Janina Esther Linnik


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Linnik

First Name

Janina Esther

ORCID

0000-0003-0540-2896

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2020 - 20234
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Publications 1 - 4 of 4
  • Interferon-λ Enhances the Differentiation of Naive B Cells into Plasmablasts via the mTORC1 Pathway
    Item type: Journal Article
    Syedbasha, Mohammedyaseen; Bonfiglio, Ferdinando; Linnik, Janina Esther; et al. (2020)
    Cell Reports
    Type III interferon (interferon lambda [IFN-λ]) is known to be a potential immune modulator, but the mechanisms behind its immune-modulatory functions and its impact on plasmablast differentiation in humans remain unknown. Human B cells and their subtypes directly respond to IFN-λ. Using B cell transcriptome profiling, we investigate the immune-modulatory role of IFN-λ in B cells. We find that IFN-λ-induced gene expression in B cells is steady, prolonged, and importantly, cell type specific. Furthermore, IFN-λ enhances the mTORC1 (mammalian/mechanistic target of rapamycin complex 1) pathway in B cells activated by the B cell receptor (BCR/anti-IgM). Engagement of mTORC1 by BCR and IFN-λ induces cell-cycle progress in B cells. Subsequently, IFN-λ boosts the differentiation of naive B cells into plasmablasts upon activation, and the cells gain effector functions such as cytokine release (IL-6 and IL-10) and antibody production. Our study shows how IFN-λ systematically boosts the differentiation of naive B cells into plasmablasts by enhancing the mTORC1 pathway and cell-cycle progression in activated B cells.
  • Mathematical models for analysing the immune response to influenza vaccination in a patient population
    Item type: Doctoral Thesis
    Linnik, Janina Esther (2021)
    Influenza viruses cause respiratory infections and spread in yearly outbreaks worldwide, causing up to 650 thousand deaths every year. Vaccination can induce protective antibodies against influenza viruses and is the most successful strategy to prevent influenza infections. Unfortunately, influenza viruses rapidly evolve and escape immunity established in previous vaccinations. Frequent updates of the influenza vaccine formulation and continuous evaluation of vaccine efficacy in large populations are necessary. A detailed molecular characterization of vaccine responses is experimentally very difficult and often unfeasible in larger populations. Instead, the hemagglutination inhibition (HI) antibody titer is commonly used as an easily accessible measure for the potency of influenza vaccine responses. In this thesis, we present three mathematical models for analysing HI titers and propose how they can be used in conjunction to characterize the vaccine response in a patient population based on easily accessible measurements and medical record information. Specifically, we apply the models to patients after hematopoietic stem cell transplantation (HSCT), a high-risk group eliciting heterogeneous vaccine responses that are not well understood. We first identify patient factors associated with HI titers for three different influenza strains. We show that sequential regression models are superior to the commonly used binary regression on conventional cut-offs (seroconversion/seroprotection) for inferring associations between patient factors and HI titers. Both approaches have a similar interpretation and yield consistent results, but sequential regression models infer associations with higher precision. Next, we present a biophysical model of the HI assay that establishes a quantitative relationship between antibody concentration, antibody avidity (binding strength) and HI titer. We apply the model to infer antibody avidities in HSCT patients from antibody concentrations and HI titers, and experimentally validate our predictions. The model predicts that influenza vaccination mostly induced an increase in antibody concentration but not in avidity. Because the model links antibody concentrations and avidities to HI titers, it enables to connect mathematical models of the immune response to HI titers assessed in vaccine studies. Finally, we integrate the identified most important patient factors into a dynamic model of the vaccine response in HSCT patients and combine it with the biophysical measurement model of the HI assay to infer patient-specific differences in immune response mechanisms. Specifically, we infer differences in memory B cells and germinal center (GC) processes that are potentially modulated by the investigated patient factors. The model predicts that vaccination induced antibody production by both plasma B cells from GCs and reactivated memory B cells. The heterogeneity in HI titer responses was well described by memory B cells and only a few patient factors that potentially affect the GC response (lymphocyte count and IFN-lambda genotype). The study demonstrates how dynamic modelling of the immune response can be combined with clinical patient information and statistical inference to characterize the vaccine response based on HI titers.
