Improvement in condition of patients with SS-ILD due to SP
The clinical characteristics of recruited patients with SS-ILD before and after treatments were documented and analyzed (Table 1, 2). The conventional therapy was set as the control group (CK). The clinical characteristics included cough and sputum, shortness of breath, weakness, HRCT scores, dry mouth, and dry eyes (Table 1, 2). There was no significant difference in the levels of these characteristics between CK and SP treatment groups (Table 1, 2). Both treatments effectively alleviated SS-ILD symptoms, and HRCT scores of patients were significantly decreased. The symptoms of cough and sputum, shortness of breath, weakness, dry mouth, and dry eyes were alleviated by both treatments (Table 1, 2). The symptoms in 26 patients were improved by SP treatment, whereas symptoms in only 18 patients were improved in the CK group, suggesting that SP treatment was more efficient than CK treatment (Table 1). The SP treatment improved cough and sputum, shortness of breath, weakness, dry mouth, and dry eyes in patients more significantly than CK treatment (Table 2). These results supported the notion that SP therapy could effectively improve SS-ILD more than conventional therapy.
Table 1 Effects of SP therapy on patients in the clinic
Variables
|
CK
|
SP
|
P value
|
Healed case (n = 28)
|
18 (64.29%)
|
26 (92.86%)
|
0.009
|
HRCT (before treatment)
|
2.00 ± 0.72
|
2.04 ± 0.64
|
0.84
|
HRCT (after treatment)
|
1.71 ± 0.53*
|
1.54 ± 0.51*
|
0.20
|
“*: P < 0.05”
Table 2 Clinical characteristics of patients after therapy (x ± s)
Group
|
Time
|
Cough and sputum
|
Breath shortness
|
Weakness
|
Dry mouth and eye
|
SP
|
Before
|
4.39 ± 1.73
|
3.96 ± 1.42
|
3.96 ± 1.43
|
2.57 ± 0.92
|
|
After
|
2.25 ± 1.75#*
|
3.00 ± 1.41#*
|
3.11 ± 0.99#*
|
2.07 ± 0.90#*
|
CK
|
Before
|
4.18 ± 1.70
|
3.75 ± 1.32
|
3.75 ± 1.32
|
2.71 ± 0.97
|
|
After
|
3.64 ± 1.25#
|
3.21 ± 1.13#
|
3.21 ± 1.13#
|
2.43 ± 0.96#
|
“#: P < 0.05 vs. Before; *: P < 0.05 vs. CK”
Transcriptomic variations in the peripheral blood of patients with SS
To investigate the detailed biological processes involved in SS-ILD pathogenesis and identify potential clinical index, we analyzed in-depth transcriptome profiles from many patients with SS. Transcriptome profiles of peripheral blood from 36 patients with severe SS were collected in the experimental group (SS; GSE66795), while profiles of 29 healthy individuals were used as controls (HT). After a data processing series, the P < 0.05 and Log2Foldchange were set as the threshold to identify DEGs. We obtained 919 genes that matched the threshold as DEGs by comparing gene expression profiles of the peripheral blood from patients with SS with those from normal healthy controls (Fig. 1A). Among all DEGs, 456 genes were upregulated in SS vs.HT, while the expression levels of 463 genes were downregulated in the peripheral blood of patients with SS (Fig. 1A; Table S1).
