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Metabolic in vivo labelling highlights differences of metabolically active microbes from the mucosal gastrointestinal microbiome between high fat and normal chow diet Andreas Oberbach, Sven-Bastiaan Haange, Nadine Schlichting, Marco Heinrich, Stefanie Lehmann, Holger Till, Floor Hugenholtz, Yvonne Kullnick, Hauke Smidt, Karin Frank, Jana Seifert, Nico Jehmlich, and Martin von Bergen J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00973 • Publication Date (Web): 02 Mar 2017 Downloaded from http://pubs.acs.org on March 3, 2017

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Journal of Proteome Research

Metabolic in vivo labelling highlights differences of metabolically active microbes from the mucosal gastrointestinal microbiome between high fat and normal chow diet 1#

2#

1,3

1,4

Andreas Oberbach , Sven-Bastiaan Haange , Nadine Schlichting , Marco Heinrich , Stefanie 4

5

6

1,4

6

7

2,8

Lehmann , Holger Till , Floor Hugenholtz , Yvonne Kullnick , Hauke Smidt , Karin Frank , Jana Seifert , 2

2,9,10 *

Nico Jehmlich , Martin von Bergen

1 2

Department of Cardiac Surgery, University of Leipzig, Heart Center Leipzig, Germany Department of Molecular Systems Biology, UFZ-Helmholtz Centre for Environmental Research, Leipzig,

Germany 3 4 5 6 7

Department of Pediatric Surgery, University of Leipzig, Leipzig, Germany Integrated Research and Treatment Center (IFB) Adiposity Diseases, University of Leipzig, Germany Department of Paediatric and Adolescent Surgery, Medical University of Graz, Austria Laboratory of Microbiology, Wageningen University, The Netherlands Department of Ecological Modelling, UFZ-Helmholtz Centre for Environmental Research, Leipzig,

Germany 8 9

Current address: Institute for Animal Science, University of Hohenheim, Stuttgart, Germany Institute of Biochemistry, Faculty of Biosciences, Pharmacy and Psychology, University of Leipzig,

Germany 10

Department of Life Sciences and Chemistry, Centre for Microbial Communities, University of Aalborg,

Denmark

#

*

Authors contributed equally to this work

corresponding author:

Tel: +49-341-235-1211, fax: +49-341-235-451211 E-mail: [email protected]

Running title: In vivo 15N incorporation highlights active mucosal microbiota

1

ACS Paragon Plus Environment


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Abstract The gastrointestinal microbiota in the gut interacts metabolically and immunologically with the host tissue in the contact zone of the mucus layer. For understanding the details of these interactions and especially their dynamics it is crucial to identify the metabolically active subset of the microbiome. This became possible by the development of stable isotope probing (SIP) techniques, which has only sparsely been applied to microbiome research. Here, we applied the in vivo stable isotope approach using

15

N labelled diet with subsequent identification of

metabolically active bacterial species. Four-week old male Sprague-Dawley rats were randomly assigned to chow diet (CD, n =15) and high fat diet (HFD, n =15). After 11 weeks, three animals from each group were sacrificed for baseline characterization of anthropometric and metabolic obesity. The remaining animals were exposed to either a

15

N-labelled (n =9) or a

14

N-unlabelled experimental diet (n =3). Three rats

from each cohort (HFD and CD) were sacrificed at 12 h, 24 h, and 72 h. The remaining three animals from each cohort, which received the 14N-unlabelled diet, were sacrificed after 72 h. The colon was harvested, divided into three equal sections (proximal, medial and distal), the mucus layer of each specimen was sampled by scraping. We identified the active subset in a high fat diet (HFD) model of obesity in comparison to lean controls rats using metaproteomics. In addition, all samples were investigated by 16S rRNA amplicon gene sequencing. The active microbiome of the HFD group showed an increase of bacterial taxa for Verrucomicrobia and Desulfovibrionaceae. In contrast to no significant changes in alpha diversity, clearly time- and localization-dependent effects in beta-diversity were observed. In terms of enzymatic functions the HFD group showed strong affected metabolic pathways such as energy production and carbohydrate metabolism.

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In vivo isotope labelling combined with metaproteomics provides a valuable method to distinguish the active from the non-active bacterial phylogenetic groups which are relevant for microbiota-host interaction. For morbid obesity such analysis may provide potentially new strategies for targeted pre- or probiotic treatments.

Keywords Protein-based stable isotope probing; gut microbiota; metaproteomics; mucus layer; 16S rRNA gene sequencing

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Introduction Progress in metagenomics has led to comprehensive assessment of the taxonomy of the human gut core microbiome 1. The metabolic capacities of certain phyla and species, and their interaction with the host, can be revealed to some extent by metagenomics 2. Due to the incomplete understanding of the functionalities of the plethora of gut microorganisms and the redundant metabolic capacities over a broad range of species, uncertainty remains about their true metabolic activity in situ. It has also recently become evident that remarkable differences occur in microbial phylogeny and, consequently, in enzyme activity between different locations of the intestinal tract. Faecal samples certainly do not represent the microbiome of proximal portions of the gastrointestinal tract 3. Moreover the “transient” microbiome of the luminal content differs from the “resident” within the mucus layer at the gut wall

3, 4

. The mucosa forms a physical and physiological

interface between the microbiota and the host, allowing for multiple immunological and metabolic interactions

5, 6

, which are supposed to result from a coevolution as well as intestinal

adaptation processes to different diets. Diet is regarded as one of the most important factors shaping the microbiome

7

with a number of studies revealing this

8-10

. Recently, David et al.

(2014) reported the consequences of changing from a plant-rich to a high meat content diet for the microbiome

11

. This study revealed that it takes at least two days before changes in microbe

phylogenetic diversity were measured in faeces. In detail the question arises of how rapidly a significant change of the diet leads to an alteration of taxonomic groups in the intestinal content and mucus layer. Clinically, for morbid obesity it seems important to address the metabolically active microbiome in particular, because there is strong evidence that the microbiome promotes obesity related

4


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comorbidities such as diabetes and hyperlipidaemia by increasing the capacity of energy harvesting from the lumen content and enhancing short chain fatty acid (SCFA) production. Stable isotope labeling in mammals (SILAM) has been achieved utilizing

13

C as well as

15

N.

