MartínJiménez2017 - Genome-scale reconstruction of the human astrocyte metabolic network
MartínJiménez2017 - Genome-scale reconstruction of the human astrocyte metabolic network
This model is described in the article:
of the Human Astrocyte Metabolic Network.
Salazar-Barreto D, Barreto GE, González J.
Front Aging Neurosci 2017; 9: 23
Astrocytes are the most abundant cells of the central
nervous system; they have a predominant role in maintaining
brain metabolism. In this sense, abnormal metabolic states have
been found in different neuropathological diseases.
Determination of metabolic states of astrocytes is difficult to
model using current experimental approaches given the high
number of reactions and metabolites present. Thus, genome-scale
metabolic networks derived from transcriptomic data can be used
as a framework to elucidate how astrocytes modulate human brain
metabolic states during normal conditions and in
neurodegenerative diseases. We performed a Genome-Scale
Reconstruction of the Human Astrocyte Metabolic Network with
the purpose of elucidating a significant portion of the
metabolic map of the astrocyte. This is the first global
high-quality, manually curated metabolic reconstruction network
of a human astrocyte. It includes 5,007 metabolites and 5,659
reactions distributed among 8 cell compartments,
(extracellular, cytoplasm, mitochondria, endoplasmic reticle,
Golgi apparatus, lysosome, peroxisome and nucleus). Using the
reconstructed network, the metabolic capabilities of human
astrocytes were calculated and compared both in normal and
ischemic conditions. We identified reactions activated in these
two states, which can be useful for understanding the
astrocytic pathways that are affected during brain disease.
Additionally, we also showed that the obtained flux
distributions in the model, are in accordance with
literature-based findings. Up to date, this is the most
complete representation of the human astrocyte in terms of
inclusion of genes, proteins, reactions and metabolic pathways,
being a useful guide for in-silico analysis of several
metabolic behaviors of the astrocyte during normal and
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Project description:Cellular decision-making is mediated by a complex interplay of external stimuli with the intracellular environment, in particular transcription factor regulatory networks. Here we have determined the expression of a network of 18 key haematopoietic transcription factors (TFs) in 597 single primary blood stem and progenitor cells isolated from mouse bone marrow. We demonstrate that different stem/progenitor populations are characterised by distinctive TF expression states, and through comprehensive bioinformatic analysis reveal positively and negatively correlated TF pairings, including previously unrecognised relationships between Gata2, Gfi1 and Gfi1b. Validation using transcriptional and transgenic assays confirmed direct regulatory interactions consistent with a novel regulatory triad in immature blood stem cells, where Gata2 may function to modulate cross-inhibition between Gfi1 and Gfi1b. Single cell expression profiling therefore identifies network states and allows reconstruction of network hierarchies involved in controlling stem cell fate choices, and provides a blueprint for studying both normal development and human disease. Examination of Gata2 and Gfi1 binding patterns in murine mast cells
Project description:By integrating sequence information from closely related bacteria with a compendium of high-throughput gene expression datasets, a large-scale transcriptional regulatory networks was constructed for Rhodobacter sphaeroides. Predictions from this network were validated in part using genome-wide analysis for 3 transcription factors (PpsR, RSP_0489 and RSP_3341). Genome-wide protein-DNA interaction analysis of 3 transcription factors predicted to be involved in photosynthesis (PpsR), carbon metabolism (RSP_0489) and iron homeostasis (RSP_3341) were used to validate predictions from a large-scale reconstruction of R. sphaeroides transcriptional regulatory network.
Project description:The reconstruction of cell type- and patient-specific metabolic models from easily and reliably measurable features such as transcriptomics data will be increasingly important at the age of personalized medicine. Current reconstruction methods suffer from high computational effort and arbitrary threshold setting. Moreover, understanding the underlying epigenetic regulation might allow the identification of putative intervention points within metabolic networks. Genes under high regulatory load from multiple enhancers or super-enhancers are known key genes for disease and cell identity. However, their role in regulation of metabolism and their placement within the metabolic networks has not been studied. Here we present FASTCORMICS, a fast and robust workflow for the creation of high-quality metabolic models from transcriptomics data. FASTCORMICS is devoid of arbitrary parameter settings and due to its low computational demand allows cross-validation assays.. Applying FASTCORMICS, we have generated models for 63 primary human cell types from microarray data, revealing significant differences in their metabolic networks. To understand the cell type-specific regulation of the alternative metabolic pathways we built multiple models during differentiation of primary human monocytes to macrophages and performed ChIP-Seq experiments for histone H3 K27 acetylation (H3K27ac) to map the active enhancers in macrophages. Focusing on the metabolic genes under high regulatory load from multiple enhancers or super-enhancers, we found these genes to show the most cell type-restricted and abundant expression profiles within their respective pathways. Importantly, the high regulatory load genes are associated to reactions enriched for transport reactions and other pathway entry points, suggesting that they are critical regulatory control points for cell type-specific metabolism. By integrating metabolic modelling and epigenomic analysis we have identified high regulatory load as a common feature of metabolic genes at pathway entry points such as transporters within the macrophage metabolic network. Analysis of these control points through further integration of metabolic and gene regulatory networks in various contexts could be beneficial in multiple fields from identification of disease intervention strategies to cellular reprogramming. ChIP-Seq was performed with chromatin from macrophages differentiated in vitro for 11 days from primary human CD14+ monocytes isolated from the blood of three different anonymous male donors. For each donor one ChIP sample using an antibody against H3K27ac and one input sample were sequenced. Please see the individual samples for further details.
