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Cells
Volume 9
Issue 2
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Cells, Volume 9, Issue 2 (February 2020) – 264 articles

Cover Story (view full-size image): Tumors release chemokines that can recruit macrophages and dendritic cells to adjacent nerves. There, localization, morphology, and lipid content of epineural adipocytes change, coinciding with downregulation of perineural tight junction proteins and appearance of microlesions. View this paper.
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24 pages, 1105 KiB  
Review
MYC’s Fine Line Between B Cell Development and Malignancy
by Oriol de Barrios, Ainara Meler and Maribel Parra
Cells 2020, 9(2), 523; https://doi.org/10.3390/cells9020523 - 24 Feb 2020
Cited by 21 | Viewed by 10767
Abstract
The transcription factor MYC is transiently expressed during B lymphocyte development, and its correct modulation is essential in defined developmental transitions. Although temporary downregulation of MYC is essential at specific points, basal levels of expression are maintained, and its protein levels are not [...] Read more.
The transcription factor MYC is transiently expressed during B lymphocyte development, and its correct modulation is essential in defined developmental transitions. Although temporary downregulation of MYC is essential at specific points, basal levels of expression are maintained, and its protein levels are not completely silenced until the B cell becomes fully differentiated into a plasma cell or a memory B cell. MYC has been described as a proto-oncogene that is closely involved in many cancers, including leukemia and lymphoma. Aberrant expression of MYC protein in these hematological malignancies results in an uncontrolled rate of proliferation and, thereby, a blockade of the differentiation process. MYC is not activated by mutations in the coding sequence, and, as reviewed here, its overexpression in leukemia and lymphoma is mainly caused by gene amplification, chromosomal translocations, and aberrant regulation of its transcription. This review provides a thorough overview of the role of MYC in the developmental steps of B cells, and of how it performs its essential function in an oncogenic context, highlighting the importance of appropriate MYC regulation circuitry. Full article
(This article belongs to the Special Issue Regulation and Function of the Myc Oncogene)
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<p>Expression and role of MYC in B lymphocyte differentiation. Schematic representation of the participation of the MYC protein throughout B-cell differentiation in the bone marrow and germinal center (GC). The percentages shown refer to the population of MYC<sup>+</sup>, BCL6<sup>+/−</sup> cells in the total number of B cells present in the GC. The blue-colored line at the top of the Figure indicates the evolution of MYC expression, where darker blue indicates steps that require higher MYC levels.</p> Full article ">Figure 2
<p>Activating mechanisms of c-MYC in leukemia with the BCR-ABL1 rearrangement. A summary of the different transduction signaling pathways that trigger the activation of MYC promoter in BCR-ABL1-rearranged leukemia. Apart from direct transcriptional activation pathways, marked in green, alternative mechanisms that induce c-MYC are depicted in black and highlighted in black squares. Dashed arrows indicate the translocation of proteins between the nucleus and the cytoplasm.</p> Full article ">
18 pages, 4642 KiB  
Article
Detection of MET Alterations Using Cell Free DNA and Circulating Tumor Cells from Cancer Patients
by Patricia Mondelo-Macía, Carmela Rodríguez-López, Laura Valiña, Santiago Aguín, Luis León-Mateos, Jorge García-González, Alicia Abalo, Oscar Rapado-González, Mercedes Suárez-Cunqueiro, Angel Díaz-Lagares, Teresa Curiel, Silvia Calabuig-Fariñas, Aitor Azkárate, Antònia Obrador-Hevia, Ihab Abdulkader, Laura Muinelo-Romay, Roberto Diaz-Peña and Rafael López-López
Cells 2020, 9(2), 522; https://doi.org/10.3390/cells9020522 - 24 Feb 2020
Cited by 23 | Viewed by 4550
Abstract
MET alterations may provide a potential biomarker to evaluate patients who will benefit from treatment with MET inhibitors. Therefore, the purpose of the present study is to investigate the utility of a liquid biopsy-based strategy to assess MET alterations in cancer patients. We [...] Read more.
MET alterations may provide a potential biomarker to evaluate patients who will benefit from treatment with MET inhibitors. Therefore, the purpose of the present study is to investigate the utility of a liquid biopsy-based strategy to assess MET alterations in cancer patients. We analyzed MET amplification in circulating free DNA (cfDNA) from 174 patients with cancer and 49 healthy controls and demonstrated the accuracy of the analysis to detect its alteration in patients. Importantly, a significant correlation between cfDNA concentration and MET copy number (CN) in cancer patients (r = 0.57, p <10−10) was determined. Furthermore, we evaluated two approaches to detect the presence of MET on circulating tumor cells (CTCs), using the CellSearch® and Parsortix systems and monitored patients under anti-EGFR treatment (n = 30) combining both cfDNA and CTCs analyses. This follow-up provides evidence for the potential of MET CN assessment when patients develop resistance to anti-EGFR therapy and a significant association between the presence of CTCs MET+ and the Overall Survival (OS) in head and neck cancer patients (P = 0.05; HR = 6.66). In conclusion, we develop specific and noninvasive assays to monitor MET status in cfDNA/CTCs and demonstrate the utility of plasma MET CN determination as a biomarker for monitoring the appearance of resistance to anti-EGFR therapy. Full article
(This article belongs to the Special Issue Liquid Biopsy)
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<p><span class="html-italic">MET</span> CN analysis. (<b>A</b>) Scatterplot representing correlation between <span class="html-italic">MET</span> CN in cancer cell lines determined by ddPCR versus single-nucleotide polymorphism (SNP) array (<span class="html-italic">n</span> = 8) using Pearson’s correlation; (<b>B</b>) Plasma <span class="html-italic">MET</span> CN detected in healthy controls (<span class="html-italic">n</span> = 49), non-metastatic patients (<span class="html-italic">n</span> = 34), and metastatic patients (<span class="html-italic">n</span> = 140) using the Mann–Whitney–Wilcoxon U-Test.</p> Full article ">Figure 2
<p><span class="html-italic">MET</span> CN analysis in circulating free DNA (cfDNA) from metastatic cancer patients. (<b>A</b>) Correlation between cfDNA levels and plasma <span class="html-italic">MET</span> CN in all metastatic cancer patients (<span class="html-italic">n</span> = 140); (<b>B</b>) Correlation between cfDNA levels and plasma <span class="html-italic">MET</span> CN in lung and head and neck cancer patients (<span class="html-italic">n</span> = 30).</p> Full article ">Figure 2 Cont.
<p><span class="html-italic">MET</span> CN analysis in circulating free DNA (cfDNA) from metastatic cancer patients. (<b>A</b>) Correlation between cfDNA levels and plasma <span class="html-italic">MET</span> CN in all metastatic cancer patients (<span class="html-italic">n</span> = 140); (<b>B</b>) Correlation between cfDNA levels and plasma <span class="html-italic">MET</span> CN in lung and head and neck cancer patients (<span class="html-italic">n</span> = 30).</p> Full article ">Figure 3
<p>Comparison of <span class="html-italic">MET</span> CN status in tissue and cfDNA. (<b>A</b>) Distribution of <span class="html-italic">MET</span> CN measured by ddPCR and fluorescence in situ hybridization (FISH) (the point larger indicates the discordant value, whereas the horizontal and vertical dotted lines indicate cut-off points of ddPCR and FISH, respectively); (<b>B</b>) Representative example of a negative case for <span class="html-italic">MET</span> amplification obtained in a NSCLC patient by FISH; and (<b>C</b>) Representative example of a positive case for <span class="html-italic">MET</span> amplification obtained in a NSCLC patient by FISH.</p> Full article ">Figure 4
<p>Percentage of spiked tumor cancer cells captured using CellSearch<sup>®</sup> and Parsortix systems. Evaluation of the enrichment capacity of CellSearch<sup>®</sup> and Parsortix systems, using healthy blood spiked with LNCaP, NCI-N87, Hs746T, AU565, SNU-5, and C32 cancer cell lines. LNCaP, NCI-N87, SNU-5, and AU565 express Epithelial cell adhesion molecule (EpCAM) while Hs746T and C32 express low levels or do not express EpCAM, respectively. <span class="html-italic">p</span>-value &lt; 5 × 10<sup>−3</sup>, in all comparisons between CellSearch<sup>®</sup> and Parsortix System in each cell line.</p> Full article ">Figure 5
<p>Detection of MET expression using tumor cancer cells with the CellSearch<sup>®</sup> and Parsortix systems (<b>A</b> and <b>B</b>, respectively). Representative images of MET expression scored on score 0 (cell line LNCaP), 1 (cell line AU565), 2 (cell line Hs746T), and 3 (cell line SNU-5).</p> Full article ">Figure 5 Cont.
<p>Detection of MET expression using tumor cancer cells with the CellSearch<sup>®</sup> and Parsortix systems (<b>A</b> and <b>B</b>, respectively). Representative images of MET expression scored on score 0 (cell line LNCaP), 1 (cell line AU565), 2 (cell line Hs746T), and 3 (cell line SNU-5).</p> Full article ">Figure 6
<p>CTCs enumeration and MET expression in blood samples evaluated by the CellSearch<sup>®</sup> (upper panel) and Parsortix (down panel) systems. Distribution of MET scores in CTCs from patients with NSCLC (<b>A</b>) and head and neck cancer (<b>B</b>).</p> Full article ">Figure 7
<p>Prognostic value to predict Overall Survival (OS) of CTCs enumeration and MET expression in head and neck cancer patients starting with anti-EGFR treatment. CTCs MET-positive ≥1: CTCs with high MET expression (scores 2+ or 3+); CTCs MET-positive &lt;1: CTC with low MET expression (score 1+).</p> Full article ">Figure 8
<p>Timeline for the clinical course of patient id60. The blue and yellow bars represent the treatments time frame, and the red drops indicate blood collection time points. Percent mutant allelic frequency (L858R and T790M) and <span class="html-italic">MET</span> CN for patient id60 are shown.</p> Full article ">
14 pages, 743 KiB  
Review
Nanoparticle-Based Delivery of Tumor Suppressor microRNA for Cancer Therapy
by Clodagh P. O’Neill and Róisín M. Dwyer
Cells 2020, 9(2), 521; https://doi.org/10.3390/cells9020521 - 24 Feb 2020
Cited by 66 | Viewed by 7475
Abstract
Improved understanding of microRNA expression and function in cancer has revealed a range of microRNAs that negatively regulate many oncogenic pathways, thus representing potent tumor suppressors. Therapeutic targeting of the expression of these microRNAs to the site of tumors and metastases provides a [...] Read more.
Improved understanding of microRNA expression and function in cancer has revealed a range of microRNAs that negatively regulate many oncogenic pathways, thus representing potent tumor suppressors. Therapeutic targeting of the expression of these microRNAs to the site of tumors and metastases provides a promising avenue for cancer therapy. To overcome challenges associated with microRNA degradation, transient expression and poor targeting, novel nanoparticles are being developed and employed to shield microRNAs for tumor-targeted delivery. This review focuses on studies describing a variety of both natural and synthetic nanoparticle delivery vehicles that have been engineered for tumor-targeted delivery of tumor suppressor microRNAs in vivo. Full article
(This article belongs to the Special Issue microRNA as Therapeutic Target)
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<p>Tumor suppressor miRNA encapsulation in nanoparticle formulations for delivery to primary tumors and metastases (image created using <a href="http://Biorender.com" target="_blank">Biorender.com</a>—paid subscription).</p> Full article ">
16 pages, 9280 KiB  
Article
Computational Identification and Analysis of Ubiquinone-Binding Proteins
by Chang Lu, Wenjie Jiang, Hang Wang, Jinxiu Jiang, Zhiqiang Ma and Han Wang
Cells 2020, 9(2), 520; https://doi.org/10.3390/cells9020520 - 24 Feb 2020
Cited by 3 | Viewed by 3203
Abstract
Ubiquinone is an important cofactor that plays vital and diverse roles in many biological processes. Ubiquinone-binding proteins (UBPs) are receptor proteins that dock with ubiquinones. Analyzing and identifying UBPs via a computational approach will provide insights into the pathways associated with ubiquinones. In [...] Read more.
Ubiquinone is an important cofactor that plays vital and diverse roles in many biological processes. Ubiquinone-binding proteins (UBPs) are receptor proteins that dock with ubiquinones. Analyzing and identifying UBPs via a computational approach will provide insights into the pathways associated with ubiquinones. In this work, we were the first to propose a UBPs predictor (UBPs-Pred). The optimal feature subset selected from three categories of sequence-derived features was fed into the extreme gradient boosting (XGBoost) classifier, and the parameters of XGBoost were tuned by multi-objective particle swarm optimization (MOPSO). The experimental results over the independent validation demonstrated considerable prediction performance with a Matthews correlation coefficient (MCC) of 0.517. After that, we analyzed the UBPs using bioinformatics methods, including the statistics of the binding domain motifs and protein distribution, as well as an enrichment analysis of the gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Full article
(This article belongs to the Special Issue Biocomputing and Synthetic Biology in Cells)
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<p>The Matthews correlation coefficient (MCC) value of the models in the process of incremental feature selection (IFS).</p> Full article ">Figure 2
<p>Distribution of each kind of feature in the optimal feature subset. AAC: amino acid composition; DC: dipeptide composition; PSSM: position-specific scoring matrix.</p> Full article ">Figure 3
<p>Illustration of the respiratory complex II of the mitochondrial respiratory chain.</p> Full article ">Figure 4
<p>Sequence logos of the motif within the ubiquinone-binding domains. The threshold of the E-value is 0.05. “Sites” represents the number of sites contributing to the construction of the motif. “Width” represents the width of the motif. The 3D visualization on the right is an example of the corresponding motif. “Protein” represents the PDB ID_Chain (domain). “Ligand” represents the type of ubiquinone.</p> Full article ">Figure 5
<p>The superfamily distribution of the selected Ubiquinone-binding proteins (UBPs). The digital labels on the chart represent the number of UBPs that the superfamily contains. The names of the categories listed in the legend are the clan name in the Pfam database. All superfamilies in the “Others” category contain one protein.</p> Full article ">Figure 6
<p>The general information of the gene ontology (GO) enrichment analysis result of human UBPs: (<b>a</b>) enriched biological processes; (<b>b</b>) enriched cell components; (<b>c</b>) enriched molecular functions. The description on the left side of the bar refers to the name of the gene term. “Percent of Genes” refers to the percentage of the number of genes involved in a given term compared to the total number of genes in the query proteins. The digital label on the right side of the bar of a gene term refers to the number of the genes involved in this term and its corresponding P-value. “Max Level” refers to the maximal annotated level of the given term in the GO graph. Different colors refer to the different max levels. Terms with the same max level are sorted according to P-value.</p> Full article ">Figure 7
<p>The top 10 significantly enriched KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways of human UBPs. The description on the left side of the bar refers to the name of the KEGG pathway. “Percent of Genes” refers to the percentage of the number of genes involved in a given pathway compared to the total number of genes in the query proteins. The digital label on the right side of the bar of a gene term refers to the number of the genes involved in this pathway and the corresponding P-value. Different colors refer to the different categories of the pathways. Pathways of the same category are sorted by P-value.</p> Full article ">
25 pages, 3951 KiB  
Article
Microfluidic Device for On-Chip Immunophenotyping and Cytogenetic Analysis of Rare Biological Cells
by Kumuditha M. Weerakoon-Ratnayake, Swarnagowri Vaidyanathan, Nicholas Larkey, Kavya Dathathreya, Mengjia Hu, Jilsha Jose, Shalee Mog, Keith August, Andrew K. Godwin, Mateusz L. Hupert, Malgorzata A. Witek and Steven A. Soper
Cells 2020, 9(2), 519; https://doi.org/10.3390/cells9020519 - 24 Feb 2020
Cited by 6 | Viewed by 5403
Abstract
The role of circulating plasma cells (CPCs) and circulating leukemic cells (CLCs) as biomarkers for several blood cancers, such as multiple myeloma and leukemia, respectively, have recently been reported. These markers can be attractive due to the minimally invasive nature of their acquisition [...] Read more.
The role of circulating plasma cells (CPCs) and circulating leukemic cells (CLCs) as biomarkers for several blood cancers, such as multiple myeloma and leukemia, respectively, have recently been reported. These markers can be attractive due to the minimally invasive nature of their acquisition through a blood draw (i.e., liquid biopsy), negating the need for painful bone marrow biopsies. CPCs or CLCs can be used for cellular/molecular analyses as well, such as immunophenotyping or fluorescence in situ hybridization (FISH). FISH, which is typically carried out on slides involving complex workflows, becomes problematic when operating on CLCs or CPCs due to their relatively modest numbers. Here, we present a microfluidic device for characterizing CPCs and CLCs using immunofluorescence or FISH that have been enriched from peripheral blood using a different microfluidic device. The microfluidic possessed an array of cross-channels (2–4 µm in depth and width) that interconnected a series of input and output fluidic channels. Placing a cover plate over the device formed microtraps, the size of which was defined by the width and depth of the cross-channels. This microfluidic chip allowed for automation of immunofluorescence and FISH, requiring the use of small volumes of reagents, such as antibodies and probes, as compared to slide-based immunophenotyping and FISH. In addition, the device could secure FISH results in <4 h compared to 2–3 days for conventional FISH. Full article
(This article belongs to the Special Issue Circulating Tumor Cells: Finding Rare Events for A Huge Knowledge of Cancer Dissemination)
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<p>Microfluidic device for performing automated immunophenotyping and FISH. (<b>A</b>) Design of the microfluidic network composed of a single bed with 7200 microtraps and the 8-bed device containing 10,000 microtraps in each bed for a total of 80,000 traps per device. Microtrap size: 4 × 2 × 50 µm (w × d × l). (<b>B</b>) Profilometer scan of the microtrap chip replicated in PDMS from a 3-level SU-8 relief and a Si master showing microchannel depth varying between input/output distribution channels, interleaving channels, and cross-channels. (<b>C</b>) Cross-channels and the deeper interleaving channels are shown in the SEM image. (<b>D</b>) Optical microscope image of a lithographically patterned 2-level SU-8 relief for preparing a single bed microtrap device. The arrows show the fluid path. (<b>E</b>) Schematic showing operation of the microtrap chip. Cells in solution (green arrows) are contained at the entrances of the microtraps, letting the fluid pass (yellow arrows) into the outlet channels of the interleaving network. (<b>F</b>) Schematic showing the 3-dimensionality of cells captured in the microtrap chip and imaging using a high magnification (60× or 100×) objective through a thin cover plate.</p> Full article ">Figure 2
<p>Simulations of the microtrap device. (<b>A</b>) 2-D CAD design of the microtrap device used for COMSOL simulations showing the interleaving network for the flow of fluid, and the cross-channels, which produce the microtraps when a cover plate is sealed to the device. The magnified image of the microtrap area is shown on the right with a single interleaving output channel (red) and two interleaving input channels (gray). (<b>B</b>) The simulated linear fluid velocity throughout the microtrap chip. The simulation shows three sections of the device: (i) input section; (ii) middle section; and (iii) outlet section. Flow was simulated across the interleaving input/output channels and the cross-channels. The dashed box shown here is the region of the device that was simulated in <a href="#app1-cells-09-00519" class="html-app">Figure S3</a> (see <a href="#app1-cells-09-00519" class="html-app">Supplementary Materials</a>). (<b>C</b>) Bar graph representing the mean velocities expressed in m/s observed for the cross-channels at different sections of the device and at a 10 μL/min volume flow rate. The sections labeled here correspond to the sections of the device simulated in (<b>B</b>). (<b>D</b>) Simulated shear rate at three different sections of the device, inlet, middle, and outlet sections. (<b>E</b>) Bar graphs representing the mean shear rates across the cross-channels at different sections of the device at a volume flow rate of 10 μL/min. The sections of the device listed here correspond to those sections shown in (<b>D</b>).</p> Full article ">Figure 3
<p>On-chip immunophenotyping of RPMI-8226 cells. (<b>A</b>) DAPI-labeled RPMI-8226 cell nucleus aligned at the entrance of the microtraps formed by the cross-channels and the cover plate assembled to the device. (<b>B</b>) CD138 expression of the RPMI-8226 cells and (<b>C</b>) CD38 expression for the same cells. (<b>D</b>) Composite image of CD138 expression (FITC channel) with the cell nucleus (DAPI channel of the microscope). (<b>E</b>) Composite image of CD38 expression (APC channel) with the cell nucleus that was DAPI stained. Exposure times were DAPI 50 ms, FITC 500 ms, and APC 1500 ms with 20× magnification. All images were collected using the Keyence fluorescence microscope. Shown in this fluorescence image are cells aligned along one interleaving input channel with cross-channels on either side of that channel.</p> Full article ">Figure 4
<p>(<b>A</b>) Brightfield image of the bifurcated entrance channels of the microtrap device. RPMI-8226 cells were injected into the device at 10 μL/min and contained at the entrance of the microtraps. Cell images were processed according to the procedure listed in the materials and methods section of this manuscript and labeled with DAPI (nuclear stain) and CD38-APC markers. (<b>B</b>) Brightfield image merged with DAPI and APC channels showing the presence of the cell nucleus and CD38 on the cell surface aligned mainly at the microtrap entrances. (<b>C</b>) Entrance of the single bed device imaged using DAPI. RPMI-8226 cells were trapped inside the device at the entrance to the microtraps. (<b>D</b>) Two consecutive beds of the 8-bed device imaged with the DAPI channel of the microscope for stained RPMI-8226 cells.</p> Full article ">Figure 5
<p>Workflow of FISH using the microtrap device. The workload was reduced from 2 days (slide method) to 4 h using the microtrap device primarily due to the hybridization time reduced from overnight to 2 h. The probe volume required for the assay was also reduced from 10 to 2 μL as well as using the microtrap device. Live cells were injected into the microtrap device at a flow rate of 10 μL/min and the washing steps were done at 5 μL/min to reduce the shear stress on the fixed cells contained within the microfluidic device.</p> Full article ">Figure 6
<p>FISH-on-chip analysis of RPMI-8226 and SUP-B15 cells. (<b>A</b>) RPMI-8226 cells after FISH processing using the microtrap chip with the D13S319 plus deletion probe; (i) a cell that shows one green and one red FISH signal; (ii) a cell with only one green signal; (iii) a cell with 2 green and 2 red signals. (<b>B</b>) FISH analysis of SUP-B15 cells processed with the TEL/AML1 translocation, dual fusion probes showing the TEL (ETV6, 12p13.2) region in red, and AML1 (RUNX1, 21q22.12) region in green. (i) Two cells contained at the entrance of two different microtraps, but the FISH probes were visible in only one cell with two green signals; (ii) shows one red and two green signals with no clear yellow signals present; (iii) two cells that show distinct red and green signals, one cell captured at the entrance of microtrap shows one red, and one green signal with a possible yellow fusion signal. Both (<b>A</b>,<b>B</b>) were imaged in one single z-plane without z-stacking. (<b>C</b>,<b>D</b>) show z-stacking planes of 15 different image planes for FISH images from SUP-B15 cells captured at the microtrap and FISH processed with BCR/ABL plus translocation, dual fusion probe. (<b>C</b>) SUP-B15 cell with two green and two red signals. (<b>D</b>) SUP-B15 cell with one yellow fusion signal (second yellow signal not visible) and one red and green signal. Each image shows 15 separate images through the 15 μm distance range taken at 1-μm imaging intervals. FISH probes were specific to the BCR/ABL gene region, Philadelphia (Ph) chromosome tagging. All images were acquired using a Nikon 60× oil objective with DAPI—200 ms, FITC—1500 ms, TRITC—2500 ms integration times. The average SNR was 59 for the green probe and 68 for the red probe.</p> Full article ">Figure 7
<p>(<b>A</b>) Immunophenotyping of cells enriched from peripheral blood of a B-ALL patient by targeting cells with that express the CD19 antigen. The cells were stained using DAPI (nucleus), and monoclonal antibodies directed against TdT (FITC), CD34 (Cy3), and CD10 (Cy5). The images were acquired using a 40× microscope objective. The CLCs shown were DAPI(+)/CD34(+) and TdT(+), but CD10(−). (<b>B</b>) Microfluidic monitoring of a B-ALL patient from day 8 to 85 of chemotherapy. Total cell count represents all DAPI(+)/CD19(+) cells selected. (<b>C</b>) Number of CLCs identified as DAPI(+)/CD19(+)/TdT(+)/CD34(±)/CD10(±). (<b>D</b>) Change in phenotype among CLCs for this patient for days 8 and 85.</p> Full article ">Figure 8
<p>On-chip FISH processed B-cells isolated from a diagnosed B-ALL patient. TEL/AML1 FISH probes were used for the chromosomal aberration of t (12;21) translocation. Cells were imaged at the microtraps. Zoomed images show (<b>a</b>) single cell with 2 green FISH signals; (<b>b</b>) single cell with one green and one yellow signal; (<b>c</b>) a single cell with one red, one green, and one yellow FISH signal; (<b>d</b>) single cell with one red, one green, and one yellow FISH signals close to each other in the cell; and (<b>e</b>) single cell with one red and two green signals. In all cases, the images were collected using a 60× objective with z-stacking.</p> Full article ">
15 pages, 2964 KiB  
Article
Protein Translocation Acquires Substrate Selectivity Through ER Stress-Induced Reassembly of Translocon Auxiliary Components
by Sohee Lee, Yejin Shin, Kyunggon Kim, Youngsup Song, Yongsub Kim and Sang-Wook Kang
Cells 2020, 9(2), 518; https://doi.org/10.3390/cells9020518 - 24 Feb 2020
Cited by 1 | Viewed by 3639
Abstract
Protein import across the endoplasmic reticulum membrane is physiologically regulated in a substrate-selective manner to ensure the protection of stressed ER from the overload of misfolded proteins. However, it is poorly understood how different types of substrates are accurately distinguished and disqualified during [...] Read more.
