@article{MTMT:34716198,
	title = {Drug repurposing platform for deciphering the druggable SARS-CoV-2 interactome},
	url = {https://m2.mtmt.hu/api/publication/34716198},
	author = {Bogacheva, M.S. and Kuivanen, S. and Potdar, S. and Hassinen, A. and Huuskonen, S. and Pöhner, I. and Luck, T.J. and Turunen, L. and Feodoroff, M. and Szirovicza, L. and Savijoki, K. and Saarela, J. and Tammela, P. and Paavolainen, L. and Poso, A. and Varjosalo, M. and Kallioniemi, O. and Pietiäinen, V. and Vapalahti, O.},
	doi = {10.1016/j.antiviral.2024.105813},
	journal-iso = {ANTIVIR RES},
	journal = {ANTIVIRAL RESEARCH},
	volume = {223},
	unique-id = {34716198},
	issn = {0166-3542},
	abstract = {The coronavirus disease 2019 (COVID-19) pandemic has heavily challenged the global healthcare system. Despite the vaccination programs, the new virus variants are circulating. Further research is required for understanding of the biology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and for discovery of therapeutic agents against the virus. Here, we took advantage of drug repurposing to identify if existing drugs could inhibit SARS-CoV-2 infection. We established an open high throughput platform for in vitro screening of drugs against SARS-CoV-2 infection. We screened ∼1000 drugs for their ability to inhibit SARS-CoV-2–induced cell death in the African green monkey kidney cell line (Vero-E6), analyzed how the hit compounds affect the viral N (nucleocapsid) protein expression in human cell lines using high-content microscopic imaging and analysis, determined the hit drug targets in silico, and assessed their ability to cause phospholipidosis, which can interfere with the viral replication. Duvelisib was found by in silico interaction assay as a potential drug targeting virus–host protein interactions. The predicted interaction between PARP1 and S protein, affected by Duvelisib, was further validated by immunoprecipitation. Our results represent a rapidly applicable platform for drug repurposing and evaluation of the new emerging viruses’ responses to the drugs. Further in silico studies help us to discover the druggable host pathways involved in the infectious cycle of SARS-CoV-2. © 2024 The Authors},
	keywords = {drug repurposing; High-throughput drug screening; COVID-19; SARS-CoV-2; Image-based data analysis},
	year = {2024},
	eissn = {1872-9096}
}

@article{MTMT:35021738,
	title = {Whole Blood as a Sample Matrix in Homogeneous Time-Resolved Assay-Förster Resonance Energy Transfer-Based Antibody Detection},
	url = {https://m2.mtmt.hu/api/publication/35021738},
	author = {Lintala, Annika and Vapalahti, Olli and Nousiainen, Arttu and Kantele, Anu and Hepojoki, Jussi},
	doi = {10.3390/diagnostics14070720},
	journal-iso = {DIAGNOSTICS},
	journal = {DIAGNOSTICS},
	volume = {14},
	unique-id = {35021738},
	issn = {2075-4418},
	keywords = {antibody detection; whole blood; COVID-19; SARS-CoV-2; LFRET},
	year = {2024},
	eissn = {2075-4418},
	orcid-numbers = {Lintala, Annika/0000-0002-2420-9357; Vapalahti, Olli/0000-0003-2270-6824; Hepojoki, Jussi/0000-0001-5699-214X}
}

@article{MTMT:35469968,
	title = {Multiplex Microscopy Assay for Assessment of Therapeutic and Serum Antibodies against Emerging Pathogens},
	url = {https://m2.mtmt.hu/api/publication/35469968},
	author = {Sartingen, N. and Stürmer, V. and Kaltenböck, M. and Müller, T.G. and Schnitzler, P. and Kreshuk, A. and Kräusslich, H.-G. and Merle, U. and Mücksch, F. and Müller, B. and Pape, C. and Laketa, V.},
	doi = {10.3390/v16091473},
	journal-iso = {VIRUSES-BASEL},
	journal = {VIRUSES},
	volume = {16},
	unique-id = {35469968},
	abstract = {The emergence of novel pathogens, exemplified recently by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), highlights the need for rapidly deployable and adaptable diagnostic assays to assess their impact on human health and guide public health responses in future pandemics. In this study, we developed an automated multiplex microscopy assay coupled with machine learning-based analysis for antibody detection. To achieve multiplexing and simultaneous detection of multiple viral antigens, we devised a barcoding strategy utilizing a panel of HeLa-based cell lines. Each cell line expressed a distinct viral antigen, along with a fluorescent protein exhibiting a unique subcellular localization pattern for cell classification. Our robust, cell segmentation and classification algorithm, combined with automated image acquisition, ensured compatibility with a high-throughput approach. As a proof of concept, we successfully applied this approach for quantitation of immunoreactivity against different variants of SARS-CoV-2 spike and nucleocapsid proteins in sera of patients or vaccinees, as well as for the study of selective reactivity of monoclonal antibodies. Importantly, our system can be rapidly adapted to accommodate other SARS-CoV-2 variants as well as any antigen of a newly emerging pathogen, thereby representing an important resource in the context of pandemic preparedness. © 2024 by the authors.},
	keywords = {ARTICLE; MOUSE; human; Sensitivity and Specificity; immunoblotting; controlled study; epitope; nonhuman; animal cell; enzyme linked immunosorbent assay; cellular distribution; RNA Splicing; Flow Cytometry; Serology; image analysis; quality control; machine learning; machine learning; human cell; virus detection; protein expression; Virus Replication; neutralizing antibody; monoclonal antibody; immune response; immunophenotyping; immunofluorescence; antibody detection; immunosuppressive treatment; vaccination; Image segmentation; Fluorescence microscopy; seroconversion; confocal microscopy; Monoclonal antibodies; genetic transfection; fluorescence activated cell sorting; artificial neural network; nucleocapsid protein; Chemiluminescence immunoassay; Sanger sequencing; NEUROPSYCHOLOGICAL ASSESSMENT; CRISPR Cas system; Herpes simplex virus 2; immunofluorescence assay; emerging pathogens; SARS coronavirus; virus nucleocapsid; Severe acute respiratory syndrome coronavirus 2; SARS-CoV-2; multiplex microscopy},
	year = {2024},
	eissn = {1999-4915}
}