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The DEK chromatin remodeling protein was previously shown to confer oncogenic phenotypes to human and mouse mammary epithelial cells using in vitro and knockout mouse models. However, its functional role in normal mammary gland epithelium remained unexplored. We developed two novel mouse models to study the role of Dek in normal mammary gland biology in vivo . Mammary gland-specific Dek over-expression in mice resulted in hyperproliferation of cells that visually resembled alveolar cells, and a transcriptional profile that indicated increased expression of cell cycle, mammary stem/progenitor, and lactation-associated genes. Conversely, Dek knockout mice exhibited an alveologenesis or lactation defect, resulting in dramatically reduced pup survival. Analysis of previously published single-cell RNA-sequencing of mouse mammary glands revealed that Dek is most highly expressed in mammary stem cells and alveolar progenitor cells, and to a lesser extent in basal epithelial cells, supporting the observed phenotypes. Mechanistically, we discovered that Dek is a modifier of Ezh2 methyltransferase activity, upregulating the levels of histone H3 trimethylation on lysine 27 (H3K27me3) to control gene transcription. Combined, this work indicates that Dek promotes proliferation of mammary epithelial cells via cell cycle deregulation. Furthermore, we report a novel function for Dek in alveologenesis and histone H3 K27 trimethylation.

Breast cancer (BC) is the second leading cause of cancer deaths among women. DEK is a known oncoprotein that is highly expressed in over 60% of breast cancers and is an independent marker of poor prognosis. However, the molecular mechanisms by which DEK promotes tumor progression are poorly understood. To identify novel oncogenic functions of DEK, we performed RNA-Seq analysis on isogenic Dek-knockout and complemented murine BC cells. Gene ontology analyses identified gene sets associated with immune system regulation and cytokine-mediated signaling and differential cytokine and chemokine expression was confirmed across Dek-proficient versus Dek-deficient cells. By exposing murine bone marrow-derived macrophages (BMDM) to tumor cell conditioned media (TCM) to mimic a tumor microenvironment, we showed that Dek-expressing breast cancer cells produce a cytokine milieu, including up-regulated Tslp and Ccl5 and down-regulated Cxcl1, Il-6, and GM-CSF, that drives the M2 polarization of macrophages. We validated this finding in primary murine mammary tumors and show that Dek expression in vivo is also associated with increased expression of M2 macrophage markers in murine tumors. Using TCGA data, we verified that DEK expression in primary human breast cancers correlates with the expression of several genes identified by RNA-Seq in our murine model and with M2 macrophage phenotypes. Together, our data demonstrate that by regulating the production of multiple secreted factors, DEK expression in BC cells creates a potentially immune suppressed tumor microenvironment, particularly by inducing M2 tumor associated macrophage (TAM) polarization.

A general multi-scale strategy is presented for modeling the mechanical environment of a group of neurons that were embedded within a collagenous matrix. The results of the multi-scale simulation are used to estimate the local strains that arise in neurons when the extracellular matrix is deformed. The distribution of local strains was found to depend strongly on the configuration of the embedded neurons relative to the loading direction, reflecting the anisotropic mechanical behavior of the neurons. More importantly, the applied strain on the surrounding extracellular matrix is amplified in the neurons for all loading configurations that are considered. In the most severe case, the applied strain is amplified by at least a factor of 2 in 10% of the neurons' volume. The approach presented in this paper provides an extension to the capability of past methods by enabling the realistic representation of complex cell geometry into a multi-scale framework. The simulation results for the embedded neurons provide local strain information that is not accessible by current experimental techniques.

Self-renewing hematopoietic stem cells and multipotent progenitor cells are responsible for maintaining hematopoiesis throughout an individual's lifetime. For overall health and survival, it is critical that the genome stability of these cells is maintained and that the cell population is not exhausted. Previous reports have indicated that the DEK protein, a chromatin structural protein that functions in numerous nuclear processes, is required for DNA damage repair in vitro and long-term engraftment of hematopoietic stem cells in vivo. Therefore, we investigated the role of DEK in normal hematopoiesis and response to DNA damaging agents in vivo. Here, we report that hematopoiesis is largely unperturbed in DEK knockout mice compared with wild-type (WT) controls. However, DEK knockout mice have fewer radioprotective units, but increased capacity to survive repeated sublethal doses of radiation exposure compared with WT mice. Furthermore, this increased survival correlated with a sustained quiescent state in which DEK knockout restricted hematopoietic progenitor cells (HPC-1) were nearly three times more likely to be quiescent following irradiation compared with WT cells and were significantly more radioresistant during the early phases of myeloid reconstitution. Together, our studies indicate that DEK functions in the normal hematopoietic stress response to recurrent radiation exposure., (Copyright © 2018 ISEH – Society for Hematology and Stem Cells. Published by Elsevier Inc. All rights reserved.)

