The University of Tokyo · 생화학·유전·분자생물학
마수루 고도 교수의 연구실은 생물의학적 데이터, 특히 공간 전사체 분석과 유전체 정보를 기반으로 질병 메커니즘을 규명하는 데 초점을 맞추고 있습니다. 주요 연구 방향은 암의 전이 및 저항성 메커니즘을 이해하기 위해 공간적 전사체 패tern과 유전자 조절 네트워크를 분석하는 것입니다. 또한 약물 유전체학과 mitochondrial 기능이 질병에 미치는 영향을 다루며, 인공지능 기반 모델링과 대규모 유전체 데이터 분석을 융합한 정밀의료 기반 연구를 수행하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
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