Protein import across the endoplasmic reticulum membrane is physiologically regulated in a substrate-selective manner to ensure the protection of stressed ER from the overload of misfolded proteins. However, it is poorly understood how different types of substrates are accurately distinguished and disqualified during translocational regulation. In this study, we found poorly assembled translocon-associated protein (TRAP) complexes in stressed ER. Immunoaffinity purification identified calnexin in the TRAP complex in which poor assembly inhibited membrane insertion of the prion protein (PrP) in a transmembrane sequence-selective manner, through translocational regulation. This reaction was induced selectively by redox perturbation, rather than calcium depletion, in the ER. The liberation of ERp57 from calnexin appeared to be the reason for the redox sensitivity. Stress-independent disruption of the TRAP complex prevented a pathogenic transmembrane form of PrP (ctmPrP) from accumulating in the ER. This study uncovered a previously unappreciated role for calnexin in assisting the redox-sensitive function of the TRAP complex and provided insights into the ER stress-induced reassembly of translocon auxiliary components as a key mechanism by which protein translocation acquires substrate selectivity. Full article
(This article belongs to the Section Intracellular and Plasma Membranes)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Analyses of newly synthesized PrP in TRAPα-deficient cells. (<b>a</b>) A TRAPα-deficient cell line (gTRAPα) was created using CRISPR/Cas9 genome editing, using a TRAPα-targeting sgRNA. Gene editing was confirmed using T7E1 assay. Heteroduplex fragments cleaved by T7E1 are indicated as arrowheads. gNT: cell line expressing non-targeting gRNA as a negative control. (<b>b</b>) Specific elimination of TRAPα protein was verified in fully solubilized TRAPα-deficient cells by immunoblotting with calnexin (CANX)-, Sec61β-, and TRAPα-specific antibodies. (<b>c</b>) Topological differences between secPrP and ctmPrP are illustrated in the upper panel. Newly synthesized secPrP and ctmPrP in pulse-labeled gNT and gTRAPα cells transiently transfected with wtPrP (secPrP) or N7a-PrP-AV3 (ctmPrP) constructs were analyzed by immunoprecipitation with the PrP-specific 3F4 antibody (lower middle panel). In this manner, the luminal localization of the N-terminal region was determined in these cell lines expressing mutant PrPs carrying the G34N mutation (lower right panel). Equal loading and translation were verified by determining total newly synthesized protein content in the cells (lower left panel).</p> Full article ">Figure 2
<p>Redox-sensitive interaction of calnexin and translocon-associated protein (TRAP) α (<b>a</b>) TRAPα complexes were recovered with anti-HA magnetic beads from detergent-solubilized microsomes isolated from stable/inducible cells expressing TRAPα-HA after doxycycline treatment (Dox; 10 ng/mL). HC: immunoglobulin heavy chain, LC: immunoglobulin light chain. (<b>b</b>) Immunoaffinity purification was performed as in (<b>a</b>) in cells treated with DTT (10 mM) or thapsigargin (Tg; 5 µM) for 1 h or 4 h, respectively. Recovery of the indicated ER membrane proteins was determined by immunoblotting with anti-HA and anti-calnexin antibodies. (<b>c</b>) TRAPα complexes recovered in (<b>a</b>) were subjected to immunoblotting with anti-HA, anti-Sec61α, and anti-calnexin antibodies. (<b>d</b>) The interaction between calnexin and TRAPα was analyzed in cells transiently transfected with a TRAPα-FLAG construct. Cells treated with DTT (10 mM) for 1 h were allowed to recover for 4 h in the absence of DTT, before being solubilized in IPM buffer. The restored interaction was assessed by detecting calnexin in TRAPα-interacting molecules precipitated with anti-FLAG antibody-conjugated magnetic beads.</p> Full article ">Figure 3
<p>Development of stress-independent PATC. (<b>a</b>) Functional residues of the primary sequence of calnexin. (<b>b</b>) The residue in calnexin required for the interaction with TRAPα was determined by co-immunoprecipitation in cells expressing various calnexin mutants fused with HA, as described in <a href="#cells-09-00518-f002" class="html-fig">Figure 2</a>D. (<b>c</b>) The sgRNA targeting the region near the sequence encoding cysteine at codon 160 of calnexin (Yellow box). (<b>d</b>) Desired genome editing (C160A mutation) as in (<b>c</b>) was confirmed by the detection of the DNA fragment cleaved by HindIII (arrowhead, left panel) and DNA sequence analysis (right panel). (<b>e</b>) Stress-independent disruption of the interaction between calnexin and TRAPα in C160A cells was verified by co-immunoprecipitation, as described in <a href="#cells-09-00518-f002" class="html-fig">Figure 2</a>D.</p> Full article ">Figure 4
<p>Analysis of PrP synthesis in PATC. (<b>a</b>) Fully solubilized non-PATC and PATC cells were subjected to immunoblotting with the indicated antibodies. (<b>b</b>) Prolactin fused with HA and newly synthesized PrP isoforms in pulse-labeled non-PATC and PATC cells transiently transfected with various mutant constructs were analyzed by immunoprecipitation with anti-HA and PrP-specific 3F4 antibodies. PrP-Ub: ubiquitinated subpopulation of cytPrP, gly: glycosylated subpopulation of PrP in the ER, +SP/-SP: uncleaved/cleaved signal sequence. (<b>c</b>) Newly synthesized ctmPrP in pulse-labeled non-PATC and PATC cells transiently transfected with the indicated constructs was analyzed by immunoprecipitation with an anti-PrP-A antibody. (<b>d</b>) The amount of ctmPrP accumulated in non-PATC and PATC cells was titrated by immunoblotting with the 3F4 antibody (upper panel). Serial dilution of protein loading was confirmed by staining the blot with Ponceaus S (lower panel).</p> Full article ">Figure 5
<p>Analyses of the interaction between ERp57 and calnexin. (<b>a</b>) Fully solubilized stable/inducible cells expressing wild-type (WT) or mutant calnexin (C160A) fused with HA were subjected to immunoblotting with an anti-HA antibody, under reducing (R) and non-reducing (NR) conditions. Equal loading was confirmed by probing the same blot with an anti-TRAPα antibody. (<b>b</b>) gNT and C160A cell lines (100 cells/well) were plated in triplicate and visualized 3 weeks later by staining with crystal violet. (<b>c</b>) Stable/inducible cells expressing wild-type (WT) or mutant calnexin (C160A) fused with HA were transiently transfected with an ERp57-FLAG construct and treated with DTT (10 mM) for 1 h or thapsigargin (Tg, 1 µM =”+” or 5 µM = ”++”) for 4 h, before being solubilized in IPM buffer. The interactions of ERp57, BiP, and PDI with calnexin were determined by immunoblotting with specific antibodies in calnexin-interacting molecules precipitated with anti-HA antibody-conjugated magnetic beads. (<b>d</b>) gNT and gTRAPα cells were transiently transfected with mutant ERp57 (K214A, R282A)-FLAG constructs. The interactions of calnexin and TRAPα with wild-type or mutant ERp57 were determined by immunoblotting with specific antibodies in ERp57-interacting molecules precipitated with anti-FLAG antibody-conjugated magnetic beads.</p> Full article ">Figure 6
<p>Working model depicting the functional link between PATC and pQC-M. Redox-sensitive selective inhibition of ctmPrP synthesis through translocational regulation is illustrated.</p> Full article ">
18 pages, 4128 KiB  
Article
Nrp1 is Activated by Konjac Ceramide Binding-Induced Structural Rigidification of the a1a2 Domain
by Seigo Usuki, Yoshiaki Yasutake, Noriko Tamura, Tomohiro Tamura, Kunikazu Tanji, Takashi Saitoh, Yuta Murai, Daisuke Mikami, Kohei Yuyama, Kenji Monde, Katsuyuki Mukai and Yasuyuki Igarashi
Cells 2020, 9(2), 517; https://doi.org/10.3390/cells9020517 - 24 Feb 2020
Cited by 3 | Viewed by 4365
Abstract
Konjac ceramide (kCer) is a plant-type ceramide composed of various long-chain bases and α-hydroxyl fatty acids. The presence of d4t,8t-sphingadienine is essential for semaphorin 3A (Sema3A)-like activity. Herein, we examined the three neuropilin 1 (Nrp1) domains (a1a2, b1b2, or c), and found that [...] Read more.
Konjac ceramide (kCer) is a plant-type ceramide composed of various long-chain bases and α-hydroxyl fatty acids. The presence of d4t,8t-sphingadienine is essential for semaphorin 3A (Sema3A)-like activity. Herein, we examined the three neuropilin 1 (Nrp1) domains (a1a2, b1b2, or c), and found that a1a2 binds to d4t,8t-kCer and possesses Sema3A-like activity. kCer binds to Nrp1 with a weak affinity of μM dissociation constant (Kd). We wondered whether bovine serum albumin could influence the ligand–receptor interaction that a1a2 has with a single high affinity binding site for kCer (Kd in nM range). In the present study we demonstrated the influence of bovine serum albumin. Thermal denaturation indicates that the a1a2 domain may include intrinsically disordered region (IDR)-like flexibility. A potential interaction site on the a1 module was explored by molecular docking, which revealed a possible Nrp1 activation mechanism, in which kCer binds to Site A close to the Sema3A-binding region of the a1a2 domain. The a1 module then accesses a2 as the IDR-like flexibility becomes ordered via kCer-induced protein rigidity of a1a2. This induces intramolecular interaction between a1 and a2 through a slight change in protein secondary structure. Full article
(This article belongs to the Special Issue Sphingolipids in Cancer Progression and Therapy)
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<p>The nomenclature of long-chain bases and ceramides following the recommendations of the IUPAC-IUBMB Joint Commission. (<b>A</b>) The endoglycoceramidase (EGCase) reaction of konjac glucosylceramide (kGlcCer). Plant-type ceramides can be prepared from plant-type glucosylceramide (GlcCer) by EGCase I treatment. A major molecular species (d18:2<sup>4t, 8c</sup>-C16h:0) of konjac ceramide (kCer) is shown. (<b>B</b>) The main long-chain bases found in plants. The long-chain bases of kCer produced by EGCase treatment are delineated by a dotted line rectangle. (<b>C</b>) kCer molecular species generated by EGCase treatment of kGlcCer. The length of the carbon chain of each hydroxyl fatty acid (C16 to C20) is shown.</p> Full article ">Figure 2
<p>Structure of Neuropilin 1 (Nrp1) and the binding mechanism of semaphorin 3A (Sema3A). (<b>A</b>) Diagram displaying the modular structure of Nrp1, which is comprised of five domains (a1, a2, b1, b2, and c), as illustrated by He et al. [<a href="#B1-cells-09-00517" class="html-bibr">1</a>]. S: signal peptide; C1r/s: complement (CUB); FV/VIII: regions homologous to coagulation factor V and VIII; MAM: a specific domain in transmembrane proteins. (<b>B</b>) Interaction between Sema3A and Nrp1. Sema3A binds to domain a1 of a1a2 via the Sema domain, the Ig-like domain, and the <span class="html-italic">C</span>-terminal basic tail, as well as to domain b1 via the <span class="html-italic">C</span>-terminal tail. Vascular endothelial growth factor (VEGF) and heparin bind to b1b2. (<b>C</b>) Proposed model for kCer binding to Nrp1 as a Sema3A agonist based on the two hypotheses. A hydrophobic part of kCer binds to the fatty acid binding pocket, forming the kCer/BSA complex, which releases kCer via formation of the kCer/BSA/Nrp1 complex. kCer is indicated by a dashed arrow (a), representing a weak flow relative to the bold arrow (b).</p> Full article ">Figure 3
<p>Co-immunoprecipitation (Co-IP) of alkaline, phosphatase-fused Sema3A (AP-Sema3A) using the His-tagged Nrp1 domain and anti-His antibody. (<b>A</b>) AP-Sema3A (0, 50, or 100 nM) was mixed with Nrp1, a1a2, b1b2, or c (100 nM) prior to Co-IP with an anti-6x-His monoclonal antibody (anti-His mAb) (2 μg). (<b>B</b>) kCer or C18Cer (100 μM) and AP-Sema3A (100 nM) were mixed with Nrp1, a1a2, b1b2, or c (100 nM) before Co-IP with anti-His mAb (2 μg).</p> Full article ">Figure 4
<p>Dot blot analysis of the binding characteristics of kCer to Nrp1 domain proteins. (<b>A</b>) The upper blot shows the results for mixtures a1a2 (of 100 nM) plus d4t,8t-NBD-Ceramide (-NBD-Cer), d4t,8c-NBD-Cer, phyto-NBD-Cer, or d 4t-NBD-Cer (all at 100 nM). The lower blot shows the results for mixtures of 100 nM d4t,8t-NBD-Cer with 100 nM a1a2, b1b2, or b. (<b>B</b>) Saturation curve of d4t,8t-NBD-Cer binding to 200 nM a1a2 (●). Control blot of d4t,8t-NBD-Cer without a1a2 is shown on the lower plot (○). Data are presented as means ± standard deviation (SD) (<span class="html-italic">n</span> = 3). <span class="html-italic">X</span> is the concentration of d4t,8t-NBD-Cer (nM). The <span class="html-italic">y</span>-axis label “FI” represents the fluorescence intensity of d4t,8t-NBD-Cer bound to 200 nM a1a2. <span class="html-italic">C</span> is a constant for non-specific binding of d4t,8t-NBDCer. Non-specific binding is represented in the equation as <math display="inline"><semantics> <mrow> <msqrt> <mrow> <mi>CX</mi> </mrow> </msqrt> </mrow> </semantics></math>. (<b>C</b>) <span class="html-italic">Kd</span> is the dissociation constant of the binding of d4t,8t-NBD-Cer to a1a2. <span class="html-italic">B<sub>max</sub></span> is the plateau of the binding of d4t,8t-NBD-Cer to a1a2. FI is the fluorescence intensity per nanomole a1a2.</p> Full article ">Figure 5
<p>Time course of NBD-Cer and Rhod-bovine serum albumin (BSA) bound to PC12 cells. (<b>A</b>) Dissociation time course analysis of FI based on binding of 100 nM d NBD-Cer (FI = 5000) and 100 nM Rhod-BSA (FI = 5510), examined using Plexin A1 gene-silencing PC12 cells. (<b>B</b>) Images showing (1 to 4) changes in d4t,8t-NBD-Cer and (5 to 8) changes in Rhod-BSA at the indicated timepoints. The left graph shows a time course plot of FI (%) relative to 0 min. Data are presented as mean ± SD (<span class="html-italic">n</span> = 3). Scale bar = 100 μm. (<b>C</b>) Images showing (1 to 4) changes in d4t,8c-NBD-Cer and (5 to 8) changes in Rhod-BSA at the indicated timepoints. The left graph is a time course plot of FI (%) relative to 0 min. Data are presented as mean ± SD (<span class="html-italic">n</span> = 3). Scale bar = 100 μm.</p> Full article ">Figure 6
<p>Inhibition profile of the Nrp1 domain of NBD-Cer and Rhod-BSA binding to PC12 cells. (<b>A</b>) Representative images of Plex A1 gene-silencing PC12 cells examined together with Nrp1 domains a1a2, bib2, and c (100 nM), or control (CNTL) plus 100 nM NBD-Cer/100 nM Rhod-BSA. Scale bar = 100 μm. Changes in FI are shown in the right upper graph (NBD-Cer) and lower right graph (Rhod-BSA). Data are presented as mean ± SD (<span class="html-italic">n</span> = 3; *<span class="html-italic">p</span> &lt; 0.01). (<b>B</b>) Colocalization of d4t,8t- or d4t,8c-NBD-Cer and Rhod-BSA during dissociation (0 to 15 min) in cells. Green (■) indicates occupation based on NBD fluorescence, and Red (□) indicates occupation based on Rhod fluorescence. Data are expressed as FI (%) at 0 min and are presented as mean ± SD (<span class="html-italic">n</span> = 3; *<span class="html-italic">p</span> &lt; 0.01).</p> Full article ">Figure 7
<p>(<b>A</b>) Molecular docking simulation for the a1 module and d4t,8t-C16kCer, and artificial species of kCer composed of C16:0 fatty acid and d4t,8t-sphingadienine. There are three binding sites (A, B, and C) on the a1 protein. Site A is located near the Sema3A binding region. (<b>B</b>) Possible activation mechanism of Nrp1 by kCer. The sole a1 module is far away from a2 and b1b2 (<a href="#app1-cells-09-00517" class="html-app">Figure S4B</a>). intrinsically disordered region (IDR)-like flexibility likely occurs due to the distance between a1 and a2 modules. When kCer binds to Site A of the a1 module, the IDR-like flexibility between a1 and a2 diminishes, and the IDR-like region rigidifies, strengthening the intermolecular interactions between a1 and a2, resulting in a slight change in protein secondary structure.</p> Full article ">
12 pages, 3591 KiB  
Article
Transcriptional and Ultrastructural Analyses Suggest Novel Insights into Epithelial Barrier Impairment in Celiac Disease
by Agnieszka Sowińska, Yasser Morsy, Elżbieta Czarnowska, Beata Oralewska, Ewa Konopka, Marek Woynarowski, Sylwia Szymańska, Maria Ejmont, Michael Scharl, Joanna B. Bierła, Marcin Wawrzyniak and Bożena Cukrowska
Cells 2020, 9(2), 516; https://doi.org/10.3390/cells9020516 - 24 Feb 2020
Cited by 11 | Viewed by 3547
Abstract
Disruption of epithelial junctional complex (EJC), especially tight junctions (TJ), resulting in increased intestinal permeability, is supposed to activate the enhanced immune response to gluten and to induce the development of celiac disease (CD). This study is aimed to present the role of [...] Read more.
