Integrating interactome, phenome, and transcriptome mapping data for the C. elegans germline

MACSPI enables tissue-selective proteomic and interactomic analyses in multicellular organisms

Multicellular organisms are composed of many tissue types that have distinct morphologies and functions, which are largely driven by specialized proteomes and interactomes. To define the proteome and interactome of a specific type of tissue in an intact animal, we developed a localized proteomics approach called Methionine Analog-based Cell-Specific Proteomics and Interactomics (MACSPI). This method uses the tissue-specific expression of an engineered methionyl-tRNA synthetase to label proteins with a bifunctional amino acid 2-amino-5-diazirinylnonynoic acid in selected cells. We applied MACSPI in Caenorhabditis elegans, a model multicellular organism, to selectively label, capture, and profile the proteomes of the body wall muscle and the nervous system, which led to the identification of tissue-specific proteins. Using the photo-cross-linker, we successfully profiled HSP90 interactors in muscles and neurons and identified tissue-specific interactors and stress-related interactors. Our study demonstrates that MACSPI can be used to profile tissue-specific proteomes and interactomes in intact multicellular organisms.

Zebrafish as a Mainstream Model for In Vivo Systems Pharmacology and Toxicology

Pharmacology and toxicology are part of a much broader effort to understand the relationship between chemistry and biology. While biomedicine has necessarily focused on specific cases, typically of direct human relevance, there are real advantages in pursuing more systematic approaches to characterizing how health and disease are influenced by small molecules and other interventions. In this context, the zebrafish is now established as the representative screenable vertebrate and, through ongoing advances in the available scale of genome editing and automated phenotyping, is beginning to address systems-level solutions to some biomedical problems. The addition of broader efforts to integrate information content across preclinical model organisms and the incorporation of rigorous analytics, including closed-loop deep learning, will facilitate efforts to create systems pharmacology and toxicology with the ability to continuously optimize chemical biological interactions around societal needs. In this review, we outline progress toward this goal.

Closing the 'phenotype gap' in precision medicine: improving what we measure to understand complex disease mechanisms

The central concept underlying precision medicine is a mechanistic understanding of each disease and its response to therapy sufficient to direct a specific intervention. To execute on this vision requires parsing incompletely defined disease syndromes into discrete mechanistic subsets and developing interventions to precisely address each of these etiologically distinct entities. This will require substantial adjustment of traditional paradigms which have tended to aggregate high-level phenotypes with very different etiologies. In the current environment, where diagnoses are not mechanistic, drug development has become so expensive that it is now impractical to imagine the cost-effective creation of new interventions for many prevalent chronic conditions. The vision of precision medicine also argues for a much more seamless integration of research and development with clinical care, where shared taxonomies will enable every clinical interaction to inform our collective understanding of disease mechanisms and drug responses. Ideally, this would be executed in ways that drive real-time and real-world discovery, innovation, translation, and implementation. Only in oncology, where at least some of the biology is accessible through surgical excision of the diseased tissue or liquid biopsy, has "co-clinical" modeling proven feasible. In most common germline disorders, while genetics often reveal the causal mutations, there still remain substantial barriers to efficient disease modeling. Aggregation of similar disorders under single diagnostic labels has directly contributed to the paucity of etiologic and mechanistic understanding by directly reducing the resolution of any subsequent studies. Existing clinical phenotypes are typically anatomic, physiologic, or histologic, and result in a substantial mismatch in information content between the phenomes in humans or in animal 'models' and the variation in the genome. This lack of one-to-one mapping of discrete mechanisms between disease and animal models causes a failure of translation and is one form of 'phenotype gap.' In this review, we will focus on the origins of the phenotyping deficit and approaches that may be considered to bridge the gap, creating shared taxonomies between human diseases and relevant models, using cardiovascular examples.

Protein interactome mapping in Caenorhabditis elegans

The systematic identification of all protein–protein interactions that take place in an organism (the ‘interactome’) is an important goal in modern biology. The nematode Caenorhabditis elegans was one of the first multicellular models for which a proteome-wide interactome mapping project was initiated. Most Caenorhabditis elegans interactome mapping efforts have utilized the yeast two-hybrid system, yielding an extensive binary interactome, while recent developments in mass spectrometry-based approaches hold great potential for further improving our understanding of protein interactome networks in a multicellular context. For example, methods like co-fractionation, proximity labeling, and tissue-specific protein purification not only identify protein–protein interactions, but have the potential to provide crucial insight into when and where interactions take place. Here we review current standards and recent improvements in protein interaction mapping in C. elegans.

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Protein interactome mapping is a key method to understanding biology.

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C. elegans was one of the first multicellular organisms whose interactome was mapped.

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Renewed efforts and improved technologies are needed to complete the interactome.

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Novel technologies are poised to provide spatial information to interactome networks.

Perturbation of Interaction Networks for Application to Cancer Therapy

We present a computational approach for studying the effect of potential drug combinations on the protein networks associated with tumor cells. The majority of therapeutics are designed to target single proteins, yet most diseased states are characterized by a combination of many interacting genes and proteins. Using the topology of protein-protein interaction networks, our methods can explicitly model the possible synergistic effect of targeting multiple proteins using drug combinations in different cancer types.

The methodology can be conceptually split into two distinct stages. Firstly, we integrate protein interaction and gene expression data to develop network representations of different tissue types and cancer types. Secondly, we model network perturbations to search for target combinations which cause significant damage to a relevant cancer network but only minimal damage to an equivalent normal network. We have developed sets of predicted target and drug combinations for multiple cancer types, which are validated using known cancer and drug associations, and are currently in experimental testing for prostate cancer. Our methods also revealed significant bias in curated interaction data sources towards targets with associations compared with high-throughput data sources from model organisms. The approach developed can potentially be applied to many other diseased cell types.

