Interferon Lambda 3/4 (IFNλ3/4) rs12979860 Polymorphisms Is Not Associated With Susceptibility to Systemic Lupus Erythematosus, Although It Regulates OASL Expression in Patients With SLE

Juárez-Vicuña, Yaneli; Pérez‐Ramos, Julia; Adalid‐Peralta, Laura; Sánchez, Fausto; Martínez-Martínez, Laura Aline; Ortiz-Segura, María Del Carmen; Pichardo-Ontiveros, Edgar; Hernández-Díazcouder, Adrián; Amezcua-Guerra, Luís M.; Ramírez‐Bello, Julián; Sánchez‐Muñoz, Fausto

doi:10.3389/fgene.2021.647487

Cited by 7 publications

(3 citation statements)

References 39 publications

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“…The connection between higher ISG expression and these clinical features has been previously shown in several studies (Hooks et al, 1979; Bennett et al, 2003; Kirou et al, 2005; Nikpour et al, 2008; Weckerle et al, 2011). The Pearson correlation between factor 1 sample scores and SLEDAI was 0.321, which is similar to that which was observed in other studies (Catalina et al, 2019; Enocsson et al, 2021; Juárez-Vicuña et al, 2021).…”

Section: Resultssupporting

confidence: 89%

Tensor decomposition reveals coordinated multicellular patterns of transcriptional variation that distinguish and stratify disease individuals

Mitchel

Gordon

Perez

et al. 2022

Preprint

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Tissue- and organism-level biological processes often involve coordinated action of multiple distinct cell types. Current computational methods for the analysis of single-cell RNA-sequencing (scRNA-seq) data, however, are not designed to capture co-variation of cell states across samples, in part due to the low number of biological samples in most scRNA-seq datasets. Recent advances in sample multiplexing have enabled population-scale scRNA-seq measurements of tens to hundreds of samples. To take advantage of such datasets, here we introduce a computational approach called single-cell Interpretable Tensor Decomposition (scITD). This method extracts multicellular gene expression patterns that vary across different biological samples. These patterns capture how changes in one cell type are connected to changes in other cell types. The multicellular patterns can be further associated with known covariates (e.g., disease, treatment, or technical batch effects) and used to stratify heterogeneous samples. We first validated the performance of scITD using in vitro experimental data and simulations. We then applied scITD to scRNA-seq data on peripheral blood mononuclear cells (PBMCs) from 115 patients with systemic lupus erythematosus and 56 healthy controls. We recapitulated a well-established pan-cell-type signature of interferon-signaling that was associated with the presence of anti-dsDNA autoantibodies and a disease activity index. We further identified a novel multicellular pattern that appears to potentiate renal involvement for patients with anti-dsDNA autoantibodies. This pattern was characterized by an expansion of activated memory B cells along with helper T cell activation and is predicted to be mediated by an increase in ICOSLG-ICOS interaction between monocytes and helper T cells. Finally, we applied scITD to two PBMC datasets from patients with COVID-19 and identified reproducible multicellular patterns that stratify patients by disease severity. Overall, scITD is a flexible method for exploring co-variation of cell states in multi-sample single-cell datasets, which can yield new insights into complex non-cell-autonomous dependencies that define and stratify disease.

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Section: Resultssupporting

confidence: 89%

Tensor decomposition reveals coordinated multicellular patterns of transcriptional variation that distinguish and stratify disease individuals

Mitchel

Gordon

Perez

et al. 2022

Preprint

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“…both are highlighted in Figure 2C . The interferon-induced GTP-binding protein Mx1 (MX1), 2’-5’-oligoadenylate synthase 3 (OAS3), and interferon-induced protein with tetratricopeptide repeats 3 (IFIT3), which are important in the type I interferon signaling pathway ( 24 – 26 ), were significantly upregulated in SLE vs . both, with MX1 being the most upregulated protein.…”

Section: Resultsmentioning

confidence: 99%

Combined proteomics and single cell RNA-sequencing analysis to identify biomarkers of disease diagnosis and disease exacerbation for systemic lupus erythematosus

Liao

et al. 2022

Front. Immunol.

