The University of Tokyo · Biochemistry, Genetics and Molecular Biology
Professor Masaru Koido's research lab specializes in computational and systems biology, focusing on integrating multi-omics data to decode complex biological mechanisms in disease, particularly cancer and drug response. The lab develops advanced machine learning and bioinformatics methods to interpret spatial transcriptomics, gene regulatory networks, and polygenic risk architectures underlying drug toxicity and tumor progression. Key research directions include predictive modeling of gene expression in spatial and pathological contexts, systems-level dissection of hypoxia and mitochondrial function in cancer, and improving genomic imputation for diverse populations. The lab bridges computational innovation with experimental validation to advance precision medicine and translational genomics.
Figures are computed from collected data and may differ slightly.
Spatial transcriptomics is an emerging technology requiring costly reagents and considerable skills, limiting the identification of transcriptional markers related to histology. Here, we show that predicted spatial gene-expression in unmeasured regions and tissues can enhance biologists' histological interpretations. We developed the Deep learning model for Spatial gene Clusters and Expression, DeepSpaCE, and confirmed its performance using the spatial-transcriptome profiles and immunohistochemi
Genome-wide association studies (GWAS) have identified genetic variations associated with adverse drug effects in pharmacogenomics (PGx) research. However, interpreting the biological implications of these associations remains a challenge. This review highlights 2 promising post-GWAS methods for PGx. First, we discuss the polygenic architecture of the PGx traits, especially for drug-induced liver injury. Experimental modelling using multiple donors' human primary hepatocytes and human liver orga
Mitochondria can be involved in regulating cellular stress response to hypoxia and tumor growth, but little is known about that mechanistic relationship. Here, we show that mitochondrial deficiency severely retards tumor xenograft growth with impairing hypoxic induction of HIF-1 transcriptional activity. Using mtDNA-deficient ρ0 cells, we found that HIF-1 pathway activation was comparable in slow-growing ρ0 xenografts and rapid-growing parental xenografts. Interestingly, we found that ex vivo ρ0
It has been difficult to elucidate the structure of gene regulatory networks under anticancer drug treatment. Here, we developed an algorithm to highlight the hub genes that play a major role in creating the upstream and downstream relationships within a given set of differentially expressed genes. The directionality of the relationships between genes was defined using information from comprehensive collections of transcriptome profiles after gene knockdown and overexpression. As expected, among
Large-scale imputation reference panels are currently available and have contributed to efficient genome-wide association studies through genotype imputation. However, whether large-size multi-ancestry or small-size population-specific reference panels are the optimal choices for under-represented populations continues to be debated. We imputed genotypes of East Asian (180k Japanese) subjects using the Trans-Omics for Precision Medicine reference panel and found that the standard imputation qual
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