Differentiating between apparent and actual rates of H2O2 metabolism by isolated rat muscle mitochondria to test a simple model of mitochondria as regulators of H2O2 concentration

AbstractMitochondria are often regarded as a major source of reactive oxygen species (ROS) in animal cells, with H2O2 being the predominant ROS released from mitochondria; however, it has been recently demonstrated that energized brain mitochondria may act as stabilizers of H2O2 concentration (Starkov et al. [1]) based on the balance between production and the consumption of H2O2, the later of which is a function of [H2O2] and follows first order kinetics. Here we test the hypothesis that isolated skeletal muscle mitochondria, from the rat, are able to modulate [H2O2] based upon the interaction between the production of ROS, as superoxide/H2O2, and the H2O2 decomposition capacity. The compartmentalization of detection systems for H2O2 and the intramitochondrial metabolism of H2O2 leads to spacial separation between these two components of the assay system. This results in an underestimation of rates when relying solely on extramitochondrial H2O2 detection. We find that differentiating between these apparent rates found when using extramitochondrial H2O2 detection and the actual rates of metabolism is important to determining the rate constant for H2O2 consumption by mitochondria in kinetic experiments. Using the high rate of ROS production by mitochondria respiring on succinate, we demonstrate that net H2O2 metabolism by mitochondria can approach a stable steady-state of extramitochondrial [H2O2]. Importantly, the rate constant determined by extrapolation of kinetic experiments is similar to the rate constant determined as the [H2O2] approaches a steady state

High-throughput optical absorption spectra for inorganic semiconductors

An optical absorption spectrum constitutes one of the most fundamental material characteristics, with relevant applications ranging from material identification to energy harvesting and optoelectronics. However, the database of both experimental and computational spectra is currently lacking. In this study, we designed a computational workflow for the optical absorption spectrum and integrated the simulated spectra into the Materials Project. Using density-functional theory, we computed the frequency-dependent dielectric function and the corresponding absorption coefficient for more than 1000 solid compounds of varying crystal structure and chemistry. The computed spectra show excellent agreement, as quantified by a high value of the Pearson correlation, with experimental results when applying the band gap correction from the HSE functional. The demonstrated calculated accuracy in the spectra suggests that the workflow can be applied in screening studies for materials with specific optical properties

A universal equivariant graph neural network for the elasticity tensors of any crystal system

The elasticity tensor that describes the elastic response of a material to external forces is among the most fundamental properties of materials. The availability of full elasticity tensors for inorganic crystalline compounds, however, is limited due to experimental and computational challenges. Here, we report the materials tensor (MatTen) model for rapid and accurate estimation of the full fourth-rank elasticity tensors of crystals. Based on equivariant graph neural networks, MatTen satisfies the two essential requirements for elasticity tensors: independence of the frame of reference and preservation of material symmetry. Consequently, it provides a universal treatment of elasticity tensors for all crystal systems across diverse chemical spaces. MatTen was trained on a dataset of first-principles elasticity tensors garnered by the Materials Project over the past several years (we are releasing the data herein) and has broad applications in predicting the isotropic elastic properties of polycrystalline materials, examining the anisotropic behavior of single crystals, and discovering new materials with exceptional mechanical properties. Using MatTen, we have discovered a hundred new crystals with extremely large maximum directional Young's modulus and eleven polymorphs of elemental cubic metals with unconventional spatial orientation of Young's modulus

Morphological differences between habitats are associated with physiological and behavioural trade-offs in stickleback (Gasterosteus aculeatus)

F.S. and A.J.W.W. were supported by the Australian Research Council, M.M.W. was supported by The University of St Andrews and R.S.J. and J.T. were supported by Coventry UniversityLocal specialization can be advantageous for individuals and may increase the resilience of the species to environmental change. However, there may be trade-offs between morphological responses and physiological performance and behaviour. Our aim was to test whether habitat-specific morphology of stickleback (Gasterosteus aculeatus) interacts with physiological performance and behaviour at different salinities. We rejected the hypothesis that deeper body shape of fish from habitats with high predation pressure led to decreases in locomotor performance. However, there was a trade-off between deeper body shape and muscle quality. Muscle of deeper-bodied fish produced less force than that of shallow-bodied saltmarsh fish. Nonetheless, saltmarsh fish had lower swimming performance, presumably because of lower muscle mass overall coupled with smaller caudal peduncles and larger heads. Saltmarsh fish performed better in saline water (20 ppt) relative to freshwater and relative to fish from freshwater habitats. However, exposure to salinity affected shoaling behaviour of fish from all habitats and shoals moved faster and closer together compared with freshwater. We show that habitat modification can alter phenotypes of native species, but local morphological specialization is associated with trade-offs that may reduce its benefits.Publisher PDFPeer reviewe

A representation-independent electronic charge density database for crystalline materials

In addition to being the core quantity in density functional theory, the charge density can be used in many tertiary analyses in materials sciences from bonding to assigning charge to specific atoms. The charge density is data-rich since it contains information about all the electrons in the system. With increasing utilization of machine-learning tools in materials sciences, a data-rich object like the charge density can be utilized in a wide range of applications. The database presented here provides a modern and user-friendly interface for a large and continuously updated collection of charge densities as part of the Materials Project. In addition to the charge density data, we provide the theory and code for changing the representation of the charge density which should enable more advanced machine-learning studies for the broader community

Proteomic profiling of urinary proteins in renal cancer by surface enhanced laser desorption ionisation (SELDI) and neural-network analysis: Identification of key issues affecting potential clinical utility.

Recent advances in proteomic profiling technologies, such as surface enhanced laser desorption ionization mass spectrometry, have allowed preliminary profiling and identification of tumor markers in biological fluids in several cancer types and establishment of clinically useful diagnostic computational models. There are currently no routinely used circulating tumor markers for renal cancer, which is often detected incidentally and is frequently advanced at the time of presentation with over half of patients having local or distant tumor spread. We have investigated the clinical utility of surface enhanced laser desorption ionization profiling of urine samples in conjunction with neural-network analysis to either detect renal cancer or to identify proteins of potential use as markers, using samples from a total of 218 individuals, and examined critical technical factors affecting the potential utility of this approach. Samples from patients before undergoing nephrectomy for clear cell renal cell carcinoma (RCC; n 48), normal volunteers (n 38), and outpatients attending with benign diseases of the urogenital tract (n 20) were used to successfully train neural-network models based on either presence/absence of peaks or peak intensity values, resulting in sensitivity and specificity values of 98.3–100%. Using an initial “blind” group of samples from 12 patients with RCC, 11 healthy controls, and 9 patients with benign diseases to test the models, sensitivities and specificities of 81.8–83.3% were achieved. The robustness of the approach was subsequently evaluated with a group of 80 samples analyzed “blind” 10 months later, (36 patients with RCC, 31 healthy volunteers, and 13 patients with benign urological conditions). However, sensitivities and specificities declined markedly, ranging from 41.0% to 76.6%. Possible contributing factors including sample stability, changing laser performance, and chip variability were examined, which may be important for the long-term robustness of such approaches, and this study highlights the need for rigorous evaluation of such factors in future studies