Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR17479774_1.fastq.gz | Fastqsanger.gz | |||
| SRR17479774_2.fastq.gz | Fastqsanger.gz | |||
| SRR17479775_1.fastq.gz | Fastqsanger.gz | |||
| SRR17479775_2.fastq.gz | Fastqsanger.gz | |||
| SRR17479776_1.fastq.gz | Fastqsanger.gz |
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Project description:This model was reconstructed from single-nucleus RNA-seq (snRNA-seq) data of human postmortem brain and curated using published metabolomics data from human iPSC-derived neurons and cerebrospinal fluid (CSF), together with gene expression data from the Human Protein Atlas. It more accurately simulates human neuronal metabolic flux in neurodegenerative conditions such as Alzheimer's disease (AD).
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Project description:RNA-binding proteins (RPBs) are deeply involved in many fundamental cellular processes in bacteria and are vital for their survival. Despite this, few studies have so far been dedicated to globally identifying bacterial RBPs. We have adapted the RNA interactome capture (RIC) technique, originally developed for eukaryotic systems, to globally identify RBPs in bacteria. RIC takes advantage of the base pairing potential of poly(A) tails to pull-down mRNA-protein complexes. By overexpressing the poly(A) polymerase I, we drastically increase the frequency of polyadenylated RNA in E. coli, allowing us to pull down RNA-protein complexes using immobilized oligo-d(T) as bait. With this approach, we identified 167 putative RBPs, roughly half of which are already annotated as RNA-binding. We experimentally verified the RNA-binding ability of several proteins previously unknown to interact with RNA, including the uncharacterized protein YhgF. YhgF is exceptionally well conserved not only in bacteria, but also in archaea and eukaryotes. We identified YhgF in vivo RNA targets using CLIP-seq, two of which were verified using electromobility shift assays. Our findings present a simple and robust strategy for RBP identification in bacteria, provide a resource of new bacterial RBPs, and lays the foundation for further studies of the strongly conserved RBP Yhg
2023-03-30 | PXD032939 | Pride
Project description:The vast number of noncoding RNAs in bacteria suggests that major post-transcriptional circuits beyond those controlled by the global RNA-binding proteins Hfq and CsrA may exist. To identify additional globally acting RNPs we have developed a method (gradient profiling by sequencing; Grad-seq) to partition the full ensemble of cellular RNAs based on their biochemical behavior. Consequently, we discovered transcripts that commonly interact with the osmoregulatory protein ProQ in Salmonella enterica. We show that ProQ is a conserved abundant RNA-binding protein with a wide range of targets, including a new class of ProQ-associated small RNAs that are highly structured and function to regulate mRNAs in trans. Based on its ability to chart the functional landscape of all cellular transcripts irrespective of their length and sequence diversity, Grad-seq promises to aid the discovery of major functional RNA classes and RNA-binding proteins in many organisms.
2017-07-21 | PXD003360 | Pride
Project description:To investigate the risk bacteria in the regulation of NPC, we established RNA-seq in NPC tissues with high/low abundance of risk bacteria. We then performed gene expression profiling analysis using data obtained from RNA-seq of NPC tissues with high/low abundance of risk bacteria.
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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.
2025-03-07 | MODEL2502180001 | BioModels
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