Transcription Factor Activity Inference in Systemic Lupus Erythematosus

Abstract

Background: Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease with diverse clinical manifestations. Although most of the SLE-associated loci are located in regulatory regions, there is a lack of global information about transcription factor (TFs) activities, the mode of regulation of the TFs, or the cell or sample-specific regulatory circuits. The aim of this work is to decipher TFs implicated in SLE. Methods: In order to decipher regulatory mechanisms in SLE, we have inferred TF activities from transcriptomic data for almost all human TFs, defined clusters of SLE patients based on the estimated TF activities and analyzed the differential activity patterns among SLE and healthy samples in two different cohorts. The Transcription Factor activity matrix was used to stratify SLE patients and define sets of TFs with statistically significant differential activity among the disease and control samples. Results: TF activities were able to identify two main subgroups of patients characterized by distinct neutrophil-to-lymphocyte ratio (NLR), with consistent patterns in two independent datasets—one from pediatric patients and other from adults. Furthermore, after contrasting all subgroups of patients and controls, we obtained a significant and robust list of 14 TFs implicated in the dysregulation of SLE by different mechanisms and pathways. Among them, well-known regulators of SLE, such as STAT or IRF, were found, but others suggest new pathways that might have important roles in SLE. Conclusions: These results provide a foundation to comprehend the regulatory mechanism underlying SLE and the established regulatory factors behind SLE heterogeneity that could be potential therapeutic targets.

The Lupus Epigenome Relates to Genetics, Transcription and Serological Profiles with Dependency on Molecular Subtypes and Informs Drug Discovery

“…It also phosphorylates SYK and promotes SYK-dependent activation of AKT1 and MAP kinase signaling. SYK is one of the top genes in the candidate drug target gene list for SLE (31,53), as STAT4 is for the IFN subytpe and VDR for the inflammatory.…”

“…Importantly, we also discovered epigenetic signals that are specific of SLE patients with an inflammatory molecular profile, as those in the EGR3 and SYNGAP genes. EGR3 is a member of a zinc finger transcription factor that plays an important role in regulating immune responses inducing also the expression of anti-inflammatory cytokines such as IL-10 and TGFB1 (31), however the role of EGR3 in autoimmunity it is not clear yet.…”

“…We inferred TFact for 119 Transcription Factors in the PRECISESADs data using whole blood RNAseq data and the R package DoRothEA (30) as previously described (31). Linear regression models were performed between DNAm at DMP and log transformed cytokine levels for 18 different inflammatory cytokines (see supplementary Table 1 ) or with TFact in different groups (SLE, CTRL, inflammatory and IFN) and corrected for the same covariates used for the EWAS/meQTLs.…”

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

“…Many studies have characterized resultant subgroups based on SLEDAI or implemented a SLEDAI cutoff as inclusion/exclusion criteria for downstream analyses. Interstudy subgroup comparison is, therefore, challenged by definitions of low or high disease activity, where some studies use SLEDAI = 4 as the cutoff [16 ▪▪ ,21 ▪▪ ] and others use SLEDAI = 6 [6 ▪ ,15 ▪▪ ], yet even others create their own definitions for ‘high’ versus ‘low’ disease activity [14 ▪ ,17 ▪▪ ].…”

“…Finally, when patient stratification is achieved, there is difficulty in appropriate interpretation of the features of these subgroups and comparison to subgroups in a validation cohort. Some studies rely on qualitative observations [16 ▪▪ ,21 ▪▪ ] and/or fail to validate patient clusters in an independent cohort [15 ▪▪ ,17 ▪▪ ,21 ▪▪ ].…”