Whole genome survey and genetic markers development of crocodile flathead Cociella crocodilus

Flatheads in family Platycephalidae are ecologically and commercially important marine fish species in the Indo-West Pacific. Due to similar morphological characters, the taxonomy and phylogenetics of flatheads are in confusion. Studies on phylogenetics and molecular marker development are required to discriminate congeners of flatheads. In the present study, we performed whole genome survey sequencing of crocodile flathead Cociella crocodilus to provide genomic information and genetic markers of this species. In total, 54.03 Gb of clean genomic data were generated. The genome size was estimated to be 732.99 Mb with the heterozygosity ratio of 0.73% and the repeat sequence ratio of 33.48%. The preliminary assembled genome sequences were 794.07 Mb with contig N50 of 1504 bp. We detected 2 624 875 genome-wide SNPs with transition/transversion ratio of 1.422. A total of 313 842 microsatellite motifs were identified, most of which were dinucleotide motifs with a frequency of 74.89%. In addition, we assembled the complete mitogenome of C. crocodilus and subsequent phylogenetic analysis were performed. Phylogenetic analyses revealed numbers of polyphyletic groups in family Platycephalidae. The reported genomic data and genetic markers in our study should be useful in further phylogeny and phylogenomics studies of flathead species.

Nitrogen-plasma doping of carbon film for a high-quality layered Si/C composite anode

The effect of the chemical component and microstructure, not to mention their facile modification, of the coating/wrapping carbon layer on the electrochemical performance of the Si/C composite anode in lithium ion batteries (LIBs) hasn't been actively explored although Si/C has been recognized as one of the most promising route for the high energy density LIBs. Herein we propose a novel nitrogen-plasma doping route to modify the top carbon film in an elaborately constructed layered Si/C composite anode. The electrochemical performance, e.g., the initial coulombic efficiency (CE), cycle stability and specific capacity of the composite anode is drastically improved by this plasma processing due to the increased kinetics of lithium ions. By means of the appropriate adjustment of the N doping ratio and N chemical configuration in the carbon layer through a N₂/H₂ plasma processing, the lithium diffusion rate in the composite anode was memorably increased as the pseudocapacitance effects promoted. The optimized Si/C composite exhibits a high capacity of 1120.7 mA h g^-1 and an initial CE of 80.8% at the current of 2 A g^-1 after a long cycle of 1500, increasing by ~40% of specific capacity and ~29% of the initial CE.

[Survival analysis of silicosis patients in Wuxi City]

Objective: To analyze the living condition and influencing factors of silicosis patients in Wuxi City form 1975 to 2019. Methods: Through the monitoring of death causes of residents, the paper-based materials and online report system of diagnosis over the years, and the combination of public security and human social system to obtain 3721 cases of silicosis patients as subjects form August to December 2019. And the combination of Kaplan Meier method and life table method were used to carry out single factor survival analysis. Through Cox regression model to analyze the factors affecting the survival time of patients. Results: From 1975 to 2019, 3721 cases of silicosis and 1274 deaths have been reported in Wuxi City, with a mortality rate of 34.24% and a median survival time of 30.9 years. With the development of diagnosis time, the mortality decreased significantly (χ(2)=747.75, P<0.05) . Compared with the first stage silicosis patients, the risk of decreased survival time of the third stage silicosis patients increased (HR=1.486, P<0.05) . Compared with the non-smoking patients, the risk of decreased survival time of the smoking patients increased (HR=1.136, P<0.05) . Compared with the patients who were less than 30 years old, the risk of decreased survival time of patients with 40-49 years old, 50-59 years old and more than 60 years old were increased (HR=9.641, 13.650, 26.794, P<0.05) . Compared with the patients who received industrial and commercial insurance, the risk of decreased survival time of patients who received compensation from employers, basic medical insurance for urban and rural residents, other social compensation and no compensation were increased (HR=3.137, 3.119, 5.129, 8.442, P<0.05) . Conclusion: The survival time of silicosis patients is related to the stage of silicosis, smoking condition, age of onset and social compensation. We should focus on controlling the above risk factors so as to improve the quality of life of patients and prolong their lives.

