Mathias Cardner


Last Name

Cardner

First Name

Mathias

ORCID

0000-0001-5873-2632

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2019 - 201922020 - 20235
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Publications 1 - 7 of 7
  • Structure-function relationships of HDL in diabetes and coronary heart disease
    Item type: Journal Article
    Cardner, Mathias; Yalcinkaya, Mustafa; Goetze, Sandra; et al. (2020)
    JCI Insight
  • Predicting tumour content of liquid biopsies from cell-free DNA
    Item type: Journal Article
    Cardner, Mathias; Marass, Francesco; Gedvilaite, Erika; et al. (2023)
    BMC Bioinformatics
    Background: Liquid biopsy is a minimally-invasive method of sampling bodily fluids, capable of revealing evidence of cancer. The distribution of cell-free DNA (cfDNA) fragment lengths has been shown to differ between healthy subjects and cancer patients, whereby the distributional shift correlates with the sample’s tumour content. These fragmentomic data have not yet been utilised to directly quantify the proportion of tumour-derived cfDNA in a liquid biopsy. Results: We used statistical learning to predict tumour content from Fourier and wavelet transforms of cfDNA length distributions in samples from 118 cancer patients. The model was validated on an independent dilution series of patient plasma. Conclusions: This proof of concept suggests that our fragmentomic methodology could be useful for predicting tumour content in liquid biopsies.
  • Inferring signalling dynamics by integrating interventional with observational data
    Item type: Journal Article
    Cardner, Mathias; Meyer-Schaller, Nathalie; Christofori, Gerhard; et al. (2019)
    Bioinformatics
    Motivation In order to infer a cell signalling network, we generally need interventional data from perturbation experiments. If the perturbation experiments are time-resolved, then signal progression through the network can be inferred. However, such designs are infeasible for large signalling networks, where it is more common to have steady-state perturbation data on the one hand, and a non-interventional time series on the other. Such was the design in a recent experiment investigating the coordination of epithelial–mesenchymal transition (EMT) in murine mammary gland cells. We aimed to infer the underlying signalling network of transcription factors and microRNAs coordinating EMT, as well as the signal progression during EMT. Results In the context of nested effects models, we developed a method for integrating perturbation data with a non-interventional time series. We applied the model to RNA sequencing data obtained from an EMT experiment. Part of the network inferred from RNA interference was validated experimentally using luciferase reporter assays. Our model extension is formulated as an integer linear programme, which can be solved efficiently using heuristic algorithms. This extension allowed us to infer the signal progression through the network during an EMT time course, and thereby assess when each regulator is necessary for EMT to advance.
  • A Hierarchical Regulatory Landscape during the Multiple Stages of EMT
    Item type: Journal Article
    Meyer-Schaller, Nathalie; Cardner, Mathias; Diepenbruck, Maren; et al. (2019)
    Developmental Cell
  • Discovery of antimicrobials by massively parallelized growth assays (Mex)
    Item type: Journal Article
    Koch, Philipp; Schmitt, Steven; Cardner, Mathias; et al. (2022)
    Scientific Reports
    The number of newly approved antimicrobial compounds has been steadily decreasing over the past 50 years emphasizing the need for novel antimicrobial substances. Here we present Mex, a method for the high-throughput discovery of novel antimicrobials, that relies on E. coli self-screening to determine the bioactivity of more than ten thousand naturally occurring peptides. Analysis of thousands of E. coli growth curves using next-generation sequencing enables the identification of more than 1000 previously unknown antimicrobial peptides. Additionally, by incorporating the kinetics of growth inhibition, a first indication of the mode of action is obtained, which has implications for the ultimate usefulness of the peptides in question. The most promising peptides of the screen are chemically synthesized and their activity is determined in standardized susceptibility assays. Ten out of 15 investigated peptides efficiently eradicate bacteria at a minimal inhibitory concentration in the lower µM or upper nM range. This work represents a step-change in the high-throughput discovery of functionally diverse antimicrobials.
  • Statistical learning from high-dimensional data in biomedicine
    Item type: Doctoral Thesis
    Cardner, Mathias (2020)
    This thesis encompasses three projects devoted to gaining biomedical insight from data gathered using high-throughput assays, such as next-generation sequencing, mass spectrometry (MS), and nuclear magnetic resonance (NMR) spectroscopy. The analyses are based on high-dimensional statistics and graphical models, with an emphasis on robustness and interpretability. In two of the projects, biological validation experiments were performed in order to assess the causal nature of relevant predictions made by the models. The first project aimed to estimate signal progression from gene expression data obtained in gene perturbation experiments as well as an unperturbed system sampled over time. To this end, we extended the framework of a nested-effects model to incorporate data from perturbation experiments with a non-interventional time series (Cardner, Meyer-Schaller et al., 2019). Jointly analysing the two types of experiment in this manner yields an estimate of how signals progress through a pathway in response to a receptor stimulus. The method's development was motivated by experiments performed by Meyer-Schaller et al. (2019), and it was applied to the corresponding sets of gene expression data. Parts of the inferred signalling pathway were validated in luciferase reporter assays. The second project concerned the function and composition of high-density lipoprotein (HDL), as well as its role in coronary heart disease (CHD) and type 2 diabetes mellitus (T2DM). Based on functional and compositional measurements of HDL in 51 healthy volunteers and 98 patients with CHD or T2DM, we aimed to understand how disease-relevant functions of HDL are determined by its composition, in terms of the hundreds of proteins and lipids which constitute HDL particles. To this end, we used a robust Gaussian graphical model to infer conditional dependence between HDL functions measured in bioassays and compositional features assayed using MS and NMR spectroscopy. We found several clinically relevant candidates, and experimentally validated novel causal links between certain HDL functions and compositional constituents (Cardner, Yalcinkaya et al., 2020). The third and final project is devoted to liquid biopsies from cancer patients, which provide a non-invasive means of monitoring systemic tumour burden and assessing treatment response. Here we analysed data from shallow whole-genome sequencing of cell-free DNA (cfDNA) in blood plasma samples taken from 118 cancer patients. By leveraging certain biochemical properties of cfDNA, we developed a method for predicting the proportion of tumour-derived cfDNA in a sample. This prediction is useful in its own right for quantifying the tumour content of a sample, for instance during relapse monitoring. In addition, we used our method to quantify copy-number aberrations across the genome, thereby identifying deletions, gains, and focal amplifications of genetic material in the circulating tumour DNA.
  • Discovery of Antimicrobials By Mex : Massively Parallelized Growth Assays
    Item type: Working Paper
    Koch, Philipp; Schmitt, Steven; Cardner, Mathias; et al. (2021)
    Research Square
    The number of newly approved antimicrobial compounds has been steadily decreasing over the past 50 years emphasizing the need for novel antimicrobial substances. Here we present Mex, a method for the high-throughput discovery of novel antimicrobials, that relies on E.coli self-screening to determine the bioactivity of more than ten thousand naturally occurring peptides. Analysis of thousands of E. coli growth curves using next-generation sequencing enables the identification of more than 1,000 previously unknown antimicrobial peptides. Additionally, by incorporating the kinetics of growth inhibition, a first indication of the mode of action is obtained, which has implications for the ultimate usefulness of the peptides in question. The most promising peptides of the screen are chemically synthesized and their activity is determined in standardized susceptibility assays. Ten out of 15 investigated peptides efficiently eradicate bacteria at a minimal inhibitory concentration in the lower µm or upper nm range. This work represents a step-change in the high-throughput discovery of functionally diverse antimicrobials.
Publications 1 - 7 of 7