Maximal Extraction of Biological Information from Genetic Interaction Data

Targeted genetic perturbation is a powerful tool for inferring gene function in model organisms. Functional relationships between genes can be inferred by observing the effects of multiple genetic perturbations in a single strain. The study of these relationships, generally referred to as genetic interactions, is a classic technique for ordering genes in pathways, thereby revealing genetic organization and gene-to-gene information flow. Genetic interaction screens are now being carried out in high-throughput experiments involving tens or hundreds of genes. These data sets have the potential to reveal genetic organization on a large scale, and require computational techniques that best reveal this organization. In this paper, we use a complexity metric based in information theory to determine the maximally informative network given a set of genetic interaction data. We find that networks with high complexity scores yield the most biological information in terms of (i) specific associations between genes and biological functions, and (ii) mapping modules of co-functional genes. This information-based approach is an automated, unsupervised classification of the biological rules underlying observed genetic interactions. It might have particular potential in genetic studies in which interactions are complex and prior gene annotation data are sparse

Predicting Quantitative Genetic Interactions by Means of Sequential Matrix Approximation

Despite the emerging experimental techniques for perturbing multiple genes and measuring their quantitative phenotypic effects, genetic interactions have remained extremely difficult to predict on a large scale. Using a recent high-resolution screen of genetic interactions in yeast as a case study, we investigated whether the extraction of pertinent information encoded in the quantitative phenotypic measurements could be improved by computational means. By taking advantage of the observation that most gene pairs in the genetic interaction screens have no significant interactions with each other, we developed a sequential approximation procedure which ranks the mutation pairs in order of evidence for a genetic interaction. The sequential approximations can efficiently remove background variation in the double-mutation screens and give increasingly accurate estimates of the single-mutant fitness measurements. Interestingly, these estimates not only provide predictions for genetic interactions which are consistent with those obtained using the measured fitness, but they can even significantly improve the accuracy with which one can distinguish functionally-related gene pairs from the non-interacting pairs. The computational approach, in general, enables an efficient exploration and classification of genetic interactions in other studies and systems as well

Structural basis for inhibition of homologous recombination by the RecX protein

The RecA/RAD51 nucleoprotein filament is central to the reaction of homologous recombination (HR). Filament activity must be tightly regulated in vivo as unrestrained HR can cause genomic instability. Our mechanistic understanding of HR is restricted by lack of structural information about the regulatory proteins that control filament activity. Here, we describe a structural and functional analysis of the HR inhibitor protein RecX and its mode of interaction with the RecA filament. RecX is a modular protein assembled of repeated three-helix motifs. The relative arrangement of the repeats generates an elongated and curved shape that is well suited for binding within the helical groove of the RecA filament. Structure-based mutagenesis confirms that conserved basic residues on the concave side of RecX are important for repression of RecA activity. Analysis of RecA filament dynamics in the presence of RecX shows that RecX actively promotes filament disassembly. Collectively, our data support a model in which RecX binding to the helical groove of the filament causes local dissociation of RecA protomers, leading to filament destabilisation and HR inhibition

Intrinsic Capability of Budding Yeast Cofilin to Promote Turnover of Tropomyosin-Bound Actin Filaments

The ability of actin filaments to function in cell morphogenesis and motility is closely coupled to their dynamic properties. Yeast cells contain two prominent actin structures, cables and patches, both of which are rapidly assembled and disassembled. Although genetic studies have shown that rapid actin turnover in patches and cables depends on cofilin, how cofilin might control cable disassembly remains unclear, because tropomyosin, a component of actin cables, is thought to protect actin filaments against the depolymerizing activity of ADF/cofilin. We have identified cofilin as a yeast tropomyosin (Tpm1) binding protein through Tpm1 affinity column and mass spectrometry. Using a variety of assays, we show that yeast cofilin can efficiently depolymerize and sever yeast actin filaments decorated with either Tpm1 or mouse tropomyosins TM1 and TM4. Our results suggest that yeast cofilin has the intrinsic ability to promote actin cable turnover, and that the severing activity may rely on its ability to bind Tpm1

Functional Maps of Protein Complexes from Quantitative Genetic Interaction Data

Recently, a number of advanced screening technologies have allowed for the comprehensive quantification of aggravating and alleviating genetic interactions among gene pairs. In parallel, TAP-MS studies (tandem affinity purification followed by mass spectroscopy) have been successful at identifying physical protein interactions that can indicate proteins participating in the same molecular complex. Here, we propose a method for the joint learning of protein complexes and their functional relationships by integration of quantitative genetic interactions and TAP-MS data. Using 3 independent benchmark datasets, we demonstrate that this method is >50% more accurate at identifying functionally related protein pairs than previous approaches. Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes, a number of which are novel or have been substantially expanded by addition of new subunits. Interestingly, we find that complexes that are enriched for aggravating genetic interactions (i.e., synthetic lethality) are more likely to contain essential genes, linking each of these interactions to an underlying mechanism. These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function

