Bibliographic record
Abstract
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.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
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".