유전체 비교분석을 통한 포유류 감염성 바이러스의 진화에 대한 통찰

학위논문(석사)--서울대학교 대학원 :농업생명과학대학 농생명공학부,2019. 8. 김희발.감염성 바이러스는 인간을 비롯한 많은 종의 동물을 감염시켜 돌이킬 수 없는 결과를 초래하기도 합니다. 수많은 사람을 죽음에 이르게 하는 것은 물론, 매 해마다 대규모 가축 감염사례로 인하여 축산업에 커다란 경제적 피해를 끼치고 있습니다. 그렇기 때문에 감염성 바이러스에 대한 충분한 연구가 필요합니다. 바이러스는 다른 미생물이나 생명체에 비하여 유전자 변형이 보다 빠르고 무작위로 이루어지는 특징이 있습니다. 대부분의 바이러스는 숙주의 종에 따라 감염 여부가 달라지지만, 뉴클레오타이드와 아미노산 서열 하나의 변형으로도 새로운 종의 숙주를 감염시키거나 그 독성이 달라지기도 하기 때문에 그들의 유전체 차원에서의 특징을 발견하고 분석하는 것은 상업적 및 과학적 주요한 가치를 제공합니다. 이러한 유전체 특징 중에서 단일 유전자 변이체(Single Nucleotide and Amino acid variant)는 많은 연구에서 연구 대상으로 사용되고 있습니다. 실제적으로 바이러스 연구에서 바이러스의 종을 동정하거나 백신 개발 등 다양한 분야에 사용되고 있습니다. 챕터 2지카바이러스는 일반적인 성인이 감염되었을 시에는 지카열, 두통 및 관절통 등의 증상을 유발하지만 임산부가 감염되었을 시에는 태아의 소두증을 일으키는 것과 연관이 있다고 알려져 있습니다. 지난 10년간 전 세계에 폭발적으로 퍼져 나갔으며 많은 학자들이 지카바이러스의 분자 메커니즘에 대한 연구를 수행했습니다. 그러나 치료와 예방을 위한 의약품 및 백신 개발은 아직까지 진행 중이며 보다 많은 유전체 수준에서의 연구가 필요합니다. 이 연구에서 공개데이터베이스로부터 이용 가능한 지카바이러스의 NGS 유전체 데이터를 수집하고 분석을 통하여 지리적, 시기적 관점을 고려한 지역 특이적 유전체 변이(Single Nucleotide and Amino Acid variants)를 유전자 마커로써 제시하였습니다. 진화적 연관분석과 자율학습 k-means 클러스터링 알고리즘을 이용하여 4개의 대표그룹을 선정하였습니다. 대표 4그룹에 초점을 맞추어 통계적으로 유의미한 유전체 변이들을 찾아내고 dN/dS 진화 분석으로 진화적으로 가속화된 단백질 암호화 영역을 확인했습니다. 이후 그룹 기능성 단백질 영역과 B-cell, T-cell 특이적 항원결정기 후보를 예측하여 찾아낸 유전체 변이들이 단백질 및 항원결정기 형성의 결정적인 역할을 확인하여 그룹별 주요 유전자 마커로써 제안하였습니다. 챕터 3인플루엔자의 새로운 타입으로 분류된 인플루엔자 D 바이러스는 소를 비롯한 반추동물을 감염시키는 호흡기성 바이러스입니다. 감염 증상은 경미하지만 다른 치명적인 호흡기성 바이러스 감염을 유발하고 인간에게도 감염될 수 있는 잠재성이 있기 때문에 유전체 차원에서의 연구를 수행하였습니다. 인플루엔자 D 바이러스의 모든 유전자 단편 NGS데이터를 이용한 유전체 특성 및 진화적 상관관계 분석으로 하나의 유전자 단편을 통한 분석의 결과와의 차이점을 밝혀냈습니다. 그 결과를 토대로 선정한 대표 그룹을 초점으로, 통계적으로 유의미한 특이적 유전체 변이를 찾아냈습니다. 이후 dN/dS 진화 분석과 단백질 코딩영역, B-cell 특이적 항원결정기 예측 분석 결과와 비교하여 그룹 특이적 유전자 마커로써 제안하였습니다. 이 연구를 통하여 감염성 바이러스의 그룹별 특이적 유전자 마커를 제시하고 이 마커가 새로운 바이러스 종의 동정과 병독성 진화에 대한 통찰, 그리고 백신 개발에 도움을 줄 수 있을 것입니다.Infectious viruses infect many species of animal, including human, and cause irreversible consequence. They bring fetal death to human and cause massive economic losses to livestock industry due to the large-scale infection. Therefore, we need more research on infectious viruses. Viruses have faster and random genetic variable features than other organisms. Most viruses are susceptible to infection depending on the host species. However, since a single nucleotide and amino acid sequence variation leads infection to a new species or alter its toxicity, genomic level of virus research provides major commercial and scientific value. Therefore, many researchers focus on the single genetic variation for identification of a new virus species or vaccine study. Chapter 1Zika virus (ZIKV) is known to be associated with a serious brain disease, fetal microcephaly in pregnant women, and has been explosively spread throughout the world over the last decade. Virologists of most countries attempted investigations of ZIKV molecular mechanisms to prevent the worldwide proliferation. However, only few genetic variants in several regions were anticipated as targets of vaccines and medicines. Here, I analyzed all of available ZIKV complete genomes from the Virus Pathogen Resource (ViPR) database to identify novel genetic markers by considering geographical and temporal perspectives. By principal component and phylogenetic analysis, ZIKV strains formed four clusters according to collected continent. Focusing on the major groups in African, Asian, Central America and Caribbean, I found single nucleotide variants (SNVs) supported by statistical significance. From the dN/dS analysis, I identified the protein coding regions that were evolutionary accelerated in each group. Out of the intercontinental SNVs, non-synonymous and synonymous variants on functional protein domains and predicted B-cell and T-cell epitopes were suggested as regional markers. I believe these local genetic markers can improve medical strategies for ZIKV prevention, diagnosis, and treatment. Chapter 2Influenza D virus (IDV), a new type of influenza, is a respiratory virus that infects ruminants, including cattle. Because the infection symptoms of IDV are mild, but, causes fatal infection of other respiratory viruses and have potential for infection in human, I conducted researches at the genomic level. Using the results of phylogeny and principal coordinate analysis (PCoA), we compared concatenated all of coding sequence dataset and each of genes coding sequence dataset. I confirmed that concatenated dataset results were more appropriately clustered into four groups with isolated region, and I selected the main three groups. Focusing on the main three groups, I found statistically significant genetic markers in comparison with dN/dS analysis, searching protein coding region, and B-cell epitope prediction analysis. Through this study, I suggest local-specific genetic markers of infectious virus, and these markers will give a deep insight for further studies.ABSTRACT IV CONTENTS VII LIST OF TABLES VIII LIST OF FIGURES IX CHAPTER 1. LITERATURE REVIEW 1 CHAPTER 2. IDENTIFICATION OF LOCAL-SPECIFIC GENETIC MARKERS OF ZIKA VIRUS ACROSS THE ENTIRE GLOBE 7 2.1 ABSTRACT 8 2.2 INTRODUCTION 9 2.3 MATERIALS AND METHODS 12 2.4 RESULTS 18 2.5 DISCUSSION 26 CHAPTER 3. LOCAL GENETIC MARKERS CLUSTERED BY CODING SEQUENCES OF INFLUENZA D VIRUS 56 3.1 ABSTRACT 57 3.2 INTRODUCTION 59 3.3 MATERIALS AND METHODS 61 3.4 RESULTS 66 3.5 DISCUSSION 72 REFERENCES 93 요약(국문초록) 100Maste

