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.     

Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes

Genome-scale metabolic models have a promising ability to describe cellular phenotypes accurately. Here we show that strains of Escherichia coli carrying a deletion of a single metabolic gene increase their growth rates (by 87% on average) during adaptive evolution and that the endpoint growth rates can be predicted computationally in 39 of 50 (78%) strains tested. These results show that computational models can be used to predict the eventual effects of genetic modifications.     

Robustness against mutations in genetic networks of yeast

There are two principal mechanisms that are responsible for the ability of an organism's physiological and developmental processes to compensate for mutations. In the first, genes have overlapping functions, and loss-of-function mutations in one gene will have little phenotypic effect if there are one or more additional genes with similar functions. The second mechanism has its origin in interactions between genes with unrelated functions, and has been documented in metabolic and regulatory gene networks. Here I analyse, on a genome-wide scale, which of these mechanisms of robustness against mutations is more prevalent. I used functional genomics data from the yeast Saccharomyces cerevisiae to test hypotheses related to the following: if gene duplications are mostly responsible for robustness, then a correlation is expected between the similarity of two duplicated genes and the effect of mutations in one of these genes. My results demonstrate that interactions among unrelated genes are the major cause of robustness against mutations. This type of robustness is probably an evolved response of genetic networks to stabilizing selection.     

The knockout mouse project

Mouse knockout technology provides a powerful means of elucidating gene function in vivo, and a publicly available genome-wide collection of mouse knockouts would be significantly enabling for biomedical discovery. To date, published knockouts exist for only about 10% of mouse genes. Furthermore, many of these are limited in utility because they have not been made or phenotyped in standardized ways, and many are not freely available to researchers. It is time to harness new technologies and efficiencies of production to mount a high-throughput international effort to produce and phenotype knockouts for all mouse genes, and place these resources into the public domain.     

Genetic similarity and quality interact in mate choice decisions by female mice

Females express mate preferences for genetically dissimilar males, especially with respect to the major histocompatibility complex, MHC, and for males whose sexually selected signals indicate high genetic quality. The balance of selection pressure on each trait will depend on how females weight these desirable qualities under different conditions, but this has not been tested empirically. Here we show in mice that although MHC dissimilarity and a 'good genes' indicator (investment in scent-marking) both have a role in determining female preference, their relative influence can vary depending on the degree of variability in each trait among available males. Such interactions between condition-dependent and disassortative mate choice criteria suggest a mechanism by which female choice can contribute to maintenance of additive genetic variance in both the MHC and condition-dependent traits, even under consistent directional selection.     

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.     

An integrated approach to characterize genetic interaction networks in yeast metabolism

Although experimental and theoretical efforts have been applied to globally map genetic interactions, we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we i, quantitatively measured genetic interactions between ～185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii, superposed the data on a detailed systems biology model of metabolism and iii, introduced a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigated the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy and gene dispensability. Last, we show the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments.     

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.     

Distributions of epistasis in microbes fit predictions from a fitness landscape model

How do the fitness effects of several mutations combine? Despite its simplicity, this question is central to the understanding of multilocus evolution. Epistasis (the interaction between alleles at different loci), especially epistasis for fitness traits such as reproduction and survival, influences evolutionary predictions "almost whenever multilocus genetics matters". Yet very few models have sought to predict epistasis, and none has been empirically tested. Here we show that the distribution of epistasis can be predicted from the distribution of single mutation effects, based on a simple fitness landscape model. We show that this prediction closely matches the empirical measures of epistasis that have been obtained for Escherichia coli and the RNA virus vesicular stomatitis virus. Our results suggest that a simple fitness landscape model may be sufficient to quantitatively capture the complex nature of gene interactions. This model may offer a simple and widely applicable alternative to complex metabolic network models, in particular for making evolutionary predictions.     