Combined Gene Expression and RNAi Screening to Identify Alkylation Damage Survival Pathways from Fly to Human

Alkylating agents are a key component of cancer chemotherapy. Several cellular mechanisms are known to be important for its survival, particularly DNA repair and xenobiotic detoxification, yet genomic screens indicate that additional cellular components may be involved. Elucidating these components has value in either identifying key processes that can be modulated to improve chemotherapeutic efficacy or may be altered in some cancers to confer chemoresistance. We therefore set out to reevaluate our prior Drosophila RNAi screening data by comparison to gene expression arrays in order to determine if we could identify any novel processes in alkylation damage survival. We noted a consistent conservation of alkylation survival pathways across platforms and species when the analysis was conducted on a pathway/process level rather than at an individual gene level. Better results were obtained when combining gene lists from two datasets (RNAi screen plus microarray) prior to analysis. In addition to previously identified DNA damage responses (p53 signaling and Nucleotide Excision Repair), DNA-mRNA-protein metabolism (transcription/translation) and proteasome machinery, we also noted a highly conserved cross-species requirement for NRF2, glutathione (GSH)-mediated drug detoxification and Endoplasmic Reticulum stress (ER stress)/Unfolded Protein Responses (UPR) in cells exposed to alkylation. The requirement for GSH, NRF2 and UPR in alkylation survival was validated by metabolomics, protein studies and functional cell assays. From this we conclude that RNAi/gene expression fusion is a valid strategy to rapidly identify key processes that may be extendable to other contexts beyond damage survival.

Integrating -Omics: Systems Biology as Explored Through C. elegans Research

-Omics data have become indispensable to systems biology, which aims to describe the full complexity of functional cells, tissues, organs and organisms. Generating vast amounts of data via such methods, researchers have invested in ways of handling and interpreting these. From the large volumes of -omics data that have been gathered over the years, it is clear that the information derived from one -ome is usually far from complete. Now, individual techniques and methods for integration are maturing to the point that researchers can focus on network-based integration rather than simply interpreting single -ome studies. This review evaluates the application of integrated -omics approaches with a focus on Caenorhabditis elegans studies, intending to direct researchers in this field to useful databases and inspiring examples.

C. elegans: an invaluable model organism for the proteomics studies of the cholesterol-mediated signaling pathway

With the availability of its complete genome sequence and unique biological features relevant to human disease, Caenorhabditis elegans has become an invaluable model organism for the studies of proteomics, leading to the elucidation of nematode gene function. A journey from the genome to proteome of C. elegans may begin with preparation of expressed proteins, which enables a large-scale analysis of all possible proteins expressed under specific physiological conditions. Although various techniques have been used for proteomic analysis of C. elegans, systematic high-throughput analysis is still to come in order to accommodate studies of post-translational modification and quantitative analysis. Given that no integrated C. elegans protein expression database is available, it is about time that a global C. elegans proteome project is launched through which datasets of transcriptomes, protein-protein interaction and functional annotation can be integrated. As an initial target of a pilot project of the C. elegans proteome project, the cholesterol-mediated signaling pathway will be an excellent example since, like in other organisms, it is one of the key controlling pathways in cell growth and development in C. elegans. As this field tends to broaden to functional proteomics, there is a high demand to develop the versatile proteome informatics tools that can mange many different data in an integrative manner.

Understanding the yeast proteome: a bioinformatics perspective

Rapid development of genomic and proteomic methodologies has provided a wealth of data for deciphering the biomolecular circuitry of a living cell. The main areas of computational research of proteomes outlined in this review are: understanding the system, its features and parameters to help plan the experiments; data integration, to help produce more reliable data sets; visualization and other forms of data representation to simplify interpretation; modeling of the functional regulation; and systems biology. With false-positive rates reaching 50% even in the more reliable data sets, handling the experimental error remains one of the most challenging tasks. Integrative approaches, incorporating results of various genome- and proteome-wide experiments, allow for minimizing the error and bring with them significant predictive power.

Systems biology for molecular life sciences and its impact in biomedicine

Modern systems biology is already contributing to a radical transformation of molecular life sciences and biomedicine, and it is expected to have a real impact in the clinical setting in the next years. In this review, the emergence of systems biology is contextualized with a historic overview, and its present state is depicted. The present and expected future contribution of systems biology to the development of molecular medicine is underscored. Concerning the present situation, this review includes a reflection on the "inflation" of biological data and the urgent need for tools and procedures to make hidden information emerge. Descriptions of the impact of networks and models and the available resources and tools for applying them in systems biology approaches to molecular medicine are provided as well. The actual current impact of systems biology in molecular medicine is illustrated, reviewing two cases, namely, those of systems pharmacology and cancer systems biology. Finally, some of the expected contributions of systems biology to the immediate future of molecular medicine are commented.

Interactome mapping for analysis of complex phenotypes: insights from benchmarking binary interaction assays

Protein interactions mediate essentially all biological processes and analysis of protein-protein interactions using both large-scale and small-scale approaches has contributed fundamental insights to the understanding of biological systems. In recent years, interactome network maps have emerged as an important tool for analyzing and interpreting genetic data of complex phenotypes. Complementary experimental approaches to test for binary, direct interactions, and for membership in protein complexes are used to explore the interactome. The two approaches are not redundant but yield orthogonal perspectives onto the complex network of physical interactions by which proteins mediate biological processes. In recent years, several publications have demonstrated that interactions from high-throughput experiments can be equally reliable as the high quality subset of interactions identified in small-scale studies. Critical for this insight was the introduction of standardized experimental benchmarking of interaction and validation assays using reference sets. The data obtained in these benchmarking experiments have resulted in greater appreciation of the limitations and the complementary strengths of different assays. Moreover, benchmarking is a central element of a conceptual framework to estimate interactome sizes and thereby measure progress toward near complete network maps. These estimates have revealed that current large-scale data sets, although often of high quality, cover only a small fraction of a given interactome. Here, I review the findings of assay benchmarking and discuss implications for quality control, and for strategies toward obtaining a near-complete map of the interactome of an organism.

