Automated construction of genetic networks from mutant data

Geneticists use mutations to investigate biological phenomena. Mutations cause changes of organism’s phenotype and may reveal which genes participate in a certain biological process and how. To represent these functional interactions between genes, a gene regulatory network is an often used formalism. We have developed a system called GenePath (1) for automated construction of genetic networks from mutant data

Cheating by Exploitation of Developmental Prestalk Patterning in Dictyostelium discoideum

The cooperative developmental system of the social amoeba Dictyostelium discoideum is susceptible to exploitation by cheaters—strains that make more than their fair share of spores in chimerae. Laboratory screens in Dictyostelium have shown that the genetic potential for facultative cheating is high, and field surveys have shown that cheaters are abundant in nature, but the cheating mechanisms are largely unknown. Here we describe cheater C (chtC), a strong facultative cheater mutant that cheats by affecting prestalk differentiation. The chtC gene is developmentally regulated and its mRNA becomes stalk-enriched at the end of development. chtC mutants are defective in maintaining the prestalk cell fate as some of their prestalk cells transdifferentiate into prespore cells, but that defect does not affect gross developmental morphology or sporulation efficiency. In chimerae between wild-type and chtC mutant cells, the wild-type cells preferentially give rise to prestalk cells, and the chtC mutants increase their representation in the spore mass. Mixing chtC mutants with other cell-type proportioning mutants revealed that the cheating is directly related to the prestalk-differentiation propensity of the victim. These findings illustrate that a cheater can victimize cooperative strains by exploiting an established developmental pathway

Learning to get along despite struggling to get by

A review of evolutionary theories for cooperation, with emphasis on the mechanisms that can favor cooperation and reduce conflict within multicellular organisms, enabling the transition from unicellular to multicellular life

GenePath: from mutations to genetic networks and back

GenePath is a web-based application for the analysis of mutant-based experiments and synthesis of genetic networks. Here, we introduce GenePath and describe a number of new approaches, including conflict resolution, handling cyclic pathways, confidence level assignment, what-if analysis and new experiment proposal. We illustrate the key concepts using data from a study of adhesion genes in Dictyostelium discoideum and show that GenePath discovered genetic interactions that were ignored in the original publication. GenePath is available at

GenePath: a System for Automated Construction of Genetic Networks from Mutant Data

Motivation: Genetic pathways are often used in the analysis of biological phenomena. In classical genetics, they are constructed manually from experimental data on mutants. The field lacks formalism to guide such analysis, and accounting for all the data becomes complicated when large amounts of data are considered. Results: We have developed GenePath, an intelligent assistant that mimics expert geneticists in the analysis of genetic data. GenePath employs expert-defined patterns to uncover gene relations from the data, and uses these relations as constraints that guide the search for a plausible genetic network. GenePath provides formalism to genetic data analysis, facilitates the consideration of all the available data in a consistent and systematic manner, and aids in the examination of the large number of possible consequences of a planned experiment. It also provides an explanation mechanism that traces back every finding to the pertinent data. GenePath was successfully tested on several genetic problems. Availability: GenePath can be accessed at http://genepath.org. Supplementary information: Supplementary material is available at http://genepath.org/bi-supp

Web-enabled knowledge-based analysis of genetic data

We present a web-based implementation of GenePath, an intelligent assistant tool for data analysis in functional genomics. GenePath considers mutant data and uses expert-defined patterns to find gene-to-gene or gene-to-outcome relations. It presents the results of analysis as genetic networks, wherein a set of genes has various influence on one another and on a biological outcome. In the paper, we particularly focus on its web-based interface and explanation mechanisms

Gene prioritization by compressive data fusion and chaining

Data integration procedures combine heterogeneous data sets into predictive models, but they are limited to data explicitly related to the target object type, such as genes. Collage is a new data fusion approach to gene prioritization. It considers data sets of various association levels with the prediction task, utilizes collective matrix factorization to compress the data, and chaining to relate different object types contained in a data compendium. Collage prioritizes genes based on their similarity to several seed genes. We tested Collage by prioritizing bacterial response genes in Dictyostelium as a novel model system for prokaryote-eukaryote interactions. Using 4 seed genes and 14 data sets, only one of which was directly related to the bacterial response, Collage proposed 8 candidate genes that were readily validated as necessary for the response of Dictyostelium to Gram-negative bacteria. These findings establish Collage as a method for inferring biological knowledge from the integration of heterogeneous and coarsely related data sets