Disruption of epithelial junctional complex (EJC), especially tight junctions (TJ), resulting in increased intestinal permeability, is supposed to activate the enhanced immune response to gluten and to induce the development of celiac disease (CD). This study is aimed to present the role of EJC in CD pathogenesis. To analyze differentially expressed genes the next-generation mRNA sequencing data from CD326+ epithelial cells isolated from non-celiac and celiac patients were involved. Ultrastructural studies with morphometry of EJC were done in potential CD, newly recognized active CD, and non-celiac controls. The transcriptional analysis suggested disturbances of epithelium and the most significant gene ontology enriched terms in epithelial cells from CD patients related to the plasma membrane, extracellular exome, extracellular region, and extracellular space. Ultrastructural analyses showed significantly tighter TJ, anomalies in desmosomes, dilatations of intercellular space, and shorter microvilli in potential and active CD compared to controls. Enterocytes of fetal-like type and significantly wider adherence junctions were observed only in active CD. In conclusion, the results do not support the hypothesis that an increased passage of gluten peptides by unsealing TJ precedes CD development. However, increased intestinal permeability due to abnormality of epithelium might play a role in CD onset. Full article
(This article belongs to the Special Issue Epithelial Cell Mechanics: From Physiology to Pathology)
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<p>Multivariate visualization of the analyzed genes. (<b>a</b>) PCA scatter plot PCA plot showing variance between CD patients’ samples and non-celiac control samples (PC1), and heterogeneity between the five biological replicates in each group (PC2). (<b>b</b>) Volcano plot representing the results of the analysis. Each dot representing one gene, and the blue highlighted genes were significantly differentially expressed (<span class="html-italic">p</span>-value &lt; 0.05) and log fold change cut-off 2. (<b>c</b>) Heatmap shows hierarchical clustering of genes on the left side and the clustering of the samples on the top. The histogram represents the expression data of the significant differentially expressed genes (green is the down-regulated and red is the up-regulated).</p> Full article ">Figure 2
<p>Results of functional enrichment analysis. The outer circle shows a scatter plot of each term, including their corresponding genes (blue dots down-regulated and red dot up-regulated). Z-score indicates the tendency to increase or decrease each of the gene ontology (GO) terms based on the ratio of the differentially expressed genes. Only the significant terms are displayed in the three main categories: (<b>a</b>) biological process, (<b>b</b>) cellular components, and (<b>c</b>) molecular function.</p> Full article ">Figure 3
<p>Ultrastructure of small intestine enterocytes from the non-CD control group, patients with potential and active CD (<b>a</b>). Cross-sections of non-CD enterocytes with anchoring filaments (AF), endosomes (e) and no intracellular dilatations between cells present in the control group. Enterocytes in potential and active CD exhibit numerous endosomes (e) and tubules of an apical canicular system (ACS), and dilated intercellular spaces (*). The length and width of the brush border microvilli (<b>b</b>). Measurements were done with the use of the morphometric iTEM program (Olympus) in 10 selected epithelial areas at a magnification of ×60,000, and at least 3 values/patient were obtained. All measurements are presented. Statistical analysis was performed with the use of one-way ANOVA with Tukey correction for multiple comparisons. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001.</p> Full article ">Figure 4
<p>The proximal region of the enterocytes with tight junctions (TJ), adherence junctions (AJ) and desmosomes (D) from the non-CD control group, patients with potential and active CD (<b>a</b>), and ultrastructural features of intercellular junctions (<b>b</b>,<b>c</b>). Desmosomes with an incorrect asymmetrical structure (D*) present in a patient with active CD. The widths (<b>b</b>) and lengths (<b>c</b>) of EJC were measured using the morphometric iTEM program (Olympus) at a magnification of ×60,000. Measurements were done in 10 selected epithelial areas through longitudinally sectioned intercellular junctions, and at least 5 values of each type of junction/patient were obtained. All measurements are presented. Statistical analysis was performed with the use of one-way ANOVA with Tukey correction for multiple comparisons. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> ≤ 0.01, **** <span class="html-italic">p</span> ≤ 0.0001.</p> Full article ">
17 pages, 2345 KiB  
Article
Non-Phosphorylatable PEA-15 Sensitises SKOV-3 Ovarian Cancer Cells to Cisplatin
by Shahana Dilruba, Alessia Grondana, Anke C. Schiedel, Naoto T. Ueno, Chandra Bartholomeusz, Jindrich Cinatl Jr, Katie-May McLaughlin, Mark N. Wass, Martin Michaelis and Ganna V. Kalayda
Cells 2020, 9(2), 515; https://doi.org/10.3390/cells9020515 - 24 Feb 2020
Cited by 5 | Viewed by 3728
Abstract
The efficacy of cisplatin-based chemotherapy in ovarian cancer is often limited by the development of drug resistance. In most ovarian cancer cells, cisplatin activates extracellular signal-regulated kinase1/2 (ERK1/2) signalling. Phosphoprotein enriched in astrocytes (PEA-15) is a ubiquitously expressed protein, capable of sequestering ERK1/2 [...] Read more.
The efficacy of cisplatin-based chemotherapy in ovarian cancer is often limited by the development of drug resistance. In most ovarian cancer cells, cisplatin activates extracellular signal-regulated kinase1/2 (ERK1/2) signalling. Phosphoprotein enriched in astrocytes (PEA-15) is a ubiquitously expressed protein, capable of sequestering ERK1/2 in the cytoplasm and inhibiting cell proliferation. This and other functions of PEA-15 are regulated by its phosphorylation status. In this study, the relevance of PEA-15 phosphorylation state for cisplatin sensitivity of ovarian carcinoma cells was examined. The results of MTT-assays indicated that overexpression of PEA-15AA (a non-phosphorylatable variant) sensitised SKOV-3 cells to cisplatin. Phosphomimetic PEA-15DD did not affect cell sensitivity to the drug. While PEA-15DD facilitates nuclear translocation of activated ERK1/2, PEA-15AA acts to sequester the kinase in the cytoplasm as shown by Western blot. Microarray data indicated deregulation of thirteen genes in PEA-15AA-transfected cells compared to non-transfected or PEA-15DD-transfected variants. Data derived from The Cancer Genome Atlas (TCGA) showed that the expression of seven of these genes including EGR1 (early growth response protein 1) and FLNA (filamin A) significantly correlated with the therapy outcome in cisplatin-treated cancer patients. Further analysis indicated the relevance of nuclear factor erythroid 2-related factor 2/antioxidant response element (Nrf2/ARE) signalling for the favourable effect of PEA-15AA on cisplatin sensitivity. The results warrant further evaluation of the PEA-15 phosphorylation status as a potential candidate biomarker of response to cisplatin-based chemotherapy. Full article
(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Ovarian Cancer)
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<p>(<b>a</b>) Expression of hemagglutinin (HA)-tagged phosphoprotein enriched in astrocytes (PEA-15) in SKOV-3 cells after transfection with the HA-tagged empty vector (EV), PEA-15AA (AA) and PEA-15DD (DD). GAPDH was used as a loading control. (<b>b</b>) Cisplatin sensitivity (pEC<sub>50</sub>, mean ± SEM, <span class="html-italic">n</span> = 8) of transfected SKOV-3-EV (EV), SKOV-3-AA (AA) and SKOV-3-DD (DD) cells. *** <span class="html-italic">p</span> &lt; 0.001, n.s. = not significant.</p> Full article ">Figure 2
<p>A representative Western blot of phosphorylated extracellular signal-regulated kinase1/2 (p-ERK1/2) expression in nuclear and cytosolic fractions of the SKOV-3 cells transfected with empty vector (EV), PEA-15AA (AA) and PEA-15DD (DD). GAPDH and Lamin B1 were used as the markers and loading controls of cytosolic (C) and nuclear fractions (N), respectively.</p> Full article ">Figure 3
<p>Heatmap of the transcriptome-wide Clariom<sup>TM</sup> S array, regulated genes with fold change cut-off at 2.0 for differentially expressed genes and a <span class="html-italic">p</span>-value cut-off at 0.05 are shown.</p> Full article ">Figure 4
<p>Heatmap indicating the relationship between low expression of the indicated genes and sensitivity/outcome, favourable (low cisplatin EC<sub>50</sub> in SKOV-3-AA cells or prolonged survival of cisplatin-treated patients, indicated in yellow) or unfavourable (high cisplatin EC<sub>50</sub> in SKOV-3-AA cells or reduced survival of cisplatin-treated patients, indicated in blue), based on the comparison of gene expression between SKOV-3-AA and EV- or PEA-15DD-transfected variants and TCGA data.</p> Full article ">Figure 5
<p>Venn diagram representing the exclusively and commonly regulated genes in different transfected cells upon cisplatin exposure. The diagram shows the total number of genes affected by cisplatin exposure in empty vector—(EV), PEA-15AA—(AA) and PEA-15DD-transfected—(DD) cells.</p> Full article ">Figure 6
<p>Twenty-one biological pathways significantly affected by cisplatin treatment in SKOV-3-AA cells, listed according to the significance level (log 2 base) in a descending order.</p> Full article ">Figure 7
<p>Representative Western blots and the corresponding densitometric quantification (mean ± SEM, <span class="html-italic">n</span> = 3) of (<b>a</b>) the relative uridine diphosphate-glucuronyl transferase (UGT)1A expression and (<b>b</b>) the relative nuclear factor erythroid 2-related factor 2 (Nrf2) expression in empty vector—(EV), PEA-15AA—(AA) and PEA-15DD-transfected—(DD) cells after treatment with 15 µM cisplatin (+Pt) for 24 h and in untreated transfected SKOV-3 cells. GAPDH was used as a loading control. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p> Full article ">Figure 8
<p>Representative Western blots and the corresponding densitometric quantification (mean ± SEM, <span class="html-italic">n</span> = 3) of (<b>a</b>) the relative Nrf2 expression and (<b>b</b>) the relative UGT1A expression in the transfected untreated SKOV-3 cells (Ctrl), after exposure to 15 µM cisplatin (Pt), to 20 µM retinoic acid (RA) and after co-incubation with 20 µM retinoic acid and 15 µM cisplatin (Pt + RA) for 24 h are shown. GAPDH was used as a loading control. *<span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, n.s. = not significant.</p> Full article ">Figure 9
<p>Sensitivity of SKOV-3 cells (pEC<sub>50</sub>, mean ± SEM, <span class="html-italic">n</span> = 9–10) of cisplatin alone (Pt), and upon co-incubation with 20 µM retinoic acid (Pt + RA) was determined over 48 h. *** <span class="html-italic">p</span> &lt; 0.001.</p> Full article ">
12 pages, 1367 KiB  
Article
Analysis of Killer Immunoglobulin-Like Receptor Genes in Colorectal Cancer
by Roberto Diaz-Peña, Patricia Mondelo-Macía, Antonio José Molina de la Torre, Rebeca Sanz-Pamplona, Víctor Moreno and Vicente Martín
Cells 2020, 9(2), 514; https://doi.org/10.3390/cells9020514 - 24 Feb 2020
Cited by 7 | Viewed by 3152
Abstract
Natural killer cells (NK cells) play a major role in the immune response to cancer. An important element of NK target recognition is the binding of human leucocyte antigen (HLA) class I molecules by killer immunoglobulin-like receptors (KIRs). Colorectal carcinoma (CRC) is one [...] Read more.
Natural killer cells (NK cells) play a major role in the immune response to cancer. An important element of NK target recognition is the binding of human leucocyte antigen (HLA) class I molecules by killer immunoglobulin-like receptors (KIRs). Colorectal carcinoma (CRC) is one of the most common types of inflammation-based cancer. The purpose of the present study was to investigate the presence of KIR genes and HLA class I and II alleles in 1074 CRC patients and 1272 controls. We imputed data from single-nucleotide polymorphism (SNP) Illumina OncoArray to identify associations at HLA (HLA–A, B, C, DPB1, DQA1, DQB1, and DRB1) and KIRs (HIBAG and KIR*IMP, respectively). For association analysis, we used PLINK (v1.9), the PyHLA software, and R version 3.4.0. Only three SNP markers showed suggestive associations (p < 10−3; rs16896742, rs28367832, and rs9277952). The frequency of KIR2DS3 was significantly increased in the CRC patients compared to healthy controls (p < 0.005). Our results suggest that the implication of NK cells in CRC may not act through allele combinations in KIR and HLA genes. Much larger studies in ethnically homogeneous populations are needed to rule out the possible role of allelic combinations in KIR and HLA genes in CRC risk. Full article
(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Colorectal Cancer)
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<p>Schematic representations of the Leukocyte Receptor Complex (LRC) region and typical KIR A and B genotypes. Map of the Leukocyte Receptor Complex. The Leukocyte Receptor Complex (LRC) is formed by a cluster of genes that encode a family of proteins that contain immunoglobulin-like domains. These include the families “killer immunoglobulin-like receptors (KIR), “leukocyte immunoglobulin-like receptor” (LILR), and “leukocyte-associated immunoglobulin-like receptor” (LAIR). The “signaling lectins” (SIGLECs) and members of the family CD66 are found close to LCR.</p> Full article ">Figure 2
<p>KIR protein structures and ligands. Individual KIR ligands are shown in small asterisks of different colors (KIR ligands for KIR3DL3, KIR2DL5, KIR2DS3, and KIR2DS5 are unknown).</p> Full article ">
22 pages, 5185 KiB  
Article
Helicobacter Pylori Targets the EPHA2 Receptor Tyrosine Kinase in Gastric Cells Modulating Key Cellular Functions
by Marina Leite, Miguel S. Marques, Joana Melo, Marta T. Pinto, Bruno Cavadas, Miguel Aroso, Maria Gomez-Lazaro, Raquel Seruca and Ceu Figueiredo
Cells 2020, 9(2), 513; https://doi.org/10.3390/cells9020513 - 24 Feb 2020
Cited by 18 | Viewed by 5148
Abstract
Helicobacter pylori, a stomach-colonizing Gram-negative bacterium, is the main etiological factor of various gastroduodenal diseases, including gastric adenocarcinoma. By establishing a life-long infection of the gastric mucosa, H. pylori continuously activates host-signaling pathways, in particular those associated with receptor tyrosine kinases. Using [...] Read more.
Helicobacter pylori, a stomach-colonizing Gram-negative bacterium, is the main etiological factor of various gastroduodenal diseases, including gastric adenocarcinoma. By establishing a life-long infection of the gastric mucosa, H. pylori continuously activates host-signaling pathways, in particular those associated with receptor tyrosine kinases. Using two different gastric epithelial cell lines, we show that H. pylori targets the receptor tyrosine kinase EPHA2. For long periods of time post-infection, H. pylori induces EPHA2 protein downregulation without affecting its mRNA levels, an effect preceded by receptor activation via phosphorylation. EPHA2 receptor downregulation occurs via the lysosomal degradation pathway and is independent of the H. pylori virulence factors CagA, VacA, and T4SS. Using small interfering RNA, we show that EPHA2 knockdown affects cell–cell and cell–matrix adhesion, invasion, and angiogenesis, which are critical cellular processes in early gastric lesions and carcinogenesis mediated by the bacteria. This work contributes to the unraveling of the underlying mechanisms of H. pylori–host interactions and associated diseases. Additionally, it raises awareness for potential interference between H. pylori infection and the efficacy of gastric cancer therapies targeting receptors tyrosine kinases, given that infection affects the steady-state levels and dynamics of some receptor tyrosine kinases (RTKs) and their signaling pathways. Full article
(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Gastric Cancer)
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Graphical abstract

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Full article ">Figure 1
<p>Prolonged exposure of gastric epithelial cell lines to <span class="html-italic">H. pylori</span> infection induced downregulation of EPHA2 receptor protein without affecting mRNA levels independently of the major virulence factors T4SS, CagA, and VacA. (<b>a</b>) EPHA2 protein expression in MKN74 (<span class="html-italic">n</span> = 5) and NCI-N87 (<span class="html-italic">n</span> = 4) cell lines either uninfected (Ø) or infected with <span class="html-italic">H. pylori</span> 60190 for different periods of time at an MOI of 100 assessed by Western blotting with corresponding densitometric analysis (left and middle panels); EPHA2 mRNA levels assessed by RT-qPCR at 24 h post-infection (<span class="html-italic">n</span> = 4; right panel); **** <span class="html-italic">p</span> &lt; 0.0001; ns—not significant (<span class="html-italic">p</span> &gt; 0.05). (<b>b</b>) Immunofluorescence of EPHA2 (green) protein in MKN24 cells at 24 h post-infection, with nuclei stained with DAPI (blue) (scale bar: 10 µm; 63× original magnification; <span class="html-italic">n</span> = 3). (<b>c</b> and <b>d</b>) EPHA2 protein expression upon coculture with (<b>c</b>) wild-type or mutants (<span class="html-italic">cagE</span> negative, <span class="html-italic">cagA</span> negative, <span class="html-italic">vacA</span> negative) of the H<span class="html-italic">. pylori</span> 60190 strain (<span class="html-italic">n</span> = 5 for MKN74 and <span class="html-italic">n</span> = 2 for NCI-N87) and (<b>d</b>) with <span class="html-italic">H. pylori</span> reference strains (60190, 26695, NCTC11637, Tx30a) and clinical isolates (CI-65, CI-64, CI-50, CI-62) (n = 4 for each cell line) in 24 h cocultures at MOI100 assessed by immunoblotting; **** <span class="html-italic">p</span> &lt; 0.0001; * <span class="html-italic">p</span> ≤ 0.05. The represented densitometric analysis are the mean ± SE of all independent experiments. One-way ANOVA with post-hoc Dunnett’s test for multiple comparisons and Student’s t-test for single comparisons.</p> Full article ">Figure 1 Cont.
<p>Prolonged exposure of gastric epithelial cell lines to <span class="html-italic">H. pylori</span> infection induced downregulation of EPHA2 receptor protein without affecting mRNA levels independently of the major virulence factors T4SS, CagA, and VacA. (<b>a</b>) EPHA2 protein expression in MKN74 (<span class="html-italic">n</span> = 5) and NCI-N87 (<span class="html-italic">n</span> = 4) cell lines either uninfected (Ø) or infected with <span class="html-italic">H. pylori</span> 60190 for different periods of time at an MOI of 100 assessed by Western blotting with corresponding densitometric analysis (left and middle panels); EPHA2 mRNA levels assessed by RT-qPCR at 24 h post-infection (<span class="html-italic">n</span> = 4; right panel); **** <span class="html-italic">p</span> &lt; 0.0001; ns—not significant (<span class="html-italic">p</span> &gt; 0.05). (<b>b</b>) Immunofluorescence of EPHA2 (green) protein in MKN24 cells at 24 h post-infection, with nuclei stained with DAPI (blue) (scale bar: 10 µm; 63× original magnification; <span class="html-italic">n</span> = 3). (<b>c</b> and <b>d</b>) EPHA2 protein expression upon coculture with (<b>c</b>) wild-type or mutants (<span class="html-italic">cagE</span> negative, <span class="html-italic">cagA</span> negative, <span class="html-italic">vacA</span> negative) of the H<span class="html-italic">. pylori</span> 60190 strain (<span class="html-italic">n</span> = 5 for MKN74 and <span class="html-italic">n</span> = 2 for NCI-N87) and (<b>d</b>) with <span class="html-italic">H. pylori</span> reference strains (60190, 26695, NCTC11637, Tx30a) and clinical isolates (CI-65, CI-64, CI-50, CI-62) (n = 4 for each cell line) in 24 h cocultures at MOI100 assessed by immunoblotting; **** <span class="html-italic">p</span> &lt; 0.0001; * <span class="html-italic">p</span> ≤ 0.05. The represented densitometric analysis are the mean ± SE of all independent experiments. One-way ANOVA with post-hoc Dunnett’s test for multiple comparisons and Student’s t-test for single comparisons.</p> Full article ">Figure 2
<p>EPHA2 receptor downregulation induced by <span class="html-italic">H. pylori</span> infection is preceded by receptor phosphorylation early on and is followed by lysosomal degradation in the MKN74 gastric cell line. (<b>a</b>) Tyrosine and serine897 phosphorylation of EPHA2 upon <span class="html-italic">H. pylori</span> exposure, as determined by ELISA and Western blot, respectively; *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> ≤ 0.05, ns—not significant (<span class="html-italic">p</span> &gt; 0.05). (<b>b</b>) Effect of PP2 (SRC family kinase inhibitor), U0126 (MEK inhibitor), and CAY10626 (PI3Kα/mTOR inhibitor) on downregulation of EPHA2 mediated by <span class="html-italic">H. pylori</span> at 24 h by Western blot and corresponding quantifications by densitometry; **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, ns—not significant (<span class="html-italic">p</span> &gt; 0.05). (<b>c</b>) Effect of SRC family kinase inhibitors (PP2 and Dasatinib inhibitors) on EPHA2-tyrosine phosphorylation 1 h after <span class="html-italic">H. pylori</span> infection, as evaluated by ELISA; **** <span class="html-italic">p</span> &lt; 0.0001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> ≤ 0.05. (<b>d</b>) Effect of lysosomal (Bafilomycin A1 and Concanamycin A) and proteasomal (bortezomib) inhibitors on EPHA2 receptor downregulation induced by <span class="html-italic">H. pylori</span> at 24 h post-infection as shown by Western blot and the respective relative density expressed as the ratio of infected/uninfected cells; *** <span class="html-italic">p</span> &lt; 0.001; ** <span class="html-italic">p</span> &lt; 0.01; ns—not significant (<span class="html-italic">p</span> &gt; 0.05). One-way ANOVA with post-hoc Dunnett’s or Tukey’s test.</p> Full article ">Figure 2 Cont.
