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Toxicogenomic analysis of publicly available transcriptomic data can predict food, drugs, and chemical-induced asthma.

ABSTRACT: Background:: With the increasing incidence of asthma, more attention is focused on the diverse and complex nutritional and environmental triggers of asthma exacerbations. Currently, there are no established risk assessment tools to evaluate asthma triggering potentials of most of the nutritional and environmental triggers encountered by asthmatic patients. Purpose: The objective of this study is to devise a reliable workflow, capable of estimating the toxicogenomic effect of such factors on key player genes in asthma pathogenesis. Methods:Gene expression extracted from publicly available datasets of asthmatic bronchial epithelium were subjected to a comprehensive analysis of differential gene expression to identify significant genes involved in asthma development and progression. The identified genes were subjected to Gene Set Enrichment Analysis using a total of 31,826 gene sets related to chemical, toxins, and drugs to identify common agents that share similar asthma-related targets genes and signaling pathways. Results:Our analysis identified 225 differentially expressed genes between severe asthmatic and healthy bronchial epithelium. Gene Set Enrichment Analysis of the identified genes showed that they are involved in response to toxic substances and organic cyclic compounds and are targeted by 41 specific diets, plants products, and plants related toxins (eg adenine, arachidonic acid, baicalein, caffeic acid, corilagin, curcumin, ellagic acid, luteolin, microcystin-RR, phytoestrogens, protoporphyrin IX, purpurogallin, rottlerin, and salazinic acid). Moreover, the identified chemicals share interesting inflammation-related pathways like NF-?B. Conclusion:Our analysis was able to explain and predict the toxicity in terms of stimulating the differentially expressed genes between severe asthmatic and healthy epithelium. Such an approach can pave the way to generate a cost-effective and reliable source for asthma-specific toxigenic reports thus allowing the asthmatic patients, physicians, and medical researchers to be aware of the potential triggering factors with fatal consequences.

SUBMITTER: Hachim MY

PROVIDER: S-EPMC6717055 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Toxicogenomic analysis of publicly available transcriptomic data can predict food, drugs, and chemical-induced asthma.

Hachim Mahmood Yaseen MY Hachim Ibrahim Yaseen IY Elemam Noha M NM Hamoudi Rifat A RA

Pharmacogenomics and personalized medicine 20190826

<h4>Background</h4>: With the increasing incidence of asthma, more attention is focused on the diverse and complex nutritional and environmental triggers of asthma exacerbations. Currently, there are no established risk assessment tools to evaluate asthma triggering potentials of most of the nutritional and environmental triggers encountered by asthmatic patients.<h4>Purpose</h4> The objective of this study is to devise a reliable workflow, capable of estimating the toxicogenomic effect of such ...[more]

PMID: 31692590

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Project description:BackgroundThe need for a better understanding of food consumption behaviour within its behavioural context has sparked the interest of nutrition researchers for user-documented food consumption data collected outside the research context using publicly available nutrition apps. The study aims to characterize the scientific, technical, legal and ethical features of this data in order to identify the opportunities and challenges associated with using this data for nutrition research.MethodA search for apps collecting food consumption data was conducted in October 2016 against UK Google Play and iTunes storefronts. 176 apps were selected based on user ratings and English language support. Publicly available information from the app stores and app-related websites was investigated and relevant data extracted and summarized. Our focus was on characteristics related to scientific relevance, data management and legal and ethical governance of user-documented food consumption data.ResultsFood diaries are the most common form of data collection, allowing for multiple inputs including generic food items, packaged products, or images. Standards and procedures for compiling food databases used for estimating energy and nutrient intakes remain largely undisclosed. Food consumption data is interlinked with various types of contextual data related to behavioural motivation, physical activity, health, and fitness. While exchange of data between apps is common practise, the majority of apps lack technical documentation regarding data export. There is a similar lack of documentation regarding the implemented terms of use and privacy policies. While users are usually the owners of their data, vendors are granted irrevocable and royalty free licenses to commercially exploit the data.ConclusionDue to its magnitude, diversity, and interconnectedness, user-documented food consumption data offers promising opportunities for a better understanding of habitual food consumption behaviour and its determinants. Non-standardized or non-documented food data compilation procedures, data exchange protocols and formats, terms of use and privacy statements, however, limit possibilities to integrate, process and share user-documented food consumption data. An ongoing research effort is required, to keep pace with the technical advancements of food consumption apps, their evolving data networks and the legal and ethical regulations related to protecting app users and their personal data.

Project description:INTRODUCTION: Breast cancer is a complex heterogeneous disease for which a substantial resource of transcriptomic data is available. Gene expression data have facilitated the division of breast cancer into, at least, five molecular subtypes, namely luminal A, luminal B, HER2, normal-like and basal. Once identified, breast cancer subtypes can inform clinical decisions surrounding patient treatment and prognosis. Indeed, it is important to identify patients at risk of developing aggressive disease so as to tailor the level of clinical intervention. METHODS: We have developed a user-friendly, web-based system to allow the evaluation of genes/microRNAs (miRNAs) that are significantly associated with survival in breast cancer and its molecular subtypes. The algorithm combines gene expression data from multiple microarray experiments which frequently also contain miRNA expression information, and detailed clinical data to correlate outcome with gene/miRNA expression levels. This algorithm integrates gene expression and survival data from 26 datasets on 12 different microarray platforms corresponding to approximately 17,000 genes in up to 4,738 samples. In addition, the prognostic potential of 341 miRNAs can be analysed. RESULTS: We demonstrated the robustness of our approach in comparison to two commercially available prognostic tests, oncotype DX and MammaPrint. Our algorithm complements these prognostic tests and is consistent with their findings. In addition, BreastMark can act as a powerful reductionist approach to these more complex gene signatures, eliminating superfluous genes, potentially reducing the cost and complexity of these multi-index assays. Known miRNA prognostic markers, mir-205 and mir-93, were used to confirm the prognostic value of this tool in a miRNA setting. We also applied the algorithm to examine expression of 58 receptor tyrosine kinases in the basal-like subtype, identifying six receptor tyrosine kinases associated with poor disease-free survival and/or overall survival (EPHA5, FGFR1, FGFR3, VEGFR1, PDGFR?, and TIE1). A web application for using this algorithm is currently available. CONCLUSIONS: BreastMark is a powerful tool for examining putative gene/miRNA prognostic markers in breast cancer. The value of this tool will be in the preliminary assessment of putative biomarkers in breast cancer. It will be of particular use to research groups with limited bioinformatics facilities.

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Toxicogenomic analysis of publicly available transcriptomic data can predict food, drugs, and chemical-induced asthma.

Publications

Toxicogenomic analysis of publicly available transcriptomic data can predict food, drugs, and chemical-induced asthma.

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