Cumulative effect of yeast extract and fructooligosaccharide supplementation on composition and metabolic activity of elderly colonic microbiota in vitro

Journal of Functional Foods 52 (2019) 43–53

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Cumulative effect of yeast extract and fructooligosaccharide supplementation on composition and metabolic activity of elderly colonic microbiota in vitro Eun-Hee Dooa, Clarissa Schwaba, Christophe Chassarda,b, Christophe Lacroixa, a b

T

⁎

ETH Zurich, Laboratory of Food Biotechnology, Department of Health Sciences and Technology, Schmelzbergstrasse 7, 8092 Zurich, Switzerland INRA Aurillac, l'Unité de Recherche Fromagères (URF), Centre de recherche Auvergne-Rhônes-Alpes, 20 rue Côte de Reyne, 15000 Aurillac, France

A R T I C LE I N FO

A B S T R A C T

Keywords: Elderly colonic microbiota Fructooligosaccharides Yeast extract Nucleosides Butyrate

Concurrent with a decline in general health, elderly gut microbiota is characterized by reduced stability compared to adults, and is impacted by living condition and diet. Dietary supplementation was suggested to stabilize and promote elderly gut microbiota. We used two PolyFermS models to investigate the impact of fructooligosaccharides (FOS), nucleosides (NS), and yeast extract (YE) supplementation on elderly colonic microbiota in vitro. NS, YE and FOS tested separately and combined at different addition levels increased the abundance of butyrate producers, lactobacilli and bifidobacteria. FOS and YE cumulatively increased butyrate levels, while propionate productions were decreased. Gene expression analysis indicated that FOS enhanced abundance of transcripts related to ‘Di-and Oligosaccharides’ and ‘Fermentation’ while treatments with FOS and YE alone and in combination altered the transcriptional profile of ‘Amino Acids and Derivatives’. Our findings suggest that YE rich in nucleotides, amino acids, vitamins and minerals, enhance the butyrogenic effect of FOS.

1. Introduction The composition of the gut microbiota is relatively stable throughout adult life, but microbiota stability and diversity reduce in old age (> 65–70 years) concurrently with a decline in general health and well-being. Health state and gut microbiota composition of the elderly has been previously linked to living condition (residential-care or community dwelling) and diet (Claesson et al., 2012; Jackson et al., 2016; Jeffery, Lynch, & O'Toole, 2016). In long term residents, healthassociated bacteria including members of the butyrate- producing Lachnospiraceae were replaced by bacterial taxa linked to increased fraility (Claesson et al., 2012). Methanogenic archaea, namely members of the Methanobrevibacteriales and Methanomassiliicoccales, were a second factor associated with health state of the elderly (Borrel et al., 2017). Methanomassiliicoccales of the free-living clade were more abundant in elderly living in residential care, while members of the host-associated clade were more prominent in community dwellers (Borrel et al., 2017). Dietary supplementation has been suggested to improve intestinal health of the elderly by beneficially modulating the gut microbiota composition and boosting metabolic activity (Toward, Montandon,

⁎

Walton, & Gibson, 2012). Short-chain fatty acids (SCFA, acetic, propionic, butyric, formic and valeric acid) and branched-chain fatty acids (BCFA, isovaleric and isobutyric acid) are produced by microbial fermentation of carbohydrates and amino acids in the large intestine. SCFA contribute to the energy supply of the host and modulate the host immune system (Morrison and Preston, 2016); SCFA formation depends on diet and microbiota composition (Ríos-Covián et al., 2016). In vitro and in vivo studies showed that dietary carbohydrates isomaltooligosaccharides, fructooligosaccharides (FOS) and inulin enhanced bifidobacterial population and SCFA production of elderly gut microbiota (Likotrafiti, Tuohy, Gibson, & Rastall, 2014; Liu, Gibson, & Walton, 2016; Toward et al., 2012). FOS and inulin are considered prebiotics, which are currently defined as a substrate that is selectively utilized by host microorganisms conferring a health benefit, for example by stimulation of bifidobacteria and eventually formation of butyrate by cross-feeding mechanisms (Gibson et al., 2017). FOS are degraded by bacterial glycoslyl hydrolases (glycosyl hydrolase family 68, β-fructofuranosidases), which frequently occur in Bifidobacterium spp. (Pokusaeva, Fitzgerald, & van Sinderen, 2011). Another less investigated class of dietary components that may impact intestinal microbiota growth and/or composition include

Corresponding author at: Schmelzbergstrasse 7, 8092 Zürich, Switzerland. E-mail address: [email protected] (C. Lacroix).

https://doi.org/10.1016/j.jff.2018.10.020 Received 23 August 2018; Received in revised form 21 October 2018; Accepted 21 October 2018 1756-4646/ © 2018 Elsevier Ltd. All rights reserved.

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during the last 3 consecutive days of each treatment period when stability was observed (Fig. S1). All reactors were operated at 37 °C and 120 rpm under anaerobic conditions by continually flushing the headspace of all reactors and medium bottles with CO2. NaOH (2.5 M) was added to control a pH of 5.7 in all reactors.

nitrogen-containing compounds such as nucleotides (NT) (Doo, Chassard, Schwab, & Lacroix, 2017; Possemiers et al., 2013). These components are present in protein-rich food, such as milk. Many bacterial species are able to synthesize NT de novo, yet competition for NT and precursors within the gut microbiota is high as they are essential for bacterial growth and proliferation (Kilstrup, Hammer, Ruhdal Jensen, & Martinussen, 2005). Auxotrophic bacteria require purines or pyrimidines, which they convert to NT using salvage pathways. For NT synthesis, prototrophic bacteria preferentially take up and metabolize nucleobases and nucleosides (NS) using salvage pathways but can also synthesize NT de novo (Kilstrup et al., 2005). NS most likely reach the colon after digestion of nucleic acids and NT by intestinal nucleotidases or alkaline phosphatases (Carver and Walker, 1995; Uauy et al., 1994). Yeast extracts (YE) represent a rich source of NT and NS and also contain proteins, amino acids, B-vitamins and minerals. Dietary NS and YE were previously shown to modify infant gut microbiota composition, and to increase SCFA formation, especially butyrate, in a dose-dependent manner (Doo et al., 2017). Dietary NT have been shown to enhance the performance in monogastric animals by promoting the renewal of epithelial cells and by influencing the activity of the gut microbiota (Sauer, Mosenthin, & Bauer, 2011). In this study, we hypothesized that dietary NS or YE supplementation alone would increase butyrate formation in elderly colonic microbiota, and that the combination with FOS, with well-characterized butyrogenic effects, would cumulatively enhance butyrate production. Such increase in butyrate could be of special importance in the elderly, considering that a decrease of butyrate producers has been associated with increased fraility (Claesson et al., 2012). Moreover, living in residential care was associated with a less diverse diet and lower butyrate levels in feces (Claesson et al., 2012). To test this hypothesis, we applied an in vitro continuous intestinal fermentation model based on the PolyFermS system and mimicking the elderly proximal colon conditions that was recently developed by Fehlbaum et al. (2015). Two PolyFermS fermentation models using immobilized fecal microbiota of healthy women were performed under the proximal colon conditions (Fehlbaum et al., 2015). NS, three YE products with low (LYE), standard (SYE) and high (HYE) NT content, and FOS were tested at different supplementation levels alone and in combination treatments for their effects on the composition (main population groups by qPCR and microbiota profile by 16S rRNA sequencing) and on the activity (short chain fatty acids by HPLC, metatranscriptomics) of the modeled gut microbiota.

