helper cells 1; Th2: T-helper cells 2; Mo: monocytes; IL: interleukin, ↑: increase; ↓: decrease; nC: naïve cells; APC: antigen-presenting cell; M1: macrophage M1-like phenotype; M2: macrophage M2-like phenotype; IFN: interferon; NT: neurotransmitters; TGF: tumor growth factor. Full article ">Figure 4
In a healthy CNS, a tightly regulated interplay among neurons, glial cells (microglia and astrocytes), and the extracellular matrix maintains functional homeostasis. Microglia monitor the environment, perform synaptic pruning, and clear cellular debris, while astrocytes regulate neurotransmitter levels, support neuronal metabolism, and reinforce the blood–brain barrier. Neurons communicate through neurotransmitters and release neurotrophins like BDNF to support synaptic stability. The ECM provides structural support and modulates growth factor availability. Low levels of cytokines facilitate intercellular communication, balancing immune activity without excessive inflammation, thus preserving CNS adaptability and resilience. BV: blood vessel; pIC: peripheral immune cells; BBB: blood–brain barrier; ECM: extracellular matrix; MG: microglia; AC: astrocytes; N: neuron; CD: cellular debris; NT: neurotransmitter; NTF: neurotrophins; NB: neuroblasts; IL: interleukin; ↑: increase; ↓: decrease; TNF: tumor necrosis factor. Full article ">Figure 5
In pathological conditions such as trauma, infection, or neurodegenerative disease, the CNS’s balanced network becomes dysregulated, leading to neuroinflammation. Microglia shift to an activated state, adopting a neurotoxic profile and releasing pro-inflammatory cytokines such as ROS and nitric oxide that exacerbate neuronal injury. Astrocytes undergo reactive astrogliosis, forming glial scars that can limit damage spread but hinder regeneration by releasing pro-inflammatory mediators that sustain inflammation. Injured neurons release danger signals, activating surrounding glia and attracting peripheral immune cells, further intensifying inflammation. Elevated cytokine levels disrupt synaptic function and neurotrophin signaling, weakening neuronal resilience. The ECM is degraded, compromising structural integrity and enabling peripheral immune cell infiltration, which perpetuates the inflammatory state. BV: blood vessel; pIC: peripheral immune cells; ↑: increase; ↓: decrease; BBB: blood–brain barrier; Mf: macrophages; Mf1: inflammatory macrophages; AC: astrocyte; dECM: depredated extracellular matrix; aMG: activated microglia; ROS: reactive oxygen species; aAC: activated astrocytes; IL: interleukin; TNF: tumor necrosis factor; iN: injured neuron; DS: danger signal; CD: cellular debris; NT: neurotransmitter; NTF: neurotrophins; dNB: disturbed neuroblasts; TC: T cells. Full article ">Figure 6
Each MLN consists of eight layers (L1–L8) with about 30 nodes in each layer. Nodes in different layers and corresponding edges are indicated by color. To illustrate the dynamic interplay in homeostatic (left) and neuroinflammatory (right) conditions or states, the first four layers (L1–L4) are split into clusters representing pro- and anti-inflammatory mechanisms. These clusters vary in node count and connectivity based on the inflammatory state; in the homeostatic state, anti-inflammatory cytokines (blue) predominate, whereas in the neuroinflammatory state, pro-inflammatory cytokines (red) become more prominent. Similar patterns apply to other cell types in layers L2 to L4. Microglia in layer 2 shift between M2 (green) and M1 (yellow) states; astrocytes in layer 3 vary between A2 (cyan) and A1 (pink) subtypes, and immune cells in layer 4 separate into Th2 (violet) and Th1 (sandy brown) T-helper subtypes, with state changes corresponding to homeostatic and neuroinflammatory conditions, respectively. For simplicity, neuronal, ECM, neurotrophic, and neurotransmitter layers (L5–L8) were not distinguished by pro- or anti-inflammatory impacts in the model; however, these layers remain dynamic, receiving inputs from the inflammatory layers and influencing the system’s behavior as described in the text. L1: cytokines; L2: microglia; L3: astrocytes; L4: immune cells; L5: neurons; L6: ECM; L7: neurotrophic factors (NTFs); L8: neurotransmitters. Full article ">

26 pages, 18146 KiB

Open AccessArticle

Trying to Kill a Killer; Impressive Killing of Patient Derived Glioblastoma Cultures Using NK-92 Natural Killer Cells Reveals Both Sensitive and Highly Resistant Glioblastoma Cells

by Jane Yu, Hyeon Joo Kim, Jordyn Reinecke, James Hucklesby, Tennille Read, Akshata Anchan, Catherine E. Angel and Euan Scott Graham

Cells 2025, 14(1), 53; https://doi.org/10.3390/cells14010053 - 5 Jan 2025

Viewed by 804

Abstract

The overall goal of this work was to assess the ability of Natural Killer cells to kill cultures of patient-derived glioblastoma cells. Herein we report impressive levels of NK-92 mediated killing of various patient-derived glioblastoma cultures observed at ET (effector: target) ratios of [...] Read more.

The overall goal of this work was to assess the ability of Natural Killer cells to kill cultures of patient-derived glioblastoma cells. Herein we report impressive levels of NK-92 mediated killing of various patient-derived glioblastoma cultures observed at ET (effector: target) ratios of 5:1 and 1:1. This enabled direct comparison of the degree of glioblastoma cell loss across a broader range of glioblastoma cultures. Importantly, even at high ET ratios of 5:1, there are always subpopulations of glioblastoma cells that prove very challenging to kill that evade the NK-92 cells. Of value in this study has been the application of ECIS (Electric Cell–Substrate Impedance Sensing) biosensor technology to monitor the glioblastoma cells in real-time, enabling temporal assessment of the NK-92 cells. ECIS has been powerful in revealing that at higher ET ratios, the glioblastoma cells are acutely sensitive to the NK-92 cells, and the observed glioblastoma cell death is supported by the high-content imaging data. Moreover, long-term ECIS experiments reveal that the surviving glioblastoma cells were then able to grow and reseed the culture, which was evident 300–500 h after the addition of the NK-92 cells. This was observed for multiple glioblastoma lines. In addition, our imaging provides evidence that some NK-92 cells appear to be compromised early, which would be consistent with potent evasive mechanisms by the glioblastoma tumour cells. This research strongly highlights the potential for NK-92 cells to kill glioblastoma tumour cells and provides a basis to identify the mechanism utilised by the surviving glioblastoma cells that we now need to target to achieve maximal cytolysis of the resistant glioblastoma cells. It is survival of the highly resistant glioblastoma clones that results in tumour relapse. Full article

(This article belongs to the Special Issue Therapeutic Targets in Glioblastoma)

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14 pages, 2786 KiB

Open AccessArticle

Local Administration of (−)-Epigallocatechin-3-Gallate as a Local Anesthetic Agent Inhibits the Excitability of Rat Nociceptive Primary Sensory Neurons

by Syogo Utugi, Risako Chida, Sana Yamaguchi, Yukito Sashide and Mamoru Takeda

Cells 2025, 14(1), 52; https://doi.org/10.3390/cells14010052 - 5 Jan 2025

Viewed by 616

Abstract

While the impact of (−)-epigallocatechin-3-gallate (EGCG) on modulating nociceptive secondary neuron activity has been documented, it is still unknown how EGCG affects the excitability of nociceptive primary neurons in vivo. The objective of the current study was to investigate whether administering EGCG locally [...] Read more.

While the impact of (−)-epigallocatechin-3-gallate (EGCG) on modulating nociceptive secondary neuron activity has been documented, it is still unknown how EGCG affects the excitability of nociceptive primary neurons in vivo. The objective of the current study was to investigate whether administering EGCG locally in rats reduces the excitability of nociceptive primary trigeminal ganglion (TG) neurons in response to mechanical stimulation in vivo. In anesthetized rats, TG neuronal extracellular single unit recordings were made in response to both non-noxious and noxious mechanical stimuli. Following the administration of EGCG, the mean firing rate of TG neurons to both non-noxious and noxious mechanical stimuli significantly decreased in a dose-dependent manner (1–10 mM), and both the non-noxious and nociceptive mechanical stimuli experienced the maximum suppression of discharge frequency within 5 min. These inhibitory effects lasted for approximately 20 min. These findings suggest that the local injection of EGCG into the peripheral receptive field suppresses the responsiveness of nociceptive primary sensory neurons in the TG, almost equal to that of the local anesthetic, 1% lidocaine. As a result, the local application of EGCG as a local anesthetic could alleviate nociceptive trigeminal pain that does not result in side effects, thereby playing a significant role in pain management. Full article

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Figure 1
Overview of TG neuron activity patterns in reaction to mechanical stimulation of facial skin. (A) The area of the facial skin activated by the whisker pad (shaded region). (B) Distribution of TG neurons activated by non-noxious and noxious mechanical stimuli on the facial skin (n = 15). The inset illustrates an example of how TG recording sites are identified (2–5 mm posterior to bregma and 1–4 mm lateral to midline). R, rostral; C, caudal; M, medial; L, lateral. Trigeminal nerve (I, II, and III). (C) An example of firing in SpVc WDR neurons, triggered by both non-noxious (2, 6, and 10 g) and noxious (15, 26, and 60 g) mechanical stimulation of the orofacial skin. Upper trace: TG neuronal activity; lower trace: post-stimulus histogram. Inset: a representative waveform of an action potential triggered by mechanical stimulation. (D) SpVc WDR neuron stimulus–response characteristics (n = 15) * p < 0.05 for comparison of 2 g vs. 6 g, 10 g, 15 g, 26 g, and 60 g. The values are mean ± standard error. (E) Effect of subcutaneous administration of vehicle (DMSO) in the peripheral receptive field on the TG neuronal activity induced by non-noxious and noxious mechanical stimulus. Full article ">Figure 2
Subcutaneous administration of EGCG in the peripheral receptive field affects TG neuronal activation induced by non-noxious and noxious mechanical stimulus. (A) Representative cases of TG neuronal activity elicited by non-noxious (2, 6, and 10 g) and noxious (15, 26, and 60 g) mechanical stimuli before and 10 min and 20 min post-administration. (B) Temporal pattern of EGCG application in the peripheral receptive field on the average firing rate of TG neurons; response to non-noxious and noxious mechanical stimulation. * p < 0.05, compared with 5 min after EGCG administration (n = 5). The values are mean ± standard error. Full article ">Figure 3
EGCG elicited a dose-dependent reduction in the mean firing frequency of TG neurons subjected to both non-noxious and noxious mechanical stimuli. * p < 0.05, 1 mM (n = 5) vs. 10 mM EGCG (n = 5). The values are mean ± standard error. Full article ">Figure 4
Evaluation of the decrease in TG neuron discharge frequency induced by EGCG between non-noxious and noxious stimuli. Non-noxious vs. noxious stimulation (n = 5). NS, not significant. The values are mean ± standard error. Full article ">Figure 5
The consequences of administering 1% lidocaine (37 mM) subcutaneously in the peripheral receptive field on TG neuron responses to non-noxious and noxious stimuli. (A) Typical examples of TG neuron activity in response to non-noxious (2, 6, and 10 g) and noxious (15, 26, and 60 g) mechanical stimulation before and 10 min and 45 min after 1% lidocaine administration (37 mM). Receptive field of the whisker pad in the facial skin. The shaded region represents the location and extent of the receptive field. (B) Temporal pattern of lidocaine application in the peripheral receptive field on the average firing rate of TG neurons responding to non-noxious and noxious mechanical stimulation. * p < 0.05, compared with 10 min after 1% lidocaine administration (n = 5). The values are mean ± standard error. Full article ">Figure 6
Comparison between 1% lidocaine and EGCG of mean magnitude of TG neuron inhibition in response to non-noxious and noxious mechanical stimulation. NS, not significant, lidocaine, n = 5; EGCG, n = 5. The values are mean ± standard error. Full article ">Figure 7
A possible mechanism underlying EGCG-induced inhibition of TG neuronal discharge in response to nociceptive mechanical stimulation. EGCG, when administered locally to peripheral tissues, inhibits the formation of both generator potentials and action potentials in the peripheral endings after nociceptive stimulation via the inhibition of mechanosensitive ionic channels (acid-sensing ion channel [ASIC] and transient receptor potential ankyrin 1 [TRPA1]), tetrodotoxin-resistant (TTX-R) and -sensitive (TTX-S) voltage-gated sodium (Nav) channels, and the facilitation of voltage-gated potassium (Kv) channels. EGCG’s potency is almost equivalent to that of Nav channel blockers, such as the commonly utilized local anesthetic lidocaine Cav, voltage-gated calcium; TG, trigeminal ganglion; and SpVc, trigeminal spinal nucleus caudalis. Full article ">

19 pages, 4549 KiB

Open AccessArticle

The Influence of Cell Isolation and Culturing on Natriuretic Peptide Receptors in Aortic Vascular Smooth Muscle Cells

by Christine Rager, Tobias Klöpper, Sabine Tasch, Michael Raymond Whittaker, Betty Exintaris, Andrea Mietens and Ralf Middendorff

Cells 2025, 14(1), 51; https://doi.org/10.3390/cells14010051 - 4 Jan 2025

Viewed by 904

Abstract

Vascular smooth muscle cell (SMC) relaxation by guanylyl cyclases (GCs) and cGMP is mediated by NO and its receptor soluble GC (sGC) or natriuretic peptides (NPs) ANP/BNP and CNP with the receptors GC-A and GC-B, respectively. It is commonly accepted that cultured SMCs [...] Read more.