To further investigate the biological processes associated with the pathogenesis of SS, we performed GO enrichment analysis on these 919 DEGs in SS vs. HT comparison. The GO enrichment results showed that 8540 GO terms were identified against these DEGs, with 116 significant GO terms among the three main groups, including biological process, molecular function, and cellular component (FDR < 0.05; Table S2). Ten important terms relevant to molecular function were structural constituent of ribosome, protein binding, hydrolase activity, nucleoside monophosphate kinase activity, binding, hemoglobin binding, and chemokine receptor binding (Fig. 1C; Table S2). Among cellular component categories, 33 terms were significantly altered in the peripheral blood of patients with severe SS, suggesting that these DEGs mainly functioned at the cytosolic ribosome, ribosome, cytoplasm, cytosol, intracellular, ribonucleoprotein complex, focal adhesion, cell-substrate adherens junction, hemoglobin complex, cell-substrate junction, extracellular organelle, extracellular vesicle, anchoring junction, and platelet alpha granules (Fig. 1B; Table S2). We noted that focal adhesion and cell-substrate adherens junction were enriched in SS vs. HT comparison, demonstrating its importance in SS pathogenesis (Fig. 1B; Table S2). For biological processes, 71 categories were significantly identified, especially genes involved in myeloid cell and erythrocyte homeostasis, immune system process, hemopoiesis, immune system development, hematopoietic or lymphoid organ development response to cytokine (Fig. 1D; Table S2). These results suggested that variations in genes relevant to the immune system, cytokines, and focal adhesion may be involved in the pathogenesis of SS.
Possible improvement in SS-ILD after Sanliangsan treatment by suppressing MUC1 essential in the SS pathogenesis
Two transcriptome profiles (GSE40611 and GSE23117) of the parotid and salivary glands from patients with SS were collected to identify the potential SS pathogenesis targets. In total, 3169 and 6025 genes were identified as DEGs in the parotid and salivary glands of patients with SS, respectively (Fig. 2A, B). We noted that 22 DEGs were shared in these three transcriptome profiles (Fig. 2C). Typically, 11 genes exhibited the same expression patterns in these three tissues, including RUFY3, CEPT1, TTC21A, BAG6, SLC24A4, KIAA1324, APP, GAA, CD86, GAS2L1, and MUC1 (Fig. 2D; Table S1). Further protein-protein interaction network showed that MUC1 and APP might be the potential hub genes involved in SS-ILD pathogenesis (Fig. 2D). Most of the genes involved in the interaction network of MUC1 were DEGs in the peripheral blood, parotid, and salivary glands of patients with SS (Fig. 2D; Table S1). RT-qPCR results showed that the expression level of MUC1 was significantly higher in the serum of patients with SS-ILD than that in healthy individuals; however, patients with SS-ILD did not exhibit significant differences in the expression level of APP (Fig. 2E). Thus, we proposed that MUC1 was the potential hub gene involved in the pathogenesis of SS-ILD and could be used as a clinical indicator for SS-ILD detection. We further determined the protein and mRNA levels of MUC1 in the serum of patients with SS-ILD before and after treatment of SP using ELISA and RT-qPCR, respectively. The results showed that the expression level of MUC1 was significantly suppressed in both CK and SP treatment groups. SP treatment could suppress MUC1 expression more effectively than CK treatment (Fig. 2F). ELISA results reached a consensus with RT-qPCR results and showed no significant difference in MUC1 levels among patients before CK and SP treatments (1098.57 ± 194.2 and 1044.64 ± 137.62 pg/mL; t = 0.775, P = 0.454; Fig. 2G). Remarkably, the MUC1 level was significantly lower in the serum of patients with SS-ILD after SP treatment (724.29 ± 72.77) than in patients before SP treatment (Fig. 2G). Moreover, the inhibitory effect of SP treatment on the MUC1 level was more significant than that of CK treatment. The MUC1 level in SS-ILD serum after CK treatment was 846.79 ± 134.13 pg/mL (Fig. 2G). Together, these lines of evidence suggest that the MUC1 level contributed to SS-ILD pathogenesis, and SP treatment could suppress the MUC1 to improve SS-ILD more effectively than CK treatment.
