A Systems Biology Approach to Investigating Host-Pathogen Interactions in Infection with Burkholderia pseudomallei

This thesis applies systems approaches in order better to understand host-pathogen interactions in infectious diseases; it focuses on the intracellular bacterium Burkholderia pseudomallei, the causative agent of the human disease melioidosis. Little is known about the epigenetic changes in host cells during infection. This study assesses genome-wide patterns of the epigenetic marker DNA methylation in host cells following infection with B. pseudomallei. The studies of this thesis concern the infection of human macrophage-like U937 cells with B. pseudomallei and the DNA methylation levels were measured during the early stages of infection. Analyses reveal significant changes in infected cells (compared to uninfected controls) at multiple locations in the host DNA. Most of the methylation changes in infected cells are losses rather than gains in methylation. Five different differential methylation patterns (constant, early, late, transient, and oscillatory) are identified. Differentially methylated sites mapped to genes that may affect virulence, e.g. genes involved in actin regulation, immune response, inflammatory response, and nitric oxide generation. The thesis also measures whole blood DNA methylation profiles of patients diagnosed with melioidosis in order to test the potential role of host DNA methylation in melioidosis. The results demonstrate that patients with melioidosis are separated from healthy subjects by their distinct methylation profiles. The differentially methylated regions reported here can potentially be used as biomarkers for classification and prognostication of infectious diseases. In addition to exploring the changes to the host, a comprehensive understanding of the pathogen interference and the search for countermeasures requires a framework that assesses how the host changes the pathogen metabolically. In this thesis, to understand the role of trehalose pathway in virulence, computational models were constructed by integrating kinetic information, genomics data and literature surveys. Existing kinetic models of the trehalose pathway were implemented and extended allowing for the in silico investigation of the trehalose mutant. Further, metabolic networks of B. pseudomallei were analysed at the genome scale to identify molecular links between trehalose and metabolic pathways such as glycolysis. The genome- scale reconstruction of the B. pseudomallei metabolic network was used to simulate growth under different conditions and predict the effects of gene knockouts. This thesis not only expands the existing knowledge about B. pseudomallei infection, the novel approaches employed here will stimulate a wider understanding of the applications of systems biology to host-pathogen research and defence needs

Tools for Assessing the Protective Efficacy of TB Vaccines in Humans: in vitro Mycobacterial Growth Inhibition Predicts Outcome of in vivo Mycobacterial Infection.

Tuberculosis (TB) remains a leading global cause of morbidity and mortality and an effective new vaccine is urgently needed. A major barrier to the rational development of novel TB vaccines is the lack of a validated immune correlate or biomarker of protection. Mycobacterial Growth Inhibition Assays (MGIAs) provide an unbiased measure of ability to control mycobacterial growth in vitro, and may represent a functional correlate of protection. However, the biological relevance of any potential correlate can only be assessed by determining the association with in vivo protection from either a controlled mycobacterial infection or natural development of TB disease. Our data demonstrate that the direct MGIA using peripheral blood mononuclear cells (PBMC) is measuring a biologically relevant response that correlates with protection from in vivo human BCG infection across two independent cohorts. This is the first report of an MGIA correlating with in vivo protection in the species-of-interest, humans, and furthermore on a per-individual as well as per-group basis. Control of mycobacterial growth in the MGIA is associated with a range of immune parameters measured post-BCG infection in vivo including the IFN-γ ELISpot response, frequency of PPD-specific IFN-γ or TNF-α producing CD4+ T cells and frequency of specific sub-populations of polyfunctional CD4+ T cells. Distinct transcriptomic profiles are associated with good vs. poor mycobacterial control in the MGIA, with good controllers showing enrichment for gene sets associated with antigen processing/presentation and the IL-23 pathway, and poor controllers showing enrichment for hypoxia-related pathways. This study represents an important step toward biologically validating the direct PBMC MGIA for use in TB vaccine development and furthermore demonstrates the utility of this assay in determining relevant immune mechanisms and pathways of protection

A Phase 1 Human Immunodeficiency Virus Vaccine Trial for Cross-Profiling the Kinetics of Serum and Mucosal Antibody Responses to CN54gp140 Modulated by Two Homologous Prime-Boost Vaccine Regimens

