Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.

Interactome networks and human disease

ConsensusPathDB consists of a comprehensive collection of human (as well as mouse and yeast) molecular interaction data integrated from 32 different public repositories and a web interface featuring a set of computational methods and visualization tools to explore these data. This protocol describes the use of ConsensusPathDB (http://consensuspathdb.org) with respect to the functional and network-based characterization of biomolecules (genes, proteins and metabolites) that are submitted to the system either as a priority list or together with associated experimental data such as RNA-seq. The tool reports interaction network modules, biochemical pathways and functional information that are significantly enriched by the user's input, applying computational methods for statistical over-representation, enrichment and graph analysis. The results of this protocol can be observed within a few minutes, even with genome-wide data. The resulting network associations can be used to interpret high-throughput data mechanistically, to characterize and prioritize biomarkers, to integrate different omics levels, to design follow-up functional assay experiments and to generate topology for kinetic models at different scales.

/pdf/analyzing-and-interpreting-genome-data-at-the-network-level-3437s6ssr5.pdf

Analyzing and interpreting genome data at the network level with ConsensusPathDB.

Studying protein interaction networks of all proteins in an organism ("interactomes") remains one of the major challenges in modern biomedicine. Such information is crucial to understanding cellular pathways and developing effective therapies for the treatment of human diseases. Over the past two decades, diverse biochemical, genetic, and cell biological methods have been developed to map interactomes. In this review, we highlight basic principles of interactome mapping. Specifically, we discuss the strengths and weaknesses of individual assays, how to select a method appropriate for the problem being studied, and provide general guidelines for carrying out the necessary follow-up analyses. In addition, we discuss computational methods to predict, map, and visualize interactomes, and provide a summary of some of the most important interactome resources. We hope that this review serves as both a useful overview of the field and a guide to help more scientists actively employ these powerful approaches in their research.

https://www.embopress.org/doi/pdf/10.15252/msb.20156351

Fundamentals of protein interaction network mapping

The current wealth of genomic variation data identified at nucleotide level presents the challenge of understanding by which mechanisms amino acid variation affects cellular processes. These effects may manifest as distinct phenotypic differences between individuals or result in the development of disease. Physical interactions between molecules are the linking steps underlying most, if not all, cellular processes. Understanding the effects that sequence variation has on a molecule's interactions is a key step towards connecting mechanistic characterization of nonsynonymous variation to phenotype. We present an open access resource created over 14 years by IMEx database curators, featuring 28,000 annotations describing the effect of small sequence changes on physical protein interactions. We describe how this resource was built, the formats in which the data is provided and offer a descriptive analysis of the data set. The data set is publicly available through the IntAct website and is enhanced with every monthly release.

/pdf/capturing-variation-impact-on-molecular-interactions-in-the-337voheaye.pdf

Capturing variation impact on molecular interactions in the IMEx Consortium mutations data set

Biochemistry and regulation of the protein arginine methyltransferases (PRMTs).

To accelerate high-density interactome mapping, we developed a yeast two-hybrid interaction screening approach involving short-read second-generation sequencing (Y2H-seq) with improved sensitivity and a quantitative scoring readout allowing rapid interaction validation. We applied Y2H-seq to investigate enzymes involved in protein methylation, a largely unexplored post-translational modification. The reported network of 523 interactions involving 22 methyltransferases or demethylases is comprehensively annotated and validated through coimmunoprecipitation experiments and defines previously undiscovered cellular roles of nonhistone protein methylation.

/pdf/a-y2h-seq-approach-defines-the-human-protein-4y034yy9d9.pdf

A Y2H-seq approach defines the human protein methyltransferase interactome.

Post-translational protein modifications, such as tyrosine phosphorylation, regulate protein–protein interactions (PPIs) critical for signal processing and cellular phenotypes. We extended an established yeast two-hybrid system employing human protein kinases for the analyses of phospho-tyrosine (pY)-dependent PPIs in a direct experimental, large-scale approach. We identified 292 mostly novel pY-dependent PPIs which showed high specificity with respect to kinases and interacting proteins and validated a large fraction in co-immunoprecipitation experiments from mammalian cells. About one-sixth of the interactions are mediated by known linear sequence binding motifs while the majority of pY-PPIs are mediated by other linear epitopes or governed by alternative recognition modes. Network analysis revealed that pY-mediated recognition events are tied to a highly connected protein module dedicated to signaling and cell growth pathways related to cancer. Using binding assays, protein complementation and phenotypic readouts to characterize the pY-dependent interactions of TSPAN2 (tetraspanin 2) and GRB2 or PIK3R3 (p55γ), we exemplarily provide evidence that the two pY-dependent PPIs dictate cellular cancer phenotypes.

https://www.embopress.org/doi/pdf/10.15252/msb.20145968

Phospho‐tyrosine dependent protein–protein interaction network

The ability of the glucocorticoid receptor (GR) to regulate the transcriptional output of genes relies on its interactions with transcriptional coregulators. However, which coregulators are required for GR-dependent activation is context-dependent and can be influenced by the sequence of the DNA bound by GR and by the nature of the GR isoform responsible for the regulation of a gene. Here, we screened for GR-interacting proteins for which the interaction signal differed between two GR isoforms GRα and GRγ. These isoforms diverge by a single amino acid insertion in a domain, the lever arm, which adopts DNA sequence-specific conformations. We identify Basic Leucine Zipper ATF-Like Transcription Factor 3 (BATF3), an AP-1 family transcription factor, as a GR coregulator whose interaction with GR is modulated by the lever arm. Further, a combination of experiments uncovered that BATF3 acts as a gene-specific coactivator of GR whose coactivator potency is influenced by the sequence of the GR binding site. Together, our findings suggest that GR isoform and the sequence of GR binding site influence the interaction of GR with BATF3, which might direct the assembly of gene-specific regulatory complexes to fine-tune the expression of individual GR target genes.

/pdf/identification-and-characterization-of-batf3-as-a-context-1gguuirsal.pdf

Petra Birth

Papers

A Y2H-seq approach defines the human protein methyltransferase interactome.

Phospho‐tyrosine dependent protein–protein interaction network

Identification and characterization of BATF3 as a context-specific coactivator of the glucocorticoid receptor.