Vascular smooth muscle cell (SMC) relaxation by guanylyl cyclases (GCs) and cGMP is mediated by NO and its receptor soluble GC (sGC) or natriuretic peptides (NPs) ANP/BNP and CNP with the receptors GC-A and GC-B, respectively. It is commonly accepted that cultured SMCs differ from those in intact vessels. Nevertheless, cell culture often remains the first step for signaling investigations and drug testing. Previously, we showed that even popular reference genes changed dramatically after SMC isolation from aorta. Regarding NP receptors, a substantial amount of data relies on cell culture. We hypothesize that the NP/cGMP system in intact aortic tunica media differs from isolated and cultured aortic SMCs. Therefore, we studied isolation and culturing effects on the expression of NP receptors GC-A, GC-B, and NP clearance receptor (NPRC) compared to sGC. We investigated intact tunica media and primary SMCs from the longitudinal halves of the same rat aorta. GC activity was monitored by cyclic guanosine monophosphate (cGMP). In addition, we hypothesize that there are sex-dependent differences in the NP/cGMP cascade in both intact tissue and cultured cells. We, therefore, analyzed a male and female cohort. Expression was quantified by RT-qPCR comparing aortic media and SMCs with our recently validated reference gene (RG) small nuclear ribonucleoprotein 2 (U2). Only GC-A was stably expressed. In intact media, GC-A exceeded GC-B and NPRC. However, GC-B, NPRC, and sGC were dramatically upregulated in cultured SMCs of the same aortae different from the stable GC-A. The expression was mirrored by NP-induced GC activity. In cultured cells, changes in GC activity were delayed compared to receptor expression. Minor differences between both sexes could also be revealed. Thus, isolation and culture fundamentally alter the cGMP system in vascular SMCs with potential impact on drug testing and scRNAseq. Especially, the dramatic increase in the clearance receptor NPRC in culture might distort all physiological ANP, BNP, and CNP effects. Full article

(This article belongs to the Special Issue Role of Vascular Smooth Muscle Cells in Cardiovascular Disease)

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15 pages, 5514 KiB

Open AccessArticle

Potassium Current Signature of Neuronal/Glial Progenitors in Amniotic Fluid Stem Cells

by Paola Sabbatini, Sabrina Cipriani, Andrea Biagini, Luana Sallicandro, Cataldo Arcuri, Rita Romani, Paolo Prontera, Alessandra Mirarchi, Rosaria Gentile, Diletta Del Bianco, Elko Gliozheni, Sandro Gerli, Irene Giardina, Maurizio Arduini, Alessandro Favilli, Antonio Malvasi, Andrea Tinelli and Bernard Fioretti

Cells 2025, 14(1), 50; https://doi.org/10.3390/cells14010050 - 4 Jan 2025

Viewed by 906

Abstract

Amniotic fluid is a complex and dynamic biological matrix that surrounds the fetus during the pregnancy. From this fluid, is possible to isolate various cell types with particular interest directed towards stem cells (AF-SCs). These cells are highly appealing due to their numerous [...] Read more.

Amniotic fluid is a complex and dynamic biological matrix that surrounds the fetus during the pregnancy. From this fluid, is possible to isolate various cell types with particular interest directed towards stem cells (AF-SCs). These cells are highly appealing due to their numerous potential applications in the field of regenerative medicine for tissues and organs as well as for treating conditions such as traumatic or ischemic injuries to the nervous system, myocardial infarction, or cancer. AF-SCs, when subcultured in the presence of basic Fibroblast Growth Factor (bFGF), have been shown to survive and migrate when transplanted into the striatum of the rat brain, exhibiting behavior characteristics of neuronal/glial progenitor cells. In this work, we performed an electrophysiological characterization to ascertain the propensity of AF-SCs to differentiate into glial and neuronal cells by bFGF. By using patch clamp technique we characterized a fibroblast-like morphology that display a barium-sensitive inward-rectifying potassium current (Kir) and calcium-activated potassium currents (KCa). The electrophysiological and calcium dynamics of histamine, a marker of undifferentiated neural progenitors, was further studied. Histamine promoted intracellular calcium increase by Fura-2 recording and calcium-activated potassium current activation with a similar temporal profile in AF-SC. The data presented in this paper ultimately confirm the expression in AF-SCs of the Kir and KCa currents, also showing regulation by endogenous stimuli such as histamine for the latter. Full article

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Figure 1
Expression of Kir currents in amniotic fluid cells with a fibroblastic morphology. (A) Representative microscopic field displaying the heterogeneous morphology of our cellular preparation; (B) GFAP immunofluorescence staining of a fibroblastic-type morphology observed in our cell preparation, with blue fluorescence due to DAPI nuclear staining; (C) a cell with fibroblast-like morphology during electrophysiological recording, note the microelectrode. (D) A family of currents recorded from a Vh of −40 mV with depolarizing pulses from −140 mV to +100 (with 10 mV increments) at 500 ms. Note the large inward component compared to the outward component, and the noise of the current traces at positive potentials. (E) The I-V relationship of the traces shown in (D) (dot points) constructed by plotting the peak currents as a function of the pulse test. The black solid line represents the current obtained by applying a potential ramp from −140 to +110 (Vh −40 mV) in the same cell as (D). The gray trace is the I-V relationship recorded from a cell with insignificant Kir current by using the ramp protocol described in (E). (F) Bar plot of currents at −120 mV derived from cells with (n = 9) and without Kir (n = 3). * p value < 0.05. Full article ">Figure 2
Activation properties of Kir currents in fibroblastic AF-SCs. (A) The current–voltage relationship obtained by the ramp potential protocol (−140 to +110, Vh −40 mV) under control conditions and in the presence of Ba2+ (300 µM). (B) The I-V relationship of the Ba-sensitive current obtained by subtraction of the control traces minus the one in Ba2+ shown in (A). The solid red line represents the best fit with the Boltzmann equation: I = (G*(V − Ek))/1 + e(V−V/2)/k, where G is the macroscopic conductance, Ek is the equilibrium potential of potassium, V/2 is the gating charge and half-activation potential and k is the voltage sensitivity. The dotted line represents the normalized Boltzmann function obtained from the fit (red line). (C) The bar plot of mean V/2 obtained from five experiments similar to that described in (B). Full article ">Figure 3
The dose and voltage dependences of the Kir Ba2+ block in amniotic fluid cells. (A) Inward currents recorded by applying a hyperpolarising pulse at −130 mV from a Vh of −40 mV with a duration of 500 ms, under control conditions (ctrl) and various concentrations of Ba2+ (10, 30, 100 and 300 µM). Note the presence of an instantaneous block (peak) and of a second block that develops during the hyperpolarization test. (B) Dose dependence of mean instantaneous peak currents (n = 3) obtained from similar experiments to those shown in (A). The black line represents the better fit with Hill’s equation. (C) The relationship between the mean reciprocal of the inactivation constant (n = 3) at various Ba2+ concentrations obtained from experiments similar to those shown in (A). The black line represents the better linear fit (see text for details). Full article ">Figure 4
Calcium-activated potassium currents in amniotic fluid cells. (A) Current ramps obtained by applying linear potential gradients from −140 to 110 from a Vh of −40 mV recorded in ctrl and after ionomycin 1 μM application. Note the increase in outward currents caused by the shift of threshold in voltage activation at negative potentials and characterized by noise. (B) The time course of the −40 mV current measured from current ramps as described in (A) repeated every 15 s. The timing of ionomycin, DCEBIO + ionomycin (100 µM + 500 nM) treatment and co-application with clotrimazole (2 µM) are shown with the bar in the upper part of the graph. Note the transient activation (run-down) of currents during ionomycin application. (C) Current ramps recorded at the times indicated in (B) under different conditions: CTRL (1), ionomycin (2), DCEBIO+ionomycin (3) and DCEBIO + ionomycin+ clotrimazole (4). The arrows in (A,C) indicate the reversal potential of potassium currents in our condition whereas the vertical dashed line indicates −40 mV. (D) A scatter plot of the current activates by DCEBIO + ionomycin at −40 mV (empty dots) and +100 mV (black dots). Full article ">Figure 5
Histamine increases intracellular calcium and activates KCa. (A) The exemplificative calcium imaging experiment that displays the effects of the application of 100 mM of histamine in amniotic cells evaluated by Fura-2-based calcium imaging. The single black traces represent the single-cell recording of the Fura-2 signal recorded in each ROI (region of interest) as indicated with circles in the inset. The red line represents the mean of all traces (n = 14). (B,C) Representative frames of Fura-2 fluorescence recorded at the time indicated in panel (A) before (1) and at the peak of the response to histamine (2), respectively. Note the presence of unresponsive cells (arrows in A and C). (D) The effects of the application of 100 μM of histamine on outward currents at −40 mV during whole-cell perforated configuration recording. (E) The I-V relationships obtained by applying voltage ramp protocols from −140 to 140 (Vh = −40 mV) recorded before (1), at the peak (2) and after peak (3) of histamine activation, respectively, as reported in (D). Full article ">

33 pages, 3394 KiB

Open AccessReview

Mechanisms of Rhodopsin-Related Inherited Retinal Degeneration and Pharmacological Treatment Strategies

by Maria Azam and Beata Jastrzebska

Cells 2025, 14(1), 49; https://doi.org/10.3390/cells14010049 - 4 Jan 2025

Viewed by 1274

Abstract

Retinitis pigmentosa (RP) is a hereditary disease characterized by progressive vision loss ultimately leading to blindness. This condition is initiated by mutations in genes expressed in retinal cells, resulting in the degeneration of rod photoreceptors, which is subsequently followed by the loss of [...] Read more.

Retinitis pigmentosa (RP) is a hereditary disease characterized by progressive vision loss ultimately leading to blindness. This condition is initiated by mutations in genes expressed in retinal cells, resulting in the degeneration of rod photoreceptors, which is subsequently followed by the loss of cone photoreceptors. Mutations in various genes expressed in the retina are associated with RP. Among them, mutations in the rhodopsin gene (RHO) are the most common cause of this condition. Due to the involvement of numerous genes and multiple mutations in a single gene, RP is a highly heterogeneous disease making the development of effective treatments particularly challenging. The progression of this disease involves complex cellular responses to restore cellular homeostasis, including the unfolded protein response (UPR) signaling, autophagy, and various cell death pathways. These mechanisms, however, often fail to prevent photoreceptor cell degradation and instead contribute to cell death under certain conditions. Current research focuses on the pharmacological modulation of the components of these pathways and the direct stabilization of mutated receptors as potential treatment strategies. Despite these efforts, the intricate interplay between these mechanisms and the diverse causative mutations involved has hindered the development of effective treatments. Advancing our understanding of the interactions between photoreceptor cell death mechanisms and the specific genetic mutations driving RP is critical to accelerate the discovery and development of therapeutic strategies for this currently incurable disease. Full article

(This article belongs to the Special Issue New Advances in Neuroinflammation)