2.2. Fecal microbiota immobilization and fermentation procedure Fresh fecal samples were collected from two healthy, communitydwelling, elderly women aged 71 (F1) and 78 (F2) years, and were processed as described previously to produce fecal beads according to Fehlbaum et al. (2015). The Ethics Committee of ETH-Zurich exempted this study from review because sample collection was not in terms of intervention. Nevertheless, an informed written consent was obtained from the donors. After microbiota immobilization, gel beads with a diameter between 1 and 2 mm were added (30% (v/v)) anaerobically to the inoculum reactor containing fresh medium for total working volumes of 200 and 225 mL for F1 and F2, respectively. Fermentation was initiated in batch, and was switched to continuous mode as described before. Briefly, five batch fermentations were performed for a total of 60 h to colonize the beads before continuous mode was initiated with medium flow rate of 25 mL h−1 for five days. 2.3. Fermentation medium and NS, YE and FOS supplementation The fermentation medium used was adapted from a previously described medium simulating the chyme arriving in the proximal colon of adults (Table S1). Medium constituents were dissolved in distilled water, pH was adjusted with 5 M NaOH to pH 5.7, and autoclaved at 121 °C for 20 min. After the medium was cooled, 1 mL of filter sterilized vitamin solution was added to 1 L medium. With the exception of soy peptone (Labo-Life Sàrl, Pully, Switzerland), YE) and KH2PO4 (VWR International, Dietikon, Switzerland), chemicals were obtained from Sigma-Aldrich (Buchs, Switzerland). Three different YE products LYE, SYE, and HYE (Alltech, Inc., Kentucky, USA), that contained 6.1, 10.6 and 23.7% NT equivalents (Table 1), respectively, FOS (Fibrulose F97, Cosucra, Warcoing, Belgium), and a NS mixed containing adenosine, cytidine, uridine, guanosine, and inosine, were tested alone and in combination. Supplements dissolved in water were sterilized before use, and treatments were added twice daily every 12 h and effluents of all reactors were collected 6 h after supplementation and were centrifuged. Cell pellets and effluents were either quick-frozen with liquid nitrogen and stored at −80 °C or were immediately further processed for analysis. YE based on the NuPro series are obtained from cytoplasmic contents of Saccharomyces cerevisae and contain a substantial proportion of protein (> 50% crude protein, dry weight basis), amino acids, inositol, and vitamins in addition to NT (Craig and McLean, 2006; Fegan et al., 2016). LYE, SYE, and HYE were tested at two levels (5 and 10 g L−1) in test reactors continuously supplied with medium not containing YE. FOS (10.0 g L−1) contained 95% inulin/oligofructose and ≤5% free sugars, 92% of the oligosaccharides had a degree of polymerization ≤20. The NS mix was formulated based on Codex guidelines for infant formula assuming NT digestibility of 10%, and reflecting the amount of NS and nucleic acid bases delivered to the proximal colon of formulafed infants (Carver and Walker, 1995; Doo et al., 2017; Mateo, 2005). It contained adenosine (0.45 g L−1), cytidine (0.43 g L−1), uridine (0.11 g L−1), guanosine (0.04 g L−1) and inosine (0.07 g L−1) and was applied in 1 and 2-fold concentration (NS1 and NS2, respectively). Treatments SYE5, SYE10 and HYE10, which were shown to strongly impact microbial composition and metabolic activity in F1, were applied in F2. In F2 different concentrations of NS (NS1 and NS2), as well as different combinations of NS1, SYE and/or FOS (NS1/FOS, SYE10/ FOS and NS1/SYE10/FOS) were also tested. Treatments SYE10, FOS and SYE10/FOS were tested in a repetitive treatment (SYE10 (2), FOS (2) and SYE10/FOS (2)) in F2.

2. Materials and methods 2.1. Experimental set-up of in vitro PolyFermS continuous intestinal fermentation model For this study, two elderly PolyFermS fermentation models (F1 and F2) were used to mimic the proximal colon conditions of the elderly as described in detail previously (Fig. 1A) (Fehlbaum et al., 2015). Briefly, both F1 and F2 consisted of two stages: an inoculum reactor inoculated (30% v/v) with immobilized elderly fecal microbiota connected to a control and three test reactors mounted in parallel and continuously inoculated with 10% inoculum reactor effluent. The initiation and operation of the elderly PolyFermS models was presented in details by Fehlbaum et al. (2015). For this study, all reactors were operated at proximal colon conditions of the elderly for 19 and 34 days including an initial stabilisation for 6 days. Composition of the fermentation medium was designed to reproduce the ileal chime entering the colon (Table S1). Inoculum reactors of F1 and F2 were provided with the fermentation medium with 2.5 g L−1 SYE. Fermentation medium without addition of NS, YE and FOS was fed to the control reactor while test reactors were supplied with fermentation medium supplemented with different levels of NS, YE, and FOS (Table 1). For microbiota and metabolite analysis, effluent samples from all reactors were collected 44

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Medium with treatments

A Medium with yeast extract

Medium without yeast extract

Flow rate (F)100 % F100 %

F50 %F10 %

ml 300

ml 300

200 100

Inoculum reactor

F90 %

F10 %

F10 %

F90 %

F10 %

F90 %

F90 %

ml 300

ml 300

ml 300

200

200

200

200

100

100

100

100

Control reactor Sampling

Test reactor 1

Test reactor 2

Test reactor 3

Sampling

Sampling

Sampling

Sampling

Waste

B

C Fermentation 1 (F1)

Period Stabilization Day

0

Period 1 5

Fermentation 2 (F2) Period 2

12

19

Inoculum reactor

Control reactor Test reactor 1 Test reactor 2 Test reactor 3

Period Day 0

Stabilization

Period 1 6

Period 2 13

Period 3

Period 4 34

27

20

Inoculum reactor

LYE5 SYE5 HYE5

SYE10 HYE10 LYE10

Control reactor Test reactor 1 Test reactor 2

NS1

SYE5

NS1/SYE10 /FOS

SYE10 (2)

SYE10

FOS

SYE10/FOS

FOS10 (2)

HYE10

NS2

NS1/FOS

SYE10/FOS (2)

Test reactor 3

Fig. 1. Experimental design of elderly PolyFermS colonic fermentation models, F1 and F2. (A) Set up of PolyFermS continuous fermentation model with immobilized elderly fecal microbiota. Treatment schedules of F1 (B) and F2 (C).

