Core Facility Hohenheim

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/16626

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Browsing Core Facility Hohenheim by Sustainable Development Goals "17"

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    Multi-omics characterization of the monkeypox virus infection
    (2024) Huang, Yiqi; Bergant, Valter; Grass, Vincent; Emslander, Quirin; Hamad, M. Sabri; Hubel, Philipp; Mergner, Julia; Piras, Antonio; Krey, Karsten; Henrici, Alexander; Öllinger, Rupert; Tesfamariam, Yonas M.; Dalla Rosa, Ilaria; Bunse, Till; Sutter, Gerd; Ebert, Gregor; Schmidt, Florian I.; Way, Michael; Rad, Roland; Bowie, Andrew G.; Protzer, Ulrike; Pichlmair, Andreas
    Multiple omics analyzes of Vaccinia virus (VACV) infection have defined molecular characteristics of poxvirus biology. However, little is known about the monkeypox (mpox) virus (MPXV) in humans, which has a different disease manifestation despite its high sequence similarity to VACV. Here, we perform an in-depth multi-omics analysis of the transcriptome, proteome, and phosphoproteome signatures of MPXV-infected primary human fibroblasts to gain insights into the virus-host interplay. In addition to expected perturbations of immune-related pathways, we uncover regulation of the HIPPO and TGF-β pathways. We identify dynamic phosphorylation of both host and viral proteins, which suggests that MAPKs are key regulators of differential phosphorylation in MPXV-infected cells. Among the viral proteins, we find dynamic phosphorylation of H5 that influenced the binding of H5 to dsDNA. Our extensive dataset highlights signaling events and hotspots perturbed by MPXV, extending the current knowledge on poxviruses. We use integrated pathway analysis and drug-target prediction approaches to identify potential drug targets that affect virus growth. Functionally, we exemplify the utility of this approach by identifying inhibitors of MTOR, CHUK/IKBKB, and splicing factor kinases with potent antiviral efficacy against MPXV and VACV.
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    Predictor preselection for mixed‐frequency dynamic factor models: a simulation study with an empirical application to GDP nowcasting
    (2025) Franjic, Domenic; Schweikert, Karsten
    We investigate the performance of dynamic factor model nowcasting with preselected predictors in a mixed‐frequency setting. The predictors are selected via the elastic net as it is common in the targeted predictor literature. A simulation study and an application to empirical data are used to evaluate different strategies for variable selection, the influence of tuning parameters, and to determine the optimal way to handle mixed‐frequency data. We propose a novel cross‐validation approach that connects the preselection and nowcasting step. In general, we find that preselecting provides more accurate nowcasts compared with the benchmark dynamic factor model using all variables. Our newly proposed cross‐validation method outperforms the other specifications in most cases.