Recent progress on topological semimetal IrO2: electronic structures, synthesis, and transport properties

5dtransition metal oxides, such as iridates, have attracted significant interest in condensed matter physics throughout the past decade owing to their fascinating physical properties that arise from intrinsically strong spin-orbit coupling (SOC) and its interplay with other interactions of comparable energy scales. Among the rich family of iridates, iridium dioxide (IrO2), a simple binary compound long known as a promising catalyst for water splitting, has recently been demonstrated to possess novel topological states and exotic transport properties. The strong SOC and the nonsymmorphic symmetry that IrO2possesses introduce symmetry-protected Dirac nodal lines (DNLs) within its band structure as well as a large spin Hall effect in the transport. Here, we review recent advances pertaining to the study of this unique SOC oxide, with an emphasis on the understanding of the topological electronic structures, syntheses of high crystalline quality nanostructures, and experimental measurements of its fundamental transport properties. In particular, the theoretical origin of the presence of the fourfold degenerate DNLs in band structure and its implications in the angle-resolved photoemission spectroscopy measurement and in the spin Hall effect are discussed. We further introduce a variety of synthesis techniques to achieve IrO2nanostructures, such as epitaxial thin films and single crystalline nanowires, with the goal of understanding the roles that each key parameter plays in the growth process. Finally, we review the electrical, spin, and thermal transport studies. The transport properties under variable temperatures and magnetic fields reveal themselves to be uniquely sensitive and modifiable by strain, dimensionality (bulk, thin film, nanowire), quantum confinement, film texture, and disorder. The sensitivity, stemming from the competing energy scales of SOC, disorder, and other interactions, enables the creation of a variety of intriguing quantum states of matter.

POS0750 THE STATUS OF BREGS AND BREG-RELATED CYTOKINES IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS

Background

Systemic lupus erythematosus (SLE) is a chronic systemic autoimmune disease which involves in multiple tissue and organ injury. Regulatory B cells (Bregs) are unique subpopulations of B cells with immune-regulating properties. Interestingly, different subsets of Bregs have distinct markers and phenotypes and participate in self immune regulation by different ways. However, the level of Bregs in SLE remains debated.

Objectives

This study aims to clarify the proportions of Bregs with special controversial cellular markers and Breg-related cytokines in SLE patients.

Methods

We explored the proportion of Bregs and Breg-related cytokines (IL-10) in SLE patients by searching literature through November 2021 from CBM, CNKI, China Science and Technology Journal Database, Wan Fang Data, PubMed, Embase, Web of Science, Cochrane Library and Medline. Random effects model was used to pool data. Heterogeneity and risk of bias was examined with I-squared index (I2) statistic. Inconsistency was evaluated by using the I2 and Egger tests were used for the evaluation of potential publication bias (STATA v.12.0).

Results

Total 14 case-control studies involving 489 PsA patients and 330 healthy controls (HCs) were included in this study (Table 1). No significant difference in the proportions of Bregs was evident between SLE patients and HCs［SMD=0.067, 95%CI (-0.924,1.059), P=0.894]. Because of a significant statistical heterogeneity observed [I2=97.1%, p<0.001], we conducted sub-analyses based on individual definitions of Bregs. We found the proportions of CD19+CD24hiCD38hi Breg cells was significantly increased in SLE [SMD=0.902, 95%CI (0.157,1.647), P<0.001](Figure 1A). The level of serum IL-10 was increased in SLE compared to that of HCs [SMD=1.062, 95%CI (0.754,1.370), P<0.001] with no publication bias based on the Egger tests (t=0.91, P=0.366)(Figure 1B).

Table 1.

Characteristics of the individual studies included in the meta-analysis.

AuthorPublish YearEIaQbCase NumbersBreg’s definitionMean % of Breg (mean(or median)±SD)% of Breg among PBMC/CD19+T cellsSLEHCBlair,P.A2010462514CD19+CD24hiCD38hiSLE: 13.9±5.21PBMCHC: 9.02±2.71Wang,T.2017475635CD19+CD24hiCD38hiSLE: 39.83±21.39PBMCHC: 8.74±3.97Wang,H.2019463630CD19+CD24hiCD38hiSLE: 12.94±5.45PBMCHC: 5.64±3.13Simon,Q2016461633CD19+CD24hiCD38hiSLE: 17.9±7.2PBMCHC: 11.65±4.01Zhuo-long Wang2018462830CD19+CD24hiCD38hiSLE: 3.62±1.25PBMCHC: 4.07±1.48Heinemann,K.2016463321CD19+CD24hiCD38hiSLE: 1.6±2.6PBMCHC: 1.5±1.1Chu,M.2015474332CD19+CD24highCD27+SLE: 8.39±7.22PBMCHC: 26.58±8.96Vadasz,Z.2015462120CD19+CD25hiFoxP3hiSLE: 18.5±3.05PBMCHC: 11±1.65Cai,X.2015476020CD19+CD5+SLE: 1.86±0.8PBMCHC: 4.35±1Yang,X.2014473015CD19+CD5+CD1dhiSLE: 4±1.57PBMCHC: 1.63±0.99Shan-feng Liu2015461010CD19+CD5+CD1dhiSLE: 0.83±0.28CD19+B cellHC: 0.2±0.21Zhong-wei Huang2014453430CD19+CD5+CD1dhiSLE: 7.86±4.1PBMCHC: 22.71±9.17Ye, Z.2019464720CD19+IL-10+SLE: 0.1±2.78CD19+B cellHC: 4.85±4.54Rong-wei Zhang2016465020CD19+IL-35+SLE: 1.77±0.79PBMCHC: 4.24±1.11

SLE: systemic lupus erythematosus. aEvidence level (EL) of each study was based on Oxford Center for Evidence-Based Medicine 2011. bQuality (Q) of each study was based on the Newcastle-Ottawa Quality Assessment Scale case.

Figure 1.

Conclusion

The levels of CD19+CD24hiCD38hi Bregs and IL-10 were significantly increased in SLE patients, suggesting that the abnormalities of Bregs numbers and function are the critical causes in the development of SLE.

Acknowledgements

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

Disclosure of Interests

None declared

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

AB0805 Levels of Natural Killer Cells in patients with Ankylosing Spondylitis: A meta-analysis

Background

Ankylosing Spondylitis(AS) is a complex chronic inflammatory autoimmune disease that mainly affects the spine. Natural Killer (NK) cells are classified among the recently discovered innate lymphoid cell subsets and have capacity to influence both innate and adaptive immune responses. However, the status of NK cells remains debated in AS.

Objectives

This study aims to clarify the level of peripheral NK cells in AS patient.

Methods

We used CNKI, CBM, Wanfang data and Chinese science, scientific and technological journal data, PubMed, Embase, Web of science, Cochrane library and Medline to collect relevant literature data, and sorted out the proportion of NK cells in AS patients. Random effects were selected to assess pooled data, inconsistency was assessed using I-squared index (I2), and Egger’s test was used to assess potential publication bias (STATA v.12.0).

Results

A total of 11 case-control studies involving 561 AS patients and 592 healthy controls (HC) were included in this study. AS patients had a significantly lower proportion of CD16+CD19+NK cells compared with HCs［SMD=-0.41, 95%CI (-0.71,-0.11), P<0.05,］(Figure 1), with no publication bias according to Egg’s test[t=0.50, P=0.63].

Conclusion

The proportion of of CD16+CD19+ NK cells in AS patients was significantly reduced, suggesting disturbance of NK closely involved in the pathogenesis of AS.

Table 1.

Summary of study characteristics of included records.

AuthorPublish yearEIaQbCase numbersNK cell’s definitionData TypeMean % of NK cell(mean(or median)±SD)ASHCQian Liu202147121120CD16+CD56+OriginalAS:17.6HC:138.9Yu Zhang2019463941CD16+CD56+OriginalAS:22.78HC:61.12Wei Hu2019466040CD16+CD56+OriginalAS:13.3HC:48.1Liyuan Zhu2016473030CD16+CD56+OriginalAS:16.23HC:44.58Hua Liu2012456030CD16+CD56+OriginalAS:29.99HC:49.5Yongfang Wang2012466044CD16+CD56+OriginalAS:14.04HC:60.12Yuehu Hua2012473632CD16+CD56+OriginalAS:31.1HC:59.8Li Ma2011464320CD16+CD56+OriginalAS:24.98HC:38.15Xuehua Ma2011463632CD16+CD56+OriginalAS:31.1HC:59.8Yong-Wook Park20094741173CD16+CD56+CalculatedAS:25.4HC:190.4Li Ma2004473530CD16+CD56+OriginalAS:33.48HC:52.37

Acknowledgements

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

Disclosure of Interests

None declared

POS0716 EFFICACY AND SAFETY OF LOW-DOSE IL-2 IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS

Background

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by disturbances of regulatory and effector CD4+T cells, which were regulated by interleukin (IL)-21,2.

Objectives

The aim of this study was to systematically evaluate the efficacy and safety of low-dose IL-2 therapy in SLE treatment.

Methods

Systematic searches of PubMed, EMBASE, Web of Science, the Cochrane Library and Medline, CNKI, CBM and Technology Journal Database were performed. Original case reports, case series, observational studies and clinical trials reporting the efficacy or safety data on SLE patients treated with IL-2 were included. A random-effects meta-analysis was performed to calculate the pooled efficacy. Inconsistency was evaluated by using the I2 and Egger tests were used for the evaluation of potential publication bias (STATA v.12.0).

Results

A total of 7 studies comprising 327 patients were identified (Table 1). After the low-dose IL-2 treatment, 54.8% Lupus nephritis patient had distinct clinical remission. The SRI-4 response rates were 0.816 (95%CI 0.730-0.901) and the SELENA-SLEDAI scores were significantly decreased [SMD=-2.504, 95%CI (-4.089,-0.919), P=0.002]. Injection site reaction and fever, which were common side effects for IL-2, occurred in 33.1% and 14.4% of patients, None serious adverse events were reported among all these studies. Besides, the proportions of CD4+T cells and Tregs were significantly increased after IL-2 injection [SMD=0.600, 95%CI (0.108,1.093), P=0.017; SMD=1.168, 95%CI (0.429,1.908), P=0.002], while there were no statistical differences in the proportions of CD8+T cells, Th17 cells, Th1 cells and Th2 cells between before and after IL-2 treatment (P>0.05)(Figure 1).

Table 1.

Available evidence including patients with SLE treated with low-dose IL-2.

Study. Year. (design)Patients (include in analysis)Gender (female %)DosageSRI-4,n(%)SELENA–SLEDAIRemission (%)Jing He.60(30)90.001 million IU every other day16 (55.17)0 w: 12.00±4.7553.852019. (PCT)12 w: 6.00±4.00Jing He.40(23)92.501 million IU every other day34 (89.50)0 w: 11.14±3.79NA2016. (PCT)12 w: 3.92±2.23Miao Shao.30(18)88.891 million IU every other dayNANA55.562019. (PCT)Chunmiao Zhao.50(50)94.001 million IU, 3-5d/monthNA0 w: 5.92±0.36NA2019. (PCT)12 w: 4.05±0.31Shengxiao Zhang. 2019. (PCT)495(54)NA0.5 million IU per day for 5 daysNANANAKai Fan.106(76)NA0.5 million IU per day for 5 daysNANANA2018. (PCT)Jing Wang.76(76)93.420.5 million IU per day for 5 daysNA0 d: 10.87±6.48NA2017. (PCT)5 d: 5.83±4.18

Conclusion

Low-dose IL-2 was promising and well-tolerated in the treatment of SLE, which could promotes the proliferation and functional recovery of Tregs.

References

[1]von Spee-Mayer C, Siegert E, Abdirama D, et al. Low-dose interleukin-2 selectively corrects regulatory T cell defects in patients with systemic lupus erythematosus. Ann Rheum Dis 2016;75(7):1407-15. doi: 10.1136/annrheumdis-2015-207776 [published Online First: 2015/08/31]

[2]Robinson S, Thomas R. Potential for Antigen-Specific Tolerizing Immunotherapy in Systematic Lupus Erythematosus. Front Immunol 2021;12:654701. doi: 10.3389/fimmu.2021.654701 [published Online First: 2021]

Acknowledgements

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

Disclosure of Interests

None declared

POS0021 CHANGES OF GUT MICROBIOTA IN CONNECTIVE TISSUE DISEASE AND ITS RELATIONSHIP WITH LYMPHOCYTE SUBSETS AND CYTOKINES

Background

Connective tissue disease (CTD) is a group of autoimmune diseases characterized by the damage of connective tissue components in various parts of the body, which involved multiple organs and systems. Dysbiosis in the gut microbiome is associated with various autoimmune diseases such as CTD.

Objectives

To explore the characters of gut microbiota and their relationship with peripheral lymphocyte subsets and cytokines in patients with CTD.

Methods

Stool samples were collected from 63 CTD patients and 63 age- and sex-matched healthy controls(HCs). Microbial genomes were extracted for 16S rRNA gene sequencing. Gut microbiota characters (alpha diversity, beta diversity, and microbial composition) were analysed by R (version 4.0.1). Peripheral lymphocyte subsets were assessed by flow cytometry. Pearson correlation analysis was used to detect the correlation between the relative abundance of genus in the sample and the activity index; correlations with p < 0.05 were considered significant.

Results

Shannon and Simpson index revealed a decreased alpha diversity in CTD compared with that of HCs (p < 0.05), though not significantly difference in ACE and Chao1 parameters (p > 0.05, Figure 1A). Bray curtis distance-based beta-diversity analysis indicated significant differences in microbial communities between CTD and HCs (p = 0.0014, ANOSIM, Figure 1B). At the genus level, CTD patients had higher abundances of Terrisporobacter (p<0.01), Paraprevotella (p<0.01), CAG−352 (p<0.01), et al. but lower abundances of Streptococcus (p<0.01), Pseudomonas (p<0.01), Bacteroides(p<0.01), et al (Figure 1D). IgM was positively correlated with Lactococcus (p<0.05), Family_XIII_AD3011_group (p<0.05), Streptococcus (p<0.05). Th17 was positively correlated with Pseudomonas (p<0.01). Th2 was positively correlated with Christensenellaceae_R−7_group (p<0.001), UCG−010 (p<0.001) was positively correlated. Treg and Th2 were positively correlated with Christensenellaceae_R−7_group (p<0.01), UCG−010 (p<0.01). Treg was positively correlated with Lachnoclostridium (p<0.05) (Figure 1E).

Conclusion

Pattients with CTD had disbiosis of gut microbiota charaterized by impared diversity and abnomal composition,which was closely correlated with peripheral lymphocyte subsets.

References

[1]Laura Ghezzi,Claudia Cantoni,Gabriela V Pinget,et al.Targeting the gut to treat multiple sclerosis.J Clin Invest.2021 Jul 1;131(13):e143774. doi: 10.1172/JCI143774.

[2]Yoshihiko Tomofuji,Toshihiro Kishikawa,Yuichi Maeda,et al.Whole gut virome analysis of 476 Japanese revealed a link between phage and autoimmune disease.Ann Rheum Dis. 2022 Feb;81(2):278-288. doi: 10.1136/annrheumdis-2021-221267. Epub 2021 Dec 8.

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

POS0449 CHARACTERISTICS OF GUT MICROBIOTA AND ITS RELATIONSHIP WITH LYMPHOCYTE SUBSETS AND CYTOKINES IN PATIENTS WITH UNDIFFERENTIATED SPONDYLOARTHRITIS

Background

Gastrointestinal microbiota, particularly dysbiosis of gut microbiota composition have been correlated with the progression of autoimmune disorders, such as undifferentiated spondyloarthritis (USPA).

Objectives

This study aimed to identify the changed gut microbiota and its relationship with lymphocyte subsets and cytokines in USPA Patients.