2.4. DNA extraction and qPCR analysis

Spectrophotometer (Witec AG). Total bacteria, and selected bacteria and archaea groups commonly found in the elderly gut (Fehlbaum et al., 2015; Vanderhaeghen, Lacroix, & Schwab, 2015) were enumerated by qPCR using an ABI PRISM 7500-PCR sequence detection system (Applied Biosystems, Rotkreuz, Switzerland). Each qPCR

The FastDNA SPIN Kit for Soil (MP Biomedicals, France) was used to extract DNA from feces and fermentation effluents. The quality and quantity of DNA were estimated by using a NanoDrop ND-1000

Table 1 Abbreviations for NS mix-, YE- and FOS-treatments used in this study. Treatment

Abbreviation

Final concentration (x–fold or g L−1)

NT equivalent in treatment (g L−1)

Without supplementation Nucleosides

Control NS1 NS2 LYE5 LYE10 SYE5 SYE10a HYE5 HYE10 FOSa NS1/FOS SYE10/FOSa NS1/SYE10/FOS

0 1× 2× 5 g L−1 10 g L−1 5 g L−1 10 g L−1 5 g L−1 10 g L−1 10 g L−1 1×/10 g L−1 10/10 g L−1 1×/10/10 g L−1

0.0 1.5 3.0 0.3 0.6 0.5 1.0 1.2 2.4 0.0 1.5 1.0 2.5

Low-nucleotide yeast extract (containing 6.1% nucleotides) Standard-nucleotide yeast extract (containing 10.6% nucleotides) High-nucleotide yeast extract (containing 23.7% nucleotides) Fructooligosaccharides Combination of nucleosides and fructooligosaccharides Combination of standard-nucleotide yeast extract and fructooligosaccharides Combination of nucleosides, standard-nucleotide yeast extract and fructooligosaccharides a

Treatments indicated with (2) were repeated in F2. 45

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reaction (25 µL) contained 1 µL DNA as template, forward and reverse primers (0.2 pM final) and the 2× SYBR Green PCR Master Mix (Applied Biosystems) (Table S2). Reactions were run in duplicates. Thermocycling conditions consisted of initial denaturation step at 95 °C for 10 min, followed by 40 cycles of denaturation at 95 °C for 15 s, annealing and extension at 60 °C for 1 min. Amplification specificity was confirmed by melting curve analysis. A standard curve was generated with 10-fold serial dilutions of the PCR amplicon of interest to calculate gene copies mL−1 effluent or g−1 feces.

Phenomenex, Le Pecq Cedex, France), a Rezex ROA-Organic Acid H+ column (8%, 300 × 7.8 mm; Phenomenex) and a refractive index detector (HPLC-RI). Effluents were centrifuged at 13,000g for 10 min at 4 °C, and supernatants were filtered prior to analysis. Samples (20 µL) were eluted with 10 mM H2SO4 at a flow rate of 0.4 mL min−1 at 40 °C. Acetate, propionate, butyrate, valerate, isobutyrate, isovalerate, lactate, and formate were quantified using external standards.

2.5. 16S rRNA gene sequencing and analysis

For detection of NH3, frozen supernatants of fermentation effluents were diluted to concentrations within the linear range of the standard curve, and were reacted with Nessler’s reagent (Sigma-Aldrich) (Streuli and Averell, 1971). Absorbance was measured using a FL600 Microplate Fluorescence Reader (Bio-Tek Instruments, Luzern, Switzerland) set to 425 nm. For preparation of a standard curve, a dilution series of ammonium chloride was prepared in the range of 0–7.2 mg L−1 NH4+. All samples were analyzed in duplicate.

2.9. NH3 analysis

Genomic DNA from elderly donor feces and fermentation effluent samples of all treatments except treatment repetitions (Table S3) was used to generate amplicon libraries of the V5-V6 hypervariable regions (Doo et al., 2017). Barcoded pyrosequencing was conducted using a 454 Life Sciences Genome Sequencer FLX with Titanium chemistry (Roche, Brussels, Belgium) at DNAVision SA (Charleroi, Belgium) as described previously (Doo et al., 2017). The Ribosomal Database Project Classifier v 2.1 was used for the taxonomic identities of sequences at 80% confidence level (Wang, Garrity, Tiedje, & Cole, 2007).

2.10. Statistical analysis Statistical analysis of qPCR data and metabolite concentrations was carried out using JMP 11.0 Statistical Discovery Software (SAS Institute). Temporal stability of the fermentation models during the experiment was monitored using abundance values of bacterial groups determined by qPCR and concentrations of metabolites (total SCFA, acetate, propionate, butyrate, valerate, formate and NH3) in effluents of the inoculum and control reactors of F1 and F2 using a non-parametric Kruskal-Wallis test and the calculated p-values of less than 0.05 were considered significant. Non-parametric Kruskal-Wallis test was also employed for pairwise comparison of abundance of selected bacterial groups, SCFA and NH3 after treatments compared to each control reactor of F1 and F2 measured during the same period, respectively. To investigate significant differences between treatments, ratios of treatment versus controls were calculated for selected bacterial groups, SCFA and NH3 concentrations, and were subjected to one-way analysis of variance (ANOVA) followed by the Tukey-Kramer-HSD test. A pvalue of less than 0.05 was considered significant for all tests.

2.6. Nucleic acid isolation and RNA purification for metatranscriptomics Nucleic acids were extracted from fermentation effluent samples obtained from the control and test reactors of F2 supplemented with SYE10 (2), FOS (2) and SYE10/FOS (2) with a phenol-chloroform beadbeating procedure as described previously (Schwab et al., 2014). Total RNA was purified from 45 µL of nucleic acid using the AllPrep DNA/ RNA Mini Kit (Qiagen, Hombrechtikon, Switzerland) following the manufacturer's specifications with additional DNase treatment using the DNA-free DNase Treatment and Removal Reagents (Ambion, Life Technologies, Switzerland). RNA isolated from the last three days of fermentation were pooled before quality and quantity of RNA were assessed using the RNA 6000 Nano Chip Kit on a Bioanalyzer 2100 (Agilent Technologies, Basel, Switzerland). Only samples with a RNA Integrity Number (RIN) value greater than 9 were submitted to RNAseq. 2.7. Metatranscriptome sequencing and bioinformatic analysis

2.11. Nucleotide sequence accession number Double-stranded cDNA libraries were paired-end sequenced using an Illumina HiSeq at Functional Genomics Center Zurich (ETH Zurich, Switzerland). A pipeline consisting of FLASH for overlapping the paired-end sequences and SortMeRNA for separation of rRNA and mRNA were used (Kopylova, Noe, & Touzet, 2012; Magoc and Salzberg, 2011) yielding between 660,000 and 926,000 putative mRNA reads per sample (Table S4). The rRNA reads were subsampled at 1 Mio reads and were classified using CREST, which conducts alignment-based classification with the lowest common ancestor logarithm, and default settings (Lanzén et al., 2012). Approximately 45% of the reads were assigned and taxonomic profiles were visualized with Megan (Table S5) (Mitra et al., 2011). Putative mRNA was compared to the NCBI RefSeq database using MALT for taxonomic profiling (Huson, Auch, Qi, & Schuster, 2007). Putative mRNA reads were also uploaded to MG-RAST for functional classification according to the SEED Subsystem scheme using default settings (Meyer et al., 2008). Statistically significant differences between metatranscriptomes of the pooled samples were identified using STAMP (Statistical Analysis between Metagenomic Profiles) (Parks and Beiko, 2010).

