Customizing cell signaling using engineered genetic logic circuits

Cells live in an ever-changing environment and continuously sense, process and react to environmental signals using their inherent signaling and gene regulatory networks. Recently, there have been great advances on rewiring the native cell signaling and gene networks to program cells to sense multiple noncognate signals and integrate them in a logical manner before initiating a desired response. Here, we summarize the current state-of-the-art of engineering synthetic genetic logic circuits to customize cellular signaling behaviors, and discuss their promising applications in biocomputing, environmental, biotechnological and biomedical areas as well as the remaining challenges in this growing field.     

Detecting susceptibility genes in case-control studies using set association

Complex diseases are generally caused by intricate interactions of multiple genes and environmental factors. Most available linkage and association methods are developed to identify individual susceptibility genes assuming a simple disease model blind to any possible gene - gene and gene - environmental interactions. We used a set association method that uses single-nucleotide polymorphism markers to locate genetic variation responsible for complex diseases in which multiple genes are involved. Here we extended the set association method from bi-allelic to multiallelic markers. In addition, we studied the type I error rates and power for both approaches using simulations based on the coalescent process. Both bi-allelic set association (BSA) and multiallelic set association (MSA) tests have the correct type I error rates. In addition, BSA and MSA can have more power than individual marker analysis when multiple genes are involved in a complex disease. We applied the MSA approach to the simulated data sets from Genetic Analysis Workshop 13. High cholesterol level was used as the definitive phenotype for a disease. MSA failed to detect markers with significant linkage disequilibrium with genes responsible for cholesterol level. This is due to the wide spacing between the markers and the lack of association between the marker loci and the simulated phenotype.     

Unraveling the genetics of human obesity

The use of modern molecular biology tools in deciphering the perturbed biochemistry and physiology underlying the obese state has proven invaluable. Identifying the hypothalamic leptin/melanocortin pathway as critical in many cases of monogenic obesity has permitted targeted, hypothesis-driven experiments to be performed, and has implicated new candidates as causative for previously uncharacterized clinical cases of obesity. Meanwhile, the effects of mutations in the melanocortin-4 receptor gene, for which the obese phenotype varies in the degree of severity among individuals, are now thought to be influenced by one's environmental surroundings. Molecular approaches have revealed that syndromes (Prader-Willi and Bardet-Biedl) previously assumed to be controlled by a single gene are, conversely, regulated by multiple elements. Finally, the application of comprehensive profiling technologies coupled with creative statistical analyses has revealed that interactions between genetic and environmental factors are responsible for the common obesity currently challenging many Westernized societies. As such, an improved understanding of the different “types” of obesity not only permits the development of potential therapies, but also proposes novel and often unexpected directions in deciphering the dysfunctional state of obesity.     

Etiology in psychiatry: embracing the reality of poly‐gene‐environmental causation of mental illness

Intriguing findings on genetic and environmental causation suggest a need to reframe the etiology of mental disorders. Molecular genetics shows that thousands of common and rare genetic variants contribute to mental illness. Epidemiological studies have identified dozens of environmental exposures that are associated with psychopathology. The effect of environment is likely conditional on genetic factors, resulting in gene‐environment interactions. The impact of environmental factors also depends on previous exposures, resulting in environment‐environment interactions. Most known genetic and environmental factors are shared across multiple mental disorders. Schizophrenia, bipolar disorder and major depressive disorder, in particular, are closely causally linked. Synthesis of findings from twin studies, molecular genetics and epidemiological research suggests that joint consideration of multiple genetic and environmental factors has much greater explanatory power than separate studies of genetic or environmental causation. Multi‐factorial gene‐environment interactions are likely to be a generic mechanism involved in the majority of cases of mental illness, which is only partially tapped by existing gene‐environment studies. Future research may cut across psychiatric disorders and address poly‐causation by considering multiple genetic and environmental measures across the life course with a specific focus on the first two decades of life. Integrative analyses of poly‐causation including gene‐environment and environment‐environment interactions can realize the potential for discovering causal types and mechanisms that are likely to generate new preventive and therapeutic tools.     

Inferring the potentially complex genetic architectures of adaptation,sexual dimorphism and genotype by environment interactions by partitioning of mean phenotypes

Genetic architecture fundamentally affects the way that traits evolve. However, the mapping of genotype to phenotype includes complex interactions with the environment or even the sex of an organism that can modulate the expressed phenotype. Line‐cross analysis is a powerful quantitative genetics method to infer genetic architecture by analysing the mean phenotype value of two diverged strains and a series of subsequent crosses and backcrosses. However, it has been difficult to account for complex interactions with the environment or sex within this framework. We have developed extensions to line‐cross analysis that allow for gene by environment and gene by sex interactions. Using extensive simulation studies and reanalysis of empirical data, we show that our approach can account for both unintended environmental variation when crosses cannot be reared in a common garden and can be used to test for the presence of gene by environment or gene by sex interactions. In analyses that fail to account for environmental variation between crosses, we find that line‐cross analysis has low power and high false‐positive rates. However, we illustrate that accounting for environmental variation allows for the inference of adaptive divergence, and that accounting for sex differences in phenotypes allows practitioners to infer the genetic architecture of sexual dimorphism.     

