Project description:Chromatin based modeling of transcription rates identifies the contribution of different regulatory layers to steady-state mRNA levels
Project description:Messenger RNA levels in eukaryotes are balanced by two consecutive regulatory layers. Primary, transcriptional regulation at the level of chromatin and secondary, post-transcriptional regulation of the initial transcript in the cytoplasm. Each layer is individually studied in mechanistic detail, while integration of both processes is required to quantify the individual contribution to steady-state RNA levels. Here we show that chromatin features are sufficient to model transcription rate but with different sensitivities in dividing versus post mitotic cells. In both cases chromatin derived transcript levels explains over 80% of variance in measured RNA level enabling to separate transcription from different post-transcriptional processes. By further inclusion of measurements of mRNA half-life and micro RNA expression data we identify a low quantitative contribution of RNA decay by either micro RNA or general differential turnover to final mRNA levels. Together this establishes a chromatin based quantitative model for the contribution of transcriptional and posttranscriptional processes to steady-state levels of messenger RNA. Strand specific expression profiling by high throughput sequencing.
Project description:Messenger RNA levels in eukaryotes are balanced by two consecutive regulatory layers. Primary, transcriptional regulation at the level of chromatin and secondary, post-transcriptional regulation of the initial transcript in the cytoplasm. Each layer is individually studied in mechanistic detail, while integration of both processes is required to quantify the individual contribution to steady-state RNA levels. Here we show that chromatin features are sufficient to model transcription rate but with different sensitivities in dividing versus post mitotic cells. In both cases chromatin derived transcript levels explains over 80% of variance in measured RNA level enabling to separate transcription from different post-transcriptional processes. By further inclusion of measurements of mRNA half-life and micro RNA expression data we identify a low quantitative contribution of RNA decay by either micro RNA or general differential turnover to final mRNA levels. Together this establishes a chromatin based quantitative model for the contribution of transcriptional and posttranscriptional processes to steady-state levels of messenger RNA. H3K36me3 ChIP followed by deep sequencing // time-series of mRNA measurement on Affymetrix microarray platform following actinomycinD treatment. MmES Samples: mRNA measurement on Affymetrix microarray platform following thioU labeling and separation of fractions according to labeled (newly synthetised) and unlabeled (pre-existing) RNA.
Project description:Messenger RNA levels in eukaryotes are balanced by two consecutive regulatory layers. Primary, transcriptional regulation at the level of chromatin and secondary, post-transcriptional regulation of the initial transcript in the cytoplasm. Each layer is individually studied in mechanistic detail, while integration of both processes is required to quantify the individual contribution to steady-state RNA levels. Here we show that chromatin features are sufficient to model transcription rate but with different sensitivities in dividing versus post mitotic cells. In both cases chromatin derived transcript levels explains over 80% of variance in measured RNA level enabling to separate transcription from different post-transcriptional processes. By further inclusion of measurements of mRNA half-life and micro RNA expression data we identify a low quantitative contribution of RNA decay by either micro RNA or general differential turnover to final mRNA levels. Together this establishes a chromatin based quantitative model for the contribution of transcriptional and posttranscriptional processes to steady-state levels of messenger RNA.
Project description:Messenger RNA levels in eukaryotes are balanced by two consecutive regulatory layers. Primary, transcriptional regulation at the level of chromatin and secondary, post-transcriptional regulation of the initial transcript in the cytoplasm. Each layer is individually studied in mechanistic detail, while integration of both processes is required to quantify the individual contribution to steady-state RNA levels. Here we show that chromatin features are sufficient to model transcription rate but with different sensitivities in dividing versus post mitotic cells. In both cases chromatin derived transcript levels explains over 80% of variance in measured RNA level enabling to separate transcription from different post-transcriptional processes. By further inclusion of measurements of mRNA half-life and micro RNA expression data we identify a low quantitative contribution of RNA decay by either micro RNA or general differential turnover to final mRNA levels. Together this establishes a chromatin based quantitative model for the contribution of transcriptional and posttranscriptional processes to steady-state levels of messenger RNA.
