Integrating HPLC-Q-TOF-MS/MS, network pharmacology and experimental validation to decipher the chemical substances and mechanism of modified Gui-shao-liu-jun-zi decoction against gastric cancer

Background and aim

Gastric cancer (GC) is a common malignant tumor worldwide. Modified Gui-shao-liu-jun-zi decoction (mGSLJZ) is a clinically effective traditional Chinese medicine (TCM) compound in GC treatment. This study aimed to analyze main chemical substances of mGSLJZ and investigate active ingredients and molecular mechanism of mGSLJZ against GC.

Experimental procedure

HPLC-Q-TOF-MS/MS was used to analyze chemical substances of mGSLJZ, and potential active ingredients were screened from TCMSP. The target set of mGSLJZ for GC was obtained based on SwissTargetPrediction. The PPI network was constructed to screen out core targets. GO and KEGG enrichment analyses were conducted to identify BPs, CCs, MFs and pathways. The "active ingredient-core target-pathway" regulatory network was constructed to obtain core substances. Subsequently, Oncomine, Proteinatlas and molecular docking were performed to validate these findings. The cell experiments were conducted to confirm the anti-GC effects of mGLSJZ.

Results and conclusion

Forty-one potential active ingredients were filtered out from 120 chemical substances in mGSLJZ, including various organic acids and flavonoids. The top 10 key targets, 20 related pathways and 6 core medicinal substances were obtained based on network pharmacology analysis. Molecular docking results indicated that the core substances and key targets had good binding activities. The cell experiments validated that mGSLJZ and the core substances inhibited the proliferation in multiple GC cells and that mGLSJZ restrained the migration of GC. Meanwhile, the top 5 targets and top 2 pathways were verified. The rescue experiments demonstrated that mGSLJZ suppressed the proliferation and migration of GC through the PI3K/AKT/HIF-1 pathway.

Dual Atomic Coherence in the Self-Assembly of Patchy Heterostructural Nanocrystals

Advances in the synthesis and self-assembly of nanocrystals have enabled researchers to create a plethora of different nanoparticle superlattices. But while many superlattices with complex types of translational order have been realized, rotational order of nanoparticle building blocks within the lattice is more difficult to achieve. Self-assembled superstructures with atomically coherent nanocrystal lattices, which are desirable due to their exceptional electronic and optical properties, have been fabricated only for a few selected systems. Here, we combine experiments with molecular dynamics (MD) simulations to study the self-assembly of heterostructural nanocrystals (HNCs), consisting of a near-spherical quantum dot (QD) host decorated with a small number of epitaxially grown gold nanocrystal (Au NC) "patches". Self-assembly of these HNCs results in face-centered-cubic (fcc) superlattices with well-defined orientational relationships between the atomic lattices of both QD hosts and Au patches. MD simulations indicate that the observed dual atomic coherence is linked to the number, size, and relative positions of gold patches. This study provides a strategy for the design and fabrication of NC superlattices with large structural complexity and delicate orientational order.

The inhibitory effect and mechanism of Yi-qi-hua-yu-jie-du decoction on the drug resistance of gastric cancer stem cells based on ABC transporters

Background

The drug resistance of tumor stem cells is an obstacle in gastric cancer (GC) treatment and the high expression of ABC transporters is a classic reason for drug resistance. This study aimed to construct a reliable GC drug-resistant stem cell model and explore the inhibitory effect and mechanism of Yi-qi-hua-yu-jie-du medicated serum (YQHY) on the drug resistance of GC stem cells based on ABC transporters.

Methods

The tumor stemness biomarker CD44 was primary identification from WGCNA. The magnetic-activated cell sorting (MACS) method was used to separate CD44( +)BGC823/5-Fu (BGC823/5–Fu-CSCs) cells and the stemness characteristics were verified from multiple dimensions. Then, the drug resistance index and expression of ABC transporter genes MDR1 and MRP1 were detected in CD44(−)/CD44(+) cells. The inhibition and apoptosis rates of the cells administrated with YQHY or/and 5-Fu were calculated to confirm that YQHY can suppress the drug resistance of BGC823/5-Fu-CSCs. Afterwards, the effects of YQHY on the expression of MDR1 and MRP1 and the activation of the PI3K/Akt/Nrf2 pathway were observed. Finally, under the administration of IGF-1 (the activator of PI3K/Akt pathway) and Nrf2 siRNA, the mechanism of YQHY on reversing the drug resistance of BGC823/5–Fu-CSCs through inhibiting the expression of MDR1 and MRP1 via PI3K/Akt/Nrf2 was verified.

