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    ๋ง์ดˆํ˜ˆ์•ก ๋‹จํ•ต๊ตฌ์—์„œ ์œ ์ „์ž ๋ฐœํ˜„์„ ํ†ตํ•œ ์ฒœ์‹ ๋ณ‘ํƒœ์ƒ๋ฆฌ์˜ ์ดํ•ด

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2021. 2. ๋ฐ•ํฅ์šฐ.์ฝ”๋ฅดํ‹ฐ์ฝ”์Šคํ…Œ๋กœ์ด๋“œ๋Š” ์ฒœ์‹ ์น˜๋ฃŒ์˜ ์ค‘์š”ํ•œ ์•ฝ์ œ์ด๋‹ค. ํ•˜์ง€๋งŒ ์ฝ”๋ฅดํ‹ฐ์ฝ”์Šคํ…Œ๋กœ์ด๋“œ์— ๋Œ€ํ•œ ์น˜๋ฃŒ ํšจ๊ณผ๋Š” ํ™˜์ž๋งˆ๋‹ค ์ƒ๋‹นํ•œ ์ฐจ์ด๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ํŠนํžˆ ์ฝ”๋ฅดํ‹ฐ์ฝ”์Šคํ…Œ๋กœ์ด๋“œ์— ๋Œ€ํ•œ ๋‚ฎ์€ ๋ฐ˜์‘์„ฑ์€ ์ค‘์ฆ ์ฒœ์‹ ๋˜๋Š” ์žฆ์€ ๊ธ‰์„ฑ ์•…ํ™”์™€ ๊ด€๋ จ์ด ์žˆ์„ ์ˆ˜ ์žˆ๋‹ค. ๋น„๋ก ๋งŽ์€ ์œ ์ „์ฒด ์—ฐ๊ตฌ๋“ค์ด ์ง„ํ–‰๋˜์—ˆ์ง€๋งŒ, ์Šคํ…Œ๋กœ์ด๋“œ ์ €๋ฐ˜์‘์„ฑ๊ณผ ๊ด€๋ จ๋œ ์ฒœ์‹์˜ ๋ณ‘ํƒœ ์ƒ๋ฆฌ์— ๋Œ€ํ•ด์„œ๋Š” ์•„์ง๊นŒ์ง€ ์ถฉ๋ถ„ํžˆ ์—ฐ๊ตฌ๋˜์ง€ ์•Š์•˜๋‹ค. ๋”ฐ๋ผ์„œ ์ƒ์ฒด์™ธ ๋ฑ์‚ฌ๋ฉ”ํƒ€์† ์ฒ˜๋ฆฌ์— ๋”ฐ๋ฅธ ์œ ์ „์ž ๋ฐœํ˜„ ์–‘์ƒ์˜ ๋ณ€ํ™”๋ฅผ ๋ถ„์„ํ•˜๋Š” ๊ฒƒ์€ ์Šคํ…Œ๋กœ์ด๋“œ ์ €๋ฐ˜์‘์„ฑ๊ณผ ๊ด€๋ จ๋œ ์ฒœ์‹ ๊ธ‰์„ฑ์•…ํ™”์˜ ๊ธฐ์ „์„ ์—ฐ๊ตฌํ•˜๋Š”๋ฐ ๋„์›€์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ฒœ์‹ ํ™˜์ž์˜ ๋ง์ดˆ ํ˜ˆ์•ก ๋‹จํ•ต๊ตฌ ์„ธํฌ์˜ ์œ ์ „์ž ๋ฐœํ˜„ ์–‘์ƒ ๋ฐ ์ƒ์ฒด์™ธ ๋ฑ์‚ฌ๋ฉ”ํƒ€์† ์ฒ˜๋ฆฌ์— ๋”ฐ๋ฅธ ๋ณ€ํ™”๋ฅผ ๋ถ„์„ํ•จ์œผ๋กœ์จ ์Šคํ…Œ๋กœ์ด๋“œ ์ €๋ฐ˜์‘์„ฑ๊ณผ ๊ด€๋ จ๋œ ๋ณ‘ํƒœ์ƒ๋ฆฌ ๊ธฐ์ „ ๋ฐ ์ƒ๋ฌผํ•™์  ๊ฒฝ๋กœ๋ฅผ ํƒ์ƒ‰ํ•ด๋ณด๊ณ ์ž ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋‘ ํŒŒํŠธ๋กœ ๋‚˜๋ˆ„์–ด ์ง„ํ–‰๋˜์—ˆ๋‹ค. ์ฒซ๋ฒˆ์งธ ํŒŒํŠธ๋Š” Weighted Gene Co-expression Network Analysis (WGCNA) ๋ฐฉ๋ฒ•๋ก ์„ ํ†ตํ•ด, ์†Œ์•„์ฒœ์‹ ํ™˜์ž์™€ ์„ฑ์ธ์ฒœ์‹ ํ™˜์ž์—์„œ ๊ณตํ†ต์ ์œผ๋กœ ๊ด€์ฐฐ๋˜๋Š” ๊ธ‰์„ฑ์•…ํ™”์™€ ๊ด€๋ จ๋œ ์œ ์ „์ฒด ๋ชจ๋“ˆ์ด ์กด์žฌํ•˜๋Š”์ง€ ๊ทธ๋ฆฌ๊ณ  ํ•ด๋‹น ๋ชจ๋“ˆ์— ์ƒ์ฒด์™ธ ๋ฑ์‚ฌ๋ฉ”ํƒ€์† ์ฒ˜๋ฆฌ๋ฅผ ์œ ์ „์ž ๋ฐœํ˜„ ์–‘์ƒ์˜ ๋ณ€ํ™”๋ฅผ ํƒ์ƒ‰ํ•˜์˜€๋‹ค. ์†Œ์•„ ์ฒœ์‹ ํ™˜์ž 107๋ช…์˜ ๋ถˆ๋ฉธํ™”๋œ ๋ฆผํ”„๋ชจ์„ธํฌ ์„ธํฌ์ฃผ์™€ ์„ฑ์ธ์ฒœ์‹ ํ™˜์ž 29๋ช…์˜ ๋ง์ดˆํ˜ˆ์•ก ๋‹จํ•ต๊ตฌ์—์„œ ์œ ์ „์ž ๋ฐœํ˜„ ์–‘์ƒ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ฒœ์‹ ๊ธ‰์„ฑ์•…ํ™”๋Š” ์ „์‹ ์Šคํ…Œ๋กœ์ด๋“œ๋ฅผ 3์ผ์ด์ƒ ๋ณต์šฉํ•˜๊ฑฐ๋‚˜ ์ฒœ์‹์œผ๋กœ ์ธํ•ด ์‘๊ธ‰ ๋ฐฉ๋ฌธ ๋˜๋Š” ์ž…์›์œผ๋กœ ์ •์˜ํ•˜์˜€๋‹ค. ์†Œ์•„์ฒœ์‹ ํ™˜์ž๊ตฐ๊ณผ ์„ฑ์ธ์ฒœ์‹ ํ™˜์ž๊ตฐ์—์„œ ๊ณตํ†ต์ ์œผ๋กœ ๊ด€์ฐฐ๋˜๋Š” ์ด 77๊ฐœ์˜ ์œ ์ „์ž๋กœ ๊ตฌ์„ฑ๋œ ๊ธ‰์„ฑ์•…ํ™”๊ณผ ๊ด€๋ จ๋œ ์œ ์ „์ฒด ๋ชจ๋“ˆ์„ ์ฐพ์•˜๋‹ค. ํ•ด๋‹น ๋ชจ๋“ˆ์˜ EIF2AK2 ์œ ์ „์ฒด์™€ NOL11 ์œ ์ „์ฒด๋Š” ๋ฑ์‚ฌ๋ฉ”ํƒ€์† ์ฒ˜๋ฆฌ์‹œ ์†Œ์•„ ์ฒœ์‹ํ™˜์ž๊ตฐ๊ณผ ์„ฑ์ธ์ฒœ์‹ ํ™˜์ž๊ตฐ ๋ชจ๋‘์—์„œ ์œ ์ „์ฒด ๋ฐœํ˜„์–‘์ด ์œ ์˜ํ•˜๊ฒŒ ๊ฐ์†Œํ•˜์˜€๋‹ค. ํ•ด๋‹น ๋ชจ๋“ˆ ์ค‘ 64๊ฐœ์˜ ์œ ์ „์ฒด๋Š” ๋ฑ์‚ฌ๋ฉ”ํƒ€์† ์ฒ˜๋ฆฌ์‹œ ์œ ์ „์ž ๋ฐœํ˜„์–‘์ด ์œ ์˜ํ•˜๊ฒŒ ๋ณ€ํ•˜์ง€ ์•Š์•˜๋Š”๋ฐ, ์ด๋“ค ์œ ์ „์ž๋“ค์€ ๋‹จ๋ฐฑ์งˆ ์ˆ˜๋ฆฌ ๊ฒฝ๋กœ (protein repair pathway) ๋“ฑ๊ณผ ๊ด€๋ จ์„ฑ์ด ์žˆ์—ˆ๋‹ค. ๋‹จ๋ฐฑ์งˆ ์ˆ˜๋ฆฌ ๊ฒฝ๋กœ์™€ ๊ด€๋ จ๋œ ์œ ์ „์ž ์ค‘์—์„œ MSRA์™€ MSRB2์˜ ์ค‘์š”ํ•œ ์—ญํ• ์€ ์‚ฐํ™” ์ŠคํŠธ๋ ˆ์Šค๋ฅผ ์กฐ์ ˆํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋‘๋ฒˆ์งธ ํŒŒํŠธ๋Š” ์œ ์ „์ž ์กฐ์ ˆ ๋„คํŠธ์›Œํฌ๋ฅผ ํ†ตํ•ด ์„ฑ์ธ ์ฒœ์‹ํ™˜์ž์—์„œ ํก์ž…์šฉ ์Šคํ…Œ๋กœ์ด๋“œ์— ๋Œ€ํ•œ ๋ฐ˜์‘์„ฑ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ๋“ค์„ ํƒ์ƒ‰ํ•˜์˜€๋‹ค. ํก์ž…์šฉ ์Šคํ…Œ๋กœ์ด๋“œ์— ๋Œ€ํ•œ ์น˜๋ฃŒ ํšจ๊ณผ๊ฐ€ ์žˆ์—ˆ๋˜ ํ™˜์ž๊ตฐ๊ณผ ์น˜๋ฃŒ ํšจ๊ณผ๊ฐ€ ์—†์—ˆ๋˜ ํ™˜์ž๊ตฐ์—์„œ ์ƒ์ฒด์™ธ ๋ฑ์‚ฌ๋ฉ”ํƒ€์† ์ฒ˜๋ฆฌ์‹œ ์ „์‚ฌ์ธ์ž ์ฐจ๋ณ„๋ฐœํ˜„์„ ๋ณด์˜€๋˜ ์ƒ์œ„ 5๊ฐœ์˜ ์ „์‚ฌ์ธ์žํšจ๊ณผ๋Š” GATA1, JUN, NFKB1, SPl1 ๊ทธ๋ฆฌ๊ณ  RELA์˜€๋‹ค. ์ด๋“ค ์ „์‚ฌ์ธ์ž๋Š” ํก์ž…์šฉ ์Šคํ…Œ๋กœ์ด๋“œ์— ๋ฐ˜์‘์ด ์žˆ์—ˆ๋˜ ํ™˜์ž๊ตฐ๊ณผ ์—†์—ˆ๋˜ ํ™˜์ž๊ตฐ์—์„œ ์„œ๋กœ ๋‹ค๋ฅธ ์œ ์ „์ž๋“ค๊ณผ ๋‹ค์–‘ํ•œ ์ƒ๋ฌผํ•™์  ๊ฒฝ๋กœ์—์„œ ์—ฐ๊ฒฐ๋˜์–ด ์žˆ์—ˆ๋‹ค. TBX4 ์œ ์ „์ž๋Š” ํก์ž…์šฉ ์Šคํ…Œ๋กœ์ด๋“œ์— ์ข‹์€ ์น˜๋ฃŒํšจ๊ณผ๋ฅผ ๋ณด์˜€๋˜ ํ™˜์ž๊ตฐ์—์„œ ์—ผ์ฆ๋ฐ˜์‘๊ณผ ๊ด€๋ จ๋œ NFKB1 ์ „์‚ฌ์ธ์ž์™€ ์—ฐ๊ฒฐ๋˜์–ด ์žˆ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๊ทœ๋ช…๋œ ์ƒˆ๋กœ์šด ์œ ์ „์ž ๋ฐ ์ƒ๋ฌผํ•™์  ๊ฒฝ๋กœ ํƒ์ƒ‰์„ ํ†ตํ•ด ์Šคํ…Œ๋กœ์ด๋“œ ์ €๋ฐ˜์‘์„ฑ๊ณผ ๊ด€๋ จ๋œ ์œ ์ „์  ํŠน์งˆ์„ ์ดํ•ดํ•˜๋Š”๋ฐ ๋„์›€์ด ๋˜์—ˆ๊ณ , ์ด๋Š” ์ฒœ์‹์˜ ๋‹ค์–‘ํ•œ ๋ณ‘ํƒœ์ƒ๋ฆฌ์— ๊ธฐ๋ฐ˜ํ•œ ์ƒˆ๋กœ์šด ์น˜๋ฃŒ์ œ ๋˜๋Š” ์ƒ๋ฌผ์ง€ํ‘œ๋ฅผ ๊ฐœ๋ฐœํ•˜๋Š”๋ฐ ์ด๋ฐ”์ง€ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.Asthma is a chronic inflammatory airway disease characterized by bronchial hyperresponsiveness and reversible airway obstruction. Corticosteroids are known to the most effective treatment for asthma. However, there is substantial variability in response to corticosteroids in asthma patients. Ineffective response to corticosteroids may result in exacerbation of asthma. Although many genetic studies have been conducted, the mechanisms of asthma pathogenesis and steroid insensitivity in asthma have not been fully elucidated. Gene expression profile represents the complete set of RNA transcripts that are produced by the genome under specific circumstances or in a specific cell. High-throughput methods such as microarray, and recent advances in biostatistics based on network-based approaches provide a quick