Systematic identification of genes that regulate neuronal wiring in the Drosophila visual system

Forward genetic screens in model organisms are an attractive means to identify those genes involved in any complex biological process, including neural circuit assembly. Although mutagenesis screens are readily performed to saturation, gene identification rarely is, being limited by the considerable effort generally required for positional cloning. Here, we apply a systematic positional cloning strategy to identify many of the genes required for neuronal wiring in the Drosophila visual system. From a large-scale forward genetic screen selecting for visual system wiring defects with a normal retinal pattern, we recovered 122 mutations in 42 genetic loci. For 6 of these loci, the underlying genetic lesions were previously identified using traditional methods. Using SNP-based mapping approaches, we have now identified 30 additional genes. Neuronal phenotypes have not previously been reported for 20 of these genes, and no mutant phenotype has been previously described for 5 genes. The genes encode a variety of proteins implicated in cellular processes such as gene regulation, cytoskeletal dynamics, axonal transport, and cell signalling. We conducted a comprehensive phenotypic analysis of 35 genes, scoring wiring defects according to 33 criteria. This work demonstrates the feasibility of combining large-scale gene identification with large-scale mutagenesis in Drosophila, and provides a comprehensive overview of the molecular mechanisms that regulate visual system wiring.     

Computational analysis of human genome polymorphism

While genome-era technologies focused on complete genome sequencing in various organisms, post-genome technologies aim at the understanding of the mechanisms of genetic information processing and elucidation of within-species variation. Single nucleotide polymorphisms (SNPs) are the most common source of genome variation in the human population. Nonsynonymous SNPs that occur in coding gene regions and result in amino acid substitutions are of particular interest. It is thought that such SNPs are responsible for phenotypic variation, quantitative traits, and the etiology of common diseases. PolyPhen is a computational tool for the prediction of putatively functional nonsynonymous SNPs by combining information of various types. The application areas of PolyPhen and similar methods include the genetics of complex diseases and congenital defects, the identification of functional mutations in model organisms, and evolutionary genetics.     

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.     

Large-scale in silico mapping of complex quantitative traits in inbred mice

Understanding the genetic basis of common disease and disease-related quantitative traits will aid in the development of diagnostics and therapeutics. The processs of gene discovery can be sped up by rapid and effective integration of well-defined mouse genome and phenome data resources. We describe here an in silico gene-discovery strategy through genome-wide association (GWA) scans in inbred mice with a wide range of genetic variation. We identified 937 quantitative trait loci (QTLs) from a survey of 173 mouse phenotypes, which include models of human disease (atherosclerosis, cardiovascular disease, cancer and obesity) as well as behavioral, hematological, immunological, metabolic, and neurological traits. 67% of QTLs were refined into genomic regions <0.5 Mb with approximately 40-fold increase in mapping precision as compared with classical linkage analysis. This makes for more efficient identification of the genes that underlie disease. We have identified two QTL genes, Adam12 and Cdh2, as causal genetic variants for atherogenic diet-induced obesity. Our findings demonstrate that GWA analysis in mice has the potential to resolve multiple tightly linked QTLs and achieve single-gene resolution. These high-resolution QTL data can serve as a primary resource for positional cloning and gene identification in the research community.     

Natural genetic variation in Arabidopsis: tools, traits and prospects for evolutionary ecology

BACKGROUND: The model plant Arabidopsis thaliana (Arabidopsis) shows a wide range of genetic and trait variation among wild accessions. Because of its unparalleled biological and genomic resources, the potential of Arabidopsis for molecular genetic analysis of this natural variation has increased dramatically in recent years. SCOPE: Advanced genomics has accelerated molecular phylogenetic analysis and gene identification by quantitative trait loci (QTL) mapping and/or association mapping in Arabidopsis. In particular, QTL mapping utilizing natural accessions is now becoming a major strategy of gene isolation, offering an alternative to artificial mutant lines. Furthermore, the genomic information is used by researchers to uncover the signature of natural selection acting on the genes that contribute to phenotypic variation. The evolutionary significance of such genes has been evaluated in traits such as disease resistance and flowering time. However, although molecular hallmarks of selection have been found for the genes in question, a corresponding ecological scenario of adaptive evolution has been difficult to prove. Ecological strategies, including reciprocal transplant experiments and competition experiments, and utilizing near-isogenic lines of alleles of interest will be a powerful tool to measure the relative fitness of phenotypic and/or allelic variants. CONCLUSIONS: As the plant model organism, Arabidopsis provides a wealth of molecular background information for evolutionary genetics. Because genetic diversity between and within Arabidopsis populations is much higher than anticipated, combining this background information with ecological approaches might well establish Arabidopsis as a model organism for plant evolutionary ecology.     

