Monitoring type 2 diabetes from volatile faecal metabolome in cushing’s syndrome and single Afmid mouse models via a longitudinal study

The analysis of volatile organic compounds (VOCs) as a non-invasive method for disease monitoring, such as type 2 diabetes (T2D) has shown potential over the years although not yet set in clinical practice. Longitudinal studies to date are limited and the understanding of the underlying VOC emission over the age is poorly understood. This study investigated longitudinal changes in VOCs present in faecal headspace in two mouse models of T2D – Cushing’s syndrome and single Afmid knockout mice. Longitudinal changes in bodyweight, blood glucose levels and plasma insulin concentration were also reported. Faecal headspace analysis was carried out using selected ion flow tube mass spectrometry (SIFT-MS) and thermal desorption coupled to gas chromatography-mass spectrometry (TD-GC-MS). Multivariate data analysis of the VOC profile showed differences mainly in acetic acid and butyric acid able to discriminate the groups Afmid and Cushing’s mice. Moreover, multivariate data analysis revealed statistically significant differences in VOCs between Cushing’s mice/wild-type (WT) littermates, mainly short-chain fatty acids (SCFAs), ketones, and alcohols, and longitudinal differences mainly attributed to methanol, ethanol and acetone. Afmid mice did not present statistically significant differences in their volatile faecal metabolome when compared to their respective WT littermates. The findings suggested that mice developed a diabetic phenotype and that the altered VOC profile may imply a related change in gut microbiota, particularly in Cushing’s mice. Furthermore, this study provided major evidence of age-related changes on the volatile profile of diabetic mice

Additional file 1: of Chronic Rho-kinase inhibition improves left ventricular contractile dysfunction in early type-1 diabetes by increasing myosin cross-bridge extension

Supplementary material including interstitial fibrosis scores and cardiomyocyte cross-sectional areas, and Western blot data for Rho-kinase and RhoA from experiment 2 are presented. Also included are relative phosphorylation states of myofilament proteins, rate of change of myosin mass transfer and the correlation between dP/dtminimum and ED intensity ratio

ST131GBK

Collection of annotated genomes used in the analysis of this stud

Maximum likelihood phylogeny of 228 <i>E</i>. <i>coli</i> ST131 isolates.

<p>Strains isolated from dogs and cats (domesticated animals), wild birds (avian), and cattle (livestock) are indicated by colour coding at the tips of the tree, with all other strains not colour coded being human isolates. Clades A, B and C are indicated by colour coding of the branches. The large black circles indicate statistically significant inferences of host jumps or ecological adaptations within the phylogeny as detected by AdaptML. The grey circles indicate phylogenetic inferences with > 99% bootstrap support. The names of the taxa match those in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006280#pgen.1006280.s009" target="_blank">S1 Table</a>.</p

Maximum likelihood phylogeny of the ST131 core genome, with gene regulatory region allele profiles overlaid.

<p>Clades A, B and C are colour coded by branch (blue, cyan, and magenta respectively). The gene regulatory region allele profiles are presented as a heatmap (red = high identity to blue = low identity) of pairwise Spearman correlations of the regulatory region alleles between each strain, such that warmer colours indicate subsets of isolates with substantially more similar regulatory region alleles between them than on average between randomly chosen isolates. The colour coding to the right indicates the accessory genome cluster of each strain as determined by Kpax2.</p

Maximum likelihood phylogeny of the ST131 core genome, with the accessory genome profile overlaid.

<p>Clades A, B and C are colour coded by branch (blue, cyan, and magenta respectively). The accessory genome is presented as a heatmap (red = high identity to blue = low identity) of pairwise Spearman correlations of the accessory gene content between each strain, such that warmer colours indicate subsets of isolates with substantially more similar gene content between them than on average between randomly chosen isolates. The colour coding to the right indicates the accessory genome cluster of each strain as determined by Kpax2.</p