A Kinetic-Model-Based Approach to Identify Malfunctioning Components in Signal Transduction Pathways from Artificial Clinical Data

Detection of malfunctioning reactions or molecules from clinical data is essential for disease treatments. In order to find an alternative to the existing oversimplistic mathematical models, a kinetic model is developed in this work to infer the malfunctioning reactions/molecules by quantifying the similarity between the clinical profile and the output profiles predicted from the model in which certain reactions/molecules malfunction. The new approach was tested in IL-6 and TNF-α/NF-κB signaling pathway, for four abnormal conditions including up/downregulation of single reaction rate constants and up/downregulation of single molecules. Since limited quantitative clinical data were available, the IL-6 ODE model was used to generate artificial clinical data for the abnormal steady-state value shown in two key molecules: nuclear STAT3 and SOCS3. Similarly, the TNF-α/NF-κB model was used to obtain the data in which abnormal oscillation dynamic was shown in the profile of NF-κB. The results show that the approach developed in this study was able to successfully identify the malfunctioning reactions and molecules from the clinical data. It was also found that this new approach was noise-robust and that it managed to reveal unique solution for the faulty components in a network

A Model-Based Investigation of Cytokine Dynamics in Immunotherapies

With the advent of effective immunotherapies to battle cancers and diseases, an obstacle in recovery has become the potential side effects, specifically cytokine release syndrome (CRS). As there is little quantitative understanding of risks for developing CRS and the degree of its severity, this work explored a model-based approach to produce a library of in silico patients through sensitivity analysis of cytokine interaction parameters and a Monte Carlo sampling. The objective of producing the in silico patients was to correlate a known grading system of cytokine release syndrome severity and thus design a new formula for grading CRS. Using our CRS grading system as the foundation, this work produced not only a formula which related the in silico patient data to the different grades, but we effectively demonstrated a selective approach to reduce the grade of CRS with sequential cytokine inhibition targets. We achieved the reduction of grades by applying the insight from the sensitivity analysis, beginning with the most sensitive targets. Cytokines IL-1, IL-8, TNF-α, INF-γ, IL-6, IL-2, IL-4, IL-10, and IL-12 were in turn the best targets for inhibition to alleviate CRS. Using this approach, patient cytokine time profiles in real-time can be related to the CRS grading system and if the grade is severe enough, action can be taken earlier during the treatment to prevent potentially life-threatening symptoms. What’s more, the identified inhibition sequence of the 9 cytokines provides guidance for clinical intervention of CRS

A Systems-Level Approach for Investigating <em>Pseudomonas aeruginosa</em> Biofilm Formation

<div><p>Prevention of the initiation of biofilm formation is the most important step for combating biofilm-associated pathogens, as the ability of pathogens to resist antibiotics is enhanced 10 to 1000 times once biofilms are formed. Genes essential to bacterial growth in the planktonic state are potential targets to treat biofilm-associated pathogens. However, the biofilm formation capability of strains with mutations in these essential genes must be evaluated, since the pathogen might form a biofilm before it is eliminated. In order to address this issue, this work proposes a systems-level approach to quantifying the biofilm formation capability of mutants to determine target genes that are essential for bacterial metabolism in the planktonic state but do not induce biofilm formation in their mutants. The changes of fluxes through the reactions associated with the genes positively related to biofilm formation are used as soft sensors in the flux balance analysis to quantify the trend of biofilm formation upon the mutation of an essential gene. The essential genes whose mutants are predicted not to induce biofilm formation are regarded as gene targets. The proposed approach was applied to identify target genes to treat <i>Pseudomonas aeruginosa</i> infections. It is interesting to find that most essential gene mutants exhibit high potential to induce the biofilm formation while most non-essential gene mutants do not. Critically, we identified four essential genes, <i>lysC</i>, <i>cysH</i>, <i>adk</i>, and <i>galU</i>, that constitute gene targets to treat <i>P. aeruginosa</i>. They have been suggested by existing experimental data as potential drug targets for their crucial role in the survival or virulence of <i>P. aeruginosa</i>. It is also interesting to find that <i>P. aeruginosa</i> tends to survive the essential-gene mutation treatment by mainly enhancing fluxes through 8 metabolic reactions that regulate acetate metabolism, arginine metabolism, and glutamate metabolism.</p> </div

Schematic description of quantifying the biofilm formation capability of single mutants of essential genes for <i>P. aeruginosa</i>.

