Peripheral mononuclear cells and systemic lupus erythematosus association: Integrated study of single-cell sequencing and mendelian randomization analysis

Abstract

Objective

Systemic Lupus Erythematosus (SLE) is a complex autoimmune disease predominantly affecting women. Despite advances in treatment, recent developments in single-cell RNA sequencing (scRNA-seq) and Mendelian randomization (MR) continue to facilitate the need for precision medicine.

Methods

Data obtained from the GSE135779 dataset underwent quality control, normalization, and dimensionality reduction using Seurat and MonacoImmuneData. Marker genes identified subgroups for analysis with CellChat and ClusterProfilerR. MR analysis of these genes’ eQTLs was performed to establish causal relationships with SLE using IEU Open GWAS project data.

Results

Single-cell analysis revealed distinct cellular subtypes and highlighted increased monocyte levels in patients with SLE. MR analysis revealed 12 genes, particularly interferon induced protein with tetratricopeptide repeats 3 (IFIT3), causally related to SLE. Gene ontology and the Kyoto encyclopedia of genes and genomes analyses identified pathways significant to SLE pathogenesis. Visualization of these genes at the single-cell level revealed their role in disease progression. Cell communication differences between IFIT3-positive and -negative groups were also observed.

Conclusion

This study demonstrates the potential of scRNA-seq and MR in identifying critical factors in SLE pathogenesis, thereby supporting the need for targeted therapies. Identifying IFIT3, among other genes, as central to SLE progression opens new avenues for precision medicine approaches in SLE management.

Keywords

Systemic lupus erythematosus single-cell sequencing mendelian randomization peripheral mononuclear cells scRNA-seq

Introduction

Systemic Lupus Erythematosus (SLE) represents a complex, multifaceted autoimmune disease characterized by the immune system’s aberrant attack on its tissues.¹ SLE can result in widespread inflammation and damage across various organs.² The epidemiology of SLE exhibits a global distribution predominantly in women,³ particularly those of childbearing age, underscoring the interplay of hormonal, genetic, and environmental factors in its pathogenesis. Despite progress in understanding its underlying causes, SLE’s etiology remains multifactorial and incompletely elucidated, involving intricate immune dysregulations and genetic predispositions.^4,5 Treatment strategies have evolved from non-specific anti-inflammatory drugs to more targeted immunosuppressive therapies.^6,7 However, the therapeutic landscape is challenged by a significant number of patients exhibiting insufficient responses or adverse effects. Therefore, precision medicine approaches to tailor interventions based on individual patient profiles are urgently required.^8,9

The emergence of single-cell RNA sequencing (scRNA-seq) technology marks a significant milestone in investigating the cellular heterogeneity and complex molecular mechanisms underlying diseases, including SLE.¹⁰ By enabling the comprehensive profiling of transcriptomic activities at the single-cell level, scRNA-seq provides an unparalleled level of detail to analyze the interplay among various cell types involved in SLE pathogenesis.^11,12 This granular perspective facilitates delineating disease-specific pathways and identifying novel cellular subsets and potential biomarkers for diagnostic and therapeutic targeting. Moreover, the ability to monitor the dynamic changes in gene expression patterns across disease states and response to treatments opens new avenues for understanding SLE’s molecular landscape and developing targeted interventions.¹³ Mendelian randomization (MR) is a powerful analytical tool for inferring causal relationships between genetic variants and complex traits or diseases. MR utilizes the random assortment of alleles at conception as a natural experiment.¹⁴ In the context of SLE, MR provides a robust framework to validate the functional relevance of candidate markers identified through scRNA-seq. Additionally, it provides a methodological advantage to circumvent confounding factors and reverse causation inherent in observational studies.¹⁵ By integrating genetic instruments associated with potential markers, MR analysis can elucidate the genetic determinants of SLE risk and progression, facilitating the translation of molecular discoveries into clinical applications.¹⁶ Combining cutting-edge scRNA-seq with MR analysis, this integrative approach can potentially advance our understanding of SLE. Additionally, it enables the identification and validation of novel biomarkers and therapeutic targets, thereby facilitating the implementation of precision medicine in SLE management.

Research methodology and data sources

Research design

This study obtained single-cell transcriptomic data from the Gene Expression Omnibus (GEO) database and analyzed data using R and Seurat. These tools were used for data retrieval, quality control, dimensionality reduction, clustering, and annotation. Marker genes were utilized to identify subpopulations within each cellular cluster. CellChat software was employed to investigate intercellular communication. MR analysis was performed to validate the expression of Quantitative Trait Loci (eQTLs) associated with SLE, utilizing single nucleotide polymorphisms (SNPs) and evaluating heterogeneity and pleiotropy to determine causal genes. Finally, transcriptome validation was performed on selected marker genes with the highest correlation with SLE. The schematic diagram of the workflow is depicted in Figure 1.

