Deduction of Novel Genes Potentially Involved in Osteoblasts of Rheumatoid Arthritis Using Next-Generation Sequencing and Bioinformatic Approaches
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE107363 | GEO | 2017/11/28
SECONDARY ACCESSION(S): PRJNA419897
REPOSITORIES: GEO
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Project description:The contribution of microRNA-mediated posttranscriptional regulation on the final proteome in differentiating cells remains elusive. Here, we evaluated the impact of microRNAs (miRNAs) on the proteome of human umbilical cord blood-derived unrestricted somatic stem cells (USSC) during retinoic acid (RA) differentiation by a systemic approach using next generation sequencing analysing mRNA and miRNA expression and quantitative mass spectrometry-based proteome analyses. Interestingly, regulation of mRNAs and their dedicated proteins highly correlated during RA-incubation. Additionally, RA-induced USSC demonstrated a clear separation from native USSC thereby shifting from a proliferating to a metabolic phenotype. Bioinformatic integration of up- and downregulated miRNAs and proteins initially implied a strong impact of the miRNome on the XXL-USSC proteome. However, quantitative proteome analysis of the miRNA contribution on the final proteome after ectopic overexpression of downregulated miR-27a-5p and miR-221-5p or inhibition of upregulated miR-34a-5p, respectively, followed by RA-induction revealed only minor proportions of differentially abundant proteins. In addition, only small overlaps of these regulated proteins with inversely abundant proteins in non-transfected RA-treated USSC were observed. Hence, mRNA transcription rather than miRNA-mediated regulation is the driving force for protein regulation upon RA-incubation, strongly suggesting that miRNAs are fine-tuning regulators rather than active primary switches during RA-induction of USSC.
2020-01-31 | PXD016915 | Pride
Project description:Objective: Fibroblast-like synovial cells (FLS) have multilineage differentiation potential including osteoblasts. We aimed to investigate the role of microRNAs during the osteogenic differentiation of rheumatoid arthritis (RA)-FLS. Methods: MicroRNA(miRNA) array analysis was performed to investigate the differentially expressed miRNAs during the osteogenic differentiation. Expression of miR-218-5p (miR-218) during the osteogenic differentiation was determined by quantitative real-time PCR. Transfection with miR-218 precursor and inhibitor were used to confirm the targets of miR-218 and to analyse the ability of miR-218 to induce osteogenic differentiation. Results: The miRNA array revealed that 12 miRNAs were up-regulated and 24 miRNAs were down-regulated after osteogenic differentiation. We observed that miR-218 rose in the early phase of osteogenic differentiation and then decreased. Micro array analysis revealed the mir-218 modulate the expression of ROBO1 in RA-FLS. The induction of miR-218 in RA-FLS decreased ROBO1 expression, and promoted osteogenic differentiation.
2018-03-26 | GSE111946 | GEO
Project description:Aims: Differential expression profiles of microRNAs (miRNAs) are associated with autoimmune diseases. This study sought to elucidate the plasma exosomal miRNA expression profiles of patients with rheumatoid arthritis (RA) and their potential clinical significance. Methods: In the screening phase, small RNA sequencing was performed to characterize dysregulated exosome-derived miRNAs in the plasma samples from six RA patients and six healthy controls. In the independent validation phase, the candidate plasma exosomal miRNAs were verified in 40 RA patients and 32 healthy controls using quantitative real-time PCR. The association of miRNA levels with clinical characteristics in RA patients was tested. The value of these miRNAs in diagnosing RA was assessed with the receiver operating characteristic curve. Results: Totally 177 and 129 miRNAs were upregulated and downregulated, respectively in RA patients versus healthy controls in the screening phase. There were 10 candidate plasma exosomal miRNAs selected for the next identification. Compared with the healthy controls, eight plasma exosomal miRNAs (let-7a-5p, let-7b-5p, let-7d-5p, let-7f-5p, let-7g-5p, let-7i-5p, miR-128-3p, and miR-25-3p) were significantly elevated in RA patients, but miR-144-3p and miR-15a-5p expression exhibited no significant changes. The let-7a-5p and miR-25-3p levels were associated with rheumatoid factor-positive phenotype in RA patients. For the eight miRNAs, the area under the receiver operating characteristic curve (AUC) ranged from 0.641 to 0.843, and their combination had a high diagnostic accuracy for RA (AUC = 0.916). Conclusions: Our study illustrates that novel exosomal miRNAs in the plasma may represent potential noninvasive biomarkers for RA.