Global Genotype-Phenotype Correlations in Pseudomonas aeruginosa

Once the genome sequence of an organism is obtained, attention turns from identifying genes to understanding their function, their organization and control of metabolic pathways and networks that determine its physiology. Recent technical advances in acquiring genome-wide data have led to substantial progress in identifying gene functions. However, we still do not know the function of a large number of genes and, even when a gene product has been assigned to a functional class, we cannot normally predict its contribution to the phenotypic behaviour of the cell or organism - the phenome. In this study, we assessed bacterial growth parameters of 4030 non-redundant PA14 transposon mutants in the pathogenic bacterium Pseudomonas aeruginosa. The genome-wide simultaneous analysis of 119 distinct growth-related phenotypes uncovered a comprehensive phenome and provided evidence that most genotypes are not phenotypically isolated but rather define specific complex phenotypic clusters of genotypes. Since phenotypic overlap was demonstrated to reflect the relatedness of genotypes on a global scale, knowledge of an organism''s phenome might significantly contribute to the advancement of functional genomics.     

Evolutionary signatures of common human cis-regulatory haplotypes

Variation in gene expression may give rise to a significant fraction of inter-individual phenotypic variation. Studies searching for the underlying genetic controls for such variation have been conducted in model organisms and humans in recent years. In our previous effort of assessing conserved underlying haplotype patterns across ethnic populations, we constructed common haplotypes using SNPs having conserved linkage disequilibrium (LD) across ethnic populations. These common haplotypes cluster into a simple evolutionary structure based on their frequencies, defining only up to three conserved clusters termed 'haplotype frameworks'. One intriguing preliminary finding was that a significant portion of reported variants strongly associated with cis-regulation tags these globally conserved haplotype frameworks. Here we expand the investigation by collecting genes showing stringently determined cis-association between genotypes and expression phenotypes from major studies. We conducted phylogenetic analysis of current major haplotypes along with the corresponding haplotypes derived from chimpanzee reference sequences. Our analysis reveals that, for the vast majority of such cis-regulatory genes, the tagging SNPs showing the strongest association also tag the haplotype lineages directly separated from ancestry, inferred from either chimpanzee reference sequences or the allele frequency-derived haplotype frameworks, suggesting that the differentially expressed phenotypes were evolved relatively early in human history. Such evolutionary signatures provide keys for a more effective identification of globally-conserved candidate regulatory haplotypes across human genes in future epidemiologic and pharmacogenetic studies.     

From phenologs to silent suppressors: Identifying potential therapeutic targets for human disease

Orthologous phenotypes, or phenologs, are seemingly unrelated phenotypes generated by mutations in a conserved set of genes. Phenologs have been widely observed and accepted by those who study model organisms, and allow one to study a set of genes in a model organism to learn more about the function of those genes in other organisms, including humans. At the cellular and molecular level, these conserved genes likely function in a very similar mode, but are doing so in different tissues or cell types and can result in different phenotypic effects. For example, the RAS‐RAF‐MEK‐MAPK pathway in animals is a highly conserved signaling pathway that animals adopted for numerous biological processes, such as vulval induction in Caenorhabditis elegans and cell proliferation in mammalian cells; but this same gene set has been co‐opted to function in a variety of cellular contexts. In this review, I give a few examples of how suppressor screens in model organisms (with a emphasis on C. elegans) can identify new genes that function in a conserved pathway in many other organisms. I also demonstrate how the identification of such genes can lead to important insights into mammalian biology. From such screens, an occasional silent suppressor that does not cause a phenotype on its own is found; such suppressors thus make for good candidates as therapeutic targets.     

Are humans increasing bacterial evolvability?

Attempts to control bacterial pathogens have led to an increase in antibiotic-resistant cells and the genetic elements that confer resistance phenotypes. These cells and genes are disseminated simultaneously with the original selective agents via human waste streams. This might lead to a second, unintended consequence of antimicrobial therapy; an increase in the evolvability of all bacterial cells. The genetic variation upon which natural selection acts is a consequence of mutation, recombination and lateral gene transfer (LGT). These processes are under selection, balancing genomic integrity against the advantages accrued by genetic innovation. Saturation of the environment with selective agents might cause directional selection for higher rates of mutation, recombination and LGT, producing unpredictable consequences for humans and the biosphere.     

