Stratification of Systemic Lupus Erythematosus Patients Using Gene Expression Data to Reveal Expression of Distinct Immune Pathways

TL;DR

This study used unsupervised machine learning on gene expression data to classify SLE patients into distinct immune pathway-based clusters, revealing a novel mitochondrial apoptosis pathway as a potential independent cause of SLE.

Contribution

It introduces a new clustering approach to stratify SLE patients and identifies mitochondrial apoptosis as a previously unrecognized pathway involved in the disease.

Findings

Three distinct immune pathways identified in SLE patients.

Mitochondrial apoptosis pathway as a novel independent factor.

Clusters correspond to different gene expression profiles.

Abstract

Systemic lupus erythematosus (SLE) is the tenth leading cause of death in females 15-24 years old in the US. The diversity of symptoms and immune pathways expressed in SLE patients causes difficulties in treating SLE as well as in new clinical trials. This study used unsupervised learning on gene expression data from adult SLE patients to separate patients into clusters. The dimensionality of the gene expression data was reduced by three separate methods (PCA, UMAP, and a simple linear autoencoder) and the results from each of these methods were used to separate patients into six clusters with k-means clustering. The clusters revealed three separate immune pathways in the SLE patients that caused SLE. These pathways were: (1) high interferon levels, (2) high autoantibody levels, and (3) dysregulation of the mitochondrial apoptosis pathway. The first two pathways have been extensively studied in SLE. However, mitochondrial apoptosis has not been investigated before to the best of our knowledge as a standalone cause of SLE, independent of autoantibody production, indicating that mitochondrial proteins could lead to a new set of therapeutic targets for SLE in future research.