TY - JOUR
T1 - Integrating microbiome, transcriptome and metabolome data to investigate gastric disease pathogenesis
T2 - A concise review
AU - Doohan, Dalla
AU - Rezkitha, Yudith Annisa Ayu
AU - Waskito, Langgeng Agung
AU - Vilaichone, Ratha Korn
AU - Yamaoka, Yoshio
AU - Miftahussurur, Muhammad
N1 - Funding Information:
Acknowledgements. This study was funded by the Grants-in-Aid for
Publisher Copyright:
© The Author(s), 2021. Published by Cambridge University Press
PY - 2021
Y1 - 2021
N2 - Microbiome, the study of microbial communities in specific environments, has developed significantly since the Human Microbiome Project began. Microbiomes have been associated with changes within environmental niches and the development of various diseases. The development of high-throughput technology such as next-generation sequencing has also allowed us to perform transcriptome studies, which provide accurate functional profiling data. Metabolome studies, which analyse the metabolites found in the environment, are the most direct environmental condition indicator. Although each dataset provides valuable information on its own, the integration of multiple datasets provides a deeper understanding of the relationship between the host, agent and environment. Therefore, network analysis using multiple datasets might give a clearer understanding of disease pathogenesis.
AB - Microbiome, the study of microbial communities in specific environments, has developed significantly since the Human Microbiome Project began. Microbiomes have been associated with changes within environmental niches and the development of various diseases. The development of high-throughput technology such as next-generation sequencing has also allowed us to perform transcriptome studies, which provide accurate functional profiling data. Metabolome studies, which analyse the metabolites found in the environment, are the most direct environmental condition indicator. Although each dataset provides valuable information on its own, the integration of multiple datasets provides a deeper understanding of the relationship between the host, agent and environment. Therefore, network analysis using multiple datasets might give a clearer understanding of disease pathogenesis.
KW - Disease burden
KW - Gastric disease
KW - Metabolome
KW - Microbiome
KW - Next-generation sequencing
KW - Transcriptome
UR - http://www.scopus.com/inward/record.url?scp=85119595016&partnerID=8YFLogxK
U2 - 10.1017/erm.2021.8
DO - 10.1017/erm.2021.8
M3 - Review article
AN - SCOPUS:85119595016
SN - 1462-3994
VL - 23
JO - Expert Reviews in Molecular Medicine
JF - Expert Reviews in Molecular Medicine
M1 - e9
ER -