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

Reconstruction of Eriocheir sinensis Protein-Protein Interaction Network Based on DGO-SVM Method

Eriocheir sinensis is an economically important aquatic animal. Its regulatory mechanisms underlying many biological processes are still vague due to the lack of systematic analysis tools. The protein-protein interaction network (PIN) is an important tool for the systematic analysis of regulatory mechanisms. In this work, a novel machine learning method, DGO-SVM, was applied to predict the protein-protein interaction (PPI) in E. sinensis, and its PIN was reconstructed. With the domain, biological process, molecular functions and subcellular locations of proteins as the features, DGO-SVM showed excellent performance in Bombyx mori, humans and five aquatic crustaceans, with 92-96% accuracy. With DGO-SVM, the PIN of E. sinensis was reconstructed, containing 14,703 proteins and 7,243,597 interactions, in which 35,604 interactions were associated with 566 novel proteins mainly involved in the response to exogenous stimuli, cellular macromolecular metabolism and regulation. The DGO-SVM demonstrated that the biological process, molecular functions and subcellular locations of proteins are significant factors for the precise prediction of PPIs. We reconstructed the largest PIN for E. sinensis, which provides a systematic tool for the regulatory mechanism analysis. Furthermore, the novel-protein-related PPIs in the PIN may provide important clues for the mechanism analysis of the underlying specific physiological processes in E. sinensis.

Inferring a spatial code of cell-cell interactions across a whole animal body

Cell-cell interactions shape cellular function and ultimately organismal phenotype. Interacting cells can sense their mutual distance using combinations of ligand-receptor pairs, suggesting the existence of a spatial code, i.e., signals encoding spatial properties of cellular organization. However, this code driving and sustaining the spatial organization of cells remains to be elucidated. Here we present a computational framework to infer the spatial code underlying cell-cell interactions from the transcriptomes of the cell types across the whole body of a multicellular organism. As core of this framework, we introduce our tool cell2cell, which uses the coexpression of ligand-receptor pairs to compute the potential for intercellular interactions, and we test it across the Caenorhabditis elegans' body. Leveraging a 3D atlas of C. elegans' cells, we also implement a genetic algorithm to identify the ligand-receptor pairs most informative of the spatial organization of cells across the whole body. Validating the spatial code extracted with this strategy, the resulting intercellular distances are negatively correlated with the inferred cell-cell interactions. Furthermore, for selected cell-cell and ligand-receptor pairs, we experimentally confirm the communicatory behavior inferred with cell2cell and the genetic algorithm. Thus, our framework helps identify a code that predicts the spatial organization of cells across a whole-animal body.

Reduced Translational Efficiency of Eukaryotic Genes after Duplication Events

Control of gene expression has been found to be predominantly determined at the level of protein translation. However, to date, reduced expression from duplicated genes in eukaryotes for dosage maintenance has only been linked to transcriptional control involving epigenetic mechanisms. Here, we hypothesize that dosage maintenance following gene duplication also involves regulation at the protein level. To test this hypothesis, we compared transcriptome and proteome data of yeast models, Saccharomyces cerevisiae and Schizosaccharomyces pombe, and worm models, Caenorhabditis elegans and Caenorhabditis briggsae, to investigate lineage-specifically duplicated genes. Duplicated genes in both eukaryotic models exhibited a reduced protein-to-mRNA abundance ratio. Moreover, dosage sensitive genes, represented by genes encoding protein complex subunits, reduced their protein-to-mRNA abundance ratios more significantly than the other genes after duplication events. An analysis of ribosome profiling (Ribo-Seq) data further showed that reduced translational efficiency was more prominent for dosage sensitive genes than for the other genes. Meanwhile, no difference in protein degradation rate was associated with duplication events. Translationally repressed duplicated genes were also more likely to be inhibited at the level of transcription. Taken together, these results suggest that translation-mediated dosage control is partially contributed by natural selection and it enhances transcriptional control in maintaining gene dosage after gene duplication events during eukaryotic genome evolution.

Analysis of Protein-Protein Interaction Networks through Computational Approaches

The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein-protein interaction prediction.

