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# Adipose tissue transcriptomic signature highlights the pathological relevance of extracellular matrix in human obesity

Genome Biology

BioMed Central

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### Abstract

Analysis of the transcriptomic signature of white adipose tissue in obese human subjects revealed increased interstitial fibrosis and an infiltration of inflammatory cells into the tissue.

### Abstract

##### Background

Investigations performed in mice and humans have acknowledged obesity as a low-grade inflammatory disease. Several molecular mechanisms have been convincingly shown to be involved in activating inflammatory processes and altering cell composition in white adipose tissue (WAT). However, the overall importance of these alterations, and their long-term impact on the metabolic functions of the WAT and on its morphology, remain unclear.

##### Results

Here, we analyzed the transcriptomic signature of the subcutaneous WAT in obese human subjects, in stable weight conditions and after weight loss following bariatric surgery. An original integrative functional genomics approach was applied to quantify relations between relevant structural and functional themes annotating differentially expressed genes in order to construct a comprehensive map of transcriptional interactions defining the obese WAT. These analyses highlighted a significant up-regulation of genes and biological themes related to extracellular matrix (ECM) constituents, including members of the integrin family, and suggested that these elements could play a major mediating role in a chain of interactions that connect local inflammatory phenomena to the alteration of WAT metabolic functions in obese subjects. Tissue and cellular investigations, driven by the analysis of transcriptional interactions, revealed an increased amount of interstitial fibrosis in obese WAT, associated with an infiltration of different types of inflammatory cells, and suggest that phenotypic alterations of human pre-adipocytes, induced by a pro-inflammatory environment, may lead to an excessive synthesis of ECM components.

##### Conclusion

This study opens new perspectives in understanding the biology of human WAT and its pathologic changes indicative of tissue deterioration associated with the development of obesity.

### Most cited references57

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### Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

(2000)
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### In silico prediction of protein-protein interactions in human macrophages

(2015)
Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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### Functional cartography of complex metabolic networks

,   (2005)
High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodology that enables us to extract and display information contained in complex networks. Specifically, we demonstrate that one can (i) find functional modules in complex networks, and (ii) classify nodes into universal roles according to their pattern of intra- and inter-module connections. The method thus yields a cartographic representation'' of complex networks. Metabolic networks are among the most challenging biological networks and, arguably, the ones with more potential for immediate applicability. We use our method to analyze the metabolic networks of twelve organisms from three different super-kingdoms. We find that, typically, 80% of the nodes are only connected to other nodes within their respective modules, and that nodes with different roles are affected by different evolutionary constraints and pressures. Remarkably, we find that low-degree metabolites that connect different modules are more conserved than hubs whose links are mostly within a single module.
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### Author and article information

###### Journal
Genome Biol
Genome Biology
BioMed Central
1465-6906
1465-6914
2008
21 January 2008
: 9
: 1
: R14
###### Affiliations
[1 ]INSERM, UMR-S 872, Les Cordeliers, Eq. 7 Nutriomique and Eq. 13, Paris, F-75006 France
[2 ]Pierre et Marie Curie-Paris 6 University, Cordeliers Research Center, UMR-S 872, Paris, F-75006 France
[3 ]Paris Descartes University, UMR-S 872, Paris, F-75006 France
[4 ]Assistance Publique-Hôpitaux de Paris (AP-HP), Pitié Salpêtrière Hospital, Nutrition and Endocrinology department, Paris, F-75013 France
[5 ]Franco-Czech Laboratory for Clinical Research on Obesity, INSERM and 3rd Faculty of Medicine, Charles University, Prague, CZ-10000, Czech Republic
[6 ]INSERM, U858, Obesity Research Laboratory, I2MR, Toulouse, F-31432 France
[7 ]Paul Sabatier University, Louis Bugnard Institute IFR31, Toulouse, F-31432 France
[8 ]Centre Hospitalier Universitaire de Toulouse, Toulouse, F-31059 France
[9 ]Assistance Publique-Hôpitaux de Paris (AP-HP), Beaujon Hospital, Pathology department, Clichy, F-92110 France
[10 ]CNRS, UMR 8149, Clichy, F-92110 France
[11 ]IRD UR Géodes, Centre IRD de l'Ile de France, Bondy, F-93143 France
###### Article
gb-2008-9-1-r14
10.1186/gb-2008-9-1-r14
2395253
18208606