Biology subjects

Hansen, N.

Publications and source records attributed to Hansen, N..

3 recordsLinked to original sources

bioRxiv2023

IL-1β promotes MPN disease initiation by favoring early clonal expansion of JAK2-mutant hematopoietic stem cells

JAK2-V617F is the most frequent somatic mutation causing myeloproliferative neoplasm (MPN). However, JAK2-V617F can also be found in healthy individuals with clonal hematopoiesis of indeterminate potential (CHIP) with a frequency much higher than the prevalence of MPN. The factors controlling the conversion of JAK2-V617F CHIP to MPN are largely unknown. We hypothesized that IL-1{beta} mediated inflammation is one of the factors that favors this progression. We examined mono- or oligoclonal evolution of MPN by performing bone marrow transplantations at limiting dilutions with only 1-3 JAK2-mutant HSCs per recipient. Genetic loss of IL-1{beta} in JAK2-mutant hematopoietic cells or inhibition by a neutralizing anti-IL-1{beta} antibody restricted the early clonal expansion of these JAK2-mutant HSCs resulting in a reduced frequency of a CHIP-like state and a lower rate of conversion to MPN. The MPN disease-promoting effects of IL-1{beta} were associated with damage to sympathetic innervation leading to loss of nestin-positive mesenchymal stromal cells and required the presence of IL-1R1 on bone marrow stromal cells. The anti-IL-1{beta} antibody protected these mesenchymal stromal cells from IL-1{beta} mediated damage and limited the expansion of the JAK2-mutant clone. Our results identify IL-1{beta} as a potential therapeutic target for preventing the transition from JAK2-V617F CHIP to MPN. Brief summaryIn a mouse model of oligo-clonal myeloproliferative neoplasm (MPN), IL-1{beta} produced by JAK2-mutant cells favored expansion of sub-clinical JAK2-V617F clones and initiation of MPN disease.

cancer biology↗

bioRxiv2022

Individualized Gaussian Process-based Prediction of Memory Performance and Biomarker Status in Ageing and Alzheimer's disease

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as informative covariates, vascular risks, brain activity, neuropsychological test etc.,) might provide useful predictions of clinical outcomes during progression towards Alzheimers disease (AD). The Bayesian approach aims to provide a trade-off by employing relevant features combinations to build decision support systems in clinical settings where uncertainties are relevant. We tested the approach in the MRI data across 959 subjects, aged 59-89 years and 453 subjects with available neuropsychological test scores and CSF biomarker status (amyloid-beta (A{beta})42/40 & and phosphorylated tau (pTau)) from a large sample multi-centric observational cohort (DELCODE). In order to explore the beneficial combinations of information from different sources, we presented a MRI-based predictive modelling of memory performance and CSF biomarker status (positive or negative) in the healthy ageing group as well as subjects at risk of Alzheimers disease using a Gaussian process multikernel framework. Furthermore, we systematically evaluated predictive combinations of input feature sets and their model variations, i.e. (A) combinations of brain tissue classes and feature type (modulated vs. unmodulated), choices of filter size of smoothing (ranging from 0 to 15 mm full width at half maximum), and image resolution (1mm, 2mm, 4mm and 8mm); (B) incorporating demography and covariates (C) the impact of the size of the training data set (i.e., number of subjects); (D) the influence of reducing the dimensions of data and (E) choice of kernel types. Finally, the approach was tested to reveal individual cognitive scores at follow-up (up to 4 years) using the baseline features. The highest accuracy for memory performance prediction was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF-biomarkers explaining 57% of outcome variance in out of sample predictions. The best accuracy for A{beta}42/40 status classification was achieved for combination demographics, ApoE4 and memory score while usage of structural MRI improved the classification of individual patients pTau status.

neuroscience↗

bioRxiv2020

A long read mapping method for highly repetitive reference sequences

About 5-10% of the human genome remains inaccessible for functional analysis due to the presence of repetitive sequences such as segmental duplications and tandem repeat arrays. To enable high-quality resequencing of personal genomes, it is crucial to support end-to-end genome variant discovery using repeat-aware read mapping methods. In this study, we highlight the fact that existing long read mappers often yield incorrect alignments and variant calls within long, near-identical repeats, as they remain vulnerable to allelic bias. In the presence of a non-reference allele within a repeat, a read sampled from that region could be mapped to an incorrect repeat copy because the standard pairwise sequence alignment scoring system penalizes true variants. To address the above problem, we propose a novel, long read mapping method that addresses allelic bias by making use of minimal confidently alignable substrings (MCASs). MCASs are formulated as minimal length substrings of a read that have unique alignments to a reference locus with sufficient mapping confidence (i.e., a mapping quality score above a user-specified threshold). This approach treats each read mapping as a collection of confident sub-alignments, which is more tolerant of structural variation and more sensitive to paralog-specific variants (PSVs) within repeats. We mathematically define MCASs and discuss an exact algorithm as well as a practical heuristic to compute them. The proposed method, referred to as Winnowmap2, is evaluated using simulated as well as real long read benchmarks using the recently completed gapless assemblies of human chromosomes X and 8 as a reference. We show that Winnowmap2 successfully addresses the issue of allelic bias, enabling more accurate downstream variant calls in repetitive sequences. As an example, using simulated PacBio HiFi reads and structural variants in chromosome 8, Winnowmap2 alignments achieved the lowest false-negative and false-positive rates (1.89%, 1.89%) for calling structural variants within near-identical repeats compared to minimap2 (39.62%, 5.88%) and NGMLR (56.60%, 36.11%) respectively. Winnowmap2 code is accessible at https://github.com/marbl/Winnowmap

bioinformatics↗