miRNA regulation of Sdf1 chemokine signaling provides genetic robustness to germ cell migration

microRNAs (miRNAs) function as genetic rheostats to control gene output. Based on their role as modulators, it has been postulated that miRNAs canalize development and provide genetic robustness. Here, we uncover a previously unidentified regulatory layer of chemokine signaling by miRNAs that confers genetic robustness on primordial germ cell (PGC) migration. In zebrafish, PGCs are guided to the gonad by the ligand Sdf1a, which is regulated by the sequestration receptor Cxcr7b. We find that miR-430 regulates sdf1a and cxcr7 mRNAs. Using target protectors, we demonstrate that miR-430-mediated regulation of endogenous sdf1a (also known as cxcl12a) and cxcr7b (i) facilitates dynamic expression of sdf1a by clearing its mRNA from previous expression domains, (ii) modulates the levels of the decoy receptor Cxcr7b to avoid excessive depletion of Sdf1a and (iii) buffers against variation in gene dosage of chemokine signaling components to ensure accurate PGC migration. Our results indicate that losing miRNA-mediated regulation can expose otherwise buffered genetic lesions leading to developmental defects.     

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.     

Mutations in CLCN2 encoding a voltage-gated chloride channel are associated with idiopathic generalized epilepsies

Idiopathic generalized epilepsy (IGE) is an inherited neurological disorder affecting about 0.4% of the world's population. Mutations in ten genes causing distinct forms of idiopathic epilepsy have been identified so far, but the genetic basis of many IGE subtypes is still unknown. Here we report a gene associated with the four most common IGE subtypes: childhood and juvenile absence epilepsy (CAE and JAE), juvenile myoclonic epilepsy (JME), and epilepsy with grand mal seizures on awakening (EGMA; ref. 8). We identified three different heterozygous mutations in the chloride-channel gene CLCN2 in three unrelated families with IGE. These mutations result in (i) a premature stop codon (M200fsX231), (ii) an atypical splicing (del74-117) and (iii) a single amino-acid substitution (G715E). All mutations produce functional alterations that provide distinct explanations for their pathogenic phenotypes. M200fsX231 and del74-117 cause a loss of function of ClC-2 channels and are expected to lower the transmembrane chloride gradient essential for GABAergic inhibition. G715E alters voltage-dependent gating, which may cause membrane depolarization and hyperexcitability.     

Natural variation in cardiac metabolism and gene expression in Fundulus heteroclitus

Individual variation in gene expression is important for evolutionary adaptation and susceptibility to diseases and pathologies. In this study, we address the functional importance of this variation by comparing cardiac metabolism to patterns of mRNA expression using microarrays. There is extensive variation in both cardiac metabolism and the expression of metabolic genes among individuals of the teleost fish Fundulus heteroclitus from natural outbred populations raised in a common environment: metabolism differed among individuals by a factor of more than 2, and expression levels of 94% of genes were significantly different (P < 0.01) between individuals in a population. This unexpectedly high variation in metabolic gene expression explains much of the variation in metabolism, suggesting that it is biologically relevant. The patterns of gene expression that are most important in explaining cardiac metabolism differ between groups of individuals. Apparently, the variation in metabolism seems to be related to different patterns of gene expression in the different groups of individuals. The magnitude of differences in gene expression in these groups is not important; large changes in expression have no greater predictive value than small changes. These data suggest that variation in physiological performance is related to the subtle variation in gene expression and that this relationship differs among individuals.     

Involvement of a novel Tnf receptor homologue in hair follicle induction.

Although inductive interactions are known to be essential for specification of cell fate in many vertebrate tissues, the signals and receptors responsible for transmitting this information remain largely unidentified. Mice with mutations in the downless (dl) gene have defects in hair follicle induction, lack sweat glands and have malformed teeth. These structures originate as ectodermal placodes, which invaginate into the underlying mesenchyme and differentiate to form specific organs. Positional cloning of the dl gene began with identification of the transgenic family OVE1. One branch of the family, dl(OVE1B), carries an approximately 600-kb deletion at the dl locus caused by transgene integration. The mutated locus has been physically mapped in this family, and a 200-kb mouse YAC clone, YAC D9, has been identified and shown to rescue the dl phenotype in the spontaneous dl(Jackson) (dl(J), recessive) and Dl(sleek) (Dl(slk), dominant negative) mutants. Here we report the positional cloning of the dl gene, which encodes a novel member of the tumour necrosis factor (Tnf) receptor (Tnfr) family. The mutant phenotype and dl expression pattern suggests that this gene encodes a receptor that specifies hair follicle fate. Its ligand is likely to be the product of the tabby (Ta) gene, as Ta mutants have a phenotype identical to that of dl mutants and Ta encodes a Tnf-like protein.