Biology subjects

Verheul, M.

Publications and source records attributed to Verheul, M..

5 recordsLinked to original sources

bioRxiv2026

Somatic variant detection using an individual-specific pangenome reference: a case study of a pediatric acute myeloid leukemia patient

Background: Reference bias can cause false or missed somatic variant calls, also in clinically relevant genes. Pangenomes aim to reduce this bias, but evidence of their diagnostic benefit is lacking. We tested how an individual-specific pangenome affects somatic variant calling in bulk samples and single cells. Results: We built an individual-specific pangenome from Nanopore assemblies of normal hematopoietic stem and progenitor cells of a child with acute myeloid leukemia. We aligned short-read whole-genome sequencing data of bulk and single-cell samples to hg38 and to the pangenome, and called somatic variants with standard downstream tools after anchoring pangenome alignments to hg38. Single nucleotide variants were mostly shared, while indel calls unique to the pangenome were fewer than those unique to hg38, mostly in repeats. Both approaches detected NUP98-NSD1 and FLT3 internal tandem duplication (FLT3-ITD) drivers. The pangenome-based phylogeny showed more parallel branching in comparison to hg38-based tree. The pangenome removed three false-positive PRSS1 variants and, in single cells, frameshift insertion calls next to polyG and polyC repeats. However, surjection to hg38 coordinates split a WT1 driver indel call in a low-complexity region into two separate calls. Validation showed that all somatic structural variant calls, except NUP98-NSD1 and FLT3-ITD, were germline variants or artifacts. Conclusions: The use of individual-specific pangenome led to fewer false-positive indel and driver calls, mainly in repeats and single cells, and changed the branching of the inferred phylogeny. However, surjection artifacts and false-positive somatic structural variants show that pangenome-based results need read-level inspection and validation before clinical use.

genomics↗

bioRxiv2025

Single-cell whole-genome sequencing reveals convergent evolution in Burkitt lymphoma

Burkitt lymphoma (BL) carries multiple oncogenic drivers yet arises predominantly in children, whose normal cells harbour few age-related mutations. To investigate this paradox, we sought to define the sequence and timing of mutational drivers underlying BL development. Here, we analysed single-cell whole-genome sequencing (WGS) data of 250 paired normal and malignant B-cells from BL patients and integrated this data with 21 bulk WGS samples and an existing dataset of 157 clonally expanded B-cells from healthy individuals. Phylogenetic reconstruction revealed an accelerated accumulation of mutations following the emergence of the most recent common ancestor, leading to the early establishment of extensive genetic intra-tumoral heterogeneity. We further provide evidence that convergent evolution shapes this diversity at both the point mutation- and copy number variation-levels. This in-depth characterisation further establishes BL as a paradigmatic model of tumorigenesis with implications for therapeutic intervention.

cancer biology↗

bioRxiv2025

Hypoxia-related immune subsets induced by Salmonella Typhi infection link early bacterial gut invasion to human infection outcomes

Salmonella Typhi (S. Typhi), the causative agent of typhoid disease, remains a major public health concern. Owing to the human-restricted nature of S. Typhi, current studies of typhoid pathogenesis in animal models are limited to a murine non-typhoidal pathogen. Furthermore, human studies are limited to analyses of peripheral immune responses which are blind to tissue-specific immunity and do not allow perturbations. What is now needed is an integrative approach that will provide mechanistic insights into S. Typhi pathogenesis and immune correlates of infection outcome. Here, we performed an integrated single-cell analysis of immune responses from the human S. Typhi challenge model and mouse model of typhoid disease, to associate biological mechanism with human infection outcome. Most prominent, we revealed immune subsets with a hypoxia-related signature in circulating immune cells from individuals that develop disease in the human challenge model. This signature was also evident in the mouse model in activated macrophages infiltrating into the Peyers patches, but not during infection with a mutant strain impaired for gut invasion. We further identified hypoxia-related signature as a general immune correlate of disease outcome in other infection- and inflammatory-related diseases. Collectively, using integrated analysis of mouse and human infection models, we revealed a hypoxia-related signature that link immune responses during bacterial invasion to increased risk of developing typhoid disease in humans, suggesting a possible causative role during the development of typhoid disease.

immunology↗

bioRxiv2023

Comprehensive single-cell genome analysis at nucleotide resolution using the PTA Analysis Toolbox

Detection of somatic mutations in single cells has been severely hampered by technical limitations of whole genome amplification. Novel technologies including primary template-directed amplification (PTA) significantly improved the accuracy of single-cell whole genome sequencing (WGS), but still generate hundreds of artefacts per amplification reaction. We developed a comprehensive bioinformatic workflow, called the PTA Analysis Toolkit (PTATO), to accurately detect single base substitutions, small insertions and deletions (indels) and structural variants in PTA-based WGS data. PTATO includes a machine learning approach to distinguish PTA-artefacts from true mutations with high sensitivity (up to 90% for base substitution and 95% for indels), outperforming existing bioinformatic approaches. Using PTATO, we demonstrate that many hematopoietic stem and progenitor cells of patients with Fanconi anemia, which cannot be analyzed using regular WGS technologies, have normal somatic single base substitution burdens, but increased numbers of deletions. Our results show that PTATO enables studying somatic mutagenesis in the genomes of single cells with unprecedented sensitivity and accuracy.

genomics↗

bioRxiv2021

MutationalPatterns: The one stop shop for the analysis of mutational processes

BackgroundThe collective of somatic mutations in a genome represents a record of mutational processes that have been operative in a cell. These processes can be investigated by extracting relevant mutational patterns from sequencing data. ResultsHere, we present the next version of MutationalPatterns, an R/Bioconductor package, which allows in-depth mutational analysis of catalogues of single and double base substitutions as well as small insertions and deletions. Major features of the package include the possibility to perform regional mutation spectra analyses and the possibility to detect strand asymmetry phenomena, such as lesion segregation. On top of this, the package also contains functions to determine how likely it is that a signature can cause damaging mutations (i.e., mutations that affect protein function). This updated package supports stricter signature refitting on known signatures in order to prevent overfitting. Using simulated mutation matrices containing varied signature contributions, we showed that reliable refitting can be achieved even when only 50 mutations are present per signature. Additionally, we incorporated bootstrapped signature refitting to assess the robustness of the signature analyses. Finally, we applied the package on genome mutation data of cell lines in which we deleted specific DNA repair processes and on large cancer datasets, to show how the package can be used to generate novel biological insights. ConclusionsThis novel version of MutationalPatterns allows for more comprehensive analyses and visualization of mutational patterns in order to study the underlying processes. Ultimately, in-depth mutational analyses may contribute to improved biological insights in mechanisms of mutation accumulation as well as aid cancer diagnostics. MutationalPatterns is freely available at http://bioconductor.org/packages/MutationalPatterns.

genomics↗