Diversity in genetic in vivo methods for protein-protein interaction studies: from the yeast two-hybrid system to the mammalian split-luciferase system

The yeast two-hybrid system pioneered the field of in vivo protein-protein interaction methods and undisputedly gave rise to a palette of ingenious techniques that are constantly pushing further the limits of the original method. Sensitivity and selectivity have improved because of various technical tricks and experimental designs. Here we present an exhaustive overview of the genetic approaches available to study in vivo binary protein interactions, based on two-hybrid and protein fragment complementation assays. These methods have been engineered and employed successfully in microorganisms such as Saccharomyces cerevisiae and Escherichia coli, but also in higher eukaryotes. From single binary pairwise interactions to whole-genome interactome mapping, the self-reassembly concept has been employed widely. Innovative studies report the use of proteins such as ubiquitin, dihydrofolate reductase, and adenylate cyclase as reconstituted reporters. Protein fragment complementation assays have extended the possibilities in protein-protein interaction studies, with technologies that enable spatial and temporal analyses of protein complexes. In addition, one-hybrid and three-hybrid systems have broadened the types of interactions that can be studied and the findings that can be obtained. Applications of these technologies are discussed, together with the advantages and limitations of the available assays.

Systematic approaches to toxicology in the zebrafish

As the current paradigms of drug discovery evolve, it has become clear that a more comprehensive understanding of the interactions between small molecules and organismal biology will be vital. The zebrafish is emerging as a complement to existing in vitro technologies and established preclinical in vivo models that can be scaled for high-throughput. In this review, we highlight the current status of zebrafish toxicology studies, identify potential future niches for the model in the drug development pipeline, and define the hurdles that must be overcome as zebrafish technologies are refined for systematic toxicology.

Challenges and solutions in proteomics

The accelerated growth of proteomics data presents both opportunities and challenges. Large-scale proteomic profiling of biological samples such as cells, organelles or biological fluids has led to discovery of numerous key and novel proteins involved in many biological/disease processes including cancers, as well as to the identification of novel disease biomarkers and potential therapeutic targets. While proteomic data analysis has been greatly assisted by the many bioinformatics tools developed in recent years, a careful analysis of the major steps and flow of data in a typical highthroughput analysis reveals a few gaps that still need to be filled to fully realize the value of the data. To facilitate functional and pathway discovery for large-scale proteomic data, we have developed an integrated proteomic expression analysis system, iProXpress, which facilitates protein identification using a comprehensive sequence library and functional interpretation using integrated data. With its modular design, iProXpress complements and can be integrated with other software in a proteomic data analysis pipeline. This novel approach to complex biological questions involves the interrogation of multiple data sources, thereby facilitating hypothesis generation and knowledge discovery from the genomic-scale studies and fostering disease diagnosis and drug development.

Caenorhabditis elegans-applications to nematode genomics

The complete genome sequence of the free-living nematode Caenorhabditis elegans was published 4 years ago. Since then, we have seen great strides in technologies that seek to exploit this data. Here we describe the application of some of these techniques and other advances that are helping us to understand about not only the biology of this important model organism but also the entire phylum Nematoda.

Interactome networks and human disease

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

Systems biology and heart failure: concepts, methods, and potential research applications

Dramatic advances in molecular biology dominated twentieth century biomedical science and delineated the function of individual genes and molecules in exquisite detail. However, biological processes cannot be fully understood based on the properties of individual genes and molecules alone, since these elements act in concert to enable the specific functions that make for living cells and organisms. The discipline of systems biology provides a novel conceptual framework for understanding biological phenomenon. Systems biology synthesizes information concerning the interactions of genes and molecules and allows characterization of the supramolecular networks and functional modules that represent the most essential aspects of cell organization and physiology.

A high-throughput screening system for Arabidopsis transcription factors and its application to Med25-dependent transcriptional regulation

The activities of transcription factors (TFs) require interactions with specific DNA sequences and other regulatory proteins. To detect such interactions in Arabidopsis, we developed a high-throughput screening system with a Gateway-compatible Gal4-AD-TF library of 1589 Arabidopsis TFs, which can be easily screened by mating-based yeast-one-hybrid (Y1H) and yeast-two-hybrid (Y2H) methods. The efficiency of the system was validated by examining two well-characterized TF-DNA and TF-protein interactions: the CHE-CCA1 promoter interaction by Y1H and NPR1-TGAs interactions by Y2H. We used this system to identify eight TFs that interact with a Mediator subunit, Med25, a key regulator in JA signaling. We identified five TFs that interacted with the GCC-box cis-element in the promoter of PDF1.2, a downstream gene of Med25. We found that three of these TFs, all from the AP2-EREBP family, interact directly both with Med25 and the GCC-box of PDF1.2, suggesting that Med25 regulates PDF1.2 expression through these three TFs. These results demonstrate that this high-throughput Y1H/Y2H screening system is an efficient tool for studying transcriptional regulation networks in Arabidopsis. This system will be available for other Arabidopsis researchers, and thus it provides a vital resource for the Arabidopsis community.

Integrative features of the yeast phosphoproteome and protein-protein interaction map

Following recent advances in high-throughput mass spectrometry (MS)-based proteomics, the numbers of identified phosphoproteins and their phosphosites have greatly increased in a wide variety of organisms. Although a critical role of phosphorylation is control of protein signaling, our understanding of the phosphoproteome remains limited. Here, we report unexpected, large-scale connections revealed between the phosphoproteome and protein interactome by integrative data-mining of yeast multi-omics data. First, new phosphoproteome data on yeast cells were obtained by MS-based proteomics and unified with publicly available yeast phosphoproteome data. This revealed that nearly 60% of ∼6,000 yeast genes encode phosphoproteins. We mapped these unified phosphoproteome data on a yeast protein-protein interaction (PPI) network with other yeast multi-omics datasets containing information about proteome abundance, proteome disorders, literature-derived signaling reactomes, and in vitro substratomes of kinases. In the phospho-PPI, phosphoproteins had more interacting partners than nonphosphoproteins, implying that a large fraction of intracellular protein interaction patterns (including those of protein complex formation) is affected by reversible and alternative phosphorylation reactions. Although highly abundant or unstructured proteins have a high chance of both interacting with other proteins and being phosphorylated within cells, the difference between the number counts of interacting partners of phosphoproteins and nonphosphoproteins was significant independently of protein abundance and disorder level. Moreover, analysis of the phospho-PPI and yeast signaling reactome data suggested that co-phosphorylation of interacting proteins by single kinases is common within cells. These multi-omics analyses illuminate how wide-ranging intracellular phosphorylation events and the diversity of physical protein interactions are largely affected by each other.

Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology

Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed by these risk variants. It has previously been observed that different genes harboring causal mutations for the same Mendelian disease often physically interact. We sought to evaluate the degree to which this is true of genes within strongly associated loci in complex disease. Using sets of loci defined in rheumatoid arthritis (RA) and Crohn's disease (CD) GWAS, we build protein-protein interaction (PPI) networks for genes within associated loci and find abundant physical interactions between protein products of associated genes. We apply multiple permutation approaches to show that these networks are more densely connected than chance expectation. To confirm biological relevance, we show that the components of the networks tend to be expressed in similar tissues relevant to the phenotypes in question, suggesting the network indicates common underlying processes perturbed by risk loci. Furthermore, we show that the RA and CD networks have predictive power by demonstrating that proteins in these networks, not encoded in the confirmed list of disease associated loci, are significantly enriched for association to the phenotypes in question in extended GWAS analysis. Finally, we test our method in 3 non-immune traits to assess its applicability to complex traits in general. We find that genes in loci associated to height and lipid levels assemble into significantly connected networks but did not detect excess connectivity among Type 2 Diabetes (T2D) loci beyond chance. Taken together, our results constitute evidence that, for many of the complex diseases studied here, common genetic associations implicate regions encoding proteins that physically interact in a preferential manner, in line with observations in Mendelian disease.

Gene-centered regulatory network mapping

The Caenorhabditis elegans hermaphrodite is a complex multicellular animal that is composed of 959 somatic cells. The C. elegans genome contains ∼20,000 protein-coding genes, 940 of which encode regulatory transcription factors (TFs). In addition, the worm genome encodes more than 100 microRNAs and many other regulatory RNA and protein molecules. Most C. elegans genes are subject to regulatory control, most likely by multiple regulators, and combined, this dictates the activation or repression of the gene and corresponding protein in the relevant cells and under the appropriate conditions. A major goal in C. elegans research is to determine the spatiotemporal expression pattern of each gene throughout development and in response to different signals, and to determine how this expression pattern is accomplished. Gene regulatory networks describe physical and/or functional interactions between genes and their regulators that result in specific spatiotemporal gene expression. Such regulators can act at transcriptional or post-transcriptional levels. Here, I will discuss the methods that can be used to delineate gene regulatory networks in C. elegans. I will mostly focus on gene-centered yeast one-hybrid (Y1H) assays that are used to map interactions between non-coding genic regions, such as promoters, and regulatory TFs. The approaches discussed here are not only relevant to C. elegans biology, but can also be applied to other model organisms and humans.

Dissection of genetic pathways in C. elegans

With unique genetic and cell biological strengths, C. elegans has emerged as a powerful model system for studying many biological processes. These processes are typically regulated by complex genetic networks consisting of genes. Identifying those genes and organizing them into genetic pathways are two major steps toward understanding the mechanisms that regulate biological events. Forward genetic screens with various designs are a traditional approach for identifying candidate genes. The completion of the genome sequencing in C. elegans and the advent of high-throughput experimental techniques have led to the development of two additional powerful approaches: functional genomics and systems biology. Genes that are discovered by these approaches can be ordered into interacting pathways through a variety of strategies, involving genetics, cell biology, biochemistry, and functional genomics, to gain a more complete understanding of how gene regulatory networks control a particular biological process. The aim of this review is to provide an overview of the approaches available to identify and construct the genetic pathways using C. elegans.

Genetic approaches to aging in budding and fission yeasts: new connections and new opportunities

Yeasts are powerful model systems to examine the evolutionarily conserved aspects of eukaryotic aging because they maintain many of the same core cellular signaling pathways and essential organelles as human cells. We constructed a strain of the budding yeast Saccharomyces cerevisiae that could monitor the distribution of proteins involved in heterochromatic silencing and aging, and isolated mutants that alter this distribution. The largest class of such mutants cause defects in mitochondrial function, and appear to cause changes in nuclear silencing separate from the well-known Rtg2p-dependent pathway that alters nuclear transcription in response to the loss of the mitochondrial genome. Mutants that inactivate the ATP2 gene, which encodes the ATPase subunit of the mitochondrial F(1)F(0)-ATPase, were isolated twice in our screen and identify a lifespan extending pathway in a gene that is conserved in both prokaryotes and eukaryotes. The budding yeast S. cerevisiae S. cerevisiae has been used with great success to identify other lifespan-extending pathways in screens using surrogate phenotypes such as stress resistance or silencing to identify random mutants, or in high throughput screens that utilize the deletion strain set resource. However, the direct selection of long-lived mutants from a pool of random mutants is more challenging. We have established a new chronological aging assay for the evolutionarily distant fission yeast Schizosaccharomyces pombe that recapitulates aspects of aging conserved in all eukaryotes. We have constructed a novel S. pombe S. pombe DNA insertion mutant bank, and used it to show that we can directly select for a long-lived mutant. The use of both the budding and fission yeast systems should continue to facilitate the identification and validation of lifespan extending pathways that are conserved in humans.