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Figure 1
Schematic rod photoreceptor and rhodopsin structure. (A) The schematic representation of the rod photoreceptor cell (left panel) and a close-up of rod outer segment disc membranes with rhodopsin (Rho) molecules. (B) The structure of bovine Rho. The PDB ID:1GZM was used to show the side view of bovine Rho in the dark state. Transmembrane helices are labeled TM1–7. Cytoplasmic helix 8 is labeled H8. Extracellular (intradiscal) loops connecting TM helices on the ligand-binding site of the receptor are labeled ECL1, ECL2, and ECL3. Intracellular (cytoplasmic) loops, connecting TM helices on the effector binding site of the receptor are labeled ICL1, ICL2, and ICL3. 11-cis-retinal is shown as red sticks. The location of residues mutated in retinitis pigmentosa (RP) is shown in orange. (C) Two-dimensional representation of human Rho structure. Residues mutated in RP are indicated with orange circles. The Lys296, which covalently binds the 11-cis-retinal, is shown with a yellow circle filled with orange. The P23H mutation is shown with a red circle filled with orange. Full article ">Figure 2
Unfolded protein response. The unfolded protein response (UPR) involves three primary sensor receptors within the ER membranes: protein kinase RNA-like ER kinase (PERK), inositol-requiring enzyme 1 (IRE1), and activating transcription factor 6 (ATF6). PERK phosphorylates eIF2α, which reduces protein translation and upregulates ATF4 transcription factor, which activates the expression of antioxidants and components of the ER-associated degradation ERAD signaling. Activated by unfolded proteins, IRE1 activates transcription factor sXBP1 which stimulates the synthesis of protein folding regulators, ERAD, and lipid biosynthesis. ATF6 (P90), upon activation, translocates to the Golgi apparatus, where it is cleaved to P50 form by proteases S1P and S2P. Cleaved ATF6 stimulates the expression of ERAD and folding regulators. Full article ">Figure 3
Schematic interplay between oxidative stress, inflammation, and photoreceptor cell death. Oxidative radicals are generated during respiration in mitochondria. Under normal physiological conditions, superoxide dismutase (SOD) catalyzes superoxide radicals (1O2) into hydrogen peroxide (H2O2) and oxygen (O2), while catalase breaks down hydroxyl radicals ·OH to water (H2O) and O2. H2O2 is converted by glutathione peroxidase to H2O. During this reaction, GSH is converted to its reduced form GSSH. The back conversion of GSSH → GSH involves NADPH → NAD+ change. Excess of reactive oxygen species (ROS) accumulated under chronic conditions of genetic mutation leads to damage of cellular content and release of pro-inflammatory markers that aggravate inflammation, ultimately leading to cell death. Full article ">Figure 4
Classical inflammation and pyroptosis signaling. In classical inflammation, damage-associated molecular patterns (DAMPs) activate phosphorylation of IκB kinase (IKK), which degrades IκB from the IκB/NFκB complex leading to the activation of NFκB. Activated NFκB stimulates the expression of proinflammatory cytokines, including IL-1β, IL-18, and TNF-α, as well as the expression of NLRP3, which leads to the formation of inflammasome. In addition, chemokine receptor CX3CR1 activated by CX3CL1 stimulates NFκB through G protein signaling. Pyroptosis is activated by DAMPs through death receptors; for example, tumor necrosis factor receptors (TNFR1 and TNRF2), which stimulate the expression of NOD-like receptor protein 3 (NLRP3) and inflammasome formation that activates caspase-1, which activates IL-1β and IL-18. Alternatively, pyroptosis is activated through Toll-like receptor 4 (TLR4) stimulated by bacterial lipopolysaccharides (LPS). Caspase-4 and -5 are activated in this pathway leading to the activation of gasdermin (GSDMD), which inserts into the membrane forming a pore that allows for the release of pro-inflammatory cytokines activated by caspase-1. Full article ">Figure 5
Apoptosis pathway. Extrinsic apoptosis is activated by extrinsic signals through death receptors (TNFRs), which recruit adaptor proteins like the Fas-associated death domain (FADD), followed by pro-caspase-8 activation. Active caspase-8 directly stimulates executioner caspase-3 and -7, leading to apoptosis. Caspase-8 can also stimulate BID, which activates BAX and BAK to permeabilize the mitochondrial membrane, linking the extrinsic and intrinsic pathways. The intrinsic pathway is activated by cellular stressors like damaged DNA or oxidative stress, which activates BAX and BAK. Permeabilized mitochondria release cytochrome c, which binds to apoptotic protease activating factor APAF1 and triggers activation of caspase-9 followed by activation of executioner caspase-3 and -7. Full article ">Figure 6
Necroptosis signaling. Necroptosis is triggered by the activation of death receptors, particularly tumor necrosis factor receptor 1 (TNFR1) upon binding of TNF-α. It could also be activated by Toll-receptor 4 (TLR4). TNFR1 recruits adaptor proteins TRADD, TRAF2, and RIPK1. In apoptosis, receptor-interacting protein kinase-1 (RIPK1) is polyubiquitinated and promotes cell survival. When caspase-8 is blocked, RIPK1 interacts with RIPK3, forming a necrosome complex. RIPK3 autophosphorylates and then phosphorylates mixed-lineage kinase domain-like protein (MLKL), a necroptosis key effector, which isomerizes and translocates to the membrane where it forms a pore enabling the release of cellular content. This can further lead to the activation of inflammatory response through released DAMPs. Full article ">Figure 7
Ferroptosis signaling. Cellular iron is imported via the transferrin receptor (TFR1), which binds Fe3+ (ferric iron)-loaded transferrin. Inside the cell, Fe3+ became reduced to Fe2+ (ferrous iron). Free Fe2+ can catalyze the Fenton reaction leading to the generation of reactive oxygen species (ROS) production, which oxidizes unsaturated membrane phospholipids. Under normal physiological conditions, an antioxidant system involving glutathione peroxidase (GPx) prevents lipid peroxidation using its cofactor GSH, which is generated in exchange for glutamate transported out of the cell through the antiporter SLC7A11. Under chronic stress of pathogenic mutations, unchecked lipid peroxidation disrupts membrane integrity and leads to photoreceptor cell death. Full article ">

17 pages, 3365 KiB

Open AccessArticle

Circulating T Cell Subsets in Type 1 Diabetes

by Aldo Ferreira-Hermosillo, Paola Santana-Sánchez, Ricardo Vaquero-García, Manuel R. García-Sáenz, Angélica Castro-Ríos, Adriana K. Chávez-Rueda, Rita A. Gómez-Díaz, Luis Chávez-Sánchez and María V. Legorreta-Haquet

Cells 2025, 14(1), 48; https://doi.org/10.3390/cells14010048 - 4 Jan 2025

Viewed by 1207

Abstract

Type 1 diabetes (T1D) is a complex disease driven by the immune system attacking the insulin-producing beta cells in the pancreas. Understanding the role of different T cell subpopulations in the development and progression of T1D is crucial. By employing flow cytometry to [...] Read more.

Type 1 diabetes (T1D) is a complex disease driven by the immune system attacking the insulin-producing beta cells in the pancreas. Understanding the role of different T cell subpopulations in the development and progression of T1D is crucial. By employing flow cytometry to compare the characteristics of T cells, we can pinpoint potential indicators of treatment response or therapeutic inefficacy. Our study reveals elevated prolactin (PRL) levels in T1D patients, along with a decreased production of key cytokines. Additionally, PD1 appears to play a significant role in T1D. Notably, PRL levels correlate with an earlier disease onset and a specific T cell phenotype, hinting at the potential influence of PRL. These findings highlight the need for further research to identify promising cellular targets for more effective and tailored therapies. Full article

(This article belongs to the Special Issue Cellular and Molecular Mechanisms in Immune Regulation)

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Figure 1
Serum levels of prolactin, glucose, and glycosylated hemoglobin. Quantification of serum levels of PRL glucose and HbA1 were compared between the patients with T1D and healthy controls. The results indicated significantly increased expression of (a) PRL (p = 0.007), (b) Glucose (p < 0.014), and (c) HbA1c (p < 0.0001) in T1D patients compared to healthy controls. Data are presented as mean ± standard division (SD). (Healthy controls, n = 29, PE women, n = 28). p < 0.05 is considered statistically significant (** p ≤ 0.01, **** p ≤ 0.0001). Full article ">Figure 2
Serum levels of cytokines. Quantification of cytokine serum levels using flow cytometry. Serum cytokine levels were compared between patients with T1D and healthy controls. The results showed significantly increased expression of (a) IL-17 (p = 0.028), (b) Perforin (p < 0.028), and (c) Granulysin (p < 0.008) in healthy controls compared to T1D patients. Data is presented as mean ± standard deviation (SD). (Healthy controls, n = 29; T1D patients, n = 28). p < 0.05 was considered statistically significant (* p ≤ 0.05, ** p ≤ 0.01). Full article ">Figure 3
Circulating T cells. (a) Flow cytometry analysis strategy to gate on T cells. FSC-H vs. FSC-A plot was used to exclude doublets; lymphocytes were gated on the FSC-A vs. SSC-A plot, and live cells were gated in the Ghost Dye negative (Viability) and CD3-BV650+. From living T cells, we selected the CD4+ or CD8+ gate, and using the pre-designed panels from each T cell subtype, we selected the following markers: (b) Exhausted T cells (CD4+TIGIT+PD1+LAG3+CTLA-4+ or CD8+TIGIT+PD1+LAG3+CTLA-4+). (c) Follicular T cells (CD4+CXCR5+PD1+ICOS+ or CD8+CXCR5+PD1+ICOS+) (d) Memory T cells (CD4+CD45RO± CCR7± or CD8+CD45RO±CCR7±). Full article ">Figure 4
Comparison of frequencies of CD4/CD8 among live CD3+. (a–d) follicular T (CD4+CXCR5+PD1+ICOS+ or CD8+CXCR5+PD1+ICOS+), and (e–h) exhausted T (CD4+TIGIT+ PD1+LAG3+CTLA-4+ or CD8+TIGIT+PD1+ LAG3+CTLA-4+). These are shown as proportions or absolute numbers of circulating T cells in T1D and healthy controls. p < 0.05 was considered statistically significant (**** p ≤ 0.0001). Full article ">Figure 5
Comparison of the frequencies of CD4/CD8 among live CD3+ cells, TCD4+ or T CD8+. (a–d) effector memory (CD45RO+CCR7-), (e–h) central memory (CD4+CD45RO+CCR7+), (i–l) naive (CD45RO-CCR7+), and (m–p) effector (CD45RO-CCR7-) T cells are shown as proportions (left) or absolute numbers (right) of circulating T cells in T1D and healthy controls. p < 0.05 was considered statistically significant (*** p ≤ 0.001). Full article ">Figure 6
Comparison of frequencies of TCD4+ or T CD8+PD1+ between those of (a,b) central memory (CD4+CD45RO+CCR7+), (c,d) effector memory (CD4+CD45RO+CCR7-), (e,f) effectors (CD45RO-CCR7-), and (g,h) naïve (CD45RO-CCR7+) are shown as the absolute number of circulating T cells in T1D and healthy controls. p < 0.05 was considered statistically significant (* p ≤ 0.05, ** p ≤ 0.01). Full article ">Figure 7
Two-way dispersion graph: prolactin and CD4 vs. TD1 diagnosis time (a) Spearman’s correlations_ TD1 diagnosis time and prolactin levels: NP r = 0.3436, p = 0.050 *, HP r = −0.5667, ns. (b) TD1 diagnosis time and CD8-CM: NP r = −0.6355, p = 0.0026 *, HP r = 0.4857, ns. (c) TD1 diagnosis time and CD8-EM_PD1: NP r = −0.4075, ns HP r = 0.200, ns (d) TD1 diagnosis time and CD8_NAIVE: NP r = −0.4849, p = 0.0289 * HP r = 0.1429, ns. (e) TD1 diagnosis time and CD8_Exhausted: NP r = 0.500, p = 0.024 *. HP r = 0.200, ns. (f) TD1 diagnosis time and CD8_ThF = −0.7580, p = 0.0001 *. HP r = 0.2571, ns. HP = Hyperprolactin, NP = Normoprolactin. Full article ">

27 pages, 831 KiB

Open AccessReview

Schwann Cells in Neuromuscular Disorders: A Spotlight on Amyotrophic Lateral Sclerosis

by Kathryn R. Moss and Smita Saxena

Cells 2025, 14(1), 47; https://doi.org/10.3390/cells14010047 - 3 Jan 2025

Viewed by 1310

Abstract

Amyotrophic Lateral Sclerosis (ALS) is a complex neurodegenerative disease primarily affecting motor neurons, leading to progressive muscle atrophy and paralysis. This review explores the role of Schwann cells in ALS pathogenesis, highlighting their influence on disease progression through mechanisms involving demyelination, neuroinflammation, and [...] Read more.

Amyotrophic Lateral Sclerosis (ALS) is a complex neurodegenerative disease primarily affecting motor neurons, leading to progressive muscle atrophy and paralysis. This review explores the role of Schwann cells in ALS pathogenesis, highlighting their influence on disease progression through mechanisms involving demyelination, neuroinflammation, and impaired synaptic function. While Schwann cells have been traditionally viewed as peripheral supportive cells, especially in motor neuron disease, recent evidence indicates that they play a significant role in ALS by impacting motor neuron survival and plasticity, influencing inflammatory responses, and altering myelination processes. Furthermore, advancements in understanding Schwann cell pathology in ALS combined with lessons learned from studying Charcot–Marie–Tooth disease Type 1 (CMT1) suggest potential therapeutic strategies targeting these cells may support nerve repair and slow disease progression. Overall, this review aims to provide comprehensive insights into Schwann cell classification, physiology, and function, underscoring the critical pathological contributions of Schwann cells in ALS and suggests new avenues for targeted therapeutic interventions aimed at modulating Schwann cell function in ALS. Full article

(This article belongs to the Special Issue Genetics and Pathomechanisms of Amyotrophic Lateral Sclerosis (ALS))

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14 pages, 2941 KiB

Open AccessArticle

Encapsulation and Melanization Are Not Correlated to Successful Immune Defense Against Parasitoid Wasps in Drosophila melanogaster

by Lilla B. Magyar, István Andó and Gyöngyi Cinege

Cells 2025, 14(1), 46; https://doi.org/10.3390/cells14010046 - 3 Jan 2025

Viewed by 930

Abstract

Parasitoid elimination in Drosophila melanogaster involves special hemocytes, called lamellocytes, which encapsulate the eggs or larvae of the parasitoid wasps. The capsules are melanized, and metabolites of the melanization reaction may play a potential role in parasitoid killing. We have observed a variation [...] Read more.

