Abstract

   Background: Systemic lupus erythematosus (SLE) is an autoimmune
   disorder characterized by immune dysregulation and chronic inflammation
   across various organ systems. While anti-dsDNA and anti-Sm antibodies
   are commonly associated with SLE, the presence of anti-RNP antibodies
   is often linked to unique gene expression profiles and immune
   responses. This study aims to investigate the gene expression profiles
   in ribonucleoprotein (RNP) autoantibody-positive SLE patients by
   analyzing publicly available transcriptomic data. Methods: This study
   analyzed transcriptomic data from the GEO dataset [26]GSE61635, which
   includes gene expression profiles from 79 anti-RNP-positive SLE
   patients and 30 healthy controls. Differentially expressed genes (DEGs)
   were identified using the GEO2R tool with a p-value < 0.05 and
   |log2fold change| > 1. Gene Ontology (GO) and Kyoto Encyclopedia of
   Genes and Genomes (KEGG) enrichment analyses were performed.
   Tissue-specific and cell-type enrichment analyses highlighted the
   involvement of immune tissues. Results: A total of 1891 DEGs were
   identified between anti-RNP-positive SLE patients and healthy controls.
   Among the identified DEGs, SLC4A1 and EPB42 were notably downregulated,
   while PIP4K2A was highly upregulated. Enrichment analyses revealed
   significant dysregulation in antiviral response and immune regulation
   pathways. PPI network analysis highlighted key hub genes, suggesting a
   heightened antiviral state in these patients. Tissue-specific
   enrichment and cell-type enrichment identified the bone marrow and
   immune tissues as being highly affected by the altered gene expression.
   Additionally, gene frequency analysis highlighted RASD2 as being
   recurrently significant across multiple studies. Conclusions: The
   findings suggest that anti-RNP-positive SLE patients exhibit distinct
   gene expression and immune dysregulation profiles, particularly in
   antiviral and immune regulation pathways. These results provide
   insights into the molecular mechanisms driving SLE in this patient
   subset and may guide future therapeutic interventions.

   Keywords: systemic lupus erythematosus, RNP autoantibodies, gene
   expression, immune regulation

1. Introduction

   Systemic lupus erythematosus (SLE) is a complex, chronic autoimmune
   disease characterized by a breakdown in self-tolerance, resulting in
   immune responses against multiple organ systems, including the skin,
   kidneys, and joints [[27]1]. Despite advances in understanding the
   immune dysfunctions of SLE, many questions remain regarding the
   specific gene expression patterns that link immune dysregulation to
   clinical manifestations.

   The presence of various autoantibodies is an important hallmark in SLE.
   Selected antinuclear antibodies (ANAs), including anti-double-stranded
   deoxyribonucleic acid (dsDNA) and anti-Sm, are highly specific for the
   diagnosis of SLE. Anti-RNP antibodies are present in a subset of
   patients with SLE, and high-titer anti-RNP antibodies are present in
   all patients with mixed connective tissue disease, a disorder that is
   closely related to SLE [[28]2]. Although anti-RNP antibodies are not a
   primary diagnostic criterion for SLE, their association with mixed
   connective tissue diseases and their potential link to specific immune
   dysregulation warrant investigation. This study aims to explore the
   gene expression profiles in anti-RNP-positive SLE patients to identify
   the pathways that might differentiate them from other serotypes,
   potentially revealing novel insights into SLE pathogenesis.

   Previous research has primarily focused on the role of the adaptive
   immune system, particularly T cells and B cells, in SLE pathogenesis. B
   cells play a critical role in the disease by not only producing
   autoantibodies, but also acting as antigen-presenting cells [[29]3]. T
   cells, especially CD4+ helper T cells, are key drivers of immune
   responses, facilitating the activation of B cells and the production of
   pathogenic autoantibodies, including anti-nuclear antibodies (ANAs)
   [[30]4]. These autoantibodies contribute to immune complex formation,
   which is a hallmark of SLE and a primary cause of tissue inflammation
   and damage to ANAs [[31]5]. The interaction between these two immune
   cell types leads to chronic inflammation and subsequent organ damage.

   More recently, attention has shifted towards the role of the innate
   immune system, especially type I interferons (IFNs) and plasmacytoid
   dendritic cells (pDCs), in SLE pathogenesis. The persistent production
   of type I IFNs, particularly IFN-α, is a major feature of SLE and has
   been shown to play a critical role in disease progression. This is
   largely driven by immune complexes containing nucleic acids, which
   activate pDCs to produce IFN-α, contributing to a loss of tolerance and
   the activation of autoreactive T and B cells [[32]6,[33]7].

