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Biology subjects

Grabherr, S.

Publications and source records attributed to Grabherr, S..

2 recordsLinked to original sources

TYPE I INTERFERON-DEPENDENT CELLULAR REMODELING DURING ACUTE PULMONARY MHV INFECTION

Impaired type I interferon (IFN) responses are predictive of severe disease during pulmonary coronavirus infection. In the periphery, dampened IFN-responsiveness is associated with viremia and hypercytokinemia, however the resolution of IFN-dependent innate immune responses in the lungs remains limited. Here, we aimed to elucidate the early dynamics of antiviral immunity and define the IFN-dependent mechanisms limiting viral spread during pulmonary infection with the murine hepatitis virus (MHV), a beta-coronavirus. While several innate immune cell types infiltrated the lungs concomitant with viral replication, the influx of type I IFN-responsive myeloid cells was essential for viral containment and prevention of fatal disease. Combining high-resolution transcriptomic analysis and genetic attenuation of interferon signaling, we delineated IFN-dependent cell-intrinsic and population-based transcriptional changes that determined viral replication and inflammatory maturation, respectively. While monocyte-derived macrophages exhibited the strongest pro-inflammatory transcriptional reprogramming during pulmonary infection, these maturation programs were impaired in the absence of IFN-signalling. Instead, IFN-deficient monocyte-derived macrophages expressed genes encoding for neutrophil attractants and IL1{beta}, resulting in enhanced lung injury. Our results reveal the requirement for type I IFN-induced antiviral states and inflammation-induced maturation programs to secure pulmonary viral containment.

immunology↗

Visualization and normalization of drift effect across batches in metabolome-wide association studies

As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and identification of metabolites having regulatory effect in various biological processes. While MS-based metabolomics assays are endowed with high-throughput and sensitivity, large-scale MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of true biologically relevant changes. We developed "dbnorm", a package in R environment, which allows visualization and removal of signal heterogeneity from large metabolomics datasets. "dbnorm" integrates advanced statistical tools to inspect dataset structure, at both macroscopic (sample batch) and microscopic (metabolic features) scales. To compare model performance on data correction, "dbnorm" assigns a score, which allows the straightforward identification of the best fitting model for each dataset. Herein, we show how "dbnorm" efficiently removes signal drift among batches to capture the true biological heterogeneity of data in two large-scale metabolomics studies.

bioinformatics↗