# Two thyroperoxidase-inhibiting chemicals induce shared transcriptional changes in hippocampus of developing rats

Louise Ramhøj, Terje Svingen, Bertrand Evrard, Frédéric Chalmel, Marta Axelstad

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Louise Ramhøj, Terje Svingen, Bertrand Evrard, Frédéric Chalmel, Marta Axelstad. Two thyroperoxidase-inhibiting chemicals induce shared transcriptional changes in hippocampus of developing rats. Toxicology, 2024, 505, pp.153822. 10.1016/j.tox.2024.153822. hal-04574290

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# Two thyroperoxidase-inhibiting chemicals induce shared transcriptional changes in hippocampus of developing rats

Louise Ramhøj<sup>a, *</sup>, Terje Svingen<sup>a</sup>, Bertrand Evrard<sup>b</sup>, Frédéric Chalmel<sup>b</sup>, Marta Axelstad<sup>a</sup>

## ARTICLE INFO

Handling Editor: Dr. Mathieu Vinken

**Keywords:**

- Endocrine disruption

- Thyroid hormone

- Hypothyroid

- Developmental neurotoxicity

- Gene expression

- AOP

## ABSTRACT

Thyroid hormone (TH) system disrupting compounds can impair brain development by perturbing TH action during critical life stages. Human exposure to TH system disrupting chemicals is therefore of great concern. To better protect humans against such chemicals, sensitive test methods that can detect effects on the developing brain are critical. Worryingly, however, current test methods are not sensitive and specific towards TH-mediated effects. To address this shortcoming, we performed RNA-sequencing of rat brains developmentally exposed to two different thyroperoxidase (TPO) inhibiting compounds, the medical drug methimazole (MMI) or the pesticide amitrole. Pregnant and lactating rats were exposed to 8 and 16 mg/kg/day(d) MMI or 25 and 50 mg/kg/d amitrole from gestational day 7 until postnatal day 16. Bulk-RNA-seq was performed on hippocampus from the 16-day old male pups. MMI and amitrole caused pronounced changes to the transcriptomes; 816 genes were differentially expressed, and 425 gene transcripts were similarly affected by both chemicals. Functional terms indicate effects from key cellular functions to changes in cell development, migration and differentiation of several cell populations. Of the total number of DEGs, 106 appeared to form a consistent transcriptional fingerprint of developmental hypothyroidism as they were similarly and dose-dependently expressed across all treatment groups. Using a filtering system, we identified 20 genes that appeared to represent the most sensitive, robust and dose-dependent markers of altered TH-mediated brain development. These markers provide inputs to the adverse outcome pathway (AOP) framework where they, in the context of linking TPO inhibiting compounds to adverse cognitive function, can be used to assess altered gene expression in the hippocampus in rat toxicity studies.

## 1. Introduction

Thyroid hormone (TH) system disrupting chemicals can disrupt brain development by interfering with TH-mediated processes during fetal and postnatal development (Bernal, 2022; Gilbert et al., 2020; Gore et al., 2015; Leemans et al., 2019). This is of major concern since human exposure can lead to cognitive impairment and neurodevelopmental disorders (Bellanger et al., 2015; Mughal et al., 2018). Acknowledgement of this issue has led to the implementation of legislative systems to test chemicals for their TH system disrupting potentials. However, instead of assessing downstream effects on brain development, identification of TH system disrupting chemicals relies almost entirely on results from animal studies showing altered TH levels in serum and changes to thyroid gland weight and histopathology (Andersson et al., 2018). The reason is that it is difficult to identify and measure

TH-dependent developmental neurotoxicity (Gilbert et al., 2020; Kortenkamp et al., 2020; Ramhøj et al., 2023). As an alternative to this traditional approach, it is envisioned that improved mechanistic assessment of causal toxicological pathways could help identify TH-specific effect biomarkers in the brain. Such biomarkers or tests could improve on current identification of TH system disruptors or be exploited to devise alternative test methods to support predictive toxicologic assessment paradigms.

The hippocampus is a region of the brain that plays a key role in TH-mediated effects on learning and memory (Crofton et al., 2019). In developmentally TH-deficient animals the hippocampus displays both morphological and molecular signs of disrupted development, including effects on neuronal migration, differentiation, interneurons, synaptic plasticity and gene expression (Koromilas et al., 2010). THs act through the thyroid receptors to directly regulate gene transcription, which is

<footnote><sup>a</sup> National Food Institute, Technical University of Denmark, Kgs. Lyngby, DK-2800, Denmark</footnote>

<footnote><sup>b</sup> Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail), UMR_S 1085, Rennes, F-35000, France</footnote>

<footnote>* Correspondence to: Technical University of Denmark, Kemitorvet, Building 202, room 5209, Kgs. Lyngby, DK-2800, Denmark.
E-mail address: louram@food.dtu.dk (L. Ramhøj).</footnote>
why changes in gene transcription represent an attractive endpoint that could aid in linking TH-mediated mechanisms to complex adverse outcomes such as impaired cognitive abilities. However, the transcriptional signature that would serve as an indicator of TH-mediated developmental neurotoxicity remains to be determined.