Collection of sequencing data

The single-cell transcriptomic data utilized in this study were obtained from the GSE135779 dataset available in the GEO database.¹⁷ The childhood systemic lupus erythematosus (cSLE) dataset comprised 33 cases represented as cSLE1 through cSLE33 and 11 age-matched healthy controls labeled cHD1 to cHD11. The adult systemic lupus erythematosus (aSLE) dataset included eight subjects designated as aSLE1 to aSLE8 alongside six age-matched healthy controls identified as aHD1 to aHD6. We utilized R and the Seurat package for data retrieval.¹⁸ Quality control criteria applied to ensure data integrity and cell viability were (1) exclusion of cells with fewer than 200 or more than 4000 genes to remove damaged or dying cells and (2) removal of cells where mitochondrial genes comprised over 10% of total gene expression to exclude cells under oxidative stress or apoptosis. Post-preprocessing involved several meticulous steps to prepare the data for downstream analyses. Initially, raw gene expression counts were normalized to total expression and scaled to 10,000 per cell. The data were logarithmically transformed to stabilize variance across cells using Seurat’s NormalizeData function. This normalization is important to adjust for differences in sequencing depth among cells. We then identified highly variable genes with the FindVariableFeatures function. We selected genes based on variance stabilizing transformation (VST), which focuses on the most informative genes for clustering. Finally, data were scaled and centered around zero using the ScaleData function to ensure that the range of expression levels does not skew downstream analyses. Principal component analysis (PCA)¹⁹ was performed for dimensionality reduction, followed by secondary dimensionality reduction using the uniform manifold approximation and projection (UMAP) algorithm. Based on PCA results, single-cell clustering visualization was performed with UMAP. The cell subpopulation clustering was further conducted using the t-distributed stochastic neighbor embedding (t-SNE) algorithm. MonacoImmuneData was utilized for cell subpopulation annotation due to its comprehensive and validated immune cell phenotypic markers, which are essential for accurately identifying and categorizing complex cell populations.²⁰ We used t-SNE and UMAP algorithms for clustering due to their ability to preserve the global data structure and highlight clusters within the high-dimensional dataset, facilitating a more nuanced interpretation of cell functionalities and interactions. The monocyte group primarily included the following: Classical monocytes, characterized by the surface markers CD14++ and CD16-, which increase in abundance during inflammation and infection; intermediate monocytes, identified by the surface markers CD14++ and CD16+, situated between classical and nonclassical monocytes²¹; myeloid dendritic cells, expressing the surface markers CD11c+ and CD123-, important for antigen presentation and the activation of immune responses²²; progenitor cells, characterized by surface markers CD34+ and CD117+, exhibit multipotent differentiation capabilities.²³ These surface antigens are essential for the identification of cell subgroups.

Single-cell RNA sequencing data analysis

The expression of marker genes facilitated further identification of relevant subgroups within each cellular population. Initially, specific cells were isolated and subjected to clustering.²⁴ Subsequently, genes within each cellular population were subjected to pseudotime and single-cell trajectory analyses. CellChat software was used to investigate intercellular communication. This software identifies overexpressed receptors in single-cell transcriptomic data and constructs protein-protein interaction networks based on gene expression, thereby computing communication probabilities and inferring intercellular communication networks. Additionally, heatmaps were constructed to visualize the quantity and intensity of intercellular interactions. By examining the distinct features of different cells, we can classify, metabolically analyze or determine changes in the Kyoto encyclopedia of genes and genomes (KEGG) pathways associated with relevant subgroups.²⁵ We conducted gene ontology (GO) functional enrichment analysis and KEGG pathway enrichment analysis on marker genes, utilizing the ClusterProfilerR software package.