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

Background:

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

Objectives:

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

Methods:

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

Results:

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

Conclusion:

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

References:

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

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

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

Acknowledgements:

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

Disclosure of Interests:

None declared

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

Background:

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

Objectives:

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

Methods:

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

Results:

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

Conclusion:

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

References:

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

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

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

Acknowledgements:

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

Disclosure of Interests:

None declared

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

Background:

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

Objectives:

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

Methods:

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

Results:

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

Conclusion:

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

References:

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

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

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

Acknowledgements:

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

Disclosure of Interests:

None declared

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

Background:

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

Objectives:

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

Methods:

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

Results:

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

Conclusion:

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

References:

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

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

Acknowledgements:

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

Disclosure of Interests:

None declared

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

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

POS0761 INVESTIGATION ON THE EFFECT AND MECHANISM OF ABNORMALLY ACTIVATED CD8+ T CELLS FROM BONE MARROW ON HEMATOPOIETIC STEM CELLS IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS

Background:

SLE is an autoimmune disease characterized by the abnormal function of lymphocytes. The impairment of hematopoietic function of bone marrow participates in its pathogenesis, in which T cells play an important role. However, study on bone marrow T cells in SLE patients is very limited.

Objectives:

This study aims to characterize the phenotype and molecular characteristics of abnormally activated CD8+T cells in bone marrow of SLE patients and explore the mechanism of hematopoietic stem cells (HSCs) reduction caused by the abnormally activated CD8+T cells in bone marrow of patients with SLE.

Methods:

A total of 8 SLE patients and 5 age- and sex-matched controls were recruited in our study. Among them, 3 SLE patients and 4 donors were collected bone marrow and peripheral blood samples for Single-cell RNA sequencing (scRNA-seq) and functional studies. BM and peripheral T cell subsets were measured by flow cytometry. Plasma cytokines and secreted immunoglobulins were detected by Luminex. Disease activity of SLE patients was measured using the SLE Disease Activity Index (SLEDAI). All analyses were performed using R language and Flowjo 9.

Results:

In the present study, SLE patients had increased CD8+T%αβT cells and decreased CD4+T%αβT cells in bone marrow of SLE, compared to healthy controls. A large number of CD38+HLADR+CD8+T cells existed in the bone marrow and peripheral blood of SLE patients. Those patients also showed reduced number of HSCs, and with a downward trend of the numbers of peripheral red blood cells, white blood cells, neutrophils, hemoglobin, and platelets. By scRNA-seq, the CD38+HLADR+CD8+T cells contained high levels of GZMK, GZMA, PRF1, IFNG, and TNF in the bone marrow of SLE patients. the CD38+HLADR+CD8+T cells exhibited significant relationship with HSCs, white blood cells, neutrophils, and platelets.

Conclusion:

These findings demonstrated that the abnormally activated CD8+T cells in bone marrow can reduce the number of HSCs by the expression of killer molecules, which contributes to the impairment of hematopoietic function and the development of SLE. This project focuses on the specific bone marrow T cell subset in SLE. The completement of this project provides information for exploring the mechanism of hematopoiesis involvement.

References:

[1]Anderson E, Shah B, Davidson A, Furie R. Lessons learned from bone marrow failure in systemic lupus erythematosus: Case reports and review of the literature. Semin Arthritis Rheum. 2018;48(1):90-104.

[2]Sun LY, Zhou KX, Feng XB, Zhang HY, Ding XQ, Jin O, Lu LW, Lau CS, Hou YY, Fan LM. Abnormal surface markers expression on bone marrow CD34+cells and correlation with disease activity in patients with systemic lupus erythematosus. Clin Rheumatol. 2007;26(12):2073-2079.

Acknowledgements:

We want to thank Lu Meng, Teng Li, Wei Zhou, and Jiaxin Guo for their assistance with this study.

Disclosure of Interests:

None declared

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

Background:

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

Objectives:

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

Methods:

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

Results:

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

Conclusion:

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

References:

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

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

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

Acknowledgements:

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

Disclosure of Interests:

None declared

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

Background:

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

Objectives:

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

Methods:

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

Results:

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

Conclusion:

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

References:

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

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

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

Acknowledgements:

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

Disclosure of Interests:

None declared

AB0146 HAND AND WRIST ACTIVE RANGE OF MOTIONS AND CONTRIBUTING FACTORS IN RHEUMATOID ARTHRITIS: A CROSS-SECTIONAL STUDY

Background:

Patients with rheumatoid arthritis (RA) usually impaired range of motions (ROMs), especially hand and wrist active ROMs (AROMs), thus influencing their ability to perform daily activities and health-related quality of life (HR-QoL). However, little is known about the potential factors of reduced hand and wrist AROMs and their relations to quality of life in Chinese RA patients.

