Transcription Factor Activity Inference in Systemic Lupus Erythematosus

Enhanced GATA4 expression in senescent systemic lupus erythematosus monocytes promotes high levels of IFNα production

Enhanced interferon α (IFNα) production has been implicated in the pathogenesis of systemic lupus erythematosus (SLE). We previously reported IFNα production by monocytes upon activation of the stimulator of IFN genes (STING) pathway was enhanced in patients with SLE. We investigated the mechanism of enhanced IFNα production in SLE monocytes. Monocytes enriched from the peripheral blood of SLE patients and healthy controls (HC) were stimulated with 2'3'-cyclic GAMP (2'3'-cGAMP), a ligand of STING. IFNα positive/negative cells were FACS-sorted for RNA-sequencing analysis. Gene expression in untreated and 2'3'-cGAMP-stimulated SLE and HC monocytes was quantified by real-time PCR. The effect of GATA binding protein 4 (GATA4) on IFNα production was investigated by overexpressing GATA4 in monocytic U937 cells by vector transfection. Chromatin immunoprecipitation was performed to identify GATA4 binding target genes in U937 cells stimulated with 2'3'-cGAMP. Differentially expressed gene analysis of cGAS-STING stimulated SLE and HC monocytes revealed the enrichment of gene sets related to cellular senescence in SLE. CDKN2A, a marker gene of cellular senescence, was upregulated in SLE monocytes at steady state, and its expression was further enhanced upon STING stimulation. GATA4 expression was upregulated in IFNα-positive SLE monocytes. Overexpression of GATA4 enhanced IFNα production in U937 cells. GATA4 bound to the enhancer region of IFIT family genes and promoted the expressions of IFIT1, IFIT2, and IFIT3, which promote type I IFN induction. SLE monocytes with accelerated cellular senescence produced high levels of IFNα related to GATA4 expression upon activation of the cGAS-STING pathway.

Transcription Factors in the Pathogenesis of Lupus Nephritis and Their Targeted Therapy

Systemic lupus erythematosus (SLE) is a prototype inflammatory autoimmune disease, characterized by breakdown of immunotolerance to self-antigens. Renal involvement, known as lupus nephritis (LN), is one of the leading causes of morbidity and a significant contributor to mortality in SLE. Despite current pathophysiological advances, further studies are needed to fully understand complex mechanisms underlying the development and progression of LN. Transcription factors (TFs) are proteins that regulate the expression of genes and play a crucial role in the development and progression of LN. The mechanisms of TF promoting or inhibiting gene expression are complex, and studies have just begun to reveal the pathological roles of TFs in LN. Understanding TFs in the pathogenesis of LN can provide valuable insights into this disease's mechanisms and potentially lead to the development of targeted therapies for its management. This review will focus on recent findings on TFs in the pathogenesis of LN and newly developed TF-targeted therapy in renal inflammation.

Analysis of transcriptomic features reveals molecular endotypes of SLE with clinical implications

BACKGROUND

Systemic lupus erythematosus (SLE) is known to be clinically heterogeneous. Previous efforts to characterize subsets of SLE patients based on gene expression analysis have not been reproduced because of small sample sizes or technical problems. The aim of this study was to develop a robust patient stratification system using gene expression profiling to characterize individual lupus patients.

METHODS

We employed gene set variation analysis (GSVA) of informative gene modules to identify molecular endotypes of SLE patients, machine learning (ML) to classify individual patients into molecular subsets, and logistic regression to develop a composite metric estimating the scope of immunologic perturbations. SHapley Additive ExPlanations (SHAP) revealed the impact of specific features on patient sub-setting.

RESULTS

Using five datasets comprising 2183 patients, eight SLE endotypes were identified. Expanded analysis of 3166 samples in 17 datasets revealed that each endotype had unique gene enrichment patterns, but not all endotypes were observed in all datasets. ML algorithms trained on 2183 patients and tested on 983 patients not used to develop the model demonstrated effective classification into one of eight endotypes. SHAP indicated a unique array of features influential in sorting individual samples into each of the endotypes. A composite molecular score was calculated for each patient and significantly correlated with standard laboratory measures. Significant differences in clinical characteristics were associated with different endotypes, with those with the least perturbed transcriptional profile manifesting lower disease severity. The more abnormal endotypes were significantly more likely to experience a severe flare over the subsequent 52 weeks while on standard-of-care medication and specific endotypes were more likely to be clinical responders to the investigational product tested in one clinical trial analyzed (tabalumab).

CONCLUSIONS

Transcriptomic profiling and ML reproducibly separated lupus patients into molecular endotypes with significant differences in clinical features, outcomes, and responsiveness to therapy. Our classification approach using a composite scoring system based on underlying molecular abnormalities has both staging and prognostic relevance.

Transcriptomics data: pointing the way to subclassification and personalized medicine in systemic lupus erythematosus

PURPOSE OF REVIEW

To summarize recent studies stratifying SLE patients into subgroups based on gene expression profiling and suggest future improvements for employing transcriptomic data to foster precision medicine.

RECENT FINDINGS

Bioinformatic & machine learning pipelines have been employed to dissect the transcriptomic heterogeneity of lupus patients and identify more homogenous subgroups. Some examples include the use of unsupervised random forest and k-means clustering to separate adult SLE patients into seven clusters and hierarchical clustering of single-cell RNA-sequencing (scRNA-seq) of immune cells yielding four clusters in a cohort of adult SLE and pediatric SLE participants. Random forest classification of bulk RNA-seq data from sorted blood cells enabled prediction of high or low disease activity in European and Asian SLE patients. Inferred transcription factor activity stratified adult and pediatric SLE into two subgroups.

SUMMARY

Several different endotypes of SLE patients with differing molecular profiles have been reported but a global consensus of clinically actionable groups has not been reached. Moreover, heterogeneity between datasets, reproducibility of predictions as well as the most effective classification approach have not been resolved. Nevertheless, gene expression-based precision medicine remains an attractive option to subset lupus patients.