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Record W326015596

IS SKELETAL MUSCLE TITIN AN ACTIVATABLE MOLECULAR SPRING

2014· article· en· W326015596 on OpenAlexvenueno aff
Jens Herzog, Tim Leonard, Azim Jinha, Walter Herzog

Bibliographic record

VenueJournal of undergraduate research in Alberta · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTitinSarcomereMyofibrilMyosinBiophysicsChemistryActinNebulinProtein filamentSpring (device)AnatomySkeletal muscleObscurinMyofilamentMyocyteMaterials scienceBiochemistryBiologyStructural engineeringCell biology
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION Titin is a viscoelastic spring protein that provides 95% of the passive force in single myofibrils (1). Recently, we discovered that titin is a virtually elastic spring if unfolding of its immunoglobulin (Ig) domains can be prevented and that this elastic property can be used at variable sarcomere lengths, thus minimizing energy loss in passive muscle function (2). There is evidence that titin changes its mechanical properties when a muscle is activated. Specifically, titin is thought to bind calcium upon muscle activation (3, 4) and attach to actin thereby increasing its spring stiffness and decreasing its spring length, respectively (5). If this is indeed the case, titin’s contribution to force in an active muscle should be much greater than in a passive muscle. However, this has never been tested for dynamic muscle contractions. Therefore, the purpose of this study was to determine titin’s force contribution to active and passive muscles. This aim was achieved by stretching active and passive single myofibrils to lengths beyond actin-myosin filament overlap where titin is known to be the only contributor to force (1, 2). METHODS Rabbit psoas muscles were harvested, the connective tissue chemically digested, and individual myofibrils mechanically separated (2). Myofibrils (n=11) were then mounted on a motorized glass lever that controlled myofibril length and a silicon nitrate lever that measured myofibril force. Myofibrils were set at sarcomere lengths of 3.0µm and were stretched actively and passively to sarcomere lengths of ~4.5-5.0µm. Activated and passive myofibrils were then subjected to ten shortening-stretch cycles of 0.5µm/sarcomere, and then returned to their starting length. All stretches were performed at a speed of 0.1µm/sarcomere/second. RESULTS Actively stretched myofibrils (Figure 1, label A) had much greater force contributions from titin (compare forces at sarcomere lengths greater than 4.0µm) than passively stretched myofibrils (Figure 1, label B). Furthermore, active myofibrils had greater hystereses and greater reductions in peak forces (Figure 1, label 2) during the ten shortening-stretch cycles compared to the passive myofibrils (Figure 1, label 3). The exemplar results shown in figure (1) for single actively and passively stretched myofibrils were the same for all myofibrils tested in this study. DISCUSSION AND CONCLUSIONS The dramatically increased forces in the region beyond actin-myosin filament overlap (sarcomere lengths>4.0µm) in the activated compared to the passive myofibrils is exclusively attributable to titin. This result unequivocally indicates that titin is “activated” in some unknown manner during muscle contraction, thereby vastly increasing its force contribution in active compared to passive muscle. The increased hystereses and peak force reduction during the repeat shortening-stretch cycles suggests that this activation is associated with an engagement of titin’s Ig domains in the active myofibrils. The molecular details of this newly found dynamic “activation” of titin requires further study to uncover the molecular details of titin’s force regulation upon muscle activation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.002

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 head0.057
GPT teacher head0.377
Teacher spread0.320 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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