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Análise da Expressão Gênica Durante a Diferenciação Osteogênica de Células Mesenquimais Estromais de Medula Óssea de Pacientes Portadores de Osteogênese Imperfeita

2011· dissertation· pt· W1612947998 on OpenAlexaff
Carla Martins Kaneto

Bibliographic record

Venuenot available
Typedissertation
Languagept
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsMesenchymal stem cellImmunophenotypingOsteogenesis imperfectaOsteoblastBone marrowPeripheral blood mononuclear cellFlow cytometryMedicineMolecular biologyAndrologyBiologyImmunologyPathologyIn vitroGenetics

Abstract

fetched live from OpenAlex

Osteogenesis imperfecta (OI) is characterized as a genetic disorder in which a generalized osteopenia leads to short stature, bone fragility and serious skeletal deformities. Mesenchymal stem cells (MSCs) are precursors present in adult bone marrow that can differentiate into osteoblasts, adipocytes and myoblasts that have been given great importance as a source cell therapy. The aim of this study was to analyze the gene expression profile during osteogenic differentiation from mesenchymal stem cells from bone marrow taken from patients diagnosed with Osteogenesis Imperfecta and control subjects. Samples were collected from three normal individuals and five samples from patients with Osteogenesis Imperfecta. Mononuclear cells (MON) were isolated to obtain mesenchymal cells that were expanded until third passage when the stimulus for osteogenic differentiation was induced. Analyses were also conducted to count the CFU-F and for four of the five samples from patients with OI, the number of CFU-F observed was lower than generally found for normal samples. Cells were collected for analysis of cell immunophenotyping by flow cytometry and RNA was extracted from the resulting sample called T0. Remaining cells were stimulated for osteogenic differentiation. After a day in culture with stimulation, cells from another bottle had their RNA extracted (T1), and the same procedure was performed on days 2 (T2), 7 (T7), 12 (T12), 17 (T17) and 21 (T21). All samples have shown potential of in vitro differentiation into osteoblasts and adipocytes. Immunophenotyping of mesenchymal cells was performed and samples of all patients had immunophenotypic profile consistent with previous works. We identified mutations in COL1A1 and / or COL1A2 responsible for developing the disease for four of five patients. For the patient with Osteogenesis Imperfecta and Bruck Syndrome, coding region of the gene PLOD2 was also sequenced, but no mutations were found. The gene expression analysis was performed by microarray and identified several genes with differential expression. Some genes of fundamental importance in osteoblast differentiation showed lower expression in samples from patients with OI, suggesting a minor involvement of MSCs of patients with osteogenic lineage. Other genes also confirmed their differential expression by Real Time PCR. We observed an increased expression of genes related to adipocytes, suggesting an increased adipogenic differentiation at the expense of osteogenic differentiation. The expression of PLOD2 gene variants proved to be different between normal samples, OI and the patient with Bruck Syndrome. There was also evidence of differential expression of 29b microRNA, with established role during osteogenic differentiation, suggesting a mechanism dependent regulation of mRNA abundance of its gene target, COL1A1.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.314
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2011
Admission routes1
Has abstractyes

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