Caenorhabditis elegans lin-35/Rb, efl-1/E2F and other synthetic multivulva genes negatively regulate the anaphase-promoting complex gene mat-3/APC8

Retinoblastoma (Rb)/E2F complexes repress expression of many genes important for G(1)-to-S transition, but also appear to regulate gene expression at other stages of the cell cycle. In C. elegans, lin-35/Rb and other synthetic Multivulva (SynMuv) group B genes function redundantly with other sets of genes to regulate G(1)/S progression, vulval and pharyngeal differentiation, and other unknown processes required for viability. Here we show that lin-35/Rb, efl-1/E2F, and other SynMuv B genes negatively regulate a component of the anaphase-promoting complex or cyclosome (APC/C). The APC/C is a multisubunit complex that promotes metaphase-to-anaphase progression and G(1) arrest by targeting different substrates for ubiquitination and proteasome-mediated destruction. The C. elegans APC/C gene mat-3/APC8 has been defined by temperature-sensitive embryonic lethal alleles that strongly affect germline meiosis and mitosis but only weakly affect somatic development. We describe severe nonconditional mat-3 alleles and a hypomorphic viable allele (ku233), all of which affect postembryonic cell divisions including those of the vulval lineage. The ku233 lesion is located outside of the mat-3 coding region and reduces mat-3 mRNA expression. Loss-of-function alleles of lin-35/Rb and other SynMuv B genes suppress mat-3(ku233) defects by restoring mat-3 mRNA to wild-type levels. Therefore, Rb/E2F complexes appear to repress mat-3 expression.     

C. elegans class B synthetic multivulva genes act in G(1) regulation

The single C. elegans member of the retinoblastoma gene family, lin-35 Rb, was originally identified as a synthetic Multivulva (synMuv) gene [1]. These genes form two redundant classes, A and B, that repress ectopic vulval cell fate induction. Recently, we demonstrated that lin-35 Rb also acts as a negative regulator of G(1) progression and likely is the major target of cyd-1 Cyclin D and cdk-4 CDK4/6. Here, we describe G(1) control functions for several other class B synMuv genes. We found that efl-1 E2F negatively regulates cell cycle entry, while dpl-1 DP appeared to act both as a positive and negative regulator. In addition, we identified a negative G(1) regulatory function for lin-9 ALY, as well as lin-15B and lin-36, which encode novel proteins. Inactivation of lin-35 Rb, efl-1, or lin-36 allowed S phase entry in the absence of cyd-1/cdk-4 and increased ectopic cell division when combined with cki-1 Cip/Kip RNAi. These data are consistent with lin-35 Rb, efl-1, and lin-36 acting in a common pathway or complex that negatively regulates G(1) progression. In contrast, lin-15B appeared to act in parallel to lin-35. Our results demonstrate the potential for genetic identification of novel G(1) regulators in C. elegans.     

A Factor Graph Nested Effects Model To Identify Networks from Genetic Perturbations

Complex phenotypes such as the transformation of a normal population of cells into cancerous tissue result from a series of molecular triggers gone awry. We describe a method that searches for a genetic network consistent with expression changes observed under the knock-down of a set of genes that share a common role in the cell, such as a disease phenotype. The method extends the Nested Effects Model of Markowetz et al. (2005) by using a probabilistic factor graph to search for a network representing interactions among these silenced genes. The method also expands the network by attaching new genes at specific downstream points, providing candidates for subsequent perturbations to further characterize the pathway. We investigated an extension provided by the factor graph approach in which the model distinguishes between inhibitory and stimulatory interactions. We found that the extension yielded significant improvements in recovering the structure of simulated and Saccharomyces cerevisae networks. We applied the approach to discover a signaling network among genes involved in a human colon cancer cell invasiveness pathway. The method predicts several genes with new roles in the invasiveness process. We knocked down two genes identified by our approach and found that both knock-downs produce loss of invasive potential in a colon cancer cell line. Nested effects models may be a powerful tool for inferring regulatory connections and genes that operate in normal and disease-related processes.     

Integrative Genomics Reveals Novel Molecular Pathways and Gene Networks for Coronary Artery Disease

The majority of the heritability of coronary artery disease (CAD) remains unexplained, despite recent successes of genome-wide association studies (GWAS) in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls) with 1) genetics of gene expression studies of CAD-relevant tissues in humans, 2) metabolic and signaling pathways from public databases, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1) and peptidylprolyl isomerase I (PPIL1), which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions.     