   Genetic predisposition also plays a significant role in SLE, with
   several studies identifying associations between susceptibility to SLE
   and polymorphisms in genes involved in the interferon pathway. In
   recent years, high-throughput technologies such as transcriptomic and
   microarray analyses have been widely applied to identify differentially
   expressed genes (DEGs) in SLE [[34]8]. These approaches have
   facilitated the identification of potential biomarkers and therapeutic
   targets by providing insight into the molecular pathways involved in
   disease progression. Numerous studies have highlighted the
   dysregulation of genes involved in immune responses, including those
   associated with interferon signaling, as critical contributors to SLE
   pathogenesis.

   Despite advances in understanding the immune dysfunctions of SLE, many
   questions remain regarding the specific gene expression patterns that
   link immune dysregulation to clinical manifestations. In this study, we
   seek to explore these gaps by analyzing gene expression in RNP
   autoantibody-positive SLE patients using publicly available datasets.
   Using bioinformatic tools, we screened DEGs from [35]GSE61635 and
   performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and
   Genomes (KEGG) pathway enrichment analyses to identify the key
   biological processes and pathways involved in SLE. A PPI network was
   constructed to identify hub genes, and further tissue- and cell
   type-specific enrichment analyses were performed to investigate the
   potential roles of these genes in disease progression. The insights
   gained from this study provide a deeper understanding of the molecular
   underpinnings of SLE and may guide future therapeutic strategies.

2. Materials and Methods

2.1. Data Source

   The GEO dataset [36]GSE61635, which profiles gene expression in RNP
   autoantibody-positive SLE patients versus healthy controls, was used
   for this study. The dataset includes mRNA expression data from 79 SLE
   patients and 30 healthy volunteers.

   RNA samples were collected from all patients following the provision of
   informed consent. The cohort consisted of 73 female and 6 male
   subjects. The duration of disease among the patients ranged from 0 to
   453 months, with a median duration of 37.5 months. The SLE Disease
   Activity Index (SLEDAI) scores varied between 0 and 31, with a median
   score of 6 [[37]9].

2.2. Data Processing

   To identify differentially expressed genes (DEGs) within the
   [38]GSE61635 dataset, we employed the GEO2R tool, a user-friendly
   web-based application that allows for the comparison of two or more
   groups of samples within the GEO series. GEO2R uses the Limma (Linear
   Models for Microarray Data) package to identify DEGs by comparing
   expression levels across different conditions. The selection criteria
   for DEGs were set as a p-value less than 0.05 and an absolute log2 fold
   change greater than 1. The DEGs identified through this process were
   considered for further downstream analyses.

2.3. Functional Enrichment Analysis

   To understand the biological significance of the identified DEGs, we
   conducted KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene
   Ontology) enrichment analyses. We utilized the KOBAS (KEGG
   Orthology-Based Annotation System) tool (Version 3.0, Peking
   University, Beijing, China, available at [39]http://bioinfo.org/kobas/;
   accessed on 3 August 2024). By inputting the DEG list into KOBAS, we
   identified pathways and gene functions that were significantly
   enriched, providing insight into the underlying biological mechanisms.

2.4. PPI Network Building

   To further understand the functional interactions between the
   identified DEGs, we constructed a protein–protein interaction (PPI)
   network. This was accomplished using STRING (Search Tool for the
   Retrieval of Interacting Genes/Proteins) and Cytoscape, (Version
   3.10.2) a robust bioinformatics software platform for visualizing
   molecular interaction networks. Initially, DEGs were input into the
   STRING database to predict interactions based on evidence from
   experimental data, co-expression, and other sources. The interaction
   data from STRING were then imported into Cytoscape to build and
   visualize the PPI network.

2.5. Tissue Enrichment Analysis

   Tissue enrichment analysis was conducted to determine the specific
   tissues where DEGs are most highly expressed. We used the TissueEnrich
   R package for this purpose [[40]10]. This package employs a statistical
   framework to analyze gene expression data, focusing on identifying the
   enrichment of genes within specific tissues. The input for this
   analysis is DEGs identified in the earlier transcriptome analysis step.
   The TissueEnrich package uses the GTEx dataset as a reference for
   tissue-specific gene expression profiles, allowing for the
   identification of significant tissue associations of DEGs.

2.6. Gene Frequency Analysis

   Gene frequency analysis was performed to identify the recurrence of
   specific genes across different studies using the same GEO dataset
   ([41]GSE61635)
   [[42]11,[43]12,[44]13,[45]14,[46]15,[47]16,[48]17,[49]18]. This helps
   in highlighting genes that are consistently reported as significant,
   suggesting their potential biological importance. Key genes were
   extracted from related studies that had employed [50]GSE61635. For each
   gene, the frequency of its occurrence was calculated across these
   studies, which provided a quantitative measure of its relevance and
   consistency.

2.7. Cell Type Annotation

   We used Enrichr ([51]https://maayanlab.cloud/Enrichr/; accessed on 23
   August 2024) for the cell type annotation on the upregulated and
   downregulated genes, and identified the primary cell types associated
   with each group. These results provided further insights into the
   cellular mechanisms involved in SLE progression.

