15,956 research outputs found

    Heterogeneity reduces sensitivity of cell death for TNF-Stimuli

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    Background Apoptosis is a form of programmed cell death essential for the maintenance of homeostasis and the removal of potentially damaged cells in multicellular organisms. By binding its cognate membrane receptor, TNF receptor type 1 (TNF-R1), the proinflammatory cytokine Tumor Necrosis Factor (TNF) activates pro-apoptotic signaling via caspase activation, but at the same time also stimulates nuclear factor kappaB (NF-kappaB)-mediated survival pathways. Differential dose-response relationships of these two major TNF signaling pathways have been described experimentally and using mathematical modeling. However, the quantitative analysis of the complex interplay between pro- and anti-apoptotic signaling pathways is an open question as it is challenging for several reasons: the overall signaling network is complex, various time scales are present, and cells respond quantitatively and qualitatively in a heterogeneous manner. Results This study analyzes the complex interplay of the crosstalk of TNF-R1 induced pro- and anti-apoptotic signaling pathways based on an experimentally validated mathematical model. The mathematical model describes the temporal responses on both the single cell level as well as the level of a heterogeneous cell population, as observed in the respective quantitative experiments using TNF-R1 stimuli of different strengths and durations. Global sensitivity of the heterogeneous population was quantified by measuring the average gradient of time of death versus each population parameter. This global sensitivity analysis uncovers the concentrations of Caspase-8 and Caspase-3, and their respective inhibitors BAR and XIAP, as key elements for deciding the cell's fate. A simulated knockout of the NF-kappaB-mediated anti-apoptotic signaling reveals the importance of this pathway for delaying the time of death, reducing the death rate in the case of pulse stimulation and significantly increasing cell-to-cell variability. Conclusions Cell ensemble modeling of a heterogeneous cell population including a global sensitivity analysis presented here allowed us to illuminate the role of the different elements and parameters on apoptotic signaling. The receptors serve to transmit the external stimulus; procaspases and their inhibitors control the switching from life to death, while NF-kappaB enhances the heterogeneity of the cell population. The global sensitivity analysis of the cell population model further revealed an unexpected impact of heterogeneity, i.e. the reduction of parametric sensitivity

    Role of AMP-activated protein kinase in adipose tissue metabolism and inflammation

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    AMPK (AMP-activated protein kinase) is a key regulator of cellular and whole-body energy balance. AMPK phosphorylates and regulates many proteins concerned with nutrient metabolism, largely acting to suppress anabolic ATP-consuming pathways while stimulating catabolic ATP-generating pathways. This has led to considerable interest in AMPK as a therapeutic target for the metabolic dysfunction observed in obesity and insulin resistance. The role of AMPK in skeletal muscle and the liver has been extensively studied, such that AMPK has been demonstrated to inhibit synthesis of fatty acids, cholesterol and isoprenoids, hepatic gluconeogenesis and translation while increasing fatty acid oxidation, muscle glucose transport, mitochondrial biogenesis and caloric intake. The role of AMPK in the other principal metabolic and insulin-sensitive tissue, adipose, remains poorly characterized in comparison, yet increasing evidence supports an important role for AMPK in adipose