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At least 145 records · Page 8

The NIH Somatic Cell Genome Editing program

The move from reading to writing the human genome offers new opportunities to improve human health. The United States National Institutes of Health (NIH) Somatic Cell Genome Editing (SCGE) Consortium aims to accelerate the development of safer and more-effective methods to edit the genomes of disease-relevant somatic cells in patients, even in tissues that are difficult to reach. Here we discuss the consortium’s plans to develop and benchmark approaches to induce and measure genome modifications, and to define downstream functional consequences of genome editing within human cells. Central to this effort is a rigorous and innovative approach that requires validation of the technology through third-party testing in small and large animals. New genome editors, delivery technologies and methods for tracking edited cells in vivo, as well as newly developed animal models and human biological systems, will be assembled—along with validated datasets—into an SCGE Toolkit, which will be disseminated widely to the biomedical research community. We visualize this toolkit—and the knowledge generated by its applications—as a means to accelerate the clinical development of new therapies for a wide range of conditions.

59 BASIC BIOLOGICAL SCIENCES↗

Graph Metric Learning Quantifies Morphological Differences between Two Genotypes of Shoot Apical Meristem Cells in Arabidopsis

We present a method for learning “spectrally descriptive” edge weights for graphs. We generalize a previously known distance measure on graphs (Graph Diffusion Distance), thereby allowing it to be tuned to minimize an arbitrary loss function. Because all steps involved in calculating this modified GDD are differentiable, we demonstrate that it is possible for a small neural network model to learn edge weights which minimize loss. We apply this method to discriminate between graphs constructed from shoot apical meristem images of two genotypes of Arabidopsis thaliana specimens: wild-type and trm678 triple mutants with cell division phenotype. Training edge weights and kernel parameters with contrastive loss produces a learned distance metric with large margins between these graph categories. We demonstrate this by showing improved performance of a simple k-nearest-neighbors classifier on the learned distance matrix. We also demonstrate a further application of this method to biological image analysis. Once trained, we use our model to compute the distance between the biological graphs and a set of graphs output by a cell division simulator. Comparing simulated cell division graphs to biological ones allows us to identify simulation parameter regimes which characterize mutant vs. wild-type Arabidopsis cells. We find that trm678 mutant cells are characterized by increased randomness of division planes and decreased ability to avoid previous vertices between cell walls.

59 BASIC BIOLOGICAL SCIENCES↗

Calculation of Heavy Ion Inactivation and Mutation Rates in Radial Dose Model of Track Structure

In the track structure model, the inactivation cross section is found by summing an inactivation probability over all impact parameters from the ion to the sensitive sites within the cell nucleus. The inactivation probability is evaluated by using the dose response of the system to gamma rays and the radial dose of the ions and may be equal to unity at small impact parameters. We apply the track structure model to recent data with heavy ion beams irradiating biological samples of E. Coli, B. Subtilis spores, and Chinese hamster (V79) cells. Heavy ions have observed cross sections for inactivation that approach and sometimes exceed the geometric size of the cell nucleus. We show how the effects of inactivation may be taken into account in the evaluation of the mutation cross sections in the track structure model through correlation of sites for gene mutation and cell inactivation. The model is fit to available data for HPRT (hypoxanthine guanine phosphoribosyl transferase) mutations in V79 cells, and good agreement is found. Calculations show the high probability for mutation by relativistic ions due to the radial extension of ions track from delta rays. The effects of inactivation on mutation rates make it very unlikely that a single parameter such as LET (linear energy transfer) can be used to specify radiation quality for heavy ion bombardment.

Cucinotta, Francis A.↗

Integrins, tensegrity, and mechanotransduction

Physical forces, such as those due to gravity, play an important role in tissue development and remodeling. Yet, little is known about how individual cells sense mechanical signals or how they transduce them into a chemical response. Rather than listing the numerous signal pathways that have been found to be sensitive to mechanical stimulation, we need to place potential molecular signaling mechanisms within the context of the entire cell. The model presented is based on the concept that cells use tensegrity architecture to organize their cytoskeleton and stabilize their form. Studies with stick and string tensegrity cell models predict that living cells are hard-wired to respond immediately to external mechanical stresses. This hard-wiring exists in the form of discrete cytoskeletal filament networks that mechanically couple specific cell surface receptors, such as integrins, to nuclear matrix scaffolds and to potential transducing molecules that physically associate with the cytoskeleton. If these signaling molecules do function in a "solid-state", then mechanical stresses may be transduced into biochemical responses through force-dependent changes in cytoskeletal geometry or through local alterations in thermodynamic or kinetic parameters. Changes in cytoskeletal tension (prestress) also may play a role in signal amplification and adaptation. Recent experimental results are described which provide direct support for the tensegrity theory.

