Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites
2017-06-02
org.kosen.entty.User@3c27ed1a
김동현(shark118)
행사&학회소개
1 Introduction
2 Methods of generating inventories of microbial metabolites
2.1 Mass measurement accuracy
2.2 Isotopic modeling
2.3 Chromatographic retention time
2.4 Size and shape by ion mobility
2.5 Ion fragmentation for structural information
2.6 Leveraging spatiotemporal metabolomics inventories to capture inter-organism interactions
3 Preparation of high content mass spectral data for metabolomics studies
3.1 Strategies for formatting data for effective comparative analysis
3.2 Methods and considerations for metabolite peak detection and alignment
4 Analysis of metabolomics data in the context of secondary metabolites
4.1 Multivariate statistical analysis and data projections for identification of abundant covarying metabolites
4.1.1 Strain prioritization via principal component analysis
4.1.2 Secondary metabolite prioritization within metabolomics data via principal component and regression analyses
4.2 Discovering molecular inventories of microbial responses via self organizing map analytics
4.3 Molecular networking to reveal structural uniqueness and relatedness in large datasets
5 Investigations of secondary metabolite bioactivity
6 Conclusions
7 Acknowledgements
8 References
2 Methods of generating inventories of microbial metabolites
2.1 Mass measurement accuracy
2.2 Isotopic modeling
2.3 Chromatographic retention time
2.4 Size and shape by ion mobility
2.5 Ion fragmentation for structural information
2.6 Leveraging spatiotemporal metabolomics inventories to capture inter-organism interactions
3 Preparation of high content mass spectral data for metabolomics studies
3.1 Strategies for formatting data for effective comparative analysis
3.2 Methods and considerations for metabolite peak detection and alignment
4 Analysis of metabolomics data in the context of secondary metabolites
4.1 Multivariate statistical analysis and data projections for identification of abundant covarying metabolites
4.1.1 Strain prioritization via principal component analysis
4.1.2 Secondary metabolite prioritization within metabolomics data via principal component and regression analyses
4.2 Discovering molecular inventories of microbial responses via self organizing map analytics
4.3 Molecular networking to reveal structural uniqueness and relatedness in large datasets
5 Investigations of secondary metabolite bioactivity
6 Conclusions
7 Acknowledgements
8 References
보고서작성신청
미생물은 여러가지 인간에게 유용한 다양한 화합물질들을 생산하지만 그것을 추출 및 분리하는 절차는 복잡하고 굉장히 큰 노력이 필요하다. 천연물화학 분야에서는 여러가지 활성이 있는 화합물들을 추적하여 분리를하고 방대한 데이터 베이스를 구축하였다. 하지만 유전자 서열이 밝혀진 후에 여러 유용한 새로운 이차 대사산물의 추출 가능성이 커졌고, 그것을 계기로 comparative metabolomics와 이차 대사 예측과의 조합은 새로운 화합물의 발견을 촉진시켰다. 본 리뷰 논문에서는 분석 및 컴퓨터 기반의 기술들이 새로운 천연물 발견에 끼치는 영향에 대하여 다루었으며, 특히 comparative metabolomics의 천연물 화학 분야에서의 미래 전망에대하여 토론하였다. 현재 대사체학은 저분자량 분석에서 큰 발전을 이루고 있기 때문에 앞으로 천연물 화학 분야에서의 대사체학의 적용은 더욱더 커질 것으로 예상되기 때문에 대사체 및 천연물 화학에서 연구를 하고 있는 연구자들에게 큰 도움을 줄 것으로 예상된다.