Finding intervention points in the pathogenesis of Dengue viral infection

Executable pathway analysis using ensemble discrete-state modeling for large-scale data

Pathway analysis is widely used to gain mechanistic insights from high-throughput omics data. However, most existing methods do not consider signal integration represented by pathway topology, resulting in enrichment of convergent pathways when downstream genes are modulated. Incorporation of signal flow and integration in pathway analysis could rank the pathways based on modulation in key regulatory genes. This implementation can be facilitated for large-scale data by discrete state network modeling due to simplicity in parameterization. Here, we model cellular heterogeneity using discrete state dynamics and measure pathway activities in cross-sectional data. We introduce a new algorithm, Boolean Omics Network Invariant-Time Analysis (BONITA), for signal propagation, signal integration, and pathway analysis. Our signal propagation approach models heterogeneity in transcriptomic data as arising from intercellular heterogeneity rather than intracellular stochasticity, and propagates binary signals repeatedly across networks. Logic rules defining signal integration are inferred by genetic algorithm and are refined by local search. The rules determine the impact of each node in a pathway, which is used to score the probability of the pathway's modulation by chance. We have comprehensively tested BONITA for application to transcriptomics data from translational studies. Comparison with state-of-the-art pathway analysis methods shows that BONITA has higher sensitivity at lower levels of source node modulation and similar sensitivity at higher levels of source node modulation. Application of BONITA pathway analysis to previously validated RNA-sequencing studies identifies additional relevant pathways in in-vitro human cell line experiments and in-vivo infant studies. Additionally, BONITA successfully detected modulation of disease specific pathways when comparing relevant RNA-sequencing data with healthy controls. Most interestingly, the two highest impact score nodes identified by BONITA included known drug targets. Thus, BONITA is a powerful approach to prioritize not only pathways but also specific mechanistic role of genes compared to existing methods. BONITA is available at: https://github.com/thakar-lab/BONITA.

Meta-analysis of biomarkers for severe dengue infections

BACKGROUND

Dengue viral infection is an acute infection that has the potential to have severe complications as its major sequela. Currently, there is no routine laboratory biomarker with which to predict the severity of dengue infection or monitor the effectiveness of standard management. Hence, this meta-analysis compared biomarker levels between dengue fever (DF) and severe dengue infections (SDI) to identify potential biomarkers for SDI.

METHODS

Data concerning levels of cytokines, chemokines, and other potential biomarkers of DF, dengue hemorrhagic fever, dengue shock syndrome, and severe dengue were obtained for patients of all ages and populations using the Scopus, PubMed, and Ovid search engines. The keywords "(IL1* or IL-1*) AND (dengue*)" were used and the same process was repeated for other potential biomarkers, according to Medical Subject Headings terms suggested by PubMed and Ovid. Meta-analysis of the mean difference in plasma or serum level of biomarkers between DF and SDI patients was performed, separated by different periods of time (days) since fever onset. Subgroup analyses comparing biomarker levels of healthy plasma and sera controls, biomarker levels of primary and secondary infection samples were also performed, as well as analyses of different levels of severity and biomarker levels upon infection by different dengue serotypes.

RESULTS

Fifty-six studies of 53 biomarkers from 3,739 dengue cases (2,021 DF and 1,728 SDI) were included in this meta-analysis. Results showed that RANTES, IL-7, IL-8, IL-10, IL-18, TGF-b, and VEGFR2 levels were significantly different between DF and SDI. IL-8, IL-10, and IL-18 levels increased during SDI (95% CI, 18.1-253.2 pg/mL, 3-13 studies, n = 177-1,909, I2 = 98.86%-99.75%). In contrast, RANTES, IL-7, TGF-b, and VEGFR2 showed a decrease in levels during SDI (95% CI, -3238.7 to -3.2 pg/mL, 1-3 studies, n = 95-418, I2 = 97.59%-99.99%). Levels of these biomarkers were also found to correlate with the severity of the dengue infection, in comparison to healthy controls. Furthermore, the results showed that IL-7, IL-8, IL-10, TGF-b, and VEGFR2 display peak differences between DF and SDI during or before the critical phase (day 4-5) of SDI.

DISCUSSION

This meta-analysis suggests that IL-7, IL-8, IL-10, TGF-b, and VEGFR2 may be used as potential early laboratory biomarkers in the diagnosis of SDI. This can be used to predict the severity of dengue infection and to monitor the effectiveness of treatment. Nevertheless, methodological and reporting limitations must be overcome in future research to minimize variables that affect the results and to confirm the findings.

optPBN: an optimisation toolbox for probabilistic Boolean networks

BACKGROUND

There exist several computational tools which allow for the optimisation and inference of biological networks using a Boolean formalism. Nevertheless, the results from such tools yield only limited quantitative insights into the complexity of biological systems because of the inherited qualitative nature of Boolean networks.

RESULTS

We introduce optPBN, a Matlab-based toolbox for the optimisation of probabilistic Boolean networks (PBN) which operates under the framework of the BN/PBN toolbox. optPBN offers an easy generation of probabilistic Boolean networks from rule-based Boolean model specification and it allows for flexible measurement data integration from multiple experiments. Subsequently, optPBN generates integrated optimisation problems which can be solved by various optimisers. In term of functionalities, optPBN allows for the construction of a probabilistic Boolean network from a given set of potential constitutive Boolean networks by optimising the selection probabilities for these networks so that the resulting PBN fits experimental data. Furthermore, the optPBN pipeline can also be operated on large-scale computational platforms to solve complex optimisation problems. Apart from exemplary case studies which we correctly inferred the original network, we also successfully applied optPBN to study a large-scale Boolean model of apoptosis where it allows identifying the inverse correlation between UVB irradiation, NFκB and Caspase 3 activations, and apoptosis in primary hepatocytes quantitatively. Also, the results from optPBN help elucidating the relevancy of crosstalk interactions in the apoptotic network.

SUMMARY

The optPBN toolbox provides a simple yet comprehensive pipeline for integrated optimisation problem generation in the PBN formalism that can readily be solved by various optimisers on local or grid-based computational platforms. optPBN can be further applied to various biological studies such as the inference of gene regulatory networks or the identification of the interaction's relevancy in signal transduction networks.

Recent development and biomedical applications of probabilistic Boolean networks

Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the study of the topology and dynamic aspects of biological systems. The combined use of rule-based representation and probability makes PBN appealing for large-scale modelling of biological networks where degrees of uncertainty need to be considered.A considerable expansion of our knowledge in the field of theoretical research on PBN can be observed over the past few years, with a focus on network inference, network intervention and control. With respect to areas of applications, PBN is mainly used for the study of gene regulatory networks though with an increasing emergence in signal transduction, metabolic, and also physiological networks. At the same time, a number of computational tools, facilitating the modelling and analysis of PBNs, are continuously developed.A concise yet comprehensive review of the state-of-the-art on PBN modelling is offered in this article, including a comparative discussion on PBN versus similar models with respect to concepts and biomedical applications. Due to their many advantages, we consider PBN to stand as a suitable modelling framework for the description and analysis of complex biological systems, ranging from molecular to physiological levels.