•A multiphase model is developed that can describe autologous chemotaxis consistent with in vitro experiments.•The role played by fluid-ECM, cell-ECM, and cell-fluid interaction forces are ...included.•The model illustrates how autologous chemotaxis can be used as a means for metastasis.
It has been demonstrated that interstitial fluid (IF) flow can play a crucial role in tumor cell progression. In the seminal works by Swartz and collaborators (Fleury et al., 2006; Shields et al., 2007) it was discovered that due to this flow, chemokine ligands secreted by tumor cells selectively tend to bind to receptors (CCR7) on the downstream side of the cells that in turn stimulate cells to migrate in the direction of the flow. This migration process was denoted as autologous chemotaxis. Previous mathematical modeling of autologous chemotaxis apparently has been restricted to single-phase considerations. The purpose of this work is to explore how a multiphase approach can be used where the fluid and cancer cells are treated as two separate phases with their own momentum balance equations. A mathematical model is derived that sheds light on essential nonlinear coupling mechanisms and interactions that are involved. The role played by fluid-ECM (friction type of term) and cell-ECM interaction forces (adhesion forces) are demonstrated. In particular, a fluid generated stress term in the mathematical expression for the cell velocity is highlighted. This term reflects how the flowing fluid will try to push the cancer cells in the downstream direction whose effect must be counterbalanced by the cancer cells by creating a sufficiently strong cell-ECM resistance force. Moreover, in order to represent the autologous chemotaxis migration mechanism we include (i) a component to represent stagnant ECM concentration (collagen); (ii) a chemical component representing chemokine that can convect with the fluid; and (iii) a third chemical component to represent protease secreted by the cancer cells which is able to release ECM-bound chemokine through proteolytic activity. The resulting model allows us to demonstrate how the autologous chemotaxis transport mechanism is governed by formation of chemokine concentration gradients that are asymmetric and skewed in the flow direction. We test the model behavior for a flow system with an external imposed pressure gradient which is comparable with the laboratory experiments by Swartz and collaborators. Sensitivity to changes in circumstances like blocking of the CCR7 receptor needed for autologous chemotaxis and elimination of the pressure driven IF flow (i.e., no flow) are explored and discussed. We also illustrate the model behavior in an envisioned tumor setting where increased IF flow is produced from leaky blood vessels that sit on the inside of the tumor. An increased fluid flow towards the region on the outside of the tumor is then generated where it is adsorbed by lymphatic vessels and gives rise to a characteristic elevated IF pressure profile that decreases at the tumor periphery. In turn, this results in an autologous chemotactic driven migration of cancer cells at the rim of the tumor. The simulation illustrates how the autologous chemotactic cell migration mechanism discovered by Swartz and collaborators possibly can be used as a means for metastasis by generating aggressive cell migration towards lymphatic vessels.
We present a model of diverse phytoplankton and zooplankton populations embedded in a global ocean circulation model. Physiological and ecological traits of the organisms are constrained by ...relationships with cell size. The model qualitatively reproduces global distributions of nutrients, biomass, and primary productivity, and captures the power-law relationship between cell size and numerical density, which has realistic slopes of between −1.3 and −0.8. We use the model to explore the global structure of marine ecosystems, highlighting the importance of both nutrient and grazer controls. The model suggests that zooplankton : phytoplankton (Z : P) biomass ratios may vary from an order of 0.1 in the oligotrophic gyres to an order of 10 in upwelling and highlatitude regions. Global estimates of the strength of bottom-up and top-down controls within plankton size classes suggest that these large-scale gradients in Z : P ratios are driven by a shift from strong bottom-up, nutrient limitation in the oligotrophic gyres to the dominance of top-down, grazing controls in more productive regions.
