Ontology highlight
ABSTRACT:
PROVIDER: PRJNA789105 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR17236220_1.fastq.gz | Fastqsanger.gz | |||
| SRR17236220_2.fastq.gz | Fastqsanger.gz | |||
| SRR17236221_1.fastq.gz | Fastqsanger.gz | |||
| SRR17236221_2.fastq.gz | Fastqsanger.gz | |||
| SRR17236222_1.fastq.gz | Fastqsanger.gz |
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Project description:Post-transcriptional control is crucial for regulating protein expression, both basally and in response to extracellular cues. Proper signal transduction requires tight control of both response induction and termination. One way protein expression might be attenuated is by targeting mRNAs to translation-dependent degradation (TDD), thus making any increase in protein expression self-limiting. However, the extent to which TDD is a general mechanism for limiting protein expression is currently unknown. Here we describe a comprehensive analysis of basal and signal-induced TDD in mouse primary CD4 T cells. Our data indicate that most cellular transcripts are decayed to some extent in a translation-dependent manner, both in resting and activated cells. Analysis of transcript features revealed that 3’UTR length and ribosome density are major determinants of the magnitude of TDD as well as GC content and amino acid identity. Consistently, upon T cell activation, all transcripts that undergo changes in ribosome density display a corresponding change in their level of TDD. Surprisingly, the amplitude of translation-independent mRNA decay appears as a mirror image of TDD. Moreover, translation-independent decay also responds to changes in ribosome density upon T cell activation but in the opposite direction to those observed for TDD. Our data demonstrate a strong interconnection between mRNA translation and decay in mammalian cells. Furthermore they indicate that ribosome loading is a major determinant of the pathway by which transcripts are degraded within cells
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Project description:Kilian2024 - Immune cell dynamics in Cue-Induced Extended Human Colitis Model
Single-cell technologies such as scRNA-seq and flow cytometry provide critical insights into immune cell behavior in inflammatory bowel disease (IBD). However, integrating these datasets into computational models for dynamic analysis remains challenging. Here, Kilian et al., (2024) developed a deterministic ODE-based model that incorporates these technologies to study immune cell population changes in murine colitis. The model parameters were optimized to fit experimental data, ensuring an accurate representation of immune cell behavior over time. It was then validated by comparing simulations with experimental data using Pearson’s correlation and further tested on independent datasets to confirm its robustness. Additionally, the model was applied to clinical bulk RNA-seq data from human IBD patients, providing valuable insights into immune system dynamics and potential therapeutic strategies.
Figure 4c, obtained from the simulation of human colitis model is highlighted here.
This model is described in the article:
Kilian, C., Ulrich, H., Zouboulis, V.A. et al. Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease. npj Syst Biol Appl 10, 69 (2024). https://doi.org/10.1038/s41540-024-00395-9
Abstract:
Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of immunological processes. Despite their high throughput, however, these measurements represent only a snapshot in time. Here, we explore how longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. We derived longitudinal changes in cell numbers of colonic cell types during inflammatory bowel disease (IBD) from flow cytometry and scRNA-seq data of murine colitis using ODE-based models. Our mathematical model generalised well across different protocols and experimental techniques, and we hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the mathematical model by deconvolution of longitudinal bulk mRNA-sequencing data from a cohort of human IBD patients treated with olamkicept. We found that neutrophil depletion may contribute to IBD patients entering remission. The predictive power of IBD deterministic modelling highlights its potential to advance our understanding of immune dynamics in health and disease.
This model was curated during the Hackathon hosted by BioMed X GmbH in 2024.
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