Mendelian randomization validation of key gene eQTLs

We obtained marker genes associated with prognostic differences and retrieved relevant eQTLs as exposure factors from the IEU Open GWAS project database (https://gwas.mrcieu.ac.uk/datasets/).²⁶ Additionally, the SLE cohort was obtained from this database, identified by the ID ebi-a-GCST90011866, comprising 8431 standard samples and 4222 SLE samples with whole-genome data, which served as outcome data. MR analysis was conducted on marker genes within each cluster and eQTLs for SLE to identify causal genes associated with SLE in each cluster. Our study had no sample overlap between populations. All participants belonged to the East Asian population, mitigating potential biases arising from racial differences. Initially, we selected SNPs that were closely associated with gene expression and had a significance threshold of p < 5 × 10⁻⁸ when using marker genes as exposure factors.²⁷ Subsequently, we computed the F-statistic to evaluate the strength of the association between instrumental variables and exposure factors. We excluded SNPs with an F-value less than 10 to address potential weak instrument bias. We employed the inverse variance-weighted fixed-effect (IVW-FE) model as the primary MR analysis method.²⁸ Cochran’s Q test determined the heterogeneity among instrumental variables, with p > .05 indicating minimal likelihood of heterogeneity.²⁹ MR Egger intercept tests were performed to evaluate horizontal pleiotropy, and if the intercept term was statistically significant, it indicated significant horizontal pleiotropy.³⁰ The most critical marker genes associated with SLE were selected for transcriptome validation. Finally, a univariate receiver operator curve (ROC) analysis in the GEO dataset was performed to determine these marker genes’ clinical and diagnostic values in patients with SLE.

Results

Different cell composition of SLE as illustrated by transcriptome data

Single-cell transcriptomic analysis indicated four distinct cell phenotypes. Utilizing expression patterns of immune cell marker genes, we generated dimensionality reduction plots and proportional representations (Figure 2(a) and (b)) with annotations for the four cell phenotypes. Monocyte levels were significantly elevated in patients with SLE compared to controls, exhibiting the strongest correlation between monocytes and SLE. Consequently, secondary dimensionality reduction and clustering analyses were conducted on monocytes and visualized (Figure 2(c) and (d)). The results indicated the most significant differences in expression levels of intermediate monocytes between healthy individuals and patients with SLE. Cell communication analysis revealed extensive interactions between intermediate monocytes and most other cell types, suggesting potential crosstalk between intermediate monocytes and various immune cells (Figure 3(a) and (b)). Intermediate monocytes were found to communicate with T cells and NK cells through the LGALS9−CD44 pathway. The interactions between intermediate monocytes and classical monocytes were observed through pathways, including ANXA1−FPR1, CCL3−CCR1, LGALS9−CD44, and MIF−(CD74+CXCR4). Additionally, they interacted with B cells through BAG6−NCR3−PS and MIF−(CD74+CXCR4) pathways. The visualization analysis of single-cell trajectories (Figure 3(c)) depicted differentiation from hematopoietic stem cells to progenitors, followed by differentiation into various monocyte types, potentially passing through multiple intermediate states, including the intermediate monocyte stage.

Utilization of MR to validate the causal relationship between key genes’ eQTL and SLE

We extracted marker genes from monocytes of patients with SLE and conducted MR analysis through eQTL analysis. A total of 12 marker genes were identified to be causally related to SLE (Figure 4(a)), including Coactosin Like F-Actin Binding Protein 1(COTL1), Cathepsin C(CTSC), interferon-induced protein with tetratricopeptide repeats 3 (IFIT3), CD68 Molecule (CD68), Placenta-associated 8 (PLAC8), docking protein 2 (DOK2), Fc epsilon receptor Ig (FCER1G), interferon induced protein with tetratricopeptide repeats 1 (IFIT1), major histocompatibility complex, class II, DR beta 1 (HLA-DRB1), major histocompatibility complex, class II, DQ alpha 1 (HLA-DQA1), major histocompatibility complex, class II, DR beta 5 (HLA-DRB5), leukocyte specific transcript 1 (LST1), and major histocompatibility complex, class II, DQ alpha 1 (HLA-DQA1). We visualized the risk effects of these genes on SLE susceptibility, with IFIT3 exhibiting the highest correlation with SLE risk (Figure 4(b)). We obtained an additional SLE cohort from the IEU Open GWAS project database (ID: ebi-a-GCST003156) to validate the robustness of our findings. This cohort comprised 9066 control samples and 5201 SLE samples of European ancestry. MR analysis confirmed the reliability of our results (Figure 4(c)). Using the ClusterProfilerR package, we performed GO and KEGG enrichment analysis on the marker genes. GO analysis included biological processes (BP), cellular components (CC), and molecular functions (MF) (Figure 5(a) to (d))). The enriched BP terms included antigen processing and presentation and peptide antigen through major histocompatibility complex (MHC) class II presentation. The enriched CC terms encompassed copii-coated endoplasmic reticulum to Golgi transport vesicle and MHC class II protein complex and coated vesicle. Conversely, enriched MF terms involved MHC class II protein complex binding, IgG binding, and MHC protein complex binding (Figure 5(a) to (b)). KEGG pathway enrichment analysis revealed enrichment in IgA production in SLE, tuberculosis, human T-cell leukemia virus one infection, and intestinal immune network (Figure 5(c) and (d)). We validated the reproducibility of the results through scatter plots (Figure S1(a)), leave-one-out plots (Figure S1(b)), forest plots (Figure S1(c)), and funnel plots (Figure S1(d)) for the eQTL of the gene IFIT3, which exhibited the highest association with SLE risk. Additionally, we conducted reverse MR analysis and found no evidence of reverse causality (Figure 4(d)).

