The intestinal microbiota is known to regulate host energy homeostasis and can be influenced by highcalorie diets. However, changes affecting the ecosystem at the functional level are still not well characterized. We measured shifts in cecal bacterial communities in mice fed a carbohydrate or high-fat (HF) diet for 12 weeks at the level of the following: (i) diversity and taxa distribution by high-throughput 16S ribosomal RNA gene sequencing; (ii) bulk and single-cell chemical composition by Fourier-transform infrared-(FT-IR) and Raman micro-spectroscopy and (iii) metaproteome and metabolome via highresolution mass spectrometry. High-fat diet caused shifts in the diversity of dominant gut bacteria and altered the proportion of Ruminococcaceae (decrease) and Rikenellaceae (increase). FT-IR spectroscopy revealed that the impact of the diet on cecal chemical fingerprints is greater than the impact of microbiota composition. Diet-driven changes in biochemical fingerprints of members of the Bacteroidales and Lachnospiraceae were also observed at the level of single cells, indicating that there were distinct differences in cellular composition of dominant phylotypes under different diets. Metaproteome and metabolome analyses based on the occurrence of 1760 bacterial proteins and 86 annotated metabolites revealed distinct HF diet-specific profiles. Alteration of hormonal and anti-microbial networks, bile acid and bilirubin metabolism and shifts towards amino acid and simple sugars metabolism were observed. We conclude that a HF diet markedly affects the gut bacterial ecosystem at the functional level.
Intestinal bacteria influence mammalian physiology, but many types of bacteria are still uncharacterized. Moreover, reference strains of mouse gut bacteria are not easily available, although mouse models are extensively used in medical research. These are major limitations for the investigation of intestinal microbiomes and their interactions with diet and host. It is thus important to study in detail the diversity and functions of gut microbiota members, including those colonizing the mouse intestine. To address these issues, we aimed at establishing the Mouse Intestinal Bacterial Collection (miBC), a public repository of bacterial strains and associated genomes from the mouse gut, and studied host-specificity of colonization and sequence-based relevance of the resource. The collection includes several strains representing novel species, genera and even one family. Genomic analyses showed that certain species are specific to the mouse intestine and that a minimal consortium of 18 strains covered 50-75% of the known functional potential of metagenomes. The present work will sustain future research on microbiota-host interactions in health and disease, as it will facilitate targeted colonization and molecular studies. The resource is available at www.dsmz.de/miBC.
Fourier-transform infrared (FT-IR) microspectroscopy was used in this study to identify yeasts. Cells were grown to microcolonies of 70 to 250 m in diameter and transferred from the agar plate by replica stamping to an IR-transparent ZnSe carrier. IR spectra of the replicas on the carrier were recorded using an IR microscope coupled to an IR spectrometer, and identification was performed by comparison to reference spectra. The method was tested by using small model libraries comprising reference spectra of 45 strains from 9 genera and 13 species, recorded with both FT-IR microspectroscopy and FT-IR macrospectroscopy. The results show that identification by FT-IR microspectroscopy is equivalent to that achieved by FT-IR macrospectroscopy but the time-consuming isolation of the organisms prior to identification is not necessary. Therefore, this method also provides a rapid tool to analyze mixed populations. Furthermore, identification of 21 Debaryomyces hansenii and 9 Saccharomyces cerevisiae strains resulted in 92% correct identification at the strain level for S. cerevisiae and 91% for D. hansenii, which demonstrates that the resolution power of FT-IR microspectroscopy may also be used for yeast typing at the strain level.Traditional identification of yeasts is achieved by applying physiological and morphological tests, which determine enzyme production profiles and growth characteristics (5, 16). Various rapid and in some cases automated identification systems for routine analyses of yeasts are commercially available and easy to use. However, identification results are questionable to a certain degree because these systems originally were developed for clinical application and databases therefore do not include an adequate number of common environmental yeast species (10,27). In recent years many techniques that identify yeasts according to the fatty acid composition of the cell membrane (3, 6) or genotypic characteristics such as temperature gradient gel electrophoresis (14), electrophoretic karyotyping (28), restriction fragment length polymorphism (12, 25) and randomly amplified polymorphic DNA (1,4,25) restriction enzyme analysis of PCR-amplified rDNA (11) or mitochondrial DNA (24), RNA probes (15,18,23), and rDNA sequencing (7, 29) have been developed. However, their application in the routine analysis of yeasts in the food industry is limited by their high cost and the requirement for highly skilled personnel.A rapid and very inexpensive method to identify microorganisms is Fourier-transform infrared (FT-IR) spectroscopy (13,19). Absorption of infrared light by cellular compounds results in a fingerprint-like spectrum that can be identified by comparison to reference spectra. Once an extensive and well designed database of reference spectra is established, reliable identification results are obtained within 25 h of starting the identification from a single colony (17,21,22).FT-IR microspectroscopy is a novel tool to characterize microorganisms (20). Microcolonies grown on a solid medium are transferred fr...
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