<p>EPHA2 receptor downregulation induced by <span class="html-italic">H. pylori</span> infection is preceded by receptor phosphorylation early on and is followed by lysosomal degradation in the MKN74 gastric cell line. (<b>a</b>) Tyrosine and serine897 phosphorylation of EPHA2 upon <span class="html-italic">H. pylori</span> exposure, as determined by ELISA and Western blot, respectively; *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> ≤ 0.05, ns—not significant (<span class="html-italic">p</span> &gt; 0.05). (<b>b</b>) Effect of PP2 (SRC family kinase inhibitor), U0126 (MEK inhibitor), and CAY10626 (PI3Kα/mTOR inhibitor) on downregulation of EPHA2 mediated by <span class="html-italic">H. pylori</span> at 24 h by Western blot and corresponding quantifications by densitometry; **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, ns—not significant (<span class="html-italic">p</span> &gt; 0.05). (<b>c</b>) Effect of SRC family kinase inhibitors (PP2 and Dasatinib inhibitors) on EPHA2-tyrosine phosphorylation 1 h after <span class="html-italic">H. pylori</span> infection, as evaluated by ELISA; **** <span class="html-italic">p</span> &lt; 0.0001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> ≤ 0.05. (<b>d</b>) Effect of lysosomal (Bafilomycin A1 and Concanamycin A) and proteasomal (bortezomib) inhibitors on EPHA2 receptor downregulation induced by <span class="html-italic">H. pylori</span> at 24 h post-infection as shown by Western blot and the respective relative density expressed as the ratio of infected/uninfected cells; *** <span class="html-italic">p</span> &lt; 0.001; ** <span class="html-italic">p</span> &lt; 0.01; ns—not significant (<span class="html-italic">p</span> &gt; 0.05). One-way ANOVA with post-hoc Dunnett’s or Tukey’s test.</p> Full article ">Figure 3
<p>Role of EPHA2 in cell–cell and cell–matrix adhesion and invasion of gastric cells. Representative images of the slow aggregation assay for (<b>a</b>) MKN74 and (<b>b</b>) NCI-N87 gastric cells untreated (cells) transfected with a nonsilencing siRNA (siNS) or with a EPHA2 siRNA (siEPHA2); **** <span class="html-italic">p</span> &lt; 0.0001, ** <span class="html-italic">p</span> &lt; 0.01, ns—not significant (<span class="html-italic">p</span> &gt; 0.05). Cell–cell aggregate size quantification for each condition using Quantity One software. Representative Western blot of the EPHA2 protein expression and the densitometric quantification of the EPHA2 levels in all slow aggregation assays (<span class="html-italic">n</span> = 4). (<b>c</b>) Cell–matrix adhesion of nontransfected siNS or siEPHA2-transfected MKN74 cells to different substrates using poly-L-lysine-coated cells as the maximal adhesion (100%); * <span class="html-italic">p</span> ≤ 0.05. (<b>d</b>) Protein expression of the integrins beta 1 (ITGB1) and alpha2 (ITGA2), a major receptor for collagen type I, assessed by Western blot and respective densitometric quantification (<span class="html-italic">n</span> = 2); * <span class="html-italic">p</span> ≤ 0.05. (<b>e</b>) <span class="html-italic">In vitro</span> Matrigel invasion assay of MKN74 and NCI-N87 cells either transfected with siNS or siEPHA2 upon infection with <span class="html-italic">H. pylori</span> 26695 strain as a control; * <span class="html-italic">p</span> ≤ 0.05. Student’s t-test and one-way ANOVA with post-hoc Tukey’s test.</p> Full article ">Figure 3 Cont.
<p>Role of EPHA2 in cell–cell and cell–matrix adhesion and invasion of gastric cells. Representative images of the slow aggregation assay for (<b>a</b>) MKN74 and (<b>b</b>) NCI-N87 gastric cells untreated (cells) transfected with a nonsilencing siRNA (siNS) or with a EPHA2 siRNA (siEPHA2); **** <span class="html-italic">p</span> &lt; 0.0001, ** <span class="html-italic">p</span> &lt; 0.01, ns—not significant (<span class="html-italic">p</span> &gt; 0.05). Cell–cell aggregate size quantification for each condition using Quantity One software. Representative Western blot of the EPHA2 protein expression and the densitometric quantification of the EPHA2 levels in all slow aggregation assays (<span class="html-italic">n</span> = 4). (<b>c</b>) Cell–matrix adhesion of nontransfected siNS or siEPHA2-transfected MKN74 cells to different substrates using poly-L-lysine-coated cells as the maximal adhesion (100%); * <span class="html-italic">p</span> ≤ 0.05. (<b>d</b>) Protein expression of the integrins beta 1 (ITGB1) and alpha2 (ITGA2), a major receptor for collagen type I, assessed by Western blot and respective densitometric quantification (<span class="html-italic">n</span> = 2); * <span class="html-italic">p</span> ≤ 0.05. (<b>e</b>) <span class="html-italic">In vitro</span> Matrigel invasion assay of MKN74 and NCI-N87 cells either transfected with siNS or siEPHA2 upon infection with <span class="html-italic">H. pylori</span> 26695 strain as a control; * <span class="html-italic">p</span> ≤ 0.05. Student’s t-test and one-way ANOVA with post-hoc Tukey’s test.</p> Full article ">Figure 4
<p>Angiogenic signature imprinted by EPHA2 in gastric cells. (<b>a</b>) A human angiogenesis antibody array composed by duplicated spots of 55 angiogenic-related factors was performed with a pool of cell lysates from siNS and siEPHA2-transfected MKN74 and siNS cells-infected with <span class="html-italic">H. pylori</span> 60190 strain (24 h; MOI100) as a control of the angiogenic response (<span class="html-italic">n</span> = 1); some representative angiogenic factors were highlighted. The map of the array and graph with fold-change variations are presented in <a href="#app1-cells-09-00513" class="html-app">Supplementary Figure S1c,d</a> (<b>b</b>) Heat map analysis representing the siNS-normalized average pixel density of the duplicated spots for each angiogenic-related protein in the array. (<b>c</b>) Validation of the array for IL-8 by ELISA (<span class="html-italic">n</span> = 6) and its comparison with array expression. One-way ANOVA with post-hoc Tukey’s test for multiple comparisons analysis: **** <span class="html-italic">p</span> &lt; 0.0001; *** <span class="html-italic">p</span> &lt; 0.001; ** <span class="html-italic">p</span> &lt; 0.01 * <span class="html-italic">p</span> ≤ 0.05; ns—not significant (<span class="html-italic">p</span> &gt; 0.05).</p> Full article ">Figure 5
<p>Role of EPHA2 in angiogenesis <span class="html-italic">in vitro</span> (<b>a</b>–<b>d</b>) and <span class="html-italic">in vivo</span> (<b>e</b>–<b>h</b>). (<b>a</b>) Representative micrographs of the <span class="html-italic">in vitro</span> capillary-like structures formed by human umbilical vein endothelial cells (HUVECs), upon treatment with conditioned medium from untreated MKN74 cells (cells), MKN74 cells treated with a nonsilencing siRNA (siNS) as a negative control, or with an siRNA for the EPHA2 (siEPHA2) 5 h post-seeding in Matrigel-coated wells. (<b>b</b>) Corresponding automatic analysis using WimTube software (scale bar: 100 µm; original magnification: ×100) with the quantification of the number of tubes (<b>c</b>) and branching points (<b>d</b>) per microscopic field from 3 independent experiments. (<b>e</b>) Representative photomicrographs of the <span class="html-italic">in vivo</span> chicken embryo chorioallantoic membrane (CAM), depicting new blood vessel formation induced by untreated MKN74 cells (cells), MKN74 cells transfected with a nonsilencing siRNA negative control (siNS), or with an siRNA against the EPHA2 (siEPHA2). Cells were inoculated on top of the CAM inside a 5 mm silicon ring under sterile conditions for 3 days (scale bar: 1 mm; original magnification: 20×). (<b>f</b>) Representative immunohistochemistry of the CAM paraffin sections stained with EPHA2 antibody (scale bar: 50 µm; original magnification: 200×). (<b>g</b>) Quantification of the number of new vessels radially formed toward the inoculation area as a measure of the angiogenic potential of the inoculated cells. Data regarding 15 fertilized eggs per condition are depicted on the box plot graph. (<b>h</b>) Representative Western blot of EPHA2 and tubulin expression in MKN74 for the different experimental conditions at the end of the experiment, and the quantification for 3 independent experiments. Data are presented as mean ± SE. One-way ANOVA analysis followed by Tukey’s multi-comparison test: **** <span class="html-italic">p</span> &lt; 0.0001; *** <span class="html-italic">p</span> &lt; 0.001; ** <span class="html-italic">p</span> &lt; 0.01; ns—not significant (<span class="html-italic">p</span> &gt; 0.05).</p> Full article ">
18 pages, 734 KiB  
Review
The Translocator Protein (TSPO) in Mitochondrial Bioenergetics and Immune Processes
by Calina Betlazar, Ryan J. Middleton, Richard Banati and Guo-Jun Liu
Cells 2020, 9(2), 512; https://doi.org/10.3390/cells9020512 - 24 Feb 2020
Cited by 78 | Viewed by 8811
Abstract
The translocator protein (TSPO) is an outer mitochondrial membrane protein that is widely used as a biomarker of neuroinflammation, being markedly upregulated in activated microglia in a range of brain pathologies. Despite its extensive use as a target in molecular imaging studies, the [...] Read more.
The translocator protein (TSPO) is an outer mitochondrial membrane protein that is widely used as a biomarker of neuroinflammation, being markedly upregulated in activated microglia in a range of brain pathologies. Despite its extensive use as a target in molecular imaging studies, the exact cellular functions of this protein remain in question. The long-held view that TSPO plays a fundamental role in the translocation of cholesterol through the mitochondrial membranes, and thus, steroidogenesis, has been disputed by several groups with the advent of TSPO knockout mouse models. Instead, much evidence is emerging that TSPO plays a fundamental role in cellular bioenergetics and associated mitochondrial functions, also part of a greater role in the innate immune processes of microglia. In this review, we examine the more direct experimental literature surrounding the immunomodulatory effects of TSPO. We also review studies which highlight a more central role for TSPO in mitochondrial processes, from energy metabolism, to the propagation of inflammatory responses through reactive oxygen species (ROS) modulation. In this way, we highlight a paradigm shift in approaches to TSPO functioning. Full article
(This article belongs to the Special Issue Study around Neuroinflammation)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Overview of the translocator protein (TSPO) in the inflammatory responses of microglia and its interaction with mitochondrial processes. Under stress conditions, TSPO is upregulated in activated, pro-inflammatory (M1) microglia. Located on the outer mitochondrial membrane, TSPO interacts with reactive oxygen species (ROS), a key part of the microglial inflammatory response. TSPO also interacts with inflammatory transcriptional pathways including MAPK and the NLRP3 inflammasome, resulting in the release of cytokines. These processes can be modulated by TSPO ligands, and by genetic deletion of TSPO, indicating a key role for TSPO in these processes.</p> Full article ">
17 pages, 4633 KiB  
Article
(R)-Salbutamol Improves Imiquimod-Induced Psoriasis-Like Skin Dermatitis by Regulating the Th17/Tregs Balance and Glycerophospholipid Metabolism
by Fei Liu, Shanping Wang, Bo Liu, Yukun Wang and Wen Tan
Cells 2020, 9(2), 511; https://doi.org/10.3390/cells9020511 - 24 Feb 2020
Cited by 21 | Viewed by 6533
Abstract
Psoriasis is a skin disease that is characterized by a high degree of inflammation caused by immune dysfunction. (R)-salbutamol is a bronchodilator for asthma and was reported to alleviate immune system reactions in several diseases. In this study, using imiquimod (IMQ)-induced [...] Read more.
Psoriasis is a skin disease that is characterized by a high degree of inflammation caused by immune dysfunction. (R)-salbutamol is a bronchodilator for asthma and was reported to alleviate immune system reactions in several diseases. In this study, using imiquimod (IMQ)-induced mouse psoriasis-like dermatitis model, we evaluated the therapeutic effects of (R)-salbutamol in psoriasis in vivo, and explored the metabolic pathway involved. The results showed that, compared with IMQ group, (R)-salbutamol treatment significantly ameliorated psoriasis, reversed the suppressive effects of IMQ on differentiation, excessive keratinocyte proliferation, and infiltration of inflammatory cells. Enzyme-linked immunosorbent assays (ELISA) showed that (R)-salbutamol markedly reduced the plasma levels of IL-17. Cell analysis using flow cytometry showed that (R)-salbutamol decreased the proportion of CD4+ Th17+ T cells (Th17), whereas it increased the percentage of CD25+ Foxp3+ regulatory T cells (Tregs) in the spleens. (R)-salbutamol also reduced the increased weight ratio of spleen to body. Furthermore, untargeted metabolomics showed that (R)-salbutamol affected three metabolic pathways, including (i) arachidonic acid metabolism, (ii) sphingolipid metabolism, and (iii) glycerophospholipid metabolism. These results demonstrated that (R)-salbutamol can alleviate IMQ-induced psoriasis through regulating Th17/Tregs cell response and glycerophospholipid metabolism. It may provide a new use of (R)-salbutamol in the management of psoriasis. Full article
(This article belongs to the Special Issue Molecular and Cellular Basis of Autoimmune Diseases)
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Figure 1

Figure 1
<p>Experimental procedure of the antipsoriatic activity evaluation of (<span class="html-italic">R</span>)-salbutamol or Dex. one hour after the administration of different doses of (<span class="html-italic">R</span>)-salbutamol or Dex twice per day, mice in all groups except for the control group received a daily topical dose of 62.50 mg of the imiquimod (IMQ) cream on the shaved area of their backs for seven consecutive days. On day 8, the mice were killed to harvest specimens for experiments.</p> Full article ">Figure 2
<p>(<span class="html-italic">R</span>)-salbutamol alleviates psoriatic dermatitis. Phenotypical presentation of mouse back skin from control, IMQ, (<span class="html-italic">R</span>)-salbutamol and Dex groups after seven days of treatment, respectively (<b>A</b>). Distinct levels of erythema (<b>B</b>), skin thickeness (<b>C</b>), scaling (<b>D</b>) of back skin was scored daily on a scale from 0 to 4. Additionally, the cumulative score (<b>E</b>) (erythema plus scaling plus thickness) is depicted. <span class="html-italic">n</span> = 8, Mean ± SD, <sup>†</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>††</sup> <span class="html-italic">p</span> &lt; 0.01 vs. IMQ group, ** <span class="html-italic">p</span> &lt; 0.01 vs. control group.</p> Full article ">Figure 3
<p>Treatment with (<span class="html-italic">R</span>)-salbutamol ameliorated the morphological changes induced by IMQ. (<b>A</b>) (<span class="html-italic">R</span>)-salbutamol improves pathological injury. (Scale bar: 200 µm) White arrows show inflammatory cell infiltration, gray arrows show parakeratosis, red arrows represent Munro’s microabscesses, and black arrows was thickened prickle cell layer of the epidermis. (<b>B</b>) (<span class="html-italic">R</span>)-salbutamol alleviates epidermal thickness of the dorsal skin on day 8. (<b>C</b>) (<span class="html-italic">R</span>)-salbutamol decreased Baker score. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, compared with IMQ group. ** <span class="html-italic">p</span> &lt; 0.01, compared with control group. Each bar represents the mean ± SD (<span class="html-italic">n</span> = 8).</p> Full article ">Figure 4
<p>(<span class="html-italic">R</span>)-salbutamol reduced the levels of leukocytes in the blood and reduced IL-17 in the plasma. (<b>A</b>) white blood cells (WBC), (<b>B</b>) Neutrophil, (<b>C</b>) Monocyte were analyzed using IDEXX ProCyte DX hematology analyzer. (<b>D</b>) Levels of IL-17 in mouse plasma were measured by ELISA. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, compared with IMQ group. ** <span class="html-italic">p</span> &lt; 0.01, compared with control group. Each bar represents the mean ± SD (<span class="html-italic">n</span> = 8).</p> Full article ">Figure 5
<p>Effect of (<span class="html-italic">R</span>)-salbutamol treatment on the ratio of spleen weight to bodyweight. (<b>A</b>) Representative photographs of spleen in different groups. (<b>B</b>) 24 h after the final administration, mice were sacrificed and the ratio of spleen weight to bodyweight was determined. <sup>###</sup> <span class="html-italic">p</span> &lt; 0.01, compared with IMQ group. *** <span class="html-italic">p</span> &lt; 0.001, compared with control group. Each bar represents the mean ± SD (<span class="html-italic">n</span> = 8).</p> Full article ">Figure 6
<p>The influence of (<span class="html-italic">R</span>)-salbutamol on Th17 cells and Treg cells levels. Spleen cells were obtained from mice on day 8 and then stimulated with cocktail (with Brefeldin) for 6 h. Thereafter, they were stained with fluorescent conjugated anti-mouse CD3, CD25, and CD4. In addition, intracellular staining of IL-17 and Foxp3 was performed using the respective antibodies. Representative contour plots showed the frequency of live CD4+ T cells, IL-17+ Th17 gated and CD25+ Foxp3+ Treg in the splenocytes isolated from mice treated with IMQ and then with (<span class="html-italic">R</span>)-salbutamol. Relative scatter plots showed the frequencies determined from live cells. (<b>A</b>) Representative dot plots showing the percentage of CD3+CD4+ T cells. (<b>B</b>) Statistical analysis of the percentage of CD3+CD4+ T cells. (<b>C</b>) Expression of intracellular cytokines IL-17 was detected by flow cytometry in cells gated for CD4+. (<b>D</b>) FoxP3 stained with a Foxp3 staining buffer set without stimulation, with CD25+ surface as the gate. (<b>E</b>,<b>F</b>) Statistical analysis of the above results. <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, relative to IMQ group. ** <span class="html-italic">p</span> &lt; 0.01, relative to the control group. Error bars represent the mean ± SD (<span class="html-italic">n</span> = 8).</p> Full article ">Figure 7
<p>Results of the metabolic effects of (<span class="html-italic">R</span>)-salbutamol in mice treated with IMQ to induce psoriasis. (<b>A</b>,<b>B</b>) PCA plot scores for the control, IMQ and (<span class="html-italic">R</span>)-salbutamol (L, M, H) groups in (<b>B</b>) ESI (−) mode and (A) ESI (+). (<b>C</b>,<b>D</b>) PLS-DA score plot for the (<span class="html-italic">R</span>)-salbutamol (L, M, H), IMQ and control on the basis of mice plasma metabolic profiles for the (<b>D</b>) ESI (−) mode and (C) ESI (+). (<b>E</b>,<b>F</b>) Venn diagrams showing the upregulated (<b>E</b>) or downregulated metabolites (<b>F</b>) based on the binary comparison of (<span class="html-italic">R</span>)-salbutamol vs. control, IMQ vs. control corresponding to the numbers shown in <a href="#app1-cells-09-00511" class="html-app">Supplemental Table S4</a>. (<b>G</b>,<b>H</b>) Volcano plots of <span class="html-italic">p</span> values in the (<b>G</b>) ESI (+) and (H) ESI (−) mode. (<b>I</b>) Visualization of candidate biomarkers among the (<span class="html-italic">R</span>)-salbutamol, IMQ, and control in the ESI (+) and ESI (−) mode using Heat map of unsupervised hierarchical clustering. Columns: samples; Rows: biomarkers. The content level of metabolites is denoted by the color key. Red stands for high metabolite level whereas blue color denotes low metabolite level. (<b>J</b>) Pathway analysis for the differential metabolism in the (<span class="html-italic">R</span>)-salbutamol (L, M, H), IMQ, and control groups based on the topology analysis (<span class="html-italic">x</span>-axis) and enrichment analysis scores (<span class="html-italic">y</span>-axis). The size and color of each circle represent the pathway impact factor and <span class="html-italic">p</span>-value, respectively. The pathways marked in red are the most significant. These analyses were performed using the MetaboAnalyst 4.0 tool.</p> Full article ">Figure 8
<p>Abundant of some metabolites in plasma of mice from control, IMQ and (<span class="html-italic">R</span>)-salbutamol groups. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, relative to IMQ group. ** <span class="html-italic">p</span> &lt; 0.01, relative to the control group.</p> Full article ">Figure 9
<p>Schematic diagram showing possible mechanisms responsible for the pharmacological efficacy of (<span class="html-italic">R</span>)-salbutamol. Oral administration of (<span class="html-italic">R</span>)-salbutamol markedly reduced the plasma levels of IL-17, decreased the proportion of CD4+ Th17+ T cells (Th17) whereas increased the percentage of CD25+ Foxp3+ regulatory T cells (Tregs) in the spleens, and affected glycerophospholipid metabolism.</p> Full article ">
23 pages, 1606 KiB  
Review
Lactate in Sarcoma Microenvironment: Much More than just a Waste Product
by Maria Letizia Taddei, Laura Pietrovito, Angela Leo and Paola Chiarugi
Cells 2020, 9(2), 510; https://doi.org/10.3390/cells9020510 - 24 Feb 2020
Cited by 28 | Viewed by 4546
Abstract
Sarcomas are rare and heterogeneous malignant tumors relatively resistant to radio- and chemotherapy. Sarcoma progression is deeply dependent on environmental conditions that sustain both cancer growth and invasive abilities. Sarcoma microenvironment is composed of different stromal cell types and extracellular proteins. In this [...] Read more.
Sarcomas are rare and heterogeneous malignant tumors relatively resistant to radio- and chemotherapy. Sarcoma progression is deeply dependent on environmental conditions that sustain both cancer growth and invasive abilities. Sarcoma microenvironment is composed of different stromal cell types and extracellular proteins. In this context, cancer cells may cooperate or compete with stromal cells for metabolic nutrients to sustain their survival and to adapt to environmental changes. The strict interplay between stromal and sarcoma cells deeply affects the extracellular metabolic milieu, thus altering the behavior of both cancer cells and other non-tumor cells, including immune cells. Cancer cells are typically dependent on glucose fermentation for growth and lactate is one of the most heavily increased metabolites in the tumor bulk. Currently, lactate is no longer considered a waste product of the Warburg metabolism, but novel signaling molecules able to regulate the behavior of tumor cells, tumor-stroma interactions and the immune response. In this review, we illustrate the role of lactate in the strong acidity microenvironment of sarcoma. Really, in the biological context of sarcoma, where novel targeted therapies are needed to improve patient outcomes in combination with current therapies or as an alternative treatment, lactate targeting could be a promising approach to future clinical trials. Full article
(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Bone Sarcomas)
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<p>Lactate has a key role in cancer progression. Microenvironmental secreted lactate increases angiogenesis, motility and migration of cancer cells. Lactate is directly involved in the ‘immune escape’ by decreasing activation of T cells and promoting Treg proliferation. Lactate increases extracellular acidosis of the tumor microenvironment (TME) stimulating chemoresistance.</p> Full article ">Figure 2
<p>Lactate and the Immune response. Lactate in the tumor microenvironment impairs immune surveillance by blocking natural killer (NK) cells and tumor infiltrating T cells. Lactate-rich milieu promotes Treg cell survival and their immunosuppressive function. Moreover Lactate stimulates the M2 pro-tumoral polarization in macrophages and activates mesenchymal stem cells (MSCs).</p> Full article ">Figure 3
<p>Lactate trafficking in tumor and stromal cells. Lactate shuttles through monocarboxylate transporter (MCT) present both in tumor and stromal cells. GPR81, a specific receptor for lactate is expressed on stromal cells and on different cancer cells, mediating chemoresistance, angiogenesis, decreasing lipolysis and up-regulating MCT1 expression.</p> Full article ">
24 pages, 6830 KiB  
Article
NOTO Transcription Factor Directs Human Induced Pluripotent Stem Cell-Derived Mesendoderm Progenitors to a Notochordal Fate
by Pauline Colombier, Boris Halgand, Claire Chédeville, Caroline Chariau, Valentin François-Campion, Stéphanie Kilens, Nicolas Vedrenne, Johann Clouet, Laurent David, Jérôme Guicheux and Anne Camus
Cells 2020, 9(2), 509; https://doi.org/10.3390/cells9020509 - 24 Feb 2020
Cited by 22 | Viewed by 5092
Abstract
The founder cells of the Nucleus pulposus, the centre of the intervertebral disc, originate in the embryonic notochord. After birth, mature notochordal cells (NC) are identified as key regulators of disc homeostasis. Better understanding of their biology has great potential in delaying the [...] Read more.
The founder cells of the Nucleus pulposus, the centre of the intervertebral disc, originate in the embryonic notochord. After birth, mature notochordal cells (NC) are identified as key regulators of disc homeostasis. Better understanding of their biology has great potential in delaying the onset of disc degeneration or as a regenerative-cell source for disc repair. Using human pluripotent stem cells, we developed a two-step method to generate a stable NC-like population with a distinct molecular signature. Time-course analysis of lineage-specific markers shows that WNT pathway activation and transfection of the notochord-related transcription factor NOTO are sufficient to induce high levels of mesendoderm progenitors and favour their commitment toward the notochordal lineage instead of paraxial and lateral mesodermal or endodermal lineages. This study results in the identification of NOTO-regulated genes including some that are found expressed in human healthy disc tissue and highlights NOTO function in coordinating the gene network to human notochord differentiation. Full article
(This article belongs to the Special Issue Highlights of the 3rd Meeting of the French Society for Stem Cell Research (FSSCR))
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<p>Schematic workflow of hiPSCs differentiation. The differentiation was initiated by single cell seeding at 35.000 cells/cm<sup>2</sup> (TryplE digestion) on matrigel-coated plates in mTser1 medium supplemented with rock inhibitor for 24 h. From day 0 to day 2, hiPSCs were cultivated in N2B27 in increasing doses of CHIR99021 and Activin A for hiPSC-derived mesendoderm progenitor cell (MEPC) specification. At Day 2, MEPC were dissociated with TryplE and transfected with Lipofectamin RNAimax (5:1) in a single cell suspension with 1500 ng of <span class="html-italic">T</span>, <span class="html-italic">FOXA2</span> or <span class="html-italic">NOTO</span> mRNA for 24 h for MEPC differentiation. Monolayer transfections were then performed on day 3 and day 4. Cells were maintained in N2B27 with 3 or 6 µM CHIR99021 with or without 50 ng/mL FGF2 from day 2 to day 5. For the stabilization phase, transfected cells were maintained in N2B27 supplemented with 3 µM CHIR99021 with or without 50 ng/mL FGF2 and 100 ng/mL SHH from day 5 to day 7. Top panel: representative brightfield images of differentiating hiPSCs upon optimal culture condition for notochordal lineage from day 0 to day 7, including undifferentiated control cells at day 2 (cells without treatment). (*) indicates optimal culture condition for notochordal differentiation at day 7.</p> Full article ">Figure 2
<p>WNT signalling pathway induces hiPSCs differentiation towards mesendoderm progenitors. (<b>A</b>) Modulation of WNT signalling by CHIR; (<b>B</b>) Relative expression of pluripotent markers (<span class="html-italic">SOX2</span>, <span class="html-italic">NANOG</span> and <span class="html-italic">POU5F1</span>), WNT and NODAL target genes (<span class="html-italic">LEF1</span>, <span class="html-italic">LEFTY1</span> and <span class="html-italic">NODAL</span>), primitive streak (<span class="html-italic">T</span>, <span class="html-italic">MIXL1</span> and <span class="html-italic">EOMES</span>) and mesendoderm markers (<span class="html-italic">FOXA2</span>, <span class="html-italic">GSC</span> and <span class="html-italic">CER1</span>), (n = 2 independent experiments, mean values); (<b>C</b>) Brightfield acquisition of differentiating hiPSCs upon CHIR treatment; (<b>D</b>) Immunostainings of T+/FOXA2+ positive cells (cell counting at day 2, n = 2 independent experiments, mean percentage ± SEM). Insets are showing nuclei staining with Hoechst. Scale bars: 100 µm. APS = anterior primitive streak; PPS = posterior primitive streak.</p> Full article ">Figure 3
<p>WNT and NODAL signalling pathways induce hiPSCs differentiation towards mesendoderm progenitors. (<b>A</b>) Modulation of WNT and NODAL signalling by CHIR and ActA; (<b>B</b>) Relative expression of pluripotent markers (<span class="html-italic">SOX2</span>, <span class="html-italic">NANOG</span> and <span class="html-italic">POU5F1)</span>, WNT and NODAL target genes (<span class="html-italic">LEF1</span>, <span class="html-italic">LEFTY1</span> and <span class="html-italic">NODAL</span>), primitive streak (<span class="html-italic">T</span>, <span class="html-italic">MIXL1</span> and <span class="html-italic">EOMES</span>) and mesendoderm markers (<span class="html-italic">FOXA2</span>, <span class="html-italic">GSC</span> and <span class="html-italic">CER1</span>), (n = 3, mean values); (<b>C</b>) Brightfield acquisition of differentiating hiPCSs upon CHIR and ActA treatment; (<b>D</b>) Immunostainings of T+/FOXA2+ positive cells (cell counting at day 2, n = 2 independent experiments, mean values ± SEM). Insets are showing nuclei staining with Hoechst. Scale bars: 100 µm. APS = anterior primitive streak; PPS = posterior primitive streak.</p> Full article ">Figure 4
<p>Generation and differentiation of mesendoderm progenitors upon WNT and NODAL signalling activation. (<b>A</b>) Effects of sustained WNT signalling activation on the differentiation of MEPC; (<b>B</b>) Brightfield acquisition of MEPC and immunostainings of T+/FOXA2+ positive cells (cell counting at day 2, n = 2 independent experiments, mean percentage ± SEM). Insets are showing nuclei staining with Hoechst; (<b>C</b>) Relative expression of axial mesoderm (<span class="html-italic">T</span>, <span class="html-italic">FOXA2</span>, <span class="html-italic">SHH</span>, <span class="html-italic">FOXJ1</span>, <span class="html-italic">NOGGIN</span> and <span class="html-italic">NOTO</span>), endoderm (<span class="html-italic">GSC, CER1</span> and <span class="html-italic">SOX17</span>) and mesoderm (<span class="html-italic">MIXL1</span>, <span class="html-italic">TBX6</span> and <span class="html-italic">FOXF1</span>) markers expression (n = 4, mean values). ND = Non-Detected Ct value. Mean and standard error of mean (SEM) values relative to experiments in panel C are shown in <a href="#app2-cells-09-00509" class="html-app">Appendix A</a>. Statistical analysis (2 way Anova test) relative to experiments in panel C to determine significant differences between conditions at day 2 and day 5 is shown in <a href="#app3-cells-09-00509" class="html-app">Appendix B</a>. Scale bars: 100 µm.</p> Full article ">Figure 5
<p>Generation and differentiation of mesendoderm progenitors upon WNT signalling activation following <span class="html-italic">T</span>, <span class="html-italic">FOXA2</span> or <span class="html-italic">NOTO</span> mRNA transfections. (<b>A</b>) Differentiation of MEPC following <span class="html-italic">T, FOXA2</span> or <span class="html-italic">NOTO</span> mRNA transfections; (<b>B</b>) Relative expression of axial mesoderm (<span class="html-italic">T</span>, <span class="html-italic">FOXA2 NOTO</span>, <span class="html-italic">SHH</span>, <span class="html-italic">NOGGIN</span> and <span class="html-italic">FOXJ1</span>), endoderm (<span class="html-italic">GSC</span>, <span class="html-italic">CER1</span> and <span class="html-italic">SOX17</span>) and mesoderm (<span class="html-italic">MIXL1</span>, <span class="html-italic">TBX6</span> and <span class="html-italic">FOXF1</span>) markers, (n = 3 independent experiments, mean values ± SEM). * indicates endogenous expression analysed by 3′UTR amplification of <span class="html-italic">T</span>, <span class="html-italic">FOXA2</span> and <span class="html-italic">NOTO transcripts</span>; (<b>C</b>,<b>D</b>) Immunostainings and quantifications of T+/FOXA2+, T+/SOX9+ and FOXA2+/SOX17+ cells (n = 2; quantification n = 2 technical replicates, mean values). Insets in C are showing nuclei staining with Hoechst. Scale bars: 100 µm.</p> Full article ">Figure 6
<p>The FGF signalling pathway does not enhance notochordal differentiation. (<b>A</b>) Assessment of MEPC differentiation into NLC following FGF2 supplementation; (<b>B</b>) Relative expression of axial mesoderm (<span class="html-italic">T</span>, <span class="html-italic">FOXA2</span>, <span class="html-italic">NOTO</span>, <span class="html-italic">FOXJ1</span>, <span class="html-italic">NOGGIN</span> and <span class="html-italic">SHH</span>), endoderm (<span class="html-italic">GSC</span>, <span class="html-italic">CER1</span> and <span class="html-italic">SOX17</span>) and mesoderm (<span class="html-italic">MIXL1</span>, <span class="html-italic">TBX6</span> and <span class="html-italic">FOXF1</span>) markers in differentiating MEPC (RT-qPCR, n = 2 independent experiments, mean values ± SEM). * endogenous expression analysed by 3′UTR amplification of <span class="html-italic">NOTO transcript</span>; (<b>C</b>) Immunostaining of T+/FOXA2+ positive cells in differentiating MEPC (n = 2). Insets are showing nuclei stained with Hoechst. Scale bar: 50 μm.</p> Full article ">Figure 7
<p><span class="html-italic">NOTO</span> mRNA transfection and WNT signalling activity are sufficient to induce a stable NLC population. (<b>A</b>) Assessment of NLC stabilization by FGF and SHH signalling activities; (<b>B</b>) Relative expression of axial mesoderm (<span class="html-italic">T</span>, <span class="html-italic">FOXA2</span>, <span class="html-italic">NOTO</span>, <span class="html-italic">SHH</span>, <span class="html-italic">FOXJ1</span>, <span class="html-italic">NOGGIN</span> and <span class="html-italic">SHH</span>), endoderm (<span class="html-italic">GSC</span>, <span class="html-italic">CER1</span>, <span class="html-italic">SOX17</span>) and mesoderm (<span class="html-italic">MIXL1</span>, <span class="html-italic">TBX6</span> and <span class="html-italic">FOXF1</span>) markers (n = 2 independent experiments, mean ± SEM). * Endogenous expression analysed by 3′UTR amplification of <span class="html-italic">NOTO transcript</span>; (<b>C</b>) Immunostainings of T+/FOXA2+ positive cells. Insets are showing nuclei staining with Hoechst. Scale bars: 50 µm; (<b>D</b>) Heatmap representation of gene expression profiles during control and <span class="html-italic">NOTO</span>-transfected cell differentiation (n = 2, mean values).</p> Full article ">Figure 8
<p><span class="html-italic">NOTO</span> mRNA transfection induced a distinct molecular signature. (<b>A</b>) Differentiation of MEPC following <span class="html-italic">NOTO</span> or <span class="html-italic">FOXA2</span> mRNA transfections; (<b>B</b>) Expression levels of genes used as markers of mesoderm, ectoderm and endoderm across our samples (left) or Tsankov et al. samples (right); (<b>C</b>) RNAseq expression profile of differentially expressed genes during the course of differentiation. Differentially expressed genes were distributed in 5 clusters based on their kinetic of expression; (<b>D</b>) Expression levels of immediate <span class="html-italic">NOTO</span> response genes during the course of <span class="html-italic">NOTO</span>- and <span class="html-italic">FOXA2</span>-driven MEPC differentiation (this study) and in hESC-derived mesoderm, ectoderm and endoderm [<a href="#B59-cells-09-00509" class="html-bibr">59</a>]; (<b>E</b>) Expression levels of delayed <span class="html-italic">NOTO</span> response genes during the course of <span class="html-italic">NOTO</span>- and <span class="html-italic">FOXA2</span>-driven MEPC differentiation (this study) and in hESC-derived mesoderm, ectoderm and endoderm [<a href="#B59-cells-09-00509" class="html-bibr">59</a>]; (<b>F</b>) Top 15 Biological Processes associated with the up-regulated genes in <span class="html-italic">NOTO</span>-transfected condition compared to <span class="html-italic">FOXA2</span>-transfected condition. Cluster details for mesendoderm genes, NOTO inhibited genes and FOXA2 response genes are presented in <a href="#app1-cells-09-00509" class="html-app">Figure S2</a>.</p> Full article ">Figure 9
<p>Visual summary of the main findings of the study. Human iPSCs were generated from dermal fibroblasts using 4 reprogramming factors (Oct4, Klf4, Sox2 and c-Myc = OKSM). Activation at day 2: WNT/β-catenin pathway activity (intermediate concentration of CHIR = Mid WNT) induced an increase in <span class="html-italic">NODAL</span> and <span class="html-italic">LEFTY1</span> gene expression. In this condition, high levels of bipotent mesendoderm progenitors (T+/FOXA2+ cells) were generated. Supplementation with Activin A resulted in hiPSCs commitment toward endoderm lineage (T-/FOXA2+ progenitor cells). High WNT pathway activation (high concentration of CHIR = High WNT) resulted in hiPSCs commitment toward mesoderm lineage (T+/FOXA2- progenitor cells). Differentiation from day 3: Mesendoderm progenitors transfected with synthetic mRNAs encoding human NOTO transcription factor and sustained with Mid WNT signalling activation generated axial mesoderm progenitors (T+/FOXA2+ cells). Stabilization up to day 7: <span class="html-italic">NOTO</span> transfection and Mid WNT signalling activation increased both SHH and FGF signalling pathway activities in axial mesoderm progenitors, which further differentiated into stable notochord-like cell population (NLC) at day 7 (T+/FOXA2+ cells and T+/SOX9+ cells). Human iPSC-derived NLC expressed embryonic notochord-related markers. Blue arrows indicate optimal culture condition for notochordal differentiation. Sets of gene markers relative to lineages or specific cell-types are indicated.</p> Full article ">
15 pages, 2730 KiB  
Article
Convolutional Neural Networks–Based Image Analysis for the Detection and Quantification of Neutrophil Extracellular Traps
by Aneta Manda-Handzlik, Krzysztof Fiok, Adrianna Cieloch, Edyta Heropolitanska-Pliszka and Urszula Demkow
Cells 2020, 9(2), 508; https://doi.org/10.3390/cells9020508 - 24 Feb 2020
Cited by 7 | Viewed by 3952
Abstract
Over a decade ago, the formation of neutrophil extracellular traps (NETs) was described as a novel mechanism employed by neutrophils to tackle infections. Currently applied methods for NETs release quantification are often limited by the use of unspecific dyes and technical difficulties. Therefore, [...] Read more.
Over a decade ago, the formation of neutrophil extracellular traps (NETs) was described as a novel mechanism employed by neutrophils to tackle infections. Currently applied methods for NETs release quantification are often limited by the use of unspecific dyes and technical difficulties. Therefore, we aimed to develop a fully automatic image processing method for the detection and quantification of NETs based on live imaging with the use of DNA-staining dyes. For this purpose, we adopted a recently proposed Convolutional Neural Network (CNN) model called Mask R-CNN. The adopted model detected objects with quality comparable to manual counting—Over 90% of detected cells were classified in the same manner as in manual labelling. Furthermore, the inhibitory effect of GW 311616A (neutrophil elastase inhibitor) on NETs release, observed microscopically, was confirmed with the use of the CNN model but not by extracellular DNA release measurement. We have demonstrated that a modern CNN model outperforms a widely used quantification method based on the measurement of DNA release and can be a valuable tool to quantitate the formation process of NETs. Full article
(This article belongs to the Special Issue Bioinformatics and Computational Biology 2019)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Representative examples of objects manually assigned as unstimulated, decondensed, neutrophil extracellular trap (NET)-releasing or dead cells. Isolated human neutrophils were seeded into plates, pre-incubated with or without neutrophil elastase inhibitor (NEi) for 30 min and stimulated with phorbol 12-myristate 13-acetate (PMA), <span class="html-italic">S</span>-nitroso-<span class="html-italic">N</span>-acetyl-<span class="html-small-caps">dl</span>-penicillamine (SNAP) or peroxynitrite or left unstimulated. After 1–3 hours of incubation, cells were simultaneously stained with Hoechst 33342 and SYTOX Green. Samples were visualized with inverted fluorescent microscope and 300 images were gathered at 40× magnification to create a NETs dataset. The observed objects were manually assigned into four categories: unstimulated, decondensed, NET-producing, and dead cells. In this figure representative examples of objects assigned into aforementioned categories are shown. Bar = 50 μm.</p> Full article ">Figure 2
<p>The adopted model trained on our preliminary dataset is able to detect objects with quality comparable to manual labeling. Images of unfixed neutrophils stained with Hoechst 33342 and SYTOX Green were analyzed by the CNN model (right) in parallel with manual analysis (left). To facilitate the assessment of labeling, colored dots are shown at one of the four vertexes of a rectangle surrounding the object. Grey dots are unstimulated cells (un), blue dots are decondensed cells (dec), violet dots are NETs, yellow dots are dead cells. Numerical values represent model’s confidence in the given cell class prediction—1 is maximum confidence, and the scale bar is 100 μm.</p> Full article ">Figure 3
<p>The adopted CNN model, contrary to DNA release measurement, confirmed the inhibitory effect of NEi on NETs release. Peripheral blood neutrophils were isolated from peripheral blood of six healthy donors, samples were pretreated with NEi for 30 min and/or stimulated with peroxynitrite for 2 h. Negative control—Unstimulated cells, incubated only with RPMI 1640. (<b>a</b>) Representative images of samples pretreated with NEi and/or stimulated with peroxynitrite using live imaging with Hoechst 33342 and SYTOX Green; (<b>b</b>) representative images of immunofluorescently-labeled samples pretreated with NEi and/or stimulated with peroxynitrite; (<b>c</b>, <b>d</b>) for each patient, 10 images of unfixed cells stained with Hoechst 33342 and SYTOX Green were taken per each one of four experimental conditions (negative control; cells pretreated with NEi; cells stimulated with peroxynitrite; cells pretreated with NEi and stimulated with peroxynitrite). The images were analyzed using the adopted model and the results were analyzed as percentage of objects of different classes and compared between groups. (<b>d</b>) n = 6, 2-way ANOVA with post-hoc Tukey’s test. (<b>e</b>) At the indicated timepoint, NETs formation was assessed fluorometrically using SYTOX green after detachment of DNA with MNase, n = 6, 1-way ANOVA with posthoc Bonferroni’s test.</p> Full article ">
16 pages, 900 KiB  
Review
Vasculogenic Mimicry: A Promising Prognosticator in Head and Neck Squamous Cell Carcinoma and Esophageal Cancer? A Systematic Review and Meta-Analysis
by Roosa Hujanen, Rabeia Almahmoudi, Sini Karinen, Bright I. Nwaru, Tuula Salo and Abdelhakim Salem
Cells 2020, 9(2), 507; https://doi.org/10.3390/cells9020507 - 24 Feb 2020
Cited by 22 | Viewed by 4510
Abstract
Vasculogenic mimicry (VM) is an intratumoral microcirculation pattern formed by aggressive cancer cells, which mediates tumor growth. In this study, we compiled the evidence from studies evaluating whether positive VM status can serve as a prognostic factor to patients with squamous cell carcinoma [...] Read more.
