Impacts of maternal microbiota and microbial metabolites on fetal intestine, brain, and placenta

BACKGROUND

The maternal microbiota modulates fetal development, but the mechanisms of these earliest host-microbe interactions are unclear. To investigate the developmental impacts of maternal microbial metabolites, we compared full-term fetuses from germ-free and specific pathogen-free mouse dams by gene expression profiling and non-targeted metabolomics.

RESULTS

In the fetal intestine, critical genes mediating host-microbe interactions, innate immunity, and epithelial barrier were differentially expressed. Interferon and inflammatory signaling genes were downregulated in the intestines and brains of the fetuses from germ-free dams. The expression of genes related to neural system development and function, translation and RNA metabolism, and regulation of energy metabolism were significantly affected. The gene coding for the insulin-degrading enzyme (Ide) was most significantly downregulated in all tissues. In the placenta, genes coding for prolactin and other essential regulators of pregnancy were downregulated in germ-free dams. These impacts on gene expression were strongly associated with microbially modulated metabolite concentrations in the fetal tissues. Aryl sulfates and other aryl hydrocarbon receptor ligands, the trimethylated compounds TMAO and 5-AVAB, Glu-Trp and other dipeptides, fatty acid derivatives, and the tRNA nucleobase queuine were among the compounds strongly associated with gene expression differences. A sex difference was observed in the fetal responses to maternal microbial status: more genes were differentially regulated in male fetuses than in females.

CONCLUSIONS

The maternal microbiota has a major impact on the developing fetus, with male fetuses potentially more susceptible to microbial modulation. The expression of genes important for the immune system, neurophysiology, translation, and energy metabolism are strongly affected by the maternal microbial status already before birth. These impacts are associated with microbially modulated metabolites. We identified several microbial metabolites which have not been previously observed in this context. Many of the potentially important metabolites remain to be identified.

Associations of altered hepatic gene expression in American lifestyle-induced obesity syndrome diet-fed mice with metabolic changes during NAFLD development and progression

Non-alcoholic fatty liver disease (NAFLD) pathogenesis remains poorly understood due to the complex metabolic and inflammatory changes in the liver. This study aimed to elucidate hepatic events related to inflammation and lipid metabolism and their linkage with metabolic alterations during NAFLD in American lifestyle-induced obesity syndrome (ALIOS) diet-fed mice. Forty-eight C57BL/6J male mice were fed with ALIOS diet (n=24) or control chow diet (n=24) for 8, 12, and 16 weeks. At the end of each timepoint, eight mice were sacrificed where plasma and liver were collected. Hepatic fat accumulation was followed using magnetic resonance imaging and confirmed with histology. Further, targeted gene expression and non-targeted metabolomics analysis were conducted. Our results showed higher hepatic steatosis, body weight, energy consumption, and liver mass in ALIOS diet-fed mice compared to control mice. ALIOS diet altered expression of genes related to inflammation (Tnfa and IL-6) and lipid metabolism (Cd36, Fasn, Scd1, Cpt1a, and Ppara). Metabolomics analysis indicated decrease of lipids containing polyunsaturated fatty acids such as LPE(20:5) and LPC(20:5) with increase of other lipid species such as LPI(16:0) and LPC(16:2) and peptides such as alanyl-phenylalanine and glutamyl-arginine. We further observed novel correlations between different metabolites including sphingolipid, lysophospholipids, peptides, and bile acid with inflammation, lipid uptake and synthesis. Together with the reduction of antioxidant metabolites and gut microbiota-derived metabolites contribute to NAFLD development and progression. The combination of non-targeted metabolomics with gene expression in future studies can further identify key metabolic routes during NAFLD which could be the targets of potential novel therapeutics.

Identification and Distribution of Sterols, Bile Acids, and Acylcarnitines by LC-MS/MS in Humans, Mice, and Pigs-A Qualitative Analysis

Sterols, bile acids, and acylcarnitines are key players in human metabolism. Precise annotations of these metabolites with mass spectrometry analytics are challenging because of the presence of several isomers and stereoisomers, variability in ionization, and their relatively low concentrations in biological samples. Herein, we present a sensitive and simple qualitative LC–MS/MS (liquid chromatography with tandem mass spectrometry) method by utilizing a set of pure chemical standards to facilitate the identification and distribution of sterols, bile acids, and acylcarnitines in biological samples including human stool and plasma; mouse ileum, cecum, jejunum content, duodenum content, and liver; and pig bile, proximal colon, cecum, heart, stool, and liver. With this method, we detected 24 sterol, 32 bile acid, and 27 acylcarnitine standards in one analysis that were separated within 13 min by reversed-phase chromatography. Further, we observed different sterol, bile acid, and acylcarnitine profiles for the different biological samples across the different species. The simultaneous detection and annotation of sterols, bile acids, and acylcarnitines from reference standards and biological samples with high precision represents a valuable tool for screening these metabolites in routine scientific research.

Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds

Prediction of retention times (RTs) is increasingly considered in untargeted metabolomics to complement MS/MS matching for annotation of unidentified peaks. We tested the performance of PredRet (http://predret.org/) to predict RTs for plant food bioactive metabolites in a data sharing initiative containing entry sets of 29-103 compounds (totalling 467 compounds, >30 families) across 24 chromatographic systems (CSs). Between 27 and 667 predictions were obtained with a median prediction error of 0.03-0.76 min and interval width of 0.33-8.78 min. An external validation test of eight CSs showed high prediction accuracy. RT prediction was dependent on shape and type of LC gradient, and number of commonly measured compounds. Our study highlights PredRet's accuracy and ability to transpose RT data acquired from one CS to another CS. We recommend extensive RT data sharing in PredRet by the community interested in plant food bioactive metabolites to achieve a powerful community-driven open-access tool for metabolomics annotation.

Mass spectrometry-based analysis of whole-grain phytochemicals

Whole grains are a rich source of several classes of phytochemicals, such as alkylresorcinols, benzoxazinoids, flavonoids, lignans, and phytosterols. A high intake of whole grains has been linked to a reduced risk of some major noncommunicable diseases, and it has been postulated that a complex mixture of phytochemicals works in synergy to generate beneficial health effects. Mass spectrometry, especially when coupled with liquid chromatography, is a widely used method for the analysis of phytochemicals owing to its high sensitivity and dynamic range. In this review, the current knowledge of the mass spectral properties of the most important classes of phytochemicals found in cereals of common wheat, barley, oats, and rye is discussed.