A Human IgSF Cell-Surface Interactome Reveals a Complex Network of Protein-Protein Interactions.

Cell-surface protein-protein interactions (PPIs) mediate cell-cell communication, recognition, and responses. We executed an interactome screen of 564 human cell-surface and secreted proteins, most of which are immunoglobulin superfamily (IgSF) proteins, using a high-throughput, automated ELISA-based screening platform employing a pooled-protein strategy to test all 318,096 PPI combinations. Screen results, augmented by phylogenetic homology analysis, revealed ∼380 previously unreported PPIs. We validated a subset using surface plasmon resonance and cell binding assays. Observed PPIs reveal a large and complex network of interactions both within and across biological systems. We identified new PPIs for receptors with well-characterized ligands and binding partners for "orphan" receptors. New PPIs include proteins expressed on multiple cell types and involved in diverse processes including immune and nervous system development and function, differentiation/proliferation, metabolism, vascularization, and reproduction. These PPIs provide a resource for further biological investigation into their functional relevance and may offer new therapeutic drug targets.

An extracellular interactome of immunoglobulin and LRR proteins reveals receptor-ligand networks.

Extracellular domains of cell surface receptors and ligands mediate cell-cell communication, adhesion, and initiation of signaling events, but most existing protein-protein "interactome" data sets lack information for extracellular interactions. We probed interactions between receptor extracellular domains, focusing on a set of 202 proteins composed of the Drosophila melanogaster immunoglobulin superfamily (IgSF), fibronectin type III (FnIII), and leucine-rich repeat (LRR) families, which are known to be important in neuronal and developmental functions. Out of 20,503 candidate protein pairs tested, we observed 106 interactions, 83 of which were previously unknown. We "deorphanized" the 20 member subfamily of defective-in-proboscis-response IgSF proteins, showing that they selectively interact with an 11 member subfamily of previously uncharacterized IgSF proteins. Both subfamilies interact with a single common "orphan" LRR protein. We also observed interactions between Hedgehog and EGFR pathway components. Several of these interactions could be visualized in live-dissected embryos, demonstrating that this approach can identify physiologically relevant receptor-ligand pairs.

Architecture of the human regulatory network derived from ENCODE data.

Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the principles of the human transcriptional regulatory network, we determined the genomic binding information of 119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations. In particular, there are significant differences in the binding proximal and distal to genes. We organized all the transcription factor binding into a hierarchy and integrated it with other genomic information (for example, microRNA regulation), forming a dense meta-network. Factors at different levels have different properties; for instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome sequences and understanding basic principles of human biology and disease.

Panel of genes transcriptionally up-regulated in squamous cell carcinoma of the cervix identified by representational difference analysis, confirmed by macroarray, and validated by real-time quantitative reverse transcription-PCR.

BACKGROUND: The Pap smear is currently the most widely used method of screening for squamous cell carcinoma of the cervix (SCCC). Because it is based on cell morphology, it is subject to variability in interpretation. Sensitive molecular markers capable of differentiating cancerous samples from noncancerous ones would be beneficial in this regard. METHODS: We performed representational difference analysis (RDA) using paired, noncancerous (normal) and cancerous (disease) tissues taken from the same specimen obtained from a single patient with a confirmed diagnosis of SCCC. Linearly amplified cDNA from normal and diseased tissues of the original patient and seven others were hybridized to DNA macroarrays containing the candidate gene transcript fragments. Real-time quantitative reverse transcription-PCR was used to validate the macroarray results. RESULTS: RDA identified a candidate pool of 65 transcript fragments up-regulated in diseased tissue compared with normal tissue. Forty-one transcripts were found to be up-regulated in diseased compared with normal tissue in at least one half the patients by macroarray hybridization. Eleven of those genes were selected for real-time quantitative reverse transcription-PCR analysis, and all were confirmed as transcriptionally up-regulated in cancer compared with normal tissue in at least one half the patients. CONCLUSIONS: RDA using tissues from a single patient identified gene fragments confirmed to be transcriptionally up-regulated in SCCC both in the original patient and in seven others. The confirmed genes have a variety of functions and also have the potential to serve as diagnostic or prognostic markers.