Chemical exposures affect the environment and may lead to adverse outcomes in its organisms. Omics-based approaches, like standardised microarray experiments, have expanded the toolbox to monitor the distribution of chemicals and assess the risk to organisms in the environment. The resulting complex data have extended the scope of toxicological knowledge bases and published literature. A plethora of computational approaches have been applied in environmental toxicology considering systems biology and data integration. Still, the complexity of environmental and biological systems given in data challenges investigations of exposure-related effects. This thesis aimed at computationally linking chemical exposure to biological effects on the molecular level considering sources of complex environmental data.
The first study employed data of an omics-based exposure study considering mixture effects in a freshwater environment. We compared three data-driven analyses in their suitability to disentangle mixture effects of chemical exposures to biological effects and their reliability in attributing potentially adverse outcomes to chemical drivers with toxicological databases on gene and pathway levels. Differential gene expression analysis and a network inference approach resulted in toxicologically meaningful outcomes and uncovered individual chemical effects — stand-alone and in combination. We developed an integrative computational strategy to harvest exposure-related gene associations from environmental samples considering mixtures of lowly concentrated compounds. The applied approaches allowed assessing the hazard of chemicals more systematically with correlation-based compound groups.
This dissertation presents another achievement toward a data-driven hypothesis generation for molecular exposure effects. The approach combined text-mining and deep learning. The study was entirely data-driven and involved state-of-the-art computational methods of artificial intelligence. We employed literature-based relational data and curated toxicological knowledge to predict chemical-biomolecule interactions. A word embedding neural network with a subsequent feed-forward network was implemented. Data augmentation and recurrent neural networks were beneficial for training with curated toxicological knowledge. The trained models reached accuracies of up to 94% for unseen test data of the employed knowledge base.
However, we could not reliably confirm known chemical-gene interactions across selected data sources. Still, the predictive models might derive unknown information from toxicological knowledge sources, like literature, databases or omics-based exposure studies. Thus, the deep learning models might allow predicting hypotheses of exposure-related molecular effects.
Both achievements of this dissertation might support the prioritisation of chemicals for testing and an intelligent selection of chemicals for monitoring in future exposure studies.:Table of Contents ... I
Abstract ... V
Acknowledgements ... VII
Prelude ... IX
1 Introduction
1.1 An overview of environmental toxicology ... 2
1.1.1 Environmental toxicology ... 2
1.1.2 Chemicals in the environment ... 4
1.1.3 Systems biological perspectives in environmental toxicology ... 7
Computational toxicology ... 11
1.2.1 Omics-based approaches ... 12
1.2.2 Linking chemical exposure to transcriptional effects ... 14
1.2.3 Up-scaling from the gene level to higher biological organisation levels ... 19
1.2.4 Biomedical literature-based discovery ... 24
1.2.5 Deep learning with knowledge representation ... 27
1.3 Research question and approaches ... 29
2 Methods and Data ... 33
2.1 Linking environmental relevant mixture exposures to transcriptional effects ... 34
2.1.1 Exposure and microarray data ... 34
2.1.2 Preprocessing ... 35
2.1.3 Differential gene expression ... 37
2.1.4 Association rule mining ... 38
2.1.5 Weighted gene correlation network analysis ... 39
2.1.6 Method comparison ... 41
Predicting exposure-related effects on a molecular level ... 44
2.2.1 Input ... 44
2.2.2 Input preparation ... 47
2.2.3 Deep learning models ... 49
2.2.4 Toxicogenomic application ... 54
3 Method comparison to link complex stream water exposures to effects on
the transcriptional level ... 57
3.1 Background and motivation ... 58
3.1.1 Workflow ... 61
3.2 Results ... 62
3.2.1 Data preprocessing ... 62
3.2.2 Differential gene expression analysis ... 67
3.2.3 Association rule mining ... 71
3.2.4 Network inference ... 78
3.2.5 Method comparison ... 84
3.2.6 Application case of method integration ... 87
3.3 Discussion ... 91
3.4 Conclusion ... 99
4 Deep learning prediction of chemical-biomolecule interactions ... 101
4.1 Motivation ... 102
4.1.1Workflow ...105
4.2 Results ... 107
4.2.1 Input preparation ... 107
4.2.2 Model selection ... 110
4.2.3 Model comparison ... 118
4.2.4 Toxicogenomic application ... 121
4.2.5 Horizontal augmentation without tail-padding ...123
4.2.6 Four-class problem formulation ... 124
4.2.7 Training with CTD data ... 125
4.3 Discussion ... 129
4.3.1 Transferring biomedical knowledge towards toxicology ... 129
4.3.2 Deep learning with biomedical knowledge representation ...133
4.3.3 Data integration ...136
4.4 Conclusion ... 141
5 Conclusion and Future perspectives ... 143
5.1 Conclusion ... 143
5.1.1 Investigating complex mixtures in the environment ... 144
5.1.2 Complex knowledge from literature and curated databases predict chemical-
biomolecule interactions ... 145
5.1.3 Linking chemical exposure to biological effects by integrating CTD ... 146
5.2 Future perspectives ... 147
S1 Supplement Chapter 1 ... 153
S1.1 Example of an estrogen bioassay ... 154
S1.2 Types of mode of action ... 154
S1.3 The dogma of molecular biology ... 157
S1.4 Transcriptomics ... 159
S2 Supplement Chapter 3 ... 161
S3 Supplement Chapter 4 ... 175
S3.1 Hyperparameter tuning results ... 176
S3.2 Functional enrichment with predicted chemical-gene interactions and CTD reference pathway genesets ... 179
S3.3 Reduction of learning rate in a model with large word embedding vectors ... 183
S3.4 Horizontal augmentation without tail-padding ... 183
S3.5 Four-relationship classification ... 185
S3.6 Interpreting loss observations for SemMedDB trained models ... 187
List of Abbreviations ... i
List of Figures ... vi
List of Tables ... x
Bibliography ... xii
Curriculum scientiae ... xxxix
Selbständigkeitserklärung ... xliii
Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:79306 |
Date | 30 May 2022 |
Creators | Krämer, Stefan |
Contributors | Universität Leipzig |
Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
Language | English |
Detected Language | English |
Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0025 seconds