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Cells
Volume 13
Issue 15
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Cells, Volume 13, Issue 15 (August-1 2024) – 77 articles

Cover Story (view full-size image): ADAR1 is crucial for maintaining smooth muscle and vascular integrity. The deletion of ADAR1 specifically in smooth muscle in mice causes lethality due to extensive hemorrhage and vascular damage. Histological analyses show structural destruction and apoptosis in smooth muscle along with mitochondrial dysfunction. RNA sequencing reveals significant changes in gene expression, such as a reduction in fibrillin-1, which disrupts the interaction between elastin and fibrillin-1. These findings highlight the essential role of ADAR1 in smooth muscle survival and vascular homeostasis. View this paper
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15 pages, 1971 KiB  
Article
RNAseq of Gingival Fibroblasts Exposed to PRF Membrane Lysates and PRF Serum
by Atefe Imani, Layla Panahipour, Hannes Kühtreiber, Michael Mildner and Reinhard Gruber
Cells 2024, 13(15), 1308; https://doi.org/10.3390/cells13151308 - 5 Aug 2024
Viewed by 1117
Abstract
Platelet-rich fibrin (PRF) is prepared by spontaneous coagulation of fractionated blood. When squeezed between two plates, PRF is separated into solid PRF membranes and a liquid exudate, the PRF serum. The question arises regarding how much the overall activity remains in the PRF [...] Read more.
Platelet-rich fibrin (PRF) is prepared by spontaneous coagulation of fractionated blood. When squeezed between two plates, PRF is separated into solid PRF membranes and a liquid exudate, the PRF serum. The question arises regarding how much the overall activity remains in the PRF membranes and what is discarded into the PRF serum. To this end, we have exposed gingival fibroblasts to lysates prepared from PRF membranes and PRF serum, followed by bulk RNA sequencing. A total of 268 up- and 136 down-regulated genes in gingival fibroblasts exposed to PRF membrane lysates were significantly regulated under the premise of a minimum log2 with 2.5-fold change and a minus log10 significance level of two, respectively. PRF serum only caused 62 up- and 32 down-regulated genes under these conditions. Among the 46 commonly up-regulated genes were CXCL1, CXCL5, CXCL6, CXCL8, IL33, IL6, and PTGS2/COX2, stanniocalcin-1—all linked to an inflammatory response. PRF membrane lysates further increased chemokines CCL2, CCL7, CXCL2, CXCL3, and IL1R1, IL1RL1, and IL1RN, as well as the paracrine factors IL11, LIF, IGF1, BMP2, BMP6, FGF2, and CCN2/CTGF, and all hyaluronan synthases. On the other hand, PRF serum increased DKK1. The genes commonly down-regulated by PRF membrane lysates and PRF serum included interferon-induced protein with tetratricopeptide repeats (IFIT1, IFIT2, IFIT3) and odd-skipped-related transcription factors (OSR1 and OSR2), as well as FGF18 and GDF15, respectively. Taken together, PRF membrane lysates, compared to PRF serum, cause a more complex response in gingival fibroblasts, but each increased chemokine expression in gingival fibroblasts. Full article
(This article belongs to the Special Issue Oral Tissue Stem Cells in Regenerative Dentistry)
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Figure 1
<p>(<b>A</b>): Principal component analysis for differentially expressed genes in gingival fibroblasts treated with PRF membrane lysates and PRF serum. The analysis shows the homogeneity of individual donors in each treatment group and the heterogenicity caused by the treatment of the cells with PRF membrane lysates (PRF; red) and PRF serum (Serum, green). Untreated cells are indicated by the blue dots. (<b>B</b>): Heat map analysis for differentially expressed genes in gingival fibroblasts treated with PRF membranes and PRF serum. Each row represents a gene; the three columns represent different preparations of PRF membrane lysates and PRF serum. Red specifies high levels of expression, while blue shows low levels. Expression levels are indicated by darker and lighter shades of red and blue. Genes with a corrected <span class="html-italic">p</span>-value &lt; 0.05 and an average log2 fold change ≥1 or ≤−1 were included in this analysis.</p> Full article ">Figure 2
<p>(<b>A</b>): Volcano plot analysis of differentially expressed genes in gingival fibroblasts treated with PRF membrane lysates. Volcano plot analysis identified up-regulated (red) and down-regulated (blue) genes in gingival fibroblasts treated with PRF membrane lysates. The annotated dots are data points at the largest (Manhattan) distance from the origin and are above the thresholds indicated by the dashed line. The threshold was set to at least a 2.5-fold change and a significance level of 2.0. (<b>B</b>): Volcano plot analysis of differentially expressed genes in gingival fibroblasts treated with <span class="html-italic">PRF serum</span>. Volcano plot analysis identified up-regulated (red) and down-regulated (blue) genes in gingival fibroblasts treated with PRF serum. The annotated dots are data points at the largest (Manhattan) distance from the origin and are above the thresholds indicated by the dashed line. The threshold was set to at least a 2.5-fold change and a significance level of 2.0.</p> Full article ">Figure 2 Cont.
<p>(<b>A</b>): Volcano plot analysis of differentially expressed genes in gingival fibroblasts treated with PRF membrane lysates. Volcano plot analysis identified up-regulated (red) and down-regulated (blue) genes in gingival fibroblasts treated with PRF membrane lysates. The annotated dots are data points at the largest (Manhattan) distance from the origin and are above the thresholds indicated by the dashed line. The threshold was set to at least a 2.5-fold change and a significance level of 2.0. (<b>B</b>): Volcano plot analysis of differentially expressed genes in gingival fibroblasts treated with <span class="html-italic">PRF serum</span>. Volcano plot analysis identified up-regulated (red) and down-regulated (blue) genes in gingival fibroblasts treated with PRF serum. The annotated dots are data points at the largest (Manhattan) distance from the origin and are above the thresholds indicated by the dashed line. The threshold was set to at least a 2.5-fold change and a significance level of 2.0.</p> Full article ">Figure 3
<p>Venn analysis of the genes that were up- and down-regulated by PRF membrane lysates and PRF serum under the premise of at least 2.5-fold change and a significance level of two. A total of 46 and 14 genes were commonly up- and down-regulated by PRF membrane lysates and PRF serum, respectively.</p> Full article ">Figure 4
<p>gProfiler analysis of differentially expressed genes in gingival fibroblasts treated with PRF membrane lysates. Functional enrichment analysis of genes that were (<b>upper</b>) up- and (<b>lower</b>) down-regulated by PRF membrane lysates. The enrichment analysis results are presented as a Manhattan plot, where the <span class="html-italic">x</span>-axis shows the functional terms grouped by the color code of the source database used. By contrast, the <span class="html-italic">y</span>-axis shows the enrichment-adjusted <span class="html-italic">p</span>-values in a negative decimal logarithmic scale. There are three types of terms listed in the gene ontology (GO): biological processes (BP), molecular functions (MF), cellular components (CC).</p> Full article ">Figure 5
<p>gProfiler analysis of differentially expressed genes in gingival fibroblasts treated with PRF serum. Functional enrichment analysis of genes that were (<b>upper</b>) up- and (<b>lower</b>) down-regulated by PRF serum. The enrichment analysis results are presented as a Manhattan plot, where the <span class="html-italic">x</span>-axis shows the functional terms grouped by the color code of the source database used. By contrast, the <span class="html-italic">y</span>-axis shows the enrichment-adjusted <span class="html-italic">p</span>-values in a negative decimal logarithmic scale. Three terms are listed in the gene ontology (GO) analysis: biological processes (BP), molecular functions (MF), cellular components (CC).</p> Full article ">
21 pages, 1926 KiB  
Review
Preserving Genome Integrity: Unveiling the Roles of ESCRT Machinery
by Mattia La Torre, Romina Burla and Isabella Saggio
Cells 2024, 13(15), 1307; https://doi.org/10.3390/cells13151307 - 5 Aug 2024
Viewed by 1325
Abstract
The endosomal sorting complex required for transport (ESCRT) machinery is composed of an articulated architecture of proteins that assemble at multiple cellular sites. The ESCRT machinery is involved in pathways that are pivotal for the physiology of the cell, including vesicle transport, cell [...] Read more.
The endosomal sorting complex required for transport (ESCRT) machinery is composed of an articulated architecture of proteins that assemble at multiple cellular sites. The ESCRT machinery is involved in pathways that are pivotal for the physiology of the cell, including vesicle transport, cell division, and membrane repair. The subunits of the ESCRT I complex are mainly responsible for anchoring the machinery to the action site. The ESCRT II subunits function to bridge and recruit the ESCRT III subunits. The latter are responsible for finalizing operations that, independently of the action site, involve the repair and fusion of membrane edges. In this review, we report on the data related to the activity of the ESCRT machinery at two sites: the nuclear membrane and the midbody and the bridge linking cells in the final stages of cytokinesis. In these contexts, the machinery plays a significant role for the protection of genome integrity by contributing to the control of the abscission checkpoint and to nuclear envelope reorganization and correlated resilience. Consistently, several studies show how the dysfunction of the ESCRT machinery causes genome damage and is a codriver of pathologies, such as laminopathies and cancer. Full article
(This article belongs to the Section Cell Proliferation and Division)
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<p>Cellular processes involving the ESCRT machinery. (<b>A</b>) Schematic representation of the functions of the ESCRT machinery. ESCRT I (red); ESCRT II (yellow); ESCRT III (blue), virus symbol (dark grey). (<b>B</b>) Schematic representation of the cascade of ESCRT complexes recruited at the site of action. ESCRT I (red); ESCRT II (yellow); ESCRT III (blue).</p> Full article ">Figure 2
<p>Structure of the ESCRT complexes. Schematic representation of the structural organization of the ESCRT complexes. The UEV domain of Vps23 is responsible for the interaction with ESCRT 0 components (black line), whereas the C-terminal domain of Vps28 interacts with the GLUE domain of Vps36. The Y shaped ESCRT II complex is responsible for the recruitment of ESCRT III subunits. Vps4 is recruited by ESCRT III subunits. ESCRT I (red); ESCRT II (yellow); ESCRT III (blue).</p> Full article ">Figure 3
<p>ESCRT recruitment and function at the nuclear envelope. (<b>A</b>) Schematic representation of the recruitment of the core proteins at the chromatin at the end of mitosis. ESCRT I (red); ESCRT II (yellow); ESCRT III (blue); BAF (purple); chromatin (dark grey); microtubule (green); lamin A (red curved line); lamin-associated proteins (blue triangle); nuclear envelope (black double dotted line). (<b>B</b>) Schematic representation of the recruitment of the ESCRT subunits during nuclear envelope sealing. ESCRT I (red); ESCRT II (yellow); ESCRT III (blue).</p> Full article ">Figure 4
<p>ESCRT recruitment and function in abscission. (<b>A</b>) Schematic of the midbody during abscission in which ESCRT I (red) and ALIX (light grey) are recruited at the middle of the tubulin (green) bridge by central spindlin and CEP55. ESCRT I and ALIX recruit ESCRT III subunits (blue). In the final stage of abscission, the ESCRT III subunits form spirals (blue spirals) and recruit spastin, which trims the microtubules, and VPS24. (<b>B</b>) Schematic representation of the abscission checkpoint activation triggered by the presence of a chromatin bridge (dark grey line). Phosphorylation (curved arrow); proteins recruited at core region of chromatin (light pink rectangle, light orange circle, light blue rectangle); actin (white circles in line and organized in patches).</p> Full article ">
5 pages, 683 KiB  
Editorial
Translating Anti-Inflammatory Strategies for Atherosclerosis: Deep Phenotyping, Next-Generation Drug Targets, and Precision Medicine
by Yaw Asare and Marios K. Georgakis
Cells 2024, 13(15), 1306; https://doi.org/10.3390/cells13151306 - 5 Aug 2024
Viewed by 1152
Abstract
Atherosclerosis is the main pathology underlying cardiovascular disease (CVD), including myocardial infarction and ischemic stroke [...] Full article
(This article belongs to the Special Issue Vascular Inflammation and Atherosclerosis: From Basic Mechanisms to Therapeutic Opportunities)
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<p>Translating anti-inflammatory strategies for atherosclerosis. (<b>a</b>) The recent emergence of new technologies including scRNA-seq, ATAC-seq, mass cytometry, and spatial omics presents unique opportunities for the deep phenotyping of atherosclerotic plaques beyond traditional immunohistochemistry techniques. (<b>b</b>) The integration of these large-scale datasets will allow the detection of druggable mechanisms and molecular targets that can be scrutinized further in experimental models to assess causality and elucidate pathways with central roles in the pathogenesis of atherosclerosis. This will aid in the identification of molecular targets for therapeutic intervention in atherosclerotic cardiovascular disease. (<b>c</b>) The development and validation of biomarkers for atheroinflammation will be crucial in selecting the patients most likely to benefit from specific treatments and in monitoring therapeutic efficacy.</p> Full article ">
2 pages, 526 KiB  
Correction
Correction: Bo et al. Atg5 Regulates Selective Autophagy of the Parental Macronucleus during Tetrahymena Sexual Reproduction. Cells 2021, 10, 3071
by Tao Bo, Yu Kang, Ya Liu, Jing Xu and Wei Wang
Cells 2024, 13(15), 1305; https://doi.org/10.3390/cells13151305 - 5 Aug 2024
Viewed by 499
Abstract
In the original publication [...] Full article
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Figure 2

Figure 2
<p>Localization of HA-Atg5 during the sexual reproduction of <span class="html-italic">Tetrahymena thermophila</span>. Cells collected at 6, 8, and 12 h after mixing were fixed and processed for immunofluorescence staining with anti-HA primary and FITC-conjugated secondary antibodies. Cellular nuclei were stained with DAPI to visualize DNA. (<b>a</b>) cells at the early-anlagen stage; (<b>b</b>) cells at the late-anlagen stage; (<b>c</b>) cells at the pair separation stage. Dashed circle represents the cell outline of <span class="html-italic">Tetrahymena</span>. The white arrows point to the paMAC to be degraded. The white box shows a sharp enlargement of the paMAC. Fluorescent images were taken with a DeltaVision deconvolution microscope. Scale bar, 10 µm.</p> Full article ">
18 pages, 11995 KiB  
Article
The Novel-m0230-3p miRNA Modulates the CSF1/CSF1R/Ras Pathway to Regulate the Cell Tight Junctions and Blood–Testis Barrier in Yak
by Qiu Yan, Qi Wang, Yong Zhang, Ligang Yuan, Junjie Hu and Xingxu Zhao
Cells 2024, 13(15), 1304; https://doi.org/10.3390/cells13151304 - 5 Aug 2024
Viewed by 881
Abstract
The yak (Bos grunniens) is a valuable livestock animal endemic to the Qinghai–Tibet Plateau in China with low reproductive rates. Cryptorchidism is one of the primary causes of infertility in male yaks. Compared with normal testes, the tight junctions (TJs) of Sertoli cells [...] Read more.
The yak (Bos grunniens) is a valuable livestock animal endemic to the Qinghai–Tibet Plateau in China with low reproductive rates. Cryptorchidism is one of the primary causes of infertility in male yaks. Compared with normal testes, the tight junctions (TJs) of Sertoli cells (SCs) and the integrity of the blood–testis barrier (BTB) in cryptorchidism are both disrupted. MicroRNAs are hairpin-derived RNAs of about 19–25 nucleotides in length and are involved in a variety of biological processes. Numerous studies have shown the involvement of microRNAs in the reproductive physiology of yak. In this study, we executed RNA sequencing (RNA-seq) to describe the expression profiles of mRNAs and microRNAs in yaks with normal testes and cryptorchidism to identify differentially expressed genes. GO and KEGG analyses were used to identify the biological processes and signaling pathways which the target genes of the differentially expressed microRNAs primarily engaged. It was found that novel-m0230-3p is an important miRNA that significantly differentiates between cryptorchidism and normal testes, and it is down-regulated in cryptorchidism with p < 0.05. Novel-m0230-3p and its target gene CSF1 both significantly contribute to the regulation of cell adhesion and tight junctions. The binding sites of novel-m0230-3p with CSF1 were validated by a dual luciferase reporter system. Then, mimics and inhibitors of novel-m0230-3p were transfected in vitro into SCs, respectively. A further analysis using qRT-PCR, immunofluorescence (IF), and Western blotting confirmed that the expression of cell adhesion and tight-junction-related proteins Occludin and ZO-1 both showed changes. Specifically, both the mRNA and protein expression levels of Occludin and ZO-1 in SCs decreased after transfection with the novel-m0230-3p mimics, while they increased after transfection with the inhibitors, with p < 0.05. These were achieved via the CSF1/CSF1R/Ras signaling pathway. In summary, our findings indicate a negative miRNA-mRNA regulatory network involving the CSF1/CSF1R/Ras signaling pathway in yak SCs. These results provide new insights into the molecular mechanisms of CSF1 and suggest that novel-m0230-3p and its target protein CSF1 could be used as potential therapeutic targets for yak cryptorchidism. Full article
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<p>Comparison of appearance and pathology of normal testis and cryptorchidism in yak. (<b>A</b>,<b>B</b>) Testes weights, ** <span class="html-italic">p</span> &lt; 0.01. (<b>C</b>) H&amp;E staining, magnification of 20×. (<b>D</b>) mRNA expression of β-catenin in normal testis and cryptorchidism of yak. Values represent mean ± SD; <span class="html-italic">n</span> = 3, ** <span class="html-italic">p</span> &lt; 0.01. (<b>E</b>) mRNA expression of ZO-1 in normal testis and cryptorchidism of yak. Values represent mean ± SD; <span class="html-italic">n</span> = 3, ** <span class="html-italic">p</span> &lt; 0.01. (<b>F</b>) Protein expression of β-catenin and ZO-1 in normal and cryptorchid testes of yaks. (<b>G</b>) Localization of β-catenin and ZO-1 proteins in yak testes, analyzed by immunofluorescence staining. β-catenin and ZO-1 in tissue are shown separately in red and nuclei are colored blue; magnification, 20×. Tes: normal testis; Cry: cryptorchidism; S: spermatogonium; SZ: spermatozoa; ST: seminiferous tubule; PS: primary spermatocyte; SC: Sertoli cell; LC: Leydig cell, PMC: peritubular myoid cell; CV: capillary vessel.</p> Full article ">Figure 2
<p>Functional annotation and enrichment analyses in normal testis and cryptorchidism. (<b>A</b>) Cluster analysis of six samples. (<b>B</b>) Clustering heatmap of differential mRNA expression. (<b>C</b>) KEGG pathway analysis of source genes of differential mRNAs. (<b>D</b>) Number of DE mRNAs, with red indicating upregulation and green indicating downregulation. (<b>E</b>) GO enrichment analysis of DE mRNAs. Tes: normal testis, Cry: cryptorchidism.</p> Full article ">Figure 3
<p>miRNA–mRNA interaction network. (<b>A</b>) Number of DE miRNAs, with purple indicating known and blue indicating novel. (<b>B</b>) Expression levels of DE miRNAs. (<b>C</b>) Clustering heatmap of DE miRNA expression. Red corresponds to upregulation, and green corresponds to downregulation. (<b>D</b>) Network plot of novel-m0230-3p and its target gene <span class="html-italic">CSF1</span>. Tes: normal testis, Cry: cryptorchidism.</p> Full article ">Figure 4
<p>qRT-PCR verification of DE genes in normal testis and cryptorchidism in yak. (<b>A</b>) mRNA expression of DE genes shown by qRT-PCR analysis. (<b>B</b>) Comparison of |log2 FC| expression levels of mRNA-seq and qRT-PCR. (<b>C</b>) mRNA expression of DE miRNAs shown by qRT-PCR analysis. (<b>D</b>) Comparison of |log2 FC| expression levels of miRNA-seq and qRT-PCR. Values represent mean ± SD; <span class="html-italic">n</span> = 3. ** <span class="html-italic">p</span> &lt; 0.01. FC, fold-change. Tes: normal testis, Cry: cryptorchidism.</p> Full article ">Figure 5
<p>Verification of CSF1 and CSF1R. (<b>A</b>) Expression patterns of CSF1 and CSF1R proteins by Western blot analysis; <span class="html-italic">n</span> = 3. (<b>B</b>–<b>E</b>) Immunofluorescence assay for expression and location of CSF1 and CSF1R in testis and cryptorchidism; magnification, 20×. Tes: normal testis; Cry: cryptorchidism; S: spermatogonium; SZ: spermatozoa; ST: seminiferous tubule; PS: primary spermatocyte; SC: Sertoli cell; LC: Leydig cell, CV: capillary vessel.</p> Full article ">Figure 6
<p>Targeting relationship between target gene CSF1 and novel-m0230-3p. (<b>A</b>) IF staining identified isolated yak SCs using antibodies against SOX9 (green) and β-tubulin (red); magnification, 20×. (<b>B</b>) Immunofluorescence staining identified isolated yak SCs using antibodies against WT1 (green) and β-tubulin (red); magnification, 20×. (<b>C</b>) Binding site of CSF1 and novel-m0230-3p. (<b>D</b>) Luciferase activity in SCs after co-transfection with mimics of novel-m0230-3p (100 nM) or mimic NC (100 nM) and pmirGLO-CSF1 3′-UTR-WT (400 ng) or pmirGLO-CSF1 3′-UTR-MUT (400 ng). Values represent mean ± SD; <span class="html-italic">n</span> = 3, ** <span class="html-italic">p</span> &lt; 0.01.</p> Full article ">Figure 7
<p>Regulation of CSF1 by novel-m0320-3p. (<b>A</b>) Optimal transfection efficiency of mimics and inhibitors of novel-m0230-3p was explored at different concentrations and transfection times. Values represent mean ± SD; <span class="html-italic">n</span> = 3. <span class="html-italic">* p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. (<b>B</b>) Localization of CSF1 protein and novel-m0230-3p mimic in SCs was analyzed by immunofluorescence staining. CSF1 was colored red, mimic is shown in green, and nuclei were counterstained with DAPI (blue); magnification, 20×. (<b>C</b>) Localization of CSF1 protein and novel-m0230-3p inhibitor in SCs was analyzed by immunofluorescence staining. CSF1 was colored red, inhibitor is shown in green, and nuclei were counterstained with DAPI (blue); magnification, 20×. (<b>D</b>) mRNA expression of novel-m0230-3p after transfection of 100 nM mimic into SCs for 48 h. Values represent mean ± SD; <span class="html-italic">n</span> = 3. ** <span class="html-italic">p &lt;</span> 0.01. (<b>E</b>,<b>F</b>) mRNA and protein expression of CSF1 after transfection of 100 nM mimic into SCs for 48 h. ** <span class="html-italic">p</span> &lt; 0.01. (<b>G</b>) mRNA expression of novel-m0230-3p after transfection of 100 nM inhibitor into Sertoli cells for 48 h. Values represent mean ± SD; <span class="html-italic">n</span> = 3. ** <span class="html-italic">p</span> &lt; 0.01. (<b>H</b>,<b>I</b>) mRNA and protein expression of CSF1 after transfection of 100 nM inhibitor into SCs for 48 h. Values represent mean ± SD; <span class="html-italic">n</span> = 3. ** <span class="html-italic">p</span> &lt; 0.01.</p> Full article ">Figure 8
<p>Novel-m0230-3p regulates CSF1 expression via CSF1/CSF1R/Ras signaling pathway to affect cellular TJs and adhesion. (<b>A</b>) mRNA expression of CSF1R, Ras, Occludin, and ZO-1 was measured by qRT-PCR after transfection of 100 nM mimic into SCs for 48 h. Values represent mean ± SD; <span class="html-italic">n</span> = 3. ** <span class="html-italic">p</span> &lt; 0.01. (<b>B</b>) Protein expression of CSF1R, Ras, Occludin, and ZO-1 was assessed by Western blotting after transfection of 100 nM mimic into SCs for 48 h. (<b>C</b>) mRNA expression of CSF1R, Ras, Occludin, and ZO-1 was measured by qRT-PCR after transfection of 100 nM inhibitor into SCs for 48 h. Values represent mean ± SD; <span class="html-italic">n</span> = 3, ** <span class="html-italic">p</span> &lt; 0.01. (<b>D</b>) Protein expression of CSF1R, Ras, Occludin, and ZO-1 was assessed by Western blotting after transfection of 100 nM inhibitor into SCs for 48 h.</p> Full article ">Figure 9
<p>Regulatory effects of novel-m0230-3p on cell TJs and adhesion via CSF1/CSF1R/Ras signaling pathway in yak SCs. Abbreviations: CSF1: colony-stimulating factor 1; CSF1R: colony-stimulating factor 1 receptor; ZO-1: TJP1 (ZO1) tight junction protein 1.</p> Full article ">
16 pages, 6678 KiB  
Article
Enhanced In Vitro Recapitulation of In Vivo Liver Regeneration by Co-Culturing Hepatocyte Organoids with Adipose-Derived Mesenchymal Stem Cells, Alleviating Steatosis and Apoptosis in Acute Alcoholic Liver Injury
by Sun A Ock, Seo-Yeon Kim, Young-Im Kim, Won Seok Ju and Poongyeon Lee
Cells 2024, 13(15), 1303; https://doi.org/10.3390/cells13151303 - 4 Aug 2024
Viewed by 1384
Abstract
Hepatocyte organoids (HOs) have superior hepatic functions to cholangiocyte-derived organoids but suffer from shorter lifespans. To counteract this, we co-cultured pig HOs with adipose-derived mesenchymal stem cells (A-MSCs) and performed transcriptome analysis. The results revealed that A-MSCs enhanced the collagen synthesis pathways, which [...] Read more.
