ABSTRACT The intestinal microbiota is known to regulate host energy homeostasis and can be influenced by high-calorie 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 high-resolution mass spectrometry. High-fat diet caused shifts in the diversity of dominant gut bacteria and altered the

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. SIMILAR CONTENT BEING VIEWED BY OTHERS

TIME-RESTRICTED FEEDING INDUCES _LACTOBACILLUS_- AND _AKKERMANSIA_-SPECIFIC FUNCTIONAL CHANGES IN THE RAT FECAL MICROBIOTA Article Open access 03 December 2021 INTERMITTENT FASTING

MODULATES THE INTESTINAL MICROBIOTA AND IMPROVES OBESITY AND HOST ENERGY METABOLISM Article Open access 07 April 2023 REDUCED CALORIE DIET COMBINED WITH NNMT INHIBITION ESTABLISHES A

DISTINCT MICROBIOME IN DIO MICE Article Open access 10 January 2022 INTRODUCTION Over the last century, the field of clinical microbiology has been driven by the study of pathogens (Raoult

et al., 2004), but recently, the importance of commensal microorganisms that colonize various body habitats has been brought to light (Lepage et al., 2013). In particular, the gut microbial

ecosystem has emerged as an important factor regulating host health and the onset of chronic diseases such as inflammatory bowel diseases, allergies and obesity (Blaut and Clavel, 2007;

Delzenne and Cani, 2011; Kau et al., 2011; Hörmannsperger et al., 2012). A proof of the causative role of gut microbes in influencing host metabolism was provided by the observation that

transfer of gut microbiota from obese donor mice to germfree mice fed a standard diet promoted adiposity (Backhed et al., 2004; Turnbaugh et al., 2009). Nonetheless, the quantitative

contribution of the gut microbiota to host energy balance remains elusive. Jumpertz et al., (2011), recently proposed that an increased energy harvest of _ca._ 150 kcal is associated with an

increase of 20% in the sequence occurrence of _Firmicutes_ and a corresponding decrease in the _Bacteroidetes_ in humans, although the impact of high inter-individual differences in the

percentage of energy lost in stools have been discussed (Heymsfield and Pietrobelli, 2011). Driven by the popularization of DNA sequencing-based approaches, many studies have described

changes in gut bacterial diversity and composition after ingestion of high-energy diets (Cani et al., 2008; Turnbaugh et al., 2008; Fleissner et al., 2010). However, the consequences of such

changes in bacterial diversity on the function of the ecosystem are still unclear. Major advances in the assessment of microbial gene occurrence by large-scale metagenomic sequencing have

shed light on the genomic potential of the gut microbiota and have indicated possible changes in microbial activity related to diet and metabolic disorders (Turnbaugh et al., 2006; Qin et

al., 2012). Nonetheless, direct proofs of changes in activity and function of the ecosystem in response to dietary challenge are urgently required. Therefore, in the present work, we used a

combination of high-resolution spectroscopic and mass spectrometric techniques for in-depth characterization of the cecal ecosystem in mice. We thereby provide novel insights into

biochemical alterations of the gut microbiota in response to a high-fat (HF) diet. MATERIALS AND METHODS ANIMALS AND SAMPLES All procedures were conducted according to the German guidelines

for animal care and approved by the state ethics committee (ref. no. 209.1/211-2531-41/03). The design of mouse trials has been described elsewhere (Desmarchelier et al., 2012, 2013).

Details are given in the Supplementary Methods. Male C57BL/6NCrl mice (_n_=6 per group) were fed an experimental carbohydrate (CARB) or HF diet for 12 weeks (Table 1). The data presented in

this paper were obtained in the course of four feeding trials with exactly the same design (Supplementary Figure S1). In trial 1–3, after cecal weight determination, the content was divided

into two portions that were snap frozen in liquid nitrogen. In trial 4, cecal contents were used in their entirety in order to obtain sufficient starting material (metaproteome via LC-MS/MS,

_n_=4; metabolome via Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS), _n_=3). Experiments with germfree mice were performed as explained in the Supplementary

Methods. HIGH-THROUGHPUT SEQUENCING Cecal samples were analyzed by sequencing the V4 region (233 bp) of 16S ribosomal RNA (rRNA) genes in paired-end modus using the MiSeq system (Illumina,

San Diego, CA, USA). Detailed instructions are given in the Supplementary Methods. The first 10 and last 20 nucleotides of all reads were trimmed using the NGS-QC toolkit (New Dehli, India)

(Patel and Jain, 2012) to avoid GC bias and non-random base composition as well as low sequence quality at 3′-end. Reads were assembled using Pandaseq with a minimum overlap of 35 bp

(Masella et al., 2012). Sequences were further analyzed using the open source software package QIIME (Boulder, CO, USA) (Caporaso et al., 2010) and the Ribosomal Database Project (East

Lansing, MI, USA) (Cole et al., 2003). Filtering parameters were as follows: minimum Phred score, 20; minimum number of high-quality calls, 0.65; maximum number of consecutive low-quality

base calls, 5. Operational taxonomic units were picked against the Greengenes database (Berkeley, CA, USA) at a threshold of 97% similarity and those occurring in less than three mice and

with a total number of less than three sequences were excluded from the analysis. FOURIER-TRANSFORM INFRARED SPECTROSCOPY In Fourier-transform infrared (FT-IR) spectroscopy, samples are

excited by an infrared beam and transmitted light is recorded, resulting in spectra showing at which wavelengths samples absorb light, depending on the nature of covalent bounds. Thereby,

FT-IR spectroscopy gives information on the overall biochemical composition of microbial cells, and can be a useful tool for the identification of pure cultures (Wenning et al., 2006).

Saline solution (0.9% NaCl in water) was used for sample preparation by centrifugation to obtain cecal microbial pellets (Supplementary Methods). Re-suspended pellets in saline solution

(referred to as cecal suspensions hereon) were analyzed by transmission using a TENSOR 27 spectrometer coupled with a HTS-XT high-throughput device (Bruker Optics, Ettlingen, Germany). The

spectrum of each sample was computed from 32 scans. Spectral similarities were assessed by hierarchical cluster analysis using the OPUS software version 6.5 (Bruker). CONFOCAL RAMAN

MICROSPECTROSCOPY Fluorescence _in situ_ hybridization (FISH) was used to identify target populations for Raman microspectroscopic analysis, which utilizes the principle of Raman scattering

to chemically fingerprint individual microbial cells (Huang et al., 2007). The probes used in the present study were Bac-0303 5′-CCA ATG TGG GGG ACC TT-3′ (Manz et al., 1996) and Erec-0482

5′-CGC GGC ATT GCT CGT TCA-3′ (Franks et al., 1998) (Thermo Fisher Scientific, Vienna, Austria). Cecal samples that had been fixed in 4% paraformaldehyde were hybridized on aluminum slides

using a previously described hybridization protocol (Berry et al., 2012). Spectra of cells from target populations were acquired using a LabRAM HR800 confocal Raman microscope (Horiba

Jobin-Yvon, Munich, Germany) equipped with a 532 nm Nd:YAG laser as described previously (Haider et al., 2010). Raman spectra were baseline corrected and mean normalized in R using the

package ‘baseline’ (Liland and Mevik, Norway) (Lieber and Mahadevan-Jansen, 2003). Machine-learning classification of spectra was performed with the ‘randomForest’ package (Liaw and Wieners

(2002); Merck Research Laboratories, Whitehouse Station, NJ, USA) in R and plotted using non-metric multidimensional scaling. PROTEIN IDENTIFICATION BY LIQUID CHROMATOGRAPHY AND TANDEM MASS

SPECTROMETRY Cecal samples were prepared by centrifugation as for FT-IR spectroscopy and microbial pellets were lysed mechanically in the presence of protease inhibitors (Supplementary

Methods). After reduction with 10 mM dithiothreitol (10 min, 95 °C) and alkylation with 50 mM iodoacetamide (30 min, room temperature), proteins were separated on 4–12% NuPAGE Bis-Tris gels

(Invitrogen, Darmstadt, Germany; cat. no. NP0321BOX) and stained with colloidal Coomassie. The complete protein separation lane of each sample was cut into 12 equal gel pieces (Supplementary