When establishing toxicological effect-markers it is important that they are broadly applicable across the chemical space, i.e. that they are not specific to single chemicals. Instead, the effect-markers should detect all chemicals that could induce a given adverse outcome (AO) by targeting a given molecular initiating event (MIE). To aid linking MIEs to AOs, the adverse outcome pathway (AOP) framework was conceptualized (Ankley et al., 2010). Now more than a decade later, the AOP concept has been broadly adopted and is envisioned to increasingly aid in the identification and regulation of chemicals based on their potential to cause harm (Bajard et al., 2023; Paini et al., 2022; Svingen et al., 2021). In an AOP, causal pathways are constructed by linking measurable key events (KE) through KE Relationships (KERs), essentially building a causal bridge between molecular events and complex *in vivo* effects, or AOs. Importantly, AOPs are chemically agnostic mechanism-based constructs that captures toxicological effects independent of single compounds, or compound classes.

Inhibition of thyroperoxidase (TPO) is well documented to cause developmental neurotoxicity by significantly reducing TH concentrations, both in the blood and the brain (Akaike et al., 1991; Ausó et al., 2004; Axelstad et al., 2008; Calvo et al., 1990; Crofton et al., 2019; Gilbert, 2011; Gilbert et al., 2014, 2016, 2020; Gilbert and Zoeller, 2011; Hassan et al., 2017; Lavado-autric et al., 2003; O'Shaughnessy, Wood, et al., 2018; Ramhøj et al., 2021, 2022; Sharlin et al., 2008; Shibutani et al., 2009; Shiraki et al., 2014). TPO is expressed in the thyroid gland where it functions as a key enzyme for TH production. It oxidizes iodide to iodine, iodinates thyroglobulin and couples iodinated tyrosine residues to form thyroxine (T4) and 3,3',5-triiodothyronine (T3) (Carvalho and Dupuy, 2017). Thus, as described in the AOP for TPO inhibition (AOP-42, externally reviewed and endorsed by the OECD) (Crofton et al., 2019), environmental chemicals that can inhibit TPO may reduce TH availability leading to altered hippocampal development and developmental neurotoxicity (Crofton et al., 2019; Friedman et al., 2016). However, assays for KEs that causally link TPO inhibition and TH deficiency to developmental neurotoxicity are not yet available in the context of toxicity testing.

We recently showed that maternal exposure to two different TPO inhibitors, the medical drug methimazole (MMI) and the pesticide amitrole, caused similar TH system disruption and developmental neurotoxicity in rat offspring (Ramhøj et al., 2022). Here, we tested if hippocampal transcriptomes from the MMI- and amitrole-exposed pups could be used to extract an AOP-suitable and compound-independent transcriptional fingerprint of TH-mediated developmental neurotoxicity. For instance, such data would provide information for KE-756 “Hippocampal gene expression, Altered”, a crucial link between TPO inhibition (MIE-279), low serum T4 (KE-281) and developmental neurotoxicity in AOP-42 (Fig. 1A).

## 2. Materials and methods

### 2.1. Animals and treatment

The animal study, including toxicity data and an evaluation of maternal and pup TH system disruption, was described in (Ramhøj et al., 2022). A brief description of methods and key results follows below and in Fig. 1.

Time-mated nulliparous Sprague-Dawley rat dams (Crl:CD(SD) bred by Charles River Europe and distributed by SCANBUR, Denmark) arrived on gestation day (GD) 3 and were exposed to test compounds from GD7 to pup day (PD) 22. The day of expected birth (GD23)

was designated as PD1. The test compounds were methimazole, also known as thiamazole, (MMI) (CAS no: 60-56-0, Sigma-Aldrich

