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 large collections of data sets covering thousands of experiments. GIANT integrates 987 data sets from approximately 14,000 publications into 144 tissue- and cell lineage-specific networks using Bayesian classification. MAGE integrates 7,463 data sets from more than 250,000 experiments into 289 tissue and cell-type networks using a masked graph autoencoder and gradient-boosted integration model. 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 pro-inflammatory 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 GIANT tissue network in predicting this experimental outcome; none of the other 143 GIANT 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.

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).