Sanliangsan treatment suppressed genes associated with MUC1 for potentially improving SS-ILD
Pathway enrichment analysis was performed on the DEGs from transcriptome profiles of the peripheral blood of patients with SS based on the KEGG database. These 919 DEGs were enriched in 281 pathways, with 18 significant pathways (Fig. 3A; P < 0.05). We noted various pathways relevant to the immune system and signal transduction were enriched in the peripheral blood of patients with SS, including cytokine-cytokine receptor interaction and signaling pathways of IL-17, NOD-like receptor, JAK-STAT, toll-like receptor, chemokine, and TNF (Fig. 3A). Additionally, numerous pathways relevant to cell growth and death were also active in the peripheral blood of patients with SS, including apoptosis, cell cycle, p53 signaling pathway, cellular senescence, ferroptosis, and necroptosis (Fig. 3A). These results suggested the involvement of these related pathways in SS pathogenesis. Considering the involvement of MUC1 in SS pathogenesis, we further constructed the interaction network of MUC1 and genes involved in these pathways (Fig. 3B). The topological network displayed complex regulation relationships among these genes; especially MUC1 performed important roles in the network (Fig. 3B). Additionally, eight genes that interacted with MUC1 were identified in the network, including CD58, CD86, CTLA4, CXCL8, HSP90AA1, PDGFRB, SIGLEC1, and STAT1 (Fig. 3B). Numerous studies have shown that the upregulation of these genes is involved in the pathogenesis of SS [7,8,9,14,]. Importantly, SP therapy effectively suppressed the upregulation of most of these genes in the serum of patients with SS-ILD, including CD58, CD86, CTLA4, CXCL8, SIGLEC1, and STAT1 (Fig. 3C), which may further result in the dysregulation of MUC1 and improve SS-ILD.
Network pharmacology identified the bioactive compounds of SP that improved SS-ILD
To investigate the bioactive compounds of SP in improving SS-ILD, we detected the metabolic compositions of SP using LC-MS. Totally, 556 metabolites were identified in SP (Table S3). These metabolites mainly belonged to Isoprenoids, Prenol lipids, Hydrocarbons, Fatty Acids and Conjugates, Benzoic acids, Flavonoids, Cinnamic acids, Linear diarylheptanoids, Fatty alcohols and Carbonyl compounds (Fig. 4A). Then, we performed network pharmacology analysis on these 556 metabolites using TCMSP online tools (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform) to identify the bioactive compounds of SP in improving SS-ILD. The TCMSP results identified 45 bioactive metabolites in SP, including eugenol, Terpineol, linalool, 1,2-Dihydrocurcumin, Curcumin, Sitogluside, vanillic acid, β-selinene, 1,8-cineole, copaene, farnesol, oleic acid, protocatechuic acid, rutin, kaempferol, quercetin and coumarin (Table S4). Totally, 600 proteins were identified as candidate targets of these 556 compounds in SP (Table S4). Then, we analyzed the function of these targeted proteins using GO and KEGG enrichment algorithm. Go enrichment analysis on these 600 targets showed that these targets mainly performed their functions in Extracellular space, Cytoplasm, Extracellular region, Vesicle, Extracellular exosome, Extracellular vesicle, Intracellular organelle lumen, Plasma membrane region, Secretory granule and Membrane raft (Table S5). These targets exhibited activities of identical protein binding, catalytic activity, enzyme binding, ion binding, small molecule binding, anion binding, oxidoreductase activity and vitamin binding (Table S5). Response to oxygen compound, Response to chemical, Response to nitrogen compound, Small molecule metabolic process, Regulation of multicellular organismal process, Response to lipid, Positive regulation of cellular process and Metabolic process (Table S5). KEGG pathway enrichment analysis on these 600 targets showed the significant enrichment of Pathways in cancer, Metabolic pathways, PI3K-Akt signaling pathway, HIF-1 signaling pathway, MicroRNAs in cancer, TNF signaling pathway, IL-17 signaling pathway and MAPK signaling pathway (Fig. 4B). Further disease enrichment analysis showed that these targets involved in the various diseases, including Breast cancer, Cancer, viral infections, Ulcerative colitis and especially Sjogren's syndrome (Table S4), supporting the involvement of SP in affecting SS-ILD. Typically, numerous pathways relevant to MUC1 were identified as targets of compounds from SP, implying that SP could improve SS-ILD via targeting MUC1. Finally, we integrated these results in the topological network to identified the hub bioactive compounds of SP in improving SS-ILD. The results showed that these targets formed a complex network, and eugenol was the hub compound that involved in the mechanisms of SP in improving UC (Fig. 4C). Importantly, compouds protocatechuic acid, eugenol, rutin, kaempferol, salicylic acid, kaempferol, acetic acid, rutin, kaempferol, coumarin and quercetin targeted the proteins (Arachidonate 5-lipoxygenase and Thioredoxin reductase, cytoplasmic) that involved in the pathogenesis of SS-ILD, of note eugenol also targeted MUC1 in the network (Fig. 4C). Further molecular docking showed that eugenol could inserted into the hydrophobic pocket of MUC1 with a credible score (Fig. 4D). Moreover, eugenol effectively attenuated the abnormal upregulation of CD86, IL6, IL10, CXCL8 and MUC1 in the model mouse with Sjogren's syndrome (Fig. 4E). Overall, our Network pharmacology analysis about SP suggested that eugenol was the bioactive compounds of SP in improving SS-ILD via targeting MUC1.
