Background/Purpose:

Inability to distinguish between infection and the inflammatory response related to SLE disease activity using clinical judgment often compromises timely and effective treatment.  Gene expression profiling of circulating leukocytes has been used to differentiate between malignancy subtypes and between sepsis and sterile inflammation. The “IFN signature” in SLE has been linked to disease pathogenesis and disease activity. A biomarker that precisely and rapidly distinguishes between active disease and infection would allow for more directed therapy, resulting in improved outcomes. RNA microarray analysis of peripheral blood leukocytes in acutely ill SLE patients was used to identify gene expression profiles that distinguish the host response to infection from inflammation related to active disease.

Methods:

37 SLE patients with suspected infection or active SLE were recruited from 3 centers in New York, Mexico and the Philippines. Subjects were excluded for pregnancy, a history of infection with Hepatitis B, C, HIV or if treatment had been initiated for the current illness. Whole blood was collected in Tempus™ Blood RNA Tubes and disease activity (SELENA SLEDAI) was measured at the time of enrollment. Infection determination was based on results of cultures, viral antibodies or PCR analyses. Microarray analyses were performed on the Illumina HT12v4 platform. Gene expression data were grouped using a modular analysis framework for blood genomics that has been applied to SLE previously (Immunity.2008; 29:150). Clinical characteristics were summarized using appropriate descriptive statistics with correction for multiple comparisons. Statistical significance for microarray modules was determined using a hypergeometric test.

Results:

31 subjects had adequate RNA for analysis; 19 met criteria for infection and subjects were grouped as either infection or flare as the cause of acute illness. There were no significant group differences in age, disease duration, ethnicity, co-morbid states, history of prior CNS or renal disease or current medication use. Presence of low serum complement or high anti-dsDNA antibody titers did not distinguish between groups, however, SLEDAI scores were significantly higher with disease activity (p=0.002). Flare subjects exhibited significant over expression of genes encoding immunoglobulin chains and CD38 (plasma cell module; p=0.001) and IFN inducible genes (p<.000). Subjects with infection demonstrated significant over expression of genes encoding molecules expressed by cells of myeloid lineage (p=0.018) and molecules inducible by or inducing inflammation (inflammation II module; p=.012).

Conclusion:

We have identified gene expression signatures that associate significantly with either infection or disease activity. Not surprisingly, genes over expressed in flare are those associated with plasma cells or are IFN inducible. Genes expressed in response to infection encode molecules such as FcγRIIA, CD86, CD163 and others associated with pattern recognition such as CD14, TLR2, MYD88, TNFR2 and BAFF. While SLEDAI scores correlate with disease activity, identification of a “sepsis signature” provides a more objective and reliable premise for treatment.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/molecular-signatures-in-sle-flare-vs-infection/