Kinome state is predictive of cell viability in pancreatic cancer tumor and cancer-associated fibroblast cell lines.

Numerous aspects of cellular signaling are regulated by the kinome-the network of over 500 protein kinases that guides and modulates information transfer throughout the cell. The key role played by both individual kinases and assemblies of kinases organized into functional subnetworks leads to kinome dysregulation driving many diseases, particularly cancer. In the case of pancreatic ductal adenocarcinoma (PDAC), a variety of kinases and associated signaling pathways have been identified for their key role in the establishment of disease as well as its progression. However, the identification of additional relevant therapeutic targets has been slow and is further confounded by interactions between the tumor and the surrounding tumor microenvironment. In this work, we attempt to link the state of the human kinome, or kinotype, with cell viability in treated, patient-derived PDAC tumor and cancer-associated fibroblast cell lines. We applied classification models to independent kinome perturbation and kinase inhibitor cell screen data, and found that the inferred kinotype of a cell has a significant and predictive relationship with cell viability. We further find that models are able to identify a set of kinases whose behavior in response to perturbation drive the majority of viability responses in these cell lines, including the understudied kinases CSNK2A1/3, CAMKK2, and PIP4K2C. We next utilized these models to predict the response of new, clinical kinase inhibitors that were not present in the initial dataset for model devlopment and conducted a validation screen that confirmed the accuracy of the models. These results suggest that characterizing the perturbed state of the human protein kinome provides significant opportunity for better understanding of signaling behavior and downstream cell phenotypes, as well as providing insight into the broader design of potential therapeutic strategies for PDAC.

Combined kinome inhibition states are predictive of cancer cell line sensitivity to kinase inhibitor combination therapies.

Protein kinases are a primary focus in targeted therapy development for cancer, owing to their role as regulators in nearly all areas of cell life. Recent strategies targeting the kinome with combination therapies have shown promise, such as trametinib and dabrafenib in advanced melanoma, but empirical design for less characterized pathways remains a challenge. Computational combination screening is an attractive alternative, allowing in-silico filtering prior to experimental testing of drastically fewer leads, increasing efficiency and effectiveness of drug development pipelines. In this work, we generated combined kinome inhibition states of 40,000 kinase inhibitor combinations from kinobeads-based kinome profiling across 64 doses. We then integrated these with transcriptomics from CCLE to build machine learning models with elastic-net feature selection to predict cell line sensitivity across nine cancer types, with accuracy R2 ∼ 0.75-0.9. We then validated the model by using a PDX-derived TNBC cell line and saw good global accuracy (R2 ∼ 0.7) as well as high accuracy in predicting synergy using four popular metrics (R2 ∼ 0.9). Additionally, the model was able to predict a highly synergistic combination of trametinib and omipalisib for TNBC treatment, which incidentally was recently in phase I clinical trials. Our choice of tree-based models for greater interpretability allowed interrogation of highly predictive kinases in each cancer type, such as the MAPK, CDK, and STK kinases. Overall, these results suggest that kinome inhibition states of kinase inhibitor combinations are strongly predictive of cell line responses and have great potential for integration into computational drug screening pipelines. This approach may facilitate the identification of effective kinase inhibitor combinations and accelerate the development of novel cancer therapies, ultimately improving patient outcomes.

Integrated single-dose kinome profiling data is predictive of cancer cell line sensitivity to kinase inhibitors.

Protein kinase activity forms the backbone of cellular information transfer, acting both individually and as part of a broader network, the kinome. Their central role in signaling leads to kinome dysfunction being a common driver of disease, and in particular cancer, where numerous kinases have been identified as having a causal or modulating role in tumor development and progression. As a result, the development of therapies targeting kinases has rapidly grown, with over 70 kinase inhibitors approved for use in the clinic and over double this number currently in clinical trials. Understanding the relationship between kinase inhibitor treatment and their effects on downstream cellular phenotype is thus of clear importance for understanding treatment mechanisms and streamlining compound screening in therapy development. In this work, we combine two large-scale kinome profiling data sets and use them to link inhibitor-kinome interactions with cell line treatment responses (AUC/IC50). We then built computational models on this data set that achieve a high degree of prediction accuracy (R2 of 0.7 and RMSE of 0.9) and were able to identify a set of well-characterized and understudied kinases that significantly affect cell responses. We further validated these models experimentally by testing predicted effects in breast cancer cell lines and extended the model scope by performing additional validation in patient-derived pancreatic cancer cell lines. Overall, these results demonstrate that broad quantification of kinome inhibition state is highly predictive of downstream cellular phenotypes.