Hepatocyte organoids (HOs) have superior hepatic functions to cholangiocyte-derived organoids but suffer from shorter lifespans. To counteract this, we co-cultured pig HOs with adipose-derived mesenchymal stem cells (A-MSCs) and performed transcriptome analysis. The results revealed that A-MSCs enhanced the collagen synthesis pathways, which are crucial for maintaining the three-dimensional structure and extracellular matrix synthesis of the organoids. A-MSCs also increased the expression of liver progenitor cell markers (KRT7, SPP1, LGR5+, and TERT). To explore HOs as a liver disease model, we exposed them to alcohol to create an alcoholic liver injury (ALI) model. The co-culture of HOs with A-MSCs inhibited the apoptosis of hepatocytes and reduced lipid accumulation of HOs. Furthermore, varying ethanol concentrations (0–400 mM) and single-versus-daily exposure to HOs showed that daily exposure significantly increased the level of PLIN2, a lipid storage marker, while decreasing CYP2E1 and increasing CYP1A2 levels, suggesting that CYP1A2 may play a critical role in alcohol detoxification during short-term exposure. Moreover, daily alcohol exposure led to excessive lipid accumulation and nuclear fragmentation in HOs cultured alone. These findings indicate that HOs mimic in vivo liver regeneration, establishing them as a valuable model for studying liver diseases, such as ALI. Full article
(This article belongs to the Special Issue Organoids as an Experimental Tool)
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<p>Transcriptomic analysis of porcine primary hepatocyte-derived organoids. (<b>A</b>) Heatmap displaying the differentially expressed genes (DEGs) with a 10-fold change cut-off. The groups are divided as follows: at passage 0, 14-day-old hepatocyte organoids (HOs) cultured alone (Group 1, G1) or co-cultured with adipose-derived mesenchymal stem cells (A-MSCs; Group 2, G2), and at passage 2, 42-day-old HOs cultured alone (Group 3, G3) or co-cultured with A-MSCs (Group 4, G4). Liver, primary hepatocytes (PH), and ear fibroblasts (EF) were used as the positive controls (PC), controls (C), and negative controls (NC), respectively; (<b>B</b>) Principal Component Analysis (PCA) plot showing inter-group differences; (<b>C</b>) Venn diagram analysis of the entire transcriptome, indicating DEGs and showing the number of up- and downregulated genes based on 2-fold (<b>C-1-1</b>,<b>C-1-2</b>) and 10-fold (<b>C-2-1</b>,<b>C-2-2</b>) change cut-offs (<span class="html-italic">p</span> &lt; 0.05). All groups were analyzed after normalization based on PH, excluding EF.</p> Full article ">Figure 2
<p>Transcriptomic insights into liver metabolism in HOs. This figure presents heat maps and Venn diagrams to analyze transcripts involved in liver metabolism within HOs. The analyses included transcripts related to hepatic protein synthesis (<b>A</b>); cholangiocytes, hepatocyte stem cells, and progenitor cells (<b>B</b>); CYP450 enzyme synthesis (<b>C</b>); and those involved in triglyceride and cholesterol metabolism, as shown in the Venn diagram (<b>D-1</b>) and heatmap (<b>D-2</b>). The experimental groups are categorized as follows: G1 (passage 0, 14-day-old HOs cultured alone), G2 (passage 0, 14-day-old HOs co-cultured with A-MSCs), G3 (passage 2, 42-day-old HOs cultured alone), G4 (passage 2, 42-day-old HOs co-cultured with A-MSCs), PH, and EF. A Venn diagram analysis was performed after normalization with PH, excluding EF. All differentially expressed gene analyses were conducted with a 2-fold change cut-off (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 3
<p>Transcriptomic analysis of the key factors involved in the 3D tissue formation of HOs. This figure presents heat maps and Venn diagrams to analyze the transcripts involved in 3D tissue formation within hepatocyte organoids. (<b>A-1</b>) Heatmaps reveal cell adhesion-related transcripts, with Venn diagrams (<b>A-2-1</b>,<b>A-2-2</b>) showing upregulated and downregulated genes. (<b>B-1</b>) Heat maps reveal the expression of genes related to the extracellular matrix, and Venn diagrams (<b>B-2-1</b>,<b>B-2-2</b>) highlight the upregulated and downregulated genes. The experimental groups are categorized as follows: G1 (passage 0, 14-day-old HOs cultured alone), G2 (passage 0, 14-day-old HOs co-cultured with A-MSCs), G3 (passage 2, 42-day-old HOs cultured alone), G4 (passage 2, 42-day-old HOs co-cultured with A-MSCs), PH, and EF. A Venn diagram analysis was performed after normalization with PH, excluding EF. All differentially expressed gene analyses were conducted with a 2-fold change cut-off (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4
<p>Effects of ethanol treatment on gene expression in co-cultures of HOs and A-MSCs. On day 11, HOs were cultured with a single or daily dose of 0, 100, or 200 mM ethanol for three days. (<b>A</b>) Genes related to lipid metabolism included <span class="html-italic">APOB</span>, <span class="html-italic">LDLR1</span>, <span class="html-italic">SREBF1</span>, and <span class="html-italic">PLIN2</span>; (<b>B</b>) Genes related to apoptotic pathways included <span class="html-italic">CASP8</span>, <span class="html-italic">BAK</span>, and <span class="html-italic">BCL2L1</span>. The data are presented as the mean relative quantification (RQ) ± the maximum and minimum values, normalized to HOs without ethanol for RQ in each of the single and daily treatment groups. Statistical significance was assessed using a one-way analysis of variance (ANOVA) with <sup>a–c</sup> <span class="html-italic">p</span> &lt; 0.05 set as the threshold after five repetitions.</p> Full article ">Figure 5
<p>Absorbance analysis after Oil Red O staining in HOs co-cultured with A-MSCs under ethanol exposure. On day 11, HOs co-cultured with A-MSCs or alone were exposed to 200 mM ethanol for three days. (<b>A</b>) Representative images of HOs before and after Oil Red O staining (ethanol-unexposed control). (<b>B</b>) Samples were prepared for absorbance measurements after eluting Oil Red O from each sample. The PC was 60% Oil Red O, while the NC was 100% isopropanol. (<b>C</b>) The quantified absorbance values were normalized to the NC. Data represent the mean ± SEM (n = 3). Statistical significance was determined using one-way ANOVA (<sup>a–f</sup> <span class="html-italic">p</span> &lt; 0.05; three replicates).</p> Full article ">Figure 6
<p>Evaluation of the alcohol-induced hepatocyte organoid injury model under various alcohol treatments. HOs were co-cultured with A-MSCs for 14 days (passage 0) and subcultured for 11 days without A-MSCs before exposure to various alcohol concentrations for 3 days. HOs received either a single dose (on day 1) or daily replacements of ethanol-supplemented medium (0, 100, 200, 400 mM) for 3 days. The genes investigated include (<b>A</b>) those involved in lipid metabolism (<span class="html-italic">APOB</span>, <span class="html-italic">LDLR1</span>, <span class="html-italic">SREBF1</span>, <span class="html-italic">PLIN2</span>), (<b>B</b>) those associated with albumin and cytochrome P450 (CYP) enzymes (<span class="html-italic">ALB</span>, <span class="html-italic">CYP3A29</span>, <span class="html-italic">CYP1A2</span>, <span class="html-italic">CYP2E1</span>), and (<b>C</b>) those involved in the apoptosis pathway (<span class="html-italic">CASP8</span>, <span class="html-italic">CDKN1A</span>, <span class="html-italic">BAK</span>, <span class="html-italic">BCL2L1</span>). Data are presented as the mean relative quantification ± the maximum and minimum values and normalized to that of the ethanol-untreated HOs for each treatment group. Statistical significance was assessed using one-way analysis of variance (ANOVA) with <sup>a–d</sup> <span class="html-italic">p</span> &lt; 0.05 as the threshold after five repetitions.</p> Full article ">Figure 7
<p>Assessing alcohol-induced hepatic steatosis in HOs. HOs were co-cultured with A-MSCs for 14 days (passage 0) and subcultured for 11 days without A-MSCs before exposure to various alcohol concentrations (0–400 mM) for 3 days. (<b>A</b>) Morphological changes in HOs exposed to various ethanol concentrations. Scale bars represent 200 μm. On day 25, HOs alone or co-cultured with A-MSCs were incubated in fresh culture medium supplemented with 200 mM ethanol for 3 days. The sections were stained with a Nile Red solution, counterstained with DAPI, and observed under a confocal microscope. (<b>B</b>) The top images show untreated HOs (left: HOs cultured alone, right: HOs co-cultured with A-MSCs), whereas the bottom images show HOs treated with 200 mM ethanol (left: HOs cultured alone, right: HOs co-cultured with A-MSCs). Red, neutral lipids; blue, nuclei; white arrows, fragmented nuclei. Scale bars represent 100 μm. White dashed circles indicate areas where the accumulated fat was distributed along the fragmented nuclei. White dashed circles highlighted by arrows indicate areas where accumulated fat was found alongside fragmented nuclei.</p> Full article ">
16 pages, 3166 KiB  
Article
Elevated Salt or Angiotensin II Levels Induce CD38+ Innate Immune Cells in the Presence of Granulocyte-Macrophage Colony Stimulating Factor
by Hannah L. Smith, Bethany L. Goodlett, Shobana Navaneethabalakrishnan and Brett M. Mitchell
Cells 2024, 13(15), 1302; https://doi.org/10.3390/cells13151302 - 4 Aug 2024
Viewed by 1093
Abstract
Hypertension (HTN) impacts almost half of adults, predisposing them to cardiovascular disease and renal damage. Salt-sensitive HTN (SSHTN) and angiotensin II (A2)-induced HTN (A2HTN) both involve immune system activation and renal innate immune cell infiltration. Subpopulations of activated [Cluster of differentiation 38 (CD38)] [...] Read more.
Hypertension (HTN) impacts almost half of adults, predisposing them to cardiovascular disease and renal damage. Salt-sensitive HTN (SSHTN) and angiotensin II (A2)-induced HTN (A2HTN) both involve immune system activation and renal innate immune cell infiltration. Subpopulations of activated [Cluster of differentiation 38 (CD38)] innate immune cells, such as macrophages and dendritic cells (DCs), play distinct roles in modulating renal function and blood pressure. It is unknown how these cells become CD38+ or which subtypes are pro-hypertensive. When bone marrow-derived monocytes (BMDMs) were grown in granulocyte-macrophage colony stimulating factor (GM-CSF) and treated with salt or A2, CD38+ macrophages and CD38+ DCs increased. The adoptive transfer of GM-CSF-primed BMDMs into mice with either SSHTN or A2HTN increased renal CD38+ macrophages and CD38+ DCs. Flow cytometry revealed increased renal M1 macrophages and type-2 conventional DCs (cDC2s), along with their CD38+ counterparts, in mice with either SSHTN or A2HTN. These results were replicable in vitro. Either salt or A2 treatment of GM-CSF-primed BMDMs significantly increased bone marrow-derived (BMD)-M1 macrophages, CD38+ BMD-M1 macrophages, BMD-cDC2s, and CD38+ BMD-cDC2s. Overall, these data suggest that GM-CSF is necessary for the salt or A2 induction of CD38+ innate immune cells, and that CD38 distinguishes pro-hypertensive immune cells. Further investigation of CD38+ M1 macrophages and CD38+ cDC2s could provide new therapeutic targets for both SSHTN and A2HTN. Full article
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Figure 1

Figure 1
<p>Hypertensive stimuli increase bone marrow-derived macrophages and CD38+ bone marrow-derived macrophages when bone marrow-derived monocytes are grown in the presence of granulocyte-macrophage colony stimulating factor but not macrophage colony stimulating factor. Macrophage populations resulting from salt or A2 treatment of BMDMs grown in macrophage media in the presence of (<b>A</b>) M-CSF or (<b>B</b>) GM-CSF. Data are presented as individual values and means ± SEM with <span class="html-italic">n</span> = 3 for control, <span class="html-italic">n</span> = 3 for salt, and <span class="html-italic">n</span> = 3 for A2. Data were analyzed with unpaired Student’s <span class="html-italic">t</span>-test, * <span class="html-italic">p</span> &lt; 0.05 vs. control.</p> Full article ">Figure 2
<p>Hypertensive stimuli increase bone marrow-derived dendritic cells and CD38+ bone marrow-derived dendritic cells only when bone marrow-derived monocytes are grown in the presence of granulocyte-macrophage colony stimulating factor but not macrophage colony stimulating factor. DC populations resulting from salt and A2 treatment of BMDMs grown in DC media in the presence of (<b>A</b>) M-CSF or (<b>B</b>) GM-CSF. Data are presented as individual values and means ± SEM with <span class="html-italic">n</span> = 3–4 for control, <span class="html-italic">n</span> = 4–6 for salt, and <span class="html-italic">n</span> = 3–4 for A2. Data were analyzed using unpaired Student’s <span class="html-italic">t</span>-test, * <span class="html-italic">p</span> &lt; 0.05 vs. control.</p> Full article ">Figure 3
<p>Adoptive transfer of granulocyte-macrophage colony stimulating factor-primed bone marrow-derived monocytes into mice with salt-sensitive hypertension and angiotensin II-induced hypertension led to increased macrophages and CD38+ macrophages in the kidneys. Populations of renal (<b>A</b>) CellTracker+ cells, (<b>B</b>) CellTracker+ macrophages, and (<b>C</b>) CellTracker+ CD38+ macrophages 12 h after adoptive transfer of BMDMs into mice with either SSHTN or A2HTN. Data are presented as individual values and means ± SEM with <span class="html-italic">n</span> = 5 for control, <span class="html-italic">n</span> = 6 for SSHTN, and <span class="html-italic">n</span> = 4–6 for A2HTN. Data were analyzed using unpaired Student’s <span class="html-italic">t</span>-test, * <span class="html-italic">p</span> &lt; 0.05 vs. control.</p> Full article ">Figure 4
<p>Adoptive transfer of granulocyte-macrophage colony stimulating factor-primed bone marrow-derived monocytes into mice with salt-sensitive hypertension and angiotensin II-induced hypertension led to increased dendritic cells and CD38+ dendritic cells in the kidneys. Populations of renal (<b>A</b>) CellTracker+ macrophages and (<b>B</b>) CellTracker+ CD38+ macrophages 12 h after adoptive transfer of BMDMs into mice with either SSHTN or A2HTN. Data are presented as individual values and means ± SEM with <span class="html-italic">n</span> = 5 for control, <span class="html-italic">n</span> = 4–6 for SSHTN, and <span class="html-italic">n</span> = 5–6 for A2HTN. Data were analyzed using unpaired Student’s <span class="html-italic">t</span>-test, * <span class="html-italic">p</span> &lt; 0.05 vs. control.</p> Full article ">Figure 5
<p>Renal macrophages and M1 macrophages are increased and activated in salt-sensitive hypertension and angiotensin II-induced hypertension. Macrophage populations in the kidneys of mice with (<b>A</b>) SSHTN and (<b>B</b>) A2HTN. Data are presented as individual values and means ± SEM with <span class="html-italic">n</span> = 3–5 for control, <span class="html-italic">n</span> = 3–5 for SSHTN, and <span class="html-italic">n</span> = 3–4 for A2HTN. Data were analyzed using unpaired Student’s <span class="html-italic">t</span>-test, * <span class="html-italic">p</span> &lt; 0.05 vs. control.</p> Full article ">Figure 6
<p>Renal dendritic cells and type-2 conventional dendritic cells are increased and activated in salt-sensitive hypertension and angiotensin II-induced hypertension. Renal DC populations in the kidneys of mice with (<b>A</b>) SSHTN and (<b>B</b>) A2HTN. Data are presented as individual values and means ± SEM with <span class="html-italic">n</span> = 3–5 for control, <span class="html-italic">n</span> = 5 for SSHTN, and <span class="html-italic">n</span> = 3–4 for A2HTN. Data were analyzed using unpaired Student’s <span class="html-italic">t</span>-test, * <span class="html-italic">p</span> &lt; 0.05 vs. control.</p> Full article ">Figure 7
<p>Hypertensive stimuli increase bone marrow-derived M1 macrophages and CD38+ bone marrow-derived M1 macrophages in vitro. BMD-M1 and CD38+ BMD-M1 macrophage populations following 24 h of treatment with salt or A2. Data are presented as individual values and means ± SEM with <span class="html-italic">n</span> = 3 for control, <span class="html-italic">n</span> = 4 for salt, and <span class="html-italic">n</span> = 3 for A2. Data were analyzed using unpaired Student’s <span class="html-italic">t</span>-test, * <span class="html-italic">p</span> &lt; 0.05 and # <span class="html-italic">p</span> &lt; 0.1 vs. control.</p> Full article ">Figure 8
<p>Hypertensive stimuli increase bone marrow-derived type-2 conventional dendritic cells and CD38+ bone marrow-derived type-2 conventional dendritic cells in vitro. cDC2 populations following 24 h of treatment with salt or A2. Data are presented as individual values and means ± SEM with <span class="html-italic">n</span> = 4 for control, <span class="html-italic">n</span> = 6 for salt, and <span class="html-italic">n</span> = 3 for A2. Data were analyzed using unpaired Student’s <span class="html-italic">t</span>-test, * <span class="html-italic">p</span> &lt; 0.05 vs. control.</p> Full article ">
20 pages, 1311 KiB  
Review
Calcium Deregulation in Neurodegeneration and Neuroinflammation in Parkinson’s Disease: Role of Calcium-Storing Organelles and Sodium–Calcium Exchanger
by Guendalina Bastioli, Silvia Piccirillo, Laura Graciotti, Marianna Carone, Giorgia Sprega, Omayema Taoussi, Alessandra Preziuso and Pasqualina Castaldo
Cells 2024, 13(15), 1301; https://doi.org/10.3390/cells13151301 - 4 Aug 2024
Cited by 1 | Viewed by 1383
Abstract
Parkinson’s disease (PD) is a progressive neurodegenerative disorder that lacks effective treatment strategies to halt or delay its progression. The homeostasis of Ca2+ ions is crucial for ensuring optimal cellular functions and survival, especially for neuronal cells. In the context of PD, [...] Read more.
