Blood samples from 40 SLE patients were collected between 26^th of December 2019 and 8^th of May 2021 from the Capital Institute of Pediatrics, Beijing. The SLE cases were divided into three subgroups according to the SLE Disease Activity Index 2000 (SLEDAI-2k): (i) IA subgroup: SLEDAI ≤4; (ii) LA subgroup: SLEDAI=5-9; and (iii) HA subgroup: SLEDAI ≥10 (8, 9). HCs were enrolled from children who underwent a health checkup at the Capital Institute of Pediatrics. This study was reviewed and approved by the Capital Institute of Pediatrics ethics committee (DWLL2018004). Written informed consent has been signed by patients or their parents.

Laboratory findings including C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), serum complement levels (C3, C4), B cell numbers and percentage, IgG, IgA and IgM were detected. Serological screening for SLE included antinuclear antibody (ANA) and anti-double stranded DNA antibodies (anti-dsDNA), anti-smith nuclear antigen (anti-Sm) and anti-nuclear-ribonuclear-protein (anti-nRNP). Pro-inflammatory cytokines including tumor necrosis factor (TNF)-α, interleukin (IL)-2R, IL-10 and IL-6 were measured by chemiluminescence immunoassay technology (CLIA) kit and detected with the Image Immunochemistry System Immulite1000 (Siemens, USA).

Firstly, serum samples from each participant were lysed at 25°C for 30 min. The lysates were then reduced with Tris phosphine (Pierce, USA) and incubated at 37°C for 30 min with shaking. Then, iodoacetamide (Sigma-Aldrich, USA) was added for alkylation at 25°C, with agitation at 300 rpm for 1 h without light. Mass spectrometry-grade trypsin gold (Promega, USA) was then added to digest proteins overnight at 37°C. Next, 20μL loading buffer (1% formic acid, FA; 1% acetonitrile, ACN) was used to dissolve the dried peptides. Ten μL of product was then used for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis on an Orbitrap Fusion Lumos in data dependent acquisition (DDA) mode coupled with Ultimate 3000 (Thermo Fisher Scientific, USA).

MS detection parameters were set as follows: the ultra-high field Orbitrap analyzer adopts full MS survey scan with a resolution of 120,000 and an ion trap size of 500,000. Mass range was set at 300 to 1400 m/z. MS/MS was carried out in an IonTrap, and the top 20 peptides with the highest energy were identified for fragmentation. Peptides were screened for fragments with 2-7 charges (5×10³ AGC target, 35 ms maximum ion time). Maxquant (Version 1.6.17) was used to analyze the mass spectrometry data. Maxquant parameters were set as follows: 20 ppm was the tolerance of precursor ion mass; full cleavage by trypsin was selected; a maximum of two missed cleavages was allowed; cysteine carbamidomethylation was set as a static modification; methionine oxidation and acetylation of peptides’ N-termini were set as variable modifications. Homo sapiens FASTA database (uniprot-proteome UP000005640.fasta) were used for protein searches with following the criteria: (1) peptide length ≥6 amino acids; (2) False Discovery Rate (FDR) ≤1% at the PSM, peptide and protein levels. Peptides were quantified using the peak area derived from their MS1 intensity by Perseus 1.6.15.0; R 3.6. The protein intensity was calculated by the intensity of unique and razor peptides.

For each sample extraction, 400 μL of methanol (MeOH)/ACN (1:1, v/v) solvent mixture was added to 100 μL of serum (2:2:1 ratio, no H₂O added) and violated vigorously for 5 min, incubated for 1 h at -20°C, and centrifuged for 10 minutes at 13,500 g at 4°C. Then, the supernatant of each sample was transferred to a new tube. To ensure data quality, quality control (QC) samples were collected, containing equal amounts of sera (0.75mL) from the 50 participants, and divided into 4 samples. The QC samples were pretreated in parallel with the study samples and added between each set of runs to monitor the stability. Two platforms, reverse-phase/ultra-performance liquid chromatography (RP/UPLC)-MS/MS methods with positive ion-mode electro spray ionization (ESI) and negative-ion mode ESI, were used to detect the metabolites.

ACQUITY 2D UPLC system (Waters, USA) and TripleTOF 5600+ (AB SCIEX, MA, USA) with ESI source and mass analyzer were applied in all UPLC-MS/MS methods. The mobile solutions used in the gradient elution were water and methanol containing 0.1% FA. The mass spectrometry analysis, with the scan range set at 70-1,500 m/z, alternated between MS and data-dependent MS2 scans using dynamic exclusion. The MS-DIAL software was used for raw data pre-processing, peak alignment and peak annotation. Then, metabolites were identified by matching the retention time, accurate mass, and MS/MS fragmentation data to the MS-DIAL software database and Human Metabolome Database (HMDB, https://hmdb.ca).