Combined kinome inhibition states are predictive of cancer cell line sensitivity to kinase inhibitor combination therapies.

Protein kinases are a primary focus in targeted therapy development for cancer, owing to their role as regulators in nearly all areas of cell life. Kinase inhibitors are one of the fastest growing drug classes in oncology, but resistance acquisition to kinase-targeting monotherapies is inevitable due to the dynamic and interconnected nature of the kinome in response to perturbation. Recent strategies targeting the kinome with combination therapies have shown promise, such as the approval of Trametinib and Dabrafenib in advanced melanoma, but similar empirical combination design for less characterized pathways remains a challenge. Computational combination screening is an attractive alternative, allowing in-silico screening prior to in-vitro or in-vivo testing of drastically fewer leads, increasing efficiency and effectiveness of drug development pipelines. In this work, we generate combined kinome inhibition states of 40,000 kinase inhibitor combinations from kinobeads-based kinome profiling across 64 doses. We then integrated these with baseline transcriptomics from CCLE to build robust machine learning models to predict cell line sensitivity from NCI-ALMANAC across nine cancer types, with model accuracy R2 ~ 0.75-0.9 after feature selection using elastic-net regression. We further validated the model's ability to extend to real-world examples by using the best-performing breast cancer model to generate predictions for kinase inhibitor combination sensitivity and synergy in a PDX-derived TNBC cell line and saw reasonable global accuracy in our experimental validation (R2 ~ 0.7) as well as high accuracy in predicting synergy using four popular metrics (R2 ~ 0.9). Additionally, the model was able to predict a highly synergistic combination of Trametinib (MEK inhibitor) and Omipalisib (PI3K inhibitor) for TNBC treatment, which incidentally was recently in phase I clinical trials for TNBC. Our choice of tree-based models over networks for greater interpretability also allowed us to further interrogate which specific kinases were highly predictive of cell sensitivity in each cancer type, and we saw confirmatory strong predictive power in the inhibition of MAPK, CDK, and STK kinases. Overall, these results suggest that kinome inhibition states of kinase inhibitor combinations are strongly predictive of cell line responses and have great potential for integration into computational drug screening pipelines. This approach may facilitate the identification of effective kinase inhibitor combinations and accelerate the development of novel cancer therapies, ultimately improving patient outcomes.

Kinome inhibition states and multiomics data enable prediction of cell viability in diverse cancer types.

Protein kinases play a vital role in a wide range of cellular processes, and compounds that inhibit kinase activity emerging as a primary focus for targeted therapy development, especially in cancer. Consequently, efforts to characterize the behavior of kinases in response to inhibitor treatment, as well as downstream cellular responses, have been performed at increasingly large scales. Previous work with smaller datasets have used baseline profiling of cell lines and limited kinome profiling data to attempt to predict small molecule effects on cell viability, but these efforts did not use multi-dose kinase profiles and achieved low accuracy with very limited external validation. This work focuses on two large-scale primary data types, kinase inhibitor profiles and gene expression, to predict the results of cell viability screening. We describe the process by which we combined these data sets, examined their properties in relation to cell viability and finally developed a set of computational models that achieve a reasonably high prediction accuracy (R2 of 0.78 and RMSE of 0.154). Using these models, we identified a set of kinases, several of which are understudied, that are strongly influential in the cell viability prediction models. In addition, we also tested to see if a wider range of multiomics data sets could improve the model results and found that proteomic kinase inhibitor profiles were the single most informative data type. Finally, we validated a small subset of the model predictions in several triple-negative and HER2 positive breast cancer cell lines demonstrating that the model performs well with compounds and cell lines that were not included in the training data set. Overall, this result demonstrates that generic knowledge of the kinome is predictive of very specific cell phenotypes, and has the potential to be integrated into targeted therapy development pipelines.

Differential Performance of Machine Learning Models in Prediction of Procedure-Specific Outcomes.

