A protein complex network of Drosophila melanogaster

Proteomics Defines Protein Interaction Network of Signaling Pathways

Protein interactions play fundamental roles in signaling transduction. Analysis of protein–protein interaction (PPI) has contributed numerous insights to the understanding of the regulation of signal pathways. Different approaches have been used to discover PPI and characterize protein complexes. In addition to conventional PPI methods, such as yeast two-hybrid (YTH), affinity purification coupled with mass spectrometry (AP-MS) is emerging as an important and popular tool to unravel protein complex and elucidate protein function through the interaction partners. With the AP-MS method, protein complexes are prepared first by affinity purification directly from cell lysates, followed by characterization of their components by mass spectrometry. In contrast to most PPI methods, AP-MS reflects PPI under near physiological conditions in the relevant organism and cell type. AP-MS is also able to probe dynamic PPI dependent on protein posttranslational modifications, which is common for signal transduction. AP-MS mapping protein interaction network of various signal pathways has dramatically increased in recent years. Here, I’ll present the strategies toward obtaining an interactome map of signal pathway and the methodology, detailed protocols, and perspectives of AP-MS.

Evidence for the robustness of protein complexes to inter-species hybridization

Despite the tremendous efforts devoted to the identification of genetic incompatibilities underlying hybrid sterility and inviability, little is known about the effect of inter-species hybridization at the protein interactome level. Here, we develop a screening platform for the comparison of protein-protein interactions (PPIs) among closely related species and their hybrids. We examine in vivo the architecture of protein complexes in two yeast species (Saccharomyces cerevisiae and Saccharomyces kudriavzevii) that diverged 5-20 million years ago and in their F1 hybrids. We focus on 24 proteins of two large complexes: the RNA polymerase II and the nuclear pore complex (NPC), which show contrasting patterns of molecular evolution. We found that, with the exception of one PPI in the NPC sub-complex, PPIs were highly conserved between species, regardless of protein divergence. Unexpectedly, we found that the architecture of the complexes in F1 hybrids could not be distinguished from that of the parental species. Our results suggest that the conservation of PPIs in hybrids likely results from the slow evolution taking place on the very few protein residues involved in the interaction or that protein complexes are inherently robust and may accommodate protein divergence up to the level that is observed among closely related species.

Using guanidine-hydrochloride for fast and efficient protein digestion and single-step affinity-purification mass spectrometry

Protein digestion is an integral part of the "shotgun" proteomics approach and commonly requires overnight incubation prior to mass spectrometry analysis. Quadruplicate "shotgun" proteomic analysis of whole yeast lysate demonstrated that Guanidine-Hydrochloride (Gnd-HCl) protein digestion can be optimally completed within 30 min with endoprotease Lys-C. No chemical artifacts were introduced when samples were incubated in Gnd-HCl at 95 °C, making Gnd-HCl an appropriate digestion buffer for shotgun proteomics. Current methodologies for investigating protein-protein interactions (PPIs) often require several preparation steps, which prolongs any parallel operation and high-throughput interaction analysis. Gnd-HCl allow the efficient elution and subsequent fast digestion of PPIs to provide a convenient high-throughput methodology for affinity-purification mass spectrometry (AP-MS) experiments. To validate the Gnd-HCl approach, label-free PPI analysis of several GFP-tagged yeast deubiquitinating enzymes was performed. The identification of known interaction partners demonstrates the utility of the optimized Gnd-HCl protocol that is also scalable to the 96 well-plate format.

Tools for label-free peptide quantification

The increasing scale and complexity of quantitative proteomics studies complicate subsequent analysis of the acquired data. Untargeted label-free quantification, based either on feature intensities or on spectral counting, is a method that scales particularly well with respect to the number of samples. It is thus an excellent alternative to labeling techniques. In order to profit from this scalability, however, data analysis has to cope with large amounts of data, process them automatically, and do a thorough statistical analysis in order to achieve reliable results. We review the state of the art with respect to computational tools for label-free quantification in untargeted proteomics. The two fundamental approaches are feature-based quantification, relying on the summed-up mass spectrometric intensity of peptides, and spectral counting, which relies on the number of MS/MS spectra acquired for a certain protein. We review the current algorithmic approaches underlying some widely used software packages and briefly discuss the statistical strategies for analyzing the data.

