Hindlimb Unloading Induces Bone Microarchitectural and Transcriptomic Changes in Murine Long Bones in an Age-Dependent Manner
Bibliographic record
Abstract
Age and disuse-related bone loss both result in decreases in bone mineral density, cortical thickness, and trabecular thickness and connectivity. Disuse induces physiological changes in bone like those seen with aging. Bone microarchitecture and biomechanical properties were compared between 6- and 22-month-old C57BL/6J male control mice and 6-month-old mice that were hindlimb unloaded (HLU) for 3 weeks. Epiphyseal trabecular bone was the compartment most affected by HLU and demonstrated an intermediate bone phenotype between age-matched controls and aged controls. RNA extracted from whole-bone marrow-flushed tibiae was sequenced and analyzed. Differential gene expression analysis additionally included 4-month-old male mice unloaded for 3 weeks compared to age-matched controls. Gene ontology analysis demonstrated that there were age-dependent differences in differentially expressed genes. Genes related to downregulation of cellular processes were most affected in 4-month-old mice after disuse whereas those related to mitochondrial function were most affected in 6- month-old mice. Cell-cycle transition was downregulated with aging. A publicly available dataset (GSE169292) from 3-month female C57BL/6N mice unloaded for 7 days was included in ingenuity pathway analysis with the other datasets. IPA was used to identify the leading canonical pathways and upstream regulators in each HLU age group. IPA identified "Senescence Pathway" as the second leading canonical pathway enriched in mice exposed to HLU. HLU induced activation of the senescence pathway in 3- month and 4-month-old mice but inhibited it in 6-month-old mice. In conclusion, we demonstrate that hindlimb unloading and aging initiate similar changes in bone microarchitecture and gene expression. However, aging is responsible for more significant transcriptome and tissue-level changes compared to hindlimb unloading. Highlights: Epiphyseal trabecular bone is most susceptible to hindlimb unloading.Hindlimb unloaded limbs resemble an intermediate phenotype between age-matched and aged controls.Hindlimb unloading induces gene expression changes that are age dependent and may lead to inflammation and/or mitochondrial dysfunction depending on context.Younger mice (3-4 months) activate the senescence pathway upon hindlimb unloading, whereas skeletally mature (6 months) mice inhibit it.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".