Comparison of the gut microbiota from soldier and worker castes of the termite Reticulitermes grassei

The bacterial microbiota from the whole gut of soldier and worker castes of the termite Reticulitermes grassei was isolated and studied. In addition, the 16S rDNA bacterial genes from gut DNA were PCR-amplified using Bacteria-selective primers, and the 16S rDNA amplicons subsequently cloned into Escherichia coli. Sequences of the cloned inserts were then used to determine closest relatives by comparison with published sequences and with sequences from our previous work. The clones were found to be affiliated with the phyla Spirochaetes, Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, Synergistetes, Verrucomicrobia, and candidate phyla Termite Group 1 (TG1) and Termite Group 2 (TG2). No significant differences were observed with respect to the relative bacterial abundances between soldier and worker phylotypes. The phylotypes obtained in this study were compared with reported sequences from other termites, especially those of phylotypes related to Spirochaetes, Wolbachia (an Alphaproteobacteria), Actinobacteria, and TG1. Many of the clone phylotypes detected in soldiers grouped with those of workers. Moreover, clones CRgS91 (soldiers) and CRgW68 (workers), both affiliated with"Endomicrobia", were the same phylotype. Soldiers and workers also seemed to have similar relative protist abundances. Heterotrophic, poly-β-hydroxyalkanoate-accumulating bacteria were isolated from the gut of soldiers and shown to be affiliated with Actinobacteria and Gammaproteobacteria. We noted that Wolbachia was detected in soldiers but not in workers. Overall, the maintenance by soldiers and workers of comparable axial and radial redox gradients in the gut is consistent with the similarities in the prokaryotes and protists comprising their microbiota.

Nutrimetabolomics fingerprinting to identify biomarkers of bread exposure in a free-living population from the PREDIMED study cohort

Bread is one of the most widely consumed foods. Its impact on human health is currently of special interest for researchers. We aimed to identify biomarkers of bread consumption by applying a nutrimetabolomic approach to a free-living population. An untargeted HPLC q-TOF-MS and multivariate analysis was applied to human urine from 155 subjects stratified by habitual bread consumption in three groups: non-consumers of bread (n = 56), white-bread consumers (n = 48) and whole-grain bread consumers (n = 51). The most differential metabolites (variable importance for projection ≥1.5) included compounds originating from cereal plant phytochemicals such as benzoxazinoids and alkylresorcinol metabolites, and compounds produced by gut microbiota (such as enterolactones, hydroxybenzoic and dihydroferulic acid metabolites). Pyrraline, riboflavin, 3-indolecarboxylic acid glucuronide, 2,8-dihydroxyquinoline glucuronide and N-α-acetylcitrulline were also tentatively identified. In order to combine multiple metabolites in a model to predict bread consumption, a stepwise logistic regression analysis was used. Receiver operating curves were constructed to evaluate the global performance of individual metabolites and their combination. The area under the curve values [AUC (95 % CI)] of combined models ranged from 77.8 % (69.1 86.4 %) to 93.7 % (89.4 98.1 %), whereas the AUC for the metabolites included in the models had weak values when they were evaluated individually: from 58.1 % (46.6 69.7 %) to 78.4 % (69.8 87.1 %). Our study showed that a daily bread intake significantly impacted on the urinary metabolome, despite being examined under uncontrolled free-living conditions. We further concluded that a combination of several biomarkers of exposure is better than a single biomarker for the predictive ability of discriminative analysis.

Nutrimetabolomics fingerprinting to identify biomarkers of bread exposure in a free-living population from the PREDIMED study cohort

Bread is one of the most widely consumed foods. Its impact on human health is currently of special interest for researchers. We aimed to identify biomarkers of bread consumption by applying a nutrimetabolomic approach to a free-living population. An untargeted HPLC q-TOF-MS and multivariate analysis was applied to human urine from 155 subjects stratified by habitual bread consumption in three groups: non-consumers of bread (n = 56), white-bread consumers (n = 48) and whole-grain bread consumers (n = 51). The most differential metabolites (variable importance for projection ≥1.5) included compounds originating from cereal plant phytochemicals such as benzoxazinoids and alkylresorcinol metabolites, and compounds produced by gut microbiota (such as enterolactones, hydroxybenzoic and dihydroferulic acid metabolites). Pyrraline, riboflavin, 3-indolecarboxylic acid glucuronide, 2,8-dihydroxyquinoline glucuronide and N-α-acetylcitrulline were also tentatively identified. In order to combine multiple metabolites in a model to predict bread consumption, a stepwise logistic regression analysis was used. Receiver operating curves were constructed to evaluate the global performance of individual metabolites and their combination. The area under the curve values [AUC (95 % CI)] of combined models ranged from 77.8 % (69.1 86.4 %) to 93.7 % (89.4 98.1 %), whereas the AUC for the metabolites included in the models had weak values when they were evaluated individually: from 58.1 % (46.6 69.7 %) to 78.4 % (69.8 87.1 %). Our study showed that a daily bread intake significantly impacted on the urinary metabolome, despite being examined under uncontrolled free-living conditions. We further concluded that a combination of several biomarkers of exposure is better than a single biomarker for the predictive ability of discriminative analysis.