Regulation of developmental processes: insights from mass spectrometry-based proteomics

Mass spectrometry (MS)-based proteomics has become an indispensable tool for protein identification and quantification. In this paper, common MS workflows are described, with an emphasis on applications of MS-based proteomics in developmental biology. Progress has been made in the analysis of proteome changes during tissue differentiation and in various genetic perturbations. MS-based proteomics has been particularly useful for identifying novel protein interactions by affinity purification-mass spectrometry (AP-MS), many of which have been subsequently functionally validated and led to the discovery of previously unknown modes of developmental regulation. Quantitative proteomics approaches can be used to study posttranslational modifications (PTMs) of proteins such as phosphorylation, to reveal the dynamics of intracellular signal transduction. Integrative approaches combine quantitative MS-based proteomics with other high-throughput methods, with the promise of a systems level understanding of developmental regulation.

Next-generation proteomics: towards an integrative view of proteome dynamics

Next-generation sequencing allows the analysis of genomes, including those representing disease states. However, the causes of most disorders are multifactorial, and systems-level approaches, including the analysis of proteomes, are required for a more comprehensive understanding. The proteome is extremely multifaceted owing to splicing and protein modifications, and this is further amplified by the interconnectivity of proteins into complexes and signalling networks that are highly divergent in time and space. Proteome analysis heavily relies on mass spectrometry (MS). MS-based proteomics is starting to mature and to deliver through a combination of developments in instrumentation, sample preparation and computational analysis. Here we describe this emerging next generation of proteomics and highlight recent applications.

An evolutionarily conserved innate immunity protein interaction network

The innate immune response plays a critical role in fighting infection; however, innate immunity also can affect the pathogenesis of a variety of diseases, including sepsis, asthma, cancer, and atherosclerosis. To identify novel regulators of innate immunity, we performed comparative genomics RNA interference screens in the nematode Caenorhabditis elegans and mouse macrophages. These screens have uncovered many candidate regulators of the response to lipopolysaccharide (LPS), several of which interact physically in multiple species to form an innate immunity protein interaction network. This protein interaction network contains several proteins in the canonical LPS-responsive TLR4 pathway as well as many novel interacting proteins. Using RNAi and overexpression studies, we show that almost every gene in this network can modulate the innate immune response in mouse cell lines. We validate the importance of this network in innate immunity regulation in vivo using available mutants in C. elegans and mice.

Mutations in the transcription elongation factor SPT5 disrupt a reporter for dosage compensation in Drosophila

In Drosophila, the MSL (Male Specific Lethal) complex up regulates transcription of active genes on the single male X-chromosome to equalize gene expression between sexes. One model argues that the MSL complex acts upon the elongation step of transcription rather than initiation. In an unbiased forward genetic screen for new factors required for dosage compensation, we found that mutations in the universally conserved transcription elongation factor Spt5 lower MSL complex dependent expression from the miniwhite reporter gene in vivo. We show that SPT5 interacts directly with MSL1 in vitro and is required downstream of MSL complex recruitment, providing the first mechanistic data corroborating the elongation model of dosage compensation.

Drosophila males hypertranscribe most of the genes along their single X chromosome to match the output of females with two X chromosomes. It had been difficult to imagine how the MSL dosage compensation complex could impose a modest, but essential, ∼two-fold increase by interacting with hundreds of different factors that control transcription initiation for such a diverse collection of genes. An alternative model proposed that dosage compensation instead acted at some step of transcription elongation common to all genes. We performed a genetic screen for mutations that subtly reduce dosage compensation and recovered mutations in the Spt5 gene that encodes a universally conserved elongation factor. SPT5 closes the RNA polymerase II clamp around the DNA template to prevent pausing or premature termination. We find that the dosage compensation complex genetically and physically interacts with SPT5 on actively transcribed genes providing direct molecular support for the elongation model of dosage compensation.