Methods

A total of 210 participants were recruited in this study, comprising 105 USPA patients and 105 age and sex-matched healthy controls (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 was used to process representative sequence clusters with a similarity cutoff of 100% (ASVs)1. Microbial diversity was estimated by the alpha diversity (observed, chao1, ACE, shannon, simpson, and ivsimpson) and beta diversity (bray distance). Biomarker species were identified based on STEMP between USPA and HC group. Correlations were analyzed with the Spearman rank correlation test.

Results

The alpha-diversity indices have no significant different between two groups (P >0.05, Figure 1A). Gut microbial community structure differed between USPA and HC, as revealed by ASV Bray–Curtis distances (P <0.05, Figure 1B). As for composition of gut microbiota, there were the increased levels of Escherichia_Shigella, Flavonifractor, Hungatella in the USPA group, and Lachnospirales, Roseburia, and Lachnospiraceae in HCs (Figure 1C). The relative abundance of Lachnospiraceae_UCG_001 and Enterobacter was negatively correlated with the absolute numbers of Th17 (P<0.05). Bifidobacterium was positively correlated with the absolute number of Th1 and Tregs (P<0.01, Figure 1D). The relative abundance of Fusobacterium, Incertae_Sedis, and Colidextribacter were negatively correlated with the absolute numbers of Il-10, IL-4, and IL-2 (P<0.05). Prevotella and Enterobacter were positively correlated with the absolute number of IL-6 and IL-4 respectively (P<0.05, Figure 1E). Bifidobacterium and Bilophila were neagtively correlated with the absolute number of NK cell (P<0.05, Figure 1F).

Figure 1.

(A) Comparison of alpha-diversity indexs between HC and USPA groups was shown using boxplot. (B) β diversity of the gut microbiome in USPA patients and HCs. Principal coordinate analysis plot generated from the bray distance analyse. (C) STEMP was used to detect difference in Flora according to USPA and HC. (D-F) Relationship between gut microbiota, and Lymphocyte subsets as well as cytokines. *P<0.05, **P<0.01.

Conclusion

Gut dysbiosis in USPA patients mainly characterized by reduced the diversity and impaired abundance of the intestinal flora, which was closely related to the disturbance of lymphocyte subpopulations and cytokines.

References

[1]Han L, Zhao K, Li Y, et al. A gut microbiota score predicting acute graft-versus-host disease following myeloablative allogeneic hematopoietic stem cell transplantation. Am J Transplant 2020;20(4):1014-27. doi: 10.1111/ajt.15654 [published Online First: 2019/10/13]

Acknowledgements

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

Disclosure of Interests

None declared.

AB1136 PREVALENCE OF COVID-19 IN SYSTEMIC LUPUS ERYTHEMATOSUS: A META-ANALYSIS

Background

Novel Coronavirus pneumonia 2019 (COVID-19) is a systemic infectious disease with prominent involvement of the respiratory tract, due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)[1]. Systemic lupus erythematosus is charcterized by an aberrant immune response with the presence of circulating autoantibodies, lymphopenia, and proinflammatory[2]. They are immune-compromised and vulnerable to infections with immune-suppressants treatment. However, data regarding the impact of COVID-19 pandemic in patients with SLE and drug use were relatively scarce.

Objectives

The prevalence of COVID-19 in SLE patients was estimated by means of meta-analysis, and the effect of the use of anti-rheumatic drugs on the clinical outcome of SLE patients with COVID-19 was investigated.

Methods

Cross-sectional investigations and case series on SLE and COVID-19 published by CBM, CNKI, China Science and Technology Journal Database, Wan Fang Data, PubMed, Embase, Web of Science, Cochrane Library and Medline from its establishment to November 10, 2021 were searched. Random effects model was used to pool data. Heterogeneity and risk of bias was examined with I-squared index (I2) statistic. Inconsistency was evaluated by using the I2. Egger tests were used for the evaluation of potential publication bias (STATA v.12.0).

Results

A total of 14 studies comprising 5365 patients were identified (Table 1). Overall prevalence of COVID-19 in SLE patients was 1.5% (95%CI: 1.2%-1.8%). Eight of the studies included patients who used hydroxychloroquine as part of their treatment regimen, with 29.8% (95%CI: 25.8%-33.8%) hospitalization rates and 14.6% (95%CI: 11.5%-17.8%) adverse outcome rates. Among patients treated with hydroxychloroquine throughout the course of disease, the prevalence was 0.7% (95%CI: 0.4%-1.0%, Figure 1).

Table 1.

Summary of study characteristics of included records.

AuthorTotal number of patientsMean/Median AgeHCQOther therapeutic drugsNumber of COVID19(n)Mean/Median AgeHCQOther therapeutic drugsHospitalization(n)supplemental oxyge(n)Combined poor outcomesGerard Espinosa40050.7GCs:47447.922GCs:12,T-DMARD:28,others:13334Emanuele Bozzalla Cassione16552.5127T-DMARD:12,others:411252.53GCs:1,others:31Ren: Cordtz253355.41170GCs:685,B-DMARD:42,T-DMARD:374,others:9261669.15GCs:416Ruth Fernandez-Ruiz226834332GCs:18,T-DMARD:5,others:5024198Zos Gendebien22551.7152GCs:92,T-DMARD:25,others:523222Wendy Wan Hui Lee8538.2858GCs:54,others:502311Giuseppe A. Ramirez, MD417others:389,combination:2891411Samar Tharwat20030.1140GCs:174,B-DMARD:4,T-DMARD:310,others:18432Dina Zucchi33247267GCs:176,B-DMARD:41,T-DMARD:118,others:118,combination:112640.36GCs:6,T-DMARD:1,others:6311Sarthak Gupta177GCs:81746.11GCs:12,T-DMARD:31022Seow Lin Chuah181838uah67Claudia Diniz Lopes Marques11033445 (31ques118GCs:134,T-DMARD:110,others:2841103578Beth Wallace31616GCs:12,others:4554 (26-67)4GCs:4,others:355Isabela Maria Bertoglio38245.138245.5206173Figure 1.

A:Forest map of a meta-analysis of COVID-19 prevalence in SLE patients.B: Forest map of a meta-analysis of hospitalization rates among patients treated with hydroxychloroquine.C:Forest map of meta-analysis of the rate of adverse outcomes in patients using hydroxychloroquine as part of a treatment regimen.D:Forest map of a meta-analysis of the prevalence of patients treated with hydroxychloroquine throughout the course.

Conclusion

Patients with SLE had a higher risk of COVID-19. Hydroxychloroquine might benefit to reduce the overall hospitalization rate and prevalence rate of COVID-19, and alleviate inflammatory damage in the chronic stage of viral infection by inhibiting over activation of the immune system.

References

[1]SCHULTZE J L, ASCHENBRENNER A C. COVID-19 and the human innate immune system [J]. Cell, 2021, 184(7): 1671-92.

[2]KIRIAKIDOU M, CHING C L. Systemic Lupus Erythematosus [J]. Ann Intern Med, 2020, 172(11): Itc81-itc96.

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

POS1353 COMPOSITION AND ASSOCIATIONS OF THE GUT MICROBIOTA IN BECHET’S DISEASE WITH PERIPHERAL LYMPHOCYTE SUBSETS AND CYTOKINES

Background

Bechet’s disease (BD) is a chronic multisystemic vasculitis with genetic and abnormal immune response. Growing evidences suggests gut microbiota compositional alteration may have an association with immune dysfunction in patients with BD.

Objectives

This study aims to investigate the gut microbiota between BD and healthy controls (HCs) and analyse relevancy between bacterial and peripheral lymphocyte subsets and cytokines.

Methods

Fecal samples obtained from 22 BD patients and 22 normal-age and gender-matched HCs in this study. The gut microbiota were assessed with 16s rRNA sequencing and the flow cytometry was used to dectect peripheral lymphocyte subsets. C-reaction protein (CRP), Erythrocyte sedimentation rate (ESR), complement C3 and C4 were also assigned for disease activity measure. The edgeR package was used for differential abundance analysis. Difference of alpha diversity indices, bacterial abundances, and the F/B ratio were carried out using the Wilcoxon rank-sum test (R v.4.0.1). The differential abundance of flora and CRP, ESR, C3 and C4 between BD patients and HCs was assessed by pearson’s correlation analysis.

Results

As for alpha diversity, the Shannon (p < 0.05) and Simpsonance analysis. Difference of alpha diversity indices, bacterial abundances, and the F/B ratio were carried out using the Wilcoxon rank-sum test (R v.4.0.1). The differential abundamicrobial community structures between BD and HCs (R = 0.053, p = 0.051; Figure 1B). The gut microbiota compositions of BD differed form those of HCs (Figure 1C). Four species of flora distinctly difference were found in BD (p < 0.05; Figure 1D). There was significant positive correlations between Tregs and Verrucomicrobiota (p < 0.05), and Proteobacteria (p < 0.05), Th1 and Proteobacteria (p < 0.05), ESR and Verrucomicrobiota (p < 0.01), but negatives correlation between TNF-α and Desulfobactbiota (p < 0.05; Figure 1E).

Conclusion

Pattients with CTD had disbiosis of gut microbiota charaterized by impared diversity and abnomal composition, which was closely correlated with peripheral lymphocyte subsets and disease activity measures.

References

[1]Margaret Alexander, Qi Yan Ang, Renuka R Nayak, et al. Human gut bacterial metabolism drives Th17 activation and colitis. Cell Host Microbe. 2022 Jan 12;30(1):17-30.e9. doi: 10.1016/j.chom.2021.11.001. Epub 2021 Nov 24.

[2]Yi-Wen Tsai, Jia-Ling Dong, Yun-Jie Jian, et al. Gut Microbiota-Modulated Metabolomic Profiling Shapes the Etiology and Pathogenesis of Autoimmune Diseases. Microorganisms. 2021 Sep 10;9(9):1930. doi: 10.3390/microorganisms9091930.

Acknowledgements

This work 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

AB0300 REDUCTION OF LACHNOSPIRA IS CLOSELY RELATED TO AUTOIMMUNE DISEASE COMPLICATED WITH PULMONARY FIBROSIS

Background

Autoimmune diseases (AD) are a group of heterogeneous disorders caused by both genetic and environmental factors. Rheumatoid arthritis (RA) and Sjögrens syndrome (SS) are typical autoimmune diseases[1], Pulmonary fibrosis (PF) is the most common complication of AD. Despite the extensive study of the human gut microbiome in AD complicated with PF(AD-PF),the question of whether there are common microbial features characterizing AD-PF still remains[2].

Objectives

This study focused on exploring differences between the microbiota diversity and peripheral lymphocyte subpopulations as well as cytokine in AD with PF is different from that of AD without PF.

Methods

A total of 64 AD patients (44 AD without PF and 20 AD with PF) as well as 100 age- and sex- matched healthy controls (HCs) were enrolled in this study. The peripheral lymphocyte subsets were analyzed by flow cytometry and the gut microbiota were investigated via 16s rRNA sequencing. Alpha and Beta diversity (bray curtis distance-based) analysis was used to define the difference of gut microbiota profiles between patients and HCs. To explore the specific bacterial taxa associated with AD-PF, the STAMP software was used to compare the fecal microbiota composition. Spearman correlation analysis was used to determine the similarities in the microbiota community with clinical meatures among fecal samples.

Results

There is a decrease that the richness and diversity index between HCs, AD and AD-PF patients. Principal co-ordinates analyses suggested that these three microbiota states explained a reasonable proportion of observed variance in gut microbiota composition (ANOSIM R2 = 0.113, p < 0.001; Figure 1b). Compared with HCs, there are obvious differences among 19 species of flora in AD without PF at the genus level, of which 2 species of flora (Lachnospira,Muribaculaceae) belong to AD-PF patients were showed much fewer. The relative abundance of Lachnospira was positive correlated with the absolute numbers of Th17, IL-6 and THF-α (P<0.05,Figure 1D-E)，which indicated Lachnospira may be the most critical among the AD-RF patients’ own species of flora. Therefore, the reduction of Lachnospira may influence the immune status of the intestinal tract of patients by producing less short-chain fatty acids.

Conclusion

Our results suggest that the decrease of Lachnospira may lead to the occurrence of AD with pulmonary interstitial fibrosis, which was closely correlated with lymphocyte subsets and Cytokines, maintaining the flora balance might be a potential therapeutic target for AD-PF.

References

[1]Ma Y, Shi N, Li M, Chen F, Niu H: Applications of Next-generation Sequencing in Systemic Autoimmune Diseases. Genomics Proteomics Bioinformatics 2015, 13(4):242-249.

[2]Belkaid Y, Hand TW: Role of the microbiota in immunity and inflammation. Cell 2014, 157(1):121-141.

Acknowledgements

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

Disclosure of Interests

None declared

POS0556 ALTERATION OF THE GUT MICROBIOTA IN CHINESE POPULATION WITH RHEUMATOID ARTHRITIS

Background

Rheumatoid arthritis (RA) is an aggressive immune-mediated joint disease characterized by synovial proliferation and inflammation, cartilage destruction, and joint destruction. Growing evidences suggests a chronic inflammatory response induced by gut microbiome critically contribute to the development of rheumatoid arthritis.

Objectives

The aim of this study was to evaluate and quantify differences in the composition of gut microbiota in RA patients and investigate the associations between flora and clinical variables in RA patients.

Methods

Fecal samples from 145 RA patients and 145 age- and gender- matched healthy controls (HCs) were collected for bacterial 16S rRNA genes sequencing. The alpha-diversity, beta-diversity and the microbial composition (at the phylum and genus level) analysis of the gut microbiome were used to define the difference of gut microbiota profiles between RA patients and HCs. The peripheral lymphocytes of these patients were assessed by flow cytometry, and inflammatory biomarkers (ESR, CRP), auto-antibodies(ACPA, MCV) and cytokines measured by ELISA were recorded. Correlations between different taxa and clinical variables, were calculated by Spearman’s rank test.

Results

Consistent with trends observed for diversity, patients with RA had a lower richness compared with those of HCs (p < 0.01, Figure 1a), suggesting gut microbiome was markedly less diverse in composition in RA. Bray curtis distance-based beta diversity analysis revealed significant differences in the microbial community between RA and HCs (ANOSIM, R2=0.061, p=0.001, Figure 1b). Ten selected taxonomic biomarkers at different phylogenetic levels showed great discriminant ability, with Log10 LDA score > 4.0 (Figure 1e-g). Detailly, at the phylum level, RA patients had a lower frequency of Firmicutes while higher Proteobacteria. RA patients presented fewer Faecalibacterium but more Escherichia_Shigella at the genus level (Figure 1c-d). PICRUSt analysis found that in the KEGG pathways, the microbial gene functions related to Propanoate metabolism were higher in the fecal microbiome of RA patients (Figure 1h). Escherichia_Shigella positively correlated with ACPA antibodies (r=0.176, p < 0.05) and IL-4 (r=0.204, p < 0.05, Figure 1i), wheras Faecalibacterium as a probiotic showed no significant correlation with our clinical measures.

Figure 1.

Conclusion

Specific gut microbiota played an important role in the pathogenesis of RA, which may aid in the diagnosis or determination of the susceptibility of individuals to RA via detection of the gut microbiome.

References

[1]de Oliveira GLV, Leite AZ, Higuchi BS, et al. Intestinal dysbiosis and probiotic applications in autoimmune diseases. Immunology 2017;152(1):1-12. doi: 10.1111/imm.12765

[2]Chen J, Wright K, Davis JM, et al. An expansion of rare lineage intestinal microbes characterizes rheumatoid arthritis. Genome Med 2016;8(1):43. doi: 10.1186/s13073-016-0299-7

Acknowledgements

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

Disclosure of Interests

None declared.

POS1558-HPR INFLUENCING FACTORS ON WORK BURNOUT OF PRE-EXAMINATION AND TRIAGE NURSES UNDER THE NORMAL EPIDEMIC PREVENTION AND CONTROL

Background

Nurse is a high-risk groups work fatigue feeling, which seriously affects the quality of conventional work efficiency and bureden pressures for contradiction between nurses and patients especially during the COVID-19 pandemic.Normalized epidemic prevention and control during the preview triage nurse need to all patients to the hospital and the accompanying personnel carries on the preliminary screening.COVID-19 fixed point hospital preview triage nurse with an infected person contact, more prone to anxiety,depression, results in the decrease of efficiency, to treat the service object formulation work sense of fatigue performance, etc.

