Correlation of disorder between S. cerevisiae interacting proteins

Protein disorder has been frequently associated with protein-protein interaction. However, our knowledge of how protein disorder evolves within a network is limited. It is expected that physically interacting proteins evolve in a coordinated manner. This has so far been shown in their evolutionary rate, and in their gene expression levels. Here we examine the percentage of predicted disorder residues within binary and complex interacting proteins (physical and functional interactions respectively) to investigate how the disorder of a protein relates to that of its interacting partners. We show that the level of disorder of interacting proteins are correlated, with a greater correlation seen among proteins that are co-members of the same complex, and a lesser correlation between proteins that are documented as binary interactors of each other. There is a striking variation among complexes not only in their disorder, but in the extent to which the proteins within the complex differ in their levels of disorder, with RNA processes and protein binding complexes showing more variation in the disorder of their proteins, whilst other complexes show very little variation in the overall disorder of their constituent proteins. There is likely to be a stronger selection for complex subunits to have similar disorder, than is seen for proteins involved in binary interactions. Thus, binary interactions may be more resilient to changes in disorder than are complex interactions. These results add a new dimension to the role of disorder in protein networks, and highlight the potential importance of maintaining similar disorder in the members of a complex.     

Erosion of interaction networks in reduced and degraded genomes

Unlike eukaryotes, which often recruit duplicated genes into existing protein-protein interaction (PPI) networks, the low levels of gene duplication coupled with the high probability of lateral transfer of novel genes alters the manner in which PPI networks can evolve in bacteria. By inferring the PPIs present in the ancestor to contemporary Gammaproteobacteria, we were able to trace the changes in gene repertoires, and their consequences on PPI network evolution, in several bacterial lineages that have independently undergone reductions in genome size and genome contents. As genomes degrade, virtually all multi-partner proteins have lost interactors; however, the overall average number of connections increases due to the preferential elimination of proteins that interact with only one other protein partner. We also studied the effect of lateral gene transfer on PPI network evolution by analyzing the connectivity of genes that have been gained along the Escherichia coli lineage, as well as those acquired genes subsequently silenced in Shigella flexneri, since diverging from the gammaproteobacterial ancestor. The situation in PPI networks, in which newly acquired genes preferentially attach to the hubs of the network, contrasts that observed in metabolic networks, which evolve by the peripheral gain and loss of genes, and in regulatory networks, in which high connectivity increases the propensity of loss.     

The atypical protein kinase Cs. Functional specificity mediated by specific protein adapters

Since its discovery more than 10 years ago, the atypical PKC (aPKC) subfamily has attracted great interest. A number of reports have shown that the kinases of this subfamily play critical roles in signaling pathways that control cell growth, differentiation and survival. Recently, several investigators have identified a number of aPKC-interacting proteins whose characterization is helping to unravel the mechanisms of action and functions of these kinases. These interactors include p62, Par-6, MEK5 and Par-4. The details of how these adapters serve to link the aPKCs to different receptor signaling pathways and substrates in response to specific stimuli are crucial not only for developing an understanding of the roles and functions of the aPKCs themselves, but also for more generally establishing a view of how specificity in signal transduction is achieved.     

Simulated evolution of protein-protein interaction networks with realistic topology

We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a new interaction with some other protein. Since many interactions are due to simple surface compatibility, we hypothesize there is an increased likelihood of interacting with other proteins in the target protein's neighborhood. We find good agreement of the model on 10 different network properties compared to high-confidence experimental PPI networks in yeast, fruit flies, and humans. Key findings are: (1) PPI networks evolve modular structures, with no need to invoke particular selection pressures. (2) Proteins in cells have on average about 6 degrees of separation, similar to some social networks, such as human-communication and actor networks. (3) Unlike social networks, which have a shrinking diameter (degree of maximum separation) over time, PPI networks are predicted to grow in diameter. (4) The model indicates that evolutionarily old proteins should have higher connectivities and be more centrally embedded in their networks. This suggests a way in which present-day proteomics data could provide insights into biological evolution.     