In the seminal work by Swartz and collaborators (Shields et al., 2007) it was discovered that autologously secreted or activated (ECM-bound) chemokine forms local pericellular diffusion gradients ...skewed by fluid convection, and the cells subsequently chemotact up the flow-directed gradient. However, in (Polacheck et al., 2011) Kamm and collaborators found that there is a competing downstream and upstream migration transport mechanism. Their study showed that both mechanisms are present at the same time and the relative strength of these two stimuli governs the directional bias in migration for a cell population and is a function of cell density, interstitial flow rate, and CCR7 receptor availability. The main objective of this work is to give a possible explanation of these two different concurrent cell migration mechanisms by means of a theoretical model. Relying on multiphase modelling, separate momentum balance equations are formulated, respectively, for the cell phase and the interstitial fluid (IF) phase. In order to represent proteolytic activity and autologous chemotaxis a non-moving ECM component is included, as well as proteases secreted by the cancer cells and chemokine that can be released from ECM. The cell and IF momentum balance equations include cell-ECM and fluid-ECM resistance force terms (i.e., classical Darcy’s equation terms), but also a cell-fluid interaction term that can account for a more indirect effect that fluid-generated stress may have on cancer cells. We illustrate how the cancer cells can work through this term and effectively avoid being pushed in the flow direction, and even create upstream migration by controlling its magnitude and sign. We think of this as the mathematical interpretation of the experimental observation by Kamm and collaborators that the fluid generated matrix adhesion tension on the upstream side of cells activates integrin adhesion complexes, resulting in activation of focal adhesion (FA) proteins. The model predicts that generally the strength of the upstream migration mechanism is sensitive to the cell volume fraction: a lower density of cells is subject to a weaker upstream migration effect; a higher density of cancer cells can more effectively generate upstream migration. This behavior is a result of the nonlinear coupling between cell-ECM, fluid-ECM, and cell-fluid interaction terms that naturally are involved in the mathematical expression for the net cell velocity.
The first hydrogenation step of benzene, which is endergonic in the electronic ground state (S
), becomes exergonic in the first triplet state (T
). This is in line with Baird's rule, which tells ...that benzene is antiaromatic and destabilized in its T
state and also in its first singlet excited state (S
), opposite to S
, where it is aromatic and remarkably unreactive. Here we utilized this feature to show that benzene and several polycyclic aromatic hydrocarbons (PAHs) to various extents undergo metal-free photochemical (hydro)silylations and transfer-hydrogenations at mild conditions, with the highest yield for naphthalene (photosilylation: 21%). Quantum chemical computations reveal that T
-state benzene is excellent at H-atom abstraction, while cyclooctatetraene, aromatic in the T
and S
states according to Baird's rule, is unreactive. Remarkably, also CVD-graphene on SiO
is efficiently transfer-photohydrogenated using formic acid/water mixtures together with white light or solar irradiation under metal-free conditions.
In this work we investigate fibroblast-enhanced tumor cell migration in an idealized tumor setting through a computational model based on a multiphase approach consisting of three phases, namely ...tumor cells, fibroblasts and interstitial fluid. The interaction between fibroblasts and tumor cells has previously been investigated through this model (Urdal et al., 2019) to comply with reported in vitro experimental results (Shieh et al., 2011). Using the information gained from in vitro single-cell behavior, what will the effect of fibroblast-enhanced tumor cell migration be in a tumor setting? In particular, how will tumor cells migrate in a heterogeneous tumor environment compared to controlled in vitro microfluidic-based experiments? From what we know about the behavior of a tumor, is that collective invasion into adjacent tissue is frequently observed. Here, we want to elucidate how fibroblasts may guide tumor cells towards draining lymphatics to which tumor cells may subsequently intravasate and thus spread to other parts of the body. Fibroblasts can act as leader cells, where they create tracks within the extracellular matrix (ECM) by matrix remodeling and contraction. In addition, a heterotypic mechanical adhesion between fibroblasts and tumor cells also assist the fibroblasts to act as leader cells. Our simulation results show how the interaction between the two cell types yields collective migration of tumor cells outwards from the tumor where fibroblasts dictate the direction of migration. The model also describes how this well-orchestrated invasive behavior is the result of a proper combination of different interaction forces between cell-ECM, fibroblast-ECM, fluid-ECM and cell-fibroblast.
Quantifying variability in the ocean carbon sink remains problematic due to sparse observations and spatiotemporal variability in surface ocean pCO2. To address this challenge, we have updated and ...improved ECCO‐Darwin, a global ocean biogeochemistry model that assimilates both physical and biogeochemical observations. The model consists of an adjoint‐based ocean circulation estimate from the Estimating the Circulation and Climate of the Ocean (ECCO) consortium and an ecosystem model developed by the Massachusetts Institute of Technology Darwin Project. In addition to the data‐constrained ECCO physics, a Green's function approach is used to optimize the biogeochemistry by adjusting initial conditions and six biogeochemical parameters. Over seasonal to multidecadal timescales (1995–2017), ECCO‐Darwin exhibits broad‐scale consistency with observed surface ocean pCO2 and air‐sea CO2 flux reconstructions in most biomes, particularly in the subtropical and equatorial regions. The largest differences between CO2 uptake occur in subpolar seasonally stratified biomes, where ECCO‐Darwin results in stronger winter uptake. Compared to the Global Carbon Project OBMs, ECCO‐Darwin has a time‐mean global ocean CO2 sink (2.47 ± 0.50 Pg C year−1) and interannual variability that are more consistent with interpolation‐based products. Compared to interpolation‐based methods, ECCO‐Darwin is less sensitive to sparse and irregularly sampled observations. Thus, ECCO‐Darwin provides a basis for identifying and predicting the consequences of natural and anthropogenic perturbations to the ocean carbon cycle, as well as the climate‐related sensitivity of marine ecosystems. Our study further highlights the importance of physically consistent, property‐conserving reconstructions, as are provided by ECCO, for ocean biogeochemistry studies.
