Consideration of multiple load cases is critical in modelling orthotropic bone adaptation in the femur

Functional adaptation of the femur has been investigated in several studies by embedding bone remodelling algorithms in finite element (FE) models, with simplifications often made to the representation of bone’s material symmetry and mechanical environment. An orthotropic strain-driven adaptation algorithm is proposed in order to predict the femur’s volumetric material property distribution and directionality of its internal structures within a continuum. The algorithm was applied to a FE model of the femur, with muscles, ligaments and joints included explicitly. Multiple load cases representing distinct frames of two activities of daily living (walking and stair climbing) were considered. It is hypothesised that low shear moduli occur in areas of bone that are simply loaded and high shear moduli in areas subjected to complex loading conditions. In addition, it is investigated whether material properties of different femoral regions are stimulated by different activities. The loading and boundary conditions were considered to provide a physiological mechanical environment. The resulting volumetric material property distribution and directionalities agreed with ex vivo imaging data for the whole femur. Regions where non-orthogonal trabecular crossing has been documented coincided with higher values of predicted shear moduli. The topological influence of the different activities modelled was analysed. The influence of stair climbing on the properties of the femoral neck region is highlighted. It is recommended that multiple load cases should be considered when modelling bone adaptation. The orthotropic model of the complete femur is released with this study.     

Effects of aortic irregularities on blood flow

Anatomic aortic anomalies are seen in many medical conditions and are known to cause disturbances in blood flow. Turner syndrome (TS) is a genetic disorder occurring only in females where cardiovascular anomalies, particularly of the aorta, are frequently encountered. In this study, numerical simulations are applied to investigate the flow characteristics in four TS patient- related aortic arches (a normal geometry, dilatation, coarctation and elongation of the transverse aorta). The Quemada viscosity model was applied to account for the non-Newtonian behavior of blood. The blood is treated as a mixture consisting of water and red blood cells (RBC) where the RBCs are modeled as a convected scalar. The results show clear geometry effects where the flow structures and RBC distribution are significantly different between the aortas. Transitional flow is observed as a jet is formed due to a constriction in the descending aorta for the coarctation case. RBC dilution is found to vary between the aortas, influencing the WSS. Moreover, the local variations in RBC volume fraction may induce large viscosity variations, stressing the importance of accounting for the non-Newtonian effects.     

Model of cellular mechanotransduction via actin stress fibers

Mechanical stresses due to blood flow regulate vascular endothelial cell structure and function and play a key role in arterial physiology and pathology. In particular, the development of atherosclerosis has been shown to correlate with regions of disturbed blood flow where endothelial cells are round and have a randomly organized cytoskeleton. Thus, deciphering the relation between the mechanical environment, cell structure, and cell function is a key step toward understanding the early development of atherosclerosis. Recent experiments have demonstrated very rapid (\(\sim \)100 ms) and long-distance (\(\sim \)10 \(\upmu \)m) cellular mechanotransduction in which prestressed actin stress fibers play a critical role. Here, we develop a model of mechanical signal transmission within a cell by describing strains in a network of prestressed viscoelastic stress fibers following the application of a force to the cell surface. We find force transmission dynamics that are consistent with experimental results. We also show that the extent of stress fiber alignment and the direction of the applied force relative to this alignment are key determinants of the efficiency of mechanical signal transmission. These results are consistent with the link observed experimentally between cytoskeletal organization, mechanical stress, and cellular responsiveness to stress. Based on these results, we suggest that mechanical strain of actin stress fibers under force constitutes a key link in the mechanotransduction chain.     

A method for three-dimensional quantification of vascular smooth muscle orientation: application in viable murine carotid arteries

When studying in vivo arterial mechanical behaviour using constitutive models, smooth muscle cells (SMCs) should be considered, while they play an important role in regulating arterial vessel tone. Current constitutive models assume a strictly circumferential SMC orientation, without any dispersion. We hypothesised that SMC orientation would show considerable dispersion in three dimensions and that helical dispersion would be greater than transversal dispersion. To test these hypotheses, we developed a method to quantify the 3D orientation of arterial SMCs. Fluorescently labelled SMC nuclei of left and right carotid arteries of ten mice were imaged using two-photon laser scanning microscopy. Arteries were imaged at a range of luminal pressures. 3D image processing was used to identify individual nuclei and their orientations. SMCs showed to be arranged in two distinct layers. Orientations were quantified by fitting a Bingham distribution to the observed orientations. As hypothesised, orientation dispersion was much larger helically than transversally. With increasing luminal pressure, transversal dispersion decreased significantly, whereas helical dispersion remained unaltered. Additionally, SMC orientations showed a statistically significant (\(p < 0.05\)) mean right-handed helix angle in both left and right arteries and in both layers, which is a relevant finding from a developmental biology perspective. In conclusion, vascular SMC orientation (1) can be quantified in 3D; (2) shows considerable dispersion, predominantly in the helical direction; and (3) has a distinct right-handed helical component in both left and right carotid arteries. The obtained quantitative distribution data are instrumental for constitutive modelling of the artery wall and illustrate the merit of our method.     

