Mechano-metabolism of adherent cells in 2D and 3D microenvironments.

Cells regulate their shape and metabolic activity in response to the mechano-chemical properties of their microenvironment. To elucidate the impact of matrix stiffness and ligand density on a cell's bioenergetics, we developed a non-equilibrium, active chemo-mechanical model that accounts for mechanical energy of the cell and matrix, chemical energy from ATP hydrolysis, interfacial energy, and mechano-sensitive regulation of stress fiber assembly through signaling. By integrating the kinetics and energetics of these processes we introduce the concept of the metabolic potential of the cell that, when minimized, gives experimentally testable predictions of the cell contractility, shape, and the ATP consumption. Specifically, we show that MDA-MB-231 breast cancer cells in 3D collagen gels follow a spherical to spindle to spherical change in morphology with increasing matrix stiffness consistent with experimental observations. This biphasic transition in cell shape emerges from a competition between increased contractility accompanied by ATP hydrolysis enabled by mechano-sensitive signaling, which lowers the volumetric contribution to the metabolic potential of elongated cells and the interfacial energy which is lower for spherical shapes. On 2D hydrogels, our model predicts a hemispherical to spindle to disc shape transition with increasing gel stiffness. In both cases, we show that increasing matrix stiffness monotonically increases the cell's contractility as well as ATP consumption. Our model also predicts how the increased energy demand in stiffer microenvironments is met by AMPK activation, which is confirmed through experimental measurement of activated AMPK levels as a function of matrix stiffness carried out here in both 2D and 3D micro-environments. Further, model predictions of increased AMPK activation on stiffer micro-environments are found to correlate strongly with experimentally measured upregulation of mitochondrial potential, glucose uptake and ATP levels. The insights from our model can be used to understand mechanosensitive regulation of metabolism in physiological events such as metastasis and tumor progression during which cells experience dynamic changes in their microenvironment and metabolic state.

CD44 and ß1-integrin are both engaged in cell traction force generation in hyaluronic acid-rich extracellular matrices.

Mechanical properties of the extracellular matrices (ECMs) critically regulate a number of important cell function including growth, differentiation and migration. Type I collagen and glycosaminoglycans (GAGs) are two primary components of ECMs that contribute to tissue mechanics with the collagen fiber network sustaining tension and GAGs withstanding compression. Collagen stiffness as well as its architecture are known to be important role players in cell-ECM mechanical interactions, however, much less is known about how GAGs within ECMs regulate cell force generation and invasion. Inspired by a recent theoretical work from the Shenoy lab that GAGs play important roles in cell - ECM interactions, we hereby present experimental studies on the role of hyaluronic acid (HA, an unsulfated GAG) in single tumor cell traction force generation within HA collagen cogels using a recently developed 3D cell traction force microscopy. Our work revealed that CD44, a cell surface adhesion receptor to HA, was engaged in cell traction force generation in conjunction with ß1-integrin. Furthermore, we found that HA significantly modified the architecture and mechanics of the collagen fiber network, decreased tumor cells' propensity to remodel the collagen network, decreased traction force generation and transmission distance, and attenuated tumor invasion in agreement with theoretical predictions. Our findings highlighted the significance of CD44 and HA engagement in cell-ECM mechanical interactions, providing new insights on the mechanical model of cellular force transmission.

ResiRole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques.

MOTIVATION: Methods to assess the quality of protein structure models are needed for user applications. To aid with the selection of structure models and further inform the development of structure prediction techniques, we describe the ResiRole method for the assessment of the quality of structure models. RESULTS: Structure prediction techniques are ranked according to the results of round-robin, head-to-head comparisons using difference scores. Each difference score was defined as the absolute value of the cumulative probability for a functional site prediction made with the FEATURE program for the reference structure minus that for the structure model. Overall, the difference scores correlate well with other model quality metrics; and based on benchmarking studies with NaïveBLAST, they are found to detect additional local structural similarities between the structure models and reference structures. AVAILABILITYAND IMPLEMENTATION: Automated analyses of models addressed in CAMEO are available via the ResiRole server, URL http://protein.som.geisinger.edu/ResiRole/. Interactive analyses with user-provided models and reference structures are also enabled. Code is available at github.com/wamclaughlin/ResiRole. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.