A census of human soluble protein complexes

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 that 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 unannotated proteins to inform on mechanism. Strikingly, whereas larger multiprotein assemblies tend to be more extensively annotated and evolutionarily conserved, human protein complexes with five or fewer subunits are far more likely to be functionally unannotated or restricted to vertebrates, suggesting more recent functional innovations.

ChIP-seq and beyond: new and improved methodologies to detect and characterize protein-DNA interactions

Chromatin immunoprecipitation experiments followed by sequencing (ChIP-seq) detect protein-DNA binding events and chemical modifications of histone proteins. Challenges in the standard ChIP-seq protocol have motivated recent enhancements in this approach, such as reducing the number of cells that are required and increasing the resolution. Complementary experimental approaches - for example, DNaseI hypersensitive site mapping and analysis of chromatin interactions that are mediated by particular proteins - provide additional information about DNA-binding proteins and their function. These data are now being used to identify variability in the functions of DNA-binding proteins across genomes and individuals. In this Review, I describe the latest advances in methods to detect and functionally characterize DNA-bound proteins.

Popular computational methods to assess multiprotein complexes derived from label-free affinity purification and mass spectrometry (AP-MS) experiments

Advances in sensitivity, resolution, mass accuracy, and throughput have considerably increased the number of protein identifications made via mass spectrometry. Despite these advances, state-of-the-art experimental methods for the study of protein-protein interactions yield more candidate interactions than may be expected biologically owing to biases and limitations in the experimental methodology. In silico methods, which distinguish between true and false interactions, have been developed and applied successfully to reduce the number of false positive results yielded by physical interaction assays. Such methods may be grouped according to: (1) the type of data used: methods based on experiment-specific measurements (e.g., spectral counts or identification scores) versus methods that extract knowledge encoded in external annotations (e.g., public interaction and functional categorisation databases); (2) the type of algorithm applied: the statistical description and estimation of physical protein properties versus predictive supervised machine learning or text-mining algorithms; (3) the type of protein relation evaluated: direct (binary) interaction of two proteins in a cocomplex versus probability of any functional relationship between two proteins (e.g., co-occurrence in a pathway, sub cellular compartment); and (4) initial motivation: elucidation of experimental data by evaluation versus prediction of novel protein-protein interaction, to be experimentally validated a posteriori. This work reviews several popular computational scoring methods and software platforms for protein-protein interactions evaluation according to their methodology, comparative strengths and weaknesses, data representation, accessibility, and availability. The scoring methods and platforms described include: CompPASS, SAINT, Decontaminator, MINT, IntAct, STRING, and FunCoup. References to related work are provided throughout in order to provide a concise but thorough introduction to a rapidly growing interdisciplinary field of investigation.

ComplexQuant: high-throughput computational pipeline for the global quantitative analysis of endogenous soluble protein complexes using high resolution protein HPLC and precision label-free LC/MS/MS

The experimental isolation and characterization of stable multi-protein complexes are essential to understanding the molecular systems biology of a cell. To this end, we have developed a high-throughput proteomic platform for the systematic identification of native protein complexes based on extensive fractionation of soluble protein extracts by multi-bed ion exchange high performance liquid chromatography (IEX-HPLC) combined with exhaustive label-free LC/MS/MS shotgun profiling. To support these studies, we have built a companion data analysis software pipeline, termed ComplexQuant. Proteins present in the hundreds of fractions typically collected per experiment are first identified by exhaustively interrogating MS/MS spectra using multiple database search engines within an integrative probabilistic framework, while accounting for possible post-translation modifications. Protein abundance is then measured across the fractions based on normalized total spectral counts and precursor ion intensities using a dedicated tool, PepQuant. This analysis allows co-complex membership to be inferred based on the similarity of extracted protein co-elution profiles. Each computational step has been optimized for processing large-scale biochemical fractionation datasets, and the reliability of the integrated pipeline has been benchmarked extensively. This article is part of a Special Issue entitled: From protein structures to clinical applications.