Yeast homologues of three BLOC-1 subunits highlight KxDL proteins as conserved interactors of BLOC-1

Biogenesis of lysosome-related organelle complex-1 (BLOC-1) is one of the four multi-subunit complexes implicated in sorting cargo to lysosome-related organelles, as loss of function of any of these complexes causes Hermansky-Pudlak syndrome. Eight subunits of BLOC-1 interact with each other and with many other proteins. Identifying new interactors of BLOC-1 will increase understanding of its mechanism of action, and studies in model organisms are useful for finding such interactors. PSI-BLAST searches identify homologues in diverse model organisms, but there are significant gaps for BLOC-1, with none of its eight subunits found in Saccharomyces cerevisiae. Here we use more sensitive searches to identify distant homologues for three BLOC-1 subunits in S. cerevisiae: Blos1, snapin and cappuccino (cno). Published data on protein interactions show that in yeast these are likely to form a complex with three other proteins. One of these is the yeast homologue of the previously uncharacterized KxDL protein, which also interacts with Blos1 and cno in higher eukaryotes, suggesting that KxDL proteins are key interactors with BLOC-1.     

The identification of similarities between biological networks: application to the metabolome and interactome

The increasing interest in systems biology has resulted in extensive experimental data describing networks of interactions (or associations) between molecules in metabolism, protein-protein interactions and gene regulation. Comparative analysis of these networks is central to understanding biological systems. We report a novel method (PHUNKEE: Pairing subgrapHs Using NetworK Environment Equivalence) by which similar subgraphs in a pair of networks can be identified. Like other methods, PHUNKEE explicitly considers the graphical form of the data and allows for gaps. However, it is novel in that it includes information about the context of the subgraph within the adjacent network. We also explore a new approach to quantifying the statistical significance of matching subgraphs. We report similar subgraphs in metabolic pathways and in protein-protein interaction networks. The most similar metabolic subgraphs were generally found to occur in processes central to all life, such as purine, pyrimidine and amino acid metabolism. The most similar pairs of subgraphs found in the protein-protein interaction networks of Drosophila melanogaster and Saccharomyces cerevisiae also include central processes such as cell division but, interestingly, also include protein sub-networks involved in pre-mRNA processing. The inclusion of network context information in the comparison of protein interaction networks increased the number of similar subgraphs found consisting of proteins involved in the same functional process. This could have implications for the prediction of protein function.     

Baboon (Papio anubis) social complexity--a network approach

Although many studies have analyzed the causes and consequences of social relationships, few studies have explicitly assessed how measures of social relationships are affected by the choice of behaviors used to quantify them. The use of many behaviors to measure social relationships in primates has long been advocated, but it was analytically difficult to implement this framework into primatological work. However, recent advances in social network analysis (SNA) now allow the comparison of multiple networks created from different behaviors. Here we use our database of baboon social behavior (Papio anubis, Gashaka Gumti National Park, Nigeria) to investigate (i) to what extent social networks created from different behaviors overlap, (ii) to what extent individuals occupy similar social positions in these networks and (iii) how sex affects social network position in this population of baboons. We used data on grooming, aggression, displacement, mounting and presenting, which were collected over a 15-month period. We calculated network parameters separately for each behavior. Networks based on displacement, mounting and presenting were very similar to each other, whereas grooming and aggression networks differed both from each other and from mounting, displacement and presenting networks. Overall, individual network positions were strongly affected by sex. Individuals central in one network tended to be central in most other networks as well, whereas other measures such as clustering coefficient were found to vary depending on the behavior analyzed. Thus, our results suggest that a baboon's social environment is best described by a multiplex network based on affiliative, aggressive and sexual behavior. Modern SNA provides a number of useful tools that will help us to better understand animals' social environment. We also discuss potential caveats related to their use.     

Interactions of Prion Protein With Intracellular Proteins: So Many Partners and no Consequences?

