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

Background

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

Objectives

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

Methods

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

Results

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

Conclusion

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

References

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

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

Acknowledgements

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

Disclosure of Interests

None declared

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

Background

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

Objectives

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

Methods

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

Results

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

Figure 1.

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

Figure 2.

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

Conclusion

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

References

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

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

Acknowledgements

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

Disclosure of Interests

None declared

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

Background

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

Objectives

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

Methods

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

Results

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

Conclusion

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

References

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

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

Acknowledgements

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

Disclosure of Interests

None declared

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.

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

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.

Updating species diversity of Colletotrichum, with a phylogenomic overview

The genus Colletotrichum includes important plant pathogens, endophytes, saprobes and human pathogens. Even though the polyphasic approach has facilitated Colletotrichum species identification, knowledge of the overall species diversity and host distribution is largely incomplete. To address this, we examined 952 Colletotrichum strains isolated from plants representing 322 species from 248 genera, or air and soil samples, from 87 locations in China, as well as 56 strains from Saudi Arabia, Thailand, Turkey, and the UK. Based on morphological characteristics and multi-locus phylogenetic analyses, the strains were assigned to 107 species, including 30 new species described in this paper and 18 new records for China. The currently most comprehensive backbone tree of Colletotrichum, comprising 16 species complexes (including a newly introduced C. bambusicola species complex) and 15 singleton species, is provided. Based on these analyses, 280 species with available molecular data are accepted in this genus, of which 139 have been reported in China, accounting for 49.6 % of the species. Colletotrichum siamense, C. karsti, C. fructicola, C. truncatum, C. fioriniae, and C. gloeosporioides were the most commonly detected species in China, as well as the species with the broadest host range. By contrast, 76 species were currently found to be associated with a single plant species or genus in China. To date, 33 Colletotrichum species have been exclusively reported as endophytes. Furthermore, we generated and assembled whole-genome sequences of the 30 new and a further 18 known species. The most comprehensive genome tree comprising 94 Colletotrichum species based on 1 893 single-copy orthologous genes was hence generated, with all nodes, except four, supported by 100 % bootstrap values. Collectively, this study represents the most comprehensive investigation of Colletotrichum diversity and host occurrence to date, and greatly enhances our understanding of the diversity and phylogenetic relationships in this genus.

[Influence of guided bone regeneration on marginal bone loss of implants in the mandible posterior region: a 10-year retrospective cohort study]

Objective: To investigate the effect of guided bone regeneration (GBR) on marginal bone loss (MBL) in the region of the mandibular posterior tooth by using a retrospective cohort study, in order to provide reference for clinical practice. Methods: The research subjects were patients who received dental implants from October 2008 to June 2011 in the region of the mandibular posterior tooth at the Department of Oral Implantology, School of Stomatology, The Fourth Military Medical University. According to whether GBR was performed or not and the time of implant insertion, the patients were divided into the controls group (patients without bone grafting), simultaneous GBR implantation group, and delayed GBR implantation group. On this basis, the MBL was measured according to radiographs by comparing the marginal bone level from that of immediate postoperation 10 years ago. General data was collected and compared among groups, including modified plaque index (mPI), modified sulcus bleeding index (mSBI), probing depth (PD), and gingival papilla height. Results: The controls group (patients without bone grafting), implantation group, and delayed GBR implantation group followed 58, 76, 26 implants in 26, 32, 13 patients aging at (46.5±9.9), (45.5±10.7), (58.3±6.4) respectively. The duration of the follow-up was (11.2±0.7), (11.1±0.8), (11.1±0.9) years respectively. The 10-year implant survival rate was 100% (58/58), 100% (76/76), 100% (26/26). The MBL was (0.91±0.28), (0.84±0.27), (1.01±0.27) mm respectively. The MBL difference of patients with simultaneous GBR implantation and delayed GBR implantation showed statistical significance (P<0.05), but these two groups showed no statistical significance compared with the controls group (P>0.05). The mPI, mSBI, PD, and gingival papilla height of the three groups all had no significance on statistics (P>0.05). Conclusions: It can be concluded that there is no difference in long-term marginal bone resorption between simultaneous and delayed implantation with or without GBR (using autologous blood mixed with granular bone meal) in the posterior mandibular area.

Reduced miR-363-3p expression in non-small cell lung cancer is associated with gemcitabine resistance via targeting of CUL4A

The article "Reduced miR-363-3p expression in non-small cell lung cancer is associated with gemcitabine resistance via targeting of CUL4A", W.-G. Bian, X.-N. Zhou, S. Song, H.-T. Chen, Y. Shen, P. Chen, published in Eur Rev Med Pharmacol Sci 2019; 23 (2): 649-659-DOI: 10.26355/eurrev_201901_16879-PMID: 30720173, has been retracted by the authors due to several inaccuracies in the research design. The Publisher apologizes for any inconvenience this may cause. https://www.europeanreview.org/article/16879.

Development of patulin certified reference material using mass balance and quantitative NMR

The certified reference materials (CRMs) are necessary for accurate quantification and insurance of comparability and traceability of results. Patulin is a typical mycotoxin in a variety of food commodities. Here, patulin CRM GBW(E)100673 was characterised and its purity was assessed by two independent orthogonal approaches including mass balance (MB) and quantitative nuclear magnetic resonance spectroscopy (qNMR) methods. From MB equation, the calculated purity was 996.9 mg/g with subtraction of water, volatile solvent, inorganic and structurally related impurities. In the other qNMR method, the calculated purity was 996.7 mg/g. This CRM was homogeneous and stable for at least 9 months under -20 °C in dark. Finally, a purity of 997 mg/g with an expanded uncertainty of 3 mg/g (k=2) was finally assigned to patulin CRM in this study. High-purity patulin CRM was fully characterised and assessed for the first time. The new CRM can be applicable to routine monitoring and risk assessment for assurance of accuracy results in food safety.

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

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