Analysis of single-cell RNA sequencing data of marker gene

Our study elucidated the causal relationship between 12 identified marker genes and SLE. We visualized the expression levels of these genes at the cellular level, including B cells, T cells, monocytes, and NK cells (Figure 6(a)). The results indicated significant expression of most marker genes in monocytes, while HLA-DQA1 expression in monocytes was not significant. Furthermore, the IFIT3 gene exhibited low expression in monocytes; however, it exhibited the strongest correlation with SLE risk at the single-cell level, with significantly higher expression in patients with SLE than in healthy individuals (Figure 6(b)). Subsequently, we performed a developmental trajectory visualization analysis of these 12 marker genes at the monocyte level (Figure 6(c)). The graph depicted that IFIT1 and IFIT3 function as downregulation switch genes in SLE, with the expression profiles of their surface proteins and associated transcription factors undergoing significant changes over time, indicating their crucial role in monocyte development. Furthermore, correlation analysis between the expression of the 12 marker genes and SLE progression suggested a negative correlation with expression levels over time (Figure 6(d)). Subsequently, we investigated cell communication and cellular metabolic pathways between intermediate monocytes and IFIT3-positive and negative groups. The intermediate monocytes and IFIT3-positive group exhibited increased IL16−CD4 and BAG6−NCR3−PS pathways in comparison to the intermediate monocytes and the IFIT3-negative group in terms of cell communication (Figure 6(e) and (f)). Differential expression was observed at the cellular metabolism level in pathways including porphyrin and chlorophyll metabolism, GPI anchor biosynthesis, glycosaminoglycan degradation, drug metabolism involving cytochrome P450, and retinol metabolism (Figure 6(g)). Additionally, the IFIT3-positive group exhibited an increase in the expression of porphyrin metabolism, suggesting a stronger correlation. Finally, we used the sequencing datasets GSE112087 and GSE50772 from the GEO database for dual validation of our results (Supplemental Table 1). In the GSE112087 dataset, the expression level of the IFIT3 gene was elevated in patients with SLE compared to healthy individuals (Figure 6(h)). Subsequently, a univariate ROC analysis was performed using the GSE50772 dataset, revealing an area under the ROC curve (AUC) of 0.906 (Figure 7(a)). The model’s ROC curve exhibited an AUC of 0.906, with a 95% CI of 0.829–0.971 (Figure 7(b)). Accordingly, IFIT3 was identified as a potential biomarker with diagnostic value for SLE.

Discussion

Our study analyzed the cellular and molecular signatures underlying SLE pathogenesis using single-cell transcriptome data and MR techniques. We identified four distinct cellular phenotypes through dimensionality reduction and clustering. The monocyte cellular phenotype exhibited a significant elevation in patients with SLE compared to controls. This observation strengthens the current comprehension of the role of mononuclear cells in SLE pathophysiology.³¹ Further investigation of the monocyte subset revealed significant disparities in intermediate monocytes’ expression levels between patients with SLE and healthy individuals, suggesting their potential as biomarkers or therapeutic targets in the SLE.³² Additionally, our cell communication analyses exhibited intricate interactions between intermediate monocytes and various immune cell types, indicating their pivotal role in modulating immune responses in SLE.³³ The relationship between intermediate monocytes and SLE is mediated through multiple mechanisms, including inflammatory responses, immune regulation, and intercellular signaling. These monocytes, with pro-inflammatory properties, produce a significant amount of inflammatory cytokines, including tumor necrosis factor-alpha (TNF-α),³⁴ interleukin-1 beta (IL-1β), and interleukin-6 (IL-6).³⁵ These monocytes play a crucial role in exacerbating the severity of SLE by promoting inflammation.³⁶ Moreover, the high expression levels of MHC class II molecules and co-stimulatory molecules enable effective antigen presentation to T cells, thereby facilitating T cell-mediated immune responses. This antigen presentation mechanism can induce and maintain autoimmunity in SLE, attacking the body’s tissues and cells.^37,38 Alterations in the immune regulatory functions of intermediate monocytes significantly contribute to the imbalance of the immune system and the development of autoimmune diseases, including SLE. These monocytes are essential for immune regulation by producing cytokines and interacting with other immune cells. In patients with SLE, these regulatory functions are likely to be altered, resulting in immune system dysregulation.³⁹ Additionally, the interaction between intermediate monocytes and other cell types, including T cells, B cells, and other monocyte subgroups, changes SLE. These alterations potentially modify the cytokine network and signaling pathways, affecting the inflammatory environment and immune response.⁴⁰ Moreover, intermediate monocytes promote the production of autoantibodies by interacting with B cells in SLE. These autoantibodies, including anti-double-stranded DNA antibodies, are hallmark features of SLE, significantly impacting disease diagnosis and progression.^41,42 Furthermore, intermediate monocytes are involved in the vasculitis and tissue damage processes within SLE, exacerbating inflammatory responses and possibly producing pro-inflammatory cytokines and angiogenic factors, resulting in clinical manifestations, including skin lesions and nephritis.⁴³ Classical monocytes, myeloid dendritic cells, and progenitor cells are intimately involved in the pathogenesis and progression of SLE. Classical monocytes contribute to inflammatory responses by producing pro-inflammatory cytokines and clearing immune complexes.⁴⁴ Myeloid dendritic cells activate autoimmune responses through abnormal antigen presentation and cytokine secretion.⁴⁵ Progenitor cells affect immune homeostasis by altering hematopoiesis and differentiation pathways.⁴⁶ The interplay of these cells and their dysregulated functions collectively promote the development and exacerbation of SLE.