Review↗

Kinetics Modeling and Reactor Design Study of Glucose-to-Terpenes Cell-Free Conversion

Cell-free systems offer many advantages over traditional biological conversion by eliminating biological growth constraints. It also offers easy manipulation and finetuning of the reaction conditions for each individual enzyme. The conversion of cellulosic glucose to Limonene, a terpene, is a promising pathway for producing fuels and chemicals. Recent advances in developing cell-free systems focuses on bench scale optimization of terpene yield and to demonstrate its feasibility towards commercialization [1,2]. There is significant knowledge gap regarding reaction kinetics of these cell-free systems to further study how it will perform at larger scale. We present here, our studies on reaction kinetics and reactor design implications of cell-free glucose to Limonene conversion to facilitate the further development and commercialization of this process. We developed a novel kinetic model based on the metabolic-network structure of the cell-free system with multi-substrate reversible Michaelis-Menten rate law. To estimate kinetic parameters for this system of rate equations, we employed Bayesian optimization to perform global search with the assistance of gaussian processes to balance exploration and exploitation. The model parameters estimated showed good results compared with experimental data. The estimated parameters were used to perform sensitivity analysis. We found that Hexokinase is one of the most critical enzymes that affect the conversion of the glucose. We also observed that abundance of co-factors is also critical to the conversion of glucose to limonene. We investigated packed bed reactors with enzymes immobilized on the surface of particles to convert glucose stream into Limonene for larger scale production. The reactor design such as particle size, enzyme loading, and flow rate are found to be critical for improving yields. [1] Dudley, Q.M., Nash, C.J. and Jewett, M.C., 2019. Synthetic Biology, 4(1), p.ysz003. [2] Korman, T.P., Opgenorth, P.H. and Bowie, J.U., 2017. Nature communications, 8(1), p.15526.

09 BIOMASS FUELS↗

RWPV bioreactor mass transport: earth-based and in microgravity

Mass transport and mixing of perfused scalar quantities in the NASA Rotating Wall Perfused Vessel bioreactor are studied using numerical models of the flow field and scalar concentration field. Operating conditions typical of both microgravity and ground-based cell cultures are studied to determine the expected vessel performance for both flight and ground-based control experiments. Results are presented for the transport of oxygen with cell densities and consumption rates typical of colon cancer cells cultured in the RWPV. The transport and mixing characteristics are first investigated with a step change in the perfusion inlet concentration by computing the time histories of the time to exceed 10% inlet concentration. The effects of a uniform cell utilization rate are then investigated with time histories of the outlet concentration, volume average concentration, and volume fraction starved. It is found that the operating conditions used in microgravity produce results that are quite different then those for ground-based conditions. Mixing times for microgravity conditions are significantly shorter than those for ground-based operation. Increasing the differential rotation rates (microgravity) increases the mixing and transport, while increasing the mean rotation rate (ground-based) suppresses both. Increasing perfusion rates enhances mass transport for both microgravity and ground-based cases, however, for the present range of operating conditions, above 5-10 cc/min there are diminishing returns as much of the inlet fluid is transported directly to the perfusion exit. The results show that exit concentration is not a good indicator of the concentration distributions in the vessel. In microgravity conditions, the NASA RWPV bioreactor with the viscous pump has been shown to provide an environment that is well mixed. Even when operated near the theoretical minimum perfusion rates, only a small fraction of the volume provides less than the required oxygen levels. 2002 Wiley Periodicals, Inc.

Models, Biological↗

The August Krogh principle applies to plants

The Krogh principle refers to the use of a large number of animals to study the large number of physiological problems, rather than limiting study to a particular organism for all problems. There may be organisms that are more suited to study of a particular problem than others. This same principle applies to plants. The authors are concerned with the recent trend in plant biology of using Arabidopsis thaliana as the "organism of choice." Arabidopsis is an excellent organism for molecular genetic research, but other plants are superior models for other research areas of plant biology. The authors present examples of the successful use of the Krogh principle in plant cell biology research, emphasizing the particular characteristics of the selected research organisms that make them the appropriate choice.