Spirulina whole cells (lyophilized powder) (15N, 98%+) have been used to uniformly label the whole proteome of rat or mice with 15N12. As a result, the labelled tissues can then be used as an internal standard when mixed with the diseased tissues from an animal model for a disease. We followed another strategy, where the applied

15

N-labelled food is used to unravel the active gut

microbiota in a time dependant analysis in the intestinal content as well as mucus layer. Such an analysis became possible by the development of stable isotope probing (SIP) techniques

13

in which the utilisation and subsequent incorporation of stable isotopes from a

substrate or nutrient into a newly synthesized biomass is used to probe metabolic activity. Protein-based stable isotope probing (protein-SIP) relies on the metabolic incorporation of heavy stable isotopes e.g. carbon (13C) or nitrogen (15N) into proteins, which are detected by mass spectrometry (MS)14. High resolution MS of peptides simultaneously yields information on the protein identity, and hence the phylogenetic origin, as well as quantification of the incorporation rate can be calculated by specific software tools 15. Despite the advantage of determining metabolically active species by stable isotope probing approaches, there are only a few microbiome studies using this approach. This is caused by the challenge of getting isotopically label into the colon. Berry et al. injecting

16

introduced the label by

15

N labelled amino acids into the tail. The detection of incorporation was achieved by

nano-SIMS analysis. This technique is sufficiently sensitive for the very low amount of label, but for taxonomically identifying the key species, other, global approaches are necessary. Hence, we introduce here a specifically

15

N labelled food for in vivo labelling the metabolically

active species in the gastrointestinal mucosal microbiota. Beside the digestion in the small

5



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intestine some parts of the diet will be hydrolysed in the colon and there the metabolically active microbes utilize the resulting short peptides, amino acids, and/or ammonium as substrates for their protein biosynthesis. In this study, we investigated the

15

N incorporation into peptides derived from proteins of

bacteria from the lower intestinal tract of rats by protein-based stable isotope labelling (SIP) in order to (i) identify the metabolically active taxa, (ii) to reveal the time course of incorporation into the microbiome, and (iii) to analyse the differences in the structure and activity of the microbiota caused by the change in nutrient availability between the CD and HFD. Therefore, we used a HFD-induced obesity rat model, where 4 weeks old rats were fed either a normal chow diet (CD) or a high fat diet (HFD) that led to differences in increased body weight and alteration of glucose and lipid homeostasis. After 11 weeks of diet both cohorts were subsequently switched to the 15N-protein labelled experimental diet for 12 h to 72 h. The primary adaptation to CD versus HFD defined the structure and the activity of the microbiota.

Experimental Procedures Animal handling, phenotyping, and sample collection The studies followed international guidelines for the prevention of animal cruelty and were approved by the local authority for animal protection (TVV 32/11). Four-week old male Sprague-Dawley rats (n =30; Medical Experimental Center, University of Leipzig, Germany) were randomly divided into two groups: chow diet (CD, n =15) and high fat diet (HFD, n =15). Three rats per cage were kept on a 12:12 light:dark cycle and provided with food (sniff EF R/M Control, sniff EF R/M D12451 modified, sniff Spezialdiäten GmbH, Soest, Germany) and water ad libitum (Figure 1). The macro- and micro-nutrient components of the diets are given in Supporting Information Table S1. Weight gain and food intake were monitored

6


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twice a week over 11 weeks. Phenotypically relevant parameters for the 2 groups (HFD, n =15) and (CD, n =15) were assessed in two additional rat cohorts, HFD (n =15) and CD (n =15) (Supporting Information Table S2). At 11 weeks, three animals from each diet group were sacrificed for baseline characterization of HFD and CD adaptation. For each cohort, the remaining animals were switched to either a labelled (n =9) or a

15

N-

14

N-nonlabelled experimental diet (n =3) [U-15N-SILAM-Mouse Diet (15N,

97%), U-14N-SILAM-Mouse Diet, Silantes, Munich, Germany]. Three rats from each cohort (HFD and CD) were sacrificed at 12 h, 24 h, and 72 h after initiation of the 15N-labelled diet. The remaining three animals from each cohort, which received the

14

N-unlabelled feed, were killed

after 72 h (Figure 1). Immediately after sacrificing each rat, the colon was removed and cut into three equal sections, designated proximal, medial, and distal colon. The mucus layer of each was sampled by cutting the section longitudinally and carefully removing the contents. The mucus was removed by scraping with a spatula. Faeces were collected from each rat, and all were snap-frozen in liquid nitrogen directly after sampling and stored at -80°C. DNA extraction and amplicon sequencing DNA was extracted from the mucus and faeces samples using bead beating plus columns (RBB+C)

17

and the bacterial composition of the samples was determined by 16S rRNA gene

amplification and pyrosequencing technology. The quantity was determined using Qubit v2.0 Fluorometer® (Invitrogen). DNA products were pyrosequenced using an FLX genome sequencer in combination with titanium chemistry (GATC-Biotech, Konstanz, Germany) (Supporting Information Table S3). QIIME tool was used to assess quality, and only sequences >200 bp and 25 and exact match to barcode and primer were selected. High-quality sequences were clustered into operational taxonomic units (OTU) at 97% sequence identity using UCLUST (v1.2.22q). The taxonomy of a representative bacterial 7



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sequence of each OTU was assigned with RDP Classifier against the Greengenes database

18

.