Parasitoid elimination in Drosophila melanogaster involves special hemocytes, called lamellocytes, which encapsulate the eggs or larvae of the parasitoid wasps. The capsules are melanized, and metabolites of the melanization reaction may play a potential role in parasitoid killing. We have observed a variation in the melanization capacity of different, commonly used D. melanogaster strains, such as Canton-S, Oregon-R, and BL5905, BL6326. In this work, we aimed to clarify a possible connection between the effectiveness of capsule melanization and the success of parasitoid elimination following infection with Leptopilina parasitoid wasps. Circulating hemocytes and lamellocyte attachment were visualized by confocal and epifluorescence microscopy using indirect immunofluorescence. Expression profiles of the PPO2 and PPO3 prophenoloxidase genes, which encode key enzymes in the melanization reaction, were detected by qRT-PCR. Parasitization assays were used to analyze fly and wasp eclosion success. Active encapsulation and melanization reactions against Leptopilina boulardi were observed in the BL5905 and the BL6326 strains, though restricted to the dead supernumerary parasitoids, while fly and wasp eclosion rates were essentially the same in the four examined D. melanogaster strains. We conclude that encapsulation and melanization carried out by D. melanogaster following L. boulardi infection have no impact on survival. Full article

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Figure 1
Hemocyte composition in D. melanogaster larvae following parasitoid wasp infection. Hemocyte isolates of wasp-infected (72 h post-infection) and age-matched naïve larvae were examined. The total hemocyte number (A) and the ratio of the lamellocytes related to the total hemocyte count (B) were analyzed in four D. melanogaster strains. Three independent experiments were performed with 20 larvae each. The error bars indicate the standard error of the mean. Kruskal–Wallis test and pairwise Wilcoxon tests were used for statistical analysis. p-values: <0.05 = *; <0.01 = **; <0.001 = ***. (C) Indirect immunofluorescence analysis of hemocytes using the anti-L1 antibody to detect lamellocytes and DAPI to visualize the nuclei. Images were generated with an epifluorescence Zeiss Axioscope 2 MOT microscope. Representative images are shown. Full article ">Figure 2
Survival of L. boulardi 72 h post-infection does not correlate with encapsulation and melanization efficiency. Three independent experiments were completed, each with 60 D. melanogaster larvae. Images were taken with an Olympus FV1000 confocal LSM microscope. The scale bars represent 100 μm. (A) Most of the host larvae carried one live and more dead L. boulardi parasitoids. The live/dead ratio of parasitoids is indicated for each strain. Representative images are shown. (B) Indirect immunofluorescence analysis of dead parasitoids using the L1 lamellocyte-specific monoclonal antibody and DAPI to visualize the nuclei. The ratio of melanized and not melanized dead parasitoids is indicated. (C) Indirect immunofluorescence analysis of live L. boulardi parasitoids using the anti-Hemese monoclonal antibody and DAPI to visualize the nuclei. As positive control, supernumerary dead L. boulardi parasitoids, isolated from BL5905, were used. Full article ">Figure 3
Expression of PPO2 (A,A’) and PPO3 (B,B’) prophenoloxidase genes following infection with L. boulardi and L. heterotoma parasitoid wasps. The same data are shown in (A,A’) and (B,B’), in different interpretations. The error bars indicate standard error of the mean of four data points. Two independent experiments were carried out, with two technical replicates in each. ANOVA and Tukey HSD were used for statistical analysis. Significant differences are labeled. * = p ≤ 0.05; ** p ≤ 0.01; *** = p ≤ 0.001. ΔΔCt was calculated by normalizing ΔCt against the lowest values, the L. heterotoma-infected Oregon-R samples in (A,A’), and the L. heterotoma-infected BL5905 in (B,B’). Full article ">Figure 4
Eclosion success following infection with L. boulardi and L. heterotoma. Four to six independent experiments were carried out with 60 D. melanogaster larvae in each. The error bars indicate the standard error of the mean. ANOVA and Tukey HSD were used for statistical analysis. p-values: <0.05 = *. Full article ">

21 pages, 4824 KiB

Open AccessArticle

TGFβ1 Restores Energy Homeostasis of Human Trophoblast Cells Under Hyperglycemia In Vitro by Inducing PPARγ Expression, AMPK Activation, and HIF1α Degradation

by Nihad Khiat, Julie Girouard, Emmanuelle Stella Kana Tsapi, Cathy Vaillancourt, Céline Van Themsche and Carlos Reyes-Moreno

Cells 2025, 14(1), 45; https://doi.org/10.3390/cells14010045 - 3 Jan 2025

Viewed by 876

Abstract

Elevated glucose levels at the fetal–maternal interface are associated with placental trophoblast dysfunction and increased incidence of pregnancy complications. Trophoblast cells predominantly utilize glucose as an energy source, metabolizing it through glycolysis in the cytoplasm and oxidative respiration in the mitochondria to produce [...] Read more.

Elevated glucose levels at the fetal–maternal interface are associated with placental trophoblast dysfunction and increased incidence of pregnancy complications. Trophoblast cells predominantly utilize glucose as an energy source, metabolizing it through glycolysis in the cytoplasm and oxidative respiration in the mitochondria to produce ATP. The TGFβ1/SMAD2 signaling pathway and the transcription factors PPARγ, HIF1α, and AMPK are key regulators of cell metabolism and are known to play critical roles in extravillous trophoblast cell differentiation and function. While HIF1α promotes glycolysis over mitochondrial respiration, PPARγ and AMPK encourage the opposite. However, the interplay between TGFβ1 and these energy-sensing regulators in trophoblast cell glucose metabolism remains unclear. This study aimed to investigate whether and how TGFβ1 regulates energy metabolism in trophoblast cells exposed to normal and high glucose conditions. The trophoblast JEG-3 cells were incubated in normal (5 mM) and high (25 mM) glucose conditions for 24 h in the absence and the presence of TGFβ1. The protein expression levels of phosphor (p)-SMAD2, GLUT1/3, HIF1α, PPARγ, p-AMPK, and specific OXPHOS protein subunits were determined by western blotting, and ATP and lactate production by bioluminescent assay kits. JEG-3 cells exposed to 25 mM glucose decreased ATP production but did not affect lactate production. These changes led to a reduction in the expression levels of GLUT1/3, mitochondrial respiratory chain proteins, and PPARγ, coinciding with an increase in HIF1α expression. Conversely, TGFβ1 treatment at 25 mM glucose reduced HIF1α expression while enhancing the expression levels of GLUT1/3, PPARγ, p-AMPK, and mitochondrial respiratory chain proteins, thereby rejuvenating ATP production. Our findings reveal that high glucose conditions disrupt cellular glucose metabolism in trophoblast cells by perturbing mitochondrial oxidative respiration and decreasing ATP production. Treatment with TGFβ1 appears to counteract this trend, probably by enhancing both glycolytic and mitochondrial metabolism, suggesting a potential regulatory role of TGFβ1 in placental trophoblast cell glucose metabolism. Full article

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22 pages, 7517 KiB

Open AccessArticle

New Insights in Microplastic Cellular Uptake Through a Cell-Based Organotypic Rainbow-Trout (Oncorhynchus mykiss) Intestinal Platform

by Nicole Verdile, Nico Cattaneo, Federica Camin, Matteo Zarantoniello, Federico Conti, Gloriana Cardinaletti, Tiziana A. L. Brevini, Ike Olivotto and Fulvio Gandolfi

Cells 2025, 14(1), 44; https://doi.org/10.3390/cells14010044 - 3 Jan 2025

Viewed by 1403

Abstract

Microplastics (MPs) in fish can cross the intestinal barrier and are often bioaccumulated in several tissues, causing adverse effects. While the impacts of MPs on fish are well documented, the mechanisms of their cellular internalization remain unclear. A rainbow-trout (Oncorhynchus mykiss) [...] Read more.

Microplastics (MPs) in fish can cross the intestinal barrier and are often bioaccumulated in several tissues, causing adverse effects. While the impacts of MPs on fish are well documented, the mechanisms of their cellular internalization remain unclear. A rainbow-trout (Oncorhynchus mykiss) intestinal platform, comprising proximal and distal intestinal epithelial cells cultured on an Alvetex scaffold, was exposed to 50 mg/L of MPs (size 1–5 µm) for 2, 4, and 6 h. MP uptake was faster in RTpi-MI compared to RTdi-MI. Exposure to microplastics compromised the cellular barrier integrity by disrupting the tight-junction protein zonula occludens-1, inducing significant decreases in the transepithelial-electrical-resistance (TEER) values. Consequently, MPs were internalized by cultured epithelial cells and fibroblasts. The expression of genes related to endocytosis (cltca, cav1), macropinocytosis (rac1), and tight junctions’ formation (oclna, cldn3a, ZO-1) was analyzed. No significant differences were observed in cltca, oclna, and cldn3a expression, while an upregulation of cav1, rac1, and ZO-1 genes was detected, suggesting macropinocytosis as the route of internalization, since also cav1 and ZO-1 are indirectly related to this mechanism. The obtained results are consistent with data previously reported in vivo, confirming its validity for identifying MP internalization pathways. This could help to develop strategies to mitigate MP absorption through ingestion. Full article

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27 pages, 2781 KiB

Open AccessReview

p63: A Master Regulator at the Crossroads Between Development, Senescence, Aging, and Cancer

by Lakshana Sruthi Sadu Murari, Sam Kunkel, Anala Shetty, Addison Bents, Aayush Bhandary and Juan Carlos Rivera-Mulia

Cells 2025, 14(1), 43; https://doi.org/10.3390/cells14010043 - 3 Jan 2025

Viewed by 1428

Abstract

The p63 protein is a master regulatory transcription factor that plays crucial roles in cell differentiation, adult tissue homeostasis, and chromatin remodeling, and its dysregulation is associated with genetic disorders, physiological and premature aging, and cancer. The effects of p63 are carried out [...] Read more.

The p63 protein is a master regulatory transcription factor that plays crucial roles in cell differentiation, adult tissue homeostasis, and chromatin remodeling, and its dysregulation is associated with genetic disorders, physiological and premature aging, and cancer. The effects of p63 are carried out by two main isoforms that regulate cell proliferation and senescence. p63 also controls the epigenome by regulating interactions with histone modulators, such as the histone acetyltransferase p300, deacetylase HDAC1/2, and DNA methyltransferases. miRNA-p63 interactions are also critical regulators in the context of cancer metastasis. This review aims to elaborate on the diverse roles of p63, focusing on disease, development, and the mechanisms controlling genome organization and function. Full article

(This article belongs to the Special Issue The Role of Cellular Senescence in Health, Disease, and Aging)

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Molecular structure of p63 and DNA binding. (A) Structure of the TP63 gene. Exons are depicted as color boxes. Alternative promoters (arrows) drive the expression of each main isoform, and alternative splicing events are shown. The domain structure of each main isoform is also shown. (B) The molecular structure of TAp63 isoform in tetrameric form was predicted based on the amino acid sequence and the p63 binding DNA motif. Four copies of the full-length TAp63 isoform and 2 tandem repeats of the p63 consensus binding motif [TTGGGCATGTCCGGACATGCCCAT] (Riege et al., 2020) were used as input for prediction using AlphaFold3 [<a href="#B37-cells-14-00043" class="html-bibr">37</a>]. Color code matches the domains shown in (A). Molecular structure file is provided in the <a href="#app1-cells-14-00043" class="html-app">Supplementary Material</a>. Full article ">Figure 2
The role of TP63 in the regulation of developmental control mechanisms. (A) Developmental defects of p63 depletion. Complete p63 knockout is lethal in mice models, while targeted depletions of each of the main isoforms result in wide abnormalities across tissues characterized by reduced proliferation and incomplete differentiation. (B) Annotated mutations with disease links of the TP63 gene. Pathological variants were obtained from the ClinVar database [<a href="#B62-cells-14-00043" class="html-bibr">62</a>] and plotted using a lollipop diagram generator [<a href="#B63-cells-14-00043" class="html-bibr">63</a>]. (C) The role of p63 in normal epidermis development. TAp63 expression induces epithelial specification in K8/K18 ectoderm progenitor cells to form a basal epithelial layer of K5/K14-expressing cells in which TAp63 acts to promote proliferation. After establishing the basal layer, a switch in p63 isoform expression (from TAp63 to ∆Np63) is triggered to induce epithelial stratification of the epidermis. Panels A and C were made in Biorender. Full article ">Figure 3
TAp63 and ΔNp63 expression across human adult tissues, glandular tissues, and epitheliums. (A) Human adult tissues enriched in TP63 expression shown as normalized transcript per million (nTPM); (B) p63 isoform expression shown as transcripts per million (TPM) in tissues enriched for TP63 expression. The data used for the analyses described here were obtained from the GTEx Portal on 07/15/24. (C) Model showing the cross-section of mammary gland epithelium composed of myoepithelial cells, luminal cells, and basal stem cells enriched for ΔNp63, which upregulates Fzd7 expression to promote stem cell self-renewal. (D) Model of human prostate gland displaying luminal, neuroendocrine, and ΔNp63-enriched basal stem cells. (E) Schematic illustration of the pseudostratified epithelium of the lung, including ciliated cells, goblet cells, club cells, and basal cells. ΔNp63 is the dominant isoform expressed in the basal cells on the basolateral side of the epithelium. Figure made in Biorender. * p < 0.05. Full article ">Figure 4
The role of p63 in chromatin remodeling and genome organization. (A) p63 recruits Brg1 and Satb1 to mediate the translocation and subsequent expression of the EDC locus near regions of SC35-positive nuclear speckles during keratinocyte differentiation. (B) A proposed model in which p63 acts as a pioneer transcription factor to reprogram the EDC locus. p63 binds to compact inactive chromatin, recruits chromatin remodelers to increase accessibility (p300), mediates the formation of the enhancer-promoter loop via the recruitment of crucial transcriptional factors and CTCF, and promotes transcriptional activation of the epidermal genes during keratinocyte differentiation. Figure made in Biorender. Full article ">

18 pages, 658 KiB

Open AccessReview

Advances in Stem Cell Therapy for Huntington’s Disease: A Comprehensive Literature Review

by Siddharth Shah, Hadeel M. Mansour and Brandon Lucke-Wold

Cells 2025, 14(1), 42; https://doi.org/10.3390/cells14010042 - 3 Jan 2025

Viewed by 1169

Abstract

Huntington’s disease (HD) is an inherited neurodegenerative disease characterized by uncontrolled movements, emotional disturbances, and progressive cognitive impairment. It is estimated to affect 4.3 to 10.6 per 100,000 people worldwide, and the mean prevalence rate among all published studies, reviews, and genetic HD [...] Read more.