Objectives

To explore the influencing factors of work burnout of pre-test and triage nurses under normal epidemic prevention and control.

Methods

A total of 110 pre-test and triage nurses from 4 Grade-A hospitals in Shanxi Province were enrolled in this study. The general data questionnaire, Nurse Job Burnout Scale, Pittsburgh Sleep Quality Index Scale, Self-Rating Anxiety Scale and Self-Rating Depression Scale were investigated towork burnout of pre-examination and triage nurses. Comparison between groups using two Independent sample t-test and single factor variance analysis. Multiple regression were applied to analysis factors affecting nurse fatigue feeling dimensions by SPSS22.0. P values<0.05 were considered significant.

Results

As shown in Table 1, different professional title, department, and the sleep quality of preview triage nurses emotional exhaustion dimension comparison(P<0.001), different department nurses to personalized level dimension comparison(P<0.05), nurse personal accomplishment dimension comparison of different cultural levels(P<0.05). Professional title, working department, sleep quality and educational level were the influencing factors of job burnout of pre-test and triage nurses.

Table 1.

Univariate analysis of job burnout of pre-examination triage nurses from different dimensions.

ItemNumberJob BurnoutEmotional exhaustionDepersonalizationPersonal accomplishmentscoreF(t)PscoreF(t)PscoreF(t)Pgender0.0200.8880.1620.6890.3190.575 female10020.10±13.676.98±6.1528.40±13.41 male1021.00±12.088.20±9.5225.98±8.70age(year)5.5110.0074.1430.0210.7500.477 18~253020.33±12.408.40±7.3328.07±6.97 26~304013.60±11.624.00±3.5526.65±10.70 31~404027.00±13.489.20±6.9924.35±8.79marriage-0.9390.352-1.1550.2530.6150.541 unmarried5218.38±13.306.04±6.4027.00±9.99 married5821.79±13.578.03±6.4025.48±8.29job title5.7390.0062.3200.1080.6110.547 junior nurse5216.12±12.945.96±5.9827.26±8.33 senior nurse3819.68±12.536.63±6.1426.50±10.62 supervisor nurse2031.70±10.3810.90±7.2223.40±5.60work experience(year)1.2770.2920.9380.4290.6590.581 ＜12024.80±15.877.60±6.9827.90±5.67 1 ~32418.42±12.056.33±6.5124.25±13.00 4 ~93416.00±10.535.47±6.0324.69±9.16 10 ~203223.06±15.109.06±6.4328.00±7.46department-3.8750.000-2.3370.0230.4010.690 out-patient5413.81±10.505.11±4.3826.70±10.65 emergency5626.32±13.239.00±7.5025.71±7.42average working time per day(hour)0.7910.4591.1250.3322.1730.124 6~6.91815.33±7.925.11±5.2830.56±8.35 7~7.94420.26±13.616.43±7.2827.09±9.33 ≥84822.00±14.888.52±5.8123.61±8.62education degree-0.6430.523-1.0000.3222.4650.017 junior college1816.00±14.764.00±5.4836.50±8.43 college9220.51±13.427.33±6.4725.39±8.70SAS(score)2.0800.0421.6370.1080.4980.621 ≤503417.74±12.716.16±5.3327.12±8.74 >507625.65±13.759.18±8.1625.79±9.31SDS(score)0.2400.8110.8250.4130.4280.671 ≤507219.58±12.946.11±3.9626.58±9.66 >503820.50±13.857.61±7.4025.47±8.05PSQI(score)2.3790.0212.0290.047-0.5210.604 ≤74017.06±12.175.80±4.9826.69±9.33 >77025.65±14.089.35±8.0225.35±8.79

Conclusion

In the COVID-19 epidemic, managers should pay more attention to the main factors that affect the sense of exhaustion of pre-test and triage nurses, and take targeted intervention measures to alleviate the sense of exhaustion of nurses, so as to ensure the safety of nursing.

Acknowledgements

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

Disclosure of Interests

None declared

POS0143 COMPOSITION AND ASSOCIATIONS OF THE GUT MICROBIOTAWITH PERIPHERAL LYMPHOCYTE SUBSETS AND CYTOKINES IN IDIOPATHIC INFLAMMATORY MYOPATHIES

Background

The idiopathic inflammatory myopathies (IIM) are a group of acquired myopathies characterized by inflammatory lymphocytes infiltrates in muscle tissue1.Gut microbiota serves as a critical environmental component of autoimmune disease pathogenesis such as IIM2.

Objectives

This study sought to investigate the composition of gut microbiota and the relationship between microbiota structure and lymphocyte subpopulations and cytokines in IIM patients to recommend feasible intervention strategies.

Methods

Faecal samples were taken from 37 IIM patients and 37 age- and gender- matched healthy controls (HCs) in a sterile environment placed into the Second Hospital of Shanxi Medical University. Microbiome profiling was performed by sequencing of the V3-V4 variable regions of the 16S rRNA gene and the peripheral T lymphocyte subsets of these participants were assessed by flow cytometry. The clinical laboratory data such as erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) and immunoglobulin were also determined. In terms of gut microbia, the diversity and richness was evaluated from two aspects: alpha diversity and beta diversity with the indices of ACE, Chao1, Shannon and Simpson. Analyses were conducted using R version 4.0.1. Pearson correlation was applied to assess the relationship between the relative abundances of bacterial genera and clinical parameters, and p < 0.05 was considered to be statistically significant.

Results

The α-diversity analysis of the richness (Chao1) and diversity (Shannon and Simpson) were reduced in IIM samples compared with those of HCs (Figure 1A, p < 0.05). Bray curtis distance-based beta diversity analysis revealed significant differences in the microbial community between IIM and HCs (Figure 1B, p = 0.001, ANOSIM). Detailly, at the genera level, IIM patients had a higher abundance of Enterococcus, Veillonella, Streptococcus, et al. and a lower abundance of Roseburia, Lachnospira, Klebsiella, et al(Figure 1D, p < 0.05). In IIM patients, Fusobacteriota correlated positively with the ratio of Th1 cells (Figure 1E, p < 0.01), and there was a significant positive correlation between Synergistota and B lymphocyte (Figure 1E, p < 0.01). Besides, Euryarchaeota and Cyanobacteria were both positively and significantly related to IL-6, IFN-γ and C-reactive protein (CRP) (Figure 1E, p < 0.001).

Conclusion

Richness and diversity of intestinal flora in IIM patients were impaired, which might participate in the pathogenesis of IIM by disturbing lymphocyte subpopulations and cytokines. Regulating intestinal flora and restoring homeostasis might become a critical therapeutic methods of IIM.

References

[1]Xu Y, Sun J, Wan K, et al. Multiparametric cardiovascular magnetic resonance characteristics and dynamic changes in myocardial and skeletal muscles in idiopathic inflammatory cardiomyopathy. J Cardiovasc Magn Reson 2020;22(1):22. doi: 10.1186/s12968-020-00616-0.

[2]Mariampillai K, Granger B, Amelin D, et al. Development of a New Classification System for Idiopathic Inflammatory Myopathies Based on Clinical Manifestations and Myositis-Specific Autoantibodies. JAMA Neurol 2018;75(12):1528-1537. doi: 10.1001/jamaneurol.2018.2598.

Acknowledgements

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

Disclosure of Interests

None declared

AB0500 THE LEVEL OF PERIPHERAL BLOOD LYMPHOCYTE SUBSETS IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS WITH RESPIRATORY TRACT INFECTION AND ITS CLINICAL SIGNIFICANCE

Background

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disorder. Infections are a most common cause of morbidity and mortality in this patient population[1] and at least 50% of patients with SLE are suffered with infections during the course of their disease [2]. Lymphocytes and Natural killer (NK) cells play an important role in the occurrence and development of SLE[3]. In this study, peripheral blood lymphocyte subsets were detected in these patients, providing reference for early diagnosis and treatment of SLE patients with respiratory tract infection.

Objectives

To analyze the detection level and clinical significance of peripheral blood lymphocyte subsets in patients with SLE with respiratory tract infection.

Methods

A total of 333 SLE patients with no recent infection, 95 SLE patients with respiratory tract infection, and 132 healthy individuals matched in age and sex were enrolled in the second Hospital of Shanxi Medical University from July 2014 to December 2016. The characteristics of lymphocyte subsets in the three groups were compared and receiver operating characteristic (ROC) curves were drawn to analyze the predictive value of lymphocyte subsets in SLE patients with respiratory tract infection.

Results

The counts of T, B, CD4 + T, CD8 + T, NK, Th1, Th2, Th17 and Tregs in SLE non-infection group and SLE infection group were [(1094.235 ± 574.495) / (702.781 ± 432.152), t= -7.169, P < 0.001], [(208.338 ± 210.448) / (177.55 ± 170.256), t = -1.306, P = 0.192], [(503.382 ± 303.498) / (304.075 ± 215.497), t = -7.168, P < 0.001], [(536.705 ± 344.218) / (358.034 ± 235.234), t = -5.802, P < 0.001], [(113.898 ± 101.48) / (61.768 ± 50.127), t = -6.831, P < 0.001], [(86.268 ± 89.081) / (47.92 ± 54.174), t = -3.367, P = 0.001], [(11.363 ± 9.834) / (6.628 ± 6.434), t = -3.622, P < 0.001], [(9.537 ± 10.12) / (5.346 ± 4.731), t = -3.646, P < 0.001], [(25.736 ± 27.013) / (20.78 ± 28.083), t =-1.037,P=0.301] (Figure 1).The above indexes in SLE infection group were lower than those in SLE non-infection groups. When lymphocyte subsets predict pulmonary infection in SLE, the AUC value of CD4 + count is the highest, and the cut-off is 387/ μ l(Table 1). The sensitivity and specificity of predicting SLE pulmonary infection were 75.8% and 38.6%(Figure 2).

Table 1.

Predictive value of peripheral blood lymphocyte subsets in SLE complicated with respiratory tract infection

IndicatorAUCP valueJordan indexcut-offSusceptibilitySpecificity(%)(%)B0.5690.04083.99500.1830.4210.238CD40.714<0.0010.3723870.7580.386CD80.682<0.0010.3254050.7260.401CD4+ T /CD8+ T0.5690.0410.0000.7850.5370.370NK0.687<0.0010.30982.50.7680.460

Conclusion

The absolute number of these subsets in infected SLE patients is significantly lower than that in uninfected patients, which indicates that the low absolute number of these cells can be used as an indicator of high infection risk in SLE patients. CD4 + T lymphocytes and NK cells in patients with respiratory tract infection are significantly lower, and can play a certain predictive value for SLE respiratory tract infection to a certain extent.

References

[1]Kedves M, Kosa F, Kunovszki P, Takacs P, Szabo MZ, et al. 2020. Large-scale mortality gap between SLE and control population is associated with increased infection-related mortality in lupus. Rheumatology (Oxford) 59:3443-51

[2]Wang J, Niu R, Jiang L, Wang Y, Shao X, et al. 2019. The diagnostic values of C-reactive protein and procalcitonin in identifying systemic lupus erythematosus infection and disease activity. Medicine (Baltimore) 98:e16798

[3]Luo Q, Kong Y, Fu B, Li X, Huang Q, et al. 2021. Increased TIM-3(+)PD-1(+) NK cells are associated with the disease activity and severity of systemic lupus erythematosus. Clin. Exp. Med.

Disclosure of Interests

None declared

POS0212 THE REDUCTION OF TURICIBACTER IN GUT MICROBIOTA ASSOCIATED WITH SJOGREN’S SYNDROME SECONDARY TO RHEUMATOID ARTHRITIS

Background

Secondary Sjogren’s syndrome(SS) is a common extra-articular manifestation of rheumatoid arthritis (RA)[1]. RA patients combined with SS have different outcomes from those without SS[2]. However, the studies investigated the characteristics of gut microbiota in patients with RA and SS is limited.

Objectives

To investigate the characteristics of gut microbiome and the associations between flora and peripheral lymphocyte subpopulations in RA patients with or without Sjogren’s syndrome.

Methods

A total of 326 samples from 145 RA patients without SS, 23 RA combined with SS patients(RA-SS) and 168 healthy controls (HCs) were recruit in this study from The Second Hospital of Shanxi Medical University (Taiyuan, Shanxi, China). The gut microbiota were investigated via 16s rRNA sequencing and the peripheral T lymphocyte subsets of these participants were assessed by flow cytometry. The Wilcoxon rank-sum test was used to compare alpha diversity indicesbetween groups. Differential abundance analysis was carried out the STAMP software. Spearman’s correlation analysis was used to assess the correlations between the relative abundances of bacterial genera and clinical meatures.

Results

Patients with RA and RA-SS exhibited a significant reduction in the richness and diversity of gut microbiota compared with those of HCs (Figure 1 A-B, p < 0.05), whereas there was no significant difference between RA and RA-SS patients. Principal co-ordinates analyses based on bray curtis distance suggested that these there microbiota states explained a definable proportion of observed variance in microbiota composition (ANOSIM R2 = 0.074, p < 0.001; Figure 1 C). Compared with HCs, 58 species of flora were discovered to be distinctly different in RA patients without SS at the genus level of which 6 species of flora unique to RA-SS patients were presented much fewer ([Eubacterium]_hallii_group, Anaerostipes, CAG-56, Fusobacterium, Turicibacter and Enterococcus). Among these RA-SS patients‘ unique species of flora, it seems that Turicibacter is the key species of flora, owing to whose has a positive correlation with most of lymphocytes such as T, B, CD4+T, CD8+T and NK cells suggesting a close association with intestinal immunity.(Figure 1 F-G,P<0.05)

Conclusion

RA patients with deficiency of Turicibacter in flora had higer occurrence of Sjögren’s syndrome sjogren’s syndrome complication, which was correlated with peripherial lymphocyte subpopulations and cytokines.

References

[1]Chen Y, Ma C, Liu L, He J, Zhu C, Zheng F, Dai W, Hong X, Liu D, Tang D et al: Analysis of gut microbiota and metabolites in patients with rheumatoid arthritis and identification of potential biomarkers. Aging 2021, 13(20):23689-23701.

[2]Brown LE, Frits ML, Iannaccone CK, Weinblatt ME, Shadick NA, Liao KP: Clinical characteristics of RA patients with secondary SS and association with joint damage. Rheumatology (Oxf) 2015, 54(5):816-820.

Acknowledgements

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

Disclosure of Interests

None declared

AB0523 THE ENTEROTYPES OF THE GUT MICROBIOTA IN CHINESE POPULATION WITH AUTOIMMUNE DISEASE

Background

An increasing number of autoimmune disorders (AD) have been associated with microbial dysbiosis[1, 2]. However, this dysbiosis is difficult to characterize for individual patients owing to the high heterogeneity of the gut microbiota. Thus, researchers must find an accurate method of characterizing the AD gut microbiota that is meaningful to clinical diagnosis.

Objectives

The aim of this study was to investigate the enterotype characters of intestinal flora in AD and their associations with peripheral lymphocyte subpopulations and cytokines.

Methods

A total of 339 AD patients and 339 age- and sex- matched healthy controls (HCs) were enrolled in this study. Mathematical modeling using Dirichlet multinomial mixtures (DMM) was applied to describe the variability in the microbiome data and cluster samples into enterotypes. The peripheral lymphocyte subsets were detected by flow cytometry and the cytokines were assessed by ELISA. Differential abundance analysis was carried out the STAMP software. R (version 4.1.0) was used for comparative statistics, and spearman’s correlation analysis was used to assess the correlations between the relative abundances of bacterial genera and clinical variables.

