Rattus norvegicus
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ABSTRACT:
PROVIDER: PRJNA144067 | ENA |
REPOSITORIES: ENA
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Project description:Appropriate nutrition during early development is essential for optimal bone mass accretion; however, linkage between early nutrition, childhood bone mass and prevention of bone loss later in life has not been extensively studied. In this report, we have demonstrated several fundamental issues in the field. 1) A significant prevention of ovariectomy (OVX) -induced bone loss from adult rats can occur with only 14 days consumption of a blueberry-containing diet immediately prior to puberty. 2) The molecular mechanisms underlying these effects involve increased myosin production and preserved a shuttle for transcription factors such as Runx2 from cytoplasm to nucleolus which stimulates osteoblast differentiation and reduces mesenchymal stromal cell senescence. 3) The effects of blueberry diet on preserving fidelity of osteoblast differentiation also overcome reduced osteoblast differentiation and activity due to OVX-induced degradation of collagen matrix. Bone quality was compared among casein control diet sham operated, casein diet ovariectomized, long term BB diet ovariectomized, and short term BB diet ovariectomized female rats
2012-06-30 | E-GEOD-30081 | biostudies-arrayexpress
Project description:Appropriate nutrition during early development is essential for optimal bone mass accretion; however, linkage between early nutrition, childhood bone mass and prevention of bone loss later in life has not been extensively studied. In this report, we have demonstrated several fundamental issues in the field. 1) A significant prevention of ovariectomy (OVX) -induced bone loss from adult rats can occur with only 14 days consumption of a blueberry-containing diet immediately prior to puberty. 2) The molecular mechanisms underlying these effects involve increased myosin production and preserved a shuttle for transcription factors such as Runx2 from cytoplasm to nucleolus which stimulates osteoblast differentiation and reduces mesenchymal stromal cell senescence. 3) The effects of blueberry diet on preserving fidelity of osteoblast differentiation also overcome reduced osteoblast differentiation and activity due to OVX-induced degradation of collagen matrix.
2012-07-01 | GSE30081 | GEO
Project description:Mice overexpressing galectin-8 (gal-8 Tg), a secreted mammalian lectin, exhibit enhanced bone turnover and reduced bone mass, similar to cases of post-menopausal osteoporosis. Gal-8 knockout (KO) mice have increased bone mass accrual at young age, but exhibit accelerated bone loss during adulthood. These phenotypes can be attributed to gal-8-mediated increase in RANKL expression that promotes osteoclastogenesis, combined with direct inhibition of osteoblasts differentiation, evident by reduced BMP signaling, SMAD phosphorylation, and reduced expression of osteoblasts differentiation markers OSX, OCN, RUNX2, DMP-1 and ALP. Gal-8 mRNA positively correlates with the mRNA levels of osteoclastogenic markers RANKL, TRAP and CTSK in human femurs. Collectively, these findings identify gal-8 as a new physiological player in the regulation of bone mass.
2017-11-08 | GSE106631 | GEO
Project description:Skeletal deformities are typical clinical manifestations of autosomal dominant hyper-immunoglobulin E syndrome (AD-HIES), but the treatment is still limited. AD-HIES is mainly caused by dominant-negative mutations in signal transducer and activator of transcription 3 (STAT3). Clarifying the mechanisms by which STAT3 inactivation destructs bone metabolism is, therefore, important. Although, a few lines of data indicate osteoporosis induced by STAT3 inactivation may be related to increased osteoclast activity, here, we firstly report that deletion of Stat3 in osteoblasts, but not in osteoclasts, induced AD-HIES-like bone defects, including craniofacial malformation, osteoporosis, and spontaneous bone fracture in mice, and these defects were resulted from impaired osteogenesis induced by STAT3 inactivation in osteoblasts, but not increased osteoclast activity. Mechanistic analysis revealed a novel regulatory model in which STAT3 drove osteoblast differentiation via promoting distal-less homeobox 5 transcription in cooperation with MSX1. Furthermore, inducible deletion of Stat3 in osteoblasts also inhibited osteogenesis and induced bone loss in adult mice. Meaningfully, pharmacological inhibition of STAT3 induced bone loss, while activation of STAT3 partially prevent bone loss due to tail sus-pension. Taken together, STAT3 is critical for both modulating skeletal development and maintaining bone homeostasis, and inactivation of STAT3 in osteoblasts, but not in osteoclasts, induced AD-HIES-related skeletal deformities by impairing osteogenesis, providing new insights for their treatment.
2021-07-28 | GSE173711 | GEO
Project description:This a model from the article:
Modeling the interactions between osteoblast and osteoclast activities in bone remodeling.
Lemaire V, Tobin FL, Greller LD, Cho CR, Suva LJ.
J Theor Biol.2004 Aug 7;229(3):293-309.