New partners in regulation of gene expression: the enhancer of Trithorax and Polycomb Corto interacts with methylated ribosomal protein l12 via its chromodomain

Chromodomains are found in many regulators of chromatin structure, and most of them recognize methylated lysines on histones. Here, we investigate the role of the Drosophila melanogaster protein Corto's chromodomain. The Enhancer of Trithorax and Polycomb Corto is involved in both silencing and activation of gene expression. Over-expression of the Corto chromodomain (CortoCD) in transgenic flies shows that it is a chromatin-targeting module, critical for Corto function. Unexpectedly, mass spectrometry analysis reveals that polypeptides pulled down by CortoCD from nuclear extracts correspond to ribosomal proteins. Furthermore, real-time interaction analyses demonstrate that CortoCD binds with high affinity RPL12 tri-methylated on lysine 3. Corto and RPL12 co-localize with active epigenetic marks on polytene chromosomes, suggesting that both are involved in fine-tuning transcription of genes in open chromatin. RNA-seq based transcriptomes of wing imaginal discs over-expressing either CortoCD or RPL12 reveal that both factors deregulate large sets of common genes, which are enriched in heat-response and ribosomal protein genes, suggesting that they could be implicated in dynamic coordination of ribosome biogenesis. Chromatin immunoprecipitation experiments show that Corto and RPL12 bind hsp70 and are similarly recruited on gene body after heat shock. Hence, Corto and RPL12 could be involved together in regulation of gene transcription. We discuss whether pseudo-ribosomal complexes composed of various ribosomal proteins might participate in regulation of gene expression in connection with chromatin regulators.

Drosophila protein interaction map (DPiM): a paradigm for metazoan protein complex interactions

Proteins perform essential cellular functions as part of protein complexes, often in conjunction with RNA, DNA, metabolites and other small molecules. The genome encodes thousands of proteins but not all of them are expressed in every cell type; and expressed proteins are not active at all times. Such diversity of protein expression and function accounts for the level of biological intricacy seen in nature. Defining protein-protein interactions in protein complexes, and establishing the when, what and where of potential interactions, is therefore crucial to understanding the cellular function of any protein-especially those that have not been well studied by traditional molecular genetic approaches. We generated a large-scale resource of affinity-tagged expression-ready clones and used co-affinity purification combined with tandem mass-spectrometry to identify protein partners of nearly 5,000 Drosophila melanogaster proteins. The resulting protein complex "map" provided a blueprint of metazoan protein complex organization. Here we describe how the map has provided valuable insights into protein function in addition to generating hundreds of testable hypotheses. We also discuss recent technological advancements that will be critical in addressing the next generation of questions arising from the map.

Coevolution reveals a network of human proteins originating with multicellularity

Protein interaction networks play central roles in biological systems, from simple metabolic pathways through complex programs permitting the development of organisms. Multicellularity could only have arisen from a careful orchestration of cellular and molecular roles and responsibilities, all properly controlled and regulated. Disease reflects a breakdown of this organismal homeostasis. To better understand the evolution of interactions whose dysfunction may be contributing factors to disease, we derived the human protein coevolution network using our MatrixMatchMaker algorithm and using the Orthologous MAtrix project (OMA) database as a source for protein orthologs from 103 eukaryotic genomes. We annotated the coevolution network using protein–protein interaction data, many functional data sources, and we explored the evolutionary rates and dates of emergence of the proteins in our data set. Strikingly, clustering based only on the topology of the coevolution network partitions it into two subnetworks, one generally representing ancient eukaryotic functions and the other functions more recently acquired during animal evolution. That latter subnetwork is enriched for proteins with roles in cell–cell communication, the control of cell division, and related multicellular functions. Further annotation using data from genetic disease databases and cancer genome sequences strongly implicates these proteins in both ciliopathies and cancer. The enrichment for such disease markers in the animal network suggests a functional link between these coevolving proteins. Genetic validation corroborates the recruitment of ancient cilia in the evolution of multicellularity.

Systematic and systemic immunology: on the future of research and its applications

This paper is a shortened English transcription of a lecture given on 13 February 2012 at the College de France. The lecture concluded a series of talks delivered the same year on the theme: "Immunity: the game of chance and specificity". The article comprises four parts: I. The game of chance and specificity. II. About the future of research in immunology. III. On the future of the applications of research in immunology. IV. The social conditions of the evolution of research and its applications.