Prion protein (PrP) plays a key role in the pathogenesis of transmissible spongiform encephalopathies (TSEs)—fatal diseases of the central nervous system. Its physiological function as well as exact role in neurodegeneration remain unclear, hence screens for proteins interacting with PrP seem to be the most promising approach to elucidating these issues. PrP is mostly a plasma membrane-anchored extracellular glycoprotein and only a small fraction resides inside the cell, yet the number of identified intracellular partners of PrP is comparable to that of its membranal or extracellular interactors. Since some TSEs are accompanied by significantly increased levels of cytoplasmic PrP and this fraction of the protein has been found to be neurotoxic, it is of particular interest to characterize the intracellular interactome of PrP. It seems reasonable that at elevated cytoplasmic levels, PrP may exert cytotoxic effect by affecting the physiological functions of its intracellular interactors. This review is focused on the cytoplasmic partners of PrP along with possible consequences of their binding.     

Survival of the sparsest: robust gene networks are parsimonious

Biological gene networks appear to be dynamically robust to mutation, stochasticity, and changes in the environment and also appear to be sparsely connected. Studies with computational models, however, have suggested that denser gene networks evolve to be more dynamically robust than sparser networks. We resolve this discrepancy by showing that misassumptions about how to measure robustness in artificial networks have inadvertently discounted the costs of network complexity. We show that when the costs of complexity are taken into account, that robustness implies a parsimonious network structure that is sparsely connected and not unnecessarily complex; and that selection will favor sparse networks when network topology is free to evolve. Because a robust system of heredity is necessary for the adaptive evolution of complex phenotypes, the maintenance of frugal network complexity is likely a crucial design constraint that underlies biological organization.     

Scalable optimal Bayesian classification of single-cell trajectories under regulatory model uncertainty

Background

Biological networks describes the mechanisms which govern cellular functions. Temporal networks show how these networks evolve over time. Studying the temporal progression of network topologies is of utmost importance since it uncovers how a network evolves and how it resists to external stimuli and internal variations. Two temporal networks have co-evolving subnetworks if the evolving topologies of these subnetworks remain similar to each other as the network topology evolves over a period of time. In this paper, we consider the problem of identifying co-evolving subnetworks given a pair of temporal networks, which aim to capture the evolution of molecules and their interactions over time. Although this problem shares some characteristics of the well-known network alignment problems, it differs from existing network alignment formulations as it seeks a mapping of the two network topologies that is invariant to temporal evolution of the given networks. This is a computationally challenging problem as it requires capturing not only similar topologies between two networks but also their similar evolution patterns.

Results

We present an efficient algorithm, Tempo, for solving identifying co-evolving subnetworks with two given temporal networks. We formally prove the correctness of our method. We experimentally demonstrate that Tempo scales efficiently with the size of network as well as the number of time points, and generates statistically significant alignments—even when evolution rates of given networks are high. Our results on a human aging dataset demonstrate that Tempo identifies novel genes contributing to the progression of Alzheimer’s, Huntington’s and Type II diabetes, while existing methods fail to do so.

Conclusions

Studying temporal networks in general and human aging specifically using Tempo enables us to identify age related genes from non age related genes successfully. More importantly, Tempo takes the network alignment problem one huge step forward by moving beyond the classical static network models.

     

Turning gold into ‘junk’: transposable elements utilize central proteins of cellular networks

The numerous discovered cases of domesticated transposable element (TE) proteins led to the recognition that TEs are a significant source of evolutionary innovation. However, much less is known about the reverse process, whether and to what degree the evolution of TEs is influenced by the genome of their hosts. We addressed this issue by searching for cases of incorporation of host genes into the sequence of TEs and examined the systems-level properties of these genes using the Saccharomyces cerevisiae and Drosophila melanogaster genomes. We identified 51 cases where the evolutionary scenario was the incorporation of a host gene fragment into a TE consensus sequence, and we show that both the yeast and fly homologues of the incorporated protein sequences have central positions in the cellular networks. An analysis of selective pressure (Ka/Ks ratio) detected significant selection in 37% of the cases. Recent research on retrovirus-host interactions shows that virus proteins preferentially target hubs of the host interaction networks enabling them to take over the host cell using only a few proteins. We propose that TEs face a similar evolutionary pressure to evolve proteins with high interacting capacities and take some of the necessary protein domains directly from their hosts.     

