Transcriptomics Analysis using Galaxy and other Online Servers for Rheumatoid Arthritis

2021 ◽  
Vol 9 (VII) ◽  
pp. 459-466
Author(s):  
Sourabh Parmar
Keyword(s):  
Rheumatoid Arthritis ◽  
Data Mining ◽  
Gene Ontology ◽  
Complex Disease ◽  
Differential Expression Analysis ◽  
Biological Data ◽  
Systemic Autoimmune Disease ◽  
Transcriptomic Data ◽  
Pathway Enrichment ◽  
User Friendly

Researchers use transcriptomics analyses for biological data mining, interpretation, and presentation. Galaxy-based tools are utilized to analyze various complex disease transcriptomic data to understand the pathogenesis of the disease, which are user-friendly. This work provides simple methods for differential expression analysis and analysis of these results in gene ontology and pathway enrichment tools like David, WebGestalt. This method is very effective in better analysis and understanding the transcriptomic data. Transcriptomics analysis has been made on rheumatoid arthritis sra data. Rheumatoid arthritis (RA) is a systemic autoimmune disease. T cells and autoantibodies mediate the pathogenesis. This article discusses the genes which are differentially expressed between the healthy (n=50) and diseased (n=51) and the functions of those genes in the pathogenesis of RA.

GEView (Gene Expression View) Tool for Intuitive and High Accessible Visualization of Expression Data for Non-Programmer Biologists

2020 ◽  
pp. 580-592
Author(s):  
Libi Hertzberg ◽  
Assif Yitzhaky ◽  
Metsada Pasmanik-Chor
Keyword(s):  
Gene Expression ◽  
Quality Control ◽  
User Interface ◽  
High Throughput ◽  
Graphical User Interface ◽  
Differential Expression Analysis ◽  
Biological Data ◽  
Expression Data ◽  
Batch Correction ◽  
User Friendly

This article describes how the last decade has been characterized by the production of huge amounts of different types of biological data. Following that, a flood of bioinformatics tools have been published. However, many of these tools are commercial, or require computational skills. In addition, not all tools provide intuitive and highly accessible visualization of the results. The authors have developed GEView (Gene Expression View), which is a free, user-friendly tool harboring several existing algorithms and statistical methods for the analysis of high-throughput gene, microRNA or protein expression data. It can be used to perform basic analysis such as quality control, outlier detection, batch correction and differential expression analysis, through a single intuitive graphical user interface. GEView is unique in its simplicity and highly accessible visualization it provides. Together with its basic and intuitive functionality it allows Bio-Medical scientists with no computational skills to independently analyze and visualize high-throughput data produced in their own labs.

scholarly journals GEView (Gene Expression View) Tool for Intuitive and High Accessible Visualization of Expression Data for Non-Programmer Biologists

2018 ◽  
Vol 8 (1) ◽  
pp. 94-105
Author(s):  
Libi Hertzberg ◽  
Assif Yitzhaky ◽  
Metsada Pasmanik-Chor
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Graphical User Interface ◽  
Differential Expression Analysis ◽  
Biological Data ◽  
Expression Data ◽  
High Throughput Data ◽  
Batch Correction ◽  
Different Types ◽  
User Friendly

This article describes how the last decade has been characterized by the production of huge amounts of different types of biological data. Following that, a flood of bioinformatics tools have been published. However, many of these tools are commercial, or require computational skills. In addition, not all tools provide intuitive and highly accessible visualization of the results. The authors have developed GEView (Gene Expression View), which is a free, user-friendly tool harboring several existing algorithms and statistical methods for the analysis of high-throughput gene, microRNA or protein expression data. It can be used to perform basic analysis such as quality control, outlier detection, batch correction and differential expression analysis, through a single intuitive graphical user interface. GEView is unique in its simplicity and highly accessible visualization it provides. Together with its basic and intuitive functionality it allows Bio-Medical scientists with no computational skills to independently analyze and visualize high-throughput data produced in their own labs.