<p>The genes positively associated with <i>P. aeruginosa</i> biofilm formation are used to determine biofilm-associated reactions in Step 1. The essential gene <i>m</i> (e.g., PA1756 gene) is used as a reference gene in Steps 2 through 3 to illustrate the proposed approach for quantifying the potential of a single mutant to form biofilms. Specifically, reactions associated with gene <i>m</i> are partially mutated, and the flux distributions of all biofilm-associated reactions for both wide-type and mutant strains are quantified in Step 2. The relative change of activity through all biofilm-associated reactions for the gene <i>m</i> mutant is quantified in Step 3. A large enhancement in the activity of biofilm-associated reactions implies a large potential for the single mutant to form biofilms. The profiles for four mutants are given as examples in Step 3. Mutant 4 exhibits the lowest potential to form biofilms. The relative activity change profiles for all single mutants are used to categorize essential genes into different clusters in Step 4. Mutant 4 is assigned to a different cluster from that for the other three mutants, as its relative activity profile is not similar to those for the other mutants in both the shape and the magnitude. The essential planktonic-growth genes whose mutants might not induce biofilm formations are identified from the cluster results and regarded as potential target genes. For example, gene PA1756, which corresponds to mutant 4 in Step 3, is one potential gene target due to the low enhanced activities through those biofilm-associated reactions in its mutant. The biofilm-associated reactions whose activity levels are apparently enhanced in most mutants are identified in Step 5. These reactions indicate the underlying mechanisms for <i>P. aeruginosa</i> biofilm formation.</p

Biofilm-associated genes and reactions that are identified via the overlay of the genes reported by Müsken, et al., 2010 [18] to be positively associated with biofilm formation onto the metabolic network presented by Oberhardt, et al., 2008 [16].

<p>Biofilm-associated genes and reactions that are identified via the overlay of the genes reported by Müsken, et al., 2010 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057050#pone.0057050-Msken1" target="_blank">[18]</a> to be positively associated with biofilm formation onto the metabolic network presented by Oberhardt, et al., 2008 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057050#pone.0057050-Oberhardt1" target="_blank">[16]</a>.</p

Relative fold change in activity levels of two biofilm-associated reactions for all single mutants.

<p>(A) Reaction Rxn# 1, and (B) Reaction Rxn# 4. The activity level of Rxn#1 is of little change for most essential mutants, while the fluxes are significantly re-distributed through Rxn# 4 for most mutants. In other words, Rxn# 4 is of much higher activity levels upon the mutation of most essential genes. This implies that Rxn# 4 stands for the mechanism utilized by mutants to form biofilms. Based upon the relative activity change profiles, the biofilm-associated reactions are categorized into two types, one with minor flux changes upon the mutation of most essential genes (represented by Rxn# 1), and the other with large flux changes upon the mutation of most essential genes (represented by Rxn# 4).</p

Genes associated with each group shown in Figure 2.

<p>The representative genes are underlined and marked in bold.</p

Clustering result for essential planktonic-growth genes based upon the ability of their mutants to form biofilms.

<p>These essential genes are separated into 30 groups by the hierarchical clustering program. The genes in each group are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057050#pone-0057050-t002" target="_blank">Table 2</a>. They are categorized into six clusters by selecting a threshold marked by the blue color line. The groups marked by red rectangles are regarded as the representatives of the cluster of genes. Two groups with the lowest similarity in the same cluster are selected as representatives if more than one group of genes are involved in that cluster.</p

Categorization of the biofilm-associated reactions based upon their relative activity changes in the mutants of 136 essential planktonic-growth genes.

<p>Categorization of the biofilm-associated reactions based upon their relative activity changes in the mutants of 136 essential planktonic-growth genes.</p