“Bronchiolitis” is a significant problem • Clinical syndrome with multiple definitions worldwide • Acute lower respiratory infection with inflammation, caused by respiratory virus(es) – e.g., RSV, RV • Acute morbidity: #1 cause of infant hospitalizations in the US (~130,000 each year); 2%-3% of all infants • Chronic morbidity: Among hospitalized infants with bronchiolitis (“severe bronchiolitis”), 30%-40% will develop childhood asthma 長谷川研究室のメインテーマ (急性)細気管支炎
Microbiome Host (eg, DNA, epigenome) Response (eg, transcriptome, proteome, metabolome) Endotype A Endotype B Endotype C “Bronchiolitis” Endotype A Endotype B Endotype C “Asthma” Internal Environment External Environment Incl. viruses Theoretical model
Nasopharyngeal metabolome profiling of 918 infants with severe bronchiolitis Consensus clustering analysis A: glycerophosphocholine-high (n=79) B: amino acid-high, polyunsaturated fatty acid-low (n=72) C: amino acid-high, glycerophospholipid-low (n=363) D: glycerophospholipid-high (n=177) E: mixed (n=227) Derived metabotypes Nasopharyngeal airway metabolome endotyping of severe bronchiolitis in infancy and risk of childhood asthma Association of metabotypes with clinical outcomes Zhu et al. JACI, 2022b Recurrent wheeze by age 3 years with asthma at age 5 years A: glycerophosphocholine-high B: amino acid-high, polyunsaturated fatty acid-low VS. Asthma at age 5 years Adjusted odds ratio=2.18 (95% CI, 1.03-4.71) Adjusted hazards ratio=2.52 (95% CI, 1.16-5.47)
33 Genetics Phenotypes (Outcome) GWAS Outcome ~ Gene (SNP) + Age + sex + PC1 + PC2 +… × 1000万SNPs (i.e., for each SNP on each chromosome) e.g., childhood asthma (Yes/No) Plot P-values for all SNPs Adjusting for ancestry difference GWAS (genome-wide association study) Manhattan plot 二値アウトカムと全ての遺伝子（塩基多型） との関連を網羅的にRegressionで調べる解析
34 Integrated Omics research with genomic data Genetics Transcriptome (mRNA) Epigenome Protein Metabolite eQTL analysis mtQTL analysis (metabolite quantitative trait loci): Metabolite ~ Gene (SNP) + Age + sex + PC1 + PC2 × 1000万SNPs (i.e., for each SNP on each chromosome) Plot P-values for all SNPs mQTL analysis mtQTL analysis pQTL analysis microRNA miR-QTL analysis Manhattan plot 遺伝子(gene)と代謝物(metabolites)との関 連が分かる 各オミックスデータの発現量（連続変数） をアウトカムにして、同じregressionをする
New analytic proposal: Convert multi-omics data into an image 35 Host Transcriptome Metabolome Transcriptome- metabolome Interaction Feature selection tPCA/ tSNE New methodology based on the DeepInsight method (Sharma et al. Sci Rep. 2019 )
36 Each red dot represents each metabolite expression Each green dot represents each mRNA expression Each blue dot represents each interaction between metabolite and mRNA Distances between each dot/cluster represent the proximity (closeness) between each variable. Dot concentration represents the expression level High expression level Low expression level “DeepInsight” image
Identify the important network cluster and variables (DeepFeature method using Grad-CAM) 39 The previous slide Based on Sharma et al. Brief Bioinformatics 2021 We can identify the important part of the image for classification using the Grad-CAM method. Grad-CAM Network analysis
Implement omics examination into daily healthcare by developing “Multi-omics healthcare App. “ 43 Phase 1 (ongoing): Health visualization App using health check-ups and digital health data Phase 2 (2025-): New health visualization App by implementing the multi-omics interface Suggesting personalized screening exams, eating, and exercise habits. You can find more personalized suggestions (e.g., condition of each organ, required nutrients, intestinal environment, etc.)
The framework of Multi-omics healthcare App 44 Individual results/ recommendations Anonymized User data Developed deep-learning models for every disease/symptom 1. Real-time digital health data 2. Longitudinal multi-omics data 3. Medication/examination data -> Realize “precision medicine “ 0. Medical history/ Questionnaire Health check-up facilities Hospitals Pretraining with latest literature/expressions database Updated with a wide variety of App users’ data Daily life
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