Sexually-dimorphic targeting of functionally-related genes in COPD

Background: There is growing evidence that many diseases develop, progress, and respond to therapy differently in men and women. This variability may manifest as a result of sex-specific structures in gene regulatory networks that influence how those networks operate. However, there are few methods to identify and characterize differences in network structure, slowing progress in understanding mechanisms driving sexual dimorphism. Results: Here we apply an integrative network inference method, PANDA (Passing Attributes between Networks for Data Assimilation), to model sex-specific networks in blood and sputum samples from subjects with Chronic Obstructive Pulmonary Disease (COPD). We used a jack-knifing approach to build an ensemble of likely networks for each sex. By adapting statistical methods to compare these network ensembles, we were able to identify strong differential-targeting patterns associated with functionally-related sets of genes, including those involved in mitochondrial function and energy metabolism. Network analysis also identified several potential sex- and disease-specific transcriptional regulators of these pathways. Conclusions: Network analysis yielded insight into potential mechanisms driving sexual dimorphism in COPD that were not evident from gene expression analysis alone. We believe our ensemble approach to network analysis provides a principled way to capture sex-specific regulatory relationships and could be applied to identify differences in gene regulatory patterns in a wide variety of diseases and contexts. Electronic supplementary material The online version of this article (doi:10.1186/s12918-014-0118-y) contains supplementary material, which is available to authorized users

High Performance Computing of Gene Regulatory Networks using a Message-Passing Model

Gene regulatory network reconstruction is a fundamental problem in computational biology. We recently developed an algorithm, called PANDA (Passing Attributes Between Networks for Data Assimilation), that integrates multiple sources of 'omics data and estimates regulatory network models. This approach was initially implemented in the C++ programming language and has since been applied to a number of biological systems. In our current research we are beginning to expand the algorithm to incorporate larger and most diverse data-sets, to reconstruct networks that contain increasing numbers of elements, and to build not only single network models, but sets of networks. In order to accomplish these "Big Data" applications, it has become critical that we increase the computational efficiency of the PANDA implementation. In this paper we show how to recast PANDA's similarity equations as matrix operations. This allows us to implement a highly readable version of the algorithm using the MATLAB/Octave programming language. We find that the resulting M-code much shorter (103 compared to 1128 lines) and more easily modifiable for potential future applications. The new implementation also runs significantly faster, with increasing efficiency as the network models increase in size. Tests comparing the C-code and M-code versions of PANDA demonstrate that this speed-up is on the order of 20-80 times faster for networks of similar dimensions to those we find in current biological applications

Estimating sample-specific regulatory networks

Biological systems are driven by intricate interactions among the complex array of molecules that comprise the cell. Many methods have been developed to reconstruct network models of those interactions. These methods often draw on large numbers of samples with measured gene expression profiles to infer connections between genes (or gene products). The result is an aggregate network model representing a single estimate for the likelihood of each interaction, or "edge," in the network. While informative, aggregate models fail to capture the heterogeneity that is represented in any population. Here we propose a method to reverse engineer sample-specific networks from aggregate network models. We demonstrate the accuracy and applicability of our approach in several data sets, including simulated data, microarray expression data from synchronized yeast cells, and RNA-seq data collected from human lymphoblastoid cell lines. We show that these sample-specific networks can be used to study changes in network topology across time and to characterize shifts in gene regulation that may not be apparent in expression data. We believe the ability to generate sample-specific networks will greatly facilitate the application of network methods to the increasingly large, complex, and heterogeneous multi-omic data sets that are currently being generated, and ultimately support the emerging field of precision network medicine

