Studies on early CNS inflammation in transgenic models of the Alzheimer's pathology
Bibliographic record
Abstract
Many advances have being made in understanding the pathological processes in Alzheimer's Disease (AD) and in testing therapeutic strategies to stop disease progression. It has become clear that neuroinflammation plays a main role in AD. Evidence is being gathered from experimental, epidemiological and clinical trials studies that this process happens even earlier, in stages where pathology is present but no symptoms are detected. Even though the late, plaque-associated central nervous system (CNS) inflammation is well characterized, many questions remain regarding the early inflammatory process. Understanding this event is important to develop biomarkers to identify those at risk for AD and to monitor disease progression, and in the search of better treatment for AD. Unfortunately, studying pre-clinical stages in AD is not possible as yet due to the lack of biomarkers signalling the conversion from normal cognition to AD. Alternatively early stages of the AD-like amyloid pathology can be study in transgenic (tg) models that mimic several aspects of the human pathology. In the studies described here we use both our AD tg rat (McGill-R-Thy1-APP) and mouse (McGill-Thy1-APP) models to investigate events on the early amyloid pathology. We first demonstrated a marked up-regulation of several classical inflammatory markers such as COX-2, IL-1β, TNF-α and fractalkine (CX3CL1) in the cerebral cortex and hippocampus. Interestingly, many of these markers were highly expressed in Aβ-burdened neurons. Activated astrocytes and microglia were found in close relationship to these Aβ-burdened neurons. These findings confirm the occurrence of a pro-inflammatory process preceeding the generation of amyloid plaques and suggest that Aβ-burdened neurons play a crucial and early role in the initial inflammation in Alzheimer's pathology.Further we investigated the impact systemic inflammation in these very early stages of both amyloid pathology and CNS inflammation, as well as its impact on cognition. We found that administration of lipopolysaccharide (LPS) to McGill-Thy1-APP tg mice – at a pre-plaque stage of the amyloid pathology – changed levels of both anti (IL-10 and TGF-β) and pro-inflammatory molecules (COX-2 and MCP-1) as well the status of the microglia, a important cell mediator of inflammation. On the other hand LPS-administration did not impact cortical human Aβ42 levels, the profile of astrocytosis in the cortex and hippocampus of these mice or their cognitive status. Here we showed a chronic and long-lasting effect of LPS-induced systemic inflammation in CNS inflammation in initial stages of the amyloid pathology which is not present in mice with no amyloid pathology. Taken together these data indicates that systemic inflammation could have a negative impact on these initial/asymptomatic stages of AD pathology. Finally we study the impact of M30, a drug with antioxidant and neuroprotective properties, regarding its potential anti-inflammatory effects and its beneficial effects on cognition in our tg rat model of AD. We found that M30 administration reverted deficits in Novel Objection Location task and prevented worsening of cognitive impairment in Novel Object Recognition paradigm. Furthermore we demonstrated that the treatment rescued microglia phenotype, decreasing activation of these cells which is associated with the release of neurotoxic molecules. This change is microglia phenotype correlates with the effects on cognition in which activated microglia is associated with poor cognitive performance. Taken together, our results suggest the presence of neuronal-driven CNS inflammatory process associated with the accumulation of oligomeric Aβ. This early pathological event is modulated by systemic inflammation which might aggravate this process. By the other hand intervention with multi-target M30 decreases the inflammatory response with beneficial impacts on cognition.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".