Results

CD44 was a reliable GC stemness biomarker and can be applied to construct the drug-resistant GC stem cell model CD44(+)BGC823/5-Fu. The growth rate, cell proliferation index, soft agar colony formation, expression of stemness specific genes and tumorigenesis ability of CD44(+)BGC823/5-Fu cells were significantly higher than those of CD44(−)BGC823/5-Fu cells. BGC823/5–Fu-CSCs exhibited strong drug resistance to 5-Fu and high expression of ABC transporter genes MDR1 and MRP1 compared to CD44(-) cells. YQHY increased the inhibition and apoptosis rates to efficiently inhibit the drug resistance of BGC823/5–Fu-CSCs. Meanwhile, it suppressed the expression of MDR1 and MRP1 and restrained the activation of PI3K/Akt/Nrf2 signaling pathway. Finally, it was found that IGF-1 partially restored the activation of PI3K/Akt/Nrf2 pathway, alleviated the inhibition of MDR1 and MRP1, blocked the proliferation-inhibitory and apoptosis-promotion effects. YQHY and si-Nrf2 synergistically suppressed the MDR1/MRP1 expression and the drug resistance of BGC823/5–Fu-CSCs.

Conclusions

CD44 was a reliable GC stemness biomarker, and the high expression of ABC transporter genes MDR1 and MRP1 was an important feature of drug-resistant stem cells. YQHY inhibited the MDR1 and MRP1 expression via PI3K/Akt/Nrf2 pathway, thus reversing the drug resistance of BGC823/5–Fu-CSCs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13020-022-00647-y.

Utilisation of village clinics in Southwest China: evidence from Yunnan Province

INTRODUCTION

Primary healthcare in rural China is underutilised, especially in village clinics in Southwest China. The aim of this study was to explore any relationships among the ethnicity of the healthcare provider, the clinical competence of the healthcare provider, and the utilisation of village clinics in Southwest China.

METHODS

This cross-sectional survey study involved 330 village healthcare providers from three prefectures in Yunnan Province in 2017. Multiple logistic regressions were adopted to investigate the utilisation of primary healthcare among different ethnic healthcare providers.

RESULTS

Primary healthcare utilisation was higher in village clinics where healthcare providers were Han Chinese than those where healthcare providers were ethnic minority (151 vs 101, P=0.008). The logistic regression analysis showed that clinical competence was positively associated with the utilisation of primary healthcare (odds ratio [OR]=1.49, 95% confidence interval [CI]=1.12-2.00; P=0.007) and that inadequate clinical competence of ethnic minority health workers may lead to a lag in the utilisation of primary healthcare (OR=0.45, 95% CI=0.23-0.89; P=0.022).

CONCLUSION

Our results confirm differences in the utilisation of primary healthcare in rural Yunnan Province among healthcare providers of different ethnicities. Appropriate enhancements of clinical competence could be conducive to improving the utilisation of primary healthcare, especially among ethnic minority healthcare providers.