We review the concept of stochasticity-i.e., unpredictable or uncontrolled fluctuations in structure, chemistry, or kinetic processes-in materials. We first define six broad classes of stochasticity: equilibrium (thermodynamic) fluctuations; structural/compositional fluctuations; kinetic fluctuations; frustration and degeneracy; imprecision in measurements; and stochasticity in modeling and simulation. In this review, we focus on the first four classes that are inherent to materials phenomena. We next develop a mathematical framework for describing materials stochasticity and then show how it can be broadly applied to these four materials-related stochastic classes. In subsequent sections, we describe structural and compositional fluctuations at small length scales that modify material properties and behavior at larger length scales; systems with engineered fluctuations, concentrating primarily on composite materials; systems in which stochasticity is developed through nucleation and kinetic phenomena; and configurations in which constraints in a given system prevent it from attaining its ground state and cause it to attain several, equally likely (degenerate) states. We next describe how stochasticity in these processes results in variations in physical properties and how these variations are then accentuated by-or amplify-stochasticity in processing and manufacturing procedures. In summary, the origins of materials stochasticity, the degree to which it can be predicted and/or controlled, and the possibility of using stochastic descriptions of materials structure, properties, and processing as a new degree of freedom in materials design are described.

Fiber networks are assemblies of one-dimensional elements representative of materials with fibrous microstructures such as collagen networks and synthetic nonwovens. The mechanics of random fiber networks has been the focus of numerous studies. However, fiber crimp has been explicitly represented only in few cases. In the present work, the mechanics of cross-linked networks with crimped athermal fibers, with and without an embedding elastic matrix, is studied. The dependence of the effective network stiffness on the fraction of nonstraight fibers and the relative crimp amplitude (or tortuosity) is studied using finite element simulations of networks with sinusoidally curved fibers. A semi-analytic model is developed to predict the dependence of network modulus on the crimp amplitude and the bounds of the stiffness reduction associated with the presence of crimp. The transition from the linear to the nonlinear elastic response of the network is rendered more gradual by the presence of crimp, and the effect of crimp on the network tangent stiffness decreases as strain increases. If the network is embedded in an elastic matrix, the effect of crimp becomes negligible even for very small, biologically relevant matrix stiffness values. However, the distribution of the maximum principal stress in the matrix becomes broader in the presence of crimp relative to the similar system with straight fibers, which indicates an increased probability of matrix failure.

Random fiber networks are assemblies of elastic elements connected in random configurations. They are used as models for a broad range of fibrous materials including biopolymer gels and synthetic nonwovens. Although the mechanics of networks made from the same type of fibers has been studied extensively, the behavior of composite systems of fibers with different properties has received less attention. In this work we numerically and theoretically study random networks of beams and springs of different mechanical properties. We observe that the overall network stiffness decreases on average as the variability of fiber stiffness increases, at constant mean fiber stiffness. Numerical results and analytical arguments show that for small variabilities in fiber stiffness the amount of network softening scales linearly with the variance of the fiber stiffness distribution. This result holds for any beam structure and is expected to apply to a broad range of materials including cellular solids.

The mechanical behavior of a three-dimensional cross-linked fiber network embedded in matrix is studied in this work. The network is composed from linear elastic fibers which store energy only in the axial deformation mode, while the matrix is also isotropic and linear elastic. Such systems are encountered in a broad range of applications, from tissue to consumer products. As the matrix modulus increases, the network is constrained to deform more affinely. This leads to internal forces acting between the network and the matrix, which produce strong stress concentration at the network cross-links. This interaction increases the apparent modulus of the network and decreases the apparent modulus of the matrix. A model is developed to predict the effective modulus of the composite and its predictions are compared with numerical data for a variety of networks.

