# Characterizing the natural history of sphingosine phosphate lyase insufficiency syndrome (SPLIS): a fundamental step in the development of a targeted cure for this novel atypical sphingolipidosis

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $693,471

## Abstract

Summary
Sphingosine-1-phosphate lyase insufficiency syndrome (SPLIS) is an ultra-rare, devastating, often lethal inborn
error of metabolism recognized just five years ago. Most affected children exhibit a rapidly progressive form of
nephrotic (protein spilling) syndrome which leads to kidney failure, the main cause of death. Affected children
may also exhibit adrenal insufficiency, hypothyroidism, skin and neurological defects, and immunodeficiency.
Although kidney transplantation may be lifesaving, there is no cure for SPLIS. SPLIS is caused by recessive
mutations in SGPL1, which encodes sphingosine-1-phosphate (S1P) lyase, a vitamin B6-dependent enzyme
responsible for catabolism of the bioactive lipid S1P in the final step of sphingolipid metabolism. SPLIS is a
member of a new family of non-lysosomal sphingolipid disorders about which relatively little is known. Despite
the dismal outcome in SPLIS, targeted therapies are rapidly being developed. Some children with milder forms
of the condition may respond to supplementation with vitamin B6, the enzyme’s cofactor. Studies in a mouse
model of SPLIS have provided proof-of-concept for the use of adeno-associated virus-mediated SGPL1 gene
therapy as a universal and potentially curative treatment for SPLIS. Clinical trials testing these two therapeutic
strategies are on the horizon. Although we have made rapid progress in methods of SPLIS patient finding,
biomarker development, and therapeutics, achieving two additional goals will enhance our readiness to
undertake SPLIS clinical trials: a) providing a comprehensive understanding of the natural history of SPLIS
including the full spectrum of clinical presentations, disease subgroups, and natural progression; b) developing
a method to identify SPLIS patients before irreversible kidney damage has occurred. We are now in a pivotal
position to achieve both these strategic goals. We hypothesize that specific disease features and biomarkers
will predict quality of life and survival in SPLIS patients. To confirm our hypothesis and achieve our goals, we
propose three Aims: 1) Characterize the spectrum of SPLIS clinical presentations and their natural progression
over time; 2) Determine how biomarker endpoints reflect or predict functional change over time in SPLIS
patients; 3) Establish a screening strategy for early detection of SPLIS based on blood S1P levels. In achieving
these Aims, we will develop a deeper understanding of the spectrum, sequence, and timing of onset of SPLIS
manifestations, and will identify predictors of disease outcomes that are meaningful to patients and families
and that will be critical for patient selection for clinical trial enrollment. Our project will create useful tools for
measuring outcomes in SPLIS, providing quantifiable endpoints to be used in clinical trials. Overall, revealing
the natural history of SPLIS will lay the foundation for evaluating the impact of targeted interventions in clinical
trials. Developing early det...

## Key facts

- **NIH application ID:** 10781687
- **Project number:** 1R01HD113778-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** JULIE D SABA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $693,471
- **Award type:** 1
- **Project period:** 2024-09-10 → 2029-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10781687

## Citation

> US National Institutes of Health, RePORTER application 10781687, Characterizing the natural history of sphingosine phosphate lyase insufficiency syndrome (SPLIS): a fundamental step in the development of a targeted cure for this novel atypical sphingolipidosis (1R01HD113778-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10781687. Licensed CC0.

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