Plain Language Summary
Data‐driven estimates of how much carbon dioxide the ocean is absorbing (the so‐called “ocean carbon sink”) have improved substantially in recent years. However, computational ocean models that include biogeochemistry continue to play a critical role as they allow us to isolate and understand the individual processes that control ocean carbon sequestration. The ideal scenario is a combination of the above two methods, where data are ingested and then used to improve a model's fit to the observed ocean, also known as, data assimilation. While the physical oceanographic community has made great progress in developing data assimilation systems, for example, the Estimating the Circulation and Climate of the Ocean (ECCO) consortium, the biogeochemical community has generally lagged behind. The ECCO‐Darwin model presented in this paper represents an important technological step forward as it is the first global ocean biogeochemistry model that (1) ingests both physical and biogeochemical observations into the model in a realistic manner and (2) considers how the nature of the ocean carbon sink has changed over multiple decades. As the ECCO ocean circulation estimates become more accurate and lengthen in time, ECCO‐Darwin will become an ever more accurate and useful tool for climate‐related ocean carbon cycle and mitigation studies.
Key Points
ECCO‐Darwin is a global ocean biogeochemistry model that assimilates physical and biogeochemical observations in a conserving manner
Air‐sea CO2 fluxes over seasonal to multidecadal timescales (1995–2017) are largely consistent with interpolation‐based products
Contrary to interpolation‐based products, ECCO‐Darwin is impervious to sparse and irregularly sampled observations
Ocean circulation shapes marine phytoplankton communities by setting environmental conditions and dispersing organisms. In addition, processes acting on the water column (e.g., heat fluxes and ...mixing) affect the community structure by modulating environmental variables that determine in situ growth and loss rates. Understanding the scales over which phytoplankton communities vary in time and space is key to elucidate the relative contributions of local processes and ocean circulation on phytoplankton distributions. Using a global ocean ecosystem model, we quantify temporal and spatial correlation scales for phytoplankton phenotypes with diverse functional traits and cell sizes. Through this analysis, we address these questions: (1) Over what timescales do perturbations in phytoplankton populations persist? and (2) over what distances are variations in phytoplankton populations synchronous? We find that correlation timescales are short in regions of strong currents, such as the Gulf Stream and Antarctic Circumpolar Current. Conversely, in the subtropical gyres, phytoplankton population anomalies persist for relatively long periods. Spatial correlation length scales are elongated near ocean fronts and narrow boundary currents, reflecting flow paths and frontal patterns. In contrast, we find nearly isotropic spatial correlation fields where current speeds are small, or where mixing acts roughly equally in all directions. Phytoplankton timescales and length scales also vary coherently with phytoplankton body size. In addition to aiding understanding of phytoplankton population dynamics, our results provide global insights to guide the design of biological ocean observing networks and to better interpret data collected at long‐term monitoring stations.
Plain Language Summary
Using a global model of the marine planktonic ecosystem, we quantify the temporal and spatial correlation scales of diverse types of phytoplankton. The timescales reflect the persistence of anomalies in time and the stability of the planktonic system. The spatial scales measure over what distances variations in phytoplankton populations are synchronous. We find that timescales and length scales vary with cell size and that global patterns of correlation are shaped by ocean currents. These results provide valuable insights for the design of ocean observing systems with a unique ecological perspective. We also discuss how regional differences in phytoplankton community correlation scales are relevant for interpreting data collected at long‐term monitoring stations.
Key Points
Correlation timescales in phytoplankton communities are longer in the subtropical gyres and shorter in regions of strong circulation
Spatial correlations in phytoplankton communities are strongly anisotropic along frontal zones and boundary currents
Ocean currents shape global patterns of temporal and spatial correlation scales in phytoplankton communities
•Introduction of ECCO2-Darwin, a new ocean biogeochemistry general circulation model.•Application of Green’s Functions to initialize high resolved biogeochemical models.•Air–sea CO2 fluxes provided ...for NASA’s Carbon Monitoring System Flux Pilot Project.•Description of residual problems of the ECCO2-Darwin estimates.
