👤 Peter A C 't Hoen

Koolen-de Vries Syndrome (KdVS) is a neurodevelopmental disorder (NDD) caused by KANSL1 haploinsufficiency with no treatment options. To investigate neuronal network activity in KdVS, human induced pluripotent stem cell (hiPSC)-derived neurons from KdVS patients and controls were cultured on microelectrode arrays (MEAs). KdVS networks exhibited reduced burst rates and increased variability in burst rhythmicity. To bridge molecular and functional aspects of the syndrome, we applied MEA-seq, integrating electrophysiological recordings with high-throughput transcriptome profiling. This analysis revealed a negative correlation between the NDD-associated gene CLCN4 and network burst rate. Knockdown of CLCN4 in KdVS neurons restored network bursting toward control levels, highlighting how transcriptome profiling can identify mediators linking genetic defects to relevant physiological phenotypes. We also identified significant correlations between mitochondrial gene expression and network activity and consequently confirmed impaired mitochondrial function in KdVS hiPSC-derived neurons. Using the KdVS transcriptomic signature for computational screening against the LINCS drug perturbation database, we predicted compounds capable of reversing dysregulated gene expression. Ten candidates were prioritized for experimental validation, focusing on mitochondrial function. Among these, the antioxidant phloretin improved multiple aspects of the KdVS-related network activity phenotype, reduced reactive oxygen species, and rescued synaptic density across patient lines, revealing its potential as a therapeutic candidate. Together, these findings demonstrate that integrative MEA-seq profiling can connect molecular and electrophysiological alterations in KdVS, providing a robust framework for identifying novel drugs and druggable pathways for KdVS and potentially other neurodevelopmental disorders. Show less

Hereditary disorders are frequently caused by genetic variants that affect pre-messenger RNA splicing. Though genetic variants in the canonical splice motifs are almost always disrupting splicing, the pathogenicity of variants in the noncanonical splice sites (NCSS) and deep intronic (DI) regions are difficult to predict. Multiple splice prediction tools have been developed for this purpose, with the latest tools employing deep learning algorithms. We benchmarked established and deep learning splice prediction tools on published gold standard sets of 71 NCSS and 81 DI variants in the ABCA4 gene and 61 NCSS variants in the MYBPC3 gene with functional assessment in midigene and minigene splice assays. The selection of splice prediction tools included CADD, DSSP, GeneSplicer, MaxEntScan, MMSplice, NNSPLICE, SPIDEX, SpliceAI, SpliceRover, and SpliceSiteFinder-like. The best-performing splice prediction tool for the different variants was SpliceRover for ABCA4 NCSS variants, SpliceAI for ABCA4 DI variants, and the Alamut 3/4 consensus approach (GeneSplicer, MaxEntScacn, NNSPLICE and SpliceSiteFinder-like) for NCSS variants in MYBPC3 based on the area under the receiver operator curve. Overall, the performance in a real-time clinical setting is much more modest than reported by the developers of the tools. Show less

Multiple single-nucleotide polymorphisms (SNPs) conferring susceptibility to osteoarthritis (OA) mark imbalanced expression of positional genes in articular cartilage, reflected by unequally expressed alleles among heterozygotes (allelic imbalance [AI]). We undertook this study to explore the articular cartilage transcriptome from OA patients for AI events to identify putative disease-driving genetic variation. AI was assessed in 42 preserved and 5 lesioned OA cartilage samples (from the Research Arthritis and Articular Cartilage study) for which RNA sequencing data were available. The count fraction of the alternative alleles among the alternative and reference alleles together (φ) was determined for heterozygous individuals. A meta-analysis was performed to generate a meta-φ and P value for each SNP with a false discovery rate (FDR) correction for multiple comparisons. To further validate AI events, we explored them as a function of multiple additional OA features. We observed a total of 2,070 SNPs that consistently marked AI of 1,031 unique genes in articular cartilage. Of these genes, 49 were found to be significantly differentially expressed (fold change <0.5 or >2, FDR <0.05) between preserved and paired lesioned cartilage, and 18 had previously been reported to confer susceptibility to OA and/or related phenotypes. Moreover, we identified notable highly significant AI SNPs in the CRLF1, WWP2, and RPS3 genes that were related to multiple OA features. We present a framework and resulting data set for researchers in the OA research field to probe for disease-relevant genetic variation that affects gene expression in pivotal disease-affected tissue. This likely includes putative novel compelling OA risk genes such as CRLF1, WWP2, and RPS3. Show less

