Biology subjects

van der Lee, M.

Publications and source records attributed to van der Lee, M..

3 recordsLinked to original sources

bioRxiv2022

Design and performance of a long-read sequencing panel for pharmacogenomics

Pharmacogenomics (PGx)-guided drug treatment is one of the cornerstones of personalized medicine. However, the genes involved in drug response are highly complex and known to carry many (rare) variants. Current technologies (short-read sequencing and SNP panels) are limited in their ability to resolve these genes and characterize all variants. Moreover, these technologies cannot always phase variants to their allele of origin. Recent advance in long-read sequencing technologies have shown promise in resolving these problems. Here we present a long-read sequencing panel-based approach for PGx using PacBio HiFi sequencing. A capture based approach was developed using a custom panel of clinically-relevant pharmacogenes including up- and downstream regions. A total of 27 samples were sequenced and panel accuracy was determined using benchmarking variant calls for 3 Genome in a Bottle samples and GeT-RM star(*)-allele calls for 21 samples.. The coverage was uniform for all samples with an average of 94% of bases covered at >30x. When compared to benchmarking results, accuracy was high with an average F1 score of 0.89 for INDELs and 0.98 for SNPs. Phasing was good with an average of 68% the target region phased (compared to ~20% for short-reads) and an average phased haploblock size of 6.6kbp. Using Aldy 4, we compared our variant calls to GeT-RM data for 8 genes (CYP2B6, CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP3A5, SLCO1B1, TPMT), and observed highly accurate star(*)-allele calling with 98.2% concordance (165/168 calls), with only one discordance in CYP2C9 leading to a different predicted phenotype. We have shown that our long-read panel-based approach results in high accuracy and target phasing for SNVs as well as for clinical star(*)-alleles.

genetics↗

bioRxiv2020

A unifying model to predict variable drug response for personalised medicine

Pharmacogenomics is a key component of personalized medicine. It promises a safer and more effective drug treatment by individualizing the choice of drug and dose based on an individuals genetic profile1,2. The majority of commonly prescribed drugs are metabolized by a small set of Cytochrome P450 (CYP) enzymes3. In clinical practice, genetic biomarkers are being used to categorize patients into predefined *-alleles to predict CYP450 enzyme activity and adjust drug dosages accordingly. Yet, this approach has important limitations as it leaves a large part of variability in drug response unexplained4,5. Here, we present a novel approach and introduce a continuous scale (instead of categorical) assignments to predict metabolic enzyme activity. The proposed strategy uses full gene sequencing data, a neural network model and CYP2D6 mediated tamoxifen metabolism from a prospective study of 561 breast cancer patients. The model explains 79% of the interindividual variability in CYP2D6 activity compared to 54% with the conventional approach. It is capable of assigning accurate enzyme activity to alleles containing previously uncharacterized combinations of variants and were replicated in an independent cohort of tamoxifen treated patients, a cohort of Venlafaxine users as well as in vitro functional assays using HEK cells. These results demonstrate the advantage of a continuous scale and a completely phased genotype for prediction of CYP450 enzyme activity and thereby enables more accurate prediction of individual drug response.

pharmacology and toxicology↗

bioRxiv2018

LCM-seq reveals unique transcriptional adaption mechanisms of resistant neurons in spinal muscular atrophy

Somatic motor neurons are selectively vulnerable in spinal muscular atrophy (SMA), a lethal disease caused by a deficiency of the ubiquitously expressed survival of motor neuron (SMN) protein. However, some brainstem motor neuron groups, including oculomotor and trochlear (ocular), which innervate the muscles around the eyes, are for unknown reasons spared. Here, using laser capture microdissection coupled with RNA sequencing (LCM-seq), we investigate the transcriptional dynamics in discrete neuronal populations in health and SMA to reveal mechanisms of vulnerability and resistance. Using gene correlation network analysis, we reveal a p53-mediated stress response that is intrinsic to all somatic motor neurons independent of their vulnerability, but absent in resistant red nucleus and visceral motor neurons. However, our temporal and spatial differential expression analysis across neuron types clearly demonstrates that the majority of SMA-induced modulations are cell-type specific. Notably, using gene ontology and protein-network analyses we show that ocular motor neurons present unique disease-adaptation mechanisms that could explain their resilience. In particular, ocular motor neurons up-regulate; i) Syt1, Syt5 and Cplx2, which modulate neurotransmitter release; ii) the motor neuron survival factors Chl1 and Lif, iii) Aldh4, that can protect cells from oxidative stress and iv) the caspase inhibitor Pak4. In conclusion, our in-depth longitudinal analysis of gene expression changes in SMA reveal novel cell-type specific changes that present compelling targets for future gene therapy studies aimed towards preserving vulnerable motor neurons.

neuroscience↗