Objectives:

To explore the contributing factors of hand and wrist AROMs and their associations with HR-QoL and functional limitation in Chinese RA population.

Methods:

In this cross-sectional study, 108 patients were enrolled from Affiliated Hospital of Nantong University between November 2018 and July 2019. We measured all the participants’ AROMs with different directions of the hand and wrist in both sides, including volar flexion, ulnar deviation, radial deviation and radial deviation of the wrist joint, the first metacarpophalangeal (MCP1) flexion, interphalangeal (IP) flexion, volar abduction, radial abduction and thumb opposition (cm) in the thumb, average flexion, hypertension and abduction of the MCP2-5, average proximal interphalangeal (PIP) 2-5 and distal interphalangeal (DIP) 2-5 flexions, total active range of motion (TAM) of the second to the fifth fingers (TAM2-TAM5). Their sociodemographic, physical, psychological, disease-related data, acute phase reactants, laboratory indicators, drug usage and HR-QoL were examined as well. Statistical analysis used Pearson’s and Spearman’s correlation analysis, univariate and multivariate linear regression analyses.

Results:

In univariate analyses, we found that living in rural area, longer disease duration, comorbidity, hospitalization, more swollen joints, higher disease activity, pain level, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), red blood cell count and glucocorticoids usage were associated with most of the decreased hand and wrist AROMs (P ≤ 0.050), while higher education and annual income were related to most of the increased hand and wrist AROMs (P ≤ 0.048). In multivariate analyses, higher disease duration (P ≤ 0.023) and higher disease activity (P ≤ 0.033) were corelated with most of the decreased hand and wrist AROMs. Interestingly, the psychological factor, anxiety, was only positively associated with thumb opposition in both univariate and multivariate analyses (P ≤ 0.001). Additionally, most of the declined hand and wrist AROMs were associated with functional impairment and poor HR-QoL, especially in physical components (P < 0.05).

Conclusion:

Various factors, especially longer disease duration and higher disease activity, were related to decreased hand and wrist AROMs, and thus causing functional impairment and poor HR-QoL in RA patients. Clinical physicians and medical faculties should pay more attention to disease activity and disease-related symptoms of these patients in order to maintain their activity of daily living (ADL) ability and improve HR-QoL.

References:

[1]Rheumatoid arthritis. Nat Rev Dis Primers. 2018;4:18002.

[2]Zhang L, Cao H, Zhang Q, Fu T, Yin R, Xia Y, et al. Motion analysis of the wrist joints in Chinese rheumatoid arthritis patients: a cross-sectional study. BMC Musculoskelet Disord. 2018;19(1):270.

Acknowledgements:

This work was funded by Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant/Award number: KYCX19_2071), National Natural Science Foundation of China (Grant/Award number: 81871278, Science and technology Project of Jiangsu Province (Grant/Award number: BE2018671)

Disclosure of Interests:

None declared

Recent developments in bio-scaffold materials as delivery strategies for therapeutics for endometrium regeneration

Intrauterine adhesions (IUAs) refer to the repair disorder after endometrial injury and may lead to uterine infertility, recurrent miscarriage, abnormal menstrual bleeding, and other obstetric complications. It is a pressing public health issue among women of childbearing age. Presently, there are limited clinical treatments for IUA, and there is no sufficient evidence that these treatment modalities can effectively promote regeneration after severe endometrial injury or improve pregnancy outcome. The inhibitory pathological micro-environment is the main factor hindering the repair of endometrial damaged tissues. To address this, tissue engineering and regenerative medicine have been achieving promising developments. Particularly, biomaterials have been used to load stem cells or therapeutic factors or construct an in situ delivery system as a treatment strategy for endometrial injury repair. This article comprehensively discusses the characteristics of various bio-scaffold materials and their application as stem cell or therapeutic factor delivery systems constructed for uterine tissue regeneration.