ABCModeller: an automatic data mining tool based on a consistent voting method with a user-friendly graphical interface

2020 ◽  
Author(s):  
Pengyi Zhang ◽  
Jiangpeng Wu ◽  
Honglin Zhai ◽  
Shuyan Li
Keyword(s):  
Data Mining ◽  
Biological Data ◽  
Significant Feature ◽  
Support Vector ◽  
Graphical Interface ◽  
Automatic Data ◽  
Data Mining Tool ◽  
Mining Tool ◽  
User Friendly ◽  
Voting Method

Abstract In order to extract useful information from a huge amount of biological data nowadays, simple and convenient tools are urgently needed for data analysis and modeling. In this paper, an automatic data mining tool, termed as ABCModeller (Automatic Binary Classification Modeller), with a user-friendly graphical interface was developed here, which includes automated functions as data preprocessing, significant feature extraction, classification modeling, model evaluation and prediction. In order to enhance the generalization ability of the final model, a consistent voting method was built here in this tool with the utilization of three popular machine-learning algorithms, as artificial neural network, support vector machine and random forest. Besides, Fibonacci search and orthogonal experimental design methods were also employed here to automatically select significant features in the data space and optimal hyperparameters of the three algorithms to achieve the best model. The reliability of this tool has been verified through multiple benchmark data sets. In addition, with the advantage of a user-friendly graphical interface of this tool, users without any programming skills can easily obtain reliable models directly from original data, which can reduce the complexity of modeling and data mining, and contribute to the development of related research including but not limited to biology. The excitable file of this tool can be downloaded from http://lishuyan.lzu.edu.cn/ABCModeller.rar.

scholarly journals OP0021 IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENES IN EARLY RHEUMATOID ARTHRITIS PATIENTS RESPONDING TO TOCILIZUMAB

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 12.2-12
Author(s):  
I. Muller ◽  
M. Verhoeven ◽  
H. Gosselt ◽  
M. Lin ◽  
T. De Jong ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
T Cells ◽  
Gene Ontology ◽  
Regulatory T Cells ◽  
Early Rheumatoid Arthritis ◽  
Whole Blood ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Double Blind