All 16S rRNA sequencing data have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under accession number PRJNA312440. mRNA reads are available at MG-RAST under mgm4644101.3-mgm4644104.3. 3. Results 3.1. Microbiota composition and activity of PolyFermS models Two elderly PolyFermS fermentation models (F1 and F2) were used in this study, F1 was described in detail previously (Fehlbaum et al., 2015). Briefly, in common with feces of the elderly donors, Firmicutes prevailed in both inoculum reactors samples (86 and 70% in F1 and F2, respectively), similarly to the corresponding fecal samples (91 and 63% in F1 and F2, respectively). Ruminococcaceae (48 and 26% in F1 and F2, respectively) and Lachnospiraceae (32 and 29% in F1 and F2, respectively) represented the dominant Firmicutes families (Figs. 2, S1). Bacteroidetes was the second most abundant phylum in feces and inoculum reactors (7 and 10%, and 34 and 15% in F1 and F2, respectively) (Figs. 2, S1). In inoculum reactors, Bacteroidetes were mainly represented by Bacteroidaceae (10%) in F1, and by Bacteroidaceae (3%), Porphyromonadaceae (10%) and Prevotellaceae (2%) in F2. These two phyla accounted for a minimum of 85% of the reads in F1 and F2. Actinobacteria (< 0.4%) encompassing the families Bifidobacteriaceae (< 0.3%) and Coriobacteriaceae (< 0.3%), were low abundant in

2.8. HPLC analysis with refractive index detection (HPLC-RI) SCFA, BCFA, formate and lactate were analyzed in effluent samples using high performance liquid chromatography (Merck-Hitachi) equipped with a SecurityGuard Cartridges Carbo-H column (4 × 3 mm; 46

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B 100

100

90

90

80

80

Relative abundance (%)

Relative abundance (%)

A

70 60 50 40 30 20

% relative abundance

% relative abundance

D

60 50 40 30 20

10

10

0

0

Bifidobacteriaceae Coriobacteriaceae Bacteroidaceae Porphyromonadaceae Prevotellaceae Rikenellaceae

C

70

Enterococcaceae Erysipelotrichaceae Incertae Sedis XI Incertae Sedis XIV Lachnospiraceae Ruminococcaceae

Lactobacillaceae Streptococcaceae Veillonellaceae Enterobacteriaceae Pseudomonadaceae others unclassified

40 Control reactor LYE5 LYE10 SYE5 SYE10 HYE5 HYE10

30 20 10 7 6 5 4 3 2 1 0

30 Control reactor SYE5 SYE10 HYE10 NS1 FOS SYE10/FOS NS1/FOS NS1/SYE10/FOS

20

10

3 2 1 us R um in oc oc c

R os eb ur ia

Pa ra pr ev ot el la

te riu m Fa ec al ib ac

us En te ro co cc

D or ea

D ia lis te r

Ba rn si el la

Ba ct

er oi de s

0

Taxon Fig. 2. Microbial composition in elderly donor feces, and in effluents of inoculum, control and test reactors. Microbial composition was determined by 16S rRNA gene sequencing. Shown are mean relative abundance of families (A, B) and selected genera (C, D) in elderly donor feces and effluents of inoculum (IR), control (CR) and test reactors supplied with YE, NS and FOS alone and in combination. The respective treatment is indicated. “Others” includes families with < 0.5% relative abundance.

47

48

0.1 0.0 0.1B 0.1B 0.0B 0.1A,† 0.0B 0.0B 0.0B 0.1B 0.1B 0.0B 0.1B 0.0B,†

9.7 10.3 10.2 10.1 10.4 10.4 10.4 10.4 10.1 10.5 10.4 10.4 10.3 10.4 10.3

10.1 10.1 10.1 10.0 10.4 10.0 10.1 10.1 10.1

± ± ± ± ± ± ± ± ± ± ± ± ± ±

± ± ± ± ± ± ± ±

0.1 0.1* 0.1B,† 0.1B 0.1B 0.1A,† 0.0B,† 0.0B,† 0.0B,† 0.1B,† 0.1B,† 0.1B 0.1B,† 0.0B,†

0.1 0.2 0.1B 0.0A,† 0.1B 0.1A,B 0.1A,B 0.1A,B

Firmicutes

± ± ± ± ± ± ± ±

0.2* 0.1* 0.1† 0.1† 0.2† 0.2† 0.2† 0.1†

9.3 9.7 ± 0.1* 10.0 ± 0.1* 9.9 ± 0.1A,B,† 10.0 ± 0.2A 9.9 ± 0.2A,B 10.0 ± 0.1A,† 9.8 ± 0.1A,B,† 9.9 ± 0.1A,B,† 8.9 ± 0.1C,† 9.5 ± 0.1B,C,† 8.9 ± 0.2C,† 9.5 ± 0.1B,C,† 8.5 ± 0.1C,† 8.6 ± 0.1C,†

9.6 9.2 9.9 9.4 9.2 9.3 9.6 9.4 9.6

BPPa

5.1 9.3 8.9 8.8 8.9 8.9 8.7 9.0 9.0 8.8 9.0 9.1 8.9 9.4 8.6

7.2 9.1 9.2 8.8 8.6 9.1 9.1 9.0 8.7

± ± ± ± ± ± ± ± ± ± ± ± ± ±

± ± ± ± ± ± ± ±

0.3* 0.1* 0.1B, C, D 0.1C, D 0.1C, D 0.0D,† 0.2C, D 0.1B,C,† 0.1D,† 0.2C, D 0.1B,† 0.1B, C, D 0.1A,† 0.0D,†

0.1 0.2 0.4 0.1† 0.3 0.1 0.1 0.1†

Entero-bacteriaceae

5.5 7.2 6.7 7.1 7.9 7.8 7.6 7.5 7.5 7.6 8.2 7.9 8.7 8.0 7.6

8.3 5.8 5.5 6.1 6.3 7.0 7.2 6.2 6.2

± ± ± ± ± ± ± ± ± ± ± ± ± ±

± ± ± ± ± ± ± ±

LLPb

0.2 0.2 0.2D,† 0.1C,D,† 0.0C,D,† 0.1C,D,† 0.0C,D,† 0.1C,D,† 0.1D,† 0.2B,† 0.2B,C,D,† 0.1A,† 0.0B,C,† 0.1C,D,†

0.3 0.1 0.0B,† 0.0B,† 0.1B,† 0.1A,† 0.1B,† 0.2B,† 8.3 8.4 8.0 8.4 9.1 9.1 9.2 8.9 9.2 9.0 8.9 9.2 9.6 9.5 9.2

9.4 9.3 8.6 8.6 9.7 8.8 9.2 9.0 9.9

± ± ± ± ± ± ± ± ± ± ± ± ± ±

± ± ± ± ± ± ± ±

0.2* 0.2 0.4C 0.1A,B,C,† 0.0A,B,C,† 0.2A,B,C,† 0.1B,C,† 0.1B,C,† 0.1A,B,C,† 0.1B,C,† 0.1B,C,† 0.1A,B,† 0.1A,† 0.1B,C,†

0.3 0.3 0.3C 0.0B,† 0.3C 0.3C,† 0.1B,C,† 0.1A,†

Roseburia spp./E. rectalec

10.1 10.5 10.5 10.7 10.5 10.5 10.8 10.4 10.7 10.6 10.8 10.3 10.3 10.5 10.4 ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.2 0.2 0.0B 0.2B 0.1B 0.1A,B 0.3B 0.1B 0.1B 0.3A 0.2B 0.2B 0.3B 0.1B

