Paired incremental uniaxial step (i.e., relaxation) and ramp tests were conducted simultaneously on four (nominally) identical samples of type I collagen gel, over a direct strain range 0 < epsilon < 0.2. The paired step and ramp responses could not both be predicted by a simple viscoelastic constitutive relation (either linear or Fung-type), but could be predicted reasonably accurately by a general nonlinear viscoelastic relation with a strain-dependent relaxation spectrum, of the form sigma(t) = f(t)-infinity g(t-tau,epsilon)[d(epsilon)(tau)/d(tau)]d(tau). Based on a four-term exponential-series approximation, we measured the stiffness moduli and time constants of the relaxation function, g(t,epsilon), for the four gel samples that we tested, and found that the time constants were independent of strain but the moduli increased strongly with strain. Further, we found that the time constants did not vary across the four gels, but the moduli varied by a factor of about 2 across the gels. Some additional tests show features of the response of collagen gels to cycles of application and removal of loading.

The fitting of quasi-linear viscoelastic (QLV) constitutive models to material data often involves somewhat cumbersome numerical convolution. A new approach to treating quasi-linearity in 1-D is described and applied to characterize the behavior of reconstituted collagen. This approach is based on a new principle for including nonlinearity and requires considerably less computation than other comparable models for both model calibration and response prediction, especially for smoothly applied stretching. Additionally, the approach allows relaxation to adapt with the strain history. The modeling approach is demonstrated through tests on pure reconstituted collagen. Sequences of "ramp-and-hold" stretching tests were applied to rectangular collagen specimens. The relaxation force data from the "hold" was used to calibrate a new "adaptive QLV model" and several models from literature, and the force data from the "ramp" was used to check the accuracy of model predictions. Additionally, the ability of the models to predict the force response on a reloading of the specimen was assessed. The "adaptive QLV model" based on this new approach predicts collagen behavior comparably to or better than existing models, with much less computation.

Characterizing how cells in three-dimensional (3D) environments or natural tissues respond to biophysical stimuli is a longstanding challenge in biology and tissue engineering. We demonstrate a strategy to monitor morphological and mechanical responses of contractile fibroblasts in a 3D environment. Cells responded to stretch through specific, cell-wide mechanisms involving staged retraction and reinforcement. Retraction responses occurred for all orientations of stress fibers and cellular protrusions relative to the stretch direction, while reinforcement responses, including extension of cellular processes and stress fiber formation, occurred predominantly in the stretch direction. A previously unreported role of F-actin clumps was observed, with clumps possibly acting as F-actin reservoirs for retraction and reinforcement responses during stretch. Responses were consistent with a model of cellular sensitivity to local physical cues. These findings suggest mechanisms for global actin cytoskeleton remodeling in non-muscle cells and provide insight into cellular responses important in pathologies such as fibrosis and hypertension.

Although much is known about the effects of uniaxial mechanical loading on fibrocartilage development, the stress fields to which fibrocartilaginous regions are subjected to during development are mutiaxial. That fibrocartilage develops at tendon-to-bone attachments and in compressive regions of tendons is well established. However, the three-dimensional (3D) nature of the stresses needed for the development of fibrocartilage is not known. Here, we developed and applied an in vitro system to determine whether fibrocartilage can develop under a state of periodic hydrostatic tension in which only a single principal component of stress is compressive. This question is vital to efforts to mechanically guide morphogenesis and matrix expression in engineered tissue replacements. Mesenchymal stromal cells in a 3D culture were exposed to compressive and tensile stresses as a result of an external tensile hydrostatic stress field. The stress field was characterized through mechanical modeling. Tensile cyclic stresses promoted spindle-shaped cells, upregulation of scleraxis and type one collagen, and cell alignment with the direction of tension. Cells experiencing a single compressive stress component exhibited rounded cell morphology and random cell orientation. No difference in mRNA expression of the genes Sox9 and aggrecan was observed when comparing tensile and compressive regions unless the medium was supplemented with the chondrogenic factor transforming growth factor beta3. In that case, Sox9 was upregulated under static loading conditions and aggrecan was upregulated under cyclic loading conditions. In conclusion, the fibrous component of fibrocartilage could be generated using only mechanical cues, but generation of the cartilaginous component of fibrocartilage required biologic factors in addition to mechanical cues. These studies support the hypothesis that the 3D stress environment influences cell activity and gene expression in fibrocartilage development. IntroductionM usculoskeletal injuries are a common cause of pain and disability, and result in significant healthcare costs.1 Many of these injuries require regeneration of fibrocartilage (tissue composed of fibrous and cartilaginous components) for effective healing.2-4 For example, meniscus healing is typically insufficient due to a lack of fibrocartilage regeneration.3 Similarly, tendon-to-bone healing and repair, as frequently required after rotator cuff injury, often fails due to a lack of fibrocartilage formation at the tendon-to-bone interface. 4 Little is known about natural fibrocartilage healing, and hence little can be done to improve it. We and others have hypothesized that rebuilding the fibrocartilaginous insertion site of the tendon or ligament into bone is critical for restoration of function and for prevention of re-injury. [4][5][6] Several studies provide evidence that the stress environment influences cell morphology and the fibrocartilage production. 7,8 Compressive loads in vivo have been shown to change tendon composition and structur...