Serum samples were mixed with 400uL methyl tert-butyl ether (MTBE) and 80uL MeOH (MTBE : MeOH:H2O=10:2:5, v/v) and vortexed for 30 s. The organic and aqueous phases were separated through centrifugation at 3,000 rpm for 15 min. The upper MTBE was transferred to a new microcentrifuge tube and dried. The dried extracts were then reconstituted with 100 μL of dichloromethane: MeOH (1:1,v/v).

Liquid chromatography (LC) was performed using an ExionLC AD system (AB SCIEX, USA). Phenomenex Kinetex 1.7u EVO C18 columns (2.1×50mm,100A; Agilent, USA) were used for LC separation and a total of 3μL of sample was loaded. The column temperature and flow rate was set to 40°C and 0.5 mL/min, respectively. The mobile phase A contained 50% water, 50% ACN, and 10mM ammonium formate while the mobile phase B contained 10% ACN, 90% isopropyl alcohol (IPA), and 10mM ammonium formate. MS was performed on a 5600+ quadrupole-time of flight/mass spectra (Q-TOF/MS, AB SCIEX, USA). The positive and negative ion ionization mode of electrospray ionization source was used. IDA-TOF-MS/MS with the quadrupole scanning rage set at 50-1500 m/z was used to collect the data. An independent reference, lock-mass ion, via the MS-DIAL (ver.3.70; 17 April 2021) was used to ensure mass accuracy during data acquisition. MS-DIAL Lipidomics MSP database was used to identify the assigned modified metabolite ions. To reduce false positive matches, chromatographic retention behavior was considered.

To elucidate the molecular characteristics of SLE patients, 10 HCs and 40 SLE patients with different activity: IAs, LAs and HAs, were collected for this multi-omics study. Throughout the course of this study, we measured autoantibodies, clinical parameters and cytokines. LC-MS/MS was applied to identify the proteomic, metabolomic, and lipidomic signatures in SLE patients. Meanwhile, these omic datasets were then integrated to provide an insight into the underlying pathogenesis of SLE ( Figure 1A ). To quantify the molecular features related to disease activity, we used pairwise comparisons between the four groups for each omic level to identify differentially expressed proteins, metabolites and lipids, respectively ( Figure 1B ).

The demographic characteristics and laboratory results of enrolled patients are shown in Table 1 . Increase in disease activity was associated with a gradual increase in disease score. Compared with LAs and HAs, IA patients had a lower percentage of positive anti-dsDNA (P = 0.048), implying a lower disease activity in IAs. Indicators of systemic inflammation, such as ESR (P = 0.0011), and pro-inflammatory cytokines, including TNF-α (P = 0.0127) and IL-10 (P = 0.0186), were increased as disease activity increased. Our observations are consistent with previous findings, which demonstrated that TNF-α and IL-10 levels were significantly increased in active SLE and correlated with clinical SLEDAI-2k (10). In contrast, expression of C3 (P <0.0001) and C4 (P = 0.013) were decreased in SLE patients compared to HCs, showing a decreased trend as disease activity increased.

Moreover, the number of B cells (P = 0.0033) as well as the levels of antibodies, including IgG (P = 0.0441), IgA (P = 0.0285) and IgM (P = 0.0189) were increased in IAs and LAs, but decreased slightly in HAs. Our observations are consistent with previous findings in SLE where abnormalities in B cells were identified to be involved in the pathogenesis of SLE (11).

After data preprocessing, the final dataset contained 3399 analytes, including 777 proteins, 1994 metabolites, and 628 complex lipids that were identified. Next, pairwise comparisons were applied to identify differentially expressed molecules, and identify the molecular signatures of SLE for each omic level.

SLE is a multifactorial and complex autoimmune disease, which is characterized by various immune-related and inflammation-related molecular aberrations. Although dysregulated immune responses and inflammation responses in SLE have been reported, the comprehensive analysis and the underling molecules participating the process at different stages of SLE disease activity is limited (14).

In this study, many complement components were significantly changed in SLEs, with the trend becoming more pronounced as disease activity increased ( Figure 4A ). As shown in Figure 4B , multiple complement-related proteins, including complement C1q subcomponent subunit C (C1QC), complement subcomponent C1r (C1R), complement C4-A (C4A), and complement C4-B (C4B) were downregulated in SLE, especially in LAs and HAs. Besides, the similar expression patterns of serum complements C3 and C4 were observed in SLEs ( Figure 4C ). The levels of C1QC, C1R, C4A, and C4B were positively associated with levels of C3 and C4 ( Figure 4D and Supplementary Data 4.1 ). More interestingly, they were also negatively related to disease score of SLE ( Figure 4E and Supplementary Data 4.2 ). This suggests that the complement system was activated in SLE and participated in the activity of SLE. Moreover, many immunoglobulins, such as immunoglobulin heavy constant gamma (IGHG)1, IGHG3, immunoglobulin kappa variable (IGKV) 2-24, and others were significantly increased in SLEs ( Figure 4F ). Interestingly, a new pattern of immune disorders at HA stage was observed. Many immunoglobulin proteins, which were increased in LA subgroup of SLEs, were specifically decreased in the HAs ( Figure 4F ). Consistently, the number of B cells and levels of IgG also showed similar expression patterns ( Figure 4G ), and were positively correlated with the immunoglobulins levels ( Supplementary Figure 5A and Supplementary Data 4.3 ). Some immunoglobulin proteins, such as IGHM, IGHV 3-34, IGLV1-47, IGLV 3-10 were positive correlated with disease score of SLE ( Figure 4H and Supplementary Data 4.2 ). Collectively, changed proteins in HAs implied the activation of complement system and suppression of immunoglobulin-related immune response at high activity stage.