Differential Requirements of Two recA Mutants for Constitutive SOS Expression in Escherichia coli K-12

Background Repairing DNA damage begins with its detection and is often followed by elicitation of a cellular response. In E. coli, RecA polymerizes on ssDNA produced after DNA damage and induces the SOS Response. The RecA-DNA filament is an allosteric effector of LexA auto-proteolysis. LexA is the repressor of the SOS Response. Not all RecA-DNA filaments, however, lead to an SOS Response. Certain recA mutants express the SOS Response (recAC) in the absence of external DNA damage in log phase cells. Methodology/Principal Findings Genetic analysis of two recAC mutants was used to determine the mechanism of constitutive SOS (SOSC) expression in a population of log phase cells using fluorescence of single cells carrying an SOS reporter system (sulAp-gfp). SOSC expression in recA4142 mutants was dependent on its initial level of transcription, recBCD, recFOR, recX, dinI, xthA and the type of medium in which the cells were grown. SOSC expression in recA730 mutants was affected by none of the mutations or conditions tested above. Conclusions/Significance It is concluded that not all recAC alleles cause SOSC expression by the same mechanism. It is hypothesized that RecA4142 is loaded on to a double-strand end of DNA and that the RecA filament is stabilized by the presence of DinI and destabilized by RecX. RecFOR regulate the activity of RecX to destabilize the RecA filament. RecA730 causes SOSC expression by binding to ssDNA in a mechanism yet to be determined

Trees on networks: resolving statistical patterns of phylogenetic similarities among interacting proteins

<p>Abstract</p> <p>Background</p> <p>Phylogenies capture the evolutionary ancestry linking extant species. Correlations and similarities among a set of species are mediated by and need to be understood in terms of the phylogenic tree. In a similar way it has been argued that biological networks also induce correlations among sets of interacting genes or their protein products.</p> <p>Results</p> <p>We develop suitable statistical resampling schemes that can incorporate these two potential sources of correlation into a single inferential framework. To illustrate our approach we apply it to protein interaction data in yeast and investigate whether the phylogenetic trees of interacting proteins in a panel of yeast species are more similar than would be expected by chance.</p> <p>Conclusions</p> <p>While we find only negligible evidence for such increased levels of similarities, our statistical approach allows us to resolve the previously reported contradictory results on the levels of co-evolution induced by protein-protein interactions. We conclude with a discussion as to how we may employ the statistical framework developed here in further functional and evolutionary analyses of biological networks and systems.</p

Predicting Protein Phenotypes Based on Protein-Protein Interaction Network

BACKGROUND: Identifying associated phenotypes of proteins is a challenge of the modern genetics since the multifactorial trait often results from contributions of many proteins. Besides the high-through phenotype assays, the computational methods are alternative ways to identify the phenotypes of proteins. METHODOLOGY/PRINCIPAL FINDINGS: Here, we proposed a new method for predicting protein phenotypes in yeast based on protein-protein interaction network. Instead of only the most likely phenotype, a series of possible phenotypes for the query protein were generated and ranked according to the tethering potential score. As a result, the first order prediction accuracy of our method achieved 65.4% evaluated by Jackknife test of 1,267 proteins in budding yeast, much higher than the success rate (15.4%) of a random guess. And the likelihood of the first 3 predicted phenotypes including all the real phenotypes of the proteins was 70.6%. CONCLUSIONS/SIGNIFICANCE: The candidate phenotypes predicted by our method provided useful clues for the further validation. In addition, the method can be easily applied to the prediction of protein associated phenotypes in other organisms

Yeast Two-Hybrid: State of the Art

Genome projects are approaching completion and are saturating sequence databases. This paper discusses the role of the two-hybrid system as a generator of hypotheses. Apart from this rather exhaustive, financially and labour intensive procedure, more refined functional studies can be undertaken. Indeed, by making hybrids of two-hybrid systems, customised approaches can be developed in order to attack specific function-related problems. For example, one could set-up a "differential" screen by combining a forward and a reverse approach in a three-hybrid set-up. Another very interesting project is the use of peptide libraries in two-hybrid approaches. This could enable the identification of peptides with very high specificity comparable to "real" antibodies. With the technology available, the only limitation is imagination