Multi-comparative systems biology analysis reveals time-course biosignatures of in vivo bovine pathway responses to B.melitensis, S.enterica Typhimurium and M.avium paratuberculosis
ABSTRACT To decipher the complexity and improve the understanding of host-pathogen interactions, biologists must adopt new system level approaches in which the hierarchy of biological interactions and dynamics can be studied. This paper presents the application of systems biology for the cross-comparative analysis and interactome modeling of three different infectious agents, leading to the identification of novel, unique and common molecular host responses (biosignatures).
A computational systems biology method was utilized to create interactome models of the host responses to Brucella melitensis (BMEL), Salmonella enterica Typhimurium (STM) and Mycobacterium avium paratuberculosis (MAP). A bovine ligated ileal loop biological model was employed to capture the host gene expression response at four time points post infection. New methods based on Dynamic Bayesian Network (DBN) machine learning were employed to conduct a systematic comparative analysis of pathway and Gene Ontology category perturbations.
A cross-comparative assessment of 219 pathways and 1620 gene ontology (GO) categories was performed on each pathogen-host condition. Both unique and common pathway and GO perturbations indicated remarkable temporal differences in pathogen-host response profiles. Highly discriminatory pathways were selected from each pathogen condition to create a common system level interactome model comprised of 622 genes. This model was trained with data from each pathogen condition to capture unique and common gene expression features and relationships leading to the identification of candidate host-pathogen points of interactions and discriminatory biosignatures.
Our results provide deeper understanding of the overall complexity of host defensive and pathogen invasion processes as well as the identification of novel host-pathogen interactions. The application of advanced computational methods for developing interactome models based on DBN has proven to be instrumental in conducting multi-conditional cross-comparative analyses. Further, this approach generates a fully simulateable model with capabilities for predictive analysis as well as for diagnostic pattern recognition. The resulting biosignatures may represent future targets for identification of emerging pathogens as well as for development of antimicrobial drugs, immunotherapeutics, or vaccines for prevention and treatment of diseases caused by known, emerging/re-emerging infectious agents.
SourceAvailable from: Sahand Khakabimamaghani[Show abstract] [Hide abstract]
ABSTRACT: This paper presents GelClust, a new software that is designed for processing gel electrophoresis images and generating the corresponding phylogenetic trees. Unlike the most of commercial and non-commercial related softwares, we found that GelClust is very user-friendly and guides the user from image toward dendrogram through seven simple steps. Furthermore, the software, which is implemented in C# programming language under Windows operating system, is more accurate than similar software regarding image processing and is the only software able to detect and correct gel 'smile' effects completely automatically. These claims are supported with experiments.Computer methods and programs in biomedicine 05/2013; DOI:10.1016/j.cmpb.2013.04.013 · 1.09 Impact Factor
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ABSTRACT: Brucella melitensis causes the most severe and acute symptoms of all Brucella species in human beings and infects hosts primarily through the oral route. The epithelium covering domed villi of jejunal-ileal Peyer's patches is an important site of entry for several pathogens, including Brucella. Here, we use the calf ligated ileal loop model to study temporal in vivo Brucella-infected host molecular and morphological responses. Our results document Brucella bacteremia occurring within 30 min after intraluminal inoculation of the ileum without histopathologic traces of lesions. Based on a system biology Dynamic Bayesian Network modeling approach (DBN) of microarray data, a very early transient perturbation of the host enteric transcriptome was associated with the initial host response to Brucella contact that is rapidly averted allowing invasion and dissemination. A detailed analysis revealed active expression of Syndecan 2, Integrin alpha L and Integrin beta 2 genes, which may favor initial Brucella adhesion. Also, two intestinal barrier-related pathways (Tight Junction and Trefoil Factors Initiated Mucosal Healing) were significantly repressed in the early stage of infection, suggesting subversion of mucosal epithelial barrier function to facilitate Brucella transepithelial migration. Simultaneously, the strong activation of the innate immune response pathways would suggest that the host mounts an appropriate protective immune response; however, the expression of the two key genes that encode innate immunity anti-Brucella cytokines such as TNF-α and IL12p40 were not significantly changed throughout the study. Furthermore, the defective expression of Toll-Like Receptor Signaling pathways may partially explain the lack of proinflammatory cytokine production and consequently the absence of morphologically detectable inflammation at the site of infection. Cumulatively, our results indicate that the in vivo pathogenesis of the early infectious process of Brucella is primarily accomplished by compromising the mucosal immune barrier and subverting critical immune response mechanisms.PLoS ONE 12/2013; 8(12):e81719. DOI:10.1371/journal.pone.0081719 · 3.53 Impact Factor