10.3 10.3 ± 0.2 10.2 ± 0.0 9.9 ± 0.3A,B 10.1 ± 0.1A,B 10.0 ± 0.2A,B 10.3 ± 0.1A 10.2 ± 0.1A,B 10.0 ± 0.0B,†

Clostridium cluster IV

4.9 7.3 6.9 7.6 7.8 7.8 8.1 7.5 8.0 8.7 8.5 8.8 9.2 8.6 8.6

8.5 6.3 5.8 6.2 6.6 7.5 7.5 6.8 7.7

± ± ± ± ± ± ± ± ± ± ± ± ± ±

± ± ± ± ± ± ± ±

0.2 0.2* 0.1B,† 0.1B,† 0.1B,† 0.1B,† 0.1B,† 0.3B,† 0.1B,† 0.2B,† 0.2B,† 0.2A,† 0.1B,† 0.0B,†

0.3 0.1 0.2C,† 0.1C,† 0.1A,B,† 0.2A,B,C,† 0.3B,C,† 0.1A,†

Bifidobacter-ium

6.7 7.8 7.2 6.4 7.2 7.1 6.7 7.5 6.7 7.4 7.5 6.6 7.5 6.3 7.3

8.1 7.3 7.3 7.2 7.6 7.3 7.3 6.8 8.1

± ± ± ± ± ± ± ± ± ± ± ± ± ±

± ± ± ± ± ± ± ±

0.5* 0.4* 0.1A,B 0.6A,B 0.1A,B 0.4A,B 0.2A,B 0.1A,B 0.2A 0.2A,B 0.2B,† 0.1A,B 0.1A,B 0.3A,B

0.8 0.5 0.7 0.1 0.3 0.3 0.4 0.3

Methanobrevi-bacteriales

7.2 NDf ND ND ND ND ND ND ND ND ND ND ND ND ND

8.0 5.3 5.2 5.4 5.1 6.0 6.1 5.9 5.5

± ± ± ± ± ± ± ±

0.9 1.0 0.4A 0.4A,B 0.8A,B 0.3B 0.3A,B 0.1A,B

Methanomassilii-coccales

b

BPP, Bacteroides/Porphyromonas/Prevotella group. LLP, Lactobacillus/Leuconostoc/Pediococcus spp. c RE, Roseburia spp./Eubacterium rectale. d Donor feces data was already partly presented in Fehlbaum et al. (2015). e Mean values during the fermentation. f ND, not detected. * An asterisk indicates a significant time change in inoculum and control reactor during the fermentation with the non-parametric Kruskal-Wallis test (P < 0.05). † Dagger indicates that abundance of bacterial group during fermentation is significantly different from the control reactor of the corresponding period with the non-parametric Kruskal-Wallis test (P < 0.05). A-D Different capital letters within the same column indicate significant differences between treatments using one-way ANOVA followed by the Tukey-Kramer-HSD test (P < 0.05).

a

10.2 10.6 10.6 10.5 10.7 10.6 10.8 10.7 10.6 10.6 10.7 10.6 10.6 10.7 10.5

F2 Donor feces Inoculum reactore Control reactore NS1 NS2 SYE5 SYE10 SYE10 (2) HYE10 FOS FOS (2) NS1/FOS SYE10/FOS SYE10/FOS (2) NS1/SYE10/FOS

± ± ± ± ± ± ± ± ± ± ± ± ± ±

11.1d 10.4 ± 10.5 ± 10.4 ± 10.3 ± 10.6 ± 10.2 ± 10.6 ± 10.3 ±

F1 Donor feces Inoculum reactore Control reactore LYE5 LYE10 SYE5 SYE10 HYE5 HYE10

0.1 0.2 0.2 0.1† 0.1 0.1† 0.1 0.1

Total bacteria

Fermentation/sample origin

Target bacterial and archaeal group (gene copies g−1 feces or mL−1 effluent)

Table 2 Abundance of selected bacterial and archaeal groups in donor feces and effluents of F1 and F2. Data are expressed as mean ± standard deviation log10 16S rRNA or functional (xfp Bifidobacterium) gene copies mL−1 effluent of the last 3 consecutive days in each fermentation period.

E.-H. Doo et al.

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Table 3 Metabolite concentrations and ratios in effluents of F1 and F2. Data are expressed as mean ± standard deviation of the last 3 consecutive days in each fermentation period. Metabolites and ratios formed Fermentation/sample origin

Total SCFA (mM)

Acetate (mM)

F1 Inoculum reactorb Control reactorb LYE5 LYE10 SYE5 SYE10 HYE5 HYE10

104.2 ± 3.4 83.5 ± 2.0 101.6 ± 3.4C,† 109.9 ± 4.9A,B,† 106.4 ± 4.1B,C,† 116.8 ± 1.5A,† 105.6 ± 3.0B,C,† 103.8 ± 2.1B,C,†

62.8 59.7 59.5 55.2 65.3 70.1 63.4 61.2

± ± ± ± ± ± ± ±

F2 Inoculum reactorb Control reactorb NS1 NS2 SYE5 SYE10 SYE10 (2) HYE10 FOS FOS (2) NS1/FOS SYE10/FOS SYE10/FOS (2) NS1/SYE10/FOS

126.0 117.9 126.4 125.8 127.5 152.1 143.8 142.4 137.3 144.6 152.0 174.3 166.7 161.2

68.6 59.5 67.1 58.5 55.4 68.0 62.3 72.1 63.2 61.6 68.2 77.4 66.5 59.1

± ± ± ± ± ± ± ± ± ± ± ± ± ±

± ± ± ± ± ± ± ± ± ± ± ± ± ±

5.0* 5.1 2.1F,† 3.6F,† 1.0F,† 3.7B,C,† 5.2E,F,† 6.7C,D,E,† 1.7D,E,F,† 1.7E,F,† 2.6B,C,D,† 5.7A,† 5.7B,C,† 1.5A,B,†

Propionate (mM)

Butyrate (mM)

Ratioa

Valerate (mM)

Formate (mM)

NH3 (mg mL−1)

0.7 0.8 0.3C, D 1.5D,† 3.5A,B,† 1.2A,† 2.0B,C,† 0.8B,C,†

17.3 ± 0.8* 4.6 ± 0.9* 17.0 ± 0.6A,† 11.4 ± 1.3B,† 13.0 ± 1.2B,† 15.8 ± 2.5A,† 12.0 ± 0.8B,† 12.2 ± 0.6B,†

19.5 19.2 25.1 39.9 28.2 30.8 27.3 29.8

± ± ± ± ± ± ± ±

1.1 1.8 3.0C,† 0.9A,† 0.8B,C,† 1.4B,† 2.4B,C,† 1.8B,†

63:17:20 71:5:23 59:17:25 52:11:37 61:12:26 60:14:26 62:12:27 59:12:29

4.7 ± NDc NDB 3.3 ± NDB NDB 3.0 ± 0.6 ±

ND ND ND ND ND ND ND ND

1.2 0.9 0.8 0.9 1.4 1.2 1.1 1.1

± ± ± ± ± ± ± ±

0.1* 0.1 1.1C 0.1B, C 0.1A,† 0.1A,† 0.1A,B,† 0.0A,†

4.0 2.2 1.6B,C,† 1.9C, D 1.1D,† 2.2B,C,† 3.1B,C,† 3.4A,B,† 0.7B,C,D,† 1.2B,C,† 2.5B,† 6.0A,† 2.1A,B,† 1.4C, D

15.1 ± 2.2* 22.3 ± 2.5* 21.0 ± 0.8B,C,† 23.7 ± 0.9A,† 16.6 ± 0.3C,† 25.5 ± 1.3A, B 19.7 ± 1.5C,† 26.5 ± 0.9A,† 9.2 ± 0.2D,E,† 9.5 ± 0.7D, E,† 8.1 ± 0.3D,E,† 10.4 ± 3.4D,† 7.1 ± 0.5E,† 7.9 ± 0.2D,E,†

