Modular epistasis in yeast metabolism

Epistatic interactions, manifested in the effects of mutations on the phenotypes caused by other mutations, may help uncover the functional organization of complex biological networks. Here, we studied system-level epistatic interactions by computing growth phenotypes of all single and double knockouts of 890 metabolic genes in Saccharomyces cerevisiae, using the framework of flux balance analysis. A new scale for epistasis identified a distinctive trimodal distribution of these epistatic effects, allowing gene pairs to be classified as buffering, aggravating or noninteracting. We found that the ensuing epistatic interaction network could be organized hierarchically into function-enriched modules that interact with each other 'monochromatically' (i.e., with purely aggravating or purely buffering epistatic links). This property extends the concept of epistasis from single genes to functional units and provides a new definition of biological modularity, which emphasizes interactions between, rather than within, functional modules. Our approach can be used to infer functional gene modules from purely phenotypic epistasis measurements.     

Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets

We present the first analysis of the human proteome with regard to interactions between proteins. We also compare the human interactome with the available interaction datasets from yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans) and fly (Drosophila melanogaster). Of >70,000 binary interactions, only 42 were common to human, worm and fly, and only 16 were common to all four datasets. An additional 36 interactions were common to fly and worm but were not observed in humans, although a coimmunoprecipitation assay showed that 9 of the interactions do occur in humans. A re-examination of the connectivity of essential genes in yeast and humans indicated that the available data do not support the presumption that the number of interaction partners can accurately predict whether a gene is essential. Finally, we found that proteins encoded by genes mutated in inherited genetic disorders are likely to interact with proteins known to cause similar disorders, suggesting the existence of disease subnetworks. The human interaction map constructed from our analysis should facilitate an integrative systems biology approach to elucidating the cellular networks that contribute to health and disease states.     

Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways

Most heritable traits, including disease susceptibility, are affected by interactions between multiple genes. However, we understand little about how genes interact because very few possible genetic interactions have been explored experimentally. We have used RNA interference in Caenorhabditis elegans to systematically test approximately 65,000 pairs of genes for their ability to interact genetically. We identify approximately 350 genetic interactions between genes functioning in signaling pathways that are mutated in human diseases, including components of the EGF/Ras, Notch and Wnt pathways. Most notably, we identify a class of highly connected 'hub' genes: inactivation of these genes can enhance the phenotypic consequences of mutation of many different genes. These hub genes all encode chromatin regulators, and their activity as genetic hubs seems to be conserved across animals. We propose that these genes function as general buffers of genetic variation and that these hub genes may act as modifier genes in multiple, mechanistically unrelated genetic diseases in humans.     

The synthetic genetic interaction spectrum of essential genes

The nature of synthetic genetic interactions involving essential genes (those required for viability) has not been previously examined in a broad and unbiased manner. We crossed yeast strains carrying promoter-replacement alleles for more than half of all essential yeast genes to a panel of 30 different mutants with defects in diverse cellular processes. The resulting genetic network is biased toward interactions between functionally related genes, enabling identification of a previously uncharacterized essential gene (PGA1) required for specific functions of the endoplasmic reticulum. But there are also many interactions between genes with dissimilar functions, suggesting that individual essential genes are required for buffering many cellular processes. The most notable feature of the essential synthetic genetic network is that it has an interaction density five times that of nonessential synthetic genetic networks, indicating that most yeast genetic interactions involve at least one essential gene.     

Systematic pathway analysis using high-resolution fitness profiling of combinatorial gene deletions

Systematic genetic interaction studies have illuminated many cellular processes. Here we quantitatively examine genetic interactions among 26 Saccharomyces cerevisiae genes conferring resistance to the DNA-damaging agent methyl methanesulfonate (MMS), as determined by chemogenomic fitness profiling of pooled deletion strains. We constructed 650 double-deletion strains, corresponding to all pairings of these 26 deletions. The fitness of single- and double-deletion strains were measured in the presence and absence of MMS. Genetic interactions were defined by combining principles from both statistical and classical genetics. The resulting network predicts that the Mph1 helicase has a role in resolving homologous recombination-derived DNA intermediates that is similar to (but distinct from) that of the Sgs1 helicase. Our results emphasize the utility of small molecules and multifactorial deletion mutants in uncovering functional relationships and pathway order.     

Mutations in the gene encoding the basal body protein RPGRIP1L, a nephrocystin-4 interactor, cause Joubert syndrome

Protein-protein interaction analyses have uncovered a ciliary and basal body protein network that, when disrupted, can result in nephronophthisis (NPHP), Leber congenital amaurosis, Senior-L?ken syndrome (SLSN) or Joubert syndrome (JBTS). However, details of the molecular mechanisms underlying these disorders remain poorly understood. RPGRIP1-like protein (RPGRIP1L) is a homolog of RPGRIP1 (RPGR-interacting protein 1), a ciliary protein defective in Leber congenital amaurosis. We show that RPGRIP1L interacts with nephrocystin-4 and that mutations in the gene encoding nephrocystin-4 (NPHP4) that are known to cause SLSN disrupt this interaction. RPGRIP1L is ubiquitously expressed, and its protein product localizes to basal bodies. Therefore, we analyzed RPGRIP1L as a candidate gene for JBTS and identified loss-of-function mutations in three families with typical JBTS, including the characteristic mid-hindbrain malformation. This work identifies RPGRIP1L as a gene responsible for JBTS and establishes a central role for cilia and basal bodies in the pathophysiology of this disorder.     