3. Results

3.1. Differential Gene Expression Analysis

   A total of 1891 DEGs (p-value < 0.05, |log2fold change| > 1) were
   identified between RNP autoantibody+ SLE patients and healthy blood
   donors. The volcano plot illustrates the distribution of upregulated
   and downregulated genes ([52]Figure 1). Notably, SLC4A1 and EPB42 are
   strongly downregulated with a log2fold change of less than −4, while
   PIP4K2A is highly upregulated with a log2fold change greater than 8.
   These genes represent the most significant expression changes in the
   dataset, suggesting their potential involvement in disease mechanisms.

Figure 1.

   [53]Figure 1
   [54]Open in a new tab

   Volcano plot of DEGs between SLE and control groups.

3.2. Functional Enrichment Analysis Results

   To further understand the biological processes and pathways associated
   with the DEGs, we performed KEGG enrichment analysis ([55]Figure 2) and
   GO enrichment analysis ([56]Figure 3) using the KOBAS tool.

Figure 2.

   [57]Figure 2
   [58]Open in a new tab

   KEGG pathway enrichment analysis of DEGs in RNP autoantibody+ SLE
   patients. The size of each bubble corresponds to the number of genes
   enriched in each pathway, while the color gradient represents the
   significance level (p-value) of the enrichment, with darker colors
   indicating higher statistical significance.

Figure 3.

   [59]Figure 3
   [60]Open in a new tab

   GO enrichment analysis of DEGs in RNP autoantibody+ SLE patients. The
   chord diagram represents the GO enrichment analysis results, displaying
   the relationships between enriched GO terms and their associated DEGs.

   Multiple pathways related to antiviral response were highly enriched,
   suggesting a heightened immune response against viral infections in RNP
   autoantibody+ SLE patients. This is consistent with previous studies
   indicating a link between viral infections and SLE pathogenesis.
   Pathways involved in immune regulation, such as innate immune response
   and the modulation of chemical synaptic transmission, were also
   significantly enriched. This indicates the dysregulation of immune
   responses in RNP autoantibody+ SLE patients, contributing to the
   development and progression of the disease. Pathways related to
   nicotine addiction and influenza A were also enriched. This suggests
   potential interactions between environmental factors and gene
   expression in SLE patients.

3.3. Protein–Protein Interaction (PPI) Network Analysis

   We constructed a PPI network using STRING and Cytoscape to identify hub
   genes and explore their functional relationships ([61]Figure 4). The
   network includes 15 hub genes. The overexpression of these hub genes in
   the SLE group suggests a heightened antiviral state and altered immune
   regulation in RNP autoantibody+ SLE patients.

Figure 4.

   [62]Figure 4
   [63]Open in a new tab

   Protein–protein interaction (PPI) network of top 15 hub genes. The
   color of the nodes reflects the degree of connectivity, and edges
   indicate protein–protein interactions, with darker nodes representing
   higher connectivity.

3.4. Tissue Enrichment Analysis Results

   We performed tissue enrichment analysis to explore the tissue-specific
   expression patterns of the DEGs ([64]Figure 5). The results revealed a
   significant enrichment of DEGs in immune tissues, such as bone marrow,
   as well as reproductive tissues, such as prostate and endometrium. This
   suggests that SLE may have a broader impact on various organ systems
   beyond the immune system.

Figure 5.

   [65]Figure 5
   [66]Open in a new tab

   (a) Tissue-specific enrichment analysis of downregulated genes in RNP
   autoantibody+ SLE patients. (b) Tissue-specific enrichment analysis of
   upregulated genes in RNP autoantibody+ SLE patients. The numbers
   displayed above each tissue bar represent the number of genes enriched
   in each tissue.

3.5. Gene Frequency Analysis Results

   To validate the findings and prioritize genes for further
   investigation, we conducted a gene frequency analysis using data from
   selected studies utilizing the same dataset ([67]GSE61635). This
   analysis revealed a high frequency of several key genes across
   different studies, with RASD2 standing out in particular, further
   supporting its critical role in SLE pathogenesis ([68]Figure 6).

Figure 6.

   [69]Figure 6
   [70]Open in a new tab

   Gene frequency analysis of key genes identified in multiple studies
   using the [71]GSE61635 dataset.

4. Discussion

   This study explores gene expression changes in systemic lupus
   erythematosus, providing important insights into both immune
   dysfunction and red blood cell (RBC) function. By analyzing
   differentially expressed genes, we identified several key pathways and
   mechanisms that may play a role in systemic lupus erythematosus
   progression, with immune cell dysfunction and RBC instability emerging
   as central contributors.