36.3 30.2 30.1 34.3 47.3 51.2 52.7 41.0 62.8 73.5 71.5 86.6 90.7 88.7

± ± ± ± ± ± ± ± ± ± ± ± ± ±

4.4* 3.7* 2.1F 0.2F,G,† 0.5D,E,† 2.7D,† 1.0D,F,† 2.1F,† 0.9B,C,† 2.4C,D,† 2.3B,† 4.5A,† 3.5B,† 1.1A,†

57:13:30 53:20:27 57:18:25 50:20:29 46:14:40 47:18:35 46:15:39 52:19:29 47:7:46 43:7:51 46:6:48 44:6:50 40:4:55 38:5:57

6.1 6.0 8.1 9.2 8.1 7.4 9.2 2.8 2.1 ND 4.2 ND 2.3 5.5

ND 0.8 ± 1.3* 2.3 ± 0.9C,† ND ND ND ND ND 5.8 ± 1.0C,† 11.2 ± 1.9A,† 6.0 ± 0.8B,† ND 1.8 ± 1.5C ND

0.9 1.0 1.2 1.0 0.9 1.1 0.9 1.0 0.4 0.3 0.4 0.5 0.4 0.6

± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.2 0.1* 0.1A,† 0.0B 0.1B, C 0.1A,† 0.1A, B 0.1A, B 0.1E,† 0.1E,† 0.0D, E,† 0.0D, E,† 0.D E,† 0.0C,D,†

± ± ± ± ± ± ± ± ±

1.6

1.6A,†

2.7A, B 0.2A,B,† 1.5 3.8* 0.2A,† 0.7B,† 0.2B,† 0.6A,B,† 0.2B 1.1A,B,† 0.4B,†

± 1.8B,† ± 0.1B,† ± 1.5B,†

a

Ratios of acetate:propionate:butyrate. Mean values during the fermentation. c ND, not detected. * An asterisk indicates a significant time change in inoculum and control reactors during the fermentation with the non-parametric Kruskal-Wallis test (P < 0.05). † Dagger indicates that metabolite concentration during fermentation is significantly different from the control reactor of the corresponding period with the nonparametric Kruskal-Wallis test (P < 0.05). A-F Different capital letters within the same column indicate significant differences between treatments using one-way ANOVA followed by the Tukey-Kramer-HSD test (P < 0.05). b

rRNA genes in F1 and F2, respectively, compared to the inoculum reactor. Levels of total SCFA were reduced in control reactors, which received fermentation medium that did not contain YE. Both acetate and butyrate were reduced in F1 and F2, compared to the corresponding inoculum reactor (Table 3, Fig. S2).

inoculum and control reactors similarly as in feces (0 and 0.2%, and 0 and 0.3% of Bifidobacteriaceae and Coriobacteriaceae in donor feces of F1 and F2, respectively) (Figs. 2, S1). qPCR results generally confirmed 16S rRNA gene sequencing data (Table 2). Both donor feces contained Methanobrevibacteriales and Methanomassiliicoccales of the host-associated clade (Borrel et al., 2017) (Table 2), which were maintained in inoculum and control reactors at log 7.2 and log 5.3 16S rRNA gene copies mL−1, respectively, of F1. In F2, only Methanobrevibacteriales were detected in the inoculum reactor (log 7.8 16S rRNA gene copies mL−1) (Table 2). Acetate was the main SCFA in both fermentations with 63 and 69 mM in inoculum reactors of F1 and F2, respectively. Levels of butyrate formed were higher in F2 than in F1 (36 compared to 19 mM). Propionate was detected at 17 and 15 mM in inoculum reactors of F1 and F2. Total SCFA levels were higher in F2 than in F1, and SCFA formation was generally stable during experimental time, indicated by standard deviations ≤5% for the main SCFA acetate, propionate and butyrate (Table 3, Fig. S2). Isobutyrate, isovalerate and lactate were not detected. The omission of YE to the feeding medium of the control reactors led to shifts in microbiota composition and activity compared to the inoculum reactor. Relative abundance of Lachospiraceae and Ruminococcaceae decreased (27 and 27%, and 24 and 14% in F1 and F2, respectively) while relative abundance of Bacteroidaceae increased (42 and 30% in F1 and F2, respectively) compared to the inoculum reactor (Fig. 2). These alterations were also observed by qPCR with decreased abundance of Roseburia spp./E. rectale group (−0.7 and −0.4 log 16S rRNA genes in F1 and F2, respectively, and increased abundance of the Bacteroides, Prevotella/Porphyromonas group (+0.7 and +0.3 log 16S

3.2. Impact of NS and YE on composition and metabolic activity of elderly colonic microbiota We compared the impacts of treatments with NS and YE in test reactors to the unsupplemented control reactors in F1 and F2, to assess changes in microbial community composition and SCFA formation (Table 2). Total 16S rRNA gene copies ranged from 10.2 to 10.7 log copies mL−1 effluent in control and test reactors (Table 2). Bacteroides/Porphyromonas/Prevotella group 16S rRNA gene copies were significantly decreased by all YE treatments in F1 and by HYE10 in F2 (Table 2). All tested YE supplementation treatments significantly increased 16S rRNA gene copies of Lactobacillus/Leuconostoc/Pediococcus spp. and Bifidobacterium spp. in both fermentations. Roseburia spp./E. rectale abundance was higher during all YE treatments, except LYE5 and SYE5 during F1. Both NS treatments tested in F2 enhanced abundance of Lactobacillus/Leuconostoc/Pediococcus spp. and Bifidobacterium spp., while Roseburia spp./E. rectale abundance was only increased for the highest NS2 level. Addition of YE or NS did not change the abundance of methanogenic archaea, except for the combined treatment NS1/FOS which significantly decreased the abundance of Methanobrevibacteriales (Table 2). We performed 16S rRNA gene sequencing of effluents derived from 49

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(from 0.3 to 15.0%). Total SCFA levels significantly increased by 44, 57 and 35 mM by NS1/SYE10/FOS, SYE10/FOS (1) and NS1/FOS, respectively, compared to the control reactor (Table 3). This increase was mainly due to an increase in butyrate formation while propionate decreased. Repetitions of SYE10/FOS (2) confirmed this effect with an increase of total SCFA production by 41 mM compared to the control reactor. As with FOS alone, NH3 levels were also lower than the control reactor after all combination treatments. Formate increased during NS1/FOS treatment (Table 3).

F1 and F2 (Table S3) confirming abundance changes of bacterial populations shown by qPCR results. After the addition of NS or YE an increase in relative abundance of the Firmicutes was observed. This increase was attributed to an increase of Lachnospiraceae, with a proportional reduction of Bacteroidetes, mainly due to a decrease of Bacteroidaceae (Fig. 2A and C). In F1, all YE treatments significantly increased the levels of total SCFA, butyrate and propionate (Table 3). Addition of SYE and HYE led to significantly higher acetate and increased NH3. In F2, total SCFA, acetate and butyrate were significantly higher during addition of YE compared to the control reactor with the exception of SYE5. YE addition of 10 g L−1 significantly enhanced propionate levels whereas 5 g L−1 YE reduced propionate. NS addition significantly increased total SCFA in F2 with NS1 and NS2 increasing acetate and butyrate, respectively, compared to the control reactor (Table 3).

3.5. Colonic microbiota gene expression after supplementation with SYE10 and/or FOS Because additions of SYE10 and FOS caused major shifts in microbial composition and metabolism, we additionally used metatranscriptomics to investigate gene expression profiles of the microbial communities of single and combined treatments.

