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Relationship: 1387

Title

The title of the KER should clearly define the two KEs being considered and the sequential relationship between them (i.e., which is upstream and which is downstream). Consequently all KER titles take the form “upstream KE leads to downstream KE”. More help

T4 in serum, Decreased leads to Hippocampal gene expression, Altered

Upstream event

Upstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help

T4 in serum, Decreased

Downstream event

Downstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help

Hippocampal gene expression, Altered

Key Event Relationship Overview

The utility of AOPs for regulatory application is defined, to a large extent, by the confidence and precision with which they facilitate extrapolation of data measured at low levels of biological organisation to predicted outcomes at higher levels of organisation and the extent to which they can link biological effect measurements to their specific causes. Within the AOP framework, the predictive relationships that facilitate extrapolation are represented by the KERs. Consequently, the overall WoE for an AOP is a reflection in part, of the level of confidence in the underlying series of KERs it encompasses. Therefore, describing the KERs in an AOP involves assembling and organising the types of information and evidence that defines the scientific basis for inferring the probable change in, or state of, a downstream KE from the known or measured state of an upstream KE. More help

AOPs Referencing Relationship

This table is automatically generated upon addition of a KER to an AOP. All of the AOPs that are linked to this KER will automatically be listed in this subsection. Clicking on the name of the AOP in the table will bring you to the individual page for that AOP. More help

AOP Name	Adjacency	Weight of Evidence	Quantitative Understanding	Point of Contact	Author Status	OECD Status
Inhibition of Thyroperoxidase and Subsequent Adverse Neurodevelopmental Outcomes in Mammals	non-adjacent	High	Low	Evgeniia Kazymova (send email)	Open for citation & comment	TFHA/WNT Endorsed

Taxonomic Applicability

Select one or more structured terms that help to define the biological applicability domain of the KER. In general, this will be dictated by the more restrictive of the two KEs being linked together by the KER. Authors can indicate the relevant taxa for this KER in this subsection. The process is similar to what is described for KEs (see pages 30-31 and 37-38 of User Handbook) More help

Term	Scientific Term	Evidence	Link
rat	Rattus norvegicus	High	NCBI
mouse	Mus musculus	High	NCBI

Sex Applicability

Authors can indicate the relevant sex for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of the User Handbook). More help

Sex	Evidence
Male	High
Female	High

Life Stage Applicability

Authors can indicate the relevant life stage for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of User Handbook). More help

Term	Evidence
During brain development	High

Key Event Relationship Description

Provide a brief, descriptive summation of the KER. While the title itself is fairly descriptive, this section can provide details that aren’t inherent in the description of the KEs themselves (see page 39 of the User Handbook). This description section can be viewed as providing the increased specificity in the nature of upstream perturbation (KEupstream) that leads to a particular downstream perturbation (KEdownstream), while allowing the KE descriptions to remain generalised so they can be linked to different AOPs. The description is also intended to provide a concise overview for readers who may want a brief summation, without needing to read through the detailed support for the relationship (covered below). Careful attention should be taken to avoid reference to other KEs that are not part of this KER, other KERs or other AOPs. This will ensure that the KER is modular and can be used by other AOPs. More help

Many of the physiological effects of thyroid hormones (THs) are mediated through regulation of gene expression by zinc finger nuclear receptor proteins that are encoded by thyroid hormone genes alpha (Thra) and beta (Thrb). It is widely accepted that TH regulates gene transcription during brain development (Bernal, 2007; Anderson et al., 2003). The sole source of TH to the brain is from the circulating levels of the prohormone, thyroxine (T4). Once taken up from the serum to reach the brain, T4 is converted to triiodothyronine (T3) which binds to TH nuclear receptors (TRα and TRβ). On binding, and in the presence of regulatory cofactors, transcription of certain genes is either up- or down-regulated (Oppenheimer, 1983). However, only a small number of genes have been shown to be directly influenced by TH receptor binding, and of these, most are transcription factors (Quignodon et al., 2008; Thompson and Potter, 2000; Horn and Heuer, 2010). In this manner, THs do influence a wide variety of genes.