Figure S2) and in-gel digestion was performed with sequencing grade trypsin (Promega) (Shevchenko et al., 1996). Peptides were measured using an Eksigent nanoLC-Ultra 1D Plus (Eksigent,

Dublin, CA, USA) coupled to a LTQ Orbitrap Velos (Thermo Scientific, Bremen, Germany), as described in detail in the Supplementary Methods. METAPROTEOME DATA ANALYSIS Peak picking and

processing of raw MS data was performed as in the Supplementary Methods. To minimize the number of hypothetical proteins, spectra were first searched against a compiled database comprising

81 well-annotated genomes recovered from the Integrated Microbial Genomes website of the Joint Genome Institute (Supplementary Methods). Confounder proteins not expected to be present in the

cecal samples were added to the database to ensure specificity. Unmatched spectra were subsequently searched against the entire NCBI database (download date 10/26/2011). For all eight

samples (each 12 gel pieces), matched spectra obtained from both database searches were compiled using Scaffold version 3.3.1 (Proteome Software, Portland, OR, USA). Threshold parameters

were as follows: protein probability, 95%; minimum number of peptides, 1; peptide probability, 95%. Protein abundances were estimated using NSAF (normalized spectral abundance factor) values

calculated from the spectral counts of each individual identified protein (Zybailov et al., 2006). Briefly, in order to account for the fact that larger proteins tend to contribute more

peptides or spectra, spectral counts were divided by protein length to provide spectral abundance factors. These factors were normalized against the sum of all spectral abundance factor

values in the corresponding run, allowing comparison of protein levels across different runs. NSAF was used as quantitative measure of protein abundances for subsequent statistical analyses.

UniProt accession numbers were obtained using the ID mapping function at www.uniprot.org. Protein sequences were downloaded via batch retrieval at the Protein Information Resource website.

Protein sequences were assigned to Clusters of Orthologous Groups (COG) by performing a BLASTP (Altschul et al., 1990) against the COG database (Tatusov et al., 2003). BLAST results were

further parsed for best hit with an e-value lower than 1e−5. HIGH-RESOLUTION METABOLOMICS Cecal samples were prepared by solid-phase extraction as described in the Supplementary Methods.

Ultrahigh resolution mass spectra were acquired using a SOLARIX FT-ICR-MS (Bruker Daltonik GmbH, Bremen, Germany), equipped with a 12 Tesla superconducting magnet and an Apollo II ESI

source. FT-ICR-MS settings are given in the Supplementary Methods. Samples were measured in parallel in positive and negative ESI mode due to ionization and detection of different compounds.

Raw spectra were processed with Data Analysis Version 4.0 SP2 (Bruker Daltonik GmbH). They were calibrated internally using reference lists of known masses with an error below 0.1 p.p.m.

Calibrated spectra were exported to .asc files with a signal-to-noise ratio of 4. The acquired peak lists were aligned by means of in-house software with an error p.p.m. of 1. The aligned

data matrix was filtered and only masses detected in at least two of the three biological replicates were kept for subsequent data processing. The filtered matrix was analyzed using

Hierarchical Clustering Explorer (College Park, MD, USA) (Seo and Shneiderman, 2002) for unsupervised multivariate data analysis and MultiExperiment Viewer (Saeed et al., 2006) for

calculation of significant masses by two-tailed Student’s _t_-test (adjusted _P_-value<0.01). Possible metabolite identities of significant masses were assigned using the MassTRIX web

server (Suhre and Schmitt-Kopplin, 2008) with a maximum error of 1 p.p.m. for both ionization modes. Masses were searched against the KEGG (Kyoto Encyclopedia of Genes and Genomes) (Kanehisa

and Goto, 2000), HMDB (Human Metabolome Database) (Wishart et al., 2007) and Lipid Maps (www.lipidmaps.org) databases using _Mus musculus_ as reference organism. STATISTICS Unless otherwise

stated, statistical tests were done using the R programming environment. For all tests, the bilateral alpha risk was α=0.05. Data were expressed as mean±s.d. and tested for normal

distribution and equality of variances before statistical testing. Non-parametric data were analyzed using the Mann–Whitney U test. The Benjamini–Hochberg procedure was used for multiple

testing corrections. For the metaproteomic NSAF data set, the Bioconductor package PLGEM (Power Law Global Error Model) (Pavelka et al., 2004) was used to fit a PLGEM to the NSAF data set.

It has been shown that the use of PLGEM-based standard deviations to calculate signal-to-noise ratios in a NSAF data set improves determination of protein expression changes, as it is more

conservative with proteins of low abundance than proteins with high abundance. The goodness-of-fit of the model to the NSAF data and the relevant algorithmic details of the PLGEM method are

explained in detail elsewhere (Pavelka et al., 2008). Principal component analysis of exported normalized FT-IR spectra was done using SIMCA-P 12.0.1. RESULTS HIGH-FAT DIET INDUCED OBESITY

AND ALTERED MICROBIAL DIVERSITY AND COMPOSITION The HF diet caused (i) an increase in body weight over the 12-week-long feeding trial (CARB, 29.9±0.63 g; HF, 43.2±4.4 g) (Supplementary

Figure S3), (ii) fasting hyperglycemia (Supplementary Table S1) and (iii) a marked reduction in mean cecal mass (tissue plus content): CARB, 342±36 mg; HF, 223±37 mg (_P_<0.001;

_t_-test). To assess the impact of experimental feeding on dominant bacterial communities, we sequenced V4 amplicons of 16S rRNA genes. After trimming, assembly and quality filtering, we

obtained a total of 82 698 sequences (6892±2419 per sample) of 233 bp length. The HF diet did not significantly affect taxa richness (Shannon diversity index: CARB, 5.37±0.63; HF 4.96±0.67)

(Supplementary Figure S4). _Beta_-diversity analysis (unweighted Unifrac) showed that samples clustered according to diet, although intra-group variations were high (Figure 1a). As expected,

phylum-level composition was dominated by members of the _Firmicutes_ (71 to 98% of total sequences) and _Bacteroidetes_ (1–16%) (Supplementary Table S2). Despite marked inter-individual

differences at the family level, sequence proportions were significantly lower for _Ruminococcaceae_ (phylum _Firmicutes_) and higher for _Rikenellaceae_ (phylum _Bacteroidetes_) in HF mice

(Figure 1b and Supplementary Table S3). Lactobacilli were detected in higher proportions in mice fed the HF diet (4–29%), but one control mouse had also a very high proportion (49%).

Proportions of _Erysipelotrichales_ were higher in three of six HF mice compared with CARB mice, reaching up to 43% in one HF mouse (Supplementary Table S3). The occurrence of 19 dominant

operational taxonomic units (OTUs) was significantly affected by the HF diet (Supplementary Table S4). Most of them belonged to the order _Clostridiales_ and their sequence numbers were

lower in HF mice. In agreement with the aforementioned results, HF mice were characterized by increased numbers of two OTUs within the genus _Alistipes_ (a genus in the _Rikenellaceae_ with

up to 3.5% total sequences). Other major OTUs with a higher prevalence in HF mice included one dominant member of the _Clostridium_ cluster XIVa (0.01 vs 4.33% in HF mice) and two

_Clostridium_ species. In order to determine if the HF diet causes not only alterations in the composition of the microbiota but also changes to the biochemical environment and microbiota

activity in the gut, we performed spectroscopic and mass spectrometric analyses of cecal samples. CECAL AND SINGLE-CELL CHEMICAL FINGERPRINTS WERE DIET-SPECIFIC To examine the effect of

diets on chemical fingerprints in the cecum, samples were analyzed using FT-IR spectroscopy. Cluster analysis of FT-IR spectra revealed a clear separation of chemical fingerprints according

to the diet (Figure 2a). There was a clear correlation between peak height and dietary composition in region 2 and 4, which corresponds to lipids and carbohydrates, respectively (Figure 2b).