BACKGROUND: Procedure-specific complications can have devastating consequences. Machine learning-based tools have the potential to outperform traditional statistical modeling in predicting their risk and guiding decision-making. We sought to develop and compare deep neural network (NN) models, a type of machine learning, to logistic regression (LR) for predicting anastomotic leak after colectomy, bile leak after hepatectomy, and pancreatic fistula after pancreaticoduodenectomy (PD). METHODS: The colectomy, hepatectomy, and PD National Surgical Quality Improvement Program (NSQIP) databases were analyzed. Each dataset was split into training, validation, and testing sets in a 60/20/20 ratio, with fivefold cross-validation. Models were created using NN and LR for each outcome. Models were evaluated primarily with area under the receiver operating characteristic curve (AUROC). RESULTS: A total of 197,488 patients were included for colectomy, 25,403 for hepatectomy, and 23,333 for PD. For anastomotic leak, AUROC for NN was 0.676 (95% 0.666-0.687), compared with 0.633 (95% CI 0.620-0.647) for LR. For bile leak, AUROC for NN was 0.750 (95% CI 0.739-0.761), compared with 0.722 (95% CI 0.698-0.746) for LR. For pancreatic fistula, AUROC for NN was 0.746 (95% CI 0.733-0.760), compared with 0.713 (95% CI 0.703-0.723) for LR. Variables related to intra-operative information, such as surgical approach, biliary reconstruction, and pancreatic gland texture were highly important for model predictions. DISCUSSION: Machine learning showed a marginal advantage over traditional statistical techniques in predicting procedure-specific outcomes. However, models that included intra-operative information performed better than those that did not, suggesting that NSQIP procedure-targeted datasets may be strengthened by including relevant intra-operative information.

Transcriptomic analyses of gastrulation-stage mouse embryos with differential susceptibility to alcohol.

Genetics are a known contributor to differences in alcohol sensitivity in humans with fetal alcohol spectrum disorders (FASDs) and in animal models. Our study profiled gene expression in gastrulation-stage embryos from two commonly used, genetically similar mouse substrains, C57BL/6J (6J) and C57BL/6NHsd (6N), that differ in alcohol sensitivity. First, we established normal gene expression patterns at three finely resolved time points during gastrulation and developed a web-based interactive tool. Baseline transcriptional differences across strains were associated with immune signaling. Second, we examined the gene networks impacted by alcohol in each strain. Alcohol caused a more pronounced transcriptional effect in the 6J versus 6N mice, matching the increased susceptibility of the 6J mice. The 6J strain exhibited dysregulation of pathways related to cell death, proliferation, morphogenic signaling and craniofacial defects, while the 6N strain showed enrichment of hypoxia and cellular metabolism pathways. These datasets provide insight into the changing transcriptional landscape across mouse gastrulation, establish a valuable resource that enables the discovery of candidate genes that may modify alcohol susceptibility that can be validated in humans, and identify novel pathogenic mechanisms of alcohol. This article has an associated First Person interview with the first author of the paper.

The Dark Kinase Knowledgebase: an online compendium of knowledge and experimental results of understudied kinases.

Kinases form the backbone of numerous cell signaling pathways, with their dysfunction similarly implicated in multiple pathologies. Further facilitated by their druggability, kinases are a major focus of therapeutic development efforts in diseases such as cancer, infectious disease and autoimmune disorders. While their importance is clear, the role or biological function of nearly one-third of kinases is largely unknown. Here, we describe a data resource, the Dark Kinase Knowledgebase (DKK; https://darkkinome.org), that is specifically focused on providing data and reagents for these understudied kinases to the broader research community. Supported through NIH's Illuminating the Druggable Genome (IDG) Program, the DKK is focused on data and knowledge generation for 162 poorly studied or 'dark' kinases. Types of data provided through the DKK include parallel reaction monitoring (PRM) peptides for quantitative proteomics, protein interactions, NanoBRET reagents, and kinase-specific compounds. Higher-level data is similarly being generated and consolidated such as tissue gene expression profiles and, longer-term, functional relationships derived through perturbation studies. Associated web tools that help investigators interrogate both internal and external data are also provided through the site. As an evolving resource, the DKK seeks to continually support and enhance knowledge on these potentially high-impact druggable targets.

Coral: Clear and Customizable Visualization of Human Kinome Data.