Modeling of cell adhesion and deformation mediated by receptor–ligand interactions

The current work is devoted to studying adhesion and deformation of biological cells mediated by receptors and ligands in order to enhance the existing models. Due to the sufficient in-plane continuity and fluidity of the phospholipid molecules, an isotropic continuum fluid membrane is proposed for modeling the cell membrane. The developed constitutive model accounts for the influence of the presence of receptors on the deformation and adhesion of the cell membrane through the introduction of spontaneous area dilation. Motivated by physics, a nonlinear receptor–ligand binding force is introduced based on charge-induced dipole interaction. Diffusion of the receptors on the membrane is governed by the receptor–ligand interaction via Fick’s Law and receptor-ligand interaction. The developed model is then applied to study the deformation and adhesion of a biological cell. The proposed model is used to study the role of the material, binding, spontaneous area dilation and environmental properties on the deformation and adhesion of the cell.     

Prediction of apparent trabecular bone stiffness through fourth-order fabric tensors

The apparent stiffness tensor is an important mechanical parameter for characterizing trabecular bone. Previous studies have modeled this parameter as a function of mechanical properties of the tissue, bone density, and a second-order fabric tensor, which encodes both anisotropy and orientation of trabecular bone. Although these models yield strong correlations between observed and predicted stiffness tensors, there is still space for reducing accuracy errors. In this paper, we propose a model that uses fourth-order instead of second-order fabric tensors. First, the totally symmetric part of the stiffness tensor is assumed proportional to the fourth-order fabric tensor in the logarithmic scale. Second, the asymmetric part of the stiffness tensor is derived from relationships among components of the harmonic tensor decomposition of the stiffness tensor. The mean intercept length (MIL), generalized MIL (GMIL), and fourth-order global structure tensor were computed from images acquired through microcomputed tomography of 264 specimens of the femur. The predicted tensors were compared to the stiffness tensors computed by using the micro-finite element method (\(\upmu \)FE), which was considered as the gold standard, yielding strong correlations (\(R^2\) above 0.962). The GMIL tensor yielded the best results among the tested fabric tensors. The Frobenius error, geodesic error, and the error of the norm were reduced by applying the proposed model by 3.75, 0.07, and 3.16 %, respectively, compared to the model by Zysset and Curnier (Mech Mater 21(4):243–250, 1995) with the second-order MIL tensor. From the results, fourth-order fabric tensors are a good alternative to the more expensive \(\upmu \)FE stiffness predictions.     

A microstructurally based continuum model of cartilage viscoelasticity and permeability incorporating measured statistical fiber orientations

The remarkable mechanical properties of cartilage derive from an interplay of isotropically distributed, densely packed and negatively charged proteoglycans; a highly anisotropic and inhomogeneously oriented fiber network of collagens; and an interstitial electrolytic fluid. We propose a new 3D finite strain constitutive model capable of simultaneously addressing both solid (reinforcement) and fluid (permeability) dependence of the tissue’s mechanical response on the patient-specific collagen fiber network. To represent fiber reinforcement, we integrate the strain energies of single collagen fibers—weighted by an orientation distribution function (ODF) defined over a unit sphere—over the distributed fiber orientations in 3D. We define the anisotropic intrinsic permeability of the tissue with a structure tensor based again on the integration of the local ODF over all spatial fiber orientations. By design, our modeling formulation accepts structural data on patient-specific collagen fiber networks as determined via diffusion tensor MRI. We implement our new model in 3D large strain finite elements and study the distributions of interstitial fluid pressure, fluid pressure load support and shear stress within a cartilage sample under indentation. Results show that the fiber network dramatically increases interstitial fluid pressure and focuses it near the surface. Inhomogeneity in the tissue’s composition also increases fluid pressure and reduces shear stress in the solid. Finally, a biphasic neo-Hookean material model, as is available in commercial finite element codes, does not capture important features of the intra-tissue response, e.g., distributions of interstitial fluid pressure and principal shear stress.     