Chimeric sequences were removed using ChimeraSlayer 19.NaCl; 50 mM Tris pH 8.0 buffer). Protein extraction and proteolytic digestion Bacteria were isolated from faeces and mucus by the method of

20

and modified 3. Briefly,

samples from three replicates were pooled and suspended in Tris buffer (0.2 M NaCl; 50 mM Tris pH 8.0) at a ratio of 3 mL per 1 g sample, shaken at 80 rpm 12 h at 4°C. The bacteria were isolated through density gradient centrifugation (10,000 g; 40 min; 4°C) using a Histodenz™ solution (40% w/v in 0.2 M QIAamp DNA Mini Kit (Qiagen, Hilden, Germany). Bacteria cells were lysed as previously described in a protein denaturating lysis buffer 3. Protein content was determined using BCA quantification assay (Thermo Fisher Scientific, Rockford, USA). In total, 150 µg of protein lysate were precipitated with a 5-fold volume of ice-cold acetone at -20°C overnight, centrifuged, and dissolved in SDS-gel sample buffer. Proteins were separated by 1DE analysis using a 12% acrylamide separating gel with the Laemmli-buffer system. After the run, each sample lane was cut into 5 slices and forwarded to proteolytic cleavage using trypsin (Supporting Information). Mass spectrometry and data analysis Mass spectrometry was performed by an Orbitrap Velos spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) coupled to a nanoACQUITY UPLC system (Waters, Milford, MA, USA). Peptide lysates were separated by a 77 min gradient from 2-40% acetonitrile, 0.1% formic acid, followed by a 15 min gradient from 40-85% acetonitrile, 0.1% formic acid on a C18 column (nanoAcquity UPLC column, C18, 75 µm×150 mm, 1.7 µm, Waters). Peptide identification was performed by a two-step database search as described

21, 22

. Briefly,

the primary search was performed by tandem mass ion search algorithms from the Mascot house servers (v.2.2.1). As a genomic database, the following taxons from the National Center for Biotechnological Information (NCBI, Rockville Pike, USA, March 2013) were selected and 8


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combined: Bacteria (taxonomic ID: 2), Archaea (taxonomic ID: 2,157) and Rattus (taxonomic ID: 10,114). FASTA sequence files were exported from all primary search results. These FASTA files were combined and redundancies were removed using dbtoolkit-v.4.1.524 23. The combined FASTA file containing all sequence data was used as a database for the subsequent secondary search. The secondary search was performed in OpenMS proteomics pipeline (TOPP) by the OMSSA search algorithm 24, 25. The peptide false discovery rate was set at 2%. The MetaProSIP node from OpenMS was used to detect the

15

N patterns in the MS spectra and to calculate the

relative isotope abundance and labelling ratio for each detected peptide15. Phylogenetic assignment of identified peptides from the metaproteomics and protein-SIP analysis was performed using UniPept 26. For functional protein classification, BlastP was performed using the Cluster of Orthologous Groups (COG) database (ftp://ftp.ncbi.nih.gov/pub/COG/COG/) from NCBI

27

(Supplement for details). The mass spectrometry proteomics data have been deposited

to the ProteomeXChange Consortium via the PRIDE partner repository with the dataset identifier PXD00311828.

Results Study design and HFD phenotype characterization The experimental setup and the workflow of the study are indicated in figure 1. The rats were adjusted to either a normal chow diet (CD) or to a high fat diet (HFD) (Supporting Information Table S1). During the treatment of 11 weeks the HFD rats developed a specific phenotype including increased body fat mass as well as alterations in glucose and lipid metabolism (Supporting Information Table S2). After exposing both groups for 12 h, 24 h, and 72 h to the experimental diet (ED) containing

15

N fully labelled protein, we investigated the microbiome in

the colon mucus to identify metabolically active taxa.

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Protein-SIP resulted in 1,850 bacterial peptides with 303 exhibiting

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15

N-incorporation. The

identified metabolically active species included a subset that was detected by 16S rRNA gene sequencing and conventional metaproteomics. For 16S rRNA gene sequencing, 823,078 reads (8,946 +/-4,847 reads per sample) were detected and annotated to 36 phyla and, at a higher resolution to 177 bacterial families. Of these, 12 phyla and 59 families were also identified as metabolically active in the mucus by protein-SIP. In metaproteomics, 8,407 non-redundant bacterial peptides were identified, annotated to 29 phyla and 228 bacterial families. All bacterial phyla (14) and families (79) identified as active by protein-SIP in the mucus were also identified using metaproteomics, indicating high reproducibility of sample extraction and measurement. Protein-SIP reveals subsets of metabolically active taxa in the mucosal community A schematic flow of possible nitrogen sources in the colon are indicated in figure 2A. The incorporation of

15

N is shown for three spectra in figure 2B. The peptide sequence was identified

by the fragmentation of the unlabelled mono-isotope peptide species (the furthest left peak in each spectra), and the incorporation, as relative isotope abundance (RIA), was calculated from the shift in the spectrum pattern caused by a change in the isotope distribution 29. In colonic mucus, the ratio

of the abundances of labelled bacterial peptides to unlabelled

bacterial peptides increased from 12 h to 72 h (Figure 2C), although the total number of identified bacterial peptides decreased over this period. There was no clear difference in the ratio of labelled to unlabelled peptides with respect to diet, although a slight delay in the labelling of rat protein was observed for the HFD cohort. The total number of bacterial peptides decreased in CD colon mucus from 346 at 12 h to 108 at 72 h, while, for HFD, the decrease was 316 at 12 h to 15 at 72 h. The decrease in the number of identified labelled peptides was assumed to be due to the dominance of the high labelling ratio and therefore a diminishing of the percentage of unlabelled mono-isotope peptide species that are necessary for protein

10


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identification (Supporting Information Figure S1B). The stronger decrease of identified peptides with HFD indicated accelerated protein turnover (Supporting Information Figure S1C). The distribution of the relative isotope abundance (RIA) values, as a measure of metabolic activity

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for all samples (Figure 2D), displayed a cluster from 20-50%. In colonic mucus, the

median RIA values for bacteria labelled peptides at 12 h after the shift to ED were 27% for CD and 26% for HFD. At 24 h, this value rose to 30% for CD and 31% for HFD. At 72 h it was 33% for CD and 30% for HFD, indicating that duration of the ED had no effect, and that there were no differences in the average