Kétoconazole et itraconazole: interactions pharmacocinétiques

Ketoconazole and itraconazole pharmacokinetic interactions are due to their inhibition power on liver and intestinal cytochromes P450 3A4 and on intestinal P-glycoprotein. Cytochromes inhibition leads to a reduced extent in metabolism which in turn responsible for a reduced hepatic first pass effect and a reduction of the elimination rate. PGP inhibition alters drugs efflux from the intestine cells to the intestinal lumen and therefore increases absorption. These mechanisms lead to increased drug concentrations, possibly toxic with severe or sometimes lethal consequences.     

Genetic dissection and prognostic modeling of overt stroke in sickle cell anemia

Sickle cell anemia (SCA) is a paradigmatic single gene disorder caused by homozygosity with respect to a unique mutation at the beta-globin locus. SCA is phenotypically complex, with different clinical courses ranging from early childhood mortality to a virtually unrecognized condition. Overt stroke is a severe complication affecting 6-8% of individuals with SCA. Modifier genes might interact to determine the susceptibility to stroke, but such genes have not yet been identified. Using Bayesian networks, we analyzed 108 SNPs in 39 candidate genes in 1,398 individuals with SCA. We found that 31 SNPs in 12 genes interact with fetal hemoglobin to modulate the risk of stroke. This network of interactions includes three genes in the TGF-beta pathway and SELP, which is associated with stroke in the general population. We validated this model in a different population by predicting the occurrence of stroke in 114 individuals with 98.2% accuracy.     

The emerging landscape of breast cancer susceptibility

The genetic basis of inherited predisposition to breast cancer has been assiduously investigated for the past two decades and has been the subject of several recent discoveries. Three reasonably well-defined classes of breast cancer susceptibility alleles with different levels of risk and prevalence in the population have become apparent: rare high-penetrance alleles, rare moderate-penetrance alleles and common low-penetrance alleles. The contribution of each component to breast cancer predisposition is still to be fully explored, as are the phenotypic characteristics of the cancers associated with them, the ways in which they interact, much of their biology and their clinical utility. These recent advances herald a new chapter in the exploration of susceptibility to breast cancer and are likely to provide insights relevant to other common, heterogeneous diseases.     

Nova regulates brain-specific splicing to shape the synapse

Alternative RNA splicing greatly increases proteome diversity and may thereby contribute to tissue-specific functions. We carried out genome-wide quantitative analysis of alternative splicing using a custom Affymetrix microarray to assess the role of the neuronal splicing factor Nova in the brain. We used a stringent algorithm to identify 591 exons that were differentially spliced in the brain relative to immune tissues, and 6.6% of these showed major splicing defects in the neocortex of Nova2-/- mice. We tested 49 exons with the largest predicted Nova-dependent splicing changes and validated all 49 by RT-PCR. We analyzed the encoded proteins and found that all those with defined brain functions acted in the synapse (34 of 40, including neurotransmitter receptors, cation channels, adhesion and scaffold proteins) or in axon guidance (8 of 40). Moreover, of the 35 proteins with known interaction partners, 74% (26) interact with each other. Validating a large set of Nova RNA targets has led us to identify a multi-tiered network in which Nova regulates the exon content of RNAs encoding proteins that interact in the synapse.     

Reverse engineering of regulatory networks in human B cells

Cellular phenotypes are determined by the differential activity of networks linking coregulated genes. Available methods for the reverse engineering of such networks from genome-wide expression profiles have been successful only in the analysis of lower eukaryotes with simple genomes. Using a new method called ARACNe (algorithm for the reconstruction of accurate cellular networks), we report the reconstruction of regulatory networks from expression profiles of human B cells. The results are suggestive a hierarchical, scale-free network, where a few highly interconnected genes (hubs) account for most of the interactions. Validation of the network against available data led to the identification of MYC as a major hub, which controls a network comprising known target genes as well as new ones, which were biochemically validated. The newly identified MYC targets include some major hubs. This approach can be generally useful for the analysis of normal and pathologic networks in mammalian cells.     

Genetic basis of individual differences in the response to small-molecule drugs in yeast

Individual response to small-molecule drugs is variable; a drug that provides a cure for some may confer no therapeutic benefit or trigger an adverse reaction in others. To begin to understand such differences systematically, we treated 104 genotyped segregants from a cross between two yeast strains with a collection of 100 diverse small molecules. We used linkage analysis to identify 124 distinct linkages between genetic markers and response to 83 compounds. The linked markers clustered at eight genomic locations, or quantitative-trait locus 'hotspots', that contain one or more polymorphisms that affect response to multiple small molecules. We also experimentally verified that a deficiency in leucine biosynthesis caused by a deletion of LEU2 underlies sensitivity to niguldipine, which is structurally related to therapeutic calcium channel blockers, and that a natural coding-region polymorphism in the inorganic phosphate transporter PHO84 underlies sensitivity to two polychlorinated phenols that uncouple oxidative phosphorylation. Our results provide a step toward a systematic understanding of small-molecule drug action in genetically distinct individuals.     

Natural variation for sulfate content in Arabidopsis thaliana is highly controlled by APR2

Most agronomic traits of importance, whether physiological (such as nutrient use efficiency) or developmental (such as flowering time), are controlled simultaneously by multiple genes and their interactions with the environment. Here, we show that variation in sulfate content between wild Arabidopsis thaliana accessions Bay-0 and Shahdara is controlled by a major quantitative trait locus that results in a strong interaction with nitrogen availability in the soil. Combining genetic and biochemical results and using a candidate gene approach, we have cloned the underlying gene, showing how a single-amino acid substitution in a key enzyme of the assimilatory sulfate reduction pathway, adenosine 5'-phosphosulfate reductase, is responsible for a decrease in enzyme activity, leading to sulfate accumulation in the plant. This work illustrates the potential of natural variation as a source of new alleles of known genes, which can aid in the study of gene function and metabolic pathway regulation. Our new insights on sulfate assimilation may have an impact on sulfur fertilizer use and stress defense improvement.