Results

Laplace approximation of DMM suggested gut microbiota of AD patients and HCs both can be divided into two distinct enterotypes (Figure 1 A-B), and AD E1 and HC E1 were primarily dominated by Prevotella while AD E2 and HC E2 by Bacteroides. Interestingly, the Prevotella-enriched enterotype (AD E1 and HC E1) had a higher alpha diversity than The Bacteroides-enriched enterotype (AD E2 and HC E2). Patients with AD always had a lower richness and diversity compared with those of HCs in each enterotype (p< 0.001), suggesting gut microbiome was markedly less diverse in composition in AD. Bray curtis distance-based beta-diversity were also different (P<0.001, ANOSIM.R =0.23, Figure 1 C-H). Significant differences in gut microbiota composition at the genus level between AD patients and HCs were found using the STAMP software in each enterotype. Compared with HCs, 37 species in AD E1 patients and 40 species in AD E2 patients of flora were discovered to be distinctly different. In the co-upregulated flora of both enterotypes, Lactobacillus was inversely associated with a variety of lymphocytes such as T, CD4+T, NK, Th2, Th17, Treg cells(P<0.05), and positive correlation with IL-10 and IFN-γ(P<0.05,Figure 1 I). However, in the co-downregulated floras Coprococcus had a positive correlation with B, NK and Treg cells, and anaerostipes had a negativate corrleation with IL-2 and IL-4(P<0.05,Figure 1 J).

Conclusion

There were both two enterotypes in patients and HCs with autoimmune disease, E2 exhibited a loss of Prevotella but a growth of Bacteroides, while E1 presented the opposite results, which were closely correlated with peripheral lymphocyte subsets and cytokines.

References

[1]Levy M, Thaiss CA, Zeevi D, Dohnalová L, Zilberman-Schapira G, Mahdi JA, David E, Savidor A, Korem T, Herzig Y et al: Microbiota-Modulated Metabolites Shape the Intestinal Microenvironment by Regulating NLRP6 Inflammasome Signaling. Cell 2015, 163(6):1428-1443.

[2]Belkaid Y, Hand TW: Role of the microbiota in immunity and inflammation. Cell 2014, 157(1):121-141.

Acknowledgements

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

Disclosure of Interests

None declared

POS1153 CHARACTERISTICS OF GUT MICROBIOME AND THEIR ASSOCIATIONS WITH PERIPHERAL LYMPHOCYTE SUBPOPULATIONS AND CYTOKINES IN RHEUMATOID ARTHRITIS PATIENTS COMPLICATED WITH OSTEOPOROSIS

Background

Osteoporosis(OP) is one of the major comorbidities of rheumatoid arthritis(RA) which is associated with immune disorders[1]. The gut microbiota has been highlighted to be an important environmental factor to influence immune system in maintaining bone health and regulating bone remodeling[2]. However, the alterations of intestinal flora and its relationship with immune system in RA patients with OP are unclear.

Objectives

To investigate the characteristics of gut microbiome as well as the associations between flora and peripheral lymphocyte subpopulations and cytokines in rheumatoid arthritis patients complicated with osteoporosis.

Methods

Total 28 RA patients were divided into 14 RA-non-OP and 14 gender- and age-matched RA-OP groups according to their bone mineral density (BMD) and the history of fragility fracture. Gut microbiota of participants were investigated by 16s rRNA and peripheral lymphocyte subsets and cytokines were assessed via flow cytometry. Indicators like erythrocyte sedimentation rate (ESR), C-reaction protein (CRP), anti-cyclic citrullinated peptide antibody (ACPA) and anti-mutated citrullinated vimentin (MCV) antibody were recorded meanwhile. Alpha diversity (ACE, Chao1, Simpson, Shannon) and beta diversity indices were analyzed using QIIME2. Biomarker species were recognized based on STEMP. Spearman analysis was adopted for correlation of two variables. All P-values reported herein were two-tailed and P-value<0.05 was taken as statistically significant.

Results

The alpha-diversity have no significant difference between RA-non-OP and RA-OP groups (P >0.05, Figure 1A). The community structure of microflora differed between two groups (P <0.05, Figure 1B). As for the composition of intestinal flora at genus level, Faecalibacterium, Proteus, Catenibacterium, Enterobacter and Erysipelatoclostridium in RA-OP group as well as Lachnospiraceae_ND3007_group, Parasutterella, Megasphaera, Tyzzerella, UCG-005, Clostridium_sensu_stricto_1, UCG-002, Lachnospiraceae_NK4A136_group, Christensenellaceae_R-7_group, Prevotella, Parabacteroides in RA-non-OP group were significantly increased (Figure 1C). There were positive correlations between Lachnospiraceae_NK4A136_group and the level of T, Th1 and Th17 cells, but negative relevance with ESR, CRP and IL-10 (P <0.05). The relative abundance of Faecalibacterium was negatively correlated with IL-2, IL-4, TNF-α and positively with MCV (P <0.05). Clostridium_sensu_stricto_1 and Lachnospiraceae_ND3007_group were negatively correlated with ACPA and MCV respectively as well as IL-2 (P <0.05, Figure 1D-E).

Conclusion

Abnormality of immune system may contribute directly or indirectly to OP in RA, which may be related to the disturbance of gut microbiota.

References

[1]Horta-Baas G, Romero-Figueroa MDS, Montiel-Jarquín AJ, et al. Intestinal Dysbiosis and Rheumatoid Arthritis: A Link between Gut Microbiota and the Pathogenesis of Rheumatoid Arthritis. J Immunol Res. 2017;2017:4835189.

[2]Raterman HG, Bultink IE, Lems WF. Osteoporosis in patients with rheumatoid arthritis: an update in epidemiology, pathogenesis, and fracture prevention. Expert Opin Pharmacother. 2020 Oct;21(14):1725-1737.

Acknowledgements

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

Disclosure of Interests

None declared

AB0844 Characterizing Gut Microbial Enterotypes in undifferentiated spondyloarthritis

Background

The presence of dysbiosis in the gut microbiome is responsible for the initiation of autoinflammatory and autoimmune diseases. However, such dysbiosis is difficult to characterize in sweeping generalization owing to the high dimensional complexity of the gut microbiota.

Objectives

This study designed to characterize the gut microbial enterotype in patients with undifferentiated spondyloarthritis (USpA) from lower dimensionality and describe the dysbiosis.

Methods

The Fecal samples of 105 patients were diagnosed with USpA and gender- and age- matched 105 healthy controls (HC) were included in the intestinal microbiota composition analyses via Illumina sequencing of bacterial 16S rRNA genes. Microbiota-derived clustering was performed using Dirichlet multinomial mixtures (DMM) modeling. To identify discriminative features in abundance between enterotypes, the Linear Discriminant Analysis Effect Size (LEfSe) algorithm was used with the online interface Galaxy (Log10 LDA score > 4.0). The phyloseq R package to compute alpha diversity (ACE, Chao1, Shannon and Simpson indices), beta diversity (Bray-Curtis dissimilarity) and the microbial composition (at the genus level) to describe the richness and diversity of the microbiota between two enterotypes.

Results

As showed in Figure 1A and C, by evaluating the Laplace approximation to the negative log mode, 2 distinctly enterotypes were identified in the USpA and HC microbiota dataset. LEfSe Analysis indicated the distinctive abundant microbial clades between the 2 enterotypes (LDA score >4) in both the USpA and HC group respectively. At the genus level, Faecalibacterium and Prevotella was the driving genus of enterotype 1 and Bacteroides contributed to enterotype 2 (Figure 1B, D). The alpha-diversity and beta diversity between the distinctive enterotypes was highly significantly different (P < 0.01, Figure 1E, F). Distinct bacterial profiles were also observed in enterotype 1 and 2 (Figure 1G). Interestingly, no significant differences were found between USpA patients and HC for the corresponding same intestinal type. This may be because USpA was at a comparatively early stage of spondyloarthritis (SpA).

Conclusion

Two significantly distinct bacterial microbiota structures existed in the USpA patients which was consistent with the general healthy population.

References

[1]Belkaid Y, Hand TW: Role of the microbiota in immunity and inflammation. Cell 2014, 157(1):121-141.

Acknowledgements

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

Disclosure of Interests

None declared

AB0005 INTEGRATED ANALYSIS OF lncRNAs AND mRNAs EXPRESSION PROFILING IN SYSTEMIC LUPUS ERYTHEMATOSUS

Background

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by over-activity of lymphocytes, production of autoantibodies and effects on multiple organs 1. Growing evidences suggest long noncoding RNAs (lncRNAs) and mRNAs widely participate in physiological and pathological processes. However, knowledge of related lncRNAs and mRNAs in SLE remains limited.

Objectives

The aim of our study is to investigate the levels of differential expression of lncRNAs and mRNAs in the peripheral blood mononuclear cells (PBMCs) of SLE patients and their correlation with disease activity, clinical features and cell differentiation.

Methods

Peripheral venous blood 4ml were collected from 11 patients with SLE before and after treatment and 11 sex-and age-matched healthy individuals and saved in EDTA tubes. PBMCs were isolated from peripheral blood samples by Ficoll-Histopaque density gradient centrifugation. Total RNA was extracted from PBMCs with TRIzol reagent. RNAs amount and quality were quantified by using a NanoDrop ND-1000. Peripheral blood samples were sent to Novogene Co. Ltd (Beijing, China) for sequencing. The DESeq package in R language was used to analyze the differential expression of lncRNAs and mRNAs in the two groups. GO and KEGG databases analyze the potential biological functions and signal transduction and disease pathways affected by abnormal expression of lncRNAs and mRNAs2.

Results

According to the RNAs expression profiles, 338 lncRNAs (173 upregulated and 165 downregulated) and 2020 mRNAs (1292 upregulated and 728 downregulated) were differentially expressed between SLE patients and control groups. In addition, 17 lncRNAs were significantly downregulated and 66 mRNAs (47 upregulated and 19 downregulated) were differentially expressed between active and treated SLE patients. There were 1645 RNAs up-expression in active SLE patients and 36 RNAs under-expression in treated SLE patients, and total 14 RNAs changed direction of expression. GO and KEGG pathway analysis showed most of mRNAs were related to transcription, inflammation and immunity. The relativity between aberrantly expressed RNAs and clinical characteristics of active and treated SLE patients were shown in Table 1.

Conclusion

Dysregulation of lncRNAs and mRNAs involves in molecular regulation of SLE, which may support for diagnosis or determination of the susceptibility of individuals of SLE.

References

[1]Tsokos GC. Systemic lupus erythematosus. N Engl J Med 2011;365(22):2110-21. doi: 10.1056/NEJMra1100359 [published Online First: 2011/12/02]

[2]Zhang Y, Xu YZ, Sun N, et al. Long noncoding RNA expression profile in fibroblast-like synoviocytes from patients with rheumatoid arthritis. Arthritis Res Ther 2016;18(1):227. doi: 10.1186/s13075-016-1129-4 [published Online First: 2016/10/08]

Figure 1.

(A-C) Analysis of DElncRNAs and DEmRNAs of pre-treated SLE and cotrols. (A) The volcano plot with the DElncRNAs. (B) The volcano plot with the DEmRNAs. (C) The hierarchical clustering heatmap of DElncRNAs and DEmRNAs. (D-F) Analysis of DElncRNAs and DEmRNAs between pre-treated and treated SLE. (D) Volcano plot with the DElncRNAs. (E) Volcano plot with the DEmRNAs. (F) The hierarchical clustering heatmap of DElncRNAs and DEmRNAs. (G1-G5) Partial RNAs expression changed in active and treated SLE patients. Table 1 showed specific changed RNAs. (H1-H4) The top 20 GO and KEGG terms related to the up-regulated and down-regulated DEmRNAs. (I1-I3) GO and KEGG analyses of DEmRNAs between active and treated SLE patients.

Table 1:

The relativity between aberrantly expressed mRNA and LncRNA and clinical characteristics of active and treated SLE patients.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 82001740) and the Natural Science Research Project of Shanxi Province (No.20210302123275).

Disclosure of Interests

None declared

OP0087 INTEGRATED SYSTEMS ANALYSIS OF THE GUT MICROBIOTA PHENOTYPES IN THE RHEUMATOID ARTHRITIS

Background

Patients with rheumatoid arthritis (RA) displays extreme dysbiosis in microbiota. However, such dysbiosis is difficult to characterize owing to the high dimensional complexity of the gut microbiota1,2.

Objectives

The aim of this study was to discover the enterotype characters of intestinal flora in RA.

Methods

Fecal samples from 145 RA patients were collected for bacterial 16S rRNA genes sequencing. Mathematical modeling using Dirichlet multinomial mixtures (DMM) was applied to describe the variability in the microbiome data and cluster samples into enterotypes. The alpha-diversity, beta-diversity and the microbial composition analysis of the gut microbiome were used to define the difference of gut microbiota profiles between different enterotypes. The nonredundant taxonomic biomarkers for each enterotype were selected by using LEfSe. Inflammatory biomarkers (ESR, CRP), auto-antibodies(ACPA, MCV), peripheral lymphocytes subsets and cytokines were analyzed in our cohort using the Kruskal-Wallis test.

Results

Laplace approximation of DMM indicated two significantly distinct bacterial microbiota structures (RAE1 and RA E2) existed in the dataset (Figure 1a). Principal co-ordinates analyses confirmed that these two microbiota states explained a reasonable proportion of observed variance in microbiota composition(ANOSIM R2 = 0.267, p = 0.001; Figure 1b), with distinct bacterial genus distribution of in each enterotype (Figure 1c). RA E1 were primarily dominated by Prevotella while RA E2 by Bacteroides. Interestingly, Chao1, ACE, Shannon and Simpson revealed a higher alpha diversity in Prevotella-enriched enterotype (p< 0.001, Figure 1d). Fourteen selected taxonomic biomarkers at different phylogenetic levels showed great discriminant ability, with Log10 LDA score > 4.0 (Figure 1e-g). Further, inflammatory biomarkers (ESR, CRP) and auto-antibodies(ACPA, MCV) as well as the number of T, B and CD4+T, Th1, Th2, Th17, and Treg were consistent in RA E1 and RA E2 (p > 0.05, Figure 2h). But CD8+T were significantly higher in RA E2 than in RA E2 (p < 0.05).

Conclusion

Despite RA gut microbiota being of different dysbiosis, two patterns of dysbiosis, designated as RA-enterotypes, were predominant among the RA patient cohort. RA E2 exhibited a loss of Prevotella but a growth of Bacteroides, while RA E1 presented the opposite results.

References

[1]Arumugam M, Raes J, Pelletier E, et al. Enterotypes of the human gut microbiome. Nature 2011;473(7346):174-80. doi: 10.1038/nature09944

[2]Costea PI, Hildebrand F, Arumugam M, et al. Enterotypes in the landscape of gut microbial community composition. Nat Microbiol 2018;3(1):8-16. doi: 10.1038/s41564-017-0072-8

Acknowledgements

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

Disclosure of Interests

None declared.

[Cost-effectiveness of anti-tumor associated antigen autoantibody screening for hepatocellular carcinoma in the population with chronic hepatitis B-related cirrhosis]

Objective: To evaluate the cost-effectiveness of anti-tumor associated antigen autoantibody (TAAb) for hepatocellular carcinoma (HCC) screening in cirrhosis population with chronic hepatitis B (CHB). Methods: A simulated cohort of 40-year-old patients with CHB cirrhosis was established with a sample size of 10 000. Using TAAb screening alone or TAAb and AFP screening in parallel (TAAb + AFP) as the research strategy, and liver ultrasound and AFP screening in parallel (liver ultrasound + AFP) as the control strategy, the decision analysis Markov model was constructed and the model validity was evaluated. The 6-month cycle was simulated using TreeAge Pro 2020 software. Cost and quality-adjusted life years (QALY) were calculated. Incremental cost-effectiveness ratio (ICER) was used to compare the two strategies, and sensitivity analysis was used to evaluate the uncertainty of results. Results: The Markov model had a total of 11 outcomes, of which 7 were natural outcomes and 4 wereclinical intervention outcomes, and the goodness of fit was 0.969. The lifetime screening cost of TAAb+AFP strategy for HCC screening was 249 612 yuan/case, and the QALY per capita was 7.704 years. Compared with liver ultrasound +AFP strategy (247 805 yuan/case), the total health cost increased by 1 807 yuan/case, and the QALY obtained was 0.014. The ICER was 127 635 yuan /QALY. When the TAAb screening fee was higher than 889.552 yuan, or the discount rate was higher than 0.068, or the antiviral treatment compliance was lower than 45.1%, ICER > 212 676 yuan /QALY. When the single TAAb screening fee was 400-600 yuan, the TAAB+AFP strategy had cost effective value. When the willingness to pay was 70 892, 141 784 and 212 676 yuan /QALY, the probability of cost-effectiveness of TAAb+AFP strategy was 70.6%, 75.3% and 77.8%, respectively. Conclusion: It is cost-effective to use TAAb+AFP for early screening of liver cancer in Chinese population with CHB cirrhosis.

