Biology subjects

Nielsen, L.

Publications and source records attributed to Nielsen, L..

4 recordsLinked to original sources

bioRxiv2025

Development of a Norway Rat Hepacivirus Reporter for High-Throughput Quantification of Neutralising Antibodies

Hepatitis C virus (HCV) remains a major global health challenge, with vaccine research hindered by lack of immunocompetent animal models. Infection with the HCV-related Norway rat hepacivirus 1 (NrHV) in rats or mice shares HCV defining characteristics, including chronicity, delayed immune responses, and liver disease, thereby providing immunocompetent small animal models for immune responses and pathology. Infectious NrHV cell culture systems allow studies of host factors and neutralising antibodies, however, quantification relies on laborious manual counting of infected cells. To permit high-throughput quantification, we engineered culture adapted NrHV to encode green fluorescent protein (GFP), firefly luciferase (FLuc), or the 11 amino acid HiBiT tag at sites corresponding to those permissive for HCV. While GFP and FLuc constructs were not viable, a reporter with a HiBiT insertion in NS5A domain III was replication competent and produced infectious virus in rat hepatoma cells. Two mutations in NS5A adapted this virus to levels comparable to wildtype, enabling robust luciferase readouts and stability. The NrHV-HiBiT-NS5A reporter was viable in rats and permitted quantification of luciferase from liver homogenates. Luciferase-based quantification of viral infection after antibody neutralisation correlated well with manual quantification. High-throughput luciferase quantification was also used to confirm dependency on the known host factors SR-BI and miR-122, and to assess sensitivity to antivirals. Finally, sensitivity to a specific inhibitor suggested that cyclophilin A, similarly to HCV, may also be a host factor for NrHV. This novel reporter system could accelerate characterisation of the NrHV model as well as ongoing vaccine studies for HCV. ImportanceDirect acting antiviral therapy provides an effective cure for hepatitis C virus (HCV) infection, but the infection burden remains high due to cost, availability of therapy, and low diagnosis rates. A vaccine therefore is critical for global control of transmission, however development is constrained by a lack of immunocompetent animal models. Norway rat hepacivirus 1 (NrHV) infection of rats and mice serves as an effective small animal model for HCV, permitting evaluation of vaccine platforms and candidates. Neutralising antibody responses are considered crucial in protection and can be quantified using the NrHV cell culture system, however, only through laborious low-throughput methods. We here present an NrHV reporter virus allowing high-throughput quantification of antibody neutralisation, virus-host interactions, and antiviral efficacy via luciferase readout. This reporter will facilitate characterisation of NrHV and support ongoing vaccine research with the potential for global control of HCV.

microbiology↗

bioRxiv2021

Transmembrane signalling by a bionic receptor: biological input and output, chemical mechanism of signal transduction

Signal transduction through sealed biological membranes is among the most important evolutionary achievements. Herein, we focus on the development of artificial signal transduction mechanisms and engineer a bionic receptor with capacity of transduction of biological signals across biological membranes using tools of chemistry. The bionic receptor described in this work exhibits similarity with the natural counterpart in the most essential characteristics: in having an exofacial ligand for signal capture, in being membrane anchored, and in featuring a releasable secondary messenger molecule, which performs enzyme activation in the endo volume. The main difference with the natural receptors is that signal transduction across the lipid bilayer was performed using the tools of organic chemistry, namely a self-immolative linker. The highest novelty of our work is that the artificial signalling cascade designed herein achieved transmembrane activation of enzymatic activity, as is the hallmark of activity by natural signalling receptors.

synthetic biology↗

bioRxiv2021

The Reactobiome Unravels a New Paradigm in Human Gut Microbiome Metabolism

Changes in microbial metabolism have been used as the main approach to assess function and elucidate environmental and host-microbiome interactions. This can be hampered by uncharacterised metagenome species and lack of metabolic annotation. To address this, we present a comprehensive computational platform for population stratification based on microbiome composition, the underlying metabolic potential and generation of metagenome species and community level metabolic models. We revisit the concepts of enterotype and microbiome richness introducing the reactobiome as a stratification method to unravel the metabolic features of the human gut microbiome. The reactobiome encapsulates resilience and microbiome dysbiosis at a functional level. We describe five reactotypes in healthy populations from 16 countries, with specific amino acid, carbohydrate and xenobiotic metabolic features. The validity of the approach was tested to unravel host-microbiome and environmental interactions by applying the reactobiome analysis on a one-year Swedish longitudinal cohort, integrating gut metagenomics, plasma metabolomics and clinical data.

systems biology↗

bioRxiv2017

A Methodology for Evaluating the Performance of Alerting and Detection Algorithms Running on Continuous Patient Data

ObjectivesClinicians in the intensive care unit (ICU) are presented with a large number of physiological data consisting of periodic and frequently sampled measurements, such as heart rate and blood pressure, as well as aperiodic measurements, such as noninvasive blood pressure and laboratory studies. Because this data can be overwhelming, there is considerable interest in designing algorithms that help integrate and interpret this data and assist ICU clinicians in detecting or predicting in advance patients who may be deteriorating. In order to decide whether to deploy such algorithms in a clinical trial, it is important to evaluate these algorithms using retrospective data. However, the fact that these algorithms will be running continuously, i.e., repeatedly sampling incoming patient data, presents some novel challenges for algorithm evaluation. Commonly used measures of performance such as sensitivity and positive predictive value (PPV) are easily applied to static \"snapshots\" of patient data, but can be very misleading when applied to indicators or alerting algorithms that are running on continuous data. Our objective is to create a method for evaluating algorithm performance on retrospective data with the algorithm running continuously throughout the patients stay as it would in a real ICU.\n\nMethodsWe introduce our evaluation methodology in the context of evaluating an algorithm, a Hemodynamic Instability Indicator (HII), for assisting bedside ICU clinicians with the early detection of hemodynamic instability before the onset of acute hypotension. Each patients ICU stay is divided into segments that are labelled as hemodynamically stable or unstable based on clinician interventions typically aimed at treating hemodynamic instability. These segments can be of varying length with varying degrees of exposure to potential alerts, whether true positive or false positive. Furthermore, to simulate how clinicians might interact with the alerting algorithm, we use a dynamic alert supervision mechanism which suppresses subsequent alerts unless the indicator has significantly deteriorated since the prior alert. Under these conditions determining what counts as a positive or negative instance, and calculations of sensitivity, specificity, and positive predictive value can be problematic. We introduce a methodology for consistently counting positive and negative instances. The methodology distinguishes between counts based on alerting events and counts based on sub-segments, and show how they can be applied in calculating measures of performance such as sensitivity, specificity, positive predictive value.\n\nResultsThe introduced methodology is applied to retrospective evaluation of two algorithms, HII and an alerting algorithm based on systolic blood pressure. We use a database, consisting of data from 41,707 patients from 25 US hospitals, to evaluate the algorithms. Both algorithms are evaluated running continuously throughout each patients stay as they would in a real ICU setting. We show how the introduced performance measures differ for different algorithms and for different assumptions.\n\nDiscussionThe standard measures of diagnostic tests in terms of true positives, false positives, etc. are based on certain assumptions which may not apply when used in the context of measuring the performance on an algorithm running continuously, and thus repeatedly sampling from the same patient. When such measures are being reported it is important that the underlying assumptions be made explicit; otherwise, the results can be very misleading.\n\nConclusionWe introduce a methodology for evaluating how an alerting algorithm or indicator will perform running continuously throughout every patients ICU stay, not just for a subset of patients for selected episodes.

bioinformatics↗