Contributions of Genetic Variation in Astrocytes to Cell and Molecular Mechanisms of Risk and Resilience to Late‐Onset Alzheimer's Disease
Bibliographic record
Abstract
Reactive astrocytes are associated with Alzheimer's disease (AD), and several AD genetic risk variants are associated with genes highly expressed in astrocytes. However, the contribution of genetic risk within astrocytes to cellular processes relevant to the pathogenesis of AD remains ill-defined. Here, we present a resource for studying AD genetic risk in astrocytes using a large collection of induced pluripotent stem cell (iPSC) lines from deeply phenotyped individuals with a range of neuropathological and cognitive outcomes. IPSC lines from 44 individuals were differentiated into astrocytes followed by unbiased molecular profiling using RNA sequencing and tandem mass tag-mass spectrometry. We demonstrate the utility of this resource in examining gene- and pathway-level associations with clinical and neuropathological traits, as well as in analyzing genetic risk and resilience factors through parallel analyses of iPSC-astrocytes and brain tissue from the same individuals. Our analyses reveal that genes and pathways altered in iPSC-derived astrocytes from individuals with AD are concordantly dysregulated in AD brain tissue. This includes increased levels of prefoldin proteins, extracellular matrix factors, COPI-mediated trafficking components and reduced levels of proteins involved in cellular respiration and fatty acid oxidation. Additionally, iPSC-derived astrocytes from individuals resilient to high AD neuropathology show elevated basal levels of interferon response proteins and increased secretion of interferon gamma. Correspondingly, higher polygenic risk scores for AD are associated with lower levels of interferon response proteins in astrocytes. This study establishes an experimental system that integrates genetic information with a matched iPSC lines and brain tissue data from a large cohort of individuals to identify genetic contributions to molecular pathways affecting AD risk and resilience.
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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".