POS0109 IDENTIFICATION OF PRIMARY SJOGREN’S SYNDROME SUBTYPES BY MACHINE LEARNING

Background:

Sjogren’s syndrome(pSS) is a chronic, progressive, and systematic autoimmune disease characterized by lymphocytic infiltration of exocrine glands 1 2. Sicca symptoms and abnormal fatigue are the main clinical presentation, but those symptoms are non-specific to patients, which lead to delayed diagnosis 1 3. The heterogeneous of clinical manifestation raise challenges regarding diagnosis and therapy in pSS, thus it’s necessary for us to sub-classify pSS.

Objectives:

To explore new biomarkers for diagnosis and subtypes of pSS based on Machine Learning Primary.

Methods:

All microarray raw datas (CEL files) were screened and downloaded from Gene Expression Omnibus (GEO). Meta-analysis to identify the consistent DEGs by MetaOmics. Weighted gene co-expression network analysis (WGCNA) was used to the modules related to SS for further analysis. Subclasses were computed using a consensus Non-negative Matrix Factorization (NMF) clustering method. Immune cell infiltration was used to evaluate the expression of immune cells and obtain various immune cell proportions from samples. P value < 0.05 were considered statistically significant. All the analyses were conducted under R environment (version 4.03).

Results:

A total of 3715 consistent DEGs were identified from the four datasets, including 1748 up-regulated and 1967 down-regulated genes. Tour meaningful modules, including yellow, turquoise, grey60 and bule, were identified (Figure 1A,1B). And 183 overlapping gene were screened from the DEGs and the Hub genes in the four modles for further analysis. We final divided pSS patients into three subtypes, of which yellow and turquoise in Sub1, grey60 in Sub2 and blue in Sub3. Sub1 and Sub3 were related to cell metabolism, while Sub2 had connection with virus infection (Figure 1C,1D). Infiltrated immune cells were also different among these three types (Figure 1E,1F).

Conclusion:

Patients with pSS could be classified into 3 subtypes, this classification might help for assessing prognosis and guiding precise treatment.

References:

[1]Ramos-Casals M, Brito-Zerón P, Sisó-Almirall A, et al. Primary Sjogren syndrome. BMJ (Clinical research ed) 2012;344:e3821. doi: 10.1136/bmj.e3821 [published Online First: 2012/06/16].

[2]Brito-Zeron P, Baldini C, Bootsma H, et al. Sjogren syndrome. Nat Rev Dis Primers 2016;2:16047. doi: 10.1038/nrdp.2016.47 [published Online First: 2016/07/08].

[3]Segal B, Bowman SJ, Fox PC, et al. Primary Sjogren’s Syndrome: health experiences and predictors of health quality among patients in the United States. Health Qual Life Outcomes 2009;7:46. doi: 10.1186/1477-7525-7-46 [published Online First: 2009/05/29].

Acknowledgements:

This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).

Disclosure of Interests:

None declared

POS0847 IDENTIFICATION OF MOLECULAR PHENOTYPES IN SYSTEMIC SCLEROSIS BY INTEGRATIVE SYSTEMS ANALYSIS

Background:

Systemic sclerosis (scleroderma, SSc) is a systemic autoimmune disease characterized by inflammation, fibrosis and vasculopathy and associated with high mortality and high morbidity1. Stratification based on whole-genome gene expression data could provide a new basis for clinical diagnosis from a micro perspective2.

Objectives:

The objective of this study is to stratify patients with SSc, combine with clinical skin scores and clinical features, and provide a preliminary assessment and novel insights for assessing disease severity, and treatment design.

Methods:

The original data mRNA expression profiles of GSE95065 (including 18 SSc patients and 4 healthy controls) and GSE130955 (including 58 SSc patients and 33 healthy controls) were downloaded from the public Gene Expression Omnibus (GEO) database. After batch correction, background adjustment, and other pre-processing, a large gene matrix was obtained to identify the differently expressed genes (DEGs) of SSc compared with healthy controls. Then the gene expression matrix decomposition was used to identify SSc subtypes by NMF algorithm. The cluster-based signature genes were applied to pathway enrichment analysis by Metascape3. Immune infiltrating cells and clinical skin scores were evaluated in all SSc subtypes.

Results:

Total 325 DEGs were imputed to NMF unsupervised machine learning algorithm. Patients were divided into 2 subtypes (Figure 1A), one of which (sub1) was mostly enriched in the defense response to bacterium and cellular response to lipopolysaccharide pathway and another subtype (sub2) was enriched in the PPAR signaling and alcohol metabolic process pathway (Figure 1B-C). According to immune infiltration, sub1 had higher level of immune cells such as B cells, CD4+T cells, DC cells, Th2 cells and Tregs compared with sub2 (P < 0.01). Sub2 had more skin-related cells, including Epithelial cells, Fibroblasts and Sebocytes (P < 0.05). Interestingly, combined with clinical information, sub1 showed a severe clinical skin score over those of Sub2 patients (P < 0.05)(Figure 1D-E).

Conclusion:

Our findings indicated that SSc patients could be stratified into 2 subtypes which had different molecular profiles of disease progression and clinical disease activities. This result could serve as a template for future studies to design stratified approaches for SSc patients.

References:

[1]Xu X, Ramanujam M, Visvanathan S, et al. Transcriptional insights into pathogenesis of cutaneous systemic sclerosis using pathway driven meta-analysis assisted by machine learning methods. PLoS One 2020;15(11):e0242863. doi: 10.1371/journal.pone.0242863 [published Online First: 2020/12/01].

[2]Xu C, Meng LB, Duan YC, et al. Screening and identification of biomarkers for systemic sclerosis via microarray technology. Int J Mol Med 2019;44(5):1753-70. doi: 10.3892/ijmm.2019.4332 [published Online First: 2019/09/24].

[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].

Acknowledgements:

This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).

Disclosure of Interests:

None declared

POS0851 IDENTIFICATION OF HUB GENES AND PATHWAYS IN DERMATOMYOSITIS BY BIOINFORMATICS ANALYSIS

Background:

Dermatomyositis (DM) is a chronic systemic autoimmune disease characterized by inflammatory infiltrates in the skin and muscle1. The genes and pathways in the inflamed myopathies in patients with DM are poorly understood2.

Objectives:

To identify the key genes and pathways associated with DM and further discover its pathogenesis.

Methods:

Muscle tissue gene expression profile (GSE143323) were acquired from the GEO database, which included 39 DM samples and 20 normal samples. The differentially expressed genes (DEGs) in DM muscle tissue were screened by adopting the R software. Gene ontology (GO) and Kyoto Encyclopedia of Genome (KEGG) pathway enrichment analysis was performed by Metascape online analysis tool. A protein-protein interaction (PPI) network was then constructed by STRING software using the genes in significantly different pathways. Network of DEGs was analyzed by Cytoscape software. And degree of nodes was used to screen key genes.

Results:

Totally, 126 DEGs were obtained, which contained 122 up-regulated and 4 down-regulated. GO analysis revealed that most of the DEGs were significantly enriched in type I interferon signaling pathway, response to interferon-gamma, collagen-containing extracellular matrix, response to interferon-alpha and bacterium, positive regulation of cell death, leukocyte chemotaxis. KEGG pathway analysis showed that upregulated DEGs enhanced pathways associated with the hepatitis C, complement and coagulation cascades, p53 signaling pathway, RIG-I-like receptor signaling, Osteoclast differentiation, and AGE-RAGE signaling pathway. Ten hub genes were identified in DM, they were ISG15, IRF7, STAT1, MX1, OASL, OAS2, OAS1, OAS3, GBP1, and IRF9 according to the Cytoscape software and cytoHubba plugin.

Conclusion:

The findings from this bioinformatics network analysis study identified the key hub genes that might provide new molecular markers for its diagnosis and treatment.

References:

[1]Olazagasti JM, Niewold TB, Reed AM. Immunological biomarkers in dermatomyositis. Curr Rheumatol Rep 2015;17(11):68. doi: 10.1007/s11926-015-0543-y [published Online First: 2015/09/26].

[2]Chen LY, Cui ZL, Hua FC, et al. Bioinformatics analysis of gene expression profiles of dermatomyositis. Mol Med Rep 2016;14(4):3785-90. doi: 10.3892/mmr.2016.5703 [published Online First: 2016/09/08].

[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].

Acknowledgements:

This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).

Disclosure of Interests:

None declared

OP0184 PHENOTYPING OF MOLECULAR SIGNATURES IN THE SYNOVIAL TISSUE OF RHEUMATOID ARTHRITIS BY INTEGRATIVE SYSTEMS ANALYSIS

Background:

Rheumatoid arthritis (RA) is an aggressive immune-mediated joint disease characterized by synovial proliferation and inflammation, cartilage destruction, and joint destruction1. Despite efforts to characterize the disease subsets and to predict the differential prognosis in RA patients, disease heterogeneity is not adequately translated into the current clinical subclassification2.

Objectives:

To develop and validate an integrative system approach for stratifying patients with RA according to disease status and whole-genome gene expression data.

Methods:

An RNA sequencing dataset of synovial tissues from 124 RA patients (including 57 patients with early RA, 95 with established RA) and 15 healthy controls (HC) was imported from the Gene Expression Omnibus (GEO) database (GSE89408) by software package R (version 4.0.3). After filtrating of differentially expressed genes (DEGs) between RA and HC, non-negative matrix factorization, functional enrichment, and immune cell infiltration were applied to illustrate the landscapes of these patients for classification. Clinical features (age, gender, and auto-antibodies) were also compared to discover the signatures of these classifications.

Results:

A matrix of 576 DEGs from RA samples was classified into 5 subtypes (early/C1–C3, established/C4-C5) with distinct molecular and cellular signatures and two sub-groups (S1 and S2) (Figure 1A-1D). New-onset patients (early C2) and established C4 patients were named as S1, they shared similar gene signatures mainly characterized by prominent immune cells and proinflammatory signatures, and enriched in the chemokine-mediated signaling pathway, lymphocyte activation, response to bacterium and Primary immunodeficiency. S2(C1, C3 and C5) were more occupied by synovial fibroblasts of destructive phenotype. They were mainly enriched in the response to external factors and PPAR signaling pathway (Figure 1E-1H). Interestingly, combined with clinical information, S1 and S2 had no significance in age and gender (P > 0.05). But patients in S1 had a stronger association with the presence of anti-citrullinated protein antibodies (ACPA) (P < 0.05) (Figure 1I-1J).

Conclusion:

We successfully deconvoluted RA synovial tissues into pathobiological discrete subsets using an unsupervised machine learning method and described their distinct molecular and cellular characteristics. These results provide important insights into divergent and shared mechanistic features of RA and serve as a template for future studies to guide drug tar-get discovery by synovial molecular signatures and de-sign stratified approaches for patients with RA.

References:

[1]Smolen JS, Aletaha D, McInnes IB. Rheumatoid arthritis. Lancet 2016;388(10055):2023-38. doi: 10.1016/S0140-6736(16)30173-8 [published Online First: 2016/10/30]

[2]Jung SM, Park KS, Kim KJ. Deep phenotyping of synovial molecular signatures by integrative systems analysis in rheumatoid arthritis. Rheumatology (Oxford) 2020 doi: 10.1093/rheumatology/keaa751 [published Online First: 2020/11/25]

Acknowledgements:

This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).

Disclosure of Interests:

None declared

POS0458 IDENTIFICATION OF HUB GENES AND MOLECULAR PATHWAYS IN PATIENTS WITH RHEUMATOID ARTHRITIS BY BIOINFORMATICS ANALYSIS

Background:

Rheumatoid arthritis (RA) is a chronic, inflammatory synovitis based systemic disease of unknown etiology1. The genes and pathways in the inflamed synovium of RA patients are poorly understood.

Objectives:

This study aims to identify differentially expressed genes (DEGs) associated with the progression of synovitis in RA using bioinformatics analysis and explore its pathogenesis2.

Methods:

RA expression profile microarray data GSE89408 were acquired from the public gene chip database (GEO), including 152 synovial tissue samples from RA and 28 healthy synovial tissue samples. The DEGs of RA synovial tissues were screened by adopting the R software. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. Protein-protein interaction (PPI) networks were assembled with Cytoscape software.

Results:

A total of 654 DEGs (268 up-regulated genes and 386 down-regulated genes) were obtained by the differential analysis. The GO enrichment results showed that the up-regulated genes were significantly enriched in the biological processes of myeloid leukocyte activation, cellular response to interferon-gamma and immune response-regulating signaling pathway, and the down-regulated genes were significantly enriched in the biological processes of extracellular matrix, retinoid metabolic process and regulation of lipid metabolic process. The KEGG annotation showed the up-regulated genes mainly participated in the staphylococcus aureus infection, chemokine signaling pathway, lysosome signaling pathway and the down-regulated genes mainly participated in the PPAR signaling pathway, AMPK signaling pathway, ECM-receptor interaction and so on. The 9 hub genes (PTPRC, TLR2, tyrobp, CTSS, CCL2, CCR5, B2M, fcgr1a and PPBP) were obtained based on the String database model by using the Cytoscape software and cytoHubba plugin3.

Conclusion:

The findings identified the molecular mechanisms and the key hub genes of pathogenesis and progression of RA.

References:

[1]Xiong Y, Mi BB, Liu MF, et al. Bioinformatics Analysis and Identification of Genes and Molecular Pathways Involved in Synovial Inflammation in Rheumatoid Arthritis. Med Sci Monit 2019;25:2246-56. doi: 10.12659/MSM.915451 [published Online First: 2019/03/28]

[2]Mun S, Lee J, Park A, et al. Proteomics Approach for the Discovery of Rheumatoid Arthritis Biomarkers Using Mass Spectrometry. Int J Mol Sci 2019;20(18) doi: 10.3390/ijms20184368 [published Online First: 2019/09/08]

[3]Zhu N, Hou J, Wu Y, et al. Identification of key genes in rheumatoid arthritis and osteoarthritis based on bioinformatics analysis. Medicine (Baltimore) 2018;97(22):e10997. doi: 10.1097/MD.0000000000010997 [published Online First: 2018/06/01]

Acknowledgements:

This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).

Disclosure of Interests:

None declared

AB0005 IDENTIFICATION OF KEY GENES AND PATHWAYS FOR PSORIASIS BASED ON GEO DATABASES BY BIOINFORMATICS ANALYSIS

Background:

Psoriasis is an immune-mediated, genetic disease manifesting in the skin or joints or both, and also has a strong genetic predisposition and autoimmune pathogenic traits1. The hallmark of psoriasis is sustained inflammation that leads to uncontrolled keratinocyte proliferation and dysfunctional differentiation. And it’s also a chronic relapsing disease, which often necessitates a long-term therapy2.

Objectives:

To investigate the molecular mechanisms of psoriasis and find the potential gene targets for diagnosis and treating psoriasis.

