Temporal Integrative Analysis of mRNA and microRNAs Expression Profiles and Epigenetic Alterations in Female SAMP8, a Model of Age-Related Cognitive Decline
Bibliographic record
Abstract
A growing body of research shows that epigenetic mechanisms are critically involved in normal and pathological aging. The Senescence-Accelerated Mouse Prone 8 (SAMP8) can be considered a useful tool to better understand the dynamics of the global epigenetic landscape during the aging process since its phenotype is not fully explained by genetic factors. Here we investigated dysfunctional age-related transcriptional profiles and epigenetic programming enzymes in the hippocampus of 2- and 9-month-old SAMP8 female mice using the Senescent-Accelerated Resistant 1 (SAMR1) mouse strain as control. SAMP8 mice presented 1,062 genes dysregulated at 2 months of age, and 1,033 genes at 9 months, with 92 genes concurrently dysregulated at both ages compared to age-matched SAMR1. SAMP8 mice showed a significant decrease in global DNA methylation (5-mC) at 2 months while hydroxymethylation (5-hmC) levels were increased in SAMP8 mice at 2 and 9 months of age compared to SAMR1. These changes were accompanied by changes in the expression of several enzymes that regulate 5-mC and methylcytosine oxidation. Acetylated H3 and H4 histone levels were significantly diminished in SAMP8 mice at 2-month-old compared to SAMR1 and altered Histone DeACetylase (HDACs) profiles were detected in both young and old SAMP8 mice. We analyzed 84 different mouse miRNAs known to be altered in neurological diseases or involved in neuronal development. Compared with SAMR1, SAMP8 mice showed 28 and 17 miRNAs differentially expressed at 2 and 9 months of age, respectively; 6 of these miRNAs overlapped at both ages. We used several bioinformatic approaches to integrate our data in mRNA:miRNA regulatory networks and functional predictions for young and aged animals. In sum, our study reveals interplay between epigenetic mechanisms and gene networks that seems to be relevant for the progression toward a pathological aging and provides several potential markers and therapeutic candidates for Alzheimer's Disease (AD) and age-related cognitive impairment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".