The NASA Carbon Monitoring System (CMS) Flux Project aims to attribute changes in the atmospheric accumulation of carbon dioxide to spatially resolved fluxes by utilizing the full suite of NASA data, models, and assimilation capabilities. For the oceanic part of this project, we introduce ECCO2-Darwin, a new ocean biogeochemistry general circulation model based on combining the following pre-existing components: (i) a full-depth, eddying, global-ocean configuration of the Massachusetts Institute of Technology general circulation model (MITgcm), (ii) an adjoint-method-based estimate of ocean circulation from the Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) project, (iii) the MIT ecosystem model “Darwin”, and (iv) a marine carbon chemistry model. Air–sea gas exchange coefficients and initial conditions of dissolved inorganic carbon, alkalinity, and oxygen are adjusted using a Green’s Functions approach in order to optimize modeled air–sea CO2 fluxes. Data constraints include observations of carbon dioxide partial pressure (pCO2) for 2009–2010, global air–sea CO2 flux estimates, and the seasonal cycle of the Takahashi et al. (2009) Atlas. The model sensitivity experiments (or Green’s Functions) include simulations that start from different initial conditions as well as experiments that perturb air–sea gas exchange parameters and the ratio of particulate inorganic to organic carbon. The Green’s Functions approach yields a linear combination of these sensitivity experiments that minimizes model-data differences. The resulting initial conditions and gas exchange coefficients are then used to integrate the ECCO2-Darwin model forward. Despite the small number (six) of control parameters, the adjusted simulation is significantly closer to the data constraints (37% cost function reduction, i.e., reduction in the model-data difference, relative to the baseline simulation) and to independent observations (e.g., alkalinity). The adjusted air–sea gas exchange parameter differs by only 3% from the baseline value and has little impact (−0.1%) on the cost function. The particulate inorganic to organic carbon ratio was increased more than threefold and reduced the cost function by 22% relative to the baseline integration, indicating a significant influence of biology on air–sea gas exchange. The largest contribution to cost reduction (35%) comes from the adjustment of initial conditions. In addition to reducing biases relative to observations, the adjusted simulation exhibits smaller model drift than the baseline. We estimate drift by integrating the model with repeated 2009 atmospheric forcing for seven years and find a volume-weighted drift reduction of, for example, 12.5% for nitrate and 30% for oxygen in the top 300 m. Although there remain several regions with large model-data discrepancies, for example, overly strong carbon uptake in the Southern Ocean, the adjusted simulation is a first step towards a more accurate representation of the ocean carbon cycle at high spatial and temporal resolution.
The electron‐accepting ability of 6,6‐dicyanopentafulvenes (DCFs) can be varied extensively through substitution on the five‐membered ring. The reduction potentials for a set of ...2,3,4,5‐tetraphenyl‐substituted DCFs, with varying substituents at the para‐position of the phenyl rings, strongly correlate with their Hammett σp‐parameters. By combining cyclic voltammetry with DFT calculations ((U)B3LYP/6‐311+G(d)), using the conductor‐like polarizable continuum model (CPCM) for implicit solvation, the absolute reduction potentials of a set of twenty DCFs were reproduced with a mean absolute deviation of 0.10 eV and a maximum deviation of 0.19 eV. Our experimentally investigated DCFs have reduction potentials within 3.67–4.41 eV, however, the computations reveal that DCFs with experimental reduction potentials as high as 5.3 eV could be achieved, higher than that of F4‐TCNQ (5.02 eV). Thus, the DCF core is a template that allows variation in the reduction potentials by about 1.6 eV.
Adjustable affinities: The reduction potentials of a group of 6,6‐dicyanopentafulvenes (DCFs) were determined both experimentally and computationally. A good agreement was found between experimentally determined reduction potentials and those calculated with inexpensive DFT methods, and were extrapolated to predict DCFs with exceptionally high reduction potentials. DCFs are thus shown to be a highly tunable electron‐accepting scaffold.
Neuronal health is essential for the long-term integrity of the brain. In this study, we characterized the novel E3 ubiquitin ligase ring finger protein 157 (RNF157), which displays a brain-dominant ...expression in mouse. RNF157 is a homolog of the E3 ligase mahogunin ring finger-1, which has been previously implicated in spongiform neurodegeneration. We identified RNF157 as a regulator of survival in cultured neurons and established that the ligase activity of RNF157 is crucial for this process. We also uncovered that independently of its ligase activity, RNF157 regulates dendrite growth and maintenance. We further identified the adaptor protein APBB1 (amyloid beta precursor protein-binding, family B, member 1 or Fe65) as an interactor and proteolytic substrate of RNF157 in the control of neuronal survival. Here, the nuclear localization of Fe65 together with its interaction partner RNA-binding protein SART3 (squamous cell carcinoma antigen recognized by T cells 3 or Tip110) is crucial to trigger apoptosis. In summary, we described that the E3 ligase RNF157 regulates important aspects of neuronal development.
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