2023-09-24 | GSE218599 | GEO
Project description:Breast Cancer is the cancer with most incidence and mortality in women. microRNAs are emerging as novel prognosis/diagnostic tools. Our aim was to identify a serum microRNA signature useful to predict cancer development. We focused on studying the expression levels of 30 microRNAs in the serum of 96 breast cancer patients versus 92 control individuals. Bioinformatic studies provide a microRNA signature, designated as a predictor, based upon the expression levels of 5 microRNAs. Then, we tested the predictor in a group of 60 randomly chosen women. Lastly, a proteomic study unveiled the over-expression and down-regulation of proteins differently expressed in the serum of breast cancer patients versus that of control individuals. Twenty-six microRNAs differentiate cancer tissue from healthy tissue and 16 microRNAs differentiate the serum of cancer patients from that of the control group. The tissue expression of miR-99a-5p, mir-497-5p, miR-362, and miR-1274, and the serum levels of miR-141 correlated with patient survival. Moreover, the predictor consisting of mir-125b-5p, miR-29c-3p, mir-16-5p, miR-1260, and miR-451a was able to differentiate breast cancer patients from controls. The predictor was validated in 20 new cases of breast cancer patients and tested in 60 volunteer women, assigning 11 out of 60 women to the cancer group. An association of low levels of mir-16-5p with a high content of CD44 protein in serum was found. Circulating microRNAs in serum can represent biomarkers for cancer prediction. Their clinical relevance and use of the predictor here described might be of potential importance for breast cancer prediction.
2020-09-18 | PXD020795 | Pride
Project description:Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease, characterized by synovial hyperplasia and progressive joint destruction. For treating inflammatory diseases, such as RA, the Janus kinase (JAK)-mediated signaling pathway has emerged as a therapeutic target. Therefore, JAK inhibitors, such as tofacitinib and baricitinib, are approved for the treatment of RA. While the immunomodulatory characteristics of JAK inhibition (JAKi) are well defined, the current knowledge of how JAKi affects bone homeostasis is limited. Addressing this question becomes increasingly relevant, as current therapeutic options can slow down RA progression, but joint damage and especially perarticular bone erosion remain. Therefore, the impact of JAKi on bone homeostasis requires further elucidation. For in vitro analysis, the impact of JAK inhibitors tofacitinib and baricitinib on bone-forming osteoblasts was analyzed with respect to their mineralization capability. JAKi, by both tofacitinib and baricitinib, increased mineralization function in mesenchymal stem cells (MSCs)-derived osteoblasts. Having observed increased mineralization capability in mesenchymal stem cells (MSCs)-derived osteoblasts, RNA-seq was performed to shed light on potential signaling pathways and mediators causing this effect. For that, MSCs were plated at 6,000 cells per well in a 24-well-plate in alpha-MEM, supplemented with 10% heat-inactivated FCS and 1% P/S. After one day medium was changed to alpha-MEM, 10% heat-inactivated FCS, 1% P/S and 568 mikromolar L-Ascorbic acid 2-phosphate 351 sesquimagnesium salt hydrate, containing either tofacitinib (500 nM), baricitinib (300 nM) or DMSO as vehicle control. After osteogenic induction, RNA was isolated via phenol-chloroform extraction. At least 3 mikrogram high quality RNA were used for RNA-seq analysis with the Illumina TrueSeq Stranded mRNA Kit and sequenced on an Illumina HiSeq 2500 platform.