A soft tissue's macroscopic behavior is largely determined by its microstructural components (often a collagen fiber network surrounded by a nonfibrillar matrix (NFM)). In the present study, a coupled fiber-matrix model was developed to fully quantify the internal stress field within such a tissue and to explore interactions between the collagen fiber network and nonfibrillar matrix (NFM). Voronoi tessellations (representing collagen networks) were embedded in a continuous three-dimensional NFM. Fibers were represented as one-dimensional nonlinear springs and the NFM, meshed via tetrahedra, was modeled as a compressible neo-Hookean solid. Multidimensional finite element modeling was employed in order to couple the two tissue components and uniaxial tension was applied to the composite representative volume element (RVE). In terms of the overall RVE response (average stress, fiber orientation, and Poisson's ratio), the coupled fiber-matrix model yielded results consistent with those obtained using a previously developed parallel model based upon superposition. The detailed stress field in the composite RVE demonstrated the high degree of inhomogeneity in NFM mechanics, which cannot be addressed by a parallel model. Distributions of maximum/minimum principal stresses in the NFM showed a transition from fiber-dominated to matrix-dominated behavior as the matrix shear modulus increased. The matrix-dominated behavior also included a shift in the fiber kinematics toward the affine limit. We conclude that if only gross averaged parameters are of interest, parallel-type models are suitable. If, however, one is concerned with phenomena, such as individual cell-fiber interactions or tissue failure that could be altered by local variations in the stress field, then the detailed model is necessary in spite of its higher computational cost.

Hydraulic permeabilities of fiber networks are of interest for many applications and have been studied extensively. There is little work, however, on permeability calculations in three-dimensional random networks. Computational power is now sufficient to calculate permeabilities directly by constructing artificial fiber networks and simulating flow through them. Even with today's high-performance computers, however, such an approach would be infeasible for large simulations. It is therefore necessary to develop a correlation based on fiber volume fraction, radius, and orientation, preferably by incorporating previous studies on isotropic or structured networks. In this work, the direct calculations were performed, using the finite element method, on networks with varying degrees of orientation, and combinations of results for flows parallel and perpendicular to a single fiber or an array thereof, using a volume-averaging theory, were compared to the detailed analysis. The detailed model agreed well with existing analytical solutions for square arrays of fibers up to fiber volume fractions of 46% for parallel flow and 33% for transverse flow. Permeability calculations were then performed for isotropic and oriented fiber networks within the fiber volume fraction range of 0.3%-15%. When drag coefficients for spatially periodic arrays were used, the results of the volume-averaging method agreed well with the direct finite element calculations. On the contrary, the use of drag coefficients for isolated fibers overpredicted the permeability for the volume fraction range that was employed. We concluded that a weighted combination of drag coefficients for spatially periodic arrays of fibers could be used as a good approximation for fiber networks, which further implies that the effect of the fiber volume fraction and orientation on the permeability of fiber networks are more important than the effect of local network structure.

We propose a simulation method which can be used to readily parallelize simulations on systems with a large spatial extent. We simulate small parts of the system with independent molecular dynamics simulations, and only occasionally pass information between them through a constitutive model free continuum approach. We illustrate the power of this method in the case of a polymeric fluid undergoing rapid one-dimensional shear flow. Since we show that this flow problem cannot be modeled by using a steady-state constitutive model, this method offers the unique capability for accessing the nonlinear viscoelasticity of complex fluids.

In this study, we present an adaptive anisotropic finite element method (FEM) and demonstrate how computational efficiency can be increased when applying the method to the simulation of blood flow in the cardiovascular system. We use the SUPG formulation for the transient 3D incompressible Navier-Stokes equations which are discretised by linear finite elements for both the pressure and the velocity field. Given the pulsatile nature of the flow in blood vessels we have pursued adaptivity based on the average flow over a cardiac cycle. Error indicators are derived to define an anisotropic mesh metric field. Mesh modification algorithms are used to anisotropically adapt the mesh according to the desired size field. We demonstrate the efficiency of the method by first applying it to pulsatile flow in a straight cylindrical vessel and then to a porcine aorta with a stenosis bypassed by a graft. We demonstrate that the use of an anisotropic adaptive FEM can result in an order of magnitude reduction in computing time with no loss of accuracy compared to analyses obtained with uniform meshes.