A Comparison Of Fecal Glucocorticoid Metabolite Concentration And Gut Microbiota Diversity In Bonobos (Pan paniscus)

Host sex, age, diet, stress, and social environment have all been found to influence the gut microbiota. In non-human primates (NHP), recent evidence from gorillas found fecal glucocorticoid metabolite concentration (FGMC) had no significant role in structuring their gut microbiota, but there was a significant differential abundance between family Anaerolineaceae and gorilla FGMC. This pattern has yet to be examined in other NHP, like bonobos (Pan paniscus). We compared FGMC to 16S rRNA amplicons for 201 bonobo fecal samples collected in the wild across five months to evaluate the impact of stress, measured with FGMC, on the gut microbiota. Simpsons index was the only alpha diversity index to have a significant linear relationship with FGMC [R2 = 0.9643, F(4, 210) = 28.56, p = 0.0023]. FGMC level explained 1.63% of the variation in beta diversity for Jensen-Shannon Distance, 2.49% for Weighted UniFrac, and 3.53% for Unweighted UniFrac using PERMANOVAs. Differential abundance models showed seventeen taxa that were significantly correlated with FGMC. We found that genus SHD-231 in the family Anaerolinaceae was significant in our differential abundance model results, similar to western lowland gorilla abundance model results. These results suggest bonobos exhibit different patterns than gorillas in alpha and beta diversity measures and that members of the family Anaerolinaceae may be differentially affected by host stress across great apes. Incorporating FGMC into gut microbiota research can provide a more robust understanding of how stress impacts the gut microbiota of primates and humans and has important ties to overall host health.

Corticosterone Metabolite Concentration Is Not Related to Problem Solving in the Fawn-Footed Mosaic-Tailed Rat Melomys cervinipes

Animals can respond physiologically, such as by adjusting glucocorticoid hormone concentrations, to sudden environmental challenges. These physiological changes can then affect behavioural and cognitive responses. While the relationships between adrenocortical activity and behaviour and cognition are well documented, results are equivocal, suggesting species-specific responses. We investigated whether adrenocortical activity, measured using corticosterone metabolite concentration, was related to problem solving in an Australian rodent, the fawn-footed mosaic-tailed rat (Melomys cervinipes). Mosaic-tailed rats live in complex environments that are prone to disturbance, suggesting a potential need to solve novel problems, and have been found to show relationships between physiology and other behaviours. We measured problem solving using five food-baited puzzles (matchbox and cylinder in the home cage, and activity board with pillars to push, tiles to slide and levers to lift in an open field), and an escape-motivated obstruction task in a light/dark box. Faecal samples were collected from individuals during routine cage cleaning. Adrenocortical activity was evaluated non-invasively by measuring faecal corticosterone metabolites using an enzyme immunoassay, which was biochemically and biologically validated. Despite varying over time, adrenocortical activity was not significantly related to problem solving success or time spent interacting for any task. However, as adrenocortical activity is reflective of multiple physiological processes, including stress and metabolism, future studies should consider how other measures of physiology are also linked to problem solving.

Comparison of seven modelling algorithms for GABA-edited 1H-MRS

Edited MRS sequences are widely used for studying GABA in the human brain. Several algorithms are available for modelling these data, deriving metabolite concentration estimates through peak fitting or a linear combination of basis spectra. The present study compares seven such algorithms, using data obtained in a large multi-site study. GABA-edited (GABA+, TE = 68 ms MEGA-PRESS) data from 222 subjects at 20 sites were processed via a standardised pipeline, before modelling with FSL-MRS, Gannet, AMARES, QUEST, LCModel, Osprey and Tarquin, using standardised vendor-specific basis sets (for GE, Philips and Siemens) where appropriate. After referencing metabolite estimates (to water or creatine), systematic differences in scale were observed between datasets acquired on different vendors' hardware, presenting across algorithms. Scale differences across algorithms were also observed. Using the correlation between metabolite estimates and voxel tissue fraction as a benchmark, most algorithms were found to be similarly effective in detecting differences in GABA+. An inter-class correlation across all algorithms showed single-rater consistency for GABA+ estimates of around 0.38, indicating moderate agreement. Upon inclusion of a basis set component explicitly modelling the macromolecule signal underlying the observed 3.0 ppm GABA peaks, single-rater consistency improved to 0.44. Correlation between discrete pairs of algorithms varied, and was concerningly weak in some cases. Our findings highlight the need for consensus on appropriate modelling parameters across different algorithms, and for detailed reporting of the parameters adopted in individual studies to ensure reproducibility and meaningful comparison of outcomes between different studies.

Longitudinal Evaluation of Magnetic Resonance Spectroscopy Metabolites as Biomarkers in Huntingtons Disease