NASA Discipline Plant Biology↗

Tracks to therapy

Studies of the structure of particle tracks have led to models of track effects based on radial dose and radiobiological target theory that have been very successful in describing and predicting track effects in physical, chemical, and biological systems. For describing mammalian cellular inactivation two inactivation modes are required, called gamma-kill and ion-kill, the first due to synergistic effects of delta rays from adjacent ion paths thus resembling the effects from gamma rays, and the second to the effects of single ion transits through a cell nucleus. The ion-kill effect is more severe, where the fraction of cells experiencing ion kill is responsible for a decrease in the oxygen enhancement ratio, and an increase in relative biological effectiveness, but these are accompanied by loss of repair, hence to a reduction in the efficiency of fractionation in high LET therapy, as shown by our calculations for radiobiological effects in the "spread out Bragg Peak".

Review, Tutorial↗

Redox-mediated activation of latent transforming growth factor-beta 1

Transforming growth factor beta 1 (TGF beta) is a multifunctional cytokine that orchestrates response to injury via ubiquitous cell surface receptors. The biological activity of TGF beta is restrained by its secretion as a latent complex (LTGF beta) such that activation determines the extent of TGF beta activity during physiological and pathological events. TGF beta action has been implicated in a variety of reactive oxygen-mediated tissue processes, particularly inflammation, and in pathologies such as reperfusion injury, rheumatoid arthritis, and atherosclerosis. It was recently shown to be rapidly activated after in vivo radiation exposure, which also generates reactive oxygen species (ROS). In the present studies, the potential for redox-mediated LTGF beta activation was investigated using a cell-free system in which ROS were generated in solution by ionizing radiation or metal ion-catalyzed ascorbate reaction. Irradiation (100 Gray) of recombinant human LTGF beta in solution induced 26% activation compared with that elicited by standard thermal activation. Metal-catalyzed ascorbate oxidation elicited extremely efficient recombinant LTGF beta activation that matched or exceeded thermal activation. The efficiency of ascorbate activation depended on ascorbate concentrations and the presence of transition metal ions. We postulate that oxidation of specific amino acids in the latency-conferring peptide leads to a conformation change in the latent complex that allows release of TGF beta. Oxidative activation offers a novel route for the involvement of TGF beta in tissue processes in which ROS are implicated and endows LTGF beta with the ability to act as a sensor of oxidative stress and, by releasing TGF beta, to function as a signal for orchestrating the response of multiple cell types. LTGF beta redox sensitivity is presumably directed toward recovery of homeostasis; however, oxidation may also be a mechanism of LTGF beta activation that can be deleterious during disease mechanisms involving chronic ROS production.

Non-NASA Center↗

Dynamics and growth rate implications of ribosomes and mRNAs interaction in E. coli

Understanding how cells grow and adapt under various nutrient conditions is pivotal in the study of biological stoichiometry. Recent studies provide empirical evidence that cells use multiple strategies to maintain an optimal protein production rate under different nutrient conditions. Mathematical models can provide a solid theoretical foundation that can explain experimental observations and generate testable hypotheses to further our understanding of the growth process. In this study, we generalize a modeling framework that centers on the translation process and study its asymptotic behaviors to validate algebraic manipulations involving the steady states. Using experimental results on the growth of E. coli under C-, N-, and P-limited environments, we simulate the expected quantitative measurements to show the feasibility of using the model to explain empirical evidence. Our results support the findings that cells employ multiple strategies to maintain a similar protein production rate across different nutrient limitations. Moreover, we find that the previous study underestimates the significance of certain biological rates, such as the binding rate of ribosomes to mRNA and the transition rate between different ribosomal stages. Furthermore, our simulation shows that the strategies used by cells under C- and P-limitations result in a faster overall growth dynamics than under N-limitation. In conclusion, the general modeling framework provides a valuable platform to study cell growth under different nutrient supply conditions, which also allows straightforward extensions to the coupling of transcription, translation, and energetics to deepen our understanding of the growth process.

59 BASIC BIOLOGICAL SCIENCES↗

Accelerating Biological Insight for Understudied Genes

Synopsis The rapid expansion of genome sequence data is increasing the discovery of protein-coding genes across all domains of life. Annotating these genes with reliable functional information is necessary to understand evolution, to define the full biochemical space accessed by nature, and to identify target genes for biotechnology improvements. The majority of proteins are annotated based on sequence conservation with no specific biological, biochemical, genetic, or cellular function identified. Recent technical advances throughout the biological sciences enable experimental research on these understudied protein-coding genes in a broader collection of species. However, scientists have incentives and biases to continue focusing on well documented genes within their preferred model organism. This perspective suggests a research model that seeks to break historic silos of research bias by enabling interdisciplinary teams to accelerate biological functional annotation. We propose an initiative to develop coordinated projects of collaborating evolutionary biologists, cell biologists, geneticists, and biochemists that will focus on subsets of target genes in multiple model organisms. Concurrent analysis in multiple organisms takes advantage of evolutionary divergence and selection, which causes individual species to be better suited as experimental models for specific genes. Most importantly, multisystem approaches would encourage transdisciplinary critical thinking and hypothesis testing that is inherently slow in current biological research.

