References of "John, Elisabeth 50002046"
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See detailThe Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease
Noronha, Alberto UL; Modamio Chamarro, Jennifer UL; Jarosz, Yohan UL et al

in Nucleic Acids Research (2018)

A multitude of factors contribute to complex diseases and can be measured with ‘omics’ methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic ... [more ▼]

A multitude of factors contribute to complex diseases and can be measured with ‘omics’ methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic Human (VMH, www.vmh.life) database encapsulating current knowledge of human metabolism within five interlinked resources ‘Human metabolism’, ‘Gut microbiome’, ‘Disease’, ‘Nutrition’, and ‘ReconMaps’. The VMH captures 5180 unique metabolites, 17 730 unique reactions, 3695 human genes, 255 Mendelian diseases, 818 microbes, 632 685 microbial genes and 8790 food items. The VMH’s unique features are (i) the hosting of the metabolic reconstructions of human and gut microbes amenable for metabolic modeling; (ii) seven human metabolic maps for data visualization; (iii) a nutrition designer; (iv) a user-friendly webpage and application-programming interface to access its content; (v) user feedback option for community engagement and (vi) the connection of its entities to 57 other web resources. The VMH represents a novel, interdisciplinary database for data interpretation and hypothesis generation to the biomedical community. [less ▲]

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See detailIntegrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network
Pacheco, Maria UL; John, Elisabeth UL; Kaoma, Tony et al

in BMC Genomics (2015), 16(809),

Background: The reconstruction of context-specific metabolic models from easily and reliably measurable features such as transcriptomics data will be increasingly important in research and medicine ... [more ▼]

Background: The reconstruction of context-specific metabolic models from easily and reliably measurable features such as transcriptomics data will be increasingly important in research and 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. Methods: 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. Results: 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. Conclusions: 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. [less ▲]

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See detailCombinatorial regulation of lipoprotein lipase by microRNAs during mouse adipogenesis
Liivrand, Maria UL; Heinäniemi, Merja UL; John, Elisabeth UL et al

in RNA Biology (2014), 11(1), 76-91

MicroRNAs (miRNAs) regulate gene expression directly through base pairing to their targets or indirectly through participating in multi-scale regulatory networks. Often miRNAs take part in feed-forward ... [more ▼]

MicroRNAs (miRNAs) regulate gene expression directly through base pairing to their targets or indirectly through participating in multi-scale regulatory networks. Often miRNAs take part in feed-forward motifs where a miRNA and a transcription factor act on shared targets to achieve accurate regulation of processes such as cell differentiation. Here we show that the expression levels of miR-27a and miR-29a inversely correlate with the mRNA levels of lipoprotein lipase (Lpl), their predicted combinatorial target, and its key transcriptional regulator peroxisome proliferator activated receptor gamma (Pparg) during 3T3-L1 adipocyte differentiation. More importantly, we show that Lpl, a key lipogenic enzyme, can be negatively regulated by the two miRNA families in a combinatorial fashion on the mRNA and functional level in maturing adipocytes. This regulation is mediated through the Lpl 3′UTR as confirmed by reporter gene assays. In addition, a small mathematical model captures the dynamics of this feed-forward motif and predicts the changes in Lpl mRNA levels upon network perturbations. The obtained results might offer an explanation to the dysregulation of LPL in diabetic conditions and could be extended to quantitative modeling of regulation of other metabolic genes under similar regulatory network motifs. [less ▲]

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See detailDataset integration identifies transcriptional regulation of microRNA genes by PPARgamma in differentiating mouse 3T3-L1 adipocytes
John, Elisabeth UL; Wienecke-Baldacchino, Anke UL; Liivrand, Maria UL et al

in Nucleic Acids Research (2012), 40(10), 4446-4460

Peroxisome proliferator-activated receptor gamma (PPARgamma) is a key transcription factor in mammalian adipogenesis. Genome-wide approaches have identified thousands of PPARgamma binding sites in mouse ... [more ▼]

Peroxisome proliferator-activated receptor gamma (PPARgamma) is a key transcription factor in mammalian adipogenesis. Genome-wide approaches have identified thousands of PPARgamma binding sites in mouse adipocytes and PPARgamma upregulates hundreds of protein-coding genes during adipogenesis. However, no microRNA (miRNA) genes have been identified as primary PPARgamma-targets. By integration of four separate datasets of genome-wide PPARgamma binding sites in 3T3-L1 adipocytes we identified 98 miRNA clusters with PPARgamma binding within 50 kb from miRNA transcription start sites. Nineteen mature miRNAs were upregulated >/=2-fold during adipogenesis and for six of these miRNA loci the PPARgamma binding sites were confirmed by at least three datasets. The upregulation of five miRNA genes miR-103-1 (host gene Pank3), miR-148b (Copz1), miR-182/96/183, miR-205 and miR-378 (Ppargc1b) followed that of Pparg. The PPARgamma-dependence of four of these miRNA loci was demonstrated by PPARgamma knock-down and the loci of miR-103-1 (Pank3), miR-205 and miR-378 (Ppargc1b) were also responsive to the PPARgamma ligand rosiglitazone. Finally, chromatin immunoprecipitation analysis validated in silico predicted PPARgamma binding sites at all three loci and H3K27 acetylation was analyzed to confirm the activity of these enhancers. In conclusion, we identified 22 putative PPARgamma target miRNA genes, showed the PPARgamma dependence of four of these genes and demonstrated three as direct PPARgamma target genes in mouse adipogenesis. [less ▲]

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