Loss of function variants in <i>ADAMTS6</i> : Connective tissue, Heart defect, thoracic Aortic aneurysm and Neuro developmental Syndrome (CHANS)
Bibliographic record
Abstract
ABSTRACT Marfan syndrome (MS), Loeys-Dietz syndrome (LDS), and heritable thoracic aortic aneurysms and dissections (hTAAD) are autosomal dominant connective tissue disorders with overlapping clinical features and underlying molecular heterogeneity. While most cases are explained by pathogenic variants in genes involved in extracellular matrix structure or TGFβ signaling, a large proportion of hTAAD cases remain idiopathic. Through exome and genome sequencing in a French diagnostic cohort, we identified rare deleterious variants in ADAMTS6 in four unrelated individuals with syndromic or isolated vascular disease. Functional studies demonstrated that these variants impair ADAMTS6 secretion or function, particularly in processing fibrillin-1 (FBN1) and fibrillin-2 (FBN2), resulting in extracellular matrix accumulation and microfibril disorganization. One variant, p.(Leu814Arg), further disrupted the Hippo and TGFβ signaling pathways and altered cell adhesion. Analysis of a patient-derived fibroblast model and Adamts6 -deficient mice supported a pathogenic role for ADAMTS6 loss-of-function in a novel connective tissue disorder. Clinical phenotypes spanned from early-onset syndromic presentations with cardiovascular, craniofacial, skeletal, and neurodevelopmental involvement to isolated adult-onset hTAAD. We propose ADAMTS6 deficiency defines a new connective tissue disorder, termed CHANS (Connective tissue, Heart defect, thoracic Aortic aneurysm, and Neurodevelopmental Syndrome), expanding the spectrum of ADAMTS-related pathologies and highlighting its key role in vascular and ECM homeostasis.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".