Background:Tocilizumab (TCZ) is a monoclonal antibody that binds to the interleukin 6 receptor (IL-6R), inhibiting IL-6R signal transduction to downstream inflammatory mediators. TCZ has shown to be effective as monotherapy in early rheumatoid arthritis (RA) patients (1). However, approximately one third of patients inadequately respond to therapy and the biological mechanisms underlying lack of efficacy for TCZ remain elusive (1). Here we report gene expression differences, in both whole blood and peripheral blood mononuclear cells (PBMC) RNA samples between early RA patients, categorized by clinical TCZ response (reaching DAS28 < 3.2 at 6 months). These findings could lead to identification of predictive biomarkers for TCZ response and improve RA treatment strategies.Objectives:To identify potential baseline gene expression markers for TCZ response in early RA patients using an RNA-sequencing approach.Methods:Two cohorts of RA patients were included and blood was collected at baseline, before initiating TCZ treatment (8 mg/kg every 4 weeks, intravenously). DAS28-ESR scores were calculated at baseline and clinical response to TCZ was defined as DAS28 < 3.2 at 6 months of treatment. In the first cohort (n=21 patients, previously treated with DMARDs), RNA-sequencing (RNA-seq) was performed on baseline whole blood PAXgene RNA (Illumina TruSeq mRNA Stranded) and differential gene expression (DGE) profiles were measured between responders (n=14) and non-responders (n=7). For external replication, in a second cohort (n=95 therapy-naïve patients receiving TCZ monotherapy), RNA-seq was conducted on baseline PBMC RNA (SMARTer Stranded Total RNA-Seq Kit, Takara Bio) from the 2-year, multicenter, double-blind, placebo-controlled, randomized U-Act-Early trial (ClinicalTrials.gov identifier: NCT01034137) and DGE was analyzed between 84 responders and 11 non-responders.Results:Whole blood DGE analysis showed two significantly higher expressed genes in TCZ non-responders (False Discovery Rate, FDR < 0.05): urotensin 2 (UTS2) and caveolin-1 (CAV1). Subsequent analysis of U-Act-Early PBMC DGE showed nine differentially expressed genes (FDR < 0.05) of which expression in clinical TCZ non-responders was significantly higher for eight genes (MTCOP12, ZNF774, UTS2, SLC4A1, FECH, IFIT1B, AHSP, and SPTB) and significantly lower for one gene (TND2P28M). Both analyses were corrected for baseline DAS28-ESR, age and gender. Expression of UTS2, with a proposed function in regulatory T-cells (2), was significantly higher in TCZ non-responders in both cohorts. Furthermore, gene ontology enrichment analysis revealed no distinct gene ontology or IL-6 related pathway(s) that were significantly different between TCZ-responders and non-responders.Conclusion:Several genes are differentially expressed at baseline between responders and non-responders to TCZ therapy at 6 months. Most notably, UTS2 expression is significantly higher in TCZ non-responders in both whole blood as well as PBMC cohorts. UTS2 could be a promising target for further analyses as a potential predictive biomarker for TCZ response in RA patients in combination with clinical parameters (3).References:[1]Bijlsma JWJ, Welsing PMJ, Woodworth TG, et al. Early rheumatoid arthritis treated with tocilizumab, methotrexate, or their combination (U-Act-Early): a multicentre, randomised, double-blind, double-dummy, strategy trial. Lancet. 2016;388(10042):343-55.[2]Bhairavabhotla R, Kim YC, Glass DD, et al. Transcriptome profiling of human FoxP3+ regulatory T cells. Human Immunology. 2016;77(2):201-13.[3]Gosselt HR, Verhoeven MMA, Bulatovic-Calasan M, et al. Complex machine-learning algorithms and multivariable logistic regression on par in the prediction of insufficient clinical response to methotrexate in rheumatoid arthritis. Journal of Personalized Medicine. 2021;11(1).Disclosure of Interests:None declared

scholarly journals Establishing a consensus for the hallmarks of cancer based on gene ontology and pathway annotations

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yi Chen ◽  
Fons. J. Verbeek ◽  
Katherine Wolstencroft
Keyword(s):  
Gene Ontology ◽  
Enrichment Analysis ◽  
Biological Data ◽  
Hallmarks Of Cancer ◽  
High Throughput Analysis ◽  
Knowledge Resources ◽  
Gene Set ◽  
Cancer Hallmarks ◽  
Starting Point ◽  
High Level

Abstract Background The hallmarks of cancer provide a highly cited and well-used conceptual framework for describing the processes involved in cancer cell development and tumourigenesis. However, methods for translating these high-level concepts into data-level associations between hallmarks and genes (for high throughput analysis), vary widely between studies. The examination of different strategies to associate and map cancer hallmarks reveals significant differences, but also consensus. Results Here we present the results of a comparative analysis of cancer hallmark mapping strategies, based on Gene Ontology and biological pathway annotation, from different studies. By analysing the semantic similarity between annotations, and the resulting gene set overlap, we identify emerging consensus knowledge. In addition, we analyse the differences between hallmark and gene set associations using Weighted Gene Co-expression Network Analysis and enrichment analysis. Conclusions Reaching a community-wide consensus on how to identify cancer hallmark activity from research data would enable more systematic data integration and comparison between studies. These results highlight the current state of the consensus and offer a starting point for further convergence. In addition, we show how a lack of consensus can lead to large differences in the biological interpretation of downstream analyses and discuss the challenges of annotating changing and accumulating biological data, using intermediate knowledge resources that are also changing over time.

scholarly journals Managing Osteoporosis and Joint Damage in Patients with Rheumatoid Arthritis: An Overview

2021 ◽  
Vol 10 (6) ◽  
pp. 1241
Author(s):  
Yoshiya Tanaka
Keyword(s):  
Rheumatoid Arthritis ◽  
Structural Damage ◽  
Kinase Inhibitors ◽  
Joint Destruction ◽  
Joint Damage ◽  
Nuclear Factor Κb ◽  
Cartilage Destruction ◽  
Janus Kinase ◽  
Systemic Autoimmune Disease ◽  
And Cartilage

