Detection of Phosphorothioated Oligonucleotides in Equine Serum
Bibliographic record
Abstract
The ability to manipulate the genome and its expression products is of great concern in both human and animal sports due to potential improvements in athletic performance. There are a variety of approaches that have been developed that may allow for alterations of genetic material, including the use antisense oligonucleotides (ASOs) and small interfering RNAs (siRNA), which can interfere with mRNA prior to protein expression. This thesis focused on developing a sensitive, non-targeted Liquid Chromatography – High Resolution Mass Spectrometry (LC-HRMS) method to detect phosphorothioated oligonucleotides in equine serum. Sample preparation involved using solid phase extraction on a mixed mode sorbent, followed by evaporation and concentration steps prior to analysis by LC-HRMS. Extracted oligonucleotides were chromatographically separated using a reverse-phase gradient with ion-pairing reagents prior to introduction to a hybrid quadrupole orbitrap mass spectrometer using negative mode electrospray ionization and all-ion-fragmentation (AIF) and parallel reaction monitoring (PRM) scan modes. The method was validated with percent difference, precision, matrix effects, recovery, limits of detection and quantification, and stability assessed using a representative 13mer synthetic oligonucleotide (PS-1) containing phosphorothioate modifications. The limits of detection (LOD) for the PS-1 oligonucleotide ranged from 10-50 ng/mL, and the limits of quantification (LOQ) ranged from 25-50 ng/mL based on the scan mode with acceptable percent difference and precision. The method was then applied for the detection of two phosphorothioated oligonucleotide sequences targeting either myostatin or EGL9 transcripts that represent gene targets. This LC-MS method successfully detected phosphorothioated oligonucleotides and has potential to be used as a screening method for modified ASOs in equine serum.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".