Characterization of genomic perturbation sensitivity using 1000 genomes population

학위논문 (박사)-- 서울대학교 대학원 : 의과대학 의과학과, 2018. 2. 김주한.연구 목적: 유전자의 발현은 수많은 유전체 돌연변이에 의해서 교란되며, 이는 세포의 기능과 개체의 표현형에 큰 영향을 준다. 최근의 대규모 차세대 염기서열분석 프로젝트에서 밝혀지고 있듯, 한사람의 유전체는 적어도 300만개의 돌연변이를 가지고 있는 것으로 알려져 있다. 본 논문에서는 이러한 유전체 교란을 해석하고 교란에 민감한 유전자의 특징을 살펴보고자 전사체 교란 네트워크를 1000 유전체 프로젝트 데이터를 통해 구성해 보았다. 연구 방법: 본 연구에서는 단백질 코딩 영역 내 비 동일 변이의 시프트 점수를 종합하여 유전자 손상 정도를 평가하였다. 이를 기반으로 전사체 교란 네트워크를 구성하고, 유전자의 내향 연결 정도를 교란 민감도로 정의하였다. 유전자를 교란 민감도에 따라 분류하고 교란 민감 유전자와 교란 둔감 유전자의 진화적, 생물학적, 그리고 임상적 특징을 조사하였다. 결과: 교란 민감 유전자는 단백질 상호작용 네트워크의 변방에 위치해 있었으나 진화적으로 보존되어 있었다. 이들은 상대적으로 적은 수의 미소 전사체와 전사인자에 의해 조절되고 있으며, 세포 간의 상호작용에 중요한 역할을 하고 있었다. 전사체 교란 네트워크의 외향 연결 정도는 중요한 생물학적 의미를 가지고 있지 않았다. 치사 유전자의 경우 교란 네트워크의 말단이면서 단백질 상호작용 네트워크의 중심부에 위치해 있었다. 반면, 대부분의 질병 유전자들의 경우 교란 네트워크의 중심이면서 단백질 상호작용 네트워크의 말단에 위치해 있었다. 두 네트워크를 모두 사용하여, 질병을 분류하기 위한 연합 네트워크 도표를 그려보았다. 결론: 효모에서의 연구와 마찬가지로, 교란 민감 유전자는 유전적으로 보존되어 있고 세포 간의 상호작용에 관여하여 개체의 생존에 필수적이었다. 또한, 내향 연결정도가 외향 연결정도에 비해 유전자 교란을 해석하는데 유용하다는 것을 확인하였다. 질병 유전자는 단백질 상호작용 네트워크와 교란 네트워크를 동시에 활용하여 시각화 되고 분류될 수 있었다. 결론적으로, 교란 민감도는 유전자의 생물학적 임상적 특성을 분석하고 유전체 교란을 평가하는데 가치 있는 지표가 될 것이다.Purpose: Transcriptome is perturbed by millions of genomic variants which could alter function of cells and phenotypes of organisms. As discovered in recent large Next-Generation Sequencing (NGS) project, Individual genome has at least 3 to 4 million variants. Here, we applied perturbation network to human data from 1000 genomes project data for interpreting genetic perturbation and characterized perturbation sensitive and tolerant genes. Methods: We integrated SIFT score of non-synonymous variants to calculate gene deleteriousness score and determine whether gene is perturbed or not. Perturbation network was constructed based on gene deleteriousness score and perturbation sensitivity was defined as in-degree of perturbation network. We categorized genes based on perturbation sensitivity and investigated evolutionarily, regulatory, and clinical properties of perturbation sensitive and tolerant genes. Results: Perturbation sensitive genes were in periphery of protein interaction network but evolutionarily conserved. They were regulated by less miRNA and transcription factor and played a key role in cell-cell interaction. Out-degree of perturbation network did not show any significant biological properties. Lethal genes were in periphery of perturbation network and hub of protein interaction network. On the contrary, most disease genes were in hub of perturbation network and showed various trends in protein-interaction network. We drew joint network map and categorized disease by degree of both network. Conclusions: As in yeast perturbation network, perturbation sensitive genes were essential in survival of organism since they were evolutionarily conserved and related to interaction between cells. We confirmed that in-degree of perturbation network is better than out-degree of perturbation network for interpreting genetic perturbation. Disease genes can be categorized and visualized using both protein-interaction network and perturbation network. In conclusion, perturbation sensitivity was valuable measure for interpreting genetic perturbation and assessing gene's biological and clinical properties.1. Introduction 1 1.1. Definition of Genetic Perturbation 1 1.2. Interpretation of genetic perturbation causing variants 2 1.3. Interpretation of genetic perturbation using biological networks 3 1.4. Perturbation Network approach in Yeast 5 1.5. Purpose of study 6 2. Materials and Methods 7 2.1. Genome and transcriptome data from 1000genomes populations. 7 2.2. Calculating gene deleteriousness scores. 8 2.3. Construction of perturbation network. 9 2.4. Construction of Protein Interaction Network. 9 2.5. Retrieving biological information for gene annotation 10 2.6. Excess retention. 10 2.7. Joint network map for visualization of gene sets. 11 2.8. Clinical annotation of PSN 11 3. Results 12 3.1. Building Perturbation network 12 3.2. Biological properties of perturbation network 13 3.2.1. Correlation between perturbation network and PPI network 14 3.2.2. Relationship of perturbation network to Evolutionary feature and regulatory feature 15 3.3. Clinical implication of perturbation network against PPI network 27 3.3.1. Lethal genes versus disease genes 27 3.3.2. Disease gene classification using both Kppi and Kin 31 4. Discussion 35 5. References 38Docto

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Development is a complex process regulated both by genetic and epigenetic and environmental clues. Recently, long non-coding RNAs (lncRNAs) have emerged as key regulators of gene expression in several tissues including the brain. Altered expression of lncRNAs has been linked to several neurodegenerative, neurodevelopmental and mental disorders. The identification and characterization of lncRNAs that are deregulated or mutated in neurodevelopmental and mental health diseases are fundamental to understanding the complex transcriptional processes in brain function. Crucially, lncRNAs can be exploited as a novel target for treating neurological disorders. In our review, we first summarize the recent advances in our understanding of lncRNA functions in the context of cell biology and then discussing their association with selected neuronal development and neurological disorders

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