A computational framework for boosting confidence in high-throughput protein-protein interaction datasets

Improving the quality and coverage of the protein interactome is of tantamount importance for biomedical research, particularly given the various sources of uncertainty in high-throughput techniques. We introduce a structure-based framework, Coev2Net, for computing a single confidence score that addresses both false-positive and false-negative rates. Coev2Net is easily applied to thousands of binary protein interactions and has superior predictive performance over existing methods. We experimentally validate selected high-confidence predictions in the human MAPK network and show that predicted interfaces are enriched for cancer -related or damaging SNPs. Coev2Net can be downloaded at http://struct2net.csail.mit.edu.

Functional genomics in Drosophila models of human disease

It is occasionally observed that common sporadic diseases have rare familial counterparts in which mutations at a single locus result in a similar disorder exhibiting simple Mendelian inheritance. Such an observation is often sufficient justification for the creation of a disease model in the fly. Whether the system is based on the over-expression of a toxic variant of a human protein or requires the loss of function of an orthologous fly gene, the consequent phenotypes can be used to understand pathogenesis through the discovery of genetic modifiers. Such genetic screening can be completed rapidly in the fly and in this review we outline how libraries of mutants are generated and how consequent changes in disease-related phenotypes are assessed. The bioinformatic approaches to processing the copious amounts of data so generated are also presented. The next phase of fly modelling will tackle the challenges of complex diseases in which many genes are associated with risk in the human. There is growing interest in the use of interactomics and epigenetics to provide proteome- and genome-scale descriptions of the regulatory dysfunction that results in disease.

The Notch signalling system: recent insights into the complexity of a conserved pathway

Notch signalling links the fate of one cell to that of an immediate neighbour and consequently controls differentiation, proliferation and apoptotic events in multiple metazoan tissues. Perturbations in this pathway activity have been linked to several human genetic disorders and cancers. Recent genome-scale studies in Drosophila melanogaster have revealed an extraordinarily complex network of genes that can affect Notch activity. This highly interconnected network contrasts our traditional view of the Notch pathway as a simple linear sequence of events. Although we now have an unprecedented insight into the way in which such a fundamental signalling mechanism is controlled by the genome, we are faced with serious challenges in analysing the underlying molecular mechanisms of Notch signal control.

Analysing signalling networks by mass spectrometry

Sequence analysis of the human genome and the association of genetic aberrations with diseases have provided a rough framework whereby the impact of individual genotypes can be assessed. To fully understand the effect of individual and co-occurring genetic aberrations, as well as their individual and collected contribution to the development of diseases, it is critical to analyse the matching proteome and to determine how the organisation, expression level and function of protein networks are affected. Sensitive mass spectrometric platforms in combination with innovative workflows allow qualitative and quantitative analyses of the cellular as well as the extracellular proteome. Importantly, in addition to specifically identifying the content of the proteome, several aspects of the proteomic organisation can be analysed including protein complexes, protein modifications, enzymatic activities and subcellular/organelle localisation. Together, these measurements will provide novel insight into the biological effect of disease-causing mutations ultimately coupling genotype and phenotype.

High-resolution protein interaction map of the Drosophila melanogaster p38 mitogen-activated protein kinases reveals limited functional redundancy

Functional redundancy is a pivotal mechanism that supports the robustness of biological systems at a molecular, cellular, and organismal level. The extensive prevalence of redundancy in molecular networks has been highlighted by recent systems biology studies; however, a detailed mechanistic understanding of redundant functions in specific signaling modules is often missing. We used affinity purification of protein complexes coupled to tandem mass spectrometry to generate a high-resolution protein interaction map of the three homologous p38 mitogen-activated protein kinases (MAPKs) in Drosophila and assessed the utility of such a map in defining the extent of common and unique functions. We found a correlation between the depth of integration of individual p38 kinases into the protein interaction network and their functional significance in cultured cells and in vivo. Based on these data, we propose a central role of p38b in the Drosophila p38 signaling module, with p38a and p38c playing more peripheral, auxiliary roles. We also present the first in vivo evidence demonstrating that an evolutionarily conserved complex of p38b with glycogen synthase links stress sensing to metabolic adaptation.