15

N uptake among bacteria species. We found RIA of 37% in CD and

24% in HFD in faeces at 12 h. At 24 h the RIA values for CD decreased to 29% but remained consistent for HFD at 23%. In colonic mucus, 209 labelled bacterial peptides were annotated to 14 phyla, of which, at a higher resolution, 188 were annotated to 79 bacterial families (Figure 3 A and B). Shifts in alpha-diversity are mostly time-dependent and beta-diversity shows strong timeand location dependence The 16S rRNA sequencing revealed that the dietary shift led to changes in the phylogeny of the active microbiota of the mucus layer when analysed by principal component analysis (PCA) (Figure 5A). These differences, however, were only partially reflected in alterations in the microbe alpha-diversity measured in terms of the Shannon Index, a combination of the richness and evenness of the distribution of taxa in a sample microbiome (Figure 5B). For the sequencing data, the alpha-diversity indicated little change in the taxa diversity in proximal colon mucus over time. In the medial and distal colon mucus, the phylogenetic make-up of the microbiota showed more variation over time but not between the prior diets. The prior diet affects most strongly the alpha-diversity in faeces, with a higher alpha-diversity on high fat diet. The number of families detected suggested that this difference was most likely the result of the number of active families (mean: CD =14, HFD =17 at baseline; CD =12; HFD =14 at 72 h) rather than of abundance. The 11



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difference in the abundance of several bacterial taxa raises the question for the effects on betadiversity, i.e. the degree of dissimilarity of the composition of the microbiota from different samples (while beta-diversity is low when all samples possess the same composition, it is maximum if they don’t share any taxon)

31

. To consider beta-diversity helps exploring

compositional changes in the course of time or the response to altered habitat conditions (Figure 6). For both dietary cohorts after the shift to ED, there are large and persistent compositional changes in comparison to the baseline detected in the proximal colon. For the medial, the distal colon and faeces there is a time dependent consistent pattern. The more distal the compartment the later the maximal change occurs. In the case of the faecal samples there is little shift in betadiversity after 12 h and after the maximal alteration after 24 h the effect is still detectable after 72 h. It has to be assumed that this pattern is caused by two overlapping effects. First the persistent difference at the proximal colon reflects the consistent delivery of nutrients from the different chow. The mucosal microbiota adapts to this new nutrition and thereby causes a further alteration of the nutrients that reach the more distal compartments. This leads to a delayed shift in beta-diversity in the other compartments.

Diet

switch

promotes

mucus-grazing

Verrucomicrobia

and

H2S-producing

Desulfovibrionaceae in HFD adapted microbiota Since diversity of the overall microbial community has only slightly altered in response to dietary change, we tested for specific changes in the community structure. Remarkable differences in relative abundance of active bacteria taxa in the two prior diets were found by 16S rRNA at the phylum level for Verrucomicrobia (Figure 4A). Akkermannsia muciniphila, was described to feed primarily on mucus proteins and has been found to be inversely correlated with obesity and hence modulating the metabolism of the host

32

. In the present study, the change is greatest for

the shift from HFD to 15N-ED but is eliminated over time, indicating a re-adaptation to reduced fat content in diet. Changes at the family level were complex with the Desulfovibrionaceae showing 12


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a sharp increase in abundance at 12 h and 24 h after dietary shift. After 72 h of the

15

N-ED we

observed a decrease even below baseline level (Figure 4B). The degree of increase in the abundance of Desulfovibrionaceae differed in the proximal and distal colon, whereas most other Proteobacteria decreased in abundance. Desulfovibrionaceae are known to produce significant amounts of H2S, which can cause oxidative stress and is correlated with inflammatory bowel disease 33. Metaproteomics reveal changes in metabolic function of mucosal bacteria We performed the metaproteomics study with animals on the unlabelled ED diet since identification of peptides from labelled samples is always more difficult due to the monoisotopic peaks used for identification being generally less abundant in labelled samples. Seventy-two hours after the change from CD and HFD to the unlabelled experimental diet (14N-ED), differences between diets were observed in the number of proteins related to tricarboxylic acid cycle (TCA) (Figure 7A). The relative percentage nearly doubled after HFD (approx. 1.2%) in comparison to CD (approx. 0.6%) and, after the shift to

14

N-ED, it decreased in HFD, while the

values for CD increased. At the functional level, the shift from CD to 14N-ED was associated with different adaptations among bacterial families (Figure 7B). While amino acid transport and metabolism increased in Actinobacteria, it was decreased in Proteobacteria. These effects were also observed accompanying the shift from HFD to

14

N-ED. Concerning Firmicutes which are

presumed to benefit from a high fat diet, there was some evidence that their reaction to the diet shift was an increase in the levels of proteins involved in coenzyme metabolism and transport as well as a decrease of proteins related to motility.

Discussion When any unnatural disturbances are avoided the delivery of heavy stable isotopes into the colon for metabolic labelling is restricted by the natural occurrence of substrates. With respect to

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carbon, only polymers reach the colon, and, in recent research using RNA-based approaches, 13

C-sialic acid or inulin were used to detect species differentially feeding on these substrates

35

. An unspecific labelling strategy was applied by Berry et al

36

34

by incubating cecum samples

with heavy water (D2O) and simultaneously providing various substrates. Quantification at the cellular level provided insight into the heterogeneity of metabolism. The need for phylum-specific FISH-probes limits this approach to a selected range of bacterial family in one experiment 37. In every approach using stable isotopes there is always the concern that an excess of labelled substrate alters the habitat and hence perturbs the phylogeny and physiology of the microbial community. The

15

N-labelled proteins used herein replaced the

14

N protein content of normal

chow and represented a physiological component of the colon content. The non-specific labelling pattern and the broad spectrum of families detected with protein-SIP support the concept of using

15

N-labelled proteins in animal diet as a general label for metabolically active

microbes. Since the uptake of proteins and amino acids is a very general part of the metabolism there may be very few highly specialized species that do not rely on this nitrogen source and will not be identified as metabolically active by

15

N-protein-SIP. In contrast, the mucus-feeding

Verrucomicrobia were also labelled, although they are considered to obtain their nitrogen source from the host. This could hint at the opportunistic metabolism of Verrucomicrobia by high fat feeding. Although the label may be derived by utilizing labeled host proteins which were also found to become labeled. A clear limitation of metaproteomics is the level of complexity of the samples, so that low abundance species will be missed. To investigate the zone of microbiome-host interaction, we analysed the mucus layer, and so we also reduced the complexity of the community. Microbiome studies have often the difficulty to demonstrate the reproducibility of community composition across cohorts. This can be due to high variations in the methods used for microbiome analysis. Furthermore, the natural biological variability of the microbiome between 14


