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Causal Biological Network Model for Inflammasome Signaling Applied for Interpreting Transcriptomic Changes in Various Inflammatory States.

ABSTRACT: Virtually any stressor that alters the cellular homeostatic state may result in an inflammatory response. As a critical component of innate immunity, inflammasomes play a prominent role in the inflammatory response. The information on inflammasome biology is rapidly growing, thus creating the need for structuring it into a model that can help visualize and enhance the understanding of underlying biological processes. Causal biological network (CBN) models provide predictive power for novel disease mechanisms and treatment outcomes. We assembled the available literature information on inflammasome activation into the CBN model and scored it with publicly available transcriptomic datasets that address viral infection of the lungs, osteo- and rheumatoid arthritis, psoriasis, and aging. The scoring inferred pathway activation leading to NLRP3 inflammasome activation in these diverse conditions, demonstrating that the CBN model provides a platform for interpreting transcriptomic data in the context of inflammasome activation.

SUBMITTER: Yepiskoposyan H

PROVIDER: S-EPMC8813300 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Causal Biological Network Model for Inflammasome Signaling Applied for Interpreting Transcriptomic Changes in Various Inflammatory States.

Yepiskoposyan Hasmik H Peitsch Manuel C MC Talikka Marja M

International journal of inflammation 20220127

Virtually any stressor that alters the cellular homeostatic state may result in an inflammatory response. As a critical component of innate immunity, inflammasomes play a prominent role in the inflammatory response. The information on inflammasome biology is rapidly growing, thus creating the need for structuring it into a model that can help visualize and enhance the understanding of underlying biological processes. Causal biological network (CBN) models provide predictive power for novel disea ...[more]

PMID: 35126992

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Project description:BackgroundInference of active regulatory cascades under specific molecular and environmental perturbations is a recurring task in transcriptional data analysis. Commercial tools based on large, manually curated networks of causal relationships offering such functionality have been used in thousands of articles in the biomedical literature. The adoption and extension of such methods in the academic community has been hampered by the lack of freely available, efficient algorithms and an accompanying demonstration of their applicability using current public networks.ResultsIn this article, we propose a new statistical method that will infer likely upstream regulators based on observed patterns of up- and down-regulated transcripts. The method is suitable for use with public interaction networks with a mix of signed and unsigned causal edges. It subsumes and extends two previously published approaches and we provide a novel algorithmic method for efficient statistical inference. Notably, we demonstrate the feasibility of using the approach to generate biological insights given current public networks in the context of controlled in-vitro overexpression experiments, stem-cell differentiation data and animal disease models. We also provide an efficient implementation of our method in the R package QuaternaryProd available to download from Bioconductor.ConclusionsIn this work, we have closed an important gap in utilizing causal networks to analyze differentially expressed genes. Our proposed Quaternary test statistic incorporates all available evidence on the potential relevance of an upstream regulator. The new approach broadens the use of these types of statistics for highly curated signed networks in which ambiguities arise but also enables the use of networks with unsigned edges. We design and implement a novel computational method that can efficiently estimate p-values for upstream regulators in current biological settings. We demonstrate the ready applicability of the implemented method to analyze differentially expressed genes using the publicly available networks.