15234198,
Abstract:
We propose a mathematical model explaining the interactions between osteoblasts and osteoclasts, two cell types specialized in the maintenance of the bone integrity. Bone is a dynamic, living tissue whose structure and shape continuously evolves during life. It has the ability to change architecture by removal of old bone and replacement with newly formed bone in a localized process called remodeling. The model described here is based on the idea that the relative proportions of immature and mature osteoblasts control the degree of osteoclastic activity. In addition, osteoclasts control osteoblasts differentially depending on their stage of differentiation. Despite the tremendous complexity of the bone regulatory system and its fragmentary understanding, we obtain surprisingly good correlations between the model simulations and the experimental observations extracted from the literature. The model results corroborate all behaviors of the bone remodeling system that we have simulated, including the tight coupling between osteoblasts and osteoclasts, the catabolic effect induced by continuous administration of PTH, the catabolic action of RANKL, as well as its reversal by soluble antagonist OPG. The model is also able to simulate metabolic bone diseases such as estrogen deficiency, vitamin D deficiency, senescence and glucocorticoid excess. Conversely, possible routes for therapeutic interventions are tested and evaluated. Our model confirms that anti-resorptive therapies are unable to partially restore bone loss, whereas bone formation therapies yield better results. The model enables us to determine and evaluate potential therapies based on their efficacy. In particular, the model predicts that combinations of anti-resorptive and anabolic therapies provide significant benefits compared with monotherapy, especially for certain type of skeletal disease. Finally, the model clearly indicates that increasing the size of the pool of preosteoblasts is an essential ingredient for the therapeutic manipulation of bone formation. This model was conceived as the first step in a bone turnover modeling platform. These initial modeling results are extremely encouraging and lead us to proceed with additional explorations into bone turnover and skeletal remodeling.
This model corresponds to the core model published in the paper. There is no corresponding plot to reproduce for this model.
To obtain each of the 9 plots in the Figure 2 of the reference publication, there are some changes to be made to the core model.
The curation figure reproduces figure 2 of the reference publication. There is a corresponding SBML and Copasi files for each of the plot. See curation tab for more details.
This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2024-09-02 | BIOMD0000000278 | BioModels
Project description:Bone is a dynamic organ in which bone formation mediated by osteoblasts balances against bone resorption mediated by osteoclasts to maintain bone homeostasis. With age, this balance gradually tilts toward bone resorption, leading to bone loss and osteoporosis. At the present study, the bone specimens from children (group A), middle-aged patients (group B), and older individuals (group C) were subjected to proteomics analysis by LC-MS/MS.
2023-02-01 | PXD039538 | Pride
Project description:This a model from the article:
Mathematical model of paracrine interactions between osteoclasts and osteoblasts predicts anabolic action of parathyroid hormone on bone.
Komarova SV.
Endocrinology.2005 Aug;146(8):3589-95.
15860557,
Abstract:
To restore falling plasma calcium levels, PTH promotes calcium liberation from bone. PTH targets bone-forming cells, osteoblasts, to increase expression of the cytokine receptor activator of nuclear factor kappaB ligand (RANKL), which then stimulates osteoclastic bone resorption. Intriguingly, whereas continuous administration of PTH decreases bone mass, intermittent PTH has an anabolic effect on bone, which was proposed to arise from direct effects of PTH on osteoblastic bone formation. However, antiresorptive therapies impair the ability of PTH to increase bone mass, indicating a complex role for osteoclasts in the process. We developed a mathematical model that describes the actions of PTH at a single site of bone remodeling, where osteoclasts and osteoblasts are regulated by local autocrine and paracrine factors. It was assumed that PTH acts only to increase the production of RANKL by osteoblasts. As a result, PTH stimulated osteoclasts upon application, followed by compensatory osteoblast activation due to the coupling of osteoblasts to osteoclasts through local paracrine factors. Continuous PTH administration resulted in net bone loss, because bone resorption preceded bone formation at all times. In contrast, over a wide range of model parameters, short application of PTH resulted in a net increase in bone mass, because osteoclasts were rapidly removed upon PTH withdrawal, enabling osteoblasts to rebuild the bone. In excellent agreement with experimental findings, increase in the rate of osteoclast death abolished the anabolic effect of PTH on bone. This study presents an original concept for the regulation of bone remodeling by PTH, currently the only approved anabolic treatment for osteoporosis.
The model reproduces Figures 1B and 2A of the reference publication. To obtain the figures 1B, the parameter g21 needs changes. To obtain the figures 1A, the parameters g21, g12 and k2 need to changed. For details look at the curation tab.
The initial concentration of Osteoclasts (x1) is corrected to 1.06066 from 10.06066.
This model was taken from the
CellML repository and automatically converted to SBML.
The original model was:
CellMLdetails
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@auckland.ac.nz
The University of Auckland
The Bioengineering Institute
This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2024-09-02 | BIOMD0000000279 | 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.
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