3.3. Change of the elderly colonic microbiota composition and metabolic activity upon FOS supplementation

3.6. Taxonomic profile of rRNA reads

FOS tested at two periods of F2 (Fig. 1C) did not change total 16S rRNA gene copies compared to the control reactor, but significantly increased abundance of Firmicutes, Lactobacillus/Leuconostoc/Pediococcus spp., Bifidobacterium spp. and Roseburia spp./E. rectale during both treatment periods (Table 2). In contrast, the Bacteroides/Porphyromonas/Prevotella group was significantly reduced in comparison with the control reactor. Addition of FOS did not significantly change the abundance of Methanobrevibacteriales (Table 2). In accordance with qPCR data, 16S rRNA gene sequencing showed a decrease of relative abundance of the Bacteroidetes from 41.1% in the control reactor to 2.5% in test reactor with FOS (1) (Fig. 2C and D). Major families of the Bacteroidetes, Bacteroidaceae (from 29.6 to 1.0%), Porphyromonadaceae (from 6.1 to 0.4%) and Prevotellaceae (from 3.6 to 1.0%) were reduced. In contrast, relative abundances of Firmicutes and Actinobacteria increased to 86.8 and 7.4% with FOS compared to 55.0 and 0.2% in the control reactor, respectively. Lachnospiraceae (from 12.4 to 47.6%) and Ruminococcaceae (from 25.6 to 31.2%) positively responded to FOS addition. Bifidobacterium spp. mainly contributed to the increase of relative abundance of Actinobacteria. Supplementation of FOS strongly stimulated butyrate formation (+116–123%), while propionate levels dropped to 56–60% compared to the control reactor, respectively (Table 3) altering the metabolite ratio from 45:7:49 (control reactor) to 53:20:28. Formate became detectable after FOS addition while NH3 significantly decreased from 0.9 ± 0.2 to 0.4 ± 0.1 mg mL−1 (Table 3).

In accordance with results obtained with 16S rRNA gene sequencing and qPCR, SYE10 addition affected relative abundance of the major orders Clostridiales and Bacteroidales (Table S5). Compared to the control reactor, the additions of SYE10, FOS and SYE10/FOS enhanced the relative abundance of of Lachnospiraceae and Ruminococcaceae (Table S5) while. in SYE10/FOS treated reactors due to a decrease of unclassified Bacteroidales, S24-7, Bacteroidaceae, Prevotellaceae and Porphyromonadaceae (Table S5). Relative abundance of Lactobacillales and Bifidobacteriales was enhanced by the addition of FOS and SYE10/FOS. Similar treatment related shifts in relative abundance were also observed when mRNA reads were taxonomically classified using MALT (Table S6). Methanobrevibacteriales were present with a relative abundance of approximately 0.04% (Table S5). When FOS was supplied alone, relative abundance of Methanobacteriales 16S rRNA gene transcripts decreased approximately 10-fold, this effect was not observed with SYE10/FOS (Table S5). 3.7. Impact of supplementations on gene expression Putative mRNA reads were assigned to SEED categories using MGRAST and were statistically analysed using STAMP (Parks and Beiko, 2010). FOS impacted the functional profile of the microbiota to a bigger extend than SYE10 (Fig. 3A) due to an increase in relative abundance of transcripts assigned to SEED categories ‘Amino Acids and Derivatives’ and ‘Carbohydrate Metabolism’, and a decrease of ‘Protein Metabolism’ (Table S7). As we also observed an increase of metabolic activity and especially butyrate formation with all treatments, we investigated transcripts assigned to ‘Carbohydrate Metabolism’ and ‘Amino Acids and Derivatives’ (Fig. 3B and C). Indeed, transcripts assigned to ‘Di- and oligosaccharides’, which encode enzymes involved in di- and oligosaccharide degradation, and to ‘Fermentation’, which include the butyrate production pathways, were significantly more abundant after FOS treatment and, to a lesser extent, after SYE10 addition compared to controls (Figs. 3B, Table S8A). SYE10 treatment alone or together with FOS enhanced relative abundance of transcripts related to polysaccharide metabolism (‘Polysaccharides’) and monosaccharide utilization (‘Monosaccharides’). In contrast, transcripts associated with ‘One Carbon Metabolism’ were reduced. Treatments affected the relative abundance of transcripts related to metabolism of amino acids (Fig. 3C, Table S8B). All treatments enhanced abundance of transcripts related to branched-chain amino acids and to ‘Lysine, threonine, methionine, and cysteine’, which contributed approximately 50% of all transcripts of the SEED categories ‘Amino

3.4. Combination effect of NS, YE and FOS on elderly colonic microbiota We were particularly interested in how elderly colonic microbiota responded to combined nitrogen, micronutrient and carbon source supplementation. In combination treatments with NS, YE, and FOS in F2, 16S rRNA gene copies of total bacteria, Firmicutes, Clostridium cluster IV and Enterobacteriaceae were not changed (Table 2) while Lactobacillus/Leuconostoc/Pediococcus spp., Bifidobacterium spp. and Roseburia spp./E. rectale significantly increased, and the Bacteroides/ Porphyromonas/Prevotella group significantly decreased compared to the control and to FOS treatment alone. Combination treatments of FOS, SYE10 and/or NS1 did not significantly change the abundance of Methanobrevibacteriales compared to single treatments (except for SYE10/FOS) (Table 2). Structural changes in microbial community composition such as increased relative abundances of Firmicutes and Actinobacteria, and decreased abundances of Bacteroidetes families Bacteroidaceae, Porphyromonadaceae and Prevotellaceae (Fig. 2C and D) were in agreement with abundance levels determined by qPCR. SYE10/FOS treatment increased relative abundance of Lactobacillus spp. (from 0.1 in the control reactor to 2.6% in the test reactor) and Bifidobacterium spp. 50

Journal of Functional Foods 52 (2019) 43–53

E.-H. Doo et al. abundance (%) 0.2

A

18.4

36.7

B abundance (%) Sugar Alcohols

0

17.3

34.6

Aminosugars Organic Acids Glycosyl Hydrolases Monosaccharides CO2 fixation One-carbon Metabolism Amino Acids and Derivatives Clustering-based Subsystems Protein Metabolism Sulfur Metabolism Regulation and Cell Signaling Cell Divison and Cell Cycle Phages, Prophages, TE, Plasmids Iron Acquisition and Metabolism Motility and Chemotaxis Virulence, Diseases and Defense Metabolism of Aromatic Compounds Dormancy and Sporulation Potassium Metabolism Photosynthesis Nitrogen Metabolism Secondary Metabolism Phosphorus Metabolism Nucleosides and Nucleotides Membrane Transport DNA Metabolism Stress Response Respiration Miscellaneous Cofactors, Vitamins, Prosthetic Groups, Pigments Fatty Acids, Lipids, and Isoprenoids RNA Metabolism Cell Wall and Capsule Carbohydrates

control FOS_SYE

Carbohydrates Polysaccharides Fermentation Di- and oligosaccharides Central Carbohydrate Metabolism

abundance (%)

C

0

23.2

46.4

Histinine Metabolism Amino Acids and Derivatives Proline and 4-Hydroxyproline Aromatic Amino Acids and Derivatives Alanine, Serine and Glycine Glutamine, Glutamate, Aspartate, Asparagine, .. Arginine: Urea Cycle, Polyamines

SYE FOS

Branched-chain Amino Acids Lysine, Threonine, Methionine, and Cysteine

Fig. 3. Bacterial gene expression profiles in the control reactor and during YE and FOS supplementation alone and in combination. Heatmap plot of reads assigned to (A) major SEED categories, (B) carbohydrate metabolism and (C) amino acid metabolism.