Protein kinases represent one of the largest gene families in eukaryotes and play roles in a wide range of cell signaling processes and human diseases. Current tools for visualizing kinase data in the context of the human kinome superfamily are limited to encoding data through the addition of nodes to a low-resolution image of the kinome tree. We present Coral, a user-friendly interactive web application for visualizing both quantitative and qualitative data. Unlike previous tools, Coral can encode data in three features (node color, node size, and branch color), allows three modes of kinome visualization (the traditional kinome tree as well as radial and dynamic force networks), and generates high-resolution scalable vector graphics files suitable for publication without the need for refinement using graphics editing software. Due to its user-friendly, interactive, and highly customizable design, Coral is broadly applicable to high-throughput studies of the human kinome. The source code and web application are available at github.com/dphansti/CORAL and phanstiel-lab.med.unc.edu/Coral, respectively.

Tunable molecular tension sensors reveal extension-based control of vinculin loading.

Molecular tension sensors have contributed to a growing understanding of mechanobiology. However, the limited dynamic range and inability to specify the mechanical sensitivity of these sensors has hindered their widespread use in diverse contexts. Here, we systematically examine the components of tension sensors that can be altered to improve their functionality. Guided by the development of a first principles model describing the mechanical behavior of these sensors, we create a collection of sensors that exhibit predictable sensitivities and significantly improved performance in cellulo. Utilized in the context of vinculin mechanobiology, a trio of these new biosensors with distinct force- and extension-sensitivities reveal that an extension-based control paradigm regulates vinculin loading in a variety of mechanical contexts. To enable the rational design of molecular tension sensors appropriate for diverse applications, we predict the mechanical behavior, in terms of force and extension, of additional 1020 distinct designs.

ß2-adrenoceptor signaling regulates invadopodia formation to enhance tumor cell invasion.

INTRODUCTION: For efficient metastatic dissemination, tumor cells form invadopodia to degrade and move through three-dimensional extracellular matrix. However, little is known about the conditions that favor invadopodia formation. Here, we investigated the effect of ß-adrenoceptor signaling - which allows cells to respond to stress neurotransmitters - on the formation of invadopodia and examined the effect on tumor cell invasion. METHODS: To characterize the molecular and cellular mechanisms of ß-adrenergic signaling on the invasive properties of breast cancer cells, we used functional cellular assays to quantify invadopodia formation and to evaluate cell invasion in two-dimensional and three-dimensional environments. The functional significance of ß-adrenergic regulation of invadopodia was investigated in an orthotopic mouse model of spontaneous breast cancer metastasis. RESULTS: ß-adrenoceptor activation increased the frequency of invadopodia-positive tumor cells and the number of invadopodia per cell. The effects were selectively mediated by the ß2-adrenoceptor subtype, which signaled through the canonical Src pathway to regulate invadopodia formation. Increased invadopodia occurred at the expense of focal adhesion formation, resulting in a switch to increased tumor cell invasion through three-dimensional extracellular matrix. ß2-adrenoceptor signaling increased invasion of tumor cells from explanted primary tumors through surrounding extracellular matrix, suggesting a possible mechanism for the observed increased spontaneous tumor cell dissemination in vivo. Selective antagonism of ß2-adrenoceptors blocked invadopodia formation, suggesting a pharmacological strategy to prevent tumor cell dissemination. CONCLUSION: These findings provide insight into conditions that control tumor cell invasion by identifying signaling through ß2-adrenoceptors as a regulator of invadopodia formation. These findings suggest novel pharmacological strategies for intervention, by using ß-blockers to target ß2-adrenoceptors to limit tumor cell dissemination and metastasis.

Construction, imaging, and analysis of FRET-based tension sensors in living cells.

Due to an increased appreciation for the importance of mechanical stimuli in many biological contexts, an interest in measuring the forces experienced by specific proteins in living cells has recently emerged. The development and use of Förster resonance energy transfer (FRET)-based molecular tension sensors has enabled these types of studies and led to important insights into the mechanisms those cells utilize to probe and respond to the mechanical nature of their surrounding environment. The process for creating and utilizing FRET-based tension sensors can be divided into three main parts: construction, imaging, and analysis. First we review several methods for the construction of genetically encoded FRET-based tension sensors, including restriction enzyme-based methods as well as the more recently developed overlap extension or Gibson Assembly protocols. Next, we discuss the intricacies associated with imaging tension sensors, including optimizing imaging parameters as well as common techniques for estimating artifacts within standard imaging systems. Then, we detail the analysis of such data and describe how to extract useful information from a FRET experiment. Finally, we provide a discussion on identifying and correcting common artifacts in the imaging of FRET-based tension sensors.

LKB1 loss in melanoma disrupts directional migration toward extracellular matrix cues.

