Dataset Information

Identification of gravity-responsive proteins in the femur of spaceflight mice using a quantitative proteomic approach

ABSTRACT: This project is “Medical proteome analysis of osteoporosis- and bone mass-related proteins using the Kibo Japanese Experiment Module of International Space Station (ISS)”. Utilizing proteomic technologies, this research aims to identify proteins involved in space flight-induced osteopenia as well as osteoporosis occurring on the ground by searching serum, and skeletal muscle and bone (femoral diaphysis, mandible) proteomes. The goal of this study is to get better understanding to protect the health of both astronauts and osteoporosis patients. Proteome analyses of C57BL/6J space flown mouse (micro-G and artificial 1-G [n = 5/group, in femoral diaphysis and n = 6/group in mandible]) were carried out tryptic digestion followed by LC-MS/MS analysis (DIA). The obtained results were analyzed using Spectronaut Pulsar X against the mouse-bone spectral library for identification and quantification of proteins.

ORGANISM(S): Mus Musculus (mouse)

SUBMITTER: Yayoi Kimura

PROVIDER: PXD041738 | JPOST Repository | Tue Aug 01 00:00:00 BST 2023

REPOSITORIES: jPOST

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Publications

Identification of gravity-responsive proteins in the femur of spaceflight mice using a quantitative proteomic approach.

Egashira Kenji K Ino Yoko Y Nakai Yusuke Y Ohira Takashi T Akiyama Tomoko T Moriyama Kayano K Yamamoto Yu Y Kimura Mitsuo M Ryo Akihide A Saito Tomoyuki T Inaba Yutaka Y Hirano Hisashi H Kumagai Ken K Kimura Yayoi Y

Journal of proteomics 20230722

Although the microgravity (μ-g) environment that astronauts encounter during spaceflight can cause severe acute bone loss, the molecular mechanism of this bone loss remains unclear. To investigate the gravity-response proteins involved in bone metabolism, it is important to comprehensively determine which proteins exhibit differential abundance associated with mechanical stimuli. However, comprehensive proteomic analysis using small bone samples is difficult because protein extraction in mineral ...[more]

PMID: 37482271

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Project description:Background This work focuses on the computational modelling of osteomyelitis, a bone pathology caused by bacteria infection (mostly Staphylococcus aureus). The infection alters the RANK/RANKL/OPG signalling dynamics that regulates osteoblasts and osteoclasts behaviour in bone remodelling, i.e. the resorption and mineralization activity. The infection rapidly leads to severe bone loss, necrosis of the affected portion, and it may even spread to other parts of the body. On the other hand, osteoporosis is not a bacterial infection but similarly is a defective bone pathology arising due to imbalances in the RANK/RANKL/OPG molecular pathway, and due to the progressive weakening of bone structure. Results Since both osteoporosis and osteomyelitis cause loss of bone mass, we focused on comparing the dynamics of these diseases by means of computational models. Firstly, we performed meta-analysis on a gene expression data of normal, osteoporotic and osteomyelitis bone conditions. We mainly focused on RANKL/OPG signalling, the TNF and TNF receptor superfamilies and the NF-kB pathway. Using information from the gene expression data we estimated parameters for a novel model of osteoporosis and of osteomyelitis. Our models could be seen as a hybrid ODE and probabilistic verification modelling framework which aims at investigating the dynamics of the effects of the infection in bone remodelling. Finally we discuss different diagnostic estimators defined by formal verification techniques, in order to assess different bone pathologies (osteopenia, osteoporosis and osteomyelitis) in an effective way. Conclusions We present a modeling framework able to reproduce aspects of the different bone remodeling defective dynamics of osteomyelitis and osteoporosis. We report that the verification-based estimators are meaningful in the light of a feed forward between computational medicine and clinical bioinformatics Model is encoded by Ruby and submitted and curated to BioModels by Ahmad Zyoud

Dataset Information

Identification of gravity-responsive proteins in the femur of spaceflight mice using a quantitative proteomic approach

Publications

Identification of gravity-responsive proteins in the femur of spaceflight mice using a quantitative proteomic approach.

Similar Datasets

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