The gene expression of type 17 T-helper cell-related cytokines in the urinary sediment of patients with systemic lupus erythematosus

Kinases have been implicated in the immunopathological mechanisms of Systemic Lupus Erythematosus (SLE). v-akt murine-thymoma viral-oncogene-homolog 1 (AKT1) and mitogen-activated-protein-kinase 1 (MAPK1) gene expressions in peripheral mononuclear cells from thirteen SLE patients with inactive or mild disease were evaluated using quantitative real-time reverse-transcription polymerase-chain-reaction and analyzed whether there was any correlation with T-helper (Th) transcription factors (TF) gene expression, cytokines, and S100A8/S100A9-(Calprotectin). Age- and gender-matched thirteen healthy controls were examined. AKT1 and MAPK1 expressions were upregulated in SLE patients and correlated with Th17-(Retinoic acid-related orphan receptor (ROR)-C), T-regulatory-(Treg)-(Transforming Growth Factor Beta (TGFB)-2), and Th2-(interleukin (IL)-5)-related genes. MAPK1 expression correlated with Th1-(IL-12A, T-box TF-(T-bet)), Th2-(GATA binding protein-(GATA)-3), and IL-10 expressions. IL-10 expression was increased and correlated with plasma Tumor Necrosis Factor (TNF)-αand Th0-(IL-2), Th1-(IL-12A, T-bet), GATA3, Treg-(Forkhead/winged-helix transcription factor- (FOXP)-3), and IL-6 expressions. FOXP3 expression, FOXP3/RORC, and FOXP3/GATA3 expression ratios were increased. Plasma IL-1β, IL-12(p70), Interferon-(IFN)-γ, and IL-6 cytokines were augmented. Plasma IL-1β, IL-6, IL-2, IFN-γ, TNF-α, IL-10, and IL-13 correlated with C-reactive protein, respectively. Increased Calprotectin correlated with neutrophils. Conclusion, SLE patients presented a systemic immunoinflammatory activity, augmented AKT1 and MAPK1 expressions, proinflammatory cytokines, and Calprotectin, together with increased expression of Treg-related genes, suggesting a regulatory feedback opposing the inflammatory activity.

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Positive Selection Affects the Expression of Systemic Lupus Erythematosus Associated Loci in Human Populations

10.21203/rs.3.rs-255082/v1 ◽

2021 ◽

Author(s):

Victoria Oberreiter ◽

Tobias Goellner ◽

David L. Morris ◽

Helmut Schaschl

Keyword(s):

Gene Expression ◽

Systemic Lupus Erythematosus ◽

Positive Selection ◽

Lupus Erythematosus ◽

Lifestyle Changes ◽

Genetic Ancestry ◽

Selection Function ◽

Human Populations ◽

Systemic Lupus ◽

A Genome

Abstract Background: Systemic lupus erythematosus (SLE) shows marked population-specific disparities in disease prevalence, including substantial variation in manifestations and complications according to genetic ancestry. Several recent studies suggest that a substantial proportion of variation of gene expression shows genetic ancestry-associated differences in gene regulation on immune responses. Positive selection may act in a population-specific manner on expression quantitative trait loci (eQTLs) and thereby contributes to the difference in the differences of SLE prevalence and manifestation in human populations. We tested the hypothesises that some of the identified SLE risk polymorphisms display pleiotropic effects or polygenicity driven by positive selection. We performed a genome-wide scan for recent positive selection by using integrated Haplotype Score (iHS) statistics in different human populations. In addition, we estimated the timing of beneficial mutations to understand what possible selective pressures drive positive selection at SLE-associated loci. Results: We identified several SLE risk loci that are population-specifically under positive selection. Almost all SNPs that are under positive selection function as cis-eQTLs in different tissue types. We determined that adaptive eQTLs affect the expression of fewer genes than non-adaptive eQTLs, suggesting a limited range of effect of an eQTL at SLE risk sites that show signatures of positive selection. Furthermore, some positively selected SNPs are located in transcription factor binding sequences. The timing of positive selection for the studied loci suggests that both environmental and recent lifestyle changes during as well as after the Neolithic Transition may have become selectively effective. We propose a novel link between positively selected eQTLs at a certain SLE risk locus in Europeans and a physiological pathway not previously considered in SLE.Conclusions: We conclude that population-specific adaptive eQTLs contribute to the observed variation in specific manifestations and complications of SLE in different ethnicities. Our results suggest also that human populations adapt more rapidly to environmental and lifestyle stimuli via modification of gene expression without having to alter the genetic code.

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Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

10.1101/647719 ◽

2019 ◽

Author(s):

William A Figgett ◽

Katherine Monaghan ◽

Milica Ng ◽

Monther Alhamdoosh ◽

Eugene Maraskovsky ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Systemic Lupus Erythematosus ◽

Clinical Trials ◽

Lupus Erythematosus ◽

Whole Blood ◽

Rna Seq ◽

Systemic Lupus ◽

Disease Heterogeneity ◽

Healthy Donors

ABSTRACTObjectiveSystemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the whole-blood transcriptomes of patients with SLE.MethodsWe applied machine learning approaches to RNA-sequencing (RNA-seq) datasets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta-analysis on two recently published whole-blood RNA-seq datasets was carried out and an additional similar dataset of 30 patients with SLE and 29 healthy donors was contributed in this research; 141 patients with SLE and 51 healthy donors were analysed in total.ResultsExamination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease-related genes relative to clinical presentation. Moreover, gene signatures correlated to flare activity were successfully identified.ConclusionGiven that disease heterogeneity has confounded research studies and clinical trials, our approach addresses current unmet medical needs and provides a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy to harness disease heterogeneity and identify patient populations that may be at an increased risk of disease symptoms. Further, this approach can be used to understand the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy.

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