Could microRNAs expressed in the tear fluids predict underlying molecular changes associated with Alzheimer’s disease (AD) at an early stage?
Bibliographic record
Abstract
Abstract Background Non‐coding small microRNAs (miRNAs) are a large family of post‐transcriptional regulators of gene expression and recent advances demonstrate their utility as disease biomarkers. Altered miRNA expression levels associated with AD have been reported in autopsy human brain samples, CSF and blood samples obtained from AD patients, and in AD animal models. However, due to the variabilities among samples and techniques from different labs, the potential applicability of miRNAs as biomarkers remains unclear. Here, we undertook a systematic study to assess the relative expression of ten candidate miRNAs in tear fluids and tissues obtained from eye and five brain regions of an AD mouse model at early and late stages. Our goal is to assess the unique differences among tear, eye and brain tissues to identify the most relevant miRNAs in tear fluids, which are collected non‐invasively, that best represent onset and progression of AD. Method Transgenic (Tg, APP/PS1), non‐Tg sibling, and wildtype (WT, C57BL/6J) female mice (n = 24, 4 per group) at two ages were studied (3‐4 months and 9‐10 months). Ten selected mature miRNAs were determined using single tube TaqMan advanced miRNA assays. A statistically significant (p <0.05, 2‐tailed Welch’s t‐test) intergroup >2‐fold difference (FD) was used to determine the differentially expressed miRNAs. Result Eight of the ten miRNAs were expressed at Ct <35.0. miR‐101a, potentially targeting amyloid precursor protein (APP), was significantly downregulated in the neocortex‐hippocampus of both young (p = 9.2×10−7, FD = 8.1) and old (p = 3.0×10−3, 3.0) Tg mice compared with its age‐matched WT controls. Importantly miRNAs ‐125b, ‐15a and ‐374c were significantly and consistently upregulated in the neocortex‐hippocampus, eye and tears of old Tg mice compared with its age‐matched WT control. Whereas all the tested miRNAs were downregulated or nonsignificant in the neocortex‐hippocampus, eye and tears of young Tg mice compared with its age‐matched WT control. Conclusion Our systematic study of miRNAs shows a consistent pattern of expression in the brain, eye and tears of an AD mouse model at two different ages. It also demonstrates the translational potential of tear fluids’ miRNAs in a Tg AD model with changes over time.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".