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Research Article
Martina Kutmon
Maastricht University Faculty of Health Medicine and Life Sciences: Universiteit Maastricht Faculty of Health Medicine and Life Sciences
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Whereas several engineered nanomaterials are able to incite toxicological effects, the underlying molecular processes are understudied. And the varied physicochemical properties complicate toxicological predictions. Gene expression data allow us to study the cell-specific responses of individual genes, whereas their role in biological processes is harder to interpret. An overrepresentation analysis allows us to identify enriched biological processes and link the experimental data to these, but still prompt broad results which complicates the analysis of detailed toxicological processes. We demonstrated a targeted filtering approach to compare the cell-specific effects of two concentrations of the widely used nanomaterial titanium dioxide (TiO2) -nanobelts.
Methods
We compared public gene expression data generated by Tilton et al. from colon endothelium cells (Caco2), lung endothelium cells (SAE), and monocytic like cells (THP1) after 24-hour exposure to low (10 μg/ml) and high (100 μg/ml) concentrations of TiO2 -nanobelts. We used pathway enrichment analysis of the WikiPathways collection to identify cell and concentration-specific affected pathways. Gene sets from selected Gene Ontology terms (apoptosis, inflammation, DNA damage response and oxidative stress) highlighted pathways with a clear toxicity focus. Finally, pathway-gene networks were created to show the genetic overlap between the altered toxicity-related pathways.
Results
All cell lines showed more differentially expressed genes after exposure to higher concentration, but our analysis found clear differences in affected molecular processes between the cell lines. Approximately half of the affected pathways are categorized with one of the selected toxicity-related processes. Caco2 cells show resilience to low and high concentrations. SAE cells display some cytotoxic response to the high concentration, while THP1 cells are already strongly affected at a low concentration. The networks show for up- and downregulation for the THP1 cells the most pathways. Additionally, the networks show gene overlap between almost all pathways for all conditions.
Conclusions
The approach allowed us to focus the analysis on affected cytotoxic processes and highlight cell-specific effects. The results showed that Caco2 cells are more resilient to TiO2 -nanobelts exposure compared to SAE cells, while THP1 cells were affected the most. The automated workflow can be easily adapted using other Gene Ontology terms focusing on other biological processes.
Keywords
nanomaterials, titaniumdioxide, nanobelts, overrepresentation analysis, gene ontology, THP1, SAE, Caco2
Figure 1
Figure 2
Figure 3
This is a list of supplementary files associated with this preprint. Click to download.
- figS1.png
Additional file 1 — Venn diagram showing the number of overlapping genes between the four GO-terms. Venn diagram showing the number of overlapping genes between the four GO-terms; “apoptotic process”, “inflammation”, “cellular response to DNA damage stimulus”, “DNA damage and Oxidative stress”.
- figS2.png
Additional file 2 — Number of significant pathways (p < 0.05) and number of genes per Gene Ontology category. Number of significant pathways (p < 0.05) and number of genes per Gene Ontology category.
- figS3.png
Additional file 3 — Overrepresentation analysis results of “cellular response to oxidative stress”. Network which shows pathway-gene-pathway interactions. Grey node represents a gene, the light brown node represents a pathway. Size depicts gene count. Pathways show genetic overlap and no separate clusters, which is an indication of these pathways being related to similar biological processes.
- figS4.png
Additional file 4 — Overrepresentation analysis results of ”apoptotic process”. Network which shows pathway-gene-pathway interactions. Grey nodes represent genes, the lightbrown node represents a pathway. Size depicts gene count. Pathways show genetic overlap and no separate clusters, which is an indication of these pathways being related to similar biological processes.
- figS5.png
Additional file 5 — Overrepresentation analysis results of “inflammatory response”. Network which shows pathway-gene-pathway interactions. Grey node represents a gene, the lightbrown node represents a pathway. Size depicts gene count. Pathways show genetic overlap and no separate clusters, which is an indication of these pathways being related to similar biological processes.
