Identification of novel susceptibility genes in childhood-onset systemic lupus erythematosus using a uniquely designed candidate gene pathway platform (original) (raw)
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Systemic lupus erythematous (SEL) is a heterogeneous, systemic autoimmune disorder which is defined by its autoantibody pattern. Transcriptomic data analysis has shown pathways and immune system responses associated with SLE. Eight up-regulated genes (SOCE, MMP9, CXCL8, JUN, IL1B, NFKBIA, TNF and FOS) have been examined with four interactions among different pathways. These genes are associated with SNPs which have been identified through two datasets from SLE genome-wide association studies (GWAS). In this investigation, the GWAS results were integrated with pathway analysis of transcriptomes and several genes were detected with known SLE-related variations (TYK2, C5, SH2B, IRF5, IL2RA, STAT4, FCGR2A, IL7R, LYN, HLA-DRB and TNFAIP3). Pathway-based analysis on the Wikipathway Human Collection allowed the identification of prioritized variants in the relevant pathways, such as thymic stromal lymphopoietin (TSLP) signaling pathway linked to LYN, IL7R, STAT4 and rs7574865. Analysis of existing transcriptomes and GWAS data identified eight upregulated candidate genes with more than four relationships among the different pathways associated with SNPs to pinpoint the relevant loci linked to SLE. The results of this investigation have expanded the number of candidate genes related to SLE and have highlighted possible pathways and GWAS-based methods for gene detection. Identification of the fundamental genes would assist in revealing the mechanisms responsible for SLE.
GENE
The identification of rare genetic variants associated to Systemic Lupus Erythematosus (SLE) could also help to understand the pathogenic mechanisms at the basis of the disease. In this study we have analyzed a cohort of 200 Italian SLE patients in order to explore the rare protein-coding variants in five genes (TNFAIP3, STAT4, IL10, TRAF3IP2, and HCP5) already investigated for commons variants found associated in our previous studies. Genomic DNA of 200 SLE patients was sequenced by whole exome sequencing. The identified variants were filtered by frequency and evaluated by in silico predictions. Allelic association analysis was performed with standard Fisher's exact test. Introducing a cutoff at MAF < 0.01, a total of 19 rare variants were identified. Seven of these variants were ultra-rare (MAF < 0.001) and six were absent in the GnomAD database. For TNFAIP3 gene, the variant c.A1939C was observed in 4 SLE patients and it is located in a region enriched in phosphorylation sites and affects the predict affinity of specific kinases. In TRAF3IP2 gene, we observed 5 different rare variants, including the novel variant c.G410A, located in the region that mediates interaction with TRAF6, and therefore a possible risk factor for SLE development. In STAT4 gene, we identified 6 different rare variants. Among these, three missense variants decrease the stability of this protein. Moreover, 3 novel rare variants were detected in 3 SLE patients. In particular, c.A767T variant was predicted as damaging by six prediction tools. Concluding, we have observed that even in genes whose common variability is associated with SLE susceptibility, it is possible to identify rare variants that could have a strong effect in the disease development and could therefore allow a better understanding of the functional domain involved.
Lupus Science & Medicine
ObjectiveTo combine targeted transcriptomic and proteomic data in an unsupervised hierarchical clustering method to stratify patients with childhood-onset SLE (cSLE) into similar biological phenotypes, and study the immunological cellular landscape that characterises the clusters.MethodsTargeted whole blood gene expression and serum cytokines were determined in patients with cSLE, preselected on disease activity state (at diagnosis, Low Lupus Disease Activity State (LLDAS), flare). Unsupervised hierarchical clustering, agnostic to disease characteristics, was used to identify clusters with distinct biological phenotypes. Disease activity was scored by clinical SELENA-SLEDAI (Safety of Estrogens in Systemic Lupus Erythematosus National Assessment-Systemic Lupus Erythematosus Disease Activity Index). High-dimensional 40-colour flow cytometry was used to identify immune cell subsets.ResultsThree unique clusters were identified, each characterised by a set of differentially expressed ge...
Identification of new SLE-associated genes with a two-step Bayesian study design
Genes and Immunity, 2009
In our previous study, we utilized a Bayesian design to probe the association of ~1,000 genes (~10,000 SNPs) with SLE on a moderate number of trios of parents and children with SLE. Two genes associated with SLE with a multitest corrected False Discovery Rate (FDR) of <0.05. were identified, and a number of noteworthy genes with FDR of <0.8 were also found, pointing out a future direction for the study. In the present report, using a large population of controls and adultor -childhood onset SLE cases, we have extended the previous investigation to explore the SLE association of ten of these noteworthy genes (109 SNPs). We have found that seven of these genes exhibit significant (FDR < 0.05) association with SLE, both confirming some genes that have previously been found to be associated with SLE (PTPN22 and IRF5) and novel findings of genes (KLRG1, IL-16, PTPRT, TLR8 and CASP10) which have not been previously reported. The results signify that the two-step candidate pathway design is an efficient way to study the genetic foundations of complex diseases. Furthermore, the novel genes identified in this study point to new directions in both the diagnosis and the eventual treatment of this debilitating disease.
Shared and Unique Gene Expression in Systemic Lupus Erythematosus Depending on Disease Activity
Annals of the New York Academy of Sciences, 2009
Patients presenting with active systemic lupus erythematosus (SLE) manifestations may exhibit distinct pathogenetic features in relation to inactive SLE. Also, cDNA microarrays may potentially discriminate the gene expression profile of a disease or disease variant. Therefore, we evaluated the expression profile of 4500 genes in peripheral blood lymphocytes (PBL) of SLE patients. We studied 11 patients with SLE (seven with active SLE and four with inactive SLE) and eight healthy controls. Total RNA was isolated from PBL, reverse transcribed into cDNA, and postlabeled with Cy3 fluorochrome. These probes were then hybridized to a glass slide cDNA microarray containing 4500 human IMAGE cDNA target sequences. An equimolar amount of total RNA from human cell lines served as reference. The microarray images were quantified, normalized, and analyzed using the R environment (ANOVA, significant analysis of microarrays, and cluster-tree view algorithms). Disease activity was assessed by the SLE disease activity index. Compared to the healthy controls, 104 genes in active SLE patients (80 repressed and 24 induced) and 52 genes in nonactive SLE patients (31 induced and 21 repressed) were differentially expressed. The modulation of 12 genes, either induced or repressed, was found in both disease variants; however, each disease variant had differential expression of different genes. Taken together, these results indicate that the two lupus variants studied have common and unique differentially expressed genes. Although the biological significance of the differentially expressed genes discussed above has not been completely understood, they may serve as a platform to further explore the molecular basis of immune deregulation in SLE.