The carbohydrate content was markedly reduced in HF mice, accompanied by a reduced amount of water probably related to reduction in hydration water of carbohydrates and proteins. In

spectral region 3 (proteins), the amide I band at 1650 cm−1 was clearly reduced in HF samples. In contrast, we observed four new peaks at 1580, 1540 and 1470 as well as one additional peak

at 725 cm−1, the absorption of which can be attributed to aromatic rings and heteroaromatic nitro compounds (Colthup et al., 1990). As a proof-of-concept for the impact of bacterial

colonization on cecal chemical fingerprints, we compared samples from mice monocolonized with _Bacteroides thetaiotaomicron_ to those from conventional mice. As illustrated by

microbiota-specific clusters of mice after multivariate analysis, cecal FT-IR spectra were dependent upon the colonization status of mice (Figure 2c). The ability to distinguish altered

ecosystem composition on the basis of FT-IR spectra was supported in another mouse cohort by the observation that cecal suspensions after antibiotic treatment clustered distinctly from

control samples (cecal preparation from mice on water without antibiotics) (Supplementary Figure S5). However, spectra were overall most affected by the diet, that is, cluster depth after

average linkage clustering of samples from conventional mice and gnotobionts on the CARB or HF diet was about threefold higher for diet vs colonization effects (data not shown). We then

aimed at refining the resolution of analysis by comparing the biochemical composition of cecal communities at the level of single cells. Therefore, we used FISH to target members of the

abundant groups _Bacteroidales_ and _Lachnospiraceae_ and measured cellular chemical composition by Raman microspectroscopy. These groups were selected because they were abundant in all mice

and alterations in their phylotype composition were observed due to diet. Using fixed cecal biomass from duplicate mice from the CARB and HF group, we acquired a total of 112 single-cell

spectra, with 7–20 cells measured for each FISH-defined population in each sample. Analysis with the Random Forests classifier showed that single-cell spectra from Bac-0303 and Erec-0482

groups were clearly distinguishable from each other under both diets (Figure 3a), due in large part to a much higher peak in the Erec-0482 group at 480–482 cm−1 (Figure 3b). An effect of the

two diets was also observed in the single-cell spectra, especially in the case of Erec-0482-positive cells (Figure 3a). No single peaks alone had high discriminative power for the

diet-related differences (Figure 3b), but rather small differences in the intensity of many wave numbers in the spectra collectively allowed for discrimination with the machine-learning

classifier. HIGH-FAT DIET ALTERED THE GUT BACTERIAL METAPROTEOME To corroborate the hypothesis that biochemical changes observed using FT-IR and Raman spectroscopy reflect changes in

microbial functions, we further analyzed cecal microbiota from mice fed the CARB and HF diet at the proteome level using LC-MS/MS (_n_=4 mice per diet). The majority of identified proteins

(94%) were of bacterial origin. A total of 114 mouse proteins were identified in the samples (Supplementary Results and Supplementary Table S5). After iterative search, 74 553 out of 1 409 

370 acquired spectra were matched to 1760 microbial proteins with a false-discovery rate of 0.6% at the protein level (Supplementary Table S5). Of these 1760 proteins, approximately 18% were

hypothetical and 29% were housekeeping proteins (ribosomal and chaperone proteins, polymerases, transcription and translation factors). The mean number of spectra and proteins per mouse

were as follows: CARB diet, 10 481±2445 and 816±68; HF diet, 5377±623 and 579±28. A proportion of 36 and 23% annotated proteins occurred only in CARB and HF mice, respectively (Figure 4a). A

heat map including 342 proteins, the occurrence of which was significantly different between the two groups, showed homogenous metaproteome patterns between replicate mice (Figure 4b).

After principal component analysis, the two groups of mice clustered very distinctly along PC1, which explained 67% of the variability within the data set (Supplementary Figure S6). For each

group, variables (differentially detected proteins) that correlated with PC1 are given in Supplementary Table S5. COG category assignment showed that overall functional patterns were

similar in the CARB and HF groups (Supplementary Figure S7) and were dominated by enzymes involved in energy production from carbohydrate metabolism originating from a variety of bacterial

species (Supplementary Results and Supplementary Table S5). However, functional assignment of differently detected proteins revealed significant variations between the dietary groups (Figure

4c). The prevalence of functional category C (energy production and conversion), G (carbohydrate metabolism and transport) and O (post-translational modification, protein turnover,

chaperone functions) was higher in mice fed the CARB diet whereas the prevalence of category E (amino-acid metabolism and transport), J (translation) and S (unknown functions) was higher in

mice fed the HF diet (Supplementary Results and Supplementary Table S5). NON-TARGETED METABOLOME ANALYSIS REVEALED DISTINCT METABOLITE PATTERNS To further describe the cecal ecosystem at the

functional level, three cecal contents in each the CARB and HF group were analyzed by FT-ICR-MS to obtain high-resolution metabolite profiles. The complete data set (filtered and

significant mass values with annotations) is provided in Supplementary Table S6. The distribution of filtered mass values between the two dietary groups showed that most masses were

discriminative (Figure 5a). HF and CARB samples shared only 15 and 28% of features in the negative and positive electrospray ionization mode, respectively, underscoring that diet has a

profound impact on cecal biochemical composition and molecular diversity. Cluster analysis resulted in a clear separation of CARB and HF samples, that is, diet-induced effects on the

metabolome in cecal contents were much higher than inter-individual differences between the three mice in each group (Figure 5b). A total of 2534 features were significantly different

between CARB and HF mice (_P_<0.01). Using MassTRIX annotation and KEGG reference pathways, 86 differently detected metabolites were identified. We noted the presence of fatty acids,

steroid hormones, anti-microbial substances (macrolides) and bacterial products such as cis-2-carboxycyclohexyl-acetic acid, cis-2,3-dihydroxy-2,3-dihydro-p-cumate, pravastatin and

urobilinogen. Among these metabolites, fatty acids and urobilinogen were specific for the HF diet (Table 2). DISCUSSION The intestinal microbiota has been extensively studied at both the

phylogenetic and metagenomic level in the context of metabolic disorders. The novelty of the present work lies in the comparative characterization of microbial communities in the mouse cecum

at the biochemical level after feeding a HF diet. We found that HF feeding alters the diversity and composition of intestinal microbiota. Mice in the HF group were characterized by

increased relative abundance of _Rikenellaceae_, which is in agreement with other reports based on qPCR and FISH that found no decrease in _Bacteroidetes_ following HF feeding (Cani et al.,

2008; Duncan et al., 2008). The presence of _Alistipes_, a genus within the _Rikenellaceae_, has also been recently associated with type-2 diabetes in humans (Qin et al., 2012). In addition,

we found that _Ruminococcaceae_ (phylum _Firmicutes_) were decreased, which makes sense in light of the fact that _ruminococci_ are major utilizers of plant polysaccharides, the amount of

which is substantially decreased in HF diets (Flint et al., 2012; Ze et al., 2012). Lactic acid bacteria have been proposed to be key players in host metabolic balance (Armougom et al.,

2009; Delzenne and Reid, 2009; Arora et al., 2012). Mean _Lactobacillus_ relative abundances were generally higher in the HF group (12.5 vs 9.7%), but diet-associated differences were not

statistically significant due to marked inter-individual variations. It is worth mentioning that, when compared with studies showing rapid changes in bacterial communities due to

high-calorie diets, the data obtained in the present work relate to adaptation of the gut ecosystem and the host to long-term feeding (12 weeks) (Desmarchelier et al., 2012, 2013), which may

explain, together with differences in HF diet composition, some of the variations observed as well as differences with data from the literature (for example, regarding abundance changes of

_Bacteroides_ or _Erysipelotrichaceae_). The fact that a HF diet may alter cellular composition of microorganisms is suggested by _in vitro_ studies showing that the addition of cholesterol

and fatty acids to growth media alter lipid and cell membrane composition of lactic acid bacteria (Dambekodi and Gilliland, 1998; Lye et al., 2010). In the present work, we used FT-IR and

Raman spectroscopy to show that a HF diet induces changes in chemical composition of the cecum and single cells as a hint for changes in bacterial physiology. The analysis of cecal samples

from mice mono-associated with _B. thetaiotaomicron_ vs conventional and from antibiotic-treated mice revealed that, for a given diet, samples clustered distinctly, testifying to the ability

to discriminate FT-IR spectra on the basis of microbial community structure. However, spectral distances between diet-dependent clusters were higher than microbiota-driven clusters. Thus,

taking into account that the monocolonized, antibiotic-treated and conventional mouse models that we used represent extreme cases, we concluded that diet alters spectra to a higher extent

than bacterial composition. It has been shown that the excess of certain nutrients, including lipids, can result in the formation of storage granules or vesicles in the microbial cells

