Mus musculus
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ABSTRACT:
PROVIDER: PRJNA247737 | ENA |
REPOSITORIES: ENA
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Project description:Multiple myeloma is largely incurable, despite development of therapies that target myeloma cell-intrinsic pathways. Disease relapse is thought to originate from dormant myeloma cells, localized in specialized niches, which resist therapy and re-populate the tumor. However, little is known about the niche, and how it exerts cell-extrinsic control over myeloma cell dormancy and re-activation. In this study we track individual myeloma cells by intravital imaging as they colonize the endosteal niche, enter a dormant state and subsequently become activated to form colonies. We demonstrate that dormancy is a reversible state which is switched ‘on’ by engagement with bone lining cells or osteoblasts, and switched ‘off’ by osteoclasts remodeling the endosteal niche. Dormant myeloma cells are resistant to chemotherapy targeting dividing cells. The demonstration that the endosteal niche is pivotal in controlling myeloma cell dormancy highlights the potential for targeting cell-extrinsic mechanisms to overcome cell-intrinsic drug resistance and prevent disease relapse.
2015-11-30 | GSE57695 | GEO
Project description:When lung adenocarcinoma cells metastasize to the bone microenvironment, they often enter a dormant state. These dormant cells can be reactivated after bone microenvironment remodeling and reemerge as metastatic disease, posing challenges for the treatment and prognosis of lung adenocarcinoma. In the bone microenvironment, it is primarily osteoclasts that activate dormant lung adenocarcinoma cells. However, the specific mechanism by which osteoclasts contact dormant lung adenocarcinoma cells and promote their proliferation remains unclear. In this experiment, dormant lung adenocarcinoma cells were induced through in vitro culture and treated with osteoclast-conditioned medium for 24 hours to obtain osteoclast-activated lung adenocarcinoma cells. RNA-seq was performed on both dormant and activated lung adenocarcinoma cells to analyze the specific mechanism of the transition from dormancy to activation in lung adenocarcinoma cells following osteoclast interaction.
2025-04-30 | GSE289262 | 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 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
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:Stem cell function is regulated by specialized microenvironments called stem cell niches. These niches maintain stem cells in a dormant state and promote self-renewal. The most potent hematopoietic stem cells (HSC) with high self-renewal potential are reportedly enriched in the endosteal compared to the central region of the bone marrow. Therefore we analyzed the global transcriptome of the endosteal region and directly compared it to that of the central bone marrow (BM). This comparative, differential analysis revealed that in addition to genes specific to the osteoblastic and osteoclastic lineage and classic regulators of HSC (CXCL12, KIT ligand, angiopoietin-1, Jagged-1, N-cadherin), the endosteum abundantly expresses prostaglandin I2 (PGI2) synthase (Ptgis), which produces PGI2. PGI2 is a highly labile, lipid metabolite with no known roles in regulating HSCs. We show in this study that PGI2 is a potent regulator of HSC function. Therefore comparing endosteal versus central BM transcriptome is a viable approach for uncovering candidate genes that may regulate the function of HSC and the HSC niche.
2021-08-01 | GSE25078 | GEO
Project description:To understand how interactions of myeloma cells with osteoclasts and mesenchymal stem cells in the bone marrow affect the clinical course of myeloma, we used microarrays to study changes in gene expression in freshly isolated myeloma plasma cells following co-cultures with osteoclasts (8 experiments) or with mesenchymal stem cells (13 experiments). Interaction with osteoclasts induced changes in the expression of 675 genes, and interaction with mesenchymal stem cells induced changes in the expression of 296 genes. Expression of only 58 genes commonly and similarly changed in both co-culture systems. Among these, we identified genes associated with overall, progression-free, and post-relapse survival, and developed survival prediction models. Gene expression data from 347 patients treated with total therapy 2 protocol, 433 with total therapy 3, and 98 patients who received various treatments (91 of them high-dose therapy with autologous stem cell support) were used for the analysis. Good predictive models were developed only for post-relapse survival, using genes involved in interaction with osteoclasts or with mesenchymal stem cells. The best predictive model used expression of first relapse of 33 probesets whose expression changed in myeloma cells following interaction with osteoclasts, with hazard ratios of 24, 20, and 12 for patients who relapsed following total therapy 2, total therapy 3 and the various other treatments, respectively. Among the probesets used for prediction, only 10, representing 8 genes, were commonly changed after both co-culture systems. These could present favorable target for therapy. Global gene expression profiling of osteoclasts (OCs) before and after co-culture with primary multiple myeloma plasma cells (MMPCs) was done using Affymetrix microarrays. Eight MMPC and OC co-culture experiments were performed using MMPC isolated from 8 patients and OC prepared from 8 different patients.