Methods:

Total 334 gene expression data of patients with psoriasis research (GSE13355 GSE14905 and GSE30999) were obtained from the Gene Expression Omnibus database. After data preprocessing and screening of differentially expressed genes (DEGs) by R software. Online toll Metascape3 was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Interactions of proteins encoded by DEGs were discovered by Protein-protein interaction network (PPI) using STRING online software. Cytoscape software was utilized to visualize PPI and the degree of each DEGs was obtained by analyzing the topological structure of the PPI network.

Results:

A total of 611 DEGs were found to be differentially expressed in psoriasis. GO analysis revealed that up-regulated DEGs were mostly associated with defense and response to external stimulus while down-regulated DEGs were mostly associated with metabolism and synthesis of lipids. KEGG enrichment analysis suggested they were mainly enriched in IL-17 signaling, Toll-like receptor signaling and PPAR signaling pathways, Cytokine-cytokine receptor interaction and lipid metabolism. In addition, top 9 key genes (CXCL10, OASL, IFIT1, IFIT3, RSAD2, MX1, OAS1, IFI44 and OAS2) were identified through Cytoscape.

Conclusion:

DEGs of psoriasis may play an essential role in disease development and may be potential pathogeneses of psoriasis.

References:

[1]Boehncke WH, Schon MP. Psoriasis. Lancet 2015;386(9997):983-94. doi: 10.1016/S0140-6736(14)61909-7 [published Online First: 2015/05/31].

[2]Zhang YJ, Sun YZ, Gao XH, et al. Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis. Mol Med Rep 2019;20(1):225-35. doi: 10.3892/mmr.2019.10241 [published Online First: 2019/05/23].

[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].

Acknowledgements:

This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).

Disclosure of Interests:

None declared

POS0199 TIME-SERIES ANALYSIS IN MODERATE TO SEVERE PLAQUE PSORIASIS UNDER DIFFERENT BIOLOGICS TREATMENTS

Background:

Moderate to Severe Plaque Psoriasis is an inflammatory skin disease that is associated with multiple comorbidities and substantially diminishes patients’ quality of life. As one of the most significant therapeutic advancements in the field of dermatology, Biologics such as TNF inhibitors, IL-12/23 inhibitor, IL-17 inhibitors, and IL-23 inhibitors, have higher efficacy compared with oral medications or phototherapy1. However, the previous studies did not focus on the simultaneous comparison of molecular changes in different classes of biologics. The identification of time-series genes (TSGs) could help to uncover the mechanisms underlying transcriptional regulation2.

Objectives:

In this study, we aimed to compare the differences in expression patterns and functions of time-series genes in Moderate to Severe Plaque Psoriasis under different biologics treatments.

Methods:

The transcription profile of GSE117239 and GSE51440 were obtained from the Gene Expression Omnibus database (GEO). The GSE117239 included 19 samples treated with Etanercept (TNF inhibitors) and 16 samples treated with Ustekinumab (IL-12/23 inhibitor). The GSE51440 included 4 samples treated with Guselkumab (IL-23 inhibitors). Skin biopsy samples (LS: lesion, NL: non-lesion) were collected at baseline, weeks 1 and 12, respectively. After background adjustment and other pre-procession, differentially expressed genes (DEGs) were extracted from LS skin biopsy and untreated NL skin biopsy at different times after three different biologics treatments, respectively. The Short Time-series Expression Miner (STEM) software was used to cluster and compare average DEGs with coherent changes. Afterward, the different expression patterns of TSGs under the three treatment groups were compared. GO analysis and KEGG pathway enrichment analysis of TSGs were performed by Metascape.

Results:

Different DEGs varied in LS skin compared with those of NL skin biopsy: 976 genes in Ustekinumab group, 996 genes in Etanercept group, and 601 genes in Guselkumab group detailly (P < 0.05 and [log FC] > 1). Gene landscapes suggested the signatures of LS gradually changed during the treatment process, and gradually converge to NL signatures (Fig.1a, 2a,3a). Time-series genes in the three treatment groups had different expression patterns and functions. In the Ustekinumab group, a total of 448 TSGs in profile 3 showed a stable-stable-decreasing expression trend and significantly associated with mitotic nuclear division and defense response to other organism, whereas in profile 4 represented a stable-stable-increasing expression trend and significantly associated with positive regulation of cellular response to organic 9 compound (Fig.1). With the treatment of Etanercept, 22 TSGs had a stable-increasing-increasing expression tendency and closely associated with fatty acid metabolism and steroid metabolic process (Fig.2). After Guselkumab treatment, 13 TSGs also represented a stable-increasing-increasing expression tendency that mainly characterized by defense response to other organism and epidermis development (Fig.3). Interestingly, both Ustekinumab and Guselkumab treatment dramatically influenced defense response to other organism-related genes, while Etanercept mainly affected genes involved in fatty acid metabolism and steroid metabolic process.

Conclusion:

Biologics effectively reconstituted the gene signatures of psoriasis in different aspects. TSG features could be one of indicator for precise intervention for psoriasis.

References:

[1]Armstrong AW, Read C. Pathophysiology, Clinical Presentation, and Treatment of Psoriasis: A Review. Jama 2020;323(19):1945-60. doi: 10.1001/jama.2020.4006 [published Online First: 2020/05/20]

[2]Ernst J, Bar-Joseph Z. STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics 2006;7:191. doi: 10.1186/1471-2105-7-191 [published Online First: 2006/04/07]

Acknowledgements:

This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).

Disclosure of Interests:

None declared

POS0363 IDENTIFICATION OF MOLECULAR PHENOTYPES AND IMMUNE CELL INFILTRATION IN PSORIATIC ARTHRITIS PATIENTS’ SKIN TISSUES BY INTEGRATED BIOINFORMATICS ANALYSIS

Background:

Psoriatic arthritis (PsA) is an inflammatory musculoskeletal disease associated with cutaneous psoriasis1. Heterogeneity of clinical manifestation often makes differential diagnosis difficult 2. Thus, the underlying molecular pathogenesis of PsA need to be further studied to diagnose early and ensure optimal management of arthritis and key comorbidities.

Objectives:

This research was conducted to identify molecular phenotypes and immune infiltration in the skin tissues of psoriatic arthritis patients according to bioinformatics analysis.

Methods:

The mRNA expression profiles of GSE13355 (116 samples), GSE14905 (56 samples) and GSE30999 (162 samples) were obtained from the publicly GEO databases. Non-negative matrix factorization (NMF), functional enrichment and cibersort algorithm were applied to illustrate the conditions of PsA patients’ skin tissues for classification after screening the differentially expressed genes (DEGs) between lesion biopsy and non-lesion biopsy.

Results:

Two subsets (Sub1 and Sub2) were identified and validated by NMF typing of 612 detected DEGs (Figure 1a). A total of 54 signature genes (18 in Sub1 and 36 in Sub2) were obtained (Figure 1b). GO and KEGG enrichment analysis showed the signature genes in Sub1 were mainly involved in proliferation and differentiation of immune cells, whereas genes in Sub2 were related to humoral immune response mediated by antimicrobial peptide (Figure 1c.1d). Further, immune cell infiltration results revealed Sub2 had higher levels of resting NK cells (P<0.001), macrophages M1(P<0.001), resting mast cells (P<0.001) and regulatory T cells (P<0.001) but lower concentrations of activated CD4+ memory T cells (P<0.001), activated NK cells (P<0.05), activated dendritric cells(P<0.001), eosinophils (P<0.05) and neutrophil (P<0.001) (Figure 1e).

Conclusion:

The pathogenesis of psoriatic arthritis is related to both cellular immunity and humoral immunity. It is indispensable to adjust the treatment strategies according to patient’s immune status.

References:

[1]Ritchlin CT, Colbert RA, Gladman DD. Psoriatic Arthritis. The New England journal of medicine 2017;376(10):957-70. doi: 10.1056/NEJMra1505557 [published Online First: 2017/03/09].

[2]Veale DJ, Fearon U. The pathogenesis of psoriatic arthritis. Lancet (London, England) 2018;391(10136):2273-84. doi: 10.1016/s0140-6736(18)30830-4 [published Online First: 2018/06/13].

Acknowledgements:

This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).

Disclosure of Interests:

None declared

POS0743 GENE EXPRESSION MICROARRAY IN LUPUS NEPHRITIS BY BIOINFORMATIC ANALYSIS

Background:

Nephritis is one of the predominant causes of morbidity and mortality in patients with lupus1 2.The lack of understanding regarding the molecular mechanisms of lupus nephritis(LN) hinders the development of specific targeted therapy for this progressive disease3.

Objectives:

In this study, we use bioinformatics method to analyze the genes involved in regulating the potential pathogenesis of LN.

Methods:

The expression profile of LN(GSE104948 and GSE32591) was obtained from the GEO database.GSE104948 was a memory chip, which included 32 LN glomerular biopsy tissues and 3 glomerular tissues from living donors.GSE32591 dataset included 32 LN glomerular biopsy tissues and 15 glomerular tissues from living donors. The Oligo package was used to process the data to obtain the expression matrix files of all the related genes.P<0.05 and |log2(FC)|>2 were setted as cut-off criteria for the DEGs.Ggplot2, heatmap packages were used to DEGs visualization. Metascape online tool was used to annotating DEGs for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis performed.We used STRING online database to construct protein-protein interaction (PPI) network. Hub genes were identified by Cytoscape.

Results:

In differential expression analysis,357 DEGs were identified,including 248 up-regulated genes and 109 down-regulated genes (Figure 1A,B).GO enrichment showed that these DEGs were primarily enriched in biological pathways, cell localization and molecular function and revealed that LN-related genes mainly involved in immune response.KEGG pathway annotation enrichment analysis revealed these DEGs were closely associated with Staphylococcus aureus infection,Complement and coagulation cascades (Figure 1D). Fourteen hub genes(IFT3,IRF7,OAS3,GBP2,RSAD2,MX1,IFIT2,IFI6,MX2,ISF15,IFIT1,QAS2,OASL,OAS1) were identified from PPI network (Figure 1C,E).

Conclusion:

Illuminating the molecular mechanisms of LN was help for deep understanding of LN.

References:

[1]Song J, Zhao L, Li Y. Comprehensive bioinformatics analysis of mRNA expression profiles and identification of a miRNA-mRNA network associated with lupus nephritis. Lupus 2020;29(8):854-61. doi: 10.1177/0961203320925155 [published Online First: 2020/05/22].

[2]Yao F, Sun L, Fang W, et al. HsamiR3715p inhibits human mesangial cell proliferation and promotes apoptosis in lupus nephritis by directly targeting hypoxiainducible factor 1alpha. Mol Med Rep 2016;14(6):5693-98. doi: 10.3892/mmr.2016.5939 [published Online First: 2016/11/24].

[3]Dall’Era M. Treatment of lupus nephritis: current paradigms and emerging strategies. Curr Opin Rheumatol 2017;29(3):241-47. doi: 10.1097/BOR.0000000000000381 [published Online First: 2017/02/17].

Acknowledgements:

This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).

Disclosure of Interests:

None declared

POS0742 SCREENING AND BIOINFORMATICS ANALYSIS OF HUB GENES AND PATHWAYS FOR PRIMARY SJÖGREN’S SYNDROME BASED ON GEO DATABASE

Background:

Primary Sjögren’s syndrome (pSS) is an autoimmune disease that featured as lymphoplasmacytic infiltration of the exocrine glands leading to sicca symptoms1. However, its underlying molecular mechanisms remain elusive.

Objectives:

This study aims to identify differentially expressed genes (DEGs) and pathways associated with the progression of pSS using bioinformatics analysis and explore its pathogenesis.

Methods:

The pSS-associated gene chip data set GSE66795 was obtained from the Gene Expression Omnibus (GEO) database, which included 131 cases of fully-phenotyped pSS patients’ whole blood samples and 29 cases of control samples. DEGs were screened Using R software. Online tool Metascape2 was used to make Gene Ontology (GO) and KEGG pathway enrichment. The PPI network was performed using String database. Hub genes were identified by Cytoscape.

Results:

A total of 108 DEGs were captured, including 101 up-regulated genes and 7 down-regulated genes. GO enrichment showed that these DEGs were primarily enriched in defense response to virus, response to interferon-gamma, regulation of innate immune response, response to interferon-beta, double-stranded RNA binding, response to interferon-alpha. KEGG pathway enrichment analysis showed these DEGs were principally enriched in Influenza A, RIG-I-like receptor signaling pathway, necroptosis, Staphylococcus aureus infection. Finally, 9 hub genes (STAT1, IRF7, OAS2, GBP1, OAS1, IFIT3, IFIH1, OAS3, DDX60) had highest degree value.

Conclusion:

The findings identified molecular mechanisms and the key hub genes that may involve in the occurrence and development of pSS.

References:

[1]Francois H, Mariette X. Renal involvement in primary Sjogren syndrome. Nat Rev Nephrol 2016;12(2):82-93. doi: 10.1038/nrneph.2015.174 [published Online First: 2015/11/17].

[2]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].

Acknowledgements:

This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).

Disclosure of Interests:

None declared

POS1211 IDENTIFICATION OF COMMON FUNCTIONAL PATHWAYS IN PATIENTS WITH LUPUS AND COVID-19 BY TIME-SERIES ANALYSIS

Background:

Systemic lupus erythematosus (SLE) is a chronic autoimmune disorder characterized by abnormal activity of the immune system, producing the autoantibodies directed against nuclear and cytoplasmic antigens1. Infection is known as one of the common trigger factors for SLE. Coronavirus disease in 2019 (COVID-19), a severe acute respiratory syndrome, is now spreading rapidly throughout the world2.Though previous studies have addressed the susceptibility of lupus patients to the virus but how patients with SLE deal with COVID-19 is unclear up until now.

Objectives:

To clarify the common pathogenesis of SLE and COVID-19, and find the appropriate treatment for Lupus and prevent COVID-19.

Methods:

The transcription profile of SLE (GSE38351) and COVID-19 (GSE161778) were obtained from the Gene Expression Omnibus database (GEO). R package was used to find differentially expressed genes (DEGs) between lupus patients and HCs. After background adjustment and other pre-procession, DEGs were extracted from the peripheral blood of patients with COVID-19 at three different disease progression(moderate, severe and remission status). The Short Time-series Expression Miner (STEM) was used to cluster and compare average DEGs with coherent changes. The different expression patterns of time-series genes (TSGs) were also compared among these patients. GO and KEGG pathway enrichment analysis of TSGs and DEGs were performed by Metascape.

Results:

Compared with HC, patients with SLE expressed 977 DEGs, which were mainly associated with defense response to virus, Epstein-Barr virus infection and response to interferon-γ(INF-γ) (Figure 1a). As for COVID-19 patients, there were 1584 DEGs obtained when compared with those of HCs (P < 0.05) (Figure 1b). Gene landscapes suggested the signatures of COVID-19 patients gradually changed during the disease progression, and gradually converge to HCs signatures. Time-series genes in the three stage of disease had different expression patterns and functions. A total of 959 TSGs in profile 3 showed a stable-stable-decreasing expression trend and significantly associated with INF signaling pathway (Figure 1c,1d). Interestingly, patients with SLE and COVID-19 shared common pathways such as INF-γ related functional pathway.

Conclusion:

INF-γ is an important common node of SLE and COVID-19. Controlling the production of INF-γ not only has therapeutic effect on SLE patients, but also may prevent COVID-19.

References:

[1]Tsokos GC. Systemic lupus erythematosus. N Engl J Med 2011;365(22):2110-21. doi: 10.1056/NEJMra1100359 [published Online First: 2011/12/02]

[2]Wan DY, Luo XY, Dong W, et al. Current practice and potential strategy in diagnosing COVID-19. Eur Rev Med Pharmacol Sci 2020;24(8):4548-53. doi: 10.26355/eurrev_202004_21039 [published Online First: 2020/05/07]

Acknowledgements:

This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).

Disclosure of Interests:

None declared.