MR analysis identified 12 marker genes with a causal relationship with SLE, including COTL1, CTSC, IFIT3, CD68, PLAC8, DOK2, FCER1G, IFIT1, HLA-DRB1, HLA-DQA1, LST1, and HLA-DRB5. IFIT3 exhibited the highest correlation with SLE risk, indicating its significance in disease pathogenesis.⁴⁷ The reliability of our findings was further validated by the use of an independent cohort, ensuring their robustness across different populations.⁴⁸ Functional enrichment analysis revealed the involvement of these marker genes in crucial biological processes, including antigen processing and presentation. Besides, it involved signaling pathways, including SLE and intestinal immune network for IgA production, providing insights into their mechanistic roles in SLE. At the single-cell level, our analysis revealed significant expression of the identified marker genes, particularly in monocytes, emphasizing their relevance in SLE pathogenesis. In addition, IFIT3 exhibited significant expression in patients with SLE, further supporting its candidacy as a diagnostic or prognostic biomarker.⁴⁹ Developmental trajectory analysis suggested the regulatory roles of IFIT1 and IFIT3 in monocyte development, implicating their potential as therapeutic targets in SLE. Correlation analysis indicated a negative association between the expression levels of the marker genes and SLE progression, underscoring their potential utility as prognostic indicators.⁵⁰ IFIT3 expression, induced by interferons, particularly under viral infections and autoimmune conditions, is upregulated in SLE, indicating a persistent activation of interferon signaling pathways—a hallmark of SLE pathogenesis.^51,52 This upregulation modulates the expression of cytokines and chemokines, thereby impacting the immune functionalities of various T cells, B cells, and dendritic cells, crucial for the autoimmune and inflammatory responses observed in SLE.⁵³ Building upon the foundation established by preceding research, this study investigated the role of IFIT3 in SLE’s mechanistic progression.

Our study exhibited a significant increase in the IL-16-CD4 and BAG6-NCR3-PS pathways in the IFIT3-positive subgroup. The IL-16-CD4 pathway, a key activator of T cells, plays a critical role in autoimmune diseases, particularly in the recruitment and activation of CD4 + T cells, thereby exacerbating inflammatory responses. Consequently, the heightened IL-16-CD4 pathway in the IFIT3-positive subgroup can indicate an intensified immune state,⁵⁴ potentially contributing to the exacerbation or persistence of SLE pathology.⁵⁵ Conversely, the augmented BAG6-NCR3-PS pathway can be associated with NK cells. The NCR3 pathway plays a crucial role in the activation and regulation of NK cells,⁵⁶ and BAG6 is involved in immune regulation and apoptosis.⁵⁷ Therefore, the upregulation of this pathway in the IFIT3-positive subgroup suggested an increased activity of NK cells, potentially impacting the SLE pathophysiology. From a therapeutic perspective, these findings offer potential therapeutic targets for the IFIT3-positive subgroup. Inhibitors targeting the IL-16-CD4 pathway can alleviate the excessive activation of the immune system, thereby delaying SLE progression. Moreover, interventions targeting the BAG6-NCR3-PS pathway can regulate the activity of NK cells, thereby affecting the balance of immune responses. These findings also provide new insights into designing more targeted therapeutic strategies, considering the availability of molecular-targeted therapies, including the anti-IFN-R1 antibody anifrolumab.⁵⁸ The potential benefits of combining anifrolumab with drugs that target IL-16 or BAG6, or utilizing IFIT3 as a therapeutic target, are worth considering. Inhibiting IFIT3 expression or function may alleviate symptoms in patients with SLE and improve their prognosis by suppressing the abnormal activation of the immune system and reducing the worsening of SLE pathology. In the IFIT3-positive subgroup, significant differences in cellular metabolism were observed, particularly a significant increase in porphyrin metabolism levels, which requires further investigation. This may indicate that the high inflammatory conditions associated with SLE result in an increased demand for energy and biosynthesis in cells. Activating porphyrin metabolism is crucial for energy production and red blood cell functionality and can be associated with increased oxidative stress.⁵⁹

