Comparison of C57Bl/KalwRij mouse bone marrow to C57BL6 mouse bone marrow
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
SUBMITTER: Jessica Fowler
PROVIDER: E-GEOD-31532 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:Multiple myeloma is a fatal hematological malignancy. In order to develop effective therapeutic approaches, it is critical to understand the pathogenesis of myeloma. The Radl 5T model of multiple myeloma is a clinically relevant murine model where myeloma spontaneously occurs in aged, in-bred C57BlKalwRij mice and can be propagated by intravenous inoculation of 5T myeloma cells into mice of the same strain. Importantly inoculation of 5T myeloma cells into C57Bl6 mice does not result in myeloma, demonstrating that the bone marrow (BM) microenvironment of the C57BlKalwRij strain provides a unique and permissive milieu for myeloma development. We hypothesized that cells of the BM microenvironment may provide essential stimuli for the development of multiple myeloma in vivo. We aim to determine the differences in expression within the bone marrow of C57Bl/KalwRij mice.
2011-08-19 | GSE31532 | GEO
Project description:<h4><strong>BACKGROUND:</strong> Multiple myeloma is characterized by clonal proliferation of malignant plasma cells in the bone marrow that produce monoclonal immunoglobulins. N-glycosylation changes of these monoclonal immunoglobulins have been reported in multiple myeloma, but previous studies only detected limited serum N-glycan features.</h4><h4><strong>METHODS:</strong> Here, a more detailed study of the human serum N-glycome of 91 multiple myeloma patients and 51 controls was performed. We additionally analyzed sequential samples from patients (n = 7) which were obtained at different time points during disease development as well as 16 paired blood serum and bone marrow plasma samples. N-glycans were enzymatically released and measured by mass spectrometry after linkage specific derivatization of sialic acids.</h4><h4><strong>RESULTS:</strong> A decrease in both α2,3- and α2,6-sialylation, galactosylation and an increase in fucosylation within complex-type N-glycans were found in multiple myeloma patients compared to controls, as well as a decrease in difucosylation of diantennary glycans. The observed glycosylation changes were present in all ISS stages, including the 'low-risk' ISS I. In individual patients, difucosylation of diantennary glycans decreased with development of the disease. Protein N-glycosylation features from blood and bone marrow showed strong correlation. Moreover, associations of monoclonal immunoglobulin (M-protein) and albumin levels with glycan traits were discovered in multiple myeloma patients.</h4><h4><strong>CONCLUSIONS & GENERAL SIGNIFICANCE: </strong>In conclusion, serum protein N-glycosylation analysis could successfully distinguish multiple myeloma from healthy controls. Further studies are needed to assess the potential roles of glycan trait changes and the associations of glycans with clinical parameters in multiple myeloma early detection and prognosis.</h4>
2022-04-27 | MTBLS676 | MetaboLights
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.
2011-11-25 | 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 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.
2011-12-06 | 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 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.
2011-11-25 | BIOMD0000000403 | BioModels
Project description:Multiple Myeloma primary myeloma cells of 131 patients, 10 human myeloma cell lines, bone marrow stromal cells of 5 myeloma patients, bone marrow CD3 cells of 5 myeloma patients, bone marrow CD14 cells of 5 myeloma patients, bone marrow CD15 cells of 5 myeloma patients, in vitro generated osteoclastic cells of 7 myeloma patients, 7 normal plasmablasts and 6 normal memory B cells.
2011-03-08 | E-TABM-937 | biostudies-arrayexpress
Project description:Bone marrow monocytes are primarily committed to osteoclast formation. It is, however, unknown whether potential primary alterations are specifically present in bone marrow monocytes from patients with multiple myeloma, smoldering myeloma or monoclonal gammopathy of undetermined significance. We analyzed the immunophenotypic and transcriptional profiles of bone marrow CD14+ monocytes in a cohort of patients with different types of monoclonal gammopathies to identify alterations involved in myeloma-enhanced osteoclastogenesis. The number of bone marrow CD14+CD16+ cells was higher in patients with active myeloma than in those with smoldering myeloma or monoclonal gammopathy of undetermined significance. Interestingly, sorted bone marrow CD14+CD16+ cells from myeloma patients were more pro-osteoclastogenic than CD14+CD16-cells in cultures ex vivo Moreover, transcriptional analysis demonstrated that bone marrow CD14+ cells from patients with multiple myeloma (but neither monoclonal gammopathy of undetermined significance nor smoldering myeloma) significantly upregulated genes involved in osteoclast formation, including IL21RIL21R mRNA over-expression by bone marrow CD14+ cells was independent of the presence of interleukin-21. Consistently, interleukin-21 production by T cells as well as levels of interleukin-21 in the bone marrow were not significantly different among monoclonal gammopathies. Thereafter, we showed that IL21R over-expression in CD14+ cells increased osteoclast formation. Consistently, interleukin-21 receptor signaling inhibition by Janex 1 suppressed osteoclast differentiation from bone marrow CD14+ cells of myeloma patients. Our results indicate that bone marrow monocytes from multiple myeloma patients show distinct features compared to those from patients with indolent monoclonal gammopathies, supporting the role of IL21R over-expression by bone marrow CD14+ cells in enhanced osteoclast formation.
2017-04-11 | GSE70345 | GEO
Project description:Multiple myeloma (MM) is the second most common hematologic malignancy, which is characterized by clonal proliferation of neoplastic plasma cells in the bone marrow. This microenvironment is characterized by low oxygen levels (1-6% O2), known as hypoxia. For MM cells, hypoxia is a physiologic feature that has been described to promote an aggressive phenotype and to confer drug resistance. However, studies on hypoxia are scarce and show little conformity. Here, we analyzed the global proteome in MM cells and the stromal cell line HS-5 to reveal hypoxia-dependent regulation of proteins.
2022-02-16 | PXD030239 | Pride
Project description:Samples in this series are pre-treatment bone marrow aspirates from multiple myeloma patients. Keywords = Multiple Myeloma, Bone Marrow, Pre-Treatment Keywords: other
2006-06-01 | GSE2658 | GEO
Project description:Bone marrow (BM) niches provide an optimal substrate for multiple myeloma (MM) cell lodgement and growth. Nevertheless, little is known about the putative mechanisms by which the BM microenvironment can lead to initiation or progression of oncogenesis in this disease. We have demonstrated that BM mesenchymal stromal cell-derived exosomes transfer their miRNA and protein content to clonal plasma cells, thus acting as synaptic vesicles responsible for molding the microenvironment surrounding multiple myeloma (MM) cells, leading to MM growth, dissemination and, therefore, disease progression. We used microarray to detail the changes in microRNA expression in MM-BM mesenchymal stromal cell (MSC)-derived exosomes, compared to normal- and monoclonal gammopathy of undetermined significance- BM-MSC-derived exosomes. Exosomes have been isolated from cell culture supernatant of BM-MSCs (MM=7; MGUS=2; Normal=4), and subsequently evaluated at ultrastructural level by using electron microscopy and immunogolf labeling. RNA was extracted; and miRNA profiling has been assessed by using TaqMan human miRNA profiling. Mean miRNA expression value has been used for miRNA RT-qPCR data normalization, as described (Mestdagh et al., 2009).
2013-07-24 | E-GEOD-39571 | biostudies-arrayexpress