Effect of wastewater treatment plant discharge on the bacterial community in a receiving river

The effluent of wastewater treatment plants (WWTPs) is an important water resource for some rivers in regions with relatively low precipitation, which may pose ecological risks. Various pollutants and microorganisms are discharged into rivers, along with the WWTP effluent, but this process has not been thoroughly studied. The objective of this study was to evaluate the effect of WWTP effluent on the bacterial community in the sediment and water column of an urban river and to identify the relationship between the total and active bacterial communities. Five sites were sampled in the river, including the most upstream site of the river (Up-most), 200 m upstream of the WWTP (Up-200), at the point of effluent discharge of the WWTP (Eff-pl) and 50 m (Down-50) and 1000 m (Down-1000) downstream of the WWTP. Compared with the two upstream sites (Up-most and Up-200), the bacterial species composition of Eff-pl was significantly different (p < 0.05) in both the sediment and water columns, while the bacterial species composition at Down-1000 was significantly different (p < 0.05) in the sediment but not in the water. The relative abundance of Proteobacteria, Actinobacteriota and Verrucomicrobiota was significantly different (p < 0.05) at Eff-pl in both the sediment and water columns compared with that at the upstream sites. The shared bacterial species between the DNA and RNA 16 S rRNA analyses were only 45.5-62.2% and 43.2-52.3% for the sediment and water, respectively. Accordingly, WWTP effluent drainage significantly alters (p < 0.05) the bacterial composition in the receiving river but can be recovered in water within a short distance. However, in sediment, a longer recovery space is probably needed. Analyses of the combination of total and active bacterial compositions are recommended to evaluate the ecological consequences of WWTP effluent drainage on the bacterial composition.

[Screening and preliminary validation of biomarkers in sputum-negative pulmonary tuberculosis based on positron emission tomography/computed tomography and transcriptomics]

Objective: To screen and perform preliminary clinical validation of biomarkers of activity based on positron emission tomography/computed tomography (PET-CT) and transcriptomics in sputum-negative pulmonary tuberculosis lesion tissue. Methods: Nine patients with sputum-negative pulmonary tuberculosis treated surgically at the Shanghai Public Health Clinical Center for Thoracic Surgery from January 1, 2017 to December 31, 2019 were retrospectively collected as the discovery group, including four males and five females, aged 20-57 years (mean 36 years). All of the patients underwent PET-CT scanning before surgery, and the resected specimens were postoperatively classified according to preoperative PET-CT. The resected specimens were divided into areas with increased fluorodeoxyglucose (FDG) metabolism (SUVmax>3) and areas with normal FDG metabolism (SUVmax ≤ 3) according to the preoperative PET-CT performance. After sample processing, total RNA was extracted from the tissues of different regions, and then whole gene transcriptome sequencing was performed. Bioinformatics analysis of the two sets of data was performed to discover the expression profiles of the differences in whole gene transcriptome data between the two regions and to screen for candidate biomarkers. Eighty patients with sputum-negative pulmonary tuberculosis admitted to Shanghai Public Health Clinical Center from January 1, 2019 to January 1, 2021 were retrospectively collected as the validation group, including 37 males and 43 females, aged 20-62 years, with an average age of 39 years. The validation group was divided into a group with increased SUV (n=40) and a group without lesions on CT imaging (n=40). Enzyme-linked immunosorbent assay (ELISA) was used to determine the protein levels of candidate biomarkers in the peripheral plasma of patients. The effect of biomarkers was assessed using subject operating characteristic (ROC) curves. Student's t-test was used to determine whether the difference in protein levels between the two groups was statistically significant. Results: Bioinformatics analysis revealed that the expression levels of C1QB, CCL19, CCL5 and HLA-DMB correlated with the metabolic activity of sputum-negative tuberculosis lesion tissue. Further screening and validation by the validation group confirmed that the difference in C1QB protein levels in the peripheral plasma of patients was statistically significant between the group with increased SUV and the group without lesions on CT imaging [(3.55±0.34) mg/L vs. (2.75±0.21) mg/L, t=4.12, P<0.001]. And the ROC curve showed that the area under the curve for C1QB protein levels was 0.731, which had potential clinical value. Conclusion: The C1QB protein level can be used to assess the activity of lesions in patients with sputum-negative tuberculosis and is a potential biomarker.