and effective way of identifying novel genes and pathways related to asthma. This study aimed to understand the pathogenesis and steroid insensitivity in asthma using gene expression profiles of blood cells from asthma patients. To obtain a comprehensive picture of the gene expression in these cells, we used network-based approaches. The study was divided into two separate parts. In the first part of the study, important genetic signatures of acute exacerbation (AE) in asthma were identified using weighted gene co-expression network analysis (WGCNA) in peripheral blood mononuclear cells (PBMCs) from 29 adult asthma patients and lymphoblastoid cell lines (LCLs) from 107 childhood asthma patients. An AE-associated gene module composed of 77 genes was identified from childhood asthma patients and the conservation of this gene module structure was validated in adult asthma patients. The identified module was found to be conserved in terms of the gene expression profile and associated with AE in both childhood and adult asthma patients, and thus it was defined as an AE-associated common gene module. Changes in the expression of genes in the AE-associated common gene module following in vitro dexamethasone (Dex) treatment were examined, to better understand the mechanisms associated with steroid insensitivity. The differential gene expression profiles were classified into two classes according to Dex-induced changes in childhood asthma patients. Thirteen genes showed significant Dex-induced differential expression and were categorized as the A gene set. Sixty-four genes were not significantly altered by Dex were categorized as the B gene set. In the A gene set, the expression of eukaryotic translation initiation factor 2-alpha kinase 2 (EIF2AK2) showed significant Dex-induced differential expression in adult asthma patients as well. In addition, the basal expression of EIF2AK2 (pre-Dex) were significantly higher in asthma patients with AE compared to those without AE in both childhood and adult asthma. In the B gene set, based on a pathway-based approach, the protein repair pathway was found to be significantly enriched. Among the genes that belong to this pathway, the basal expression of methionine sulfoxide reductase A (MSRA) and methionine sulfoxide reductase B2 (MSRB2) were significantly