Inferring secretory and metabolic pathway activity from omic data with secCellFie
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1043633 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR26901289_1.fastq.gz | Fastqsanger.gz | |||
| SRR26901289_2.fastq.gz | Fastqsanger.gz | |||
| SRR26901290_1.fastq.gz | Fastqsanger.gz | |||
| SRR26901290_2.fastq.gz | Fastqsanger.gz | |||
| SRR26901291_1.fastq.gz | Fastqsanger.gz |
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Project description:Understanding protein secretion has considerable importance in the biotechnology industry and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer protein secretion capabilities from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can be used to predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.
2024-06-20 | GSE248376 | GEO
Project description:Understanding protein secretion has considerable importance in the biotechnology industry and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer protein secretion capabilities from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can be used to predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.
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Project description:We used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features that are critical for macrophage activation. We constructed a genome-scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of activation. Metabolites well-known to be associated with immunoactivation (glucose and arginine) and immunosuppression (tryptophan and vitamin D3) were among the most critical effectors. Intracellular metabolic mechanisms were assessed, identifying a suppressive role for de-novo nucleotide synthesis. Finally, underlying metabolic mechanisms of macrophage activation are identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions. Two condition (flagellin and LPS) time course exposure of RAW 264.7 cell line at 1, 2, 4, and 24 hours. Two replicates for each condition and time point. All conditions compared to a pool of untreated cells at a 0 hour time point.
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Project description:Abstract: Secretion occurs in all cells, with relatively low levels in most cells and extremely high levels in specialized secretory cells, such as those of the pancreas, salivary and mammary glands. Here, we report that the CrebA/Creb3-like family of bZip transcription factors functions to upregulate expression of both the general protein machinery required in all cells for secretion and of cell-type specific secreted proteins. Drosophila CrebA directly binds the enhancers of secretory pathway genes and is both necessary and sufficient to activate expression of every secretory pathway component gene examined thus far. Microarray profiling reveals that CrebA also upregulates expression of genes encoding cell type specific secreted components. Finally, we find that the human CrebA orthologues, Creb3L1 and Creb3L2, have the ability to upregulate the secretory pathway in non-secretory cell types. Goals: The goals of the microarray experiments were to identify additional targets of the CrebA transcription factor to learn the range of genes regulated by this transcription factor during embryogenesis. Previous work had indicated that CrebA upregulates the protein machinery of the early secretory pathway. Our new data now shows that in addition to the protein machinery, CrebA also upregulates genes encoding the protein cargo that is secreted from specialized secretory organs. RNA was isolated from stage 11-15 wild type Drosophila embryos and compared to RNA from CrebA null mutant embryos of the same age; all samples were hybridized to the Drosophila Genome 2.0 Affymetrix array. Three individual replicates were obtained for each sample.
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Project description:Abstract: Secretion occurs in all cells, with relatively low levels in most cells and extremely high levels in specialized secretory cells, such as those of the pancreas, salivary and mammary glands. Here, we report that the CrebA/Creb3-like family of bZip transcription factors functions to upregulate expression of both the general protein machinery required in all cells for secretion and of cell-type specific secreted proteins. Drosophila CrebA directly binds the enhancers of secretory pathway genes and is both necessary and sufficient to activate expression of every secretory pathway component gene examined thus far. Microarray profiling reveals that CrebA also upregulates expression of genes encoding cell type specific secreted components. Finally, we find that the human CrebA orthologues, Creb3L1 and Creb3L2, have the ability to upregulate the secretory pathway in non-secretory cell types. Goals: The goals of the microarray experiments were to identify additional targets of the CrebA transcription factor to learn the range of genes regulated by this transcription factor during embryogenesis. Previous work had indicated that CrebA upregulates the protein machinery of the early secretory pathway. Our new data now shows that in addition to the protein machinery, CrebA also upregulates genes encoding the protein cargo that is secreted from specialized secretory organs.
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