Ontology highlight
ABSTRACT:
PROVIDER: PRJNA625414 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR11547671_1.fastq.gz | Fastqsanger.gz | |||
| SRR11547671_2.fastq.gz | Fastqsanger.gz | |||
| SRR11547672_1.fastq.gz | Fastqsanger.gz | |||
| SRR11547672_2.fastq.gz | Fastqsanger.gz | |||
| SRR11547673_1.fastq.gz | Fastqsanger.gz |
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Project description:We investigated the toxicity of soil samples derived from a former municipal landfill site in the South of the Netherlands, where a bioremediation project is running aiming at reusing the site for recreation. Both an organic soil extract and the original soil sample was investigated using the ISO standardised Folsomia soil ecotoxicological testing and gene expression analysis. The 28 day survival/reproduction test revealed that the ecologically more relevant original soil sample was more toxic than the organic soil extract. Microarray analysis showed that the more toxic soil samples induced gene regulatory changes in twice as less genes compared to the soil extract. Consequently gene regulatory changes were highly dependent on sample type, and were to a lesser extent caused by exposure level. An important biological process shared among the two sample types was the detoxification pathway for xenobiotics (biotransformation I, II and III) suggesting a link between compound type and observed adverse effects. Finally, we were able to retrieve a selected group of genes that show highly significant dose-dependent gene expression and thus were tightly linked with adverse effects on reproduction. Expression of four cytochrome P450 genes showed highest correlation values with reproduction, and maybe promising genetic markers for soil quality. However, a more elaborate set of environmental soil samples is needed to validate the correlation between gene expression induction and adverse phenotypic effects.
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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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