Mutations in sphingosine-1-phosphate lyase cause nephrosis with ichthyosis and adrenal insufficiency
Bibliographic record
Abstract
Steroid-resistant nephrotic syndrome (SRNS) causes 15% of chronic kidney disease cases. A mutation in 1 of over 40 monogenic genes can be detected in approximately 30% of individuals with SRNS whose symptoms manifest before 25 years of age. However, in many patients, the genetic etiology remains unknown. Here, we have performed whole exome sequencing to identify recessive causes of SRNS. In 7 families with SRNS and facultative ichthyosis, adrenal insufficiency, immunodeficiency, and neurological defects, we identified 9 different recessive mutations in SGPL1, which encodes sphingosine-1-phosphate (S1P) lyase. All mutations resulted in reduced or absent SGPL1 protein and/or enzyme activity. Overexpression of cDNA representing SGPL1 mutations resulted in subcellular mislocalization of SGPL1. Furthermore, expression of WT human SGPL1 rescued growth of SGPL1-deficient dpl1Δ yeast strains, whereas expression of disease-associated variants did not. Immunofluorescence revealed SGPL1 expression in mouse podocytes and mesangial cells. Knockdown of Sgpl1 in rat mesangial cells inhibited cell migration, which was partially rescued by VPC23109, an S1P receptor antagonist. In Drosophila, Sply mutants, which lack SGPL1, displayed a phenotype reminiscent of nephrotic syndrome in nephrocytes. WT Sply, but not the disease-associated variants, rescued this phenotype. Together, these results indicate that SGPL1 mutations cause a syndromic form of SRNS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".