Phosphorylation networks regulating JNK activity in diverse genetic backgrounds

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Genetic Interactions in Various Environmental Conditions in Caenorhabditis elegans

Although it is well known that epistasis plays an important role in many evolutionary processes (e.g., speciation, evolution of sex), our knowledge on the frequency and prevalent sign of epistatic interactions is mainly limited to unicellular organisms or cell cultures of multicellular organisms. This is even more pronounced in regard to how the environment can influence genetic interactions. To broaden our knowledge in that respect we studied gene-gene interactions in a whole multicellular organism, Caenorhabditis elegans. We screened over one thousand gene interactions, each one in standard laboratory conditions, and under three different stressors: heat shock, oxidative stress, and genotoxic stress. Depending on the condition, between 7% and 22% of gene pairs showed significant genetic interactions and an overall sign of epistasis changed depending on the condition. Sign epistasis was quite common, but reciprocal sign epistasis was extremally rare. One interaction was common to all conditions, whereas 78% of interactions were specific to only one environment. Although epistatic interactions are quite common, their impact on evolutionary processes will strongly depend on environmental factors.

A global genetic interaction network by single-cell imaging and machine learning

Cellular and organismal phenotypes are controlled by complex gene regulatory networks. However, reference maps of gene function are still scarce across different organisms. Here, we generated synthetic genetic interaction and cell morphology profiles of more than 6,800 genes in cultured Drosophila cells. The resulting map of genetic interactions was used for machine learning-based gene function discovery, assigning functions to genes in 47 modules. Furthermore, we devised Cytoclass as a method to dissect genetic interactions for discrete cell states at the single-cell resolution. This approach identified an interaction of Cdk2 and the Cop9 signalosome complex, triggering senescence-associated secretory phenotypes and immunogenic conversion in hemocytic cells. Together, our data constitute a genome-scale resource of functional gene profiles to uncover the mechanisms underlying genetic interactions and their plasticity at the single-cell level.

Protein kinase sensors: an overview of new designs for visualizing kinase dynamics in single plant cells

Protein kinase dynamics play key roles in regulation of cell differentiation, growth, development and in diverse cell signaling networks. Protein kinase sensors enable visualization of protein kinase activity in living cells and tissues in time and space. These sensors have therefore become important and powerful molecular tools for investigation of diverse kinase activities and can resolve long-standing and challenging biological questions. In the present Update, we review new advanced approaches for genetically encoded protein kinase biosensor designs developed in animal systems together with the basis of each biosensor's working principle and components. In addition, we review recent first examples of real time plant protein kinase activity biosensor development and application. We discuss how these sensors have helped to resolve how stomatal signal transduction in response to elevated CO2 merges with abscisic acid signaling downstream of a resolved basal SnRK2 kinase activity in guard cells. Furthermore, recent advances, combined with the new strategies described in this Update, can help deepen the understanding of how signaling networks regulate unique functions and responses in distinct plant cell types and tissues and how different stimuli and signaling pathways can interact.

Recent advances in the use of genetically encodable optical tools to elicit and monitor signaling events

Cells rely on a complex network of spatiotemporally regulated signaling activities to effectively transduce information from extracellular cues to intracellular machinery. To probe this activity architecture, researchers have developed an extensive molecular tool kit of fluorescent biosensors and optogenetic actuators capable of monitoring and manipulating various signaling activities with high spatiotemporal precision. The goal of this review is to provide readers with an overview of basic concepts and recent advances in the development and application of genetically encodable biosensors and optogenetic tools for understanding signaling activity.

In silico structural and functional characterization and phylogenetic study of alkaline phosphatase in bacterium, Rhizobium leguminosarum (Frank 1879)

Phosphorus is one of the primary macronutrient of plants, which is present in soil. It is essential for normal growth and development of plants. Plants use inorganic form of phosphate but organic form can also be assimilated with the help of soil inhabiting bacteria. Alkaline phosphatase is an enzyme present in Rizobium bacteria. This enzyme is responsible for solubilization and mineralization of organic phosphate and makes it readily available for plants. In the present study, nine different strains of Rhizobium leguminosarum were selected for a detailed computational structural and functional characterization and phylogenetic studies of alkaline phosphatase. Amino acid sequences were retrieved from UniProt and saved in FASTA format for use in analysis. Phylogenetic analysis of these strains was done by using MEGA7. 3D structure prediction was performed by using online server I-Tasser. Galaxy Web and 3D Refine were used for structure refinement. The refined structures were evaluated using two validation servers, QMEAN and SAVES. Protein-protein interaction analysis was done by using STRING. For detailed functional characterization, Cofactor, Coach, RaptorX, PSORT and MEME were used. Overall quality of predicted protein models was above 80%. Refined and validated models were submitted into PMDB. Seven out of nine strains were closely related and other two were distantly related. Protein-Protein interaction showed no significant co-expression among the interaction partners.

The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern

Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several hypothetical molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in baker's yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.

Genetically Encoded Fluorescent Biosensors Illuminate the Spatiotemporal Regulation of Signaling Networks

Cellular signaling networks are the foundation which determines the fate and function of cells as they respond to various cues and stimuli. The discovery of fluorescent proteins over 25 years ago enabled the development of a diverse array of genetically encodable fluorescent biosensors that are capable of measuring the spatiotemporal dynamics of signal transduction pathways in live cells. In an effort to encapsulate the breadth over which fluorescent biosensors have expanded, we endeavored to assemble a comprehensive list of published engineered biosensors, and we discuss many of the molecular designs utilized in their development. Then, we review how the high temporal and spatial resolution afforded by fluorescent biosensors has aided our understanding of the spatiotemporal regulation of signaling networks at the cellular and subcellular level. Finally, we highlight some emerging areas of research in both biosensor design and applications that are on the forefront of biosensor development.