(Bauchart et al., 1990), which can have pronounced effects on FT-IR fingerprints (Naumann, 2000) and may even mask differences in microbial composition (Bosch et al., 2008). Still, it is

very likely that part of the spectral differences that we observed were not of microbial origin, that is, due to dietary constituents remaining in the sample or adhering to bacteria. We

therefore tested the hypothesis that the differences in diet would result in accumulation of storage compounds by employing single-cell analysis with Raman microspectroscopy. Surprisingly,

we found no evidence of increase in storage compounds under either diet, though Erec-0482-positive cells had a higher peak at 480–482 cm−1 than Bac-0303-positive cells. This peak is due to

intracellular glycogen or polyglucan storage (Movasaghi et al., 2007), which likely reflects the presence of the clostridial-equivalent of glycogen, granulose, a high molecular weight

polyglucan important for sporulation (Reysenbach et al., 1986). Nonetheless, analysis of chemical composition at the single-cell level confirmed that the overall cellular composition of

bacteria within the abundant bacterial groups _Bacteroidales_ and _Lachnospiraceae_ was altered by HF feeding, which may be due to the shifting phylotype dynamics that were observed within

this group and possibly also altered activity. While many studies have shown that microbial diversity is altered by dietary changes (Clavel et al., 2005; Martinez et al., 2009; Jumpertz et

al., 2011; Kau et al., 2011), much less is known about the impact of diet on the metabolic potential of gut microbiota (Martin et al., 2010; McNulty et al., 2011; Muegge et al., 2011). To

our knowledge, this is the first report on the adaptive response of the gut metaproteome to dietary challenge. Since the first gel-based metaproteome published in 2007 (Klaassens et al.,

2007) and the state-of-the art LC-MS/MS work by Verberkmoes et al. (2009), the size of genomic databases has been rising exponentially. Nevertheless, annotation of peptide spectra remains

challenging and functional interpretation based on the occurrence of dominant proteins is limited, that is, taking into account the high degree of diversity of the intestinal microbial

ecosystem, we still profit only from a narrow window of analysis at the proteome level (Rooijers et al., 2011; Haange et al., 2012; Kolmeder et al., 2012; Perez-Cobas et al., 2012). This

challenge is reflected in the present study by the fact that only 5% of all tandem MS spectra acquired could be matched to protein sequences from the databases. In contrast, up to 50% of all

spectra can be commonly identified for sequenced microorganisms. The total number of proteins identified in the mouse cecal samples that we analyzed (_n_=1760) is comparable to results

obtained using human fecal samples (_n_=1500 to 1800 proteins) (Verberkmoes et al., 2009; Kolmeder et al., 2012). After functional category assignment of identified proteins, our data

confirmed that, regardless of diet, the dominant mammalian gut metaproteome is involved in energy production from carbohydrate metabolism. However, in the HF group, we observed a lower

spectra occurrence for proteins classified in COG category C (energy production and conversion). These findings suggest that (i) the microbial ecosystem is not well prepared for efficient

energy harvest when 60% of dietary energy originates from fat. The HF diet-induced increase in spectral abundance factors related to category J (translation) may reflect adaptation of

microbial cells to meet their needs for survival in a milieu with low energy originating from carbohydrates. For example, _in vitro_ studies showed that levels of protein synthesis in marine

_Sphingomonas_ during starvation were low but preformed ribosomes and whole-cell proteins were retained for at least 7 days in culture (Fegatella and Cavicchioli, 2000). The authors

proposed that in starvation, the number of ribosomes is in large excess relative to protein synthesis requirements, which may corroborate increased signals for ribosomal proteins in our HF

data sets; and (ii) the depth of analysis (both in terms of biological replicates and protein annotation) is still limiting, that is, only most dominant proteins are detected, which likely

prevents better discrimination of the effect of the two diets at the metaproteome level. Bacterial response to the HF feeding included a sharp decrease in the occurrence of proteins involved

in carbohydrate metabolism as well as a reorganization of amino acid metabolism. Indeed, among proteins that best characterized microbiota from mice fed the HF diet, we noted the presence

of enzymes metabolizing amino acids (aminotransferases and proteases) that were not detected in CARB mice. We thus propose that shifts in the metabolism of amino acids such as histidine

(ammonia-lyase, glutamate formimidoyltransferase, urocanate hydratase) and alanine (alanine and glutamate dehydrogenase) as well as arginine and proline coupled with the use of glutamate as

a source of pyruvate for energy production (acetylornithine aminotransferase, glutamate dehydrogenase, (R)-2-hydroxyglutaryl-CoA dehydratase, urease) represent the most prominent metabolic

adaptations of the microbial ecosystem to the HF diet (Potrykus et al., 2008). This may corroborate (i) the higher protein to carbohydrate ratio in the HF vs CARB diet (ca. 1:1 vs 1:3), and

(ii) increased production of branched-chain fatty acids from the amino acid leucine, isoleucine and valine reported by others after HF feeding in humans or protein fermentation _in vitro_

(MacFarlane et al., 1992; Russell et al., 2011). The occurrence of only two of seven enzymes involved in the metabolism of the aforementioned branched-chain amino acids was higher in the HF

metaproteome. Of note, in contrast to the suggested increase in the production of short-chain fatty acids linked to higher prevalence of specific members of the _Firmicutes_ (for example,

_Erysipelotrichaceae_) in diet-induced obesity (Turnbaugh et al., 2008), evidence from human intervention studies clearly indicates decreased short-chain fatty acids, in particular butyrate,

yet increased branched-chain fatty acids concentrations after HF feeding (Brinkworth et al., 2009; Russell et al., 2011). Thus, the notion of increased capacity of the microbiome for energy

harvest in the context of diet-induced obesity should be taken with caution, although results may depend on diet composition (for example, content of simple sugars or proteins) and duration

of feeding as well as the host phenotype (lean or obese). Most recent metagenomic data suggest that low-diversity (gene count) fecal microbiomes, which seem to be associated with adiposity

and metabolic disturbances, are characterized by decreased capability of producing butyrate (Le Chatelier et al., 2013). The metaproteomic data set is also in agreement with previously

published transcriptome analysis and functional prediction of metagenomic sequencing reads (Turnbaugh et al., 2008, 2009). In the aforementioned studies, the authors reported that the cecal

metagenome from mice fed a HF/high-sugar Western diet was enriched in genes assigned to glutamate metabolic pathways. They also proposed that the microbiome adapted to the Western diet by

increasing transport and conversion of simple sugars and host-derived glycoproteins, which agrees with the higher occurrence of 2-dehydro-3-deoxygluconokinase, 6-phosphofructokinase,

_N_-acetylglucosamine-6-phosphate deacetylase and two sugar-binding proteins (gi:282600834 and gi:266619140) in the present HF metaproteomic data set. Also consistent with our results,

Yatsunenko et al. (2012) recently proposed that a Westernized diet is associated with metagenomes enriched in amino acid- and simple sugar-degrading enzymes when compared with African

populations on rural diets high in complex carbohydrates. Finally, the cecal HF metaproteome was characterized by three enzymes (glutaredoxin, alkyl hydroperoxide and thioredoxin reductase)

involved in oxidative stress responses, which may reflect adaptation to an environment with altered redox potential (Xiao et al., 2010). Holmes et al. (2012) have provided evidence that the

gut microbiome influences host metabolic phenotypes, based primarily on NMR studies (Claus et al., 2008; Calvani et al., 2010). However, only few papers focused on the analysis of

metabolites in intestinal content (Martin et al., 2010). In line with the pioneering work by Jansson et al., (2009), we used a non-targeted metabolomic approach based on ultrahigh resolution

mass spectrometry to identify diet-derived, host and microbial metabolites in cecal samples. Antunes et al. (2011) have also recently used FT-ICR-MS to assess the effect of antibiotics and

_Salmonella_ infection on the metabolome in mouse feces. They found that both treatments altered host hormone metabolism, for example, production of steroids and eicosanoids. In the present

study, we detected prostaglandins, thromboxanes and several steroids and conjugates thereof, which were essentially absent in cecal samples from mice fed the HF diet. This implies that host

steroid hormone homeostasis can also be affected by a HF diet. Although literature data are inconsistent, studies have demonstrated that HF diets can influence, for instance, serum

testosterone levels in mice and humans (Reed et al., 1987; Meikle et al., 1990; Whyte et al., 2007). Interestingly, the absence of cholesterol-derived products like steroids in the cecum of

