Lopez-Pedrera, C. | Search bioRxiv

bioRxiv2024

Systemic autoimmune disease patients' blood immunome reveals specificities and commonalities among different diagnostic entities

1BackgroundSystemic autoimmune diseases (SADs) are characterized by internal heterogeneity, overlapping clinical symptoms, and shared molecular pathways. Therefore, they are difficult to diagnose and new tools allowing precise diagnosis are needed. Molecular-based reclassification studies enable to find patterns in a diagnosis-independent way. ObjectiveTo evaluate the possibility of using high-content immunophenotyping for detecting patient subgroups in the context of precise treatment. MethodsWhole blood high-content immunophenotyping of 101 patients with 7 systemic autoimmune diseases and 22 controls was performed using 36-plex mass cytometry panel. Patients were compared across diagnostic entities and re-classified using Monte Carlo reference-based consensus clustering. Levels of 45-plex multiplexed cytokine were measured and used for cluster characterization. ResultsDifferential analysis by diagnosis did not reveal any disease-specific pattern in the cellular compositions and phenotypes but rather their relative similarities. Accordingly, patients were classified into phenotypically distinct groups composed of different diagnostic entities sharing common immunophenotypes and cytokine signatures. These features were mainly based on granulocyte activation and CD38 expression in discrete lymphocyte populations and were related to Th17 or IFN-dependent cytokines. ConclusionsOur data indicate that specific individuals could potentially benefit from the same line of treatment independently of their diagnosis and emphasize the possibility of using immunophenotyping as a stratification tool in precision rheumatology. 2 Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/594621v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1098351org.highwire.dtl.DTLVardef@18c45a1org.highwire.dtl.DTLVardef@2f5f42org.highwire.dtl.DTLVardef@170e28_HPS_FORMAT_FIGEXP M_FIG C_FIG Key messagesO_LIWhole blood immmunophenotyping could be used to stratify systemic autoimmune patients, thus it is a useful tool in precision medicine. C_LIO_LIPatients groups could benefit from the same line of treatment. C_LI

immunology↗

bioRxiv2020

Genomic convergence of locus-based GWAS meta-analysis identifies DDX11 as a novel Systemic Lupus Erythematosus gene

Genome-wide association studies (GWAS) of systemic lupus erythematosus (SLE) explain only [~]15% of genetic risk, indicating genes of modest effect remain to be discovered. Association clustering methods such as OASIS are more apt at identifying modest genetic effects. 410 genes were mapped to previously identified OASIS GWAS SLE loci and investigated for expression in SLE GEO datasets. GSE50395 dataset from Cordoba was used for validation. Blood eQTL for significant SNPs in SLE loci and STRING for functional pathways of differentially expressed genes was used. Confirmatory qPCR on monocytes of 12 SLE patients and controls was performed. We identified 55 genes that were differentially expressed in at least 2 SLE GEO datasets with all probes directionally aligned. DDX11 was downregulated in both GEO (P=3.60E-02) and Cordoba (P=8.02E-03) datasets and confirmed by qPCR (P=0.001). The most significant SNP, rs3741869 (P=3.2E-05) in OASIS locus 12p11.21, containing DDX11, was a cis-eQTL regulating DDX11 expression (P=8.62E-05). DDX11 interacted with multiple genes including STAT1/STAT4. Genomic convergence with OASIS and multiple expression datasets identifies novel genes. DDX11, RNA helicase involved in genome stability, is repressed in SLE. Summary StatementMore than 100 genes for SLE have been identified but they explain only [~]15% of heritability. GWAS are challenged by risk genes of modest effect. Using locus-based GWAS mapping and multiple gene expression replications, DDX11 was identified as a novel SLE gene. DDX11, repressed in SLE, may be used as a clinical diagnostic tool.

genomics↗