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1.
Figure 5

Figure 5. From: Enhancing the Role of Veterinary Vaccines Reducing Zoonotic Diseases of Humans: Linking Systems Biology with Vaccine Development.

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 5 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 spreadsheet 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 modified to identify significantly perturbed genes (annotated with orange circles, Figure 5). The strength of correlation between gene pairs is indicated by the color and thickness of the arcs connecting the genes.

L. Garry Adams, et al. Vaccine. ;29(41):7197-7206.
2.
Figure 2

Figure 2. From: Enhancing the Role of Veterinary Vaccines Reducing Zoonotic Diseases of Humans: Linking Systems Biology with Vaccine Development.

A DBGGA analysis was 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 2(a). Figure 2 (b) illustrates the comparative analysis of gene scores for just the phagocytosis GO term category. The gene ontology group scores show a very diverse pattern over time. As can be seen, the induction or suppression of groups of genes allows us to identify specific biological process groups that define the pathogenicity biosignatures of each pathogen. Individual gene patterns within the groups can be further compared as show in Figure 2(b). For example the gene encoding ADORA1 (adenosine A1 receptor) is up modulated in BMEL for at all four time points, but is not significantly expressed in MAP or STM. Comparative pathogenicity can provide important insights into the mechanistic differences and guiding the research biologists to identify unique and common mechanisms that may be new targets for immunotherapeutic drugs or indicators of immunogenicity of novel vaccine candidates. Such comparative modeling could also be utilized to compare the effectiveness of vaccine candidates across multiple species.

L. Garry Adams, et al. Vaccine. ;29(41):7197-7206.
3.
Figure 3

Figure 3. From: Enhancing the Role of Veterinary Vaccines Reducing Zoonotic Diseases of Humans: Linking Systems Biology with Vaccine Development.

Of the 219 signaling/metabolic pathways scored, we focused on a subset of immune response related pathways as listed in Figure 3. This figure shows a heat map comparison of pathway Bayesian z-scores between pathogen conditions over time post infection. There were considerable differences between the host response profiles. MAP had strong early (30 minute) induction of the majority of its pathways and appeared to reverse to a more suppressive state by 240 minutes. STM's early response indicated mild perturbations at 30 minutes that increased over time until several pathways were strongly induced by 240 minutes. BMEL was more strongly suppressive for the majority of pathways over time. At early times (30, 60 minutes) there were a few commonly induced pathways: Antigen Processing and Presentation, B Cell Receptor Signaling, Fc epsilon RI Signaling, Hedgehog Signaling, and Natural Killer Cell Mediated Cytotoxicity. In contrast, only ECM-receptor Interaction, Apoptotic Signaling and Apoptotic DNA Fragmentation had similar suppressions at 30 and 60 minutes. Interestingly, there was no single pathway at later times (120, 240 minutes) with similar perturbed states, implying that the host defenses have divergent biosignatures against the various virulent mechanisms presented by the pathogens.

L. Garry Adams, et al. Vaccine. ;29(41):7197-7206.
4.
Figure 4

Figure 4. From: Enhancing the Role of Veterinary Vaccines Reducing Zoonotic Diseases of Humans: Linking Systems Biology with Vaccine Development.

The MAPK pathway was selected as a potential candidate gene for more detailed discussion with regard to gene perturbations, mechanistic interpretations, and gene knockout simulation. Figure 4 is a heat map of significantly perturbed genes for the MAPK pathway by pathogen condition. In this figure, the genes are sorted in order of highest up modulation to lowest down modulation, and for a gene to be included in this figure, a Bayesian z-score>|2.24| at any one time point was required. The Bayesian z-score > |2.24| reflects 99% confidence in the data. It is easy to observe that the perturbed genes and their expression patterns are quite different between conditions. Surprisingly, of the 171 measured genes in this pathway, only two genes in Figure 4 were found to be commonly perturbed across all three pathogen conditions: 1) IL1A, which encodes interleukin 1 protein involved in various immune responses, inflammatory processes, and hematopoiesis; and 2) RASGRP1, which encodes a protein characterized by the presence of a Ras superfamily guanine nucleotide exchange factor (GEF) domain that activates the Erk/MAP kinase cascade and regulates T-cell and B-cell development, homeostasis and differentiation. The perturbation of IL1A and RASGRP1 is consistent with genes involved in immune response, but the expression patterns for these two genes vary significantly between pathogens.