RNA Interference (RNAi) Screening in Drosophila

In the last decade, RNA interference (RNAi), a cellular mechanism that uses RNA-guided degradation of messenger RNA transcripts, has had an important impact on identifying and characterizing gene function. First discovered in Caenorhabditis elegans, RNAi can be used to silence the expression of genes through introduction of exogenous double-stranded RNA into cells. In Drosophila, RNAi has been applied in cultured cells or in vivo to perturb the function of single genes or to systematically probe gene function on a genome-wide scale. In this review, we will describe the use of RNAi to study gene function in Drosophila with a particular focus on high-throughput screening methods applied in cultured cells. We will discuss available reagent libraries and cell lines, methodological approaches for cell-based assays, and computational methods for the analysis of high-throughput screens. Furthermore, we will review the generation and use of genome-scale RNAi libraries for tissue-specific knockdown analysis in vivo and discuss the differences and similarities with the use of genome-engineering methods such as CRISPR/Cas9 for functional analysis.

Endocrine regulation of a dispersal polymorphism in winged insects: a short review

Changes in food availability and crowding are two critical environmental conditions that impact an animal's trajectory toward either reproduction or migration. Many insects facing this challenge have evolved wing polymorpisms that allow them to respond to changing conditions. When conditions favor reproduction, wing polymorphic species produce adults that either have no wings or short, non-functional wings; however, when conditions favor migration, adults with functional wings and robust flight muscles develop. Here we review three recently reported signaling pathways regulating wing polyphenism in wing polymorphic crickets, aphids, and brown planthoppers: juvenile horomone/ecdysone signaling, insulin signaling, and Jun-N-terminal Kinase (JNK) signaling. Understanding how these pathways respond to nutrition, stress and crowding with the appropriate adaptive phenotype is an important step in understanding how life-history trade-offs evolve.

Next generation of network medicine: interdisciplinary signaling approaches

In the last decade, network approaches have transformed our understanding of biological systems. Network analyses and visualizations have allowed us to identify essential molecules and modules in biological systems, and improved our understanding of how changes in cellular processes can lead to complex diseases, such as cancer, infectious and neurodegenerative diseases. "Network medicine" involves unbiased large-scale network-based analyses of diverse data describing interactions between genes, diseases, phenotypes, drug targets, drug transport, drug side-effects, disease trajectories and more. In terms of drug discovery, network medicine exploits our understanding of the network connectivity and signaling system dynamics to help identify optimal, often novel, drug targets. Contrary to initial expectations, however, network approaches have not yet delivered a revolution in molecular medicine. In this review, we propose that a key reason for the limited impact, so far, of network medicine is a lack of quantitative multi-disciplinary studies involving scientists from different backgrounds. To support this argument, we present existing approaches from structural biology, 'omics' technologies (e.g., genomics, proteomics, lipidomics) and computational modeling that point towards how multi-disciplinary efforts allow for important new insights. We also highlight some breakthrough studies as examples of the potential of these approaches, and suggest ways to make greater use of the power of interdisciplinarity. This review reflects discussions held at an interdisciplinary signaling workshop which facilitated knowledge exchange from experts from several different fields, including in silico modelers, computational biologists, biochemists, geneticists, molecular and cell biologists as well as cancer biologists and pharmacologists.

An Integrative Analysis of the InR/PI3K/Akt Network Identifies the Dynamic Response to Insulin Signaling

Insulin regulates an essential conserved signaling pathway affecting growth, proliferation, and metabolism. To expand our understanding of the insulin pathway, we combine biochemical, genetic, and computational approaches to build a comprehensive Drosophila InR/PI3K/Akt network. First, we map the dynamic protein-protein interaction network surrounding the insulin core pathway using bait-prey interactions connecting 566 proteins. Combining RNAi screening and phospho-specific antibodies, we find that 47% of interacting proteins affect pathway activity, and, using quantitative phosphoproteomics, we demonstrate that ∼10% of interacting proteins are regulated by insulin stimulation at the level of phosphorylation. Next, we integrate these orthogonal datasets to characterize the structure and dynamics of the insulin network at the level of protein complexes and validate our method by identifying regulatory roles for the Protein Phosphatase 2A (PP2A) and Reptin-Pontin chromatin-remodeling complexes as negative and positive regulators of ribosome biogenesis, respectively. Altogether, our study represents a comprehensive resource for the study of the evolutionary conserved insulin network.

Kinase activity ranking using phosphoproteomics data (KARP) quantifies the contribution of protein kinases to the regulation of cell viability

Cell survival is regulated by a signaling network driven by the activity of protein kinases; however, determining the contribution that each kinase in the network makes to such regulation remains challenging. Here, we report a computational approach that uses mass spectrometry-based phosphoproteomics data to rank protein kinases based on their contribution to cell regulation. We found that the scores returned by this algorithm, which we have termed kinase activity ranking using phosphoproteomics data (KARP), were a quantitative measure of the contribution that individual kinases make to the signaling output. Application of KARP to the analysis of eight hematological cell lines revealed that cyclin-dependent kinase (CDK) 1/2, casein kinase (CK) 2, extracellular signal-related kinase (ERK), and p21-activated kinase (PAK) were the most frequently highly ranked kinases in these cell models. The patterns of kinase activation were cell-line specific yet showed a significant association with cell viability as a function of kinase inhibitor treatment. Thus, our study exemplifies KARP as an untargeted approach to empirically and systematically identify regulatory kinases within signaling networks.

A strategy to apply quantitative epistasis analysis on developmental traits

BACKGROUND

Genetic interactions are keys to understand complex traits and evolution. Epistasis analysis is an effective method to map genetic interactions. Large-scale quantitative epistasis analysis has been well established for single cells. However, there is a substantial lack of such studies in multicellular organisms and their complex phenotypes such as development. Here we present a method to extend quantitative epistasis analysis to developmental traits.

METHODS

In the nematode Caenorhabditis elegans, we applied RNA interference on mutants to inactivate two genes, used an imaging system to quantitatively measure phenotypes, and developed a set of statistical methods to extract genetic interactions from phenotypic measurement.

RESULTS

Using two different C. elegans developmental phenotypes, body length and sex ratio, as examples, we showed that this method could accommodate various metazoan phenotypes with performances comparable to those methods in single cell growth studies. Comparing with qualitative observations, this method of quantitative epistasis enabled detection of new interactions involving subtle phenotypes. For example, several sex-ratio genes were found to interact with brc-1 and brd-1, the orthologs of the human breast cancer genes BRCA1 and BARD1, respectively. We confirmed the brc-1 interactions with the following genes in DNA damage response: C34F6.1, him-3 (ortholog of HORMAD1, HORMAD2), sdc-1, and set-2 (ortholog of SETD1A, SETD1B, KMT2C, KMT2D), validating the effectiveness of our method in detecting genetic interactions.

