\u3ci\u3eIn Vitro\u3c/i\u3e Gene Regulatory Networks Predict In Vivo Function of Liver

Background: Evolution of toxicity testing is predicated upon using in vitro cell based systems to rapidly screen and predict how a chemical might cause toxicity to an organ in vivo. However, the degree to which we can extend in vitro results to in vivo activity and possible mechanisms of action remains to be fully addressed. Results: Here we use the nitroaromatic 2,4,6-trinitrotoluene (TNT) as a model chemical to compare and determine how we might extrapolate from in vitro data to in vivo effects. We found 341 transcripts differentially expressed in common among in vitro and in vivo assays in response to TNT. The major functional term corresponding to these transcripts was cell cycle. Similarly modulated common pathways were identified between in vitro and in vivo. Furthermore, we uncovered the conserved common transcriptional gene regulatory networks between in vitro and in vivo cellular liver systems that responded to TNT exposure, which mainly contain 2 subnetwork modules: PTTG1 and PIR centered networks. Interestingly, all 7 genes in the PTTG1 module were involved in cell cycle and downregulated by TNT both in vitro and in vivo. Conclusions: The results of our investigation of TNT effects on gene expression in liver suggest that gene regulatory networks obtained from an in vitro system can predict in vivo function and mechanisms. Inhibiting PTTG1 and its targeted cell cyle related genes could be key machanism for TNT induced liver toxicity

MicroRNA and messenger RNA profiling reveals new biomarkers and mechanisms for RDX induced neurotoxicity

Background RDX is a well-known pollutant to induce neurotoxicity. MicroRNAs (miRNA) and messenger RNA (mRNA) profiles are useful tools for toxicogenomics studies. It is worthy to integrate MiRNA and mRNA expression data to understand RDX-induced neurotoxicity. Results Rats were treated with or without RDX for 48 h. Both miRNA and mRNA profiles were conducted using brain tissues. Nine miRNAs were significantly regulated by RDX. Of these, 6 and 3 miRNAs were up- and down-regulated respectively. The putative target genes of RDX-regulated miRNAs were highly nervous system function genes and pathways enriched. Fifteen differentially genes altered by RDX from mRNA profiles were the putative targets of regulated miRNAs. The induction of miR-71, miR-27ab, miR-98, and miR-135a expression by RDX, could reduce the expression of the genes POLE4, C5ORF13, SULF1 and ROCK2, and eventually induce neurotoxicity. Over-expression of miR-27ab, or reduction of the expression of unknown miRNAs by RDX, could up-regulate HMGCR expression and contribute to neurotoxicity. RDX regulated immune and inflammation response miRNAs and genes could contribute to RDX- induced neurotoxicity and other toxicities as well as animal defending reaction response to RDX exposure. Conclusions Our results demonstrate that integrating miRNA and mRNA profiles is valuable to indentify novel biomarkers and molecular mechanisms for RDX-induced neurological disorder and neurotoxicity.published_or_final_versio

Multiple functions of microfluidic platforms: Characterization and applications in tissue engineering and diagnosis of cancer

Microfluidic system, or lab-on-a-chip, has grown explosively. This system has been used in research for the first time and then entered in the clinical section. Due to economic reasons, this technique has been used for screening of laboratory and clinical indices. The microfluidic system solves some difficulties accompanied by clinical and biological applications. In this review, the interpretation and analysis of some recent developments in microfluidic systems in biomedical applications with more emphasis on tissue engineering and cancer will be discussed. Moreover, we try to discuss the features and functions of microfluidic systems. © 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Identification of Biomarkers That Distinguish Chemical Contaminants Based on Gene Expression Profiles

