The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease.

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.

Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network.

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.

Combinatorial regulation of lipoprotein lipase by microRNAs during mouse adipogenesis.

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.

Killer Ig-like receptor 2DL4 does not mediate NK cell IFN-γ responses to soluble HLA-G preparations.

The MHC class Ib molecule HLA-G has previously been reported to be the ligand for the NK cell receptor killer Ig-like receptor (KIR)2DL4, but this remains controversial. In this study, we investigated IFN-γ production by freshly isolated NK cells in response to both soluble and solid-phase ligands, including anti-KIR2DL4 mAbs and rHLA-G. Although freshly isolated CD56(bright) NK cells produced IFN-γ in response to soluble HLA-G preparations, the response was found to be absolutely dependent on the presence of small numbers of contaminating CD56(-), CD14(-), CD11c(+) myeloid dendritic cells (mDCs). HLA-G tetramers bound only to the contaminating mDCs in the NK preparations, and Abs to KIR2DL4 and HLA-G did not block NK cell IFN-γ production. NK cells did not respond to plate-bound HLA-G. Freshly isolated NK cells also produced IFN-γ in response to unpurified soluble anti-KIR2DL4 mAb but not to low endotoxin affinity-purified Ab. The data suggest that previous reports of functional interactions between KIR2DL4 and HLA-G may have resulted from the use of purified NK cells that were contaminated with mDCs and HLA-G preparations that were contaminated with material capable of stimulating mDCs to produce cytokines that stimulate NK cells to produce IFN-γ.

Dataset integration identifies transcriptional regulation of microRNA genes by PPARγ in differentiating mouse 3T3-L1 adipocytes.

Peroxisome proliferator-activated receptor γ (PPARγ) is a key transcription factor in mammalian adipogenesis. Genome-wide approaches have identified thousands of PPARγ binding sites in mouse adipocytes and PPARγ upregulates hundreds of protein-coding genes during adipogenesis. However, no microRNA (miRNA) genes have been identified as primary PPARγ-targets. By integration of four separate datasets of genome-wide PPARγ binding sites in 3T3-L1 adipocytes we identified 98 miRNA clusters with PPARγ 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 PPARγ 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 PPARγ-dependence of four of these miRNA loci was demonstrated by PPARγ knock-down and the loci of miR-103-1 (Pank3), miR-205 and miR-378 (Ppargc1b) were also responsive to the PPARγ ligand rosiglitazone. Finally, chromatin immunoprecipitation analysis validated in silico predicted PPARγ binding sites at all three loci and H3K27 acetylation was analyzed to confirm the activity of these enhancers. In conclusion, we identified 22 putative PPARγ target miRNA genes, showed the PPARγ dependence of four of these genes and demonstrated three as direct PPARγ target genes in mouse adipogenesis.

Rapid, flow cytometric assay for NK alloreactivity reveals exceptions to rules governing alloreactivity.

Alloreactive NK cells lyse target cells lacking self-HLA-C or the HLA-B-Bw4 epitope. Prior to haploidentical stem cell transplants, donor alloreactivity toward the patient is evaluated by natural killer (NK) cloning followed by testing of the clones in the (51)Cr-release assay. As only a few percent of NK clones are alloreactive, a large number of NK clones must be established and evaluated. This approach is laborious and time consuming, with a complete evaluation taking up to 6 weeks. We developed a flow cytometry-based cytotoxicity assay utilizing CD107a expression on 12-day polyclonally expanded NK cells and showed that NK alloreactivity mediated by inhibitory and activating KIR can be detected by measuring CD107a expression following incubation with targets lacking the appropriate class I epitope. The percentage of alloreactive NK cells varied greatly between individuals and was easily estimated by the CD107a assay. For each epitope (C1, C2, Bw4), donors were found who did not have alloreactivity, although alloreactivity was predicted by the current rules thought to govern alloreactivity. The data emphasize the importance of demonstrating alloreactivity in a functional assay.

Further evidence for genetic heterogeneity of distal HMN type V, CMT2 with predominant hand involvement and Silver syndrome.

OBJECTIVE: Distal hereditary motor neuropathy type V (dHMN-V) and Charcot-Marie-Tooth syndrome (CMT) type 2 presenting with predominant hand involvement, also known as CMT2D and Silver syndrome (SS) are rare phenotypically overlapping diseases which can be caused by mutations in the Berardinelli-Seip Congenital Lipodystrophy 2 (BSCL2) and in the glycyl-tRNA synthetase encoding (GARS) genes. Mutations in the heat-shock proteins HSPB1 and HSPB8 can cause related distal hereditary motor neuropathies (dHMN) and are considered candidates for dHMN-V, CMT2, and SS. DESIGN: To define the frequency and distribution of mutations in the GARS, BSCL2, HSPB1 and HSPB8 genes we screened 33 unrelated sporadic and familial patients diagnosed as either dHMN-V, CMT2D or SS. Exon 3 of the BSCL2 gene was screened in further 69 individuals with an unclassified dHMN phenotype or diagnosed as hereditary spastic paraplegia (HSP) complicated by pure motor neuropathy. RESULTS: Four patients diagnosed with dHMN-V or SS carried known heterozygous BSCL2 mutations (N88S and S90L). In one dHMN-V patient we detected a putative GARS mutation (A57V). No mutations were detected in HSPB1 and HSPB8. The diagnostic yield gained in the series of 33 probands was 12% for BSCL2 mutations and 3% for GARS mutations. In the series of unclassified dHMN and complicated HSP cases no mutations were found. CONCLUSIONS: Our data confirm that most likely only two mutations (N88S, S90L) in exon 3 of BSCL2 may lead to dHMN-V or SS phenotypes. Mutations in GARS, HSPB1 and HSPB8. are not a common cause of dHMN-V, SS and CMT2D. We would therefore suggest that a genetic testing of dHMN-V and SS patients should begin with screening of exon 3 of the BSCL2 gene. Screening of the GARS gene is useful in patients with CMT2 with predominant hand involvement and dHMN-V. The rather low frequencies of BSCL2, GARS, HSPB1 and HSPB8 mutations in dHMN-V, CMT2D and SS patients strongly point to further genetic heterogeneity of these related disorders.