Multi-omic network signatures of disease

Abstract

To better understand dynamic disease processes, integrated multi-omic methods are needed, yet comparing different types of omic data remains difficult. Integrative solutions benefit experimenters by eliminating potential biases that come with single omic analysis. We have developed the methods needed to explore whether a relationship exists between co-expression network models built from transcriptomic and proteomic data types, and whether this relationship can be used to improve the disease signature discovery process. A naïve, correlation based method is utilized for comparison. Using publicly available infectious disease time series data, we analyzed the related co-expression structure of the transcriptome and proteome in response to SARS-CoV infection in mice. Transcript and peptide expression data was filtered using quality scores and subset by taking the intersection on mapped Entrez IDs. Using this data set, independent co-expression networks were built. The networks were integrated by constructing a bipartite module graph based on module member overlap, module summary correlation, and correlation to phenotypes of interest. Compared to the module level results, the naïve approach is hindered by a lack of correlation across data types, less significant enrichment results, and little functional overlap across data types. Our module graph approach avoids these problems, resulting in an integrated omic signature of disease progression, which allows prioritization across data types for down-stream experiment planning. Integrated modules exhibited related functional enrichments and could suggest novel interactions in response to infection. These disease and platform-independent methods can be used to realize the full potential of multi-omic network signatures. The data (experiment SM001) are publically available through the NIAID Systems Virology (https://www.systemsvirology.org) and PNNL (http://omics.pnl.gov) web portals. Phenotype data is found in the supplementary information. The ProCoNA package is available as part of Bioconductor 2.13.

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

“…WGCNA is one of the most used approaches to build and to analyze gene co-expression networks [59], and it has been recently adapted for proteomics use also [14][15][16][17][18][19][20]. It provides a weighted network model by converting a co-expression measure to a connection weight.…”

“…In this scenario, a common procedure to evaluate gene expression levels is based on statistics that measure the dependence between variables, and the resulting co-expression networks are used to identify genes functionally related or controlled by the same transcriptional regulatory program [11][12][13]. Unlike gene expression levels, the use of proteomic data to infer co-expression networks has been explored through few studies [14][15][16][17][18][19][20]. Similar to PPI and gene co-expression networks, these networks have been evaluated at topological level in terms of edge rearrangement, as well as of modules associated with common cellular functions.…”

Taking Bioinformatics to Systems Medicine

“…WGCNA can also be used when multiple omics data types are available. One example of such an approach involved the integration of transcriptomic and proteomic data from a study investigating the response to SARS-CoV infection in mice [ 111 ]. In this study WGCNA-based gene and protein co-expression modules were constructed and…”

Development of Broad-Spectrum Antibiofilm Drugs: Strategies and Challenges

“…This could be achieved by undertaking an integrated multiomics analysis of the global transcriptomic and proteomic data involving biofilm stage of different bacteria (Figure 1). This integrated approach will allow us to identify conserved genes and pathways specific to the biofilm stage of a particular bacterial family or genera [78].…”