CONCLUSIONS

We developed a reliable, high-throughput method for quantitative epistasis analysis of developmental phenotypes.

Systematic epistatic mapping of cellular processes

Genetic screens have identified many novel components of various biological processes, such as components required for cell cycle and cell division. While forward genetic screens typically generate unstructured ‘hit’ lists, genetic interaction mapping approaches can identify functional relations in a systematic fashion. Here, we discuss a recent study by our group demonstrating a two-step approach to first screen for regulators of the mitotic cell cycle, and subsequently guide hypothesis generation by using genetic interaction analysis. The screen used a high-content microscopy assay and automated image analysis to capture defects during mitotic progression and cytokinesis. Genetic interaction networks derived from process-specific features generate a snapshot of functional gene relations in those processes, which follow a temporal order during the cell cycle. This complements a recently published approach, which inferred directional genetic interactions reconstructing hierarchical relationships between genes across different phases during mitotic progression. In conclusion, this strategy leverages unbiased, genome-wide, yet highly sensitive and process-focused functional screening in cells.

Strategic Integration of Multiple Bioinformatics Resources for System Level Analysis of Biological Networks

Recent technological advances in genomics allow the production of biological data at unprecedented tera- and petabyte scales. Efficient mining of these vast and complex datasets for the needs of biomedical research critically depends on a seamless integration of the clinical, genomic, and experimental information with prior knowledge about genotype-phenotype relationships. Such experimental data accumulated in publicly available databases should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining.We present an integrated computational platform Lynx (Sulakhe et al., Nucleic Acids Res 44:D882-D887, 2016) ( http://lynx.cri.uchicago.edu ), a web-based database and knowledge extraction engine. It provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization. It gives public access to the Lynx integrated knowledge base (LynxKB) and its analytical tools via user-friendly web services and interfaces. The Lynx service-oriented architecture supports annotation and analysis of high-throughput experimental data. Lynx tools assist the user in extracting meaningful knowledge from LynxKB and experimental data, and in the generation of weighted hypotheses regarding the genes and molecular mechanisms contributing to human phenotypes or conditions of interest. The goal of this integrated platform is to support the end-to-end analytical needs of various translational projects.

High-Content Imaging Platform for Profiling Intracellular Signaling Network Activity in Living Cells

Essential characteristics of cellular signaling networks include a complex interconnected architecture and temporal dynamics of protein activity. The latter can be monitored by Förster resonance energy transfer (FRET) biosensors at a single-live-cell level with high temporal resolution. However, these experiments are typically limited to the use of a couple of FRET biosensors. Here, we describe a FRET-based multi-parameter imaging platform (FMIP) that allows simultaneous high-throughput monitoring of multiple signaling pathways. We apply FMIP to monitor the crosstalk between epidermal growth factor receptor (EGFR) and insulin-like growth factor-1 receptor signaling, signaling perturbations caused by pathophysiologically relevant EGFR mutations, and the effects of a clinically important MEK inhibitor (selumetinib) on the EGFR network. We expect that in the future the platform will be applied to develop comprehensive models of signaling networks and will help to investigate the mechanism of action as well as side effects of therapeutic treatments.

Localized JNK signaling regulates organ size during development

A fundamental question of biology is what determines organ size. Despite demonstrations that factors within organs determine their sizes, intrinsic size control mechanisms remain elusive. Here we show that Drosophila wing size is regulated by JNK signaling during development. JNK is active in a stripe along the center of developing wings, and modulating JNK signaling within this stripe changes organ size. This JNK stripe influences proliferation in a non-canonical, Jun-independent manner by inhibiting the Hippo pathway. Localized JNK activity is established by Hedgehog signaling, where Ci elevates dTRAF1 expression. As the dTRAF1 homolog, TRAF4, is amplified in numerous cancers, these findings provide a new mechanism for how the Hedgehog pathway could contribute to tumorigenesis, and, more importantly, provides a new strategy for cancer therapies. Finally, modulation of JNK signaling centers in developing antennae and legs changes their sizes, suggesting a more generalizable role for JNK signaling in developmental organ size control.

DOI:http://dx.doi.org/10.7554/eLife.11491.001

A key challenge in biology is to understand what determines size. As an animal grows, signals are produced that control the size of its organs. Many of the signaling pathways that regulate size during normal animal development also contribute to the formation of tumors. Therefore, it is important to find out exactly how the signaling molecules that regulate size are linked to those that regulate tumor growth.

A protein called JNK activates a signaling pathway that triggers tumor growth. JNK signaling also stimulates cells to multiply in tissues that need repair, but it is not known whether it also regulates the size of organs during animal development. Here, Willsey et al. investigate whether JNK is active in the developing wings of fruit flies, which are commonly used as models of animal development.

The experiments show that JNK is active in a stripe across the developing wing and is required for the wing to grow to its proper size. A master signal protein called Hedgehog is responsible for establishing this stripe of JNK activity. Unexpectedly, rather than acting through its usual signaling pathway, JNK activates the Hippo pathway in the wing to control organ size during development.

Willsey et al.’s findings highlight potential new targets for cancer therapies. A future challenge will be to find out whether small patches of JNK signaling are found in the developing organs of other animals, and whether they can help explain how size changes between species.

DOI:http://dx.doi.org/10.7554/eLife.11491.002

A genetic interaction map of cell cycle regulators

A combination of genome-scale RNA interference screening and genetic interaction analysis using process-directed phenotypes is used to assign components to specific pathways and complexes for modulators of mitosis and cytokinesis in Drosophila S2 cells.

Cell-based RNA interference (RNAi) is a powerful approach to screen for modulators of many cellular processes. However, resulting candidate gene lists from cell-based assays comprise diverse effectors, both direct and indirect, and further dissecting their functions can be challenging. Here we screened a genome-wide RNAi library for modulators of mitosis and cytokinesis in Drosophila S2 cells. The screen identified many previously known genes as well as modulators that have previously not been connected to cell cycle control. We then characterized ∼300 candidate modifiers further by genetic interaction analysis using double RNAi and a multiparametric, imaging-based assay. We found that analyzing cell cycle–relevant phenotypes increased the sensitivity for associating novel gene function. Genetic interaction maps based on mitotic index and nuclear size grouped candidates into known regulatory complexes of mitosis or cytokinesis, respectively, and predicted previously uncharacterized components of known processes. For example, we confirmed a role for the Drosophila CCR4 mRNA processing complex component l(2)NC136 during the mitotic exit. Our results show that the combination of genome-scale RNAi screening and genetic interaction analysis using process-directed phenotypes provides a powerful two-step approach to assigning components to specific pathways and complexes.

