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Record W2151078631 · doi:10.1677/joe.0.1670417

Myostatin and insulin-like growth factor-I and -II expression in the muscle of rats exposed to the microgravity environment of the NeuroLab space shuttle flight

2000· article· en· W2151078631 on OpenAlexaff
Rukhsana Lalani, Shalender Bhasin, Frances Byhower, Roy Tarnuzzer, Maria B. Grant, R. Shen, Shereen Ezzat, NF Gonzalez-Cadavid

Bibliographic record

VenueJournal of Endocrinology · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsUniversity of Toronto
FundersNational Center for Research ResourcesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute on AgingKennedy Space Center
KeywordsMyostatinEndocrinologyInternal medicineSpaceflightSkeletal muscleBiologyChemistryMedicine

Abstract

fetched live from OpenAlex

The mechanism of the loss of skeletal muscle mass that occurs during spaceflight is not well understood. Myostatin has been proposed as a negative modulator of muscle mass, and IGF-I and IGF-II are known positive regulators of muscle differentiation and growth. We investigated whether muscle loss associated with spaceflight is accompanied by increased levels of myostatin and a reduction in IGF-I and -II levels in the muscle, and whether these changes correlate with an increase in muscle proteolysis and apoptosis. Twelve male adult rats sent on the 17-day NASA STS-90 NeuroLab space flight were divided upon return to earth into two groups, and killed either 1 day later (R1) or after 13 days of acclimatization (R13). Ground-based control rats were maintained for the same periods in either vivarium (R3 and R15, respectively), or flight-simulated cages (R5 and R17, respectively). RNA and protein were isolated from the tibialis anterior, biceps femoris, quadriceps, and gastrocnemius muscles. Myostatin, IGF-I, IGF-II and proteasome 2c mRNA concentrations were determined by reverse transcription/PCR; myostatin and ubiquitin mRNA were also measured by Northern blot analysis; myostatin protein was estimated by immunohistochemistry; the apoptotic index and the release of 3-methylhistidine were determined respectively by the TUNEL assay and by HPLC. Muscle weights were 19-24% lower in the R1 rats compared with the control R3 and R5 rats, but were not significantly different after the recovery period. The myostatin/beta-actin mRNA ratios (means+/-s.e.m. ) were higher in the muscles of the R1 rats compared with the control R5 rats: 5.0-fold in tibialis (5.35 +/- 1.85 vs 1.07 +/- 0.26), 3.0-fold in biceps (2.46+/-0.70 vs 0.81 +/- 0.04), 1.9-fold in quadriceps (7.84 +/- 1.73 vs 4.08 +/- 0.52), and 2.2-fold in gastrocnemius (0.99 +/- 0.35 vs 0.44 +/- 0.17). These values also normalized upon acclimatization. Our antibody against a myostatin peptide was validated by detection of the recombinant human myostatin protein on Western blots, which also showed that myostatin immunostaining was increased in muscle sections from R1 rats, compared with control R3 rats, and normalized upon acclimatization. In contrast, IGF-II mRNA concentrations in the muscles from R1 rats were 64-89% lower than those in R3 animals. With the exception of the gastrocnemius, IGF-II was also decreased in R5 animals maintained in flight-simulated cages, and normalized upon acclimatization. The intramuscular IGF-I mRNA levels were not significantly different between the spaceflight rats and the controls. No increase was found in the proteolysis markers 3-methyl histidine, ubiquitin mRNA, and proteasome 2C mRNA. In conclusion, the loss of skeletal muscle mass that occurs during spaceflight is associated with increased myostatin mRNA and protein levels in the skeletal muscle, and a decrease in IGF-II mRNA levels. These alterations are normalized upon restoration of normal gravity and caging conditions. These data suggest that reciprocal changes in the expression of myostatin and IGF-II may contribute to the multifactorial pathophysiology of muscle atrophy that occurs during spaceflight.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
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 score0.478
Threshold uncertainty score0.182

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

Quick stats

Citations176
Published2000
Admission routes1
Has abstractyes

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