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.     

Genetic engineering of the multicellular green alga Volvox: a modified and multiplied bacterial antibiotic resistance gene as a dominant selectable marker

The green alga Volvox represents the simplest multicellular organism: Volvax is composed of only two cell types, somatic and reproductive. Volvox, therefore, is an attractive model system for studying various aspects of multicellularity. With the biolistic nuclear transformation of Volvox carteri, the powerful molecular genetic manipulation of this organism has been established, but applications have been restricted to an auxotrophic mutant serving as the DNA recipient. Therefore, a dominant selectable marker working in all strains and mutants of this organism is required. Among several gene constructs tested, the most advantageous results were obtained with a chimeric gene composed of the coding sequence of the bacterial ble gene, conferring resistance to the antibiotic zeocin, modified with insertions of two endogenous introns from the Volvox arylsulfatase gene and fused to 5' and 3' untranslated regions from the Volvox beta 2-tubulin gene. In the most suitable plasmid used, the gene dosage was increased 16-fold by a technique that allows exponential multiplication of a DNA fragment. Co-transformation of this plasmid and a non-selectable plasmid allowed the identification of zeocin resistant transformants with nuclear integration of both selectable and non-selectable plasmids. Stable expression of the ble gene and of genes from several non-selectable plasmids is demonstrated. The modified ble gene provides the first dominant marker for transformation of both wild-type and mutant strains of Volvox.     

Parallel reverse genetic screening in mutant human cells using transcriptomics

Reverse genetic screens have driven gene annotation and target discovery in model organisms. However, many disease‐relevant genotypes and phenotypes cannot be studied in lower organisms. It is therefore essential to overcome technical hurdles associated with large‐scale reverse genetics in human cells. Here, we establish a reverse genetic approach based on highly robust and sensitive multiplexed RNA sequencing of mutant human cells. We conduct 10 parallel screens using a collection of engineered haploid isogenic cell lines with knockouts covering tyrosine kinases and identify known and unexpected effects on signaling pathways. Our study provides proof of concept for a scalable approach to link genotype to phenotype in human cells, which has broad applications. In particular, it clears the way for systematic phenotyping of still poorly characterized human genes and for systematic study of uncharacterized genomic features associated with human disease.     

Network-based Phenome-Genome Association Prediction by Bi-Random Walk

MotivationThe availability of ontologies and systematic documentations of phenotypes and their genetic associations has enabled large-scale network-based global analyses of the association between the complete collection of phenotypes (phenome) and genes. To provide a fundamental understanding of how the network information is relevant to phenotype-gene associations, we analyze the circular bigraphs (CBGs) in OMIM human disease phenotype-gene association network and MGI mouse phentoype-gene association network, and introduce a bi-random walk (BiRW) algorithm to capture the CBG patterns in the networks for unveiling human and mouse phenome-genome association. BiRW performs separate random walk simultaneously on gene interaction network and phenotype similarity network to explore gene paths and phenotype paths in CBGs of different sizes to summarize their associations as predictions.ResultsThe analysis of both OMIM and MGI associations revealed that majority of the phenotype-gene associations are covered by CBG patterns of small path lengths, and there is a clear correlation between the CBG coverage and the predictability of the phenotype-gene associations. In the experiments on recovering known associations in cross-validations on human disease phenotypes and mouse phenotypes, BiRW effectively improved prediction performance over the compared methods. The constructed global human disease phenome-genome association map also revealed interesting new predictions and phenotype-gene modules by disease classes.     

Comparative genomics: the key to understanding the Human Genome Project

The sequencing of the human genome is well underway. Technology has advanced, such that the total genomic sequence is possible, along with an extensive catalogue of genes via comprehensive cDNA libraries. With the recent completion of the Saccharomyces cerevisiae sequencing project and the imminent completion of that of Caenorhabditis elegans, the most frequently asked question is how much can sequence data alone tell us? The answer is that that a DNA sequence taken in isolation from a single organism reveals very little. The vast majority of DNA in most organisms is noncoding. Protein coding sequences or genes cannot function as isolated units without interaction with noncoding DNA and neighboring genes. This genomic environment is specific to each organism. In order to understand this we need to look at similar genes in different organisms, to determine how function and position has changed over the course of evolution. By understanding evolutionary processes we can gain a greater insight into what makes a gene and the wider processes of genetics and inheritance. Comparative genomics (with model organisms), once the poor relation of the human genome project, is starting to provide the key to unlock the DNA code.