Background: Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed nonviable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree classifier, integrate diverse biological networks and show that our method outperforms established methods.
Innovations in metazoan development arise from evolutionary modification of gene regulatory networks (GRNs). We report widespread cryptic variation in the requirement for two key regulatory inputs, SKN-1/Nrf2 and MOM-2/Wnt, into the C. elegans endoderm GRN. While some natural isolates show a nearly absolute requirement for these two regulators, in others, most embryos differentiate endoderm in their absence. GWAS and analysis of recombinant inbred lines reveal multiple genetic regions underlying this broad phenotypic variation. We observe a reciprocal trend, in which genomic variants, or knockdown of endoderm regulatory genes, that result in a high SKN-1 requirement often show low MOM-2/Wnt requirement and vice-versa, suggesting that cryptic variation in the endoderm GRN may be tuned by opposing requirements for these two key regulatory inputs. These findings reveal that while the downstream components in the endoderm GRN are common across metazoan phylogeny, initiating regulatory inputs are remarkably plastic even within a single species.
The Ralstonia species complex is a genetically diverse group of plant wilt pathogens. Ralstonia strains are classified by a “phylotype-sequevar” phylogenetic system. Since the development of the phylotype-sequevar system, over one hundred papers have described the genetic diversity of Ralstonia strains isolated from agriculturally important crops, ornamental plants, and plants in natural ecosystems. Our goal is to create a database that contains the reported global distribution and host range of Ralstonia sequevars. In this first release, we have catalogued information from 35 manuscripts that report one or more Ralstonia strain isolated from 50 geographic regions. The database is hosted as a GitHub repository (https://github.com/lowepowerlab/Ralstonia_Global_Diversity) that will be updated regularly.
Complex animals display bilaterally asymmetric motor behavior, or “motor handedness,” often revealed by preferential use of limbs on one side. For example, use of right limbs is dominant in a strong majority of humans. While the mechanisms that establish bilateral asymmetry in motor function are unknown in humans, they appear to be distinct from those for other handedness asymmetries, including bilateral visceral organ asymmetry, brain laterality, and ocular dominance. We report here that a simple, genetically homogeneous animal comprised of only ∼1000 somatic cells, the nematode C. elegans, also shows a distinct motor handedness preference: on a population basis, males show a pronounced right-hand turning bias during mating. The handedness bias persists through much of adult lifespan, suggesting that, as in more complex animals, it is an intrinsic trait of each individual, which can differ from the population mean. Our observations imply that the laterality of motor handedness preference in C. elegans is driven by epigenetic factors rather than by genetic variation. The preference for right-hand turns is also seen in animals with mirror-reversed anatomical handedness and is not attributable to stochastic asymmetric loss of male sensory rays that occurs by programmed cell death. As with C. elegans, we also observed a substantial handedness bias, though not necessarily the same preference in direction, in several gonochoristic Caenorhabditis species. These findings indicate that the independence of bilaterally asymmetric motor dominance from overall anatomical asymmetry, and a population-level tendency away from ambidexterity, occur even in simple invertebrates, suggesting that these may be common features of bilaterian metazoans.
Uncovering subgraphs with an abnormal distribution of attributes reveals much insight into network behaviors. For example in social or communication networks, diseases or intrusions usually do not propagate uniformly, which makes it critical to find anomalous regions with high concentrations of a specific disease or intrusion. In this paper, we introduce a probabilistic model to identify anomalous subgraphs containing a significantly different percentage of a certain vertex attribute, such as a specific disease or an intrusion, compared to the rest of the graph. Our framework, gAnomaly, models generative processes of vertex attributes and divides the graph into regions that are governed by background and anomaly processes. Two types of regularizers are employed to smoothen the regions and to facilitate vertex assignment. We utilize deterministic annealing EM to learn the model parameters, which is less initialization-dependent and better at avoiding local optima. In order to find fine-grained anomalies, an iterative procedure is further proposed. Experiments show gAnomaly outperforms a state-of-the-art algorithm at uncovering anomalous subgraphs in attributed graphs.
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