Dataset Information

A method to disentangle and quantify host anabolic turnover in photosymbiotic holobionts with subcellular resolution.

ABSTRACT: A wide range of organisms host photosynthesizing symbionts. In these animals the metabolic exchange between host and symbionts has prevented in situ host anabolic turnover to be studied without the confounding effect of translocated photosynthates. Using the symbiotic coral Stylophora pistillata as a model organism and [1-¹³C]-pyruvate and [2,3-¹³C]-pyruvate in different incubation conditions (light, light?+?DCMU, and darkness), we employed NanoSIMS isotopic imaging to quantify host anabolism, with and without translocated metabolites from their photosynthesizing dinoflagellate symbionts. Under our experimental conditions, host de novo lipid synthesis accounted for ~40% of the total holobiont lipid reserve, and dinoflagellate recycling of metabolic ¹³CO₂ enhanced host tissue ¹³C-enrichment by 13-22% in the epidermis, 40-58% in the gastrodermis, and 135-169% in host lipid bodies. Furthermore, we show that host anabolic turnover in different tissue structures differs, in a manner consistent with the localisation, function and cellular composition of these structures.

SUBMITTER: Gibbin E

PROVIDER: S-EPMC6949218 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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A method to disentangle and quantify host anabolic turnover in photosymbiotic holobionts with subcellular resolution.

Gibbin Emma E Banc-Prandi Guilhem G Fine Maoz M Comment Arnaud A Meibom Anders A

Communications biology 20200108 1

A wide range of organisms host photosynthesizing symbionts. In these animals the metabolic exchange between host and symbionts has prevented in situ host anabolic turnover to be studied without the confounding effect of translocated photosynthates. Using the symbiotic coral Stylophora pistillata as a model organism and [1-<sup>13</sup>C]-pyruvate and [2,3-<sup>13</sup>C]-pyruvate in different incubation conditions (light, light + DCMU, and darkness), we employed NanoSIMS isotopic imaging to quan ...[more]

PMID: 31925332

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Project description:The diffusion-weighted magnetic resonance imaging (dMRI) signal measured in vivo arises from multiple diffusion domains, including hindered and restricted water pools, free water and blood pseudo-diffusion. Not accounting for the correct number of components can bias metrics obtained from model fitting because of partial volume effects that are present in, for instance, diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). Approaches that aim to overcome this shortcoming generally make assumptions about the number of considered components, which are not likely to hold for all voxels. The spectral analysis of the dMRI signal has been proposed to relax assumptions on the number of components. However, it currently requires a clinically challenging signal-to-noise ratio (SNR) and accounts only for two diffusion processes defined by hard thresholds. In this work, we developed a method to automatically identify the number of components in the spectral analysis, and enforced its robustness to noise, including outlier rejection and a data-driven regularization term. Furthermore, we showed how this method can be used to take into account partial volume effects in DTI and DKI fitting. The proof of concept and performance of the method were evaluated through numerical simulations and in vivo MRI data acquired at 3 T. With simulations our method reliably decomposed three diffusion components from SNR = 30. Biases in metrics derived from DTI and DKI were considerably reduced when components beyond hindered diffusion were taken into account. With the in vivo data our method determined three macro-compartments, which were consistent with hindered diffusion, free water and pseudo-diffusion. Taking free water and pseudo-diffusion into account in DKI resulted in lower mean diffusivity and higher fractional anisotropy values in both gray and white matter. In conclusion, the proposed method allows one to determine co-existing diffusion compartments without prior assumptions on their number, and to account for undesired signal contaminations within clinically achievable SNR levels.

Dataset Information

A method to disentangle and quantify host anabolic turnover in photosymbiotic holobionts with subcellular resolution.

Publications

A method to disentangle and quantify host anabolic turnover in photosymbiotic holobionts with subcellular resolution.

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