High-Content,Image-Based Screening for Drug Targets in Yeast

Background

Drug discovery and development are predicated on elucidation of the potential mechanisms of action and cellular targets of candidate chemical compounds. Recent advances in high-content imaging techniques allow simultaneous analysis of a range of cellular events. In this study, we propose a novel strategy to identify drug targets by combining genetic screening and high-content imaging in yeast.

Methodology

In this approach, we infer the cellular functions affected by candidate drugs by comparing morphologic changes induced by the compounds with the phenotypes of yeast mutants.

Conclusions

Using this method and four well-characterized reagents, we successfully identified previously known target genes of the compounds as well as other genes involved with functionally related cellular pathways. This is the first demonstration of a genetic high-content assay that can be used to identify drug targets based on morphologic phenotypes of a reference mutant panel.     

Enabling inverse metabolic engineering through genomics

Inverse metabolic engineering (IME) is a powerful framework for engineering cellular phenotypes. Progress in this field has been limited by a lack of comprehensive methods for efficiently identifying the genetic basis of relevant phenotypes. Advances in genomics technologies, including DNA microarrays and gene sequencing, have dramatically improved our ability to relate changes in phenotype with associated changes in genotype. When applied in the context of IME, these tools should enable the integration of "evolutionary" and "direct" approaches to engineering cell physiology, which should improve our understanding of the complex interactions affecting the expression, evolution and engineering of traits in natural and industrial hosts.     

A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF,OMIM and PubMed records

Background

Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic profile of genes with respect to their connection to disease phenotypes. The importance of protein-protein interaction networks in the genetic heterogeneity of common diseases or complex traits is becoming increasingly recognized. Thus, the development of a network-based approach combined with phenotypic profiling would be useful for disease gene prioritization.

Results

We developed a random-set scoring model and implemented it to quantify phenotype relevance in a network-based disease gene-prioritization approach. We validated our approach based on different gene phenotypic profiles, which were generated from PubMed abstracts, OMIM, and GeneRIF records. We also investigated the validity of several vocabulary filters and different likelihood thresholds for predicted protein-protein interactions in terms of their effect on the network-based gene-prioritization approach, which relies on text-mining of the phenotype data. Our method demonstrated good precision and sensitivity compared with those of two alternative complex-based prioritization approaches. We then conducted a global ranking of all human genes according to their relevance to a range of human diseases. The resulting accurate ranking of known causal genes supported the reliability of our approach. Moreover, these data suggest many promising novel candidate genes for human disorders that have a complex mode of inheritance.

Conclusion

We have implemented and validated a network-based approach to prioritize genes for human diseases based on their phenotypic profile. We have devised a powerful and transparent tool to identify and rank candidate genes. Our global gene prioritization provides a unique resource for the biological interpretation of data from genome-wide association studies, and will help in the understanding of how the associated genetic variants influence disease or quantitative phenotypes.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-315) contains supplementary material, which is available to authorized users.     

High content screening in neurodegenerative diseases

The functional annotation of genomes, construction of molecular networks and novel drug target identification, are important challenges that need to be addressed as a matter of great urgency. Multiple complementary 'omics' approaches have provided clues as to the genetic risk factors and pathogenic mechanisms underlying numerous neurodegenerative diseases, but most findings still require functional validation. For example, a recent genome wide association study for Parkinson's Disease (PD), identified many new loci as risk factors for the disease, but the underlying causative variant(s) or pathogenic mechanism is not known. As each associated region can contain several genes, the functional evaluation of each of the genes on phenotypes associated with the disease, using traditional cell biology techniques would take too long. There is also a need to understand the molecular networks that link genetic mutations to the phenotypes they cause. It is expected that disease phenotypes are the result of multiple interactions that have been disrupted. Reconstruction of these networks using traditional molecular methods would be time consuming. Moreover, network predictions from independent studies of individual components, the reductionism approach, will probably underestimate the network complexity. This underestimation could, in part, explain the low success rate of drug approval due to undesirable or toxic side effects. Gaining a network perspective of disease related pathways using HT/HC cellular screening approaches, and identifying key nodes within these pathways, could lead to the identification of targets that are more suited for therapeutic intervention. High-throughput screening (HTS) is an ideal methodology to address these issues. but traditional methods were one dimensional whole-well cell assays, that used simplistic readouts for complex biological processes. They were unable to simultaneously quantify the many phenotypes observed in neurodegenerative diseases such as axonal transport deficits or alterations in morphology properties. This approach could not be used to investigate the dynamic nature of cellular processes or pathogenic events that occur in a subset of cells. To quantify such features one has to move to multi-dimensional phenotypes termed high-content screening (HCS). HCS is the cell-based quantification of several processes simultaneously, which provides a more detailed representation of the cellular response to various perturbations compared to HTS. HCS has many advantages over HTS, but conducting a high-throughput (HT)-high-content (HC) screen in neuronal models is problematic due to high cost, environmental variation and human error. In order to detect cellular responses on a 'phenomics' scale using HC imaging one has to reduce variation and error, while increasing sensitivity and reproducibility. Herein we describe a method to accurately and reliably conduct shRNA screens using automated cell culturing and HC imaging in neuronal cellular models. We describe how we have used this methodology to identify modulators for one particular protein, DJ1, which when mutated causes autosomal recessive parkinsonism. Combining the versatility of HC imaging with HT methods, it is possible to accurately quantify a plethora of phenotypes. This could subsequently be utilized to advance our understanding of the genome, the pathways involved in disease pathogenesis as well as identify potential therapeutic targets.     