2015-08-13 | E-GEOD-31156 | biostudies-arrayexpress
Project description:Hematopoietic aging is defined by a loss of regenerative capacity and skewed differentiation from hematopoietic stem cells (HSC) leading to dysfunctional blood production. Signals from the bone marrow (BM) microenvironment dynamically tailor hematopoiesis, but the effect of aging on the niche and the contribution of the aging niche to blood aging still remains unclear. Here, we show the development of an inflammatory milieu in the aged marrow cavity, which drives both niche and hematopoietic system remodeling. We find decreased numbers and functionality of osteogenic endosteal mesenchymal stromal cells (MSC), expansion of pro-inflammatory perisinusoidal MSCs, and deterioration of the central marrow sinusoidal endothelium, which together create a self-reinforcing inflamed BM milieu. Single cell molecular mapping of old niche cells further confirms disruption of cell identities and enrichment of inflammatory response genes. Inflammation, in turn, drives chronic activation of emergency myelopoiesis pathways in old HSCs and multipotent progenitors, which promotes myeloid differentiation at the expense of lymphoid and erythroid commitment, and hinders hematopoietic regeneration. Remarkably, both defective hematopoietic regeneration, niche deterioration and HSC aging can be improved by blocking inflammatory IL-1 signaling. Our results indicate that targeting the pro-inflammatory niche milieu can be instrumental in restoring blood production during aging.
2022-11-02 | GSE168613 | GEO
Project description:To understand how interactions of myeloma cells with osteoclasts and mesenchymal stem cells in the bone marrow affect the clinical course of myeloma, we used microarrays to study changes in gene expression in freshly isolated myeloma plasma cells following co-cultures with osteoclasts (8 experiments) or with mesenchymal stem cells (13 experiments). Interaction with osteoclasts induced changes in the expression of 675 genes, and interaction with mesenchymal stem cells induced changes in the expression of 296 genes. Expression of only 58 genes commonly and similarly changed in both co-culture systems. Among these, we identified genes associated with overall, progression-free, and post-relapse survival, and developed survival prediction models. Gene expression data from 347 patients treated with total therapy 2 protocol, 433 with total therapy 3, and 98 patients who received various treatments (91 of them high-dose therapy with autologous stem cell support) were used for the analysis. Good predictive models were developed only for post-relapse survival, using genes involved in interaction with osteoclasts or with mesenchymal stem cells. The best predictive model used expression of first relapse of 33 probesets whose expression changed in myeloma cells following interaction with osteoclasts, with hazard ratios of 24, 20, and 12 for patients who relapsed following total therapy 2, total therapy 3 and the various other treatments, respectively. Among the probesets used for prediction, only 10, representing 8 genes, were commonly changed after both co-culture systems. These could present favorable target for therapy. Global gene expression profiling of primary multiple myeloma plasma cells (MMPCs) and mesenchymal stem cells (MSCs) before and after co-culture was done using Affymetrix microarrays. Thirteen MMPC and MSC co-culture experiments using MMPCs from 8 patients and MSCs from 5 healthy donors were performed.
2015-08-13 | E-GEOD-31159 | biostudies-arrayexpress
Project description:Myeloma bone disease is a devastating complication of multiple myeloma (MM) and is caused by dysregulation of bone remodeling processes in the bone marrow microenvironment. Previous studies showed that microRNA-138 (miR-138) is a negative regulator of osteogenic differentiation of mesenchymal stromal cells (MSCs) and that inhibiting its function enhances bone formation in vitro. In this study, we explored the role of miR-138 in myeloma bone disease and evaluated the potential of systemically delivered locked nucleic acid (LNA)-modified anti-miR-138 oligonucleotides in suppressing myeloma bone disease. We showed that expression of miR-138 was significantly increased in MSCs from MM patients (MM-MSCs) and myeloma cells compared to those from healthy subjects. Furthermore, inhibition of miR-138 resulted in enhanced osteogenic differentiation of MM-MSCs in vitro and increased number of endosteal osteoblastic lineage cells (OBCs) and bone formation rate in mouse models of myeloma bone disease. RNA sequencing of the OBCs identified TRPS1 and SULF2 as potential miR-138 targets that were de-repressed in anti-miR-138 treated mice. In summary, these data indicate that inhibition of miR-138 enhances bone formation in MM and that pharmacological inhibition of miR-138 could represent a new therapeutic strategy for treatment of myeloma bone disease.
2018-06-01 | GSE111651 | GEO