Parkinson’s disease (PD) is a progressive neurodegenerative disorder that lacks effective treatment strategies to halt or delay its progression. The homeostasis of Ca2+ ions is crucial for ensuring optimal cellular functions and survival, especially for neuronal cells. In the context of PD, the systems regulating cellular Ca2+ are compromised, leading to Ca2+-dependent synaptic dysfunction, impaired neuronal plasticity, and ultimately, neuronal loss. Recent research efforts directed toward understanding the pathology of PD have yielded significant insights, particularly highlighting the close relationship between Ca2+ dysregulation, neuroinflammation, and neurodegeneration. However, the precise mechanisms driving the selective loss of dopaminergic neurons in PD remain elusive. The disruption of Ca2+ homeostasis is a key factor, engaging various neurodegenerative and neuroinflammatory pathways and affecting intracellular organelles that store Ca2+. Specifically, impaired functioning of mitochondria, lysosomes, and the endoplasmic reticulum (ER) in Ca2+ metabolism is believed to contribute to the disease’s pathophysiology. The Na+-Ca2+ exchanger (NCX) is considered an important key regulator of Ca2+ homeostasis in various cell types, including neurons, astrocytes, and microglia. Alterations in NCX activity are associated with neurodegenerative processes in different models of PD. In this review, we will explore the role of Ca2+ dysregulation and neuroinflammation as primary drivers of PD-related neurodegeneration, with an emphasis on the pivotal role of NCX in the pathology of PD. Consequently, NCXs and their interplay with intracellular organelles may emerge as potentially pivotal players in the mechanisms underlying PD neurodegeneration, providing a promising avenue for therapeutic intervention aimed at halting neurodegeneration. Full article
(This article belongs to the Special Issue Calcium Signaling in Immune Cells)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Regulation of Ca<sup>2+</sup> homeostasis in physiological state and Parkinson’s disease (PD). In the physiological state (left panel), Ca<sup>2+</sup> homeostasis is maintained through the balanced activity of various Ca<sup>2+</sup> channels and exchangers. The endoplasmic reticulum (ER) functions as the primary intracellular reservoir for Ca<sup>2+</sup> ions actively managed by sarco/endoplasmic reticulum Ca<sup>2+</sup> ATPases (SERCAs) that pump Ca<sup>2+</sup> into the ER. The controlled release of Ca<sup>2+</sup> from the ER is mediated by ryanodine receptors (RyRs) and inositol 1,4,5-triphosphate receptors (Ins(1,4,5)P3Rs). Mitochondria, located in close proximity to the ER, capture the released Ca<sup>2+</sup> through the mitochondrial Ca<sup>2+</sup> uniporter (MCU) complex, thereby regulating cellular metabolism. Lysosomes are recognized as the second-largest reservoir of intracellular Ca<sup>2+</sup>. The lysosomal release of Ca<sup>2+</sup> is mediated by TRPC mucolipin 1 (TRPML1), whose activity is critical for maintaining proper lysosomal membrane trafficking. The sodium–calcium exchanger (NCX) in both the plasma membrane and mitochondria functions normally, extruding Ca<sup>2+</sup> in the forward mode and maintaining cellular Ca<sup>2+</sup> balance; the sodium–calcium–lithium exchanger (NCLX) in the mitochondria operates effectively, contributing to the regulation of mitochondrial Ca<sup>2+</sup> levels. Red arrows are related to Ca²⁺ influx, while black arrows indicate Ca²⁺ efflux.</p> Full article ">Figure 2
<p>Schematic representation of the key neuropathological features of PD and the role of NCX. In PD, disrupted Ca<sup>2+</sup> homeostasis, mitochondrial dysfunction, oxidative stress, misfolded protein aggregation, and neuroinflammation represent the key neuropathological features. NCX plays a crucial role as it becomes dysregulated and contributes to Ca<sup>2+</sup> overload, exacerbating mitochondrial damage and neuronal cell death. Additionally, the accumulation of misfolded α-syn proteins and heightened oxidative stress further amplify both neurodegenerative and neuroinflammatory processes. DA = dopamine; BBB = blood–brain barrier; α-syn = α-synuclein; ROS = reactive oxygen species; NCX = Na<sup>+</sup>/Ca<sup>2+</sup> exchanger.</p> Full article ">
13 pages, 3095 KiB  
Article
Overexpression of Toxic Poly(Glycine-Alanine) Aggregates in Primary Neuronal Cultures Induces Time-Dependent Autophagic and Synaptic Alterations but Subtle Activity Impairments
by Christina Steffke, Shreya Agarwal, Edor Kabashi and Alberto Catanese
Cells 2024, 13(15), 1300; https://doi.org/10.3390/cells13151300 - 3 Aug 2024
Viewed by 989
Abstract
The pathogenic expansion of the intronic GGGGCC hexanucleotide located in the non-coding region of the C9orf72 gene represents the most frequent genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). This mutation leads to the accumulation of toxic RNA foci and [...] Read more.
The pathogenic expansion of the intronic GGGGCC hexanucleotide located in the non-coding region of the C9orf72 gene represents the most frequent genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). This mutation leads to the accumulation of toxic RNA foci and dipeptide repeats (DPRs), as well as reduced levels of the C9orf72 protein. Thus, both gain and loss of function are coexisting pathogenic aspects linked to C9orf72-ALS/FTD. Synaptic alterations have been largely described in C9orf72 models, but it is still not clear which aspect of the pathology mostly contributes to these impairments. To address this question, we investigated the dynamic changes occurring over time at the synapse upon accumulation of poly(GA), the most abundant DPR. Overexpression of this toxic form induced a drastic loss of synaptic proteins in primary neuron cultures, anticipating autophagic defects. Surprisingly, the dramatic impairment characterizing the synaptic proteome was not fully matched by changes in network properties. In fact, high-density multi-electrode array analysis highlighted only minor reductions in the spike number and firing rate of poly(GA) neurons. Our data show that the toxic gain of function linked to C9orf72 affects the synaptic proteome but exerts only minor effects on the network activity. Full article
(This article belongs to the Special Issue Motor Neuron Diseases: From Molecular Basic Research to Diagnosis and Therapeutics Implications)
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Figure 1
<p>Time-dependent alterations in autophagic pathway upon poly(GA) overexpression independent from C9orf72 levels. (<b>A</b>) Workflow: Primary cortical neurons from rat embryos (E19) were plated at DIV0 and transduced with AAV9-poly(GA) to overexpress poly(GA) aggregates or AAV9-GFP as control at DIV10. Cells were cultured up to DIV 14, 21, and 28, and Western Blotting, immunocytochemistry, and microelectrode array (MEA) were performed at all 3 time points to elucidate changes over time. Microscope pictures were acquired with a 10x objective and an additional 3x digital zoom. (<b>B</b>) Representative blots. (<b>C</b>) Canonical proteins in early phases of autophagy, Beclin and Atg5, reveal a time-dependent decrease in poly(GA) and control cultures, with significantly lower protein levels at DIV28 upon toxic Poly(GA) aggregates (Beclin: DIV21-PolyGA vs. DIV21-GFP <span class="html-italic">p</span> = 0.0448; DIV28-PolyGA vs. DIV28-GFP <span class="html-italic">p</span> &lt; 0.0001; Atg5: DIV28-PolyGA vs. DIV28-GFP <span class="html-italic">p</span> = 0.0039). Together with distinctly accumulating p62 protein over time (DIV21-PolyGA vs. DIV21-GFP <span class="html-italic">p</span> = 0.0010; DIV28-PolyGA vs. DIV28-GFP <span class="html-italic">p</span> = 0.0070), this indicates an impaired autophagic flux aggravated by poly(GA). Interestingly, cultures overexpressing poly(GA) lack increasing phosphorylated mTor within 2 weeks (DIV28-PolyGA 0.272 vs. DIV28-GFP <span class="html-italic">p</span> = 0.0057), while total mTor only shows minor differences, implying an overall catabolic blockade. Intriguingly, (<b>D</b>) C9orf72 levels remain comparable between both conditions. Protein levels were normalized to β-Actin levels as loading control. Values are shown as fold change to GFP DIV14. Experiments were performed in <span class="html-italic">N</span> = 3 independent replicates. Data are displayed as mean value ± SD (two-way ANOVA; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; **** <span class="html-italic">p</span> &lt; 0.0001; all other comparisons are not significant).</p> Full article ">Figure 2
<p>Poly(GA) aggregates induce reduction in pre- and postsynaptic proteins. Representative blots of (<b>A</b>) post- and (<b>B</b>) presynaptic proteins. (<b>C</b>) Protein levels of pre- and postsynaptic scaffold- and vesicle-related proteins show a significant decrease in poly(GA)-transduced cultures already at DIV21 (Syp: DIV21-PolyGA vs. DIV21-GFP <span class="html-italic">p</span> = 0.0099; Snap47: DIV21-PolyGA vs. DIV21-GFP <span class="html-italic">p</span> = 0.0740; Homer1: DIV21-PolyGA vs. DIV21-GFP <span class="html-italic">p</span> = 0.0640) with aggravation up to DIV28 (Syp: DIV28-PolyGA vs. DIV28-GFP <span class="html-italic">p</span> = 0.0409; Syn1: DIV28-PolyGA vs. DIV28-GFP <span class="html-italic">p</span> = 0.0493; Syn2: DIV28-PolyGA vs. DIV28-GFP <span class="html-italic">p</span> = 0.0534; Snap47: DIV28-PolyGA vs. DIV28-GFP <span class="html-italic">p</span> = 0.0360; PSD-95: DIV28-PolyGA vs. DIV28-GFP <span class="html-italic">p</span> = 0.0288; Gephyrin: DIV28-PolyGA vs. DIV28-GFP <span class="html-italic">p</span> = 0.0460; and Homer1: DIV28-PolyGA vs. DIV28-GFP <span class="html-italic">p</span> = 0.0268). The opposite synaptic dynamics of decreasing protein expression upon poly(GA), in contrast to increasing synaptic proteins in the sense of neuronal maturation over time in control cultures (DIV28-PolyGA vs. DIV28-GFP <span class="html-italic">p</span> &lt; 0.0001), is highlighted in (<b>D</b>). Protein levels were normalized to β-Actin levels as loading control. Values are shown as fold change to GFP DIV14. Experiments were performed in <span class="html-italic">N</span> = 3 independent replicates. Data are displayed as mean value ± SD (2way-ANOVA; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; all other comparisons are not significant).</p> Full article ">Figure 3
<p>Subtle impairment of network properties in primary neurons upon poly(GA) exposition. (<b>A</b>) Representative network activity plots of neuronal cultures. (<b>B</b>) Surprisingly, network activity represented by firing property, interspike interval (ISI), burst frequency, and burst peak firing rate remains unaltered between control and poly(GA)-overexpressing cultures at all timepoints and even displays a maturing-dependent signature of increased activity. (<b>C</b>) Axonal tracking assay reveals (<b>D</b>) a significantly decreased number of spikes (PolyGA vs. GFP <span class="html-italic">p</span> = 0.0027) and firing rate (PolyGA vs. GFP <span class="html-italic">p</span> = 0.0027) at the axonal initiation site, while the amplitude is significantly higher (PolyGA vs. GFP <span class="html-italic">p</span> = 0.0002) in poly(GA)-transduced cultures. Values are shown as fold change to GFP (DIV14). Experiments were performed in <span class="html-italic">N</span> = 3 independent replicates. Data are displayed as mean value ± SD (two-way ANOVA or Welch’s <span class="html-italic">t</span>-test; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; all other comparisons are not significant).</p> Full article ">Figure 4
<p>AMPA-receptor subunits seem to be more resistant to toxicity of poly(GA) aggregates. Protein levels of GluA1 and GluA2 display significant downregulation upon poly(GA)-transduction only at DIV28 (GluA1: DIV28-PolyGA vs. DIV28-GFP <span class="html-italic">p</span> = 0.032; GluA2: DIV28-PolyGA vs. DIV28-GFP <span class="html-italic">p</span> = 0.0485), while up until DIV21, the receptor subunits still show comparable levels between poly(GA) and GFP, in contrast to scaffold- and vesicle-related synaptic proteins. Protein levels were normalized to β-Actin levels as loading control. Values are shown as fold change to GFP DIV14. Experiments were performed in <span class="html-italic">N</span> = 3 independent replicates. Data are displayed as mean value ± SD (two-way-ANOVA; * <span class="html-italic">p</span> &lt; 0.05; all other comparisons are not significant).</p> Full article ">
14 pages, 2836 KiB  
Article
Identification of Poliovirus Receptor-like 3 Protein as a Prognostic Factor in Triple-Negative Breast Cancer
by Gian Marco Leone, Katia Mangano, Salvatore Caponnetto, Paolo Fagone and Ferdinando Nicoletti
Cells 2024, 13(15), 1299; https://doi.org/10.3390/cells13151299 - 3 Aug 2024
Viewed by 1361
Abstract
Triple-negative breast cancer (TNBC) represents an aggressive subtype of breast cancer, with a bad prognosis and lack of targeted therapeutic options. Characterized by the absence of estrogen receptors, progesterone receptors, and HER2 expression, TNBC is often associated with a significantly lower survival rate [...] Read more.
Triple-negative breast cancer (TNBC) represents an aggressive subtype of breast cancer, with a bad prognosis and lack of targeted therapeutic options. Characterized by the absence of estrogen receptors, progesterone receptors, and HER2 expression, TNBC is often associated with a significantly lower survival rate compared to other breast cancer subtypes. Our study aimed to explore the prognostic significance of 83 immune-related genes, by using transcriptomic data from the TCGA database. Our analysis identified the Poliovirus Receptor-Like 3 protein (PVRL3) as a critical negative prognostic marker in TNBC patients. Furthermore, we found that the Enhancer of Zeste Homolog 2 (EZH2), a well-known epigenetic regulator, plays a pivotal role in modulating PVRL3 levels in TNBC cancer cell lines expressing EZH2 along with high levels of PVRL3. The elucidation of the EZH2-PVRL3 regulatory axis provides valuable insights into the molecular mechanisms underlying TNBC aggressiveness and opens up potential pathways for personalized therapeutic intervention. Full article
(This article belongs to the Special Issue Molecular Mechanisms of Tumor Pathogenesis)
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Figure 1

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<p>Survival analysis. The TCGA TNBC patients were stratified in quartiles based on the expression of the 83 genes of interest, following normalization for tumor purity, and samples in the upper and lower quartiles were selected for comparison. Cox Proportional Hazards Regression Model, with the stage of the disease included as a covariate, was used to assess the prognostic values of each gene. Kaplan–Meier curves only for the statistically significant genes are shown.</p> Full article ">Figure 2
<p>PVRL3 expression levels in 28 breast cancer cell lines (<b>A</b>); PVRL3 expression in breast cancer cell lines aggregated based on the PAM50 molecular classification of breast tumors (<a href="https://www.ebi.ac.uk/gxa/experiments/E-MTAB-4801/Results" target="_blank">https://www.ebi.ac.uk/gxa/experiments/E-MTAB-4801/Results</a>, accessed on 1 April 2024) (<b>B</b>). Comparisons between two groups on a single parameter were conducted using the two-tailed unpaired <span class="html-italic">t</span>-test.</p> Full article ">Figure 3
<p>Study workflow (<b>A</b>); PVRL3 expression levels in MDA-MB-231 cell line after 72 h of treatment with DMSO (CTRL), BIX01294, GSK343, GSK2801, JQ1, and Vorinostat (<b>B</b>).</p> Full article ">Figure 4
<p>Study workflow (<b>A</b>). EZH2 expression levels in 24 TNBC cell lines (<b>B</b>). Modulation of EZH2 and PVRL3 expression in MDA-MB-231 in control cells (CTRL) upon overexpression (upEZH2) and following EZH2 silencing (shEZH2) (<b>C</b>,<b>D</b>). Modulation of EZH2 and PVRL3 expression in MDA-MB-436 in control cells (CTRL) upon overexpression (upEZH2) and following EZH2 silencing (shEZH2) (<b>E</b>,<b>F</b>). Modulation of PVRL3 expression in MDA-MB-453 in control cells (CTRL) upon overexpression (upEZH2) and following EZH2 silencing (shEZH2) (<b>G</b>,<b>H</b>). Modulation of PVRL3 expression in HCC1937 in control cells (CTRL) upon overexpression (upEZH2) and following EZH2 silencing (shEZH2) (<b>I</b>,<b>J</b>).</p> Full article ">Figure 5
<p>The 20 potential TFs (transcription factors) capable of binding to different PVRL3 promoter regions (<b>A</b>). Top 100 PVRL3 co-expressed genes based on Arch4 database (<b>B</b>). Top 10 TFs regulating the top 100 PVRL3 co-expressed genes (<b>C</b>).</p> Full article ">
16 pages, 2602 KiB  
Article
MicroRNA Profile of Mouse Adipocyte-Derived Extracellular Vesicles
by Tamás Röszer
Cells 2024, 13(15), 1298; https://doi.org/10.3390/cells13151298 - 1 Aug 2024
Viewed by 986
Abstract
The post-transcriptional control of gene expression is a complex and evolving field in adipocyte biology, with the premise that the delivery of microRNA (miRNA) species to the obese adipose tissue may facilitate weight loss. Cells shed extracellular vesicles (EVs) that may deliver miRNAs [...] Read more.
The post-transcriptional control of gene expression is a complex and evolving field in adipocyte biology, with the premise that the delivery of microRNA (miRNA) species to the obese adipose tissue may facilitate weight loss. Cells shed extracellular vesicles (EVs) that may deliver miRNAs as intercellular messengers. However, we know little about the miRNA profile of EVs secreted by adipocytes during postnatal development. Here, we defined the miRNA cargo of EVs secreted by mouse adipocytes in two distinct phases of development: on postnatal day 6, when adipocytes are lipolytic and thermogenic, and on postnatal day 56, when adipocytes have active lipogenesis. EVs were collected from cell culture supernatants, and their miRNA profile was defined by small RNA sequencing. The most abundant miRNA of mouse adipocyte-derived EVs was mmu-miR-148a-3p. Adipocyte EVs on postnatal day 6 were hallmarked with mmu-miR-98-5p, and some miRNAs were specific to this developmental stage, such as mmu-miR-466i-5p and 12 novel miRNAs. Adipocytes on postnatal day 56 secreted mmu-miR-365-3p, and 16 miRNAs were specific to this developmental stage. The miRNA cargo of adipocyte EVs targeted gene networks of cell proliferation, insulin signaling, interferon response, thermogenesis, and lipogenesis. We provided here a database of miRNAs secreted by developing mouse adipocytes, which may be a tool for further studies on the regulation of gene networks that control mouse adipocyte development. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Mouse adipocytes shed EVs in the endosomal pathway. (<b>A</b>) TEM image of EVs released by young (postnatal day 6) and adult (postnatal day 56) mouse adipocytes in vitro. Scale: 1 μm. Representative images from three independent EV pellets. (<b>B</b>) FACS analysis of EVs secreted by young and adult mouse adipocytes; representative histograms from three assays. Free beads: remainder of capture beads used to enrich EVs. (<b>C</b>) Size distribution of adipocyte EVs using TEM images of three independent EV pellets. Gray background labels show the expected size range for EVs. (<b>D</b>) Transmission electron microscopy images of mouse adipocytes, cultured in vitro, showing clathrin-coated pits, endosome budding (1–4), and multivesicular bodies (MVBs) (5). Pm: plasma membrane; En: endosome; Aly: autolysosome; Cav: caveolae; arrowhead: extracellular vesicles (EVs) in MVBs. Scale: 1 μm. (<b>E</b>) The endosomal pathway of EV generation was tested by incubating adipocytes with FITC-conjugated dextran, a marker of fluid-phase endocytosis (pinocytosis). Dextran is taken up by endosomes and may later accumulate in MVBs or in lysosomes. FACS analysis of mouse adipocytes cultured without FITC-conjugated dextran (–Dextran) or with FITC-conjugated dextran (+Dextran). (<b>F</b>) Fluorescence microscopy showing endocytosed FITC–dextran within adipocytes. nc: nucleus; scale: 10 μm. (<b>G</b>) EVs secreted by the dextran-incubated adipocytes were collected and analyzed further with FACS. Dextran was present in the EVs, showing that the endosomal pathway contributes to EV generation. (<b>H</b>) TEM image of an MVB; scale: 1 μm. (<b>I</b>) Scheme of the endosomal pathway of EV generation. (<b>J</b>) Workflow of the analysis of the miRNA cargo of EVs. The inset shows FACS analysis of the nucleic acid content of EVs. We used SytoxGreen to stain nucleic acids in EVs. Histogram showing SytoxGreen fluorescence intensity of EVs.</p> Full article ">Figure 2
<p>Abundantly expressed miRNA species in mouse adipocyte EVs. (<b>A</b>) Copy numbers of miRNA species in EVs secreted by young (postnatal day 6) mouse adipocytes. The highest-copy-number miRNA species are highlighted. (<b>B</b>) Copy numbers of miRNA species in EVs secreted by adult (postnatal day 56) mouse adipocytes, with highlighting of the most abundant miRNAs. (<b>C</b>) Comparison of average copy numbers of the most abundantly expressed miRNAs. Average copy numbers were determined by using small RNA sequencing data from 3–3 EV samples from young and adult mouse adipocyte cultures. (<b>D</b>) STRING network formed by target genes of mmu-miR-148a-3p. (<b>E</b>) Gene ontology (GO) terms of target genes of mmu-miR-148a-3p. (<b>F</b>) Transcript levels of <span class="html-italic">Pten</span> and cell-cycle-regulating gene products in young and adult mouse adipose tissue. Next-generation sequencing data. ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05, Student’s 2-tailed unpaired <span class="html-italic">t</span>-test.</p> Full article ">Figure 3
<p>EVs of young mouse adipocytes secrete mmu-miR-98-5p. (<b>A</b>) STRING network showing an interactome map of mmu-miR-98-5p target genes. (<b>B</b>) Comparison of the transcript levels of insulin receptor (<span class="html-italic">Insr</span>), insulin signal pathway gene products, and beta-adrenergic receptors in young and adult mouse adipocytes using NGS data. (<b>C</b>) GO analysis of mmu-miR-98-5p target genes. (<b>D</b>) Venn diagrams summarizing the numbers of interferon-stimulated genes (ISGs) that are targets of mmu-miR-98-5p (Top). Numbers of ISGs underrepresented in young mouse adipocytes using NGS data [<a href="#B22-cells-13-01298" class="html-bibr">22</a>] (Bottom). IFN-I: type I IFN response; IFN-II: type II IFNs; IFN-III: type III IFNs.</p> Full article ">Figure 4
<p>Sequences and predicted structures of novel miRNAs of mouse adipocyte EVs. Novel miRNA species have been named according to their order of identification during sequencing. (<b>Top</b>): Novel miRNAs in EVs secreted by young mouse adipocytes. (<b>Bottom</b>): Novel miRNAs secreted in EVs by adult mouse adipocytes.</p> Full article ">Figure 5
<p>Possible targets of miRNAs specific to young and adult adipocyte EVs. (<b>A</b>) STRING network of target genes of mmu-miR-466i-5p. This miRNA was present only in young adipocyte EVs—lacking in adult adipocyte EVs—and had the highest copy number among miRNAs that were specific to young adipocyte EVs. (<b>B</b>) GO enrichment analysis of mmu-miR-466i-5p. (<b>C</b>) STRING network and GO enrichment analysis of target genes of mmu-miR-365-3p. (<b>D</b>) Proposed molecular targets of miRNAs secreted in adipocyte EVs.</p> Full article ">
3 pages, 877 KiB  
Correction
Correction: Kundu et al. Regression of Triple-Negative Breast Cancer in a Patient-Derived Xenograft Mouse Model by Monoclonal Antibodies against IL-12 p40 Monomer. Cells 2022, 11, 259
by Madhuchhanda Kundu, Sumita Raha, Avik Roy and Kalipada Pahan
Cells 2024, 13(15), 1297; https://doi.org/10.3390/cells13151297 - 1 Aug 2024
Viewed by 611
Abstract
Error in Figures 3 and 9 [...] Full article
(This article belongs to the Collection Advances in Immune Monitoring)
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Figure 3

Figure 3
<p>Regression of TNBC tumor in vivo in patient-derived xenograft (PDX) mice by p40 mAb. (<b>A</b>) Female 6–8 week old NOD scid gamma (NSG) mice were engrafted TNBC tumor fragments at passage P1-P9 (invasive ductal carcinoma; TNBC ER<sup>−/−</sup>/PR<sup>−/−</sup>/HER2<sup>−/−</sup>) in the flank. After 6 weeks of engraftment, levels of p40 and p40<sub>2</sub> were measured in serum by sandwich ELISA. Results are mean ± SEM of four mice (<span class="html-italic">n</span> = 4) per group. ** <span class="html-italic">p</span> &lt; 0.01. (<b>B</b>) After about 4 weeks of engraftment, when tumors of PDX mice (<span class="html-italic">n</span> = 5 per group) were 0.6–0.8 cm<sup>2</sup> in size, mice were treated with p40 mAb (right panel) and hamster IgG (middle panel) at a dose of 2 mg/kg body wt once a week. After 2 weeks, tumors were labeled with Alexa800 conjugated 2DG dye via tail vein injection and then imaged in Licor Odyssey infrared imaging system. Results were compared with control group (Left panel). (<b>C</b>) Tumors were excised from the flank of all groups of mice. Five mice (<span class="html-italic">n</span> = 5) were included in each group. (<b>D</b>) Tumor size was monitored every alternate day. Results are mean ± SEM of five different mice.</p> Full article ">Figure 9
<p>Neutralization of p40 by p40 mAb decreases the level of TGFβin TNBC tumor of PDX mice. Female 6–8 week old NSG mice were engrafted TNBC tumor in the flank. After about 4 weeks of engraftment, when tumors of PDX mice were 0.6–0.8 cm<sup>2</sup> in size, mice were treated with p40 mAb and hamster IgG at a dose of 2 mg/kg body wt once a week. After 2 weeks of treatment, tumor cross sections were double-immunolabeled for Iba1 and TGFβ (<b>A</b>). DAPI was used to stain nuclei. Cells positive for Iba1 and TGFβ (<b>B</b>) were counted in one section (2–3 images per section) of each of five different mice per group. * <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.001.</p> Full article ">
12 pages, 1931 KiB  
Review
PPAR-Mediated Bile Acid Glucuronidation: Therapeutic Targets for the Treatment of Cholestatic Liver Diseases
by Gina M. Gallucci, Colleen M. Hayes, James L. Boyer, Olivier Barbier, David N. Assis and Nisanne S. Ghonem
Cells 2024, 13(15), 1296; https://doi.org/10.3390/cells13151296 - 1 Aug 2024
Viewed by 1527
Abstract
Cholestatic liver diseases, including primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC), result from an impairment of bile flow that leads to the hepatic retention of bile acids, causing liver injury. Until recently, the only approved treatments for PBC were ursodeoxycholic acid [...] Read more.