A sensitised RNAi screen reveals a ch-TOG genetic interaction network required for spindle assembly

How multiple spindle assembly pathways are integrated to drive bipolar spindle assembly is poorly understood. We performed an image-based double RNAi screen to identify genes encoding Microtubule-Associated Proteins (MAPs) that interact with the highly conserved ch-TOG gene to regulate bipolar spindle assembly in human cells. We identified a ch-TOG centred network of genetic interactions which promotes ensures centrosome-mediated microtubule polymerisation, leading to the incorporation of microtubules polymerised by all pathways into a bipolar structure. Our genetic screen also reveals that ch-TOG maintains a dynamic microtubule population, in part, through modulating HSET activity. ch-TOG ensures that spindle assembly is robust to perturbation but sufficiently dynamic such that spindles can explore a diverse shape space in search of structures that can align chromosomes.

An integrated metabonomic and proteomic study on Kidney-Yin Deficiency Syndrome patients with diabetes mellitus in China

AIM

To investigate specific changes in metabolites and proteins of Kidney-Yin Deficiency Syndrome (KYDS) patients with diabetes mellitus (DM) in China.

METHODS

KYDS (n=29) and non-KYDS (n=23) patients with DM were recruited for this study. The KYDS was diagnosed by two senior TCM clinicians separately. The metabonomic and proteomic profiles of the patients were assessed using a metabonomic strategy based on NMR with multivariate analysis and a proteomic strategy based on MALDI-TOF-MS, respectively.

RESULTS

Eighteen upregulated peptides and thirty downregulated peptides were observed in the plasma of the KYDS patients. Comparing the proteomic profiles of the KYDS and non-KYDS groups, however, no significantly differentially expressed peptides were found. At the same time, major metabolic alterations were found to distinguish the two groups, including eight significantly changed metabolites (creatinine, citrate, TMAO, phenylalanine, tyrosine, alanine, glycine and taurine). The levels of creatinine, citrate, TMAO, phenylalanine and tyrosine were decreased, whereas the levels of alanine, glycine and taurine were increased in the KYDS patients. These biochemical changes were found to be associated with alterations in amino acid metabolism, energy metabolism and gut microflora.

CONCLUSION

The identification of distinct expression profiles of metabolites and signaling pathways in KYDS patients with DM suggests that there are indeed molecular signatures underlying the principles of 'Syndrome Differentiation' in traditional Chinese medicine.

Super-low dose endotoxin pre-conditioning exacerbates sepsis mortality

Sepsis mortality varies dramatically in individuals of variable immune conditions, with poorly defined mechanisms. This phenomenon complements the hypothesis that innate immunity may adopt rudimentary memory, as demonstrated in vitro with endotoxin priming and tolerance in cultured monocytes. However, previous in vivo studies only examined the protective effect of endotoxin tolerance in the context of sepsis. In sharp contrast, we report herein that pre-conditioning with super-low or low dose endotoxin lipopolysaccharide (LPS) cause strikingly opposite survival outcomes. Mice pre-conditioned with super-low dose LPS experienced severe tissue damage, inflammation, increased bacterial load in circulation, and elevated mortality when they were subjected to cecal-ligation and puncture (CLP). This is in contrast to the well-reported protective phenomenon with CLP mice pre-conditioned with low dose LPS. Mechanistically, we demonstrated that super-low and low dose LPS differentially modulate the formation of neutrophil extracellular trap (NET) in neutrophils. Instead of increased ERK activation and NET formation in neutrophils pre-conditioned with low dose LPS, we observed significantly reduced ERK activation and compromised NET generation in neutrophils pre-conditioned with super-low dose LPS. Collectively, our findings reveal a mechanism potentially responsible for the dynamic programming of innate immunity in vivo as it relates to sepsis risks.

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Super-low dose endotoxin pre-conditioning exacerbates, while higher dose endotoxin alleviates sepsis mortality.

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Super-low dose endotoxin reduces, while higher dose endotoxin facilitates neutrophil extracellular trap (NET) formation.

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Super-low dose endotoxin suppresses, while higher dose endotoxin induces ERK activation required for NET formation.

A map of directional genetic interactions in a metazoan cell

Gene–gene interactions shape complex phenotypes and modify the effects of mutations during development and disease. The effects of statistical gene–gene interactions on phenotypes have been used to assign genes to functional modules. However, directional, epistatic interactions, which reflect regulatory relationships between genes, have been challenging to map at large-scale. Here, we used combinatorial RNA interference and automated single-cell phenotyping to generate a large genetic interaction map for 21 phenotypic features of Drosophila cells. We devised a method that combines genetic interactions on multiple phenotypes to reveal directional relationships. This network reconstructed the sequence of protein activities in mitosis. Moreover, it revealed that the Ras pathway interacts with the SWI/SNF chromatin-remodelling complex, an interaction that we show is conserved in human cancer cells. Our study presents a powerful approach for reconstructing directional regulatory networks and provides a resource for the interpretation of functional consequences of genetic alterations.

DOI:http://dx.doi.org/10.7554/eLife.05464.001

Genes encode instructions that control our physical characteristics, known as traits. Although some traits are controlled by the activity of a single gene, most traits are influenced by the activities of multiple genes.

The genes that influence a particular trait may work independently of each other. However, it is also possible for the genes to interact so that one gene may mask or amplify the effect of another gene. Although gene interactions were first described almost 100 years ago, it has been difficult to identify them and work out the direction of these interactions (i.e., does gene A affect gene B, or vice versa?).

Fischer, Sandmann et al. have now studied the interactions between the genes involved in 21 different traits of fruit fly cells. A technique called RNA interference was used to lower the expression of the genes in different combinations, which made it possible to analyze any changes in the traits that occurred when particular genes were not working properly. Fischer, Sandmann et al. took hundreds of thousands images of the cells and analyzed the changes in cell shape, cell size, cell division and other traits. Next, they developed a method to infer the directions of the interactions between individual pairs of genes from the data and then made a map of the genetic interactions for the traits.