The stress fiber network within contractile fibroblasts structurally reinforces and provides tension, or "tone", to tissues such as those found in healing wounds. Stress fibers have previously been observed to polymerize in response to mechanical forces. We observed that, when stretched sufficiently, contractile fibroblasts diminished the mechanical tractions they exert on their environment through depolymerization of actin filaments then restored tissue tension and rebuilt actin stress fibers through staged Ca ++ -dependent processes. These staged Ca ++ -modulated contractions consisted of a rapid phase that ended less than a minute after stretching, a plateau of inactivity, and a final gradual phase that required several minutes to complete. Active contractile forces during recovery scaled with the degree of rebuilding of the actin cytoskeleton. This complementary action demonstrates a programmed regulatory mechanism that protects cells from excessive stretch through choreographed active mechanical and biochemical healing responses.

The mechanics of bio-artificial tissue constructs result from active and passive contributions of cells and extracellular matrix (ECM). We delineated these for a fibroblast-populated matrix (FPM) consisting of chick embryo fibroblast cells in a type I collagen ECM through mechanical testing, mechanical modeling, and selective biochemical elimination of tissue components. From a series of relaxation tests, we found that contributions to overall tissue mechanics from both cells and ECM increase exponentially with the cell concentration. The force responses in these relaxation tests exhibited a logarithmic decay over the 3600 second test duration. The amplitudes of these responses were nearly linear with the amplitude of the applied stretch. The active component of cellular forces rose dramatically for FPMs containing higher cell concentrations.

Straight chain fatty acid ␣-oxidation increases during differentiation of 3T3-L1 adipocytes, leading to a marked accumulation of odd chain length fatty acyl moieties. Potential roles of this pathway in adipocyte differentiation and lipogenesis are unknown. Mammalian fatty acid 2-hydroxylase (FA2H) was recently identified and suggested to catalyze the initial step of straight chain fatty acid ␣-oxidation. Accordingly, we examined whether FA2H modulates adipocyte differentiation and lipogenesis in mature adipocytes. FA2H level markedly increases during differentiation of 3T3-L1 adipocytes, and small interfering RNAs against FA2H inhibit the differentiation process. In mature adipocytes, depletion of FA2H inhibits basal and insulin-stimulated glucose uptake and lipogenesis, which are partially rescued by the enzymatic product of FA2H, 2-hydroxy palmitic acid. Expression of fatty-acid synthase and SCD1 was decreased in FA2H-depleted cells, and levels of GLUT4 and insulin receptor proteins were reduced. 2-Hydroxy fatty acids are enriched in cellular sphingolipids, which are components of membrane rafts. Accelerated diffusional mobility of raft-associated lipids was shown to enhance degradation of GLUT4 and insulin receptor in adipocytes. Consistent with this, depletion of FA2H appeared to increase raft lipid mobility as it significantly accelerated the rates of fluorescence recovery after photobleaching measurements of lipid rafts labeled with Alexa 488-conjugated cholera toxin subunit B. Moreover, the enhanced recovery rates were partially reversed by treatment with 2-hydroxy palmitic acid. In conclusion, our findings document the novel role of FA2H in adipocyte lipogenesis possibly by modulation of raft fluidity and level of GLUT4.In addition to mitochondrial ␤-oxidation, fatty acids (FA) 2 can also be oxidized in peroxisomes, where they can undergo ␤-as well as ␣-oxidation. Peroxisomal ␣-oxidation produces fatty acyl moieties with one less carbon (odd chain length) without generating acetyl-CoA (1). It is well known that peroxisomal ␣-oxidation is important for catabolism of branched-chain FA and also for very long chain FA (2). Its contribution to catabolism of other long chain FA is not known, so its role in whole cell energy homeostasis remains unclear. There is evidence for up-regulation of FA ␣-oxidation during the preadipocyte to adipocyte transition as cells begin accumulating lipids. Using electrospray ionization-mass spectrometry and gas chromatography-coupled mass spectrometry, we and others demonstrated during differentiation of 3T3-L1 cells a marked increase in activity of peroxisomal FA ␣-oxidation, which was reflected in accumulation of odd chain length acyl moieties in major lipid species (3, 4). The underlying mechanisms for this change and the functions of the ␣-oxidation pathway in adipocyte differentiation and lipid metabolism in mature adipocytes have not been explored.Although straight chain FA ␣-oxidation was first reported in 1964 (5), a detailed understanding of the enzymatic pathway is sti...

Viscoelastic relaxation spectra are essential for predicting and interpreting the mechanical responses of materials and structures. For biological tissues, these spectra must usually be estimated from viscoelastic relaxation tests. Interpreting viscoelastic relaxation tests is challenging because the inverse problem is expensive computationally. We present here an efficient algorithm that enables rapid identification of viscoelastic relaxation spectra. The algorithm was tested against trial data to characterize its robustness and identify its limitations and strengths. The algorithm was then applied to identify the viscoelastic response of reconstituted collagen, revealing an extensive distribution of viscoelastic time constants.