Project description:Two human acute lymphoblastic leukemia cell lines (Molt-4 and CCRF-CEM) were treated with direct (A-769662) and indirect (AICAR) AMPK activators. Molt-4 and CCRF-CEM cells were obtained from ATCC (CRL-1582 and CCL-119). Control samples were used for the analysis of metabolic differences between cell lines. Therefore the data was analyzed in combination with, metabolomic data, and the genome-scale reconstruction of human metabolism. For experiments cells were grown in serum-free medium containing DMSO (0.67%) at a cell concentration of 5 x 105 cells/mL. Overall design: We analyzed acute lymphoblastic leukemia cell lines (Molt-4 and CCRF-CEM) treated with direct (A-769662) and indirect (AICAR) AMPK activators, along with controls. We used the Affymetrix GeneChip Human Exon 1.0 ST Array to measure whole genome exon expression.
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Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions.
Bordbar A, Lewis NE, Schellenberger J, Palsson BØ, Jamshidi N. Mol Syst Biol.
2010 Oct 19;6:422. PMID: 20959820
, DOI: 10.1038/msb.2010.68
Metabolic coupling of Mycobacterium tuberculosis to its host is foundational to its pathogenesis. Computational genome-scale metabolic models have shown utility in integrating -omic as well as physiologic data for systemic, mechanistic analysis of metabolism. To date, integrative analysis of host-pathogen interactions using in silico mass-balanced, genome-scale models has not been performed. We, therefore, constructed a cell-specific alveolar macrophage model, iAB-AMØ-1410, from the global human metabolic reconstruction, Recon 1. The model successfully predicted experimentally verified ATP and nitric oxide production rates in macrophages. This model was then integrated with an M. tuberculosis H37Rv model, iNJ661, to build an integrated host-pathogen genome-scale reconstruction, iAB-AMØ-1410-Mt-661. The integrated host-pathogen network enables simulation of the metabolic changes during infection. The resulting reaction activity and gene essentiality targets of the integrated model represent an altered infectious state. High-throughput data from infected macrophages were mapped onto the host-pathogen network and were able to describe three distinct pathological states. Integrated host-pathogen reconstructions thus form a foundation upon which understanding the biology and pathophysiology of infections can be developed.
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To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication
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To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
Project description:Large-scale sequencing of RNAs from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states. However, current single-cell RNA-sequencing (scRNA-seq) methods have limited ability to count RNAs at allele- and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells. Here, we introduce Smart-seq3 that combines full-length transcriptome coverage with a 5’ unique molecular identifier (UMI) RNA counting strategy that enabled in silico reconstruction of thousands of RNA molecules per cell. Importantly, a large portion of counted and reconstructed RNA molecules could be directly assigned to specific isoforms and allelic origin, and we identified significant transcript isoform regulation in mouse strains and human cell types. Moreover, Smart-seq3 showed a dramatic increase in sensitivity and typically detected thousands more genes per cell than Smart-seq2. Altogether, we developed a short-read sequencing strategy for single-cell RNA counting at isoform and allele-resolution applicable to large-scale characterization of cell types and states across tissues and organisms.
Project description:Malassezia species are lipophilic and lipid dependent yeasts belonging to the human and animal microbiota. Typically, they are isolated from regions rich in sebaceous glands. They have been associated with dermatological diseases such as seborrheic dermatitis, tinea versicolor, atopic dermatitis, and folliculitis. Genome sequences of Malassezia globosa, Malassezia sympodialis, and Malassezia pachydermatis lack genes related to fatty acid synthesis. Here, lipid synthesis pathways of M. furfur, M. pachydermatis, M. globosa, M. sympodialis and an atypical variant of M. furfur were reconstructed using genome data and Constraints Based Reconstruction and Analysis. The metabolic reconstruction allowed us to predict variation in the fluxes of each reaction over the network to satisfy the biomass objective function. Proteomic profiling improved and validated the models through data integration. Results suggest that several mechanisms including steroid and butanoate metabolism explain the yeast’s growth under different lipid conditions. Flux differences were observed in production of riboflavin in M. furfur and the biosynthesis of glycerolipids in the atypical variant of M. furfur and Malassezia sympodialis.