SalmoNet, an integrated network of ten Salmonella enterica strains reveals common and distinct pathways to host adaptation

Salmonella enterica is a prominent bacterial pathogen with implications on human and animal health. Salmonella serovars could be classified as gastro-intestinal or extra-intestinal. Genome-wide comparisons revealed that extra-intestinal strains are closer relatives of gastro-intestinal strains than to each other indicating a parallel evolution of this trait. Given the complexity of the differences, a systems-level comparison could reveal key mechanisms enabling extra-intestinal serovars to cause systemic infections. Accordingly, in this work, we introduce a unique resource, SalmoNet, which combines manual curation, high-throughput data and computational predictions to provide an integrated network for Salmonella at the metabolic, transcriptional regulatory and protein-protein interaction levels. SalmoNet provides the networks separately for five gastro-intestinal and five extra-intestinal strains. As a multi-layered, multi-strain database containing experimental data, SalmoNet is the first dedicated network resource for Salmonella. It comprehensively contains interactions between proteins encoded in Salmonella pathogenicity islands, as well as regulatory mechanisms of metabolic processes with the option to zoom-in and analyze the interactions at specific loci in more detail. Application of SalmoNet is not limited to strain comparisons as it also provides a Salmonella resource for biochemical network modeling, host-pathogen interaction studies, drug discovery, experimental validation of novel interactions, uncovering new pathological mechanisms from emergent properties and epidemiological studies. SalmoNet is available at http://salmonet.org

Network Biology Approaches to Identify Molecular and Systems-Level Differences Between Salmonella Pathovars

The field of systems biology endeavors to map, study, and simulate cellular systems and their underlying mechanisms. The internal mechanisms of biological systems can be represented with networks comprising nodes and edges. Nodes denote the constituents of the biological system whereas edges indicate the relationships among them. Likewise, every layer of cellular organization can be represented by networks. Multilayered networks capture interactions between various network types, such as transcriptional regulatory networks, protein–protein interaction networks, and metabolic networks from the same biological system. This property makes multilayered networks representative of the system while its internal mechanisms are investigated. However, there are not many multilayered networks containing integrated data for nonmodel organisms including the bacterial pathogens Salmonella. Here, we outline the steps to create such an integrated network database, through the example of SalmoNet, the first integrated multilayered data resource for multiple strains belonging to distinct Salmonella serovars