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).
Project description:The first GSSM of V. vinifera was reconstructed (MODEL2408120001). Tissue-specific models for stem, leaf, and berry of the Cabernet Sauvignon cultivar were generated from the original model, through the integration of RNA-Seq data. These models have been merged into diel multi-tissue models to study the interactions between tissues at light and dark phases.
Project description:This experiment aimed to understand stress responses of microbial communities differing in chronic exposure to the photosynthesis inhibitor diuron, combining untargeted metatranscriptomics (RNA-seq) and dose-response design. First, river microbial communities were incubated for 5-weeks in microcosms 1/ under constant exposure to 4µg/L of diuron (stressed community) or 2/ without contamination (non-stressed community). Then, both communities were exposed for 1 hour to a gradient of diuron concentrations to investigate differences in stress responses after chronic exposure. This experimental design enabled the determination of contig response trends as well as sensitivity thresholds.
Project description:We performed RNA-Seq based gene expression analysis of Arabidopsis Col-0 plants grown under axenic and holoxenic conditions in FlowPot system. Holoxenic plants were grown in the presence of soil slurries containing microbial communities derived from natural soils and under axenic condition in presence of heat-killed soil slurries for three weeks. We identified genes differentially enriched in response to presence of microbial communities. Our results suggested that in presence of microbiota there is a differential expression of immunity/defense-related genes in holoxenic compared to axenic plants.
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.
Project description:Rationale: Recent studies suggest a potential link between gut bacterial microbiota dysbiosis and PAH, but the exact role of gut microbial communities, including bacteria, archaea, and fungi, in PAH remains unclear. Objectives: To investigate the role of gut microbiota dysbiosis in idiopathic pulmonary arterial hypertension (IPAH) and to assess the therapeutic potential of fecal microbiota transplantation (FMT) in modulating PAH progression. Methods: Using shotgun metagenomics, we analyzed gut microbial communities in IPAH patients and healthy controls. FMT was performed to transfer gut microbiota from IPAH patients or MCT-PAH rats to normal rats and from healthy rats to MCT-PAH rats. Hemodynamic measurements, echocardiography, histological examination, metabolomic and RNA-seq analysis were conducted to evaluate the effects of FMT on PAH phenotypes. Measurements and Main Results: Gut microbiota analysis revealed significant alterations in the bacterial, archaeal, and fungal communities in IPAH patients compared to healthy controls. FMT from IPAH patients induced PAH phenotypes in recipient rats. Conversely, FMT from healthy rats to IPAH rats significantly ameliorated PAH symptoms, restored gut microbiota composition, and normalized serum metabolite profiles. Specific microbial species were identified with high diagnostic potential for IPAH, improving predictive performance beyond individual or combined microbial communities. Conclusions: This study establishes a causal link between gut microbiota dysbiosis and IPAH and demonstrates the therapeutic potential of FMT in reversing PAH phenotypes. The findings highlight the critical role of bacterial, archaeal, and fungal communities in PAH pathogenesis and suggest that modulation of the gut microbiome could be a promising treatment strategy for PAH.
Project description:Data analysis is a critical part of quantitative proteomics studies in interpreting biological questions. Numerous computational tools including protein quantification, imputation, and differential expression (DE) analysis were generated in the past decade. However, searching optimized tools is still an unsolved issue. Moreover, due to the rapid development of RNA-Seq technology, a vast number of DE analysis methods are created. Applying these newly developed RNA-Seq-oriented tools to proteomics data is still a question that needs to be addressed. In order to benchmark these analysis methods, a proteomics dataset constituted the proteins derived from human, yeast, and drosophila with different ratios were generated. Based on this dataset, DE analysis tools (including array-based and RNA-Seq based), imputation algorithms, and protein quantification methods were compared and benchmarked. This study provided useful information on analyzing quantitative proteomics datasets. All the methods used in this study were integrated into Perseus which are available at https://www.maxquant.org/perseus.
Project description:<p>Gene expression is a biological process regulated at different molecular levels, including chromatin accessibility, transcription, and RNA maturation and transport. In addition, these regulatory mechanisms have strong links with cellular metabolism. Here we present a multi-omics dataset that captures different aspects of this multi-layered process in yeast. We obtained RNA-seq, metabolomics, and H4K12Ac ChIP-seq data for wild-type and mip6delta strains during a heat-shock time course. Mip6 is an RNA-binding protein that contributes to RNA export during environmental stress and is informative of the contribution of post-transcriptional regulation to control cellular adaptations to environmental changes. The experiment was performed in quadruplicate, and the different omics measurements were obtained from the same biological samples, which facilitates the integration and analysis of data using covariance-based methods. We validate our dataset by showing that ChIP-seq, RNA-seq and metabolomics signals recapitulate existing knowledge about the response of ribosomal genes and the contribution of trehalose metabolism to heat stress.</p>
Project description:Here we report a direct tRNA sequencing protocol and software to simultaneously examine the composition and biological activity of naturally occurring microbial communities. Our analysis of mouse gut microbiome with tRNA-seq and 16S ribosomal RNA gene amplicons revealed comparable microbial community structures, and additional physiological insights into the microbiome through tRNA abundance and modifications.
Project description:This dataset contains data-independent acquisition (DIA) proteomics data generated from K562 human leukemia cells treated with coffee extract or vehicle control. Quantitative proteomic analysis was performed to investigate molecular mechanisms underlying coffee-induced epigenetic remodeling. The proteomics dataset was integrated with histone modification profiling, ChIP-seq and RNA-seq analyses to characterize pathways associated with histone acetylation and MYC transcriptional regulation.