Vasculogenic mimicry (VM) is an intratumoral microcirculation pattern formed by aggressive cancer cells, which mediates tumor growth. In this study, we compiled the evidence from studies evaluating whether positive VM status can serve as a prognostic factor to patients with squamous cell carcinoma of the head and neck (HNSCC) or esophagus (ESCC). Comprehensive systematic searches were conducted using Cochrane Library, Ovid Medline, PubMed, and Scopus databases. We appraised the quality of studies and the potential for bias, and performed random-effect meta-analysis to assess the prognostic impact of VM on the overall survival (OS). Seven studies with 990 patients were eligible, where VM was detected in 34.24% of patients. Positive-VM was strongly associated with poor OS (hazard ratio = 0.50; 95% confidence interval: 0.38–0.64), which remained consistent following the subgroup analysis of the studies. Furthermore, VM was associated with more metastasis to local lymph nodes and more advanced stages of HNSCC and ESCC. In conclusion, this study provides clear evidence showing that VM could serve as a promising prognosticator for patients with either HNSCC or ESCC. Further studies are warranted to assess how VM can be implemented as a reliable staging element in clinical practice and whether it could provide a new target for therapeutic intervention. Full article
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<p>Flowchart diagram of literature search and selection. Irrelevant article types and other exclusion criteria are listed in <a href="#cells-09-00507-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 2
<p>Angiogenesis versus vasculogenic mimicry (VM) in the tumorigenesis of solid tumors. In angiogenesis: (<b>A</b>) tumor cells secrete potent angiogenic factors; (<b>B</b>) these factors enhance the budding and growth of pre-existing blood vessels; (<b>C</b>) new endothelial cell-lined blood vessels are formed and enrich the tumor microenvironment. In VM: (<b>D</b>) aggressive starved tumor cells can utilize an alternative non-angiogenic vascularization method; (<b>E</b>) tumor cells start to generate new patterned vessel-like anastomoses; (<b>F</b>) these new tumor cell-lined channels invade host vessels and increase nutrient retrieval to nourish tumor tissue.</p> Full article ">
28 pages, 810 KiB  
Review
G-Protein-Coupled Receptors in CNS: A Potential Therapeutic Target for Intervention in Neurodegenerative Disorders and Associated Cognitive Deficits
by Shofiul Azam, Md. Ezazul Haque, Md. Jakaria, Song-Hee Jo, In-Su Kim and Dong-Kug Choi
Cells 2020, 9(2), 506; https://doi.org/10.3390/cells9020506 - 23 Feb 2020
Cited by 60 | Viewed by 12196
Abstract
Neurodegenerative diseases are a large group of neurological disorders with diverse etiological and pathological phenomena. However, current therapeutics rely mostly on symptomatic relief while failing to target the underlying disease pathobiology. G-protein-coupled receptors (GPCRs) are one of the most frequently targeted receptors for [...] Read more.
Neurodegenerative diseases are a large group of neurological disorders with diverse etiological and pathological phenomena. However, current therapeutics rely mostly on symptomatic relief while failing to target the underlying disease pathobiology. G-protein-coupled receptors (GPCRs) are one of the most frequently targeted receptors for developing novel therapeutics for central nervous system (CNS) disorders. Many currently available antipsychotic therapeutics also act as either antagonists or agonists of different GPCRs. Therefore, GPCR-based drug development is spreading widely to regulate neurodegeneration and associated cognitive deficits through the modulation of canonical and noncanonical signals. Here, GPCRs’ role in the pathophysiology of different neurodegenerative disease progressions and cognitive deficits has been highlighted, and an emphasis has been placed on the current pharmacological developments with GPCRs to provide an insight into a potential therapeutic target in the treatment of neurodegeneration. Full article
(This article belongs to the Special Issue Key Signalling Molecules in Aging and Neurodegeneration)
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<p>Schematic display of G-protein-coupled receptors (GPCRs) signalling in cognitive impairment. Depending on the agonist or inverse agonist ligand binding, the PI3/Akt-signalling pathway signals Bax and Casp-9 while increasing neurofibrillary tangle (NFT) formation via phosphorylation. PI3/Akt-signalling hyperphosphorylation also activates glycogen synthase kinase-3β (GSK-3β), which increases either or both tau protein phosphorylation and amyloid precursor protein (APP). Phosphorylated tau protein forms neurofibrillary tangles (NFTs) and regulates cognitive function. Similarly, APP metabolism regulates Aβ-plaque formation and controls cognition. Moreover, neuronal and dendritic plasticity is required for synaptic growth, regeneration, and memory formation, and it depends on extracellular signal-regulated kinase (ERK ½) modulation. Depending on the ligands, GPCRs activate cAMP-response element-binding protein (CREB) via the cAMP/ERK ½ pathway and regulate cognition (based on [<a href="#B20-cells-09-00506" class="html-bibr">20</a>,<a href="#B21-cells-09-00506" class="html-bibr">21</a>]).</p> Full article ">Figure 2
<p>Schematic display of allosteric modulator action on GPCRs. (<b>A</b>) Conventional agonist binding makes conformational changes and activates downstream signalling. Positive allosteric modulators bind to a distinct site and enhance conventional ligand-induced signalling. Negative allosteric modulators binding decreases conventional agonist efficacy and reduces downstream signalling. (<b>B</b>) In normal physiology, neurotransmitters are released into the synaptic cleft, binding to postsynaptic GPCRs, and activating downstream signalling. The duration of signalling can be degraded by metabolizing enzymes. A positive allosteric modulator (green rectangle) cobinding with the metabolites can extend the duration of receptor activation and enhance signalling (based on [<a href="#B177-cells-09-00506" class="html-bibr">177</a>]).</p> Full article ">
14 pages, 1670 KiB  
Review
Microtubule-Based Mechanisms of Pronuclear Positioning
by Johnathan L. Meaders and David R. Burgess
Cells 2020, 9(2), 505; https://doi.org/10.3390/cells9020505 - 23 Feb 2020
Cited by 17 | Viewed by 5248
Abstract
The zygote is defined as a diploid cell resulting from the fusion of two haploid gametes. Union of haploid male and female pronuclei in many animals occurs through rearrangements of the microtubule cytoskeleton into a radial array of microtubules known as the sperm [...] Read more.
The zygote is defined as a diploid cell resulting from the fusion of two haploid gametes. Union of haploid male and female pronuclei in many animals occurs through rearrangements of the microtubule cytoskeleton into a radial array of microtubules known as the sperm aster. The sperm aster nucleates from paternally-derived centrioles attached to the male pronucleus after fertilization. Nematode, echinoderm, and amphibian eggs have proven as invaluable models to investigate the biophysical principles for how the sperm aster unites male and female pronuclei with precise spatial and temporal regulation. In this review, we compare these model organisms, discussing the dynamics of sperm aster formation and the different force generating mechanism for sperm aster and pronuclear migration. Finally, we provide new mechanistic insights for how sperm aster growth may influence sperm aster positioning. Full article
(This article belongs to the Special Issue Manufacturing a Female Gamete: An Oocyte Story)
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<p>Fertilization and pronuclear migration in <span class="html-italic">C. elegans</span>. (<b>A</b>) The <span class="html-italic">C. elegans</span> oocyte is arrested in metaphase of meiosis I just prior to fertilization. The meiotic spindle is located on the future anterior end of the oocyte, while the sperm/male pronucleus enters on the future posterior end. (<b>B</b>) Early centration phase. Fertilization prompts the completion of meiosis and formation of the female pronucleus (red circle). After sperm entry and maturation of the paternally derived centrioles, two sperm asters form oriented on opposite sides of the male pronucleus (purple circle), perpendicular to the anterior-posterior axis. These asters help define the posterior half (bright blue plasma membrane). The asters migrate toward the egg center due to cytoplasmic dynein-dependent pulling forces that scale with MT length (inset). Force (black arrows) is generated in the opposite direction of movement (orange arrows). Therefore, more force is generated on the longer front MTs relative to the short rear/cortical facing MTs. (<b>C</b>) Late centration phase. The aster pairs expand during the centration phase, enlarging the posterior half relative to the anterior half of the egg (blue and orange membrane, respectively). The female pronucleus is captured by long front astral MTs and is transported to the male pronucleus by dynein. (<b>D</b>) Maintenance phase. The combined male and female pronucleus (pronuclear complex or PNC) finish migrating to the egg center and rotate. This rotation orients centrosomes parallel to the anterior-posterior axis. (<b>E</b>) Posteriorization phase. Nuclear envelope breakdown occurs, combining maternal and paternal chromosomes as the first mitotic apparatus forms in the zygote. The apparatus is pulled toward the posterior side by more dynein activity at the posterior half relative to the anterior (inset). MT catastrophe is also considered as a potential mechanism to generate forces (inset).</p> Full article ">Figure 2
<p>Fertilization and pronuclear migration in the sea urchin (echinoderm). (<b>A</b>) The sea urchin oocyte has already completed meiosis resulting in formation of the female pronucleus (red circle), which is located randomly within the oocyte cytoplasm. Fertilization may also occur anywhere around the oocyte. (<b>B</b>) Almost immediately after fertilization, the paternally-derived centrosome is attached to the male pronucleus (purple circle) and begins forming the interphase sperm aster near the cortex. During this early time-point the sperm aster does not begin to migrate until astral MTs reach the rear cortex. (<b>C</b> and <b>D</b>) As the sperm aster grows, it enters the centration phase where it reaches a constant maximum speed. This velocity is either set by growth rates of rear cortical facing MTs pushing against the cortex as in (<b>C</b>), cytoplasmic dynein-dependent pulling forces that scale with MT lengths as in (<b>D</b>), or a combination of the two. The female pronucleus is captured by astral MTs and is presumably transported towards the aster center/male pronucleus by dynein. Transport causes the female pronucleus to form a “tear drop” shape (<b>E</b>) The sperm aster slows down as it approaches the egg center, prophase centrosomes separation occurs, and pronuclei fuse forming the zygote nucleus (blue oval).</p> Full article ">Figure 3
<p>Fertilization and pronuclear migration in <span class="html-italic">Xenopus.</span> (<b>A</b>) The frog oocyte is arrested in metaphase II of meiosis. The meiotic spindle is located at the pole of the animal half of the egg (top beige hemisphere). The sperm can fertilize the egg along the side of the animal half. The yolky vegetal half is illustrated as the lower dark yellow hemisphere. (<b>B</b>) Fertilization resumes the cell cycle, resulting in formation of the female pronucleus (red circle) near the animal pole after meiosis completes. The paternally derived centrosomes begin forming the interphase sperm aster attached to the male pronucleus (purple circle). (<b>C</b>) The sperm aster expands and migrates toward the center of the egg, just above the vegetal half. As the astral MTs contact the female pronucleus it is transported retrograde along astral MTs in a dynein dependent manner (inset). Furthermore, cytoplasmic dynein/cargo (inset) likely generates pulling forces through retrograde transport. (<b>D</b>) Simplification of sperm aster growth according the standard growth model (top) and the collective growth model (bottom). The standard growth model predicts that asters are formed solely from centrosome-nucleated MTs, while the collective growth model includes MT-dependent MT nucleation, or MT branching. When considering pushing forces due to MT polymerization against the cell cortex, long individual MTs (numbered 1–3) nucleate from the centrosome and bear a high compression load, which can lead to MT buckling and decentering (see text for details). However, this problem is solved by the collective growth model in which the compression load is redistributed to a greater number of short MTs (numbered 1–6) polymerizing against the cortex.</p> Full article ">
19 pages, 4264 KiB  
Article
RNA-Based Strategies for Cardiac Reprogramming of Human Mesenchymal Stromal Cells
by Paula Mueller, Markus Wolfien, Katharina Ekat, Cajetan Immanuel Lang, Dirk Koczan, Olaf Wolkenhauer, Olga Hahn, Kirsten Peters, Hermann Lang, Robert David and Heiko Lemcke
Cells 2020, 9(2), 504; https://doi.org/10.3390/cells9020504 - 22 Feb 2020
Cited by 9 | Viewed by 4339
Abstract
Multipotent adult mesenchymal stromal cells (MSCs) could represent an elegant source for the generation of patient-specific cardiomyocytes needed for regenerative medicine, cardiovascular research, and pharmacological studies. However, the differentiation of adult MSC into a cardiac lineage is challenging compared to embryonic stem cells [...] Read more.
Multipotent adult mesenchymal stromal cells (MSCs) could represent an elegant source for the generation of patient-specific cardiomyocytes needed for regenerative medicine, cardiovascular research, and pharmacological studies. However, the differentiation of adult MSC into a cardiac lineage is challenging compared to embryonic stem cells or induced pluripotent stem cells. Here we used non-integrative methods, including microRNA and mRNA, for cardiac reprogramming of adult MSC derived from bone marrow, dental follicle, and adipose tissue. We found that MSC derived from adipose tissue can partly be reprogrammed into the cardiac lineage by transient overexpression of GATA4, TBX5, MEF2C, and MESP1, while cells isolated from bone marrow, and dental follicle exhibit only weak reprogramming efficiency. qRT-PCR and transcriptomic analysis revealed activation of a cardiac-specific gene program and up-regulation of genes known to promote cardiac development. Although we did not observe the formation of fully mature cardiomyocytes, our data suggests that adult MSC have the capability to acquire a cardiac-like phenotype when treated with mRNA coding for transcription factors that regulate heart development. Yet, further optimization of the reprogramming process is mandatory to increase the reprogramming efficiency. Full article
(This article belongs to the Special Issue Stem Cell Research on Cardiology)
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<p>Phenotype-related and functional characterization of mesenchymal stromal cells (MSC): (<b>A</b>) Flow cytometric measurements revealed a high expression of common MSC surface markers (CD29, CD44, CD73, CD90, CD105), while very low levels were found for hematopoietic surface markers (CD45 and CD117). Representative flow cytometry charts of adipose tissue-derived MSC (adMSC) demonstrate the expression level of surface markers. Blue histograms represent measurement of CD surface marker with corresponding isotype control, shown in red. (<b>B</b>) Tri-lineage differentiation assay indicated adipogenic, osteogenic, and chondrogenic differentiation of MSC. Detection of adipocytes was performed by labelling of FABP4, while osteocytes and chondrocytes were identified by fluorescence staining of osteocalcein and aggrecan, respectively. Scale bar: 50 µm. Results in (<b>A</b>) are shown as mean ± SEM, obtained by analysis of three different donors for each MSC cell type.</p> Full article ">Figure 2
<p>Comparative microarray analysis of undifferentiated dental follicle stem cells (DFSCs), bone marrow (BM) MSC, and adMSC. (<b>A</b>) Comparison of signal intensity for .cel files (blue) and .chp files (red) after normalization demonstrates sufficient data quality. (<b>B</b>) MSC from different sources are clearly distinct in regard to their transcription profile. A high patient-dependent variety was found for BM MSC, while adMSC and DFSCs demonstrate a more homogenous distribution. (<b>C</b>) Venn diagram visualizes expressed genes overlapping between different MSC cell types. (<b>D</b>) The numbers of up- and down-regulated transcripts is significantly differentially expressed in all three cell types.</p> Full article ">Figure 3
<p>miRNA transfection and programming efficiency in MSC. (<b>A</b>) Uptake of miRNA was determined using Cy3-labelled miRNA and flow cytometry. (<b>B</b>) Detection of dead cells revealed low cytotoxicity induced by miRNA transfection. (<b>C</b>) Relative expression of cardiac marker genes among all tested cell types, four weeks after transfection and cultivation under different culture conditions. Reprogramming efficiency with cardiac induction medium I, II and myo-miRNAs (miR-1, miR-499, miR-208, miR-133) resulted in an up-regulation of cardiac specific markers in all types of MSC, while most profound up-regulation was found for cardiac induction medium II. Among tested MSC, the strongest increase of cardiac gene expression was observed for adMSC. Note, no beneficial effects on cardiac programming were observed following myo-miRNA transfection. Data are shown as mean ± SEM, obtained from three donors for each MSC type. Statistical analysis was performed using ANOVA test, followed by Bonferroni post-hoc analysis. * <span class="html-italic">p</span> ≤ 0.5, ** <span class="html-italic">p</span> ≤ 0.05, *** <span class="html-italic">p</span> ≤ 0.001.</p> Full article ">Figure 4
<p>mRNA-based cardiac programming of adMSC. (<b>A</b>) Concentration-dependent expression of transfected mRNAs was evaluated with mRNA coding GFP. The quantative flow cytometry analysis demonstrated maximum transfection efficiency of ~80% when ≤ 1000 ng mRNA were applied. (<b>B</b>) Representative scatterplots of control cells (left) and cells transfected with GFP mRNA (right). (<b>C</b>) Corresponding microscopy images of cells expressing GFP following mRNA treatment. (<b>D</b>) Cytotoxic effects were only induced when mRNA amounts higher than 1000 ng were used for transfection. (<b>E</b>) Compared to untreated control cells, higher gene expression levels of selected cardiac markers were detected for all reprogramming conditions, in particular for α-actinin. (<b>F</b>) Immunolabeling of cells using anti α-actinin antibody results in a faint fluorescence signal in cells transfected with MESP1 and GATA4, MEF2C, and TBX5 (GMT) mRNAs, Scale Bar: 25 µm. (<b>G</b>) Moreover, GMT treated cells also demonstrated protein expression of MEF2C, an early cardiac transcription factor. Flow cytometry and qRT-PCR data are shown as mean ± SEM, obtained from three different donors. Statistical analysis was performed using one-way ANOVA. * <span class="html-italic">p</span> ≤ 0.5, ** <span class="html-italic">p</span> ≤ 0.05, *** <span class="html-italic">p</span> ≤ 0.001.</p> Full article ">Figure 5
<p>Transcriptome based comparison of reprogrammed adMSC. (<b>A</b>) Quality control of microarray data. Box plot of signal intensity of performed microarrays on .cel (blue) and .chp files normalization (red) confirm good data quality. (<b>B</b>) Principal component analysis (PCA) demonstrates clustering of treated groups, clearly showing the impact of respective reprogramming conditions on the transcriptomic profile compared to control cells (blue). Yet, cells subjected to MESP1 (green), GMT (purple) or cardiac induction medium II solely (red) remain distinguishable. (<b>C</b>) Up-and down-regulated transcripts and corresponding Venn diagram (<b>D</b>) showing the impact of reprogrammed cells compared to control. Most differentially expressed transcripts were regulated by all three reprogramming treatments (2828 genes), while 1816 transcripts are shared by GMT vs. control and MESP1 vs. control. (<b>E</b>) Detailed comparison of common and distinct up-regulated (red) and down-regulated (green) transcripts among the three reprogrammed groups. The differences found for optimized medium vs. MESP1 and GMT transfections are much more prominent than the differences between MESP1 and GMT.</p> Full article ">Figure 6
<p>The impact of reprogramming on cardiac-differentiation pathways. Up-regulated and down-regulated transcripts of respective programming conditions are labelled in red or green color. (<b>A</b>,<b>B</b>) Strongest up-regulation of transcripts involved in cardiac development ((<b>A</b>) heart development, (<b>B</b>) cardiac progenitor differentiation) was mainly found in GMT reprogrammed cells, followed by MESP1 treatment and cardiac induction medium II. Key cardiac transcription factors and signaling molecules were significantly up-regulated, including TBX5, GATA4, MEF2C, HAND2, BMP4, and IGF.</p> Full article ">
12 pages, 508 KiB  
Review
Telomeres and Telomerase in Heart Ontogenesis, Aging and Regeneration
by Denis Nalobin, Svetlana Alipkina, Anna Gaidamaka, Alexander Glukhov and Zaza Khuchua
Cells 2020, 9(2), 503; https://doi.org/10.3390/cells9020503 - 22 Feb 2020
Cited by 10 | Viewed by 4665
Abstract
The main purpose of the review article is to assess the contributions of telomere length and telomerase activity to the cardiac function at different stages of development and clarify their role in cardiac disorders. It has been shown that the telomerase complex and [...] Read more.
