McFarland2020 - Totally Asymmetric Simple Exclusion Process (TASEP) model describing the movement of particles (ribosomes) along a lattice (the mRNA)
Ontology highlight
ABSTRACT:
SUBMITTER: 
Ian Stansfield
PROVIDER: MODEL2001080004 | BioModels | 2020-02-05
REPOSITORIES: BioModels
Dataset's files
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| MODEL2001080004?filename=GTM%20code_v2.zip | Other | |||
| MODEL2001080004?filename=README_Global%20Translation%20Model_McFarland_et_al%20v2.txt | Txt |
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Project description:Asymmetric mRNA localization facilitates efficient translation in cells such as neurons and fibroblasts. However, the extent and significance of mRNA polarization in epithelial tissues is unclear. Here, we used single molecule transcript imaging and subcellular transcriptomics to uncover global apical-basal intracellular polarization of mRNA in the mouse intestinal epithelium. The localization of mRNAs did not generally overlap protein localization. Instead, ribosomes were more abundant on the apical sides, and apical transcripts were consequently more efficiently translated. Refeeding of fasted mice elicited a basal to apical shift in polarization of mRNAs encoding ribosomal proteins, which was associated with a specific boost in their translation. This lead to increased protein production, required for efficient nutrient absorption. These findings reveal a post-transcriptional regulatory mechanism involving dynamic polarization of mRNA and polarized translation.
2017-08-10 | GSE95415 | GEO
Project description:Asymmetric mRNA localization facilitates efficient translation in cells such as neurons and fibroblasts. However, the extent and significance of mRNA polarization in epithelial tissues is unclear. Here, we used single molecule transcript imaging and subcellular transcriptomics to uncover global apical-basal intracellular polarization of mRNA in the mouse intestinal epithelium. The localization of mRNAs did not generally overlap protein localization. Instead, ribosomes were more abundant on the apical sides, and apical transcripts were consequently more efficiently translated. Refeeding of fasted mice elicited a basal to apical shift in polarization of mRNAs encoding ribosomal proteins, which was associated with a specific boost in their translation. This lead to increased protein production, required for efficient nutrient absorption. These findings reveal a post-transcriptional regulatory mechanism involving dynamic polarization of mRNA and polarized translation.
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Project description:Work summary: Full-transcriptome methods have brought versatile power to protein biosynthesis research, but remain difficult to apply for the quantification of absolute protein synthesis rates. Here we propose and, using modified translation complex profiling, confirm co-localisation of ribosomes on messenger(m)RNA resulting from the ribosomal diffusional dynamics. We demonstrate that the stochastically co-localised ribosomes are linked with the translation initiation rate and provide a robust variable to model and quantify specific absolute protein output from mRNA. The new type of signal originating from the stochastic co-localisation of ribosomes on mRNA is evidenced by long disome-derived (potentially but rarely, multisome-derived) footprints outside the regions of non-random elongation stalls. Using the stochastic co-localisation component of the translation complex profiling data, we propose a new approach to assess absolute translation rates across mRNA, based on the calculation of Stochastic Translation Efficiency (STE) measure. STE employs a machine learning model trained with multiple TCP-seq-sourced variables. The variables, among others, include stochastic disome signal derived from the total disome footprint data and normalised to the ribosome (RS) footprint abundance across the respective open reading frames (ORFs). The variables also include SSU footprint occurrences over the respective start codons normalised to the RS signal averaged and normalised by the ORF length, to better distinguish between the cases of actively initiated or densely populated but slowly initiated mRNAs. Importantly, STE does not employ any variables resulting from normalisation of the signals of different nature, such as any normalisation of the footprint data by relative RNA abundance measured by RNA-seq. We propose STE as a more robust, sometimes more convenient, alternative to the classical TE (translation efficiency). The main distinguishing features of the STE are the inherent single-molecule-event nature of the main measure component (the long stochastic disome footprint signal), similar type of data used for the numerator(s) and denominator(s) of the calculated variables, resistance to the abundance changes and variations in the differential localisation of RNA and insensitivity to the library preparation techniques. We further hypothesise that similar high-throughput data-derived measures based on parameters dependent on random individual molecular co-incidences can be utilised to provide robust assessment of many molecular processes in dynamic biological systems. 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Results summary: Preliminary theoretical investigations revealed monotonous dependence of the stochastic DS frequency on translation initiation rate in a broad range of initiation and elongation rates and variances, well-covering all biologically meaningful combination of parameters. Biomimetic in silico modelling utilising elongation and initiation rate distributions extrapolated from in vivo single-molecule measurements available in the published data confirmed the link of stochastic DS signal with the initiation frequency. The in silico-modelled stochastic DS frequency has shown resistance to local alterations of the elongation rate over ORFs and monotonous dependence on the initiation rate, even in the extreme cases of elongation rate discontinuity. DS footprint fractions derived from the TCP-seq material prominently demonstrated the presence of longer footprints, and revealed a complex pattern of footprint lengths. The TCP-seq DS footprint pattern allowed us to propose a generalised case of ribosomal co-localisation, whereby DS can form as a result of non-random co-localisations of serially occurring ribosomes, as well as co-localisations resulting from the spatial proximity and the geometry and spatial arrangement of the respective source poly(ribo)somes. Bioinformatically segregating all of the DS signal into its stochastic and non-random components, and combining the derived components with the other TCP-seq-sourced variables and known ORF lengths, we further implemented a machine learning approach that results in Stochastic Translation Efficiency (STE) measure with high correlation (Pearson’s r ~0.7 for train and ~0.6 for test data, respectively) to the absolute protein synthesis rates as directly measured in the published data using metabolic labelling and mass spectrometry. 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