Sequence analysis methods for the design of cancer vaccines that target tumor-specific mutant antigens (neoantigens)

The human adaptive immune system is programmed to distinguish between self and non-self proteins and if trained to recognize markers unique to a cancer, it may be possible to stimulate the selective destruction of cancer cells. Therapeutic cancer vaccines aim to boost the immune system by selectively increasing the population of T cells specifically targeted to the tumor-unique antigens, thereby initiating cancer cell death.. In the past, this approach has primarily focused on targeted selection of ‘shared’ tumor antigens, found across many patients. The advent of massively parallel sequencing and specialized analytical approaches has enabled more efficient characterization of tumor-specific mutant antigens, or neoantigens. Specifically, methods to predict which tumor-specific mutant peptides (neoantigens) can elicit anti-tumor T cell recognition improve predictions of immune checkpoint therapy response and identify one or more neoantigens as targets for personalized vaccines. Selecting the best/most immunogenic neoantigens from a large number of mutations is an important challenge, in particular in cancers with a high mutational load, such as melanomas and smoker-associated lung cancers. To address such a challenging task, Chapter 1 of this thesis describes a genome-guided in silico approach to identifying tumor neoantigens that integrates tumor mutation and expression data (DNA- and RNA-Seq). The cancer vaccine design process, from read alignment to variant calling and neoantigen prediction, typically assumes that the genotype of the Human Reference Genome sequence surrounding each somatic variant is representative of the patient’s genome sequence, and does not account for the effect of nearby variants (somatic or germline) in the neoantigenic peptide sequence. Because the accuracy of neoantigen identification has important implications for many clinical trials and studies of basic cancer immunology, Chapter 2 describes and supports the need for patient-specific inclusion of proximal variants to address this previously oversimplified assumption in the identification of neoantigens. The method of neoantigen identification described in Chapter 1 was subsequently extended (Chapter 3) and improved by the addition of a modular workflow that aids in each component of the neoantigen prediction process from neoantigen identification, prioritization, data visualization, and DNA vaccine design. These chapters describe massively parallel sequence analysis methods that will help in the identification and subsequent refinement of patient-specific antigens for use in personalized immunotherapy