with cumulative effects (Fig. 4). A combination treatment of YE containing micronutrients and nitrogen sources together with a prebiotic carbohydrate source could thus be a way to selectively promote the abundance of bacterial groups while also enhancing overall metabolic activity leading especially to the formation of butyrate. In both in vitro fermentation models Lactobacillus/Leuconostoc/ Pediococcus spp., Bifidobacterium spp. and the Roseburia spp./E. rectale group increased after YE addition. Despite the dominance of different butyrate producers in both models (F1: Roseburia and Ruminococcus, F2: Faecalibacterium and Roseburia), the addition of YE increased butyrate while propionate was reduced by most treatments. This decrease might be due to decreased abundance or shifts within the the Bacteroides/ Prevotella/Porphyromonas group. These results imply that despite differences in initial microbiota composition, the compositional shifts and the metabolic response of an elderly colonic microbiota to YE addition were similar. Inter-individual differences in host response to carbohydrate based dietary interventions were observed both in human and animal studies (Smits, Marcobal, Higginbottom, Sonnenburg, & Kashyap, 2016). In agreement with previous observations, YE exerted a stronger effect than NS on SCFA formation and microbiota composition likely due to the presence of additional components in YE, such as minerals, vitamins, and amino acids, which play important roles in a wide range of biological processes (Doo et al., 2017; Thanissery et al., 2010; Wang et al., 2009). Interestingly, the combined SYE10 and FOS supplementation cumulatively elevated levels of butyrate and total SCFA by 55 and 41% compared to 17 and 18% increases with SYE10, and 38 and

acids and derivatives’, and reduced abundance of transcripts involved in ‘Glutamine, glutamate, aspartate, asparagine; ammonia assimilation’, ‘Histidine Metabolism’ and ‘Proline and 4-hydroxyproline’. Only the SYE10/FOS addition decreased abundance of ‘Alanine, serine, and glycine’ related transcripts.

4. Discussion Dietary interventions are considered an effective strategy to beneficially modify gut microbiota composition and activity to enhance host health (Gibson et al., 2017; Sanders et al., 2014; Xu and Knight, 2015). Using a combined analytical approach to study microbial community structure, gene expression and metabolic activity, we investigated the impact of supplementation of NS, YE and FOS alone and in combination using two elderly PolyFermS models. In vitro models allow to selectively investigate the impact of supplementation on the microbiota, as YE was recently shown to also impact bowel movement in constipated patients (Pinheiro et al., 2017) which might lead to altered composition of the microbial community. We found that SCFA production of elderly colonic microbiota increased dose-dependent up to addition of 1.0 equivalent NT g L-1. This observation was in agreement with previous studies (Fig. 4) (Doo et al., 2017; Possemiers et al., 2013). Despite different initial SCFA levels in both models, the absolute increase in total SCFA formed during a treatment was similar in both models (Fig. 4). We also confirmed our initial hypothesis that supplementing YE with FOS, a non-digestible but fermentable dietary carbohydrate, further enhanced SCFA formation 51

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180

B

A

total SCFA (mM)

160

LYE SYE HYE

140

120 NS SYE HYE SYE10/FOS NS1/SYE10/FOS

100

80 0.0

0.5

1.0

1.5

2.0

2.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

NT equivalent (g L-1) Fig. 4. Total SCFA formation in dependence of equivalent NT content. SCFA formation in response to NS, YE and FOS supplementation alone and in combination in F1 (A) and F2 (B).

Methanobrevibacteriales rRNA transcripts was reduced approximately 10-fold after FOS addition while rRNA gene counts were not significantly different from the control reactor. This could suggest that FOS decreased the metabolic activity of Methanobrevibacteriales while abundance was maintained. FOS supplementation could therefore be a way to reduce intestinal methane formation, which should be investigated in further studies.

19% increases with FOS alone. Gene expression analysis indicated that supplementations affected different metabolic pathways: FOS enhanced transcripts related to ‘Fermentation’ while YE enhanced transcripts of ‘Amino Acid Metabolism’. A combination of dietary carbohydrate/and YE micronutrient containing supplements can therefore supply metabolically different microbial groups. FOS can be used by several butyrate producing gut microbes including Faecalibacterium prausnitzii, Roseburia spp., and Eubacterium rectale (Scott, Martin, Duncan, & Flint, 2014). Butyrate forming bacteria might also cross-feed on fermentation endproducts, or on monosaccharides of FOS utilizers and degraders (Belenguer et al., 2006; de Vuyst and Leroy, 2011). Amino acid catabolism by for example proteolytic clostridia can likewise lead to the formation of SCFA (Davila et al., 2013). Data obtained from the elderly colonic microbiota supplemented with NS and YE were in agreement with findings of previous in vitro fermentation studies on the swine and infant colonic microbiota with an increase of the level of lactobacilli and total SCFA production, especially butyrate (Doo et al., 2017; Sauer, Bauer, Vahjen, Zentek, & Mosenthin, 2010). On the other hand, the supplementation of NS and YE impacted bifidobacteria to a lesser extent in the infant microbiota model compared to the elderly colonic microbiota. The small impact of YE on bifidobacteria abundance in the infant colonic microbiota can be explained by an approximately 100-times higher abundance of bifidobacteria in the infant model (Doo et al., 2017), while in the elderly microbiota, Bifidobacterium spp. constituted only about 0.1%. Under favorable conditions, low abundant populations can become abundant (Hibbing, Fuqua, Parsek, & Peterson, 2010). Methanobrevibacter smithii of the Methanobrevibacteriales and Methanomassiliicoccales of the free-living and host-associated clade are regular constituents of the elderly gut microbiota (Borrel et al., 2017). In agreement with previous observations, the two community dwelling donors harboured Methanomassiliicoccales of the host-associated clade. Methanomassiliicoccales are of especial interest in regard to elderly health, as some members are able to utilize trimethylamine (TMA), a precursor for trimethyl-N-oxide which has been linked to the development of cardiovascular disease and atherosclerosis (Manor et al., 2018; Qi et al., 2018). Indeed, abundance of TMA using Methanomassiliicoccales correlated negatively to fecal TMA concentrations (Borrel et al., 2017). We showed here that Methanomassiliicoccales can be maintained in the elderly PolyFermS model, and that addition of NS or YE did not impact their abundance. To our best knowledge, no previous study investigated the impact of dietary supplementation on abundance of abundance of human Methanomassiliicoccales. Relative

4.1. Conclusion In conclusion, this study highlights the health-promoting potential of dietary supplementation of YE alone and in combination with FOS on the elderly colonic microbiota, particularly leading to enhanced abundance of butyrate producers, lactobacilli and bifidobacteria and elevated butyrate levels. In the elderly, a lifestyle and diet-dependent decrease of butyrate producers, and an increase of Bacteroidetes, was linked to increased fraility. We show here that supplementation of YE and FOS in combination might counteract these microbial shifts with potential benefits on host health and without adverse impacts on methanogen populations. Acknowledgements The authors thank the Functional Genomics Center Zurich for conducting the RNAseq and Lennart Oppitz from FGCZ for help with the sequencing analysis. Alfonso Die and Alain Körner are thanked for technical assistance. Funding source This work was supported by a research grant from the Worldwide Research Alltech, Inc. of USA. Conflict of interest The authors declare that they have no conflict of interest. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jff.2018.10.020. 52

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