A

B

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** This study in the context of the adverse outcome pathway (AOP) framework, its study design and previous results. (A) Simplified schematic representation of an AOP and the focus of this study in the context of the AOP-42 (externally reviewed and endorsed by the OECD) (Crofton et al., 2019). This AOP, “Inhibition of thyroperoxidase in mammals”, describes how thyroid hormone (TH) disruption can lead to developmental neurotoxicity. The AOP describes the sequence of events leading from thyroperoxidase (TPO) inhibition through a series of Key Events (KE), including reduced serum T4, to culminate at the Adverse Outcome (AO) “Cognitive function, decreased”. An intermediate KE is “hippocampal gene expression, altered”, which was the target for this study. (B) The animal study design and previous results as reported in (Ramhøj et al., 2022). Here, we performed bulk-RNA-barcoding and sequencing (BRB-seq) on hippocampus from male PD16 pups (n = 11-12) exposed to TPO inhibiting compounds throughout pre- and postnatal development. Exposure doses given in mg/kg bodyweight/day by oral gavage. AO: adverse outcome, BRB-seq: Bulk-RNA-barcoding and sequencing, GD: gestational day, KE: Key event, KER: Key event relationship, MIE: molecular initiating event, MMI: methimazole, PD: pup day, T4: thyroxine TH: thyroid hormones, TPO: thyroperoxidase.</figcaption></figure>

301507–5 G, batch: WXBC9951 V, purity 99.0 %)

and 3-Amino-1 H-1,2,4-triazole (amitrole or Ami) (CAS no: 61–82–5, Sigma-Aldrich 8144950100, batch: S7075495 925, purity 99.7 %). Administration of test compounds took place once daily by oral gavage to animals pseudo-randomly divided into 5 groups with equal body-weight distribution (n = 11–12): control (vehicle, corn oil, Sigma-Aldrich), 8 mg/kg bodyweight/day (mg/kg/d) MMI, 16 mg/kg/d MMI, 25 mg/kg/d amitrole and 50 mg/kg/d amitrole. The sample size was chosen to provide adequate power for examinations of adverse histological and neurobehavioral effects in the offspring, and it ensures a robust dataset for transcriptomics. Test compounds were dissolved in corn oil and exposure of all groups was performed at a constant volume of 2 mL/kg/d. The dams, and their litters, were housed in semi-transparent polysulfone (PSU) cages of 15×27×43 cm (PSU) (Techniplast S.p.A, Buguggiate, Italy) in ventilated cabinets (ScanTainers, Scanbur, Karlslunde, Denmark) with ScanClime (Scanbur, Karlslunde, Denmark) controlled environment: 55 ± 5 % humidity, 21 ± 1°C, air-change 50 times/hr, reversed light/dark cycles (light form 9 PM to 9 AM, dark form 9 AM to 9 PM). The animals had *ad libitum* access to water (acidified, in PSU bottles from Techniplast) and soy and alfalfa-free
Altromin 1314 feed (Altromin GmbH, Lage, Germany). The diet con-
tained 1.52 mg/kg iodine and 0.26 mg/kg selenium.

As previously reported (Ramhøj et al., 2021, 2022), the medicinal
drug MMI and the pesticide amitrole were chosen as potent and pre-
sumably selective inhibitors of TPO. By studying two compounds acting
through the same mechanism we expect that any observed effects reflect
the mechanism, the so-called molecular initiating event, per se and not
compound-specific toxicity, therefore that they are chemically agnostic
and could be caused by all chemicals with TPO inhibiting properties. We
previously reported (Ramhøj et al., 2022) that MMI and amitrole caused
dose-dependent hypothyroidism in dams (GD15 and PD22) and in
16-day old male pups (female pups were similarly affected). The male
offspring were severely hypothyroid with reductions in serum T4 (down
80–90 % in high dose groups) and T3 (down 25–35 % in high dose
groups) and increased TSH (up 330–450 % in high dose groups). In late
gestation fetuses and postnatal day 3 and 6 male pups severe reductions
in TH in the groups exposed to high dose MMI and both doses of amitrole
were evident (unpublished data). The low dose MMI group stood out, as
the fetuses and PD3 pups were less affected (unpublished data). Further,
we reported TH-mediated effects in the cerebral cortex where there was
a dose-dependent effect on 9 gene transcripts shown to be
dose-dependently reduced in hypothyroid animals (O'Shaughnessy,
Wood, et al., 2018), and increased motor activity in the prepubertal pups
(Ramhøj et al., 2022).

The animal study was overseen by the Animal Welfare Committee for
animal care and use at the National Food Institute. The study was carried
out in accordance with the EU directive 2010/63/EU for animal ex-
periments and was ethically approved by the Danish Animal Experi-
ments Inspectorate under the authorization number
2020-15-0201-00539. The reporting of the animal study was in
accordance with the ARRIVE guidelines.