tissue function. Obesity is characterized by hypertrophy of adipocytes and the development of a chronic sub-clinical pro-inflammatory environment in adipose tissue, leading to increased infiltration of immune cells. This combination of dysfunctional hypertrophic adipocytes and a pro-inflammatory environment contributes to insulin resistance and the development of Type 2 diabetes. Exciting recent studies indicate that AMPK may not only influence metabolism in adipocytes, but also act to suppress this pro-inflammatory environment, such that targeting AMPK in adipose tissue may be desirable to normalize adipose dysfunction and inflammation. In the present review, we discuss the role of AMPK in adipose tissue, focussing on the regulation of carbohydrate and lipid metabolism, adipogenesis and pro-inflammatory pathways in physiological and pathophysiological conditions

    Resistance to Cell Death and Its Modulation in Cancer Stem Cells

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    Accumulating evidence has demonstrated that human cancers arise from various tissues of origin that initiate from cancer stem cells (CSCs) or cancer-initiating cells. The extrinsic and intrinsic apoptotic pathways are dysregulated in CSCs, and these cells play crucial roles in tumor initiation, progression, cell death resistance, chemo- and radiotherapy resistance, and tumor recurrence. Understanding CSC-specific signaling proteins and pathways is necessary to identify specific therapeutic targets that may lead to the development of more efficient therapies selectively targeting CSCs. Several signaling pathways-including the phosphatidylinositol 3-kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR), maternal embryonic leucine zipper kinase (MELK), NOTCH1, and Wnt/ฮ’-catenin&and expression of the CSC markers CD133, CD24, CD44, Oct4, Sox2, Nanog, and ALDH1A1 maintain CSC properties. Studying such pathways may help to understand CSC biology and lead to the development of potential therapeutic interventions to render CSCs more sensitive to cell death triggered by chemotherapy and radiation therapy. Moreover, recent demonstrations of dedifferentiation of differentiated cancer cells into CSC-like cells have created significant complexity in the CSCs hypothesis. Therefore, any successful therapeutic agent or combination of drugs for cancer therapy must eliminate not only CSCs but differentiated cancer cells and the entire bulk of tumor cells. This review article expands on the CSC hypothesis and paradigm with respect to major signaling pathways and effectors that regulate CSC apoptosis resistance. Moreover, selective CSC apoptotic modulators and their therapeutic potential for making tumors more responsive to therapy are discussed. The use of novel therapies, including small-molecule inhibitors of specific proteins in signaling pathways that regulate stemness, proliferation and migration of CSCs, immunotherapy, and noncoding microRNAs may provide better means of treating CSCs

    Novel properties of mature adipocytes in obesity and hyperinsulinemia

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    Adipose tissue expansion and dysfunction, which leads to obesity and related metabolic diseases (e.g., diabetes and hypertension) are currently the most costly challenges for public health, yet the mechanisms underlying adipocyte functional dysregulation have not been fully elucidated . Investigation is largely hindered by the unique technical limitations associated with handling these large, lipid-filled fat cells. New techniques need to be developed in order to better understand the physiology and pathology of adipocytes. In Paper I, by using an adipocyte-specific reporter mouse, we proved that previously reported adipocyte flow cytometry techniques missed the major