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different hosts can also be a cause

38

.One widely used applied method for taxonomic microbiota

profiling is based on 16S rRNA gene sequencing. The complex steps process of 16S rRNA gene microbiome analysis involves sample collection, DNA extraction and purification, PCR and amplicon purification, sequencing and bioinformatics. But PCR primers for 16S rRNA amplification were observed to cause the highest variations among the datasets39. Taxonomicbased approaches are sufficient to identify the broad community structure, but multi-molecular approaches (16S rRNA gene sequencing, proteomics, stable isotope probing) provide a more reliable assessment of the bacterial community structure. Less abundant or rare taxa are often ignored in data interpretation because the identification is often seen as unreliable. However, we observed relevant low abundant taxa identified by two different methods (16S rRNA gene sequencing and protein-SIP). The SIP approach holds promise because only metabolically active taxa will be identified. In addition to labelling by

15

N, a general labelling by deuterium, as recently reported 40, might be

feasible in in vivo models. A potential drawback of deuterium might be the rapid dilution into the host and back. This is not likely for

15

N labelled protein since, for labelling time shorter than 24 h

there is no substantial reflux of nitrogen from the host into the microbiome. Labelling with 15N will also allow analysis of the flux of labelled compounds in the host body in terms of nitrogen containing metabolites in the serum of the host. A drawback of the applied strategy is that

15

N

labelling lacks specificity, and, for detailed studies of metabolic activity of single members of the microbiome, labelling of fatty acids or carbohydrates would be necessary. A reverse labelling strategy, from host to the microbiome, has been used by

16

using nano-SIMS and FISH to

identify specific families as mucus-feeding. The outcomes of the study are in line with ecological theory. The results on the dietary effects on the composition of the microbiome can be related to Metacommunity Assembly Theory

41

that

underpins the importance of the environmental conditions like diet to which the microbiome 15



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adapts. Lozupone et al. (2014) highlight that each host has a relatively stable microbiome which can be shifted through a long-term dietary change 42, 43 7 but which is highly resilient to any shortterm dietary change. Evidently, the duration of a dietary change determines the persistence of the induced assembly shift. Diet influences the self-regulating feedback between microbiome and host. Berry et al. (2014) add that the induced assembly shifts do not alter the overall microbial diversity (alpha-diversity, Shannon-index), but the similarity of the microbiomes under the different diets (beta-diversity, Bray-Curtis index) 11. Despite this structural insensitivity, we found hints on diet-mediated alterations of the metabolic activity of the microbiome both in CD and HFD. This can be related to the theories of Ecological Energetics

44

and Dynamic Energy Budget

45

. Both theories address the response of organisms

to alterations in the energy supply and their strategies to allocate the up-taken energy to various activities (e.g. growth, motility, metabolic activity). Altered energy supply, here through a dietary change, can alter the activity patterns of microbes, their ecological interaction (competition, trophic interaction) and response to the environment. It can even alter the pathways of resource uptake (indirectly via the host or directly from the diet). Accumulated energy reserves can also explain the emergence of memory effects in the functional adaptation of the microbiome to a dietary change. This calls for a systematic assessment of the interplay of structural and functional aspects when trying to understand the human microbiome and its response to environmental change. Various studies address situations where the community assembly is changing, while certain ecological functions remain unchanged. This is indicated as sign for functional redundancy

42

. In the present case, the constellation is opposite that can be

interpreted as indication for adaptability on the level of the individual functional traits which is of increasing importance in community ecology 46. In conclusion, our study reveals some evidence for differences in response of the mucosal microbiota with prior adaption to either CD or HFD to a shift in diet. Noteworthy, are the differences along the gut and over time which stresses the necessity to take longitudinal 16


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sectional studies of the colon into account. The usage of an index marker for metabolic activity will support future studies focussing on functional aspects in microbiome research.

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Supporting Information The following files are available free of charge at ACS website http://pubs.acs.org: Supporting Information Table S 1: Macro- and micronutrient components of the diet Supporting Information Table S 2: Measured phenotype parameters for samples rats Supporting Information Table S 3: PCR primers Supporting Information Figure S 1: Protein-SIP experiment of feces samples Supporting Information Figure S 2: 15N-protein-SIP labelled bacterial taxa. Supporting Information Figure S 3: 16S rRNA gene sequencing Supporting Information Figure S 4: Metaproteomic analysis Supporting Information Material and Methods Supporting Information Results

Author Contribution Study design: AO, MvB, SH and JS. Experimental work and data analysis: AO, SH, NS, MH, SL, FH, YK, HK, and NJ. Interpretation of data: AO, SH, NJ, KF and MvB. Drafting of paper: MvB, SH, and NJ with partial contributions of NS, AO, HT, HS, FH, JS, and KF. Contributions to the revision: MvB, NJ, SH, and KF. Approval of final draft: all authors.

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Acknowledgements We are also grateful for funding by IFB project K723 for HT, AO, SH, and YK. .We are also grateful for funding of SH by the DFG priority program SPP1656, partial funding of JS by DFG priority program SPP1319, and partial funding of MvB and NJ by DFG SFB Aquadiva.

Conflict of interest We declare that there is no conflict of interest for any of the authors.