HF mice corroborates with the finding that mice on the same HF diet were characterized by lower intestinal and hepatic levels of cholesterol in spite of plasma hypercholesterolemia, probably

due to increased demand for lipid absorption (Desmarchelier et al., 2012). Samples from CARB mice were also characterized by the identification of (i) metabolites involved in naphthalene

and xylene/cymene degradation by bacteria, and (ii) pravastatin, a potent inhibitor of hydroxymethylglutaryl-CoA reductase with cholesterol-lowering effects, which can also be produced by a

variety of bacteria (Serizawa, 1996; Park et al., 2003). In addition, the occurrence of various macrolides in CARB samples may partly explain disturbances in gut microbial composition after

HF feeding due to changes in the pool of anti-microbial substances present in the cecum. In cecal samples from mice fed the HF diet, fatty acid levels were much higher than in CARB mice,

reflecting the proportion of major fatty acids in the HF diet (12% oleic acid, 8% palmitic acid, 6% stearic acid, 2% linoleic acid). We also noted the presence of a ceramide

(_N_-acylsphingosine), for which _de novo_ synthesis may be promoted by high levels of palmitate in the HF diet. Ceramide production may also be enhanced by hydrolysis of sphingomyelin,

which corroborates with the detection of a secreted protein of the sphingomyelinase family (sp|P58242|ASM3B_MOUSE) in the mouse cecal proteome after HF feeding. Of note, ceramides have been

implicated in diet-induced insulin resistance (Longato et al., 2011) and have cytotoxic and pro-apoptotic properties (Haimovitz-Friedman et al., 1997; Jarvis and Grant, 1998). Finally, the

conversion of bilirubin to urobilinogen is considered to be a specific feature of the gut microbiota, especially _Clostridium_ spp. (Becker et al., 2011). Thus, higher levels of urobilinogen

and its oxidized product urobilin in the cecum of HF mice (i) testify to diet-induced functional alterations of the microbial ecosystem, (ii) may be related to the observed HF-induced

increase in phylotypes within the _Clostridiales_, an order that includes known converters of bilirubin, and (iii) could explain the appearance of the FT-IR spectral features attributed to

aromatic and heteroaromatic ring vibrations in the cecum of HF mice. Due to the relatively limited number of samples analyzed and to the fact that data sets were obtained from different mice

and that a minor fraction of metabolite masses can be annotated, we did not integrate data and extrapolate on functional links between the metaproteome and metabolome. In summary, we

demonstrated that (i) diet can alter the biochemical composition of the gut microbiota either by shifting phylotype composition or the activity of bacterial cells, (ii) changes in bacterial

metaproteome after HF feeding are most pronounced for pathways of amino acid metabolism, and (iii) cecal metabolic pathways affected by HF feeding include eicosanoid, steroid hormone,

macrolide, bile acid and bilirubin metabolism. These findings show that a HF diet has a major impact on the mouse cecal microbiota that extends beyond compositional changes to major

alterations in bacterial physiology and metabolite landscape. Molecular mechanisms underlying the conversion of steroids and amino acids by specific gut bacteria in relation with the onset

of metabolic disorders appear to be of particular relevance for future targeted experimental work. REFERENCES * Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ . (1990). Basic local

alignment search tool. _J Mol Biol_ 215: 403–410. Article  CAS  PubMed  Google Scholar  * Antunes LC, Arena ET, Menendez A, Han J, Ferreira RB, Buckner MM _et al_ (2011). Impact of

salmonella infection on host hormone metabolism revealed by metabolomics. _Infect Immun_ 79: 1759–1769. Article  CAS  PubMed  PubMed Central  Google Scholar  * Antunes LC, Han J, Ferreira

RB, Lolic P, Borchers CH, Finlay BB . (2011). Effect of antibiotic treatment on the intestinal metabolome. _Antimicrob Agents Chemother_ 55: 1494–1503. Article  CAS  PubMed  PubMed Central 

Google Scholar  * Armougom F, Henry M, Vialettes B, Raccah D, Raoult D . (2009). Monitoring bacterial community of human gut microbiota reveals an increase in Lactobacillus in obese patients

and Methanogens in anorexic patients. _PLoS One_ 4: e7125. Article  PubMed  PubMed Central  CAS  Google Scholar  * Arora T, Anastasovska J, Gibson G, Tuohy K, Sharma RK, Bell J _et al_

(2012). Effect of _Lactobacillus acidophilus_ NCDC 13 supplementation on the progression of obesity in diet-induced obese mice. _Br J Nutr_ 108: 1382–1389. Article  CAS  PubMed  Google

Scholar  * Backhed F, Ding H, Wang T, Hooper LV, Koh GY, Nagy A _et al_ (2004). The gut microbiota as an environmental factor that regulates fat storage. _Proc Natl Acad Sci USA_ 101:

15718–15723. Article  CAS  PubMed  PubMed Central  Google Scholar  * Bauchart D, Legay-Carmier F, Doreau M, Gaillard B . (1990). Lipid metabolism of liquid-associated and solid-adherent

bacteria in rumen contents of dairy cows offered lipid-supplemented diets. _Br J Nutr_ 63: 563–578. Article  CAS  PubMed  Google Scholar  * Becker N, Kunath J, Loh G, Blaut M . (2011). Human

intestinal microbiota: characterization of a simplified and stable gnotobiotic rat model. _Gut Microbes_ 2: 25–33. Article  PubMed  Google Scholar  * Berry D, Schwab C, Milinovich G,

Reichert J, Ben Mahfoudh K, Decker T _et al_ (2012). Phylotype-level 16S rRNA analysis reveals new bacterial indicators of health state in acute murine colitis. _ISME J_ 6: 2091–2106.

Article  CAS  PubMed  PubMed Central  Google Scholar  * Blaut M, Clavel T . (2007). Metabolic diversity of the intestinal microbiota: implications for health and disease. _J Nutr_ 137:

751S–755S. Article  CAS  PubMed  Google Scholar  * Bosch A, Minan A, Vescina C, Degrossi J, Gatti B, Montanaro P _et al_ (2008). Fourier transform infrared spectroscopy for rapid

identification of nonfermenting gram-negative bacteria isolated from sputum samples from cystic fibrosis patients. _J Clin Microbiol_ 46: 2535–2546. Article  CAS  PubMed  PubMed Central 

Google Scholar  * Brinkworth GD, Noakes M, Clifton PM, Bird AR . (2009). Comparative effects of very low-carbohydrate, high-fat and high-carbohydrate, low-fat weight-loss diets on bowel

habit and faecal short-chain fatty acids and bacterial populations. _Br J Nutr_ 101: 1493–1502. Article  CAS  PubMed  Google Scholar  * Calvani R, Miccheli A, Capuani G, Tomassini Miccheli

A, Puccetti C, Delfini M _et al_ (2010). Gut microbiome-derived metabolites characterize a peculiar obese urinary metabotype. _Int J Obes (Lond)_ 34: 1095–1098. Article  CAS  Google Scholar

  * Cani PD, Bibiloni R, Knauf C, Waget A, Neyrinck AM, Delzenne NM _et al_ (2008). Changes in gut microbiota control metabolic endotoxemia-induced inflammation in high-fat diet-induced

obesity and diabetes in mice. _Diabetes_ 57: 1470–1481. Article  CAS  PubMed  Google Scholar  * Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK _et al_ (2010).

