Signaling pathway perturbation analysis for assessment of biological impact of cigarette smoke on lung cells

Exposure to cigarette smoke (CS) results in injury to the epithelial cells of the human respiratory tract and has been implicated as a causative factor in the development of chronic obstructive pulmonary disease and lung cancers. The application of omics-scale methodologies has improved the capacity to understand cellular signaling processes underlying response to CS exposure. We report here the development of an algorithm based on quantitative assessment of transcriptomic profiles and signaling pathway perturbation analysis (SPPA) of human bronchial epithelial cells (HBEC) exposed to the toxic components present in CS. HBEC were exposed to CS of different compositions and for different durations using an ISO3308 smoking regime and the impact of exposure was monitored in 2263 signaling pathways in the cell to generate a total effect score that reflects the quantitative degree of impact of external stimuli on the cells. These findings support the conclusion that the SPPA algorithm provides an objective, systematic, sensitive means to evaluate the biological impact of exposures to CS of different compositions making a powerful comparative tool for commercial product evaluation and potentially for other known or potentially toxic environmental smoke substances.

The phenylpropanoid pathway inhibitor piperonylic acid induces broad-spectrum pest and disease resistance in plants

While many phenylpropanoid pathway-derived molecules act as physical and chemical barriers to pests and pathogens, comparatively little is known about their role in regulating plant immunity. To explore this research field, we transiently perturbed the phenylpropanoid pathway through application of the CINNAMIC ACID-4-HYDROXYLASE (C4H) inhibitor piperonylic acid (PA). Using bioassays involving diverse pests and pathogens, we show that transient C4H inhibition triggers systemic, broad-spectrum resistance in higher plant without affecting growth. PA treatment enhances tomato (Solanum lycopersicum) resistance in field and laboratory conditions, thereby illustrating the potential of phenylpropanoid pathway perturbation in crop protection. At the molecular level, transcriptome and metabolome analyses reveal that transient C4H inhibition in tomato reprograms phenylpropanoid and flavonoid metabolism, systemically induces immune signaling and pathogenesis-related genes, and locally affects reactive oxygen species metabolism. Furthermore, C4H inhibition primes cell wall modification and phenolic compound accumulation in response to root-knot nematode infection. Although PA treatment induces local accumulation of the phytohormone salicylic acid, the PA resistance phenotype is preserved in tomato plants expressing the salicylic acid-degrading NahG construct. Together, our results demonstrate that transient phenylpropanoid pathway perturbation is a conserved inducer of plant resistance and thus highlight the crucial regulatory role of this pathway in plant immunity.

ORN: Inferring patient-specific dysregulation status of pathway modules in cancer with OR-gate Network

Pathway level understanding of cancer plays a key role in precision oncology. However, the current amount of high-throughput data cannot support the elucidation of full pathway topology. In this study, instead of directly learning the pathway network, we adapted the probabilistic OR gate to model the modular structure of pathways and regulon. The resulting model, OR-gate Network (ORN), can simultaneously infer pathway modules of somatic alterations, patient-specific pathway dysregulation status, and downstream regulon. In a trained ORN, the differentially expressed genes (DEGs) in each tumour can be explained by somatic mutations perturbing a pathway module. Furthermore, the ORN handles one of the most important properties of pathway perturbation in tumours, the mutual exclusivity. We have applied the ORN to lower-grade glioma (LGG) samples and liver hepatocellular carcinoma (LIHC) samples in TCGA and breast cancer samples from METABRIC. Both datasets have shown abnormal pathway activities related to immune response and cell cycles. In LGG samples, ORN identified pathway modules closely related to glioma development and revealed two pathways closely related to patient survival. We had similar results with LIHC samples. Additional results from the METABRIC datasets showed that ORN could characterize critical mechanisms of cancer and connect them to less studied somatic mutations (e.g., BAP1, MIR604, MICAL3, and telomere activities), which may generate novel hypothesis for targeted therapy.

Proteomic and Metabolomic Analyses Provide Insights into the Mechanism on Arginine Metabolism Regulated by tRNA Modification Enzymes GidA and MnmE of Streptococcus suis

GidA and MnmE, two important tRNA modification enzymes, are contributed to the addition of the carboxymethylaminomethyl (cmnm) group onto wobble uridine of tRNA. GidA-MnmE modification pathway is evolutionarily conserved among Bacteria and Eukarya, which is crucial in efficient and accurate protein translation. However, its function remains poorly elucidated in zoonotic Streptococcus suis (SS). Here, a gidA and mnmE double knock-out (DKO) strain was constructed to systematically decode regulatory characteristics of GidA-MnmE pathway via proteomic. TMT labelled proteomics analysis identified that many proteins associated with cell divison and growth, fatty acid biosynthesis, virulence, especially arginine deiminase system (ADS) responsible for arginine metabolism were down-regulated in DKO mutant compared with the wild-type (WT) SC19. Accordingly, phenotypic experiments showed that the DKO strain displayed decreased in arginine consumption and ammonia production, deficient growth, and attenuated pathogenicity. Moreover, targeted metabolomic analysis identified that arginine was accumulated in DKO mutant as well. Therefore, these data provide molecular mechanisms for GidA-MnmE modification pathway in regulation of arginine metabolism, cell growth and pathogenicity of SS. Through proteomic and metabolomic analysis, we have identified arginine metabolism that is the links between a framework of protein level and the metabolic level of GidA-MnmE modification pathway perturbation.

e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks

Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the interaction profile mediated by a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath (edgetic Mutation-mediated Pathway perturbations), a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect.

e-MutPath: Computational modelling reveals the functional landscape of genetic mutations rewiring interactome networks

ABSTRACTUnderstanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the ‘edgotype’ of a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath, a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect. The code for e-MutPath is available as a user-friendly R package at the GitHub website (https://github.com/lyshaerbin/eMutPath).

A comparative review of computational methods for pathway perturbation analysis: dynamical and topological perspectives

Stem cells offer great promise within the field of regenerative medicine but despite encouraging results, the large scale use of stem cells for therapeutic applications still faces challenges when it comes to controlling signaling pathway responses with respect to environmental perturbations.