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  • 1.
    Castro Dopico, Xaquin
    et al.
    Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.
    Muschiol, Sandra
    Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Microbiology, Karolinska University Hospital, Stockholm, Sweden.
    Grinberg, Nastasiya F.
    Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge, United Kingdom.
    Aleman, Soo
    Department of Infectious Diseases, Karolinska University Hospital, Huddinge, Sweden.
    Sheward, Daniel J.
    Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.
    Hanke, Leo
    Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.
    Ahl, Marcus
    Department of Infectious Diseases, Karolinska University Hospital, Huddinge, Sweden.
    Vikström, Linnea
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology.
    Forsell, Mattias N. E.
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology.
    Coquet, Jonathan M.
    Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.
    McInerney, Gerald
    Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.
    Dillner, Joakim
    Division of Pathology, Department of Laboratory Medicine, Karolinska Institutet, Huddinge, Sweden.
    Bogdanovic, Gordana
    Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge, United Kingdom.
    Murrell, Ben
    Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.
    Albert, Jan
    Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Microbiology, Karolinska University Hospital, Stockholm, Sweden.
    Wallace, Chris
    Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge, United Kingdom; Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom.
    Karlsson Hedestam, Gunilla B.
    Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.
    Probabilistic classification of anti-SARS-CoV-2 antibody responses improves seroprevalence estimates2022In: Clinical & Translational Immunology (CTI), E-ISSN 2050-0068, Vol. 11, no 3, article id e1379Article in journal (Refereed)
    Abstract [en]

    Objectives: Population-level measures of seropositivity are critical for understanding the epidemiology of an emerging pathogen, yet most antibody tests apply a strict cutoff for seropositivity that is not learnt in a data-driven manner, leading to uncertainty when classifying low-titer responses. To improve upon this, we evaluated cutoff-independent methods for their ability to assign likelihood of SARS-CoV-2 seropositivity to individual samples. Methods: Using robust ELISAs based on SARS-CoV-2 spike (S) and the receptor-binding domain (RBD), we profiled antibody responses in a group of SARS-CoV-2 PCR+ individuals (n = 138). Using these data, we trained probabilistic learners to assign likelihood of seropositivity to test samples of unknown serostatus (n = 5100), identifying a support vector machines-linear discriminant analysis learner (SVM-LDA) suited for this purpose. Results: In the training data from confirmed ancestral SARS-CoV-2 infections, 99% of participants had detectable anti-S and -RBD IgG in the circulation, with titers differing > 1000-fold between persons. In data of otherwise healthy individuals, 7.2% (n = 367) of samples were of uncertain serostatus, with values in the range of 3-6SD from the mean of pre-pandemic negative controls (n = 595). In contrast, SVM-LDA classified 6.4% (n = 328) of test samples as having a high likelihood (> 99% chance) of past infection, 4.5% (n = 230) to have a 50–99% likelihood, and 4.0% (n = 203) to have a 10–49% likelihood. As different probabilistic approaches were more consistent with each other than conventional SD-based methods, such tools allow for more statistically-sound seropositivity estimates in large cohorts. Conclusion: Probabilistic antibody testing frameworks can improve seropositivity estimates in populations with large titer variability.

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  • 2.
    Kerkman, Priscilla
    et al.
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology. Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR).
    Dernstedt, Andy
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology. Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR).
    Tadala, Lalitha
    Umeå University, Faculty of Medicine, Molecular Infection Medicine Sweden (MIMS). Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine). Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR).
    Mittler, Eva
    Dannborg, Mirjam
    Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR). Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine). Umeå University, Faculty of Medicine, Molecular Infection Medicine Sweden (MIMS).
    Sundling, Christopher
    Maleki, Kimia T.
    Tauriainen, Johanna
    Tuiskunen-Bäck, Anne
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology. Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR).
    Wigren Byström, Julia
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology. Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR).
    Ocaya, Pauline
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology. Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR).
    Thunberg, Therese
    Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR). Umeå University, Faculty of Medicine, Department of Clinical Microbiology.
    Jangra, Rohit K
    Román-Sosa, Gleyder
    Guardado-Calvo, Pablo
    Rey, Feilx A.
    Klingström, Jonas
    Chandran, Kartik
    Puhar, Andrea
    Umeå University, Faculty of Medicine, Molecular Infection Medicine Sweden (MIMS). Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR). Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Ahlm, Clas
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology. Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR).
    Forsell, Mattias N. E.
    Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR). Umeå University, Faculty of Medicine, Department of Clinical Microbiology.
    Generation of plasma cells and CD27-IgD- B cells during hantavirus infection is associated with distinct pathological findings2021In: Clinical & Translational Immunology (CTI), E-ISSN 2050-0068, Vol. 10, article id e1313Article in journal (Refereed)
    Abstract [en]

    Objective: Human hantavirus infections can cause haemorrhagic fever with renal syndrome (HFRS). The pathogenic mechanisms arenot fully understood, nor if they affect the humoral immune system. The objective of this study was to investigate humoral immune responses to hantavirus infection and to correlate them to the typical features of HFRS: thrombocytopenia and transient kidney dysfunction.

    Methods: We performed a comprehensive characterisation of longitudinal antiviral B-cell responses of 26 hantavirus patients and combined this with paired clinical data. In addition, we measured extracellular adenosine triphosphate (ATP)and its breakdown products in circulation and performed in vitro stimulations to address its effect on B cells.

    Results: We found that thrombocytopenia was correlated to an elevated frequency of plasmablasts in circulation. In contrast, kidney dysfunction was indicative of an accumulation of CD27-IgD- B cells and CD27/low plasmablasts. Finally, we provide evidence that high levels of extracellular ATP and matrix metalloproteinase 8 can contribute to shedding of CD27 during human hantavirus infection.

    Conclusion:  Our findings demonstrate that thrombocytopenia and kidneydysfunction associate with distinctly different effects on the humoral immune system. Moreover, hantavirus-infectedindividuals have significantly elevated levels of extracellular ATP incirculation.

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