The Identification of Novel Protein-Protein Interactions in Liver that Affect Glucagon Receptor Activity

Glucagon regulates glucose homeostasis by controlling glycogenolysis and gluconeogenesis in the liver. Exaggerated and dysregulated glucagon secretion can exacerbate hyperglycemia contributing to type 2 diabetes (T2D). Thus, it is important to understand how glucagon receptor (GCGR) activity and signaling is controlled in hepatocytes. To better understand this, we sought to identify proteins that interact with the GCGR to affect ligand-dependent receptor activation. A Flag-tagged human GCGR was recombinantly expressed in Chinese hamster ovary (CHO) cells, and GCGR complexes were isolated by affinity purification (AP). Complexes were then analyzed by mass spectrometry (MS), and protein-GCGR interactions were validated by co-immunoprecipitation (Co-IP) and Western blot. This was followed by studies in primary hepatocytes to assess the effects of each interactor on glucagon-dependent glucose production and intracellular cAMP accumulation, and then in immortalized CHO and liver cell lines to further examine cell signaling. Thirty-three unique interactors were identified from the AP-MS screening of GCGR expressing CHO cells in both glucagon liganded and unliganded states. These studies revealed a particularly robust interaction between GCGR and 5 proteins, further validated by Co-IP, Western blot and qPCR. Overexpression of selected interactors in mouse hepatocytes indicated that two interactors, LDLR and TMED2, significantly enhanced glucagon-stimulated glucose production, while YWHAB inhibited glucose production. This was mirrored with glucagon-stimulated cAMP production, with LDLR and TMED2 enhancing and YWHAB inhibiting cAMP accumulation. To further link these interactors to glucose production, key gluconeogenic genes were assessed. Both LDLR and TMED2 stimulated while YWHAB inhibited PEPCK and G6Pase gene expression. In the present study, we have probed the GCGR interactome and found three novel GCGR interactors that control glucagon-stimulated glucose production by modulating cAMP accumulation and genes that control gluconeogenesis. These interactors may be useful targets to control glucose homeostasis in T2D.     

An integrated view of protein evolution

Why do proteins evolve at different rates? Advances in systems biology and genomics have facilitated a move from studying individual proteins to characterizing global cellular factors. Systematic surveys indicate that protein evolution is not determined exclusively by selection on protein structure and function, but is also affected by the genomic position of the encoding genes, their expression patterns, their position in biological networks and possibly their robustness to mistranslation. Recent work has allowed insights into the relative importance of these factors. We discuss the status of a much-needed coherent view that integrates studies on protein evolution with biochemistry and functional and structural genomics.     

Quantitative Proteomics Identified TTC4 as a TBK1 Interactor and a Positive Regulator of SeV‐Induced Innate Immunity

TBK1, STING, and MDA5 are important players within the antiviral innate immune response network. We mapped the interactome of endogenous TBK1, STING, and MDA5 by affinity enrichment MS in virally infected or uninfected THP‐1 cells. Based on quantitative data of more than 2000 proteins and stringent statistical analysis, 58 proteins were identified as high‐confidence interactors for at least one of three bait proteins. Our data indicated that TBK1 and MDA5 mostly interacted within preexisting protein networks, while STING interacted with different proteins with different viral infections. Functional analysis was performed on 17 interactors, and six were found to have functions in innate immune responses. We identified TTC4 as a TBK1 interactor and positive regulator of sendai virus‐induced innate immunity.     

