Thyroid hormone (TH) is an important regulator of human brain development, and maternal TH imbalance is linked to adverse outcomes such as reduced IQ and decreased motor function in children. As part of the VHP4Safety initiative, using the human neural progenitor test (hNPT), we investigated the molecular effects of elevated levels and absence of T3. Neural progenitor cells were differentiated for 10 days under no (0nM), normal (4.1nM; the reference), or high (410nM) triiodothyronine (T3) conditions. RNA-Seq was used to assess gene expression, and a molecular Adverse Outcome Pathway (AOP) network was applied to interpret transcriptomic changes compared to the reference alongside Gene Ontology (GO) enrichment analysis. Data show that normal T3 levels induce key neurodevelopmental processes which are not active without T3 exposure, based on significantly altered gene expression in pathways related to neurogenesis, synaptogenesis, differentiation and metabolism, including pathways of brain-derived neurotrophic factor (BDNF) signalling, proliferation, oligodendrocyte specification, and glycolysis. Key Events (KEs) such as reduced BDNF and impaired proliferation were, compared to normal levels of T3, significantly affected in the molecular AOP network in absence of T3, while high T3 levels showed minimal differences in transcriptional effects. Our findings demonstrate that absence of T3 inhibits brain development since no T3 condition affects key neurodevelopmental processes in vitro. This study illustrates the utility of integrating transcriptomics with molecular AOPs to provide a structured, mechanistic framework for evaluating the regulation of neuronal cell proliferation and differentiation in brain development. The framework may be useful for chemical risk assessment.
Marvin Martens, N. Dierichs, J. L. Pennings et al.· Toxicology· 0 citations
Abstract Motivation Huntington’s disease (HD) exhibits substantial variability in age of onset and disease progression that is not fully explained by CAG repeat length alone. Part of this residual variation is heritable, implicating additional genetic mechanisms. cis-regulatory variation, genetic variants that alter transcription and splicing of nearby genes, represents one such mechanism that can be quantified through allele-specific expression (ASE) analysis. However, methods for integrating ASE profiles into patient stratification frameworks remain underdeveloped, particularly for rare diseases with small cohorts and sparse data. Results We adapt a network-based stratification algorithm, originally developed for somatic tumour mutations, to ASE data. By propagating gene-level ASE imbalance profiles through a protein-protein interaction network, we stratified 20 HD patients into three distinct biological patient subgroups. Differential gene expression analysis highlights neuroinflammatory pathways, including microglial activation, immune cell activation, and cytokine regulation, as key sources of inter-patient heterogeneity, while differential ASE analysis implicates proteasomal and ubiquitin-dependent protein catabolic processes, immune activation, and central nervous system development. Intersection of differentially imbalanced and expressed genes identified FAM181B as a candidate gene with potential eQTL-mediated regulation, supported by independent cis-eQTL evidence for rs3780 in the caudate and putamen, the primary HD-affected striatal regions. FAM181B encodes a nuclear protein expressed in neural tissues acting as an interactor of the Hippo pathway TEAD transcription factors, implicating transcriptional regulatory variation as a potential contributor to molecular heterogeneity between patient subgroups. Differences in cortical and striatal neuropathological scores between clusters, even when adjusted for CAG repeat length, provide clinical support for the biological relevance of the identified subgroups. Availability All analysis code, Docker containers, and conda environments are available at https://github.com/macsbio/HD-ASE-NBS.
D. van Beek, Aishwarya Iyer, Friederike Ehrhart et al.· Bioinformatics· 0 citations