Evidence Supporting this KER

Assembly and description of the scientific evidence supporting KERs in an AOP is an important step in the AOP development process that sets the stage for overall assessment of the AOP (see pages 49-56 of the User Handbook). To do this, biological plausibility, empirical support, and the current quantitative understanding of the KER are evaluated with regard to the predictive relationships/associations between defined pairs of KEs as a basis for considering WoE (page 55 of User Handbook). In addition, uncertainties and inconsistencies are considered. More help

The weight of evidence for this indirect relationship is strong. It is well established that serum TH is the primary source of brain T4 from which neuronal T3, the active hormone, is locally generated and presented to the receptors in the nucleus of neurons to control gene transcription.

Biological Plausibility

Define, in free text, the biological rationale for a connection between KEupstream and KEdownstream. What are the structural or functional relationships between the KEs? For example, there is a functional relationship between an enzyme’s activity and the product of a reaction it catalyses. Supporting references should be included. However, it is recognised that there may be cases where the biological relationship between two KEs is very well established, to the extent that it is widely accepted and consistently supported by so much literature that it is unnecessary and impractical to cite the relevant primary literature. Citation of review articles or other secondary sources, like text books, may be reasonable in such cases. The primary intent is to provide scientifically credible support for the structural and/or functional relationship between the pair of KEs if one is known. The description of biological plausibility can also incorporate additional mechanistic details that help inform the relationship between KEs, this is useful when it is not practical/pragmatic to represent these details as separate KEs due to the difficulty or relative infrequency with which it is likely to be measured (see page 40 of the User Handbook for further information). More help

The biological plausibility of this KER is rated as strong. This is consistent with the known biology of the relationship between serum TH concentrations and brain TH concentrations, and the known action of TH to mediate gene transcription in brain and many other tissues.

Empirical Evidence

In this section authors are encouraged to cite specific evidence that supports the idea that a change in the upstream KE (KEupstream) will lead to, or is associated with, a subsequent change in the downstream KE (KEdownstream), assuming the perturbation of KEupstream is sufficient. Given the likelihood that new empirical support will be developed over time, particularly as various AOPs are tested and applied, it is most practical to provide empirical support in the form of bulleted lists or tables that include a short description of the nature of the empirical support along with the corresponding reference(s).In particular, it is useful to cite evidence showing that stressors that perturb KEupstream also perturb KEdownstream. Because this section of the KER description cites evidence from specific studies, it is also helpful to provide as much detail about the toxicological and biological context in which the measurements were made, as is feasible, including the stressor(s) tested, the effective doses at each KE, etc. While the KER itself is not intended to be stressor-specific, those details can aid the overall assessment of the individual AOPs that include that KER. These details also help inform the question of consistency of supporting data, consistency across different biological contexts for which the KER is relevant, and the applicability domain of the KER. However, authors are cautioned that this evidence should focus on data that only relate KEupstream to KEdownstream, and should avoid reference to other KEs, KERs and AOPs as much as possible in order to maintain modularity of the KER. Please see pages 40-41 of the User Handbook for more information. More help

The empirical support for this KER is strong. A global transcriptome analysis of primary cerebrocoritical cells was recently published in which a number of genes regulated by T3 were identified (Gil-Ibanez et al., 2015). Although the bulk of literature in which serum TH reductions have been associated with gene expression changes in the brain have been focused on the cortex, several reports in hippocampus are available. Genes directly regulated by TH include the transcription factors Hr and Klf9 (Bteb) (Thompson and Potter, 2000; Cayrou et al., 2002; Denver and Williamson, 2009). The expression of a number of genes modulated by TH are expressed in the hippocampus. Many of genes that regulate processes involved in hippocampal development are also present in the developing cortex. Thus, Table 1 lists TH responsive genes whose expression in either area are altered by TH reduction. This list is not meant to be exhaustive, just exemplary.