Drosophila CTCF tandemly aligns with other insulator proteins at the borders of H3K27me3 domains

Several multiprotein DNA complexes capable of insulator activity have been identified in Drosophila melanogaster, yet only CTCF, a highly conserved zinc finger protein, and the transcription factor TFIIIC have been shown to function in mammals. CTCF is involved in diverse nuclear activities, and recent studies suggest that the proteins with which it associates and the DNA sequences that it targets may underlie these various roles. Here we show that the Drosophila homolog of CTCF (dCTCF) aligns in the genome with other Drosophila insulator proteins such as Suppressor of Hairy wing [SU(HW)] and Boundary Element Associated Factor of 32 kDa (BEAF-32) at the borders of H3K27me3 domains, which are also enriched for associated insulator proteins and additional cofactors. RNAi depletion of dCTCF and combinatorial knockdown of gene expression for other Drosophila insulator proteins leads to a reduction in H3K27me3 levels within repressed domains, suggesting that insulators are important for the maintenance of appropriate repressive chromatin structure in Polycomb (Pc) domains. These results shed new insights into the roles of insulators in chromatin domain organization and support recent models suggesting that insulators underlie interactions important for Pc-mediated repression. We reveal an important relationship between dCTCF and other Drosophila insulator proteins and speculate that vertebrate CTCF may also align with other nuclear proteins to accomplish similar functions.

DroPNet: a web portal for integrated analysis of Drosophila protein-protein interaction networks

DroPNet (Drosophila Protein Network) is a Drosophila-dedicated web portal for generating and analyzing protein–protein interaction (PPI) networks. This platform integrates users’ experimental data provided as one or two lists of genes with PPI data from Drosophila and other species. These experimental data can, for example, come from RNAi screens, for which this approach is known to be valuable. DroPNet, therefore, provides an essential basis for further biological analysis by linking functional and physical interactions and reinforcing the relevance of each. DroPNet focuses on the search of PPIs between genes of the entry list, and includes the possibility of searching for intermediate genes for which the corresponding protein indirectly links two entry data. It also offers multiple functions for editing the networks obtained, providing users with interactive possibilities to progressively improve and refine the results. This approach gives a global view of the studied process and makes it possible to highlight specific interactions that have so far been understudied. DroPNet is freely available at http://dropnet.isima.fr.

The SUMO pathway promotes basic helix-loop-helix proneural factor activity via a direct effect on the Zn finger protein senseless

During development, proneural transcription factors of the basic helix-loop-helix (bHLH) family are required to commit cells to a neural fate. In Drosophila neurogenesis, a key mechanism promoting sense organ precursor (SOP) fate is the synergy between proneural factors and their coactivator Senseless in transcriptional activation of target genes. Here we present evidence that posttranslational modification by SUMO enhances this synergy via an effect on Senseless protein. We show that Senseless is a direct target for SUMO modification and that mutagenesis of a predicted SUMOylation motif in Senseless reduces Senseless/proneural synergy both in vivo and in cell culture. We propose that SUMOylation of Senseless via lysine 509 promotes its synergy with proneural proteins during transcriptional activation and hence regulates an important step in neurogenesis leading to the formation and maturation of the SOPs.

Mediation of Drosophila autosomal dosage effects and compensation by network interactions

BACKGROUND

Gene dosage change is a mild perturbation that is a valuable tool for pathway reconstruction in Drosophila. While it is often assumed that reducing gene dose by half leads to two-fold less expression, there is partial autosomal dosage compensation in Drosophila, which may be mediated by feedback or buffering in expression networks.

RESULTS

We profiled expression in engineered flies where gene dose was reduced from two to one. While expression of most one-dose genes was reduced, the gene-specific dose responses were heterogeneous. Expression of two-dose genes that are first-degree neighbors of one-dose genes in novel network models also changed, and the directionality of change depended on the response of one-dose genes.

CONCLUSIONS

Our data indicate that expression perturbation propagates in network space. Autosomal compensation, or the lack thereof, is a gene-specific response, largely mediated by interactions with the rest of the transcriptome.