POS0732 IDENTIFICATION OF AUTOPHAGY-RELATED PHENOTYPES IN PRIMARY SJOGREN’S SYNDROME

Background:

Primary Sjogren’s syndrome (pSS) is a chronic systemic autoimmune disease characterized by disorders of effector T cell subpopulations such as Th1, Th2, Th17, regulatory T cells, and follicular helper T cells 1 2. Autophagy is an evolutionarily conserved self-digestion process that plays an important role in T cell-mediated immune response3. The relationship between autophagy and T cell subsets was unclear in pSS up till now.

Objectives:

To landscape the autophagy-related multiple gene expression signature in pSS classification and discover the influence of autophagy in T cell subsets.

Methods:

Gene expression profiles of pSS samples (GSE66795, GSE51092, GSE154926) were acquired from GEO database. A set of significant G-ATGs were intersected from the global gene of patients and 232 autophagy genes (ATGs) which were obtained from the Human Autophagy Database (HADb, http://www.autophagy.lu/). In training dataset (GSE66795, including 155 patients and 29 healthy controls), non-negative matrix factorization was used to divided patients by G-ATGs expression microarray data. An autophagy score model divided patients into the high-autophagy score and low groups by ssGSEA scores of gene according to normalized G-ATGs training data. Further, new classifications were validated by both peripheral blood samples (GSE51092, 90 patients) and salivary gland tissue (GSE154926, 43 participants).

Results:

Two distinct subtypes were identified and validated by 206 selected significant G-ATGs in training datasets (figure 1A,B) and validation datasets according to the autophagy score (figure 1D,E,F) Combined with clinical information of salivary gland dataset, it was found that most patients with early pSS were grouped in the high autophagy, while advanced patients were grouped in the low (figure 1G). Patients in high-autophagy group had higher levels of Treg cells and Th2 cells but lower concentrations of Th17 and Th1 in peripheral blood (figure 1C, P <0.05). Similar results were also observed in salivary gland tissue (figure 1H, P <0.05).

Conclusion:

Patients with different autophagy status differs from each other. Autophagy is closely corelated with lymphocyte subpopulations in patients with pSS. This work may help inform therapeutic decision-making for the treatment of pSS.

References:

[1]Colafrancesco S, Vomero M, Iannizzotto V, et al. Autophagy occurs in lymphocytes infiltrating Sjögren’s syndrome minor salivary glands and correlates with histological severity of salivary gland lesions. Arthritis research & therapy 2020;22(1):238. doi: 10.1186/s13075-020-02317-6 [published Online First: 2020/10/15].

[2]Alessandri C, Ciccia F, Priori R, et al. CD4 T lymphocyte autophagy is upregulated in the salivary glands of primary Sjögren’s syndrome patients and correlates with focus score and disease activity. Arthritis research & therapy 2017;19(1):178. doi: 10.1186/s13075-017-1385-y [published Online First: 2017/07/27].

[3]Wei J, Long L, Yang K, et al. Autophagy enforces functional integrity of regulatory T cells by coupling environmental cues and metabolic homeostasis. Nature immunology 2016;17(3):277-85. doi: 10.1038/ni.3365 [published Online First: 2016/01/26].

Acknowledgements:

This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).

Disclosure of Interests:

None declared

AB0824 ANALYSIS OF GUT MICROBIOTA IN RHEUMATOID ARTHRITIS PATIENTS

Background:

Gastrointestinal microbiota, particularly gut microbiota is an indispensable environmental factor in the progression of Rheumatoid Arthritis (RA). Understanding the diversity and function of the intestinal flora in patients with RA is part and parcel to understand the relationship between microbiota and human health.

Objectives:

This study aimed to identify the diversity and function of the intestinal flora in patients with RA.

Methods:

A total of 166 participants were recruited in this study, comprising 93 RA patients and 73 age and sex-matched healthy controls (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. Sequence data were compiled and processed using Qiime2. Sequences were grouped into operational taxonomic units (ASVs)1. Microbial diversity was estimated by the Simpson index. PICRUSt2 was used to predict KEGG functional pathway differences between RA and HC intestinal flora functions based on ASV Tables2. Linear Discriminant Analysis (LDA) Effect Size (LEfSe) analysis was performed using LEfSe software to discovery the different intestinal flora and functions between RA and healthy.

Results:

The alpha-diversity index of Simpson the microbiome in RA patients was lower than that of HCs (Figure 1a, P <0.05). Compared with HCs, bacterial Bacilli and Lactobacillales were more abundant in patients with RA (Figure 1b, P <0.05). In contrast, Marinifilaceae, Peptococcaceae, Peptococcales and Phascolarcto bacterium were less abundant in the RA group (Figure 1b, P <0.05). As shown in Figure 1c, propanoate metabolism, taurine and hypotaurine metabolism, ascorbate and aldarate metabolism, biosynthesis of siderophore group nonribosomal peptides and glutathione metabolism were the most significantly altered pathways in RA (P <0.05). Epithelial cell signaling in Helicobacter pylori infection, RNA transport, RNA degradation and plant-pathogen interaction were the most significantly altered pathways in HC (P <0.05). The different KEGG metabolic pathways were mainly concentrated in carbohydrate and amino acid metabolism.

Conclusion:

Gut dysbiosis in RA patients mainly characterized by reduced the diversity and impaired abundance of the intestinal flora, which severely influence the metabolism of gastrointestinal microbiota. The discovery of the associated intestinal microbiota of RA may provide a new idea for RA treatment.

References:

[1]Han L, Zhao K, Li Y, et al. A gut microbiota score predicting acute graft-versus-host disease following myeloablative allogeneic hematopoietic stem cell transplantation. Am J Transplant 2020;20(4):1014-27. doi: 10.1111/ajt.15654 [published Online First: 2019/10/13]

[2]Liss MA, White JR, Goros M, et al. Metabolic Biosynthesis Pathways Identified from Fecal Microbiome Associated with Prostate Cancer. Eur Urol 2018;74(5):575-82. doi: 10.1016/j.eururo.2018.06.033 [published Online First: 2018/07/17]

Acknowledgements:

This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).

Disclosure of Interests:

None declared

POS0736 IDENTIFICATION OF MOLECULAR PHENOTYPES AND IMMUNE CELL INFILTRATION IN SYSTEMIC LUPUS ERYTHEMATOSUS PATIENTS ACCORDING TO LONGITUDINAL GENE EXPRESSION

Background:

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with highly heterogeneous clinical presentation characterized by disease unpredictable flares and multi-systemic involvement1 2. This clinical heterogeneity calls for design a molecular stratification to improve clinical trial design and formulate personalization treatment therapies.

Objectives:

This research was conducted to develop a reliable method to stratify SLE patients combined gene expression information and disease status.

Methods:

The mRNA expression profile of GSE138458 (contained 307 patients and 23 controls) and GSE49454 (contained 111 patients and 16 controls) were downloaded from the publicly GEO databases. After background adjustment, batch correction, and other pre-procession, obtaining a big gene matrix to identify the differentially expressed genes (DEGs) in SLE compared with healthy controls, which were screened by P value < 0.01. SLE subtypes were identified by non-negative matrix factorization (NMF) based on DEGs. Acquired signature genes in different SLE subtypes were conducted to process pathway enrichment analysis in Metascape. SLEDAI score and immune cell infiltration was also performed between subtypes by software package R (version 4.0.3).

Results:

Total 1202 DEGs were imputed to NMF unsupervised machine learning method. Patients with SLE were stratified into two subsets based on 184 signature genes derived from obtained DEGs(Fig.1A, 1B). GO and KEGG enrichment analysis showed that signature genes were mainly involved in negative regulation of innate immune response, toll-like receptor signaling pathway, regulation of immune effector process and so on(Fig.1C). Patients in Sub1 group had severe disease activity measures compared with those in Sub2(Fig.1D). SLEDAI scores from GSE49454 dataset were also higher in Sub1 compare with Sub2(Fig.1E). Further, immune cell infiltration results revealed an insufficient of regulatory T cell, CD8 T cells and naive CD4 T cells in Sub1 and neutrophils cells in Sub2(P<0.05)(Fig.1F).

Conclusion:

Our findings indicate that patients with SLE could be stratified into 2 subtypes which had different lymphocyte status and closely related to disease activity. This phenotyping may help us understand the etiology of the disease, inform patient in the design of clinical trials and guide treatment decision.

References:

[1]Dorner T, Furie R. Novel paradigms in systemic lupus erythematosus. Lancet 2019;393(10188):2344-58. doi: 10.1016/S0140-6736(19)30546-X [published Online First: 2019/06/11].

[2]Fanouriakis A, Tziolos N, Bertsias G, et al. Update οn the diagnosis and management of systemic lupus erythematosus. Annals of the rheumatic diseases 2021;80(1):14-25. doi: 10.1136/annrheumdis-2020-218272 [published Online First: 2020/10/15].

Acknowledgements:

This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).

Disclosure of Interests:

None declared

POS0932 REGULATION OF INTESTINAL FLORA RESTORES IMMUNE BALANCE IN PATIENTS WITH UNDIFFERENTIATED SPONDYLOARTHRITIS

Background:

Undifferentiated spondyloarthritis (USpA) is the most common subtype of the spondyloarthritides with a prevalence between 0.7% and 2.0%1. Inflammatory back pain, peripheral arthritis and less frequently enthesitis are the main clinical features of USpA2. Resently the role of dysregulated microbiome along with migration of T lymphocytes and other cells from gut to the joint (“gut-joint” axis) has been recognized3 4. However, the detailed lymphocyte statuses of USpA patients and the effect of regulating the intestinal flora on the lymphocyte subsets is unclear.

Objectives:

To investigate the status of lymphocyte subsets in peripheral blood (PB) of USpA patients and the variation after regulation of intestinal flora.

Methods:

A total of 39 newly diagnosed patients with USpA who fulfilled the European Spondyloarthropathy Study Group (ESSG) classification criteria and 60 age- and sex- matched healthy controls (HC) were enrolled in this study. All patients were given intestinal flora regulation therapy for six months, including clostridium butyricum capsule or bacillus coagulans tablet. The peripheral lymphocyte subsets of these participants were assessed by flow cytometry. Methane hydrogen breath test as well as cytokines were measured in all patients before and after treatment. Mann-Whitney U test was applied for the lymphocyte status between USpA patients and HC and Wilcoxon test for the comparison before and after treatment. The results of methane hydrogen breath were counted by the Chi-Square test. All P-values reported herein are two-tailed and P-value<0.05 was taken as statistically significant.

Results:

Compared with HC, patients with USpA had a lower numbers of T cells (P=0.001), NK cells (P=0.026), CD8+T cells (P=0.046) and Treg cells (P<0.05) but higher ratios of Th17/Tregs (P=0.001), indicating a disturbance of immune microenvironment (Figure 1). After given therapy, T cells (P=0.003), B cells (P=0.018), NK cells (P=0.003), CD8+T cells (P=0.001) and Treg cells (P=0.009) were distinctly increased while the ratio of Th17/Treg decreased (P=0.046), suggesting a rebalance of immune systems (Figure 2a-c). Moreover, there were increase in the level of IL-6 (P<0.001), IL-17 (P=0.029) and TNF-α (P=0.003) as well as decrease in IL-10 (P=0.045) and IFN-γ (P=0.001) (Figure 2d). Further, the positive rate of intestinal bacterial overgrowth decreased significantly after regulation (P=0.029) (Figure 2e).

Conclusion:

Imbalance of immune environment is closely related to the incidence of undifferentiated spondyloarthrosis. The regulation of intestinal flora restores the balance and improve the growth of bacteria in the small intestine simultaneously. Therefore it is essential to focus on the alteration of intestinal flora to prevent the outbreak of inflammation and disease progression.

References:

[1]Cruzat V, Cuchacovich R, Espinoza LR. Undifferentiated spondyloarthritis: recent clinical and therapeutic advances. Curr Rheumatol Rep 2010;12(5):311-7. doi: 10.1007/s11926-010-0115-0 [published Online First: 2010/07/16].

[2]Deodhar A, Miossec P, Baraliakos X. Is undifferentiated spondyloarthritis a discrete entity? A debate. Autoimmun Rev 2018;17(1):29-32. doi: 10.1016/j.autrev.2017.11.006 [published Online First: 2017/11/08].

[3]Sheth T, Pitchumoni CS, Das KM. Management of Musculoskeletal Manifestations in Inflammatory Bowel Disease. Gastroenterol Res Pract 2015;2015:387891. doi: 10.1155/2015/387891 [published Online First: 2015/07/15].

[4]Fragoulis GE, Liava C, Daoussis D, et al. Inflammatory bowel diseases and spondyloarthropathies: From pathogenesis to treatment. World J Gastroenterol 2019;25(18):2162-76. doi: 10.3748/wjg.v25.i18.2162 [published Online First: 2019/05/31].

Acknowledgements:

This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).

Disclosure of Interests:

None declared

POS0859 DEEP PHENOTYPING OF DERMATOMYOSITIS BASED ON LIPID FERROPTOSIS-RELATED GENES BY MACHINE LEARNING

Background:

Dermatomyositis (DM) is an idiopathic inflammatory myopathy with heterogeneous clinical manifestation that raise challenges regarding diagnosis and therapy1. Ferroptosis is a newly discovered form of regulated cell death that is the nexus between metabolism, redox biology, and rheumatic immune diseases2. However, how ferroptosis maintains the balance of lymphocyte T cells and affect disease activity in DM is unclear.

Objectives:

To investigate an ferroptosis-related multiple gene expression signature for classification by assessing the global gene expression profile, and calculate the lymphocyte T cells status in the different subsets.

Methods:

Gene expression profiles of skeletal muscle from DM samples were acquired from GEO database. GSE143323 (30 patients and 20 HCs) was selected as the training set. The GSE3307 contained 21 DM patients and was selected as the validation set. The 60 ferroptosis genes were obtained from previous literature3. The intersection of the global gene and ferroptosis genes was considered the set of significant G-Ferroptosis genes for further analysis. The “NMF” (R-package) was applied as an unsupervised clustering method for sample classification by using G-Ferroptosis genes expression microarray data from the training datasets. An ferroptosis score model was constructed. The performance of the ferroptosis genes-based risk score model constructed by the DM training set was validated in the batch-1 and batch-2 DM sets. Normalized ferroptosis genes training data was used to compare the ssGSEA scores of gene sets between the high risk and low risk group. The statistical software package R (version 4.0.3) was used for all analyses. P value < 0.05 were considered statistically significant.

Results:

We selected 54 significant G-Ferroptosis genes for further analysis in training set. There were 2 distinct subtypes (high-ferroptosis-score groups and low-ferroptosis-score groups) identified in G-Ferroptosis genes cohort which were also identified in validation datasets (Fig.1A, C, D). Metallothionein 1G (MT1G) was a characteristic gene of low-ferroptosis-score group. The characteristic genes of high-ferroptosis-score group were acyl-CoA synthetase family member 2(ACSF2) and aconitase 1(ACO1) (Fig.1B). Patients in high-ferroptosis-score group had a lower level of Tregs compared with that of low-ferroptosis-score patients in both training and validation set (P <0.05, Fig.1E).

Conclusion:

The biological process of ferroptosis is associated with the lever of Tregs, suggesting the process of ferroptosis may be involved in the disease progression of DM. Identificating ferroptosis-related features for DM might provide a new idea for clinical treatment.

References:

[1]DeWane ME, Waldman R, Lu J. Dermatomyositis: Clinical features and pathogenesis. Journal of the American Academy of Dermatology 2020;82(2):267-81. doi: 10.1016/j.jaad.2019.06.1309 [published Online First: 2019/07/08].

[2]Liang C, Zhang X, Yang M, et al. Recent Progress in Ferroptosis Inducers for Cancer Therapy. Advanced materials (Deerfield Beach, Fla) 2019;31(51):e1904197. doi: 10.1002/adma.201904197 [published Online First: 2019/10/09].

[3]Liang JY, Wang DS, Lin HC, et al. A Novel Ferroptosis-related Gene Signature for Overall Survival Prediction in Patients with Hepatocellular Carcinoma. International journal of biological sciences 2020;16(13):2430-41. doi: 10.7150/ijbs.45050 [published Online First: 2020/08/08].