Cholestatic liver diseases, including primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC), result from an impairment of bile flow that leads to the hepatic retention of bile acids, causing liver injury. Until recently, the only approved treatments for PBC were ursodeoxycholic acid (UDCA) and obeticholic acid (OCA). While these therapies slow the progression of PBC in the early stage of the disease, approximately 40% of patients respond incompletely to UDCA, and advanced cases do not respond. UDCA does not improve survival in patients with PSC, and patients often have dose-limiting pruritus reactions to OCA. Left untreated, these diseases can progress to fibrosis and cirrhosis, resulting in liver failure and the need for transplantation. These shortcomings emphasize the urgent need for alternative treatment strategies. Recently, nuclear hormone receptors have been explored as pharmacological targets for adjunct therapy because they regulate enzymes involved in bile acid metabolism and detoxification. In particular, the peroxisome proliferator-activated receptor (PPAR) has emerged as a therapeutic target for patients with PBC or PSC who experience an incomplete response to UDCA. PPARα is predominantly expressed in the liver, and it plays an essential role in the regulation of cytochrome P450 (CYP) and uridine 5’-diphospho-glucuronosyltransferase (UGT) enzymes, both of which are critical enzyme families involved in the regulation of bile acid metabolism and glucuronidation, respectively. Importantly, PPARα agonists, e.g., fenofibrate, have shown therapeutic benefits in reducing elevated markers of cholestasis in patients with PBC and PSC, and elafibranor, the first PPAR (dual α, β/δ) agonist, has been FDA-approved for the second-line treatment of PBC. Additionally, newer PPAR agonists that target various PPAR isoforms (β/δ, γ) are under development as an adjunct therapy for PBC or PSC, although their impact on glucuronidation pathways are less characterized. This review will focus on PPAR-mediated bile acid glucuronidation as a therapeutic pathway to improve outcomes for patients with PBC and PSC. Full article
(This article belongs to the Special Issue The Role of PPARs in Disease - Volume III)
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<p>Two major bile acid biosynthetic pathways are shown. In the classic pathway, cytochrome P450 (CYP)7A1 converts cholesterol to 7α-hydroxycholesterol, which is converted to 7α-hydroxy-4-cholesten-3-one (C4) by 3<span class="html-italic">β</span>-hydroxysteroid dehydrogenase (HSD3B7). C4 is converted to cholic acid (CA) by CYP8B1 or to chenodeoxycholic acid (CDCA). In the alternative pathway, CYP27A1 or CYP3A4 converts cholesterol to 25(R)-26-hydroxycholesterol, followed by hydroxylation by CYP7B1 for the eventual synthesis of CA and CDCA. CA is converted to deoxycholic acid (DCA), and CDCA can be converted to lithocholic acid (LCA) or hyocholic acid (HCA). LCA can be hydroxylated to form hyodeoxycholic acid (HDCA). Uridine 5’-diphospho-glucuronosyltransferase (UGT) enzymes catalyze the glucuronidation of these bile acids to form -3, -6, or -24-glucuronides (-G). Bile acid glucuronides (BA-Gs) are transported out of the liver and into the bile canalicular space via the multidrug resistance-associated protein (MRP)-2 and pass through the common bile duct, gallbladder, and intestines, where they can be recycled back to the liver via enterohepatic recirculation. Alternatively, BA-Gs can be transported out of the liver and into systemic circulation via MRP3 and MRP4 for eventual renal excretion into urine. Image created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p> Full article ">Figure 2
<p>Combination treatment with UDCA and fenofibrate alters the concentration and composition of -3/6 bile acid glucuronides during cholestatic liver diseases. Serum was collected from patients treated with either UDCA monotherapy (13–15 mg/kg/day)—“mono”—or combination treatment with UDCA and fenofibrate (145–160 mg/day)—“combo”. Serum bile acid glucuronide concentrations were measured using LC-MS/MS methodology. Data represent the total concentration of -3 and -6 bile acid glucuronides within each patient cohort [<a href="#B18-cells-13-01296" class="html-bibr">18</a>]. PBC (<span class="html-italic">n</span> = 16 “mono”, <span class="html-italic">n</span> = 16 “combo”) and PSC (<span class="html-italic">n</span> = 11 “mono”, <span class="html-italic">n</span> = 12 “combo”). UDCA: ursodeoxycholic acid; PBC: primary biliary cholangitis; PSC: primary sclerosing cholangitis; LCA: lithocholic acid; DCA: deoxycholic acid; HCA: hyocholic acid; HDCA: hyodeoxycholic acid; CDCA: chenodeoxycholic acid.</p> Full article ">
23 pages, 9450 KiB  
Article
Neonicotinoid Pesticides Affect Developing Neurons in Experimental Mouse Models and in Human Induced Pluripotent Stem Cell (iPSC)-Derived Neural Cultures and Organoids
by Alessandro Mariani, Davide Comolli, Roberto Fanelli, Gianluigi Forloni and Massimiliano De Paola
Cells 2024, 13(15), 1295; https://doi.org/10.3390/cells13151295 - 31 Jul 2024
Viewed by 1056
Abstract
Neonicotinoids are synthetic, nicotine-derived insecticides used worldwide to protect crops and domestic animals from pest insects. The reported evidence shows that they are also able to interact with mammalian nicotine receptors (nAChRs), triggering detrimental responses in cultured neurons. Exposure to high neonicotinoid levels [...] Read more.
Neonicotinoids are synthetic, nicotine-derived insecticides used worldwide to protect crops and domestic animals from pest insects. The reported evidence shows that they are also able to interact with mammalian nicotine receptors (nAChRs), triggering detrimental responses in cultured neurons. Exposure to high neonicotinoid levels during the fetal period induces neurotoxicity in animal models. Considering the persistent exposure to these insecticides and the key role of nAChRs in brain development, their potential neurotoxicity on mammal central nervous system (CNS) needs further investigations. We studied here the neurodevelopmental effects of different generations of neonicotinoids on CNS cells in mouse fetal brain and primary cultures and in neuronal cells and organoids obtained from human induced pluripotent stem cells (iPSC). Neonicotinoids significantly affect neuron viability, with imidacloprid (IMI) inducing relevant alterations in synaptic protein expression, neurofilament structures, and microglia activation in vitro, and in the brain of prenatally exposed mouse fetuses. IMI induces neurotoxic effects also on developing human iPSC-derived neurons and cortical organoids. Collectively, the current findings show that neonicotinoids might induce impairment during neuro/immune-development in mouse and human CNS cells and provide new insights in the characterization of risk for the exposure to this class of pesticides. Full article
(This article belongs to the Section Cells of the Nervous System)
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<p>Expression of α7-nAChR in cultured neurons and glia. Primary neuron/glia cocultures obtained from different areas of mouse embryo brain were immunostained for NF200 (<b>A</b>,<b>D</b>,<b>G</b>; green), CD11b (<b>J</b>) or GFAP (<b>M</b>), and α7-nAChR (<b>B</b>,<b>E</b>,<b>H</b>,<b>K</b>,<b>N</b>; red) at 14 DIV. Images were acquired by a confocal microscope at 400× magnification, scale bars 50 µm. Neurons derived from different brain areas showed a widespread α7-nAChR distribution colocalizing with NF200-positive cells (<b>C</b>,<b>F</b>,<b>I</b>; merge). CD11b-positive microglia (<b>J</b>) also expressed α7-nAChR (<b>K</b>, red) as shown by colocalized signals in the merge picture (<b>L</b>). A very weak non-specific α7-nAChR signal (<b>N</b>,<b>O</b>) was detected for GFAP-positive astrocytes (<b>M</b>).</p> Full article ">Figure 2
<p>Effects of neonicotinoids on neuron viability. Primary neuronal, cultures obtained from different mouse embryo brain areas (HN: hippocampal neurons, CBN: cerebellar neurons, CXN: cortical neurons) were treated with different concentrations of neonics (up to 10,000× aFBC) or comparable doses of nicotine at 3 DIV (<b>A</b>–<b>D</b>) or 13 DIV (<b>E</b>–<b>H</b>) for 72 h. Neuron viability was evaluated from 3 independent experiments via MTS test. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001 versus control. Two-way ANOVA and Dunnett’s post-test.</p> Full article ">Figure 3
<p>Dose–response effects of mecamylamine. Hippocampal cultures were treated with the mecamylamine for 72 h (from 1 nM up to 6.2 mM). **** <span class="html-italic">p</span> &lt; 0.0001 versus control. Data are expressed as mean ± st.dev of 3 replicates/conditions from 3 independent experiments. One-way ANOVA and Dunnett’s post-test. The LC50, LOAEL, and NOAEL were calculated using GraphPad Prism software 6.01 (logarithmic transformation of X-values and non-linear regression, sigmoidal dose–response analysis with variable slope, with bottom and top constrains set at 0 and 100, respectively).</p> Full article ">Figure 4
<p>Mecamylamine reverts imidacloprid- and nicotine-induced neurotoxic effects. Primary hippocampal neurons were cotreated with 10 μM mecamylamine and 50 μM imidacloprid or nicotine (NIC) at 13 DIV for 72 h. Neuron viability was evaluated via MTS test. Data are from 3 independent experiments (N = 18). ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05 versus control; ° <span class="html-italic">p</span> &lt; 0.05, °°°° <span class="html-italic">p</span> &lt; 0.0001 vs. treatment with imidacloprid or nicotine alone, respectively. F<sub>interaction</sub> (2.102) = 6.23; <span class="html-italic">p</span> &lt; 0.01. Two-way ANOVA and Tukey’s post-test.</p> Full article ">Figure 5
<p>Neonicotinoid effects on microglia inflammatory response. Neonic effects on microglia activation and their responses to a pro-inflammatory stimulus (1 μg/mL LPS) were evaluated with in purified cultures treated with imidacloprid (up to 1000× aFBC, 170 μM) for 72 h. (<b>A</b>) Quantification of the expression levels of mRNA related to M1 (TNFα) or M2 (YM1) phenotype markers (by RT-PCR). The exposure to imidacloprid did not induce alterations in TNFα and YM1 mRNA. (<b>B</b>) LPS treatment induced TNFα expression, which was significantly inhibited by pre-incubation with imidacloprid (170 μM). (<b>C</b>) IMI significantly reduced the TNFα release in conditioned media in LPS-stimulated microglia cultures, as measured by ELISA assay. Data were obtained from 3-6 independent experiments. ° <span class="html-italic">p</span> &lt; 0.05; °°° <span class="html-italic">p</span> &lt; 0.001 versus 1 μg/mL LPS. One-way ANOVA and Dunnett’s post-test.</p> Full article ">Figure 6
<p>Imidacloprid induced neurodevelopmental alterations in cultured neurons. The in vitro effects of imidacloprid on immature neurofilaments and presynaptic protein (synaptophysin) expression and on dendritic arborization were analyzed in hippocampal “sandwich” cocultures treated with the insecticide for 72 h at 13 DIV. Cells were stained with DCX, NF200, and synaptophysin antibodies; nuclei were counterstained with Hoechst 33258 dye (blue). Three-dimensional neuron reconstruction was then performed, and the different marker expressions were analyzed. (<b>A</b>) As an index of neuron maturation, the ratio between DCX (green) and NF200 (red) volumes was quantified. Exposure to imidacloprid (up to 17 μM, 100× aFBC) did not significantly affect the DCX/NF200 ratio. (<b>B</b>) Automated Sholl analyses, for detecting alterations in neuron branching complexity, were performed on the acquired images (NF200 in red). IMI reduced the number of dendritic branches per neuron from the lower tested concentration (170 nM). (<b>C</b>) The number of synaptophysin-positive puncta (green) were determined and normalized for NF200 (red) volume. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 versus control; one-way ANOVA and Dunnett’s post-test. At least 5 fields (600×) for each condition were analyzed from three independent experiments.</p> Full article ">Figure 7
<p>α7-nAChR expression in human iPSC-derived neurons and astrocytes. iPSC-derived neurons (i-neu) and astrocytes (i-astro) were differentiated from NPCs for 28 DIV. Immunocytochemistry was performed on differentiated cells using neuron (NF200; <b>A</b>, green)- or astrocyte (S100β; <b>D</b>, green)- and α7-nAChR (<b>B</b>,<b>E</b>, red)-specific antibodies. Images were acquired via a confocal microscope at 400x magnification, scale bars 50 µm. Mature i-neu (<b>A</b>–<b>C</b>) and i-astro (<b>D</b>–<b>F</b>) showed a widespread α7-nAChR distribution colocalizing with NF200-positive (<b>C</b>, merged signal) or S100β-positive (<b>F</b>, merged signal) cells.</p> Full article ">Figure 8
<p>Human iPSC-derived neuron and astrocyte viability is affected by imidacloprid in specific developmental stages. (<b>A</b>–<b>E</b>) I-neu were exposed to IMI by repetitive treatments with different pesticide concentrations, from 3 to 28 DIV of differentiation. Cell viability was evaluated with MTT assay at different time points: 3 (<b>A</b>), 10 (<b>B</b>), 14 (<b>C</b>), 21 (<b>D</b>) and 28 (<b>E</b>) DIV. (<b>F</b>) I-astro were differentiated for 28 days, then treated with IMI for 72h. Cell death was quantified via LDH release assay. Data were obtained from 3-10 replicates in 3 independent experiments. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 versus untreated cultures. One-way ANOVA and Dunnett’s post-test.</p> Full article ">Figure 9
<p>Chronic exposure to imidacloprid induces cell death in human iPSC-derived cortical organoids. Representative image of mature hCO stained for nuclei (blue), astrocytes (green), and neurons (red) showing cell interaction complexity (merge). hCOs obtained from confluent NPCs were cultured for 60 DIV and treated with 5 nM or 17 μM IMI (eFBC and aFBC, respectively) three times a week starting from 11 DIV. Conditioned media were collected before the treatment (0 DIV) and at 25, 35 49, and 60 DIV, and LDH release was measured at the different time points. An amount of 17 μM IMI induced an increase in LDH release at 25, 35, and 60 DIV compared to untreated cultures. No significant effects were revealed for 5 nM IMI. Data were obtained from 3–15 replicates for each time point. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001 versus untreated cultures, ° <span class="html-italic">p</span> &lt; 0.05 versus 5 nM IMI. One-way ANOVA and Dunnett’s post-test at each time point.</p> Full article ">Figure 10
<p>Prenatal exposure to imidacloprid or nicotine induced synaptic alterations in the hippocampus and cerebellum of newborn mice. The effects of prenatal exposure to imidacloprid on synaptic protein expression were evaluated by immunohistochemical analyses performed on single brains isolated from prenatally exposed (up to 10× ADI for imidacloprid) newborn mice of at least three different litters. (<b>A</b>) Representative images of synaptophysin staining in hippocampus and cerebellum of control and imidacloprid-treated mouse brains. Scale bar, 250 µm. (<b>B</b>) Quantification of the integrated density of synaptophysin staining in the hippocampus (left) and cerebellum (right). Imidacloprid increased synaptophysin expression starting from ADI. Nicotine induced an increase in synaptophysin expression already at the lower dose (<span class="html-italic">p</span> &lt; 0.05 compared to control group) only in the hippocampus. Data obtained from at least 3 different litters from 3 independent experiments. * <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.001 versus control group; one-way ANOVA and Dunnett’s post-test.</p> Full article ">Figure 11
<p>Prenatal exposure to imidacloprid or nicotine induced impairments in doublecortin expression in the hippocampus and cerebellum of newborn mice. The effects of prenatal exposure to imidacloprid and nicotine on doublecortin expression were evaluated by immunohistochemical analyses performed on single brains isolated from prenatally exposed newborn mice (up to 10× ADI for imidacloprid) of at least three different litters. (<b>A</b>) Representative images of DCX fluorescent signal (green: nuclei in blue) in hippocampus and cerebellum of control and imidacloprid-treated mouse brains. Scale bar, 200 µm. (<b>B</b>) Quantification of the area stained by DCX antibody on the total area analyzed in the hippocampus (left) and cerebellum (right). Data obtained from at least 3 different litters from 3 independent experiments. * <span class="html-italic">p</span> &lt; 0.05 versus control group; one-way ANOVA and Dunnett’s post-test.</p> Full article ">Figure 12
<p>Prenatal exposure to imidacloprid or nicotine induced microglial alterations in the hippocampus and cerebellum of newborn mice. The effects of prenatal exposure to imidacloprid and nicotine on microglial markers (IBA-1 or CD11b) were evaluated by immunohistochemical analyses performed on single brains isolated from prenatally exposed newborn mice (up to 10× ADI for imidacloprid) from at least three different litters. (<b>A</b>) Representative images of IBA-1-positive cells in hippocampus and cerebellum of control and imidacloprid-treated mouse brains. Scale bar, 500 µm for the hippocampus and 300 µm for the cerebellum. (<b>B</b>) Quantification of the density of IBA-1-positive cells (n cells/mm<sup>2</sup>) in the hippocampus (left) and cerebellum (right). Data obtained from at least 3 different litters from 3 independent experiments. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001 versus control group; one-way ANOVA and Dunnett’s post-test.</p> Full article ">Scheme 1
<p>Overall experimental design of this study is reported.</p> Full article ">
28 pages, 1193 KiB  
Review
Effects of Angiogenic Factors on the Epithelial-to-Mesenchymal Transition and Their Impact on the Onset and Progression of Oral Squamous Cell Carcinoma: An Overview
by Silvia Pomella, Ombretta Melaiu, Maria Dri, Mirko Martelli, Marco Gargari and Giovanni Barillari
Cells 2024, 13(15), 1294; https://doi.org/10.3390/cells13151294 - 31 Jul 2024
Viewed by 1159
Abstract
High levels of vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF)-2 and angiopoietin (ANG)-2 are found in tissues from oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMDs). As might be expected, VEGF, FGF-2, and ANG-2 overexpression parallels the development [...] Read more.
High levels of vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF)-2 and angiopoietin (ANG)-2 are found in tissues from oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMDs). As might be expected, VEGF, FGF-2, and ANG-2 overexpression parallels the development of new blood and lymphatic vessels that nourish the growing OPMDs or OSCCs and provide the latter with metastatic routes. Notably, VEGF, FGF-2, and ANG-2 are also linked to the epithelial-to-mesenchymal transition (EMT), a trans-differentiation process that respectively promotes or exasperates the invasiveness of normal and neoplastic oral epithelial cells. Here, we have summarized published work regarding the impact that the interplay among VEGF, FGF-2, ANG-2, vessel generation, and EMT has on oral carcinogenesis. Results from the reviewed studies indicate that VEGF, FGF-2, and ANG-2 spark either protein kinase B (AKT) or mitogen-activated protein kinases (MAPK), two signaling pathways that can promote both EMT and new vessels’ formation in OPMDs and OSCCs. Since EMT and vessel generation are key to the onset and progression of OSCC, as well as to its radio- and chemo-resistance, these data encourage including AKT or MAPK inhibitors and/or antiangiogenic drugs in the treatment of this malignancy. Full article
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<p>Molecular mechanisms leading to EMT in oral mucosa. Arrows symbolize directions of connections. Abbreviations: AKT, protein kinase B; E-cadherin, epithelial-cadherin; EGF, epidermal growth factor; EMT, epithelial-to-mesenchymal transition; GLUT, glucose transporter protein; HIF, hypoxia-inducible factor; IC, inflammatory cytokines; MAPK, mitogen-activated protein kinase; MMPs, matrix metalloproteinases; N-cadherin, neuronal-cadherin; NF-κB, nuclear factor kappa B; OPMDs, oral potentially malignant disorders; OSCC, oral squamous cell carcinoma; SNAI, zinc finger snail homolog; TGF, transforming growth factor; TWIST, basic helix–loop–helix twist homolog; VEGF, vascular endothelial growth factor; ZEB, zinc finger E-box-binding homeobox. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p> Full article ">Figure 2
<p>FGF-2 promotes EMT in oral mucosa. Arrows symbolize directions of connections. Abbreviations: AKT, protein kinase B; CSCs, cancer stem cells; EMT, epithelial-to-mesenchymal transition; FGF, fibroblast growth factor; GLUT, glucose transporter protein; HIF, hypoxia-inducible factor; MAPK, mitogen-activated protein kinase; MMPs, matrix metalloproteinases; OSCC, oral squamous cell carcinoma; SNAI, zinc finger snail homolog; TWIST, basic helix–loop–helix twist homolog; VEGF, vascular endothelial growth factor; ZEB, zinc finger E-box-binding homeobox. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p> Full article ">
17 pages, 1975 KiB  
Review
Sequence of Molecular Events in the Development of Alzheimer’s Disease: Cascade Interactions from Beta-Amyloid to Other Involved Proteins
by Soghra Bagheri, Ali Akbar Saboury and Luciano Saso
Cells 2024, 13(15), 1293; https://doi.org/10.3390/cells13151293 - 31 Jul 2024
Viewed by 1365
Abstract
Alzheimer’s disease is the primary neurodegenerative disease affecting the elderly population. Despite the first description of its pathology over a century ago, its precise cause and molecular mechanism remain unknown. Numerous factors, including beta-amyloid, tau protein, the APOEε4 gene, and different metals, have [...] Read more.