Huntington’s disease (HD) is an inherited neurodegenerative disease characterized by uncontrolled movements, emotional disturbances, and progressive cognitive impairment. It is estimated to affect 4.3 to 10.6 per 100,000 people worldwide, and the mean prevalence rate among all published studies, reviews, and genetic HD registries is 5.7 per 100,000. A key feature of HD is the loss of striatal neurons and cortical atrophy. Although there is no cure at present, the discovery of the gene causing HD has brought us into a new DNA era and therapeutic advances for several neurological disorders. PubMed was systematically searched using three search strings: ‘“Huntington disease” + “stem cell”’, ‘”Huntington disease” + Mesenchymal stromal cell’, and ‘”Huntington disease” + “induced pluripotent stem cell”’. For each string, the search results were categorized based on cell type, and papers that included a clinical analysis were categorized as well. The data were extracted up to 2024. We did not include other databases in our search to have a comparable and systematic review of the literature on the topic. The collected data were analyzed and used for critical interpretation in the present review. Data are presented chronologically as clinical studies were published. Therapeutic strategies based on stem cells have drawn a lot of interest as possible HD therapies. Recent research indicates that NSCs have been the most often utilized stem cell type for treating HD. NSCs have been generated and extracted from a variety of sources, including HD patients’ somatic cells and the brain itself. There is strong evidence supporting the transplantation of stem cells or their derivatives in HD animal models, even if stem-cell-based preclinical and clinical trials are still in their early stages. Current treatment only aims at relieving the symptoms rather than treating the pathogenesis of the disease. Although preclinical trials in HD models have shown promise in improving cognitive and motor functions, stem cell therapy still faces many challenges and disadvantages including immunosuppression and immunorejection as well as ethical, technical, and safety concerns. Further research is required for a definitive conclusion. Full article

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19 pages, 552 KiB

Open AccessReview

CAR-T Therapy Beyond B-Cell Hematological Malignancies

by Martina Canichella and Paolo de Fabritiis

Cells 2025, 14(1), 41; https://doi.org/10.3390/cells14010041 - 3 Jan 2025

Viewed by 1369

Abstract

Despite the advances of CAR-T cells in certain hematological malignancies, mostly from B-cell derivations such as non-Hodgkin lymphomas, acute lymphoblastic leukemia and multiple myeloma, a significant portion of other hematological and non-hematological pathologies can benefit from this innovative treatment, as the results of [...] Read more.

Despite the advances of CAR-T cells in certain hematological malignancies, mostly from B-cell derivations such as non-Hodgkin lymphomas, acute lymphoblastic leukemia and multiple myeloma, a significant portion of other hematological and non-hematological pathologies can benefit from this innovative treatment, as the results of clinical studies are demonstrating. The clinical application of CAR-T in the setting of acute T-lymphoid leukemia, acute myeloid leukemia, solid tumors, autoimmune diseases and infections has encountered limitations that are different from those of hematological B-cell diseases. To overcome these restrictions, strategies based on different molecular engineering platforms have been devised and will be illustrated below. The aim of this manuscript is to provide an overview of the CAR-T application in pathologies other than those currently treated, highlighting both the limits and results obtained with these settings. Full article

(This article belongs to the Special Issue Potential of CAR-Based Cellular Immunotherapy for Non-oncologic Disorders)

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17 pages, 3642 KiB

Open AccessArticle

Mesenchymal Stem/Stromal Cells Reverse Adipose Tissue Inflammation in Pigs with Metabolic Syndrome and Renovascular Hypertension

by Alexander B. C. Krueger, Xiangyang Zhu, Sarosh Siddiqi, Emma C. Whitehead, Hui Tang, Kyra L. Jordan, Amir Lerman and Lilach O. Lerman

Cells 2025, 14(1), 40; https://doi.org/10.3390/cells14010040 - 2 Jan 2025

Viewed by 752

Abstract

Metabolic syndrome (MetS) is associated with low-grade inflammation, which can be exacerbated by renal artery stenosis (RAS) and renovascular hypertension, potentially worsening outcomes through pro-inflammatory cytokines. This study investigated whether mesenchymal stem/stromal cells (MSCs) could reduce fat inflammation in pigs with MetS and [...] Read more.

Metabolic syndrome (MetS) is associated with low-grade inflammation, which can be exacerbated by renal artery stenosis (RAS) and renovascular hypertension, potentially worsening outcomes through pro-inflammatory cytokines. This study investigated whether mesenchymal stem/stromal cells (MSCs) could reduce fat inflammation in pigs with MetS and RAS. Twenty-four pigs were divided into Lean (control), MetS, MetS + RAS, and MetS + RAS + MSCs. In the MSC-treated group, autologous adipose-derived MSCs (10⁷ cells) were injected into the renal artery six weeks after RAS induction. After four weeks, fat volumes and inflammatory markers were assessed. MSC treatment reduced levels of pro-inflammatory cytokines (MCP-1, TNF-a, IL-6) in the renal vein blood and in perirenal fat. The MSCs also decreased fat fibrosis, restored adipocyte size, and altered adipogenesis-related gene expression, particularly in the perirenal fat. These effects were less pronounced in subcutaneous fat. The MSC therapy attenuated fat inflammation and improved metabolic outcomes in pigs with MetS + RAS, suggesting that adipose-derived MSCs may offer a promising therapeutic approach for metabolic disorders. Full article

(This article belongs to the Special Issue Adipose-Derived Stem Cells: Current Applications and Future Directions)

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Schematic of the experimental protocol and treatment timeline. Full article ">Figure 2
MSCs downregulate visceral adipose tissue accumulation. (A) Abdominal CT axial images with post-imaging processing highlighting subcutaneous (red) and visceral (yellow) fat. (B) Visceral fat fraction was augmented in MetS and MetS + RAS, but not in MetS + RAS + MSC. (C) The subcutaneous fat fraction was increased in all 3 experimental groups relative to Lean. * p < 0.05 vs. Lean. MetS, metabolic syndrome; RAS, renal artery stenosis; MSC, mesenchymal stem/stromal cells. Full article ">Figure 3
MSCs attenuate adipose tissue fibrosis and hypertrophy. Representative images of trichrome-stained (blue) perirenal (A) and subcutaneous (B) fat (10× magnification). Pink: eosin counterstain. (C) Perirenal fat fibrosis increased in MetS + RAS but was attenuated by the delivery of MSCs. (D) Subcutaneous fat fibrosis was only higher in MetS + RAS vs. MetS. (E) Adipocyte cross-sectional area was higher in the perirenal fat of MetS + RAS vs. Lean, suggesting hypertrophy, but decreased in MetS + RAS + MSC. (F) In the subcutaneous fat, MetS had smaller adipocytes compared to Lean and MetS + RAS. * p < 0.05 vs. Lean; † p < 0.05 vs. MetS; $ p < 0.05 vs. MetS + RAS. MetS, metabolic syndrome; RAS, renal artery stenosis; MSC, mesenchymal stem/stromal cells. Full article ">Figure 4
MSCs downregulate perirenal adipose tissue inflammation. (A) Representative immunofluorescence images (40×) showing the expression of MCP-1 (red), TNF-a (green), and IL-6 (red) in pig perirenal fat. MCP-1 (B), TNF-a (C), and IL-6 (D) were upregulated in MetS + RAS relative to Lean and MetS but were downregulated in MetS + RAS + MSC. * p < 0.05 vs. Lean; † p < 0.05 vs. MetS; $ p < 0.05 vs. MetS + RAS. MetS, metabolic syndrome; RAS, renal artery stenosis; MSC, mesenchymal stem/stromal cell. Full article ">Figure 5
MSCs downregulate subcutaneous adipose tissue inflammation. (A) Representative immunofluorescence staining images (40×) showing the expression of MCP-1 (red), TNF-a (green), and IL-6 (red) in pig subcutaneous fat. MCP-1 (B) and TNF-a (C) were upregulated in MetS + RAS vs. Lean. MCP-1 significantly decreased in MetS + RAS + MSC and TNF-a tended to decrease as well. (D) IL-6 expression was not elevated in MetS or MetS + RAS vs. Lean. Nevertheless, MSCs reduced IL-6 expression compared to MetS + RAS. * p < 0.05 vs. Lean; † p < 0.05 vs. MetS; $ p < 0.05 vs. MetS + RAS. MetS, metabolic syndrome; RAS, renal artery stenosis; MSC, mesenchymal stem/stromal cell. Full article ">Figure 6
MSCs downregulate proinflammatory cytokine gene expression. (A,C,E) MCP-1 and IL-6 gene expression was upregulated in the MetS + RAS perirenal fat relative to Lean, but MSC treatment attenuated this effect. A slight elevation of TNF-a gene expression (p = 0.09) in the perirenal fat of MetS + RAS did not achieve statistical significance. (B,D,F) In the subcutaneous fat, no differences in MCP-1 or TNF-a gene expression were observed among the four groups. However, IL-6 mRNA expression was reduced in the MetS + RAS pigs, while in the MetS + RAS + MSC group, IL-6 levels trended lower relative to the Lean group (p = 0.08) but did not reach statistical significance. * p < 0.05 vs. Lean; † p < 0.05 vs. MetS. MetS, metabolic syndrome; RAS, renal artery stenosis; MSC, mesenchymal stem cells. Full article ">Figure 7
MSCs alter perirenal and subcutaneous anti-inflammatory and adipogenic gene expression. (A) Perirenal fat showed upregulated TSG-6 expression in MetS and MetS + RAS + MSC relative to Lean. (B) In contrast, subcutaneous fat showed downregulated TSG-6 expression in MetS and MetS + RAS relative to Lean. MetS + RAS + MSC tended to be lower than Lean, but this did not reach statistical significance (p = 0.06). C/EBPa expression increased only in the perirenal fat of MetS + RAS + MSC pigs relative to Lean (C) and in the subcutaneous fat of MetS + RAS relative to Lean and MetS (D). (E) Perirenal fat C/EBPb expression was upregulated in MetS relative to Lean and in MetS + RAS + MSC relative to Lean and MetS + RAS. (F) Subcutaneous fat expression of C/EBPb was upregulated in MetS + RAS relative to Lean and MetS. PPARy was upregulated in the perirenal fat of MetS + RAS relative to Lean (G) but was unchanged in the subcutaneous fat of all pig groups (H). * p < 0.05 vs. Lean; † p < 0.05 vs. MetS; $ p < 0.05 vs. MetS + RAS. MetS, metabolic syndrome; RAS, renal artery stenosis; MSC, mesenchymal stem cells. Full article ">

14 pages, 2266 KiB

Open AccessArticle

The Ivermectin Related Compound Moxidectin Can Target Apicomplexan Importin α and Limit Growth of Malarial Parasites

by Sujata B. Walunj, Geetanjali Mishra, Kylie M. Wagstaff, Swati Patankar and David A. Jans

Cells 2025, 14(1), 39; https://doi.org/10.3390/cells14010039 - 2 Jan 2025

Viewed by 1200

Abstract

Signal-dependent transport into and out of the nucleus mediated by members of the importin (IMP) superfamily is crucial for eukaryotic function, with inhibitors targeting IMPα being of key interest as anti-infectious agents, including against the apicomplexan Plasmodium species and Toxoplasma gondii, causative [...] Read more.