[Research Progress on Estimation of Postmortem Submersion Interval]

Abstract

Postmortem interval （PMI） estimation is one of the most important and difficult academic tasks in forensic sciences. Due to the influence of the corpse itself and the water environment, corpses in water have unique corruption phenomenon and laws. Based on the experience of traditional PMI studies of corpses on land, forensic practitioners across the world have proposed a variety of practical methods for estimating postmortem submersion interval （PMSI）. This paper summarizes the literatures related to PMSI in recent years, and introduces methods to infer PMSI according to the phenomenon of corpses, the development of insects, the succession pattern of aquatic organisms, and the changes of other physical and chemical indexes of corpses, in order to provide some reference for the study of PMSI of corpses in water.

FHOD3 promotes carcinogenesis by regulating RhoA/ROCK1/LIMK1 signaling pathway in medulloblastoma

PURPOSE

Medulloblastoma (MB) is a malignant brain disease in young children. The overall survival of MB patients is disappointing due to absence of effective therapeutics and this could be attributed to the lack of molecular mechanism underlying MB. FHOD3 was an important gene during cardio-genesis and was reported to promote cell migration in cancer. However, its role in MB is not clear to date.

METHODS

RT-qPCR and IHC analysis were used to determine expression of FHOD3. Survival curve was drawn by K-M analysis. FHOD3 was knocked down by RNAi technology. The effects of FHOD3 on medulloblastoma cells were determined by CCK-8 assay, colony formation assay, transwell assay and FACs analysis.

RESULTS

FHOD3 expression increased by 1.5 fold in tumor tissues compared to the control and IHC analysis further confirmed strong expression of FHOD3 in medulloblastoma tissues. Then higher FHOD3 expression was associated with shorter survival time in MB patients (13.0 months versus 43.8 months). In medulloblastoma cells such as Daoy and D283med, FHOD3 also displayed abundant expression. When FHOD3 was knocked down, the ability of cell proliferation and colony formation was reduced over greatly. The capability of cell migration and invasion was also inhibited significantly. However, cell apoptotic rate increased significantly reversely. Mechanistically, the phosphorylation level of RhoA, ROCK1, and LIMK1 was decreased when FHOD3 was knocked down but increased reversely when FHOD3 was over-expressed in Daoy cells.

CONCLUSIONS

FHOD3 was associated with overall survival time in medulloblastoma patients and was essential to cell proliferation, growth and survival in medulloblastoma and might regulates activation of RhoA/ROCK1/LIMK1 signaling pathway.

Cognitive frailty and falls in Chinese elderly people: a population-based longitudinal study

BACKGROUND AND PURPOSE

Falling is considered an important public health problem among older people. A recent cross-sectional study suggested that cognitive frailty (CF) is associated with falls. We aimed to explore whether CF is a risk factor for falls in a population-based longitudinal study.

METHODS

Using data from the Rugao Longevity and Aging Study, physical frailty was assessed according to the modified Fried's phenotype, and the 20% of participants with the lowest scores on the Revised Hasegawa Dementia Scale were defined as having cognitive impairment (CoI). Cognitive frailty (CF) was defined as the coexistence of physical frailty and CoI, but excluded severe CoI (revised Hasegawa Dementia Scale score ≤ 10). The outcome of number of falls in the previous 12 months was measured using a questionnaire.

RESULTS

At baseline, the prevalence of CF was 2.6% and the prevalence of two or more falls was 6.7%. Cross-sectional analysis found that two or more falls was associated with physical frailty without CoI (odds ratio [OR] 6.79, 95% confidence interval [CI] 3.17-14.56), pre-frailty with CoI (OR 4.54, 95% CI 2.44-8.44) and CF (OR 3.51, 95% CI 1.18-10.44). Slow gait with CoI was associated with two or more falls (OR 2.21, 95% CI 1.08-4.53). At 3-year follow-up, the prevalence of two or more falls was 10.6%. Logistic regression analysis showed that, compared with the robust and non-CoI elderly groups, the CF elderly group had a higher risk of two or more falls (OR 3.41, 95% CI 1.11-10.50).

CONCLUSIONS

Cognitive frailty was associated with two or more falls at baseline and might be a risk factor for two or more falls after 3 years. Early screening of CF might be beneficial in the prevention of falls.