Stanford Advisors

Contact

  • Academic
    mokino@stanford.edu
    University - Scholar Department: Pediatrics Position: Postdoctoral Scholar

All Publications

  • Deciphering the impact of genomic variation on function. Nature 2024; 633 (8028): 47-57

    Abstract

    Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.

    View details for DOI 10.1038/s41586-024-07510-0

    View details for PubMedID 39232149

    View details for PubMedCentralID 7405896

  • Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes. Nature genetics Wang, G., Chiou, J., Zeng, C., Miller, M., Matta, I., Han, J. Y., Kadakia, N., Okino, M., Beebe, E., Mallick, M., Camunas-Soler, J., Dos Santos, T., Dai, X., Ellis, C., Hang, Y., Kim, S. K., MacDonald, P. E., Kandeel, F. R., Preissl, S., Gaulton, K. J., Sander, M. 2023

    Abstract

    Dysfunctional pancreatic islet beta cells are a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of the underlying mechanisms, including gene dysregulation, is lacking. Here we integrate information from measurements of chromatin accessibility, gene expression and function in single beta cells with genetic association data to nominate disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 nondiabetic, pre-T2D and T2D donors, we identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift during T2D progression. Subtype-defining accessible chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both beta cell subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is probably induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for characterizing mechanisms of complex diseases.

    View details for DOI 10.1038/s41588-023-01397-9

    View details for PubMedID 37231096