Network pharmacology and experimental verification based research into the effect and mechanism of Aucklandiae Radix-Amomi Fructus against gastric cancer

To investigate the mechanism of the Aucklandiae Radix–Amomi Fructus (AR–AF) herb pair in treating gastric cancer (GC) by using network pharmacology and experimental verification. Using the traditional Chinese medicine system pharmacology database and analysis platform (TCMSP), the major active components and their corresponding targets were estimated and screened out. Using Cytoscape 3.7.2 software, a visual network was established using the active components of AR–AF and the targets of GC. Based on STRING online database, the protein interaction network of vital targets was built and analyzed. With the Database for Annotation, Visualization, and Integrated Discovery (DAVID) server, the gene ontology (GO) biological processes and the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways of the target enrichment were performed. AutoDock Vina was used to perform molecular docking and calculate the binding affinity. The mRNA and protein expression levels of the hub targets were analyzed by the Oncomine, GEPIA, HPA databases and TIMER online tool, and the predicted targets were verified by qRT–PCR in vitro. Eremanthin, cynaropicrin, and aceteugenol were identified as vital active compounds, and AKT1, MAPK3, IL6, MAPK1, as well as EGFR were considered as the major targets. These targets exerted therapeutic effects on GC by regulating the cAMP signaling pathway, and PI3K-Akt signaling pathway. Molecular docking revealed that these active compounds and targets showed good binding interactions. The validation in different databases showed that most of the results were consistent with this paper. The experimental results confirmed that eremanthin could inhibit the proliferation of AGS by reducing the mRNA expression of hub targets. As predicted by network pharmacology and validated by the experimental results, AR–AF exerts antitumor effects through multiple components, targets, and pathways, thereby providing novel ideas and clues for the development of preparations and the treatment of GC.

POS0811 CHARACTERISTICS OF INTESTINAL MICROBIOTA AND ITS RELATIONSHIP WITH LYMPHOCYTE SUBSETS AND CYTOKINES IN PATIENTS WITH VASCULITIS

Background

Vasculitis include a group of autoimmune inflammatory diseases with clinical heterogeneous characterized by inflammation of vascular wall, inflammation of perivascular tissues, and cell-like necrosis[1]. Disorder of gut microbiota, which plays a crucial role in regulating immune cells such as Th1, Th17 and Treg, is associated with other autoimmune diseases[2], and may also be involved in the pathogenesis of vasculitis.

Objectives

To investigate the changes of intestinal microbiota and its correlation with peripheral lymphocyte subsets and inflammatory factors levels in patients with vasculitis.

Methods

Combined with clinical manifestations and laboratory examination, 33 patients with vasculitis who met the 2012 revised International Chapel Hill Consensus Conference Nomenclature of Vasculitides[3] and 33 of age- and gender- matched healthy controls (HCs) were selected from the Second Hospital of Shanxi Medical University. The demographic characteristics, general laboratory indicators such as erythrocyte sedimentation rate (ESR), C-reaction protein (CRP), levels of peripheral lymphocyte subpopulations and serum cytokines detected by modified flow cytometry. Fecal microbiota detected by 16S rRNA gene sequencing and compiled and processed using Qiime2 and OTU-profiling tables were collected and analyzed in this study.

Results

Compared with HCs, the richness and diversity of intestinal flora in patients with vasculitis tended to decrease with a statistically significant difference in β diversity (P = 0.025, Figure 1 A and B). More specifically, vasculitis patients had a lower frequency of Firmicutes while higher Proteobacteria and Bacteroidota at the phylum level (P < 0.001, Figure 1C). In vasculitis patients, the relative abundances of 23 bacteria differed from HCs at the genus level was all decreased, including Gemella, Anaeroglobus, Campylobacter, Fournierella, et al (P < 0.001, Figure 1D and E). More importantly, the relative abundance of Muribaculaceae were positively correlated with the absolute count of Th2 and the proportions of Th1 and CD4+T cells and negatively correlated with CRP and ESR, while relative abundance of [Eubacterium]_ventriosum were positively associated with the absolute number of Treg cells and negatively correlated with the percentages of Th2 and CD8+T cells (Figure 1F).

Figure 1.

Differences in α diversity (A), β diversity (B), phylum (C), genus (D), and microbial composition (E) between vasculitis patients and HC and correlation analysis between differential microflora and clinical data in patients with vasculitis (F).