2.2. Tissue samples and RNA extraction

Animals were killed by decapitation under CO₂/O₂ anesthesia.
Hippocampi were extracted from one PD16 male pup per litter (n =
11-12), flash frozen in liquid nitrogen and stored at -80°C. The samples
were homogenized using a TissueLyser II (Qiagen, Hilden, Germany) at
30 oscillations per sec for 2 minutes. The Qiagen RNeasy Mini Kit with
on-column DNase I digestion was used to extract total RNA. RNA quality
and quantity was assessed according to manufacturer's instructions
using Agilent RNA 6000 Nano Kit on an Agilent 2100 Bioanalyzer Sys-
tem (Agilent Technologies, Waldbroon, Germany). All samples had RNA
integrity number (RIN) ≥9.

2.3. Library preparation and Bulk RNA barcoding and sequencing (BRB-seq)

3'Bulk-RNA-barcoding and sequencing (BRB-seq) (Alpern et al., 2019) was performed at the Research Institute for Environmental and occupational Health (Irset, Rennes, France). In brief, sample-specific barcoded oligo-dT and 4 µL total RNA at a concentration of 2.5 ng/µL were used for reverse transcription and template switching. The cDNA was purified and PCR used to make Double-strand (ds) cDNAs. 50 ng of cDNA was used to make sequencing libraries by tagmentation and using the Illumina Nextera XT Kit (Illumina, #FC-131-1024) following manufacturer's protocol. According to Illuminas instructions the IntegraGen Company (https://integragen.com/fr/) performed the sequencing as Paired-End 100 base reads on a NovaSeq sequencer. RTA 2.7.7 and bcl2fastq 2.17.1.14 were used for image analysis and base calling. DimerRemover (https://sourceforge.net/projects/dimerremover/) was used to remove Adapter dimer reads.

2.4. Data processing and quality control

SciLicium (Rennes, France) performed data preprocessing and

quality controls according to the pipeline described by (Kugathas et al., 2023). Briefly, the first 16 base pairs (bp) make up the first read and these had a quality score >10. The sample-specific barcode was the first 6 bp and the next 10 were the molecular identifier. After the first read, the parameter "-124" from BWA version 0.7.4.4 was used to align the second read to the rat reference transcriptome in the UCSC website release rn6 from August 2020. If reads mapped to multiple places in the genome they were removed from the analysis. A gene count matrix was constructed by counting the UMIs associated with each gene in each sample. Regularized log (rlog) transformation package from DeSeq2 package (Love et al., 2014) was used to further normalize the UMI matrix. The raw- and preprocessed data were deposited with the accession number GSE246890 at the GEO repository (Edgar et al., 2002) and the transcriptomic results were also deposited in the TOXIsgN repository (https://toxsign.genouest.org/) (Darde, Gaudriault, et al., 2018).

2.5. Differential gene expression analysis

Statistical comparisons between exposure groups and controls, and
between low and high dose for each compound, were made in the AMEN
suite of tools (Chalmel and Primig, 2008). Genes showing an expression
signal higher than the background cut-off determined by the overall
median of the rlog-transformed UMI dataset (0.861) and a fold-change
of at least 1.3 relative to the control were selected. Identification of
statistically significantly differentially expressed genes (DEGs) was done
using the Linear Models for MicroArray data (LIMMA) package (Smyth,
2004) with the F-value adjusted with Benjamini and Hochberg (BH)
false discovery rate approach (p ≤ 0.05) (Benjamini and Hochberg,
1995). Partitioning of the DEGs into distinct expression patterns was
performed with the k-means algorithm.

2.6. Functional analysis

The AMEN software (Chalmel and Primig, 2008) was used for Gene Ontology enrichment analysis. A False Discovery Rate (FDR)-adjusted p-value ≤0.05 was used to determine enriched specific annotation terms in a gene cluster.

3. Results

3.1. MMI and amitrole caused similar and dose-dependent effects on the developing hippocampal transcriptome

We analyzed the hippocampal transcriptome in animals that were
exposed to MMI or amitrole during development. The pups were TH-
deficient on PD16 and we found pronounced changes in the tran-
scriptomes at this developmental stage. The two first components of a
principal component analysis (PCA) (Fig. 2A) showed that MMI-16
(16 mg/kg/d MMI), Ami-25 (25 mg/kg/d amitrole) and Ami-50
(50 mg/kg/d amitrole) cluster distinctly different from the controls.
The differences were less pronounced between controls and the MMI low
dose group and the individual animals were distributed amongst the