adipocyte population. Therefore, we defined several crucial cytometer settings required for large cell types that allow us to analyze and sort both white and brown mature adipocytes. This improved strategy is applicable to sort adipocytes based on size without fixation, which greatly facilitates subsequent downstream analyses. In combination with immunostaining, the presented approach can effectively sort UCP1 positive adipocytes from mouse white and brown adipose tissue. Furthermore, we demonstrated a heterogeneous ADRB2 expression pattern in human adipocytes, which confirmed the applicability of our newly developed technique to further explore other aspects of adipocyte identity. In Paper II, we developed a MAAC (membrane mature adipocyte aggregate culture) system as a novel, high-viability model for human mature adipocytes. With a permeable membrane insert sitting on top to facilitate cell aggregation and nutrition access, adipocytes cultured in the MAAC system maintained adipogenic properties, did not dedifferentiate and had reduced hypoxia. This newly developed in vitro system allows us to compare depot-specific adipocyte gene expression, and analyze the crosstalk between adipocytes and macrophages. In particular, we demonstrated that human adipocytes can be transdifferentiated to brown-like adipocytes under the conditions of rosiglitazone stimulation or PGC-1ฮฑ overexpression. Taken together, we provided a versatile tool for modulating primary adipocytes, opening up numerous downstream applications. In Paper III, we revealed that a large group of mature human adipocytes express an array of cell cycle-specific markers indicative of a cell cycle re-entry profile, and that this is associated with whole-body insulin resistance. We demonstrated that insulin is a critical driver of adipocyte cell cycle re-entry, subsequently making them vulnerable to undergo cellularsenescence. Our data showed that hyperinsulinemia in obese patients is associated with increased p16 and senescence associated ฮฒ-galactosidase activity in mature adipocytes. Furthermore, we showed that senescent adipocytes are hypertrophic and develop a senescence- associated secretory phenotype (SASP), defined by the secretion of IL-6, IL-8, and MCP1. These findings challenge the dogma that adipocytes permanently exit the cell cycle upon differentiation and reveals cellular senescence as a new mechanism associated with inflammation-related adipocyte pathology. In conclusion, the research within this thesis has provided important techniques for both in vitro adipocyte modulation and high throughput flowcytometric adipocyte analysis, supporting multiple downstream research applications to investigate mechanisms regulating adipocyte physiology and pathology. Furthermore, we demonstrated the phenomenon of cell cycle re- entry and senescence in human mature adipocytes, thereby introducing novel insights into obesity and hyperinsulinemia-induced adipocyte dysfunction, suggesting potential targets for treating obesity-related metabolic diseases

    Role of Diacylglycerol kinase alpha (DGKA) as a therapeutic target in Glioblastoma (GB)

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    Glioblastoma (GB) is the most common high-grade fatal brain tumor. The standard of care treatment is surgery, followed by radiotherapy and chemotherapy. Despite decades of research, the median life expectancy of patients is still between 12 to 15 months. The activation of multiple receptor tyrosine kinases (RTKs) and/or downstream tumour-intrinsic mutations provide oncogenic stimuli to GB progression and accounts for resistance to current therapies. Identifying a target that is capable of simultaneously disabling of multiple, parallel oncogenic signals can represent an effective therapy. Mounting reports indicate