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References 1. Li, J.; Jia, H.; Cai, X.; Zhong, H.; Feng, Q.; Sunagawa, S.; Arumugam, M.; Kultima, J. R.; Prifti, E.; Nielsen, T.; Juncker, A. S.; Manichanh, C.; Chen, B.; Zhang, W.; Levenez, F.; Wang, J.; Xu, X.; Xiao, L.; Liang, S.; Zhang, D.; Zhang, Z.; Chen, W.; Zhao, H.; Al-Aama, J. Y.; Edris, S.; Yang, H.; Wang, J.; Hansen, T.; Nielsen, H. B.; Brunak, S.; Kristiansen, K.; Guarner, F.; Pedersen, O.; Dore, J.; Ehrlich, S. D.; Meta, H. I. T. C.; Bork, P.; Wang, J.; Meta, H. I. T. C., An integrated catalog of reference genes in the human gut microbiome. Nat Biotechnol 2014, 32, (8), 834-41. 2. Jacobsen, U. P.; Nielsen, H. B.; Hildebrand, F.; Raes, J.; Sicheritz-Ponten, T.; Kouskoumvekaki, I.; Panagiotou, G., The chemical interactome space between the human host and the genetically defined gut metabotypes. Isme J 2013, 7, (4), 730-42. 3. Haange, S. B.; Oberbach, A.; Schlichting, N.; Hugenholtz, F.; Smidt, H.; von Bergen, M.; Till, H.; Seifert, J., Metaproteome analysis and molecular genetics of rat intestinal microbiota reveals section and localization resolved species distribution and enzymatic functionalities. J Proteome Res 2012, 11, (11), 5406-17. 4. Li, H.; Limenitakis, J. P.; Fuhrer, T.; Geuking, M. B.; Lawson, M. A.; Wyss, M.; Brugiroux, S.; Keller, I.; Macpherson, J. A.; Rupp, S.; Stolp, B.; Stein, J. V.; Stecher, B.; Sauer, U.; McCoy, K. D.; Macpherson, A. J., The outer mucus layer hosts a distinct intestinal microbial niche. Nature communications 2015, 6, 8292. 5. Leone, V. A.; Cham, C. M.; Chang, E. B., Diet, gut microbes, and genetics in immune function: can we leverage our current knowledge to achieve better outcomes in inflammatory bowel diseases? Current opinion in immunology 2014, 31c, 16-23. 6. Tremaroli, V.; Backhed, F., Functional interactions between the gut microbiota and host metabolism. Nature 2012, 489, (7415), 242-249. 7. Xu, Z.; Knight, R., Dietary effects on human gut microbiome diversity. Br J Nutr 2015, 113 Suppl, S1-5. 8. Muegge, B. D.; Kuczynski, J.; Knights, D.; Clemente, J. C.; Gonzalez, A.; Fontana, L.; Henrissat, B.; Knight, R.; Gordon, J. I., Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 2011, 332, (6032), 970-4. 9. Wu, G. D.; Chen, J.; Hoffmann, C.; Bittinger, K.; Chen, Y. Y.; Keilbaugh, S. A.; Bewtra, M.; Knights, D.; Walters, W. A.; Knight, R.; Sinha, R.; Gilroy, E.; Gupta, K.; Baldassano, R.; Nessel, L.; Li, H.; Bushman, F. D.; Lewis, J. D., Linking long-term dietary patterns with gut microbial enterotypes. Science 2011, 334, (6052), 105-8. 10. De Filippo, C.; Cavalieri, D.; Di Paola, M.; Ramazzotti, M.; Poullet, J. B.; Massart, S.; Collini, S.; Pieraccini, G.; Lionetti, P., Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc Natl Acad Sci U S A 2010, 107, (33), 14691-14696. 11. David, L. A.; Maurice, C. F.; Carmody, R. N.; Gootenberg, D. B.; Button, J. E.; Wolfe, B. E.; Ling, A. V.; Devlin, A. S.; Varma, Y.; Fischbach, M. A.; Biddinger, S. B.; Dutton, R. J.; Turnbaugh, P. J., Diet rapidly and reproducibly alters the human gut microbiome. Nature 2014, 505, (7484), 559-+. 12. Wu, C. C.; MacCoss, M. J.; Howell, K. E.; Matthews, D. E.; Yates, J. R., 3rd, Metabolic labeling of mammalian organisms with stable isotopes for quantitative proteomic analysis. Anal Chem 2004, 76, (17), 4951-9. 13. Neufeld, J. D.; Wagner, M.; Murrell, J. C., Who eats what, where and when? Isotope-labelling experiments are coming of age. Isme J 2007, 1, (2), 103-10. 14. Jehmlich, N.; Schmidt, F.; Taubert, M.; Seifert, J.; von Bergen, M.; Richnow, H. H.; Vogt, C., Comparison of methods for simultaneous identification of bacterial species and determination of metabolic activity by protein-based stable isotope probing (Protein-SIP) experiments. Rapid Commun Mass Spectrom 2009, 23, (12), 1871-8. 20


