Novel insights into adipogenesis from omics data

Abstract

Obesity, the excess accumulation of adipose tissue, is one of the most pressing health problems in both the Western world and in developing countries. Adipose tissue growth results from two processes: the increase in number of adipocytes (hyperplasia) that develop from precursor cells, and the growth of individual fat cells (hypertrophy) due to incorporation of triglycerides. Adipogenesis, the process of fat cell development, has been extensively studied using various cell and animal models. While these studies pointed out a number of key factors involved in adipogenesis, the list of molecular components is far from complete. The advance of high-throughput technologies has sparked many experimental studies aimed at the identification of novel molecular components regulating adipogenesis. This paper examines the results of recent studies on adipogenesis using high-throughput technologies. Specifically, it provides an overview of studies employing microarrays for gene expression profiling and studies using gel based and non-gel based proteomics as well as a chromatin immunoprecipitation followed by microarray analysis (ChIP-chip) or sequencing (ChIP-seq). Due to the maturity of the technology, the bulk of the available data was generated using microarrays. Therefore these data sets were not only reviewed but also underwent meta analysis. The review also shows that large-scale omics technologies in conjunction with sophisticated bioinformatics analyses can provide not only a list of novel players, but also a global view on biological processes and molecular networks. Finally, developing technologies and computational challenges associated with the data analyses are highlighted, and an outlook on the questions not previously addressed is provided.

Fig. (1)

Development of mature adipocytes from preadipocytes. (Adopted from [119]). Dlk1 (Pref-1), Cebpb and Cebpd are expressed at the early stages of the differentiation process. After commitment, Pparg and Cebpa are upregulated and target many genes relevant for the function of mature adipocytes.

Fig. (2)

Gene expression profiles of selected candidates from 3T3-L1 time series experiments. ¹Raw data were downloaded from Gene Expression Omnibus (GEO) GSE6794, normalized using GCRMA and annotated with recent annotation files for Mu11kA and Mu11kB downloaded from the Affymetrix website. Log2ratios are visualized for each time point in relation to the preconfluent stage; ²Normalized expression data from Ross et al. 2002 were downloaded from the authors’ website http://wwwpersonal. umich.edu/~macdouga/MacDougaldLab.html and data set were filtered for genes, which did not show an average difference of >100 in at least one condition. Relative expression levels (log2ratios) were related to the 3T3-L1 preconfluent stage and averaged over two measurements; ³Data for Hackl et al. 2005 was derived from ArrayExpress (E-MARS-02), normalized as stated by the authors and ESTs were annotated using a current Refseq version. Relative expression levels (log2ratios) from different time points were related to the preconfluent state and averaged over 3 independent experiments; ⁴Raw data (CEL files) were downloaded from http://wwww.arcs.uams.edu/ directory/microarray.asp , normalized using GCRMA and annotated using an annotation file provided by Affymetrix for U74Av2 mouse microarrays. Relative gene expressions were related to time point 0 immediately before hormonal induction and data were averaged over 2 replicated experiments; data were color coded according to the color scheme added to the figure).