DGK\u3b1 relevance as a therapeutic target across multiple cancers, given its role in different aspects of tumour biology. DGK\u3b1 phosphorylates diacylglycerol (DG) resulting in the production of phosphatidic acid (PA). Both DG and PA are membrane bound secondary messengers that regulate signalling molecules involved in cancer. DGKs act simultaneously as both terminators and activators of DG- and PA-mediated signalling. In order to exploit DGK\u3b1 as a therapeutic target we investigated the role of DGK\u3b1 in GB biology and signalling. Our results show that DGK\u3b1 is required for GB stem-like cell long term viability and stemness maintenance and sensitize tumor cells to temozolomide. Inhibition of DGK\u3b1 strongly impairs NF-\u3baB transcriptional activity and analysis of the TNFR signalling showed that DGK\u3b1 is necessary for FAK and AKT activation downstream TNFa stimulation. Taken together, the results of this study strongly suggest that DGK\u3b1 plays a key role in stemness maintenance contributing FAK, Akt and NF-kB activation upon TNF stimulation and for this reason DGKa might represent a targetable oncogene that links in\ufb02ammation and tumor growth and progression

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋†์ƒ๋ช…๊ณตํ•™๋ถ€(๋ฐ”์ด์˜ค๋ชจ๋“ˆ๋ ˆ์ด์…˜์ „๊ณต), 2021. 2. ์†ก์šฉ์ƒ.Ovarian cancer is the most lethal type of gynecologic malignancy with high rates of mortality. About 70% of patients diagnosed with the cancer show recurrence of cancer despite the cytoreductive surgery and platinum-based chemotherapy. This is mainly due to the highly metastatic capacity of ovarian cancer and extrinsic mechanisms of chemoresistance exerted by extrinsic factors known as the tumor microenvironment. In this series of studies on the ovarian cancer tumor microenvironment, the effects of the tumor microenvironmental factors (hypoxia, inflammation and malignant ascites) affecting chemoresistance and metastatic potential were investigated. Mitochondria are often referred to as the power plants of a cell. They are vital organelles within a eukaryotic cell, constantly undergoing fission and fusion processes in response to the external stimuli in the surrounding cues. Dysregulation of mitochondrial fission and fusion processes has been shown to be linked to onsets and the progression of many serious diseases such as neurodegenerative diseases, cardiovascular diseases and cancer. Hypoxia is a critical feature of the tumor microenvironment affecting cancer progression through modulation of cancer hallmarks. Hypoxia stimulates the production of reactive oxygen species (ROS), inducing changes in mitochondrial function, metabolism and structures. In this study, we sought to investigate the effect of hypoxia on mitochondrial dynamics of ovarian cancer cells. We investigated alterations in mitochondrial networks under hypoxic conditions (< 1% O2) and the subsequent effect of the mitochondrial alteration on drug response in ovarian cancer cells. Hypoxia-induced ROS caused an increase in mitochondrial fission, a response abolished by free radical scavenging with N-acetylcysteine (NAC) and Trolox. Also, the treatment of hydrogen peroxide (H2O2) decreased inhibitory p-Drp1 (Ser637) content and increased mitochondrial fission. Suppression of mitochondrial fission enhanced the CDDP sensitivity of hypoxic ovarian cancer cells. Lastly, in tumor spheroids from malignant ascites or tissues of patients with advanced-stage ovarian cancer, pre-treatment with Mdivi-1 increased the CDDP sensitivity. Taken together, our results implicate that hypoxia-induced ROS triggers mitochondrial fission and CDDP resistance in ovarian cancer cells. Mitochondrial dynamics of cancer cells adapting to the hypoxic tumor microenvironment could be a promising target for ovarian cancer treatment. Next, a