Gene Name	Tissue	Model	Age	Reference
FETAL
Klf9 (Bteb)	Rat- Cortex	MMI+CLO4	Fetus-GD17	Dong et al., 2015
Nurr1	Mouse-cortex	Thyroidectomy, MMI + ClO4	Fetal GD17; PN90	Navarro et al., 2014
Bdnf	Rat- Cortex	MMI	Fetus GD14-18	Pathak et al., 2011
Trkb	Rat- Cortex	MMI	Fetus GD14-18	Pathak et al., 2011
MCT8	Rat- Cortex	MMI	Fetus GD14-18	Mohan et al, 2012
Dio2	Rat -Cortex	MMI	Fetus GD14-18	Mohan et al, 2012
CyclinD1	Rat- Cortex	MMI	Fetus GD14-18	Mohan et al, 2012
Cyclin D2	Rat- Cortex	MMi	Fetus GD14-18	Mohan et al, 2012
Pax6	Rat- Cortex	MMI	Fetus GD14	Mohan et al, 2012
Hr	Mouse- cortex	MMI + ClO4	Fetal GD17	Morte et al., 2010
Sema7a	Mouse- Cortex	MMI + ClO4	Fetal GD17	Morte et al., 2010
RC3 (Neurogranin)	Rat- Hippocampus, Cortex	MMI	Fetus-GD16	Dowling and Zoeller, 2001
Camk4	Mouse-cortex	Thyroidectomy, MMI + ClO4	Fetal GD17; PN90	Morte et al., 2010; Navarro et al., 2014
NEONATAL
Klf9 (Bteb)	Rat, Mouse- Cortex	PTU	Neonate-PN14	Royland et al., 2008; Bastian et al., 2012; Denver and Williamson, 2009; Denver et al., 1999
Hr	Rat- Cortex, Hippocampus, Cerebellum	PTU, MMI	Neonate-PN14	Royland et al., 2008; Bastian et al., 2012; Thompson and Potter, 2000; Morte et al., 2010
Parv	Rat- cortex	PTU	Neonate-PN14/21	Royland et al., 2008; Bastian et al., 2012; 2014, Shiraki et al., 2014
Ngf	Rat- Hippocampus, cortex	PTU	Neonate-PN14, PN90	Royland et al., 2008; Bastian et al., 2012; Gilbert et al., 2016
Agt	Rat- Cortex	PTU	Neonate, PN14	Royland et al., 2008; Bastian et al., 2012; 2014
Col11a2	Rat- Cortex	PTU	Neonate, PN14	Royland et al., 2008
Itih2	Rat- Cortex	PTU	Neonate, PN14	Royland et al., 2008
Sema7a	Rat- Cortex	PTU	Neonate-PN14	Royland et al., 2008
Reelin	Rat- Hippocampus, cortex, cerebellum	Thyroidectomy, PTU		Alvarez-Dolado et al. 1999; Shiraki et al., 2014
Mbp	Rat- Hippocampus, Cortex, Cerebellum	PTU, MMI	Neonate- PN14/21	Ibarrola et al., 1997; Royland et al., 2008; Bastian et al., 2012; 2014, Shiraki et al, 2014
Plp2	Hippocampus, Cortex	PTU, MMI		Royland et al., 2008; Bastian et al., 2012
Camk4	Rat/Mouse- cortex	PTU	Neonate-PN14	Royland et al., 2008
RC3 (Neurogranin)	Rat-hippocampus	Thyroidectomy + MMI	Neonate-PN5, PN21	Iniquez et al., 1993; Dong et al., 2010

Temporal Evidence: The temporal nature of this KER is developmental (Seed et al., 2005). It is a well-recognized fact that there are critical developmental windows for disruption of the serum THs that result in altered gene expression in the developing brain, including the hippocampus. Rescue experiments for this endpoint of gene expression in hypothyroid models are limited. In one, a combination of T3 and T4 treatment delivered on the last day of a 3-day gestational MMI hypothyroxinemia mouse model altered the pattern of gene expression observed in the cortex of offspring relative to euthyroid controls and MMI alone (Dong et al., 2015).