controls and towards the MMI high dose group. This coincides with
the observation that the low dose of MMI caused a less severe effect on
T4 levels in the fetuses and neonates. There were pronounced treatment-
related changes in the transcriptomes, with 816 differentially expressed
genes (DEGs) (Fig. 2C and Supplementary Table S1). Fewest effects were
found in the MMI-8 group with 141 DEGs whereas >470 DEGs were
detected in MMI-16, Ami-25 and Ami-50. Notably, there was a large
overlap in downregulated transcripts between MMI and amitrole: 425
DEGs were downregulated in both MMI and amitrole high dose groups
including 129 DEGs which were found to be downregulated in all
exposure groups (Fig. 2B). Conversely, the intersection plot (Fig. 2B)
showed that only few genes were upregulated in more than one group:
only 9 genes were found to be upregulated in both MMI and amitrole-
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Effects of MMI and amitrole on the hippocampal transcriptome in developmentally hypothyroid rat offspring. (A) Principal component analysis of all samples, principal components 1 and 2 are shown (n = 11 for MMI-16, Ami-25 and Ami-50, n = 12 for control and MMI-8). There was distinct separation of controls from the high dose MMI, and both doses of amitrole. Animals in the low dose MMI group was distributed within the controls and towards the high dose MMI. (B) Intersection plots for down- and up-regulated DEGs. Plots indicate the number of DEGs in each dose group and whether they are in common between groups. Notably, 129 DEGs were downregulated in all 4 exposure groups. (C) data analysis flow and statistical filtration of the transcriptional data. Comparisons of controls with exposed groups led to a total of 816 differentially expressed genes (DEGs) across MMI and amitrole exposed animals. Data clustered in 5 distinct patterns (P, also see Fig. 3) and number of DEGs within each pattern for each comparison is indicated. (D) Functional analysis of DEGs in the four treatment groups. Select enriched biological terms are shown with the size of the spot indicating the number of DEGs in the associated gene ontology term relative to the number of DEGs in the gene cluster. Number of affected gene transcripts are shown next to spots and * denotes statistical significance with color intensity indicating the adj. p-value. r: number of DEGs retrieved in the associated term, R: number of DEGs in the associated gene cluster. Ami: amitrole, Ctrl: control, DEG: differentially expressed gene, MMI: methimazole, P: pattern. Doses shown in mg/kg/d.</figcaption></figure>
exposed pups regardless of the dose. We compared the low and high dose groups for each chemical in the dose-dependent contrast (Fig. 2C). This contrast showed dose-dependency in the number of DEGs. For MMI there were considerably more DEGs in the high dose group, whereas for amitrole this simple contrast suggest that the two doses affect the same transcripts compared to controls, but that the nominal difference between the low and high dose is smaller than for MMI.

We next performed functional analysis to identify the biological processes associated with the affected gene transcripts (Fig. 2D and Supplementary Table S2). The similarity between MMI- and amitrole-induced effects, as well as their dose-dependency, also showed a large proportion of enriched terms in the functional analysis for both chemicals (Fig. 2D). Again, the fewest effects were found in MMI-8. The enriched terms were related to development: “regulation of nervous system development” (17 DEGs, p-value <6.5e-3) and “animal organ development” (51, <3.4e-2) but also to the more specific processes “regulation of neurogenesis” (14, <1.8e-2) and “structural constituent of myelin sheath” (4, <3.2e-3).

Additionally, a portion of the functional terms concerned metabolic processes e.g. “organic substance metabolic process” (104, <2.0e-2) and “macromolecule metabolic process” (92,

<2.5e-2). The remaining 3 groups share the terms found in MMI-8 but many more processes were affected (Fig. 2D and supplementary materials S2). Again, the biological terms span many different terms for global developmental processes, including nervous system

development, and to effects such as development and differentiation of specific cell populations including neurons, astrocytes, and oligodendrocytes. Additionally, key processes and organelles were affected by developmental exposure to MMI and amitrole: gene expression, metabolic processes, cell migration, localization, adhesion, myelination, axons and cell projections.

## 3.2. The transcriptional fingerprint of developmental hypothyroidism is associated to disrupted neuronal development

The 816 DEGs partitioned into five distinct expression patterns (named P1-P5). To associate the expression patterns to biological functions we performed functional analysis (Fig. 3 and Supplementary Table S2). Genes in P1 (210 DEGs) displayed a dose-dependent down-regulation with the strongest effects found in MMI-16 and Ami-50, and they were significantly associated to functional terms related to development and the nervous system, including: neuron development (40 DEGs, p-value <4.7e-5), myelination (15, <6.5e-6) and glial cell differentiation (16, <3.8e-4), glutamatergic synapse (9, <2.1e-3), cholinergic synapse (7, <3.8e-2) and serotonergic synapse (7, <3.8e-2).

