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

Constructing Biological Pathways by a Two-Step Counting Approach

Networks are widely used in biology to represent the relationships between genes and gene functions. In Boolean biological models, it is mainly assumed that there are two states to represent a gene: on-state and off-state. It is typically assumed that the relationship between two genes can be characterized by two kinds of pairwise relationships: similarity and prerequisite. Many approaches have been proposed in the literature to reconstruct biological relationships. In this article, we propose a two-step method to reconstruct the biological pathway when the binary array data have measurement error. For a pair of genes in a sample, the first step of this approach is to assign counting numbers for every relationship and select the relationship with counting number greater than a threshold. The second step is to calculate the asymptotic p-values for hypotheses of possible relationships and select relationships with a large p-value. This new method has the advantages of easy calculation for the counting numbers and simple closed forms for the p-value. The simulation study and real data example show that the two-step counting method can accurately reconstruct the biological pathway and outperform the existing methods. Compared with the other existing methods, this two-step method can provide a more accurate and efficient alternative approach for reconstructing the biological network

Short RNA Guides Cleavage by Eukaryotic RNase III

In eukaryotes, short RNAs guide a variety of enzymatic activities that range from RNA editing to translation repression. It is hypothesized that pre-existing proteins evolved to bind and use guide RNA during evolution. However, the capacity of modern proteins to adopt new RNA guides has never been demonstrated. Here we show that Rnt1p, the yeast orthologue of the bacterial dsRNA-specific RNase III, can bind short RNA transcripts and use them as guides for sequence-specific cleavage. Target cleavage occurred at a constant distance from the Rnt1p binding site, leaving the guide RNA intact for subsequent cleavage. Our results indicate that RNase III may trigger sequence-specific RNA degradation independent of the RNAi machinery, and they open the road for a new generation of precise RNA silencing tools that do not trigger a dsRNA-mediated immune response

Multiple Signals Converge on a Differentiation MAPK Pathway

An important emerging question in the area of signal transduction is how information from different pathways becomes integrated into a highly coordinated response. In budding yeast, multiple pathways regulate filamentous growth, a complex differentiation response that occurs under specific environmental conditions. To identify new aspects of filamentous growth regulation, we used a novel screening approach (called secretion profiling) that measures release of the extracellular domain of Msb2p, the signaling mucin which functions at the head of the filamentous growth (FG) MAPK pathway. Secretion profiling of complementary genomic collections showed that many of the pathways that regulate filamentous growth (RAS, RIM101, OPI1, and RTG) were also required for FG pathway activation. This regulation sensitized the FG pathway to multiple stimuli and synchronized it to the global signaling network. Several of the regulators were required for MSB2 expression, which identifies the MSB2 promoter as a target “hub” where multiple signals converge. Accessibility to the MSB2 promoter was further regulated by the histone deacetylase (HDAC) Rpd3p(L), which positively regulated FG pathway activity and filamentous growth. Our findings provide the first glimpse of a global regulatory hierarchy among the pathways that control filamentous growth. Systems-level integration of signaling circuitry is likely to coordinate other regulatory networks that control complex behaviors

Nuclear localization and function of polypeptide ligands and their receptors: a new paradigm for hormone specificity within the mammary gland?

The specific effects triggered by polypeptide hormone/growth factor stimulation of mammary cells were considered mediated solely by receptor-associated signaling networks. A compelling body of new data, however, clearly indicates that polypeptide ligands and/or their receptors are transported into the nucleus, where they function directly to regulate the expression of specific transcription factors and gene loci. The intranuclear function of these complexes may contribute to the explicit functions associated with a given ligand, and may serve as new targets for pharmacologic intervention

PathFinder: mining signal transduction pathway segments from protein-protein interaction networks

<p>Abstract</p> <p>Background</p> <p>A Signal transduction pathway is the chain of processes by which a cell converts an extracellular signal into a response. In most unicellular organisms, the number of signal transduction pathways influences the number of ways the cell can react and respond to the environment. Discovering signal transduction pathways is an arduous problem, even with the use of systematic genomic, proteomic and metabolomic technologies. These techniques lead to an enormous amount of data and how to interpret and process this data becomes a challenging computational problem.</p> <p>Results</p> <p>In this study we present a new framework for identifying signaling pathways in protein-protein interaction networks. Our goal is to find biologically significant pathway segments in a given interaction network. Currently, protein-protein interaction data has excessive amount of noise, e.g., false positive and false negative interactions. First, we eliminate false positives in the protein-protein interaction network by integrating the network with microarray expression profiles, protein subcellular localization and sequence information. In addition, protein families are used to repair false negative interactions. Then the characteristics of known signal transduction pathways and their functional annotations are extracted in the form of association rules.</p> <p>Conclusion</p> <p>Given a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins with possible missing links (recovered false negatives). In our study, <it>S. cerevisiae </it>(yeast) data is used to demonstrate the effectiveness of our method.</p

The Caenorhabditis elegans Gene mfap-1 Encodes a Nuclear Protein That Affects Alternative Splicing

RNA splicing is a major regulatory mechanism for controlling eukaryotic gene expression. By generating various splice isoforms from a single pre–mRNA, alternative splicing plays a key role in promoting the evolving complexity of metazoans. Numerous splicing factors have been identified. However, the in vivo functions of many splicing factors remain to be understood. In vivo studies are essential for understanding the molecular mechanisms of RNA splicing and the biology of numerous RNA splicing-related diseases. We previously isolated a Caenorhabditis elegans mutant defective in an essential gene from a genetic screen for suppressors of the rubberband Unc phenotype of unc-93(e1500) animals. This mutant contains missense mutations in two adjacent codons of the C. elegans microfibrillar-associated protein 1 gene mfap-1. mfap-1(n4564 n5214) suppresses the Unc phenotypes of different rubberband Unc mutants in a pattern similar to that of mutations in the splicing factor genes uaf-1 (the C. elegans U2AF large subunit gene) and sfa-1 (the C. elegans SF1/BBP gene). We used the endogenous gene tos-1 as a reporter for splicing and detected increased intron 1 retention and exon 3 skipping of tos-1 transcripts in mfap-1(n4564 n5214) animals. Using a yeast two-hybrid screen, we isolated splicing factors as potential MFAP-1 interactors. Our studies indicate that C. elegans mfap-1 encodes a splicing factor that can affect alternative splicing.National Natural Science Foundation (China) (Grant 30971639)United States. National Institutes of Health (Grant GM24663

N-Acetylglucosamine Induces White to Opaque Switching, a Mating Prerequisite in Candida albicans

To mate, the fungal pathogen Candida albicans must undergo homozygosis at the mating-type locus and then switch from the white to opaque phenotype. Paradoxically, opaque cells were found to be unstable at physiological temperature, suggesting that mating had little chance of occurring in the host, the main niche of C. albicans. Recently, however, it was demonstrated that high levels of CO2, equivalent to those found in the host gastrointestinal tract and select tissues, induced the white to opaque switch at physiological temperature, providing a possible resolution to the paradox. Here, we demonstrate that a second signal, N-acetylglucosamine (GlcNAc), a monosaccharide produced primarily by gastrointestinal tract bacteria, also serves as a potent inducer of white to opaque switching and functions primarily through the Ras1/cAMP pathway and phosphorylated Wor1, the gene product of the master switch locus. Our results therefore suggest that signals produced by bacterial co-members of the gastrointestinal tract microbiota regulate switching and therefore mating of C. albicans