2020-02-12 | E-MTAB-8536 | biostudies-arrayexpress
Project description:<p><strong>OBJECTIVE:</strong> Rheumatoid arthritis (RA) is a progressive disease including four stages, where gut microbiome is associated with pathogenesis. We aimed to investigate stage-specific roles of microbial dysbiosis and metabolic disorders in RA.</p><p><strong>METHODS:</strong> We investigated stage-based profiles of faecal metagenome and plasma metabolome of 76 individuals with RA grouped into four stages (stages I-IV) according to 2010 RA classification criteria, 19 individuals with osteroarthritis and 27 healthy individuals. To verify bacterial invasion of joint synovial fluid, 16S rRNA gene sequencing, bacterial isolation and scanning electron microscopy were conducted on another validation cohort of 271 patients from four RA stages.</p><p><strong>RESULTS:</strong> First, depletion of <em>Bacteroides uniformis</em> and <em>Bacteroides plebeius</em> weakened glycosaminoglycan metabolism (p<0.001), continuously hurting articular cartilage across four stages. Second, elevation of <em>Escherichia coli</em> enhanced arginine succinyltransferase pathway in the stage II and stage III (p<0.001), which was correlated with the increase of the rheumatoid factor (p=1.35 x 10^-3) and could induce bone loss. Third, abnormally high levels of methoxyacetic acid (p=1.28 x 10^-8) and cysteine-S-sulfate (p=4.66 x 10^-12) inhibited osteoblasts in the stage II and enhanced osteoclasts in the stage III, respectively, promoting bone erosion. Fourth, continuous increase of gut permeability may induce gut microbial invasion of the joint synovial fluid in the stage IV.</p><p><strong>CONCLUSIONS:</strong> Clinical microbial intervention should consider the RA stage, where microbial dysbiosis and metabolic disorders present distinct patterns and played stage-specific roles. Our work provides a new insight in understanding gut-joint axis from a perspective of stages, which opens up new avenues for RA prognosis and therapy.</p>
2023-01-31 | MTBLS5297 | MetaboLights
Project description:MicroRNAs (miRNAs) are important regulators of gene expression, with documented roles in bone metabolism and osteoporosis, suggesting potential therapeutic targets. Our aim was to identify miRNAs differentially expressed in fractured vs nonfractured bones. Additionally, we performed a miRNA profiling of primary osteoblasts to assess the origin of these differentially expressed miRNAs. Total RNA was extracted from (a) fresh femoral neck trabecular bone from women undergoing hip replacement due to either osteoporotic fracture (OP group, n=6) or osteoarthritis in the absence of osteoporosis (Control group, n=6), matching the two groups by age and body mass index, and (b) primary osteoblasts obtained from knee replacement due to osteoarthritis (n=4). Samples were hybridized to a microRNA array containing more than 1900 miRNAs. Principal component analysis (PCA) plots and heat map hierarchical clustering were performed. For comparison of expression levels, the threshold was set at log fold change > 1.5 and a p-value < 0.05 (corrected for multiple testing). Both PCA and heat map analyses showed that the samples clustered according to the presence or absence of fracture. Overall, 790 and 315 different miRNAs were detected in fresh bone samples and in primary osteoblasts, respectively, 293 of which were common to both groups. A subset of 82 miRNAs was differentially expressed (p < 0.05) between osteoporotic and non-osteoporotic samples. The eight miRNAs with the lowest p-values (and for which a validated miRNA qPCR assay was available) were assayed, and two were confirmed: miR-320a and miR-483-5p. Both were over-expressed in the osteoporotic samples and expressed in primary osteoblasts. miR-320a is known to target CTNNB1 and predicted to regulate RUNX2, while miR-483-5p down-regulates IGF2. We observed a reduction trend for this target gene in the osteoporotic bone.In conclusion, we identified two osteoblast miRNAs over-expressed in osteoporotic fractures, which opens novel prospects for research and therapy.