From single genes to gene networks: high-throughput-high-content screening for neurological disease

Neuronal development, function, and the subsequent degeneration of the brain are still an enigma in both the normal and pathologic states, and there is an urgent need to find better targets for developing therapeutic intervention. Current techniques to deconstruct the architecture of brain and disease-related pathways are best suited for following up on single genes but would take an impractical amount of time for the leads from the current wave of genetic and genomic data. New technical developments have made combined high-throughput-high-content (HT-HC) cellular screens possible, which have the potential to contextualize the information, gathered from a combination of genetic and genomic approaches, into networks and functional biology and can be utilized for the identification of therapeutic targets. Herein we discuss the potential impact of HT-HC cellular screens on medical neuroscience.

The zebrafish: scalable in vivo modeling for systems biology

The zebrafish offers a scalable vertebrate model for many areas of biologic investigation. There is substantial conservation of genetic and genomic features and, at a higher order, conservation of intermolecular networks, as well as physiologic systems and phenotypes. We highlight recent work demonstrating the extent of this homology, and efforts to develop high-throughput phenotyping strategies suited to genetic or chemical screening on a scale compatible with in vivo validation for systems biology. We discuss the implications of these approaches for functional annotation of the genome, elucidation of multicellular processes in vivo, and mechanistic exploration of hypotheses generated by a broad range of 'unbiased' 'omic technologies such as expression profiling and genome-wide association. Finally, we outline potential strategies for the application of the zebrafish to the systematic study of phenotypic architecture, disease heterogeneity and drug responses.

Elucidating gene regulatory mechanisms for sperm function through the integration of classical and systems approaches in C. elegans

From worms to mammals, successful spermatogenesis depends on a gene expression profile that balances activating and repressive mechanisms. Besides developmental control of specific spermatogenic genes, male fertility requires temporal shifts in global gene expression and dramatic changes in chromatin structure and condensation. Recent studies are beginning to elucidate the molecular processes that both drive these temporal changes in gene expression and underlie fertility. In this review, we provide an overview of relevant C. elegans studies that have laid the groundwork for modern approaches. Next, we highlight recent studies that investigate how gene expression in C. elegans is modulated during spermatogenesis. These studies use large-scale genomic profiling in combination with bioinformatics, genetics, biochemistry, and in vitro methods to target specific stages or processes during sperm formation. Such studies are beginning to elucidate the multiple layers of gene regulation required during spermatogenesis, i.e., transcriptional, post-transcriptional, and epigenetic. Moreover, knowledge of how C. elegans coordinately regulates gene expression during spermatogenesis promises to provide key insights into parallel processes in mammals that are vital for fertility.

The genetics of congestive heart failure

The heart failure syndrome is known to represent a final common pathway for a broad range of etiologies, but there is tremendous variation in the propensity to develop congestive heart failure after a given insult. This variation is thought to result in part from inherited differences in myocardial, vascular or systemic responses, but the nature of the underlying traits responsible ultimately for the development of heart failure has remained elusive. There has been limited progress in the genetic exploration of the key clinical phenotype itself: heart failure. In this article, the author attempts to place the results of genetic studies of cardiomyopathy in the broader context of the clinical syndrome of heart failure, highlighting some of the key questions for future study.

Analysis of combinatorial regulation: scaling of partnerships between regulators with the number of governed targets

Through combinatorial regulation, regulators partner with each other to control common targets and this allows a small number of regulators to govern many targets. One interesting question is that given this combinatorial regulation, how does the number of regulators scale with the number of targets? Here, we address this question by building and analyzing co-regulation (co-transcription and co-phosphorylation) networks that describe partnerships between regulators controlling common genes. We carry out analyses across five diverse species: Escherichia coli to human. These reveal many properties of partnership networks, such as the absence of a classical power-law degree distribution despite the existence of nodes with many partners. We also find that the number of co-regulatory partnerships follows an exponential saturation curve in relation to the number of targets. (For E. coli and Bacillus subtilis, only the beginning linear part of this curve is evident due to arrangement of genes into operons.) To gain intuition into the saturation process, we relate the biological regulation to more commonplace social contexts where a small number of individuals can form an intricate web of connections on the internet. Indeed, we find that the size of partnership networks saturates even as the complexity of their output increases. We also present a variety of models to account for the saturation phenomenon. In particular, we develop a simple analytical model to show how new partnerships are acquired with an increasing number of target genes; with certain assumptions, it reproduces the observed saturation. Then, we build a more general simulation of network growth and find agreement with a wide range of real networks. Finally, we perform various down-sampling calculations on the observed data to illustrate the robustness of our conclusions.

A regulatory network consists of regulators such as transcription factors or kinases that control the expression or activity of their target genes. Almost always, there are multiple regulators partnering together to control their targets. Compared to more commonplace contexts, these regulators can be thought of as managers in a social or corporate setting controlling their common subordinates. One interesting question that we address here in this study is how the number of governing regulators scales with the number of governed targets. We build and analyze co-regulation (co-transcription and co-phosphorylation) networks that describe partnerships between regulators controlling common genes. We use a simple framework across five species that demonstrate a wide range of evolution: Escherichia coli to human. The analysis reveals many properties of partnership networks and shows that the number of co-regulatory partnerships follows an exponential saturation curve with the number of targets. To gain more intuition, we explore more commonplace contexts and find that exponential saturation relationship also exists in several social networks. Finally, we propose a simple model to explain this relationship that also exists in a simulated evolutionary environment.

