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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.

Adams LG, Khare S, Lawhon SD, Rossetti CA, Lewin HA, Lipton MS, Turse JE, Wylie DC, Bai Y, Drake KL - BMC Proc (2011)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Veterinary Pathobiology, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX 77843-4467, USA. gadams@cvm.tamu.edu.

ABSTRACT

Background: 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).

Methods: 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.

Results: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus

(a) Gene ontology group scores using DBGGA A few groups are displayed here to illustrate the GO group scoring difference between pathogen conditions. (b) Gene scores for phagocytosis GO group Illustrating the temporal difference of gene expression Bayesian z-score for each pathogen condition.
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Figure 4: (a) Gene ontology group scores using DBGGA A few groups are displayed here to illustrate the GO group scoring difference between pathogen conditions. (b) Gene scores for phagocytosis GO group Illustrating the temporal difference of gene expression Bayesian z-score for each pathogen condition.

Mentions: A DBGGA analysis was also conducted for gene GO categories. For each pathogen condition, 1620 biological process GO categories were scored. Each condition produced its own unique set of highly scored GO functions, but for comparison purposes, we chose a small subset of highly perturbed categories to illustrate the different temporal responses as shown in Figure 4(a). Figure 4(b) illustrates the comparative analysis of gene scores for the phagocytosis GO Group.


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.

Adams LG, Khare S, Lawhon SD, Rossetti CA, Lewin HA, Lipton MS, Turse JE, Wylie DC, Bai Y, Drake KL - BMC Proc (2011)

(a) Gene ontology group scores using DBGGA A few groups are displayed here to illustrate the GO group scoring difference between pathogen conditions. (b) Gene scores for phagocytosis GO group Illustrating the temporal difference of gene expression Bayesian z-score for each pathogen condition.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3108236&req=5

Figure 4: (a) Gene ontology group scores using DBGGA A few groups are displayed here to illustrate the GO group scoring difference between pathogen conditions. (b) Gene scores for phagocytosis GO group Illustrating the temporal difference of gene expression Bayesian z-score for each pathogen condition.
Mentions: A DBGGA analysis was also conducted for gene GO categories. For each pathogen condition, 1620 biological process GO categories were scored. Each condition produced its own unique set of highly scored GO functions, but for comparison purposes, we chose a small subset of highly perturbed categories to illustrate the different temporal responses as shown in Figure 4(a). Figure 4(b) illustrates the comparative analysis of gene scores for the phagocytosis GO Group.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Veterinary Pathobiology, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX 77843-4467, USA. gadams@cvm.tamu.edu.

ABSTRACT

Background: 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).

Methods: 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.

Results: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus