# Longitudinal deep multi-omics profiling in a CLN3Δex7/8 minipig model identifies biomarker signatures of disease

## TL;DR

Researchers identified blood-based biomarkers for CLN3 disease in a pig model, which could help monitor disease progression and treatment response.

## Contribution

The study provides the most comprehensive multi-omics profiling of serum in a porcine model of CLN3 disease, enabling the discovery of novel biomarker signatures.

## Key findings

- Presymptomatic disease is marked by elevated glycerophosphodiester species and lysosomal proteases.
- Later disease stages show increased immune cell activation and sphingolipid metabolism markers.
- A biomarker index score based on CTSS, CTSB, and specific glycerophospholipids distinguishes healthy from diseased animals.

## Abstract

Development of therapies for CLN3 disease, a rare pediatric lysosomal storage disorder, has been hindered by the lack of etiological insights and translatable biomarkers to clinics.

We used a deep multi-omics approach to discover blood-based biomarkers using longitudinal serum samples from a porcine model of CLN3 disease. Comprehensive metabolomics was combined with a nanoparticle-based LC-MS-based proteomic profiling coupled with TMTpro 18-plex to generate quantitative data on 769 metabolites and 2634 proteins, collectively the most exhaustive multi-omics profile conducted on serum from a porcine model. This was previously impossible due to lack of efficient deep serum proteome profiling technologies compatible with model organisms.

Here we show that the presymptomatic disease state is characterized by elevations in glycerophosphodiester species and lysosomal proteases, while later timepoints are enriched with species involved in immune cell activation and sphingolipid metabolism. Cathepsin S (CTSS), Cathepsin B (CTSB), glycerophosphoinositol, and glycerophosphoethanolamine captured a large portion of the genotype-correlated variation between healthy and diseased animals, suggesting that an index score based on these analytes could have great utility in the clinic.

This study’s findings demonstrate the potential of deep multi-omics profiling for uncovering disease-specific biomarkers, providing valuable insights for understanding disease and facilitating the identification of potential drug targets, thus offering valuable insights for therapeutic interventions.

Batten disease is a rare childhood disorder in which brain cells become damaged and there is a decline in physical and mental abilities. It is difficult to monitor disease progression or response to treatments as there are no established components of the blood (biomarkers) that can be used to identify people with the disease. Identifying such biomarkers is critical to understanding why disease develops and to enable development of treatments. We used a large animal model of Batten disease to investigate whether blood-based biomarkers could be identified. We developed a scoring framework that accurately distinguishes disease from control samples. These findings suggest that the identified biomarkers have the potential to be used to determine response in clinical trials.

Rechtzigel et al. perform a comprehensive multi-omics study using serum from CLN3Δex7-8 minipigs, identifying blood-based molecular changes linked to disease progression. These data are used to create a biomarker scoring framework accurately distinguishing healthy from affected pigs.