Genome-wide screening for gene function using RNAi in mammalian cells

Mammalian genome sequencing has identified numerous genes requiring functional annotation. The discovery that dsRNA can direct gene-specific silencing in both model organisms and mammalian cells through RNA interference (RNAi) has provided a platform for dissecting the function of independent genes. The generation of large-scale RNAi libraries targeting all predicted genes within mouse, rat and human cells, combined with the large number of cell-based assays, provides a unique opportunity to perform high-throughput genetics in these complex cell systems. Many different formats exist for the generation of genome-wide RNAi libraries for use in mammalian cells. Furthermore, the use of these libraries in either genetic screens or genetic selections allows for the identification of known and novel genes involved in complex cellular phenotypes and biological processes, some of which underpin human disease. In this review, we examine genome-wide RNAi libraries used in model organisms and mammalian cells and provide examples of how these information rich reagents can be used for determining gene function, discovering novel therapeutic targets and dissecting signalling pathways, cellular processes and complex phenotypes.     

Differential dynamic properties of scleroderma fibroblasts in response to perturbation of environmental stimuli

Diseases are believed to arise from dysregulation of biological systems (pathways) perturbed by environmental triggers. Biological systems as a whole are not just the sum of their components, rather ever-changing, complex and dynamic systems over time in response to internal and external perturbation. In the past, biologists have mainly focused on studying either functions of isolated genes or steady-states of small biological pathways. However, it is systems dynamics that play an essential role in giving rise to cellular function/dysfunction which cause diseases, such as growth, differentiation, division and apoptosis. Biological phenomena of the entire organism are not only determined by steady-state characteristics of the biological systems, but also by intrinsic dynamic properties of biological systems, including stability, transient-response, and controllability, which determine how the systems maintain their functions and performance under a broad range of random internal and external perturbations. As a proof of principle, we examine signal transduction pathways and genetic regulatory pathways as biological systems. We employ widely used state-space equations in systems science to model biological systems, and use expectation-maximization (EM) algorithms and Kalman filter to estimate the parameters in the models. We apply the developed state-space models to human fibroblasts obtained from the autoimmune fibrosing disease, scleroderma, and then perform dynamic analysis of partial TGF-beta pathway in both normal and scleroderma fibroblasts stimulated by silica. We find that TGF-beta pathway under perturbation of silica shows significant differences in dynamic properties between normal and scleroderma fibroblasts. Our findings may open a new avenue in exploring the functions of cells and mechanism operative in disease development.     

Phenotypic Activation to Discover Biological Pathways and Kinase Substrates

Observing the effects of gene perturbation on cells or organisms has long been a standard strategy in biological research. We developed a genome-wide gene overexpression library as a new tool for large-scale functional analysis in budding yeast. Previous large-scale genetic studies have focused on applications of the deletion mutant collection, which has arguably revolutionized the functional characterization of yeast genes. While extremely powerful, deletion mutant experiments are generally limited to the characterization of loss-of-function phenotypes. We have explored the potential for using the Synthetic Genetic Array (SGA) method, a platform for high-throughput genetic analysis, with a genome-wide “overexpression array”, in which each strain on the array overexpresses a unique yeast gene. The overexpression array enables gain-of-function phenotypes to be examined on a large scale, providing a unique insight into gene function and a novel source of reagents for the global mapping of genetic networks and functional relationships amongst genes and pathways. Understanding the molecular bases of overexpression phenotypes should also shed new light on the nature of genetic dominance.     

Learning Gene Networks under SNP Perturbations Using eQTL Datasets

The standard approach for identifying gene networks is based on experimental perturbations of gene regulatory systems such as gene knock-out experiments, followed by a genome-wide profiling of differential gene expressions. However, this approach is significantly limited in that it is not possible to perturb more than one or two genes simultaneously to discover complex gene interactions or to distinguish between direct and indirect downstream regulations of the differentially-expressed genes. As an alternative, genetical genomics study has been proposed to treat naturally-occurring genetic variants as potential perturbants of gene regulatory system and to recover gene networks via analysis of population gene-expression and genotype data. Despite many advantages of genetical genomics data analysis, the computational challenge that the effects of multifactorial genetic perturbations should be decoded simultaneously from data has prevented a widespread application of genetical genomics analysis. In this article, we propose a statistical framework for learning gene networks that overcomes the limitations of experimental perturbation methods and addresses the challenges of genetical genomics analysis. We introduce a new statistical model, called a sparse conditional Gaussian graphical model, and describe an efficient learning algorithm that simultaneously decodes the perturbations of gene regulatory system by a large number of SNPs to identify a gene network along with expression quantitative trait loci (eQTLs) that perturb this network. While our statistical model captures direct genetic perturbations of gene network, by performing inference on the probabilistic graphical model, we obtain detailed characterizations of how the direct SNP perturbation effects propagate through the gene network to perturb other genes indirectly. We demonstrate our statistical method using HapMap-simulated and yeast eQTL datasets. In particular, the yeast gene network identified computationally by our method under SNP perturbations is well supported by the results from experimental perturbation studies related to DNA replication stress response.     