Dose-Response Evidence: There are a limited number of studies that have reported on the dose-dependent nature of the correlation between serum THs and hippocampal gene expression (Bastian et al., 2012; 2014; Royland et al., 2008).

Uncertainties and Inconsistencies

In addition to outlining the evidence supporting a particular linkage, it is also important to identify inconsistencies or uncertainties in the relationship. Additionally, while there are expected patterns of concordance that support a causal linkage between the KEs in the pair, it is also helpful to identify experimental details that may explain apparent deviations from the expected patterns of concordance. Identification of uncertainties and inconsistencies contribute to evaluation of the overall WoE supporting the AOPs that contain a given KER and to the identification of research gaps that warrant investigation (seep pages 41-42 of the User Handbook).Given that AOPs are intended to support regulatory applications, AOP developers should focus on those inconsistencies or gaps that would have a direct bearing or impact on the confidence in the KER and its use as a basis for inference or extrapolation in a regulatory setting. Uncertainties that may be of academic interest but would have little impact on regulatory application don’t need to be described. In general, this section details evidence that may raise questions regarding the overall validity and predictive utility of the KER (including consideration of both biological plausibility and empirical support). It also contributes along with several other elements to the overall evaluation of the WoE for the KER (see Section 4 of the User Handbook). More help

There are no inconsistencies in this KER, but there are some uncertainties. It is widely accepted that changes in serum THs will result in alterations in hippocampal gene expression. Several different animal models have been used to manipulate serum TH concentrations that also measure gene expression changes. Varying windows of exposure to TH disruption and developmental sample time and region examined have also varied across studies. However, dose-response data is lacking. Most investigations of hippocampal gene expression have employed treatments that induce severe hormone reductions induced by PTU or MMI, or by thyroidectomy. In addition, few reports have studied the genes in the hippocampus, the cortex being more accessible in young animals. Finally, when the hippocampus is the target, different genes at different ages are reported, making it difficult to compare findings.

Quantitative Understanding of the Linkage

The quantitative understanding section of the KER description is intended to capture information that helps to define how much change in the upstream KE, and/or for how long, is needed to elicit a detectable and defined change in the downstream KE. Empirical support addresses whether data between the two KEs are consistent with the patterns that are expected if the upstream event is causing the downstream event to occur, while the quantitative understanding section helps to define the precision with which the state of the downstream KE can be predicted from knowledge of the state of the upstream KE. These quantitative relationships may be defined in terms of correlations, response-response relationships, dose-dependent transitions or points of departure (i.e., a threshold of change in KEupstream needed to elicit a change in KEdownstream), etc. They may take the form of simple mathematical equations or sophisticated biologically-based computational models that consider other modulating factors such as compensatory responses, or interactions with other biological or environmental variables. Regardless of form, the idea is to briefly describe what is known regarding the quantitative relationship between the KEs and cite appropriate literature that defines those relationships and/or provides support for them.Data that confer quantitative understanding of a KER are not necessarily mutually exclusive from those addressing other weight of evidence considerations. In that respect, the quantitative understanding section of the KER description is not intended to be redundant with the other WoE sections. Rather, it is intended to aid application of the AOP by allowing a reader to rapidly identify the relationships that would support a quantitative prediction of the probability or magnitude of change in KEdownstream based on a known state of KEupstream. For transparency, the toxicological and biological context in which the quantitative relationships were defined should be indicated within the description. However, the ultimate goal is to identify quantitative relationships that generalize across the entire applicability domain of the two KEs being linked via the KER. More help

