Introduction
Gene expression Abstract Systemic lupus erythematosus SLE is an autoimmune disease in which outcomes vary among different racial groups. To understand the identified clusters, correlation analysis revealed significant positive associations between the clusters and clinical parameters including disease activity as well as ethnicity. Lupus Research Paper then explored differentially expressed genes between Asian and White groups for each cell-type.
The shared differentially expressed genes across cells were involved in SLE or other autoimmune-related pathways. Co-expression analysis identified similarly regulated genes across samples and grouped these genes into modules. The results from these analyses will help stratify patients based on their gene expression signatures to enable SLE precision medicine.
Download PDF Introduction Systemic lupus erythematosus SLE is a complex multisystem autoimmune here characterized by dysregulation of the innate and adaptive arms of the immune system 1. The predisposition and clinical phenotype of SLE, a disease associated with compelling morbidity and mortality, are attributed to a combination of genetics, hormones, and environmental factors 2.
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Heterogenous clinical and serologic manifestations, a waxing and waning course, and delays in diagnosis contribute to the complexity of this disease. Many of these loci are unique to European-Americans patients while others have been identified in African American or Hispanic-Americans 45. Susceptibility to SLE see more a strong genetic component, Lupus Research Paper trans-ancestral genetic studies have revealed a substantial commonality of shared genetic risk variants across different genetic ancestries that predispose to the development of SLE 6. Scientific evidences have identified that the dominant group most commonly diagnosed with SLE are women of minority status in low socio-economic environments 8.
Systemic Lupus Research Paper
It is believed that an increased genetic Lupus Research Paper burden in these populations, associated with increased autoantibody reactivity in non-white individuals with SLE, may explain the more severe lupus phenotype 6. As patients with similar Systemic Lupus Erythematosus Disease Activity Index, or SLEDAI Luupsscores may have different prognoses and link responses 11there is an urgent need to establish a new method of stratification of lupus patients. With the recent advances in molecular measurements and computational technologies, there are incredible opportunities to characterize disease-associated genes and pathways.
Previous studies have used machine learning ML and clustering approaches to try to stratify patients with SLE based on different parameters including clinical data, expression quantitative trait loci eQTLsmethylation, and transcriptomic data 121314 However, these studies have been mainly conducted on whole blood or Lulus blood mononuclear cells PBMCstherefore not considering the involvement of different Lupus Research Paper cell types in disease, and in White cohorts, thus without taking into account the relationship between disease activity and ethnic background.
Research Paper On Systemic Lupus Erythematosus
In our own previous Lupus Research Paper, unsupervised clustering of the 11 American College of Rheumatology ACR classification criteria in an ethnically diverse lupus cohort revealed three stable clusters, characterized by significant differences in several SLE features, as well as the lupus severity index Following up on the clinical clustering study, the objective of this study which leverages the same cohort is to use large-scale transcriptomic data from diverse Lupus Research Paper populations to identify transcriptomic signatures in SLE relating to various clinical and demographic factors and better sub-classify SLE patients into more clinically actionable groups.
This study aims to look for specific-transcriptomic effects in each of these immune cell types using a four-tier approach: unsupervised clustering, differential expression analyses, gene co-expression analyses, and machine learning approaches Fig. This classification might provide insightful information to improve predictions of disease outcomes and subtype-specific mechanistic pathways that could be strategically targeted.
We further explore molecular pathways that underlie the clinical and demographic differences in SLE.]
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