Next, we analyzed the expression of inflammation markers in different groups. Levels of ESR and pro-inflammatory cytokines, including TNF-α, IL-2R, IL-6 and IL-10 were elevated in SLE and this was associated with disease activity ( Figure 5A ). Besides, many proteins associated with inflammation identified by proteomics were also up-regulated ( Figure 5B and Figure 5C ). For example, the levels of acute-phase proteins (serum amyloid A [SAA] 1, SAA2, SAA2-SAA4) were increased in SLEs ( Figure 5C ). Circulatory SAA concentration was reported to be significantly increased during acute phase responses (15). Here, the expression of SAA1 was higher in SLEs compared to HCs, and was also further elevated in HAs compared to IAs ( Figure 5C ). The level of SAA2 was highest in HAs while the levels of SAA2-SAA4 was higher in LAs and HAs compared to HCs. More interestingly, the levels of SAA1 and SAA2 were positively correlated with SLE disease score ( Figure 5D and Supplementary Data 4.2 ), and SAA1 alone was correlated with ESR and TNF-α ( Figure 5F and Supplementary Data 4.4 ). Except for acute inflammatory-related pathway, mitogen-activated protein kinase (MAPK) signaling pathways and extracellular signal-regulated kinase (ERK) 1/2 signaling pathway were also changed in SLE patients ( Figure 5E ).

Since chronic inflammation in patients with SLE can lead to accelerated atherosclerosis, it is reasonable to suspect that these risk factors can induce specific alterations in lipoprotein metabolism (16). In this study, we found a large number of apolipoproteins that were up-regulated in SLE, especially in HAs ( Figure 6A ). Compared with HCs, the expressions of apolipoprotein B (APOB), apolipoprotein C (APOC), apolipoprotein D (APOD), apolipoprotein E (APOE) and apolipoprotein L1 (APOL1) were increased in SLE patients ( Figure 6B ). Moreover, the expression of APOB and APOE were correlated to SLE disease score ( Figure 6C ). These results are consistent with previous findings demonstrating that APOE correlated with disease activity and related cytokines in SLE patients (17). Besides, APOC3 was strongly related to cardiovascular disease risk (18). Thus, high levels of APOC3 in HAs may be involved in the occurrence of atherosclerosis induced SLE.

As a lipid transport carrier, apolipoproteins participate in the pathogenesis of SLE, however, their relationship with lipids in SLE is still unclear (17). Here, we conducted a correlation analysis for apolipoproteins and lipid metabolism. As mentioned above, Cer, PI-Cer and DAG were increased, while LPC, LPE and PI were decreased as SLE disease activity increased ( Figure 3E ). Interestingly, we found the expression of APOB were positively connected with Cer, while negatively associated with LPC and LPE ( Figure 6D and Supplementary Data 4.5 ). Further correlation between apolipoproteins and lipids are provided in Supplementary Figure 5B . In addition, many lipids were also observed to be correlated with SLE disease score ( Supplementary Figure 6A ). It has been reported that dyslipidemia occurs frequently in SLE and is characterized by elevated plasma levels of low-density lipoprotein (LDL), APOB, and decreased level of high-density lipoprotein (HDL) (16). Many studies have shown that dyslipidemia, is an important contributing factor that can also accelerate inflammation, autoimmunity, and atherosclerosis in autoimmune diseases (19). Therefore, an imbalance in lipid metabolism together with dysregulated apolipoproteins combined involved in the SLE activity.

As mentioned above, the metabolomics data revealed a significant impact on sphingolipid metabolism ( Figure 3B ). To determine whether this pathway is also involved in high disease activity, differentially expressed metabolites between HAs and other groups were identified ( Figure 7A ). Of note, sphingolipid metabolism was the top pathway affected in the high activity group from our metabolomics analysis ( Figure 7B and Supplementary Data 3.2 ). As such, further analysis of this pathway highlighted significant increases in sphinganine, glucosylceramide, and phytosphingosine levels ( Figure 7C ). In contrast, decreases in sulfatide, sphingomyelin (SM), and sphingosine-1P suggested hyperactivation of the ceramide pathway in activity stage of SLE. Among them, glucosylceramide and SM levels were also associated with SLE disease score ( Supplementary Figure 6B ). Consistently, sphinganine and SM were reported to be changed in SLEs and related to complications, thus supporting our data (20). These data show the potential value of assessing sphingolipid changes in SLEs, especially in the HA group, and supports the use of sphingolipidomics as a tool to pinpoint a biomarker for early identification (12).