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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

MAPK pathway network model for bovine host infected with Salmonella enterica Typhimurium (STM). The model is trained and then used to score the pathway and individual genes. This figure is the MAPK pathway as a screen capture taken from BioSignatureDS™ user interface. This figure is a snapshot of the perturbed state of the MAPK pathway at 60 minutes post infection for the STM-Host condition. Mechanistic genes are encircled with an orange ring. In the pathway, genes aligned along the green vertical bar are typically receptor/membrane related, and those on the blue bar are nucleus related. Gene nodes with an attached “TF” subscript are transcription factors or transcription factor related. The arcs connecting genes are coded to indicate the correlation between connected genes. Brown arcs indicate positive correlation while turquoise arcs represent negative correlations, and the thickness of the arc indicating the magnitude of correlation. Arcs with an encircled number and arrow correspond to those arcs labeled accordingly in Table 1.
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Figure 3: MAPK pathway network model for bovine host infected with Salmonella enterica Typhimurium (STM). The model is trained and then used to score the pathway and individual genes. This figure is the MAPK pathway as a screen capture taken from BioSignatureDS™ user interface. This figure is a snapshot of the perturbed state of the MAPK pathway at 60 minutes post infection for the STM-Host condition. Mechanistic genes are encircled with an orange ring. In the pathway, genes aligned along the green vertical bar are typically receptor/membrane related, and those on the blue bar are nucleus related. Gene nodes with an attached “TF” subscript are transcription factors or transcription factor related. The arcs connecting genes are coded to indicate the correlation between connected genes. Brown arcs indicate positive correlation while turquoise arcs represent negative correlations, and the thickness of the arc indicating the magnitude of correlation. Arcs with an encircled number and arrow correspond to those arcs labeled accordingly in Table 1.

Mentions: Simply comparing and contrasting the expression patterns of perturbed genes was inadequate for deciphering the MAPK pathway response dynamics. Clearly, the uniqueness of the MAPK pathway responses suggested that very different invasion/evasion mechanisms have evolved for each pathogen. More sophisticated methods are needed to identify potential points of host response disruptions. This is done by interrogating the trained DBN model for the MAPK Pathway for genes that exceed threshold Bayesian z-scores>/2.24/ (“mechanistic genes”) and gene-gene network relationships (arcs). For example, Figure 3 shows the visualization of the MAPK pathway network. The network can be employed to visualize several key features that would otherwise be difficult to discern by looking at spread sheet lists of genes. For example, the state of gene modulation is distinguished by color coded nodes. The state of upstream and downstream genes can be easily identified. Various threshold levels can be entered to identify significantly perturbed genes (annotated with orange circles, Fig 3). The strength of correlation between gene pairs is indicated by the color and thickness of the arcs connecting the genes.


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)

MAPK pathway network model for bovine host infected with Salmonella enterica Typhimurium (STM). The model is trained and then used to score the pathway and individual genes. This figure is the MAPK pathway as a screen capture taken from BioSignatureDS™ user interface. This figure is a snapshot of the perturbed state of the MAPK pathway at 60 minutes post infection for the STM-Host condition. Mechanistic genes are encircled with an orange ring. In the pathway, genes aligned along the green vertical bar are typically receptor/membrane related, and those on the blue bar are nucleus related. Gene nodes with an attached “TF” subscript are transcription factors or transcription factor related. The arcs connecting genes are coded to indicate the correlation between connected genes. Brown arcs indicate positive correlation while turquoise arcs represent negative correlations, and the thickness of the arc indicating the magnitude of correlation. Arcs with an encircled number and arrow correspond to those arcs labeled accordingly in Table 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: MAPK pathway network model for bovine host infected with Salmonella enterica Typhimurium (STM). The model is trained and then used to score the pathway and individual genes. This figure is the MAPK pathway as a screen capture taken from BioSignatureDS™ user interface. This figure is a snapshot of the perturbed state of the MAPK pathway at 60 minutes post infection for the STM-Host condition. Mechanistic genes are encircled with an orange ring. In the pathway, genes aligned along the green vertical bar are typically receptor/membrane related, and those on the blue bar are nucleus related. Gene nodes with an attached “TF” subscript are transcription factors or transcription factor related. The arcs connecting genes are coded to indicate the correlation between connected genes. Brown arcs indicate positive correlation while turquoise arcs represent negative correlations, and the thickness of the arc indicating the magnitude of correlation. Arcs with an encircled number and arrow correspond to those arcs labeled accordingly in Table 1.
Mentions: Simply comparing and contrasting the expression patterns of perturbed genes was inadequate for deciphering the MAPK pathway response dynamics. Clearly, the uniqueness of the MAPK pathway responses suggested that very different invasion/evasion mechanisms have evolved for each pathogen. More sophisticated methods are needed to identify potential points of host response disruptions. This is done by interrogating the trained DBN model for the MAPK Pathway for genes that exceed threshold Bayesian z-scores>/2.24/ (“mechanistic genes”) and gene-gene network relationships (arcs). For example, Figure 3 shows the visualization of the MAPK pathway network. The network can be employed to visualize several key features that would otherwise be difficult to discern by looking at spread sheet lists of genes. For example, the state of gene modulation is distinguished by color coded nodes. The state of upstream and downstream genes can be easily identified. Various threshold levels can be entered to identify significantly perturbed genes (annotated with orange circles, Fig 3). The strength of correlation between gene pairs is indicated by the color and thickness of the arcs connecting the genes.

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