Abstract
Staphylococcus aureus is a gram-positive bacterium that causes a wide range of infections. Recently, the spread of methicillin-resistant S. aureus (MRSA) strains has seriously reduced antibiotic treatment options. Anti-virulence strategies, the objective of which is to target the virulence instead of the viability of the pathogen, have become widely accepted as a means of avoiding the emergence of new antibiotic-resistant strains. To increase the number of anti-virulence therapeutic options, it is necessary to identify as many novel virulence-associated genes as possible in MRSA. Co-functional networks have proved useful for mapping gene-to-phenotype associations in various organisms. Herein, we present StaphNet (www.inetbio.org/staphnet), a genome-scale co-functional network for an MRSA strain, S. aureus subsp. USA300_FPR3757. StaphNet, which was constructed by the integration of seven distinct types of genomics data within a Bayesian statistics framework, covers approximately 94% of the coding genome with a high degree of accuracy. We implemented a companion web server for network-based gene prioritization of the phenotypes of 31 different S. aureus strains. We demonstrated that StaphNet can effectively identify genes for virulence-associated phenotypes in MRSA. These results suggest that StaphNet can facilitate target discovery for the development of anti-virulence drugs to treat MRSA infection.
Highlights
Staphylococcus aureus is an opportunistic human pathogen that can cause disorders ranging from minor skin infections to life-threatening invasive diseases[1,2,3,4]
StaphNet effectively reconstructed various pathways involved in S. aureus virulence, and experimentally validated novel genes predicted for hemolysis and biofilm formation
For all the virulence-associated phenotypes in our analysis, we found that the genes for each phenotype were highly interconnected in StaphNet (P-value < 1 × 10−4 for all tested phenotypes except genome-wide association study (GWAS) toxicity, which had a P-value = 0.0067, according to a permutation test of 106 randomized samples) (Fig. 2A)
Summary
Staphylococcus aureus is an opportunistic human pathogen that can cause disorders ranging from minor skin infections to life-threatening invasive diseases[1,2,3,4]. USA300_FPR3757 has approximately 2,700 coding genes, 78 of which are responsible for virulence-associated phenotypes. This suggests that many more virulence genes remain to be discovered, and the efficient identification of such genes could facilitate the development of anti-virulence drugs. Prioritization for disease research has increased in popularity[13], and co-functional networks of various organisms including hosts and pathogens can be constructed using a Bayesian statistics approach[14]. We previously constructed a co-functional network for the opportunistic fungal pathogen Cryptococcus neoformans, and demonstrated its usefulness for identifying novel genes involved in fungal pathogenicity and drug resistance[15]. To apply a similar network-based approach to identifying virulence genes in S. aureus, we constructed StaphNet, a genome-scale co-functional network for S. aureus subsp. StaphNet effectively reconstructed various pathways involved in S. aureus virulence, and experimentally validated novel genes predicted for hemolysis and biofilm formation
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