Conclusion

Disturbance of intestinal flora, mainly manifested by decreased diversity and richness, may be involved in the occurrence and development of vasculitis by inducing disroders in lymphocyte subsets and cytokines. Consequently, further studies on the immune mechanisms and influencing factors of intestinal flora may provide new ideas for the diagnosis and treatment of the disease for vasculitis patients.

References

[1]Aierken X, Zhu Q, Wu T, et al. Increased Urinary CD163 Levels in Systemic Vasculitis with Renal Involvement[J]. Biomed Res Int, 2021, 2021: 6637235. DOI: 10.1155/2021/6637235.

[2]Zhang X, Zhang D, Jia H, et al. The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment[J]. Nat Med, 2015, 21(8): 895-905. DOI: 10.1038/nm.3914.

[3]Jennette JC, Falk RJ, Bacon PA, et al. 2012 revised International Chapel Hill Consensus Conference Nomenclature of Vasculitides[J]. Arthritis Rheum, 2013, 65(1): 1-11. DOI: 10.1002/art.37715.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No.82001740).

Disclosure of Interests

None declared

AB0202 GUT MICROBIOTA DYSBIOSIS WERE CLOSELY CORRELATED WITH LYMPHOCYTE SUBSETS AND CYTOKINES IN PATIENTS WITH INFLAMMATORY ARTHRITIS

Background

Inflammatory arthritis includes a group of chronic conditions, particularly rheumatoid arthritis (RA), ankylosing spondylitis (AS) and psoriatic arthritis (PsA)[1].Growing evidences link gut microbiota dysbiosis with the development of inflammatory arthritis[2].

Objectives

The aim of this study was to discover the characters of microbiota in inflammatory arthritis patients and compare the relationship between the microbiota and peripheral lymphocyte subsets and cytokines.

Methods

Fecal samples were collected from 73 arthritis patients (13 PsA, 30 AS, 30 RA patients) and 140 sex- and age-matched healthy controls (HCs). The gut microbiota was studied by sequencing the V3-V4 variable regions of bacterial 16S rRNA genes by the Illumina Miseq PE300 system. Peripheral lymphocyte subsets in these participants were assessed by flow cytometry. Measures of disease activity such as erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) were recorded. Alpha and Beta diversity was assessed using results from QIIME2 and gut microbiome profiles were compared using linear discriminant analysis (LDA) effect size (LEfSe). R (version 4.0.1) was used for comparative statistics, using pearson correlation analysis to assess the correlation between the relative abundance of genus in the sample and clinical parameters.

Results

Compared with HCs, the richness of gut microbiota (ACE and Chao 1) was significantly lower (p < 0.05) in arthritis patients, and bacterial diversity including Shannon and Simpson indices (p < 0.001) was also significant in arthritis decreased (Figure 1A). β-diversity analysis based on Bray-curtis distance revealed significant differences in microbial communities between arthritis and HCs (Figure 1B, r=0.098, p=0.001, ANOSIM). In addition, compared with HCs at the genus level, 9 bacterial groups were significantly different in PsA (p < 0.05), 19 bacterial groups in AS (p < 0.05), and 17 bacterial groups in RA(p < 0.05) (Figure 1C). There was a significant positive correlation between CD4+T and Prevotella(p<0.01), T and Prevotella(p<0.05), Blautia(p<0.05) as well as Megamonas(p<0.05), Th17 and Prevotella(p<0.01), CD8+T and Megamonas(p<0.01), Th1 and Megamonas(p<0.05), Prevotella(p<0.01),Coprococcus(p<0.05), B and Erysipelotricbaceae_UCG-003(p<0.01), and Erysipelotricbaceae_UCG-003(p<0.01), Anaerostipes(p<0.01), CRP and Fusobacterium(p<0.05) as well as Roseburia(p<0.05). There were negative correlations between T and Erysipelotricbaceae_UCG-003 (p<0.05),CD8+T and Fusobacterium(p<0.01), CD4+T and Fusobacterium(p<0.05), NK and Fusicatenibacter(p<0.05).