This map was able to reconstruct the known order of activity of genes during cell division and other cell processes. Furthermore, it revealed previously unknown interactions between genes. For example, genes involved in the Ras signaling pathway—which promotes cell growth and is frequently mutated in human tumors—interacted with genes that encode a group of proteins called the SWI/SNF complex. This complex alters how DNA is packaged in cells to control the expression of genes, and these gene interactions may play an important role in the control of cell growth by Ras signaling.

The approach developed by Fischer, Sandmann et al. can shed light on the interactions between genes that produce complex traits of cells. In future, this approach might be helpful to find out which genetic differences between individuals alter the effectiveness of drug treatments, and the impact of using combinations of drugs to treat diseases.

DOI:http://dx.doi.org/10.7554/eLife.05464.002

Molecular and Physiological Factors of Neuroprotection in Hypoxia-tolerant Models: Pharmacological Clues for the Treatment of Stroke

The naked mole-rat possesses several unique physiological and molecular features that underlie their remarkably and exceptional resistance to tissue hypoxia. Elevated pattern of Epo, an erythropoietin (Epo) factor; c-fos; vascular endothelial growth factor (VEGF); and hypoxia-inducible factors (HIF-1α) contribute to the adaptive strategy to cope with hypoxic stress. Moreover, the naked mole-rat has a lower metabolic rate than any other eutherian mammal of comparable size that has been studied. The ability to actively reduce metabolic rate represents a strategy widely used in the face of decreased tissue oxygen availability. Understanding the different molecular and physiological factors that induce metabolic suppression could guide the development of pharmacological agents for the clinical management of stroke patient.

Visualization of the spatial and temporal dynamics of MAPK signaling using fluorescence imaging techniques

Conserved mitogen-activated protein kinase (MAPK) signaling pathways are major mechanisms through which cells perceive and respond properly to their surrounding environment. Such homeostatic responses maintain the life of the organism. Since errors in MAPK signaling pathways can lead to cancers and to defects in immune responses, in the nervous system and metabolism, these pathways have been extensively studied as potential therapeutic targets. Although much has been studied about the roles of MAPKs in various cellular functions, less is known regarding regulation of MAPK in living organisms. This review will focus on the latest understanding of the dynamic regulation of MAPK signaling in intact cells that was revealed by using novel fluorescence imaging techniques and advanced systems-analytical methods. These techniques allowed quantitative analyses of signal transduction in situ with high spatio-temporal resolution and have revealed the nature of the molecular dynamics that determine cellular responses and fates.

Computational phosphorylation network reconstruction: methods and resources

The succession of protein activation and deactivation mediated by phosphorylation and dephosphorylation events constitutes a key mechanism of molecular information transfer in cellular systems. To deduce the details of those molecular information cascades and networks has been a central goal pursued by both experimental and computational approaches. Many computational network reconstruction methods employing an array of different statistical learning methods have been developed to infer phosphorylation networks based on different types of molecular data sets such as protein sequence, protein structure, or phosphoproteomics data. In this chapter, different computational network inference methods and resources for biological network reconstruction with a particular focus on phosphorylation networks are surveyed.

Single-cell and multivariate approaches in genetic perturbation screens

Large-scale genetic perturbation screens are a classical approach in biology and have been crucial for many discoveries. New technologies can now provide unbiased quantification of multiple molecular and phenotypic changes across tens of thousands of individual cells from large numbers of perturbed cell populations simultaneously. In this Review, we describe how these developments have enabled the discovery of new principles of intracellular and intercellular organization, novel interpretations of genetic perturbation effects and the inference of novel functional genetic interactions. These advances now allow more accurate and comprehensive analyses of gene function in cells using genetic perturbation screens.

The PERK pathway independently triggers apoptosis and a Rac1/Slpr/JNK/Dilp8 signaling favoring tissue homeostasis in a chronic ER stress Drosophila model

The endoplasmic reticulum (ER) has a major role in protein folding. The accumulation of unfolded proteins in the ER induces a stress, which can be resolved by the unfolded protein response (UPR). Chronicity of ER stress leads to UPR-induced apoptosis and in turn to an unbalance of tissue homeostasis. Although ER stress-dependent apoptosis is observed in a great number of devastating human diseases, how cells activate apoptosis and promote tissue homeostasis after chronic ER stress remains poorly understood. Here, using the Drosophila wing imaginal disc as a model system, we validated that Presenilin overexpression induces chronic ER stress in vivo. We observed, in this novel model of chronic ER-stress, a PERK/ATF4-dependent apoptosis requiring downregulation of the antiapoptotic diap1 gene. PERK/ATF4 also activated the JNK pathway through Rac1 and Slpr activation in apoptotic cells, leading to the expression of Dilp8. This insulin-like peptide caused a developmental delay, which partially allowed the replacement of apoptotic cells. Thanks to a novel chronic ER stress model, these results establish a new pathway that both participates in tissue homeostasis and triggers apoptosis through an original regulation.

Plasticity of the MAPK signaling network in response to mechanical stress

Cells display versatile responses to mechanical inputs and recent studies have identified the mitogen-activated protein kinase (MAPK) cascades mediating the biological effects observed upon mechanical stimulation. Although, MAPK pathways can act insulated from each other, several mechanisms facilitate the crosstalk between the components of these cascades. Yet, the combinatorial complexity of potential molecular interactions between these elements have prevented the understanding of their concerted functions. To analyze the plasticity of the MAPK signaling network in response to mechanical stress we performed a non-saturating epistatic screen in resting and stretched conditions employing as readout a JNK responsive dJun-FRET biosensor. By knocking down MAPKs, and JNK pathway regulators, singly or in pairs in Drosophila S2R+ cells, we have uncovered unexpected regulatory links between JNK cascade kinases, Rho GTPases, MAPKs and the JNK phosphatase Puc. These relationships have been integrated in a system network model at equilibrium accounting for all experimentally validated interactions. This model allows predicting the global reaction of the network to its modulation in response to mechanical stress. It also highlights its context-dependent sensitivity.