The main purpose of the review article is to assess the contributions of telomere length and telomerase activity to the cardiac function at different stages of development and clarify their role in cardiac disorders. It has been shown that the telomerase complex and telomeres are of great importance in many periods of ontogenesis due to the regulation of the proliferative capacity of heart cells. The review article also discusses the problems of heart regeneration and the identification of possible causes of dysfunction of telomeres and telomerase. Full article
(This article belongs to the Section Mitochondria)
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<p>Problems associated with chromosomes’ terminal under-replication [<a href="#B2-cells-09-00503" class="html-bibr">2</a>].</p> Full article ">
14 pages, 5020 KiB  
Communication
The Timing and Extent of Motor Neuron Vulnerability in ALS Correlates with Accumulation of Misfolded SOD1 Protein in the Cortex and in the Spinal Cord
by Baris Genc, Oge Gozutok, Nuran Kocak and P. Hande Ozdinler
Cells 2020, 9(2), 502; https://doi.org/10.3390/cells9020502 - 22 Feb 2020
Cited by 9 | Viewed by 4557
Abstract
Understanding the cellular and molecular basis of selective vulnerability has been challenging, especially for motor neuron diseases. Developing drugs that improve the health of neurons that display selective vulnerability relies on in vivo cell-based models and quantitative readout measures that translate to patient [...] Read more.
Understanding the cellular and molecular basis of selective vulnerability has been challenging, especially for motor neuron diseases. Developing drugs that improve the health of neurons that display selective vulnerability relies on in vivo cell-based models and quantitative readout measures that translate to patient outcome. We initially developed and characterized UCHL1-eGFP mice, in which motor neurons are labeled with eGFP that is stable and long-lasting. By crossing UCHL1-eGFP to amyotrophic lateral sclerosis (ALS) disease models, we generated ALS mouse models with fluorescently labeled motor neurons. Their examination over time began to reveal the cellular basis of selective vulnerability even within the related motor neuron pools. Accumulation of misfolded SOD1 protein both in the corticospinal and spinal motor neurons over time correlated with the timing and extent of degeneration. This further proved simultaneous degeneration of both upper and lower motor neurons, and the requirement to consider both upper and lower motor neuron populations in drug discovery efforts. Demonstration of the direct correlation between misfolded SOD1 accumulation and motor neuron degeneration in both cortex and spinal cord is important for building cell-based assays in vivo. Our report sets the stage for shifting focus from mice to diseased neurons for drug discovery efforts, especially for motor neuron diseases. Full article
(This article belongs to the Section Cellular Pathology)
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<p>Generation of hSOD1<sup>G93A</sup>-UeGFP amyotrophic lateral sclerosis (ALS) reporter mouse model and detection of misfolded SOD1 protein. (<b>a</b>) hSOD1<sup>G93A</sup>-UeGFP mice were generated by breeding hSOD1<sup>G93A</sup> male mice carrying high copy number of human SOD1 protein with a point mutation at position 93 with female UCHL1-eGFP reporter mice that label corticospinal motor neurons (CSMN) with eGFP expression; (<b>b</b>,<b>c</b>) B8H10 antibody detects misfolded human SOD1 protein in the primary motor cortex of hSOD1<sup>G93A</sup>-UeGFP (<b>c</b>) but not WT-UeGFP (<b>b</b>) mice. Misfolded SOD1 signal is the brightest in layer 5 where GFP+ CSMN are located. Scale bar, 250 μm.</p> Full article ">Figure 2
<p>Misfolded SOD1 protein in the primary motor cortex. (<b>a</b>) B8H10 antibody does not detect any misfolded SOD1 protein in the primary motor cortex of WT-UeGFP control mice; (<b>b</b>–<b>e</b>) misfolded SOD1 protein can be detected in the primary motor cortex of hSOD1<sup>G93A</sup>-UeGFP mice by the B8H10 antibody at P30 (<b>b</b>), P60 (<b>c</b>), P90 (<b>d</b>), and P140 (<b>e</b>). Boxed areas enlarged in the right panels. Scale bar, 250 μm (left, low mag) and 50 μm (right, high mag).</p> Full article ">Figure 3
<p>Misfolded SOD1 protein in the ventral horn of the lumbar spinal cord. (<b>a</b>) No B8H10 signal is detected in the spinal cord of WT-UeGFP control mice; (<b>b</b>) misfolded SOD1 protein can be detected in the lumbar spinal cord of hSOD1<sup>G93A</sup>-UeGFP mice by the B8H10 antibody in ChAT+ SMN. Arrows point to eGFP+ ChAT+ SMN without a misfolded SOD1 signal. Boxed areas enlarged in the panels below. Scale bar, 100 μm (top, low mag) and 50 μm (bottom, high mag).</p> Full article ">Figure 4
<p>Misfolded SOD1 protein in the ventral horn of the lumbar spinal cord. (<b>a</b>) There is no misfolded SOD1 protein in the spinal cord of WT-UeGFP control mice; (<b>b</b>–<b>e</b>) misfolded SOD1 protein are detected in the lumbar spinal cord of hSOD1<sup>G93A</sup>-UeGFP mice by the B8H10 antibody and degeneration resistant eGFP+ SMN do not have misfolded SOD1 at P30 (<b>b</b>), P60 (<b>c</b>), P90 (<b>d</b>), and P140 (<b>e</b>). Boxed areas enlarged in the right panels. Scale bar, 100 μm (left, low mag) and 50 μm (right, high mag).</p> Full article ">
15 pages, 567 KiB  
Review
Arteriogenesis of the Spinal Cord—The Network Challenge
by Florian Simon, Markus Udo Wagenhäuser, Albert Busch, Hubert Schelzig and Alexander Gombert
Cells 2020, 9(2), 501; https://doi.org/10.3390/cells9020501 - 22 Feb 2020
Cited by 18 | Viewed by 6888
Abstract
Spinal cord ischemia (SCI) is a clinical complication following aortic repair that significantly impairs the quality and expectancy of life. Despite some strategies, like cerebrospinal fluid drainage, the occurrence of neurological symptoms, such as paraplegia and paraparesis, remains unpredictable. Beside the major blood [...] Read more.
Spinal cord ischemia (SCI) is a clinical complication following aortic repair that significantly impairs the quality and expectancy of life. Despite some strategies, like cerebrospinal fluid drainage, the occurrence of neurological symptoms, such as paraplegia and paraparesis, remains unpredictable. Beside the major blood supply through conduit arteries, a huge collateral network protects the central nervous system from ischemia—the paraspinous and the intraspinal compartment. The intraspinal arcades maintain perfusion pressure following a sudden inflow interruption, whereas the paraspinal system first needs to undergo arteriogenesis to ensure sufficient blood supply after an acute ischemic insult. The so-called steal phenomenon can even worsen the postoperative situation by causing the hypoperfusion of the spine when, shortly after thoracoabdominal aortic aneurysm (TAAA) surgery, muscles connected with the network divert blood and cause additional stress. Vessels are a conglomeration of different cell types involved in adapting to stress, like endothelial cells, smooth muscle cells, and pericytes. This adaption to stress is subdivided in three phases—initiation, growth, and the maturation phase. In fields of endovascular aortic aneurysm repair, pre-operative selective segmental artery occlusion may enable the development of a sufficient collateral network by stimulating collateral vessel growth, which, again, may prevent spinal cord ischemia. Among others, the major signaling pathways include the phosphoinositide 3 kinase (PI3K) pathway/the antiapoptotic kinase (AKT) pathway/the endothelial nitric oxide synthase (eNOS) pathway, the Erk1, the delta-like ligand (DII), the jagged (Jag)/NOTCH pathway, and the midkine regulatory cytokine signaling pathways. Full article
(This article belongs to the Special Issue Arteriogenesis and Therapeutic Neovascularization)
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<p>During a steal phenomenon blood becomes redistributed endangering spinal cord blood supply by hypoperfusion.</p> Full article ">
14 pages, 563 KiB  
Review
Molecular Diagnostics in Human Papillomavirus-Related Head and Neck Squamous Cell Carcinoma
by Katherine C. Wai, Madeleine P. Strohl, Annemieke van Zante and Patrick K. Ha
Cells 2020, 9(2), 500; https://doi.org/10.3390/cells9020500 - 22 Feb 2020
Cited by 22 | Viewed by 5654
Abstract
The incidence of human papillomavirus (HPV)-related head and neck squamous cell carcinoma continues to increase. Accurate diagnosis of the HPV status of a tumor is vital, as HPV+ versus HPV– tumors represent two unique biological and clinical entities with different treatment strategies. High-risk [...] Read more.
The incidence of human papillomavirus (HPV)-related head and neck squamous cell carcinoma continues to increase. Accurate diagnosis of the HPV status of a tumor is vital, as HPV+ versus HPV– tumors represent two unique biological and clinical entities with different treatment strategies. High-risk HPV subtypes encode oncoproteins E6 and E7 that disrupt cellular senescence and ultimately drive tumorigenesis. Current methods for detection of HPV take advantage of this established oncogenic pathway and detect HPV at various biological stages. This review article provides an overview of the existing technologies employed for the detection of HPV and their current or potential future role in management and prognostication. Full article
(This article belongs to the Special Issue HPV-Associated Malignancies: Screening, Prevention and Treatment)
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<p>Human papillomavirus (HPV)+ cancer increases expression of p16. Left panel: Normal, uninfected cell. Cyclin D–cyclin dependent kinase (CDK) 4/6 complex initiates phosphorylation of the tumor suppressor protein, pRb. The hyperphosphorylation of pRb leads to release of the transcription factor E2F into its active state, which drives the expression of downstream gene products allowing the cell to transition from the G1 to S phase. As a cyclin kinase inhibitor, p16 is a tumor suppressor and negative regulator of the cyclin D–CDK 4/6 complex. Right panel: HPV infected cell. When the transcription factor E2F is bound to pRb, it remains inactive. The overexpression of the E7 oncoprotein by high-risk HPV subtypes disrupts the E2F–pRb complex by displacing E2F and binding to pRb. The subsequent release of E2F into its active state drives the expression of downstream gene products, allowing the cell to transition from the G1 to S phase. In a regulatory feedback attempt to inhibit further cell proliferation, p16 is upregulated, and thus can be a surrogate for HPV+ tumors. The overexpression E6 oncoprotein acts via a separate mechanism. E6 binds to the tumor suppressor protein, p53, and ultimately leads to degradation of p53. Loss of the regulatory function of p53 causes aberrant propagation of the cell cycle and prevents apoptosis.</p> Full article ">
18 pages, 5527 KiB  
Article
High Levels of Class I Major Histocompatibility Complex mRNA Are Present in Epstein–Barr Virus-Associated Gastric Adenocarcinomas
by Farhad Ghasemi, Steven F. Gameiro, Tanner M. Tessier, Allison H. Maciver and Joe S. Mymryk
Cells 2020, 9(2), 499; https://doi.org/10.3390/cells9020499 - 21 Feb 2020
Cited by 20 | Viewed by 3170
Abstract
Epstein–Barr virus (EBV) is responsible for approximately 9% of stomach adenocarcinomas. EBV-encoded microRNAs have been reported as reducing the function of the class I major histocompatibility complex (MHC-I) antigen presentation apparatus, which could allow infected cells to evade adaptive immune responses. Using data [...] Read more.
Epstein–Barr virus (EBV) is responsible for approximately 9% of stomach adenocarcinomas. EBV-encoded microRNAs have been reported as reducing the function of the class I major histocompatibility complex (MHC-I) antigen presentation apparatus, which could allow infected cells to evade adaptive immune responses. Using data from nearly 400 human gastric carcinomas (GCs), we assessed the impact of EBV on MHC-I heavy and light chain mRNA levels, as well as multiple other components essential for antigen processing and presentation. Unexpectedly, mRNA levels of these genes were as high, or higher, in EBV-associated gastric carcinomas (EBVaGCs) compared to normal control tissues or other GC subtypes. This coordinated upregulation could have been a consequence of the higher intratumoral levels of interferon γ in EBVaGCs, which correlated with signatures of increased infiltration by T and natural killer (NK) cells. These results indicate that EBV-encoded products do not effectively reduce mRNA levels of the MHC-I antigen presentation apparatus in human GCs. Full article
(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Gastric Cancer)
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<p>Expression of classical MHC-I heavy chain gene mRNA in gastric carcinoma subtypes and normal gastric tissue. RNA-Sequencing by Expectation Maximization (RSEM) normalized data for the HLA-A (<b>A</b>), HLA-B (<b>B</b>) and HLA-C (<b>C</b>) MHC-I heavy chain genes were extracted from The Cancer Genome Atlas (TCGA) database for the TCGA/PanCancer Atlas gastric/stomach adenocarcinoma (STAD) cohort for EBV-associated gastric carcinomas (EBVaGCs), normal control tissues, and three other gastric cancer (GC) subtypes. False discovery rate (FDR)-adjusted <span class="html-italic">p</span>-values for each statistical comparison are shown on the right for each gene panel. CIN: chromosomal instability; GS: genomically stable; MSI: microsatellite instability.</p> Full article ">Figure 2
<p>Expression of non-classical MHC-I heavy chain genes and light chain in gastric carcinoma subtypes and normal gastric tissue. Normalized RNA-seq data for the HLA-E (<b>A</b>), HLA-F (<b>B</b>) and HLA-G (<b>C</b>) MHC-I heavy chain and B2M (<b>D</b>) light chain genes were extracted from the TCGA database for the STAD cohort for EBVaGCs, normal control tissues, and three other GC subtypes. FDR-adjusted <span class="html-italic">p</span>-values for each statistical comparison are shown on the right for each gene panel.</p> Full article ">Figure 3
<p>Expression levels of the TAP genes involved in MHC-I-dependent antigen presentation in gastric carcinoma subtypes and normal gastric tissue. Normalized RNA-seq data for the TAP1 (<b>A</b>), TAP2 (<b>B</b>) and TAPBP (<b>C</b>) genes involved in MHC-I-dependent antigen presentation were extracted from the TCGA database for the STAD cohort for EBVaGCs, normal control tissues, and three other GC subtypes. FDR-adjusted <span class="html-italic">p</span>-values for each statistical comparison are shown on the right for each gene panel.</p> Full article ">Figure 4
<p>Expression levels of other genes involved in MHC-I-dependent antigen loading in gastric carcinoma subtypes and normal gastric tissue. Normalized RNA-seq data for the CANX (<b>A</b>), CALR (<b>B</b>), PDIA3 (<b>C</b>), ERAP1 (<b>D</b>) and ERAP2 (<b>E</b>) genes involved in MHC-I-dependent antigen presentation were extracted from the TCGA database for the STAD cohort for EBVaGCs, normal control tissues, and three other GC subtypes. FDR-adjusted <span class="html-italic">p</span>-values for each statistical comparison are shown on the right for each gene panel.</p> Full article ">Figure 5
<p>Detection of tumor infiltrating T cells and natural killer (NK) cells in gastric carcinoma subtypes and normal gastric tissue. Normalized RNA-seq data for genes indicative of tumor infiltrating T cells including CD3D (<b>A</b>), CD3E (<b>B</b>), and CD3G (<b>C</b>), or FCGR3A (<b>D</b>) for NK cells were extracted from the TCGA database for the STAD cohort for EBVaGCs, normal control tissues, and three other GC subtypes. FDR-adjusted <span class="html-italic">p</span>-values for each statistical comparison are shown on the right for each gene panel.</p> Full article ">Figure 6
<p>Expression of mRNA encoding IFN-γ and transcription factors involved in regulating the interferon-dependent activation of MHC-I-dependent antigen presentation and loading genes. Normalized RNA-seq data for the IFN-γ (IFNG) gene (<b>A</b>) and the genes encoding the nucleotide-binding oligomerization domain (NOD)-like receptor caspase recruitment domain containing protein 5 (NLRC5)/MHC-I transactivator (CITA; panel (<b>B</b>)) and Regulatory Factor X5 (RFX5; panel (<b>C</b>)) transcription factors involved in interferon-induced activation of expression of genes involved in MHC-I-dependent antigen presentation were extracted from the TCGA database for the STAD cohort for EBVaGCs, normal control tissues, and three other GC subtypes. FDR-adjusted <span class="html-italic">p</span>-values for each statistical comparison are shown on the right for each gene panel.</p> Full article ">Figure 7
<p>Correlation matrix of selected genes involved in the MHC-I antigen presentation pathway. Heatmap of Spearman correlation analysis of mRNA expression of the indicated MHC-I pathway genes in EBVaGC (<b>A</b>). Comparisons with EBV genes reported to antagonize interferon-γ response are also shown. For comparison, Spearman correlations between mRNA levels for interferon-γ (IFNG) and MHC-I pathway genes are also shown for the CIN (<b>B</b>), GS (<b>C</b>), and MSI (<b>D</b>) subtypes. RSEM normalized RNA-seq data for the genes listed above were extracted from the TCGA database for the STAD cohort for EBVaGCs. Pairwise spearman correlations were performed. Numbers in boxes indicate Spearman’s rank correlation coefficient of analyzed gene pairs and <span class="html-italic">p</span>-values.</p> Full article ">
12 pages, 562 KiB  
Review
Plasma Membrane Transporters as Biomarkers and Molecular Targets in Cholangiocarcinoma
by Jose J.G. Marin, Rocio I.R. Macias, Candela Cives-Losada, Ana Peleteiro-Vigil, Elisa Herraez and Elisa Lozano
Cells 2020, 9(2), 498; https://doi.org/10.3390/cells9020498 - 21 Feb 2020
Cited by 6 | Viewed by 4034
Abstract
The dismal prognosis of patients with advanced cholangiocarcinoma (CCA) is due, in part, to the extreme resistance of this type of liver cancer to available chemotherapeutic agents. Among the complex mechanisms accounting for CCA chemoresistance are those involving the impairment of drug uptake, [...] Read more.
The dismal prognosis of patients with advanced cholangiocarcinoma (CCA) is due, in part, to the extreme resistance of this type of liver cancer to available chemotherapeutic agents. Among the complex mechanisms accounting for CCA chemoresistance are those involving the impairment of drug uptake, which mainly occurs through transporters of the superfamily of solute carrier (SLC) proteins, and the active export of drugs from cancer cells, mainly through members of families B, C and G of ATP-binding cassette (ABC) proteins. Both mechanisms result in decreased amounts of active drugs able to reach their intracellular targets. Therefore, the “cancer transportome”, defined as the set of transporters expressed at a given moment in the tumor, is an essential element for defining the multidrug resistance (MDR) phenotype of cancer cells. For this reason, during the last two decades, plasma membrane transporters have been envisaged as targets for the development of strategies aimed at sensitizing cancer cells to chemotherapy, either by increasing the uptake or reducing the export of antitumor agents by modulating the expression/function of SLC and ABC proteins, respectively. Moreover, since some elements of the transportome are differentially expressed in CCA, their usefulness as biomarkers with diagnostic and prognostic purposes in CCA patients has been evaluated. Full article
(This article belongs to the Special Issue Molecular Mechanism and Therapeutic Opportunities of Cholangiocarcinoma)
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Graphical abstract

Graphical abstract
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<p>Schematic representation of the role in cholangiocarcinoma of uptake (up) and export (down) transporters as biomarkers for diagnosis and prediction of response to chemotherapy (left) or as targets for strategies of chemosensitization to antitumor drugs (right). Aquaporin-1/5 (AQP-1/5); apical sodium-dependent bile acid transporter (ASBT); cooper transporter (CTR1); equilibrative nucleoside transporter 1 (ENT1); glucose transporter 1/2 (GLUT1/2); L-type amino acid transporter-1 (LAT1); multidrug resistance protein 1 (MDR1); multidrug resistance-associated protein 1/3 (MRP1/3); sodium–iodide symporter (NIS); organic cation transporter 1 (OCT1); Phosphohippolin (PPH); sodium-dependent vitamin C transporter 2 (SVCT2).</p> Full article ">
31 pages, 3670 KiB  
Review
Ribosome and Translational Control in Stem Cells
by Mathieu Gabut, Fleur Bourdelais and Sébastien Durand
Cells 2020, 9(2), 497; https://doi.org/10.3390/cells9020497 - 21 Feb 2020
Cited by 54 | Viewed by 7668
Abstract
Embryonic stem cells (ESCs) and adult stem cells (ASCs) possess the remarkable capacity to self-renew while remaining poised to differentiate into multiple progenies in the context of a rapidly developing embryo or in steady-state tissues, respectively. This ability is controlled by complex genetic [...] Read more.