QIIME allows analysis of high-throughput community sequencing data. _Nat Methods_ 7: 335–336. Article  CAS  PubMed  PubMed Central  Google Scholar  * Claus SP, Tsang TM, Wang Y, Cloarec O,

Skordi E, Martin FP _et al_ (2008). Systemic multicompartmental effects of the gut microbiome on mouse metabolic phenotypes. _Mol Syst Biol_ 4: 219. Article  PubMed  PubMed Central  CAS 

Google Scholar  * Clavel T, Fallani M, Lepage P, Levenez F, Mathey J, Rochet V _et al_ (2005). Isoflavones and functional foods alter the dominant intestinal microbiota in postmenopausal

women. _J Nutr_ 135: 2786–2792. Article  CAS  PubMed  Google Scholar  * Cole JR, Chai B, Marsh TL, Farris RJ, Wang Q, Kulam SA _et al_ (2003). The Ribosomal Database Project (RDP-II):

previewing a new autoaligner that allows regular updates and the new prokaryotic taxonomy. _Nucleic Acids Res_ 31: 442–443. Article  CAS  PubMed  PubMed Central  Google Scholar  * Colthup

NB, Daly LH, Wiberley SE . (1990) _Introduction to Infrared and Raman Spectroscopy_ 3rd edn Academic Press: Boston. Google Scholar  * Dambekodi PC, Gilliland SE . (1998). Incorporation of

cholesterol into the cellular membrane of Bifidobacterium longum. _J Dairy Sci_ 81: 1818–1824. Article  CAS  PubMed  Google Scholar  * Delzenne N, Reid G . (2009). No causal link between

obesity and probiotics. _Nat Rev Microbiol_ 7: 901, author reply 901. Article  CAS  Google Scholar  * Delzenne NM, Cani PD . (2011). Interaction between obesity and the gut microbiota:

relevance in nutrition. _Annu Rev Nutr_ 31: 15–31. Article  CAS  PubMed  Google Scholar  * Desmarchelier C, Dahlhoff C, Keller S, Sailer M, Jahreis G, Daniel H . (2012). C57Bl/6 N mice on a

western diet display reduced intestinal and hepatic cholesterol levels despite a plasma hypercholesterolemia. _BMC Genomics_ 13: 84. Article  CAS  PubMed  PubMed Central  Google Scholar  *

Desmarchelier C, Ludwig T, Scheundel R, Rink N, Bader BL, Klingenspor M _et al_ (2013). Diet-induced obesity in _ad libitum_-fed mice: food texture overrides the effect of macronutrient

composition. _Br J Nutr_ 109: 1518–1527. Article  CAS  PubMed  Google Scholar  * Duncan SH, Lobley GE, Holtrop G, Ince J, Johnstone AM, Louis P _et al_ (2008). Human colonic microbiota

associated with diet, obesity and weight loss. _Int J Obes (Lond)_ 32: 1720–1724. Article  CAS  Google Scholar  * Fegatella F, Cavicchioli R . (2000). Physiological responses to starvation

in the marine oligotrophic _ultramicrobacterium Sphingomonas_ sp. strain RB2256. _Appl Environ Microbiol_ 66: 2037–2044. Article  CAS  PubMed  PubMed Central  Google Scholar  * Fleissner CK,

Huebel N, Abd El-Bary MM, Loh G, Klaus S, Blaut M . (2010). Absence of intestinal microbiota does not protect mice from diet-induced obesity. _Br J Nutr_ 104: 919–929. Article  CAS  PubMed

  Google Scholar  * Flint HJ, Scott KP, Duncan SH, Louis P, Forano E . (2012). Microbial degradation of complex carbohydrates in the gut. _Gut Microbes_ 3: 289–306. Article  PubMed  PubMed

Central  Google Scholar  * Franks AH, Harmsen HJ, Raangs GC, Jansen GJ, Schut F, Welling GW . (1998). Variations of bacterial populations in human feces measured by fluorescent in situ

hybridization with group-specific 16S rRNA-targeted oligonucleotide probes. _Appl Environ Microbiol_ 64: 3336–3345. CAS  PubMed  PubMed Central  Google Scholar  * Haange SB, Oberbach A,

Schlichting N, Hugenholtz F, Smidt H, von Bergen M _et al_ (2012). Metaproteome analysis and molecular genetics of rat intestinal microbiota reveals section and localization resolved species

distribution and enzymatic functionalities. _J Proteome Res_ 11: 5406–5417. Article  CAS  PubMed  Google Scholar  * Haider S, Wagner M, Schmid MC, Sixt BS, Christian JG, Hacker G _et al_

(2010). Raman microspectroscopy reveals long-term extracellular activity of Chlamydiae. _Mol Microbiol_ 77: 687–700. Article  CAS  PubMed  Google Scholar  * Haimovitz-Friedman A,

Cordon-Cardo C, Bayoumy S, Garzotto M, McLoughlin M, Gallily R _et al_ (1997). Lipopolysaccharide induces disseminated endothelial apoptosis requiring ceramide generation. _J Exp Med_ 186:

1831–1841. Article  CAS  PubMed  PubMed Central  Google Scholar  * Heymsfield SB, Pietrobelli A . (2011). Individual differences in apparent energy digestibility are larger than generally

recognized. _Am J Clin Nutr_ 94: 1650–1651. Article  CAS  PubMed  Google Scholar  * Holmes E, Li JV, Marchesi JR, Nicholson JK . (2012). Gut microbiota composition and activity in relation

to host metabolic phenotype and disease risk. _Cell Metab_ 16: 559–564. Article  CAS  PubMed  Google Scholar  * Huang WE, Stoecker K, Griffiths R, Newbold L, Daims H, Whiteley AS _et al_

(2007). Raman-FISH: combining stable-isotope Raman spectroscopy and fluorescence _in situ_ hybridization for the single cell analysis of identity and function. _Environ Microbiol_ 9:

1878–1889. Article  CAS  PubMed  Google Scholar  * Hörmannsperger G, Clavel T, Haller D . (2012). Gut matters: microbe-host interactions in allergic diseases. _J Allergy Clin Immunol_ 129:

1452–1459. Article  PubMed  CAS  Google Scholar  * Jansson J, Willing B, Lucio M, Fekete A, Dicksved J, Halfvarson J _et al_ (2009). Metabolomics reveals metabolic biomarkers of Crohn’s

disease. _PLoS One_ 4: e6386. Article  PubMed  PubMed Central  CAS  Google Scholar  * Jarvis WD, Grant S . (1998). The role of ceramide in the cellular response to cytotoxic agents. _Curr

Opin Oncol_ 10: 552–559. Article  CAS  PubMed  Google Scholar  * Jumpertz R, Le DS, Turnbaugh PJ, Trinidad C, Bogardus C, Gordon JI _et al_ (2011). Energy-balance studies reveal associations

between gut microbes, caloric load, and nutrient absorption in humans. _Am J Clin Nutr_ 94: 58–65. Article  CAS  PubMed  PubMed Central  Google Scholar  * Kanehisa M, Goto S . (2000). KEGG:

kyoto encyclopedia of genes and genomes. _Nucleic Acids Res_ 28: 27–30. Article  CAS  PubMed  PubMed Central  Google Scholar  * Kau AL, Ahern PP, Griffin NW, Goodman AL, Gordon JI . (2011).

Human nutrition, the gut microbiome and the immune system. _Nature_ 474: 327–336. Article  CAS  PubMed  PubMed Central  Google Scholar  * Klaassens ES, de Vos WM, Vaughan EE . (2007).

Metaproteomics approach to study the functionality of the microbiota in the human infant gastrointestinal tract. _Appl Environ Microbiol_ 73: 1388–1392. Article  CAS  PubMed  Google Scholar

  * Kolmeder CA, de Been M, Nikkila J, Ritamo I, Matto J, Valmu L _et al_ (2012). Comparative metaproteomics and diversity analysis of human intestinal microbiota testifies for its temporal

stability and expression of core functions. _PLoS One_ 7: e29913. Article  CAS  PubMed  PubMed Central  Google Scholar  * Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G

_et al_ (2013). Richness of human gut microbiome correlates with metabolic markers. _Nature_ 500: 541–546. Article  CAS  PubMed  Google Scholar  * Lepage P, Leclerc MC, Joossens M, Mondot S,

Blottiere HM, Raes J _et al_ (2013). A metagenomic insight into our gut's microbiome. _Gut_ 62: 146–158. Article  PubMed  Google Scholar  * Liaw A, Wiener M . (2002). Classification

and Regression by randomForest. _R News_ 2: 18–22. Google Scholar  * Lieber CA, Mahadevan-Jansen A . (2003). Automated method for subtraction of fluorescence from biological Raman spectra.

