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Scaria, S. M.

Publications and source records attributed to Scaria, S. M..

2 recordsLinked to original sources

Dihydroxyacetone phosphate signals glucose availability to mTORC1

In response to nutrients, the mTOR complex 1 (mTORC1) kinase regulates cell growth by setting the balance between anabolic and catabolic processes. To be active, mTORC1 requires the environmental presence of amino acids and glucose, which provide the building blocks for the biosynthesis of most macromolecules. While a mechanistic understanding of amino acid sensing by mTORC1 is emerging, how glucose activates mTORC1 remains mysterious. Here, we used metabolically engineered human cells to identify glucose-derived metabolites required to activate mTORC1. We find that mTORC1 senses a metabolite downstream of the aldolase and upstream of the glyceraldehyde 3-phosphate dehydrogenase steps of glycolysis and pinpoint dihydroxyacetone phosphate (DHAP) as the key molecule. In cells expressing a triose kinase, the synthesis of DHAP from dihydroxyacetone is sufficient to activate mTORC1 even in the absence of glucose. Genetic perturbations in the GATOR-Rag signaling axis abrogate glucose sensing by mTORC1. DHAP is the glycolytic metabolite along with glyceraldehyde 3-phosphate (GAP) that has the greatest fold-change between cells in high and low glucose. DHAP is a precursor for lipid synthesis, a process under the control of mTORC1, which provides a potential rationale for the sensing of DHAP by mTORC1.

cell biology↗

Titrating gene expression with series of systematically compromised CRISPR guide RNAs

Biological phenotypes arise from the degrees to which genes are expressed, but the lack of tools to precisely control gene expression limits our ability to evaluate the underlying expression-phenotype relationships. Here, we describe a readily implementable approach to titrate expression of human genes using series of systematically compromised sgRNAs and CRISPR interference. We empirically characterize the activities of compromised sgRNAs using large-scale measurements across multiple cell models and derive the rules governing sgRNA activity using deep learning, enabling construction of a compact sgRNA library to titrate expression of [~]2,400 genes involved in central cell biology and a genome-wide in silico library. Staging cells along a continuum of gene expression levels combined with rich single-cell RNA-seq readout reveals gene-specific expression-phenotype relationships with expression level-specific responses. Our work provides a general tool to control gene expression, with applications ranging from tuning biochemical pathways to identifying suppressors for diseases of dysregulated gene expression.

genomics↗