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Enregistrement W3190598211

Systems and Synthetic Biology Approaches to Mechanobiology

2018· article· en· W3190598211 sur OpenAlexfundno aff
Jasmine H. Hughes

Notice bibliographique

RevueeScholarship (California Digital Library) · 2018
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCellular Mechanics and Interactions
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaSiebel Scholars FoundationW. M. Keck FoundationDivision of Civil, Mechanical and Manufacturing InnovationNational Institutes of HealthNational Science Foundation
Mots-clésStress fiberPhosphorylationKinaseCell biologyBiologyCrosstalkSignal transductionMechanobiologyNeurosciencePhysicsFocal adhesion
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Over the last several decades, it has become increasingly clear that cells throughout the body sense and respond to mechanical and biophysical cues and actively apply forces to their surroundings. Mechanobiologists have developed an increasingly clear picture of how this signal transduction occurs. However, many questions have been challenging to answer due to the often nonlinear relationships between protein activity and cell function and due to the crosstalk with other signaling pathways. Over the same period of time, the fields of synthetic biology, which aims to create de novo signaling systems using biological parts, and systems biology, which aims to describe cell behavior from a holistic perspective, have become rich fields with new tools for understanding and controlling cell behavior. We first apply an inducible promoter system to answer fundamental questions about the regulation of stress fiber viscoelasticity. MLCK and the rho-associated kinases 1 and 2 (ROCK1 and 2) both activate myosin’s motor activity through phosphorylation of its regulatory light chains (RLCs), and these light chains can be phosphorylated once at serine 19 (p-RLC) or twice at serine 19 and threonine 18 (pp-RLC). Prior on-off approaches using pharmacological inhibitors showed that these kinases act on distinct pools of stress fibers, with MLCK inhibition primarily regulating the viscoelastic properties of peripheral stress fibers and ROCK inhibition primarily influencing those of central stress fibers. It was unclear, however, whether these kinases had distinct effects on the two RLC phosphorylation states and how these phosphorylation states influenced these populations of stress fibers. To answer these questions, we stably transduced cells with a doxycycline-inducible promoter governing the expression of constitutively active (CA) mutants of ROCK or MLCK. By varying the concentration of doxycycline in the cell culture media, we show that graded MLCK activity produces a graded increase in p-RLC, which localizes to peripheral SFs. In contrast, graded ROCK activity produces a graded increase in pp-RLC, which localizes to central SFs. Viscoelasticity measurements of individual stress fibers through subcellular laser ablation further reveal that MLCK and ROCK regulate the mechanical properties of peripheral and central stress fibers, respectively, with phosphomimetic mutants phenocopying these findings. Interestingly, we observe nonlinear relationships between levels of phosphorylation and viscoelasticity parameters, demonstrating the power of our synthetic biology approach.Next, we build on this toolkit by incorporating CRISPR-Cas9 gene editing to remove the endogenous protein. This strategy permits us to probe the relationship between a protein of interest and its role in cell signaling along a gradient of values from virtually zero activity to supraphysiological levels. We apply this approach to MLCK, allowing us to modulating p-RLC activity. Interestingly, we find that knockout of endogenous MLCK has very little effect on levels of p-RLC or on mechanobiological phenotypes like migration and focal adhesion formation, but that these phenotypes are sensitive to overexpression of CA-MLCK. One possible explanation is that the smooth muscle MLCK isoform, which is expressed from the same gene as the full-length non-muscle MLCK isoform but from an internal promoter site, is able to compensate for the loss of non-muscle MLCK.Finally, we turn to the systems biology toolkit to determine why patient-derived stem-like glioblastoma (GBM) tumor-initiating cells (TICs) show reduced sensitivity to mechanical cues. GBM’s poor prognosis is associated with a subpopulation of cells that share characteristics with neural stem cells in that they are able to self-renew and differentiate in response to morphogens. These cells are highly invasive and evade treatment, differentiating to re-establish the heterogenous tumor population after treatment. Prior work by our lab and by others found that some of these TICs are insensitive to mechanical cues, with reduced mechanosensitivity correlating with increased invasiveness. Furthermore, we previously found that differentiation of these cells with bone morphogenetic protein 4 (BMP4), which greatly limits their tumor-initiating capacity, sensitizes TIC spreading to substrate stiffness. In this chapter, we use RNA sequencing to identify transcriptomic changes in response to different matrix mechanical properties and to differentiation. We find that BMP4 suppresses expression of proteins associated with extracellular matrix signaling. Interestingly, we find that matrix stiffness has a pronounced effect on metabolic function, with stiff gels promoting expression of oxidative phosphorylation genes. This finding is not impacted by differentiation state, although the number of genes influenced by gel stiffness is amplified in differentiated cells. Guided by these bioinformatics analyses, we then inhibited oxidative phosphorylation, finding that cell spreading was dependent on ATP synthase activity in a stiffness- and differentiation-dependent manner. Together, this work integrated systems biology tools with mechanobiology assays to yield new insight into how morphogens influence mechanical signaling in GBM TICs.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Synthèse · Signal consensuel: aucune
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,024

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,004
Communication savante0,0020,002
Science ouverte0,0010,002
Intégrité de la recherche0,0010,004
Charge utile insuffisante (le modèle a refusé de juger)0,0070,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,032
Tête enseignante GPT0,221
Écart entre enseignants0,188 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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