Understanding metabolic adaptation by using bacterial laboratory evolution and trans-omics analysis.
Takaaki HorinouchiChikara FurusawaPublished in: Biophysical reviews (2020)
Many diseases such as metabolic syndrome, cancer, inflammatory diseases, and pathological phenomena can be understood as an adaptive reconstitution of the metabolic state (metabolic adaptation). One of the effective approaches to reveal the property of metabolic networks is using model organisms such as microorganisms that are easier to experiment with than higher organisms. Using the laboratory evolution approach, we can elucidate the evolutionary dynamics in various stress environments, which provide us an understanding of the metabolic adaptation. In addition, the integration of omics data and phenotypic data enables us to clarify the genetic and phenotypic alterations during adaptation. In this review, we describe our recent studies on bacterial laboratory evolution and the omics approach to clarify the stress adaptation process. We have also obtained high-dimensional phenotypic data using our automated culture system. By combining these genomic and transcriptomic data within high-throughput phenotypic data, we can discuss the complex trans-omics network of metabolic adaptation.
Keyphrases
- single cell
- electronic health record
- high throughput
- metabolic syndrome
- big data
- genome wide
- rna seq
- gene expression
- machine learning
- squamous cell carcinoma
- cardiovascular disease
- data analysis
- young adults
- stress induced
- copy number
- papillary thyroid
- adipose tissue
- cardiovascular risk factors
- lymph node metastasis
- heat stress