Protein-protein interaction networks studies and importance of 3D structure knowledge

Protein-protein interaction networks (PPINs) are a powerful tool to study biological processes in living cells. In this review, we present the progress of PPIN studies from abstract to more detailed representations. We will focus on 3D interactome networks, which offer detailed information at the atomic level. This information can be exploited in understanding not only the underlying cellular mechanisms, but also how human variants and disease-causing mutations affect protein functions and complexes' stability. Recent studies have used structural information on PPINs to also understand the molecular mechanisms of binding partner selection. We will address the challenges in generating 3D PPINs due to the restricted number of solved protein structures. Finally, some of the current use of 3D PPINs will be discussed, highlighting their contribution to the studies in genotype-phenotype relationships and in the optimization of targeted studies to design novel chemical compounds for medical treatments.

RNAi screening in Drosophila cells and in vivo

Here, I discuss how RNAi screening can be used effectively to uncover gene function. Specifically, I discuss the types of high-throughput assays that can be done in Drosophila cells and in vivo, RNAi reagent design and available reagent collections, automated screen pipelines, analysis of screen results, and approaches to RNAi results verification.

Experimental and computational tools for analysis of signaling networks in primary cells

Cellular information processing in signaling networks forms the basis of responses to environmental stimuli. At any given time, cells receive multiple simultaneous input cues, which are processed and integrated to determine cellular responses such as migration, proliferation, apoptosis, or differentiation. Protein phosphorylation events play a major role in this process and are often involved in fundamental biological and cellular processes such as protein-protein interactions, enzyme activity, and immune responses. Determining which kinases phosphorylate specific phospho sites poses a challenge; this information is critical when trying to elucidate key proteins involved in specific cellular responses. Here, methods to generate high-quality quantitative phosphorylation data from cell lysates originating from primary cells, and how to analyze the generated data to construct quantitative signaling network models, are presented. These models can subsequently be used to guide follow-up in vitro/in vivo validation studies.

Chris Bakal: Look and learn. Interviewed by Caitlin Sedwick

Bakal studies the signaling networks that control cell shape.

High-resolution network biology: connecting sequence with function

Proteins are not monolithic entities; rather, they can contain multiple domains that mediate distinct interactions, and their functionality can be regulated through post-translational modifications at multiple distinct sites. Traditionally, network biology has ignored such properties of proteins and has instead examined either the physical interactions of whole proteins or the consequences of removing entire genes. In this Review, we discuss experimental and computational methods to increase the resolution of protein-protein, genetic and drug-gene interaction studies to the domain and residue levels. Such work will be crucial for using interaction networks to connect sequence and structural information, and to understand the biological consequences of disease-associated mutations, which will hopefully lead to more effective therapeutic strategies.

Metabolomics and proteomics annotate therapeutic properties of geniposide: targeting and regulating multiple perturbed pathways

Geniposide is an important constituent of Gardenia jasminoides Ellis, a famous Chinese medicinal plant, and has displayed bright prospects in prevention and therapy of hepatic injury (HI). Unfortunately, the working mechanisms of this compound are difficult to determine and thus remain unknown. To determine the mechanisms that underlie this compound, we conducted a systematic analysis of the therapeutic effects of geniposide using biochemistry, metabolomics and proteomics. Geniposide significantly intensified the therapeutic efficacy as indicated by our modern biochemical analysis. Metabolomics results indicate 9 ions in the positive mode as differentiating metabolites which were associated with perturbations in primary bile acid biosynthesis, butanoate metabolism, citrate cycle (TCA cycle), alanine, aspartate and glutamate metabolism. Of note, geniposide has potential pharmacological effect through regulating multiple perturbed pathways to normal state. In an attempt to address the benefits of geniposide based on the proteomics approaches, the protein-interacting networks were constructed to aid identifying the drug targets of geniposide. Six identified differential proteins appear to be involved in antioxidation and signal transduction, energy production, immunity, metabolism, chaperoning. These proteins were closely related in the protein-protein interaction network and the modulation of multiple vital physiological pathways. These data will help to understand the molecular therapeutic mechanisms of geniposide on hepatic damage rats. We also conclude that metabolomics and proteomics are powerful and versatile tools for both biomarker discovery and exploring the complex relationships between biological pathways and drug response, highlighting insights into drug discovery.

Phosphoproteomics-based network medicine

One of the major tasks of phosphoproteomics is providing potential biomarkers for either diagnosis or drug targets in medical applications. Because most complex diseases are due to the actions of multiple genes/proteins, the identification of complex phospho-signatures containing multiple phosphorylation events within phosphoproteomics-based networks generates more efficient and robust biomarkers than a single, differentially phosphorylated substrate or site. Here, we briefly summarize the current efforts and progress in this newly emerging field of phosphoproteomics-based network medicine by reviewing the computational (re)construction of phosphorylation-mediated signaling networks from unannotated phosphoproteomic data, the discovery of robust network phospho-signatures and the application of these signatures for classifying cancers and predicting drug responses. The challenges as well as the potential advantages are evaluated and discussed. Although the current techniques are at present far from mature, we believe that such a systematic approach as we describe can generate more useful and robust biomarkers for biomedical usage, even at the current stage of development.

Chapter 17: bioimage informatics for systems pharmacology

Recent advances in automated high-resolution fluorescence microscopy and robotic handling have made the systematic and cost effective study of diverse morphological changes within a large population of cells possible under a variety of perturbations, e.g., drugs, compounds, metal catalysts, RNA interference (RNAi). Cell population-based studies deviate from conventional microscopy studies on a few cells, and could provide stronger statistical power for drawing experimental observations and conclusions. However, it is challenging to manually extract and quantify phenotypic changes from the large amounts of complex image data generated. Thus, bioimage informatics approaches are needed to rapidly and objectively quantify and analyze the image data. This paper provides an overview of the bioimage informatics challenges and approaches in image-based studies for drug and target discovery. The concepts and capabilities of image-based screening are first illustrated by a few practical examples investigating different kinds of phenotypic changes caEditorsused by drugs, compounds, or RNAi. The bioimage analysis approaches, including object detection, segmentation, and tracking, are then described. Subsequently, the quantitative features, phenotype identification, and multidimensional profile analysis for profiling the effects of drugs and targets are summarized. Moreover, a number of publicly available software packages for bioimage informatics are listed for further reference. It is expected that this review will help readers, including those without bioimage informatics expertise, understand the capabilities, approaches, and tools of bioimage informatics and apply them to advance their own studies.

