Whole blood transcriptome analysis for age- and gender-specific gene expression profiling in Japanese individuals.
Yu-Ichi AokiKeiko TaguchiHayato AnzawaJunko KawashimaNoriko IshidaAkihito OtsukiAtsushi HasegawaLiam BairdTakafumi SuzukiIkuko N MotoikeKinuko OhnedaKazuki KumadaFumiki KatsuokaKengo KinoshitaMasayuki YamamotoPublished in: Journal of biochemistry (2024)
Whole blood transcriptome analysis is a valuable approachin medical research, primarily due to the ease of sample collection and the richness of the information obtained. Since the expression profile of individual genes in the analysis is influenced by medical traits and demographic attributes such as age and gender, there has been a growing demand for a comprehensive database for blood transcriptome analysis. Here, we performed whole blood RNA sequencing (RNA-seq) analysis on 576 participants stratified by age (20-30s and 60-70s) and gender from cohorts of the Tohoku Medical Megabank (TMM). A part of female segment included pregnant women. We did not exclude the globin gene family in our RNA-seq study, which enabled us to identify instances of hereditary persistence of fetal hemoglobin based on the HBG1 and HBG2 expression information. Comparing stratified populations allowed us to identify groups of genes associated with age-related changes and gender differences. We also found that the immune response status, particularly measured by neutrophil-to-lymphocyte ratio (NLR), strongly influences the diversity of individual gene expression profiles in whole blood transcriptome analysis. This stratification has resulted in a data set that will be highly beneficial for future whole blood transcriptome analysis in the Japanese population.
Keyphrases
- rna seq
- single cell
- gene expression
- pregnant women
- healthcare
- immune response
- genome wide
- mental health
- poor prognosis
- health information
- genome wide identification
- machine learning
- toll like receptor
- emergency department
- electronic health record
- inflammatory response
- social media
- deep learning
- binding protein
- transcription factor
- genome wide analysis