In rheumatoid arthritis, a representative systemic autoimmune disease, immune abnormality and accompanying persistent synovitis cause bone and cartilage destruction and systemic osteoporosis. Biologics targeting tumor necrosis factor, which plays a central role in the inflammatory process, and Janus kinase inhibitors have been introduced in the treatment of rheumatoid arthritis, making clinical remission a realistic treatment goal. These drugs can prevent structural damage to bone and cartilage. In addition, osteoporosis, caused by factors such as menopause, aging, immobility, and glucocorticoid use, can be treated with bisphosphonates and the anti-receptor activator of the nuclear factor-κB ligand antibody. An imbalance in the immune system in rheumatoid arthritis induces an imbalance in bone metabolism. However, osteoporosis and bone and cartilage destruction occur through totally different mechanisms. Understanding the mechanisms underlying osteoporosis and joint destruction in rheumatoid arthritis leads to improved care and the development of new treatments.

scholarly journals AB0162 OCULAR MANIFESTATIONS IN PATIENTS WITH RHEUMATOID ARTHRITIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1108.1-1108
Author(s):  
D. Monova ◽  
S. Monov ◽  
R. Shumnalieva ◽  
D. Dimova ◽  
M. Sotirova
Keyword(s):  
Rheumatoid Arthritis ◽  
Intraocular Pressure ◽  
Disease Duration ◽  
Keratoconjunctivitis Sicca ◽  
Retinal Vasculitis ◽  
Standard Test ◽  
Systemic Autoimmune Disease ◽  
Ophthalmological Examination ◽  
Schirmer’S Test ◽  
Ocular Symptoms

Background:Rheumatoid arthritis (RA) is the most common systemic autoimmune disease and is associated with a number of extra-articular organ manifestations, including ocular complications.Objectives:The aim of this study is to evaluate the frequency and characteristics of ocular manifestation in patients with rheumatoid arthritis (RA).Methods:The study involved 87 patients with RA. All the study subjects underwent complete ophthalmological examination involving visual acuity assessment, examination of anterior and posterior eye segments, Schirmer’s test, diameter and mobility of pupils, as well as eyeball mobility assessment of intraocular pressure. Data regarding age, gender, disease duration, age at diagnosis, systemic corticosteroid use, blood pressure, ocular symptoms and detailed ophthalmic history were recorded. The presence of rheumatoid factor in serum was evaluated by standard test methods based on principle of agglutination. All patients were seropositive.Results:87 patients (26 male, 59 female, mean age 45,6 ± 13,1 years; mean disease duration 7,4 ± 6,2 years) with RA were enrolled in this study. 31 (35,63 %) of them had no ocular symptoms. Among the patients with ocular symptoms, 39 (69,64 %) complained of decreased vision, 33 (58,93 %) - of dry eye, 32 (57,14 %) - of burning, 29 (51,78 %) -photophobia, 28 (50 %) - of gritty sensation, 27 (48,21 %) - of itching, 18 (32,14 %) - of redness, 13 (23,21 %) - of ocular pain, 3 (5,36 %) - of floaters. Ophthalmological examination revealed higher incidence of the following abnormalities in the study group: myopic astigmatism - in 10 (5,74 %) eyes, vascular abnormalities within fundus - in 22 (12,64 %) eyes, increased intraocular pressure (> 21 mm Hg) - in 11 (6,32 %) eyes. Mean IOP values were 17,34 ± 5,12 mm Hg. In 48 eyes Schirmer’s test results were below 10 mm, and in 18 eyes - below 5 mm. Keratoconjunctivitis sicca was present in 31 (35,63 %) of all patients. Episcleritis was diagnosed in 4 patients (4,6 %), scleritis – in 3 (3,45 %). Retinal vasculitis was present in 2 (2,3 %) patients and involves veins and arteries peripheral branches. Lens opacity was found in 13 (14,94 %) patients (21 eyes), mostly in the form of posterior subcapsular cataract (in 16 eyes) and nuclear cataract (in 5 eyes). The mean age of patients with cataracts was 52,3 ± 14,2 years. 13 of the patients with cataracts were either currently taking or had previously taken systemic corticosteroids.Conclusion:In patients with RA numerous abnormalities within the vision of organ may be found. Ocular symptoms are relatively common complications of RA, and may result in irreversible changes in the organ of vision. Regular ophthalmological examinations are essential among the patients with RA.Disclosure of Interests:None declared