Alzheimer’s disease is the primary neurodegenerative disease affecting the elderly population. Despite the first description of its pathology over a century ago, its precise cause and molecular mechanism remain unknown. Numerous factors, including beta-amyloid, tau protein, the APOEε4 gene, and different metals, have been extensively investigated in relation to this disease. However, none of them have been proven to have a decisive causal relationship. Furthermore, no single theory has successfully integrated these puzzle pieces thus far. In this review article, we propose the most probable molecular mechanism for AD, which clearly shows the relationship between the main aspects of the disease, and addresses fundamental questions such as: Why is aging the major risk factor for the disease? Are amyloid plaques and tau tangles the causes or consequences of AD? Why are the distributions of senile plaques and tau tangles in the brain different and independent of each other? Why is the APOEε4 gene a risk factor for AD? Finally, why is the disease more prevalent in women? Full article
(This article belongs to the Special Issue Alzheimer's Disease: Molecular Mechanisms and Novel Treatment Strategies)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Sequential events in the development of Alzheimer’s disease.</p> Full article ">Figure 2
<p>Differences in the formation of diffuse plaques and senile plaques in Alzheimer’s disease.</p> Full article ">Figure 3
<p>Aβ clearance by APOE isoforms. APOEε4 hinders the clearance of Aβ by attaching to LRP1 and VLDLR, whereas APOEε2 and APOEε3 facilitate the uptake of Aβ by binding to LRP1.</p> Full article ">Figure 4
<p>The impact of cholesterol on APOEε4 function. APOEε4 strongly impairs Aβ clearance in the presence of cholesterol.</p> Full article ">
19 pages, 3839 KiB  
Article
Senolytics and Senomorphics Targeting p38MAPK/NF-κB Pathway Protect Endothelial Cells from Oxidative Stress-Mediated Premature Senescence
by Jingyuan Ya and Ulvi Bayraktutan
Cells 2024, 13(15), 1292; https://doi.org/10.3390/cells13151292 - 31 Jul 2024
Viewed by 1431
Abstract
Oxidative stress is a prominent causal factor in the premature senescence of microvascular endothelial cells and the ensuing blood–brain barrier (BBB) dysfunction. Through the exposure of an in vitro model of human BBB, composed of brain microvascular endothelial cells (BMECs), astrocytes, and pericytes [...] Read more.
Oxidative stress is a prominent causal factor in the premature senescence of microvascular endothelial cells and the ensuing blood–brain barrier (BBB) dysfunction. Through the exposure of an in vitro model of human BBB, composed of brain microvascular endothelial cells (BMECs), astrocytes, and pericytes to H2O2, this study examined whether a specific targeting of the p38MAPK/NF-κB pathway and/or senescent cells could delay oxidative stress-mediated EC senescence and protect the BBB. Enlarged BMECs, displaying higher β-galactosidase activity, γH2AX staining, p16 expression, and impaired tubulogenic capacity, were regarded as senescent. The BBB established with senescent BMECs had reduced transendothelial electrical resistance and increased paracellular flux, which are markers of BBB integrity and function, respectively. Premature senescence disrupted plasma-membrane localization of the tight junction protein, zonula occludens-1, and elevated basement membrane-degrading matrix metalloproteinase-2 activity and pro-inflammatory cytokine release. Inhibition of p38MAPK by BIRB796 and NF-κB by QNZ and the elimination of senescent cells by a combination of dasatinib and quercetin attenuated the effects of H2O2 on senescence markers; suppressed release of the pro-inflammatory cytokines interleukin-8, monocyte chemoattractant protein-1, and intercellular adhesion molecule-1; restored tight junctional unity; and improved BBB function. In conclusion, therapeutic approaches that mitigate p38MAPK/NF-κB activity and senescent cell accumulation in the cerebrovasculature may successfully protect BBB from oxidative stress-induced BBB dysfunction. Full article
(This article belongs to the Section Cellular Aging)
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<p>A schematic representation of mechanisms involved in oxidative stress-mediated induction of cellular senescence. Excessive bioavailability of oxidative stress promotes DNA damage and activates p38MAPK/p53 and p16/RB pathways which, in turn, inhibit the CDK2 and CDK4/6 enzymatic activities and induce cell-cycle arrest. Activation of p38MAPK/NF-κB pathway also promotes senescence through release of SASP. Therapeutic strategies targeting p38MAPK/NF-κB pathway or senescent cells themselves effectively attenuate accumulation of senescent cells. P38MAPK, p38 mitogen-activated protein kinase; MSK1, mitogen- and stress-activated protein kinase-1; NF-κB, nuclear factor kappa B; SASP, senescence-associated secretory phenotype; MK5, map kinase-activated protein kinase 5; CDK, cyclin-dependent kinase; pRB, phosphorylate retinoblastoma protein.</p> Full article ">Figure 2
<p>Time- and concentration-dependent effects of H<sub>2</sub>O<sub>2</sub> on HBMECs. (<b>A</b>) Senescent cells emerged in cells treated with increasing concentrations of H<sub>2</sub>O<sub>2</sub> for 24–72 h. (<b>B</b>) Twelve days appeared to be the best time period for the development of senescence following exposure to 400 μM of H<sub>2</sub>O<sub>2</sub>. (<b>C</b>,<b>D</b>) Percentage of cells reaching senescence, as ascertained by SA-β-gal positive staining, in response to different concentrations of H<sub>2</sub>O<sub>2</sub> and post-H<sub>2</sub>O<sub>2</sub> incubation period. (<b>E</b>) Level of LDH cytotoxicity in response to different concentrations of H<sub>2</sub>O<sub>2</sub>. The green arrows indicate some of the SA-β-gal positive cells in different experimental groups. * indicates statistically significant differences compared to control group. <sup>#</sup> indicates statistically significant differences between 100 and 1000 μM treatment groups and corresponding 400 μM group. Data are expressed as mean ± SEM from three independent experiments.</p> Full article ">Figure 3
<p>Exposure to H<sub>2</sub>O<sub>2</sub> led to enlarged cellular morphology (<b>A</b>) and elevated the number of cells stained positive for SA-β-gal (<b>B</b>, orange arrow) and γH2AX (<b>C</b>, green arrow) where normal nuclear staining with DAPI is illustrated by a white arrow. * <span class="html-italic">p</span> &lt; 0.05 compared to the control group, <sup>#</sup> <span class="html-italic">p </span>&lt; 0.05 compared to the SIPS group.</p> Full article ">Figure 4
<p>Effect of treatments with H<sub>2</sub>O<sub>2</sub> on p16 expression and telomere length. A significant increase in the expression of cyclin-dependent kinase inhibitor p16 was detected by Western analyses in prematurely senescent cells that were selectively suppressed by treatments with an inhibitor of NF-κB (QNZ) and D+Q (<b>A</b>). No significant difference was observed in telomere length in any of the experimental groups in the absence or presence of inhibitors (<b>B</b>). Data are presented as mean ± SEM from three independent experiments. * <span class="html-italic">p</span> &lt; 0.05 compared to the control group, ns = not significant compared to control and/or SIPS group.</p> Full article ">Figure 5
<p>Representative images showing the impact of H<sub>2</sub>O<sub>2</sub> on p38MAPK and NF-κB phosphorylation. H<sub>2</sub>O<sub>2</sub> evoked p38MAPK (<b>A</b>) and NF-κB (<b>B</b>) phosphorylation in a time-dependent manner. The increases observed in both p38MAPK and NF-κB activity within 30 min of exposure to H<sub>2</sub>O<sub>2</sub> were significantly suppressed by BIRB796, a p38MAPK inhibitor where the impact of QNZ was specific to NF-κB (<b>C</b>,<b>D</b>). Data are presented as mean ± SEM from 3 independent experiments. * <span class="html-italic">p </span>&lt; 0.05 compared to the control group, <sup>#</sup> <span class="html-italic">p </span>&lt; 0.05 compared to the SIPS group, ns = not significant compared to control and/or SIPS group.</p> Full article ">Figure 6
<p>HBMECs senescence adversely affect the characteristics of blood–brain barrier. An in vitro model of human BBB established with human astrocytes, pericytes, and senescent HBMECs led to significantly decreased transendothelial electrical resistance (TEER) (<b>A</b>) and increased paracellular flux (<b>B</b>) of a low molecular weight permeability marker, sodium fluorescein (NaF). Treatments with BIRB796 (a p38MAPK inhibitor), QNZ (an NF-κB inhibitor), and a combination of dasatinib and quercetin (DQ) markedly reduced the impact of SIPS on both parameters. Data are expressed as mean ± SEM from three independent experiments. * <span class="html-italic">p</span> &lt; 0.05 compared to the control group, <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 compared to the SIPS group.</p> Full article ">Figure 7
<p>Senescence diminishes the angiogenic capacity of HBMECs. Cells subjected to H<sub>2</sub>O<sub>2</sub> formed significantly fewer and shorter tubules on Matrigel compared to those cultured under normal conditions. Treatment with BIRB796, a p38MAPK inhibitor, completely restored the angiogenic capacity while treatments with QNZ, an NF-κB inhibitor, and a cocktail of dasatinib and quercetin (DQ) were not as effective despite attenuating the impact of H<sub>2</sub>O<sub>2.</sub> Data are expressed as mean ± SEM from three independent experiments. * <span class="html-italic">p </span>&lt; 0.05 compared to the control group, <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 compared to the SIPS group, ns = not significant compared to control and/or SIPS group.</p> Full article ">Figure 8
<p>Stress-induced premature senescence affected the subcellular distribution of tight junction proteins. Compared to controls, H<sub>2</sub>O<sub>2</sub> increased total expression of occludin and claudin-5 without affecting that of ZO-1 (<b>A</b>). Treatment with H<sub>2</sub>O<sub>2</sub> perturbed plasma-membrane localization of zonula occludens-1 (ZO-1) without affecting those of occludin and claudin-5. Co-treatment of cells with H<sub>2</sub>O<sub>2</sub> and an inhibitor for p38MAPK (BIRB796), NF-κB (QNZ), or a combination of dasatinib and quercetin (DQ) restored the plasma-membrane expression of ZO-1 (<b>B</b>). While levels of pro-MMP-2 remained unaffected in all experimental groups, a significantly increased level of activated MMP-2 was observed in SIPS group only. Treatments with BIRB796, QNZ, and DQ suppressed the oxidative stress-evoked MMP-2 activation (<b>C</b>). Due to high molecular weight of ZO-1, the same protein samples used for the detection of occludin, claudin-5, and β-actin were run on separate gels. As cell-culture media rather than cell lysates were used for gelatin zymography studies, no housekeeping protein could be used as loading control [<a href="#B22-cells-13-01292" class="html-bibr">22</a>,<a href="#B23-cells-13-01292" class="html-bibr">23</a>]. Data are expressed as mean ± SEM from three independent experiments. * <span class="html-italic">p </span>&lt; 0.05 compared to the control group, <sup>#</sup> <span class="html-italic">p </span>&lt; 0.05 compared to the SIPS group, ns = not significant compared to control and/or SIPS group.</p> Full article ">Figure 9
<p>Senescence-induced adoption of a pro-inflammatory phenotype in human brain microvascular endothelial cells. Cells exposed to H<sub>2</sub>O<sub>2</sub> secreted significantly higher levels of MCP-1, CXCL-1, ICAM-1, IL-6, IL-8, and MIF-1 and lower levels of PAI-1. Inhibition of p38MAPK (by BIRB796) and NF-κB (by QNZ) and treatment with a cocktail of dasatinib and quercetin (DQ) partially suppressed the overexpression of the MCP-1, ICAM-1, and IL-8. Treatment with BIRB796 also suppressed the expression of CXCL-1. The expression of cytokines was quantified using ImageJ based on the densitometric analysis of the dot blot duplicates. * <span class="html-italic">p </span>&lt; 0.05 compared to the control group, <sup>#</sup> <span class="html-italic">p </span>&lt; 0.05 compared to the SIPS group, ns = not significant compared to control and/or SIPS group.</p> Full article ">
22 pages, 27479 KiB  
Article
CTRP13-Mediated Effects on Endothelial Cell Function and Their Potential Role in Obesity
by Muhammad Aslam, Ling Li, Sina Nürnberger, Bernd Niemann and Susanne Rohrbach
Cells 2024, 13(15), 1291; https://doi.org/10.3390/cells13151291 - 31 Jul 2024
Viewed by 833
Abstract
Background: Obesity, a major component of cardiometabolic syndrome, contributes to the imbalance between pro- and anti-atherosclerotic factors via dysregulation of adipocytokine secretion. Among these adipocytokines, the C1q/TNF-related proteins (CTRPs) play a role in the modulation of atherosclerosis development and progression. Here, we investigated [...] Read more.
Background: Obesity, a major component of cardiometabolic syndrome, contributes to the imbalance between pro- and anti-atherosclerotic factors via dysregulation of adipocytokine secretion. Among these adipocytokines, the C1q/TNF-related proteins (CTRPs) play a role in the modulation of atherosclerosis development and progression. Here, we investigated the vascular effects of CTRP13. Results: CTRP13 is not only expressed in adipose tissue but also in vessels/endothelial cells (ECs) of mice, rats, and humans. Obese individuals (mice, rats, and humans) showed higher vascular CTRP13 expression. Human Umbilical Vein Endothelial Cells (HUVECs), cultured in the presence of serum from obese mice, mimicked this obesity-associated effect on CTRP13 protein expression. Similarly, high glucose conditions and TNF-alpha, but not insulin, resulted in a strong increase in CTRP13 in these cells. Recombinant CTRP13 induced a reduction in EC proliferation via AMPK. In addition, CTRP13 reduced cell cycle progression and increased p53 phosphorylation and p21 protein expression, but reduced Rb phosphorylation, with the effects largely depending on alpha-2 AMPK as suggested by adenoviral overexpression of dominant-negative (DN) or wild-type (WT) alpha 1/alpha 2 AMPK. Conclusion: The present study demonstrates that CTRP13 expression is induced in ECs under diabetic conditions and that CTRP13 possesses significant vaso-modulatory properties which may have an impact on vascular disease progression in patients. Full article
(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cardiovascular and Metabolic Diseases)
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<p>CTRP13 expression in obese and lean mice or rats. (<b>A</b>) CTRP13 mRNA and protein expression in adipose tissue of male and female wild-type (WT) or ob/ob mice. (<b>B</b>) CTRP13 mRNA and protein expression in aortic tissue of male and female WT or ob/ob mice. Representative CTRP13 western blots are shown. GAPDH served as loading control. Uncropped images with size markers are presented in <a href="#app1-cells-13-01291" class="html-app">Supplementary Figure S2</a>. Data are mean ± SEM from 6 to 12 animals per group and gender. **** <span class="html-italic">p</span> &lt; 0.0001 vs. WT female. (<b>C</b>) CTRP13 mRNA and protein expression in aortic tissue of male obese ZDF (fa/fa) rats, lean heterozygous (Fa/fa) rats, and wild-type (Fa/Fa) rats. Representative CTRP13 western blots are shown. GAPDH served as loading control. Uncropped images with size markers are presented in <a href="#app1-cells-13-01291" class="html-app">Supplementary Figure S3</a>. (<b>D</b>) CTRP13 mRNA in the left ventricle (LV), LV ECs, and LV cardiomyocytes of male obese ZDF (fa/fa) rats, lean heterozygous (Fa/fa) rats, and wild-type (Fa/Fa) rats. Data are mean ± SEM from five animals per group and gender. * <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001 vs. wild-type (Fa/Fa) rats.</p> Full article ">Figure 2
<p>CTRP13 expression in the mammary artery of patients. CTRP13 mRNA and protein expression were analyzed in mammary arteries of lean (BMI 18.5–25 kg/m<sup>2</sup>) or obese (30–35 kg/m<sup>2</sup>) patients. Representative CTRP13 estern blots are shown. GAPDH served as loading control. Uncropped images with size markers are presented in <a href="#app1-cells-13-01291" class="html-app">Supplementary Figure S5</a>. Data are mean ± SEM from 12 to 18 patients per group. **** <span class="html-italic">p</span> &lt; 0.0001 vs. lean patients.</p> Full article ">Figure 3
<p>Intracellular localization and induction of CTRP13 in HUVECs. (<b>A</b>) Cells cultured on glass coverslips were stained with CTRP13 and CD31 followed by Cy3 or FITC-coupled secondary antibodies. The cell nuclei were stained with TO-PRO-3™ iodide (Ex./Em.642/661). (<b>B</b>) Cells were cultured in Opti-MEM™ supplemented with either 10% serum from wild-type (WT) or ob/ob mice for 48 h. Representative CTRP13 Western blots are shown. GAPDH served as loading control. Uncropped images with size markers are presented in <a href="#app1-cells-13-01291" class="html-app">Supplementary Figure S6</a>. Data are mean ± SEM from 4 independent experiments with 3–4 biological replicates each. **** <span class="html-italic">p</span> &lt; 0.0001 vs. WT serum.</p> Full article ">Figure 4
<p>Impact of high glucose and TNF-alpha on CTRP13 protein expression in HUVECs. (<b>A</b>) HUVECs were cultured either under normal glucose (5 mM D-glucose, low glu) or high glucose (25 mM D-glucose, high glu) conditions for 24 h. (<b>B</b>) HUVECs were cultured under normal glucose conditions (5 mM D-glucose) and treated with 1 ng/mL (low TNF), 10 ng/mL (high TNF) TNF-alpha or buffer (control) for 24 h. (<b>C</b>) HUVECs were cultured under normal glucose conditions (5 mM D-glucose, low glu) or high glucose conditions (25 mM D-glucose, high glu) and treated ± 1 ng/mL TNF-alpha for 24 h. (<b>D</b>) HUVECs were cultured under normal glucose conditions (5 mM D-glucose, low glu), treated ± 1 ng/mL TNF-alpha for 24 h, and preincubated with the TNF-alpha-specific small-molecule inhibitor C87 (2 µM) for 60 min as indicated. (<b>E</b>) HUVECs were cultured under normal glucose conditions (5 mM D-glucose, low glu) or high glucose conditions (25 mM D-glucose, high glu) and treated ± C87 (2 µM) for 24 h. Representative CTRP13 Western blots are shown. GAPDH served as loading control. Uncropped images with size markers are presented in <a href="#app1-cells-13-01291" class="html-app">Supplementary Figure S7</a>. (<b>F</b>) Cells were treated as described in (<b>E</b>) and analyzed for the mRNA expression of TNF-alpha, ICAM-1, or IL-8 by qPCR. Data are mean ± SEM from 4 independent experiments with 3–5 biological replicates each. * <span class="html-italic">p</span> &lt; 0.05, **** <span class="html-italic">p</span> &lt; 0.0001 vs. normal glucose unless otherwise indicated.</p> Full article ">Figure 5
<p>Time course of signaling activation in response to CTRP13 in HUVECs. Cells were treated with 4 µg/mL CTRP13 in Opti-MEM™ supplemented with 1% serum for the indicated duration. Western blots were performed to assess the phosphorylation of AMPK (Thr172), p44/42 MAPK (Thr202/Tyr204), and Akt (Thr308 or Ser473). Total AMPK, total p44/42 MAPK, or total Akt served as loading control. Uncropped images with size markers are shown in <a href="#app1-cells-13-01291" class="html-app">Supplementary Figure S8</a>. Data are mean ± SEM from five independent experiments with two biological replicates each. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01; **** <span class="html-italic">p</span> &lt; 0.0001 vs. control.</p> Full article ">Figure 6
<p>Impact of CTRP13 on EC proliferation and migration. (<b>A</b>) Cells were treated with 4 µg/mL CTRP13 in Opti-MEM™ supplemented with 1% serum for 24 h. Sixty min prior to CTRP13 treatment, HUVECs were incubated with the AMPK inhibitor AraA (500 µM), the p44/42 MAPKinase inhibitor UO126 (10 µM), or the Akt inhibitor VIII (0.1 µM). Subsequently, the cell numbers were counted (upper panel) or a BrdU assay was performed (lower panel). Data are mean ± SEM from 4 independent experiments with 2–5 biological replicates each. **** <span class="html-italic">p</span> &lt; 0.0001 vs. control. (<b>B</b>) Cells were left untreated in Opti-MEM™ supplemented with 1% serum (control) or treated with 4 µg/mL CTRP13 for 24 h. Sixty min prior to CTRP13 treatment, HUVECs were incubated with the AMPK inhibitor AraA (500 µM) as indicated. To investigate the effect of CTRP13 on the migration of ECs, time-dependent closure of a cell-free gap with a width of 500 μm was monitored. Representative photomicrographs with a marked boundary of the cell-free gap are shown (<b>upper panel</b>) together with the quantification of 4 independent experiments with 1 biological replicate each (<b>lower panel</b>).</p> Full article ">Figure 7
<p>Impact of CTRP13 on cell cycle regulators and cell cycle progression. (<b>A</b>) Cells were left untreated in Opti-MEM™ supplemented with 1% serum or treated with 4 µg/mL CTRP13 for 24 h. Sixty min prior to CTRP13 treatment, HUVECs were incubated with the AMPK inhibitor AraA (500 µM) as indicated. Western blots were performed for phosphorylation of p53 (Ser15 or Ser20) or Rb and total protein expression of p53 and p21. Representative Western blots are shown (<b>left panel</b>). GAPDH served as loading control. Uncropped images with size markers are presented in <a href="#app1-cells-13-01291" class="html-app">Supplementary Figure S10</a>. Results from FACS-based cell cycle analyses in accordingly treated cells are shown in the right panel. (<b>B</b>) Adenoviral overexpression of wild-type (WT) or dominant negative (DN) alpha 1 and alpha 2 AMPK was performed in HUVECs. After 48 h, cells were left untreated in Opti-MEM™ supplemented with 1% serum or treated with 4 µg/mL CTRP13 for 24 h. Western blots and FACS-based cell cycle analyses were performed as described in (<b>A</b>). Data are mean ± SEM from 4 independent experiments with 2–3 biological replicates each. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001 vs. control. Results regarding the impact of alpha AMPK isoforms in mediating the CTRP13 effects on the G2/M phase are presented in <a href="#app1-cells-13-01291" class="html-app">Supplementary Figure S12</a>.</p> Full article ">Figure 8
<p>Impact of endothelial CTRP13 on human fibroblasts. Adenoviral overexpression of CTRP13 or GFP in HUVECs was performed. Conditioned medium from HUVECs overexpressing either CTRP13 or GFP or HUVECs treated with conditioned medium from 293A cells without overexpression was utilized to culture human fibroblasts in 50% (<span class="html-italic">v</span>/<span class="html-italic">v</span>) HUVECs medium in DMEM for 24 h (<b>B</b>,<b>C</b>) or as indicated (<b>E</b>). Overexpression of V5-tagged CTRP13 or GFP in HUVEC lysates (<b>upper panel</b>) or secretion of CTRP13 into cell culture medium (<b>lower panel</b>) was analyzed by Western blotting (<b>A</b>). GAPDH served as a loading control. (<b>B</b>) Western blots were performed for phosphorylation of p53 (Ser15), total protein expression of p53, p21, and alpha-AMPK and AMPK phosphorylation (Thr172). GAPDH served as loading control. (<b>C</b>) Western blots were performed for phosphorylation of Smad2 (Ser465/467)/Smad3 (Ser423/425). Representative Western blots are shown and uncropped images with size markers are presented in <a href="#app1-cells-13-01291" class="html-app">Supplementary Figure S14</a>. (<b>D</b>) Cells were analyzed for the mRNA expression of Col1A1, Col3A1, TGF-beta1, and alpha-SMA by qPCR. Data are mean ± SEM from 4 independent experiments with 4–5 biological replicates each. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001 vs. control. (<b>E</b>) To investigate the effect of conditioned medium from HUVECs overexpressing either CTRP13 or GFP on the migration of fibroblasts, time-dependent closure of a cell-free gap with a width of 500 μm was monitored. Representative photomicrographs with a marked boundary of the cell-free gap are shown.</p> Full article ">
15 pages, 1991 KiB  
Article
Culture and Immunomodulation of Equine Muscle-Derived Mesenchymal Stromal Cells: A Comparative Study of Innovative 2D versus 3D Models Using Equine Platelet Lysate
by J. Duysens, H. Graide, A. Niesten, A. Mouithys-Mickalad, G. Deby-Dupont, T. Franck, J. Ceusters and D. Serteyn
Cells 2024, 13(15), 1290; https://doi.org/10.3390/cells13151290 - 31 Jul 2024
Viewed by 796
Abstract
Muscle-derived mesenchymal stromal cells (mdMSCs) hold great promise in regenerative medicine due to their immunomodulatory properties, multipotent differentiation capacity and ease of collection. However, traditional in vitro expansion methods use fetal bovine serum (FBS) and have numerous limitations including ethical concerns, batch-to-batch variability, [...] Read more.
