The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamics

It has been a long-standing goal in systems biology to find relations between the topological properties and functional features of protein networks. However, most of the focus in network studies has been on highly connected proteins (“hubs”). As a complementary notion, it is possible to define bottlenecks as proteins with a high betweenness centrality (i.e., network nodes that have many “shortest paths” going through them, analogous to major bridges and tunnels on a highway map). Bottlenecks are, in fact, key connector proteins with surprising functional and dynamic properties. In particular, they are more likely to be essential proteins. In fact, in regulatory and other directed networks, betweenness (i.e., “bottleneck-ness”) is a much more significant indicator of essentiality than degree (i.e., “hub-ness”). Furthermore, bottlenecks correspond to the dynamic components of the interaction network—they are significantly less well coexpressed with their neighbors than nonbottlenecks, implying that expression dynamics is wired into the network topology

Positional artifacts in microarrays: experimental verification and construction of COP, an automated detection tool

Microarray technology is currently one of the most widely-used technologies in biology. Many studies focus on inferring the function of an unknown gene from its co-expressed genes. Here, we are able to show that there are two types of positional artifacts in microarray data introducing spurious correlations between genes. First, we find that genes that are close on the microarray chips tend to have higher correlations between their expression profiles. We call this the ‘chip artifact’. Our calculations suggest that the carry-over during the printing process is one of the major sources of this type of artifact, which is later confirmed by our experiments. Based on our experiments, the measured intensity of a microarray spot contains 0.1% (for fully-hybridized spots) to 93% (for un-hybridized ones) of noise resulting from this artifact. Secondly, we, for the first time, show that genes that are close on the microtiter plates in microarray experiments also tend to have higher correlations. We call this the ‘plate artifact’. Both types of artifacts exist with different severity in all cDNA microarray experiments that we analyzed. Therefore, we develop an automated web tool—COP (COrrelations by Positional artifacts) to detect these artifacts in microarray experiments. COP has been integrated with the microarray data normalization tool, ExpressYourself, which is available at . Together, the two can eliminate most of the common noises in microarray data

iRegNet3D: three-dimensional integrated regulatory network for the genomic analysis of coding and non-coding disease mutations

The mechanistic details of most disease-causing mutations remain poorly explored within the context of regulatory networks. We present a high-resolution three-dimensional integrated regulatory network (iRegNet3D) in the form of a web tool, where we resolve the interfaces of all known transcription factor (TF)-TF, TF-DNA and chromatinchromatin interactions for the analysis of both coding and non-coding disease-associated mutations to obtain mechanistic insights into their functional impact. Using iRegNet3D, we find that disease-associated mutations may perturb the regulatory network through diverse mechanisms including chromatin looping. iRegNet3D promises to be an indispensable tool in large-scale sequencing and disease association studies

nature biotechnology VOLUME

To better understand the molecular mechanisms and genetic basis of human disease, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders by generating a three-dimensional, structurally resolved human interactome. This network consists of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense point mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, and that the disease specificity for different mutations of the same gene can be explained by their location within an interface. We also predict 292 candidate genes for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses. This work indicates that knowledge of how in-frame disease mutations alter specific interactions is critical to understanding pathogenesis. Structurally resolved interaction networks should be valuable tools for interpreting the wealth of data being generated by large-scale structural genomics and disease association studies. Over the past few decades, a tremendous amount of resources and effort have been invested in mapping human disease loci genetically and later physically 1 . Since the completion of the human genome sequence, especially with advances in genome-wide association studies and ongoing cancer genome sequencing projects, an impressive list of disease-associated genes and their mutations have been produced 2 . However, it has rarely been possible to translate this wealth of information on individual mutations and their association with disease into biological or therapeutic insights 3 . Most of the drugs approved by the US Food and Drug Administration today are palliative 4 -they merely treat symptoms, rather than targeting specific genes or pathways responsible, even if associated genes are known. One main reason for this lack of success is the complex genotype-tophenotype relationships among diseases and their associated genes and mutations. In particular, (i) the same gene can be associated with multiple disorders (gene pleiotropy); and (ii) mutations in any one of many genes can cause the same clinical disorder (locus heterogeneity). For example, mutations in TP53 are linked to 32 clinically distinguishable forms of cancer and cancer-related disorders, whereas mutations in any of at least 12 different genes can lead to long QT syndrome. With the publication of several large-scale protein-protein interaction networks in human 5-8 , researchers have recently begun to use complex cellular networks to explore these genotype-to-phenotype relationships 2,9 , on the basis that many proteins function by interacting with other proteins. However, most analyses model proteins as graph-theoretical nodes, ignoring the structural details of individual proteins and the spatial constraints of their interactions. Here, we investigate on a large-scale the underlying molecular mechanisms for the complex genotype-to-phenotype relationships by integrating three-dimensional (3D) atomic-level protein structure information with high-quality large-scale protein-protein interaction data. Within the framework of this structurally resolved protein interactome, we examine the relationships among human diseases and their associated genes and mutations. RESULTS Structurally resolved protein interactome for human disease We first combined 12,577 reliable literature-curated binary interactions filtered from six widely used databases 10-15 (Online Methods) and 8,173 well-verified, high-throughput, yeast two-hybrid (Y2H) interactions Next, we structurally resolved the interfaces of these interactions using a homology modeling approach 16 . We used both iPfam 17 and 3did 18 to identify the interfaces of two interacting proteins by mapping them to known atomic-resolution 3D structures of interactions in the Protein Data Bank (PDB) Finally, to compile a comprehensive list of disease-associated genes and their mutations, we combined information from both Online Mendelian Inheritance in Man (OMIM

Structural basis of TRAPPIII‐mediated Rab1 activation

The GTPase Rab1 is a master regulator of the early secretory pathway and is critical for autophagy. Rab1 activation is controlled by its guanine nucleotide exchange factor, the multisubunit TRAPPIII complex. Here, we report the 3.7 Å cryo‐EM structure of the Saccharomyces cerevisiae TRAPPIII complex bound to its substrate Rab1/Ypt1. The structure reveals the binding site for the Rab1/Ypt1 hypervariable domain, leading to a model for how the complex interacts with membranes during the activation reaction. We determined that stable membrane binding by the TRAPPIII complex is required for robust activation of Rab1/Ypt1 in vitro and in vivo, and is mediated by a conserved amphipathic α‐helix within the regulatory Trs85 subunit. Our results show that the Trs85 subunit serves as a membrane anchor, via its amphipathic helix, for the entire TRAPPIII complex. These findings provide a structural understanding of Rab activation on organelle and vesicle membranes