Response-response Relationship

This subsection should be used to define sources of data that define the response-response relationships between the KEs. In particular, information regarding the general form of the relationship (e.g., linear, exponential, sigmoidal, threshold, etc.) should be captured if possible. If there are specific mathematical functions or computational models relevant to the KER in question that have been defined, those should also be cited and/or described where possible, along with information concerning the approximate range of certainty with which the state of the KEdownstream can be predicted based on the measured state of the KEupstream (i.e., can it be predicted within a factor of two, or within three orders of magnitude?). For example, a regression equation may reasonably describe the response-response relationship between the two KERs, but that relationship may have only been validated/tested in a single species under steady state exposure conditions. Those types of details would be useful to capture. More help

There are no quantitative models that predict the degree of serum TH reduction that is required to alter hippocampal gene transcription. Most investigations for hippocampus have been conducted in the neonate after severe hormone reductions. Only four publications have reported dose-dependent effects on gene expression in at less than maximal hormone depletion (Bastian et al., 2012; 2014; O'Shaughnessy et al., 2018; Royland et al., 2008). O'Shaughnessy et al (2018) demostrates dose-response relationships between cortical T4 and T3 concentrations and changes in a variety of neocortical genes (e.g., Parv, Col11a2, Hr, Ngf) that were "statistically significant at doses that decreased brain t4 and/or T3". There was no quantitation of this relationship reported.

In addition, there is very little known about whether compensatory processes are available in the developing hippocampus that may modulate the impact of serum levels on hippocampal gene transcription. These available data suggest that a 40-50% decrement in serum T4 in the pup, is sufficient to observe changes in hippocampal gene expression. This is similar to finding for loss of hearing function in rats following postnatal chemical-induced hypothyroxinemia (Crofton, 2004).

Time-scale

This sub-section should be used to provide information regarding the approximate time-scale of the changes in KEdownstream relative to changes in KEupstream (i.e., do effects on KEdownstream lag those on KEupstream by seconds, minutes, hours, or days?). This can be useful information both in terms of modelling the KER, as well as for analyzing the critical or dominant paths through an AOP network (e.g., identification of an AO that could kill an organism in a matter of hours will generally be of higher priority than other potential AOs that take weeks or months to develop). Identification of time-scale can also aid the assessment of temporal concordance. For example, for a KER that operates on a time-scale of days, measurement of both KEs after just hours of exposure in a short-term experiment could lead to incorrect conclusions regarding dose-response or temporal concordance if the time-scale of the upstream to downstream transition was not considered. More help

Known modulating factors

This sub-section presents information regarding modulating factors/variables known to alter the shape of the response-response function that describes the quantitative relationship between the two KEs (for example, an iodine deficient diet causes a significant increase in the slope of the relationship; a particular genotype doubles the sensitivity of KEdownstream to changes in KEupstream). Information on these known modulating factors should be listed in this subsection, along with relevant information regarding the manner in which the modulating factor can be expected to alter the relationship (if known). Note, this section should focus on those modulating factors for which solid evidence supported by relevant data and literature is available. It should NOT list all possible/plausible modulating factors. In this regard, it is useful to bear in mind that many risk assessments conducted through conventional apical guideline testing-based approaches generally consider few if any modulating factors. More help

Known Feedforward/Feedback loops influencing this KER

This subsection should define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits? In some cases where feedback processes are measurable and causally linked to the outcome, they should be represented as KEs. However, in most cases these features are expected to predominantly influence the shape of the response-response, time-course, behaviours between selected KEs. For example, if a feedback loop acts as compensatory mechanism that aims to restore homeostasis following initial perturbation of a KE, the feedback loop will directly shape the response-response relationship between the KERs. Given interest in formally identifying these positive or negative feedback, it is recommended that a graphical annotation (page 44) indicating a positive or negative feedback loop is involved in a particular upstream to downstream KE transition (KER) be added to the graphical representation, and that details be provided in this subsection of the KER description (see pages 44-45 of the User Handbook). More help

Domain of Applicability

As for the KEs, there is also a free-text section of the KER description that the developer can use to explain his/her rationale for the structured terms selected with regard to taxonomic, life stage, or sex applicability, or provide a more generalizable or nuanced description of the applicability domain than may be feasible using standardized terms. More help

Most of the data available has come from rodent models.