Repeated small perturbation approach reveals transcriptomic steady states

The study of biological systems dynamics requires elucidation of the transitions of steady states. A "small perturbation" approach can provide important information on the "steady state" of a biological system. In our experiments, small perturbations were generated by applying a series of repeating small doses of ultraviolet radiation to a human keratinocyte cell line, HaCaT. The biological response was assessed by monitoring the gene expression profiles using cDNA microarrays. Repeated small doses (10 J/m2) of ultraviolet B (UVB) exposure modulated the expression profiles of two groups of genes in opposite directions. The genes that were up-regulated have functions mainly associated with anti-proliferation/anti-mitogenesis/apoptosis, and the genes that were down-regulated were mainly related to proliferation/mitogenesis/anti-apoptosis. For both groups of genes, repetition of the small doses of UVB caused an immediate response followed by relaxation between successive small perturbations. This cyclic pattern was suppressed when large doses (233 or 582.5 J/m2) of UVB were applied. Our method and results contribute to a foundation for computational systems biology, which implicitly uses the concept of steady state.     

Stochastic synchronization of genetic oscillator networks

Background  

The study of synchronization among genetic oscillators is essential for the understanding of the rhythmic phenomena of living organisms at both molecular and cellular levels. Genetic networks are intrinsically noisy due to natural random intra- and inter-cellular fluctuations. Therefore, it is important to study the effects of noise perturbation on the synchronous dynamics of genetic oscillators. From the synthetic biology viewpoint, it is also important to implement biological systems that minimizing the negative influence of the perturbations.     

High-throughput RNAi screening to dissect cellular pathways: A how-to guide

RNA interference (RNAi) has become a powerful tool to dissect cellular pathways and characterize gene functions. The availability of genome-wide RNAi libraries for various model organisms and mammalian cells has enabled high-throughput RNAi screenings. These RNAi screens successfully identified key components that had previously been missed in classical forward genetic screening approaches and allowed the assessment of combined loss-of-function phenotypes. Crucially, the quality of RNAi screening results depends on quantitative assays and the choice of the right biological context. In this review, we provide an overview on the design and application of high-throughput RNAi screens as well as data analysis and candidate validation strategies.     

Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles

MOTIVATION: Gene expression profiling experiments in cell lines and animal models characterized by specific genetic or molecular perturbations have yielded sets of genes annotated by the perturbation. These gene sets can serve as a reference base for interrogating other expression datasets. For example, a new dataset in which a specific pathway gene set appears to be enriched, in terms of multiple genes in that set evidencing expression changes, can then be annotated by that reference pathway. We introduce in this paper a formal statistical method to measure the enrichment of each sample in an expression dataset. This allows us to assay the natural variation of pathway activity in observed gene expression data sets from clinical cancer and other studies. RESULTS: Validation of the method and illustrations of biological insights gleaned are demonstrated on cell line data, mouse models, and cancer-related datasets. Using oncogenic pathway signatures, we show that gene sets built from a model system are indeed enriched in the model system. We employ ASSESS for the use of molecular classification by pathways. This provides an accurate classifier that can be interpreted at the level of pathways instead of individual genes. Finally, ASSESS can be used for cross-platform expression models where data on the same type of cancer are integrated over different platforms into a space of enrichment scores. AVAILABILITY: Versions are available in Octave and Java (with a graphical user interface). Software can be downloaded at http://people.genome.duke.edu/assess.     

Design and implementation of cell-based assays to model human disease.

Cell-based assays, if appropriately designed, can be used to rapidly identify molecular mechanisms of human disease and develop novel therapeutics. In the last 20 years, many genes that cause or contribute to diverse disorders, including cancer and neurodegenerative disease, have been identified. With such genes in hand, scientists have created numerous model systems to dissect the molecular mechanisms of basic cellular and developmental biology. Meanwhile, techniques for high-throughput screening that use large chemical libraries have been developed, as have cDNA and RNA interference libraries that cover the entire human genome. By combining cell-based assays with chemical and genetic screens, we now have vastly improved our ability to dissect molecular mechanisms of disease and to identify therapeutic targets and therapeutic lead compounds. However, cell-based screening systems have yet to yield many fundamental insights into disease pathogenesis, and the development of therapeutic leads is frustratingly slow. This may be due to a failure of such assays to accurately reflect key aspects of pathogenesis. This Review attempts to guide the design of productive cellular models of human disease that may be used in high-throughput chemical and genetic screens. We emphasize two points: (i) model systems should use quantifiable molecular indicators of a pathogenic process, and (ii) small chemical libraries that include molecules with known biological activity and/or acceptable safety profiles are very useful.     

