Abstract

Background

   Systemic lupus erythematosus (SLE) is an autoimmune disease that
   involves multiple organs. However, the current SLE-related biomarkers
   still lack sufficient sensitivity, specificity and predictive power for
   clinical application. Thus, it is significant to explore new
   immune-related biomarkers for SLE diagnosis and development.

Methods

   We obtained seven SLE gene expression profile microarrays
   ([33]GSE121239/11907/81622/65391/100163/45291/49454) from the GEO
   database. First, differentially expressed genes (DEGs) were screened
   using GEO2R, and SLE biomarkers were screened by performing WGCNA,
   Random Forest, SVM-REF, correlation with SLEDAI and differential gene
   analysis. Receiver operating characteristic curves (ROCs) and AUC
   values were used to determine the clinical value. The expression level
   of the biomarker was verified by RT‒qPCR. Subsequently, functional
   enrichment analysis was utilized to identify biomarker-associated
   pathways. ssGSEA, CIBERSORT, xCell and ImmuCellAI algorithms were
   applied to calculate the sample immune cell infiltration abundance.
   Single-cell data were analyzed for gene expression specificity in
   immune cells. Finally, the transcriptional regulatory network of the
   biomarker was constructed, and the corresponding therapeutic drugs were
   predicted.

Results

   Multiple algorithms were screened together for a unique marker gene,
   MX2, and expression analysis of multiple datasets revealed that MX2 was
   highly expressed in SLE compared to the normal group (all P < 0.05),
   with the same trend validated by RT‒qPCR (P = 0.026). Functional
   enrichment analysis identified the main pathway of MX2 promotion in SLE
   as the NOD-like receptor signaling pathway (NES=2.492, P < 0.001,
   etc.). Immuno-infiltration analysis showed that MX2 was closely
   associated with neutrophils, and single-cell and transcriptomic data
   revealed that MX2 was specifically expressed in neutrophils. The
   NOD-like receptor signaling pathway was also remarkably correlated with
   neutrophils (r >0.3, P < 0.001, etc.). Most of the MX2-related
   interacting proteins were associated with SLE, and potential
   transcription factors of MX2 and its related genes were also
   significantly associated with the immune response.

Conclusion

   Our study found that MX2 can serve as an immune-related biomarker for
   predicting the diagnosis and disease activity of SLE. It activates the
   NOD-like receptor signaling pathway and promotes neutrophil
   infiltration to aggravate SLE.

   Keywords: systemic lupus erythematosus, MX2, machine learning,
   biomarker, immune infiltration

Introduction

   Systemic lupus erythematosus (SLE), a systemic autoimmune disease
   characterized by abnormal activity of the immune system, is a serious
   threat to human health ([34]1, [35]2). Recently, accumulating evidence
   has demonstrated that some biomarkers have important reference values
   for the early diagnosis and treatment of SLE. For instance,
   hsa_circ_0000479 and TCONS_00483150 are novel diagnostic markers for
   SLE ([36]3, [37]4). PGLYRP2 and DTX1 can predict disease activity in
   SLE patients ([38]5, [39]6). DKK-1 can be used as a biomarker for
   active lupus erythematosus nephritis ([40]7). However, the current
   SLE-related biomarkers still lack sufficient sensitivity, specificity
   and predictive power for clinical application. Based on the
   pathological changes in the immune microenvironment, it is necessary to
   discover new immune-related biomarkers for SLE diagnosis and
   development.

   MX2 (MX Dynamin Like GTPase 2), encodes a protein with nuclear and
   cytoplasmic forms and is a member of the dynamin family and large
   GTPase family. The nuclear form is localized in a granular pattern in
   the heterochromatin region below the nuclear envelope. In the present
   study, we found that MX2 is closely related to inflammation-related
   pathways. Interestingly, inflammation-related signals, such as
   Toll-like receptor, NF-κB and Nod-like receptor, also play an important
   role in the development of SLE. For instance, in SLE patients, loss of
   B-cell tolerance to autoantigens is controlled by Toll-like receptors
   in a cell-intrinsic manner ([41]8). Toll-like receptor signaling
   inhibitory peptides ameliorate inflammation in animal models and human
   SLE ([42]9). A recent study found that reduced TNFAIP3 and enhanced
   UBE2L3 synergistically activate NF-κB, thereby synergistically
   increasing lupus risk ([43]10). Peli1 negatively regulates noncanonical
   NF-κB signaling to inhibit SLE ([44]11). Curcumin attenuates murine
   lupus by inhibiting NLRP3 inflammatory vesicles ([45]12). Dietary olive
   bitter glycosides and their acyl derivatives ameliorated the
   pristane-induced inflammatory response in peritoneal macrophages of SLE
   mice and murine lupus nephritis by inhibiting the NLRP3 inflammatory
   vesicle pathway ([46]13, [47]14). Meanwhile, MX2 has been found to play
   an important role in immune-related diseases and immune cells, but the
   function of MX2 and its detailed regulatory mechanisms in SLE remain
   unclear ([48]15–[49]18).

