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제126회 대한화학회 학술발표회 및 총회 Metabolomics in human disease : advances and applications

등록일
2020년 9월 28일 09시 58분 16초
접수번호
1301
발표코드
ANAL1-5 이곳을 클릭하시면 발표코드에 대한 설명을 보실 수 있습니다.
발표시간
화 14시 : 30분
발표형식
분과기념
발표분야
Analytical Chemistry - Recent Advances in Mass Spectrometry based on Immunoaffinity
저자 및
공동저자
Geum-Sook Hwang
Korea Basic Science Institute, Korea
The development of global metabolic profiling and the study of the metabolomics are particularly important in human disease where small molecules such as endogenous metabolites play fundamental signaling roles. Metabolomic approaches not only can help unravel the underlying molecular mechanism of disease but also identify the signature of pathway characteristic for specific disease that can be used for diagnosis, prognosis, and targeted therapy. Therefore, metabolomics-drived approaches have the potential to map biochemical changes in disease and provide an opportunity to unveil the metabolic pathways. This approach can also improve our understanding of the biological processes that are associated with a certain phenotype and also allow studying how the dysregulation of specific biological pathways is propagated across the biological system.
Analytical platforms, including NMR and Mass spectrometry, were used to generate a molecular fingerprint of biofluid or tissue samples, and then pattern recognition technique was applied to identify molecular signatures associated with the specific diseases or drug efficiency. Several metabolites that differentiate disease samples from the control were thoroughly characterized and the metabolic changes in human and animal model were investigated using analytical platforms. Spectral data were applied to targeted profiling and spectral binning method, and then multivariate statistical data analysis (MVDA) was used to examine in detail the modulation of small molecule candidate biomarkers. The metabolomic profiling process generates accurate metabolite concentration data, and provides data that can be used to help understand metabolic differences between healthy and disease or drug treated models. Such metabolic signatures could provide diagnostic markers for a disease state or biomarkers for drug response phenotypes, and mechanistic information on cellular perturbations and pathways associated with diseases.

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