SUMMARY Cellular processes often depend on stable physical associations between proteins. Despite recent progress, knowledge of the composition of human protein complexes remains limited. To close this gap, we applied an integrative global proteomic profiling approach, based on chromatographic separation of cultured human cell extracts into more than one thousand biochemical fractions which were subsequently analyzed by quantitative tandem mass spectrometry, to systematically identify a network of 13,993 high-confidence physical interactions among 3,006 stably-associated soluble human proteins. Most of the 622 putative protein complexes we report are linked to core biological processes, and encompass both candidate disease genes and unnanotated proteins to inform on mechanism. Strikingly, whereas larger multi-protein assemblies tend to be more extensively annotated and evolutionarily conserved, human protein complexes with 5 or fewer subunits are far more likely to be functionally un-annotated or restricted to vertebrates, suggesting more recent functional innovations.

Summary Mechanistic roles for many lncRNAs are poorly understood in part because their direct interactions with genomic loci and proteins are difficult to assess. Using a method to purify endogenous RNAs and their associated factors, we mapped the genomic binding sites for two highly expressed human lncRNAs, NEAT1 and MALAT1. We show that NEAT1 and MALAT1 localize to hundreds of genomic sites in human cells, primarily over active genes. NEAT1 and MALAT1 exhibit colocalization to many of these loci, but display distinct gene body binding patterns at these sites, suggesting independent but complementary functions for these RNAs. We also identified numerous proteins enriched by both lncRNAs, supporting complementary binding and function, in addition to unique associated proteins. Transcriptional inhibition or stimulation alters localization of NEAT1 on active chromatin sites, implying that underlying DNA sequence does not target NEAT1 to chromatin and that localization responds to cues involved in the transcription process.

Network “guilt by association” (GBA) is a proven approach for identifying novel disease genes based on the observation that similar mutational phenotypes arise from functionally related genes. In principle, this approach could account even for nonadditive genetic interactions, which underlie the synergistic combinations of mutations often linked to complex diseases. Here, we analyze a large-scale, human gene functional interaction network (dubbed HumanNet). We show that candidate disease genes can be effectively identified by GBA in cross-validated tests using label propagation algorithms related to Google's PageRank. However, GBA has been shown to work poorly in genome-wide association studies (GWAS), where many genes are somewhat implicated, but few are known with very high certainty. Here, we resolve this by explicitly modeling the uncertainty of the associations and incorporating the uncertainty for the seed set into the GBA framework. We observe a significant boost in the power to detect validated candidate genes for Crohn's disease and type 2 diabetes by comparing our predictions to results from follow-up meta-analyses, with incorporation of the network serving to highlight the JAK–STAT pathway and associated adaptors GRB2/SHC1 in Crohn's disease and BACH2 in type 2 diabetes. Consideration of the network during GWAS thus conveys some of the benefits of enrolling more participants in the GWAS study. More generally, we demonstrate that a functional network of human genes provides a valuable statistical framework for prioritizing candidate disease genes, both for candidate gene-based and GWAS-based studies.

Nucleosomes play important structural and regulatory roles by tightly wrapping the DNA that constitutes the metazoan genome. The Polycomb group (PcG) proteins modulate nucleosomes to maintain repression of key developmental genes, including Hox genes whose temporal and spatial expression is tightly regulated to guide patterning of the anterior-posterior body axis. CBX2, a component of the mammalian Polycomb Repressive Complex 1 (PRC1), contains a ‘compaction region’ that has the biochemically-defined activity of bridging adjacent nucleosomes. Here we demonstrate that a functional compaction region is necessary for proper body patterning, as mutating this region leads to homeotic transformations similar to those observed with PcG loss-of-function mutations. We propose that CBX2-driven nucleosome compaction is a key mechanism by which PcG proteins maintain gene silencing during mouse development.