lower in asthma patients with AE compared to those without AE in both childhood and adult asthma. These findings suggest that alternate treatment options, apart from corticosteroids, may be needed to prevent AE in asthma. Expression of EIF2AK2, MSRA, and MSRB2 in blood cells may help us to identify AE-susceptible asthma patients and adjust treatments to prevent AE events. In the second study, gene regulatory networks identified gene expression profiles of PBMCs from 23 adult asthma patients were assessed to elucidate the differences in responsiveness to inhaled corticosteroids (ICSs). Among these the top five (top-5) transcriptional factors (TFs; Top-5 TFs: GATA1, JUN, NFฮบB1, SPl1, and RELA) showing differential connections between good-responders (GRs) and poor-responders (PRs) were identified. Interestingly, GATA1 and JUN also showed differential connections in the gene regulatory networks identified gene expression profiles of LCLs from 107 childhood asthma patients in a previous study. The top-5 TFs and their connected genes were significantly enriched in distinct biological pathways associated with asthma. Among the genes connected to the top-5 TFs, the expression of TBX4, which is regulated by the TF, NFฮบB1, may be helpful in identifying GRs to ICS treatment. In conclusion, the novel genes and biological pathways identified in this study may deepen our understanding of asthma pathophysiology and steroid insensitivity in asthma.Table of Contents Chapter 1 Introduction 1 Chapter 2 Part I 10 2.1 Introduction 10 2.2 Methods 12 2.3 Results 23 2.4 Discussion 43 Chapter 3 Part II 48 3.1 Introduction 48 3.2 Methods 50 3.3 Results 53 3.4 Discussion 79 Chapter 4 Conclusions 84 Bibliography 86 Abstract in korean 101 List of Tables Table 1 26 Table 2 27 Table 3 29 Table 4 34 Table 5 55 Table 6 56 Table 7 57 Table 8 59 List of Figures Figure 1 14 Figure 2 35 Figure 3 36 Figure 4 37 Figure 5 38 Figure 6 39 Figure 7 40 Figure 8 41 Figure 9 42 Figure 10 77 Figure 11 78Docto

    The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks

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    Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods

    Falsifiable Network Models. A Network-based Approach to Predict Treatment Efficacy in Ulcerative Colitis