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.     

Screens, maps & networks: from genome sequences to personalized medicine

Genome sequencing of tumors provides a wealth of information on mutations and structural variations, instilling hope that this data can be used to predict individual tumor progression and response to treatment. Yet currently, our ability to predict the functional consequences of these aberrations remains poor. How do cancer-associated mutations give rise to the hallmark phenotypes of cancer? Recently, information about the genetic makeup of cancer cells has been combined with novel functional genomics approaches to identify novel targets, exploit synthetic lethality and explore the rewiring of cellular pathways. Here, we highlight recent developments revealing the hidden landscape of genetic interactions in model organisms and cancer cells, a key step toward personalized cancer diagnostics and therapy.     

A Drosophila model for genetic analysis of influenza viral/host interactions

Influenza viruses impose a constant threat to vertebrates susceptible to this family of viruses. We have developed a new tool to study virus-host interactions that play key roles in viral replication and to help identify novel anti-influenza drug targets. Via the UAS/Gal4 system we ectopically expressed the influenza virus M2 gene in Drosophila melanogaster and generated dose-sensitive phenotypes in the eye and wing. We have confirmed that the M2 proton channel is properly targeted to cell membranes in Drosophila tissues and functions as a proton channel by altering intracellular pH. As part of the efficacy for potential anti-influenza drug screens, we have also demonstrated that the anti-influenza drug amantadine, which targets the M2 proton channel, suppressed the UAS-M2 mutant phenotype when fed to larvae. In a candidate gene screen we identified mutations in components of the vacuolar V1V0 ATPase that modify the UAS-M2 phenotype. Importantly, in this study we demonstrate that Drosophila genetic interactions translate directly to physiological requirements of the influenza A virus for these components in mammalian cells. Overexpressing specific V1 subunits altered the replication capacity of influenza virus in cell culture and suggests that drugs targeting the enzyme complex via these subunits may be useful in anti-influenza drug therapies. Moreover, this study adds credence to the idea of using the M2 "flu fly" to identify new and previously unconsidered cellular genes as potential drug targets and to provide insight into basic mechanisms of influenza virus biology.     

Gene expression during Drosophila wing morphogenesis and differentiation

The simple cellular composition and array of distally pointing hairs has made the Drosophila wing a favored system for studying planar polarity and the coordination of cellular and tissue level morphogenesis. We carried out a gene expression screen to identify candidate genes that functioned in wing and wing hair morphogenesis. Pupal wing RNA was isolated from tissue prior to, during, and after hair growth and used to probe Affymetrix Drosophila gene chips. We identified 435 genes whose expression changed at least fivefold during this period and 1335 whose expression changed at least twofold. As a functional validation we chose 10 genes where genetic reagents existed but where there was little or no evidence for a wing phenotype. New phenotypes were found for 9 of these genes, providing functional validation for the collection of identified genes. Among the phenotypes seen were a delay in hair initiation, defects in hair maturation, defects in cuticle formation and pigmentation, and abnormal wing hair polarity. The collection of identified genes should be a valuable data set for future studies on hair and bristle morphogenesis, cuticle synthesis, and planar polarity.     

