Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis

Julia Åkesson, Sara Hojjati, Sandra Hellberg, Johanna Raffetseder, Mohsen Khademi, Robert Rynkowski, Ingrid Kockum, Claudio Altafini, Zelmina Lubovac-Pilav, Johan Mellergård, Maria C. Jenmalm, Fredrik Piehl, Tomas Olsson, Jan Ernerudh and Mika Gustafsson ()
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Julia Åkesson: Linköping University
Sara Hojjati: Linköping University
Sandra Hellberg: Linköping University
Johanna Raffetseder: Linköping University
Mohsen Khademi: Karolinska University Hospital, Karolinska Institute
Robert Rynkowski: Linköping University
Ingrid Kockum: Karolinska University Hospital, Karolinska Institute
Claudio Altafini: Linköping University
Zelmina Lubovac-Pilav: University of Skövde
Johan Mellergård: Linköping University
Maria C. Jenmalm: Linköping University
Fredrik Piehl: Karolinska University Hospital, Karolinska Institute
Tomas Olsson: Karolinska University Hospital, Karolinska Institute
Jan Ernerudh: Linköping University
Mika Gustafsson: Linköping University

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract Sensitive and reliable protein biomarkers are needed to predict disease trajectory and personalize treatment strategies for multiple sclerosis (MS). Here, we use the highly sensitive proximity-extension assay combined with next-generation sequencing (Olink Explore) to quantify 1463 proteins in cerebrospinal fluid (CSF) and plasma from 143 people with early-stage MS and 43 healthy controls. With longitudinally followed discovery and replication cohorts, we identify CSF proteins that consistently predicted both short- and long-term disease progression. Lower levels of neurofilament light chain (NfL) in CSF is superior in predicting the absence of disease activity two years after sampling (replication AUC = 0.77) compared to all other tested proteins. Importantly, we also identify a combination of 11 CSF proteins (CXCL13, LTA, FCN2, ICAM3, LY9, SLAMF7, TYMP, CHI3L1, FYB1, TNFRSF1B and NfL) that predict the severity of disability worsening according to the normalized age-related MS severity score (replication AUC = 0.90). The identification of these proteins may help elucidate pathogenetic processes and might aid decisions on treatment strategies for persons with MS.

Date: 2023
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DOI: 10.1038/s41467-023-42682-9

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