In summary, the elevated IFIT3 expression in patients with SLE underscores its potential as a diagnostic biomarker, facilitating earlier and more precise disease detection. Therapeutically, targeting the IFIT3 pathway could alleviate the aberrant immune responses characteristic of SLE. Developing IFIT3 inhibitors or monoclonal antibodies presents new therapeutic avenues. Moreover, IFIT3 can be utilized to stratify patients based on their risk, enabling personalized treatment plans tailored to individual molecular profiles. Regular monitoring of IFIT3 levels could indicate disease progression and treatment efficacy, allowing for dynamic and responsive therapeutic adjustments.

Strengths and limitations

The novelty of our study lies in several aspects. First, through single-cell transcriptomic analysis, we elucidated the compositional differences in various cell types among patients with SLE, particularly significant alterations in the monocyte expression profile. Second, we employed MR analysis to identify 12 monocyte marker genes causally linked to SLE and successfully validated them across different populations, further affirming their significance in the disease mechanism. Furthermore, at the single-cell level, we reported the significant correlation between IFIT3 expression in monocytes and the risk of SLE and its potential role in disease progression, thereby providing novel theoretical foundations for targeting IFIT3 in therapy. However, our study has several limitations. The small and homogeneous dataset from the GEO database could limit the generalizability of our findings. The methodologies employed, including advanced computational tools and MR, critically hinge on accurate data input and certain assumptions, potentially introducing bias. Although validation was performed using datasets from the IEU Open GWAS Project database and two GEO databases, additional independent, multicenter, and diverse samples are required to ensure the reliability and generalizability of our results.

Supplemental Material

Supplemental Material - Peripheral mononuclear cells and systemic lupus erythematosus association: Integrated study of single-cell sequencing and mendelian randomization analysis

Supplemental Material for Peripheral mononuclear cells and systemic lupus erythematosus association: Integrated study of single-cell sequencing and mendelian randomization analysis by Shi Jian and Han Li in Lupus

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Han Li

Supplemental Material

Supplemental material for this article is available online.

References

Liu

Zhang

, et al. Pathogenesis and treatment of neuropsychiatric systemic lupus erythematosus: a review. Front Cell Dev Biol 2022; 10: 998328.

Möckel

Basta

Weinmann-Menke

, et al. B cell activating factor (BAFF): structure, functions, autoimmunity and clinical implications in Systemic Lupus Erythematosus (SLE). Autoimmun Rev 2021; 20(2): 102736.

Weinstein

Alexander

Zack

. A review of complement activation in SLE. Curr Rheumatol Rep 2021; 23(3): 16.

Pan

Wang

, et al. Immunological pathogenesis and treatment of systemic lupus erythematosus. World J Pediatr 2020; 16(1): 19–30.

Lamri

Charles

. IgE in the pathogenesis of SLE: from pathogenic role to therapeutic target. Antibodies 2020; 9(4).

Baechler

Batliwalla

Karypis

, et al. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc Natl Acad Sci U S A 2003; 100(5): 2610–2615.

Chiche

Jourde-Chiche

Whalen

, et al. Modular transcriptional repertoire analyses of adults with systemic lupus erythematosus reveal distinct type I and type II interferon signatures. Arthritis Rheumatol 2014; 66(6): 1583–1595.

Lander

Wallace

Weisman

. Celecoxib for systemic lupus erythematosus: case series and literature review of the use of NSAIDs in SLE. Lupus 2002; 11(6): 340–347.

Schneider

. Target therapy in SLE. Autoimmun Rev 2019; 18(1): 21–24.

10.

Guerrero-Juarez

Dedhia

Jin

, et al. Single-cell analysis reveals fibroblast heterogeneity and myeloid-derived adipocyte progenitors in murine skin wounds. Nat Commun 2019; 10(1): 650.

11.

Han

Zhou

Fei

, et al. Construction of a human cell landscape at single-cell level. Nature 2020; 581(7808): 303–309.

12.

