Tissue-specific Networks

The precise actions of genes are frequently dependent on their tissue context, and human diseases result from the disordered interplay of tissue- and cell lineage–specific processes. These factors combine to make the understanding of tissue-specific gene functions, disease pathophysiology and gene-disease associations particularly challenging.

Functional interactions

HumanBase builds genome-scale functional maps of human tissues by integrating a collection of data sets covering thousands of experiments contained in more than 14,000 distinct publications. To integrate these data, we automatically assess each data set for its relevance to each of 144 tissue- and cell lineage–specific functional contexts. The resulting functional maps provide a detailed portrait of protein function and interactions in specific human tissues and cell lineages ranging from B lymphocytes to the renal glomerulus and the whole brain. This approach allows us to profile the specialized function of genes in a high-throughput manner, even in tissues and cell lineages for which no or few tissue-specific data exist.

These maps can answer biological questions that are specific to a single gene in a single tissue. For example, we have used these maps for the gene IL1B (encoding interleukin (IL)-1β) in the blood vessel network, where it has a key role in inflammation, to predict lineage-specific responses to IL-1β stimulation, which we experimentally confirmed.

Examples

IL1B in blood vessel

We examined and experimentally verified the tissue-specific molecular response of blood vessel cells to stimulation by IL-1β (IL1B), a proinflammatory cytokine. We anticipated that the genes most tightly connected to IL1B in the blood vessel network would be among those responding to IL-1β stimulation in blood vessel cells. We tested this hypothesis by profiling the gene expression of human aortic smooth muscle cells (HASMCs; the predominant cell type in blood vessels) stimulated with IL-1β.

Examination of the genes whose expression was significantly upregulated at 2 h after stimulation showed that 18 of the 20 IL1B network neighbors were among the top 500 most upregulated genes in the experiment (P = 2.07 × 10−23). The blood vessel network was the most accurate tissue network in predicting this experimental outcome; none of the other 143 tissue-specific networks or the tissue-naive network performed as well when evaluated by each network’s ability to predict the result of IL-1β stimulation on the cells.

http://www.nature.com/ng/journal/v47/n6/images/ng.3259-F2.jpg

We anticipated that the genes most tightly connected to IL1B in the blood vessel network would be among those responding to IL-1β stimulation in blood vessel cells (a) The 20 genes most tightly connected to IL1B in the blood vessel network are shown. These genes are predicted to respond to IL-1β stimulation in blood vessel. (b) The bar plot shows the differential expression levels of the 20 IL1B neighbors measured in a microarray experiment at 0 h and 2 h after IL-1β stimulation in HASMCs, which constitute most of the blood vessel. Each bar represents the gene’s log ratio of mean expression at 2 h over its mean expression at 0 h. Error bars represent regularized pooled standard errors estimated by LIMMA (n = 4 biological replicates). Eighteen of the 20 IL1B network neighbors (labeled in bold) were found to be among the most significantly differentially expressed genes at 2 h relative to 0 h (P = 1.95 × 10−23).