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    This work is focused on understanding the treatment efficacy of patients with ulcerative colitis (UC) using a network-based approach. UC is one of two forms of inflammatory bowel disease (IBD) along with Crohnโ€™s disease. UC is a debilitating condition characterized by chronic inflammation and ulceration of the colon and rectum. UC symptoms occur gradually rather than abruptly, and the degree of symptoms differs across UC patients. Only around 20% of all UC cases can be explained by known genetic variations, implying a more ambiguous aetiology that is yet not fully understood but is thought to involve a complex interplay between genetic and environmental factors. The available therapy for UC substantially reduces symptoms and achieves long-term remission. However, about one-third of UC patients fail to respond to anti-TNFฮฑ therapy and consequently develop long-term side effects due to medication. Non-response to existing antibody-based therapies in subgroups of UC patients is a major challenge and incurs a healthcare burden. Therefore, the disease markers for predicting therapy response to assist individualized therapy decisions are needed. To date, no quantitative computational framework is available to predict treatment response in UC. We developed a quantitative framework that uses gene expression data and existing biological background information on signalling pathways to quantify network connectivity from receptors to transcription factors (TF) that are involved in UC pathogenesis. Variations in network connectivity in UC patients can be used to identify responders and non-responders to anti-TNFฮฑ and anti-Integrin treatment. Our findings allow us to summarize the effect of small gene expression changes on the overall connectivity of a signalling network and estimate the effect this will have on the individual patients' responses. Estimating the network connectivity associated with varied drug responses may provide an understanding of individualized treatment outcomes. Our model could be used to generate testable hypotheses about how individual genes act together in networks to cause inflammation in UC as well as other immune-inflammatory diseases such as psoriasis, asthma, and rheumatoid arthritis
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