Nehar-Belaid

Hong

Marches

, et al. Mapping systemic lupus erythematosus heterogeneity at the single-cell level. Nat Immunol 2020; 21(9): 1094–1106.

13.

Papalexi

Satija

. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat Rev Immunol 2018; 18(1): 35–45.

14.

Larsson

Butterworth

Burgess

. Mendelian randomization for cardiovascular diseases: principles and applications. Eur Heart J 2023; 44(47): 4913–4924.

15.

Sekula

Del Greco M

Pattaro

, et al. Mendelian randomization as an approach to assess causality using observational data. J Am Soc Nephrol 2016; 27(11): 3253–3265.

16.

Birney

. Mendelian randomization. Cold Spring Harb Perspect Med 2022; 12(4): a041302.

17.

Barrett

Wilhite

Ledoux

, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res 2013; 41(Database issue): D991–D995.

18.

Satija

Farrell

Gennert

, et al. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 2015; 33(5): 495–502.

19.

Lall

Sinha

Bandyopadhyay

, et al. Structure-aware principal component analysis for single-cell RNA-seq data. J Comput Biol 2018; 3: 2.

20.

Canzar

. A generalization of t-SNE and UMAP to single-cell multimodal omics. Genome Biol 2021; 22(1): 130.

21.

Geissmann

Manz

Jung

, et al. Development of monocytes, macrophages, and dendritic cells. Science 2010; 327(5966): 656–661.

22.

Ziegler-Heitbrock

Hofer

. Toward a refined definition of monocyte subsets. Front Immunol 2013; 4: 23.

23.

Wong

Tai

JJY

Wong

, et al. Gene expression profiling reveals the defining features of the classical, intermediate, and nonclassical human monocyte subsets. Blood 2011; 118(5): e16–e61.

24.

Tanay

Sebé-Pedrós

. Evolutionary cell type mapping with single-cell genomics. Trends Genet 2021; 37(10): 919–932.

25.

McCracken

Taylor

Kok

, et al. Transcriptional dynamics of pluripotent stem cell-derived endothelial cell differentiation revealed by single-cell RNA sequencing. Eur Heart J 2020; 41(9): 1024–1036.

26.

Yazar

Alquicira-Hernandez

Wing

, et al. Single-cell eQTL mapping identifies cell type-specific genetic control of autoimmune disease. Science 2022; 376(6589): eabf3041.

27.

Burgess

Thompson

CRP CHD Genetics Collaboration . Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol 2011; 40(3): 755–764.

28.

Burgess

Thompson

. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 2017; 32(5): 377–389.

29.

Ference

. Interpreting the clinical implications of drug-target mendelian randomization studies. J Am Coll Cardiol 2022; 80(7): 663–665.

30.

Liu

Jin

Jiang

. Interleukin-6 receptor and inflammatory bowel disease: a mendelian randomization study. Gastroenterology 2019; 156(3): 823–824.

31.

Perez-Sanchez

Barbarroja

Messineo

, et al. Gene profiling reveals specific molecular pathways in the pathogenesis of atherosclerosis and cardiovascular disease in antiphospholipid syndrome, systemic lupus erythematosus and antiphospholipid syndrome with lupus. Ann Rheum Dis 2015; 74(7): 1441–1449.

32.

Sasaki

Bracero

Keegan

, et al. Longitudinal immune cell profiling in patients with early systemic lupus erythematosus. Arthritis Rheumatol 2022; 74(11): 1808–1821.

33.

Hirose

Lin

Ohtsuji

, et al. Monocyte subsets involved in the development of systemic lupus erythematosus and rheumatoid arthritis. Int Immunol 2019; 31(11): 687–696.

34.

Richter

Macovei

Mihai

, et al. Cytokines in systemic lupus erythematosus-focus on TNF-α and IL-17. Int J Mol Sci 2023; 24(19): 14413.

35.

Grebenciucova

VanHaerents

. Interleukin 6: at the interface of human health and disease. Front Immunol 2023; 14: 1255533.

36.

Ferreté-Bonastre

. Disease activity drives divergent epigenetic and transcriptomic reprogramming of monocyte subpopulations in systemic lupus erythematosus. Ann Rheum Dis 2024; 2: 1.

37.

Hersh

Arkin

Prahalad

. Immunogenetics of cutaneous lupus erythematosus. Curr Opin Pediatr 2016; 28(4): 470–475.

38.

Lin

Sui

Tan

, et al. Integrated analyses of a major histocompatibility complex, methylation and transcribed ultra-conserved regions in systemic lupus erythematosus. Int J Mol Med 2016; 37(1): 139–148.

39.