Overall, effects were more pronounced in P2 (428 DEGs) where genes were downregulated in all exposure groups with the exception of the MMI low dose group where some, but not all, animals were affected. Again, the biological processes were related to nervous system development such as neurogenesis (80, <4.45e-5), neuron projection

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Heatmap of hippocampal expression patterns and the associated functional analysis. Developing rat pups were exposed to thyroperoxidase (TPO) inhibiting compounds MMI and amitrole throughout perinatal development and the hippocampal transcriptome analysed in the 16-days old pups. Expression levels shown as rlog2-scaled expression changes from the controls. Data clustered in 5 expression patterns and there were enriched functional terms identified for P1–3 (representative selection shown). Left side color code indicates patterns and number of DEGs. Left side dose-response-column indicates DEGs that display dose-dependent expression in MMI and/or amitrole. Ami: amitrole, MMI: methimazole, P: Pattern.</figcaption></figure>
development (52, <7.81), neuron differentiation (69, <1.3e-4) and cell migration (56, <4.4e-2).

P3 (128 DEGs) contained DEGs that were dose-dependently upregulated by exposure to both compounds, and it was strongly associated with intracellular anatomical structure (106 genes out of 128 DEGs, p-value <2.0e-2) but also cytoplasm (92, <2.0e-2) and motile cilium (8, 4.2e-2).

Patterns P4 (17 DEGs) and P5 (33 DEGs) presented a more mixed expression pattern across groups and there were no associated functional terms.

### 3.3. Putative biomarkers for pragmatic identification of TH system disrupting compounds

Our data showed marked transcriptional changes in the hippocampus across compounds and doses. To improve the detection of such effects in future toxicity studies we proceeded to identify candidate biomarkers for the effects of developmental TH deficiency. First, we filtered for the DEGs displaying dose-dependent expression; that is, DEGs where the effect size was smaller in the low dose group than in the respective high dose group (Fig. 3 and Supplementary Table S1) (note

that this analysis is more refined than the dose-response contrast presented in Fig. 2C). This yielded 314 DEGs (Fig. 3 and Supplementary Table S1): 287 downregulated genes (P1 and P2) and 27 upregulated genes (P3). Secondly, we refined the selection for those that were DEGs in all four groups: this yielded 79 downregulated genes. For the upregulated genes none were DEGs in all four dose groups and there were only 4 (*Cpne8*, *Dcn*, *Pnisr* and *Slc7a3*) in common between the 3 dose groups. Therefore, we concentrated on the 27 genes that were upregulated in the animals exposed to high dose MMI and amitrole. The select 106 DEGs (79 + 27) from P1, P2 and P3 are depicted in Fig. 4 and Supplementary Table S1. We displayed the median expression of each gene in each dose group (thin grey curves) and the global median expression of all DEGs in each dose group (thick colored curves). This illustrates how the DEGs in each pattern showed similar expression changes in both MMI and amitrole-exposed offspring. Since the gene transcripts displayed dose-dependent effects, and were affected across dose-groups and compounds, we consider the list to be the most robust transcriptional fingerprint of developmental hypothyroidism. Possibly, the list provides a compound-independent readout of TH-mediated effects on the developing brain – a fingerprint that can link TH system disruption to adverse effects.

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Dose response effects on transcription and identification of biomarker candidates in the hippocampus. A) Gene expression changes for select candidate biomarker genes across control, low and high doses of MMI (8 and 16 mg/kg/d) and amitrole (25 and 50 mg/kg/d) for each pattern (P). In pattern 1 and 2 there were respectively 13 and 66 DEGs displaying dose-response effects and affected in all four exposure groups. The difference between the low dose MMI and the low dose amitrole coincides with more marked effects on serum T4 levels in the fetuses and neonates in the animals exposed to the low dose of amitrole (unpublished data). In P3 no transcripts were DEGs in all groups, but there were 27 transcripts affected in both MMI and amitrole high dose groups. Graphs show scaled log-transformed counts with light grey lines the median expression of each DEG. Colored thick lines indicate median expression of all dose-response DEGs in the pattern and grey shaded areas the density of datapoints for each exposure group. B-D) Dose-response expression for the chosen putative biomarker transcripts. Five transcripts belong to P1 (B), 11 to P2 (C) and 4 to P3 (D). Ami: amitrole, DEGs: differentially expressed genes, MMI: methimazole, P: pattern.</figcaption></figure>
To select a representative subset of genes, for instance for RT-qPCR profiling, the most useful would be to further refine our selection towards more sensitive (towards TH deficiency) and robust (large fold-changes and dynamic range) gene biomarker candidates. Among the 106 DEGs we chose a subset (Fig. 4B) representing a set of biomarker

candidates, chosen as representatives of different characteristics: effect direction, expression levels, some with large fold changes in the least affected MMI-8 group (Mobp, Fscn1, Gpr37l1, Slc39a1), a TR target gene (Pnisr (according to GTRD (via GSEA gene lists) and TTRUST)), some from each pattern (Fig. 4 B-D), some previously identified in developmental hypothyroidism studies (Mal, Mobp, Mog, Ntf3, Gpr37l1, Fxyd6, Padi2, int5, Samd4b, Sema4d) and some with large %-changes representing, to our knowledge, newly identified markers of developmental TH deficiency (Slc39a1, Ythdf2, Ube4d, Dcn). We suggest this battery to be tested as markers of developmental TH deficiency and TH-mediated adverse brain development.