Acknowledgements:

This project was supported by National Science Foundation of China (82001740).Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).

Disclosure of Interests:

None declared

POS0848 IDENTIFICATION OF POTENTIAL CRUCIAL GENES AND KEY PATHWAYS IN PULMONARY ARTERIAL HYPERTENSION WITH SYSTEMIC SCLEROSIS BY BIOINFORMATIC ANALYSIS

Background:

Pulmonary arterial hypertension with systemic sclerosis (SSc-PAH) is the main cause of death in patients with SSc. Early diagnosis and timely treatment are very important to reduce the mortality of patients with SSc-PAH1. At present, there are not many sensitive markers for the diagnosis of SSc-PAH. Therefore, it is necessary to mine more sensitive markers as more accurate and practical predictors, which is of great significance for the diagnosis and treatment of SSc-PAH.

Objectives:

To discover the differentially expressed genes (DEGs) and activated signaling pathways in SSc-PAH.

Methods:

Fifty-five samples (27 SSc-PAH v.s 28 normal controls) in GSE33463 chip data obtained from Gene Expression Omnibus (GEO) were included in this study. DEGs in SSc-PAH patients were screened by R, key pathways and hub genes were discoved by Metascape2, STRING3 and Cytoscape.

Results:

Total 431 genes with large differences were identified, including 238 up-regulated genes and 193 down-regulated genes, after standardizing the data (|logFC| > 1; P < 0.05). GO analysis showed that the upregulated genes were mainly involved in defense response to virus, hemoglobin complex, platelet alpha granule membrane and cytokine binding. The downregulated genes were mainly characterized by positive regulation of cell death, regulation of MAPK cascade, regulation of DNA-binding transcription factor activity and transcription factor AP-1 complex. Several significant enriched pathways obtained in the KEGG pathway analysis were Influenza A, Hepatitis C, IL-17 signaling pathway, MAPK signaling pathway, Toll-like receptor signaling pathway. Finally, after the selected differential genes were introduced into STRING online software, the data information of protein interaction network was derived, and 12 core genes in the network were identified, they were CXCL8, PPBP, LPAR1, FPR2, GNG11, CXCL10, LPAR5, JUN, C3AR1, CCR2, CCR3, IRF2.

Conclusion:

The genes and signal pathways related to SSc-PAH discovered by bioinformatics methods could not only provided new molecular markers for its diagnosis and treatment, but also provided new ideas for its related biological research.

References:

[1]Zheng JN, Li Y, Yan YM, et al. Identification and Validation of Key Genes Associated With Systemic Sclerosis-Related Pulmonary Hypertension. Front Genet 2020;11:816. doi: 10.3389/fgene.2020.00816 [published Online First: 2020/08/15].

[2]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].

[3]Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019;47(D1):D607-D13. doi: 10.1093/nar/gky1131 [published Online First: 2018/11/27].

Acknowledgements:

This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).

Disclosure of Interests:

None declared

Measurement of the Cosmic Ray Helium Energy Spectrum from 70 GeV to 80 TeV with the DAMPE Space Mission

The measurement of the energy spectrum of cosmic ray helium nuclei from 70 GeV to 80 TeV using 4.5 years of data recorded by the Dark Matter Particle Explorer (DAMPE) is reported in this work. A hardening of the spectrum is observed at an energy of about 1.3 TeV, similar to previous observations. In addition, a spectral softening at about 34 TeV is revealed for the first time with large statistics and well controlled systematic uncertainties, with an overall significance of 4.3σ. The DAMPE spectral measurements of both cosmic protons and helium nuclei suggest a particle charge dependent softening energy, although with current uncertainties a dependence on the number of nucleons cannot be ruled out.

OP0307 GUT MICROBIOTA AND ITS RELEVANCE TO PERIPHERAL LYMPHOCYTE SUBPOPULATION IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS

Background:

Systemic lupus erythematosus (SLE) is an autoimmune disease with disturbance of lymphocyte subpopulations1. Growing experimental and clinical evidence suggests that chronic inflammatory response induced by gut microbiome critically contribute to the development of SLE2 3.

Objectives:

To investigate the characteristics of gut microbiome and the associations between flora and peripheral lymphocyte subpopulations in SLE patients.

Methods:

A total of 19 SLE patients who fulfilled the 2019 American college of Rheumatology (ACR) and European League Against Rheumatism (EULAR) classification criteria and 16 age- and sex- matched healthy controls (HC) were enrolled in this study. The peripheral T lymphocyte subsets of these participants were assessed by flow cytometry and the gut microbiota were investigated via 16s rRNA. Indicators of disease activity such as erythrocyte sedimentation rate (ESR), complement C3 and C4 were recorded at the same time. Mann-Whitney U test was applied to compare T lymphocyte subsets between SLE patients and HC. Spearman analysis was used for calculating correlation between T subsets and highly expressed differential flora as well as their correlation with disease activity indicators. All P-values reported herein were two-tailed and P-value<0.05 was taken as statistically significant.

Results:

SLE patients had higher proportions of Th17 cells (P=0.020) and γδT cells (P=0.018) but lower levels of Treg cells (P=0.001), Tfh cells (P=0.018) and Naïve CD4+T cells (P=0.004) (Figure 1a-e). The diversity and relative abundance of intestinal flora in patients with SLE were significantly different from those in HCs. Detailly, the α-diversity was decreased in SLE (P<0.05) (Figure 2a-c). Compared with HC, 11 species of flora were discovered to be distinctly different(P<0.05) (Figure 2d-e). Moreover, there was a significant positive correlation between Treg levels and Ruminococcus2 (P=0.042), Th17 and Megamonas (P=0.009), γδT and Streptococcus (P=0.004) as well as Megamonas (P=0.003), Tfh and Bacteroides (P=0.040). Whereas Th1 levels and Bifidobacterium were negatively correlated in these participants (P=0.005). As for clinical disease measures, there were negative correlations not only between ESR and Treg cells (P=0.031) but also C4 and the amount of Unclassified Ruminococcaceae (P=0.032).

Conclusion:

Abnormality of T cell subsets, especially the level of Naïve CD4+T, γδT, Tfh, Treg, and Th17 cells contributes to the occurrence and progression of SLE, which may be related to the disturbance of gut microbiota. Therefore it is necessary to attach importance to the alteration of gut microbiota to prevent the outbreak of inflammation and maybe they can be identified as biomarkers predicting disease activity.

References:

[1]Katsuyama T, Tsokos GC, Moulton VR. Aberrant T Cell Signaling and Subsets in Systemic Lupus Erythematosus. Front Immunol 2018;9:1088. doi: 10.3389/fimmu.2018.01088 [published Online First: 2018/06/06]

[2]López P, de Paz B, Rodríguez-Carrio J, et al. Th17 responses and natural IgM antibodies are related to gut microbiota composition in systemic lupus erythematosus patients. Sci Rep 2016;6:24072. doi: 10.1038/srep24072 [published Online First: 2016/04/06]

[3]Esmaeili SA, Mahmoudi M, Momtazi AA, et al. Tolerogenic probiotics: potential immunoregulators in Systemic Lupus Erythematosus. J Cell Physiol 2017;232(8):1994-2007. doi: 10.1002/jcp.25748 [published Online First: 2016/12/21]

Acknowledgements:

This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).

Disclosure of Interests:

None declared.

[Clinical characteristics of 288 cases of pathologically confirmed benign pulmonary nodules post-surgery]

Objective: To analyze retrospectively the clinical characteristics of pathologically confirmed benign pulmonary nodules post-surgery, and therefore to provide evidence for the diagnosis of benign pulmonary nodules. Methods: 288 cases of pulmonary nodules were selected in the Ruijin Hospital from 1^st January 2017 to 31^st October 2019. All the lesions of these patients were confirmed by surgery and had definite pathological diagnosis. The clinical data were collected, including demography, clinical data, radiological features. Features that indicated benign pulmonary nodules were summarized. Results: The main etiologies of benign pulmonary nodules were granulomas, hamartomas, cryptococcus infection, organizing pneumonia and non-specific inflammation. In our cohort, we found that the radiological characteristics of benign nodules were single, solid, less than 10 mm in average diameter, with well-defined margins, absence of vacuole sign or vascular convergence , and negative functional imaging. Conclusion: The most common etiologies of post-surgical benign nodules were granulomas, hamartomas, and cryptococcus infection, characterized by being single, solid and with well-defined margins. Caution should be taken before considering surgery for such nodules.

[A study of LEF1 protein expression in diagnosis and differential diagnosis of lymphoblastic lymphoma/acute lymphoblastic leukemia]

Objective: To evaluate the expression of LEF1 protein in lymphoblastic lymphoma/acute lymphoblastic leukemia (LBL/ALL) and small B-cell lymphomas, and its value in pathologic diagnosis and differential diagnosis of LBL/ALL. Methods: 53 cases of LBL/ALL were collected at shanghai Tongji Hospital from January 2012 to December 2019. The protein expression of LEF1 and TdT was detected by immunohistochemistry in 53 paraffin-embedded tissue samples of LBL/ALL. The specificity and sensitivity of LEF1 and TdT in the diagnosis of LBL/ALL were compared. The expression of LEF1 protein in 77 cases of small B-cell lymphomas including chronic lymphocytic leukemia/small lymphoid lymphoma (CLL/SLL), follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma and Waldenstrom's macroglobulinemia/lymphoplasmacytic lymphoma was studied. The correlation between LEF1 expression and overall survival (OS) and progression-free survival (PFS) was performed by univariate analysis. Results: The expression of LEF1 in LBL/ALL was 100% (53/53), the median value was 90%; the expression of TdT was 84.9% (T-LBL/ALL 78.1%, B-LBL/ALL 95.2%), the median value was 80%; the expression rate and median value of LEF1 and TdT were significantly different (P=0.008 and 0.001 respectively). The expression of LEF1 in CLL/SLL was 14/18, the median value was 45%; LEF1 was not expressed in follicular lymphoma (0/16), mantle cell lymphoma (0/16), marginal zone lymphoma (0/19), and Waldenstrom's macroglobulinemia/lymphoplasmacytic lymphoma (0/8). LEF1 expression was significantly different between B-LBL/ALL and small B-cell lymphomas. The median follow-up time of LBL/ALL cases in this group was 16 months. There was no statistical difference between LEF1 expression and the OS and PFS in LBL/ALL patients. Conclusions: Immunohistochemical staining of LEF1 has high sensitivity and good specificity in the diagnosis of LBL/ALL, and its combination with TdT can improve the diagnostic rate of LBL/ALL.

[Clinical effect of one-stage total knee arthroplasty for knee osteoarthritis with femoral extra-articular deformity]

Objective: To investigate the application and efficacy of the one-stage total knee arthroplasty (TKA) of intra-articular compensation osteotomy in knee osteoarthritis(KOA) patients with extra-articular deformity (EAD). Methods: A retrospective study of 9 patients with end-stage KOA and EAD undergoing one-stage TKA from January 2014 to December 2017 in the First Affiliated Hospital of Zhejiang Chinese Medical University was performed. There were 3 males and 6 females with an average age of 56 years(range, 19-77 years);5 cases of simple coronal deformity (varus 10°-27°, mean 18.2°), 3 cases of sagittal deformity (recurvatum15°-35°, mean 22.6°), 1 case combined with coronal and sagittal deformity (varus 16°, recurvatum 31°); hemophilia dysplasia in 1 case, fracture malformation in 8 cases. Main outcome measures included the mechanical axis, range of motion (ROM) and Hospital for Special Surgery Knee Score (HSS). Results: The mean follow-up period was 33.2 months (range, 25-47 months). The mechanical axis angle was restored from 12.4°±4.1°to 1.4°±0.9°(t=7.954, P<0.01). The HSS was improved from 28±14 preoperatively to 87±7 postoperatively (t=-11.174, P=0.013). The ROM increased from 56°±22°to 99°±8° (t=-5.480, P=0.010). There was no complications such as joint instability, infection, fracture, common peroneal nerve injury and early prosthesis loosening. Conclusions: For KOA patients with femoral EAD, one-stage TKA with intra-articular compensatory osteotomy can effectively restore the mechanical axis and obtain satisfying joint function. Through a series of measures such as preoperative measurement, soft tissue evaluation and 3D printing, the accuracy of surgery can be improved and the difficulty of surgery can be reduced.

THU0104 THE GUT MICROBIOTA AND ITS RELEVANCE TO PERIPHERAL T REGULATORY CELLS AND T HELPER 17 IN PATIENTS WITH RHEUMATOID ARTHRITIS

Background:

Rheumatoid arthritis (RA) is a common autoimmune disorder with joint destruction and synovial inflammation characterized by abnormal immune responses to autoantigens. Our previous studies have demonstrated that impaired peripheral lymphocytes especially insufficiency of regulatory T cells (Tregs) played an important role in pathogenesis of RA1 2. Interestingly, the dysbiosis of gut microbiota triggers several types of autoimmune diseases through the imbalance of T lymphocyte subsets3. However, the detailed gut microbiota of RA patients and its correlation with Tregs and helper T cells 17 (Th17) are unclear up until now.

Objectives:

To compare the difference of gut microbiota between RA and healthy controls (HCs), and to investigate the relevance of gut microbiota with circulating Tregs and Th17 in patients with RA.

Methods:

From December 2018 to August 2019, a total of 205 diagnosed patients with RA and 199 age and sex-matched HCs were enrolled in this study. Stool of Every participant was collected for bacterial DNA extraction and 16S ribosomal RNA (rRNA) gene sequencing. The absolute numbers of Tregs and Th17 in PB of these individuals were measured by Flow Cytometer (FCM) combined with standard absolute counting beads. Data were expressed as mean ± standard deviation to the distribution. Independent-samples T test and Spearman rank correlation test. P value <0.05 were considered statistically significant.

Results:

Patients with RA had a significantly difference of diversity and abundance of intestinal microbiota compared with those of HCs (P< 0.05). Detailedly, the abundance of Proteobacteria was significantly increased in RA patients (P< 0.05), and the abundance of Firmicutes, Fusobacteria and Verrucomicrobia were significantly reduced (P<0.05) at the level of Phylum (Figure 1). At the genus level, in the RA group, the abundance of Escherichia, Ruminococcus2 and Clostridium_sensu_stricto were significantly increased (P< 0.05), but the abundance of Lachnospiracea_incertae_sedis, Prevotella, Clostridium_XlVa, Roseburia, Dialister, Blautia, Megamonas and Gemmiger were significantly lower than the healthy controls (P< 0.05) (Figure 2). Moreover, Blautia, Anaerostipes and Ruminococcus2 have negative correlation with the absolute number of Tregs, and Cloacibacillus and Streptophyta have positive correlation with the absolute number of Th17.

Conclusion:

Patients with RA had a dysbiosis of the gut microbiota in both diversity and abundance, which is closely related to the impaired peripheral lymphocyte subsets, that may be related to the pathogenesis of RA, which might provide a new idea for RA treatment.

References:

[1]Wen HY, Wang J, Zhang SX, et al. Low-Dose Sirolimus Immunoregulation Therapy in Patients with Active Rheumatoid Arthritis: A 24-Week Follow-Up of the Randomized, Open-Label, Parallel-Controlled Trial.J Immunol Res2019;2019:7684352. doi: 10.1155/2019/7684352 [published Online First: 2019/11/30]

[2]Niu HQ, Li ZH, Zhao WP, et al. Sirolimus selectively increases circulating Treg cell numbers and restores the Th17/Treg balance in rheumatoid arthritis patients with low disease activity or in DAS28 remission who previously received conventional disease-modifying anti-rheumatic drugs.Clin Exp Rheumatol2019 [published Online First: 2019/05/11]

[3]Lee N, Kim WU. Microbiota in T-cell homeostasis and inflammatory diseases.Exp Mol Med2017;49(5):e340. doi: 10.1038/emm.2017.36 [published Online First: 2017/05/27]

Acknowledgments:

None

Disclosure of Interests:

None declared