Surfactant metabolism dysfunction, pulmonary, 1: genes and variants

Genes linked to Surfactant metabolism dysfunction, pulmonary, 1

• SFTPC: Surfactant protein C

  It contributes to pulmonary surfactant organization and is produced specifically by alveolar type II cells. Dominant pathogenic variants can cause interstitial lung disease across infancy and adulthood, often through protein misfolding and toxic cellular stress.

  7 disease-causing and 18 uncertain variants in SFTPC are linked to Surfactant metabolism dysfunction, pulmonary, 1.

• ABCA3: Phospholipid-transporting ATPase ABCA3

  It transports phospholipids into lamellar bodies of alveolar type II cells, supporting pulmonary surfactant assembly and normal gas exchange. Biallelic loss-of-function variants can cause neonatal respiratory failure or childhood interstitial lung disease.

  1 disease-causing and 2 uncertain variants in ABCA3 are linked to Surfactant metabolism dysfunction, pulmonary, 1.

• SFTPB: Pulmonary surfactant-associated protein B

  It lowers surface tension and stabilizes pulmonary surfactant films during repeated breathing cycles, preventing alveolar collapse at end expiration. Biallelic loss-of-function variants cause severe neonatal surfactant dysfunction and respiratory failure.

  0 disease-causing and 24 uncertain variants in SFTPB are linked to Surfactant metabolism dysfunction, pulmonary, 1.

Known disease-causing variants in Surfactant metabolism dysfunction, pulmonary, 1

Which prediction tools work for Surfactant metabolism dysfunction, pulmonary, 1

How often each tool ranks a disease-causing variant above a harmless one (AUROC × 100).

• CATVariant: 96 out of 100 (learned from overlapping clinical labels, so this is optimistic)
• SIFT: 94 out of 100

Same protein, different disease

• Hereditary pulmonary alveolar proteinosis is also caused by SFTPC variants; they fall mostly in different places as the Surfactant metabolism dysfunction, pulmonary, 1 variants (4 disease-causing).
• Interstitial lung disease due to ABCA3 deficiency is also caused by ABCA3 variants; they fall mostly in different places as the Surfactant metabolism dysfunction, pulmonary, 1 variants (30 disease-causing).
• Hereditary pulmonary alveolar proteinosis is also caused by ABCA3 variants; they fall mostly in different places as the Surfactant metabolism dysfunction, pulmonary, 1 variants (12 disease-causing).

Diseases related to Surfactant metabolism dysfunction, pulmonary, 1

• Hereditary pulmonary alveolar proteinosis, also linked to ABCA3, SFTPB and SFTPC
• Interstitial lung disease, also linked to ABCA3 and SFTPC
• Loeys-Dietz syndrome, also linked to ABCA3
• Interstitial lung disease due to ABCA3 deficiency, also linked to ABCA3
• Idiopathic pulmonary fibrosis, also linked to SFTPC

Frequently asked questions

Which genes are linked to Surfactant metabolism dysfunction, pulmonary, 1?

In CATVariant, Surfactant metabolism dysfunction, pulmonary, 1 is linked to 3 analyzed proteins: SFTPC (Surfactant protein C), ABCA3 (Phospholipid-transporting ATPase ABCA3) and SFTPB (Pulmonary surfactant-associated protein B).

How many genetic variants are linked to Surfactant metabolism dysfunction, pulmonary, 1?

66 variants: 8 are classified as disease-causing (pathogenic or likely pathogenic) in ClinVar and 44 are of uncertain significance or have conflicting reports.

Which uncertain variants in Surfactant metabolism dysfunction, pulmonary, 1 look disease-causing?

None of the uncertain variants currently reaches the likely-pathogenic range on computable evidence alone.

Which variant effect predictor works best for Surfactant metabolism dysfunction, pulmonary, 1?

Among tools not trained on clinical labels, SIFT separates this disease's known disease-causing variants from harmless ones best (AUROC 0.94, based on 8 disease-causing and 44 harmless variants).

About this data

Variant–disease links come from ClinVar, Open Targets and UniProt, pooled from the latest public CATVariant analysis of each human protein. Evidence scores use the ACMG/AMP Bayesian points scale with computable criteria only (position among known disease variants, rarity in gnomAD, calibrated predictors, deep mutational scanning); there is no family or patient data, so they prioritise variants for expert review and never classify them.

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