Networks in Caenorhabditis elegans

Gene regulatory network inference in long-lived C. elegans reveals modular properties that are predictive of novel aging genes

We design a “wisdom-of-the-crowds” GRN inference pipeline and couple it to complex network analysis to understand the organizational principles governing gene regulation in long-lived glp-1/Notch Caenorhabditis elegans. The GRN has three layers (input, core, and output) and is topologically equivalent to bow-tie/hourglass structures prevalent among metabolic networks. To assess the functional importance of structural layers, we screened 80% of regulators and discovered 50 new aging genes, 86% with human orthologues. Genes essential for longevity—including ones involved in insulin-like signaling (ILS)—are at the core, indicating that GRN's structure is predictive of functionality. We used in vivo reporters and a novel functional network covering 5,497 genetic interactions to make mechanistic predictions. We used genetic epistasis to test some of these predictions, uncovering a novel transcriptional regulator, sup-37, that works alongside DAF-16/FOXO. We present a framework with predictive power that can accelerate discovery in C. elegans and potentially humans.

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Gene-regulatory inference provides global network of long-lived animals

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The large-scale topology of the network has an hourglass structure

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Membership to the core of the hourglass is a good predictor of functionality

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Discovered 50 novel aging genes, including sup-37, a DAF-16 dependent gene

Evolution, Chance, and Aging

Aging has provided fruitful challenges for evolutionary theory, and evolutionary theory has deepened our understanding of aging. A great deal of genetic and molecular data now exists concerning mortality regulation and there is a growing body of knowledge concerning the life histories of diverse species. Assimilating all relevant data into a framework for the evolution of aging promises to significantly advance the field. We propose extensions of some key concepts to provide greater precision when applying these concepts to age-structured contexts. Secondary or byproduct effects of mutations are proposed as an important factor affecting survival patterns, including effects that may operate in small populations subject to genetic drift, widening the possibilities for mutation accumulation and pleiotropy. Molecular and genetic studies have indicated a diverse array of mechanisms that can modify aging and mortality rates, while transcriptome data indicate a high level of tissue and species specificity for genes affected by aging. The diversity of mechanisms and gene effects that can contribute to the pattern of aging in different organisms may mirror the complex evolutionary processes behind aging.

Transcriptional Control of Developmental Cell Behaviors

Tissue-specific transcription regulators emerged as key developmental control genes, which operate in the context of complex gene regulatory networks (GRNs) to coordinate progressive cell fate specification and tissue morphogenesis. We discuss how GRNs control the individual cell behaviors underlying complex morphogenetic events. Cell behaviors classically range from mesenchymal cell motility to cell shape changes in epithelial sheets. These behaviors emerge from the tissue-specific, multiscale integration of the local activities of universal and pleiotropic effectors, which underlie modular subcellular processes including cytoskeletal dynamics, cell-cell and cell-matrix adhesion, signaling, polarity, and vesicle trafficking. Extrinsic cues and intrinsic cell competence determine the subcellular spatiotemporal patterns of effector activities. GRNs influence most subcellular activities by controlling only a fraction of the effector-coding genes, which we argue is enriched in effectors involved in reading and processing the extrinsic cues to contextualize intrinsic subcellular processes and canalize developmental cell behaviors. The properties of the transcription-cell behavior interface have profound implications for evolution and disease.

An integrative C. elegans protein-protein interaction network with reliability assessment based on a probabilistic graphical model

In Caenorhabditis elegans, a large number of protein-protein interactions (PPIs) are identified by different experiments. However, a comprehensive weighted PPI network, which is essential for signaling pathway inference, is not yet available in this model organism. Therefore, we firstly construct an integrative PPI network in C. elegans with 12,951 interactions involving 5039 proteins from seven molecular interaction databases. Then, a reliability score based on a probabilistic graphical model (RSPGM) is proposed to assess PPIs. It assumes that the random number of interactions between two proteins comes from the Bernoulli distribution to avoid multi-links. The main parameter of the RSPGM score contains a few latent variables which can be considered as several common properties between two proteins. Validations on high-confidence yeast datasets show that RSPGM provides more accurate evaluation than other approaches, and the PPIs in the reconstructed PPI network have higher biological relevance than that in the original network in terms of gene ontology, gene expression, essentiality and the prediction of known protein complexes. Furthermore, this weighted integrative PPI network in C. elegans is employed on inferring interaction path of the canonical Wnt/β-catenin pathway as well. Most genes on the inferred interaction path have been validated to be Wnt pathway components. Therefore, RSPGM is essential and effective for evaluating PPIs and inferring interaction path. Finally, the PPI network with RSPGM scores can be queried and visualized on a user interactive website, which is freely available at .

Bioinformatics tools for achieving better gene silencing in plants

RNA interference (RNAi) is one of the most popular and effective molecular technologies for knocking down the expression of an individual gene of interest in living organisms. Yet the technology still faces the major issue of nonspecific gene silencing, which can compromise gene functional characterization and the interpretation of phenotypes associated with individual gene knockdown. Designing an effective and target-specific small interfering RNA (siRNA) for induction of RNAi is therefore the major challenge in RNAi-based gene silencing. A 'good' siRNA molecule must possess three key features: (a) the ability to specifically silence an individual gene of interest, (b) little or no effect on the expressions of unintended siRNA gene targets (off-target genes), and (c) no cell toxicity. Although several siRNA design and analysis algorithms have been developed, only a few of them are specifically focused on gene silencing in plants. Furthermore, current algorithms lack a comprehensive consideration of siRNA specificity, efficacy, and nontoxicity in siRNA design, mainly due to lack of integration of all known rules that govern different steps in the RNAi pathway. In this review, we first describe popular RNAi methods that have been used for gene silencing in plants and their serious limitations regarding gene-silencing potency and specificity. We then present novel, rationale-based strategies in combination with computational and experimental approaches to induce potent, specific, and nontoxic gene silencing in plants.

Boolean genetic network model for the control of C. elegans early embryonic cell cycles

Background

In Caenorhabditis elegans early embryo, cell cycles only have two phases: DNA synthesis and mitosis, which are different from the typical 4-phase cell cycle. Modeling this cell-cycle process into network can fill up the gap in C. elegans cell-cycle study and provide a thorough understanding on the cell-cycle regulations and progressions at the network level.

Methods

In this paper, C. elegans early embryonic cell-cycle network has been constructed based on the knowledge of key regulators and their interactions from literature studies. A discrete dynamical Boolean model has been applied in computer simulations to study dynamical properties of this network. The cell-cycle network is compared with random networks and tested under several perturbations to analyze its robustness. To investigate whether our proposed network could explain biological experiment results, we have also compared the network simulation results with gene knock down experiment data.

Results

With the Boolean model, this study showed that the cell-cycle network was stable with a set of attractors (fixed points). A biological pathway was observed in the simulation, which corresponded to a whole cell-cycle progression. The C. elegans network was significantly robust when compared with random networks of the same size because there were less attractors and larger basins than random networks. Moreover, the network was also robust under perturbations with no significant change of the basin size. In addition, the smaller number of attractors and the shorter biological pathway from gene knock down network simulation interpreted the shorter cell-cycle lengths in mutant from the RNAi gene knock down experiment data. Hence, we demonstrated that the results in network simulation could be verified by the RNAi gene knock down experiment data.

Conclusions

A C. elegans early embryonic cell cycles network was constructed and its properties were analyzed and compared with those of random networks. Computer simulation results provided biologically meaningful interpretations of RNAi gene knock down experiment data.

Frizzled 2 and frizzled 7 function redundantly in convergent extension and closure of the ventricular septum and palate: evidence for a network of interacting genes

Frizzled (Fz) 2 and Fz7, together with Fz1, form a distinct subfamily within the Frizzled family of Wnt receptors. Using targeted gene deletion, we show that: Fz7(-/-) mice exhibit tail truncation and kinking with 100% penetrance and ventricular septal defects (VSDs) with ~15% penetrance; Fz2(+/-);Fz7(-/-) mice exhibit VSDs with ~50% penetrance and cleft palate with less than 10% penetrance; and Fz2(-/-);Fz7(-/-) mice exhibit convergent extension defects and mid-gestational lethality with 100% penetrance. When Fz2 and/or Fz7 mutations are combined with mutations in Vangl2, Dvl3, Wnt3a, Wnt5a or Wnt11, an increased frequency of VSDs is observed with Dvl3, Wnt3a and Wnt11; an increased frequency of palate closure defects is observed with Vangl2; and early lethality and enhanced tail shortening are observed with Wnt5a. To assess the signaling pathways that underlie these and other Frizzled-mediated genetic interactions, we used transfected mammalian cells to analyze (1) canonical Wnt signaling induced by all pairwise combinations of the ten mouse Frizzleds and the 19 mouse Wnts and (2) localization of each Frizzled at cell-cell junctional complexes formed by mouse Celsr1, a likely indicator of competence for planar cell polarity signaling. These in vitro experiments indicate that Fz2 and Fz7 are competent to signal via the canonical pathway. Taken together, the data suggest that genetic interactions between Fz2, Fz7 and Vangl2, Dvl3 and Wnt genes reflect interactions among different signaling pathways in developmental processes that are highly sensitive to perturbations in Frizzled signaling.