Computational approaches for analyzing information flow in biological networks

The advancements in "omics" (proteomics, genomics, lipidomics, and metabolomics) technologies have yielded large inventories of genes, transcripts, proteins, and metabolites. The challenge is to find out how these entities work together to regulate the processes by which cells respond to external and internal signals. Mathematical and computational modeling of signaling networks has a key role in this task, and network analysis provides insights into biological systems and has applications for medicine. Here, we review experimental and theoretical progress and future challenges toward this goal. We focus on how networks are reconstructed from data, how these networks are structured to control the flow of biological information, and how the design features of the networks specify biological decisions.

Beyond hairballs: The use of quantitative mass spectrometry data to understand protein-protein interactions

The past 10 years have witnessed a dramatic proliferation in the availability of protein interaction data. However, for interaction mapping based on affinity purification coupled with mass spectrometry (AP-MS), there is a wealth of information present in the datasets that often goes unrecorded in public repositories, and as such remains largely unexplored. Further, how this type of data is represented and used by bioinformaticians has not been well established. Here, we point out some common mistakes in how AP-MS data are handled, and describe how protein complex organization and interaction dynamics can be inferred using quantitative AP-MS approaches.

Protein landscape at Drosophila melanogaster telomere-associated sequence repeats

The specific set of proteins bound at each genomic locus contributes decisively to regulatory processes and to the identity of a cell. Understanding of the function of a particular locus requires the knowledge of what factors interact with that locus and how the protein composition changes in different cell types or during the response to internal and external signals. Proteomic analysis of isolated chromatin segments (PICh) was developed as a tool to target, purify, and identify proteins associated with a defined locus and was shown to allow the purification of human telomeric chromatin. Here we have developed this method to identify proteins that interact with the Drosophila telomere-associated sequence (TAS) repeats. Several of the purified factors were validated as novel TAS-bound proteins by chromatin immunoprecipitation, and the Brahma complex was confirmed as a dominant modifier of telomeric position effect through the use of a genetic test. These results offer information on the efficacy of applying the PICh protocol to loci with sequence more complex than that found at human telomeres and identify proteins that bind to the TAS repeats, which might contribute to TAS biology and chromatin silencing.

Selfish cellular networks and the evolution of complex organisms

Human gametogenesis takes years and involves many cellular divisions, particularly in males. Consequently, gametogenesis provides the opportunity to acquire multiple de novo mutations. A significant portion of these is likely to impact the cellular networks linking genes, proteins, RNA and metabolites, which constitute the functional units of cells. A wealth of literature shows that these individual cellular networks are complex, robust and evolvable. To some extent, they are able to monitor their own performance, and display sufficient autonomy to be termed "selfish". Their robustness is linked to quality control mechanisms which are embedded in and act upon the individual networks, thereby providing a basis for selection during gametogenesis. These selective processes are equally likely to affect cellular functions that are not gamete-specific, and the evolution of the most complex organisms, including man, is therefore likely to occur via two pathways: essential housekeeping functions would be regulated and evolve during gametogenesis within the parents before being transmitted to their progeny, while classical selection would operate on other traits of the organisms that shape their fitness with respect to the environment.

Synergy between the ESCRT-III complex and Deltex defines a ligand-independent Notch signal

The ESCRT-III complex component Shrub plays a pivotal rate-limiting step in late endosomal ligand-independent Notch activation.

The Notch signaling pathway defines a conserved mechanism that regulates cell fate decisions in metazoans. Signaling is modulated by a broad and multifaceted genetic circuitry, including members of the endocytic machinery. Several individual steps in the endocytic pathway have been linked to the positive or negative regulation of the Notch receptor. In seeking genetic elements involved in regulating the endosomal/lysosomal degradation of Notch, mediated by the molecular synergy between the ubiquitin ligase Deltex and Kurtz, the nonvisual β-arrestin in Drosophila, we identified Shrub, a core component of the ESCRT-III complex as a key modulator of this synergy. Shrub promotes the lysosomal degradation of the receptor by mediating its delivery into multivesicular bodies (MVBs). However, the interplay between Deltex, Kurtz, and Shrub can bypass this path, leading to the activation of the receptor. Our analysis shows that Shrub plays a pivotal rate-limiting step in late endosomal ligand-independent Notch activation, depending on the Deltex-dependent ubiquitinylation state of the receptor. This activation mode of the receptor emphasizes the complexity of Notch signal modulation in a cell and has significant implications for both development and disease.