Signal-dependent transport into and out of the nucleus mediated by members of the importin (IMP) superfamily is crucial for eukaryotic function, with inhibitors targeting IMPα being of key interest as anti-infectious agents, including against the apicomplexan Plasmodium species and Toxoplasma gondii, causative agents of malaria and toxoplasmosis, respectively. We recently showed that the FDA-approved macrocyclic lactone ivermectin, as well as several other different small molecule inhibitors, can specifically bind to and inhibit P. falciparum and T. gondii IMPα functions, as well as limit parasite growth. Here we focus on the FDA-approved antiparasitic moxidectin, a structural analogue of ivermectin, for its IMPα-targeting and anti-apicomplexan properties for the first time. We use circular dichroism and intrinsic tryptophan fluorescence measurements to show that moxidectin can bind directly to apicomplexan IMPαs, thereby inhibiting their key binding functions at low μM concentrations, as well as possessing anti-parasitic activity against P. falciparum in culture. The results imply a class effect in terms of IMPα’s ability to be targeted by macrocyclic lactone compounds. Importantly, in the face of rising global emergence of resistance to approved anti-parasitic agents, the findings highlight the potential of moxidectin and possibly other macrocyclic lactone compounds as antimalarial agents. Full article

(This article belongs to the Topic Bioactive Compounds and Therapeutics: Molecular Aspects, Metabolic Profiles, and Omics Studies)

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Figure 1
Close similarities of ivermectin and moxidectin in structure and IMPα inhibitory properties. (a). Structures of ivermectin and moxidectin are shown, with the shared chemical scaffold in black, and distinct groups highlighted in colour; an example is the C13 residue that is attached to sugar groups in the case of avermectins such as ivermectin, but is protonated in moxidectin and other members of the milbemycin family. (b). AlphaScreen technology was used to determine the IC50 values for inhibition of the binding of various IMPαs (5 nM) to NLS-containing proteins (30 nM) by ivermectin and moxidectin. Data represent the mean ± SEM for quadruplet wells from a single experiment, from a series of 3 independent experiments (see <a href="#cells-14-00039-t001" class="html-table">Table 1</a> for pooled data). Full article ">Figure 2
Like ivermectin, moxidectin can inhibit IMPα–IMPβ1 binding. AlphaScreen technology was used to determine the IC50 for inhibition by ivermectin and moxidectin of binding of MmIMPβ1 (30 nM) to various IMPαs (30 nM). Data represent the mean ± SEM for quadruplet wells from a single typical experiment, from a series of three independent experiments (see <a href="#cells-14-00039-t001" class="html-table">Table 1</a> for pooled data). Full article ">Figure 3
CD spectra for apicomplexan and mammalian IMPαs in the absence and presence of moxidectin and ivermectin. CD spectra were collected for PfIMPα, TgIMPα, and MmIMPα in the absence or presence of 30 or 80 μM ivermectin or moxidectin. (a). Spectra are shown from a single experiment, representative of 2 independent experiments for IMPαs without or with 30 μM ivermectin or moxidectin. Note: θ is ellipticity in mdeg cm2 dmol−1. (b). The α-helical content of the respective IMPαs was estimated as previously (see <a href="#sec2dot4-cells-14-00039" class="html-sec">Section 2.4</a>) from spectra as per <a href="#cells-14-00039-f003" class="html-fig">Figure 3</a>a. Results represent the mean ± SD for 2 independent experiments. Full article ">Figure 4
Intrinsic tryptophan fluorescence assay to study the interaction between moxidectin and ivermectin with PfIMPα, TgIMPα, and MmIMPα. (a) Intrinsic tryptophan fluorescence spectra of PfIMPα, TgIMPα, and MmIMPα were collected (excitation wavelength: 295 nm and emission from 315 nm to 400 nm) in the presence or absence of moxidectin and ivermectin in the indicated concentration ranges. (b). Changes in fluorescence intensity at 340 nm (λmax) from (a) with increasing concentrations of compound were plotted using GraphPad prism for a single typical set of measurements, representative of a series of 3 independent experiments (see <a href="#cells-14-00039-t002" class="html-table">Table 2</a>). Full article ">Figure 5
Ivermectin and moxidectin inhibit P. falciparum parasites in culture at low µM concentrations. P. falciparum cultures (0.25% parasitemia) were treated with increasing concentrations of the indicated compounds for 72 h, after which the HRP2-based sandwich ELISA was used to measure the HRP2 levels, determined by optical density. The results shown are from a single typical experiment performed in duplicate (SD shown), representative of a series of three independent experiments (see <a href="#cells-14-00039-t003" class="html-table">Table 3</a> for pooled data). Full article ">

24 pages, 2160 KiB

Open AccessReview

In Vitro 3D Models of Haematological Malignancies: Current Trends and the Road Ahead?

by Carlotta Mattioda, Claudia Voena, Gianluca Ciardelli and Clara Mattu

Cells 2025, 14(1), 38; https://doi.org/10.3390/cells14010038 - 2 Jan 2025

Viewed by 1672

Abstract

Haematological malignancies comprise a diverse group of life-threatening systemic diseases, including leukaemia, lymphoma, and multiple myeloma. Currently available therapies, including chemotherapy, immunotherapy, and CAR-T cells, are often associated with important side effects and with the development of drug resistance and, consequently, disease relapse. [...] Read more.

Haematological malignancies comprise a diverse group of life-threatening systemic diseases, including leukaemia, lymphoma, and multiple myeloma. Currently available therapies, including chemotherapy, immunotherapy, and CAR-T cells, are often associated with important side effects and with the development of drug resistance and, consequently, disease relapse. In the last decades, it was largely demonstrated that the tumor microenvironment significantly affects cancer cell proliferation and tumor response to treatment. The development of biomimetic, in vitro models may promote the investigation of the interactions between cancer cells and the tumor microenvironment and may help to better understand the mechanisms leading to drug resistance. Although advanced in vitro models have been largely explored in the field of solid tumors, due to the complex nature of the blood cancer tumor microenvironment, the mimicking of haematological malignancies mostly relies on simpler systems, often limited to two-dimensional cell culture, which intrinsically excludes the microenvironmental niche, or to ethically debated animal models. This review aims at reporting an updated overview of state-of-the-art hematological malignancies 3D in vitro models, emphasizing the key features and limitations of existing systems to inspire further research in this underexplored field. Full article

(This article belongs to the Special Issue State-of-the-Art of Cell Cultures in Drug Validation and Toxicity Assessment)

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19 pages, 4850 KiB

Open AccessArticle

Single-Nucleus RNA Sequencing Reveals Cellular Transcriptome Features at Different Growth Stages in Porcine Skeletal Muscle

by Ziyu Chen, Xiaoqian Wu, Dongbin Zheng, Yuling Wang, Jie Chai, Tinghuan Zhang, Pingxian Wu, Minghong Wei, Ting Zhou, Keren Long, Mingzhou Li, Long Jin and Li Chen

Cells 2025, 14(1), 37; https://doi.org/10.3390/cells14010037 - 2 Jan 2025

Viewed by 969

Abstract

Porcine latissimus dorsi muscle (LDM) is a crucial source of pork products. Meat quality indicators, such as the proportion of muscle fibers and intramuscular fat (IMF) deposition, vary during the growth and development of pigs. Numerous studies have highlighted the heterogeneous nature of [...] Read more.

Porcine latissimus dorsi muscle (LDM) is a crucial source of pork products. Meat quality indicators, such as the proportion of muscle fibers and intramuscular fat (IMF) deposition, vary during the growth and development of pigs. Numerous studies have highlighted the heterogeneous nature of skeletal muscle, with phenotypic differences reflecting variations in cellular composition and transcriptional profiles. This study investigates the cellular-level transcriptional characteristics of LDM in large white pigs at two growth stages (170 days vs. 245 days) using single-nucleus RNA sequencing (snRNA-seq). We identified 56,072 cells across 12 clusters, including myofibers, fibro/adipogenic progenitor (FAP) cells, muscle satellite cells (MUSCs), and other resident cell types. The same cell types were present in the LDM at both growth stages, but their proportions and states differed. A higher proportion of FAPs was observed in the skeletal muscle of 245-day-old pigs. Additionally, these cells exhibited more active communication with other cell types compared to 170-day-old pigs. For instance, more interactions were found between FAPs and pericytes or endothelial cells in 245-day-old pigs, including collagen and integrin family signaling. Three subclasses of FAPs was identified, comprising FAPs_COL3A1⁺, FAPs_PDE4D⁺, and FAPs_EBF1⁺, while adipocytes were categorized into Ad_PDE4D⁺ and Ad_DGAT2⁺ subclasses. The proportions of these subclasses differed between the two age groups. We also constructed differentiation trajectories for FAPs and adipocytes, revealing that FAPs in 245-day-old pigs differentiated more toward fibrosis, a characteristic reminiscent of the high prevalence of skeletal muscle fibrosis in aging humans. Furthermore, the Ad_PDE4D⁺ adipocyte subclass, predominant in 245-day-old pigs, originated from FAPs_PDE4D⁺ expressing the same gene, while the Ad_DGAT2⁺ subclass stemmed from FAPs_EBF1⁺. In conclusion, our study elucidates transcriptional differences in skeletal muscle between two growth stages of pigs and provides insights into mechanisms relevant to pork meat quality and skeletal muscle diseases. Full article

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(A) Differences in body weight, backfat thickness, and intramuscular fat content of the longest dorsal muscle in 170-day-old and 245-day-old pigs (n = 2; *** indicates p < 0.001, * indicates p < 0.05). (B) Results of cell nuclear population clustering and annotation. (C) Main marker genes and expression levels of cell types in a violin plot. (D) Expression of marker genes in different cell clusters. (E) Heatmap of the top 10 differentially expressed genes across different cell types. (F) Myofiber enrichment pathway results. (G) FAP enrichment pathway results. Full article ">Figure 2
(A) The top five regulons for each cell type. Regulon specificity scores (RSS) of each annotated cell population. A point in a panel shows the RSS of one TF regulon. TF regulons are sorted by the RSSs in each cell type. The top five specific regulons are highlighted in red. (B) The expression activity of the top five regulons in each cell. (C) The unsupervised clustering results of the CSI matrix. Heatmap displays clustered regulon modules based on the CSI matrix along with the included regulons being shown in the right, indicating whether a given regulon is specific to a cell type. Top five RSS for each cluster shown. (D) The correspondence between regulon modules and the cells with the highest average activity of regulons. (E) The top 20 regulons in each cell appear in different modules. (F–I) Pathway enrichment results for M1, M2, M3, and M4, respectively. Full article ">Figure 3
(A) Uniformity of cell types between two stages. (B) Cell composition of pigs at two stages. (C) Number of differentially expressed genes for each cell type between two stages (|logFC| > 1, p.adj < 0.05). (D) Enrichment results for differentially upregulated gene pathways in FAPs and MUSCs. (E) Number of cellular communications between two stages. (F) Violin plot of cell-to-cell communication between FAPs and other cells in the 245-day-old pigs. (G) Differences in expression levels of cell-to-cell communication between the two stages. Full article ">Figure 4
(A) Original clustering results and cell annotation of FAP cells. (B) Expression of marker genes in different subtypes. (C) Violin plot of marker gene expression in major cell types. (D) Proportion of FAP subtypes at different developmental stages. (E) Pseudotime trajectory of FAPs. (F) Developmental pseudotime of FAP subtypes. (G) Distribution of FAP cells along the pseudotime trajectory in individuals at different developmental stages. (H) Differences in differentiation levels of FAP subtypes. Full article ">Figure 5
(A) Original clustering results and cell annotation of adipocyte cells. (B) Expression of marker genes in different subtypes. (C) Violin plot of marker gene expression in major cell types. (D) Proportion of adipocyte subtypes at different developmental stages. (E) RNA velocity plot of FAPs and adipocyte cells. (F) Differences in differentiation levels between FAPs and adipocyte cells. (G) Distribution of FAPs and adipocyte cells along the pseudotime trajectory in individuals at different developmental stages. (H) Pathway enrichment analysis of Ad_DGAT2+ adipocyte cell-specific expressed genes. Full article ">

18 pages, 304 KiB

Open AccessReview

Oxidative Stress and the NLRP3 Inflammasome: Focus on Female Fertility and Reproductive Health

by Efthalia Moustakli, Sofoklis Stavros, Periklis Katopodis, Charikleia Skentou, Anastasios Potiris, Periklis Panagopoulos, Ekaterini Domali, Ioannis Arkoulis, Theodoros Karampitsakos, Eleftheria Sarafi, Theologos M. Michaelidis, Athanasios Zachariou and Athanasios Zikopoulos

Cells 2025, 14(1), 36; https://doi.org/10.3390/cells14010036 - 2 Jan 2025

Viewed by 964

Abstract

Chronic inflammation is increasingly recognized as a critical factor in female reproductive health; influencing natural conception and the outcomes of assisted reproductive technologies such as in vitro fertilization (IVF). An essential component of innate immunity, the NLR family pyrin domain-containing 3 (NLRP3) inflammasome [...] Read more.