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IntroductionSystemic lupus erythematosus (SLE) is a chronic autoimmune disease for which there is no cure. Effective diagnosis and precise assessment of disease exacerbation remains a major challenge.MethodsWe performed peripheral blood mononuclear cell (PBMC) proteomics of a discovery cohort, including patients with active SLE and inactive SLE, patients with rheumatoid arthritis (RA), and healthy controls (HC). Then, we performed a machine learning pipeline to identify biomarker combinations. The biomarker combinations were further validated using enzyme-linked immunosorbent assays (ELISAs) in another cohort. Single-cell RNA sequencing (scRNA-seq) data from active SLE, inactive SLE, and HC PBMC samples further elucidated the potential immune cellular sources of each of these PBMC biomarkers.ResultsScreening of the PBMC proteome identified 1023, 168, and 124 proteins that were significantly different between SLE vs. HC, SLE vs. RA, and active SLE vs. inactive SLE, respectively. The machine learning pipeline identified two biomarker combinations that accurately distinguished patients with SLE from controls and discriminated between active and inactive SLE. The validated results of ELISAs for two biomarker combinations were in line with the discovery cohort results. Among them, the six-protein combination (IFIT3, MX1, TOMM40, STAT1, STAT2, and OAS3) exhibited good performance for SLE disease diagnosis, with AUC of 0.723 and 0.815 for distinguishing SLE from HC and RA, respectively. Nine-protein combination (PHACTR2, GOT2, L-selectin, CMC4, MAP2K1, CMPK2, ECPAS, SRA1, and STAT2) showed a robust performance in assessing disease exacerbation (AUC=0.990). Further, the potential immune cellular sources of nine PBMC biomarkers, which had the consistent changes with the proteomics data, were elucidated by PBMC scRNAseq.DiscussionUnbiased proteomic quantification and experimental validation of PBMC samples from two cohorts of patients with SLE were identified as biomarker combinations for diagnosis and activity monitoring. Furthermore, the immune cell subtype origin of the biomarkers in the transcript expression level was determined using PBMC scRNAseq. These findings present valuable PBMC biomarkers associated with SLE and may reveal potential therapeutic targets.

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“…However, the lack of RIG-I protein in chicken cells indicates that MDA5 acts as a complementary ligand in the recognition of RNA ( 33 ). 2′-5′-oligoadenylate synthetase-like (OASL) and myxovirus resistance 1 (MX1) are two of the most induced proteins in cells following IFN-I or viral infection ( 34 ). To further confirm the inhibitory effect of aMPV/C infection on IFN-I production, the mRNA levels of OASL and MX1 were measured using RT-qPCR.…”

Section: Resultsmentioning

confidence: 99%

Avian Metapneumovirus Subgroup C Phosphoprotein Suppresses Type I Interferon Production by Blocking Interferon Regulatory Factor 3 Nuclear Translocation

et al. 2023

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Interferon Lambda 3/4 (IFNλ3/4) rs12979860 Polymorphisms Is Not Associated With Susceptibility to Systemic Lupus Erythematosus, Although It Regulates OASL Expression in Patients With SLE

Cited by 7 publications

References 39 publications

Tensor decomposition reveals coordinated multicellular patterns of transcriptional variation that distinguish and stratify disease individuals

Tensor decomposition reveals coordinated multicellular patterns of transcriptional variation that distinguish and stratify disease individuals

Combined proteomics and single cell RNA-sequencing analysis to identify biomarkers of disease diagnosis and disease exacerbation for systemic lupus erythematosus

Avian Metapneumovirus Subgroup C Phosphoprotein Suppresses Type I Interferon Production by Blocking Interferon Regulatory Factor 3 Nuclear Translocation

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