The Drosophila foraging Gene Mediates Adult Plasticity and Gene–Environment Interactions in Behaviour,Metabolites, and Gene Expression in Response to Food Deprivation

Nutrition is known to interact with genotype in human metabolic syndromes, obesity, and diabetes, and also in Drosophila metabolism. Plasticity in metabolic responses, such as changes in body fat or blood sugar in response to changes in dietary alterations, may also be affected by genotype. Here we show that variants of the foraging (for) gene in Drosophila melanogaster affect the response to food deprivation in a large suite of adult phenotypes by measuring gene by environment interactions (GEI) in a suite of food-related traits. for affects body fat, carbohydrates, food-leaving behavior, metabolite, and gene expression levels in response to food deprivation. This results in broad patterns of metabolic, genomic, and behavioral gene by environment interactions (GEI), in part by interaction with the insulin signaling pathway. Our results show that a single gene that varies in nature can have far reaching effects on behavior and metabolism by acting through multiple other genes and pathways.     

The Drosophila foraging Gene Mediates Adult Plasticity and Gene–Environment Interactions in Behaviour, Metabolites, and Gene Expression in Response to Food Deprivation

Nutrition is known to interact with genotype in human metabolic syndromes, obesity, and diabetes, and also in Drosophila metabolism. Plasticity in metabolic responses, such as changes in body fat or blood sugar in response to changes in dietary alterations, may also be affected by genotype. Here we show that variants of the foraging (for) gene in Drosophila melanogaster affect the response to food deprivation in a large suite of adult phenotypes by measuring gene by environment interactions (GEI) in a suite of food-related traits. for affects body fat, carbohydrates, food-leaving behavior, metabolite, and gene expression levels in response to food deprivation. This results in broad patterns of metabolic, genomic, and behavioral gene by environment interactions (GEI), in part by interaction with the insulin signaling pathway. Our results show that a single gene that varies in nature can have far reaching effects on behavior and metabolism by acting through multiple other genes and pathways.     

Contribution of genetics to the concept of risk status for alcohol dependence

The factors involved in the variability of one's own risk for alcohol dependence is multifactorial and mostly unknown. Nevertheless, genetic factors are clearly involved in the risk for the disorder, the impact of addictive genetic factors being evaluated between 30% and 50%. Aggregation studies underline the difficulties of delimiting the boundaries of the phenotype, as some subgroups of alcohol-dependent patients have genetic factors with an increased weight (severe and young-onset disorder, presence of antisocial behavior...). Furthermore, familial studies also showed that the genetic of "addiction" may be more relevant than studying the genetic of one specific dependence. The use of one substance lately abused being probably more dependent of familial and/or environmental features. The discover of susceptibility genes had a greater impact on defining the phenotype than proposing new treatments. Three examples are given in this review. Firstly, le gene coding for the second dopamine receptor may be more specifically involved in severe and comorbid alcohol-dependence. Secondly, the gene coding for the serotonin transporter may increase the suicidal risk in alcohol-dependent patients. Thirdly, the quality of the withdrawal process is partly explained by the existence of a specific genotype of the dopamine transport gene.     

Genetics of Obesity: What have we Learned?

Candidate gene and genome-wide association studies have led to the discovery of nine loci involved in Mendelian forms of obesity and 58 loci contributing to polygenic obesity. These loci explain a small fraction of the heritability for obesity and many genes remain to be discovered. However, efforts in obesity gene identification greatly modified our understanding of this disorder. In this review, we propose an overlook of major lessons learned from 15 years of research in the field of genetics and obesity. We comment on the existence of the genetic continuum between monogenic and polygenic forms of obesity that pinpoints the role of genes involved in the central regulation of food intake and genetic predisposition to obesity. We explain how the identification of novel obesity predisposing genes has clarified unsuspected biological pathways involved in the control of energy balance that have helped to understand past human history and to explore causality in epidemiology. We provide evidence that obesity predisposing genes interact with the environment and influence the response to treatment relevant to disease prediction.     

Creation and implications of a phenome-genome network

Although gene and protein measurements are increasing in quantity and comprehensiveness, they do not characterize a sample's entire phenotype in an environmental or experimental context. Here we comprehensively consider associations between components of phenotype, genotype and environment to identify genes that may govern phenotype and responses to the environment. Context from the annotations of gene expression data sets in the Gene Expression Omnibus is represented using the Unified Medical Language System, a compendium of biomedical vocabularies with nearly 1-million concepts. After showing how data sets can be clustered by annotative concepts, we find a network of relations between phenotypic, disease, environmental and experimental contexts as well as genes with differential expression associated with these concepts. We identify novel genes related to concepts such as aging. Comprehensively identifying genes related to phenotype and environment is a step toward the Human Phenome Project.     