Yeasts from peat in a tropical peat swamp forest in Thailand and their ability to produce ethanol,indole-3-acetic acid and extracellular enzymes

This study aimed to isolate and identify yeasts from peat in To Daeng peat swamp forest in southern of Thailand, and to investigate their ability to produce ethanol from glucose and xylose and to produce indole-3-acetic acid (IAA) and extracellular enzymes. A total of 65 yeast strains were obtained from 15 peat samples using an enrichment technique, and 61 strains were identified to be five species belonging to the phylum Ascomycota, namely Cyberlindnera subsufficiens, Debaryomyces fabryi, Meyerozyma guilliermondii, Saturnispora diversa and Schwanniomyces polymorphus var. africanus, and five species of the phylum Basidiomycota, namely Cryptococcus taiwanensis pro tem, Cutaneotrichosporon mucoides, Papiliotrema flavescens, Papiliotrema laurentii and Rhodotorula mucilaginosa. Four strains were unidentified and require further analysis. They differed from the type strain of P. flavescens by two nucleotide substitutions in the D1/D2 region of the LSU rRNA gene and nine nucleotide substitutions in the ITS region. R. mucilaginosa was the most prevalent yeast species, followed by S. polymorphus var. africanus, Cy. subsufficiens and D. fabryi. None of the yeast strains obtained in this study were able to ferment xylose to ethanol, but all ascomycetous yeast strains produced ethanol from glucose in a range of 9.0–58.0 g/L, with Cy. subsufficiens DMKU-YNB42-1 producing the highest ethanol concentration. A total of 62 strains produced IAA in a range of 9.0 to 66.9 mg/L, with the highest IAA produced by R. mucilaginosa DMKU-Y33-A. Investigation of the production of cellulases, xylanase, pectinase, amylase, protease and lipase revealed that all 65 yeast strains produced at least one extracellular enzyme, a lipase.     