The regulation of translation initiation factor 2 (eIF2) is important for erythroid survival and differentiation. Lack of iron, a critical component of heme and hemoglobin, activates Heme Regulated Inhibitor (HRI). This results in phosphorylation of eIF2 and reduced eIF2 availability, which inhibits protein synthesis. Translation of specific transcripts such as Atf4, however, is enhanced. Upstream open reading frames (uORFs) are key to this regulation. The aim of this study is to investigate how tunicamycin treatment, that induces eIF2 phosphorylation, affects mRNA translation in erythroblasts. Ribosome profiling combined with RNA sequencing was used to determine translation initiation sites and ribosome density on individual transcripts. Treatment of erythroblasts with Tunicamycin (Tm) increased phosphorylation of eIF2 2-fold. At a false discovery rate of 1%, ribosome density was increased for 147 transcripts, among which transcriptional regulators such as Atf4, Tis7/Ifrd1, Pnrc2, Gtf2h, Mbd3, JunB and Kmt2e. Translation of 337 transcripts decreased more than average, among which Dym and Csde1. Ribosome profiling following Harringtonine treatment uncovered novel translation initiation sites and uORFs. Surprisingly, translated uORFs did not predict the sensitivity of transcripts to altered ribosome recruitment in presence or absence of Tm. The regulation of transcription and translation factors in reponse to eIF2 phosphorylation may explain the large overall response to iron deficiency in erythroblasts. Show less

Erythropoiesis is regulated at many levels, including control of mRNA translation. Changing environmental conditions, such as hypoxia or the availability of nutrients and growth factors, require a rapid response enacted by the enhanced or repressed translation of existing transcripts. Cold shock domain protein e1 (Csde1/Unr) is an RNA-binding protein required for erythropoiesis and strongly upregulated in erythroblasts relative to other hematopoietic progenitors. The aim of this study is to identify the Csde1-containing protein complexes and investigate their role in post-transcriptional expression control of Csde1-bound transcripts. We show that Serine/Threonine kinase receptor-associated protein (Strap/Unrip), was the protein most strongly associated with Csde1 in erythroblasts. Strap is a WD40 protein involved in signaling and RNA splicing, but its role when associated with Csde1 is unknown. Reduced expression of Strap did not alter the pool of transcripts bound by Csde1. Instead, it altered the mRNA and/or protein expression of several Csde1-bound transcripts that encode for proteins essential for translational regulation during hypoxia, such as Hmbs, eIF4g3 and Pabpc4. Also affected by Strap knockdown were Vim, a Gata-1 target crucial for erythrocyte enucleation, and Elavl1, which stabilizes Gata-1 mRNA. The major cellular processes affected by both Csde1 and Strap were ribosome function and cell cycle control. Show less

Metabolite quantitative traits carry great promise for epidemiological studies, and their genetic background has been addressed using Genome-Wide Association Studies (GWAS). Thus far, the role of less common variants has not been exhaustively studied. Here, we set out a GWAS for metabolite quantitative traits in serum, followed by exome sequence analysis to zoom in on putative causal variants in the associated genes. 1H Nuclear Magnetic Resonance (1H-NMR) spectroscopy experiments yielded successful quantification of 42 unique metabolites in 2,482 individuals from The Erasmus Rucphen Family (ERF) study. Heritability of metabolites were estimated by SOLAR. GWAS was performed by linear mixed models, using HapMap imputations. Based on physical vicinity and pathway analyses, candidate genes were screened for coding region variation using exome sequence data. Heritability estimates for metabolites ranged between 10% and 52%. GWAS replicated three known loci in the metabolome wide significance: CPS1 with glycine (P-value  = 1.27×10-32), PRODH with proline (P-value  = 1.11×10-19), SLC16A9 with carnitine level (P-value  = 4.81×10-14) and uncovered a novel association between DMGDH and dimethyl-glycine (P-value  = 1.65×10-19) level. In addition, we found three novel, suggestively significant loci: TNP1 with pyruvate (P-value  = 1.26×10-8), KCNJ16 with 3-hydroxybutyrate (P-value  = 1.65×10-8) and 2p12 locus with valine (P-value  = 3.49×10-8). Exome sequence analysis identified potentially causal coding and regulatory variants located in the genes CPS1, KCNJ2 and PRODH, and revealed allelic heterogeneity for CPS1 and PRODH. Combined GWAS and exome analyses of metabolites detected by high-resolution 1H-NMR is a robust approach to uncover metabolite quantitative trait loci (mQTL), and the likely causative variants in these loci. It is anticipated that insight in the genetics of intermediate phenotypes will provide additional insight into the genetics of complex traits. Show less