## 4. Discussion

In this study we scrutinized the transcriptome of the hippocampus from chemically induced hypothyroid rats. The aim was to potentially identify a transcriptional fingerprint represented by a smaller battery of DEGs that can be used as a toxicological predictive marker of whether a compound may cause adverse TH-mediated effects in the brain of intact animals, including humans. We chose to analyze tissues from rats after exposure to the two TPO inhibitors MMI and amitrole since TPO inhibition is a prominent mechanism for chemically induced developmental hypothyroidism. The inclusion of two chemicals also allowed for first line identification of transcriptional effects caused by hypothyroidism per se and not simply a chemical-specific effect. We have previously shown changes to cortical gene expression as well as adverse effects on pup behavior (Ramhøj et al., 2022) in the animals included herein; and these two chemicals can also induce periventricular heterotopia in rats – a TH dependent irreversible malformation of the brain (Goodman and Gilbert, 2007; Ramhøj et al., 2021; Shibutani et al., 2009; Shiraki et al., 2012). Thus, the adverse effect has been established in this and similar studies and the transcriptome reflects the molecular changes associated with adverse brain development.

The hippocampal transcriptional fingerprint included changes to gene transcripts and their related functions and described the effects of developmental hypothyroidism as induced by TPO inhibitors. We found alterations in transcripts related to neurons, oligodendrocytes, and astrocytes, as well as in key functions such as cell cycle, cell differentiation, synapses, migration, myelination and axonogenesis. These effects are consistent with findings in previous studies of developmental hypothyroidism (Bernal, 2022; Gilbert et al., 2020; Kobayashi et al., 2008; Morte et al., 2010; Royland et al., 2008; Shiraki et al., 2014). We compared our 816 DEGs to the 51 gene transcripts found in a dose response study of 1–3 ppm propylthiouracil (PTU) administered in the drinking water throughout gestation and lactation (Royland et al., 2008). This exposure regimen renders pups hypothyroid during development, the 3 ppm dose to a similar degree as MMI and amitrole high dose groups (>80 % reduction) while 1 ppm causes a more moderate sustained reduction similarly to the low dose MMI (O'Shaughnessy, Wood, et al., 2018; Royland et al., 2008). Hippocampi from the 14-day old male rats were analyzed by Affymetrix Rat Gene Chips. The Royland et al. study used different compounds to us (PTU versus MMI and amitrole), as well as different transcriptomics technologies (microarray versus BRB-seq), and we identified 23 of the 51 DEGs from the PTU exposure study (Royland et al., 2008). The common genes were related to processes such as cell adhesion (Col11a2, Hapln1), differentiation (including Hopx, Septin4, Ntf3), myelination (including Cldn11, Plp, Mag, Mal, Mobp, Mog), and synaptic function (Fxyd6, Gpr37). Having established the transcriptional fingerprint, we proceeded to narrow down a selection of genes that were the most robust and sensitive markers of developmental TH deficiency. We extracted a subset of 106 gene

transcripts that were DEGs in both low and high dose groups (only high dose groups for the upregulated DEGs), displayed dose-response expression patterns and were affected by both MMI and amitrole. Expression of these genes could represent the most robust fingerprint of developmental TH disruption caused by TPO inhibitors.

Previously, a selection of dose-dependent DEGs in cortex of developmentally hypothyroid rats have been defined (O'Shaughnessy, Wood, et al., 2018) and later used to evaluate TH action in the developing cortex (Gilbert et al., 2021, 2023; Ramhøj et al., 2020, 2022). Because the affected gene transcripts can differ between cortex and hippocampus (Bastian et al., 2012; Kobayashi et al., 2008; Royland et al., 2008; Shiraki et al., 2014) we aimed to define a similar battery of candidate biomarker genes to detect TH deficiency in the hippocampus. Clearly, the full transcriptional fingerprint represents the gold standard for effect identification. Future toxicity testing frameworks will likely incorporate the use of omics technologies, including toxicogenomics (Darde et al., 2018; Martins et al., 2019; Rahman et al., 2022). However, until this has been implemented, the use of gene batteries represents a pragmatic approach for molecular identification. Thus, we propose a list of 20 selected candidate genes which we expect would be the most accurate indicators of the effects in the hippocampus. This gene battery was chosen based on them being easy to assess by standard RT-qPCR, having lowest possible interindividual variation, the largest possible dynamic range and being relatively sensitive to more modest reductions in TH levels. Half of the genes has been identified before and six of them were DEGs in the low-dose PTU study by (Royland et al., 2008). Ideally, however, the gene transcripts would display larger fold-changes, and the sensitivity, including the gene expression response at the lower ends of the dose-response curves, should be the focus of future studies. We envision that they can be used as a measurable KE within the AOP framework. Specifically, KE-756 “Hippocampal gene expression, Altered” currently a KE in AOP-42, and others, describing a sequence of events from TPO inhibition, through TH reductions to decreased cognitive function.