A key aspect to finding an efficacious human immunodeficiency virus (HIV) vaccine is the optimization of vaccine schedules that can mediate the efficient maturation of protective immune responses. In the present study, we investigated the effect of alternate booster regimens on the immune responses to a candidate HIV-1 clade C CN54gp140 envelope protein, which was coadministered with the TLR4-agonist glucopyranosyl lipid A-aqueous formulation. Twelve study participants received a common three-dose intramuscular priming series followed by a final booster at either 6 or 12 months. The two homologous prime-boost regimens were well tolerated and induced CN54gp140-specific responses that were observed in both the systemic and mucosal compartments. Levels of vaccine-induced IgG-subclass antibodies correlated significantly with Fc gamma R engagement, and both vaccine regimens were associated with strikingly similar patterns in antibody titer and Fc gamma R-binding profiles. In both groups, identical changes in the antigen (Ag)-specific IgG-subclass fingerprint, leading to a decrease in IgG1 and an increase in IgG4 levels, were modulated by booster injections. Here, the dissection of immune profiles further supports the notion that prime-boost strategies are essential for the induction of diverse Ag-specific HIV-1 responses. The results reported here clearly demonstrate that identical responses were effectively and safely induced by both vaccine regimens, indicating that an accelerated 6-month regimen could be employed for the rapid induction of immune responses against CN54gp140 with no apparent impact on the overall quality of the induced immune response. (This study has been registered at http://ClinicalTrials.gov under registration no.NCT01966900.

Steady State Solutions and Trajectories Predicted by ANGII-AKT Model.

<p>S₁, S₂ and S₃ denote the three steady-states when insulin level λ = 0.5. The steady-states are at the intersection of red curves and black curve. Solid blue lines are the trajectories that start from different initial conditions I₁, I₂, I₃ and I₄. NO and pAKT both exhibit the desired bistability and switching dynamics behavior.</p

Comparison of Base Case (k₇ = 0.1) with the Case k₇ = 100.

<p>Red curves are the steady-state curves for the base case (k₇ = 0.1). Green curves are the same curves when k₇ = 100. Intersections of red and green curves with the black define the steady-states for the two cases. Solid blue curve is the state trajectory for the base case. Dashed blue curve is the state trajectory for the case when k₇ = 100. As the effect of Ang II is increased by increasing k₇, the system loses bistability. Trajectories converge to the single steady-state S₁ at low levels of pAKT and NO.</p

Feedback Loops.

<p>Feedback Loops.</p

Regulatory Networks and Complex Interactions between the Insulin and Angiotensin II Signalling Systems: Models and Implications for Hypertension and Diabetes

<div><p>The cross-talk between insulin and angiotensin II signalling pathways plays a significant role in the co-occurrence of diabetes and hypertension. We developed a mathematical model of the system of interactions among the biomolecules that are involved in the cross-talk between the insulin and angiotensin II signalling pathways. We have identified several feedback structures that regulate the dynamic behavior of the individual signalling pathways and their interactions. Different scenarios are simulated and dominant steady-state, dynamic and stability characteristics are revealed. The proposed mechanistic model describes how angiotensin II inhibits the actions of insulin and impairs the insulin-mediated vasodilation. The model also predicts that poor glycaemic control induced by diabetes contributes to hypertension by activating the renin angiotensin aystem.</p></div

Response Curves of the AKT model. Insulin level is redefined as [45].

<p>(<b>A</b>) Steady-state response curves of pAKT vs insulin for different feedback strengths . . The curves are calculated by setting <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083640#pone.0083640.e030" target="_blank">equations (10</a>) and (11) equal to zero. (<b>B</b>) Dynamic response curve in red superimposed on the steady-state curve shows the normal insulin cycle between states A and B. At State A, the cell has low nutrient level and requires glucose uptake. By stimulating insulin, the system switches to State B, where pAKT is activated and glucose is taken into the cell. Withdrawing insulin enables the switch back to low pAKT levels. (<b>C</b>) Bistability is lost under excess negative feedback Steady-state curves in (A) are identical to those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083640#pone.0083640-Wang1" target="_blank">[45]</a>. Dynamic responses in (B) and (C) are new and they are generated form the dynamic model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083640#pone.0083640.e030" target="_blank">equations (10</a>) and (11).</p

Steady State Response Curves for Different Levels of Inhibition by ONOO.

<p>The strength of inhibition is represented by the parameter. Increasing or inhibition reduces the sensitivity to insulin.</p

Comparison of Base Case (k₄ = 0.01) and the Case k₄ = 0.015.

<p>Red curves are the steady-state curves for both cases. Black curve is the steady-state curve for the base case (k₄ = 0.01). Green curve is the new curve when k₄ = 0.015. Intersections of red curves with the black define the steady-states for the two cases. Solid blue curve is the state trajectory for the base case. Dashed blue curve is the state trajectory for the case when k₄ = 0.015. NO production by pAKT is enhanced by Increasing the value of parameter increases availability of NO and production of ONOO which impairs insulin signalling. Bistability is lost and the system settles to the single steady-state S₁ where both pAKT and NO are low. In normal conditions ( = 0.01), pIRS1-pAKT-pIRS1 and pAKT-NO-pAKT feedback loops work in coordination to maintain both insulin sensitivity and the right amount of NO by switching between S₁ and S₃ as necessary.</p