Interactome and Gene Ontology provide congruent yet subtly different views of a eukaryotic cell

Estrogen-responsive gene networks in the teleost liver: What are the key molecular indicators?

An overarching goal of environmental genomics is to leverage sensitive suites of markers that are robust and reliable to assess biological responses in a range of species inhabiting variable environments. The objective of this study was to identify core groups of transcripts and molecular signaling pathways that respond to 17alpha-ethylinestadiol (EE2), a ubiquitous estrogenic contaminant, using transcriptome datasets generated from six independent laboratories. We sought to determine which biomarkers and gene networks were those most robust and reliably detected in multiple laboratories. Six laboratories conducted microarray analysis in pieces of the same liver from male fathead minnows exposed to ∼15ng/L EE2 for 96h. There were common transcriptional networks identified in every dataset. These included down-regulation of gene networks associated with blood clotting, complement activation, triglyceride storage, and xenobiotic metabolism. Noteworthy was that more than ∼85% of the gene networks were suppressed by EE2. Leveraging both these data and those mined from the Comparative Toxicogenomics Database (CTD), we narrowed in on an EE2-responsive transcriptional network. All transcripts in this network responded ∼±5-fold or more to EE2, increasing reliability of detection. This network included estrogen receptor alpha, transferrin, myeloid cell leukemia 1, insulin like growth factor 1, insulin like growth factor binding protein 2, and methionine adenosyltransferase 2A. This estrogen-responsive interactome has the advantage over single markers (e.g. vitellogenin) in that these entities are directly connected to each other based upon evidence of expression regulation and protein binding. Thus, it represents an interacting functional suite of estrogenic markers. Vitellogenin, the gold standard for estrogenic exposures, can show high individual variability in its response to estrogens, and the use of a multi-gene approach for estrogenic chemicals is expected to improve sensitivity. In our case, the coefficient of variation was significantly lowered by the gene network (∼67%) compared to Vtg alone, supporting the use of this transcriptional network as a sensitive alternative for detecting estrogenic effluents and chemicals. We propose that screening chemicals for estrogenicity using interacting genes within a defined expression network will improve sensitivity, accuracy, and reduce the number of animals required for endocrine disruption assessments.

Network analysis reveals stage-specific changes in zebrafish embryo development using time course whole transcriptome profiling and prior biological knowledge

BACKGROUND

Molecular networks act as the backbone of molecular activities within cells, offering a unique opportunity to better understand the mechanism of diseases. While network data usually constitute only static network maps, integrating them with time course gene expression information can provide clues to the dynamic features of these networks and unravel the mechanistic driver genes characterizing cellular responses. Time course gene expression data allow us to broadly "watch" the dynamics of the system. However, one challenge in the analysis of such data is to establish and characterize the interplay among genes that are altered at different time points in the context of a biological process or functional category. Integrative analysis of these data sources will lead us a more complete understanding of how biological entities (e.g., genes and proteins) coordinately perform their biological functions in biological systems.

RESULTS

In this paper, we introduced a novel network-based approach to extract functional knowledge from time-dependent biological processes at a system level using time course mRNA sequencing data in zebrafish embryo development. The proposed method was applied to investigate 1α, 25(OH)2D3-altered mechanisms in zebrafish embryo development. We applied the proposed method to a public zebrafish time course mRNA-Seq dataset, containing two different treatments along four time points. We constructed networks between gene ontology biological process categories, which were enriched in differential expressed genes between consecutive time points and different conditions. The temporal propagation of 1α, 25-Dihydroxyvitamin D3-altered transcriptional changes started from a few genes that were altered initially at earlier stage, to large groups of biological coherent genes at later stages. The most notable biological processes included neuronal and retinal development and generalized stress response. In addition, we also investigated the relationship among biological processes enriched in co-expressed genes under different conditions. The enriched biological processes include translation elongation, nucleosome assembly, and retina development. These network dynamics provide new insights into the impact of 1α, 25-Dihydroxyvitamin D3 treatment in bone and cartilage development.

CONCLUSION

We developed a network-based approach to analyzing the DEGs at different time points by integrating molecular interactions and gene ontology information. These results demonstrate that the proposed approach can provide insight on the molecular mechanisms taking place in vertebrate embryo development upon treatment with 1α, 25(OH)2D3. Our approach enables the monitoring of biological processes that can serve as a basis for generating new testable hypotheses. Such network-based integration approach can be easily extended to any temporal- or condition-dependent genomic data analyses.

Deciphering network community structure by surprise

The analysis of complex networks permeates all sciences, from biology to sociology. A fundamental, unsolved problem is how to characterize the community structure of a network. Here, using both standard and novel benchmarks, we show that maximization of a simple global parameter, which we call Surprise (S), leads to a very efficient characterization of the community structure of complex synthetic networks. Particularly, S qualitatively outperforms the most commonly used criterion to define communities, Newman and Girvan's modularity (Q). Applying S maximization to real networks often provides natural, well-supported partitions, but also sometimes counterintuitive solutions that expose the limitations of our previous knowledge. These results indicate that it is possible to define an effective global criterion for community structure and open new routes for the understanding of complex networks.

Jerarca: efficient analysis of complex networks using hierarchical clustering

Background

How to extract useful information from complex biological networks is a major goal in many fields, especially in genomics and proteomics. We have shown in several works that iterative hierarchical clustering, as implemented in the UVCluster program, is a powerful tool to analyze many of those networks. However, the amount of computation time required to perform UVCluster analyses imposed significant limitations to its use.

Methodology/Principal Findings

We describe the suite Jerarca, designed to efficiently convert networks of interacting units into dendrograms by means of iterative hierarchical clustering. Jerarca is divided into three main sections. First, weighted distances among units are computed using up to three different approaches: a more efficient version of UVCluster and two new, related algorithms called RCluster and SCluster. Second, Jerarca builds dendrograms based on those distances, using well-known phylogenetic algorithms, such as UPGMA or Neighbor-Joining. Finally, Jerarca provides optimal partitions of the trees using statistical criteria based on the distribution of intra- and intercluster connections. Outputs compatible with the phylogenetic software MEGA and the Cytoscape package are generated, allowing the results to be easily visualized.

Conclusions/Significance

The four main advantages of Jerarca in respect to UVCluster are: 1) Improved speed of a novel UVCluster algorithm; 2) Additional, alternative strategies to perform iterative hierarchical clustering; 3) Automatic evaluation of the hierarchical trees to obtain optimal partitions; and, 4) Outputs compatible with popular software such as MEGA and Cytoscape.