Comparative transcriptomic and proteomic profiling of industrial wine yeast strains

The geno- and phenotypic diversity of commercial Saccharomyces cerevisiae wine yeast strains provides an opportunity to apply the system-wide approaches that are reasonably well established for laboratory strains to generate insight into the functioning of complex cellular networks in industrial environments. We have previously analyzed the transcriptomes of five industrial wine yeast strains at three time points during alcoholic fermentation. Here, we extend the comparative approach to include an isobaric tag for relative and absolute quantitation (iTRAQ)-based proteomic analysis of two of the previously analyzed wine yeast strains at the same three time points during fermentation in synthetic wine must. The data show that differences in the transcriptomes of the two strains at a given time point rather accurately reflect differences in the corresponding proteomes independently of the gene ontology (GO) category, providing strong support for the biological relevance of comparative transcriptomic data sets in yeast. In line with previous observations, the alignment proves to be less accurate when assessing intrastrain changes at different time points. In this case, differences between the transcriptome and proteome appear to be strongly dependent on the GO category of the corresponding genes. The data in particular suggest that metabolic enzymes and the corresponding genes appear to be strongly correlated over time and between strains, suggesting a strong transcriptional control of such enzymes. The data also allow the generation of hypotheses regarding the molecular origin of significant differences in phenotypic traits between the two strains.

A unifying view of 21st century systems biology

The idea that multi-scale dynamic complex systems formed by interacting macromolecules and metabolites, cells, organs and organisms underlie some of the most fundamental aspects of life was proposed by a few visionaries half a century ago. We are witnessing a powerful resurgence of this idea made possible by the availability of nearly complete genome sequences, ever improving gene annotations and interactome network maps, the development of sophisticated informatic and imaging tools, and importantly, the use of engineering and physics concepts such as control and graph theory. Alongside four other fundamental "great ideas" as suggested by Sir Paul Nurse, namely, the gene, the cell, the role of chemistry in biological processes, and evolution by natural selection, systems-level understanding of "What is Life" may materialize as one of the major ideas of biology.

Aging mice show a decreasing correlation of gene expression within genetic modules

In this work we present a method for the differential analysis of gene co-expression networks and apply this method to look for large-scale transcriptional changes in aging. We derived synonymous gene co-expression networks from AGEMAP expression data for 16-month-old and 24-month-old mice. We identified a number of functional gene groups that change co-expression with age. Among these changing groups we found a trend towards declining correlation with age. In particular, we identified a modular (as opposed to uniform) decline in general correlation with age. We identified potential transcriptional mechanisms that may aid in modular correlation decline. We found that computationally identified targets of the NF-ΚB transcription factor decrease expression correlation with age. Finally, we found that genes that are prone to declining co-expression tend to be co-located on the chromosome. Our results conclude that there is a modular decline in co-expression with age in mice. They also indicate that factors relating to both chromosome domains and specific transcription factors may contribute to the decline.

There is mounting evidence that mammalian aging is marked by increased gene transcriptional variation. This trend was shown not only by studying gene expression in single cells (Bahar et al. 2006), but at the coarse tissue resolution as well (Somel et al. 2006; Li et al. 2009). These led us to believe that looking at absolute changes in expression level alone may not tell the whole story of transcriptional changes in age. Instead the story may be in the more subtle changes in the coordination of expression among multiple genes. For this reason, we decided to look at changes in co-expression relationships with age. To this end, we developed a methodology for differential co-expression network analysis for the comparison gene co-expression on a global scale. We applied this methodology to compare co-expression between young (16-month) and old (24-month) mice. This allowed us to find both gene groups whose coordination appear to be affected by age and to propose potential mechanisms for the change. We believe our work is of broad importance because it represents a different paradigm for looking not only at aging but also at any complex condition or disease—away from changes in individual genes towards changes in gene relationships.

Scaffolds: interaction platforms for cellular signalling circuits

Scaffold proteins influence cellular signalling by binding to multiple signalling enzymes, receptors or ion channels. Although normally devoid of catalytic activity, they have a big impact on controlling the flow of signalling information. By assembling signalling proteins into complexes, they play the part of signal processing hubs. As we learn more about the way signalling components are linked into natural signalling circuits, researchers are becoming interested in building non-natural signalling pathways to test our knowledge and/or to intentionally reprogram cellular behaviour. In this review, we discuss the role of scaffold proteins as efficient tools for assembling intracellular signalling complexes, both natural and artificial.

Evolution of early embryogenesis in rhabditid nematodes

The cell-biological events that guide early-embryonic development occur with great precision within species but can be quite diverse across species. How these cellular processes evolve and which molecular components underlie evolutionary changes is poorly understood. To begin to address these questions, we systematically investigated early embryogenesis, from the one- to the four-cell embryo, in 34 nematode species related to C. elegans. We found 40 cell-biological characters that captured the phenotypic differences between these species. By tracing the evolutionary changes on a molecular phylogeny, we found that these characters evolved multiple times and independently of one another. Strikingly, all these phenotypes are mimicked by single-gene RNAi experiments in C. elegans. We use these comparisons to hypothesize the molecular mechanisms underlying the evolutionary changes. For example, we predict that a cell polarity module was altered during the evolution of the Protorhabditis group and show that PAR-1, a kinase localized asymmetrically in C. elegans early embryos, is symmetrically localized in the one-cell stage of Protorhabditis group species. Our genome-wide approach identifies candidate molecules-and thereby modules-associated with evolutionary changes in cell-biological phenotypes.

Evidence for gene length as a determinant of gene coexpression in protein complexes

Variation of gene length imposes a challenge on genes requiring coexpression. Using a large human protein complex data set, we show that genes encoding subunits of the same protein complex tend to have similar length. The length uniformity is greater for complexes with stronger coexpression. We also show that the rate of gene length evolution is associated with gene coexpression level within a complex. These results suggest a new angle in understanding the evolution of protein complexes as well as the regulation of gene coexpression.

Marian Walhout: transcriptional mapmaker. Interviewed by Ben Short

Walhout uses the genome as a base camp for exploring transcriptional regulation.

Identification of genes expressed in the hermaphrodite germ line of C. elegans using SAGE

BACKGROUND

Germ cells must progress through elaborate developmental stages from an undifferentiated germ cell to a fully differentiated gamete. Some of these stages include exiting mitosis and entering meiosis, progressing through the various stages of meiotic prophase, adopting either a male (sperm) or female (oocyte) fate, and completing meiosis. Additionally, many of the factors needed to drive embryogenesis are synthesized in the germ line. To increase our understanding of the genes that might be necessary for the formation and function of the germ line, we have constructed a SAGE library from hand dissected C. elegans hermaphrodite gonads.