Chronic inflammation is increasingly recognized as a critical factor in female reproductive health; influencing natural conception and the outcomes of assisted reproductive technologies such as in vitro fertilization (IVF). An essential component of innate immunity, the NLR family pyrin domain-containing 3 (NLRP3) inflammasome is one of the major mediators of inflammatory responses, and its activation is closely linked to oxidative stress. This interaction contributes to a decline in oocyte quality, reduced fertilization potential, and impaired embryo development. In the ovarian milieu, oxidative stress and NLRP3 inflammasome activation interact intricately, and their combined effects on oocyte competence and reproductive outcomes are significant. The aims of this review are to examine these molecular mechanisms and to explore therapeutic strategies targeting oxidative stress and NLRP3 inflammasome activity, with the goal of enhancing female fertility and improving clinical outcomes in reproductive health. Full article

(This article belongs to the Special Issue Cellular and Molecular Mechanisms in Gynecological Disorders)

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18 pages, 3235 KiB

Open AccessArticle

Dysregulation of the NLRP3 Inflammasome and Promotion of Disease by IL-1β in a Murine Model of Sandhoff Disease

by Nick Platt, Dawn Shepherd, David A. Smith, Claire Smith, Kerri-Lee Wallom, Raashid Luqmani, Grant C. Churchill, Antony Galione and Frances M. Platt

Cells 2025, 14(1), 35; https://doi.org/10.3390/cells14010035 - 1 Jan 2025

Viewed by 898

Abstract

Sandhoff disease (SD) is a progressive neurodegenerative lysosomal storage disorder characterized by GM2 ganglioside accumulation as a result of mutations in the HEXB gene, which encodes the β-subunit of the enzyme β-hexosaminidase. Lysosomal storage of GM2 triggers inflammation in the CNS and periphery. [...] Read more.

Sandhoff disease (SD) is a progressive neurodegenerative lysosomal storage disorder characterized by GM2 ganglioside accumulation as a result of mutations in the HEXB gene, which encodes the β-subunit of the enzyme β-hexosaminidase. Lysosomal storage of GM2 triggers inflammation in the CNS and periphery. The NLRP3 inflammasome is an important coordinator of pro-inflammatory responses, and we have investigated its regulation in murine SD. The NLRP3 inflammasome requires two signals, lipopolysaccharide (LPS) and ATP, to prime and activate the complex, respectively, leading to IL-1β secretion. Peritoneal, but not bone-marrow-derived, macrophages from symptomatic SD mice, but not those from pre-symptomatic animals, secrete the cytokine following priming with LPS with no requirement for activation with ATP, suggesting that such NLRP3 deregulation is related to the extent of glycosphingolipid storage. Dysregulated production of IL-1β was dependent upon caspase activity but not cathepsin B. We investigated the role of IL-1β in SD pathology using two approaches: the creation of hexb^−/−Il1r1^−/− double knockout mice or by treating hexb^−/− animals with anakinra, a recombinant form of the IL-1 receptor antagonist, IL-1Ra. Both resulted in modest but significant extensions in lifespan and improvement of neurological function. These data demonstrate that IL-1β actively participates in the disease process and provides proof-of-principle that blockade of the pro-inflammatory cytokine IL-1β may provide benefits to patients. Full article

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11 pages, 2149 KiB

Open AccessArticle

Bi-Hormonal Endocrine Cell Presence Within the Islets of Langerhans of the Human Pancreas Throughout Life

by Jiwon Hahm, Dawn Kumar, Juan Andres Fernandez Andrade, Edith Arany and David J. Hill

Cells 2025, 14(1), 34; https://doi.org/10.3390/cells14010034 - 1 Jan 2025

Viewed by 1174

Abstract

Bi-hormonal islet endocrine cells have been proposed to represent an intermediate state of cellular transdifferentiation, enabling an increase in beta-cell mass in response to severe metabolic stress. Beta-cell plasticity and regenerative capacity are thought to decrease with age. We investigated the ontogeny of [...] Read more.

Bi-hormonal islet endocrine cells have been proposed to represent an intermediate state of cellular transdifferentiation, enabling an increase in beta-cell mass in response to severe metabolic stress. Beta-cell plasticity and regenerative capacity are thought to decrease with age. We investigated the ontogeny of bi-hormonal islet endocrine cell populations throughout the human lifespan. Immunofluorescence microscopy was performed for insulin, glucagon, and somatostatin presence on paraffin-embedded sections of pancreata from 20 donors without diabetes aged between 11 days and 79 years of age. The mean proportional presence of glucagon-, insulin-, and somatostatin-immunoreactive cells within islets was 27.5%, 62.1%, and 12.1%, respectively. There was no change in the relative presence of alpha- or beta-cells with advancing age, but delta-cell presence showed a decline with age (R² = 0.59, p < 0.001). The most abundant bi-hormonal cell phenotype observed co-stained for glucagon and insulin, representing 3.1 ± 0.3% of all islet cells. Glucagon/somatostatin and insulin/somatostatin bi-hormonal cells were also observed representing 2–3% abundance relative to islet cell number. Glucagon/insulin bi-hormonal cells increased with age (R² = 0.30, p < 0.05) whilst insulin/somatostatin (R² = 0.50, p < 0.01) and glucagon/somatostatin (R² = 0.35, p < 0.05) cells decreased with age of donor. Findings show that bi-hormonal cells are present within human pancreatic islets throughout life, perhaps reflecting an ongoing potential for endocrine cell plasticity. Full article

(This article belongs to the Section Tissues and Organs)

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Distribution of alpha-cells (glucagon—red), beta-cells (insulin—green), and delta-cells (somatostatin—white) in representative human pancreatic islets from (A) an 11-day-old donor and (B) a 52-year-old individual. Cell nuclei are visualized in blue. The bars indicate islet size. Full article ">Figure 2
Mean percentage of beta-, alpha-, and delta-cells within islets of Langerhans for individual pancreas donors distributed by donor age. The best-fit line is shown following non-linear regression analysis. Full article ">Figure 3
Examples of bi-hormonal endocrine cells in human islets (arrows). Red and green fluorescence filters were assigned to identify cells bi-hormonal for the co-presence (arrows) of insulin (Ins) and glucagon (Glu) ((A)—male, 11 years); insulin and somatostatin (Sst) ((B)—female, 28 years); or Sst and Glu ((C)—female, 5 years), or examples of tri-hormonal cells containing Ins, Glu, and Sst ((D)—female, 5 years). Islet size is indicated in each panel by a size bar on the merged image. Full article ">Figure 4
Percentage of multi-hormonal cell phenotypes within islets of Langerhans for individual pancreas donors distributed by donor age and expressed relative to all islet cells ((A) insulin/glucagon bi-hormonal cells, (B) insulin/somatostatin bi-hormonal cells, (C) glucagon/somatostatin bi-hormonal cells, (D) insulin/glucagon/somatostatin tri-hormonal cells). Data were analyzed by linear regression and the regression lines are shown. Full article ">

11 pages, 3447 KiB

Open AccessCommunication

CCL4 Affects Eosinophil Survival via the Shedding of the MUC1 N-Terminal Domain in Airway Inflammation

by Yoshiki Kobayashi, Chu Hong Hanh, Naoto Yagi, Nhi Kieu Thi Le, Yasutaka Yun, Akihiro Shimamura, Kenta Fukui, Akitoshi Mitani, Kensuke Suzuki, Akira Kanda and Hiroshi Iwai

Cells 2025, 14(1), 33; https://doi.org/10.3390/cells14010033 - 31 Dec 2024

Viewed by 697

Abstract

Eosinophilic chronic rhinosinusitis (ECRS), a CRS with nasal polyps (CRSwNP), is characterized by eosinophilic infiltration with type 2 inflammation and is highly associated with bronchial asthma. Intractable ECRS with poorly controlled asthma is recognized as a difficult-to-treat eosinophilic airway inflammation. Although eosinophils are [...] Read more.

Eosinophilic chronic rhinosinusitis (ECRS), a CRS with nasal polyps (CRSwNP), is characterized by eosinophilic infiltration with type 2 inflammation and is highly associated with bronchial asthma. Intractable ECRS with poorly controlled asthma is recognized as a difficult-to-treat eosinophilic airway inflammation. Although eosinophils are activated and coincubation with airway epithelial cells prolongs their survival, the interaction mechanism between eosinophils and epithelial cells is unclear. This study examined the effect of eosinophils on mucin glycoprotein 1 (MUC1), a member of membrane-bound mucin, in the airway epithelial cells, to elucidate the mechanisms of the eosinophil–airway epithelial cell interaction. Nasal polyp samples from patients with CRSwNP and BEAS-2B airway epithelial cells, coincubated with purified eosinophils, were stained with two MUC1 antibodies. To confirm the involvement of CCL4, an anti-CCL4 neutralizing antibody or recombinant CCL4 was used as needed. The immunofluorescence results revealed a negative correlation between the expression of full-length MUC1 and eosinophil count in nasal polyps. In BEAS-2B coincubated with eosinophils, full-length MUC1, but not the C-terminal domain, was reduced, and eosinophil survival was prolonged, which was concomitant with CCL4 increase, whereas the anti-CCL4 neutralizing antibody decreased these reactions. The survival of eosinophils that contacted recombinant MUC1 without the N-terminal domain was prolonged, and recombinant CCL4 increased the expression of metalloproteases. Increased CCL4 induces the contact between eosinophils and airway epithelial cells by shedding the MUC1 N-terminal domain and enhances eosinophil survival in eosinophilic airway inflammation. This novel mechanism may be a therapeutic target for difficult-to-treat eosinophilic airway inflammation. Full article

(This article belongs to the Special Issue Eosinophils and Their Role in Allergy and Related Diseases)

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Full-length MUC1 (MUC1-FL) expression in the epithelial cells of nasal polyps. (A) Immunofluorescence analysis of nasal polyps obtained from patients with chronic rhinosinusitis with nasal polyps (CRSwNP) with high eosinophil count (left panels; i–iii) or those with low eosinophil count (right panels; iv–vi). MUC1-FL (green), C-terminal domain (MUC1-C, red), and the nucleus (blue) are stained. Images were captured by an FV3000 confocal microscope (400× objectives). The scale bars in the bottom-right corner indicate 10 μm. (B) Correlation of MUC1-FL expression with eosinophil count in nasal polyps. MUC1-FL intensity is indicated as a ratio to epithelial cell adhesion molecule (EpCAM) (n = 39). Full article ">Figure 2
Effect of eosinophils on full-length MUC1 (MUC1-FL) expression in airway epithelial cells. (A–D) BEAS-2B cells were coincubated overnight with purified peripheral blood eosinophils. MUC1-FL mRNA levels (A), MUC1-FL protein levels (B), and MUC1 C-terminal domain (MUC1-C) protein levels (C) were evaluated. (D) Immunofluorescence analysis of MUC1-FL (green), MUC1-C (red), and the nucleus (blue) are shown in the upper (without eosinophils) and lower (with eosinophils) panels. Images were captured by an FV3000 confocal microscope (400× objectives). Scale bars in the bottom-right corner indicate 10 μm. Results were representative of at least three experiments. (E) MUC1-FL protein expression in BEAS-2B coincubated with the supernatants of eosinophilic mucin overnight. Patients underwent endoscopic sinus surgery under general anesthesia. Mucin samples were collected from the sinuses of refractory ECRS subjects. Values in (A–C,E) represent the mean ± SEM of four experiments; # p < 0.05, ## p < 0.01 (vs. vehicle). Full article ">Figure 3
Relation between full-length MUC1 (MUC1-FL) and CCL4 expression in the epithelial cells of nasal polyps. (A) Immunofluorescence staining of nasal polyps obtained from patients with CRSwNP with high or low eosinophil count. MUC1-FL, CCL4, and epithelial cell adhesion molecule (EpCAM) expression levels were evaluated. MUC1-FL (pink), CCL4 (green), EpCAM (orange), MBP (red), and the nucleus (blue) are stained with hematoxylin and eosin (H&E). Images were captured by an FV3000 confocal microscope (100× objectives). The scale bars in the bottom-right corner indicate 100 μm. (B,C) Correlation of CCL4 expression with the eosinophil count in nasal polyps (B) and MUC1-FL expression (C). MUC1-FL and CCL4 intensities are indicated as a ratio to EpCAM (n = 39). Full article ">Figure 4
Effect of CCL4-mediated reduction of full-length MUC1 (MUC1-FL) on eosinophil survival. (A,B) BEAS-2B was stimulated overnight with recombinant human CCL4 (10 μg/mL). MUC1-FL expression (A) and matrix metalloproteases (ADAM17 and MMP14) mRNA levels (B) in BEAS-2B. (C–E) BEAS-2B and purified eosinophils were coincubated with or without anti-CCL4 neutralizing antibody (10 μg/mL). MUC1-FL protein levels in BEAS-2B (C), CCL4 concentration in supernatants of cell culture (D), and eosinophil survival (E) were evaluated. (F) Purified eosinophils were incubated overnight on a recombinant human MUC1-coated plate, followed by the evaluation of their survival. Images (MUC1-FL, green; nucleus, blue) in A were captured by an FV3000 confocal microscope (400× objectives) with scale bars (20 μm) in the bottom-right corner, which were representative of at least three experiments. The values in (B–F) represent the mean ± SEM of four experiments. # p < 0.05, ## p < 0.01 (vs. without rhCCL4 in (B), without eosinophils in (C,D), without BEAS-2B in (E), and without rhMUC1 in (F)). ** p < 0.01 (between the two groups). Full article ">Figure 5
Mechanism of prolonged eosinophil survival associated with MUC1 in airway epithelial cells. Eosinophils–airway epithelial cells contact activates both cells and induces CCL4 release from them. CCL4 might be involved in the shedding of the MUC1 N-terminal domain by increased expression of these metalloproteases (e.g., ADAM17 and MMP14). The viability of eosinophils bound to the MUC1 C-terminal domain could be upregulated. Full article ">

18 pages, 2911 KiB

Open AccessArticle

Flow Cytometric Assessment of FcγRIIIa-V158F Polymorphisms and NK Cell Mediated ADCC Revealed Reduced NK Cell Functionality in Colorectal Cancer Patients

by Phillip Schiele, Stefan Kolling, Stanislav Rosnev, Charlotte Junkuhn, Anna Luzie Walter, Jobst Christian von Einem, Sebastian Stintzing, Wenzel Schöning, Igor Maximilian Sauer, Dominik Paul Modest, Kathrin Heinrich, Lena Weiss, Volker Heinemann, Lars Bullinger, Marco Frentsch and Il-Kang Na

Cells 2025, 14(1), 32; https://doi.org/10.3390/cells14010032 - 31 Dec 2024

Viewed by 1110

Abstract

Antibody-dependent cell-mediated cytotoxicity (ADCC) by NK cells is a key mechanism in anti-cancer therapies with monoclonal antibodies, including cetuximab (EGFR-targeting) and avelumab (PDL1-targeting). Fc gamma receptor IIIa (FcγRIIIa) polymorphisms impact ADCC, yet their clinical relevance in NK cell functionality remains debated. We developed [...] Read more.

