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Description
Diagnosis of Lyme borreliosis (LB), an infection caused by spirochaetes of the B. burgdorferi sl species complex, is mainly based on clinical symptoms supported with serology and is often misdiagnosed in areas of endemicity. The complexity of the antigenic composition among the Borrelia genospecies and differential expression of proteins in host and vector (temporal and spatial antigenic variability) has posed challenges for the serodiagnosis of LB. Despite numerous efforts, little is still known about proteins or their fragments which are conserved for all pathogenic genospecies and can be used in the detection of specific immunoglobulins from human serum samples. Therefore, the correct serodiagnosis of this disease still faces many difficulties. For this reason, it is extremely important to conduct basic research aimed at selecting, obtaining and testing the antigenic properties of new forms of recombinant proteins. Thus, the essence of the problem that we intend to solve is to ascertain whether it is possible to obtain new variants of B. burgdorferi sl recombinant proteins which have significant antigenic properties and may detect immunoglobulins G (IgG) from patients’ sera with Lyme disease that may be infected with different genospecies of B. burgdorferi sl. This dataset contains data regarding the construction of a recombinant plasmid coding a Borrelia burgdorferii sl protein named "F".
Data includes the methodology, cloning diagram, primer sequences, nucleotide sequence of the obtained recombinant plasmid and image files with results of agarose gel electrophoresis of obtained PCR products and restriction analysis.
This research was funded by the National Science Centre, Poland, under the research project no. 2023/49/B/NZ6/02881
Dataset file
hexmd5(md5(part1)+md5(part2)+...)-{parts_count} where a single part of the file is 512 MB in size.Example script for calculation:
https://github.com/antespi/s3md5
File details
- License:
-
open in new tab
CC BYAttribution - File embargo:
- 2026-12-17
Details
- Year of publication:
- 2024
- Verification date:
- 2024-12-31
- Dataset language:
- English
- Fields of science:
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- chemical sciences (Natural sciences)
- health sciences (Medical and Health Sciences )
- DOI:
- DOI ID 10.34808/bkp8-ae70 open in new tab
- Funding:
- Series:
- Verified by:
- Gdańsk University of Technology
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