Zoology↗

A down-to-earth model of gravisensing

The statolith hypothesis for gravity sensing is the most accepted model of gravity sensing in plants. However, the lukewarm acceptance of the statolith hypothesis among plant physiologists is most likely due to inertia and not due to any one experiment done over the past one hundred years that unambiguously pitted the statolith hypothesis against any of the competing hypotheses. Here we emphasize the weaknesses of the statolith hypothesis and suggest that the gravitational pressure hypothesis is superior in describing gravisensing in all plant cells.

Non-NASA Center↗

Fundamental space radiobiology

The unique feature of the space radiation environment is the dominance of high-energy charged particles (HZE or high LET radiation) emitted by the Sun and galactic sources, or trapped in the Van Allen radiation belts. These charged particles present a significant hazard to space flight crews, and accelerator-based experiments are underway to quantify the health risks due to unavoidable radiation exposure. There are three principal properties of charged particles that distinguish them from conventional radiation, i.e. gamma rays and x-rays. First, they have a defined range in matter rather than an exponential absorption profile. Second, they undergo nuclear reactions to produce secondary particles. Third, and most important, they deposit their energy along well-defined linear paths or tracks rather than diffuse fields. The structured energy deposition pattern interacts on multiple scales with the biological structures of DNA, cells and tissues to produce correlated patterns of damage that evade repair systems. Traditional concepts of dose and its associated normalization parameter, RBE (relative biological effectiveness), break down under experimental scrutiny, and probabilistic models of risk based on the number of particle traversals per cell may be more appropriate. Unique patterns of DNA damage, gene expression, mobilization of repair proteins, activation of cytokines and remodeling of cellular microenvironment are observed following exposure to high LET radiation. At low levels of exposure the communication of bioactive substances from irradiated to unirradiated "bystander" cells can amplify the damage and cause a significant deviation from linearity in dose vs. response relations. Under some circumstances, there is even a multigenerational delay in the expression of radiation-induced genetic damage (genomic instability) which is not strictly dose dependent. These issues and the experimental evidence derived from ground based experiments at particle accelerators are presented along with speculation about how modified inertial conditions might perturb homeostatic responses to radiation to further complicate risk assessment for space flight.

NASA Discipline Radiation Health↗

Multi-level Force-dependent Allosteric Enhancement of αE-catenin Binding to F-actin by Vinculin

Classical cadherins are transmembrane proteins whose extracellular domains link neighboring cells, and whose intracellular domains connect to the actin cytoskeleton via β-catenin and α-catenin. The cadherin-catenin complex transmits forces that drive tissue morphogenesis and wound healing. In addition, tension-dependent changes in αE-catenin conformation enables it to recruit the actin-binding protein vinculin to cell-cell junctions, which contributes to junctional strengthening. How and whether multiple cadherin-complexes cooperate to reinforce cell-cell junctions in response to load remains poorly understood. Here, we used single-molecule optical trap measurements to examine how multiple cadherin-catenin complexes interact with F-actin under load, and how this interaction is influenced by the presence of vinculin. We show that force oriented toward the (-) end of the actin filament results in mean lifetimes 3-fold longer than when force was applied towards the barbed (+) end. We also measured force-dependent actin binding by a quaternary complex comprising the cadherin-catenin complex and the vinculin head region, which cannot itself bind actin. Binding lifetimes of this quaternary complex increased as additional complexes bound F-actin, but only when load was oriented toward the (-) end. In contrast, the cadherin-catenin complex alone did not show this form of cooperativity. Furthermore, these findings reveal multi-level, force-dependent regulation that enhances the strength of the association of multiple cadherin/catenin complexes with F-actin, conferring positive feedback that may strengthen the junction and polarize F-actin to facilitate the emergence of higher-order cytoskeletal organization.

59 BASIC BIOLOGICAL SCIENCES↗

Motility Analysis of T-Cell Histories in Activation (Matcha) v0.1.0

Despite advances in microscopy, imaging techniques only capture a very small spatial and temporal fraction of T cell activity within a tissue. For this reason, we have pursued the development of a virtual T cell model. Matcha's objective is to design virtual T cells that move like biological T cells. The virtual T cells will match the speed and turning angle distributions of biological cells. In addition, Matcha will also attempt to match the volume patrolled by the biological cells and the dependence on speed and turning angle on the previous speed.