Muscle-derived mesenchymal stromal cells (mdMSCs) hold great promise in regenerative medicine due to their immunomodulatory properties, multipotent differentiation capacity and ease of collection. However, traditional in vitro expansion methods use fetal bovine serum (FBS) and have numerous limitations including ethical concerns, batch-to-batch variability, immunogenicity, xenogenic contamination and regulatory compliance issues. This study investigates the use of 10% equine platelet lysate (ePL) obtained by plasmapheresis as a substitute for FBS in the culture of mdMSCs in innovative 2D and 3D models. Using muscle microbiopsies as the primary cell source in both models showed promising results. Initial investigations indicated that small variations in heparin concentration in 2D cultures strongly influenced medium coagulation with an optimal proliferation observed at final heparin concentrations of 1.44 IU/mL. The two novel models investigated showed that expansion of mdMSCs is achievable. At the end of expansion, the 3D model revealed a higher total number of cells harvested (64.60 ± 5.32 million) compared to the 2D culture (57.20 ± 7.66 million). Trilineage differentiation assays confirmed the multipotency (osteoblasts, chondroblasts and adipocytes) of the mdMSCs generated in both models with no significant difference observed. Immunophenotyping confirmed the expression of the mesenchymal stem cell (MSC) markers CD-90 and CD-44, with low expression of CD-45 and MHCII markers for mdMSCs derived from the two models. The generated mdMSCs also had great immunomodulatory properties. Specific immunological extraction followed by enzymatic detection (SIEFED) analysis demonstrated that mdMSCs from both models inhibited myeloperoxidase (MPO) activity in a strong dose-dependent manner. Moreover, they were also able to reduce reactive oxygen species (ROS) activity, with mdMSCs from the 3D model showing significantly higher dose-dependent inhibition compared to the 2D model. These results highlighted for the first time the feasibility and efficacy of using 10% ePL for mdMSC expansion in novel 2D and 3D approaches and also that mdMSCs have strong immunomodulatory properties that can be exploited to advance the field of regenerative medicine and cell therapy instead of using FBS with all its drawbacks. Full article
(This article belongs to the Collection Stem Cells in Tissue Engineering and Regeneration)
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<p>Collection of ePL from an awake horse on which apheresis is being carried out using the COM.tec plasmapheresis device.</p> Full article ">Figure 2
<p>Representative microphotographs by optical microscope (×100) of mdMSCs cultured after 7 days with 10% platelet lysate and 1.44 IU/mL of heparin (<b>left</b>) and 3 IU/mL of heparin (<b>right</b>) (Horse 1).</p> Full article ">Figure 3
<p>Representative microphotographs by optical microscope (×100) of mdMSCs cultured after 4 days (<b>left</b>) and 8 days (<b>right</b>) with 10% platelet lysate and 1.44 IU/mL of heparin (Horse 3).</p> Full article ">Figure 4
<p>Representative microphotographs by optical microscope (×100) of mdMSCs cultured in 3D after 4 days (<b>left</b>) and 8 days (<b>right</b>) with DMEM/Ham’s F12 medium supplemented with 10% platelet lysate (Horse 3).</p> Full article ">Figure 5
<p>Total number of mdMSCs harvested with the 3D and 2D models at passage 3.</p> Full article ">Figure 6
<p>Representative microphotographs obtained by optical microscope (×100) of trilineage differentiations of mdMSCs (from the 3D model at passage 3) with, respectively, for the upper line chondroblast (<b>left</b>), adipocyte (<b>between</b>), and osteoblast (<b>right</b>) cells cultured without differentiation media. Lower line is composed of differentiated chondroblasts (<b>left</b>), adipocytes (<b>between</b>), and osteoblasts (<b>right</b>).</p> Full article ">Figure 7
<p>Effect of mdMSCs (passage 3) on the activity of equine MPO measured by SIEFED. Results from five independent experiments with two technical replicates for each concentration (<span class="html-italic">n</span> = 10). The means ± SD are shown as relative percentages compared to the MPO control, which was performed without mdMSCs and defined as 100% response.</p> Full article ">Figure 8
<p>Effects of different concentrations of mdMSCs (passage 3) in Ringer lactate on the ROS production by neutrophils (<span class="html-italic">n</span> = 10). NA and A represent, respectively, non-activated and activated neutrophils alone. Means ± SD are shown in relative percentages. Stimulated neutrophils without mdMSCs (A) is defined as 100% response. Means ± SD are shown in relative percentages of A.</p> Full article ">
16 pages, 1100 KiB  
Review
The Role of the MiR-181 Family in Hepatocellular Carcinoma
by Jinbiao Chen, Ken Liu, Mathew A. Vadas, Jennifer R. Gamble and Geoffrey W. McCaughan
Cells 2024, 13(15), 1289; https://doi.org/10.3390/cells13151289 - 31 Jul 2024
Viewed by 1162
Abstract
Hepatocellular carcinoma (HCC) is the fourth-leading cause of cancer-related death worldwide. Due to the high mortality rate in HCC patients, discovering and developing novel systemic treatment options for HCC is a vital unmet medical need. Among the numerous molecular alterations in HCCs, microRNAs [...] Read more.
Hepatocellular carcinoma (HCC) is the fourth-leading cause of cancer-related death worldwide. Due to the high mortality rate in HCC patients, discovering and developing novel systemic treatment options for HCC is a vital unmet medical need. Among the numerous molecular alterations in HCCs, microRNAs (miRNAs) have been increasingly recognised to play critical roles in hepatocarcinogenesis. We and others have recently revealed that members of the microRNA-181 (miR-181) family were up-regulated in some, though not all, human cirrhotic and HCC tissues—this up-regulation induced epithelial–mesenchymal transition (EMT) in hepatocytes and tumour cells, promoting HCC progression. MiR-181s play crucial roles in governing the fate and function of various cells, such as endothelial cells, immune cells, and tumour cells. Previous reviews have extensively covered these aspects in detail. This review aims to give some insights into miR-181s, their targets and roles in modulating signal transduction pathways, factors regulating miR-181 expression and function, and their roles in HCC. Full article
(This article belongs to the Collection MicroRNAs in Cancer: From Molecular Mechanisms to Applications in Diagnostic and Therapeutic Opportunities)
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<p>Chromosomal position of the three miR-181 clusters. (<b>I</b>) Mir181A/B1 and (<b>II</b>) mir181A/B2 transcription start sites (TSS) have been mapped to 78.3 kb and 34.0 kb upstream of the mature miRNAs, respectively, consistent with the position of H3K27Ac (in blue/pink), which is associated with the higher activation of transcription [<a href="#B38-cells-13-01289" class="html-bibr">38</a>]. (<b>III</b>) The putative TSS of miR181C/D might be 9 kb upstream of the miR-181C/D precursor. Please note that the clusters are not drawn to scale.</p> Full article ">Figure 2
<p>The TGF-β-miR-181-CBX7 axis. Arrow end lines mean activating, inducing, promoting; blunt end lines mean inhibiting, blocking, or reducing. EMT: epithelial–mesenchymal transition. Pathways such as Wnt-TCF/LEF-miR-181-CDX2/GATA6/NLK and KRAS-GATA3-miR-181a/b1 are partially shown. Several miR-181 targets other than CBX7 are also shown (in grey).</p> Full article ">
29 pages, 920 KiB  
Review
The Potential of Metabolomics to Find Proper Biomarkers for Addressing the Neuroprotective Efficacy of Drugs Aimed at Delaying Parkinson’s and Alzheimer’s Disease Progression
by Rafael Franco, Claudia Garrigós, Jaume Lillo and Rafael Rivas-Santisteban
Cells 2024, 13(15), 1288; https://doi.org/10.3390/cells13151288 - 31 Jul 2024
Cited by 1 | Viewed by 1225
Abstract
The first objective is to highlight the lack of tools to measure whether a given intervention affords neuroprotection in patients with Alzheimer’s or Parkinson’s diseases. A second aim is to present the primary outcome measures used in clinical trials in cohorts of patients [...] Read more.
The first objective is to highlight the lack of tools to measure whether a given intervention affords neuroprotection in patients with Alzheimer’s or Parkinson’s diseases. A second aim is to present the primary outcome measures used in clinical trials in cohorts of patients with neurodegenerative diseases. The final aim is to discuss whether metabolomics using body fluids may lead to the discovery of biomarkers of neuroprotection. Information on the primary outcome measures in clinical trials related to Alzheimer’s and Parkinson’s disease registered since 2018 was collected. We analysed the type of measures selected to assess efficacy, not in terms of neuroprotection since, as stated in the aims, there is not yet any marker of neuroprotection. Proteomic approaches using plasma or CSF have been proposed. PET could estimate the extent of lesions, but disease progression does not necessarily correlate with a change in tracer uptake. We propose some alternatives based on considering the metabolome. A new opportunity opens with metabolomics because there have been impressive technological advances that allow the detection, among others, of metabolites related to mitochondrial function and mitochondrial structure in serum and/or cerebrospinal fluid; some of the differentially concentrated metabolites can become reliable biomarkers of neuroprotection. Full article
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<p>Pros and cons of proteomics and metabolomics for AD/PD diagnostics and therapeutics. Proteomics allows the detection of potentially useful biomarkers, but in clinical practice, it would be difficult to differentiate among the different versions (phosphorylated, glycosylated, etc.) of a given protein. Metabolomics may simultaneously detect hundreds of metabolites, including those that reflect mitochondrial dysfunction, but the combination of various metabolite levels may be necessary to achieve the right information on disease progression and/or the effectiveness of presumably neuroprotective interventions.</p> Full article ">
25 pages, 7822 KiB  
Article
Regulatory B Cells Expressing Granzyme B from Tolerant Renal Transplant Patients: Highly Differentiated B Cells with a Unique Pathway with a Specific Regulatory Profile and Strong Interactions with Immune System Cells
by Nicolas Sailliet, Amandine Dupuy, François Brinas, Karine Renaudin, Luc Colas, Clarisse Kerleau, Thi-Van-Ha Nguyen, Cynthia Fourgeux, Jérémie Poschmann, Clément Gosset, Magali Giral, Nicolas Degauque, Hoa Le Mai, Richard Danger and Sophie Brouard
Cells 2024, 13(15), 1287; https://doi.org/10.3390/cells13151287 - 31 Jul 2024
Viewed by 1173
Abstract
The aim of our study was to determine whether granzyme B-expressing regulatory B cells (GZMB+ B cells) are enriched in the blood of transplant patients with renal graft tolerance. To achieve this goal, we analysed two single-cell RNA sequencing (scRNAseq) datasets: (1) [...] Read more.
The aim of our study was to determine whether granzyme B-expressing regulatory B cells (GZMB+ B cells) are enriched in the blood of transplant patients with renal graft tolerance. To achieve this goal, we analysed two single-cell RNA sequencing (scRNAseq) datasets: (1) peripheral blood mononuclear cells (PBMCs), including GZMB+ B cells from renal transplant patients, i.e., patients with stable graft function on conventional immunosuppressive treatment (STA, n = 3), drug-free tolerant patients (TOL, n = 3), and patients with antibody-mediated rejection (ABMR, n = 3), and (2) ex-vivo-induced GZMB+ B cells from these groups. In the patient PBMCs, we first showed that natural GZMB+ B cells were enriched in genes specific to Natural Killer (NK) cells (such as NKG7 and KLRD1) and regulatory B cells (such as GZMB, IL10, and CCL4). We performed a pseudotemporal trajectory analysis of natural GZMB+ B cells and showed that they were highly differentiated B cells with a trajectory that is very different from that of conventional memory B cells and linked to the transcription factor KLF13. By specifically analysing GZMB+ natural B cells in TOLs, we found that these cells had a very specific transcriptomic profile associated with a reduction in the expression of HLA molecules, apoptosis, and the inflammatory response (in general) in the blood and that this signature was conserved after ex vivo induction, with the induction of genes associated with migration processes, such as CCR7, CCL3, or CCL4. An analysis of receptor/ligand interactions between these GZMB+/− natural B cells and all of the immune cells present in PBMCs also demonstrated that GZMB+ B cells were the B cells that carried the most ligands and had the most interactions with other immune cells, particularly in tolerant patients. Finally, we showed that these GZMB+ B cells were able to infiltrate the graft under inflammatory conditions, thus suggesting that they can act in locations where immune events occur. Full article
(This article belongs to the Special Issue Immune Functions and Therapeutic Potential of B Lymphocytes in Autoimmune Disorders)
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<p>Single-cell RNA sequencing of PBMC from kidney-transplanted patients. Schematic representation of the experiment. (<b>A</b>) scRNAseq was performed on PBMCs from kidney-transplanted patients (STA = 3/TOL = 3/ABMR = 3). (<b>B</b>) Identical sampling methods were performed for all patients. PBMCs were sequenced using multiplexed CITE-seq protocols. (<b>C</b>) Genes identifying immune cell sub-populations are shown in violin plots. (<b>D</b>,<b>E</b>) UMAP represents the main populations of NK/CD4 T cells/CD8 T cells/DC/monocytes and B cells in blood from transplanted patients. Each dot represents a cell, and each group of coloured dots represents one cell population. UMAPs show all cells of the dataset (<b>D</b>) or are split according to the group of patients (<b>E</b>).</p> Full article ">Figure 2
<p>Single-cell sequencing gene expression across B cell clusters. (<b>A</b>) UMAP representation of PBMC clusters made using seurat FindClusters() function. Clusters 5 and 7 were associated with MS4A1<sup>+</sup> B cells. (<b>B</b>) UMAP representation of the PBMC Clusters 5 and 7 sub-clusterization leading to the identification of 6 B cell sub-clusters. (<b>C</b>) UMAP of GZMB expression in B cells. Cluster 3 was identified as GZMB<sup>+</sup> B cells. (<b>D</b>) Dotplot representation of DEG in GZMB<sup>+</sup> B cell Cluster 3 and GZMB<sup>−</sup> B cell Clusters 0, 1, 2, 4, and 5. Only the DEGs within each group of patients are shown. Dots are coloured based on the average expression of the gene in the cluster, and the dot size represents the percentage of cells expressing the gene. (<b>E</b>) Scatter plots showing the co-expression of <span class="html-italic">MS4A1</span> (CD20) with either <span class="html-italic">NKG7</span>, <span class="html-italic">KLRD1</span>, <span class="html-italic">CD160</span>, or <span class="html-italic">CD247</span> in Cluster 3. (<b>F</b>) Scatter plot of the quality control metrics “nFeatures_RNA” and “nCounts_RNA” used to determine cell doublets. (<b>G</b>) Aggregated average expression levels of each gene of the regulatory B cell signature described by Dubois et al. [<a href="#B37-cells-13-01287" class="html-bibr">37</a>] at the single-cell level, subtracted by the aggregated expression of 100 control features, within B cell clusters. Differences were defined as statistically significant when <span class="html-italic">p</span> &lt; 0.01 (*), <span class="html-italic">p</span> &lt; 0.001 (**), and <span class="html-italic">p</span> &lt; 0.0001 (****).</p> Full article ">Figure 3
<p>Single-cell RNA sequencing trajectory analysis of B cells: (<b>A</b>) UMAP of B cell trajectories using Monocle v3. TCL1A<sup>+</sup> IgD<sup>+</sup> IgM<sup>+</sup> B cells were chosen as the origin cell cluster, and the arrows highlight the trajectories. Cells are coloured according to their differentiated state, ranging from blue (naive) to red (terminally differentiated). (<b>B</b>) Heatmap of the significantly enriched target gene sets’ downstream transcription factors grouped according to the B cell clusters. (<b>C</b>) Violin plot of <span class="html-italic">KLF13</span> expression across B cell clusters.</p> Full article ">Figure 4
<p>Characterisation of TOL GZMB<sup>+</sup> B cells’ specific genes. DEGs in TOL patients compared to STA and ABMR within each B cell cluster were summarised in an UpSetPlot representing the number of DEGs in different B cell clusters. Solid black dots represent clusters, and genes differentially expressed in several B clusters are indicated by two or more dots connected by a line. The vertical bar plot indicates the number of DEGs representing each combination, while the horizontal bar plot indicates the number of DEGs between TOL and other groups in each B cell cluster.</p> Full article ">Figure 5
<p>Single-cell RNA sequencing from GZMB<sup>+</sup> B cells generated in vitro. (<b>A</b>) Schematic representation of the groups. RNA sequencing was performed on ex-vivo-induced GZMB<sup>+</sup> B cells from kidney-transplanted patients (STA, TOL, ABMR) prior to scRNAseq. (<b>B</b>) GZMB<sup>+</sup> B cells or GZMB<sup>−</sup> B cells were generated from sorted blood B cells for 3 days prior to scRNAseq. (<b>C</b>,<b>D</b>) UMAP representing the clustering of B cells according to the experimental design in all groups and per group, and violin plot showing the aggregated average expression levels of each gene of the regulatory B cell signature described by Dubois et al. [<a href="#B37-cells-13-01287" class="html-bibr">37</a>] at the single-cell level, subtracted by the aggregated expression of 100 control features within B cell clusters. Each dot represents a cell, and each colour represents either the GZMB<sup>−</sup> B cells (unstimulated B cells) or the GZMB<sup>+</sup> B cells (stimulated B cells) across UMAPs. UMAPs show all cells of the dataset (<b>C</b>) or are split according to the clinical groups (<b>D</b>). Differences were defined as statistically significant when <span class="html-italic">p</span> &lt; 0.0001 (****).</p> Full article ">Figure 6
<p>DEG specific to induced GZMB<sup>+</sup> B cells from TOL. (<b>A</b>) DEGs in GZMB<sup>+</sup> B cells compared to GZMB<sup>−</sup> B cells within groups were summarised in an UpSetPlot with the number of DEGs between GZMB<sup>+</sup> and GZMB<sup>−</sup> B cells. Solid black dots represent groups, and genes differentially expressed in several groups are indicated by two or more dots connected by a line. The vertical bar plot indicates the number of DEGs representing each combination, while the horizontal bar plot indicates the number of DEGs between GZMB<sup>+</sup> and GZMB<sup>−</sup> B cells in each group. (<b>B</b>) Similarity matrix of the 198 ontologies associated with the 316 differentially expressed genes common to all groups.</p> Full article ">Figure 7
<p>Induced GZMB<sup>+</sup> B cells’ and natural GZMB<sup>+</sup> B cells’ gene overlap. (<b>A</b>) The signatures of GZMB<sup>+</sup> B cells generated from the two datasets of induced GZMB<sup>+</sup> B cells (316 genes) and natural GZMB<sup>+</sup> B cells (114 genes) have been crossed. The 10 resulting genes are common to natural and induced GZMB<sup>+</sup> B cells and common to TOL, STA, and ABMR. (<b>B</b>) The signatures of GZMB<sup>+</sup> B cells in TOL generated from the two datasets of induced GZMB<sup>+</sup> B cells (449 genes) and natural GZMB<sup>+</sup> B cells (397 genes) have been crossed. The 56 resulting genes are common to natural and induced GZMB<sup>+</sup> B cells from TOL. The expression levels are shown by dotplots. The width of the dots represents the percentage of expressing cells for each condition. Genes upregulated in GZMB<sup>+</sup> B cells are associated with blue dots, and genes downregulated in GZMB<sup>+</sup> B cells are associated with yellow dots.</p> Full article ">Figure 8
<p>Natural GZMB<sup>+</sup> B cells ligand–receptor pairs formed with blood immune cells. Nichenet analysis was performed, and communications contributing to signalling from GZMB<sup>−</sup> and GZMB<sup>+</sup> B cells to other immune cells are shown in mushroom plots as ligand (blue) and receptor (red) expression across clusters. Each plot is associated with one population of target cells. The size of the semi-circles represents the percentage of cells expressing the gene, and the colour intensity represents the relative expression across B cell clusters (ligand) and immune populations (receptor).</p> Full article ">Figure 9
<p>GZMB<sup>+/−</sup> B cell communication with blood immune cells in TOL. (<b>A</b>) DEGs between TOL and STA or ABMR among dendritic cells (yellow), monocytes (dark blue), CD8 T (dark green), CD4 T (light blue), NK (vermilion), and B cells (orange) were used to infer ligand activity based on the Nichenet model. Only the 50 most common DEGs are represented in the dotplots. (<b>B</b>) Nichenet uses correlation matrix of ligand–target regulatory potential generated from public databases to infer ligand and receptor activity. (<b>C</b>) Ligands predicted to be associated with the transcriptional profile in TOL were then visualised within B cell clusters, as represented with dotplots. (<b>D</b>) Circos plot visualisation of predicted ligands on GZMB<sup>+</sup> and GZMB<sup>−</sup> B cell clusters and their targets on the different immune populations. The width of the arrows represents the strength of the interactions according to the Nichenet model. Target genes are coloured according to the immune population in which they are differentially expressed (same colours as <a href="#cells-13-01287-f007" class="html-fig">Figure 7</a>A). Arrows are coloured according to the B cell cluster overexpressing the associated ligand (same colours as <a href="#cells-13-01287-f007" class="html-fig">Figure 7</a>C). Only the 2000 strongest associations are represented (ligand to target pairs, represented by the arrows). (<b>E</b>) Expressions of ligands assigned to GZMB<sup>+</sup> B cell Cluster 3 are represented by violin plots.</p> Full article ">Figure 10
<p>GZMB<sup>+</sup> B cells infiltrate the graft under inflammatory conditions. IHC staining using the OPAL multiplex system in one representative biopsy of patient with plasma-cell-rich rejection (first row), mixed rejection (second row), tolerance (third row), and stability (last row) at 50 and 200 µM. Enlargement of one representative CD19<sup>+</sup> GZMB<sup>+</sup> cell was performed for all patients. For all images, the 3 fluorescence channels are merged to form one picture coloured by canal (DAPI—blue; CD19—green; GZMB—red).</p> Full article ">Figure 10 Cont.