Project description:BackgroundMediation analysis aims at estimating to what extent the effect of an exposure on an outcome is explained by a set of mediators on the causal pathway between the exposure and the outcome. The total effect of the exposure on the outcome can be decomposed into an indirect effect, i.e. the effect explained by the mediators jointly, and a direct effect, i.e. the effect unexplained by the mediators. However finer decompositions are possible in presence of independent or sequential mediators.MethodsWe review four statistical methods to analyse multiple sequential mediators, the inverse odds ratio weighting approach, the inverse probability weighting approach, the imputation approach and the extended imputation approach. These approaches are compared and implemented using a case-study with the aim to investigate the mediating role of adverse reproductive outcomes and infant respiratory infections in the effect of maternal pregnancy mental health on infant wheezing in the Ninfea birth cohort.ResultsUsing the inverse odds ratio weighting approach, the direct effect of maternal depression or anxiety in pregnancy is equal to a 59% (95% CI: 27%,94%) increased prevalence of infant wheezing and the mediated effect through adverse reproductive outcomes is equal to a 3% (95% CI: -6%,12%) increased prevalence of infant wheezing. When including infant lower respiratory infections in the mediation pathway, the direct effect decreases to 57% (95% CI: 25%,92%) and the indirect effect increases to 5% (95% CI: -5%,15%). The estimates of the effects obtained using the weighting and the imputation approaches are similar. The extended imputation approach suggests that the small joint indirect effect through adverse reproductive outcomes and lower respiratory infections is due entirely to the contribution of infant lower respiratory infections, and not to an increased prevalence of adverse reproductive outcomes.ConclusionsThe four methods revealed similar results of small mediating role of adverse reproductive outcomes and early respiratory tract infections in the effect of maternal pregnancy mental health on infant wheezing. The choice of the method depends on what is the effect of main interest, the type of the variables involved in the analysis (binary, categorical, count or continuous) and the confidence in specifying the models for the exposure, the mediators and the outcome.

Project description:E-cigarette vaping is a major aspect of nicotine consumption, especially for children and young adults. Although it is branded as a safer alternative to cigarette smoking, murine and rat models of subacute and chronic e-cigarette vaping exposure have shown many proinflammatory changes in the respiratory tract. An acute vaping exposure paradigm has not been demonstrated in the golden Syrian hamster, and the hamster is a readily available small animal model that has the unique benefit of becoming infected with and transmitting respiratory viruses, including SARS-CoV-2, without genetic alteration of the animal or virus. Using a 2-day, whole body vaping exposure protocol in male golden Syrian hamsters, we evaluated serum cotinine, bronchoalveolar lavage cells, lung, and nasal histopathology, and gene expression in the nasopharynx and lung through reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Depending on the presence of nonnormality or outliers, statistical analysis was performed by ANOVA or Kruskal-Wallis tests. For tests that were statistically significant (P < 0.05), post hoc Tukey-Kramer and Dunn's tests, respectively, were performed to make pairwise comparisons between groups. In nasal tissue, RT-qPCR analysis revealed nicotine-dependent increases in gene expression associated with type 1 inflammation (CCL-5 and CXCL-10), fibrosis [transforming growth factor-β (TGF-β)], nicotine-independent increase oxidative stress response (SOD-2), and a nicotine-independent decrease in vasculogenesis/angiogenesis (VEGF-A). In the lung, nicotine-dependent increases in the expression of genes involved in the renin-angiotensin pathway [angiotensin-converting enzyme (ACE), ACE2], coagulation (tissue factor, Serpine-1), extracellular matrix remodeling (MMP-2, MMP-9), type 1 inflammation (IL-1β, TNF-α, and CXCL-10), fibrosis (TGF-β and Serpine-1), oxidative stress response (SOD-2), neutrophil extracellular traps release (ELANE), and vasculogenesis and angiogenesis (VEGF-A) were identified. To our knowledge, this is the first demonstration that the Syrian hamster is a viable model of e-cigarette vaping. In addition, this is the first report that e-cigarette vaping with nicotine can increase tissue factor gene expression in the lung. Our results show that even an acute exposure to e-cigarette vaping causes significant upregulation of mRNAs in the respiratory tract from pathways involving the renin-angiotensin system, coagulation, extracellular matrix remodeling, type 1 inflammation, fibrosis, oxidative stress response, neutrophil extracellular trap release (NETosis), vasculogenesis, and angiogenesis.

Dataset Information

Causal Biological Network Model for Inflammasome Signaling Applied for Interpreting Transcriptomic Changes in Various Inflammatory States.

Publications

Causal Biological Network Model for Inflammasome Signaling Applied for Interpreting Transcriptomic Changes in Various Inflammatory States.

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