Biology subjects

Koeleman, B. P. C.

Publications and source records attributed to Koeleman, B. P. C..

5 recordsLinked to original sources

bioRxiv2025

Predicting Drug Response with Multi-Task Gradient-Boosted Trees in Epilepsy

MotivationDespite the availability of numerous anti-seizure medications (ASMs), drug resistance remains a major issue for people with epilepsy. The probability of achieving seizure freedom diminishes with each unsuccessful drug trial, and the impact of genetic and clinical markers on ASM response remains unclear. To address this issue, we used state-of-the-art machine learning (ML) methods to predict the response of people with epilepsy to individual ASMs based on their clinical and genomic information. ResultsTo overcome data sparsity for less common drugs, we implement a multi-task (MT) learning approach for gradient-boosted trees (GBTs), assuming that predicting responses to different ASMs involves similar tasks. This strategy allows models for less prevalent drugs to leverage the more abundant data available for other drugs during training. The proposed model outperforms individual and combined drug-response predictions for most drugs. Our findings identify key genomic and clinical features influencing drug response, enhancing understanding of individual drug responses in people with epilepsy, and aiding clinicians in making informed treatment decisions. Availability and ImplementationDue to privacy reasons data is not publically available. The code will be made available upon acceptance under https://github.com/pfeiferAI/MT-GBT

bioinformatics↗

bioRxiv2022

Efficient and accurate prime editing strategy to correct genetic alterations in hiPSC using single EF-1alpha driven all-in-one plasmids

Prime editing (PE) is currently the most effective and versatile technology to make targeted alterations in the genome. Several improvements to the PE machinery have recently been made, and have been tested in a range of model systems, including immortalized cell lines, stem-cells and animal models. While nick RNA (ncRNA)-dependent PE systems like PE3 and PE5 are currently considered to be the most effective, they come with undesired indels or SNVs at the edit locus. Here, we aimed to improve ncRNA-independent systems PE2 and PE4max by generating novel all-in-one (pAIO) plasmids, driven by a tissue-broad EF-1alpha promoter, that is especially suitable for human iPSC models, and linked to a GFP tag for fluorescent based sorting. These novel pAIO systems effectively corrected mutations observed in patients suffering from epileptic encephalopathy, including a truncating SCN1A R612* variant in HEK293T cells and a gain-of-function KCNQ2 R201C variant in patient-derived hiPSC, with edit efficiency up to 50%. We also show that introducing additional silent PAM-removing mutations can negatively influence edit efficiency. Finally, we observed an absence of genome-wide PE off-target effects at pegRNA homology sites. Taken together, our study shows an improved efficacy and accuracy of EF-1alpha driven ncRNA-independent pAIO PE plasmids in hiPSC.

molecular biology↗

bioRxiv2019

NaV1.1 and NaV1.6 selective compounds reduce the behavior phenotype in a novel zebrafish model for Dravet Syndrome

Dravet syndrome is caused by dominant loss-of-function mutations in SCN1A which cause reduced activity of Nav1.1 leading to lack of neuronal inhibition. On the other hand, gain-of-function mutations in SCN8A can lead to a severe epileptic encephalopathy subtype by over activating NaV1.6 channels. These observations suggest that Nav1.1 and Nav1.6 represent two opposing sides of the neuronal balance between inhibition and activation. Here, we hypothesize that Dravet syndrome may be treated by either enhancing Nav1.1 or reducing Nav1.6 activity. To test this hypothesis we generated and characterized a novel DS zebrafish model and tested new compounds that selectively activate or inhibit the human NaV1.1 or NaV1.6 channel respectively. We used CRISPR/Cas9 to generate two separate Scn1Lab knockout lines as an alternative to previous knock-down models. Using an optimized locomotor assay, spontaneous burst movements were detected that were unique to Scn1Lab knockouts and disappear when introducing human SCN1A mRNA. Besides the behavioral phenotype, Scn1Lab knockouts show sudden, electrical discharges in the brain that indicate epileptic seizures in zebrafish. Scn1Lab knockouts showed increased sensitivity to the convulsant pentylenetetrazole and a reduction in whole organism GABA levels. Drug screenings further validated a Dravet syndrome phenotype. We tested the NaV1.1 activator AA43279 and our newly synthesized NaV1.6 inhibitors MV1369 and MV1312 in the Scn1Lab knockouts. Both type of compounds significantly reduced the number of burst movements. Our results show that selective inhibition of NaV1.6 could be just as efficient as selective activation of NaV1.1 and these approaches could prove to be novel potential treatment strategies for Dravet syndrome and other (genetic) epilepsies. Compounds tested in zebrafish however, should always be further validated in other model systems, preferably human derived.

genetics↗

bioRxiv2018

EXtrACtor, a tool for multiple queries and data extractions from the EXAC and gnomAD database

IntroductionEXtrACtor provides an easy and flexible way to perform multiple queries of the ExAC and gnomAD databases (Lek M, al. e, 2016) and process the results. The ExAC browser is an often-used tool to gather information from a large number of exomes while the gnomAD database expands on this by increasing the number of exomes but also adding genomes. However the websites only allow the users to query a single gene, transcript or location at a time. On the other hand downloading the entire database results in other challenges for the average user, especially with the download size for the gnomAD database approaching 100GB. EXtrACtor allows the user to supply a list of genes, transcripts or regions to be queried against either the ExAC or the gnomAD database. At the same time it gives the user a flexible user interface in order to quickly filter on various properties such as cohort, variant type and more.\n\nAvailability and implementationEXtrACtor source code is freely available from https://github.com/dagrim1/extractor and is written in platform independent software Java for use on windows, UNIX and mac.\n\nContactf.mulder@umcutrecht.nl

bioinformatics↗

bioRxiv2017

The Spectrum Of De Novo Variants In Neurodevelopmental Disorders With Epilepsy

Epilepsy is a frequent feature of neurodevelopmental disorders (NDD) but little is known about genetic differences between NDD with and without epilepsy. We analyzed de novo variants (DNV) in 6753 parent-offspring trios ascertained for different NDD. In the subset of 1942 individuals with NDD with epilepsy, we identified 33 genes with a significant excess of DNV, of which SNAP25 and GABRB2 had previously only limited evidence for disease association. Joint analysis of all individuals with NDD also implicated CACNA1E as a novel disease gene. Comparing NDD with and without epilepsy, we found missense DNV, DNV in specific genes, age of recruitment and severity of intellectual disability to be associated with epilepsy. We further demonstrate to what extent our results impact current genetic testing as well as treatment, emphasizing the benefit of accurate genetic diagnosis in NDD with epilepsy.

genetics↗