Exploration of methods to identify polymorphisms associated with variation in DNA repair capacity phenotypes

Elucidating the relationship between polymorphic sequences and risk of common disease is a challenge. For example, although it is clear that variation in DNA repair genes is associated with familial cancer, aging and neurological disease, progress toward identifying polymorphisms associated with elevated risk of sporadic disease has been slow. This is partly due to the complexity of the genetic variation, the existence of large numbers of mostly low frequency variants and the contribution of many genes to variation in susceptibility. There has been limited development of methods to find associations between genotypes having many polymorphisms and pathway function or health outcome. We have explored several statistical methods for identifying polymorphisms associated with variation in DNA repair phenotypes. The model system used was 80 cell lines that had been resequenced to identify variation; 191 single nucleotide substitution polymorphisms (SNPs) are included, of which 172 are in 31 base excision repair pathway genes, 19 in 5 anti-oxidation genes, and DNA repair phenotypes based on single strand breaks measured by the alkaline Comet assay. Univariate analyses were of limited value in identifying SNPs associated with phenotype variation. Of the multivariable model selection methods tested: the easiest that provided reduced error of prediction of phenotype was simple counting of the variant alleles predicted to encode proteins with reduced activity, which led to a genotype including 52 SNPs; the best and most parsimonious model was achieved using a two-step analysis without regard to potential functional relevance: first SNPs were ranked by importance determined by random forests regression (RFR), followed by cross-validation in a second round of RFR modeling that included ever more SNPs in declining order of importance. With this approach six SNPs were found to minimize prediction error. The results should encourage research into utilization of multivariate analytical methods for epidemiological studies of the association of genetic variation in complex genotypes with risk of common diseases.     

Defining the phenotype in human genetic studies: forward genetics and reverse phenotyping

The definition of phenotypes for genetic study is a challenging endeavor. Just as we apply strict quality standards to genotype data, we should expect that phenotypes meet consistently high standards of reproducibility and validity. The methods for achieving accurate phenotype assignment in the research setting--the 'research diagnosis'--are different from the methods used in clinical diagnosis in the patient care setting. We evaluate some of the main challenges of phenotype definition in human genetics, and begin to outline a set of standards to which phenotypes used in genetics studies may aspire with the goal of increasing the quality and reproducibility of linkage and association studies. Revisiting the traditional phenotype definitions through a focus on familial components and heritable endophenotypes is a time-honored approach. Reverse phenotyping, where phenotypes are refined based on genetic marker data, may be a promising new approach. The stakes are high, since the success of gene mapping in genetically complex disorders hinges on the ability to delineate the target phenotype with accuracy and precision.     

Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses

Identification of complex molecular networks underlying common human phenotypes is a major challenge of modern genetics. In this study, we develop a method for network-based analysis of genetic associations (NETBAG). We use NETBAG to identify a large biological network of genes affected by rare de novo CNVs in autism. The genes forming the network are primarily related to synapse development, axon targeting, and neuron motility. The identified network is strongly related to genes previously implicated in autism and intellectual disability phenotypes. Our results are also consistent with the hypothesis that significantly stronger functional perturbations are required to trigger the autistic phenotype in females compared to males. Overall, the presented analysis of de novo variants supports the hypothesis that perturbed synaptogenesis is at the heart of autism. More generally, our study provides proof of the principle that networks underlying complex human phenotypes can be identified by a network-based functional analysis of rare genetic variants.     

Functional genomics and target validation approaches using antisense oligonucleotide technology

The recent increase in the amount and rate of accumulation of genomic information has created new challenges for the pharmaceutical industry. These include how best to rapidly and efficiently identify key genes responsible for complex disease phenotypes and how to use this information to develop new and specific classes of drugs. Antisense technology offers a powerful approach to identify novel cellular networks and signaling "cassettes" and provides a method to validate genes in vivo as attractive drug targets.     

Osteoporosis: A Silent Disease with Complex Genetic Contribution

Osteoporosis is the most common multifactorial metabolic bone disorder worldwide with a strong genetic component. In this review, the evidence for a genetic contribution to osteoporosis and related phenotypes is summarized alongside with methods used to identify osteoporosis susceptibility genes. The key biological pathways involved in the skeleton and bone development are discussed with a particular focus on master genes clustered in these pathways and their mode of action. Furthermore, the most studied single nucleotide polymorphisms(SNPs) analyzed for their importance as genetic markers of the disease are presented. New data generated by nextgeneration sequencing in conjunction with extensive meta-analyses should contribute to a better understanding of the genetic basis of osteoporosis and related phenotype variability. These data could be ultimately used for identifying at-risk patients for disease prevention by both controlling environmental factors and providing possible therapeutic targets.     

Divergent cellular phenotypes of human and mouse cells lacking the Werner syndrome RecQ helicase

Werner syndrome (WS) is a human autosomal recessive genetic instability and cancer predisposition syndrome with features of premature aging. Several genetically determined mouse models of WS have been generated, however, none develops features of premature aging or an elevated risk of neoplasia unless additional genetic perturbations are introduced. In order to determine whether differences in cellular phenotype could explain the discrepant phenotypes of Wrn^?/? mice and WRN-deficient humans, we compared the cellular phenotype of newly derived Wrn^?/? mouse primary fibroblasts with previous analyses of primary and transformed fibroblasts from WS patients and with newly derived, WRN-depleted human primary fibroblasts. These analyses confirmed previously reported cellular phenotypes of WRN-mutant and WRN-deficient human fibroblasts, and demonstrated that the human WRN-deficient cellular phenotype can be detected in cells grown in 5% or in 20% oxygen. In contrast, we did not identify prominent cellular phenotypes present in WRN-deficient human cells in Wrn^?/? mouse fibroblasts. Our results indicate that human and mouse fibroblasts have different functional requirements for WRN protein, and that the absence of a strong cellular phenotype may in part explain the failure of Wrn^?/? mice to develop an organismal phenotype resembling Werner syndrome.     

