Network Pharmacology Study and Experimental Validation of Yiqi Huayu Decoction Inducing Ferroptosis in Gastric Cancer

Objective

This study aimed to identify the mechanism of Yiqi Huayu Decoction (YQHY) induced ferroptosis in gastric cancer (GC) by using network pharmacology and experimental validation.

Methods

The targets of YQHY, ferroptosis-related targets, and targets related to GC were derived from databases. Following the protein–protein interaction (PPI) network, the hub targets for YQHY induced ferroptosis in GC were identified. Furthermore, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were used to analyze the hub targets from a macro perspective. We verified the hub targets by molecular docking, GEPIA, HPA, and the cBioPortal database. Finally, we performed cell viability assays, quantitative real-time polymerase chain reaction (qRT-PCR), western blotting, lipid peroxidation, and GSH assays to explore the mechanism of YQHY induced ferroptosis in GC.

Results

We identified the main active compounds and hub targets: Quercetin, DIBP, DBP, Mipax, Phaseol and TP53, ATM, SMAD4, PTGS2, and ACSL4. KEGG enrichment analyses indicated that the JAK2-STAT3 signaling pathway may be a significant pathway. Molecular docking results showed that the main active compounds had a good binding activity with the hub targets. The experimental results proved that YQHY could induce ferroptosis in AGS by increasing the MDA content and reducing the GSH content. qRT–PCR and Western blot results showed that YQHY can induce ferroptosis in GC by affecting the JAK2-STAT3 pathway and the expression of ACSL4.

Conclusions

This study indicated that YQHY can induce ferroptosis in GC by affecting the JAK2–STAT3 pathway and the expression of ACSL4, and induction of ferroptosis may be one of the possible mechanisms of YQHY’s anti-recurrence and metastasis of GC.

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.

Emerging materials for the electrochemical detection of COVID-19

The SARS-CoV-2 virus is still causing a dramatic loss of human lives worldwide, constituting an unprecedented challenge for the society, public health and economy, to overcome. The up-to-date diagnostic tests, PCR, antibody ELISA and Rapid Antigen, require special equipment, hours of analysis and special staff. For this reason, many research groups have focused recently on the design and development of electrochemical biosensors for the SARS-CoV-2 detection, indicating that they can play a significant role in controlling COVID disease. In this review we thoroughly discuss the transducer electrode nanomaterials investigated in order to improve the sensitivity, specificity and response time of the as-developed SARS-CoV-2 electrochemical biosensors. Particularly, we mainly focus on the results appeard on Au-based and carbon or graphene-based electrodes, which are the main material groups recently investigated worldwidely. Additionally, the adopted electrochemical detection techniques are also discussed, highlighting their pros and cos. The nanomaterial-based electrochemical biosensors could enable a fast, accurate and without special cost, virus detection. However, further research is required in terms of new nanomaterials and synthesis strategies in order the SARS-CoV-2 electrochemical biosensors to be commercialized.

Combination of the Herbs Radix Rehmanniae and Cornus Officinalis Mitigated Testicular Damage From Diabetes Mellitus by Enhancing Glycolysis via the AGEs/RAGE/HIF-1α Axis

Radix Rehmanniae and Cornus Officinalis (RR-CO) have been widely used as "nourishing Yin and tonifying kidney" herb pairs for the treatment of diabetes mellitus (DM) and its complications in traditional Chinese medicine (TCM). Based on the theory of "kidney governing reproduction" in TCM, the aim of this study was to investigate the therapeutic effects of RR-CO on DM-induced reproduction damage through regulating testicular glycolysis. Moreover, the regulation of AGEs/RAGE/HIF-1α axis on the testicular glycolysis process has also been studied. Spontaneous DM model KK-Ay mice were used to investigate the protective effect of RR, CO, RR-CO on DM-induced reproductive disturbances. RR, CO, RR-CO improved DM-induced renal and testicular morphology damages. Moreover, the impaired spermatogenesis, germ cell apoptosis and motility in testis induced upon DM were also attenuated by RR, CO or RR-CO, accompanied by an increased level of glycolysis metabolomics such as l-lactate, d-Fructose 1,6-bisphosphate, etc. Meanwhile, glucose membrane transporters (GLUT1, GLUT3), monocarboxylate transporter 4 (MCT4) expression, lactate dehydrogenase (LDH) activity, HIF-1α were upregulated by RR, CO and RR-CO treatment compared with the model group, whereas AGE level and RAGE expression were decreased with the drug administration. The RR-CO group was associated with superior protective effects in comparison to RR, CO use only. Aminoguanidine (Ami) and FPS-ZM1, the AGEs and RAGE inhibitors, were used as a tool drug to study the mechanism, showing different degrees of protection against DM-induced reproductive damage. This work preliminarily sheds light on the herb pair RR-CO exhibited favorable effects against DM-induced reproductive disturbances through enhancing testicular glycolysis, which might be mediated by AGEs/RAGE/HIF-1α axis.

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

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

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