To further investigate what happened in the treated organism, I decided to have a closer look on the ’death pathways’ (apotosis, necrosis ans autophagy) and transcriptional pro- cesses. Apoptosis, necrosis and autophagy are of course very common in toxicity in- duced expression patterns. With this analysis, I wanted to get an idea how prominent this pathways are in the treatments. To study the transcriptional processes, I focused on the differentially regulation of the transcription factors.
Apoptosis, Necrosis and Autophagy Genes
Unfortunately, no data set was available containing a list of that genes. On that account, I checked Gene Ontology terms and the gene descriptions provided by Ensembl for the occurrence of the terms death and apotosis, necrosis or autophagy. The resulting list of genes was mapped to the Agilent zebrafish v2 array. For necrosis no genes could be iden- tified and only 11 transcripts were linked to autophagy. Therefore, this two pathways were not further investigated. But 271 transcripts on the array could be linked to apoptosis. The percentage of ‘apoptotic’ transcripts for the significantly regulated gene sets (p-value < 0.05) for each compound is shown in Figure 5.13. Even if not all genes involved in apo- totic processes have been identified, this list should give a good overview of the general degree of apoptotic damage in the treated organism.
Figure 5.13: Overview over the induced apoptotic Genes. The bars represent the percent- age of genes, which could be linked to apoptosis in the regulated (p-value < 0.05) data set.
The percentage of apoptotic transcripts was very low for all compounds. Although chlorophenol showed a large number of differentially expressed transcripts, there was no enrichment of apoptotic genes detectable as compared to the other compounds. In Table 5.15, the results from the enrichment analysis (Chapter 4.3) of the apotosis genes are shown.
5.1 10 COMPOUNDSTUDY 71
Regulated data set
chlorophenol dibutylphthalate methoxychlor esfenvalerate propoxur
Ratio of enrichment 1.29 1.16 1.13 1.36 1.78
P-value 1.06E-002 2.51E-001 3.17E-001 7.44E-002 1.70E-003
dibromoethane dimethylphenol flucythrinate chlorpyrifos chlorthalonil
Ratio of enrichment 1.21 1.23 0.96 1.4 1.7
P-value 2.19E-001 1.96E-001 5.95E-001 1.51E-001 7.31E-002
Table 5.15: Enrichment analysis for the apotosis genes. A p-value < 0.05 shows that the enrichment of the immune response genes in a data set is statistically significant. Ratio of enrichment values > 1 indicate an over representation of apoptosis genes in the data set, compared to what would be expected by chance.
Only for chlorophenol and propoxur a significant enrichment (P-value < 0.05) was found. This means that there are more apoptotic genes differentially expressed than would be expected by chance. Therefore, one can assume that the exposure concentrations of this compounds are in a range were apoptosis is induced. Nevertheless, no high enrichment was found, so the influence of apoptosis on the whole expression data set is small and other processes seem to be more prominent.
Transcription Factors
In order to investigate the regulation of transcriptional process by the compounds, a gene set analysis for transcription factors was performed. Therefore, a list of possible transcrip- tion factors was used (Chapter 2.1.3). 2626 transcripts related to transcriptional processes could be found on the Agilent v2 Array in total. Figure 5.14 gives an overview of the percentage of transcription factors in the different compound data sets.
Generally, less than 16% of the regulated transcripts belong to transcription factor genes. For methoxychlor and chlorthalonil the occurence of genes involved in transcrip- tion in the very highly differentially expressed transcripts was lower than for the other compounds. Other processes might be more important in these data sets than transcrip- tion. For dibromoethane, dimethylphenol and flucythrinate even more transcripts anno- tated with transcription were differentially expressed in the high regulated data set than in the regulated data set.
In Table 5.16 the results of the enrichment analysis are presented. Based on all differ- entially expressed transcripts, chlorophenol, dibutylphthalate, dimethylphenol, and chlor- pyrifos showed a significant enrichment (p-value < 0.05) of transcriptional genes. If only the highly expressed transcripts were taken into account, only dibromoethane and dimethylphenol were statistically significant enriched for transcriptional processes.
Figure 5.14: Overview over the induced transcription factors. The bars represent the percentage of genes involved in transcription, of the regulated (p-value < 0.05) and highly regulated data sets (p-value < 0.05; M > |1.4|).
Regulated data set
chlorophenol dibutylphthalate methoxychlor esfenvalerate propoxur
Ratio of enrichment 1.10 1.16 0.95 1.05 0.79
P-value 2.50E-003 6.10E-003 7.73E-001 2.52E-001 9.99E-001
dibromoethane dimethylphenol flucythrinate chlorpyrifos chlorthalonil
Ratio of enrichment 1.09 1.15 0.99 1.22 0.93
P-value 1.06E-001 2.19E-002 5.48E-001 1.97E-002 7.40E-001
Highly regulated data set
chlorophenol dibutylphthalate methoxychlor esfenvalerate propoxur
Ratio of enrichment 1.14 0.92 0.15 1.10 0.82
P-value 6.36E-002 7.15E-001 9.99E-001 2.84E-001 9.09E-001
dibromoethane dimethylphenol flucythrinate chlorpyrifos chlorthalonil
Ratio of enrichment 1.29 1.26 1.18 0.75 0.16
P-value 2.17E-002 3.71E-002 3.99E-001 7.64E-001 9.99E-001
Table 5.16: Transcription factor enrichment statistics. An p-value < 0.05 shows that the enrichment of the transcription factor genes in a data set is statistically significant. Ratio of enrichment values > 1 indicate an over representation of transcription factor genes in the data set, compared to what would be expected by chance.
5.2 WHOLEGENOMEARRAY 73
5.1.6
Gene Function Analysis
For gaining a better understanding of the mechanisms in the gene expression patterns of the different compounds, a gene function analysis like described in Chapter 4.4 was performed. Since it is not clear where in the data set the information about the toxicity mechanism is located. A specific toxicity mechanism might be stronger induced than a general toxicity response. Therefore the gene set of the highly regulated transcripts might be better suited to find them. But it would also be possible that the complete set of differentially expressed transcripts is need to find the underlaying mechanisms. Pathways that show up- or down-regulation might be of higher interest than pathways that show a more mixed regulation. For this reason, I created several data sets and performed a gene function analysis of each of them. This should help to obtain more information and a better understanding of the regulation of specific pathways. The following data sets were used:
• All: All differentially expressed transcripts (p-value < 0.05). • All up: All up-regulated transcripts.
• All down: All down-regulated transcripts.
• Highly: Highly regulated transcripts (p-value < 0.05, M > |1.4|). • Highly up: Highly up-regulated transcripts.
• Highly down: Highly down-regulated transcripts.
The Gene Ontology and two pathway databases (KEGG and WikiPathwas) were used to find enriched functions or processes in the data sets. To improve the Gene Ontology analysis, the GO categories were summarized via similarity measures (Chapter 4.5). The results of the analysis for each compound can be found in the appendix Chapter A. The interpretation is done in the discussion of the individual compound results in Chapter 6.1.1.