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Record W4389609369 · doi:10.1101/2023.12.11.571192

Functional analysis of cell lines derived from SMAD3-related Loeys-Dietz Syndrome patients provides insights into genotype-phenotype relations

2023· preprint· en· W4389609369 on OpenAlexaff
Nathalie P. de Wagenaar, Lisa M. van den Bersselaar, Hanny Odijk, Sanne J. M. Stefens, Dieter P. Reinhardt, Jolien W. Roos‐Hesselink, Roland Kanaar, Judith M.A. Verhagen, Hennie T. Brüggenwirth, Ingrid M.B.H. van de Laar, Ingrid van der Pluijm, Jeroen Essers

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsHaploinsufficiencyPhenotypeMedicineMyofibroblastGenotypeInternal medicineGenePathologyBiologyGeneticsFibrosis

Abstract

fetched live from OpenAlex

Abstract Introduction Pathogenic (P) and likely pathogenic (LP) variants in the SMAD3 gene cause Loeys-Dietz syndrome type 3 (LDS3), also known as aneurysms-osteoarthritis syndrome (AOS). The phenotype of LDS3 is highly variable and characterized by arterial aneurysms, dissections and tortuosity throughout the vascular system combined with skeletal, cutaneous and facial features. Objectives Investigate the impact of P/LP SMAD3 variants through conducting functional tests on patient-derived fibroblasts and vascular smooth muscle cells (VSMCs).The resulting knowledge will optimize interpretation of SMAD3 variants. Material and methods We conducted a retrospective analysis on clinical data from individuals with a P/LP SMAD3 variant and utilized patient-derived VSMCs to investigate the functional impacts of dominant negative (DN) and haploinsufficient (HI) variants in SMAD3. Additionally, to broaden our cell model accessibility, we performed similar functional analyses on patient-derived fibroblasts carrying SMAD3 variants, differentiating them into myofibroblasts with the same variants. This enabled us to study the functional effects of DN and HI variants in SMAD3 across both patient-derived myofibroblasts and VSMCs. Results Individuals with dominant negative (DN) variants in the MH2 protein interaction domain of SMAD3 exhibited a higher frequency of major events (66.7% vs. 44.0%, p=0.054), occurring at a younger age compared to those with haploinsufficient (HI) variants. Moreover, the age at the onset of the first major event was notably younger in individuals with DN variants in MH2, 35.0 years [IQR 29.0-47.0], compared to 46.0 years [IQR 40.0-54.0] in those with HI variants (p=0.065). In functional assays, fibroblasts carrying DN SMAD3 variants displayed reduced differentiation potential, contrasting with increased differentiation potential observed in fibroblasts with HI SMAD3 variants. Additionally, HI SMAD3 variant VSMCs showed elevated SMA expression, while exhibiting altered expression of alternative MYH11 isoforms. Conversely, DN SMAD3 variant myofibroblasts demonstrated reduced extracellular matrix (ECM) formation compared to control cell lines. These findings collectively indicate distinct functional consequences between DN and HI variants in SMAD3 across fibroblasts and VSMCs, potentially contributing to the observed differences in disease manifestation and age of onset of major events. Conclusion Distinguishing between P/LP HI and DN SMAD3 variants can be achieved by assessing differentiation potential, and evaluating SMA and MYH11 expression. Notably, myofibroblast differentiation seems to be a suitable alternative in vitro test system in comparison to VSMCs. Moreover, there is a notable trend of aortic events occurring at younger age in individuals with a DN SMAD3 variant in the MH2 domain, distinguishing them from those with a DN variant in the MH1 domain or a HI variant.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.016
GPT teacher head0.232
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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