Conclusion

The gut microbiota of patients with inflammatory arthritis differs from HC and also varies among individual arthritis, which was closely related to lymphocyte subsets.

References

[1]Wu X. Innate Lymphocytes in Inflammatory Arthritis[J]. Front Immunol, 2020, 11: 565275.DOI: 10.3389/fimmu.2020.565275

[2]Breban M. Gut microbiota and inflammatory joint diseases[J]. Joint Bone Spine, 2016, 83(6): 645-649.DOI: 10.1016/j.jbspin.2016.04.005

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 82001740).

Disclosure of Interests

None declared

POS0745 GUT DYSBIOSIS ASSOCIATED WITH PERIPHERAL LYMPHOCYTES AND CYTOKINES IN PATIENTS WITH SJÖGREN’S SYNDROME

Background

Primary Sjögren’s syndrome (pSS) is a systemic autoimmune disease characterized by disorders of lymphocyte subpopulations with various cytokines and auto-antibodies1. Growing evidences suggest that gut microbiome dysbiosis may contribute to the development of pSS2.

Objectives

To investigate the alterations to the gut microbiome and the correlation with peripheral lymphocytes and serum cytokines as well as inflammatory factors in pSS patients.

Methods

A total of 101 pSS patients and 101 age- and sex- matched healthy controls (HCs) were enrolled in this study from The Second Hospital of Shanxi Medical University (Taiyuan, Shanxi, China). Patients fulfilled the 2019 ACR/EULAR classification criteria. We conducted 16S rRNA gene sequencing using fecal microbiota samples and analyzed the peripheral lymphocyte subsets by flow cytometry. Serum cytokines, erythrocyte sedimentation rate (ESR), C-reaction protein (CRP), unstimulated and stimulated whole saliva (UWS and SWS) secretion rate was also collected, respectively. Sequence data were compiled and processed using Qiime2 and OTU-profiling tables were constructed. Correlations between different taxa and gut microbiome, as well as clinical variables, were calculated by Spearman’s rank test.

Results

Patients with pSS exhibited a significant reduction in the richness and diversity of gut microbiota compared with those of HCs (Figure 1A-B, p < 0.05). Detailly, at the phylum level, pSS patients had a lower frequency of Firmicutes while higher Proteobacteria (Figure 1C, p < 0.05). Compared with HCs, 11 species of flora were discovered to be distinctly different at the genus level (p < 0.05). Patients presented fewer Faecalibacterium and Roseburia but more Lactobacillus (Figure 1D, p < 0.05). Lactobacillus negatively correlated with T cells (r=-0.407), CD8+T (r=-0.417) and Th2 (r=-0.323). There was a significant positive correlation between Faecalibacterium and IL-2(r=0.312), IFN-γ(r=0.338), TNF-α levels(r=0.322) (Figure 1E, p < 0.05). As for clinical disease measures, IL-6 increases were in line with ESR and CRP, while IL-2 levels inversely related to CRP. Additional UWS secretion rate and SWS secretion rate had negative correlation with ESR (Figure 1F, p < 0.05).

Conclusion

The structural disorder of gut microbiota was distinct in pSS which were associated with peripheral lymphocyte subsets and cytokines. Disorders of gut microbiota and immune systems may contribute to the occurrence and development of pSS.

References

[1]Mariette X, Criswell LA. Primary Sjogren’s Syndrome. N Engl J Med 2018;378(10):931-39. doi: 10.1056/NEJMcp1702514

[2]Trujillo-Vargas CM, Schaefer L, Alam J, et al. The gut-eye-lacrimal gland-microbiome axis in Sjogren Syndrome. Ocul Surf 2020;18(2):335-44. doi: 10.1016/j.jtos.2019.10.006

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 82001740).

Disclosure of Interests

None declared

POS0330 DIFFERENCES IN GUT MICROBIOTA ASSOCIATED WITH LYMPHOCYTE SUBSETS, CYTOKINES AND DISEASE ACTIVITY IN ANKYLOSING SPONDYLITIS

Background

Ankylosing spondylitis (AS), a common chronic inflammatory disease, is a prototype of spondyloarthritis affecting sacroiliac joints and spine with or without peripheral arthritis and other systemic symptoms[1]. Environmental factors, especially microorganisms have been suggested to implicate with AS pathogenesis[2].