References

List of the literature that was cited for this KER description using the appropriate format. Ideally, the list of references should conform, to the extent possible, with the OECD Style Guide (OECD, 2015). More help

Alvarez-Dolado M, Ruiz M, Del Rio JA, Alcantara S, Burgaya F, Sheldon M, Nakajima K, Bernal J, Howell BW, Curran T, Soriano E, Munoz A (1999) Thyroid hormone regulates reelin and dab1 expression during brain development. J Neurosci 19:6979-6993.

Anderson GW, Schoonover CM, Jones SA (2003) Control of thyroid hormone action in the developing rat brain. Thyroid 13:1039-56.

Bastian TW, Anderson JA, Fretham SJ, Prohaska JR, Georgieff MK, Anderson GW (2012), Fetal and neonatal iron deficiency reduces thyroid hormone-responsive gene mRNA levels in the neonatal rat hippocampus and cerebral cortex. Endocrinology 153:5668-5680.

Bastian TW, Prohaska JR, Georgieff MK, Anderson GW. 2014. Fetal and neonatal iron deficiency exacerbates mild thyroid hormone insufficiency effects on male thyroid hormone levels and brain thyroid hormone-responsive gene expression. Endocrinology. 155:1157-1167.

Bernal J. 2007. Thyroid hormone receptors in brain development and function. Nature clinical practice Endocrinology & metabolism. 3:249-259.

Cayrou C, Denver RJ, Puymirat J. Suppression of the basic transcription element-binding protein in brain neuronal cultures inhibits thyroid hormone-induced neurite branching. Endocrinology. 2002 Jun;143(6):2242-9.

Crofton KM. Developmental disruption of thyroid hormone: correlations with hearing dysfunction in rats. Risk Anal. 2004 Dec;24(6):1665-71.

Denver RJ, Ouellet L, Furling D, Kobayashi A, Fujii-Kuriyama Y, Puymirat J. Basic transcription element binding protein (BTEB) is a thyroid hormone-regulated gene in the developing central nervous system. Evidence for a role in neurite outgrowth. J Biol Chem. 1999 Aug 13;274(33):23128-34.

Denver RJ, Williamson KE (2009) Identification of a thyroid hormone response element in the mouse Kruppel-like factor 9 gene to explain its postnatal expression in the brain. Endocrinology 150:3935-3943.

Dong J, Liu W, Wang Y, Xi Q, Chen J. 2010. Hypothyroidism following developmental iodine deficiency reduces hippocampal neurogranin, CaMK II and calmodulin and elevates calcineurin in lactational rats. International journal of developmental neuroscience 28:589-596.

Dong H, You SH, Williams A, Wade MG, Yauk CL, Thomas Zoeller R (2015) Transient Maternal Hypothyroxinemia Potentiates the Transcriptional Response to Exogenous Thyroid Hormone in the Fetal Cerebral Cortex Before the Onset of Fetal Thyroid Function: A Messenger and MicroRNA Profiling Study. Cereb Cortex 25:1735-1745.

Dowling AL, Zoeller RT. Thyroid hormone of maternal origin regulates the expression of RC3/neurogranin mRNA in the fetal rat brain. Brain research Molecular brain research. 2000. 82:126-132.

Gil-Ibanez P, Garcia-Garcia F, Dopazo J, Bernal J, Morte B. 2015. Global Transcriptome Analysis of Primary Cerebrocortical Cells: Identification of Genes Regulated by Triiodothyronine in Specific Cell Types. Cerebral cortex. Nov 2.