It is challenging to identify and measure TH-mediated developmental neurotoxicity caused by chemical exposure. During brain development many processes take place in a spatiotemporal manner and some of these are mediated by TH-signaling (Bernal, 2022). The TH system is a complex endocrine system that also changes during development and many chemicals can target one or several processes from the central hypothalamic-pituitary-thyroid (HPT) axis to TH action, metabolism and excretion (Gilbert et al., 2020; Howdeshell, 2002; Miller et al., 2009; Mughal et al., 2018). Furthermore, when exposure takes place during fetal and neonatal life ADME (absorption, distribution, metabolism and excretion) properties of specific chemicals may change over time. This is likely the case for the low dose MMI group which was less affected around birth than the other 3 groups (unpublished data). Thus, spatiotemporality, TH system targets, and ADME properties all interact to cause the final *in vivo* effect outcomes. This may have implications for the transcriptional fingerprint identified in this study. We chose PD16 pups for consistency with previous studies, ease of assessment as well as the developmental stage of the rat brain. It is thus important to consider that the fingerprint has been determined in 16-day old rat offspring and that, possibly, the specific signature would look different if assessed at a different developmental stage. Additionally, MMI and amitrole induced developmental TH deficiency sustained at >50 % reduction in T4 throughout development. Thus, if a specific chemical only affects fetuses or postnatal pups, the fingerprint and effect profile may differ. This phenomenon is seen, for instance, with TH-mediated endpoints with specific critical windows including neuronal migration errors, parvalbumin expression and hearing loss (Ausó et al., 2004; Crofton, 2000; Gilbert et al., 2007; Lavado-autric et al., 2003; O'Shaughnessy et al., 2019; O'Shaughnessy, Ford, et al., 2018; Ramhøj et al., 2021; Uchida et al., 2021). Thus, careful evaluations of biology and toxicity should be considered when evaluating chemicals for TH system disrupting properties. Nevertheless, the
approach presented herein can contribute to improved assessments of TH system disrupting chemicals.

## 5. Conclusions

We have identified a transcriptional fingerprint of developmental hypothyroidism in the hippocampus. The gene signature is likely a readout of altered TH action caused by TH deficiency rather than representing a chemical specific fingerprint since data is derived from two different compounds. Therefore, the fingerprint represents a measurable link between TPO inhibition, low TH and adverse brain development in toxicity studies, and it could help improve the identification of TH system disrupting chemicals. Specifically, upon further validation, a suite of 20 genes provide a potential test method to assess KE-758 of AOP-42 describing events from TPO inhibition to impaired cognition. Although this will not address the full scope of TH mediated effects on the developing brain, it could provide an important first line of evidence to assess TH disrupting potential of chemical substances.

## Funding

This work was funded by the Danish Environmental Protection Agency through the Danish Centre on Endocrine Disrupters (CeHoS) and the EU Horizon 2020 programme, grant number 825161 for the project ATHENA: Assays for the identification of Thyroid Hormone axis-disrupting chemicals: Elaborating Novel assessment strategies, (Kortenkamp et al., 2020). LR received funding from Hørslev-Fonden.

## CRediT authorship contribution statement

**Marta Axelstad:** Writing – review & editing, Conceptualization. **Frédéric Chalmel:** Writing – review & editing, Visualization, Resources, Formal analysis. **Terje Svingen:** Writing – review & editing, Resources, Funding acquisition, Conceptualization. **Bertrand Evrard:** Writing – review & editing, Investigation. **Louise Ramhøj:** Writing – review & editing, Writing – original draft, Visualization, Investigation, Funding acquisition, Formal analysis, Conceptualization.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data Availability

The data is available as stated in the manuscript text

## Acknowledgements

We thank Mette Voigt Jessen, Heidi Letting, Lillian Sztuk and Dorte Lykkegaard Korsbech for their invaluable technical assistance. Likewise, we thank the BioFacility at the Technical University of Denmark for animal care and husbandry. BRB-sequencing was performed by SciLicium.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.tox.2024.153822.

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