Project description:Eukaryotic cells are constantly challenged by the presence of reactive oxygen species, which play an important role in aging and human disease progression. In particular, acute oxidative stress can lead to extensive damage to cellular DNA, proteins, and lipids and can trigger a response that remodels the transcriptional and translational state of the cell. Although a number of previous studies have profiled the relative changes in mRNA and protein and more studies revealing the dynamics of transcription and translation in response to stress are starting to emerge, a quantitative view of this response has been lacking. Here, we have applied quantitative methods to characterize the time dynamics of mRNA and protein levels in the oxidative stress response of the fission yeast Schizosaccharomyces pombe, which has allowed us to perform dynamic modeling of responsive genes in units of copies per cell. Analysis of the resulting time dynamics provided a new genome-wide view of the scale, timing and rates of transcription and translation in the transient response. The majority of dynamic genes were observed to be responsive in their mRNA or protein levels alone implying extensive translational regulation. Nevertheless, modeling of genes with responsive mRNA and protein levels showed that protein levels could, in a majority of these cases, be accurately predicted with constant translation and decay rates while a minority benefited from explicit translation delay parameters. A number of independent features, e.g. measures of codon bias, ribosome occupancy, etc., were found to be less correlated to maximally perturbed protein levels than steady-state levels. Codon bias measures were more correlated than mRNA levels to quantitative protein levels at both perturbed and un-perturbed states. Measures of translation activity, on the other hand, were only significantly correlated at steady state. In total 32 samples: 11 for stressed time series R1, 11 for stressed time series R2, 5 for control time series C1 and 5 for control time series C2
Project description:To determine the contribution at protein levels to the steady state of the cells in different sexes, we estimated the relative amounts of the microglia cells by mass spectrometry-based proteomics approach and further label-free quantification analysis (LFQ). We analysed the differences of protein abundance, comparing the normalized intensity on distinct proteins from four different mice per sex.
Project description:Gene expression levels are determined by the balance between rates of mRNA transcription and decay, and genetic variation in either of these processes can result in heritable differences in transcript abundance. Although the genetics of gene expression has been the subject of intense interest, the contribution of heritable variation in mRNA decay rates to gene expression variation has received far less attention. To this end, we developed a novel statistical framework and measured allele-specific differences in mRNA decay rates in a diploid yeast hybrid created by mating two genetically diverse parental strains. In total, we estimate that 31% of genes exhibit allelic differences in mRNA decay rate, of which 350 can be identified at a false discovery rate of 10%. Genes with significant allele-specific differences in mRNA decay rate have higher levels of polymorphism compared to other genes, with all gene regions contributing to allelic differences in mRNA decay rate. Strikingly, we find widespread evidence for compensatory evolution, such that variants influencing transcriptional initiation and decay having opposite effects, suggesting steady-state gene expression levels are subject to pervasive stabilizing selection. Our results demonstrate that heritable differences in mRNA decay rates are widespread, and are an important target for natural selection to maintain or fine-tune steady-state gene expression levels.
Project description:Eukaryotic cells are constantly challenged by the presence of reactive oxygen species, which play an important role in aging and human disease progression. In particular, acute oxidative stress can lead to extensive damage to cellular DNA, proteins, and lipids and can trigger a response that remodels the transcriptional and translational state of the cell. Although a number of previous studies have profiled the relative changes in mRNA and protein and more studies revealing the dynamics of transcription and translation in response to stress are starting to emerge, a quantitative view of this response has been lacking. Here, we have applied quantitative methods to characterize the time dynamics of mRNA and protein levels in the oxidative stress response of the fission yeast Schizosaccharomyces pombe, which has allowed us to perform dynamic modeling of responsive genes in units of copies per cell. Analysis of the resulting time dynamics provided a new genome-wide view of the scale, timing and rates of transcription and translation in the transient response. The majority of dynamic genes were observed to be responsive in their mRNA or protein levels alone implying extensive translational regulation. Nevertheless, modeling of genes with responsive mRNA and protein levels showed that protein levels could, in a majority of these cases, be accurately predicted with constant translation and decay rates while a minority benefited from explicit translation delay parameters. A number of independent features, e.g. measures of codon bias, ribosome occupancy, etc., were found to be less correlated to maximally perturbed protein levels than steady-state levels. Codon bias measures were more correlated than mRNA levels to quantitative protein levels at both perturbed and un-perturbed states. Measures of translation activity, on the other hand, were only significantly correlated at steady state.