Selection and characterization of DNA aptamers targeting the surface Borrelia protein CspZ with high-throughput cross-over SELEX
Bibliographic record
Abstract
Lyme borreliosis (LB) is the most prevalent tick-borne illness, with an estimated 700 000 cases annually in the United States and Europe. The LB diagnosis based on a two-tiered serology remains controversial due to its indirect nature and low sensitivity during the early stage of the disease. Aptamers are single-stranded DNA or RNA oligonucleotides that exhibit high selectivity and specificity for their target due to their unique three-dimensional structure. By applying cross-over-SELEX process, an enrichment of DNA oligonucleotide sequences against a surface protein of Borrelia, named CspZ, has been performed and monitored using absorbance at 260 nm, melting curves and NGS analyses. Beyond sequence enrichment, oligonucleotides binding to CspZ were observed during the selection rounds by Dot Blot and beads assays. Thirteen unique and highly redundant oligonucleotide sequences were further characterized using multiple approaches such as Dot Blot, BioLayer Interferometry and Surface Plasmon Resonance. The selected aptamers showed KD values from tens of nanomolar to the micromolar range by BLI and SPR. Two aptamers, Apta9 and Apta10, characterized by flow cytometry and epifluorescence microscopy, were able to specifically recognize Borrelia burgdorferi sensu stricto. This strategy holds promise for the development of an improved diagnostic assay. Lyme borreliosis is challenging to diagnose, particularly in its early stages. Aptamers targeting CspZ from Borrelia, selected through cross-over SELEX, bind both the recombinant protein and bacteria, demonstrating potential for enhanced diagnostics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".