Predicting microbial traits with phylogenies

Phylogeny reflects genetic and phenotypic traits in Bacteria and Archaea. The phylogenetic conservatism of microbial traits has prompted the application of phylogeny-based algorithms to predict unknown trait values of extant taxa based on the traits of their evolutionary relatives to estimate, for instance, rRNA gene copy numbers, gene contents or tolerance to abiotic conditions. Unlike the ‘macrobial'' world, microbial ecologists face scenarios potentially compromising the accuracy of trait reconstruction methods, as, for example, extremely large phylogenies and limited information on the traits of interest. We review 990 bacterial and archaeal traits from the literature and support that phylogenetic trait conservatism is widespread through the tree of life, while revealing that it is generally weak for ecologically relevant phenotypic traits and high for genetically complex traits. We then perform a simulation exercise to assess the accuracy of phylogeny-based trait predictions in common scenarios faced by microbial ecologists. Our simulations show that ca. 60% of the variation in phylogeny-based trait predictions depends on the magnitude of the trait conservatism, the number of species in the tree, the proportion of species with unknown trait values and the mean distance in the tree to the nearest neighbour with a known trait value. Results are similar for both binary and continuous traits. We discuss these results under the light of the reviewed traits and provide recommendations for the use of phylogeny-based trait predictions for microbial ecologists.Trait-based approaches in community ecology studies are becoming increasingly appealing for microbial ecologists partly because metagenomic sequencing allows surveying molecular functions (Green et al., 2008; Lauro et al., 2009; Burke et al., 2011; Raes et al., 2011; Brown et al., 2014; Fierer et al., 2014). Although genetic data can provide precise information on cellular processes or metabolic pathways, they are generally blind to other ecologically relevant phenotypic traits such as the tolerance to certain abiotic conditions or the specific growth rate (but see Vieira-Silva and Rocha, 2010). Unlike ‘macrobial'' ecologists, who can directly observe phenotypic characters of plants and animals, microbial ecologists usually face situations where most of the phenotypes of their study organisms are unknown. This difficulty relies on the fact that gathering phenotypic (physiological, morphological, biochemical) data requires culturing microbial species. The unbalanced growth of genotypic vs phenotypic information is currently challenging microbial ecologists to work with phylogenetic trees of increasing size (hundreds to thousands of species) in which the percentage of species with unknown traits becomes larger and larger.Recent evidence indicate that phylogeny reflects molecular functions and phenotypes in Bacteria and Archaea (Langille et al., 2013; Martiny et al., 2013). This is due to the phylogenetic conservatism of microbial traits (Martiny et al., 2013), which likely arises from microbial evolution mostly proceeding by vertical gene inheritance rather than horizontal gene transfer (Kurland et al., 2003, see Fraser et al., 2007 for theoretical models on the role of horizontal gene transfer in bacterial speciation). At present, the massive sequencing of microbes in the environment is providing a huge amount of genetic information that is extremely useful to reconstruct the phylogenetic relationships among microbial lineages. This fact has triggered the interest of microbial ecologists to apply the methods developed to predict unobserved trait values of extant taxa based on the traits observed in their evolutionary relatives (Kembel et al., 2012; Langille et al., 2013; Angly et al., 2014, see review in Zaneveld and Thurber, 2014). All these methods are based on the existence of a significant phylogenetic signal or, in other words, in the fact that close relatives have more similar traits than expected by chance. Phylogeny-based trait prediction procedures (PTP hereafter) in microbes have been mainly performed under the phylogenetic generalized least squares framework (Martins and Hansen, 1997; Garland and Ives, 2000). Specifically, the trait value (for continuous traits) or state (for binary traits) of the focal species have been reconstructed through ancestral state reconstructions after rerooting the phylogeny at the most recent common ancestor of the taxon with unobserved trait and the rest of the tree (Kembel et al., 2012). The accuracy of PTP methods has been typically assessed under ‘macrobial'' scenarios containing phylogenies of moderate size, with low-to-medium proportion of species with unknown traits and significant phylogenetic signals. For example, Fagan et al. (2013) predicted population growth rates of mammals in phylogenies of 42–65 species containing 54–64% of unknowns and a significant phylogenetic signal (Blomberg et al., 2003; Blomberg''s K) ranging from 0.68 to 1.42. However, the current microbial scenarios derived from high-throughput sequencing projects face large-sized phylogenies (hundreds to thousands tips) with a high number of species with unknown traits and varying phylogenetic signals jeopardizing the applicability of PTP methods (Zaneveld and Thurber, 2014).The extent to which phylogeny reflects phenotype is strongly dependent on the degree of conservatism with which the focal trait has evolved. For instance, complex traits that involve many genes (for example, photosynthesis or methanogenesis) show higher conservatism than simpler traits, such as the consumption of a specific carbon source (Martiny et al., 2013). Furthermore, certain traits such as those related to genes encoding antibiotic or metal resistance are particularly prone to be horizontally transferred (Bruins et al., 2000), a process that can blur their phylogenetic signal. Therefore, if phylogenetic relatedness is to be used to infer the phenotype, the phylogenetic conservatism of the target trait needs to be quantified in every case.Altogether, the abovementioned observations indicate that the possibility to estimate phenotypes from phylogenies depends on the amount of phylogenetic and phenotypic information available to predict the unobserved trait values. Here we provide a simulation exercise to test the accuracy of the most widely used PTP method in microbial ecology to predict continuous trait values and binary trait states of extant taxa with different amount of phenotypic and phylogenetic information. We simulated several situations faced by microbial ecologists, including phylogenies of different sizes in which a small (P=0.3), medium (P=0.6) or large (P=0.9) proportion of species have unknown trait values. The correlations between the actual and the predicted trait values were obtained for characters evolved under different degree of conservatism. Finally, we put these values in the context of the phylogenetic signals described in the literature for different continuous and binary microbial traits and provide some recommendations for future analyses aimed to predict microbial traits with the help of the phylogenetic information.     

Lipid remodelling is a widespread strategy in marine heterotrophic bacteria upon phosphorus deficiency

Upon phosphorus (P) deficiency, marine phytoplankton reduce their requirements for P by replacing membrane phospholipids with alternative non-phosphorus lipids. It was very recently demonstrated that a SAR11 isolate also shares this capability when phosphate starved in culture. Yet, the extent to which this process occurs in other marine heterotrophic bacteria and in the natural environment is unknown. Here, we demonstrate that the substitution of membrane phospholipids for a variety of non-phosphorus lipids is a conserved response to P deficiency among phylogenetically diverse marine heterotrophic bacteria, including members of the Alphaproteobacteria and Flavobacteria. By deletion mutagenesis and complementation in the model marine bacterium Phaeobacter sp. MED193 and heterologous expression in recombinant Escherichia coli, we confirm the roles of a phospholipase C (PlcP) and a glycosyltransferase in lipid remodelling. Analyses of the Global Ocean Sampling and Tara Oceans metagenome data sets demonstrate that PlcP is particularly abundant in areas characterized by low phosphate concentrations. Furthermore, we show that lipid remodelling occurs seasonally and responds to changing nutrient conditions in natural microbial communities from the Mediterranean Sea. Together, our results point to the key role of lipid substitution as an adaptive strategy enabling heterotrophic bacteria to thrive in the vast P-depleted areas of the ocean.