Scavenging acrolein with 2-HDP preserves neurovascular integrity in a rat model of diabetic retinal disease
Bibliographic record
Abstract
AIMS/HYPOTHESIS: Diabetic retinal disease (DRD) is characterised by progressive neurovascular unit (NVU) dysfunction, often occurring before visible microvascular damage. Our previous studies suggested that the accumulation of acrolein (ACR)-derived protein adducts on retinal Müller cells and neuronal proteins may contribute to NVU dysfunction in diabetes, although this has yet to be directly tested. In this study, we evaluated the effects of the novel ACR-scavenging drug 2-hydrazino-4,6-dimethylpyrimidine (2-HDP) on retinal NVU dysfunction in experimental diabetes and explored its potential for systemic delivery in humans. METHODS: Sprague Dawley rats were divided into three groups: non-diabetic rats; streptozocin (STZ)-induced diabetic rats; and STZ-induced diabetic rats treated with 2-HDP in their drinking water throughout the duration of diabetes. Endpoint measures were taken at varying time points, ranging from 1 to 6 months post-diabetes induction. Retinal function and structure were evaluated using electroretinography (ERG) and spectral-domain optical coherence tomography (SD-OCT). Retinal vessel calibre, BP and vasopermeability (assessed by Evans Blue leakage) were also monitored. Immunohistochemistry was employed to assess retinal neurodegenerative and vasodegenerative changes, while cytokine arrays were used to investigate the effect of 2-HDP on diabetes-induced retinal inflammation. The accumulation of the ACR-protein adduct Nε-(3-formyl-3,4-dehydropiperidino)lysine (FDP-Lys) in human diabetic retinas was analysed. Computational chemistry simulations were performed to predict 2-HDP's passive permeability properties and its potential for systemic delivery. RESULTS: levels or BP in diabetic rats (p>0.05). However, it protected against retinal FDP-Lys accumulation (p<0.05) and neurophysiological dysfunction, preserving ERG waveforms at 3 and 6 months post-diabetes induction (p<0.05 to p<0.001 for scotopic for a-wave, b-wave and summed oscillatory potentials). SD-OCT imaging revealed that 2-HDP prevented retinal thinning at 3 months (p<0.01) and protected against synaptic dysfunction, as evidenced by preserved synaptophysin expression (p<0.01 and p<0.001 for inner and outer plexiform layers, respectively). It also prevented neurodegeneration by maintaining retinal ganglion cells, amacrine cells, bipolar cells, and photoreceptors (p<0.05 to p<0.01). In addition, 2-HDP prevented retinal arteriolar dilation (p<0.01), reduced microvascular permeability (p<0.05) and attenuated microvascular damage, as indicated by preserved pericyte numbers and reduced acellular capillary formation (p<0.05). Mechanistically, 2-HDP inhibited microglial activation (p<0.05), suppressed the upregulation of proinflammatory molecules associated with NVU dysfunction in the diabetic retina (p<0.05 to p<0.001) and preserved the expression of the Müller cell glutamate-handling proteins, glutamate aspartate transporter 1 and glutamine synthetase (p<0.05 to p<0.01). FDP-Lys accumulation was observed in post-mortem human retinas from individuals with type 2 diabetes (p<0.05), in a pattern that was similar to that in the rat model of diabetes. Molecular dynamics simulations showed that the neutral form of 2-HDP readily crosses cell membranes, with enhanced permeation in the presence of ACR, highlighting its potential for systemic delivery. CONCLUSIONS/INTERPRETATION: 2-HDP protects against retinal NVU dysfunction in diabetic rats by reducing FDP-Lys accumulation, preserving neuroretinal function and preventing microvascular damage, independent of glycaemic control. These results, combined with evidence from human diabetic retinas and molecular dynamics simulations, support 2-HDP's potential as a promising therapeutic agent for DRD, warranting further preclinical and clinical investigation.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| 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".