Objectives

Utilizing 16S rRNA genes sequencing on the feces of untreated AS patients and healthy controls (HCs), our study aimed to provide an in-depth understanding of AS gut microbiota and identifying a feasible diagnostic strategy for AS.

Methods

Fecal samples were collected from 62 AS patients and 62 age-and-gender- matched HCs. Microbial genome was extracted from approximately 250mg fresh fecal samples from all participants using QIAamp PowerFecal DNA Kit (Qiagen). The V3-V4 variable regions of bacterial 16S rRNA genes were sequenced with the Illumina Miseq PE300 system. QIIME2 based pipeline was used to process the raw sequence data. Alpha and beta diversities were assessed using result from QIIME2, and comparisons of gut microbiome profile were performed using linear discriminant analysis (LDA) effect size (LEfSe) to examine differences between AS and HCs. R (version 4. 0.1) was used for comparative statistics, and pearson’s correlation was used to assess the correlations between the relative abundances of bacterial genera and clinical parameters; correlations with p<0.05 were considered significant.

Results

AS for alpha-diversity, ACE and Chao1 indices were lower in AS compared with those HCs(Figure 1A, p<0.05), though no significant differences observed in Shannon and Simpson index. Bray curtis distance-based beta-diversity analysis revealed significant differences in the microbial community between AS and HCs (Figure 1B, p=0.003, ANOSIM). Fecal microbial communities in AS differed significantly from those in HCs, driven by higher abundances of Escherichia-Shigella, Turicibacter, Enterococcus, et al. and a lower abundance of Agathobacter, Roseburia, Eubacterium_eligens_group, et al (Figure 1C, p<0.05). There was a significant positive correlation between ESR and Klebsiella, Butyricicoccus, Roseburia, CRP and Faecalibacterium, Muribaculaceae, ASDAS-CRP score and Faecalibacterium, Ruminococcus, total lymphocyte cells and Agathobacter, Ruminococcus, T cell and Agathobacter, CD4+T cell and Agathobacter, B cell and Agathobacter, Streptococcus, Th1 and Prevotella, CAG−352, Th2 and Agathobacter, Th17 and Prevotella, Agathobacter, IL-2 and Agathobacter, IL-4 and Agathobacter, IL-6 and Lachnospiraceae_UCG−004, Muribaculaceae, IL-17 and Eubacterium_hallii_group, IFN-gama and Phascolarctobacterium.There were negative correlations between total lymphocytes and Escherichia−Shigella, CD4+T cell and Enterobacteriaceae, Th2 cell and Escherichia−Shigella, IL-10 and CAG−352, Ruminococcus (Figure 2, p<0.05).

Figure 1.

Feature of gut microbiota in AS patients and HCs. (A) Alpha-diversity assessed by richness (Chao1, ACE) and diversity (Shannon, Simpson), Median estimates compared across cohorts. (B) PCoA plot based on the Bray curtis distance of gut microbiota samples from AS patients vs. HC group(p=0.003, ANOSIM). (C) Panel demonstrated the average relative abundance of different genus in AS and HCs. (D) Distribution of gut microbiota at genus level.

Figure 2.

Correlations between the relative abundance of significantly different bacteria and clinical variables. *p<0.05, **p < 0.01, ***p <0 .001, ****p < 0.0001.

Conclusion

Human gut microbiome in patients with AS differed from that of the HCs. Characters of bacteria communities were associated with disease activity.

References

[1]Simone D, Al Mossawi M H, Bowness P. Progress in our understanding of the pathogenesis of ankylosing spondylitis [J]. Rheumatology (Oxford), 2018, 57(suppl_6): vi4-vi9.

[2]Zhou C, Zhao H, Xiao X Y, et al. Metagenomic profiling of the pro-inflammatory gut microbiota in ankylosing spondylitis [J]. J Autoimmun, 2020, 107(102360.