말초혈액 단핵구에서 유전자 발현을 통한 천식 병태생리의 이해

학위논문 (박사) -- 서울대학교 대학원 : 의과대학 의학과, 2021. 2. 박흥우.코르티코스테로이드는 천식 치료의 중요한 약제이다. 하지만 코르티코스테로이드에 대한 치료 효과는 환자마다 상당한 차이가 있는 것으로 알려져 있다. 특히 코르티코스테로이드에 대한 낮은 반응성은 중증 천식 또는 잦은 급성 악화와 관련이 있을 수 있다. 비록 많은 유전체 연구들이 진행되었지만, 스테로이드 저반응성과 관련된 천식의 병태 생리에 대해서는 아직까지 충분히 연구되지 않았다. 따라서 생체외 덱사메타손 처리에 따른 유전자 발현 양상의 변화를 분석하는 것은 스테로이드 저반응성과 관련된 천식 급성악화의 기전을 연구하는데 도움이 될 수 있다. 본 연구는 천식 환자의 말초 혈액 단핵구 세포의 유전자 발현 양상 및 생체외 덱사메타손 처리에 따른 변화를 분석함으로써 스테로이드 저반응성과 관련된 병태생리 기전 및 생물학적 경로를 탐색해보고자 한다. 본 연구는 두 파트로 나누어 진행되었다. 첫번째 파트는 Weighted Gene Co-expression Network Analysis (WGCNA) 방법론을 통해, 소아천식 환자와 성인천식 환자에서 공통적으로 관찰되는 급성악화와 관련된 유전체 모듈이 존재하는지 그리고 해당 모듈에 생체외 덱사메타손 처리를 유전자 발현 양상의 변화를 탐색하였다. 소아 천식 환자 107명의 불멸화된 림프모세포 세포주와 성인천식 환자 29명의 말초혈액 단핵구에서 유전자 발현 양상을 분석하였다. 천식 급성악화는 전신스테로이드를 3일이상 복용하거나 천식으로 인해 응급 방문 또는 입원으로 정의하였다. 소아천식 환자군과 성인천식 환자군에서 공통적으로 관찰되는 총 77개의 유전자로 구성된 급성악화과 관련된 유전체 모듈을 찾았다. 해당 모듈의 EIF2AK2 유전체와 NOL11 유전체는 덱사메타손 처리시 소아 천식환자군과 성인천식 환자군 모두에서 유전체 발현양이 유의하게 감소하였다. 해당 모듈 중 64개의 유전체는 덱사메타손 처리시 유전자 발현양이 유의하게 변하지 않았는데, 이들 유전자들은 단백질 수리 경로 (protein repair pathway) 등과 관련성이 있었다. 단백질 수리 경로와 관련된 유전자 중에서 MSRA와 MSRB2의 중요한 역할은 산화 스트레스를 조절하는 것으로 알려져 있다. 본 연구의 두번째 파트는 유전자 조절 네트워크를 통해 성인 천식환자에서 흡입용 스테로이드에 대한 반응성에 영향을 미치는 요인들을 탐색하였다. 흡입용 스테로이드에 대한 치료 효과가 있었던 환자군과 치료 효과가 없었던 환자군에서 생체외 덱사메타손 처리시 전사인자 차별발현을 보였던 상위 5개의 전사인자효과는 GATA1, JUN, NFKB1, SPl1 그리고 RELA였다. 이들 전사인자는 흡입용 스테로이드에 반응이 있었던 환자군과 없었던 환자군에서 서로 다른 유전자들과 다양한 생물학적 경로에서 연결되어 있었다. TBX4 유전자는 흡입용 스테로이드에 좋은 치료효과를 보였던 환자군에서 염증반응과 관련된 NFKB1 전사인자와 연결되어 있었다. 본 연구를 통해 규명된 새로운 유전자 및 생물학적 경로 탐색을 통해 스테로이드 저반응성과 관련된 유전적 특질을 이해하는데 도움이 되었고, 이는 천식의 다양한 병태생리에 기반한 새로운 치료제 또는 생물지표를 개발하는데 이바지할 수 있을 것이다.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, NFκB1, 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, NFκB1, 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

Network Medicine in the Age of Biomedical Big Data

Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity and environmental perturbations on the observed phenotype. Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level. In this review, we survey existing network types and biomedical data sources. More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied. We provide three paradigms, mainly dealing with three major biological network archetypes: protein-protein interaction, expression-based, and gene regulatory networks. For each of these paradigms, we discuss a broad overview of philosophies under which various network methods work. We also provide a few examples in each paradigm as a test case of its successful application. Finally, we delineate several opportunities and challenges in the field of network medicine. We hope this review provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise. Taken together, the understanding gained from combining biomedical data with networks can be useful for characterizing disease etiologies and identifying therapeutic targets, which, in turn, will lead to better preventive medicine with translational impact on personalized healthcare