Project description:Series of regulatory mechanisms control eukaryotic mRNAs, from their production on chromatin to their eventual degradation. Dissecting these pathways requires quantitative measurements of mRNA flow across the cell. We developed subcellular TimeLapse-seq to measure the rates at which RNAs are released from chromatin, exported from the nucleus, loaded onto polysomes, and degraded within the nucleus and the cytoplasm. All rates displayed substantial variability genome-wide, and transcripts from genes with related functions flowed across subcellular compartments with similar kinetics. For some genes, most transcripts were rapidly degraded within the nucleus, while the remaining molecules were exported and persisted with stable lifespans. With compartment-specific measurements of poly(A) tail lengths, we found that relationships between tail length and stability differ across the cell. The targets of RNA binding proteins experience distinct RNA flow kinetics, and we found that two, DDX3X and PABPC4, act by controlling the nuclear export of their targets. Finally, we developed a machine learning model to propose additional molecular features that underlie the diverse life cycle of mammalian mRNAs.
Project description:Series of regulatory mechanisms control eukaryotic mRNAs, from their production on chromatin to their eventual degradation. Dissecting these pathways requires quantitative measurements of mRNA flow across the cell. We developed subcellular TimeLapse-seq to measure the rates at which RNAs are released from chromatin, exported from the nucleus, loaded onto polysomes, and degraded within the nucleus and the cytoplasm. All rates displayed substantial variability genome-wide, and transcripts from genes with related functions flowed across subcellular compartments with similar kinetics. For some genes, most transcripts were rapidly degraded within the nucleus, while the remaining molecules were exported and persisted with stable lifespans. With compartment-specific measurements of poly(A) tail lengths, we found that relationships between tail length and stability differ across the cell. The targets of RNA binding proteins experience distinct RNA flow kinetics, and we found that two, DDX3X and PABPC4, act by controlling the nuclear export of their targets. Finally, we developed a machine learning model to propose additional molecular features that underlie the diverse life cycle of mammalian mRNAs.
Project description:Dissecting the myriad regulatory mechanisms controlling eukaryotic transcripts from production to degradation requires quantitative measurements of mRNA flow across the cell. We developed subcellular TimeLapse-seq to measure the rates at which RNAs are released from chromatin, exported from the nucleus, loaded onto polysomes, and degraded within the nucleus and cytoplasm. These rates varied substantially, yet transcripts from genes with related functions or targeted by the same transcription factors and RNA binding proteins flowed across subcellular compartments with similar kinetics. Verifying these associations uncovered roles for DDX3X and PABPC4 in nuclear export. For hundreds of genes, most transcripts were degraded within the nucleus, while the remaining molecules were exported and persisted with stable lifespans. Transcripts residing on chromatin for longer had extended poly(A) tails, whereas the reverse was observed for cytoplasmic mRNAs. Finally, a machine learning model identified additional molecular features that underlie the diverse life cycles of mammalian mRNAs.
Project description:Dissecting the myriad regulatory mechanisms controlling eukaryotic transcripts from production to degradation requires quantitative measurements of mRNA flow across the cell. We developed subcellular TimeLapse-seq to measure the rates at which RNAs are released from chromatin, exported from the nucleus, loaded onto polysomes, and degraded within the nucleus and cytoplasm. These rates varied substantially, yet transcripts from genes with related functions or targeted by the same transcription factors and RNA binding proteins flowed across subcellular compartments with similar kinetics. Verifying these associations uncovered roles for DDX3X and PABPC4 in nuclear export. For hundreds of genes, most transcripts were degraded within the nucleus, while the remaining molecules were exported and persisted with stable lifespans. Transcripts residing on chromatin for longer had extended poly(A) tails, whereas the reverse was observed for cytoplasmic mRNAs. Finally, a machine learning model identified additional molecular features that underlie the diverse life cycles of mammalian mRNAs.
Project description:Series of regulatory mechanisms control eukaryotic mRNAs, from their production on chromatin to their eventual degradation. Dissecting these pathways requires quantitative measurements of mRNA flow across the cell. We developed subcellular TimeLapse-seq to measure the rates at which RNAs are released from chromatin, exported from the nucleus, loaded onto polysomes, and degraded within the nucleus and the cytoplasm. All rates displayed substantial variability genome-wide, and transcripts from genes with related functions flowed across subcellular compartments with similar kinetics. For some genes, most transcripts were rapidly degraded within the nucleus, while the remaining molecules were exported and persisted with stable lifespans. With compartment-specific measurements of poly(A) tail lengths, we found that relationships between tail length and stability differ across the cell. The targets of RNA binding proteins experience distinct RNA flow kinetics, and we found that two, DDX3X and PABPC4, act by controlling the nuclear export of their targets. Finally, we developed a machine learning model to propose additional molecular features that underlie the diverse life cycle of mammalian mRNAs.
Project description:Dissecting the regulatory mechanisms controlling mammalian transcripts from production to degradation requires quantitative measurements of mRNA flow across the cell. We developed subcellular TimeLapse-seq to measure the rates at which RNAs are released from chromatin, exported from the nucleus, loaded onto polysomes, and degraded within the nucleus and cytoplasm in human and mouse cells. These rates varied substantially, yet transcripts from genes with related functions or targeted by the same transcription factors and RNA-binding proteins flowed across subcellular compartments with similar kinetics. Verifying these associations uncovered a link between DDX3X and nuclear export. For hundreds of RNA metabolism genes, most transcripts with retained introns were degraded by the nuclear exosome, while the remaining molecules were exported with stable cytoplasmic lifespans. Transcripts residing on chromatin for longer had extended poly(A) tails, whereas the reverse was observed for cytoplasmic mRNAs. Finally, machine learning identified molecular features that predicted the diverse life cycles of mRNAs.
Project description:Dissecting the myriad regulatory mechanisms controlling eukaryotic transcripts from production to degradation requires quantitative measurements of mRNA flow across the cell. We developed subcellular TimeLapse-seq to measure the rates at which RNAs are released from chromatin, exported from the nucleus, loaded onto polysomes, and degraded within the nucleus and cytoplasm. These rates varied substantially, yet transcripts from genes with related functions or targeted by the same transcription factors and RNA binding proteins flowed across subcellular compartments with similar kinetics. Verifying these associations uncovered roles for DDX3X and PABPC4 in nuclear export. For hundreds of genes, most transcripts were degraded within the nucleus, while the remaining molecules were exported and persisted with stable lifespans. Transcripts residing on chromatin for longer had extended poly(A) tails, whereas the reverse was observed for cytoplasmic mRNAs. Finally, a machine learning model identified additional molecular features that underlie the diverse life cycles of mammalian mRNAs.
Project description:Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the mammalian transcript life cycle.