Biology subjects

Linthorst, J.

Publications and source records attributed to Linthorst, J..

4 recordsLinked to original sources

bioRxiv2026

A machine learning approach to infer DNase1L3 activity from plasma cell-free DNA fragmentomics

DNase1L3 is an endonuclease that fragments DNA during apoptosis and digests DNA from microparticles in plasma, shaping key features of cell-free DNA (cfDNA) and maintaining extracellular DNA homeostasis, a process implicated in autoimmunity. The common missense variant p.Arg206Cys (R206C) affects cfDNA through a non-linear allele dosage effect, with limited effects in heterozygotes and strong effects in homozygotes. Therefore, commonly used models trained on fragmentomics from individuals with normal DNase1L3 activity perform poorly in R206C homozygotes. To address this, we analyzed cfDNA sequencing data from 129,676 Non-Invasive Prenatal Tests and validated R206C genotypes in a selection of 169 matching plasma samples. Supervised and unsupervised learning were used to infer DNase1L3 activity from cfDNA fragmentation properties. Our models accurately identify R206C homozygotes using as little as 10,000 cfDNA fragments, outperforming genotype imputation. However, unsupervised analysis reveals few samples that cluster with homozygotes but lack the corresponding genotype, suggesting that our method also identifies other or downstream effects of DNase1L3 impairment. Conversely, some R206C homozygotes initially lacked the aberrant fragmentome, but longitudinal follow-up across subsequent pregnancies shows that they develop aberrant fragmentomes over time. These findings enable the identification of samples with impaired DNase1L3 activity directly from sequencing data, providing a practical approach to improve interpretation and robustness of cfDNA-based diagnostics across clinical applications.

genomics↗

bioRxiv2025

Breaking Bad: Exploring & Complementing the Effects of the DNASE1L3 p.Arg206Cys Variant on Cell-Free DNA from an Isogenic Cell Line Model

DNASE1L3 is a key endonuclease, essential for proper fragmentation and clearance of cell-free DNA (cfDNA). The p.R206C common variant impairs DNASE1L3 secretion and activity, causing aberrant cfDNA fragmentation and therefore affecting liquid biopsy-based screening and diagnostics. Existing studies on DNASE1L3 relied on resource-intensive murine models or plasmid-based overexpression, which do not accurately represent native expression. To address this, we developed an isogenic HEK293T cell line model by using CRISPR Prime Editing for endogenous expression of DNASE1L3R206C. We analyzed the cfDNA composition directly from conditioned culture medium and found that fragment size distributions in mutant cells mimics the hypofragmented profiles previously observed in plasma samples from p.R206C carriers. We also showed that in vitro treatment of hypofragmented cfDNA with recombinant wildtype DNASE1L3 could enrich for mononucleosomal fragments, with fragment end-motifs characteristic of DNASE1L3 cleavage activity. This could open avenues for DNASE1L3 as a candidate pre-treatment agent to improve the accuracy and efficiency of cfDNA sequencing-based diagnostics in hypofragmented liquid biopsies. These findings demonstrate that our isogenic cell line model provides a controlled system to study cfDNA fragmentation biology and DNASE1L3 function.

molecular biology↗

bioRxiv2020

VNTR prediction on sequence characteristics using long-read annotation and validation by short-read pileup

Tandem repeats (TRs) are contiguously repetitive sequences with a high mutation rate. Several human diseases have been associated with an expansion of TR, a mutation which constitutes a change in their number of repetitions. Nevertheless, these Variable Number Tandem Repeats (VNTRs) have not been included in many genome-wide studies. The reason is that VNTR genotyping is inaccurate using short-read sequencing while new technology like long-read sequencing is expensive and lacks throughput. Here, we propose a sequence based random forest classifier that is able to predict variable expansion of TR regions, given by incomplete VNTR annotation from long-read sequencing of 5 haplotypes. The classifier mainly predicted VNTRs using the features TR length. The second most used feature is a novel finding: the Mfold predicted likelihood of self-folding for which more stable foldings are correlated with VNTRs. We validated VNTR candidates predicted by this classifier by clustering short-read pileup patterns compared across 17 genomes. TRs labeled VNTR by the classifier showed similar local variance in their pileup profiles. Contactdiederik.cvb@gmail.com Supplementary informationSupplementary data are available at bioRxiv

bioinformatics↗

bioRxiv2018

CHOP: Haplotype-aware path indexing in population graphs

MotivationAlthough the characterization of within species genomic diversity continues to increase, this information is usually not incorporated in the sequencing analysis process. Existing reference genomes can easily be converted to graph-based reference genomes by extending them with known sequence variations. However, the practical use of these graph-based reference genomes depends on the ability to align reads to them. Performing substring queries to paths through these graphs lies at the core of this task. The combination of increasing pattern length and encoded variations inevitably leads to a combinatorial explosion of the search space. We present a solution that uses haplotype information to prevent this from happening.\n\nResultsWe present CHOP, a method that uses haplotype information to retrieve the constrained sequence search space of a graph-based reference genome. Our approach enables typical sequence aligners to perform read alignments to graphs that store any type of variation. CHOP performs similarly to another graph alignment method that unnecessarily indexes all combinations of sequence, while aligning reads to a population reference graph of Mycobacterium tuberculosis genomes. CHOP can achieve increased sensitivity for variation detection by iteratively integrating variation into a graph. Finally, we show that CHOP can be applied to large and complex datasets, by applying it on a graph-based representation of chromosome 6 of the human genome encoding the variants reported by the 1000 Genomes project.

bioinformatics↗