# From dose-response modelling of transcriptomic data to biological interpretation: an application to embryonic exposure of zebrafish (Danio rerio) to di-n-butyl phthalate (DBP)

Ellis Franklin, Elise Billoir, Marie Laure Delignette-Muller, Sophie M Prud'Homme

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## To cite this version:

Ellis Franklin, Elise Billoir, Marie Laure Delignette-Muller, Sophie M Prud'Homme. From dose-response modelling of transcriptomic data to biological interpretation: an application to embryonic exposure of zebrafish (Danio rerio) to di-n-butyl phthalate (DBP). 22nd Pollutant Responses in Marine Organisms Symposium (PRIMO22), May 2024, Nantes, France. . [hal-05282671](https://hal.science/hal-05282671v1)

HAL Id: hal-05282671

https://hal.science/hal-05282671v1

Submitted on 25 Sep 2025

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# From dose-response modelling of transcriptomic data to biological interpretation: an application to embryonic exposure of zebrafish (*Danio rerio*) to di-n-butyl phthalate (DBP)

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LBBE

Ellis Franklin<fnref n="1" />,<fnref n="2" />*, Elise Billoir<fnref n="1" />, Marie Laure Delignette-Muller<fnref n="2" />, Sophie M. Prud'homme<fnref n="1" />

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*ellis.franklin@univ-lorraine.fr

## Context & Objectives

The advancement of analysis tools for transcriptomic data and molecular databases has provided an unprecedented opportunity to delve deeply into the intricate mechanisms underlying the toxicity of contaminants. Given the intricate and multifaceted nature of these mechanisms, deciphering and comprehending the complex data becomes particularly challenging, especially when dealing with well-characterized model organisms like the zebrafish (*Danio rerio*).

Our transcriptomic study is dedicated to unravelling the dose-dependent mechanisms of action of di-n-butyl phthalate (DBP), a prevalent phthalate compound commonly identified in surface waters worldwide. To address this challenge, we have devised a systematic and replicable approach for the biological interpretation of this data, with the primary goal of enhancing the objectivity of the approach and relevance of the mechanistic hypotheses we formulate.

## Methods

### Data analysis

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* Selection of transcripts regulated along the concentration gradient

* Modelling of corresponding dose-response curves

* Calculation of « benchmark doses » : $BMD_{1SD} = \text{critical effect dose}$ at which the studied response is > to 1 residual standard error of the control response

* 2433 deregulated transcripts : 6% of the transcriptome

### Proposed workflow for interpretation

Transcript list

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n = 2433

Gene list

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n = 2365

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STRING

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2279 transcripts
2212 genes
3477 interactions

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409 transcripts
397 genes
53 clusters

Cluster filter
on size

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777 transcripts
752 genes
207 clusters

Markov
clustering

g: Profiler

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Functional
enrichment
per cluster

Over-representation analysis (ORA)

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KEGG

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WIKIPATHWAYS

Cluster A

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Term A

Term B

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Enriched term B

Lonely genes

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Cluster A

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Cluster A

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Term B

Cluster B

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Term B

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Lonely gene
fishing

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Term B

*Protein-Protein Interaction

This work was carried out with the financial support of the ANR as part of the JCJC Chroco project (ANR-21-CE34-0003) and the Observatoire Terre et Environnement de Lorraine (OTELo).

My doctoral scholarship is co-funded by ANR and VetAgro Sup.

Raw sequencing data was processed using computation and storage tools and resources made freely available by the Institut Français de Bioinformatique (IFB core)

## Experimental design & dataset

### Exposition to DBP

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Measurable concentration in surface waters

NOEC – long term toxicity - ECHA

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Zebrafish embryos
3h post-fertilisation

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Young zebrafish larvae
120h post-fertilisation

**mRNA sequencing** 1x75 pb - 40M reads per sample

3 replicates per condition

10 larvae per replicate

## Results

### Comparing the standard & proposed approaches

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ORA on all deregulated transcripts' genes

<table><thead><tr><th></th><th>Standard</th><th>Proposed</th><th>Shared</th></tr></thead><tbody><tr><td>cluster count</td><td>X</td><td>46</td><td>X</td></tr><tr><td>enriched GO count</td><td>12</td><td>41</td><td>11</td></tr><tr><td>enriched KEGG count</td><td>6</td><td>45</td><td>6</td></tr><tr><td>enriched Wikipathways count</td><td>2</td><td>10</td><td>2</td></tr><tr><td>considered transcripts</td><td>332</td><td>13%</td><td>1056</td></tr><tr><td>un-considered transcripts</td><td>2101</td><td>77%</td><td>1377</td></tr><tr><td></td><td></td><td>43%</td><td>12%</td></tr><tr><td></td><td></td><td>57%</td><td>55%</td></tr></tbody></table>

Lonely cluster

* Much more deregulated transcripts grouped into 46 clusters

* Additional 30 GO, 39 KEGG and 8 Wikipathway terms

* Lonely cluster enriches additional biological functions

### Road to interpretation: an example of exploration

#### Summary of BMD values per cluster

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Benchmark dose median (µg.L⁻<fnref n="1" />) with
interquartile range

Most sensitive cluster: 43

#### Fitted curves

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#### Enriched term

→ KEGG pathway: Retinol metabolism

#### Table summary

<table><thead><tr><th>Gene name</th><th>BMD-1SD</th><th>Trend</th></tr></thead><tbody><tr><td>cyp27c1</td><td>7.3</td><td></td></tr><tr><td>cyp3c1_t2</td><td>42</td><td></td></tr><tr><td>cyp3c3_g2t1</td><td>1.2</td><td></td></tr><tr><td>Iratb</td><td>37.8</td><td></td></tr><tr><td>Iratb.2</td><td>1.9</td><td></td></tr><tr><td>ugt1b1_t2</td><td>3.8</td><td></td></tr><tr><td>ugt2a7_g2t2</td><td>2.5</td><td></td></tr><tr><td>ugt2a7_g3t1</td><td>1.7</td><td></td></tr><tr><td>zgc:77938</td><td>2.9</td><td></td></tr></tbody></table>

* From the workflow, an **interactive** quarto® report was generated, providing an initial overview that paves the way for detailed cluster interpretation.

* Exploration within the report can be guided by either global cluster **sensitivity** or **trend**, ensuring an objective and systematic approach to exploration.

## Conclusion & Perspectives

* The proposed approach offers an untargeted exploration of the data, unveiling novel biological functions and pathways beyond the standard approach.

* The workflow is reproducible and semi-automated, leveraging **R** to seamlessly integrate DRomics modelling metrics with clustering and functional enrichment tools.

* The interactive Quarto report streamlines result exploration, aiding in the formulation of insightful hypotheses regarding the mechanisms of action behind pollutants.

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<footnote n="1">¹ Université de Lorraine, CNRS, LIEC, F-57000, Metz, France</footnote>

<footnote n="2">² Université de Lyon, CNRS, VetAgro Sup, LBBE, F-69622, Villeurbanne, France</footnote>