<p>GZMB<sup>+</sup> B cells infiltrate the graft under inflammatory conditions. IHC staining using the OPAL multiplex system in one representative biopsy of patient with plasma-cell-rich rejection (first row), mixed rejection (second row), tolerance (third row), and stability (last row) at 50 and 200 µM. Enlargement of one representative CD19<sup>+</sup> GZMB<sup>+</sup> cell was performed for all patients. For all images, the 3 fluorescence channels are merged to form one picture coloured by canal (DAPI—blue; CD19—green; GZMB—red).</p> Full article ">
18 pages, 4874 KiB  
Article
A Novel Liquid Biopsy Method Based on Specific Combinations of Vesicular Markers Allows Us to Discriminate Prostate Cancer from Hyperplasia
by Emanuele Martorana, Gabriele Raciti, Raffaella Giuffrida, Elena Bruno, Vincenzo Ficarra, Giuseppe Mario Ludovico, Nazareno Roberto Suardi, Nunzio Iraci, Loredana Leggio, Benedetta Bussolati, Cristina Grange, Aurelio Lorico, Rosario Leonardi and Stefano Forte
Cells 2024, 13(15), 1286; https://doi.org/10.3390/cells13151286 - 31 Jul 2024
Cited by 1 | Viewed by 1541
Abstract
Background: Prostate cancer is the second most common cancer in males worldwide, and its incidence is rising. Early detection is crucial for improving the outcomes, but the current screening methods have limitations. While prostate-specific antigen (PSA) testing is the most widely used screening [...] Read more.
Background: Prostate cancer is the second most common cancer in males worldwide, and its incidence is rising. Early detection is crucial for improving the outcomes, but the current screening methods have limitations. While prostate-specific antigen (PSA) testing is the most widely used screening tool, it has poor specificity, leading to a high rate of false positives and unnecessary biopsies. The existing biopsy techniques are invasive and are associated with complications. The liquid biopsy methods that analyze the biomarkers in blood or other bodily fluids offer a non-invasive and more accurate alternative for detecting and characterizing prostate tumors. Methods: Here, we present a novel liquid biopsy method for prostate cancer based on the identification of specific proteins in the extracellular vesicles isolated from the blood of patients with prostate cancer. Results: We observed that a specific combination of sEV proteins is a sensitive indicator of prostate cancer. Indeed, we found that the number of clusters expressed by specific combinations of either intra-vesicular (STAT3 and CyclinD1) or surface proteins (ERBB3, ALK, and CD81) allowed us to significantly discriminate the patients with prostate cancer from the individuals with hyperplasia. Conclusion: This new liquid biopsy method has the potential to improve prostate cancer screening by providing a non-invasive and more accurate diagnostic tool. Full article
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<p>EV characterization. (<b>A</b>) Nanotracking analysis of PCa and BPH samples showing EV size and concentration. (<b>B</b>) Representative images of sEV acquisitions by SEM at different magnifications. (<b>C</b>) Western Blot analysis on Jurkat (J) and vesicular lysates (1, 2, 3, and 4) to detect CD45, Alix, and β-actin. Abbreviations: PCa: prostate cancer; BPH: benign prostate hyperplasia; sEVs: small extracellular vesicles; SEM: scanning electron microscopy; J: Jurkat cell lysate; M: marker.</p> Full article ">Figure 2
<p>Super-resolution microscopy-based EV characterization. Representative images of single sEVs expressing only one (single labelling) of the analyzed markers (<b>A</b>) and co-expressing two (<b>B</b>) or three (<b>C</b>) markers contemporarily (double and triple labelling, respectively). Super-resolution microscopy field of view of sEVs isolated from PCa sample with scale bar of 20 µm (<b>D</b>). Pie charts of BPH surface-only (<b>E</b>) and surface/intra-vesicular (<b>F</b>) markers distribution and of PCa surface-only (<b>G</b>) and surface/intra-vesicular (<b>H</b>) ones. Surface-only (<b>I</b>) and surface/intra-vesicular (<b>J</b>) cluster counts for both types of all analyzed samples. Abbreviations: sEVs: small extracellular vesicles; PCa: prostate cancer; BPH: benign prostate hyperplasia; AF: alexa fluor dye; CF: cyanine-based far red fluorescent dye.</p> Full article ">Figure 2 Cont.
<p>Super-resolution microscopy-based EV characterization. Representative images of single sEVs expressing only one (single labelling) of the analyzed markers (<b>A</b>) and co-expressing two (<b>B</b>) or three (<b>C</b>) markers contemporarily (double and triple labelling, respectively). Super-resolution microscopy field of view of sEVs isolated from PCa sample with scale bar of 20 µm (<b>D</b>). Pie charts of BPH surface-only (<b>E</b>) and surface/intra-vesicular (<b>F</b>) markers distribution and of PCa surface-only (<b>G</b>) and surface/intra-vesicular (<b>H</b>) ones. Surface-only (<b>I</b>) and surface/intra-vesicular (<b>J</b>) cluster counts for both types of all analyzed samples. Abbreviations: sEVs: small extracellular vesicles; PCa: prostate cancer; BPH: benign prostate hyperplasia; AF: alexa fluor dye; CF: cyanine-based far red fluorescent dye.</p> Full article ">Figure 3
<p>The cluster counts in the tumor and hyperplasia samples for the surface and intra-vesicular targets. The subfigures represent clusters expressing ERBB3 and ALK (<b>A</b>); ERBB3, ALK, and CD81 (<b>B</b>); STAT3 (<b>C</b>); STAT3 and CD81 (<b>D</b>); STAT3 and CyclinD1 (<b>E</b>); and STAT3, CyclinD1, and CD81 (<b>F</b>). Abbreviations: PCa: prostate cancer; BPH: benign prostate hyperplasia. Symbols represent: “*” <span class="html-italic">p</span> &lt; 0.05; “ns” <span class="html-italic">p</span> ≥ 0.05; “•”refer to values out of ±1.5 * IQR.</p> Full article ">Figure 4
<p>The receiver operating characteristic curves for the intra-vesicular and surface targets. ROC curves for intra-vesicular and surface targets with the most differentiated counts: ERBB3, ALK (<b>A</b>); ERBB3, ALK, and CD81 (<b>B</b>); STAT3 (<b>C</b>); STAT3 and CD81 (<b>D</b>); STAT3 and CyclinD1 (<b>E</b>); and STAT3, CyclinD1, and CD81 (<b>F</b>).</p> Full article ">Figure 5
<p>K-means cluster analysis. The above graph was generated from the analysis of the four most characterizing targets: STAT3; STAT3 and CD81; STAT3 and CyclinD1; and STAT3, CyclinD1, and CD81.</p> Full article ">Figure 6
<p>Cluster counts according to Gleason score for ERBB3 and CD81 markers. Boxplots show higher ERBB3 and CD81 levels in GS7(4 + 3) than those in GS7(3 + 4). These <span class="html-italic">p</span>-values were not adjusted due to small sample size. Symbol “•”represent values out of ±1.5 * IQR.</p> Full article ">Figure 7
<p>Correlation plots of surface markers with PSA values. The presented plots highlight the correlation between the PSA values and the cluster counts for both the CD81, ALK surface protein (<b>A</b>) and CD81 alone (<b>B</b>), showing a weak-to-moderate but statistically significant correlation.</p> Full article ">
2 pages, 562 KiB  
Correction
Correction: Bellu et al. Smart Nanofibers with Natural Extracts Prevent Senescence Patterning in a Dynamic Cell Culture Model of Human Skin. Cells 2020, 9, 2530
by Emanuela Bellu, Giuseppe Garroni, Sara Cruciani, Francesca Balzano, Diletta Serra, Rosanna Satta, Maria Antonia Montesu, Angela Fadda, Maurizio Mulas, Giorgia Sarais, Pasquale Bandiera, Elena Torreggiani, Fernanda Martini, Mauro Tognon, Carlo Ventura, Jiří Beznoska, Evzen Amler and Margherita Maioli
Cells 2024, 13(15), 1285; https://doi.org/10.3390/cells13151285 - 31 Jul 2024
Viewed by 554
Abstract
In the original publication [...] Full article
(This article belongs to the Special Issue Stem Cell-Immune Function and Cardiac Regeneration)
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<p>Senescence-associated β-galactosidase activity evaluated in HFF1 (<b>a</b>) and skin stem cells (<b>b</b>) after seven days. Cells pretreated with NanoPCL-M (T) are compared to control untreated cells (C) and UV stress control (CS). Scale bar = 100 µm. The number of blue positive HFF1 (<b>c</b>) and skin stem (<b>d</b>) was calculated using ImageJ. Data are expressed as mean ± SD.</p> Full article ">
23 pages, 2669 KiB  
Review
Nuclear Receptors and the Hidden Language of the Metabolome
by Yujie Chen, Matthew Tom Anderson, Nathaniel Payne, Fabio R. Santori and Natalia B. Ivanova
Cells 2024, 13(15), 1284; https://doi.org/10.3390/cells13151284 - 31 Jul 2024
Viewed by 1210
Abstract
Nuclear hormone receptors (NHRs) are a family of ligand-regulated transcription factors that control key aspects of development and physiology. The regulation of NHRs by ligands derived from metabolism or diet makes them excellent pharmacological targets, and the mechanistic understanding of how NHRs interact [...] Read more.
Nuclear hormone receptors (NHRs) are a family of ligand-regulated transcription factors that control key aspects of development and physiology. The regulation of NHRs by ligands derived from metabolism or diet makes them excellent pharmacological targets, and the mechanistic understanding of how NHRs interact with their ligands to regulate downstream gene networks, along with the identification of ligands for orphan NHRs, could enable innovative approaches for cellular engineering, disease modeling and regenerative medicine. We review recent discoveries in the identification of physiologic ligands for NHRs. We propose new models of ligand-receptor co-evolution, the emergence of hormonal function and models of regulation of NHR specificity and activity via one-ligand and two-ligand models as well as feedback loops. Lastly, we discuss limitations on the processes for the identification of physiologic NHR ligands and emerging new methodologies that could be used to identify the natural ligands for the remaining 17 orphan NHRs in the human genome. Full article
(This article belongs to the Collection Functions of Nuclear Receptors)
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<p>NHRs are classified using a standardized nomenclature [<a href="#B8-cells-13-01284" class="html-bibr">8</a>]. Functionally, the 7 subfamilies of NHRs in the human genome can be grouped into 4 classes [<a href="#B9-cells-13-01284" class="html-bibr">9</a>]: (<b>A</b>) Class I NHRs are ligand induced receptors that bind to DNA palindromic repeats as homodimers such as the steroid hormone receptors [<a href="#B9-cells-13-01284" class="html-bibr">9</a>,<a href="#B10-cells-13-01284" class="html-bibr">10</a>]. (<b>B</b>) Class II NHRs form heterodimers with the members of the NR2B group (RXRs) and bind to direct repeats in the DNA sequence [<a href="#B9-cells-13-01284" class="html-bibr">9</a>,<a href="#B10-cells-13-01284" class="html-bibr">10</a>]. Some examples of Class II NHRs are the members of the NR1B group (RAR) and NR1I1 (VDR) [<a href="#B11-cells-13-01284" class="html-bibr">11</a>]. (<b>C</b>) Class III receptors form homodimers that bind to direct repeats in the DNA [<a href="#B9-cells-13-01284" class="html-bibr">9</a>]. An example of Class III NHR are COUP-TFs (NR2F1 and NR2F2) [<a href="#B12-cells-13-01284" class="html-bibr">12</a>]. (<b>D</b>) Class IV NHRs bind to DNA extended core sites as monomers [<a href="#B9-cells-13-01284" class="html-bibr">9</a>]. NR4A1 (NUR77) and NR5A1 (SF-1) are Class IV NHRs [<a href="#B13-cells-13-01284" class="html-bibr">13</a>,<a href="#B14-cells-13-01284" class="html-bibr">14</a>]. (<b>E</b>) NHRs of all classes have a modular structure with 4 domains: Two transactivation domains, activation function (AF-1) at the N-terminus and AF-2 at the C-terminus [<a href="#B15-cells-13-01284" class="html-bibr">15</a>] and between AF-1 and AF-2 there is a DBD and LBD [<a href="#B15-cells-13-01284" class="html-bibr">15</a>]. The modular structure is not rigid and variations in the basic design are common. For example, the estrogen receptor (NR3A1) has isoforms that lack the AF-1 domain [<a href="#B16-cells-13-01284" class="html-bibr">16</a>] and the glucocorticoid receptor (NR3C1) has isoforms that lack the AF-2 domain [<a href="#B17-cells-13-01284" class="html-bibr">17</a>]. The loss or truncation of the AF-1 domain in steroid receptors can result in tissue-specific patterns of ligand action [<a href="#B16-cells-13-01284" class="html-bibr">16</a>]. The loss of the AF-2 domain affects both ligand binding and transcriptional activity generating isoforms that are transcriptionally repressive [<a href="#B17-cells-13-01284" class="html-bibr">17</a>]. Furthermore, there are isoforms of the androgen receptor (NR3C4) that lack an LBD domain [<a href="#B18-cells-13-01284" class="html-bibr">18</a>]. Finally, some NHRs that function as transcriptional repressors lack the DBD domain. These include NR0B1 (DAX1) and NR0B2 (SHP1) and isoforms of NR2F2 (COUP-TFII) [<a href="#B19-cells-13-01284" class="html-bibr">19</a>,<a href="#B20-cells-13-01284" class="html-bibr">20</a>,<a href="#B21-cells-13-01284" class="html-bibr">21</a>]. (<b>F</b>) NHR nomenclature and class distribution. The table contains the standard NHR nomenclature [<a href="#B8-cells-13-01284" class="html-bibr">8</a>], the common name, the official gene symbol defined by the HUGO gene nomenclature committee and the official gene name. The last column shows the classification of each receptor by class I to IV (panels (<b>A</b>–<b>D</b>)), which is based on references [<a href="#B22-cells-13-01284" class="html-bibr">22</a>,<a href="#B23-cells-13-01284" class="html-bibr">23</a>,<a href="#B24-cells-13-01284" class="html-bibr">24</a>,<a href="#B25-cells-13-01284" class="html-bibr">25</a>,<a href="#B26-cells-13-01284" class="html-bibr">26</a>].</p> Full article ">Figure 2
<p>Orthosteric and Allosteric NHR ligands. Orthosteric ligands bind inside the NHR LBP while allosteric ligands bind on alternative sites in the LBD. Most known physiologic ligands for NHRs, such as retinoic acid and steroid hormones, are orthosteric. In contrast, the concept of physiologic allosteric ligands for NHRs is relatively novel [<a href="#B32-cells-13-01284" class="html-bibr">32</a>,<a href="#B33-cells-13-01284" class="html-bibr">33</a>] and understudied. However, they add an important conceptual tool for understanding NHR transcriptional activity. Physiologic allosteric ligands are defined as those metabolites that regulate receptor activity by binding outside the NHR LBP. Allosteric ligands were first described for estrogen receptors. The oxysterols 24(S), 25 and 27-hydroxycholesterol are natural metabolites that bind and inhibit the transcriptional activity of the estrogen receptors ERα (NR3A1) and ERβ (NR3A2) at physiologic concentrations [<a href="#B32-cells-13-01284" class="html-bibr">32</a>,<a href="#B33-cells-13-01284" class="html-bibr">33</a>]. Other examples of natural allosteric ligands are metabolites of vitamin E (α-tocopherol) which bind to PPARα (NR1C1) and PPARγ (NR1C3) [<a href="#B34-cells-13-01284" class="html-bibr">34</a>,<a href="#B35-cells-13-01284" class="html-bibr">35</a>] in vitro, and thyroid hormone (T3), which binds to an allosteric site of the androgen receptor [<a href="#B36-cells-13-01284" class="html-bibr">36</a>]. However, the low concentration of metabolites of α-tocopherol [<a href="#B37-cells-13-01284" class="html-bibr">37</a>] and T3 [<a href="#B38-cells-13-01284" class="html-bibr">38</a>] in serum suggests that these allosteric ligands are not physiologic.</p> Full article ">Figure 3
<p>NHR families in the animal kingdom. The first authentic NHRs are found in sponges like the demosponge <span class="html-italic">Amphimedon queenslandica</span>. These NHRs recognize saturated and monounsaturated fatty acids and include an ortholog of the mammalian NR2A group [<a href="#B49-cells-13-01284" class="html-bibr">49</a>], which are known receptors for saturated and monounsaturated fatty acids [<a href="#B50-cells-13-01284" class="html-bibr">50</a>,<a href="#B51-cells-13-01284" class="html-bibr">51</a>,<a href="#B52-cells-13-01284" class="html-bibr">52</a>]. Next, the placozoan <span class="html-italic">Trichoplax adhaerens</span> has 4 NHRs, including orthologs for the NR2A, NR2B [<a href="#B53-cells-13-01284" class="html-bibr">53</a>] and the NR2F groups [<a href="#B54-cells-13-01284" class="html-bibr">54</a>]. The RXR of <span class="html-italic">Trichoplax adhaerens</span> recognizes a terpenoid [<a href="#B55-cells-13-01284" class="html-bibr">55</a>,<a href="#B56-cells-13-01284" class="html-bibr">56</a>,<a href="#B57-cells-13-01284" class="html-bibr">57</a>], suggesting that the branching of fatty acid versus terpenoid ligands occurred early in evolution. In cnidarians such as <span class="html-italic">Nematostella vectensis</span>, there are 17 NHRs, including orthologs for all members of the NR2 subfamily as well as potential ancestors for the remaining NHR subfamilies [<a href="#B58-cells-13-01284" class="html-bibr">58</a>].</p> Full article ">Figure 4
<p>NHR–ligand co-evolution. Ligands exert selective pressure on NHRs. (<b>A</b>) Structure of cholesterol. (<b>B</b>) Evolution of steroid hormones from a cholesterol-like metabolite. (<b>C</b>) Adaptation of FXR to recognize bile acids by differences of 2D and 3D structures.</p> Full article ">Figure 5
<p>Homeostasis of Vitamin D action is regulated through a direct feedback loop. In the presence of vitamin D (1α,25-dihydroxyD3), the VDR binds to the promoters and enhancers of the enzymes that are responsible for the activation of vitamin D (CYP27B1) [<a href="#B137-cells-13-01284" class="html-bibr">137</a>,<a href="#B138-cells-13-01284" class="html-bibr">138</a>] and its catabolism (CYP24A1) [<a href="#B139-cells-13-01284" class="html-bibr">139</a>,<a href="#B140-cells-13-01284" class="html-bibr">140</a>,<a href="#B141-cells-13-01284" class="html-bibr">141</a>]. VDR binding leads to the transcriptional repression of the vitamin D activating enzyme CYP27B1 [<a href="#B137-cells-13-01284" class="html-bibr">137</a>,<a href="#B138-cells-13-01284" class="html-bibr">138</a>,<a href="#B142-cells-13-01284" class="html-bibr">142</a>], and transcriptional activation of the vitamin D inactivating enzyme CYP24A1 [<a href="#B139-cells-13-01284" class="html-bibr">139</a>,<a href="#B140-cells-13-01284" class="html-bibr">140</a>,<a href="#B141-cells-13-01284" class="html-bibr">141</a>]. This is reflected by increased levels of vitamin D in VDR knockout mice caused by lack of both transcriptional repression of Cyp27b1 and transcriptional induction of Cyp24a1 [<a href="#B142-cells-13-01284" class="html-bibr">142</a>].</p> Full article ">Figure 6
<p>The isolation and identification of ecdysone as the hormone that regulates molting and metamorphosis in insects. Insects have a chitin-based exoskeleton and need to molt in order to grow. Molting in the kissing bug <span class="html-italic">Rhodnius prolixus</span> is inhibited by decapitation [<a href="#B178-cells-13-01284" class="html-bibr">178</a>] and metamorphosis in butterflies and moths (<span class="html-italic">Lepidoptera</span>) is blocked by removing the brain in these animals [<a href="#B179-cells-13-01284" class="html-bibr">179</a>]. This suggested that the head of insects produces a “hormone” that is released into the hemolymph to promote molting or metamorphosis [<a href="#B178-cells-13-01284" class="html-bibr">178</a>]. To identify this hormone, a sensitive bioassay was developed in which a ligature was used to block the circulation of hemolymph from the head to the rest of the body of blow fly larvae [<a href="#B180-cells-13-01284" class="html-bibr">180</a>]. Under these circumstances, the head will undergo metamorphosis while the rest of the body remains in the larval stage. However, these larvae will undergo metamorphosis when injected with insect extracts that contain the hormone [<a href="#B180-cells-13-01284" class="html-bibr">180</a>]. In this manner, ecdysone was isolated [<a href="#B181-cells-13-01284" class="html-bibr">181</a>], characterized [<a href="#B182-cells-13-01284" class="html-bibr">182</a>,<a href="#B183-cells-13-01284" class="html-bibr">183</a>] and synthesized [<a href="#B184-cells-13-01284" class="html-bibr">184</a>,<a href="#B185-cells-13-01284" class="html-bibr">185</a>,<a href="#B186-cells-13-01284" class="html-bibr">186</a>].</p> Full article ">
15 pages, 1224 KiB  
Review
The Need to Identify Novel Markers for Early Renal Injury in Cardiorenal Syndrome
by Anna Lisa, Federico Carbone, Luca Liberale and Fabrizio Montecucco
Cells 2024, 13(15), 1283; https://doi.org/10.3390/cells13151283 - 30 Jul 2024
Cited by 1 | Viewed by 2207
Abstract
The term “Cardiorenal Syndrome” (CRS) refers to the complex interplay between heart and kidney dysfunction. First described by Robert Bright in 1836, CRS was brought to its modern view by Ronco et al. in 2008, who defined it as one organ’s primary dysfunction [...] Read more.