_Appl Spectrosc_ 57: 1363–1367. Article  CAS  PubMed  Google Scholar  * Longato L, Tong M, Wands JR, de la Monte SM . (2011). High fat diet induced hepatic steatosis and insulin resistance:

Role of dysregulated ceramide metabolism. _Hepatol Res_ 42: 412–427. Article  PubMed  PubMed Central  CAS  Google Scholar  * Lye HS, Rusul G, Liong MT . (2010). Removal of cholesterol by

lactobacilli via incorporation and conversion to coprostanol. _J Dairy Sci_ 93: 1383–1392. Article  CAS  PubMed  Google Scholar  * MacFarlane GT, Gibson G, Beatty E, Cummings JH . (1992).

Estimation of short-chain fatty acid production from protein by human intestinal bacteria based on branched-chain fatty acid measurments. _FEMS Microbiol Ecol_ 101: 81–88. CAS  Google

Scholar  * Manz W, Amann R, Ludwig W, Vancanneyt M, Schleifer KH . (1996). Application of a suite of 16S rRNA-specific oligonucleotide probes designed to investigate bacteria of the phylum

Cytophaga-Flavobacter-Bacteroides in the natural environment. _Microbiology_ 142: 1097–1106. Article  CAS  PubMed  Google Scholar  * Martin FP, Sprenger N, Montoliu I, Rezzi S, Kochhar S,

Nicholson JK . (2010). Dietary modulation of gut functional ecology studied by fecal metabonomics. _J Proteome Res_ 9: 5284–5295. Article  CAS  PubMed  Google Scholar  * Martinez I, Wallace

G, Zhang C, Legge R, Benson AK, Carr TP _et al_ (2009). Diet-induced metabolic improvements in a hamster model of hypercholesterolemia are strongly linked to alterations of the gut

microbiota. _Appl Environ Microbiol_ 75: 4175–4184. Article  CAS  PubMed  PubMed Central  Google Scholar  * Masella AP, Bartram AK, Truszkowski JM, Brown DG, Neufeld JD . (2012). PANDAseq:

paired-end assembler for illumina sequences. _BMC Bioinformatics_ 13: 31. Article  CAS  PubMed  PubMed Central  Google Scholar  * McNulty NP, Yatsunenko T, Hsiao A, Faith JJ, Muegge BD,

Goodman AL _et al_ (2011). The impact of a consortium of fermented milk strains on the gut microbiome of gnotobiotic mice and monozygotic twins. _Sci Transl Med_ 3: 106ra106. Article  PubMed

  PubMed Central  CAS  Google Scholar  * Meikle AW, Stringham JD, Woodward MG, McMurry MP . (1990). Effects of a fat-containing meal on sex hormones in men. _Metabolism_ 39: 943–946. Article

  CAS  PubMed  Google Scholar  * Movasaghi Z, Rehman S, Rehman I . (2007). Raman spectroscopy of biological tissues. _Appl Spectrosc Rev_ 42: 493–541. Article  CAS  Google Scholar  * Muegge

BD, Kuczynski J, Knights D, Clemente JC, Gonzalez A, Fontana L _et al_ (2011). Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. _Science_

332: 970–974. Article  CAS  PubMed  PubMed Central  Google Scholar  * Naumann D . (2000) _Infrared Spectroscopy in Microbiology_ In. Encyclopedia of analytical chemistry. In RA Meyers (ed.)

John Wiley & Sons: Chichester;, pp 102–131. Google Scholar  * Park JW, Lee JK, Kwon TJ, Yi DH, Kim YJ, Moon SH _et al_ (2003). Bioconversion of compactin into pravastatin by

_Streptomyces_ sp. _Biotechnol Lett_ 25: 1827–1831. Article  CAS  PubMed  Google Scholar  * Patel RK, Jain M . (2012). NGS QC Toolkit: a toolkit for quality control of next generation

sequencing data. _PLoS One_ 7: e30619. Article  CAS  PubMed  PubMed Central  Google Scholar  * Pavelka N, Fournier ML, Swanson SK, Pelizzola M, Ricciardi-Castagnoli P, Florens L _et al_

(2008). Statistical similarities between transcriptomics and quantitative shotgun proteomics data. _Mol Cell Proteomics_ 7: 631–644. Article  CAS  PubMed  Google Scholar  * Pavelka N,

Pelizzola M, Vizzardelli C, Capozzoli M, Splendiani A, Granucci F _et al_ (2004). A power law global error model for the identification of differentially expressed genes in microarray data.

_BMC Bioinformatics_ 5: 203. Article  PubMed  PubMed Central  CAS  Google Scholar  * Perez-Cobas AE, Gosalbes MJ, Friedrichs A, Knecht H, Artacho A, Eismann K _et al_ (2012). Gut microbiota

disturbance during antibiotic therapy: a multi-omic approach. _Gut_ doi:10.1136/gutjnl-2012-303184. * Potrykus J, White RL, Bearne SL . (2008). Proteomic investigation of amino acid

catabolism in the indigenous gut anaerobe _Fusobacterium varium_. _Proteomics_ 8: 2691–2703. Article  CAS  PubMed  Google Scholar  * Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F _et al_ (2012).

A metagenome-wide association study of gut microbiota in type 2 diabetes. _Nature_ 490: 55–60. Article  CAS  PubMed  Google Scholar  * Raoult D, Fournier PE, Drancourt M . (2004). What does

the future hold for clinical microbiology? _Nat Rev Microbiol_ 2: 151–159. Article  CAS  PubMed  PubMed Central  Google Scholar  * Reed MJ, Cheng RW, Simmonds M, Richmond W, James VH .

(1987). Dietary lipids: an additional regulator of plasma levels of sex hormone binding globulin. _J Clin Endocrinol Metab_ 64: 1083–1085. Article  CAS  PubMed  Google Scholar  * Reysenbach

AL, Ravenscroft N, Long S, Jones DT, Woods DR . (1986). Characterization, biosynthesis, and regulation of granulose in _clostridium acetobutylicum_. _Appl Environ Microbiol_ 52: 185–190. CAS

  PubMed  PubMed Central  Google Scholar  * Rooijers K, Kolmeder C, Juste C, Dore J, de Been M, Boeren S _et al_ (2011). An iterative workflow for mining the human intestinal metaproteome.

_BMC Genomics_ 12: 6. Article  CAS  PubMed  PubMed Central  Google Scholar  * Russell WR, Gratz SW, Duncan SH, Holtrop G, Ince J, Scobbie L _et al_ (2011). High-protein, reduced-carbohydrate

weight-loss diets promote metabolite profiles likely to be detrimental to colonic health. _Am J Clin Nutr_ 93: 1062–1072. Article  CAS  PubMed  Google Scholar  * Saeed A, Bhagabati N,

Braisted J, Liang W, Sharov V, Howe E _et al_ (2006). TM4 microarray software suite. DNA microarrays, part B: databases and statistics. _A. Kimmel and B. Oliver_. Elsevier Academic Press:

San Diego, Vol. 411: 134–193. Google Scholar  * Seo J, Shneiderman B . (2002). Interactively exploring hierarchical clustering results. _IEEE Computer_ 35: 80–86. Google Scholar  * Serizawa

N . (1996). Biochemical and molecular approaches for production of pravastatin, a potent cholesterol-lowering drug. _Biotechnol Annu Rev_ 2: 373–389. Article  CAS  PubMed  Google Scholar  *

Shevchenko A, Wilm M, Vorm O, Mann M . (1996). Mass spectrometric sequencing of proteins silver-stained polyacrylamide gels. _Anal Chem_ 68: 850–858. Article  CAS  PubMed  Google Scholar  *

Suhre K, Schmitt-Kopplin P . (2008). MassTRIX: mass translator into pathways. _Nucleic Acids Res_ 36: W481–W484. Article  CAS  PubMed  PubMed Central  Google Scholar  * Tatusov RL, Fedorova

ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV _et al_ (2003). The COG database: an updated version includes eukaryotes. _BMC Bioinformatics_ 4: 41. Article  PubMed  PubMed Central  Google

Scholar  * Turnbaugh P, Ridaura V, Faith J, Rey F, Knight R, Gordon J . (2009). The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. _Sci