Jha

Joseph

Prabhu

, et al. Utility of peripheral blood monocyte subsets, circulating immune complexes and serum cytokines in assessment of SLE activity: an observational, cross-sectional study. Clin Rheumatol 2024; 43(1): 209–217.

40.

López

Rodríguez-Carrio

Martínez-Zapico

, et al. Low-density granulocytes and monocytes as biomarkers of cardiovascular risk in systemic lupus erythematosus. Rheumatology 2020; 59(7): 1795–17114.

41.

Giordano

Kuley

Draves

, et al. B cell-activating factor (BAFF) from dendritic cells, monocytes and neutrophils is required for B cell maturation and autoantibody production in SLE-like autoimmune disease. Front Immunol 2023; 14: 1050528.

42.

Dieker

van der Vlag

Berden

. Triggers for anti-chromatin autoantibody production in SLE. Lupus 2002; 11(12): 856–864.

43.

Crow

. Pathogenesis of systemic lupus erythematosus: risks, mechanisms and therapeutic targets. Ann Rheum Dis 2023; 82(8): 999–1014.

44.

Ferreté-Bonastre

Martínez-Gallo

Morante-Palacios

, et al. Disease activity drives divergent epigenetic and transcriptomic reprogramming of monocyte subpopulations in systemic lupus erythematosus. Ann Rheum Dis 2024; 83(7): 865–878.

45.

Crispin

Alcocer-Varela

. The role myeloid dendritic cells play in the pathogenesis of systemic lupus erythematosus. Autoimmun Rev 2007; 6(7): 450–456.

46.

Westerweel

Luijten

RKMAC

Hoefer

, et al. Haematopoietic and endothelial progenitor cells are deficient in quiescent systemic lupus erythematosus. Ann Rheum Dis 2007; 66(7): 865–870.

47.

Shen

Duan

Xie

, et al. Identification of key interferon-stimulated genes for indicating the condition of patients with systemic lupus erythematosus. Front Immunol 2022; 13: 962393.

48.

Wang

Dai

Cui

, et al. Association of abnormal elevations in IFIT3 with overactive cyclic GMP-AMP synthase/stimulator of interferon genes signaling in human systemic lupus erythematosus monocytes. Arthritis Rheumatol 2018; 70(12): 2036–2045.

49.

Xiao

Zhan

Lin

. Clinical values of the identified hub genes in systemic lupus erythematosus. Front Immunol 2022; 13: 844025.

50.

Zhou

Yang

, et al. Comprehensive analysis of epigenetic modifications and immune-cell infiltration in tissues from patients with systemic lupus erythematosus. Epigenomics 2022; 14(2): 81–100.

51.

Huang

Shen

Bao

, et al. Interferon-induced protein IFIT4 is associated with systemic lupus erythematosus and promotes differentiation of monocytes into dendritic cell-like cells. Arthritis Res Ther 2008; 10(4): R91.

52.

Siddiqi

Wilhelm

Ulff-Møller

, et al. Cluster of highly expressed interferon-stimulated genes associate more with African ancestry than disease activity in patients with systemic lupus erythematosus. A systematic review of cross-sectional studies. Transl Res 2021; 238: 63–75.

53.

Teng

Ding

, et al. Identification of significant genes and pathways for the chronic and subacute cutaneous lupus erythematosus via bioinformatics analysis. Dis Markers 2022; 2022: 9891299.

54.

Skundric

Cruikshank

Drulovic

. Role of IL-16 in CD4+ T cell-mediated regulation of relapsing multiple sclerosis. J Neuroinflammation 2015; 12: 78.

55.

Glass

Sarisky

Vecchio

. Not-so-sweet sixteen: the role of IL-16 in infectious and immune-mediated inflammatory diseases. J Interferon Cytokine Res 2006; 26(8): 511–520.

56.

Mulcahy

O'Rourke

Adams

, et al. LST1 and NCR3 expression in autoimmune inflammation and in response to IFN-gamma, LPS and microbial infection. Immunogenetics 2006; 57(12): 893–903.

57.

Daßler-Plenker

. RIG-I activation induces the release of extracellular vesicles with antitumor activity. OncoImmunology 2016; 5(10): e1219827.

58.

Furie

Khamashta

Merrill

, et al. Anifrolumab, an anti-interferon-α receptor monoclonal antibody, in moderate-to-severe systemic lupus erythematosus. Arthritis Rheumatol 2017; 69(2): 376–386.

59.

Maitra

Bragazzi Cunha

Elenbaas

, et al. Porphyrin-induced protein oxidation and aggregation as a mechanism of porphyria-associated cell injury. Cell Mol Gastroenterol Hepatol 2019; 8(4): 535–548.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.09 MB