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 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
Project description:Like normal HSCs, LSCs appear to reside in endosteal BM niche and utilize hypoxic niche for their stemness maintenance. To determine the molecular mechanism that regulating the stemness of LSCs, we performed RNA-seq analyses on leukemia cells isolated from collagenase treated bone (endosteal BM, EBM) and from conventional flushed BM (central BM, CBM) of AE9a leukemia mice.
2025-12-04 | GSE272011 | GEO
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:Endosteal bone marrow (BM) niches are crucial to sustain non-steady-state hematopoiesis but are challenging to be modelled in their cellular and molecular complexity in standardized, human settings. We report a developmentally-guided approach to generate a macro-scale organotypic model of BM endosteal niches (engineered vascularized osteoblastic niche, eVON) based on human induced pluripotent stem cells (hiPSC) and porous hydroxyapatite scaffolds. Vascular and osteoblastic cells derived from the same hiPSC self-assembled into complex and long-lasting vascular networks integrated within osteogenic matrix. The system supported hematopoiesis in vitro and was stable upon implantation in vivo. Transcriptomic analysis revealed osteogenic, vascular and neural cells expressing key niche signals (e.g., CXCL12, KITLG and VEGFA) in human-specific patterns. The eVON could be perturbed at cellular (removing vascular cells) and molecular (deregulating VEGF signaling) levels to study contribution of the endosteal vasculature to myelopoiesis. The eVON offers unprecedented possibilities to dissect human pathophysiological hematopoiesis in endosteal BM.
2025-11-18 | GSE285292 | 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