L. Garry Adams, et al. Vaccine. ;29(41):7197-7206.
5.
Figure 7

Figure 7. From: Enhancing the Role of Veterinary Vaccines Reducing Zoonotic Diseases of Humans: Linking Systems Biology with Vaccine Development.

To gain better insight regarding the influence of MAPK1 on other genes, we employed the interactome model to simulate the MAPK1 knockdown in both the MAPK Signaling and Regulation of Actin Cytoskeleton models. The simulation identified a set of genes that were heavily influenced by MAPK1 as shown in the gene expression plots of Figure 7 in which the simulation data is plotted in comparison to the actual data used to train the interactome model. Interestingly, the simulation identified genes that were both in correlation with the reduced MAPK1 knockdown expression as well as several that had an increase in expression (anti-correlated). Either set of correlated or anti-correlated genes could be considered important contributors to the observed internalized reduction of BMEL in the HeLa host cells. For example, a correlated gene, SRF (serum response factor), is known to be involved in actin filament organization, regulation of cell adhesion, negative regulation of cell migration, negative regulation of cell proliferation, and regulation of transcription. Another correlated gene, MAPT (Microtubule-associated protein tau) is associated with regulation of microtubule depolymerization. The anti-correlated gene, YWHAZ (14-3-3 protein zeta/delta), is known to be involved in the biological processes of anti-apoptosis, histamine secretion by mast cell and signal transduction. The anti-correlated gene RHOA (Transforming protein RhoA) is associated with actin cytoskeleton organization, regulation of I-kappaB kinase/NF-kappaB cascade, and cell adhesion. This type of analysis is an integral part of the “incremental systems biology interactome modeling” process and introduced here as preliminary illustration as to how simulation/inferencing of the interactome model can be employed to guide next phases of in vitro and in vivo experimentation.

L. Garry Adams, et al. Vaccine. ;29(41):7197-7206.
6.
Figure 6

Figure 6. From: Enhancing the Role of Veterinary Vaccines Reducing Zoonotic Diseases of Humans: Linking Systems Biology with Vaccine Development.

More detailed interrogation of the BMEL-Host model found that the gene, MAPK1, was significantly upregulated in the BMEL condition while not in MAP or STM. Further, it was observed that MAPK1 had a number of interactions with other genes within the MAPK Signaling pathway model that showed either very strong anti-correlated relationship such as the MAPK1->YWHAZ or strongly correlated relationship such as MAPK1->MAPT, while in MAP and STM host interactome models, the influence of MAPK1 was negligible. Specifically for the BMEL condition, we found that MAPK1 has a series of direct relationships with highly significant correlations as listed in Table 2. This could imply that MAPK1 has a unique role in BMEL pathogenesis during early host cell invasion. Mitogen-activated protein kinase 1 (MAPK1 or ERK 1/2) controls many biological functions (Johnson and Lapadat 2002). The MAPK signaling cascade, represented by 3 well characterized subfamilies of MAPKs (ERK1/2, JNK and p38), has been implicated in bacterial internalization (Tang, Sutherland et al. 1998) and intracellular survival and replication (Hobbie, Chen et al. 1997; Palmer, Hobbie et al. 1998; Schorey and Cooper 2003). Jimenez-Bagues et al. (Bagués, Gross et al. 2005) demonstrated the importance that the integrity of the MEK - MAPK - ERK 1/2 pathway has on the elimination of rough Brucella suis in macrophages. To identify the importance of this MAPK signaling pathway in BMEL invasion and intracellular survival in HeLa cells, a siRNA molecule (ID1449) was used to knock-down MAPK1 expression. Our results confirmed that the internalization of BMEL decreased more than 60% when the gene was knocked-down with the siRNA molecule as shown in Figure 6.

