Alzheimer’s disease PSEN-2 N141I mutation reveals altered and shear-sensitive brain endothelial cell-like phenotype in human iPSC-derived models
Bibliographic record
Abstract
Abstract Drug discovery efforts in neurological diseases, such as Alzheimer’s disease (AD), have had particularly poor outcomes due to the lack of models that recapitulate drug interactions at the cerebral vasculature. There is an unmet need to develop physiologically relevant models to study the impacts of blood flow-induced shear stress. In this work, we use a microfluidic platform to model the cerebral vasculature in AD using patient-derived brain endothelial-like cells (BECs). Induced pluripotent stem cells derived from a patient with familial AD (PSEN-2 N141I) and an unaffected control line were differentiated into BECs (AD2-BEC and fControl-BEC, respectively). BECs were exposed to static conditions or 12 dynes/cm 2 of shear stress for 72 h prior to assessment of barrier permeability using fluorescent tracer assays, monocyte adhesion, and efflux transport function using receptor-inhibition assays. Upon shear conditioning, BECs demonstrated shear responsiveness through greater cell alignment in the direction of flow. AD2-BECs demonstrated reduced capacity for efflux transport by p-glycoprotein (P-gp), breast cancer resistant protein (BCRP), and multidrug resistant protein (MRP1) compared to controls (fControl-BECs, p = 0.0017, p = 0.0004, p = 0.0002, respectively). Upon application of shear conditioning, impairments to efflux transport in AD2-BECs were ameliorated. AD2-BECs also exhibited increased monocyte adhesion (2.2 ± 0.4-fold; p < 0.0001) which was further reduced by the application of shear stress in both lines. Taken together, these observations suggest the lack of shear stress exacerbates altered BEC phenotype in fAD. To our knowledge, we present the first in depth functional characterization of in vitro AD patient-derived BECs in both static and physiologically relevant shear conditions in which lack of shear reveals dysfunction of the cerebral endothelium in AD relevant to drug transport and immune cell trafficking.
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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) | 0.000 | 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 | 0.001 |
| 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".