Biology subjects

Walton, L.

Publications and source records attributed to Walton, L..

3 recordsLinked to original sources

bioRxiv2022

C4 Grasses Employ Various Strategies to Acclimate Rubisco Activase to Heat Stress

1Rising temperatures due to the current climate crisis will have devastating impacts on crop performance and resilience in the near future. One key step that limits plant photosynthetic performance under higher temperatures is the activity of the thermolabile enzyme rubisco activase (RCA). RCA is highly conserved in photosynthetic organisms, including C4 crops such as Zea mays (maize) and Sorghum bicolor (sorghum) which are crucial components of global food supply and the bioenergy sector. While rubisco is the most abundant protein on earth and responsible for carbon fixation, RCA is an essential chaperone required to remove inhibitory sugar phosphates from the active site of rubisco to allow for continued CO2 fixation. We set out to understand temperature-dependent RCA regulation in four different C4 plants, with a focus on the crop plants maize (two cultivars) and sorghum, as well as the model grass Setaria viridis (setaria). Gas exchange measurements confirm that CO2 assimilation is indeed limited by Ribulose 1,5-bisphosphate (RuBP) carboxylation in these organisms and at high temperatures. All three species express distinct sets of RCA isoforms and each species alters the isoform and proteoform abundances in response to heat; however, the changes are species-specific. In order to understand how even subtle changes in the molecular environment of the chloroplast stroma affect RCA function during heat acclimation, we examined the regulation of RCA activity directly with respect thermostability, the ratio of ADP to ATP and the concentration of Mg2+ ions. As shown previously, the activity of RCA is modulated by a combination of these variables, but surprisingly, how these biochemical environment factors affect RCA function differs vastly between the different C4 species, and differences are even apparent between different cultivars within a single species, both with respect to proteoform abundance and regulation. Our results suggest that each grass evolved different parts of the RCA regulation portfolio and we conclude that a successful engineering approach aimed at improving carbon capture in C4 grasses will need to accommodate these individual regulatory mechanisms.

plant biology↗

bioRxiv2021

3D U-Net improves automatic brain extraction for isotropic rat brain MRI data

Brain extraction is a critical pre-processing step in brain magnetic resonance imaging (MRI) analytical pipelines. In rodents, this is often achieved by manually editing brain masks slice-by-slice, a time-consuming task where workloads increase with higher spatial resolution datasets. We recently demonstrated successful automatic brain extraction via a deep-learning-based framework, U-Net, using 2D convolutions. However, such an approach cannot make use of the rich 3D spatial-context information from volumetric MRI data. In this study, we advanced our previously proposed U-Net architecture by replacing all 2D operations with their 3D counterparts and created a 3D U-Net framework. We trained and validated our model using a recently released CAMRI rat brain database acquired at isotropic spatial resolution, including T2-weighted turbo-spin-echo structural MRI and T2*-weighted echo-planar-imaging functional MRI. The performance of our 3D U-Net model was compared with existing rodent brain extraction tools, including Rapid Automatic Tissue Segmentation (RATS), Pulse-Coupled Neural Network (PCNN), SHape descriptor selected External Regions after Morphologically filtering (SHERM), and our previously proposed 2D U-Net model. 3D U-Net demonstrated superior performance in Dice, Jaccard, Hausdorff distance, and sensitivity. Additionally, we demonstrated the reliability of 3D U-Net under various noise levels, evaluated the optimal training sample sizes, and disseminated all source codes publicly, with a hope that this approach will benefit rodent MRI research community. Significant methodological contributionWe proposed a deep-learning-based framework to automatically identify the rodent brain boundaries in MRI. With a fully 3D convolutional network model, 3D U-Net, our proposed method demonstrated improved performance compared to current automatic brain extraction methods, as shown in several qualitative metrics (Dice, Jaccard, PPV, SEN, and Hausdorff). We trust that this tool will avoid human bias and streamline pre-processing steps during 3D high resolution rodent brain MRI data analysis. The software developed herein has been disseminated freely to the community.

bioinformatics↗

bioRxiv2021

Simultaneous fMRI and fast-scan cyclic voltammetry bridges oxygenation and neurotransmitter dynamics across spatiotemporal scales

The vascular contributions of neurotransmitters to the hemodynamic response are gaining more attention in neuroimaging studies, as many neurotransmitters are vasomodulatory. To date, well-established electrochemical techniques that detect neurotransmission in high magnetic field environments are limited. Here, we propose an experimental setting enabling simultaneous fast-scan cyclic voltammetry (FSCV) and blood oxygenation-dependent functional magnetic imaging (BOLD fMRI) to measure both local tissue oxygen and dopamine responses, and global BOLD changes, respectively. By using MR-compatible materials and the proposed data acquisition schemes, FSCV detected physiological analyte concentrations with high spatiotemporal resolution inside of a 9.4 T MRI bore. We found that tissue oxygen and BOLD correlate strongly, and brain regions that encode dopamine amplitude differences can be identified via modeling simultaneously acquired dopamine FSCV and BOLD fMRI time-courses. This technique provides complementary neurochemical and hemodynamic information and expands the scope of studying the influence of local neurotransmitter release over the entire brain.

neuroscience↗