RESULTS

We found that 4699 genes, roughly 21% of all known C. elegans genes, are expressed in the adult hermaphrodite germ line. Ribosomal genes are highly expressed in the germ line; roughly four fold above their expression levels in the soma. We further found that 1063 of the germline-expressed genes have enriched expression in the germ line as compared to the soma. A comparison of these 1063 germline-enriched genes with a similar list of genes prepared using microarrays revealed an overlap of 460 genes, mutually reinforcing the two lists. Additionally, we identified 603 germline-enriched genes, supported by in situ expression data, which were not previously identified. We also found >4 fold enrichment for RNA binding proteins in the germ line as compared to the soma.

CONCLUSION

Using multiple technological platforms provides a more complete picture of global gene expression patterns. Genes involved in RNA metabolism are expressed at a significantly higher level in the germ line than the soma, suggesting a stronger reliance on RNA metabolism for control of the expression of genes in the germ line. Additionally, the number and expression level of germ line expressed genes on the X chromosome is lower than expected based on a random distribution.

Hsm3/S5b participates in the assembly pathway of the 19S regulatory particle of the proteasome

The 26S proteasome, the central enzyme of the ubiquitin-proteasome system, is comprised of the 20S catalytic core particle (CP) and the 19S regulatory particle (RP), itself composed of two subcomplexes, the base and the lid. 20S proteasome assembly is assisted by several chaperones. Integral subunits of the RP participate in its assembly, but no external factors have been identified so far. Here we characterize the yeast Hsm3 protein, which displays unique features regarding 19S assembly. Hsm3 associates with 19S subcomplexes via a carboxy-terminal domain of the Rpt1 base subunit but is missing in the final 26S proteasome. Moreover, Hsm3 is specifically required for the base subcomplex assembly. Finally, we identify the putative species-specific 19S subunit S5b as a functional homolog of the Hsm3 chaperone in mammals. These findings shed light on chaperone-assisted proteasome assembly in eukaryotes.

Zebrafish genetic models for arrhythmia

Over the last decade the zebrafish has emerged as a major genetic model organism. While stimulated originally by the utility of its transparent embryos for the study of vertebrate organogenesis, the success of the zebrafish was consolidated through multiple genetic screens, sequencing of the fish genome by the Sanger Center, and the advent of extensive genomic resources. In the last few years the potential of the zebrafish for in vivo cell biology, physiology, disease modeling and drug discovery has begun to be realized. This review will highlight work on cardiac electrophysiology, emphasizing the arenas in which the zebrafish complements other in vivo and in vitro models; developmental physiology, large-scale screens, high-throughput disease modeling and drug discovery. Much of this work is at an early stage, and so the focus will be on the general principles, the specific advantages of the zebrafish and on future potential.

Exploring phenotype-associated modules in an oral cavity tumor using an integrated framework

MOTIVATION

Like most human diseases, tumors are complex traits, the genesis and development of which recruit a number of genes and several important biological processes. As proteins involved in common processes tend to be centralized in the same local area of protein-protein interaction networks, here a novel framework has been developed to identify which areas of the networks are most relevant to a phenotype.

RESULTS

These areas termed 'coherent modules' can be regarded as gene sets dynamically defined in the networks. Compared with previous analogous approaches, one critical feature of our method is the optimization of coherent modules for two distinct aspects balanced by tuning a parameter in the framework. First, we seek the low coupling between coherent modules and then maximize the intrinsic similarity within a module. The framework has good expansibility, with classical expression data analysis methods generalized as particular cases. This coherent module approach was applied to an oral cavity tumor dataset with 18 significant coherent modules identified, which could indicate the presence of lymph node metastasis. Further examination shows that most of the modules are responsible for comparatively independent biological processes. Our framework is helpful for the prognosis of tumors and offers a new perspective for tumor research.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network

To provide accurate biological hypotheses and elucidate global properties of cellular networks, systematic identification of protein-protein interactions must meet high quality standards.We present an expanded C. elegans protein-protein interaction network, or 'interactome' map, derived from testing a matrix of approximately 10,000 x approximately 10,000 proteins using a highly specific, high-throughput yeast two-hybrid system. Through a new empirical quality control framework, we show that the resulting data set (Worm Interactome 2007, or WI-2007) was similar in quality to low-throughput data curated from the literature. We filtered previous interaction data sets and integrated them with WI-2007 to generate a high-confidence consolidated map (Worm Interactome version 8, or WI8). This work allowed us to estimate the size of the worm interactome at approximately 116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationships between genes or proteins

Interaction networks: from protein functions to drug discovery. A review

Most genes, proteins and other components carry out their functions within a complex network of interactions and a single molecule can affect a wide range of other cell components. A global, integrative, approach has been developed for several years, including protein-protein interaction networks (interactomes). In this review, we describe the high-throughput methods used to identify new interactions and to build large interaction datasets. The minimum information required for reporting a molecular interaction experiment (MIMIx) has been defined as a standard for storing data in publicly available interaction databases. Several examples of interaction networks from molecular machines (proteasome) or organelles (phagosome, mitochondrion) to whole organisms (viruses, bacteria, yeast, fly, and worm) are given and attempts to cover the entire human interaction network are discussed. The methods used to perform the topological analysis of interaction networks and to extract biological information from them are presented. These investigations have provided clues on protein functions, signalling and metabolic pathways, and physiological processes, unraveled the molecular basis of some diseases (cancer, infectious diseases), and will be very useful to identify new therapeutic targets and for drug discovery. A major challenge is now to integrate data from different sources (interactome, transcriptome, phenome, localization) to switch from static to dynamic interaction networks. The merging of a viral interactome and the human interactome has been used to simulate viral infection, paving the way for future studies aiming at providing molecular basis of human diseases.