Multilevel regulation of muscle-specific transcription factor hlh-1 during Caenorhabditis elegans embryogenesis

hlh-1 is a myogenic transcription factor required for body-wall muscle specification during embryogenesis in Caenorhabditis elegans. Despite its well-known role in muscle specification, comprehensive regulatory control upstream of hlh-1 remains poorly defined. Here, we first established a statistical reference for the spatiotemporal expression of hlh-1 at single-cell resolution up to the second last round of divisions for most of the cell lineages (from 4- to 350-cell stage) using 13 wild-type embryos. We next generated lineal expression of hlh-1 after RNA interference (RNAi) perturbation of 65 genes, which were selected based on their degree of conservation, mutant phenotypes, and known roles in development. We then compared the expression profiles between wild-type and RNAi embryos by clustering according to their lineal expression patterns using mean-shift and density-based clustering algorithms, which not only confirmed the roles of existing genes but also uncovered the potential functions of novel genes in muscle specification at multiple levels, including cellular, lineal, and embryonic levels. By combining the public data on protein-protein interactions, protein-DNA interactions, and genetic interactions with our RNAi data, we inferred regulatory pathways upstream of hlh-1 that function globally or locally. This work not only revealed diverse and multilevel regulatory mechanisms coordinating muscle differentiation during C. elegans embryogenesis but also laid a foundation for further characterizing the regulatory pathways controlling muscle specification at the cellular, lineal (local), or embryonic (global) level.

Fast Maximum Likelihood Estimation via Equilibrium Expectation for Large Network Data

A major line of contemporary research on complex networks is based on the development of statistical models that specify the local motifs associated with macro-structural properties observed in actual networks. This statistical approach becomes increasingly problematic as network size increases. In the context of current research on efficient estimation of models for large network data sets, we propose a fast algorithm for maximum likelihood estimation (MLE) that affords a significant increase in the size of networks amenable to direct empirical analysis. The algorithm we propose in this paper relies on properties of Markov chains at equilibrium, and for this reason it is called equilibrium expectation (EE). We demonstrate the performance of the EE algorithm in the context of exponential random graph models (ERGMs) a family of statistical models commonly used in empirical research based on network data observed at a single period in time. Thus far, the lack of efficient computational strategies has limited the empirical scope of ERGMs to relatively small networks with a few thousand nodes. The approach we propose allows a dramatic increase in the size of networks that may be analyzed using ERGMs. This is illustrated in an analysis of several biological networks and one social network with 104,103 nodes.

Recruitment of histone modifications to assist mRNA dosage maintenance after degeneration of cytosine DNA methylation during animal evolution

Following gene duplication, mRNA expression of the duplicated gene is reduced to maintain mRNA dosage. In mammals, this process is achieved with increased cytosine DNA methylation of the promoters of duplicated genes to suppress transcriptional initiation. However, not all animal species possess a full apparatus for cytosine DNA methylation. For such species, such as the roundworm (Caenorhabditis elegans, "worm" hereafter) or fruit fly (Drosophila melanogaster, "fly" hereafter), it is unclear how reduced expression of duplicated genes has been achieved evolutionarily. Here, we hypothesize that in the absence of a classical cytosine DNA methylation pathway, histone modifications play an increasing role in maintaining mRNA dosage following gene duplication. We initially verified that reduced gene expression of duplicated genes had occurred in the worm, fly, and mouse (Mus musculus). Next, several histone marks, with the capacity to control mRNA abundance in the models studied, were examined. In the worm and fly, but not in the mouse, multiple histone modifications were found to assist mRNA dosage maintenance following gene duplication events and the possible involvement of adenine DNA methylation in this process was excluded. Furthermore, the histone marks and acting regions that mediated the reduction in duplicated gene expression were found to be largely organism specific. Thus, it appears that many of the histone marks that maintain mRNA dosage were independently recruited during the evolution of worms and flies to compensate for the loss of cytosine DNA methylation machinery from their genomes.

Prediction of Protein-Protein Interactions by Evidence Combining Methods

Most cellular functions involve proteins' features based on their physical interactions with other partner proteins. Sketching a map of protein-protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.