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Potential classification of chemical immunologic response based on gene expression profiles.

ABSTRACT: Occupational immune diseases are a serious public health burden and are often a result of exposure to low molecular weight (LMW) chemicals. The complete immunological mechanisms driving these responses are not fully understood which has made the classification of chemical allergens difficult. Antimicrobials are a large group of immunologically-diverse LMW agents. In these studies, mice were dermally exposed to representative antimicrobial chemicals (sensitizers: didecyldimethylammonium chloride (DDAC), ortho-phthalaldehyde (OPA), irritants: benzal-konium chloride (BAC), and adjuvant: triclosan (TCS)) and the mRNA expression of cytokines and cellular mediators was evaluated using real-time qPCR in various tissues over a 7-days period. All antimicrobials caused increases in the mRNA expression of the danger signals Tslp (skin), and S100a8 (skin, blood, lung). Expression of the T_H2 cytokine Il4 peaked at different timepoints for the chemicals based on exposure duration. Unique expression profiles were identified for OPA (Il10 in lymph node, Il4 and Il13 in lung) and TCS (Tlr4 in skin). Additionally, all chemicals except OPA induced decreased expression of the cellular adhesion molecule Ecad. Overall, the results from these studies suggest that unique gene expression profiles are implicated following dermal exposure to various antimicrobial agents, warranting the need for additional studies. In order to advance the development of preventative and therapeutic strategies to combat immunological disease, underlying mechanisms of antimicrobial-induced immunomodulation must be fully understood. This understanding will aid in the development of more effective methods to screen for chemical toxicity, and may potentially lead to more effective treatment strategies for those suffering from immune diseases.

SUBMITTER: Anderson SE

PROVIDER: S-EPMC7673648 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Potential classification of chemical immunologic response based on gene expression profiles.

Anderson Stacey E SE Baur Rachel R Kashon Michael M Lukomska Ewa E Weatherly Lisa L Shane Hillary L HL

Journal of immunotoxicology 20201201 1

Occupational immune diseases are a serious public health burden and are often a result of exposure to low molecular weight (LMW) chemicals. The complete immunological mechanisms driving these responses are not fully understood which has made the classification of chemical allergens difficult. Antimicrobials are a large group of immunologically-diverse LMW agents. In these studies, mice were dermally exposed to representative antimicrobial chemicals (sensitizers: didecyldimethylammonium chloride ...[more]

PMID: 32449871

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Project description:Development of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. The accumulated experiment evidence supports the assumption that genes express and perform their functions in modular fashions in cells. Therefore, there is an open space for development of the timely and relevant computational algorithms that use robust functional expression profiles towards precise classification of complex human diseases at the modular level.Inspired by the insight that genes act as a module to carry out a highly integrated cellular function, we thus define a low dimension functional expression profile for data reduction. After annotating each individual gene to functional categories defined in a proper gene function classification system such as Gene Ontology applied in this study, we identify those functional categories enriched with differentially expressed genes. For each functional category or functional module, we compute a summary measure (s) for the raw expression values of the annotated genes to capture the overall activity level of the module. In this way, we can treat the gene expressions within a functional module as an integrative data point to replace the multiple values of individual genes. We compare the classification performance of decision trees based on functional expression profiles with the conventional gene expression profiles using four publicly available datasets, which indicates that precise classification of tumour types and improved interpretation can be achieved with the reduced functional expression profiles.This modular approach is demonstrated to be a powerful alternative approach to analyzing high dimension microarray data and is robust to high measurement noise and intrinsic biological variance inherent in microarray data. Furthermore, efficient integration with current biological knowledge has facilitated the interpretation of the underlying molecular mechanisms for complex human diseases at the modular level.

Dataset Information

Potential classification of chemical immunologic response based on gene expression profiles.

Publications

Potential classification of chemical immunologic response based on gene expression profiles.

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