Information-based methods for predicting gene function from systematic gene knock-downs

BACKGROUND

The rapid annotation of genes on a genome-wide scale is now possible for several organisms using high-throughput RNA interference assays to knock down the expression of a specific gene. To date, dozens of RNA interference phenotypes have been recorded for the nematode Caenorhabditis elegans. Although previous studies have demonstrated the merit of using knock-down phenotypes to predict gene function, it is unclear how the data can be used most effectively. An open question is how to optimally make use of phenotypic observations, possibly in combination with other functional genomics datasets, to identify genes that share a common role.

RESULTS

We compared several methods for detecting gene-gene functional similarity from phenotypic knock-down profiles. We found that information-based measures, which explicitly incorporate a phenotype's genomic frequency when calculating gene-gene similarity, outperform non-information-based methods. We report the presence of newly predicted modules identified from an integrated functional network containing phenotypic congruency links derived from an information-based measure. One such module is a set of genes predicted to play a role in regulating body morphology based on their multiply-supported interactions with members of the TGF-beta signaling pathway.

CONCLUSION

Information-based metrics significantly improve the comparison of phenotypic knock-down profiles, based upon their ability to enhance gene function prediction and identify novel functional modules.

Cell biology and evolution: molecular modules link it all?

Classical studies comparing developing embryos have suggested the importance of modified cell biological processes in the evolution of new phenotypes. Here, I revisit this connection focusing on embryonic development, in particular nematode embryogenesis. I compare phenotypic differences in nematode embryogenesis in two basic cell biological processes, the cell cycle and the localization of the first division axis. The analysis of these and other processes shows that, at the cell biological level, exhaustive variation is found that does not necessarily translate into morphological differences. Modern molecular analyses have led to a view in which molecular complexes, made up of groups of proteins, or modules, that are working together, are responsible for the proper execution of cell biological programs. I discuss how this modular architecture could facilitate the phenotypic changes observed in cell biological processes. Ultimately, understanding the connection between cellular behavior and phenotypic outcome will further elucidate the mechanisms responsible for phenotypic evolution.

The FLYWCH transcription factors FLH-1, FLH-2, and FLH-3 repress embryonic expression of microRNA genes in C. elegans

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression post-transcriptionally via antisense base-pairing. Although miRNAs are involved in a variety of important biological functions, little is known about their transcriptional regulation. Using yeast one-hybrid assays, we identified transcription factors with a FLYWCH Zn-finger DNA-binding domain that bind to the promoters of several Caenorhabditis elegans miRNA genes. The products of the flh-1 and flh-2 genes function redundantly to repress embryonic expression of lin-4, mir-48, and mir-241, miRNA genes that are normally expressed only post-embryonically. Although single mutations in either flh-1 or flh-2 genes result in a viable phenotype, double mutation of flh-1 and flh-2 results in early larval lethality and an enhanced derepression of their target miRNAs in embryos. Double mutations in flh-2 and a third FLYWCH Zn-finger-containing transcription factor, flh-3, also result in enhanced precocious expression of target miRNAs. Mutations of lin-4 or mir-48&mir-241 do not rescue the lethal flh-1; flh-2 double-mutant phenotype, suggesting that the inviability is not solely the result of precocious expression of these miRNAs. Therefore, the FLH-1 and FLH-2 proteins likely play a more general role in regulating gene expression in embryos.

Mapping the human protein interactome

Interactions are the essence of all biomolecules because they cannot fulfill their roles without interacting with other molecules. Hence, mapping the interactions of biomolecules can be useful for understanding their roles and functions. Furthermore, the development of molecular based systems biology requires an understanding of the biomolecular interactions. In recent years, the mapping of protein-protein interactions in different species has been reported, but few reports have focused on the large-scale mapping of protein-protein interactions in human. Here, we review the developments in protein interaction mapping and we discuss issues and strategies for the mapping of the human protein interactome.

Identification of Drosophila mitotic genes by combining co-expression analysis and RNA interference

RNAi screens have, to date, identified many genes required for mitotic divisions of Drosophila tissue culture cells. However, the inventory of such genes remains incomplete. We have combined the powers of bioinformatics and RNAi technology to detect novel mitotic genes. We found that Drosophila genes involved in mitosis tend to be transcriptionally co-expressed. We thus constructed a co-expression-based list of 1,000 genes that are highly enriched in mitotic functions, and we performed RNAi for each of these genes. By limiting the number of genes to be examined, we were able to perform a very detailed phenotypic analysis of RNAi cells. We examined dsRNA-treated cells for possible abnormalities in both chromosome structure and spindle organization. This analysis allowed the identification of 142 mitotic genes, which were subdivided into 18 phenoclusters. Seventy of these genes have not previously been associated with mitotic defects; 30 of them are required for spindle assembly and/or chromosome segregation, and 40 are required to prevent spontaneous chromosome breakage. We note that the latter type of genes has never been detected in previous RNAi screens in any system. Finally, we found that RNAi against genes encoding kinetochore components or highly conserved splicing factors results in identical defects in chromosome segregation, highlighting an unanticipated role of splicing factors in centromere function. These findings indicate that our co-expression-based method for the detection of mitotic functions works remarkably well. We can foresee that elaboration of co-expression lists using genes in the same phenocluster will provide many candidate genes for small-scale RNAi screens aimed at completing the inventory of mitotic proteins.

Lessons from genetics: interpreting complex phenotypes in RNAi screens

Mammalian cell biology is witnessing a new era in which cellular processes are explained through dynamic networks of interacting cellular components. In this fast-pacing field, where image-based RNAi screening is taking a central role, there is a strong need to improve ways to capture such interactions in space and time. Cell biologists traditionally depict these events by confining themselves to the level of a single cell, or to many population-averaged cells. Similarly, classical geneticists observe and interpret phenotypes in a single organism to delineate signaling processes, but have also described genetic phenomena in populations of organisms. The analogy in the two approaches inspired us to draw parallels with, and take lessons from concepts in classical genetics.