Acknowledgements

This project was supported by the National Natural Science Foundation of China (No. 82001740).

Disclosure of Interests

None declared

AB0492 INTESTINAL MICROBIOLOGICAL DISORDER CLOSELY ASSOCIATED WITH PERIPHERAL LYMPHOCYTE SUBSETS AND CYTOKINES IN SYSTEMIC LUPUS ERYTHEMATOSUS

Background

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by widespread inflammation and tissue damage in multiple organs[1]. Microbiome is one of environmental factors that has been suggested to contribute to the occurrence and development of SLE[2].

Objectives

This study aims to the understanding of the pathogenesis of SLE from the perspective of intestinal microorganisms and investigate the associations between flora and peripheral lymphocyte subpopulations and cytokines in SLE patients.

Methods

Fecal samples were collected from 96 patients with SLE, and 96 sex-and age-matched healthy controls (HCs). The gut microbiota were investigated via 16s rRNA sequencing and the peripheral T lymphocyte subsets of these participants were assessed by flow cytometry. Indicators of disease activity such as erythrocyte sedimentation rate (ESR), C-reaction protein (CRP), complement C3 and C4 were recorded. Differential abundance analysis was carried out using the edgeR algorithm. The Wilcoxon rank-sum test was used to compare alpha diversity indices, bacterial abundances, and the F/B ratio between groups. R (version 4.0.1) was used for comparative statistics, and pearson’s correlation analysis was used to assess the correlations between the relative abundances of bacterial genera and serum levels of ESR, CRP, C3 and C4 in the samples; correlations with p < 0.05 were considered significant.

Results

The alpha estimators of richness (ACE and Chao 1) were significantly reduced in SLE feces samples compared with those of HCs (p < 0.0001). Bacterial diversity estimators, including the Shannon (p < 0.001) and Simpson’s (p < 0.01) indices, were also significantly lower in SLE (Figure 1A-D). The microbial community structures of the SLE and HCs could be separated by unweighted UnFrac-based principal coordinates analysis (PCoA) (R = 0.186, and p = 0.001; Figure 1E). Significant differences in gut microbiota composition between SLE and HCs were found using the edgeR algorithm. Compared with HCs, 24 species of flora were discovered to be distinctly different(p < 0.05). Moreover, there was a significant positive correlation between Tregs and Corynebacterium(p < 0.05), CD8+T and Corynebacterium (p < 0.05), CD4+T and Corynebacterium (p < 0.05), T and Corynebacterium (p < 0.05), Th1 and Escherichia−Shigella (p < 0.01), Th2 and Dielma (P<0.001) as well as Eubacterium eligens group (p < 0.05), NK and Faecalibacterium (p < 0.01). as well as Corynebacterium (p < 0.001), IL-6 and Coprococcus (p < 0.05), IL-10 and Eubacterium eligens group (p < 0.001) as well as Veillonella (p < 0.05). and Lachnospira (p < 0.01). As for clinical disease measures, there were positive correlations between CRP and Eubacterium ventriosum (p < 0.05). and Coprococcus (p < 0.05), C4 and the abundance of Corynebacterium (p < 0.05) (Figure 1F).

Conclusion

Patients with gut dysbiosis that mainly characterized by reduced the diversity and impaired abundance of the intestinal flora. Abnormality of T cell subsets and cytokines, especially the level of CD4+T, CD8+T, NK, Treg, Th, IL-6 and IL-10 cells contributes to the occurrence and progression of SLE, which may be related to the disturbance of gut microbiota. The discovery of the associated intestinal microbiota of SLE may provide a new idea for treatment.

References

[1]Fava A, Petri M. Systemic lupus erythematosus: diagnosis and clinical management. J Autoimmun. (2019) 96:1–13. 10.1016/j.jaut.2018.11.001

[2]He Z, Shao T, Li H, Xie Z, Wen C: Alterations of the gut microbiome in Chinese patients with systemic lupus erythematosus. Gut pathogens 2016, 8:64.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 82001740).

Disclosure of Interests

None declared