The nucleolar protein Viriato/Nol12 is required for the growth and differentiation progression activities of the Dpp pathway during Drosophila eye development

Drosophila Decapentaplegic (Dpp), a member of the BMP2/4 class of the TGF-βs, is required for organ growth, patterning and differentiation. However, much remains to be understood about the mechanisms acting downstream of these multiple roles. Here we investigate this issue during the development of the Drosophila eye. We have previously identified viriato (vito) as a dMyc-target gene encoding a nucleolar protein that is required for proper tissue growth in the developing eye. By carrying out a targeted in vivo double-RNAi screen to identify genes and pathways functioning with Vito during eye development, we found a strong genetic interaction between vito and members of the Dpp signaling pathway including the TGF-β receptors tkv (type I), put (type II), and the co-Smad medea (med). Analyzing the expression of the Dpp receptor Tkv and the activation pattern of the pathway's transducer, p-Mad, we found that vito is required for a correct signal transduction in Dpp-receiving cells. Overall, we validate the use of double RNAi to find specific genetic interactions and, in particular, we uncover a link between the Dpp pathway and Vito, a nucleolar component. vito would act genetically downstream of Dpp, playing an important role in maintaining a sufficient level of Dpp activity for the promotion of eye disc growth and regulation of photoreceptor differentiation in eye development.

Differential RNAi screening provides insights into the rewiring of signalling networks during oxidative stress

Reactive Oxygen Species (ROS) are a natural by-product of cellular growth and proliferation, and are required for fundamental processes such as protein-folding and signal transduction. However, ROS accumulation, and the onset of oxidative stress, can negatively impact cellular and genomic integrity. Signalling networks have evolved to respond to oxidative stress by engaging diverse enzymatic and non-enzymatic antioxidant mechanisms to restore redox homeostasis. The architecture of oxidative stress response networks during periods of normal growth, and how increased ROS levels dynamically reconfigure these networks are largely unknown. In order to gain insight into the structure of signalling networks that promote redox homeostasis we first performed genome-scale RNAi screens to identify novel suppressors of superoxide accumulation. We then infer relationships between redox regulators by hierarchical clustering of phenotypic signatures describing how gene inhibition affects superoxide levels, cellular viability, and morphology across different genetic backgrounds. Genes that cluster together are likely to act in the same signalling pathway/complex and thus make "functional interactions". Moreover we also calculate differential phenotypic signatures describing the difference in cellular phenotypes following RNAi between untreated cells and cells submitted to oxidative stress. Using both phenotypic signatures and differential signatures we construct a network model of functional interactions that occur between components of the redox homeostasis network, and how such interactions become rewired in the presence of oxidative stress. This network model predicts a functional interaction between the transcription factor Jun and the IRE1 kinase, which we validate in an orthogonal assay. We thus demonstrate the ability of systems-biology approaches to identify novel signalling events.

Gliotactin and Discs-large are co-regulated to maintain epithelial integrity

Establishment and maintenance of permeability barriers is one of the most important functions of epithelial cells. Tricellular junctions (TCJs) maintain the permeability barriers at the contact site of three epithelial cells. Gliotactin (Gli), a member of the Neuroligin family, is the only known Drosophila protein exclusively localized to the TCJ and is necessary for maintenance of the permeability barrier. Over-expression triggers the spread of Gliotactin away from the TCJ and causes epithelial cells to delaminate, migrate and die. Furthermore, excess Gli at the cell membrane results in an extensive down regulation of Dlg at the septate junctions. The intracellular domain of Gli contains two highly conserved tyrosine residues, and a PDZ binding motif. We previously found that phosphorylation of the tyrosine residues are necessary to control the level of Gliotactin at the TCJ. In this study we demonstrate that the phenotypes associated with excess Gliotactin is due to a functional interaction between Gliotactin and Dlg that is dependent on both tyrosine phosphorylation as well as the PDZ binding motif. We further show that elevated levels of Dlg strongly enhance Gliotactin over-expression phenotypes to the point where tissue over-growth is observed. The exhibition of these phenotypes require phosphorylation of Dlg on serine 797, a known Par1 phosphorylation target. Blocking this phosphorylation completely suppresses the cell invasiveness and apoptotic phenotypes associated with Gliotactin overexpression. Additionally, we show that Drosophila JNK acts downstream of Gliotactin and Dlg to mediate the overgrowth and apoptosis caused by the functional interaction of Gliotactin and Dlg.

Proteomic identification network analysis of haptoglobin as a key regulator associated with liver fibrosis

Liver fibrosis (LF) is the final stage of liver dysfunction, characterized by diffuse fibrosis which is the main response to the liver injury. Haptoglobin (HP) protein, produced as an acute phase reactant during LF, preventing liver damage, may be potential molecular targets for early LF diagnostics and therapeutic applications. However, protein networks associated with the HP are largely unknown. To address this issue, we used a pathological mouse model of LF that was induced by treatment with carbon tetrachloride for 8 days. HP protein was separated and identified by two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionization time-of-flight/time-of-flight mass spectrometry. HP protein was subjected to functional pathway analysis using STRING and Cytoscape software for better understanding of the protein-protein interaction (PPI) networks in biological context. Bioinformatics analyses revealed that HP expression associated with fibrosis was upregulated, and suggested that HP responsible for fibrosis may precede the onset and progression of LF. Using the web-based database, functional pathway analysis suggested the modulation of multiple vital physiological pathways, including antioxidation immunity, signal transduction, metabolic process, energy production, cell apoptosis, oxidation reduction, DNA repair process, cell communication, and regulation of cellular process. The generation of protein interaction networks clearly enhances the interpretation and understanding of the molecular mechanisms of HP. HP protein represents targets for further experimental investigation that will provide biological insight and potentially could be exploited for novel therapeutic approaches to combat LF.