L. Garry Adams, et al. Vaccine. ;29(41):7197-7206.
7.
Figure 1

Figure 1. From: Enhancing the Role of Veterinary Vaccines Reducing Zoonotic Diseases of Humans: Linking Systems Biology with Vaccine Development.

As researchers hypothesize and deduce the sequences and structures of pathogenic proteins and develop detailed knowledge of their regulatory roles in the host, they can rationally design vaccines with defined components in order to maximize effectiveness and minimize safety concerns. Computational capabilities are emerging for creating host-pathogen interactome models. Such models, utilizing data at the genomic, transcriptomic, proteomic, metabolomics, etc. levels, can be used to learn and understand the underlying mechanisms and points of interaction governing the host innate and adaptive responses to pathogens and their vaccines. Such models are envisioned to play an increasingly integral part in the vaccine and immunotherapeutic development process, with incremental model improvements accruing as new biological knowledge is collected from translational in vivo and ex vivo efficacy and safety studies (non-clinical through clinical trials). An exciting prospect of such incremental modeling is the role these models can play in a forward -looking vaccine rational design strategy. Figure 1 illustrates the strategy of employing a vaccine-immunotherapeutic development methodology referred to as incremental systems biology interactome modeling. Multiple elements must come together to implement such a methodology. Prior biological knowledge (molecular and functional biology) must be current for both the host and pathogen biological systems. Often such knowledge is minimal for many of the veterinary animal species and extra steps of obtaining latest genome and proteome annotations and interaction predictions are necessary and labor intensive. The role of the computer scientist, statistician, and biologist is integral to the successful development, refinement and verification of such models. The interactome model cannot just be a list of possible interaction prediction, but must be part of a dynamic model in which the relationships governing the host immune response can be captured, interpreted and refined. The interactome model becomes a tool that can be interrogated and employed in simulation to help guide vaccine development and/or immunotherapeutic drug candidate selections. Experimental verification will always be a necessary element, and as such experiments are conducted, the resulting biological information should be retained and employed as new biological knowledge for creating the next refined interactome model.

L. Garry Adams, et al. Vaccine. ;29(41):7197-7206.
8.
Figure 8

Figure 8. From: Enhancing the Role of Veterinary Vaccines Reducing Zoonotic Diseases of Humans: Linking Systems Biology with Vaccine Development.

Comparative pathogenicity is a method by which the host response between different pathogens or pathogen vaccine candidates can be utilized to elicit unique and/or common biomarkers of immunogenicity. Utilizing BioSignatureDS™, the significantly perturbed pathways and gene groups from DBGGA were integrated to construct a plausible system level model of the STM wild type (WT) condition versus an isogenic ΔsipA, sopABDE2 mutant. The system model encompasses whole time-course patterns and multi-conditional behaviors of larger groups of genes and proteins than utilized only in the pathways. The system model expands the relationship of genes across related pathways and can be used for more efficient comparative modeling, pattern recognition and simulation supporting “what-if” type of analyses as previously described at the pathway level. The system model is constructed from a method based on merging of pathways with known gene/protein relationships and produces a trained and optimized network model similar to the MAPK signaling pathway network shown in Figure 5. Following this procedure, a system model was constructed from 10 selected pathways (listed in Figure 8) showing significant perturbation between the host infected with STM WT and STM mutant. The resulting model has a common network structure that is trained using the host response data for the WT, mutant and control conditions and was comprised of 930 genes and over 1500 gene-to-gene relations. By interrogating the model, we identified a number of significantly differentially expressed genes (|Bayesian z-score| >= 2.24) as shown in the center heatmap of Figure 8. From this heatmap, the difference in the STM mutant shown as green in the heatmap are a subset of genes which were found to be very highly up regulated in the STM mutant compared to the wild type and could be considered candidate genes governing the effective immune response of the host. These genes also form the basis of a biosignature that can be correlated to immunogenicity for more rational vaccine development. As expected for the WT, we found increased expression of genes associated with immune response such as those encoding IFNG, TNF, TLR4, and as well as genes associated with signaling and regulation of the actin cytoskeleton.

L. Garry Adams, et al. Vaccine. ;29(41):7197-7206.

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