Bioinformatics Resources and Tools for Conformational B-Cell Epitope Prediction

Identification of epitopes which invoke strong humoral responses is an essential issue in the field of immunology. Localizing epitopes by experimental methods is expensive in terms of time, cost, and effort; therefore, computational methods feature for its low cost and high speed was employed to predict B-cell epitopes. In this paper, we review the recent advance of bioinformatics resources and tools in conformational B-cell epitope prediction, including databases, algorithms, web servers, and their applications in solving problems in related areas. To stimulate the development of better tools, some promising directions are also extensively discussed

Insights to Protein Pathogenicity from the Lens of Protein Evolution

As protein sequences evolve, differences in selective constraints may lead to outcomes ranging from sequence conservation to structural and functional divergence. Evolutionary protein family analysis can illuminate which protein regions are likely to diverge or remain conserved in sequence, structure, and function. Moreover, nonsynonymous mutations in pathogens may result in the emergence of protein regions that affect the behavior of pathogenic proteins within a host and host response. I aimed to gain insight on pathogenic proteins from cancer and viruses using an evolutionary perspective. First, I examined p53, a conformationally flexible, multifunctional protein mutated in ~50% of human cancers. Multifunctional proteins may experience rapid sequence divergence given trade-offs between functions, while proteins with important functions may be more constrained. How, then, does a protein like p53 evolve? I assessed the evolutionary dynamics of structural and regulatory properties in the p53 family, revealing paralog-specific patterns of functional divergence. I also studied flaviviruses, like Dengue and Zika virus, whose conformational flexibility contributes to antibody-dependent enhancement (ADE). ADE has long complicated vaccine development for these viruses, making antiviral drug development an attractive alternative. I identified fitness-critical sites conserved in sequence and structure in the proteome of flaviviruses with the potential to act as broadly neutralizing antiviral drug target sites. I later developed Epitopedia, a computational method for epitope-based prediction of molecular mimicry. Molecular mimicry occurs when regions of antigenic proteins resemble protein regions from the host or other pathogens, leading to antibody cross-reactivity at these sites which can result in autoimmunity or have a protective effect. I applied Epitopedia to the antigenic Spike protein from SARS-CoV-2, the causative agent of COVID-19. Molecular mimicry may explain the varied symptoms and outcomes seen in COVID-19 patients. I found instances of molecular mimicry in Spike associated with COVID-19-related blood-clotting disorders and cardiac disease, with implications on disease treatment and vaccine design

Computational design with flexible backbone sampling for protein remodeling and scaffolding of complex binding sites

Dissertation presented to obtain the Doutoramento (Ph.D.) degree in Biochemistry at the Instituto de Tecnologia Qu mica e Biol ogica da Universidade Nova de LisboaComputational protein design has achieved several milestones, including the design of a new protein fold, the design of enzymes for reactions that lack natural catalysts, and the re-engineering of protein-protein and protein-DNA binding speci city. These achievements have spurred demand to apply protein design methods to a wider array of research problems. However, the existing computational methods have largely relied on xed-backbone approaches that may limit the scope of problems that can be tackled. Here, we describe four computational protocols - side chain grafting, exible backbone remodeling, backbone grafting, and de novo sca old design - that expand the methodological protein design repertoire, three of which incorporate backbone exibility. Brie y, in the side chain grafting method, side chains of a structural motif are transplanted to a protein with a similar backbone conformation; in exible backbone remodeling, de novo segments of backbone are built and designed; in backbone grafting, structural motifs are explicitly grafted onto other proteins; and in de novo sca olding, a protein is folded and designed around a structural motif. We developed these new methods for the design of epitope-sca old vaccines in which viral neutralization epitopes of known three-dimensional structure were transplanted onto nonviral sca old proteins for conformational stabilization and immune presentation.(...