- figS6.png
Additional file 6 — Overrepresentation analysis results of “cellular response to DNA damage stimulus”. Network which shows pathway-gene-pathway interactions. Grey node represents a gene, the lightbrown node represents a pathway. Size depicts gene count. Pathways show genetic overlap and no separate clusters, which is an indication of these pathways being related to similar biological processes.
- figS7.png
Additional file 7 — Visualization of log fold change on the Apoptosis pathway (wikipathways:WP254) for all six conditions. Cell lines are depicted from left to right as Caco2, SAE and THP1. The top row depicts the low concentration whereas the bottom row depicts the high concentration. Gradient goes from blue (log fold change < -0.58) via white (log fold change = 0.0) to red (log fold change > 0.58).
- figS8.png
Additional file 8 — Visualization of log fold change on the DNA Mismatch Repair pathway (wikipathways:WP531) for all six conditions. Cell lines are depicted from left to right as Caco2, SAE and THP1. The top row depicts the low concentration whereas the bottom row depicts the high concentration. Gradient goes from blue (log fold change < -0.58) via white (log fold change = 0.0) to red (log fold change > 0.58).
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On 30 Mar, 2021
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Reviewer #1 agreed
On 27 Mar, 2021
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On 17 Mar, 2021
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On 17 Mar, 2021
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This preprint is under consideration at Particle and Fibre Toxicology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Martina Kutmon
Maastricht University Faculty of Health Medicine and Life Sciences: Universiteit Maastricht Faculty of Health Medicine and Life Sciences
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 16 Mar, 2021
No community comments so far
Reviewer #2 agreed
On 30 Mar, 2021
Review #? received
Received 30 Mar, 2021
Reviewer #1 agreed
On 27 Mar, 2021
Reviewers invited
Invitations sent on 24 Mar, 2021
Editor assigned
On 17 Mar, 2021
Submission checks complete
On 17 Mar, 2021
Editor invited
On 17 Mar, 2021
First submitted
On 15 Mar, 2021
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Whereas several engineered nanomaterials are able to incite toxicological effects, the underlying molecular processes are understudied. And the varied physicochemical properties complicate toxicological predictions. Gene expression data allow us to study the cell-specific responses of individual genes, whereas their role in biological processes is harder to interpret. An overrepresentation analysis allows us to identify enriched biological processes and link the experimental data to these, but still prompt broad results which complicates the analysis of detailed toxicological processes. We demonstrated a targeted filtering approach to compare the cell-specific effects of two concentrations of the widely used nanomaterial titanium dioxide (TiO2) -nanobelts.
Methods
We compared public gene expression data generated by Tilton et al. from colon endothelium cells (Caco2), lung endothelium cells (SAE), and monocytic like cells (THP1) after 24-hour exposure to low (10 μg/ml) and high (100 μg/ml) concentrations of TiO2 -nanobelts. We used pathway enrichment analysis of the WikiPathways collection to identify cell and concentration-specific affected pathways. Gene sets from selected Gene Ontology terms (apoptosis, inflammation, DNA damage response and oxidative stress) highlighted pathways with a clear toxicity focus. Finally, pathway-gene networks were created to show the genetic overlap between the altered toxicity-related pathways.
Results
All cell lines showed more differentially expressed genes after exposure to higher concentration, but our analysis found clear differences in affected molecular processes between the cell lines. Approximately half of the affected pathways are categorized with one of the selected toxicity-related processes. Caco2 cells show resilience to low and high concentrations. SAE cells display some cytotoxic response to the high concentration, while THP1 cells are already strongly affected at a low concentration. The networks show for up- and downregulation for the THP1 cells the most pathways. Additionally, the networks show gene overlap between almost all pathways for all conditions.
Conclusions
The approach allowed us to focus the analysis on affected cytotoxic processes and highlight cell-specific effects. The results showed that Caco2 cells are more resilient to TiO2 -nanobelts exposure compared to SAE cells, while THP1 cells were affected the most. The automated workflow can be easily adapted using other Gene Ontology terms focusing on other biological processes.
Figure 1
Figure 2
Figure 3
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