   In the present study, a total of five methods, namely, weighted gene
   co-expression network analysis (WGCNA), support vector
   machine-recursive feature elimination (SVM-RFE), random forest (RF),
   correlation with SLEDAI and differential expressed genes (DEGs)
   analysis, were initially applied to screen MX2, a potential biomarker
   for SLE. The differential expression levels of MX2 in SLE vs. healthy
   individuals were validated by real-time quantitative PCR (RT‒qPCR).
   Then, the relationship between MX2 and clinical parameters in SLE
   patients was analyzed. Thereafter, the mechanism by which MX2 promotes
   SLE was investigated by enrichKEGG and GSEA. Four immune infiltration
   analysis algorithms, including ssGSEA, CIBERSORT, xCell and ImmuCellAI,
   were performed to explore the relationship between MX2 and immune
   cells. Finally, the transcriptional regulatory network of MX2 was
   constructed, and the corresponding therapeutic drugs were predicted.
   The flowchart of this study was shown in [50]Figure 1 .

Figure 1.

   [51]Figure 1
   [52]Open in a new tab

   The flowchart of the analysis process.

Material and methods

Data collection and processing

   SLE-related public data were first downloaded from the Gene Expression
   Omnibus (GEO) database ([53]19, [54]20), and seven datasets were
   collected: three datasets from PBMC samples [[55]GSE121239
   ([56]GPL13158, Normal: 20, SLE: 292) ([57]21, [58]22), [59]GSE11907
   ([60]GPL96, Normal: 12, SLE: 110) ([61]23), [62]GSE81622 ([63]GPL10558,
   Normal: 25, SLE: 30) ([64]24)]; four datasets from whole blood samples
   [[65]GSE65391 ([66]GPL10558, Normal: 72, SLE: 924) ([67]25),
   [68]GSE100163 ([69]GPL6884, Normal: 14, SLE: 55) ([70]26–[71]28),
   [72]GSE45291 ([73]GPL13158, Normal: 20, SLE: 292) ([74]29),
   [75]GSE49454 ([76]GPL1261, Normal: 20, SLE: 157) ([77]30)]. GEO2R was
   used for differential expression analysis of the [78]GSE121239,
   [79]GSE11907 and [80]GSE81622 datasets. |log2 FC| > 0.5 and adj.P.Val
   <0.05 were screened as differentially expressed genes (DEGs).
   Information on the datasets was displayed in [81]Supplementary File S1
   , and the UMAP plots of the datasets were shown in [82]Supplementary
   Figure S1 .

Biomarker screening

   Five methods were used for the screening of biomarkers of SLE: WGCNA
   ([83]31), SVM-RFE and RF ([84]32, [85]33) based on DEGs results from
   the [86]GSE121239 dataset, SLEDAI correlation analysis, and the
   difference analysis of two SLE datasets ([87]GSE11907, [88]GSE81622).
   Gene module classification and identification of DEGs was performed
   using the R package “WGCNA” ([89]34, [90]35). First, the samples were
   clustered to assess whether there were any significant outliers.
   Second, the automatic network construction function was used to
   construct the co-expression network. A soft threshold β is calculated.
   Third, hierarchical clustering and dynamic tree cutting functions were
   used to detect modules. Finally, gene importance (GS) and module
   membership (MM) were calculated. Linking modules to clinical traits.
   The corresponding module extracted the corresponding module gene
   information for further analysis. The SVM classifier was constructed
   using the R package “e1071” and cross-validated to eliminate regression
   features. The “randomForest” package was used for the RF classification
   process. For correlation analysis, the Spearman correlation coefficient
   was applied, and GEO2R was used for DEGs analysis of the SLE dataset
   ([91]GSE11907, [92]GSE81622). Then, overlapping genes were selected
   from the above five methods for further analysis in this study. The
   specific screening conditions for the analytic methods were shown in
   [93]Supplementary File S1 .

PBMC isolation

   A total of 47 samples, including 23 SLE samples and 24 healthy
   controls, were collected from the First Affiliated Hospital of USTC
   (Anhui Provincial Hospital). This study was approved by the ethics
   committee of the First Affiliated Hospital of USTC, and informed
   consent forms were signed by the patients. Peripheral blood mononuclear
   cells (PBMCs) were isolated from EDTA anticoagulated by FicollPaque
   density gradient centrifugation. Then, the cells were cultured in
   RPMI-1640 medium. Cells were maintained in a humidified incubator at
   37°C with 5% CO[2].

Real-time quantitative PCR

   RNA was extracted using total RNA isolation reagent (Epizyme, Shanghai,
   China). A cDNA synthesis kit (Bioer, Hangzhou, China) was used for
   reverse transcription of total RNA. GAPDH (forward:
   5’-GTCTCCTCTGACTTCAACAGCG-3’, reverse: 5’-ACCACCCTGTTGCTGTAGCCAA-3’),
   MX2 (forward: 5’-CACCGAGCTAGAGCTTCAGGA-3’, reverse:
   5’-CCGGGAAGGTCAATGATGGT-3’), and GAPDH was subsequently processed as
   internal references. Relative expression was calculated by the 2^-ΔΔCt