Embryonic stem cells (ESCs) and adult stem cells (ASCs) possess the remarkable capacity to self-renew while remaining poised to differentiate into multiple progenies in the context of a rapidly developing embryo or in steady-state tissues, respectively. This ability is controlled by complex genetic programs, which are dynamically orchestrated at different steps of gene expression, including chromatin remodeling, mRNA transcription, processing, and stability. In addition to maintaining stem cell homeostasis, these molecular processes need to be rapidly rewired to coordinate complex physiological modifications required to redirect cell fate in response to environmental clues, such as differentiation signals or tissue injuries. Although chromatin remodeling and mRNA expression have been extensively studied in stem cells, accumulating evidence suggests that stem cell transcriptomes and proteomes are poorly correlated and that stem cell properties require finely tuned protein synthesis. In addition, many studies have shown that the biogenesis of the translation machinery, the ribosome, is decisive for sustaining ESC and ASC properties. Therefore, these observations emphasize the importance of translational control in stem cell homeostasis and fate decisions. In this review, we will provide the most recent literature describing how ribosome biogenesis and translational control regulate stem cell functions and are crucial for accommodating proteome remodeling in response to changes in stem cell fate. Full article
(This article belongs to the Special Issue Translational Machinery to Understand and Fight Cancer)
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Figure 1

Figure 1
<p>Translational control in stem cells. Upper panel: main signaling pathways that may regulate translational efficiency in embryonic stem cells. Translation regulators that promote stemness or in contrast stimulate differentiation are depicted in purple or orange, respectively. Lower panel: Similar to the upper panel in somatic stem cells. Translation regulators that promote stemness or induce differentiation are depicted in purple or orange, respectively. Images of cell membranes were kindly provided by the SMART Servier medical art database [<a href="#B9-cells-09-00497" class="html-bibr">9</a>].</p> Full article ">Figure 2
<p>Ribosome biogenesis is highly regulated in embryonic and adult stem cells. Key RBFs described in this review and their implication in ribosome biogenesis in stem cells are presented. Factors stimulating ESC and ASC maintenance are depicted in purple and green, respectively. Direct protein/protein interactions are indicated by double-headed black arrows. Images of nucleic acids, the nucleus, and proteins were kindly provided by the SMART Servier medical art database [<a href="#B9-cells-09-00497" class="html-bibr">9</a>].</p> Full article ">Figure 3
<p>Ribosomes regulate gene-specific expression. Schematic diagram illustrating two models describing the impact of ribosome concentration on translation of specific mRNA subsets (upper panel) and the concept of functionally specialized ribosomes, which consists in heterogeneous ribosomes endowed with substrate selectivity (lower panel).</p> Full article ">
13 pages, 3433 KiB  
Article
The Cytokine Nicotinamide Phosphoribosyltransferase (eNAMPT; PBEF; Visfatin) Acts as a Natural Antagonist of C-C Chemokine Receptor Type 5 (CCR5)
by Simone Torretta, Giorgia Colombo, Cristina Travelli, Sara Boumya, Dmitry Lim, Armando A. Genazzani and Ambra A. Grolla
Cells 2020, 9(2), 496; https://doi.org/10.3390/cells9020496 - 21 Feb 2020
Cited by 23 | Viewed by 3588
Abstract
(1) Background: Extracellular nicotinamide phosphoribosyltrasferase (eNAMPT) is released by various cell types with pro-tumoral and pro-inflammatory properties. In cancer, eNAMPT regulates tumor growth through the activation of intracellular pathways, suggesting that it acts through a putative receptor, although its nature is still elusive. [...] Read more.
(1) Background: Extracellular nicotinamide phosphoribosyltrasferase (eNAMPT) is released by various cell types with pro-tumoral and pro-inflammatory properties. In cancer, eNAMPT regulates tumor growth through the activation of intracellular pathways, suggesting that it acts through a putative receptor, although its nature is still elusive. It has been shown, using surface plasma resonance, that eNAMPT binds to the C-C chemokine receptor type 5 (CCR5), although the physiological meaning of this finding is unknown. The aim of the present work was to characterize the pharmacodynamics of eNAMPT on CCR5. (2) Methods: HeLa CCR5-overexpressing stable cell line and B16 melanoma cells were used. We focused on some phenotypic effects of CCR5 activation, such as calcium release and migration, to evaluate eNAMPT actions on this receptor. (3) Results: eNAMPT did not induce ERK activation or cytosolic Ca2+-rises alone. Furthermore, eNAMPT prevents CCR5 internalization mediated by Rantes. eNAMPT pretreatment inhibits CCR5-mediated PKC activation and Rantes-dependent calcium signaling. The effect of eNAMPT on CCR5 was specific, as the responses to ATP and carbachol were unaffected. This was strengthened by the observation that eNAMPT inhibited Rantes-induced Ca2+-rises and Rantes-induced migration in a melanoma cell line. (4) Conclusions: Our work shows that eNAMPT binds to CCR5 and acts as a natural antagonist of this receptor. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Extracellular nicotinamide phosphoribosyltrasferase (eNAMPT) binds to C-C chemokine receptor type 5 (CCR5) in HeLa cancer cells. (<b>A</b>) Representative flow cytometry analysis of CCR5 expression in HeLa-SCR and HeLa-CCR5 cells, using Rat anti-mouse CCR5 antibody. (<b>B</b>) Western blot analysis of CCR5 expression in HeLa-SCR and HeLa-CCR5 cells. The CCR5 antibody recognizes both human endogenous CCR5 and murine exogenous CCR5. (<b>C</b>) Representative FACS analysis and calculated percentage of positive cells of Rantes-PE (16 nM) binding on HeLa-CCR5 cells incubated in the presence or absence of eNAMPT (2.25 µM) or maraviroc (10 µM). Mean ± S.E.M. of five separate experiments; *** <span class="html-italic">p</span> &lt; 0.001.</p> Full article ">Figure 2
<p>eNAMPT is not an agonist of CCR5. (<b>A</b>) Representative Western blot and densitometry analysis of phosphorylated ERK after 2 h of starvation followed by treatment for 5–30 min with recombinant Rantes (25 ng/mL; 3 nM) or eNAMPT (500 ng/mL; 9 nM) in serum-free conditions. Data from four separate experiments. (<b>B</b>) Representative calcium traces of HeLa-CCR5 loaded with FURA-2AM and stimulated with Rantes (25 ng/mL) or eNAMPT (500 ng/mL). Representative traces of 98–110 cells from 5–7 independent experiments. (<b>C</b>) Flow cytometry analysis of surface expression of CCR5 in HeLa-CCR5 cells treated for 1 h with Rantes (100 ng/mL = 12 nM), CCL3 (100 ng/mL = 9.9 nM), CCL7 (250 ng/mL = 22 nM), maraviroc (10 µM), and eNAMPT (2.5 µg/mL= 45 nM). The graph shows the mean ± S.E.M. of 12 determinations from four separate experiments. ** <span class="html-italic">p</span> &lt; 0.01 *** <span class="html-italic">p</span> &lt; 0.001; ns not statistically significant.</p> Full article ">Figure 3
<p>eNAMPT reduces CCR5 internalization and prevents pPKC activation. (<b>A</b>) Representative flow cytometry analysis of CCR5 expression in HeLa-CCR5 cells after the indicated stimuli. (<b>B</b>) Flow cytometry analysis of surface expression of CCR5 in HeLa-CCR5 cells treated for 1 h with Rantes (100 ng/mL; 12 nM) alone or pretreated for 20 min with eNAMPT (2.5 µg/mL; 45 nM) or CCL7 (250 ng/mL; 22 nM) or maraviroc (10 µM). Mean ± S.E.M. of four separate experiments. * <span class="html-italic">p</span> &lt; 0.05. (<b>C</b>) Representative Western blot and densitometry of two independent experiments of phosphoPKC (pPKC) in Hela-CCR5 cells treated in serum-free for 20 min with Rantes (25 ng/mL; 3 nM), eNAMPT (500 ng/mL; 9 nM), CCL7 (250 ng/mL; 22 nM), or CCL3 (100 ng/mL; 9.9 nM) alone or combined.</p> Full article ">Figure 4
<p>eNAMPT inhibits Rantes-induced cytosolic Ca2+-signals. (<b>A</b>) Representative calcium traces of HeLa-CCR5 loaded with FURA-2AM and treated with vehicle, eNAMPT (250 ng/mL; 4.5 nM or 500 ng/mL = 9 nM), or maraviroc (10 µM) 100 s before the addition of 25 ng/mL (3 nM) of Rantes. Histograms of responding cells (middle panel) and percentage of Area Under the Curve ( AUC )(right panel) as mean ± S.E.M. (248–410 cells from 6–11 independent experiments). (<b>B</b>,<b>C</b>) Representative calcium traces and percentage of AUC of HeLa-SCR and HeLa-CCR5 loaded with FURA-2AM and treated with 3 µM of ATP (<b>B</b>) or 300 µM of CCh (<b>C</b>) alone or pretreated with eNAMPT (500 ng/mL = 9 nM) for 5 min. The data are summarized in histograms and expressed as mean ± S.E.M of 120–190 cells (from 5–9 independent experiments) and 105–185 cells (from 5–9 independent experiments), respectively. ** <span class="html-italic">p</span> &lt; 0.01 *** <span class="html-italic">p</span> &lt; 0.001.</p> Full article ">Figure 5
<p>The catalytic activity of eNAMPT is dispensable for CCR5 antagonism. (<b>A</b>) Left, representative calcium traces of HeLa-CCR5 loaded with FURA-2AM and treated with RANTES (25 ng/mL; 3 nM) alone or pretreated with eNAMPT/eNAMPT H247E (500 ng/mL; 9 nM) for 100 s. Right, histograms of percentage of AUC (middle panel) and responding cells (right panel) as mean ± S.E.M. (180–210 cells from four independent experiments). (<b>B</b>) Left, representative calcium traces of HeLa-CCR5 loaded with FURA-2AM and pretreated for 100 s with vehicle or NMN (100 µM) before the addition of Rantes (25 ng/mL = 3 nM). Histograms of percentage of AUC (right panel) and responding cells (left panel) as mean ± S.E.M. (99–126 cells from three independent experiments). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p> Full article ">Figure 6
<p>eNAMPT antagonizes the CCR5-mediated migration of B16 melanoma cells. (<b>A</b>) CCR5 expression in B16 cells stained with anti-mouse CCR5-PE and analyzed by flow cytometry. (<b>B</b>,<b>C</b>) AUC and percentage of responding cells of B16 cells loaded with FURA-2AM and pretreated with eNAMPT (500 ng/mL; 9 nM) for 100 s before the addition of RANTES (100 ng/mL; 12 nM) at 100 s. Mean ± S.E.M. of 180–210 cells from four independent experiments. (<b>D</b>) Left, representative wound healing images of B16 cells treated with vehicle, Rantes (100 ng/mL; 12 nM), eNAMPT (500 ng/mL; 9 nM) and/or maraviroc (10 µM), and/or CCL7 (250 ng/mL; 22 nM) and CCL3 (100 ng/mL; 9.9 nM) at time 0 and after 24 h of treatment. Right, percentage of wound closure (compared to % of the control) after 24 h of treatment. Mean ± S.E.M. of six determinations from two separate 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.</p> Full article ">
3 pages, 199 KiB  
Editorial
Novel Therapeutic Approach to Induce Autophagy in a Drosophila Model for Huntington’s Disease
by Marta Martinez-Vicente
Cells 2020, 9(2), 495; https://doi.org/10.3390/cells9020495 - 21 Feb 2020
Cited by 1 | Viewed by 2600
Abstract
Autophagy induction is an attractive therapeutic approach to ameliorate aggregate accumulation in many neurodegenerative diseases [...] Full article
(This article belongs to the Special Issue Autophagy in Neurodegenerative Diseases)
18 pages, 5613 KiB  
Article
TLR2 Signaling Pathway Combats Streptococcus uberis Infection by Inducing Mitochondrial Reactive Oxygen Species Production
by Bin Li, Zhixin Wan, Zhenglei Wang, Jiakun Zuo, Yuanyuan Xu, Xiangan Han, Vanhnaseng Phouthapane and Jinfeng Miao
Cells 2020, 9(2), 494; https://doi.org/10.3390/cells9020494 - 21 Feb 2020
Cited by 24 | Viewed by 3496
Abstract
Mastitis caused by Streptococcus uberis (S. uberis) is a common and difficult-to-cure clinical disease in dairy cows. In this study, the role of Toll-like receptors (TLRs) and TLR-mediated signaling pathways in mastitis caused by S. uberis was investigated using mouse models [...] Read more.
Mastitis caused by Streptococcus uberis (S. uberis) is a common and difficult-to-cure clinical disease in dairy cows. In this study, the role of Toll-like receptors (TLRs) and TLR-mediated signaling pathways in mastitis caused by S. uberis was investigated using mouse models and mammary epithelial cells (MECs). We used S. uberis to infect mammary glands of wild type, TLR2−/− and TLR4−/− mice and quantified the adaptor molecules in TLR signaling pathways, proinflammatory cytokines, tissue damage, and bacterial count. When compared with TLR4 deficiency, TLR2 deficiency induced more severe pathological changes through myeloid differentiation primary response 88 (MyD88)-mediated signaling pathways during S. uberis infection. In MECs, TLR2 detected S. uberis infection and induced mitochondrial reactive oxygen species (mROS) to assist host in controlling the secretion of inflammatory factors and the elimination of intracellular S. uberis. Our results demonstrated that TLR2-mediated mROS has a significant effect on S. uberis-induced host defense responses in mammary glands as well as in MECs. Full article
(This article belongs to the Special Issue Innate-Acquired Linkage in Immunotherapy)
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Figure 1

Figure 1
<p>TLR2 mediates tissue damage and anti-<span class="html-italic">S. uberis</span> infection in mammary gland. (<b>A</b>) Mammary gland staining by hematoxylin and eosin of WTB6 (isotype-matched negative control of TLR2<sup>−/−</sup>), WTB10 (isotype-matched negative control of TLR4<sup>−/−</sup>), TLR2<sup>−/−</sup>, TLR4<sup>−/−</sup> mice with or without <span class="html-italic">S. uberis</span>. Polymorphonuclear neutrophilic leukocyte (PMN) infiltration (black arrow), the bleeding and degeneration (white arrow), adipose tissue (blue arrow). Images are representative of <span class="html-italic">n</span> = 6 animals per genotype. Scale bars, 50 μm. (<b>B</b>,<b>C</b>) The bleeding and degeneration, PMN infiltration, and adipose tissue were observed by light microscopic and scored by double blind method. B stands the scores for TLR2<sup>−/−</sup> group and C is for TLR4<sup>−/−</sup> group (<b>D</b>) NAGase activity was analyzed in mammary gland. (<b>E</b>) Viable bacteria were counted via the plate with Todd–Hewitt broth (THB) agar medium. Experiments D and E were repeated three times. All data were presented as the means ± SEM (<span class="html-italic">n</span> = 6). * (<span class="html-italic">p</span> &lt; 0.05) = significantly different between the indicated groups.</p> Full article ">Figure 2
<p>TLR2 and TLR4 deficiencies affect the secretion of cytokines in <span class="html-italic">S.uberis</span> infection. (<b>A</b>,<b>B</b>) The protein expressions of TNF-α, IL-1β and IL-6 were determined by ELISA in mammary gland of WTB6, WTB10, TLR2<sup>−/−</sup> and TLR4<sup>−/−</sup> mice with or without <span class="html-italic">S. uberis</span>. A is for the protein expressions for TLR2<sup>−/−</sup> group and B is for the TLR4<sup>−/−</sup> group. Experiments were repeated three times and all data were presented as the means ± SEM (<span class="html-italic">n</span> = 6). * (<span class="html-italic">p</span> &lt; 0.05) = significantly different between the indicated groups.</p> Full article ">Figure 3
<p>MyD88 dependent pathway predominates in <span class="html-italic">S. uberis</span> infection. (<b>A</b>) Immunohistochemistry was used to analyze the protein expression levels of MyD88 and TRIF in mammary gland of WTB6 and TLR2<sup>−/−</sup> mice with or without <span class="html-italic">S. uberis</span>. (<b>B</b>) Immunohistochemistry was used to analyze the protein expression levels of MyD88 and TRIF in mammary gland of WTB10 and TLR4<sup>−/−</sup> mice with or without <span class="html-italic">S. uberis</span>. Images are representative of <span class="html-italic">n</span> = 6 animals per genotype. Scale bars, 100 μm. Data were presented as the means ± SEM (<span class="html-italic">n</span> = 6). * (<span class="html-italic">p</span> &lt; 0.05) = significantly different between the indicated groups. (<b>C</b>,<b>D</b>,<b>E</b>) The protein expression levels of MyD88 and TRIF of WTB6, WTB10, TLR2<sup>−/−</sup>, TLR4<sup>−/−</sup> mice with or without <span class="html-italic">S. uberis</span> were determined by Western blot in mammary epithelial cells (MECs). For quantitative analysis, bands were evaluated densitometrically with Image J analyzer software normalized for GAPDH density. Experiments were repeated three times and data were presented as the means ± SEM (<span class="html-italic">n</span> = 3). * (<span class="html-italic">p</span> &lt; 0.05) = significantly different between the indicated groups.</p> Full article ">Figure 4
<p>TRAF6 and ECSIT participate in sensing signal from Toll-like receptors (TLRs) in <span class="html-italic">S. uberis</span> infection. (<b>A</b>) Immunohistochemistry was used to analyze the protein expression levels of TRAF6 and ECSIT in mammary gland of WTB6 and TLR2<sup>−/−</sup> mice with or without <span class="html-italic">S. uberis</span>. (<b>B</b>) Immunohistochemistry was used to analyze the protein expression levels of TRAF6 and ECSIT in mammary gland of WTB10 and TLR4<sup>−/−</sup> mice with or without <span class="html-italic">S. uberis</span>. Images are representative of <span class="html-italic">n</span> = 6 animals per genotype. Scale bars, 100 μm. Data were presented as the means ± SEM (<span class="html-italic">n</span> = 6). * (<span class="html-italic">p</span>&lt; 0.05) = significantly different between the indicated groups. (<b>C</b>,<b>D</b>,<b>E</b>) The protein expression levels of TRAF6 and ECSIT of WTB6, WTB10, TLR2<sup>−/−</sup>, TLR4<sup>−/−</sup> mice with or without <span class="html-italic">S. uberis</span> were determined by Western blot in MECs. For quantitative analysis, bands were evaluated densitometrically with Image J analyzer software normalized for GAPDH density. Experiments were repeated three times and data were presented as the means ± SEM (<span class="html-italic">n</span> = 3). * (<span class="html-italic">p</span> &lt; 0.05) = significantly different between the indicated groups.</p> Full article ">Figure 5
<p>TLRs mediate redox status in mammary gland during <span class="html-italic">S. uberis</span> infection. (<b>A</b>,<b>B</b>) The protein expressions of total antioxidant capacity (T-AOC), superoxide dismutase (SOD), malondialdehyde (MDA), and uncoupling protein 2 (UCP2) were determined by kits in mammary gland of WTB6, WTB10, TLR2<sup>−/−</sup>, TLR4<sup>−/−</sup> mice with or without <span class="html-italic">S. uberis</span>. A is for the protein expressions for TLR2<sup>−/−</sup> group and B is for the TLR4<sup>−/−</sup> group. Data are presented as the means ± SEM (<span class="html-italic">n</span> = 6). * (<span class="html-italic">p</span> &lt; 0.05) = significantly different between the indicated groups. (<b>C</b>) CellQuest Pro acquisition and analysis software analyzed the levels of reactive oxygen species (ROS) and mitochondrial reactive oxygen species (mROS) in MECs of Control, siTLR2, siTLR4, and siTLR2/4 groups with or without <span class="html-italic">S. uberis</span>. (<b>D</b>) The activity of UCP2 was determined by ELISA in MECs of Control, siTLR2, siTLR4 and siTLR2/4 groups with or without <span class="html-italic">S. uberis</span>. Data were presented as the means ± SEM (<span class="html-italic">n</span> = 3). * (<span class="html-italic">p</span> &lt; 0.05) = significantly different between the indicated groups. All experiments were repeated three times.</p> Full article ">Figure 6
<p>mROS plays an important role in anti-<span class="html-italic">S. uberis</span> infection in MECs. (<b>A</b>) The levels of ROS and mROS after using GKT137831 and NG25 simultaneously or separately with or without <span class="html-italic">S. uberis</span> infection in MECs. (<b>B</b>) Viable bacteria were counted via the plate with THB agar medium after using GKT137831 and NG25 simultaneously or separately during <span class="html-italic">S. uberis</span> infection in MECs. (<b>C</b>) The levels of ROS and mROS after using siECSIT in MECs of Control and siECSIT groups with or without <span class="html-italic">S. uberis</span> infection. (<b>D</b>) The expressions of TNF-α, IL-1β and IL-6 after using siECSIT in MECs of control and siECSIT groups with or without <span class="html-italic">S. uberis</span> infection. (<b>E</b>) Bacteria counts after using siECSIT in MECs of Control and siECSIT groups during <span class="html-italic">S. uberis</span> infection. All experiments were repeated three times and all data were presented as the means ± SEM (<span class="html-italic">n</span> = 3). * (<span class="html-italic">p</span> &lt; 0.05) = significantly different between the indicated groups.</p> Full article ">
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