Somatic inactivation of the serine/threonine kinase gene STK11/LKB1/PAR-4 occurs in a variety of cancers, including ∼10% of melanoma. However, how the loss of LKB1 activity facilitates melanoma invasion and metastasis remains poorly understood. In LKB1-null cells derived from an autochthonous murine model of melanoma with activated Kras and Lkb1 loss and matched reconstituted controls, we have investigated the mechanism by which LKB1 loss increases melanoma invasive motility. Using a microfluidic gradient chamber system and time-lapse microscopy, in this paper, we uncover a new function for LKB1 as a directional migration sensor of gradients of extracellular matrix (haptotaxis) but not soluble growth factor cues (chemotaxis). Systematic perturbation of known LKB1 effectors demonstrated that this response does not require canonical adenosine monophosphate-activated protein kinase (AMPK) activity but instead requires the activity of the AMPK-related microtubule affinity-regulating kinase (MARK)/PAR-1 family kinases. Inhibition of the LKB1-MARK pathway facilitated invasive motility, suggesting that loss of the ability to sense inhibitory matrix cues may promote melanoma invasion.

Engineered kinase activation reveals unique morphodynamic phenotypes and associated trafficking for Src family isoforms.

The Src kinase family comprises nine homologous members whose distinct expression patterns and cellular distributions indicate that they have unique roles. These roles have not been determined because genetic manipulation has not produced clearly distinct phenotypes, and the kinases' homology complicates generation of specific inhibitors. Through insertion of a modified FK506 binding protein (insertable FKBP12, iFKBP) into the protein kinase isoforms Fyn, Src, Lyn, and Yes, we engineered kinase analogs that can be activated within minutes in living cells (RapR analogs). Combining our RapR analogs with computational tools for quantifying and characterizing cellular dynamics, we demonstrate that Src family isoforms produce very different phenotypes, encompassing cell spreading, polarized motility, and production of long, thin cell extensions. Activation of Src and Fyn led to patterns of kinase translocation that correlated with morphological changes in temporally distinct stages. Phenotypes were dependent on N-terminal acylation, not on Src homology 3 (SH3) and Src homology 2 (SH2) domains, and correlated with movement between a perinuclear compartment, adhesions, and the plasma membrane.

Automated analysis of invadopodia dynamics in live cells.

Multiple cell types form specialized protein complexes that are used by the cell to actively degrade the surrounding extracellular matrix. These structures are called podosomes or invadopodia and collectively referred to as invadosomes. Due to their potential importance in both healthy physiology as well as in pathological conditions such as cancer, the characterization of these structures has been of increasing interest. Following early descriptions of invadopodia, assays were developed which labelled the matrix underneath metastatic cancer cells allowing for the assessment of invadopodia activity in motile cells. However, characterization of invadopodia using these methods has traditionally been done manually with time-consuming and potentially biased quantification methods, limiting the number of experiments and the quantity of data that can be analysed. We have developed a system to automate the segmentation, tracking and quantification of invadopodia in time-lapse fluorescence image sets at both the single invadopodia level and whole cell level. We rigorously tested the ability of the method to detect changes in invadopodia formation and dynamics through the use of well-characterized small molecule inhibitors, with known effects on invadopodia. Our results demonstrate the ability of this analysis method to quantify changes in invadopodia formation from live cell imaging data in a high throughput, automated manner.

The Focal Adhesion Analysis Server: a web tool for analyzing focal adhesion dynamics.

The Focal Adhesion Analysis Server (FAAS) is a web-based implementation of a set of computer vision algorithms designed to quantify the behavior of focal adhesions in cells imaged in 2D cultures. The input consists of one or more images of a labeled focal adhesion protein. The outputs of the system include a range of static and dynamic measurements for the adhesions present in each image as well as how these properties change over time. The user is able to adjust several parameters important for proper focal adhesion identification. This system provides a straightforward tool for the global, unbiased assessment of focal adhesion behavior common in optical microscopy studies. The webserver is available at: http://faas.bme.unc.edu/.

Nck enables directional cell migration through the coordination of polarized membrane protrusion with adhesion dynamics.