  • HLA antibody affinity determination: From HLA-specific monoclonal antibodies to donor HLA specific antibodies (DSA) in patient serum
    Item type: Journal Article
    Hug, Melanie N.; Keller, Sabrina; Marty, Talea; et al. (2023)
    HLA
    Organs transplanted across donor-specific HLA antibodies (DSA) are associated with a variety of clinical outcomes, including a high risk of acute kidney graft rejection. Unfortunately, the currently available assays to determine DSA characteristics are insufficient to clearly discriminate between potentially harmless and harmful DSA. To further explore the hazard potential of DSA, their concentration and binding strength to their natural target, using soluble HLA, may be informative. There are currently a number of biophysical technologies available that allow the assessment of antibody binding strength. However, these methods require prior knowledge of antibody concentrations. Our objective within this study was to develop a novel approach that combines the determination of DSA-affinity as well as DSA-concentration for patient sample evaluation within one assay. We initially tested the reproducibility of previously reported affinities of human HLA-specific monoclonal antibodies and assessed the technology-specific precision of the obtained results on multiple platforms, including surface plasmon resonance (SPR), bio-layer interferometry (BLI), Luminex (single antigen beads; SAB), and flow-induced dispersion analysis (FIDA). While the first three (solid-phase) technologies revealed comparable high binding-strengths, suggesting measurement of avidity, the latter (in-solution) approach revealed slightly lower binding-strengths, presumably indicating measurement of affinity. We believe that our newly developed in-solution FIDA-assay is particularly suitable to provide useful clinical information by not just measuring DSA-affinities in patient serum samples but simultaneously delivering a particular DSA-concentration. Here, we investigated DSA from 20 pre-transplant patients, all of whom showed negative CDC-crossmatch results with donor cells and SAB signals ranging between 571 and 14899 mean fluorescence intensity (MFI). DSA-concentrations were found in the range between 11.2 and 1223 nM (median 81.1 nM), and their measured affinities fall between 0.055 and 24.7 nM (median 5.34 nM; 449-fold difference). In 13 of 20 sera (65%), DSA accounted for more than 0.1% of total serum antibodies, and 4/20 sera (20%) revealed a proportion of DSA even higher than 1%. To conclude, this study strengthens the presumption that pre-transplant patient DSA consists of various concentrations and different net affinities. Validation of these results in a larger patient cohort with clinical outcomes will be essential in a further step to assess the clinical relevance of DSA-concentration and DSA-affinity.
  • Model-based inference of neutralizing antibody avidities against influenza virus
    Item type: Journal Article
    Linnik, Janina Esther; Syedbasha, Mohammedyaseen; Hollenstein, Yvonne; et al. (2022)
    PLoS Pathogens
    To assess the response to vaccination, quantity (concentration) and quality (avidity) of neutralizing antibodies are the most important parameters. Specifically, an increase in avidity indicates germinal center formation, which is required for establishing long-term protection. For influenza, the classical hemagglutination inhibition (HI) assay, however, quantifies a combination of both, and to separately determine avidity requires high experimental effort. We developed from first principles a biophysical model of hemagglutination inhibition to infer IgG antibody avidities from measured HI titers and IgG concentrations. The model accurately describes the relationship between neutralizing antibody concentration/avidity and HI titer, and explains quantitative aspects of the HI assay, such as robustness to pipetting errors and detection limit. We applied our model to infer avidities against the pandemic 2009 H1N1 influenza virus in vaccinated patients (n = 45) after hematopoietic stem cell transplantation (HSCT) and validated our results with independent avidity measurements using an enzyme-linked immunosorbent assay with urea elution. Avidities inferred by the model correlated with experimentally determined avidities (ρ = 0.54, 95% CI = [0.31, 0.70], P < 10−4). The model predicted that increases in IgG concentration mainly contribute to the observed HI titer increases in HSCT patients and that immunosuppressive treatment is associated with lower baseline avidities. Since our approach requires only easy-to-establish measurements as input, we anticipate that it will help to disentangle causes for poor vaccination outcomes also in larger patient populations. This study demonstrates that biophysical modelling can provide quantitative insights into agglutination assays and complement experimental measurements to refine antibody response analyses.
Publications 1 - 4 of 4