Networks Underpinning Symbiosis Revealed Through Cross-Species eQTL Mapping.

Organisms engage in extensive cross-species molecular dialog, yet the underlying molecular actors are known for only a few interactions. Many techniques have been designed to uncover genes involved in signaling between organisms. Typically, these focus on only one of the partners. We developed an expression quantitative trait locus (eQTL) mapping-based approach to identify cause-and-effect relationships between genes from two partners engaged in an interspecific interaction. We demonstrated the approach by assaying expression of 98 isogenic plants (Medicago truncatula), each inoculated with a genetically distinct line of the diploid parasitic nematode Meloidogyne hapla With this design, systematic differences in gene expression across host plants could be mapped to genetic polymorphisms of their infecting parasites. The effects of parasite genotypes on plant gene expression were often substantial, with up to 90-fold (P = 3.2 × 10-52) changes in expression levels caused by individual parasite loci. Mapped loci included a number of pleiotropic sites, including one 87-kb parasite locus that modulated expression of >60 host genes. The 213 host genes identified were substantially enriched for transcription factors. We distilled higher-order connections between polymorphisms and genes from both species via network inference. To replicate our results and test whether effects were conserved across a broader host range, we performed a confirmatory experiment using M. hapla-infected tomato. This revealed that homologous genes were similarly affected. Finally, to validate the broader utility of cross-species eQTL mapping, we applied the strategy to data from a Salmonella infection study, successfully identifying polymorphisms in the human genome affecting bacterial expression

Species-level functional profiling of metagenomes and metatranscriptomes.

Functional profiles of microbial communities are typically generated using comprehensive metagenomic or metatranscriptomic sequence read searches, which are time-consuming, prone to spurious mapping, and often limited to community-level quantification. We developed HUMAnN2, a tiered search strategy that enables fast, accurate, and species-resolved functional profiling of host-associated and environmental communities. HUMAnN2 identifies a community's known species, aligns reads to their pangenomes, performs translated search on unclassified reads, and finally quantifies gene families and pathways. Relative to pure translated search, HUMAnN2 is faster and produces more accurate gene family profiles. We applied HUMAnN2 to study clinal variation in marine metabolism, ecological contribution patterns among human microbiome pathways, variation in species' genomic versus transcriptional contributions, and strain profiling. Further, we introduce 'contributional diversity' to explain patterns of ecological assembly across different microbial community types

Composite structural motifs of binding sites for delineating biological functions of proteins

Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs which represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures.Comment: 34 pages, 7 figure

Fine-Scale Haplotype Structure Reveals Strong Signatures of Positive Selection in a Recombining Bacterial Pathogen

Identifying genetic variation in bacteria that has been shaped by ecological differences remains an important challenge. For recombining bacteria, the sign and strength of linkage provide a unique lens into ongoing selection. We show that derived allelesPeer reviewe

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

A bioinformatics toolkit: in silico tools and online resources for investigating genetic variation

With the advent of large-scale next-generation sequencing initiatives, there is an increasing importance to interpret and understand the potential phenotypic influence of identified genetic variation and its significance in the human genome. Bioinformatics analyses can provide useful information to assist with variant interpretation. This review provides an overview of tools/resources currently available, and how they can help predict the impact of genetic variation at the deoxyribonucleic acid, ribonucleic acid, and protein level

Understanding The Intra And Inter-Cellular Interaction Complexities And Flexibilities Using Systems And Sequence Analysis Approach

The present thesis work has been undertaken to gain an understanding of intra-cellular or inter-cellular interactions between bio-molecular entities utilizing either a systems analysis based perspective or different sequence analysis approaches. During this study different principles likely to be prevalent among intra-cellular and inter-cellular interactions have been studied with the help of computational approaches. Broadly, the complexities in intra-cellular interactions have been studied by determining the effect of perturbations such as over-expression or down-regulation of a key regulator on the intra-cellular interaction network architecture or its components. In particular, network analysis of regulatory network proteins in association with the intra-cellular proteinprotein interaction network, led to a key observation that topologically important effector proteins in the regulatory network could be important signaling proteins. Identification of such important effector proteins essential for the regulatory network integrity of a key regulator may be performed by network analysis. It is likely that alterations in these important effector proteins may lead to disruptions in cellular physiology and as such in this manner probable disease associated entities can be determined. Alternately, the flexibility among protein-protein interactions has been studied by analyzing homologous sequence families of interacting proteins with the help of information theory based measures like mutual information and Bhattacharyya co-efficient. Since interacting proteins may co-evolve, co-variation may allow the preservation of a functional interaction between co-evolving proteins and interdependent residue pair alterations may occur as a result of evolutionary pressure. Analysis of molecular co-evolution in inter-cellular protein interaction complexes determined that co-evolutionary pairings may be present among interface and noninterface residue pairs and such positions are likely to be crucial for a functional interaction between these sets of proteins. Therefore, utilising information contained in biological sequences, co-evolutionary pairings involving structurally or functionally crucial residue positions in disease associated inter-cellular protein-protein interaction complexes were predicted. Thus, different computational approaches have been utilised to study a particular hypothesis in a disease scenario in order to delineate certain themes prevalent in intra-cellular or inter-cellular interactions among bio-molecular entities while predicting disease associated entities or studying interaction patterns among them

Development of a sequence based method for the prediction of protein function

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