scholarly journals Genetic Vectors as a Tool in Association Studies: Definitions and Application for Study of Rheumatoid Arthritis

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Igor Sandalov ◽  
Leonid Padyukov
Keyword(s):  
Rheumatoid Arthritis ◽  
Complex Disease ◽  
Association Studies ◽  
Healthy Population ◽  
Common Complex Disease ◽  
New Approach ◽  
Functional Studies ◽  
Biological Interpretation ◽  
Genetic Vectors ◽  
Efficient Selection

To identify putative relations between different genetic factors in the human genome in the development of common complex disease, we mapped the genetic data to an ensemble of spin chains and analysed the data as a quantum system. Each SNP is considered as a spin with three states corresponding to possible genotypes. The combined genotype represents a multispin state, described by the product of individual-spin states. Each person is characterized by a single genetic vector (GV) and individuals with identical GVs comprise the GV group. This consolidation of genotypes into GVs provides integration of multiple genetic variants for a single statistical test and excludes ambiguity of biological interpretation known for allele and haplotype associations. We analyzed two independent cohorts, with 2633 rheumatoid arthritis cases and 2108 healthy controls, and data for 6 SNPs from the HTR2A locus plus shared epitope allele. We found that GVs based on selected markers are highly informative and overlap for 98.3% of the healthy population between two cohorts. Interestingly, some of the GV groups contain either only controls or only cases, thus demonstrating extreme susceptibility or protection features. By using this new approach we confirmed previously detected univariate associations and demonstrated the most efficient selection of SNPs for combined analyses for functional studies.

scholarly journals Effects of Antitumor Necrosis Factor Therapy on Osteoprotegerin, Neopterin, and sRANKL Concentrations in Patients with Rheumatoid Arthritis

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Katharina Kurz ◽  
Manfred Herold ◽  
Elisabeth Russe ◽  
Werner Klotz ◽  
Guenter Weiss ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Disease Activity ◽  
Systemic Autoimmune Disease ◽  
Das28 Score ◽  
Reactive Protein ◽  
Adalimumab Therapy ◽  
Receptor Activator ◽  
Joint Erosions ◽  
Factor Therapy ◽  
Necrosis Factor

Background. Rheumatoid arthritis is a systemic autoimmune disease characterized by joint erosions, progressive focal bone loss, and chronic inflammation.Methods. 20 female patients with moderate-to-severe rheumatoid arthritis were treated with anti-TNF-antibody adalimumab in addition to concomitant antirheumatic therapies. Patients were assessed for overall disease activity using the DAS28 score, and neopterin, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP) concentrations as well as osteoprotegerin (OPG) and soluble receptor activator of NF-κB ligand (sRANKL) concentrations were determined before therapy and at week 12. Neopterin as well as OPG and sRANKL were determined by commercial ELISAs.Results. Before anti-TNF therapy patients presented with high disease activity and elevated concentrations of circulating inflammatory markers. OPG concentrations correlated with neopterin (rs=0.494,p=0.027), but not with DAS28. OPG concentrations and disease activity scores declined during anti-TNF-treatment (bothp<0.02). Patients who achieved remission (n=7) or showed a good response according to EULAR criteria (n=13) presented with initially higher baseline OPG levels, which subsequently decreased significantly during treatment (p=0.018for remission,p=0.011for good response).Conclusions. Adalimumab therapy was effective in modifying disease activity and reducing proinflammatory and bone remodelling cascades.

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