   Tissue-specific enrichment analysis revealed significant gene
   expression changes in the bone marrow, cerebral cortex, and testes,
   suggesting these tissues also play crucial roles in disease
   pathogenesis. The bone marrow is a primary site for immune cell
   production, and disruptions here may reflect altered lymphocyte
   development. Changes in the cerebral cortex may contribute to the
   neurological manifestations seen in SLE, while alterations in the
   testes highlight potential reproductive health impacts in male patients
   [[72]19,[73]20].

   One notable finding is the downregulation of genes critical for RBC
   stability, including SLC4A1 and EPB42. SLC4A1 is responsible for
   maintaining membrane integrity and facilitating CO[2] transport, while
   EPB42 supports the shape and mechanical properties of red blood cells
   [[74]21,[75]22]. The decreased expression of these genes may explain
   symptoms like anemia and fatigue, expanding the conventional view of
   systemic lupus erythematosus as not just an immune disorder, but also
   one that affects RBCs.

   Our tissue and cell type enrichment analyses revealed distinct patterns
   of immune cell regulation. Downregulated genes were found mainly in
   granulocytes and myeloid cells, suggesting that innate immunity may be
   compromised, contributing to chronic inflammation and increased
   susceptibility to infections in patients with systemic lupus
   erythematosus [[76]23,[77]24]. On the other hand, upregulated genes
   were predominantly associated with inflammatory cells, such as M1
   macrophages and neutrophils, which is consistent with the heightened
   inflammatory response typically seen in the disease [[78]25]. These
   findings align with previous research that suggests an overactivation
   of the innate immune system contributes to tissue damage in systemic
   lupus erythematosus [[79]26].

   Previous studies have shown that B cells not only produce pathogenic
   autoantibodies, but also act as antigen-presenting cells, further
   activating T cells and perpetuating the autoimmune response. T cells,
   especially the CD4+ and CD8+ subsets, have been implicated in driving
   direct tissue damage and contributing to the inflammatory response
   [[80]7]. While much of the previous research has focused on the
   adaptive immune response—particularly involving T and B cells—our
   results highlight the importance of innate immunity and RBC dysfunction
   in the disease. This broader understanding opens new possibilities for
   diagnosis and treatment, suggesting that addressing both immune
   dysregulation and RBC instability could help alleviate symptoms like
   anemia.

   However, this study has its limitations. First, we relied on publicly
   available GEO datasets, which may affect the generalizability of our
   findings due to variations in populations and study conditions. Second,
   the accuracy of tissue enrichment analyses depends on the quality of
   reference datasets, which could introduce bias. Most importantly, we
   did not perform functional experiments to validate the roles of the
   identified genes. Future research will need to conduct such experiments
   to confirm how these genes contribute to systemic lupus erythematosus
   and their impact on both immune cells and RBCs.

   In the future, functional studies such as gene knockdown or
   overexpression experiments should be conducted to determine the precise
   role of these genes in systemic lupus erythematosus. Additionally,
   larger studies involving diverse patient populations and more detailed
   immune cell analyses are necessary to fully understand the complexity
   of the disease and to identify new therapeutic targets.

5. Conclusions

   In this study, we explored the distinct gene expression profiles and
   immune dysregulation in anti-RNP-positive SLE patients, identifying
   significant alterations in immune and antiviral response pathways. The
   analysis of DEGs, including the downregulation of SLC4A1 and EPB42 and
   the upregulation of PIP4K2A, highlighted potential molecular mechanisms
   contributing to SLE pathogenesis, particularly in antiviral pathways.
   Our findings also underscore the involvement of tissues such as bone
   marrow and immune cells in the disease process, further suggesting that
   both immune and red blood cell dysfunction play pivotal roles in the
   progression of SLE. These insights open new avenues for therapeutic
   intervention, potentially targeting these dysregulated pathways to
   alleviate both immune and hematological symptoms in patients. Future
   studies should focus on functional validation of these key genes and
   their roles in SLE progression to enhance our understanding of disease
   mechanisms and treatment strategies.

Author Contributions

   Conceptualization, S.T. and T.D.; methodology, S.T.; formal analysis,
   S.T.; writing—original draft preparation, S.T.; writing—review and
   editing, T.D.; supervision, T.D. All authors have read and agreed to
   the published version of the manuscript.

Institutional Review Board Statement

   Ethical review and approval were waived for this study, as the data
   used were obtained from a publicly available GEO database
   ([81]GSE61635), which had already undergone ethical approval in the
   original study.

Informed Consent Statement

   The data utilized in this study were sourced from the publicly
   available GEO database ([82]GSE61635). According to the original study,
   all patients provided RNA samples after giving informed consent.

Data Availability Statement

   The authors declare that the data supporting the findings of this study
   are available within the paper.

Conflicts of Interest

   The authors declare no conflicts of interest.

Funding Statement

   This research received no external funding.

Footnotes

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References