Targeting Alpha-Synuclein using chemically modified nucleic acid scaffolds
Bibliographic record
Abstract
Parkinson’s disease is the second most prevalent neurodegenerative disease and a rising problem worldwide, where the lack of early and specific diagnosis is causing significant concerns. To date, diagnosis relies on clinical determination of symptoms that may develop decades after the disease’s onset. Biomarkers reflecting the progression of Parkinson’s disease have arisen as potential diagnostic targets, such as -Synuclein (-SN) aggregates. -SN oligomers are small aggregated species which play a key role in disease initiation and progression. These appear promising biomarkers for early detection, although specific targeting remains challenging. Nucleic acid technology has transpired in diagnostic applications due to its unique properties of self-assembly and sequence programmability, facilitated by Watson-Crick base pairing, along with the possibility of easy chemical functionalisation. Nucleic acids are commonly applied in biosensing and target recognition, as they can be utilised to build various architectures, either binding a target specifically, such as aptamers, or scaffolding known binding partners. This thesis presents three projects approaching specific targeting of -SN by employing the functionalisation of nucleic acids. In the first project, RNA was modified in a site-specific manner using small molecules reported to interact with -SN. The modifications were incorporated, using the RNA as a spatially designed scaffold or employing systematic evolution of ligands by exponential enrichment (SELEX) for specific targeting of -SN. The incorporation of modifications did increase interactions of -SN, though specificity was not obtained. The second project applied the conjugation of known -SN binding partners to DNA oligonucleotides, spatially scaffolding the binding partners, such as a nanobody, a peptide or DNA aptamers to obtain increased binding through multimerisation. Multimerisation of the nanobody resulted in an increased binding affinity. The final project approached the employment of modified RNA in standard applications, such as fibrillation assays and histochemistry. Small -SN binding molecules were conjugated to RNA to study if the conjugation affected binding properties or fibrillation of -SN. And whether modified RNA could be utilised as an imaging agent of human tissue containing pathological -SN. The application of modified RNA did not yield conclusive results when investigating -SN, as none of the RNA constructs exhibited the desired specificity. Collectively, these projects demonstrate the potential of chemically modifying nucleic acids by altering the sequence in a site-specific manner or utilising oligonucleotides to scaffold binding partners and detection agents. Nucleic acid scaffolds can display a diverse range of molecules with high spatial control, making them suitable for applications in bioimaging and molecular targeting.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".