Towards the prediction of protein interaction partners using physical docking

Deciphering the whole network of protein interactions for a given proteome (‘interactome’) is the goal of many experimental and computational efforts in Systems Biology. Separately the prediction of the structure of protein complexes by docking methods is a well‐established scientific area. To date, docking programs have not been used to predict interaction partners. We provide a proof of principle for such an approach. Using a set of protein complexes representing known interactors in their unbound form, we show that a standard docking program can distinguish the true interactors from a background of 922 non‐redundant potential interactors. We additionally show that true interactions can be distinguished from non‐likely interacting proteins within the same structural family. Our approach may be put in the context of the proposed ‘funnel‐energy model’; the docking algorithm may not find the native complex, but it distinguishes binding partners because of the higher probability of favourable models compared with a collection of non‐binders. The potential exists to develop this proof of principle into new approaches for predicting interaction partners and reconstructing biological networks.     

The Central Region of the Drosophila Co-repressor Groucho as a Regulatory Hub

Groucho (Gro) is a Drosophila co-repressor that regulates the expression of a large number of genes, many of which are involved in developmental control. Previous studies have shown that its central region is essential for function even though its three domains are poorly conserved and intrinsically disordered. Using these disordered domains as affinity reagents, we have now identified multiple embryonic Gro-interacting proteins. The interactors include protein complexes involved in chromosome organization, mRNA processing, and signaling. Further investigation of the interacting proteins using a reporter assay showed that many of them modulate Gro-mediated repression either positively or negatively. The positive regulators include components of the spliceosomal subcomplex U1 small nuclear ribonucleoprotein (U1 snRNP). A co-immunoprecipitation experiment confirms this finding and suggests that a sizable fraction of nuclear U1 snRNP is associated with Gro. The use of RNA-seq to analyze the gene expression profile of cells subjected to knockdown of Gro or snRNP-U1-C (a component of U1 snRNP) showed a significant overlap between genes regulated by these two factors. Furthermore, comparison of our RNA-seq data with Gro and RNA polymerase II ChIP data led to a number of insights, including the finding that Gro-repressed genes are enriched for promoter-proximal RNA polymerase II. We conclude that the Gro central domains mediate multiple interactions required for repression, thus functioning as a regulatory hub. Furthermore, interactions with the spliceosome may contribute to repression by Gro.     

Systematic Screens for Proteins That Interact with the Mucolipidosis Type IV Protein TRPML1

Mucolipidosis type IV is a lysosomal storage disorder resulting from mutations in the MCOLN1 gene, which encodes the endosomal/lysosomal Transient Receptor Potential channel protein mucolipin-1/TRPML1. Cells isolated from Mucolipidosis type IV patients and grown in vitro and in in vivo models of this disease both show several lysosome-associated defects. However, it is still unclear how TRPML1 regulates the transport steps implicated by these defects. Identifying proteins that associate with TRPML1 will facilitate the elucidation of its cellular and biochemical functions. We report here two saturation screens for proteins that interact with TRPML1: one that is based on immunoprecipitation/mass spectrometry and the other using a genetic yeast two-hybrid approach. From these screens, we identified largely non-overlapping proteins, which represent potential TRPML1-interactors., Using additional interaction assays on some of the potential interactors from each screen, we validated some proteins as candidate TRPML1 interactors In addition, our analysis indicates that each of the two screens not only identified some false-positive interactors, as expected from any screen, but also failed to uncover potential TRPML1 interactors. Future studies on the true interactors, first identified in these screens, will help elucidate the structure and function of protein complexes containing TRPML1.     

Prediction of C. elegans Longevity Genes by Human and Worm Longevity Networks

Intricate and interconnected pathways modulate longevity, but screens to identify the components of these pathways have not been saturating. Because biological processes are often executed by protein complexes and fine-tuned by regulatory factors, the first-order protein-protein interactors of known longevity genes are likely to participate in the regulation of longevity. Data-rich maps of protein interactions have been established for many cardinal organisms such as yeast, worms, and humans. We propose that these interaction maps could be mined for the identification of new putative regulators of longevity. For this purpose, we have constructed longevity networks in both humans and worms. We reasoned that the essential first-order interactors of known longevity-associated genes in these networks are more likely to have longevity phenotypes than randomly chosen genes. We have used C. elegans to determine whether post-developmental inactivation of these essential genes modulates lifespan. Our results suggest that the worm and human longevity networks are functionally relevant and possess a high predictive power for identifying new longevity regulators.