Background: High throughput transcriptomics profiles such as those generated using microarrays have been useful in identifying biomarkers for different classification and toxicity prediction purposes. Here, we investigated the use of microarrays to predict chemical toxicants and their possible mechanisms of action. Results: In this study, in vitro cultures of primary rat hepatocytes were exposed to 105 chemicals and vehicle controls, representing 14 compound classes. We comprehensively compared various normalization of gene expression profiles, feature selection and classification algorithms for the classification of these 105 chemicals into14 compound classes. We found that normalization had little effect on the averaged classification accuracy. Two support vector machine (SVM) methods, LibSVM and sequential minimal optimization, had better classification performance than other methods. SVM recursive feature selection (SVM-RFE) had the highest overfitting rate when an independent dataset was used for a prediction. Therefore, we developed a new feature selection algorithm called gradient method that had a relatively high training classification as well as prediction accuracy with the lowest overfitting rate of the methods tested. Analysis of biomarkers that distinguished the 14 classes of compounds identified a group of genes principally involved in cell cycle function that were significantly downregulated by metal and inflammatory compounds, but were induced by anti-microbial, cancer related drugs, pesticides, and PXR mediators. Conclusions: Our results indicate that using microarrays and a supervised machine learning approach to predict chemical toxicants, their potential toxicity and mechanisms of action is practical and efficient. Choosing the right feature and classification algorithms for this multiple category classification and prediction is critical

Precision environmental health - an omics-based whole-mixture approach

In the natural waters, hundreds to thousands of chemicals co-exist as complex mixtures, which needs a holistic assessment of their health effects. Identifying and testing each individual chemicals in the environment is undoubtably an insurmountable challenge to ecotoxicological studies and an unrealistic approach to reveal mixture effect at environmental relevant concentration, which may require insight from toxicogenomic studies. In this thesis, a new way of understanding and potentially discovering solutions to the mixture effect problem of safeguarding the health of human populations and the environment from the unknown effects of real-world chemical mixtures, specifically targeting pollutants of inland waters. In Chapter 1, the current status of environmental monitoring, its challenges and limitations by highlighting environmental sample classification and harmful chemical component prioritisation are described and discussed as the major issues. The conceptual framework of Precision Environmental Health is then proposed in Chapter 2, emphasising the importance of chemical mixture modes of action in the view of multi-omics. The Precision Environmental Health framework applies an omics-based bioassay approach to comprehensively characterise the effect of environmental chemical mixtures. The core of this framework focuses on the identification and interpretation of the molecular key event (mKE), which is responsive of foreign chemical exposure and indicative of potential adverse outcome. The mKEs are subsequently applied to classify the mixture effect and identify associated chemical components. This conceptual framework aims at I integrating the data-driven biological signatures generated by omics profiles and prior knowledge of gene functions and pathways of counterpart genetic model species. Chapter 3 explains and verifies the mathematical basis of the framework, which relies on multi-block correlation analysis. Two case studies are included to demonstrate this framework in action, and two chemical components (caffeine and carbamazepine) are selected as prove-of-concept. The Data-driven biological features are compared with prior knowledge and compared between two case, in order to prove the effectiveness and robustness of the mathematical assumption behind this framework. Derived from PEH framework, the mKE was used to group and classify the mixture effects of chemicals at environmentally relevant concentrations in two case studies, as gene clusters of highly variable genes in the transcriptomic profiles were identified and grouping pattern of gene clusters associated with chemical responses in Chapter 4 and further identify chemical component associated signatures that may reflect the chemicals’ modes of action in Chapter 5. In Chapter 4, expressionbased clustering analysis of five gene clusters revealed that the environmental chemical mixture of a single site (M16) induced relatively higher expression levels in stress response and cellular homeostasis, and these differences are significantly related to Dibenz[a,h]anthracene, Erythromycin and Trimethoprim in the Chaobai case study. In Chapter 5, similarity analysis of chemical profiles and transcriptomic profiles reveal similar grouping pattern, as expression-based clustering analysis of gene clusters revealed that distinctive transcriptomic profiles of two sites (D11 and D12) reveal down-regulation of xenobiotic biodegradation and antioxidative response pathways. This thesis ends by highlighting in Chapter 6 the promise of Precision Environmental Health to address harm caused by real world chemical pollutants based on my findings and discusses need for future verification