Cell type-selective disease-association of genes under high regulatory load

We previously showed that disease-linked metabolic genes are often under combinatorial regulation. Using the genome-wide ChIP-Seq binding profiles for 93 transcription factors in nine different cell lines, we show that genes under high regulatory load are significantly enriched for disease-association across cell types. We find that transcription factor load correlates with the enhancer load of the genes and thereby allows the identification of genes under high regulatory load by epigenomic mapping of active enhancers. Identification of the high enhancer load genes across 139 samples from 96 different cell and tissue types reveals a consistent enrichment for disease-associated genes in a cell type-selective manner. The underlying genes are not limited to super-enhancer genes and show several types of disease-association evidence beyond genetic variation (such as biomarkers). Interestingly, the high regulatory load genes are involved in more KEGG pathways than expected by chance, exhibit increased betweenness centrality in the interaction network of liver disease genes, and carry longer 3'UTRs with more microRNA (miRNA) binding sites than genes on average, suggesting a role as hubs integrating signals within regulatory networks. In summary, epigenetic mapping of active enhancers presents a promising and unbiased approach for identification of novel disease genes in a cell type-selective manner

INTEGRATED ANALYSIS OF TRANSCRIPT-LEVEL REGULATION OF HUMAN ADIPOGENESIS AND CELL TYPE-SELECTIVE DISEASE ASSOCIATION OF HIGH REGULATORY LOAD GENES

Most diseases are characterized by altered epigenetic and metabolic states, pointing to the need of a global and combined understanding of mechanisms underlying epigenetic and metabolic changes as an important piece to enable disease eradication. Adipocytes impact systemic homeostasis and their differentiation encompasses a phenotypic change whose function becomes impaired with diseases such as obesity and metabolic syndrome. Following an integrative systems biology approach, we combined different omics data from the differentiation of Simpson-Golabi-Behmel syndrome (SGBS) adipocytes with a human metabolic model to observe key metabolic changes upon differentiation, their regulation and relevance for disease. Pursuing the link to disease, we used public data from the genome-wide binding of TFs and location of active enhancers to test for disease association in function of the regulatory load, revealing a cell type-selective enrichment for disease of the high regulatory load genes. Diverse experimental data were collected, covering a gene expression time-course during adipogenesis, with identification of miR-27a, miR-29a and miR-222 target genes, the genome-wide binding profiles of PPARg, C/EBPa and LXRa , and the H3K4me3 histone modification mark for actively transcribed transcription start sites (TSSs). Metabolic genes showed a highly dynamic expression pattern during adipogenesis, most being targeted by PPARg and C/EBPa . Lipid metabolism pathways including triacylglyceride synthesis showed extensive and combinatorial regulation by TFs and miRNAs, converging on known dyslipidemia genes. For data visualization, we developed a web portal that interactively renders metabolic pathways with omics data overlaid (IDARE, http://systemsbiology.uni.lu/idare.html). Public ChIP-seq data revealed a general principle of higher disease association of genes under higher regulatory control in a cell type-selective manner. First, data from the genome-wide binding of 10 TFs in HUVEC cells showed an enrichment for vascular diseases on metabolic genes targeted by > 6 TFs. Second, data from the binding of a total of 93 TFs confirmed the enrichment for disease association of genes with the top TF load in 8 additional cell lines. Finally, active enhancer data from 139 samples spanning 96 cell types and tissues demonstrated the cell type-selective disease enrichment of the genes with the highest active enhancer load. High regulatory load genes enriched for disease association beyond genetic variation, including association types like “altered expression” and “biomarker”, among others. Additionally, high regulatory load genes appeared on average in more KEGG pathways and had higher betweenness centrality in a liver disease network than other genes, showing longer 3’UTRs harboring more binding sites for diverse microRNA families, suggesting also a higher post-transcriptional regulatory load and a role as signal integrators within biological networks. Our results point to the pertinence of including high regulatory load genes for unbiased prioritization of novel candidate genes for disease association

Transcriptomics profiling of human SGBS adipogenesis

Obesity is an ever-growing epidemic where tissue homeostasis is influenced by the differentiation of adipocytes that function in lipid metabolism, endocrine and inflammatory processes. While this differentiation process has been well-characterized in mice, limited data is available from human cells. Applying microarray expression profiling in the human SGBS pre-adipocyte cell line, we identified genes with differential expression during differentiation in combination with constraint-based modeling of metabolic pathway activity. Here we describe the experimental design and quality controls in detail for the gene expression and related results published by Galhardo et al. in Nucleic Acids Research 2014 associated with the data uploaded to NCBI Gene Expression Omnibus ()

Integrated analysis of transcript-level regulation of metabolism reveals disease-relevant nodes of the human metabolic network

Metabolic diseases and comorbidities represent an ever-growing epidemic where multiple cell types impact tissue homeostasis. Here, the link between the metabolic and gene regulatory networks was studied through experimental and computational analysis. Integrating gene regulation data with a human metabolic network prompted the establishment of an open-sourced web portal, IDARE (Integrated Data Nodes of Regulation), for visualizing various gene-related data in context of metabolic pathways. Motivated by increasing availability of deep sequencing studies, we obtained ChIP-seq data from widely studied human umbilical vein endothelial cells. Interestingly, we found that association of metabolic genes with multiple transcription factors (TFs) enriched disease-associated genes. To demonstrate further extensions enabled by examining these networks together, constraintbased modeling was applied to data from human preadipocyte differentiation. In parallel, data on gene expression, genome-wide ChIP-seq profiles for peroxisome proliferator-activated receptor (PPAR) c, CCAAT/enhancer binding protein (CEBP) a, liver X receptor (LXR) and H3K4me3 and microRNA target identification for miR-27a, miR-29a and miR-222 were collected. Disease-relevant key nodes, including mitochondrial glycerol-phosphateacyltransferase (GPAM), were exposed from metabolic pathways predicted to change activity by focusing on association with multiple regulators. In both cell types, our analysis reveals the convergence of microRNAs and TFs within the branched chain amino acid (BCAA) metabolic pathway, possibly providing an explanation for its downregulation in obese and diabetic conditions

Characterisation of microbial attack on archaeological bone

As part of an EU funded project to investigate the factors influencing bone preservation in the archaeological record, more than 250 bones from 41 archaeological sites in five countries spanning four climatic regions were studied for diagenetic alteration. Sites were selected to cover a range of environmental conditions and archaeological contexts. Microscopic and physical (mercury intrusion porosimetry) analyses of these bones revealed that the majority (68%) had suffered microbial attack. Furthermore, significant differences were found between animal and human bone in both the state of preservation and the type of microbial attack present. These differences in preservation might result from differences in early taphonomy of the bones. © 2003 Elsevier Science Ltd. All rights reserved