Planta Med 2022; 88(15): 1445-1446
DOI: 10.1055/s-0042-1759025
Poster Session I

Identification of biomarkers of Lyme borreliosis by non-targeted metabolomics

I Kuukkanen∗
1   Department of Chemistry, University of Turku, Turku, Finland
,
J Hytönen
2   Institute of Biomedicine, University of Turku, Turku, Finland
,
M Karonen
1   Department of Chemistry, University of Turku, Turku, Finland
› Author Affiliations
› Further Information
 

Lyme borreliosis (LB) is the most notable vector-borne infectious disease caused by members of spirochete group Borrelia burgdorferi sensu lato and is spread by ticks from Ixodes family. LB metabolomics have been studied before, but currently, there is no routine analytical method in use for identifying the early stages of the LB, before antibodies are formed after several weeks from the infection [1], [2], [3].

The aim of this study is to develop a non-targeted metabolomics-based method for identifying small specific biomarkers and metabolic routes from human blood serum samples that are affected by the neurological form of LB, Lyme neuroborreliosis (LNB), by utilizing UHPLC-DAD-HESI-Orbitrap-MS platform together with cutting edge in silico tools. This metabolomics data workflow was applied to select a biosignature for classifying the healthy controls from LNB samples of two different time points: acute infection and treated infection (12 months after the infection). Proteins were precipitated from the sample matrix by using methanol before the high-resolution MS acquisition via negative ESI mode.

The used metabolomic UHPLC-MS/MS-data workflow approach pointed towards different biomarker groups from the sample matrix including different acylcarnitines, hydroxy-, nitro- and polyunsaturated fatty acids, diacylglycerols, peptides, triglycerides and sphingolipids. The results show promise for applying non-targeted LB metabolomics to create targeted methods in the future for early LB diagnostics.



Publication History

Article published online:
12 December 2022

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