The serine protease Corin is a novel modifier of the Agouti pathway

The hair follicle is a model system for studying epithelial-mesenchymal interactions during organogenesis. Although analysis of the epithelial contribution to these interactions has progressed rapidly, the lack of tools to manipulate gene expression in the mesenchymal component, the dermal papilla, has hampered progress towards understanding the contribution of these cells. In this work, Corin was identified in a screen to detect genes specifically expressed in the dermal papilla. It is expressed in the dermal papilla of all pelage hair follicle types from the earliest stages of their formation, but is not expressed elsewhere in the skin. Mutation of the Corin gene reveals that it is not required for morphogenesis of the hair follicle. However, analysis of the ;dirty blonde' phenotype of these mice reveals that the transmembrane protease encoded by Corin plays a critical role in specifying coat color and acts downstream of agouti gene expression as a suppressor of the agouti pathway.     

Gene regulatory network models for plant development

Accumulated genetic data are stimulating the use of mathematical and computational tools for studying the concerted action of genes during cell differentiation and morphogenetic processes. At the same time, network theory has flourished, enabling analyses of complex systems that have multiple elements and interactions. Reverse engineering methods that use genomic data or detailed experiments on gene interactions have been used to propose gene network architectures. Experiments on gene interactions incorporate enough detail for relatively small developmental modules and thus allow dynamical analyses that have direct functional interpretations. Generalities are beginning to emerge. For example, biological genetic networks are robust to environmental and genetic perturbations. Such dynamical studies also enable novel predictions that can lead to further experimental tests, which might then feedback to the theoretical analyses. This interplay is proving productive for understanding plant development. Finally, both experiments on gene interactions and theoretical analyses allow the identification of frequent or fixed evolutionary solutions to developmental problems, and thus are contributing to an understanding of the genetic basis of the evolution of development and body plan.     

Detection of epistatic interactions between exotic alleles introgressed from wild barley (H. vulgare ssp. spontaneum)

The expression of a quantitative phenotype can be controlled through genotype, environment and genotype by environment interaction effects. Further, genotype effects can be attributed to major genes, quantitative trait loci (QTL) and gene by gene interactions, which are also termed epistatic interactions. The present study demonstrates that two-way epistatic interactions can play an important role for the expression of domestication-related traits like heading date, plant height and yield. In the BC₂DH population S42, carrying wild barley introgressions in the genetic background of the spring barley cultivar Scarlett, 13, 8 and 12 marker by marker interaction effects could be detected for the traits heading date, plant height and yield, respectively. Significant allelic combinations at interacting loci coincided for heading date, plant height and yield suggesting the presence of pleiotropic effects rather than several linked QTL. The mode of epistasis observed was primarily characterised by either (1) compensatory effects, where allelic combinations from the same genotype buffered the phenotype, or (2) augmented effects, where only the combination of the exotic allele at both interacting loci caused an altered phenotype. The present study shows that estimates of main effects of QTL can be confounded by interactions with background loci, suggesting that the identification of epistatic effects is important for gene cloning and marker-assisted selection. Furthermore, interaction effects between loci and putative candidate genes detected in the present study reveal potential functional relationships, which can be used to further elucidate gene networks in barley.     

PGMapper: a web-based tool linking phenotype to genes

SUMMARY: With the availability of whole genome sequence in many species, linkage analysis, positional cloning and microarray are gradually becoming powerful tools for investigating the links between phenotype and genotype or genes. However, in these methods, causative genes underlying a quantitative trait locus, or a disease, are usually located within a large genomic region or a large set of genes. Examining the function of every gene is very time consuming and needs to retrieve and integrate the information from multiple databases or genome resources. PGMapper is a software tool for automatically matching phenotype to genes from a defined genome region or a group of given genes by combining the mapping information from the Ensembl database and gene function information from the OMIM and PubMed databases. PGMapper is currently available for candidate gene search of human, mouse, rat, zebrafish and 12 other species. AVAILABILITY: Available online at http://www.genediscovery.org/pgmapper/index.jsp.     

Evolutionary History and Attenuation of Myxoma Virus on Two Continents

The attenuation of myxoma virus (MYXV) following its introduction as a biological control into the European rabbit populations of Australia and Europe is the canonical study of the evolution of virulence. However, the evolutionary genetics of this profound change in host-pathogen relationship is unknown. We describe the genome-scale evolution of MYXV covering a range of virulence grades sampled over 49 years from the parallel Australian and European epidemics, including the high-virulence progenitor strains released in the early 1950s. MYXV evolved rapidly over the sampling period, exhibiting one of the highest nucleotide substitution rates ever reported for a double-stranded DNA virus, and indicative of a relatively high mutation rate and/or a continually changing selective environment. Our comparative sequence data reveal that changes in virulence involved multiple genes, likely losses of gene function due to insertion-deletion events, and no mutations common to specific virulence grades. Hence, despite the similarity in selection pressures there are multiple genetic routes to attain either highly virulent or attenuated phenotypes in MYXV, resulting in convergence for phenotype but not genotype.