Transl Med_ 1: 6ra14. Article  PubMed  PubMed Central  CAS  Google Scholar  * Turnbaugh PJ, Backhed F, Fulton L, Gordon JI . (2008). Diet-induced obesity is linked to marked but reversible

alterations in the mouse distal gut microbiome. _Cell Host Microbe_ 3: 213–223. Article  CAS  PubMed  PubMed Central  Google Scholar  * Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis

ER, Gordon JI . (2006). An obesity-associated gut microbiome with increased capacity for energy harvest. _Nature_ 444: 1027–1031. Article  PubMed  Google Scholar  * Verberkmoes NC, Russell

AL, Shah M, Godzik A, Rosenquist M, Halfvarson J _et al_ (2009). Shotgun metaproteomics of the human distal gut microbiota. _Isme J_ 3: 179–189. Article  CAS  PubMed  Google Scholar  *

Wenning M, Theilmann V, Scherer S . (2006). Rapid analysis of two food-borne microbial communities at the species level by Fourier-transform infrared microspectroscopy. _Environ Microbiol_

8: 848–857. Article  CAS  PubMed  Google Scholar  * Whyte JJ, Alexenko AP, Davis AM, Ellersieck MR, Fountain ED, Rosenfeld CS . (2007). Maternal diet composition alters serum steroid and

free fatty acid concentrations and vaginal pH in mice. _J Endocrinol_ 192: 75–81. Article  CAS  PubMed  Google Scholar  * Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N _et al_

(2007). HMDB: the Human Metabolome Database. _Nucleic Acids Res_ 35: D521–D526. Article  CAS  PubMed  PubMed Central  Google Scholar  * Xiao Y, Cui J, Shi YH, Sun J, Wang ZP, Le GW . (2010).

Effects of duodenal redox status on calcium absorption and related genes expression in high-fat diet-fed mice. _Nutrition_ 26: 1188–1194. Article  CAS  PubMed  Google Scholar  * Yatsunenko

T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M _et al_ (2012). Human gut microbiome viewed across age and geography. _Nature_ 486: 222–227. Article  CAS  PubMed  PubMed

Central  Google Scholar  * Ze X, Duncan SH, Louis P, Flint HJ . (2012). Ruminococcus bromii is a keystone species for the degradation of resistant starch in the human colon. _ISME J_ 6:

1535–1543. Article  CAS  PubMed  PubMed Central  Google Scholar  * Zybailov B, Mosley AL, Sardiu ME, Coleman MK, Florens L, Washburn MP . (2006). Statistical analysis of membrane proteome

expression changes in Saccharomyces cerevisiae. _J Prot Res_ 5: 2339–2347. Article  CAS  Google Scholar  Download references ACKNOWLEDGEMENTS Raw files obtained by metaproteomic and

metabolomic spectrometry measurements are available upon request. We thank Adelmar Stamfort and Pia Baur for helping with statistical analysis, Nico Gebhardt, Ines Grüner, Elmar Jocham,

Melanie Klein, Ute Lehmann, Ronny Scheundel and Caroline Ziegler for technical assistance, and Gabriele Hörmannsperger for reviewing the manuscript. Charles Desmarchelier was financed by the

EU FP6 project _Nutrient Sensing in Satiety Control and Obesity_ (NuSISCO, grant no. MEST-CT-2005-020494). Chrisoph Böhm, David Berry, and Michael Wagner were supported by the European

Research Council (Advanced Grant NITRICARE 294343) and David Berry was supported by the Vienna Science and Technology Fund (WWTF) through project LS12-001. AUTHOR INFORMATION Author notes *

Amin Moghaddas Gholami, David Berry, Charles Desmarchelier and Hannes Hahne: These authors contributed equally to the work. AUTHORS AND AFFILIATIONS * Molecular Nutrition Unit, ZIEL-Research

Center for Nutrition and Food Sciences, Technische Universität München, Freising-Weihenstephan, Germany Hannelore Daniel & Charles Desmarchelier * Proteomics and Bioanalytics,

Technische Universität München, Freising-Weihenstephan, Germany Amin Moghaddas Gholami, Hannes Hahne & Bernhard Kuster * Division of Microbial Ecology, Department of Microbiology and

Ecosystem Science, Faculty of Life Sciences, University of Vienna, Vienna, Austria David Berry, Christoph Böhm & Michael Wagner * Department of Gastrointestinal Microbiology, German

Institute of Human Nutrition Potsdam-Rehbrücke (DIfE), Nuthetal, Germany Gunnar Loh & Michael Blaut * INRA/AgroParisTech, Micalis UMR1319, Jouy-en-Josas, France Stanislas Mondot & 

Patricia Lepage * CSIRO Livestock Industries, Queensland Bioscience Precinct, St Lucia, Australia Stanislas Mondot * Research Unit Plant-Microbe Interactions, Helmholtz Zentrum München,

Neuherberg, Germany Michael Rothballer * Analytical BioGeoChemistry, Helmoltz Zentrum München, Neuherberg, Germany; Chair of Analytical Food Chemistry, Technische Universität München, Alesia

Walker & Philippe Schmitt-Kopplin * Chair of Analytical Food Chemistry, Technische Universität München, Freising-Weihenstephan, Germany Alesia Walker & Philippe Schmitt-Kopplin *

Microbiology, ZIEL-Research Center for Nutrition and Food Sciences, Technische Universität München, Freising-Weihenstephan, Germany Mareike Wenning * Center for Integrated Protein Science

Munich, Germany Bernhard Kuster * Chair of Nutrition and Immunology, Technische Universität München, Freising-Weihenstephan, Germany Dirk Haller & Thomas Clavel * Junior Research Group

Intestinal Microbiome, ZIEL-Research Center for Nutrition and Food Sciences, Technische Universität München, Freising-Weihenstephan, Germany Thomas Clavel Authors * Hannelore Daniel View

author publications You can also search for this author inPubMed Google Scholar * Amin Moghaddas Gholami View author publications You can also search for this author inPubMed Google Scholar

* David Berry View author publications You can also search for this author inPubMed Google Scholar * Charles Desmarchelier View author publications You can also search for this author

inPubMed Google Scholar * Hannes Hahne View author publications You can also search for this author inPubMed Google Scholar * Gunnar Loh View author publications You can also search for this

author inPubMed Google Scholar * Stanislas Mondot View author publications You can also search for this author inPubMed Google Scholar * Patricia Lepage View author publications You can

also search for this author inPubMed Google Scholar * Michael Rothballer View author publications You can also search for this author inPubMed Google Scholar * Alesia Walker View author

publications You can also search for this author inPubMed Google Scholar * Christoph Böhm View author publications You can also search for this author inPubMed Google Scholar * Mareike

Wenning View author publications You can also search for this author inPubMed Google Scholar * Michael Wagner View author publications You can also search for this author inPubMed Google

Scholar * Michael Blaut View author publications You can also search for this author inPubMed Google Scholar * Philippe Schmitt-Kopplin View author publications You can also search for this

author inPubMed Google Scholar * Bernhard Kuster View author publications You can also search for this author inPubMed Google Scholar * Dirk Haller View author publications You can also

search for this author inPubMed Google Scholar * Thomas Clavel View author publications You can also search for this author inPubMed Google Scholar CORRESPONDING AUTHOR Correspondence to

Thomas Clavel. ADDITIONAL INFORMATION Supplementary Information accompanies this paper on The ISME Journal website SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION (DOC 2295 KB)

SUPPLEMENTARY TABLE S2 (XLS 18 KB) SUPPLEMENTARY TABLE S3 (XLS 27 KB) SUPPLEMENTARY TABLE S4 (XLS 25 KB) SUPPLEMENTARY TABLE S5 (XLS 790 KB) SUPPLEMENTARY TABLE S6 (XLS 1564 KB)

SUPPLEMENTARY TABLE LEGENDS (DOC 23 KB) RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Daniel, H., Gholami, A., Berry, D. _et al._ High-fat diet alters

gut microbiota physiology in mice. _ISME J_ 8, 295–308 (2014). https://doi.org/10.1038/ismej.2013.155 Download citation * Received: 13 June 2013 * Accepted: 04 August 2013 * Published: 12

September 2013 * Issue Date: February 2014 * DOI: https://doi.org/10.1038/ismej.2013.155 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get

shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative KEYWORDS *

diet-induced obesity * high-fat diet * intestinal microbiota * metabolomics * metaproteome * spectroscopy