Antibody-dependent cell-mediated cytotoxicity (ADCC) by NK cells is a key mechanism in anti-cancer therapies with monoclonal antibodies, including cetuximab (EGFR-targeting) and avelumab (PDL1-targeting). Fc gamma receptor IIIa (FcγRIIIa) polymorphisms impact ADCC, yet their clinical relevance in NK cell functionality remains debated. We developed two complementary flow cytometry assays: one to predict the FcγRIIIa-V158F polymorphism using a machine learning model, and a 15-color flow cytometry panel to assess antibody-induced NK cell functionality and cancer-immune cell interactions. Samples were collected from healthy donors and metastatic colorectal cancer (mCRC) patients from the FIRE-6-Avelumab phase II study. The machine learning model accurately predicted the FcγRIIIa-V158F polymorphism in 94% of samples. FF homozygous patients showed diminished cetuximab-mediated ADCC compared to VF or VV carriers. In mCRC patients, NK cell dysfunctions were evident as impaired ADCC, decreased CD16 downregulation, and reduced CD137/CD107a induction. Elevated PD1+ NK cell levels, reduced lysis of PDL1-expressing CRC cells and improved NK cell activation in combination with the PDL1-targeting avelumab indicate that the PD1-PDL1 axis contributes to impaired cetuximab-induced NK cell function. Together, these optimized assays effectively identify NK cell dysfunctions in mCRC patients and offer potential for broader application in evaluating NK cell functionality across cancers and therapeutic settings. Full article

(This article belongs to the Special Issue Advances in the Study of Natural Killer (NK) Cells)

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Figure 1
Detection of FcγRIIIa-158 phenotypes by flow cytometry. (A) Summary of the assay development to detect the FcγRIIIa-V158F polymorphism including sample source, preparation, and bioinformatics. FcγRIIIa-typing was established on 39 healthy donors followed by validation on a cohort of 52 mCRC patients from the FIRE-6 study. At each point, FcγRIIIa polymorphisms were detected by PCR sequencing and flow cytometry. (B) Gating strategy to detect FcγRIIIa-V158F phenotypes using two different CD16 clones. After selecting lymphocytes, NK cells were identified as CD3-CD14-CD56+ cells, and LNK16 and MEM154 binding was analyzed. Representative examples for each FcγRIIIa phenotype are shown. (C) Scatter plot of MEM154 and LNK16 binding (MFI) on NK cells. Dots represent individuals and FcgRIIIa-158 phenotypes are color-coded. Full article ">Figure 2
Machine learning model predicts FcγRIIIa polymorphisms. (A) Visualization of the generated LDA model for a specific training set to which Logistic Regression was subsequently applied for prediction. As highlighted, the major contributors for distinct clustering were the ratios between both CD16 clones (M = MEM154, L = LNK16) either in terms of frequency (F) or mean fluorescent intensity (MFI) on CD56dim NK cells. (B,C) Bar graphs show the mean prediction performance as defined by F1 scores of 10-fold cross-validation for the flow cytometry assay regarding all FcγRIIIa phenotypes (B) or the F1 scores of leave-one-out cross-validation per time point during therapy for all FcγRIIIa phenotypes and weighted for the study cohort (C). Highlighted are the correctly assigned FcγRIIIa phenotypes with respect to PCR-based detection. (D) Performance of prediction model built from gradually smaller size of training sets. For each sample size, a new prediction model was repeatedly trained and validated on the same testing set (repetitions n = 100) using two different approaches for FcγRIIIa phenotype proportions: approach 4:4:2 (blue) simulates the approximate prevalence of each phenotype in the Caucasian population, whereas approach 1:1:1 (green) assumes equal proportions of each phenotype in the training set. Full article ">Figure 3
ADCC assay development and gating strategy. (A) Schematic representation of the established assay set-up. Briefly, 3 × 104 SNU-C5 cancer cells were co-cultivated with 3 × 105 PBMCs and stimulated with 100 ng/mL cetuximab or avelumab for 24 h. Anti-cancer response was then analyzed by LDH release and flow cytometry. (B,C) Gating strategy to analyze the viability of cancer cells and activation and regulations of immune checkpoints on immune cells. (B) After doublet exclusion (comparable to <a href="#cells-14-00032-f001" class="html-fig">Figure 1</a>) and physical discrimination using control samples with either cancer cell or PBMCs alone for pre-gating of respective cell types, EpCAM+ cancer cells, CD14+ monocytes, CD3+ T cells and CD3-CD14-CD56+ NK cells were distinguished. (C) For cancer cells, viability was detected by DAPI and Annexin V staining along with checkpoint expression of PDL1 and CD40. CD56+ NK cells were separated in CD56dimCD16+ and CD56hiCD16low/−. All NK cell subsets were analyzed for CD107a, CD137, NKG2A, NKG2D, CD62L and PD1 expression while T cells were measured for NKG2A, NKG2D, CD137, CD62L and PD1 expression. CD14+ monocytes were gated for CD40, PDL1, CD62L and PD1 expression. (D) Scatter plot of ADCC values from experiments with PBMCs from healthy donors or mCRC patients detected by LDH release assay (ADCC LysisLDH) or flow cytometry (∆Tumour cellsFACS). Each dot represents matched values from both readouts and the Pearson’s correlation coefficient together with the p-value for correlation fit is depicted. Full article ">Figure 4
Reduced cytotoxic potential in mCRC patients. (A,B) Healthy donors (n = 10) and baseline samples from mCRC patients (n = 35) of the FIRE-6 study prior to therapy initiation were grouped according to their FcγRIIIa polymorphism and (A) assayed for cetuximab (Cet) mediated ADCC against SNU-C5 cancer cells. Additionally, samples from these donors were also stimulated with a combination of cetuximab and avelumab (Cet+Ave) to assess (B) ADCC and regulations of CD16, CD137 and CD107a expressions according to different stimulations. (C) After z-score standardization, t-Distributed Stochastic Neighbor Embedding (tSNE) dimensionality reduction of 154 flow cytometry parameters shows the response to ex vivo cetuximab stimulation in healthy donors (blue, HD) or mCRC patients (red, mCRC). (D,E) Examples of differentially regulated parameters representing (D) NK cell functionalities or (E) regulations on cancer cells and monocytes. ∆-values are calculated as the difference between unstimulated and cetuximab-treated samples. Statistics: (A,B) One-way ANOVA followed by Tukey’s multiple comparison test. (D,E) Mann–Whitney U test. (A,E) Each dot represents the mean of technical triplicates from individual donors. Full article ">

12 pages, 3119 KiB

Open AccessArticle

Epigenetic Inhibitors Differentially Impact TGF-β1 Signaling Cascades in COPD Airway Smooth Muscle Cells

by Karosham Diren Reddy, Dikaia Xenaki, Ian M. Adcock, Brian G. G. Oliver and Razia Zakarya

Cells 2025, 14(1), 31; https://doi.org/10.3390/cells14010031 - 31 Dec 2024

Viewed by 841

Abstract

Background: Chronic obstructive pulmonary disease (COPD) is characterized by progressive and incurable airflow obstruction and chronic inflammation. Both TGF-β1 and CXCL8 have been well described as fundamental to COPD progression. DNA methylation and histone acetylation, which are well-understood epigenetic mechanisms regulating gene expression, [...] Read more.

Background: Chronic obstructive pulmonary disease (COPD) is characterized by progressive and incurable airflow obstruction and chronic inflammation. Both TGF-β1 and CXCL8 have been well described as fundamental to COPD progression. DNA methylation and histone acetylation, which are well-understood epigenetic mechanisms regulating gene expression, are associated with COPD progression. However, a deeper understanding of the complex mechanisms associated with DNA methylation, histone post-translational changes and RNA methylation in the context of regulatory pathways remains to be elucidated. We here report on how DNA methylation and histone acetylation inhibition differentially affect CXCL8 signaling in primary human non-COPD and COPD airway cells. Methods: Airway smooth muscle (ASM) cells, a pivotal cell type in COPD, were isolated from the small airways of heavy smokers with and without COPD. Histone acetylation and DNA methylation were inhibited before the TGF-β1 stimulation of cells. Subsequently, CXCL8 production and the abundance and activation of pertinent transcription regulatory proteins (NF-κB, p38 MAPK and JNK) were analyzed. Results: TGF-β1-stimulated CXCL8 release from ASM cells from ‘healthy’ smoker subjects was significantly modulated by DNA methylation (56.32 pg/mL and 56.60 pg/mL) and acetylation inhibitors (27.50 pg/mL and 48.85 pg/mL) at 24 and 48 h, respectively. However, modulation via the inhibition of DNA methylation (34.06 pg/mL and 43.18 pg/mL) and acetylation (23.14 pg/mL and 27.18 pg/mL) was observed to a lesser extent in COPD ASM cells. These changes were associated with differences in the TGF-β1 activation of NF-κB and MAPK pathways at 10 and 20 min. Conclusions: Our findings offer insight into differential epigenetics in controlling COPD ASM cells and provide a foundation warranting future studies on epigenetic differences associated with COPD diagnosis. This would provide a scope for developing therapeutic interventions targeting signaling and epigenetic pathways to improve patient outcomes. Full article

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Graphical abstract
Full article ">Figure 1
CXCL8 production (pg/mL) from TGF-β1-stimulated (10 ng/mL) non-COPD (gray) and COPD (black) ASM cells. Cells were pre-treated with either trichostatin A (TSA, 100 nM) (a,b) or 5-azacytidine (5-aza, 10 μM) (c,d) and incubated for 24 or 48 h. CXCL8 was determined in cell-free supernatant by ELISA. Data are presented as the median with the interquartile range and analyzed by two-way ANOVA with post hoc Fisher’s LSD test for multiple comparisons; n = 5–7. Statistical significance is represented by * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001. Full article ">Figure 2
Western blot quantification of total and phosphorylated NF-κB abundance in non-COPD (gray) and COPD (black) ASM cells. Shown are total (a,b)/phosphorylated (c,d) NF-κB after 10 min TGF-β1 stimulation (10 ng/mL) in the presence and absence of trichostatin A (TSA, 100 nM) or 5-azacytidine (5-aza, 10 μM). Data are presented as median and the interquartile range and analyzed by two-way ANOVA with post hoc Fisher’s LSD test for multiple comparisons; n = 6–7. Statistical significance is represented as * p < 0.05 and ** p < 0.01. Full article ">Figure 3
Western blot quantification of total and phosphorylated p38 MAPK abundance in non-COPD (gray) and COPD (black) ASM cells. Shown are total (a,b)/phosphorylated (c,d) p38 MAPK after 10 min TGF-β1 stimulation (10 ng/mL) in the presence and absence of trichostatin A (TSA, 100 nM) or 5-azacytidine (5-aza, 10 μM). Data are presented as median and the interquartile range and analyzed by two-way ANOVA with post hoc Fisher’s LSD test for multiple comparisons; n = 6–7. Statistical significance is represented as ** p < 0.01. Full article ">Figure 4
Western blot quantification of total and phosphorylated JNK abundance in non-COPD (gray) and COPD (black) ASM cells. Shown are total (a,b)/phosphorylated (c,d) JNK was measured after 20 min TGF-β1 stimulation (10 ng/mL) in the presence and absence of trichostatin A (TSA, 100 nM) or 5-azacytidine (5-aza, 10 μM). Data are presented as median and the interquartile range and analyzed by two-way ANOVA with post hoc Fisher’s LSD test for multiple comparisons; n = 6–7. Statistical significance is represented as ** p < 0.01 and *** p < 0.001. Full article ">Figure 5
Schematic of the TGF-β1 signaling cascade. The straight solid arrows represent the direction of protein interactions and activation of signaling molecules. The dotted arrows represent signaling proteins translocating from the cytosol to the nucleus. The bent solid arrows represent the initiation of gene expression for CXCL8 due to activation of the respective transcription factor. The red circles containing a ‘P’ represent proteins that have been phosphorylated. Figure generated using Biorender. Full article ">

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Cells, Volume 14, Issue 1 (January-1 2025) – 60 articles

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