2015-12-21 | GSE74209 | GEO
Project description:This a model from the article:
A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease
Bruce P Ayati, Claire M Edwards, Glenn F Webb and John P Wikswo.
Biology Direct2010 Apr 20;5(28).
20406449,
Abstract:
BACKGROUND:
Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.
RESULTS:
Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.
CONCLUSIONS:
The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.
Note:
The paper describes three models 1) Zero-dimensional Bone Model without Tumour, 2) Zero-dimensional Bone Model with Tumour and 3) Zero-dimensional Bone Model with Tumour and Drug Treatment. This model corresponds to the Zero-dimensional Bone Model without Tumour.
Typos in the publication:
Equation (4): The first term should be (β1/α1)^(g12/Γ) and not (β2/α2)^(g12/Γ)
Equation (14): The first term should be (β1/α1)^(((g12/(1+r12))/Γ) and not (β2/α2)^(((g12/(1+r12))/Γ)
Equation (13): The first term should be (β1/α1)^((1-g22+r22)/Γ) and not (β1/α1)^((1-g22-r22)/Γ)
All these corrections has been implemented in the model, with the authors agreement.
Beyond these, there are several mismatches between the equation numbers that are mentioned in for each equation and the reference that has been made to these equations in the figure legend.
2024-09-02 | BIOMD0000000401 | BioModels
Project description:This a model from the article:
A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease
Bruce P Ayati, Claire M Edwards, Glenn F Webb and John P Wikswo.
Biology Direct2010 Apr 20;5(28).
20406449,
Abstract:
BACKGROUND:
Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.
RESULTS:
Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.
CONCLUSIONS:
The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.
Note:
The paper describes three models 1) Zero-dimensional Bone Model without Tumour, 2) Zero-dimensional Bone Model with Tumour and 3) Zero-dimensional Bone Model with Tumour and Drug Treatment. This model corresponds to the Zero-dimensional Bone Model with Tumour.
Typos in the publication:
Equation (4): The first term should be (β1/α1)^(g12/Γ) and not (β2/α2)^(g12/Γ)
Equation (14): The first term should be (β1/α1)^(((g12/(1+r12))/Γ) and not (β2/α2)^(((g12/(1+r12))/Γ)
Equation (13): The first term should be (β1/α1)^((1-g22+r22)/Γ) and not (β1/α1)^((1-g22-r22)/Γ)
All these corrections has been implemented in the model, with the authors agreement.
Beyond these, there are several mismatches between the equation numbers that are mentioned in for each equation and the reference that has been made to these equations in the figure legend.
2024-09-02 | BIOMD0000000402 | BioModels
Project description:This a model from the article:
A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease
Bruce P Ayati, Claire M Edwards, Glenn F Webb and John P Wikswo.
Biology Direct2010 Apr 20;5(28).
20406449,
Abstract:
BACKGROUND:
Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.
RESULTS:
Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.
CONCLUSIONS:
The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.
Note:
The paper describes three models 1) Zero-dimensional Bone Model without Tumou
r, 2) Zero-dimensional Bone Model with Tumour and 3) Zero-dimensional Bone Model with Tumour and Drug Treatment. This model corresponds to the Zero-dimensional Bo
ne Model with Tumour and Drug Treatment.
Typos in the publication:
Equation (4): The first term should be (β1/α1)^(g12/Γ) and not (β2/α2)^(g12/Γ)
Equation (14): The first term should be (β1/α1)^(((g12/(1+r12))/Γ) and not (β2/α2)^(((g12/(1+r12))/Γ)
Equation (13): The first term should be (β1/α1)^((1-g22+r22)/Γ) and not (β1/α1)^((1-g22-r22)/Γ)
All these corrections has been implemented in the model, with the authors agreement.
Beyond these, there are several mismatches between the equation numbers that are mentioned in for each equation and the reference that has been made to these equations in the figure legend.
2024-09-02 | BIOMD0000000403 | BioModels