Abstract 288: Development Of A Phenotypic Screen With 56 Marfan Syndrome Patient-derived Fibroblasts
Bibliographic record
Abstract
Marfan syndrome (MFS) is a connective tissue disorder caused by mutations in the extracellular matrix gene coding for fibrillin-1 (FBN1). MFS leads to aortic aneurysms as key feature. Due to the high genetic diversity within the MFS patient population, genotype-phenotype predictions on disease progression and response to therapy remain challenging. Furthermore, current preclinical murine models represent only a subset of FBN1 mutations, which limits translational studies. In this study, we developed a preclinical model using primary cells of MFS patients that represent genetic and clinical variability in order to develop improved drug strategies. Human primary fibroblasts were isolated from skin biopsies derived from MFS patients (n=56) participating in the COMPARE trial. Human aortic smooth muscle cells (AoSMCs) were isolated from aortic tissue derived from MFS patients undergoing prophylactic aortic surgery (n=4). Long-term cultures in 384-well plates were assessed for the extracellular matrix and disease associated pathways. Analysis of all MFS fibroblasts showed either a reduced or abolished fibrillin-1 extracellular matrix network. Moreover, substantially reduced secretion of fibrillin-1 and asprosin in the culture medium was measured, as compared to healthy control fibroblasts. MFS fibroblasts with an abolished fibrillin-1 matrix were associated with patients with more rapid aortic root diameter growth. Exploring disease-relevant pathways revealed reduced nuclear localization of transcription factor KLF4 in MFS fibroblasts, which associated with the level of fibrillin-1 matrix. As a marker for myofibroblasts, α-smooth muscle actin (ASMA) staining was increased in these MFS fibroblasts. These findings were validated in AoSMC cultures, showing similar MFS fibrillin-1 matrix, nuclear KLF4, and ASMA expression profiles. In conclusion, phenotypic screening of patient-derived cells contributes to improved understanding of disease development and is valuable as preclinical tool for risk assessment and drug development for MFS and related conditions.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".