A new social gene in Dictyostelium discoideum, chtB

Background: Competitive social interactions are ubiquitous in nature, but their genetic basis is difficult to determine. Much can be learned from single gene knockouts in a eukaryote microbe. The mutants can be competed with the parent to discern the social impact of that specific gene. Dictyostelium discoideum is a social amoeba that exhibits cooperative behavior in the construction of a multicellular fruiting body. It is a good model organism to study the genetic basis of cooperation since it has a sequenced genome and it is amenable to genetic manipulation. When two strains of D. discoideum are mixed, a cheater strain can exploit its social partner by differentiating more spore than its fair share relative to stalk cells. Cheater strains can be generated in the lab or found in the wild and genetic analyses have shown that cheating behavior can be achieved through many pathways. Results: We have characterized the knockout mutant chtB, which was isolated from a screen for cheater mutants that were also able to form normal fruiting bodies on their own. When mixed in equal proportions with parental strain cells, chtB mutants contributed almost 60% of the total number of spores. To do so, chtB cells inhibit wild type cells from becoming spores, as indicated by counts and by the wild type cells’ reduced expression of the prespore gene, cotB. We found no obvious fitness costs (morphology, doubling time in liquid medium, spore production, and germination efficiency) associated with the cheating ability of the chtB knockout. Conclusions: In this study we describe a new gene in D. discoideum, chtB, which when knocked out inhibits the parental strain from producing spores. Moreover, under lab conditions, we did not detect any fitness costs associated with this behavior

New components of the Dictyostelium PKA pathway revealed by Bayesian analysis of expression data

<p>Abstract</p> <p>Background</p> <p>Identifying candidate genes in genetic networks is important for understanding regulation and biological function. Large gene expression datasets contain relevant information about genetic networks, but mining the data is not a trivial task. Algorithms that infer Bayesian networks from expression data are powerful tools for learning complex genetic networks, since they can incorporate prior knowledge and uncover higher-order dependencies among genes. However, these algorithms are computationally demanding, so novel techniques that allow targeted exploration for discovering new members of known pathways are essential.</p> <p>Results</p> <p>Here we describe a Bayesian network approach that addresses a specific network within a large dataset to discover new components. Our algorithm draws individual genes from a large gene-expression repository, and ranks them as potential members of a known pathway. We apply this method to discover new components of the cAMP-dependent protein kinase (PKA) pathway, a central regulator of <it>Dictyostelium discoideum </it>development. The PKA network is well studied in <it>D. discoideum </it>but the transcriptional networks that regulate PKA activity and the transcriptional outcomes of PKA function are largely unknown. Most of the genes highly ranked by our method encode either known components of the PKA pathway or are good candidates. We tested 5 uncharacterized highly ranked genes by creating mutant strains and identified a candidate cAMP-response element-binding protein, yet undiscovered in <it>D. discoideum</it>, and a histidine kinase, a candidate upstream regulator of PKA activity.</p> <p>Conclusions</p> <p>The single-gene expansion method is useful in identifying new components of known pathways. The method takes advantage of the Bayesian framework to incorporate prior biological knowledge and discovers higher-order dependencies among genes while greatly reducing the computational resources required to process high-throughput datasets.</p

Polymorphic members of the lag gene family mediate kin discrimination in Dictyostelium

Self and kin discrimination are observed in most kingdoms of life and are mediated by highly polymorphic plasma membrane proteins. Sequence polymorphism, which is essential for effective recognition, is maintained by balancing selection. Dictyostelium discoideum are social amoebas that propagate as unicellular organisms but aggregate upon starvation and form fruiting bodies with viable spores and dead stalk cells. Aggregative development exposes Dictyostelium to the perils of chimerism, including cheating, which raises questions about how the victims survive in nature and how social cooperation persists. Dictyostelids can minimize the cost of chimerism by preferential cooperation with kin, but the mechanisms of kin discrimination are largely unknown. Dictyostelium lag genes encode transmembrane proteins with multiple immunoglobulin (Ig) repeats that participate in cell adhesion and signaling. Here, we describe their role in kin discrimination. We show that lagB1 and lagC1 are highly polymorphic in natural populations and that their sequence dissimilarity correlates well with wild-strain segregation. Deleting lagB1 and lagC1 results in strain segregation in chimeras with wild-type cells, whereas elimination of the nearly invariant homolog lagD1 has no such consequences. These findings reveal an early evolutionary origin of kin discrimination and provide insight into the mechanism of social recognition and immunity