PROTEINCHALLENGE: crowd sourcing in proteomics analysis and software development

In large-scale proteomics studies there is a temptation, after months of experimental work, to plug resulting data into a convenient-if poorly implemented-set of tools, which may neither do the data justice nor help answer the scientific question. In this paper we have captured key concerns, including arguments for community-wide open source software development and "big data" compatible solutions for the future. For the meantime, we have laid out ten top tips for data processing. With these at hand, a first large-scale proteomics analysis hopefully becomes less daunting to navigate. However there is clearly a real need for robust tools, standard operating procedures and general acceptance of best practises. Thus we submit to the proteomics community a call for a community-wide open set of proteomics analysis challenges--PROTEINCHALLENGE--that directly target and compare data analysis workflows, with the aim of setting a community-driven gold standard for data handling, reporting and sharing.

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.

JNK pathway activation is controlled by Tao/TAOK3 to modulate ethanol sensitivity

Neuronal signal transduction by the JNK MAP kinase pathway is altered by a broad array of stimuli including exposure to the widely abused drug ethanol, but the behavioral relevance and the regulation of JNK signaling is unclear. Here we demonstrate that JNK signaling functions downstream of the Sterile20 kinase family gene tao/Taok3 to regulate the behavioral effects of acute ethanol exposure in both the fruit fly Drosophila and mice. In flies tao is required in neurons to promote sensitivity to the locomotor stimulant effects of acute ethanol exposure and to establish specific brain structures. Reduced expression of key JNK pathway genes substantially rescued the structural and behavioral phenotypes of tao mutants. Decreasing and increasing JNK pathway activity resulted in increased and decreased sensitivity to the locomotor stimulant properties of acute ethanol exposure, respectively. Further, JNK expression in a limited pattern of neurons that included brain regions implicated in ethanol responses was sufficient to restore normal behavior. Mice heterozygous for a disrupted allele of the homologous Taok3 gene (Taok3Gt) were resistant to the acute sedative effects of ethanol. JNK activity was constitutively increased in brains of Taok3Gt/+ mice, and acute induction of phospho-JNK in brain tissue by ethanol was occluded in Taok3Gt/+ mice. Finally, acute administration of a JNK inhibitor conferred resistance to the sedative effects of ethanol in wild-type but not Taok3Gt/+ mice. Taken together, these data support a role of a TAO/TAOK3-JNK neuronal signaling pathway in regulating sensitivity to acute ethanol exposure in flies and in mice.

Proteomics study on the hepatoprotective effects of traditional Chinese medicine formulae Yin-Chen-Hao-Tang by a combination of two-dimensional polyacrylamide gel electrophoresis and matrix-assisted laser desorption/ionization-time of flight mass spectrometry

Proteomics can bring breakthroughs in the study of traditional Chinese medicine (TCM). Yin-Chen-Hao-Tang (YCHT), a famous TCM formulae, has been used to alleviate various types of liver injury. However, the underlying mechanisms and drug targets of YCHT associated with the hepatic injury are largely unknown. To identify the possible target proteins of YCHT, two-dimensional gel electrophoresis (2-DE)-based proteomics was performed and proteins altered after YCHT treatment were identified by MALDI-TOF/TOF-MS. Interestingly, 15 modulated proteins were identified, out of which 7 were found to be significantly altered by YCHT. YCHT treatment caused a statistically significant down-regulation of zinc finger protein 407, haptoglobin, macroglobulin, alpha-1-antitrypsin; significant up-regulation of transthyretin, vitamin D-binding protein, and prothrombin, appear to be involved in metabolism, energy generation, chaperone, antioxidation, signal transduction, protein folding and apoptosis. Finally, interaction network from 7 differentially expressed protein to the signal-related proteins was established using bioinformatic analysis. Of note, these signal-related proteins could be included in a network together with 7 proteins through direct interaction or only one intermediate partner. Functional pathway analysis suggested that these proteins were closely related in the protein-protein interaction network and the modulation of multiple vital physiological pathways. Thus, our data will help to understand the molecular mechanisms of hepatoprotective effects of YCHT.

A genome-wide RNAi screen reveals a Trio-regulated Rho GTPase circuitry transducing mitogenic signals initiated by G protein-coupled receptors

Activating mutations in GNAQ and GNA11, encoding members of the Gα(q) family of G protein α subunits, are the driver oncogenes in uveal melanoma, and mutations in Gq-linked G protein-coupled receptors have been identified recently in numerous human malignancies. How Gα(q) and its coupled receptors transduce mitogenic signals is still unclear because of the complexity of signaling events perturbed upon Gq activation. Using a synthetic-biology approach and a genome-wide RNAi screen, we found that a highly conserved guanine nucleotide exchange factor, Trio, is essential for activating Rho- and Rac-regulated signaling pathways acting on JNK and p38, and thereby transducing proliferative signals from Gα(q) to the nucleus independently of phospholipase C-β. Indeed, whereas many biological responses elicited by Gq depend on the transient activation of second-messenger systems, Gq utilizes a hard-wired protein-protein-interaction-based signaling circuitry to achieve the sustained stimulation of proliferative pathways, thereby controlling normal and aberrant cell growth.

Reducing false-positive prediction of minimotifs with a genetic interaction filter

Background

Minimotifs are short contiguous peptide sequences in proteins that have known functions. At its simplest level, the minimotif sequence is present in a source protein and has an activity relationship with a target, most of which are proteins. While many scientists routinely investigate new minimotif functions in proteins, the major web-based discovery tools have a high rate of false-positive prediction. Any new approach that reduces false-positives will be of great help to biologists.

Methods and Findings

We have built three filters that use genetic interactions to reduce false-positive minimotif predictions. The basic filter identifies those minimotifs where the source/target protein pairs have a known genetic interaction. The HomoloGene genetic interaction filter extends these predictions to predicted genetic interactions of orthologous proteins and the node-based filter identifies those minimotifs where proteins that have a genetic interaction with the source or target have a genetic interaction. Each filter was evaluated with a test data set containing thousands of true and false-positives. Based on sensitivity and selectivity performance metrics, the basic filter had the best discrimination for true positives, whereas the node-based filter had the highest sensitivity. We have implemented these genetic interaction filters on the Minimotif Miner 2.3 website. The genetic interaction filter is particularly useful for improving predictions of posttranslational modifications such as phosphorylation and proteolytic cleavage sites.

Conclusions

Genetic interaction data sets can be used to reduce false-positive minimotif predictions. Minimotif prediction in known genetic interactions can help to refine the mechanisms behind the functional connection between genes revealed by genetic experimentation and screens.