Drosophila Pez acts in Hippo signaling to restrict intestinal stem cell proliferation

The conserved Hippo signaling pathway acts in growth control and is fundamental to animal development and oncogenesis. Hippo signaling has also been implicated in adult midgut homeostasis in Drosophila. Regulated divisions of intestinal stem cells (ISCs), giving rise to an ISC and an enteroblast (EB) that differentiates into an enterocyte (EC) or an enteroendocrine (EE) cell, enable rapid tissue turnover in response to intestinal stress. The damage-related increase in ISC proliferation requires deactivation of the Hippo pathway and consequential activation of the transcriptional coactivator Yorkie (Yki) in both ECs and ISCs. Here, we identify Pez, an evolutionarily conserved FERM domain protein containing a protein tyrosine phosphatase (PTP) domain, as a novel binding partner of the upstream Hippo signaling component Kibra. Pez function--but not its PTP domain--is essential for Hippo pathway activity specifically in the fly midgut epithelium. Thus, Pez displays a tissue-specific requirement and functions as a negative upstream regulator of Yki in the regulation of ISC proliferation.

SPPS: a sequence-based method for predicting probability of protein-protein interaction partners

Background

The molecular network sustained by different types of interactions among proteins is widely manifested as the fundamental driving force of cellular operations. Many biological functions are determined by the crosstalk between proteins rather than by the characteristics of their individual components. Thus, the searches for protein partners in global networks are imperative when attempting to address the principles of biology.

Results

We have developed a web-based tool “Sequence-based Protein Partners Search” (SPPS) to explore interacting partners of proteins, by searching over a large repertoire of proteins across many species. SPPS provides a database containing more than 60,000 protein sequences with annotations and a protein-partner search engine in two modes (Single Query and Multiple Query). Two interacting proteins of human FBXO6 protein have been found using the service in the study. In addition, users can refine potential protein partner hits by using annotations and possible interactive network in the SPPS web server.

Conclusions

SPPS provides a new type of tool to facilitate the identification of direct or indirect protein partners which may guide scientists on the investigation of new signaling pathways. The SPPS server is available to the public at http://mdl.shsmu.edu.cn/SPPS/.

Comparative interaction networks: bridging genotype to phenotype

Over the past decade, biomedical research has witnessed an exponential increase in the throughput of the characterization of biological systems. Here we review the recent progress in large-scale methods to determine protein-protein, genetic and chemical-genetic interaction networks. We discuss some of the limitations and advantages of the different methods and give examples of how these networks are being used to study the evolutionary process. Comparative studies have revealed that different types of protein-protein interactions diverge at different rates with high conservation of co-complex membership but rapid divergence of more promiscuous interactions like those that mediate post-translational modifications. These evolutionary trends have consistent genetic consequences with highly conserved epistatic interactions within complex subunits but faster divergence of epistatic interactions across complexes or pathways. Finally, we discuss how these evolutionary observations are being used to interpret cross-species chemical-genetic studies and how they might shape therapeutic strategies. Together, these interaction networks offer us an unprecedented level of detail into how genotypes are translated to phenotypes, and we envision that they will be increasingly useful in the interpretation of genetic and phenotypic variation occurring within populations as well as the rational design of combinatorial therapeutics.

Stringent analysis of gene function and protein-protein interactions using fluorescently tagged genes

In Drosophila collections of green fluorescent protein (GFP) trap lines have been used to probe the endogenous expression patterns of trapped genes or the subcellular localization of their protein products. Here, we describe a method, based on nonoverlapping, highly specific, shRNA transgenes directed against GFP, that extends the utility of these collections to loss-of-function studies. Furthermore, we used a MiMIC transposon to generate GFP traps in Drosophila cell lines with distinct subcellular localization patterns, which will permit high-throughput screens using fluorescently tagged proteins. Finally, we show that fluorescent traps, paired with recombinant nanobodies and mass spectrometry, allow the study of endogenous protein complexes in Drosophila.