Inferring subnetworks from perturbed expression profiles

Genome-wide expression profiles of genetic mutants provide a wide variety of measurements of cellular responses to perturbations. Typical analysis of such data identifies genes affected by perturbation and uses clustering to group genes of similar function. In this paper we discover a finer structure of interactions between genes, such as causality, mediation, activation, and inhibition by using a Bayesian network framework. We extend this framework to correctly handle perturbations, and to identify significant subnetworks of interacting genes. We apply this method to expression data of S. cerevisiae mutants and uncover a variety of structured metabolic, signaling and regulatory pathways.     

A Genetic Strategy for Probing the Functional Diversity of Magnetosome Formation

Model genetic systems are invaluable, but limit us to understanding only a few organisms in detail, missing the variations in biological processes that are performed by related organisms. One such diverse process is the formation of magnetosome organelles by magnetotactic bacteria. Studies of model magnetotactic α-proteobacteria have demonstrated that magnetosomes are cubo-octahedral magnetite crystals that are synthesized within pre-existing membrane compartments derived from the inner membrane and orchestrated by a specific set of genes encoded within a genomic island. However, this model cannot explain all magnetosome formation, which is phenotypically and genetically diverse. For example, Desulfovibrio magneticus RS-1, a δ-proteobacterium for which we lack genetic tools, produces tooth-shaped magnetite crystals that may or may not be encased by a membrane with a magnetosome gene island that diverges significantly from those of the α-proteobacteria. To probe the functional diversity of magnetosome formation, we used modern sequencing technology to identify hits in RS-1 mutated with UV or chemical mutagens. We isolated and characterized mutant alleles of 10 magnetosome genes in RS-1, 7 of which are not found in the α-proteobacterial models. These findings have implications for our understanding of magnetosome formation in general and demonstrate the feasibility of applying a modern genetic approach to an organism for which classic genetic tools are not available.     

Two sides of lifespan regulating genes: pro-longevity or anti-longevity?

Traditionally, ageing has been considered a passive and entropic process, in which damages accumulate on biological macromolecules over time and the accumulated damages lead to a decline in overall physiological functions. However, the discovery of a longevity mutant in the nematode Caenorhabditis elegans has challenged this view. A longevity mutant is a mutant organism, in which a reduction-of-function of a certain gene prolongs the lifespan. Thus, the discovery of longevity mutants has shown the existence of genes, which function to shorten lifespan in wild-type organisms, promoting extensive hunting for longevity-regulating genes in short-lived model organisms, such as yeast, worms and flies. These studies have revealed remarkable conservation of longevity-regulating genes and their networks among species. Decreased insulin/IGF-like signalling and decreased target of rapamycin (TOR) signalling are both shown to extend lifespan in evolutionarily divergent species, from unicellular organisms to mammals. Intriguingly, most of these longevity-regulating pathways reveal pro-longevity and anti-longevity effects on lifespan, depending on biological and environmental contexts. This review summarizes pleiotropic functions of the conserved longevity-regulating genes or pathways, focusing on studies in C. elegans.     

Quantitative Genome-Wide Genetic Interaction Screens Reveal Global Epistatic Relationships of Protein Complexes in Escherichia coli

Large-scale proteomic analyses in Escherichia coli have documented the composition and physical relationships of multiprotein complexes, but not their functional organization into biological pathways and processes. Conversely, genetic interaction (GI) screens can provide insights into the biological role(s) of individual gene and higher order associations. Combining the information from both approaches should elucidate how complexes and pathways intersect functionally at a systems level. However, such integrative analysis has been hindered due to the lack of relevant GI data. Here we present a systematic, unbiased, and quantitative synthetic genetic array screen in E. coli describing the genetic dependencies and functional cross-talk among over 600,000 digenic mutant combinations. Combining this epistasis information with putative functional modules derived from previous proteomic data and genomic context-based methods revealed unexpected associations, including new components required for the biogenesis of iron-sulphur and ribosome integrity, and the interplay between molecular chaperones and proteases. We find that functionally-linked genes co-conserved among γ-proteobacteria are far more likely to have correlated GI profiles than genes with divergent patterns of evolution. Overall, examining bacterial GIs in the context of protein complexes provides avenues for a deeper mechanistic understanding of core microbial systems.     

Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.     

Mapping of signaling networks through synthetic genetic interaction analysis by RNAi

The analysis of synthetic genetic interaction networks can reveal how biological systems achieve a high level of complexity with a limited repertoire of components. Studies in yeast and bacteria have taken advantage of collections of deletion strains to construct matrices of quantitative interaction profiles and infer gene function. Yet comparable approaches in higher organisms have been difficult to implement in a robust manner. Here we report a method to identify genetic interactions in tissue culture cells through RNAi. By performing more than 70,000 pairwise perturbations of signaling factors, we identified >600 interactions affecting different quantitative phenotypes of Drosophila melanogaster cells. Computational analysis of this interaction matrix allowed us to reconstruct signaling pathways and identify a conserved regulator of Ras-MAPK signaling. Large-scale genetic interaction mapping by RNAi is a versatile, scalable approach for revealing gene function and the connectivity of cellular networks.