Metabolic landscape in serum associated with SS-ILD before and after SP treatment
Our previous results showed that SS-ILD and MUC1 were associated with numerous metabolisms. Here, we further investigate the metabolic changes in SS-ILD individuals before and after SP treatment. Totally, five biological replicates of serum from each group (SS-ILD and SP) were collected for metabolomics, and normal individuals were used as control (Nor) (Fig. 5A). Principal component analysis (PCA) was performed on all metabolic profiles to global metabolic variations of rats from each group. The two-dimensional results showed that the samples observably distributed into four regions according to their metabolic compositions (Fig. 5A). The principal components 1 and 2 explained 24.6% and 16.5%, and most variations were generated between SS-ILD vs. Nor and SP vs. SS-ILD comparisons (Fig. 5A). PCA results suggested that SS-ILD pathogenesis was closely associated with metabolic changes, whereas SP improved the abnormal metabolic changes in serum with SS-ILD (Fig. 5A). Then, we used P < 0.05 and foldchange > 2.0 to identified differentially expressed metabolites (DEMs). Totally, 30 (21 downregulated and 9 upregulated) and 43 (20 downregulated and 23 upregulated) DEMs were identified in SS-ILD vs. Nor and SP vs. SS-ILD comparisons, respectively (Fig. 5B). OPLSDA analysis on SS-ILD vs. Nor comparison showed that top 10 metabolites with VIP > 1.0 were Inosine, Pyrrole-2-carboxylic acid, Uridine, Arborinine, Hypoxanthine, Inosine, PC(18:3(6Z,9Z,12Z)/15:0), PC(20:3(8Z,11Z,14Z)/14:0), PE(P-18:1(11Z)/18:4(6Z,9Z,12Z,15Z)) and 3-Hydroxycapric acid, thus these metabolites could be used as clinical indicators for SS-ILD diagnosis. Further pathway enrichment analysis on 30 DEMs from SS-ILD vs. Nor showed that these metabolites involved in 7 pathways, including Glycerophospholipid metabolism, Purine metabolism, Glycosylphosphatidylinositol (GPI)-anchor biosynthesis, Pentose phosphate pathway, Biosynthesis of unsaturated fatty acids, Arginine and proline metabolism and Pyrimidine metabolism (Fig. 5C). Similarly, DEMs from SP vs. SS-ILD also involved in these pathways that 5 related pathways from SS-ILD vs. Nor were also enriched in SP vs. SS-ILD (Fig. 5C, D, E), suggesting that SP could affected the abnormal metabolisms associated with SS-ILD pathogenesis. Among these DEMs from both SS-ILD vs. Nor and SP vs. SS-ILD comparisons, we found that the SP performed opposite effect on their levels compared to SS-ILD. The metabolites that triggered to increase in SS-ILD were decrease by SP treatment, whereas most metabolites that decreased in SS-ILD were increased by SP treatment in vivo (Fig. 5F). Taken together, these results suggested that SP could partially restore the abnormal metabolisms associated with SS-ILD pathogenesis.