The coordinate regulation of pharyngeal development in C. elegans by lin-35/Rb, pha-1, and ubc-18

Organ development is a complex process involving the coordination of cell proliferation, differentiation, and morphogenetic events. Using a screen to identify genes that function coordinately with lin-35/Rb during animal development, we have isolated a weak loss-of-function (LOF) mutation in pha-1. lin-35; pha-1 double mutants are defective at an early step in pharyngeal morphogenesis leading to an abnormal pharyngeal architecture. pha-1 is also synthetically lethal with other class B synthetic multivulval (SynMuv) genes including the C. elegans E2F homolog, efl-1. Reporter analyses indicate that pha-1 is broadly expressed during embryonic development and that its functions reside in the cytoplasm. We also provide genetic and phenotypic evidence to support the model that PHA-1, a novel protein, and UBC-18, a ubiquitin-conjugating enzyme that we have previously shown to function with lin-35 during pharyngeal development, act in parallel pathways to regulate the activity of a common cellular target.     

Multiple genetic interaction experiments provide complementary information useful for gene function prediction

Genetic interactions help map biological processes and their functional relationships. A genetic interaction is defined as a deviation from the expected phenotype when combining multiple genetic mutations. In Saccharomyces cerevisiae, most genetic interactions are measured under a single phenotype - growth rate in standard laboratory conditions. Recently genetic interactions have been collected under different phenotypic readouts and experimental conditions. How different are these networks and what can we learn from their differences? We conducted a systematic analysis of quantitative genetic interaction networks in yeast performed under different experimental conditions. We find that networks obtained using different phenotypic readouts, in different conditions and from different laboratories overlap less than expected and provide significant unique information. To exploit this information, we develop a novel method to combine individual genetic interaction data sets and show that the resulting network improves gene function prediction performance, demonstrating that individual networks provide complementary information. Our results support the notion that using diverse phenotypic readouts and experimental conditions will substantially increase the amount of gene function information produced by genetic interaction screens.     

Function approximation approach to the inference of reduced NGnet models of genetic networks

Background  

The inference of a genetic network is a problem in which mutual interactions among genes are deduced using time-series of gene expression patterns. While a number of models have been proposed to describe genetic regulatory networks, this study focuses on a set of differential equations since it has the ability to model dynamic behavior of gene expression. When we use a set of differential equations to describe genetic networks, the inference problem can be defined as a function approximation problem. On the basis of this problem definition, we propose in this study a new method to infer reduced NGnet models of genetic networks.     

Disease-driven detection of differential inherited SNP modules from SNP network

Detection of the synergetic effects between variants, such as single-nucleotide polymorphisms (SNPs), is crucial for understanding the genetic characters of complex diseases. Here, we proposed a two-step approach to detect differentially inherited SNP modules (synergetic SNP units) from a SNP network. First, SNP-SNP interactions are identified based on prior biological knowledge, such as their adjacency on the chromosome or degree of relatedness between the functional relationships of their genes. These interactions form SNP networks. Second, disease-risk SNP modules (or sub-networks) are prioritised by their differentially inherited properties in IBD (Identity by Descent) profiles of affected and unaffected sibpairs. The search process is driven by the disease information and follows the structure of a SNP network. Simulation studies have indicated that this approach achieves high accuracy and a low false-positive rate in the identification of known disease-susceptible SNPs. Applying this method to an alcoholism dataset, we found that flexible patterns of susceptible SNP combinations do play a role in complex diseases, and some known genes were detected through these risk SNP modules. One example is GRM7, a known alcoholism gene successfully detected by a SNP module comprised of two SNPs, but neither of the two SNPs was significantly associated with the disease in single-locus analysis. These identified genes are also enriched in some pathways associated with alcoholism, including the calcium signalling pathway, axon guidance and neuroactive ligand-receptor interaction. The integration of network biology and genetic analysis provides putative functional bridges between genetic variants and candidate genes or pathways, thereby providing new insight into the aetiology of complex diseases.     

Tracking of time-varying genomic regulatory networks with a LASSO-Kalman smoother

It is widely accepted that cellular requirements and environmental conditions dictate the architecture of genetic regulatory networks. Nonetheless, the status quo in regulatory network modeling and analysis assumes an invariant network topology over time. In this paper, we refocus on a dynamic perspective of genetic networks, one that can uncover substantial topological changes in network structure during biological processes such as developmental growth. We propose a novel outlook on the inference of time-varying genetic networks, from a limited number of noisy observations, by formulating the network estimation as a target tracking problem. We overcome the limited number of observations (small n large p problem) by performing tracking in a compressed domain. Assuming linear dynamics, we derive the LASSO-Kalman smoother, which recursively computes the minimum mean-square sparse estimate of the network connectivity at each time point. The LASSO operator, motivated by the sparsity of the genetic regulatory networks, allows simultaneous signal recovery and compression, thereby reducing the amount of required observations. The smoothing improves the estimation by incorporating all observations. We track the time-varying networks during the life cycle of the Drosophila melanogaster. The recovered networks show that few genes are permanent, whereas most are transient, acting only during specific developmental phases of the organism.