Horn S. and Heuer H. Thyroid hormone action during brain development: more questions than answers. Mol Cell Endocrinol. 2010 Feb 5;315(1-2):19-26.

Ibarrola N, Rodriguez-Pena A (1997) Hypothyroidism coordinately and transiently affects myelin protein gene expression in most rat brain regions during postnatal development. Brain Res 752:285-293.

Iñiguez MA, Rodriguez-Peña A, Ibarrola N, Aguilera M, Muñoz A, Bernal J. Thyroid hormone regulation of RC3, a brain-specific gene encoding a protein kinase-C substrate. Endocrinology. 1993 Aug;133(2):467-73.

Mohan V, Sinha RA, Pathak A, Rastogi L, Kumar P, Pal A, Godbole MM (2012) Maternal thyroid hormone deficiency affects the fetal neocorticogenesis by reducing the proliferating pool, rate of neurogenesis and indirect neurogenesis. Exp Neurol 237:477-488.

Morte B, Diez D, Auso E, Belinchon MM, Gil-Ibanez P, Grijota-Martinez C, Navarro D, de Escobar GM, Berbel P, Bernal J) Thyroid hormone regulation of gene expression in the developing rat fetal cerebral cortex: prominent role of the Ca2+/calmodulin-dependent protein kinase IV pathway. Endocrinology 2010a. 151:810-820.

Morte B, Ceballos A, Diez D, Grijota-Martinez C, Dumitrescu AM, Di Cosmo C, Galton VA, Refetoff S, Bernal J. Thyroid hormone-regulated mouse cerebral cortex genes are differentially dependent on the source of the hormone: a study in monocarboxylate transporter-8- and deiodinase-2-deficient mice. Endocrinology. 2010b. 151:2381-2387.

Navarro D, Alvarado M, Morte B, Berbel D, Sesma J, Pacheco P, Morreale de Escobar G, Bernal J, Berbel P. Late Maternal Hypothyroidism Alters the Expression of Camk4 in Neocortical Subplate Neurons: A Comparison with Nurr1 Labeling. Cereb Cortex 2014. 10:2694-2706.

Oppenheimer J. The nuclear-receptor-triiodothyronine complex: Relationship to thyroid hormone distribution, metabolism, and biological action, In: Samuels HH, eds: Molecular Basis of Thyroid Hormone Action. Academic Press: New York. 1983: 1-34.

Pathak A, Sinha RA, Mohan V, Mitra K, Godbole MM (2011). Maternal thyroid hormone before the onset of fetal thyroid function regulates reelin and downstream signaling cascade affecting neocortical neuronal migration. Cereb Cortex 21:11-21.

Quignodon L, et al. Thyroid hormone signaling is highly heterogeneous during pre- and postnatal brain development. J Mol Endocrinol 2004, 33(2), 467-476.

Royland JE, Parker JS, Gilbert ME. A genomic analysis of subclinical hypothyroidism in hippocampus and neocortex of the developing rat brain. J Neuroendocrinol. 2008 Dec;20(12):1319-38.

Seed J, Carney EW, Corley RA, Crofton KM, DeSesso JM, Foster PM, Kavlock R, Kimmel G, Klaunig J, Meek ME, Preston RJ, Slikker W Jr, Tabacova S, Williams GM, Wiltse J, Zoeller RT, Fenner-Crisp P, Patton DE. Overview: Using mode of action and life stage information to evaluate the human relevance of animal toxicity data. Crit Rev Toxicol. 2005 35:664-72.

Shiraki A, Saito F, Akane H, Takeyoshi M, Imatanaka N, Itahashi M, Yoshida T, Shibutani M (2014) Expression alterations of genes on both neuronal and glial development in rats after developmental exposure to 6-propyl-2-thiouracil. Toxicol Lett 228:225-234.

Thompson CC, Potter GB. Thyroid hormone action in neural development. Cereb Cortex 2000, 10(10), 939-945.