Directional migration requires the coordination of cytoskeletal changes essential for cell polarization and adhesion turnover. Extracellular signals that alter tyrosine phosphorylation drive directional migration by inducing reorganization of the actin cytoskeleton. It is recognized that Nck is an important link between tyrosine phosphorylation and actin dynamics; however, the role of Nck in cytoskeletal remodeling during directional migration and the underlying molecular mechanisms remain largely undetermined. In this study, a combination of molecular genetics and quantitative live cell microscopy was used to show that Nck is essential in the establishment of front-back polarity and directional migration of endothelial cells. Time-lapse differential interference contrast and total internal reflection fluorescence microscopy showed that Nck couples the formation of polarized membrane protrusions with their stabilization through the assembly and maturation of cell-substratum adhesions. Measurements by atomic force microscopy showed that Nck also modulates integrin α5ß1-fibronectin adhesion force and cell stiffness. Fluorescence resonance energy transfer imaging revealed that Nck depletion results in delocalized and increased activity of Cdc42 and Rac. By contrast, the activity of RhoA and myosin II phosphorylation were reduced by Nck knockdown. Thus, this study identifies Nck as a key coordinator of cytoskeletal changes that enable cell polarization and directional migration, which are crucial processes in development and disease.

Gleevec, an Abl family inhibitor, produces a profound change in cell shape and migration.

The issue of how contractility and adhesion are related to cell shape and migration pattern remains largely unresolved. In this paper we report that Gleevec (Imatinib), an Abl family kinase inhibitor, produces a profound change in the shape and migration of rat bladder tumor cells (NBTII) plated on collagen-coated substrates. Cells treated with Gleevec adopt a highly spread D-shape and migrate more rapidly with greater persistence. Accompanying this more spread state is an increase in integrin-mediated adhesion coupled with increases in the size and number of discrete adhesions. In addition, both total internal reflection fluorescence microscopy (TIRFM) and interference reflection microscopy (IRM) revealed a band of small punctate adhesions with rapid turnover near the cell leading margin. These changes led to an increase in global cell-substrate adhesion strength, as assessed by laminar flow experiments. Gleevec-treated cells have greater RhoA activity which, via myosin activation, led to an increase in the magnitude of total traction force applied to the substrate. These chemical and physical alterations upon Gleevec treatment produce the dramatic change in morphology and migration that is observed.

Arp2/3 is critical for lamellipodia and response to extracellular matrix cues but is dispensable for chemotaxis.

Lamellipodia are sheet-like, leading edge protrusions in firmly adherent cells that contain Arp2/3-generated dendritic actin networks. Although lamellipodia are widely believed to be critical for directional cell motility, this notion has not been rigorously tested. Using fibroblasts derived from Ink4a/Arf-deficient mice, we generated a stable line depleted of Arp2/3 complex that lacks lamellipodia. This line shows defective random cell motility and relies on a filopodia-based protrusion system. Utilizing a microfluidic gradient generation system, we tested the role of Arp2/3 complex and lamellipodia in directional cell migration. Surprisingly, Arp2/3-depleted cells respond normally to shallow gradients of PDGF, indicating that lamellipodia are not required for fibroblast chemotaxis. Conversely, these cells cannot respond to a surface-bound gradient of extracellular matrix (haptotaxis). Consistent with this finding, cells depleted of Arp2/3 fail to globally align focal adhesions, suggesting that one principle function of lamellipodia is to organize cell-matrix adhesions in a spatially coherent manner.

The vinculin C-terminal hairpin mediates F-actin bundle formation, focal adhesion, and cell mechanical properties.

Vinculin is an essential and highly conserved cell adhesion protein, found at both focal adhesions and adherens junctions, where it couples integrins or cadherins to the actin cytoskeleton. Vinculin is involved in controlling cell shape, motility, and cell survival, and has more recently been shown to play a role in force transduction. The tail domain of vinculin (Vt) contains determinants necessary for binding and bundling of actin filaments. Actin binding to Vt has been proposed to induce formation of a Vt dimer that is necessary for cross-linking actin filaments. Results from this study provide additional support for actin-induced Vt self-association. Moreover, the actin-induced Vt dimer appears distinct from the dimer formed in the absence of actin. To better characterize the role of the Vt strap and carboxyl terminus (CT) in actin binding, Vt self-association, and actin bundling, we employed smaller amino-terminal (NT) and CT deletions that do not perturb the structural integrity of Vt. Although both NT and CT deletions retain actin binding, removal of the CT hairpin (1061-1066) selectively impairs actin bundling in vitro. Moreover, expression of vinculin lacking the CT hairpin in vinculin knock-out murine embryonic fibroblasts affects the number of focal adhesions formed, cell spreading as well as cellular stiffening in response to mechanical force.