<Emphasis Type="Italic">In-silico</Emphasis> identification of phenotype-biased functional modules

Background

Phenotypes exhibited by microorganisms can be useful for several purposes, e.g., ethanol as an alternate fuel. Sometimes, the target phenotype maybe required in combination with other phenotypes, in order to be useful, for e.g., an industrial process may require that the organism survive in an anaerobic, alcohol rich environment and be able to feed on both hexose and pentose sugars to produce ethanol. This combination of traits may not be available in any existing organism or if they do exist, the mechanisms involved in the phenotype-expression may not be efficient enough to be useful. Thus, it may be required to genetically modify microorganisms. However, before any genetic modification can take place, it is important to identify the underlying cellular subsystems responsible for the expression of the target phenotype.

Results

In this paper, we develop a method to identify statistically significant and phenotypically-biased functional modules. The method can compare the organismal network information from hundreds of phenotype expressing and phenotype non-expressing organisms to identify cellular subsystems that are more prone to occur in phenotype-expressing organisms than in phenotype non-expressing organisms. We have provided literature evidence that the phenotype-biased modules identified for phenotypes such as hydrogen production (dark and light fermentation), respiration, gram-positive, gram-negative and motility, are indeed phenotype-related.

Conclusion

Thus we have proposed a methodology to identify phenotype-biased cellular subsystems. We have shown the effectiveness of our methodology by applying it to several target phenotypes. The code and all supplemental files can be downloaded from (http://freescience.org/cs/phenotype-biased-biclusters/).

     

The use of mouse models to unravel genetic architecture of physical activity: a review

The discovery of genetic variants that underlie a complex phenotype is challenging. One possible approach to facilitate this endeavor is to identify quantitative trait loci (QTL) that contribute to the phenotype and consequently unravel the candidate genes within these loci. Each proposed candidate locus contains multiple genes and, therefore, further analysis is required to choose plausible candidate genes. One of such methods is to use comparative genomics in order to narrow down the QTL to a region containing only a few genes. We illustrate this strategy by applying it to genetic findings regarding physical activity (PA) in mice and human. Here, we show that PA is a complex phenotype with a strong biological basis and complex genetic architecture. Furthermore, we provide considerations for the translatability of this phenotype between species. Finally, we review studies which point to candidate genetic regions for PA in humans (genetic association and linkage studies) or use mouse models of PA (QTL studies) and we identify candidate genetic regions that overlap between species. On the basis of a large variety of studies in mice and human, statistical analysis reveals that the number of overlapping regions is not higher than expected on a chance level. We conclude that the discovery of new candidate genes for complex phenotypes, such as PA levels, is hampered by various factors, including genetic background differences, phenotype definition and a wide variety of methodological differences between studies .     

Coexisting/Coexpressing Genomic Libraries (CoGeL) identify interactions among distantly located genetic loci for developing complex microbial phenotypes

In engineering novel microbial strains for biotechnological applications, beyond a priori identifiable pathways to be engineered, it is becoming increasingly important to develop complex, ill-defined cellular phenotypes. One approach is to screen genomic or metagenomic libraries to identify genes imparting desirable phenotypes, such as tolerance to stressors or novel catabolic programs. Such libraries are limited by their inability to identify interactions among distant genetic loci. To solve this problem, we constructed plasmid- and fosmid-based Escherichia coli Coexisting/Coexpressing Genomic Libraries (CoGeLs). As a proof of principle, four sets of two genes of the l-lysine biosynthesis pathway distantly located on the E. coli chromosome were knocked out. Upon transformation of these auxotrophs with CoGeLs, cells growing without supplementation were found to harbor library inserts containing the knocked-out genes demonstrating the interaction between the two libraries. CoGeLs were also screened to identify genetic loci that work synergistically to create the considerably more complex acid-tolerance phenotype. CoGeL screening identified combination of genes known to enhance acid tolerance (gadBC operon and adiC), but also identified the novel combination of arcZ and recA that greatly enhanced acid tolerance by 9000-fold. arcZ is a small RNA that we show increases pH tolerance alone and together with recA.