growing body of evidence suggests that inflammation is closely associated with the development and progression of ovarian cancer. Herein, we postulated that TNF-mediated activation of the alternative NFฮบB pathway promotes invasiveness and resistance to cell death induced by TNF. Among the inflammation-related cytokines, we found that TNF is highly expressed in ovarian cancer tissues, compared to that in the other types of solid tumors. Ovarian cancer cells exposed to TNF for 48 hours showed increased colony-forming capacity and cell cycle progression. Additionally, the gene expression levels of TNF and it receptors (TNFRSF1A and TNFRSF1B) were elevated in the metastatic ovarian cancer tissues than the primary tissues from the same patient. Also, the results from the Matrigel-coated transwell insert assay showed that TNF promotes invasiveness of ovarian cancer cells. To investigate the mechanisms involved in this process, we examined the protein expression of classical (p50:p65) and alternative (RelB:P52) NFฮบB pathway regulators. higher expression of the alternative NFKB transcription factor, RelB was associated with poor survival rates. Using 307 ovarian tumor samples from The Cancer Genome Atlas (TCGA), we found a significant positive correlation between the alternative NFKB pathway genes (RelB and P52) and cancer invasion-related protease (MMP9, PLAU). Protease array revealed that PLAU was upregulated by TNF but this response was absent in the RelB knockdown ovarian cancer cell. Collectively, these findings highlight the involvement of the alternative NFฮบB pathway-mediated by RelB expression in metastatic processes in ovarian cancer cells. Lastly, to understand cellular interactions, present within the metastatic niche of the ovarian cancer tumor microenvironment, malignant ascites, we utilized single-cell RNA sequencing data from 5 patients with ovarian cancer. We identified 7 distinct cell types based on the expression patterns of cell-type-specific signatures. Macrophages were the most heterogeneous cell type with eleven sub-clusters in the ascites tumor microenvironment. Annotation of macrophage subpopulations was done using MacSpectrum. Also, intra- and inter-tumoral heterogeneities of OC cells in the ascites were assessed. The communication between immune and OC cells was predicted through ligand-receptor interaction analysis with NicheNet. We uncovered that CCL5 is enriched in T cells and NK cells, modulating OC cell survival in the ascites presumably through SDC4. Average SDC4 expression positively correlates with OC cell proportion in each sample. Elevated SDC4 expression predicted poor overall survival of OC patients. Altogether, our study highlights the potential tumor-promoting role of T cells and NK cells in long-term survival outcomes, suggesting that SDC4 is a vital molecule for OC cell survival and a prognostic marker in OC patients.๋‚œ์†Œ์•”์€ ์‚ฌ๋ง๋ฅ ์ด ๋†’์€ ๊ฐ€์žฅ ์น˜๋ช…์ ์ธ ์œ ํ˜•์˜ ๋ถ€์ธ๊ณผ ์•…์„ฑ ์ข…์–‘์ด๋‹ค. ์•” ์ง„๋‹จ์„๋ฐ›์€ ํ™˜์ž์˜ ์•ฝ 70%๋Š” ์ข…์–‘ ๊ฐ์ถ• ์ˆ˜์ˆ ๊ณผ ๋ฐฑ๊ธˆ ๊ธฐ๋ฐ˜ ํ™”ํ•™ ์š”๋ฒ•์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์•”์˜ ์žฌ๋ฐœ์„ ๋ณด์ธ๋‹ค. ์ด๊ฒƒ์€ ์ฃผ๋กœ ๋‚œ์†Œ ์•”์˜ ๊ณ ๋„๋กœ ์ „์ด๋˜๋Š” ๋Šฅ๋ ฅ๊ณผ ์ข…์–‘ ๋ฏธ์„ธ ํ™˜๊ฒฝ์œผ๋กœ ์•Œ๋ ค์ง„ ์™ธ์ธ์„ฑ ์š”์ธ์— ์˜ํ•ด ๋ฐœํœ˜๋˜๋Š” ํ™”ํ•™ ๋‚ด์„ฑ์˜ ์™ธ์ธ์„ฑ ๋ฉ”์ปค๋‹ˆ์ฆ˜ ๋•Œ๋ฌธ์ด๋‹ค. ๋‚œ์†Œ์•” ์ข…์–‘ ๋ฏธ์„ธ ํ™˜๊ฒฝ์— ๋Œ€ํ•œ ์ผ๋ จ์˜ ์—ฐ๊ตฌ์—์„œ, ์ข…์–‘ ๋ฏธ์„ธ ํ™˜๊ฒฝ ์š”์ธ (์ €์‚ฐ์†Œ์ฆ, ์—ผ์ฆ ๋ฐ ์•…์„ฑ ๋ณต์ˆ˜)์ด ํ™”ํ•™ ์š”๋ฒ•๊ณผ ์ „์ด ๊ฐ€๋Šฅ์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์กฐ์‚ฌํ–ˆ๋‹ค. ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„๋Š” ์ข…์ข… ์„ธํฌ์˜ ๋ฐœ์ „์†Œ๋ผ๊ณ  ๋ถˆ๋ฆฐ๋‹ค. ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„๋Š” ์ง„ํ•ต ์„ธํฌ ๋‚ด์—์„œ ์ค‘์š”ํ•œ ์†Œ๊ธฐ๊ด€์ด๋ฉฐ, ์ฃผ๋ณ€ ํ™˜๊ฒฝ์˜ ์™ธ๋ถ€ ์ž๊ทน์— ๋ฐ˜์‘ํ•˜์—ฌ ๋ถ„์—ด๊ณผ ์œตํ•ฉ ๊ณผ์ •์„ ๋ฐ˜๋ณตํ•œ๋‹ค. ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„์˜ ํ˜•ํƒœ์  ๋ณ€ํ™”๋Š” ํ‡ดํ–‰์„ฑ ์‹ ๊ฒฝ๋ณ‘, ์‹ฌํ˜ˆ๊ด€ ์งˆํ™˜ ๋ฐ ์•”์„ ํฌํ•จํ•œ ์—ฌ๋Ÿฌ ์‹ฌ๊ฐํ•œ ์งˆ๋ณ‘์˜ ๋ฐœ๋ณ‘ ๋ฐ ์ง„ํ–‰๊ณผ ๊ด€๋ จ์ด ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ €์‚ฐ์†Œ์ฆ์€ ์•” ํŠน์ง•(cancer hallmark)์˜ ์กฐ์ ˆ์„ ํ†ตํ•ด ์•” ์ง„ํ–‰์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ข…์–‘ ๋ฏธ์„ธํ™˜๊ฒฝ์˜ ์ค‘์š”ํ•œ ํŠน์ง•์ด๋‹ค. ์ €์‚ฐ์†Œ์ฆ์€ ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„์˜ ๊ธฐ๋Šฅ, ๋Œ€์‚ฌ ๋ฐ ๊ตฌ์กฐ์˜ ๋ณ€ํ™”๋ฅผ ์œ ๋„ํ•˜์—ฌ ํ™œ์„ฑ ์‚ฐ์†Œ ์ข…(ROS)์˜ ์ƒ์„ฑ์„ ์ž๊ทนํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ €์‚ฐ์†Œํ™˜๊ฒฝ์˜ ๋‚œ์†Œ์•” ์„ธํฌ์—์„œ ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ์—ญํ•™์„ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ €์‚ฐ์†Œ ์กฐ๊ฑด(<1% O2)์—์„œ ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ๋„คํŠธ์›Œํฌ๊ฐ€ ์•”์„ธํฌ์—์„œ ์•ฝ๋ฌผ ๋ฐ˜์‘์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ €์‚ฐ์†Œํ™˜๊ฒฝ์—์„œ์˜ ํ™œ์„ฑ ์‚ฐ์†Œ ์ข…์€ ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ๋ถ„์—ด์„ ์ฆ๊ฐ€์‹œ์ผฐ๊ณ , ์ด๋Ÿฌํ•œ ํ˜„์ƒ์€ ํ•ญ์‚ฐํ™”์ œ์ธ N-์•„์„ธํ‹ธ ์‹œ์Šคํ…Œ์ธ(NAC) ๋ฐ ํŠธ๋กค๋ก์Šค(Trolox) ์ฒ˜๋ฆฌ ์‹œ ์™„ํ™”๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ๊ณผ์‚ฐํ™”์ˆ˜์†Œ(H2O2) ์ฒ˜๋ฆฌ ์‹œ pDrp1(Ser637)์˜ ๋ฐœํ˜„์˜ ๊ฐ์†Œ์™€ ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„์˜ ๋ถ„์—ด์ด ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ๋ถ„์—ด ์ €ํ•ด ์‹œ, ์ €์‚ฐ์†Œ์„ฑ ๋‚œ์†Œ์•” ์„ธํฌ์˜ ์‹œ์Šคํ”Œ๋ผํ‹ด ๊ฐ์ˆ˜์„ฑ์„ ์ฆ๊ฐ€์‹œ์ผฐ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์ง„ํ–‰์„ฑ ๋‚œ์†Œ์•” ํ™˜์ž์˜ ์•…์„ฑ ๋ณต์ˆ˜ ๋˜๋Š” ์กฐ์ง์œผ๋กœ๋ถ€ํ„ฐ ๋ถ„๋ฆฌ ํ•œ ์ผ์ฐจ ์ข…์–‘ ์ŠคํŽ˜๋กœ์ด๋“œ(Speroids)์— Drp1 ์–ต์ œ์ œ์ธ Mdivi-1 ์ „์ฒ˜๋ฆฌ์‹œ, ์‹œ์Šคํ”Œ๋ผํ‹ด ๊ฐ์ˆ˜์„ฑ์ด ์ฆ๊ฐ€๋˜์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ, ๋‚œ์†Œ์•”์„ธํฌ๋Š” ์ €์‚ฐ์†Œ ํ™˜๊ฒฝ์—์„œ ํ•ญ์•”์ œ ์‹œ์Šคํ”Œ๋ผํ‹ด์˜ ์ €ํ•ญ์„ฑ์„ ์ผ์œผํ‚ค๊ณ , ์ด๊ฒƒ์€ ์ €์‚ฐ์†Œ ํ™˜๊ฒฝ์—์„œ ์ฆ๊ฐ€ํ•œ ํ™œ์„ฑ์‚ฐ์†Œ๋ฅผ ํ†ตํ•œ ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ๋ถ„์—ด ๋•Œ๋ฌธ์ด๋ผ๋Š” ๊ฒƒ์„ ํ™•์ธํ–ˆ๋‹ค. ๋‹ค์Œ์œผ๋กœ, ์—ผ์ฆ์ด ๋‚œ์†Œ ์•”์˜ ๋ฐœ์ƒ ๋ฐ ์ง„ํ–‰๊ณผ ๋ฐ€์ ‘ํ•œ ๊ด€๋ จ์ด ์žˆ๋‹ค๋Š” ์ฆ๊ฑฐ๊ฐ€ ๋Š˜์–ด๋‚˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์—ฌ๊ธฐ์—์„œ, ์šฐ๋ฆฌ๋Š” ๋Œ€์ฒด NFฮบB ๊ฒฝ๋กœ์˜ TNF ๋งค๊ฐœ ํ™œ์„ฑํ™”๊ฐ€ TNF์— ์˜ํ•ด ์œ ๋„ ๋œ ์„ธํฌ ์‚ฌ๋ฉธ์— ๋Œ€ํ•œ ์นจ์ž… ์„ฑ๊ณผ ์ €ํ•ญ์„ฑ์„ ์ด‰์ง„ํ•œ๋‹ค๊ณ  ๊ฐ€์„ค์„ ์„ธ์› ๋‹ค. ์—ผ์ฆ ๊ด€๋ จ ์‚ฌ์ดํ†  ์นด์ธ ์ค‘ TNF๋Š” ๋‹ค๋ฅธ ์œ ํ˜•์˜ ๊ณ ํ˜• ์ข…์–‘์— ๋น„ํ•ด ๋‚œ์†Œ ์•” ์กฐ์ง์—์„œ ๊ณ ๋„๋กœ ๋ฐœํ˜„๋œ๋‹ค๋Š” ๊ฒƒ์„ ๋ฐœ๊ฒฌํ–ˆ์Šต๋‹ˆ๋‹ค. 48 ์‹œ๊ฐ„ ๋™์•ˆ TNF์— ๋…ธ์ถœ ๋œ ๋‚œ์†Œ ์•” ์„ธํฌ๋Š” ์ฆ๊ฐ€ ๋œ ์ฝœ๋กœ๋‹ˆ ํ˜•์„ฑ ๋Šฅ๋ ฅ๊ณผ ์„ธํฌ์ฃผ๊ธฐ ์ง„ํ–‰์„ ๋ณด์—ฌ ์ฃผ์—ˆ๋‹ค. ๋˜ํ•œ TNF ๋ฐ ์ˆ˜์šฉ์ฒด(TNFRSF1A ๋ฐ TNFRSF1B)์˜ ์œ ์ „์ž ๋ฐœํ˜„ ์ˆ˜์ค€์€ ์ „์ด์„ฑ ๋‚œ์†Œ ์•” ์กฐ์ง์—์„œ ๋™์ผํ•œ ํ™˜์ž์˜ 1 ์ฐจ ์กฐ์ง๋ณด๋‹ค ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ Matrigel ์ฝ”ํŒ… ๋œ transwell inserts ๋ถ„์„ ๊ฒฐ๊ณผ๋Š” TNF๊ฐ€ ๋‚œ์†Œ ์•” ์„ธํฌ์˜ ์นจ์Šต์„ฑ์„ ์ด‰์ง„ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด ๊ณผ์ •์— ๊ด€๋ จ๋œ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด ์šฐ๋ฆฌ๋Š” ๊ณ ์ „์  (p50 : p65) ๋ฐ ๋Œ€์•ˆ์  (RELB : P52) NFฮบB ๊ฒฝ๋กœ ์กฐ์ ˆ์ œ์˜ ๋‹จ๋ฐฑ์งˆ ๋ฐœํ˜„์„ ์กฐ์‚ฌํ–ˆ์Šต๋‹ˆ๋‹ค. ๋Œ€์ฒด NFKB ๊ฒฝ๋กœ์˜ ์ „์‚ฌ ์ธ์ž ์ธ RELB์˜ ๋” ๋†’์€ ๋ฐœํ˜„์€ ๋‚ฎ์€ ์ƒ์กด์œจ๊ณผ ๊ด€๋ จ์ด ์žˆ์—ˆ๋‹ค. The Cancer Genome Atlas (TCGA)์˜ 307 ๊ฐœ์˜ ๋‚œ์†Œ ์ข…์–‘ ์ƒ˜ํ”Œ์„ ์‚ฌ์šฉํ•˜์—ฌ ๋Œ€์ฒด NFKB ๊ฒฝ๋กœ ์œ ์ „์ž (RELB ๋ฐ P52)์™€ ์•” ์นจ์Šต ๊ด€๋ จ ํ”„๋กœํ…Œ์•„์ œ (MMP9, PLAU)๊ฐ„์— ์œ ์˜ ํ•œ ์–‘์˜ ์ƒ๊ด€ ๊ด€๊ณ„๋ฅผ ๋ฐœ๊ฒฌํ–ˆ๋‹ค. ํ”„๋กœํ…Œ์•„์ œ ์–ด๋ ˆ์ด๋Š” PLAU๊ฐ€ TNF์— ์˜ํ•ด ์ƒํ–ฅ ์กฐ์ ˆ๋˜๋Š” ๊ฒƒ์œผ๋กœ ๋ฐํ˜€์กŒ์ง€๋งŒ์ด ๋ฐ˜์‘์€ RELB ๋…น๋‹ค์šด ๋‚œ์†Œ ์•” ์„ธํฌ์—๋Š” ์—†์—ˆ๋‹ค. ์ข…ํ•ฉ์ ์œผ๋กœ, ์ด๋Ÿฌํ•œ ๋ฐœ๊ฒฌ์€ ๋‚œ์†Œ ์•” ์„ธํฌ์˜ ์ „์ด ๊ณผ์ •์—์„œ RelB ๋ฐœํ˜„์— ์˜ํ•ด ๋งค๊ฐœ๋˜๋Š” ๋Œ€์ฒด NFฮบB ๊ฒฝ๋กœ์˜ ๊ด€์—ฌ๋ฅผ ์‹œ์‚ฌํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๋‚œ์†Œ ์•” ์ข…์–‘ ๋ฏธ์„ธํ™˜๊ฒฝ์ธ ์•…์„ฑ ๋ณต์ˆ˜์— ์กด์žฌํ•˜๋Š” ์„ธํฌ ์ƒํ˜ธ ์ž‘์šฉ์„ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด ๋‚œ์†Œ ์•” ํ™˜์ž 5 ๋ช…์˜ ๋‹จ์ผ ์„ธํฌ RNA ์‹œํ€€์‹ฑ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ–ˆ๋‹ค. ์šฐ๋ฆฌ๋Š” ์„ธํฌ ์œ ํ˜•๋ณ„ ์‹œ๊ทธ๋‹ˆ์ฒ˜์˜ ๋ฐœํ˜„ ํŒจํ„ด์„ ๊ธฐ๋ฐ˜์œผ๋กœ 7 ๊ฐœ์˜ ๋ณ„๊ฐœ์˜ ์„ธํฌ ์œ ํ˜•์„ ์‹๋ณ„ํ–ˆ๋‹ค. ๋Œ€์‹์„ธํฌ๋Š” ๋ณต์ˆ˜ ์ข…์–‘ ๋ฏธ์„ธ ํ™˜๊ฒฝ์—์„œ 11 ๊ฐœ์˜ ํ•˜์œ„ ํด๋Ÿฌ์Šคํ„ฐ๋ฅผ ๊ฐ€์ง„ ๊ฐ€์žฅ ์ด์งˆ์ ์ธ ์„ธํฌ ์œ ํ˜•์ด์—ˆ๋‹ค. ๋Œ€์‹์„ธํฌ ๋ถ€๋ถ„ ์ง‘๋‹จ์˜ ํ•ด์„์€ MacSpectrum์„ ์‚ฌ์šฉํ•˜์—ฌ ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ๋˜ํ•œ ๋ณต์ˆ˜์—์„œ OC ์„ธํฌ์˜ ์ข…์–‘ ๋‚ด ๋ฐ ์ข…์–‘ ๊ฐ„ ์ด์งˆ์„ฑ์„ ํ‰๊ฐ€ํ–ˆ๋‹ค. NicheNet์„ ํ†ตํ•œ ๋ฆฌ๊ฐ„๋“œ-์ˆ˜์šฉ์ฒด ์ƒํ˜ธ ์ž‘์šฉ ๋ถ„์„์„ ํ†ตํ•ด ๋ฉด์—ญ ์„ธํฌ์™€ OC ์„ธํฌ ๊ฐ„์˜ ์ƒํ˜ธ์ž‘์šฉ์„ ์˜ˆ์ธกํ–ˆ๋‹ค. ์šฐ๋ฆฌ๋Š” CCL5๊ฐ€ T ์„ธํฌ์™€ NK ์„ธํฌ์—์„œ ๋ฐœํ˜„ํ•˜์—ฌ ์•„๋งˆ๋„ SDC4๋ฅผ ํ†ตํ•ด ๋ณต์ˆ˜์—์„œ OC ์„ธํฌ ์ƒ์กด์„ ์กฐ์ ˆํ•œ๋‹ค๋Š” ๊ฒƒ์„ ๋ฐœ๊ฒฌํ–ˆ๋‹ค. ํ‰๊ท  SDC4 ๋ฐœํ˜„์€ ๊ฐ ์ƒ˜ํ”Œ์—์„œ OC ์„ธํฌ ๋น„์œจ๊ณผ ์–‘์˜ ์ƒ๊ด€ ๊ด€๊ณ„๊ฐ€ ์žˆ์—ˆ๋‹ค. ์ฆ๊ฐ€ ๋œ SDC4 ๋ฐœํ˜„์€ OC ํ™˜์ž์˜ ์ „์ฒด ์ƒ์กด์œจ์ด ๋‚ฎ์Œ์„ ์˜ˆ์ธกํ–ˆ๋‹ค. ์ „์ฒด์ ์œผ๋กœ, ์šฐ๋ฆฌ์˜ ์—ฐ๊ตฌ๋Š” ์žฅ๊ธฐ ์ƒ์กด ๊ฒฐ๊ณผ์—์„œ T ์„ธํฌ์™€ NK ์„ธํฌ์˜ ์ž ์žฌ์ ์ธ ์ข…์–‘ ์ด‰์ง„ ์—ญํ• ์„ ๊ฐ•์กฐํ•˜์—ฌ SDC4๊ฐ€ OC ์„ธํฌ ์ƒ์กด์„์œ„ํ•œ ํ•„์ˆ˜ ๋ถ„์ž์ด์ž OC ํ™˜์ž์˜ ์˜ˆํ›„ ๋งˆ์ปค์ž„์„ ์‹œ์‚ฌํ•œ๋‹ค.Table of contents Abstract 2 Table of contents 5 List of figures and table 9 List of abbreviations 12 1. Chapter I: Literature review and study summary 14 1.1 Introduction 15 1.2 Molecular regulators of mitochondrial dynamics 16 1.2.1 Mitochondrial fission 20 1.2.2 Mitochondrial fusion 23 1.2.3 Motility and distribution 24 1.3 Tumor microenvironment and mitochondrial dynamics 25 1.3.1 ROS and mitochondrial dynamics 28 1.3.2 Inflammatory microenvironment and mitochondrial dynamics 30 1.3.3 Reduced blood supply and mitochondrial dynamics 31 1.3.4 Intracellular communication and mitochondrial dynamics 32 1.4 Drug resistance and mitochondrial dynamics in cancer 34 1.4.1 Mitochondrial fusion and chemoresistance 40 1.4.2 Mitochondrial fission and chemoresistance 41 1.4.3 Mitochondrial trafficking and chemoresistance 42 1.5 Study summary 43 2. Chapter II: Mitochondrial fission causes cisplatin resistance under hypoxic conditions via ROS in ovarian cancer cells 45 2.1 Introduction 46 2.2 Materials and Methods 48 2.2.1 Cell lines 48 2.2.2 Chemical reagents 48 2.2.3 Bioinformatics 48 2.2.4 Immunoblotting 49 2.2.5 MTT assay 49 2.2.6 Apoptosis analysis 50 2.2.7 Analysis of relative mitochondrial activity and mass 50 2.2.8 Immunocytochemistry and confocal microscopy 50 2.2.9 Detection of relative ROS levels 51 2.2.10 TUNEL assay 51 2.2.11 siRNA transfection 51 2.2.12 Isolation and culture of primary cells and patient-derived spheroids 52 2.2.13 Statistical analysis 53 2.3 Results 54 2.3.1 Hypoxia makes ovarian cancer cells resistant to CDDP irrespective of p53 status 54 2.3.2 Hypoxia increases mitochondrial fission in ovarian cancer cells 62 2.3.3 Hypoxia changes expression and activation status of mitochondrial dynamics protein 72 2.3.4 ROS exerted by hypoxia promotes mitochondrial fission 72 2.3.5 Expression of mRNAs in cancer tissue samples from GEO dataset shows the relevance of hypoxia-induced mitochondrial fission and chemoresistance 81 2.3.6 Inhibition of mitochondrial fission increases the sensitivity of ovarian cancer cells to CDDP in hypoxic conditions 85 2.3.7 Mdivi-1 pretreatment increases CDDP sensitivity of patient-derived spheroids (PDS) from tumor tissues and malignant ascites 104 2.4 Discussion 111 3. Chapter III: RELB stimulated by TNFฮฑ promotes invasive capacity of ovarian cancer cells through secretion of urokinase (PLAU/uPa) 116 3.1 Introduction 117 3.2 Materials and Methods 119 3.2.1 Cell culture 