Rouson, Damian↗

A microstructural approach to cytoskeletal mechanics based on tensegrity

Mechanical properties of living cells are commonly described in terms of the laws of continuum mechanics. The purpose of this report is to consider the implications of an alternative approach that emphasizes the discrete nature of stress bearing elements in the cell and is based on the known structural properties of the cytoskeleton. We have noted previously that tensegrity architecture seems to capture essential qualitative features of cytoskeletal shape distortion in adherent cells (Ingber, 1993a; Wang et al., 1993). Here we extend those qualitative notions into a formal microstructural analysis. On the basis of that analysis we attempt to identify unifying principles that might underlie the shape stability of the cytoskeleton. For simplicity, we focus on a tensegrity structure containing six rigid struts interconnected by 24 linearly elastic cables. Cables carry initial tension ("prestress") counterbalanced by compression of struts. Two cases of interconnectedness between cables and struts are considered: one where they are connected by pin-joints, and the other where the cables run through frictionless loops at the junctions. At the molecular level, the pinned structure may represent the case in which different cytoskeletal filaments are cross-linked whereas the looped structure represents the case where they are free to slip past one another. The system is then subjected to uniaxial stretching. Using the principal of virtual work, stretching force vs. extension and structural stiffness vs. stretching force relationships are calculated for different prestresses. The stiffness is found to increase with increasing prestress and, at a given prestress, to increase approximately linearly with increasing stretching force. This behavior is consistent with observations in living endothelial cells exposed to shear stresses (Wang & Ingber, 1994). At a given prestress, the pinned structure is found to be stiffer than the looped one, a result consistent with data on mechanical behavior of isolated, cross-linked and uncross-linked actin networks (Wachsstock et al., 1993). On the basis of our analysis we concluded that architecture and the prestress of the cytoskeleton might be key features that underlie a cell's ability to regulate its shape.

NASA Discipline Cell Biology↗

Examining metastatic behavior within 3D bioprinted vasculature for the validation of a 3D computational flow model

Understanding the dynamics of circulating tumor cell (CTC) behavior within the vasculature has remained an elusive goal in cancer biology. To elucidate the contribution of hydrodynamics in determining sites of CTC vascular colonization, the physical forces affecting these cells must be evaluated in a highly controlled manner. To this end, we have bioprinted endothelialized vascular beds and perfused these constructs with metastatic mammary gland cells under physiological flow rates. By pairing these in vitro devices with an advanced computational flow model, we found that the bioprinted analog was readily capable of evaluating the accuracy and integrated complexity of a computational flow model, while also highlighting the discrete contribution of hydrodynamics in vascular colonization. This intersection of these two technologies, bioprinting and computational simulation, is a key demonstration in the establishment of an experimentation pipeline for the understanding of complex biophysical events.

42 ENGINEERING↗

Studying Host-Pathogen Interactions In 3-D: Organotypic Models For Infectious Disease And Drug Development

Representative, reproducible and high-throughput models of human cells and tissues are critical for a meaningful evaluation of host-pathogen interactions and are an essential component of the research developmental pipeline. The most informative infection models - animals, organ explants and human trials - are not suited for extensive evaluation of pathogenesis mechanisms and screening of candidate drugs. At the other extreme, more cost effective and accessible infection models such as conventional cell culture and static co-culture may not capture physiological and three-dimensional aspects of tissue biology that are important in assessing pathogenesis, and effectiveness and cytotoxicity of therapeutics. Our lab has used innovative bioengineering technology to establish biologically meaningful 3-D models of human tissues that recapitulate many aspects of the differentiated structure and function of the parental tissue in vivo, and we have applied these models to study infectious disease. We have established a variety of different 3-D models that are currently being used in infection studies - including small intestine, colon, lung, placenta, bladder, periodontal ligament, and neuronal models. Published work from our lab has shown that our 3-D models respond to infection with bacterial and viral pathogens in ways that reflect the infection process in vivo. By virtue of their physiological relevance, 3-D cell cultures may also hold significant potential as models to provide insight into the neuropathogenesis of HIV infection. Furthermore, the experimental flexibility, reproducibility, cost-efficiency, and high throughput platform afforded by these 3-D models may have important implications for the design and development of drugs with which to effectively treat neurological complications of HIV infection.

Nickerson, Cheryl A.↗