Dataset Information

Time-series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations.

ABSTRACT: The cell stress-responsive transcription factor p53 influences the expression of its target genes and subsequent cellular responses based in part on its dynamics (changes in level over time). The mechanisms decoding p53 dynamics into subsequent target mRNA and protein dynamics remain unclear. We systematically quantified p53 target mRNA and protein expression over time under two p53 dynamical regimes, oscillatory and rising, using RNA-sequencing and TMT mass spectrometry. Oscillatory dynamics allowed for a greater variety of dynamical patterns for both mRNAs and proteins. Mathematical modeling of empirical data revealed three distinct mechanisms that decode p53 dynamics. Specific combinations of these mechanisms at the transcriptional and post-transcriptional levels enabled exclusive induction of proteins under particular dynamics. In addition, rising induction of p53 led to higher induction of proteins regardless of their functional class, including proteins promoting arrest of proliferation, the primary cellular outcome under rising p53. Our results highlight the diverse mechanisms cells employ to distinguish complex transcription factor dynamics to regulate gene expression.

SUBMITTER: Jimenez A

PROVIDER: S-EPMC8919251 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Time-series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations.

Jiménez Alba A Lu Dan D Kalocsay Marian M Berberich Matthew J MJ Balbi Petra P Jambhekar Ashwini A Lahav Galit G

Molecular systems biology 20220301 3

The cell stress-responsive transcription factor p53 influences the expression of its target genes and subsequent cellular responses based in part on its dynamics (changes in level over time). The mechanisms decoding p53 dynamics into subsequent target mRNA and protein dynamics remain unclear. We systematically quantified p53 target mRNA and protein expression over time under two p53 dynamical regimes, oscillatory and rising, using RNA-sequencing and TMT mass spectrometry. Oscillatory dynamics al ...[more]

PMID: 35285572

Similar Datasets

Project description:BackgroundIdiopathic pulmonary fibrosis (IPF) is a fatal lung disease with a significant unmet medical need. Development of transformational therapies for IPF is challenging in part to due to lack of robust predictive biomarkers of prognosis and treatment response. Importantly, circulating biomarkers of IPF are limited and none are in clinical use.MethodsWe previously reported dysregulated pathways and new disease biomarkers in advanced IPF through RNA sequencing of lung tissues from a cohort of transplant-stage IPF patients (n = 36) in comparison to normal healthy donors (n = 19) and patients with acute lung injury (n = 11). Here we performed proteomic profiling of matching plasma samples from these cohorts through the Somascan-1300 SomaLogics platform.ResultsComparative analyses of lung transcriptomic and plasma proteomic signatures identified a set of 34 differentially expressed analytes (fold change (FC) ≥ ± 1.5, false discovery ratio (FDR) ≤ 0.1) in IPF samples compared to healthy controls. IPF samples showed strong enrichment of chemotaxis, tumor infiltration and mast cell migration pathways and downregulated extracellular matrix (ECM) degradation. Mucosal (CCL25 and CCL28) and Th2 (CCL17 and CCL22) chemokines were markedly upregulated in IPF and highly correlated within the subjects. The mast cell maturation chemokine, CXCL12, was also upregulated in IPF plasma (fold change 1.92, FDR 0.006) and significantly correlated (Pearson r = - 0.38, p = 0.022) to lung function (%predicted FVC), with a concomitant increase in the mast cell Tryptase, TPSB2. Markers of collagen III and VI degradation (C3M and C6M) were significantly downregulated (C3M p < 0.001 and C6M p < 0.0001 IPF vs control) and correlated, Pearson r = 0.77) in advanced IPF consistent with altered ECM homeostasis.ConclusionsOur study identifies a panel of tissue and circulating biomarkers with clinical utility in IPF that can be validated in future studies across larger cohorts.

Project description:The human gut microbiome is crucial in health and disease. Longitudinal studies are becoming increasingly important compared to traditional cross-sectional approaches, as precision medicine and individualized interventions are coming to the forefront. Investigating the temporal dynamics of the microbiome is essential for comprehending its function and impact on health. This knowledge has implications for targeted therapeutic strategies, such as personalized diets or probiotic therapy. In this study, we focused on developing and implementing methods specifically designed for analyzing gut microbiome time series. Our statistical framework provides researchers with tools to examine the temporal behavior of the gut microbiome. Key features of our framework include statistical tests for time series properties, predictive modeling, classification of bacterial species based on stability and noise, and clustering analyses to identify groups of bacteria with similar temporal patterns. We analyzed dense amplicon sequencing time series from four generally healthy subjects. Using our developed statistical framework, we analyzed both the overall community dynamics and the behavior of individual bacterial species. We showed six longitudinal regimes within the gut microbiome and discussed their features. Additionally, we explored whether specific bacterial clusters undergo similar fluctuations, suggesting potential functional relationships and interactions within the microbiome. Our development of specialized methods for analyzing human gut microbiome time series significantly enhances the understanding of its dynamic nature and implications for human health. The guidelines and tools provided by our framework support scientists in studying the complex dynamics of the gut microbiome, fostering further research and advancements in microbiome analysis. The gut microbiome is integral to human health, influencing various diseases. Longitudinal studies offer deeper insights into its temporal dynamics compared to cross-sectional approaches. In this study, we developed a statistical framework for analyzing the time series of the human gut microbiome. This framework provides robust tools for examining microbial community dynamics over time. It includes statistical tests for time series properties, predictive modeling, classification of bacterial species based on stability and noise, and clustering analyses. Our approach significantly enhances the methodologies available to researchers, promoting further exploration and innovation in microbiome analysis.ImportanceThis project developed innovative methods to analyze gut microbiome time series data, offering fresh insights into its dynamic nature. Unlike many studies that focus on static snapshots, we found that the healthy gut microbiome is predictably stable over time, with only a small subset of bacteria showing significant changes. By identifying groups of bacteria with diverse temporal behaviors and clusters that change together, we pave the way for more effective probiotic therapies and dietary interventions, addressing the overlooked dynamic aspects of gut microbiome changes.

Dataset Information

Time-series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations.

Publications

Time-series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets