Despite decades of lupus research, it remains poorly understood why certain ethnic or ancestral groups have higher prevalence of lupus than others.

Lupus symptoms occur when a person’s immune system improperly attacks the body’s own tissues and organs. The damaging behavior of immune cells in lupus is thought to be determined by the genetic makeup of the individuals. However, the genetic differences between people from different ancestral groups that contribute to the disease variations are still poorly defined.

In this project, the global multi-disciplinary team from Australia and the United Kingdom led by Dr. Morand will identify the distinct molecular pathways – a series of interactions among molecules – that are associated with lupus symptoms in each ancestral group. Applying the latest scientific technological approaches, the team will use biological samples and clinical data from lupus patients from five ancestral groups (Europeans, Afro-Caribbean, South and East Asians, and Indigenous Australians) to identify the genes and molecules associated with systemic lupus erythematosus (SLE) symptoms specific for each ancestral group as well as those shared among groups. Furthermore, the team will identify the ancestry-specific genetic variations that dictate the behavior of molecular pathways and disease symptoms. With the generation of a large data trove, the team will use advanced computational analysis to identify potential genes that drive SLE among varying groups.

What this study means for people with lupus

The outcomes of this research may pinpoint the genetic variations and genes responsible for the variations in SLE symptoms and disease severity in different ancestral groups. These findings could potentially identify novel targets for treatment or prevention of lupus.

SLE clinical phenotypes are renowned for heterogeneity. Despite this, prominent patterns of clinical expression are observed when comparing patients of different ancestry. In non-European compared to European ancestry, these include higher prevalence, younger onset, greater serological activity, and increased prevalence of several organ manifestations including nephritis that in turn are associated with worse long-term outcomes. The molecular underpinnings of this variation represent a potential opportunity for intervention. Phenotypic variation in SLE manifests across diverse ‘axes’ – age of onset, organ involvement, damage accrual. We propose that this variation is mediated via complex combinations of transcriptional and translational events that yield a spectrum of biological endotypes and corresponding clinical phenotypes. In turn, ancestry-linked clinical phenotypes, and their corresponding biological endotypes, must be linked with inherited traits. Several recent studies have begun to demonstrate links between ancestry and measurable molecular traits in SLE. Until now, however, these have focused, largely for technical reasons, on single layers of the ‘-omics stack’ of genome, transcriptome, or proteome. We believe that the molecular variation which underpins ancestry-related differences in SLE phenotype can be represented in high-dimensional space but can only be properly assessed through measurements across multiple molecular layers. To address this, we propose to undertake multi-modal-omics measurements in large and ancestrally diverse cohorts of SLE patients from three major centers, using advanced sequencing and other technologies, and interdisciplinary expertise ranging from clinical measurement through genetics and advanced bioinformatic methods. We will generate of map of functional endotypes (transcriptome (whole blood and single cell RNASeq) and proteome (wide angle multianalyte panels and mass spectroscopy) in these patients, and examine how these are associated both with clinical phenotypes and with different ancestries. Subsequently, we will investigate how genome level variation is linked to these endotypes, expecting that a finite number of biological endotypes will enable discovery of genomic explanation of ancestry-dependent variation in SLE phenotype. A key feature of our proposal is the use of genomics as an ‘anchor’ for the other-omic layers, enabling us leverage to move beyond descriptive, correlative analysis and identify causal pathways. The outcomes of this work will be a set of maps explaining ancestrally-determined variation in SLE phenotype, potentially unveiling novel therapeutic targets through which to address the marked variation in SLE outcomes that are observed. We believe this will also provide a lens through which to better understand SLE heterogeneity at large, and a massive dataset for future researchers to exploit.