119 3.2.2 Invasion assay 119 3.2.3 Cell cycle analysis 119 3.2.4 Reagents and antibodies 120 3.2.5 Cell viability assay 120 3.2.6 Transfection of siRNA 120 3.2.7 Western blotting 120 3.2.8 Bioinformatics analysis 120 3.2.9 Proteome profiler array 121 3.2.10 Statistical analysis 121 3.3 Results 122 3.3.1 TNF is highly expressed in ovarian malignancy compared with other types of solid malignancies 122 3.3.2 TNF promotes ovarian cancer cell growth through cell cycle progression 122 3.3.3 TNF enhances invasion of ovarian cancer cells 125 3.3.4 TNF increases expression of RelB, an alternative NFฮบB gene, predicting poor prognosis in ovarian cancer patients 125 3.3.5 Knockdown of RelB prevents TNF induced invasion of ovarian cancer cells 125 3.3.6 TNF induces secretion of prometastatic proteases through RelB signaling 132 3.4 Discussion 135 4. Chapter IV: Communication between immune and cancer cells in ovarian cancer ascites through CCL5-SDC4 interaction 138 4.1 Introduction 139 4.2 Materials and Methods 141 4.2.1 Patient samples and sample processing 141 4.2.2 scRNA-seq library preparation and data pre-processing 141 4.2.3 scRNA-seq Data analysis 141 4.2.4 MacSpectrum analysis 142 4.2.5 Pathway enrichment analysis 142 4.2.6 NicheNet analysis 143 4.2.7 TCGA, GEO and GTEx dataset analysis 143 4.2.8 Statistical analysis 144 4.3 Results 145 4.3.1 Clustering of single-cell transcriptomic data identifies cellular heterogeneity of malignant ascites 145 4.3.2 Profiling of macrophage polarization states in ascites TME of ovarian cancer patients with MacSpectrum analysis 149 4.3.3 Comparison of molecular pathways enriched in ovarian cancer cell clusters in malignant ascites reveals intra- and inter-patient heterogeneity 156 4.3.4 Elevated expression of ligands related to the anoikis-resistant phenotype may provide survival advantages to cancer cells in the malignant ascites 164 4.3.5 Effector and exhausted CD8+ T cells and NK cells highly express CCL5 in the malignant ascites 171 4.3.6 SDC4 expression correlates with poor prognosis in ovarian cancer patients 175 4.4 Discussion 180 5. References 184 6. Abstract (Korean version) 206Docto

    Crosstalk between Macrophages and Pancreatic ฮฒ-Cells in Islet Development, Homeostasis and Disease.

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    Macrophages are highly heterogeneous and plastic immune cells with peculiar characteristics dependent on their origin and microenvironment. Following pathogen infection or damage, circulating monocytes can be recruited in different tissues where they differentiate into macrophages. Stimuli present in the surrounding milieu induce the polarisation of macrophages towards a pro-inflammatory or anti-inflammatory profile, mediating inflammatory or homeostatic responses, respectively. However, macrophages can also derive from embryonic hematopoietic precursors and reside in specific tissues, actively participating in the development and the homeostasis in physiological conditions. Pancreatic islet resident macrophages are present from the prenatal stages onwards and show specific surface markers and functions. They localise in close proximity to ฮฒ-cells, being exquisite sensors of their secretory ability and viability. Over the years, the crucial role of macrophages in ฮฒ-cell differentiation and homeostasis has been highlighted. In addition, macrophages are emerging as central players in the initiation of autoimmune insulitis in type 1 diabetes and in the low-grade chronic inflammation characteristic of obesity and type 2 diabetes pathogenesis. The present work reviews the current knowledge in the field, with a particular focus on the mechanisms of communication between ฮฒ-cells and macrophages that have been described so far
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