The term “Cardiorenal Syndrome” (CRS) refers to the complex interplay between heart and kidney dysfunction. First described by Robert Bright in 1836, CRS was brought to its modern view by Ronco et al. in 2008, who defined it as one organ’s primary dysfunction leading to secondary dysfunction in the other, a view that led to the distinction of five different types depending on the organ of primary dysfunction and the temporal pattern (acute vs. chronic). Their pathophysiology is intricate, involving various hemodynamic, neurohormonal, and inflammatory processes that result in damage to both organs. While traditional biomarkers have been utilized for diagnosing and prognosticating CRS, they are inadequate for the early detection of acute renal damage. Hence, there is a pressing need to discover new biomarkers to enhance clinical outcomes and treatment approaches. Full article
(This article belongs to the Special Issue Acute Kidney Injury: From Molecular Mechanisms to Diseases)
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<p>Classification of cardiorenal syndromes. CRSs are classified based on the primary involvement of the kidney or the heart and based on its temporal progression. Type I and II include cardiac conditions causing a secondary damage to the kidney in an acute or chronic fashion, respectively. Type III and IV encompass kidney dysfunction that damages the heart in an acute or chronic fashion, respectively. Finally, type V includes systemic afflictions that affects both cardiac and kidney functions.</p> Full article ">Figure 2
<p>Underlying mechanisms for CRS type I and type II. CRS type I and II are due to cardiac afflictions primarily altering kidney function. Its pathogenesis involves the increased activation of the sympathetic nervous system (SNS), as well as a rise in inflammatory mediators and the renin–angiotensin–aldosterone system (RAAS). Additionally, hemodynamic alterations such as renal hypoperfusion play a significant role.</p> Full article ">Figure 3
<p>Underlying mechanisms for the CRS type III and type IV. CRS type III and IV are characterized by acute or chronic alterations in renal function leading to cardiac disease. The underlying pathophysiology is not well understood but suggests a bidirectional relationship between the kidney and heart, involving inflammatory processes and physiological imbalances such as acid–base disturbances, electrolyte abnormalities, volume overload, chronic inflammation, endothelial dysfunction, and the toxic effects of the uremic environment.</p> Full article ">Figure 4
<p>Underlying mechanisms for CRS type V. CRS type V occurs due to simultaneous kidney and heart dysfunction in systemic conditions. It can be acute, often due to sepsis or toxic drugs, or chronic, commonly associated with diabetes, hypertension, or amyloidosis.</p> Full article ">
18 pages, 850 KiB  
Review
Cell-Based Therapy and Genome Editing as Emerging Therapeutic Approaches to Treat Rheumatoid Arthritis
by Vitaly Chasov, Irina Ganeeva, Ekaterina Zmievskaya, Damir Davletshin, Elvina Gilyazova, Aygul Valiullina and Emil Bulatov
Cells 2024, 13(15), 1282; https://doi.org/10.3390/cells13151282 - 30 Jul 2024
Viewed by 1287
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation of the joints. Although much remains unknown about the pathogenesis of RA, there is evidence that impaired immune tolerance and the development of RA are related. And it is precisely the restoration [...] Read more.
Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation of the joints. Although much remains unknown about the pathogenesis of RA, there is evidence that impaired immune tolerance and the development of RA are related. And it is precisely the restoration of immune tolerance at the site of the inflammation that is the ultimate goal of the treatment of RA. Over the past few decades, significant progress has been made in the treatment of RA, with higher rates of disease remission and improved long-term outcomes. Unfortunately, despite these successes, the proportion of patients with persistent, difficult-to-treat disease remains high, and the task of improving our understanding of the basic mechanisms of disease development and developing new ways to treat RA remains relevant. This review focuses on describing new treatments for RA, including cell therapies and gene editing technologies that have shown potential in preclinical and early clinical trials. In addition, we discuss the opportunities and limitations associated with the use of these new approaches in the treatment of RA. Full article
(This article belongs to the Special Issue Advances in Cellular and Molecular Treatment of Autoimmune Diseases)
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<p>Potential novel strategies for targeting RA. Cell-based and genome-editing therapies for the treatment of RA include microRNA therapy, genome-editing using the CRISPR-Cas9 system, mesenchymal stem cell therapy, adoptive Treg cell transfer, and CAR T-cell therapy.</p> Full article ">Figure 2
<p>Four different approaches in CAR-T cell therapy for treatment of RA. (<b>A</b>) shows universal anti-FITC CAR-T cells. Using FITC-labeled autoantibody-positive citrullinated peptides, including citrullinated vimentin (1), citrullinated type II collagen (2), citrullinated fibrinogen (3), and tenascin C (4), as mediators, anti-FITC CAR-T cells can eliminate autoreactive B cells through peptide-mediated CAR-T cytotoxicity. (<b>B</b>) shows anti-DR1-CII CAR-T cells. In these CAR-T cells, the HLA-DRB1*01:01 (DR1) CAR molecule contains a covalently linked type II collagen autoantigenic peptide (CII) to specifically recognize and deplete autoimmune CD4+ T cells. (<b>C</b>) shows CAAR-T cell that recognizes autoantibody on the surface of the target B cell and induces a cytotoxic effect. (<b>D</b>) shows CAR-Treg that recognizes an antigen on the target cell and induces a regulatory response.</p> Full article ">
38 pages, 1881 KiB  
Review
Therapy-Induced Cellular Senescence: Potentiating Tumor Elimination or Driving Cancer Resistance and Recurrence?
by Yue Liu, Isabelle Lomeli and Stephen J. Kron
Cells 2024, 13(15), 1281; https://doi.org/10.3390/cells13151281 - 30 Jul 2024
Viewed by 2534
Abstract
Cellular senescence has been increasingly recognized as a hallmark of cancer, reflecting its association with aging and inflammation, its role as a response to deregulated proliferation and oncogenic stress, and its induction by cancer therapies. While therapy-induced senescence (TIS) has been linked to [...] Read more.
Cellular senescence has been increasingly recognized as a hallmark of cancer, reflecting its association with aging and inflammation, its role as a response to deregulated proliferation and oncogenic stress, and its induction by cancer therapies. While therapy-induced senescence (TIS) has been linked to resistance, recurrence, metastasis, and normal tissue toxicity, TIS also has the potential to enhance therapy response and stimulate anti-tumor immunity. In this review, we examine the Jekyll and Hyde nature of senescent cells (SnCs), focusing on how their persistence while expressing the senescence-associated secretory phenotype (SASP) modulates the tumor microenvironment through autocrine and paracrine mechanisms. Through the SASP, SnCs can mediate both resistance and response to cancer therapies. To fulfill the unmet potential of cancer immunotherapy, we consider how SnCs may influence tumor inflammation and serve as an antigen source to potentiate anti-tumor immune response. This new perspective suggests treatment approaches based on TIS to enhance immune checkpoint blockade. Finally, we describe strategies for mitigating the detrimental effects of senescence, such as modulating the SASP or targeting SnC persistence, which may enhance the overall benefits of cancer treatment. Full article
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<p><b>Hallmarks of cellular senescence.</b> Cellular senescence is characterized by an exit from proliferation and persistent cell cycle arrest, accompanied by a host of cellular changes. In culture, SnCs display an enlarged and flattened morphology, altered nuclear shape, and prominent intracellular vesicles, which can be identified through phase contrast or brightfield microscopy. The increased vesicular content likely underlies the activation of GLB1 lysosomal β-galactosidase, a biochemical marker known as senescence-associated β-galactosidase (SA-β-gal), detectable with X-Gal or other chromogenic and fluorogenic β-galactosidase reporters. Other features include a chronically active DNA damage response (DDR), characterized by persistent nuclear foci of phosphorylated histone H2AX. These foci may form DNA segments with chromatin alterations reinforcing senescence (DNA-SCARS) or telomere dysfunction-induced foci (TIF) when associated with promyelocytic leukemia protein (PML) nuclear bodies. Additional nuclear changes in SnCs include decreased lamins, chromatin remodeling, and senescence-associated heterochromatin foci (SAHF). SnC persistence reflects resistance to apoptosis through the overexpression of anti-apoptotic proteins such as the Bcl-2 family members. Another hallmark of senescence is the accumulation of dysfunctional mitochondria, contributing to increased levels of reactive oxygen species (ROS) and oxidative damage. SnCs also display altered surface protein expression. Despite growth arrest, SnCs remain metabolically active and experience significant metabolic changes. A key feature of SnCs is the senescence-associated secretory phenotype (SASP), characterized by the release of a wide range of bioactive molecules and extracellular vesicles. The accumulation of single and double-stranded cytosolic DNA and chromatin fragments, including those from retrotransposon reverse transcription via LINE1, chromosomal DNA damage processing, dysfunctional mitochondria, and micronuclei, can contribute to SASP through the cGAS-STING pathway. MHC, major histocompatibility complex; DPP4, dipeptidyl peptidase 4.</p> Full article ">Figure 2
<p><b>Metabolic changes in senescent cells.</b> SnCs can display a Warburg effect-like pattern by continuing glycolysis regardless of oxygen availability. This shift is often accompanied by the upregulation of glycolytic enzymes. SnCs may also exhibit enhanced beta-oxidation and oxygen consumption. Lipid metabolism shifts toward increased fatty acid synthesis, along with increased lipid uptake, resulting in lipid droplet accumulation. Overall, SnC metabolic changes can contribute to elevated ROS levels. As ROS levels rise, oxidative damage occurs across various cellular components, causing depletion of antioxidants, lipid peroxidation, DNA damage, and protein carbonylation. Labile iron pools in SnCs can exacerbate oxidative damage by facilitating the conversion of relatively mild oxidants into highly reactive free radicals via the Fenton reaction. Furthermore, oxidative stress compromises the lysosomal and proteasomal systems. These changes contribute to the formation of lipofuscin, a complex aggregate of highly cross-linked non-degradable proteins, carbohydrates, lipids, and transition metals.</p> Full article ">Figure 3
<p><b>Positive and negative effects of cellular senescence in the tumor microenvironment:</b> (<b>Left</b>) The anti-tumor effects of cellular senescence. SnCs contribute to anti-tumor defenses by facilitating the recruitment and activation of immune cells such as phagocytes (e.g., Mφ), dendritic cells (DCs), T cells, and natural killer (NK) cells. This is achieved through SASP factors and surface proteins, including class I MHC, NKG2D ligands, and ICAM-1. Ds activation by SnCs provides an additional mechanism for cytotoxic lymphocyte-mediated tumor control. Senescence can also promote angiogenesis, which contributes to the mobilization of immune cells to the tumor site. Beyond immune-mediated actions, SnCs can maintain and spread the senescent state through autocrine and paracrine mechanisms that suppress cell proliferation, with interleukin signals, particularly IL-1, implicated in paracrine senescence. (<b>Right</b>) The pro-tumor effects of cellular senescence. SnCs can dampen cytotoxic lymphocyte function by expressing certain surface molecules, including the non-canonical class I MHC molecule HLA-E and immune checkpoint PD-L1/2. The SASP also facilitates recruitment and differentiation of immunosuppressive cells, such as myeloid-derived suppressor cells (MDSCs) and M2-like macrophages, which inhibit NK and T cell function. Additionally, SnCs may contribute to tumor progression by promoting non-immunologic processes such as epithelial–mesenchymal transition (EMT), vasculogenesis, cancer cell reprogramming, malignant transformation, and hyperproliferation.</p> Full article ">
18 pages, 3802 KiB  
Article
Discovery of Cell Number-Interstitial Fluid Volume (CIF) Ratio Reveals Secretory Autophagy Pathway to Supply eHsp90α for Wound Healing
by Cheng Chang, Xin Tang, Axel H. Schönthal, Mei Chen, David T. Woodley, Yanzhuang Wang, Chengyu Liang and Wei Li
Cells 2024, 13(15), 1280; https://doi.org/10.3390/cells13151280 - 30 Jul 2024
Viewed by 1049
Abstract
Cell secretion repairs tissue damage and restores homeostasis throughout adult life. The extracellular heat shock protein-90alpha (eHsp90α) has been reported as an exosome cargo and a potential driver of wound healing. However, neither the mechanism of secretion nor the genetic evidence for eHsp90α [...] Read more.
Cell secretion repairs tissue damage and restores homeostasis throughout adult life. The extracellular heat shock protein-90alpha (eHsp90α) has been reported as an exosome cargo and a potential driver of wound healing. However, neither the mechanism of secretion nor the genetic evidence for eHsp90α in wound healing has been substantiated. Herein, we show that tissue injury causes massive deposition of eHsp90α in tissues and secretion of eHsp90α by cells. Sequential centrifugations of conditioned medium from relevant cell lines revealed the relative distributions of eHsp90α in microvesicle, exosome and trypsin-sensitive supernatant fractions to be approximately <2%, <4% and >95%, respectively. Establishing the cell-number-to-interstitial-fluid-volume (CIF) ratio for the microenvironment of human tissues as 1 × 109 cells: 1 mL interstitial fluid enabled us to predict the corresponding tissue concentrations of eHsp90α in these fractions as 3.74 μg/mL, 5.61 μg/mL and 178 μg/mL. Remarkably, the 178 μg/mL eHsp90α matches the previously reported 100–300 μg/mL of recombinant eHsp90α whose topical application promotes maximum wound healing in animal models. More importantly, we demonstrate that two parallel secretory autophagy-regulating gene families, the autophagy-regulating (AR) genes and the Golgi reassembly-stacking protein (GRASP) genes work together to mediate the secretion of the physiological concentration of eHsp90α to promote wound healing. Thus, utilization of the CIF ratio-based extrapolation method may enable investigators to rapidly predict biomarker targets from cell-conditioned-medium data. Full article
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Figure 1

Figure 1
<p>Tissue injury induces massive secretion of eHsp90α in vivo and in vitro. (<b>A</b>) Three types of cells, as indicated, were subjected to three kinds of known wound-stress signals: TGFα, hypoxia and nutrient deprivation. CM from the equalized number of each cell type were concentrated and analyzed by western blot analysis with anti-Hsp90α antibodies. (<b>B</b>) Wedge biopsies of 1.5 cm full-thickness pig skin wounds on day 0 and day 4 were subjected to anti-Hsp90α antibody staining. The red arrows point out the areas of the specific antibody staining (brown). Gabriel Landini’s color deconvolution plugin and ImageJ analysis were used to quantitate the intensity of the staining as optical density (OD) (dashed blue boxes). (<b>C</b>) Similar wedge biopsies were stained with an anti-keratin antibody to indicate the location of epidermis. The data of lung-injury and liver-tumor mouse models, as well as the original films of the western blots, are included on pages 1 and 2 in <a href="#app1-cells-13-01280" class="html-app">Supplemental Data</a>. Epi. Epidermis; Der. Dermis.</p> Full article ">Figure 2
<p>Exosome-independent secretion controls 95% of secreted eHsp90α. (<b>A</b>) Comparison of eHsp90α secretion among four cell types under serum-free conditions. CMs of equalized numbers of cells were blotted with an anti-Hsp90α antibody and ECL on films bands quantitated using ImageJ. (<b>B</b>) A schematic illustration of a modified CM fractionation protocol, resulting in MV, Exo and Sup fractions for further analyses. (<b>C</b>) In order to visualize eHsp90α in all three fractions all at once, the MV and Exo fractions from CM of an entire 15-cm dish and the Sup fraction from 8% CM of a 15-cm dish were loaded on an SDS-PAGE and subjected to western blot analysis with an anti-Hsp90α antibody. The exosomal marker CD9 was included to show the success of the fraction procedures. ImageJ data are shown below the bands. (<b>D</b>) Calculation of the percentage of eHsp90α in each of the three fractions. (<b>E</b>) EV-depleted Sup was concentrated and subjected to digestion with increasing concentrations of trypsin (panel d) with rHsp90α as the positive control (panel e). Samples were subjected to western blot analysis with an anti-Hsp90α antibody. (<b>F</b>) Increasing known amounts of rHsp90α proteins (ng) were loaded together with CMs of five million cells from three independent experiments on an SDS-PAGE and the intensity of anti-Hsp90α body western blotting was compared using ImageJ. (<b>G</b>) Based on a chart of band intensity versus ng of rHsp90α protein, an average amount of eHsp90α from three independent experiments was obtained. The original films of the western blots are included on page 3 in <a href="#app1-cells-13-01280" class="html-app">Supplemental Data</a>.</p> Full article ">Figure 3
<p>Establishment of human tissue CIF ratio to connect in vitro CM to in vivo interstitial fluid. (<b>A</b>) Differences in the medium volume that supports the same number of cells in 2-D versus 3-D cultures. Three different types of cells were collected and pelleted to reach 0.1 cm<sup>3</sup> and the cell numbers in each tube counted, reaching the final calculation of 3 × 10<sup>8</sup> cells in 1 cm<sup>3</sup> volume with a surrounding 1 mL of liquid medium. In comparison, 3 × 10<sup>8</sup> cells in 2-D culture requires 30 of 15 cm dishes with 600 mL medium to cover the cells. (<b>B</b>,<b>C</b>) Based on reported human body parameters, the ratio of cell number to volume of interstitial fluid surrounding the cells, or the CIF ratio, was calculated. (<b>D</b>) Using the CIF ratio, the projected physiological concentration of secreted Hsp90α in tissue interstitial fluid was calculated, based on the amount of secreted Hsp90α in CM, 0.85 μg/5 × 10<sup>6</sup> cells (see <a href="#cells-13-01280-f002" class="html-fig">Figure 2</a>G).</p> Full article ">Figure 4
<p>GRASP-55 is necessary and sufficient for partial secretion of eHsp90α. (<b>A</b>) Based on known requirements for each of the four types of UPS pathways, the type III pathway is the only possibility for eHsp90α secretion, which does not have any signal peptide and is not found inside vesicles in CM. (<b>B</b>) GRASP-55- and GRASP-65-KO Hela cells and their CM of equalized cell numbers were subjected to immune blotting analyses with indicated antibodies. (<b>C</b>) The CM from the three cell lines were resolved in SDS gel and stained with Coomassie brilliant blue. (<b>D</b>) A GFP-GRASP55 cDNA was expressed in GRASP-55-KO Hela cells to the similar level of the endogenous GRASP-55 in parental Hela cells. (<b>E</b>) GFP-GRASP55 rescued the reduced portion of eHsp90α in GRASP-55-KO Hela cells (panel i). (<b>F</b>) Lentiviral infection-mediated downregulation of GRASP-55, but not GRASP-65, partially inhibited secretion of eHsp90α in MDA-MB-231 cells (panel l). (<b>G</b>) Lentiviral infection-mediated downregulation of GRASP-55 partially inhibited eHsp90α secretion in human keratinocytes (panel n). The original data from the western blot films are included on page 4 in <a href="#app1-cells-13-01280" class="html-app">Supplemental Data</a>.</p> Full article ">Figure 5
<p>Autophagosome-regulating (AR) genes and GRASP-55 work additively to regulate 95% secretion of eHsp90α. The CIF ratio defines eHsp90α as an essential wound-healing factor. (<b>A</b>) Lentiviral infection-mediated downregulation of various AR genes in MDA-MB-231 cells. (<b>B</b>) Effect of AR gene downregulation on (late) autophagosome surface marker LC-3. (<b>C</b>) The EV-depleted CM or Sup of each of the cell lines were equalized for the same numbers of cells in the culture, concentrated and subjected to western blot analysis with an anti-Hsp90α antibody. (<b>D</b>) The EV-depleted CM or Sup of the parental, single or double gene knockout cells were subjected to western blot analysis with an anti-Hsp90α antibody. The original films of the western blots are included on page 5 in <a href="#app1-cells-13-01280" class="html-app">Supplemental Data</a>.</p> Full article ">Figure 6
<p>Confirmation of the main findings in human keratinocytes. (<b>A</b>) CM of human keratinocytes was separated into MV, Exo and Sup fractions, loaded on an SDS gel with indicated portions, and immunoblotted with anti-Hsp90α antibody. Exosomal marker CD9 was included to show successful fractionation. ImageJ data are shown below the bands. (<b>B</b>) Calculation of the percentage of eHsp90α in each of the three fractions based on ImageJ data and portions of loaded fractions. (<b>C</b>) EV-depleted CM or Sup from double gene knockout keratinocytes were subjected to western blot analysis with an anti-Hsp90α antibody. (<b>D</b>) A schematic summary of the main findings from various cell types. The original films of the western blots are included on page 6 in <a href="#app1-cells-13-01280" class="html-app">Supplemental Data</a>.</p> Full article ">Figure 7
<p>Essential role for eHsp90α from secretory autophagy supply for wound healing in Hsp90α-knockout mice. (<b>A</b>) Eight mm (8 × 8 mm) full thickness wounds were created in wild-type and Hsp90α-knockout mice. (<b>B</b>) Wounds were treated with or without topical treatment of 300 μg/mL rHsp90α protein (in red). Wound closure was measured as % of the open wound area over time in reference to day 0 wounds (Methods). (<b>C</b>) Quantitation of the wound closure as shown in panel B with <span class="html-italic">p</span> values, where wounds in Hsp90α-knockout mice still remained open on day 12. (<b>D</b>) Section of partial wounds on day 10 were subjected to H&amp;E staining. Red vertical dashed lines divide the unwounded (left) and wounded (right) areas. Yellow horizontal dashed lines show the re-epithelialization tongue (Re-epi T), i.e., epidermal cell migration. Representative images are shown, while additional data of three independent experiments and wound bandages are included on pages 7–10 in <a href="#app1-cells-13-01280" class="html-app">Supplemental Data</a>.</p> Full article ">
37 pages, 1284 KiB  
Review
Intratumoral Microbiome: Foe or Friend in Reshaping the Tumor Microenvironment Landscape?
by Athina A. Kyriazi, Makrina Karaglani, Sofia Agelaki and Stavroula Baritaki
Cells 2024, 13(15), 1279; https://doi.org/10.3390/cells13151279 - 30 Jul 2024
Viewed by 1496
Abstract
The role of the microbiome in cancer and its crosstalk with the tumor microenvironment (TME) has been extensively studied and characterized. An emerging field in the cancer microbiome research is the concept of the intratumoral microbiome, which refers to the microbiome residing within [...] Read more.
The role of the microbiome in cancer and its crosstalk with the tumor microenvironment (TME) has been extensively studied and characterized. An emerging field in the cancer microbiome research is the concept of the intratumoral microbiome, which refers to the microbiome residing within the tumor. This microbiome primarily originates from the local microbiome of the tumor-bearing tissue or from translocating microbiome from distant sites, such as the gut. Despite the increasing number of studies on intratumoral microbiome, it remains unclear whether it is a driver or a bystander of oncogenesis and tumor progression. This review aims to elucidate the intricate role of the intratumoral microbiome in tumor development by exploring its effects on reshaping the multileveled ecosystem in which tumors thrive, the TME. To dissect the complexity and the multitude of layers within the TME, we distinguish six specialized tumor microenvironments, namely, the immune, metabolic, hypoxic, acidic, mechanical and innervated microenvironments. Accordingly, we attempt to decipher the effects of the intratumoral microbiome on each specialized microenvironment and ultimately decode its tumor-promoting or tumor-suppressive impact. Additionally, we portray the intratumoral microbiome as an orchestrator in the tumor milieu, fine-tuning the responses in distinct, specialized microenvironments and remodeling the TME in a multileveled and multifaceted manner. Full article
(This article belongs to the Special Issue Recent Advances in Tumor Immunological Microenvironment Research)
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<p>Diagrammatic representation of the six specialized sub-microenvironments found in the TME, accompanied with their main features.</p> Full article ">Figure 2
<p>Graphic illustration portraying intratumoral phyla, genera or species, promoting either immunostimulation (left side) or immunosuppression (right side) in different tumor types. More details regarding their specific impact on the immune TME found in the text.</p> Full article ">Figure 3
<p>Graphic summary of the intratumoral phyla, genera or species associated with the metabolic (blue), hypoxic (green), acidic (purple), mechanical (pink) and innervated (orange) sub-microenvironments of the TME, in different tumor types. More details on their role on the respective sub-microenvironment can be found in the text.</p> Full article ">
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