Magnetic resonance spectroscopy (MRS) is a non-invasive method of exploring cerebral metabolism. In Huntingtons disease, altered MRS-determined concentrations of several metabolites have been described; however, findings are often discrepant and longitudinal studies of metabolite trajectory are lacking. MRS metabolites may represent a valuable source of biomarkers, thus their relationship with established biofluid and structural imaging markers of disease progression require further exploration to assess prognostic value and elucidate biochemical pathways associated with neurodegeneration. In a prospective single-site controlled cohort study with standardised collection of CSF, blood, phenotypic and imaging data, we used MRS to evaluate metabolic profiles in the putamen of 56 participants at baseline (15 healthy controls, 15 premanifest and 26 manifest gene expansion carriers) and at 2-year follow-up. Intergroup differences and associations with established measures were assessed cross-sectionally using generalized linear models and partial correlation, controlling for age and CAG repeat length. We report no significant groupwise differences in metabolite concentration but found several metabolites to be associated with measures of disease progression; however, only two relationships were replicated across both time points, with total Creatine (creatine + phosphocreatine) and myo-inositol displaying significant associations with reduced caudate volume. Although relationships were observed between MRS metabolites and biofluid measures, these were not consistent across time points. To further assess prognostic value of the metabolites, we examined whether baseline MRS values, or rate of change, predicted subsequent change in established measures of disease progression. Several associations were found but were inconsistent across known indicators of disease progression. Finally, longitudinal mixed effects models, controlling for age, revealed no significant change in metabolite concentration over time in gene expansion carriers. Altogether, our findings show some interesting cross-sectional associations between select metabolites, namely total creatine and myo-inositol, and markers of disease progression, potentially highlighting the proposed roles of neuroinflammation and metabolic dysfunction in disease pathogenesis. However, the absence of group differences, inconsistency between baseline and follow-up, and lack of clear longitudinal change over two years suggests that MRS metabolites have limited potential as biomarkers in Huntingtons disease.

Clarifying intercellular signalling in yeast: Saccharomyces cerevisiae does not undergo a quorum sensing-dependent switch to filamentous growth

Saccharomyces cerevisiae can alter its morphology to a filamentous form associated with unipolar budding in response to environmental stressors. Induction of filamentous growth is suggested under nitrogen deficiency in response to alcoholic signalling molecules through a quorum sensing mechanism. To investigate this claim, we analysed the budding pattern of S. cerevisiae cells over time under low nitrogen while concurrently measuring cell density and extracellular metabolite concentration. We found that the proportion of cells displaying unipolar budding increased between local cell densities of 4.8x106 and 5.3x107 cells/ml within 10 to 20 hours of growth. However, the observed increase in unipolar budding could not be reproduced when cells were prepared at the critical cell density and in conditioned media. Removing the nutrient restriction by growth in high nitrogen conditions also resulted in an increase in unipolar budding between local cell densities of 5.2x106 and 8.2x107 cells/ml within 10 to 20 hours of growth, but there were differences in metabolite concentration compared to the low nitrogen conditions. This suggests that neither cell density, metabolite concentration, nor nitrogen deficiency were necessary or sufficient to increase the proportion of unipolar budding cells. It is therefore unlikely that quorum sensing is the mechanism controlling the switch to filamentous growth in S. cerevisiae. Only a high concentration of the putative signalling molecule, 2-phenylethanol resulted in an increase in unipolar budding, but this concentration was not physiologically relevant. We suggest that the compound 2-phenylethanol acts through a toxicity mechanism, rather than quorum sensing, to induce filamentous growth.

Determination of Cell Volume as an Integral Part of Metabolomics

Cells regulate their cell volume, but cell volumes may change in response to metabolic and other perturbations. Many metabolomics experiments use cultured cells to measure changes in metabolites in response to physiological and other experimental perturbations, but the metabolomics workflow by mass spectrometry only determines total metabolite amounts in cell culture extracts. To convert metabolite amount to metabolite concentration requires knowledge of the number and volume of the cells. Measuring only metabolite amount can lead to incorrect or skewed results in cell culture experiments because cell size may change due to experimental conditions independent of change in metabolite concentration. We have developed a novel method to determine cell volume in cell culture experiments using a pair of stable isotopically labeled phenylalanine internal standards incorporated within the normal liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics workflow. This method relies on the flooding-dose technique where the intracellular concentration of a particular compound (in this case phenylalanine) is forced to equal its extracellular concentration. We illustrate the LC-MS/MS technique for two different mammalian cell lines. Although the method is applicable in general for determining cell volume, the major advantage of the method is its seamless incorporation within the normal metabolomics workflow.

Evaluation of BOLD effects in the rat cortex

Purpose: This study aimed to characterize Blood oxygen level-dependent (BOLD) effects in 1H- MR spectra obtained during optogenetic activation of the rat forelimb cortex for the correction and estimation of accurate metabolite concentration changes. Methods : T2*-induced effects were characterized by linewidth changes and amplitude changes of water, NAA and tCr spectral peaks during the stimulation paradigm. Spectral linewidth-matching procedures were used to correct for the line-narrowing effect induced by BOLD. For an increased understanding of spectroscopic BOLD effects and the optimized way to correct them, a 1 Hz line-narrowing effect was also simulated on mouseproton MR spectrum1H-fMRS data acquired using STEAM acquisitions at 9.4T in rats (n=8) upon optogenetic stimulation of the primary somatosensory cortex were used. Data were analyzed with MATLAB routines and LCModel. Uncorrected and corrected 1H-MR spectra of simulated and in-vivo data were quantified and compared. BOLD-corrected difference spectra were also calculated and analyzed. Results: Significant mean increases in water and NAA peak heights (+ 1.1% and +4.5%, respectively) were found accompanied by decreased linewidths (-0.5 Hz and -2.8%) upon optogenetic stimulation. These estimates were used for further definition of an accurate line-broadening factor (lb). Usage of an erroneous lb introduced false-positive errors in metabolite concentration change estimates thereby altering the specificity of findings. Using different water scalings within LCModel, the water and metabolite BOLD contributions were separated. Conclusion : The linewidth-matching procedure using a precise lb factor remains the most performant approach for the accurate quantification of small (0.3 micromol/g) metabolic changes in 1H-fMRS studies. A simple and preliminary compartmentation of BOLD effects was proposed, which will require validation.