Page 21 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


15. Sachsenberg, T.; Herbst, F. A.; Taubert, M.; Kermer, R.; Jehmlich, N.; von Bergen, M.; Seifert, J.; Kohlbacher, O., MetaProSIP: Automated Inference of Stable Isotope Incorporation Rates in Proteins for Functional Metaproteomics. J Proteome Res 2015, 14, (2), 619-627. 16. Berry, D.; Stecher, B.; Schintlmeister, A.; Reichert, J.; Brugiroux, S.; Wild, B.; Wanek, W.; Richter, A.; Rauch, I.; Decker, T.; Loy, A.; Wagner, M., Host-compound foraging by intestinal microbiota revealed by single-cell stable isotope probing. Proceedings of the National Academy of Sciences of the United States of America 2013, 110, (12), 4720-5. 17. Yu, Z.; Morrison, M., Improved extraction of PCR-quality community DNA from digesta and fecal samples. BioTechniques 2004, 36, (5), 808-12. 18. McDonald, D.; Price, M. N.; Goodrich, J.; Nawrocki, E. P.; DeSantis, T. Z.; Probst, A.; Andersen, G. L.; Knight, R.; Hugenholtz, P., An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. Isme J 2012, 6, (3), 610-8. 19. Haas, B. J.; Gevers, D.; Earl, A. M.; Feldgarden, M.; Ward, D. V.; Giannoukos, G.; Ciulla, D.; Tabbaa, D.; Highlander, S. K.; Sodergren, E.; Methe, B.; DeSantis, T. Z.; Human Microbiome, C.; Petrosino, J. F.; Knight, R.; Birren, B. W., Chimeric 16S rRNA sequence formation and detection in Sanger and 454pyrosequenced PCR amplicons. Genome Res 2011, 21, (3), 494-504. 20. Beloqui, A.; Guazzaroni, M. E.; Ferrer, M., Procedures for Protein Isolation in Pure Culture and Microbial Communities. In Handbook of Hydrocarbon and Lipid Microbiology, Timmis, K. N., Ed. Springer Berlin Heidelberg: Berlin, Heidelberg, 2010; pp 4183-4194. 21. Jagtap, P.; Goslinga, J.; Kooren, J. A.; McGowan, T.; Wroblewski, M. S.; Seymour, S. L.; Griffin, T. J., A two-step database search method improves sensitivity in peptide sequence matches for metaproteomics and proteogenomics studies. Proteomics 2013, 13, (8), 1352-7. 22. Herbst, F. A.; Bahr, A.; Duarte, M.; Pieper, D. H.; Richnow, H. H.; von Bergen, M.; Seifert, J.; Bombach, P., Elucidation of in situ polycyclic aromatic hydrocarbon degradation by functional metaproteomics (protein-SIP). Proteomics 2013, 13, (18-19), 2910-20. 23. Martens, L.; Vandekerckhove, J.; Gevaert, K., DBToolkit: processing protein databases for peptide-centric proteomics. Bioinformatics 2005, 21, (17), 3584-5. 24. Kohlbacher, O.; Reinert, K.; Gropl, C.; Lange, E.; Pfeifer, N.; Schulz-Trieglaff, O.; Sturm, M., TOPP-the OpenMS proteomics pipeline. Bioinformatics 2007, 23, (2), e191-7. 25. Sturm, M.; Bertsch, A.; Gropl, C.; Hildebrandt, A.; Hussong, R.; Lange, E.; Pfeifer, N.; SchulzTrieglaff, O.; Zerck, A.; Reinert, K.; Kohlbacher, O., OpenMS - an open-source software framework for mass spectrometry. BMC bioinformatics 2008, 9, 163. 26. Mesuere, B.; Devreese, B.; Debyser, G.; Aerts, M.; Vandamme, P.; Dawyndt, P., Unipept: tryptic peptide-based biodiversity analysis of metaproteome samples. Journal of proteome research 2012, 11, (12), 5773-80. 27. Tatusov, R. L.; Galperin, M. Y.; Natale, D. A.; Koonin, E. V., The COG database: a tool for genomescale analysis of protein functions and evolution. Nucleic acids research 2000, 28, (1), 33-6. 28. Vizcaino, J. A.; Cote, R. G.; Csordas, A.; Dianes, J. A.; Fabregat, A.; Foster, J. M.; Griss, J.; Alpi, E.; Birim, M.; Contell, J.; O'Kelly, G.; Schoenegger, A.; Ovelleiro, D.; Perez-Riverol, Y.; Reisinger, F.; Rios, D.; Wang, R.; Hermjakob, H., The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic acids research 2013, 41, (Database issue), D1063-9. 29. Jehmlich, N.; Schmidt, F.; Taubert, M.; Seifert, J.; Bastida, F.; von Bergen, M.; Richnow, H. H.; Vogt, C., Protein-based stable isotope probing. Nat Protoc 2010, 5, (12), 1957-66. 30. Seifert, J.; Taubert, M.; Jehmlich, N.; Schmidt, F.; Volker, U.; Vogt, C.; Richnow, H. H.; von Bergen, M., Protein-based stable isotope probing (protein-SIP) in functional metaproteomics. Mass spectrometry reviews 2012, 31, (6), 683-697. 31. Whittaker, R. H., Evolution and Measurement of Species Diversity. Taxon 1972, 21, (2/3), 213251. 21



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 33

32. Everard, A.; Belzer, C.; Geurts, L.; Ouwerkerk, J. P.; Druart, C.; Bindels, L. B.; Guiot, Y.; Derrien, M.; Muccioli, G. G.; Delzenne, N. M.; de Vos, W. M.; Cani, P. D., Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Proc Natl Acad Sci U S A 2013, 110, (22), 9066-71. 33. Macfarlane, S.; Steed, H.; Macfarlane, G. T., Intestinal bacteria and inflammatory bowel disease. Crit Rev Clin Lab Sci 2009, 46, (1), 25-54. 34. Young, W.; Egert, M.; Bassett, S. A.; Bibiloni, R., Detection of sialic acid-utilising bacteria in a caecal community batch culture using RNA-based stable isotope probing. Nutrients 2015, 7, (4), 2109-24. 35. Shao, Y.; Arias-Cordero, E.; Guo, H.; Bartram, S.; Boland, W., In vivo Pyro-SIP assessing active gut microbiota of the cotton leafworm, Spodoptera littoralis. PLoS One 2014, 9, (1), e85948. 36. Berry, D.; Mader, E.; Lee, T. K.; Woebken, D.; Wang, Y.; Zhu, D.; Palatinszky, M.; Schintlmeister, A.; Schmid, M. C.; Hanson, B. T.; Shterzer, N.; Mizrahi, I.; Rauch, I.; Decker, T.; Bocklitz, T.; Popp, J.; Gibson, C. M.; Fowler, P. W.; Huang, W. E.; Wagner, M., Tracking heavy water (D2O) incorporation for identifying and sorting active microbial cells. Proc Natl Acad Sci U S A 2015, 112, (2), E194-203. 37. Musat, N.; Foster, R.; Vagner, T.; Adam, B.; Kuypers, M. M., Detecting metabolic activities in single cells, with emphasis on nanoSIMS. FEMS Microbiol Rev 2012, 36, (2), 486-511. 38. Shafquat, A.; Joice, R.; Simmons, S. L.; Huttenhower, C., Functional and phylogenetic assembly of microbial communities in the human microbiome. Trends Microbiol 2014, 22, (5), 261-6. 39. Hiergeist, A.; Reischl, U.; Priority Program Intestinal Microbiota Consortium/ quality assessment, p.; Gessner, A., Multicenter quality assessment of 16S ribosomal DNA-sequencing for microbiome analyses reveals high inter-center variability. International journal of medical microbiology : IJMM 2016, 306, (5), 334-42. 40. Justice, N. B.; Li, Z.; Wang, Y.; Spaudling, S. E.; Mosier, A. C.; Hettich, R. L.; Pan, C.; Banfield, J. F., (15)N- and (2)H proteomic stable isotope probing links nitrogen flow to archaeal heterotrophic activity. Environ Microbiol 2014, 16, (10), 3224-37. 41. Costello, E. K.; Stagaman, K.; Dethlefsen, L.; Bohannan, B. J.; Relman, D. A., The application of ecological theory toward an understanding of the human microbiome. Science 2012, 336, (6086), 125562. 42. Lozupone, C. A.; Stombaugh, J. I.; Gordon, J. I.; Jansson, J. K.; Knight, R., Diversity, stability and resilience of the human gut microbiota. Nature 2012, 489, (7415), 220-30. 43. Daniel, H.; Moghaddas Gholami, A.; Berry, D.; Desmarchelier, C.; Hahne, H.; Loh, G.; Mondot, S.; Lepage, P.; Rothballer, M.; Walker, A.; Bohm, C.; Wenning, M.; Wagner, M.; Blaut, M.; Schmitt-Kopplin, P.; Kuster, B.; Haller, D.; Clavel, T., High-fat diet alters gut microbiota physiology in mice. The ISME journal 2014, 8, (2), 295-308. 44. Tomlinson, S.; Arnall, S. G.; Munn, A.; Bradshaw, S. D.; Maloney, S. K.; Dixon, K. W.; Didham, R. K., Applications and implications of ecological energetics. Trends in ecology & evolution 2014, 29, (5), 280-90. 45. Sousa, T.; Domingos, T.; Poggiale, J. C.; Kooijman, S. A., Dynamic energy budget theory restores coherence in biology. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 2010, 365, (1557), 3413-28. 46. Bolnick, D. I.; Amarasekare, P.; Araujo, M. S.; Burger, R.; Levine, J. M.; Novak, M.; Rudolf, V. H.; Schreiber, S. J.; Urban, M. C.; Vasseur, D. A., Why intraspecific trait variation matters in community ecology. Trends in ecology & evolution 2011, 26, (4), 183-92.

