Large-scale analysis of genome-wide gene expression signatures allows researchers to discriminate between patients with active and inactive systemic lupus erythematosus — and could help physicians in adjusting treatment to control disease activity while minimizing the risk of side effects.
The study reporting the method, “Clinical Application of a Modular Genomics Technique in Systemic Lupus Erythematosus: Progress towards Precision Medicine,” was published in the International Journal of Genomics.
The complexity of lupus, with all the various symptoms and underlying changes in immune system players, makes the disease a particularly challenging condition to treat. There are currently no blood tests to shed light on the entire lupus population, and researchers have turned to studying gene expression in a search for markers that would allow physicians to track the ups and downs of lupus presentations.
Earlier studies have suggested gene expression signatures related to immune factors called interferons, or immune cells called neutrophils. But researchers at the Medical University of South Carolina thought these early attempts at genetic markers suffered high rates of variability.
Instead, the research team used what it called a modular approach to study gene expression changes in lupus patients. Such an approach looks for differences in clusters of genes that can produce signatures linked to certain disease characteristics.
For the study, the team recruited lupus patients, 95 of whom had typical lupus, 13 with high disease activity, and 25 with low disease activity. In addition, 51 healthy controls were used for comparison.
Researchers analyzed a total of 799 genes, using the modular analysis to identify clusters of genes that could be representative of high and low disease activity. They found gene expression signatures that could differentiate child lupus patients from healthy controls, nearly exactly repeating the results of earlier studies.
Comparisons between adult lupus patients with a typical mixture of disease symptoms, and healthy controls, also noted some of the gene expression signatures reported earlier, particularly differences in interferon gene expression.
The team then looked for differences between lupus patients with high and low disease activity, hypothesizing that genes linked to diseases state would not be the same as those differing between lupus patients and healthy people.
Researchers again discovered that genes related to interferon signaling were increased in people with high disease activity, compared to those with no current activity. They also noted that genes related to neutrophils were linked to the presence of lupus kidney disease.
“There is great promise in the use of data-driven analysis in the exploration of complex, heterogeneous diseases such as lupus. We show here an example of how this can be used in evaluating active versus inactive disease within SLE. As we move to precision medicine, methods such as these will lead to better characterization of disease, better therapies, and better response to therapies,” the team concluded.
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