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Figure Legends Figure 1: Experimental design (CD: chow diet, HFD: high fat diet, experimental diet,

15

N-ED:

15

N-fully labelled

14

N-ED: non-labelled experimental diet). The rats were adapted to a CD or

HFD from age 4 weeks to 14 weeks (upper left). After baseline measures, rats were fed with an experimental diet with either

15

N-fully labelled or unlabelled protein (lower left). Three areas of

the colon were sampled (upper right), and mucus was harvested separately. Analysis of metabolic activity was performed by

15

N-protein stable isotope probing, of metabolic function by

metaproteomics of unlabelled samples, and of bacterial diversity by 16SrRNA sequencing (midand lower right) taxa identified as metabolically active by protein-SIP were used for further analysis of genetic diversity and functionality. Rat and colon graphics courtesy of Sven-Bastiaan Haange.

Figure 2: Protein-SIP analysis of colon mucus (CD: chow diet; HFD: high fat diet;

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N-ED:

15

N-

labelled experimental diet). A: Possible colon nitrogen sources. Blue arrows represent nitrogencontaining compound flux into microbiota. B: MS spectra of measured peptides (left) and theoretical spectra (right) modelled with the assigned relative isotope abundance (RIA) and labelling ratio (LabR) values. In the theoretical peptide model, blue represents natural isotope abundance (before introduction of label) and red that part of the spectra attributed to the introduced

15

N-label. C: Relative abundance of labelled bacteria and rat peptides identified in

colon mucus at 12 h, 24 h, and 72 h for chow diet fed rats and high fat diet fed rats after the change to the experimental diet. Black =peptides identified with labelling. Grey =without labelling. Numbers over columns are the number of peptides identified. D: Histogram depicting the relative abundance of identified labelled bacterial peptides binned into 5% RIA increments. Figure 3: Bacteria phyla (A) and families (B) detected as metabolically active in rat colon mucus by

15

N-protein-SIP. The heat map depicts the number of the bacterial peptides identified with

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15

N-label in which the phylogeny could be resolved to the level of phyla and family. The numbers

of identified peptides from each mucus location are summed. The three time points represent the sampling points after the

15

N labelled experimental diet (15N-ED) was provided. CD =chow

diet. HFD =high fat diet. Figure 4: Change in distribution of bacterial phylogeny in the colon mucus identified by 16S rRNA gene sequencing considering the bacteria phyla (A) or families (B) identified as active by protein-SIP after the shift to the experimental diet. [proximal: proximal colon mucus; medial: medial colon mucus; distal: distal colon mucus] A: Distribution of mean relative abundance of bacteria phyla determined by 16S rRNA gene sequencing and filtered by protein-SIP. CD =chow diet, HFD =high fat diet,

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N-ED =15N fully labelled experimental diet. 12 h, 24 h, and 72 h =time

post-shift to the experimental diet. B: Heat map visualises the log2 fold-change in mean relative abundance at 12 h, 24 h, or 72 h after switch to the experimental diet compared to the baseline (CD or HFD). Only bacterial families identified as being active in the protein-SIP experiment and those which were unique for before or after diet change were considered. Bacteria families identified in at least two replicates only at a single time point were considered unique. Red: higher relative abundance after change to the experimental diet. Blue: lower relative abundance after change to the experimental diet. Black: family unique before change to the experimental diet. Green: family unique after change to the experimental diet. Figure 5: Adaptation of the active microbiota to the experimental diet (ED) from baseline high fat diet (HFD) and chow diet (CD) rats. A: Principal Component Analysis (PCA) of the bacterial family distribution in 16S rRNA gene sequencing filtered for active families as identified by

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N-

labelled peptides in the Protein-SIP analysis. B: Diversity as determined by the Shannon Index for the baseline chow diet (CD), high fat diet (HFD), and experimental diet (ED). Only active bacterial families determined by 15N-labelled peptides in the protein-SIP analysis were analysed.

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Figure 6: Time and location dependent effects on beta-diversity. Dissimilarity between baseline (CD chow diet; HFD high fat diet) and time point after shift to the experimental diet (ED). BrayCurtis Dissimilarity values were calculated from the 16S rRNA sequencing data for each possible combination of baseline replicates and time point replicates (either 6 or 9 values) and using only the bacterial families identified as being active by Protein-SIP. (p-value: *

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